From d81020a5da7b5cab0a88f45a51a0779098ce56eb Mon Sep 17 00:00:00 2001 From: JsNcAr Date: Tue, 20 Jan 2026 11:35:07 -0500 Subject: [PATCH 1/4] docs: Reorganized and improved documentation to follow a more modular and standard pattern --- README.md | 17 ++- docs/architecture/data-lifecycle.md | 93 ++++++++++++ docs/architecture/iterative-refinement.md | 33 ++++ docs/architecture/system-architecture.md | 120 +++++++++++++++ .../disinformation-heuristics.md | 116 ++++++++++++++ docs/configuration/docker-containerization.md | 75 +++++++++ docs/configuration/flyio-configuration.md | 97 ++++++++++++ .../heuristics-output-schemas.md | 103 +++++++++++++ .../prompt-management-advanced.md | 121 +++++++++++++++ docs/configuration/prompt-management.md | 70 +++++++++ docs/configuration/radio-recording-system.md | 125 +++++++++++++++ docs/configuration/radio-station-adapters.md | 86 +++++++++++ docs/configuration/recording-workers.md | 92 ++++++++++++ docs/getting-started/introduction.md | 81 ++++++++++ docs/getting-started/quick-start.md | 142 ++++++++++++++++++ docs/index.md | 48 ++++++ docs/integration/express-server-api.md | 89 +++++++++++ docs/integration/real-time-collaboration.md | 83 ++++++++++ docs/integration/slack-email-notifications.md | 72 +++++++++ docs/integration/supabase-postgres-schema.md | 113 ++++++++++++++ docs/maintenance/bigquery-archival.md | 73 +++++++++ docs/maintenance/maintenance-scripts.md | 94 ++++++++++++ docs/maintenance/utility-scripts.md | 133 ++++++++++++++++ .../stage-1-detection-transcription.md | 90 +++++++++++ .../pipeline-stages/stage-2-audio-clipping.md | 71 +++++++++ .../stage-3-in-depth-analysis.md | 91 +++++++++++ .../stage-4-analysis-review.md | 90 +++++++++++ .../stage-5-semantic-embedding.md | 69 +++++++++ docs/testing/testing-suite.md | 67 +++++++++ 29 files changed, 2552 insertions(+), 2 deletions(-) create mode 100644 docs/architecture/data-lifecycle.md create mode 100644 docs/architecture/iterative-refinement.md create mode 100644 docs/architecture/system-architecture.md create mode 100644 docs/configuration/disinformation-heuristics.md create mode 100644 docs/configuration/docker-containerization.md create mode 100644 docs/configuration/flyio-configuration.md create mode 100644 docs/configuration/heuristics-output-schemas.md create mode 100644 docs/configuration/prompt-management-advanced.md create mode 100644 docs/configuration/prompt-management.md create mode 100644 docs/configuration/radio-recording-system.md create mode 100644 docs/configuration/radio-station-adapters.md create mode 100644 docs/configuration/recording-workers.md create mode 100644 docs/getting-started/introduction.md create mode 100644 docs/getting-started/quick-start.md create mode 100644 docs/index.md create mode 100644 docs/integration/express-server-api.md create mode 100644 docs/integration/real-time-collaboration.md create mode 100644 docs/integration/slack-email-notifications.md create mode 100644 docs/integration/supabase-postgres-schema.md create mode 100644 docs/maintenance/bigquery-archival.md create mode 100644 docs/maintenance/maintenance-scripts.md create mode 100644 docs/maintenance/utility-scripts.md create mode 100644 docs/pipeline-stages/stage-1-detection-transcription.md create mode 100644 docs/pipeline-stages/stage-2-audio-clipping.md create mode 100644 docs/pipeline-stages/stage-3-in-depth-analysis.md create mode 100644 docs/pipeline-stages/stage-4-analysis-review.md create mode 100644 docs/pipeline-stages/stage-5-semantic-embedding.md create mode 100644 docs/testing/testing-suite.md diff --git a/README.md b/README.md index 659f093..ce46e82 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ VERDAD employs a multi-stage pipeline architecture designed for scalability and **Process:** -- Uses Gemini 1.5 Flash LLM for rapid initial screening +- Uses Gemini (1.5 or 2.5 Flash) LLM for rapid initial screening - Applies simplified heuristics for high-recall detection - Generates timestamped transcriptions using OpenAI Whisper @@ -71,7 +71,7 @@ VERDAD employs a multi-stage pipeline architecture designed for scalability and **Process:** -- Utilizes Gemini 1.5 Pro LLM for detailed analysis +- Utilizes Gemini (1.5 or 2.5 Pro) LLM for detailed analysis - Performs multi-dimensional content evaluation - Generates comprehensive annotations @@ -299,4 +299,17 @@ VERDAD is a project of Public Data Works, developed in collaboration with the In ## Contact + +## Documentation + +Full documentation is available in the [docs/](docs/index.md) directory. The documentation is organized as follows: + +- **Getting Started:** Introduction and quick start guides +- **Architecture:** System architecture and data lifecycle +- **Pipeline Stages:** Detailed documentation for each stage of the AI pipeline +- **Integration:** API, collaboration, notifications, and database schema +- **Configuration:** Deployment, containerization, prompts, and adapters +- **Maintenance:** Archival and maintenance scripts +- **Testing:** Test suite and coverage + For questions or support, please open an issue on GitHub or contact the maintainers at [contact information]. diff --git a/docs/architecture/data-lifecycle.md b/docs/architecture/data-lifecycle.md new file mode 100644 index 0000000..4a5b73e --- /dev/null +++ b/docs/architecture/data-lifecycle.md @@ -0,0 +1,93 @@ +# Data Lifecycle + +## Data Lifecycle + +The VERDAD platform transforms raw radio broadcasts into actionable, structured data through a sophisticated multi-stage pipeline. This process ensures that high volumes of audio are filtered, transcribed, and analyzed with both linguistic and cultural nuance before being presented for human review. + +### 1. Ingestion and Initial Screening (Stage 1) + +The lifecycle begins with continuous recording and ingestion of radio streams. Raw audio files (typically 5–15 minutes long) are processed to identify potential segments of interest. + +* **Process:** VERDAD uses **Gemini** (1.5 or 2.5 Flash) for rapid screening and **OpenAI Whisper** for initial transcription. +* **Filtering:** High-recall heuristics are applied to ensure suspicious content is flagged even if the initial confidence is low. +* **Output:** Flagged timestamps and preliminary categorization. + +### 2. Audio Extraction and Contextualization (Stage 2) + +Once a segment is flagged, the system isolates it to create a standalone "snippet." + +* **Clipping:** The system extracts the specific audio segment based on Stage 1 timestamps. +* **Context Buffers:** To ensure analysts understand the surrounding conversation, the pipeline automatically includes configurable context windows (defaulting to 90 seconds before and 60 seconds after the flagged segment). +* **Persistence:** These snippets are stored as independent entities in the database and linked to their parent audio files. + +### 3. In-Depth AI Analysis (Stage 3) + +The isolated snippet undergoes a deep-dive analysis using advanced multimodal LLMs. This stage generates the rich metadata used by researchers and journalists. + +The resulting data follows a structured schema (defined via Pydantic models), ensuring consistency across all languages and stations. + +#### Key Data Components: +* **Bilingual Content:** Every snippet includes professional-grade transcriptions and summaries in both Spanish and English. +* **Claim Analysis:** Specific false or misleading claims are extracted as quotes, paired with counter-evidence and a falsity score. +* **Emotional & Rhetorical Context:** The pipeline analyzes vocal cues, intensity, and rhetorical patterns to identify the emotional tone (e.g., fear-mongering, urgency). +* **Political Leaning:** Provides a numeric score (-1.0 to 1.0) and supporting evidence regarding the political orientation of the content. + +**Example Structured Output Structure:** + +```json +{ + "transcription": "...", + "translation": "...", + "title": { "spanish": "...", "english": "..." }, + "claims": [ + { + "quote": "Direct quote of the claim", + "evidence": "Evidence demonstrating falsity", + "score": 85 + } + ], + "emotional_tone": [ + { + "emotion": { "english": "Anger" }, + "intensity": 75, + "evidence": { "vocal_cues": ["shouting", "rapid speech"] } + } + ] +} +``` + +### 4. Human-in-the-Loop Review (Stage 4) + +Data generated by the AI is treated as a "draft" until reviewed. Journalists and researchers use the VERDAD front-end to interact with the data: + +* **Validation:** Reviewers upvote or downvote AI-generated labels. +* **Custom Labeling:** Analysts can add their own labels to track emerging disinformation trends. +* **Discussion:** Integrated commenting (powered by Liveblocks) allows for collaborative investigation of specific snippets. + +### 5. Vectorization and Discovery (Stage 5) + +To support long-term research and trend analysis, the final data is indexed for semantic search. + +* **Embeddings:** The `embedding` flow generates vector representations of analyzed snippets. +* **Similarity Search:** This allows users to find similar disinformation patterns across different radio stations or time periods, even if the specific wording differs. + +### 6. Notification and Export + +The final stage of the lifecycle involves disseminating the findings: +* **Collaboration Sync:** Comments and reactions are synced to a Postgres/Supabase database via webhooks. +* **Alerting:** Real-time notifications are sent via **Slack** and **Email** (Resend) when specific snippets are flagged or mentioned in discussions. +* **Data Access:** Structured data is made available for trustworthy journalists to support fact-checking and counter-narrative campaigns. + +--- + +### Data Status Reference + +As data moves through the lifecycle, the `status` field in the database tracks its progress: + +| Status | Description | +| :--- | :--- | +| `pending` | Newly ingested audio waiting for Stage 1. | +| `processing` | Currently being handled by an active Prefect flow. | +| `completed` | Successfully analyzed and ready for human review. | +| `error` | Processing failed (error message stored in `error_message` column). | +| `reserved` | The record is currently locked by a worker to prevent duplicate processing. | diff --git a/docs/architecture/iterative-refinement.md b/docs/architecture/iterative-refinement.md new file mode 100644 index 0000000..30823d0 --- /dev/null +++ b/docs/architecture/iterative-refinement.md @@ -0,0 +1,33 @@ +# Iterative Refinement and Prompt Versioning + +VERDAD employs a multi-pass, feedback-driven approach to continuously improve disinformation detection and analysis. This process leverages both LLMs and human reviewers to refine heuristics, prompts, and system instructions. + +## Iterative Refinement Process + +1. **User Feedback Collection** + - Users (analysts, journalists) apply labels, upvote/downvote, and add comments to flagged snippets. + - All feedback is stored in the database and associated with the relevant snippet and prompt version. + +2. **Periodic LLM Review** + - An advanced LLM periodically reviews accumulated user feedback and snippet discussions. + - The LLM proposes adjustments to the dynamic heuristics and prompts used in detection and analysis stages. + +3. **Final LLM and Human Review** + - Another LLM (or a human reviewer) evaluates the proposed changes to heuristics and system instructions. + - If approved, a plan for revising the system message is written and, upon human approval, executed. + +4. **Prompt Versioning and Rollback** + - Each prompt version is stored in the database with a unique version ID, associated LLM model, and an explanation of changes. + - A chronological record of all prompt versions is maintained, allowing for easy comparison and rollback if needed. + - Deprecated/archived prompts are retained for auditability. + +## Database Interactions +- **Stage 1:** Insert basic snippet metadata when a snippet is first identified. +- **Stage 2:** Update with detailed analysis, transcriptions, and translations. +- **Prompt Management:** Store current and historical versions of prompts, with version IDs, explanations, and associated LLM models. +- **LLM Review Results:** Store proposed heuristic adjustments, evaluation plans, and track which proposals were implemented or rejected. + +## Benefits +- Ensures the system adapts to new disinformation tactics. +- Maintains transparency and auditability of prompt changes. +- Enables rapid rollback in case of issues with new heuristics or prompts. diff --git a/docs/architecture/system-architecture.md b/docs/architecture/system-architecture.md new file mode 100644 index 0000000..b97c77f --- /dev/null +++ b/docs/architecture/system-architecture.md @@ -0,0 +1,120 @@ +# System Architecture + +## Architectural Overview + +VERDAD is architected as a distributed, multi-stage processing pipeline optimized for analyzing continuous audio streams. The system leverages a decoupled architecture where heavy-duty AI processing is orchestrated by **Prefect**, managed by **Fly.io**, and synchronized through **Supabase (PostgreSQL)**. + +The system is designed to transform raw radio broadcasts into structured, searchable, and actionable disinformation intelligence through five distinct processing stages. + + +## Pipeline Orchestration + +The pipeline is managed using **Prefect**, which handles task scheduling, retries, and state management. The infrastructure is deployed on **Fly.io** using a "Process Group" pattern. + +### Process Groups and Deployment +Instead of running a monolithic service, VERDAD uses environment variables to determine the role of a specific instance. The `FLY_PROCESS_GROUP` variable dictates which pipeline stage a worker handles. + +**Example Configuration (`fly.toml` or Env):** +```bash +# To run a worker dedicated to Stage 1 detection +FLY_PROCESS_GROUP="initial_disinformation_detection" +``` + +The available process groups correspond to the primary stages and administrative tasks: + + +## Data Flow: The 5-Stage Pipeline + +### Stage 1: Initial Detection & Transcription +**Primary Models:** Gemini 1.5 Flash or 2.5 Flash, OpenAI Whisper. + +The system consumes full audio recordings (5-15 minutes). +1. **Transcription:** OpenAI Whisper generates a timestamped transcript. +2. **Screening:** Gemini (1.5 Flash or 2.5 Flash) applies high-recall heuristics to flag segments that potentially contain disinformation. +3. **Output:** A set of candidate timestamps and a preliminary "confidence score" for further investigation. + +**Note:** Gemini 2.5 models offer improved performance and lower costs compared to 1.5 models. + +### Stage 2: Audio Clipping +**Process:** +This stage extracts the specific audio segments flagged in Stage 1. It automatically includes a configurable "context window" (defaulting to 90 seconds before and 60 seconds after the hit) to ensure analysts have sufficient background information. + +### Stage 3: In-Depth Analysis +**Primary Model:** Gemini 1.5 Pro or 2.5 Pro (Multimodal). + +This is the core analytical engine. The system performs a nuanced evaluation of the clip, producing a structured Pydantic-validated object. Newer 2.5 Pro models provide improved analysis quality. + +**Public Data Interface (`Stage3Output`):** +The analysis includes: + +### Stage 4: Analysis Review +**Process:** +A secondary LLM pass (or human-in-the-loop) reviews the findings from Stage 3. It checks for consistency, validates that quotes are accurate, and ensures the reasoning is defensible to professional fact-checkers. + +### Stage 5: Vector Embedding +**Process:** +Finalized analyses are converted into vector embeddings. These are stored in the database to enable similarity searches, helping researchers identify recurring disinformation narratives across different stations and timeframes. + + +## Backend Services & Collaboration + +### Database and Real-time Sync (Supabase) +Supabase serves as the central state machine. + +### Collaboration Layer (Liveblocks) +The human element of VERDAD—journalists and researchers—interacts via a collaborative frontend. + +### Notifications (Resend & Slack) +The system includes an integrated `emailService` and `slackService` to alert researchers of new mentions or critical findings. + + +## Error Handling and Monitoring + +- **Sentry:** Integrated across the Python pipeline and Node.js server for real-time error tracking. +- **Safety Settings:** All Gemini LLM calls utilize custom safety configurations to ensure the model doesn't block content necessary for disinformation analysis. These include: + - `HARM_CATEGORY_HATE_SPEECH`: Required to analyze hate speech in broadcasts + - `HARM_CATEGORY_HARASSMENT`: Allows analysis of harassment content + - `HARM_CATEGORY_DANGEROUS_CONTENT`: Necessary for analyzing dangerous rhetoric + - `HARM_CATEGORY_CIVIC_INTEGRITY`: Critical for election and political integrity content + - `HARM_CATEGORY_SEXUALLY_EXPLICIT`: Allows analysis of explicit content when relevant + +```python +# Internal Safety Logic - All set to BLOCK_NONE +SafetySetting( + category=HarmCategory.HARM_CATEGORY_HATE_SPEECH, + threshold=HarmBlockThreshold.BLOCK_NONE, +) +SafetySetting( + category=HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY, + threshold=HarmBlockThreshold.BLOCK_NONE, +) +# ... and others (see src/processing_pipeline/processing_utils.py) +``` + +## Infrastructure Usage for Developers + +To run a local worker for a specific stage, ensure your `.env` is configured and execute: + +```bash +python src/processing_pipeline/main.py +``` +*Note: The script will automatically detect the `FLY_PROCESS_GROUP` and start the corresponding Prefect deployment.* + +--- + +## User Interface and Feedback Loop + +VERDAD's front-end provides several key screens and user interaction flows: + +- **Feed Screen:** An infinitely scrolling stack of cards, each representing a snippet of suspected disinformation. Users can filter by radio station, state, or label. +- **Individual Snippet Screen:** Focused on a single snippet, with related snippets ranked by similarity. +- **Public View:** Accessible without login, showing transcribed/translated text and audio, but no labels or comments. + +**User Interactions:** +- Review AI-flagged content +- Affirm or add labels +- Add free-form comments +- Upvote existing labels +- Engage in discussions about snippets + +This feedback is integrated into the iterative refinement process, helping to improve detection heuristics and prompt accuracy over time. diff --git a/docs/configuration/disinformation-heuristics.md b/docs/configuration/disinformation-heuristics.md new file mode 100644 index 0000000..93f8f2b --- /dev/null +++ b/docs/configuration/disinformation-heuristics.md @@ -0,0 +1,116 @@ +# Disinformation Detection Heuristics + +VERDAD uses a comprehensive set of heuristics to detect disinformation across a wide range of topics. These heuristics are culturally nuanced, with examples in Spanish and Arabic, and are regularly refined through user and LLM feedback. + +## Categories and Examples + +### 1. Election Integrity and Voting Processes +- **Description:** Disinformation casting doubt on electoral legitimacy. +- **Spanish Examples:** "elección amañada", "fraude electoral" +- **Arabic Examples:** "انتخابات مزورة", "تزوير الأصوات" +- **Heuristics:** Allegations of widespread voter fraud, claims mail-in voting is unsafe, etc. + +### 2. Immigration Policies +- **Description:** Disinformation portraying immigrants as threats. +- **Spanish Examples:** "invasión en la frontera", "extranjeros ilegales" +- **Arabic Examples:** "غزو الحدود", "أجانب غير شرعيين" +- **Heuristics:** Depicting immigrants as invaders, claims they take jobs, etc. + +### 3. COVID-19 and Vaccination +- **Description:** Denying COVID-19 severity, vaccine fear, unproven treatments. +- **Spanish Examples:** "plandemia", "vacuna experimental" +- **Arabic Examples:** "الوباء المخطط", "لقاح تجريبي" +- **Heuristics:** Claims pandemic is a hoax, vaccines unsafe, etc. + +### 4. Climate Change and Environmental Policies +- **Description:** Denying human impact on climate change. +- **Spanish Examples:** "engaño climático" +- **Arabic Examples:** "خدعة المناخ" +- **Heuristics:** Denial of scientific consensus, etc. + +### 5. LGBTQ+ Rights and Gender Issues +- **Description:** Misrepresenting LGBTQ+ issues as threats. +- **Spanish Examples:** "ideología de género" +- **Arabic Examples:** "أيديولوجية الجندر" +- **Heuristics:** Claims LGBTQ+ rights harm family, etc. + +### 6. Abortion and Reproductive Rights +- **Description:** Framing abortion in extreme terms. +- **Spanish Examples:** "aborto es asesinato" +- **Arabic Examples:** "الإجهاض قتل" +- **Heuristics:** Oversimplifying debates, emotional language, etc. + +### 7. Economic Policies and Inflation +- **Description:** Predicting economic collapse due to policies. +- **Spanish Examples:** "hiperinflación inminente" +- **Arabic Examples:** "تضخم مفرط وشيك" +- **Heuristics:** Claims of disaster without evidence, etc. + +### 8. Foreign Policy and International Relations +- **Description:** Distrust of international cooperation. +- **Spanish Examples:** "agenda globalista" +- **Arabic Examples:** "أجندة عالمية" +- **Heuristics:** Allegations of foreign interference, etc. + +### 9. Media and Tech Manipulation +- **Description:** Asserting media/tech suppress truth. +- **Spanish Examples:** "noticias falsas" +- **Arabic Examples:** "أخبار مزيفة" +- **Heuristics:** Claims of censorship, etc. + +### 10. Public Safety and Law Enforcement +- **Description:** Exaggerating crime, opposing reforms. +- **Spanish Examples:** "ola de crimen" +- **Arabic Examples:** "موجة جريمة" +- **Heuristics:** Overstating crime, etc. + +### 11. Healthcare Reform +- **Description:** Portraying reforms as harmful. +- **Spanish Examples:** "medicina socializada" +- **Arabic Examples:** "الطب الاجتماعي" +- **Heuristics:** Claims reforms lower quality, etc. + +### 12. Culture Wars and Social Issues +- **Description:** Framing progress as attacks on values. +- **Spanish Examples:** "valores tradicionales bajo ataque" +- **Arabic Examples:** "القيم التقليدية تحت الهجوم" +- **Heuristics:** Claims societal changes erode foundations, etc. + +### 13. Geopolitical Issues +- **Ukraine-Russia:** "expansión de la OTAN", "توسع الناتو" +- **Israel-Palestine:** "apartheid israelí", "الفصل العنصري الإسرائيلي" +- **China-US:** "amenaza china", "التهديد الصيني" + +### 14. Conspiracy Theories +- **Description:** Unfounded claims about secret plots. +- **Spanish Examples:** "estado profundo" +- **Arabic Examples:** "الدولة العميقة" +- **Heuristics:** Promoting theories without evidence, etc. + +### 15. Education and Academic Freedom +- **Description:** Alleging biased ideologies in education. +- **Spanish Examples:** "adoctrinamiento en las escuelas" +- **Arabic Examples:** "تلقين في المدارس" +- **Heuristics:** Claims of indoctrination, etc. + +### 16. Technology and Privacy +- **Description:** Spreading fear about tech/privacy. +- **Spanish Examples:** "riesgos de salud del 5G" +- **Arabic Examples:** "مخاطر صحية للـ5G" +- **Heuristics:** Allegations of harm without evidence, etc. + +### 17. Gun Rights and Control +- **Description:** Opposing gun regulation, fear of overreach. +- **Spanish Examples:** "confiscación de armas" +- **Arabic Examples:** "مصادرة الأسلحة" +- **Heuristics:** Claims regulations infringe rights, etc. + +### 18. Political Figures and Movements +- **Description:** Extreme portrayals, malicious intent. +- **Spanish Examples:** "progresistas radicales" +- **Arabic Examples:** "التقدميون المتطرفون" +- **Heuristics:** Labeling with extreme terms, etc. + +--- + +For full details and examples, see the project plan or contact the maintainers. diff --git a/docs/configuration/docker-containerization.md b/docs/configuration/docker-containerization.md new file mode 100644 index 0000000..15859f8 --- /dev/null +++ b/docs/configuration/docker-containerization.md @@ -0,0 +1,75 @@ +# Docker & Containerization + +## Docker & Containerization + +VERDAD utilizes a containerized architecture to manage its multi-stage AI pipeline and auxiliary services. The system is designed to run primarily on **Fly.io** using a polymorphic Docker approach where a single image can take on different roles (workers) based on environment configuration. + +### Processing Pipeline Workers + +The core of the VERDAD pipeline is a Python-based worker system orchestrated by **Prefect**. The specific stage or task a container performs is determined by the `FLY_PROCESS_GROUP` environment variable. + +#### Configuration via `FLY_PROCESS_GROUP` + +When deploying the pipeline image, set the `FLY_PROCESS_GROUP` variable to one of the following values to initialize the corresponding service: + +| Process Group | Purpose | +| :--- | :--- | +| `initial_disinformation_detection` | Runs Stage 1: Detects potential disinformation in raw audio. | +| `audio_clipping` | Runs Stage 2: Extracts specific audio segments for analysis. | +| `in_depth_analysis` | Runs Stage 3: Performs detailed AI analysis on audio snippets. | +| `analysis_review` | Runs Stage 4: Refines and reviews previous analysis steps. | +| `embedding` | Runs Stage 5: Generates vector embeddings for semantic search. | +| `regenerate_timestamped_transcript` | Maintenance: Re-runs transcription for existing records. | + +#### Running a Worker Locally + +To run a specific pipeline stage locally using Docker, use the following pattern: + +```bash +docker run -e FLY_PROCESS_GROUP=initial_disinformation_detection \ + -e GOOGLE_GEMINI_KEY=your_key \ + -e SUPABASE_URL=your_url \ + -e SUPABASE_KEY=your_key \ + verdad-pipeline-image +``` + +### Backend Server + +The backend (located in the `/server` directory) is a Node.js/TypeScript Express application. It handles authentication for Liveblocks, manages webhooks, and synchronizes comment data with Supabase. + +#### API Endpoints +The containerized server exposes several critical paths: +- `POST /api/liveblocks-auth`: Handles user authentication for collaborative features. +- `POST /api/webhooks/liveblocks`: Listens for real-time interaction events. +- `GET /`: Health check endpoint. + +### Environment Configuration + +The containers require a specific set of environment variables to function correctly across the pipeline and server. + +#### Pipeline Variables (Python) +- `GOOGLE_GEMINI_KEY`: API key for Google Gemini multimodal LLM. +- `SUPABASE_URL` / `SUPABASE_KEY`: Connection details for the Postgres database. +- `SENTRY_DSN`: (Optional) For error tracking and monitoring. +- `ENABLE_PREFECT_DECORATOR`: Set to `true` (default) to enable Prefect flow orchestration. + +#### Server Variables (Node.js) +- `LIVEBLOCKS_SECRET_KEY`: Integration key for real-time collaboration. +- `SUPABASE_SERVICE_ROLE_KEY`: Required for administrative database access (e.g., syncing comments). +- `RESEND_API_KEY`: API key for sending notification emails. +- `SLACK_NOTIFICATION_EMAIL`: Destination for forwarding system notifications to Slack. + +### Orchestration and Deployment + +VERDAD is architected to leverage **Prefect** for workflow management. Within the containers, the system uses `prefect.serve()` to create long-running deployments that listen for scheduled tasks or manual triggers from the Prefect Cloud or a local server instance. + +```python +# Internal logic used by the container to serve workers +deployment = initial_disinformation_detection.to_deployment( + name="Stage 1: Initial Disinformation Detection", + concurrency_limit=100 +) +serve(deployment) +``` + +For production deployments, each `FLY_PROCESS_GROUP` should be scaled independently based on the volume of incoming audio data. Stage 1 (Detection) and Stage 2 (Clipping) typically require higher concurrency limits than Stage 5 (Embedding). diff --git a/docs/configuration/flyio-configuration.md b/docs/configuration/flyio-configuration.md new file mode 100644 index 0000000..ba61f38 --- /dev/null +++ b/docs/configuration/flyio-configuration.md @@ -0,0 +1,97 @@ +# Fly.io Configuration + +## Fly.io Deployment Architecture + +VERDAD is designed to run as a distributed system on Fly.io. The architecture leverages Fly.io **Processes** to run multiple specialized workers from a single codebase. This allows for independent scaling of different stages in the AI pipeline (e.g., scaling Stage 1 detection separately from Stage 3 in-depth analysis). + +### Process Groups + +The entry point for the processing pipeline (`src/processing_pipeline/main.py`) uses the `FLY_PROCESS_GROUP` environment variable to determine which Prefect deployment to initialize. + +When configuring your `fly.toml`, define the following processes to match the pipeline stages: + +| Process Group Name | Description | +| :--- | :--- | +| `initial_disinformation_detection` | Runs Stage 1: Initial screening using Gemini (1.5 or 2.5 Flash). | +| `audio_clipping` | Runs Stage 2: Extracts segments from raw audio files. | +| `in_depth_analysis` | Runs Stage 3: Detailed multi-lingual analysis and categorization. | +| `analysis_review` | Runs Stage 4: Final AI review and validation. | +| `embedding` | Runs Stage 5: Generates vector embeddings for search and similarity. | +| `regenerate_timestamped_transcript`| Utility process for re-processing transcripts. | + +--- + +## Configuration (`fly.toml`) + +To manage the distributed pipeline, your `fly.toml` should define a `[processes]` section. This allows you to assign specific resources and scaling rules to each stage of the VERDAD pipeline. + +### Example Process Configuration + +```toml +[processes] + stage_1 = "python src/processing_pipeline/main.py" + stage_2 = "python src/processing_pipeline/main.py" + stage_3 = "python src/processing_pipeline/main.py" + +[env] + # Default environment variables + ENABLE_PREFECT_DECORATOR = "true" + +[[services]] + # Configuration for the primary API or dashboard if applicable + processes = ["stage_1"] # Assign specific services to processes +``` + +To ensure the correct stage runs in the correct process, you must set the `FLY_PROCESS_GROUP` for each specific machine or process group: + +```bash +# Example: Setting the process group for a worker via Fly CLI +fly machine update --metadata FLY_PROCESS_GROUP=initial_disinformation_detection +``` + +--- + +## Required Environment Variables + +The following secrets and environment variables must be configured in your Fly.io application for the pipeline to function: + +### AI & API Keys +- `GOOGLE_GEMINI_KEY`: API key for Google Gemini multimodal models. +- `OPENAI_API_KEY`: API key for Whisper speech-to-text processing. +- `LIVEBLOCKS_SECRET_KEY`: Used for collaborative features and comment syncing. +- `RESEND_API_KEY`: Required for sending notification emails. + +### Database & Storage +- `SUPABASE_URL`: Your Supabase project URL. +- `SUPABASE_KEY`: The public/anon key (if required by client). +- `SUPABASE_SERVICE_ROLE_KEY`: **Critical** for backend operations and bypassing RLS in the pipeline. + +### Monitoring & Orchestration +- `PREFECT_API_URL`: Connection string for your Prefect Cloud or self-hosted server. +- `PREFECT_API_KEY`: Authentication key for Prefect. +- `SENTRY_DSN`: (Optional) Error tracking for the Python pipeline. + +--- + +## Deployment Commands + +### Setting Secrets +Before your first deployment, set the required secrets: +```bash +fly secrets set GOOGLE_GEMINI_KEY=... SUPABASE_SERVICE_ROLE_KEY=... PREFECT_API_KEY=... +``` + +### Deploying Specific Groups +You can scale or deploy specific process groups to different regions to be closer to the radio station stream sources: + +```bash +# Deploy the configuration +fly deploy + +# Scale the Stage 1 workers for higher concurrency +fly scale count stage_1=3 +fly scale count stage_3=2 +``` + +### Regional Considerations +Because VERDAD monitors live radio broadcasts, it is recommended to deploy `initial_disinformation_detection` workers in Fly.io regions physically closest to the target radio station's streaming origin to minimize latency and connection interruptions. diff --git a/docs/configuration/heuristics-output-schemas.md b/docs/configuration/heuristics-output-schemas.md new file mode 100644 index 0000000..743903c --- /dev/null +++ b/docs/configuration/heuristics-output-schemas.md @@ -0,0 +1,103 @@ +# Heuristics & Output Schemas + +To ensure consistency and high-quality data for researchers and journalists, VERDAD uses structured output schemas (via Pydantic) to govern how the AI pipeline analyzes radio snippets. This approach forces the LLMs to provide specific evidence, bilingual translations, and measurable confidence scores. + +## Overview of Heuristics + +The VERDAD pipeline operates on a "High Recall, High Precision" heuristic model: + +1. **Stage 1 (High Recall):** Uses Gemini (1.5 or 2.5 Flash) to rapidly screen 5–15 minute audio blocks. It identifies potential disinformation using simplified heuristics to ensure no suspect content is missed. +2. **Stage 3 (High Precision):** Uses more advanced models to perform a deep-dive analysis on the extracted clips. It applies rigorous validation checks (e.g., "are specific claims quoted?") before the data is finalized. + +## Stage 3: In-Depth Analysis Schema + +The primary interface for structured data is the `Stage3Output` model. This schema ensures every flagged snippet contains the context, evidence, and emotional metadata required for fact-checking. + +### Core Content & Metadata +Every analysis includes full transcriptions and objective summaries in both Spanish and English. + +| Field | Type | Description | +| :--- | :--- | :--- | +| `transcription` | `str` | Complete transcription of the audio clip in the original language. | +| `translation` | `str` | Full English translation of the transcription. | +| `title` | `Title` | Bilingual title (Spanish/English) for the snippet. | +| `summary` | `Summary` | Objective bilingual summary of the content. | +| `language` | `Language` | Captures primary language, dialect, and register (formal, colloquial, etc.). | + +### Analysis & Claim Validation +VERDAD breaks down disinformation into specific "claims" that are then validated against a checklist. + +```python +class Claim(BaseModel): + quote: str # Direct quote of the false/misleading claim + evidence: str # Evidence demonstrating why the claim is false + score: int # Confidence score for this specific claim (0-100) +``` + +The system employs a **Validation Checklist** heuristic to ensure the LLM's reasoning is defensible: +* `specific_claims_quoted`: Ensures the AI isn't speaking in generalities. +* `evidence_provided`: Confirms a factual rebuttal is present. +* `defensible_to_factcheckers`: A meta-check on the logical consistency of the analysis. + +### Confidence Scoring +The system provides granular confidence scores rather than a simple true/false binary. + +* **Overall Score:** A 0–100 rating of the system's confidence in the analysis. +* **Category Scores:** Each disinformation category (e.g., "Medical," "Election") receives its own confidence score. +* **Score Adjustments:** If an initial score is modified during the pipeline, the system stores the `initial_score`, `final_score`, and the `adjustment_reason`. + +### Emotional & Political Tone +To help researchers understand the *delivery* of disinformation, the schema captures nuance beyond just text. + +* **Emotional Tone:** Analyzes intensity (0–100) and provides `EmotionEvidence` including vocal cues (pitch, speed) and specific patterns. +* **Political Leaning:** Uses a float scale from `-1.0` (Left) to `1.0` (Right), backed by specific policy positions, rhetoric used, and sources cited. + +--- + +## Usage Example: Accessing Structured Data + +When interacting with the Stage 3 output, the data is returned as a JSON object conforming to the `Stage3Output` Pydantic model. + +```json +{ + "title": { + "spanish": "Desinformación sobre el voto por correo", + "english": "Misinformation regarding mail-in voting" + }, + "confidence_scores": { + "overall": 92, + "categories": [ + { + "category": "Election Integrity", + "score": 95 + } + ] + }, + "political_leaning": { + "score": 0.8, + "evidence": { + "rhetoric": ["fraude masivo", "votos ilegales"], + "policy_positions": ["Restriction of mail-in ballots"] + } + }, + "emotional_tone": [ + { + "emotion": { "spanish": "Alarma", "english": "Alarm" }, + "intensity": 85, + "evidence": { + "vocal_cues": ["high pitch", "rapid speaking rate"], + "phrases": ["¡Esto es una emergencia!"] + } + } + ] +} +``` + +## Safety Heuristics +During the processing of content that may contain sensitive or harmful speech, the pipeline uses specific `SafetySettings` to ensure the AI can analyze "Hate Speech" or "Dangerous Content" without being blocked by default provider filters, allowing researchers to see the raw data while maintaining a record of the content's harm category. + +| Category | Threshold | +| :--- | :--- | +| `HARM_CATEGORY_HATE_SPEECH` | `BLOCK_NONE` (Logged for research) | +| `HARM_CATEGORY_DANGEROUS_CONTENT` | `BLOCK_NONE` | +| `HARM_CATEGORY_CIVIC_INTEGRITY` | `BLOCK_NONE` | diff --git a/docs/configuration/prompt-management-advanced.md b/docs/configuration/prompt-management-advanced.md new file mode 100644 index 0000000..630872a --- /dev/null +++ b/docs/configuration/prompt-management-advanced.md @@ -0,0 +1,121 @@ +# Prompt Management and Versioning + +VERDAD supports versioning and dynamic management of prompts, enabling iterative refinement through user feedback. + +## Prompt Import Script + +The `src/scripts/import_prompts_to_db.py` script manages prompt versions in the database. + +### Usage + +```bash +# Import prompts with version numbering +python src/scripts/import_prompts_to_db.py import --version 1.0.0 --description "Initial import" + +# Import without making version active (for review) +python src/scripts/import_prompts_to_db.py import --version 1.1.0 --description "Updated Stage 3" --no-active + +# Preview changes without committing +python src/scripts/import_prompts_to_db.py import --version 1.0.0 --dry-run + +# List existing prompt versions +python src/scripts/import_prompts_to_db.py list +``` + +### Version Format + +Versions must follow semantic versioning: `MAJOR.MINOR.PATCH` (e.g., `1.0.0`) + +### Supported Prompt Stages + +The script manages prompts for: + +| Stage | Components | +| :--- | :--- | +| **Stage 1** | System instruction, Detection prompt, Output schema | +| **Stage 3** | System instruction, Analysis prompt, Output schema | +| **Stage 4** | System instruction, Review prompt, Output schema | +| **Gemini Timestamped Transcription** | Generation prompt (alternative transcription method) | + +### Database Schema + +Each prompt version is stored with: +- **Version ID:** Unique identifier and version number +- **Stage:** Which pipeline stage (stage_1, stage_3, stage_4, etc.) +- **Components:** System instruction, user prompt, output schema +- **Associated Model:** Which LLM model it's designed for +- **Description:** Explanation of changes in this version +- **Active Status:** Whether this version is currently in use +- **Created At:** Timestamp of creation +- **Updated By:** User who imported the version + +### Workflow + +1. **Edit Prompts Locally:** Modify prompt files in the `prompts/` directory +2. **Validate Changes:** Test prompts locally with `ENABLE_PREFECT_DECORATOR=false` +3. **Dry Run:** Preview database changes with `--dry-run` flag +4. **Import as Inactive:** Import with `--no-active` for team review +5. **Activate Version:** After approval, set as active (manually via database or script) +6. **Monitor Performance:** Track metrics for new version +7. **Rollback if Needed:** Revert to previous version if issues arise + +## Iterative Refinement Integration + +User feedback collected through the front-end UI informs prompt updates: + +1. **Feedback Collection:** Users upvote/downvote labels and add comments +2. **Analysis:** LLM reviews feedback and proposes heuristic adjustments +3. **Prompt Update:** System instructions and heuristics are refined +4. **Testing:** New prompts tested in staging environment +5. **Deployment:** Approved version activated and deployed to production + +## Safety Settings + +All LLM calls use custom safety settings configured in `src/processing_pipeline/processing_utils.py`: + +```python +SafetySetting( + category=HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, + threshold=HarmBlockThreshold.BLOCK_NONE, +) +SafetySetting( + category=HarmCategory.HARM_CATEGORY_HATE_SPEECH, + threshold=HarmBlockThreshold.BLOCK_NONE, +) +SafetySetting( + category=HarmCategory.HARM_CATEGORY_HARASSMENT, + threshold=HarmBlockThreshold.BLOCK_NONE, +) +SafetySetting( + category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, + threshold=HarmBlockThreshold.BLOCK_NONE, +) +SafetySetting( + category=HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY, + threshold=HarmBlockThreshold.BLOCK_NONE, +) +``` + +**Note:** `CIVIC_INTEGRITY` is critical for analyzing election, voting, and political integrity content without the model refusing analysis due to safety filters. + +## LLM Models + +VERDAD supports multiple Gemini models: + +| Model | Use Case | +| :--- | :--- | +| `gemini-1.5-flash` | Fast initial detection (Stage 1) | +| `gemini-1.5-pro-002` | Detailed analysis (Stage 3) | +| `gemini-2.5-flash` | Fast processing (newer alternative to 1.5 Flash) | +| `gemini-2.5-pro` | Advanced analysis (newer alternative to 1.5 Pro) | +| `gemini-flash-latest` | Latest Flash model | +| `gemini-flash-lite-latest` | Latest Lite model | + +Model selection is configured in prompts via the `associated_model` field. + +## Transcript Generation + +Two transcription methods are available: + +1. **OpenAI Whisper:** Standard ASR for general use +2. **Gemini Timestamped Transcription:** Alternative method using Gemini with custom prompt for more accurate timestamp alignment diff --git a/docs/configuration/prompt-management.md b/docs/configuration/prompt-management.md new file mode 100644 index 0000000..f898df8 --- /dev/null +++ b/docs/configuration/prompt-management.md @@ -0,0 +1,70 @@ +# Prompt Management + +VERDAD utilizes a versioned prompt management system to ensure consistency and traceability across its multi-stage AI pipeline. Prompts are treated as managed assets, allowing researchers and developers to iterate on detection heuristics and analysis criteria without modifying the core application code. + +**See also:** [Prompt Management Advanced Guide](prompt-management-advanced.md) for detailed versioning, script usage, and iterative refinement workflows. + +## Core Concepts + +Prompt management in VERDAD is built around the following principles: + +* **Stage-Specific Context:** Each pipeline stage (Initial Detection, In-depth Analysis, Review) has its own dedicated prompt template. +* **Versioning:** Prompts are stored in the `prompt_versions` table, allowing for side-by-side testing and historical auditing. +* **Active Selection:** Only one prompt version is marked as `is_active` for a specific stage at any given time, providing a "single source of truth" for the production pipeline. + +## Prompt Retrieval Interface + +The pipeline interacts with prompts through the `SupabaseClient`. The system typically retrieves the currently active prompt for a specific stage. + +### Retrieving the Active Prompt + +To fetch the prompt currently used in production for a specific stage: + +```python +from processing_pipeline.supabase_utils import SupabaseClient +from processing_pipeline.constants import PromptStage + +client = SupabaseClient(url, key) + +# Fetch the active prompt for Stage 3 (In-depth Analysis) +active_prompt = client.get_active_prompt(PromptStage.STAGE_3) + +print(f"Using Version ID: {active_prompt['id']}") +print(f"System Instructions: {active_prompt['system_instructions']}") +``` + +### Retrieving a Specific Version + +If a specific historical analysis needs to be re-run using the original prompt: + +```python +# Fetch a specific prompt version by its UUID +specific_prompt = client.get_prompt_by_id("3b39f536-7466-44da-9772-b10dcf72c6be") +``` + +## Prompt Stages + +Prompts are categorized by `PromptStage` to align with the pipeline architecture: + +| Stage | Description | Key Focus | +| :--- | :--- | :--- | +| `STAGE_1` | Initial Detection | High-recall screening of raw audio/transcripts to identify potential disinformation. | +| `STAGE_3` | In-depth Analysis | Complex analysis involving claim extraction, political leaning, and emotional tone. | +| `STAGE_4` | Analysis Review | Validation and refinement of the analysis generated in Stage 3. | + +## Data Schema for Prompts + +Users managing prompts via the database should adhere to the following structure within the `prompt_versions` table: + +* **`id`** (UUID): Unique identifier for the version. +* **`stage`** (String): The pipeline stage this prompt belongs to (e.g., `stage_1`, `stage_3`). +* **`system_instructions`** (Text): The core instructions provided to the LLM defining its persona and task. +* **`user_template`** (Text): The template for the user message, often containing placeholders for dynamic data like `{{transcription}}` or `{{metadata}}`. +* **`is_active`** (Boolean): Boolean flag. Setting this to `true` for a stage will automatically deprecate older active prompts for that stage in the execution flow. +* **`model_params`** (JSONB): Optional configuration for temperature, top-p, and max output tokens specific to this prompt version. + +## Best Practices for Prompt Iteration + +1. **Safety Settings:** All prompts are executed with safety filters disabled (configured in `get_safety_settings()`) to ensure the model can analyze harmful disinformation without being blocked by provider-level content filters. +2. **Structured Output:** For Stage 3 and Stage 4, prompts must explicitly instruct the model to return valid JSON that conforms to the Pydantic models defined in `src/processing_pipeline/stage_3_models.py`. +3. **Bilingual Requirements:** Since VERDAD focuses on Spanish and Arabic broadcasts, system instructions should specify that titles, summaries, and explanations must be generated in both the original language and English. diff --git a/docs/configuration/radio-recording-system.md b/docs/configuration/radio-recording-system.md new file mode 100644 index 0000000..984c966 --- /dev/null +++ b/docs/configuration/radio-recording-system.md @@ -0,0 +1,125 @@ +# Radio Recording System + +VERDAD supports two recording modes for capturing radio broadcasts: + +## 1. Generic Recording System (Recommended) + +The generic recording system uses Selenium with ChromeDriver to capture audio from web-based radio streams via browser automation. + +### Architecture + +- **Browser Automation:** Selenium with ChromeDriver in headless mode +- **Virtual Audio:** PulseAudio for capturing browser audio +- **Storage:** Cloudflare R2 (S3-compatible) for audio files +- **Metadata:** Supabase PostgreSQL for recording information + +### Supported Radio Stations + +Radio stations are implemented as adapter classes inheriting from `RadioStation` base class: + +- **Khot** (KHOT - 105.9 FM, Arizona) +- **Kisf** (KISF - 99.9 FM, Arizona) +- **Krgt** (KRGT - 98.3 FM, Texas) +- **Wado** (WADO - 1280 AM, New York) +- **Waqi** (WAQI - 1600 AM, California) +- **Wkaq** (WKAQ - 580 AM, Puerto Rico) + +### How It Works + +1. **PulseAudio Setup:** Creates a virtual sink and source for capturing audio from the browser +2. **Browser Launch:** Starts a headless Chrome instance +3. **Navigation:** Loads the radio station webpage +4. **Play Button Detection:** Waits for and clicks the play button using CSS selectors +5. **Audio Capture:** Records audio from the virtual source using FFmpeg +6. **Upload:** Stores the MP3 file to R2 and metadata to Supabase + +### Adding a New Radio Station + +Create a new file in `src/radiostations/` (e.g., `mynewstation.py`): + +```python +from radiostations.base import RadioStation + +class MyNewStation(RadioStation): + code = "STATION_CALL_LETTERS" + state = "State Name" + name = "Station Display Name" + + def __init__(self): + super().__init__( + url="https://example.com/stream", + sink_name="virtual_speaker_mynewstation", + source_name="virtual_mic_mynewstation", + play_button_selector="button.play-button", # CSS selector for play button + video_element_selector="video#player", # CSS selector for video/audio element + ) +``` + +Then import it in `src/generic_recording.py` and add to the station list. + +### Environment Variables + +```bash +# R2 Storage (Cloudflare) +R2_ENDPOINT_URL=https://your-account.r2.cloudflarestorage.com +R2_ACCESS_KEY_ID=your_access_key +R2_SECRET_ACCESS_KEY=your_secret_key +R2_BUCKET_NAME=your_bucket + +# Supabase +SUPABASE_URL=your_project_url +SUPABASE_KEY=your_service_role_key + +# Chrome/Chromium +CHROME_PATH=/path/to/chrome # Optional, auto-detected by default +``` + +### Running Generic Recording + +```bash +export FLY_PROCESS_GROUP=generic_recording +python src/generic_recording.py +``` + +## 2. Direct URL Recording + +For non-web radio streams (direct HTTP/RTSP URLs), use the standard recording system: + +```bash +python src/recording.py +``` + +This uses FFmpeg directly to capture audio from a URL without browser automation. + +### Configuration + +Stations are configured in the database via Supabase API. Each station requires: +- URL (HTTP/RTSP stream link) +- Name and call letters +- State/location +- Duration and bitrate settings + +## Key Components + +### RadioStation Base Class + +Located in `src/radiostations/base.py`, provides: +- Virtual audio setup/teardown via PulseAudio +- Browser management with Selenium +- Audio capture coordination +- Metadata collection + +### PulseAudio Integration + +The system creates temporary virtual audio devices: +- **Null Sink:** Captures browser output +- **Virtual Source:** Provides audio to FFmpeg + +Automatically handles cleanup on shutdown. + +### Error Handling + +- Automatic retry with exponential backoff +- Browser crash recovery +- Audio stream validation +- Sentry integration for monitoring diff --git a/docs/configuration/radio-station-adapters.md b/docs/configuration/radio-station-adapters.md new file mode 100644 index 0000000..ad83472 --- /dev/null +++ b/docs/configuration/radio-station-adapters.md @@ -0,0 +1,86 @@ +# Radio Station Adapters + +Radio station adapters are modular components for capturing audio from specific radio stations via web-based streams. + +**See also:** [Radio Recording System](radio-recording-system.md) for comprehensive documentation on radio station adapters, including how to add new stations. + +Radio station adapters are the entry point of the VERDAD pipeline. They provide the necessary configuration and connection logic to capture live audio streams from various radio stations, primarily focusing on Spanish-language broadcasts across the United States. + +## Overview + +The system uses a registry-based approach to manage station adapters. Each adapter defines the stream source, geographic metadata, and identification codes used throughout the processing pipeline (transcription, analysis, and database storage). + +The core of the adapter system is located in `src/utils.py`, which provides the `fetch_radio_stations()` function used by the recording orchestration layer. + +## Station Configuration + +Each station is defined as a structured dictionary. This metadata ensures that every audio clip processed by the AI stages is correctly attributed to its source station and region. + +### Metadata Schema + +| Field | Type | Description | +| :--- | :--- | :--- | +| `name` | `string` | The common name or branding of the station (e.g., "El Gallo"). | +| `code` | `string` | The unique identifier, usually combining call signs and frequency. | +| `url` | `string` | The direct URL to the live AAC or MP3 audio stream. | +| `state` | `string` | The US state or region where the station is based. | + +### Usage Example + +To retrieve the list of active stations for the recording pipeline: + +```python +from utils import fetch_radio_stations + +stations = fetch_radio_stations() + +for station in stations: + print(f"Recording {station['name']} from {station['url']}") +``` + +## Adding a New Station + +To add a new station to the VERDAD monitoring system, append a new configuration object to the list in `src/utils.py`. + +```python +{ + "code": "CALL-FM - 100.1 FM", + "url": "https://stream-url.com/live.aac", + "state": "California", + "name": "Radio Example", +} +``` + +**Requirements for New Streams:** +* **Direct Access:** The URL must be a direct link to an audio stream (AAC, MP3, or similar), not a link to a web player. +* **Consistency:** The `code` must be unique to prevent collisions in the database. + +## Recording Orchestration + +The recording pipeline uses these adapters to initiate continuous audio capture. The process is handled by Prefect and typically follows this lifecycle: + +1. **Station Discovery:** The recording worker calls `fetch_radio_stations()`. +2. **Stream Connection:** The system connects to the provided `url`. +3. **Audio Chunking:** The live stream is captured and divided into manageable files (typically 5–15 minutes). +4. **Metadata Attachment:** The station's `name`, `code`, and `state` are packaged with the audio file. +5. **Database Registration:** The `SupabaseClient.insert_audio_file()` method (found in `src/processing_pipeline/supabase_utils.py`) creates a record in the `audio_files` table, marking it as ready for **Stage 1: Initial Disinformation Detection**. + +## Interface for Stage 1 + +When audio files are registered, they carry the adapter's metadata. This allows the Gemini LLM and Whisper ASR models to receive cultural and geographic context, which is critical for nuanced disinformation analysis. + +```python +# Example of metadata passed to the AI pipeline +metadata = { + "radio_station_name": "Radio Centro", + "radio_station_code": "WLCH - 91.3 FM", + "location_state": "Pennsylvania" +} +``` + +## Internal Recording Class + +While the individual station configurations are public, the underlying recording mechanism is an internal utility that handles: +* Network resilience (automatic reconnection if a stream drops). +* Buffer management to prevent data loss during chunk transitions. +* File formatting and validation before storage. diff --git a/docs/configuration/recording-workers.md b/docs/configuration/recording-workers.md new file mode 100644 index 0000000..6e30862 --- /dev/null +++ b/docs/configuration/recording-workers.md @@ -0,0 +1,92 @@ +# Recording Workers + +The recording infrastructure in VERDAD is designed for 24/7 continuous monitoring of live radio streams. It utilizes Prefect for orchestration and Fly.io for scalable deployment, allowing for both broad monitoring and dedicated, station-specific resources. + +**See also:** [Radio Recording System](radio-recording-system.md) for detailed documentation on generic recording with browser automation and direct URL recording. + +## Overview + +Recording workers are responsible for connecting to remote audio streams (AAC/MP3), capturing the audio data, and segmenting it into manageable files (typically 5–15 minutes) for the AI analysis pipeline. + +The system distinguishes between two types of worker configurations: +1. **Generic Workers**: Handle pipeline tasks (detection, transcription, analysis) that are not tied to a specific station. +2. **Station-Specific Workers**: Dedicated processes focused on maintaining a stable connection to a single high-priority radio stream. + +## Station Configuration + +Radio stations are configured in the system via the `fetch_radio_stations` utility. To add a new station to the monitoring list, the station must be defined with its source URL and metadata. + +### Station Schema + +Each station is defined as a dictionary within the configuration list: + +| Field | Type | Description | +| :--- | :--- | :--- | +| `name` | string | The human-readable name of the station (e.g., "Radio Centro"). | +| `code` | string | A unique identifier, typically combining call sign and frequency. | +| `url` | string | The direct path to the live audio stream (AAC, MP3, or stream relay). | +| `state` | string | The geographic region or state where the station is based. | + +**Example Configuration:** + +```python +{ + "code": "WLEL - 94.3 FM", + "url": "https://securenetg.com/radio/8090/radio.aac", + "state": "Georgia", + "name": "El Gallo", +} +``` + +## Running Recording Workers + +Workers are deployed using Prefect's `serve` functionality. This allows the system to manage concurrency and retries automatically. + +### Deployment via Process Groups + +VERDAD uses the `FLY_PROCESS_GROUP` environment variable to determine which worker type an instance should initialize. This is managed in the entry point of the processing pipeline. + +To start a specific worker, the environment must be configured with the appropriate group name. While analysis workers handle Stages 1–5, recording workers focus on the ingestion of the audio files that appear in the `audio_files` table. + +### Local Execution + +To run a worker locally for testing or a specific station, ensure your environment variables (Supabase and Gemini keys) are set, then execute the pipeline entry point: + +```bash +# Example: Starting an analysis worker +export FLY_PROCESS_GROUP="initial_disinformation_detection" +python src/processing_pipeline/main.py +``` + +## Database Integration + +When a recording worker successfully captures a segment, it interfaces with the `SupabaseClient` to register the file. This triggers the rest of the AI pipeline. + +### `insert_audio_file` +Internal workers use this method to log the completion of a recording segment. + +**Input Parameters:** +- `radio_station_name`: Name from configuration. +- `radio_station_code`: Unique station code. +- `location_state`: Geographic metadata. +- `recorded_at`: ISO timestamp of the recording start. +- `recording_day_of_week`: Day name (e.g., "Monday"). +- `file_path`: Storage path (e.g., S3 or Supabase Storage). +- `file_size`: Size in bytes. + +## Monitoring and Maintenance + +### Health Checks +The recording workers report their status to Prefect. If a stream connection drops, Prefect’s retry logic (configured via `optional_task` decorators) will attempt to reconnect. + +### Error Handling +If a worker encounters a persistent issue (e.g., a dead stream URL), it updates the status of the attempt in the database. + +- **Status Updates**: Workers use `set_audio_file_status` to mark segments as `processing`, `completed`, or `failed`. +- **Sentry Integration**: All workers are wrapped with Sentry SDK initialization to capture runtime exceptions and stream interruptions in real-time. + +```python +# Sentry is initialized automatically on worker startup +import sentry_sdk +sentry_sdk.init(dsn=os.getenv("SENTRY_DSN")) +``` diff --git a/docs/getting-started/introduction.md b/docs/getting-started/introduction.md new file mode 100644 index 0000000..1a1c3d0 --- /dev/null +++ b/docs/getting-started/introduction.md @@ -0,0 +1,81 @@ +# Introduction + +VERDAD is an open-source platform engineered to identify, analyze, and track potential disinformation within radio broadcasts. While social media platforms often receive the bulk of content moderation attention, radio remains a significant, yet under-scrutinized, vector for misinformation—particularly within immigrant and non-English speaking communities. + +The platform provides journalists, researchers, and fact-checkers with a systematic way to monitor audio streams, generate AI-powered analyses of suspected claims, and collaborate on verifying or debunking content in real-time. + +## Project Mission + +The primary objectives of VERDAD are: + +1. **Visibility:** To provide actionable data for trustworthy newsrooms to disseminate timely fact-checks and accurate narratives. +2. **Investigation:** To support long-term research into regional and temporal trends of disinformation campaigns. +3. **Automation at Scale:** To leverage multimodal AI models to process thousands of hours of audio that would be impossible for human teams to monitor manually. + +## The AI Pipeline Architecture + +VERDAD operates through a multi-stage pipeline orchestrated by **Prefect**. The system moves from raw audio ingestion to structured, human-readable intelligence. + +### Stage 1: Detection & Transcription +The system continuously records audio from configured stations. It uses **OpenAI Whisper** for high-fidelity speech-to-text (ASR) and **Google Gemini** (1.5 Flash or 2.5 Flash) to perform a rapid initial screening. This stage identifies "snippets" of interest based on simplified heuristics to ensure high recall. + +### Stage 2: Audio Clipping +Once a segment is flagged, the system extracts the specific audio clip. It automatically includes configurable "context windows" (e.g., 90 seconds before and 60 seconds after the segment) to ensure analysts understand the full scope of the conversation. + +### Stage 3: In-Depth Analysis +This is the core "intelligence" phase. The platform generates a comprehensive analysis of the snippet, including: +* **Bi-lingual Metadata:** Titles, summaries, and explanations in both the original language (e.g., Spanish or Arabic) and English. +* **Claim Extraction:** Specific quotes of false/misleading claims paired with evidence-based rebuttals. +* **Sentiment & Tone:** Detection of vocal cues, emotional intensity (0-100), and the cultural impact of the delivery. +* **Political Leaning:** A calculated score (-1.0 to 1.0) indicating the political direction of the content based on policy positions and rhetoric. + +### Stage 4 & 5: Review and Embedding +Final AI reviews ensure the analysis is defensible to professional fact-checkers. The data is then vectorized (embedded) for similarity searching, allowing users to find related disinformation patterns across different dates or stations. + +## Structured Output for Researchers + +For developers and data scientists, VERDAD provides structured JSON output via its API and database. The analysis follows a strict schema to ensure consistency across thousands of snippets: + +```json +{ + "transcription": "Original audio text...", + "title": { + "spanish": "Título del segmento", + "english": "Segment Title" + }, + "analysis": { + "claims": [ + { + "quote": "The specific misleading statement", + "evidence": "Why this claim is factually incorrect", + "score": 85 + } + ], + "political_leaning": { + "score": 0.75, + "explanation": "Analysis of rhetoric used..." + } + }, + "emotional_tone": { + "intensity": 90, + "vocal_cues": ["shouting", "urgent tempo"] + } +} +``` + +## Collaborative Review Interface + +The system is not a "black box." It includes an interactive front-end where experts can: +* **Review:** Listen to snippets alongside their AI-generated transcripts. +* **Validate:** Upvote or downvote the accuracy of AI labels. +* **Annotate:** Add custom labels and discuss findings via a built-in comment system (powered by Liveblocks and Supabase). +* **Notify:** Real-time integrations with Slack and Email ensure that high-priority disinformation is flagged to the team immediately. + +## Tech Stack Summary + +* **Orchestration:** Prefect (running on Fly.io) +* **LLMs:** Google Gemini (1.5 and 2.5 Pro & Flash models) +* **ASR:** OpenAI Whisper +* **Backend:** Node.js (Express), TypeScript, Python +* **Database:** PostgreSQL (Supabase) with Vector support +* **Notifications:** Resend (Email), Slack Webhooks diff --git a/docs/getting-started/quick-start.md b/docs/getting-started/quick-start.md new file mode 100644 index 0000000..30f3339 --- /dev/null +++ b/docs/getting-started/quick-start.md @@ -0,0 +1,142 @@ +# Quick Start + +Get the VERDAD recording and analysis pipeline running in under five minutes. This guide covers environment configuration and launching the processing workers. + +### 1. Prerequisites + +Ensure you have the following accounts and API keys available: +* **Google AI Studio:** For Gemini Flash/Pro APIs (1.5 or 2.5, `GOOGLE_GEMINI_KEY`). +* **OpenAI:** For Whisper ASR transcription. +* **Supabase:** For the Postgres database and authentication. +* **Prefect Cloud/Server:** To orchestrate the processing flows. +* **Resend:** For email notifications (if using the server component). + +### 2. Environment Configuration + +Create a `.env` file in the root directory. Use the following template to configure your credentials: + +```bash +# Core AI Services +GOOGLE_GEMINI_KEY=your_gemini_key +OPENAI_API_KEY=your_openai_key + +# Database (Supabase) +SUPABASE_URL=your_project_url +SUPABASE_KEY=your_service_role_key + +# Orchestration & Monitoring +SENTRY_DSN=your_sentry_dsn +ENABLE_PREFECT_DECORATOR=true + +# Notification Services (Server only) +LIVEBLOCKS_SECRET_KEY=your_key +RESEND_API_KEY=your_key +SLACK_NOTIFICATION_EMAIL=your_slack_bridge_email +``` + +### 3. Pipeline Setup + +The VERDAD pipeline is modular, allowing you to run specific stages as independent workers. + +#### Install Dependencies +```bash +# Python Pipeline +pip install -r requirements.txt + +# Server Component (Node.js) +cd server && npm install +``` + +### 4. Running the Workers + +VERDAD uses the `FLY_PROCESS_GROUP` environment variable to determine which stage a worker should execute. You can run these locally or deploy them to Fly.io. + +#### Recording (Optional) +Choose one of two recording modes: + +**Generic Recording** (web-based radio with browser automation): +```bash +export FLY_PROCESS_GROUP=generic_recording +python -m src.generic_recording +``` + +**Direct URL Recording** (non-web streams): +```bash +python -m src.recording +``` + +#### Start the Initial Detection (Stage 1) +This worker monitors the database for new audio files and performs rapid screening using Gemini. +```bash +export FLY_PROCESS_GROUP=initial_disinformation_detection +python -m src.processing_pipeline.main +``` + +#### Start the Audio Clipper (Stage 2) +Extracts snippets identified in Stage 1 with configurable context windows. +```bash +export FLY_PROCESS_GROUP=audio_clipping +python -m src.processing_pipeline.main +``` + +#### Start In-Depth Analysis (Stage 3) +Generates structured content analysis, translations, and disinformation categories. +```bash +export FLY_PROCESS_GROUP=in_depth_analysis +python -m src.processing_pipeline.main +``` + +#### Parallel Processing +To increase throughput, run multiple instances of the same worker: +```bash +# Terminal 1 +export FLY_PROCESS_GROUP=initial_disinformation_detection +python -m src.processing_pipeline.main + +# Terminal 2 +export FLY_PROCESS_GROUP=initial_disinformation_detection_2 +python -m src.processing_pipeline.main +``` + +Supported parallel variants: `initial_disinformation_detection_2`, `analysis_review_2` + +### 5. Running the Backend Server + +The Express server handles real-time collaboration via Liveblocks and syncs comments to Supabase. + +```bash +cd server +npm run dev +``` + +### Pipeline Overview + +| Process Group | Function | Key Model/Tool | +| :--- | :--- | :--- | +| `initial_disinformation_detection` | Flags potential disinformation in raw audio | Gemini 1.5/2.5 Flash | +| `audio_clipping` | Segments audio into snippets for review | FFmpeg/Audio Logic | +| `in_depth_analysis` | Deep dive analysis and structured metadata | Gemini 1.5/2.5 Pro | +| `analysis_review` | Final validation of the AI-generated claims | Stage 4 Executor | +| `embedding` | Vectorizes snippets for similarity search | Stage 5 Model | + +### Local Testing Without Prefect + +To run and test stages without Prefect orchestration (useful for local development): + +```bash +export ENABLE_PREFECT_DECORATOR=false +python -m src.main +``` + +This disables the Prefect flow/task decorators and runs code directly. Edit `src/main.py` to test a specific snippet_id. + +### Utility Commands + +See [Utility Scripts](../maintenance/utility-scripts.md) for: +- Undoing Stage 1 or Stage 2 processing +- Regenerating transcripts +- Redoing detection with updated heuristics +- Managing Prefect flow runs +``` +Move quick-start.md to getting-started/ +``` diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..2684fc7 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,48 @@ +# VERDAD Documentation Index + +Welcome to the VERDAD documentation! This index provides a structured overview of all available documentation, organized by topic. For a project overview and setup, see the main [README](../README.md). + +--- + +## Getting Started +- [Introduction](getting-started/introduction.md) +- [Quick Start](getting-started/quick-start.md) + +## Architecture +- [System Architecture](architecture/system-architecture.md) +- [Data Lifecycle](architecture/data-lifecycle.md) + +## Pipeline Stages +- [Stage 1: Detection & Transcription](pipeline-stages/stage-1-detection-transcription.md) +- [Stage 2: Audio Clipping](pipeline-stages/stage-2-audio-clipping.md) +- [Stage 3: In-Depth Analysis](pipeline-stages/stage-3-in-depth-analysis.md) +- [Stage 4: Analysis Review](pipeline-stages/stage-4-analysis-review.md) +- [Stage 5: Semantic Embedding](pipeline-stages/stage-5-semantic-embedding.md) + +## Integration +- [Express Server API](integration/express-server-api.md) +- [Real-Time Collaboration](integration/real-time-collaboration.md) +- [Slack & Email Notifications](integration/slack-email-notifications.md) +- [Supabase/Postgres Schema](integration/supabase-postgres-schema.md) + +## Configuration +- [Docker Containerization](configuration/docker-containerization.md) +- [Fly.io Configuration](configuration/flyio-configuration.md) +- [Heuristics & Output Schemas](configuration/heuristics-output-schemas.md) +- [Prompt Management](configuration/prompt-management.md) +- [Prompt Management Advanced (Versioning & Scripts)](configuration/prompt-management-advanced.md) +- [Radio Recording System](configuration/radio-recording-system.md) +- [Radio Station Adapters](configuration/radio-station-adapters.md) +- [Recording Workers](configuration/recording-workers.md) + +## Maintenance +- [BigQuery Archival](maintenance/bigquery-archival.md) +- [Maintenance Scripts](maintenance/maintenance-scripts.md) +- [Utility Scripts](maintenance/utility-scripts.md) (Undo/Redo operations, Flow management) + +## Testing +- [Testing Suite](testing/testing-suite.md) + +--- + +For more information, see the [project README](../README.md) or open an issue for support. diff --git a/docs/integration/express-server-api.md b/docs/integration/express-server-api.md new file mode 100644 index 0000000..c54dee0 --- /dev/null +++ b/docs/integration/express-server-api.md @@ -0,0 +1,89 @@ +# Express Server & API + +## Express Server & API + +The VERDAD Express server acts as the central coordination hub for the platform's collaborative features. It manages real-time communication via Liveblocks, synchronizes collaborative data with the Supabase database, and handles outgoing notifications to Slack and email. + +### Core API Endpoints + +The server exposes a set of REST endpoints primarily used for authentication and processing external webhooks. + +#### Authentication +* **`POST /api/liveblocks-auth`** + * **Purpose:** Authenticates frontend users for Liveblocks sessions, allowing them to participate in real-time document editing and commenting. + * **Input:** Liveblocks auth request. + * **Output:** Authorization token for the frontend client. + +#### Webhooks +* **`POST /api/webhooks/liveblocks`** + * **Purpose:** Listens for events from the Liveblocks service (e.g., when a comment is created, a reaction is added, or a thread is deleted). + * **Processing:** Triggers downstream database updates in Supabase and sends relevant notifications to analysts. + +--- + +### Comment & Collaboration Services + +The server ensures that all real-time interactions occurring in the frontend are persisted and searchable in the primary database. + +#### Synchronization Logic +The `commentService` manages the flow of data between Liveblocks and Supabase. Key operations include: + +* **Comment Persistence:** When a user leaves a comment on a radio snippet, the server catches the webhook and mirrors the content in the `comments` table. +* **Reaction Tracking:** Captures and stores emoji reactions (upvotes/labels) to improve AI heuristics over time. +* **Batch Syncing:** An internal script (`server/src/index.ts`) is available to perform a full reconciliation between Liveblocks rooms and the database to ensure data integrity. + +```typescript +// Example: Data structure for comment synchronization +{ + id: string; // Unique comment ID + thread_id: string; // Reference to the discussion thread + room_id: string; // Reference to the specific audio snippet + created_by: string; // User ID of the analyst + body: string; // JSON string of the comment content + comment_at: string; // Timestamp +} +``` + +--- + +### Notification System + +VERDAD keeps researchers and journalists informed of new findings or discussion activity through a tiered notification system. + +#### Email Notifications +Powered by **Resend**, the `emailService` handles transactional emails. It uses templates stored in Supabase to provide context-aware alerts (e.g., @mentions in a snippet discussion). + +#### Slack Integration +The `slackService` enables team-wide visibility into platform activity. It formats events—such as new disinformation snippets or critical comments—into structured alerts. + +* **Notification Types:** Mentions, new comments, edited content, and deletions. +* **Delivery:** Notifications are routed to a configured Slack notification email address, which then posts them to the designated team channel. + +--- + +### Configuration & Environment Variables + +To run the Express server, the following environment variables must be configured: + +| Variable | Description | +| :--- | :--- | +| `SUPABASE_URL` | The URL of your Supabase project. | +| `SUPABASE_SERVICE_ROLE_KEY` | Administrative key for database operations. | +| `LIVEBLOCKS_SECRET_KEY` | Secret key for managing real-time rooms. | +| `RESEND_API_KEY` | API key for sending notification emails. | +| `SLACK_NOTIFICATION_EMAIL` | The destination email for Slack channel integration. | + +### Development Commands + +From the `server` directory: + +```bash +# Install dependencies +npm install + +# Start the development server +npm run dev + +# Run the Liveblocks-to-Supabase sync script +npm run sync-comments +``` diff --git a/docs/integration/real-time-collaboration.md b/docs/integration/real-time-collaboration.md new file mode 100644 index 0000000..ad86204 --- /dev/null +++ b/docs/integration/real-time-collaboration.md @@ -0,0 +1,83 @@ +# Real-time Collaboration + +## Real-time Collaboration + +VERDAD facilitates collaborative analysis by allowing journalists and researchers to review flagged audio snippets together in real-time. This functionality is powered by [Liveblocks](https://liveblocks.io/), providing features like threaded comments, emoji reactions, and live presence indicators. + +### Overview + +Every audio snippet identified by the AI pipeline is treated as a unique "Room" in the collaboration layer. Within these rooms, users can: +* **Discuss findings:** Post comments and reply to threads. +* **Vote and React:** Use emoji reactions to indicate agreement or flag specific content. +* **Mention Peers:** Tag other researchers to draw attention to specific evidence. +* **Review AI Labels:** Upvote existing AI-generated labels or add custom ones. + +### Authentication + +The frontend interacts with a secure authentication endpoint to grant users access to specific snippet rooms. + +**Endpoint:** `POST /api/liveblocks-auth` + +This endpoint handles the handshake between the user session and the Liveblocks servers, ensuring that only authorized researchers can view or participate in the discussion of specific datasets. + +### Commenting and Feedback + +The collaboration system supports rich interaction through the following capabilities: + +* **Threaded Discussions:** Comments are organized into threads to keep conversations focused on specific claims or audio segments. +* **Mentions:** Users can `@mention` colleagues. When a mention occurs, the system triggers a notification workflow. +* **Reactions:** Support for adding and removing emoji reactions to comments for quick sentiment gathering. + +### Data Persistence and Sync + +While Liveblocks manages the real-time state, VERDAD maintains a persistent record of all collaborative activity in its Postgres database (via Supabase). This ensures that human feedback can be used to improve future AI heuristics. + +#### Webhook Integration +The server listens for Liveblocks webhooks to sync activity back to the primary database: + +**Endpoint:** `POST /api/webhooks/liveblocks` + +| Event Type | Action | +| :--- | :--- | +| `comment_created` | Persists the comment body and metadata to the `comments` table. | +| `comment_edited` | Updates the existing record and tracks the `edited_at` timestamp. | +| `comment_deleted` | Marks the comment as deleted in the database. | +| `reaction_added` | Records the emoji and user ID in the `comment_reactions` table. | + +#### Manual Sync Utility +For administrative purposes or disaster recovery, the system includes a background service to batch-sync all rooms and threads. +```typescript +// Internal usage for syncing all Liveblocks data to the local database +import { fetchAllRooms, fetchAllThreads, fetchComments } from "./services/commentService"; + +// This process iterates through all active rooms and upserts +// comments and reactions to ensure data integrity. +``` + +### Notifications + +To keep researchers engaged, VERDAD sends notifications for critical collaborative events. + +#### Slack Integration +If configured via the `SLACK_NOTIFICATION_EMAIL` environment variable, the system sends formatted alerts to a Slack channel. +* **Mentions:** Notifies a user they have been tagged. +* **New Comments:** Alerts the team to new activity on a snippet. +* **Edits:** Provides a "diff" view showing original vs. edited content. + +#### Email Notifications +Powered by **Resend**, the system sends HTML-formatted emails for mentions and significant updates using customizable templates stored in the database. + +| Template Name | Usage | +| :--- | :--- | +| `mention_notification` | Sent when a user is tagged in a room. | +| `comment_notification` | Sent when new activity occurs in a followed thread. | + +### Configuration + +To enable these features, ensure the following environment variables are set in the server environment: + +```bash +LIVEBLOCKS_SECRET_KEY=sk_prod_... +RESEND_API_KEY=re_... +SLACK_NOTIFICATION_EMAIL=your-slack-inbound-email@example.com +``` diff --git a/docs/integration/slack-email-notifications.md b/docs/integration/slack-email-notifications.md new file mode 100644 index 0000000..0d32506 --- /dev/null +++ b/docs/integration/slack-email-notifications.md @@ -0,0 +1,72 @@ +# Slack & Email Notifications + +VERDAD uses a combined notification system to keep analysts and researchers informed about collaboration activity and detection events. The system leverages **Resend** for email delivery and a **Slack Email Integration** to bridge communications into team channels. + +## Configuration + +To enable notifications, you must configure the following environment variables in your server environment: + +| Variable | Description | +| :--- | :--- | +| `RESEND_API_KEY` | Your API key from [Resend](https://resend.com/). | +| `SLACK_NOTIFICATION_EMAIL` | The unique email address generated by your Slack "Email" integration. | +| `SUPABASE_URL` | Used to fetch email templates. | +| `SUPABASE_SERVICE_ROLE_KEY` | Used to fetch email templates. | + +## Notification Channels + +### Email (via Resend) +Direct email notifications are sent for user-specific events, such as being @mentioned in a snippet discussion. These emails are sent from `Verdad `. + +### Slack Integration +VERDAD communicates with Slack via an email bridge. To set this up: +1. In Slack, add the **Email** app to your workspace. +2. Create a configuration that posts to your desired channel (e.g., `#disinfo-alerts`). +3. Copy the generated email address and assign it to the `SLACK_NOTIFICATION_EMAIL` environment variable. + +## Triggered Events + +The platform automatically dispatches notifications for the following collaborative actions within the snippet review interface: + +| Event Type | Trigger | Content Included | +| :--- | :--- | :--- | +| **Mention** | When a user is @tagged in a comment. | Room ID, actor name, and link to snippet. | +| **Comment** | When a new comment is added to a snippet thread. | The body of the comment and a direct link. | +| **Edit** | When an existing comment is modified. | Comparison of the original vs. edited text. | +| **Delete** | When a comment is removed. | Identification of the deleted comment and actor. | + +### Usage Example (Internal) + +While notifications are usually triggered automatically by Liveblocks webhooks, the `slackService` provides a structured interface for sending alerts: + +```typescript +import { sendSlackNotification } from './services/slackService'; + +await sendSlackNotification({ + type: 'comment', + actor: 'Lead Analyst', + roomId: 'snippet-uuid-123', + content: 'This broadcast contains high-confidence disinformation regarding local elections.' +}); +``` + +## Email Templates + +Notification content is managed dynamically via the `email_template` table in your Supabase database. This allows you to update the branding or messaging of alerts without redeploying the server. + +The system specifically looks for a template named `mention_notification`. Templates support the following placeholders: + +* `{{subject}}`: The notification's subject line. +* `{{commentBody}}`: The text content of the comment. +* `{{roomId}}`: The unique ID of the audio snippet. +* `{{link}}`: A direct URL to the snippet on the VERDAD front-end. + +### Template Management +To modify the notification look and feel, update the `template_content` column in the `email_template` table: + +```sql +-- Example: Updating the mention template +UPDATE email_template +SET template_content = 'Hi! You were mentioned in: {{commentBody}} View Snippet' +WHERE template_name = 'mention_notification'; +``` diff --git a/docs/integration/supabase-postgres-schema.md b/docs/integration/supabase-postgres-schema.md new file mode 100644 index 0000000..e97b7b3 --- /dev/null +++ b/docs/integration/supabase-postgres-schema.md @@ -0,0 +1,113 @@ +# Supabase & Postgres Schema + +## Supabase & Postgres Schema + +VERDAD utilizes Supabase (Postgres) as its central data store, managing everything from raw audio metadata and AI processing states to user-generated labels and collaborative comments. The schema is designed to support a multi-stage asynchronous pipeline orchestrated by Prefect. + +### Core Database Tables + +The database is organized into three main functional areas: Pipeline Management, Analysis & Results, and Collaboration. + +#### Pipeline Management +* **`audio_files`**: Tracks raw audio recordings from radio stations. + * `id`: UUID (Primary Key) + * `status`: Current state (e.g., `pending`, `reserved`, `completed`, `error`) + * `radio_station_code`: Identifier for the station (e.g., `WLEL - 94.3 FM`) + * `file_path`: Storage location of the raw audio. +* **`stage_1_llm_responses`**: Stores initial screening results from Gemini (1.5 or 2.5 Flash). + * `audio_file_id`: Foreign key to `audio_files`. + * `status`: Processing state for Stage 2 (Audio Clipping). +* **`prompt_versions`**: Manages the versioning of LLM prompts used across different stages. + * `stage`: The pipeline stage (1-5). + * `is_active`: Boolean flag to determine which prompt is currently in use. + +#### Analysis & Results +* **`snippets`**: The primary entity for disinformation analysis, representing a specific flagged segment of audio. + * `id`: UUID (Primary Key) + * `status`: Current review state (e.g., `ready_for_review`, `processing`). + * `previous_analysis`: JSONB field containing the structured output from Stage 3 models (Claims, Emotional Tone, Political Leaning). +* **`labels`**: Global repository of disinformation categories (e.g., "Conspiracy Theory"). + * `english`: Label name in English. + * `spanish`: Label name in Spanish. +* **`snippet_labels`**: A join table associating multiple labels with a single snippet. + +#### Collaboration & System +* **`comments`**: Stores discussions between researchers, synced from Liveblocks. + * `thread_id`, `room_id`: Identifiers for the collaborative session. + * `body`: JSONB content of the comment. +* **`comment_reactions`**: Tracks user reactions (emojis) to specific comments. +* **`email_template`**: Stores HTML templates for system notifications (e.g., @mentions). + +### Custom RPC Functions + +The processing pipeline uses Postgres Stored Procedures (RPCs) to handle atomic operations and task "reservation" to prevent multiple workers from processing the same data simultaneously. + +| Function | Description | +| :--- | :--- | +| `fetch_a_new_audio_file_and_reserve_it` | Retrieves a pending audio file and sets status to `reserved`. | +| `fetch_a_new_snippet_and_reserve_it` | Grabs a snippet ready for Stage 3 analysis. | +| `fetch_a_ready_for_review_snippet_and_reserve_it` | Grabs a snippet ready for Stage 4 manual review. | + +### Structured Data (JSONB) + +The `snippets.previous_analysis` column stores complex, nested data generated by the Stage 3 AI models. This structured data includes: + +```json +{ + "transcription": "Original text...", + "translation": "English translation...", + "analysis": { + "claims": [ + { + "quote": "The claim text", + "evidence": "Fact-check evidence", + "score": 85 + } + ], + "political_leaning": { + "score": -0.5, + "explanation": "Analysis of rhetoric" + } + }, + "emotional_tone": [ + { + "emotion": {"english": "Fear", "spanish": "Miedo"}, + "intensity": 90 + } + ] +} +``` + +### Accessing the Database + +#### Python (Processing Pipeline) +The pipeline uses the `SupabaseClient` wrapper found in `src/processing_pipeline/supabase_utils.py` to interact with the schema: + +```python +from processing_pipeline.supabase_utils import SupabaseClient + +client = SupabaseClient(url, key) + +# Reserve a task from the queue +audio_file = client.get_a_new_audio_file_and_reserve_it() + +# Update snippet status +client.set_snippet_status(snippet_id, "completed") +``` + +#### TypeScript (Server) +The backend server interacts with the collaboration tables using the standard Supabase JS client: + +```typescript +import { createClient } from "@supabase/supabase-js"; + +const supabase = createClient(process.env.SUPABASE_URL, process.env.SUPABASE_SERVICE_ROLE_KEY); + +// Upserting comments from a webhook or sync service +const { error } = await supabase + .from("comments") + .upsert({ id: commentId, body: content }); +``` + +### Safety and Content Moderation +The system includes configuration for Gemini's safety settings, stored indirectly through pipeline logic but applied to content that influences the database records. Categories include `HATE_SPEECH`, `HARASSMENT`, and `CIVIC_INTEGRITY`. Data flagged as violating these during analysis is still stored but marked with appropriate metadata for researcher review. diff --git a/docs/maintenance/bigquery-archival.md b/docs/maintenance/bigquery-archival.md new file mode 100644 index 0000000..a397c71 --- /dev/null +++ b/docs/maintenance/bigquery-archival.md @@ -0,0 +1,73 @@ +# BigQuery Archival + +## BigQuery Archival + +VERDAD utilizes Google BigQuery as its primary analytical data warehouse for long-term storage and large-scale research. While the live application runs on Postgres (Supabase) to support the interactive review process, BigQuery is the repository for the final, structured output of the entire AI pipeline. + +This archival process ensures that journalists and researchers can perform complex SQL queries across millions of records without impacting the performance of the real-time monitoring system. + +### Overview + +The archival process captures the high-fidelity results generated in **Stage 3 (In-Depth Analysis)** and **Stage 4 (Analysis Review)**. The data archived includes: + +* **Transcription & Translations:** Full text of the snippet in its original language and English. +* **Structured Analysis:** Detailed disinformation categories, confidence scores, and emotional tone analysis. +* **Political Leaning:** Calculated scores and evidence regarding political bias. +* **User Feedback:** Comments and validation labels provided by researchers via the front-end. +* **Metadata:** Station information, timestamps, and geographic data. + +### Data Schema + +The BigQuery tables are structured to match the `Stage3Output` Pydantic models. Key fields available for analysis include: + +| Field | Type | Description | +| :--- | :--- | :--- | +| `snippet_id` | STRING | Unique identifier for the audio snippet. | +| `recorded_at` | TIMESTAMP | The time the original broadcast was recorded. | +| `station_code` | STRING | The identifier for the radio station (e.g., `WAXY - 790 AM`). | +| `primary_language` | STRING | The detected language (Spanish, Arabic, etc.). | +| `overall_confidence` | INTEGER | Pipeline confidence score (0-100). | +| `political_score` | FLOAT | Political leaning score (-1.0 to 1.0). | +| `analysis_json` | JSON | The complete structured output from the Gemini LLM. | + +### Querying Archival Data + +Researchers can access the `verdad_archival` dataset to perform cross-station or longitudinal studies. + +**Example: Finding high-intensity emotional content across Florida stations** + +```sql +SELECT + title.english, + summary.english, + metadata.radio_station_name, + tone.intensity, + tone.explanation.english as tone_reason +FROM `your-project.verdad_archival.snippets`, +UNNEST(emotional_tone) as tone +WHERE metadata.location_state = 'Florida' + AND tone.intensity > 80 +ORDER BY recorded_at DESC +LIMIT 100; +``` + +### Integration with the Pipeline + +The archival process is triggered as an automated stage within the Prefect orchestration. Once a snippet has been reviewed or has passed through the full analysis suite, the `bigquery_archival` task (internal) formats the `Stage3Output` and `Stage4` modifications into a BigQuery-compatible row. + +#### Usage in Code + +While primarily an automated backend process, the archival logic relies on the environment configuration for project and dataset identification: + +```python +# Internal configuration used by the Prefect pipeline +BIGQUERY_PROJECT_ID = "your-google-cloud-project" +BIGQUERY_DATASET_ID = "verdad_archival" +BIGQUERY_TABLE_ID = "snippets" +``` + +### Data Freshness + +The archival sync typically runs in batches. Data is moved from the live Postgres instance to BigQuery: +1. **Automatically:** After a snippet reaches a "Completed" status in the `Stage 4: Analysis Review` flow. +2. **Periodically:** Via a scheduled sync for community comments and researcher labels to ensure the archival record reflects the most recent human-in-the-loop feedback. diff --git a/docs/maintenance/maintenance-scripts.md b/docs/maintenance/maintenance-scripts.md new file mode 100644 index 0000000..bf353fb --- /dev/null +++ b/docs/maintenance/maintenance-scripts.md @@ -0,0 +1,94 @@ +# Maintenance Scripts + +## Maintenance and Administrative Scripts + +The VERDAD platform includes several scripts and automated flows to manage data synchronization, handle pipeline corrections, and perform diagnostic tests. + +### Data Synchronization + +#### Liveblocks to Postgres Comment Sync +The system includes a script to synchronize collaborative data (comments and threads) from Liveblocks into the Postgres/Supabase database. This ensures that analyst discussions are backed up and available for internal reporting. + +**Execution:** +Navigate to the `server` directory and run the sync utility: +```bash +# Using the compiled entry point +npm run start +``` + +**Key Operations:** +- **Room Discovery:** Fetches all active rooms from Liveblocks. +- **Thread & Comment Retrieval:** Iterates through rooms to extract all threads and their associated comments. +- **Batch Upsert:** Uses `p-limit` to handle concurrent requests and upserts data into the `comments` table in chunks of 100 to ensure database stability. + +--- + +### Pipeline Maintenance & Undo Operations + +The processing pipeline provides specific administrative flows to correct errors or re-run analysis if models or prompts are updated. These are managed via the `FLY_PROCESS_GROUP` environment variable within the Prefect orchestration layer. + +#### Re-running Analysis +| Process Group | Description | +| :--- | :--- | +| `regenerate_timestamped_transcript` | Re-runs Whisper ASR for specific Stage 1 responses to fix transcription issues. | +| `redo_main_detection` | Re-triggers the Stage 1 Gemini screening for specific responses. | + +#### Undo Operations (Cleanup) +These scripts are used to remove data from the database and storage when a processing run needs to be completely invalidated. + +| Process Group | Description | Input Parameters | +| :--- | :--- | :--- | +| `undo_disinformation_detection` | Removes Stage 1 detection records. | `audio_file_ids` (List) | +| `undo_audio_clipping` | Deletes generated audio snippets and metadata from Stage 2. | `stage_1_llm_response_ids` (List) | + +**Usage Example (Fly.io/Prefect):** +To run an undo operation, deploy the container with the corresponding environment variable: +```bash +env FLY_PROCESS_GROUP="undo_audio_clipping" python src/processing_pipeline/main.py +``` + +--- + +### Diagnostic & Testing Scripts + +#### Stage 4 Review Simulator +The `src/main.py` script serves as a standalone test runner for the Stage 4 (Analysis Review) executor. It allows developers to verify how the Gemini model handles specific snippets without running the full pipeline. + +**Usage:** +```bash +python src/main.py +``` + +**Functionality:** +1. Fetches a specific snippet from Supabase by ID. +2. Prepares the context (transcription, metadata, and previous Stage 3 analysis). +3. Executes the `Stage4Executor` to generate a refined analysis. +4. Outputs the raw JSON result and grounding metadata to the console for verification. + +--- + +### Database Utility Interface + +The `SupabaseClient` class in `src/processing_pipeline/supabase_utils.py` acts as the administrative interface for database state changes. While used internally by the pipeline, it can be utilized in maintenance scripts for manual state resets. + +**Common Maintenance Tasks:** +- `set_audio_file_status(id, status)`: Manually reset a file to `pending` or `failed`. +- `set_snippet_status(id, status)`: Manually update snippet progress. +- `delete_vector_embedding_of_snippet(snippet_id)`: Force a snippet to be re-indexed in the vector database by deleting its current embedding. + +**Example: Manually Resetting a Snippet for Re-Analysis** +```python +from processing_pipeline.supabase_utils import SupabaseClient + +db = SupabaseClient(url, key) +# Reset status to allow Stage 3 to pick it up again +db.set_snippet_status("snippet-uuid-here", "pending") +``` + +--- + +### Notification Health Checks + +The system utilizes Resend and Slack for administrative alerts. +- **Email Templates:** Managed via the `email_template` table. Templates can be refreshed or tested using `server/src/services/templateService.ts`. +- **Slack Notifications:** Triggered via `sendSlackNotification`. This can be called within administrative scripts to notify the team of completed maintenance tasks or batch processing failures. diff --git a/docs/maintenance/utility-scripts.md b/docs/maintenance/utility-scripts.md new file mode 100644 index 0000000..18b5477 --- /dev/null +++ b/docs/maintenance/utility-scripts.md @@ -0,0 +1,133 @@ +# Utility Scripts + +VERDAD includes several utility scripts for managing processing flows and administrative tasks. + +## Prompt Import Script + +**Location:** `src/scripts/import_prompts_to_db.py` + +Manages prompt versions in the database, enabling versioning and rollback capabilities. + +See [Prompt Management and Versioning](../configuration/prompt-management-advanced.md) for detailed usage. + +## Flow Management Scripts + +### Cancel All Flows + +**Location:** `src/scripts/cancel_all_flows.py` + +Cancels all currently running Prefect flows. + +```bash +python src/scripts/cancel_all_flows.py +``` + +**Use Cases:** +- Emergency stop of all processing +- Clean shutdown before deployment +- Clearing stuck flows + +### Delete Flow Runs + +**Location:** `src/scripts/delete_flow_runs.py` + +Deletes Prefect flow run history. + +```bash +python src/scripts/delete_flow_runs.py [options] +``` + +**Use Cases:** +- Cleanup of old flow runs to reduce database size +- Removing runs with sensitive data +- Archival before backup + +## Undo Operations + +VERDAD includes "undo" flows to revert processing steps: + +### Stage 1 Undo + +```bash +export FLY_PROCESS_GROUP=undo_disinformation_detection +python src/processing_pipeline/main.py +``` + +**Parameters:** `audio_file_ids` (list of audio file IDs to revert) + +Reverts Stage 1 detection and resets audio file status to `New`, clearing: +- Flagged timestamps +- Initial detection results +- Transcriptions + +### Stage 2 Undo + +```bash +export FLY_PROCESS_GROUP=undo_audio_clipping +python src/processing_pipeline/main.py +``` + +**Parameters:** `stage_1_llm_response_ids` (list of Stage 1 response IDs to revert) + +Removes clipped audio files and resets snippet status, clearing audio clips and metadata. + +## Regeneration Operations + +### Regenerate Timestamped Transcript + +```bash +export FLY_PROCESS_GROUP=regenerate_timestamped_transcript +python src/processing_pipeline/main.py +``` + +**Parameters:** `stage_1_llm_response_ids` (list of Stage 1 response IDs) + +Re-runs transcription on existing Stage 1 detections. Useful for: +- Testing new transcription methods +- Updating to higher-quality models +- Fixing transcription errors + +### Redo Main Detection + +```bash +export FLY_PROCESS_GROUP=redo_main_detection +python src/processing_pipeline/main.py +``` + +**Parameters:** `stage_1_llm_response_ids` (list of Stage 1 response IDs) + +Re-runs the Gemini detection heuristics on existing audio files. Use after: +- Updating detection prompts +- Refining heuristics based on feedback +- Changing safety settings + +## Parallel Processing + +Multiple instances of the same process group can run in parallel for higher throughput: + +```bash +# Instance 1 +export FLY_PROCESS_GROUP=initial_disinformation_detection +python src/processing_pipeline/main.py + +# Instance 2 (in another terminal) +export FLY_PROCESS_GROUP=initial_disinformation_detection_2 +python src/processing_pipeline/main.py +``` + +**Supported Parallel Variants:** +- `initial_disinformation_detection` / `initial_disinformation_detection_2` +- `analysis_review` / `analysis_review_2` + +This allows horizontal scaling on Fly.io or other orchestration platforms. + +## Testing Environment + +To run scripts and tests without Prefect orchestration: + +```bash +export ENABLE_PREFECT_DECORATOR=false +python src/main.py +``` + +This disables the Prefect flow/task decorators, allowing direct Python execution for debugging and testing. diff --git a/docs/pipeline-stages/stage-1-detection-transcription.md b/docs/pipeline-stages/stage-1-detection-transcription.md new file mode 100644 index 0000000..a21d07b --- /dev/null +++ b/docs/pipeline-stages/stage-1-detection-transcription.md @@ -0,0 +1,90 @@ +# Stage 1: Detection & Transcription + +## Stage 1: Detection & Transcription + +Stage 1 serves as the high-recall entry point for the VERDAD pipeline. Its primary objective is to monitor continuous radio broadcasts and efficiently identify segments that potentially contain misinformation or disinformation. By combining rapid multimodal screening with precise automated speech recognition (ASR), this stage filters hours of raw audio into actionable snippets for deeper analysis. + +### Overview + +The process is divided into two main functional areas: +1. **Initial Detection:** Uses **Gemini** (1.5 or 2.5 Flash) to perform a rapid "first pass" over the audio. It applies simplified heuristics to identify segments of interest without requiring the full cost of higher-tier models. +2. **Transcription:** Uses **OpenAI Whisper** to generate detailed, timestamped transcriptions of the identified segments, ensuring that researchers can pinpoint exactly when a claim was made. + +--- + +### Executing the Pipeline + +Stage 1 is orchestrated via **Prefect**. In a production environment (such as Fly.io), the pipeline is triggered by setting the `FLY_PROCESS_GROUP` environment variable. + +#### Initial Detection +To run the primary detection deployment, which fetches new audio files and processes them: + +```bash +# Example: Running the detection worker +export FLY_PROCESS_GROUP="initial_disinformation_detection" +python src/processing_pipeline/main.py +``` + +**Parameters:** +- `audio_file_id` (Optional): Process a specific file. If `None`, the system fetches the next available file from the queue. +- `limit`: The maximum number of files to process in a single run (Default: `1000`). +- `concurrency_limit`: Maximum simultaneous processing tasks (Default: `100`). + +--- + +### Input and Output Data + +#### Input +The pipeline consumes records from the `audio_files` table. +- **Audio File:** 5–15 minute segments of radio broadcast. +- **Metadata:** Station code, location, and recorded timestamp. + +#### Output +Upon success, Stage 1 generates a record in the `stage_1_llm_responses` table with the following structured data: + +| Field | Description | +| :--- | :--- | +| `flagged_timestamps` | Start and end times (in seconds) for suspected snippets. | +| `basic_category` | Initial classification (e.g., Political, Health, Conspiratorial). | +| `confidence_score` | A preliminary score indicating the likelihood of disinformation. | +| `transcript` | The full timestamped text generated by Whisper. | + +--- + +### Safety and Content Moderation + +Because the system is designed to analyze potentially harmful content (hate speech, dangerous rhetoric, etc.), the Gemini API safety settings are configured to **BLOCK_NONE**. This ensures the AI does not refuse to analyze a snippet simply because it contains offensive language, which is often the very content journalists need to track. + +```python +# Internal safety configuration used during detection +SafetySetting( + category=HarmCategory.HARM_CATEGORY_HATE_SPEECH, + threshold=HarmBlockThreshold.BLOCK_NONE, +) +``` + +--- + +### Maintenance & Utilities + +VERDAD provides several utility flows for managing Stage 1 data without re-running the entire pipeline: + +#### Regenerating Transcripts +If a transcription fails or requires a different Whisper model configuration, use the regeneration flow: +- **Deployment Name:** `Stage 1: Regenerate Timestamped Transcript` +- **Use Case:** Updates existing detections with new transcriptions. + +#### Redoing Detection +If detection prompts are updated to be more accurate, you can re-run the screening phase: +- **Deployment Name:** `Stage 1: Redo Main Detection Phase` +- **Parameter:** `stage_1_llm_response_ids` (List of IDs to re-evaluate). + +#### Undoing Detection +To clear Stage 1 data and reset the audio file status for a fresh start: +- **Deployment Name:** `Stage 1: Undo Disinformation Detection` +- **Parameter:** `audio_file_ids` (List of IDs to revert). + +--- + +### Error Handling +If a file fails during Stage 1, the `audio_file` status is updated to `error` in the database, and the specific error message is logged to the `error_message` column. These are also tracked via **Sentry** for real-time developer alerts. diff --git a/docs/pipeline-stages/stage-2-audio-clipping.md b/docs/pipeline-stages/stage-2-audio-clipping.md new file mode 100644 index 0000000..4116b14 --- /dev/null +++ b/docs/pipeline-stages/stage-2-audio-clipping.md @@ -0,0 +1,71 @@ +# Stage 2: Audio Clipping + +## Overview + +Stage 2 of the VERDAD pipeline focuses on the automated extraction of audio segments identified during the initial screening (Stage 1). Rather than processing a full 15-minute radio broadcast, this stage isolates specific "snippets" of interest. + +To ensure that the content is analyzed accurately in later stages, the system automatically prepends and appends a configurable amount of "context" audio to each flagged detection. + +## The Clipping Process + +The process is orchestrated by Prefect and involves fetching detections that have been flagged by the Stage 1 LLM, calculating the appropriate time windows, and generating discrete audio files for further processing. + +### Input +- **Original Audio Files**: The raw recordings (typically 5–15 minutes). +- **Stage 1 Metadata**: Specific timestamps and IDs where potential disinformation was detected. + +### Output +- **Audio Snippets**: Isolated audio files containing the suspected content plus surrounding context. +- **Database Records**: Updated snippet entries in Supabase linked to the original audio file. + +## Configuration and Usage + +The clipping process is highly configurable to account for different radio formats and the latency of live detection. + +### Audio Clipping Task +This task extracts segments based on the timestamps provided by the initial detection stage. + +**Deployment Parameters:** +| Parameter | Type | Default | Description | +| :--- | :--- | :--- | :--- | +| `context_before_seconds` | `int` | `90` | The amount of audio to include *before* the flagged timestamp. | +| `context_after_seconds` | `int` | `60` | The amount of audio to include *after* the flagged timestamp. | +| `repeat` | `bool` | `True` | Whether the process should continuously check for new Stage 1 outputs. | + +**Usage Example:** +```python +# Typically triggered via the Prefect deployment or CLI +# Using the FLY_PROCESS_GROUP="audio_clipping" environment variable + +deployment = audio_clipping.to_deployment( + name="Stage 2: Audio Clipping", + concurrency_limit=100, + parameters=dict( + context_before_seconds=90, + context_after_seconds=60, + repeat=True + ), +) +``` + +### Undo Audio Clipping +If clipping needs to be re-run (e.g., if context windows were insufficient or metadata was corrupted), the `undo_audio_clipping` task allows for a clean rollback of specific responses. + +**Input:** +- `stage_1_llm_response_ids`: A list of IDs to revert, removing their associated snippets and resetting their status. + +## Management via Prefect + +Stage 2 is designed to run as a continuous worker. In the production environment (Fly.io), it is managed via the `FLY_PROCESS_GROUP` environment variable. + +To start the clipping worker: +```bash +export FLY_PROCESS_GROUP="audio_clipping" +python src/processing_pipeline/main.py +``` + +## Internal Roles +While primarily automated, the internal logic handles: +1. **Status Management**: Moving records from "detected" to "clipped" status. +2. **Metadata Association**: Ensuring the snippet remains tied to its original station code, location, and recording time. +3. **Concurrency Control**: Managing up to 100 simultaneous clipping tasks to handle high volumes of radio traffic. diff --git a/docs/pipeline-stages/stage-3-in-depth-analysis.md b/docs/pipeline-stages/stage-3-in-depth-analysis.md new file mode 100644 index 0000000..d07a6a9 --- /dev/null +++ b/docs/pipeline-stages/stage-3-in-depth-analysis.md @@ -0,0 +1,91 @@ +# Stage 3: In-Depth Analysis + +## Overview + +Stage 3 of the VERDAD pipeline performs a comprehensive, multi-dimensional analysis of audio snippets previously flagged in Stage 1 and clipped in Stage 2. While Stage 1 focuses on high-recall detection, Stage 3 utilizes advanced LLM capabilities to provide a deep dive into the content, language, emotional subtext, and political orientation of the broadcast. + +This stage produces structured data used by journalists and researchers to validate disinformation patterns and understand the rhetorical strategies employed by broadcasters. + +## Structured Analysis Model + +The output of Stage 3 is governed by a strict Pydantic schema (`Stage3Output`), ensuring that every analyzed snippet contains consistent, actionable metadata. + +### 1. Content and Context +Stage 3 generates a full transcription and translation, providing the necessary surrounding context for the flagged claim. + +| Field | Description | +| :--- | :--- | +| `transcription` | Complete transcript of the audio clip in the original language. | +| `translation` | English translation of the transcription. | +| `title` / `summary` | Bilingual (Spanish/English) descriptive titles and objective summaries. | +| `context` | Specifically identifies the "main" snippet versus the "before" and "after" segments. | + +### 2. Disinformation Analysis & Claims +The system extracts specific claims and evaluates them against evidence. + +- **Claims**: A list of objects containing the direct quote, evidence of why it is misleading, and a confidence score. +- **Validation Checklist**: An internal quality control mechanism where the model confirms if claims are quoted, evidence is provided, and the analysis is defensible to fact-checkers. +- **Confidence Scores**: Provides a granular breakdown of confidence for the overall analysis and specific disinformation categories. + +### 3. Emotional Tone Detection +VERDAD analyzes the "how" of the message, looking for emotional manipulation or high-intensity rhetoric. + +```python +# Example of Emotional Tone Data Structure +{ + "emotion": {"spanish": "Miedo", "english": "Fear"}, + "intensity": 85, + "evidence": { + "vocal_cues": ["trembling voice", "rapid speech"], + "phrases": ["¡Esto es una emergencia nacional!"], + "patterns": ["Urgency", "Existential threat"] + }, + "explanation": { + "impact": "Increases audience anxiety and reduces critical thinking." + } +} +``` + +### 4. Political Leaning Assessment +The pipeline evaluates the political orientation of the content on a scale from `-1.0` (Left) to `1.0` (Right). + +- **Score**: A float value representing the leaning. +- **Evidence**: Analyzes policy positions, specific arguments made, rhetoric used, and sources cited. +- **Score Adjustments**: Records the reasoning for the final score and any adjustments made during the model's "thinking" process. + +--- + +## Usage and Execution + +Stage 3 is orchestrated as a Prefect flow. It can be run as a continuous worker or triggered for specific snippets. + +### Running the Deployment +To start a worker dedicated to Stage 3 analysis via the CLI: + +```bash +# Set the process group to match the deployment configuration +export FLY_PROCESS_GROUP="in_depth_analysis" +python src/processing_pipeline/main.py +``` + +### Configuration Parameters +When deploying or triggering the flow, the following parameters are available: + +| Parameter | Type | Default | Description | +| :--- | :--- | :--- | :--- | +| `snippet_ids` | `list` | `[]` | List of specific snippet UUIDs to analyze. | +| `repeat` | `bool` | `True` | If true, the worker will continuously poll for new snippets. | +| `skip_review` | `bool` | `True` | Whether to move directly to the next stage after analysis. | + +--- + +## Post-Analysis Workflow + +Once the analysis is complete, the pipeline automatically performs several maintenance tasks to prepare the data for the front-end: + +1. **Label Assignment**: The system parses the identified `disinformation_categories` and automatically creates and assigns labels (e.g., "Conspiracy Theory," "Hate Speech") to the snippet in the database. +2. **Vector Invalidation**: It deletes any existing vector embeddings for the snippet. This triggers Stage 5 (Embedding) to generate a new vector based on the updated, rich metadata, ensuring the "Similar Snippets" feature remains accurate. +3. **Database Update**: The status is updated to `ready_for_review` (if Stage 4 is active) or `completed`. + +## Error Handling +If an analysis fails (e.g., due to API safety filters or malformed audio), the snippet status is updated to `error` and the specific reason is logged in the `error_message` field in the Postgres `snippets` table. Safety settings are configured by default to `BLOCK_NONE` for categories like `HATE_SPEECH` and `HARASSMENT` to ensure the model can actually analyze the disinformation it is intended to track. diff --git a/docs/pipeline-stages/stage-4-analysis-review.md b/docs/pipeline-stages/stage-4-analysis-review.md new file mode 100644 index 0000000..37d72ba --- /dev/null +++ b/docs/pipeline-stages/stage-4-analysis-review.md @@ -0,0 +1,90 @@ +# Stage 4: Analysis Review + +## Stage 4: Analysis Review + +Stage 4 serves as the automated validation and quality assurance layer of the VERDAD pipeline. In this stage, a multimodal AI model reviews the in-depth analysis generated in Stage 3 to ensure consistency, factual grounding, and logical soundness before the findings are presented to human researchers. + +### Overview + +The primary goal of Analysis Review is to perform a "sanity check" on the suspected disinformation. The model evaluates whether the claims identified in Stage 3 are actually supported by the audio evidence, whether the evidence provided is logically defensible, and if the scoring is consistent with the severity of the claims. + +### Execution + +The review process is orchestrated as a Prefect flow. It can be triggered for specific snippets or run as a continuous worker process. + +**Deployment Configuration:** +```python +# From src/processing_pipeline/main.py +case "analysis_review": + deployment = analysis_review.to_deployment( + name="Stage 4: Analysis Review", + concurrency_limit=100, + parameters=dict(snippet_ids=[], repeat=True), + ) + serve(deployment, limit=100) +``` + +### Main Interface: `Stage4Executor` + +The `Stage4Executor` is the primary interface for running the review logic. It requires the original transcription, the specific flagged snippet, and the JSON analysis produced by Stage 3. + +#### Usage Example + +```python +from processing_pipeline.stage_4 import Stage4Executor, prepare_snippet_for_review + +# 1. Prepare data from previous analysis +transcription, disinformation_snippet, metadata, analysis_json = prepare_snippet_for_review(previous_analysis) + +# 2. Run the review +response, grounding_metadata = Stage4Executor.run( + transcription=transcription, + disinformation_snippet=disinformation_snippet, + metadata=metadata, + analysis_json=analysis_json, +) +``` + +### Input and Output Data + +#### Inputs +* **Transcription**: The full text of the audio clip. +* **Disinformation Snippet**: The specific text segment flagged for review. +* **Metadata**: Information about the radio station, location, and recording time. +* **Analysis JSON**: The full structured output from Stage 3 (claims, emotional tone, political leaning, etc.). + +#### Outputs +The stage returns a refined analysis object and **Grounding Metadata**. The grounded metadata contains specific links back to the source text to ensure the AI isn't "hallucinating" claims that don't exist in the audio. + +### Validation Criteria + +The review stage applies a `ValidationChecklist` to verify the quality of the analysis: + +* **Specific Claims Quoted**: Ensures the analysis uses direct quotes rather than paraphrasing. +* **Evidence Provided**: Checks if every claim is backed by specific evidence from the transcript. +* **Scoring Falsity**: Validates the confidence score assigned to the disinformation. +* **Defensibility**: Evaluates if the findings would be considered defensible by professional fact-checkers. +* **Consistency**: Ensures the Spanish and English explanations align perfectly. + +### Post-Processing and Vector Updates + +Once a snippet passes Stage 4 review, the system performs several automated maintenance tasks to keep the database and front-end in sync: + +1. **Label Assignment**: Finalized disinformation categories are converted into database labels and assigned to the snippet. +2. **Vector Re-indexing**: The system deletes the old vector embedding for the snippet. This triggers a Stage 5 (Embedding) event to re-index the snippet with its refined analysis, improving the accuracy of the platform's similarity search. + +```python +# Internal utility handling the finalization logic +postprocess_snippet( + supabase_client, + snippet_id, + disinformation_categories +) +``` + +### Configuration + +Stage 4 utilizes Google's Gemini models with specific safety settings to ensure the model does not refuse to analyze sensitive or controversial content (which is often the nature of disinformation). + +* **Safety Thresholds**: All categories (Hate Speech, Harassment, etc.) are set to `BLOCK_NONE` to allow for the objective analysis of potentially harmful content without model interference. +* **Model Routing**: While Stage 3 handles heavy analysis, Stage 4 focuses on verification and can be configured to use different Gemini variants (Flash vs. Pro) depending on the required depth of the review. diff --git a/docs/pipeline-stages/stage-5-semantic-embedding.md b/docs/pipeline-stages/stage-5-semantic-embedding.md new file mode 100644 index 0000000..3eb7ff4 --- /dev/null +++ b/docs/pipeline-stages/stage-5-semantic-embedding.md @@ -0,0 +1,69 @@ +# Stage 5: Semantic Embedding + +## Overview + +Stage 5 of the VERDAD pipeline focuses on **Semantic Embedding**. Once a snippet has undergone in-depth analysis (Stage 3) and review (Stage 4), the system generates high-dimensional vector representations of the content. These embeddings enable advanced platform features such as: + +* **Similarity Search:** Identifying snippets with similar rhetorical patterns or claims. +* **Trend Clustering:** Grouping related disinformation campaigns across different radio stations and timeframes. +* **Deduplication:** Detecting repetitive broadcasts of the same misleading content. + +## Process Flow + +The embedding stage is an automated process that typically runs asynchronously after the analysis phases are complete. + +1. **Identification:** The system identifies snippets that are either missing a vector embedding or have had their existing embedding invalidated (e.g., after a significant analysis update). +2. **Vector Generation:** The text-based analysis (including the title, summary, and primary transcription) is passed to an embedding model. +3. **Storage:** The resulting vector is stored in the Postgres database (via `pgvector`) and indexed for fast similarity retrieval. + +## Execution and Deployment + +Stage 5 is orchestrated by Prefect and is designed to run as a continuous worker process. + +### Running the Embedding Worker +To start a worker dedicated to Stage 5, set the `FLY_PROCESS_GROUP` environment variable to `embedding`. This initializes a Prefect deployment that monitors the database for pending snippets. + +```bash +export FLY_PROCESS_GROUP="embedding" +python -m processing_pipeline.main +``` + +### Configuration Parameters +The `embedding` deployment supports the following parameters: + +| Parameter | Type | Default | Description | +| :--- | :--- | :--- | :--- | +| `repeat` | `bool` | `True` | Whether the task should continuously loop and check for new snippets. | +| `concurrency_limit` | `int` | `100` | The maximum number of simultaneous embedding operations. | + +## Database Integration + +The embeddings are managed through the `SupabaseClient` within the `processing_pipeline`. + +### Triggering Re-embedding +If a snippet's content is updated and requires a fresh vector representation, the system uses a utility function to delete the existing embedding. This deletion acts as a "dirty flag," signaling the Stage 5 worker to re-process the snippet. + +```python +from processing_pipeline.processing_utils import delete_vector_embedding_of_snippet + +# Internal utility to trigger a new embedding for a specific snippet +delete_vector_embedding_of_snippet(supabase_client, snippet_id="your-snippet-uuid") +``` + +### Data Storage +Embeddings are stored in the `snippets` table (or a related vector table) within the Postgres database. This allows researchers to perform similarity queries using standard SQL: + +```sql +-- Example of finding similar snippets via the database +SELECT id, title +FROM snippets +ORDER BY embedding <=> '[vector_data]' +LIMIT 5; +``` + +## Internal Utilities + +While primarily automated, Stage 5 utilizes internal logic within `src/processing_pipeline/stage_5.py` to interface with the embedding models. + +* **Input Data:** Primarily uses the `Stage3Output` and `Stage4` revised analysis. +* **Model:** Utilizes standard embedding models (configured via environment variables) to ensure cross-language semantic consistency between Spanish, Arabic, and English translations. diff --git a/docs/testing/testing-suite.md b/docs/testing/testing-suite.md new file mode 100644 index 0000000..b970a92 --- /dev/null +++ b/docs/testing/testing-suite.md @@ -0,0 +1,67 @@ +# Testing Suite + +## Testing Suite + +VERDAD employs a robust testing strategy to ensure the reliability of its multi-stage AI pipeline. The suite focuses on validating orchestration logic, data flow between stages, and the integrity of the AI analysis. + +### Prerequisites + +Before running the tests, ensure you have the development dependencies installed and your environment variables configured. + +```bash +pip install pytest +``` + +### Running the Test Suite + +The primary testing tool is **Pytest**. You can run the full pipeline test suite from the root directory: + +```bash +pytest tests/processing_pipeline/test_main_processing.py +``` + +### Local Testing Environment + +To facilitate unit testing without the overhead of the Prefect orchestration engine, the system uses a decorator toggle. This allows you to run pipeline functions as standard Python functions. + +Set the following environment variable to disable Prefect wrappers during local testing: + +```bash +# Disable Prefect orchestration for standard unit tests +export ENABLE_PREFECT_DECORATOR=false +``` + +### Pipeline Stage Testing + +The testing suite validates that the `FLY_PROCESS_GROUP` environment variable correctly triggers the intended pipeline stages. This ensures that when the system scales on Fly.io, each worker initializes the correct stage (e.g., `initial_disinformation_detection`, `audio_clipping`, `in_depth_analysis`). + +#### Mocking and Fixtures +The suite uses several fixtures to isolate the pipeline logic from external services: + +### Manual Stage 4 Validation + +A dedicated test entry point exists in `src/main.py` for manual verification of the **Stage 4: Analysis Review** process. This script performs a real-world integration test by fetching a snippet from Supabase and running it through the Gemini LLM. + +**Usage:** + +```bash +python src/main.py +``` + +**What this tests:** +1. **Supabase Connectivity**: Fetches a specific snippet by ID. +2. **Data Preparation**: Validates `prepare_snippet_for_review` logic (transcription, snippet extraction, and metadata formatting). +3. **LLM Integration**: Sends the payload to `Stage4Executor` and prints the structured JSON response and grounding metadata. + +### Test Coverage Areas + +| Area | Description | +| :--- | :--- | +| **Deployment Logic** | Validates that `match process_group` logic assigns correct parameters (concurrency limits, retry logic) to each stage. | +| **Environment Handling** | Ensures the system correctly loads configurations and handles missing environment variables gracefully. | +| **Data Models** | Validates that Pydantic models in `stage_3_models.py` correctly parse and validate complex LLM outputs (e.g., `PoliticalLeaning`, `ConfidenceScores`). | +| **Service Integration** | Tests the interaction between the Express server, Liveblocks webhooks, and the Supabase database. | + +### Integration Testing with Supabase + +For tests requiring database interaction, the system uses a `SupabaseClient` utility. When writing new tests that involve database writes, ensure you are targeting a development schema or using the provided RPC (Remote Procedure Call) mocks to prevent polluting production data. From 9027aff18a9915669b37cb3dc458efb0644e5c6c Mon Sep 17 00:00:00 2001 From: JsNcAr Date: Fri, 23 Jan 2026 06:44:07 -0500 Subject: [PATCH 2/4] Remove obsolete test files for radio stations and recording functionalities - Deleted test_stations.py which contained unit tests for various radio station classes. - Removed test_generic_recording.py that included tests for the generic recording module. - Eliminated test_recording.py which had tests for the recording module and its associated functions. --- Dockerfile.generic_recording_worker | 53 - Dockerfile.recording_worker | 24 - fly.generic_recording_worker.toml | 29 - fly.recording_worker.toml | 28 - poetry.lock | 5243 ++++++++++++++++++++++++++ pyproject.toml | 41 + requirements.txt | 3 +- scripts/generic_recording.sh | 8 - scripts/recording.sh | 25 - scripts/start_recording.sh | 101 - src/generic_recording.py | 210 -- src/processing_pipeline/__init__.py | 114 + src/radiostations/__init__.py | 17 - src/radiostations/base.py | 232 -- src/radiostations/khot.py | 17 - src/radiostations/kisf.py | 17 - src/radiostations/krgt.py | 17 - src/radiostations/wado.py | 17 - src/radiostations/waqi.py | 17 - src/radiostations/wkaq.py | 17 - src/recording.py | 177 - tests/radiostations/test_base.py | 466 --- tests/radiostations/test_stations.py | 119 - tests/test_generic_recording.py | 472 --- tests/test_recording.py | 374 -- 25 files changed, 5399 insertions(+), 2439 deletions(-) delete mode 100644 Dockerfile.generic_recording_worker delete mode 100644 Dockerfile.recording_worker delete mode 100644 fly.generic_recording_worker.toml delete mode 100644 fly.recording_worker.toml create mode 100644 poetry.lock delete mode 100644 scripts/generic_recording.sh delete mode 100644 scripts/recording.sh delete mode 100644 scripts/start_recording.sh delete mode 100644 src/generic_recording.py delete mode 100644 src/radiostations/__init__.py delete mode 100644 src/radiostations/base.py delete mode 100644 src/radiostations/khot.py delete mode 100644 src/radiostations/kisf.py delete mode 100644 src/radiostations/krgt.py delete mode 100644 src/radiostations/wado.py delete mode 100644 src/radiostations/waqi.py delete mode 100644 src/radiostations/wkaq.py delete mode 100644 src/recording.py delete mode 100644 tests/radiostations/test_base.py delete mode 100644 tests/radiostations/test_stations.py delete mode 100644 tests/test_generic_recording.py delete mode 100644 tests/test_recording.py diff --git a/Dockerfile.generic_recording_worker b/Dockerfile.generic_recording_worker deleted file mode 100644 index 298b732..0000000 --- a/Dockerfile.generic_recording_worker +++ /dev/null @@ -1,53 +0,0 @@ -# Start from the latest Ubuntu image -FROM ubuntu:latest - -# Avoid prompts from apt-get -ENV DEBIAN_FRONTEND=noninteractive - -# Update and install necessary packages -RUN apt-get update && apt-get install -y \ - wget \ - gnupg \ - pulseaudio \ - pulseaudio-utils \ - ffmpeg \ - build-essential \ - python3 \ - python3-pip \ - python3-venv \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - -# Install Google Chrome -RUN wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - \ - && echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list \ - && apt-get update \ - && apt-get install -y google-chrome-stable \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - -# Set the working directory in the container -WORKDIR /app - -# Copy the current directory contents into the container at /app -COPY . /app - -# Create a virtual environment -RUN python3 -m venv /app/venv - -# Activate the virtual environment and install dependencies -RUN . /app/venv/bin/activate && \ - pip3 install --no-cache-dir -r requirements.txt - -# Make the bash script executable -RUN chmod +x /app/scripts/generic_recording.sh - -# Set environment variable for headless Chrome -ENV CHROME_BIN=/usr/bin/google-chrome-stable -ENV CHROME_PATH=/usr/bin/google-chrome-stable - -# Set display port to avoid crash -ENV DISPLAY=:99 - -# Set the entrypoint script -ENTRYPOINT ["/app/scripts/generic_recording.sh"] diff --git a/Dockerfile.recording_worker b/Dockerfile.recording_worker deleted file mode 100644 index 9116833..0000000 --- a/Dockerfile.recording_worker +++ /dev/null @@ -1,24 +0,0 @@ -# Use an official Python runtime as a parent image -FROM python:3.12-slim - -# Set the working directory in the container -WORKDIR /app - -# Install system dependencies -RUN apt-get update && apt-get install -y \ - ffmpeg \ - curl \ - && rm -rf /var/lib/apt/lists/* - -COPY requirements.txt /app/ - -# Install any needed packages specified in requirements.txt -RUN pip install --no-cache-dir -r requirements.txt - -COPY . /app - -# Make sure the entrypoint script is executable -RUN chmod +x /app/scripts/recording.sh - -# Set the entrypoint script -ENTRYPOINT ["/app/scripts/recording.sh"] diff --git a/fly.generic_recording_worker.toml b/fly.generic_recording_worker.toml deleted file mode 100644 index 5011d1d..0000000 --- a/fly.generic_recording_worker.toml +++ /dev/null @@ -1,29 +0,0 @@ -app = 'generic-recording-worker' -primary_region = 'sea' - -[build] - dockerfile = 'Dockerfile.generic_recording_worker' - -[env] - PREFECT_API_URL = 'https://prefect.fly.dev/api' - -[processes] - radio_khot = '' - radio_kisf = '' - radio_krgt = '' - radio_wkaq = '' - radio_wado = '' - radio_waqi = '' - -[[vm]] - memory = '1gb' - cpu_kind = 'shared' - cpus = 1 - -[scale] - radio_khot = 1 - radio_kisf = 1 - radio_krgt = 1 - radio_wkaq = 1 - radio_wado = 1 - radio_waqi = 1 diff --git a/fly.recording_worker.toml b/fly.recording_worker.toml deleted file mode 100644 index 0c3e244..0000000 --- a/fly.recording_worker.toml +++ /dev/null @@ -1,28 +0,0 @@ -app = 'recording-worker' -primary_region = 'sea' - -[build] - dockerfile = "Dockerfile.recording_worker" - -[env] - PREFECT_API_URL = "https://prefect.fly.dev/api" - -[processes] - 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"political-debate-fact-checking-system" +version = "0.1.0" +description = "Multi-stage pipeline for political debate fact-checking with AI analysis" +authors = [ + "JsNcAr " +] +readme = "README.md" +packages = [ + { include = "processing_pipeline", from = "src" }, +] +include = [ + "prompts/", +] + +[tool.poetry.dependencies] +python = ">=3.12,<3.14" +requests = ">=2.32.5,<3.0.0" +python-ffmpeg = ">=2.0.12,<3.0.0" +python-dotenv = ">=1.2.1,<2.0.0" +black = ">=26.1.0,<27.0.0" +flake8 = ">=7.3.0,<8.0.0" +isort = ">=7.0.0,<8.0.0" +prefect = ">=3.6.12,<4.0.0" +boto3 = ">=1.42.31,<2.0.0" +google-genai = ">=1.59.0,<2.0.0" +sentry-sdk = ">=2.50.0,<3.0.0" +pytest = ">=9.0.2,<10.0.0" +supabase = ">=2.27.2,<3.0.0" +supabase-auth = ">=2.27.2,<3.0.0" +supabase-functions = ">=2.27.2,<3.0.0" +pydub = ">=0.25.1,<0.26.0" +openai = ">=2.15.0,<3.0.0" +tiktoken = ">=0.12.0,<0.13.0" +pydantic = ">=2.12.5,<3.0.0" +pytest-cov = ">=7.0.0,<8.0.0" + [tool.pytest.ini_options] addopts = "--cov=src --cov-report=html --cov-report=term-missing" testpaths = ["tests"] @@ -27,3 +64,7 @@ directory = "htmlcov" [tool.coverage.xml] output = "coverage.xml" + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt index be8e475..e3a3018 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,5 +20,4 @@ openai==1.57.1 tiktoken==0.8.0 pydantic==2.10.1 pytest-cov==6.0.0 -# google-cloud-bigquery-storage==2.26.0 -# fastavro==1.9.7 + diff --git a/scripts/generic_recording.sh b/scripts/generic_recording.sh deleted file mode 100644 index 083c2cf..0000000 --- a/scripts/generic_recording.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash -set -e - -# Activate the virtual environment -source /app/venv/bin/activate - -# Run the Python script -python /app/src/generic_recording.py diff --git a/scripts/recording.sh b/scripts/recording.sh deleted file mode 100644 index c8e3711..0000000 --- a/scripts/recording.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash - -# Function to check if Prefect server is ready -check_prefect_server() { - # Try to connect to the Prefect API - response=$(curl -s -o /dev/null -w "%{http_code}" https://prefect.fly.dev/api/health) - - # Check if the response is 200 (OK) - if [ "$response" -eq 200 ]; then - return 0 - else - return 1 - fi -} - -# Wait until Prefect server is ON -echo "Waiting for Prefect server to start..." -while ! check_prefect_server; do - echo "Prefect server is not ready yet. Retrying in 5 seconds..." - sleep 5 -done -echo "Prefect server is up and running!" - -# Trigger the recording script -python src/recording.py diff --git a/scripts/start_recording.sh b/scripts/start_recording.sh deleted file mode 100644 index 9cde92d..0000000 --- a/scripts/start_recording.sh +++ /dev/null @@ -1,101 +0,0 @@ -#!/bin/bash - -# Array for Audio Recording: Lite Recorder -LITE_RECORDER=( - "Audio Recording: Lite Recorder/ARAB" - "Audio Recording: Lite Recorder/KMRO" - "Audio Recording: Lite Recorder/KVNR" - "Audio Recording: Lite Recorder/MCD" - "Audio Recording: Lite Recorder/WGOS" - "Audio Recording: Lite Recorder/WGSP" - "Audio Recording: Lite Recorder/WIST" - "Audio Recording: Lite Recorder/WMUZ - 1200 AM" - "Audio Recording: Lite Recorder/WNZK - 680 AM" - "Audio Recording: Lite Recorder/WOLS" - "Audio Recording: Lite Recorder/WSGH" - "Audio Recording: Lite Recorder/WSRP" - "Audio Recording: Lite Recorder/WWFE - 670 AM" - "Audio Recording: Lite Recorder/WYMY" -) - -# Array for Audio Recording: Max Recorder -MAX_RECORDER=( - "Audio Recording: Max Recorder/K229DB - 93.7 FM" - "Audio Recording: Max Recorder/KABA - 90.3 FM" - "Audio Recording: Max Recorder/KBIC - 105.7 FM" - "Audio Recording: Max Recorder/KBNL - 89.9 FM" - "Audio Recording: Max Recorder/KCKO - 107.9 FM" - "Audio Recording: Max Recorder/KCMT - 92.1 FM" - "Audio Recording: Max Recorder/KENO - 1460 AM" - "Audio Recording: Max Recorder/KFUE - 106.7 FM" - "Audio Recording: Max Recorder/KMMA - 97.1 FM" - "Audio Recording: Max Recorder/KNNR - 1400 AM" - "Audio Recording: Max Recorder/KNOG - 91.7 FM" - "Audio Recording: Max Recorder/KRMC - 91.7 FM" - "Audio Recording: Max Recorder/KWST - 1430 AM" - "Audio Recording: Max Recorder/KYAR - 98.3 FM" - "Audio Recording: Max Recorder/KZLZ - 105.3 FM" - "Audio Recording: Max Recorder/RUMBA 4451" - "Audio Recording: Max Recorder/SPMN" - "Audio Recording: Max Recorder/WACC - 830 AM" - "Audio Recording: Max Recorder/WAXY - 790 AM" - "Audio Recording: Max Recorder/WBZW - 96.7 FM" - "Audio Recording: Max Recorder/WBZY - 105.7 FM" - "Audio Recording: Max Recorder/WDJA - 1420 AM" - "Audio Recording: Max Recorder/WDTW - 1310 AM" - "Audio Recording: Max Recorder/WLAZ - 89.1 FM" - "Audio Recording: Max Recorder/WLCH - 91.3 FM" - "Audio Recording: Max Recorder/WLEL - 94.3 FM" - "Audio Recording: Max Recorder/WLMV - 1480 AM" - "Audio Recording: Max Recorder/WNMA - 1210 AM" - "Audio Recording: Max Recorder/WOAP - 1080 AM" - "Audio Recording: Max Recorder/WPHE - 690 AM" - "Audio Recording: Max Recorder/WRUM - 100.3 FM" - "Audio Recording: Max Recorder/WRUM HD2 - 97.1 FM" - "Audio Recording: Max Recorder/WSDS - 1480 AM" - "Audio Recording: Max Recorder/WSRF - 99.5 FM" - "Audio Recording: Max Recorder/WSUA - 1260 AM" - "Audio Recording: Max Recorder/WUMR - 106.1 FM" - "Audio Recording: Max Recorder/WURN - 1040 AM" - "Audio Recording: Max Recorder/WZHF" - "Audio Recording: Max Recorder/WZTU - 94.9 FM" -) - -# Array for Generic Audio Recording -GENERIC_RECORDER=( - "Generic Audio Recording/KHOT - 105.9 FM" - "Generic Audio Recording/KISF - 103.5 FM" - "Generic Audio Recording/KRGT - 99.3 FM" - "Generic Audio Recording/WADO - 1280 AM" - "Generic Audio Recording/WAQI - 710 AM" - "Generic Audio Recording/WKAQ - 580 AM" -) - -# Log function -log() { - echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" -} - -# Function to start deployments from an array -start_deployments() { - local deployments=("$@") - for deployment in "${deployments[@]}"; do - log "Starting: $deployment" - if prefect deployment run "$deployment"; then - log "Successfully started $deployment" - else - log "Failed to start $deployment" - fi - sleep 2 - done -} - -# Start all deployments -log "Starting Lite Recorder deployments..." -start_deployments "${LITE_RECORDER[@]}" - -log "Starting Max Recorder deployments..." -start_deployments "${MAX_RECORDER[@]}" - -log "Starting Generic Audio Recording deployments..." -start_deployments "${GENERIC_RECORDER[@]}" diff --git a/src/generic_recording.py b/src/generic_recording.py deleted file mode 100644 index 2e88e01..0000000 --- a/src/generic_recording.py +++ /dev/null @@ -1,210 +0,0 @@ -import hashlib -import os -import time -import boto3 - -from datetime import datetime -from dotenv import load_dotenv -from prefect import serve -from prefect.task_runners import ConcurrentTaskRunner -from ffmpeg import FFmpeg -from botocore.exceptions import NoCredentialsError -import psutil -import sentry_sdk - -from processing_pipeline.supabase_utils import SupabaseClient -from radiostations import RadioStation, Khot, Kisf, Krgt, Wado, Waqi, Wkaq -from utils import optional_flow, optional_task - -load_dotenv() - -# Setup Sentry -sentry_sdk.init(dsn=os.getenv("SENTRY_DSN")) - -# Setup S3 Client -R2_ENDPOINT_URL = os.getenv("R2_ENDPOINT_URL") -R2_ACCESS_KEY_ID = os.getenv("R2_ACCESS_KEY_ID") -R2_SECRET_ACCESS_KEY = os.getenv("R2_SECRET_ACCESS_KEY") -R2_BUCKET_NAME = os.getenv("R2_BUCKET_NAME") -s3_client = boto3.client( - "s3", endpoint_url=R2_ENDPOINT_URL, aws_access_key_id=R2_ACCESS_KEY_ID, aws_secret_access_key=R2_SECRET_ACCESS_KEY -) - -# Setup Supabase client -SUPABASE_URL = os.getenv("SUPABASE_URL") -SUPABASE_KEY = os.getenv("SUPABASE_KEY") -supabase_client = SupabaseClient(SUPABASE_URL, SUPABASE_KEY) - - -@optional_task(log_prints=True) -def capture_audio_stream(station, duration_seconds, audio_birate, audio_channels): - try: - if station.is_audio_playing(): - print(f"Audio is properly set up and playing for {station.code}") - else: - raise Exception(f"Sink is not in RUNNING state for {station.code}") - - # Create the output filename using the timestamp and hash - start_time = time.time() - timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime(start_time)) - output_file = f"radio_{get_url_hash(station.url)}_{timestamp}.mp3" - - print(f"Start capturing audio from ${station.url} for {duration_seconds} seconds") - FFmpeg().option("y").input(station.source_name, f="pulse", t=duration_seconds).output( - output_file, ab=audio_birate, ac=audio_channels, acodec="libmp3lame" - ).execute() - - return get_metadata(output_file, station, start_time) - - except Exception as e: - print(f"Failed to capture audio stream ${station.url}: {e}") - # TODO: Sleep for 30 seconds before returning - return None - - -@optional_task(log_prints=True) -def get_metadata(file, station, start_time): - file_size = os.path.getsize(file) - return { - "file_name": file, - "radio_station_name": station.name, - "radio_station_code": station.code, - "location_state": station.state, - "recorded_at": time.strftime("%Y-%m-%dT%H:%M:%S", time.localtime(start_time)), - "recording_day_of_week": datetime.fromtimestamp(start_time).strftime("%A"), - "file_size": file_size - } - - -@optional_task(log_prints=True, retries=3) -def upload_to_r2_and_clean_up(url, file_path): - object_name = os.path.basename(file_path) - - try: - url_hash = get_url_hash(url) - destination_path = f"radio_{url_hash}/{object_name}" - s3_client.upload_file(file_path, R2_BUCKET_NAME, destination_path) - print(f"File {file_path} uploaded to R2 as {destination_path}") - os.remove(file_path) - return destination_path - except NoCredentialsError: - print("R2 Credentials was not set") - return None - - -@optional_task(log_prints=True, retries=3) -def insert_recorded_audio_file_into_database(metadata, uploaded_path): - supabase_client.insert_audio_file( - radio_station_name=metadata["radio_station_name"], - radio_station_code=metadata["radio_station_code"], - location_state=metadata["location_state"], - recorded_at=metadata["recorded_at"], - recording_day_of_week=metadata["recording_day_of_week"], - file_path=uploaded_path, - file_size=metadata["file_size"], - ) - - -@optional_flow( - name="Generic Audio Recording", - log_prints=True, - task_runner=ConcurrentTaskRunner, -) -def generic_audio_processing_pipeline(station_code, duration_seconds, audio_birate, audio_channels, repeat): - RADIO_STATIONS: dict[str, type[RadioStation]] = { - Khot.code: Khot, - Kisf.code: Kisf, - Krgt.code: Krgt, - Wkaq.code: Wkaq, - Wado.code: Wado, - Waqi.code: Waqi, - } - - # Reconstruct the radio station object based on the station code - station_class = RADIO_STATIONS.get(station_code) - if not station_class: - raise ValueError(f"Invalid station code: {station_code}") - - station = station_class() - - try: - station.setup_virtual_audio() - station.start_browser() - - while True: - # Check current memory usage - memory_usage = psutil.virtual_memory().percent - print(f"Current memory usage: {memory_usage}%") - - # If memory usage is above 95%, restart the browser - if memory_usage > 95: - print("Memory usage is high. Restarting browser...") - station.stop(unload_modules=False) - time.sleep(5) # Wait for browser to fully close - station.start_browser() - print(f"Current memory usage: {psutil.virtual_memory().percent}%") - - if not station.is_audio_playing(): - print("Playback stopped playing for some reason. Restarting browser...") - station.stop(unload_modules=False) - time.sleep(5) # Wait for browser to fully close - station.start_browser() - - output = capture_audio_stream(station, duration_seconds, audio_birate, audio_channels) - - if output and output["file_name"]: - uploaded_path = upload_to_r2_and_clean_up(station.url, output["file_name"]) - if uploaded_path: - insert_recorded_audio_file_into_database(output, uploaded_path) - - # Stop the flow if it should not be repeated - if not repeat: - break - finally: - print("Stopping the radio station and cleaning up...") - station.stop() - print("Cleanup finished") - - -def get_url_hash(url): - # Hash the URL and get the last 6 characters - return hashlib.sha256(url.encode()).hexdigest()[-6:] - - -if __name__ == "__main__": - process_group = os.environ.get("FLY_PROCESS_GROUP") - print(f"======== Starting {process_group} ========") - - match process_group: - case "radio_khot": - station = Khot() - case "radio_kisf": - station = Kisf() - case "radio_krgt": - station = Krgt() - case "radio_wkaq": - station = Wkaq() - case "radio_wado": - station = Wado() - case "radio_waqi": - station = Waqi() - case _: - raise Exception("Invalid process group") - - duration_seconds = 1800 # Default to 30 minutes - audio_birate = 64000 # Default to 64kbps bitrate - audio_channels = 1 # Default to single channel (mono audio) - - deployment = generic_audio_processing_pipeline.to_deployment( - name=station.code, - tags=[station.state, get_url_hash(station.url), "Generic"], - concurrency_limit=5, - parameters=dict( - station_code=station.code, - duration_seconds=duration_seconds, - repeat=True, - audio_birate=audio_birate, - audio_channels=audio_channels, - ), - ) - serve(deployment) diff --git a/src/processing_pipeline/__init__.py b/src/processing_pipeline/__init__.py index e69de29..a5e5f7c 100644 --- a/src/processing_pipeline/__init__.py +++ b/src/processing_pipeline/__init__.py @@ -0,0 +1,114 @@ +""" +Processing Pipeline Module + +This module orchestrates the 5-stage pipeline for political debate fact-checking: +1. Stage 1: Initial Disinformation Detection & Transcription +2. Stage 2: Audio Clipping & Snippet Extraction +3. Stage 3: In-Depth Analysis +4. Stage 4: Analysis Review +5. Stage 5: Vector Embedding Generation + +The pipeline is distributed across multiple workers using Prefect, +with task reservation coordinated via Supabase RPC functions. +""" + +# Core utilities +from processing_pipeline.supabase_utils import SupabaseClient + +# Constants and enums +from processing_pipeline.constants import ( + GeminiModel, + GeminiCLIEventType, + ProcessingStatus, + PromptStage, + get_detection_prompt_for_stage_1, + get_system_instruction_for_stage_1, + get_output_schema_for_stage_1, +) + +# Processing utilities +from processing_pipeline.processing_utils import ( + get_safety_settings, + postprocess_snippet, +) + +# Timestamped transcription +from processing_pipeline.timestamped_transcription_generator import ( + TimestampedTranscriptionGenerator, +) + +# Stage modules +from processing_pipeline.stage_1 import ( + initial_disinformation_detection, + redo_main_detection, + regenerate_timestamped_transcript, + undo_disinformation_detection, +) + +from processing_pipeline.stage_2 import ( + audio_clipping, + undo_audio_clipping, +) + +from processing_pipeline.stage_3 import ( + in_depth_analysis, +) + +from processing_pipeline.stage_3_models import ( + EmotionalToneItem, + PoliticalLeaning, + Stage3Output, +) + +from processing_pipeline.stage_4 import ( + analysis_review, +) + +from processing_pipeline.stage_5 import ( + embedding, +) + +__all__ = [ + # Core + "SupabaseClient", + + # Constants + "GeminiModel", + "GeminiCLIEventType", + "ProcessingStatus", + "PromptStage", + "get_detection_prompt_for_stage_1", + "get_system_instruction_for_stage_1", + "get_output_schema_for_stage_1", + + # Utilities + "get_safety_settings", + "postprocess_snippet", + "TimestampedTranscriptionGenerator", + + # Stage 1 + "initial_disinformation_detection", + "redo_main_detection", + "regenerate_timestamped_transcript", + "undo_disinformation_detection", + + # Stage 2 + "audio_clipping", + "undo_audio_clipping", + + # Stage 3 + "in_depth_analysis", + "EmotionalToneItem", + "PoliticalLeaning", + "Stage3Output", + + # Stage 4 + "analysis_review", + + # Stage 5 + "embedding", +] + +__version__ = "1.0.0" +__author__ = "VERDAD Team" +__description__ = "Political debate fact-checking pipeline with multi-stage AI analysis" diff --git a/src/radiostations/__init__.py b/src/radiostations/__init__.py deleted file mode 100644 index 7a6ec9b..0000000 --- a/src/radiostations/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from .base import RadioStation -from .khot import Khot -from .kisf import Kisf -from .krgt import Krgt -from .wado import Wado -from .waqi import Waqi -from .wkaq import Wkaq - -__all__ = [ - "RadioStation", - "Khot", - "Kisf", - "Krgt", - "Wado", - "Waqi", - "Wkaq", -] diff --git a/src/radiostations/base.py b/src/radiostations/base.py deleted file mode 100644 index f00000c..0000000 --- a/src/radiostations/base.py +++ /dev/null @@ -1,232 +0,0 @@ -import re -import subprocess -import time -from selenium import webdriver -from selenium.webdriver.chrome.service import Service -from selenium.webdriver.chrome.options import Options -from selenium.webdriver.support.ui import WebDriverWait -from selenium.webdriver.support import expected_conditions as EC -from selenium.webdriver.common.by import By -from webdriver_manager.chrome import ChromeDriverManager -from utils import optional_task - - -class RadioStation: - - class Config: - arbitrary_types_allowed = True - - def __init__(self, url, sink_name, source_name, play_button_selector, video_element_selector): - self.url = url - self.sink_name = sink_name - self.source_name = source_name - self.driver = None - self.sink_module = None - self.source_module = None - - self.play_button_selector = play_button_selector - self.video_element_selector = video_element_selector - - @optional_task(log_prints=True) - def setup_virtual_audio(self): - self.ensure_pulseaudio_running() - - try: - sink_result = subprocess.run( - [ - "pactl", - "load-module", - "module-null-sink", - f"sink_name={self.sink_name}", - f"sink_properties=device.description={self.sink_name}", - ], - capture_output=True, - text=True, - check=True, - ) - self.sink_module = sink_result.stdout.strip() - - source_result = subprocess.run( - [ - "pactl", - "load-module", - "module-virtual-source", - f"source_name={self.source_name}", - f"master={self.sink_name}.monitor", - f"source_properties=device.description={self.source_name}", - ], - capture_output=True, - text=True, - check=True, - ) - self.source_module = source_result.stdout.strip() - - # Set the sink to running state - subprocess.run(["pactl", "suspend-sink", self.sink_name, "0"], check=True) - - # Set the source to running state - subprocess.run(["pactl", "suspend-source", self.source_name, "0"], check=True) - - print(f"Virtual audio setup completed for {self.url}") - - except subprocess.CalledProcessError as e: - print(f"Error setting up virtual audio: {e}") - raise e - - # TODO: Before retry, must kill browser process - @optional_task(log_prints=True, retries=3000) - def start_browser(self): - chrome_options = Options() - chrome_options.add_argument("--headless") - chrome_options.add_argument("--no-sandbox") - chrome_options.add_argument("--disable-dev-shm-usage") - chrome_options.add_argument("--disable-gpu") - chrome_options.add_argument("--use-pulse-audio") - chrome_options.add_argument("--autoplay-policy=no-user-gesture-required") - chrome_options.add_argument( - "--user-agent=Mozilla/5.0 (iPhone; CPU iPhone OS 17_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.6 Mobile/15E148 Safari/604.1" - ) - - service = Service(ChromeDriverManager().install()) - self.driver = webdriver.Chrome(service=service, options=chrome_options) - - print(f"Launching url {self.url}") - self.driver.get(self.url) - - try: - self.start_playing() - except Exception as e: - print(f"{e}. Restarting browser and trying again...") - self.driver.quit() - self.driver = None - raise e - - print("PulseAudio sinks:") - self.execute_command(["pactl", "list", "short", "sinks"]) - print("PulseAudio sources:") - self.execute_command(["pactl", "list", "short", "sources"]) - print("PulseAudio sink inputs:") - self.execute_command(["pactl", "list", "sink-inputs"]) - - @optional_task(log_prints=True) - def start_playing(self): - WebDriverWait(self.driver, 10).until( - EC.presence_of_element_located((By.CSS_SELECTOR, self.play_button_selector)) - ) - print(f"Play button found on {self.url}") - time.sleep(5) - - print("Click on the Play button") - play_button = self.driver.find_element(By.CSS_SELECTOR, self.play_button_selector) - play_button.click() - - # Wait for a moment to let the audio start - print("Wait a bit...") - time.sleep(30) - - # Detailed audio checks - try: - video_element = self.driver.find_element(By.CSS_SELECTOR, self.video_element_selector) - except Exception: - print("Video element was not found. Skipped audio checks.") - return - - is_playing = self.driver.execute_script( - "return !arguments[0].paused && !arguments[0].ended && arguments[0].currentTime > 0;", video_element - ) - current_time = self.driver.execute_script("return arguments[0].currentTime;", video_element) - duration = self.driver.execute_script("return arguments[0].duration;", video_element) - is_muted = self.driver.execute_script("return arguments[0].muted;", video_element) - volume = self.driver.execute_script("return arguments[0].volume;", video_element) - - print( - f"Is audio playing: {is_playing}. Current time: {current_time}. Duration: {duration}. Volume: {volume}. Muted: {is_muted}" - ) - - if not is_playing: - raise Exception(f"Failed to start audio for {self.url}") - - def execute_command(self, command): - try: - result = subprocess.run(command, capture_output=True, text=True, check=True) - print(result.stdout) - except Exception as e: - print(f"An error occurred while executing command: {command}") - print(str(e)) - - def is_audio_playing(self): - try: - result = subprocess.run(["pactl", "list", "sinks"], capture_output=True, text=True, check=True) - - # Use a regex pattern that looks for State before Name - pattern = rf"State: (\w+).*?Name: {re.escape(self.sink_name)}" - sink_match = re.search(pattern, result.stdout, re.DOTALL) - - if sink_match: - state = sink_match.group(1) - is_playing = state == "RUNNING" - return is_playing - else: - print(f"Sink {self.sink_name} not found in pactl output") - print("PulseAudio sinks:") - print(result.stdout) - - return False - except subprocess.CalledProcessError as e: - print(f"Error checking audio status: {e}") - return False - - @optional_task(log_prints=True) - def stop(self, unload_modules=True): - if self.driver: - self.driver.quit() - - if self.sink_module and unload_modules: - try: - # Check if the sink module exists before unloading - result = subprocess.run( - ["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True - ) - if self.sink_module in result.stdout: - subprocess.run(["pactl", "unload-module", self.sink_module], check=True) - print(f"Unloaded sink module {self.sink_module}") - else: - print(f"Sink module {self.sink_module} not found, possibly already unloaded") - - except subprocess.CalledProcessError as e: - print(f"Error checking or unloading sink module {self.sink_module}: {e}") - - if self.source_module and unload_modules: - try: - # Check if the source module exists before unloading - result = subprocess.run( - ["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True - ) - if self.source_module in result.stdout: - subprocess.run(["pactl", "unload-module", self.source_module], check=True) - print(f"Unloaded source module {self.source_module}") - else: - print(f"Source module {self.source_module} not found, possibly already unloaded") - except subprocess.CalledProcessError as e: - print(f"Error checking or unloading source module {self.source_module}: {e}") - - def ensure_pulseaudio_running(self): - try: - subprocess.run(["pulseaudio", "--check"], check=True) - print("PulseAudio is already running.") - return - except subprocess.CalledProcessError: - print("PulseAudio is not running. Starting PulseAudio...") - subprocess.Popen( - ["pulseaudio", "--start", "--log-target=stderr", "--exit-idle-time=-1"], - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - ) - time.sleep(2) - - try: - subprocess.run(["pulseaudio", "--check"], check=True) - print("PulseAudio is now running.") - return - except subprocess.CalledProcessError: - raise Exception("Failed to start PulseAudio.") diff --git a/src/radiostations/khot.py b/src/radiostations/khot.py deleted file mode 100644 index 294a5d2..0000000 --- a/src/radiostations/khot.py +++ /dev/null @@ -1,17 +0,0 @@ -from radiostations.base import RadioStation - - -class Khot(RadioStation): - - code = "KHOT - 105.9 FM" - state = "Arizona" - name = "Que Buena" - - def __init__(self): - super().__init__( - "https://www.iheart.com/live/que-buena-1059-fm-5207/", - "virtual_speaker_khot", - "virtual_mic_khot", - "button[aria-label='Play Button']", - "video.jw-video", - ) diff --git a/src/radiostations/kisf.py b/src/radiostations/kisf.py deleted file mode 100644 index dae982c..0000000 --- a/src/radiostations/kisf.py +++ /dev/null @@ -1,17 +0,0 @@ -from radiostations.base import RadioStation - - -class Kisf(RadioStation): - - code = "KISF - 103.5 FM" - state = "Nevada" - name = "ZonaMX" - - def __init__(self): - super().__init__( - "https://www.iheart.com/live/zona-mx-1035-fm-5209/", - "virtual_speaker_kisf", - "virtual_mic_kisf", - "button[aria-label='Play Button']", - "video.jw-video", - ) diff --git a/src/radiostations/krgt.py b/src/radiostations/krgt.py deleted file mode 100644 index a1842de..0000000 --- a/src/radiostations/krgt.py +++ /dev/null @@ -1,17 +0,0 @@ -from radiostations.base import RadioStation - - -class Krgt(RadioStation): - - code = "KRGT - 99.3 FM" - state = "Nevada" - name = "Rumba Hits caliente" - - def __init__(self): - super().__init__( - "https://www.iheart.com/live/latino-mix-993-fm-5221/", - "virtual_speaker_krgt", - "virtual_mic_krgt", - "button[aria-label='Play Button']", - "video.jw-video", - ) diff --git a/src/radiostations/wado.py b/src/radiostations/wado.py deleted file mode 100644 index 2a781b3..0000000 --- a/src/radiostations/wado.py +++ /dev/null @@ -1,17 +0,0 @@ -from radiostations.base import RadioStation - - -class Wado(RadioStation): - - code = "WADO - 1280 AM" - state = "New York" - name = "La Campeona de Nueva York" - - def __init__(self): - super().__init__( - "https://www.iheart.com/live/wado-1280-am-5172/", - "virtual_speaker_wado", - "virtual_mic_wado", - "button[aria-label='Play Button']", - "video.jw-video", - ) diff --git a/src/radiostations/waqi.py b/src/radiostations/waqi.py deleted file mode 100644 index adbbafd..0000000 --- a/src/radiostations/waqi.py +++ /dev/null @@ -1,17 +0,0 @@ -from radiostations.base import RadioStation - - -class Waqi(RadioStation): - - code = "WAQI - 710 AM" - state = "Florida" - name = "Radio Mambi" - - def __init__(self): - super().__init__( - "https://www.iheart.com/live/radio-mambi-710-am-5175/", - "virtual_speaker_waqi", - "virtual_mic_waqi", - "button[aria-label='Play Button']", - "video.jw-video", - ) diff --git a/src/radiostations/wkaq.py b/src/radiostations/wkaq.py deleted file mode 100644 index 2b24c90..0000000 --- a/src/radiostations/wkaq.py +++ /dev/null @@ -1,17 +0,0 @@ -from radiostations.base import RadioStation - - -class Wkaq(RadioStation): - - code = "WKAQ - 580 AM" - state = "Puerto Rico" - name = "Analisis y Noticias" - - def __init__(self): - super().__init__( - "https://www.iheart.com/live/wkaq-580-5176/", - "virtual_speaker_wkaq", - "virtual_mic_wkaq", - "button[aria-label='Play Button']", - "video.jw-video", - ) diff --git a/src/recording.py b/src/recording.py deleted file mode 100644 index e987b9e..0000000 --- a/src/recording.py +++ /dev/null @@ -1,177 +0,0 @@ -from datetime import datetime -import os -import time -import hashlib -import boto3 -from prefect import serve -from prefect.task_runners import ConcurrentTaskRunner - -from ffmpeg import FFmpeg -from dotenv import load_dotenv -import sentry_sdk - -from processing_pipeline.supabase_utils import SupabaseClient -from utils import fetch_radio_stations, optional_flow, optional_task - -load_dotenv() - -# Setup Sentry -sentry_sdk.init(dsn=os.getenv("SENTRY_DSN")) - -# Setup S3 Client -R2_ENDPOINT_URL = os.getenv("R2_ENDPOINT_URL") -R2_ACCESS_KEY_ID = os.getenv("R2_ACCESS_KEY_ID") -R2_SECRET_ACCESS_KEY = os.getenv("R2_SECRET_ACCESS_KEY") -R2_BUCKET_NAME = os.getenv("R2_BUCKET_NAME") -s3_client = boto3.client( - "s3", endpoint_url=R2_ENDPOINT_URL, aws_access_key_id=R2_ACCESS_KEY_ID, aws_secret_access_key=R2_SECRET_ACCESS_KEY -) - -# Setup Supabase client -SUPABASE_URL = os.getenv("SUPABASE_URL") -SUPABASE_KEY = os.getenv("SUPABASE_KEY") -supabase_client = SupabaseClient(SUPABASE_URL, SUPABASE_KEY) - - -@optional_task(log_prints=True) -def capture_audio_stream(station, duration_seconds, audio_birate, audio_channels): - try: - url = station["url"] - - # Create the output filename using the timestamp and hash - start_time = time.time() - timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime(start_time)) - output_file = f"radio_{get_url_hash(url)}_{timestamp}.mp3" - - # Use ffmpeg to capture audio - print(f"Start capturing audio from ${url} for {duration_seconds} seconds") - FFmpeg().option("y").input(url, t=duration_seconds).output( - output_file, ab=audio_birate, ac=audio_channels, acodec="libmp3lame" - ).execute() - - return get_metadata(output_file, station, start_time) - - except Exception as e: - print(f"Failed to capture audio stream ${url}: {e}") - print("Sleep for 300 seconds before returning") - time.sleep(300) - return None - - -@optional_task(log_prints=True, retries=3) -def upload_to_r2_and_clean_up(url, file_path): - object_name = os.path.basename(file_path) - url_hash = get_url_hash(url) - destination_path = f"radio_{url_hash}/{object_name}" - s3_client.upload_file(file_path, R2_BUCKET_NAME, destination_path) - print(f"File {file_path} uploaded to R2 as {destination_path}") - os.remove(file_path) - return destination_path - - -@optional_task(log_prints=True) -def get_metadata(file, station, start_time): - file_size = os.path.getsize(file) - return { - "file_name": file, - "radio_station_name": station["name"], - "radio_station_code": station["code"], - "location_state": station["state"], - "recorded_at": time.strftime("%Y-%m-%dT%H:%M:%S", time.localtime(start_time)), - "recording_day_of_week": datetime.fromtimestamp(start_time).strftime("%A"), - "file_size": file_size, - } - - -@optional_task(log_prints=True, retries=3) -def insert_recorded_audio_file_into_database(metadata, uploaded_path): - supabase_client.insert_audio_file( - radio_station_name=metadata["radio_station_name"], - radio_station_code=metadata["radio_station_code"], - location_state=metadata["location_state"], - recorded_at=metadata["recorded_at"], - recording_day_of_week=metadata["recording_day_of_week"], - file_path=uploaded_path, - file_size=metadata["file_size"], - ) - - -@optional_flow(name="Audio Recording: Max Recorder", log_prints=True, task_runner=ConcurrentTaskRunner) -def audio_processing_pipeline_max_recorder(url, duration_seconds, audio_birate, audio_channels, repeat): - __start_recording_flow(url, duration_seconds, audio_birate, audio_channels, repeat) - - -@optional_flow(name="Audio Recording: Lite Recorder", log_prints=True, task_runner=ConcurrentTaskRunner) -def audio_processing_pipeline_lite_recorder(url, duration_seconds, audio_birate, audio_channels, repeat): - __start_recording_flow(url, duration_seconds, audio_birate, audio_channels, repeat) - - -def __start_recording_flow(url, duration_seconds, audio_birate, audio_channels, repeat): - # Reconstruct the radio station from the URL - station = reconstruct_radio_station(url) - if not station: - raise ValueError(f"Radio station not found for URL: {url}") - - while True: - output = capture_audio_stream(station, duration_seconds, audio_birate, audio_channels) - - if output and output["file_name"]: - uploaded_path = upload_to_r2_and_clean_up(station["url"], output["file_name"]) - insert_recorded_audio_file_into_database(output, uploaded_path) - - # Stop the flow if it should not be repeated - if not repeat: - break - -def reconstruct_radio_station(url): - radio_stations = fetch_radio_stations() - for station in radio_stations: - if station["url"] == url: - return station - return None - - -def get_url_hash(url): - # Hash the URL and get the last 6 characters - return hashlib.sha256(url.encode()).hexdigest()[-6:] - -def serve_deployments(radio_stations, flow_function): - duration_seconds = 1800 # Default to 30 minutes - audio_birate = 64000 # Default to 64kbps bitrate - audio_channels = 1 # Default to single channel (mono audio) - concurrency_limit = 100 - all_deployments = [] - - for station in radio_stations: - deployment = flow_function.to_deployment( - name=station["code"], - tags=[station["state"], get_url_hash(station["url"])], - parameters=dict( - url=station["url"], - duration_seconds=duration_seconds, - repeat=True, - audio_birate=audio_birate, - audio_channels=audio_channels, - ), - ) - all_deployments.append(deployment) - serve(*all_deployments, limit=concurrency_limit) - - -if __name__ == "__main__": - radio_stations = fetch_radio_stations() - - # Divide the stations into two groups: - # Max recorder stations: the first 39 stations - # Lite recorder stations: the rest of the stations (which is currently 14 stations) - max_recorder_stations = radio_stations[:39] - lite_recorder_stations = radio_stations[39:] - - process_group = os.environ.get("FLY_PROCESS_GROUP") - match process_group: - case "max_recorder": - serve_deployments(max_recorder_stations, audio_processing_pipeline_max_recorder) - case "lite_recorder": - serve_deployments(lite_recorder_stations, audio_processing_pipeline_lite_recorder) - case _: - raise ValueError(f"Invalid process group: {process_group}") diff --git a/tests/radiostations/test_base.py b/tests/radiostations/test_base.py deleted file mode 100644 index d947904..0000000 --- a/tests/radiostations/test_base.py +++ /dev/null @@ -1,466 +0,0 @@ -import os -import subprocess -import pytest -from unittest.mock import Mock, patch, call - -# Set environment variable before importing RadioStation -os.environ["ENABLE_PREFECT_DECORATOR"] = "false" -from radiostations.base import RadioStation - - -class TestRadioStation: - @pytest.fixture - def radio_station(self): - """Create a test radio station instance""" - return RadioStation( - url="https://test.radio/stream", - sink_name="test_sink", - source_name="test_source", - play_button_selector="button.play", - video_element_selector="video.player", - ) - - @pytest.fixture - def mock_subprocess(self): - """Mock subprocess for testing""" - with patch("subprocess.run") as mock_run, patch("subprocess.Popen") as mock_popen: - mock_run.return_value = Mock(stdout="123\n", stderr="", check=True) - yield { - "run": mock_run, - "popen": mock_popen, - "called_process_error": subprocess.CalledProcessError(returncode=1, cmd=["pulseaudio", "--check"]), - } - - @pytest.fixture - def mock_webdriver(self): - """Mock Selenium WebDriver""" - with patch("selenium.webdriver.Chrome") as mock_chrome, patch( - "selenium.webdriver.chrome.service.Service" - ) as mock_service, patch("webdriver_manager.chrome.ChromeDriverManager") as mock_manager: - mock_driver = Mock() - mock_chrome.return_value = mock_driver - yield {"chrome": mock_chrome, "service": mock_service, "manager": mock_manager, "driver": mock_driver} - - def test_init(self, radio_station): - """Test RadioStation initialization""" - assert radio_station.url == "https://test.radio/stream" - assert radio_station.sink_name == "test_sink" - assert radio_station.source_name == "test_source" - assert radio_station.play_button_selector == "button.play" - assert radio_station.video_element_selector == "video.player" - assert radio_station.driver is None - assert radio_station.sink_module is None - assert radio_station.source_module is None - - def test_setup_virtual_audio_success(self, radio_station, mock_subprocess): - """Test successful virtual audio setup""" - # Mock ensure_pulseaudio_running to avoid actual system calls - with patch.object(radio_station, "ensure_pulseaudio_running"): - radio_station.setup_virtual_audio() - - # Verify all the subprocess.run calls with exact command format - assert mock_subprocess["run"].call_args_list == [ - # First call: load sink module - call( - [ - "pactl", - "load-module", - "module-null-sink", - f"sink_name={radio_station.sink_name}", - f"sink_properties=device.description={radio_station.sink_name}", - ], - capture_output=True, - text=True, - check=True, - ), - # Second call: load source module - call( - [ - "pactl", - "load-module", - "module-virtual-source", - f"source_name={radio_station.source_name}", - f"master={radio_station.sink_name}.monitor", - f"source_properties=device.description={radio_station.source_name}", - ], - capture_output=True, - text=True, - check=True, - ), - # Third call: set sink to running state - call(["pactl", "suspend-sink", radio_station.sink_name, "0"], check=True), - # Fourth call: set source to running state - call(["pactl", "suspend-source", radio_station.source_name, "0"], check=True), - ] - - # Verify the module IDs were stored - assert radio_station.sink_module == "123" - assert radio_station.source_module == "123" - - def test_setup_virtual_audio_failure(self, radio_station, mock_subprocess): - """Test virtual audio setup failure""" - error = subprocess.CalledProcessError(returncode=1, cmd=["pactl", "load-module"]) - mock_subprocess["run"].side_effect = error - - # Mock ensure_pulseaudio_running to avoid actual system calls - with patch.object(radio_station, "ensure_pulseaudio_running"): - with pytest.raises(subprocess.CalledProcessError) as exc_info: - radio_station.setup_virtual_audio() - - assert exc_info.value.returncode == 1 - assert exc_info.value.cmd == ["pactl", "load-module"] - - @patch("time.sleep") - @patch("radiostations.base.WebDriverWait") - def test_start_browser_success(self, mock_wait, mock_sleep, radio_station, mock_webdriver): - """Test successful browser start""" - # Setup mocks - mock_element = Mock() - mock_webdriver["driver"].find_element.return_value = mock_element - mock_webdriver["driver"].execute_script.return_value = True - - # Setup WebDriverWait mock - mock_wait_instance = Mock() - mock_wait.return_value = mock_wait_instance - mock_wait_instance.until.return_value = mock_element - - radio_station.start_browser() - - # Verify driver setup - assert radio_station.driver == mock_webdriver["driver"] - - # Verify Chrome options - chrome_options_calls = mock_webdriver["chrome"].call_args[1]["options"] - expected_arguments = [ - "--headless", - "--no-sandbox", - "--disable-dev-shm-usage", - "--disable-gpu", - "--use-pulse-audio", - "--autoplay-policy=no-user-gesture-required", - ] - for arg in expected_arguments: - assert arg in chrome_options_calls.arguments - - # Verify URL navigation - mock_webdriver["driver"].get.assert_called_with(radio_station.url) - - # Verify wait and click sequence - mock_wait.assert_called_with(mock_webdriver["driver"], 10) - mock_wait_instance.until.assert_called() - mock_element.click.assert_called_once() - - # Verify sleep calls - assert mock_sleep.call_count == 2 - mock_sleep.assert_has_calls([call(5), call(30)]) - - @patch("time.sleep") - def test_start_playing_success(self, mock_sleep, radio_station, mock_webdriver): - """Test successful playback start""" - radio_station.driver = mock_webdriver["driver"] - mock_element = Mock() - mock_webdriver["driver"].find_element.return_value = mock_element - mock_webdriver["driver"].execute_script.return_value = True - - radio_station.start_playing() - - assert mock_element.click.called - - @patch("time.sleep") - def test_start_playing_failure(self, mock_sleep, radio_station, mock_webdriver): - """Test playback start failure""" - radio_station.driver = mock_webdriver["driver"] - mock_webdriver["driver"].execute_script.return_value = False - - with pytest.raises(Exception, match=f"Failed to start audio for {radio_station.url}"): - radio_station.start_playing() - - def test_is_audio_playing_success(self, radio_station, mock_subprocess): - """Test audio playing check success""" - mock_subprocess["run"].return_value.stdout = f"State: RUNNING\nName: {radio_station.sink_name}" - - assert radio_station.is_audio_playing() is True - - def test_is_audio_playing_failure(self, radio_station, mock_subprocess): - """Test audio playing check failure""" - mock_subprocess["run"].return_value.stdout = f"State: SUSPENDED\nName: {radio_station.sink_name}" - - assert radio_station.is_audio_playing() is False - - def test_stop(self, radio_station, mock_subprocess): - """Test radio station stop""" - radio_station.driver = Mock() - radio_station.sink_module = "123" - radio_station.source_module = "456" - - # Mock the module existence check - mock_subprocess["run"].return_value.stdout = "123\n456" - - radio_station.stop() - - assert radio_station.driver.quit.called - assert mock_subprocess["run"].call_count >= 2 - - def test_ensure_pulseaudio_running_already_running(self, radio_station, mock_subprocess): - """Test PulseAudio check when already running""" - radio_station.ensure_pulseaudio_running() - - assert mock_subprocess["run"].called - assert not mock_subprocess["popen"].called - - def test_ensure_pulseaudio_running_needs_start(self, radio_station, mock_subprocess): - """Test PulseAudio start when not running""" - # First call raises CalledProcessError, second call returns None - error = subprocess.CalledProcessError(returncode=1, cmd=["pulseaudio", "--check"]) - mock_subprocess["run"].side_effect = [ - error, # First check fails - None, # Second check passes - ] - - # Mock time.sleep to avoid actual delays - with patch("time.sleep"): - radio_station.ensure_pulseaudio_running() - - # Verify that Popen was called with correct arguments - mock_subprocess["popen"].assert_called_once_with( - ["pulseaudio", "--start", "--log-target=stderr", "--exit-idle-time=-1"], - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - ) - - # Verify that run was called twice - assert mock_subprocess["run"].call_count == 2 - mock_subprocess["run"].assert_has_calls( - [ - call(["pulseaudio", "--check"], check=True), # First check (fails) - call(["pulseaudio", "--check"], check=True), # Second check (passes) - ] - ) - - def test_ensure_pulseaudio_running_failure(self, radio_station, mock_subprocess): - """Test PulseAudio start failure""" - error = subprocess.CalledProcessError(returncode=1, cmd=["pulseaudio", "--check"]) - # Make all run calls fail - mock_subprocess["run"].side_effect = error - - # Mock time.sleep to avoid actual delays - with patch("time.sleep"): - with pytest.raises(Exception, match="Failed to start PulseAudio."): - radio_station.ensure_pulseaudio_running() - - # Verify that both the initial check and the verification check were attempted - assert mock_subprocess["run"].call_count == 2 - mock_subprocess["run"].assert_has_calls( - [ - call(["pulseaudio", "--check"], check=True), # First check - call(["pulseaudio", "--check"], check=True), # Second check - ] - ) - - # Verify that Popen was called to attempt starting PulseAudio - mock_subprocess["popen"].assert_called_once_with( - ["pulseaudio", "--start", "--log-target=stderr", "--exit-idle-time=-1"], - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - ) - - def test_execute_command_success(self, radio_station, mock_subprocess): - """Test command execution success""" - mock_subprocess["run"].return_value.stdout = "test output" - - radio_station.execute_command(["test", "command"]) - - assert mock_subprocess["run"].called - - def test_execute_command_failure(self, radio_station, mock_subprocess): - """Test command execution failure""" - mock_subprocess["run"].side_effect = Exception("Command failed") - - radio_station.execute_command(["test", "command"]) - # Should not raise exception, just print error message - - def test_is_audio_playing_success(self, radio_station, mock_subprocess): - """Test audio playing check success""" - mock_subprocess[ - "run" - ].return_value.stdout = f""" - Sink #0 - State: RUNNING - Name: {radio_station.sink_name} - Description: Test Sink - """ - - assert radio_station.is_audio_playing() is True - - def test_is_audio_playing_failure_sink_not_found(self, radio_station, mock_subprocess): - """Test audio playing check when sink is not found""" - mock_subprocess["run"].return_value.stdout = "State: RUNNING\nName: different_sink" - - assert radio_station.is_audio_playing() is False - - def test_is_audio_playing_failure_not_running(self, radio_station, mock_subprocess): - """Test audio playing check when sink is not running""" - mock_subprocess[ - "run" - ].return_value.stdout = f""" - State: SUSPENDED - Name: {radio_station.sink_name} - """ - - assert radio_station.is_audio_playing() is False - - def test_is_audio_playing_command_error(self, radio_station, mock_subprocess): - """Test audio playing check when command fails""" - mock_subprocess["run"].side_effect = subprocess.CalledProcessError(1, ["pactl", "list", "sinks"]) - - assert radio_station.is_audio_playing() is False - - def test_stop_with_driver(self, radio_station, mock_subprocess): - """Test stop with driver""" - radio_station.driver = Mock() - radio_station.sink_module = "123" - radio_station.source_module = "456" - - # Mock the module existence check - mock_subprocess["run"].return_value = Mock(stdout="123\n456", stderr="", check=True) - - radio_station.stop() - - # Verify driver quit was called - radio_station.driver.quit.assert_called_once() - - # Verify all subprocess calls in order - mock_subprocess["run"].assert_has_calls( - [ - call(["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True), - call(["pactl", "unload-module", "123"], check=True), - call(["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True), - call(["pactl", "unload-module", "456"], check=True), - ] - ) - - def test_stop_without_driver(self, radio_station, mock_subprocess): - """Test stop without driver""" - radio_station.driver = None - radio_station.sink_module = "123" - radio_station.source_module = "456" - - # Mock the module existence check - mock_subprocess["run"].return_value = Mock(stdout="123\n456", stderr="", check=True) - - radio_station.stop() - - # Verify all subprocess calls in order - mock_subprocess["run"].assert_has_calls( - [ - call(["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True), - call(["pactl", "unload-module", "123"], check=True), - call(["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True), - call(["pactl", "unload-module", "456"], check=True), - ] - ) - - def test_stop_module_already_unloaded(self, radio_station, mock_subprocess): - """Test stop when modules are already unloaded""" - radio_station.driver = None - radio_station.sink_module = "123" - radio_station.source_module = "456" - - # Mock the module list to show modules don't exist - mock_subprocess["run"].return_value = Mock(stdout="789\n012", stderr="", check=True) - - radio_station.stop() - - # Verify list checks were made for both modules - mock_subprocess["run"].assert_has_calls( - [ - call(["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True), - call(["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True), - ] - ) - - def test_stop_unload_error(self, radio_station, mock_subprocess): - """Test stop when unload command fails""" - radio_station.driver = None - radio_station.sink_module = "123" - radio_station.source_module = "456" - - # Setup mock responses - list_modules_response = Mock(stdout="123\n456", stderr="", check=True) - error = subprocess.CalledProcessError(1, ["pactl", "unload-module", "123"]) - - mock_subprocess["run"].side_effect = [ - list_modules_response, # First list modules check - error, # First unload attempt fails - list_modules_response, # Second list modules check - None, # Second unload succeeds - ] - - radio_station.stop() - - # Verify the expected sequence of calls - mock_subprocess["run"].assert_has_calls( - [ - call(["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True), - call(["pactl", "unload-module", "123"], check=True), - call(["pactl", "list", "short", "modules"], capture_output=True, text=True, check=True), - call(["pactl", "unload-module", "456"], check=True), - ] - ) - - def test_execute_command_success(self, radio_station, mock_subprocess): - """Test successful command execution""" - mock_subprocess["run"].return_value.stdout = "test output" - - radio_station.execute_command(["test", "command"]) - - mock_subprocess["run"].assert_called_once_with(["test", "command"], capture_output=True, text=True, check=True) - - def test_execute_command_failure(self, radio_station, mock_subprocess): - """Test command execution failure""" - error = Exception("Command failed") - mock_subprocess["run"].side_effect = error - - radio_station.execute_command(["test", "command"]) - # Should not raise exception, just print error message - - @patch("time.sleep") - def test_start_browser_element_not_found(self, mock_sleep, radio_station, mock_webdriver): - """Test browser start when element is not found""" - from selenium.common.exceptions import TimeoutException - - # Setup WebDriverWait mock - with patch("radiostations.base.WebDriverWait") as mock_wait: - # Setup the wait mock to raise TimeoutException - mock_wait_instance = Mock() - mock_wait.return_value = mock_wait_instance - mock_wait_instance.until.side_effect = TimeoutException() - - # Run the test and verify the exception message - with pytest.raises(TimeoutException): - radio_station.start_browser() - - # Verify browser was launched - mock_webdriver["driver"].get.assert_called_with(radio_station.url) - mock_wait.assert_called_once_with(mock_webdriver["driver"], 10) - - @patch("time.sleep") - def test_start_browser_click_fails(self, mock_sleep, radio_station, mock_webdriver): - """Test browser start when click fails""" - # Setup WebDriverWait mock - with patch("radiostations.base.WebDriverWait") as mock_wait: - # Setup the wait mock to return an element that fails on click - mock_wait_instance = Mock() - mock_wait.return_value = mock_wait_instance - mock_element = Mock() - mock_element.click.side_effect = Exception("Click failed") - mock_wait_instance.until.return_value = mock_element - mock_webdriver["driver"].find_element.return_value = mock_element - - # Run the test and verify the exception message - with pytest.raises(Exception, match="Click failed"): - radio_station.start_browser() - - # Verify browser was launched and click was attempted - mock_webdriver["driver"].get.assert_called_with(radio_station.url) - mock_element.click.assert_called_once() diff --git a/tests/radiostations/test_stations.py b/tests/radiostations/test_stations.py deleted file mode 100644 index 4eae1ea..0000000 --- a/tests/radiostations/test_stations.py +++ /dev/null @@ -1,119 +0,0 @@ -from radiostations.khot import Khot -from radiostations.kisf import Kisf -from radiostations.krgt import Krgt -from radiostations.wado import Wado -from radiostations.waqi import Waqi -from radiostations.wkaq import Wkaq - -class TestKhot: - def test_init(self): - """Test Khot radio station initialization""" - station = Khot() - assert station.code == "KHOT - 105.9 FM" - assert station.state == "Arizona" - assert station.name == "Que Buena" - assert station.url == "https://www.iheart.com/live/que-buena-1059-fm-5207/" - assert station.sink_name == "virtual_speaker_khot" - assert station.source_name == "virtual_mic_khot" - assert station.play_button_selector == "button[aria-label='Play Button']" - assert station.video_element_selector == "video.jw-video" - -class TestKisf: - def test_init(self): - """Test Kisf radio station initialization""" - station = Kisf() - assert station.code == "KISF - 103.5 FM" - assert station.state == "Nevada" - assert station.name == "ZonaMX" - assert station.url == "https://www.iheart.com/live/zona-mx-1035-fm-5209/" - assert station.sink_name == "virtual_speaker_kisf" - assert station.source_name == "virtual_mic_kisf" - assert station.play_button_selector == "button[aria-label='Play Button']" - assert station.video_element_selector == "video.jw-video" - -class TestKrgt: - def test_init(self): - """Test Krgt radio station initialization""" - station = Krgt() - assert station.code == "KRGT - 99.3 FM" - assert station.state == "Nevada" - assert station.name == "Rumba Hits caliente" - assert station.url == "https://www.iheart.com/live/latino-mix-993-fm-5221/" - assert station.sink_name == "virtual_speaker_krgt" - assert station.source_name == "virtual_mic_krgt" - assert station.play_button_selector == "button[aria-label='Play Button']" - assert station.video_element_selector == "video.jw-video" - -class TestWado: - def test_init(self): - """Test Wado radio station initialization""" - station = Wado() - assert station.code == "WADO - 1280 AM" - assert station.state == "New York" - assert station.name == "La Campeona de Nueva York" - assert station.url == "https://www.iheart.com/live/wado-1280-am-5172/" - assert station.sink_name == "virtual_speaker_wado" - assert station.source_name == "virtual_mic_wado" - assert station.play_button_selector == "button[aria-label='Play Button']" - assert station.video_element_selector == "video.jw-video" - -class TestWaqi: - def test_init(self): - """Test Waqi radio station initialization""" - station = Waqi() - assert station.code == "WAQI - 710 AM" - assert station.state == "Florida" - assert station.name == "Radio Mambi" - assert station.url == "https://www.iheart.com/live/radio-mambi-710-am-5175/" - assert station.sink_name == "virtual_speaker_waqi" - assert station.source_name == "virtual_mic_waqi" - assert station.play_button_selector == "button[aria-label='Play Button']" - assert station.video_element_selector == "video.jw-video" - -class TestWkaq: - def test_init(self): - """Test Wkaq radio station initialization""" - station = Wkaq() - assert station.code == "WKAQ - 580 AM" - assert station.state == "Puerto Rico" - assert station.name == "Analisis y Noticias" - assert station.url == "https://www.iheart.com/live/wkaq-580-5176/" - assert station.sink_name == "virtual_speaker_wkaq" - assert station.source_name == "virtual_mic_wkaq" - assert station.play_button_selector == "button[aria-label='Play Button']" - assert station.video_element_selector == "video.jw-video" - -def test_all_stations_have_unique_codes(): - """Test that all station codes are unique""" - stations = [Khot(), Kisf(), Krgt(), Wado(), Waqi(), Wkaq()] - codes = [station.code for station in stations] - assert len(codes) == len(set(codes)), "Duplicate station codes found" - -def test_all_stations_have_unique_sink_names(): - """Test that all station sink names are unique""" - stations = [Khot(), Kisf(), Krgt(), Wado(), Waqi(), Wkaq()] - sink_names = [station.sink_name for station in stations] - assert len(sink_names) == len(set(sink_names)), "Duplicate sink names found" - -def test_all_stations_have_unique_source_names(): - """Test that all station source names are unique""" - stations = [Khot(), Kisf(), Krgt(), Wado(), Waqi(), Wkaq()] - source_names = [station.source_name for station in stations] - assert len(source_names) == len(set(source_names)), "Duplicate source names found" - -def test_all_stations_have_valid_urls(): - """Test that all station URLs are valid iheart.com URLs""" - stations = [Khot(), Kisf(), Krgt(), Wado(), Waqi(), Wkaq()] - for station in stations: - assert station.url.startswith("https://www.iheart.com/live/") - assert station.url.endswith("/") - -def test_all_stations_have_same_selectors(): - """Test that all stations use the same selectors""" - stations = [Khot(), Kisf(), Krgt(), Wado(), Waqi(), Wkaq()] - expected_play_button_selector = "button[aria-label='Play Button']" - expected_video_element_selector = "video.jw-video" - - for station in stations: - assert station.play_button_selector == expected_play_button_selector - assert station.video_element_selector == expected_video_element_selector diff --git a/tests/test_generic_recording.py b/tests/test_generic_recording.py deleted file mode 100644 index 022342a..0000000 --- a/tests/test_generic_recording.py +++ /dev/null @@ -1,472 +0,0 @@ -import os -from unittest.mock import Mock, call, patch -import pytest -from generic_recording import ( - capture_audio_stream, - upload_to_r2_and_clean_up, - get_metadata, - insert_recorded_audio_file_into_database, - generic_audio_processing_pipeline, - get_url_hash -) -from radiostations.base import RadioStation - -class TestGenericRecording: - @pytest.fixture - def mock_ffmpeg(self): - """Setup mock FFmpeg""" - with patch('generic_recording.FFmpeg') as mock: - ffmpeg_instance = Mock() - mock.return_value.option.return_value.input.return_value.output.return_value = ffmpeg_instance - yield mock, ffmpeg_instance - - @pytest.fixture - def mock_radio_station(self): - """Setup mock radio station""" - station = Mock(spec=RadioStation) - station.code = "TEST-FM" - station.name = "Test Radio" - station.state = "Test State" - station.url = "https://test.radio/stream" - station.sink_name = "virtual_speaker_test" - station.source_name = "virtual_mic_test" - return station - - - @pytest.fixture - def mock_supabase_client(self): - """Setup mock Supabase client""" - with patch('generic_recording.supabase_client') as mock: - yield mock - - @pytest.fixture - def mock_s3_client(self): - """Setup mock S3 client""" - with patch('generic_recording.s3_client') as mock: - yield mock - - @pytest.fixture - def mock_radio_stations(self, mock_radio_station): - """Setup mock radio stations dictionary""" - return { - "TEST-FM": lambda: mock_radio_station - } - - def test_capture_audio_stream_success(self, mock_ffmpeg, mock_radio_station): - """Test successful audio capture""" - mock_ffmpeg_class, mock_ffmpeg_instance = mock_ffmpeg - mock_radio_station.is_audio_playing.return_value = True - - with patch('os.path.getsize', return_value=1000): - result = capture_audio_stream(mock_radio_station, 1800, 64000, 1) - - mock_radio_station.is_audio_playing.assert_called_once() - mock_ffmpeg_instance.execute.assert_called_once() - - assert result["radio_station_name"] == mock_radio_station.name - assert result["radio_station_code"] == mock_radio_station.code - assert result["location_state"] == mock_radio_station.state - assert result["file_size"] == 1000 - assert "recorded_at" in result - assert "recording_day_of_week" in result - - def test_capture_audio_stream_not_playing(self, mock_ffmpeg, mock_radio_station): - """Test audio capture when stream is not playing""" - mock_radio_station.is_audio_playing.return_value = False - - with patch('time.sleep'): - result = capture_audio_stream(mock_radio_station, 1800, 64000, 1) - - mock_radio_station.is_audio_playing.assert_called_once() - assert result is None - - def test_capture_audio_stream_ffmpeg_error(self, mock_ffmpeg, mock_radio_station): - """Test audio capture when FFmpeg fails""" - mock_ffmpeg_class, mock_ffmpeg_instance = mock_ffmpeg - mock_radio_station.is_audio_playing.return_value = True - mock_ffmpeg_instance.execute.side_effect = Exception("FFmpeg error") - - with patch('time.sleep'): - result = capture_audio_stream(mock_radio_station, 1800, 64000, 1) - - assert result is None - - def test_get_metadata(self, mock_radio_station): - """Test metadata generation""" - file_name = "test.mp3" - start_time = 1704067200 # 2024-01-01 00:00:00 - - def mock_strftime(format_string, *args): - if format_string == "%Y-%m-%dT%H:%M:%S": - return "2024-01-01T00:00:00" - elif format_string == "%A": - return "Monday" - return "" - - with patch('os.path.getsize', return_value=1000), \ - patch('time.strftime', side_effect=mock_strftime): - metadata = get_metadata(file_name, mock_radio_station, start_time) - - assert metadata["file_name"] == file_name - assert metadata["radio_station_name"] == mock_radio_station.name - assert metadata["radio_station_code"] == mock_radio_station.code - assert metadata["location_state"] == mock_radio_station.state - assert metadata["file_size"] == 1000 - assert metadata["recorded_at"] == "2024-01-01T00:00:00" - assert metadata["recording_day_of_week"] == "Monday" - - def test_upload_to_r2_success(self, mock_s3_client): - """Test successful file upload to R2""" - url = "https://test.radio/stream" - file_path = "test.mp3" - url_hash = get_url_hash(url) - expected_destination = f"radio_{url_hash}/test.mp3" - - with patch('os.remove') as mock_remove: - result = upload_to_r2_and_clean_up(url, file_path) - - assert result == expected_destination - mock_s3_client.upload_file.assert_called_once() - mock_remove.assert_called_once_with(file_path) - - def test_upload_to_r2_failure(self, mock_s3_client): - """Test file upload failure""" - with patch('generic_recording.R2_BUCKET_NAME', 'test-bucket'): - mock_s3_client.upload_file.side_effect = Exception("Upload failed") - - # Expect the exception to be raised - with pytest.raises(Exception, match="Upload failed"): - upload_to_r2_and_clean_up("https://test.radio/stream", "test.mp3") - - def test_insert_recorded_audio_file_success(self, mock_supabase_client): - """Test successful database insertion""" - metadata = { - "radio_station_name": "Test Radio", - "radio_station_code": "TEST-FM", - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - uploaded_path = "radio_123456/test.mp3" - - insert_recorded_audio_file_into_database(metadata, uploaded_path) - - mock_supabase_client.insert_audio_file.assert_called_once_with( - radio_station_name=metadata["radio_station_name"], - radio_station_code=metadata["radio_station_code"], - location_state=metadata["location_state"], - recorded_at=metadata["recorded_at"], - recording_day_of_week=metadata["recording_day_of_week"], - file_path=uploaded_path, - file_size=metadata["file_size"] - ) - - def test_get_url_hash(self): - """Test URL hash generation""" - url = "https://test.radio/stream" - hash_value = get_url_hash(url) - - assert len(hash_value) == 6 - assert isinstance(hash_value, str) - - def test_generic_audio_processing_pipeline_success(self, mock_radio_station): - """Test successful pipeline execution""" - station_code = "KHOT - 105.9 FM" - - with patch('generic_recording.Khot', return_value=mock_radio_station) as mock_khot_class, \ - patch('generic_recording.Kisf'), \ - patch('generic_recording.Krgt'), \ - patch('generic_recording.Wkaq'), \ - patch('generic_recording.Wado'), \ - patch('generic_recording.Waqi'), \ - patch('generic_recording.capture_audio_stream') as mock_capture, \ - patch('generic_recording.upload_to_r2_and_clean_up') as mock_upload, \ - patch('generic_recording.insert_recorded_audio_file_into_database') as mock_insert, \ - patch('psutil.virtual_memory') as mock_memory, \ - patch('time.sleep') as mock_sleep: - - # Setup mock Khot class code - mock_khot_class.code = station_code - - # Setup mock returns - mock_radio_station.is_audio_playing.side_effect = [True, False] # Play once then stop - mock_memory.return_value.percent = 50 # Normal memory usage - mock_capture.return_value = { - "file_name": "test.mp3", - "radio_station_name": "Test Radio", - "radio_station_code": station_code, - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - mock_upload.return_value = "radio_123456/test.mp3" - - # Run pipeline - generic_audio_processing_pipeline( - station_code=station_code, - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False - ) - - # Verify the flow - mock_capture.assert_called_once() - mock_upload.assert_called_once() - mock_insert.assert_called_once() - - def test_generic_audio_processing_pipeline_high_memory(self, mock_radio_station, mock_supabase_client): - """Test pipeline with high memory usage""" - station_code = "KHOT - 105.9 FM" - - # Setup mock Supabase response - mock_response = Mock() - mock_response.data = [{"id": 1}] # Simulate Supabase response structure - mock_supabase_client.insert_audio_file.return_value = {"id": 1} # Set return value for insert_audio_file - - with patch('generic_recording.Khot', return_value=mock_radio_station) as mock_khot_class, \ - patch('generic_recording.Kisf'), \ - patch('generic_recording.Krgt'), \ - patch('generic_recording.Wkaq'), \ - patch('generic_recording.Wado'), \ - patch('generic_recording.Waqi'), \ - patch('generic_recording.capture_audio_stream') as mock_capture, \ - patch('generic_recording.upload_to_r2_and_clean_up') as mock_upload, \ - patch('psutil.virtual_memory') as mock_memory, \ - patch('time.sleep') as mock_sleep: - - # Setup mock Khot class code - mock_khot_class.code = station_code - mock_radio_station.url = "https://test.radio/stream" - - # Setup mock returns - mock_memory.return_value.percent = 96 # High memory usage - mock_radio_station.is_audio_playing.return_value = True - mock_capture.return_value = { - "file_name": "test.mp3", - "radio_station_name": "Test Radio", - "radio_station_code": station_code, - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - mock_upload.return_value = "radio_123456/test.mp3" - - # Run pipeline - generic_audio_processing_pipeline( - station_code=station_code, - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False - ) - - # Verify both stop and start_browser calls - assert mock_radio_station.stop.call_count == 2 - assert mock_radio_station.start_browser.call_count == 2 - - # Verify the sequence of calls - mock_radio_station.stop.assert_has_calls([ - call(unload_modules=False), # First call during browser restart - call() # Second call during cleanup - ]) - mock_radio_station.start_browser.assert_has_calls([ - call(), # Initial setup - call() # After browser restart - ]) - - # Verify Supabase interaction - mock_supabase_client.insert_audio_file.assert_called_once_with( - radio_station_name="Test Radio", - radio_station_code=station_code, - location_state="Test State", - recorded_at="2024-01-01T00:00:00", - recording_day_of_week="Monday", - file_path="radio_123456/test.mp3", - file_size=1000 - ) - - def test_generic_audio_processing_pipeline_playback_stopped(self, mock_radio_station, mock_supabase_client): - """Test pipeline when playback stops""" - station_code = "KHOT - 105.9 FM" - - with patch('generic_recording.Khot', return_value=mock_radio_station) as mock_khot_class, \ - patch('generic_recording.Kisf'), \ - patch('generic_recording.Krgt'), \ - patch('generic_recording.Wkaq'), \ - patch('generic_recording.Wado'), \ - patch('generic_recording.Waqi'), \ - patch('generic_recording.capture_audio_stream') as mock_capture, \ - patch('generic_recording.upload_to_r2_and_clean_up') as mock_upload, \ - patch('psutil.virtual_memory') as mock_memory, \ - patch('time.sleep') as mock_sleep: - - # Setup mock Khot class code - mock_khot_class.code = station_code - mock_radio_station.url = "https://test.radio/stream" - - # Setup mock returns - mock_memory.return_value.percent = 50 - mock_radio_station.is_audio_playing.return_value = False - mock_capture.return_value = { - "file_name": "test.mp3", - "radio_station_name": "Test Radio", - "radio_station_code": station_code, - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - mock_upload.return_value = "radio_123456/test.mp3" - - # Run pipeline - generic_audio_processing_pipeline( - station_code=station_code, - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False - ) - - # Verify both stop and start_browser calls - assert mock_radio_station.stop.call_count == 2 - assert mock_radio_station.start_browser.call_count == 2 - - # Verify the sequence of calls - mock_radio_station.stop.assert_has_calls([ - call(unload_modules=False), # First call during browser restart - call() # Second call during cleanup - ]) - mock_radio_station.start_browser.assert_has_calls([ - call(), # Initial setup - call() # After browser restart - ]) - - def test_generic_audio_processing_pipeline_cleanup(self, mock_radio_station, mock_supabase_client): - """Test pipeline cleanup""" - station_code = "KHOT - 105.9 FM" - - with patch('generic_recording.Khot', return_value=mock_radio_station) as mock_khot_class, \ - patch('generic_recording.Kisf'), \ - patch('generic_recording.Krgt'), \ - patch('generic_recording.Wkaq'), \ - patch('generic_recording.Wado'), \ - patch('generic_recording.Waqi'), \ - patch('generic_recording.capture_audio_stream') as mock_capture, \ - patch('generic_recording.upload_to_r2_and_clean_up') as mock_upload, \ - patch('psutil.virtual_memory') as mock_memory, \ - patch('time.sleep'): - - # Setup mock Khot class code - mock_khot_class.code = station_code - mock_radio_station.url = "https://test.radio/stream" - - # Setup mock returns - mock_memory.return_value.percent = 50 - mock_radio_station.is_audio_playing.return_value = True - mock_capture.return_value = { - "file_name": "test.mp3", - "radio_station_name": "Test Radio", - "radio_station_code": station_code, - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - mock_upload.return_value = "radio_123456/test.mp3" - - # Run pipeline - generic_audio_processing_pipeline( - station_code=station_code, - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False - ) - - # Verify cleanup - mock_radio_station.stop.assert_called_once() - - def test_generic_audio_processing_pipeline_invalid_station(self): - """Test pipeline with invalid station code""" - with patch('generic_recording.Khot') as mock_khot, \ - patch('generic_recording.Kisf') as mock_kisf, \ - patch('generic_recording.Krgt') as mock_krgt, \ - patch('generic_recording.Wkaq') as mock_wkaq, \ - patch('generic_recording.Wado') as mock_wado, \ - patch('generic_recording.Waqi') as mock_waqi: - - # Set up the codes for all station classes - mock_khot.code = "KHOT - 105.9 FM" - mock_kisf.code = "KISF - 103.5 FM" - mock_krgt.code = "KRGT - 99.3 FM" - mock_wkaq.code = "WKAQ - 580 AM" - mock_wado.code = "WADO - 1280 AM" - mock_waqi.code = "WAQI - 710 AM" - - with pytest.raises(ValueError, match="Invalid station code: INVALID-FM"): - generic_audio_processing_pipeline( - station_code="INVALID-FM", - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False - ) - - def test_main_execution(self): - """Test main execution with process groups""" - mock_deployment = Mock() - mock_flow = Mock() - mock_flow.to_deployment.return_value = mock_deployment - - with patch.dict('os.environ', {'FLY_PROCESS_GROUP': 'radio_khot'}), \ - patch('generic_recording.serve') as mock_serve, \ - patch('generic_recording.generic_audio_processing_pipeline', mock_flow): - - # Execute the main block code directly - process_group = os.environ.get("FLY_PROCESS_GROUP") - match process_group: - case "radio_khot": - deployment = mock_flow.to_deployment( # Use mock_flow instead of generic_audio_processing_pipeline - "KHOT - 105.9 FM", - tags=["Arizona", "1853b3", "Generic"], - parameters=dict( - station_code="KHOT - 105.9 FM", - duration_seconds=1800, - repeat=True, - audio_birate=64000, - audio_channels=1, - ), - ) - mock_serve(deployment) - case _: - raise ValueError(f"Invalid process group: {process_group}") - - # Verify serve was called with the mock deployment - mock_serve.assert_called_once_with(mock_deployment) - # Verify to_deployment was called with correct parameters - mock_flow.to_deployment.assert_called_once_with( - "KHOT - 105.9 FM", - tags=["Arizona", "1853b3", "Generic"], - parameters=dict( - station_code="KHOT - 105.9 FM", - duration_seconds=1800, - repeat=True, - audio_birate=64000, - audio_channels=1, - ), - ) - - def test_main_execution_invalid_process_group(self): - """Test main execution with invalid process group""" - with patch.dict('os.environ', {'FLY_PROCESS_GROUP': 'invalid_group'}): - process_group = os.environ.get("FLY_PROCESS_GROUP") - with pytest.raises(ValueError, match="Invalid process group: invalid_group"): - match process_group: - case "radio_khot": - pass - case _: - raise ValueError(f"Invalid process group: {process_group}") diff --git a/tests/test_recording.py b/tests/test_recording.py deleted file mode 100644 index 39656a5..0000000 --- a/tests/test_recording.py +++ /dev/null @@ -1,374 +0,0 @@ -import os -import time -from unittest.mock import Mock, patch -import pytest -from recording import ( - capture_audio_stream, - serve_deployments, - upload_to_r2_and_clean_up, - get_metadata, - insert_recorded_audio_file_into_database, - audio_processing_pipeline_max_recorder, - audio_processing_pipeline_lite_recorder, - get_url_hash, - reconstruct_radio_station -) - -class TestRecording: - @pytest.fixture - def mock_ffmpeg(self): - with patch('recording.FFmpeg') as mock: - ffmpeg_instance = Mock() - mock.return_value.option.return_value.input.return_value.output.return_value = ffmpeg_instance - yield mock, ffmpeg_instance - - @pytest.fixture - def sample_station(self): - return { - "code": "TEST-FM", - "url": "https://test.radio/stream", - "state": "Test State", - "name": "Test Radio" - } - - @pytest.fixture - def mock_supabase_client(self): - with patch('recording.supabase_client') as mock: - yield mock - - @pytest.fixture - def mock_s3_client(self): - with patch('recording.s3_client') as mock: - yield mock - - def test_capture_audio_stream_success(self, mock_ffmpeg, sample_station): - """Test successful audio capture""" - mock_ffmpeg_class, mock_ffmpeg_instance = mock_ffmpeg - - # Create a temporary file that will be "created" by FFmpeg - with patch('os.path.getsize', return_value=1000): - result = capture_audio_stream(sample_station, 1800, 64000, 1) - - # Verify FFmpeg was called with correct parameters - assert mock_ffmpeg_class.called - assert mock_ffmpeg_instance.execute.called - - # Verify the returned metadata - assert result["radio_station_name"] == sample_station["name"] - assert result["radio_station_code"] == sample_station["code"] - assert result["location_state"] == sample_station["state"] - assert result["file_size"] == 1000 - assert "recorded_at" in result - assert "recording_day_of_week" in result - - def test_capture_audio_stream_failure(self, mock_ffmpeg, sample_station): - """Test audio capture failure""" - mock_ffmpeg_class, mock_ffmpeg_instance = mock_ffmpeg - mock_ffmpeg_instance.execute.side_effect = Exception("FFmpeg error") - - with patch('time.sleep'): # Avoid actual sleep in test - result = capture_audio_stream(sample_station, 1800, 64000, 1) - - assert result is None - - def test_get_metadata(self, sample_station): - """Test metadata generation""" - file_name = "test.mp3" - start_time = time.time() - - with patch('os.path.getsize', return_value=1000): - metadata = get_metadata(file_name, sample_station, start_time) - - assert metadata["file_name"] == file_name - assert metadata["radio_station_name"] == sample_station["name"] - assert metadata["radio_station_code"] == sample_station["code"] - assert metadata["location_state"] == sample_station["state"] - assert metadata["file_size"] == 1000 - assert isinstance(metadata["recorded_at"], str) - assert isinstance(metadata["recording_day_of_week"], str) - - def test_upload_to_r2_success(self, mock_s3_client): - """Test successful file upload to R2""" - url = "https://test.radio/stream" - file_path = "test.mp3" - url_hash = get_url_hash(url) - expected_destination = f"radio_{url_hash}/test.mp3" - - with patch('os.remove') as mock_remove: - result = upload_to_r2_and_clean_up(url, file_path) - - assert result == expected_destination - mock_s3_client.upload_file.assert_called_once() - mock_remove.assert_called_once_with(file_path) - - def test_upload_to_r2_failure(self, mock_s3_client): - """Test file upload failure""" - # Mock both the environment variable and the s3_client in the recording module - with patch('recording.R2_BUCKET_NAME', 'test-bucket'), \ - patch('recording.s3_client', mock_s3_client): - - mock_s3_client.upload_file.side_effect = Exception("Upload failed") - - # Expect the exception to be raised - with pytest.raises(Exception, match="Upload failed"): - upload_to_r2_and_clean_up("https://test.radio/stream", "test.mp3") - - # Verify upload was attempted - mock_s3_client.upload_file.assert_called_once() - - def test_insert_recorded_audio_file_success(self, mock_supabase_client): - """Test successful database insertion""" - metadata = { - "radio_station_name": "Test Radio", - "radio_station_code": "TEST-FM", - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - uploaded_path = "radio_123456/test.mp3" - - insert_recorded_audio_file_into_database(metadata, uploaded_path) - - mock_supabase_client.insert_audio_file.assert_called_once_with( - radio_station_name=metadata["radio_station_name"], - radio_station_code=metadata["radio_station_code"], - location_state=metadata["location_state"], - recorded_at=metadata["recorded_at"], - recording_day_of_week=metadata["recording_day_of_week"], - file_path=uploaded_path, - file_size=metadata["file_size"] - ) - - def test_get_url_hash(self): - """Test URL hash generation""" - url = "https://test.radio/stream" - hash_value = get_url_hash(url) - - assert len(hash_value) == 6 - assert isinstance(hash_value, str) - - def test_reconstruct_radio_station(self): - """Test radio station reconstruction from URL""" - test_url = "https://test.radio/stream" - test_station = { - "code": "TEST-FM", - "url": test_url, - "state": "Test State", - "name": "Test Radio" - } - - with patch('recording.fetch_radio_stations', return_value=[test_station]): - result = reconstruct_radio_station(test_url) - - assert result == test_station - - def test_reconstruct_radio_station_not_found(self): - """Test radio station reconstruction with unknown URL""" - with patch('recording.fetch_radio_stations', return_value=[]): - result = reconstruct_radio_station("https://unknown.radio/stream") - - assert result is None - - def test_audio_processing_pipeline_max_recorder(self, sample_station): - """Test max recorder pipeline""" - with patch('recording.capture_audio_stream') as mock_capture, \ - patch('recording.upload_to_r2_and_clean_up') as mock_upload, \ - patch('recording.insert_recorded_audio_file_into_database') as mock_insert, \ - patch('recording.reconstruct_radio_station', return_value=sample_station): - - # Setup mock returns - mock_capture.return_value = { - "file_name": "test.mp3", - "radio_station_name": "Test Radio", - "radio_station_code": "TEST-FM", - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - mock_upload.return_value = "radio_123456/test.mp3" - - # Run the pipeline - audio_processing_pipeline_max_recorder( - url=sample_station["url"], - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False - ) - - # Verify the pipeline flow - mock_capture.assert_called_once_with( - sample_station, 1800, 64000, 1 - ) - mock_upload.assert_called_once_with( - sample_station["url"], - mock_capture.return_value["file_name"] - ) - mock_insert.assert_called_once_with( - mock_capture.return_value, - mock_upload.return_value - ) - - def test_audio_processing_pipeline_lite_recorder(self, sample_station): - """Test lite recorder pipeline""" - with patch('recording.capture_audio_stream') as mock_capture, \ - patch('recording.upload_to_r2_and_clean_up') as mock_upload, \ - patch('recording.insert_recorded_audio_file_into_database') as mock_insert, \ - patch('recording.reconstruct_radio_station', return_value=sample_station): - - # Setup mock returns - mock_capture.return_value = { - "file_name": "test.mp3", - "radio_station_name": "Test Radio", - "radio_station_code": "TEST-FM", - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - mock_upload.return_value = "radio_123456/test.mp3" - - # Run the pipeline - audio_processing_pipeline_lite_recorder( - url=sample_station["url"], - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False - ) - - # Verify the pipeline flow - mock_capture.assert_called_once_with( - sample_station, 1800, 64000, 1 - ) - mock_upload.assert_called_once_with( - sample_station["url"], - mock_capture.return_value["file_name"] - ) - mock_insert.assert_called_once_with( - mock_capture.return_value, - mock_upload.return_value - ) - - def test_serve_deployments(self): - """Test serve_deployments function""" - test_stations = [ - { - "code": "TEST-FM", - "url": "https://test.radio/stream", - "state": "Test State", - "name": "Test Radio" - } - ] - - # Create a mock flow function with to_deployment attribute - mock_flow = Mock() - mock_deployment = Mock() - mock_flow.to_deployment = Mock(return_value=mock_deployment) - - with patch('recording.serve') as mock_serve: - serve_deployments(test_stations, mock_flow) - mock_serve.assert_called_once() - - def test_audio_processing_pipeline_with_repeat(self, sample_station): - """Test pipeline with repeat enabled""" - with patch('recording.capture_audio_stream') as mock_capture, \ - patch('recording.upload_to_r2_and_clean_up') as mock_upload, \ - patch('recording.insert_recorded_audio_file_into_database') as mock_insert, \ - patch('recording.reconstruct_radio_station', return_value=sample_station), \ - patch('time.sleep') as mock_sleep: # Mock sleep to speed up test - - # Setup mock to run once then return None to break the loop - mock_capture.return_value = { - "file_name": "test1.mp3", - "radio_station_name": "Test Radio", - "radio_station_code": "TEST-FM", - "location_state": "Test State", - "recorded_at": "2024-01-01T00:00:00", - "recording_day_of_week": "Monday", - "file_size": 1000 - } - mock_upload.return_value = "path1" - - # Run the pipeline with repeat enabled - audio_processing_pipeline_max_recorder( - url=sample_station["url"], - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False # Changed to False to run only once - ) - - # Verify one iteration occurred - mock_capture.assert_called_once() - mock_upload.assert_called_once() - mock_insert.assert_called_once() - - def test_pipeline_with_invalid_station(self): - """Test pipeline behavior with invalid station URL""" - with patch('recording.reconstruct_radio_station', return_value=None): - with pytest.raises(ValueError, match="Radio station not found for URL:"): - audio_processing_pipeline_max_recorder( - url="https://invalid.radio/stream", - duration_seconds=1800, - audio_birate=64000, - audio_channels=1, - repeat=False - ) - - def test_main_process_group_handling(self): - """Test main process group handling""" - test_stations = [ - { - "code": "TEST-FM", - "url": "https://test.radio/stream", - "state": "Test State", - "name": "Test Radio" - } - ] - - with patch('recording.fetch_radio_stations', return_value=test_stations), \ - patch('recording.serve_deployments') as mock_serve_deployments, \ - patch.dict('os.environ', {'FLY_PROCESS_GROUP': 'max_recorder'}): - - # Execute the main logic directly - process_group = os.environ.get("FLY_PROCESS_GROUP") - match process_group: - case "max_recorder": - mock_serve_deployments(test_stations, audio_processing_pipeline_max_recorder) - case "lite_recorder": - mock_serve_deployments(test_stations, audio_processing_pipeline_lite_recorder) - case _: - raise ValueError(f"Invalid process group: {process_group}") - - # Verify serve_deployments was called with correct parameters - mock_serve_deployments.assert_called_once_with( - test_stations, - audio_processing_pipeline_max_recorder - ) - - def test_invalid_process_group(self): - """Test handling of invalid process group""" - with patch.dict('os.environ', {'FLY_PROCESS_GROUP': 'invalid_group'}): - with pytest.raises(ValueError, match="Invalid process group: invalid_group"): - process_group = os.environ.get("FLY_PROCESS_GROUP") - match process_group: - case "max_recorder": - pass - case "lite_recorder": - pass - case _: - raise ValueError(f"Invalid process group: {process_group}") - - def test_capture_audio_stream_with_sleep(self, mock_ffmpeg, sample_station): - """Test audio capture with sleep on failure""" - mock_ffmpeg_class, mock_ffmpeg_instance = mock_ffmpeg - mock_ffmpeg_instance.execute.side_effect = Exception("FFmpeg error") - - with patch('time.sleep') as mock_sleep: - result = capture_audio_stream(sample_station, 1800, 64000, 1) - - assert result is None - mock_sleep.assert_called_once_with(300) # Verify sleep duration From c0590e3047490cfd90c31964737b33a8c22301b2 Mon Sep 17 00:00:00 2001 From: JsNcAr Date: Fri, 23 Jan 2026 11:11:50 -0500 Subject: [PATCH 3/4] Refactor processing pipeline to use PostgreSQL and local storage - Replaced SupabaseClient with PostgresClient in stages 1 to 5 for database interactions. - Introduced LocalStorage for file handling instead of S3. - Updated audio file download and upload functions to work with the new storage client. - Enhanced audio file metadata handling to support both ISO strings and datetime objects. - Added environment variable loading with dotenv for better configuration management. - Created SQL migration scripts to reset the database and establish a minimal local schema. - Loaded initial prompt versions and heuristics into the database through migration scripts. --- poetry.lock | 994 ++------------------- pyproject.toml | 4 +- scripts/load_prompts.py | 196 ++++ src/main.py | 4 +- src/processing_pipeline/__init__.py | 8 +- src/processing_pipeline/local_storage.py | 141 +++ src/processing_pipeline/postgres_client.py | 498 +++++++++++ src/processing_pipeline/stage_1.py | 72 +- src/processing_pipeline/stage_2.py | 62 +- src/processing_pipeline/stage_3.py | 30 +- src/processing_pipeline/stage_4.py | 4 +- src/processing_pipeline/stage_5.py | 4 +- supabase/migrations/00_reset_database.sql | 11 + supabase/migrations/01_local_schema.sql | 269 ++++++ supabase/migrations/02_load_prompts.sql | 98 ++ 15 files changed, 1369 insertions(+), 1026 deletions(-) create mode 100755 scripts/load_prompts.py create mode 100644 src/processing_pipeline/local_storage.py create mode 100644 src/processing_pipeline/postgres_client.py create mode 100644 supabase/migrations/00_reset_database.sql create mode 100644 supabase/migrations/01_local_schema.sql create mode 100644 supabase/migrations/02_load_prompts.sql diff --git a/poetry.lock b/poetry.lock index 1c492c8..7246415 100644 --- a/poetry.lock +++ b/poetry.lock @@ -814,21 +814,6 @@ calendars = ["convertdate (>=2.2.1)", "hijridate"] fasttext = ["fasttext (>=0.9.1)", "numpy (>=1.19.3,<2)"] langdetect = ["langdetect (>=1.0.0)"] -[[package]] -name = "deprecation" -version = "2.1.0" -description = "A library to handle automated deprecations" -optional = false -python-versions = "*" -groups = ["main"] -files = [ - 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= ">=1.42.31,<2.0.0" google-genai = ">=1.59.0,<2.0.0" sentry-sdk = ">=2.50.0,<3.0.0" pytest = ">=9.0.2,<10.0.0" -supabase = ">=2.27.2,<3.0.0" -supabase-auth = ">=2.27.2,<3.0.0" -supabase-functions = ">=2.27.2,<3.0.0" +psycopg2-binary = ">=2.9.9,<3.0.0" pydub = ">=0.25.1,<0.26.0" openai = ">=2.15.0,<3.0.0" tiktoken = ">=0.12.0,<0.13.0" diff --git a/scripts/load_prompts.py b/scripts/load_prompts.py new file mode 100755 index 0000000..0d9ec15 --- /dev/null +++ b/scripts/load_prompts.py @@ -0,0 +1,196 @@ +#!/usr/bin/env python3 +""" +Load prompt files from the prompts/ directory into the PostgreSQL database. +This script reads markdown and JSON files and inserts them into the prompt_versions table. +""" + +import json +import os +import sys +from pathlib import Path + +# Add project root to path +project_root = Path(__file__).parent.parent +sys.path.insert(0, str(project_root)) + +from src.processing_pipeline.postgres_client import PostgresClient + + +def read_file(filepath): + """Read file content.""" + with open(filepath, 'r', encoding='utf-8') as f: + return f.read() + + +def read_json(filepath): + """Read JSON file content.""" + with open(filepath, 'r', encoding='utf-8') as f: + return json.load(f) + + +def load_prompts(): + """Load all prompts into the database.""" + db = PostgresClient() + prompts_dir = project_root / 'prompts' + + print("🚀 Loading prompts into database...") + + # Define prompt configurations + prompt_configs = [ + { + 'stage': 'gemini_timestamped_transcription', + 'version_number': 1, + 'llm_model': 'gemini-2.5-flash', + 'prompt_file': 'Gemini_timestamped_transcription_generation_prompt.md', + 'system_instruction_file': None, + 'output_schema_file': 'Timestamped_transcription_generation_output_schema.json', + 'change_explanation': 'Initial prompt version for Gemini timestamped transcription' + }, + { + 'stage': 'stage_1', + 'version_number': 1, + 'llm_model': 'gemini-2.5-flash', + 'prompt_file': 'Stage_1_detection_prompt.md', + 'system_instruction_file': 'Stage_1_system_instruction.md', + 'output_schema_file': 'Stage_1_output_schema.json', + 'change_explanation': 'Initial prompt version for Stage 1 detection' + }, + { + 'stage': 'stage_3', + 'version_number': 1, + 'llm_model': 'gemini-2.5-flash', + 'prompt_file': 'Stage_3_analysis_prompt.md', + 'system_instruction_file': 'Stage_3_system_instruction.md', + 'output_schema_file': 'Stage_3_output_schema.json', + 'change_explanation': 'Initial prompt version for Stage 3 analysis' + }, + { + 'stage': 'stage_4', + 'version_number': 1, + 'llm_model': 'gemini-2.5-flash', + 'prompt_file': 'Stage_4_review_prompt.md', + 'system_instruction_file': 'Stage_4_system_instruction.md', + 'output_schema_file': 'Stage_4_output_schema.json', + 'change_explanation': 'Initial prompt version for Stage 4 review' + } + ] + + # Load each prompt configuration + for config in prompt_configs: + stage = config['stage'] + + # Check if prompt already exists + existing = db._execute( + "SELECT id FROM prompt_versions WHERE stage = %s AND is_active = TRUE", + (stage,), + fetch_one=True + ) + + if existing: + print(f"⏭️ Skipping {stage} - already exists") + continue + + # Read prompt text + prompt_text = read_file(prompts_dir / config['prompt_file']) + + # Read system instruction if exists + system_instruction = None + if config['system_instruction_file']: + system_instruction = read_file(prompts_dir / config['system_instruction_file']) + + # Read output schema if exists + output_schema = None + if config['output_schema_file']: + output_schema = read_json(prompts_dir / config['output_schema_file']) + + # Insert into database + db._execute( + """ + INSERT INTO prompt_versions ( + stage, + version_number, + llm_model, + prompt_text, + system_instruction, + output_schema, + is_active, + change_explanation + ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s) + """, + ( + stage, + config['version_number'], + config['llm_model'], + prompt_text, + system_instruction, + json.dumps(output_schema) if output_schema else None, + True, + config['change_explanation'] + ), + fetch_one=False + ) + + print(f"✅ Loaded {stage} prompt (v{config['version_number']})") + + # Load heuristics + heuristics_configs = [ + { + 'stage': 'stage_1', + 'version_number': 1, + 'content_file': 'Stage_1_heuristics.md', + 'change_explanation': 'Initial heuristics for Stage 1' + }, + { + 'stage': 'stage_3', + 'version_number': 1, + 'content_file': 'Stage_3_heuristics.md', + 'change_explanation': 'Initial heuristics for Stage 3' + } + ] + + for config in heuristics_configs: + stage = config['stage'] + + # Check if heuristics already exist + existing = db._execute( + "SELECT id FROM heuristics WHERE stage = %s AND is_active = TRUE", + (stage,), + fetch_one=True + ) + + if existing: + print(f"⏭️ Skipping {stage} heuristics - already exists") + continue + + # Read heuristics content + content = read_file(prompts_dir / config['content_file']) + + # Insert into database + db._execute( + """ + INSERT INTO heuristics ( + stage, + version_number, + content, + is_active, + change_explanation + ) VALUES (%s, %s, %s, %s, %s) + """, + ( + stage, + config['version_number'], + content, + True, + config['change_explanation'] + ), + fetch_one=False + ) + + print(f"✅ Loaded {stage} heuristics (v{config['version_number']})") + + db.close() + print("\n🎉 All prompts and heuristics loaded successfully!") + + +if __name__ == '__main__': + load_prompts() diff --git a/src/main.py b/src/main.py index a3e4be1..99ea3e6 100644 --- a/src/main.py +++ b/src/main.py @@ -4,7 +4,7 @@ from dotenv import load_dotenv from processing_pipeline.stage_4 import Stage4Executor, prepare_snippet_for_review -from processing_pipeline.supabase_utils import SupabaseClient +from processing_pipeline.postgres_client import PostgresClient load_dotenv() @@ -13,7 +13,7 @@ def test_stage_4(): - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + supabase_client = PostgresClient() snippet = supabase_client.get_snippet_by_id(id="3b39f536-7466-44da-9772-b10dcf72c6be") previous_analysis = snippet["previous_analysis"] transcription, disinformation_snippet, metadata, analysis_json = prepare_snippet_for_review(previous_analysis) diff --git a/src/processing_pipeline/__init__.py b/src/processing_pipeline/__init__.py index a5e5f7c..c9ecd67 100644 --- a/src/processing_pipeline/__init__.py +++ b/src/processing_pipeline/__init__.py @@ -9,11 +9,12 @@ 5. Stage 5: Vector Embedding Generation The pipeline is distributed across multiple workers using Prefect, -with task reservation coordinated via Supabase RPC functions. +with task reservation coordinated via PostgreSQL RPC functions. """ # Core utilities -from processing_pipeline.supabase_utils import SupabaseClient +from processing_pipeline.postgres_client import PostgresClient +from processing_pipeline.local_storage import LocalStorage # Constants and enums from processing_pipeline.constants import ( @@ -70,7 +71,8 @@ __all__ = [ # Core - "SupabaseClient", + "PostgresClient", + "LocalStorage", # Constants "GeminiModel", diff --git a/src/processing_pipeline/local_storage.py b/src/processing_pipeline/local_storage.py new file mode 100644 index 0000000..af52322 --- /dev/null +++ b/src/processing_pipeline/local_storage.py @@ -0,0 +1,141 @@ +""" +Simple filesystem storage client that replaces boto3 S3 client. +No S3 API emulation - direct filesystem operations. +""" + +import os +import shutil +from pathlib import Path + + +class LocalStorage: + """ + Drop-in replacement for boto3 S3 client using local filesystem. + Simpler than MinIO - no S3 protocol overhead. + """ + + def __init__(self, base_path=None): + """ + Initialize local storage. + + Args: + base_path: Base directory for file storage. + If None, reads from STORAGE_PATH environment variable. + Default: ~/verdad_debates_storage + """ + if base_path is None: + base_path = os.getenv( + 'STORAGE_PATH', + '~/verdad_debates_storage' + ) + + # Expand ~ and ensure absolute path + self.base_path = os.path.abspath(os.path.expanduser(base_path)) + Path(self.base_path).mkdir(parents=True, exist_ok=True) + + print(f"LocalStorage initialized at: {self.base_path}") + + def upload_file(self, local_path, bucket_name, remote_path): + """ + Upload file to local storage. + + Args: + local_path: Path to local file + bucket_name: Ignored (kept for compatibility with boto3) + remote_path: Destination path relative to base_path + + Returns: + Absolute path to stored file + """ + # Create full destination path + dest_path = os.path.join(self.base_path, remote_path) + + # Create parent directories + os.makedirs(os.path.dirname(dest_path), exist_ok=True) + + # Copy file + shutil.copy2(local_path, dest_path) + + print(f"✓ Uploaded: {local_path} → {dest_path}") + return dest_path + + def download_file(self, bucket_name, remote_path, local_path): + """ + Download file from local storage. + + Args: + bucket_name: Ignored (kept for compatibility with boto3) + remote_path: Source path relative to base_path + local_path: Destination path + + Returns: + Absolute path to downloaded file + """ + # Create full source path + source_path = os.path.join(self.base_path, remote_path) + + # Check if file exists + if not os.path.exists(source_path): + raise FileNotFoundError(f"File not found: {source_path}") + + # Create parent directories for destination + os.makedirs(os.path.dirname(os.path.abspath(local_path)), exist_ok=True) + + # Copy file + shutil.copy2(source_path, local_path) + + print(f"✓ Downloaded: {source_path} → {local_path}") + return os.path.abspath(local_path) + + def delete_object(self, Bucket, Key): + """ + Delete file from local storage. + + Args: + Bucket: Ignored (kept for compatibility with boto3) + Key: File path relative to base_path + """ + file_path = os.path.join(self.base_path, Key) + + if os.path.exists(file_path): + os.remove(file_path) + print(f"✓ Deleted: {file_path}") + else: + print(f"⚠ File not found (already deleted?): {file_path}") + + def get_file_path(self, remote_path): + """ + Get absolute path to a file in storage. + Useful for direct file access without copying. + + Args: + remote_path: File path relative to base_path + + Returns: + Absolute path to file + """ + return os.path.join(self.base_path, remote_path) + + def list_files(self, prefix=""): + """ + List all files under a prefix. + + Args: + prefix: Directory prefix to list + + Returns: + List of file paths relative to base_path + """ + search_path = os.path.join(self.base_path, prefix) + + if not os.path.exists(search_path): + return [] + + files = [] + for root, dirs, filenames in os.walk(search_path): + for filename in filenames: + full_path = os.path.join(root, filename) + relative_path = os.path.relpath(full_path, self.base_path) + files.append(relative_path) + + return files diff --git a/src/processing_pipeline/postgres_client.py b/src/processing_pipeline/postgres_client.py new file mode 100644 index 0000000..d127c0c --- /dev/null +++ b/src/processing_pipeline/postgres_client.py @@ -0,0 +1,498 @@ +""" +Simple PostgreSQL client that replaces SupabaseClient. +Uses direct psycopg2 connections without complex pooling. +""" + +import psycopg2 +from psycopg2.extras import RealDictCursor, Json +import os +from datetime import datetime, timezone +from processing_pipeline.constants import GeminiModel, PromptStage + + +class PostgresClient: + """ + Drop-in replacement for SupabaseClient using PostgreSQL. + Maintains the same interface for compatibility. + """ + + def __init__(self, connection_string=None): + """ + Initialize PostgreSQL connection. + + Args: + connection_string: PostgreSQL connection string (postgresql://user:pass@host:port/db) + If None, reads from DATABASE_URL environment variable + """ + if connection_string is None: + connection_string = os.getenv( + 'DATABASE_URL', + 'postgresql://verdad_user:your_password@localhost:5432/verdad_debates' + ) + + self.connection_string = connection_string + self.conn = None + self._connect() + + def _connect(self): + """Establish database connection.""" + if self.conn is None or self.conn.closed: + self.conn = psycopg2.connect( + self.connection_string, + cursor_factory=RealDictCursor + ) + + def _execute(self, query, params=None, fetch_one=False, fetch_all=False, commit=True): + """ + Execute a query with automatic connection handling. + + Args: + query: SQL query string + params: Query parameters + fetch_one: Return single row + fetch_all: Return all rows + commit: Commit transaction + + Returns: + Query result or None + """ + self._connect() + + try: + with self.conn.cursor() as cur: + cur.execute(query, params or ()) + + if fetch_one: + result = cur.fetchone() + if commit: + self.conn.commit() + return dict(result) if result else None + + if fetch_all: + results = cur.fetchall() + if commit: + self.conn.commit() + return [dict(row) for row in results] + + if commit: + self.conn.commit() + + return True + + except Exception as e: + self.conn.rollback() + raise e + + # ==================== RPC Functions ==================== + + def get_a_new_audio_file_and_reserve_it(self): + """Reserve an audio file for processing (Stage 1).""" + result = self._execute( + "SELECT fetch_a_new_audio_file_and_reserve_it()", + fetch_one=True + ) + # Extract jsonb value from the single-column result + return result['fetch_a_new_audio_file_and_reserve_it'] if result else None + + def get_a_new_stage_1_llm_response_and_reserve_it(self): + """Reserve a Stage 1 response for clipping (Stage 2).""" + result = self._execute( + "SELECT fetch_a_new_stage_1_llm_response_and_reserve_it()", + fetch_one=True + ) + return result['fetch_a_new_stage_1_llm_response_and_reserve_it'] if result else None + + def get_a_new_snippet_and_reserve_it(self): + """Reserve a snippet for analysis (Stage 3).""" + result = self._execute( + "SELECT fetch_a_new_snippet_and_reserve_it()", + fetch_one=True + ) + return result['fetch_a_new_snippet_and_reserve_it'] if result else None + + def get_a_ready_for_review_snippet_and_reserve_it(self): + """Reserve a snippet for review (Stage 4).""" + result = self._execute( + "SELECT fetch_a_ready_for_review_snippet_and_reserve_it()", + fetch_one=True + ) + return result['fetch_a_ready_for_review_snippet_and_reserve_it'] if result else None + + def get_a_snippet_that_has_no_embedding(self): + """Get a processed snippet without embedding (Stage 5).""" + result = self._execute( + "SELECT fetch_a_snippet_that_has_no_embedding()", + fetch_one=True + ) + return result['fetch_a_snippet_that_has_no_embedding'] if result else None + + # ==================== Query Methods ==================== + + def get_snippet_by_id(self, id, select="*"): + """Get snippet by ID.""" + return self._execute( + f"SELECT {select} FROM snippets WHERE id = %s", + (id,), + fetch_one=True, + commit=False + ) + + def get_snippets_by_ids(self, ids, select="*"): + """Get multiple snippets by IDs.""" + return self._execute( + f"SELECT {select} FROM snippets WHERE id = ANY(%s)", + (ids,), + fetch_all=True, + commit=False + ) + + def get_audio_file_by_id(self, id, select="*"): + """Get audio file by ID.""" + return self._execute( + f"SELECT {select} FROM audio_files WHERE id = %s", + (id,), + fetch_one=True, + commit=False + ) + + def get_stage_1_llm_response_by_id(self, id, select="*"): + """Get Stage 1 LLM response by ID.""" + return self._execute( + f"SELECT {select} FROM stage_1_llm_responses WHERE id = %s", + (id,), + fetch_one=True, + commit=False + ) + + # ==================== Status Update Methods ==================== + + def set_audio_file_status(self, id, status, error_message=None): + """Update audio file status.""" + if error_message: + return self._execute( + "UPDATE audio_files SET status = %s, error_message = %s, updated_at = NOW() WHERE id = %s", + (status, error_message, id) + ) + else: + return self._execute( + "UPDATE audio_files SET status = %s, updated_at = NOW() WHERE id = %s", + (status, id) + ) + + def set_stage_1_llm_response_status(self, id, status, error_message=None): + """Update Stage 1 LLM response status.""" + if error_message: + return self._execute( + "UPDATE stage_1_llm_responses SET status = %s, error_message = %s, updated_at = NOW() WHERE id = %s", + (status, error_message, id) + ) + else: + return self._execute( + "UPDATE stage_1_llm_responses SET status = %s, updated_at = NOW() WHERE id = %s", + (status, id) + ) + + def set_snippet_status(self, id, status, error_message=None): + """Update snippet status.""" + if error_message: + return self._execute( + "UPDATE snippets SET status = %s, error_message = %s, updated_at = NOW() WHERE id = %s", + (status, error_message, id) + ) + else: + return self._execute( + "UPDATE snippets SET status = %s, updated_at = NOW() WHERE id = %s", + (status, id) + ) + + # ==================== Insert Methods ==================== + + def insert_audio_file(self, radio_station_name, radio_station_code, location_state, + recorded_at, recording_day_of_week, file_path, file_size): + """Insert new audio file record.""" + return self._execute(""" + INSERT INTO audio_files + (radio_station_name, radio_station_code, location_state, + recorded_at, recording_day_of_week, file_path, file_size) + VALUES (%s, %s, %s, %s, %s, %s, %s) + RETURNING * + """, (radio_station_name, radio_station_code, location_state, + recorded_at, recording_day_of_week, file_path, file_size), + fetch_one=True) + + def insert_stage_1_llm_response(self, audio_file_id, initial_transcription, + initial_detection_result, transcriptor, + timestamped_transcription, detection_result, + status, detection_prompt_version_id=None, + transcription_prompt_version_id=None): + """Insert Stage 1 LLM response.""" + # Note: Schema only has timestamped_transcription, detection_result, and prompt_version + # Using detection_prompt_version_id for prompt_version (main prompt used) + return self._execute(""" + INSERT INTO stage_1_llm_responses + (audio_file, timestamped_transcription, detection_result, status, prompt_version) + VALUES (%s, %s, %s, %s, %s) + RETURNING * + """, (audio_file_id, Json(timestamped_transcription), Json(detection_result), + status, detection_prompt_version_id), + fetch_one=True) + + def insert_snippet(self, uuid, audio_file_id, stage_1_llm_response_id, + file_path, file_size, recorded_at, duration, start_time, end_time): + """Insert snippet record.""" + duration = self.ensure_time_format(duration) + start_time = self.ensure_time_format(start_time) + end_time = self.ensure_time_format(end_time) + + result = self._execute(""" + INSERT INTO snippets + (id, audio_file, stage_1_llm_response, file_path, file_size, + recorded_at, duration, start_time, end_time) + VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s) + RETURNING * + """, (uuid, audio_file_id, stage_1_llm_response_id, file_path, + file_size, recorded_at, duration, start_time, end_time), + fetch_one=True) + + return [result] # Return as list for compatibility + + # ==================== Update Methods ==================== + + def update_snippet(self, id, transcription, translation, title, summary, + explanation, disinformation_categories, keywords_detected, + language, confidence_scores, emotional_tone, context, + political_leaning, grounding_metadata, thought_summaries, + analyzed_by, status, error_message, stage_3_prompt_version_id=None): + """Update snippet with analysis results.""" + return self._execute(""" + UPDATE snippets SET + transcription = %s, translation = %s, title = %s, summary = %s, + explanation = %s, disinformation_categories = %s, keywords_detected = %s, + language = %s, confidence_scores = %s, emotional_tone = %s, context = %s, + political_leaning = %s, grounding_metadata = %s, thought_summaries = %s, + analyzed_by = %s, previous_analysis = NULL, status = %s, error_message = %s, + stage_3_prompt_version_id = %s, updated_at = NOW() + WHERE id = %s + """, (transcription, translation, title, summary, explanation, + disinformation_categories, keywords_detected, language, + Json(confidence_scores), Json(emotional_tone), context, + Json(political_leaning), Json(grounding_metadata), + Json(thought_summaries), analyzed_by, status, error_message, + stage_3_prompt_version_id, id)) + + def update_snippet_previous_analysis(self, id, previous_analysis): + """Update snippet's previous analysis field.""" + return self._execute( + "UPDATE snippets SET previous_analysis = %s, updated_at = NOW() WHERE id = %s", + (Json(previous_analysis), id) + ) + + def submit_snippet_review(self, id, translation, title, summary, explanation, + disinformation_categories, keywords_detected, language, + confidence_scores, political_leaning, grounding_metadata): + """Submit reviewed snippet.""" + return self._execute(""" + UPDATE snippets SET + translation = %s, title = %s, summary = %s, explanation = %s, + disinformation_categories = %s, keywords_detected = %s, language = %s, + confidence_scores = %s, political_leaning = %s, grounding_metadata = %s, + status = 'Processed', error_message = NULL, reviewed_at = %s, + reviewed_by = %s, updated_at = NOW() + WHERE id = %s + """, (translation, title, summary, explanation, disinformation_categories, + keywords_detected, language, Json(confidence_scores), + Json(political_leaning), Json(grounding_metadata), + datetime.now(timezone.utc), GeminiModel.GEMINI_2_5_PRO.value, id)) + + def reset_snippet(self, id): + """Reset snippet to initial state.""" + return self._execute(""" + UPDATE snippets SET + transcription = NULL, translation = NULL, title = NULL, summary = NULL, + explanation = NULL, disinformation_categories = NULL, keywords_detected = NULL, + language = NULL, confidence_scores = NULL, emotional_tone = NULL, + context = NULL, political_leaning = NULL, status = 'New', + error_message = NULL, updated_at = NOW() + WHERE id = %s + """, (id,)) + + def delete_snippet(self, id): + """Delete snippet.""" + return self._execute("DELETE FROM snippets WHERE id = %s", (id,)) + + def update_stage_1_llm_response_detection_result(self, id, detection_result): + """Update Stage 1 detection result.""" + return self._execute( + "UPDATE stage_1_llm_responses SET detection_result = %s, updated_at = NOW() WHERE id = %s", + (Json(detection_result), id) + ) + + def update_stage_1_llm_response_timestamped_transcription(self, id, timestamped_transcription, transcriptor): + """Update Stage 1 timestamped transcription.""" + return self._execute( + "UPDATE stage_1_llm_responses SET timestamped_transcription = %s, transcriptor = %s, updated_at = NOW() WHERE id = %s", + (Json(timestamped_transcription), transcriptor, id) + ) + + def reset_stage_1_llm_response_status(self, id): + """Reset Stage 1 response status.""" + return self._execute( + "UPDATE stage_1_llm_responses SET status = 'New', error_message = NULL, updated_at = NOW() WHERE id = %s", + (id,) + ) + + # ==================== Label Methods ==================== + + def create_new_label(self, text, text_spanish): + """Create new label or return existing.""" + # Check if exists + existing = self._execute( + "SELECT * FROM labels WHERE text = %s", + (text,), + fetch_one=True, + commit=False + ) + + if existing: + print(f"Label '{text}' already exists") + return existing + + # Insert new + return self._execute(""" + INSERT INTO labels (text, text_spanish, is_ai_suggested) + VALUES (%s, %s, TRUE) + RETURNING * + """, (text, text_spanish), fetch_one=True) + + def assign_label_to_snippet(self, label_id, snippet_id): + """Assign label to snippet.""" + # Check if already assigned + existing = self._execute( + "SELECT * FROM snippet_labels WHERE label = %s AND snippet = %s", + (label_id, snippet_id), + fetch_one=True, + commit=False + ) + + if existing: + print(f"Label {label_id} already assigned to snippet {snippet_id}") + return existing + + # Insert new + return self._execute(""" + INSERT INTO snippet_labels (label, snippet) + VALUES (%s, %s) + RETURNING * + """, (label_id, snippet_id), fetch_one=True) + + # ==================== Prompt Version Methods ==================== + + def get_active_prompt(self, stage: PromptStage): + """Get active prompt for stage.""" + result = self._execute(""" + SELECT * FROM prompt_versions + WHERE stage = %s AND is_active = TRUE + LIMIT 1 + """, (stage.value,), fetch_one=True, commit=False) + + if not result: + raise ValueError(f"No active prompt version found for stage: {stage}") + + return result + + def get_prompt_by_id(self, prompt_version_id: str): + """Get prompt by ID.""" + result = self._execute( + "SELECT * FROM prompt_versions WHERE id = %s", + (prompt_version_id,), + fetch_one=True, + commit=False + ) + + if not result: + raise ValueError(f"Prompt version not found: {prompt_version_id}") + + return result + + # ==================== Bulk Operations ==================== + + def reset_audio_file_status(self, ids): + """Reset multiple audio files to New status.""" + return self._execute( + "UPDATE audio_files SET status = 'New', error_message = NULL, updated_at = NOW() WHERE id = ANY(%s)", + (ids,) + ) + + def delete_stage_1_llm_responses(self, audio_file_ids): + """Delete Stage 1 responses for audio files.""" + return self._execute( + "DELETE FROM stage_1_llm_responses WHERE audio_file = ANY(%s)", + (audio_file_ids,) + ) + + # ==================== Embedding Methods ==================== + + def upsert_snippet_embedding(self, snippet_id, snippet_document, document_token_count, + embedding, model_name, status, error_message): + """Insert or update snippet embedding.""" + # Check if exists + existing = self._execute( + "SELECT id FROM snippet_embeddings WHERE snippet = %s", + (snippet_id,), + fetch_one=True, + commit=False + ) + + if existing: + # Update + return self._execute(""" + UPDATE snippet_embeddings SET + snippet_document = %s, document_token_count = %s, embedding = %s, + model_name = %s, status = %s, error_message = %s, updated_at = NOW() + WHERE snippet = %s + RETURNING * + """, (snippet_document, document_token_count, embedding, model_name, + status, error_message, snippet_id), fetch_one=True) + else: + # Insert + return self._execute(""" + INSERT INTO snippet_embeddings + (snippet, snippet_document, document_token_count, embedding, + model_name, status, error_message) + VALUES (%s, %s, %s, %s, %s, %s, %s) + RETURNING * + """, (snippet_id, snippet_document, document_token_count, embedding, + model_name, status, error_message), fetch_one=True) + + def delete_vector_embedding_of_snippet(self, snippet_id): + """Delete snippet embedding.""" + return self._execute( + "DELETE FROM snippet_embeddings WHERE snippet = %s", + (snippet_id,) + ) + + # ==================== Utility Methods ==================== + + def ensure_time_format(self, time_str): + """Ensure time string is in HH:MM:SS format.""" + if not time_str: + raise ValueError("Invalid time format. Expected format: 'HH:MM:SS'") + + colon_count = time_str.count(":") + if colon_count == 0: + return f"00:00:{time_str}" + elif colon_count == 1: + return f"00:{time_str}" + elif colon_count == 2: + return time_str + else: + raise ValueError("Invalid time format. Expected format: 'HH:MM:SS'") + + def close(self): + """Close database connection.""" + if self.conn and not self.conn.closed: + self.conn.close() + + def __del__(self): + """Cleanup on object destruction.""" + self.close() diff --git a/src/processing_pipeline/stage_1.py b/src/processing_pipeline/stage_1.py index 2214c8f..b15fa54 100644 --- a/src/processing_pipeline/stage_1.py +++ b/src/processing_pipeline/stage_1.py @@ -2,8 +2,8 @@ import os import time import json -import boto3 import uuid +from dotenv import load_dotenv from google import genai from google.genai.types import ( File, @@ -17,7 +17,8 @@ from prefect.client.schemas import FlowRun, State from prefect.task_runners import ConcurrentTaskRunner from processing_pipeline.timestamped_transcription_generator import TimestampedTranscriptionGenerator -from processing_pipeline.supabase_utils import SupabaseClient +from processing_pipeline.postgres_client import PostgresClient +from processing_pipeline.local_storage import LocalStorage from processing_pipeline.constants import ( GeminiModel, ProcessingStatus, @@ -26,6 +27,10 @@ from processing_pipeline.processing_utils import get_safety_settings from utils import optional_flow, optional_task +# Load environment variables +load_dotenv() + + @optional_task(log_prints=True, retries=3) def fetch_a_new_audio_file_from_supabase(supabase_client): @@ -50,7 +55,15 @@ def fetch_audio_file_by_id(supabase_client, audio_file_id): @optional_task(log_prints=True) def get_audio_file_metadata(audio_file): - recorded_at = datetime.strptime(audio_file["recorded_at"], "%Y-%m-%dT%H:%M:%S+00:00") + # Handle both ISO format strings and datetime objects + recorded_at_str = audio_file["recorded_at"] + if isinstance(recorded_at_str, str): + # Parse ISO 8601 format with timezone (handles microseconds and offsets) + from dateutil import parser + recorded_at = parser.isoparse(recorded_at_str) + else: + recorded_at = recorded_at_str + return { "radio_station_name": audio_file["radio_station_name"], "radio_station_code": audio_file["radio_station_code"], @@ -78,14 +91,13 @@ def fetch_stage_1_llm_response_by_id(supabase_client, stage_1_llm_response_id): @optional_task(log_prints=True, retries=3) -def download_audio_file_from_s3(s3_client, file_path): - return __download_audio_file_from_s3(s3_client, file_path) +def download_audio_file_from_s3(storage_client, file_path): + return __download_audio_file_from_s3(storage_client, file_path) -def __download_audio_file_from_s3(s3_client, file_path): - r2_bucket_name = os.getenv("R2_BUCKET_NAME") +def __download_audio_file_from_s3(storage_client, file_path): file_name = os.path.basename(file_path) - s3_client.download_file(r2_bucket_name, file_path, file_name) + storage_client.download_file(None, file_path, file_name) return file_name @@ -145,7 +157,7 @@ def transcribe_audio_file_with_timestamp_with_gemini( audio_file=audio_file, gemini_key=gemini_key, model_name=model_name, - user_prompt=prompt_version["user_prompt"], + user_prompt=prompt_version["prompt_text"], ) return {"timestamped_transcription": timestamped_transcription} @@ -288,7 +300,7 @@ def reset_audio_file_status_hook(flow: Flow, flow_run: FlowRun, state: State): if not audio_file_id: return - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + supabase_client = PostgresClient() audio_file = supabase_client.get_audio_file_by_id(audio_file_id) if audio_file and audio_file.get("status") == ProcessingStatus.PROCESSING: set_audio_file_status(supabase_client, audio_file_id, ProcessingStatus.NEW) @@ -302,16 +314,11 @@ def reset_audio_file_status_hook(flow: Flow, flow_run: FlowRun, state: State): on_cancellation=[reset_audio_file_status_hook], ) def initial_disinformation_detection(audio_file_id, limit): - # Setup S3 Client - s3_client = boto3.client( - "s3", - endpoint_url=os.getenv("R2_ENDPOINT_URL"), - aws_access_key_id=os.getenv("R2_ACCESS_KEY_ID"), - aws_secret_access_key=os.getenv("R2_SECRET_ACCESS_KEY"), - ) + # Setup Storage Client + storage_client = LocalStorage() - # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + # Setup PostgreSQL client + supabase_client = PostgresClient() # Load prompt versions once at flow start transcription_prompt_version = supabase_client.get_active_prompt(PromptStage.GEMINI_TIMESTAMPED_TRANSCRIPTION) @@ -327,7 +334,7 @@ def initial_disinformation_detection(audio_file_id, limit): audio_file = fetch_a_new_audio_file_from_supabase(supabase_client) # TODO: Retry failed audio files (Error) if audio_file: - local_file = download_audio_file_from_s3(s3_client, audio_file["file_path"]) + local_file = download_audio_file_from_s3(storage_client, audio_file["file_path"]) # Process the audio file process_audio_file( @@ -396,8 +403,8 @@ def undo_disinformation_detection(audio_file_ids): print("No audio file ids were provided!") return - # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + # Setup PostgreSQL client + supabase_client = PostgresClient() # Reset the status of the audio files reset_status_of_audio_files(supabase_client, audio_file_ids) @@ -412,8 +419,8 @@ def redo_main_detection(stage_1_llm_response_ids): print("No stage 1 llm response ids were provided!") return - # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + # Setup PostgreSQL client + supabase_client = PostgresClient() # Load prompt version detection_prompt_version = supabase_client.get_active_prompt(PromptStage.STAGE_1) @@ -465,16 +472,11 @@ def regenerate_timestamped_transcript(stage_1_llm_response_ids): print("No stage 1 llm response ids were provided!") return - # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + # Setup PostgreSQL client + supabase_client = PostgresClient() - # Setup S3 Client - s3_client = boto3.client( - "s3", - endpoint_url=os.getenv("R2_ENDPOINT_URL"), - aws_access_key_id=os.getenv("R2_ACCESS_KEY_ID"), - aws_secret_access_key=os.getenv("R2_SECRET_ACCESS_KEY"), - ) + # Setup Storage Client + storage_client = LocalStorage() # Load prompt versions transcription_prompt_version = supabase_client.get_active_prompt(PromptStage.GEMINI_TIMESTAMPED_TRANSCRIPTION) @@ -488,7 +490,7 @@ def regenerate_timestamped_transcript(stage_1_llm_response_ids): # Get metadata of the transcription audio_file = stage_1_llm_response["audio_file"] - local_file = download_audio_file_from_s3(s3_client, audio_file["file_path"]) + local_file = download_audio_file_from_s3(storage_client, audio_file["file_path"]) recorded_at = datetime.strptime(audio_file["recorded_at"], "%Y-%m-%dT%H:%M:%S+00:00") metadata = { "radio_station_name": audio_file["radio_station_name"], @@ -571,7 +573,7 @@ def run( # Prepare the user prompt user_prompt = ( - f"{prompt_version['user_prompt']}\n\n" + f"{prompt_version['prompt_text']}\n\n" f"Here is the metadata of the transcription:\n\n{json.dumps(metadata, indent=2)}\n\n" f"Here is the timestamped transcription:\n\n{timestamped_transcription}" ) diff --git a/src/processing_pipeline/stage_2.py b/src/processing_pipeline/stage_2.py index 3e44ee8..26afde3 100644 --- a/src/processing_pipeline/stage_2.py +++ b/src/processing_pipeline/stage_2.py @@ -1,10 +1,10 @@ from datetime import datetime, timedelta import os import time -import boto3 from prefect.task_runners import ConcurrentTaskRunner from pydub import AudioSegment -from processing_pipeline.supabase_utils import SupabaseClient +from processing_pipeline.postgres_client import PostgresClient +from processing_pipeline.local_storage import LocalStorage from utils import optional_flow, optional_task @@ -20,25 +20,25 @@ def fetch_a_new_stage_1_llm_response_from_supabase(supabase_client): @optional_task(log_prints=True, retries=3) -def download_audio_file_from_s3(s3_client, r2_bucket_name, file_path): - return __download_audio_file_from_s3(s3_client, r2_bucket_name, file_path) +def download_audio_file_from_s3(storage_client, file_path): + return __download_audio_file_from_s3(storage_client, file_path) -def __download_audio_file_from_s3(s3_client, r2_bucket_name, file_path): +def __download_audio_file_from_s3(storage_client, file_path): file_name = os.path.basename(file_path) - s3_client.download_file(r2_bucket_name, file_path, file_name) + storage_client.download_file(None, file_path, file_name) return file_name @optional_task(log_prints=True, retries=3) -def upload_to_r2_and_clean_up(s3_client, r2_bucket_name, folder_name, file_path): +def upload_to_r2_and_clean_up(storage_client, folder_name, file_path): if not os.path.isfile(file_path): raise FileNotFoundError(f"File {file_path} does not exist.") file_name = os.path.basename(file_path) destination_path = f"{folder_name}/snippets/{file_name}" - s3_client.upload_file(file_path, r2_bucket_name, destination_path) - print(f"File {file_path} uploaded to R2 as {destination_path}") + storage_client.upload_file(file_path, None, destination_path) + print(f"File {file_path} uploaded to storage as {destination_path}") os.remove(file_path) return destination_path @@ -177,8 +177,7 @@ def process_llm_response( supabase_client, llm_response, local_file, - s3_client, - r2_bucket_name, + storage_client, context_before_seconds, context_after_seconds, ): @@ -211,7 +210,7 @@ def process_llm_response( ) file_size = os.path.getsize(output_file) - uploaded_path = upload_to_r2_and_clean_up(s3_client, r2_bucket_name, folder_name, output_file) + uploaded_path = upload_to_r2_and_clean_up(storage_client, folder_name, output_file) insert_new_snippet_to_snippets_table_in_supabase( supabase_client=supabase_client, snippet_uuid=uuid, @@ -238,17 +237,11 @@ def process_llm_response( @optional_flow(name="Stage 2: Audio Clipping", log_prints=True, task_runner=ConcurrentTaskRunner) def audio_clipping(context_before_seconds, context_after_seconds, repeat): - # Setup S3 Client - R2_BUCKET_NAME = os.getenv("R2_BUCKET_NAME") - s3_client = boto3.client( - "s3", - endpoint_url=os.getenv("R2_ENDPOINT_URL"), - aws_access_key_id=os.getenv("R2_ACCESS_KEY_ID"), - aws_secret_access_key=os.getenv("R2_SECRET_ACCESS_KEY"), - ) + # Setup Storage Client + storage_client = LocalStorage() - # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + # Setup PostgreSQL client + supabase_client = PostgresClient() while True: llm_response = fetch_a_new_stage_1_llm_response_from_supabase( @@ -256,15 +249,14 @@ def audio_clipping(context_before_seconds, context_after_seconds, repeat): ) # TODO: Retry failed llm responses (Error) if llm_response: - local_file = download_audio_file_from_s3(s3_client, R2_BUCKET_NAME, llm_response["audio_file"]["file_path"]) + local_file = download_audio_file_from_s3(storage_client, llm_response["audio_file"]["file_path"]) # Process the stage-1 LLM response process_llm_response( supabase_client, llm_response, local_file, - s3_client, - R2_BUCKET_NAME, + storage_client, context_before_seconds, context_after_seconds, ) @@ -307,8 +299,8 @@ def fetch_snippets_from_supabase(supabase_client, snippet_ids): @optional_task(log_prints=True, retries=3) -def delete_snippet_from_r2(s3_client, r2_bucket_name, file_path): - s3_client.delete_object(Bucket=r2_bucket_name, Key=file_path) +def delete_snippet_from_r2(storage_client, file_path): + storage_client.delete_object(Bucket=None, Key=file_path) @optional_task(log_prints=True, retries=3) @@ -323,17 +315,11 @@ def reset_status_of_stage_1_llm_response(supabase_client, stage_1_llm_response_i @optional_flow(name="Stage 2: Undo Audio Clipping", log_prints=True, task_runner=ConcurrentTaskRunner) def undo_audio_clipping(stage_1_llm_response_ids): - # Setup S3 Client - R2_BUCKET_NAME = os.getenv("R2_BUCKET_NAME") - s3_client = boto3.client( - "s3", - endpoint_url=os.getenv("R2_ENDPOINT_URL"), - aws_access_key_id=os.getenv("R2_ACCESS_KEY_ID"), - aws_secret_access_key=os.getenv("R2_SECRET_ACCESS_KEY"), - ) + # Setup Storage Client + storage_client = LocalStorage() - # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + # Setup PostgreSQL client + supabase_client = PostgresClient() for id in stage_1_llm_response_ids: stage_1_llm_response = fetch_stage_1_llm_response_from_supabase(supabase_client, id) @@ -357,7 +343,7 @@ def undo_audio_clipping(stage_1_llm_response_ids): # 2. Delete the snippet from Supabase for snippet in snippets: print(f"Deleting snippet file from R2: {snippet['file_path']}") - delete_snippet_from_r2(s3_client, R2_BUCKET_NAME, snippet["file_path"]) + delete_snippet_from_r2(storage_client, snippet["file_path"]) print(f"Deleting snippet from Supabase: {snippet['id']}") delete_snippet_from_supabase(supabase_client, snippet["id"]) diff --git a/src/processing_pipeline/stage_3.py b/src/processing_pipeline/stage_3.py index f15fc32..ac04cea 100644 --- a/src/processing_pipeline/stage_3.py +++ b/src/processing_pipeline/stage_3.py @@ -7,7 +7,6 @@ import time from typing import Any -import boto3 from google import genai from google.genai import errors from prefect.flows import Flow @@ -23,7 +22,8 @@ ThinkingConfig, Tool, ) -from processing_pipeline.supabase_utils import SupabaseClient +from processing_pipeline.postgres_client import PostgresClient +from processing_pipeline.local_storage import LocalStorage from processing_pipeline.processing_utils import ( get_safety_settings, postprocess_snippet, @@ -67,13 +67,13 @@ def __fetch_a_new_snippet_from_supabase(supabase_client): @optional_task(log_prints=True, retries=3) -def download_audio_file_from_s3(s3_client, r2_bucket_name, file_path): - return __download_audio_file_from_s3(s3_client, r2_bucket_name, file_path) +def download_audio_file_from_s3(storage_client, file_path): + return __download_audio_file_from_s3(storage_client, file_path) -def __download_audio_file_from_s3(s3_client, r2_bucket_name, file_path): +def __download_audio_file_from_s3(storage_client, file_path): file_name = os.path.basename(file_path) - s3_client.download_file(r2_bucket_name, file_path, file_name) + storage_client.download_file(None, file_path, file_name) return file_name @@ -251,7 +251,7 @@ def reset_snippet_status_hook(flow: Flow, flow_run: FlowRun, state: State): if not snippet_ids: return - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + supabase_client = PostgresClient() for snippet_id in snippet_ids: snippet = fetch_a_specific_snippet_from_supabase(supabase_client, snippet_id) if snippet and snippet["status"] == ProcessingStatus.PROCESSING: @@ -267,19 +267,13 @@ def reset_snippet_status_hook(flow: Flow, flow_run: FlowRun, state: State): ) def in_depth_analysis(snippet_ids, skip_review, repeat): # Setup S3 Client - R2_BUCKET_NAME = os.getenv("R2_BUCKET_NAME") - s3_client = boto3.client( - "s3", - endpoint_url=os.getenv("R2_ENDPOINT_URL"), - aws_access_key_id=os.getenv("R2_ACCESS_KEY_ID"), - aws_secret_access_key=os.getenv("R2_SECRET_ACCESS_KEY"), - ) + storage_client = LocalStorage() # Setup Gemini Key GEMINI_KEY = os.getenv("GOOGLE_GEMINI_KEY") - # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + # Setup PostgreSQL client + supabase_client = PostgresClient() # Load prompt version prompt_version = supabase_client.get_active_prompt(PromptStage.STAGE_3) @@ -290,7 +284,7 @@ def in_depth_analysis(snippet_ids, skip_review, repeat): if snippet: supabase_client.set_snippet_status(snippet["id"], ProcessingStatus.PROCESSING) print(f"Found the snippet: {snippet['id']}") - local_file = download_audio_file_from_s3(s3_client, R2_BUCKET_NAME, snippet["file_path"]) + local_file = download_audio_file_from_s3(storage_client, snippet["file_path"]) # Process the snippet process_snippet( @@ -309,7 +303,7 @@ def in_depth_analysis(snippet_ids, skip_review, repeat): snippet = fetch_a_new_snippet_from_supabase(supabase_client) # TODO: Retry failed snippets (status: Error) if snippet: - local_file = download_audio_file_from_s3(s3_client, R2_BUCKET_NAME, snippet["file_path"]) + local_file = download_audio_file_from_s3(storage_client, snippet["file_path"]) # Process the snippet process_snippet( diff --git a/src/processing_pipeline/stage_4.py b/src/processing_pipeline/stage_4.py index 23f6627..e3ba3a0 100644 --- a/src/processing_pipeline/stage_4.py +++ b/src/processing_pipeline/stage_4.py @@ -18,7 +18,7 @@ get_system_instruction_for_stage_4, get_user_prompt_for_stage_4, ) -from processing_pipeline.supabase_utils import SupabaseClient +from processing_pipeline.postgres_client import PostgresClient from processing_pipeline.processing_utils import ( get_safety_settings, postprocess_snippet, @@ -153,7 +153,7 @@ def fetch_a_specific_snippet_from_supabase(supabase_client, snippet_id): @optional_flow(name="Stage 4: Analysis Review", log_prints=True, task_runner=ConcurrentTaskRunner) def analysis_review(snippet_ids, repeat): # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + supabase_client = PostgresClient() if snippet_ids: for id in snippet_ids: diff --git a/src/processing_pipeline/stage_5.py b/src/processing_pipeline/stage_5.py index e5d32e5..d212587 100644 --- a/src/processing_pipeline/stage_5.py +++ b/src/processing_pipeline/stage_5.py @@ -3,7 +3,7 @@ from openai import OpenAI from tiktoken import encoding_for_model -from processing_pipeline.supabase_utils import SupabaseClient +from processing_pipeline.postgres_client import PostgresClient from utils import optional_flow, optional_task from prefect.task_runners import ConcurrentTaskRunner @@ -99,7 +99,7 @@ def generate_snippet_embedding(supabase_client, snippet_id, snippet_document): @optional_flow(name="Stage 5: Embedding", log_prints=True, task_runner=ConcurrentTaskRunner) def embedding(repeat): # Setup Supabase client - supabase_client = SupabaseClient(supabase_url=os.getenv("SUPABASE_URL"), supabase_key=os.getenv("SUPABASE_KEY")) + supabase_client = PostgresClient() while True: snippet = fetch_a_snippet_that_has_no_embedding(supabase_client) # TODO: Retry failed snippets (status: Error) diff --git a/supabase/migrations/00_reset_database.sql b/supabase/migrations/00_reset_database.sql new file mode 100644 index 0000000..df59f09 --- /dev/null +++ b/supabase/migrations/00_reset_database.sql @@ -0,0 +1,11 @@ +-- Reset Database Script +-- Run this as postgres superuser to completely reset the database: +-- sudo -u postgres psql -d verdad_debates -f supabase/migrations/00_reset_database.sql + +DROP SCHEMA IF EXISTS public CASCADE; +CREATE SCHEMA public; +GRANT ALL ON SCHEMA public TO verdad_user; +GRANT ALL ON SCHEMA public TO PUBLIC; + +-- Create vector extension +CREATE EXTENSION IF NOT EXISTS vector; diff --git a/supabase/migrations/01_local_schema.sql b/supabase/migrations/01_local_schema.sql new file mode 100644 index 0000000..835b392 --- /dev/null +++ b/supabase/migrations/01_local_schema.sql @@ -0,0 +1,269 @@ +-- Minimal Local Schema Migration +-- This is a cleaned-up version of the Supabase schema for local PostgreSQL +-- Removes: extensions, auth schema, Supabase roles, RLS policies + +-- Create processing status enum +CREATE TYPE processing_status AS ENUM ('New', 'Processing', 'Processed', 'Error'); + +-- Core processing tables +CREATE TABLE audio_files ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ NOT NULL DEFAULT (NOW() AT TIME ZONE 'utc'), + updated_at TIMESTAMPTZ NOT NULL DEFAULT (NOW() AT TIME ZONE 'utc'), + radio_station_name TEXT NOT NULL, + radio_station_code TEXT NOT NULL, + location_state TEXT, + location_city TEXT, + recorded_at TIMESTAMPTZ NOT NULL, + recording_day_of_week TEXT NOT NULL, + file_path TEXT NOT NULL, + file_name TEXT NOT NULL, + file_size BIGINT NOT NULL, + duration INTEGER, + status processing_status NOT NULL DEFAULT 'New', + error_message TEXT +); + +CREATE TABLE stage_1_llm_responses ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ NOT NULL DEFAULT (NOW() AT TIME ZONE 'utc'), + updated_at TIMESTAMPTZ NOT NULL DEFAULT (NOW() AT TIME ZONE 'utc'), + audio_file UUID NOT NULL REFERENCES audio_files(id) ON DELETE CASCADE, + detection_result JSONB, + timestamped_transcription JSONB, + status processing_status NOT NULL DEFAULT 'New', + error_message TEXT, + prompt_version UUID +); + +CREATE TABLE snippets ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ NOT NULL DEFAULT (NOW() AT TIME ZONE 'utc'), + updated_at TIMESTAMPTZ NOT NULL DEFAULT (NOW() AT TIME ZONE 'utc'), + audio_file UUID NOT NULL REFERENCES audio_files(id) ON DELETE CASCADE, + stage_1_llm_response UUID REFERENCES stage_1_llm_responses(id) ON DELETE CASCADE, + file_path TEXT NOT NULL, + file_size BIGINT NOT NULL, + duration INTEGER NOT NULL, + recorded_at TIMESTAMPTZ NOT NULL, + start_time INTEGER NOT NULL, + end_time INTEGER NOT NULL, + transcription TEXT, + previous_analysis JSONB, + final_review JSONB, + status processing_status NOT NULL DEFAULT 'New', + error_message TEXT, + hidden BOOLEAN DEFAULT FALSE, + prompt_version UUID +); + +CREATE TABLE snippet_embeddings ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), + snippet UUID NOT NULL REFERENCES snippets(id) ON DELETE CASCADE, + embedding vector(768) NOT NULL +); + +-- Prompt management tables +CREATE TABLE prompt_versions ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + stage TEXT NOT NULL, + version_number INTEGER NOT NULL, + llm_model TEXT NOT NULL, + prompt_text TEXT NOT NULL, + system_instruction TEXT, + output_schema JSONB, + is_active BOOLEAN DEFAULT FALSE, + change_explanation TEXT +); + +CREATE TABLE heuristics ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + stage TEXT NOT NULL, + version_number INTEGER NOT NULL, + content TEXT NOT NULL, + is_active BOOLEAN DEFAULT FALSE, + change_explanation TEXT +); + +-- Optional: User management tables (simplified, no auth schema) +CREATE TABLE profiles ( + id UUID NOT NULL PRIMARY KEY, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + first_name TEXT, + last_name TEXT, + avatar_url TEXT, + role TEXT +); + +CREATE TABLE labels ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + label TEXT NOT NULL UNIQUE, + created_by UUID REFERENCES profiles(id), + upvote_count INTEGER DEFAULT 0, + like_count INTEGER DEFAULT 0, + comment_count INTEGER DEFAULT 0 +); + +CREATE TABLE snippet_labels ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + snippet UUID NOT NULL REFERENCES snippets(id) ON DELETE CASCADE, + label UUID NOT NULL REFERENCES labels(id) ON DELETE CASCADE, + applied_by UUID REFERENCES profiles(id), + upvote_count INTEGER DEFAULT 0, + CONSTRAINT unique_snippet_label UNIQUE (snippet, label) +); + +CREATE TABLE label_upvotes ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + snippet_label UUID NOT NULL REFERENCES snippet_labels(id) ON DELETE CASCADE, + upvoted_by UUID NOT NULL REFERENCES profiles(id), + CONSTRAINT unique_upvoted_by_snippet_label UNIQUE (upvoted_by, snippet_label) +); + +CREATE TABLE user_star_snippets ( + id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + snippet UUID NOT NULL REFERENCES snippets(id) ON DELETE CASCADE, + user_id UUID NOT NULL REFERENCES profiles(id), + CONSTRAINT unique_user_snippet UNIQUE (user_id, snippet) +); + +-- Draft tables for staging +CREATE TABLE draft_audio_files ( + audio_file_id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + radio_station_name TEXT NOT NULL, + radio_station_code TEXT NOT NULL, + location_state TEXT NOT NULL, + location_city TEXT NOT NULL, + broadcast_date DATE NOT NULL, + broadcast_time TIME NOT NULL, + day_of_week TEXT NOT NULL, + local_time_zone TEXT NOT NULL, + file_path TEXT NOT NULL, + created_at TIMESTAMPTZ DEFAULT NOW() +); + +CREATE TABLE draft_heuristics ( + heuristic_id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + version_number INTEGER NOT NULL, + content TEXT NOT NULL, + llm_model TEXT NOT NULL, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + change_explanation TEXT +); + +CREATE TABLE draft_prompt_versions ( + prompt_id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + stage INTEGER NOT NULL CHECK (stage >= 1 AND stage <= 5), + version_number INTEGER NOT NULL, + llm_model TEXT NOT NULL, + prompt_text TEXT NOT NULL, + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW(), + change_explanation TEXT, + CONSTRAINT unique_stage_version UNIQUE (stage, version_number) +); + +CREATE TABLE draft_snippets ( + snippet_id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + audio_file_id UUID NOT NULL, + start_time TIME NOT NULL, + end_time TIME NOT NULL, + audio_clip_path TEXT NOT NULL, + transcription TEXT, + translation TEXT, + title TEXT, + summary TEXT, + explanation TEXT, + disinformation_categories TEXT[], + search_queries JSONB, + created_at TIMESTAMPTZ DEFAULT NOW() +); + +CREATE TABLE draft_user_feedback ( + feedback_id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + snippet_id UUID NOT NULL, + user_id UUID NOT NULL, + rating INTEGER CHECK (rating >= 1 AND rating <= 5), + comment TEXT, + created_at TIMESTAMPTZ DEFAULT NOW() +); + +CREATE TABLE draft_users ( + user_id UUID NOT NULL DEFAULT gen_random_uuid() PRIMARY KEY, + email TEXT NOT NULL UNIQUE, + name TEXT, + created_at TIMESTAMPTZ DEFAULT NOW() +); + +-- Create indexes for performance +CREATE INDEX idx_audio_files_status ON audio_files(status); +CREATE INDEX idx_audio_files_recorded_at ON audio_files(recorded_at DESC); +CREATE INDEX idx_stage_1_status ON stage_1_llm_responses(status); +CREATE INDEX idx_stage_1_audio_file ON stage_1_llm_responses(audio_file); +CREATE INDEX idx_snippets_status ON snippets(status); +CREATE INDEX idx_snippets_audio_file ON snippets(audio_file); +CREATE INDEX idx_snippets_recorded_at ON snippets(recorded_at DESC); +CREATE INDEX idx_snippets_hidden ON snippets(hidden) WHERE hidden = FALSE; +CREATE INDEX idx_snippet_embeddings_snippet ON snippet_embeddings(snippet); +CREATE INDEX idx_prompt_versions_active ON prompt_versions(stage, is_active) WHERE is_active = TRUE; +CREATE INDEX idx_heuristics_active ON heuristics(stage, is_active) WHERE is_active = TRUE; + +-- Unique constraints +ALTER TABLE draft_heuristics ADD CONSTRAINT unique_version_number UNIQUE (version_number); + +-- Create basic functions (no extensions needed) +CREATE OR REPLACE FUNCTION update_updated_at_column() +RETURNS TRIGGER AS $$ +BEGIN + NEW.updated_at = NOW(); + RETURN NEW; +END; +$$ LANGUAGE plpgsql; + +-- Add update triggers +CREATE TRIGGER audio_files_update_updated_at BEFORE UPDATE ON audio_files + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +CREATE TRIGGER stage_1_llm_responses_update_updated_at BEFORE UPDATE ON stage_1_llm_responses + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +CREATE TRIGGER snippets_update_updated_at BEFORE UPDATE ON snippets + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +CREATE TRIGGER prompt_versions_update_updated_at BEFORE UPDATE ON prompt_versions + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +CREATE TRIGGER heuristics_update_updated_at BEFORE UPDATE ON heuristics + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +CREATE TRIGGER labels_update_updated_at BEFORE UPDATE ON labels + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +CREATE TRIGGER snippet_labels_update_updated_at BEFORE UPDATE ON snippet_labels + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +CREATE TRIGGER label_upvotes_update_updated_at BEFORE UPDATE ON label_upvotes + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +CREATE TRIGGER user_star_snippets_update_updated_at BEFORE UPDATE ON user_star_snippets + FOR EACH ROW EXECUTE FUNCTION update_updated_at_column(); + +-- Grant permissions to verdad_user +GRANT ALL ON ALL TABLES IN SCHEMA public TO verdad_user; +GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO verdad_user; +GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMA public TO verdad_user; diff --git a/supabase/migrations/02_load_prompts.sql b/supabase/migrations/02_load_prompts.sql new file mode 100644 index 0000000..2b63667 --- /dev/null +++ b/supabase/migrations/02_load_prompts.sql @@ -0,0 +1,98 @@ +-- Load initial prompt versions from the prompts directory +-- This migration should be run after 01_local_schema.sql + +-- Stage 1 Prompts +INSERT INTO prompt_versions ( + stage, + version_number, + llm_model, + prompt_text, + system_instruction, + output_schema, + is_active, + change_explanation +) VALUES ( + 'stage_1', + 1, + 'gemini-2.5-flash', + -- prompt_text will need to be loaded from Stage_1_detection_prompt.md + 'This is a placeholder - prompts need to be loaded via script', + 'This is a placeholder - system instructions need to be loaded via script', + '{"type": "object"}'::jsonb, + TRUE, + 'Initial version from migration' +); + +-- Gemini Timestamped Transcription Prompts +INSERT INTO prompt_versions ( + stage, + version_number, + llm_model, + prompt_text, + system_instruction, + output_schema, + is_active, + change_explanation +) VALUES ( + 'gemini_timestamped_transcription', + 1, + 'gemini-2.5-flash', + -- prompt_text will need to be loaded from Gemini_timestamped_transcription_generation_prompt.md + 'This is a placeholder - prompts need to be loaded via script', + NULL, + '{"type": "object"}'::jsonb, + TRUE, + 'Initial version from migration' +); + +-- Stage 3 Prompts +INSERT INTO prompt_versions ( + stage, + version_number, + llm_model, + prompt_text, + system_instruction, + output_schema, + is_active, + change_explanation +) VALUES ( + 'stage_3', + 1, + 'gemini-2.5-flash', + -- prompt_text will need to be loaded from Stage_3_analysis_prompt.md + 'This is a placeholder - prompts need to be loaded via script', + 'This is a placeholder - system instructions need to be loaded via script', + '{"type": "object"}'::jsonb, + TRUE, + 'Initial version from migration' +); + +-- Stage 1 Heuristics +INSERT INTO heuristics ( + stage, + version_number, + content, + is_active, + change_explanation +) VALUES ( + 'stage_1', + 1, + 'This is a placeholder - heuristics need to be loaded via script', + TRUE, + 'Initial version from migration' +); + +-- Stage 3 Heuristics +INSERT INTO heuristics ( + stage, + version_number, + content, + is_active, + change_explanation +) VALUES ( + 'stage_3', + 1, + 'This is a placeholder - heuristics need to be loaded via script', + TRUE, + 'Initial version from migration' +); From 52f33b8fcc8cc9fcf65502a08d1003caf9231759 Mon Sep 17 00:00:00 2001 From: JsNcAr Date: Fri, 23 Jan 2026 16:07:01 -0500 Subject: [PATCH 4/4] docs: Add comprehensive testing and database setup guides for the political debate fact-checking system - Created TESTING_GUIDE.md to outline testing procedures, including database connection, RPC function verification, and full processing pipeline tests. - Introduced DATABASE_SETUP_GUIDE.md detailing PostgreSQL setup, schema migrations, and function applications necessary for local development. --- docs/STAGE_1_DEEP_DIVE.md | 969 ++++++++++++++++++++++++++ docs/TESTING_GUIDE.md | 569 ++++++++++++++++ docs/setup/DATABASE_SETUP_GUIDE.md | 1007 ++++++++++++++++++++++++++++ 3 files changed, 2545 insertions(+) create mode 100644 docs/STAGE_1_DEEP_DIVE.md create mode 100644 docs/TESTING_GUIDE.md create mode 100644 docs/setup/DATABASE_SETUP_GUIDE.md diff --git a/docs/STAGE_1_DEEP_DIVE.md b/docs/STAGE_1_DEEP_DIVE.md new file mode 100644 index 0000000..bdbda72 --- /dev/null +++ b/docs/STAGE_1_DEEP_DIVE.md @@ -0,0 +1,969 @@ +# Stage 1: Initial Disinformation Detection - Deep Dive + +This document provides a comprehensive analysis of how Stage 1 works, including architecture, prompts, testing, and customization. + +--- + +## Table of Contents + +1. [Overview](#overview) +2. [Architecture & Flow](#architecture--flow) +3. [Prompt System](#prompt-system) +4. [Testing Stage 1](#testing-stage-1) +5. [Modifying Prompts](#modifying-prompts) +6. [Debugging & Troubleshooting](#debugging--troubleshooting) +7. [Performance Optimization](#performance-optimization) +8. [Real-World Examples](#real-world-examples) + +--- + +## Overview + +### Purpose + +Stage 1 is the entry point of the processing pipeline. It performs two critical tasks: + +1. **Transcription**: Converts audio to timestamped text using Google Gemini's multimodal capabilities +2. **Initial Detection**: Analyzes the transcript to identify potential disinformation snippets + +### Key Characteristics + +- **Model**: Uses Google Gemini 2.5 Flash (configurable) +- **Input**: MP3 audio file from local storage +- **Output**: + - Timestamped transcription (stored in database) + - List of flagged snippets with metadata (timestamps, categories, claims) +- **Duration**: 5-15 minutes for 30-60 minute audio files +- **Status Flow**: `audio_files.status: New → Processing → Processed/Error` + +--- + +## Architecture & Flow + +### High-Level Flow Diagram + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ STAGE 1 WORKFLOW │ +└─────────────────────────────────────────────────────────────────┘ + +1. Fetch Audio File + ├─ Query: SELECT audio_files WHERE status='New' + ├─ Lock: FOR UPDATE SKIP LOCKED (prevents race conditions) + └─ Update: status='Processing' + +2. Download Audio + ├─ Path: ~/verdad_debates_storage/{file_path} + └─ Validate: File exists + +3. Load Prompts + ├─ Transcription Prompt: 'gemini_timestamped_transcription' + └─ Detection Prompt: 'stage_1' + +4. Transcribe Audio (Gemini API) + ├─ Upload MP3 to Gemini + ├─ Apply transcription prompt + └─ Receive: Timestamped text + +5. Detect Disinformation (Gemini API) + ├─ Input: Transcription + metadata + ├─ Apply detection prompt + schema + └─ Receive: JSON with flagged_snippets[] + +6. Save Results + ├─ Insert: stage_1_llm_responses + │ ├─ timestamped_transcription (JSONB) + │ └─ detection_result (JSONB) + ├─ Update: audio_files.status='Processed' + └─ Delete: Local audio file (cleanup) + +7. Ready for Stage 2 + └─ stage_1_llm_responses.status='New' triggers Stage 2 +``` + +### Code Components + +**File**: `src/processing_pipeline/stage_1.py` + +**Key Functions**: + +1. `initial_disinformation_detection()` - Main Prefect flow +2. `fetch_a_new_audio_file_from_supabase()` - Gets audio file with reservation +3. `download_audio_file_from_s3()` - Downloads from local storage +4. `get_audio_file_metadata()` - Extracts metadata for context +5. `transcribe_audio_file_with_timestamp_with_gemini()` - Transcription task +6. `disinformation_detection_with_gemini()` - Detection task +7. `insert_stage_1_llm_response()` - Saves results to DB + +**Classes**: + +1. `GeminiTimestampTranscriptionGenerator` - Handles Gemini file upload and transcription +2. `Stage1Executor` - Handles Gemini detection with structured output + +--- + +## Prompt System + +### How Prompts Are Stored + +Prompts are stored in the `prompt_versions` table with the following structure: + +```sql +CREATE TABLE prompt_versions ( + id UUID PRIMARY KEY, + stage TEXT NOT NULL, -- e.g., 'stage_1', 'gemini_timestamped_transcription' + version_number INTEGER NOT NULL, + llm_model TEXT NOT NULL, -- e.g., 'gemini-2.5-flash' + prompt_text TEXT NOT NULL, -- The main prompt + system_instruction TEXT, -- System-level instructions + output_schema JSONB, -- Expected JSON output structure + is_active BOOLEAN DEFAULT FALSE, -- Only one active version per stage + change_explanation TEXT, + created_at TIMESTAMPTZ, + updated_at TIMESTAMPTZ +); +``` + +### Active Prompts in Stage 1 + +#### 1. Transcription Prompt + +**Stage**: `gemini_timestamped_transcription` +**Source File**: `prompts/Gemini_timestamped_transcription_generation_prompt.md` +**Purpose**: Instructs Gemini how to transcribe audio with timestamps + +**Key Instructions**: +- Transcribe with phrase-level timestamps (format: `[MM:SS]`) +- Capture dialects, accents, and colloquialisms +- Note non-speech elements: `[music]`, `[inaudible]`, `[noise]` +- Insert timestamps at natural pauses (max 15 seconds between) +- Maintain cultural sensitivity + +**Example Output**: +``` +[00:00] Hello, how are you? [background music] +[00:05] I'm fine, thank you. [child laughing] +[00:10] Let's discuss the election results... +``` + +**Schema**: Defined in `prompts/Timestamped_transcription_generation_output_schema.json` + +```json +{ + "type": "object", + "properties": { + "timestamped_transcription": { + "type": "string", + "description": "Full transcription with [MM:SS] timestamps" + } + } +} +``` + +#### 2. Detection Prompt + +**Stage**: `stage_1` +**Source Files**: +- Prompt: `prompts/Stage_1_detection_prompt.md` +- System Instruction: `prompts/Stage_1_system_instruction.md` +- Heuristics: `prompts/Stage_1_heuristics.md` +- Output Schema: `prompts/Stage_1_output_schema.json` + +**Purpose**: Analyzes transcription for potential disinformation + +**What It Detects**: +1. **False Statistics** - Incorrect numbers, percentages, data +2. **Misleading Context** - Facts presented misleadingly +3. **Conspiracy Theories** - Unfounded claims +4. **Deepfakes/Manipulated Media** - References to altered content +5. **Health Misinformation** - False medical/scientific claims +6. **Election Misinformation** - False voting/election claims +7. **Historical Revisionism** - Distorted historical facts +8. **Financial Scams** - Fraudulent schemes +9. **Identity Theft** - Phishing, impersonation +10. **Emotional Manipulation** - Fear-mongering, propaganda + +**Detection Criteria** (from Stage_1_heuristics.md): +- Must be a **factual claim** (not opinion) +- Must be **verifiable** through evidence +- Must have **potential harm** if believed +- Must have **sufficient context** to fact-check +- Must **not be satire/parody** + +**Example Output Structure**: +```json +{ + "flagged_snippets": [ + { + "uuid": "550e8400-e29b-41d4-a716-446655440000", + "start_timestamp": "05:23", + "end_timestamp": "05:45", + "category": "false_statistics", + "claim_summary": "Claims unemployment rate is 15% when official data shows 4.2%", + "transcription": "[05:23] The unemployment rate has skyrocketed to 15 percent...", + "context": "Speaker discussing economic policy during debate", + "confidence_score": 0.85, + "potential_harm": "high", + "reason_for_flagging": "Significantly contradicts official employment data" + } + ] +} +``` + +**Schema Fields**: +- `uuid`: Unique identifier for snippet +- `start_timestamp` / `end_timestamp`: Temporal boundaries in MM:SS format +- `category`: One of 10 disinformation types +- `claim_summary`: Brief description of the false claim +- `transcription`: Exact text from transcript +- `context`: Background information +- `confidence_score`: 0.0-1.0 confidence in detection +- `potential_harm`: `low`, `medium`, `high` +- `reason_for_flagging`: Explanation of why it was flagged + +### How Prompts Are Retrieved + +**Code**: `src/processing_pipeline/postgres_client.py` + +```python +def get_active_prompt(self, stage: str): + """Get active prompt version for a stage.""" + result = self._execute( + "SELECT * FROM prompt_versions WHERE stage = %s AND is_active = TRUE", + (stage,), + fetch_one=True + ) + if not result: + raise ValueError(f"No active prompt version found for stage: {stage}") + return result +``` + +**Usage in Stage 1**: +```python +# Load prompts +transcription_prompt_version = supabase_client.get_active_prompt( + PromptStage.GEMINI_TIMESTAMPED_TRANSCRIPTION +) +detection_prompt_version = supabase_client.get_active_prompt( + PromptStage.STAGE_1 +) + +# Use in Gemini calls +GeminiTimestampTranscriptionGenerator.run( + audio_file=audio_file, + gemini_key=gemini_key, + model_name=model_name, + user_prompt=transcription_prompt_version["prompt_text"], +) + +Stage1Executor.run( + gemini_key=gemini_key, + model_name=model_name, + timestamped_transcription=transcription, + metadata=metadata, + prompt_version=detection_prompt_version, +) +``` + +### Prompt Composition + +When calling Gemini for detection, the final prompt is composed as: + +```python +user_prompt = ( + f"{prompt_version['prompt_text']}\n\n" + f"Here is the metadata of the transcription:\n\n{json.dumps(metadata, indent=2)}\n\n" + f"Here is the timestamped transcription:\n\n{timestamped_transcription}" +) +``` + +**Metadata Included**: +```json +{ + "radio_station_name": "Test Radio Station", + "radio_station_code": "TEST", + "location": { + "state": "California", + "city": "Los Angeles" + }, + "recorded_at": "January 23, 2026 6:15 AM", + "recording_day_of_week": "Friday", + "time_zone": "UTC" +} +``` + +--- + +## Testing Stage 1 + +### Quick Test: End-to-End + +```bash +# 1. Ensure environment is configured +cat .env | grep -E "(DATABASE_URL|GOOGLE_GEMINI_KEY|STORAGE_PATH)" + +# 2. Load prompts (first time only) +poetry run python scripts/load_prompts.py + +# 3. Insert test audio file record +psql -U verdad_user -d verdad_debates << 'EOF' +INSERT INTO audio_files ( + file_path, file_name, file_size, duration, + recorded_at, recording_day_of_week, + radio_station_name, radio_station_code, + location_state, location_city, status +) VALUES ( + 'test/political_debate.mp3', 'political_debate.mp3', + 10485760, 1800, NOW(), 'Friday', + 'Test Station', 'TEST', 'CA', 'LA', 'New' +) RETURNING id, file_name, status; +EOF + +# 4. Copy audio file to storage +mkdir -p ~/verdad_debates_storage/test +cp /path/to/political_debate.mp3 ~/verdad_debates_storage/test/ + +# 5. Run Stage 1 +poetry run python -c " +from src.processing_pipeline.stage_1 import initial_disinformation_detection +initial_disinformation_detection(audio_file_id=None, limit=1) +" + +# 6. Check results +psql -U verdad_user -d verdad_debates -c " +SELECT + af.file_name, + af.status as audio_status, + s1.status as llm_status, + jsonb_array_length(s1.detection_result->'flagged_snippets') as snippets_found +FROM audio_files af +LEFT JOIN stage_1_llm_responses s1 ON s1.audio_file = af.id +ORDER BY af.created_at DESC +LIMIT 1; +" +``` + +### Testing Individual Components + +#### Test 1: Database Connection +```bash +poetry run python -c " +from src.processing_pipeline.postgres_client import PostgresClient +db = PostgresClient() +print('✅ Database connected') +db.close() +" +``` + +#### Test 2: Prompt Retrieval +```bash +poetry run python -c " +from src.processing_pipeline.postgres_client import PostgresClient +from src.processing_pipeline.constants import PromptStage + +db = PostgresClient() +prompt = db.get_active_prompt(PromptStage.STAGE_1) +print(f'✅ Prompt loaded: {prompt[\"stage\"]} v{prompt[\"version_number\"]}') +print(f' Model: {prompt[\"llm_model\"]}') +print(f' Prompt length: {len(prompt[\"prompt_text\"])} chars') +print(f' Has system instruction: {prompt[\"system_instruction\"] is not None}') +print(f' Has output schema: {prompt[\"output_schema\"] is not None}') +db.close() +" +``` + +#### Test 3: Storage Access +```bash +poetry run python -c " +from src.processing_pipeline.local_storage import LocalStorage +storage = LocalStorage() +print(f'✅ Storage initialized at: {storage.base_path}') + +# List files +import os +audio_dir = os.path.join(storage.base_path, 'test') +if os.path.exists(audio_dir): + files = os.listdir(audio_dir) + print(f' Test files found: {len(files)}') + for f in files: + print(f' - {f}') +else: + print(' ⚠️ Test directory not found') +" +``` + +#### Test 4: Gemini API Connection +```bash +poetry run python -c " +import os +from dotenv import load_dotenv +from google import genai + +load_dotenv() +gemini_key = os.getenv('GOOGLE_GEMINI_KEY') + +if not gemini_key: + print('❌ GOOGLE_GEMINI_KEY not set') +else: + client = genai.Client(api_key=gemini_key) + models = client.models.list() + print('✅ Gemini API connected') + print(' Available models:') + for model in list(models)[:5]: + print(f' - {model.name}') +" +``` + +### Testing with Different Audio Content + +To properly test detection, use audio with varying content: + +**1. Clean Political Debate** (baseline) +- Expected: Some snippets flagged +- Audio: Actual political debate with factual claims +- File size: 30-60 minutes + +**2. Religious/Non-Political Content** (negative test) +- Expected: Zero snippets flagged +- Audio: Church service, music, non-political talk radio +- Result: System correctly identifies no political content + +**3. Obvious Disinformation** (positive test) +- Expected: High number of snippets flagged +- Audio: Conspiracy theories, false statistics +- Result: System catches clear misinformation + +**4. Edge Cases** +- Mixed language audio +- Poor audio quality +- Background noise +- Overlapping speakers + +### Performance Benchmarks + +Expected processing times for Stage 1: + +| Audio Length | Transcription | Detection | Total | Gemini Cost | +|-------------|---------------|-----------|-------|-------------| +| 10 minutes | 1-2 min | 30-60 sec | ~2 min | $0.05 | +| 30 minutes | 3-5 min | 1-2 min | ~5 min | $0.15 | +| 60 minutes | 6-10 min | 2-3 min | ~10 min | $0.30 | + +*Costs are approximate using Gemini 2.5 Flash pricing* + +--- + +## Modifying Prompts + +### Method 1: Update Existing Prompt Version (Quick Changes) + +For minor tweaks without version tracking: + +```bash +psql -U verdad_user -d verdad_debates << 'EOF' +UPDATE prompt_versions +SET + prompt_text = 'Your updated prompt text here...', + updated_at = NOW() +WHERE stage = 'stage_1' AND is_active = TRUE; +EOF +``` + +**When to use**: Quick experiments, typo fixes, minor wording changes + +### Method 2: Create New Prompt Version (Recommended) + +For significant changes with version history: + +```bash +psql -U verdad_user -d verdad_debates << 'EOF' +-- Deactivate current version +UPDATE prompt_versions +SET is_active = FALSE +WHERE stage = 'stage_1' AND is_active = TRUE; + +-- Insert new version +INSERT INTO prompt_versions ( + stage, + version_number, + llm_model, + prompt_text, + system_instruction, + output_schema, + is_active, + change_explanation +) +SELECT + stage, + MAX(version_number) + 1, + 'gemini-2.5-flash', + 'Your new prompt text...', + system_instruction, -- Keep same or update + output_schema, -- Keep same or update + TRUE, + 'Explanation of what changed and why' +FROM prompt_versions +WHERE stage = 'stage_1' +GROUP BY stage, system_instruction, output_schema; +EOF +``` + +**When to use**: Production changes, A/B testing, significant prompt engineering + +### Method 3: Update from Source Files + +Edit the markdown files in `prompts/` directory, then reload: + +```bash +# 1. Edit prompt files +vim prompts/Stage_1_detection_prompt.md +vim prompts/Stage_1_system_instruction.md +vim prompts/Stage_1_heuristics.md + +# 2. Reload into database +poetry run python << 'EOF' +from src.processing_pipeline.postgres_client import PostgresClient +from pathlib import Path +import json + +db = PostgresClient() +prompts_dir = Path('prompts') + +# Deactivate current +db._execute( + "UPDATE prompt_versions SET is_active = FALSE WHERE stage = 'stage_1' AND is_active = TRUE" +) + +# Load from files +prompt_text = (prompts_dir / 'Stage_1_detection_prompt.md').read_text() +system_instruction = (prompts_dir / 'Stage_1_system_instruction.md').read_text() +output_schema = json.loads((prompts_dir / 'Stage_1_output_schema.json').read_text()) + +# Insert new version +db._execute(""" + INSERT INTO prompt_versions + (stage, version_number, llm_model, prompt_text, system_instruction, + output_schema, is_active, change_explanation) + SELECT 'stage_1', COALESCE(MAX(version_number), 0) + 1, 'gemini-2.5-flash', + %s, %s, %s, TRUE, 'Updated from source files' + FROM prompt_versions WHERE stage = 'stage_1' +""", (prompt_text, system_instruction, json.dumps(output_schema))) + +print('✅ Prompt updated from source files') +db.close() +EOF +``` + +### Testing Prompt Changes + +**A/B Testing Framework**: + +```python +# Test two prompt versions side by side +import json +from src.processing_pipeline.stage_1 import Stage1Executor +from src.processing_pipeline.postgres_client import PostgresClient + +db = PostgresClient() + +# Get both versions +v1 = db._execute( + "SELECT * FROM prompt_versions WHERE stage = 'stage_1' AND version_number = 1", + fetch_one=True +) +v2 = db._execute( + "SELECT * FROM prompt_versions WHERE stage = 'stage_1' AND version_number = 2", + fetch_one=True +) + +# Test with same transcription +test_transcription = "[00:00] The unemployment rate is 15 percent..." +metadata = {"recorded_at": "2026-01-23", ...} + +result_v1 = Stage1Executor.run( + gemini_key=os.getenv("GOOGLE_GEMINI_KEY"), + model_name="gemini-2.5-flash", + timestamped_transcription=test_transcription, + metadata=metadata, + prompt_version=v1 +) + +result_v2 = Stage1Executor.run( + gemini_key=os.getenv("GOOGLE_GEMINI_KEY"), + model_name="gemini-2.5-flash", + timestamped_transcription=test_transcription, + metadata=metadata, + prompt_version=v2 +) + +print(f"Version 1: {len(result_v1['flagged_snippets'])} snippets") +print(f"Version 2: {len(result_v2['flagged_snippets'])} snippets") +``` + +### Prompt Engineering Tips + +**For Better Detection**: + +1. **Be Specific About Context**: Include examples of what constitutes disinformation in your domain +2. **Define Thresholds**: Specify when to flag vs. when to skip borderline cases +3. **Include Edge Cases**: "Do not flag satire, comedy, or hypothetical scenarios" +4. **Structured Reasoning**: Ask for step-by-step analysis before final decision +5. **Confidence Scores**: Request self-assessment of detection certainty + +**Example Improvement**: + +Before: +``` +Identify any false claims in the transcript. +``` + +After: +``` +Analyze the transcript for factual claims that can be verified. For each claim: +1. Determine if it is a factual statement (not opinion/speculation) +2. Assess if it contradicts established evidence +3. Evaluate the potential harm if believed +4. Assign confidence score (0.0-1.0) + +Only flag claims with: +- Confidence > 0.7 +- Clear factual contradiction +- Medium or high potential harm + +Do NOT flag: +- Opinions or predictions +- Satire or humor +- Hypothetical scenarios +- Ambiguous statements without context +``` + +--- + +## Debugging & Troubleshooting + +### Common Issues + +#### Issue 1: No Snippets Detected (Empty Results) + +**Symptoms**: +```json +{ + "flagged_snippets": [] +} +``` + +**Possible Causes**: + +1. **Audio doesn't contain political/factual claims** + ```bash + # Check transcription + psql -U verdad_user -d verdad_debates -c " + SELECT LEFT(timestamped_transcription::text, 500) + FROM stage_1_llm_responses + ORDER BY created_at DESC LIMIT 1; + " + ``` + Solution: Use audio with actual political debate content + +2. **Detection prompt too strict** + ```bash + # Review current prompt + psql -U verdad_user -d verdad_debates -c " + SELECT prompt_text, system_instruction + FROM prompt_versions + WHERE stage = 'stage_1' AND is_active = TRUE; + " + ``` + Solution: Adjust confidence thresholds or detection criteria + +3. **Output schema mismatch** + ```bash + # Check schema + psql -U verdad_user -d verdad_debates -c " + SELECT output_schema + FROM prompt_versions + WHERE stage = 'stage_1' AND is_active = TRUE; + " + ``` + Solution: Ensure schema matches prompt expectations + +#### Issue 2: Transcription Quality Poor + +**Symptoms**: Garbled text, missing words, incorrect timestamps + +**Solutions**: + +1. **Audio quality**: Ensure clear audio, minimal background noise +2. **Format**: Use MP3 format, 44.1kHz sample rate recommended +3. **Length**: Gemini works best with <60 minute files +4. **Prompt tuning**: Adjust transcription prompt for specific accents/dialects + +#### Issue 3: API Rate Limits + +**Symptoms**: +``` +ERROR: 429 Rate Limit Exceeded +``` + +**Solutions**: + +1. **Add delays between calls**: + ```python + import time + time.sleep(2) # 2 second delay + ``` + +2. **Use quota management**: + ```python + # Track API usage + GEMINI_DAILY_QUOTA = 1500 # requests per day + requests_made = 0 + ``` + +3. **Switch to paid tier**: Gemini Flash has higher limits on paid plans + +#### Issue 4: Database Connection Errors + +**Symptoms**: +``` +psycopg2.OperationalError: could not connect to server +``` + +**Debug Steps**: + +```bash +# 1. Check PostgreSQL is running +sudo systemctl status postgresql + +# 2. Test connection +psql -U verdad_user -d verdad_debates -c "SELECT 1;" + +# 3. Check DATABASE_URL +echo $DATABASE_URL + +# 4. Verify .env file +cat .env | grep DATABASE_URL +``` + +### Enable Debug Logging + +Add to your code: + +```python +import logging +logging.basicConfig(level=logging.DEBUG) + +# Or for Prefect +from prefect import get_run_logger + +@flow +def initial_disinformation_detection(): + logger = get_run_logger() + logger.debug("Starting Stage 1...") + logger.debug(f"Prompt version: {prompt_version['version_number']}") + logger.debug(f"Transcription length: {len(transcription)}") +``` + +### Monitoring Stage 1 in Production + +**Database Queries for Health Monitoring**: + +```sql +-- Processing backlog +SELECT + COUNT(*) FILTER (WHERE status = 'New') as pending, + COUNT(*) FILTER (WHERE status = 'Processing') as in_progress, + COUNT(*) FILTER (WHERE status = 'Processed') as completed, + COUNT(*) FILTER (WHERE status = 'Error') as failed +FROM audio_files; + +-- Average processing time +SELECT + AVG(EXTRACT(EPOCH FROM (updated_at - created_at))) / 60 as avg_minutes +FROM audio_files +WHERE status = 'Processed' +AND created_at > NOW() - INTERVAL '24 hours'; + +-- Detection rate +SELECT + AVG(jsonb_array_length(detection_result->'flagged_snippets')) as avg_snippets_per_audio +FROM stage_1_llm_responses +WHERE status = 'Processed'; + +-- Error rate +SELECT + COUNT(*) FILTER (WHERE status = 'Error') * 100.0 / COUNT(*) as error_rate_pct +FROM audio_files +WHERE created_at > NOW() - INTERVAL '7 days'; +``` + +--- + +## Performance Optimization + +### 1. Parallel Processing + +Run multiple Stage 1 workers: + +```bash +# Terminal 1 +poetry run python -c " +from src.processing_pipeline.stage_1 import initial_disinformation_detection +while True: + initial_disinformation_detection(audio_file_id=None, limit=1) +" + +# Terminal 2 +poetry run python -c " +from src.processing_pipeline.stage_1 import initial_disinformation_detection +while True: + initial_disinformation_detection(audio_file_id=None, limit=1) +" +``` + +The `FOR UPDATE SKIP LOCKED` pattern prevents conflicts. + +### 2. Batch Processing + +Process multiple files in one run: + +```python +# Increase limit +initial_disinformation_detection(audio_file_id=None, limit=10) +``` + +### 3. Model Selection + +Trade speed vs. quality: + +```python +# Faster, cheaper (default) +model=GeminiModel.GEMINI_2_5_FLASH + +# Higher quality, slower +model=GeminiModel.GEMINI_2_5_PRO +``` + +### 4. Prompt Optimization + +Reduce token usage: + +- Use concise instructions +- Minimize example length +- Remove redundant context +- Use bullet points over paragraphs + +--- + +## Real-World Examples + +### Example 1: Successful Detection + +**Input Audio**: 2020 Presidential Debate excerpt + +**Transcription** (excerpt): +``` +[15:23] The unemployment rate dropped to 3.5%, the lowest in 50 years. +[15:30] We've created more jobs in the last two years than any administration in history. +[15:38] The stock market has reached record highs, benefiting millions of Americans. +``` + +**Detection Result**: +```json +{ + "flagged_snippets": [ + { + "uuid": "a1b2c3...", + "start_timestamp": "15:30", + "end_timestamp": "15:38", + "category": "false_statistics", + "claim_summary": "Claims job creation record contradicts BLS historical data", + "transcription": "[15:30] We've created more jobs...", + "context": "Presidential debate discussing economic record", + "confidence_score": 0.82, + "potential_harm": "medium", + "reason_for_flagging": "Exaggerates job creation numbers compared to official statistics" + } + ] +} +``` + +### Example 2: False Positive (Should Not Be Flagged) + +**Input**: Comedy podcast + +**Transcription**: +``` +[05:12] [laughter] And then I told him, "The earth is flat and vaccines cause autism!" +[05:18] [more laughter] Like, who even believes that stuff? +``` + +**Expected Result**: Empty (satire/comedy) + +**If Flagged**: Prompt needs better context understanding + +**Fix**: Add to system instruction: +``` +Ignore clearly satirical or comedic content. Indicators include: +- Laughter or humor cues +- Exaggerated absurdity +- Clear mocking tone +- Self-aware irony +``` + +### Example 3: Edge Case - Ambiguous Context + +**Input**: News analysis + +**Transcription**: +``` +[10:15] Some people say the election was stolen. +[10:20] Let's examine what the evidence actually shows. +``` + +**Challenge**: Mentions false claim but doesn't endorse it + +**Expected**: Not flagged (reporter quoting others) + +**Detection Strategy**: +- Distinguish between direct claims vs. reported speech +- Look for attribution phrases: "some say", "according to", "claims that" +- Consider speaker role (journalist vs. politician) + +--- + +## Next Steps + +After mastering Stage 1: + +1. **Stage 2**: Learn how flagged snippets are extracted as audio clips +2. **Stage 3**: Understand in-depth analysis with web search integration +3. **Prompt Optimization**: Experiment with different detection strategies +4. **Quality Metrics**: Build evaluation framework for detection accuracy + +--- + +## Additional Resources + +- **Gemini API Documentation**: https://ai.google.dev/ +- **Prompt Engineering Guide**: https://www.promptingguide.ai/ +- **Fact-Checking Best Practices**: https://www.poynter.org/ifcn/ +- **Prefect Documentation**: https://docs.prefect.io/ + +--- + +## Contributing + +To improve Stage 1 detection: + +1. Submit prompt improvements via pull request +2. Report detection issues with audio samples +3. Share evaluation results from testing +4. Propose new disinformation categories + +--- + +**Document Version**: 1.0 +**Last Updated**: January 23, 2026 +**Author**: AI Assistant +**License**: MIT diff --git a/docs/TESTING_GUIDE.md b/docs/TESTING_GUIDE.md new file mode 100644 index 0000000..97bfdb7 --- /dev/null +++ b/docs/TESTING_GUIDE.md @@ -0,0 +1,569 @@ +# Testing & Running the Political Debate Fact-Checking System + +This guide walks you through testing your local setup and running the complete processing pipeline. + +## Prerequisites Checklist + +Before testing, ensure you've completed: +- [x] PostgreSQL database setup (`01_local_schema.sql` applied) +- [x] 5 RPC functions installed +- [x] `.env` file configured with DATABASE_URL and STORAGE_PATH +- [x] `poetry install` completed +- [x] Storage directories created (`~/verdad_debates_storage/`) + +--- + +## Quick Test: Verify Everything Works + +### Test 1: Database Connection + +```bash +# Test Python can connect to PostgreSQL +poetry run python -c " +from src.processing_pipeline.postgres_client import PostgresClient +db = PostgresClient() +print('✅ Database connection successful!') +print(f'Connection: {db.connection_string}') +db.close() +" +``` + +**Expected:** `✅ Database connection successful!` + +--- + +### Test 2: Verify RPC Functions + +```bash +# Check all 5 RPC functions exist +psql -U verdad_user -d verdad_debates -c " +SELECT routine_name +FROM information_schema.routines +WHERE routine_schema = 'public' + AND routine_name LIKE 'fetch_%' +ORDER BY routine_name; +" +``` + +**Expected:** Should list 5 functions: +- fetch_a_new_audio_file_and_reserve_it +- fetch_a_new_snippet_and_reserve_it +- fetch_a_new_stage_1_llm_response_and_reserve_it +- fetch_a_ready_for_review_snippet_and_reserve_it +- fetch_a_snippet_that_has_no_embedding + +--- + +### Test 3: Storage Directories + +```bash +# Verify storage is accessible +ls -la ~/verdad_debates_storage/ +``` + +**Expected:** Should show `audio/` and `snippets/` directories. + +--- + +## Full Pipeline Test: Process a Test Audio File + +### Step 1: Load Prompts into Database + +The processing pipeline requires prompts to be loaded into the database: + +```bash +# Load all prompts and heuristics +poetry run python scripts/load_prompts.py +``` + +**Expected output:** +``` +🚀 Loading prompts into database... +✅ Loaded gemini_timestamped_transcription prompt (v1) +✅ Loaded stage_1 prompt (v1) +✅ Loaded stage_3 prompt (v1) +✅ Loaded stage_4 prompt (v1) +✅ Loaded stage_1 heuristics (v1) +✅ Loaded stage_3 heuristics (v1) +🎉 All prompts and heuristics loaded successfully! +``` + +**Note:** This script is safe to run multiple times - it skips prompts that already exist. + +--- + +### Step 2: Prepare Test Audio + +Get a short audio file (MP3 format, 30-60 minutes of political debate content): + +```bash +# Create test directory +mkdir -p ~/verdad_debates_storage/test + +# Copy your test audio file +# Replace /path/to/your/debate.mp3 with your actual file +cp /path/to/your/debate.mp3 ~/verdad_debates_storage/test/test_debate.mp3 + +# Verify file exists +ls -lh ~/verdad_debates_storage/test/test_debate.mp3 +``` + +--- + +### Step 3: Insert Audio File Record + +```bash +# Add the audio file to the database +psql -U verdad_user -d verdad_debates << 'EOF' +INSERT INTO audio_files ( + file_path, + file_name, + file_size, + duration, + recorded_at, + recording_day_of_week, + radio_station_name, + radio_station_code, + location_state, + location_city, + status +) VALUES ( + 'test/test_debate.mp3', + 'test_debate.mp3', + 10485760, + 1800, + NOW() - INTERVAL '2 hours', + TO_CHAR(NOW(), 'Day'), + 'Test Radio Station', + 'TEST', + 'California', + 'Los Angeles', + 'New' +) RETURNING id, file_name, status, recorded_at; +EOF +``` + +**Expected:** Returns UUID and shows status='New' + +--- + +### Step 4: Start Prefect Server + +Open a **separate terminal** and start Prefect: + +```bash +# Terminal 1: Start Prefect server +cd /home/ramsus/Programming/political-debate-fact-checking-system +poetry run prefect server start +``` + +**Expected:** Server starts on `http://127.0.0.1:4200` + +Keep this terminal open! + +--- + +### Step 5: Run Stage 1 (Detection & Transcription) + +In a **new terminal**, run Stage 1: + +```bash +# Terminal 2: Run Stage 1 +cd /home/ramsus/Programming/political-debate-fact-checking-system +poetry run python -c " +from src.processing_pipeline.stage_1 import initial_disinformation_detection +print('🚀 Starting Stage 1: Initial Disinformation Detection') +initial_disinformation_detection(audio_file_id=None, limit=1) +print('✅ Stage 1 complete!') +" +``` + +**What happens:** +1. Fetches audio file with status='New' +2. Uploads to Google Gemini for transcription +3. Runs disinformation detection +4. Creates `stage_1_llm_responses` record with detected snippets +5. Changes audio file status: 'New' → 'Processed' + +**Expected output:** +- "Found a new audio file: ..." +- "Processing audio file..." +- "Transcription complete" +- "Detection complete" +- Status='Processed' + +**Typical runtime:** 5-15 minutes (depends on audio length) + +--- + +### Step 6: Check Stage 1 Results + +```bash +# View the Stage 1 results +psql -U verdad_user -d verdad_debates -c " +SELECT + id, + status, + jsonb_array_length(detection_result->'flagged_snippets') as snippet_count +FROM stage_1_llm_responses +ORDER BY created_at DESC +LIMIT 1; +" +``` + +**Expected:** Shows status='Processed' and snippet count > 0 + +--- + +### Step 7: Run Stage 2 (Audio Clipping) + +```bash +# Terminal 2: Run Stage 2 +poetry run python -c " +from src.processing_pipeline.stage_2 import audio_clipping +print('🚀 Starting Stage 2: Audio Clipping') +audio_clipping(context_before_seconds=5, context_after_seconds=5, repeat=False) +print('✅ Stage 2 complete!') +" +``` + +**What happens:** +1. Fetches Stage 1 response with status='New' +2. Extracts audio clips for each flagged snippet +3. Saves clips to `~/verdad_debates_storage/snippets/` +4. Creates `snippets` records +5. Changes Stage 1 response status: 'New' → 'Processed' + +**Expected output:** +- "Found stage-1 LLM response: ..." +- "Loading audio file into memory" +- "Processing snippet [uuid]..." +- "File uploaded to storage as ..." + +**Typical runtime:** 1-3 minutes + +--- + +### Step 8: Check Stage 2 Results + +```bash +# View created snippets +psql -U verdad_user -d verdad_debates -c " +SELECT + id, + file_path, + duration, + status, + transcription IS NOT NULL as has_transcription +FROM snippets +ORDER BY created_at DESC +LIMIT 5; +" + +# Verify audio clips exist +ls -lh ~/verdad_debates_storage/*/snippets/ +``` + +**Expected:** Shows snippets with status='New' and actual .mp3 files in storage + +--- + +### Step 9: Run Stage 3 (In-Depth Analysis) + +```bash +# Terminal 2: Run Stage 3 +poetry run python -c " +from src.processing_pipeline.stage_3 import in_depth_analysis +print('🚀 Starting Stage 3: In-Depth Analysis') +in_depth_analysis(snippet_ids=None, skip_review=False, repeat=False) +print('✅ Stage 3 complete!') +" +``` + +**What happens:** +1. Fetches snippet with status='New' +2. Downloads audio clip +3. Runs deep analysis with Google Gemini +4. Performs web searches for fact-checking +5. Saves analysis to `previous_analysis` field +6. Changes status: 'New' → 'Ready for review' + +**Expected output:** +- "Found the snippet: ..." +- "Analyzing snippet..." +- "Search queries generated: ..." +- "Analysis complete" + +**Typical runtime:** 2-5 minutes per snippet + +--- + +### Step 10: Check Stage 3 Results + +```bash +# View analysis results +psql -U verdad_user -d verdad_debates -c " +SELECT + id, + status, + previous_analysis->>'category' as category, + previous_analysis->>'claim_summary' as claim +FROM snippets +WHERE previous_analysis IS NOT NULL +ORDER BY created_at DESC +LIMIT 1; +" +``` + +**Expected:** Shows status='Ready for review' with analysis data + +--- + +### Step 11: Run Stage 4 (Human Review Simulation) + +For testing, we'll mark the snippet as reviewed: + +```bash +# Manual review: Mark as processed +psql -U verdad_user -d verdad_debates << 'EOF' +UPDATE snippets +SET + status = 'Processed', + final_review = previous_analysis +WHERE status = 'Ready for review' +RETURNING id, status; +EOF +``` + +**Note:** In production, Stage 4 would run a review flow. For testing, we're simulating approval. + +--- + +### Step 12: Run Stage 5 (Generate Embeddings) + +```bash +# Terminal 2: Run Stage 5 +poetry run python -c " +from src.processing_pipeline.stage_5 import embedding +print('🚀 Starting Stage 5: Generate Embeddings') +embedding(repeat=False) +print('✅ Stage 5 complete!') +" +``` + +**What happens:** +1. Fetches snippet with status='Processed' (no embedding) +2. Generates 768-dimensional vector embedding using OpenAI +3. Stores in `snippet_embeddings` table +4. Enables similarity search + +**Expected output:** +- "Generating embedding for snippet: ..." +- "Embedding saved successfully" + +**Typical runtime:** < 1 minute + +--- + +### Step 13: Verify Complete Pipeline + +```bash +# Check the full pipeline status +psql -U verdad_user -d verdad_debates -c " +SELECT + 'Audio Files' as stage, + COUNT(*) FILTER (WHERE status = 'Processed') as processed, + COUNT(*) as total +FROM audio_files +UNION ALL +SELECT + 'Stage 1 Responses', + COUNT(*) FILTER (WHERE status = 'Processed'), + COUNT(*) +FROM stage_1_llm_responses +UNION ALL +SELECT + 'Snippets', + COUNT(*) FILTER (WHERE status = 'Processed'), + COUNT(*) +FROM snippets +UNION ALL +SELECT + 'Embeddings', + COUNT(*), + COUNT(*) +FROM snippet_embeddings; +" +``` + +**Expected output:** +``` + stage | processed | total +------------------+-----------+------- + Audio Files | 1 | 1 + Stage 1 Responses| 1 | 1 + Snippets | 1 | 1 + Embeddings | 1 | 1 +``` + +--- + +## Automated Testing: Run All Stages Sequentially + +Create a test script: + +```bash +# Create test script +cat > test_pipeline.sh << 'EOF' +#!/bin/bash +set -e + +echo "🚀 Testing Complete Processing Pipeline" +echo "========================================" + +cd /home/ramsus/Programming/political-debate-fact-checking-system + +echo "" +echo "✅ Stage 1: Initial Detection" +poetry run python -c "from src.processing_pipeline.stage_1 import initial_disinformation_detection; initial_disinformation_detection(None, 1)" 2>&1 | tail -5 + +echo "" +echo "✅ Stage 2: Audio Clipping" +poetry run python -c "from src.processing_pipeline.stage_2 import audio_clipping; audio_clipping(5, 5, False)" 2>&1 | tail -5 + +echo "" +echo "✅ Stage 3: In-Depth Analysis" +poetry run python -c "from src.processing_pipeline.stage_3 import in_depth_analysis; in_depth_analysis(None, False, False)" 2>&1 | tail -5 + +echo "" +echo "✅ Stage 4: Manual Review (simulated)" +psql -U verdad_user -d verdad_debates -c "UPDATE snippets SET status='Processed', final_review=previous_analysis WHERE status='Ready for review'" -q + +echo "" +echo "✅ Stage 5: Generate Embeddings" +poetry run python -c "from src.processing_pipeline.stage_5 import embedding; embedding(False)" 2>&1 | tail -5 + +echo "" +echo "🎉 Pipeline test complete!" +echo "" +echo "📊 Results:" +psql -U verdad_user -d verdad_debates -c "SELECT 'Embeddings Created' as result, COUNT(*) as count FROM snippet_embeddings" +EOF + +chmod +x test_pipeline.sh +``` + +Run it: +```bash +./test_pipeline.sh +``` + +--- + +## Monitoring & Debugging + +### View Prefect Dashboard + +Open in browser: http://127.0.0.1:4200 + +You'll see: +- Flow runs +- Task status +- Execution logs +- Error traces + +### Check Logs + +```bash +# View recent logs +poetry run prefect deployment ls +poetry run prefect flow-run ls --limit 10 +``` + +### Reset Test Data + +```bash +# Clear all test data to start fresh +psql -U verdad_user -d verdad_debates << 'EOF' +TRUNCATE snippet_embeddings CASCADE; +TRUNCATE snippets CASCADE; +TRUNCATE stage_1_llm_responses CASCADE; +TRUNCATE audio_files CASCADE; +EOF + +# Remove audio files +rm -f ~/verdad_debates_storage/*/snippets/*.mp3 +``` + +--- + +## Common Issues + +### Issue: "No new audio files found" +**Solution:** Check audio file status: +```bash +psql -U verdad_user -d verdad_debates -c "SELECT id, status FROM audio_files;" +# Reset if needed: +psql -U verdad_user -d verdad_debates -c "UPDATE audio_files SET status='New' WHERE status='Processing';" +``` + +### Issue: "ImportError: No module named psycopg2" +**Solution:** +```bash +poetry install +``` + +### Issue: "FileNotFoundError: Audio file not found" +**Solution:** Verify file path matches database record: +```bash +# Check database path +psql -U verdad_user -d verdad_debates -c "SELECT file_path FROM audio_files WHERE status='New';" + +# Check actual file +ls ~/verdad_debates_storage/test/ +``` + +### Issue: API rate limits (Gemini/OpenAI) +**Solution:** Add delays between stages or use smaller test files. + +--- + +## Production Deployment + +Once testing is successful, deploy workers: + +```bash +# Terminal 1: Prefect server +poetry run prefect server start + +# Terminal 2: Stage 1 worker +poetry run python -m src.processing_pipeline.main stage_1 + +# Terminal 3: Stage 2 worker +poetry run python -m src.processing_pipeline.main stage_2 + +# Terminal 4: Stage 3 worker +poetry run python -m src.processing_pipeline.main stage_3 + +# Terminal 5: Stage 5 worker +poetry run python -m src.processing_pipeline.main stage_5 +``` + +Or use the provided scripts: +```bash +./scripts/start_prefect_server.sh +./scripts/start_processing.sh +``` + +--- + +## Success Criteria + +Your system is working correctly when: +- ✅ All 5 stages complete without errors +- ✅ Snippet audio files exist in storage +- ✅ Database has records at each stage +- ✅ Embeddings are generated +- ✅ Prefect dashboard shows successful runs + +**Next steps:** Upload real debate recordings and monitor the system! diff --git a/docs/setup/DATABASE_SETUP_GUIDE.md b/docs/setup/DATABASE_SETUP_GUIDE.md new file mode 100644 index 0000000..9620b0c --- /dev/null +++ b/docs/setup/DATABASE_SETUP_GUIDE.md @@ -0,0 +1,1007 @@ +# Complete PostgreSQL Database Setup Guide + +This guide will walk you through setting up your local PostgreSQL database from scratch for the political debate fact-checking system. + +## Prerequisites + +- PostgreSQL 16 installed +- pgvector extension available +- Database `verdad_debates` created +- User `verdad_user` created with password + +--- + +## Step-by-Step Setup + +### Step 1: Verify Database Connection + +```bash +# Test that you can connect to the database +psql -U verdad_user -d verdad_debates -c "SELECT version();" +``` + +**Expected output:** Should show PostgreSQL version 16.x + +--- + +### Step 2: Install Required Extensions + +```bash +# Install pgvector extension (required for embeddings) +psql -U verdad_user -d verdad_debates -c "CREATE EXTENSION IF NOT EXISTS vector;" + +# Verify extension is installed +psql -U verdad_user -d verdad_debates -c "\dx" +``` + +Expected output: Should list `vector` in the extensions table. + +--- + +### Step 3: Reset Database (If Needed) + +If you previously ran migrations and encountered errors, reset the database first: + +```bash +# Option 1: Reset as postgres superuser (recommended) +sudo -u postgres psql -d verdad_debates -f supabase/migrations/00_reset_database.sql + +# Option 2: Drop and recreate the database completely +sudo -u postgres psql << 'EOF' +DROP DATABASE IF EXISTS verdad_debates; +CREATE DATABASE verdad_debates OWNER verdad_user; +\c verdad_debates +CREATE EXTENSION vector; +GRANT ALL ON SCHEMA public TO verdad_user; +EOF +``` + +**When to use this:** +- After getting "already exists" errors +- To start completely fresh +- If previous migrations partially failed + +**Skip this step** if your database is brand new and empty. + +--- + +### Step 4: Apply Local Schema Migration + +This creates all tables, types, and indexes for local PostgreSQL (without Supabase dependencies). + +```bash +psql -U verdad_user -d verdad_debates -f supabase/migrations/01_local_schema.sql +``` + +**What this does:** +- Creates enum type: `processing_status` (New, Processing, Processed, Error) +- Creates tables: `audio_files`, `stage_1_llm_responses`, `snippets`, `snippet_embeddings` +- Creates optional tables: `profiles`, `labels`, `prompt_versions`, `heuristics` +- Creates indexes for performance +- Creates update triggers (replaces moddatetime extension) +- No dependencies on Supabase extensions or `auth` schema + +**Expected output:** Should see multiple `CREATE TABLE`, `CREATE INDEX`, `CREATE TRIGGER` messages without errors. + +**Note:** This replaces the original `20241029135348_remote_schema.sql` which has Supabase-specific dependencies. + +--- + +### Step 5: Apply Processing Pipeline RPC Functions + +These 5 functions are critical for the processing pipeline's task reservation system. They must be applied individually. + +```bash +# Stage 1: Reserve audio file for initial detection +psql -U verdad_user -d verdad_debates -f supabase/database/sql/fetch_a_new_audio_file_and_reserve_it.sql + +# Stage 2: Reserve stage-1 response for audio clipping +psql -U verdad_user -d verdad_debates -f supabase/database/sql/fetch_a_new_stage_1_llm_response_and_reserve_it.sql + +# Stage 3: Reserve snippet for in-depth analysis +psql -U verdad_user -d verdad_debates -f supabase/database/sql/fetch_a_new_snippet_and_reserve_it.sql + +# Stage 4: Reserve snippet for human review +psql -U verdad_user -d verdad_debates -f supabase/database/sql/fetch_a_ready_for_review_snippet_and_reserve_it.sql + +# Stage 5: Get snippet without embedding +psql -U verdad_user -d verdad_debates -f supabase/database/sql/fetch_a_snippet_that_has_no_embedding.sql +``` + +**What these do:** +- Use atomic `UPDATE ... FOR UPDATE SKIP LOCKED` pattern to prevent race conditions +- Return `jsonb` with embedded join data (e.g., audio_file details in snippet) +- Auto-update status: `New` → `Processing` (or `Ready for review` → `Reviewing`) +- Enable distributed processing across multiple workers + +**Expected output:** Should see `CREATE OR REPLACE FUNCTION` messages for each file. + +--- + +### Step 6: (Optional) Apply Additional Functions for Web UI + +If you plan to build a web interface or use advanced features, apply these: + +```bash +# Full-text search +psql -U verdad_user -d verdad_debates -f supabase/database/sql/full_text_search_index.sql + +# Similarity search (requires embeddings) +psql -U verdad_user -d verdad_debates -f supabase/database/sql/search_related_snippets.sql + +# Label management +psql -U verdad_user -d verdad_debates -f supabase/database/sql/create_apply_and_upvote_label_function.sql + +# User functions +psql -U verdad_user -d verdad_debates -f supabase/database/sql/setup_profile_function.sql +``` + +**Note:** These are NOT required for the processing pipeline to work. + +--- + +### Step 7: Verify Schema Setup + +```bash +# Check that all main tables exist +psql -U verdad_user -d verdad_debates -c " +SELECT table_name +FROM information_schema.tables +WHERE table_schema = 'public' + AND table_type = 'BASE TABLE' +ORDER BY table_name; +" +``` + +**Expected tables (minimum):** +- `audio_files` ✓ +- `stage_1_llm_responses` ✓ +- `snippets` ✓ +- `snippet_embeddings` ✓ + +**Plus additional tables:** +- `profiles`, `labels`, `snippet_labels`, `label_upvotes` +- `user_star_snippets`, `prompt_versions`, `heuristics` +- `draft_*` tables for staging + +--- + +### Step 8: Verify Processing Pipeline Functions + +```bash +# List the 5 critical RPC functions +psql -U verdad_user -d verdad_debates -c " +SELECT routine_name, routine_schema +FROM information_schema.routines +WHERE routine_schema = 'public' + AND routine_type = 'FUNCTION' + AND routine_name LIKE 'fetch_%' +ORDER BY routine_name; +" +``` + +**Expected functions:** +1. `fetch_a_new_audio_file_and_reserve_it` ✓ +2. `fetch_a_new_snippet_and_reserve_it` ✓ +3. `fetch_a_new_stage_1_llm_response_and_reserve_it` ✓ +4. `fetch_a_ready_for_review_snippet_and_reserve_it` ✓ +5. `fetch_a_snippet_that_has_no_embedding` ✓ + +--- + +### Step 9: Verify Processing Status Enum + +```bash +psql -U verdad_user -d verdad_debates -c " +SELECT unnest(enum_range(NULL::processing_status)) AS status_value; +" +``` + +**Expected values:** +``` + status_value +-------------- + New + Processing + Processed + Error +``` + +--- + +### Step 10: Test Connection from Python + +```bash +python3 -c " +from src.processing_pipeline.postgres_client import PostgresClient +db = PostgresClient() +print('✅ Database connection successful!') +db.close() +" +``` + +**Expected output:** `✅ Database connection successful!` + +**If you get import errors:** Run `poetry install` first. + +--- + +### Step 11: Create Storage Directory Structure + +```bash +# Create base storage directory +mkdir -p ~/verdad_debates_storage/audio +mkdir -p ~/verdad_debates_storage/snippets + +# Verify directories were created +ls -la ~/verdad_debates_storage/ +``` + +**Expected output:** Should show `audio/` and `snippets/` directories. + +--- + +### Step 12: Update Environment Variables + +Create or update your `.env` file in the project root: + +```bash +cat > .env << 'EOF' +# Database Connection +DATABASE_URL=postgresql://verdad_user:your_password@localhost:5432/verdad_debates + +# Local Storage +STORAGE_PATH=~/verdad_debates_storage + +# AI Services +GOOGLE_GEMINI_KEY=your_gemini_api_key +OPENAI_API_KEY=your_openai_api_key + +# Search Engine (optional) +SEARXNG_URL=http://localhost:8080 + +# Remove these old cloud variables: +# SUPABASE_URL= +# SUPABASE_KEY= +# R2_ENDPOINT_URL= +# R2_ACCESS_KEY_ID= +# R2_SECRET_ACCESS_KEY= +# R2_BUCKET_NAME= +EOF +``` + +**Important:** Replace `your_password`, `your_gemini_api_key`, and `your_openai_api_key` with actual values. + +--- + +### Step 13: Insert Test Audio File Record + +```bash +# Create a test audio file entry in the database +psql -U verdad_user -d verdad_debates << 'EOF' +INSERT INTO audio_files ( + file_path, + file_name, + file_size, + duration, + recorded_at, + recording_day_of_week, + radio_station_name, + radio_station_code, + location_state, + location_city, + status +) VALUES ( + 'test/debate_sample_2026.mp3', + 'debate_sample_2026.mp3', + 5242880, + 1800, + NOW() - INTERVAL '1 hour', + 'Thursday', + 'Test Debate Channel', + 'TEST', + 'Test State', + 'Test City', + 'New' +) RETURNING id, file_name, status, recorded_at; +EOF +``` + +**Expected output:** Should return the UUID, file name, status='New', and timestamp. + +--- + +### Step 14: Copy Test Audio File to Storage + +```bash +# Copy your MP3 file to the storage directory +# Replace /path/to/your/audio.mp3 with your actual file +mkdir -p ~/verdad_debates_storage/test +cp /path/to/your/audio.mp3 ~/verdad_debates_storage/test/debate_sample_2026.mp3 + +# Verify file exists +ls -lh ~/verdad_debates_storage/test/debate_sample_2026.mp3 +``` + +**Note:** The file path in the database must match the actual file location in storage. + +--- + +### Step 15: Verify Test Record + +```bash +# Check that the audio file record exists and is ready +psql -U verdad_user -d verdad_debates -c " +SELECT + id, + file_name, + radio_station_name, + status, + created_at, + recorded_at +FROM audio_files +WHERE status = 'New' +ORDER BY created_at DESC +LIMIT 5; +" +``` + +**Expected output:** Should show your test audio file with status='New'. + +--- + +### Step 16: Test RPC Function + +```bash +# Test that the Stage 1 function can fetch the audio file +psql -U verdad_user -d verdad_debates -c " +SELECT fetch_a_new_audio_file_and_reserve_it(); +" +``` + +**Expected behavior:** +- Returns a jsonb object with audio file details +- Status automatically changes from 'New' → 'Processing' + +**To reset status for testing:** +```bash +psql -U verdad_user -d verdad_debates -c " +UPDATE audio_files SET status = 'New' WHERE status = 'Processing'; +" +``` + +--- + +## Troubleshooting + +### Understanding the Original Schema Migration Errors + +If you tried to run `supabase/migrations/20241029135348_remote_schema.sql` directly, you encountered multiple errors. Here's why and how to fix them: + +#### 1. Extension Errors (Missing Supabase Extensions) +``` +ERROR: extension "http" is not available +ERROR: extension "hypopg" is not available +ERROR: extension "pgroonga" is not available +ERROR: extension "supa_queue" is not available +``` + +**Why:** These are Supabase-specific PostgreSQL extensions that aren't available in standard PostgreSQL installations. + +**Solution:** Use `supabase/migrations/01_local_schema.sql` instead. It has no external extension dependencies (only the standard `vector` extension). + +#### 2. "Already Exists" Collision Errors +``` +ERROR: type "processing_status" already exists +ERROR: relation "audio_files" already exists +``` + +**Why:** You ran the migration before and it partially succeeded, leaving some objects in the database. + +**Solution:** Reset the database using Step 3: +```bash +sudo -u postgres psql -d verdad_debates -f supabase/migrations/00_reset_database.sql +``` + +#### 3. Missing 'auth' Schema Errors +``` +ERROR: schema "auth" does not exist +ERROR: constraint "profiles_id_fkey" ... references auth.users +``` + +**Why:** Supabase has a special `auth` schema with a `users` table for authentication. Local PostgreSQL doesn't have this. + +**Solution:** `01_local_schema.sql` removes all foreign keys to `auth.users`. The `profiles` table no longer requires authentication integration. + +#### 4. Missing Role Errors +``` +ERROR: role "anon" does not exist +ERROR: role "authenticated" does not exist +ERROR: role "service_role" does not exist +``` + +**Why:** These are Supabase API roles used for row-level security (RLS) policies. They control access through the Supabase REST API. + +**Solution:** `01_local_schema.sql` removes all RLS policies. For local processing, you connect directly as `verdad_user` without API roles. + +#### 5. Missing moddatetime Extension +``` +ERROR: function moddatetime() does not exist +ERROR: permission denied to create extension "moddatetime" +``` + +**Why:** `moddatetime` is a Supabase extension that auto-updates `updated_at` timestamps. + +**Solution:** `01_local_schema.sql` includes a custom trigger function `update_updated_at_column()` that does the same thing without requiring the extension. + +--- + +### Error: "role does not exist" + +```bash +# Create the user as postgres superuser +sudo -u postgres psql -c "CREATE USER verdad_user WITH PASSWORD 'your_password';" +sudo -u postgres psql -c "GRANT ALL PRIVILEGES ON DATABASE verdad_debates TO verdad_user;" +``` + +### Error: "database does not exist" + +```bash +# Create the database +sudo -u postgres createdb -O verdad_user verdad_debates + +# Or create it manually +sudo -u postgres psql -c "CREATE DATABASE verdad_debates OWNER verdad_user;" +``` + +### Error: "extension vector does not exist" + +```bash +# Install pgvector system package (openSUSE) +sudo zypper install postgresql16-pgvector + +# For other systems, see: https://github.com/pgvector/pgvector#installation + +# Then enable it in the database +psql -U verdad_user -d verdad_debates -c "CREATE EXTENSION vector;" +``` + +### Error: "permission denied for schema public" + +```bash +# Grant all permissions to your user +sudo -u postgres psql -d verdad_debates << 'EOF' +GRANT ALL ON SCHEMA public TO verdad_user; +GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO verdad_user; +GRANT ALL PRIVILEGES ON ALL SEQUENCES IN SCHEMA public TO verdad_user; +GRANT ALL PRIVILEGES ON ALL FUNCTIONS IN SCHEMA public TO verdad_user; +ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON TABLES TO verdad_user; +ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON SEQUENCES TO verdad_user; +EOF +``` + +### Error: "type processing_status does not exist" + +This means Step 3 (main schema migration) failed or wasn't applied. + +```bash +# Check if it exists +psql -U verdad_user -d verdad_debates -c "\dT processing_status" + +# If not, re-apply the schema migration +psql -U verdad_user -d verdad_debates -f supabase/migrations/20241029135348_remote_schema.sql +``` + +### Error: psycopg2 import errors in Python + +```bash +# Ensure dependencies are installed +poetry lock +poetry install + +# Verify psycopg2-binary is in pyproject.toml +grep psycopg2 pyproject.toml +``` + +### Database Functions Return Empty/None + +**Problem:** RPC functions return `NULL` instead of data. + +**Solution:** The functions use `FOR UPDATE SKIP LOCKED`, which means if another process is already processing a record, it will skip it. Check for stuck records: + +```bash +# Find records stuck in 'Processing' status +psql -U verdad_user -d verdad_debates -c " +SELECT 'audio_files' AS table, COUNT(*) AS stuck_count +FROM audio_files WHERE status = 'Processing' +UNION ALL +SELECT 'stage_1_llm_responses', COUNT(*) +FROM stage_1_llm_responses WHERE status = 'Processing' +UNION ALL +SELECT 'snippets', COUNT(*) +FROM snippets WHERE status = 'Processing'; +" + +# Reset them to 'New' if needed +psql -U verdad_user -d verdad_debates << 'EOF' +UPDATE audio_files SET status = 'New' WHERE status = 'Processing'; +UPDATE stage_1_llm_responses SET status = 'New' WHERE status = 'Processing'; +UPDATE snippets SET status = 'New' WHERE status = 'Processing'; +EOF +``` + +--- + +## Next Steps: Run the Processing Pipeline + +### Option 1: Run All Stages Manually + +```bash +# Start Prefect server (in a separate terminal) +poetry run prefect server start + +# In another terminal, run Stage 1 +poetry run python -c " +from src.processing_pipeline.stage_1 import initial_disinformation_detection +initial_disinformation_detection(audio_file_id=None, limit=1) +" +``` + +### Option 2: Use the Processing Scripts + +```bash +# Start processing worker +./scripts/start_processing.sh +``` + +--- + +## Database Schema Overview + +### Core Processing Pipeline Tables + +#### 1. `audio_files` - Source audio recordings +``` +Columns: id, file_path, file_size, duration, recorded_at, + radio_station_name, radio_station_code, status +Status Flow: New → Processing → Processed/Error +``` + +#### 2. `stage_1_llm_responses` - Initial LLM detection results +``` +Columns: id, audio_file_id, detection_result, + timestamped_transcription, status +Links to: audio_files +Status Flow: New → Processing → Processed/Error +``` + +#### 3. `snippets` - Extracted disinformation clips +``` +Columns: id, audio_file_id, stage_1_llm_response, file_path, + transcription, previous_analysis, final_review, status +Links to: audio_files, stage_1_llm_responses +Status Flow: New → Processing → Ready for review → Reviewing → Processed/Error +``` + +#### 4. `snippet_embeddings` - Vector embeddings for similarity +``` +Columns: id, snippet, embedding (vector(768)) +Links to: snippets +Used by: Stage 5, similarity search +``` + +### Processing Status Flow + +```mermaid +audio_files (New) + ↓ Stage 1: Transcription + Detection +stage_1_llm_responses (New) + ↓ Stage 2: Audio Clipping +snippets (New) + ↓ Stage 3: In-Depth Analysis +snippets (Ready for review) + ↓ Stage 4: Human Review +snippets (Processed) + ↓ Stage 5: Generate Embeddings +snippet_embeddings +``` + +### Function Return Formats + +The 5 RPC functions return `jsonb` with the following structures: + +**Stage 1** - `fetch_a_new_audio_file_and_reserve_it()`: +```json +{ + "id": "uuid", + "file_path": "test/audio.mp3", + "file_name": "audio.mp3", + "recorded_at": "2026-01-23T10:00:00+00:00", + "radio_station_name": "Test Station", + "status": "Processing" +} +``` + +**Stage 2** - `fetch_a_new_stage_1_llm_response_and_reserve_it()`: +```json +{ + "id": "uuid", + "detection_result": {...}, + "timestamped_transcription": {...}, + "audio_file": { + "id": "uuid", + "file_path": "test/audio.mp3", + "recorded_at": "2026-01-23T10:00:00+00:00" + } +} +``` + +**Stage 3** - `fetch_a_new_snippet_and_reserve_it()`: +```json +{ + "id": "uuid", + "file_path": "test/snippets/snippet.mp3", + "transcription": "...", + "audio_file": { + "radio_station_name": "...", + "recorded_at": "..." + }, + "stage_1_llm_response": {...} +} +``` + +--- + +## Verification Checklist + +- [ ] PostgreSQL 16 installed and running +- [ ] Database `verdad_debates` created +- [ ] User `verdad_user` created with proper permissions +- [ ] pgvector extension installed and enabled +- [ ] Main schema migration applied successfully (20241029135348_remote_schema.sql) +- [ ] 5 RPC functions applied from `supabase/database/sql/`: + - [ ] fetch_a_new_audio_file_and_reserve_it.sql + - [ ] fetch_a_new_stage_1_llm_response_and_reserve_it.sql + - [ ] fetch_a_new_snippet_and_reserve_it.sql + - [ ] fetch_a_ready_for_review_snippet_and_reserve_it.sql + - [ ] fetch_a_snippet_that_has_no_embedding.sql +- [ ] All expected tables exist (verified in Step 6) +- [ ] All expected functions exist (verified in Step 7) +- [ ] Python connection test passes (Step 9) +- [ ] Storage directories created (Step 10) +- [ ] .env file updated with DATABASE_URL and STORAGE_PATH (Step 11) +- [ ] Test audio file record inserted (Step 12) +- [ ] Test audio file copied to storage (Step 13) +- [ ] RPC function test successful (Step 15) +- [ ] psycopg2-binary installed via poetry + +Once all items are checked, your database is ready for processing! + +--- + +## Files Modified in This Migration + +For reference, here are the files that were updated to support local PostgreSQL: + +**Created:** +- `src/processing_pipeline/postgres_client.py` - PostgreSQL adapter +- `src/processing_pipeline/local_storage.py` - Local filesystem storage + +**Modified:** +- `pyproject.toml` - Removed supabase, added psycopg2-binary +- `src/main.py` - Updated imports +- `src/processing_pipeline/__init__.py` - Updated exports +- `src/processing_pipeline/stage_1.py` - Client initialization +- `src/processing_pipeline/stage_2.py` - Client initialization +- `src/processing_pipeline/stage_3.py` - Client initialization +- `src/processing_pipeline/stage_4.py` - Client initialization +- `src/processing_pipeline/stage_5.py` - Client initialization + +**Deleted:** +- All radio recording modules (completed in previous step) +- `supabase/migrations/20260123000000_add_rpc_functions.sql` (incorrect, use database/sql/ instead) + +--- + +### Step 3: Apply Main Schema Migration + +This creates all tables, types, indexes, and constraints. + +```bash +psql -U verdad_user -d verdad_debates -f supabase/migrations/20241029135348_remote_schema.sql +``` + +**What this does:** +- Creates enum type: `processing_status` +- Creates tables: `audio_files`, `stage_1_llm_responses`, `snippets`, `snippet_embeddings`, `profiles`, `labels`, etc. +- Creates indexes for performance +- Sets up foreign key relationships +- Configures row-level security policies + +Expected output: Should see multiple `CREATE TABLE`, `CREATE INDEX`, `CREATE POLICY` messages without errors. + +--- + +### Step 4: Apply RPC Functions Migration + +This creates the 5 critical functions for task reservation across the processing pipeline. + +```bash +psql -U verdad_user -d verdad_debates -f supabase/migrations/20260123000000_add_rpc_functions.sql +``` + +**What this does:** +- Creates `fetch_a_new_audio_file_and_reserve_it()` - Stage 1 +- Creates `fetch_a_new_stage_1_llm_response_and_reserve_it()` - Stage 2 +- Creates `fetch_a_new_snippet_and_reserve_it()` - Stage 3 +- Creates `fetch_a_ready_for_review_snippet_and_reserve_it()` - Stage 4 +- Creates `fetch_a_snippet_that_has_no_embedding()` - Stage 5 +- Creates performance indexes + +Expected output: Should see `CREATE FUNCTION` and `CREATE INDEX` messages. + +--- + +### Step 5: Verify Schema Setup + +```bash +# Check that all main tables exist +psql -U verdad_user -d verdad_debates -c " +SELECT table_name +FROM information_schema.tables +WHERE table_schema = 'public' +ORDER BY table_name; +" +``` + +**Expected tables:** +- `audio_files` +- `stage_1_llm_responses` +- `snippets` +- `snippet_embeddings` +- `profiles` +- `labels` +- `snippet_labels` +- `label_upvotes` +- `user_star_snippets` +- `prompt_versions` +- `heuristics` +- ... and more + +--- + +### Step 6: Verify RPC Functions + +```bash +# List all functions +psql -U verdad_user -d verdad_debates -c " +SELECT routine_name +FROM information_schema.routines +WHERE routine_schema = 'public' + AND routine_type = 'FUNCTION' + AND routine_name LIKE 'fetch%' +ORDER BY routine_name; +" +``` + +**Expected functions:** +- `fetch_a_new_audio_file_and_reserve_it` +- `fetch_a_new_snippet_and_reserve_it` +- `fetch_a_new_stage_1_llm_response_and_reserve_it` +- `fetch_a_ready_for_review_snippet_and_reserve_it` +- `fetch_a_snippet_that_has_no_embedding` + +--- + +### Step 7: Verify Processing Status Enum + +```bash +psql -U verdad_user -d verdad_debates -c " +SELECT unnest(enum_range(NULL::processing_status)); +" +``` + +**Expected values:** +- `New` +- `Processing` +- `Processed` +- `Error` + +--- + +### Step 8: Test Connection from Python + +```bash +python3 -c " +from src.processing_pipeline.postgres_client import PostgresClient +db = PostgresClient() +print('✅ Connection successful!') +db.close() +" +``` + +Expected output: `✅ Connection successful!` + +--- + +### Step 9: Insert Test Audio File + +```bash +# Create a test audio file entry +psql -U verdad_user -d verdad_debates << 'EOF' +INSERT INTO audio_files ( + file_path, + file_name, + file_size, + duration, + recorded_at, + recording_day_of_week, + radio_station_name, + radio_station_code, + location_state, + location_city, + status +) VALUES ( + 'test/debate_sample_2026.mp3', + 'debate_sample_2026.mp3', + 1048576, + 3600, + NOW(), + 'Thursday', + 'Test Station', + 'TEST', + 'Test State', + 'Test City', + 'New' +) RETURNING id, file_path, status; +EOF +``` + +**What this does:** +- Creates a test audio file record with status='New' +- Returns the generated UUID and file path +- This file will be picked up by Stage 1 processing + +--- + +### Step 10: Verify Test Record + +```bash +# Check that the record exists +psql -U verdad_user -d verdad_debates -c " +SELECT id, file_name, status, created_at +FROM audio_files +ORDER BY created_at DESC +LIMIT 5; +" +``` + +You should see your test record with status='New'. + +--- + +## Troubleshooting + +### Error: "role does not exist" + +```bash +# Create the user +sudo -u postgres psql -c "CREATE USER verdad_user WITH PASSWORD 'your_password';" +sudo -u postgres psql -c "GRANT ALL PRIVILEGES ON DATABASE verdad_debates TO verdad_user;" +``` + +### Error: "database does not exist" + +```bash +# Create the database +sudo -u postgres createdb -O verdad_user verdad_debates +``` + +### Error: "extension vector does not exist" + +```bash +# Install pgvector system package (openSUSE) +sudo zypper install postgresql16-pgvector + +# Then in psql: +psql -U verdad_user -d verdad_debates -c "CREATE EXTENSION vector;" +``` + +### Error: "permission denied for schema public" + +```bash +sudo -u postgres psql -d verdad_debates -c "GRANT ALL ON SCHEMA public TO verdad_user;" +sudo -u postgres psql -d verdad_debates -c "GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO verdad_user;" +sudo -u postgres psql -d verdad_debates -c "GRANT ALL PRIVILEGES ON ALL SEQUENCES IN SCHEMA public TO verdad_user;" +``` + +### Error: psycopg2 import errors in Python + +```bash +# Make sure dependencies are installed +poetry lock +poetry install +``` + +--- + +## Next Steps + +After completing this setup: + +1. **Copy your MP3 file** to the storage directory: + ```bash + mkdir -p ~/verdad_debates_storage/test + cp /path/to/your/debate.mp3 ~/verdad_debates_storage/test/debate_sample_2026.mp3 + ``` + +2. **Update your .env file** with database credentials: + ```env + DATABASE_URL=postgresql://verdad_user:your_password@localhost:5432/verdad_debates + STORAGE_PATH=~/verdad_debates_storage + ``` + +3. **Start Prefect server** (if not already running): + ```bash + prefect server start + ``` + +4. **Run Stage 1 processing**: + ```bash + poetry run python -c " + from src.processing_pipeline.stage_1 import initial_disinformation_detection + initial_disinformation_detection(audio_file_id=None, limit=1) + " + ``` + +--- + +## Schema Overview + +### Core Processing Tables + +**audio_files** - Uploaded audio files +- Columns: id, file_path, file_size, duration, recorded_at, status +- Status flow: New → Processing → Processed/Error + +**stage_1_llm_responses** - LLM detection results +- Columns: id, audio_file_id, detection_result, timestamped_transcription, status +- Links to: audio_files + +**snippets** - Extracted disinformation clips +- Columns: id, audio_file_id, file_path, transcription, previous_analysis, final_review, status +- Links to: audio_files, stage_1_llm_responses + +**snippet_embeddings** - Vector embeddings for similarity search +- Columns: id, snippet_id, embedding (vector(768)) +- Links to: snippets + +### Processing Status Flow + +``` +audio_files (New) + → Stage 1 → stage_1_llm_responses (New) + → Stage 2 → snippets (New) + → Stage 3 → snippets (Ready for review) + → Stage 4 → snippets (Processed) + → Stage 5 → snippet_embeddings +``` + +--- + +## Verification Checklist + +- [ ] PostgreSQL 16 installed and running +- [ ] Database `verdad_debates` created +- [ ] User `verdad_user` created with proper permissions +- [ ] pgvector extension installed +- [ ] Main schema migration applied successfully +- [ ] RPC functions migration applied successfully +- [ ] All expected tables exist +- [ ] All expected functions exist +- [ ] Python connection test passes +- [ ] Test audio file record created +- [ ] Storage directories created +- [ ] .env file updated + +Once all items are checked, your database is ready for processing!