feat: add simple ingestion workflow and document processing activities

hivemind_etl/activities.py

@@ -13,6 +13,9 @@
     transform_mediawiki_data,
     load_mediawiki_data,
 )
+from hivemind_etl.simple_ingestion.pipeline import (
+    process_document,
+)
 
 from temporalio import activity
 

hivemind_etl/simple_ingestion/pipeline.py

@@ -0,0 +1,89 @@
+from datetime import timedelta
+
+from temporalio import activity, workflow
+from temporalio.common import RetryPolicy
+from temporalio.workflow import execute_activity
+from .schema import IngestionRequest
+
+with workflow.unsafe.imports_passed_through():
+    from tc_hivemind_backend.ingest_qdrant import CustomIngestionPipeline
+    from llama_index.core import Document
+
+
+@workflow.defn
+class IngestionWorkflow:
+    """A Temporal workflow for processing document ingestion requests.
+
+    This workflow handles the orchestration of document processing activities,
+    including retry logic and timeout configurations.
+    """
+
+    @workflow.run
+    async def run(self, ingestion_request: IngestionRequest) -> None:
+        """Execute the ingestion workflow.
+
+        Parameters
+        ----------
+        ingestion_request : IngestionRequest
+            The request containing all necessary information for document processing,
+            including community ID, platform ID, text content, and metadata.
+
+        Notes
+        -----
+        The workflow implements a retry policy with the following configuration:
+        - Initial retry interval: 1 second
+        - Maximum retry interval: 1 minute
+        - Maximum retry attempts: 3
+        - Activity timeout: 5 minutes
+        """
+        retry_policy = RetryPolicy(
+            initial_interval=timedelta(seconds=1),
+            maximum_interval=timedelta(minutes=1),
+            maximum_attempts=3,
+        )
+
+        await execute_activity(
+            process_document,
+            ingestion_request,
+            retry_policy=retry_policy,
+            start_to_close_timeout=timedelta(minutes=5),
+        )
+
+
+@activity.defn
+async def process_document(
+    ingestion_request: IngestionRequest,
+) -> None:
+    """Process the document according to the ingestion request specifications.
+
+    Parameters
+    ----------
+    ingestion_request : IngestionRequest
+        The request containing all necessary information for document processing,
+        including community ID, platform ID, text content, and metadata.
+
+    Notes
+    -----
+    This activity will be implemented by the user to handle the actual document
+    processing logic, including any necessary embedding or LLM operations.
+    """
+    if ingestion_request.collectionName is None:
+        collection_name = (
+            f"{ingestion_request.communityId}_{ingestion_request.platformId}"
+        )
+    else:
+        collection_name = ingestion_request.collectionName
+
+    # Initialize the ingestion pipeline
+    pipeline = CustomIngestionPipeline(
+        community_id=ingestion_request.communityId,
+        collection_name=collection_name,
+    )
+
+    document = Document(
+        doc_id=ingestion_request.docId,
+        text=ingestion_request.text,
+        metadata=ingestion_request.metadata,
+    )
+
+    pipeline.run_pipeline(docs=[document])

hivemind_etl/simple_ingestion/schema.py

@@ -0,0 +1,39 @@
+from pydantic import BaseModel
+from uuid import uuid4
+
+
+class IngestionRequest(BaseModel):
+    """A model representing an ingestion request for document processing.
+
+    Parameters
+    ----------
+    communityId : str
+        The unique identifier of the community.
+    platformId : str
+        The unique identifier of the platform.
+    text : str
+        The text content to be processed.
+    metadata : dict
+        Additional metadata associated with the document.
+    docId : str, optional
+        Unique identifier for the document. If not provided, a UUID will be generated.
+        Default is a new UUID.
+    excludedEmbedMetadataKeys : list[str], optional
+        List of metadata keys to exclude from embedding process.
+        Default is an empty list.
+    excludedLlmMetadataKeys : list[str], optional
+        List of metadata keys to exclude from LLM processing.
+        Default is an empty list.
+    collectionName : str | None, optional
+        The name of the collection to use for the document.
+        Default is `None` means it would follow the default pattern of `[communityId]_[platformId]`
+    """
+
+    communityId: str
+    platformId: str
+    text: str
+    metadata: dict
+    docId: str = str(uuid4())
+    excludedEmbedMetadataKeys: list[str] = []
+    excludedLlmMetadataKeys: list[str] = []
+    collectionName: str | None = None

hivemind_etl/website/website_etl.py

@@ -19,7 +19,7 @@ def __init__(
             the community to save its data
         platform_id : str
             the platform to save its data
-        
+
         Note: the collection name would be `community_id_platform_id`
         """
         if not community_id or not isinstance(community_id, str):

registry.py

@@ -8,6 +8,7 @@
     get_hivemind_mediawiki_platforms,
     transform_mediawiki_data,
     load_mediawiki_data,
+    process_document,
 )
 from hivemind_summarizer.activities import (
     fetch_platform_summaries_by_date,
@@ -16,18 +17,18 @@
 )
 from workflows import (
     CommunityWebsiteWorkflow,
-    SayHello,
     WebsiteIngestionSchedulerWorkflow,
     MediaWikiETLWorkflow,
     PlatformSummariesWorkflow,
+    IngestionWorkflow,
 )
 
 WORKFLOWS = [
     CommunityWebsiteWorkflow,
-    SayHello,
     WebsiteIngestionSchedulerWorkflow,
     MediaWikiETLWorkflow,
     PlatformSummariesWorkflow,
+    IngestionWorkflow,
 ]
 
 ACTIVITIES = [
@@ -43,4 +44,5 @@
     fetch_platform_summaries_by_date,
     fetch_platform_summaries_by_date_range,
     get_platform_name,
+    process_document,
 ]

workflows.py

@@ -10,22 +10,13 @@
 from hivemind_etl.mediawiki.workflows import (
     MediaWikiETLWorkflow,
 )
+from hivemind_etl.simple_ingestion.pipeline import (
+    IngestionWorkflow,
+)
 from hivemind_summarizer.workflows import PlatformSummariesWorkflow
 
 from temporalio import workflow
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
-
-
-# For test purposes
-# To be deleted in future
-@workflow.defn
-class SayHello:
-    @workflow.run
-    async def run(self) -> int:
-        logger.info(f"Hello at time {workflow.now()}!")
-        return await workflow.start_activity(
-            say_hello, start_to_close_timeout=timedelta(seconds=5)
-        )