From 24493bfe2df326ef18adc0e47f4169691f64d8f1 Mon Sep 17 00:00:00 2001 From: rootflo-hardik Date: Wed, 22 Oct 2025 20:19:01 +0530 Subject: [PATCH 1/3] added rootflo_llm for llm proxy calls - added LLMProvider Enum, and having model_id, base_url, llm_model, llm_provider and api_token as RootFloLLM parameters - initialized llm according to the llm_provider. - modified generate and stream signature of gemini_llm, openai_llm and anthropic_llm for consistency - modified gemini_llm client initialized for proper base_url passing alongside the Authorization header. - passed kwargs after self.kwargs in openai_llm --- flo_ai/flo_ai/llm/anthropic_llm.py | 20 +++-- flo_ai/flo_ai/llm/gemini_llm.py | 19 ++++- flo_ai/flo_ai/llm/openai_llm.py | 17 +++- flo_ai/flo_ai/llm/rootflo_llm.py | 121 +++++++++++++++++++++++++++++ 4 files changed, 162 insertions(+), 15 deletions(-) create mode 100644 flo_ai/flo_ai/llm/rootflo_llm.py diff --git a/flo_ai/flo_ai/llm/anthropic_llm.py b/flo_ai/flo_ai/llm/anthropic_llm.py index 39247f67..33f164a4 100644 --- a/flo_ai/flo_ai/llm/anthropic_llm.py +++ b/flo_ai/flo_ai/llm/anthropic_llm.py @@ -31,6 +31,7 @@ async def generate( messages: List[Dict[str, str]], functions: Optional[List[Dict[str, Any]]] = None, output_schema: Optional[Dict[str, Any]] = None, + **kwargs, ) -> Dict[str, Any]: # Convert messages to Claude format system_message = next( @@ -54,20 +55,21 @@ async def generate( ) try: - kwargs = { + anthropic_kwargs = { 'model': self.model, 'messages': conversation, 'temperature': self.temperature, **self.kwargs, + **kwargs, } if system_message: - kwargs['system'] = system_message + anthropic_kwargs['system'] = system_message if functions: - kwargs['tools'] = functions + anthropic_kwargs['tools'] = functions - response = await self.client.messages.create(**kwargs) + response = await self.client.messages.create(**anthropic_kwargs) # Record token usage if available if hasattr(response, 'usage') and response.usage: @@ -116,6 +118,7 @@ async def stream( self, messages: List[Dict[str, str]], functions: Optional[List[Dict[str, Any]]] = None, + **kwargs, ) -> AsyncIterator[Dict[str, Any]]: """Stream partial responses from the LLM as they are generated""" # Convert messages to Claude format @@ -133,21 +136,22 @@ async def stream( } ) - kwargs = { + anthropic_kwargs = { 'model': self.model, 'messages': conversation, 'temperature': self.temperature, 'max_tokens': self.kwargs.get('max_tokens', 1024), **self.kwargs, + **kwargs, } if system_message: - kwargs['system'] = system_message + anthropic_kwargs['system'] = system_message if functions: - kwargs['tools'] = functions + anthropic_kwargs['tools'] = functions # Use Anthropic SDK streaming API and yield text deltas - async with self.client.messages.stream(**kwargs) as stream: + async with self.client.messages.stream(**anthropic_kwargs) as stream: async for event in stream: if ( getattr(event, 'type', None) == 'content_block_delta' diff --git a/flo_ai/flo_ai/llm/gemini_llm.py b/flo_ai/flo_ai/llm/gemini_llm.py index 55065527..b0dc302d 100644 --- a/flo_ai/flo_ai/llm/gemini_llm.py +++ b/flo_ai/flo_ai/llm/gemini_llm.py @@ -24,8 +24,15 @@ def __init__( **kwargs, ): super().__init__(model, api_key, temperature, **kwargs) + # Configure http_options for proxy or custom base_url + http_options = {'base_url': base_url} if base_url else {} + if base_url and self.api_key: + # For custom base_url (proxy), set Authorization header explicitly + http_options['headers'] = {'Authorization': f'Bearer {self.api_key}'} self.client = ( - genai.Client(api_key=self.api_key) if self.api_key else genai.Client() + genai.Client(http_options=http_options) + if http_options + else genai.Client(api_key=self.api_key) ) @trace_llm_call(provider='gemini') @@ -34,6 +41,7 @@ async def generate( messages: List[Dict[str, str]], functions: Optional[List[Dict[str, Any]]] = None, output_schema: Optional[Dict[str, Any]] = None, + **kwargs, ) -> Dict[str, Any]: # Convert messages to Gemini format contents = [] @@ -50,10 +58,12 @@ async def generate( try: # Prepare generation config + # Merge instance kwargs with method kwargs + config_kwargs = {**self.kwargs, **kwargs} generation_config = types.GenerateContentConfig( temperature=self.temperature, system_instruction=system_prompt, - **self.kwargs, + **config_kwargs, ) # Add tools if functions are provided @@ -132,6 +142,7 @@ async def stream( self, messages: List[Dict[str, str]], functions: Optional[List[Dict[str, Any]]] = None, + **kwargs, ) -> AsyncIterator[Dict[str, Any]]: """Stream partial responses from Gemini as they are generated""" # Convert messages to Gemini format @@ -148,10 +159,12 @@ async def stream( contents.append(message_content) # Prepare generation config + # Merge instance kwargs with method kwargs + config_kwargs = {**self.kwargs, **kwargs} generation_config = types.GenerateContentConfig( temperature=self.temperature, system_instruction=system_prompt, - **self.kwargs, + **config_kwargs, ) # Add tools if functions are provided diff --git a/flo_ai/flo_ai/llm/openai_llm.py b/flo_ai/flo_ai/llm/openai_llm.py index 5fd721c3..9e1f280e 100644 --- a/flo_ai/flo_ai/llm/openai_llm.py +++ b/flo_ai/flo_ai/llm/openai_llm.py @@ -30,10 +30,16 @@ def __init__( @trace_llm_call(provider='openai') async def generate( - self, messages: list[dict], output_schema: dict = None, **kwargs + self, + messages: list[dict], + functions: Optional[List[Dict[str, Any]]] = None, + output_schema: dict = None, + **kwargs, ) -> Any: - # Convert output_schema to OpenAI format if provided + # Handle structured output vs tool calling + # Priority: output_schema takes precedence over functions for structured output if output_schema: + # Convert output_schema to OpenAI format for structured output kwargs['response_format'] = {'type': 'json_object'} kwargs['functions'] = [ { @@ -57,14 +63,17 @@ async def generate( 'content': 'Please provide your response in JSON format according to the specified schema.', }, ) + elif functions: + # Use functions for tool calling when output_schema is not provided + kwargs['functions'] = functions # Prepare OpenAI API parameters openai_kwargs = { 'model': self.model, 'messages': messages, 'temperature': self.temperature, - **kwargs, **self.kwargs, + **kwargs, } # Make the API call @@ -112,8 +121,8 @@ async def stream( 'messages': messages, 'temperature': self.temperature, 'stream': True, - **kwargs, **self.kwargs, + **kwargs, } if functions: diff --git a/flo_ai/flo_ai/llm/rootflo_llm.py b/flo_ai/flo_ai/llm/rootflo_llm.py new file mode 100644 index 00000000..a419056e --- /dev/null +++ b/flo_ai/flo_ai/llm/rootflo_llm.py @@ -0,0 +1,121 @@ +from enum import Enum +from typing import AsyncIterator, Dict, Any, List, Optional +from .base_llm import BaseLLM, ImageMessage +from .openai_llm import OpenAI +from .gemini_llm import Gemini +from .anthropic_llm import Anthropic +from flo_ai.tool.base_tool import Tool + + +class LLMProvider(Enum): + """Enum for supported LLM providers""" + + OPENAI = 'openai' + GEMINI = 'gemini' + ANTHROPIC = 'anthropic' + + +class RootFloLLM(BaseLLM): + """ + Proxy LLM class that routes to different SDK implementations based on type. + Acts as a unified interface to OpenAI, Gemini, and Anthropic SDKs via a proxy URL. + """ + + def __init__( + self, + base_url: str, + model_id: str, + llm_model: str, + llm_provider: LLMProvider, + api_token: str, + temperature: float = 0.7, + **kwargs, + ): + """ + Initialize RootFloLLM proxy. + + Args: + base_url: The base URL of the proxy server + model_id: The model identifier + llm_provider: Type of LLM SDK to use (LLMProvider enum) + api_token: API token (e.g., JWT Bearer token without bearer prefix) for authenticating with the proxy server + temperature: Temperature parameter for generation + **kwargs: Additional parameters to pass to the underlying SDK + """ + super().__init__( + model=llm_model, api_key=api_token, temperature=temperature, **kwargs + ) + + self.base_url = base_url + self.model_id = model_id + self.llm_provider = llm_provider + + # Construct full URL + full_url = f'{base_url}/{model_id}' + + # Instantiate appropriate SDK wrapper based on llm_provider + if llm_provider == LLMProvider.OPENAI: + self._llm = OpenAI( + model=llm_model, + base_url=full_url, + api_key=api_token, + temperature=temperature, + **kwargs, + ) + elif llm_provider == LLMProvider.ANTHROPIC: + self._llm = Anthropic( + model=llm_model, + base_url=full_url, + api_key=api_token, + temperature=temperature, + **kwargs, + ) + elif llm_provider == LLMProvider.GEMINI: + # Gemini SDK - pass base_url which will be handled via http_options + self._llm = Gemini( + model=llm_model, + api_key=api_token, + temperature=temperature, + base_url=full_url, + **kwargs, + ) + else: + raise ValueError(f'Unsupported LLM provider: {llm_provider}') + + async def generate( + self, + messages: List[Dict[str, str]], + functions: Optional[List[Dict[str, Any]]] = None, + output_schema: Optional[Dict[str, Any]] = None, + **kwargs, + ) -> Dict[str, Any]: + """Generate a response from the LLM""" + return await self._llm.generate( + messages, functions=functions, output_schema=output_schema, **kwargs + ) + + async def stream( + self, + messages: List[Dict[str, Any]], + functions: Optional[List[Dict[str, Any]]] = None, + **kwargs: Any, + ) -> AsyncIterator[Dict[str, Any]]: + """Generate a streaming response from the LLM""" + async for chunk in self._llm.stream(messages, functions=functions, **kwargs): + yield chunk + + def get_message_content(self, response: Any) -> str: + """Extract message content from response""" + return self._llm.get_message_content(response) + + def format_tool_for_llm(self, tool: 'Tool') -> Dict[str, Any]: + """Format a tool for the specific LLM's API""" + return self._llm.format_tool_for_llm(tool) + + def format_tools_for_llm(self, tools: List['Tool']) -> List[Dict[str, Any]]: + """Format a list of tools for the specific LLM's API""" + return self._llm.format_tools_for_llm(tools) + + def format_image_in_message(self, image: ImageMessage) -> str: + """Format a image in the message""" + return self._llm.format_image_in_message(image) From 2bd7e923758c7d1eb7806060c131a937b546ffc0 Mon Sep 17 00:00:00 2001 From: rootflo-hardik Date: Thu, 23 Oct 2025 14:45:25 +0530 Subject: [PATCH 2/3] gemini_llm client initialization fix --- flo_ai/flo_ai/llm/gemini_llm.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/flo_ai/flo_ai/llm/gemini_llm.py b/flo_ai/flo_ai/llm/gemini_llm.py index b0dc302d..54bd9cf6 100644 --- a/flo_ai/flo_ai/llm/gemini_llm.py +++ b/flo_ai/flo_ai/llm/gemini_llm.py @@ -29,11 +29,14 @@ def __init__( if base_url and self.api_key: # For custom base_url (proxy), set Authorization header explicitly http_options['headers'] = {'Authorization': f'Bearer {self.api_key}'} - self.client = ( - genai.Client(http_options=http_options) - if http_options - else genai.Client(api_key=self.api_key) - ) + + # Initialize client based on configuration + if http_options: + self.client = genai.Client(http_options=http_options) + elif self.api_key: + self.client = genai.Client(api_key=self.api_key) + else: + self.client = genai.Client() @trace_llm_call(provider='gemini') async def generate( From ae92e7345e40ca9c6299c28f10b65a0db127d89e Mon Sep 17 00:00:00 2001 From: rootflo-hardik Date: Mon, 27 Oct 2025 16:31:16 +0530 Subject: [PATCH 3/3] have app_key, app_secret to create token - have issuer and audience as parameters as well - also have a custom header X-Rootflo-Key passed - added jwt and cryptography package --- flo_ai/flo_ai/llm/anthropic_llm.py | 8 +- flo_ai/flo_ai/llm/gemini_llm.py | 4 + flo_ai/flo_ai/llm/openai_llm.py | 8 +- flo_ai/flo_ai/llm/rootflo_llm.py | 32 +++- flo_ai/poetry.lock | 267 ++++++++++++++++++++--------- flo_ai/pyproject.toml | 2 + 6 files changed, 240 insertions(+), 81 deletions(-) diff --git a/flo_ai/flo_ai/llm/anthropic_llm.py b/flo_ai/flo_ai/llm/anthropic_llm.py index 33f164a4..bcf525ef 100644 --- a/flo_ai/flo_ai/llm/anthropic_llm.py +++ b/flo_ai/flo_ai/llm/anthropic_llm.py @@ -20,10 +20,16 @@ def __init__( temperature: float = 0.7, api_key: Optional[str] = None, base_url: str = None, + custom_headers: Optional[Dict[str, str]] = None, **kwargs, ): super().__init__(model, api_key, temperature, **kwargs) - self.client = AsyncAnthropic(api_key=self.api_key, base_url=base_url) + # Add custom headers if base_url is provided (proxy scenario) + client_kwargs = {'api_key': self.api_key, 'base_url': base_url} + if base_url and custom_headers: + client_kwargs['default_headers'] = custom_headers + + self.client = AsyncAnthropic(**client_kwargs) @trace_llm_call(provider='anthropic') async def generate( diff --git a/flo_ai/flo_ai/llm/gemini_llm.py b/flo_ai/flo_ai/llm/gemini_llm.py index 54bd9cf6..1b521a76 100644 --- a/flo_ai/flo_ai/llm/gemini_llm.py +++ b/flo_ai/flo_ai/llm/gemini_llm.py @@ -21,6 +21,7 @@ def __init__( temperature: float = 0.7, api_key: Optional[str] = None, base_url: str = None, + custom_headers: Optional[Dict[str, str]] = None, **kwargs, ): super().__init__(model, api_key, temperature, **kwargs) @@ -29,6 +30,9 @@ def __init__( if base_url and self.api_key: # For custom base_url (proxy), set Authorization header explicitly http_options['headers'] = {'Authorization': f'Bearer {self.api_key}'} + # Merge custom headers if provided (proxy scenario) + if custom_headers: + http_options['headers'].update(custom_headers) # Initialize client based on configuration if http_options: diff --git a/flo_ai/flo_ai/llm/openai_llm.py b/flo_ai/flo_ai/llm/openai_llm.py index 9e1f280e..c69eae26 100644 --- a/flo_ai/flo_ai/llm/openai_llm.py +++ b/flo_ai/flo_ai/llm/openai_llm.py @@ -19,12 +19,18 @@ def __init__( api_key: str = None, temperature: float = 0.7, base_url: str = None, + custom_headers: Optional[Dict[str, str]] = None, **kwargs, ): super().__init__( model=model, api_key=api_key, temperature=temperature, **kwargs ) - self.client = AsyncOpenAI(api_key=api_key, base_url=base_url) + # Add custom headers if base_url is provided (proxy scenario) + client_kwargs = {'api_key': api_key, 'base_url': base_url} + if base_url and custom_headers: + client_kwargs['default_headers'] = custom_headers + + self.client = AsyncOpenAI(**client_kwargs) self.model = model self.kwargs = kwargs diff --git a/flo_ai/flo_ai/llm/rootflo_llm.py b/flo_ai/flo_ai/llm/rootflo_llm.py index a419056e..ccb792aa 100644 --- a/flo_ai/flo_ai/llm/rootflo_llm.py +++ b/flo_ai/flo_ai/llm/rootflo_llm.py @@ -1,5 +1,7 @@ from enum import Enum from typing import AsyncIterator, Dict, Any, List, Optional +from datetime import datetime, timedelta +import jwt from .base_llm import BaseLLM, ImageMessage from .openai_llm import OpenAI from .gemini_llm import Gemini @@ -27,7 +29,10 @@ def __init__( model_id: str, llm_model: str, llm_provider: LLMProvider, - api_token: str, + app_key: str, + app_secret: str, + issuer: str, + audience: str, temperature: float = 0.7, **kwargs, ): @@ -38,10 +43,27 @@ def __init__( base_url: The base URL of the proxy server model_id: The model identifier llm_provider: Type of LLM SDK to use (LLMProvider enum) - api_token: API token (e.g., JWT Bearer token without bearer prefix) for authenticating with the proxy server + app_key: Application key for X-Rootflo-Key header + app_secret: Application secret for JWT signing + issuer: JWT issuer claim + audience: JWT audience claim temperature: Temperature parameter for generation **kwargs: Additional parameters to pass to the underlying SDK """ + # Generate JWT token + now = datetime.now() + payload = { + 'iss': issuer, + 'aud': audience, + 'iat': int(now.timestamp()), + 'exp': int((now + timedelta(seconds=3600)).timestamp()), + 'role_id': 'floconsole-service', + 'user_id': 'service', + 'service_auth': True, + } + service_token = jwt.encode(payload, app_secret, algorithm='HS256') + api_token = f'fc_{service_token}' + super().__init__( model=llm_model, api_key=api_token, temperature=temperature, **kwargs ) @@ -53,6 +75,9 @@ def __init__( # Construct full URL full_url = f'{base_url}/{model_id}' + # Prepare custom headers for proxy authentication + custom_headers = {'X-Rootflo-Key': app_key} + # Instantiate appropriate SDK wrapper based on llm_provider if llm_provider == LLMProvider.OPENAI: self._llm = OpenAI( @@ -60,6 +85,7 @@ def __init__( base_url=full_url, api_key=api_token, temperature=temperature, + custom_headers=custom_headers, **kwargs, ) elif llm_provider == LLMProvider.ANTHROPIC: @@ -68,6 +94,7 @@ def __init__( base_url=full_url, api_key=api_token, temperature=temperature, + custom_headers=custom_headers, **kwargs, ) elif llm_provider == LLMProvider.GEMINI: @@ -77,6 +104,7 @@ def __init__( api_key=api_token, temperature=temperature, base_url=full_url, + custom_headers=custom_headers, **kwargs, ) else: diff --git a/flo_ai/poetry.lock 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