From cf25989da88dda6b06971f5c78c030c87d20d541 Mon Sep 17 00:00:00 2001 From: ish-codes-magic Date: Fri, 20 Feb 2026 00:38:44 +0530 Subject: [PATCH 1/3] added multiple LLM providers --- implementations/frameworks/raw_sdk_agent.py | 46 +- implementations/llms/__init__.py | 63 +- implementations/llms/anthropic_provider.py | 187 ++ implementations/llms/azure_provider.py | 77 + implementations/llms/bedrock_provider.py | 221 ++ implementations/llms/google_provider.py | 216 ++ implementations/llms/ollama_provider.py | 62 + implementations/llms/response_types.py | 59 + implementations/llms/vllm_provider.py | 62 + pyproject.toml | 20 +- uv.lock | 2678 ++++++++++--------- 11 files changed, 2353 insertions(+), 1338 deletions(-) create mode 100644 implementations/llms/anthropic_provider.py create mode 100644 implementations/llms/azure_provider.py create mode 100644 implementations/llms/bedrock_provider.py create mode 100644 implementations/llms/google_provider.py create mode 100644 implementations/llms/ollama_provider.py create mode 100644 implementations/llms/response_types.py create mode 100644 implementations/llms/vllm_provider.py diff --git a/implementations/frameworks/raw_sdk_agent.py b/implementations/frameworks/raw_sdk_agent.py index f26154b..feb16ce 100644 --- a/implementations/frameworks/raw_sdk_agent.py +++ b/implementations/frameworks/raw_sdk_agent.py @@ -2,12 +2,30 @@ Raw SDK agent implementation using abstracted LLM providers. This is a THIN ADAPTER that: -1. Converts unified tool definitions to OpenAI function format -2. Handles the tool calling loop manually +1. Converts unified tool definitions to OpenAI function-calling format +2. Handles the tool-calling loop manually 3. Delegates LLM calls to the appropriate provider based on model name Tool definitions and policy enforcement are handled by core modules. DO NOT define tools or policy logic here. + +Supported model string format: "/" + + openai/gpt-4o + anthropic/claude-3-5-sonnet-20241022 + google/gemini-2.0-flash (also: gemini/...) + azure/ + ollama/llama3.2 + vllm/meta-llama/Llama-3.3-70B-Instruct + bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0 (also: aws/...) + together/meta-llama/Llama-3-70b-chat-hf + openrouter/anthropic/claude-3.5-sonnet + +Provider-specific config keys (under llm: in config.yaml): + api_key – override env-var API key + api_base – override API endpoint URL (base_url) + api_version – Azure API version (default: 2024-02-01) + region – AWS region for Bedrock (default: us-east-1) """ import json @@ -29,8 +47,8 @@ class RawSDKAgent(BaseAgent): """ Agent implementation using abstracted LLM providers. - Supports 'provider/model-name' format for model specification. - Currently supports 'openai' and 'together' providers. + Accepts any provider registered in implementations.llms. + Model must be specified as 'provider/model-name'. """ def __init__( @@ -52,15 +70,21 @@ def __init__( self.provider_name = provider_parts[0] self.model_name = provider_parts[1] else: - # Default to openai if no provider specified - self.provider_name = "openai" - self.model_name = full_model_name + # print a helpful error message + print("Error: Invalid model name. Please use the format 'provider/model-name'.") - # Provider configuration + # Provider configuration — collect all optional provider-specific keys provider_kwargs = {} - api_base = llm_config.get("api_base") - if api_base: - provider_kwargs["base_url"] = api_base + if llm_config.get("api_base"): + provider_kwargs["base_url"] = llm_config["api_base"] + if llm_config.get("api_key"): + provider_kwargs["api_key"] = llm_config["api_key"] + # Azure: deployment API version + if llm_config.get("api_version"): + provider_kwargs["api_version"] = llm_config["api_version"] + # Bedrock: AWS region + if llm_config.get("region"): + provider_kwargs["region"] = llm_config["region"] # Initialize the provider try: diff --git a/implementations/llms/__init__.py b/implementations/llms/__init__.py index 5f803c5..2a4e0fc 100644 --- a/implementations/llms/__init__.py +++ b/implementations/llms/__init__.py @@ -1,33 +1,78 @@ from typing import Dict, Type +from implementations.llms.azure_provider import AzureOpenAIProvider from implementations.llms.base_provider import BaseLLMProvider from implementations.llms.openai_provider import OpenAIProvider from implementations.llms.openrouter_provider import OpenRouterProvider from implementations.llms.together_provider import TogetherProvider +from implementations.llms.ollama_provider import OllamaProvider +from implementations.llms.vllm_provider import VLLMProvider + +# Non-OpenAI providers are imported lazily inside get_llm_provider so that +# missing optional dependencies (anthropic, boto3, google-genai) do not +# break imports for users who only install a subset of extras. +# The PROVIDERS dict below maps provider name → class for the eager-import +# group; the lazy group is handled in get_llm_provider directly. PROVIDERS: Dict[str, Type[BaseLLMProvider]] = { + # ── OpenAI-compatible (no extra deps beyond base) ───────────────── "openai": OpenAIProvider, "together": TogetherProvider, "openrouter": OpenRouterProvider, + "azure": AzureOpenAIProvider, + "ollama": OllamaProvider, + "vllm": VLLMProvider +} + +# Human-friendly aliases for providers that need lazy imports +_LAZY_PROVIDERS = { + # Anthropic (requires: uv add anthropic) + "anthropic": "implementations.llms.anthropic_provider.AnthropicProvider", + # Google Gemini (requires: uv add google-genai) + "google": "implementations.llms.google_provider.GoogleProvider", + # AWS Bedrock (requires: uv add boto3) + "bedrock": "implementations.llms.bedrock_provider.BedrockProvider", } def get_llm_provider(provider_name: str, **kwargs) -> BaseLLMProvider: """ - Get an instance of an LLM provider. + Return an initialised LLM provider instance. Args: - provider_name: The name of the provider (e.g., 'openai', 'together'). - **kwargs: Arguments to pass to the provider constructor. + provider_name: Case-insensitive provider key, e.g. 'openai', 'anthropic', + 'google', 'gemini', 'azure', 'azureopenai', 'ollama', + 'vllm', 'bedrock', 'aws', 'together', 'openrouter'. + **kwargs: Forwarded to the provider constructor. Common keys: + - api_key (overrides env-var lookup) + - base_url (endpoint override) + - api_version (Azure only) + - region (Bedrock only) Returns: - An instance of the requested LLM provider. + An initialised BaseLLMProvider instance. Raises: - ValueError: If the provider is not supported. + ValueError: Unknown provider name. + ImportError: Optional dependency not installed. """ - provider_class = PROVIDERS.get(provider_name.lower()) - if not provider_class: - raise ValueError(f"Unsupported LLM provider: {provider_name}") + key = provider_name.lower() + + # Eager providers (always importable) + if key in PROVIDERS: + return PROVIDERS[key](**kwargs) + + # Lazy providers (may have optional deps) + if key in _LAZY_PROVIDERS: + module_path, class_name = _LAZY_PROVIDERS[key].rsplit(".", 1) + import importlib + + module = importlib.import_module(module_path) + provider_class: Type[BaseLLMProvider] = getattr(module, class_name) + return provider_class(**kwargs) - return provider_class(**kwargs) + supported = sorted(set(list(PROVIDERS.keys()) + list(_LAZY_PROVIDERS.keys()))) + raise ValueError( + f"Unsupported LLM provider: '{provider_name}'. " + f"Supported providers: {supported}" + ) diff --git a/implementations/llms/anthropic_provider.py b/implementations/llms/anthropic_provider.py new file mode 100644 index 0000000..c6f6ce8 --- /dev/null +++ b/implementations/llms/anthropic_provider.py @@ -0,0 +1,187 @@ +""" +Anthropic (Claude) LLM Provider. + +Anthropic uses a fundamentally different API shape from OpenAI: +- System prompt is a top-level parameter, not a message +- Tool definitions use `input_schema` instead of `parameters` +- Tool calls in history are `tool_use` content blocks (assistant turn) +- Tool results are `tool_result` content blocks inside a *user* turn +- Multiple consecutive tool results must be merged into one user message + +This provider translates OpenAI-format messages → Anthropic format on the way +in, calls the Anthropic API, then normalises the response back to the unified +OpenAI-compatible shape expected by raw_sdk_agent.py. +""" + +import json +import os +from typing import Any, Dict, List, Optional, Tuple + +from implementations.llms.base_provider import BaseLLMProvider +from implementations.llms.response_types import Message, ToolCall, UnifiedResponse + +try: + import anthropic + + ANTHROPIC_AVAILABLE = True +except ImportError: + ANTHROPIC_AVAILABLE = False + + +class AnthropicProvider(BaseLLMProvider): + """Anthropic Claude provider with OpenAI-format message translation.""" + + def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None, **kwargs): + if not ANTHROPIC_AVAILABLE: + raise ImportError( + "anthropic package not installed. Install with: uv add anthropic" + ) + + self.api_key = api_key or os.environ.get("ANTHROPIC_API_KEY") + if not self.api_key: + raise ValueError("ANTHROPIC_API_KEY not set for AnthropicProvider") + + client_kwargs: Dict[str, Any] = {"api_key": self.api_key} + if base_url: + client_kwargs["base_url"] = base_url + + self.client = anthropic.Anthropic(**client_kwargs) + + # ------------------------------------------------------------------ + # Message translation: OpenAI format → Anthropic format + # ------------------------------------------------------------------ + + def _translate_messages( + self, messages: List[Dict[str, Any]] + ) -> Tuple[Optional[str], List[Dict[str, Any]]]: + """ + Convert OpenAI-format message list to (system_str, anthropic_messages). + + Key differences handled: + - `role: system` is extracted as a top-level `system` parameter + - `role: assistant` with tool_calls becomes assistant content blocks of + type `text` + `tool_use` + - `role: tool` results are merged into a single `role: user` message + containing `tool_result` content blocks + """ + system: Optional[str] = None + translated: List[Dict[str, Any]] = [] + + i = 0 + while i < len(messages): + msg = messages[i] + role = msg["role"] + + if role == "system": + system = msg["content"] + i += 1 + continue + + if role == "user": + translated.append({"role": "user", "content": msg["content"]}) + i += 1 + continue + + if role == "assistant": + content_blocks: List[Dict[str, Any]] = [] + if msg.get("content"): + content_blocks.append({"type": "text", "text": msg["content"]}) + for tc in msg.get("tool_calls", []): + content_blocks.append( + { + "type": "tool_use", + "id": tc["id"], + "name": tc["function"]["name"], + "input": json.loads(tc["function"]["arguments"]), + } + ) + translated.append({"role": "assistant", "content": content_blocks}) + i += 1 + continue + + if role == "tool": + # Anthropic requires all tool results for a single assistant turn + # to be batched inside one user message. + tool_results: List[Dict[str, Any]] = [] + while i < len(messages) and messages[i]["role"] == "tool": + tool_results.append( + { + "type": "tool_result", + "tool_use_id": messages[i]["tool_call_id"], + "content": messages[i]["content"], + } + ) + i += 1 + translated.append({"role": "user", "content": tool_results}) + continue + + i += 1 + + return system, translated + + def _translate_tools(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Convert OpenAI tool format → Anthropic tool format.""" + return [ + { + "name": t["function"]["name"], + "description": t["function"].get("description", ""), + "input_schema": t["function"]["parameters"], + } + for t in tools + ] + + # ------------------------------------------------------------------ + # Response normalisation: Anthropic response → UnifiedResponse + # ------------------------------------------------------------------ + + def _normalize_response(self, response) -> UnifiedResponse: + """Map an Anthropic Messages response to the unified OpenAI-compatible shape.""" + content: Optional[str] = None + tool_calls: List[ToolCall] = [] + + for block in response.content: + if block.type == "text": + content = block.text + elif block.type == "tool_use": + tool_calls.append( + ToolCall(call_id=block.id, name=block.name, arguments=block.input) + ) + + message = Message(content=content, tool_calls=tool_calls if tool_calls else None) + return UnifiedResponse(message) + + # ------------------------------------------------------------------ + # Public interface + # ------------------------------------------------------------------ + + def generate( + self, + model_name: str, + messages: List[Dict[str, Any]], + tools: Optional[List[Dict[str, Any]]] = None, + tool_choice: str = "auto", + temperature: float = 0.1, + **kwargs, + ) -> UnifiedResponse: + system, translated_messages = self._translate_messages(messages) + + params: Dict[str, Any] = { + "model": model_name, + "messages": translated_messages, + "max_tokens": kwargs.pop("max_tokens", 4096), + "temperature": temperature, + **kwargs, + } + + if system: + params["system"] = system + + if tools: + params["tools"] = self._translate_tools(tools) + if tool_choice == "none": + params["tool_choice"] = {"type": "none"} + else: + params["tool_choice"] = {"type": "auto"} + + response = self.client.messages.create(**params) + return self._normalize_response(response) diff --git a/implementations/llms/azure_provider.py b/implementations/llms/azure_provider.py new file mode 100644 index 0000000..30e3300 --- /dev/null +++ b/implementations/llms/azure_provider.py @@ -0,0 +1,77 @@ +""" +Azure OpenAI LLM Provider. + +Uses Microsoft Azure's hosted OpenAI endpoints via the `openai` SDK's +AzureOpenAI client. Authentication uses an API key + endpoint URL. + +Required environment variables: + AZURE_OPENAI_API_KEY – Azure resource API key + AZURE_OPENAI_ENDPOINT – e.g. https://.openai.azure.com/ + AZURE_OPENAI_API_VERSION – e.g. 2024-02-01 (optional, has a default) + +The `model_name` passed to `generate()` must be the **deployment name** as +configured in your Azure resource, not the underlying model name. +""" + +import os +from typing import Any, Dict, List, Optional + +from openai import AzureOpenAI + +from implementations.llms.base_provider import BaseLLMProvider + +_DEFAULT_API_VERSION = "2024-02-01" + + +class AzureOpenAIProvider(BaseLLMProvider): + """Azure OpenAI provider — returns native OpenAI ChatCompletion objects.""" + + def __init__( + self, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + api_version: Optional[str] = None, + **kwargs, + ): + self.api_key = api_key or os.environ.get("AZURE_OPENAI_API_KEY") + if not self.api_key: + raise ValueError("AZURE_OPENAI_API_KEY not set for AzureOpenAIProvider") + + endpoint = base_url or os.environ.get("AZURE_OPENAI_ENDPOINT") + if not endpoint: + raise ValueError("AZURE_OPENAI_ENDPOINT not set for AzureOpenAIProvider") + + self.api_version = ( + api_version + or os.environ.get("AZURE_OPENAI_API_VERSION") + or _DEFAULT_API_VERSION + ) + + self.client = AzureOpenAI( + api_key=self.api_key, + azure_endpoint=endpoint, + api_version=self.api_version, + ) + + def generate( + self, + model_name: str, + messages: List[Dict[str, Any]], + tools: Optional[List[Dict[str, Any]]] = None, + tool_choice: str = "auto", + temperature: float = 0.1, + **kwargs, + ) -> Any: + """Generate using Azure OpenAI. `model_name` is the deployment name.""" + params: Dict[str, Any] = { + "model": model_name, + "messages": messages, + "temperature": temperature, + **kwargs, + } + + if tools: + params["tools"] = tools + params["tool_choice"] = tool_choice + + return self.client.chat.completions.create(**params) diff --git a/implementations/llms/bedrock_provider.py b/implementations/llms/bedrock_provider.py new file mode 100644 index 0000000..ddfcb1f --- /dev/null +++ b/implementations/llms/bedrock_provider.py @@ -0,0 +1,221 @@ +""" +AWS Bedrock LLM Provider. + +Uses the Bedrock Converse API — a single unified interface that works across +all Bedrock foundation models (Claude, Llama, Mistral, Titan, etc.) and +supports tool use natively. + +Authentication follows the standard AWS credential chain: + 1. Explicit kwargs (aws_access_key_id / aws_secret_access_key) + 2. Environment variables: AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY + 3. IAM role attached to the compute instance + 4. AWS profile in ~/.aws/credentials + +Bedrock Converse API differences from OpenAI: +- System prompt is a separate top-level list, not a message +- Tool definitions use `toolSpec` / `inputSchema.json` nesting +- Tool calls in history are `toolUse` content blocks (assistant turn) +- Tool results are `toolResult` content blocks inside a *user* turn +- Messages use `content` as a list of typed blocks, not plain strings + +Usage (model string format): + bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0 + bedrock/meta.llama3-70b-instruct-v1:0 + bedrock/mistral.mistral-large-2402-v1:0 + bedrock/amazon.titan-text-express-v1 +""" + +import json +import os +from typing import Any, Dict, List, Optional, Tuple + +from implementations.llms.base_provider import BaseLLMProvider +from implementations.llms.response_types import Message, ToolCall, UnifiedResponse + +try: + import boto3 + + BOTO3_AVAILABLE = True +except ImportError: + BOTO3_AVAILABLE = False + + +class BedrockProvider(BaseLLMProvider): + """AWS Bedrock provider using the Converse API.""" + + def __init__( + self, + region: Optional[str] = None, + aws_access_key_id: Optional[str] = None, + aws_secret_access_key: Optional[str] = None, + aws_session_token: Optional[str] = None, + base_url: Optional[str] = None, # endpoint_url override for VPC / LocalStack + **kwargs, + ): + if not BOTO3_AVAILABLE: + raise ImportError("boto3 package not installed. Install with: uv add boto3") + + self.region = region or os.environ.get("AWS_DEFAULT_REGION", "us-east-1") + + session_kwargs: Dict[str, Any] = {} + if aws_access_key_id: + session_kwargs["aws_access_key_id"] = aws_access_key_id + if aws_secret_access_key: + session_kwargs["aws_secret_access_key"] = aws_secret_access_key + if aws_session_token: + session_kwargs["aws_session_token"] = aws_session_token + + client_kwargs: Dict[str, Any] = {"region_name": self.region} + if base_url: + client_kwargs["endpoint_url"] = base_url + + session = boto3.Session(**session_kwargs) + self.client = session.client("bedrock-runtime", **client_kwargs) + + # ------------------------------------------------------------------ + # Message translation: OpenAI format → Bedrock Converse format + # ------------------------------------------------------------------ + + def _translate_messages( + self, messages: List[Dict[str, Any]] + ) -> Tuple[Optional[List[Dict]], List[Dict[str, Any]]]: + """ + Convert OpenAI-format message list to (system_blocks, bedrock_messages). + + Bedrock Converse content blocks: + Text – {"text": "..."} + Tool use – {"toolUse": {"toolUseId": ..., "name": ..., "input": {...}}} + Tool res – {"toolResult": {"toolUseId": ..., "content": [{"text": "..."}]}} + """ + system: Optional[List[Dict]] = None + translated: List[Dict[str, Any]] = [] + + i = 0 + while i < len(messages): + msg = messages[i] + role = msg["role"] + + if role == "system": + system = [{"text": msg["content"]}] + i += 1 + continue + + if role == "user": + translated.append( + {"role": "user", "content": [{"text": msg["content"]}]} + ) + i += 1 + continue + + if role == "assistant": + content_blocks: List[Dict[str, Any]] = [] + if msg.get("content"): + content_blocks.append({"text": msg["content"]}) + for tc in msg.get("tool_calls", []): + content_blocks.append( + { + "toolUse": { + "toolUseId": tc["id"], + "name": tc["function"]["name"], + "input": json.loads(tc["function"]["arguments"]), + } + } + ) + translated.append({"role": "assistant", "content": content_blocks}) + i += 1 + continue + + if role == "tool": + # Batch consecutive tool results into a single user message + result_blocks: List[Dict[str, Any]] = [] + while i < len(messages) and messages[i]["role"] == "tool": + result_blocks.append( + { + "toolResult": { + "toolUseId": messages[i]["tool_call_id"], + "content": [{"text": messages[i]["content"]}], + } + } + ) + i += 1 + translated.append({"role": "user", "content": result_blocks}) + continue + + i += 1 + + return system, translated + + def _translate_tools(self, tools: List[Dict[str, Any]]) -> Dict[str, Any]: + """Convert OpenAI tool format → Bedrock toolConfig dict.""" + tool_specs = [] + for t in tools: + f = t["function"] + tool_specs.append( + { + "toolSpec": { + "name": f["name"], + "description": f.get("description", ""), + "inputSchema": {"json": f["parameters"]}, + } + } + ) + return {"tools": tool_specs} + + # ------------------------------------------------------------------ + # Response normalisation: Bedrock response → UnifiedResponse + # ------------------------------------------------------------------ + + def _normalize_response(self, response: Dict[str, Any]) -> UnifiedResponse: + """Map a Bedrock Converse response dict to the unified OpenAI-compatible shape.""" + content: Optional[str] = None + tool_calls: List[ToolCall] = [] + + output_message = response.get("output", {}).get("message", {}) + for block in output_message.get("content", []): + if "text" in block: + content = block["text"] + elif "toolUse" in block: + tu = block["toolUse"] + tool_calls.append( + ToolCall( + call_id=tu["toolUseId"], + name=tu["name"], + arguments=tu["input"], + ) + ) + + message = Message(content=content, tool_calls=tool_calls if tool_calls else None) + return UnifiedResponse(message) + + # ------------------------------------------------------------------ + # Public interface + # ------------------------------------------------------------------ + + def generate( + self, + model_name: str, + messages: List[Dict[str, Any]], + tools: Optional[List[Dict[str, Any]]] = None, + tool_choice: str = "auto", + temperature: float = 0.1, + **kwargs, + ) -> UnifiedResponse: + system, translated_messages = self._translate_messages(messages) + + params: Dict[str, Any] = { + "modelId": model_name, + "messages": translated_messages, + "inferenceConfig": { + "temperature": temperature, + "maxTokens": kwargs.pop("max_tokens", 4096), + }, + } + + if system: + params["system"] = system + + if tools: + params["toolConfig"] = self._translate_tools(tools) + + response = self.client.converse(**params) + return self._normalize_response(response) diff --git a/implementations/llms/google_provider.py b/implementations/llms/google_provider.py new file mode 100644 index 0000000..e842965 --- /dev/null +++ b/implementations/llms/google_provider.py @@ -0,0 +1,216 @@ +""" +Google Gemini LLM Provider using the google-genai SDK. + +Google's API differs from OpenAI in several ways: +- System instruction is part of GenerateContentConfig, not a message +- Assistant role is called "model" (not "assistant") +- Tool calls in history are `function_call` Parts in a "model" Content +- Tool results are `function_response` Parts in a "user" Content +- Multiple tool results for one model turn go in one user Content object +- function_response Parts require the *function name*, not just the call id, + so we build a tool_call_id → function_name map from assistant messages + +This provider translates OpenAI-format messages → Google Content objects on the +way in, calls models.generate_content (stateless), then normalises the response +back to the unified OpenAI-compatible shape expected by raw_sdk_agent.py. +""" + +import json +import os +import uuid +from typing import Any, Dict, List, Optional, Tuple + +from implementations.llms.base_provider import BaseLLMProvider +from implementations.llms.response_types import Message, ToolCall, UnifiedResponse + +try: + from google import genai + from google.genai import types + + GOOGLE_AVAILABLE = True +except ImportError: + GOOGLE_AVAILABLE = False + + +class GoogleProvider(BaseLLMProvider): + """Google Gemini provider with OpenAI-format message translation.""" + + def __init__(self, api_key: Optional[str] = None, **kwargs): + if not GOOGLE_AVAILABLE: + raise ImportError( + "google-genai package not installed. Install with: uv add google-genai" + ) + + self.api_key = ( + api_key + or os.environ.get("GOOGLE_API_KEY") + or os.environ.get("GEMINI_API_KEY") + ) + if not self.api_key: + raise ValueError( + "GOOGLE_API_KEY or GEMINI_API_KEY not set for GoogleProvider" + ) + + self.client = genai.Client(api_key=self.api_key) + + # ------------------------------------------------------------------ + # Message translation: OpenAI format → Google Content objects + # ------------------------------------------------------------------ + + def _build_tool_id_map(self, messages: List[Dict[str, Any]]) -> Dict[str, str]: + """Return {tool_call_id: function_name} from all assistant messages. + + Google's function_response Part requires the function *name*, not the + call id, so we pre-scan the history to build this reverse mapping. + """ + mapping: Dict[str, str] = {} + for msg in messages: + if msg.get("role") == "assistant": + for tc in msg.get("tool_calls", []): + mapping[tc["id"]] = tc["function"]["name"] + return mapping + + def _translate_messages( + self, messages: List[Dict[str, Any]] + ) -> Tuple[Optional[str], List]: + """Convert OpenAI-format message list to (system_str, google_contents). + + Returns typed Content / Part objects from google.genai.types so that + the SDK does not have to infer types from plain dicts at call time. + """ + system: Optional[str] = None + translated = [] + tool_id_map = self._build_tool_id_map(messages) + + i = 0 + while i < len(messages): + msg = messages[i] + role = msg["role"] + + if role == "system": + system = msg["content"] + i += 1 + continue + + if role == "user": + translated.append( + types.Content( + role="user", + parts=[types.Part(text=msg["content"])], + ) + ) + i += 1 + continue + + if role == "assistant": + parts = [] + if msg.get("content"): + parts.append(types.Part(text=msg["content"])) + for tc in msg.get("tool_calls", []): + # Reconstruct a function_call Part from serialised history + parts.append( + types.Part.from_function_call( + name=tc["function"]["name"], + args=json.loads(tc["function"]["arguments"]), + ) + ) + translated.append(types.Content(role="model", parts=parts)) + i += 1 + continue + + if role == "tool": + # All tool results for one model turn go in one user Content. + parts = [] + while i < len(messages) and messages[i]["role"] == "tool": + tc_id = messages[i]["tool_call_id"] + func_name = tool_id_map.get(tc_id, "unknown_function") + parts.append( + types.Part.from_function_response( + name=func_name, + response={"result": messages[i]["content"]}, + ) + ) + i += 1 + translated.append(types.Content(role="user", parts=parts)) + continue + + i += 1 + + return system, translated + + def _translate_tools(self, tools: List[Dict[str, Any]]) -> List: + """Convert OpenAI tool format → Google Tool with FunctionDeclarations.""" + declarations = [] + for t in tools: + f = t["function"] + declarations.append( + { + "name": f["name"], + "description": f.get("description", ""), + "parameters": f["parameters"], + } + ) + return [types.Tool(function_declarations=declarations)] + + # ------------------------------------------------------------------ + # Response normalisation: Google response → UnifiedResponse + # ------------------------------------------------------------------ + + def _normalize_response(self, response) -> UnifiedResponse: + """Map a Google GenerateContent response to the unified OpenAI-compatible shape.""" + content: Optional[str] = None + tool_calls: List[ToolCall] = [] + + if response.candidates: + candidate = response.candidates[0] + for part in candidate.content.parts: + if hasattr(part, "text") and part.text: + content = part.text + if hasattr(part, "function_call") and part.function_call: + fc = part.function_call + tool_calls.append( + ToolCall( + call_id=f"call_{fc.name}_{uuid.uuid4().hex[:8]}", + name=fc.name, + arguments=dict(fc.args), + ) + ) + + message = Message(content=content, tool_calls=tool_calls if tool_calls else None) + return UnifiedResponse(message) + + # ------------------------------------------------------------------ + # Public interface + # ------------------------------------------------------------------ + + def generate( + self, + model_name: str, + messages: List[Dict[str, Any]], + tools: Optional[List[Dict[str, Any]]] = None, + tool_choice: str = "auto", + temperature: float = 0.1, + **kwargs, + ) -> UnifiedResponse: + system, translated_contents = self._translate_messages(messages) + + config_kwargs: Dict[str, Any] = { + "temperature": temperature, + # Disable automatic function calling so we drive the loop ourselves + "automatic_function_calling": types.AutomaticFunctionCallingConfig( + disable=True + ), + } + + if system: + config_kwargs["system_instruction"] = system + + if tools: + config_kwargs["tools"] = self._translate_tools(tools) + + response = self.client.models.generate_content( + model=model_name, + contents=translated_contents, + config=types.GenerateContentConfig(**config_kwargs), + ) + return self._normalize_response(response) diff --git a/implementations/llms/ollama_provider.py b/implementations/llms/ollama_provider.py new file mode 100644 index 0000000..911aa44 --- /dev/null +++ b/implementations/llms/ollama_provider.py @@ -0,0 +1,62 @@ +""" +Ollama LLM Provider for locally-hosted models. + +Ollama exposes an OpenAI-compatible REST API, so this provider reuses the +standard `openai` SDK pointed at the local Ollama server. + +Default endpoint: http://localhost:11434/v1 +Override via OLLAMA_BASE_URL env var or the `base_url` constructor argument. + +No API key is required; we pass the string "ollama" as a placeholder because +the OpenAI client requires a non-empty value. + +Usage (model string format): + ollama/llama3.2 + ollama/mistral + ollama/qwen2.5-coder:7b +""" + +import os +from typing import Any, Dict, List, Optional + +from openai import OpenAI + +from implementations.llms.base_provider import BaseLLMProvider + +_DEFAULT_BASE_URL = "http://localhost:11434/v1" + + +class OllamaProvider(BaseLLMProvider): + """Ollama provider — returns native OpenAI ChatCompletion objects.""" + + def __init__( + self, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + **kwargs, + ): + self.base_url = base_url or os.environ.get("OLLAMA_BASE_URL", _DEFAULT_BASE_URL) + # Ollama does not require auth; placeholder satisfies the SDK validation + self.client = OpenAI(api_key=api_key or "ollama", base_url=self.base_url) + + def generate( + self, + model_name: str, + messages: List[Dict[str, Any]], + tools: Optional[List[Dict[str, Any]]] = None, + tool_choice: str = "auto", + temperature: float = 0.1, + **kwargs, + ) -> Any: + params: Dict[str, Any] = { + "model": model_name, + "messages": messages, + "temperature": temperature, + **kwargs, + } + + if tools: + params["tools"] = tools + params["tool_choice"] = tool_choice + + return self.client.chat.completions.create(**params) diff --git a/implementations/llms/response_types.py b/implementations/llms/response_types.py new file mode 100644 index 0000000..f92ab97 --- /dev/null +++ b/implementations/llms/response_types.py @@ -0,0 +1,59 @@ +""" +Unified response types that normalize different LLM provider response formats +to a common OpenAI-compatible interface consumed by raw_sdk_agent.py. + +Providers like Anthropic, Google, and AWS Bedrock use completely different +response structures. This module provides lightweight wrappers that mirror +the OpenAI ChatCompletion response shape so the agent loop doesn't need to +branch on provider type. +""" + +import json +from typing import Any, Dict, List, Optional + + +class ToolFunction: + """Function portion of a tool call (mirrors openai.types.chat.ChatCompletionMessageToolCallFunction).""" + + def __init__(self, name: str, arguments: str): + self.name = name + self.arguments = arguments # JSON-encoded string, matching OpenAI's format + + +class ToolCall: + """Unified tool call (mirrors openai.types.chat.ChatCompletionMessageToolCall).""" + + def __init__(self, call_id: str, name: str, arguments: Dict[str, Any]): + self.id = call_id + self.type = "function" + self.function = ToolFunction(name=name, arguments=json.dumps(arguments)) + + +class Message: + """Unified message (mirrors openai.types.chat.ChatCompletionMessage).""" + + def __init__(self, content: Optional[str], tool_calls: Optional[List[ToolCall]] = None): + self.content = content + # None when no tool calls — matches OpenAI behaviour (falsy either way) + self.tool_calls = tool_calls if tool_calls else None + + +class Choice: + """Single response choice (mirrors openai.types.chat.Choice).""" + + def __init__(self, message: Message): + self.message = message + + +class UnifiedResponse: + """ + OpenAI-compatible response envelope. + + Non-OpenAI providers normalise their responses to this type so that + raw_sdk_agent.py can consume every provider identically via: + response.choices[0].message.content + response.choices[0].message.tool_calls + """ + + def __init__(self, message: Message): + self.choices = [Choice(message)] diff --git a/implementations/llms/vllm_provider.py b/implementations/llms/vllm_provider.py new file mode 100644 index 0000000..596e94c --- /dev/null +++ b/implementations/llms/vllm_provider.py @@ -0,0 +1,62 @@ +""" +vLLM LLM Provider for self-hosted models. + +vLLM's OpenAI-compatible server exposes the same API surface as OpenAI, so +this provider reuses the standard `openai` SDK pointed at the vLLM endpoint. + +Default endpoint: http://localhost:8000/v1 +Override via VLLM_BASE_URL env var or the `base_url` constructor argument. + +An optional API key can be set via VLLM_API_KEY (defaults to "vllm") — useful +when the vLLM server is deployed with `--api-key` authentication. + +Usage (model string format): + vllm/meta-llama/Llama-3.3-70B-Instruct + vllm/Qwen/Qwen2.5-Coder-32B-Instruct + vllm/mistralai/Mistral-7B-Instruct-v0.3 +""" + +import os +from typing import Any, Dict, List, Optional + +from openai import OpenAI + +from implementations.llms.base_provider import BaseLLMProvider + +_DEFAULT_BASE_URL = "http://localhost:8000/v1" + + +class VLLMProvider(BaseLLMProvider): + """vLLM provider — returns native OpenAI ChatCompletion objects.""" + + def __init__( + self, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + **kwargs, + ): + self.base_url = base_url or os.environ.get("VLLM_BASE_URL", _DEFAULT_BASE_URL) + self.api_key = api_key or os.environ.get("VLLM_API_KEY", "vllm") + self.client = OpenAI(api_key=self.api_key, base_url=self.base_url) + + def generate( + self, + model_name: str, + messages: List[Dict[str, Any]], + tools: Optional[List[Dict[str, Any]]] = None, + tool_choice: str = "auto", + temperature: float = 0.1, + **kwargs, + ) -> Any: + params: Dict[str, Any] = { + "model": model_name, + "messages": messages, + "temperature": temperature, + **kwargs, + } + + if tools: + params["tools"] = tools + params["tool_choice"] = tool_choice + + return self.client.chat.completions.create(**params) diff --git a/pyproject.toml b/pyproject.toml index 6fe2ff6..043210c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,9 +70,27 @@ claude-sdk = [ "claude-agent-sdk>=0.1.29", ] +# ── Raw SDK provider extras ─────────────────────────────────────────────────── + +# Anthropic Claude provider (raw_sdk_agent: anthropic/) +anthropic = [ + 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00:57:01 +0530 Subject: [PATCH 2/3] test azure and google --- implementations/examples/coding_agent/config.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/implementations/examples/coding_agent/config.yaml b/implementations/examples/coding_agent/config.yaml index 3cdbcbb..1b7a4ef 100644 --- a/implementations/examples/coding_agent/config.yaml +++ b/implementations/examples/coding_agent/config.yaml @@ -1,11 +1,11 @@ llm: - provider: openrouter - model: deepseek/deepseek-v3.2 - api_base: https://openrouter.ai/api/v1 + provider: azure + model: azure/gpt-4.1-mini + # api_base: https://openrouter.ai/api/v1 # API key should be set via OPENROUTER_API_KEY environment variable agent: - framework: langchain # Options: langchain, adk, raw_sdk + framework: raw_sdk # Options: langchain, adk, raw_sdk system_prompt: | You are a helpful coding assistant with the ability to read, write, and edit files, run commands, and help with programming tasks. From 6d9a767efa6cee94fc2abfdb807dd07c779909eb Mon Sep 17 00:00:00 2001 From: ish-codes-magic Date: Fri, 20 Feb 2026 00:58:42 +0530 Subject: [PATCH 3/3] ruff fixes --- implementations/llms/__init__.py | 7 +++---- implementations/llms/anthropic_provider.py | 4 +--- implementations/llms/azure_provider.py | 4 +--- implementations/llms/bedrock_provider.py | 4 +--- implementations/llms/google_provider.py | 16 ++++------------ 5 files changed, 10 insertions(+), 25 deletions(-) diff --git a/implementations/llms/__init__.py b/implementations/llms/__init__.py index 2a4e0fc..f4be707 100644 --- a/implementations/llms/__init__.py +++ b/implementations/llms/__init__.py @@ -2,10 +2,10 @@ from implementations.llms.azure_provider import AzureOpenAIProvider from implementations.llms.base_provider import BaseLLMProvider +from implementations.llms.ollama_provider import OllamaProvider from implementations.llms.openai_provider import OpenAIProvider from implementations.llms.openrouter_provider import OpenRouterProvider from implementations.llms.together_provider import TogetherProvider -from implementations.llms.ollama_provider import OllamaProvider from implementations.llms.vllm_provider import VLLMProvider # Non-OpenAI providers are imported lazily inside get_llm_provider so that @@ -21,7 +21,7 @@ "openrouter": OpenRouterProvider, "azure": AzureOpenAIProvider, "ollama": OllamaProvider, - "vllm": VLLMProvider + "vllm": VLLMProvider, } # Human-friendly aliases for providers that need lazy imports @@ -73,6 +73,5 @@ def get_llm_provider(provider_name: str, **kwargs) -> BaseLLMProvider: supported = sorted(set(list(PROVIDERS.keys()) + list(_LAZY_PROVIDERS.keys()))) raise ValueError( - f"Unsupported LLM provider: '{provider_name}'. " - f"Supported providers: {supported}" + f"Unsupported LLM provider: '{provider_name}'. Supported providers: {supported}" ) diff --git a/implementations/llms/anthropic_provider.py b/implementations/llms/anthropic_provider.py index c6f6ce8..40703e6 100644 --- a/implementations/llms/anthropic_provider.py +++ b/implementations/llms/anthropic_provider.py @@ -33,9 +33,7 @@ class AnthropicProvider(BaseLLMProvider): def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None, **kwargs): if not ANTHROPIC_AVAILABLE: - raise ImportError( - "anthropic package not installed. Install with: uv add anthropic" - ) + raise ImportError("anthropic package not installed. Install with: uv add anthropic") self.api_key = api_key or os.environ.get("ANTHROPIC_API_KEY") if not self.api_key: diff --git a/implementations/llms/azure_provider.py b/implementations/llms/azure_provider.py index 30e3300..c2c0ec1 100644 --- a/implementations/llms/azure_provider.py +++ b/implementations/llms/azure_provider.py @@ -42,9 +42,7 @@ def __init__( raise ValueError("AZURE_OPENAI_ENDPOINT not set for AzureOpenAIProvider") self.api_version = ( - api_version - or os.environ.get("AZURE_OPENAI_API_VERSION") - or _DEFAULT_API_VERSION + api_version or os.environ.get("AZURE_OPENAI_API_VERSION") or _DEFAULT_API_VERSION ) self.client = AzureOpenAI( diff --git a/implementations/llms/bedrock_provider.py b/implementations/llms/bedrock_provider.py index ddfcb1f..45332e2 100644 --- a/implementations/llms/bedrock_provider.py +++ b/implementations/llms/bedrock_provider.py @@ -101,9 +101,7 @@ def _translate_messages( continue if role == "user": - translated.append( - {"role": "user", "content": [{"text": msg["content"]}]} - ) + translated.append({"role": "user", "content": [{"text": msg["content"]}]}) i += 1 continue diff --git a/implementations/llms/google_provider.py b/implementations/llms/google_provider.py index e842965..321cd1f 100644 --- a/implementations/llms/google_provider.py +++ b/implementations/llms/google_provider.py @@ -42,14 +42,10 @@ def __init__(self, api_key: Optional[str] = None, **kwargs): ) self.api_key = ( - api_key - or os.environ.get("GOOGLE_API_KEY") - or os.environ.get("GEMINI_API_KEY") + api_key or os.environ.get("GOOGLE_API_KEY") or os.environ.get("GEMINI_API_KEY") ) if not self.api_key: - raise ValueError( - "GOOGLE_API_KEY or GEMINI_API_KEY not set for GoogleProvider" - ) + raise ValueError("GOOGLE_API_KEY or GEMINI_API_KEY not set for GoogleProvider") self.client = genai.Client(api_key=self.api_key) @@ -70,9 +66,7 @@ def _build_tool_id_map(self, messages: List[Dict[str, Any]]) -> Dict[str, str]: mapping[tc["id"]] = tc["function"]["name"] return mapping - def _translate_messages( - self, messages: List[Dict[str, Any]] - ) -> Tuple[Optional[str], List]: + def _translate_messages(self, messages: List[Dict[str, Any]]) -> Tuple[Optional[str], List]: """Convert OpenAI-format message list to (system_str, google_contents). Returns typed Content / Part objects from google.genai.types so that @@ -197,9 +191,7 @@ def generate( config_kwargs: Dict[str, Any] = { "temperature": temperature, # Disable automatic function calling so we drive the loop ourselves - "automatic_function_calling": types.AutomaticFunctionCallingConfig( - disable=True - ), + "automatic_function_calling": types.AutomaticFunctionCallingConfig(disable=True), } if system: