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"""
OpenRouter Provider Implementation
OpenRouter API provider with function calling support.
Fallback provider for when Gemini rate limits are exceeded.
Uses OpenAI-compatible API format.
"""
import logging
from typing import List, Dict, Any
import httpx
from .base import LLMProvider, LLMResponse
logger = logging.getLogger(__name__)
class OpenRouterProvider(LLMProvider):
"""
OpenRouter API provider implementation.
Features:
- OpenAI-compatible API
- Access to multiple free models
- Function calling support
- Recommended free model: google/gemini-flash-1.5
"""
def __init__(
self,
api_key: str,
model: str = "google/gemini-flash-1.5",
temperature: float = 0.7,
max_tokens: int = 8192
):
super().__init__(api_key, model, temperature, max_tokens)
self.base_url = "https://openrouter.ai/api/v1"
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
logger.info(f"Initialized OpenRouterProvider with model: {model}")
def _convert_tools_to_openai_format(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Convert MCP tool definitions to OpenAI function format.
Args:
tools: MCP tool definitions
Returns:
List of OpenAI-formatted function definitions
"""
return [
{
"type": "function",
"function": {
"name": tool["name"],
"description": tool["description"],
"parameters": tool["parameters"]
}
}
for tool in tools
]
async def generate_response_with_tools(
self,
messages: List[Dict[str, str]],
system_prompt: str,
tools: List[Dict[str, Any]]
) -> LLMResponse:
"""
Generate a response with function calling support.
Args:
messages: Conversation history
system_prompt: System instructions
tools: Tool definitions
Returns:
LLMResponse with content and/or tool_calls
"""
try:
# Prepare messages with system prompt
formatted_messages = [{"role": "system", "content": system_prompt}] + messages
# Convert tools to OpenAI format
openai_tools = self._convert_tools_to_openai_format(tools)
# Make API request
async with httpx.AsyncClient(timeout=30.0) as client:
response = await client.post(
f"{self.base_url}/chat/completions",
headers=self.headers,
json={
"model": self.model,
"messages": formatted_messages,
"tools": openai_tools,
"temperature": self.temperature,
"max_tokens": self.max_tokens
}
)
response.raise_for_status()
data = response.json()
# Parse response
choice = data["choices"][0]
message = choice["message"]
# Check for function calls
if "tool_calls" in message and message["tool_calls"]:
tool_calls = [
{
"name": tc["function"]["name"],
"arguments": tc["function"]["arguments"]
}
for tc in message["tool_calls"]
]
logger.info(f"OpenRouter requested function calls: {[tc['name'] for tc in tool_calls]}")
return LLMResponse(
content=None,
tool_calls=tool_calls,
finish_reason=choice.get("finish_reason", "function_call")
)
# Regular text response
content = message.get("content")
logger.info("OpenRouter generated text response")
return LLMResponse(
content=content,
finish_reason=choice.get("finish_reason", "stop")
)
except httpx.HTTPStatusError as e:
logger.error(f"OpenRouter API HTTP error: {e.response.status_code} - {e.response.text}")
raise
except Exception as e:
logger.error(f"OpenRouter API error: {str(e)}")
raise
async def generate_response_with_tool_results(
self,
messages: List[Dict[str, str]],
tool_calls: List[Dict[str, Any]],
tool_results: List[Dict[str, Any]]
) -> LLMResponse:
"""
Generate a final response after tool execution.
Args:
messages: Original conversation history
tool_calls: Tool calls that were made
tool_results: Results from tool execution
Returns:
LLMResponse with final content
"""
try:
# Format tool results as messages
messages_with_results = messages.copy()
# Add assistant message with tool calls
messages_with_results.append({
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": f"call_{i}",
"type": "function",
"function": {
"name": call["name"],
"arguments": str(call["arguments"])
}
}
for i, call in enumerate(tool_calls)
]
})
# Add tool result messages
for i, (call, result) in enumerate(zip(tool_calls, tool_results)):
messages_with_results.append({
"role": "tool",
"tool_call_id": f"call_{i}",
"content": str(result)
})
# Generate final response
async with httpx.AsyncClient(timeout=30.0) as client:
response = await client.post(
f"{self.base_url}/chat/completions",
headers=self.headers,
json={
"model": self.model,
"messages": messages_with_results,
"temperature": self.temperature,
"max_tokens": self.max_tokens
}
)
response.raise_for_status()
data = response.json()
choice = data["choices"][0]
content = choice["message"].get("content")
logger.info("OpenRouter generated final response after tool execution")
return LLMResponse(
content=content,
finish_reason=choice.get("finish_reason", "stop")
)
except httpx.HTTPStatusError as e:
logger.error(f"OpenRouter API HTTP error: {e.response.status_code} - {e.response.text}")
raise
except Exception as e:
logger.error(f"OpenRouter API error in tool results: {str(e)}")
raise
async def generate_simple_response(
self,
messages: List[Dict[str, str]],
system_prompt: str
) -> LLMResponse:
"""
Generate a simple response without function calling.
Args:
messages: Conversation history
system_prompt: System instructions
Returns:
LLMResponse with content
"""
try:
# Prepare messages with system prompt
formatted_messages = [{"role": "system", "content": system_prompt}] + messages
# Make API request
async with httpx.AsyncClient(timeout=30.0) as client:
response = await client.post(
f"{self.base_url}/chat/completions",
headers=self.headers,
json={
"model": self.model,
"messages": formatted_messages,
"temperature": self.temperature,
"max_tokens": self.max_tokens
}
)
response.raise_for_status()
data = response.json()
choice = data["choices"][0]
content = choice["message"].get("content")
logger.info("OpenRouter generated simple response")
return LLMResponse(
content=content,
finish_reason=choice.get("finish_reason", "stop")
)
except httpx.HTTPStatusError as e:
logger.error(f"OpenRouter API HTTP error: {e.response.status_code} - {e.response.text}")
raise
except Exception as e:
logger.error(f"OpenRouter API error: {str(e)}")
raise
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