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"""
Cohere Provider Implementation
Cohere API provider with function calling support.
Optional provider (trial only, not recommended for production).
"""
import logging
from typing import List, Dict, Any
import cohere
from .base import LLMProvider, LLMResponse
logger = logging.getLogger(__name__)
class CohereProvider(LLMProvider):
"""
Cohere API provider implementation.
Features:
- Native function calling support
- Trial tier only (not recommended for production)
- Model: command-r-plus (best for function calling)
Note: Cohere requires a paid plan after trial expires.
Use Gemini or OpenRouter for free-tier operation.
"""
def __init__(
self,
api_key: str,
model: str = "command-r-plus",
temperature: float = 0.7,
max_tokens: int = 8192
):
super().__init__(api_key, model, temperature, max_tokens)
self.client = cohere.Client(api_key)
logger.info(f"Initialized CohereProvider with model: {model}")
def _convert_tools_to_cohere_format(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Convert MCP tool definitions to Cohere tool format.
Args:
tools: MCP tool definitions
Returns:
List of Cohere-formatted tool definitions
"""
return [
{
"name": tool["name"],
"description": tool["description"],
"parameter_definitions": tool["parameters"].get("properties", {})
}
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:
# Convert tools to Cohere format
cohere_tools = self._convert_tools_to_cohere_format(tools)
# Format chat history for Cohere
chat_history = []
for msg in messages[:-1]: # All except last message
chat_history.append({
"role": "USER" if msg["role"] == "user" else "CHATBOT",
"message": msg["content"]
})
# Last message is the current user message
current_message = messages[-1]["content"] if messages else ""
# Generate response with function calling
response = self.client.chat(
message=current_message,
chat_history=chat_history,
preamble=system_prompt,
model=self.model,
temperature=self.temperature,
max_tokens=self.max_tokens,
tools=cohere_tools
)
# Check for tool calls
if response.tool_calls:
tool_calls = [
{
"name": tc.name,
"arguments": tc.parameters
}
for tc in response.tool_calls
]
logger.info(f"Cohere requested function calls: {[tc['name'] for tc in tool_calls]}")
return LLMResponse(
content=None,
tool_calls=tool_calls,
finish_reason="tool_calls"
)
# Regular text response
content = response.text
logger.info("Cohere generated text response")
return LLMResponse(
content=content,
finish_reason="COMPLETE"
)
except Exception as e:
logger.error(f"Cohere 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 chat history
chat_history = []
for msg in messages:
chat_history.append({
"role": "USER" if msg["role"] == "user" else "CHATBOT",
"message": msg["content"]
})
# Format tool results for Cohere
tool_results_formatted = [
{
"call": {"name": call["name"], "parameters": call["arguments"]},
"outputs": [{"result": str(result)}]
}
for call, result in zip(tool_calls, tool_results)
]
# Generate final response
response = self.client.chat(
message="Based on the tool results, provide a natural language response.",
chat_history=chat_history,
model=self.model,
temperature=self.temperature,
max_tokens=self.max_tokens,
tool_results=tool_results_formatted
)
content = response.text
logger.info("Cohere generated final response after tool execution")
return LLMResponse(
content=content,
finish_reason="COMPLETE"
)
except Exception as e:
logger.error(f"Cohere 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:
# Format chat history
chat_history = []
for msg in messages[:-1]:
chat_history.append({
"role": "USER" if msg["role"] == "user" else "CHATBOT",
"message": msg["content"]
})
current_message = messages[-1]["content"] if messages else ""
# Generate response
response = self.client.chat(
message=current_message,
chat_history=chat_history,
preamble=system_prompt,
model=self.model,
temperature=self.temperature,
max_tokens=self.max_tokens
)
content = response.text
logger.info("Cohere generated simple response")
return LLMResponse(
content=content,
finish_reason="COMPLETE"
)
except Exception as e:
logger.error(f"Cohere API error: {str(e)}")
raise
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