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# client_agent.py
import asyncio
from contextlib import AsyncExitStack
from typing import Optional
from dotenv import load_dotenv
from anthropic import Anthropic
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
load_dotenv()
class DocsNavigatorClient:
def __init__(self):
self.session: Optional[ClientSession] = None
self.exit_stack = AsyncExitStack()
self.anthropic = Anthropic()
self._tools_cache = None
async def connect(self, server_script_path: str = "src/server/server.py"):
"""
Start the docs MCP server (via stdio) and initialize a session.
"""
import os
import sys
# Try to use uv run first, then fall back to the virtual environment python
if os.path.exists(".venv/Scripts/python.exe"):
# Windows virtual environment
python_path = ".venv/Scripts/python.exe"
params = StdioServerParameters(
command=python_path,
args=[server_script_path],
env=None,
)
elif os.path.exists(".venv/bin/python"):
# Unix virtual environment
python_path = ".venv/bin/python"
params = StdioServerParameters(
command=python_path,
args=[server_script_path],
env=None,
)
else:
# Fallback to system python
params = StdioServerParameters(
command="python",
args=[server_script_path],
env=None,
)
stdio_transport = await self.exit_stack.enter_async_context(
stdio_client(params)
)
self.stdio, self.write = stdio_transport
self.session = await self.exit_stack.enter_async_context(
ClientSession(self.stdio, self.write)
)
await self.session.initialize()
tools_response = await self.session.list_tools()
self._tools_cache = [
{
"name": t.name,
"description": t.description,
"input_schema": t.inputSchema,
}
for t in tools_response.tools
]
async def close(self):
await self.exit_stack.aclose()
async def answer(self, user_query: str) -> str:
"""
Ask the LLM to answer a question, using docs tools when needed.
Supports multi-turn conversations with multiple tool calls.
"""
if not self.session:
raise RuntimeError("MCP session not initialized. Call connect() first.")
if self._tools_cache is None:
tools_response = await self.session.list_tools()
self._tools_cache = [
{
"name": t.name,
"description": t.description,
"input_schema": t.inputSchema,
}
for t in tools_response.tools
]
messages = [
{
"role": "user",
"content": (
"You are a documentation assistant. "
"Use the available MCP tools to search and read docs in order "
"to answer the question. You can use multiple tools and think through "
"your response step by step. Always reference the files you used.\n\n"
f"User question: {user_query}"
),
}
]
tools = self._tools_cache
max_iterations = 10 # Prevent infinite loops
iteration = 0
while iteration < max_iterations:
iteration += 1
# Call the LLM
response = self.anthropic.messages.create(
model="claude-3-haiku-20240307",
max_tokens=2500, # Increased token limit for longer responses
messages=messages,
tools=tools,
)
# Add assistant's response to conversation
messages.append({
"role": "assistant",
"content": response.content,
})
# Check if there are any tool calls to execute
tool_calls = [content for content in response.content if content.type == "tool_use"]
if not tool_calls:
# No more tool calls - we're done
text_content = [content.text for content in response.content if content.type == "text"]
return "\n".join(text_content) if text_content else "[no text response from model]"
# Execute all tool calls in this round
tool_results = []
for tool_call in tool_calls:
try:
tool_name = tool_call.name
tool_args = tool_call.input
# Call the MCP tool
result = await self.session.call_tool(tool_name, tool_args)
tool_results.append({
"type": "tool_result",
"tool_use_id": tool_call.id,
"content": result.content,
})
except Exception as e:
# Handle tool errors gracefully
tool_results.append({
"type": "tool_result",
"tool_use_id": tool_call.id,
"content": f"Error calling tool {tool_call.name}: {str(e)}",
"is_error": True,
})
# Add tool results to conversation
messages.append({
"role": "user",
"content": tool_results,
})
# If we hit max iterations, return what we have so far
text_content = []
for message in messages:
if message["role"] == "assistant":
for content in message["content"]:
if hasattr(content, 'type') and content.type == "text":
text_content.append(content.text)
elif isinstance(content, dict) and content.get("type") == "text":
text_content.append(content.get("text", ""))
return "\n".join(text_content) if text_content else "[reached max iterations without final response]"
# Thread-local storage for client instances
import threading
_thread_local = threading.local()
def answer_sync(user_query: str) -> str:
"""
Synchronous wrapper so Gradio can call into our async flow easily.
Creates a new client for each request to avoid event loop conflicts.
"""
import concurrent.futures
def run_in_new_loop():
# Create a new event loop in this thread
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
return loop.run_until_complete(_answer_async(user_query))
finally:
loop.close()
# Run in a separate thread to avoid conflicts with Gradio's event loop
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(run_in_new_loop)
return future.result()
async def _answer_async(user_query: str) -> str:
"""
Create a fresh client for each request to avoid event loop issues.
"""
client = DocsNavigatorClient()
try:
await client.connect("src/server/server.py")
return await client.answer(user_query)
finally:
await client.close() |