Add initial implementation of chatbot with PostgreSQL query tool and environment configuration

.env

@@ -0,0 +1 @@
+OPENAI_API_KEY=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

agent.py

@@ -0,0 +1,59 @@
+from typing import Annotated
+from typing_extensions import TypedDict
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from langchain.chat_models import init_chat_model
+import os
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+graph_builder = StateGraph(State)
+
+key = os.getenv("OPENAI_API_KEY")
+
+llm = init_chat_model("openai:gpt-4o-mini")
+tool = {"type": "web_search_preview"}
+llm_with_tools = llm.bind_tools(
+    [
+        {
+            "type": "mcp",
+            "server_label": "clickup_data",
+            "server_url": "http://127.0.0.1:8000",
+            "require_approval": "never",
+        }
+    ]
+    + [tool]
+    
+)
+
+def chatbot(state: State):
+    return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+graph_builder.add_node("chatbot", chatbot)
+graph_builder.add_edge(START, "chatbot")
+graph_builder.add_edge("chatbot", END)
+graph = graph_builder.compile()
+
+if __name__ == "__main__":
+    print("Chatbot started! Type 'quit' to exit.")
+    
+    while True:
+        user_input = input("\nYou: ")
+        
+        if user_input.lower() in ['quit', 'exit', 'bye']:
+            print("Goodbye!")
+            break
+            
+        # Create initial state with user message
+        initial_state = {
+            "messages": [{"role": "user", "content": user_input}]
+        }
+        
+        # Run the graph
+        result = graph.invoke(initial_state)
+        
+        # Get the bot's response
+        bot_response = result["messages"][-1].content
+        print(f"Bot: {bot_response}")
+

app.py

@@ -1,13 +1,12 @@
 import os
+import asyncpg
 from fastapi import FastAPI, HTTPException
 from fastapi_mcp import FastApiMCP
 from pydantic import BaseModel
 from typing import Optional
 
-# Create a FastAPI instance
 app = FastAPI()
 
-# --- Keep this root endpoint for health checks and basic access ---
 @app.get("/")
 async def read_root():
     return {"message": "Server is running and healthy!"}
@@ -19,27 +18,51 @@ class ToolInput(BaseModel):
 class ToolOutput(BaseModel):
     result: str
 
+class QueryInput(BaseModel):
+    query: str
+
 @app.post("/tools/echo", operation_id="echo_tool")
 async def echo_tool(input_data: ToolInput) -> ToolOutput:
     """
     A simple echo tool that returns the input text with a prefix
     """
     try:
-        # Process the input
         result = f"Echo: {input_data.text}"
         return ToolOutput(result=result)
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
-# Initialize MCP with the tool endpoint
+@app.post("/tools/query_postgres", operation_id="query_postgres")
+async def query_postgres(input_data: QueryInput) -> ToolOutput:
+    """
+    Query PostgreSQL database
+    """
+    try:
+        # Only allow SELECT queries
+        if not input_data.query.lower().strip().startswith('select'):
+            return ToolOutput(result="Error: Only SELECT queries are allowed")
+        
+        conn = await asyncpg.connect('postgres://postgres:apple@172.17.21.23:5432/agency_data')
+        rows = await conn.fetch(input_data.query + " LIMIT 10")
+        await conn.close()
+        
+        if not rows:
+            return ToolOutput(result="No results found")
+        
+        results = [dict(row) for row in rows]
+        return ToolOutput(result=f"Found {len(results)} rows: {results}")
+        
+    except Exception as e:
+        return ToolOutput(result=f"Database error: {str(e)}")
+
+# Initialize MCP with both tools
 mcp = FastApiMCP(
     app,
-    include_operations=["echo_tool"]
-    )
+    include_operations=["echo_tool", "query_postgres"]
+)
 mcp.mount()
 
 if __name__ == "__main__":
     import uvicorn
-    # Use the PORT environment variable provided by Hugging Face, default to 7860
     port = int(os.environ.get("PORT", "7860"))
     uvicorn.run(app, host="0.0.0.0", port=port)

mcp_config.json

@@ -0,0 +1,7 @@
+{
+    "mcpServers": {
+      "fastapi-mcp": {
+        "serverUrl": "https://roniorque-fastapi-test-server.hf.space/mcp"
+      }
+    }
+}

requirements.txt

@@ -1,4 +1,15 @@
-fastapi
-uvicorn
-fastapi-mcp
-pydantic
+fastapi>=0.104.0
+fastapi-mcp>=0.1.0
+uvicorn[standard]>=0.24.0
+pydantic>=2.5.0
+asyncpg>=0.29.0
+psycopg2-binary>=2.9.7
+sqlalchemy[asyncio]>=2.0.0
+python-dotenv>=1.0.0
+httpx>=0.25.0
+langchain>=0.1.0
+langchain-community>=0.0.10
+langchain-core>=0.1.0
+langchain-openai>=0.3.1
+langgraph>=0.0.30
+typing-extensions>=4.8.0