Update agent.py

agent.py

@@ -1,74 +1,99 @@
-"""
+"""Main script for the RAG agent with ChromaDB and Tavily.
-Agent setup with tools for local ChromaDB search and Tavily web search.
+
+The script:
+1. Loads or creates the Chroma vector store.
+2. Loads documents from the `documents/` folder.
+3. Sets up the LangChain agent with two tools: `search_local_kb` and `web_search`.
+4. Runs a simple CLI loop.
 """
 
 import os
-from typing import List
+from pathlib import Path
 
 from langchain_ollama import ChatOllama
-from langchain.tools import tool
-from langchain.schema import Document
-from langchain_chroma import Chroma
-from langchain_ollama import OllamaEmbeddings
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.document_loaders import TextLoader
-from langchain_community.document_loaders import MarkdownLoader
-
-from tavily import TavilySearchResults
-
-# Load environment variables
-from dotenv import load_dotenv
-load_dotenv()
-
-# Load vectorstore
-from vectorstore import create_vectorstore, load_documents
-
-# Persist directory
-PERSIST_DIR = "./chroma_db"
-
-# Create or load vectorstore
-vectorstore = create_vectorstore(persist_directory=PERSIST_DIR)
-
-# Load documents from documents folder if not already loaded
-if not os.path.exists(PERSIST_DIR) or not os.listdir(PERSIST_DIR):
-    print("Loading documents into vector store...")
-    load_documents("documents", vectorstore)
-
-# Define tools
-@tool
-def search_local_kb(query: str, top_k: int = 3) -> str:
-    """Semantic search in local ChromaDB knowledge base."""
-    retriever = vectorstore.as_retriever(search_kwargs={"k": top_k})
-    docs = retriever.get_relevant_documents(query)
-    if not docs:
-        return "No relevant documents found in local knowledge base."
-    # Concatenate content
-    content = "\n\n".join([f"Source: {doc.metadata.get('source', 'unknown')}\n{doc.page_content}" for doc in docs])
-    return f"[Local KB]\n{content}"
-
-@tool
-def web_search(query: str) -> str:
-    """Web search using Tavily."""
-    tavily = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))
-    results = tavily.run(query)
-    if not results:
-        return "No web results found."
-    # Format results
-    formatted = "\n\n".join([f"{i+1}. {r.get('title', 'No title')}\n{r.get('url', '')}\n{r.get('content', '')}" for i, r in enumerate(results)])
-    return f"[Web Search]\n{formatted}"
-
-# Create agent
-llm = ChatOllama(model="llama3")
-
 from langchain.agents import initialize_agent, AgentType
+from langchain.tools import Tool
 
-agent_executor = initialize_agent(
+from vectorstore import create_vectorstore, load_documents
-    tools=[search_local_kb, web_search],
+from rag_tools import search_local_kb, web_search
-    llm=llm,
+
-    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+# ---------------------------------------------------------------------------
-    verbose=True,
+# Configuration
-    handle_parsing_errors=True,
+# ---------------------------------------------------------------------------
+CHROMA_DIR = "./chroma_db"
+DOCS_DIR = "./documents"
+MODEL = "llama3"
+
+# ---------------------------------------------------------------------------
+# Helper: load or create vector store
+# ---------------------------------------------------------------------------
+vectorstore = create_vectorstore(persist_directory=CHROMA_DIR)
+
+# Load documents – we always load; Chroma will deduplicate by ID if same content
+print("Loading documents into ChromaDB (if not already present)...")
+load_documents(DOCS_DIR, vectorstore)
+print("Documents loaded.")
+
+# ---------------------------------------------------------------------------
+# Define tools – pass the vectorstore to the local search tool
+# ---------------------------------------------------------------------------
+# We wrap the tool functions to include the vectorstore argument
+
+def local_kb_tool(query: str, top_k: int = 3):
+    return search_local_kb(query=query, top_k=top_k, vectorstore=vectorstore)
+
+# Create LangChain Tool objects
+local_tool = Tool(
+    name="search_local_kb",
+    func=local_kb_tool,
+    description="Semantic search in the local knowledge base. Use when the answer is in the local documents.",
+)
+web_tool = Tool(
+    name="web_search",
+    func=web_search,
+    description="Search the web using Tavily. Use for up‑to‑date facts or news.",
 )
 
-# Expose agent_executor
+# ---------------------------------------------------------------------------
-__all__ = ["agent_executor"]
+# Agent setup
+# ---------------------------------------------------------------------------
+llm = ChatOllama(model=MODEL, temperature=0.0)
+
+system_prompt = (
+    "You are an assistant that answers user questions. "
+    "If the answer is likely to be in the local knowledge base, use the tool "
+    "search_local_kb. If the answer requires up‑to‑date information, use the "
+    "web_search tool. After retrieving information, provide the answer and "
+    "state the source: either 'chromadb' or 'tavily'."
+)
+
+agent = initialize_agent(
+    tools=[local_tool, web_tool],
+    llm=llm,
+    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+    verbose=True,
+    prefix=system_prompt,
+)
+
+# ---------------------------------------------------------------------------
+# CLI loop
+# ---------------------------------------------------------------------------
+print("\nRAG Agent ready. Type your question (or 'exit' to quit).\n")
+while True:
+    try:
+        query = input("Query: ")
+    except (KeyboardInterrupt, EOFError):
+        print("\nExiting.")
+        break
+    if query.strip().lower() in {"exit", "quit", "q"}:
+        print("Exiting.")
+        break
+    if not query.strip():
+        continue
+    # Run the agent
+    try:
+        result = agent.run(query)
+        print(f"\nAnswer:\n{result}\n")
+    except Exception as e:
+        print(f"Error: {e}")
+        continue