# ---------------------------------------------------------- # Section 0: Imports # ---------------------------------------------------------- import json import os import pickle import re import subprocess import textwrap import base64 import functools # Used to pre-fill arguments for our tool functions from io import BytesIO from pathlib import Path # Third-party libraries import requests from cachetools import TTLCache from PIL import Image # LangChain and associated libraries from langchain.schema import Document from langchain.tools.retriever import create_retriever_tool from langchain_community.vectorstores import FAISS from langchain_community.tools.tavily_search import TavilySearchResults from langchain_community.document_loaders import WikipediaLoader, ArxivLoader from langchain_core.messages import SystemMessage, HumanMessage, AIMessage from langchain_core.tools import Tool, tool # Import Tool for manual tool creation from langchain_google_genai import ChatGoogleGenerativeAI from langchain_groq import ChatGroq from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpoint, ChatHuggingFace from langgraph.graph import START, StateGraph, MessagesState from langgraph.prebuilt import ToolNode, tools_condition # Environment variable loading from dotenv import load_dotenv load_dotenv() # ---------------------------------------------------------- # Section 1: Constants and Configuration # ---------------------------------------------------------- JSONL_PATH = Path("metadata.jsonl") FAISS_CACHE = Path("faiss_index.pkl") EMBED_MODEL = "sentence-transformers/all-mpnet-base-v2" RETRIEVER_K = 5 CACHE_TTL = 600 API_CACHE = TTLCache(maxsize=256, ttl=CACHE_TTL) # Global helper for caching API calls def cached_get(key: str, fetch_fn): if key in API_CACHE: return API_CACHE[key] val = fetch_fn() API_CACHE[key] = val return val # ---------------------------------------------------------- # Section 2: Standalone Tool Functions (No 'self' parameter) # ---------------------------------------------------------- @tool def python_repl(code: str) -> str: """Executes a string of Python code and returns the stdout/stderr.""" code = textwrap.dedent(code).strip() try: result = subprocess.run(["python", "-c", code], capture_output=True, text=True, timeout=10, check=False) if result.returncode == 0: return f"Execution successful.\nSTDOUT:\n```\n{result.stdout}\n```" else: return f"Execution failed.\nSTDOUT:\n```\n{result.stdout}\n```\nSTDERR:\n```\n{result.stderr}\n```" except subprocess.TimeoutExpired: return "Execution timed out (>10s)." # These functions now accept their dependencies (like an llm instance or a cache function) as arguments. @tool def describe_image_func(image_source: str, vision_llm_instance) -> str: """Describes an image from a local file path or a URL using a provided vision LLM.""" try: if image_source.startswith("http"): img = Image.open(BytesIO(requests.get(image_source, timeout=10).content)) else: img = Image.open(image_source) buffered = BytesIO() img.convert("RGB").save(buffered, format="JPEG") b64_string = base64.b64encode(buffered.getvalue()).decode() msg = HumanMessage(content=[{"type": "text", "text": "Describe this image in detail."}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64_string}"}}]) return vision_llm_instance.invoke([msg]).content except Exception as e: return f"Error processing image: {e}" @tool def web_search_func(query: str, cache_func) -> str: """Performs a web search using Tavily and returns a compilation of results.""" key = f"web:{query}" results = cache_func(key, lambda: TavilySearchResults(max_results=5).invoke(query)) return "\n\n---\n\n".join([f"Source: {res['url']}\nContent: {res['content']}" for res in results]) @tool def wiki_search_func(query: str, cache_func) -> str: """Searches Wikipedia and returns the top 2 results.""" key = f"wiki:{query}" docs = cache_func(key, lambda: WikipediaLoader(query=query, load_max_docs=2, doc_content_chars_max=2000).load()) return "\n\n---\n\n".join([f"Source: {d.metadata['source']}\n\n{d.page_content}" for d in docs]) @tool def arxiv_search_func(query: str, cache_func) -> str: """Searches Arxiv for scientific papers and returns the top 2 results.""" key = f"arxiv:{query}" docs = cache_func(key, lambda: ArxivLoader(query=query, load_max_docs=2).load()) return "\n\n---\n\n".join([f"Source: {d.metadata['source']}\nPublished: {d.metadata['Published']}\nTitle: {d.metadata['Title']}\n\nSummary:\n{d.page_content}" for d in docs]) # ---------------------------------------------------------- # Section 3: System Prompt # ---------------------------------------------------------- SYSTEM_PROMPT = ( """You are an expert-level research assistant designed to answer questions accurately. **Your Reasoning Process:** 1. **Think Step-by-Step:** Break down the user's question into logical steps. Plan which tools you need. 2. **Use Your Tools:** Execute your plan by calling one tool at a time. Analyze the results. 3. **Iterate:** If needed, use more tools until you have enough information. 4. **Synthesize:** Formulate a concise final answer based on the information. **Output Format:** - Your final response MUST strictly follow this format: `FINAL ANSWER: [Your concise and accurate answer here]` **Crucial Instructions:** - If your tools **cannot possibly answer the question** (e.g., it requires watching a video or listening to audio), you MUST respond by stating the limitation. - In case of a limitation, your response should be: `FINAL ANSWER: I am unable to answer this question because it requires a capability I do not possess, such as [describe the missing capability].` """ ) # ---------------------------------------------------------- # Section 4: Factory Function for Agent Executor # ---------------------------------------------------------- def create_agent_executor(provider: str = "groq"): """ Factory function to create and compile the LangGraph agent executor. This version creates tools from standalone functions to ensure model compatibility. """ print(f"Initializing agent with provider: {provider}") # Step 1: Build LLMs if provider == "google": main_llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest", temperature=0) elif provider == "groq": main_llm = ChatGroq(model_name="meta-llama/llama-4-maverick-17b-128e-instruct", temperature=0) elif provider == "huggingface": main_llm = ChatHuggingFace(llm=HuggingFaceEndpoint(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", temperature=0.1)) else: raise ValueError("Invalid provider selected") vision_llm = ChatGroq(model_name="meta-llama/llama-4-maverick-17b-128e-instruct", temperature=0) # Step 2: Build Retriever embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL) if FAISS_CACHE.exists(): with open(FAISS_CACHE, "rb") as f: vector_store = pickle.load(f) else: docs = [Document(page_content=f"Question: {rec['Question']}\n\nFinal answer: {rec['Final answer']}", metadata={"source": rec["task_id"]}) for rec in (json.loads(line) for line in open(JSONL_PATH, "rt", encoding="utf-8"))] vector_store = FAISS.from_documents(docs, embeddings) with open(FAISS_CACHE, "wb") as f: pickle.dump(vector_store, f) retriever = vector_store.as_retriever(search_kwargs={"k": RETRIEVER_K}) # Step 3: Create the final list of tools # We use functools.partial to "bake in" the dependencies (like the LLM or cache) into our standalone functions. # This creates new functions with a simpler signature that the agent can easily call. tools_list = [ python_repl, Tool(name="describe_image", func=functools.partial(describe_image_func, vision_llm_instance=vision_llm), description="Describes an image from a local file path or a URL."), Tool(name="web_search", func=functools.partial(web_search_func, cache_func=cached_get), description="Performs a web search using Tavily."), Tool(name="wiki_search", func=functools.partial(wiki_search_func, cache_func=cached_get), description="Searches Wikipedia."), Tool(name="arxiv_search", func=functools.partial(arxiv_search_func, cache_func=cached_get), description="Searches Arxiv for scientific papers."), create_retriever_tool(retriever=retriever, name="retrieve_examples", description="Retrieve solved questions similar to the user's query."), ] llm_with_tools = main_llm.bind_tools(tools_list) # Step 4: Define Graph Nodes def retriever_node(state: MessagesState): user_query = state["messages"][-1].content docs = retriever.invoke(user_query) messages = [SystemMessage(content=SYSTEM_PROMPT)] if docs: example_text = "\n\n---\n\n".join(d.page_content for d in docs) messages.append(AIMessage(content=f"I have found {len(docs)} similar solved examples:\n\n{example_text}", name="ExampleRetriever")) messages.extend(state["messages"]) return {"messages": messages} def assistant_node(state: MessagesState): result = llm_with_tools.invoke(state["messages"]) return {"messages": [result]} # Step 5: Build Graph builder = StateGraph(MessagesState) builder.add_node("retriever", retriever_node) builder.add_node("assistant", assistant_node) builder.add_node("tools", ToolNode(tools_list)) builder.add_edge(START, "retriever") builder.add_edge("retriever", "assistant") builder.add_conditional_edges("assistant", tools_condition, {"tools": "tools", "__end__": "__end__"}) builder.add_edge("tools", "assistant") agent_executor = builder.compile() print("Agent Executor created successfully.") return agent_executor # ---------------------------------------------------------- # Section 5: Pre-flight check and Direct Execution Block # ---------------------------------------------------------- def test_llm_connection(provider: str = "google"): """Performs a quick test to see if the LLM provider is accessible.""" print(f"--- Performing pre-flight check for LLM provider: {provider} ---") try: if provider == "google": llm, name = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest"), "Google Gemini" elif provider == "groq": llm, name = ChatGroq(model_name="llama3-70b-8192"), "Groq" elif provider == "huggingface": llm, name = ChatHuggingFace(llm=HuggingFaceEndpoint(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1")), "Hugging Face" else: return "❌ **LLM Status:** Invalid provider configured." llm.invoke("hello") success_message = f"✅ **LLM Status:** Connection to {name} is successful." print(success_message) return success_message except Exception as e: error_message = f"❌ **LLM Status:** FAILED to connect. Check API keys/credits. Details: {e}" print(error_message) return error_message if __name__ == "__main__": """Allows for direct testing of the agent's logic.""" print("--- Running Agent in Test Mode ---") agent = create_agent_executor(provider="google") question = "According to wikipedia, what is the main difference between a lama and an alpaca?" print(f"\nTest Question: {question}\n\n--- Agent Thinking... ---\n") for chunk in agent.stream({"messages": [("user", question)]}): for key, value in chunk.items(): if value['messages']: message = value['messages'][-1] if message.content: print(f"--- Node: {key} ---\n{message.content}\n")