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63f13b5 1f5e7a7 63f13b5 1488b1e 63f13b5 ac38a11 63f13b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | """LangGraph agent using retriever fallback with LLM + tools."""
import os
from langgraph.graph import StateGraph, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_core.messages import HumanMessage, AIMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.runnables import Runnable
# from llama_index.core.agent.workflow import AgentWorkflow, ReActAgent
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
from tools import TOOLS
import pandas as pd
# Load metadata from local jsonl
QA_PATH = "metadata.jsonl"
qa_pairs = pd.read_json(QA_PATH, lines=True)
qa_dict = {row["Question"].strip(): row["Final answer"].strip() for _, row in qa_pairs.iterrows()}
def build_graph():
"""Construct a LangGraph agent with a QA retriever and fallback LLM+tools."""
# Initialize the LLM (e.g., Gemini Flash, zero temperature)
# llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0)
# llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
llm_with_tools = llm.bind_tools(TOOLS)
# Step 1: Retriever node
def retriever_node(state: MessagesState):
query = state["messages"][-1].content.strip()
if query in qa_dict:
print(f"✅ Exact match found in retriever.")
return {"messages": [AIMessage(content=qa_dict[query])]}
print(f"🔍 No match found. Falling back to LLM.")
return {"messages": state["messages"]} # Continue to LLM if no match
# Step 2: LLM + Tools node
def assistant_node(state: MessagesState):
return {"messages": [llm_with_tools.invoke(state["messages"])]}
# Build LangGraph
builder = StateGraph(MessagesState)
builder.add_node("retriever", retriever_node)
builder.add_node("assistant", assistant_node)
builder.add_node("tools", ToolNode(TOOLS))
# Edges
builder.set_entry_point("retriever")
builder.add_edge("retriever", "assistant")
builder.add_conditional_edges("assistant", tools_condition)
builder.add_edge("tools", "assistant")
builder.set_finish_point("assistant")
return builder.compile()
# Final agent interface
class BasicAgent:
def __init__(self):
print("BasicAgent initialized with retriever + LLM.")
self.graph = build_graph()
def __call__(self, question: str) -> str:
print(f"Agent received question: {question[:80]}")
result = self.graph.invoke({"messages": [HumanMessage(content=question)]})
return result['messages'][-1].content.strip()
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