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.env

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+GROQ_API_KEY=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
+SERPAPI_API_KEY=e92e6c6b0f63d2352fedc24c5f5db7cc2977e075ac048e3ab916449d3b536200

langgraph_chain.py

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+from langgraph.graph import StateGraph, END
+from langchain.chains import RetrievalQA
+from typing import TypedDict, Optional
+from tools import llm, load_vectorstore, search_tool
+
+# Load your vectorstore
+vectorstore = load_vectorstore()
+
+# --- TypedDict to define graph state schema ---
+class GraphState(TypedDict):
+    question: str
+    pdf_answer: Optional[str]
+    llm_answer: Optional[str]
+    web_answer: Optional[str]
+
+# --- LangGraph Node Functions ---
+
+def pdf_qa_node(state: GraphState) -> GraphState:
+    query = state["question"]
+    qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())
+    result = qa.run(query)
+    return {**state, "pdf_answer": result}
+
+def check_pdf_relevance(state: GraphState) -> str:
+    ans = state.get("pdf_answer", "").lower()
+    if (
+        "i don't know" in ans
+        or "i don't have information" in ans
+        or "no relevant" in ans
+        or "not available" in ans
+        or len(ans.strip()) < 20
+    ):
+        return "llm_fallback"
+    return "respond_pdf"
+
+def llm_fallback_node(state: GraphState) -> GraphState:
+    query = state["question"]
+    prompt = f"""You are a helpful AI assistant. The user asked a question, and no relevant documents were found.
+
+Try your best to answer this:
+
+Question: {query}
+Answer:"""
+    res = llm.invoke(prompt)
+    return {**state, "llm_answer": res.content}
+
+def check_llm_confidence(state: GraphState) -> str:
+    ans = state.get("llm_answer", "").lower()
+    if "i don't know" in ans or "not sure" in ans or "no idea" in ans:
+        return "web_search"
+    return "respond_llm"
+
+def web_search_node(state: GraphState) -> GraphState:
+    query = state["question"]
+    result = search_tool(query)
+    return {**state, "web_answer": result}
+
+def respond_pdf(state: GraphState) -> dict:
+    print("📄 Responding from PDF")
+    return {"answer": state["pdf_answer"]}
+
+def respond_llm(state: GraphState) -> dict:
+    print("🤖 Responding from LLM")
+    return {"answer": state["llm_answer"]}
+
+def respond_web(state: GraphState) -> dict:
+    print("🌐 Responding from Web Search")
+    return {"answer": state["web_answer"]}
+
+# --- Graph Creation Function ---
+def create_graph():
+    builder = StateGraph(GraphState)  # Pass schema
+
+    builder.add_node("pdf_qa", pdf_qa_node)
+    builder.add_node("llm_fallback", llm_fallback_node)
+    builder.add_node("web_search", web_search_node)
+
+    builder.add_node("respond_pdf", respond_pdf)
+    builder.add_node("respond_llm", respond_llm)
+    builder.add_node("respond_web", respond_web)
+
+    builder.set_entry_point("pdf_qa")
+
+    builder.add_conditional_edges("pdf_qa", check_pdf_relevance, {
+        "respond_pdf": "respond_pdf",
+        "llm_fallback": "llm_fallback"
+    })
+
+    builder.add_conditional_edges("llm_fallback", check_llm_confidence, {
+        "respond_llm": "respond_llm",
+        "web_search": "web_search"
+    })
+
+    builder.add_edge("web_search", "respond_web")
+
+    # Set all end nodes
+    builder.add_edge("respond_pdf", END)
+    builder.add_edge("respond_llm", END)
+    builder.add_edge("respond_web", END)
+
+    return builder.compile()

main.py

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+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from langgraph_chain import create_graph
+from fastapi import Form
+
+app = FastAPI()
+graph = create_graph()
+
+class Query(BaseModel):
+    question: str
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+
+@app.post("/ask")
+def ask_q(question: str = Form(...)):
+    result = graph.invoke({"question": question})
+    return {"response": result}

requirements.txt

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+langchain
+langgraph
+openai
+groq
+faiss-cpu
+sentence-transformers
+pypdf
+python-dotenv
+fastapi
+uvicorn
+serpapi
+langchain_community

tools.py

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+import os
+from dotenv import load_dotenv
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+import serpapi
+
+load_dotenv()
+
+# LLM (Groq + LLaMA3)
+llm = ChatOpenAI(
+    model="llama3-8b-8192",
+    openai_api_base="https://api.groq.com/openai/v1",
+    openai_api_key=os.environ["GROQ_API_KEY"]
+)
+
+# Embeddings (HuggingFace)
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+# Load PDFs and create FAISS vectorstore
+def load_vectorstore(pdf_dir="pdfs/"):
+    docs = []
+    for file in os.listdir(pdf_dir):
+        if file.endswith(".pdf"):
+            loader = PyPDFLoader(os.path.join(pdf_dir, file))
+            docs.extend(loader.load())
+    splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = splitter.split_documents(docs)
+    return FAISS.from_documents(chunks, embedding=embeddings)
+
+# Custom Web Search tool using SerpAPI
+def search_tool(query: str):
+    client = serpapi.Client(api_key=os.getenv("SERPAPI_API_KEY"))
+    search = client.search({
+        "engine": "google",
+        "q": query,
+    })
+    results = dict(search)
+    return results["organic_results"][0]["snippet"]  # Return the snippet or any part of the result