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src/rag_pipeline.py

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+import os
+from datasets import load_dataset
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_text_splitters import CharacterTextSplitter
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.documents import Document
+from langgraph.graph import START, StateGraph
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.prebuilt import create_react_agent
+from langchain_core.prompts import ChatPromptTemplate
+from huggingface_hub import login
+from dotenv import load_dotenv
+from typing import TypedDict, List
+
+# Load environment variables
+load_dotenv()
+
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Authenticate Hugging Face
+if HF_TOKEN:
+    try:
+        login(token=HF_TOKEN)
+        print("✅ Logged in to Hugging Face using HF_TOKEN.")
+    except Exception as e:
+        print(f"⚠️ Hugging Face login failed: {e}")
+else:
+    print("⚠️ No HF_TOKEN found in .env file. Using public mode.")
+
+
+# --- STATE DEFINITION ---
+class RAGState(TypedDict):
+    question: str
+    context: str
+    answer: str
+    chat_history: List[str]
+    source_documents: List[Document]
+
+
+def build_rag_pipeline():
+    """Builds a LangGraph-based RAG pipeline compatible with LangChain 1.x."""
+
+    # --- Load dataset ---
+    try:
+        dataset = load_dataset("fadodr/mental_health_therapy", split="train[:300]")
+        print("✅ Loaded dataset: fadodr/mental_health_therapy")
+    except Exception as e:
+        print(f"⚠️ Could not load dataset: {e}")
+        dataset = load_dataset("mental_health_therapy", split="train[:300]", token=HF_TOKEN)
+
+    # --- Prepare documents ---
+    texts = [f"Q: {d['instruction']}\nA: {d['input']}" for d in dataset if d.get("input", "").strip()]
+    if not texts:
+        raise ValueError("No valid text found in dataset to create embeddings!")
+
+    splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    docs = [Document(page_content=t) for t in texts]
+    split_docs = splitter.split_documents(docs)
+
+    # --- Embeddings + Chroma DB ---
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_db = Chroma.from_documents(split_docs, embeddings, persist_directory="chroma_db")
+    retriever = vector_db.as_retriever(search_kwargs={"k": 3})
+
+    # --- LLM ---
+    llm = ChatGoogleGenerativeAI(model="models/gemini-2.5-flash", google_api_key=GOOGLE_API_KEY)
+
+    # --- PROMPT TEMPLATE ---
+    prompt = ChatPromptTemplate.from_template(
+        """
+        You are a helpful assistant. Use the following retrieved context to answer the user's question.
+        If the context doesn't contain the answer, say so politely.
+        Context:
+        {context}
+
+        Question:
+        {question}
+
+        Answer:
+        """
+    )
+
+    # --- NODES (GRAPH FUNCTIONS) ---
+    def retrieve_docs(state: RAGState):
+        query = state["question"]
+        docs = retriever.invoke(query)
+        context = "\n\n".join([d.page_content for d in docs])
+        return {"context": context, "source_documents": docs}
+
+    def generate_answer(state: RAGState):
+        prompt_text = prompt.format(context=state["context"], question=state["question"])
+        response = llm.invoke(prompt_text)
+        return {"answer": response.content}
+
+    # --- BUILD THE GRAPH ---
+    graph_builder = StateGraph(RAGState)
+    graph_builder.add_node("retrieve", retrieve_docs)
+    graph_builder.add_node("generate", generate_answer)
+    graph_builder.add_edge(START, "retrieve")
+    graph_builder.add_edge("retrieve", "generate")
+
+    # Add in-memory checkpointing (conversation memory)
+    memory = MemorySaver()
+
+    graph = graph_builder.compile(checkpointer=memory)
+
+    # Wrap in a callable interface so app.py still works
+    class RAGChainWrapper:
+        def __init__(self, graph):
+            self.graph = graph
+
+        def __call__(self, inputs: dict):
+            question = inputs.get("question", "")
+            state = {"question": question, "chat_history": []}
+            result = self.graph.invoke(
+                state,
+                config={"configurable": {"thread_id": "default"}}
+            )
+            return {
+                "answer": result.get("answer", ""),
+                "source_documents": result.get("source_documents", [])
+            }
+
+    rag_chain = RAGChainWrapper(graph)
+
+    return llm, retriever, rag_chain