Update Chat History Logic

app/page_speed/config.py

@@ -1,39 +1,57 @@
 from pydantic_settings import BaseSettings, SettingsConfigDict
-from urllib.parse import quote_plus
 
 class Settings(BaseSettings):
+    """Application settings loaded from environment variables."""
+
+    # ───────────────────────────────────────────────────────────────────────────
     # Google API Keys
+    # ───────────────────────────────────────────────────────────────────────────
     pagespeed_api_key: str
     gemini_api_key: str
 
+    # ───────────────────────────────────────────────────────────────────────────
     # Chat & RAG Configuration
+    # ───────────────────────────────────────────────────────────────────────────
     groq_api_key: str
     vectorstore_base_path: str = "./vectorstores"
 
-    # Hugging Face
+    # ───────────────────────────────────────────────────────────────────────────
+    # Hugging Face Hub
+    # ───────────────────────────────────────────────────────────────────────────
     huggingfacehub_api_token: str
 
-    # MongoDB Config
-    mongo_password: str
+    # ───────────────────────────────────────────────────────────────────────────
+    # MongoDB Configuration (Local)
+    # ───────────────────────────────────────────────────────────────────────────
+    mongo_uri: str = "mongodb://localhost:27017"
     mongo_chat_db: str = "MAAS"
     mongo_chat_collection: str = "chat_histories"
 
-    # FastAPI Server Config
+    # ───────────────────────────────────────────────────────────────────────────
+    # FastAPI Server Configuration
+    # ───────────────────────────────────────────────────────────────────────────
     host: str = "0.0.0.0"
     port: int = 8000
     debug: bool = False
 
-    # MongoDB Atlas URI (Dynamically Constructed)
-    @property
-    def mongo_uri(self):
-        encoded_pwd = quote_plus(self.mongo_password)
-        return f"mongodb+srv://Hammad:{encoded_pwd}@cluster0.oi9z5ig.mongodb.net/{self.mongo_chat_db}?retryWrites=true&w=majority"
+    # ───────────────────────────────────────────────────────────────────────────
+    # App Metadata (unchanged)
+    # ───────────────────────────────────────────────────────────────────────────
+    app_name: str = "PageSpeed Insights Report Generator"
+    app_version: str = "1.0.0"
+    app_description: str = (
+        "Professional API for generating PageSpeed Insights reports "
+        "using Google's APIs and Gemini AI"
+    )
 
+    # ───────────────────────────────────────────────────────────────────────────
+    # Tell Pydantic to load from .env and ignore extras
+    # ───────────────────────────────────────────────────────────────────────────
     model_config = SettingsConfigDict(
         env_file=".env",
         env_file_encoding="utf-8",
-        extra="ignore"
+        extra="ignore",
     )
 
-# Global settings instance
-settings = Settings()
+# Single shared Settings instance
+settings = Settings()

app/rag/routes.py

@@ -4,6 +4,7 @@ from fastapi import APIRouter, HTTPException
 
 from .schemas import SetupRequest, ChatRequest, SetupResponse, ChatResponse
 from .utils import (
+    get_vectorstore_metadata,
     text_splitter,
     embeddings,
     save_vectorstore_to_disk,
@@ -31,6 +32,23 @@ async def setup_rag_session(onboarding_id: str, body: SetupRequest):
             onboarding_id, vectorstore_path
         )
         vs_path = vectorstore_path
+
+        chat_session = get_vectorstore_metadata(onboarding_id)
+
+        if chat_session:
+            chat_id = str(chat_session["chat_id"])
+            logger.info("Using existing chat session id=%s for onboarding_id=%s", chat_id, onboarding_id)
+        else:
+            logger.warning("No chat session found for onboarding_id=%s", onboarding_id)
+        
+        return SetupResponse(
+            success=True,
+            message="RAG setup completed with existing vectorstore.",
+            onboarding_id=onboarding_id,
+            chat_id=chat_id,
+            vectorstore_path=vs_path
+        )
+
     else:
         if not body.documents:
             logger.error(
@@ -40,6 +58,15 @@ async def setup_rag_session(onboarding_id: str, body: SetupRequest):
                 status_code=400,
                 detail="Vectorstore does not exist; please provide documents to ingest."
             )
+        
+        # Create new chat session
+        chat_id = str(uuid.uuid4())
+        ChatHistoryManager.create_session(chat_id)
+        logger.info(
+            "Created new chat session %s for onboarding_id=%s",
+            chat_id, onboarding_id
+        )
+
         # Ingest new vectorstore
         all_text = "\n\n".join(body.documents)
         text_chunks = text_splitter.split_text(all_text)
@@ -48,18 +75,11 @@ async def setup_rag_session(onboarding_id: str, body: SetupRequest):
         vs = _FAISS.from_texts(texts=text_chunks, embedding=embeddings)
         vs_path = save_vectorstore_to_disk(vs, onboarding_id)
         logger.info("Saved FAISS index to %s", vs_path)
-        upsert_vectorstore_metadata(onboarding_id, vs_path)
+        upsert_vectorstore_metadata(onboarding_id, vs_path, chat_id)
         logger.info(
             "Upserted vectorstore metadata for onboarding_id=%s", onboarding_id
         )
-
-    # Create new chat session
-    chat_id = str(uuid.uuid4())
-    ChatHistoryManager.create_session(chat_id)
-    logger.info(
-        "Created new chat session %s for onboarding_id=%s",
-        chat_id, onboarding_id
-    )
+        
 
     return SetupResponse(
         success=True,

app/rag/utils.py

@@ -16,25 +16,25 @@ from app.rag.prompt_library import page_speed_prompt, default_user_prompt,seo_pr
 # ──────────────────────────────────────────────────────────────────────────────
 # 1. Helper: Path to Store (or Load) a User's FAISS Vectorstore on Disk
 # ──────────────────────────────────────────────────────────────────────────────
-def get_vectorstore_path(user_id: str) -> str:
+def get_vectorstore_path(onboarding_id: str) -> str:
     """
     Ensure a local directory exists for this user's vectorstore.
-    Returns a path like './vectorstores/{user_id}'.
+    Returns a path like './vectorstores/{onboarding_id}'.
     """
     base_dir = settings.vectorstore_base_path
-    user_dir = os.path.join(base_dir, user_id)
+    user_dir = os.path.join(base_dir, onboarding_id)
     # os.makedirs(user_dir, exist_ok=True)
     return user_dir
 
 # ──────────────────────────────────────────────────────────────────────────────
 # 2. Build or Load an Existing FAISS Index for a User
 # ──────────────────────────────────────────────────────────────────────────────
-def build_or_load_vectorstore(user_id: str) -> FAISS:
+def build_or_load_vectorstore(onboarding_id: str) -> FAISS:
     """
     Attempt to load an existing FAISS index for this user.
     If not found on disk, raise a FileNotFoundError.
     """
-    user_dir = get_vectorstore_path(user_id)
+    user_dir = get_vectorstore_path(onboarding_id)
     faiss_index_path = os.path.join(user_dir, "faiss_index")
 
     if not os.path.isdir(faiss_index_path):
@@ -50,12 +50,12 @@ def build_or_load_vectorstore(user_id: str) -> FAISS:
 # ──────────────────────────────────────────────────────────────────────────────
 # 3. Save a FAISS Vectorstore to Disk for a User
 # ──────────────────────────────────────────────────────────────────────────────
-def save_vectorstore_to_disk(vectorstore: FAISS, user_id: str) -> str:
+def save_vectorstore_to_disk(vectorstore: FAISS, vectorstore_name: str) -> str:
     """
     Save the FAISS vectorstore under './vectorstores/{user_id}/faiss_index'.
     Returns the path to that saved folder.
     """
-    user_dir = get_vectorstore_path(user_id)
+    user_dir = get_vectorstore_path(vectorstore_name)
     faiss_index_path = os.path.join(user_dir, "faiss_index")
     os.makedirs(faiss_index_path, exist_ok=True)
     vectorstore.save_local(folder_path=faiss_index_path)
@@ -64,21 +64,22 @@ def save_vectorstore_to_disk(vectorstore: FAISS, user_id: str) -> str:
 # ──────────────────────────────────────────────────────────────────────────────
 # 4. Upsert or Fetch Vectorstore Metadata in MongoDB
 # ──────────────────────────────────────────────────────────────────────────────
-def upsert_vectorstore_metadata(user_id: str, vectorstore_path: str) -> None:
+def upsert_vectorstore_metadata(onboarding_id: str, vectorstore_path: str, chat_id: str) -> None:
     """
     Insert or update a document mapping user_id → vectorstore_path in MongoDB.
     """
     vectorstore_meta_coll.update_one(
-        {"user_id": user_id},
-        {"$set": {"vectorstore_path": vectorstore_path}},
+        {"onboarding_id": onboarding_id},
+        {"$set": {"vectorstore_path": vectorstore_path}, 
+         "$setOnInsert": {"chat_id": chat_id}},
         upsert=True
     )
 
-def get_vectorstore_metadata(user_id: str) -> Optional[Dict[str, Any]]:
+def get_vectorstore_metadata(onboarding_id: str) -> Optional[Dict[str, Any]]:
     """
-    Retrieve the metadata doc (if any) for this user_id.
+    Retrieve the metadata doc (if any) for this onboarding_id.
     """
-    return vectorstore_meta_coll.find_one({"user_id": user_id})
+    return vectorstore_meta_coll.find_one({"onboarding_id": onboarding_id})
 
 # ────────────────────────────────────────────────────��─────────────────────────
 # 5. Initialize (or Return) a MongoDBChatMessageHistory for chat_id
@@ -95,20 +96,20 @@ def initialize_chat_history(chat_id: str) -> MongoDBChatMessageHistory:
     )
 
 # ──────────────────────────────────────────────────────────────────────────────
-# 6. Build a ConversationalRetrievalChain (RAG Chain) for user_id + chat_id
+# 6. Build a ConversationalRetrievalChain (RAG Chain) for onboarding_id + chat_id
 # ──────────────────────────────────────────────────────────────────────────────
-def build_rag_chain(user_id: str, chat_id: str, prompt_type: str) -> ConversationalRetrievalChain:
+def build_rag_chain(onboarding_id: str, chat_id: str, prompt_type: str) -> ConversationalRetrievalChain:
     """
-    - Loads the FAISS index for user_id.
+    - Loads the FAISS index for onboarding_id.
     - Creates a retriever (k=3).
     - Wraps MongoDBChatMessageHistory in a ConversationBufferMemory.
     - Attaches the ChatGroq LLM + user_prompt.
     """
     # 1. Load FAISS index (or 404 if not found)
     try:
-        faiss_vs = build_or_load_vectorstore(user_id)
+        faiss_vs = build_or_load_vectorstore(onboarding_id)
     except FileNotFoundError:
-        raise HTTPException(status_code=404, detail="Vectorstore not found for this user. Call /rag/ingest first.")
+        raise HTTPException(status_code=404, detail="Vectorstore not found for this onboarding_id. Call /rag/ingest first.")
 
     retriever = faiss_vs.as_retriever(search_kwargs={"k": 5})