pr/1

.gitignore

@@ -1,5 +1,4 @@
 venv
 .env
 __pycache__
-.vscode
-corpus
+.vscode

app.py

@@ -1,14 +1,8 @@
 from fastapi import FastAPI
 from langgraph.agents.summarize_agent.graph import graph   
-from langgraph.agents.rag_agent.graph import graph as rag_graph
 from fastapi import Request
 from fastapi.middleware.cors import CORSMiddleware
 
-from langchain_core.documents import Document
-
-from utils.create_vectordb import create_chroma_db_and_document,query_chroma_db
-
-
 
 
 
@@ -38,73 +32,5 @@ async def summarize(request: Request):
     return  graph.invoke({"urls": urls, "codes": codes, "notes": notes})
 
 
-@app.post("/save_summary")
-async def save_summary(request: Request):
-    data = await request.json()
-    summary = data.get("summary", "")
-    post_id = data.get("post_id", None)
-    title = data.get("title", "")
-    category = data.get("category", "") 
-    tags = data.get("tags", [])
-    references = data.get("references", [])
-    
-    page_content = f"""
-    Title: {title}
-    Category: {category}
-    Tags: {', '.join(tags)}
-    Summary: {summary}
-    """
-    
-    document = Document(
-        page_content=page_content,
-        id = str(post_id)
-        
-    )
-    
-    is_added = create_chroma_db_and_document(document)
-    
-    if not is_added:
-        return {"error": "Failed to save summary to the database." , "status": "error"}
-    
-    return {"message": "Summary saved successfully." , "status": "success"}
-
-@app.post("/summaries")
-async def get_summaries(request: Request):
-  
-    data = await request.json()
-    print(data)
-    query = data.get("query" , "")
-    
-    print(f"Query received: {query}")
-    results = query_chroma_db(query=query)
-    return results
-    
-    
-    
-    
-@app.post("/chat")
-async def chat(request: Request):
-    data = await request.json()
-    
-    print(f"Chat request data: {data}")
-  
-    user_input = data.get("message", "")
-    chat_history = data.get("chat_history", [])
-    
-    print(f"User input: {user_input}")
-    print(f"Chat history: {chat_history}")
-    
-    # Invoke the RAG chatbot graph
-    result = rag_graph.invoke({
-        "user_input": user_input,
-        "chat_history": chat_history
-    })
-    
-    return {
-        "response": result["response"],
-        "chat_history": result["chat_history"]
-    }
-
-
 
 

langgraph/agents/rag_agent/graph.py

@@ -1,207 +0,0 @@
-import os
-from typing import Dict, List, Any, Literal
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-from langgraph.graph import StateGraph
-from langgraph.graph.graph import END
-from dotenv import load_dotenv
-import google.generativeai as genai
-from google.generativeai import GenerativeModel
-import sys
-
-# Add the parent directory to the path to import utils
-sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
-from utils.create_vectordb import query_chroma_db
-
-load_dotenv()
-
-# Initialize Gemini model
-api_key = os.getenv("GOOGLE_API_KEY")
-genai.configure(api_key=api_key)
-model = GenerativeModel("gemini-2.5-flash-preview-05-20")
-
-def retrieve_context(state: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Retrieve relevant context from the vector database based on the user query.
-    """
-    query = state.get("user_input", "")
-    if not query:
-        return {"context": "No query provided.", "user_input": query, "next": "request_clarification"}
-    
-    # Check if query is clear enough
-    if len(query.split()) < 3 or "?" not in query and not any(w in query.lower() for w in ["what", "how", "why", "when", "where", "who", "which"]):
-        return {"context": "", "user_input": query, "next": "request_clarification"}
-    
-    # Query the vector database
-    results = query_chroma_db(query, n_results=3)
-    
-    # Extract the retrieved documents
-    documents = results.get("documents", [[]])[0]
-    metadatas = results.get("metadatas", [[]])[0]
-    
-    # Format the context
-    formatted_context = []
-    for i, (doc, metadata) in enumerate(zip(documents, metadatas)):
-        source = metadata.get("source", "Unknown")
-        formatted_context.append(f"Document {i+1} (Source: {source}):\n{doc}\n")
-    
-    context = "\n".join(formatted_context) if formatted_context else ""
-    
-    # Determine next step based on context quality
-    if not context or len(context) < 50:
-        next_step = "use_gemini_knowledge"
-    else:
-        next_step = "generate_response"
-    
-    return {"context": context, "user_input": query, "next": next_step}
-
-def request_clarification(state: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Request clarification from the user when the query is unclear.
-    """
-    query = state.get("user_input", "")
-    
-    clarification_message = model.generate_content(
-        f"""The user asked: "{query}"
-        
-        This query seems vague or unclear. Generate a polite response asking for more specific details.
-        Focus on what additional information would help you understand their request better.
-        Keep your response under 3 sentences and make it conversational."""
-    )
-    
-    response = clarification_message.text
-    
-    # Update chat history
-    chat_history = state.get("chat_history", [])
-    new_chat_history = chat_history + [
-        {"role": "user", "content": query},
-        {"role": "assistant", "content": response}
-    ]
-    
-    
-    return {
-        "response": response,
-        "chat_history": new_chat_history,
-        "needs_clarification": True
-    }
-
-def use_gemini_knowledge(state: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Use Gemini's knowledge base when no relevant information is found in the vector database.
-    """
-    query = state.get("user_input", "")
-    chat_history = state.get("chat_history", [])
-    
-    # Construct the prompt
-    prompt_template = """
-    I couldn't find specific information about this in my local database. However, I can try to answer based on my general knowledge.
-    
-    User Question: {query}
-    
-    First, acknowledge that you're answering from general knowledge rather than the specific database.
-    Then provide a helpful, accurate response based on what you know about the topic.
-    """
-    
-    # Generate response
-    response = model.generate_content(
-        prompt_template.format(query=query)
-    )
-    
-    # Update chat history
-    new_chat_history = chat_history + [
-        {"role": "user", "content": query},
-        {"role": "assistant", "content": response.text}
-    ]
-    
-    return {
-        "response": response.text,
-        "chat_history": new_chat_history
-    }
-
-def generate_response(state: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Generate a response using the LLM based on the retrieved context and user query.
-    """
-    context = state.get("context", "")
-    query = state.get("user_input", "")
-    chat_history = state.get("chat_history", [])
-    
-    # Construct the prompt
-    prompt_template = """
-    You are a helpful assistant that answers questions based on the provided context.
-    
-    Context:
-    {context}
-    
-    Chat History:
-    {chat_history}
-    
-    User Question: {query}
-    
-    Answer the question based only on the provided context. If the context doesn't contain enough information,
-    acknowledge this but still try to provide a helpful response based on the available information.
-    Provide a clear, concise, and helpful response.
-    """
-    
-    # Format chat history for the prompt
-    formatted_chat_history = "\n".join([f"{msg['role']}: {msg['content']}" for msg in chat_history])
-    
-    # Generate response
-    response = model.generate_content(
-        prompt_template.format(
-            context=context,
-            chat_history=formatted_chat_history,
-            query=query
-        )
-    )
-    
-    # Update chat history
-    new_chat_history = chat_history + [
-        {"role": "user", "content": query},
-        {"role": "assistant", "content": response.text}
-    ]
-    
-    return {
-        "response": response.text,
-        "chat_history": new_chat_history
-    }
-
-def decide_next_step(state: Dict[str, Any]) -> Literal["request_clarification", "use_gemini_knowledge", "generate_response"]:
-    """
-    Decide the next step in the workflow based on the state.
-    """
-    return state["next"]
-
-# Define the workflow
-def build_graph():
-    workflow = StateGraph(state_schema=Dict[str, Any])
-    
-    # Add nodes
-    workflow.add_node("retrieve_context", retrieve_context)
-    workflow.add_node("request_clarification", request_clarification)
-    workflow.add_node("use_gemini_knowledge", use_gemini_knowledge)
-    workflow.add_node("generate_response", generate_response)
-    
-    # Define edges with conditional routing
-    workflow.set_entry_point("retrieve_context")
-    workflow.add_conditional_edges(
-        "retrieve_context",
-        decide_next_step,
-        {
-            "request_clarification": "request_clarification",
-            "use_gemini_knowledge": "use_gemini_knowledge",
-            "generate_response": "generate_response"
-        }
-    )
-    
-    # Set finish points
-    workflow.add_edge("request_clarification", END)
-    workflow.add_edge("use_gemini_knowledge", END)
-    workflow.add_edge("generate_response", END)
-    
-    # Compile the graph
-    return workflow.compile()
-
-# Create the graph
-graph = build_graph()

requirements.txt

@@ -3,10 +3,6 @@ uvicorn[standard]
 langgraph 
 langsmith
 google-genai
-google-generativeai
-chromadb
-langchain
-langchain-community
+
 python-dotenv
-pypdf
 

utils/__init__.py

@@ -1 +0,0 @@
-# This file is intentionally left empty to make the directory a Python package

utils/create_vectordb.py

@@ -1,153 +0,0 @@
-import os
-from typing import Optional, List
-import chromadb
-from chromadb.utils import embedding_functions
-from langchain_community.document_loaders import DirectoryLoader, PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from dotenv import load_dotenv
-import google.generativeai as genai
-
-load_dotenv()
-
-# Configure paths
-CORPUS_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "corpus")
-DB_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "vectordb")
-
-# Ensure directories exist
-os.makedirs(CORPUS_DIR, exist_ok=True)
-os.makedirs(DB_DIR, exist_ok=True)
-
-def load_documents(corpus_dir: str = CORPUS_DIR) -> List:
-    """Load documents from the corpus directory."""
-    if not os.path.exists(corpus_dir):
-        raise FileNotFoundError(f"Corpus directory not found: {corpus_dir}")
-    print(f"Loading documents from {corpus_dir}...")
-    
-    # Initialize loaders for different file types
-    loaders = {
-        # "txt": DirectoryLoader(corpus_dir, glob="**/*.txt", loader_cls=TextLoader),
-        "pdf": DirectoryLoader(corpus_dir, glob="**/*.pdf", loader_cls=PyPDFLoader),
-        # "docx": DirectoryLoader(corpus_dir, glob="**/*.docx", loader_cls=Docx2txtLoader),
-    }
-    
-    documents = []
-    for file_type, loader in loaders.items():
-        try:
-            docs = loader.load()
-            print(f"Loaded {len(docs)} {file_type} documents")
-            documents.extend(docs)
-        except Exception as e:
-            print(f"Error loading {file_type} documents: {e}")
-    
-    return documents
-
-def split_documents(documents, chunk_size=1000, chunk_overlap=200):
-    """Split documents into chunks."""
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=chunk_size,
-        chunk_overlap=chunk_overlap,
-        length_function=len,
-    )
-    
-    splits = text_splitter.split_documents(documents)
-    print(f"Split {len(documents)} documents into {len(splits)} chunks")
-    
-    return splits
-
-def create_chroma_db_and_document(document, collection_name="corpus_collection", db_dir=DB_DIR):
-    """Create a Chroma vector database from documents."""
-    # Initialize the Gemini embedding function
-    gemini_ef = embedding_functions.GoogleGenerativeAiEmbeddingFunction(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        model_name="models/embedding-001"
-    )
-    
-    # Initialize Chroma client
-    client = chromadb.PersistentClient(path=db_dir)
-    
-    # Create or get collection
-    try:
-        collection = client.get_collection(name=collection_name)
-        print(f"Using existing collection: {collection_name}")
-    except:
-        collection = client.create_collection(
-            name=collection_name,
-            embedding_function=gemini_ef
-        )
-        print(f"Created new collection: {collection_name}")
-        
-    
-    try:
-    
-        collection.add(
-            documents = [document.page_content],
-            ids = [document.id]
-        )
-        
-        print("Document added to collection successfully.")
-        return True
-    
-    except Exception as e:
-        print(f"Error adding document to collection: {e}")
-        
-        return False
-        
-        
- 
-
-def query_chroma_db(query: str, collection_name="corpus_collection", n_results=5, db_dir=DB_DIR):
-    """Query the Chroma vector database."""
-    # Initialize the Gemini embedding function
-    gemini_ef = embedding_functions.GoogleGenerativeAiEmbeddingFunction(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        model_name="models/embedding-001"
-    )
-    
-    # Initialize Chroma client
-    client = chromadb.PersistentClient(path=db_dir)
-    
-    # Get collection
-    collection = client.get_collection(name=collection_name, embedding_function=gemini_ef)
-    
-    # Query collection
-    results = collection.query(
-        query_texts=[query],
-        n_results=n_results
-    )
-    
-    return results
-
-def main():
-    """Main function to create and test the vector database."""
-    print("Starting vector database creation...")
-    
-    # Load documents
-    documents = load_documents()
-    if not documents:
-        print("No documents found in corpus directory. Please add documents to proceed.")
-        return
-    
-    # Split documents
-    splits = split_documents(documents)
-    
-    # Create vector database
-    collection = create_chroma_db(splits)
-    
-    # Test query
-    test_query = "What is this corpus about?"
-    print(f"\nTesting query: '{test_query}'")
-    results = query_chroma_db(test_query)
-    print(f"Found {len(results['documents'][0])} matching documents")
-    for i, (doc, metadata) in enumerate(zip(results['documents'][0], results['metadatas'][0])):
-        print(f"\nResult {i+1}:")
-        print(f"Document: {doc[:150]}...")
-        print(f"Source: {metadata.get('source', 'Unknown')}")
-    
-    print("\nVector database creation and testing complete!")
-    
-
-
-
-
-if __name__ == "__main__":
-    main()