mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
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Merge pull request #115 from CodeWithCharan/main
Updated AI Blog Search Project with Qdrant and Gemini 2.0 Flash
This commit is contained in:
Shubham Saboo
GitHub
56
rag_tutorials/ai_blog_search/README.md
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56
rag_tutorials/ai_blog_search/README.md
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# Agentic RAG with LangGraph: AI Blog Search
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## Overview
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AI Blog Search is an Agentic RAG application designed to enhance information retrieval from AI-related blog posts. This system leverages LangChain, LangGraph, and Google's Gemini model to fetch, process, and analyze blog content, providing users with accurate and contextually relevant answers.
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## LangGraph Workflow
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## Demo
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https://github.com/user-attachments/assets/cee07380-d3dc-45f4-ad26-7d944ba9c32b
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## Features
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- **Document Retrieval:** Uses Qdrant as a vector database to store and retrieve blog content based on embeddings.
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- **Agentic Query Processing:** Uses an AI-powered agent to determine whether a query should be rewritten, answered, or require more retrieval.
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- **Relevance Assessment:** Implements an automated relevance grading system using Google's Gemini model.
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- **Query Refinement:** Enhances poorly structured queries for better retrieval results.
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- **Streamlit UI:** Provides a user-friendly interface for entering blog URLs, queries and retrieving insightful responses.
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- **Graph-Based Workflow:** Implements a structured state graph using LangGraph for efficient decision-making.
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## Technologies Used
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- **Programming Language**: [Python 3.10+](https://www.python.org/downloads/release/python-31011/)
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- **Framework**: [LangChain](https://www.langchain.com/) and [LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/)
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- **Database**: [Qdrant](https://qdrant.tech/)
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- **Models**:
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- Embeddings: [Google Gemini API (embedding-001)](https://ai.google.dev/gemini-api/docs/embeddings)
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- Chat: [Google Gemini API (gemini-2.0-flash)](https://ai.google.dev/gemini-api/docs/models/gemini#gemini-2.0-flash)
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- **Blogs Loader**: [Langchain WebBaseLoader](https://python.langchain.com/docs/integrations/document_loaders/web_base/)
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- **Document Splitter**: [RecursiveCharacterTextSplitter](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/recursive_text_splitter/)
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- **User Interface (UI)**: [Streamlit](https://docs.streamlit.io/)
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## Requirements
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1. **Install Dependencies**:
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```bash
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pip install -r requirements.txt
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```
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2. **Run the Application**:
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```bash
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streamlit run app.py
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```
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3. **Use the Application**:
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- Paste your Google API Key in the sidebar.
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- Paste the blog link.
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- Enter your query about the blog post.
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## :mailbox: Connect With Me
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<img align="right" src="https://media.giphy.com/media/2HtWpp60NQ9CU/giphy.gif" alt="handshake gif" width="150">
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<p align="left">
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<a href="https://linkedin.com/in/codewithcharan" target="blank"><img align="center" src="https://raw.githubusercontent.com/rahuldkjain/github-profile-readme-generator/master/src/images/icons/Social/linked-in-alt.svg" alt="codewithcharan" height="30" width="40" style="margin-right: 10px" /></a>
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<a href="https://instagram.com/joyboy._.ig" target="blank"><img align="center" src="https://raw.githubusercontent.com/rahuldkjain/github-profile-readme-generator/master/src/images/icons/Social/instagram.svg" alt="__mr.__.unique" height="30" width="40" /></a>
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<a href="https://twitter.com/Joyboy_x_" target="blank"><img align="center" src="https://raw.githubusercontent.com/rahuldkjain/github-profile-readme-generator/master/src/images/icons/Social/twitter.svg" alt="codewithcharan" height="30" width="40" style="margin-right: 10px" /></a>
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</p>
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<img src="https://readme-typing-svg.herokuapp.com/?font=Righteous&size=35¢er=true&vCenter=true&width=500&height=70&duration=4000&lines=Thanks+for+visiting!+👋;+Message+me+on+Linkedin!;+I'm+always+down+to+collab+:)"/>
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373
rag_tutorials/ai_blog_search/app.py
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373
rag_tutorials/ai_blog_search/app.py
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from langchain_google_genai import GoogleGenerativeAIEmbeddings
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from langchain_qdrant import QdrantVectorStore
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from qdrant_client import QdrantClient
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from uuid import uuid4
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from langchain_community.document_loaders import WebBaseLoader
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain.tools.retriever import create_retriever_tool
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from typing import Annotated, Literal, Sequence
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from typing_extensions import TypedDict
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from functools import partial
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from langchain import hub
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from langchain_core.messages import BaseMessage, HumanMessage
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from langgraph.graph.message import add_messages
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import PromptTemplate
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from langchain_google_genai import ChatGoogleGenerativeAI
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from pydantic import BaseModel, Field
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from langgraph.graph import END, StateGraph, START
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from langgraph.prebuilt import ToolNode, tools_condition
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import streamlit as st
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st.set_page_config(page_title="AI Blog Search", page_icon=":mag_right:")
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st.header(":blue[Agentic RAG with LangGraph:] :green[AI Blog Search]")
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# Initialize session state variables if they don't exist
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if 'qdrant_host' not in st.session_state:
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st.session_state.qdrant_host = ""
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if 'qdrant_api_key' not in st.session_state:
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st.session_state.qdrant_api_key = ""
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if 'gemini_api_key' not in st.session_state:
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st.session_state.gemini_api_key = ""
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def set_sidebar():
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"""Setup sidebar for API keys and configuration."""
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with st.sidebar:
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st.subheader("API Configuration")
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qdrant_host = st.text_input("Enter your Qdrant Host URL:", type="password")
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qdrant_api_key = st.text_input("Enter your Qdrant API key:", type="password")
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gemini_api_key = st.text_input("Enter your Gemini API key:", type="password")
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if st.button("Done"):
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if qdrant_host and qdrant_api_key and gemini_api_key:
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st.session_state.qdrant_host = qdrant_host
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st.session_state.qdrant_api_key = qdrant_api_key
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st.session_state.gemini_api_key = gemini_api_key
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st.success("API keys saved!")
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else:
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st.warning("Please fill all API fields")
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def initialize_components():
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"""Initialize components that require API keys"""
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if not all([st.session_state.qdrant_host,
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st.session_state.qdrant_api_key,
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st.session_state.gemini_api_key]):
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return None, None, None
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try:
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# Initialize embedding model with API key
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embedding_model = GoogleGenerativeAIEmbeddings(
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model="models/embedding-001",
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google_api_key=st.session_state.gemini_api_key
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)
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# Initialize Qdrant client
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client = QdrantClient(
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st.session_state.qdrant_host,
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api_key=st.session_state.qdrant_api_key
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)
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# Initialize vector store
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db = QdrantVectorStore(
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client=client,
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collection_name="qdrant_db",
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embedding=embedding_model
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)
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return embedding_model, client, db
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except Exception as e:
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st.error(f"Initialization error: {str(e)}")
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return None, None, None
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class AgentState(TypedDict):
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messages: Annotated[Sequence[BaseMessage], add_messages]
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# Edges
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## Check Relevance
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def grade_documents(state) -> Literal["generate", "rewrite"]:
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"""
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Determines whether the retrieved documents are relevant to the question.
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Args:
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state (messages): The current state
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Returns:
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str: A decision for whether the documents are relevant or not
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"""
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print("---CHECK RELEVANCE---")
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# Data model
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class grade(BaseModel):
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"""Binary score for relevance check."""
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binary_score: str = Field(description="Relevance score 'yes' or 'no'")
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# LLM
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model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, model="gemini-2.0-flash", streaming=True)
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# LLM with tool and validation
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llm_with_tool = model.with_structured_output(grade)
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# Prompt
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prompt = PromptTemplate(
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template="""You are a grader assessing relevance of a retrieved document to a user question. \n
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Here is the retrieved document: \n\n {context} \n\n
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Here is the user question: {question} \n
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If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n
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Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.""",
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input_variables=["context", "question"],
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)
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# Chain
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chain = prompt | llm_with_tool
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messages = state["messages"]
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last_message = messages[-1]
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question = messages[0].content
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docs = last_message.content
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scored_result = chain.invoke({"question": question, "context": docs})
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score = scored_result.binary_score
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if score == "yes":
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print("---DECISION: DOCS RELEVANT---")
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return "generate"
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else:
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print("---DECISION: DOCS NOT RELEVANT---")
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print(score)
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return "rewrite"
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# Nodes
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## agent node
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def agent(state, tools):
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"""
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Invokes the agent model to generate a response based on the current state. Given
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the question, it will decide to retrieve using the retriever tool, or simply end.
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Args:
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state (messages): The current state
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Returns:
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dict: The updated state with the agent response appended to messages
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"""
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print("---CALL AGENT---")
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messages = state["messages"]
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model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, streaming=True, model="gemini-2.0-flash")
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model = model.bind_tools(tools)
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response = model.invoke(messages)
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# We return a list, because this will get added to the existing list
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return {"messages": [response]}
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## rewrite node
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def rewrite(state):
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"""
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Transform the query to produce a better question.
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Args:
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state (messages): The current state
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Returns:
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dict: The updated state with re-phrased question
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"""
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print("---TRANSFORM QUERY---")
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messages = state["messages"]
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question = messages[0].content
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msg = [
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HumanMessage(
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content=f""" \n
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Look at the input and try to reason about the underlying semantic intent / meaning. \n
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Here is the initial question:
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\n ------- \n
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{question}
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\n ------- \n
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Formulate an improved question: """,
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)
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]
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# Grader
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model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, model="gemini-2.0-flash", streaming=True)
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response = model.invoke(msg)
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return {"messages": [response]}
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## generate node
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def generate(state):
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"""
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Generate answer
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Args:
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state (messages): The current state
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Returns:
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dict: The updated state with re-phrased question
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"""
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print("---GENERATE---")
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messages = state["messages"]
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question = messages[0].content
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last_message = messages[-1]
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docs = last_message.content
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# Initialize a Chat Prompt Template
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prompt_template = hub.pull("rlm/rag-prompt")
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# Initialize a Generator (i.e. Chat Model)
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chat_model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, model="gemini-2.0-flash", temperature=0, streaming=True)
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# Initialize a Output Parser
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output_parser = StrOutputParser()
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# RAG Chain
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rag_chain = prompt_template | chat_model | output_parser
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response = rag_chain.invoke({"context": docs, "question": question})
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return {"messages": [response]}
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# graph function
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def get_graph(retriever_tool):
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tools = [retriever_tool] # Create tools list here
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# Define a new graph
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workflow = StateGraph(AgentState)
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# Use partial to pass tools to the agent function
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workflow.add_node("agent", partial(agent, tools=tools))
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# Rest of the graph setup remains the same
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retrieve = ToolNode(tools)
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workflow.add_node("retrieve", retrieve)
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workflow.add_node("rewrite", rewrite) # Re-writing the question
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workflow.add_node(
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"generate", generate
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) # Generating a response after we know the documents are relevant
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# Call agent node to decide to retrieve or not
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workflow.add_edge(START, "agent")
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# Decide whether to retrieve
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workflow.add_conditional_edges(
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"agent",
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# Assess agent decision
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tools_condition,
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{
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# Translate the condition outputs to nodes in our graph
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"tools": "retrieve",
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END: END,
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},
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)
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# Edges taken after the `action` node is called.
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workflow.add_conditional_edges(
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"retrieve",
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# Assess agent decision
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grade_documents,
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)
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workflow.add_edge("generate", END)
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workflow.add_edge("rewrite", "agent")
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# Compile
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graph = workflow.compile()
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return graph
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def generate_message(graph, inputs):
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generated_message = ""
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for output in graph.stream(inputs):
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for key, value in output.items():
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if key == "generate" and isinstance(value, dict):
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generated_message = value.get("messages", [""])[0]
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return generated_message
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def add_documents_to_qdrant(url, db):
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try:
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docs = WebBaseLoader(url).load()
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text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
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chunk_size=100, chunk_overlap=50
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)
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doc_chunks = text_splitter.split_documents(docs)
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uuids = [str(uuid4()) for _ in range(len(doc_chunks))]
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db.add_documents(documents=doc_chunks, ids=uuids)
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return True
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except Exception as e:
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st.error(f"Error adding documents: {str(e)}")
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return False
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def main():
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set_sidebar()
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# Check if API keys are set
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if not all([st.session_state.qdrant_host,
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st.session_state.qdrant_api_key,
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st.session_state.gemini_api_key]):
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st.warning("Please configure your API keys in the sidebar first")
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return
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# Initialize components
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embedding_model, client, db = initialize_components()
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if not all([embedding_model, client, db]):
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return
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# Initialize retriever and tools
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retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5})
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retriever_tool = create_retriever_tool(
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retriever,
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"retrieve_blog_posts",
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"Search and return information about blog posts on LLMs, LLM agents, prompt engineering, and adversarial attacks on LLMs.",
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)
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tools = [retriever_tool]
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# URL input section
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url = st.text_input(
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":link: Paste the blog link:",
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placeholder="e.g., https://lilianweng.github.io/posts/2023-06-23-agent/"
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)
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if st.button("Enter URL"):
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if url:
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with st.spinner("Processing documents..."):
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if add_documents_to_qdrant(url, db):
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st.success("Documents added successfully!")
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else:
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st.error("Failed to add documents")
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else:
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st.warning("Please enter a URL")
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# Query section
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graph = get_graph(retriever_tool)
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query = st.text_area(
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":bulb: Enter your query about the blog post:",
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placeholder="e.g., What does Lilian Weng say about the types of agent memory?"
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)
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if st.button("Submit Query"):
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if not query:
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st.warning("Please enter a query")
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return
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inputs = {"messages": [HumanMessage(content=query)]}
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with st.spinner("Generating response..."):
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try:
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response = generate_message(graph, inputs)
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st.write(response)
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except Exception as e:
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st.error(f"Error generating response: {str(e)}")
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st.markdown("---")
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st.write("Built with :blue-background[LangChain] | :blue-background[LangGraph] by [Charan](https://www.linkedin.com/in/codewithcharan/)")
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if __name__ == "__main__":
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main()
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10
rag_tutorials/ai_blog_search/requirements.txt
Normal file
10
rag_tutorials/ai_blog_search/requirements.txt
Normal file
@@ -0,0 +1,10 @@
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langchain
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langgraph
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langchainhub
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langchain-community
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langchain-google-genai
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langchain-qdrant
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langchain-text-splitters
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tiktoken
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beautifulsoup4
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python-dotenv
|
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Reference in New Issue
Block a user