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.gitignore

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README.md

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----
-title: Query Url
-emoji: 💻
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 4.19.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# LLM x Website URLs
+
+Most of the language models like GPT 4 and even Gemini Pro have the issue of not having the latest information considering they were trained with a set of data for pretraining.
+
+In cases ,We may need to access real-time information from websites to get up-to-date information. LLMs do not have this functionality by default. But through Retrieval Augmented Generation(RAG), we can allow them to access the data that we provide. 
+
+Here, we use Langchain's Webloader and get the data from the site and store the data into a vectorstore, from where we can get the necessary answers for our queries. 
+
+## Usage Instructions:
+
+1. **Install Dependencies:**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+2. **Run the Application:**
+    ```bash
+    python main.py
+    ```
+
+3. **Access the Gradio Interface:**
+   - Open your web browser and navigate to the provided URL.
+
+4. **Input Parameters:**
+   - Query: Enter your question or input.
+   - URL: Provide the website URL for context.
+   - OpenAI Key: Enter your OpenAI key for authentication.
+
+5. **Interact with the Chatbot:**
+   - Receive responses based on your input and website context.
+
+## Code Overview:
+
+- **main.py:** Python script containing the main application code.
+- **Functions:**
+    - `getvecstore(url)`: Retrieves a vector store from the given website URL.
+    - `getcontext(vector_store)`: Obtains a context-aware retriever chain.
+    - `getragchain(retriever_chain)`: Retrieves a conversational RAG (Retrieval-Augmented Generation) chain.
+    - `getresp(user_input, website_url)`: Gets a response from the chatbot based on user input and website URL.
+    - `interact(Query, URL, OpenAI_Key)`: Function to interact with the chatbot through Gradio interface.
+
+## Future Improvements:
+
+For future enhancements, I am considering incorporating a complete local and private workflow using the following technologies:
+
+- **Ollama for Embeddings:** Explore the use of Ollama for embeddings to enhance local processing and privacy.
+- **LLM (Language Model):** The use of local LLM inference through LM Studio and Ollama can be added to make it completely private to our system alone.
+
+## Important Note:
+
+- Keep your OpenAI key secure and avoid sharing it publicly.
+- Implement additional security measures for handling sensitive information.
+
+Feel free to explore and contribute to the codebase for further improvements and customization.

main.py

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+# Importing necessary libraries
+import gradio as gr
+import os
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_community.document_loaders import WebBaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+
+# Function to get vector store from a given URL
+def getvecstore(url):
+    loader = WebBaseLoader(url)
+    document = loader.load()
+
+    # Split the document into chunks
+    text_splitter = RecursiveCharacterTextSplitter()
+    document_chunks = text_splitter.split_documents(document)
+
+    # Create a vector store from the chunks
+    vector_store = Chroma.from_documents(document_chunks, OpenAIEmbeddings())
+
+    return vector_store
+
+# Function to get a context-aware retriever chain
+def getcontext(vector_store):
+    llm = ChatOpenAI()
+
+    retriever = vector_store.as_retriever()
+
+    # Define the prompt for context-aware retrieval
+    prompt = ChatPromptTemplate.from_messages([
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+        ("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
+    ])
+
+    retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
+
+    return retriever_chain
+
+# Function to get a conversational RAG (Retrieval-Augmented Generation) chain
+def getragchain(retriever_chain):
+    llm = ChatOpenAI()
+
+    # Define the prompt for conversational RAG
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", "Answer the user's questions based on the below context:\n\n{context}"),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+    ])
+
+    # Create a chain for generating responses based on context
+    stuff_documents_chain = create_stuff_documents_chain(llm, prompt)
+
+    return create_retrieval_chain(retriever_chain, stuff_documents_chain)
+
+# Global variables
+chat_history = [AIMessage(content="I am an assistant made to answer your questions?")]
+vector_store = None
+
+# Function to get response from the chatbot
+def getresp(user_input, website_url):
+    global chat_history, vector_store
+    if vector_store is None:
+        vector_store = getvecstore(website_url)
+
+    retriever_chain = getcontext(vector_store)
+    conversation_rag_chain = getragchain(retriever_chain)
+
+    # Invoke the chain to get the response
+    response = conversation_rag_chain.invoke({
+        "chat_history": chat_history,
+        "input": user_input
+    })
+
+    # Update chat history
+    chat_history.append(HumanMessage(content=user_input))
+    chat_history.append(AIMessage(content=response))
+
+    return response['answer']
+
+# Function to interact with the chatbot through Gradio interface
+def interact(Query, URL, OpenAI_Key):
+    # Set the OpenAI key
+    os.environ['OPENAI_API_KEY'] = OpenAI_Key
+    # Get the response from the chatbot
+    response = getresp(Query, URL)
+    return response
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=interact,
+    inputs=["text", "text", "text"],
+    outputs="text",
+    title="Chat with LLM",
+    description="Interact with a chatbot based on your website URL. Also Provide your OpenAI key as well to get it working."
+)
+
+# Launch the Gradio interface
+iface.launch()

requirements.txt

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+streamlit
+langchain
+langchain-openai
+beautifulsoup4
+python-dotenv
+chromadb