chore: updated README

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-  <a href="https://unwindai.substack.com">
+  <a href="http://www.theunwindai.com">
     <img src="docs/banner/unwind.png" width="600px" alt="Unwind AI">
   </a>
 </p>

llama3_local_rag/README.md

@@ -1,34 +0,0 @@
-## 💻 Local Lllama-3 with RAG
-Streamlit app that allows you to chat with any webpage using local Llama-3 and Retrieval Augmented Generation (RAG). This app runs entirely on your computer, making it 100% free and without the need for an internet connection.
-
-
-### Features
-- Input a webpage URL
-- Ask questions about the content of the webpage
-- Get accurate answers using RAG and the Llama-3 model running locally on your computer
-
-### How to get Started?
-
-1. Clone the GitHub repository
-
-```bash
-git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
-```
-2. Install the required dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-3. Run the Streamlit App
-```bash
-streamlit run llama3_local_rag.py
-```
-
-### How it Works?
-
-- The app loads the webpage data using WebBaseLoader and splits it into chunks using RecursiveCharacterTextSplitter.
-- It creates Ollama embeddings and a vector store using Chroma.
-- The app sets up a RAG (Retrieval-Augmented Generation) chain, which retrieves relevant documents based on the user's question.
-- The Llama-3 model is called to generate an answer using the retrieved context.
-- The app displays the answer to the user's question.
-

llama3_local_rag/llama3_local_rag.py

@@ -1,50 +0,0 @@
-import streamlit as st
-import ollama
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_community.vectorstores import Chroma
-from langchain_community.embeddings import OllamaEmbeddings
-
-st.title("Chat with Webpage 🌐")
-st.caption("This app allows you to chat with a webpage using local llama3 and RAG")
-
-# Get the webpage URL from the user
-webpage_url = st.text_input("Enter Webpage URL", type="default")
-
-if webpage_url:
-    # 1. Load the data
-    loader = WebBaseLoader(webpage_url)
-    docs = loader.load()
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)
-    splits = text_splitter.split_documents(docs)
-
-    # 2. Create Ollama embeddings and vector store
-    embeddings = OllamaEmbeddings(model="llama3")
-    vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
-
-    # 3. Call Ollama Llama3 model
-    def ollama_llm(question, context):
-        formatted_prompt = f"Question: {question}\n\nContext: {context}"
-        response = ollama.chat(model='llama3', messages=[{'role': 'user', 'content': formatted_prompt}])
-        return response['message']['content']
-
-    # 4. RAG Setup
-    retriever = vectorstore.as_retriever()
-
-    def combine_docs(docs):
-        return "\n\n".join(doc.page_content for doc in docs)
-
-    def rag_chain(question):
-        retrieved_docs = retriever.invoke(question)
-        formatted_context = combine_docs(retrieved_docs)
-        return ollama_llm(question, formatted_context)
-
-    st.success(f"Loaded {webpage_url} successfully!")
-
-    # Ask a question about the webpage
-    prompt = st.text_input("Ask any question about the webpage")
-
-    # Chat with the webpage
-    if prompt:
-        result = rag_chain(prompt)
-        st.write(result)

llama3_local_rag/requirements.txt

@@ -1,4 +0,0 @@
-streamlit 
-ollama 
-langchain 
-langchain_community