Listen to this Post
Source:
Shantanu Ladhwe demonstrates how to build a fully local RAG (Retrieval-Augmented Generation) system without cloud dependencies. The system leverages hybrid retrieval (BM25 + semantic search) for efficient document processing and question-answering.
Key Components
1. Streamlit App
- Simple UI for file uploads, queries, and responses.
pip install streamlit streamlit run app.py
2. OCR (PyTesseract)
- Converts PDFs/images to text.
sudo apt install tesseract-ocr Linux pip install pytesseract pillow
3. Ingestion Pipeline
- Cleans, chunks, and enriches text with embeddings.
from langchain.text_splitter import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
4. Vector Store (OpenSearch)
- Stores embeddings for hybrid search.
docker run -p 9200:9200 -e "discovery.type=single-node" opensearchproject/opensearch
5. Hybrid Search (BM25 + Semantic)
- Combines keyword and vector search.
from opensearchpy import OpenSearch client = OpenSearch("http://localhost:9200")
6. Prompt Template
- Structures LLM inputs for better responses.
template = """Answer based on context: {context}\nQuestion: {question}"""
7. Local LLM (Ollama)
- Runs models like LLaMA, Mistral locally.
ollama pull llama3 ollama run llama3
You Should Know:
1. Setting Up OpenSearch for Hybrid Search
Install & run OpenSearch docker pull opensearchproject/opensearch docker run -d -p 9200:9200 -e "discovery.type=single-node" opensearchproject/opensearch
Verify:
curl -X GET "http://localhost:9200"
2. Running Ollama for Local LLM
Install Ollama curl -fsSL https://ollama.com/install.sh | sh Run a model ollama pull mistral ollama run mistral
3. Hybrid Search Query Example
from opensearchpy import OpenSearch
client = OpenSearch("http://localhost:9200")
query = {
"query": {
"hybrid": {
"queries": [
{"match": {"text": "RAG systems"}}, BM25
{"knn": {"embedding": {"vector": [0.1, 0.2, ...], "k": 5}}} Semantic
]
}
}
}
response = client.search(index="documents", body=query)
4. Streamlit UI for RAG
import streamlit as st
st.title("Local RAG System")
uploaded_file = st.file_uploader("Upload a PDF")
if uploaded_file:
text = extract_text(uploaded_file)
st.write("Extracted Text:", text[:500])
What Undercode Say:
Hybrid RAG systems offer cost efficiency, privacy, and control by avoiding cloud dependencies. Key takeaways:
– Start small (local LLMs + OpenSearch).
– Optimize retrieval (BM25 + embeddings).
– Experiment with models (Mistral, LLaMA, Phi).
Expected Output:
A fully functional RAG system running locally with hybrid search, OCR support, and a Streamlit UI.
Prediction:
Hybrid RAG will dominate enterprise AI due to its balance of accuracy and cost, reducing reliance on expensive agentic frameworks.
References:
Reported By: Shantanuladhwe 90 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
