https://github.com/md-emon-hasan/retrieval-augmented-generation-rag
RAG enhances LLMs by retrieving relevant external knowledge before generating responses, improving accuracy and reducing hallucinations.
https://github.com/md-emon-hasan/retrieval-augmented-generation-rag
ai-chatbot chromadb custom-llm document-retrieval embedding-models faiss huggingface-rag knowledge-augmented-llm knowledge-graph langchain-rag llm-applications llm-retrieval multi-modal-rag prompt-engineering rag-pipeline retrieval-augmented-generation retrieval-qa semantic-search text-embedding vector-search
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RAG enhances LLMs by retrieving relevant external knowledge before generating responses, improving accuracy and reducing hallucinations.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/retrieval-augmented-generation-rag
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2025-02-14T14:58:40.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-02-14T15:13:20.000Z (4 months ago)
- Last Synced: 2025-02-14T16:22:36.500Z (4 months ago)
- Topics: ai-chatbot, chromadb, custom-llm, document-retrieval, embedding-models, faiss, huggingface-rag, knowledge-augmented-llm, knowledge-graph, langchain-rag, llm-applications, llm-retrieval, multi-modal-rag, prompt-engineering, rag-pipeline, retrieval-augmented-generation, retrieval-qa, semantic-search, text-embedding, vector-search
- Language: Jupyter Notebook
- Homepage:
- Size: 569 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Retrieval-Augmented Generation (RAG)
## Overview
Retrieval-Augmented Generation (RAG) is an advanced approach that enhances large language models (LLMs) by retrieving relevant information from external knowledge sources before generating responses. This technique improves factual accuracy, reduces hallucinations, and enables dynamic knowledge updates without retraining the model.
## Key Components
1. **Retriever**: Searches and fetches relevant documents from a knowledge base.
2. **Embedder**: Converts text into dense vector representations for efficient retrieval.
3. **Vector Store**: Stores and indexes embeddings for fast similarity search.
4. **Generator (LLM)**: Generates responses based on retrieved documents.
5. **Pipeline**: Integrates all components to process user queries efficiently.
## Implementation Steps
### 1. Environment Setup
```bash
pip install langchain transformers faiss-cpu sentence-transformers chromadb
```
### 2. Load and Preprocess Documents
```python
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Load documents
doc_loader = TextLoader("data/documents.txt")
documents = doc_loader.load()
# Split text into smaller chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
document_chunks = text_splitter.split_documents(documents)
```
### 3. Create Embeddings and Store in Vector Database
```python
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
# Initialize embedding model
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# Store embeddings in FAISS vector database
vector_store = FAISS.from_documents(document_chunks, embedding_model)
```
### 4. Build the RAG Pipeline
```python
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.llms import HuggingFacePipeline
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
# Load LLM
model_name = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Create pipeline for text generation
hf_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
llm = HuggingFacePipeline(pipeline=hf_pipeline, max_new_tokens=150)
# Initialize retrieval-based QA system
qa_chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vector_store.as_retriever())
```
### 5. Query the RAG System
```python
query = "What is Retrieval-Augmented Generation?"
response = qa_chain({"question": query}, return_only_outputs=True)
print("Answer:", response['answer'])
```
## Deployment Considerations
- **Scalability**: Use **Weaviate, Pinecone, or ChromaDB** for large-scale vector storage.
- **Latency Optimization**: Use optimized embedding models like **BGE-M3** or **FAISS-HNSW**.
- **Fine-Tuning**: Adapt the LLM to domain-specific knowledge.
- **API Integration**: Deploy using **FastAPI** or **Flask** for production use.
## Conclusion
Retrieval-Augmented Generation significantly improves LLM performance by incorporating external knowledge retrieval. Implementing RAG with LangChain and Hugging Face provides a powerful framework for knowledge-grounded AI applications.
---
### Contact Information
- **Email:** [[email protected]](mailto:[email protected])
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)