Building RAG Systems
Generative AI
Implement Retrieval-Augmented Generation for accurate and grounded AI applications
75 mins
Overview
- •Understanding Retrieval-Augmented Generation fundamentals
- •Building efficient document processing pipelines
- •Vector database selection and optimization
- •Advanced retrieval techniques and strategies
- •Prompt engineering for effective augmentation
- •Evaluation metrics and continuous improvement
Implementation Scenarios
Document Ingestion Pipeline
Data ProcessingCreating an efficient pipeline for processing and chunking documents
Implementation Steps
- Document loading from multiple sources (PDF, HTML, Markdown, etc.)
- Text extraction and cleaning techniques
- Chunking strategies: size, overlap, and semantic coherence
- Metadata extraction and enrichment
- Handling document updates and versioning
- Parallel processing for large document collections
Code Example
# Example code for document processing pipeline
from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
# Load documents from a directory
loader = DirectoryLoader('./documents/', glob="**/*.pdf", loader_cls=PyPDFLoader)
documents = loader.load()
print(f"Loaded {len(documents)} document pages")
# Text splitting with overlap
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len,
add_start_index=True,
)
chunks = text_splitter.split_documents(documents)
print(f"Split into {len(chunks)} chunks")
# Add metadata to chunks
for i, chunk in enumerate(chunks):
chunk.metadata["chunk_id"] = i
# Extract and add more metadata as needed
if "page" in chunk.metadata:
chunk.metadata["source"] = f"Page {chunk.metadata['page']} from {chunk.metadata['source']}"
Tools & Libraries
LangChainUnstructuredPyPDFBeautiful Soup
Instructor
Nim Hewage
Co-founder
Generative AI specialist with extensive experience building production-ready RAG systems for enterprise applications. Focuses on creating accurate, reliable, and scalable AI solutions that leverage the latest advancements in LLMs.
Related Resources
Tutorial Materials
Additional Learning Resources
LangChain RAG Documentation
Comprehensive guide to implementing RAG with LangChain
View documentation →