rag-pipeline

Implement document chunking strategies (fixed, semantic, recursive, hybrid)

Configure embedding models (OpenAI, HuggingFace, Cohere, Voyage)

Integrate Google File Search API for managed RAG with Gemini - handles store creation, file uploads, chunking, and citations

Implement hybrid search (vector + keyword with RRF)

Add metadata filtering and multi-tenant support

Add observability (LangSmith/LlamaCloud integration)

Add document parsers (LlamaParse, Unstructured, PyPDF, PDFPlumber)

Add web scraping capability (Playwright, Selenium, BeautifulSoup, Scrapy)

Configure vector database (pgvector, Chroma, Pinecone, Weaviate, Qdrant, FAISS)

Build RAG generation pipeline with streaming support

Build document ingestion pipeline (load, parse, chunk, embed, store)

Build retrieval pipeline (simple, hybrid, rerank)

Deploy RAG application to production platforms

Initialize RAG project with framework selection (LlamaIndex/LangChain)

Optimize RAG performance and reduce costs

Run comprehensive RAG pipeline tests