Blueprint · intermediate · 10 steps

Build a RAG Pipeline from Scratch

Build a document Q&A system with Python, LlamaIndex, and Qdrant — ingest your docs and query them with natural language.

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STEP 01Step 1: What We're BuildingA document Q&A system that stores your files in Supabase, embeds them with pgvector, and answers questions using Claude — all with just one platform and two API calls.3 min→STEP 02Step 2: Project SetupCreate the project directory, set up a Python virtual environment, install the four dependencies, and configure your API keys.2 min→STEP 03Step 3: Set Up SupabaseCreate a Supabase project, enable the pgvector extension, and create the documents table that will store your text chunks and their embeddings.3 min→STEP 04Step 4: Document LoaderLoad text files from a directory using plain Python and inspect the results — no frameworks needed.3 min→STEP 05Step 5: Chunking and EmbeddingSplit documents into overlapping chunks with a simple Python function, then generate vector embeddings using OpenAI's API directly.3 min→STEP 06Step 6: Store VectorsInsert your embedded chunks into the Supabase documents table and verify they are stored correctly.4 min→STEP 07Step 7: Query EngineBuild the query engine that embeds a question, searches Supabase for relevant chunks, and generates an answer using Claude.3 min→STEP 08Step 8: Test and IterateEvaluate your pipeline with real questions, tune chunk size and retrieval parameters, and improve answer quality systematically.3 min→STEP 09Step 9: What's NextProduction considerations, extensions, and where to go from here with your RAG pipeline.3 min→