Building a RAG application in Python

Set up a basic python application that connects to an llm via the OpenAI sdk, creates a virtual environment, and builds a structured project with a command line interface.

Configure a config-driven LLM client with OpenAI SDK, define message roles (system, user, assistant), and enable streaming responses via a chat client and from_config.

Set up a Python command line interface for a RAG app using Typer, load config, and a chat client, then run a streaming chat loop with reset and exit.

Pull a model from Ollama and configure the .on file, then start ollama serve to test a local llm; outline the rag workflow with a retriever, vector store, and rewriter.

Set up postgres with the pgvector extension via docker-compose and initialize the database using the provided init.sql to create documents and chunks for vector storage in Python.

Implement a Postgres vector store by creating postgres.py, establishing a lazy, single connection, and upserting documents with chunks inside a transactional, rollback-capable process.

Define and wire a vector store by implementing the abstract VectorStore interface and a Postgres backend, then create a factory to instantiate the configured store via pg_dsn.

Learn to upsert a document into a Postgres store from Python, including creating the stores package, a root demo.txt, and verifying data in a Postgres client.

Ingest on-disk documents by splitting into chunks with vectors and feed them to the vector store, while configuring embed model, embed dim, and chunk parameters.

Chunk large documents for a RAG store by splitting text into around 1,000 characters per chunk, paragraph-aware chunks with overlap, using a Python chunking module before embedding.

Set up the ingestor for the RAG application, building the input pipeline to hash content for idempotence, chunk text, embed in batches, and upsert chunks to a Postgres vector store.

Wire up the cli for the ingestor by configuring logging and building with config, embedder, and store, then expose ingest and scan commands to process files and the documents dir.

Test the ingestion pipeline end-to-end by fixing typos, preparing documents, running pyrag ingest, populating a Postgres-backed vector store with 75 chunks and embeddings, and preparing for retrieval implementation.

Implement search in the Postgres vector store using HNSW when beneficial and cosine distance to rank results. Retrieve source paths, chunk indices, content, and metadata, returning the top matching chunks.

Test retrieval in a python rag app by running queries in the interpreter, using a vector store and embedder to fetch top hits via dense search.

Configure a default system prompt to make the assistant an expert in mythological creatures and to use vector store documents, avoid inventing facts, and allow file-based overrides.

Learn to implement a stateless ask command in a Python RAG app, wiring config, top-k retrieval, and end-to-end vector store querying for one-shot use.

Explore implementing a custom external system prompt by loading from prompts/system-prompt.txt and using the system_prompt_file environment variable to override the default prompt in pyrag with vector store support.

Modify the chat command to use rag by querying the vector store and incorporating retrieved context into the response, with configurable top k and dynamic system prompts.

Improve the spinner by silencing log noise and implementing a terminal clear, by setting httpx and httpCore log levels to warning and clearing the line after thinking.

Set up a watchdog-based file watcher to auto-ingest files into the vector store and move ingested files into the process directory with a timestamp to prevent overwrites.

Implement a per-path debouncing handler that waits 750 milliseconds after file system events—created, moved, or modified—before ingesting files.

Set up a watcher that uses a watchdog observer and a debounced handler to monitor the documents directory non-recursively, perform initial scan, ensure directories exist, and stop cleanly on interruption.

add the watch command to the cli to monitor ingester activity, set up logging, manage the ingester lifecycle, and ensure the database connection closes after use.

Combine dense vector search with lexical ranking to create a hybrid retrieval for a Python rag app in Postgres, using reciprocal rank fusion and content TSV.

Improve rag retrieval by replacing pure dense search with hybrid retrieval that combines dense vector semantic ranking and lexical content tsv ranking, fused via reciprocal rank fusion in postgres.py.

Extend the vector store contract to support metadata by adding a metadata dict to upsert_document and adding document_metadata and chunks_metadata fields, updating base.py and postgres.py.

Update the search method to return document metadata and ingested at by selecting documents.metadata as document_metadata and including it in each search hit, without changing semantic, lexical, or fusion logic.

Update ingest logic to populate document and chunk metadata by extending upsertDocument with suffix, kind, and type text, then ingest and verify metadata in Postgres and the vector store.

Define upsertDocument in the Weave 8 store, delete old chunks, batch-insert new chunks with metadata and vector embedding, and ensure atomicity with error handling.

Implement delete document by removing all chunks for a given source path using deleteMany with a where clause on the source path. Proceed to the upcoming search method.