Show HN: Raggy – A lightweight CLI tool for RAG over local documents

A lightweight CLI tool for Retrieval-Augmented Generation (RAG) over local documents built with LangChain, Chroma, and Ollama. Hybrid database (vector + BM25 index) and embedding generation run fully locally. Answer generation can run either via a local LLM or remotely using an API key. raggy supports most common document formats and handles images/scans automatically via OCR.

Usage example -- CLI returns an answer based on your documents, and citations along with their locations and relevance scores:

These are all currently supported file formats (all other formats are ignored):

Ollama is required for running the local embedding model (which feeds the on-disk vector DB), and also a local LLM (if needed). To install Ollama:

curl -fsSL https://ollama.com/install.sh | sh

ollama
ollama serve

If an API key will be used for accessing an LLM remotely, a standard environment variable needs to be set (one of the following):

export GEMINI_API_KEY=...
export OPENAI_API_KEY=...
export ANTHROPIC_API_KEY=...

Python 3.10 or newer is required; installing uv is recommended:

curl -LsSf https://astral.sh/uv/install.sh | sh

Clone the repo:

git clone https://github.com/paulknysh/raggy.git && cd raggy

Then install using:

uv tool install -e .

pipx install -e .

For now, cloning + editable install is picked as a preferred installation method, as it allows you to experiment with the demo dataset, run the eval harness, and edit/debug code if needed. In the future, direct install via uv tool install git+https .../ pipx install git+https ... will be used instead.

First, run this command inside the cloned repo:

make config

It creates your own user config (config.yaml) where all your execution parameters live. While config.yaml comes with defaults you can test, you should populate sources (your input folders/files) and db_directory (DB location) sections with your preferred paths. For a detailed overview of all config parameters, see Configuration.

To start the CLI, use the raggy <path-to-config-file> command:

raggy config.yaml

Important

CLI automatically pulls all models listed in config.yaml and (re-)indexes your documents -- this might take a while on the first run, depending on models chosen, document count/size, and whether OCR is needed (scans, images, etc).

Important

Relative paths in config.yaml resolve against the current directory (from where raggy command is executed). Keep that in mind if you want to run raggy from other locations. To be safe, just always use absolute paths in your config file.

Here is the basic snippet you can run via uv run snippet.py:

from raggy import run_pipeline, source_label

query = "What is TS-RAG?"

response, retrieved_docs = run_pipeline(query, config_path="config.yaml")

print(f"\n*** RESPONSE:\n\n{response}\n\n***")

for i, doc in enumerate(retrieved_docs, 1):
    print(f"\n\n=== Doc {i} [{source_label(doc)}] ===\n\n")
    print(doc.page_content)

All runtime settings are defined in the config file:

Notes:

The current default config parameters were tested on a basic MacBook Air with 8GB RAM. Switching to much heavier local models likely needs appropriate GPU/memory. #

Chroma doesn't seem to be able to embed all chunks in one go; therefore, embed_batch_size was introduced so it's done in batches instead. 100 seems like a reasonable default, but if you get Chroma errors during embedding (such asError: Post "http://127.0.0.1:50175/tokenize": EOF (status code: 400) ), try loweringembed_batch_size further.

Below are the main steps in the RAG pipeline (assuming the DB is already created):

[1] Hybrid retrieval. retrieve_k is a total candidate budget, split by hybrid_alpha between two retrievals over the whole corpus:

• dense retrieval -- nearest chunks in Chroma by embedding similarity (good at paraphrase and synonyms);
• lexical retrieval -- the persistedbm25s index (good at exact terms: identifiers, names, acronyms, numbers).

So retrieve_k: 50 with hybrid_alpha: 0.5 takes 25 chunks from each arm. The two ranked lists are merged by reciprocal rank fusion, which collapses duplicates and needs no score calibration between the two very different scales. Fusion weights are uniform on purpose: hybrid_alpha already sets each arm's influence by deciding how many candidates it contributes.

[2] Cross-encoder reranking. Stage 1 favors recall and is noisy. The reranker (rerank_model, run locally on onnxruntime) pushes the query and the chunk through the model together and emits one relevance score per pair -- far sharper than cosine distance between separately embedded texts, and affordable on ~50 chunks though not on the whole corpus. The top rerank_k chunks survive.

[3] Score threshold. Each chunk carries its reranker score in doc.metadata["relevance_score"], and anything below rerank_threshold is dropped. This keeps rerank_k from polluting the context when the corpus has no good answer; 0.0 disables it.

[4] Generation. The survivors are concatenated into {context} in system_prompt and sent to the configured LLM, along with the chat history (in chat mode) and the question. The exact chunks the model saw are returned to the caller -- retrieved_docs above, and the source table the CLI prints.

DB creates/updates itself automatically, so you don't need to think about it. Below is just a high-level overview of the mechanics.

On the first run, initialize_db (in raggy/indexing.py) loads every supported file under each entry in sources, splits them into overlapping chunks, and embeds them into Chroma. It also writes a manifest.yaml into the persist directory recording these parameters:

• sources
• chunk_size
• chunk_overlap
• embedding_model
• files — a{file path: SHA-256 content hash} map of every indexed file

The same chunks are also indexed with bm25s (a lexical BM25 index), stored in <db_directory>/bm25_index/, so the lexical half of hybrid retrieval runs without re-indexing at retrieval time.

The manifest.yaml is cheap to recompute, so for every new run it is computed and compared against the existing one. What happens next depends on what changed:

• Incremental update (the common case) — the source files changed butchunk_size ,chunk_overlap , andembedding_model did not. In this case, only added and modified files are reloaded and embedded. Untouched files are never re-embedded, so editing one file in a large corpus costs one file's worth of work.
• Full rebuild —chunk_size ,chunk_overlap , orembedding_model changed (every stored vector is then invalid), or there is no manifest yet. The persist directory is wiped, and everything is indexed from scratch.

The BM25 index has no incremental update path, so it is rebuilt after every update — from the chunks already stored in Chroma, which needs no embedding calls and no re-reading of source files. This should be very fast anyway.

This article (https://arxiv.org/abs/2608.06223v1) is used here as a demo dataset. It's an 8-page document -- each page is saved in different file formats (including PDF, plaintext, images, MS Office) and saved inside the sample_docs directory. This directory is specified in config.yaml by default.

eval folder currently contains a basic harness to test pipeline performance on the demo dataset. You can run it by:

uv run eval/run_eval.py

It computes basic retrieval/generation metrics and produces a summary (both printed and saved to eval/results.json). The Q&A pairs are about sample_docs, so the harness always runs against default_config/default_config.yaml rather than your own config.yaml.

If some features are not working or missing, feel free to open an issue or a PR.

MIT