raglite

🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite

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RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.

README:

🥤 RAGLite

RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite.

Features

Configurable

🧠 Choose any LLM provider with LiteLLM, including local llama-cpp-python models
💾 Choose either PostgreSQL or SQLite as a keyword & vector search database
🥇 Choose any reranker with rerankers, including multilingual FlashRank as the default

Fast and permissive

❤️ Only lightweight and permissive open source dependencies (e.g., no PyTorch or LangChain)
🚀 Acceleration with Metal on macOS, and CUDA on Linux and Windows

Unhobbled

📖 PDF to Markdown conversion on top of pdftext and pypdfium2
🧬 Multi-vector chunk embedding with late chunking and contextual chunk headings
✏️ Optimal sentence splitting with wtpsplit-lite by solving a binary integer programming problem
✂️ Optimal semantic chunking by solving a binary integer programming problem
🔍 Hybrid search with the database's native keyword & vector search (tsvector+pgvector, FTS5+sqlite-vec¹)
💭 Adaptive retrieval where the LLM decides whether to and what to retrieve based on the query
💰 Improved cost and latency with a prompt caching-aware message array structure
🍰 Improved output quality with Anthropic's long-context prompt format
🌀 Optimal closed-form linear query adapter by solving an orthogonal Procrustes problem

Extensible

🔌 A built-in Model Context Protocol (MCP) server that any MCP client like Claude desktop can connect with
💬 Optional customizable ChatGPT-like frontend for web, Slack, and Teams with Chainlit
✍️ Optional conversion of any input document to Markdown with Pandoc
✅ Optional evaluation of retrieval and generation performance with Ragas

Installing

[!TIP] 🚀 If you want to use local models, it is recommended to install an accelerated llama-cpp-python precompiled binary with:

# Configure which llama-cpp-python precompiled binary to install (⚠️ not every combination is available):
LLAMA_CPP_PYTHON_VERSION=0.3.4
PYTHON_VERSION=310|311|312
ACCELERATOR=metal|cu121|cu122|cu123|cu124
PLATFORM=macosx_11_0_arm64|linux_x86_64|win_amd64

# Install llama-cpp-python:
pip install "https://github.com/abetlen/llama-cpp-python/releases/download/v$LLAMA_CPP_PYTHON_VERSION-$ACCELERATOR/llama_cpp_python-$LLAMA_CPP_PYTHON_VERSION-cp$PYTHON_VERSION-cp$PYTHON_VERSION-$PLATFORM.whl"

Install RAGLite with:

pip install raglite

To add support for a customizable ChatGPT-like frontend, use the chainlit extra:

pip install raglite[chainlit]

To add support for filetypes other than PDF, use the pandoc extra:

pip install raglite[pandoc]

To add support for evaluation, use the ragas extra:

pip install raglite[ragas]

Using

Overview

Configuring RAGLite
Inserting documents
Retrieval-Augmented Generation (RAG)
Computing and using an optimal query adapter
Evaluation of retrieval and generation
Running a Model Context Protocol (MCP) server
Serving a customizable ChatGPT-like frontend

1. Configuring RAGLite

[!TIP] 🧠 RAGLite extends LiteLLM with support for llama.cpp models using llama-cpp-python. To select a llama.cpp model (e.g., from bartowski's collection), use a model identifier of the form "llama-cpp-python/<hugging_face_repo_id>/<filename>@<n_ctx>", where n_ctx is an optional parameter that specifies the context size of the model.

[!TIP] 💾 You can create a PostgreSQL database in a few clicks at neon.tech.

First, configure RAGLite with your preferred PostgreSQL or SQLite database and any LLM supported by LiteLLM:

from raglite import RAGLiteConfig

# Example 'remote' config with a PostgreSQL database and an OpenAI LLM:
my_config = RAGLiteConfig(
    db_url="postgresql://my_username:my_password@my_host:5432/my_database",
    llm="gpt-4o-mini",  # Or any LLM supported by LiteLLM
    embedder="text-embedding-3-large",  # Or any embedder supported by LiteLLM
)

# Example 'local' config with a SQLite database and a llama.cpp LLM:
my_config = RAGLiteConfig(
    db_url="sqlite:///raglite.db",
    llm="llama-cpp-python/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/*Q4_K_M.gguf@8192",
    embedder="llama-cpp-python/lm-kit/bge-m3-gguf/*F16.gguf@1024",  # A context size of 1024 tokens is the sweet spot for bge-m3
)

You can also configure any reranker supported by rerankers:

from rerankers import Reranker

# Example remote API-based reranker:
my_config = RAGLiteConfig(
    db_url="postgresql://my_username:my_password@my_host:5432/my_database"
    reranker=Reranker("cohere", lang="en", api_key=COHERE_API_KEY)
)

# Example local cross-encoder reranker per language (this is the default):
my_config = RAGLiteConfig(
    db_url="sqlite:///raglite.db",
    reranker=(
        ("en", Reranker("ms-marco-MiniLM-L-12-v2", model_type="flashrank")),  # English
        ("other", Reranker("ms-marco-MultiBERT-L-12", model_type="flashrank")),  # Other languages
    )
)

2. Inserting documents

[!TIP] ✍️ To insert documents other than PDF, install the pandoc extra with pip install raglite[pandoc].

Next, insert some documents into the database. RAGLite will take care of the conversion to Markdown, optimal level 4 semantic chunking, and multi-vector embedding with late chunking:

# Insert a document given its file path
from pathlib import Path
from raglite import insert_document

insert_document(Path("On the Measure of Intelligence.pdf"), config=my_config)
insert_document(Path("Special Relativity.pdf"), config=my_config)

# Insert a document given its Markdown content
markdown_content = """
# ON THE ELECTRODYNAMICS OF MOVING BODIES
## By A. EINSTEIN  June 30, 1905
It is known that Maxwell
"""
insert_document(markdown_content, config=my_config)

3. Retrieval-Augmented Generation (RAG)

3.1 Adaptive RAG

Now you can run an adaptive RAG pipeline that consists of adding the user prompt to the message history and streaming the LLM response:

from raglite import rag

# Create a user message
messages = []  # Or start with an existing message history
messages.append({
    "role": "user",
    "content": "How is intelligence measured?"
})

# Adaptively decide whether to retrieve and then stream the response
chunk_spans = []
stream = rag(messages, on_retrieval=lambda x: chunk_spans.extend(x), config=my_config)
for update in stream:
    print(update, end="")

# Access the documents referenced in the RAG context
documents = [chunk_span.document for chunk_span in chunk_spans]

The LLM will adaptively decide whether to retrieve information based on the complexity of the user prompt. If retrieval is necessary, the LLM generates the search query and RAGLite applies hybrid search and reranking to retrieve the most relevant chunk spans (each of which is a list of consecutive chunks). The retrieval results are sent to the on_retrieval callback and are appended to the message history as a tool output. Finally, the assistant response is streamed and appended to the message history.

3.2 Programmable RAG

If you need manual control over the RAG pipeline, you can run a basic but powerful pipeline that consists of retrieving the most relevant chunk spans with hybrid search and reranking, converting the user prompt to a RAG instruction and appending it to the message history, and finally generating the RAG response:

from raglite import create_rag_instruction, rag, retrieve_rag_context

# Retrieve relevant chunk spans with hybrid search and reranking
user_prompt = "How is intelligence measured?"
chunk_spans = retrieve_rag_context(query=user_prompt, num_chunks=5, config=my_config)

# Append a RAG instruction based on the user prompt and context to the message history
messages = []  # Or start with an existing message history
messages.append(create_rag_instruction(user_prompt=user_prompt, context=chunk_spans))

# Stream the RAG response and append it to the message history
stream = rag(messages, config=my_config)
for update in stream:
    print(update, end="")

# Access the documents referenced in the RAG context
documents = [chunk_span.document for chunk_span in chunk_spans]

[!TIP] 🥇 Reranking can significantly improve the output quality of a RAG application. To add reranking to your application: first search for a larger set of 20 relevant chunks, then rerank them with a rerankers reranker, and finally keep the top 5 chunks.

RAGLite also offers more advanced control over the individual steps of a full RAG pipeline:

Searching for relevant chunks with keyword, vector, or hybrid search
Retrieving the chunks from the database
Reranking the chunks and selecting the top 5 results
Extending the chunks with their neighbors and grouping them into chunk spans
Converting the user prompt to a RAG instruction and appending it to the message history
Streaming an LLM response to the message history
Accessing the cited documents from the chunk spans

A full RAG pipeline is straightforward to implement with RAGLite:

# Search for chunks
from raglite import hybrid_search, keyword_search, vector_search

user_prompt = "How is intelligence measured?"
chunk_ids_vector, _ = vector_search(user_prompt, num_results=20, config=my_config)
chunk_ids_keyword, _ = keyword_search(user_prompt, num_results=20, config=my_config)
chunk_ids_hybrid, _ = hybrid_search(user_prompt, num_results=20, config=my_config)

# Retrieve chunks
from raglite import retrieve_chunks

chunks_hybrid = retrieve_chunks(chunk_ids_hybrid, config=my_config)

# Rerank chunks and keep the top 5 (optional, but recommended)
from raglite import rerank_chunks

chunks_reranked = rerank_chunks(user_prompt, chunks_hybrid, config=my_config)
chunks_reranked = chunks_reranked[:5]

# Extend chunks with their neighbors and group them into chunk spans
from raglite import retrieve_chunk_spans

chunk_spans = retrieve_chunk_spans(chunks_reranked, config=my_config)

# Append a RAG instruction based on the user prompt and context to the message history
from raglite import create_rag_instruction

messages = []  # Or start with an existing message history
messages.append(create_rag_instruction(user_prompt=user_prompt, context=chunk_spans))

# Stream the RAG response and append it to the message history
from raglite import rag

stream = rag(messages, config=my_config)
for update in stream:
    print(update, end="")

# Access the documents referenced in the RAG context
documents = [chunk_span.document for chunk_span in chunk_spans]

4. Computing and using an optimal query adapter

RAGLite can compute and apply an optimal closed-form query adapter to the prompt embedding to improve the output quality of RAG. To benefit from this, first generate a set of evals with insert_evals and then compute and store the optimal query adapter with update_query_adapter:

# Improve RAG with an optimal query adapter
from raglite import insert_evals, update_query_adapter

insert_evals(num_evals=100, config=my_config)
update_query_adapter(config=my_config)  # From here, every vector search will use the query adapter

5. Evaluation of retrieval and generation

If you installed the ragas extra, you can use RAGLite to answer the evals and then evaluate the quality of both the retrieval and generation steps of RAG using Ragas:

# Evaluate retrieval and generation
from raglite import answer_evals, evaluate, insert_evals

insert_evals(num_evals=100, config=my_config)
answered_evals_df = answer_evals(num_evals=10, config=my_config)
evaluation_df = evaluate(answered_evals_df, config=my_config)

6. Running a Model Context Protocol (MCP) server

RAGLite comes with an MCP server implemented with FastMCP that exposes a search_knowledge_base tool. To use the server:

Install Claude desktop
Install uv so that Claude desktop can start the server
Configure Claude desktop to use uv to start the MCP server with:

raglite \
    --db-url sqlite:///raglite.db \
    --llm llama-cpp-python/bartowski/Llama-3.2-3B-Instruct-GGUF/*Q4_K_M.gguf@4096 \
    --embedder llama-cpp-python/lm-kit/bge-m3-gguf/*F16.gguf@1024 \
    mcp install

To use an API-based LLM, make sure to include your credentials in a .env file or supply them inline:

export OPENAI_API_KEY=sk-...
raglite \
    --llm gpt-4o-mini \
    --embedder text-embedding-3-large \
    mcp install

Now, when you start Claude desktop you should see a 🔨 icon at the bottom right of your prompt indicating that the Claude has successfully connected with the MCP server.

When relevant, Claude will suggest to use the search_knowledge_base tool that the MCP server provides. You can also explicitly ask Claude to search the knowledge base if you want to be certain that it does.

7. Serving a customizable ChatGPT-like frontend

If you installed the chainlit extra, you can serve a customizable ChatGPT-like frontend with:

raglite chainlit

The application is also deployable to web, Slack, and Teams.

You can specify the database URL, LLM, and embedder directly in the Chainlit frontend, or with the CLI as follows:

raglite \
    --db-url sqlite:///raglite.db \
    --llm llama-cpp-python/bartowski/Llama-3.2-3B-Instruct-GGUF/*Q4_K_M.gguf@4096 \
    --embedder llama-cpp-python/lm-kit/bge-m3-gguf/*F16.gguf@1024 \
    chainlit

To use an API-based LLM, make sure to include your credentials in a .env file or supply them inline:

OPENAI_API_KEY=sk-... raglite --llm gpt-4o-mini --embedder text-embedding-3-large chainlit

Contributing

Prerequisites

Generate an SSH key and add the SSH key to your GitHub account.

Configure SSH to automatically load your SSH keys:

cat << EOF >> ~/.ssh/config

Host *
  AddKeysToAgent yes
  IgnoreUnknown UseKeychain
  UseKeychain yes
  ForwardAgent yes
EOF

Install Docker Desktop.
Install VS Code and VS Code's Dev Containers extension. Alternatively, install PyCharm.
Optional: install a Nerd Font such as FiraCode Nerd Font and configure VS Code or PyCharm to use it.

Development environments

The following development environments are supported:

⭐️ GitHub Codespaces: click on Open in GitHub Codespaces to start developing in your browser.
⭐️ VS Code Dev Container (with container volume): click on Open in Dev Containers to clone this repository in a container volume and create a Dev Container with VS Code.

⭐️ uv: clone this repository and run the following from root of the repository:

# Create and install a virtual environment
uv sync --python 3.10 --all-extras

# Activate the virtual environment
source .venv/bin/activate

# Install the pre-commit hooks
pre-commit install --install-hooks

VS Code Dev Container: clone this repository, open it with VS Code, and run Ctrl/⌘ + ⇧ + P → Dev Containers: Reopen in Container.
PyCharm Dev Container: clone this repository, open it with PyCharm, create a Dev Container with Mount Sources, and configure an existing Python interpreter at /opt/venv/bin/python.

Developing

This project follows the Conventional Commits standard to automate Semantic Versioning and Keep A Changelog with Commitizen.
Run poe from within the development environment to print a list of Poe the Poet tasks available to run on this project.
Run uv add {package} from within the development environment to install a run time dependency and add it to pyproject.toml and uv.lock. Add --dev to install a development dependency.
Run uv sync --upgrade from within the development environment to upgrade all dependencies to the latest versions allowed by pyproject.toml. Add --only-dev to upgrade the development dependencies only.
Run cz bump to bump the package's version, update the CHANGELOG.md, and create a git tag. Then push the changes and the git tag with git push origin main --tags.

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We use PyNNDescent until sqlite-vec is more mature. ↩

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kaito

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

github

: 405

PyRIT

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

github

: 2.3k

tabby

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

github

: 30.6k

spear

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

github

: 224

Magick

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

github

: 675