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Top 23 Python rag Projects
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txtai
đź’ˇ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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ragflow
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
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swirl-search
Swirl is an open-source search platform that uses AI to search multiple content and data sources simultaneously and return AI-ranked results. And provides summaries of your answers from searches using LLMs. It's a one-click, easy-to-use Retrieval Augmented Generation (RAG) Solution.
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GenerativeAIExamples
Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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raptor
The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval (by parthsarthi03)
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cognita
Cognita by TrueFoundry - Framework for building modular, open source RAG applications for production.
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tonic_validate
Metrics to evaluate the quality of responses of your Retrieval Augmented Generation (RAG) applications.
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open-assistant-api
The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework, supporting customized extensions for LLM, RAG, function call, and tools capabilities. It also supports seamless integration with the openai/langchain sdk.
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enterprise-h2ogpte
Client Code Examples, Use Cases and Benchmarks for Enterprise h2oGPTe RAG-Based GenAI Platform
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NeoGPT
Your Local AI Assistant: Seamlessly Chat, Execute Commands, and Interpret Code with Local Models for Ultimate Privacy.
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vectorboard
Open Source Embeddings Optimisation and Eval Framework for RAG/LLM Applications. Documentations at https://docs.vectorboard.ai/introduction
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Project mention: LlamaIndex: A data framework for your LLM applications | news.ycombinator.com | 2024-04-07
Project mention: What contributing to Open-source is, and what it isn't | news.ycombinator.com | 2024-04-27I tend to agree with this sentiment. Many junior devs and/or those in college want to contribute. Then they feel entitled to merge a PR that they worked hard on often without guidance. I'm all for working with people but projects have standards and not all ideas make sense. In many cases, especially with commercial open source, the project is the base of a companies identity. So it's not just for drive-by ideas to pad a resume or finish a school project.
For those who do want to do this, I'd recommend writing an issue and/or reaching out to the developers to engage in a dialogue. This takes work but it will increase the likelihood of a PR being merged.
Disclaimer: I'm the primary developer of txtai (https://github.com/neuml/txtai), an open-source vector database + RAG framework
Project mention: RAGFlow is an open-source RAG engine based on deep document understanding | news.ycombinator.com | 2024-04-01Just link them to https://github.com/infiniflow/ragflow/blob/main/rag/llm/chat... :)
Project mention: GitHub - swirlai/swirl-search: Swirl is an open-source search platform that uses AI to search multiple content and data sources simultaneously, finds the best results using a reader LLM, then prompts Generative AI, enabling you to get answers based on your data. | /r/programming | 2023-12-05
Pinecone: A scalable vector database service that facilitates efficient similarity search in high-dimensional spaces. Ideal for building real-time applications in AI, such as personalized recommendation engines and content-based retrieval systems.
Project mention: FastLLM by Qdrant – lightweight LLM tailored For RAG | news.ycombinator.com | 2024-04-01
Project mention: Raptor: Recursive Abstractive Processing for Tree-Organized Retrieval | news.ycombinator.com | 2024-03-06
Project mention: Show HN: GPT-Powered Video Retrieval and Streaming | news.ycombinator.com | 2024-02-08
Project mention: Show HN: Cognita – open-source RAG framework for modular applications | news.ycombinator.com | 2024-04-27
Project mention: Launch HN: Relari (YC W24) – Identify the root cause of problems in LLM apps | news.ycombinator.com | 2024-03-08Hi HN, we are the founders of Relari, the company behind continuous-eval (https://github.com/relari-ai/continuous-eval), an evaluation framework that lets you test your GenAI systems at the component level, pinpointing issues where they originate.
We experienced the need for this when we were building a copilot for bankers. Our RAG pipeline blew up in complexity as we added components: a query classifier (to triage user intent), multiple retrievers (to grab information from different sources), filtering LLM (to rerank / compress context), a calculator agent (to call financial functions) and finally the synthesizer LLM that gives the answer. Ensuring reliability became more difficult with each of these we added.
When a bad response was detected by our answer evaluator, we had to backtrack multiple steps to understand which component(s) made a mistake. But this quickly became unscalable beyond a few samples.
I did my Ph.D. in fault detection for autonomous vehicles, and I see a strong parallel between the complexity of autonomous driving software and today's LLM pipelines. In self-driving systems, sensors, perception, prediction, planning, and control modules are all chained together. To ensure system-level safety, we use granular metrics to measure the performance of each module individually. When the vehicle makes an unexpected decision, we use these metrics to pinpoint the problem to a specific component. Only then we can make targeted improvements, systematically.
Based on this thinking, we developed the first version of continuous-eval for ourselves. Since then we’ve made it more flexible to fit various types of GenAI pipelines. Continuous-eval allows you to describe (programmatically) your pipeline and modules, and select metrics for each module. We developed 30+ metrics to cover retrieval, text generation, code generation, classification, agent tool use, etc. We now have a number of companies using us to test complex pipelines like finance copilots, enterprise search, coding agents, etc.
As an example, one customer was trying to understand why their RAG system did poorly on trend analysis queries. Through continuous-eval, they realized that the “retriever” component was retrieving 80%+ of all relevant chunks, but the “reranker” component, that filters out “irrelevant” context, was dropping that to below 50%. This enabled them to fix the problem, in their case by skipping the reranker for certain queries.
We’ve also built ensemble metrics that do a surprisingly good job of predicting user feedback. Users often rate LLM-generated answers by giving a thumbs up/down about how good the answer was. We train our custom metrics on this user data, and then use those metrics to generate thumbs up/down ratings on future LLM answers. The results turn out to be 90% aligned with what the users say. This gives developers a feedback loop from production data to offline testing and development. Some customers have found this to be our most unique advantage.
Lastly, to make the most out of evaluation, you should use a diverse dataset—ideally with ground truth labels for comprehensive and consistent assessment. Because ground truth labels are costly and time-consuming to curate manually, we also have a synthetic data generation pipeline that allows you to get started quickly. Try it here (https://www.relari.ai/#synthetic_data_demo)
What’s been your experience testing and iterating LLM apps? Please let us know your thoughts and feedback on our approaches (modular framework, leveraging user feedback, testing with synthetic data).
Project mention: Instrukt: a TUI AI assistant to explore and understand any complex code base. | /r/programming | 2023-09-07
Project mention: Validating the RAG Performance of Amazon Titan vs. Cohere Using Amazon Bedrock | news.ycombinator.com | 2024-02-09I tried out Amazon Bedrock, and used Tonic Validate to do a head to head comparison of very simple RAG system's built using embedding and text models available in Amazon Bedrock. I compared Amazon Titan's embedding and text models to Cohere's embedding and text models in RAG systems that employ Amazon Bedrock Knowledge Bases as the vector db and retrieval components of the system.
The code for the comparison is in this jupyter notebook https://github.com/TonicAI/tonic_validate/blob/main/examples...
Let me know what you think, And your experiences building RAG with Amazon Bedrock!
git clone https://github.com/Quansight/ragna.git cd ragna pip install 'ragna[all]'
One of the most interesting projects I came across this month was NeoGPT. It's a GPT based application that is being built to converse with documents and videos. While still in its infancy, the project has outlined a cool roadmap and has a very active base of contributors continuously expanding on its functionality. The project appeals to my desire to learn how to work with AI and neural networks. It is also at a development stage that it is not outside of the reach of my comprehension. Icing on the cake being it's Py based, which is my sharpest tool at the moment. I see it as a decent project to stay tapped into and grow my skills as the application develops.
Project mention: Exploring Methods to Improve Text Chunking in RAG Models (and other things...) | /r/GPT3 | 2023-10-22Hi, about chunking, if the text is structured (markdown or html), you can take headding and paragraph as a chunking unit, but the result is also affected by the applied embeddings, which can be evaluated separately first, for standard chunking methods with different chunk lengths, for example with this tool https://github.com/VectorBoard/vectorboard.
We'll use Vercel's Generative UI library to render the chat window and Dewy to implement the information storage and retrieval used by our tool and Dewy as the knowledge store backing the tool.
Project mention: Wouldn't it be cool to have a Supabase for SQLite? | news.ycombinator.com | 2024-04-25Not directly an answer, but similar what if thinking got me wondering: wouldn't it be cool to be able to use postgres much like how you use sqlite with python? I implemented this idea as a pip-installable python package, https://github.com/orm011/pgserver, and your feedback would be great :) I use it for my projects.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python rag related posts
- Show HN: R2R – Open-source framework for production-grade RAG
- Show HN: GPT-Powered Video Retrieval and Streaming
- Using Vector Embeddings to Overengineer 404 pages
- Pinecone: Build Knowledgeable AI
- Show HN: Talk to any ArXiv paper just by changing the URL
- Open source alternative to ChatGPT and ChatPDF-like AI tools
- Canopy is an open-source Retrieval Augmented Generation (RAG) framework
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A note from our sponsor - SaaSHub
www.saashub.com | 27 Apr 2024
Index
What are some of the best open-source rag projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | llama_index | 30,910 |
| 2 | txtai | 6,990 |
| 3 | ragflow | 5,516 |
| 4 | TaskingAI | 4,233 |
| 5 | swirl-search | 1,509 |
| 6 | GenerativeAIExamples | 1,502 |
| 7 | canopy | 873 |
| 8 | fastembed | 781 |
| 9 | llama_parse | 758 |
| 10 | raptor | 424 |
| 11 | StreamRAG | 392 |
| 12 | cognita | 349 |
| 13 | continuous-eval | 302 |
| 14 | txtchat | 221 |
| 15 | Instrukt | 214 |
| 16 | tonic_validate | 199 |
| 17 | ragna | 160 |
| 18 | open-assistant-api | 148 |
| 19 | enterprise-h2ogpte | 63 |
| 20 | NeoGPT | 55 |
| 21 | vectorboard | 42 |
| 22 | dewy | 34 |
| 23 | pgserver | 34 |
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