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Top 23 Python rag Projects
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chatgpt-on-wechat
基于大模型搭建的聊天机器人，同时支持 微信 公众号、企业微信应用、飞书、钉钉 等接入，可选择GPT3.5/GPT-4o/GPT4.0/ Claude/文心一言/讯飞星火/通义千问/ Gemini/GLM-4/Claude/Kimi/LinkAI，能处理文本、语音和图片，访问操作系统和互联网，支持基于自有知识库进行定制企业智能客服。
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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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txtai
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
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GenerativeAIExamples
Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
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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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SaaSHub
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cognita
RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
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raptor
The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
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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
Chat effortlessly, execute commands, and interpret code with Llama3, Phi3, and more - your local AI assistant. Enjoy seamless interaction while ensuring ultimate privacy
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SaaSHub
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Project mention: LlamaIndex: A data framework for your LLM applications | news.ycombinator.com | 2024-04-07
Project mention: DeepSeek-V2 integrated, RAGFlow v0.5.0 is released | news.ycombinator.com | 2024-05-07
Project mention: Show HN: FileKitty – Combine and label text files for LLM prompt contexts | news.ycombinator.com | 2024-05-01
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
Project mention: Adding an Amazon Bedrock Knowledge Base to the Forex Rate Assistant | dev.to | 2024-05-16It's fair to think that undesirable artifacts and lack of structural context would impact search accuracy, performance, and ultimately cost. Consequently, it makes sense to perform some data pre-processing before passing the source documents to the RAG workflow. Third-party APIs and tools, such as LlamaParse and LayoutPDFReader, can help with pre-processing PDF data, however keep in mind that source documents may take any forms and there is no one-size-fits-all solution. You may have to resort to developing custom processes for pre-processing and search your unique data.
To create a PineCone account, sign up via this link: https://www.pinecone.io/
Project mention: FastLLM by Qdrant – lightweight LLM tailored For RAG | news.ycombinator.com | 2024-04-01
Project mention: Show HN: A phone number to text with questions about current events | news.ycombinator.com | 2024-05-10Hi HN! For my senior thesis in CS, I built an SMS-based application to make journalism more accessible. It works like this:
1) You text the topics you're interested in to my phone number. Every day, you'll receive a text with 5 headlines from The Associated Press (https://apnews.com/) related to those topics.
2) If you have questions about any of the current events the headlines describe, you just text them back. A response is generated from the contents of the articles using the RAPTOR retrieval framework (https://github.com/parthsarthi03/raptor) and texted right back to you.
The repo can be found here: https://github.com/tdh15/pressText
I'd really appreciate any and all feedback. Whatever you got, I'd love to hear it :)
Project mention: Show HN: GPT-Powered Video Retrieval and Streaming | news.ycombinator.com | 2024-02-08
Project mention: Show HN: Ellipsis – Automated PR reviews and bug fixes | news.ycombinator.com | 2024-05-09Hi HN, hunterbrooks and nbrad here from Ellipsis (https://www.ellipsis.dev). Ellipsis automatically reviews your PRs when opened and on each new commit. If you tag @ellipsis-dev in a comment, it can make changes to the PR (via direct commit or side PR) and answer questions, just like a human.
Demo video: https://www.youtube.com/watch?v=X61NGZpaNQA
So far, we have dozens of open source projects and companies using Ellipsis. We seem to have landed in a kind of sweet spot where there’s a good match between the current capabilities of AI tools and the actual needs of software engineers - this doesn’t replace human review, but it saves you time by catching/fixing lots of small silly stuff.
Here’s an example in the wild: https://github.com/relari-ai/continuous-eval/pull/38, where Ellipsis (1) adds a PR summary; (2) finds a bug and adds a review comment; (3) after a [human] user comments, generates a side PR with the fix; and (4) after a (human) user merges the side PR and adds another commit, re-reviews the PR and approves it
Here’s another example: https://github.com/SciPhi-AI/R2R/pull/350#pullrequestreview-..., where Ellipsis adds several comments with inline suggestions that were directly merged by the developer.
You can configure Ellipsis in natural language to enforce custom rules, style guides, or conventions. For example, here’s how the `jxnl/instructor` repo uses natural language rules to make sure that docs are kept in sync: https://github.com/jxnl/instructor/blob/main/ellipsis.yaml#L..., and here’s an example PR that Ellipsis came up with based on those rules: https://github.com/jxnl/instructor/pull/346.
Don’t worry, your code is never stored or used to train models (https://docs.ellipsis.dev/security).
Installing into your repo takes 2 clicks at https://www.ellipsis.dev. We’d really appreciate your feedback, thoughts, and ideas!
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]'
Project mention: Can I let AI read a group of information from books and what not and then let it answer questions? | /r/ArtificialInteligence | 2023-06-04
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.
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
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Build a simple RAG chatbot with LangChain...
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Adding an Amazon Bedrock Knowledge Base to the Forex Rate Assistant
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Show HN: A phone number to text with questions about current events
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DeepSeek-V2 integrated, RAGFlow v0.5.0 is released
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RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation
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Show HN: R2R – Open-source framework for production-grade RAG
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Show HN: GPT-Powered Video Retrieval and Streaming
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A note from our sponsor - InfluxDB
www.influxdata.com | 17 May 2024
Index
What are some of the best open-source rag projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | llama_index | 31,628 |
| 2 | chatgpt-on-wechat | 25,427 |
| 3 | ragflow | 7,404 |
| 4 | txtai | 7,080 |
| 5 | TaskingAI | 4,837 |
| 6 | GenerativeAIExamples | 1,575 |
| 7 | swirl-search | 1,542 |
| 8 | cognita | 1,320 |
| 9 | llama_parse | 1,108 |
| 10 | canopy | 895 |
| 11 | fastembed | 822 |
| 12 | raptor | 491 |
| 13 | StreamRAG | 400 |
| 14 | continuous-eval | 327 |
| 15 | txtchat | 226 |
| 16 | Instrukt | 221 |
| 17 | tonic_validate | 210 |
| 18 | open-assistant-api | 172 |
| 19 | ragna | 163 |
| 20 | mychatGPT | 123 |
| 21 | enterprise-h2ogpte | 66 |
| 22 | beyondllm | 62 |
| 23 | NeoGPT | 63 |
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