superduperdb
langroid
superduperdb | langroid | |
---|---|---|
24 | 15 | |
4,415 | 1,698 | |
3.7% | 21.4% | |
9.9 | 9.8 | |
5 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
superduperdb
- FLaNK Stack Weekly 12 February 2024
- FLaNK Stack Weekly 11 Dec 2023
- Trending on GitHub top 10 globally for the 4th day in a row: Open-source framework for integrating OpenAI with major databases
- Trending on GitHub top 10 for the 4th day in a row: Open-source framework for integrating AI models and APIs directly with all major SQL databases
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Trending on GitHub top 10 for the 4th day in a row and official technology partner of MongoDB: Open-source framework for integrating AI with MongoDB and MongoDB Atlas
Definitely check it out: https://github.com/SuperDuperDB/superduperdb and find it here: https://cloud.mongodb.com/ecosystem/
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Trending on GitHub top 10 globally for the 4th day in a row: Open-source framework for integrating OpenAI and GPT with major databases
Build a chatbot with OpenAI: https://github.com/SuperDuperDB/superduperdb/blob/main/examples/question_the_docs.ipynb
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
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Trending on GitHub globally 3 days in a row: SuperDuperDB, a framework for integrating AI with major databases (making them super-duper)
It is for building AI (into your) apps easily without complex pipelines and make your database intelligent (including vector search), definitely check it out: https://github.com/SuperDuperDB/superduperdb
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🔮 SuperDuperDB is #3 on GitHub Trending globally! 🥉
VentureBeat already covered the launch This is our website This is our main GitHub repository
langroid
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OpenAI: Streaming is now available in the Assistants API
This was indeed true in the beginning, and I don’t know if this has changed. Inserting messages with Assistant role is crucial for many reasons, such as if you want to implement caching, or otherwise edit/compress a previous assistant response for cost or other reason.
At the time I implemented a work-around in Langroid[1]: since you can only insert a “user” role message, prepend the content with ASSISTANT: whenever you want it to be treated as an assistant role. This actually works as expected and I was able to do caching. I explained it in this forum:
https://community.openai.com/t/add-custom-roles-to-messages-...
[1] the Langroid code that adds a message with a given role, using this above “assistant spoofing trick”:
https://github.com/langroid/langroid/blob/main/langroid/agen...
- FLaNK Stack 29 Jan 2024
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Ollama Python and JavaScript Libraries
Same question here. Ollama is fantastic as it makes it very easy to run models locally, But if you already have a lot of code that processes OpenAI API responses (with retry, streaming, async, caching etc), it would be nice to be able to simply switch the API client to Ollama, without having to have a whole other branch of code that handles Alama API responses. One way to do an easy switch is using the litellm library as a go-between but it’s not ideal (and I also recently found issues with their chat formatting for mistral models).
For an OpenAI compatible API my current favorite method is to spin up models using oobabooga TGW. Your OpenAI API code then works seamlessly by simply switching out the api_base to the ooba endpoint. Regarding chat formatting, even ooba’s Mistral formatting has issues[1] so I am doing my own in Langroid using HuggingFace tokenizer.apply_chat_template [2]
[1] https://github.com/oobabooga/text-generation-webui/issues/53...
[2] https://github.com/langroid/langroid/blob/main/langroid/lang...
Related question - I assume ollama auto detects and applies the right chat formatting template for a model?
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Pushing ChatGPT's Structured Data Support to Its Limits
we (like simpleaichat from OP) leverage Pydantic to specify the desired structured output, and under the hood Langroid translates it to either the OpenAI function-calling params or (for LLMs that don’t natively support fn-calling), auto-insert appropriate instructions into tje system-prompt. We call this mechanism a ToolMessage:
https://github.com/langroid/langroid/blob/main/langroid/agen...
We take this idea much further — you can define a method in a ChatAgent to “handle” the tool and attach the tool to the agent. For stateless tools you can define a “handle” method in the tool itself and it gets patched into the ChatAgent as the handler for the tool.
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Many services/platforms are careless/disingenuous when they claim they “train” on your documents, where they actually mean they do RAG.
An under-appreciate benefit of RAG is the ability to have the LLM cite sources for its answers (which are in principle automatically/manually verifiable). You lose this citation ability when you finetune on your documents.
In Langroid (the Multi-Agent framework from ex-CMU/UW-Madison researchers) https://github.com/langroid/langroid
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Build a search engine, not a vector DB
This resonates with the approach we’ve taken in Langroid (the Multi-Agent framework from ex-CMU/UW-Madison researchers): our DocChatAgent uses a combination of lexical and semantic retrieval, reranking and relevance extraction to improve precision and recall:
https://github.com/langroid/langroid/blob/main/langroid/agen...
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HuggingChat – ChatGPT alternative with open source models
In the Langroid library (a multi-agent framework from ex-CMU/UW-Madison researchers) we have these and more. For example here’s a script that combines web search and RAG:
https://github.com/langroid/langroid/blob/main/examples/docq...
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SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
Thanks, also found Langdroid: https://github.com/langroid/langroid/blob/main/README.md
- memory in ConversationalRetrievalChain removed
- [D] github repositories for ai web search agents
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