agency
langroid
agency | langroid | |
---|---|---|
3 | 15 | |
379 | 1,594 | |
4.2% | 16.2% | |
8.3 | 9.8 | |
27 days ago | 5 days ago | |
Go | Python | |
MIT License | MIT License |
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agency
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Agency: Pure Go LangChain Alternative
I would, at the very least, wrap the errors being returned inside the process function https://github.com/neurocult/agency/blob/14b14e50a7570189388...
Or, I suppose the user must handle exception behavior in their custom `OperationHandler`
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šµ Agency: The Go Way to AI. Part 1
Recognizing the need for a Go-friendly tool thatās simple yet powerful, we developed Agency. This Go library, designed with a clean approach, matches Go's strengths in a static type system and high performance. It's our answer to bringing easy-to-use, efficient AI capabilities to Go developers.
langroid
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OpenAI: Streaming is now available in the Assistants API
This was indeed true in the beginning, and I donāt 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 āuserā 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 āassistant spoofing trickā:
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 itās 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 oobaās 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 donāt 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 ā you can define a method in a ChatAgent to āhandleā the tool and attach the tool to the agent. For stateless tools you can define a āhandleā 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 ātrainā 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 weāve 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 ā 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 hereās 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
What are some alternatives?
langchain - š¦š Build context-aware reasoning applications
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
go-openai - OpenAI ChatGPT, GPT-3, GPT-4, DALLĀ·E, Whisper API wrapper for Go
modelfusion - The TypeScript library for building AI applications.
humanscript - A truly natural scripting language
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
yay - š¹ interact with openai api from command line
vectordb - A minimal Python package for storing and retrieving text using chunking, embeddings, and vector search.
langchaingo - LangChain for Go, the easiest way to write LLM-based programs in Go
Adala - Adala: Autonomous DAta (Labeling) Agent framework
go-gpt3 - OpenAI ChatGPT, GPT-3, DALLĀ·E, Whisper API wrapper for Go [Moved to: https://github.com/sashabaranov/go-openai]
chidori - A reactive runtime for building durable AI agents