rag-demystified
TypeChat
rag-demystified | TypeChat | |
---|---|---|
3 | 12 | |
754 | 8,048 | |
- | 1.5% | |
8.2 | 9.0 | |
5 months ago | 15 days ago | |
Python | TypeScript | |
Apache License 2.0 | MIT License |
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rag-demystified
- Show HN: Demystifying Advanced RAG Pipelines
- Show HN: Demystifying Advanced Rag Pipelines
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Good LLM Validation Is Just Good Validation
This was exactly my experience trying to write a RAG pipeline that answers complex questions by generating sub-questions [1]. The hidden prompts in the complex libraries are quite difficult to get to and then start tweaking, whereas writing my own pipeline boiled down the code to 4 LLM calls.
[1] https://github.com/pchunduri6/rag-demystified
TypeChat
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Fuck You, Show Me the Prompt
Not sure it's related to function calling. GPT4 can do function calling without using the specific function-calling API just by injecting the schema you want into the prompt with directions and asking it to return JSON. It works like >99% of the time. Same with 3.5-turbo.
The problem is these libraries convert pydantic models into json schemas and inject them into the prompt, which uses up like 80% more tokens than just describing the schema using typescript type syntax for example. See https://microsoft.github.io/TypeChat/, where they prompt using typescript type descriptions to get json data from LLMs. It's similar to what we built but with more boilerplate.
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Semantic Kernel
Semantic Memory (renamed to Kernel Memory - https://github.com/microsoft/kernel-memory) complements SK. Guidance's features are being absorbed into SK, following the departure of that team from Microsoft. Additionally, we have TypeChat (https://github.com/microsoft/TypeChat), which aims to ensure type-safe responses from LLMs. Most features of Autogen are also being integrated into SK, along with Assistants. SK serves as the orchestration engine powering Microsoft Copilots.
- Good LLM Validation Is Just Good Validation
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Show HN: Symphony – Make functions invokable by GPT-4
I tried TypeChat for my use case and ended up defining functions as typescript data types. This approach sounds much better, and leverages the newer OpenAI function calling, which should be more reliable I would think. Thanks for creating+sharing.
https://microsoft.github.io/TypeChat/
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Show HN: LLMs can generate valid JSON 100% of the time
That re-prompting error on is what this new Microsoft library does, too: https://github.com/microsoft/TypeChat
Here's their prompt for that: https://github.com/microsoft/TypeChat/blob/c45460f4030938da3...
I think the approach using grammars (seen here, but also in things like https://github.com/ggerganov/llama.cpp/pull/1773 ) is a much more elegant solution.
- TypeChat replaces prompt engineering with schema engineering
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Introducing TypeChat from Microsoft
I'm very surprised that they're not using `guidance` [0] here.
It not only would allow them to suggest that required fields be completed (avoiding the need for validation [1]) and probably save them GPU time in the end.
There must be a reason and I'm dying to know what it is! :)
[0] https://github.com/microsoft/guidance
[1] https://github.com/microsoft/TypeChat/blob/main/src/typechat...
What are some alternatives?
waggle-dance - Knowledge work automation with AI agents
outlines - Structured Text Generation
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
guidance - A guidance language for controlling large language models.
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
shelby_as_a_service - Production-ready LLM Agents. Just add API keys
ai-agents-laravel - Build AI Agents for popular LLMs quick and easy in Laravel
ts-patch - Augment the TypeScript compiler to support extended functionality
llm-mlc - LLM plugin for running models using MLC
CopilotKit - Build in-app AI chatbots 🤖, and AI-powered Textareas ✨, into react web apps. [Moved to: https://github.com/CopilotKit/CopilotKit]
LLM-OpenAPI-minifier - Making openapi spec swagger documents friendly for GPT and other LLMs.