fructose
lmql
| fructose | lmql | |
|---|---|---|
| 3 | 30 | |
| 700 | 3,375 | |
| 13.1% | 4.4% | |
| 9.1 | 9.5 | |
| about 1 month ago | 11 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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fructose
- FLaNK AI Weekly 18 March 2024
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Show HN: Fructose, LLM calls as strongly typed functions
This approach may be too high-level "magic" to the point of being difficult to work with and iterate upon.
Looking at the prompt templates (https://github.com/bananaml/fructose/tree/main/src/fructose/... ), they use LangChain-esque "just try to make the output to be valid JSON" when APIs such as the GPT-4 turbo which this model uses by defauly now support function calling/structured data natively, and libraries such as outlines (https://github.com/outlines-dev/outlines) which is more complex but can better ensure a dictionary output for local LLMs
lmql
- Show HN: Fructose, LLM calls as strongly typed functions
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Prompting LLMs to constrain output
have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.
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[D] Prompt Engineering Seems Like Guesswork - How To Evaluate LLM Application Properly?
the only time i've ever felt like it was anything other than guesswork was using LMQL . not coincidentally, LMQL works with LLMs as autocomplete engines rather than q&a ones.
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Guidance for selecting a function-calling library?
lqml
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Show HN: Magentic – Use LLMs as simple Python functions
This is also similar in spirit to LMQL
https://github.com/eth-sri/lmql
- Show HN: LLMs can generate valid JSON 100% of the time
- LangChain Agent Simulation – Multi-Player Dungeons and Dragons
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The Problem with LangChain
LLM calls are just function calls, so most functional composition is already afforded by any general-purpose language out there. If you need fancy stuff, use something like Python‘s functools.
Working on https://github.com/eth-sri/lmql (shameless plug, sorry), we have always found that compositional abstractions on top of LMQL are mostly there already, once you internalize prompts being functions.
- Is there a UI that can limit LLM tokens to a preset list?
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Local LLMs: After Novelty Wanes
LMQL is another.
What are some alternatives?
outlines - Structured Text Generation
guidance - A guidance language for controlling large language models.
grok-1 - Grok open release
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
guardrails - Adding guardrails to large language models.
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
clownfish - Constrained Decoding for LLMs against JSON Schema
sketch - AI code-writing assistant that understands data content
OpenChat - LLMs custom-chatbots console ⚡