factool
haystack
| factool | haystack | |
|---|---|---|
| 1 | 55 | |
| 775 | 14,279 | |
| 0.4% | 4.5% | |
| 7.3 | 9.9 | |
| 8 months ago | 3 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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.
factool
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How to Detect AI Hallucinations
FACTOOL is a task and domain-agnostic framework designed to tackle the escalating challenge of factual error detection in generative AI. It is a five-step tool-augmented framework that consists of claim extraction, query generation, tool querying, evidence collection, and verification. FACTOOL uses tools like Google Search, Google Scholar, code interpreters, Python, and even LLMs themselves to detect factual errors in knowledge-based QA, code generation, math problem solving, and scientific literature review writing. It outperforms all other baselines across all scenarios and is shown to be highly robust in performing its specified tasks compared to LLMs themselves.
haystack
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Haystack DB – 10x faster than FAISS with binary embeddings by default
I was confused for a bit but there is no relation to https://haystack.deepset.ai/
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Release Radar • March 2024 Edition
View on GitHub
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First 15 Open Source Advent projects
4. Haystack by Deepset | Github | tutorial
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Generative AI Frameworks and Tools Every Developer Should Know!
Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:
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Best way to programmatically extract data from a set of .pdf files?
But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.
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Which LLM framework(s) do you use in production and why?
Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??
- Overview: AI Assembly Architectures
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Llama2 and Haystack on Colab
I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.
The notebook can be helpful for those trying to load Llama2 on Colab.
1) Installed Transformers from the main branch (and other libraries)
- Build with LLMs for production with Haystack – has 10k stars on GitHub
- Show HN: Haystack – Production-Ready LLM Framework
What are some alternatives?
langchain - 🦜🔗 Build context-aware reasoning applications
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
BentoML - The easiest way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Multi-model Inference Graph/Pipelines, LLM/RAG apps, and more!
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
jina - ☁️ Build multimodal AI applications with cloud-native stack
BERT-pytorch - Google AI 2018 BERT pytorch implementation
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
jina-financial-qa-search
scibert - A BERT model for scientific text.
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap