haystack

I created a simple app to explore how Agents & MCP can help with medical research. It connects to ClinicalTrials.gov, PubMed/PubTator, and MyVariant.info using the BioMCP Server(https://github.com/genomoncology/biomcp), and uses Haystack(https://github.com/deepset-ai/haystack) as the MCP Client. The idea is to let users ask natural-language questions like: - Are there new studies on treating acne? - What trials exist for migraine prevention? - Show me trials for managing hay fever symptoms - What are some ongoing trails for never-smokers with lung cancer? If you’re curious, here are the links: - MedOryx App: https://med-oryx.streamlit.app - MedOryx GitHub: https://github.com/oryx1729/med-oryx Would love any feedback or thoughts.

Location: Italy Remote: Only Willing to relocate: No Technologies: Python, JS/TS, React, Go, C++, Docker, CI/CD processes Résumé/CV: https://resume.silvanocerza.com Email: You can find it in my CV Hey people! I'm actively searching for a job right now, but I'm also open to contracting if you need help integrating AI into your products. I’ve spent the past couple of years working on Haystack (https://github.com/deepset-ai/haystack) and am now building my own agent orchestrator framework. I love tackling interesting challenges, so if you’re working on something exciting and could use an extra hand let’s chat!

Did you really have to pick the same name as the Haystack open source AI framework? https://haystack.deepset.ai/ https://github.com/deepset-ai/haystack It's a very active project and it's confusing to have two projects with the same name. Besides, I don't understand why you'd give a "2D digital whiteboard that automatically draws connections between code as you navigate and edit files" the name haystack.

Namespace conflict, and yes it does matter. Just as "mamba" was an anaconda package manager re-implementation before it got commandeered by the LLM research crowd, now Haystack is being stolen away from Deepset: https://github.com/deepset-ai/haystack https://haystack.deepset.ai/

Haystack [1] is another good option. It‘s modular, doesn’t get in your way and is particularly strong at retrieval. People like the documentation too. Disclaimer: I work at deepset [1] https://github.com/deepset-ai/haystack

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) 2) Loaded Llama-2-13b-chat-hf on Colab using 4-bit quantizazion, thanks to the material shared by Younes Belkada 3) Disabled Tensor Parallelism, which caused some issues 4) Installed a minimal version of Haystack 5) Found a hacky way to load the model in Haystack's PromptNode 6) Had a fun chat session with the model, discussing everything from David Guetta to Don Matteo (an Italian TV series)!

banks is a side project by my colleague Massi. We work on the open source NLP framework Haystack together (https://github.com/deepset-ai/haystack) and prompt templating is obviously a 'hot' topic and it's something to design these types of projects around. Massi had this concept in mind to use Jinja (or any other templating language for that matter) as the basis, and clearly decided to take it for a spin. I personally am in love with this style of prompt templating and think it makes the whole process insanely flexible and easy to read. Wanted to share it here to see what others think. Hope you enjoy.

These are the similar projects I've come across: - [GitHub - e-johnstonn/BriefGPT: Locally hosted tool that connects documents to LLMs for summarization and querying, with a simple GUI.](https://github.com/e-johnstonn/BriefGPT) - [GitHub - go-skynet/LocalAI: Self-hosted, community-driven, local OpenAI-compatible API. Drop-in replacement for OpenAI running LLMs on consumer-grade hardware. No GPU required. LocalAI is a RESTful API to run ggml compatible models: llama.cpp, alpaca.cpp, gpt4all.cpp, rwkv.cpp, whisper.cpp, vicuna, koala, gpt4all-j, cerebras and many others!](https://github.com/go-skynet/LocalAI) - [GitHub - paulpierre/RasaGPT: RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram](https://github.com/paulpierre/Ra

Yes! https://github.com/deepset-ai/haystack

Haystack Agents are designed in a way so that you can easily use them with different LLM providers. You just need to implement one standardized wrapper class for your modelprovider of choice (https://github.com/deepset-ai/haystack/blob/7c5f9313ff5eedf2...) So back to your question: We will enable both ways in Haystack: 1) Loading a local model directly via Haystack AND 2) quering self-hosted models via REST (e.g. Huggingface running on AWS SageMaker). Our philosophy here: The model provider should be independent from your application logic and easy to switch. In the current version, we support for local models only option 1. This works for many of the provided models provided by HuggingFace, e.g. flan-t5. We are already working on adding support for more open-source models (e.g. alpaca) as models like Flan-T5 don't perform great when used