Txtai Alternatives

txtai reviews and mentions

• Embeddings index format for open data access
  4 projects | dev.to | 6 Sep 2024

  txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.

• Just use Postgres
  3 projects | news.ycombinator.com | 17 Aug 2024

  I'm always cautious with a one-size-fits-all approach. If a team is working on a small project and SQLite works then great. You can use a SQLite database on something like a $4/month DigitalOcean droplet. Can't say the same for Postgres.

  > AI is a bubble

  Many say this but Generative AI and LLMs have gotten bunched up with everything else. There is a clear need for vectors and multimodal search. There is no core SQL statement to find concepts within an image for example. Machine learning models support that with arrays of numbers (i.e. vectors). pgvector adds vector storage and similarity search for Postgres. There was a recent post about storing vectors in SQLite (https://github.com/asg017/sqlite-vec).

  > Even if your business is another AI grift, you probably only need to import openai.

  There's much more than this. There are frameworks such as LangChain, LlamaIndex and txtai (disclaimer I'm the primary author of https://github.com/neuml/txtai) that handle generating embeddings locally or with APIs and storing them in databases such as Postgres.

• Ask HN: Discernment Lattice [Prompting]
  1 project | news.ycombinator.com | 11 Aug 2024
• Ask HN: Company wants us to upskill in AI. What is the best approach?
  2 projects | news.ycombinator.com | 9 Aug 2024

  I posted this to a CS students Q -- and it stands, but with your skillset - you can go deeper faster:

  But since we dont know what you do, or what you need, "upskilling in AI" in a meaningful way to your employer, and such that youre already proficient in python - then taking what you do, and wrapping it with something like txtai - and then showing that you can effectively create txtai workflows surrounding things you already do, would allow you to then NLP /GPT your infra.

  https://github.com/neuml/txtai/tree/master/examples

  Use AI to help you document everything. Feed it a piece of code, and tell it to explain its functionality, give you a swim and mermaid diagram and write documentation of a readme.md.

  Use it to write scripts to crawl whatever filestructures/whatever you have and document them - write internal tools that will simulate user behavior in different departments (such as how there will be pupeteer/orchistration in browsers - create an AI version of a role in a particular department that mocks through a daily workflow of the users and report it to a statuspage.internalCompany.com -- and have it report that the pipeline for that roles workflow is green.

  Find ways to create tests, or things to give automation tools to your users for certain things.

  Use it to do compliance check if you need to adhere to some ISO/10DLC/SOX whatever.

  Have it create boilerplate functions that your users need...

  chatbots are cool and all - but you want to find utility.

  Also - when you close a prompt, I find the following helpful:

  As the last line of the prompt:

  "Review, explain, propose, confirm, execute"

  ---

• txtai: Open-source vector search and RAG for minimalists
  3 projects | news.ycombinator.com | 21 Jul 2024
• txtai 7.3 released: Adds new RAG Web Apps and streaming LLM/RAG support
  1 project | news.ycombinator.com | 15 Jul 2024
• txtai: Vector search, Knowledge Graphs, RAG and LLM workflows locally run
  2 projects | news.ycombinator.com | 29 Jun 2024

  Hello, author of txtai here. txtai was created back in 2020 starting with semantic search of medical literature. It has since grown into a framework for vector search, retrieval augmented generation (RAG) and large language model (LLM) orchestration.

  The goal of txtai is to be simple and easy-to-use. Semantic Graphs were added in 2022 before the Generative AI wave of 2023/2024. Knowledge Graphs are now a hot topic, so this component is even more relevant today. txtai also has a robust RAG and workflow framework.

  I often see others complain about AI/LLM/RAG frameworks, so I wanted to share this project as many don't know it exists.

  txtai is Apache 2.0 licensed and all code is available at https://github.com/neuml/txtai

• How RAG with txtai works
  1 project | dev.to | 21 Jun 2024

  # Set base image ARG BASE_IMAGE=neuml/txtai-gpu FROM $BASE_IMAGE # Copy configuration COPY config.yml . # Install latest version of txtai from GitHub RUN \ apt-get update && \ apt-get -y --no-install-recommends install git && \ rm -rf /var/lib/apt/lists && \ python -m pip install git+https://github.com/neuml/txtai # Run local API instance to cache models in container RUN python -c "from txtai.api import API; API('config.yml')" # Start server and listen on all interfaces ENV CONFIG "config.yml" ENTRYPOINT ["uvicorn", "--host", "0.0.0.0", "txtai.api:app"]

• We no longer use LangChain for building our AI agents
  10 projects | news.ycombinator.com | 20 Jun 2024

  An alternative is using txtai (https://github.com/neuml/txtai). It's lightweight and works with both local and remote LLMs.

  Here is an example article that shows how to use OpenAI calls with txtai: https://neuml.hashnode.dev/rag-with-llamacpp-and-external-ap...

• Show HN: FileKitty – Combine and label text files for LLM prompt contexts
  5 projects | news.ycombinator.com | 1 May 2024
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  www.influxdata.com | 20 Sep 2024

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