NeuML/neuml-linkedin-202501 huggingface dataset:
LinkedIn post by David Mezzetti (01/09/2025):
🚀 Time for our first release of 2025 - txtai 8.2!

This release simplifies LLM chat messages, adds attribute filtering to Graph RAG and enables multi-cpu/gpu vector encoding.

txtai is the best framework around for Agents, RAG and Vector Search! Read more below.

GitHub: https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/e89BtUZS
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb
LinkedIn post by David Mezzetti (01/08/2025):
🦠 Here is a dataset with PubMed article metadata related to Human metapneumovirus (HMPV). It's also a resource for those who want to get more educated on the matter with reliable data. Don't just believe what you see on social media.

Dataset: https://lnkd.in/eVrkKgHB
CSV: https://lnkd.in/egYqgmuF
LinkedIn post by David Mezzetti (01/07/2025):
📄⚙️ Want to parse medical and scientific papers? Then check out paperetl. 

With paperetl, you can select a subset of the PubMed archive using id's, MeSH codes and/or keywords. Build your own medical knowledge base. It also supports parsing full PDFs. All code no AI in this project.

https://lnkd.in/ecuuVG2
LinkedIn post by David Mezzetti (01/06/2025):
🦠 We're hearing a lot about H5N1. Here is a dataset with PubMed article metadata related to H5N1 in hopes it can help those looking to prevent this from growing into something bigger. It's also a resource for those who want to get more educated on the matter with reliable data.

Dataset: https://lnkd.in/eK82xAWq
CSV: https://lnkd.in/epFGB6AY
LinkedIn post by David Mezzetti (01/03/2025):
🧬⚕️🔬 What if we said you can have a competitive 8 million parameter model that can index as fast as BM25? Or a 100K parameter 200KB model that retains knowledge?

We're excited to release static PubMedBERT Embeddings models! These are a set of static models distilled with the great Model2Vec library by The Minish Lab Thank you to Stéphan Tulkens and Thomas van Dongen for creating Model2Vec!

https://lnkd.in/esgtC_2X
LinkedIn post by David Mezzetti (01/03/2025):
🤖📄 It's 2025 and you want to get into AI? Perhaps you've tried ChatGPT and want to see how something can be done with your own data locally?

Then check out our RAG application. It's an easy-to-run Docker container that has Vector RAG, Graph RAG, PDF text extraction, vector search and and more all built-in with a UI! It's also a great example on how to use txtai.

https://lnkd.in/evdB5HgN
LinkedIn post by David Mezzetti (01/01/2025):
📰🔥 NeuML - 2024 Year in Review

Agents, GraphRAG, Speech to Speech RAG, Postgres, Streaming LLMs, Docling, Model2Vec and Consulting! 

Lots of fun ahead in 2025. This article is worth a read!

https://lnkd.in/e2PWVEjS
LinkedIn post by David Mezzetti (12/28/2024):
💥 Surprise! One last release in 2024. 

🧬⚕️🔬 This time it's paperai and paperetl 2.3. paperai is a semantic search and workflow application for medical/scientific papers. paperetl is a companion library for parsing papers.

This update enables the latest txtai features and fixes some long standing bugs. More to come in 2025 and maybe one more thing in 2024!

paperai: https://lnkd.in/egFSSP4
paperetl: https://lnkd.in/ecuuVG2
LinkedIn post by David Mezzetti (12/19/2024):
🎄🎅🎶 With the year winding down and us heading into the holidays, it's amazing to think what is now and what will be possible soon.

Still 🤯 that a set of open source tools can clone my voice and recite the night before Christmas with an AI-generated music track to go along with it!

Read more: https://lnkd.in/dPmt-bZm
LinkedIn post by David Mezzetti (12/18/2024):
🚚 📦 We're shipping today! This time it's AnnotationAI v0.2!

This updates the app to be able to run embeddings searches against txtai's ArXiv database when there is no active url.

GitHub: https://lnkd.in/ev4xH34m
Release Notes: https://lnkd.in/eWvD66tf
PyPI: https://lnkd.in/ehSq8uAA
Docker Hub: https://lnkd.in/e9RURbPD
LinkedIn post by David Mezzetti (12/18/2024):
📚 Excited to release another new project, RAG Data.

While we have long provided publicly available embeddings databases for ArXiv and Wikipedia, the code wasn't easy to find. This project fixes that!

This project can be used as a starting point for those building their own large embeddings databases. It has multiprocessing and other performance enhancements built-in.

GitHub: https://lnkd.in/eTBJCiRr
PyPI: https://lnkd.in/eW2W6yVW
LinkedIn post by David Mezzetti (12/17/2024):
🚀 Happy to release v0.9.0 of the txtai rag app! This version adds support for extracting text from documents via Docling.

GitHub: https://lnkd.in/evdB5HgN
Release Notes: https://lnkd.in/eJsxDBX7
Docker Hub: https://lnkd.in/e-3Cx_68
LinkedIn post by David Mezzetti (12/13/2024):
💥📝 We're excited to release a new project.....AnnotateAI

Who reads papers around here? Lots of us do we're sure! There are plenty of projects to summarize, search and build generative systems with papers. What about helping us read them?

Well that's where AnnotateAI comes in! AnnotateAI automatically annotates papers using LLMs. This project focuses on providing human readers with context as they read.

Click through the link below to learn more!

https://lnkd.in/dCjtYPeR
LinkedIn post by David Mezzetti (12/10/2024):
💥 txtai 8.1 is out!

txtai 8.1 adds Docling integration, Embeddings context managers and significant database component enhancements. The latest version of the txtai RAG application is also available on Docker Hub.

See below for more.

GitHub: https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/ecK4KnU5
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb
LinkedIn post by David Mezzetti (12/08/2024):
txtai has a robust and growing integration with Postgres. It has the ability to persist each of it's components to Postgres as follows:

- Vectors via pgvector
- Sparse vectors via full-text search
- Documents are persisted as JSON columns and fully searchable
- Graph edges and nodes

More features are underway.

https://lnkd.in/eFeFNgYK
LinkedIn post by David Mezzetti (12/05/2024):
When building AI apps moving into 2025, you have a choice. A framework where with a couple config changes you can move quickly from prototyping locally to production. Or you can use frameworks that require you to write a bunch of gobbledygook code and imports for everything. And maybe you'll get it into production 😀

Want to learn more, take a look at txtai: https://lnkd.in/dxWDeey
LinkedIn post by David Mezzetti (12/01/2024):
The LLM pipeline with txtai is designed to be flexible. It supports running models via Transformers, llama.cpp and LLM API services (via LiteLLM) through a single interface. Provide prompts as strings or chat messages. GGUF, AWQ and more. 

Read more here: https://lnkd.in/ebMr77S2
LinkedIn post by David Mezzetti (11/27/2024):
🧬⚕️🔬 For those working with medical and scientific literature, the OpenScholar project from Ai2 is quite promising.

We like to work with AWQ quantization. So, we've added a AWQ-quantized version to the Hugging Face Hub!

Read more below.

AWQ Model: https://lnkd.in/eDtPvZgx
AI2 Blog: https://lnkd.in/dBZv5EPf
AI2 Paper: https://lnkd.in/ezQUsgTJ
LinkedIn post by David Mezzetti (11/23/2024):
The foundation of txtai is it's embeddings database. This is where knowledge is stored and it powers other components. From here we can build autonomous agents, retrieval augmented generation (RAG) processes and multi-model workflows.

Default configuration is provided out of the box for all these components, so you can get up and running fast!

https://lnkd.in/dxWDeey
LinkedIn post by David Mezzetti (11/20/2024):
Our PubMedBERT Embeddings model has over 100K downloads and 100 likes on the Hugging Face Hub. It also has a growing number of citations, albeit sadly it's often paired with LangChain 🙁.

Model: https://lnkd.in/egnEKcqd
Google Scholar Search: https://lnkd.in/exFepgQV
LinkedIn post by David Mezzetti (11/19/2024):
🥁 Alright....so here we go. We're proud to announce the release of txtai 8.0! This release adds an agent framework to txtai (built on top of Transformers Agents 🤗 with all LLMs supported).

txtai is now one of the most straightforward ways to add real-world agents to production without the bloat. LFG to the 🚀🌕!

GitHub: https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/e47jPNKZ
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb
Article: https://lnkd.in/e7XPs6Ub
LinkedIn post by David Mezzetti (11/05/2024):
🚀 We're thrilled to share a preview version of txtai agents. Inspired by the simplicity of frameworks like OpenAI Swarm, txtai agents are built on top of the Transformers Agent framework.

This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI + Claude + AWS Bedrock via LiteLLM).

Available in GitHub now, will be released soon!

Example code: https://lnkd.in/eapczzk3
LinkedIn post by David Mezzetti (10/29/2024):
The future of AI will be driven by your knowledge and your data. Those with the best knowledge bases win the day.

That's why txtai makes it easy to build all sorts of knowledge bases and store them in all sorts of places. Postgres, SQLite, S3, Hugging Face Hub and more!

https://lnkd.in/eMGY7uRB
LinkedIn post by David Mezzetti (10/23/2024):
🚀 Thank you to the 9K people who have given txtai a ⭐! 

A goal set from the beginning was to reach 10K ⭐'s on GitHub which will happen at some point over the coming months. While some projects race out to a fast start and are fortunate to trend, txtai has been more of a steady incline. 

It's been a journey full of ups and downs, thank you to all those following! If you haven't given txtai a star yet and like it, what are you waiting for?

https://lnkd.in/dxWDeey
LinkedIn post by David Mezzetti (10/23/2024):
🚀 txtai is built on the shoulders of open-source giants. Transformers, SQLite, NetworkX, Postgres, Faiss, NumPy are all major players.

Small projects just work without any configuration. Larger projects can integrate with Postgres, which is built on almost 30 years of production experience.

Don't settle for less just because "AI Influencers" say so. We're not paying anyone to do our bidding - we work hard to build a solid project.

https://lnkd.in/eh4-rVEr
LinkedIn post by David Mezzetti (10/21/2024):
Running an embeddings database with limited compute? Model2Vec is a technique to turn any sentence transformer into a really small static model.

The next release of txtai will have support for this new vectorization method!

Example txtai code: https://lnkd.in/eFSdT7B5
GitHub Project: https://lnkd.in/eEpWxZx8
Blogpost: https://lnkd.in/e4iPg4wi
LinkedIn post by David Mezzetti (10/15/2024):
LLMs are more artist than analyst. What's called a hallucination in business is good in the creative space. Imagine a world where with a few words we can generate stories with automated voice narration and music. We're trending in that direction and it's applications are plentiful.

We're on a path where creativity will be in the hands of many.

https://lnkd.in/e7kd8_TE
LinkedIn post by David Mezzetti (10/14/2024):
🚀 Start your week with the txtai 7.5 release!

txtai 7.5 adds Speech to Speech RAG, new TTS models and Generative Audio features. The latest version of the txtai RAG application is also available on Docker Hub.

See below for more.

GitHub: https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/eWuMe4FW
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb
LinkedIn post by David Mezzetti (10/13/2024):
📚 New from txtai - Generative storytelling!

Check out these Generative Audio workflows that takes a story ("The Raven" by Edgar Allan Poe) and joins speech with generated background audio. The audio is generated with a LLM building a prompt for a music generation model.

Article: https://lnkd.in/e7kd8_TE
LinkedIn post by David Mezzetti (10/04/2024):
See how a RAG process can write a poem about Machine Learning!

This example runs a vector search with Wikipedia, finds the best matching articles to use as context and runs a prompt to generate the poem using that context. The answer is then converted to speech!

Can you guess who the generated voice is? 😂 This is all machine generated!

Video on YouTube: https://lnkd.in/ez_ABNvG
LinkedIn post by David Mezzetti (09/29/2024):
Heading into October🍂 - it's been quite a year for txtai. Just this year alone, txtai added:

- llama.cpp support
- LLM API support (i.e. OpenAI, Claude, Bedrock etc)
- Graph RAG
- Binary quantization and support for Matryoshka Embeddings
- Postgres integration for all vector database components
- Streaming LLM, Streaming RAG and chat message support
- Significant text extraction improvements

There's much more but these are the most crucial changes. It's hard to remember a time when txtai didn't have these features but it wasn't long ago!

https://lnkd.in/dxWDeey
LinkedIn post by David Mezzetti (09/28/2024):
A great thing about txtai's new Speech to Speech RAG workflow is that it's modular. It's simple to swap out a local LLM for a LLM API, same for transcription and text to speech. Want RAG with Postgres + pgvector? No problem! That all comes with txtai and it's why it's the "all-in-one embeddings database"

https://lnkd.in/eEH-gZz2
LinkedIn post by David Mezzetti (09/27/2024):
💥 Dropping one of the most powerful and capable workflows txtai has to date: Introducing the Speech to Speech RAG workflow!

Lots of hard work went into this from end-to-end and we're confident txtai is the easiest way to build your own Speech to Speech RAG workflow. With the simplicity of txtai, you can swap in your own Embeddings database and be off the the races 🏇 - enjoy!

Video
https://lnkd.in/eEGGsXBQ

Article
https://lnkd.in/eEH-gZz2
LinkedIn post by David Mezzetti (09/17/2024):
🔥 Check out this RAG with CoT + Self-Reflection example using the Wikipedia Embeddings index from txtai!

The release of OpenAI's o1 model has many trying to glean how it works without knowing for sure since it's a closed model. There is much speculation that CoT + Self-Reflection is part of the process.

Code: https://lnkd.in/eyFzupDq
LinkedIn post by David Mezzetti (09/14/2024):
Want to build a RAG system at scale with your own data? Then focus on the R in RAG - Retrieval!

Vector storage is one of the major challenges at scale. Vectors tend to take up more space than the data itself. But there are options available such as quantization and concepts like Matryoshka Embeddings.

Check out the following articles to learn more.

Vector Quantization: https://lnkd.in/db5Jx_fE
Matryoshka Embeddings: https://lnkd.in/gr25HsBF
LinkedIn post by David Mezzetti (09/12/2024):
From prototyping, small-scale to enterprise production, txtai has you covered. One interface gives easy access to the following:

Vector Search:

✅ In-memory
✅ Local indexes
✅ Postgres (via pgvector)

LLM / RAG Inference:

✅ Local Hugging Face models
✅ llama.cpp
✅ GPT-4, Claude, Bedrock, Cohere etc

https://lnkd.in/emd_5mNr
LinkedIn post by David Mezzetti (09/11/2024):
☁️ Want an AWS-hosted version of txtai? Then check out this RAG example! It uses AWS Bedrock for embeddings + LLM calls. Content is stored in Postgres/pgvector via Aurora or RDS.

Other frameworks like LangChain and LlamaIndex require code changes to switch from local to cloud. The same code can handle both with minor configuration changes in txtai! 

Code: https://lnkd.in/eWjbci_k
LinkedIn post by David Mezzetti (09/06/2024):
💯🤯 Big time release with txtai 7.4!

txtai 7.4 adds the SQLite ANN, new text extraction features and a programming language neutral embeddings index format. The latest version of the txtai RAG application is also available on Docker Hub.

The embeddings index format is a contract to enable open data access in a programmatic and platform independent way. New ways to bind index components will be coming soon!

See below for more.

GitHub: https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/eDGp3dAi
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb
LinkedIn post by David Mezzetti (08/29/2024):
With LLMs growing more and more powerful⚡, one of the most important tasks is ensuring the best content is provided.

Did you know that txtai has a robust text extraction pipeline that can convert HTML, PDF, DOCX, XLSX and more to LLM-friendly Markdown? 

Read more at the links below. 

https://lnkd.in/en-wT8zT
https://lnkd.in/eSmxjk7t
LinkedIn post by David Mezzetti (08/26/2024):
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. It uses a number of index formats to store data with each of it's components.

Each component is designed to ensure open access to the underlying data in a programmatic and platform independent way. The next release is making some backwards-compatible changes to ensure this is the case for all components.

Read more here: https://lnkd.in/eiX-WtQn
LinkedIn post by David Mezzetti (08/13/2024):
Did you know that most of the functionality in txtai can be run with configuration? That's right, txtai can dynamically load Embeddings, LLM, RAG and other pipelines with YAML configuration.

Check out this example that loads an Embeddings database via Docker with a couple of lines of YAML config. The example then runs a graph search via the local API and plots the results with Sigma.js. 

Code: https://lnkd.in/e5CwDjBb
Docs: https://lnkd.in/dz7v_UPb
LinkedIn post by David Mezzetti (08/12/2024):
An array of search results show the top N best matches for a query. But what if some of those matches aren't related to the others? That's where txtai's semantic graph and Graph RAG patterns help. Graphs open up different possibilities such as looking at the most central results and/or walking a specific path to pull in only certain types of results.

Check out this article for more on this topic: https://lnkd.in/e9vGkZ2x
LinkedIn post by David Mezzetti (08/11/2024):
🥳🎂 August 11, 2020 - txtai was born. This image and Reddit post started it all!

The last 4 years have been amazing! The community that has developed around txtai has been humbling. 8.5K ⭐'s later there is still much work to do. Only a small fraction of the space knows about txtai. The more that know, the more that will see that it's a better approach than other "popular" frameworks. Let's go!

GitHub: https://lnkd.in/dxWDeey
Original Reddit Post: https://lnkd.in/eA9G7m6M
LinkedIn post by David Mezzetti (08/11/2024):
📈 While LLMs are prone to mistakes, they are a great way to learn the lingo of a new domain. Take this example of a Llama 3.1 prompt that analyzes publicly traded stocks. If you run this example with a couple different stocks, you'll quickly learn the indicators that are common: P/E Ratio, market cap, trailing EPS, cash on hand etc. While LLMs shouldn't be trusted blindly, they are a great tool.

Code: https://lnkd.in/e9zn7UNA
LinkedIn post by David Mezzetti (08/10/2024):
One of the most powerful pipelines available in txtai is it's textractor pipeline. It can convert a large number of document formats to Markdown for LLM/RAG consumption. One common concern is with it's Apache Tika/Java dependency.

Did you know that Apache Tika can instead be started via this Docker Image?

https://lnkd.in/eMUHkrBS
LinkedIn post by David Mezzetti (08/10/2024):
One of the more underappreciated components of txtai is it's cloud sync. Say you're doing market/sales/academic or even medical research, you parse and build an embeddings index over a series of papers/websites/documents. Given the portable nature of txtai's index format, an Embeddings index can easily be synced to cloud storage (i.e. AWS S3/Azure Blob/Google Cloud) or even the Hugging Face Hub. From there, anyone can spin up a RAG process using this data upon being granted access. No need for servers and complex setups.

Learn more below.

https://lnkd.in/eMGY7uRB
LinkedIn post by David Mezzetti (08/09/2024):
🙏 Thank you for the amazing feedback with the txtai RAG v0.2 release. There were some great ideas that were too good to sit on. With that, we're happy to announce v0.3 is now available!

GitHub: https://lnkd.in/evdB5HgN
Docker Hub: https://lnkd.in/e-3Cx_68
LinkedIn post by David Mezzetti (08/09/2024):
📄 Transforming HTML to Markdown is easy until it isn't! Most websites have headers, footers and sidebars with little consistency. Just naively converting a website to Markdown leads to a lot of irrelevant content which can throw LLMs off. 

That's where txtai's textractor pipeline can help🚀 This pipeline has logic to detect the most likely sections with the main content removing noisy sections such as headers, footers and sidebars. This helps improve the overall RAG accuracy.

Check out this example extraction: https://lnkd.in/eh6d8Nau

See how only the main content is extracted!
LinkedIn post by David Mezzetti (08/08/2024):
🔥 v0.2 of our txtai RAG application is out! This is an easy-to-use application for exploring your own data with retrieval augmented generation (RAG) backed by txtai.

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. txtai has a feature to automatically create knowledge graphs using semantic similarity. This enables running Graph RAG queries with path traversals. This RAG application generates a visual network to illustrate the path traversals and help understand the context from which answers are generated from.

Embeddings databases are used as the knowledge store. The application can start with a blank database or an existing one such as Wikipedia. In both cases, new data can be added. This enables augmenting a large data source with new/custom information.

Adding new data is done with the textractor pipeline. This pipeline can extract content from documents (PDF, Word, etc) along with websites. The website extraction logic detects the likely sections with main content removing noisy sections such as headers and sidebars. This helps improve the overall RAG accuracy.

See more at the links below.

GitHub: https://lnkd.in/evdB5HgN
Docker Hub: https://lnkd.in/e-3Cx_68
LinkedIn post by David Mezzetti (08/07/2024):
📑 Did you know that Markdown formatted text helps improve RAG accuracy? While retrieval and prompt engineering are important components of a RAG pipeline, Markdown can help give an additional boost ⚡

The Textractor pipeline supports generating tables, lists, code, blockquotes and emphasis sections as Markdown.

https://lnkd.in/e8nfE-Zp
LinkedIn post by David Mezzetti (07/27/2024):
As many go down the "agentic path", we're choosing a different path.....graph path traversals! 🔵->🟨->🟢

Graph path traversals use vector similarity and/or relationships of your choosing to walk a graph and enable LLMs to explain complex concepts and relationships. This example walks a path and automatically generates an explanation of the network in the form of a short article. 

Paths can be set directly (i.e. Roman Empire -> Reasons for collapse) or inferred from a query (Tell me the reasons why the Roman Empire collapsed).

Learn more here: https://lnkd.in/evdB5HgN
LinkedIn post by David Mezzetti (07/26/2024):
🚀 We're thrilled to release an innovative and easy-to-use application for RAG and 📈 GraphRAG. We believe this application has features that are novel and not seen anywhere else. 

txtai has been out ahead with semantic graphs for a while now. We've long known that graph path traversals are a great way to build contexts even before we knew LLMs would be the spot for this context.

The RAG application allows uploading your own data and documents. Graphs are automatically constructed, relationships automatically derived. Each node in the graph is given a LLM-generated topic.

With one pull of a Docker image, you can be up and running with a full-featured GraphRAG application on your own data. Enjoy!

GitHub: https://lnkd.in/evdB5HgN
Docker: https://lnkd.in/e-3Cx_68