posts.csv

Post title,Post link,Post type,Posted by,Created date,Audience,Impressions,Clicks,Click through rate (CTR),Likes,Comments,Reposts,Engagement rate
"🚀 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",https://www.linkedin.com/feed/update/urn:li:activity:7283225139486326784,Organic,David Mezzetti,01/09/2025,All followers,2044,112,0.054794520139694214,23,0,4,0.06800391525030136
"125 ⭐'s to go for txtai 10K!

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7282595210184863744,Organic,David Mezzetti,01/08/2025,All followers,320,10,0.03125,3,0,0,0.04062499850988388
"🦠 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",https://www.linkedin.com/feed/update/urn:li:activity:7282582586344800257,Organic,David Mezzetti,01/08/2025,All followers,259,4,0.0154440151527524,3,0,1,0.0308880303055048
"📄⚙️ 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
",https://www.linkedin.com/feed/update/urn:li:activity:7282485854973902851,Organic,David Mezzetti,01/07/2025,All followers,448,19,0.0424107126891613,4,1,1,0.0558035708963871
"Want to build your own Speech to Speech RAG pipeline? Then check out this video tutorial!

https://lnkd.in/eEGGsXBQ",https://www.linkedin.com/feed/update/urn:li:activity:7282473058655715328,Organic,David Mezzetti,01/07/2025,All followers,231,8,0.034632034599781036,2,0,1,0.0476190485060215
"🦠 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",https://www.linkedin.com/feed/update/urn:li:activity:7282054158587682817,Organic,David Mezzetti,01/06/2025,All followers,417,14,0.03357314318418503,5,0,1,0.047961629927158356
"🧬⚕️🔬 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",https://www.linkedin.com/feed/update/urn:li:activity:7281057260766695424,Organic,David Mezzetti,01/03/2025,All followers,2850,179,0.06280701607465744,44,5,3,0.08105263113975525
"🤖📄 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",https://www.linkedin.com/feed/update/urn:li:activity:7280955767967346688,Organic,David Mezzetti,01/03/2025,All followers,690,30,0.043478261679410934,9,0,1,0.05797101557254791
"🔥 Happy to share this new article on using txtai with LLM APIs by Igor Ribeiro Lima!

https://lnkd.in/ercixQKS",https://www.linkedin.com/feed/update/urn:li:activity:7280647029133832193,Organic,David Mezzetti,01/02/2025,All followers,584,22,0.03767123445868492,3,0,1,0.0445205494761467
"📰🔥 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",https://www.linkedin.com/feed/update/urn:li:activity:7280323095284834304,Organic,David Mezzetti,01/01/2025,All followers,660,23,0.03484848514199257,9,1,2,0.05303030461072922
Happy New Year! Wishing you all the best in 2025 🍾🥂🎉,https://www.linkedin.com/feed/update/urn:li:activity:7280031297962405888,Organic,David Mezzetti,01/01/2025,All followers,241,1,0.004149377811700106,2,0,1,0.016597511246800423
"💥 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",https://www.linkedin.com/feed/update/urn:li:activity:7278873244105904128,Organic,David Mezzetti,12/28/2024,All followers,5229,337,0.0644482672214508,77,4,3,0.08051252365112305
Merry Christmas and Happy Holidays to all! 🎄🎅❄️,https://www.linkedin.com/feed/update/urn:li:activity:7277350467746107392,Organic,David Mezzetti,12/24/2024,All followers,342,5,0.01461988314986229,3,0,1,0.02631578966975212
See why txtai had over 4000 (real) ⭐'s in 2024,https://www.linkedin.com/feed/update/urn:li:activity:7276223906695319552,Organic,David Mezzetti,12/21/2024,All followers,576,15,0.02604166604578495,7,2,1,0.0434027761220932
"The NeuML Holiday 🎄 🎅 ❄️ 🎆 newsletter is out!

https://lnkd.in/eEAhfu9N
",https://www.linkedin.com/feed/update/urn:li:activity:7275902067007709184,Organic,David Mezzetti,12/20/2024,All followers,364,2,0.005494505632668734,1,0,1,0.010989011265337467
"🎄🎅🎶 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",https://www.linkedin.com/feed/update/urn:li:activity:7275539285963882498,Organic,David Mezzetti,12/19/2024,All followers,353,11,0.03116147220134735,3,0,1,0.04249291867017746
2025 is the year of the AI vertical you say?,https://www.linkedin.com/feed/update/urn:li:activity:7275488346255085568,Organic,David Mezzetti,12/19/2024,All followers,264,5,0.018939394503831863,3,0,1,0.034090910106897354
"Medium article covering the AnnotateAI project.

https://lnkd.in/ec6KGvsg",https://www.linkedin.com/feed/update/urn:li:activity:7275330519762972672,Organic,David Mezzetti,12/19/2024,All followers,312,8,0.025641025975346565,1,0,1,0.03205128386616707
"🚚 📦 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",https://www.linkedin.com/feed/update/urn:li:activity:7275213835186126849,Organic,David Mezzetti,12/18/2024,All followers,1012,87,0.08596838265657425,13,1,1,0.10079051554203033
"The txtai-arxiv embeddings database on the Hugging Face Hub has been updated with data through December 2024!

https://lnkd.in/eSCCs-Jz",https://www.linkedin.com/feed/update/urn:li:activity:7275211742740201472,Organic,David Mezzetti,12/18/2024,All followers,413,17,0.04116222634911537,6,0,1,0.05811138078570366
"📚 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
",https://www.linkedin.com/feed/update/urn:li:activity:7275211468378144768,Organic,David Mezzetti,12/18/2024,All followers,516,34,0.06589147448539734,8,0,1,0.0833333358168602
"🚀 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",https://www.linkedin.com/feed/update/urn:li:activity:7274821495594315778,Organic,David Mezzetti,12/17/2024,All followers,396,24,0.06060606241226196,2,0,1,0.06818182021379471
"Want to add highlights and text annotations to PDFs, then check out txtmarker!

https://lnkd.in/dqkstw5",https://www.linkedin.com/feed/update/urn:li:activity:7274065142089240577,Organic,David Mezzetti,12/15/2024,All followers,515,13,0.025242717936635017,2,0,1,0.03106796182692051
"There is now a Docker app available for AnnotateAI. The web app automatically annotates papers from URLs. It also renders a version of the annotated PDF right in the browser along with being available for download.

Check it out!

https://lnkd.in/e9RURbPD",https://www.linkedin.com/feed/update/urn:li:activity:7274049801124462592,Organic,David Mezzetti,12/15/2024,All followers,817,60,0.07343941181898117,10,0,1,0.08690330386161804
"💥📝 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",https://www.linkedin.com/feed/update/urn:li:activity:7273400490766315530,Organic,David Mezzetti,12/13/2024,All followers,1382,112,0.08104196935892105,23,3,2,0.10130245983600616
"Postgres is all you need for vectors. Start small with SQLite and move up to Postgres for the win with txtai.

https://lnkd.in/eFg3zE4A
",https://www.linkedin.com/feed/update/urn:li:activity:7272704947547168768,Organic,David Mezzetti,12/11/2024,All followers,377,3,0.007957560010254383,5,0,0,0.02122015878558159
"💥 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",https://www.linkedin.com/feed/update/urn:li:activity:7272309319247740929,Organic,David Mezzetti,12/10/2024,All followers,1898,123,0.0648050606250763,27,15,1,0.0874604880809784
"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
",https://www.linkedin.com/feed/update/urn:li:activity:7271642302928850944,Organic,David Mezzetti,12/08/2024,All followers,398,4,0.01005025114864111,1,0,1,0.015075377188622952
"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",https://www.linkedin.com/feed/update/urn:li:activity:7270474156771577856,Organic,David Mezzetti,12/05/2024,All followers,319,11,0.03448275849223137,2,0,1,0.043887145817279816
"Coming in txtai 8.1 - support for Docling!

Docling is rapidly growing in popularity due to robust PDF text extraction and table parsing support.

Support is in the main txtai in GitHub now.

https://lnkd.in/gUYgQByS
",https://www.linkedin.com/feed/update/urn:li:activity:7270031724686843905,Organic,David Mezzetti,12/04/2024,All followers,517,27,0.05222437158226967,5,0,1,0.06382978707551956
"With AWS re:Invent upon us, did you know that txtai can flexibly plug into AWS?

Here is a diagram for hosting a RAG service.",https://www.linkedin.com/feed/update/urn:li:activity:7269046575736270851,Organic,David Mezzetti,12/01/2024,All followers,312,6,0.01923076994717121,4,0,1,0.035256411880254745
"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",https://www.linkedin.com/feed/update/urn:li:activity:7269010664717799424,Organic,David Mezzetti,12/01/2024,All followers,369,7,0.0189701896160841,1,0,1,0.024390242993831635
Happy Thanksgiving! 🦃 🍂 🍻 🏈,https://www.linkedin.com/feed/update/urn:li:activity:7267676372100169728,Organic,David Mezzetti,11/27/2024,All followers,225,1,0.004444444552063942,2,0,1,0.017777778208255768
"🧬⚕️🔬 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
",https://www.linkedin.com/feed/update/urn:li:activity:7267573987772096512,Organic,David Mezzetti,11/27/2024,All followers,464,21,0.045258618891239166,5,3,1,0.06465516984462738
"Cool to see txtai holding it's own against the big dogs 🐕‍🦺

https://lnkd.in/d2-HKx_d",https://www.linkedin.com/feed/update/urn:li:activity:7267524131661701120,Organic,David Mezzetti,11/27/2024,All followers,312,19,0.06089743599295616,7,2,1,0.09294871985912323
"🚀 The txtai journey over the last two years in one article. Plus a bunch of tips on how you can do the same.

https://lnkd.in/eX4bn5V8",https://www.linkedin.com/feed/update/urn:li:activity:7267212351513284608,Organic,David Mezzetti,11/26/2024,All followers,319,11,0.03448275849223137,4,0,1,0.050156738609075546
"🤖✨ Build autonomous agents with txtai!

https://lnkd.in/eQzJQz6B",https://www.linkedin.com/feed/update/urn:li:activity:7266924019315093506,Organic,David Mezzetti,11/25/2024,All followers,245,6,0.02448979578912258,2,0,1,0.0367346927523613
"There are lots of AI project charts out there with tiny logos that fit nicely into tiny boxes of functionality. Then it's only you to connect all the tiny logos together into a working system.

What if there is an all-in-one solution?

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7266476728049446912,Organic,David Mezzetti,11/24/2024,All followers,588,27,0.04591836780309677,6,0,2,0.0595238097012043
"Did you know txtai agents can load embeddings databases right from the Hugging Face Hub?

Want to add your own knowledge base contribution? Then what are you waiting for!

https://lnkd.in/eyTV9uwR",https://www.linkedin.com/feed/update/urn:li:activity:7266047086054227968,Organic,David Mezzetti,11/23/2024,All followers,352,2,0.005681818351149559,3,0,1,0.017045455053448677
"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",https://www.linkedin.com/feed/update/urn:li:activity:7266038865071587328,Organic,David Mezzetti,11/23/2024,All followers,298,5,0.016778523102402687,4,0,1,0.033557046204805374
"Agents are great but what if you have a known and repeatable process? Then don't overengineer it, use a workflow! Check out this example that builds a scheduled workflow that pushes out notifications.

https://lnkd.in/dvSkSepJ
",https://www.linkedin.com/feed/update/urn:li:activity:7265822308466839552,Organic,David Mezzetti,11/22/2024,All followers,235,3,0.012765957042574883,1,0,1,0.021276595070958138
"The NeuML Thanksgiving 🦃 🍂 🍻 🏈 newsletter is out!

https://lnkd.in/e-veEUhS
",https://www.linkedin.com/feed/update/urn:li:activity:7265772152988078080,Organic,David Mezzetti,11/22/2024,All followers,136,4,0.029411764815449715,1,0,0,0.036764707416296005
"Analyzing Hugging Face Posts with Graphs and Agents! Check out this article that explores graph analysis and agent execution.

Learn what and who's popular and trending in the world of AI!

https://lnkd.in/eFmdz6bF",https://www.linkedin.com/feed/update/urn:li:activity:7265544322547171329,Organic,David Mezzetti,11/22/2024,All followers,722,47,0.06509695202112198,12,3,1,0.08725761622190475
↪️️ Workflows vs 🤖 Agents in txtai - there's a place for both! ,https://www.linkedin.com/feed/update/urn:li:activity:7265138396157648896,Organic,David Mezzetti,11/20/2024,All followers,358,17,0.04748603329062462,3,0,1,0.05865921825170517
"txtai has a lot of features. Sometimes we miss the easy use cases!

https://lnkd.in/eP3ppFaY",https://www.linkedin.com/feed/update/urn:li:activity:7265055770658881537,Organic,David Mezzetti,11/20/2024,All followers,258,6,0.023255813866853714,3,0,1,0.03875968977808952
"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
",https://www.linkedin.com/feed/update/urn:li:activity:7265021014332293120,Organic,David Mezzetti,11/20/2024,All followers,544,19,0.03492647036910057,8,0,1,0.05147058889269829
"That was fast! Check out this excellent video on how to build AI Agents with txtai from the community!

https://lnkd.in/evRmtZBT",https://www.linkedin.com/feed/update/urn:li:activity:7265018068584558593,Organic,David Mezzetti,11/20/2024,All followers,310,8,0.025806451216340065,3,0,1,0.03870967775583267
"Check out this video on RAG with txtai from the community!

https://lnkd.in/eQEw6bDi",https://www.linkedin.com/feed/update/urn:li:activity:7265017977404624896,Organic,David Mezzetti,11/20/2024,All followers,179,3,0.016759777441620827,1,0,1,0.02793296054005623
"🥁 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",https://www.linkedin.com/feed/update/urn:li:activity:7264489976346636288,Organic,David Mezzetti,11/19/2024,All followers,1593,109,0.06842435896396637,28,1,3,0.08851224184036255
"💥 The txtai 8.0 release is coming very soon! The big leap forward is a full agent framework. Simplicity, easy of use and integration with the txtai ecosystem inbound. 

2025 is going to be 🔥",https://www.linkedin.com/feed/update/urn:li:activity:7263523258203795456,Organic,David Mezzetti,11/16/2024,All followers,612,21,0.03431372717022896,12,2,1,0.05882352963089943
"Bored? Want to read something mildly interesting? Then check out this article on how to run txtai in assembly 😀

https://lnkd.in/emHFyU98",https://www.linkedin.com/feed/update/urn:li:activity:7263365469854744576,Organic,David Mezzetti,11/16/2024,All followers,335,8,0.023880597203969955,3,0,1,0.035820893943309784
"Looking to ""hire"" an ""Agent Team""? Then check out the link below. Work smarter not harder 🔥

https://lnkd.in/eaAwEyyq",https://www.linkedin.com/feed/update/urn:li:activity:7260662463963058177,Organic,David Mezzetti,11/08/2024,All followers,425,22,0.051764704287052155,4,1,1,0.0658823549747467
"🚀 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",https://www.linkedin.com/feed/update/urn:li:activity:7259584341209485312,Organic,David Mezzetti,11/05/2024,All followers,608,29,0.04769736900925636,7,0,2,0.0625
"🎃👻 Happy Halloween! Recently txtai generated this ""spooky""🕸️ audio of ""The Raven"" using a speech + audio workflow.

Hope everyone has a fun evening!",https://www.linkedin.com/feed/update/urn:li:activity:7257765442440556544,Organic,David Mezzetti,10/31/2024,All followers,213,10,0.04694835841655731,2,0,1,0.061032865196466446
"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
",https://www.linkedin.com/feed/update/urn:li:activity:7257094886057742336,Organic,David Mezzetti,10/29/2024,All followers,317,8,0.025236593559384346,3,0,2,0.041009463369846344
"✨ Did you know that each txtai embeddings, pipeline and/or workflow can easily be served with a built-in API?

txtai has a scaffolding framework to automatically build FastAPI endpoints. In addition to that, any custom API endpoint can be added as shown in the article below.

https://lnkd.in/ei-u7grV
",https://www.linkedin.com/feed/update/urn:li:activity:7256623503003521024,Organic,David Mezzetti,10/28/2024,All followers,372,8,0.02150537632405758,5,2,1,0.04301075264811516
"🚀 The next release of txtai will have easy-to-use agents support. It's going to be 🔥!

https://lnkd.in/dxWDeey
",https://www.linkedin.com/feed/update/urn:li:activity:7255893422522208256,Organic,David Mezzetti,10/26/2024,All followers,451,17,0.037694014608860016,4,0,1,0.04878048598766327
"The NeuML Halloween 🎃 👻 🦇 🕷️ newsletter is out!

https://lnkd.in/efTYfZwD",https://www.linkedin.com/feed/update/urn:li:activity:7255544570149572608,Organic,David Mezzetti,10/25/2024,All followers,180,3,0.01666666753590107,1,0,1,0.02777777798473835
"A hotfix release of txtai (7.5.1) is out! This addresses a breaking change from the huggingface-hub library.

https://lnkd.in/ekz77NG8
",https://www.linkedin.com/feed/update/urn:li:activity:7255535693475598336,Organic,David Mezzetti,10/25/2024,All followers,243,10,0.041152264922857285,2,0,1,0.05349794402718544
"The AI space is growing fast but it's often hard to know how to apply AI to your business. 

Did you know that in addition to our open-source development that NeuML also provides advisory and strategic support services (i.e. Fractional CTO)?

Reach out to learn more!

https://neuml.com",https://www.linkedin.com/feed/update/urn:li:activity:7255193102636781569,Organic,David Mezzetti,10/24/2024,All followers,444,16,0.036036036908626556,6,0,1,0.05180180072784424
"🚀 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",https://www.linkedin.com/feed/update/urn:li:activity:7254998940314427393,Organic,David Mezzetti,10/23/2024,All followers,353,11,0.03116147220134735,7,1,1,0.056657224893569946
"🚀 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",https://www.linkedin.com/feed/update/urn:li:activity:7254854842664312832,Organic,David Mezzetti,10/23/2024,All followers,387,4,0.010335917584598064,3,0,1,0.02067183516919613
"txtai 7.5 added a number of features to add Speech to Speech RAG. One of the more complicated tasks was voice activity detection.

If you like FFTs and Butterworth filters, then click through to read more!

https://lnkd.in/e3EB743g",https://www.linkedin.com/feed/update/urn:li:activity:7254515978724691969,Organic,David Mezzetti,10/22/2024,All followers,271,5,0.018450183793902397,1,0,1,0.025830257683992386
"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",https://www.linkedin.com/feed/update/urn:li:activity:7254193006306635777,Organic,David Mezzetti,10/21/2024,All followers,4042,135,0.0333993062376976,65,4,4,0.051459673792123795
"Think Python always has to be slow? Well think again. Check out this article on how txtai was able to achieve near native performance with it's sparse indexes.

https://lnkd.in/eA3ui6cQ",https://www.linkedin.com/feed/update/urn:li:activity:7253716963309375488,Organic,David Mezzetti,10/20/2024,All followers,461,8,0.01735357940196991,3,0,2,0.028199566528201103
"PSA from txtai: RAG doesn't have to be hard. LLM frameworks don't have to be convoluted and poorly written.

💯 Have a great weekend.

https://lnkd.in/eExBX_3A
",https://www.linkedin.com/feed/update/urn:li:activity:7253077188688695297,Organic,David Mezzetti,10/18/2024,All followers,555,21,0.037837836891412735,10,0,1,0.05765765905380249
"Want to use ONNX models through Sentence Transformers with txtai? No problem!

https://lnkd.in/ehsEizjW",https://www.linkedin.com/feed/update/urn:li:activity:7252696467696422913,Organic,David Mezzetti,10/17/2024,All followers,287,11,0.038327526301145554,5,0,1,0.05923344939947128
"There are more RAG frameworks than ever. If you want to see how txtai compares, then check out this article.

https://lnkd.in/eu4frpZ6
",https://www.linkedin.com/feed/update/urn:li:activity:7252284910332166145,Organic,David Mezzetti,10/16/2024,All followers,343,7,0.020408162847161293,5,0,1,0.03790087625384331
"Generative Audio with txtai

Learn how txtai 7.5 supports Speech to Speech RAG and Generative Audio

https://lnkd.in/dPmt-bZm",https://www.linkedin.com/feed/update/urn:li:activity:7252014551863336960,Organic,David Mezzetti,10/15/2024,All followers,521,13,0.024952014908194542,11,0,1,0.04798464477062225
"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",https://www.linkedin.com/feed/update/urn:li:activity:7251955738476965888,Organic,David Mezzetti,10/15/2024,All followers,296,2,0.006756756920367479,1,0,1,0.013513513840734959
"🚀 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",https://www.linkedin.com/feed/update/urn:li:activity:7251669798709772289,Organic,David Mezzetti,10/14/2024,All followers,853,24,0.028135990723967552,13,0,1,0.044548653066158295
"Generative storytelling Part 2

Reading ""The Night Before Christmas"" with generated background music using the story text!

Article: https://lnkd.in/e7kd8_TE",https://www.linkedin.com/feed/update/urn:li:activity:7251203271141289985,Organic,David Mezzetti,10/13/2024,All followers,400,19,0.04749999940395355,3,0,1,0.057500001043081284
"📚 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",https://www.linkedin.com/feed/update/urn:li:activity:7251202372645851136,Organic,David Mezzetti,10/13/2024,All followers,456,35,0.0767543837428093,3,1,1,0.08771929889917374
🤯 My AI generated voice reading a poem with AI generated music in the background. All with a txtai RAG workflow!,https://www.linkedin.com/feed/update/urn:li:activity:7247986217286299651,Organic,David Mezzetti,10/04/2024,All followers,331,14,0.04229607433080673,1,0,1,0.04833836853504181
"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",https://www.linkedin.com/feed/update/urn:li:activity:7247959534634201089,Organic,David Mezzetti,10/04/2024,All followers,518,28,0.054054055362939835,5,1,2,0.06949806958436966
"🔥 Check out this snippet of audio from the recently released Speech to Speech RAG workflow with txtai.

This is blending the facts from a knowledge base with the creativity of a LLM!

Full video on YouTube: https://lnkd.in/eEGGsXBQ",https://www.linkedin.com/feed/update/urn:li:activity:7246708419729006592,Organic,David Mezzetti,10/01/2024,All followers,422,41,0.09715639799833298,3,0,1,0.10663507133722305
"If you're running on constrained hardware and/or with no GPU, LLaMA 3.2 1B models with 4-bit quantization work surprisingly well! The model is only 800MB.

Here's an easy-to-try example with txtai!

Example: https://lnkd.in/etd5w-9Q",https://www.linkedin.com/feed/update/urn:li:activity:7246583112908906496,Organic,David Mezzetti,09/30/2024,All followers,705,32,0.04539006948471069,8,2,1,0.06099290773272514
"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",https://www.linkedin.com/feed/update/urn:li:activity:7246089678221717504,Organic,David Mezzetti,09/29/2024,All followers,857,30,0.035005833953619,8,3,1,0.049008168280124664
"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
",https://www.linkedin.com/feed/update/urn:li:activity:7245734487353880576,Organic,David Mezzetti,09/28/2024,All followers,359,5,0.013927577063441277,5,0,1,0.03064066916704178
"💥 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",https://www.linkedin.com/feed/update/urn:li:activity:7245465896457052162,Organic,David Mezzetti,09/27/2024,All followers,1230,123,0.10000000149011612,22,11,2,0.1284552812576294
"Want your TextToSpeech (TTS) pipeline to sound British 🇬🇧? Then check out this model with over 100 variations of English accents!

https://lnkd.in/e_gg4nfD
",https://www.linkedin.com/feed/update/urn:li:activity:7244670426629517312,Organic,David Mezzetti,09/25/2024,All followers,514,21,0.04085602983832359,9,0,1,0.06031128391623497
"Want a Hugging Face dataset with the most recent (September 2024) copy of Wikipedia?

https://lnkd.in/eZsaMb3A
",https://www.linkedin.com/feed/update/urn:li:activity:7242134292942761984,Organic,David Mezzetti,09/18/2024,All followers,289,3,0.010380622930824757,1,0,1,0.017301037907600403
"🔥 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",https://www.linkedin.com/feed/update/urn:li:activity:7241820975690838017,Organic,David Mezzetti,09/17/2024,All followers,944,40,0.04237288236618042,11,0,2,0.05614406615495682
"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",https://www.linkedin.com/feed/update/urn:li:activity:7240680798092165120,Organic,David Mezzetti,09/14/2024,All followers,595,18,0.03025210089981556,4,0,2,0.040336135774850845
"✅ Check out the NeuML fall newsletter. This issue covers all the developments over the last couple of months!

https://lnkd.in/eqcBMAXj",https://www.linkedin.com/feed/update/urn:li:activity:7240412660146532353,Organic,David Mezzetti,09/13/2024,All followers,275,6,0.02181818149983883,1,0,1,0.02909090928733349
"🔥 Happy to release the September 2024 version of txtai's Wikipedia Embedding indexes!

Link on HF Hub: https://lnkd.in/e4newZeM
",https://www.linkedin.com/feed/update/urn:li:activity:7240380020227997696,Organic,David Mezzetti,09/13/2024,All followers,407,19,0.04668304696679115,4,0,1,0.058968059718608856
🚀 Want to try the new OpenAI o1 model with txtai? No problem!,https://www.linkedin.com/feed/update/urn:li:activity:7240341524104839168,Organic,David Mezzetti,09/13/2024,All followers,686,14,0.020408162847161293,11,0,1,0.03790087625384331
"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",https://www.linkedin.com/feed/update/urn:li:activity:7239997328827297793,Organic,David Mezzetti,09/12/2024,All followers,273,5,0.018315019086003304,4,0,1,0.03663003817200661
"⚡ Looking to build with txtai on AWS? Then check out this reference architecture. 

Code: https://lnkd.in/eWjbci_k",https://www.linkedin.com/feed/update/urn:li:activity:7239656871681306624,Organic,David Mezzetti,09/11/2024,All followers,530,16,0.030188679695129395,4,3,1,0.04528301954269409
"☁️ 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",https://www.linkedin.com/feed/update/urn:li:activity:7239623524896690176,Organic,David Mezzetti,09/11/2024,All followers,456,17,0.03728070110082626,8,1,1,0.05921052768826485
"Want a vector embeddings model trained for medical literature?

https://lnkd.in/egnEKcqd
",https://www.linkedin.com/feed/update/urn:li:activity:7239241118700179457,Organic,David Mezzetti,09/10/2024,All followers,426,11,0.025821596384048462,3,0,1,0.035211268812417984
"Curious about Graph RAG? Did you know that txtai has an ready-to-use Embeddings graph database for popular Wikipedia articles on the HF Hub?

https://lnkd.in/e3fPr6fd",https://www.linkedin.com/feed/update/urn:li:activity:7238920785522798594,Organic,David Mezzetti,09/09/2024,All followers,490,10,0.020408162847161293,7,0,1,0.0367346927523613
"🔥 Learn about txtai's embedding index format for open data access.

https://lnkd.in/eh4-rVEr",https://www.linkedin.com/feed/update/urn:li:activity:7237881069146902528,Organic,David Mezzetti,09/06/2024,All followers,403,9,0.022332506254315376,4,0,1,0.034739453345537186
"💯🤯 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",https://www.linkedin.com/feed/update/urn:li:activity:7237636783453220864,Organic,David Mezzetti,09/06/2024,All followers,877,28,0.03192702308297157,12,0,1,0.04675028473138809
"🔥 Coming soon - txtai stores vectors in SQLite via sqlite-vec!

https://lnkd.in/dDi9_4FF",https://www.linkedin.com/feed/update/urn:li:activity:7237136492538548224,Organic,David Mezzetti,09/04/2024,All followers,406,9,0.02216748706996441,3,0,1,0.03201970458030701
"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
",https://www.linkedin.com/feed/update/urn:li:activity:7234997730945691648,Organic,David Mezzetti,08/29/2024,All followers,505,20,0.039603959769010544,3,0,1,0.04752475395798683
"AI/ML/NLP workflows? 🥱 txtai had that back in 2021.

https://lnkd.in/eDj8NZtb",https://www.linkedin.com/feed/update/urn:li:activity:7233815946593726465,Organic,David Mezzetti,08/26/2024,All followers,370,6,0.01621621660888195,3,0,1,0.027027027681469917
"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",https://www.linkedin.com/feed/update/urn:li:activity:7233812131312201729,Organic,David Mezzetti,08/26/2024,All followers,465,8,0.017204301431775093,3,0,1,0.025806451216340065
"If you're evaluating LangChain, LlamaIndex and/or Chroma DB for your projects, then check out this article that contrasts those libraries with txtai.

https://lnkd.in/eu4frpZ6
",https://www.linkedin.com/feed/update/urn:li:activity:7231647335338909696,Organic,David Mezzetti,08/20/2024,All followers,606,21,0.03465346619486809,8,0,1,0.049504950642585754
"txtai RAG v0.4 is out! This release adds a couple new parameters to change the context and max generation sizes. 

GitHub: https://lnkd.in/evdB5HgN
Docker Hub: https://lnkd.in/e-3Cx_68
",https://www.linkedin.com/feed/update/urn:li:activity:7231458302478413824,Organic,David Mezzetti,08/20/2024,All followers,531,25,0.047080978751182556,4,0,1,0.05649717524647713
"🔥 Want to use LLMs and RAG to analyze the stock market with current data? Then check out this article!

https://lnkd.in/db5PCc98
",https://www.linkedin.com/feed/update/urn:li:activity:7231377944626044928,Organic,David Mezzetti,08/19/2024,All followers,452,24,0.05309734493494034,5,0,1,0.06637167930603027
"🚀 Great to see how each new release of txtai brings new users into the fold. Look how adding llama.cpp and LLM APIs bumped up the number of downloads. And how better examples with streaming RAG with local document text extraction did the same!

https://lnkd.in/ecEgQDYM",https://www.linkedin.com/feed/update/urn:li:activity:7230542476527218688,Organic,David Mezzetti,08/17/2024,All followers,537,15,0.02793296054005623,6,0,1,0.04096834361553192
"🚀 Just published this comprehensive article covering our new RAG application with txtai, check it out!

https://lnkd.in/dPEPv4Fm
",https://www.linkedin.com/feed/update/urn:li:activity:7229533817089269763,Organic,David Mezzetti,08/14/2024,All followers,646,33,0.05108359083533287,7,0,2,0.06501547992229462
"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",https://www.linkedin.com/feed/update/urn:li:activity:7229090247416975360,Organic,David Mezzetti,08/13/2024,All followers,632,7,0.011075949296355247,7,0,3,0.026898734271526337
"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",https://www.linkedin.com/feed/update/urn:li:activity:7228883439846895617,Organic,David Mezzetti,08/12/2024,All followers,780,23,0.029487179592251778,6,0,1,0.03846153989434242
"🥳🎂 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",https://www.linkedin.com/feed/update/urn:li:activity:7228379520309825537,Organic,David Mezzetti,08/11/2024,All followers,780,38,0.04871794953942299,13,2,1,0.06923077255487442
"📈 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",https://www.linkedin.com/feed/update/urn:li:activity:7228373119634210816,Organic,David Mezzetti,08/11/2024,All followers,531,24,0.0451977401971817,6,1,1,0.060263652354478836
"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
",https://www.linkedin.com/feed/update/urn:li:activity:7228017143827566592,Organic,David Mezzetti,08/10/2024,All followers,497,9,0.018108651041984558,5,0,1,0.03018108569085598
"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
",https://www.linkedin.com/feed/update/urn:li:activity:7227995148129882112,Organic,David Mezzetti,08/10/2024,All followers,401,11,0.027431420981884003,1,0,1,0.03241895139217377
"💥 Did you know that txtai search results can be loaded as a Pandas DataFrame?

https://lnkd.in/euPdWUe2",https://www.linkedin.com/feed/update/urn:li:activity:7227781117427302400,Organic,David Mezzetti,08/09/2024,All followers,510,8,0.01568627543747425,7,0,1,0.0313725508749485
"🙏 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
",https://www.linkedin.com/feed/update/urn:li:activity:7227731402933387264,Organic,David Mezzetti,08/09/2024,All followers,502,27,0.05378486216068268,6,0,1,0.06772908568382263
"📄 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!",https://www.linkedin.com/feed/update/urn:li:activity:7227498189661052928,Organic,David Mezzetti,08/09/2024,All followers,542,28,0.05166051536798477,4,0,1,0.060885608196258545
"🔥 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",https://www.linkedin.com/feed/update/urn:li:activity:7227396692638138368,Organic,David Mezzetti,08/08/2024,All followers,1129,59,0.05225863680243492,16,0,4,0.0699734315276146
"📑 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",https://www.linkedin.com/feed/update/urn:li:activity:7227000688088621056,Organic,David Mezzetti,08/07/2024,All followers,793,29,0.0365699864923954,10,5,1,0.056746531277894974
"Nice to see a continued bump in growth for txtai over the last couple of weeks!

https://lnkd.in/eXrGAb6W",https://www.linkedin.com/feed/update/urn:li:activity:7225931632002691072,Organic,David Mezzetti,08/04/2024,All followers,850,28,0.03294117748737335,8,2,1,0.04588235169649124
"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",https://www.linkedin.com/feed/update/urn:li:activity:7222921388091682816,Organic,David Mezzetti,07/27/2024,All followers,1211,36,0.029727498069405556,8,0,1,0.03715937212109566
"Want to learn more on how the txtai RAG app works? Then check it out on GitHub!

https://lnkd.in/evdB5HgN
",https://www.linkedin.com/feed/update/urn:li:activity:7222630553475256320,Organic,David Mezzetti,07/26/2024,All followers,515,14,0.027184465900063515,3,0,1,0.034951455891132355
"📋 The 2024 txtai survey is out!

We don't do telemetry (we'll be on the right side of this future issue). We're old fashioned and ask 🗩

Please submit your thoughts if you'd like to help guide the future direction of the project.

https://lnkd.in/equGWe9d",https://www.linkedin.com/feed/update/urn:li:activity:7222606179376529411,Organic,David Mezzetti,07/26/2024,All followers,417,14,0.03357314318418503,2,0,1,0.04076738655567169
"🚀 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",https://www.linkedin.com/feed/update/urn:li:activity:7222462570320793600,Organic,David Mezzetti,07/26/2024,All followers,1533,115,0.07501630485057831,23,1,2,0.09197651594877243
"🥴 Text extraction is a messy business!

There are libraries that don't work well, require numerous dependencies, have licenses that are commercially untenable (AGPL-3) and/or require sending data to a remote API for processing!

With txtai, we use Apache Tika as our main text extraction library. It works well for most formats but it does require Java. Keep in mind that a headless JRE is smaller than dependencies other text extraction libraries require such as LibreOffice.

txtai also plays nice with others. If you'd like to use an external library for PDF parsing, check this example out. Just be aware of the license for this library!

Link to code: https://lnkd.in/dt2z7W_j",https://www.linkedin.com/feed/update/urn:li:activity:7222242898904293376,Organic,David Mezzetti,07/25/2024,All followers,1410,58,0.041134752333164215,9,2,2,0.05035461112856865
"🚀 Happy to see txtai is a trending Python project today on GitHub

https://lnkd.in/dUixHvq",https://www.linkedin.com/feed/update/urn:li:activity:7221481859354894338,Organic,David Mezzetti,07/23/2024,All followers,994,39,0.03923541307449341,18,1,1,0.05935613811016083
"🚀 Nice to see txtai trending on Hacker News today!

https://lnkd.in/ez3tmEa",https://www.linkedin.com/feed/update/urn:li:activity:7220876861659119616,Organic,David Mezzetti,07/21/2024,All followers,758,27,0.03562005236744881,6,3,1,0.04881266504526138
"Did you know that a txtai embeddings database is a file format? 

While txtai can integrate with a number of external components, it's base components all save content locally. The entire database can be saved to a singled compressed file. There is built-in support for saving these indexes to cloud storage (i.e. S3) and the HF Hub.

Learn more about the file formats behind this here: https://lnkd.in/e3j5Pf-n",https://www.linkedin.com/feed/update/urn:li:activity:7220747351454355456,Organic,David Mezzetti,07/21/2024,All followers,675,13,0.0192592591047287,3,0,1,0.02518518455326557
"📈 Let's talk about Graph RAG. We've been looking at graph-based approaches for context generation since 2022. The best use case we've seen for Graph RAG is for more complex questions and research. For example, think of a problem as a road trip with multiple stops. A graph path traversal is a great way to pick up various concepts as context, concepts which may not be directly related and not picked up by a simple keyword/vector search.

The attached image shows two graph path traversal examples. The first shows the path between a squirrel and the Red Sox winning the world series. The 2nd shows an image path from a person parachuting and someone holding a french horn. Note the progression of both the text and images along the way. There is also another example of traversing history from the end of the Roman Empire to the Norman Conquest of England.

For problems like this, graphs do a great job. If the answer is a simple retrieval of a single entry, Graph RAG doesn't add much value. Like all things, Graph RAG isn't the be-all and end-all.

Read more in the articles below.

Semantic Graph Intro: https://lnkd.in/eMsAXarn
Graph RAG: https://lnkd.in/d-BSjuj7",https://www.linkedin.com/feed/update/urn:li:activity:7220390554633732097,Organic,David Mezzetti,07/20/2024,All followers,1174,38,0.032367974519729614,15,0,3,0.047700170427560806
"🔒 Want to work with vectors, LLMs and RAG but worried about security?

Did you know that txtai has full Postgres integration (dense vectors, sparse vectors, content and graph)? This can be combined with standard row level security to limit what content a user can utilize for GenAI processes.

Learn more at the links below.

Article: https://lnkd.in/eFeFNgYK
Postgres docs: https://lnkd.in/e9aFks5Y
",https://www.linkedin.com/feed/update/urn:li:activity:7220068323353337857,Organic,David Mezzetti,07/19/2024,All followers,510,5,0.009803921915590763,7,0,1,0.02549019642174244
"txtai now supports building vector databases and/or RAG pipelines exclusively with llama.cpp and/or API integrations (i.e OpenAI, Claude, Ollama etc)

https://lnkd.in/e_rud4Ff",https://www.linkedin.com/feed/update/urn:li:activity:7219653044861370368,Organic,David Mezzetti,07/18/2024,All followers,851,14,0.016451233997941017,9,0,1,0.02820211462676525
"🔥 We'll have a couple new videos in the coming weeks on our YouTube channel covering txtai. Stay tuned.

https://lnkd.in/dREtaaM7",https://www.linkedin.com/feed/update/urn:li:activity:7219451260075212800,Organic,David Mezzetti,07/17/2024,All followers,277,5,0.018050542101264,2,0,1,0.02888086624443531
"Earlier this month we ran an experiment to compare txtai with other popular open-source frameworks.

The conclusion is that txtai should be on your list

Read more here: https://lnkd.in/eu4frpZ6
",https://www.linkedin.com/feed/update/urn:li:activity:7219385511302307840,Organic,David Mezzetti,07/17/2024,All followers,252,6,0.02380952425301075,2,0,1,0.0357142873108387
"✂️ New in txtai 7.3: Better Text Extraction. This release brings significant improvements to parsing web content (HTML). Supports parsing sections, lists, tables and more!

Link to code: https://lnkd.in/ebWwY473",https://www.linkedin.com/feed/update/urn:li:activity:7219027434950586368,Organic,David Mezzetti,07/16/2024,All followers,989,48,0.04853387176990509,12,3,2,0.06572294980287552
"🚀 New in txtai 7.3: Streaming RAG. This feature builds off streaming LLMs which iteratively return chunks of content as a stream vs waiting for the entire generation call. Streaming is supported with Transformers, llama.cpp and LLM APIs (i.e. GPT-4, Claude, Ollama)

Try it out with txtai's new RAG application. Plug in a new txtai embeddings index to use this application with your own data.

Docker image: https://lnkd.in/d-fJiVXR
RAG Application: https://lnkd.in/e82fddyY",https://www.linkedin.com/feed/update/urn:li:activity:7219009499846619136,Organic,David Mezzetti,07/16/2024,All followers,676,44,0.06508875638246536,5,0,1,0.07396449893712997
"txtai is a grassroots project dedicated to the developer-experience. We're passionate about building a quality project. GitHub ⭐'s and 🔥 comments go a long way.

NeuML is building a sustainable and profitable company from the start. We provide consulting services in situations where it's an interesting business problem. It's not about generating buzz to get the next funding round or rubbing elbows with big names in Silicon Valley.

The majority of our work is open-source and from our desk to yours with ❤️ Thank you!",https://www.linkedin.com/feed/update/urn:li:activity:7218951378222596097,Organic,David Mezzetti,07/16/2024,All followers,570,38,0.06666667014360428,5,2,1,0.08070175349712372
"🚀 txtai 7.3 is out! 

This release adds a new RAG front-end template, streaming LLM and streaming RAG support along with significant text extraction improvements.

From local to remote vectorization, model inference and data storage - txtai has you covered. It's the easiest way to build vector search, LLM and RAG systems without the bloat. 

See below for more.

GitHub:https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/e_4kYXma
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb

API Clients:
Python: https://lnkd.in/eqVx_nqt
JavaScript​: https://lnkd.in/dM8ua2y
Rust: https://lnkd.in/d2MAae2
Java: https://lnkd.in/dqmmjTw
Go: https://lnkd.in/dq7Ujv4",https://www.linkedin.com/feed/update/urn:li:activity:7218684906715889664,Organic,David Mezzetti,07/15/2024,All followers,1658,87,0.052472859621047974,25,1,2,0.06936067342758179
"😎 All the cool kids have a RAG framework in 2024. Why? Because it's really easy. A simple RAG framework can be a couple lines of code. If all you do is call API services and stitch the results together, what value does this add?

txtai is much more than this. It's a local vector database that can also store data in Postgres. A LLM framework that works with multiple LLM backends local and remote. A sophisticated RAG pipeline. Not to mention components for graphs, BM25 and other traditional ML model pipelines. It does all this without creating unnecessary complexity and abstraction.

And it's open-source, check it out: https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7217524333554925570,Organic,David Mezzetti,07/12/2024,All followers,1418,38,0.0267983078956604,11,0,4,0.037376586347818375
"We often hear one say they have a LLM and they want to solve a problem. LLMs aren't always the best tool for the job. Let's take text classification using a sentiment dataset.

Running LLM prompts for this dataset only leads to 58% accuracy! Training with a 4.4M parameter model has 91% accuracy. BERT is 93%. Sure we can fine-tune the LLM for this task but why spend an hour vs 8 minutes?

Be willing to accept the simpler solution.

Link to code: https://lnkd.in/d38xBm6X",https://www.linkedin.com/feed/update/urn:li:activity:7217120867527385090,Organic,David Mezzetti,07/11/2024,All followers,1710,49,0.028654970228672028,20,1,1,0.04152046889066696
"🤔 Machine translation just ask a LLM to do it?

While LLMs can translate that doesn't mean they should. What if we could utilize smaller models that were trained to translate between specific languages? What if there was a pipeline that automatically loads models based on the source to target language? 

🚀 Enter txtai's translation pipeline! The Translation pipeline automatically detects languages and searches the Hugging Face Hub for the best specialized model to perform the translation. These specialized models are often smaller than LLMs and much faster. 

Link to code: https://lnkd.in/eefCwRDu",https://www.linkedin.com/feed/update/urn:li:activity:7216410295450120192,Organic,David Mezzetti,07/09/2024,All followers,1884,46,0.024416135624051094,14,3,1,0.03397027775645256
"No GPU available? Only using external API services? Want llama.cpp GPU models with txtai? 

Did you know that Torch has a CPU-only install that brings a significantly smaller dependency chain (no PyPI CUDA libraries). The txtai-cpu Docker image employs this same strategy. It reduces the image size from 3.1 GB to 700 MB.

Link to code: https://lnkd.in/exnasWHf",https://www.linkedin.com/feed/update/urn:li:activity:7216057535986839552,Organic,David Mezzetti,07/08/2024,All followers,1202,17,0.014143094420433044,20,0,5,0.03494176268577576
"🗎 Want to summarize webpages, word documents, PDFs and more? Did you know there are models pre-built for summarization that pre-date the latest LLMs? And that they do a decent job and are faster? 

txtai supports pre-trained summarization models and LLMs for summarization. Either can be run as Python workflows or FastAPI services.

Link to code: https://lnkd.in/emg4QUfm
",https://www.linkedin.com/feed/update/urn:li:activity:7215768857762762752,Organic,David Mezzetti,07/07/2024,All followers,1326,59,0.04449472203850746,11,0,2,0.05429864302277565
"✨ Let's build on our previous BM25 post and take tokenization into account. We'll compare LangChain's BM25 retriever, the recently released bm25s library (built with Scipy sparse matrices) and txtai. We'll use the same tokenization method for all 3, the Unicode Text Segmentation algorithm (UAX 29). Keep in mind that this is relevant to those using hybrid search (vector + keyword).

1.6M ArXiv abstracts were evaluated. txtai's index time was slower but search times were significantly faster. txtai used almost 6x less RAM.

Link to code: https://lnkd.in/ekSxN9pJ",https://www.linkedin.com/feed/update/urn:li:activity:7215679457859121152,Organic,David Mezzetti,07/07/2024,All followers,2393,107,0.04471374675631523,33,3,2,0.06059339642524719
"✂️ Let's talk tokenization, an underappreciated part of the NLP pipeline! Naive methods like splitting on whitespace work for European languages but not with others. Stop words were once a common pattern but have since fallen out of favor.

Modern keyword tokenizers split using the Unicode Text Segmentation algorithm (UAX 29). This enables broader language support. Many Transformers models use either word/subword or BPE tokenizers.

txtai has a built-in tokenizer that implements UAX 29. This functionality is similar to what's found in systems like Elasticsearch/Apache Lucene and it's used with txtai's sparse keyword indexing.

Link to the code: https://lnkd.in/egVRgEAT",https://www.linkedin.com/feed/update/urn:li:activity:7215458584950681601,Organic,David Mezzetti,07/06/2024,All followers,1938,56,0.028895769268274307,28,1,3,0.04540763795375824
"⚡ LangChain and LlamaIndex both use the Rank-BM25 library to provide in-line BM25 document retrieval. Rank-BM25 is a great way to quickly stand up a BM25 search index for a small number of documents. But it doesn't scale as it's built to run in memory.

txtai has it's own BM25 implementation in Python. Term vectors are built harnessing the native performance of the Python arrays package. These term vectors are stored in a SQLite database. LRU caching stores frequently used vectors in memory. This combination of factors enables a highly performant index.

For this comparison, 2.3M ArXiv abstracts were used. LangChain ran out of memory (32 GB of RAM). The test was scaled to 1.6M abstracts. txtai had 2x slower index times but 13x faster search times than LangChain. LangChain used 25 GB of RAM, txtai used 3.8 GB of RAM.

Link to code: https://lnkd.in/eD7qn6qn",https://www.linkedin.com/feed/update/urn:li:activity:7215085459910070272,Organic,David Mezzetti,07/05/2024,All followers,2096,92,0.043893128633499146,20,0,3,0.05486641079187393
"The latest from NeuML in one place.

https://lnkd.in/emkngSiK",https://www.linkedin.com/feed/update/urn:li:activity:7215020830399832064,Organic,David Mezzetti,07/05/2024,All followers,336,3,0.008928571827709675,2,0,1,0.01785714365541935
"💥⚾ BM25 continues to be a heavy hitter in the information retrieval space. Did you know that txtai has a BM25 component built for speed💨? 

BM25 term vectors are built harnessing the native performance of the Python arrays package. These term vectors are stored in a SQLite database. LRU caching stores frequently used vectors in memory. This combination of factors enables a highly performant index. 

https://lnkd.in/eA3ui6cQ",https://www.linkedin.com/feed/update/urn:li:activity:7214952352154292225,Organic,David Mezzetti,07/05/2024,All followers,1673,20,0.01195457298308611,5,0,2,0.016138672828674316
"How does txtai stack up against other open source frameworks for Vector Search & RAG?

Short answer: it is up to the task 💯

https://lnkd.in/eu4frpZ6
",https://www.linkedin.com/feed/update/urn:li:activity:7214681508152832000,Organic,David Mezzetti,07/04/2024,All followers,569,15,0.026362039148807526,5,0,1,0.036906853318214417
"One LLM pipeline many tasks with txtai. The LLM pipeline supports many models local and remote. Simply change the model path.

Inputs can be prompt strings or chat messages. Easily run in Python or as an API service.

Link to code: https://lnkd.in/emT6SChu",https://www.linkedin.com/feed/update/urn:li:activity:7214580572268937216,Organic,David Mezzetti,07/04/2024,All followers,611,18,0.02945990115404129,6,0,1,0.040916528552770615
"💡 Retrieval Augmented Generation (RAG) is one of the most practical use cases of the Generative AI era. An LLM when presented with a bounding context often will generate factually grounded answers.

txtai makes RAG with your documents easy. It has pipelines to extract text from Office and PDF documents while preserving structured formatting (i.e. tables, lists). It has an easy-to-use LLM pipeline that automatically loads models from Hugging Face, llama.cpp and APIs (OpenAI, Ollama etc).

See how this compares to RAG with LangChain (txtai was able to generate the correct answer given it preserves table formatting): https://lnkd.in/evv_7gi6",https://www.linkedin.com/feed/update/urn:li:activity:7214432494748581888,Organic,David Mezzetti,07/04/2024,All followers,898,40,0.04454343020915985,11,3,1,0.061247214674949646
"💥 Hnswlib is a great vector indexing library. It's integrated into a number of vector databases.

txtai utilizes the same core pipeline for generating embeddings and storing vectors regardless of the end components. There has been a careful focus in building a highly performant and efficient vector database implementation that runs great locally.

txtai uses mmap-ing and other techniques to ensure that memory limits are respected. Streaming vector generation and offloading those vectors during index creation allows txtai to build large local indexes whereas other implementations run out of memory. 

See how this compares to Chroma DB (txtai is 3x faster for the same dataset): https://lnkd.in/eY_nMA85",https://www.linkedin.com/feed/update/urn:li:activity:7214335924824850432,Organic,David Mezzetti,07/03/2024,All followers,747,31,0.04149933159351349,10,0,1,0.05622490122914314
"Breadth vs depth? Support the maximum number of integrations or build a few deep and meaningful integrations?

txtai has taken the depth approach to ensure that integrations it adds are performant and support a large number of the underlying libraries features. We're not into box checking.

https://lnkd.in/dxWDeey
",https://www.linkedin.com/feed/update/urn:li:activity:7213901962503712768,Organic,David Mezzetti,07/02/2024,All followers,505,8,0.015841584652662277,4,0,1,0.02574257366359234
"Came across this txtai mention in Star History's blog on open-source AI search. Thank you!

https://lnkd.in/eJU9TkpM",https://www.linkedin.com/feed/update/urn:li:activity:7213638147979554817,Organic,David Mezzetti,07/01/2024,All followers,342,8,0.023391813039779663,3,0,1,0.035087719559669495
"🔥 Did you know that the original ""Introducing txtai"" notebook from the 1.0 release in August 2020 by and large still works today? 1300+ commits later. Why? Because user experience and good engineering practices matter to us.

See the original notebook for yourself: https://lnkd.in/ecfhQsSV",https://www.linkedin.com/feed/update/urn:li:activity:7213545801132765186,Organic,David Mezzetti,07/01/2024,All followers,546,7,0.012820512987673283,7,0,1,0.02747252769768238
"⚡ Faiss is a great vector indexing library. It has so many features past just a flat index. txtai automatically creates a performant Faiss index scaled by the size of the incoming data. The index type can also be fully customized with configuration. This shows the power of a full-featured and long-standing integration.

See how this compares to LlamaIndex: https://lnkd.in/eWqU5z3U",https://www.linkedin.com/feed/update/urn:li:activity:7213531377437155328,Organic,David Mezzetti,07/01/2024,All followers,1095,46,0.0420091338455677,9,0,1,0.05114155262708664
"🚀 Want to run RAG with Ollama and txtai? No problem! txtai supports Ollama models for both embeddings and LLM generation.

Link to code: https://lnkd.in/e8tvbp7m",https://www.linkedin.com/feed/update/urn:li:activity:7213252452555329536,Organic,David Mezzetti,06/30/2024,All followers,531,12,0.02259887009859085,4,0,1,0.03201506659388542
"What do we get with txtai out of the box? txtai vector indexes use SQLite + Faiss by default. This enables search with SQL and dynamic columns. Results are standard Python dictionaries and that allows direct integration with Pandas/Polars DataFrames.

See how this compares to LangChain: https://lnkd.in/esX88rPR",https://www.linkedin.com/feed/update/urn:li:activity:7213202454392270848,Organic,David Mezzetti,06/30/2024,All followers,2051,133,0.06484641879796982,26,0,5,0.07996099442243576
"A fundamental part of any RAG solution is the data source.

txtai is an all-in-one embeddings database with support for storing data as local file-based indexes. Did you know that txtai has built-in support for storing these indexes as a Hugging Face model and cloud storage such as with AWS S3 buckets? These composable indexes can be built and shared for RAG.

https://lnkd.in/eMGY7uRB

A couple example datasets are linked below.

txtai-wikipedia: https://lnkd.in/eQz5dKtG
txtai-arxiv: https://lnkd.in/eSCCs-Jz",https://www.linkedin.com/feed/update/urn:li:activity:7213138432556969984,Organic,David Mezzetti,06/30/2024,All followers,1454,11,0.007565337233245373,2,0,1,0.00962861068546772
"Did you know that txtai has a full-featured workflow framework? It can run tasks sequentially, multi-threaded and/or with multiple processes (to work around Python's GIL).

Parse a directory of files, files in a S3 bucket, multi-step prompt action and more!

https://lnkd.in/eDj8NZtb
",https://www.linkedin.com/feed/update/urn:li:activity:7212840421532536832,Organic,David Mezzetti,06/29/2024,All followers,685,17,0.024817518889904022,6,0,1,0.03503649681806564
"Frustrated by convoluted AI/LLM/RAG frameworks? Don't settle for 🗑️. Take a look at txtai!

https://lnkd.in/dxWDeey
",https://www.linkedin.com/feed/update/urn:li:activity:7212432020172349440,Organic,David Mezzetti,06/28/2024,All followers,358,10,0.02793296054005623,6,0,1,0.04748603329062462
"Curious about RAG? Not a programmer and want to experiment? Well check out these easy-to-use series of RAG applications packaged as Docker images! Everything needed is built in.

Wikipedia: https://lnkd.in/d-fJiVXR
ArXiv: https://lnkd.in/dMx8Sfk2

All code and configuration used to build these images can be found on txtai's GitHub repo: https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7212193563953012736,Organic,David Mezzetti,06/27/2024,All followers,344,25,0.07267441600561142,2,0,1,0.08139535039663315
"Did you know that txtai has an application for building language model workflows? Try it out on the HF Hub.

https://lnkd.in/dQxbucux
",https://www.linkedin.com/feed/update/urn:li:activity:7211020252254498818,Organic,David Mezzetti,06/24/2024,All followers,546,11,0.02014652080833912,4,0,1,0.029304029420018196
"Want your own local RAG API service? Did you know that txtai can automatically start an API service using YAML? And that it can be run as a Docker container?

Read more here: https://lnkd.in/eC2_HkEi",https://www.linkedin.com/feed/update/urn:li:activity:7210716942779777024,Organic,David Mezzetti,06/23/2024,All followers,538,8,0.014869888313114643,1,0,1,0.01858736015856266
"Did you know that txtai provides a schemaless database? Metadata can be persisted in SQLite, Postgres, MariaDB and DuckDB. Vectors can be stored with Faiss, HNSWLib and PGVector.

Read more on how this all works here: https://lnkd.in/e3j5Pf-n
",https://www.linkedin.com/feed/update/urn:li:activity:7210609072092389377,Organic,David Mezzetti,06/23/2024,All followers,528,4,0.007575757801532745,2,0,1,0.013257576152682304
"txtai has published a lot of content lately covering RAG. This article puts all of the best content in one place!

https://lnkd.in/eDigfyYd
",https://www.linkedin.com/feed/update/urn:li:activity:7210321373389336578,Organic,David Mezzetti,06/22/2024,All followers,919,43,0.0467899888753891,11,2,2,0.06311208009719849
"🤔 Curious about how Retrieval Augmented Generation (RAG) works? Then check out this easy-to-understand article covering how txtai RAG works!

This article shows how to create RAG processes in Python. It also covers standing up low code RAG API services with FastAPI and Docker.

https://lnkd.in/eExBX_3A",https://www.linkedin.com/feed/update/urn:li:activity:7210243904963538944,Organic,David Mezzetti,06/22/2024,All followers,839,10,0.011918950825929642,4,0,1,0.017878426238894463
"Why are so many AI projects failing? Unrealistic expectations has to be at the top. But another often overlooked item is picking too complex a stack. Many of the popular AI frameworks try to support integrating everything leading to unnecessary complexity.

txtai follows the KISS principle with it's architecture. It's designed to get up and running fast but also scale to production

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7208574391343820801,Organic,David Mezzetti,06/17/2024,All followers,678,10,0.014749262481927872,9,0,1,0.029498524963855743
"Did you know that txtai has a customizable FastAPI integration? Check out this example on how to create a custom endpoint that can easily be run as an API service.

https://lnkd.in/ei-u7grV
",https://www.linkedin.com/feed/update/urn:li:activity:7208535149259427842,Organic,David Mezzetti,06/17/2024,All followers,461,8,0.01735357940196991,2,0,1,0.023861171677708626
"Looking for a fun Sunday project? Then check out this article that covers how to load Python code via C/C++ and x86 assembly. Step through an example using txtai. 

https://lnkd.in/emHFyU98
",https://www.linkedin.com/feed/update/urn:li:activity:7208079799032950791,Organic,David Mezzetti,06/16/2024,All followers,477,6,0.012578615918755531,4,0,3,0.027253668755292892
"Want to use LLMs to automatically extraction entity-relationship models? And  load them into a knowledge graph? Then check out this article.

https://lnkd.in/dBy_H4C2",https://www.linkedin.com/feed/update/urn:li:activity:7208074938795053056,Organic,David Mezzetti,06/16/2024,All followers,2141,108,0.05044371634721756,25,2,5,0.06539000570774078
"If you're new to LLMs/Vector Search/RAG/GenAI, then this article is worth a read. It covers a basic overview of semantic search, which is often the foundation of a RAG system.

https://lnkd.in/eqZs96D3",https://www.linkedin.com/feed/update/urn:li:activity:7207373729729761282,Organic,David Mezzetti,06/14/2024,All followers,595,14,0.0235294122248888,4,0,2,0.03361344709992409
"Cool 😎 to see the jump in txtai installs over the last couple of weeks!

https://lnkd.in/ecEgQDYM",https://www.linkedin.com/feed/update/urn:li:activity:7207111803388973058,Organic,David Mezzetti,06/13/2024,All followers,468,29,0.061965811997652054,7,0,1,0.07905983179807663
"Graph RAG is a 🔥 topic right now. Did you know that txtai has Graph RAG support using Cypher queries?

https://lnkd.in/d-BSjuj7
",https://www.linkedin.com/feed/update/urn:li:activity:7207108256278740994,Organic,David Mezzetti,06/13/2024,All followers,725,34,0.0468965508043766,12,2,2,0.06896551698446274
"Knowledge Graphs (KGs) are a 🔥 topic now. But how do you build them? Check out this article that uses embeddings models to automatically build a semantic graph. And it's multimodal!

https://lnkd.in/eMsAXarn
",https://www.linkedin.com/feed/update/urn:li:activity:7205552493202685952,Organic,David Mezzetti,06/09/2024,All followers,2567,140,0.05453837290406227,39,0,3,0.07089988142251968
"Have an existing database of questions or a FAQ? Then check out this article. RAG can also be considered but semantic search might be enough and will use fewer resources.

https://lnkd.in/epjYtMaj",https://www.linkedin.com/feed/update/urn:li:activity:7205549947088162816,Organic,David Mezzetti,06/09/2024,All followers,626,18,0.028753994032740593,6,0,1,0.03993610292673111
"Did you know that txtai has prebuilt Docker images for CPU and GPU?

https://lnkd.in/e598zTHb",https://www.linkedin.com/feed/update/urn:li:activity:7205152576298741761,Organic,David Mezzetti,06/08/2024,All followers,500,17,0.03400000184774399,8,0,2,0.05400000140070915
"txtai 7.2 added full integration (data, vectors, graph, keyword) with Postgres. If txtai could integrate with something else, what would it be? Add a comment to share.

https://lnkd.in/eFeFNgYK
",https://www.linkedin.com/feed/update/urn:li:activity:7205141267171717120,Organic,David Mezzetti,06/08/2024,All followers,645,24,0.03720930218696594,9,0,1,0.05271317809820175
"Want a RAG solution using only local llama.cpp GGUF models? Then check this article out.

https://lnkd.in/e_rud4Ff",https://www.linkedin.com/feed/update/urn:li:activity:7204476462714798081,Organic,David Mezzetti,06/06/2024,All followers,518,19,0.03667953610420227,4,0,3,0.050193049013614655
Want external vectorization for vector search? It's simple with txtai.,https://www.linkedin.com/feed/update/urn:li:activity:7203794434872721409,Organic,David Mezzetti,06/04/2024,All followers,377,4,0.010610079392790794,5,0,1,0.02652519941329956
"Congratulations to DuckDB on their 1.0.0 ""Nivis"" release!

Did you know that txtai can store metadata and content in DuckDB?

https://lnkd.in/edem7iNX",https://www.linkedin.com/feed/update/urn:li:activity:7203398999981047809,Organic,David Mezzetti,06/03/2024,All followers,546,7,0.012820512987673283,8,1,2,0.032967034727334976
"🚀 txtai 7.2 is out! 

This release adds Postgres integration for all components, LLM Chat Messages and vectorization with llama.cpp/LiteLLM

From local to remote vectorization, model inference and data storage - txtai has you covered. It's the easiest way to build vector search, LLM and RAG systems without the bloat. 

See below for more.

GitHub: https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/eKe56dwE
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb

API Clients:
Python: https://lnkd.in/eqVx_nqt
JavaScript: https://lnkd.in/dM8ua2y
Rust: https://lnkd.in/d2MAae2
Java: https://lnkd.in/dqmmjTw
Go: https://lnkd.in/dq7Ujv4
",https://www.linkedin.com/feed/update/urn:li:activity:7202340108505677824,Organic,David Mezzetti,05/31/2024,All followers,971,36,0.03707518056035042,21,0,4,0.06282183527946472
"Want txtai vectorization and/or LLM inference with llama.cpp or API services like OpenAI/Cohere/Azure? Then this article is for you 🔥

https://lnkd.in/e_rud4Ff
",https://www.linkedin.com/feed/update/urn:li:activity:7202300570601209857,Organic,David Mezzetti,05/31/2024,All followers,694,20,0.028818443417549133,10,0,2,0.046109508723020554
"LLMs can translate and summarize but that doesn't mean they should. Check out this simple summarization method that's still quite popular.

https://lnkd.in/dM6XB_Rc
",https://www.linkedin.com/feed/update/urn:li:activity:7198436489842618368,Organic,David Mezzetti,05/20/2024,All followers,2703,125,0.04624491184949875,9,0,2,0.050314463675022125
"txtai has a unique feature where it can persist indexes to object storage (i.e. S3) along with other systems such as the Hugging Face Hub. This adds a large level of customizability with the same code.

From Postgres to S3 and the Hugging Face Hub, txtai has you covered.

https://lnkd.in/eMGY7uRB
",https://www.linkedin.com/feed/update/urn:li:activity:7195411178435608576,Organic,David Mezzetti,05/12/2024,All followers,432,4,0.009259259328246117,3,0,1,0.018518518656492233
"There has been considerable buzz on Knowledge Graph-driven LLM orchestration. txtai has been on it since 2022, check out this article for more.

https://lnkd.in/d-BSjuj7",https://www.linkedin.com/feed/update/urn:li:activity:7195037571674947584,Organic,David Mezzetti,05/11/2024,All followers,2253,128,0.05681313946843147,32,2,3,0.07323568314313889
"🔥 Building on the recent pgvector integration is pgtext! pgtext makes it possible to build sparse (keyword) indexes with txtai and Postgres. It also enables full hybrid search with Postgres.

https://lnkd.in/eES4uruV",https://www.linkedin.com/feed/update/urn:li:activity:7194744256752746496,Organic,David Mezzetti,05/10/2024,All followers,626,24,0.03833865746855736,9,0,2,0.055910542607307434
"Want RAG over scientific knowledge? Then check out this txtai datasource.

https://lnkd.in/eSCCs-Jz
",https://www.linkedin.com/feed/update/urn:li:activity:7194025197471985666,Organic,David Mezzetti,05/08/2024,All followers,880,33,0.03750000149011612,21,0,3,0.06477272510528564
"Is Postgres all you need? Is a vector just a data type? That's a tough question. On one hand, dedicated vector databases have a lot of catching up to do in terms of almost 30 years of functionality. On the other, there are advantages to reimagining the architecture factoring in all we know in 2024. 

The good news with txtai is that it's capable of working with multiple setups. It can persist data to Postgres. It can store data in Faiss + SQLite. It can also integrate content with other vector databases. The idea is to have everything needed to get started fast and be flexible to change as the requirements and landscape evolves.

https://lnkd.in/dxWDeey
",https://www.linkedin.com/feed/update/urn:li:activity:7193593763565322242,Organic,David Mezzetti,05/07/2024,All followers,731,16,0.021887825801968575,13,1,1,0.04240766167640686
"Check out the latest newsletter for a summary of what's happening with txtai.

https://lnkd.in/eSJdBPNf",https://www.linkedin.com/feed/update/urn:li:activity:7192123946622746625,Organic,David Mezzetti,05/03/2024,All followers,445,10,0.02247191034257412,6,0,1,0.03820224851369858
"One unique feature of txtai is it's ability to mix and match vector, content, graph and keyword index systems together. Out of the box, local defaults are set to get up and running fast. But txtai provides a high level of flexibility in integrating different components together. It also provides it's own SQL dialect for querying regardless of the underlying choices made. The architecture is designed to make it easy to add new file formats and integrations.

https://lnkd.in/edem7iNX
",https://www.linkedin.com/feed/update/urn:li:activity:7191041678437199874,Organic,David Mezzetti,04/30/2024,All followers,802,20,0.02493765577673912,12,0,1,0.041147131472826004
"⭐ Excited to announce that txtai has crossed 7K stars on GitHub!

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7190743859981615104,Organic,David Mezzetti,04/29/2024,All followers,1008,33,0.032738097012043,21,3,2,0.0585317462682724
"One unique feature of txtai is that it can load and save content as Hugging Face models. Read the article for more details and see the examples below.

Article: https://lnkd.in/eMGY7uRB
Examples: https://lnkd.in/ejr8e2Wy",https://www.linkedin.com/feed/update/urn:li:activity:7189933359651803136,Organic,David Mezzetti,04/27/2024,All followers,538,5,0.00929368007928133,3,0,1,0.01672862470149994
"🚀 Big news! We're excited to release this new Postgres + pgvector integration for txtai. It's now possible to fully persist txtai content, vectors and graph data to Postgres. From there it can be queried through txtai and/or directly with any Postgres client!

From prototyping to production, txtai has you covered.

https://lnkd.in/eFeFNgYK",https://www.linkedin.com/feed/update/urn:li:activity:7189323577396011010,Organic,David Mezzetti,04/25/2024,All followers,1087,45,0.041398342698812485,22,3,4,0.06807727366685867
"txtai has long had the ability to build serverless vector search. With this method, one can build a vector search system with Cloud Functions (i.e. AWS Lambda, Google Cloud Run, Azure Functions) and Object Storage. This also works with Kubernetes paired with KNative.

https://lnkd.in/ek2TaG9a",https://www.linkedin.com/feed/update/urn:li:activity:7187779947241877504,Organic,David Mezzetti,04/21/2024,All followers,537,9,0.016759777441620827,3,0,1,0.024208566173911095
"Want to extract structured information with RAG? Then check out this article.

https://lnkd.in/euhTG2Gj",https://www.linkedin.com/feed/update/urn:li:activity:7187447546787569664,Organic,David Mezzetti,04/20/2024,All followers,966,63,0.06521739065647125,11,2,3,0.08178053796291351
"🎉 We're excited to release txtai 7.1

This release adds dynamic embeddings vector support along with semantic graph and RAG improvements.

See below for more.

GitHub: https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/eiJWhTA6
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb

API Clients:
Python: https://lnkd.in/eqVx_nqt
JavaScript: https://lnkd.in/dM8ua2y
Rust: https://lnkd.in/d2MAae2
Java: https://lnkd.in/dqmmjTw
Go: https://lnkd.in/dq7Ujv4",https://www.linkedin.com/feed/update/urn:li:activity:7187168885161230338,Organic,David Mezzetti,04/19/2024,All followers,950,46,0.04842105135321617,11,0,1,0.061052631586790085
"🔥 Check out this new article introducing Retrieval Augmented and Guided Generation (RAGG).

This article combines txtai with the great outlines library to generate structured output. See how knowledge can be stored as Pydantic models!

https://lnkd.in/euhTG2Gj",https://www.linkedin.com/feed/update/urn:li:activity:7186782380420968448,Organic,David Mezzetti,04/18/2024,All followers,977,52,0.05322415381669998,15,5,3,0.07676561176776886
"Check out this interesting article that uses txtai to solve crossword puzzles.

https://lnkd.in/ewdqZWzi",https://www.linkedin.com/feed/update/urn:li:activity:7183437619777671169,Organic,David Mezzetti,04/09/2024,All followers,1046,10,0.009560229256749153,4,0,1,0.01434034388512373
"Check out this blog post that uses txtai to build a private chat RAG solution

https://lnkd.in/e-Mvk4-D",https://www.linkedin.com/feed/update/urn:li:activity:7183436717469638656,Organic,David Mezzetti,04/09/2024,All followers,455,6,0.013186813332140446,2,0,1,0.019780220463871956
"As an open-source project, it's always great to get feedback like what's in the comment below.

""Thanks for this, txtai looks like the most production focused library in this space.""

Thank you!

https://lnkd.in/e9gYiEGv",https://www.linkedin.com/feed/update/urn:li:activity:7182006310836543488,Organic,David Mezzetti,04/05/2024,All followers,579,19,0.032815199345350266,5,0,2,0.044905006885528564
"Want to build agent workflows? Then take a look at txtai.

txtai has long (since 2021) had a framework for connecting different pipelines into unified workflows. 

This can be used to connect LLM prompts and/or specialized models for translation/summarization/text extraction.

Read this to learn more: https://lnkd.in/em2ew5ia",https://www.linkedin.com/feed/update/urn:li:activity:7180942247956217856,Organic,David Mezzetti,04/02/2024,All followers,442,11,0.024886878207325935,2,0,2,0.03393665328621864
"🚀 We're excited to announce a new 500M parameter model 🌌 Space Time LLM. 

Recent breakthroughs in LLMs have resulted in an uncanny and game changing ability to predict future outcomes. Impressive advances in quantization and compression such as 1-bit LLMs have contributed to this phenomenal breakthrough in predictive capabilities. This model redefines our understanding of what and how LLMs learn.

Check out this model and see what you can predict today!

https://lnkd.in/exdy4R5G
",https://www.linkedin.com/feed/update/urn:li:activity:7180594108413943809,Organic,David Mezzetti,04/01/2024,All followers,2605,206,0.07907869666814804,19,9,2,0.09059500694274902
"txtai is developed in the open. The full project history and design decisions are documented. What tests run and all code is easily accessible. Documentation is a priority and examples are provided for all major features. There are no secrets. It takes courage to develop with this level of transparency.

We're proud of our code quality, design decisions and code consistency. There is no ""just get it done and we'll fix it later"" mentality here.

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7179166473456635906,Organic,David Mezzetti,03/28/2024,All followers,604,14,0.023178808391094208,4,0,1,0.03145695477724075
"Thrilled to see that our PubMedBERT Embeddings model has over 200K downloads and is one of the most popular sentence similarity models on the HF Hub!

Add it to your list if you're looking to build semantic search apps for medical literature.

https://lnkd.in/egnEKcqd",https://www.linkedin.com/feed/update/urn:li:activity:7178844946857111552,Organic,David Mezzetti,03/27/2024,All followers,782,27,0.03452685475349426,13,0,1,0.052429668605327606
"Nice video covering how to build an AI Search engine with txtai.

https://lnkd.in/e2NGemzv

Check out the NeuML YouTube channel for links to this video and more: https://lnkd.in/e9cJQ79k",https://www.linkedin.com/feed/update/urn:li:activity:7177633277652938752,Organic,David Mezzetti,03/24/2024,All followers,628,19,0.030254777520895004,4,0,1,0.03821656107902527
"🔥 Excellent tutorial on building a product search engine with txtai from NeuralNine!

https://lnkd.in/e8xsvbm2",https://www.linkedin.com/feed/update/urn:li:activity:7176989768348532737,Organic,David Mezzetti,03/22/2024,All followers,908,35,0.038546256721019745,10,0,3,0.05286343768239021
"🚀 Exciting to see this new txtai model for Swedish Wikipedia!

https://lnkd.in/eKEdG6cC",https://www.linkedin.com/feed/update/urn:li:activity:7176674937917571072,Organic,David Mezzetti,03/21/2024,All followers,655,22,0.03358778730034828,7,0,2,0.047328244894742966
"We're seeing a lot of progress with methods to improve the efficiency of vector embeddings generation. For example, vector models trained with Matryoshka Representation Learning, push the most important information to the front of the vector, enabling us to only keep a portion of an embeddings vector.

Another method, quantization, compresses the number of values that can be represented by each bucket in an embeddings vector. With binary quantization, the values can even be reduced down to a single bit.

While all this is exciting, how do we know that these methods will work well enough for our requirements? We have to test it of course! The BEIR evaluation framework has a number of sources and it's easy to add new custom sources to test. Check out this article for a full example on this topic.

https://lnkd.in/db5Jx_fE",https://www.linkedin.com/feed/update/urn:li:activity:7175105277338222592,Organic,David Mezzetti,03/17/2024,All followers,2651,85,0.03206337243318558,37,0,2,0.04677480086684227
"LLMs are great summarizers. But did you know that txtai can utilize smaller models specialized for summarization? 

https://lnkd.in/dM6XB_Rc
",https://www.linkedin.com/feed/update/urn:li:activity:7173640816895148034,Organic,David Mezzetti,03/13/2024,All followers,734,23,0.031335148960351944,7,0,2,0.04359672963619232
"While LLMs can translate text between languages, it's often not the most efficient way to do it. 

Did you know that txtai has a comprehensive translation pipeline built on specialized translation models? The translation pipeline autodetects languages and retrieves the best model for each translation task. 

https://lnkd.in/e6QK5M8D
",https://www.linkedin.com/feed/update/urn:li:activity:7173640097601384448,Organic,David Mezzetti,03/13/2024,All followers,627,18,0.028708133846521378,7,0,1,0.04146730527281761
"⚡ The new mxbai-embed-2d-large-v1 embeddings model from mixedbread is impressive! Performance with 384 dimensions using 2D Matryoshka Sentence Embeddings is superb. This model works out of the box with txtai - see below.

Model: https://lnkd.in/dnGAMnaH
Gist: https://lnkd.in/e5PeHxBB",https://www.linkedin.com/feed/update/urn:li:activity:7172768223786975232,Organic,David Mezzetti,03/11/2024,All followers,938,28,0.02985074557363987,13,0,2,0.04584221914410591
"One powerful feature of txtai is that graphs can be automatically created using semantic similarity. Did you know that relationships can also be manually loaded using techniques such as relationship extraction with LLMs?

Check out this article on building knowledge graphs with LLMs and txtai.

https://lnkd.in/dBy_H4C2",https://www.linkedin.com/feed/update/urn:li:activity:7172738954000089088,Organic,David Mezzetti,03/10/2024,All followers,2356,97,0.04117147624492645,30,0,2,0.05475382134318352
"Graph RAG is a developing feature. Lots to explore in this space. If you haven't had a chance to read about it, check out this article.

https://lnkd.in/d-BSjuj7",https://www.linkedin.com/feed/update/urn:li:activity:7170930503800332288,Organic,David Mezzetti,03/05/2024,All followers,1803,95,0.05268996208906174,28,4,3,0.07210205495357513
"The best compliment on txtai is that it's easy to get up and running. The goal is to have a well-documented project that has a number of reliable, easy-to-use features of the box. It's great to see the number of positive comments over on Reddit's r/LocalLLaMA subreddit. 

https://lnkd.in/ePMrZXi6",https://www.linkedin.com/feed/update/urn:li:activity:7169701136348733440,Organic,David Mezzetti,03/02/2024,All followers,706,15,0.02124645933508873,8,0,2,0.035410765558481216
"Would you trade 1% of accuracy to only have to store 1% of the data?

Exciting to see the innovation happening in the vector space and we're not talking about 1.58-bit LLMs.

With Matryoshka Embeddings, we can drastically reduce vector dimensionality while maintaining a strong level of accuracy. Check out this example that combines Matryoshka Embeddings with Faiss 4-bit scalar quantization 🚀",https://www.linkedin.com/feed/update/urn:li:activity:7169424983956398081,Organic,David Mezzetti,03/01/2024,All followers,1722,58,0.03368176519870758,18,0,2,0.045296166092157364
"ICYMI: Check out what's new in txtai 7.0

https://lnkd.in/efuQipp9
",https://www.linkedin.com/feed/update/urn:li:activity:7168694537660399617,Organic,David Mezzetti,02/28/2024,All followers,605,24,0.03966942057013512,10,0,1,0.057851240038871765
"🚀 Excited to release an updated version of PubMedBERT Embeddings with Matryoshka Representation Learning support! With this model, dynamic embeddings sizes of 64, 128, 256, 384 and 512 can be used in addition to the full size of 768. It's a great way to save space with a relatively low level of accuracy tradeoff.

Thank you to Tom Aarsen, Philipp Schmid and the Hugging Face team for adding this feature to Sentence Transformers!

https://lnkd.in/edNYMyrF",https://www.linkedin.com/feed/update/urn:li:activity:7167123461675425792,Organic,David Mezzetti,02/24/2024,All followers,7202,223,0.03096362203359604,81,1,7,0.04332130029797554
"🚀 🌕 Exploring and pushing the frontier forward!

https://lnkd.in/ebJEfzNf",https://www.linkedin.com/feed/update/urn:li:activity:7166781857160765440,Organic,David Mezzetti,02/23/2024,All followers,470,9,0.0191489364951849,3,0,1,0.027659574523568153
"🚀 2024 Goals

✅ Generative knowledge graphs
☐ Micromodels
☐ Cloud offering
☐ Consulting 2x
☐ Community engagement and training

What's next 🤔 ?

https://lnkd.in/eVTpBhsV",https://www.linkedin.com/feed/update/urn:li:activity:7166392533768462336,Organic,David Mezzetti,02/22/2024,All followers,579,12,0.020725388079881668,8,0,2,0.03799654543399811
"🚀 🌕 We're excited to release txtai 7.0 🎉

This major release introduces the next generation of the semantic graph. It adds support for graph search, advanced graph traversal and graph RAG. It also adds binary support to the API, index format improvements and training LoRA/QLoRA models.

See below for more.

GitHub: https://lnkd.in/dxWDeey
Release Notes: https://lnkd.in/eD_vkBFu
Article: https://lnkd.in/efuQipp9
PyPI: https://lnkd.in/eE_Jvft
Docker Hub: https://lnkd.in/e598zTHb

API Clients:
Python: https://lnkd.in/eqVx_nqt
JavaScript: https://lnkd.in/dM8ua2y
Rust: https://lnkd.in/d2MAae2
Java: https://lnkd.in/dqmmjTw
Go: https://lnkd.in/dq7Ujv4
",https://www.linkedin.com/feed/update/urn:li:activity:7166245671933571073,Organic,David Mezzetti,02/22/2024,All followers,1538,70,0.04551365226507187,24,2,1,0.0630689188838005
"Check out this article on how to use Graph RAG to write a short book covering early medieval English history!

https://lnkd.in/d-BSjuj7",https://www.linkedin.com/feed/update/urn:li:activity:7166138938124890113,Organic,David Mezzetti,02/21/2024,All followers,617,29,0.04700161889195442,9,0,1,0.06320907920598984
"🕒 The countdown to txtai 7.0 is on. In the meantime, checkout this notebook with the code showing how all these graphs you've seen work!

https://lnkd.in/dBy_H4C2",https://www.linkedin.com/feed/update/urn:li:activity:7166138216532566016,Organic,David Mezzetti,02/21/2024,All followers,1322,58,0.04387291893362999,20,0,5,0.06278365850448608
"🔥 Happy to announce that the next release of txtai will be 7.0, a major release. Graph networks are now an integral part of txtai and it merits more than a dot release! Some exciting stuff inbound.

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7164953687193284610,Organic,David Mezzetti,02/18/2024,All followers,649,18,0.027734976261854172,13,2,1,0.05238828808069229
"Hoping this model gets more visibility! It's a great resource for RAG over scientific literature (CS, Physics, Math and more)

https://lnkd.in/eSCCs-Jz
",https://www.linkedin.com/feed/update/urn:li:activity:7164951114352705536,Organic,David Mezzetti,02/18/2024,All followers,665,25,0.03759398311376572,12,0,1,0.05714285746216774
"Happy to see this model is one of the top downloaded models in the medical literature domain!

https://lnkd.in/egnEKcqd
",https://www.linkedin.com/feed/update/urn:li:activity:7164950616656568321,Organic,David Mezzetti,02/18/2024,All followers,281,10,0.035587187856435776,11,0,1,0.07829181849956512
"If you're looking to get started in RAG, vector search and/or with LLMs, txtai-wikipedia is a great datasource. It's a txtai index with all Wikipedia article abstracts organized by popularity (views). 

It's one of the easiest ways to have vector search across a broad range of topics.

https://lnkd.in/eQz5dKtG",https://www.linkedin.com/feed/update/urn:li:activity:7164591070733922304,Organic,David Mezzetti,02/17/2024,All followers,455,15,0.032967034727334976,7,0,1,0.050549451261758804
"Simple RAG systems run a vector query and use that as context. Graph RAG enables much more complexity and nuance.

Let's say you're a geopolitical analyst and you want to study the events from WW2 to the 1989 revolutions in Eastern Europe. Just searching for ""Cold War"" isn't going to pull everything you need. With Graph RAG you can draw a path between WW2->Cold War->1989 and pull in other concepts along the way. Like having a shopping cart and going down the aisles. You can also study the first two decades of the 2000s. 

Keep in mind that each node here is a Wikipedia article. Imagine what this could do with your data.",https://www.linkedin.com/feed/update/urn:li:activity:7164274921576062976,Organic,David Mezzetti,02/16/2024,All followers,527,36,0.0683111920952797,10,1,3,0.09487666189670563
"The new Graph RAG feature from txtai is a gamechanger. It's a whole new way to pull information as context, a composable graph. Check out these graphs of knowledge built using Wikipedia articles (txtai-wikipedia).

Keep in mind that these graphs are automatically built using vector similarity and path finding queries. Think of it like taking a highlighter and circling a series of concepts to research.

More to come soon.",https://www.linkedin.com/feed/update/urn:li:activity:7164083598944362496,Organic,David Mezzetti,02/16/2024,All followers,501,73,0.1457085758447647,16,2,1,0.1836327314376831
"RAG in it's simplest form is a vector search as context with a LLM prompt.

With the next version of txtai, we'll have a series of new graph-based RAG techniques. Think of this like a road trip with a number of pit stops. 

Say you're researching the early medieval history of England. Sure we can run a vector search for that. But what if we can instruct a query to traverse a number of concepts we're interested in? 

Let's take the example below. This is a network of Wikipedia articles (via txtai-wikipedia). The query traverses paths of history between the Roman Empire, Anglo-Saxon period, Viking period and ends with the Norman conquest. This rich dataset is then available as a library of context to downstream LLM prompts.

Graph databases aren't new. The difference here is that txtai builds a vector store and uses that to automatically build a graph network weighted by vector similarity. Load your data and you get this for free. 😀",https://www.linkedin.com/feed/update/urn:li:activity:7163326849538846722,Organic,David Mezzetti,02/14/2024,All followers,1069,61,0.057062674313783646,17,3,3,0.07857810705900192
"Did you know that txtai can run LLM prompts with llama.cpp and LLM API services through LiteLLM?

https://lnkd.in/emYziqwS",https://www.linkedin.com/feed/update/urn:li:activity:7163172682534998016,Organic,David Mezzetti,02/13/2024,All followers,573,13,0.022687608376145363,5,2,1,0.03664921596646309
"txtai is one of the easiest ways to get start with RAG. It has a vector database and LLM framework built-in. 

While releases are frequent, we don't have a reckless release cycle that constantly changes/breaks things. There is a focus on consistency and stability which is perhaps a bit more old fashioned.

https://lnkd.in/e8nfE-Zp
",https://www.linkedin.com/feed/update/urn:li:activity:7162452578503483392,Organic,David Mezzetti,02/11/2024,All followers,1074,35,0.03258845582604408,12,4,2,0.04934823140501976
"The txtai graph component is growing strong💪

While the semantic graph was originally released in 2022, it's features have been relatively limited. Check out what's coming in the next release. 

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7161380528145772545,Organic,David Mezzetti,02/08/2024,All followers,568,17,0.02992957830429077,8,1,1,0.047535210847854614
🔥 Coming with the next release of txtai: Advanced Graph Traversal! This new feature enables complex graph queries to control how a graph is traversed. The full graph path is consumed and can be used as a RAG context 💥,https://www.linkedin.com/feed/update/urn:li:activity:7161077323088248832,Organic,David Mezzetti,02/07/2024,All followers,608,87,0.1430921107530594,7,3,1,0.16118420660495758
"👀 Exciting new change coming with the next release of txtai - better support for binary data via the API.

File uploads will be supported for embeddings and pipelines, which should significantly improve the overall experience. 

MessagePack response encoding will also be supported. ",https://www.linkedin.com/feed/update/urn:li:activity:7160684758144864257,Organic,David Mezzetti,02/06/2024,All followers,457,10,0.021881837397813797,4,0,3,0.037199124693870544
"Did you know that txtai pipelines work with models trained with scikit-learn and XGBoost? Check out this example for more.

https://lnkd.in/dZybUdVF",https://www.linkedin.com/feed/update/urn:li:activity:7160630687333507072,Organic,David Mezzetti,02/06/2024,All followers,326,4,0.012269938364624977,5,0,1,0.030674846842885017
"txtai has a full-featured API, backed by FastAPI, that can be enabled for any txtai process. A full API implementation is automatically generated based on the txtai configuration selected.

But did you know that fully customized end points can also be added in?

https://lnkd.in/ei-u7grV
",https://www.linkedin.com/feed/update/urn:li:activity:7159871787109900288,Organic,David Mezzetti,02/04/2024,All followers,589,12,0.0203735139220953,3,0,1,0.02716468647122383
"txtai can run vector search queries with SQL. But did you know that natural language queries can also be translated into SQL? 

Depending on the model used, this can handle quite a few complex use cases.

Read the article below for more. 

https://lnkd.in/eRXaY3-Q",https://www.linkedin.com/feed/update/urn:li:activity:7159611472744992768,Organic,David Mezzetti,02/03/2024,All followers,1336,26,0.019461078569293022,12,0,2,0.02994011901319027
"⚡ Here's a powerful txtai feature: machine learning models, workflows and LLM chains can be directly embedded in vector search queries! 

This means we can translate, summarize, transform and even generate new results right at query time.

https://lnkd.in/e56xaXg8",https://www.linkedin.com/feed/update/urn:li:activity:7158898026512601088,Organic,David Mezzetti,02/01/2024,All followers,481,9,0.018711019307374954,6,0,1,0.03326403349637985
"🚀 Coming with the next release of txtai: PEFT training support! 

txtai has long had a trainer pipeline that's designed for ease-of-use. Now we can simply set quantize=True and/or lora=True to automatically train a QLoRA or LoftQ model. Of course, the settings are also fully customizable. 

https://lnkd.in/dsZrtTiV",https://www.linkedin.com/feed/update/urn:li:activity:7158534231252480001,Organic,David Mezzetti,01/31/2024,All followers,375,5,0.013333333656191826,7,0,2,0.03733333200216293
"⚡ Moving up the charts

Source: https://lnkd.in/d2-HKx_d",https://www.linkedin.com/feed/update/urn:li:activity:7158487396982964224,Organic,David Mezzetti,01/31/2024,All followers,301,16,0.05315614491701126,6,0,1,0.07641196250915527
"💡 Great article by Sethu I. on building dynamically weighted txtai queries. 

Article: https://lnkd.in/eW2y8WEc
GitHub: https://lnkd.in/epufMZ5k
",https://www.linkedin.com/feed/update/urn:li:activity:7157909215267704834,Organic,David Mezzetti,01/30/2024,All followers,592,25,0.042229730635881424,7,2,1,0.059121619910001755
"Did you know that txtai can integrate with external embeddings datasets and APIs? 

Read more here: https://lnkd.in/eJh-Q7Bs",https://www.linkedin.com/feed/update/urn:li:activity:7157875562932289537,Organic,David Mezzetti,01/29/2024,All followers,432,6,0.013888888992369175,5,0,2,0.030092593282461166
"Did you know that txtai has text-to-speech (TTS) pipelines?

https://lnkd.in/e4niD6Ad",https://www.linkedin.com/feed/update/urn:li:activity:7157394809140232192,Organic,David Mezzetti,01/28/2024,All followers,545,16,0.029357798397541046,6,0,1,0.04220183566212654
"Did you know that txtai has a robust and mature framework for chaining models and tasks together called Workflows?

Workflows can be built in Python and with configuration (YAML). They can also easily run as an API service directly and/or with containers.

https://lnkd.in/eNjvZMZ8",https://www.linkedin.com/feed/update/urn:li:activity:7156981949700448256,Organic,David Mezzetti,01/27/2024,All followers,524,7,0.013358778320252895,6,0,1,0.02671755664050579
"Hearing about semantic search (also known as vector search) but unsure on why we even need it? Then take a look at this article for a quick overview.

https://lnkd.in/eqZs96D3",https://www.linkedin.com/feed/update/urn:li:activity:7156614709033521152,Organic,David Mezzetti,01/26/2024,All followers,424,7,0.016509434208273888,2,0,1,0.02358490601181984
"Typical solutions for multi-tenancy store all data in the same database. Access controls are then applied either in code or the database to control what users see. There is growing interest in evaluating if SQLite or similar can be a multi-tenant solution. In other words, every user or account has it's own database.

With txtai, a similar approach could be used for multi-tenancy. An embeddings database per account or user. In this setup, each embeddings database would be stored on a filesystem or cloud storage like S3.

Some would say this is CRAZY🤪 talk. But it's a setup that could potentially work in some situations. 

Read more here: https://lnkd.in/eMGY7uRB",https://www.linkedin.com/feed/update/urn:li:activity:7156340502588510208,Organic,David Mezzetti,01/25/2024,All followers,438,4,0.00913241971284151,7,0,1,0.027397260069847107
"Great to see this model on the Hugging Face Hub using txtai's hub integration!

Model: https://lnkd.in/eJHcrBrs

Learn more on how to do this in this article: https://lnkd.in/eMGY7uRB",https://www.linkedin.com/feed/update/urn:li:activity:7156261840228945920,Organic,David Mezzetti,01/25/2024,All followers,1582,83,0.05246523395180702,19,2,2,0.06700379401445389
"🚀 Great to see that LlamaIndex has provided a txtai integration with their latest release!

While there are overlaps between the projects, it's always great to see two popular open source projects work together. OSS for the win. 

LlamaIndex: https://www.llamaindex.ai/
TxtaiVectorStore: https://lnkd.in/e8R2N5NY",https://www.linkedin.com/feed/update/urn:li:activity:7156257409772978176,Organic,David Mezzetti,01/25/2024,All followers,522,20,0.038314174860715866,7,0,1,0.05363984778523445
"📰 🔥 Big news: the follow-up to semantic graphs is here! Semantic graphs, as constructed in txtai, are a novel approach that few if any other systems have. Expect some disruption once this becomes more popular and known!

https://lnkd.in/e9vGkZ2x",https://www.linkedin.com/feed/update/urn:li:activity:7155709239926116352,Organic,David Mezzetti,01/23/2024,All followers,1245,69,0.05542168766260147,22,5,1,0.07791164517402649
"👋 txtai says hello, it's here for your vector search, LLM and RAG needs. 

Don't settle for low quality code - expect more for less. 

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7155266059577491457,Organic,David Mezzetti,01/22/2024,All followers,225,6,0.02666666731238365,3,0,1,0.04444444552063942
"Did you know that txtai can batch process large tensor arrays?

https://lnkd.in/ecNiUAgb",https://www.linkedin.com/feed/update/urn:li:activity:7155173660830416896,Organic,David Mezzetti,01/22/2024,All followers,260,4,0.015384615398943424,1,0,1,0.023076923564076424
"txtai provides sensible defaults to get up and running fast. But did you know that each of it's components are customizable? This unique approach enables txtai to adapt to the environments it runs in vs forcing developers to adapt to what txtai prescribes.

https://lnkd.in/edem7iNX",https://www.linkedin.com/feed/update/urn:li:activity:7154787006659641344,Organic,David Mezzetti,01/21/2024,All followers,568,8,0.014084506779909134,8,0,1,0.02992957830429077
"NeuML ❤️'s Hugging Face. We have a growing number of models available on the hub. For example, given that txtai is a fully encapsulated index format, we have ArXiv and Wikipedia txtai embeddings databases available. These vector databases can be used directly or as a source for RAG. There's also models for generating medical literature embeddings, text to sql, text to speech and more. Check it out!

https://lnkd.in/eJTK9NRr",https://www.linkedin.com/feed/update/urn:li:activity:7154428289732780032,Organic,David Mezzetti,01/20/2024,All followers,849,36,0.04240282624959946,12,0,1,0.05771495774388313
"Did you know that txtai has a workflow processing framework? Workflows can be built in Python and YAML. YAML workflows can be automatically spun up as API services (even supports serverless apps via KNative). 

https://lnkd.in/em2ew5ia",https://www.linkedin.com/feed/update/urn:li:activity:7154094095731159040,Organic,David Mezzetti,01/19/2024,All followers,494,22,0.044534411281347275,7,0,1,0.06072874367237091
"Looking for a fast, logic-driven way to find near duplicate images? Then check out this article on the ImageHash pipeline.

https://lnkd.in/e6Afs6pa",https://www.linkedin.com/feed/update/urn:li:activity:7153732539168219136,Organic,David Mezzetti,01/18/2024,All followers,323,6,0.018575850874185562,2,0,1,0.027863776311278343
"🔥 Read this great article by Mrinal Walia covering how to build RAG pipelines with txtai!

https://lnkd.in/eKySBWR6",https://www.linkedin.com/feed/update/urn:li:activity:7153389333897445376,Organic,David Mezzetti,01/17/2024,All followers,425,23,0.05411764606833458,7,3,2,0.08235294371843338
"Check out this site covering vector databases and their features. Great to see how well txtai stacks up against the alternatives.

https://lnkd.in/dRvHpSwQ",https://www.linkedin.com/feed/update/urn:li:activity:7153168638198325248,Organic,David Mezzetti,01/16/2024,All followers,377,12,0.03183024004101753,4,0,2,0.04774535819888115
"Did you know that txtai has had serverless vector search since 2022? And did you also know that Faiss supports mmap-ed indexes, so they don't have to be fully loaded into RAM.

https://lnkd.in/ek2TaG9a
",https://www.linkedin.com/feed/update/urn:li:activity:7153097814300905473,Organic,David Mezzetti,01/16/2024,All followers,378,9,0.02380952425301075,7,1,1,0.0476190485060215
"Excited to add a new knowledge base for RAG and more. Check out the txtai-arxiv embeddings index on the HF Hub!

The arXiv index works well as a fact-based context source for retrieval augmented generation (RAG). Additionally, this model can identify articles to cite in research. Passing a title + abstract pair will find similar existing articles.

Many of us look at arXiv for CS papers but it has much more. Articles on sports analytics, astronomy, physics and even the search for ET. Happy exploring!

https://lnkd.in/eSCCs-Jz",https://www.linkedin.com/feed/update/urn:li:activity:7153023172945018881,Organic,David Mezzetti,01/16/2024,All followers,777,31,0.03989703953266144,10,0,1,0.054054055362939835
"🚀 txtai just crossed 6K ⭐'s on GitHub. Thank you to all those following along!

https://lnkd.in/dxWDeey",https://www.linkedin.com/feed/update/urn:li:activity:7152662057543225344,Organic,David Mezzetti,01/15/2024,All followers,445,7,0.01573033630847931,11,0,1,0.04269662871956825
"💭 txtchat is our solution for ""quick up and running"" self-hosted RAG applications. But it's much more than that! 

Given that integrates with Rocket Chat, you get user management, security, compliance and a mobile app. txtchat can also be easily extended to integrate with hosted solutions such as Slack and Teams. 

There are a lot of ""toy"" RAG apps built using Streamlit and/or a custom web framework. We all know that is far from a production solution, especially for an enterprise company.

Read more here: https://lnkd.in/eMzMMtTV",https://www.linkedin.com/feed/update/urn:li:activity:7152282895712231424,Organic,David Mezzetti,01/14/2024,All followers,1372,30,0.02186588943004608,9,0,1,0.029154518619179726
"Did you know that txtai can store content in Postgres?

Read the article below for more.

https://lnkd.in/evUBX-bC",https://www.linkedin.com/feed/update/urn:li:activity:7152051178191609858,Organic,David Mezzetti,01/13/2024,All followers,415,6,0.014457831159234047,5,0,1,0.028915662318468094
"The best way to hear about txtai is from the community. It's great that these talented individuals, from around the world, choose to dedicate their time to share how txtai works. It's much appreciated! Check out the community YouTube playlist below. 

https://lnkd.in/e9_GrFBH",https://www.linkedin.com/feed/update/urn:li:activity:7151906798176780288,Organic,David Mezzetti,01/13/2024,All followers,344,23,0.06686046719551086,6,0,1,0.0872092992067337