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🔥 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 | 0.038546 | 10 | 0 | 3 | 0.052863 |
🚀 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 | 0.033588 | 7 | 0 | 2 | 0.047328 |
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 | 2,651 | 85 | 0.032063 | 37 | 0 | 2 | 0.046775 |
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 | 0.031335 | 7 | 0 | 2 | 0.043597 |
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 | 0.028708 | 7 | 0 | 1 | 0.041467 |
⚡ 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 | 0.029851 | 13 | 0 | 2 | 0.045842 |
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 | 2,356 | 97 | 0.041171 | 30 | 0 | 2 | 0.054754 |
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 | 1,803 | 95 | 0.05269 | 28 | 4 | 3 | 0.072102 |
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 | 0.021246 | 8 | 0 | 2 | 0.035411 |
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 | 1,722 | 58 | 0.033682 | 18 | 0 | 2 | 0.045296 |
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 | 0.039669 | 10 | 0 | 1 | 0.057851 |
🚀 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 | 7,202 | 223 | 0.030964 | 81 | 1 | 7 | 0.043321 |
🚀 🌕 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 | 0.019149 | 3 | 0 | 1 | 0.02766 |
🚀 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 | 0.020725 | 8 | 0 | 2 | 0.037997 |
🚀 🌕 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 | 1,538 | 70 | 0.045514 | 24 | 2 | 1 | 0.063069 |
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 | 0.047002 | 9 | 0 | 1 | 0.063209 |
🕒 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 | 1,322 | 58 | 0.043873 | 20 | 0 | 5 | 0.062784 |
🔥 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 | 0.027735 | 13 | 2 | 1 | 0.052388 |
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 | 0.037594 | 12 | 0 | 1 | 0.057143 |
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 | 0.035587 | 11 | 0 | 1 | 0.078292 |
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 | 0.032967 | 7 | 0 | 1 | 0.050549 |
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 | 0.068311 | 10 | 1 | 3 | 0.094877 |
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 | 0.145709 | 16 | 2 | 1 | 0.183633 |
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 | 1,069 | 61 | 0.057063 | 17 | 3 | 3 | 0.078578 |
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 | 0.022688 | 5 | 2 | 1 | 0.036649 |
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 | 1,074 | 35 | 0.032588 | 12 | 4 | 2 | 0.049348 |
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 | 0.02993 | 8 | 1 | 1 | 0.047535 |
🔥 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 | 0.143092 | 7 | 3 | 1 | 0.161184 |
👀 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 | 0.021882 | 4 | 0 | 3 | 0.037199 |
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 | 0.01227 | 5 | 0 | 1 | 0.030675 |
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 | 0.020374 | 3 | 0 | 1 | 0.027165 |
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 | 1,336 | 26 | 0.019461 | 12 | 0 | 2 | 0.02994 |
⚡ 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 | 0.018711 | 6 | 0 | 1 | 0.033264 |
🚀 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 | 0.013333 | 7 | 0 | 2 | 0.037333 |
⚡ 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 | 0.053156 | 6 | 0 | 1 | 0.076412 |
💡 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 | 0.04223 | 7 | 2 | 1 | 0.059122 |
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 | 0.013889 | 5 | 0 | 2 | 0.030093 |
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 | 0.029358 | 6 | 0 | 1 | 0.042202 |
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 | 0.013359 | 6 | 0 | 1 | 0.026718 |
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 | 0.016509 | 2 | 0 | 1 | 0.023585 |
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 | 0.009132 | 7 | 0 | 1 | 0.027397 |
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 | 1,582 | 83 | 0.052465 | 19 | 2 | 2 | 0.067004 |
🚀 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 | 0.038314 | 7 | 0 | 1 | 0.05364 |
📰 🔥 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 | 1,245 | 69 | 0.055422 | 22 | 5 | 1 | 0.077912 |
👋 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 | 0.026667 | 3 | 0 | 1 | 0.044444 |
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 | 0.015385 | 1 | 0 | 1 | 0.023077 |
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 | 0.014085 | 8 | 0 | 1 | 0.02993 |
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 | 0.042403 | 12 | 0 | 1 | 0.057715 |
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 | 0.044534 | 7 | 0 | 1 | 0.060729 |
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 | 0.018576 | 2 | 0 | 1 | 0.027864 |
🔥 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 | 0.054118 | 7 | 3 | 2 | 0.082353 |
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 | 0.03183 | 4 | 0 | 2 | 0.047745 |
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 | 0.02381 | 7 | 1 | 1 | 0.047619 |
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 | 0.039897 | 10 | 0 | 1 | 0.054054 |
🚀 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 | 0.01573 | 11 | 0 | 1 | 0.042697 |
💭 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 | 1,372 | 30 | 0.021866 | 9 | 0 | 1 | 0.029155 |
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 | 0.014458 | 5 | 0 | 1 | 0.028916 |
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 | 0.06686 | 6 | 0 | 1 | 0.087209 |
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