tika-docker
txtai
Our great sponsors
| tika-docker | txtai | |
|---|---|---|
| 20 | 354 | |
| 100 | 6,910 | |
| - | 5.7% | |
| 4.1 | 9.3 | |
| 16 days ago | 19 days ago | |
| Shell | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tika-docker
- Text Extraction from Documents
- Apache Tika – Extract text and metadata from doc types (the backbone of RAG)
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Demystifying Text Data with the Unstructured Python Library
If you accept running Java, the Apache Tika is extremely good at parsing content (https://tika.apache.org/)
- Ajuda com Buscador
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How do you manage and find large amount of files?
Apache Tika can spit out text from lots of formats. I've used it with grep (or rg) to make a small scale searching of local folders. Tika does a really good job at OCR for finding if text is in a file.
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40 Containers & Counting...
https://tika.apache.org Meta data from things.
- Hosted app to manage server inventory
- Best FOSS (ideally Docker) that can split PDF files ?
- OK, ElasticSearch works, text files are indexed. How about images? Can images be indexed in NextCloud and fulltextsearched?
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Document Parsing - an unsolved problem?
At my previous job we had the same problem which we solved by using Tika. We called it on the server along with other stuff, but there is also a Python binding.
txtai
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Build knowledge graphs with LLM-driven entity extraction
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Bootstrap or VC?
Bootstrapping only works if you have the runway to do it and you don't feel the need to grow fast.
With NeuML (https://neuml.com), I've went the bootstrapping route. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. It's a "live within your means" strategy.
VC funding can have a snowball effect where you need more and more. Then you're in the loop of needing funding rounds to survive. The hope is someday you're acquired or start turning a profit.
I would say both have their pros and cons. Not all ideas have the luxury of time.
- txtai: An embeddings database for semantic search, graph networks and RAG
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Ask HN: What happened to startups, why is everything so polished?
I agree that in many cases people are puffing their feathers to try to be something they're not (at least not yet). Some believe in the fake it until you make it mentality.
With NeuML (https://neuml.com), the website is a simple HTML page. On social media, I'm honest about what NeuML is, that I'm in my 40s with a family and not striving to be the next Steve Jobs. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. For me, authenticity and being genuine is most important. I would say that being genuine has been way more of an asset than liability.
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Are we at peak vector database?
I'll add txtai (https://github.com/neuml/txtai) to the list.
There is still plenty of room for innovation in this space. Just need to focus on the right projects that are innovating and not the ones (re)working on problems solved in 2020/2021.
- Txtai: An all-in-one embeddings database for semantic search and LLM workflows
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Generate knowledge with Semantic Graphs and RAG
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Show HN: Open-source Rule-based PDF parser for RAG
Nice project! I've long used Tika for document parsing given it's maturity and wide number of formats supported. The XHTML output helps with chunking documents for RAG.
Here's a couple examples:
- https://neuml.hashnode.dev/build-rag-pipelines-with-txtai
- https://neuml.hashnode.dev/extract-text-from-documents
Disclaimer: I'm the primary author of txtai (https://github.com/neuml/txtai).
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RAG Using Unstructured Data and Role of Knowledge Graphs
If you're interested in graphs + RAG and want an alternate approach, txtai has a semantic graph component.
https://neuml.hashnode.dev/introducing-the-semantic-graph
https://github.com/neuml/txtai
Disclaimer: I'm the primary author of txtai
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Ten Noteworthy AI Research Papers of 2023
fwiw this link looks interesting, everyone
What are some alternatives?
Paperless-ng - A supercharged version of paperless: scan, index and archive all your physical documents
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
sist2 - Lightning-fast file system indexer and search tool
tika-python - Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.
spyglass - A personal search engine: Create a searchable library from your personal documents, interests, and more!
faiss - A library for efficient similarity search and clustering of dense vectors.
spacedrive - Spacedrive is an open source cross-platform file explorer, powered by a virtual distributed filesystem written in Rust.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
yew - Rust / Wasm framework for creating reliable and efficient web applications
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
self-hosted_docker_setups - A collection of my docker-compose files used to setup self-hosted services on Raspberry Pi 4 running 64-bit Raspberry Pi OS
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers