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<b>All-in-one embeddings database</b>
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txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
Embeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases. This enables vector search with SQL, topic modeling, retrieval augmented generation (RAG) and more.
Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts.
Summary of txtai features:
- 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing
- 📄 Create embeddings for text, documents, audio, images and video
- 💡 Pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarization and more
- ↪️️ Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.
- ⚙️ Build with Python or YAML. API bindings available for [JavaScript](https://github.com/neuml/txtai.js), [Java](https://github.com/neuml/txtai.java), [Rust](https://github.com/neuml/txtai.rs) and [Go](https://github.com/neuml/txtai.go).
- ☁️ Run local or scale out with container orchestration
txtai is built with Python 3.9+, [Hugging Face Transformers](https://github.com/huggingface/transformers), [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and [FastAPI](https://github.com/tiangolo/fastapi). txtai is open-source under an Apache 2.0 license.
*Interested in an easy and secure way to run hosted txtai applications? Then join the [txtai.cloud](https://txtai.cloud) preview to learn more.*
## Why txtai?
New vector databases, LLM frameworks and everything in between are sprouting up daily. Why build with txtai?
- Up and running in minutes with [pip](https://neuml.github.io/txtai/install/) or [Docker](https://neuml.github.io/txtai/cloud/)
```python
# Get started in a couple lines
import txtai
embeddings = txtai.Embeddings()
embeddings.index(["Correct", "Not what we hoped"])
embeddings.search("positive", 1)
#[(0, 0.29862046241760254)]
```
- Built-in API makes it easy to develop applications using your programming language of choice
```yaml
# app.yml
embeddings:
path: sentence-transformers/all-MiniLM-L6-v2
```
```bash
CONFIG=app.yml uvicorn "txtai.api:app"
curl -X GET "http://localhost:8000/search?query=positive"
```
- Run local - no need to ship data off to disparate remote services
- Work with micromodels all the way up to large language models (LLMs)
- Low footprint - install additional dependencies and scale up when needed
- [Learn by example](https://neuml.github.io/txtai/examples) - notebooks cover all available functionality
## Use Cases
The following sections introduce common txtai use cases. A comprehensive set of over 50 [example notebooks and applications](https://neuml.github.io/txtai/examples) are also available.
### Semantic Search
Build semantic/similarity/vector/neural search applications.
Traditional search systems use keywords to find data. Semantic search has an understanding of natural language and identifies results that have the same meaning, not necessarily the same keywords.
Get started with the following examples.
| Notebook | Description | |
|:----------|:-------------|------:|
| [Introducing txtai](https://github.com/neuml/txtai/blob/master/examples/01_Introducing_txtai.ipynb) [▶️](https://www.youtube.com/watch?v=SIezMnVdmMs) | Overview of the functionality provided by txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/01_Introducing_txtai.ipynb) |
| [Similarity search with images](https://github.com/neuml/txtai/blob/master/examples/13_Similarity_search_with_images.ipynb) | Embed images and text into the same space for search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/13_Similarity_search_with_images.ipynb) |
| [Build a QA database](https://github.com/neuml/txtai/blob/master/examples/34_Build_a_QA_database.ipynb) | Question matching with semantic search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/34_Build_a_QA_database.ipynb) |
| [Semantic Graphs](https://github.com/neuml/txtai/blob/master/examples/38_Introducing_the_Semantic_Graph.ipynb) | Explore topics, data connectivity and run network analysis| [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/38_Introducing_the_Semantic_Graph.ipynb) |
### LLM Orchestration
LLM chains, retrieval augmented generation (RAG), chat with your data, pipelines and workflows that interface with large language models (LLMs).
#### Chains
Integrate LLM chains (known as workflows in txtai), multiple LLM agents and self-critique.
See below to learn more.
| Notebook | Description | |
|:----------|:-------------|------:|
| [Prompt templates and task chains](https://github.com/neuml/txtai/blob/master/examples/44_Prompt_templates_and_task_chains.ipynb) | Build model prompts and connect tasks together with workflows | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/44_Prompt_templates_and_task_chains.ipynb) |
| [Integrate LLM frameworks](https://github.com/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb) | Integrate llama.cpp, LiteLLM and custom generation frameworks | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb) |
| [Build knowledge graphs with LLMs](https://github.com/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) | Build knowledge graphs with LLM-driven entity extraction | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) |
#### Retrieval augmented generation
Retrieval augmented generation (RAG) reduces the risk of LLM hallucinations by constraining the output with a knowledge base as context. RAG is commonly used to "chat with your data".
A novel feature of txtai is that it can provide both an answer and source citation.
| Notebook | Description | |
|:----------|:-------------|------:|
| [Build RAG pipelines with txtai](https://github.com/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) | Guide on retrieval augmented generation including how to create citations | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) |
| [How RAG with txtai works](https://github.com/neuml/txtai/blob/master/examples/63_How_RAG_with_txtai_works.ipynb) | Create RAG processes, API services and Docker instances | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/63_How_RAG_with_txtai_works.ipynb) |
| [Advanced RAG with graph path traversal](https://github.com/neuml/txtai/blob/master/examples/58_Advanced_RAG_with_graph_path_traversal.ipynb) | Graph path traversal to collect complex sets of data for advanced RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/58_Advanced_RAG_with_graph_path_traversal.ipynb) |
| [Speech to Speech RAG](https://github.com/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) [▶️](https://www.youtube.com/watch?v=tH8QWwkVMKA) | Full cycle speech to speech workflow with RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) |
### Language Model Workflows
Language model workflows, also known as semantic workflows, connect language models together to build intelligent applications.
While LLMs are powerful, there are plenty of smaller, more specialized models that work better and faster for specific tasks. This includes models for extractive question-answering, automatic summarization, text-to-speech, transcription and translation.
| Notebook | Description | |
|:----------|:-------------|------:|
| [Run pipeline workflows](https://github.com/neuml/txtai/blob/master/examples/14_Run_pipeline_workflows.ipynb) [▶️](https://www.youtube.com/watch?v=UBMPDCn1gEU) | Simple yet powerful constructs to efficiently process data | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/14_Run_pipeline_workflows.ipynb) |
| [Building abstractive text summaries](https://github.com/neuml/txtai/blob/master/examples/09_Building_abstractive_text_summaries.ipynb) | Run abstractive text summarization | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/09_Building_abstractive_text_summaries.ipynb) |
| [Transcribe audio to text](https://github.com/neuml/txtai/blob/master/examples/11_Transcribe_audio_to_text.ipynb) | Convert audio files to text | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/11_Transcribe_audio_to_text.ipynb) |
| [Translate text between languages](https://github.com/neuml/txtai/blob/master/examples/12_Translate_text_between_languages.ipynb) | Streamline machine translation and language detection | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/12_Translate_text_between_languages.ipynb) |
## Installation
The easiest way to install is via pip and PyPI
```
pip install txtai
```
Python 3.9+ is supported. Using a Python [virtual environment](https://docs.python.org/3/library/venv.html) is recommended.
See the detailed [install instructions](https://neuml.github.io/txtai/install) for more information covering [optional dependencies](https://neuml.github.io/txtai/install/#optional-dependencies), [environment specific prerequisites](https://neuml.github.io/txtai/install/#environment-specific-prerequisites), [installing from source](https://neuml.github.io/txtai/install/#install-from-source), [conda support](https://neuml.github.io/txtai/install/#conda) and how to [run with containers](https://neuml.github.io/txtai/cloud).
## Model guide
See the table below for the current recommended models. These models all allow commercial use and offer a blend of speed and performance.
| Component | Model(s) |
| ----------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| [Embeddings](https://neuml.github.io/txtai/embeddings) | [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) |
| [Image Captions](https://neuml.github.io/txtai/pipeline/image/caption) | [BLIP](https://hf.co/Salesforce/blip-image-captioning-base) |
| [Labels - Zero Shot](https://neuml.github.io/txtai/pipeline/text/labels) | [BART-Large-MNLI](https://hf.co/facebook/bart-large) |
| [Labels - Fixed](https://neuml.github.io/txtai/pipeline/text/labels) | Fine-tune with [training pipeline](https://neuml.github.io/txtai/pipeline/train/trainer) |
| [Large Language Model (LLM)](https://neuml.github.io/txtai/pipeline/text/llm) | [Mistral 7B OpenOrca](https://hf.co/Open-Orca/Mistral-7B-OpenOrca) |
| [Summarization](https://neuml.github.io/txtai/pipeline/text/summary) | [DistilBART](https://hf.co/sshleifer/distilbart-cnn-12-6) |
| [Text-to-Speech](https://neuml.github.io/txtai/pipeline/audio/texttospeech) | [ESPnet JETS](https://hf.co/NeuML/ljspeech-jets-onnx) |
| [Transcription](https://neuml.github.io/txtai/pipeline/audio/transcription) | [Whisper](https://hf.co/openai/whisper-base) |
| [Translation](https://neuml.github.io/txtai/pipeline/text/translation) | [OPUS Model Series](https://hf.co/Helsinki-NLP) |
Models can be loaded as either a path from the Hugging Face Hub or a local directory. Model paths are optional, defaults are loaded when not specified. For tasks with no recommended model, txtai uses the default models as shown in the Hugging Face Tasks guide.
See the following links to learn more.
- [Hugging Face Tasks](https://hf.co/tasks)
- [Hugging Face Model Hub](https://hf.co/models)
- [MTEB Leaderboard](https://hf.co/spaces/mteb/leaderboard)
- [LMSYS LLM Leaderboard](https://chat.lmsys.org/?leaderboard)
- [Open LLM Leaderboard](https://hf.co/spaces/HuggingFaceH4/open_llm_leaderboard)
## Powered by txtai
The following applications are powered by txtai.
| Application | Description |
|:------------ |:-------------|
| [txtchat](https://github.com/neuml/txtchat) | Retrieval Augmented Generation (RAG) powered search |
| [paperai](https://github.com/neuml/paperai) | Semantic search and workflows for medical/scientific papers |
| [codequestion](https://github.com/neuml/codequestion) | Semantic search for developers |
| [tldrstory](https://github.com/neuml/tldrstory) | Semantic search for headlines and story text |
In addition to this list, there are also many other [open-source projects](https://github.com/neuml/txtai/network/dependents), [published research](https://scholar.google.com/scholar?q=txtai&hl=en&as_ylo=2022) and closed proprietary/commercial projects that have built on txtai in production.
## Further Reading
- [Introducing txtai, the all-in-one embeddings database](https://medium.com/neuml/introducing-txtai-the-all-in-one-embeddings-database-c721f4ff91ad)
- [Tutorial series on Hashnode](https://neuml.hashnode.dev/series/txtai-tutorial) | [dev.to](https://dev.to/neuml/tutorial-series-on-txtai-ibg)
- [What's new in txtai 7.0](https://medium.com/neuml/whats-new-in-txtai-7-0-855ad6a55440) | [6.0](https://medium.com/neuml/whats-new-in-txtai-6-0-7d93eeedf804) | [5.0](https://medium.com/neuml/whats-new-in-txtai-5-0-e5c75a13b101) | [4.0](https://medium.com/neuml/whats-new-in-txtai-4-0-bbc3a65c3d1c)
- [Getting started with semantic search](https://medium.com/neuml/getting-started-with-semantic-search-a9fd9d8a48cf) | [workflows](https://medium.com/neuml/getting-started-with-semantic-workflows-2fefda6165d9) | [rag](https://medium.com/neuml/getting-started-with-rag-9a0cca75f748)
- [Running txtai at scale](https://medium.com/neuml/running-at-scale-with-txtai-71196cdd99f9)
- [Vector search & RAG Landscape: A review with txtai](https://medium.com/neuml/vector-search-rag-landscape-a-review-with-txtai-a7f37ad0e187)
## Documentation
[Full documentation on txtai](https://neuml.github.io/txtai) including configuration settings for embeddings, pipelines, workflows, API and a FAQ with common questions/issues is available.
## Contributing
For those who would like to contribute to txtai, please see [this guide](https://github.com/neuml/.github/blob/master/CONTRIBUTING.md).
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"description": "<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/neuml/txtai/master/logo.png\"/>\n</p>\n\n<p align=\"center\">\n <b>All-in-one embeddings database</b>\n</p>\n\n<p align=\"center\">\n <a href=\"https://github.com/neuml/txtai/releases\">\n <img src=\"https://img.shields.io/github/release/neuml/txtai.svg?style=flat&color=success\" alt=\"Version\"/>\n </a>\n <a href=\"https://github.com/neuml/txtai\">\n <img src=\"https://img.shields.io/github/last-commit/neuml/txtai.svg?style=flat&color=blue\" alt=\"GitHub last commit\"/>\n </a>\n <a href=\"https://github.com/neuml/txtai/issues\">\n <img src=\"https://img.shields.io/github/issues/neuml/txtai.svg?style=flat&color=success\" alt=\"GitHub issues\"/>\n </a>\n <a href=\"https://join.slack.com/t/txtai/shared_invite/zt-1cagya4yf-DQeuZbd~aMwH5pckBU4vPg\">\n <img src=\"https://img.shields.io/badge/slack-join-blue?style=flat&logo=slack&logocolor=white\" alt=\"Join Slack\"/>\n </a>\n <a href=\"https://github.com/neuml/txtai/actions?query=workflow%3Abuild\">\n <img src=\"https://github.com/neuml/txtai/workflows/build/badge.svg\" alt=\"Build Status\"/>\n </a>\n <a href=\"https://coveralls.io/github/neuml/txtai?branch=master\">\n <img src=\"https://img.shields.io/coverallsCoverage/github/neuml/txtai\" alt=\"Coverage Status\">\n </a>\n</p>\n\ntxtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.\n\n\n\nEmbeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases. This enables vector search with SQL, topic modeling, retrieval augmented generation (RAG) and more.\n\nEmbeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts.\n\nSummary of txtai features:\n\n- \ud83d\udd0e Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing\n- \ud83d\udcc4 Create embeddings for text, documents, audio, images and video\n- \ud83d\udca1 Pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarization and more\n- \u21aa\ufe0f\ufe0f Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.\n- \u2699\ufe0f Build with Python or YAML. API bindings available for [JavaScript](https://github.com/neuml/txtai.js), [Java](https://github.com/neuml/txtai.java), [Rust](https://github.com/neuml/txtai.rs) and [Go](https://github.com/neuml/txtai.go).\n- \u2601\ufe0f Run local or scale out with container orchestration\n\ntxtai is built with Python 3.9+, [Hugging Face Transformers](https://github.com/huggingface/transformers), [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and [FastAPI](https://github.com/tiangolo/fastapi). txtai is open-source under an Apache 2.0 license.\n\n*Interested in an easy and secure way to run hosted txtai applications? Then join the [txtai.cloud](https://txtai.cloud) preview to learn more.*\n\n## Why txtai?\n\n\n\nNew vector databases, LLM frameworks and everything in between are sprouting up daily. Why build with txtai?\n\n- Up and running in minutes with [pip](https://neuml.github.io/txtai/install/) or [Docker](https://neuml.github.io/txtai/cloud/)\n```python\n# Get started in a couple lines\nimport txtai\n\nembeddings = txtai.Embeddings()\nembeddings.index([\"Correct\", \"Not what we hoped\"])\nembeddings.search(\"positive\", 1)\n#[(0, 0.29862046241760254)]\n```\n- Built-in API makes it easy to develop applications using your programming language of choice\n```yaml\n# app.yml\nembeddings:\n path: sentence-transformers/all-MiniLM-L6-v2\n```\n```bash\nCONFIG=app.yml uvicorn \"txtai.api:app\"\ncurl -X GET \"http://localhost:8000/search?query=positive\"\n```\n- Run local - no need to ship data off to disparate remote services\n- Work with micromodels all the way up to large language models (LLMs)\n- Low footprint - install additional dependencies and scale up when needed\n- [Learn by example](https://neuml.github.io/txtai/examples) - notebooks cover all available functionality\n\n## Use Cases\n\nThe following sections introduce common txtai use cases. A comprehensive set of over 50 [example notebooks and applications](https://neuml.github.io/txtai/examples) are also available.\n\n### Semantic Search\n\nBuild semantic/similarity/vector/neural search applications.\n\n\n\nTraditional search systems use keywords to find data. Semantic search has an understanding of natural language and identifies results that have the same meaning, not necessarily the same keywords.\n\n\n\nGet started with the following examples.\n\n| Notebook | Description | |\n|:----------|:-------------|------:|\n| [Introducing txtai](https://github.com/neuml/txtai/blob/master/examples/01_Introducing_txtai.ipynb) [\u25b6\ufe0f](https://www.youtube.com/watch?v=SIezMnVdmMs) | Overview of the functionality provided by txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/01_Introducing_txtai.ipynb) |\n| [Similarity search with images](https://github.com/neuml/txtai/blob/master/examples/13_Similarity_search_with_images.ipynb) | Embed images and text into the same space for search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/13_Similarity_search_with_images.ipynb) |\n| [Build a QA database](https://github.com/neuml/txtai/blob/master/examples/34_Build_a_QA_database.ipynb) | Question matching with semantic search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/34_Build_a_QA_database.ipynb) |\n| [Semantic Graphs](https://github.com/neuml/txtai/blob/master/examples/38_Introducing_the_Semantic_Graph.ipynb) | Explore topics, data connectivity and run network analysis| [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/38_Introducing_the_Semantic_Graph.ipynb) |\n\n### LLM Orchestration\n\nLLM chains, retrieval augmented generation (RAG), chat with your data, pipelines and workflows that interface with large language models (LLMs).\n\n#### Chains\n\nIntegrate LLM chains (known as workflows in txtai), multiple LLM agents and self-critique.\n\n\n\nSee below to learn more.\n\n| Notebook | Description | |\n|:----------|:-------------|------:|\n| [Prompt templates and task chains](https://github.com/neuml/txtai/blob/master/examples/44_Prompt_templates_and_task_chains.ipynb) | Build model prompts and connect tasks together with workflows | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/44_Prompt_templates_and_task_chains.ipynb) |\n| [Integrate LLM frameworks](https://github.com/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb) | Integrate llama.cpp, LiteLLM and custom generation frameworks | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb) |\n| [Build knowledge graphs with LLMs](https://github.com/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) | Build knowledge graphs with LLM-driven entity extraction | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) |\n\n#### Retrieval augmented generation\n\nRetrieval augmented generation (RAG) reduces the risk of LLM hallucinations by constraining the output with a knowledge base as context. RAG is commonly used to \"chat with your data\".\n\n\n\nA novel feature of txtai is that it can provide both an answer and source citation.\n\n| Notebook | Description | |\n|:----------|:-------------|------:|\n| [Build RAG pipelines with txtai](https://github.com/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) | Guide on retrieval augmented generation including how to create citations | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) |\n| [How RAG with txtai works](https://github.com/neuml/txtai/blob/master/examples/63_How_RAG_with_txtai_works.ipynb) | Create RAG processes, API services and Docker instances | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/63_How_RAG_with_txtai_works.ipynb) |\n| [Advanced RAG with graph path traversal](https://github.com/neuml/txtai/blob/master/examples/58_Advanced_RAG_with_graph_path_traversal.ipynb) | Graph path traversal to collect complex sets of data for advanced RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/58_Advanced_RAG_with_graph_path_traversal.ipynb) |\n| [Speech to Speech RAG](https://github.com/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) [\u25b6\ufe0f](https://www.youtube.com/watch?v=tH8QWwkVMKA) | Full cycle speech to speech workflow with RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) |\n\n### Language Model Workflows\n\nLanguage model workflows, also known as semantic workflows, connect language models together to build intelligent applications.\n\n\n\nWhile LLMs are powerful, there are plenty of smaller, more specialized models that work better and faster for specific tasks. This includes models for extractive question-answering, automatic summarization, text-to-speech, transcription and translation.\n\n| Notebook | Description | |\n|:----------|:-------------|------:|\n| [Run pipeline workflows](https://github.com/neuml/txtai/blob/master/examples/14_Run_pipeline_workflows.ipynb) [\u25b6\ufe0f](https://www.youtube.com/watch?v=UBMPDCn1gEU) | Simple yet powerful constructs to efficiently process data | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/14_Run_pipeline_workflows.ipynb) |\n| [Building abstractive text summaries](https://github.com/neuml/txtai/blob/master/examples/09_Building_abstractive_text_summaries.ipynb) | Run abstractive text summarization | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/09_Building_abstractive_text_summaries.ipynb) |\n| [Transcribe audio to text](https://github.com/neuml/txtai/blob/master/examples/11_Transcribe_audio_to_text.ipynb) | Convert audio files to text | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/11_Transcribe_audio_to_text.ipynb) |\n| [Translate text between languages](https://github.com/neuml/txtai/blob/master/examples/12_Translate_text_between_languages.ipynb) | Streamline machine translation and language detection | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/12_Translate_text_between_languages.ipynb) |\n\n## Installation\n\n\n\nThe easiest way to install is via pip and PyPI\n\n```\npip install txtai\n```\n\nPython 3.9+ is supported. Using a Python [virtual environment](https://docs.python.org/3/library/venv.html) is recommended.\n\nSee the detailed [install instructions](https://neuml.github.io/txtai/install) for more information covering [optional dependencies](https://neuml.github.io/txtai/install/#optional-dependencies), [environment specific prerequisites](https://neuml.github.io/txtai/install/#environment-specific-prerequisites), [installing from source](https://neuml.github.io/txtai/install/#install-from-source), [conda support](https://neuml.github.io/txtai/install/#conda) and how to [run with containers](https://neuml.github.io/txtai/cloud).\n\n## Model guide\n\n\n\nSee the table below for the current recommended models. These models all allow commercial use and offer a blend of speed and performance.\n\n| Component | Model(s) |\n| ----------------------------------------------------------------------------- | ------------------------------------------------------------------------ |\n| [Embeddings](https://neuml.github.io/txtai/embeddings) | [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | \n| [Image Captions](https://neuml.github.io/txtai/pipeline/image/caption) | [BLIP](https://hf.co/Salesforce/blip-image-captioning-base) |\n| [Labels - Zero Shot](https://neuml.github.io/txtai/pipeline/text/labels) | [BART-Large-MNLI](https://hf.co/facebook/bart-large) |\n| [Labels - Fixed](https://neuml.github.io/txtai/pipeline/text/labels) | Fine-tune with [training pipeline](https://neuml.github.io/txtai/pipeline/train/trainer) |\n| [Large Language Model (LLM)](https://neuml.github.io/txtai/pipeline/text/llm) | [Mistral 7B OpenOrca](https://hf.co/Open-Orca/Mistral-7B-OpenOrca) |\n| [Summarization](https://neuml.github.io/txtai/pipeline/text/summary) | [DistilBART](https://hf.co/sshleifer/distilbart-cnn-12-6) |\n| [Text-to-Speech](https://neuml.github.io/txtai/pipeline/audio/texttospeech) | [ESPnet JETS](https://hf.co/NeuML/ljspeech-jets-onnx) |\n| [Transcription](https://neuml.github.io/txtai/pipeline/audio/transcription) | [Whisper](https://hf.co/openai/whisper-base) | \n| [Translation](https://neuml.github.io/txtai/pipeline/text/translation) | [OPUS Model Series](https://hf.co/Helsinki-NLP) |\n\nModels can be loaded as either a path from the Hugging Face Hub or a local directory. Model paths are optional, defaults are loaded when not specified. For tasks with no recommended model, txtai uses the default models as shown in the Hugging Face Tasks guide.\n\nSee the following links to learn more.\n\n- [Hugging Face Tasks](https://hf.co/tasks)\n- [Hugging Face Model Hub](https://hf.co/models)\n- [MTEB Leaderboard](https://hf.co/spaces/mteb/leaderboard)\n- [LMSYS LLM Leaderboard](https://chat.lmsys.org/?leaderboard)\n- [Open LLM Leaderboard](https://hf.co/spaces/HuggingFaceH4/open_llm_leaderboard)\n\n## Powered by txtai\n\nThe following applications are powered by txtai.\n\n\n\n| Application | Description |\n|:------------ |:-------------|\n| [txtchat](https://github.com/neuml/txtchat) | Retrieval Augmented Generation (RAG) powered search |\n| [paperai](https://github.com/neuml/paperai) | Semantic search and workflows for medical/scientific papers |\n| [codequestion](https://github.com/neuml/codequestion) | Semantic search for developers |\n| [tldrstory](https://github.com/neuml/tldrstory) | Semantic search for headlines and story text |\n\nIn addition to this list, there are also many other [open-source projects](https://github.com/neuml/txtai/network/dependents), [published research](https://scholar.google.com/scholar?q=txtai&hl=en&as_ylo=2022) and closed proprietary/commercial projects that have built on txtai in production.\n\n## Further Reading\n\n\n\n- [Introducing txtai, the all-in-one embeddings database](https://medium.com/neuml/introducing-txtai-the-all-in-one-embeddings-database-c721f4ff91ad)\n- [Tutorial series on Hashnode](https://neuml.hashnode.dev/series/txtai-tutorial) | [dev.to](https://dev.to/neuml/tutorial-series-on-txtai-ibg)\n- [What's new in txtai 7.0](https://medium.com/neuml/whats-new-in-txtai-7-0-855ad6a55440) | [6.0](https://medium.com/neuml/whats-new-in-txtai-6-0-7d93eeedf804) | [5.0](https://medium.com/neuml/whats-new-in-txtai-5-0-e5c75a13b101) | [4.0](https://medium.com/neuml/whats-new-in-txtai-4-0-bbc3a65c3d1c)\n- [Getting started with semantic search](https://medium.com/neuml/getting-started-with-semantic-search-a9fd9d8a48cf) | [workflows](https://medium.com/neuml/getting-started-with-semantic-workflows-2fefda6165d9) | [rag](https://medium.com/neuml/getting-started-with-rag-9a0cca75f748)\n- [Running txtai at scale](https://medium.com/neuml/running-at-scale-with-txtai-71196cdd99f9)\n- [Vector search & RAG Landscape: A review with txtai](https://medium.com/neuml/vector-search-rag-landscape-a-review-with-txtai-a7f37ad0e187)\n\n## Documentation\n\n[Full documentation on txtai](https://neuml.github.io/txtai) including configuration settings for embeddings, pipelines, workflows, API and a FAQ with common questions/issues is available.\n\n## Contributing\n\nFor those who would like to contribute to txtai, please see [this guide](https://github.com/neuml/.github/blob/master/CONTRIBUTING.md).\n",
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