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| author | CoprDistGit <infra@openeuler.org> | 2023-05-05 11:43:58 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-05 11:43:58 +0000 |
| commit | 8ded03be28cf8c8012cb6d05e9ac1cd174bb2147 (patch) | |
| tree | 4d590e376ee87d3e8c694cb6ba62adfed670e6bd /python-txtai.spec | |
| parent | 145a7a296ddb450fa1bda2e78474388dfe3e20ed (diff) | |
automatic import of python-txtaiopeneuler20.03
Diffstat (limited to 'python-txtai.spec')
| -rw-r--r-- | python-txtai.spec | 611 |
1 files changed, 611 insertions, 0 deletions
diff --git a/python-txtai.spec b/python-txtai.spec new file mode 100644 index 0000000..fa811d6 --- /dev/null +++ b/python-txtai.spec @@ -0,0 +1,611 @@ +%global _empty_manifest_terminate_build 0 +Name: python-txtai +Version: 5.5.1 +Release: 1 +Summary: Semantic search and workflows powered by language models +License: Apache 2.0: http://www.apache.org/licenses/LICENSE-2.0 +URL: https://github.com/neuml/txtai +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d2/44/96ff97fdc15bba78fd4394138ad8ca4aa4d522034f928f6cdb54eb3512ff/txtai-5.5.1.tar.gz +BuildArch: noarch + +Requires: python3-faiss-cpu +Requires: python3-numpy +Requires: python3-pyyaml +Requires: python3-torch +Requires: python3-transformers +Requires: python3-aiohttp +Requires: python3-fastapi +Requires: python3-uvicorn +Requires: python3-apache-libcloud +Requires: python3-rich +Requires: python3-duckdb +Requires: python3-pillow +Requires: python3-networkx +Requires: python3-louvain +Requires: python3-onnx +Requires: python3-onnxruntime +Requires: python3-soundfile +Requires: python3-scipy +Requires: python3-ttstokenizer +Requires: python3-beautifulsoup4 +Requires: python3-nltk +Requires: python3-pandas +Requires: python3-tika +Requires: python3-imagehash +Requires: python3-timm +Requires: python3-fasttext +Requires: python3-sentencepiece +Requires: python3-onnxmltools +Requires: python3-annoy +Requires: python3-hnswlib +Requires: python3-pymagnitude-lite +Requires: python3-scikit-learn +Requires: python3-sentence-transformers +Requires: python3-croniter +Requires: python3-openpyxl +Requires: python3-requests +Requires: python3-xmltodict +Requires: python3-aiohttp +Requires: python3-fastapi +Requires: python3-uvicorn +Requires: python3-apache-libcloud +Requires: python3-rich +Requires: python3-duckdb +Requires: python3-pillow +Requires: python3-black +Requires: python3-coverage +Requires: python3-coveralls +Requires: python3-httpx +Requires: python3-mkdocs-material +Requires: python3-mkdocstrings[python-legacy] +Requires: python3-pre-commit +Requires: python3-pylint +Requires: python3-networkx +Requires: python3-louvain +Requires: python3-onnx +Requires: python3-onnxruntime +Requires: python3-onnx +Requires: python3-onnxruntime +Requires: python3-soundfile +Requires: python3-scipy +Requires: python3-ttstokenizer +Requires: python3-beautifulsoup4 +Requires: python3-nltk +Requires: python3-pandas +Requires: python3-tika +Requires: python3-imagehash +Requires: python3-pillow +Requires: python3-timm +Requires: python3-fasttext +Requires: python3-sentencepiece +Requires: python3-onnxmltools +Requires: python3-onnx +Requires: python3-onnxruntime +Requires: python3-soundfile +Requires: python3-scipy +Requires: python3-ttstokenizer +Requires: python3-beautifulsoup4 +Requires: python3-nltk +Requires: python3-pandas +Requires: python3-tika +Requires: python3-imagehash +Requires: python3-pillow +Requires: python3-timm +Requires: python3-fasttext +Requires: python3-sentencepiece +Requires: python3-onnx +Requires: python3-onnxmltools +Requires: python3-onnxruntime +Requires: python3-annoy +Requires: python3-fasttext +Requires: python3-hnswlib +Requires: python3-pymagnitude-lite +Requires: python3-scikit-learn +Requires: python3-sentence-transformers +Requires: python3-apache-libcloud +Requires: python3-croniter +Requires: python3-openpyxl +Requires: python3-pandas +Requires: python3-pillow +Requires: python3-requests +Requires: python3-xmltodict + +%description +txtai is an open-source platform for semantic search and workflows powered by language models. + +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. + +txtai builds embeddings databases, which are a union of vector indexes and relational databases. This enables similarity search with SQL. Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts. +Semantic workflows connect language models together to build intelligent applications. + +Integrate vector search, conversational search, automatic summarization, transcription, translation and more. +Summary of txtai features: +- 🔎 Similarity search with SQL, object storage, topic modeling, graph analysis, multiple vector index backends ([Faiss](https://github.com/facebookresearch/faiss), [Annoy](https://github.com/spotify/annoy), [Hnswlib](https://github.com/nmslib/hnswlib)) and support for external vector databases +- 📄 Create embeddings for text, documents, audio, images and video +- 💡 Pipelines powered by language models that run question-answering, labeling, transcription, translation, summarization, LLM prompts 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). +- ☁️ Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes) +The following applications are powered by txtai. + +| Application | Description | +|:----------|:-------------| +| [txtchat](https://github.com/neuml/txtchat) | Conversational search and workflows for all | +| [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 | +txtai is built with Python 3.7+, [Hugging Face Transformers](https://github.com/huggingface/transformers), [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and [FastAPI](https://github.com/tiangolo/fastapi) +## Why txtai? + +In addition to traditional search systems, a growing number of language model backed solutions are available, so why 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 +from txtai.embeddings import Embeddings +embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2"}) +embeddings.index([(0, "Correct", None), (1, "Not what we hoped", None)]) +embeddings.search("positive", 1) +#[(0, 0.2986203730106354)] +``` +- 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](#examples) - notebooks cover all available functionality +## Installation + +The easiest way to install is via pip and PyPI +``` +pip install txtai +``` +Python 3.7+ 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). +## Examples + +The examples directory has a series of notebooks and applications giving an overview of txtai. See the sections below. +### Semantic Search +Build semantic/similarity/vector/neural search applications. +| 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) | +| [Build an Embeddings index with Hugging Face Datasets](https://github.com/neuml/txtai/blob/master/examples/02_Build_an_Embeddings_index_with_Hugging_Face_Datasets.ipynb) | Index and search Hugging Face Datasets | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/02_Build_an_Embeddings_index_with_Hugging_Face_Datasets.ipynb) | +| [Build an Embeddings index from a data source](https://github.com/neuml/txtai/blob/master/examples/03_Build_an_Embeddings_index_from_a_data_source.ipynb) | Index and search a data source with word embeddings | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/03_Build_an_Embeddings_index_from_a_data_source.ipynb) | +| [Add semantic search to Elasticsearch](https://github.com/neuml/txtai/blob/master/examples/04_Add_semantic_search_to_Elasticsearch.ipynb) | Add semantic search to existing search systems | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/04_Add_semantic_search_to_Elasticsearch.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) | +| [Distributed embeddings cluster](https://github.com/neuml/txtai/blob/master/examples/15_Distributed_embeddings_cluster.ipynb) | Distribute an embeddings index across multiple data nodes | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/15_Distributed_embeddings_cluster.ipynb) | +| [What's new in txtai 4.0](https://github.com/neuml/txtai/blob/master/examples/24_Whats_new_in_txtai_4_0.ipynb) | Content storage, SQL, object storage, reindex and compressed indexes | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/24_Whats_new_in_txtai_4_0.ipynb) | +| [Anatomy of a txtai index](https://github.com/neuml/txtai/blob/master/examples/29_Anatomy_of_a_txtai_index.ipynb) | Deep dive into the file formats behind a txtai embeddings index | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/29_Anatomy_of_a_txtai_index.ipynb) | +| [Custom Embeddings SQL functions](https://github.com/neuml/txtai/blob/master/examples/30_Embeddings_SQL_custom_functions.ipynb) | Add user-defined functions to Embeddings SQL | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/30_Embeddings_SQL_custom_functions.ipynb) | +| [Model explainability](https://github.com/neuml/txtai/blob/master/examples/32_Model_explainability.ipynb) | Explainability for semantic search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/32_Model_explainability.ipynb) | +| [Query translation](https://github.com/neuml/txtai/blob/master/examples/33_Query_translation.ipynb) | Domain-specific natural language queries with query translation | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/33_Query_translation.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) | +| [Embeddings components](https://github.com/neuml/txtai/blob/master/examples/37_Embeddings_index_components.ipynb) | Composable search with vector, SQL and scoring components | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/37_Embeddings_index_components.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) | +| [Topic Modeling with BM25](https://github.com/neuml/txtai/blob/master/examples/39_Classic_Topic_Modeling_with_BM25.ipynb) | Topic modeling backed by a BM25 index | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/39_Classic_Topic_Modeling_with_BM25.ipynb) | +| [Prompt-driven search with LLMs](https://github.com/neuml/txtai/blob/master/examples/42_Prompt_driven_search_with_LLMs.ipynb) | Embeddings-guided and Prompt-driven search with Large Language Models (LLMs) | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/42_Prompt_driven_search_with_LLMs.ipynb) | +| [Embeddings in the Cloud](https://github.com/neuml/txtai/blob/master/examples/43_Embeddings_in_the_Cloud.ipynb) | Load and use an embeddings index from the Hugging Face Hub | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/43_Embeddings_in_the_Cloud.ipynb) | +### Pipelines +Transform data with language model backed pipelines. +| Notebook | Description | | +|:----------|:-------------|------:| +| [Extractive QA with txtai](https://github.com/neuml/txtai/blob/master/examples/05_Extractive_QA_with_txtai.ipynb) | Introduction to extractive question-answering with txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/05_Extractive_QA_with_txtai.ipynb) | +| [Extractive QA with Elasticsearch](https://github.com/neuml/txtai/blob/master/examples/06_Extractive_QA_with_Elasticsearch.ipynb) | Run extractive question-answering queries with Elasticsearch | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/06_Extractive_QA_with_Elasticsearch.ipynb) | +| [Extractive QA to build structured data](https://github.com/neuml/txtai/blob/master/examples/20_Extractive_QA_to_build_structured_data.ipynb) | Build structured datasets using extractive question-answering | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/20_Extractive_QA_to_build_structured_data.ipynb) | +| [Apply labels with zero shot classification](https://github.com/neuml/txtai/blob/master/examples/07_Apply_labels_with_zero_shot_classification.ipynb) | Use zero shot learning for labeling, classification and topic modeling | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/07_Apply_labels_with_zero_shot_classification.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) | +| [Extract text from documents](https://github.com/neuml/txtai/blob/master/examples/10_Extract_text_from_documents.ipynb) | Extract text from PDF, Office, HTML and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/10_Extract_text_from_documents.ipynb) | +| [Text to speech generation](https://github.com/neuml/txtai/blob/master/examples/40_Text_to_Speech_Generation.ipynb) | Generate speech from text | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/40_Text_to_Speech_Generation.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) | +| [Generate image captions and detect objects](https://github.com/neuml/txtai/blob/master/examples/25_Generate_image_captions_and_detect_objects.ipynb) | Captions and object detection for images | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/25_Generate_image_captions_and_detect_objects.ipynb) | +| [Near duplicate image detection](https://github.com/neuml/txtai/blob/master/examples/31_Near_duplicate_image_detection.ipynb) | Identify duplicate and near-duplicate images | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/31_Near_duplicate_image_detection.ipynb) | +| [API Gallery](https://github.com/neuml/txtai/blob/master/examples/08_API_Gallery.ipynb) | Using txtai in JavaScript, Java, Rust and Go | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/08_API_Gallery.ipynb) | +### Workflows +Efficiently process data at scale. +| 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) | +| [Transform tabular data with composable workflows](https://github.com/neuml/txtai/blob/master/examples/22_Transform_tabular_data_with_composable_workflows.ipynb) | Transform, index and search tabular data | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/22_Transform_tabular_data_with_composable_workflows.ipynb) | +| [Tensor workflows](https://github.com/neuml/txtai/blob/master/examples/23_Tensor_workflows.ipynb) | Performant processing of large tensor arrays | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/23_Tensor_workflows.ipynb) | +| [Entity extraction workflows](https://github.com/neuml/txtai/blob/master/examples/26_Entity_extraction_workflows.ipynb) | Identify entity/label combinations | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/26_Entity_extraction_workflows.ipynb) | +| [Workflow Scheduling](https://github.com/neuml/txtai/blob/master/examples/27_Workflow_scheduling.ipynb) | Schedule workflows with cron expressions | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/27_Workflow_scheduling.ipynb) | +| [Push notifications with workflows](https://github.com/neuml/txtai/blob/master/examples/28_Push_notifications_with_workflows.ipynb) | Generate and push notifications with workflows | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/28_Push_notifications_with_workflows.ipynb) | +| [Pictures are a worth a thousand words](https://github.com/neuml/txtai/blob/master/examples/35_Pictures_are_worth_a_thousand_words.ipynb) | Generate webpage summary images with DALL-E mini | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/35_Pictures_are_worth_a_thousand_words.ipynb) | +| [Run txtai with native code](https://github.com/neuml/txtai/blob/master/examples/36_Run_txtai_in_native_code.ipynb) | Execute workflows in native code with the Python C API | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/36_Run_txtai_in_native_code.ipynb) | +| [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) | +### Model Training +Train NLP models. +| Notebook | Description | | +|:----------|:-------------|------:| +| [Train a text labeler](https://github.com/neuml/txtai/blob/master/examples/16_Train_a_text_labeler.ipynb) | Build text sequence classification models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/16_Train_a_text_labeler.ipynb) | +| [Train without labels](https://github.com/neuml/txtai/blob/master/examples/17_Train_without_labels.ipynb) | Use zero-shot classifiers to train new models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/17_Train_without_labels.ipynb) | +| [Train a QA model](https://github.com/neuml/txtai/blob/master/examples/19_Train_a_QA_model.ipynb) | Build and fine-tune question-answering models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/19_Train_a_QA_model.ipynb) | +| [Train a language model from scratch](https://github.com/neuml/txtai/blob/master/examples/41_Train_a_language_model_from_scratch.ipynb) | Build new language models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/41_Train_a_language_model_from_scratch.ipynb) | +| [Export and run models with ONNX](https://github.com/neuml/txtai/blob/master/examples/18_Export_and_run_models_with_ONNX.ipynb) | Export models with ONNX, run natively in JavaScript, Java and Rust | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/18_Export_and_run_models_with_ONNX.ipynb) | +| [Export and run other machine learning models](https://github.com/neuml/txtai/blob/master/examples/21_Export_and_run_other_machine_learning_models.ipynb) | Export and run models from scikit-learn, PyTorch and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/21_Export_and_run_other_machine_learning_models.ipynb) | +### Applications +Series of example applications with txtai. Links to hosted versions on [Hugging Face Spaces](https://hf.co/spaces) also provided. +| Application | Description | | +|:-------------|:-------------|------:| +| [Basic similarity search](https://github.com/neuml/txtai/blob/master/examples/similarity.py) | Basic similarity search example. Data from the original txtai demo. |[🤗](https://hf.co/spaces/NeuML/similarity)| +| [Book search](https://github.com/neuml/txtai/blob/master/examples/books.py) | Book similarity search application. Index book descriptions and query using natural language statements. |*Local run only*| +| [Image search](https://github.com/neuml/txtai/blob/master/examples/images.py) | Image similarity search application. Index a directory of images and run searches to identify images similar to the input query. |[🤗](https://hf.co/spaces/NeuML/imagesearch)| +| [Summarize an article](https://github.com/neuml/txtai/blob/master/examples/article.py) | Summarize an article. Workflow that extracts text from a webpage and builds a summary. |[🤗](https://hf.co/spaces/NeuML/articlesummary)| +| [Wiki search](https://github.com/neuml/txtai/blob/master/examples/wiki.py) | Wikipedia search application. Queries Wikipedia API and summarizes the top result. |[🤗](https://hf.co/spaces/NeuML/wikisummary)| +| [Workflow builder](https://github.com/neuml/txtai/blob/master/examples/workflows.py) | Build and execute txtai workflows. Connect summarization, text extraction, transcription, translation and similarity search pipelines together to run unified workflows. |[🤗](https://hf.co/spaces/NeuML/txtai)| +## Documentation +[Full documentation on txtai](https://neuml.github.io/txtai) including configuration settings for pipelines, workflows, indexing and the API. +## Further Reading + +- [Introducing txtai, semantic search and workflows built on Transformers](https://medium.com/neuml/introducing-txtai-an-ai-powered-search-engine-built-on-transformers-37674be252ec) +- [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 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) +- [Run workflows to transform data and build semantic search applications with txtai](https://medium.com/neuml/run-machine-learning-workflows-to-transform-data-and-build-ai-powered-text-indices-with-txtai-43d769b566a7) +- [Semantic search on the cheap](https://medium.com/neuml/semantic-search-on-the-cheap-55940c0fcdab) +- [Serverless vector search with txtai](https://medium.com/neuml/serverless-vector-search-with-txtai-96f6163ab972) +- [Insights from the txtai console](https://medium.com/neuml/insights-from-the-txtai-console-d307c28e149e) +- [The big and small of txtai](https://medium.com/neuml/the-big-and-small-of-txtai-4ca405c1b82) +## Contributing +For those who would like to contribute to txtai, please see [this guide](https://github.com/neuml/.github/blob/master/CONTRIBUTING.md). + +%package -n python3-txtai +Summary: Semantic search and workflows powered by language models +Provides: python-txtai +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-txtai +txtai is an open-source platform for semantic search and workflows powered by language models. + +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. + +txtai builds embeddings databases, which are a union of vector indexes and relational databases. This enables similarity search with SQL. Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts. +Semantic workflows connect language models together to build intelligent applications. + +Integrate vector search, conversational search, automatic summarization, transcription, translation and more. +Summary of txtai features: +- 🔎 Similarity search with SQL, object storage, topic modeling, graph analysis, multiple vector index backends ([Faiss](https://github.com/facebookresearch/faiss), [Annoy](https://github.com/spotify/annoy), [Hnswlib](https://github.com/nmslib/hnswlib)) and support for external vector databases +- 📄 Create embeddings for text, documents, audio, images and video +- 💡 Pipelines powered by language models that run question-answering, labeling, transcription, translation, summarization, LLM prompts 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). +- ☁️ Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes) +The following applications are powered by txtai. + +| Application | Description | +|:----------|:-------------| +| [txtchat](https://github.com/neuml/txtchat) | Conversational search and workflows for all | +| [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 | +txtai is built with Python 3.7+, [Hugging Face Transformers](https://github.com/huggingface/transformers), [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and [FastAPI](https://github.com/tiangolo/fastapi) +## Why txtai? + +In addition to traditional search systems, a growing number of language model backed solutions are available, so why 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 +from txtai.embeddings import Embeddings +embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2"}) +embeddings.index([(0, "Correct", None), (1, "Not what we hoped", None)]) +embeddings.search("positive", 1) +#[(0, 0.2986203730106354)] +``` +- 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](#examples) - notebooks cover all available functionality +## Installation + +The easiest way to install is via pip and PyPI +``` +pip install txtai +``` +Python 3.7+ 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). +## Examples + +The examples directory has a series of notebooks and applications giving an overview of txtai. See the sections below. +### Semantic Search +Build semantic/similarity/vector/neural search applications. +| 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) | +| [Build an Embeddings index with Hugging Face Datasets](https://github.com/neuml/txtai/blob/master/examples/02_Build_an_Embeddings_index_with_Hugging_Face_Datasets.ipynb) | Index and search Hugging Face Datasets | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/02_Build_an_Embeddings_index_with_Hugging_Face_Datasets.ipynb) | +| [Build an Embeddings index from a data source](https://github.com/neuml/txtai/blob/master/examples/03_Build_an_Embeddings_index_from_a_data_source.ipynb) | Index and search a data source with word embeddings | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/03_Build_an_Embeddings_index_from_a_data_source.ipynb) | +| [Add semantic search to Elasticsearch](https://github.com/neuml/txtai/blob/master/examples/04_Add_semantic_search_to_Elasticsearch.ipynb) | Add semantic search to existing search systems | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/04_Add_semantic_search_to_Elasticsearch.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) | +| [Distributed embeddings cluster](https://github.com/neuml/txtai/blob/master/examples/15_Distributed_embeddings_cluster.ipynb) | Distribute an embeddings index across multiple data nodes | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/15_Distributed_embeddings_cluster.ipynb) | +| [What's new in txtai 4.0](https://github.com/neuml/txtai/blob/master/examples/24_Whats_new_in_txtai_4_0.ipynb) | Content storage, SQL, object storage, reindex and compressed indexes | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/24_Whats_new_in_txtai_4_0.ipynb) | +| [Anatomy of a txtai index](https://github.com/neuml/txtai/blob/master/examples/29_Anatomy_of_a_txtai_index.ipynb) | Deep dive into the file formats behind a txtai embeddings index | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/29_Anatomy_of_a_txtai_index.ipynb) | +| [Custom Embeddings SQL functions](https://github.com/neuml/txtai/blob/master/examples/30_Embeddings_SQL_custom_functions.ipynb) | Add user-defined functions to Embeddings SQL | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/30_Embeddings_SQL_custom_functions.ipynb) | +| [Model explainability](https://github.com/neuml/txtai/blob/master/examples/32_Model_explainability.ipynb) | Explainability for semantic search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/32_Model_explainability.ipynb) | +| [Query translation](https://github.com/neuml/txtai/blob/master/examples/33_Query_translation.ipynb) | Domain-specific natural language queries with query translation | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/33_Query_translation.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) | +| [Embeddings components](https://github.com/neuml/txtai/blob/master/examples/37_Embeddings_index_components.ipynb) | Composable search with vector, SQL and scoring components | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/37_Embeddings_index_components.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) | +| [Topic Modeling with BM25](https://github.com/neuml/txtai/blob/master/examples/39_Classic_Topic_Modeling_with_BM25.ipynb) | Topic modeling backed by a BM25 index | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/39_Classic_Topic_Modeling_with_BM25.ipynb) | +| [Prompt-driven search with LLMs](https://github.com/neuml/txtai/blob/master/examples/42_Prompt_driven_search_with_LLMs.ipynb) | Embeddings-guided and Prompt-driven search with Large Language Models (LLMs) | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/42_Prompt_driven_search_with_LLMs.ipynb) | +| [Embeddings in the Cloud](https://github.com/neuml/txtai/blob/master/examples/43_Embeddings_in_the_Cloud.ipynb) | Load and use an embeddings index from the Hugging Face Hub | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/43_Embeddings_in_the_Cloud.ipynb) | +### Pipelines +Transform data with language model backed pipelines. +| Notebook | Description | | +|:----------|:-------------|------:| +| [Extractive QA with txtai](https://github.com/neuml/txtai/blob/master/examples/05_Extractive_QA_with_txtai.ipynb) | Introduction to extractive question-answering with txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/05_Extractive_QA_with_txtai.ipynb) | +| [Extractive QA with Elasticsearch](https://github.com/neuml/txtai/blob/master/examples/06_Extractive_QA_with_Elasticsearch.ipynb) | Run extractive question-answering queries with Elasticsearch | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/06_Extractive_QA_with_Elasticsearch.ipynb) | +| [Extractive QA to build structured data](https://github.com/neuml/txtai/blob/master/examples/20_Extractive_QA_to_build_structured_data.ipynb) | Build structured datasets using extractive question-answering | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/20_Extractive_QA_to_build_structured_data.ipynb) | +| [Apply labels with zero shot classification](https://github.com/neuml/txtai/blob/master/examples/07_Apply_labels_with_zero_shot_classification.ipynb) | Use zero shot learning for labeling, classification and topic modeling | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/07_Apply_labels_with_zero_shot_classification.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) | +| [Extract text from documents](https://github.com/neuml/txtai/blob/master/examples/10_Extract_text_from_documents.ipynb) | Extract text from PDF, Office, HTML and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/10_Extract_text_from_documents.ipynb) | +| [Text to speech generation](https://github.com/neuml/txtai/blob/master/examples/40_Text_to_Speech_Generation.ipynb) | Generate speech from text | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/40_Text_to_Speech_Generation.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) | +| [Generate image captions and detect objects](https://github.com/neuml/txtai/blob/master/examples/25_Generate_image_captions_and_detect_objects.ipynb) | Captions and object detection for images | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/25_Generate_image_captions_and_detect_objects.ipynb) | +| [Near duplicate image detection](https://github.com/neuml/txtai/blob/master/examples/31_Near_duplicate_image_detection.ipynb) | Identify duplicate and near-duplicate images | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/31_Near_duplicate_image_detection.ipynb) | +| [API Gallery](https://github.com/neuml/txtai/blob/master/examples/08_API_Gallery.ipynb) | Using txtai in JavaScript, Java, Rust and Go | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/08_API_Gallery.ipynb) | +### Workflows +Efficiently process data at scale. +| 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) | +| [Transform tabular data with composable workflows](https://github.com/neuml/txtai/blob/master/examples/22_Transform_tabular_data_with_composable_workflows.ipynb) | Transform, index and search tabular data | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/22_Transform_tabular_data_with_composable_workflows.ipynb) | +| [Tensor workflows](https://github.com/neuml/txtai/blob/master/examples/23_Tensor_workflows.ipynb) | Performant processing of large tensor arrays | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/23_Tensor_workflows.ipynb) | +| [Entity extraction workflows](https://github.com/neuml/txtai/blob/master/examples/26_Entity_extraction_workflows.ipynb) | Identify entity/label combinations | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/26_Entity_extraction_workflows.ipynb) | +| [Workflow Scheduling](https://github.com/neuml/txtai/blob/master/examples/27_Workflow_scheduling.ipynb) | Schedule workflows with cron expressions | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/27_Workflow_scheduling.ipynb) | +| [Push notifications with workflows](https://github.com/neuml/txtai/blob/master/examples/28_Push_notifications_with_workflows.ipynb) | Generate and push notifications with workflows | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/28_Push_notifications_with_workflows.ipynb) | +| [Pictures are a worth a thousand words](https://github.com/neuml/txtai/blob/master/examples/35_Pictures_are_worth_a_thousand_words.ipynb) | Generate webpage summary images with DALL-E mini | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/35_Pictures_are_worth_a_thousand_words.ipynb) | +| [Run txtai with native code](https://github.com/neuml/txtai/blob/master/examples/36_Run_txtai_in_native_code.ipynb) | Execute workflows in native code with the Python C API | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/36_Run_txtai_in_native_code.ipynb) | +| [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) | +### Model Training +Train NLP models. +| Notebook | Description | | +|:----------|:-------------|------:| +| [Train a text labeler](https://github.com/neuml/txtai/blob/master/examples/16_Train_a_text_labeler.ipynb) | Build text sequence classification models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/16_Train_a_text_labeler.ipynb) | +| [Train without labels](https://github.com/neuml/txtai/blob/master/examples/17_Train_without_labels.ipynb) | Use zero-shot classifiers to train new models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/17_Train_without_labels.ipynb) | +| [Train a QA model](https://github.com/neuml/txtai/blob/master/examples/19_Train_a_QA_model.ipynb) | Build and fine-tune question-answering models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/19_Train_a_QA_model.ipynb) | +| [Train a language model from scratch](https://github.com/neuml/txtai/blob/master/examples/41_Train_a_language_model_from_scratch.ipynb) | Build new language models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/41_Train_a_language_model_from_scratch.ipynb) | +| [Export and run models with ONNX](https://github.com/neuml/txtai/blob/master/examples/18_Export_and_run_models_with_ONNX.ipynb) | Export models with ONNX, run natively in JavaScript, Java and Rust | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/18_Export_and_run_models_with_ONNX.ipynb) | +| [Export and run other machine learning models](https://github.com/neuml/txtai/blob/master/examples/21_Export_and_run_other_machine_learning_models.ipynb) | Export and run models from scikit-learn, PyTorch and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/21_Export_and_run_other_machine_learning_models.ipynb) | +### Applications +Series of example applications with txtai. Links to hosted versions on [Hugging Face Spaces](https://hf.co/spaces) also provided. +| Application | Description | | +|:-------------|:-------------|------:| +| [Basic similarity search](https://github.com/neuml/txtai/blob/master/examples/similarity.py) | Basic similarity search example. Data from the original txtai demo. |[🤗](https://hf.co/spaces/NeuML/similarity)| +| [Book search](https://github.com/neuml/txtai/blob/master/examples/books.py) | Book similarity search application. Index book descriptions and query using natural language statements. |*Local run only*| +| [Image search](https://github.com/neuml/txtai/blob/master/examples/images.py) | Image similarity search application. Index a directory of images and run searches to identify images similar to the input query. |[🤗](https://hf.co/spaces/NeuML/imagesearch)| +| [Summarize an article](https://github.com/neuml/txtai/blob/master/examples/article.py) | Summarize an article. Workflow that extracts text from a webpage and builds a summary. |[🤗](https://hf.co/spaces/NeuML/articlesummary)| +| [Wiki search](https://github.com/neuml/txtai/blob/master/examples/wiki.py) | Wikipedia search application. Queries Wikipedia API and summarizes the top result. |[🤗](https://hf.co/spaces/NeuML/wikisummary)| +| [Workflow builder](https://github.com/neuml/txtai/blob/master/examples/workflows.py) | Build and execute txtai workflows. Connect summarization, text extraction, transcription, translation and similarity search pipelines together to run unified workflows. |[🤗](https://hf.co/spaces/NeuML/txtai)| +## Documentation +[Full documentation on txtai](https://neuml.github.io/txtai) including configuration settings for pipelines, workflows, indexing and the API. +## Further Reading + +- [Introducing txtai, semantic search and workflows built on Transformers](https://medium.com/neuml/introducing-txtai-an-ai-powered-search-engine-built-on-transformers-37674be252ec) +- [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 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) +- [Run workflows to transform data and build semantic search applications with txtai](https://medium.com/neuml/run-machine-learning-workflows-to-transform-data-and-build-ai-powered-text-indices-with-txtai-43d769b566a7) +- [Semantic search on the cheap](https://medium.com/neuml/semantic-search-on-the-cheap-55940c0fcdab) +- [Serverless vector search with txtai](https://medium.com/neuml/serverless-vector-search-with-txtai-96f6163ab972) +- [Insights from the txtai console](https://medium.com/neuml/insights-from-the-txtai-console-d307c28e149e) +- [The big and small of txtai](https://medium.com/neuml/the-big-and-small-of-txtai-4ca405c1b82) +## Contributing +For those who would like to contribute to txtai, please see [this guide](https://github.com/neuml/.github/blob/master/CONTRIBUTING.md). + +%package help +Summary: Development documents and examples for txtai +Provides: python3-txtai-doc +%description help +txtai is an open-source platform for semantic search and workflows powered by language models. + +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. + +txtai builds embeddings databases, which are a union of vector indexes and relational databases. This enables similarity search with SQL. Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts. +Semantic workflows connect language models together to build intelligent applications. + +Integrate vector search, conversational search, automatic summarization, transcription, translation and more. +Summary of txtai features: +- 🔎 Similarity search with SQL, object storage, topic modeling, graph analysis, multiple vector index backends ([Faiss](https://github.com/facebookresearch/faiss), [Annoy](https://github.com/spotify/annoy), [Hnswlib](https://github.com/nmslib/hnswlib)) and support for external vector databases +- 📄 Create embeddings for text, documents, audio, images and video +- 💡 Pipelines powered by language models that run question-answering, labeling, transcription, translation, summarization, LLM prompts 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). +- ☁️ Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes) +The following applications are powered by txtai. + +| Application | Description | +|:----------|:-------------| +| [txtchat](https://github.com/neuml/txtchat) | Conversational search and workflows for all | +| [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 | +txtai is built with Python 3.7+, [Hugging Face Transformers](https://github.com/huggingface/transformers), [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and [FastAPI](https://github.com/tiangolo/fastapi) +## Why txtai? + +In addition to traditional search systems, a growing number of language model backed solutions are available, so why 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 +from txtai.embeddings import Embeddings +embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2"}) +embeddings.index([(0, "Correct", None), (1, "Not what we hoped", None)]) +embeddings.search("positive", 1) +#[(0, 0.2986203730106354)] +``` +- 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](#examples) - notebooks cover all available functionality +## Installation + +The easiest way to install is via pip and PyPI +``` +pip install txtai +``` +Python 3.7+ 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). +## Examples + +The examples directory has a series of notebooks and applications giving an overview of txtai. See the sections below. +### Semantic Search +Build semantic/similarity/vector/neural search applications. +| 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) | +| [Build an Embeddings index with Hugging Face Datasets](https://github.com/neuml/txtai/blob/master/examples/02_Build_an_Embeddings_index_with_Hugging_Face_Datasets.ipynb) | Index and search Hugging Face Datasets | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/02_Build_an_Embeddings_index_with_Hugging_Face_Datasets.ipynb) | +| [Build an Embeddings index from a data source](https://github.com/neuml/txtai/blob/master/examples/03_Build_an_Embeddings_index_from_a_data_source.ipynb) | Index and search a data source with word embeddings | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/03_Build_an_Embeddings_index_from_a_data_source.ipynb) | +| [Add semantic search to Elasticsearch](https://github.com/neuml/txtai/blob/master/examples/04_Add_semantic_search_to_Elasticsearch.ipynb) | Add semantic search to existing search systems | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/04_Add_semantic_search_to_Elasticsearch.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) | +| [Distributed embeddings cluster](https://github.com/neuml/txtai/blob/master/examples/15_Distributed_embeddings_cluster.ipynb) | Distribute an embeddings index across multiple data nodes | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/15_Distributed_embeddings_cluster.ipynb) | +| [What's new in txtai 4.0](https://github.com/neuml/txtai/blob/master/examples/24_Whats_new_in_txtai_4_0.ipynb) | Content storage, SQL, object storage, reindex and compressed indexes | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/24_Whats_new_in_txtai_4_0.ipynb) | +| [Anatomy of a txtai index](https://github.com/neuml/txtai/blob/master/examples/29_Anatomy_of_a_txtai_index.ipynb) | Deep dive into the file formats behind a txtai embeddings index | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/29_Anatomy_of_a_txtai_index.ipynb) | +| [Custom Embeddings SQL functions](https://github.com/neuml/txtai/blob/master/examples/30_Embeddings_SQL_custom_functions.ipynb) | Add user-defined functions to Embeddings SQL | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/30_Embeddings_SQL_custom_functions.ipynb) | +| [Model explainability](https://github.com/neuml/txtai/blob/master/examples/32_Model_explainability.ipynb) | Explainability for semantic search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/32_Model_explainability.ipynb) | +| [Query translation](https://github.com/neuml/txtai/blob/master/examples/33_Query_translation.ipynb) | Domain-specific natural language queries with query translation | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/33_Query_translation.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) | +| [Embeddings components](https://github.com/neuml/txtai/blob/master/examples/37_Embeddings_index_components.ipynb) | Composable search with vector, SQL and scoring components | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/37_Embeddings_index_components.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) | +| [Topic Modeling with BM25](https://github.com/neuml/txtai/blob/master/examples/39_Classic_Topic_Modeling_with_BM25.ipynb) | Topic modeling backed by a BM25 index | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/39_Classic_Topic_Modeling_with_BM25.ipynb) | +| [Prompt-driven search with LLMs](https://github.com/neuml/txtai/blob/master/examples/42_Prompt_driven_search_with_LLMs.ipynb) | Embeddings-guided and Prompt-driven search with Large Language Models (LLMs) | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/42_Prompt_driven_search_with_LLMs.ipynb) | +| [Embeddings in the Cloud](https://github.com/neuml/txtai/blob/master/examples/43_Embeddings_in_the_Cloud.ipynb) | Load and use an embeddings index from the Hugging Face Hub | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/43_Embeddings_in_the_Cloud.ipynb) | +### Pipelines +Transform data with language model backed pipelines. +| Notebook | Description | | +|:----------|:-------------|------:| +| [Extractive QA with txtai](https://github.com/neuml/txtai/blob/master/examples/05_Extractive_QA_with_txtai.ipynb) | Introduction to extractive question-answering with txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/05_Extractive_QA_with_txtai.ipynb) | +| [Extractive QA with Elasticsearch](https://github.com/neuml/txtai/blob/master/examples/06_Extractive_QA_with_Elasticsearch.ipynb) | Run extractive question-answering queries with Elasticsearch | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/06_Extractive_QA_with_Elasticsearch.ipynb) | +| [Extractive QA to build structured data](https://github.com/neuml/txtai/blob/master/examples/20_Extractive_QA_to_build_structured_data.ipynb) | Build structured datasets using extractive question-answering | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/20_Extractive_QA_to_build_structured_data.ipynb) | +| [Apply labels with zero shot classification](https://github.com/neuml/txtai/blob/master/examples/07_Apply_labels_with_zero_shot_classification.ipynb) | Use zero shot learning for labeling, classification and topic modeling | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/07_Apply_labels_with_zero_shot_classification.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) | +| [Extract text from documents](https://github.com/neuml/txtai/blob/master/examples/10_Extract_text_from_documents.ipynb) | Extract text from PDF, Office, HTML and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/10_Extract_text_from_documents.ipynb) | +| [Text to speech generation](https://github.com/neuml/txtai/blob/master/examples/40_Text_to_Speech_Generation.ipynb) | Generate speech from text | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/40_Text_to_Speech_Generation.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) | +| [Generate image captions and detect objects](https://github.com/neuml/txtai/blob/master/examples/25_Generate_image_captions_and_detect_objects.ipynb) | Captions and object detection for images | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/25_Generate_image_captions_and_detect_objects.ipynb) | +| [Near duplicate image detection](https://github.com/neuml/txtai/blob/master/examples/31_Near_duplicate_image_detection.ipynb) | Identify duplicate and near-duplicate images | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/31_Near_duplicate_image_detection.ipynb) | +| [API Gallery](https://github.com/neuml/txtai/blob/master/examples/08_API_Gallery.ipynb) | Using txtai in JavaScript, Java, Rust and Go | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/08_API_Gallery.ipynb) | +### Workflows +Efficiently process data at scale. +| 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) | +| [Transform tabular data with composable workflows](https://github.com/neuml/txtai/blob/master/examples/22_Transform_tabular_data_with_composable_workflows.ipynb) | Transform, index and search tabular data | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/22_Transform_tabular_data_with_composable_workflows.ipynb) | +| [Tensor workflows](https://github.com/neuml/txtai/blob/master/examples/23_Tensor_workflows.ipynb) | Performant processing of large tensor arrays | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/23_Tensor_workflows.ipynb) | +| [Entity extraction workflows](https://github.com/neuml/txtai/blob/master/examples/26_Entity_extraction_workflows.ipynb) | Identify entity/label combinations | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/26_Entity_extraction_workflows.ipynb) | +| [Workflow Scheduling](https://github.com/neuml/txtai/blob/master/examples/27_Workflow_scheduling.ipynb) | Schedule workflows with cron expressions | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/27_Workflow_scheduling.ipynb) | +| [Push notifications with workflows](https://github.com/neuml/txtai/blob/master/examples/28_Push_notifications_with_workflows.ipynb) | Generate and push notifications with workflows | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/28_Push_notifications_with_workflows.ipynb) | +| [Pictures are a worth a thousand words](https://github.com/neuml/txtai/blob/master/examples/35_Pictures_are_worth_a_thousand_words.ipynb) | Generate webpage summary images with DALL-E mini | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/35_Pictures_are_worth_a_thousand_words.ipynb) | +| [Run txtai with native code](https://github.com/neuml/txtai/blob/master/examples/36_Run_txtai_in_native_code.ipynb) | Execute workflows in native code with the Python C API | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/36_Run_txtai_in_native_code.ipynb) | +| [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) | +### Model Training +Train NLP models. +| Notebook | Description | | +|:----------|:-------------|------:| +| [Train a text labeler](https://github.com/neuml/txtai/blob/master/examples/16_Train_a_text_labeler.ipynb) | Build text sequence classification models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/16_Train_a_text_labeler.ipynb) | +| [Train without labels](https://github.com/neuml/txtai/blob/master/examples/17_Train_without_labels.ipynb) | Use zero-shot classifiers to train new models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/17_Train_without_labels.ipynb) | +| [Train a QA model](https://github.com/neuml/txtai/blob/master/examples/19_Train_a_QA_model.ipynb) | Build and fine-tune question-answering models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/19_Train_a_QA_model.ipynb) | +| [Train a language model from scratch](https://github.com/neuml/txtai/blob/master/examples/41_Train_a_language_model_from_scratch.ipynb) | Build new language models | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/41_Train_a_language_model_from_scratch.ipynb) | +| [Export and run models with ONNX](https://github.com/neuml/txtai/blob/master/examples/18_Export_and_run_models_with_ONNX.ipynb) | Export models with ONNX, run natively in JavaScript, Java and Rust | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/18_Export_and_run_models_with_ONNX.ipynb) | +| [Export and run other machine learning models](https://github.com/neuml/txtai/blob/master/examples/21_Export_and_run_other_machine_learning_models.ipynb) | Export and run models from scikit-learn, PyTorch and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/21_Export_and_run_other_machine_learning_models.ipynb) | +### Applications +Series of example applications with txtai. Links to hosted versions on [Hugging Face Spaces](https://hf.co/spaces) also provided. +| Application | Description | | +|:-------------|:-------------|------:| +| [Basic similarity search](https://github.com/neuml/txtai/blob/master/examples/similarity.py) | Basic similarity search example. Data from the original txtai demo. |[🤗](https://hf.co/spaces/NeuML/similarity)| +| [Book search](https://github.com/neuml/txtai/blob/master/examples/books.py) | Book similarity search application. Index book descriptions and query using natural language statements. |*Local run only*| +| [Image search](https://github.com/neuml/txtai/blob/master/examples/images.py) | Image similarity search application. Index a directory of images and run searches to identify images similar to the input query. |[🤗](https://hf.co/spaces/NeuML/imagesearch)| +| [Summarize an article](https://github.com/neuml/txtai/blob/master/examples/article.py) | Summarize an article. Workflow that extracts text from a webpage and builds a summary. |[🤗](https://hf.co/spaces/NeuML/articlesummary)| +| [Wiki search](https://github.com/neuml/txtai/blob/master/examples/wiki.py) | Wikipedia search application. Queries Wikipedia API and summarizes the top result. |[🤗](https://hf.co/spaces/NeuML/wikisummary)| +| [Workflow builder](https://github.com/neuml/txtai/blob/master/examples/workflows.py) | Build and execute txtai workflows. Connect summarization, text extraction, transcription, translation and similarity search pipelines together to run unified workflows. |[🤗](https://hf.co/spaces/NeuML/txtai)| +## Documentation +[Full documentation on txtai](https://neuml.github.io/txtai) including configuration settings for pipelines, workflows, indexing and the API. +## Further Reading + +- [Introducing txtai, semantic search and workflows built on Transformers](https://medium.com/neuml/introducing-txtai-an-ai-powered-search-engine-built-on-transformers-37674be252ec) +- [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 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) +- [Run workflows to transform data and build semantic search applications with txtai](https://medium.com/neuml/run-machine-learning-workflows-to-transform-data-and-build-ai-powered-text-indices-with-txtai-43d769b566a7) +- [Semantic search on the cheap](https://medium.com/neuml/semantic-search-on-the-cheap-55940c0fcdab) +- [Serverless vector search with txtai](https://medium.com/neuml/serverless-vector-search-with-txtai-96f6163ab972) +- [Insights from the txtai console](https://medium.com/neuml/insights-from-the-txtai-console-d307c28e149e) +- [The big and small of txtai](https://medium.com/neuml/the-big-and-small-of-txtai-4ca405c1b82) +## Contributing +For those who would like to contribute to txtai, please see [this guide](https://github.com/neuml/.github/blob/master/CONTRIBUTING.md). + +%prep +%autosetup -n txtai-5.5.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-txtai -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 5.5.1-1 +- Package Spec generated |