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Require Python 3.11, closes #1273

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davidmezzetti
2026-09-24 16:58:32 -04:00
parent 37500887ae
commit 793ecc8dc2
14 changed files with 35 additions and 35 deletions
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.github/workflows/build.yml
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@@ -18,7 +18,7 @@ jobs:
- name: Install Python
uses: actions/setup-python@v6
with:
python-version: "3.10"
python-version: "3.11"
- name: Install Java
uses: actions/setup-java@v5
.github/workflows/docs.yml
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@@ -13,7 +13,7 @@ jobs:
- name: Install Python
uses: actions/setup-python@v6
with:
python-version: "3.10"
python-version: "3.11"
- name: Install txtai
run: |
.github/workflows/minimal.yml
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@@ -13,7 +13,7 @@ jobs:
- name: Install Python
uses: actions/setup-python@v6
with:
python-version: "3.10"
python-version: "3.11"
- name: Minimal install
run: |
README.md
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@@ -49,7 +49,7 @@ Summary of txtai features:
- 🔋 Batteries included with defaults to get up and running fast
- ☁️ Run local or scale out with container orchestration
txtai is built with Python 3.10+, [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.
txtai is built with Python 3.11+, [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.
> [!NOTE]
>
@@ -190,7 +190,7 @@ The easiest way to install is via pip and PyPI
pip install txtai
```
Python 3.10+ is supported. Using a Python [virtual environment](https://docs.python.org/3/library/venv.html) is recommended.
Python 3.11+ 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), [lightweight minimal installation](https://neuml.github.io/txtai/install/#minimal-install) and how to [run with containers](https://neuml.github.io/txtai/cloud).
docker/base/Dockerfile
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@@ -1,5 +1,5 @@
# Set base image
ARG BASE_IMAGE=python:3.10-slim
ARG BASE_IMAGE=python:3.11-slim
FROM $BASE_IMAGE
# Install GPU-enabled version of PyTorch if set
@@ -8,7 +8,7 @@ ARG GPU
# Target CPU architecture
ARG TARGETARCH
# Set Python version (i.e. 3, 3.10)
# Set Python version (i.e. 3, 3.11)
ARG PYTHON_VERSION=3
# List of txtai components to install
docker/minimal/Dockerfile
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@@ -1,11 +1,11 @@
# Set base image
ARG BASE_IMAGE=python:3.10-slim
ARG BASE_IMAGE=python:3.11-slim
FROM $BASE_IMAGE
# Target CPU architecture
ARG TARGETARCH
# Set Python version (i.e. 3, 3.10)
# Set Python version (i.e. 3, 3.11)
ARG PYTHON_VERSION=3
# Locale environment variables
docs/cloud.md
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@@ -30,8 +30,8 @@ Examples build commands below.
# Get Dockerfile
wget https://raw.githubusercontent.com/neuml/txtai/master/docker/base/Dockerfile
# Build Ubuntu 22.04 image running Python 3.10
docker build -t txtai --build-arg BASE_IMAGE=ubuntu:22.04 --build-arg PYTHON_VERSION=3.10 .
# Build Ubuntu 22.04 image running Python 3.11
docker build -t txtai --build-arg BASE_IMAGE=ubuntu:22.04 --build-arg PYTHON_VERSION=3.11 .
# Build image with GPU support
docker build -t txtai --build-arg GPU=1 .
docs/index.md
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@@ -51,7 +51,7 @@ Summary of txtai features:
- 🔋 Batteries included with defaults to get up and running fast
- ☁️ Run local or scale out with container orchestration
txtai is built with Python 3.10+, [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.
txtai is built with Python 3.11+, [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.
!!! note
docs/install.md
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@@ -9,7 +9,7 @@ The easiest way to install is via pip and PyPI
pip install txtai
```
Python 3.10+ is supported. Using a Python [virtual environment](https://docs.python.org/3/library/venv.html) is recommended.
Python 3.11+ is supported. Using a Python [virtual environment](https://docs.python.org/3/library/venv.html) is recommended.
## Optional dependencies
examples/01_Introducing_txtai.ipynb
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@@ -27,7 +27,7 @@
"- 🔋 Batteries included with defaults to get up and running fast\n",
"- ☁️ Run local or scale out with container orchestration\n",
"\n",
"txtai is built with Python 3.10+, [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",
"txtai is built with Python 3.11+, [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",
"> [NeuML](https://neuml.com) is the company behind txtai and we provide AI consulting services around our stack. [Schedule a meeting](https://cal.com/neuml/intro) or [send a message](mailto:[email protected]) to learn more.\n",
">\n",
examples/52_Build_RAG_pipelines_with_txtai.ipynb
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@@ -125,7 +125,7 @@
"## Install\n",
"The easiest way to install is via pip and PyPI\n",
"pip install txtai\n",
"Python 3.10+ is supported. Using a Python virtual environment is **recommended** .\n",
"Python 3.11+ is supported. Using a Python virtual environment is **recommended** .\n",
"See the detailed install instructions for more information covering optional dependencies, environment specific prerequisites, installing from source, conda support and how to run with containers.\n",
"\n",
"\n",
@@ -525,8 +525,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: Python 3.10 and later versions (Python 3.10+) are supported.\n",
"CITATION: [{'id': '24', 'text': 'Python 3.10+ is supported. Using a Python virtual environment is recommended.'}]\n"
"ANSWER: Python 3.11 and later versions (Python 3.11+) are supported.\n",
"CITATION: [{'id': '24', 'text': 'Python 3.11+ is supported. Using a Python virtual environment is recommended.'}]\n"
]
}
],
examples/73_Chunking_your_data_for_RAG.ipynb
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@@ -100,12 +100,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"{'indexid': 0, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': '**GitHub - neuml/txtai: 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows**\\n\\n*💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows - neuml/txtai*\\n\\n\\n\\n**All-in-one embeddings database** \\ntxtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.\\n\\nEmbeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases.\\n\\nThis foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.\\n\\nBuild autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more.\\n\\nSummary of txtai features:\\n\\n- 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing\\n- 📄 Create embeddings for text, documents, audio, images and video\\n- 💡 Pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarization and more\\n- ↪️️ Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.\\n- 🤖 Agents that intelligently connect embeddings, pipelines, workflows and other agents together to autonomously solve complex problems\\n- ⚙️ 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- 🔋 Batteries included with defaults to get up and running fast\\n- ☁️ Run local or scale out with container orchestration\\ntxtai is built with Python 3.10+, [Hugging Face Transformers](https://github.com/huggingface/transformers) , [Sentence Transformers](https://github.'}\n",
"{'indexid': 0, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': '**GitHub - neuml/txtai: 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows**\\n\\n*💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows - neuml/txtai*\\n\\n\\n\\n**All-in-one embeddings database** \\ntxtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.\\n\\nEmbeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases.\\n\\nThis foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.\\n\\nBuild autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more.\\n\\nSummary of txtai features:\\n\\n- 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing\\n- 📄 Create embeddings for text, documents, audio, images and video\\n- 💡 Pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarization and more\\n- ↪️️ Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.\\n- 🤖 Agents that intelligently connect embeddings, pipelines, workflows and other agents together to autonomously solve complex problems\\n- ⚙️ 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- 🔋 Batteries included with defaults to get up and running fast\\n- ☁️ Run local or scale out with container orchestration\\ntxtai is built with Python 3.11+, [Hugging Face Transformers](https://github.com/huggingface/transformers) , [Sentence Transformers](https://github.'}\n",
"{'indexid': 1, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': '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?\\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```\\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\\n- Built-in API makes it easy to develop applications using your programming language of choice\\n```\\n# app.yml\\nembeddings:\\n path: sentence-transformers/all-MiniLM-L6-v2\\n```\\n\\n```\\nCONFIG=app.yml uvicorn \"txtai.api:app\"\\ncurl -X GET \"http://localhost:8000/search?query=positive\"\\n```\\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\\nThe following sections introduce common txtai use cases. A comprehensive set of over 60 [example notebooks and applications](https://neuml.github.io/txtai/examples) are also available.\\n\\n\\n### Semantic Search\\nBuild semantic/similarity/vector/neural search applications.'}\n",
"{'indexid': 2, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': '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.\\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) |Overview of the functionality provided by txtai||\\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||\\n|[Build a QA database](https://github.com/neuml/txtai/blob/master/examples/34_Build_a_QA_database.ipynb) |Question matching with semantic search||\\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||\\n\\n### LLM Orchestration\\nAutonomous agents, retrieval augmented generation (RAG), chat with your data, pipelines and workflows that interface with large language models (LLMs).\\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||\\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||\\n|[Build knowledge graphs with LLMs](https://github.'}\n",
"{'indexid': 3, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': 'com/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) |Build knowledge graphs with LLM-driven entity extraction||\\n\\n#### Agents\\nAgents connect embeddings, pipelines, workflows and other agents together to autonomously solve complex problems.\\n\\ntxtai agents are built on top of the Transformers Agent framework. This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI / Claude / AWS Bedrock via LiteLLM).\\n\\nSee the link below to learn more.\\n\\n|Notebook|Description||\\n|---|---|---|\\n|[Analyzing Hugging Face Posts with Graphs and Agents](https://github.com/neuml/txtai/blob/master/examples/68_Analyzing_Hugging_Face_Posts_with_Graphs_and_Agents.ipynb) |Explore a rich dataset with Graph Analysis and Agents||\\n|[Granting autonomy to agents](https://github.com/neuml/txtai/blob/master/examples/69_Granting_autonomy_to_agents.ipynb) |Agents that iteratively solve problems as they see fit||\\n|[Analyzing LinkedIn Company Posts with Graphs and Agents](https://github.com/neuml/txtai/blob/master/examples/71_Analyzing_LinkedIn_Company_Posts_with_Graphs_and_Agents.ipynb) |Exploring how to improve social media engagement with AI||\\n\\n#### Retrieval augmented generation\\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\\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.'}\n",
"{'indexid': 4, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': 'com/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) |Guide on retrieval augmented generation including how to create citations||\\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||\\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||\\n|[Speech to Speech RAG](https://github.com/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) |Full cycle speech to speech workflow with RAG||\\n\\n### Language Model Workflows\\nLanguage model workflows, also known as semantic workflows, connect language models together to build intelligent applications.\\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) |Simple yet powerful constructs to efficiently process data||\\n|[Building abstractive text summaries](https://github.com/neuml/txtai/blob/master/examples/09_Building_abstractive_text_summaries.ipynb) |Run abstractive text summarization||\\n|[Transcribe audio to text](https://github.com/neuml/txtai/blob/master/examples/11_Transcribe_audio_to_text.'}\n",
"{'indexid': 5, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': 'ipynb) |Convert audio files to text||\\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||\\n\\n## Installation\\nThe easiest way to install is via pip and PyPI\\n\\n```\\npip install txtai\\n```\\n\\nPython 3.10+ 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\\n## Model guide\\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",
"{'indexid': 5, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': 'ipynb) |Convert audio files to text||\\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||\\n\\n## Installation\\nThe easiest way to install is via pip and PyPI\\n\\n```\\npip install txtai\\n```\\n\\nPython 3.11+ 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\\n## Model guide\\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",
"{'indexid': 6, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': '|[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) |[Llama 3.1 Instruct](https://hf.co/meta-llama/Llama-3.1-8B-Instruct) |\\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) |\\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\\n## Powered by txtai\\nThe following applications are powered by txtai.'}\n",
"{'indexid': 7, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': \"|Application|Description|\\n|---|---|\\n|[rag](https://github.com/neuml/rag) |Retrieval Augmented Generation (RAG) application|\\n|[ragdata](https://github.com/neuml/ragdata) |Build knowledge bases for RAG|\\n|[paperai](https://github.com/neuml/paperai) |Semantic search and workflows for medical/scientific papers|\\n|[annotateai](https://github.com/neuml/annotateai) |Automatically annotate papers with LLMs|\\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\\n## Further Reading\\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 8.0](https://medium.com/neuml/whats-new-in-txtai-8-0-2d7d0ab4506b) | [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.\"}\n",
"{'indexid': 8, 'id': '0', 'url': 'https://github.com/neuml/txtai', 'text': '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\\n## Documentation\\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\\n## Contributing\\nFor those who would like to contribute to txtai, please see [this guide](https://github.com/neuml/.github/blob/master/CONTRIBUTING.md) .'}\n",
examples/84_Agent_Tools.ipynb
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Observations: ## Search Results\n",
"\n",
"|txtai · PyPI](https://pypi.org/project/txtai/)\n",
"☁️ Run local or scale out with container orchestration txtai is built with Python 3.10+, Hugging Face Transformers,\n",
"☁️ Run local or scale out with container orchestration txtai is built with Python 3.11+, Hugging Face Transformers,\n",
"Sentence Transformers and FastAPI. txtai is open-source under an Apache 2.0 license. |!NOTE] NeuML is the company \n",
"behind txtai and we provide AI consulting services around our stack. Schedule a meeting or send a message to ...\n",
"\n",
@@ -767,7 +767,7 @@
"Observations: ## Search Results\n",
"\n",
"|txtai · PyPI](https://pypi.org/project/txtai/)\n",
"☁️ Run local or scale out with container orchestration txtai is built with Python 3.10+, Hugging Face Transformers,\n",
"☁️ Run local or scale out with container orchestration txtai is built with Python 3.11+, Hugging Face Transformers,\n",
"Sentence Transformers and FastAPI. txtai is open-source under an Apache 2.0 license. |!NOTE] NeuML is the company \n",
"behind txtai and we provide AI consulting services around our stack. Schedule a meeting or send a message to ...\n",
"\n",
@@ -896,7 +896,7 @@
"|Rust](https://github.com/neuml/txtai.rs) and |Go](https://github.com/neuml/txtai.go) .\n",
"- 🔋 Batteries included with defaults to get up and running fast\n",
"- ☁️ Run local or scale out with container orchestration\n",
"txtai is built with Python 3.10+, |Hugging Face Transformers](https://github.com/huggingface/transformers) , \n",
"txtai is built with Python 3.11+, |Hugging Face Transformers](https://github.com/huggingface/transformers) , \n",
"|Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and \n",
"|FastAPI](https://github.com/tiangolo/fastapi) . txtai is open-source under an Apache 2.0 license.\n",
"\n",
@@ -1060,7 +1060,7 @@
"pip install txtai\n",
"```\n",
"\n",
"Python 3.10+ is supported. Using a Python |virtual environment](https://docs.python.org/3/library/venv.html) is \n",
"Python 3.11+ is supported. Using a Python |virtual environment](https://docs.python.org/3/library/venv.html) is \n",
"recommended.\n",
"\n",
"See the detailed |install instructions](https://neuml.github.io/txtai/install) for more information covering \n",
@@ -1169,7 +1169,7 @@
"|Rust](https://github.com/neuml/txtai.rs) and |Go](https://github.com/neuml/txtai.go) .\n",
"- 🔋 Batteries included with defaults to get up and running fast\n",
"- ☁️ Run local or scale out with container orchestration\n",
"txtai is built with Python 3.10+, |Hugging Face Transformers](https://github.com/huggingface/transformers) , \n",
"txtai is built with Python 3.11+, |Hugging Face Transformers](https://github.com/huggingface/transformers) , \n",
"|Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and \n",
"|FastAPI](https://github.com/tiangolo/fastapi) . txtai is open-source under an Apache 2.0 license.\n",
"\n",
@@ -1333,7 +1333,7 @@
"pip install txtai\n",
"```\n",
"\n",
"Python 3.10+ is supported. Using a Python |virtual environment](https://docs.python.org/3/library/venv.html) is \n",
"Python 3.11+ is supported. Using a Python |virtual environment](https://docs.python.org/3/library/venv.html) is \n",
"recommended.\n",
"\n",
"See the detailed |install instructions](https://neuml.github.io/txtai/install) for more information covering \n",
@@ -1554,7 +1554,7 @@
"|Rust](https://github.com/neuml/txtai.rs) and |Go](https://github.com/neuml/txtai.go) .\n",
"- 🔋 Batteries included with defaults to get up and running fast\n",
"- ☁️ Run local or scale out with container orchestration\n",
"txtai is built with Python 3.10+, |Hugging Face Transformers](https://github.com/huggingface/transformers) , \n",
"txtai is built with Python 3.11+, |Hugging Face Transformers](https://github.com/huggingface/transformers) , \n",
"|Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and \n",
"|FastAPI](https://github.com/tiangolo/fastapi) . txtai is open-source under an Apache 2.0 license.\n",
"\n",
@@ -1599,7 +1599,7 @@
"|Rust](https://github.com/neuml/txtai.rs) and |Go](https://github.com/neuml/txtai.go) .\n",
"- 🔋 Batteries included with defaults to get up and running fast\n",
"- ☁️ Run local or scale out with container orchestration\n",
"txtai is built with Python 3.10+, |Hugging Face Transformers](https://github.com/huggingface/transformers) , \n",
"txtai is built with Python 3.11+, |Hugging Face Transformers](https://github.com/huggingface/transformers) , \n",
"|Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and \n",
"|FastAPI](https://github.com/tiangolo/fastapi) . txtai is open-source under an Apache 2.0 license.\n",
"\n",
@@ -1673,7 +1673,7 @@
"pip install txtai\n",
"```\n",
"\n",
"Python 3.10+ is supported. Using a Python |virtual environment](https://docs.python.org/3/library/venv.html) is \n",
"Python 3.11+ is supported. Using a Python |virtual environment](https://docs.python.org/3/library/venv.html) is \n",
"recommended.\n",
"\n",
"## Optional dependencies\n",
@@ -1847,7 +1847,7 @@
"pip install txtai\n",
"```\n",
"\n",
"Python 3.10+ is supported. Using a Python |virtual environment](https://docs.python.org/3/library/venv.html) is \n",
"Python 3.11+ is supported. Using a Python |virtual environment](https://docs.python.org/3/library/venv.html) is \n",
"recommended.\n",
"\n",
"## Optional dependencies\n",
@@ -2043,7 +2043,7 @@
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
"│ Calling tool: 'write' with arguments: {'path': 'txtai_facts.md', 'content': '# txtai - All-in-One AI │\n",
"│ Framework\\n\\n## Overview\\n\\ntxtai is an all-in-one open-source AI framework for semantic search, LLM │\n",
"│ orchestration, and language model workflows. It\\'s built with Python 3.10+, Hugging Face Transformers, Sentence │\n",
"│ orchestration, and language model workflows. It\\'s built with Python 3.11+, Hugging Face Transformers, Sentence │\n",
"│ Transformers, and FastAPI, and is licensed under Apache 2.0.\\n\\n## Key Features\\n\\n- **Vector Search**: │\n",
"│ Semantic search with SQL, object storage, topic modeling, graph analysis, and multimodal indexing\\n- │\n",
"│ **Embeddings**: Create embeddings for text, documents, audio, images, and video\\n- **Language Model │\n",
@@ -2063,7 +2063,7 @@
"│ Installation\\n\\nThe easiest way to install is via pip:\\n\\n```bash\\npip install txtai\\n```\\n\\nOptional │\n",
"│ dependencies can be installed as extras:\\n\\n```bash\\npip install txtai[all] # Install all dependencies\\npip │\n",
"│ install txtai[api] # Serve txtai via a web API\\npip install txtai[workflow] # All workflow tasks\\n```\\n\\n## │\n",
"│ Technology Stack\\n\\n- Built with Python 3.10+\\n- Hugging Face Transformers\\n- Sentence Transformers\\n- │\n",
"│ Technology Stack\\n\\n- Built with Python 3.11+\\n- Hugging Face Transformers\\n- Sentence Transformers\\n- │\n",
"│ FastAPI\\n\\n## Supported Models\\n\\n- Embeddings: all-MiniLM-L6-v2\\n- Image Captions: BLIP\\n- Labels (Zero Shot): │\n",
"│ BART-Large-MNLI\\n- Large Language Model (LLM): gpt-oss-20b\\n- Summarization: DistilBART\\n- Text-to-Speech: │\n",
"│ ESPnet JETS\\n- Transcription: Whisper\\n- Translation: OPUS Model Series\\n\\n## Company Behind txtai\\n\\nNeuML is │\n",
@@ -2081,7 +2081,7 @@
"╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
"│ Calling tool: 'write' with arguments: {'path': 'txtai_facts.md', 'content': '# txtai - All-in-One AI │\n",
"│ Framework\\n\\n## Overview\\n\\ntxtai is an all-in-one open-source AI framework for semantic search, LLM │\n",
"│ orchestration, and language model workflows. It\\'s built with Python 3.10+, Hugging Face Transformers, Sentence │\n",
"│ orchestration, and language model workflows. It\\'s built with Python 3.11+, Hugging Face Transformers, Sentence │\n",
"│ Transformers, and FastAPI, and is licensed under Apache 2.0.\\n\\n## Key Features\\n\\n- **Vector Search**: │\n",
"│ Semantic search with SQL, object storage, topic modeling, graph analysis, and multimodal indexing\\n- │\n",
"│ **Embeddings**: Create embeddings for text, documents, audio, images, and video\\n- **Language Model │\n",
@@ -2101,7 +2101,7 @@
"│ Installation\\n\\nThe easiest way to install is via pip:\\n\\n```bash\\npip install txtai\\n```\\n\\nOptional │\n",
"│ dependencies can be installed as extras:\\n\\n```bash\\npip install txtai[all] # Install all dependencies\\npip │\n",
"│ install txtai[api] # Serve txtai via a web API\\npip install txtai[workflow] # All workflow tasks\\n```\\n\\n## │\n",
"│ Technology Stack\\n\\n- Built with Python 3.10+\\n- Hugging Face Transformers\\n- Sentence Transformers\\n- │\n",
"│ Technology Stack\\n\\n- Built with Python 3.11+\\n- Hugging Face Transformers\\n- Sentence Transformers\\n- │\n",
"│ FastAPI\\n\\n## Supported Models\\n\\n- Embeddings: all-MiniLM-L6-v2\\n- Image Captions: BLIP\\n- Labels (Zero Shot): │\n",
"│ BART-Large-MNLI\\n- Large Language Model (LLM): gpt-oss-20b\\n- Summarization: DistilBART\\n- Text-to-Speech: │\n",
"│ ESPnet JETS\\n- Transcription: Whisper\\n- Translation: OPUS Model Series\\n\\n## Company Behind txtai\\n\\nNeuML is │\n",
@@ -2252,7 +2252,7 @@
"\n",
"## Overview\n",
"\n",
"txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration, and language model workflows. It's built with Python 3.10+, Hugging Face Transformers, Sentence Transformers, and FastAPI, and is licensed under Apache 2.0.\n",
"txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration, and language model workflows. It's built with Python 3.11+, Hugging Face Transformers, Sentence Transformers, and FastAPI, and is licensed under Apache 2.0.\n",
"\n",
"## Key Features\n",
"\n",
setup.py
+1 -1
View File
@@ -195,7 +195,7 @@ setup(
packages=find_packages(where="src/python"),
package_dir={"": "src/python"},
keywords="search embedding machine-learning nlp",
python_requires=">=3.10",
python_requires=">=3.11",
install_requires=install,
extras_require=extras,
classifiers=[