chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,8 @@
|
||||
[run]
|
||||
source = src/python
|
||||
concurrency = multiprocessing,thread
|
||||
disable_warnings = no-data-collected
|
||||
omit = **/__main__.py
|
||||
|
||||
[combine]
|
||||
disable_warnings = no-data-collected
|
||||
@@ -0,0 +1,71 @@
|
||||
# GitHub Actions build workflow
|
||||
name: build
|
||||
|
||||
on: ["push", "pull_request"]
|
||||
|
||||
jobs:
|
||||
build:
|
||||
runs-on: ${{ matrix.os }}
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ubuntu-latest, macos-latest, windows-latest]
|
||||
|
||||
timeout-minutes: 60
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v6
|
||||
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: "3.10"
|
||||
|
||||
- name: Install Java
|
||||
uses: actions/setup-java@v5
|
||||
with:
|
||||
distribution: "zulu"
|
||||
java-version: 21
|
||||
|
||||
- name: Install dependencies - Linux
|
||||
run: |
|
||||
sudo apt-get update
|
||||
sudo apt-get install libportaudio2 libsndfile1 libegl1 libgles2
|
||||
sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc
|
||||
if: matrix.os == 'ubuntu-latest'
|
||||
|
||||
- name: Install dependencies - macOS
|
||||
run: |
|
||||
echo "PYTORCH_MPS_DISABLE=1" >> $GITHUB_ENV
|
||||
echo "LLAMA_NO_METAL=1" >> $GITHUB_ENV
|
||||
echo "TIKA_STARTUP_SLEEP=30" >> $GITHUB_ENV
|
||||
echo "TIKA_STARTUP_MAX_RETRY=10" >> $GITHUB_ENV
|
||||
brew install portaudio
|
||||
if: matrix.os == 'macos-latest'
|
||||
|
||||
- name: Install dependencies - Windows
|
||||
run: |
|
||||
"PYTHONIOENCODING=utf-8" >> $env:GITHUB_ENV
|
||||
choco install wget
|
||||
if: matrix.os == 'windows-latest'
|
||||
|
||||
- name: Build
|
||||
run: |
|
||||
pip install -U wheel
|
||||
pip install .[all,dev]
|
||||
pip cache purge
|
||||
python -c "import nltk; nltk.download(['punkt', 'punkt_tab', 'averaged_perceptron_tagger_eng'])"
|
||||
python --version
|
||||
make data coverage
|
||||
env:
|
||||
HF_HUB_ETAG_TIMEOUT: 100
|
||||
HF_HUB_DOWNLOAD_TIMEOUT: 100
|
||||
HF_XET_CHUNK_CACHE_SIZE_BYTES: 0
|
||||
|
||||
- uses: pre-commit/action@v3.0.1
|
||||
if: matrix.os == 'ubuntu-latest'
|
||||
|
||||
- name: Test Coverage
|
||||
run: coveralls --service=github
|
||||
if: matrix.os == 'ubuntu-latest'
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
@@ -0,0 +1,25 @@
|
||||
name: docs
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
jobs:
|
||||
deploy:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v6
|
||||
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: "3.10"
|
||||
|
||||
- name: Install txtai
|
||||
run: |
|
||||
pip install -U pip wheel
|
||||
pip install .[all,dev]
|
||||
|
||||
- name: Deploy documentation
|
||||
run: mkdocs gh-deploy --force
|
||||
|
||||
@@ -0,0 +1,22 @@
|
||||
# GitHub Actions minimal build
|
||||
name: minimal
|
||||
|
||||
on: ["push", "pull_request"]
|
||||
|
||||
jobs:
|
||||
deploy:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v6
|
||||
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: "3.10"
|
||||
|
||||
- name: Minimal install
|
||||
run: |
|
||||
pip install -U pip wheel
|
||||
MINIMAL=1 pip install .
|
||||
python -c "import txtai"
|
||||
+10
@@ -0,0 +1,10 @@
|
||||
build/
|
||||
dist/
|
||||
docker/**/*.yml
|
||||
htmlcov/
|
||||
*egg-info/
|
||||
__pycache__/
|
||||
.coverage
|
||||
.coverage.*
|
||||
*.pyc
|
||||
.vscode/
|
||||
@@ -0,0 +1,14 @@
|
||||
repos:
|
||||
- repo: https://github.com/pycqa/pylint
|
||||
rev: v3.3.1
|
||||
hooks:
|
||||
- id: pylint
|
||||
args:
|
||||
- -d import-error
|
||||
- -d duplicate-code
|
||||
- -d too-many-positional-arguments
|
||||
- repo: https://github.com/ambv/black
|
||||
rev: 24.10.0
|
||||
hooks:
|
||||
- id: black
|
||||
language_version: python3
|
||||
@@ -0,0 +1,17 @@
|
||||
[BASIC]
|
||||
module-rgx=[a-z_][a-zA-Z0-9_]{2,30}$
|
||||
method-rgx=[a-z_][a-zA-Z0-9_]{2,30}$
|
||||
function-rgx=[a-z_][a-zA-Z0-9_]{2,30}$
|
||||
argument-rgx=[a-z_][a-zA-Z0-9_]{0,30}$
|
||||
variable-rgx=[a-z_][a-zA-Z0-9_]{0,30}$
|
||||
attr-rgx=[a-z_][a-zA-Z0-9_]{0,30}$
|
||||
|
||||
[DESIGN]
|
||||
max-args=10
|
||||
max-locals=40
|
||||
max-returns=10
|
||||
max-attributes=20
|
||||
min-public-methods=0
|
||||
|
||||
[FORMAT]
|
||||
max-line-length=150
|
||||
@@ -0,0 +1,11 @@
|
||||
cff-version: 1.2.0
|
||||
date-released: 2020-08-11
|
||||
message: "If you use this software, please cite it as below."
|
||||
title: "txtai: the all-in-one AI framework"
|
||||
abstract: "txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows"
|
||||
url: "https://github.com/neuml/txtai"
|
||||
authors:
|
||||
- family-names: "Mezzetti"
|
||||
given-names: "David"
|
||||
affiliation: NeuML
|
||||
license: Apache-2.0
|
||||
@@ -0,0 +1,190 @@
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
1. Definitions.
|
||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
|
||||
and distribution as defined by Sections 1 through 9 of this document.
|
||||
|
||||
"Licensor" shall mean the copyright owner or entity authorized by
|
||||
the copyright owner that is granting the License.
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|
||||
"Legal Entity" shall mean the union of the acting entity and all
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"control" means (i) the power, direct or indirect, to cause the
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END OF TERMS AND CONDITIONS
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|
||||
Copyright 2020- NeuML LLC
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||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
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||||
You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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||||
limitations under the License.
|
||||
@@ -0,0 +1,58 @@
|
||||
# Project utility scripts
|
||||
.PHONY: test
|
||||
|
||||
# Setup environment
|
||||
export SRC_DIR := ./src/python
|
||||
export TEST_DIR := ./test/python
|
||||
export PYTHONPATH := ${SRC_DIR}:${TEST_DIR}:${PYTHONPATH}
|
||||
export PATH := ${TEST_DIR}:${PATH}
|
||||
export PYTHONWARNINGS := ignore
|
||||
|
||||
# Disable tokenizer parallelism for tests
|
||||
export TOKENIZERS_PARALLELISM := false
|
||||
|
||||
# Default python executable if not provided
|
||||
PYTHON ?= python
|
||||
|
||||
# Check for wget
|
||||
WGET := $(shell wget --version 2> /dev/null)
|
||||
ifndef WGET
|
||||
$(error "Required binary `wget` not found, please install wget OS package")
|
||||
endif
|
||||
|
||||
# Download test data
|
||||
data:
|
||||
mkdir -p /tmp/txtai
|
||||
wget -N https://github.com/neuml/txtai/releases/download/v6.2.0/tests.tar.gz -P /tmp
|
||||
tar -xvzf /tmp/tests.tar.gz -C /tmp
|
||||
|
||||
# Unit tests
|
||||
test:
|
||||
${PYTHON} -m unittest discover -v -s ${TEST_DIR}
|
||||
|
||||
# Run tests while calculating code coverage
|
||||
coverage:
|
||||
coverage run -m unittest discover -v -k testagent -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testann -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testapi -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testapp -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testarchive -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testcloud -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testconsole -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testdatabase -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testembeddings -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testgraph -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testlibrary -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testmodels -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testoptional -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testpipeline.testaudio -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testpipeline.testdata -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testpipeline.testimage -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testpipeline.testllm -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testpipeline.testtext -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testpipeline.testtrain -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testscoring -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testserialize -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testvectors -s ${TEST_DIR}
|
||||
coverage run -m unittest discover -v -k testworkflow -s ${TEST_DIR}
|
||||
coverage combine
|
||||
@@ -0,0 +1,258 @@
|
||||
<p align="center">
|
||||
<img src="https://raw.githubusercontent.com/neuml/txtai/master/logo.png"/>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<b>All-in-one AI framework</b>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://github.com/neuml/txtai/releases">
|
||||
<img src="https://img.shields.io/github/release/neuml/txtai.svg?style=flat&color=success" alt="Version"/>
|
||||
</a>
|
||||
<a href="https://github.com/neuml/txtai">
|
||||
<img src="https://img.shields.io/github/last-commit/neuml/txtai.svg?style=flat&color=blue" alt="GitHub last commit"/>
|
||||
</a>
|
||||
<a href="https://github.com/neuml/txtai/issues">
|
||||
<img src="https://img.shields.io/github/issues/neuml/txtai.svg?style=flat&color=success" alt="GitHub issues"/>
|
||||
</a>
|
||||
<a href="https://join.slack.com/t/txtai/shared_invite/zt-37c1zfijp-Y57wMty6YOx_hyIHEQvQJA">
|
||||
<img src="https://img.shields.io/badge/slack-join-blue?style=flat&logo=slack&logocolor=white" alt="Join Slack"/>
|
||||
</a>
|
||||
<a href="https://github.com/neuml/txtai/actions?query=workflow%3Abuild">
|
||||
<img src="https://github.com/neuml/txtai/workflows/build/badge.svg" alt="Build Status"/>
|
||||
</a>
|
||||
<a href="https://coveralls.io/github/neuml/txtai?branch=master">
|
||||
<img src="https://img.shields.io/coverallsCoverage/github/neuml/txtai" alt="Coverage Status">
|
||||
</a>
|
||||
</p>
|
||||
|
||||
txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows.
|
||||
|
||||
|
||||
|
||||
|
||||
The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases.
|
||||
|
||||
This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.
|
||||
|
||||
Build autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more.
|
||||
|
||||
Summary of txtai features:
|
||||
|
||||
- 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing
|
||||
- 📄 Create embeddings for text, documents, audio, images and video
|
||||
- 💡 Pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarization and more
|
||||
- ↪️️ Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.
|
||||
- 🤖 Agents that intelligently connect embeddings, pipelines, workflows and other agents together to autonomously solve complex problems
|
||||
- ⚙️ Web and Model Context Protocol (MCP) APIs. 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).
|
||||
- 🔋 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.
|
||||
|
||||
> [!NOTE]
|
||||
>
|
||||
> [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:info@neuml.com) to learn more.
|
||||
>
|
||||
> We're also building an easy and secure way to run hosted txtai applications with [txtai.cloud](https://txtai.cloud).
|
||||
|
||||
## Why txtai?
|
||||
|
||||
|
||||
|
||||
|
||||
New vector databases, LLM frameworks and everything in between are sprouting up daily. Why build with txtai?
|
||||
|
||||
- Up and running in minutes with [pip](https://neuml.github.io/txtai/install/) or [Docker](https://neuml.github.io/txtai/cloud/)
|
||||
```python
|
||||
# Get started in a couple lines
|
||||
import txtai
|
||||
|
||||
embeddings = txtai.Embeddings()
|
||||
embeddings.index(["Correct", "Not what we hoped"])
|
||||
embeddings.search("positive", 1)
|
||||
#[(0, 0.29862046241760254)]
|
||||
```
|
||||
- Built-in API makes it easy to develop applications using your programming language of choice
|
||||
```yaml
|
||||
# app.yml
|
||||
embeddings:
|
||||
path: sentence-transformers/all-MiniLM-L6-v2
|
||||
```
|
||||
```bash
|
||||
CONFIG=app.yml uvicorn "txtai.api:app"
|
||||
curl -X GET "http://localhost:8000/search?query=positive"
|
||||
```
|
||||
- Run local - no need to ship data off to disparate remote services
|
||||
- Work with micromodels all the way up to large language models (LLMs)
|
||||
- Low footprint - install additional dependencies and scale up when needed
|
||||
- [Learn by example](https://neuml.github.io/txtai/examples) - notebooks cover all available functionality
|
||||
|
||||
## Use Cases
|
||||
|
||||
The following sections introduce common txtai use cases. A comprehensive set of over 70 [example notebooks and applications](https://neuml.github.io/txtai/examples) are also available.
|
||||
|
||||
### Semantic Search
|
||||
|
||||
Build semantic/similarity/vector/neural search applications.
|
||||
|
||||
|
||||
|
||||
Traditional search systems use keywords to find data. Semantic search has an understanding of natural language and identifies results that have the same meaning, not necessarily the same keywords.
|
||||
|
||||
|
||||
|
||||
|
||||
Get started with the following examples.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [Introducing txtai](https://github.com/neuml/txtai/blob/master/examples/01_Introducing_txtai.ipynb) [▶️](https://www.youtube.com/watch?v=SIezMnVdmMs) | Overview of the functionality provided by txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/01_Introducing_txtai.ipynb) |
|
||||
| [Similarity search with images](https://github.com/neuml/txtai/blob/master/examples/13_Similarity_search_with_images.ipynb) | Embed images and text into the same space for search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/13_Similarity_search_with_images.ipynb) |
|
||||
| [Build a QA database](https://github.com/neuml/txtai/blob/master/examples/34_Build_a_QA_database.ipynb) | Question matching with semantic search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/34_Build_a_QA_database.ipynb) |
|
||||
| [Semantic Graphs](https://github.com/neuml/txtai/blob/master/examples/38_Introducing_the_Semantic_Graph.ipynb) | Explore topics, data connectivity and run network analysis| [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/38_Introducing_the_Semantic_Graph.ipynb) |
|
||||
|
||||
### LLM Orchestration
|
||||
|
||||
Autonomous agents, retrieval augmented generation (RAG), chat with your data, pipelines and workflows that interface with large language models (LLMs).
|
||||
|
||||
|
||||
|
||||
See below to learn more.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [Prompt templates and task chains](https://github.com/neuml/txtai/blob/master/examples/44_Prompt_templates_and_task_chains.ipynb) | Build model prompts and connect tasks together with workflows | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/44_Prompt_templates_and_task_chains.ipynb) |
|
||||
| [Integrate LLM frameworks](https://github.com/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb) | Integrate llama.cpp, LiteLLM and custom generation frameworks | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb) |
|
||||
| [Build knowledge graphs with LLMs](https://github.com/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) | Build knowledge graphs with LLM-driven entity extraction | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) |
|
||||
| [Parsing the stars with txtai](https://github.com/neuml/txtai/blob/master/examples/72_Parsing_the_stars_with_txtai.ipynb) | Explore an astronomical knowledge graph of known stars, planets, galaxies | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/72_Parsing_the_stars_with_txtai.ipynb) |
|
||||
|
||||
#### Agents
|
||||
|
||||
Agents connect embeddings, pipelines, workflows and other agents together to autonomously solve complex problems.
|
||||
|
||||
|
||||
|
||||
txtai agents are built on top of the [smolagents](https://github.com/huggingface/smolagents) framework. This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI / Claude / AWS Bedrock via LiteLLM). Agent prompting with [`agents.md`](https://github.com/agentsmd/agents.md) and [`skill.md`](https://agentskills.io/specification) are also supported.
|
||||
|
||||
Check out this [Agent Quickstart Example](https://github.com/neuml/txtai/blob/master/examples/agent_quickstart.py). Additional examples are listed below.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [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 | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/69_Granting_autonomy_to_agents.ipynb) |
|
||||
| [TxtAI got skills](https://github.com/neuml/txtai/blob/master/examples/83_TxtAI_got_skills.ipynb) | Integrate skill.md files with your agent | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/83_TxtAI_got_skills.ipynb) |
|
||||
| [Agent Tools](https://github.com/neuml/txtai/blob/master/examples/84_Agent_Tools.ipynb) [▶️](https://www.youtube.com/watch?v=RDNaFXQy3GQ) | Learn about the txtai agent toolkit | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/84_Agent_Tools.ipynb) |
|
||||
| [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 | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/71_Analyzing_LinkedIn_Company_Posts_with_Graphs_and_Agents.ipynb) |
|
||||
|
||||
#### Retrieval augmented generation
|
||||
|
||||
Retrieval augmented generation (RAG) reduces the risk of LLM hallucinations by constraining the output with a knowledge base as context. RAG is commonly used to "chat with your data".
|
||||
|
||||
|
||||
|
||||
|
||||
Check out this [RAG Quickstart Example](https://github.com/neuml/txtai/blob/master/examples/rag_quickstart.py). Additional examples are listed below.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [Build RAG pipelines with txtai](https://github.com/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) [▶️](https://www.youtube.com/watch?v=t_OeAc8NVfQ) | Guide on retrieval augmented generation including how to create citations | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) |
|
||||
| [RAG is more than Vector Search](https://github.com/neuml/txtai/blob/master/examples/79_RAG_is_more_than_Vector_Search.ipynb) | Context retrieval via Web, SQL and other sources | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/79_RAG_is_more_than_Vector_Search.ipynb) |
|
||||
| [GraphRAG with Wikipedia and GPT OSS](https://github.com/neuml/txtai/blob/master/examples/77_GraphRAG_with_Wikipedia_and_GPT_OSS.ipynb) | Deep graph search powered RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/77_GraphRAG_with_Wikipedia_and_GPT_OSS.ipynb) |
|
||||
| [Speech to Speech RAG](https://github.com/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) [▶️](https://www.youtube.com/watch?v=tH8QWwkVMKA) | Full cycle speech to speech workflow with RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) |
|
||||
|
||||
### Language Model Workflows
|
||||
|
||||
Language model workflows, also known as semantic workflows, connect language models together to build intelligent applications.
|
||||
|
||||
|
||||
|
||||
|
||||
While LLMs are powerful, there are plenty of smaller, more specialized models that work better and faster for specific tasks. This includes models for extractive question-answering, automatic summarization, text-to-speech, transcription and translation.
|
||||
|
||||
Check out this [Workflow Quickstart Example](https://github.com/neuml/txtai/blob/master/examples/workflow_quickstart.py). Additional examples are listed below.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [Run pipeline workflows](https://github.com/neuml/txtai/blob/master/examples/14_Run_pipeline_workflows.ipynb) [▶️](https://www.youtube.com/watch?v=UBMPDCn1gEU) | Simple yet powerful constructs to efficiently process data | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/14_Run_pipeline_workflows.ipynb) |
|
||||
| [Building abstractive text summaries](https://github.com/neuml/txtai/blob/master/examples/09_Building_abstractive_text_summaries.ipynb) | Run abstractive text summarization | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/09_Building_abstractive_text_summaries.ipynb) |
|
||||
| [Transcribe audio to text](https://github.com/neuml/txtai/blob/master/examples/11_Transcribe_audio_to_text.ipynb) | Convert audio files to text | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/11_Transcribe_audio_to_text.ipynb) |
|
||||
| [Translate text between languages](https://github.com/neuml/txtai/blob/master/examples/12_Translate_text_between_languages.ipynb) | Streamline machine translation and language detection | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/12_Translate_text_between_languages.ipynb) |
|
||||
|
||||
## Installation
|
||||
|
||||
|
||||
|
||||
|
||||
The easiest way to install is via pip and PyPI
|
||||
|
||||
```
|
||||
pip install txtai
|
||||
```
|
||||
|
||||
Python 3.10+ 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).
|
||||
|
||||
## Model guide
|
||||
|
||||
|
||||
|
||||
See the table below for the current recommended models. These models all allow commercial use and offer a blend of speed and performance.
|
||||
|
||||
| Component | Model(s) |
|
||||
| ----------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
|
||||
| [Embeddings](https://neuml.github.io/txtai/embeddings) | [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) |
|
||||
| [Image Captions](https://neuml.github.io/txtai/pipeline/image/caption) | [BLIP](https://hf.co/Salesforce/blip-image-captioning-base) |
|
||||
| [Labels - Zero Shot](https://neuml.github.io/txtai/pipeline/text/labels) | [DeBERTa v3 Zeroshot](https://hf.co/MoritzLaurer/deberta-v3-base-zeroshot-v2.0-c) |
|
||||
| [Labels - Fixed](https://neuml.github.io/txtai/pipeline/text/labels) | Fine-tune with [training pipeline](https://neuml.github.io/txtai/pipeline/train/trainer) |
|
||||
| [Large Language Model (LLM)](https://neuml.github.io/txtai/pipeline/text/llm) | [Gemma 4 31B](https://hf.co/google/gemma-4-31B) |
|
||||
| [Summarization](https://neuml.github.io/txtai/pipeline/text/summary) | [DistilBART](https://hf.co/sshleifer/distilbart-cnn-12-6) |
|
||||
| [Text-to-Speech](https://neuml.github.io/txtai/pipeline/audio/texttospeech) | [ESPnet JETS](https://hf.co/NeuML/ljspeech-jets-onnx) |
|
||||
| [Transcription](https://neuml.github.io/txtai/pipeline/audio/transcription) | [Whisper](https://hf.co/openai/whisper-base) |
|
||||
| [Translation](https://neuml.github.io/txtai/pipeline/text/translation) | [OPUS Model Series](https://hf.co/Helsinki-NLP) |
|
||||
|
||||
Models can be loaded as either a path from the Hugging Face Hub or a local directory. Model paths are optional, defaults are loaded when not specified. For tasks with no recommended model, txtai uses the default models as shown in the Hugging Face Tasks guide.
|
||||
|
||||
See the following links to learn more.
|
||||
|
||||
- [Hugging Face Tasks](https://hf.co/tasks)
|
||||
- [Hugging Face Model Hub](https://hf.co/models)
|
||||
- [Embeddings Leaderboard](https://hf.co/spaces/mteb/leaderboard)
|
||||
- [LLM Leaderboard](https://hf.co/spaces/lmarena-ai/arena-leaderboard)
|
||||
|
||||
## Powered by txtai
|
||||
|
||||
The following applications are powered by txtai.
|
||||
|
||||
|
||||
|
||||
| Application | Description |
|
||||
|:------------ |:-------------|
|
||||
| [rag](https://github.com/neuml/rag) | Retrieval Augmented Generation (RAG) application |
|
||||
| [ncoder](https://github.com/neuml/ncoder) | Open-Source AI coding agent |
|
||||
| [paperai](https://github.com/neuml/paperai) | AI for medical and scientific papers |
|
||||
| [annotateai](https://github.com/neuml/annotateai) | Automatically annotate papers with LLMs |
|
||||
|
||||
In addition to this list, there are also many other [open-source projects](https://github.com/neuml/txtai/network/dependents), [published research](https://scholar.google.com/scholar?q=txtai&hl=en&as_ylo=2022) and closed proprietary/commercial projects that have built on txtai in production.
|
||||
|
||||
## Further Reading
|
||||
|
||||
|
||||
|
||||
|
||||
- [Introducing txtai, the all-in-one AI framework](https://medium.com/neuml/introducing-txtai-the-all-in-one-ai-framework-0660ecfc39d7)
|
||||
- [txtai: An All-in-One AI Framework for Semantic Search and LLM Workflows](https://github.com/neuml/papers/blob/master/txtai/txtai.pdf)
|
||||
- [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 9.0](https://medium.com/neuml/whats-new-in-txtai-9-0-d522bb150afa) | [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.com/neuml/whats-new-in-txtai-4-0-bbc3a65c3d1c)
|
||||
- [Getting started with semantic search](https://medium.com/neuml/getting-started-with-semantic-search-a9fd9d8a48cf) | [workflows](https://medium.com/neuml/getting-started-with-semantic-workflows-2fefda6165d9) | [rag](https://medium.com/neuml/getting-started-with-rag-9a0cca75f748)
|
||||
- [Running txtai at scale](https://medium.com/neuml/running-at-scale-with-txtai-71196cdd99f9)
|
||||
- [Vector search & RAG Landscape: A review with txtai](https://medium.com/neuml/vector-search-rag-landscape-a-review-with-txtai-a7f37ad0e187)
|
||||
|
||||
## Documentation
|
||||
|
||||
[Full documentation on txtai](https://neuml.github.io/txtai) including configuration settings for embeddings, pipelines, workflows, API and a FAQ with common questions/issues is available.
|
||||
|
||||
## Contributing
|
||||
|
||||
For those who would like to contribute to txtai, please see [this guide](https://github.com/neuml/.github/blob/master/CONTRIBUTING.md).
|
||||
@@ -0,0 +1,7 @@
|
||||
# WeHub 来源说明
|
||||
|
||||
- 原始项目：`neuml/txtai`
|
||||
- 原始仓库：https://github.com/neuml/txtai
|
||||
- 导入方式：上游默认分支的最新快照
|
||||
- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准
|
||||
- 本文件仅用于记录来源，不代表 WeHub 是原项目作者
|
||||
@@ -0,0 +1,13 @@
|
||||
# Set base image
|
||||
ARG BASE_IMAGE=neuml/txtai-cpu
|
||||
FROM $BASE_IMAGE
|
||||
|
||||
# Copy configuration
|
||||
COPY config.yml .
|
||||
|
||||
# Run local API instance to cache models in container
|
||||
RUN python -c "from txtai.api import API; API('config.yml', False)"
|
||||
|
||||
# Start server and listen on all interfaces
|
||||
ENV CONFIG "config.yml"
|
||||
ENTRYPOINT ["uvicorn", "--host", "0.0.0.0", "txtai.api:app"]
|
||||
@@ -0,0 +1,23 @@
|
||||
# Set base image
|
||||
ARG BASE_IMAGE=neuml/txtai-cpu
|
||||
FROM $BASE_IMAGE
|
||||
|
||||
# Application script to copy into image
|
||||
ARG APP=api.py
|
||||
|
||||
# Install Lambda Runtime Interface Client and Mangum ASGI bindings
|
||||
RUN pip install awslambdaric mangum
|
||||
|
||||
# Copy configuration
|
||||
COPY config.yml .
|
||||
|
||||
# Run local API instance to cache models in container
|
||||
RUN python -c "from txtai.api import API; API('config.yml', False)"
|
||||
|
||||
# Copy application
|
||||
COPY $APP ./app.py
|
||||
|
||||
# Start runtime client using default application handler
|
||||
ENV CONFIG "config.yml"
|
||||
ENTRYPOINT ["python", "-m", "awslambdaric"]
|
||||
CMD ["app.handler"]
|
||||
@@ -0,0 +1,17 @@
|
||||
"""
|
||||
Lambda handler for a txtai API instance
|
||||
"""
|
||||
|
||||
from mangum import Mangum
|
||||
|
||||
from txtai.api import app, start
|
||||
|
||||
# pylint: disable=C0103
|
||||
# Create FastAPI application instance wrapped by Mangum
|
||||
handler = None
|
||||
if not handler:
|
||||
# Start application
|
||||
start()
|
||||
|
||||
# Create handler
|
||||
handler = Mangum(app, lifespan="off")
|
||||
@@ -0,0 +1,33 @@
|
||||
"""
|
||||
Lambda handler for txtai workflows
|
||||
"""
|
||||
|
||||
import json
|
||||
|
||||
from txtai.api import API
|
||||
|
||||
APP = None
|
||||
|
||||
|
||||
# pylint: disable=W0603,W0613
|
||||
def handler(event, context):
|
||||
"""
|
||||
Runs a workflow using input event parameters.
|
||||
|
||||
Args:
|
||||
event: input event
|
||||
context: input context
|
||||
|
||||
Returns:
|
||||
Workflow results
|
||||
"""
|
||||
|
||||
# Create (or get) global app instance
|
||||
global APP
|
||||
APP = APP if APP else API("config.yml")
|
||||
|
||||
# Get parameters from event body
|
||||
event = json.loads(event["body"])
|
||||
|
||||
# Run workflow and return results
|
||||
return {"statusCode": 200, "headers": {"Content-Type": "application/json"}, "body": list(APP.workflow(event["name"], event["elements"]))}
|
||||
@@ -0,0 +1,38 @@
|
||||
# Set base image
|
||||
ARG BASE_IMAGE=python:3.10-slim
|
||||
FROM $BASE_IMAGE
|
||||
|
||||
# Install GPU-enabled version of PyTorch if set
|
||||
ARG GPU
|
||||
|
||||
# Target CPU architecture
|
||||
ARG TARGETARCH
|
||||
|
||||
# Set Python version (i.e. 3, 3.10)
|
||||
ARG PYTHON_VERSION=3
|
||||
|
||||
# List of txtai components to install
|
||||
ARG COMPONENTS=[all]
|
||||
|
||||
# Locale environment variables
|
||||
ENV LC_ALL=C.UTF-8
|
||||
ENV LANG=C.UTF-8
|
||||
|
||||
RUN \
|
||||
# Install required packages
|
||||
apt-get update && \
|
||||
apt-get -y --no-install-recommends install libgomp1 libportaudio2 libsndfile1 libegl1 libgles2 libvulkan1 git gcc g++ python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python3-pip && \
|
||||
rm -rf /var/lib/apt/lists && \
|
||||
\
|
||||
# Install txtai project and dependencies
|
||||
ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python && \
|
||||
python -m pip install --no-cache-dir -U pip wheel setuptools && \
|
||||
if [ -z ${GPU} ]; then pip install --no-cache-dir torch==2.12.1+cpu torchvision==0.27.1+cpu -f https://download.pytorch.org/whl/torch -f https://download.pytorch.org/whl/torchvision; fi && \
|
||||
python -m pip install --no-cache-dir txtai${COMPONENTS} && \
|
||||
python -c "import sys, importlib.util as util; 1 if util.find_spec('nltk') else sys.exit(); import nltk; nltk.download(['punkt', 'punkt_tab', 'averaged_perceptron_tagger_eng'])" && \
|
||||
\
|
||||
# Cleanup build packages
|
||||
apt-get -y purge git gcc g++ python${PYTHON_VERSION}-dev && apt-get -y autoremove
|
||||
|
||||
# Set default working directory
|
||||
WORKDIR /app
|
||||
@@ -0,0 +1,20 @@
|
||||
# Set base image
|
||||
ARG BASE_IMAGE=python:3.10-slim
|
||||
FROM $BASE_IMAGE
|
||||
|
||||
# Target CPU architecture
|
||||
ARG TARGETARCH
|
||||
|
||||
# Set Python version (i.e. 3, 3.10)
|
||||
ARG PYTHON_VERSION=3
|
||||
|
||||
# Locale environment variables
|
||||
ENV LC_ALL=C.UTF-8
|
||||
ENV LANG=C.UTF-8
|
||||
|
||||
RUN \
|
||||
# Install minimal txtai project and dependencies
|
||||
MINIMAL=1 python -m pip install --no-cache-dir txtai_minimal
|
||||
|
||||
# Set default working directory
|
||||
WORKDIR /app
|
||||
@@ -0,0 +1,12 @@
|
||||
# Set base image
|
||||
ARG BASE_IMAGE=neuml/txtai-cpu
|
||||
FROM $BASE_IMAGE
|
||||
|
||||
# Copy configuration
|
||||
COPY config.yml .
|
||||
|
||||
# Run local API instance to cache models in container
|
||||
RUN python -c "from txtai.api import API; API('config.yml', False)"
|
||||
|
||||
# Start application and wait for completion. Scheduled workflows can run indefinitely.
|
||||
ENTRYPOINT ["python", "-c", "from txtai.api import API; API('config.yml').wait()"]
|
||||
@@ -0,0 +1,13 @@
|
||||
# Set base image
|
||||
ARG BASE_IMAGE=neuml/txtai-cpu
|
||||
FROM $BASE_IMAGE
|
||||
|
||||
# Copy configuration
|
||||
COPY config.yml .
|
||||
|
||||
# Run local API instance to cache models in container
|
||||
RUN python -c "from txtai.api import API; API('config.yml', False)"
|
||||
|
||||
# Run workflow. Requires two command line arguments: name of workflow and input elements
|
||||
ENTRYPOINT ["python", "-c", "import sys; from txtai.api import API\nfor _ in API('config.yml').workflow(sys.argv[1], sys.argv[2:]): pass"]
|
||||
CMD ["workflow"]
|
||||
@@ -0,0 +1,141 @@
|
||||
# Configuration
|
||||
|
||||
An agent takes two main arguments, an LLM and a list of tools.
|
||||
|
||||
The txtai agent framework is built with [smolagents](https://github.com/huggingface/smolagents). Additional options can be passed in the `Agent` constructor.
|
||||
|
||||
```python
|
||||
from datetime import datetime
|
||||
|
||||
from txtai import Agent
|
||||
|
||||
wikipedia = {
|
||||
"name": "wikipedia",
|
||||
"description": "Searches a Wikipedia database",
|
||||
"provider": "huggingface-hub",
|
||||
"container": "neuml/txtai-wikipedia"
|
||||
}
|
||||
|
||||
arxiv = {
|
||||
"name": "arxiv",
|
||||
"description": "Searches a database of scientific papers",
|
||||
"provider": "huggingface-hub",
|
||||
"container": "neuml/txtai-arxiv"
|
||||
}
|
||||
|
||||
def today() -> str:
|
||||
"""
|
||||
Gets the current date and time
|
||||
|
||||
Returns:
|
||||
current date and time
|
||||
"""
|
||||
|
||||
return datetime.today().isoformat()
|
||||
|
||||
agent = Agent(
|
||||
model="Qwen/Qwen3-4B-Instruct-2507",
|
||||
tools=[today, wikipedia, arxiv, "websearch"],
|
||||
)
|
||||
```
|
||||
|
||||
## model
|
||||
|
||||
```yaml
|
||||
model: string|llm instance
|
||||
```
|
||||
|
||||
LLM model path or LLM pipeline instance. The `llm` parameter is also supported for backwards compatibility.
|
||||
|
||||
See the [LLM pipeline](../../pipeline/text/llm) for more information.
|
||||
|
||||
## tools
|
||||
|
||||
```yaml
|
||||
tools: list
|
||||
```
|
||||
|
||||
List of tools to supply to the agent. Supports the following configurations.
|
||||
|
||||
### function
|
||||
|
||||
A function tool takes the following dictionary fields.
|
||||
|
||||
| Field | Description |
|
||||
|:------------|:-------------------------|
|
||||
| name | name of the tool |
|
||||
| description | tool description |
|
||||
| target | target method / callable |
|
||||
|
||||
A function or callable method can also be directly supplied in the `tools` list. In this case, the fields are inferred from the method documentation.
|
||||
|
||||
### embeddings
|
||||
|
||||
Embeddings indexes have built-in support. Provide the following dictionary configuration to add an embeddings index as a tool.
|
||||
|
||||
| Field | Description |
|
||||
|:------------|:-------------------------------------------|
|
||||
| name | embeddings index name |
|
||||
| description | embeddings index description |
|
||||
| **kwargs | Parameters to pass to [embeddings.load](../../embeddings/methods/#txtai.embeddings.Embeddings.load) |
|
||||
|
||||
### tool
|
||||
|
||||
The following shortcut strings load tools directly. Passing a Tool instance is also supported.
|
||||
|
||||
| Tool | Description |
|
||||
|:------------|:----------------------------------------------------------|
|
||||
| bash | Runs a shell command through subprocess |
|
||||
| defaults | Loads all of these tools as the default toolkit |
|
||||
| edit | Edits a file in place and returns a diff |
|
||||
| glob | Finds matching file patterns in a directory |
|
||||
| grep | Finds matching file content in a directory |
|
||||
| http.* | HTTP Path to a [Model Context Protocol (MCP)](https://modelcontextprotocol.io/docs/getting-started/intro) server |
|
||||
| python | Runs a Python action |
|
||||
| read | Reads file or url content, supports text extraction |
|
||||
| todowrite | Generates a task list to organize complex tasks |
|
||||
| websearch | Runs a websearch using the built-in websearch tool |
|
||||
| webview | Extracts content from a web page. Alias for `read` tool |
|
||||
| write | Writes content to file |
|
||||
| *.md | Loads a [`skill.md`](https://agentskills.io/specification) file |
|
||||
|
||||
## instructions
|
||||
|
||||
```yaml
|
||||
instructions: string|path
|
||||
```
|
||||
|
||||
Supports loading an `agents.md` file. Can be provided directly as a string or as a path to a file.
|
||||
|
||||
[Read more about agents.md here](https://github.com/agentsmd/agents.md)
|
||||
|
||||
## template
|
||||
|
||||
```yaml
|
||||
template: string
|
||||
```
|
||||
|
||||
Customize the prompt template used by this agent. Supports Jinja templates. Uses a default template when this parameter is not provided.
|
||||
Must include `{{ text }}` and `{{ memory }}` placeholders.
|
||||
|
||||
## memory
|
||||
|
||||
```yaml
|
||||
memory: int
|
||||
```
|
||||
|
||||
Keeps a rolling window of `memory` inputs and outputs. These are added to future prompts and serve as "agent memory".
|
||||
|
||||
Supports storing memory by `session` to enable multiple conversation threads. Defaults to shared memory when not set. See the [method documentation](../methods#txtai.agent.base.Agent.__call__) for more information.
|
||||
|
||||
## method
|
||||
|
||||
```yaml
|
||||
method: code|tool
|
||||
```
|
||||
|
||||
Sets the agent method. Supports either a `code` or `tool` (default) calling agent. A code agent generates Python code and executes that. A tool calling agent generates JSON blocks and calls the agents within those blocks.
|
||||
|
||||
Additional options can be directly passed. See [CodeAgent](https://huggingface.co/docs/smolagents/main/en/reference/agents#smolagents.CodeAgent) or [ToolCallingAgent](https://huggingface.co/docs/smolagents/main/en/reference/agents#smolagents.ToolCallingAgent) for a list of parameters.
|
||||
|
||||
[Read more here](https://huggingface.co/docs/smolagents/main/en/guided_tour).
|
||||
@@ -0,0 +1,156 @@
|
||||
# Agent
|
||||
|
||||
|
||||
|
||||
An agent automatically creates workflows to answer multi-faceted user requests. Agents iteratively prompt and/or interface with tools to
|
||||
step through a process and ultimately come to an answer for a request.
|
||||
|
||||
Agent prompting with [`agents.md`](https://github.com/agentsmd/agents.md) and [`skill.md`](https://agentskills.io/specification) are also supported. [Read the configuration](./configuration/#tool) for more on how to setup those up.
|
||||
|
||||
Agents excel at complex tasks where multiple tools and/or methods are required. They incorporate a level of randomness similar to different
|
||||
people working on the same task. When the request is simple and/or there is a rule-based process, other methods such as RAG and Workflows
|
||||
should be explored.
|
||||
|
||||
The following code snippet defines a basic agent.
|
||||
|
||||
```python
|
||||
from datetime import datetime
|
||||
|
||||
from txtai import Agent
|
||||
|
||||
wikipedia = {
|
||||
"name": "wikipedia",
|
||||
"description": "Searches a Wikipedia database",
|
||||
"provider": "huggingface-hub",
|
||||
"container": "neuml/txtai-wikipedia"
|
||||
}
|
||||
|
||||
arxiv = {
|
||||
"name": "arxiv",
|
||||
"description": "Searches a database of scientific papers",
|
||||
"provider": "huggingface-hub",
|
||||
"container": "neuml/txtai-arxiv"
|
||||
}
|
||||
|
||||
def today() -> str:
|
||||
"""
|
||||
Gets the current date and time
|
||||
|
||||
Returns:
|
||||
current date and time
|
||||
"""
|
||||
|
||||
return datetime.today().isoformat()
|
||||
|
||||
agent = Agent(
|
||||
model="Qwen/Qwen3-4B-Instruct-2507",
|
||||
tools=[today, wikipedia, arxiv, "websearch"],
|
||||
max_steps=10,
|
||||
)
|
||||
```
|
||||
|
||||
The agent above has access to two embeddings databases (Wikipedia and ArXiv) and the web. Given the user's input request, the agent decides the best tool to solve the task.
|
||||
|
||||
## Example
|
||||
|
||||
The first example will solve a problem with multiple data points. See below.
|
||||
|
||||
```python
|
||||
agent("Which city has the highest population, Boston or New York?")
|
||||
```
|
||||
|
||||
This requires looking up the population of each city before knowing how to answer the question. Multiple search requests are run to generate a final answer.
|
||||
|
||||
## Agentic RAG
|
||||
|
||||
Standard retrieval augmented generation (RAG) runs a single vector search to obtain a context and builds a prompt with the context + input question. Agentic RAG is a more complex process that goes through multiple iterations. It can also utilize multiple databases to come to a final conclusion.
|
||||
|
||||
The example below aggregates information from multiple sources and builds a report on a topic.
|
||||
|
||||
```python
|
||||
researcher = """
|
||||
You're an expert researcher looking to write a paper on {topic}.
|
||||
Search for websites, scientific papers and Wikipedia related to the topic.
|
||||
Write a report with summaries and references (with hyperlinks).
|
||||
Write the text as Markdown.
|
||||
"""
|
||||
|
||||
agent(researcher.format(topic="alien life"))
|
||||
```
|
||||
|
||||
## Agent Teams
|
||||
|
||||
Agents can also be tools. This enables the concept of building "Agent Teams" to solve problems. The previous example can be rewritten as a list of agents.
|
||||
|
||||
```python
|
||||
from txtai import Agent, LLM
|
||||
|
||||
llm = LLM("Qwen/Qwen3-4B-Instruct-2507")
|
||||
|
||||
websearcher = Agent(
|
||||
model=llm,
|
||||
tools=["websearch"],
|
||||
)
|
||||
|
||||
wikiman = Agent(
|
||||
model=llm,
|
||||
tools=[{
|
||||
"name": "wikipedia",
|
||||
"description": "Searches a Wikipedia database",
|
||||
"provider": "huggingface-hub",
|
||||
"container": "neuml/txtai-wikipedia"
|
||||
}],
|
||||
)
|
||||
|
||||
researcher = Agent(
|
||||
model=llm,
|
||||
tools=[{
|
||||
"name": "arxiv",
|
||||
"description": "Searches a database of scientific papers",
|
||||
"provider": "huggingface-hub",
|
||||
"container": "neuml/txtai-arxiv"
|
||||
}],
|
||||
)
|
||||
|
||||
agent = Agent(
|
||||
model=llm,
|
||||
tools=[{
|
||||
"name": "websearcher",
|
||||
"description": "I run web searches, there is no answer a web search can't solve!",
|
||||
"target": websearcher
|
||||
}, {
|
||||
"name": "wikiman",
|
||||
"description": "Wikipedia has all the answers, I search Wikipedia and answer questions",
|
||||
"target": wikiman
|
||||
}, {
|
||||
"name": "researcher",
|
||||
"description": "I'm a science guy. I search arXiv to get all my answers.",
|
||||
"target": researcher
|
||||
}],
|
||||
max_steps=10
|
||||
)
|
||||
```
|
||||
|
||||
This provides another level of intelligence to the process. Instead of just a single tool execution, each agent-tool combination has it's own reasoning engine.
|
||||
|
||||
```python
|
||||
agent("""
|
||||
Research fundamental concepts about Signal Processing and build a comprehensive report.
|
||||
Write the output in Markdown.
|
||||
""")
|
||||
```
|
||||
|
||||
# More examples
|
||||
|
||||
Check out this [Agent Quickstart Example](https://github.com/neuml/txtai/blob/master/examples/agent_quickstart.py). Additional examples are listed below.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [What's new in txtai 8.0](https://github.com/neuml/txtai/blob/master/examples/67_Whats_new_in_txtai_8_0.ipynb) | Agents with txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/67_Whats_new_in_txtai_8_0.ipynb) |
|
||||
| [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 | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/68_Analyzing_Hugging_Face_Posts_with_Graphs_and_Agents.ipynb) |
|
||||
| [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 | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/69_Granting_autonomy_to_agents.ipynb) |
|
||||
| [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 | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/71_Analyzing_LinkedIn_Company_Posts_with_Graphs_and_Agents.ipynb) |
|
||||
| [Parsing the stars with txtai](https://github.com/neuml/txtai/blob/master/examples/72_Parsing_the_stars_with_txtai.ipynb) | Explore an astronomical knowledge graph of known stars, planets, galaxies | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/72_Parsing_the_stars_with_txtai.ipynb) |
|
||||
| [Agentic College Search](https://github.com/neuml/txtai/blob/master/examples/82_Agentic_College_Search.ipynb) | Identify list of strong engineering colleges | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/82_Agentic_College_Search.ipynb) |
|
||||
| [TxtAI got skills](https://github.com/neuml/txtai/blob/master/examples/83_TxtAI_got_skills.ipynb) | Integrate skill.md files with your agent | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/83_TxtAI_got_skills.ipynb) |
|
||||
| [Agent Tools](https://github.com/neuml/txtai/blob/master/examples/84_Agent_Tools.ipynb) [▶️](https://www.youtube.com/watch?v=RDNaFXQy3GQ) | Learn about the txtai agent toolkit | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/84_Agent_Tools.ipynb) |
|
||||
@@ -0,0 +1,4 @@
|
||||
# Methods
|
||||
|
||||
## ::: txtai.agent.base.Agent.__init__
|
||||
## ::: txtai.agent.base.Agent.__call__
|
||||
@@ -0,0 +1,20 @@
|
||||
# Distributed embeddings clusters
|
||||
|
||||
The API supports combining multiple API instances into a single logical embeddings index. An example configuration is shown below.
|
||||
|
||||
```yaml
|
||||
cluster:
|
||||
shards:
|
||||
- http://127.0.0.1:8002
|
||||
- http://127.0.0.1:8003
|
||||
```
|
||||
|
||||
This configuration aggregates the API instances above as index shards. Data is evenly split among each of the shards at index time. Queries are run in parallel against each shard and the results are joined together. This method allows horizontal scaling and supports very large index clusters.
|
||||
|
||||
This method is only recommended for data sets in the 1 billion+ records. The ANN libraries can easily support smaller data sizes and this method is not worth the additional complexity. At this time, new shards can not be added after building the initial index.
|
||||
|
||||
See the link below for a detailed example covering distributed embeddings clusters.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [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) |
|
||||
@@ -0,0 +1,165 @@
|
||||
# Configuration
|
||||
|
||||
Configuration is set through YAML. In most cases, YAML keys map to fields names in Python. The [example in the previous section](../) gave a full-featured example covering a wide array of configuration options.
|
||||
|
||||
Each section below describes the available configuration settings.
|
||||
|
||||
## Embeddings
|
||||
|
||||
The configuration parser expects a top level `embeddings` key to be present in the YAML. All [embeddings configuration](../../embeddings/configuration) is supported.
|
||||
|
||||
The following example defines an embeddings index.
|
||||
|
||||
```yaml
|
||||
path: index path
|
||||
writable: true
|
||||
|
||||
embeddings:
|
||||
path: vector model
|
||||
content: true
|
||||
```
|
||||
|
||||
Three top level settings are available to control where indexes are saved and if an index is a read-only index.
|
||||
|
||||
### path
|
||||
```yaml
|
||||
path: string
|
||||
```
|
||||
|
||||
Path to save and load the embeddings index. Each API instance can only access a single index at a time.
|
||||
|
||||
### writable
|
||||
```yaml
|
||||
writable: boolean
|
||||
```
|
||||
|
||||
Sets this embeddings index as writable (true) or read-only (false). This allows serving a read-only index. Defaults to read-only.
|
||||
|
||||
### reindex
|
||||
```yaml
|
||||
reindex: boolean
|
||||
```
|
||||
|
||||
Enables re-indexing for this embeddings index (defaults to disabled).
|
||||
|
||||
Re-indexing accepts new configuration and should only be enabled when the configuration comes from trusted sources.
|
||||
|
||||
### cloud
|
||||
[Cloud storage settings](../../embeddings/configuration/cloud) can be set under a `cloud` top level configuration group.
|
||||
|
||||
## Agent
|
||||
|
||||
Agents are defined under a top level `agent` key. Each key under the `agent` key is the name of the agent. Constructor parameters can be passed under this key.
|
||||
|
||||
The following example defines an agent.
|
||||
|
||||
```yaml
|
||||
agent:
|
||||
researcher:
|
||||
tools:
|
||||
- websearch
|
||||
|
||||
llm:
|
||||
path: Qwen/Qwen3-4B-Instruct-2507
|
||||
```
|
||||
|
||||
## Pipeline
|
||||
|
||||
Pipelines are loaded as top level configuration parameters. Pipeline names are automatically detected in the YAML configuration and created upon startup. All [pipelines](../../pipeline) are supported.
|
||||
|
||||
The following example defines a series of pipelines. Note that entries below are the lower-case names of the pipeline class.
|
||||
|
||||
```yaml
|
||||
caption:
|
||||
|
||||
extractor:
|
||||
path: model path
|
||||
|
||||
labels:
|
||||
|
||||
summary:
|
||||
|
||||
tabular:
|
||||
|
||||
translation:
|
||||
```
|
||||
|
||||
Under each pipeline name, configuration settings for the pipeline can be set.
|
||||
|
||||
## Workflow
|
||||
|
||||
Workflows are defined under a top level `workflow` key. Each key under the `workflow` key is the name of the workflow. Under that is a `tasks` key with each task definition.
|
||||
|
||||
The following example defines a workflow.
|
||||
|
||||
```yaml
|
||||
workflow:
|
||||
sumtranslate:
|
||||
tasks:
|
||||
- action: summary
|
||||
- action: translation
|
||||
```
|
||||
|
||||
### schedule
|
||||
|
||||
Schedules a workflow using a [cron expression](../../workflow/schedule).
|
||||
|
||||
```yaml
|
||||
workflow:
|
||||
index:
|
||||
schedule:
|
||||
cron: 0/10 * * * * *
|
||||
elements: ["api params"]
|
||||
tasks:
|
||||
- task: service
|
||||
url: api url
|
||||
- action: index
|
||||
```
|
||||
|
||||
### tasks
|
||||
```yaml
|
||||
tasks: list
|
||||
```
|
||||
|
||||
Expects a list of workflow tasks. Each element defines a single workflow task. All [task configuration](../../workflow/task) is supported.
|
||||
|
||||
A shorthand syntax for creating tasks is supported. This syntax will automatically map task strings to an `action:value` pair.
|
||||
|
||||
Example below.
|
||||
|
||||
```yaml
|
||||
workflow:
|
||||
index:
|
||||
tasks:
|
||||
- action1
|
||||
- action2
|
||||
```
|
||||
|
||||
Each task element supports the following additional arguments.
|
||||
|
||||
#### action
|
||||
```yaml
|
||||
action: string|list
|
||||
```
|
||||
|
||||
Both single and multi-action tasks are supported.
|
||||
|
||||
The action parameter works slightly different when passed via configuration. The parameter(s) needs to be converted into callable method(s). If action is a pipeline that has been defined in the current configuration, it will use that pipeline as the action.
|
||||
|
||||
There are three special action names `index`, `upsert` and `search`. If `index` or `upsert` are used as the action, the task will collect workflow data elements and load them into defined the embeddings index. If `search` is used, the task will execute embeddings queries for each input data element.
|
||||
|
||||
Otherwise, the action must be a path to a callable object or function. The configuration parser will resolve the function name and use that as the task action.
|
||||
|
||||
#### task
|
||||
```yaml
|
||||
task: string
|
||||
```
|
||||
|
||||
Optionally sets the type of task to create. For example, this could be a `file` task or a `retrieve` task. If this is not specified, a generic task is created. [The list of workflow tasks can be found here](../../workflow).
|
||||
|
||||
#### args
|
||||
```yaml
|
||||
args: list
|
||||
```
|
||||
|
||||
Optional list of static arguments to pass to the workflow task. These are combined with workflow data to pass to each `__call__`.
|
||||
@@ -0,0 +1,25 @@
|
||||
# Customization
|
||||
|
||||
The txtai API has a number of features out of the box that are designed to help get started quickly. API services can also be augmented with custom code and functionality. The two main ways to do this are with extensions and dependencies.
|
||||
|
||||
Extensions add a custom endpoint. Dependencies add middleware that executes with each request. See the sections below for more.
|
||||
|
||||
## Extensions
|
||||
|
||||
While the API is extremely flexible and complex logic can be executed through YAML-driven workflows, some may prefer to create an endpoint in Python. API extensions define custom Python endpoints that interact with txtai applications.
|
||||
|
||||
See the link below for a detailed example.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [Custom API Endpoints](https://github.com/neuml/txtai/blob/master/examples/51_Custom_API_Endpoints.ipynb) | Extend the API with custom endpoints | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/51_Custom_API_Endpoints.ipynb) |
|
||||
|
||||
## Dependencies
|
||||
|
||||
txtai has a default API token authorization method that works well in many cases. Dependencies can also add custom logic with each request. This could be an additional authorization step and/or an authentication method.
|
||||
|
||||
See the link below for a detailed example.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [API Authorization and Authentication](https://github.com/neuml/txtai/blob/master/examples/54_API_Authorization_and_Authentication.ipynb) | Add authorization, authentication and middleware dependencies to the API | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/54_API_Authorization_and_Authentication.ipynb) |
|
||||
@@ -0,0 +1,134 @@
|
||||
# API
|
||||
|
||||
|
||||
|
||||
|
||||
txtai has a full-featured API, backed by [FastAPI](https://github.com/tiangolo/fastapi), that can optionally be enabled for any txtai process. All functionality found in txtai can be accessed via the API.
|
||||
|
||||
The following is an example configuration and startup script for the API.
|
||||
|
||||
Note: This configuration file enables all functionality. For memory-bound systems, splitting pipelines into multiple instances is a best practice.
|
||||
|
||||
```yaml
|
||||
# Index file path
|
||||
path: /tmp/index
|
||||
|
||||
# Allow indexing of documents
|
||||
writable: True
|
||||
|
||||
# Enbeddings index
|
||||
embeddings:
|
||||
path: sentence-transformers/nli-mpnet-base-v2
|
||||
|
||||
# Extractive QA
|
||||
extractor:
|
||||
path: distilbert-base-cased-distilled-squad
|
||||
|
||||
# Zero-shot labeling
|
||||
labels:
|
||||
|
||||
# Similarity
|
||||
similarity:
|
||||
|
||||
# Text segmentation
|
||||
segmentation:
|
||||
sentences: true
|
||||
|
||||
# Text summarization
|
||||
summary:
|
||||
|
||||
# Text extraction
|
||||
textractor:
|
||||
paragraphs: true
|
||||
minlength: 100
|
||||
join: true
|
||||
|
||||
# Transcribe audio to text
|
||||
transcription:
|
||||
|
||||
# Translate text between languages
|
||||
translation:
|
||||
|
||||
# Workflow definitions
|
||||
workflow:
|
||||
sumfrench:
|
||||
tasks:
|
||||
- action: textractor
|
||||
task: url
|
||||
- action: summary
|
||||
- action: translation
|
||||
args: ["fr"]
|
||||
sumspanish:
|
||||
tasks:
|
||||
- action: textractor
|
||||
task: url
|
||||
- action: summary
|
||||
- action: translation
|
||||
args: ["es"]
|
||||
```
|
||||
|
||||
Assuming this YAML content is stored in a file named config.yml, the following command starts the API process.
|
||||
|
||||
```bash
|
||||
CONFIG=config.yml uvicorn "txtai.api:app"
|
||||
```
|
||||
|
||||
Uvicorn is a full-featured production-ready server. See the [Uvicorn deployment guide](https://www.uvicorn.org/deployment/) for more on configuration options.
|
||||
|
||||
## Connect to API
|
||||
|
||||
The default port for the API is 8000. See the uvicorn link above to change this.
|
||||
|
||||
txtai has a number of language bindings which abstract the API (see links below). Alternatively, code can be written to connect directly to the API. Documentation for a live running instance can be found at the `/docs` url (i.e. http://localhost:8000/docs). The following example runs a workflow using cURL.
|
||||
|
||||
```bash
|
||||
curl \
|
||||
-X POST "http://localhost:8000/workflow" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"name":"sumfrench", "elements": ["https://github.com/neuml/txtai"]}'
|
||||
```
|
||||
|
||||
## Local instance
|
||||
|
||||
A local instance can be instantiated. In this case, a txtai application runs internally, without any network connections, providing the same consolidated functionality. This enables running txtai in Python with configuration.
|
||||
|
||||
The configuration above can be run in Python with:
|
||||
|
||||
```python
|
||||
from txtai import Application
|
||||
|
||||
# Load and run workflow
|
||||
app = Application(config.yml)
|
||||
app.workflow("sumfrench", ["https://github.com/neuml/txtai"])
|
||||
```
|
||||
|
||||
See this [link for a full list of methods](./methods).
|
||||
|
||||
## Run with containers
|
||||
|
||||
The API can be containerized and run. This will bring up an API instance without having to install Python, txtai or any dependencies on your machine!
|
||||
|
||||
[See this section for more information](../cloud/#api).
|
||||
|
||||
## Supported language bindings
|
||||
|
||||
The following programming languages have bindings with the txtai API:
|
||||
|
||||
- [Python](https://github.com/neuml/txtai.py)
|
||||
- [JavaScript](https://github.com/neuml/txtai.js)
|
||||
- [Java](https://github.com/neuml/txtai.java)
|
||||
- [Rust](https://github.com/neuml/txtai.rs)
|
||||
- [Go](https://github.com/neuml/txtai.go)
|
||||
|
||||
The API also supports hosting [OpenAI-compatible](./openai) and [Model Context Protocol (MCP)](./mcp) endpoints.
|
||||
|
||||
See the links below for detailed examples covering the API.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [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) |
|
||||
| [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) |
|
||||
| [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) |
|
||||
| [Custom API Endpoints](https://github.com/neuml/txtai/blob/master/examples/51_Custom_API_Endpoints.ipynb) | Extend the API with custom endpoints | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/51_Custom_API_Endpoints.ipynb) |
|
||||
| [API Authorization and Authentication](https://github.com/neuml/txtai/blob/master/examples/54_API_Authorization_and_Authentication.ipynb) | Add authorization, authentication and middleware dependencies to the API | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/54_API_Authorization_and_Authentication.ipynb) |
|
||||
| [OpenAI Compatible API](https://github.com/neuml/txtai/blob/master/examples/74_OpenAI_Compatible_API.ipynb) | Connect to txtai with a standard OpenAI client library | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/74_OpenAI_Compatible_API.ipynb) |
|
||||
@@ -0,0 +1,34 @@
|
||||
# Model Context Protocol
|
||||
|
||||
The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools.
|
||||
|
||||
The API can be configured to handle MCP requests. All enabled endpoints set in the API configuration are automatically added as MCP tools.
|
||||
|
||||
```yaml
|
||||
mcp: boolean|dict
|
||||
```
|
||||
|
||||
When `mcp` is a boolean, default arguments are used. When `mcp` is a dictionary it supports the following options.
|
||||
|
||||
```yaml
|
||||
mcp:
|
||||
clientargs: http client options (dict)
|
||||
mcpargs: FastApiMCP options (dict)
|
||||
```
|
||||
|
||||
See the following links for details on the options.
|
||||
|
||||
- [HTTP Client Arguments](https://www.python-httpx.org/api/#asyncclient)
|
||||
- [FastApiMCP Arguments](https://github.com/tadata-org/fastapi_mcp/blob/main/fastapi_mcp/server.py#L22)
|
||||
|
||||
Once this configuration option is added, a new route is added to the application `/mcp`.
|
||||
|
||||
The [Model Context Protocol Inspector tool](https://www.npmjs.com/package/@modelcontextprotocol/inspector) is a quick way to explore how the MCP tools are exported through this interface.
|
||||
|
||||
Run the following and go to the local URL specified.
|
||||
|
||||
```
|
||||
npx @modelcontextprotocol/inspector node build/index.js
|
||||
```
|
||||
|
||||
Enter `http://localhost:8000/mcp` to see the full list of tools available.
|
||||
@@ -0,0 +1,15 @@
|
||||
# Methods
|
||||
|
||||
::: txtai.api.API
|
||||
options:
|
||||
inherited_members: true
|
||||
filters:
|
||||
- "!__del__"
|
||||
- "!flows"
|
||||
- "!function"
|
||||
- "!indexes"
|
||||
- "!limit"
|
||||
- "!pipes"
|
||||
- "!read"
|
||||
- "!resolve"
|
||||
- "!weights"
|
||||
@@ -0,0 +1,13 @@
|
||||
# OpenAI-compatible API
|
||||
|
||||
The API can be configured to serve an OpenAI-compatible API as shown below.
|
||||
|
||||
```yaml
|
||||
openai: True
|
||||
```
|
||||
|
||||
See the link below for a detailed example.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [OpenAI Compatible API](https://github.com/neuml/txtai/blob/master/examples/74_OpenAI_Compatible_API.ipynb) | Connect to txtai with a standard OpenAI client library | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/74_OpenAI_Compatible_API.ipynb) |
|
||||
@@ -0,0 +1,33 @@
|
||||
# Security
|
||||
|
||||
The default implementation of an API service runs via HTTP and is fully open. If the service is being run as a prototype on an internal network, that may be fine. In most scenarios, the connection should at least be encrypted. Authorization is another built-in feature that requires a valid API token with each request. See below for more.
|
||||
|
||||
## HTTPS
|
||||
|
||||
The default API service command starts a Uvicorn server as a HTTP service on port 8000. To run a HTTPS service, consider the following options.
|
||||
|
||||
- [TLS Proxy Server](https://fastapi.tiangolo.com/deployment/https/). *Recommended choice*. With this configuration, the txtai API service runs as a HTTP service only accessible on the localhost/local network. The proxy server handles all encryption and redirects requests to local services. See this [example configuration](https://www.uvicorn.org/deployment/#running-behind-nginx) for more.
|
||||
|
||||
- [Uvicorn SSL Certificate](https://www.uvicorn.org/deployment/). Another option is setting the SSL certificate on the Uvicorn service. This works in simple situations but gets complex when hosting multiple txtai or other related services.
|
||||
|
||||
## Authorization
|
||||
|
||||
Authorization requires a valid API token with each API request. This token is sent as a HTTP `Authorization` header.
|
||||
|
||||
*Server*
|
||||
```bash
|
||||
CONFIG=config.yml TOKEN=<sha256 encoded token> uvicorn "txtai.api:app"
|
||||
```
|
||||
|
||||
*Client*
|
||||
```bash
|
||||
curl \
|
||||
-X POST "http://localhost:8000/workflow" \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "Authorization: Bearer <token>" \
|
||||
-d '{"name":"sumfrench", "elements": ["https://github.com/neuml/txtai"]}'
|
||||
```
|
||||
|
||||
It's important to note that HTTPS **must** be enabled using one of the methods mentioned above. Otherwise, tokens will be exchanged as clear text.
|
||||
|
||||
Authentication and Authorization can be fully customized. See the [dependencies](../customization#dependencies) section for more.
|
||||
+182
@@ -0,0 +1,182 @@
|
||||
# Cloud
|
||||
|
||||
|
||||
|
||||
|
||||
Scalable cloud-native applications can be built with txtai. The following cloud runtimes are supported.
|
||||
|
||||
- Container Orchestration Systems (i.e. Kubernetes)
|
||||
- Docker Engine
|
||||
- Serverless Compute
|
||||
- txtai.cloud (planned for future)
|
||||
|
||||
Images for txtai are available on Docker Hub for [CPU](https://hub.docker.com/r/neuml/txtai-cpu) and [GPU](https://hub.docker.com/r/neuml/txtai-gpu) installs. The CPU install is recommended when GPUs aren't available given the image is significantly smaller.
|
||||
|
||||
The base txtai images have no models installed and models will be downloaded each time the container starts. Caching the models is recommended as that will significantly reduce container start times. This can be done a couple different ways.
|
||||
|
||||
- Create a container with the [models cached](#container-image-model-caching)
|
||||
- Set the transformers cache environment variable and mount that volume when starting the image
|
||||
```bash
|
||||
docker run -v <local dir>:/models -e TRANSFORMERS_CACHE=/models --rm -it <docker image>
|
||||
```
|
||||
|
||||
## Build txtai images
|
||||
|
||||
The txtai images found on Docker Hub are configured to support most situations. This image can be locally built with different options as desired.
|
||||
|
||||
Examples build commands below.
|
||||
|
||||
```bash
|
||||
# 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 image with GPU support
|
||||
docker build -t txtai --build-arg GPU=1 .
|
||||
|
||||
# Build minimal image with the base txtai components
|
||||
docker build -t txtai --build-arg COMPONENTS= .
|
||||
```
|
||||
|
||||
## Container image model caching
|
||||
|
||||
As mentioned previously, model caching is recommended to reduce container start times. The following commands demonstrate this. In all cases, it is assumed a config.yml file is present in the local directory with the desired configuration set.
|
||||
|
||||
### API
|
||||
This section builds an image that caches models and starts an API service. The config.yml file should be configured with the desired components to expose via the API.
|
||||
|
||||
The following is a sample config.yml file that creates an Embeddings API service.
|
||||
|
||||
```yaml
|
||||
# config.yml
|
||||
writable: true
|
||||
|
||||
embeddings:
|
||||
path: sentence-transformers/nli-mpnet-base-v2
|
||||
content: true
|
||||
```
|
||||
|
||||
The next section builds the image and starts an instance.
|
||||
|
||||
```bash
|
||||
# Get Dockerfile
|
||||
wget https://raw.githubusercontent.com/neuml/txtai/master/docker/api/Dockerfile
|
||||
|
||||
# CPU build
|
||||
docker build -t txtai-api .
|
||||
|
||||
# GPU build
|
||||
docker build -t txtai-api --build-arg BASE_IMAGE=neuml/txtai-gpu .
|
||||
|
||||
# Run
|
||||
docker run -p 8000:8000 --rm -it txtai-api
|
||||
```
|
||||
|
||||
### Service
|
||||
This section builds a scheduled workflow service. [More on scheduled workflows can be found here.](../workflow/schedule)
|
||||
|
||||
```bash
|
||||
# Get Dockerfile
|
||||
wget https://raw.githubusercontent.com/neuml/txtai/master/docker/service/Dockerfile
|
||||
|
||||
# CPU build
|
||||
docker build -t txtai-service .
|
||||
|
||||
# GPU build
|
||||
docker build -t txtai-service --build-arg BASE_IMAGE=neuml/txtai-gpu .
|
||||
|
||||
# Run
|
||||
docker run --rm -it txtai-service
|
||||
```
|
||||
|
||||
### Workflow
|
||||
This section builds a single run workflow. [Example workflows can be found here.](../examples/#workflows)
|
||||
|
||||
```bash
|
||||
# Get Dockerfile
|
||||
wget https://raw.githubusercontent.com/neuml/txtai/master/docker/workflow/Dockerfile
|
||||
|
||||
# CPU build
|
||||
docker build -t txtai-workflow .
|
||||
|
||||
# GPU build
|
||||
docker build -t txtai-workflow --build-arg BASE_IMAGE=neuml/txtai-gpu .
|
||||
|
||||
# Run
|
||||
docker run --rm -it txtai-workflow <workflow name> <workflow parameters>
|
||||
```
|
||||
|
||||
## Serverless Compute
|
||||
|
||||
One of the most powerful features of txtai is building YAML-configured applications with the "build once, run anywhere" approach. API instances and workflows can run locally, on a server, on a cluster or serverless.
|
||||
|
||||
Serverless instances of txtai are supported on frameworks such as [AWS Lambda](https://aws.amazon.com/lambda/), [Google Cloud Functions](https://cloud.google.com/functions), [Azure Cloud Functions](https://azure.microsoft.com/en-us/services/functions/) and [Kubernetes](https://kubernetes.io/) with [Knative](https://knative.dev/docs/).
|
||||
|
||||
### AWS Lambda
|
||||
|
||||
The following steps show a basic example of how to build a serverless API instance with [AWS SAM](https://github.com/aws/serverless-application-model).
|
||||
|
||||
- Create config.yml and template.yml
|
||||
|
||||
```yaml
|
||||
# config.yml
|
||||
writable: true
|
||||
|
||||
embeddings:
|
||||
path: sentence-transformers/nli-mpnet-base-v2
|
||||
content: true
|
||||
```
|
||||
|
||||
```yaml
|
||||
# template.yml
|
||||
Resources:
|
||||
txtai:
|
||||
Type: AWS::Serverless::Function
|
||||
Properties:
|
||||
PackageType: Image
|
||||
MemorySize: 3000
|
||||
Timeout: 20
|
||||
Events:
|
||||
Api:
|
||||
Type: Api
|
||||
Properties:
|
||||
Path: "/{proxy+}"
|
||||
Method: ANY
|
||||
Metadata:
|
||||
Dockerfile: Dockerfile
|
||||
DockerContext: ./
|
||||
DockerTag: api
|
||||
```
|
||||
|
||||
- Install [AWS SAM](https://pypi.org/project/aws-sam-cli/)
|
||||
|
||||
- Run following
|
||||
|
||||
```bash
|
||||
# Get Dockerfile and application
|
||||
wget https://raw.githubusercontent.com/neuml/txtai/master/docker/aws/api.py
|
||||
wget https://raw.githubusercontent.com/neuml/txtai/master/docker/aws/Dockerfile
|
||||
|
||||
# Build the docker image
|
||||
sam build
|
||||
|
||||
# Start API gateway and Lambda instance locally
|
||||
sam local start-api -p 8000 --warm-containers LAZY
|
||||
|
||||
# Verify instance running (should return 0)
|
||||
curl http://localhost:8080/count
|
||||
```
|
||||
|
||||
If successful, a local API instance is now running in a "serverless" fashion. This configuration can be deployed to AWS using SAM. [See this link for more information.](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-cli-command-reference-sam-deploy.html)
|
||||
|
||||
### Kubernetes with Knative
|
||||
|
||||
txtai scales with container orchestration systems. This can be self-hosted or with a cloud provider such as [Amazon Elastic Kubernetes Service](https://aws.amazon.com/eks/), [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine) and [Azure Kubernetes Service](https://azure.microsoft.com/en-us/services/kubernetes-service/). There are also other smaller providers with a managed Kubernetes offering.
|
||||
|
||||
A full example covering how to build a serverless txtai application on Kubernetes with Knative [can be found here](https://medium.com/neuml/serverless-vector-search-with-txtai-96f6163ab972).
|
||||
|
||||
## txtai.cloud
|
||||
|
||||
[txtai.cloud](https://txtai.cloud) is a planned effort that will offer an easy and secure way to run hosted txtai applications.
|
||||
@@ -0,0 +1,136 @@
|
||||
# ANN
|
||||
|
||||
Approximate Nearest Neighbor (ANN) index configuration for storing vector embeddings.
|
||||
|
||||
## backend
|
||||
```yaml
|
||||
backend: faiss|hnsw|annoy|ggml|numpy|torch|turbovec|pgvector|sqlite|custom
|
||||
```
|
||||
|
||||
Sets the ANN backend. Defaults to `faiss`. Additional backends are available via the [ann](../../../install/#ann) extras package. Set custom backends via setting this parameter to the fully resolvable class string.
|
||||
|
||||
Backend-specific settings are set with a corresponding configuration object having the same name as the backend (i.e. annoy, faiss, or hnsw). These are optional and set to defaults if omitted.
|
||||
|
||||
### faiss
|
||||
```yaml
|
||||
faiss:
|
||||
components: comma separated list of components - defaults to "IDMap,Flat" for small
|
||||
indices and "IVFx,Flat" for larger indexes where
|
||||
x = min(4 * sqrt(embeddings count), embeddings count / 39)
|
||||
automatically calculates number of IVF cells when omitted (supports "IVF,Flat")
|
||||
nprobe: search probe setting (int) - defaults to x/16 (as defined above)
|
||||
for larger indexes
|
||||
nflip: same as nprobe - only used with binary hash indexes
|
||||
quantize: store vectors with x-bit precision vs 32-bit (boolean|int)
|
||||
true sets 8-bit precision, false disables, int sets specified
|
||||
precision
|
||||
mmap: load as on-disk index (boolean) - trade query response time for a
|
||||
smaller RAM footprint, defaults to false
|
||||
sample: percent of data to use for model training (0.0 - 1.0)
|
||||
reduces indexing time for larger (>1M+ row) indexes, defaults to 1.0
|
||||
```
|
||||
|
||||
Faiss supports both floating point and binary indexes. Floating point indexes are the default. Binary indexes are used when indexing scalar-quantized datasets.
|
||||
|
||||
See the following Faiss documentation links for more information.
|
||||
|
||||
- [Guidelines for choosing an index](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index)
|
||||
- [Index configuration summary](https://github.com/facebookresearch/faiss/wiki/Faiss-indexes)
|
||||
- [Index Factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)
|
||||
- [Binary Indexes](https://github.com/facebookresearch/faiss/wiki/Binary-indexes)
|
||||
- [Search Tuning](https://github.com/facebookresearch/faiss/wiki/Faster-search)
|
||||
|
||||
Note: For macOS users, an existing bug in an upstream package restricts the number of processing threads to 1. This limitation is managed internally to prevent system crashes.
|
||||
|
||||
### hnsw
|
||||
```yaml
|
||||
hnsw:
|
||||
efconstruction: ef_construction param for init_index (int) - defaults to 200
|
||||
m: M param for init_index (int) - defaults to 16
|
||||
randomseed: random-seed param for init_index (int) - defaults to 100
|
||||
efsearch: ef search param (int) - defaults to None and not set
|
||||
```
|
||||
|
||||
See [Hnswlib documentation](https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md) for more information on these parameters.
|
||||
|
||||
### annoy
|
||||
```yaml
|
||||
annoy:
|
||||
ntrees: number of trees (int) - defaults to 10
|
||||
searchk: search_k search setting (int) - defaults to -1
|
||||
```
|
||||
|
||||
See [Annoy documentation](https://github.com/spotify/annoy#full-python-api) for more information on these parameters. Note that annoy indexes can not be modified after creation, upserts/deletes and other modifications are not supported.
|
||||
|
||||
### ggml
|
||||
```yaml
|
||||
ggml:
|
||||
gpu: enable GPU - defaults to True
|
||||
quantize: sets the tensor quantization - defaults to F32
|
||||
querysize: query buffer size - defaults to 64
|
||||
```
|
||||
|
||||
The [GGML](https://github.com/ggml-org/ggml) backend is a k-nearest neighbors backend. It stores tensors using GGML and [GGUF](https://huggingface.co/docs/hub/en/gguf). It supports GPU-enabled operations and supports quantization. GGML is the framework used by [llama.cpp](https://github.com/ggml-org/llama.cpp).
|
||||
|
||||
[See this](https://github.com/ggml-org/ggml/blob/master/include/ggml.h#L379) for a list of quantization types.
|
||||
|
||||
### numpy
|
||||
|
||||
The NumPy backend is a k-nearest neighbors backend. It's designed for simplicity and works well with smaller datasets that fit into memory.
|
||||
|
||||
```yaml
|
||||
numpy:
|
||||
safetensors: stores vectors using the safetensors format
|
||||
defaults to NumPy array storage
|
||||
```
|
||||
|
||||
### torch
|
||||
|
||||
The Torch backend is a k-nearest neighbors backend like NumPy. It supports GPU-enabled operations. It also has support for quantization which enables fitting larger arrays into GPU memory.
|
||||
|
||||
When quantization is enabled, vectors are _always_ stored in safetensors. _Note that macOS support for quantization is limited._
|
||||
|
||||
```yaml
|
||||
torch:
|
||||
safetensors: stores vectors using the safetensors format - defaults
|
||||
to NumPy array storage if quantization is disabled
|
||||
quantize:
|
||||
type: quantization type (fp4, nf4, int8)
|
||||
blocksize: quantization block size parameter
|
||||
```
|
||||
|
||||
### turbovec
|
||||
|
||||
The [turbovec](https://github.com/RyanCodrai/turbovec) backend is a k-nearest neighbors backend powered by the [TurboQuant algorithm](https://arxiv.org/abs/2504.19874).
|
||||
|
||||
```yaml
|
||||
turbovec:
|
||||
bitwidth: number of bits to store each vector dimension as. Supports 2, 3 or 4.
|
||||
|
||||
### pgvector
|
||||
```yaml
|
||||
pgvector:
|
||||
url: database url connection string, alternatively can be set via
|
||||
ANN_URL environment variable
|
||||
schema: database schema to store vectors - defaults to being
|
||||
determined by the database
|
||||
table: database table to store vectors - defaults to `vectors`
|
||||
precision: vector float precision (half or full) - defaults to `full`
|
||||
efconstruction: ef_construction param (int) - defaults to 200
|
||||
m: M param for init_index (int) - defaults to 16
|
||||
```
|
||||
|
||||
The pgvector backend stores embeddings in a Postgres database. See the [pgvector documentation](https://github.com/pgvector/pgvector-python?tab=readme-ov-file#sqlalchemy) for more information on these parameters. See the [SQLAlchemy](https://docs.sqlalchemy.org/en/20/core/engines.html#database-urls) documentation for more information on how to construct url connection strings.
|
||||
|
||||
### sqlite
|
||||
```yaml
|
||||
sqlite:
|
||||
quantize: store vectors with x-bit precision vs 32-bit (boolean|int)
|
||||
true sets 8-bit precision, false disables, int sets specified
|
||||
precision
|
||||
table: database table to store vectors - defaults to `vectors`
|
||||
```
|
||||
|
||||
The SQLite backend stores embeddings in a SQLite database using [sqlite-vec](https://github.com/asg017/sqlite-vec). This backend supports 1-bit and 8-bit quantization at the storage level.
|
||||
|
||||
See [this note](https://alexgarcia.xyz/sqlite-vec/python.html#macos-blocks-sqlite-extensions-by-default) on how to run this ANN on MacOS.
|
||||
@@ -0,0 +1,110 @@
|
||||
# Cloud
|
||||
|
||||
The following describes parameters used to sync indexes with cloud storage. Cloud object storage, the [Hugging Face Hub](https://huggingface.co/models) and custom providers are all supported.
|
||||
|
||||
Parameters are set via the [embeddings.load](../../methods/#txtai.embeddings.base.Embeddings.load) and [embeddings.save](../../methods/#txtai.embeddings.base.Embeddings.save) methods.
|
||||
|
||||
## provider
|
||||
```yaml
|
||||
provider: string
|
||||
```
|
||||
|
||||
Cloud provider. Can be one of the following:
|
||||
|
||||
- Cloud object storage. Set to one of these [providers](https://libcloud.readthedocs.io/en/stable/storage/supported_providers.html). Use the text shown in the `Provider Constant` column as lower case.
|
||||
|
||||
- Hugging Face Hub. Set to `huggingface-hub`.
|
||||
|
||||
- Custom providers. Set to the full class path of the custom provider.
|
||||
|
||||
## container
|
||||
```yaml
|
||||
container: string
|
||||
```
|
||||
|
||||
Container/bucket/directory/repository name. Embeddings will be stored in the container with the filename specified by the `path` configuration.
|
||||
|
||||
## Cloud object storage configuration
|
||||
|
||||
In addition to the above common configuration, the cloud object storage provider has the following additional configuration parameters. Note that some cloud providers do not need any of these parameters and can use implicit authentication with service accounts.
|
||||
|
||||
See the [libcloud documentation](https://libcloud.readthedocs.io/en/stable/apidocs/libcloud.common.html#module-libcloud.common.base) for more information on these parameters.
|
||||
|
||||
### key
|
||||
```yaml
|
||||
key: string
|
||||
```
|
||||
|
||||
Provider-specific access key. Can also be set via `ACCESS_KEY` environment variable. Ensure the configuration file is secured if added to the file. When using implicit authentication, set this to a value such as 'using-implicit-auth'.
|
||||
|
||||
### secret
|
||||
```yaml
|
||||
secret: string
|
||||
```
|
||||
|
||||
Provider-specific access secret. Can also be set via `ACCESS_SECRET` environment variable. Ensure the configuration file is secured if added to the file. When using implicit authentication, this option is not required.
|
||||
|
||||
### prefix
|
||||
```yaml
|
||||
prefix: string
|
||||
```
|
||||
|
||||
Optional object prefix. Object storage doesn't have the concept of a directory but a prefix is similar. For example, a prefix could be `base/dir`. This helps with organizing data in an object storage bucket.
|
||||
|
||||
More can be found at the following links.
|
||||
|
||||
- [Organizing objects using prefixes](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-prefixes.html)
|
||||
- [libcloud container method documentation](https://libcloud.readthedocs.io/en/stable/storage/api.html#libcloud.storage.base.StorageDriver.iterate_container_objects)
|
||||
|
||||
### host
|
||||
```yaml
|
||||
host: string
|
||||
```
|
||||
|
||||
Optional server host name. Set when using a local cloud storage server.
|
||||
|
||||
### port
|
||||
```yaml
|
||||
port: int
|
||||
```
|
||||
|
||||
Optional server port. Set when using a local cloud storage server.
|
||||
|
||||
### token
|
||||
```yaml
|
||||
token: string
|
||||
```
|
||||
|
||||
Optional temporary session token
|
||||
|
||||
### region
|
||||
```yaml
|
||||
region: string
|
||||
```
|
||||
|
||||
Optional parameter to specify the storage region, provider-specific.
|
||||
|
||||
## Hugging Face Hub configuration
|
||||
|
||||
The huggingface-hub provider supports the following additional configuration parameters. More on these parameters can be found in the [Hugging Face Hub's documentation](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/overview).
|
||||
|
||||
### revision
|
||||
```yaml
|
||||
revision: string
|
||||
```
|
||||
|
||||
Optional Git revision id which can be a branch name, a tag, or a commit hash
|
||||
|
||||
### cache
|
||||
```yaml
|
||||
cache: string
|
||||
```
|
||||
|
||||
Path to the folder where cached files are stored
|
||||
|
||||
### token
|
||||
```yaml
|
||||
token: string|boolean
|
||||
```
|
||||
|
||||
Token to be used for the download. If set to True, the token will be read from the Hugging Face config folder.
|
||||
@@ -0,0 +1,83 @@
|
||||
# Database
|
||||
|
||||
Databases store metadata, text and binary content.
|
||||
|
||||
## content
|
||||
```yaml
|
||||
content: boolean|sqlite|duckdb|client|url|custom
|
||||
```
|
||||
|
||||
Enables content storage. When true, the default storage engine, `sqlite` will be used to save metadata.
|
||||
|
||||
Client-server connections are supported with either `client` or a full connection URL. When set to `client`, the CLIENT_URL environment variable must be set to the full connection URL. See the [SQLAlchemy](https://docs.sqlalchemy.org/en/20/core/engines.html#database-urls) documentation for more information on how to construct connection strings for client-server databases.
|
||||
|
||||
Add custom storage engines via setting this parameter to the fully resolvable class string.
|
||||
|
||||
Content storage specific settings are set with a corresponding configuration object having the same name as the content storage engine (i.e. duckdb or sqlite). These are optional and set to defaults if omitted.
|
||||
|
||||
### client
|
||||
```yaml
|
||||
schema: default database schema for the session - defaults to being
|
||||
determined by the database
|
||||
```
|
||||
|
||||
Additional settings for client-server databases. Also supported when the `content=url`.
|
||||
|
||||
### sqlite
|
||||
```yaml
|
||||
sqlite:
|
||||
wal: enable write-ahead logging - allows concurrent read/write operations,
|
||||
defaults to false
|
||||
```
|
||||
|
||||
Additional settings for SQLite.
|
||||
|
||||
## objects
|
||||
```yaml
|
||||
objects: boolean|image|pickle
|
||||
```
|
||||
|
||||
Enables object storage. Supports storing binary content. Requires content storage to also be enabled.
|
||||
|
||||
Object encoding options are:
|
||||
|
||||
- `standard`: Default encoder when boolean set. Encodes and decodes objects as byte arrays.
|
||||
- `image`: Image encoder. Encodes and decodes objects as image objects.
|
||||
- `pickle`: Pickle encoder. Encodes and decodes objects with the pickle module. Supports arbitrary objects.
|
||||
|
||||
## functions
|
||||
```yaml
|
||||
functions: list
|
||||
```
|
||||
|
||||
List of functions with user-defined SQL functions. Each list element must be one of the following:
|
||||
|
||||
- function
|
||||
- callable object
|
||||
- dict with fields for name, argcount, function and deterministic
|
||||
|
||||
[An example can be found here](../../query#custom-sql-functions).
|
||||
|
||||
## expressions
|
||||
```yaml
|
||||
expressions: list
|
||||
```
|
||||
|
||||
List of expression shortcuts. Each list element must be a dict with the following fields.
|
||||
|
||||
- `name`: name of the expression
|
||||
- `expression`: SQL expression, defaults to `name` when empty
|
||||
- `index`: if this expression should have a database index, defaults to False when not provided
|
||||
|
||||
The expression can be a json data column, sql function or anything that can be run as a SQL snippet.
|
||||
|
||||
## query
|
||||
```yaml
|
||||
query:
|
||||
path: sets the path for the query model - this can be any model on the
|
||||
Hugging Face Model Hub or a local file path.
|
||||
prefix: text prefix to prepend to all inputs
|
||||
maxlength: maximum generated sequence length
|
||||
```
|
||||
|
||||
Query translation model. Translates natural language queries to txtai compatible SQL statements.
|
||||
@@ -0,0 +1,83 @@
|
||||
# General
|
||||
|
||||
General configuration options.
|
||||
|
||||
## keyword
|
||||
```yaml
|
||||
keyword: boolean|string
|
||||
```
|
||||
|
||||
Enables sparse keyword indexing for this embeddings.
|
||||
|
||||
When set to a boolean, this parameter creates a BM25 index for full text search. When set to a string, it expects a [keyword method](../scoring#method).
|
||||
|
||||
It also implicitly disables the [defaults](#defaults) setting for vector search.
|
||||
|
||||
## sparse
|
||||
```yaml
|
||||
sparse: boolean|path
|
||||
```
|
||||
|
||||
Enables sparse vector indexing for this embeddings.
|
||||
|
||||
When set to `True`, this parameter creates a sparse vector index using the [default sparse index model](https://huggingface.co/prithivida/Splade_PP_en_v2). When set to a string, it expects a local or Hugging Face model path.
|
||||
|
||||
It also implicitly disables the [defaults](#defaults) setting for vector search.
|
||||
|
||||
## dense
|
||||
```yaml
|
||||
dense: boolean|string
|
||||
```
|
||||
|
||||
Alias for the [vector model path](../vectors/#path). When set to `True`, the [default transformers vector model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) is used.
|
||||
|
||||
## hybrid
|
||||
```yaml
|
||||
hybrid: boolean
|
||||
```
|
||||
|
||||
Enables hybrid (sparse + dense) indexing for this embeddings.
|
||||
|
||||
When enabled, this parameter creates a BM25 index for full text search. It has no effect on the [defaults](#defaults) or [path](../vectors/#path) settings.
|
||||
|
||||
## defaults
|
||||
```yaml
|
||||
defaults: boolean
|
||||
```
|
||||
|
||||
Uses default vector model path when enabled (default setting is True) and `path` is not provided. See [this link](../) for an example.
|
||||
|
||||
## indexes
|
||||
```yaml
|
||||
indexes: dict
|
||||
```
|
||||
|
||||
Key value pairs defining subindexes for this embeddings. Each key is the index name and the value is the full configuration. This configuration can use any of the available configurations in a standard embeddings instance.
|
||||
|
||||
## autoid
|
||||
```yaml
|
||||
format: int|uuid function
|
||||
```
|
||||
|
||||
Sets the auto id generation method. When this is not set, an autogenerated numeric sequence is used. This also supports [UUID generation functions](https://docs.python.org/3/library/uuid.html#uuid.uuid1). For example, setting this value to `uuid4` will generate random UUIDs. Setting this to `uuid5` will generate deterministic UUIDs for each input data row.
|
||||
|
||||
## columns
|
||||
```yaml
|
||||
columns:
|
||||
text: name of the text column
|
||||
object: name of the object column
|
||||
store: limit json data fields to this list of columns
|
||||
```
|
||||
|
||||
Sets the `text` and `object` column names. Defaults to `text` and `object` if not provided.
|
||||
|
||||
`store` sets a list of columns to store in the JSON data field. When this isn't provided, all columns are stored (default). When `store` is set to `None`, no JSON columns are stored. This is useful is a field is only needed at indexing time but not search time.
|
||||
|
||||
## format
|
||||
```yaml
|
||||
format: json|pickle
|
||||
```
|
||||
|
||||
Sets the configuration storage format. Defaults to `json`.
|
||||
|
||||
Note that `pickle` configuration is deprecated and will raise an error with the default settings. Its only kept for reading legacy indexes and requires setting the `ALLOW_PICKLE` environment variable. Only enable for local and/or trusted sources.
|
||||
@@ -0,0 +1,80 @@
|
||||
# Graph
|
||||
|
||||
Enable graph storage via the `graph` parameter. This component requires the [graph](../../../install/#graph) extras package.
|
||||
|
||||
When enabled, a graph network is built using the embeddings index. Graph nodes are synced with each embeddings index operation (index/upsert/delete). Graph edges are created using the embeddings index upon completion of each index/upsert/delete embeddings index call.
|
||||
|
||||
## backend
|
||||
```yaml
|
||||
backend: networkx|rdbms|custom
|
||||
```
|
||||
|
||||
Sets the graph backend. Defaults to `networkx`.
|
||||
|
||||
Add custom graph storage engines via setting this parameter to the fully resolvable class string.
|
||||
|
||||
The `rdbms` backend has the following additional settings.
|
||||
|
||||
### rdbms
|
||||
```yaml
|
||||
url: database url connection string, alternatively can be set via the
|
||||
GRAPH_URL environment variable
|
||||
schema: database schema to store graph - defaults to being
|
||||
determined by the database
|
||||
nodes: table to store node data, defaults to `nodes`
|
||||
edges: table to store edge data, defaults to `edges`
|
||||
```
|
||||
|
||||
## batchsize
|
||||
```yaml
|
||||
batchsize: int
|
||||
```
|
||||
|
||||
Batch query size, used to query embeddings index - defaults to 256.
|
||||
|
||||
## limit
|
||||
```yaml
|
||||
limit: int
|
||||
```
|
||||
|
||||
Maximum number of results to return per embeddings query - defaults to 15.
|
||||
|
||||
## minscore
|
||||
```yaml
|
||||
minscore: float
|
||||
```
|
||||
|
||||
Minimum score required to consider embeddings query matches - defaults to 0.1.
|
||||
|
||||
## approximate
|
||||
```yaml
|
||||
approximate: boolean
|
||||
```
|
||||
|
||||
When true, queries only run for nodes without edges - defaults to true.
|
||||
|
||||
## topics
|
||||
```yaml
|
||||
topics:
|
||||
algorithm: community detection algorithm (string), options are
|
||||
louvain (default), greedy, lpa
|
||||
level: controls number of topics (string), options are best (default) or first
|
||||
resolution: controls number of topics (int), larger values create more
|
||||
topics (int), defaults to 100
|
||||
labels: scoring index method used to build topic labels (string)
|
||||
options are bm25 (default), tfidf, sif
|
||||
terms: number of frequent terms to use for topic labels (int), defaults to 4
|
||||
stopwords: optional list of stop words to exclude from topic labels
|
||||
categories: optional list of categories used to group topics, allows
|
||||
granular topics with broad categories grouping topics
|
||||
```
|
||||
|
||||
Enables topic modeling. Defaults are tuned so that in most cases these values don't need to be changed (except for categories). These parameters are available for advanced use cases where one wants full control over the community detection process.
|
||||
|
||||
## copyattributes
|
||||
```yaml
|
||||
copyattributes: boolean|list
|
||||
```
|
||||
|
||||
Copy these attributes from input dictionaries in the `insert` method. If this is set to `True`, all attributes are copied. Otherwise, only the
|
||||
attributes specified in this list are copied to the graph as attributes.
|
||||
@@ -0,0 +1,51 @@
|
||||
# Configuration
|
||||
|
||||
The following describes available embeddings configuration. These parameters are set in the [Embeddings constructor](../methods#txtai.embeddings.base.Embeddings.__init__) via either the `config` parameter or as keyword arguments.
|
||||
|
||||
Configuration is designed to be optional and set only when needed. Out of the box, sensible defaults are picked to get up and running fast. For example:
|
||||
|
||||
```python
|
||||
from txtai import Embeddings
|
||||
|
||||
embeddings = Embeddings()
|
||||
```
|
||||
|
||||
Creates a new embeddings instance, using [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) as the vector model, [Faiss](https://faiss.ai/) as the ANN index backend and content disabled.
|
||||
|
||||
```python
|
||||
from txtai import Embeddings
|
||||
|
||||
embeddings = Embeddings(content=True)
|
||||
```
|
||||
|
||||
Is the same as above except it adds in [SQLite](https://www.sqlite.org/index.html) for content storage.
|
||||
|
||||
The following sections link to all the available configuration options.
|
||||
|
||||
## [ANN](./ann)
|
||||
|
||||
The default vector index backend is Faiss.
|
||||
|
||||
## [Cloud](./cloud)
|
||||
|
||||
Embeddings databases can optionally be synced with cloud storage.
|
||||
|
||||
## [Database](./database)
|
||||
|
||||
Content storage is disabled by default. When enabled, SQLite is the default storage engine.
|
||||
|
||||
## [General](./general)
|
||||
|
||||
General configuration that doesn't fit elsewhere.
|
||||
|
||||
## [Graph](./graph)
|
||||
|
||||
An accomplying graph index can be created with an embeddings database. This enables topic modeling, path traversal and more. [NetworkX](https://github.com/networkx/networkx) is the default graph index.
|
||||
|
||||
## [Scoring](./scoring)
|
||||
|
||||
Sparse keyword indexing and word vectors term weighting.
|
||||
|
||||
## [Vectors](./vectors)
|
||||
|
||||
Vector search is enabled by converting text and other binary data into embeddings vectors. These vectors are then stored in an ANN index. The vector model is optional and a default model is used when not provided.
|
||||
@@ -0,0 +1,109 @@
|
||||
# Scoring
|
||||
|
||||
Enable scoring support via the `scoring` parameter.
|
||||
|
||||
This scoring instance can serve two purposes, depending on the settings.
|
||||
|
||||
One use case is building sparse/keyword indexes. This occurs when the `terms` parameter is set to `True`.
|
||||
|
||||
The other use case is with word vector term weighting. This feature has been available since the initial version but isn't quite as common anymore.
|
||||
|
||||
The following covers the available options.
|
||||
|
||||
## method
|
||||
```yaml
|
||||
method: bm25|tfidf|sif|pgtext|sparse|custom
|
||||
```
|
||||
|
||||
Sets the scoring method. Add custom scoring via setting this parameter to the fully resolvable class string.
|
||||
|
||||
### pgtext
|
||||
```yaml
|
||||
schema: database schema to store keyword index - defaults to being
|
||||
determined by the database
|
||||
```
|
||||
|
||||
Additional settings for Postgres full-text keyword indexes.
|
||||
|
||||
### sparse
|
||||
```yaml
|
||||
path: sparse vector model path
|
||||
vectormethod: vector embeddings method
|
||||
vectornormalize: enable vector embeddings normalization (boolean)
|
||||
gpu: boolean|int|string|device
|
||||
normalize: enable score normalization (boolean|float|string|dict)
|
||||
batch: Sets the transform batch size
|
||||
encodebatch: Sets the encode batch size
|
||||
vectors: additional model init args
|
||||
encodeargs: additional encode() args
|
||||
backend: ivfsparse|pgsparse
|
||||
```
|
||||
|
||||
Sparse vector scoring options. The sparse scoring instance combines a sparse vector model with a sparse approximate nearest neighbor index (ANN). This method supports both vector normalization and score normalization.
|
||||
|
||||
Vector normalization normalizes all vectors to have a magnitude of 1. By extension, all generated scores will be 0 to 1.
|
||||
|
||||
Score normalization scales the output between 0 and 1. This setting supports:
|
||||
|
||||
- `True` for default scale normalization
|
||||
- `float` normalize using this as the scale factor
|
||||
- `"bayes"` for Bayesian normalization using dynamic candidate score statistics
|
||||
- `{method: "bayes", alpha: 1.0, beta: null}` for Bayesian normalization with optional custom parameters
|
||||
|
||||
#### ivfsparse
|
||||
```yaml
|
||||
ivfsparse:
|
||||
sample: percent of data to use for model training (0.0 - 1.0)
|
||||
nfeatures: top n features to use for model training (int)
|
||||
nlist: desired number of clusters (int)
|
||||
nprobe: search probe setting (int)
|
||||
minpoints: minimum number of points for a cluster (int)
|
||||
```
|
||||
|
||||
Inverted file (IVF) index with flat vector file storage and sparse array support.
|
||||
|
||||
#### pgsparse
|
||||
|
||||
Sparse ANN backed by Postgres. Supports same options as the [pgvector](../ann/#pgvector) ANN.
|
||||
|
||||
## terms
|
||||
```yaml
|
||||
terms: boolean|dict
|
||||
```
|
||||
|
||||
Enables term frequency sparse arrays for a scoring instance. This is the backend for sparse keyword indexes.
|
||||
|
||||
Supports a `dict` with the parameters `cachelimit` and `cutoff`.
|
||||
|
||||
`cachelimit` is the maximum amount of resident memory in bytes to use during indexing before flushing to disk. This parameter is an `int`.
|
||||
|
||||
`cutoff` is used during search to determine what constitutes a common term. This parameter is a `float`, i.e. 0.1 for a cutoff of 10%.
|
||||
|
||||
When `terms` is set to `True`, default parameters are used for the `cachelimit` and `cutoff`. Normally, these defaults are sufficient.
|
||||
|
||||
## normalize
|
||||
```yaml
|
||||
normalize: boolean|str|dict
|
||||
```
|
||||
|
||||
Enables normalized scoring (ranging from 0 to 1). This setting supports:
|
||||
|
||||
- `True` for standard score normalization
|
||||
- `"bayes"` | `"bb25"` for Bayesian normalization using dynamic candidate score statistics
|
||||
- `{method: "bayes", alpha: 1.0, beta: null}` for Bayesian normalization with optional custom parameters
|
||||
|
||||
When standard normalization is enabled, statistics from the index are used to calculate normalized scores.
|
||||
When Bayesian/BB25 normalization is enabled, it uses positive-score candidates, dynamic `beta=median(scores)`, adaptive
|
||||
`alpha_eff=alpha/std(scores)` and a sigmoid transform (likelihood-only variant with flat prior) to map scores to `[0, 1]`.
|
||||
|
||||
Bayesian normalization references:
|
||||
|
||||
- [https://github.com/instructkr/bb25](https://github.com/instructkr/bb25)
|
||||
- [https://github.com/cognica-io/bayesian-bm25](https://github.com/cognica-io/bayesian-bm25)
|
||||
|
||||
## tokenizer
|
||||
```yaml
|
||||
tokenizer: dict
|
||||
```
|
||||
|
||||
Set tokenization rules. Passes these arguments to the underlying [Tokenization pipeline](../../../pipeline/data/tokenizer#txtai.pipeline.Tokenizer.__init__).
|
||||
@@ -0,0 +1,161 @@
|
||||
# Vectors
|
||||
|
||||
The following covers available vector model configuration options.
|
||||
|
||||
## path
|
||||
```yaml
|
||||
path: string
|
||||
```
|
||||
|
||||
Sets the path for a vectors model. When using a transformers/sentence-transformers model, this can be any model on the
|
||||
[Hugging Face Hub](https://huggingface.co/models) or a local file path. Otherwise, it must be a local file path to a word embeddings model.
|
||||
|
||||
## method
|
||||
```yaml
|
||||
method: transformers|sentence-transformers|llama.cpp|litellm|model2vec|external|
|
||||
words
|
||||
```
|
||||
|
||||
Embeddings method to use. If the method is not provided, it is inferred using the `path`.
|
||||
|
||||
`sentence-transformers`, `llama.cpp`, `litellm`, `model2vec` and `words` require the [vectors](../../../install/#vectors) extras package to be installed.
|
||||
|
||||
### transformers
|
||||
|
||||
Builds embeddings using a transformers model. While this can be any transformers model, it works best with
|
||||
[models trained](https://huggingface.co/models?pipeline_tag=sentence-similarity) to build embeddings.
|
||||
|
||||
`mean`, `cls`, `last` and `late` pooling are supported and automatically inferred from the model. The pooling method can be overwritten by changing the method
|
||||
from `transformers` to `meanpooling`, `clspooling`, `lastpooling` or `latepooling` respectively.
|
||||
|
||||
Setting `maxlength` to `True` enables truncating inputs to the `max_seq_length`. Setting `maxlength` to an integer will truncate inputs to that value. When omitted (default), the `maxlength` will be set to either the model or tokenizer maxlength.
|
||||
|
||||
Supports loading ONNX models directly. [See this section for more](../../../examples/#model-training).
|
||||
|
||||
### sentence-transformers
|
||||
|
||||
Same as transformers but loads models with the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) library.
|
||||
|
||||
### llama.cpp
|
||||
|
||||
Builds embeddings using a [llama.cpp](https://github.com/abetlen/llama-cpp-python) model. Supports both local and remote GGUF paths on the HF Hub.
|
||||
|
||||
### litellm
|
||||
|
||||
Builds embeddings using a LiteLLM model. See the [LiteLLM documentation](https://litellm.vercel.app/docs/providers) for the options available with LiteLLM models.
|
||||
|
||||
### model2vec
|
||||
|
||||
Builds embeddings using a [Model2Vec](https://github.com/MinishLab/model2vec) model. Model2Vec is a knowledge-distilled version of a transformers model with static vectors.
|
||||
|
||||
### words
|
||||
|
||||
Builds embeddings using a word embeddings model and static vectors. While Transformers models are preferred in most cases, this method can be useful for low resource and historical languages where there isn't much linguistic data available.
|
||||
|
||||
#### pca
|
||||
```yaml
|
||||
pca: int
|
||||
```
|
||||
|
||||
Removes _n_ principal components from generated embeddings. When enabled, a TruncatedSVD model is built to help with dimensionality reduction. After pooling of vectors creates a single embedding, this method is applied.
|
||||
|
||||
### external
|
||||
|
||||
Embeddings are created via an external model or API. Requires setting the [transform](#transform) parameter to a function that translates data into embeddings.
|
||||
|
||||
#### transform
|
||||
```yaml
|
||||
transform: function
|
||||
```
|
||||
|
||||
When method is `external`, this function transforms input content into embeddings. The input to this function is a list of data. This method must return either a numpy array or list of numpy arrays.
|
||||
|
||||
## gpu
|
||||
```yaml
|
||||
gpu: boolean|int|string|device
|
||||
```
|
||||
|
||||
Set the target device. Supports true/false, device id, device string and torch device instance. This is automatically derived if omitted.
|
||||
|
||||
The `sentence-transformers` method supports encoding with multiple GPUs. This can be enabled by setting the gpu parameter to `all`.
|
||||
|
||||
## batch
|
||||
```yaml
|
||||
batch: int
|
||||
```
|
||||
|
||||
Sets the transform batch size. This parameter controls how input streams are chunked and vectorized.
|
||||
|
||||
## encodebatch
|
||||
```yaml
|
||||
encodebatch: int
|
||||
```
|
||||
|
||||
Sets the encode batch size. This parameter controls the underlying vector model batch size. This often corresponds to a GPU batch size, which controls GPU memory usage.
|
||||
|
||||
## dimensionality
|
||||
```yaml
|
||||
dimensionality: int
|
||||
```
|
||||
|
||||
Enables truncation of vectors to this dimensionality. This is only useful for models trained to store more important information in earlier dimensions such as [Matryoshka Representation Learning (MRL)](https://huggingface.co/blog/matryoshka).
|
||||
|
||||
## quantize
|
||||
```yaml
|
||||
quantize: int|boolean
|
||||
```
|
||||
|
||||
Enables scalar vector quantization at the specified precision. Supports 1-bit through 8-bit quantization. Scalar quantization transforms continuous floating point values to discrete unsigned integers. The `faiss`, `pgvector`, `numpy` and `torch` ANN backends support storing these vectors.
|
||||
|
||||
This parameter supports booleans for backwards compatability. When set to true/false, this flag sets [faiss.quantize](../ann/#faiss).
|
||||
|
||||
In addition to vector-level quantization, some ANN backends have the ability to quantize vectors at the storage layer. See the [ANN](../ann) configuration options for more.
|
||||
|
||||
## instructions
|
||||
```yaml
|
||||
instructions:
|
||||
query: prefix for queries
|
||||
data: prefix for indexing
|
||||
```
|
||||
|
||||
Instruction-based models use prefixes to modify how embeddings are computed. This is especially useful with asymmetric search, which is when the query and indexed data are of vastly different lengths. In other words, short queries with long documents.
|
||||
|
||||
`txtai` automatically loads prompts stored in `config_sentence_transformers.json` except if this parameter is set. For some older models such as [E5-base](https://huggingface.co/intfloat/e5-base), instructions still need to be provided via this parameter.
|
||||
|
||||
## models
|
||||
```yaml
|
||||
models: dict
|
||||
```
|
||||
|
||||
Loads and stores vector models in this cache. This is primarily used with subindexes but can be set on any embeddings instance. This prevents the same model from being loaded multiple times when working with multiple embeddings instances.
|
||||
|
||||
## tokenize
|
||||
```yaml
|
||||
tokenize: boolean
|
||||
```
|
||||
|
||||
Enables string tokenization (defaults to false). This method applies tokenization rules that only work with English language text. It's not recommended for use with recent vector models.
|
||||
|
||||
## vectors
|
||||
```yaml
|
||||
vectors: dict
|
||||
```
|
||||
|
||||
Passes these additional parameters to the underlying vector model.
|
||||
|
||||
### muvera
|
||||
```yaml
|
||||
muvera:
|
||||
repetitions: defaults 20
|
||||
hashes: defaults to 5
|
||||
projection: defaults 16
|
||||
```
|
||||
|
||||
Settings to control the size of MUVERA fixed dimensional outputs. Default is 20 * 2^5 * 16 = 10,240 dimensions.
|
||||
|
||||
### trust_remote_code
|
||||
```yaml
|
||||
trust_remote_code: boolean
|
||||
```
|
||||
|
||||
Parameter for trusting the code from Hugging Face models with custom implementations.
|
||||
@@ -0,0 +1,59 @@
|
||||
# Index format
|
||||
|
||||
|
||||
|
||||
|
||||
This section documents the txtai index format. Each component is designed to ensure open access to the underlying data in a programmatic and platform independent way
|
||||
|
||||
If an underlying library has an index format, that is used. Otherwise, txtai persists content with [MessagePack](https://msgpack.org/index.html) serialization.
|
||||
|
||||
To learn more about how these components work together, read the [Index Guide](../indexing) and [Query Guide](../query).
|
||||
|
||||
## ANN
|
||||
|
||||
Approximate Nearest Neighbor (ANN) index configuration for storing vector embeddings.
|
||||
|
||||
| Component | Storage Format |
|
||||
| ------------------------------------------------------------- | ---------------------------------------------------------------------------- |
|
||||
| [Faiss](https://github.com/facebookresearch/faiss) | Local file format provided by library |
|
||||
| [Hnswlib](https://github.com/nmslib/hnswlib) | Local file format provided by library |
|
||||
| [Annoy](https://github.com/spotify/annoy) | Local file format provided by library |
|
||||
| [NumPy](https://github.com/numpy/numpy) | Local NumPy array files via np.save / np.load |
|
||||
| [Postgres via pgvector](https://github.com/pgvector/pgvector) | Vector tables in a Postgres database |
|
||||
|
||||
## Core
|
||||
|
||||
Core embeddings index files.
|
||||
|
||||
| Component | Storage Format |
|
||||
| ------------------------------------------------------------- | ---------------------------------------------------------------------------- |
|
||||
| [Configuration](https://www.json.org/) | Embeddings index configuration stored as JSON |
|
||||
| [Index Ids](https://msgpack.org/index.html) | Embeddings index ids serialized with MessagePack. Only enabled when when content storage (database) is disabled. |
|
||||
|
||||
## Database
|
||||
|
||||
Databases store metadata, text and binary content.
|
||||
|
||||
| Component | Storage Format |
|
||||
| ------------------------------------------------------------- | ---------------------------------------------------------------------------- |
|
||||
| [SQLite](https://www.sqlite.org/) | Local database files with SQLite |
|
||||
| [DuckDB](https://github.com/duckdb/duckdb) | Local database files with DuckDB |
|
||||
| [Postgres](https://www.postgresql.org/) | Postgres relational database via [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy). Supports additional databases via this library. |
|
||||
|
||||
## Graph
|
||||
|
||||
Graph nodes and edges for an embeddings index
|
||||
|
||||
| Component | Storage Format |
|
||||
| ------------------------------------------------------------- | ----------------------------------------------------------------------------- |
|
||||
| [NetworkX](https://github.com/networkx/networkx) | Nodes and edges exported to local file serialized with MessagePack |
|
||||
| [Postgres](https://github.com/aplbrain/grand) | Nodes and edges stored in a Postgres database. Supports additional databases. |
|
||||
|
||||
## Scoring
|
||||
|
||||
Sparse/keyword indexing
|
||||
|
||||
| Component | Storage Format |
|
||||
| ------------------------------------------------------------- | ----------------------------------------------------------------------------- |
|
||||
| [Local index](https://www.sqlite.org/) | Metadata serialized with MessagePack. Terms stored in SQLite. |
|
||||
| [Postgres](https://www.postgresql.org/docs/current/textsearch.html) | Text indexed with Postgres Full Text Search (FTS) |
|
||||
@@ -0,0 +1,122 @@
|
||||
# Embeddings
|
||||
|
||||
|
||||
|
||||
|
||||
Embeddings databases are the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts will produce similar vectors. Indexes both large and small are built with these vectors. The indexes are used to find results that have the same meaning, not necessarily the same keywords.
|
||||
|
||||
The following code snippet shows how to build and search an embeddings index.
|
||||
|
||||
```python
|
||||
from txtai import Embeddings
|
||||
|
||||
# Create embeddings model, backed by sentence-transformers & transformers
|
||||
embeddings = Embeddings(path="sentence-transformers/nli-mpnet-base-v2")
|
||||
|
||||
data = [
|
||||
"US tops 5 million confirmed virus cases",
|
||||
"Canada's last fully intact ice shelf has suddenly collapsed, " +
|
||||
"forming a Manhattan-sized iceberg",
|
||||
"Beijing mobilises invasion craft along coast as Taiwan tensions escalate",
|
||||
"The National Park Service warns against sacrificing slower friends " +
|
||||
"in a bear attack",
|
||||
"Maine man wins $1M from $25 lottery ticket",
|
||||
"Make huge profits without work, earn up to $100,000 a day"
|
||||
]
|
||||
|
||||
# Index the list of text
|
||||
embeddings.index(data)
|
||||
|
||||
print(f"{'Query':20} Best Match")
|
||||
print("-" * 50)
|
||||
|
||||
# Run an embeddings search for each query
|
||||
for query in ("feel good story", "climate change", "public health story", "war",
|
||||
"wildlife", "asia", "lucky", "dishonest junk"):
|
||||
# Extract uid of first result
|
||||
# search result format: (uid, score)
|
||||
uid = embeddings.search(query, 1)[0][0]
|
||||
|
||||
# Print text
|
||||
print(f"{query:20} {data[uid]}")
|
||||
```
|
||||
|
||||
## Build
|
||||
|
||||
An embeddings instance is [configuration-driven](configuration) based on what is passed in the constructor. Vectors are stored with the option to also [store content](configuration/database#content). Content storage enables additional filtering and data retrieval options.
|
||||
|
||||
The example above sets a specific embeddings vector model via the [path](configuration/vectors/#path) parameter. An embeddings instance with no configuration can also be created.
|
||||
|
||||
```python
|
||||
embeddings = Embeddings()
|
||||
```
|
||||
|
||||
In this case, when loading and searching for data, the [default transformers vector model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) is used to vectorize data. See the [model guide](../models) for current model recommentations.
|
||||
|
||||
## Index
|
||||
|
||||
After creating a new embeddings instance, the next step is adding data to it.
|
||||
|
||||
```python
|
||||
embeddings.index(rows)
|
||||
```
|
||||
|
||||
The index method takes an iterable and supports the following formats for each element.
|
||||
|
||||
- `(id, data, tags)` - default processing format
|
||||
|
||||
| Element | Description |
|
||||
| ----------- | ------------------------------------------------------------- |
|
||||
| id | unique record id |
|
||||
| data | input data to index, can be text, a dictionary or object |
|
||||
| tags | optional tags string, used to mark/label data as it's indexed |
|
||||
|
||||
- `(id, data)`
|
||||
|
||||
Same as above but without tags.
|
||||
|
||||
- `data`
|
||||
|
||||
Single element to index. In this case, unique id's will automatically be generated. Note that for generated id's, [upsert](methods/#txtai.embeddings.base.Embeddings.upsert) and [delete](methods/#txtai.embeddings.base.Embeddings.delete) calls require a separate search to get the target ids.
|
||||
|
||||
When the data field is a dictionary, text is passed via the `text` key, binary objects via the `object` key. Note that [content](configuration/database#content) must be enabled to store metadata and [objects](configuration/database#objects) to store binary object data. The `id` and `tags` keys will be extracted, if provided.
|
||||
|
||||
The input iterable can be a list or generator. [Generators](https://wiki.python.org/moin/Generators) help with indexing very large datasets as only portions of the data is in memory at any given time.
|
||||
|
||||
More information on indexing can be found in the [index guide](indexing).
|
||||
|
||||
## Search
|
||||
|
||||
Once data is indexed, it is ready for search.
|
||||
|
||||
```python
|
||||
embeddings.search(query, limit)
|
||||
```
|
||||
|
||||
The search method takes two parameters, the query and query limit. The results format is different based on whether [content](configuration/database#content) is stored or not.
|
||||
|
||||
- List of `(id, score)` when content is _not_ stored
|
||||
- List of `{**query columns}` when content is stored
|
||||
|
||||
Both natural language and SQL queries are supported. More information can be found in the [query guide](query).
|
||||
|
||||
## Resource management
|
||||
|
||||
Embeddings databases are context managers. The following blocks automatically [close](methods/#txtai.embeddings.base.Embeddings.close) and free resources upon completion.
|
||||
|
||||
```python
|
||||
# Create a new Embeddings database, index data and save
|
||||
with Embeddings() as embeddings:
|
||||
embeddings.index(rows)
|
||||
embeddings.save(path)
|
||||
|
||||
# Search a saved Embeddings database
|
||||
with Embeddings().load(path) as embeddings:
|
||||
embeddings.search(query)
|
||||
```
|
||||
|
||||
While calling `close` isn't always necessary (resources will be garbage collected), it's best to free shared resources like database connections as soon as they aren't needed.
|
||||
|
||||
## More examples
|
||||
|
||||
See [this link](../examples/#semantic-search) for a full list of embeddings examples.
|
||||
@@ -0,0 +1,136 @@
|
||||
# Index guide
|
||||
|
||||
|
||||
|
||||
|
||||
This section gives an in-depth overview on how to index data with txtai. We'll cover vectorization, indexing/updating/deleting data and the various components of an embeddings database.
|
||||
|
||||
## Vectorization
|
||||
|
||||
The most compute intensive step in building an index is vectorization. The [path](../configuration/vectors#path) parameter sets the path to the vector model. There is logic to automatically detect the vector model [method](../configuration/vectors#method) but it can also be set directly.
|
||||
|
||||
The [batch](../configuration/vectors#batch) and [encodebatch](../configuration/vectors#encodebatch) parameters control the vectorization process. Larger values for `batch` will pass larger batches to the vectorization method. Larger values for `encodebatch` will pass larger batches for each vector encode call. In the case of GPU vector models, larger values will consume more GPU memory.
|
||||
|
||||
Data is buffered to temporary storage during indexing as embeddings vectors can be quite large (for example 768 dimensions of float32 is 768 * 4 = 3072 bytes per vector). Once vectorization is complete, a mmapped array is created with all vectors for [Approximate Nearest Neighbor (ANN)](../configuration/vectors#backend) indexing.
|
||||
|
||||
The terms `ANN` and `dense vector index` are used interchangeably throughout txtai's documentation.
|
||||
|
||||
## Setting a backend
|
||||
|
||||
As mentioned above, computed vectors are stored in an ANN. There are various index [backends](../configuration/ann#backend) that can be configured. Faiss is the default backend.
|
||||
|
||||
## Content storage
|
||||
|
||||
Embeddings indexes can optionally [store content](../configuration/database#content). When this is enabled, the input content is saved in a database alongside the computed vectors. This enables filtering on additional fields and content retrieval.
|
||||
|
||||
The columns used for text, object and JSON data storage are set via [column configuration](../configuration/general#columns).
|
||||
|
||||
## Index vs Upsert
|
||||
|
||||
Data is loaded into an index with either an [index](../methods#txtai.embeddings.base.Embeddings.index) or [upsert](../methods#txtai.embeddings.base.Embeddings.upsert) call.
|
||||
|
||||
```python
|
||||
embeddings.index([(uid, text, None) for uid, text in enumerate(data)])
|
||||
embeddings.upsert([(uid, text, None) for uid, text in enumerate(data)])
|
||||
```
|
||||
|
||||
The `index` call will build a brand new index replacing an existing one. `upsert` will insert or update records. `upsert` ops do _not_ require a full index rebuild.
|
||||
|
||||
## Save
|
||||
|
||||
Indexes can be stored in a directory using the [save](../methods/#txtai.embeddings.base.Embeddings.save) method.
|
||||
|
||||
```python
|
||||
embeddings.save("/path/to/save")
|
||||
```
|
||||
|
||||
Compressed indexes are also supported.
|
||||
|
||||
```python
|
||||
embeddings.save("/path/to/save/index.tar.gz")
|
||||
```
|
||||
|
||||
In addition to saving indexes locally, they can also be persisted to [cloud storage](../configuration/cloud).
|
||||
|
||||
```python
|
||||
embeddings.save("/path/to/save/index.tar.gz", cloud={...})
|
||||
```
|
||||
|
||||
This is especially useful when running in a serverless context or otherwise running on temporary compute. Cloud storage is only supported with compressed indexes.
|
||||
|
||||
Embeddings indexes can be restored using the [load](../methods/#txtai.embeddings.base.Embeddings.load) method.
|
||||
|
||||
```python
|
||||
embeddings.load("/path/to/load")
|
||||
```
|
||||
|
||||
## Delete
|
||||
|
||||
Content can be removed from the index with the [delete](../methods#txtai.embeddings.base.Embeddings.delete) method. This method takes a list of ids to delete.
|
||||
|
||||
```python
|
||||
embeddings.delete(ids)
|
||||
```
|
||||
|
||||
## Reindex
|
||||
|
||||
When [content storage](../configuration/database#content) is enabled, [reindex](../methods#txtai.embeddings.base.Embeddings.reindex) can be called to rebuild the index with new settings. For example, the backend can be switched from faiss to hnsw or the vector model can be updated. This prevents having to go back to the original raw data.
|
||||
|
||||
```python
|
||||
embeddings.reindex(path="sentence-transformers/all-MiniLM-L6-v2", backend="hnsw")
|
||||
```
|
||||
|
||||
## Graph
|
||||
|
||||
Enabling a [graph network](../configuration/graph) adds a semantic graph at index time as data is being vectorized. Vector embeddings are used to automatically create relationships in the graph. Relationships can also be manually specified at index time.
|
||||
|
||||
```python
|
||||
# Manual relationships by id
|
||||
embeddings.index([{"id": "0", "text": "...", "relationships": ["2"]}])
|
||||
|
||||
# Manual relationships with additional edge attributes
|
||||
embeddings.index(["id": "0", "text": "...", "relationships": [
|
||||
{"id": "2", "type": "MEMBER_OF"}
|
||||
]])
|
||||
```
|
||||
|
||||
Additionally, graphs can be used for topic modeling. Dimensionality reduction with UMAP combined with HDBSCAN is a popular topic modeling method found in a number of libraries. txtai takes a different approach using community detection algorithms to build topic clusters.
|
||||
|
||||
This approach has the advantage of only having to vectorize data once. It also has the advantage of better topic precision given there isn't a dimensionality reduction operation (UMAP). Semantic graph examples are shown below.
|
||||
|
||||
Get a mapping of discovered topics to associated ids.
|
||||
|
||||
```python
|
||||
embeddings.graph.topics
|
||||
```
|
||||
|
||||
Show the most central nodes in the index.
|
||||
|
||||
```python
|
||||
embeddings.graph.centrality()
|
||||
```
|
||||
|
||||
Graphs are persisted alongside an embeddings index. Each save and load will also save and load the graph.
|
||||
|
||||
## Sparse vectors
|
||||
|
||||
Scoring instances can create a standalone [sparse keyword indexes](../configuration/general#keyword) (BM25, TF-IDF) and [sparse vector indexes](../configuration/general#sparse) (SPLADE). This enables [hybrid](../configuration/general/#hybrid) search when there is an available dense vector index.
|
||||
|
||||
The terms `sparse vector index`, `keyword index`, `terms index` and `scoring index` are used interchangeably throughout txtai's documentation.
|
||||
|
||||
See [this link](../../examples/#semantic-search) to learn more.
|
||||
|
||||
## Subindexes
|
||||
|
||||
An embeddings instance can optionally have associated [subindexes](../configuration/general/#indexes), which are also embeddings databases. This enables indexing additional fields, vector models and much more.
|
||||
|
||||
## Word vectors
|
||||
|
||||
When using [word vector backed models](../configuration/vectors#words) with scoring set, a separate call is required before calling `index` as follows:
|
||||
|
||||
```python
|
||||
embeddings.score(rows)
|
||||
embeddings.index(rows)
|
||||
```
|
||||
|
||||
Both calls are required to support generator-backed iteration of data with word vectors models.
|
||||
@@ -0,0 +1,20 @@
|
||||
# Methods
|
||||
|
||||
::: txtai.embeddings.Embeddings
|
||||
options:
|
||||
filters:
|
||||
- "!columns"
|
||||
- "!createann"
|
||||
- "!createcloud"
|
||||
- "!createdatabase"
|
||||
- "!creategraph"
|
||||
- "!createids"
|
||||
- "!createindexes"
|
||||
- "!createscoring"
|
||||
- "!checkarchive"
|
||||
- "!configure"
|
||||
- "!defaultallowed"
|
||||
- "!defaults"
|
||||
- "!initindex"
|
||||
- "!loadquery"
|
||||
- "!loadvectors"
|
||||
@@ -0,0 +1,285 @@
|
||||
# Query guide
|
||||
|
||||
|
||||
|
||||
|
||||
This section covers how to query data with txtai. The simplest way to search for data is building a natural language string with the desired content to find. txtai also supports querying with SQL. We'll cover both methods here.
|
||||
|
||||
## Natural language queries
|
||||
|
||||
In the simplest case, the query is text and the results are index text that is most similar to the query text.
|
||||
|
||||
```python
|
||||
embeddings.search("feel good story")
|
||||
embeddings.search("wildlife")
|
||||
```
|
||||
|
||||
The queries above [search](../methods#txtai.embeddings.base.Embeddings.search) the index for similarity matches on `feel good story` and `wildlife`. If content storage is enabled, a list of `{**query columns}` is returned. Otherwise, a list of `(id, score)` tuples are returned.
|
||||
|
||||
## SQL
|
||||
|
||||
txtai supports more complex queries with SQL. This is only supported if [content storage](../configuration/database#content) is enabled. txtai has a translation layer that analyzes input SQL statements and combines similarity results with content stored in a relational database.
|
||||
|
||||
SQL queries are run through `embeddings.search` like natural language queries but the examples below only show the SQL query for conciseness.
|
||||
|
||||
```python
|
||||
embeddings.search("SQL query")
|
||||
```
|
||||
|
||||
### Similar clause
|
||||
|
||||
The similar clause is a txtai function that enables similarity searches with SQL.
|
||||
|
||||
```sql
|
||||
SELECT id, text, score FROM txtai WHERE similar('feel good story')
|
||||
```
|
||||
|
||||
The similar clause takes the following arguments:
|
||||
|
||||
```sql
|
||||
similar("query", "number of candidates", "index", "weights")
|
||||
```
|
||||
|
||||
| Argument | Description |
|
||||
| --------------------- | ---------------------------------------|
|
||||
| query | natural language query to run |
|
||||
| number of candidates | number of candidate results to return |
|
||||
| index | target index name |
|
||||
| weights | hybrid score weights |
|
||||
|
||||
The txtai query layer joins results from two separate components, a relational store and a similarity index. With a similar clause, a similarity search is run and those ids are fed to the underlying database query.
|
||||
|
||||
The number of candidates should be larger than the desired number of results when applying additional filter clauses. This ensures that `limit` results are still returned after applying additional filters. If the number of candidates is not specified, it is defaulted as follows:
|
||||
|
||||
- For a single query filter clause, the default is the query limit
|
||||
- With multiple filtering clauses, the default is 10x the query limit
|
||||
|
||||
The index name is only applicable when [subindexes](../configuration/general/#indexes) are enabled. This specifies the index to use for the query.
|
||||
|
||||
Weights sets the hybrid score weights when an index has both a sparse and dense index.
|
||||
|
||||
### Dynamic columns
|
||||
|
||||
Content can be indexed in multiple ways when content storage is enabled. [Remember that input documents](../#index) take the form of `(id, data, tags)` tuples. If data is a string or binary content, it's indexed and searchable with `similar()` clauses.
|
||||
|
||||
If data is a dictionary, then all fields in the dictionary are stored and available via SQL. The `text` field or [field specified in the index configuration](../configuration/general/#columns) is indexed and searchable with `similar()` clauses.
|
||||
|
||||
For example:
|
||||
|
||||
```python
|
||||
embeddings.index([{"text": "text to index", "flag": True,
|
||||
"actiondate": "2022-01-01"}])
|
||||
```
|
||||
|
||||
With the above input data, queries can now have more complex filters.
|
||||
|
||||
```sql
|
||||
SELECT text, flag, actiondate FROM txtai WHERE similar('query') AND flag = 1
|
||||
AND actiondate >= '2022-01-01'
|
||||
```
|
||||
|
||||
txtai's query layer automatically detects columns and translates queries into a format that can be understood by the underlying database.
|
||||
|
||||
Nested dictionaries/JSON is supported and can be escaped with bracket statements.
|
||||
|
||||
```python
|
||||
embeddings.index([{"text": "text to index",
|
||||
"parent": {"child element": "abc"}}])
|
||||
```
|
||||
|
||||
```sql
|
||||
SELECT text FROM txtai WHERE [parent.child element] = 'abc'
|
||||
```
|
||||
|
||||
Note the bracket statement escaping the nested column with spaces in the name.
|
||||
|
||||
### Bind parameters
|
||||
|
||||
txtai has support for SQL bind parameters.
|
||||
|
||||
```python
|
||||
# Query with a bind parameter for similar clause
|
||||
query = "SELECT id, text, score FROM txtai WHERE similar(:x)"
|
||||
results = embeddings.search(query, parameters={"x": "feel good story"})
|
||||
|
||||
# Query with a bind parameter for column filter
|
||||
query = "SELECT text, flag, actiondate FROM txtai WHERE flag = :x"
|
||||
results = embeddings.search(query, parameters={"x": 1})
|
||||
```
|
||||
|
||||
### Aggregation queries
|
||||
|
||||
The goal of txtai's query language is to closely support all functions in the underlying database engine. The main challenge is ensuring dynamic columns are properly escaped into the engines native query function.
|
||||
|
||||
Aggregation query examples.
|
||||
|
||||
```sql
|
||||
SELECT count(*) FROM txtai WHERE similar('feel good story') AND score >= 0.15
|
||||
SELECT max(length(text)) FROM txtai WHERE similar('feel good story')
|
||||
AND score >= 0.15
|
||||
SELECT count(*), flag FROM txtai GROUP BY flag ORDER BY count(*) DESC
|
||||
```
|
||||
|
||||
## Binary objects
|
||||
|
||||
txtai has support for storing and retrieving binary objects. Binary objects can be retrieved as shown in the example below.
|
||||
|
||||
```python
|
||||
# Create embeddings index with content and object storage enabled
|
||||
embeddings = Embeddings(content=True, objects=True)
|
||||
|
||||
# Get an image
|
||||
request = open("demo.gif", "rb")
|
||||
|
||||
# Insert record
|
||||
embeddings.index([(
|
||||
"txtai",
|
||||
{"text": "txtai executes machine-learning workflows.",
|
||||
"object": request.read()}
|
||||
)])
|
||||
|
||||
# Query txtai and get associated object
|
||||
query = "SELECT object FROM txtai WHERE similar('machine learning') LIMIT 1"
|
||||
result = embeddings.search(query)[0]["object"]
|
||||
|
||||
# Query binary content with a bind parameter
|
||||
query = "SELECT object FROM txtai WHERE similar(:x) LIMIT 1"
|
||||
results = embeddings.search(query, parameters={"x": request.read()})
|
||||
```
|
||||
|
||||
## Custom SQL functions
|
||||
|
||||
Custom, user-defined SQL functions extend selection, filtering and ordering clauses with additional logic. For example, the following snippet defines a function that translates text using a translation pipeline.
|
||||
|
||||
```python
|
||||
# Translation pipeline
|
||||
translate = Translation()
|
||||
|
||||
# Create embeddings index
|
||||
embeddings = Embeddings(path="sentence-transformers/nli-mpnet-base-v2",
|
||||
content=True,
|
||||
functions=[translate]})
|
||||
|
||||
# Run a search using a custom SQL function
|
||||
embeddings.search("""
|
||||
SELECT
|
||||
text,
|
||||
translation(text, 'de', null) 'text (DE)',
|
||||
translation(text, 'es', null) 'text (ES)',
|
||||
translation(text, 'fr', null) 'text (FR)'
|
||||
FROM txtai WHERE similar('feel good story')
|
||||
LIMIT 1
|
||||
""")
|
||||
```
|
||||
|
||||
## Expressions
|
||||
|
||||
Expression shortcuts expand into more complex SQL snippets. This is useful for making SQL queries more concise. Indexing is also available on expressions as a performance improvement.
|
||||
|
||||
The following example indexes a json extraction field (`filepath`) and the length of each field.
|
||||
|
||||
```python
|
||||
# Create embeddings index
|
||||
embeddings = Embeddings(
|
||||
path="sentence-transformers/nli-mpnet-base-v2",
|
||||
content=True,
|
||||
expressions=[
|
||||
{"name": "filepath", "index": True},
|
||||
{"name": "textlength", "expression": "length(text)", "index": True}
|
||||
]
|
||||
)
|
||||
|
||||
embeddings.search("SELECT textlength, filepath FROM txtai LIMIT 1")
|
||||
```
|
||||
|
||||
## Query translation
|
||||
|
||||
Natural language queries with filters can be converted to txtai-compatible SQL statements with query translation. For example:
|
||||
|
||||
```python
|
||||
embeddings.search("feel good story since yesterday")
|
||||
```
|
||||
|
||||
can be converted to a SQL statement with a similar clause and date filter.
|
||||
|
||||
```sql
|
||||
select id, text, score from txtai where similar('feel good story') and
|
||||
entry >= date('now', '-1 day')
|
||||
```
|
||||
|
||||
This requires setting a [query translation model](../configuration/database#query). The default query translation model is [t5-small-txtsql](https://huggingface.co/NeuML/t5-small-txtsql) but this can easily be finetuned to handle different use cases.
|
||||
|
||||
## Hybrid search
|
||||
|
||||
When an embeddings database has both a sparse and dense index, both indexes will be queried and the results will be equally weighted unless otherwise specified.
|
||||
|
||||
```python
|
||||
embeddings.search("query", weights=0.5)
|
||||
embeddings.search(
|
||||
"SELECT id, text, score FROM txtai WHERE similar('query', 0.5)"
|
||||
)
|
||||
```
|
||||
|
||||
## Graph search
|
||||
|
||||
If an embeddings database has an associated graph network, graph searches can be run. The search syntax below uses [openCypher](https://github.com/opencypher/openCypher). Follow the preceding link to learn more about this syntax.
|
||||
|
||||
Additionally, standard embeddings searches can be returned as graphs.
|
||||
|
||||
```python
|
||||
# Find all paths between id: 0 and id: 5 between 1 and 3 hops away
|
||||
embeddings.search("""
|
||||
MATCH P=({id: 0})-[*1..3]->({id: 5})
|
||||
RETURN P
|
||||
""")
|
||||
|
||||
# Find related nodes for query matches
|
||||
embeddings.search("""
|
||||
MATCH P=(A)-[]->(B)
|
||||
WHERE SIMILAR(A, "query")
|
||||
RETURN B
|
||||
ORDER BY A.score DESC
|
||||
LIMIT 10
|
||||
""")
|
||||
|
||||
# Standard embeddings search as graph
|
||||
embeddings.search("query", graph=True)
|
||||
```
|
||||
|
||||
## Subindexes
|
||||
|
||||
Subindexes can be queried as follows:
|
||||
|
||||
```python
|
||||
# Build index with subindexes
|
||||
embeddings = Embeddings(
|
||||
content=True,
|
||||
defaults=False,
|
||||
indexes={
|
||||
"keyword": {
|
||||
"keyword": True
|
||||
},
|
||||
"dense":{
|
||||
"dense": True
|
||||
}
|
||||
}
|
||||
)
|
||||
embeddings.index(stream())
|
||||
|
||||
# Query with index parameter
|
||||
embeddings.search("query", index="keyword")
|
||||
|
||||
# Specify with SQL
|
||||
embeddings.search("""
|
||||
SELECT id, text, score FROM txtai
|
||||
WHERE similar('query', 'keyword')
|
||||
""")
|
||||
```
|
||||
|
||||
## Combined index architecture
|
||||
|
||||
txtai has multiple storage and indexing components. Content is stored in an underlying database along with an approximate nearest neighbor (ANN) index, keyword index and graph network. These components combine to deliver similarity search alongside traditional structured search.
|
||||
|
||||
The ANN index stores ids and vectors for each input element. When a natural language query is run, the query is translated into a vector and a similarity query finds the best matching ids. When a database is added into the mix, an additional step is executed. This step takes those ids and effectively inserts them as part of the underlying database query. The same steps apply with keyword indexes except a term frequency index is used to find the best matching ids.
|
||||
|
||||
Dynamic columns are supported via the underlying engine. For SQLite, data is stored as JSON and dynamic columns are converted into `json_extract` clauses. Client-server databases are supported via [SQLAlchemy](https://docs.sqlalchemy.org/en/20/dialects/) and dynamic columns are supported provided the underlying engine has [JSON](https://docs.sqlalchemy.org/en/20/core/type_basics.html#sqlalchemy.types.JSON) support.
|
||||
@@ -0,0 +1,163 @@
|
||||
# Examples
|
||||
|
||||
|
||||
|
||||
|
||||
See below for a comprehensive series of example notebooks and applications covering txtai.
|
||||
|
||||
## 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) |
|
||||
| [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) |
|
||||
| [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) |
|
||||
|
||||
## LLM
|
||||
|
||||
Autonomous agents, retrieval augmented generation (RAG), chat with your data, pipelines and workflows that interface with large language models (LLMs).
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [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) |
|
||||
| [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) |
|
||||
| [Build RAG pipelines with txtai](https://github.com/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) [▶️](https://www.youtube.com/watch?v=t_OeAc8NVfQ) | Guide on retrieval augmented generation including how to create citations | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/52_Build_RAG_pipelines_with_txtai.ipynb) |
|
||||
| [Integrate LLM frameworks](https://github.com/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb) | Integrate llama.cpp, LiteLLM and custom generation frameworks | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb) |
|
||||
| [Generate knowledge with Semantic Graphs and RAG](https://github.com/neuml/txtai/blob/master/examples/55_Generate_knowledge_with_Semantic_Graphs_and_RAG.ipynb) | Knowledge exploration and discovery with Semantic Graphs and RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/55_Generate_knowledge_with_Semantic_Graphs_and_RAG.ipynb) |
|
||||
| [Build knowledge graphs with LLMs](https://github.com/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) | Build knowledge graphs with LLM-driven entity extraction | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb) |
|
||||
| [Advanced RAG with graph path traversal](https://github.com/neuml/txtai/blob/master/examples/58_Advanced_RAG_with_graph_path_traversal.ipynb) | Graph path traversal to collect complex sets of data for advanced RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/58_Advanced_RAG_with_graph_path_traversal.ipynb) |
|
||||
| [Advanced RAG with guided generation](https://github.com/neuml/txtai/blob/master/examples/60_Advanced_RAG_with_guided_generation.ipynb) | Retrieval Augmented and Guided Generation | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/60_Advanced_RAG_with_guided_generation.ipynb) |
|
||||
| [RAG with llama.cpp and external API services](https://github.com/neuml/txtai/blob/master/examples/62_RAG_with_llama_cpp_and_external_API_services.ipynb) | RAG with additional vector and LLM frameworks | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/62_RAG_with_llama_cpp_and_external_API_services.ipynb) |
|
||||
| [How RAG with txtai works](https://github.com/neuml/txtai/blob/master/examples/63_How_RAG_with_txtai_works.ipynb) | Create RAG processes, API services and Docker instances | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/63_How_RAG_with_txtai_works.ipynb) |
|
||||
| [Speech to Speech RAG](https://github.com/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) [▶️](https://www.youtube.com/watch?v=tH8QWwkVMKA) | Full cycle speech to speech workflow with RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/65_Speech_to_Speech_RAG.ipynb) |
|
||||
| [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 | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/68_Analyzing_Hugging_Face_Posts_with_Graphs_and_Agents.ipynb) |
|
||||
| [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 | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/69_Granting_autonomy_to_agents.ipynb) |
|
||||
| [Getting started with LLM APIs](https://github.com/neuml/txtai/blob/master/examples/70_Getting_started_with_LLM_APIs.ipynb) | Generate embeddings and run LLMs with OpenAI, Claude, Gemini, Bedrock and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/70_Getting_started_with_LLM_APIs.ipynb) |
|
||||
| [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 | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/71_Analyzing_LinkedIn_Company_Posts_with_Graphs_and_Agents.ipynb) |
|
||||
| [Parsing the stars with txtai](https://github.com/neuml/txtai/blob/master/examples/72_Parsing_the_stars_with_txtai.ipynb) | Explore an astronomical knowledge graph of known stars, planets, galaxies | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/72_Parsing_the_stars_with_txtai.ipynb) |
|
||||
| [Chunking your data for RAG](https://github.com/neuml/txtai/blob/master/examples/73_Chunking_your_data_for_RAG.ipynb) | Extract, chunk and index content for effective retrieval | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/73_Chunking_your_data_for_RAG.ipynb) |
|
||||
| [Medical RAG Research with txtai](https://github.com/neuml/txtai/blob/master/examples/75_Medical_RAG_Research_with_txtai.ipynb) | Analyze PubMed article metadata with RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/75_Medical_RAG_Research_with_txtai.ipynb) |
|
||||
| [GraphRAG with Wikipedia and GPT OSS](https://github.com/neuml/txtai/blob/master/examples/77_GraphRAG_with_Wikipedia_and_GPT_OSS.ipynb) | Deep graph search powered RAG | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/77_GraphRAG_with_Wikipedia_and_GPT_OSS.ipynb) |
|
||||
| [RAG is more than Vector Search](https://github.com/neuml/txtai/blob/master/examples/79_RAG_is_more_than_Vector_Search.ipynb) | Context retrieval via Web, SQL and other sources | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/79_RAG_is_more_than_Vector_Search.ipynb) |
|
||||
| [OpenCode as a txtai LLM](https://github.com/neuml/txtai/blob/master/examples/81_OpenCode_as_a_txtai_LLM.ipynb) | Integrate OpenCode with the txtai ecosystem | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/81_OpenCode_as_a_txtai_LLM.ipynb) |
|
||||
| [Agentic College Search](https://github.com/neuml/txtai/blob/master/examples/82_Agentic_College_Search.ipynb) | Identify a list of strong engineering colleges | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/82_Agentic_College_Search.ipynb) |
|
||||
| [TxtAI got skills](https://github.com/neuml/txtai/blob/master/examples/83_TxtAI_got_skills.ipynb) | Integrate skill.md files with your agent | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/83_TxtAI_got_skills.ipynb) |
|
||||
| [Agent Tools](https://github.com/neuml/txtai/blob/master/examples/84_Agent_Tools.ipynb) [▶️](https://www.youtube.com/watch?v=RDNaFXQy3GQ) | Learn about the txtai agent toolkit | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/84_Agent_Tools.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) |
|
||||
|
||||
## 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) |
|
||||
| [Generative Audio](https://github.com/neuml/txtai/blob/master/examples/66_Generative_Audio.ipynb) | Storytelling with generative audio workflows | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/66_Generative_Audio.ipynb) |
|
||||
|
||||
## Model Training
|
||||
|
||||
Train, distill, fine-tune and export 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) |
|
||||
| [Distilling Knowledge into Tiny LLMs](https://github.com/neuml/txtai/blob/master/examples/80_Distilling_Knowledge_into_Tiny_LLMs.ipynb) [▶️](https://www.youtube.com/watch?v=Ol560ktgkf0) | Finetune tiny LLMs to enable inference using less resources | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/80_Distilling_Knowledge_into_Tiny_LLMs.ipynb) |
|
||||
| [Progressive Distillation](https://github.com/neuml/txtai/blob/master/examples/85_Progressive_Distillation.ipynb) | Incrementally compress knowledge from a series of teachers | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/85_Progressive_Distillation.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) |
|
||||
|
||||
## API
|
||||
|
||||
Run distributed txtai, integrate with the API and cloud endpoints.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [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) |
|
||||
| [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) |
|
||||
| [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) |
|
||||
| [Custom API Endpoints](https://github.com/neuml/txtai/blob/master/examples/51_Custom_API_Endpoints.ipynb) | Extend the API with custom endpoints | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/51_Custom_API_Endpoints.ipynb) |
|
||||
| [API Authorization and Authentication](https://github.com/neuml/txtai/blob/master/examples/54_API_Authorization_and_Authentication.ipynb) | Add authorization, authentication and middleware dependencies to the API | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/54_API_Authorization_and_Authentication.ipynb) |
|
||||
| [OpenAI Compatible API](https://github.com/neuml/txtai/blob/master/examples/74_OpenAI_Compatible_API.ipynb) | Connect to txtai with a standard OpenAI client library | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/74_OpenAI_Compatible_API.ipynb) |
|
||||
|
||||
## Architecture
|
||||
|
||||
Project architecture, data formats, external integrations, scale to production, benchmarks, and performance.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [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) |
|
||||
| [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) |
|
||||
| [Customize your own embeddings database](https://github.com/neuml/txtai/blob/master/examples/45_Customize_your_own_embeddings_database.ipynb) | Ways to combine vector indexes with relational databases | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/45_Customize_your_own_embeddings_database.ipynb) |
|
||||
| [Building an efficient sparse keyword index in Python](https://github.com/neuml/txtai/blob/master/examples/47_Building_an_efficient_sparse_keyword_index_in_Python.ipynb) | Fast and accurate sparse keyword indexing | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/47_Building_an_efficient_sparse_keyword_index_in_Python.ipynb) |
|
||||
| [Benefits of hybrid search](https://github.com/neuml/txtai/blob/master/examples/48_Benefits_of_hybrid_search.ipynb) | Improve accuracy with a combination of semantic and keyword search | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/48_Benefits_of_hybrid_search.ipynb) |
|
||||
| [External database integration](https://github.com/neuml/txtai/blob/master/examples/49_External_database_integration.ipynb) | Store metadata in PostgreSQL, MariaDB, MySQL and more | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/49_External_database_integration.ipynb) |
|
||||
| [All about vector quantization](https://github.com/neuml/txtai/blob/master/examples/50_All_about_vector_quantization.ipynb) | Benchmarking scalar and product quantization methods | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/50_All_about_vector_quantization.ipynb) |
|
||||
| [External vectorization](https://github.com/neuml/txtai/blob/master/examples/56_External_vectorization.ipynb) | Vectorization with precomputed embeddings datasets and APIs | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/56_External_vectorization.ipynb) |
|
||||
| [Integrate txtai with Postgres](https://github.com/neuml/txtai/blob/master/examples/61_Integrate_txtai_with_Postgres.ipynb) | Persist content, vectors and graph data in Postgres | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/61_Integrate_txtai_with_Postgres.ipynb) |
|
||||
| [Embeddings index format for open data access](https://github.com/neuml/txtai/blob/master/examples/64_Embeddings_index_format_for_open_data_access.ipynb) | Platform and programming language independent data storage with txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/64_Embeddings_index_format_for_open_data_access.ipynb) |
|
||||
| [Accessing Low Level Vector APIs](https://github.com/neuml/txtai/blob/master/examples/78_Accessing_Low_Level_Vector_APIs.ipynb) | Build a vector database using txtai's low-level APIs | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/78_Accessing_Low_Level_Vector_APIs.ipynb) |
|
||||
|
||||
## Releases
|
||||
|
||||
New functionality added in major releases.
|
||||
|
||||
| Notebook | Description | |
|
||||
|:----------|:-------------|------:|
|
||||
| [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) |
|
||||
| [What's new in txtai 6.0](https://github.com/neuml/txtai/blob/master/examples/46_Whats_new_in_txtai_6_0.ipynb) | Sparse, hybrid and subindexes for embeddings, LLM improvements | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/46_Whats_new_in_txtai_6_0.ipynb) |
|
||||
| [What's new in txtai 7.0](https://github.com/neuml/txtai/blob/master/examples/59_Whats_new_in_txtai_7_0.ipynb) | Semantic graph 2.0, LoRA/QLoRA training and binary API support | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/59_Whats_new_in_txtai_7_0.ipynb) |
|
||||
| [What's new in txtai 8.0](https://github.com/neuml/txtai/blob/master/examples/67_Whats_new_in_txtai_8_0.ipynb) | Agents with txtai | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/67_Whats_new_in_txtai_8_0.ipynb) |
|
||||
| [What's new in txtai 9.0](https://github.com/neuml/txtai/blob/master/examples/76_Whats_new_in_txtai_9_0.ipynb) | Learned sparse vectors, late interaction models and rerankers | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/76_Whats_new_in_txtai_9_0.ipynb) |
|
||||
|
||||
## Applications
|
||||
|
||||
Series of example applications with txtai. Links to hosted versions on [Hugging Face Spaces](https://hf.co/spaces) are also provided, when available.
|
||||
|
||||
| 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)|
|
||||
| [Baseball stats](https://github.com/neuml/txtai/blob/master/examples/baseball.py) | Match historical baseball player stats using vector search. |[🤗](https://hf.co/spaces/NeuML/baseball)|
|
||||
| [Benchmarks](https://github.com/neuml/txtai/blob/master/examples/benchmarks.py) | Calculate performance metrics for the BEIR datasets. |*Local run only*|
|
||||
| [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)|
|
||||
| [Retrieval Augmented Generation](https://github.com/neuml/rag/) | RAG with txtai embeddings databases. Ask questions and get answers from LLMs bound by a context. |*Local run only*|
|
||||
| [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)|
|
||||
+151
@@ -0,0 +1,151 @@
|
||||
# FAQ
|
||||
|
||||
|
||||
|
||||
Below is a list of frequently asked questions and common issues encountered.
|
||||
|
||||
## Questions
|
||||
|
||||
----------
|
||||
|
||||
__Question__
|
||||
|
||||
What models are recommended?
|
||||
|
||||
__Answer__
|
||||
|
||||
See the [model guide](../models).
|
||||
|
||||
----------
|
||||
|
||||
__Question__
|
||||
|
||||
What is the best way to track the progress of an `embeddings.index` call?
|
||||
|
||||
__Answer__
|
||||
|
||||
Wrap the list or generator passed to the index call with tqdm. See [#478](https://github.com/neuml/txtai/issues/478) for more.
|
||||
|
||||
----------
|
||||
|
||||
__Question__
|
||||
|
||||
What is the best way to analyze and debug a txtai process?
|
||||
|
||||
__Answer__
|
||||
|
||||
See the [observability](../observability) section for more on how this can be enabled in txtai processes.
|
||||
|
||||
txtai also has a console application. [This article](https://medium.com/neuml/insights-from-the-txtai-console-d307c28e149e) has more details.
|
||||
|
||||
----------
|
||||
|
||||
__Question__
|
||||
|
||||
How can models be externally loaded and passed to embeddings and pipelines?
|
||||
|
||||
__Answer__
|
||||
|
||||
Embeddings example.
|
||||
|
||||
```python
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
from txtai import Embeddings
|
||||
|
||||
# Load model externally
|
||||
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
|
||||
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
|
||||
|
||||
# Pass to embeddings instance
|
||||
embeddings = Embeddings(path=model, tokenizer=tokenizer)
|
||||
```
|
||||
|
||||
LLM pipeline example.
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
from txtai import LLM
|
||||
|
||||
# Load Qwen3 0.6B
|
||||
path = "Qwen/Qwen3-0.6B"
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
path,
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(path)
|
||||
|
||||
llm = LLM((model, tokenizer))
|
||||
```
|
||||
|
||||
## Common issues
|
||||
|
||||
----------
|
||||
|
||||
__Issue__
|
||||
|
||||
Embeddings query errors like this:
|
||||
|
||||
```
|
||||
SQLError: no such function: json_extract
|
||||
```
|
||||
|
||||
__Solution__
|
||||
|
||||
Upgrade Python version as it doesn't have SQLite support for `json_extract`
|
||||
|
||||
----------
|
||||
|
||||
__Issue__
|
||||
|
||||
Segmentation faults and similar errors on macOS
|
||||
|
||||
__Solution__
|
||||
|
||||
Set the following environment parameters.
|
||||
|
||||
- Disable OpenMP multithreading via `export OMP_NUM_THREADS=1`
|
||||
- Workaround `OMP: Error #15` errors via `export KMP_DUPLICATE_LIB_OK=TRUE`
|
||||
- Disable PyTorch MPS device via `export PYTORCH_MPS_DISABLE=1`
|
||||
- Disable llama.cpp metal via `export LLAMA_NO_METAL=1`
|
||||
|
||||
For more details, refer to [this issue on GitHub](https://github.com/kyamagu/faiss-wheels/issues/73).
|
||||
|
||||
----------
|
||||
|
||||
__Issue__
|
||||
|
||||
Error running SQLite ANN on macOS
|
||||
|
||||
```
|
||||
AttributeError: 'sqlite3.Connection' object has no attribute 'enable_load_extension'
|
||||
```
|
||||
|
||||
__Solution__
|
||||
|
||||
See [this note](https://alexgarcia.xyz/sqlite-vec/python.html#macos-blocks-sqlite-extensions-by-default) for options on how to fix this.
|
||||
|
||||
----------
|
||||
|
||||
__Issue__
|
||||
|
||||
`ContextualVersionConflict` and/or package METADATA exception while running one of the [examples](../examples) notebooks on Google Colab
|
||||
|
||||
__Solution__
|
||||
|
||||
Restart the kernel. See issue [#409](https://github.com/neuml/txtai/issues/409) for more on this issue.
|
||||
|
||||
----------
|
||||
|
||||
__Issue__
|
||||
|
||||
Error installing optional/extra dependencies such as `pipeline`
|
||||
|
||||
__Solution__
|
||||
|
||||
The default MacOS shell (zsh) and Windows PowerShell require escaping square brackets
|
||||
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||||
```
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||||
pip install 'txtai[pipeline]'
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||||
```
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@@ -0,0 +1,12 @@
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||||
# Further reading
|
||||
|
||||
|
||||
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||||
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||||
- [Introducing txtai, the all-in-one AI framework](https://medium.com/neuml/introducing-txtai-the-all-in-one-ai-framework-0660ecfc39d7)
|
||||
- [txtai: An All-in-One AI Framework for Semantic Search and LLM Workflows](https://github.com/neuml/papers/blob/master/txtai/txtai.pdf)
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||||
- [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 9.0](https://medium.com/neuml/whats-new-in-txtai-9-0-d522bb150afa) | [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.com/neuml/whats-new-in-txtai-4-0-bbc3a65c3d1c)
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- [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)
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||||
- [Running txtai at scale](https://medium.com/neuml/running-at-scale-with-txtai-71196cdd99f9)
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||||
- [Vector search & RAG Landscape: A review with txtai](https://medium.com/neuml/vector-search-rag-landscape-a-review-with-txtai-a7f37ad0e187)
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Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user