Add dynamic quantization from @Xenova

* check for existing files

* (chore: model_management.py):  Add comment

---------

Co-authored-by: Nirant <NirantK@users.noreply.github.com>

.github/workflows/ci.yml

@@ -0,0 +1,25 @@
+name: ci
+on:
+  push:
+    branches:
+      - master
+      - main
+permissions:
+  contents: write
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: 3.x
+      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
+      - uses: actions/cache@v3
+        with:
+          key: mkdocs-material-${{ env.cache_id }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+      - run: pip install mkdocs-material mkdocstrings pillow cairosvg mknotebooks
+      - run: mkdocs gh-deploy --force

.github/workflows/python-publish.yml

@@ -0,0 +1,40 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  workflow_dispatch:
+  push:
+    # Pattern matched against refs/tags
+    tags:
+      - 'v*'           # Push events to every version tag
+
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install poetry
+        poetry install
+    - name: Build package
+      run: poetry build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}

.github/workflows/python-tests.yml

@@ -0,0 +1,46 @@
+name: Tests
+
+on:
+  push:
+    branches: [ master, main ]
+  pull_request:
+
+env:
+  CARGO_TERM_COLOR: always
+
+jobs:
+  test:
+
+    strategy:
+      matrix:
+        python-version:
+          - '3.8.x'
+          - '3.9.x'
+          - '3.10.x'
+          - '3.11.x'
+          - '3.12.x'
+        os:
+          - ubuntu-latest
+          - macos-latest
+          - windows-latest
+
+    runs-on: ${{ matrix.os }}
+
+    name: Python ${{ matrix.python-version }} on ${{ matrix.os }} test
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install poetry
+          poetry config virtualenvs.create false
+          poetry install --no-interaction --no-ansi
+      - name: Run tests
+        run: |
+          export IS_UBUNTU_CI=$(test "${{ matrix.os }}" = "ubuntu-latest" && echo "true" || echo "false")
+          pytest
+        shell: bash

.gitignore

@@ -0,0 +1,178 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+.DS_Store
+nbs/*.tar.gz
+*.tar.gz
+nbs/fast-*/*
+local_cache/*/*
+*/local_cache/*/*
+*/*/local_cache/*/*
+docs/experimental/*.parquet
+docs/experimental/*.bin
+qdrant_storage/*
+fooling_around/fast-multilingual-e5-large/config.json
+fooling_around/fast-multilingual-e5-large/model_optimized.onnx
+fooling_around/fast-multilingual-e5-large/model_optimized.onnx.data
+fooling_around/fast-multilingual-e5-large/ort_config.json
+fooling_around/fast-multilingual-e5-large/sentencepiece.bpe.model
+fooling_around/fast-multilingual-e5-large/special_tokens_map.json
+fooling_around/fast-multilingual-e5-large/tokenizer_config.json
+fooling_around/fast-multilingual-e5-large/tokenizer.json

.pre-commit-config.yaml

@@ -0,0 +1,9 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.13
+    hooks:
+      - id: ruff
+        types_or: [ python, pyi, jupyter ]
+        args: [ --fix ]
+      - id: ruff-format
+        types_or: [ python, pyi, jupyter ]

404.html

@@ -1,961 +0,0 @@
-
-<!doctype html>
-<html lang="en" class="no-js">
-  <head>
-    
-      <meta charset="utf-8">
-      <meta name="viewport" content="width=device-width,initial-scale=1">
-      
-      
-        <meta name="author" content="Nirant Kasliwal">
-      
-      
-      
-      
-      
-        
-      
-      
-      <link rel="icon" href="/fastembed/assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.7.7">
-    
-    
-      
-        <title>FastEmbed</title>
-      
-    
-    
-      <link rel="stylesheet" href="/fastembed/assets/stylesheets/main.ec1eaa64.min.css">
-      
-        
-        <link rel="stylesheet" href="/fastembed/assets/stylesheets/palette.ab4e12ef.min.css">
-      
-      
-
-
-    
-    
-      
-    
-    
-      
-        
-        
-        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
-        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback">
-        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
-      
-    
-    
-      <link rel="stylesheet" href="/fastembed/assets/_mkdocstrings.css">
-    
-      <link rel="stylesheet" href="/fastembed/css/ansi-colours.css">
-    
-      <link rel="stylesheet" href="/fastembed/css/jupyter-cells.css">
-    
-      <link rel="stylesheet" href="/fastembed/css/pandas-dataframe.css">
-    
-    <script>__md_scope=new URL("/fastembed/",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
-    
-      
-
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-    
-  </head>
-  
-  
-    
-    
-      
-    
-    
-    
-    
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="indigo" data-md-color-accent="indigo">
-  
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-    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
-    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
-    <label class="md-overlay" for="__drawer"></label>
-    <div data-md-component="skip">
-      
-    </div>
-    <div data-md-component="announce">
-      
-        <aside class="md-banner">
-          <div class="md-banner__inner md-grid md-typeset">
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-                
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README.md

@@ -0,0 +1,90 @@
+# ⚡️ What is FastEmbed?
+
+FastEmbed is a lightweight, fast, Python library built for embedding generation. We [support popular text models](https://qdrant.github.io/fastembed/examples/Supported_Models/). Please [open a GitHub issue](https://github.com/qdrant/fastembed/issues/new) if you want us to add a new model.
+
+The default text embedding (`TextEmbedding`) model is Flag Embedding, the top model in the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. It supports "query" and "passage" prefixes for the input text. Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/examples/Retrieval_with_FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/examples/Usage_With_Qdrant/).
+
+1. Light & Fast
+    - Quantized model weights
+    - ONNX Runtime, no PyTorch dependency
+    - CPU-first design
+    - Data-parallelism for encoding of large datasets
+
+2. Accuracy/Recall
+    - Better than OpenAI Ada-002
+    - Default is Flag Embedding, which is top of the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard
+    - List of [supported models](https://qdrant.github.io/fastembed/examples/Supported_Models/) - including multilingual models
+
+## 🚀 Installation
+
+To install the FastEmbed library, pip works:
+
+```bash
+pip install fastembed
+```
+
+## 📖 Quickstart
+
+```python
+from fastembed import TextEmbedding
+from typing import List
+import numpy as np
+
+documents: List[str] = [
+    "passage: Hello, World!",
+    "query: Hello, World!", # these are two different embedding
+    "passage: This is an example passage.",
+    "fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended
+]
+embedding_model = TextEmbedding(model_name="BAAI/bge-base-en")
+embeddings: List[np.ndarray] = list(embedding_model.embed(documents)) # Note the list() call - this is a generator
+```
+
+## Usage with Qdrant
+
+Installation with Qdrant Client in Python:
+
+```bash
+pip install qdrant-client[fastembed]
+```
+
+You might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh.
+
+```python
+from qdrant_client import QdrantClient
+
+# Initialize the client
+client = QdrantClient("localhost", port=6333) # For production
+# client = QdrantClient(":memory:") # For small experiments
+
+# Prepare your documents, metadata, and IDs
+docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
+metadata = [
+    {"source": "Langchain-docs"},
+    {"source": "Llama-index-docs"},
+]
+ids = [42, 2]
+
+# If you want to change the model:
+# client.set_model("sentence-transformers/all-MiniLM-L6-v2")
+# List of supported models: https://qdrant.github.io/fastembed/examples/Supported_Models
+
+# Use the new add() instead of upsert()
+# This internally calls embed() of the configured embedding model
+client.add(
+    collection_name="demo_collection",
+    documents=docs,
+    metadata=metadata,
+    ids=ids
+)
+
+search_result = client.query(
+    collection_name="demo_collection",
+    query_text="This is a query document"
+)
+print(search_result)
+```
+
+#### Similar Work
+
+Ilyas M. wrote about using [FlagEmbeddings with Optimum](https://twitter.com/IlysMoutawwakil/status/1705215192425288017) over CUDA.

assets/javascripts/lunr/min/lunr.da.min.js

@@ -1,18 +0,0 @@
-/*!
- * Lunr languages, `Danish` language
- * https://github.com/MihaiValentin/lunr-languages
- *
- * Copyright 2014, Mihai Valentin
- * http://www.mozilla.org/MPL/
- */
-/*!
- * based on
- * Snowball JavaScript Library v0.3
- * http://code.google.com/p/urim/
- * http://snowball.tartarus.org/
- *
- * Copyright 2010, Oleg Mazko
- * http://www.mozilla.org/MPL/
- */
-
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){var e,r=f.cursor+3;if(d=f.limit,0<=r&&r<=f.limit){for(a=r;;){if(e=f.cursor,f.in_grouping(w,97,248)){f.cursor=e;break}if(f.cursor=e,e>=f.limit)return;f.cursor++}for(;!f.out_grouping(w,97,248);){if(f.cursor>=f.limit)return;f.cursor++}d=f.cursor,d<a&&(d=a)}}function n(){var e,r;if(f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(c,32),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del();break;case 2:f.in_grouping_b(p,97,229)&&f.slice_del()}}function t(){var e,r=f.limit-f.cursor;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.find_among_b(l,4)?(f.bra=f.cursor,f.limit_backward=e,f.cursor=f.limit-r,f.cursor>f.limit_backward&&(f.cursor--,f.bra=f.cursor,f.slice_del())):f.limit_backward=e)}function s(){var e,r,i,n=f.limit-f.cursor;if(f.ket=f.cursor,f.eq_s_b(2,"st")&&(f.bra=f.cursor,f.eq_s_b(2,"ig")&&f.slice_del()),f.cursor=f.limit-n,f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(m,5),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del(),i=f.limit-f.cursor,t(),f.cursor=f.limit-i;break;case 2:f.slice_from("løs")}}function o(){var e;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.out_grouping_b(w,97,248)?(f.bra=f.cursor,u=f.slice_to(u),f.limit_backward=e,f.eq_v_b(u)&&f.slice_del()):f.limit_backward=e)}var a,d,u,c=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],l=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],p=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],f=new i;this.setCurrent=function(e){f.setCurrent(e)},this.getCurrent=function(){return f.getCurrent()},this.stem=function(){var r=f.cursor;return e(),f.limit_backward=r,f.cursor=f.limit,n(),f.cursor=f.limit,t(),f.cursor=f.limit,s(),f.cursor=f.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});

assets/javascripts/lunr/min/lunr.hi.min.js

@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}});

assets/javascripts/lunr/min/lunr.hy.min.js

@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hy=function(){this.pipeline.reset(),this.pipeline.add(e.hy.trimmer,e.hy.stopWordFilter)},e.hy.wordCharacters="[A-Za-z԰-֏ff-ﭏ]",e.hy.trimmer=e.trimmerSupport.generateTrimmer(e.hy.wordCharacters),e.Pipeline.registerFunction(e.hy.trimmer,"trimmer-hy"),e.hy.stopWordFilter=e.generateStopWordFilter("դու և եք էիր էիք հետո նաև նրանք որը վրա է որ պիտի են այս մեջ ն իր ու ի այդ որոնք այն կամ էր մի ես համար այլ իսկ էին ենք հետ ին թ էինք մենք նրա նա դուք եմ էի ըստ որպես ում".split(" ")),e.Pipeline.registerFunction(e.hy.stopWordFilter,"stopWordFilter-hy"),e.hy.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.hy.stemmer,"stemmer-hy")}});

assets/javascripts/lunr/min/lunr.ja.min.js

@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.ja=function(){this.pipeline.reset(),this.pipeline.add(e.ja.trimmer,e.ja.stopWordFilter,e.ja.stemmer),r?this.tokenizer=e.ja.tokenizer:(e.tokenizer&&(e.tokenizer=e.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.ja.tokenizer))};var t=new e.TinySegmenter;e.ja.tokenizer=function(i){var n,o,s,p,a,u,m,l,c,f;if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t.toLowerCase()):t.toLowerCase()});for(o=i.toString().toLowerCase().replace(/^\s+/,""),n=o.length-1;n>=0;n--)if(/\S/.test(o.charAt(n))){o=o.substring(0,n+1);break}for(a=[],s=o.length,c=0,l=0;c<=s;c++)if(u=o.charAt(c),m=c-l,u.match(/\s/)||c==s){if(m>0)for(p=t.segment(o.slice(l,c)).filter(function(e){return!!e}),f=l,n=0;n<p.length;n++)r?a.push(new e.Token(p[n],{position:[f,p[n].length],index:a.length})):a.push(p[n]),f+=p[n].length;l=c+1}return a},e.ja.stemmer=function(){return function(e){return e}}(),e.Pipeline.registerFunction(e.ja.stemmer,"stemmer-ja"),e.ja.wordCharacters="一二三四五六七八九十百千万億兆一-龠々〆ヵヶぁ-んァ-ヴーｱ-ﾝﾞa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ja.trimmer=e.trimmerSupport.generateTrimmer(e.ja.wordCharacters),e.Pipeline.registerFunction(e.ja.trimmer,"trimmer-ja"),e.ja.stopWordFilter=e.generateStopWordFilter("これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし".split(" ")),e.Pipeline.registerFunction(e.ja.stopWordFilter,"stopWordFilter-ja"),e.jp=e.ja,e.Pipeline.registerFunction(e.jp.stemmer,"stemmer-jp"),e.Pipeline.registerFunction(e.jp.trimmer,"trimmer-jp"),e.Pipeline.registerFunction(e.jp.stopWordFilter,"stopWordFilter-jp")}});

assets/javascripts/lunr/min/lunr.jp.min.js

@@ -1 +0,0 @@
-module.exports=require("./lunr.ja");

assets/javascripts/lunr/min/lunr.kn.min.js

@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.kn=function(){this.pipeline.reset(),this.pipeline.add(e.kn.trimmer,e.kn.stopWordFilter,e.kn.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.kn.stemmer))},e.kn.wordCharacters="ಀ-಄ಅ-ಔಕ-ಹಾ-ೌ಼-ಽೕ-ೖೝ-ೞೠ-ೡೢ-ೣ೤೥೦-೯ೱ-ೳ",e.kn.trimmer=e.trimmerSupport.generateTrimmer(e.kn.wordCharacters),e.Pipeline.registerFunction(e.kn.trimmer,"trimmer-kn"),e.kn.stopWordFilter=e.generateStopWordFilter("ಮತ್ತು ಈ ಒಂದು ರಲ್ಲಿ ಹಾಗೂ ಎಂದು ಅಥವಾ ಇದು ರ ಅವರು ಎಂಬ ಮೇಲೆ ಅವರ ತನ್ನ ಆದರೆ ತಮ್ಮ ನಂತರ ಮೂಲಕ ಹೆಚ್ಚು ನ ಆ ಕೆಲವು ಅನೇಕ ಎರಡು ಹಾಗು ಪ್ರಮುಖ ಇದನ್ನು ಇದರ ಸುಮಾರು ಅದರ ಅದು ಮೊದಲ ಬಗ್ಗೆ ನಲ್ಲಿ ರಂದು ಇತರ ಅತ್ಯಂತ ಹೆಚ್ಚಿನ ಸಹ ಸಾಮಾನ್ಯವಾಗಿ ನೇ ಹಲವಾರು ಹೊಸ ದಿ ಕಡಿಮೆ ಯಾವುದೇ ಹೊಂದಿದೆ ದೊಡ್ಡ ಅನ್ನು ಇವರು ಪ್ರಕಾರ ಇದೆ ಮಾತ್ರ ಕೂಡ ಇಲ್ಲಿ ಎಲ್ಲಾ ವಿವಿಧ ಅದನ್ನು ಹಲವು ರಿಂದ ಕೇವಲ ದ ದಕ್ಷಿಣ ಗೆ ಅವನ ಅತಿ ನೆಯ ಬಹಳ ಕೆಲಸ ಎಲ್ಲ ಪ್ರತಿ ಇತ್ಯಾದಿ ಇವು ಬೇರೆ ಹೀಗೆ ನಡುವೆ ಇದಕ್ಕೆ ಎಸ್ ಇವರ ಮೊದಲು ಶ್ರೀ ಮಾಡುವ ಇದರಲ್ಲಿ ರೀತಿಯ ಮಾಡಿದ ಕಾಲ ಅಲ್ಲಿ ಮಾಡಲು ಅದೇ ಈಗ ಅವು ಗಳು ಎ ಎಂಬುದು ಅವನು ಅಂದರೆ ಅವರಿಗೆ ಇರುವ ವಿಶೇಷ ಮುಂದೆ ಅವುಗಳ ಮುಂತಾದ ಮೂಲ ಬಿ ಮೀ ಒಂದೇ ಇನ್ನೂ ಹೆಚ್ಚಾಗಿ ಮಾಡಿ ಅವರನ್ನು ಇದೇ ಯ ರೀತಿಯಲ್ಲಿ ಜೊತೆ ಅದರಲ್ಲಿ ಮಾಡಿದರು ನಡೆದ ಆಗ ಮತ್ತೆ ಪೂರ್ವ ಆತ ಬಂದ ಯಾವ ಒಟ್ಟು ಇತರೆ ಹಿಂದೆ ಪ್ರಮಾಣದ ಗಳನ್ನು ಕುರಿತು ಯು ಆದ್ದರಿಂದ ಅಲ್ಲದೆ ನಗರದ ಮೇಲಿನ ಏಕೆಂದರೆ ರಷ್ಟು ಎಂಬುದನ್ನು ಬಾರಿ ಎಂದರೆ ಹಿಂದಿನ ಆದರೂ ಆದ ಸಂಬಂಧಿಸಿದ ಮತ್ತೊಂದು ಸಿ ಆತನ ".split(" ")),e.kn.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.kn.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var n=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(n).split("|")},e.Pipeline.registerFunction(e.kn.stemmer,"stemmer-kn"),e.Pipeline.registerFunction(e.kn.stopWordFilter,"stopWordFilter-kn")}});

assets/javascripts/lunr/min/lunr.multi.min.js

@@ -1 +0,0 @@
-!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){e.multiLanguage=function(){for(var t=Array.prototype.slice.call(arguments),i=t.join("-"),r="",n=[],s=[],p=0;p<t.length;++p)"en"==t[p]?(r+="\\w",n.unshift(e.stopWordFilter),n.push(e.stemmer),s.push(e.stemmer)):(r+=e[t[p]].wordCharacters,e[t[p]].stopWordFilter&&n.unshift(e[t[p]].stopWordFilter),e[t[p]].stemmer&&(n.push(e[t[p]].stemmer),s.push(e[t[p]].stemmer)));var o=e.trimmerSupport.generateTrimmer(r);return e.Pipeline.registerFunction(o,"lunr-multi-trimmer-"+i),n.unshift(o),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,n),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,s))}}}});

assets/javascripts/lunr/min/lunr.no.min.js

@@ -1,18 +0,0 @@
-/*!
- * Lunr languages, `Norwegian` language
- * https://github.com/MihaiValentin/lunr-languages
- *
- * Copyright 2014, Mihai Valentin
- * http://www.mozilla.org/MPL/
- */
-/*!
- * based on
- * Snowball JavaScript Library v0.3
- * http://code.google.com/p/urim/
- * http://snowball.tartarus.org/
- *
- * Copyright 2010, Oleg Mazko
- * http://www.mozilla.org/MPL/
- */
-
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,r=w.cursor+3;if(a=w.limit,0<=r||r<=w.limit){for(s=r;;){if(e=w.cursor,w.in_grouping(d,97,248)){w.cursor=e;break}if(e>=w.limit)return;w.cursor=e+1}for(;!w.out_grouping(d,97,248);){if(w.cursor>=w.limit)return;w.cursor++}a=w.cursor,a<s&&(a=s)}}function i(){var e,r,n;if(w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(m,29),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:n=w.limit-w.cursor,w.in_grouping_b(c,98,122)?w.slice_del():(w.cursor=w.limit-n,w.eq_s_b(1,"k")&&w.out_grouping_b(d,97,248)&&w.slice_del());break;case 3:w.slice_from("er")}}function t(){var e,r=w.limit-w.cursor;w.cursor>=a&&(e=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,w.find_among_b(u,2)?(w.bra=w.cursor,w.limit_backward=e,w.cursor=w.limit-r,w.cursor>w.limit_backward&&(w.cursor--,w.bra=w.cursor,w.slice_del())):w.limit_backward=e)}function o(){var e,r;w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(l,11),e?(w.bra=w.cursor,w.limit_backward=r,1==e&&w.slice_del()):w.limit_backward=r)}var s,a,m=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],u=[new r("dt",-1,-1),new r("vt",-1,-1)],l=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],c=[119,125,149,1],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,i(),w.cursor=w.limit,t(),w.cursor=w.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}});

assets/javascripts/lunr/min/lunr.sa.min.js

@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sa=function(){this.pipeline.reset(),this.pipeline.add(e.sa.trimmer,e.sa.stopWordFilter,e.sa.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sa.stemmer))},e.sa.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿ꣠-꣱ꣲ-ꣷ꣸-ꣻ꣼-ꣽꣾ-ꣿᆰ0-ᆰ9",e.sa.trimmer=e.trimmerSupport.generateTrimmer(e.sa.wordCharacters),e.Pipeline.registerFunction(e.sa.trimmer,"trimmer-sa"),e.sa.stopWordFilter=e.generateStopWordFilter('तथा अयम्‌ एकम्‌ इत्यस्मिन्‌ तथा तत्‌ वा अयम्‌ इत्यस्य ते आहूत उपरि तेषाम्‌  किन्तु तेषाम्‌ तदा इत्यनेन अधिकः इत्यस्य तत्‌ केचन बहवः द्वि तथा महत्वपूर्णः अयम्‌ अस्य  विषये अयं अस्ति तत्‌ प्रथमः विषये इत्युपरि इत्युपरि इतर अधिकतमः अधिकः अपि सामान्यतया ठ इतरेतर नूतनम्‌ द  न्यूनम्‌ कश्चित्‌ वा विशालः द  सः अस्ति तदनुसारम् तत्र अस्ति केवलम्‌ अपि अत्र सर्वे विविधाः तत्‌ बहवः यतः इदानीम्‌ द  दक्षिण इत्यस्मै तस्य उपरि नथ अतीव कार्यम्‌ सर्वे एकैकम्‌ इत्यादि। एते सन्ति  उत इत्थम्‌ मध्ये एतदर्थं . स कस्य प्रथमः श्री. करोति अस्मिन् प्रकारः निर्मिता कालः तत्र कर्तुं  समान अधुना ते सन्ति स एकः अस्ति सः अर्थात् तेषां कृते . स्थितम्  विशेषः अग्रिम तेषाम्‌ समान स्रोतः ख म समान इदानीमपि अधिकतया करोतु ते समान इत्यस्य वीथी सह यस्मिन्  कृतवान्‌ धृतः तदा पुनः पूर्वं सः आगतः किम्‌ कुल इतर पुरा  मात्रा स विषये उ अतएव अपि नगरस्य  उपरि यतः प्रतिशतं  कतरः कालः साधनानि भूत तथापि जात सम्बन्धि अन्यत्‌ ग अतः अस्माकं स्वकीयाः अस्माकं इदानीं अन्तः इत्यादयः भवन्तः इत्यादयः एते एताः तस्य अस्य इदम् एते तेषां तेषां तेषां तान् तेषां तेषां तेषां समानः सः एकः च तादृशाः बहवः अन्ये च वदन्ति यत् कियत् कस्मै  कस्मै  यस्मै  यस्मै  यस्मै  यस्मै न अतिनीचः किन्तु प्रथमं सम्पूर्णतया  ततः चिरकालानन्तरं पुस्तकं सम्पूर्णतया अन्तः  किन्तु अत्र वा इह इव श्रद्धाय अवशिष्यते  परन्तु अन्ये वर्गाः सन्ति ते सन्ति शक्नुवन्ति सर्वे मिलित्वा सर्वे एकत्र"'.split(" ")),e.sa.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.sa.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var i=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(i).split("|")},e.Pipeline.registerFunction(e.sa.stemmer,"stemmer-sa"),e.Pipeline.registerFunction(e.sa.stopWordFilter,"stopWordFilter-sa")}});

assets/javascripts/lunr/min/lunr.stemmer.support.min.js

@@ -1 +0,0 @@
-!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r||!t&&0!=t||!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var r;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(t){r=t,this.cursor=0,this.limit=t.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var t=r;return r=null,t},in_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e>s||e<i)return this.cursor++,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s||e<i)return this.cursor--,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor+s)!=i.charCodeAt(s))return!1;return this.cursor+=t,!0},eq_s_b:function(t,i){if(this.cursor-this.limit_backward<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor-t+s)!=i.charCodeAt(s))return!1;return this.cursor-=t,!0},find_among:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=l;m<_.s_size;m++){if(n+l==u){f=-1;break}if(f=r.charCodeAt(n+l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},find_among_b:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit_backward,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=_.s_size-1-l;m>=0;m--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n-_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n-_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});

assets/javascripts/lunr/min/lunr.sv.min.js

@@ -1,18 +0,0 @@
-/*!
- * Lunr languages, `Swedish` language
- * https://github.com/MihaiValentin/lunr-languages
- *
- * Copyright 2014, Mihai Valentin
- * http://www.mozilla.org/MPL/
- */
-/*!
- * based on
- * Snowball JavaScript Library v0.3
- * http://code.google.com/p/urim/
- * http://snowball.tartarus.org/
- *
- * Copyright 2010, Oleg Mazko
- * http://www.mozilla.org/MPL/
- */
-
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){function e(){var e,r=w.cursor+3;if(o=w.limit,0<=r||r<=w.limit){for(a=r;;){if(e=w.cursor,w.in_grouping(l,97,246)){w.cursor=e;break}if(w.cursor=e,w.cursor>=w.limit)return;w.cursor++}for(;!w.out_grouping(l,97,246);){if(w.cursor>=w.limit)return;w.cursor++}o=w.cursor,o<a&&(o=a)}}function t(){var e,r=w.limit_backward;if(w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(u,37),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.in_grouping_b(d,98,121)&&w.slice_del()}}function i(){var e=w.limit_backward;w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.find_among_b(c,7)&&(w.cursor=w.limit,w.ket=w.cursor,w.cursor>w.limit_backward&&(w.bra=--w.cursor,w.slice_del())),w.limit_backward=e)}function s(){var e,r;if(w.cursor>=o){if(r=w.limit_backward,w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(m,5))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.slice_from("lös");break;case 3:w.slice_from("full")}w.limit_backward=r}}var a,o,u=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],c=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],l=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],d=[119,127,149],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,t(),w.cursor=w.limit,i(),w.cursor=w.limit,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});

assets/javascripts/lunr/min/lunr.ta.min.js

@@ -1 +0,0 @@
-!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ta=function(){this.pipeline.reset(),this.pipeline.add(e.ta.trimmer,e.ta.stopWordFilter,e.ta.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ta.stemmer))},e.ta.wordCharacters="஀-உஊ-ஏஐ-ஙச-ட஠-னப-யர-ஹ஺-ிீ-௉ொ-௏ௐ-௙௚-௟௠-௩௪-௯௰-௹௺-௿a-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ta.trimmer=e.trimmerSupport.generateTrimmer(e.ta.wordCharacters),e.Pipeline.registerFunction(e.ta.trimmer,"trimmer-ta"),e.ta.stopWordFilter=e.generateStopWordFilter("அங்கு அங்கே அது அதை அந்த அவர் அவர்கள் அவள் அவன் அவை ஆக ஆகவே ஆகையால் ஆதலால் ஆதலினால் ஆனாலும் ஆனால் இங்கு இங்கே இது இதை இந்த இப்படி இவர் இவர்கள் இவள் இவன் இவை இவ்வளவு உனக்கு உனது உன் உன்னால் எங்கு எங்கே எது எதை எந்த எப்படி எவர் எவர்கள் எவள் எவன் எவை எவ்வளவு எனக்கு எனது எனவே என் என்ன என்னால் ஏது ஏன் தனது தன்னால் தானே தான் நாங்கள் நாம் நான் நீ நீங்கள்".split(" ")),e.ta.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.ta.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.ta.stemmer,"stemmer-ta"),e.Pipeline.registerFunction(e.ta.stopWordFilter,"stopWordFilter-ta")}});

assets/javascripts/lunr/min/lunr.te.min.js

@@ -1 +0,0 @@
-!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.te=function(){this.pipeline.reset(),this.pipeline.add(e.te.trimmer,e.te.stopWordFilter,e.te.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.te.stemmer))},e.te.wordCharacters="ఀ-ఄఅ-ఔక-హా-ౌౕ-ౖౘ-ౚౠ-ౡౢ-ౣ౦-౯౸-౿఼ఽ్ౝ౷౤౥",e.te.trimmer=e.trimmerSupport.generateTrimmer(e.te.wordCharacters),e.Pipeline.registerFunction(e.te.trimmer,"trimmer-te"),e.te.stopWordFilter=e.generateStopWordFilter("అందరూ అందుబాటులో అడగండి అడగడం అడ్డంగా అనుగుణంగా అనుమతించు అనుమతిస్తుంది అయితే ఇప్పటికే ఉన్నారు ఎక్కడైనా ఎప్పుడు ఎవరైనా ఎవరో ఏ ఏదైనా ఏమైనప్పటికి ఒక ఒకరు కనిపిస్తాయి కాదు కూడా గా గురించి చుట్టూ చేయగలిగింది తగిన తర్వాత దాదాపు దూరంగా నిజంగా పై ప్రకారం ప్రక్కన మధ్య మరియు మరొక మళ్ళీ మాత్రమే మెచ్చుకో వద్ద వెంట వేరుగా వ్యతిరేకంగా సంబంధం".split(" ")),e.te.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.te.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.te.stemmer,"stemmer-te"),e.Pipeline.registerFunction(e.te.stopWordFilter,"stopWordFilter-te")}});

assets/javascripts/lunr/min/lunr.th.min.js

@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.th=function(){this.pipeline.reset(),this.pipeline.add(e.th.trimmer),r?this.tokenizer=e.th.tokenizer:(e.tokenizer&&(e.tokenizer=e.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.th.tokenizer))},e.th.wordCharacters="[฀-๿]",e.th.trimmer=e.trimmerSupport.generateTrimmer(e.th.wordCharacters),e.Pipeline.registerFunction(e.th.trimmer,"trimmer-th");var t=e.wordcut;t.init(),e.th.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t):t});var n=i.toString().replace(/^\s+/,"");return t.cut(n).split("|")}}});

assets/javascripts/lunr/min/lunr.vi.min.js

@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.vi=function(){this.pipeline.reset(),this.pipeline.add(e.vi.stopWordFilter,e.vi.trimmer)},e.vi.wordCharacters="[A-Za-ẓ̀͐́͑̉̃̓ÂâÊêÔôĂ-ăĐ-đƠ-ơƯ-ư]",e.vi.trimmer=e.trimmerSupport.generateTrimmer(e.vi.wordCharacters),e.Pipeline.registerFunction(e.vi.trimmer,"trimmer-vi"),e.vi.stopWordFilter=e.generateStopWordFilter("là cái nhưng mà".split(" "))}});

assets/javascripts/lunr/min/lunr.zh.min.js

@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 為 以 于 於 上 他 而 后 後 之 来 來 及 了 因 下 可 到 由 这 這 与 與 也 此 但 并 並 个 個 其 已 无 無 小 我 们 們 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 當 从 從 得 打 凡 儿 兒 尔 爾 该 該 各 给 給 跟 和 何 还 還 即 几 幾 既 看 据 據 距 靠 啦 另 么 麽 每 嘛 拿 哪 您 凭 憑 且 却 卻 让 讓 仍 啥 如 若 使 谁 誰 虽 雖 随 隨 同 所 她 哇 嗡 往 些 向 沿 哟 喲 用 咱 则 則 怎 曾 至 致 着 著 诸 諸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});

assets/javascripts/lunr/tinyseg.js

@@ -1,206 +0,0 @@
-/**
- * export the module via AMD, CommonJS or as a browser global
- * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js
- */
-;(function (root, factory) {
-    if (typeof define === 'function' && define.amd) {
-        // AMD. Register as an anonymous module.
-        define(factory)
-    } else if (typeof exports === 'object') {
-        /**
-         * Node. Does not work with strict CommonJS, but
-         * only CommonJS-like environments that support module.exports,
-         * like Node.
-         */
-        module.exports = factory()
-    } else {
-        // Browser globals (root is window)
-        factory()(root.lunr);
-    }
-}(this, function () {
-    /**
-     * Just return a value to define the module export.
-     * This example returns an object, but the module
-     * can return a function as the exported value.
-     */
-
-    return function(lunr) {
-        // TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript
-        // (c) 2008 Taku Kudo <taku@chasen.org>
-        // TinySegmenter is freely distributable under the terms of a new BSD licence.
-        // For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt
-
-        function TinySegmenter() {
-          var patterns = {
-            "[一二三四五六七八九十百千万億兆]":"M",
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css/ansi-colours.css

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css/jupyter-cells.css

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css/pandas-dataframe.css

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-    padding-right: 10px;
-}

docs/Getting Started.ipynb

@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3f159fb4",
+   "metadata": {},
+   "source": [
+    "# 🚶🏻‍♂️ Getting Started\n",
+    "\n",
+    "Here you will learn how to use the fastembed package to embed your data into a vector space. The package is designed to be easy to use and fast. It is built on top of the [ONNX](https://onnx.ai/) standard, which allows for fast inference on a variety of hardware (called Runtimes in ONNX). \n",
+    "\n",
+    "## Quick Start\n",
+    "\n",
+    "The fastembed package is designed to be easy to use. The main class is the `Embedding` class. It takes a list of strings as input and returns a list of vectors as output. The `Embedding` class is initialized with a model file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ada95c6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install fastembed --upgrade --quiet # Install fastembed "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed81d725",
+   "metadata": {},
+   "source": [
+    "Make the necessary imports, initialize the `Embedding` class, and embed your data into vectors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b61c6552",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 76.7M/76.7M [00:05<00:00, 15.0MiB/s]\n",
+      "100%|██████████| 3/3 [00:00<00:00, 455.37it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(384,)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "import numpy as np\n",
+    "from fastembed.embedding import DefaultEmbedding\n",
+    "\n",
+    "# Example list of documents\n",
+    "documents: List[str] = [\n",
+    "    \"Hello, World!\",\n",
+    "    \"This is an example document.\",\n",
+    "    \"fastembed is supported by and maintained by Qdrant.\",\n",
+    "]\n",
+    "# Initialize the DefaultEmbedding class\n",
+    "embedding_model = DefaultEmbedding()\n",
+    "embeddings: List[np.ndarray] = list(embedding_model.embed(documents))\n",
+    "print(embeddings[0].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c49ae50",
+   "metadata": {},
+   "source": [
+    "## Let's think step by step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92cf4b76",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "\n",
+    "Importing the required classes and modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c0a6f634",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "import numpy as np\n",
+    "from fastembed.embedding import DefaultEmbedding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fd03a71",
+   "metadata": {},
+   "source": [
+    "Notice that we are using the DefaultEmbedding -- which is a quantized, state of the Art Flag Embedding model which beats OpenAI's Embedding by a large margin. \n",
+    "\n",
+    "### Prepare your Documents\n",
+    "You can define a list of documents that you'd like to embed. These can be sentences, paragraphs, or even entire documents. \n",
+    "\n",
+    "#### Format of the Document List\n",
+    "1. List of Strings: Your documents must be in a list, and each document must be a string\n",
+    "2. For Retrieval Tasks: If you're working with queries and passages, you can add special labels to them:\n",
+    "- **Queries**: Add \"query:\" at the beginning of each query string\n",
+    "- **Passages**: Add \"passage:\" at the beginning of each passage string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "145a56ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example list of documents\n",
+    "documents: List[str] = [\n",
+    "    \"passage: Hello, World!\",\n",
+    "    \"query: Hello, World!\",  # these are two different embedding\n",
+    "    \"passage: This is an example passage.\",\n",
+    "    # You can leave out the prefix but it's recommended\n",
+    "    \"fastembed is supported by and maintained by Qdrant.\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cb3cc87",
+   "metadata": {},
+   "source": [
+    "### Load the Embedding Model Weights\n",
+    "Next, initialize the Embedding class with the desired parameters. Here, \"BAAI/bge-small-en\" is the pre-trained model name, and max_length=512 is the maximum token length for each document.\n",
+    "\n",
+    "This will download the model weights, decompress to directory `local_cache` and load them into the Embedding class.\n",
+    "\n",
+    "#### Initialize DefaultEmbedding\n",
+    "\n",
+    "We will initialize Flag Embeddings with the model name and the maximum token length. That is the DefaultEmbedding class with the model name \"BAAI/bge-small-en\" and max_length=512."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "272c8915",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_model = DefaultEmbedding()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5549d501",
+   "metadata": {},
+   "source": [
+    "### Embed your Documents\n",
+    "\n",
+    "Use the embed method of the embedding model to transform the documents into a List of np.array. The method returns a generator, so we cast it to a list to get the embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8013eee9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4/4 [00:00<00:00, 361.82it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "embeddings: List[np.ndarray] = list(embedding_model.embed(documents))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5b5a6ad",
+   "metadata": {},
+   "source": [
+    "You can print the shape of the embeddings to understand their dimensions. Typically, the shape will indicate the number of dimensions in the vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0d8c8e08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(384,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(embeddings[0].shape)  # (384,) or similar output"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/Retrieval_with_FastEmbed.ipynb

@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ⚓️ Retrieval with FastEmbed\n",
+    "\n",
+    "This notebook demonstrates how to use FastEmbed to perform vector search and retrieval. It consists of the following sections:\n",
+    "\n",
+    "1. Setup: Installing the necessary packages.\n",
+    "2. Importing Libraries: Importing FastEmbed and other libraries.\n",
+    "3. Data Preparation: Example data and embedding generation.\n",
+    "4. Querying: Defining a function to search documents based on a query.\n",
+    "5. Running Queries: Running example queries.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, we need to install the dependencies. `fastembed` to create embeddings and perform retrieval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install fastembed --quiet --upgrade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Importing the necessary libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-02-07 22:20:57.013\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfastembed.embedding\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m7\u001b[0m - \u001b[33m\u001b[1mDefaultEmbedding, FlagEmbedding, JinaEmbedding are deprecated. Use TextEmbedding instead.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "import numpy as np\n",
+    "from fastembed import TextEmbedding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preparation\n",
+    "We initialize the embedding model and generate embeddings for the documents.\n",
+    "\n",
+    "### 💡 Tip: Prefer using `query_embed` for queries and `passage_embed` for documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(384,) 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example list of documents\n",
+    "documents: List[str] = [\n",
+    "    \"Maharana Pratap was a Rajput warrior king from Mewar\",\n",
+    "    \"He fought against the Mughal Empire led by Akbar\",\n",
+    "    \"The Battle of Haldighati in 1576 was his most famous battle\",\n",
+    "    \"He refused to submit to Akbar and continued guerrilla warfare\",\n",
+    "    \"His capital was Chittorgarh, which he lost to the Mughals\",\n",
+    "    \"He died in 1597 at the age of 57\",\n",
+    "    \"Maharana Pratap is considered a symbol of Rajput resistance against foreign rule\",\n",
+    "    \"His legacy is celebrated in Rajasthan through festivals and monuments\",\n",
+    "    \"He had 11 wives and 17 sons, including Amar Singh I who succeeded him as ruler of Mewar\",\n",
+    "    \"His life has been depicted in various films, TV shows, and books\",\n",
+    "]\n",
+    "# Initialize the DefaultEmbedding class with the desired parameters\n",
+    "embedding_model = TextEmbedding(model_name=\"BAAI/bge-small-en\", max_length=512)\n",
+    "\n",
+    "# We'll use the passage_embed method to get the embeddings for the documents\n",
+    "embeddings: List[np.ndarray] = list(\n",
+    "    embedding_model.passage_embed(documents)\n",
+    ")  # notice that we are casting the generator to a list\n",
+    "\n",
+    "print(embeddings[0].shape, len(embeddings))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Querying\n",
+    "\n",
+    "We'll define a function to print the top k documents based on a query, and prepare a sample query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"Who was Maharana Pratap?\"\n",
+    "query_embedding = list(embedding_model.query_embed(query))[0]\n",
+    "plain_query_embedding = list(embedding_model.embed(query))[0]\n",
+    "\n",
+    "\n",
+    "def print_top_k(query_embedding, embeddings, documents, k=5):\n",
+    "    # use numpy to calculate the cosine similarity between the query and the documents\n",
+    "    scores = np.dot(embeddings, query_embedding)\n",
+    "    # sort the scores in descending order\n",
+    "    sorted_scores = np.argsort(scores)[::-1]\n",
+    "    # print the top 5\n",
+    "    for i in range(k):\n",
+    "        print(f\"Rank {i+1}: {documents[sorted_scores[i]]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.04393955,  0.04452892, -0.00760788, -0.03399807,  0.01951348],\n",
+       "       dtype=float32),\n",
+       " array([-0.06002192,  0.04322132, -0.00545516, -0.04419701, -0.00542277],\n",
+       "       dtype=float32))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_embedding[:5], plain_query_embedding[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `query_embed` is specifically designed for queries, leading to more relevant and context-aware results. The retrieved documents tend to align closely with the query's intent.\n",
+    "\n",
+    "In contrast, `embed` is a more general-purpose representation that might not capture the nuances of the query as effectively. The retrieved documents using plain embeddings might be less relevant or ordered differently compared to the results obtained using query embeddings.\n",
+    "\n",
+    "Conclusion: Using query and passage embeddings leads to more relevant and context-aware results."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fst",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/examples/Supported_Models.ipynb

@@ -0,0 +1,289 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>dim</th>\n",
+       "      <th>description</th>\n",
+       "      <th>size_in_GB</th>\n",
+       "      <th>sources</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BAAI/bge-base-en</td>\n",
+       "      <td>768</td>\n",
+       "      <td>Base English model</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BAAI/bge-base-en-v1.5</td>\n",
+       "      <td>768</td>\n",
+       "      <td>Base English model, v1.5</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz', 'hf': 'qdrant/bge-base-en-v1.5-onnx-q'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BAAI/bge-large-en-v1.5-quantized</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>Large English model, v1.5</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>{'hf': 'qdrant/bge-large-en-v1.5-onnx-q'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BAAI/bge-large-en-v1.5</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>Large English model, v1.5</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>{'hf': 'qdrant/bge-large-en-v1.5-onnx'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BAAI/bge-small-en</td>\n",
+       "      <td>384</td>\n",
+       "      <td>Fast English model</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BAAI/bge-small-en-v1.5</td>\n",
+       "      <td>384</td>\n",
+       "      <td>Fast and Default English model</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-en-v1.5.tar.gz', 'hf': 'qdrant/bge-small-en-v1.5-onnx-q'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BAAI/bge-small-zh-v1.5</td>\n",
+       "      <td>512</td>\n",
+       "      <td>Fast and recommended Chinese model</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
+       "      <td>384</td>\n",
+       "      <td>Sentence Transformer model, MiniLM-L6-v2</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz', 'hf': 'qdrant/all-MiniLM-L6-v2-onnx'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>nomic-ai/nomic-embed-text-v1</td>\n",
+       "      <td>768</td>\n",
+       "      <td>8192 context length english model</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>{'hf': 'nomic-ai/nomic-embed-text-v1'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>nomic-ai/nomic-embed-text-v1.5</td>\n",
+       "      <td>768</td>\n",
+       "      <td>8192 context length english model</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>{'hf': 'nomic-ai/nomic-embed-text-v1.5'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>thenlper/gte-large</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>Large general text embeddings model</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>{'hf': 'qdrant/gte-large-onnx'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>intfloat/multilingual-e5-large</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>Multilingual model, e5-large. Recommend using this model for non-English languages</td>\n",
+       "      <td>2.24</td>\n",
+       "      <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz', 'hf': 'qdrant/multilingual-e5-large-onnx'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sentence-transformers/paraphrase-multilingual-mpnet-base-v2</td>\n",
+       "      <td>768</td>\n",
+       "      <td>Sentence-transformers model for tasks like clustering or semantic search</td>\n",
+       "      <td>1.11</td>\n",
+       "      <td>{'hf': 'xenova/paraphrase-multilingual-mpnet-base-v2'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2</td>\n",
+       "      <td>384</td>\n",
+       "      <td>Sentence Transformer model, paraphrase-multilingual-MiniLM-L12-v2</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>{'hf': 'qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>jinaai/jina-embeddings-v2-base-en</td>\n",
+       "      <td>768</td>\n",
+       "      <td>English embedding model supporting 8192 sequence length</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>{'hf': 'xenova/jina-embeddings-v2-base-en'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>jinaai/jina-embeddings-v2-small-en</td>\n",
+       "      <td>512</td>\n",
+       "      <td>English embedding model supporting 8192 sequence length</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>{'hf': 'xenova/jina-embeddings-v2-small-en'}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                          model   dim  \\\n",
+       "0                                              BAAI/bge-base-en   768   \n",
+       "1                                         BAAI/bge-base-en-v1.5   768   \n",
+       "2                              BAAI/bge-large-en-v1.5-quantized  1024   \n",
+       "3                                        BAAI/bge-large-en-v1.5  1024   \n",
+       "4                                             BAAI/bge-small-en   384   \n",
+       "5                                        BAAI/bge-small-en-v1.5   384   \n",
+       "6                                        BAAI/bge-small-zh-v1.5   512   \n",
+       "7                        sentence-transformers/all-MiniLM-L6-v2   384   \n",
+       "8                                  nomic-ai/nomic-embed-text-v1   768   \n",
+       "9                                nomic-ai/nomic-embed-text-v1.5   768   \n",
+       "10                                           thenlper/gte-large  1024   \n",
+       "11                               intfloat/multilingual-e5-large  1024   \n",
+       "12  sentence-transformers/paraphrase-multilingual-mpnet-base-v2   768   \n",
+       "13  sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2   384   \n",
+       "14                            jinaai/jina-embeddings-v2-base-en   768   \n",
+       "15                           jinaai/jina-embeddings-v2-small-en   512   \n",
+       "\n",
+       "                                                                           description  \\\n",
+       "0                                                                   Base English model   \n",
+       "1                                                             Base English model, v1.5   \n",
+       "2                                                            Large English model, v1.5   \n",
+       "3                                                            Large English model, v1.5   \n",
+       "4                                                                   Fast English model   \n",
+       "5                                                       Fast and Default English model   \n",
+       "6                                                   Fast and recommended Chinese model   \n",
+       "7                                             Sentence Transformer model, MiniLM-L6-v2   \n",
+       "8                                                    8192 context length english model   \n",
+       "9                                                    8192 context length english model   \n",
+       "10                                                 Large general text embeddings model   \n",
+       "11  Multilingual model, e5-large. Recommend using this model for non-English languages   \n",
+       "12            Sentence-transformers model for tasks like clustering or semantic search   \n",
+       "13                   Sentence Transformer model, paraphrase-multilingual-MiniLM-L12-v2   \n",
+       "14                             English embedding model supporting 8192 sequence length   \n",
+       "15                             English embedding model supporting 8192 sequence length   \n",
+       "\n",
+       "    size_in_GB  \\\n",
+       "0         0.50   \n",
+       "1         0.44   \n",
+       "2         1.34   \n",
+       "3         1.34   \n",
+       "4         0.20   \n",
+       "5         0.13   \n",
+       "6         0.10   \n",
+       "7         0.09   \n",
+       "8         0.54   \n",
+       "9         0.54   \n",
+       "10        1.34   \n",
+       "11        2.24   \n",
+       "12        1.11   \n",
+       "13        0.46   \n",
+       "14        0.55   \n",
+       "15        0.13   \n",
+       "\n",
+       "                                                                                                                                           sources  \n",
+       "0                                                               {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz'}  \n",
+       "1                  {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz', 'hf': 'qdrant/bge-base-en-v1.5-onnx-q'}  \n",
+       "2                                                                                                        {'hf': 'qdrant/bge-large-en-v1.5-onnx-q'}  \n",
+       "3                                                                                                          {'hf': 'qdrant/bge-large-en-v1.5-onnx'}  \n",
+       "4                                                              {'url': 'https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz'}  \n",
+       "5                {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-en-v1.5.tar.gz', 'hf': 'qdrant/bge-small-en-v1.5-onnx-q'}  \n",
+       "6                                                         {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz'}  \n",
+       "7   {'url': 'https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz', 'hf': 'qdrant/all-MiniLM-L6-v2-onnx'}  \n",
+       "8                                                                                                           {'hf': 'nomic-ai/nomic-embed-text-v1'}  \n",
+       "9                                                                                                         {'hf': 'nomic-ai/nomic-embed-text-v1.5'}  \n",
+       "10                                                                                                                 {'hf': 'qdrant/gte-large-onnx'}  \n",
+       "11         {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz', 'hf': 'qdrant/multilingual-e5-large-onnx'}  \n",
+       "12                                                                                          {'hf': 'xenova/paraphrase-multilingual-mpnet-base-v2'}  \n",
+       "13                                                                                   {'hf': 'qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q'}  \n",
+       "14                                                                                                     {'hf': 'xenova/jina-embeddings-v2-base-en'}  \n",
+       "15                                                                                                    {'hf': 'xenova/jina-embeddings-v2-small-en'}  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastembed import TextEmbedding\n",
+    "import pandas as pd\n",
+    "\n",
+    "pd.set_option(\"display.max_colwidth\", None)\n",
+    "pd.DataFrame(TextEmbedding.list_supported_models())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fst",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/examples/Usage_With_Qdrant.ipynb

@@ -0,0 +1,242 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Usage With Qdrant\n",
+    "\n",
+    "This notebook demonstrates how to use FastEmbed and Qdrant to perform vector search and retrieval. Qdrant is an open-source vector similarity search engine that is used to store, organize, and query collections of high-dimensional vectors. \n",
+    "\n",
+    "We will use the Qdrant to add a collection of documents to the engine and then query the collection to retrieve the most relevant documents.\n",
+    "\n",
+    "It consists of the following sections:\n",
+    "\n",
+    "1. Setup: Installing necessary packages, including the Qdrant Client and FastEmbed.\n",
+    "2. Importing Libraries: Importing FastEmbed and other libraries\n",
+    "3. Data Preparation: Example data and embedding generation\n",
+    "4. Querying: Defining a function to search documents based on a query\n",
+    "5. Running Queries: Running example queries\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, we need to install the dependencies. `fastembed` to create embeddings and perform retrieval, and `qdrant-client` to interact with the Qdrant database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install 'qdrant-client[fastembed]' --quiet --upgrade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Importing the necessary libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "from qdrant_client import QdrantClient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preparation\n",
+    "We initialize the embedding model and generate embeddings for the documents.\n",
+    "\n",
+    "### 💡 Tip: Prefer using `query_embed` for queries and `passage_embed` for documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example list of documents\n",
+    "documents: List[str] = [\n",
+    "    \"Maharana Pratap was a Rajput warrior king from Mewar\",\n",
+    "    \"He fought against the Mughal Empire led by Akbar\",\n",
+    "    \"The Battle of Haldighati in 1576 was his most famous battle\",\n",
+    "    \"He refused to submit to Akbar and continued guerrilla warfare\",\n",
+    "    \"His capital was Chittorgarh, which he lost to the Mughals\",\n",
+    "    \"He died in 1597 at the age of 57\",\n",
+    "    \"Maharana Pratap is considered a symbol of Rajput resistance against foreign rule\",\n",
+    "    \"His legacy is celebrated in Rajasthan through festivals and monuments\",\n",
+    "    \"He had 11 wives and 17 sons, including Amar Singh I who succeeded him as ruler of Mewar\",\n",
+    "    \"His life has been depicted in various films, TV shows, and books\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tutorial demonstrates how to utilize the QdrantClient to add documents to a collection and query the collection for relevant documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ➕ Adding Documents\n",
+    "\n",
+    "The `add` creates a collection if it does not already exist. Now, we can add the documents to the collection:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 77.7M/77.7M [00:05<00:00, 14.6MiB/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['4fa8b10c78da4b18ba0830ba8a57367a',\n",
+       " '2eae04b515ee4e9185a9a0e6be812bba',\n",
+       " 'c6039f88486f47f1835ae3b069c5823c',\n",
+       " 'c2c8c51e305144d1917b373125fb4d95',\n",
+       " '79fd23b9ec0648cdab38d1947c6b933e',\n",
+       " '036aa200d8c3492b8a438e4f825f5e7f',\n",
+       " 'c35c77f3ea37460a9a13723fb77b7367',\n",
+       " '6ebccbca571b40d0ab6e83e5e0f2f562',\n",
+       " '38048c2ccc1d4962a4f8f1bd89c8357a',\n",
+       " 'c6b09308360140c7b4f106af3658a31e']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client = QdrantClient(\":memory:\")\n",
+    "client.add(collection_name=\"test_collection\", documents=documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the ids of the documents we just added. We don't have a use for them in this tutorial, but they can be used to update or delete documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📝 Running Queries\n",
+    "We'll define a function to print the top k documents based on a query, and prepare a sample query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[42, 2]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Prepare your documents, metadata, and IDs\n",
+    "docs = [\"Qdrant has Langchain integrations\", \"Qdrant also has Llama Index integrations\"]\n",
+    "metadata = [\n",
+    "    {\"source\": \"Langchain-docs\"},\n",
+    "    {\"source\": \"Linkedin-docs\"},\n",
+    "]\n",
+    "ids = [42, 2]\n",
+    "\n",
+    "# Use the new add method\n",
+    "client.add(collection_name=\"demo_collection\", documents=docs, metadata=metadata, ids=ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Behind the scenes, Qdrant Client uses the FastEmbed library to make a passage embedding and then uses the Qdrant API to upsert the documents with metadata, put together as a Points into the collection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[QueryResponse(id=42, embedding=None, metadata={'document': 'Qdrant has Langchain integrations', 'source': 'Langchain-docs'}, document='Qdrant has Langchain integrations', score=0.8276550115796268), QueryResponse(id=2, embedding=None, metadata={'document': 'Qdrant also has Llama Index integrations', 'source': 'Linkedin-docs'}, document='Qdrant also has Llama Index integrations', score=0.8265536935180283)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "search_result = client.query(collection_name=\"demo_collection\", query_text=\"This is a query document\")\n",
+    "print(search_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎬 Conclusion\n",
+    "\n",
+    "This tutorial demonstrates the basics of working with the QdrantClient to add and query documents. By following this guide, you can easily integrate Qdrant into your projects for vector similarity search and retrieval.\n",
+    "\n",
+    "Remember to properly handle the closing of the client connection and further customization of the query parameters according to your specific needs.\n",
+    "\n",
+    "The official Qdrant Python client documentation can be found [here](https://github.com/qdrant/qdrant-client) for more details on customization and advanced features."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fst",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/experimental/Binary Quantization from Scratch.ipynb

@@ -0,0 +1,468 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Binary Quantization of OpenAI Embedding\n",
+    "---\n",
+    "\n",
+    "In the world of large-scale data retrieval and processing, efficiency is crucial. With the exponential growth of data, the ability to retrieve information quickly and accurately can significantly affect system performance. This blog post explores a technique known as binary quantization applied to OpenAI embeddings, demonstrating how it can enhance **retrieval latency by 20x** or more.\n",
+    "\n",
+    "## What Are OpenAI Embeddings?\n",
+    "OpenAI embeddings are numerical representations of textual information. They transform text into a vector space where semantically similar texts are mapped close together. This mathematical representation enables computers to understand and process human language more effectively.\n",
+    "\n",
+    "## Binary Quantization\n",
+    "Binary quantization is a method which converts continuous numerical values into binary values (0 or 1). It simplifies the data structure, allowing faster computations. Here's a brief overview of the binary quantization process applied to OpenAI embeddings:\n",
+    "\n",
+    "1. **Load Embeddings**: OpenAI embeddings are loaded from parquet files.\n",
+    "2. **Binary Transformation**: The continuous valued vectors are converted into binary form. Here, values greater than 0 are set to 1, and others remain 0.\n",
+    "3. **Comparison & Retrieval**: Binary vectors are used for comparison using logical XOR operations and other efficient algorithms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup: Install Dependencies, Imports & Download Embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install matplotlib tqdm pandas numpy --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "WBVTItUX4yyr"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 👨🏾‍💻 Code Walkthrough\n",
+    "Here's an explanation of the code structure provided:\n",
+    "\n",
+    "1. **Loading Data**: OpenAI embeddings are loaded from a parquet files (we can load upto 1M embedding) and concatenated into one array.\n",
+    "2. **Binary Conversion**: A new array with the same shape is initialized with zeros, and the positive values in the original vectors are set to 1.\n",
+    "3. **Accuracy Function**: The accuracy function compares original vectors with binary vectors for a given index, limit, and oversampling rate. The comparison is done using dot products and logical XOR, sorting the results, and measuring the intersection.\n",
+    "4. **Testing**: The accuracy is tested for different oversampling rates (1, 2, 4), revealing a correctness of ~0.96 for an oversampling of 4.\n",
+    "\n",
+    "\n",
+    "## 💿 Loading Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 250
+    },
+    "id": "REJpFqkG7EG2",
+    "outputId": "7a43c0ae-fbcc-45fe-fd58-bfe691297b22"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 26/26 [00:10<00:00,  2.45it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(1000000, 1536)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_openai_vectors(force_download: bool = False):\n",
+    "    res = []\n",
+    "    for i in tqdm(range(26)):\n",
+    "        if force_download:\n",
+    "            !wget https://huggingface.co/api/datasets/KShivendu/dbpedia-entities-openai-1M/parquet/KShivendu--dbpedia-entities-openai-1M/train/{i}.parquet\n",
+    "        df = pd.read_parquet(f\"{i}.parquet\", engine=\"pyarrow\")\n",
+    "        res.append(np.stack(df.openai))\n",
+    "        del df\n",
+    "\n",
+    "    openai_vectors = np.concatenate(res)\n",
+    "    del res\n",
+    "    return openai_vectors\n",
+    "\n",
+    "\n",
+    "openai_vectors = get_openai_vectors(force_download=False)\n",
+    "openai_vectors.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ㆓ Binary Conversion\n",
+    "\n",
+    "Here, we will use 0 as the threshold for the binary conversion. All values greater than 0 will be set to 1, and others will remain 0. This is a simple and effective way to convert continuous values into binary values for OpenAI embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "0JM2-Bj2Jkab"
+   },
+   "outputs": [],
+   "source": [
+    "openai_bin = np.zeros_like(openai_vectors, dtype=np.int8)\n",
+    "openai_bin[openai_vectors > 0] = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎯 Accuracy Function\n",
+    "\n",
+    "We will use the accuracy function to compare the original vectors with the binary vectors for a given index, limit, and oversampling rate. The comparison is done using dot products and logical XOR, sorting the results, and measuring the intersection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "FqshI-GlIERd"
+   },
+   "outputs": [],
+   "source": [
+    "def accuracy(idx, limit: int, oversampling: int):\n",
+    "    scores = np.dot(openai_vectors, openai_vectors[idx])\n",
+    "    dot_results = np.argsort(scores)[-limit:][::-1]\n",
+    "\n",
+    "    bin_scores = 1536 - np.logical_xor(openai_bin, openai_bin[idx]).sum(axis=1)\n",
+    "    bin_results = np.argsort(bin_scores)[-(limit * oversampling) :][::-1]\n",
+    "\n",
+    "    return len(set(dot_results).intersection(set(bin_results))) / limit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📊 Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qtzUlq_sFTRf",
+    "outputId": "17fe04ea-4f73-4a57-990b-180f1c04b472"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/4 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 1, 'limit': 10, 'recall': 0.8}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2/2 [00:33<00:00, 16.98s/it]\n",
+      " 25%|██▌       | 1/4 [00:33<01:41, 33.96s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 1, 'limit': 100, 'recall': 0.708}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 2, 'limit': 10, 'recall': 0.95}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2/2 [00:32<00:00, 16.38s/it]\n",
+      " 50%|█████     | 2/4 [01:06<01:06, 33.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 2, 'limit': 100, 'recall': 0.877}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 3, 'limit': 10, 'recall': 0.96}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2/2 [00:32<00:00, 16.49s/it]\n",
+      " 75%|███████▌  | 3/4 [01:39<00:33, 33.13s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 3, 'limit': 100, 'recall': 0.937}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 5, 'limit': 10, 'recall': 0.9800000000000001}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2/2 [00:32<00:00, 16.47s/it]\n",
+      "100%|██████████| 4/4 [02:12<00:00, 33.17s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 5, 'limit': 100, 'recall': 0.977}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "number_of_samples = 10\n",
+    "limits = [10, 100]\n",
+    "sampling_rate = [1, 2, 3, 5]\n",
+    "results = []\n",
+    "\n",
+    "\n",
+    "def mean_accuracy(number_of_samples, limit, sampling_rate):\n",
+    "    return np.mean([accuracy(i, limit=limit, oversampling=sampling_rate) for i in range(number_of_samples)])\n",
+    "\n",
+    "\n",
+    "for i in tqdm(sampling_rate):\n",
+    "    for j in tqdm(limits):\n",
+    "        result = {\"sampling_rate\": i, \"limit\": j, \"recall\": mean_accuracy(number_of_samples, j, i)}\n",
+    "        print(result)\n",
+    "        results.append(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sampling_rate</th>\n",
+       "      <th>limit</th>\n",
+       "      <th>recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.977</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sampling_rate  limit  recall\n",
+       "0              1     10   0.800\n",
+       "1              1    100   0.708\n",
+       "2              2     10   0.950\n",
+       "3              2    100   0.877\n",
+       "4              3     10   0.960\n",
+       "5              3    100   0.937\n",
+       "6              5     10   0.980\n",
+       "7              5    100   0.977"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = pd.DataFrame(results)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| sampling_rate | limit | accuracy |\n",
+    "|---------------|-------|----------|\n",
+    "| 1             | 10    | 0.800    |\n",
+    "| 1             | 100   | 0.708    |\n",
+    "| 2             | 10    | 0.950    |\n",
+    "| 2             | 100   | 0.877    |\n",
+    "| 4             | 10    | 0.970    |\n",
+    "| 4             | 100   | 0.956    |\n",
+    "| 8             | 10    | 0.990    |\n",
+    "| 8             | 100   | 0.990    |\n",
+    "| 16            | 10    | 1.000    |\n",
+    "| 16            | 100   | 0.998    |"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

docs/index.md

@@ -0,0 +1,76 @@
+# ⚡️ What is FastEmbed?
+
+FastEmbed is a lightweight, fast, Python library built for embedding generation. We [support popular text models](https://qdrant.github.io/fastembed/examples/Supported_Models/). Please [open a Github issue](https://github.com/qdrant/fastembed/issues/new) if you want us to add a new model.
+
+The default embedding supports "query" and "passage" prefixes for the input text. The default model is Flag Embedding, which is top of the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/examples/Retrieval%20with%20FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/examples/Usage_With_Qdrant/).
+
+1. Light & Fast
+    - Quantized model weights
+    - ONNX Runtime for inference via [Optimum](github.com/huggingface/optimum)
+
+2. Accuracy/Recall
+    - Better than OpenAI Ada-002
+    - Default is Flag Embedding, which is top of the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard
+    - List of [supported models](https://qdrant.github.io/fastembed/examples/Supported_Models/) - including multilingual models
+
+## 🚀 Installation
+
+To install the FastEmbed library, pip works:
+
+```bash
+pip install fastembed
+```
+
+## 📖 Usage
+
+```python
+from fastembed.embedding import FlagEmbedding as Embedding
+
+documents: List[str] = [
+    "passage: Hello, World!",
+    "query: Hello, World!", # these are two different embedding
+    "passage: This is an example passage.",
+    "fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended
+]
+embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512)
+embeddings: List[np.ndarray] = embedding_model.embed(documents) # If you use
+```
+
+## Usage with Qdrant
+
+Installation with Qdrant Client in Python:
+
+```bash
+pip install qdrant-client[fastembed]
+```
+
+Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh.
+
+```python
+from qdrant_client import QdrantClient
+
+# Initialize the client
+client = QdrantClient(":memory:")  # or QdrantClient(path="path/to/db")
+
+# Prepare your documents, metadata, and IDs
+docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
+metadata = [
+    {"source": "Langchain-docs"},
+    {"source": "Linkedin-docs"},
+]
+ids = [42, 2]
+
+# Use the new add method
+client.add(
+    collection_name="demo_collection",
+    documents=docs,
+    metadata=metadata,
+    ids=ids
+)
+
+search_result = client.query(
+    collection_name="demo_collection",
+    query_text="This is a query document"
+)
+print(search_result)
+```

experiments/01_ONNX_Port.ipynb

@@ -0,0 +1,357 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e9dbcde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c37e1fda-c7f1-46e7-a5d4-19fa05c36ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from typing import List, Tuple, Any\n",
+    "\n",
+    "import numpy as np\n",
+    "import time\n",
+    "from torch import Tensor\n",
+    "from transformers import AutoTokenizer, AutoModel\n",
+    "\n",
+    "from optimum.onnxruntime import AutoOptimizationConfig, ORTModelForFeatureExtraction, ORTOptimizer\n",
+    "from optimum.pipelines import pipeline\n",
+    "import torch.nn.functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78a65856",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the tokenizer and export the model to the ONNX format\n",
+    "# model_id = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
+    "# model_id = \"thenlper/gte-base\"\n",
+    "# model_id = \"intfloat/multilingual-e5-large\"\n",
+    "model_id = \"BAAI/bge-small-en-v1.5\"\n",
+    "save_dir = f\"fast-{model_id.split('/')[1]}\"\n",
+    "print(save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1ecf0b6-db81-4da3-b47f-e31460ccfbf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hf_model = AutoModel.from_pretrained(model_id)\n",
+    "hf_tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "\n",
+    "# The input texts can be in any language, not just English.\n",
+    "# Each input text should start with \"query: \" or \"passage: \", even for non-English texts.\n",
+    "# For tasks other than retrieval, you can simply use the \"query: \" prefix.\n",
+    "input_texts = [\n",
+    "    \"query: how much protein should a female eat\",\n",
+    "    \"query: 南瓜的家常做法\",\n",
+    "    \"query: भारत का राष्ट्रीय खेल कौन-सा है?\",  # Hindi text\n",
+    "    \"query: భారత్ దేశంలో రాష్ట్రపతి ఎవరు?\",  # Telugu text\n",
+    "    \"query: இந்தியாவின் தேசிய கோப்பை எது?\",  # Tamil text\n",
+    "    \"query: ಭಾರತದಲ್ಲಿ ರಾಷ್ಟ್ರಪತಿ ಯಾರು?\",  # Kannada text\n",
+    "    \"query: ഇന്ത്യയുടെ രാഷ്ട്രീയ ഗാനം എന്താണ്?\",  # Malayalam text\n",
+    "]\n",
+    "\n",
+    "english_texts = [\n",
+    "    \"India: Where the Taj Mahal meets spicy curry.\",\n",
+    "    \"Machine Learning: Turning data into knowledge, one algorithm at a time.\",\n",
+    "    \"Python: The language that makes programming a piece of cake.\",\n",
+    "    \"fastembed: Accelerating embeddings for lightning-fast similarity search.\",\n",
+    "    \"Qdrant: The ultimate tool for high-dimensional indexing and search.\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f8c761c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:\n",
+    "    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)\n",
+    "    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]\n",
+    "\n",
+    "\n",
+    "def hf_embed(model_id: str, inputs: List[str]):\n",
+    "    # Tokenize the input texts\n",
+    "    batch_dict = hf_tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors=\"pt\")\n",
+    "\n",
+    "    outputs = hf_model(**batch_dict)\n",
+    "    embeddings = average_pool(outputs.last_hidden_state, batch_dict[\"attention_mask\"])\n",
+    "\n",
+    "    # normalize embeddings\n",
+    "    embeddings = F.normalize(embeddings, p=2, dim=1)\n",
+    "    return embeddings.detach().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69bb4501",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hf_embed(inputs=english_texts, model_id=model_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "451dbd16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "model = ORTModelForFeatureExtraction.from_pretrained(model_id, export=True)\n",
+    "\n",
+    "# Remove all existing files in the save_dir using Path.unlink()\n",
+    "save_dir = Path(save_dir)\n",
+    "save_dir.mkdir(parents=True, exist_ok=True)\n",
+    "for p in save_dir.iterdir():\n",
+    "    p.unlink()\n",
+    "\n",
+    "# Load the optimization configuration detailing the optimization we wish to apply\n",
+    "optimization_config = AutoOptimizationConfig.O4()\n",
+    "optimizer = ORTOptimizer.from_pretrained(model)\n",
+    "\n",
+    "optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config, use_external_data_format=True)\n",
+    "model = ORTModelForFeatureExtraction.from_pretrained(save_dir)\n",
+    "\n",
+    "tokenizer.save_pretrained(save_dir)\n",
+    "# model.save_pretrained(save_dir)\n",
+    "# model.push_to_hub(\"new_path_for_directory\", repository_id=\"my-onnx-repo\", use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8422cddd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onnx_quant_embed = pipeline(\n",
+    "    \"feature-extraction\", model=model, accelerator=\"ort\", tokenizer=tokenizer, return_tensors=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51fa5775",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = onnx_quant_embed(inputs=english_texts)\n",
+    "F.normalize(embeddings[4])[:, 0], english_texts[4], len(embeddings), len(english_texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df405d70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def measure_pipeline_time(pipeline, input_texts: List[str], num_runs=10, **kwargs: Any) -> Tuple[float, float]:\n",
+    "    \"\"\"Measures the time it takes to run the pipeline on the input texts.\"\"\"\n",
+    "    times = []\n",
+    "    total_chars = sum(len(text) for text in input_texts)\n",
+    "    for _ in range(num_runs):\n",
+    "        start_time = time.time()\n",
+    "        _ = pipeline(inputs=input_texts, **kwargs)\n",
+    "        end_time = time.time()\n",
+    "        times.append(end_time - start_time)\n",
+    "\n",
+    "    mean_time = np.mean(times)\n",
+    "    std_dev = np.std(times)\n",
+    "    chars_per_second = total_chars / mean_time\n",
+    "    return mean_time, std_dev, chars_per_second"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "2d72aba5",
+   "metadata": {},
+   "source": [
+    "# Ours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b881152",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, _, chars_per_sec = measure_pipeline_time(onnx_quant_embed, input_texts)\n",
+    "print(f\"Multilingual Speed: {chars_per_sec:.2f} chars/sec\")\n",
+    "_, _, chars_per_sec = measure_pipeline_time(onnx_quant_embed, english_texts)\n",
+    "print(f\"English Speed: {chars_per_sec:.2f} chars/sec\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "49e1daf8",
+   "metadata": {},
+   "source": [
+    "# Original"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61b3bf53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, _, chars_per_sec = measure_pipeline_time(hf_embed, input_texts=input_texts, model_id=model_id)\n",
+    "print(f\"Multilingual Speed: {chars_per_sec:.2f} chars/sec\")\n",
+    "_, _, chars_per_sec = measure_pipeline_time(hf_embed, input_texts=english_texts, model_id=model_id)\n",
+    "print(f\"English Speed: {chars_per_sec:.2f} chars/sec\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "f0b7da8f-ffe7-4f58-95dd-7e9836f19328",
+   "metadata": {},
+   "source": [
+    "# Compress & Upload\n",
+    "\n",
+    "## Compress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "578b1d74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "import tarfile\n",
+    "\n",
+    "save_dir = Path(\"../local_cache/fast-bge-small-en-v1.5\")\n",
+    "\n",
+    "\n",
+    "def compress(directory_path):\n",
+    "    directory_path = Path(directory_path)\n",
+    "    assert directory_path.exists(), f\"{directory_path} does not exist\"\n",
+    "    output_filename = directory_path.name + \".tar.gz\"\n",
+    "    if Path(output_filename).exists():\n",
+    "        print(\"We've an output file already? Manually delete that first\")\n",
+    "        return output_filename\n",
+    "\n",
+    "    with tarfile.open(output_filename, \"w:gz\") as tar:\n",
+    "        tar.add(directory_path, arcname=os.path.basename(directory_path))\n",
+    "    return output_filename\n",
+    "\n",
+    "\n",
+    "compressed_file_name = compress(save_dir)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "96cdf140-eca8-4778-9ebe-947988b4cfcb",
+   "metadata": {},
+   "source": [
+    "## Upload to Qdrant Google Cloud Storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1dab9595",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.9/site-packages/google/auth/_default.py:76: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. See the following page for troubleshooting: https://cloud.google.com/docs/authentication/adc-troubleshooting/user-creds. \n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File fast-bge-small-en-v1.5.tar.gz uploaded to qdrant-fastembed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.cloud import storage\n",
+    "\n",
+    "\n",
+    "def upload(bucket_name, source_file_path):\n",
+    "    storage_client = storage.Client(project=\"main\")\n",
+    "    bucket = storage_client.bucket(bucket_name)\n",
+    "    blob = bucket.blob(os.path.basename(source_file_path))\n",
+    "\n",
+    "    blob.upload_from_filename(source_file_path)\n",
+    "\n",
+    "    print(f\"File {source_file_path} uploaded to {bucket_name}.\")\n",
+    "\n",
+    "\n",
+    "upload(\"qdrant-fastembed\", source_file_path=compressed_file_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "731554f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the directory and compressed file\n",
+    "!rm -rvf {save_dir}\n",
+    "!rm -vf {save_dir}.tar.gz"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

experiments/dynamic_quantization_export.py

@@ -0,0 +1,473 @@
+"""
+This script is used to convert HuggingFace models to ONNX format and optionally quantize the model using dynamic quantization. 
+This is courtesy of Joshua aka @Xenova
+"""
+
+import json
+import os
+import shutil
+from dataclasses import dataclass, field
+from typing import Optional, Set
+
+import onnx
+from onnxruntime.quantization import QuantType, quantize_dynamic
+from optimum.exporters.onnx import export_models, main_export
+from optimum.exporters.tasks import TasksManager
+from tqdm import tqdm
+from transformers import AutoConfig, AutoTokenizer, HfArgumentParser
+
+DEFAULT_QUANTIZE_PARAMS = {
+    "per_channel": True,
+    "reduce_range": True,
+}
+
+MODEL_SPECIFIC_QUANTIZE_PARAMS = {
+    # Decoder-only models
+    "codegen": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "gpt2": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "gpt_bigcode": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "gptj": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "gpt-neo": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "gpt-neox": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "mpt": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "bloom": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "llama": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "opt": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "mistral": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "falcon": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "phi": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "qwen2": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    # Encoder-decoder models
+    "whisper": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    "vision-encoder-decoder": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+    # Encoder-only models
+    "owlv2": {
+        "per_channel": False,
+        "reduce_range": False,
+    },
+}
+
+MODELS_WITHOUT_TOKENIZERS = [
+    "wav2vec2",
+    "wav2vec2-bert",
+    "wavlm",
+    "hubert",
+]
+
+
+@dataclass
+class ConversionArguments:
+    """
+    Arguments used for converting HuggingFace models to onnx.
+    """
+
+    model_id: str = field(metadata={"help": "Model identifier"})
+    tokenizer_id: str = field(default=None, metadata={"help": "Tokenizer identifier (if different to `model_id`)"})
+    quantize: bool = field(default=False, metadata={"help": "Whether to quantize the model."})
+    output_parent_dir: str = field(
+        default="./models/", metadata={"help": "Path where the converted model will be saved to."}
+    )
+
+    task: Optional[str] = field(
+        default="auto",
+        metadata={
+            "help": (
+                "The task to export the model for. If not specified, the task will be auto-inferred based on the model. Available tasks depend on the model, but are among:"
+                f" {str(TasksManager.get_all_tasks())}. For decoder models, use `xxx-with-past` to export the model using past key values in the decoder."
+            )
+        },
+    )
+
+    opset: int = field(
+        default=None,
+        metadata={
+            "help": (
+                "If specified, ONNX opset version to export the model with. Otherwise, the default opset will be used."
+            )
+        },
+    )
+
+    device: str = field(default="cpu", metadata={"help": "The device to use to do the export."})
+    skip_validation: bool = field(default=False, metadata={"help": "Whether to skip validation of the converted model"})
+
+    per_channel: bool = field(default=None, metadata={"help": "Whether to quantize weights per channel"})
+    reduce_range: bool = field(
+        default=None,
+        metadata={
+            "help": "Whether to quantize weights with 7-bits. It may improve the accuracy for some models running on non-VNNI machine, especially for per-channel mode"
+        },
+    )
+
+    output_attentions: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to output attentions from the model. NOTE: This is only supported for whisper models right now."
+        },
+    )
+
+    split_modalities: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to split multimodal models. NOTE: This is only supported for CLIP models right now."
+        },
+    )
+
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": "Allows to use custom code for the modeling hosted in the model repository. This option should only be set for repositories"
+            "you trust and in which you have read the code, as it will execute on your local machine arbitrary code present in the model repository."
+        },
+    )
+
+    custom_onnx_configs: str = field(
+        default=None,
+        metadata={
+            "help": "Experimental usage: override the default ONNX config used for the given model. This argument may be useful for advanced users "
+            "that desire a finer-grained control on the export."
+        },
+    )
+
+
+def get_operators(model: onnx.ModelProto) -> Set[str]:
+    operators = set()
+
+    def traverse_graph(graph):
+        for node in graph.node:
+            operators.add(node.op_type)
+            for attr in node.attribute:
+                if attr.type == onnx.AttributeProto.GRAPH:
+                    subgraph = attr.g
+                    traverse_graph(subgraph)
+
+    traverse_graph(model.graph)
+    return operators
+
+
+def quantize(model_names_or_paths, **quantize_kwargs):
+    """
+    Quantize the weights of the model from float32 to int8 to allow very efficient inference on modern CPU
+
+    Uses unsigned ints for activation values, signed ints for weights, per
+    https://onnxruntime.ai/docs/performance/quantization.html#data-type-selection
+    it is faster on most CPU architectures
+    Args:
+        onnx_model_path: Path to location the exported ONNX model is stored
+    Returns: The Path generated for the quantized
+    """
+
+    quantize_config = dict(**quantize_kwargs, per_model_config={})
+
+    for model in tqdm(model_names_or_paths, desc="Quantizing"):
+        directory_path = os.path.dirname(model)
+        file_name_without_extension = os.path.splitext(os.path.basename(model))[0]
+
+        # NOTE:
+        # As of 2023/04/20, the current latest version of onnxruntime-web is 1.14.0, and does not support INT8 weights for Conv layers.
+        # For this reason, we choose model weight types to ensure compatibility with onnxruntime-web.
+        #
+        # As per docs, signed weight type (QInt8) is faster on most CPUs, so, we use that unless the model contains a Conv layer.
+        # For more information, see:
+        #  - https://github.com/microsoft/onnxruntime/issues/3130#issuecomment-1105200621
+        #  - https://github.com/microsoft/onnxruntime/issues/2339
+
+        loaded_model = onnx.load_model(model)
+        op_types = get_operators(loaded_model)
+        weight_type = QuantType.QUInt8 if "Conv" in op_types else QuantType.QInt8
+
+        quantize_dynamic(
+            model_input=model,
+            model_output=os.path.join(directory_path, f"{file_name_without_extension}_quantized.onnx"),
+            weight_type=weight_type,
+            # TODO allow user to specify these
+            # op_types_to_quantize=['MatMul', 'Add', 'Conv'],
+            extra_options=dict(EnableSubgraph=True),
+            **quantize_kwargs,
+        )
+
+        quantize_config["per_model_config"][file_name_without_extension] = dict(
+            op_types=list(op_types),
+            weight_type=str(weight_type),
+        )
+
+    # Save quantization config
+    with open(os.path.join(directory_path, "quantize_config.json"), "w") as fp:
+        json.dump(quantize_config, fp, indent=4)
+
+
+def main():
+    parser = HfArgumentParser((ConversionArguments,))
+    (conv_args,) = parser.parse_args_into_dataclasses()
+
+    model_id = conv_args.model_id
+    tokenizer_id = conv_args.tokenizer_id or model_id
+
+    output_model_folder = os.path.join(conv_args.output_parent_dir, model_id)
+
+    # Create output folder
+    os.makedirs(output_model_folder, exist_ok=True)
+
+    from_pretrained_kwargs = dict(
+        trust_remote_code=conv_args.trust_remote_code,
+    )
+
+    # Saving the model config
+    config = AutoConfig.from_pretrained(model_id, **from_pretrained_kwargs)
+
+    custom_kwargs = {}
+    if conv_args.custom_onnx_configs is not None:
+        if conv_args.task == "auto":
+            raise Exception("`--task` must be set when exporting with `--custom_onnx_configs`")
+        custom_onnx_configs = json.loads(conv_args.custom_onnx_configs)
+
+        for key in custom_onnx_configs:
+            onnx_configs = TasksManager._SUPPORTED_MODEL_TYPE[custom_onnx_configs[key]]["onnx"]
+            mapping = onnx_configs[conv_args.task]
+            custom_onnx_configs[key] = mapping.func(config, **mapping.keywords)
+
+        custom_kwargs["custom_onnx_configs"] = custom_onnx_configs
+
+    tokenizer = None
+    try:
+        # Load tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, **from_pretrained_kwargs)
+
+        # To avoid inserting all chat templates into tokenizers.js, we save the chat template
+        # to the tokenizer_config.json file, and load it when the tokenizer is loaded.
+        if getattr(tokenizer, "chat_template", None) is None and getattr(tokenizer, "use_default_system_prompt", False):
+            # No chat template specified, and we use the default
+            setattr(tokenizer, "chat_template", tokenizer.default_chat_template)
+
+    except KeyError:
+        pass  # No Tokenizer
+
+    except Exception as e:
+        if config.model_type not in MODELS_WITHOUT_TOKENIZERS:
+            raise e
+
+    core_export_kwargs = dict(
+        opset=conv_args.opset,
+        device=conv_args.device,
+        trust_remote_code=conv_args.trust_remote_code,
+        **custom_kwargs,
+    )
+
+    export_kwargs = dict(
+        model_name_or_path=model_id,
+        output=output_model_folder,
+        task=conv_args.task,
+        do_validation=not conv_args.skip_validation,
+        library_name="transformers",
+        **core_export_kwargs,
+    )
+
+    # Handle special cases
+    if config.model_type == "marian":
+        from .extra.marian import generate_tokenizer_json
+
+        tokenizer_json = generate_tokenizer_json(model_id, tokenizer)
+
+        with open(os.path.join(output_model_folder, "tokenizer.json"), "w", encoding="utf-8") as fp:
+            json.dump(tokenizer_json, fp, indent=4)
+
+    elif config.model_type == "esm":
+        from .extra.esm import generate_fast_tokenizer
+
+        fast_tokenizer = generate_fast_tokenizer(tokenizer)
+        fast_tokenizer.save(os.path.join(output_model_folder, "tokenizer.json"))
+
+    elif config.model_type == "whisper":
+        if conv_args.output_attentions:
+            from .extra.whisper import get_main_export_kwargs
+
+            export_kwargs.update(**get_main_export_kwargs(config, "automatic-speech-recognition"))
+
+    elif config.model_type in ("wav2vec2", "wav2vec2-bert", "hubert"):
+        if tokenizer is not None:
+            from .extra.wav2vec2 import generate_tokenizer_json
+
+            tokenizer_json = generate_tokenizer_json(tokenizer)
+
+            with open(os.path.join(output_model_folder, "tokenizer.json"), "w", encoding="utf-8") as fp:
+                json.dump(tokenizer_json, fp, indent=4)
+
+    elif config.model_type == "vits":
+        if tokenizer is not None:
+            from .extra.vits import generate_tokenizer_json
+
+            tokenizer_json = generate_tokenizer_json(tokenizer)
+
+            with open(os.path.join(output_model_folder, "tokenizer.json"), "w", encoding="utf-8") as fp:
+                json.dump(tokenizer_json, fp, indent=4)
+
+    elif config.model_type == "speecht5":
+        # TODO allow user to specify vocoder path
+        export_kwargs["model_kwargs"] = {"vocoder": "microsoft/speecht5_hifigan"}
+
+        if tokenizer is not None:
+            from .extra.speecht5 import generate_tokenizer_json
+
+            tokenizer_json = generate_tokenizer_json(tokenizer)
+
+            with open(os.path.join(output_model_folder, "tokenizer.json"), "w", encoding="utf-8") as fp:
+                json.dump(tokenizer_json, fp, indent=4)
+
+    elif config.model_type in ("owlvit", "owlv2"):
+        # Override default batch size to 1, needed because non-maximum suppression is performed for exporting.
+        # For more information, see https://github.com/huggingface/optimum/blob/e3b7efb1257c011db907ef40ab340e795cc5684c/optimum/exporters/onnx/model_configs.py#L1028-L1032
+        export_kwargs["batch_size"] = 1
+
+    else:
+        pass  # TODO
+
+    # Step 1. convert huggingface model to onnx
+    if not conv_args.split_modalities:
+        main_export(**export_kwargs)
+    else:
+        custom_export_kwargs = dict(
+            output_dir=output_model_folder,
+            **core_export_kwargs,
+        )
+
+        if config.model_type == "clip":
+            # Handle special case for exporting text and vision models separately
+            from transformers.models.clip import CLIPTextModelWithProjection, CLIPVisionModelWithProjection
+
+            from .extra.clip import CLIPTextModelWithProjectionOnnxConfig, CLIPVisionModelWithProjectionOnnxConfig
+
+            text_model = CLIPTextModelWithProjection.from_pretrained(model_id, **from_pretrained_kwargs)
+            vision_model = CLIPVisionModelWithProjection.from_pretrained(model_id, **from_pretrained_kwargs)
+
+            export_models(
+                models_and_onnx_configs={
+                    "text_model": (text_model, CLIPTextModelWithProjectionOnnxConfig(text_model.config)),
+                    "vision_model": (vision_model, CLIPVisionModelWithProjectionOnnxConfig(vision_model.config)),
+                },
+                **custom_export_kwargs,
+            )
+
+        elif config.model_type == "siglip":
+            # Handle special case for exporting text and vision models separately
+            from transformers.models.siglip import SiglipTextModel, SiglipVisionModel
+
+            from .extra.siglip import SiglipTextModelOnnxConfig, SiglipVisionModelOnnxConfig
+
+            text_model = SiglipTextModel.from_pretrained(model_id, **from_pretrained_kwargs)
+            vision_model = SiglipVisionModel.from_pretrained(model_id, **from_pretrained_kwargs)
+
+            export_models(
+                models_and_onnx_configs={
+                    "text_model": (text_model, SiglipTextModelOnnxConfig(text_model.config)),
+                    "vision_model": (vision_model, SiglipVisionModelOnnxConfig(vision_model.config)),
+                },
+                **custom_export_kwargs,
+            )
+
+        # TODO: Enable once https://github.com/huggingface/optimum/pull/1552 is merged
+        # elif config.model_type == 'clap':
+        #     # Handle special case for exporting text and audio models separately
+        #     from .extra.clap import ClapTextModelWithProjectionOnnxConfig, ClapAudioModelWithProjectionOnnxConfig
+        #     from transformers.models.clap import ClapTextModelWithProjection, ClapAudioModelWithProjection
+
+        #     text_model = ClapTextModelWithProjection.from_pretrained(model_id, **from_pretrained_kwargs)
+        #     audio_model = ClapAudioModelWithProjection.from_pretrained(model_id, **from_pretrained_kwargs)
+
+        #     export_models(
+        #         models_and_onnx_configs={
+        #             "text_model": (text_model, ClapTextModelWithProjectionOnnxConfig(text_model.config)),
+        #             "audio_model": (audio_model, ClapAudioModelWithProjectionOnnxConfig(audio_model.config)),
+        #         },
+        #         **custom_export_kwargs,
+        #     )
+
+        else:
+            raise Exception(f"Unable to export {config.model_type} model with `--split_modalities`.")
+
+    # Step 2. (optional, recommended) quantize the converted model for fast inference and to reduce model size.
+    if conv_args.quantize:
+        # Update quantize config with model specific defaults
+        quantize_config = MODEL_SPECIFIC_QUANTIZE_PARAMS.get(config.model_type, DEFAULT_QUANTIZE_PARAMS)
+
+        # Update if user specified values
+        if conv_args.per_channel is not None:
+            quantize_config["per_channel"] = conv_args.per_channel
+
+        if conv_args.reduce_range is not None:
+            quantize_config["reduce_range"] = conv_args.reduce_range
+
+        quantize(
+            [
+                os.path.join(output_model_folder, x)
+                for x in os.listdir(output_model_folder)
+                if x.endswith(".onnx") and not x.endswith("_quantized.onnx")
+            ],
+            **quantize_config,
+        )
+
+    # Step 3. Move .onnx files to the 'onnx' subfolder
+    os.makedirs(os.path.join(output_model_folder, "onnx"), exist_ok=True)
+    for file in os.listdir(output_model_folder):
+        if file.endswith((".onnx", ".onnx_data")):
+            shutil.move(os.path.join(output_model_folder, file), os.path.join(output_model_folder, "onnx", file))
+
+    # Step 4. Update the generation config if necessary
+    if config.model_type == "whisper":
+        from transformers import GenerationConfig
+
+        from .extra.whisper import get_alignment_heads
+
+        generation_config = GenerationConfig.from_pretrained(model_id, **from_pretrained_kwargs)
+        generation_config.alignment_heads = get_alignment_heads(config)
+        generation_config.save_pretrained(output_model_folder)
+
+
+if __name__ == "__main__":
+    main()

experiments/try_quantized_model.py

@@ -0,0 +1,56 @@
+from pathlib import Path
+from typing import List
+
+import click
+import numpy as np
+import torch
+import torch.nn.functional as F
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+from optimum.pipelines import pipeline
+from torch import Tensor
+from transformers import AutoModel, AutoTokenizer
+
+
+def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
+    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
+    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+
+
+def hf_embed(model_id: str, texts: List[str], tokenizer):
+    # Tokenize the input texts
+    model = AutoModel.from_pretrained(model_id)
+    model.eval()
+    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+
+    model_output = model(**encoded_input)
+    sentence_embeddings = model_output[0][:, 0]
+    sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
+    return sentence_embeddings
+
+
+@click.command()
+@click.option("--model_id", help="model id from huggingface.co/models")
+@click.option("--model_dir", help="The person to greet.")
+def setup(model_id, model_dir):
+    text = "This is a test sentence"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    output_dir = Path(model_dir)
+    model = ORTModelForFeatureExtraction.from_pretrained(output_dir)
+    onnx_quant_embed = pipeline(
+        "feature-extraction", model=model, accelerator="ort", tokenizer=tokenizer, return_tensors=True
+    )
+    quant_embeddings = onnx_quant_embed([text])
+    quant_embeddings = F.normalize(quant_embeddings[0][:,0], p=2, dim=1)
+    quant_embeddings = quant_embeddings.detach().numpy()
+    print(quant_embeddings.shape)
+
+    torch_embeddings = hf_embed(model_id, texts=[text], tokenizer=tokenizer)
+    torch_embeddings = F.normalize(torch_embeddings, p=2, dim=1)
+    torch_embeddings = torch_embeddings.detach().numpy()
+    print(torch_embeddings.shape)
+    assert quant_embeddings.shape == torch_embeddings.shape
+    print(np.allclose(quant_embeddings, torch_embeddings, atol=1e-5))
+
+
+if __name__ == "__main__":
+    setup()

fastembed/__init__.py

@@ -0,0 +1,3 @@
+from fastembed.text.text_embedding import TextEmbedding
+
+__all__ = ["TextEmbedding"]

fastembed/common/model_management.py

@@ -0,0 +1,197 @@
+import os
+import shutil
+import tarfile
+from pathlib import Path
+from typing import List, Optional, Dict, Any
+
+import requests
+from huggingface_hub import snapshot_download
+from huggingface_hub.utils import RepositoryNotFoundError
+from tqdm import tqdm
+from loguru import logger
+
+
+def locate_model_file(model_dir: Path, file_names: List[str]) -> Path:
+    """
+    Find model path for both TransformerJS style `onnx`  subdirectory structure and direct model weights structure used
+    by Optimum and Qdrant
+    """
+    if not model_dir.is_dir():
+        raise ValueError(f"Provided model path '{model_dir}' is not a directory.")
+
+    for file_name in file_names:
+        file_paths = [path for path in model_dir.rglob(file_name) if path.is_file()]
+
+        if file_paths:
+            return file_paths[0]
+
+    raise ValueError(f"Could not find either of {', '.join(file_names)} in {model_dir}")
+
+
+class ModelManagement:
+    @classmethod
+    def download_file_from_gcs(cls, url: str, output_path: str, show_progress: bool = True) -> str:
+        """
+        Downloads a file from Google Cloud Storage.
+
+        Args:
+            url (str): The URL to download the file from.
+            output_path (str): The path to save the downloaded file to.
+            show_progress (bool, optional): Whether to show a progress bar. Defaults to True.
+
+        Returns:
+            str: The path to the downloaded file.
+        """
+
+        if os.path.exists(output_path):
+            return output_path
+        response = requests.get(url, stream=True)
+
+        # Handle HTTP errors
+        if response.status_code == 403:
+            raise PermissionError(
+                "Authentication Error: You do not have permission to access this resource. "
+                "Please check your credentials."
+            )
+
+        # Get the total size of the file
+        total_size_in_bytes = int(response.headers.get("content-length", 0))
+
+        # Warn if the total size is zero
+        if total_size_in_bytes == 0:
+            print(f"Warning: Content-length header is missing or zero in the response from {url}.")
+
+        show_progress = total_size_in_bytes and show_progress
+
+        with tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True, disable=not show_progress) as progress_bar:
+            with open(output_path, "wb") as file:
+                for chunk in response.iter_content(chunk_size=1024):
+                    if chunk:  # Filter out keep-alive new chunks
+                        progress_bar.update(len(chunk))
+                        file.write(chunk)
+        return output_path
+
+    @classmethod
+    def download_files_from_huggingface(cls, hf_source_repo: str, cache_dir: Optional[str] = None) -> str:
+        """
+        Downloads a model from HuggingFace Hub.
+        Args:
+            hf_source_repo (str): Name of the model on HuggingFace Hub, e.g. "qdrant/all-MiniLM-L6-v2-onnx".
+            cache_dir (Optional[str]): The path to the cache directory.
+        Returns:
+            Path: The path to the model directory.
+        """
+
+        return snapshot_download(
+            repo_id=hf_source_repo,
+            ignore_patterns=["model.safetensors", "pytorch_model.bin"],
+            cache_dir=cache_dir,
+        )
+
+    @classmethod
+    def decompress_to_cache(cls, targz_path: str, cache_dir: str):
+        """
+        Decompresses a .tar.gz file to a cache directory.
+
+        Args:
+            targz_path (str): Path to the .tar.gz file.
+            cache_dir (str): Path to the cache directory.
+
+        Returns:
+            cache_dir (str): Path to the cache directory.
+        """
+        # Check if targz_path exists and is a file
+        if not os.path.isfile(targz_path):
+            raise ValueError(f"{targz_path} does not exist or is not a file.")
+
+        # Check if targz_path is a .tar.gz file
+        if not targz_path.endswith(".tar.gz"):
+            raise ValueError(f"{targz_path} is not a .tar.gz file.")
+
+        try:
+            # Open the tar.gz file
+            with tarfile.open(targz_path, "r:gz") as tar:
+                # Extract all files into the cache directory
+                tar.extractall(path=cache_dir)
+        except tarfile.TarError as e:
+            # If any error occurs while opening or extracting the tar.gz file,
+            # delete the cache directory (if it was created in this function)
+            # and raise the error again
+            if "tmp" in cache_dir:
+                shutil.rmtree(cache_dir)
+            raise ValueError(f"An error occurred while decompressing {targz_path}: {e}")
+
+        return cache_dir
+
+    @classmethod
+    def retrieve_model_gcs(cls, model_name: str, source_url: str, cache_dir: str) -> Path:
+        fast_model_name = f"fast-{model_name.split('/')[-1]}"
+
+        cache_tmp_dir = Path(cache_dir) / "tmp"
+        model_tmp_dir = cache_tmp_dir / fast_model_name
+        model_dir = Path(cache_dir) / fast_model_name
+
+        # check if the model_dir and the model files are both present for macOS
+        if model_dir.exists() and len(list(model_dir.glob("*"))) > 0:
+            return model_dir
+
+        if model_tmp_dir.exists():
+            shutil.rmtree(model_tmp_dir)
+
+        cache_tmp_dir.mkdir(parents=True, exist_ok=True)
+
+        model_tar_gz = Path(cache_dir) / f"{fast_model_name}.tar.gz"
+
+        cls.download_file_from_gcs(
+            source_url,
+            output_path=str(model_tar_gz),
+        )
+
+        cls.decompress_to_cache(targz_path=str(model_tar_gz), cache_dir=str(cache_tmp_dir))
+        assert model_tmp_dir.exists(), f"Could not find {model_tmp_dir} in {cache_tmp_dir}"
+
+        model_tar_gz.unlink()
+        # Rename from tmp to final name is atomic
+        model_tmp_dir.rename(model_dir)
+
+        return model_dir
+
+    @classmethod
+    def download_model(cls, model: Dict[str, Any], cache_dir: Path) -> Path:
+        """
+        Downloads a model from HuggingFace Hub or Google Cloud Storage.
+
+        Args:
+            model (Dict[str, Any]): The model description.
+                Example:
+                ```
+                {
+                    "model": "BAAI/bge-base-en-v1.5",
+                    "dim": 768,
+                    "description": "Base English model, v1.5",
+                    "size_in_GB": 0.44,
+                    "sources": {
+                        "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz",
+                        "hf": "qdrant/bge-base-en-v1.5-onnx-q",
+                    }
+                }
+                ```
+            cache_dir (str): The path to the cache directory.
+
+        Returns:
+            Path: The path to the downloaded model directory.
+        """
+
+        hf_source = model.get("sources", {}).get("hf")
+        url_source = model.get("sources", {}).get("url")
+
+        if hf_source:
+            try:
+                return Path(cls.download_files_from_huggingface(hf_source, cache_dir=str(cache_dir)))
+            except (EnvironmentError, RepositoryNotFoundError, ValueError) as e:
+                logger.error(f"Could not download model from HuggingFace: {e}" "Falling back to other sources.")
+
+        if url_source:
+            return cls.retrieve_model_gcs(model["model"], url_source, str(cache_dir))
+
+        raise ValueError(f"Could not download model {model['model']} from any source.")

fastembed/common/models.py

@@ -0,0 +1,52 @@
+import json
+from pathlib import Path
+
+import numpy as np
+from tokenizers import Tokenizer, AddedToken
+
+
+def load_tokenizer(model_dir: Path, max_length: int = 512) -> Tokenizer:
+    config_path = model_dir / "config.json"
+    if not config_path.exists():
+        raise ValueError(f"Could not find config.json in {model_dir}")
+
+    tokenizer_path = model_dir / "tokenizer.json"
+    if not tokenizer_path.exists():
+        raise ValueError(f"Could not find tokenizer.json in {model_dir}")
+
+    tokenizer_config_path = model_dir / "tokenizer_config.json"
+    if not tokenizer_config_path.exists():
+        raise ValueError(f"Could not find tokenizer_config.json in {model_dir}")
+
+    tokens_map_path = model_dir / "special_tokens_map.json"
+    if not tokens_map_path.exists():
+        raise ValueError(f"Could not find special_tokens_map.json in {model_dir}")
+
+    with open(str(config_path)) as config_file:
+        config = json.load(config_file)
+
+    with open(str(tokenizer_config_path)) as tokenizer_config_file:
+        tokenizer_config = json.load(tokenizer_config_file)
+
+    with open(str(tokens_map_path)) as tokens_map_file:
+        tokens_map = json.load(tokens_map_file)
+
+    tokenizer = Tokenizer.from_file(str(tokenizer_path))
+    tokenizer.enable_truncation(max_length=min(tokenizer_config["model_max_length"], max_length))
+    tokenizer.enable_padding(pad_id=config.get("pad_token_id", 0), pad_token=tokenizer_config["pad_token"])
+
+    for token in tokens_map.values():
+        if isinstance(token, str):
+            tokenizer.add_special_tokens([token])
+        elif isinstance(token, dict):
+            tokenizer.add_special_tokens([AddedToken(**token)])
+
+    return tokenizer
+
+
+def normalize(input_array, p=2, dim=1, eps=1e-12) -> np.ndarray:
+    # Calculate the Lp norm along the specified dimension
+    norm = np.linalg.norm(input_array, ord=p, axis=dim, keepdims=True)
+    norm = np.maximum(norm, eps)  # Avoid division by zero
+    normalized_array = input_array / norm
+    return normalized_array

fastembed/common/utils.py

@@ -0,0 +1,33 @@
+import os
+import tempfile
+from itertools import islice
+from pathlib import Path
+from typing import Union, Iterable, Generator, Optional
+
+
+def iter_batch(iterable: Union[Iterable, Generator], size: int) -> Iterable:
+    """
+    >>> list(iter_batch([1,2,3,4,5], 3))
+    [[1, 2, 3], [4, 5]]
+    """
+    source_iter = iter(iterable)
+    while source_iter:
+        b = list(islice(source_iter, size))
+        if len(b) == 0:
+            break
+        yield b
+
+
+def define_cache_dir(cache_dir: Optional[str] = None) -> Path:
+    """
+    Define the cache directory for fastembed
+    """
+    if cache_dir is None:
+        default_cache_dir = os.path.join(tempfile.gettempdir(), "fastembed_cache")
+        cache_path = Path(os.getenv("FASTEMBED_CACHE_PATH", default_cache_dir))
+    else:
+        cache_path = Path(cache_dir)
+
+    cache_path.mkdir(parents=True, exist_ok=True)
+
+    return cache_path

fastembed/embedding.py

@@ -0,0 +1,21 @@
+from typing import Optional
+
+from loguru import logger
+
+from fastembed.text.text_embedding import TextEmbedding
+
+logger.warning("DefaultEmbedding, FlagEmbedding, JinaEmbedding are deprecated." "Use from fastembed import TextEmbedding instead.")
+
+DefaultEmbedding = TextEmbedding
+FlagEmbedding = TextEmbedding
+
+
+class JinaEmbedding(TextEmbedding):
+    def __init__(
+        self,
+        model_name: str = "jinaai/jina-embeddings-v2-base-en",
+        cache_dir: Optional[str] = None,
+        threads: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__(model_name, cache_dir, threads, **kwargs)

fastembed/parallel_processor.py

@@ -0,0 +1,207 @@
+import logging
+import os
+from collections import defaultdict
+from enum import Enum
+from multiprocessing import Queue, get_context
+from multiprocessing.context import BaseContext
+from multiprocessing.process import BaseProcess
+from multiprocessing.sharedctypes import Synchronized as BaseValue
+from queue import Empty
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
+
+# Single item should be processed in less than:
+processing_timeout = 10 * 60  # seconds
+
+max_internal_batch_size = 200
+
+
+class QueueSignals(str, Enum):
+    stop = "stop"
+    confirm = "confirm"
+    error = "error"
+
+
+class Worker:
+    @classmethod
+    def start(cls, **kwargs: Any) -> "Worker":
+        raise NotImplementedError()
+
+    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
+        raise NotImplementedError()
+
+
+def _worker(
+    worker_class: Type[Worker],
+    input_queue: Queue,
+    output_queue: Queue,
+    num_active_workers: BaseValue,
+    worker_id: int,
+    kwargs: Optional[Dict[str, Any]] = None,
+) -> None:
+    """
+    A worker that pulls data pints off the input queue, and places the execution result on the output queue.
+    When there are no data pints left on the input queue, it decrements
+    num_active_workers to signal completion.
+    """
+
+    if kwargs is None:
+        kwargs = {}
+
+    logging.info(f"Reader worker: {worker_id} PID: {os.getpid()}")
+    try:
+        worker = worker_class.start(**kwargs)
+
+        # Keep going until you get an item that's None.
+        def input_queue_iterable() -> Iterable[Any]:
+            while True:
+                item = input_queue.get()
+                if item == QueueSignals.stop:
+                    break
+                yield item
+
+        for processed_item in worker.process(input_queue_iterable()):
+            output_queue.put(processed_item)
+    except Exception as e:  # pylint: disable=broad-except
+        logging.exception(e)
+        output_queue.put(QueueSignals.error)
+    finally:
+        # It's important that we close and join the queue here before
+        # decrementing num_active_workers. Otherwise our parent may join us
+        # before the queue's feeder thread has passed all buffered items to
+        # the underlying pipe resulting in a deadlock.
+        #
+        # See:
+        # https://docs.python.org/3.6/library/multiprocessing.html?highlight=process#pipes-and-queues
+        # https://docs.python.org/3.6/library/multiprocessing.html?highlight=process#programming-guidelines
+        output_queue.close()
+        output_queue.join_thread()
+
+        with num_active_workers.get_lock():
+            num_active_workers.value -= 1
+
+        logging.info(f"Reader worker {worker_id} finished")
+
+
+class ParallelWorkerPool:
+    def __init__(self, num_workers: int, worker: Type[Worker], start_method: Optional[str] = None):
+        self.worker_class = worker
+        self.num_workers = num_workers
+        self.input_queue: Optional[Queue] = None
+        self.output_queue: Optional[Queue] = None
+        self.ctx: BaseContext = get_context(start_method)
+        self.processes: List[BaseProcess] = []
+        self.queue_size = self.num_workers * max_internal_batch_size
+
+        self.num_active_workers: Optional[BaseValue] = None
+
+    def start(self, **kwargs: Any) -> None:
+        self.input_queue = self.ctx.Queue(self.queue_size)
+        self.output_queue = self.ctx.Queue(self.queue_size)
+
+        ctx_value = self.ctx.Value("i", self.num_workers)
+        assert isinstance(ctx_value, BaseValue)
+        self.num_active_workers = ctx_value
+
+        for worker_id in range(0, self.num_workers):
+            assert hasattr(self.ctx, "Process")
+            process = self.ctx.Process(
+                target=_worker,
+                args=(
+                    self.worker_class,
+                    self.input_queue,
+                    self.output_queue,
+                    self.num_active_workers,
+                    worker_id,
+                    kwargs.copy(),
+                ),
+            )
+            process.start()
+            self.processes.append(process)
+
+    def ordered_map(self, stream: Iterable[Any], *args: Any, **kwargs: Any) -> Iterable[Any]:
+        buffer = defaultdict(Any)
+        next_expected = 0
+
+        for idx, item in self.semi_ordered_map(stream, *args, **kwargs):
+            buffer[idx] = item
+            while next_expected in buffer:
+                yield buffer.pop(next_expected)
+                next_expected += 1
+
+    def semi_ordered_map(self, stream: Iterable[Any], *args: Any, **kwargs: Any) -> Iterable[Tuple[int, Any]]:
+        try:
+            self.start(**kwargs)
+
+            assert self.input_queue is not None, "Input queue was not initialized"
+            assert self.output_queue is not None, "Output queue was not initialized"
+
+            pushed = 0
+            read = 0
+            for idx, item in enumerate(stream):
+                if pushed - read < self.queue_size:
+                    try:
+                        out_item = self.output_queue.get_nowait()
+                    except Empty:
+                        out_item = None
+                else:
+                    try:
+                        out_item = self.output_queue.get(timeout=processing_timeout)
+                    except Empty as e:
+                        self.join_or_terminate()
+                        raise e
+
+                if out_item is not None:
+                    if out_item == QueueSignals.error:
+                        self.join_or_terminate()
+                        raise RuntimeError("Thread unexpectedly terminated")
+                    yield out_item
+                    read += 1
+
+                self.input_queue.put((idx, item))
+                pushed += 1
+
+            for _ in range(self.num_workers):
+                self.input_queue.put(QueueSignals.stop)
+
+            while read < pushed:
+                out_item = self.output_queue.get(timeout=processing_timeout)
+                if out_item == QueueSignals.error:
+                    self.join_or_terminate()
+                    raise RuntimeError("Thread unexpectedly terminated")
+                yield out_item
+                read += 1
+        finally:
+            assert self.input_queue is not None, "Input queue is None"
+            assert self.output_queue is not None, "Output queue is None"
+            self.input_queue.close()
+            self.output_queue.close()
+
+    def join_or_terminate(self, timeout: Optional[int] = 1) -> None:
+        """
+        Emergency shutdown
+        @param timeout:
+        @return:
+        """
+        for process in self.processes:
+            process.join(timeout=timeout)
+            if process.is_alive():
+                process.terminate()
+        self.processes.clear()
+
+    def join(self) -> None:
+        for process in self.processes:
+            process.join()
+        self.processes.clear()
+
+    def __del__(self) -> None:
+        """
+        Terminate processes if the user hasn't joined. This is necessary as
+        leaving stray processes running can corrupt shared state. In brief,
+        we've observed shared memory counters being reused (when the memory was
+        free from the perspective of the parent process) while the stray
+        workers still held a reference to them.
+        For a discussion of using destructors in Python in this manner, see
+        https://eli.thegreenplace.net/2009/06/12/safely-using-destructors-in-python/.
+        """
+        for process in self.processes:
+            process.terminate()

fastembed/text/e5_onnx_embedding.py

@@ -0,0 +1,58 @@
+from typing import Type, List, Dict, Any
+
+import numpy as np
+
+from fastembed.text.onnx_embedding import OnnxTextEmbedding, OnnxTextEmbeddingWorker, EmbeddingWorker
+
+supported_multilingual_e5_models = [
+    {
+        "model": "intfloat/multilingual-e5-large",
+        "dim": 1024,
+        "description": "Multilingual model, e5-large. Recommend using this model for non-English languages",
+        "size_in_GB": 2.24,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz",
+            "hf": "qdrant/multilingual-e5-large-onnx",
+        },
+    },
+    {
+        "model": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+        "dim": 768,
+        "description": "Sentence-transformers model for tasks like clustering or semantic search",
+        "size_in_GB": 1.11,
+        "sources": {
+            "hf": "xenova/paraphrase-multilingual-mpnet-base-v2",
+        },
+    },
+]
+
+
+class E5OnnxEmbedding(OnnxTextEmbedding):
+    @classmethod
+    def _get_worker_class(cls) -> Type["EmbeddingWorker"]:
+        return E5OnnxEmbeddingWorker
+
+    @classmethod
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
+        """Lists the supported models.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
+        """
+        return supported_multilingual_e5_models
+
+    def _preprocess_onnx_input(self, onnx_input: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
+        """
+        Preprocess the onnx input.
+        """
+        onnx_input.pop("token_type_ids", None)
+        return onnx_input
+
+
+class E5OnnxEmbeddingWorker(OnnxTextEmbeddingWorker):
+    def init_embedding(
+        self,
+        model_name: str,
+        cache_dir: str,
+    ) -> E5OnnxEmbedding:
+        return E5OnnxEmbedding(model_name=model_name, cache_dir=cache_dir, threads=1)

fastembed/text/jina_onnx_embedding.py

@@ -0,0 +1,62 @@
+from typing import Type, List, Dict, Any, Tuple
+
+import numpy as np
+
+from fastembed.common.models import normalize
+from fastembed.text.onnx_embedding import OnnxTextEmbedding, EmbeddingWorker, OnnxTextEmbeddingWorker
+
+supported_jina_models = [
+    {
+        "model": "jinaai/jina-embeddings-v2-base-en",
+        "dim": 768,
+        "description": "English embedding model supporting 8192 sequence length",
+        "size_in_GB": 0.55,
+        "sources": {"hf": "xenova/jina-embeddings-v2-base-en"},
+    },
+    {
+        "model": "jinaai/jina-embeddings-v2-small-en",
+        "dim": 512,
+        "description": "English embedding model supporting 8192 sequence length",
+        "size_in_GB": 0.13,
+        "sources": {"hf": "xenova/jina-embeddings-v2-small-en"},
+    },
+]
+
+
+class JinaOnnxEmbedding(OnnxTextEmbedding):
+    @classmethod
+    def _get_worker_class(cls) -> Type[EmbeddingWorker]:
+        return JinaEmbeddingWorker
+
+    @classmethod
+    def mean_pooling(cls, model_output, attention_mask) -> np.ndarray:
+        token_embeddings = model_output
+        input_mask_expanded = (np.expand_dims(attention_mask, axis=-1)).astype(float)
+
+        sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)
+        mask_sum = np.clip(np.sum(input_mask_expanded, axis=1), a_min=1e-9, a_max=None)
+
+        return sum_embeddings / mask_sum
+
+    @classmethod
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
+        """Lists the supported models.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
+        """
+        return supported_jina_models
+
+    @classmethod
+    def _post_process_onnx_output(cls, output: Tuple[np.ndarray, np.ndarray]) -> np.ndarray:
+        embeddings, attn_mask = output
+        return normalize(cls.mean_pooling(embeddings, attn_mask)).astype(np.float32)
+
+
+class JinaEmbeddingWorker(OnnxTextEmbeddingWorker):
+    def init_embedding(
+        self,
+        model_name: str,
+        cache_dir: str,
+    ) -> OnnxTextEmbedding:
+        return JinaOnnxEmbedding(model_name=model_name, cache_dir=cache_dir, threads=1)

fastembed/text/onnx_embedding.py

@@ -0,0 +1,343 @@
+import os
+from multiprocessing import get_all_start_methods
+from typing import List, Dict, Any, Optional, Tuple, Union, Iterable, Type
+
+import numpy as np
+import onnxruntime as ort
+
+from fastembed.common.model_management import locate_model_file
+from fastembed.common.models import load_tokenizer, normalize
+from fastembed.common.utils import define_cache_dir, iter_batch
+from fastembed.parallel_processor import ParallelWorkerPool, Worker
+from fastembed.text.text_embedding_base import TextEmbeddingBase
+
+supported_onnx_models = [
+    {
+        "model": "BAAI/bge-base-en",
+        "dim": 768,
+        "description": "Base English model",
+        "size_in_GB": 0.5,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz",
+        },
+    },
+    {
+        "model": "BAAI/bge-base-en-v1.5",
+        "dim": 768,
+        "description": "Base English model, v1.5",
+        "size_in_GB": 0.44,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz",
+            "hf": "qdrant/bge-base-en-v1.5-onnx-q",
+        },
+    },
+    {
+        "model": "BAAI/bge-large-en-v1.5-quantized",
+        "dim": 1024,
+        "description": "Large English model, v1.5",
+        "size_in_GB": 1.34,
+        "sources": {
+            "hf": "qdrant/bge-large-en-v1.5-onnx-q",
+        },
+    },
+    {
+        "model": "BAAI/bge-large-en-v1.5",
+        "dim": 1024,
+        "description": "Large English model, v1.5",
+        "size_in_GB": 1.34,
+        "sources": {
+            "hf": "qdrant/bge-large-en-v1.5-onnx",
+        },
+    },
+    {
+        "model": "BAAI/bge-small-en",
+        "dim": 384,
+        "description": "Fast English model",
+        "size_in_GB": 0.2,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz",
+        },
+    },
+    # {
+    #     "model": "BAAI/bge-small-en",
+    #     "dim": 384,
+    #     "description": "Fast English model",
+    #     "size_in_GB": 0.2,
+    #     "hf_sources": [],
+    #     "compressed_url_sources": [
+    #         "https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-en.tar.gz",
+    #         "https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz"
+    #     ]
+    # },
+    {
+        "model": "BAAI/bge-small-en-v1.5",
+        "dim": 384,
+        "description": "Fast and Default English model",
+        "size_in_GB": 0.13,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-en-v1.5.tar.gz",
+            "hf": "qdrant/bge-small-en-v1.5-onnx-q",
+        },
+    },
+    {
+        "model": "BAAI/bge-small-zh-v1.5",
+        "dim": 512,
+        "description": "Fast and recommended Chinese model",
+        "size_in_GB": 0.1,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz",
+        },
+    },
+    {  # todo: it is not a flag embedding
+        "model": "sentence-transformers/all-MiniLM-L6-v2",
+        "dim": 384,
+        "description": "Sentence Transformer model, MiniLM-L6-v2",
+        "size_in_GB": 0.09,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz",
+            "hf": "qdrant/all-MiniLM-L6-v2-onnx",
+        },
+    },
+    {
+        "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+        "dim": 384,
+        "description": "Sentence Transformer model, paraphrase-multilingual-MiniLM-L12-v2",
+        "size_in_GB": 0.46,
+        "sources": {
+            "hf": "qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q",
+        },
+    },
+    {
+        "model": "nomic-ai/nomic-embed-text-v1",
+        "dim": 768,
+        "description": "8192 context length english model",
+        "size_in_GB": 0.54,
+        "sources": {
+            "hf": "nomic-ai/nomic-embed-text-v1",
+        },
+    },
+    {
+        "model": "nomic-ai/nomic-embed-text-v1.5",
+        "dim": 768,
+        "description": "8192 context length english model",
+        "size_in_GB": 0.54,
+        "sources": {
+            "hf": "nomic-ai/nomic-embed-text-v1.5",
+        },
+    },
+    {
+        "model": "thenlper/gte-large",
+        "dim": 1024,
+        "description": "Large general text embeddings model",
+        "size_in_GB": 1.34,
+        "sources": {
+            "hf": "qdrant/gte-large-onnx",
+        },
+    },
+    # {
+    #     "model": "sentence-transformers/all-MiniLM-L6-v2",
+    #     "dim": 384,
+    #     "description": "Sentence Transformer model, MiniLM-L6-v2",
+    #     "size_in_GB": 0.09,
+    #     "hf_sources": [
+    #         "qdrant/all-MiniLM-L6-v2-onnx"
+    #     ],
+    #     "compressed_url_sources": [
+    #         "https://storage.googleapis.com/qdrant-fastembed/fast-all-MiniLM-L6-v2.tar.gz",
+    #         "https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz"
+    #     ]
+    # }
+]
+
+
+class OnnxTextEmbedding(TextEmbeddingBase):
+    """Implementation of the Flag Embedding model."""
+
+    @classmethod
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
+        """Lists the supported models.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
+        """
+        return supported_onnx_models
+
+    @classmethod
+    def _get_model_description(cls, model_name: str) -> Dict[str, Any]:
+        """
+        Gets the model description from the model_name.
+
+        Args:
+            model_name (str): The name of the model.
+
+        raises:
+            ValueError: If the model_name is not supported.
+
+        Returns:
+            Dict[str, Any]: The model description.
+        """
+        for model in cls.list_supported_models():
+            if model_name == model["model"]:
+                return model
+
+        raise ValueError(f"Model {model_name} is not supported in FlagEmbedding.")
+
+    def __init__(
+        self,
+        model_name: str = "BAAI/bge-small-en-v1.5",
+        cache_dir: Optional[str] = None,
+        threads: Optional[int] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            model_name (str): The name of the model to use.
+            cache_dir (str, optional): The path to the cache directory.
+                                       Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+                                       Defaults to `fastembed_cache` in the system's temp directory.
+            threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
+
+        Raises:
+            ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
+        """
+
+        super().__init__(model_name, cache_dir, threads, **kwargs)
+
+        self.model_name = model_name
+        self._model_description = self._get_model_description(model_name)
+
+        self._cache_dir = define_cache_dir(cache_dir)
+        self._model_dir = self.download_model(self._model_description, self._cache_dir)
+        self._max_length = 512
+
+        model_path = locate_model_file(self._model_dir, ["model.onnx", "model_optimized.onnx"])
+
+        # List of Execution Providers: https://onnxruntime.ai/docs/execution-providers
+        onnx_providers = ["CPUExecutionProvider"]
+
+        so = ort.SessionOptions()
+        so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+        if self.threads is not None:
+            so.intra_op_num_threads = self.threads
+            so.inter_op_num_threads = self.threads
+
+        self.tokenizer = load_tokenizer(model_dir=self._model_dir, max_length=self._max_length)
+        self.model = ort.InferenceSession(str(model_path), providers=onnx_providers, sess_options=so)
+
+    def embed(
+        self,
+        documents: Union[str, Iterable[str]],
+        batch_size: int = 256,
+        parallel: Optional[int] = None,
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
+        """
+        Encode a list of documents into list of embeddings.
+        We use mean pooling with attention so that the model can handle variable-length inputs.
+
+        Args:
+            documents: Iterator of documents or single document to embed
+            batch_size: Batch size for encoding -- higher values will use more memory, but be faster
+            parallel:
+                If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+                If 0, use all available cores.
+                If None, don't use data-parallel processing, use default onnxruntime threading instead.
+
+        Returns:
+            List of embeddings, one per document
+        """
+        is_small = False
+
+        if isinstance(documents, str):
+            documents = [documents]
+            is_small = True
+
+        if isinstance(documents, list):
+            if len(documents) < batch_size:
+                is_small = True
+
+        if parallel == 0:
+            parallel = os.cpu_count()
+
+        if parallel is None or is_small:
+            for batch in iter_batch(documents, batch_size):
+                yield from self._post_process_onnx_output(self.onnx_embed(batch))
+        else:
+            start_method = "forkserver" if "forkserver" in get_all_start_methods() else "spawn"
+            params = {
+                "model_name": self.model_name,
+                "cache_dir": str(self._cache_dir),
+            }
+            pool = ParallelWorkerPool(parallel, self._get_worker_class(), start_method=start_method)
+            for batch in pool.ordered_map(iter_batch(documents, batch_size), **params):
+                yield from self._post_process_onnx_output(batch)
+
+    @classmethod
+    def _get_worker_class(cls) -> Type["EmbeddingWorker"]:
+        return OnnxTextEmbeddingWorker
+
+    def _preprocess_onnx_input(self, onnx_input: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
+        """
+        Preprocess the onnx input.
+        """
+        return onnx_input
+
+    @classmethod
+    def _post_process_onnx_output(cls, output: Tuple[np.ndarray, np.ndarray]):
+        embeddings, _ = output
+        return normalize(embeddings[:, 0]).astype(np.float32)
+
+    def onnx_embed(self, documents: List[str]) -> Tuple[np.ndarray, np.ndarray]:
+        encoded = self.tokenizer.encode_batch(documents)
+        input_ids = np.array([e.ids for e in encoded])
+        attention_mask = np.array([e.attention_mask for e in encoded])
+
+        onnx_input = {
+            "input_ids": np.array(input_ids, dtype=np.int64),
+            "attention_mask": np.array(attention_mask, dtype=np.int64),
+            "token_type_ids": np.array([np.zeros(len(e), dtype=np.int64) for e in input_ids], dtype=np.int64),
+        }
+
+        onnx_input = self._preprocess_onnx_input(onnx_input)
+
+        model_output = self.model.run(None, onnx_input)
+        embeddings = model_output[0]
+        return embeddings, attention_mask
+
+
+class EmbeddingWorker(Worker):
+    def init_embedding(
+        self,
+        model_name: str,
+        cache_dir: str,
+    ) -> OnnxTextEmbedding:
+        raise NotImplementedError()
+
+    def __init__(
+        self,
+        model_name: str,
+        cache_dir: str,
+    ):
+        self.model = self.init_embedding(model_name, cache_dir)
+
+    @classmethod
+    def start(cls, model_name: str, cache_dir: str, **kwargs: Any) -> "EmbeddingWorker":
+        return cls(
+            model_name=model_name,
+            cache_dir=cache_dir,
+        )
+
+    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
+        for idx, batch in items:
+            embeddings, attn_mask = self.model.onnx_embed(batch)
+            yield idx, (embeddings, attn_mask)
+
+
+class OnnxTextEmbeddingWorker(EmbeddingWorker):
+    def init_embedding(
+        self,
+        model_name: str,
+        cache_dir: str,
+    ) -> OnnxTextEmbedding:
+        return OnnxTextEmbedding(model_name=model_name, cache_dir=cache_dir, threads=1)

fastembed/text/text_embedding.py

@@ -0,0 +1,89 @@
+from typing import Optional, Union, Iterable, List, Dict, Any, Type
+
+import numpy as np
+
+from fastembed.text.e5_onnx_embedding import E5OnnxEmbedding
+from fastembed.text.jina_onnx_embedding import JinaOnnxEmbedding
+from fastembed.text.onnx_embedding import OnnxTextEmbedding
+from fastembed.text.text_embedding_base import TextEmbeddingBase
+
+
+class TextEmbedding(TextEmbeddingBase):
+    EMBEDDINGS_REGISTRY: List[Type[TextEmbeddingBase]] = [
+        OnnxTextEmbedding,
+        E5OnnxEmbedding,
+        JinaOnnxEmbedding,
+    ]
+
+    @classmethod
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
+        """
+        Lists the supported models.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
+
+            Example:
+                ```
+                [
+                    {
+                        "model": "intfloat/multilingual-e5-large",
+                        "dim": 1024,
+                        "description": "Multilingual model, e5-large. Recommend using this model for non-English languages",
+                        "size_in_GB": 2.24,
+                        "sources": {
+                            "gcp": "https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz",
+                            "hf": "qdrant/multilingual-e5-large-onnx",
+                        }
+                    }
+                ]
+                ```
+        """
+        result = []
+        for embedding in cls.EMBEDDINGS_REGISTRY:
+            result.extend(embedding.list_supported_models())
+        return result
+
+    def __init__(
+        self,
+        model_name: str = "BAAI/bge-small-en-v1.5",
+        cache_dir: Optional[str] = None,
+        threads: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__(model_name, cache_dir, threads, **kwargs)
+
+        for embedding in self.EMBEDDINGS_REGISTRY:
+            supported_models = embedding.list_supported_models()
+            if any(model_name == model["model"] for model in supported_models):
+                self.model = embedding(model_name, cache_dir, threads, **kwargs)
+                return
+
+        raise ValueError(
+            f"Model {model_name} is not supported in TextEmbedding."
+            "Please check the supported models using `TextEmbedding.list_supported_models()`"
+        )
+
+    def embed(
+        self,
+        documents: Union[str, Iterable[str]],
+        batch_size: int = 256,
+        parallel: Optional[int] = None,
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
+        """
+        Encode a list of documents into list of embeddings.
+        We use mean pooling with attention so that the model can handle variable-length inputs.
+
+        Args:
+            documents: Iterator of documents or single document to embed
+            batch_size: Batch size for encoding -- higher values will use more memory, but be faster
+            parallel:
+                If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+                If 0, use all available cores.
+                If None, don't use data-parallel processing, use default onnxruntime threading instead.
+
+        Returns:
+            List of embeddings, one per document
+        """
+        yield from self.model.embed(documents, batch_size, parallel, **kwargs)

fastembed/text/text_embedding_base.py

@@ -0,0 +1,57 @@
+from typing import Any, Dict, Iterable, List, Optional, Union
+
+import numpy as np
+
+from fastembed.common.model_management import ModelManagement
+
+
+class TextEmbeddingBase(ModelManagement):
+    @classmethod
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
+        raise NotImplementedError()
+
+    def __init__(self, model_name: str, cache_dir: Optional[str] = None, threads: Optional[int] = None, **kwargs):
+        self.model_name = model_name
+        self.cache_dir = cache_dir
+        self.threads = threads
+
+    def embed(
+        self,
+        documents: Union[str, Iterable[str]],
+        batch_size: int = 256,
+        parallel: Optional[int] = None,
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
+        raise NotImplementedError()
+
+    def passage_embed(self, texts: Iterable[str], **kwargs) -> Iterable[np.ndarray]:
+        """
+        Embeds a list of text passages into a list of embeddings.
+
+        Args:
+            texts (Iterable[str]): The list of texts to embed.
+            **kwargs: Additional keyword argument to pass to the embed method.
+
+        Yields:
+            Iterable[np.ndarray]: The embeddings.
+        """
+
+        # This is model-specific, so that different models can have specialized implementations
+        yield from self.embed(texts, **kwargs)
+
+    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[np.ndarray]:
+        """
+        Embeds queries
+
+        Args:
+            query (Union[str, Iterable[str]]): The query to embed, or an iterable e.g. list of queries.
+
+        Returns:
+            Iterable[np.ndarray]: The embeddings.
+        """
+
+        # This is model-specific, so that different models can have specialized implementations
+        if isinstance(query, str):
+            yield from self.embed([query], **kwargs)
+        if isinstance(query, Iterable):
+            yield from self.embed(query, **kwargs)

mkdocs.yml

@@ -0,0 +1,93 @@
+site_name: FastEmbed
+site_url: https://qdrant.github.io/fastembed/
+site_author: Nirant Kasliwal
+repo_url: https://github.com/qdrant/fastembed/
+repo_name: qdrant/fastembed
+
+remote_branch: gh-pages
+remote_name: origin
+
+copyright: |
+  Maintained by <a href="https://github.com/qdrant">Qdrant</a>. Originally created by <a href="https://nirantk.com/about">Nirant Kasliwal</a>.
+
+theme:
+  name: material
+  logo: assets/favicon.png
+  custom_dir: docs/overrides
+  icon:
+    repo: fontawesome/brands/github
+  features:
+    - search.suggest
+    - search.highlight
+    - navigation.instant
+    - navigation.tracking
+    - navigation.expand
+    - navigation.sections
+    - content.code.annotate
+    - toc.follow
+    - header.autohide
+    - announce.dismiss
+  accent:
+    # Primary color
+    color: "#3f51b5"
+    # Text color for primary color
+    text: "#ffffff"
+
+  palette:
+    # Palette toggle for light mode
+    - scheme: default
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+
+    # Palette toggle for dark mode
+    - scheme: slate
+      toggle:
+        icon: material/brightness-4
+        name: Switch to light mode
+
+markdown_extensions:
+  - abbr
+  - admonition
+  - attr_list
+  # - highlight
+  - def_list
+  - toc:
+      permalink: true
+      toc_depth: 3
+
+plugins:
+  - search
+  - mkdocstrings:
+      default_handler: python
+      handlers:
+        python:
+          options:
+            show_source: false
+            show_bases: false
+            show_if_no_docstring: true
+            merge_init_into_class: true
+            show_root_toc_entry: false
+            show_inheritance: true
+            show_private: false
+            show_special_members: false
+  - mknotebooks:
+      execute: false
+      timeout: 100
+      allow_errors: false
+      tag_remove_configs:
+        remove_cell_tags:
+          - Remove_cell
+        remove_all_outputs_tags:
+          - Remove_all_output
+        remove_single_output_tags:
+          - Remove_single_output
+        remove_input_tags:
+          - Remove_input
+
+markdown_extensions:
+  - pymdownx.superfences:
+      custom_fences:
+        - name: mermaid
+          class: mermaid
+          format: !!python/name:pymdownx.superfences.fence_code_format