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community[minor]: Add support for modle2vec embeddings (#28507)
This PR add an embeddings integration for model2vec, the `Model2vecEmbeddings` class. - **Description**: [Model2Vec](https://github.com/MinishLab/model2vec) lets you turn any sentence transformer into a really small static model and makes running the model faster. - **Issue**: - **Dependencies**: model2vec ([pypi](https://pypi.org/project/model2vec/)) - **Twitter handle:**: - [x] **Add tests and docs**: - [Test](https://github.com/blacksmithop/langchain/blob/model2vec_embeddings/libs/community/langchain_community/embeddings/model2vec.py), [docs](https://github.com/blacksmithop/langchain/blob/model2vec_embeddings/docs/docs/integrations/text_embedding/model2vec.ipynb) - [x] **Lint and test**: --------- Co-authored-by: Abhinav KM <abhinav.m@zerone-consulting.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
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Abhinav
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317a38b83e
201
docs/docs/integrations/text_embedding/model2vec.ipynb
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201
docs/docs/integrations/text_embedding/model2vec.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e8712110",
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"metadata": {},
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"source": [
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"## Overview\n",
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"\n",
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"Model2Vec is a technique to turn any sentence transformer into a really small static model\n",
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"[model2vec](https://github.com/MinishLab/model2vec) can be used to generate embeddings."
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]
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},
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{
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"cell_type": "markdown",
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"id": "266dd424",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"```bash\n",
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"pip install -U langchain-community\n",
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"```\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "78ab91a6",
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"metadata": {},
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"source": [
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"## Instantiation"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d06e7719",
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"metadata": {},
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"source": [
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"Ensure that `model2vec` is installed\n",
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"\n",
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"```bash\n",
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"pip install -U model2vec\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8ea1ed5",
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"metadata": {},
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"source": [
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"## Indexing and Retrieval"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d25dc22d-b656-46c6-a42d-eace958590cd",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:17.176956Z",
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"start_time": "2023-05-24T15:13:15.399076Z"
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},
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"execution": {
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"iopub.execute_input": "2024-03-29T15:39:19.252281Z",
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"shell.execute_reply.started": "2024-03-29T15:39:19.252260Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain_community.embeddings import Model2vecEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8397b91f-a1f9-4be6-a699-fedaada7c37a",
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"metadata": {
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},
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"execution": {
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"shell.execute_reply.started": "2024-03-29T15:39:19.901529Z"
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}
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},
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"outputs": [],
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"source": [
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"embeddings = Model2vecEmbeddings(\"minishlab/potion-base-8M\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "abcf98b7-424c-4691-a1cd-862c3d53be11",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:17.844903Z",
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"start_time": "2023-05-24T15:13:17.198751Z"
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"execution": {
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"shell.execute_reply.started": "2024-03-29T15:39:20.434501Z"
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},
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"query_text = \"This is a test query.\"\n",
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"query_result = embeddings.embed_query(query_text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "98897454-b280-4ee1-bbb9-2c6c15342f87",
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"metadata": {
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"ExecuteTime": {
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"start_time": "2023-05-24T15:13:17.845906Z"
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},
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"execution": {
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"iopub.execute_input": "2024-03-29T15:39:28.164009Z",
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"shell.execute_reply.started": "2024-03-29T15:39:28.163876Z"
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},
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"document_text = \"This is a test document.\"\n",
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"document_result = embeddings.embed_documents([document_text])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "11bac134",
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"metadata": {},
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"source": [
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"## Direct Usage\n",
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"\n",
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"Here's how you would directly make use of `model2vec`\n",
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"\n",
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"```python\n",
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"from model2vec import StaticModel\n",
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"\n",
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"# Load a model from the HuggingFace hub (in this case the potion-base-8M model)\n",
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"model = StaticModel.from_pretrained(\"minishlab/potion-base-8M\")\n",
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"\n",
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"# Make embeddings\n",
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"embeddings = model.encode([\"It's dangerous to go alone!\", \"It's a secret to everybody.\"])\n",
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"\n",
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"# Make sequences of token embeddings\n",
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"token_embeddings = model.encode_as_sequence([\"It's dangerous to go alone!\", \"It's a secret to everybody.\"])\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d81e21aa",
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"metadata": {},
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"source": [
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"## API Reference\n",
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"\n",
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"For more information check out the model2vec github [repo](https://github.com/MinishLab/model2vec)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -145,6 +145,9 @@ if TYPE_CHECKING:
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from langchain_community.embeddings.mlflow_gateway import (
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MlflowAIGatewayEmbeddings,
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)
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from langchain_community.embeddings.model2vec import (
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Model2vecEmbeddings,
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)
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from langchain_community.embeddings.modelscope_hub import (
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ModelScopeEmbeddings,
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)
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@ -289,6 +292,7 @@ __all__ = [
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"MlflowAIGatewayEmbeddings",
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"MlflowCohereEmbeddings",
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"MlflowEmbeddings",
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"Model2vecEmbeddings",
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"ModelScopeEmbeddings",
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"MosaicMLInstructorEmbeddings",
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"NLPCloudEmbeddings",
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@ -372,6 +376,7 @@ _module_lookup = {
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"MlflowAIGatewayEmbeddings": "langchain_community.embeddings.mlflow_gateway",
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"MlflowCohereEmbeddings": "langchain_community.embeddings.mlflow",
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"MlflowEmbeddings": "langchain_community.embeddings.mlflow",
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"Model2vecEmbeddings": "langchain_community.embeddings.model2vec",
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"ModelScopeEmbeddings": "langchain_community.embeddings.modelscope_hub",
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"MosaicMLInstructorEmbeddings": "langchain_community.embeddings.mosaicml",
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"NLPCloudEmbeddings": "langchain_community.embeddings.nlpcloud",
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66
libs/community/langchain_community/embeddings/model2vec.py
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66
libs/community/langchain_community/embeddings/model2vec.py
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"""Wrapper around model2vec embedding models."""
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from typing import List
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from langchain_core.embeddings import Embeddings
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class Model2vecEmbeddings(Embeddings):
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"""model2v embedding models.
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Install model2vec first, run 'pip install -U model2vec'.
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The github repository for model2vec is : https://github.com/MinishLab/model2vec
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Example:
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.. code-block:: python
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from langchain_community.embeddings import Model2vecEmbeddings
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embedding = Model2vecEmbeddings("minishlab/potion-base-8M")
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embedding.embed_documents([
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"It's dangerous to go alone!",
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"It's a secret to everybody.",
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])
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embedding.embed_query(
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"Take this with you."
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)
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"""
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def __init__(self, model: str):
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"""Initialize embeddings.
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Args:
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model: Model name.
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"""
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try:
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from model2vec import StaticModel
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except ImportError as e:
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raise ImportError(
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"Unable to import model2vec, please install with "
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"`pip install -U model2vec`."
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) from e
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self._model = StaticModel.from_pretrained(model)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed documents using the model2vec embeddings model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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return self._model.encode_as_sequence(texts)
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using the model2vec embeddings model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return self._model.encode(text)
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@ -26,6 +26,7 @@ EXPECTED_ALL = [
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"MlflowAIGatewayEmbeddings",
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"MlflowEmbeddings",
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"MlflowCohereEmbeddings",
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"Model2vecEmbeddings",
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"ModelScopeEmbeddings",
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"TensorflowHubEmbeddings",
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"SagemakerEndpointEmbeddings",
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11
libs/community/tests/unit_tests/embeddings/test_model2vec.py
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libs/community/tests/unit_tests/embeddings/test_model2vec.py
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from langchain_community.embeddings.model2vec import Model2vecEmbeddings
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def test_hugginggface_inferenceapi_embedding_documents_init() -> None:
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"""Test model2vec embeddings."""
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try:
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embedding = Model2vecEmbeddings("minishlab/potion-base-8M")
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assert len(embedding.embed_query("hi")) == 256
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except Exception:
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# model2vec is not installed
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assert True
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