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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>
This commit is contained in:
Abhinav
GitHub
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docs/docs/integrations/text_embedding/model2vec.ipynb
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docs/docs/integrations/text_embedding/model2vec.ipynb
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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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"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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"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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"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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"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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"cell_type": "code",
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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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},
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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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"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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"scrolled": true
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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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"name": "ipython",
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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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