forked from Archives/langchain
Add Sentence Transformers Embeddings (#3409)
Add embeddings based on the sentence transformers library. Add a notebook and integration tests. Co-authored-by: khimaros <me@khimaros.com>fix_agent_callbacks
Zander Chase
1 year ago
committed by
GitHub
parent
73bc70b4fa
commit
20f530e9c5
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "ed47bb62",
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"metadata": {},
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"source": [
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"# Sentence Transformers Embeddings\n",
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"\n",
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"Let's generate embeddings using the [SentenceTransformers](https://www.sbert.net/) integration. SentenceTransformers is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
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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": 7,
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"id": "06c9f47d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
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"To disable this warning, you can either:\n",
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"\t- Avoid using `tokenizers` before the fork if possible\n",
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"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
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]
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}
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],
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"source": [
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"!pip install sentence_transformers > /dev/null"
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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": 8,
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"id": "861521a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import SentenceTransformerEmbeddings "
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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": 9,
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"id": "ff9be586",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = SentenceTransformerEmbeddings(model=\"all-MiniLM-L6-v2\")"
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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": 10,
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"id": "d0a98ae9",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": 11,
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"id": "5d6c682b",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_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": 12,
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"id": "bb5e74c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
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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": null,
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"id": "aaad49f8",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.11.2"
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},
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"vscode": {
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"interpreter": {
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"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
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}
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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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"""Wrapper around sentence transformer embedding models."""
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Extra, Field, root_validator
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from langchain.embeddings.base import Embeddings
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class SentenceTransformerEmbeddings(BaseModel, Embeddings):
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embedding_function: Any #: :meta private:
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model: Optional[str] = Field("all-MiniLM-L6-v2", alias="model")
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"""Transformer model to use."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that sentence_transformers library is installed."""
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model = values["model"]
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try:
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from sentence_transformers import SentenceTransformer
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values["embedding_function"] = SentenceTransformer(model)
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except ImportError:
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raise ModuleNotFoundError(
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"Could not import sentence_transformers library. "
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"Please install the sentence_transformers library to "
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"use this embedding model: pip install sentence_transformers"
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)
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except Exception:
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raise NameError(f"Could not load SentenceTransformer model {model}.")
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed a list of documents using the SentenceTransformer 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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embeddings = self.embedding_function.encode(
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texts, convert_to_numpy=True
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).tolist()
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return [list(map(float, e)) for e in embeddings]
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using the SentenceTransformer model.
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Args:
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text: The text to embed.
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Returns:
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Embedding for the text.
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"""
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return self.embed_documents([text])[0]
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@ -0,0 +1,38 @@
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# flake8: noqa
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"""Test sentence_transformer embeddings."""
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from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
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from langchain.vectorstores import Chroma
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def test_sentence_transformer_embedding_documents() -> None:
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"""Test sentence_transformer embeddings."""
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embedding = SentenceTransformerEmbeddings()
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documents = ["foo bar"]
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) == 384
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def test_sentence_transformer_embedding_query() -> None:
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"""Test sentence_transformer embeddings."""
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embedding = SentenceTransformerEmbeddings()
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query = "what the foo is a bar?"
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query_vector = embedding.embed_query(query)
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assert len(query_vector) == 384
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def test_sentence_transformer_db_query() -> None:
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"""Test sentence_transformer similarity search."""
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embedding = SentenceTransformerEmbeddings()
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texts = [
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"we will foo your bar until you can't foo any more",
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"the quick brown fox jumped over the lazy dog",
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]
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query = "what the foo is a bar?"
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query_vector = embedding.embed_query(query)
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assert len(query_vector) == 384
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db = Chroma(embedding_function=embedding)
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db.add_texts(texts)
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docs = db.similarity_search_by_vector(query_vector, k=2)
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assert docs[0].page_content == "we will foo your bar until you can't foo any more"
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