Add Sentence Transformers Embeddings (langchain-ai#3409)

Add embeddings based on the sentence transformers library.
Add a notebook and integration tests.

Co-authored-by: khimaros <me@khimaros.com>

Author: vowelparrot

docs/modules/models/text_embedding/examples/sentence_transformers.ipynb

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ed47bb62",
+   "metadata": {},
+   "source": [
+    "# Sentence Transformers Embeddings\n",
+    "\n",
+    "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "06c9f47d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install sentence_transformers > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "861521a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import SentenceTransformerEmbeddings "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ff9be586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = SentenceTransformerEmbeddings(model=\"all-MiniLM-L6-v2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d0a98ae9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5d6c682b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bb5e74c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaad49f8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

langchain/embeddings/__init__.py

@@ -22,6 +22,7 @@
     SelfHostedHuggingFaceEmbeddings,
     SelfHostedHuggingFaceInstructEmbeddings,
 )
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
 from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings
 
 logger = logging.getLogger(__name__)
@@ -42,6 +43,7 @@
     "FakeEmbeddings",
     "AlephAlphaAsymmetricSemanticEmbedding",
     "AlephAlphaSymmetricSemanticEmbedding",
+    "SentenceTransformerEmbeddings",
 ]
 
 

langchain/embeddings/sentence_transformer.py

@@ -0,0 +1,63 @@
+"""Wrapper around sentence transformer embedding models."""
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Extra, Field, root_validator
+
+from langchain.embeddings.base import Embeddings
+
+
+class SentenceTransformerEmbeddings(BaseModel, Embeddings):
+    embedding_function: Any  #: :meta private:
+
+    model: Optional[str] = Field("all-MiniLM-L6-v2", alias="model")
+    """Transformer model to use."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that sentence_transformers library is installed."""
+        model = values["model"]
+
+        try:
+            from sentence_transformers import SentenceTransformer
+
+            values["embedding_function"] = SentenceTransformer(model)
+        except ImportError:
+            raise ModuleNotFoundError(
+                "Could not import sentence_transformers library. "
+                "Please install the sentence_transformers library to "
+                "use this embedding model: pip install sentence_transformers"
+            )
+        except Exception:
+            raise NameError(f"Could not load SentenceTransformer model {model}.")
+
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents using the SentenceTransformer model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = self.embedding_function.encode(
+            texts, convert_to_numpy=True
+        ).tolist()
+        return [list(map(float, e)) for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query using the SentenceTransformer model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embedding for the text.
+        """
+        return self.embed_documents([text])[0]

pyproject.toml

@@ -117,6 +117,7 @@ torch = "^1.0.0"
 chromadb = "^0.3.21"
 tiktoken = "^0.3.3"
 python-dotenv = "^1.0.0"
+sentence-transformers = "^2"
 gptcache = "^0.1.9"
 promptlayer = "^0.1.80"
 
@@ -144,7 +145,8 @@ llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifes
 qdrant = ["qdrant-client"]
 openai = ["openai"]
 cohere = ["cohere"]
-all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "boto3", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect"]
+embeddings = ["sentence-transformers"]
+all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "boto3", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect"]
 
 [tool.ruff]
 select = [

tests/integration_tests/embeddings/test_sentence_transformer.py

@@ -0,0 +1,38 @@
+# flake8: noqa
+"""Test sentence_transformer embeddings."""
+
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain.vectorstores import Chroma
+
+
+def test_sentence_transformer_embedding_documents() -> None:
+    """Test sentence_transformer embeddings."""
+    embedding = SentenceTransformerEmbeddings()
+    documents = ["foo bar"]
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 384
+
+
+def test_sentence_transformer_embedding_query() -> None:
+    """Test sentence_transformer embeddings."""
+    embedding = SentenceTransformerEmbeddings()
+    query = "what the foo is a bar?"
+    query_vector = embedding.embed_query(query)
+    assert len(query_vector) == 384
+
+
+def test_sentence_transformer_db_query() -> None:
+    """Test sentence_transformer similarity search."""
+    embedding = SentenceTransformerEmbeddings()
+    texts = [
+        "we will foo your bar until you can't foo any more",
+        "the quick brown fox jumped over the lazy dog",
+    ]
+    query = "what the foo is a bar?"
+    query_vector = embedding.embed_query(query)
+    assert len(query_vector) == 384
+    db = Chroma(embedding_function=embedding)
+    db.add_texts(texts)
+    docs = db.similarity_search_by_vector(query_vector, k=2)
+    assert docs[0].page_content == "we will foo your bar until you can't foo any more"