mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-11 05:45:01 +00:00
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
parent
fbf0704e48
commit
317a38b83e
201
docs/docs/integrations/text_embedding/model2vec.ipynb
Normal file
201
docs/docs/integrations/text_embedding/model2vec.ipynb
Normal file
@ -0,0 +1,201 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e8712110",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Overview\n",
|
||||
"\n",
|
||||
"Model2Vec is a technique to turn any sentence transformer into a really small static model\n",
|
||||
"[model2vec](https://github.com/MinishLab/model2vec) can be used to generate embeddings."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "266dd424",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"```bash\n",
|
||||
"pip install -U langchain-community\n",
|
||||
"```\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "78ab91a6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Instantiation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d06e7719",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Ensure that `model2vec` is installed\n",
|
||||
"\n",
|
||||
"```bash\n",
|
||||
"pip install -U model2vec\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f8ea1ed5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Indexing and Retrieval"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "d25dc22d-b656-46c6-a42d-eace958590cd",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-05-24T15:13:17.176956Z",
|
||||
"start_time": "2023-05-24T15:13:15.399076Z"
|
||||
},
|
||||
"execution": {
|
||||
"iopub.execute_input": "2024-03-29T15:39:19.252281Z",
|
||||
"iopub.status.busy": "2024-03-29T15:39:19.252101Z",
|
||||
"iopub.status.idle": "2024-03-29T15:39:19.339106Z",
|
||||
"shell.execute_reply": "2024-03-29T15:39:19.338614Z",
|
||||
"shell.execute_reply.started": "2024-03-29T15:39:19.252260Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.embeddings import Model2vecEmbeddings"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "8397b91f-a1f9-4be6-a699-fedaada7c37a",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-05-24T15:13:17.193751Z",
|
||||
"start_time": "2023-05-24T15:13:17.182053Z"
|
||||
},
|
||||
"execution": {
|
||||
"iopub.execute_input": "2024-03-29T15:39:19.901573Z",
|
||||
"iopub.status.busy": "2024-03-29T15:39:19.900935Z",
|
||||
"iopub.status.idle": "2024-03-29T15:39:19.906540Z",
|
||||
"shell.execute_reply": "2024-03-29T15:39:19.905345Z",
|
||||
"shell.execute_reply.started": "2024-03-29T15:39:19.901529Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = Model2vecEmbeddings(\"minishlab/potion-base-8M\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "abcf98b7-424c-4691-a1cd-862c3d53be11",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-05-24T15:13:17.844903Z",
|
||||
"start_time": "2023-05-24T15:13:17.198751Z"
|
||||
},
|
||||
"execution": {
|
||||
"iopub.execute_input": "2024-03-29T15:39:20.434581Z",
|
||||
"iopub.status.busy": "2024-03-29T15:39:20.433117Z",
|
||||
"iopub.status.idle": "2024-03-29T15:39:22.178650Z",
|
||||
"shell.execute_reply": "2024-03-29T15:39:22.176058Z",
|
||||
"shell.execute_reply.started": "2024-03-29T15:39:20.434501Z"
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_text = \"This is a test query.\"\n",
|
||||
"query_result = embeddings.embed_query(query_text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "98897454-b280-4ee1-bbb9-2c6c15342f87",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-05-24T15:13:18.605339Z",
|
||||
"start_time": "2023-05-24T15:13:17.845906Z"
|
||||
},
|
||||
"execution": {
|
||||
"iopub.execute_input": "2024-03-29T15:39:28.164009Z",
|
||||
"iopub.status.busy": "2024-03-29T15:39:28.161759Z",
|
||||
"iopub.status.idle": "2024-03-29T15:39:30.217232Z",
|
||||
"shell.execute_reply": "2024-03-29T15:39:30.215348Z",
|
||||
"shell.execute_reply.started": "2024-03-29T15:39:28.163876Z"
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"document_text = \"This is a test document.\"\n",
|
||||
"document_result = embeddings.embed_documents([document_text])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "11bac134",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Direct Usage\n",
|
||||
"\n",
|
||||
"Here's how you would directly make use of `model2vec`\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from model2vec import StaticModel\n",
|
||||
"\n",
|
||||
"# Load a model from the HuggingFace hub (in this case the potion-base-8M model)\n",
|
||||
"model = StaticModel.from_pretrained(\"minishlab/potion-base-8M\")\n",
|
||||
"\n",
|
||||
"# Make embeddings\n",
|
||||
"embeddings = model.encode([\"It's dangerous to go alone!\", \"It's a secret to everybody.\"])\n",
|
||||
"\n",
|
||||
"# Make sequences of token embeddings\n",
|
||||
"token_embeddings = model.encode_as_sequence([\"It's dangerous to go alone!\", \"It's a secret to everybody.\"])\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d81e21aa",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API Reference\n",
|
||||
"\n",
|
||||
"For more information check out the model2vec github [repo](https://github.com/MinishLab/model2vec)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.11.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -145,6 +145,9 @@ if TYPE_CHECKING:
|
||||
from langchain_community.embeddings.mlflow_gateway import (
|
||||
MlflowAIGatewayEmbeddings,
|
||||
)
|
||||
from langchain_community.embeddings.model2vec import (
|
||||
Model2vecEmbeddings,
|
||||
)
|
||||
from langchain_community.embeddings.modelscope_hub import (
|
||||
ModelScopeEmbeddings,
|
||||
)
|
||||
@ -289,6 +292,7 @@ __all__ = [
|
||||
"MlflowAIGatewayEmbeddings",
|
||||
"MlflowCohereEmbeddings",
|
||||
"MlflowEmbeddings",
|
||||
"Model2vecEmbeddings",
|
||||
"ModelScopeEmbeddings",
|
||||
"MosaicMLInstructorEmbeddings",
|
||||
"NLPCloudEmbeddings",
|
||||
@ -372,6 +376,7 @@ _module_lookup = {
|
||||
"MlflowAIGatewayEmbeddings": "langchain_community.embeddings.mlflow_gateway",
|
||||
"MlflowCohereEmbeddings": "langchain_community.embeddings.mlflow",
|
||||
"MlflowEmbeddings": "langchain_community.embeddings.mlflow",
|
||||
"Model2vecEmbeddings": "langchain_community.embeddings.model2vec",
|
||||
"ModelScopeEmbeddings": "langchain_community.embeddings.modelscope_hub",
|
||||
"MosaicMLInstructorEmbeddings": "langchain_community.embeddings.mosaicml",
|
||||
"NLPCloudEmbeddings": "langchain_community.embeddings.nlpcloud",
|
||||
|
||||
66
libs/community/langchain_community/embeddings/model2vec.py
Normal file
66
libs/community/langchain_community/embeddings/model2vec.py
Normal file
@ -0,0 +1,66 @@
|
||||
"""Wrapper around model2vec embedding models."""
|
||||
|
||||
from typing import List
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
|
||||
class Model2vecEmbeddings(Embeddings):
|
||||
"""model2v embedding models.
|
||||
|
||||
Install model2vec first, run 'pip install -U model2vec'.
|
||||
The github repository for model2vec is : https://github.com/MinishLab/model2vec
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.embeddings import Model2vecEmbeddings
|
||||
|
||||
embedding = Model2vecEmbeddings("minishlab/potion-base-8M")
|
||||
embedding.embed_documents([
|
||||
"It's dangerous to go alone!",
|
||||
"It's a secret to everybody.",
|
||||
])
|
||||
embedding.embed_query(
|
||||
"Take this with you."
|
||||
)
|
||||
"""
|
||||
|
||||
def __init__(self, model: str):
|
||||
"""Initialize embeddings.
|
||||
|
||||
Args:
|
||||
model: Model name.
|
||||
"""
|
||||
try:
|
||||
from model2vec import StaticModel
|
||||
except ImportError as e:
|
||||
raise ImportError(
|
||||
"Unable to import model2vec, please install with "
|
||||
"`pip install -U model2vec`."
|
||||
) from e
|
||||
self._model = StaticModel.from_pretrained(model)
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Embed documents using the model2vec embeddings model.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
|
||||
return self._model.encode_as_sequence(texts)
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Embed a query using the model2vec embeddings model.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embeddings for the text.
|
||||
"""
|
||||
|
||||
return self._model.encode(text)
|
||||
@ -26,6 +26,7 @@ EXPECTED_ALL = [
|
||||
"MlflowAIGatewayEmbeddings",
|
||||
"MlflowEmbeddings",
|
||||
"MlflowCohereEmbeddings",
|
||||
"Model2vecEmbeddings",
|
||||
"ModelScopeEmbeddings",
|
||||
"TensorflowHubEmbeddings",
|
||||
"SagemakerEndpointEmbeddings",
|
||||
|
||||
11
libs/community/tests/unit_tests/embeddings/test_model2vec.py
Normal file
11
libs/community/tests/unit_tests/embeddings/test_model2vec.py
Normal file
@ -0,0 +1,11 @@
|
||||
from langchain_community.embeddings.model2vec import Model2vecEmbeddings
|
||||
|
||||
|
||||
def test_hugginggface_inferenceapi_embedding_documents_init() -> None:
|
||||
"""Test model2vec embeddings."""
|
||||
try:
|
||||
embedding = Model2vecEmbeddings("minishlab/potion-base-8M")
|
||||
assert len(embedding.embed_query("hi")) == 256
|
||||
except Exception:
|
||||
# model2vec is not installed
|
||||
assert True
|
||||
Loading…
Reference in New Issue
Block a user