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feat(embeddings): text-embeddings-inference (#14288)
- **Description:** Added a notebook to illustrate how to use `text-embeddings-inference` from huggingface. As `HuggingFaceHubEmbeddings` was using a deprecated client, I made the most of this PR updating that too. - **Issue:** #13286 - **Dependencies**: None - **Tag maintainer:** @baskaryan
This commit is contained in:
Massimiliano Pronesti
GitHub
parent
85b88c33f3
commit
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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": "ceabf1eb-ca96-4791-90ad-e9acb31edf5c",
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"metadata": {},
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"source": [
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"# Text Embeddings Inference\n",
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"\n",
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"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.\n",
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"\n",
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"To use it within langchain, first install `huggingface-hub`."
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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": "579f0677-aa06-4ad8-a816-3520c8d6923c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install huggingface-hub -q"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c6b1015-bc3f-4283-93d5-11387be1b98d",
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"metadata": {},
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"source": [
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"Then expose an embedding model using TEI. For instance, using Docker, you can serve `BAAI/bge-large-en-v1.5` as follows:\n",
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"\n",
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"```bash\n",
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"model=BAAI/bge-large-en-v1.5\n",
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"revision=refs/pr/5\n",
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"volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run\n",
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"\n",
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"docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:0.6 --model-id $model --revision $revision\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": "48eebefc-a631-48dd-9bde-4a987f81aa20",
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"metadata": {},
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"source": [
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"Finally, instantiate the client and embed your texts."
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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": "22b09777-5ba3-4fbe-81cf-a702a55df9c4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings import HuggingFaceHubEmbeddings"
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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": "c26fca9f-cfdb-45e5-a0bd-f677ff8b9d92",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Enter your HF API Key:\n",
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"\n",
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" ········\n"
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]
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}
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],
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"source": [
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"from getpass import getpass\n",
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"\n",
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"huggingfacehub_api_token = getpass(\"Enter your HF API Key:\\n\\n\")"
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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": "f9a92970-16f4-458c-b186-2a83e9f7d840",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"embeddings = HuggingFaceHubEmbeddings(\n",
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" model=\"http://localhost:8080\", huggingfacehub_api_token=huggingfacehub_api_token\n",
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")"
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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": "42105438-9fee-460a-9c52-b7c595722758",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"text = \"What is deep learning?\""
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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": "20167762-0988-4205-bbd4-1f20fd9dd247",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0.018113142, 0.00302585, -0.049911194]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"query_result = embeddings.embed_query(text)\n",
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"query_result[:3]"
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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": "54b87cf6-86ad-46f5-b2cd-17eb43cb4d0b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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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": "conda_python3",
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"language": "python",
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"name": "conda_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": "3.10.13"
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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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@ -1,3 +1,4 @@
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import json
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from typing import Any, Dict, List, Optional
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from langchain_core.embeddings import Embeddings
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@ -5,7 +6,7 @@ from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
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from langchain.utils import get_from_dict_or_env
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DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"
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DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2"
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VALID_TASKS = ("feature-extraction",)
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@ -20,17 +21,19 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
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.. code-block:: python
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from langchain.embeddings import HuggingFaceHubEmbeddings
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repo_id = "sentence-transformers/all-mpnet-base-v2"
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model = "sentence-transformers/all-mpnet-base-v2"
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hf = HuggingFaceHubEmbeddings(
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repo_id=repo_id,
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model=model,
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task="feature-extraction",
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huggingfacehub_api_token="my-api-key",
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)
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"""
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client: Any #: :meta private:
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repo_id: str = DEFAULT_REPO_ID
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model: Optional[str] = None
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"""Model name to use."""
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repo_id: Optional[str] = None
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"""Huggingfacehub repository id, for backward compatibility."""
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task: Optional[str] = "feature-extraction"
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"""Task to call the model with."""
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model_kwargs: Optional[dict] = None
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@ -50,22 +53,23 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
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values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
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)
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try:
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from huggingface_hub.inference_api import InferenceApi
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from huggingface_hub import InferenceClient
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repo_id = values["repo_id"]
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if not repo_id.startswith("sentence-transformers"):
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raise ValueError(
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"Currently only 'sentence-transformers' embedding models "
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f"are supported. Got invalid 'repo_id' {repo_id}."
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)
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client = InferenceApi(
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repo_id=repo_id,
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if values["model"]:
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values["repo_id"] = values["model"]
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elif values["repo_id"]:
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values["model"] = values["repo_id"]
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else:
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values["model"] = DEFAULT_MODEL
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values["repo_id"] = DEFAULT_MODEL
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client = InferenceClient(
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model=values["model"],
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token=huggingfacehub_api_token,
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task=values.get("task"),
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)
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if client.task not in VALID_TASKS:
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if values["task"] not in VALID_TASKS:
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raise ValueError(
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f"Got invalid task {client.task}, "
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f"Got invalid task {values['task']}, "
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f"currently only {VALID_TASKS} are supported"
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)
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values["client"] = client
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@ -88,8 +92,10 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
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# replace newlines, which can negatively affect performance.
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texts = [text.replace("\n", " ") for text in texts]
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_model_kwargs = self.model_kwargs or {}
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responses = self.client(inputs=texts, params=_model_kwargs)
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return responses
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responses = self.client.post(
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json={"inputs": texts, "parameters": _model_kwargs, "task": self.task}
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)
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return json.loads(responses.decode())
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def embed_query(self, text: str) -> List[float]:
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"""Call out to HuggingFaceHub's embedding endpoint for embedding query text.
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