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https://github.com/hwchase17/langchain
synced 2024-10-27 21:46:30 +00:00
Added Hugging face inference api (#10280)
Embed documents without locally downloading the HF model --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
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parent
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@ -5,13 +5,23 @@
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"id": "ed47bb62",
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"metadata": {},
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"source": [
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"# Hugging Face Hub\n",
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"# Hugging Face\n",
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"Let's load the Hugging Face Embedding class."
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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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"execution_count": null,
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"id": "16b20335-da1d-46ba-aa23-fbf3e2c6fe60",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install langchain sentence_transformers"
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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": "861521a9",
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"metadata": {},
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"outputs": [],
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@ -21,7 +31,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 3,
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"id": "ff9be586",
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"metadata": {},
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"outputs": [],
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@ -31,7 +41,7 @@
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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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"execution_count": 3,
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"id": "d0a98ae9",
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"metadata": {},
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"outputs": [],
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@ -41,7 +51,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 5,
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"id": "5d6c682b",
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"metadata": {},
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"outputs": [],
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@ -51,7 +61,28 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 6,
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"id": "b57b8ce9-ef7d-4e63-979e-aa8763d1f9a8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[-0.04895168915390968, -0.03986193612217903, -0.021562768146395683]"
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]
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},
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"execution_count": 6,
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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[: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": 7,
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"id": "bb5e74c0",
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"metadata": {},
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"outputs": [],
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@ -60,19 +91,71 @@
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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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"cell_type": "markdown",
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"id": "92019ef1-5d30-4985-b4e6-c0d98bdfe265",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"## Hugging Face Inference API\n",
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"We can also access embedding models via the Hugging Face Inference API, which does not require us to install ``sentence_transformers`` and download models locally."
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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": 1,
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"id": "66f5c6ba-1446-43e1-b012-800d17cef300",
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"metadata": {},
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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 Inference 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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"import getpass\n",
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"\n",
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"inference_api_key = getpass.getpass(\"Enter your HF Inference 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": 4,
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"id": "d0623c1f-cd82-4862-9bce-3655cb9b66ac",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[-0.038338541984558105, 0.1234646737575531, -0.028642963618040085]"
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]
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},
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"execution_count": 4,
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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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"from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings\n",
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"\n",
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"embeddings = HuggingFaceInferenceAPIEmbeddings(\n",
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" api_key=inference_api_key,\n",
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" model_name=\"sentence-transformers/all-MiniLM-l6-v2\"\n",
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")\n",
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"\n",
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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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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "poetry-venv",
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"language": "python",
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"name": "python3"
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"name": "poetry-venv"
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},
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"language_info": {
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"codemirror_mode": {
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@ -35,6 +35,7 @@ from langchain.embeddings.gpt4all import GPT4AllEmbeddings
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from langchain.embeddings.huggingface import (
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HuggingFaceBgeEmbeddings,
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HuggingFaceEmbeddings,
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HuggingFaceInferenceAPIEmbeddings,
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HuggingFaceInstructEmbeddings,
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)
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from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
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@ -69,6 +70,7 @@ __all__ = [
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"CohereEmbeddings",
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"ElasticsearchEmbeddings",
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"HuggingFaceEmbeddings",
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"HuggingFaceInferenceAPIEmbeddings",
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"JinaEmbeddings",
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"LlamaCppEmbeddings",
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"HuggingFaceHubEmbeddings",
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@ -1,5 +1,7 @@
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from typing import Any, Dict, List, Optional
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import requests
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from langchain.embeddings.base import Embeddings
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from langchain.pydantic_v1 import BaseModel, Extra, Field
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@ -58,7 +60,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
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except ImportError as exc:
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raise ImportError(
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"Could not import sentence_transformers python package. "
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"Please install it with `pip install sentence_transformers`."
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"Please install it with `pip install sentence-transformers`."
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) from exc
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self.client = sentence_transformers.SentenceTransformer(
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@ -266,3 +268,71 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
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self.query_instruction + text, **self.encode_kwargs
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)
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return embedding.tolist()
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class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings):
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"""Embed texts using the HuggingFace API.
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Requires a HuggingFace Inference API key and a model name.
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"""
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api_key: str
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"""Your API key for the HuggingFace Inference API."""
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model_name: str = "sentence-transformers/all-MiniLM-L6-v2"
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"""The name of the model to use for text embeddings."""
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@property
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def _api_url(self) -> str:
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return (
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"https://api-inference.huggingface.co"
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"/pipeline"
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"/feature-extraction"
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f"/{self.model_name}"
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)
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@property
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def _headers(self) -> dict:
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return {"Authorization": f"Bearer {self.api_key}"}
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Get the embeddings for a list of texts.
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Args:
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texts (Documents): A list of texts to get embeddings for.
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Returns:
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Embedded texts as List[List[float]], where each inner List[float]
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corresponds to a single input text.
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Example:
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.. code-block:: python
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from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
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hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
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api_key="your_api_key",
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model_name="sentence-transformers/all-MiniLM-l6-v2"
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)
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texts = ["Hello, world!", "How are you?"]
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hf_embeddings.embed_documents(texts)
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"""
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response = requests.post(
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self._api_url,
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headers=self._headers,
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json={
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"inputs": texts,
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"options": {"wait_for_model": True, "use_cache": True},
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},
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)
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return response.json()
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a HuggingFace transformer model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return self.embed_documents([text])[0]
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