forked from Archives/langchain
Add DeepInfra embeddings integration with tests and examples, better exception handling for Deep Infra LLM (#5854)
#### Who can review? Tag maintainers/contributors who might be interested: @hwchase17 - project lead - @agola11 --------- Co-authored-by: Yessen Kanapin <yessen@deepinfra.com>searx_updates
Yessen Kanapin
11 months ago
committed by
GitHub
parent
4d8cda1c3b
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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"metadata": {},
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"source": [
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"# DeepInfra\n",
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"\n",
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"[DeepInfra](https://deepinfra.com/?utm_source=langchain) is a serverless inference as a service that provides access to a [variety of LLMs](https://deepinfra.com/models?utm_source=langchain) and [embeddings models](https://deepinfra.com/models?type=embeddings&utm_source=langchain). This notebook goes over how to use LangChain with DeepInfra for text embeddings."
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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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"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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" ········\n"
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]
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}
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],
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"source": [
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"# sign up for an account: https://deepinfra.com/login?utm_source=langchain\n",
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"\n",
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"from getpass import getpass\n",
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"\n",
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"DEEPINFRA_API_TOKEN = getpass()"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"DEEPINFRA_API_TOKEN\"] = DEEPINFRA_API_TOKEN"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import DeepInfraEmbeddings"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = DeepInfraEmbeddings(\n",
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" model_id=\"sentence-transformers/clip-ViT-B-32\",\n",
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" query_instruction=\"\",\n",
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" embed_instruction=\"\",\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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = [\"Dog is not a cat\",\n",
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" \"Beta is the second letter of Greek alphabet\"]\n",
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"document_result = embeddings.embed_documents(docs)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What is the first letter of Greek alphabet\"\n",
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"query_result = embeddings.embed_query(query)"
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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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"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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"Cosine similarity between \"Dog is not a cat\" and query: 0.7489097144129355\n",
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"Cosine similarity between \"Beta is the second letter of Greek alphabet\" and query: 0.9519380640702013\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"query_numpy = np.array(query_result)\n",
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"for doc_res, doc in zip(document_result, docs):\n",
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" document_numpy = np.array(doc_res)\n",
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" similarity = np.dot(query_numpy, document_numpy) / (np.linalg.norm(query_numpy)*np.linalg.norm(document_numpy))\n",
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" print(f\"Cosine similarity between \\\"{doc}\\\" and query: {similarity}\")"
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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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"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": "3.10.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@ -0,0 +1,129 @@
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from typing import Any, Dict, List, Mapping, Optional
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import requests
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from pydantic import BaseModel, Extra, root_validator
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from langchain.embeddings.base import Embeddings
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from langchain.utils import get_from_dict_or_env
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DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32"
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class DeepInfraEmbeddings(BaseModel, Embeddings):
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"""Wrapper around Deep Infra's embedding inference service.
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To use, you should have the
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environment variable ``DEEPINFRA_API_TOKEN`` set with your API token, or pass
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it as a named parameter to the constructor.
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There are multiple embeddings models available,
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see https://deepinfra.com/models?type=embeddings.
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Example:
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.. code-block:: python
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from langchain.embeddings import DeepInfraEmbeddings
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deepinfra_emb = DeepInfraEmbeddings(
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model_id="sentence-transformers/clip-ViT-B-32",
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deepinfra_api_token="my-api-key"
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)
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r1 = deepinfra_emb.embed_documents(
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[
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"Alpha is the first letter of Greek alphabet",
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"Beta is the second letter of Greek alphabet",
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]
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)
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r2 = deepinfra_emb.embed_query(
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"What is the second letter of Greek alphabet"
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)
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"""
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model_id: str = DEFAULT_MODEL_ID
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"""Embeddings model to use."""
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normalize: bool = False
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"""whether to normalize the computed embeddings"""
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embed_instruction: str = "passage: "
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"""Instruction used to embed documents."""
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query_instruction: str = "query: "
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"""Instruction used to embed the query."""
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model_kwargs: Optional[dict] = None
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"""Other model keyword args"""
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deepinfra_api_token: Optional[str] = None
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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 api key and python package exists in environment."""
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deepinfra_api_token = get_from_dict_or_env(
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values, "deepinfra_api_token", "DEEPINFRA_API_TOKEN"
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)
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values["deepinfra_api_token"] = deepinfra_api_token
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {"model_id": self.model_id}
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def _embed(self, input: List[str]) -> List[List[float]]:
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_model_kwargs = self.model_kwargs or {}
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# HTTP headers for authorization
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headers = {
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"Authorization": f"bearer {self.deepinfra_api_token}",
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"Content-Type": "application/json",
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}
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# send request
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try:
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res = requests.post(
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f"https://api.deepinfra.com/v1/inference/{self.model_id}",
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headers=headers,
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json={"inputs": input, "normalize": self.normalize, **_model_kwargs},
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)
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except requests.exceptions.RequestException as e:
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raise ValueError(f"Error raised by inference endpoint: {e}")
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if res.status_code != 200:
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raise ValueError(
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"Error raised by inference API HTTP code: %s, %s"
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% (res.status_code, res.text)
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)
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try:
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t = res.json()
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embeddings = t["embeddings"]
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except requests.exceptions.JSONDecodeError as e:
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raise ValueError(
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f"Error raised by inference API: {e}.\nResponse: {res.text}"
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)
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return embeddings
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed documents using a Deep Infra deployed embedding 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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instruction_pairs = [f"{self.query_instruction}{text}" for text in texts]
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embeddings = self._embed(instruction_pairs)
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using a Deep Infra deployed embedding 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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instruction_pair = f"{self.query_instruction}{text}"
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embedding = self._embed([instruction_pair])[0]
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return embedding
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"""Test DeepInfra API wrapper."""
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from langchain.embeddings import DeepInfraEmbeddings
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def test_deepinfra_call() -> None:
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"""Test valid call to DeepInfra."""
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deepinfra_emb = DeepInfraEmbeddings(model_id="sentence-transformers/clip-ViT-B-32")
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r1 = deepinfra_emb.embed_documents(
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[
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"Alpha is the first letter of Greek alphabet",
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"Beta is the second letter of Greek alphabet",
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]
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)
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assert len(r1) == 2
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assert len(r1[0]) == 512
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assert len(r1[1]) == 512
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r2 = deepinfra_emb.embed_query("What is the third letter of Greek alphabet")
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assert len(r2) == 512
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"""Test DeepInfra API wrapper."""
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from langchain.llms.deepinfra import DeepInfra
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def test_deepinfra_call() -> None:
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"""Test valid call to DeepInfra."""
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llm = DeepInfra(model_id="google/flan-t5-small")
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output = llm("What is 2 + 2?")
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assert isinstance(output, str)
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