mirror of https://github.com/hwchase17/langchain
Add MiniMax embeddings (#5174)
- Add support for MiniMax embeddings Doc: [MiniMax embeddings](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a) --------- Co-authored-by: Archon <archongum@outlook.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>pull/5291/head
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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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"# MiniMax\n",
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"\n",
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"[MiniMax](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a) offers an embeddings service.\n",
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"\n",
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"This example goes over how to use LangChain to interact with MiniMax Inference for text embedding."
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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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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:15.397075Z",
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"start_time": "2023-05-24T15:13:15.387540Z"
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}
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},
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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[\"MINIMAX_GROUP_ID\"] = \"MINIMAX_GROUP_ID\"\n",
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"os.environ[\"MINIMAX_API_KEY\"] = \"MINIMAX_API_KEY\""
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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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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:17.176956Z",
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"start_time": "2023-05-24T15:13:15.399076Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings import MiniMaxEmbeddings"
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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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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:17.193751Z",
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"start_time": "2023-05-24T15:13:17.182053Z"
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}
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},
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"outputs": [],
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"source": [
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"embeddings = MiniMaxEmbeddings()"
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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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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:17.844903Z",
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"start_time": "2023-05-24T15:13:17.198751Z"
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}
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},
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"outputs": [],
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"source": [
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"query_text = \"This is a test query.\"\n",
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"query_result = embeddings.embed_query(query_text)"
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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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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:18.605339Z",
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"start_time": "2023-05-24T15:13:17.845906Z"
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}
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},
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"outputs": [],
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"source": [
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"document_text = \"This is a test document.\"\n",
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"document_result = embeddings.embed_documents([document_text])"
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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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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:18.620432Z",
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"start_time": "2023-05-24T15:13:18.608335Z"
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}
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},
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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 document and query: 0.1573236279277012\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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"document_numpy = np.array(document_result[0])\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 document and query: {similarity}\")"
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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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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,163 @@
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"""Wrapper around MiniMax APIs."""
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from __future__ import annotations
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import logging
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from typing import Any, Callable, Dict, List, Optional
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import requests
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from pydantic import BaseModel, Extra, root_validator
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from tenacity import (
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before_sleep_log,
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retry,
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stop_after_attempt,
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wait_exponential,
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)
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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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logger = logging.getLogger(__name__)
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def _create_retry_decorator() -> Callable[[Any], Any]:
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"""Returns a tenacity retry decorator."""
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multiplier = 1
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min_seconds = 1
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max_seconds = 4
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max_retries = 6
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return retry(
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reraise=True,
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stop=stop_after_attempt(max_retries),
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wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator()
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@retry_decorator
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def _embed_with_retry(*args: Any, **kwargs: Any) -> Any:
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return embeddings.embed(*args, **kwargs)
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return _embed_with_retry(*args, **kwargs)
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class MiniMaxEmbeddings(BaseModel, Embeddings):
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"""Wrapper around MiniMax's embedding inference service.
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To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
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``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to
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the constructor.
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Example:
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.. code-block:: python
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from langchain.embeddings import MiniMaxEmbeddings
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embeddings = MiniMaxEmbeddings()
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query_text = "This is a test query."
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query_result = embeddings.embed_query(query_text)
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document_text = "This is a test document."
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document_result = embeddings.embed_documents([document_text])
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"""
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endpoint_url: str = "https://api.minimax.chat/v1/embeddings"
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"""Endpoint URL to use."""
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model: str = "embo-01"
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"""Embeddings model name to use."""
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embed_type_db: str = "db"
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"""For embed_documents"""
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embed_type_query: str = "query"
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"""For embed_query"""
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minimax_group_id: Optional[str] = None
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"""Group ID for MiniMax API."""
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minimax_api_key: Optional[str] = None
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"""API Key for MiniMax API."""
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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 group id and api key exists in environment."""
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minimax_group_id = get_from_dict_or_env(
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values, "minimax_group_id", "MINIMAX_GROUP_ID"
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)
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minimax_api_key = get_from_dict_or_env(
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values, "minimax_api_key", "MINIMAX_API_KEY"
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)
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values["minimax_group_id"] = minimax_group_id
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values["minimax_api_key"] = minimax_api_key
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return values
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def embed(
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self,
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texts: List[str],
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embed_type: str,
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) -> List[List[float]]:
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payload = {
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"model": self.model,
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"type": embed_type,
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"texts": texts,
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}
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# HTTP headers for authorization
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headers = {
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"Authorization": f"Bearer {self.minimax_api_key}",
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"Content-Type": "application/json",
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}
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params = {
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"GroupId": self.minimax_group_id,
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}
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# send request
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response = requests.post(
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self.endpoint_url, params=params, headers=headers, json=payload
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)
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parsed_response = response.json()
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# check for errors
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if parsed_response["base_resp"]["status_code"] != 0:
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raise ValueError(
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f"MiniMax API returned an error: {parsed_response['base_resp']}"
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)
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embeddings = parsed_response["vectors"]
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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 MiniMax embedding endpoint.
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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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embeddings = embed_with_retry(self, texts=texts, embed_type=self.embed_type_db)
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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 MiniMax embedding endpoint.
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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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embeddings = embed_with_retry(
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self, texts=[text], embed_type=self.embed_type_query
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)
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return embeddings[0]
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