mirror of https://github.com/hwchase17/langchain
Add Tencent Hunyuan chat model (#12022)
### Description: The Tencent Hunyuan model, developed by Tencent, is a large language model by robust Chinese text generation capabilities, adeptness in logical reasoning within complex contexts, and reliable task execution proficiency.For more information, see [https://cloud.tencent.com/document/product/1729](https://cloud.tencent.com/document/product/1729)pull/12047/head
John Mai
12 months ago
committed by
GitHub
parent
68599d98c2
commit
4188f046ec
@ -0,0 +1,160 @@
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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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"# Tencent Hunyuan\n",
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"\n",
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"Hunyuan chat model API by Tencent. For more information, see [https://cloud.tencent.com/document/product/1729](https://cloud.tencent.com/document/product/1729)"
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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": "2023-10-19T10:20:38.718834Z",
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"start_time": "2023-10-19T10:20:38.264050Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatHunyuan\n",
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"from langchain.schema import HumanMessage"
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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": "2023-10-19T10:19:53.529876Z",
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"start_time": "2023-10-19T10:19:53.526210Z"
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}
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},
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"outputs": [],
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"source": [
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"chat = ChatHunyuan(\n",
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" hunyuan_app_id='YOUR_APP_ID',\n",
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" hunyuan_secret_id='YOUR_SECRET_ID',\n",
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" hunyuan_secret_key='YOUR_SECRET_KEY',\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": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-10-19T10:19:56.054289Z",
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"start_time": "2023-10-19T10:19:53.531078Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "AIMessage(content=\"J'aime programmer.\")"
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},
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"execution_count": 3,
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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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"chat([\n",
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" HumanMessage(content='You are a helpful assistant that translates English to French.Translate this sentence from English to French. I love programming.')\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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"source": [
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"## For ChatHunyuan with Streaming"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"chat = ChatHunyuan(\n",
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" hunyuan_app_id='YOUR_APP_ID',\n",
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" hunyuan_secret_id='YOUR_SECRET_ID',\n",
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" hunyuan_secret_key='YOUR_SECRET_KEY',\n",
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" streaming=True,\n",
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")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-10-19T10:20:41.507720Z",
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"start_time": "2023-10-19T10:20:41.496456Z"
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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": 3,
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"outputs": [
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{
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"data": {
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"text/plain": "AIMessageChunk(content=\"J'aime programmer.\")"
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},
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"execution_count": 3,
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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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"chat([\n",
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" HumanMessage(content='You are a helpful assistant that translates English to French.Translate this sentence from English to French. I love programming.')\n",
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"])"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-10-19T10:20:46.275673Z",
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"start_time": "2023-10-19T10:20:44.241097Z"
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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": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2023-10-19T10:19:56.233477Z"
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}
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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": "3.11.4"
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},
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"orig_nbformat": 4
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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 +1,325 @@
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import base64
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import hashlib
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import hmac
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import json
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import logging
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import time
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from typing import Any, Dict, Iterator, List, Mapping, Optional, Type, Union
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from urllib.parse import urlparse
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import requests
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.chat_models.base import BaseChatModel, _generate_from_stream
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from langchain.pydantic_v1 import Field, SecretStr, root_validator
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from langchain.schema import (
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AIMessage,
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BaseMessage,
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ChatGeneration,
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ChatMessage,
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ChatResult,
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HumanMessage,
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)
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from langchain.schema.messages import (
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AIMessageChunk,
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BaseMessageChunk,
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ChatMessageChunk,
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HumanMessageChunk,
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)
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from langchain.schema.output import ChatGenerationChunk
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from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
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logger = logging.getLogger(__name__)
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DEFAULT_HUNYUAN_API_BASE = "https://hunyuan.cloud.tencent.com"
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DEFAULT_HUNYUAN_PATH = "/hyllm/v1/chat/completions"
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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message_dict: Dict[str, Any]
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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else:
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raise TypeError(f"Got unknown type {message}")
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return message_dict
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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return AIMessage(content=_dict.get("content", "") or "")
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") or ""
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content)
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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# signature generation
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# https://cloud.tencent.com/document/product/1729/97732#532252ce-e960-48a7-8821-940a9ce2ccf3
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def _signature(secret_key: SecretStr, url: str, payload: Dict[str, Any]) -> str:
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sorted_keys = sorted(payload.keys())
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url_info = urlparse(url)
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sign_str = url_info.netloc + url_info.path + "?"
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for key in sorted_keys:
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value = payload[key]
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if isinstance(value, list) or isinstance(value, dict):
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value = json.dumps(value, separators=(",", ":"))
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elif isinstance(value, float):
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value = "%g" % value
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sign_str = sign_str + key + "=" + str(value) + "&"
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sign_str = sign_str[:-1]
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hmacstr = hmac.new(
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key=secret_key.get_secret_value().encode("utf-8"),
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msg=sign_str.encode("utf-8"),
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digestmod=hashlib.sha1,
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).digest()
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return base64.b64encode(hmacstr).decode("utf-8")
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def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
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generations = []
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for choice in response["choices"]:
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message = _convert_dict_to_message(choice["messages"])
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generations.append(ChatGeneration(message=message))
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token_usage = response["usage"]
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llm_output = {"token_usage": token_usage}
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return ChatResult(generations=generations, llm_output=llm_output)
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def _to_secret(value: Union[SecretStr, str]) -> SecretStr:
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"""Convert a string to a SecretStr if needed."""
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if isinstance(value, SecretStr):
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return value
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return SecretStr(value)
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class ChatHunyuan(BaseChatModel):
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"""Tencent Hunyuan chat models API by Tencent.
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For more information, see https://cloud.tencent.com/document/product/1729
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"""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {
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"hunyuan_app_id": "HUNYUAN_APP_ID",
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"hunyuan_secret_id": "HUNYUAN_SECRET_ID",
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"hunyuan_secret_key": "HUNYUAN_SECRET_KEY",
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}
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@property
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def lc_serializable(self) -> bool:
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return True
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hunyuan_api_base: str = "https://hunyuan.cloud.tencent.com"
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"""Hunyuan custom endpoints"""
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hunyuan_app_id: Optional[str] = None
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"""Hunyuan App ID"""
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hunyuan_secret_id: Optional[str] = None
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"""Hunyuan Secret ID"""
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hunyuan_secret_key: Optional[SecretStr] = None
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"""Hunyuan Secret Key"""
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streaming: bool = False
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"""Whether to stream the results or not."""
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request_timeout: int = 60
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"""Timeout for requests to Hunyuan API. Default is 60 seconds."""
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query_id: Optional[str] = None
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"""Query id for troubleshooting"""
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temperature: float = 1.0
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"""What sampling temperature to use."""
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top_p: float = 1.0
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"""What probability mass to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for API call not explicitly specified."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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logger.warning(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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values["model_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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values["hunyuan_api_base"] = get_from_dict_or_env(
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values,
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"hunyuan_api_base",
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"HUNYUAN_API_BASE",
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)
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values["hunyuan_app_id"] = get_from_dict_or_env(
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values,
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"hunyuan_app_id",
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"HUNYUAN_APP_ID",
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)
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values["hunyuan_secret_id"] = get_from_dict_or_env(
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values,
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"hunyuan_secret_id",
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"HUNYUAN_SECRET_ID",
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)
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values["hunyuan_secret_key"] = _to_secret(
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get_from_dict_or_env(
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values,
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"hunyuan_secret_key",
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"HUNYUAN_SECRET_KEY",
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)
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Hunyuan API."""
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normal_params = {
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"app_id": self.hunyuan_app_id,
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"secret_id": self.hunyuan_secret_id,
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"temperature": self.temperature,
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"top_p": self.top_p,
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}
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if self.query_id is not None:
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normal_params["query_id"] = self.query_id
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return {**normal_params, **self.model_kwargs}
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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stream_iter = self._stream(
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messages=messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return _generate_from_stream(stream_iter)
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res = self._chat(messages, **kwargs)
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response = res.json()
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if "error" in response:
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raise ValueError(f"Error from Hunyuan api response: {response}")
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return _create_chat_result(response)
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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res = self._chat(messages, **kwargs)
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default_chunk_class = AIMessageChunk
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for chunk in res.iter_lines():
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response = json.loads(chunk)
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if "error" in response:
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raise ValueError(f"Error from Hunyuan api response: {response}")
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for choice in response["choices"]:
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chunk = _convert_delta_to_message_chunk(
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choice["delta"], default_chunk_class
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)
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default_chunk_class = chunk.__class__
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yield ChatGenerationChunk(message=chunk)
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if run_manager:
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run_manager.on_llm_new_token(chunk.content)
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def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
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if self.hunyuan_secret_key is None:
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raise ValueError("Hunyuan secret key is not set.")
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parameters = {**self._default_params, **kwargs}
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headers = parameters.pop("headers", {})
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timestamp = parameters.pop("timestamp", int(time.time()))
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expired = parameters.pop("expired", timestamp + 24 * 60 * 60)
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payload = {
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"timestamp": timestamp,
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"expired": expired,
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"messages": [_convert_message_to_dict(m) for m in messages],
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**parameters,
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}
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if self.streaming:
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payload["stream"] = 1
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url = self.hunyuan_api_base + DEFAULT_HUNYUAN_PATH
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res = requests.post(
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url=url,
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timeout=self.request_timeout,
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headers={
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"Content-Type": "application/json",
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"Authorization": _signature(
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secret_key=self.hunyuan_secret_key, url=url, payload=payload
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),
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**headers,
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},
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json=payload,
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stream=self.streaming,
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)
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return res
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@property
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||||
def _llm_type(self) -> str:
|
||||
return "hunyuan-chat"
|
||||
@ -0,0 +1,24 @@
|
||||
from langchain.chat_models.hunyuan import ChatHunyuan
|
||||
from langchain.schema.messages import AIMessage, HumanMessage
|
||||
|
||||
|
||||
def test_chat_hunyuan() -> None:
|
||||
chat = ChatHunyuan()
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert isinstance(response, AIMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_chat_hunyuan_with_temperature() -> None:
|
||||
chat = ChatHunyuan(temperature=0.6)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert isinstance(response, AIMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_extra_kwargs() -> None:
|
||||
chat = ChatHunyuan(temperature=0.88, top_p=0.7)
|
||||
assert chat.temperature == 0.88
|
||||
assert chat.top_p == 0.7
|
||||
@ -0,0 +1,114 @@
|
||||
import pytest
|
||||
|
||||
from langchain.chat_models.hunyuan import (
|
||||
_convert_delta_to_message_chunk,
|
||||
_convert_dict_to_message,
|
||||
_convert_message_to_dict,
|
||||
_signature,
|
||||
)
|
||||
from langchain.pydantic_v1 import SecretStr
|
||||
from langchain.schema.messages import (
|
||||
AIMessage,
|
||||
AIMessageChunk,
|
||||
ChatMessage,
|
||||
FunctionMessage,
|
||||
HumanMessage,
|
||||
HumanMessageChunk,
|
||||
SystemMessage,
|
||||
)
|
||||
|
||||
|
||||
def test__convert_message_to_dict_human() -> None:
|
||||
message = HumanMessage(content="foo")
|
||||
result = _convert_message_to_dict(message)
|
||||
expected_output = {"role": "user", "content": "foo"}
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_message_to_dict_ai() -> None:
|
||||
message = AIMessage(content="foo")
|
||||
result = _convert_message_to_dict(message)
|
||||
expected_output = {"role": "assistant", "content": "foo"}
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_message_to_dict_system() -> None:
|
||||
message = SystemMessage(content="foo")
|
||||
with pytest.raises(TypeError) as e:
|
||||
_convert_message_to_dict(message)
|
||||
assert "Got unknown type" in str(e)
|
||||
|
||||
|
||||
def test__convert_message_to_dict_function() -> None:
|
||||
message = FunctionMessage(name="foo", content="bar")
|
||||
with pytest.raises(TypeError) as e:
|
||||
_convert_message_to_dict(message)
|
||||
assert "Got unknown type" in str(e)
|
||||
|
||||
|
||||
def test__convert_dict_to_message_human() -> None:
|
||||
message_dict = {"role": "user", "content": "foo"}
|
||||
result = _convert_dict_to_message(message_dict)
|
||||
expected_output = HumanMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_dict_to_message_ai() -> None:
|
||||
message_dict = {"role": "assistant", "content": "foo"}
|
||||
result = _convert_dict_to_message(message_dict)
|
||||
expected_output = AIMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_dict_to_message_other_role() -> None:
|
||||
message_dict = {"role": "system", "content": "foo"}
|
||||
result = _convert_dict_to_message(message_dict)
|
||||
expected_output = ChatMessage(role="system", content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_delta_to_message_assistant() -> None:
|
||||
delta = {"role": "assistant", "content": "foo"}
|
||||
result = _convert_delta_to_message_chunk(delta, AIMessageChunk)
|
||||
expected_output = AIMessageChunk(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_delta_to_message_human() -> None:
|
||||
delta = {"role": "user", "content": "foo"}
|
||||
result = _convert_delta_to_message_chunk(delta, HumanMessageChunk)
|
||||
expected_output = HumanMessageChunk(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__signature() -> None:
|
||||
secret_key = SecretStr("YOUR_SECRET_KEY")
|
||||
url = "https://hunyuan.cloud.tencent.com/hyllm/v1/chat/completions"
|
||||
|
||||
result = _signature(
|
||||
secret_key=secret_key,
|
||||
url=url,
|
||||
payload={
|
||||
"app_id": "YOUR_APP_ID",
|
||||
"secret_id": "YOUR_SECRET_ID",
|
||||
"query_id": "test_query_id_cb5d8156-0ce2-45af-86b4-d02f5c26a142",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "You are a helpful assistant that translates English"
|
||||
" to French.Translate this sentence from English to"
|
||||
" French. I love programming.",
|
||||
}
|
||||
],
|
||||
"temperature": 0.0,
|
||||
"top_p": 0.8,
|
||||
"stream": 1,
|
||||
"timestamp": 1697738378,
|
||||
"expired": 1697824778,
|
||||
},
|
||||
)
|
||||
|
||||
# The signature was generated by the demo provided by Huanyuan.
|
||||
# https://hunyuan-sdk-1256237915.cos.ap-guangzhou.myqcloud.com/python.zip
|
||||
expected_output = "MXBvqNCXyxJWfEyBwk1pYBVnxzo="
|
||||
assert result == expected_output
|
||||
Loading…
Reference in New Issue