mirror of https://github.com/hwchase17/langchain
community[minor]: add support for Moonshot llm and chat model (#17100)
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824dccf5e2
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{
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Moonshot\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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"metadata": {
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"collapsed": false
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},
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"source": [
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"# MoonshotChat\n",
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"\n",
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"[Moonshot](https://platform.moonshot.cn/) is a Chinese startup that provides LLM service for companies and individuals.\n",
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"\n",
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"This example goes over how to use LangChain to interact with Moonshot Inference for Chat."
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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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"import os\n",
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"\n",
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"# Generate your api key from: https://platform.moonshot.cn/console/api-keys\n",
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"os.environ[\"MOONSHOT_API_KEY\"] = \"MOONSHOT_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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.chat_models.moonshot import MoonshotChat\n",
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"from langchain_core.messages import HumanMessage, SystemMessage"
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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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"chat = MoonshotChat()\n",
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"# or use a specific model\n",
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"# Available models: https://platform.moonshot.cn/docs\n",
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"# chat = MoonshotChat(model=\"moonshot-v1-128k\")"
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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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"messages = [\n",
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" SystemMessage(\n",
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" content=\"You are a helpful assistant that translates English to French.\"\n",
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" ),\n",
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" HumanMessage(\n",
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" content=\"Translate this sentence from English to French. I love programming.\"\n",
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" ),\n",
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"]\n",
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"\n",
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"chat.invoke(messages)"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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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 +1,85 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# MoonshotChat\n",
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"\n",
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"[Moonshot](https://platform.moonshot.cn/) is a Chinese startup that provides LLM service for companies and individuals.\n",
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"\n",
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"This example goes over how to use LangChain to interact with Moonshot."
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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": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.llms.moonshot import Moonshot"
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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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"import os\n",
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"\n",
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"# Generate your api key from: https://platform.moonshot.cn/console/api-keys\n",
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"os.environ[\"MOONSHOT_API_KEY\"] = \"MOONSHOT_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": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = Moonshot()\n",
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"# or use a specific model\n",
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"# Available models: https://platform.moonshot.cn/docs\n",
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"# llm = Moonshot(model=\"moonshot-v1-128k\")"
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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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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"# Prompt the model\n",
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"llm.invoke(\"What is the difference between panda and bear?\")"
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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": ".venv",
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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": "3.10.4"
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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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"""Wrapper around Moonshot chat models."""
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from typing import Dict
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from langchain_core.pydantic_v1 import root_validator
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from langchain_core.utils import get_from_dict_or_env
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.llms.moonshot import MOONSHOT_SERVICE_URL_BASE, MoonshotCommon
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class MoonshotChat(MoonshotCommon, ChatOpenAI):
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"""Wrapper around Moonshot large language models.
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To use, you should have the ``openai`` python package installed, and the
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environment variable ``MOONSHOT_API_KEY`` set with your API key.
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(Moonshot's chat API is compatible with OpenAI's SDK.)
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Referenced from https://platform.moonshot.cn/docs
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Example:
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.. code-block:: python
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from langchain_community.chat_models.moonshot import MoonshotChat
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moonshot = MoonshotChat(model="moonshot-v1-8k")
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"""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that the environment is set up correctly."""
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values["moonshot_api_key"] = get_from_dict_or_env(
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values, "moonshot_api_key", "MOONSHOT_API_KEY"
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)
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try:
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import openai
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except ImportError:
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raise ImportError(
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"Could not import openai python package. "
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"Please install it with `pip install openai`."
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)
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client_params = {
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"api_key": values["moonshot_api_key"],
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"base_url": values["base_url"]
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if "base_url" in values
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else MOONSHOT_SERVICE_URL_BASE,
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}
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if not values.get("client"):
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values["client"] = openai.OpenAI(**client_params).chat.completions
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if not values.get("async_client"):
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values["async_client"] = openai.AsyncOpenAI(
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**client_params
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).chat.completions
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return values
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from typing import Any, Dict, List, Optional
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models import LLM
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from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from langchain_community.llms.utils import enforce_stop_tokens
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MOONSHOT_SERVICE_URL_BASE = "https://api.moonshot.cn/v1"
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class _MoonshotClient(BaseModel):
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"""An API client that talks to the Moonshot server."""
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api_key: SecretStr
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"""The API key to use for authentication."""
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base_url: str = MOONSHOT_SERVICE_URL_BASE
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def completion(self, request: Any) -> Any:
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headers = {"Authorization": f"Bearer {self.api_key.get_secret_value()}"}
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response = requests.post(
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=request,
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)
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if not response.ok:
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raise ValueError(f"HTTP {response.status_code} error: {response.text}")
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return response.json()["choices"][0]["message"]["content"]
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class MoonshotCommon(BaseModel):
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_client: _MoonshotClient
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base_url: str = MOONSHOT_SERVICE_URL_BASE
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moonshot_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
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"""Moonshot API key. Get it here: https://platform.moonshot.cn/console/api-keys"""
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model_name: str = Field(default="moonshot-v1-8k", alias="model")
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"""Model name. Available models listed here: https://platform.moonshot.cn/pricing"""
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max_tokens = 1024
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"""Maximum number of tokens to generate."""
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temperature = 0.3
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"""Temperature parameter (higher values make the model more creative)."""
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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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@property
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def lc_secrets(self) -> dict:
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"""A map of constructor argument names to secret ids.
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For example,
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{"moonshot_api_key": "MOONSHOT_API_KEY"}
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"""
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return {"moonshot_api_key": "MOONSHOT_API_KEY"}
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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 OpenAI API."""
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return {
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"model": self.model_name,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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}
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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return {**{"model": self.model_name}, **self._default_params}
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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 parameters.
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Override the superclass method, prevent the model parameter from being
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overridden.
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"""
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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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"""Validate that api key and python package exists in environment."""
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values["moonshot_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "moonshot_api_key", "MOONSHOT_API_KEY")
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)
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values["_client"] = _MoonshotClient(
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api_key=values["moonshot_api_key"],
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base_url=values["base_url"]
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if "base_url" in values
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else MOONSHOT_SERVICE_URL_BASE,
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)
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return values
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "moonshot"
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class Moonshot(MoonshotCommon, LLM):
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"""Moonshot large language models.
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To use, you should have the environment variable ``MOONSHOT_API_KEY`` set with your
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API key. Referenced from https://platform.moonshot.cn/docs
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Example:
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.. code-block:: python
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from langchain_community.llms.moonshot import Moonshot
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moonshot = Moonshot(model="moonshot-v1-8k")
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"""
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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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def _call(
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self,
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prompt: str,
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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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) -> str:
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request = self._invocation_params
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request["messages"] = [{"role": "user", "content": prompt}]
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request.update(kwargs)
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text = self._client.completion(request)
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if stop is not None:
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# This is required since the stop tokens
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# are not enforced by the model parameters
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text = enforce_stop_tokens(text, stop)
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return text
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import os
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import pytest
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from langchain_community.llms.moonshot import Moonshot
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os.environ["MOONSHOT_API_KEY"] = "key"
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@pytest.mark.requires("openai")
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def test_moonshot_model_param() -> None:
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llm = Moonshot(model="foo")
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assert llm.model_name == "foo"
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llm = Moonshot(model_name="bar")
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assert llm.model_name == "bar"
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