mirror of https://github.com/hwchase17/langchain
community[minor]: Add iFlyTek Spark LLM chat model support (#13389)
- **Description:** This PR enables LangChain to access the iFlyTek's Spark LLM via the chat_models wrapper. - **Dependencies:** websocket-client ^1.6.1 - **Tag maintainer:** @baskaryan ### SparkLLM chat model usage Get SparkLLM's app_id, api_key and api_secret from [iFlyTek SparkLLM API Console](https://console.xfyun.cn/services/bm3) (for more info, see [iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi) ), then set environment variables `IFLYTEK_SPARK_APP_ID`, `IFLYTEK_SPARK_API_KEY` and `IFLYTEK_SPARK_API_SECRET` or pass parameters when using it like the demo below: ```python3 from langchain.chat_models.sparkllm import ChatSparkLLM client = ChatSparkLLM( spark_app_id="<app_id>", spark_api_key="<api_key>", spark_api_secret="<api_secret>" ) ```pull/15761/head^2
Xudong Sun
6 months ago
committed by
GitHub
parent
80fcc50c65
commit
019b6ebe8d
@ -0,0 +1,99 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3ddface67cd10a87",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"# SparkLLM Chat\n",
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"\n",
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"SparkLLM chat models API by iFlyTek. For more information, see [iFlyTek Open Platform](https://www.xfyun.cn/)."
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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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"source": [
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"## Basic use"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "43daa39972d4c533",
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"metadata": {
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"collapsed": false,
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"is_executing": true
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},
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"outputs": [],
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"source": [
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"\"\"\"For basic init and call\"\"\"\n",
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"from langchain.chat_models import ChatSparkLLM\n",
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"from langchain.schema import HumanMessage\n",
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"\n",
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"chat = ChatSparkLLM(\n",
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" spark_app_id=\"<app_id>\", spark_api_key=\"<api_key>\", spark_api_secret=\"<api_secret>\"\n",
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")\n",
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"message = HumanMessage(content=\"Hello\")\n",
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"chat([message])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "df755f4c5689510",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"- Get SparkLLM's app_id, api_key and api_secret from [iFlyTek SparkLLM API Console](https://console.xfyun.cn/services/bm3) (for more info, see [iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi) ), then set environment variables `IFLYTEK_SPARK_APP_ID`, `IFLYTEK_SPARK_API_KEY` and `IFLYTEK_SPARK_API_SECRET` or pass parameters when creating `ChatSparkLLM` as the demo above."
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]
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},
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{
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"cell_type": "markdown",
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"id": "984e32ee47bc6772",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## For ChatSparkLLM with Streaming"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7dc162bd65fec08f",
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"chat = ChatSparkLLM(streaming=True)\n",
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"for chunk in chat.stream(\"Hello!\"):\n",
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" print(chunk.content, end=\"\")"
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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",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,473 @@
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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 queue
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import threading
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from datetime import datetime
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from queue import Queue
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from time import mktime
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from typing import Any, Dict, Generator, Iterator, List, Mapping, Optional, Type
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from urllib.parse import urlencode, urlparse, urlunparse
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from wsgiref.handlers import format_date_time
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from langchain_core.callbacks import (
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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generate_from_stream,
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)
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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)
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from langchain_core.outputs import (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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)
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.utils import (
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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logger = logging.getLogger(__name__)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": "user", "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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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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else:
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raise ValueError(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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msg_role = _dict["role"]
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msg_content = _dict["content"]
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if msg_role == "user":
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return HumanMessage(content=msg_content)
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elif msg_role == "assistant":
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content = msg_content or ""
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return AIMessage(content=content)
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elif msg_role == "system":
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return SystemMessage(content=msg_content)
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else:
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return ChatMessage(content=msg_content, role=msg_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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msg_role = _dict["role"]
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msg_content = _dict.get("content", "")
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if msg_role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=msg_content)
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elif msg_role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=msg_content)
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elif msg_role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=msg_content, role=msg_role)
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else:
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return default_class(content=msg_content)
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class ChatSparkLLM(BaseChatModel):
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"""Wrapper around iFlyTek's Spark large language model.
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To use, you should pass `app_id`, `api_key`, `api_secret`
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as a named parameter to the constructor OR set environment
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variables ``IFLYTEK_SPARK_APP_ID``, ``IFLYTEK_SPARK_API_KEY`` and
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``IFLYTEK_SPARK_API_SECRET``
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Example:
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.. code-block:: python
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client = ChatSparkLLM(
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spark_app_id="<app_id>",
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spark_api_key="<api_key>",
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spark_api_secret="<api_secret>"
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)
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"""
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this model can be serialized by Langchain."""
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return False
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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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"spark_app_id": "IFLYTEK_SPARK_APP_ID",
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"spark_api_key": "IFLYTEK_SPARK_API_KEY",
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"spark_api_secret": "IFLYTEK_SPARK_API_SECRET",
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"spark_api_url": "IFLYTEK_SPARK_API_URL",
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"spark_llm_domain": "IFLYTEK_SPARK_LLM_DOMAIN",
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}
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client: Any = None #: :meta private:
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spark_app_id: Optional[str] = None
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spark_api_key: Optional[str] = None
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spark_api_secret: Optional[str] = None
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spark_api_url: Optional[str] = None
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spark_llm_domain: Optional[str] = None
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spark_user_id: str = "lc_user"
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streaming: bool = False
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request_timeout: int = 30
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temperature: float = 0.5
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top_k: int = 4
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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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["spark_app_id"] = get_from_dict_or_env(
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values,
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"spark_app_id",
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"IFLYTEK_SPARK_APP_ID",
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)
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values["spark_api_key"] = get_from_dict_or_env(
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values,
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"spark_api_key",
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"IFLYTEK_SPARK_API_KEY",
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)
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values["spark_api_secret"] = get_from_dict_or_env(
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values,
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"spark_api_secret",
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"IFLYTEK_SPARK_API_SECRET",
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)
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values["spark_app_url"] = get_from_dict_or_env(
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values,
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"spark_app_url",
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"IFLYTEK_SPARK_APP_URL",
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"wss://spark-api.xf-yun.com/v3.1/chat",
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)
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values["spark_llm_domain"] = get_from_dict_or_env(
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values,
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"spark_llm_domain",
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"IFLYTEK_SPARK_LLM_DOMAIN",
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"generalv3",
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)
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# put extra params into model_kwargs
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values["model_kwargs"]["temperature"] = values["temperature"] or cls.temperature
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values["model_kwargs"]["top_k"] = values["top_k"] or cls.top_k
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values["client"] = _SparkLLMClient(
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app_id=values["spark_app_id"],
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api_key=values["spark_api_key"],
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api_secret=values["spark_api_secret"],
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api_url=values["spark_api_url"],
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spark_domain=values["spark_llm_domain"],
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model_kwargs=values["model_kwargs"],
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)
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return values
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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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default_chunk_class = AIMessageChunk
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self.client.arun(
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[_convert_message_to_dict(m) for m in messages],
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self.spark_user_id,
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self.model_kwargs,
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self.streaming,
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)
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for content in self.client.subscribe(timeout=self.request_timeout):
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if "data" not in content:
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continue
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delta = content["data"]
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chunk = _convert_delta_to_message_chunk(delta, default_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(str(chunk.content))
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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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self.client.arun(
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[_convert_message_to_dict(m) for m in messages],
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self.spark_user_id,
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self.model_kwargs,
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False,
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)
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completion = {}
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llm_output = {}
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for content in self.client.subscribe(timeout=self.request_timeout):
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if "usage" in content:
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llm_output["token_usage"] = content["usage"]
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if "data" not in content:
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continue
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completion = content["data"]
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message = _convert_dict_to_message(completion)
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generations = [ChatGeneration(message=message)]
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return ChatResult(generations=generations, llm_output=llm_output)
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@property
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def _llm_type(self) -> str:
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return "spark-llm-chat"
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class _SparkLLMClient:
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"""
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Use websocket-client to call the SparkLLM interface provided by Xfyun,
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which is the iFlyTek's open platform for AI capabilities
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"""
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def __init__(
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self,
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app_id: str,
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api_key: str,
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api_secret: str,
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api_url: Optional[str] = None,
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spark_domain: Optional[str] = None,
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model_kwargs: Optional[dict] = None,
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):
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try:
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import websocket
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self.websocket_client = websocket
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except ImportError:
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raise ImportError(
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"Could not import websocket client python package. "
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"Please install it with `pip install websocket-client`."
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)
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self.api_url = (
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"wss://spark-api.xf-yun.com/v3.1/chat" if not api_url else api_url
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)
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self.app_id = app_id
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self.ws_url = _SparkLLMClient._create_url(
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self.api_url,
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api_key,
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api_secret,
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)
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self.model_kwargs = model_kwargs
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self.spark_domain = spark_domain or "generalv3"
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self.queue: Queue[Dict] = Queue()
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self.blocking_message = {"content": "", "role": "assistant"}
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@staticmethod
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def _create_url(api_url: str, api_key: str, api_secret: str) -> str:
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"""
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Generate a request url with an api key and an api secret.
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"""
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# generate timestamp by RFC1123
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date = format_date_time(mktime(datetime.now().timetuple()))
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# urlparse
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parsed_url = urlparse(api_url)
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host = parsed_url.netloc
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path = parsed_url.path
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signature_origin = f"host: {host}\ndate: {date}\nGET {path} HTTP/1.1"
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# encrypt using hmac-sha256
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signature_sha = hmac.new(
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api_secret.encode("utf-8"),
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signature_origin.encode("utf-8"),
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digestmod=hashlib.sha256,
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).digest()
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signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding="utf-8")
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authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", \
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headers="host date request-line", signature="{signature_sha_base64}"'
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authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(
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encoding="utf-8"
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)
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# generate url
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params_dict = {"authorization": authorization, "date": date, "host": host}
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encoded_params = urlencode(params_dict)
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url = urlunparse(
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(
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parsed_url.scheme,
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parsed_url.netloc,
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parsed_url.path,
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parsed_url.params,
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encoded_params,
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parsed_url.fragment,
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)
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)
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return url
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def run(
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self,
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messages: List[Dict],
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user_id: str,
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model_kwargs: Optional[dict] = None,
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streaming: bool = False,
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) -> None:
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self.websocket_client.enableTrace(False)
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ws = self.websocket_client.WebSocketApp(
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self.ws_url,
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on_message=self.on_message,
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on_error=self.on_error,
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on_close=self.on_close,
|
||||
on_open=self.on_open,
|
||||
)
|
||||
ws.messages = messages
|
||||
ws.user_id = user_id
|
||||
ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs
|
||||
ws.streaming = streaming
|
||||
ws.run_forever()
|
||||
|
||||
def arun(
|
||||
self,
|
||||
messages: List[Dict],
|
||||
user_id: str,
|
||||
model_kwargs: Optional[dict] = None,
|
||||
streaming: bool = False,
|
||||
) -> threading.Thread:
|
||||
ws_thread = threading.Thread(
|
||||
target=self.run,
|
||||
args=(
|
||||
messages,
|
||||
user_id,
|
||||
model_kwargs,
|
||||
streaming,
|
||||
),
|
||||
)
|
||||
ws_thread.start()
|
||||
return ws_thread
|
||||
|
||||
def on_error(self, ws: Any, error: Optional[Any]) -> None:
|
||||
self.queue.put({"error": error})
|
||||
ws.close()
|
||||
|
||||
def on_close(self, ws: Any, close_status_code: int, close_reason: str) -> None:
|
||||
logger.debug(
|
||||
{
|
||||
"log": {
|
||||
"close_status_code": close_status_code,
|
||||
"close_reason": close_reason,
|
||||
}
|
||||
}
|
||||
)
|
||||
self.queue.put({"done": True})
|
||||
|
||||
def on_open(self, ws: Any) -> None:
|
||||
self.blocking_message = {"content": "", "role": "assistant"}
|
||||
data = json.dumps(
|
||||
self.gen_params(
|
||||
messages=ws.messages, user_id=ws.user_id, model_kwargs=ws.model_kwargs
|
||||
)
|
||||
)
|
||||
ws.send(data)
|
||||
|
||||
def on_message(self, ws: Any, message: str) -> None:
|
||||
data = json.loads(message)
|
||||
code = data["header"]["code"]
|
||||
if code != 0:
|
||||
self.queue.put(
|
||||
{"error": f"Code: {code}, Error: {data['header']['message']}"}
|
||||
)
|
||||
ws.close()
|
||||
else:
|
||||
choices = data["payload"]["choices"]
|
||||
status = choices["status"]
|
||||
content = choices["text"][0]["content"]
|
||||
if ws.streaming:
|
||||
self.queue.put({"data": choices["text"][0]})
|
||||
else:
|
||||
self.blocking_message["content"] += content
|
||||
if status == 2:
|
||||
if not ws.streaming:
|
||||
self.queue.put({"data": self.blocking_message})
|
||||
usage_data = (
|
||||
data.get("payload", {}).get("usage", {}).get("text", {})
|
||||
if data
|
||||
else {}
|
||||
)
|
||||
self.queue.put({"usage": usage_data})
|
||||
ws.close()
|
||||
|
||||
def gen_params(
|
||||
self, messages: list, user_id: str, model_kwargs: Optional[dict] = None
|
||||
) -> dict:
|
||||
data: Dict = {
|
||||
"header": {"app_id": self.app_id, "uid": user_id},
|
||||
"parameter": {"chat": {"domain": self.spark_domain}},
|
||||
"payload": {"message": {"text": messages}},
|
||||
}
|
||||
|
||||
if model_kwargs:
|
||||
data["parameter"]["chat"].update(model_kwargs)
|
||||
logger.debug(f"Spark Request Parameters: {data}")
|
||||
return data
|
||||
|
||||
def subscribe(self, timeout: Optional[int] = 30) -> Generator[Dict, None, None]:
|
||||
while True:
|
||||
try:
|
||||
content = self.queue.get(timeout=timeout)
|
||||
except queue.Empty as _:
|
||||
raise TimeoutError(
|
||||
f"SparkLLMClient wait LLM api response timeout {timeout} seconds"
|
||||
)
|
||||
if "error" in content:
|
||||
raise ConnectionError(content["error"])
|
||||
if "usage" in content:
|
||||
yield content
|
||||
continue
|
||||
if "done" in content:
|
||||
break
|
||||
if "data" not in content:
|
||||
break
|
||||
yield content
|
||||
@ -0,0 +1,36 @@
|
||||
from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage
|
||||
|
||||
from langchain_community.chat_models.sparkllm import ChatSparkLLM
|
||||
|
||||
|
||||
def test_chat_spark_llm() -> None:
|
||||
chat = ChatSparkLLM()
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert isinstance(response, AIMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_chat_spark_llm_streaming() -> None:
|
||||
chat = ChatSparkLLM(streaming=True)
|
||||
for chunk in chat.stream("Hello!"):
|
||||
assert isinstance(chunk, AIMessageChunk)
|
||||
assert isinstance(chunk.content, str)
|
||||
|
||||
|
||||
def test_chat_spark_llm_with_domain() -> None:
|
||||
chat = ChatSparkLLM(spark_llm_domain="generalv3")
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
print(response)
|
||||
assert isinstance(response, AIMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_chat_spark_llm_with_temperature() -> None:
|
||||
chat = ChatSparkLLM(temperature=0.9, top_k=2)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
print(response)
|
||||
assert isinstance(response, AIMessage)
|
||||
assert isinstance(response.content, str)
|
||||
Loading…
Reference in New Issue