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Implement Alibaba Tongyi chat model apis. (#10922)
Hi there This PR is aim to implement chat model for Alibaba Tongyi LLM model. It contains work below: 1.Implement ChatTongyi chat model in langchain.chat_models.tongyi. Note this is different with tongyi llm model to another PR https://github.com/langchain-ai/langchain/pull/10878. For detail it implements _generate() and _stream() function in ChatTongyi. 2. Add some examples in chat/tongyi.ipynb. 3. Add integration test in chat_models/test_tongyi.py Note async completion for the Text API is not yet supported. Dependencies: dashscope. It will be installed manually cause it is not need by everyone.
This commit is contained in:
163
docs/extras/integrations/chat/tongyi.ipynb
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163
docs/extras/integrations/chat/tongyi.ipynb
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"# Tongyi Qwen\n",
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"Tongyi Qwen is a large language model developed by Alibaba's Damo Academy. It is capable of understanding user intent through natural language understanding and semantic analysis, based on user input in natural language. It provides services and assistance to users in different domains and tasks. By providing clear and detailed instructions, you can obtain results that better align with your expectations.\n",
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"In this notebook, we will introduce how to use langchain with [Tongyi](https://www.aliyun.com/product/dashscope) mainly in `Chat` corresponding\n",
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" to the package `langchain/chat_models` in langchain"
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"# Install the package\n",
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"!pip install dashscope"
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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" ········\n"
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]
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}
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],
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"source": [
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"# Get a new token: https://help.aliyun.com/document_detail/611472.html?spm=a2c4g.2399481.0.0\n",
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"from getpass import getpass\n",
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"\n",
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"DASHSCOPE_API_KEY = getpass()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"DASHSCOPE_API_KEY\"] = DASHSCOPE_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": 4,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"chat resp: content='Hello! How' additional_kwargs={} example=False\n",
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"chat resp: content=' can I assist you today?' additional_kwargs={} example=False\n"
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]
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}
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],
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"source": [
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"from langchain.chat_models.tongyi import ChatTongyi\n",
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"from langchain.schema import HumanMessage\n",
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"\n",
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"chatLLM = ChatTongyi(\n",
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" streaming=True,\n",
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")\n",
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"res = chatLLM.stream([HumanMessage(content=\"hi\")], streaming=True)\n",
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"for r in res:\n",
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" print(\"chat resp:\", r)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessageChunk(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 5,
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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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"from langchain.schema import AIMessage, HumanMessage, SystemMessage\n",
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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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"chatLLM(messages)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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