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https://github.com/hwchase17/langchain.git
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fix recent docs integrations file loc (#11782)
This commit is contained in:
Bagatur
GitHub
@@ -1,163 +0,0 @@
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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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@@ -1,186 +0,0 @@
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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": "683953b3",
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"metadata": {
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"id": "683953b3"
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},
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"source": [
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"# Elasticsearch Chat Message History\n",
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"\n",
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">[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library.\n",
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"\n",
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"This notebook shows how to use chat message history functionality with Elasticsearch."
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c7720c3",
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"metadata": {},
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"source": [
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"## Set up Elasticsearch\n",
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"\n",
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"There are two main ways to set up an Elasticsearch instance:\n",
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"\n",
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"1. **Elastic Cloud.** Elastic Cloud is a managed Elasticsearch service. Sign up for a [free trial](https://cloud.elastic.co/registration?storm=langchain-notebook).\n",
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"\n",
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"2. **Local Elasticsearch installation.** Get started with Elasticsearch by running it locally. The easiest way is to use the official Elasticsearch Docker image. See the [Elasticsearch Docker documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html) for more information."
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]
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},
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{
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"cell_type": "markdown",
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"id": "cdf1d2b7",
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"metadata": {},
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"source": [
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"## Install dependencies"
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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": "e5bbffe2",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install elasticsearch langchain"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8be8fcc3",
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"metadata": {},
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"source": [
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"## Initialize Elasticsearch client and chat message history"
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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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"id": "8e2ee0fa",
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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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"from langchain.memory import ElasticsearchChatMessageHistory\n",
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"\n",
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"es_url = os.environ.get(\"ES_URL\", \"http://localhost:9200\")\n",
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"\n",
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"# If using Elastic Cloud:\n",
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"# es_cloud_id = os.environ.get(\"ES_CLOUD_ID\")\n",
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"\n",
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"# Note: see Authentication section for various authentication methods\n",
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"\n",
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"history = ElasticsearchChatMessageHistory(\n",
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" es_url=es_url,\n",
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" index=\"test-history\",\n",
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" session_id=\"test-session\"\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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"id": "a63942e2",
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"metadata": {},
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"source": [
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"## Use the chat message history"
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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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"id": "c1c7be79",
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"metadata": {},
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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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"indexing message content='hi!' additional_kwargs={} example=False\n",
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"indexing message content='whats up?' 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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"history.add_user_message(\"hi!\")\n",
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"history.add_ai_message(\"whats up?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c46c216c",
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"metadata": {},
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"source": [
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"# Authentication\n",
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"\n",
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"## Username/password\n",
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"\n",
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"```python\n",
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"es_username = os.environ.get(\"ES_USERNAME\", \"elastic\")\n",
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"es_password = os.environ.get(\"ES_PASSWORD\", \"changeme\")\n",
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"\n",
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"history = ElasticsearchChatMessageHistory(\n",
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" es_url=es_url,\n",
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" es_user=es_username,\n",
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" es_password=es_password,\n",
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" index=\"test-history\",\n",
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" session_id=\"test-session\"\n",
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")\n",
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"```\n",
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"\n",
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"### How to obtain a password for the default \"elastic\" user\n",
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"\n",
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"To obtain your Elastic Cloud password for the default \"elastic\" user:\n",
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"1. Log in to the Elastic Cloud console at https://cloud.elastic.co\n",
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"2. Go to \"Security\" > \"Users\"\n",
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"3. Locate the \"elastic\" user and click \"Edit\"\n",
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"4. Click \"Reset password\"\n",
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"5. Follow the prompts to reset the password\n",
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"\n",
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"## API key\n",
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"\n",
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"```python\n",
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"es_api_key = os.environ.get(\"ES_API_KEY\")\n",
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"\n",
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"history = ElasticsearchChatMessageHistory(\n",
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" es_api_key=es_api_key,\n",
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" index=\"test-history\",\n",
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" session_id=\"test-session\"\n",
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")\n",
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"```\n",
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"\n",
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"### How to obtain an API key\n",
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"\n",
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"To obtain an API key:\n",
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"1. Log in to the Elastic Cloud console at https://cloud.elastic.co\n",
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"2. Open Kibana and go to Stack Management > API Keys\n",
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"3. Click \"Create API key\"\n",
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"4. Enter a name for the API key and click \"Create\""
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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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": "3.10.9"
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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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