mirror of
https://github.com/hwchase17/langchain.git
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164 lines
4.3 KiB
Plaintext
164 lines
4.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"id": "fbc66410",
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"metadata": {
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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"---\n",
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"sidebar_label: Bedrock\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": "bf733a38-db84-4363-89e2-de6735c37230",
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"metadata": {},
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"source": [
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"# ChatBedrock\n",
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"\n",
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">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of \n",
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"> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, \n",
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"> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to \n",
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"> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, \n",
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"> you can easily experiment with and evaluate top FMs for your use case, privately customize them with \n",
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"> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
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"> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
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"> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
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"> generative AI capabilities into your applications using the AWS services you are already familiar with.\n"
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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": "d51edc81",
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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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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install --upgrade --quiet langchain-aws"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain_aws import ChatBedrock\n",
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"from langchain_core.messages import HumanMessage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat = ChatBedrock(\n",
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" model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
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" model_kwargs={\"temperature\": 0.1},\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", additional_kwargs={'usage': {'prompt_tokens': 20, 'completion_tokens': 21, 'total_tokens': 41}}, response_metadata={'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0', 'usage': {'prompt_tokens': 20, 'completion_tokens': 21, 'total_tokens': 41}}, id='run-994f0362-0e50-4524-afad-3c4f5bb11328-0')"
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]
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},
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"execution_count": 12,
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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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"messages = [\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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"chat.invoke(messages)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "a4a4f4d4",
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"metadata": {},
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"source": [
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"### Streaming\n",
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"\n",
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"To stream responses, you can use the runnable `.stream()` method."
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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": 14,
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"id": "d9e52838",
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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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"Voici la traduction en français :\n",
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"\n",
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"J'aime la programmation."
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]
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}
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],
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"source": [
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"for chunk in chat.stream(messages):\n",
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" print(chunk.content, end=\"\", flush=True)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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