mirror of
https://github.com/hwchase17/langchain
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docs[patch]: AWS platform page update (#14160)
The `AWS` platform page has many missed integrations. - added missed integration references to the `AWS` platform page - added/updated descriptions and links in the referenced notebooks - renamed two notebook files. They have file names != page Title, which generate unordered ToC. - reroute the URLs for renamed files - fixed `amazon_textract` notebook: removed failed cell outputs
This commit is contained in:
Leonid Ganeline
GitHub
parent
74d4154bcc
commit
94bf733dae
@ -7,7 +7,9 @@
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"source": [
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"# Amazon Comprehend Moderation Chain\n",
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"\n",
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"This notebook shows how to use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle `Personally Identifiable Information` (`PII`) and toxicity.\n",
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">[Amazon Comprehend](https://aws.amazon.com/comprehend/) is a natural-language processing (NLP) service that uses machine learning to uncover valuable insights and connections in text.\n",
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"\n",
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"This notebook shows how to use `Amazon Comprehend` to detect and handle `Personally Identifiable Information` (`PII`) and toxicity.\n",
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"\n",
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"## Setting up"
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]
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@ -1417,7 +1419,7 @@
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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": "3.9.1"
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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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@ -7,7 +7,15 @@
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"source": [
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"# Bedrock Chat\n",
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"\n",
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"[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case"
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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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@ -131,7 +139,7 @@
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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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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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884
docs/docs/integrations/document_loaders/amazon_textract.ipynb
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884
docs/docs/integrations/document_loaders/amazon_textract.ipynb
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@ -0,0 +1,884 @@
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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": "1f3cebbe-079a-4bfe-b1a1-07bdac882ce2",
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"metadata": {},
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"source": [
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"# Amazon Textract \n",
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"\n",
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">[Amazon Textract](https://docs.aws.amazon.com/managedservices/latest/userguide/textract.html) is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents.\n",
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">\n",
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">It goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables. Today, many companies manually extract data from scanned documents such as PDFs, images, tables, and forms, or through simple OCR software that requires manual configuration (which often must be updated when the form changes). To overcome these manual and expensive processes, `Textract` uses ML to read and process any type of document, accurately extracting text, handwriting, tables, and other data with no manual effort. \n",
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"\n",
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"This sample demonstrates the use of `Amazon Textract` in combination with LangChain as a DocumentLoader.\n",
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"\n",
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"`Textract` supports`PDF`, `TIF`F, `PNG` and `JPEG` format.\n",
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"\n",
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"`Textract` supports these [document sizes, languages and characters](https://docs.aws.amazon.com/textract/latest/dg/limits-document.html)."
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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": "a1aa66d4-85f2-42ad-a8d3-de7cea8d6c35",
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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install boto3 openai tiktoken python-dotenv"
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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": "e4305a0d-37da-41f9-a52c-7d166d7dbabf",
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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install \"amazon-textract-caller>=0.2.0\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "400b25c6-befa-4730-a201-39ff112c8858",
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"metadata": {},
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"source": [
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"## Sample 1\n",
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"\n",
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"The first example uses a local file, which internally will be send to Amazon Textract sync API [DetectDocumentText](https://docs.aws.amazon.com/textract/latest/dg/API_DetectDocumentText.html). \n",
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"\n",
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"Local files or URL endpoints like HTTP:// are limited to one page documents for Textract.\n",
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"Multi-page documents have to reside on S3. This sample file is a jpeg."
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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": "1becee92-e82f-42d4-9b4e-b23d77cbe88d",
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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.document_loaders import AmazonTextractPDFLoader\n",
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"\n",
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"loader = AmazonTextractPDFLoader(\"example_data/alejandro_rosalez_sample-small.jpeg\")\n",
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"documents = loader.load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d566dc56-c9a9-44ec-84fb-a81928f90d40",
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"metadata": {},
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"source": [
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"Output from the file"
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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": 10,
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"id": "1272ce8c-d298-4059-ac0a-780bf5f82302",
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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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"[Document(page_content='Patient Information First Name: ALEJANDRO Last Name: ROSALEZ Date of Birth: 10/10/1982 Sex: M Marital Status: MARRIED Email Address: Address: 123 ANY STREET City: ANYTOWN State: CA Zip Code: 12345 Phone: 646-555-0111 Emergency Contact 1: First Name: CARLOS Last Name: SALAZAR Phone: 212-555-0150 Relationship to Patient: BROTHER Emergency Contact 2: First Name: JANE Last Name: DOE Phone: 650-555-0123 Relationship FRIEND to Patient: Did you feel fever or feverish lately? Yes No Are you having shortness of breath? Yes No Do you have a cough? Yes No Did you experience loss of taste or smell? Yes No Where you in contact with any confirmed COVID-19 positive patients? Yes No Did you travel in the past 14 days to any regions affected by COVID-19? Yes No Patient Information First Name: ALEJANDRO Last Name: ROSALEZ Date of Birth: 10/10/1982 Sex: M Marital Status: MARRIED Email Address: Address: 123 ANY STREET City: ANYTOWN State: CA Zip Code: 12345 Phone: 646-555-0111 Emergency Contact 1: First Name: CARLOS Last Name: SALAZAR Phone: 212-555-0150 Relationship to Patient: BROTHER Emergency Contact 2: First Name: JANE Last Name: DOE Phone: 650-555-0123 Relationship FRIEND to Patient: Did you feel fever or feverish lately? Yes No Are you having shortness of breath? Yes No Do you have a cough? Yes No Did you experience loss of taste or smell? Yes No Where you in contact with any confirmed COVID-19 positive patients? Yes No Did you travel in the past 14 days to any regions affected by COVID-19? Yes No ', metadata={'source': 'example_data/alejandro_rosalez_sample-small.jpeg', 'page': 1})]"
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]
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},
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"execution_count": 10,
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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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"documents"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4cf7f19c-3635-453a-9c76-4baf98b8d7f4",
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"metadata": {},
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"source": [
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"## Sample 2\n",
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"The next sample loads a file from an HTTPS endpoint. \n",
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"It has to be single page, as Amazon Textract requires all multi-page documents to be stored on S3."
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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": 7,
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"id": "10374bfb-b325-451f-8bd0-c686710ab68c",
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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.document_loaders import AmazonTextractPDFLoader\n",
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"\n",
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"loader = AmazonTextractPDFLoader(\n",
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" \"https://amazon-textract-public-content.s3.us-east-2.amazonaws.com/langchain/alejandro_rosalez_sample_1.jpg\"\n",
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")\n",
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"documents = loader.load()"
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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": "16a2b6a3-7514-4c2c-a427-6847169af473",
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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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"[Document(page_content='Patient Information First Name: ALEJANDRO Last Name: ROSALEZ Date of Birth: 10/10/1982 Sex: M Marital Status: MARRIED Email Address: Address: 123 ANY STREET City: ANYTOWN State: CA Zip Code: 12345 Phone: 646-555-0111 Emergency Contact 1: First Name: CARLOS Last Name: SALAZAR Phone: 212-555-0150 Relationship to Patient: BROTHER Emergency Contact 2: First Name: JANE Last Name: DOE Phone: 650-555-0123 Relationship FRIEND to Patient: Did you feel fever or feverish lately? Yes No Are you having shortness of breath? Yes No Do you have a cough? Yes No Did you experience loss of taste or smell? Yes No Where you in contact with any confirmed COVID-19 positive patients? Yes No Did you travel in the past 14 days to any regions affected by COVID-19? Yes No Patient Information First Name: ALEJANDRO Last Name: ROSALEZ Date of Birth: 10/10/1982 Sex: M Marital Status: MARRIED Email Address: Address: 123 ANY STREET City: ANYTOWN State: CA Zip Code: 12345 Phone: 646-555-0111 Emergency Contact 1: First Name: CARLOS Last Name: SALAZAR Phone: 212-555-0150 Relationship to Patient: BROTHER Emergency Contact 2: First Name: JANE Last Name: DOE Phone: 650-555-0123 Relationship FRIEND to Patient: Did you feel fever or feverish lately? Yes No Are you having shortness of breath? Yes No Do you have a cough? Yes No Did you experience loss of taste or smell? Yes No Where you in contact with any confirmed COVID-19 positive patients? Yes No Did you travel in the past 14 days to any regions affected by COVID-19? Yes No ', metadata={'source': 'example_data/alejandro_rosalez_sample-small.jpeg', 'page': 1})]"
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]
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},
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"execution_count": 11,
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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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"documents"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a9cd8ec-e663-4dc7-9db1-d2f575253141",
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"metadata": {},
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"source": [
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"## Sample 3\n",
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"\n",
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"Processing a multi-page document requires the document to be on S3. The sample document resides in a bucket in us-east-2 and Textract needs to be called in that same region to be successful, so we set the region_name on the client and pass that in to the loader to ensure Textract is called from us-east-2. You could also to have your notebook running in us-east-2, setting the AWS_DEFAULT_REGION set to us-east-2 or when running in a different environment, pass in a boto3 Textract client with that region name like in the cell below."
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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": "8185e3e6-9599-4a47-8969-d6dcef3e6404",
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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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"import boto3\n",
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"\n",
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"textract_client = boto3.client(\"textract\", region_name=\"us-east-2\")\n",
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"\n",
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"file_path = \"s3://amazon-textract-public-content/langchain/layout-parser-paper.pdf\"\n",
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"loader = AmazonTextractPDFLoader(file_path, client=textract_client)\n",
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"documents = loader.load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b8901eec-070d-4fd6-9d65-52211d332441",
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"metadata": {},
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"source": [
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"Now getting the number of pages to validate the response (printing out the full response would be quite long...). We expect 16 pages."
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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": 13,
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"id": "b23c01c8-cf69-4fe2-8141-4621edb7d79c",
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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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"16"
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]
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},
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"execution_count": 13,
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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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"len(documents)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3e41b4d-b159-4274-89be-80d8159134ef",
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"metadata": {},
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"source": [
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"## Using the AmazonTextractPDFLoader in an LangChain chain (e. g. OpenAI)\n",
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"\n",
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"The AmazonTextractPDFLoader can be used in a chain the same way the other loaders are used.\n",
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"Textract itself does have a [Query feature](https://docs.aws.amazon.com/textract/latest/dg/API_Query.html), which offers similar functionality to the QA chain in this sample, which is worth checking out as well."
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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": "53c47b24-cc06-4256-9e5b-a82fc80bc55d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# You can store your OPENAI_API_KEY in a .env file as well\n",
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"# import os\n",
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"# from dotenv import load_dotenv\n",
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"\n",
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"# load_dotenv()"
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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": 15,
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"id": "a9ae004c-246c-4c7f-8458-191cd7424a9b",
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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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"# Or set the OpenAI key in the environment directly\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"your-OpenAI-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": 16,
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"id": "d52b089c-10ca-45fb-8669-8a1c5fee10d5",
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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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"' The authors are Zejiang Shen, Ruochen Zhang, Melissa Dell, Benjamin Charles Germain Lee, Jacob Carlson, Weining Li, Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N., Peters, M., Schmitz, M., Zettlemoyer, L., Lukasz Garncarek, Powalski, R., Stanislawek, T., Topolski, B., Halama, P., Gralinski, F., Graves, A., Fernández, S., Gomez, F., Schmidhuber, J., Harley, A.W., Ufkes, A., Derpanis, K.G., He, K., Gkioxari, G., Dollár, P., Girshick, R., He, K., Zhang, X., Ren, S., Sun, J., Kay, A., Lamiroy, B., Lopresti, D., Mears, J., Jakeway, E., Ferriter, M., Adams, C., Yarasavage, N., Thomas, D., Zwaard, K., Li, M., Cui, L., Huang,'"
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]
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},
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"execution_count": 16,
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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.chains.question_answering import load_qa_chain\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"chain = load_qa_chain(llm=OpenAI(), chain_type=\"map_reduce\")\n",
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"query = [\"Who are the autors?\"]\n",
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"\n",
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"chain.run(input_documents=documents, question=query)"
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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": "1a09d18b-ab7b-468e-ae66-f92abf666b9b",
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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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"availableInstances": [
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{
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"_defaultOrder": 0,
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"_isFastLaunch": true,
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"category": "General purpose",
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"gpuNum": 0,
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"hideHardwareSpecs": false,
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"memoryGiB": 4,
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"name": "ml.t3.medium",
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"vcpuNum": 2
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},
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{
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"_defaultOrder": 1,
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"_isFastLaunch": false,
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"category": "General purpose",
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"gpuNum": 0,
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"hideHardwareSpecs": false,
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"memoryGiB": 8,
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"name": "ml.t3.large",
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"vcpuNum": 2
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},
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{
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"_defaultOrder": 2,
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"_isFastLaunch": false,
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"category": "General purpose",
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"gpuNum": 0,
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"hideHardwareSpecs": false,
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"memoryGiB": 16,
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"name": "ml.t3.xlarge",
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"vcpuNum": 4
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},
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{
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"_defaultOrder": 3,
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"_isFastLaunch": false,
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"category": "General purpose",
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"gpuNum": 0,
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"hideHardwareSpecs": false,
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"memoryGiB": 32,
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"name": "ml.t3.2xlarge",
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"vcpuNum": 8
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},
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{
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"_defaultOrder": 4,
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"_isFastLaunch": true,
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"category": "General purpose",
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"gpuNum": 0,
|
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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||||
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|
||||
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||||
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|
||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
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|
||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
"category": "Memory Optimized",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
"category": "Accelerated computing",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"vcpuNum": 48
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"name": "ml.g5.48xlarge",
|
||||
"vcpuNum": 192
|
||||
},
|
||||
{
|
||||
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|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
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|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 1152,
|
||||
"name": "ml.p4d.24xlarge",
|
||||
"vcpuNum": 96
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 56,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 8,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 1152,
|
||||
"name": "ml.p4de.24xlarge",
|
||||
"vcpuNum": 96
|
||||
}
|
||||
],
|
||||
"instance_type": "ml.t3.medium",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -11,9 +11,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[Amazon API Gateway](https://aws.amazon.com/api-gateway/) is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the \"front door\" for applications to access data, business logic, or functionality from your backend services. Using API Gateway, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication applications. API Gateway supports containerized and serverless workloads, as well as web applications.\n",
|
||||
">[Amazon API Gateway](https://aws.amazon.com/api-gateway/) is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any >scale. APIs act as the \"front door\" for applications to access data, business logic, or functionality from your backend services. Using `API Gateway`, you can create RESTful APIs and >WebSocket APIs that enable real-time two-way communication applications. API Gateway supports containerized and serverless workloads, as well as web applications.\n",
|
||||
"\n",
|
||||
"API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, CORS support, authorization and access control, throttling, monitoring, and API version management. API Gateway has no minimum fees or startup costs. You pay for the API calls you receive and the amount of data transferred out and, with the API Gateway tiered pricing model, you can reduce your cost as your API usage scales."
|
||||
">`API Gateway` handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, CORS support, authorization >and access control, throttling, monitoring, and API version management. `API Gateway` has no minimum fees or startup costs. You pay for the API calls you receive and the amount of data >transferred out and, with the `API Gateway` tiered pricing model, you can reduce your cost as your API usage scales."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -11,7 +11,15 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case"
|
||||
">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of \n",
|
||||
"> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, \n",
|
||||
"> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to \n",
|
||||
"> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, \n",
|
||||
"> you can easily experiment with and evaluate top FMs for your use case, privately customize them with \n",
|
||||
"> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
|
||||
"> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
|
||||
"> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
|
||||
"> generative AI capabilities into your applications using the AWS services you are already familiar with.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -116,7 +124,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -1,11 +1,22 @@
|
||||
# AWS
|
||||
|
||||
All functionality related to [Amazon AWS](https://aws.amazon.com/) platform
|
||||
The `LangChain` integrations related to [Amazon AWS](https://aws.amazon.com/) platform.
|
||||
|
||||
## LLMs
|
||||
|
||||
### Bedrock
|
||||
|
||||
>[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of
|
||||
> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`,
|
||||
> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to
|
||||
> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`,
|
||||
> you can easily experiment with and evaluate top FMs for your use case, privately customize them with
|
||||
> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build
|
||||
> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is
|
||||
> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy
|
||||
> generative AI capabilities into your applications using the AWS services you are already familiar with.
|
||||
|
||||
|
||||
See a [usage example](/docs/integrations/llms/bedrock).
|
||||
|
||||
```python
|
||||
@ -14,32 +25,28 @@ from langchain.llms.bedrock import Bedrock
|
||||
|
||||
### Amazon API Gateway
|
||||
|
||||
[Amazon API Gateway](https://aws.amazon.com/api-gateway/) is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the "front door" for applications to access data, business logic, or functionality from your backend services. Using API Gateway, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication applications. API Gateway supports containerized and serverless workloads, as well as web applications.
|
||||
>[Amazon API Gateway](https://aws.amazon.com/api-gateway/) is a fully managed service that makes it easy for
|
||||
> developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the "front door"
|
||||
> for applications to access data, business logic, or functionality from your backend services. Using
|
||||
> `API Gateway`, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication
|
||||
> applications. `API Gateway` supports containerized and serverless workloads, as well as web applications.
|
||||
>
|
||||
> `API Gateway` handles all the tasks involved in accepting and processing up to hundreds of thousands of
|
||||
> concurrent API calls, including traffic management, CORS support, authorization and access control,
|
||||
> throttling, monitoring, and API version management. `API Gateway` has no minimum fees or startup costs.
|
||||
> You pay for the API calls you receive and the amount of data transferred out and, with the `API Gateway`
|
||||
> tiered pricing model, you can reduce your cost as your API usage scales.
|
||||
|
||||
API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, CORS support, authorization and access control, throttling, monitoring, and API version management. API Gateway has no minimum fees or startup costs. You pay for the API calls you receive and the amount of data transferred out and, with the API Gateway tiered pricing model, you can reduce your cost as your API usage scales.
|
||||
|
||||
See a [usage example](/docs/integrations/llms/amazon_api_gateway_example).
|
||||
See a [usage example](/docs/integrations/llms/amazon_api_gateway).
|
||||
|
||||
```python
|
||||
from langchain.llms import AmazonAPIGateway
|
||||
|
||||
api_url = "https://<api_gateway_id>.execute-api.<region>.amazonaws.com/LATEST/HF"
|
||||
# These are sample parameters for Falcon 40B Instruct Deployed from Amazon SageMaker JumpStart
|
||||
model_kwargs = {
|
||||
"max_new_tokens": 100,
|
||||
"num_return_sequences": 1,
|
||||
"top_k": 50,
|
||||
"top_p": 0.95,
|
||||
"do_sample": False,
|
||||
"return_full_text": True,
|
||||
"temperature": 0.2,
|
||||
}
|
||||
llm = AmazonAPIGateway(api_url=api_url, model_kwargs=model_kwargs)
|
||||
```
|
||||
|
||||
### SageMaker Endpoint
|
||||
|
||||
>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a system that can build, train, and deploy machine learning (ML) models with fully managed infrastructure, tools, and workflows.
|
||||
>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a system that can build, train, and deploy
|
||||
> machine learning (ML) models with fully managed infrastructure, tools, and workflows.
|
||||
|
||||
We use `SageMaker` to host our model and expose it as the `SageMaker Endpoint`.
|
||||
|
||||
@ -50,6 +57,16 @@ from langchain.llms import SagemakerEndpoint
|
||||
from langchain.llms.sagemaker_endpoint import LLMContentHandler
|
||||
```
|
||||
|
||||
## Chat models
|
||||
|
||||
### Bedrock Chat
|
||||
|
||||
See a [usage example](/docs/integrations/chat/bedrock).
|
||||
|
||||
```python
|
||||
from langchain.chat_models import BedrockChat
|
||||
```
|
||||
|
||||
## Text Embedding Models
|
||||
|
||||
### Bedrock
|
||||
@ -67,11 +84,32 @@ from langchain.embeddings import SagemakerEndpointEmbeddings
|
||||
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
|
||||
```
|
||||
|
||||
## Chains
|
||||
|
||||
### Amazon Comprehend Moderation Chain
|
||||
|
||||
>[Amazon Comprehend](https://aws.amazon.com/comprehend/) is a natural-language processing (NLP) service that
|
||||
> uses machine learning to uncover valuable insights and connections in text.
|
||||
|
||||
|
||||
We need to install the `boto3` and `nltk` libraries.
|
||||
|
||||
```bash
|
||||
pip install boto3 nltk
|
||||
```
|
||||
|
||||
See a [usage example](/docs/guides/safety/amazon_comprehend_chain).
|
||||
|
||||
```python
|
||||
from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain
|
||||
```
|
||||
|
||||
## Document loaders
|
||||
|
||||
### AWS S3 Directory and File
|
||||
>[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.
|
||||
|
||||
>[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
|
||||
> is an object storage service.
|
||||
>[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
|
||||
>[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)
|
||||
|
||||
@ -83,6 +121,17 @@ See a [usage example for S3FileLoader](/docs/integrations/document_loaders/aws_s
|
||||
from langchain.document_loaders import S3DirectoryLoader, S3FileLoader
|
||||
```
|
||||
|
||||
### Amazon Textract
|
||||
|
||||
>[Amazon Textract](https://docs.aws.amazon.com/managedservices/latest/userguide/textract.html) is a machine
|
||||
> learning (ML) service that automatically extracts text, handwriting, and data from scanned documents.
|
||||
|
||||
See a [usage example](/docs/integrations/document_loaders/amazon_textract).
|
||||
|
||||
```python
|
||||
from langchain.document_loaders import AmazonTextractPDFLoader
|
||||
```
|
||||
|
||||
## Memory
|
||||
|
||||
### AWS DynamoDB
|
||||
@ -103,3 +152,112 @@ See a [usage example](/docs/integrations/memory/aws_dynamodb).
|
||||
```python
|
||||
from langchain.memory import DynamoDBChatMessageHistory
|
||||
```
|
||||
|
||||
## Retrievers
|
||||
|
||||
### Amazon Kendra
|
||||
|
||||
> [Amazon Kendra](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html) is an intelligent search service
|
||||
> provided by `Amazon Web Services` (`AWS`). It utilizes advanced natural language processing (NLP) and machine
|
||||
> learning algorithms to enable powerful search capabilities across various data sources within an organization.
|
||||
> `Kendra` is designed to help users find the information they need quickly and accurately,
|
||||
> improving productivity and decision-making.
|
||||
|
||||
> With `Kendra`, we can search across a wide range of content types, including documents, FAQs, knowledge bases,
|
||||
> manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and
|
||||
> contextual meanings to provide highly relevant search results.
|
||||
|
||||
We need to install the `boto3` library.
|
||||
|
||||
```bash
|
||||
pip install boto3
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/retrievers/amazon_kendra_retriever).
|
||||
|
||||
```python
|
||||
from langchain.retrievers import AmazonKendraRetriever
|
||||
```
|
||||
|
||||
### Amazon Bedrock (Knowledge Bases)
|
||||
|
||||
> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an
|
||||
> `Amazon Web Services` (`AWS`) offering which lets you quickly build RAG applications by using your
|
||||
> private data to customize foundation model response.
|
||||
|
||||
We need to install the `boto3` library.
|
||||
|
||||
```bash
|
||||
pip install boto3
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/retrievers/amazon_bedrock_knowledge_bases).
|
||||
|
||||
```python
|
||||
from langchain.retrievers import AmazonKnowledgeBasesRetriever
|
||||
```
|
||||
|
||||
## Vector stores
|
||||
|
||||
### Amazon OpenSearch Service
|
||||
|
||||
> [Amazon OpenSearch Service](https://aws.amazon.com/opensearch-service/) performs
|
||||
> interactive log analytics, real-time application monitoring, website search, and more. `OpenSearch` is
|
||||
> an open source,
|
||||
> distributed search and analytics suite derived from `Elasticsearch`. `Amazon OpenSearch Service` offers the
|
||||
> latest versions of `OpenSearch`, support for many versions of `Elasticsearch`, as well as
|
||||
> visualization capabilities powered by `OpenSearch Dashboards` and `Kibana`.
|
||||
|
||||
We need to install several python libraries.
|
||||
|
||||
```bash
|
||||
pip install boto3 requests requests-aws4auth
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/vectorstores/opensearch#using-aos-amazon-opensearch-service).
|
||||
|
||||
```python
|
||||
from langchain.vectorstores import OpenSearchVectorSearch
|
||||
```
|
||||
|
||||
## Tools
|
||||
|
||||
### AWS Lambda
|
||||
|
||||
>[`Amazon AWS Lambda`](https://aws.amazon.com/pm/lambda/) is a serverless computing service provided by
|
||||
> `Amazon Web Services` (`AWS`). It helps developers to build and run applications and services without
|
||||
> provisioning or managing servers. This serverless architecture enables you to focus on writing and
|
||||
> deploying code, while AWS automatically takes care of scaling, patching, and managing the
|
||||
> infrastructure required to run your applications.
|
||||
|
||||
We need to install `boto3` python library.
|
||||
|
||||
```bash
|
||||
pip install boto3
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/tools/awslambda).
|
||||
|
||||
|
||||
## Callbacks
|
||||
|
||||
### SageMaker Tracking
|
||||
|
||||
>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly
|
||||
> and easily build, train and deploy machine learning (ML) models.
|
||||
|
||||
>[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability
|
||||
> of `Amazon SageMaker` that lets you organize, track,
|
||||
> compare and evaluate ML experiments and model versions.
|
||||
|
||||
We need to install several python libraries.
|
||||
|
||||
```bash
|
||||
pip install google-search-results sagemaker
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/callbacks/sagemaker_tracking).
|
||||
|
||||
```python
|
||||
from langchain.callbacks import SageMakerCallbackHandler
|
||||
```
|
||||
|
||||
@ -7,9 +7,9 @@
|
||||
"source": [
|
||||
"# Amazon Kendra\n",
|
||||
"\n",
|
||||
"> Amazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.\n",
|
||||
"> [Amazon Kendra](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html) is an intelligent search service provided by `Amazon Web Services` (`AWS`). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. `Kendra` is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.\n",
|
||||
"\n",
|
||||
"> With Kendra, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results."
|
||||
"> With `Kendra`, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -74,11 +74,24 @@
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -5,13 +5,13 @@
|
||||
"id": "b6636c27-35da-4ba7-8313-eca21660cab3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Amazon Bedrock (Knowledge Bases)\n",
|
||||
"# Bedrock (Knowledge Bases)\n",
|
||||
"\n",
|
||||
"> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.\n",
|
||||
"\n",
|
||||
"> Implementing RAG requires organizations to perform several cumbersome steps to convert data into embeddings (vectors), store the embeddings in a specialized vector database, and build custom integrations into the database to search and retrieve text relevant to the user’s query. This can be time-consuming and inefficient.\n",
|
||||
"> Implementing `RAG` requires organizations to perform several cumbersome steps to convert data into embeddings (vectors), store the embeddings in a specialized vector database, and build custom integrations into the database to search and retrieve text relevant to the user’s query. This can be time-consuming and inefficient.\n",
|
||||
"\n",
|
||||
"> With Knowledge Bases for Amazon Bedrock, simply point to the location of your data in Amazon S3, and Knowledge Bases for Amazon Bedrock takes care of the entire ingestion workflow into your vector database. If you do not have an existing vector database, Amazon Bedrock creates an Amazon OpenSearch Serverless vector store for you. For retrievals, use the Langchain - Amazon Bedrock integration via the Retrieve API to retrieve relevant results for a user query from knowledge bases.\n",
|
||||
"> With `Knowledge Bases for Amazon Bedrock`, simply point to the location of your data in `Amazon S3`, and `Knowledge Bases for Amazon Bedrock` takes care of the entire ingestion workflow into your vector database. If you do not have an existing vector database, Amazon Bedrock creates an Amazon OpenSearch Serverless vector store for you. For retrievals, use the Langchain - Amazon Bedrock integration via the Retrieve API to retrieve relevant results for a user query from knowledge bases.\n",
|
||||
"\n",
|
||||
"> Knowledge base can be configured through [AWS Console](https://aws.amazon.com/console/) or by using [AWS SDKs](https://aws.amazon.com/developer/tools/)."
|
||||
]
|
||||
@ -108,7 +108,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.13"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -7,7 +7,16 @@
|
||||
"source": [
|
||||
"# Bedrock\n",
|
||||
"\n",
|
||||
">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.\n"
|
||||
">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of \n",
|
||||
"> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, \n",
|
||||
"> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to \n",
|
||||
"> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, \n",
|
||||
"> you can easily experiment with and evaluate top FMs for your use case, privately customize them with \n",
|
||||
"> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
|
||||
"> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
|
||||
"> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
|
||||
"> generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -11,11 +11,11 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
">`Amazon AWS Lambda` is a serverless computing service provided by `Amazon Web Services` (`AWS`). It helps developers to build and run applications and services without provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.\n",
|
||||
">[`Amazon AWS Lambda`](https://aws.amazon.com/pm/lambda/) is a serverless computing service provided by `Amazon Web Services` (`AWS`). It helps developers to build and run applications and services without provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.\n",
|
||||
"\n",
|
||||
"This notebook goes over how to use the `AWS Lambda` Tool.\n",
|
||||
"\n",
|
||||
"By including a `awslambda` in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.\n",
|
||||
"By including the `AWS Lambda` in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.\n",
|
||||
"\n",
|
||||
"When an Agent uses the `AWS Lambda` tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.\n",
|
||||
"\n",
|
||||
|
||||
@ -63,7 +63,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 1,
|
||||
"id": "aac9563e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -321,12 +321,30 @@
|
||||
"id": "5f590d35",
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Using AOSS (Amazon OpenSearch Service Serverless)"
|
||||
"## Using AOSS (Amazon OpenSearch Service Serverless)\n",
|
||||
"\n",
|
||||
"It is an example of the `AOSS` with `faiss` engine and `efficient_filter`.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"We need to install several `python` packages."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "279bfc5c-b7f4-4553-ad15-2df7baebec47",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install requests requests-aws4auth"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -335,13 +353,16 @@
|
||||
"id": "de397be7",
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This is just an example to show how to use AOSS with faiss engine and efficient_filter, you need to set proper values.\n",
|
||||
"from requests_aws4auth import AWS4Auth\n",
|
||||
"\n",
|
||||
"service = \"aoss\" # must set the service as 'aoss'\n",
|
||||
"region = \"us-east-2\"\n",
|
||||
@ -374,7 +395,10 @@
|
||||
"cell_type": "markdown",
|
||||
"id": "0aa012c8",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Using AOS (Amazon OpenSearch Service)"
|
||||
@ -386,6 +410,9 @@
|
||||
"id": "2c47e408",
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
@ -436,7 +463,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -1576,6 +1576,10 @@
|
||||
"source": "/en/latest/modules/chains/generic/serialization.html",
|
||||
"destination": "/docs/modules/chains/how_to/serialization"
|
||||
},
|
||||
{
|
||||
"source": "/docs/integrations/document_loaders/pdf-amazonTextractPDFLoader",
|
||||
"destination": "/docs/integrations/document_loaders/amazon_textract"
|
||||
},
|
||||
{
|
||||
"source": "/en/latest/modules/indexes/document_loaders/examples/acreom.html",
|
||||
"destination": "/docs/integrations/document_loaders/acreom"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user