Amazon Textract
Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. 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. You can quickly automate document processing and act on the information extracted, whether you’re automating loans processing or extracting information from invoices and receipts. Textract can extract the data in minutes instead of hours or days.
This sample demonstrates the use of Amazon Textract in combination with LangChain as a DocumentLoader.
Textract supports PDF, TIFF, PNG and JPEG format.
Check https://docs.aws.amazon.com/textract/latest/dg/limits-document.html for supported document sizes, languages and characters.
# !pip install langchain boto3 openai tiktoken python-dotenv -q
pip install boto3 openai tiktoken python-dotenv -q
pip install -e /Users/schadem/code/github/schadem/langchain/libs/langchain
DEPRECATION: amazon-textract-pipeline-pagedimensions 0.0.8 has a non-standard dependency specifier Pillow>=9.4.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of amazon-textract-pipeline-pagedimensions or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
DEPRECATION: amazon-textract-pipeline-pagedimensions 0.0.8 has a non-standard dependency specifier pypdf>=2.5.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of amazon-textract-pipeline-pagedimensions or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
[notice] A new release of pip is available: 23.2 -> 23.3
[notice] To update, run: python -m pip install --upgrade pip
Obtaining file:///Users/schadem/code/github/schadem/langchain/libs/langchain
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DEPRECATION: amazon-textract-pipeline-pagedimensions 0.0.8 has a non-standard dependency specifier Pillow>=9.4.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of amazon-textract-pipeline-pagedimensions or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
DEPRECATION: amazon-textract-pipeline-pagedimensions 0.0.8 has a non-standard dependency specifier pypdf>=2.5.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of amazon-textract-pipeline-pagedimensions or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
Installing collected packages: dataclasses-json, amazon-textract-caller, langchain
Attempting uninstall: dataclasses-json
Found existing installation: dataclasses-json 0.6.1
Uninstalling dataclasses-json-0.6.1:
Successfully uninstalled dataclasses-json-0.6.1
Attempting uninstall: amazon-textract-caller
Found existing installation: amazon-textract-caller 0.2.0
Uninstalling amazon-textract-caller-0.2.0:
Successfully uninstalled amazon-textract-caller-0.2.0
Attempting uninstall: langchain
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[notice] A new release of pip is available: 23.2 -> 23.3
[notice] To update, run: python -m pip install --upgrade pip
pip install "amazon-textract-caller>=0.2.0"
Collecting amazon-textract-caller>=0.2.0
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DEPRECATION: amazon-textract-pipeline-pagedimensions 0.0.8 has a non-standard dependency specifier Pillow>=9.4.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of amazon-textract-pipeline-pagedimensions or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
DEPRECATION: amazon-textract-pipeline-pagedimensions 0.0.8 has a non-standard dependency specifier pypdf>=2.5.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of amazon-textract-pipeline-pagedimensions or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
Installing collected packages: amazon-textract-caller
Attempting uninstall: amazon-textract-caller
Found existing installation: amazon-textract-caller 0.0.29
Uninstalling amazon-textract-caller-0.0.29:
Successfully uninstalled amazon-textract-caller-0.0.29
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
amazon-textract-textractor 1.4.1 requires amazon-textract-caller<0.1.0,>=0.0.27, but you have amazon-textract-caller 0.2.0 which is incompatible.
Successfully installed amazon-textract-caller-0.2.0
[notice] A new release of pip is available: 23.2 -> 23.3
[notice] To update, run: python -m pip install --upgrade pip
Sample 1​
The first example uses a local file, which internally will be send to Amazon Textract sync API DetectDocumentText.
Local files or URL endpoints like HTTP:// are limited to one page documents for Textract. Multi-page documents have to reside on S3. This sample file is a jpeg.
from langchain.document_loaders import AmazonTextractPDFLoader
loader = AmazonTextractPDFLoader("example_data/alejandro_rosalez_sample-small.jpeg")
documents = loader.load()
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[6], line 1
----> 1 from langchain.document_loaders import AmazonTextractPDFLoader
2 loader = AmazonTextractPDFLoader("example_data/alejandro_rosalez_sample-small.jpeg")
3 documents = loader.load()
File ~/code/github/schadem/langchain/libs/langchain/langchain/document_loaders/__init__.py:46
44 from langchain.document_loaders.bigquery import BigQueryLoader
45 from langchain.document_loaders.bilibili import BiliBiliLoader
---> 46 from langchain.document_loaders.blackboard import BlackboardLoader
47 from langchain.document_loaders.blob_loaders import (
48 Blob,
49 BlobLoader,
50 FileSystemBlobLoader,
51 YoutubeAudioLoader,
52 )
53 from langchain.document_loaders.blockchain import BlockchainDocumentLoader
File ~/code/github/schadem/langchain/libs/langchain/langchain/document_loaders/blackboard.py:9
7 from langchain.docstore.document import Document
8 from langchain.document_loaders.directory import DirectoryLoader
----> 9 from langchain.document_loaders.pdf import PyPDFLoader
10 from langchain.document_loaders.web_base import WebBaseLoader
13 class BlackboardLoader(WebBaseLoader):
File ~/code/github/schadem/langchain/libs/langchain/langchain/document_loaders/pdf.py:17
15 from langchain.document_loaders.base import BaseLoader
16 from langchain.document_loaders.blob_loaders import Blob
---> 17 from langchain.document_loaders.parsers.pdf import (
18 AmazonTextractPDFParser,
19 DocumentIntelligenceParser,
20 PDFMinerParser,
21 PDFPlumberParser,
22 PyMuPDFParser,
23 PyPDFium2Parser,
24 PyPDFParser,
25 )
26 from langchain.document_loaders.unstructured import UnstructuredFileLoader
27 from langchain.utils import get_from_dict_or_env
ImportError: cannot import name 'DocumentIntelligenceParser' from 'langchain.document_loaders.parsers.pdf' (/Users/schadem/code/github/schadem/langchain/libs/langchain/langchain/document_loaders/parsers/pdf.py)
Output from the file
documents
[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})]
Sample 2​
The next sample loads a file from an HTTPS endpoint. It has to be single page, as Amazon Textract requires all multi-page documents to be stored on S3.
from langchain.document_loaders import AmazonTextractPDFLoader
loader = AmazonTextractPDFLoader(
"https://amazon-textract-public-content.s3.us-east-2.amazonaws.com/langchain/alejandro_rosalez_sample_1.jpg"
)
documents = loader.load()
documents
[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})]
Sample 3​
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.
import boto3
textract_client = boto3.client("textract", region_name="us-east-2")
file_path = "s3://amazon-textract-public-content/langchain/layout-parser-paper.pdf"
loader = AmazonTextractPDFLoader(file_path, client=textract_client)
documents = loader.load()
Now getting the number of pages to validate the response (printing out the full response would be quite long...). We expect 16 pages.
len(documents)
16
Using the AmazonTextractPDFLoader in an LangChain chain (e. g. OpenAI)​
The AmazonTextractPDFLoader can be used in a chain the same way the other loaders are used. Textract itself does have a Query feature, which offers similar functionality to the QA chain in this sample, which is worth checking out as well.
# You can store your OPENAI_API_KEY in a .env file as well
# import os
# from dotenv import load_dotenv
# load_dotenv()
# Or set the OpenAI key in the environment directly
import os
os.environ["OPENAI_API_KEY"] = "your-OpenAI-API-key"
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
chain = load_qa_chain(llm=OpenAI(), chain_type="map_reduce")
query = ["Who are the autors?"]
chain.run(input_documents=documents, question=query)
' 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,'