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 directlyimport os os.environ["OPENAI_API_KEY"] = "your-OpenAI-API-key"from langchain.llms import OpenAIfrom langchain.chains.question_answering import load_qa_chainchain = 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., Fern√°ndez, S., Gomez, F., Schmidhuber, J., Harley, A.W., Ufkes, A., Derpanis, K.G., He, K., Gkioxari, G., Doll√°r, 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,'PreviousPandas DataFrameNextPolars DataFrameSample 1Sample 2Sample 3Using the AmazonTextractPDFLoader in an LangChain chain (e. g. OpenAI)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
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‚Äôre automating loans processing or extracting information from invoices and receipts. Textract can extract the data in minutes instead of hours or days.
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‚Äôre automating loans processing or extracting information from invoices and receipts. Textract can extract the data in minutes instead of hours or days. ->: 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 directlyimport os os.environ["OPENAI_API_KEY"] = "your-OpenAI-API-key"from langchain.llms import OpenAIfrom langchain.chains.question_answering import load_qa_chainchain = 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., Fern√°ndez, S., Gomez, F., Schmidhuber, J., Harley, A.W., Ufkes, A., Derpanis, K.G., He, K., Gkioxari, G., Doll√°r, 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,'PreviousPandas DataFrameNextPolars DataFrameSample 1Sample 2Sample 3Using the AmazonTextractPDFLoader in an LangChain chain (e. g. OpenAI)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.