Welcome! Today, we're diving into the concept of Question Answering in Document Analysis. Specifically, we're looking at how a tool like can enhance this process. Question Answering (QA) in document analysis is the art and science of extracting precise information from a collection of documents in response to a specific query. With the advancement in AI and Natural Language Processing (NLP), this process has become increasingly automated, reliable, and efficient. That's where LangChain comes into play. LangChain LangChain, when integrated with DeepInfra, becomes a potent tool for Document Analysis. DeepInfra provides a ) that you can harness for various AI applications, with LangChain providing the infrastructure to link these models together in a pipeline, tailored to specific needs. You can find an extensive list of LLMs to use with LangChain at . This platform lets you search, filter, and sort AI models, making finding the right model for your AI project easier. For instance, you could utilize , , or based on your specific needs. suite of large language models (LLMs AIModels.fyi gpt-neo-125M dolly-v2-12b flan-t5 Setting Up DeepInfra with LangChain Let's now set up the DeepInfra ecosystem within LangChain. This setup involves a series of steps such as setting the environment API key, creating a DeepInfra instance, setting up a prompt template for question and answer, and finally running an LLMChain. For the uninitiated, an configured in a certain way to achieve a specific task. LLMChain is essentially a chain of LLMs Setting the Environment API Key First, make sure to obtain your API key from DeepInfra. You'll have to and get a new token. If you're new to DeepInfra, you'll be glad to know that you're given a 1-hour free serverless GPU compute to test different models. login Once you have your API key, you can set it in your environment: from getpass import getpass
import os
DEEPINFRA_API_TOKEN = getpass()
os.environ["DEEPINFRA_API_TOKEN"] = DEEPINFRA_API_TOKEN Creating the DeepInfra Instance Next, create a DeepInfra instance using your model of choice. In this case, we are using the ' ' model. databricks/dolly-v2-12b from langchain.llms import DeepInfra

llm = DeepInfra(model_id="databricks/dolly-v2-12b")
llm.model_kwargs = {'temperature': 0.7, 'repetition_penalty': 1.2, 'max_new_tokens': 250, 'top_p': 0.9} Creating a Prompt Template To streamline the question-answering process, we'll create a prompt template for the question and answer. This provides a structured approach for our queries. from langchain import PromptTemplate

template = """Question: {question}\nAnswer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"]) Initiating and Running the LLMChain Finally, we can initiate the LLMChain and run it: from langchain import LLMChain
llm_chain = LLMChain(prompt=prompt, llm=llm)
# Provide a question and run the LLMChain
question = "Can penguins reach the North pole?"
llm_chain.run(question) This setup will provide you with an LLMChain ready for document analysis and question-answering tasks. In-depth Explanation of Document Analysis and Question Answering Process LangChain and DeepInfra make document analysis and question answering a smooth process. To further illustrate how this process works, let's dive into a detailed explanation. Loading Documents The first step in this journey involves loading your documents. You can use a provided by LangChain: TextLoader from langchain.document_loaders import TextLoader
loader = TextLoader('../state_of_the_union.txt') Creating Your Index Once the documents are loaded, create an index over your data. This index is used to efficiently retrieve relevant documents given a query, thus saving you time and resources. In LangChain, this can be done using a : VectorstoreIndexCreator from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator().from_loaders([loader]) Querying Your Index Finally, query your index to fetch relevant documents. Let's say you want to know what the president said about a certain individual. You could do: query = "What did the president say about Ketanji Brown Jackson"
index.query(query, llm=llm) You can also use to also get back the sources involved: query_with_sources query = "What did the president say about Ketanji Brown Jackson"
index.query_with_sources(query, llm=llm) Additional Features and Advanced Usage LangChain also provides advanced features such as question answering with sources, where the language model cites the documents used to generate the response. Here's how you can get started: from langchain.chains.qa_with_sources import load_qa_with_sources_chain
chain = load_qa_with_sources_chain(llm, chain_type="stuff")
chain({"input_documents": docs, "question": query}, return_only_outputs=True) Conclusion LangChain, when integrated with DeepInfra, provides a and powerful tool for question-answering in document analysis. It allows you to use AI models efficiently, easily create workflows, and make the process of understanding and extracting information from your documents a breeze. We encourage you to explore LangChain and DeepInfra, and utilize their capabilities in your applications. Happy experimenting! versatile Subscribe or follow me on for more content like this! Twitter Also published . here

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