Ensure to install the necessary dependencies using the following commands:
!pip install langchain==0.0.350 openai faiss-gpu tiktoken
!pip install wandbSet up the required environment variables and configurations:
Fetch and set the OPENAI_API_KEY environment variable for authentication. This key is essential for OpenAI's functionality and can be retrieved from Google Colab's user data.
Enable the tracing feature for wandb in the langchain library by setting LANGCHAIN_WANDB_TRACING to "true" in the environment.
This code handles a PDF document ("CommonInsuranceTerms.pdf") through the following steps:
Utilizes langchain.document_loaders.PyPDFLoader to load the PDF file into the environment.
Divides the loaded document into smaller text chunks using langchain.text_splitter.RecursiveCharacterTextSplitter. These chunks are used for further processing.
Convert text chunks into embeddings using OpenAI's language model (langchain.embeddings.openai.OpenAIEmbeddings) and store these embeddings in a vector store using langchain.vectorstores.faiss.FAISS.
Construct a retrieval-based question-answering chain (langchain.chains.RetrievalQA) that uses OpenAI's language model for answering questions. Configuration details include:
- Language Model: Utilizes OpenAI's language model with a temperature parameter set to 0.3.
- Chain Type: Configures the chain type as "stuff".
- Retriever Setup: Configures the chain with a retriever generated from the earlier vector store.
After setting up the environment and processing the document, you can interact with the QA chain by asking specific questions. For instance:
print(qa_chain.run("What is Mortality Charge?"))
print(qa_chain.run("Tell me about Preferred provider organization (PPO)."))