Package: qnabot
QnA BotCreate a question answering over docs bot with one line of code:pipinstallqnabotfromqnabotimportQnABotimportosos.environ["OPENAI_API_KEY"]="my key"# Create a bot 👉 with one line of codebot=QnABot(directory="./mydata")# Ask a questionanswer=bot.ask("How do I use this bot?")# Save the index to save costs (GPT is used to create the index)bot.save_index("index.pickle")# Load the index from a previous runbot=QnABot(directory="./mydata",index="index.pickle")You can also create a FastAPI app that will expose the bot as an API using the create_app function.
Assuming you file is calledmain.pyrunuvicorn main:app --reloadto run the API locally.
You should then be able to visithttp://localhost:8000/docsto see the API documentation.fromqnabotimportQnABot,create_appapp=create_app(QnABot("./mydata"))You can expose a gradio UI for the bot usingcreate_interfacefunction.
Assuming your file is calledui.pyrungradio qnabot/ui.pyto run the UI locally.
You should then be able to visithttp://127.0.0.1:7860to see the API documentation.fromqnabotimportQnABot,create_interfacedemo=create_interface(QnABot("./mydata"))FeaturesCreate a question answering bot over your documents with one line of code using GPTSave / load index to reduce costs (Open AI embedings are used to create the index)Local data source (directory of documents) or S3 data sourceFAISS for storing vectors / indexExpose bot over API using FastAPIGradio UIIntegration withguardrailsIntegration withGPTCacheSupport for other vector databases (e.g. Weaviate, Pinecone)Customise promptSupport for LLaMA modelHere's how it worksLarge language models (LLMs) are powerful, but they can't answer questions about documents they haven't seen. If you want to use an LLM to answer questions about documents it was not trained on, you have to give it information about those documents. To solve this, we use "retrieval augmented generation."In simple terms, when you have a question, you first search for relevant documents. Then, you give the documents and the question to the language model to generate an answer. To make this work, you need your documents in a searchable format (an index). This process involves two main steps: (1) preparing your documents for easy querying, and (2) using the retrieval augmented generation method.QnABotuses FAISS to create an index of documents and GPT to generate answers.sequenceDiagram
    actor User
    participant API
    participant LLM
    participant Vectorstore
    participant IngestionEngine
    participant DataLake
    autonumber

    Note over API, DataLake: Ingestion phase
    loop Every X time
    IngestionEngine ->> DataLake: Load documents
    DataLake -->> IngestionEngine: Return data
    IngestionEngine -->> IngestionEngine: Split documents and Create embeddings
    IngestionEngine ->> Vectorstore: Store documents and embeddings
    end

    Note over API, DataLake: Generation phase

    User ->> API: Receive user question
    API ->> Vectorstore: Lookup documents in the index relevant to the question
    API ->> API: Construct a prompt from the question and any relevant documents
    API ->> LLM: Pass the prompt to the model
    LLM -->> API: Get response from model
    API -->> User: Return response