Welcome to this step-by-step tutorial that will guide you through the process of creating a powerful document search engine using LlamaIndex. Let's get started!

Step 1: Import the Required Modules

from llama_index import VectorStoreIndex, SimpleDirectoryReader, download_loader

# Download the PDFReader loader
PDFReader = download_loader("PDFReader")

# Create a SimpleDirectoryReader object
loader = PDFReader()

Step 2: Load and Index Your Documents

# Load the PDF documents
documents = loader.load_data(file=Path('amdpt.pdf'))

# Create a VectorStoreIndex object
index = VectorStoreIndex.from_documents(documents)

Step 3: Set Up the Query Engine

# Set the OpenAI API key
os.environ["OPENAI_API_KEY"] = ""

# Create a query engine object
query_engine = index.as_query_engine()

Step 4: Search and Retrieve Information

# Query the index with your question
question = "?"
response  = query_engine.query(question)

# Print the response
print(response)

Congratulations! You have successfully built a document search engine using LlamaIndex. Experiment with different questions and explore the results.

For more advanced features and in-depth documentation, please visit the LlamaIndex documentation.

Butterfish is a CLI tool for large language models (LLMs). It can be used to index and search text, generate text, and answer questions.

• Index text: Butterfish can index text files and then search them using the OpenAI embedding API.
• Generate text: Butterfish can generate text using the OpenAI API.
• Answer questions: Butterfish can answer questions using the OpenAI API.

To use Butterfish, you will need an OpenAI account and … click here to read