Tutorial: Building a LlamaIndex for Efficient Document Searching

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

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.

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