Getting Started with LlamaIndex: Build Your First Document Q&A System in 10 Minutes

Getting Started with LlamaIndex: Build Your First Document Q&A System in 10 Minutes

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In today’s AI era, how to enable AI models to understand and answer questions based on specific documents has become a hot topic. LlamaIndex, as a powerful data framework, can help us easily build document Q&A systems based on large language models. This article will guide you to set up a simple yet fully functional document Q&A system in 10 minutes.

Getting Started with LlamaIndex: Build Your First Document Q&A System in 10 Minutes

1. Environment Preparation

First, we need to install the necessary dependencies. Create a requirements.txt file containing the following:

llama-index-core
python-dotenv
openai

Then run:

pip install -r requirements.txt

2. Configure Environment Variables

Create a .env file and add your OpenAI API key:

OPENAI_API_KEY=your_OpenAI_API_key

3. Code Implementation

Let’s look at a basic implementation:

# Import necessary libraries
# VectorStoreIndex is used to create vector indexes
# SimpleDirectoryReader is used to read documents
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# For loading environment variables
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Read all PDF documents from the pdf directory
documents = SimpleDirectoryReader("pdf/").load_data()

# Convert documents to vector index
index = VectorStoreIndex.from_documents(documents) 

# Create query engine
query_engine = index.as_query_engine()

# Execute query, asking about design goals and details
response = query_engine.query("What are the design goals and give details about it please.")

# Print query results
print(response)

4. Code Analysis

Let’s analyze this code line by line:

  • Import Necessary Libraries

    • VectorStoreIndex: Used to create a vector index for documents

    • SimpleDirectoryReader: Used to read document directories

    • load_dotenv: Used to load environment variables

  • Load Documents

    • SimpleDirectoryReader("pdf/").load_data(): This line reads all documents in the pdf directory

    • LlamaIndex supports various document formats, including PDF, TXT, DOCX, etc.

  • Create Index

    • VectorStoreIndex.from_documents(documents): This step converts the documents into vector form

    • Under the hood, LlamaIndex uses language models to split documents into smaller chunks and create vector embeddings

Query Engine

  • index.as_query_engine(): Creates a query engine

  • The query engine is responsible for handling user questions and generating answers

5. Core Concept Explanation

  • Vector Index

    • The vector index is a core concept of LlamaIndex

    • It converts documents into vector form, enabling AI to understand and retrieve document content

  • Query Engine

    • The query engine is responsible for understanding user questions

    • Retrieving relevant content from the vector index

    • Using language models to generate final answers

6. Practical Application Example

Suppose we have some PDF documents, we can use it like this:

Create Query Engine

query_engine = index.as_query_engine()

Example Questions

questions = [
    "What is the main content of the document?",
    "What key concepts are mentioned in the document?",
    "Can you summarize the core points of the document?"
]

Iterate Through Questions and Get Answers

for question in questions:
    response = query_engine.query(question)
    print(f"Question: {question}")
    print(f"Answer: {response}\n")

7. Performance Optimization Suggestions

  • Document Storage

    • Consider persisting the index to avoid recreating it each time

    • You can use storage_context to achieve this

  • Memory Management

    • For large documents, it is recommended to adjust the document chunk size appropriately

    • You can control this by setting the chunk_size parameter

Conclusion

Through this simple example, we have implemented a basic document Q&A system. LlamaIndex‘s strength lies in its simplicity and extensibility; you can build upon this basic version:

  • Add support for more document formats

  • Implement more complex Q&A logic

  • Integrate it into existing applications

This is just the tip of the iceberg of LlamaIndex’s capabilities; in the upcoming series of articles, we will explore more advanced features and application scenarios.

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