Introduction to Index Types in LlamaIndex

When working with large language models (LLMs) and vast amounts of data, efficient indexing and retrieval become crucial. LlamaIndex provides several index types to help you organize and access your data effectively. Let's explore the main index types and learn how to choose the right one for your project.

Vector Index

The Vector Index is the most commonly used index type in LlamaIndex. It's based on embedding vectors, which are numerical representations of text that capture semantic meaning.

How it works:

1. Each document or chunk of text is converted into a vector using an embedding model.
2. These vectors are stored in a vector database.
3. When querying, the input is also converted to a vector, and the most similar vectors are retrieved.

Use cases:

• Semantic search
• Content recommendation
• Document clustering

Example:

from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)

query_engine = index.as_query_engine()
response = query_engine.query("What is the capital of France?")
print(response)

List Index

The List Index is a simple, yet powerful index type that stores documents in a list format.

How it works:

1. Documents are stored sequentially in a list.
2. During query time, each document is compared to the query using an LLM.

Use cases:

• Small to medium-sized datasets
• When you need to preserve the original order of documents

Example:

from llama_index import ListIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
index = ListIndex.from_documents(documents)

query_engine = index.as_query_engine()
response = query_engine.query("What are the main topics covered in the documents?")
print(response)

Tree Index

The Tree Index organizes documents in a hierarchical structure, allowing for efficient traversal and retrieval.

How it works:

1. Documents are organized into a tree structure based on their content.
2. Queries traverse the tree to find the most relevant information.

Use cases:

• Large datasets with hierarchical relationships
• When you need to capture document structure or categories

Example:

from llama_index import TreeIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
index = TreeIndex.from_documents(documents)

query_engine = index.as_query_engine()
response = query_engine.query("What are the main categories of products?")
print(response)

Keyword Index

The Keyword Index uses traditional keyword-based indexing techniques for fast retrieval.

How it works:

1. Documents are indexed based on keywords or phrases.
2. Queries are matched against these keywords for quick lookup.

Use cases:

• When exact keyword matching is important
• Complementing other index types for hybrid search

Example:

from llama_index import KeywordTableIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
index = KeywordTableIndex.from_documents(documents)

query_engine = index.as_query_engine()
response = query_engine.query("Find documents containing 'artificial intelligence'")
print(response)

Selecting the Right Index Type

Choosing the appropriate index type depends on various factors:

1. Dataset size: For small datasets, List Index might suffice. For larger datasets, consider Vector or Tree Index.

2. Query complexity: If you need semantic understanding, Vector Index is ideal. For hierarchical queries, use Tree Index.

3. Update frequency: If your data changes often, Vector Index might be more suitable than Tree Index.

4. Performance requirements: Keyword Index offers fast retrieval for exact matches, while Vector Index provides better semantic search capabilities.

5. Memory constraints: List Index is memory-efficient for small datasets, while Vector Index might require more resources for large collections.

Hybrid Approaches

Sometimes, combining multiple index types can yield better results. For example:

from llama_index import VectorStoreIndex, KeywordTableIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
vector_index = VectorStoreIndex.from_documents(documents)
keyword_index = KeywordTableIndex.from_documents(documents)

query_engine = vector_index.as_query_engine()
keyword_engine = keyword_index.as_query_engine()

response = query_engine.query("What are the latest trends in AI?")
keyword_response = keyword_engine.query("Find documents mentioning 'machine learning'")

print("Vector Index Response:", response)
print("Keyword Index Response:", keyword_response)

By using multiple index types, you can leverage the strengths of each to create a more robust and flexible querying system.

Conclusion

Understanding index types and selection strategies in LlamaIndex is crucial for building efficient LLM-powered applications. By choosing the right index type or combination of types, you can optimize your data retrieval process and create more responsive and accurate systems.