Introduction to Query Engines

Query engines are the heart of LlamaIndex's information retrieval system. They're responsible for taking a user's query, searching through the indexed data, and returning the most relevant information. In the context of LLM applications, query engines play a crucial role in providing accurate and contextual responses.

How Query Engines Work

At a high level, query engines in LlamaIndex follow these steps:

1. Receive a user query
2. Process and understand the query
3. Search the indexed data
4. Retrieve relevant information
5. Format and return the results

Let's break this down with a simple example:

from llama_index import VectorStoreIndex, SimpleDirectoryReader

# Load documents
documents = SimpleDirectoryReader('data').load_data()

# Create an index
index = VectorStoreIndex.from_documents(documents)

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

# Run a query
response = query_engine.query("What is the capital of France?")
print(response)

In this example, the query engine searches the indexed documents for information about the capital of France and returns a relevant response.

Types of Query Engines

LlamaIndex offers several types of query engines, each with its own strengths:

1. Vector Store Query Engine: This is the default query engine that uses vector embeddings to find similar documents.

2. List Query Engine: Useful for querying a list of documents or nodes.

3. Tree Query Engine: Designed for hierarchical data structures, allowing for efficient traversal of tree-like information.

4. Keyword Table Query Engine: Utilizes keyword matching for faster retrieval in certain scenarios.

Let's look at how to use a Vector Store Query Engine with custom parameters:

from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores import FaissVectorStore

# Load documents
documents = SimpleDirectoryReader('data').load_data()

# Create a FAISS vector store
vector_store = FaissVectorStore(dim=512)  # Set the embedding dimension

# Create an index with the custom vector store
index = VectorStoreIndex.from_documents(
    documents,
    vector_store=vector_store
)

# Create a query engine with custom parameters
query_engine = index.as_query_engine(
    similarity_top_k=5,  # Return top 5 similar results
    response_mode="compact"  # Get a concise response
)

# Run a query
response = query_engine.query("What are the main features of Python?")
print(response)

Advanced Query Engine Techniques

1. Query Transformations

Query transformations allow you to modify the user's query before it's processed by the query engine. This can be useful for adding context, expanding abbreviations, or correcting common mistakes.

from llama_index.indices.query.query_transform import HydeQueryTransform

hyde_transform = HydeQueryTransform(index)
query_engine = index.as_query_engine(
    query_transform=hyde_transform
)

2. Response Synthesis

You can customize how the query engine synthesizes responses from retrieved information:

from llama_index.response_synthesizers import CompactAndRefine

synthesizer = CompactAndRefine(
    service_context=service_context,
    streaming=True
)

query_engine = index.as_query_engine(
    response_synthesizer=synthesizer
)

3. Combining Query Engines

LlamaIndex allows you to combine multiple query engines for more complex retrieval strategies:

from llama_index.query_engine import RouterQueryEngine

query_engine1 = index1.as_query_engine()
query_engine2 = index2.as_query_engine()

router_query_engine = RouterQueryEngine.from_defaults(
    [query_engine1, query_engine2],
    service_context=service_context
)

Optimizing Query Engine Performance

To get the best results from your query engine, consider these tips:

1. Choose the right index: Different index types (e.g., VectorStoreIndex, ListIndex) perform better for different types of data and queries.

2. Tune similarity parameters: Adjust similarity_top_k to balance between retrieval accuracy and speed.

3. Use caching: Implement caching mechanisms to store frequently accessed results and reduce computation time.

4. Experiment with embedding models: Try different embedding models to find the one that best represents your data.

Conclusion

Query engines are a fundamental component of LlamaIndex, enabling efficient and accurate information retrieval for LLM applications. By understanding their workings and exploring different types and techniques, you can build more powerful and responsive AI-powered systems.