----------------------
The RetrieverQueryEngine is an important component in LlamaIndex that you would use when you want to combine retrieval and response synthesis in a flexible way. Here are the main reasons to use RetrieverQueryEngine:
- Customizable Retrieval: RetrieverQueryEngine allows you to plug in different retriever implementations. This is useful when you want to use a specific retrieval strategy or combine multiple retrievers.
- Separate Retrieval from Synthesis: It separates the retrieval step from the response synthesis step. This separation allows you to have more control over each part of the query process.
- Flexible Response Generation: You can customize how the final response is generated by configuring the response synthesizer.
- Integration with Custom Retrievers: If you've implemented a custom retriever, RetrieverQueryEngine provides an easy way to use it within a full query pipeline.
- Postprocessing of Retrieved Nodes: RetrieverQueryEngine supports postprocessing of retrieved nodes before they are sent to the response synthesizer.
Here's a basic example of how to use RetrieverQueryEngine:
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = RetrieverQueryEngine.from_args(
retriever,
response_synthesizer=response_synthesizer,
)
response = query_engine.query("Your query here")In this example, you provide your own retriever and response synthesizer, allowing for a high degree of customization.
RetrieverQueryEngine is particularly useful when you need more control over the retrieval and synthesis process than what's provided by the default query engines of various index types.
