SQLAutoVectorQueryEngine for multiple vector indexes

[telekosmos]

Ultimately I want to use SQLAutoVectorQueryEngine. It needs as a parameter a vector query tool but I have multiple collections in Chroma and, as far as I know, each one makes for a different vector index, retriever and query engine and tool. How can I combine or merge the collections, indexes, retriever or query engines to get a single tool?

[dosubot[bot]]

To combine or merge multiple collections, indexes, retrievers, or query engines in Chroma for use with a single SQLAutoVectorQueryEngine tool, you can utilize the from_sql_and_vector_query_engines class method. This method allows you to create an instance of SQLAutoVectorQueryEngine by providing both a SQL query engine and a vector retriever. Here's an example of how you can do it:

from llama_index.core.query_engine.sql_vector_query_engine import SQLAutoVectorQueryEngine
from llama_index.core.indices.struct_store.sql_query import BaseSQLTableQueryEngine
from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine

# Assume sql_query_engine and vector_auto_retriever are already created instances
sql_query_engine = BaseSQLTableQueryEngine(...)  # Your SQL query engine instance
vector_auto_retriever = RetrieverQueryEngine(...)  # Your vector retriever instance

sql_tool_name = "SQL Tool"
sql_tool_description = "Description for SQL Tool"
vector_tool_name = "Vector Tool"
vector_tool_description = "Description for Vector Tool"

# Create the SQLAutoVectorQueryEngine instance
sql_auto_vector_query_engine = SQLAutoVectorQueryEngine.from_sql_and_vector_query_engines(
    sql_query_engine=sql_query_engine,
    sql_tool_name=sql_tool_name,
    sql_tool_description=sql_tool_description,
    vector_auto_retriever=vector_auto_retriever,
    vector_tool_name=vector_tool_name,
    vector_tool_description=vector_tool_description
)

This approach combines the SQL and vector query engines into a single query engine that can handle queries for both types of data sources, allowing for seamless integration of structured and unstructured data insights ^[1][2].

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[telekosmos]

I need a single vector retriever for multiple vector indexes in the previous snippet.

[dosubot[bot]]

To create a single vector retriever for multiple vector indexes, you can use the QueryFusionRetriever class. This class allows you to combine retrieval results from multiple vector indexes into a single retriever. Here's how you can modify the previous setup to achieve this:

from llama_index.core.retrievers import QueryFusionRetriever
from llama_index.core.query_engine import RetrieverQueryEngine

# Assume index_1 and index_2 are your vector indexes
retriever = QueryFusionRetriever(
    [index_1.as_retriever(), index_2.as_retriever()],
    similarity_top_k=2,
    num_queries=4,
    use_async=True,
    verbose=True,
)

# Create a RetrieverQueryEngine using the combined retriever
vector_auto_retriever = RetrieverQueryEngine.from_args(retriever)

This setup will allow you to handle multiple vector indexes as a single retriever, which can then be used with the SQLAutoVectorQueryEngine or any other component that requires a single vector retriever ^[1].

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[telekosmos]

Ok, but QueryFusionRetriever won't work in SQLAutoVectorQueryEngine as it needs the retriever to be a VectorIndexAutoRetriever... So, any way to merge or combine multiple indexes in a single VectorIndexAutoRetriever?

[dosubot[bot]]

Currently, there isn't a direct method or class in the LlamaIndex framework specifically designed to combine multiple vector indexes into a single VectorIndexAutoRetriever. The VectorIndexAutoRetriever is primarily designed to work with a single vector store index. However, you can explore using the AutoMergingRetriever class, which provides utilities for merging vector indexes into a single index. This class attempts to merge context into a parent context by retrieving chunks from a vector store and merging them into a single context ^[1][2].

While this doesn't directly create a VectorIndexAutoRetriever for multiple indexes, it might help in consolidating your vector data into a single index that can then be used with VectorIndexAutoRetriever.

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