How to use Auto merging retriever alongside chromadb

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Please stand by @bidda7287 as I search the knowledge base. I normally take about 20 seconds or so. :wave:

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I'm sorry, but the provided knowledge sources do not contain information on how to use the AutoMergingRetriever alongside ChromaDB. The sources provide separate examples of how to use the AutoMergingRetriever and how to set up a ChromaDB collection, but they do not provide an example of using them together.

Here's what we know from the sources:

The AutoMergingRetriever is a part of the LlamaIndex project. It is designed to merge context into parent context. It first retrieves chunks from a vector store and then tries to merge the chunks into a single context. You can initialize and use it as follows:

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from llama_index.core.retrievers import AutoMergingRetriever

base_retriever = base_index.as_retriever(similarity_top_k=6)
retriever = AutoMergingRetriever(base_retriever, storage_context, verbose=True)

query_str = "Your query string here"
nodes = retriever.retrieve(query_str)

(Source: GitHub ())

ChromaDB is a vector database that can be used with LlamaIndex. You can set up an empty Chroma collection as follows:

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import chromadb

chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")

(Source: GitHub ())

kkapa.ai

However, to use the AutoMergingRetriever with ChromaDB, you would likely need to set up ChromaDB as the vector store for the retriever. Unfortunately, the provided sources do not provide an example or instructions on how to do this. You may want to refer to the LlamaIndex and ChromaDB documentation or their respective communities for more specific guidance.
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How to use Auto merging retriever alongside chromadb