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Updated 7 months ago

Is there a way to query an index using an embedding?

@kapa.ai Is there a way to query an index using an embedding?
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Yes, there is a way to query an index using an embedding in LlamaIndex. You can use the query_engine object to query the index. The query_engine object is created from the index object using the as_query_engine() method.

If you are using a vector store that uses vector search, such as Typesense, you need to provide the embedding yourself. You can use the OpenAIEmbedding model to generate the embedding for your query. Here is an example of how to do it:

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from llama_index.core import QueryBundle
from llama_index.embeddings.openai import OpenAIEmbedding

# By default, typesense vector store uses vector search. You need to provide the embedding yourself.
query_str = "What did the author do growing up?"
embed_model = OpenAIEmbedding()
query_embedding = embed_model.get_agg_embedding_from_queries(query_str)
query_bundle = QueryBundle(query_str, embedding=query_embedding)
response = index.as_query_engine().query(query_bundle)


In this example, the QueryBundle object is created with the query string and its corresponding embedding. The query() method of the query_engine object is then used to query the index with this QueryBundle.

You can find more details in the LlamaIndex documentation ().
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How can I retrieve multipel documents?
@kapa.ai How can I retrieve multiple documents?
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You can retrieve multiple documents in LlamaIndex using the retrieve_documents method of the MetaphorToolSpec class. This method takes a list of document IDs and returns a list of corresponding documents. Here is an example of how to use it:

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from llama_index.tools.metaphor import MetaphorToolSpec

# Assuming you have a MetaphorToolSpec instance named metaphor_tool
document_ids = ["doc1", "doc2", "doc3"]  # replace with your actual document IDs
documents = metaphor_tool.retrieve_documents(document_ids)


In this example, documents will be a list of Document objects corresponding to the IDs in document_ids.

Here is the method signature and description from the LlamaIndex API reference ():

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retrieve_documents(ids: List[str]) -> List[Document]


Retrieve a list of document summaries returned by metaphor_search, using the ID field.

Parameters:

| Name | Type | Description | Default |
| --- | --- | --- | --- |
| ids | List[str] | the ids of the documents to retrieve | required |

Please note that you need to replace "doc1", "doc2", and "doc3" with the actual IDs of the documents you want to retrieve.
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I want to retrieve multiple documents via query_engine.retrieve() @kapa.ai
Let me check the knowledge base for you. One moment! :books:
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In LlamaIndex, you can retrieve multiple documents by configuring the similarity_top_k parameter when building the query_engine. This parameter determines the number of most similar documents to return. Here is an example:

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=5)  # retrieve top 5 most similar documents
response = query_engine.query("What did the author do growing up?")
print(response)


In this example, the query_engine is configured to return the top 5 most similar documents to the query "What did the author do growing up?".

You can find more details in the LlamaIndex documentation ().
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I want to use .retrieve() not .query()
I want to use .retrieve() not .query() @kapa.ai
Let me check the knowledge base for you. One moment! :books:
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In LlamaIndex, you can use the retrieve() method of a retriever object to retrieve multiple documents relevant to a query. Here's an example:

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retriever = index.as_retriever()
nodes = retriever.retrieve("Who is Paul Graham?")


In this example, nodes will be a list of NodeWithScore objects, which are the retrieved documents relevant to the query "Who is Paul Graham?". The number of documents retrieved depends on the configuration of the retriever.

You can find more details in the LlamaIndex documentation ().
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@kapa.ai How can I set the retriever to return diverse documents?
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In LlamaIndex, you can set the retriever to return diverse documents by setting the mmr_diversity_bias parameter when creating the retriever. The mmr_diversity_bias parameter controls the trade-off between relevance and diversity. Here's an example:

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retriever = index.as_retriever(mmr_diversity_bias=1)
nodes = retriever.retrieve("movie about toys")


In this example, the mmr_diversity_bias is set to 1, which means the retriever will prioritize diversity over relevance. The retrieve() method is then used to retrieve documents relevant to the query "movie about toys".

You can find more details in the LlamaIndex documentation ().
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