Embedding is None

[johnhu33]

Embedding is None after inserting nodes to a VectorStoreIndex that is initialised using from_vector_store, how can I store embeddings?

[dosubot[bot]]

Hey @johnhu33! 👋 I'm here to help you with any bugs, questions, or contributions while you wait for a human maintainer. Let's squash those bugs together!

I found a similar closed issue that might be relevant: [Question]: Retriever documents, embeddings = None. The suggested solution is to modify the _build_node_list_from_query_result method to set the embedding attribute in the NodeWithScore objects. Alternatively, you can use index.vector_store.get(node.node_id) to get the associated embedding ^[1].

To store embeddings after inserting nodes into a VectorStoreIndex initialized using from_vector_store, follow these steps:

1. Generate Embeddings: Use the _get_node_with_embedding method to generate embeddings for the nodes.
2. Insert Nodes: Use the _add_nodes_to_index method to insert the nodes along with their embeddings into the index.

Here is a code snippet demonstrating these steps:

# Assuming you have a VectorStoreIndex instance `index` and a list of nodes `nodes`

# Step 1: Generate embeddings for the nodes
nodes_with_embeddings = index._get_node_with_embedding(nodes)

# Step 2: Insert nodes with embeddings into the index
index._add_nodes_to_index(index._index_struct, nodes_with_embeddings)

The _get_node_with_embedding method generates the embeddings for the nodes, and the _add_nodes_to_index method handles the insertion of these nodes into the index, ensuring that the embeddings are stored properly ^[2][3][4][5].

To continue talking to Dosu, mention @dosu.

[dosubot[bot]]

To retrieve a node's embedding using the index as a retriever, you can use the SummaryIndexEmbeddingRetriever class from the LlamaIndex framework. This class generates embeddings in a lazy fashion for all nodes that are traversed and retrieves nodes based on their similarity to a query.

Here is an example of how you can achieve this:

from llama_index.core.indices.list.retrievers import SummaryIndexEmbeddingRetriever
from llama_index.core.schema import QueryBundle

# Assuming you have an instance of SummaryIndex named `summary_index`
retriever = SummaryIndexEmbeddingRetriever(index=summary_index)

# Create a QueryBundle with the query string
query_bundle = QueryBundle(query_str="your_query_string")

# Now you can use the retriever to get nodes and their embeddings
nodes_with_scores = retriever._retrieve(query_bundle)

# The nodes_with_scores list will contain nodes and their embeddings
for node_with_score in nodes_with_scores:
    node = node_with_score.node
    embedding = node.embedding
    print(embedding)

This code snippet demonstrates how to use the SummaryIndexEmbeddingRetriever to retrieve nodes and their embeddings based on a query. The QueryBundle is used to encapsulate the query string, and the _retrieve method is called to get the nodes with their embeddings ^[1][2].

To continue talking to Dosu, mention @dosu.

[johnhu33]

@dosu, please give me some code to retrieve embedding using retriever = index.as_retriever(), result_nodes = retriever.retrieve(query)

[dosubot[bot]]

To retrieve embeddings using retriever = index.as_retriever() and result_nodes = retriever.retrieve(query), you can follow these steps:

1. Initialize the Retriever: Convert your index to a retriever using index.as_retriever().
2. Retrieve Results: Use the retrieve method on the retriever with your query.

Here is a code example:

from llama_index.core.schema import QueryBundle

# Assuming `index` is an instance of VectorStoreIndex
retriever = index.as_retriever(similarity_top_k=1)

# Define your query
query = "Your query string here"

# Create a QueryBundle with the query string
query_bundle = QueryBundle(query_str=query)

# Retrieve the results
result_nodes = retriever.retrieve(query_bundle)

# Extract embeddings from the result nodes
for node_with_score in result_nodes:
    node = node_with_score.node
    embedding = node.embedding
    print(embedding)

This code snippet demonstrates how to use the retriever to get nodes and their embeddings based on a query. The QueryBundle is used to encapsulate the query string, and the retrieve method is called to get the nodes with their embeddings ^[1][2].

To continue talking to Dosu, mention @dosu.

[johnhu33]

@dosu, using this method, the embedding is None

[dosubot[bot]]

To get a node's corresponding embedding using its node_id, you can use the embed_nodes function provided in the llama_index.legacy.indices.utils module. This function takes a sequence of nodes and an embedding model, and returns a dictionary mapping node IDs to their embeddings.

Here is an example of how you can use this function:

from llama_index.legacy.indices.utils import embed_nodes
from llama_index.legacy.embeddings.base import BaseEmbedding
from llama_index.legacy.schema import BaseNode

# Assuming you have a list of nodes and an embedding model
nodes = [...]  # List of BaseNode objects
embed_model = BaseEmbedding()  # Your embedding model instance

# Get the embeddings
id_to_embed_map = embed_nodes(nodes, embed_model)

# Retrieve the embedding for a specific node_id
node_id = "your_node_id"
embedding = id_to_embed_map.get(node_id)
print(embedding)

This code will give you the embedding for the node with the specified node_id. If the node does not have a precomputed embedding, the function will compute it using the provided embedding model ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[johnhu33]

How to get node's corresponding embedding with node_id?

[johnhu33]

vector_store = VectorStoreIndex(redis_client=redis_client, overwrite=True, schema=schema)

load storage context

storage_context = StorageContext.from_defaults(vector_store=vector_store)
service_context = ServiceContext.from_defaults()
service_context.embed_model = BGEEmbeddings()

build and load index from documents and storage context

index = VectorStoreIndex.from_vector_store(vector_store=vector_store, embed_model=BGEEmbeddings())

index.insert_nodes(nodes)
retriever = index.as_retriever()
result_nodes = retriever.retrieve(query)

result_nodes[0].embedding is None

How could I store index in the VectorStoreIndex?

[dosubot[bot]]

To ensure that embeddings are correctly stored and retrieved in the VectorStoreIndex, follow these steps:

1. Initialize the VectorStoreIndex with the correct vector_store and embed_model:

   vector_store = VectorStoreIndex(redis_client=redis_client, overwrite=True, schema=schema)
   
   # Load storage context
   storage_context = StorageContext.from_defaults(vector_store=vector_store)
   service_context = ServiceContext.from_defaults()
   service_context.embed_model = BGEEmbeddings()
   
   # Build and load index from documents and storage context
   index = VectorStoreIndex.from_vector_store(vector_store=vector_store, embed_model=BGEEmbeddings())

2. Insert nodes with embeddings into the index:
   Ensure that the nodes have embeddings before inserting them into the index. You can use the _get_node_with_embedding method to generate embeddings for the nodes.

   # Assuming `nodes` is a list of nodes to be inserted
   nodes_with_embeddings = index._get_node_with_embedding(nodes)
   
   # Insert nodes with embeddings into the index
   index._add_nodes_to_index(index._index_struct, nodes_with_embeddings)

3. Retrieve nodes and their embeddings:
   Use the retriever to get nodes and their embeddings based on a query.

   retriever = index.as_retriever(similarity_top_k=1)
   
   # Define your query
   query = "Your query string here"
   
   # Create a QueryBundle with the query string
   query_bundle = QueryBundle(query_str=query)
   
   # Retrieve the results
   result_nodes = retriever.retrieve(query_bundle)
   
   # Extract embeddings from the result nodes
   for node_with_score in result_nodes:
       node = node_with_score.node
       embedding = node.embedding
       print(embedding)

By following these steps, you ensure that the embeddings are correctly stored and retrieved in the VectorStoreIndex. If result_nodes[0].embedding is still None, it might be due to an issue in the VectorStoreIndex implementation or the way embeddings are stored/retrieved in the database ^[1][2].

To continue talking to Dosu, mention @dosu.

[hdotking]

I had a similar issue where the input embeddings were going in as one size and coming out as 1536.

This happened because llama-index was defaulting to _Settings singleton which used an OpenAI model with 1536 embedding length by default.

I resolved this by explicitly passing my embedding model during the indexing process. E.g.

# Create your chosen embedding model at the top of the file
my_embed_model = FastEmbedEmbedding(model_name="BAAI/bge-base-en-v1.5")

# Create storage context
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Create the index from documents with chosen embeddings
index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
    embed_model=my_embed_model,  # Explicitly pass the embedding model
)

[hdotking]

NGL It would have saved me a lot of time if there was a warning that this was happening...