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...

[danem]

For anyone dealing with this:

Depending on the vector store you use, it might not return the embeddings from the retriever. For instance, PGVectorStore does NOT return embeddings from queries, and they are only accessible with an additional call to get_nodes. This is really an unfortunate design choice and this behavior ought to be documented somewhere.