[Question]: VectorStoreIndex log status for each file

[mirallm]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

I would like to be able to do log what's happening to each file, what was embedded, what was not etc..
I use llamaindex VectorStoreIndex with Qdrant.
`
async def create_index_nodes(self, nodes, embed_model):
"""
Asynchronously creates an index in Qdrant based on the provided nodes.

              :param nodes: List of TextNodes to index.
              :param vector_store: The Qdrant vector store instance.
              """
          
           
             
              self.vector_index =   VectorStoreIndex(
                  nodes=nodes,
                  embed_model=embed_model,
                  storage_context=self.storage_context, 
                  show_progress=True
              )
          `

I thought someone gave an advice to log file per file:

                 index = VectorStoreIndex(nodes=[], ...)
                for doc in reader.iter_data():
                  index.insert(doc)

1)However I was kind of suprised because I use VectorStoreIndex to create embedding and store them inside QDRANT. if we call again index.insert, doesnt' mean that we will embed them again?
2) i get nodes from all documents, should i insert doc or nodes? how would i know the end node of a file?
3) what is the way to be able to store embeddings and log things for each file.

@dosu

[dosubot]

To address your questions about logging and embedding with llamaindex and Qdrant:

1. Re-embedding with index.insert: If you call index.insert again on the same documents, it will indeed re-embed them. To avoid this, you should manage the state of your nodes and ensure that you are not re-inserting nodes that have already been embedded and stored. You can keep track of which nodes have been processed to prevent re-embedding.

2. Inserting documents or nodes: You should insert nodes rather than documents to avoid re-embedding. The add method in the QdrantVectorStore class is designed to handle nodes, which are instances of BaseNode. This method is optimized for handling nodes with embeddings, so you should focus on inserting nodes to ensure efficient processing ^[1].

3. Logging the embedding process: To log the embedding process for each file, you can add logging statements in the _build_points method of the QdrantVectorStore class. This method is responsible for building the points (embeddings) for each node. By adding logging statements here, you can track the embedding process for each node. Here's an example of how you might add logging:

   # Log the embedding process
   logger.info(f"Processed embedding for node ID: {node.node_id}")

   This will log the node ID each time an embedding is processed, allowing you to track which nodes have been embedded.

By following these steps, you can effectively manage the embedding process and logging for each file when using llamaindex with Qdrant.

To continue talking to Dosu, mention @dosu.

[mirallm]

@dosu

can you write me the code

[dosubot]

Certainly! Here's an example of how you might implement logging for the embedding process in the QdrantVectorStore class, specifically within the _build_points method. This will help you track the embedding process for each node:

import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class QdrantVectorStore:
    # Existing methods...

    def _build_points(self, nodes, sparse_vector_name):
        points = []
        node_ids = []

        for node in nodes:
            # Assuming `node` has an `embedding` attribute
            point = {
                'id': node.node_id,
                'vector': node.embedding,
                'payload': {
                    'text': node.text,
                    'metadata': node.metadata
                }
            }
            points.append(point)
            node_ids.append(node.node_id)

            # Log the embedding process
            logger.info(f"Processed embedding for node ID: {node.node_id}")

        return points, node_ids

# Example usage
async def create_index_nodes(nodes, embed_model):
    vector_index = VectorStoreIndex(
        nodes=nodes,
        embed_model=embed_model,
        storage_context=storage_context, 
        show_progress=True
    )

    # Assuming `reader` is an instance of a class with `iter_data` method
    for doc in reader.iter_data():
        # Convert documents to nodes if necessary
        nodes = convert_documents_to_nodes(doc)
        vector_index.insert(nodes)

# Helper function to convert documents to nodes
def convert_documents_to_nodes(documents):
    # Implement conversion logic here
    return nodes

In this code:

• Logging is set up using Python's logging module.
• The _build_points method logs each node's ID when its embedding is processed.
• The create_index_nodes function demonstrates how to use the VectorStoreIndex to insert nodes, ensuring that you handle nodes rather than raw documents to avoid re-embedding.

Make sure to replace convert_documents_to_nodes with your actual logic for converting documents to nodes. This setup will help you track the embedding process for each file and node.

To continue talking to Dosu, mention @dosu.

[mirallm]

       vector_index = VectorStoreIndex(
              nodes=nodes,
              embed_model=embed_model,
              storage_context=storage_context, 
              show_progress=True
          )

is already generating embeddings for me then i dont see the use of vector_index.insert(nodes) @dosu

[mirallm]

@dosu

[dosubot]

The VectorStoreIndex is designed to generate embeddings for nodes when it is initially created with the nodes parameter. This means that if you provide nodes during the initialization of VectorStoreIndex, it will automatically handle the embedding process for those nodes.

Using vector_index.insert(nodes) is typically unnecessary if you've already embedded the nodes during the initial creation of the index. The insert method is useful when you want to add new nodes to an existing index after its initial creation. If your nodes are already embedded and stored during the initial setup, you don't need to call insert again unless you have new nodes to add later on.

In your case, since the embeddings are generated during the initialization with nodes=nodes, you can skip the insert step unless you have additional nodes to process later.

To continue talking to Dosu, mention @dosu.

[mirallm]

yes but i want to be able to do things such as logging for each file after it is finished. if VectorStoreIndex will embed everything, hence I won't be able to control that. what can I do in this case. @dosu , ca i just initialize a vectorstoreindex?

[dosubot]

To achieve logging or perform actions after each file is processed while using VectorStoreIndex, you can customize the embedding process. Here are a few strategies you can consider:

1. Custom Embedding Model: Implement a custom embedding model that includes logging. You can pass this model to the VectorStoreIndex during initialization. This way, you can log details each time an embedding is generated.

2. Modify Node Processing Methods: You can modify the _get_node_with_embedding and _aget_node_with_embedding methods in the VectorStoreIndex class to include logging after each node is processed. This allows you to track the progress of embedding for each node.

   Example:

   def _get_node_with_embedding(self, nodes, show_progress=False):
       id_to_embed_map = embed_nodes(nodes, self._embed_model, show_progress=show_progress)
       results = []
       for node in nodes:
           embedding = id_to_embed_map[node.node_id]
           result = node.model_copy()
           result.embedding = embedding
           results.append(result)
           # Log after processing each node
           logger.info(f"Processed node {node.node_id} with embedding.")
       return results

3. Batch Processing Control: If you want to control the batch processing, you can manage the nodes manually. Instead of passing all nodes at once, process them in smaller batches or one by one, and log after each batch or node is processed.

4. Use CallbackManager: Utilize the CallbackManager to add hooks or callbacks that execute custom code after each node is processed. This can be useful for logging or other actions.

By implementing one or more of these strategies, you can gain more control over the embedding process and perform logging or other actions after each file or node is processed.

To continue talking to Dosu, mention @dosu.

[mirallm]

can you write me the code using the CallbackManager. I will send documents, then i need to parse it and get_nodes_from_documents and send it to VectorStoreIndex. make sure in the callbackmanager to log when all the documents has been stored. so i can log node by node but also when the whole document is finished, to log that too. I want to know at the end how many documents has been sucessfully stored. @dosu

[dosubot]

Here's how you can use the CallbackManager to log the embedding process for each node and also log when all documents have been successfully stored, including the total number of documents stored:

from llama_index import CallbackManager, VectorStoreIndex
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class CustomCallback:
    def __init__(self):
        self.total_documents = 0

    def on_embedding(self, event):
        # Log the embedding process for each node
        logger.info(f"Embedding {event['num_texts']} texts.")

    def on_node_parsing(self, event):
        # Increment the total number of documents stored
        self.total_documents += len(event['nodes'])

    def on_all_documents_stored(self):
        # Log when all documents have been successfully stored
        logger.info(f"All documents have been stored. Total documents: {self.total_documents}")

# Create a callback manager and add the custom callback
callback_manager = CallbackManager()
custom_callback = CustomCallback()

# Register the custom callback for the EMBEDDING and NODE_PARSING events
callback_manager.register_callback('EMBEDDING', custom_callback.on_embedding)
callback_manager.register_callback('NODE_PARSING', custom_callback.on_node_parsing)

# Function to process documents
def process_documents(documents, embed_model, storage_context):
    # Parse documents into nodes
    nodes = get_nodes_from_documents(documents, callback_manager=callback_manager)

    # Create a VectorStoreIndex with the nodes
    vector_index = VectorStoreIndex(
        nodes=nodes,
        embed_model=embed_model,
        storage_context=storage_context,
        show_progress=True
    )

    # Log when all documents have been stored
    custom_callback.on_all_documents_stored()

# Example usage
documents = [...]  # Your list of documents
embed_model = ...  # Your embedding model
storage_context = ...  # Your storage context

process_documents(documents, embed_model, storage_context)

In this code:

• A CustomCallback class is defined to handle logging for each node's embedding and when all documents are stored.
• The CallbackManager is used to register these callbacks.
• The process_documents function parses documents into nodes and creates a VectorStoreIndex, logging the necessary information throughout the process.

To continue talking to Dosu, mention @dosu.

[mirallm]

@dosu how can i insert_nodes using vectorstoreIndex? imagine that we have never initialized before a vectorstoreindex @dosu

[dosubot]

To insert nodes using VectorStoreIndex if you have never initialized a VectorStoreIndex before, you need to first initialize the VectorStoreIndex with an empty list or with your initial set of documents. Here's how you can do it:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Load documents and build index
documents = SimpleDirectoryReader("../../examples/data/paul_graham").load_data()
index = VectorStoreIndex.from_documents(documents)

Alternatively, if you want more control over how your documents are indexed, you can use the ingestion pipeline to create nodes:

from llama_index.core import Document
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.extractors import TitleExtractor
from llama_index.core.ingestion import IngestionPipeline

# Create the pipeline with transformations
pipeline = IngestionPipeline(
    transformations=[
        SentenceSplitter(chunk_size=25, chunk_overlap=0),
        TitleExtractor(),
        OpenAIEmbedding(),
    ]
)

# Run the pipeline
nodes = pipeline.run(documents=[Document.example()])
index = VectorStoreIndex(nodes)

This approach allows you to customize the chunking, metadata, and embedding of the nodes before inserting them into the index ^[1][2].

To continue talking to Dosu, mention @dosu.

[mirallm]

ok but how can i log after the end of each document, from a set of document that i send to vectorstoreindex using the pipeline approach. @dosu

[dosubot]

To log the completion of each document after processing a set of documents using the pipeline approach with VectorStoreIndex, you can modify the _wait_for_documents_ingestion method to include logging for each document's completion. Here's an example of how you can achieve this:

import logging
import time
from urllib.parse import quote_plus

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class CustomVectorStoreIndex(VectorStoreIndex):
    def _wait_for_documents_ingestion(self, doc_ids, verbose=False, raise_on_error=False):
        if verbose:
            print("Loading data: ", end="")

        # Wait until all documents are loaded
        pending_docs = set(doc_ids)
        while pending_docs:
            docs_to_remove = set()
            for doc in pending_docs:
                # Check the status of each document
                status = self._client.pipelines.get_pipeline_document_status(
                    pipeline_id=self.pipeline.id,
                    document_id=quote_plus(quote_plus(doc)),
                )
                if status in [
                    ManagedIngestionStatus.NOT_STARTED,
                    ManagedIngestionStatus.IN_PROGRESS,
                ]:
                    continue
                if status == ManagedIngestionStatus.ERROR:
                    if verbose:
                        print(f"Document ingestion failed for {doc}")
                    if raise_on_error:
                        raise ValueError(f"Document ingestion failed for {doc}")

                # Log the completion of the document
                logger.info(f"Document {doc} ingestion completed successfully.")

                docs_to_remove.add(doc)

            pending_docs -= docs_to_remove

            if pending_docs:
                if verbose:
                    print(".", end="")
                time.sleep(0.5)

        if verbose:
            print("Done!")

        # Wait for pipeline ingestion to complete
        self.wait_for_completion(verbose, raise_on_error)

# Example usage
documents = [...]  # Your list of documents
embed_model = ...  # Your embedding model
storage_context = ...  # Your storage context

# Initialize the custom index
index = CustomVectorStoreIndex(
    nodes=[],
    embed_model=embed_model,
    storage_context=storage_context,
    show_progress=True
)

# Process documents and log completion
index._wait_for_documents_ingestion(doc_ids=[doc.id for doc in documents])

In this code:

• The _wait_for_documents_ingestion method is modified to log the completion of each document.
• The logger.info statement logs when each document's ingestion is completed successfully.
• This setup allows you to track the progress and completion of each document in the pipeline ^[1].

To continue talking to Dosu, mention @dosu.

[mirallm]

@logan-markewich is there a simple way to do that?