llamaIndex - 💡(How to fix) Fix [Question]: Potential lack of source chunks when injecting ontology-based data [1 comments, 2 participants]

Fix Plan

To address the issue of ontology-inserted nodes lacking sufficient grounding or context, we recommend manually attaching source text to ontology nodes. Here are the concrete steps:

• Step 1: Modify the upsert mechanism to include source text for each node.
• Step 2: Update the node creation process to link nodes to their corresponding text chunks.
• Step 3: Implement a context retrieval mechanism to ensure sufficient context is retrieved during queries.

Example code snippet (Python):

# Step 1: Modify the upsert mechanism
def upsert_node(node_id, node_data, source_text):
    # Upsert node data into the graph
    graph.upsert(node_id, node_data)
    # Attach source text to the node
    graph.add_edge(node_id, source_text, relation="source_text")

# Step 2: Update the node creation process
def create_node(node_data, source_text):
    node_id = graph.add_node(node_data)
    upsert_node(node_id, node_data, source_text)
    return node_id

# Step 3: Implement a context retrieval mechanism
def get_context(node_id, query):
    # Retrieve the source text linked to the node
    source_text = graph.get_edge(node_id, relation="source_text")
    # Use the source text to retrieve sufficient context
    context = retrieve_context(source_text, query)
    return context

Verification

To verify that the fix worked, test the following scenarios:

• Query a node with attached source text and verify that the retrieved context is accurate.
• Query a node without attached source text and verify that the retrieved context is incomplete or inaccurate.

Extra Tips

• Ensure that the source text is properly indexed and linked to the corresponding nodes in the graph.
• Consider implementing a caching mechanism to improve the performance of context retrieval during queries.