[Question]: How do I load a KnowledgeGraphIndex from an existing Neptune Knowledge Graph without from_documents()?

[sumanthnallamotu]

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Question

I am trying to load an existing Neptune knowledge graph as an index in LlamaIndex without using the from_documents() argument, as the graph has already been creating using said documents. For example, this is how I'm currently doing things:

documents = SimpleDirectoryReader( "documents" ).load_data()

graph_store = NeptuneAnalyticsGraphStore( graph_identifier="" ) storage_context = StorageContext.from_defaults(graph_store=graph_store)

index = KnowledgeGraphIndex.from_documents( documents, storage_context=storage_context, max_triplets_per_chunk=4 )

query_engine = index.as_query_engine( include_text=True, response_mode="tree_summarize", embedding_mode="hybrid", similarity_top_k=10 )

This works just fine when I send queries about the documents. However, I don't want to have to create a new knowledge graph from the documents every time I want to ask questions. If I want to use the query engine behind a server for example, I want to be able to send questions to the existing Neptune knowledge graph index that has the documents loaded. Any guidance?

[dosubot[bot]]

Hey there, @sumanthnallamotu! I'm here to help you with any bugs, questions, or contributions you have. Let's tackle this together!

To load an existing Neptune knowledge graph as an index in LlamaIndex without using the from_documents() method, you can directly instantiate the NeptuneAnalyticsGraphStore with your existing Neptune graph and then create a StorageContext using this graph store. Here is how you can do it:

1. Instantiate the Neptune Graph Store:

   from llama_index.graph_stores.neptune import NeptuneAnalyticsGraphStore
   
   graph_store = NeptuneAnalyticsGraphStore(
      graph_identifier="<IDENTIFIER>"
   )

2. Create the Storage Context:

   from llama_index.core import StorageContext
   
   storage_context = StorageContext.from_defaults(graph_store=graph_store)

3. Initialize the KnowledgeGraphIndex:

   from llama_index.core.indices.knowledge_graph.base import KnowledgeGraphIndex
   
   index = KnowledgeGraphIndex(storage_context=storage_context)

4. Query the Knowledge Graph:

   query_engine = index.as_query_engine(
      include_text=True,
      response_mode="tree_summarize",
      embedding_mode="hybrid",
      similarity_top_k=10
   )
   response = query_engine.query("Your query here")

Here is the complete code snippet:

from llama_index.graph_stores.neptune import NeptuneAnalyticsGraphStore
from llama_index.core import StorageContext, KnowledgeGraphIndex

# Instantiate Neptune Graph Store
graph_store = NeptuneAnalyticsGraphStore(
    graph_identifier="<IDENTIFIER>"
)

# Create the Storage Context
storage_context = StorageContext.from_defaults(graph_store=graph_store)

# Initialize the KnowledgeGraphIndex
index = KnowledgeGraphIndex(storage_context=storage_context)

# Query the Knowledge Graph
query_engine = index.as_query_engine(
    include_text=True,
    response_mode="tree_summarize",
    embedding_mode="hybrid",
    similarity_top_k=10
)
response = query_engine.query("Your query here")

# Display the response
print(response)

This way, you can use your existing Neptune knowledge graph as an index in LlamaIndex without needing to recreate it from documents each time ^[1].

To continue talking to Dosu, mention @dosu.

[sumanthnallamotu]

`ValueError Traceback (most recent call last) Cell In[9], line 2 1 # Initialize the KnowledgeGraphIndex ----> 2 index = KnowledgeGraphIndex(storage_context=storage_context) 4 # Query the Knowledge Graph 5 query_engine = index.as_query_engine( 6 include_text=True, 7 response_mode="tree_summarize", 8 embedding_mode="hybrid", 9 similarity_top_k=10 10 )

File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/llama_index/core/indices/knowledge_graph/base.py:99, in KnowledgeGraphIndex.init(self, nodes, objects, index_struct, llm, embed_model, storage_context, kg_triple_extract_template, max_triplets_per_chunk, include_embeddings, show_progress, max_object_length, kg_triplet_extract_fn, service_context, kwargs) 94 self._llm = llm or llm_from_settings_or_context(Settings, service_context) 95 self._embed_model = embed_model or embed_model_from_settings_or_context( 96 Settings, service_context 97 ) ---> 99 super().init( 100 nodes=nodes, 101 index_struct=index_struct, 102 service_context=service_context, 103 storage_context=storage_context, 104 show_progress=show_progress, 105 objects=objects, 106 kwargs, 107 ) 109 # TODO: legacy conversion - remove in next release 110 if ( 111 len(self.index_struct.table) > 0 112 and isinstance(self.graph_store, SimpleGraphStore) 113 and len(self.graph_store._data.graph_dict) == 0 114 ):

File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/llama_index/core/indices/base.py:59, in BaseIndex.init(self, nodes, objects, index_struct, storage_context, callback_manager, transformations, show_progress, service_context, **kwargs) 57 """Initialize with parameters.""" 58 if index_struct is None and nodes is None and objects is None: ---> 59 raise ValueError("One of nodes, objects, or index_struct must be provided.") 60 if index_struct is not None and nodes is not None: 61 raise ValueError("Only one of nodes or index_struct can be provided.")

ValueError: One of nodes, objects, or index_struct must be provided. ​`

I get the above error when running your code

[dosubot[bot]]

To resolve the error "One of nodes, objects, or index_struct must be provided" and successfully load the existing Neptune knowledge graph as an index in LlamaIndex, you need to ensure that you provide at least one of the required parameters (nodes, objects, or index_struct) when initializing the KnowledgeGraphIndex.

Here is an example of how you can initialize the KnowledgeGraphIndex with an index_struct:

from llama_index.core.indices.knowledge_graph.base import KnowledgeGraphIndex
from llama_index.core.schema import IndexStruct

# Assuming you have an existing index_struct
existing_index_struct = IndexStruct(...)  # Replace with your actual IndexStruct

# Initialize the KnowledgeGraphIndex with the existing index_struct
index = KnowledgeGraphIndex(index_struct=existing_index_struct, storage_context=storage_context)

Alternatively, if you have a list of nodes or objects, you can initialize the KnowledgeGraphIndex with those:

from llama_index.core.indices.knowledge_graph.base import KnowledgeGraphIndex
from llama_index.core.schema import BaseNode, IndexNode

# Assuming you have a list of nodes or objects
nodes = [BaseNode(...), BaseNode(...)]  # Replace with your actual nodes
objects = [IndexNode(...), IndexNode(...)]  # Replace with your actual objects

# Initialize the KnowledgeGraphIndex with nodes
index = KnowledgeGraphIndex(nodes=nodes, storage_context=storage_context)

# Or initialize the KnowledgeGraphIndex with objects
index = KnowledgeGraphIndex(objects=objects, storage_context=storage_context)

Make sure to replace the placeholders with your actual data. This should resolve the error and allow you to load the existing Neptune knowledge graph as an index in LlamaIndex ^[1].

To continue talking to Dosu, mention @dosu.

[sumanthnallamotu]

`# Persist index to disk index.storage_context.persist("naval_index")

from llama_index import StorageContext, load_index_from_storage

Rebuild storage context

storage_context = StorageContext.from_defaults(persist_dir="naval_index")

Load index from the storage context

new_index = load_index_from_storage(storage_context)

new_query_engine = new_index.as_query_engine() response = new_query_engine.query("who is this text about?") print(response)`

Thanks @dosu! Using your answer + this code snippet from [(https://medium.com/@reddyyashu20/llamaindex-create-save-load-indexes-customize-llms-prompts-embeddings-abb581df6dac)], I was able to get it working