[Question]: nl2graphquery.

[ghost]

Question Validation

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

Question

@wey-gu
While using the nl2graphquery i am getting this error. For the context i havent index anything but this nl2graphquery suppose to give the answer from the knowledge graph itself or do I need to index some document for reference. Please help to find the solution

Param: {}Error message: Error found in optimization stage: IndexNotFound: No valid index found
Thanks.

[logan-markewich]

@yashdeepyds you need to have some existing knowledge graph to connect to. Can you share more details?

[wey-gu]

Could you plz share your code? In case it’s kgquery engine no index should be required indeed.

[ghost]

Before doing this i have created a knowledge graph.

from llama_index.query_engine import RetrieverQueryEngine
from llama_index.retrievers import KnowledgeGraphRAGRetriever
from llama_index.storage.storage_context import StorageContext
from llama_index.graph_stores import NebulaGraphStore
from llama_index.llms import OpenAI
from IPython.display import Markdown, display

space_name = "abc"
edge_types = ["has_column"]
tags = ["table", "column"]

graph_store = NebulaGraphStore(
space_name=space_name,
edge_types=edge_types,
tags=tags,
)
storage_context = StorageContext.from_defaults(graph_store=graph_store)

graph_rag_retriever_with_nl2graphquery = KnowledgeGraphRAGRetriever(
storage_context=storage_context,
service_context=service_context,
llm=llm,
verbose=True,
with_nl2graphquery=True,
)

query_engine_with_nl2graphquery = RetrieverQueryEngine.from_args(
graph_rag_retriever_with_nl2graphquery, service_context=service_context
)

response = query_engine_with_nl2graphquery.query(
"what is this graph about",
)
display(Markdown(f"{response}"))

[ghost]

Hi @wey-gu
Any suggestions on this .
Thanks.

[dosubot[bot]]

🤖

Hello @wey-gu,

Yes, the nl2graphquery function does require indexing some documents for reference. The error message "IndexNotFound: No valid index found" suggests that the function is trying to access an index that hasn't been created or loaded properly.

The KnowledgeGraphRAGRetriever class, which is part of the LlamaIndex framework, uses an instance of KnowledgeGraphIndex to retrieve information. This index is used in various methods such as _get_keywords, _retrieve, and others to search for nodes by keywords, get embeddings, and perform other operations. Therefore, if the index is not properly created or loaded, these operations would fail, leading to the "IndexNotFound: No valid index found" error.

To properly create or load an index in the LlamaIndex framework, you can follow these steps:

1. Create an instance of the BaseIndex class using the from_documents method. This method requires a list of documents, a storage_context, and a service_context.

index = BaseIndex.from_documents(
    documents=your_documents,
    storage_context=your_storage_context,
    service_context=your_service_context,
)

2. Persist the storage context to disk using the persist method of the StorageContext instance.

your_storage_context.persist(your_directory)

3. Load the storage context from the disk using the from_defaults method of the StorageContext class with the persist_dir argument.

new_storage_context = StorageContext.from_defaults(persist_dir=your_directory)

4. Load the index from the storage using the load_index_from_storage function. This function requires a storage_context and a service_context.

new_index = load_index_from_storage(
    storage_context=new_storage_context, 
    service_context=your_service_context
)

5. After loading the data, you can search the information using the retrieve method of the Retriever instance obtained from the as_retriever method of the BaseIndex instance.

new_nodes = new_index.as_retriever().retrieve(your_query)

Please replace your_documents, your_storage_context, your_service_context, your_directory, and your_query with your actual values.

I hope this helps! If you have any other questions, feel free to ask.

Best,
Dosu

Sources

• llama_index/indices/knowledge_graph/retrievers.py
• tests/indices/knowledge_graph/test_base.py
• llama_index/tools/tool_spec/load_and_search/base.py
• tests/indices/test_loading.py
• llama_index/indices/base.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[dosubot[bot]]

🤖

Hello @wey-gu,

Yes, the nl2graphquery function does require indexing some documents for reference. The error message "IndexNotFound: No valid index found" suggests that the function is trying to access an index that hasn't been created or loaded properly.

The KnowledgeGraphRAGRetriever class, which is part of the LlamaIndex framework, uses an instance of KnowledgeGraphIndex to retrieve information. This index is used in various methods such as _get_keywords, _retrieve, and others to search for nodes by keywords, get embeddings, and perform other operations. Therefore, if the index is not properly created or loaded, these operations would fail, leading to the "IndexNotFound: No valid index found" error.

To properly create or load an index in the LlamaIndex framework, you can follow these steps:

1. Create an instance of the BaseIndex class using the from_documents method. This method requires a list of documents, a storage_context, and a service_context.

index = BaseIndex.from_documents(
    documents=your_documents,
    storage_context=your_storage_context,
    service_context=your_service_context,
)

2. Persist the storage context to disk using the persist method of the StorageContext instance.

your_storage_context.persist(your_directory)

3. Load the storage context from the disk using the from_defaults method of the StorageContext class with the persist_dir argument.

new_storage_context = StorageContext.from_defaults(persist_dir=your_directory)

4. Load the index from the storage using the load_index_from_storage function. This function requires a storage_context and a service_context.

new_index = load_index_from_storage(
    storage_context=new_storage_context, 
    service_context=your_service_context
)

5. After loading the data, you can search the information using the retrieve method of the Retriever instance obtained from the as_retriever method of the BaseIndex instance.

new_nodes = new_index.as_retriever().retrieve(your_query)

Please replace your_documents, your_storage_context, your_service_context, your_directory, and your_query with your actual values.

I hope this helps! If you have any other questions, feel free to ask.

Best,
Dosu

Sources

• llama_index/indices/knowledge_graph/retrievers.py
• tests/indices/knowledge_graph/test_base.py
• llama_index/tools/tool_spec/load_and_search/base.py
• tests/indices/test_loading.py
• llama_index/indices/base.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.