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Updated 3 months ago

best ways to do vector index and knowledge graph index at the same time to generate respon

aandysingal
·
best ways to do vector index and knowledge graph index at the same time to generate response based on query
k
a
12 comments
kkapa.ai
Please stand by @andysingal as I search the knowledge base. I normally take about 20 seconds or so. :wave:
kkapa.ai
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To generate a response based on a query using both vector index and knowledge graph index, you can follow these steps:

  1. Vector Index Query:
    First, you can use the vector index to query the data. Here is an example of how to do it:
Plain Text
   query_engine = vector_index.as_query_engine()
   vector_response = query_engine.query("Your query here")


This code creates a query engine from the vector index and uses it to query the data. The query should be replaced with your specific question. You can find this example in the RedisDocstoreIndexStoreDemo () and AzureDocstoreDemo ().

  1. Knowledge Graph Index Query:
    Next, you can use the knowledge graph index to query the data. Here is an example of how to do it:
Plain Text
   query_engine = index.as_query_engine(
       include_text=True, response_mode="tree_summarize"
   )
   response = query_engine.query(
       "Your query here",
   )


This code creates a query engine from the knowledge graph index and uses it to query the data. The query should be replaced with your specific question. You can find this example in the KnowledgeGraphDemo ().

Remember, the index in the above examples should be replaced with your specific index object. Also, the query string should be replaced with your specific question.
kkapa.ai
For more detailed guides on each index and their corresponding query engines, you can check out the module guide on Indexes () provided by LlamaIndex.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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aandysingal
@kapa.ai do you have example combining vector index and knowledge graph index to generate response
kkapa.ai
Let me check the knowledge base for you. One moment! :books:
kkapa.ai
----------------------
I'm sorry, but the provided knowledge sources do not contain a specific example of combining a vector index and a knowledge graph index to generate a response.

However, you can independently use both indexes and then combine the results according to your needs. Here are the steps to use each index:

Vector Index Query:
Plain Text
query_engine = index.as_query_engine()
response = query_engine.query("Your query here")
print(response)

You can find this example in the Tair Vector Store ().

Knowledge Graph Index Query:
Plain Text
query_engine = index.as_query_engine(
    include_text=True, response_mode="tree_summarize"
)
response = query_engine.query(
    "Your query here",
)

You can find this example in the Knowledge Graph Demo ().

Remember to replace "Your query here" with your specific question and index with your specific index object.

For more detailed guides on each index and their corresponding query engines, you can check out the module guide on Indexes () provided by LlamaIndex.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
aandysingal
@kapa.ai I want a combined version of vector index + knowledge graph index which includes entity extraction for generating response
kkapa.ai
Let me check the knowledge base for you. One moment! :books:
kkapa.ai
----------------------
I'm sorry, but the provided knowledge sources do not contain a specific example of combining a vector index and a knowledge graph index with entity extraction to generate a response.

However, you can independently use both indexes and then combine the results according to your needs. Here are the steps to use each index:

Vector Index Query:
Plain Text
query_engine = index.as_query_engine()
response = query_engine.query("Your query here")
print(response)

You can find this example in the Tair Vector Store ().

Knowledge Graph Index Query:
Plain Text
query_engine = index.as_query_engine(
    include_text=True, response_mode="tree_summarize"
)
response = query_engine.query(
    "Your query here",
)

You can find this example in the Knowledge Graph Demo ().

For entity extraction, you can use the WikiFilter class as shown in the Knowledge Graph Demo (). This class can be used to filter out entities from the text.

Remember to replace "Your query here" with your specific question and index with your specific index object.

For more detailed guides on each index and their corresponding query engines, you can check out the module guide on Indexes () provided by LlamaIndex.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
aandysingal
@kapa.ai entity extraction + knowledge graph index?
kkapa.ai
Let me check the knowledge base for you. One moment! :books:
kkapa.ai
----------------------
In LlamaIndex, you can use the KnowledgeGraphRAGRetriever for entity extraction and knowledge graph indexing. This retriever performs the following steps:

  1. Search related Entities of the question/task
  2. Get SubGraph of those Entities (default 2-depth) from the Knowledge Graph (KG)
  3. Build Context based on the SubGraph
Here is an example of how to use KnowledgeGraphRAGRetriever:

Plain Text
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import KnowledgeGraphRAGRetriever

graph_rag_retriever = KnowledgeGraphRAGRetriever(
    storage_context=storage_context,
    verbose=True,
)

query_engine = RetrieverQueryEngine.from_args(
    graph_rag_retriever,
)

Then you can query it like:

Plain Text
from IPython.display import display, Markdown

response = query_engine.query(
    "Tell me about Peter Quill?",
)
display(Markdown(f"<b>{response}</b>"))

This example is from the Knowledge Graph RAG Query Engine () example.

Please note, the way to Search related Entities could be either Keyword extraction based or Embedding based, which is controlled by argument retriever_mode of the KnowledgeGraphRAGRetriever, and supported options are:
  • "keyword"
  • "embedding"(not yet implemented)
  • "keyword_embedding"(not yet implemented)
Remember to replace storage_context with your specific storage context object.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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