dosubot[bot]
commented
1 year ago 🤖
Hello,
Thank you for your interest in LlamaIndex and for your detailed question. It seems like you're on the right track with your approach to using a Knowledge Graph (KG) for your use case.
To address your concerns:
-
Missing Information in the Graph: The graph generation process in LlamaIndex is based on extracting triplets from the text. This process might not capture all the information in the text, especially if the information is not in a format that can be easily converted into a triplet. This is a known limitation of the current version of LlamaIndex.
-
Adding Triplets Manually: The example you provided is a manual way of adding triplets to the graph. The resulting graph might not make sense if the triplets are not related or if the relationships between the nodes are not clearly defined.
-
Using Local Model for Extracting Triplets: The effectiveness of the local model in extracting triplets can vary depending on the quality and format of the input text. If the text is not well-structured or if it contains complex sentences, the model might not be able to extract all the relevant triplets.
For your use case, you might need to preprocess your data to ensure that it is in a format that can be easily parsed by the model. This might involve converting your excel data into a structured format like JSON or CSV, and structuring your text in a way that makes it easier for the model to extract the relevant triplets.
Here's a sample code snippet on how you can use LlamaIndex for your use case:
from llama_index import (
KnowledgeGraphIndex,
LLMPredictor,
ServiceContext,
SimpleDirectoryReader,
)
from llama_index.storage.storage_context import StorageContext
from llama_index.graph_stores import NebulaGraphStore
from llama_index.llms import OpenAI
llm = OpenAI(temperature=0, model="text-davinci-002")
service_context = ServiceContext.from_defaults(llm=llm, chunk_size_limit=512)
graph_store = NebulaGraphStore(
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
)
storage_context = StorageContext.from_defaults(graph_store=graph_store)
kg_index = KnowledgeGraphIndex.from_documents(
documents,
storage_context=storage_context,
max_triplets_per_chunk=10,
service_context=service_context,
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
include_embeddings=True,
)
from llama_index.query_engine import KnowledgeGraphQueryEngine
query_engine = KnowledgeGraphQueryEngine(
storage_context=storage_context,
service_context=service_context,
llm=llm,
verbose=True,
)
response = query_engine.query(
"Your question here",
)Please replace the placeholders with your actual data and parameters.
I hope this helps! If you have any more questions or need further clarification, feel free to ask.
Best, Dosu
Sources
- docs/examples/query_engine/knowledge_graph_query_engine.ipynb
- llama_index/query_engine/knowledge_graph_query_engine.py
- docs/end_to_end_tutorials/question_and_answer.md
- llama_index/indices/knowledge_graph/base.py
- llama_index/readers/file/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.
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vishnu9000
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Question Validation
Question
Hi guys I am new to llama index and llm. I am working on an use case for document question answering and I am confused on which path to take. I am lot of excel files that contain test case details like component, test case id, test case name, test case steps, expected results. I want to create a QA system such that it should be able to answer following questions:
Here first 2 questions can be done with simple vector store. But last two cannot be achieved by vector store due to limitations of vector store RAG where chunks are save. Let say I said give me test case for y component. The pipeline will retrieve 5 best matched chunk. But I may have 20 such test cases. And for last question the model might need to go to multiple chunks to get the accurate results. In my case for each test case component, test case id, test case name, test case steps, expected results is a chunk so itself will be having a good amount of size.
That when I thought of Knowledge Graph and looked into llama index framework. I am using local llama 2 quantized model for security reasons of data. I have checked out following sections.
I have tried to add triplets manually as in docs node_0_tups = [ ("author", "worked on", "writing"), ("author", "worked on", "programming"), ] for tup in node_0_tups: index.upsert_triplet_and_node(tup, nodes[0])
But this the graph its generating, I am really confused by how the relation ship became like this.
I was hoping KG could help with with all the use case I have listed above. Can some one help me with this. I am stuck. Am I doing anything wrong? Is this not the correct approach?