[Question]: Using LSP to generate a knowledge graph for a code base.

[YassineElyk]

Question Validation

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

Question

Hello,

I am very new to machine learning and to Llama_index and LLM's in general. Still exploring the concepts.

I was looking into possible ways of running a LLM locally against a large codebase and so far not much success.

One idea I've had while reading through the documentation and how Llama_index works is to potentially build a knowledge graph (Or perhaps another type of index will be more suitable for a codebase?) using The Language Server Protocol (LSP).
Is this an idea worth pursuing?

For example, I have found this project released recently that seems to do something similar : https://github.com/chanhx/crabviz

Potentially the components of the graph, using LSP, can be methods and functions or more granular parts of the code. Perhaps it will optimise the process of querying a codebase much better!

[jon-chuang]

Hello, this is very interesting. Certainly, for code use case, leveraging more semantic structure in the codebase could lead to better results.

Some questions:

1. Which languages does LSP work best for?

If you have a specific repo, perhaps you could try to create a notebook around using crabviz to aid in node splitting and metadata extraction, we could work towards merging that if it makes sense.

[YassineElyk]

LSP is actually language agnostic.

Crabviz was just to illustrate what's possible.

In an actual implementation that can be integrated with llama_index, I guess one needs to use an LSP python client library to interface with a specific LSP server. Get the data from it and build Documents, nodes and eventually the knowledge graph from that.
Will try to give this some thought.

[dosubot[bot]]

🤖

Hello,

Your idea of using the Language Server Protocol (LSP) to build a knowledge graph with LlamaIndex for querying a large codebase is indeed feasible and worth pursuing.

In LlamaIndex, the KnowledgeGraphIndex class provides a way to build a knowledge graph by extracting triplets and leveraging the KG during query-time. It includes methods like _extract_triplets, _llm_extract_triplets, _parse_triplet_response, _build_index_from_nodes, _insert, upsert_triplet, add_node, upsert_triplet_and_node, and get_networkx_graph.

The KnowledgeGraphIndex class also includes a method as_retriever which returns a KGTableRetriever object. This object can be used to retrieve information from the knowledge graph.

Here is a sample code snippet from the KnowledgeGraphIndex class:

class KnowledgeGraphIndex(BaseIndex[KG]):
    ...
    def as_retriever(self, **kwargs: Any) -> BaseRetriever:
        from llama_index.indices.knowledge_graph.retrievers import (
            KGRetrieverMode,
            KGTableRetriever,
        )

        if len(self.index_struct.embedding_dict) > 0 and "retriever_mode" not in kwargs:
            kwargs["retriever_mode"] = KGRetrieverMode.HYBRID

        return KGTableRetriever(self, **kwargs)
    ...

This code shows that the LlamaIndex framework can be used to build a knowledge graph and query it using keywords or embeddings or both. The Language Server Protocol (LSP) can be used to provide the query string to the KGTableRetriever object.

You can find more details about the KnowledgeGraphIndex class and its methods in the LlamaIndex codebase at the following links:

• llama_index/indices/knowledge_graph/retrievers.py
• llama_index/query_engine/init.py
• docs/examples/query_engine/knowledge_graph_query_engine.ipynb
• llama_index/query_engine/knowledge_graph_query_engine.py
• llama_index/indices/knowledge_graph/base.py

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

Best,
Dosu

Sources

• llama_index/indices/knowledge_graph/retrievers.py
• llama_index/query_engine/init.py
• docs/examples/query_engine/knowledge_graph_query_engine.ipynb
• llama_index/query_engine/knowledge_graph_query_engine.py
• llama_index/indices/knowledge_graph/base.py
• tests/indices/knowledge_graph/test_base.py

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