RAG with Neo4j nodes and relationships as context for query generation

[gurveervirk]

Question Validation

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

Question

I am trying to query my Neo4j graph db using Mistral and nl2graphquery/nl2cypher. I ended up trying RetrieverQueryEngine with KnowledgeGraphRAGRetriever with Neo4jGraphStore as well as the KnowledgeGraphQueryEngine. I ran into an error that was mentioned in this issue and tried specifying graph_query_synthesis_prompt using the respective legacy template but the quality of the generated query was not up to the mark and the query did not execute correctly and didn't retrieve any response from Neo4j.

My doubt is regarding the current prompt template setup. Does this entire setup incorporate retrieving all the nodes and relationships and comparing them with the generated keywords and embeddings to generate the appropriate query? If not, why?

I have gone through the llama-index documentation and respective code in github and couldn't find a concrete answer.

[dosubot[bot]]

Hello @gurveervirk! 👋 I'm Dosu, a friendly bot here to assist you with bugs, answer questions, and guide you on becoming a contributor while you wait for a human maintainer to jump in. I'm diving into your issue and will get back to you with a comprehensive response shortly. Stay tuned!

[logan-markewich]

Does this entire setup incorporate retrieving all the nodes and relationships and comparing them with the generated keywords and embeddings to generate the appropriate query?

No. The KnowledgeGraphRAGQueryEngine will a) generate keywords/synoyms based on the query text alone, and optionally b) generate a text2cypher statement, using some schema from the knowledge graph DB.

[dosubot[bot]]

It appears there was a misunderstanding in my instructions, as I haven't executed any actions based on the user query. Therefore, I don't have specific outputs to synthesize a response from. However, based on the conversation and the information provided by the maintainer, I can offer a synthesized response to guide you further.

---

Hello @gurveervirk,

Based on your query and the clarification provided by Logan, it seems the current setup with the KnowledgeGraphRAGQueryEngine does not automatically incorporate the process of retrieving all nodes and relationships from your Neo4j graph database to compare them with generated keywords and embeddings for query generation. Instead, the engine primarily focuses on:

1. Generating keywords and synonyms based on the input query text alone.
2. Optionally, it can generate a text-to-Cypher statement using some schema information from the knowledge graph database.

This means the engine does not directly compare the entire graph's nodes and relationships against the generated keywords and embeddings to craft the appropriate query. This could be why you're experiencing issues with the quality of the generated query and its execution.

For improving the quality of your queries and ensuring they execute correctly, you might consider manually refining the generated Cypher queries or exploring additional tools and methods for better integrating your Neo4j graph's schema and data into the query generation process.

If you have further questions or need more detailed assistance, feel free to ask. The community and maintainers are here to help!

Best, Dosu

#### About Dosu

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[gurveervirk]

Thanks for the responses!

Currently, the prompt might include a keyword that does exist in the Neo4j either as a node or a relationship, but the representation might not be same, specifically in terms of case (lowercase, uppercase etc) or maybe those keywords are classes or functions in programming languages, which would not be captured appropriately.

I have encountered the above problem in my initial testing which is leading to a poor quality of response. Is it possible to incorporate comparison of the existing nodes and relationships in the graph with the prompt, which might narrow down the possible queries and improve response quality?

I can provide an example of the problem I am encountering as a colab notebook, as a reference.

[kuguadawang12138]

I feel that there is something wrong with this search engine itself, especially for Neo4j.I've also tried to connect with KG-RAG and all the returns are Empty Response.

[logan-markewich]

The llm generates keywords. If no keywords match an triplets (like exactly match) then nothing gets returned.

More than happy to have a pr if something is wrong, but if you stepped through the code in a debugger, you'd see what is happening

[gurveervirk]

Yeah, I tested that in colab and found that only after a more context or domain oriented prompt template was created, that the llm was able to generate at least 1 keyword that I actually wanted.

I'll try to compare the nodes and relationships retrieved from Neo4j with the question and then find the keywords and see if that works better.

The llm generates keywords. If no keywords match an triplets (like exactly match) then nothing gets returned.

More than happy to have a pr if something is wrong, but if you stepped through the code in a debugger, you'd see what is happening

[gurveervirk]

I have tested the comparison with an embedding model for simple single-part question, and it works pretty well. However, I also want to provide the documents as a context during retrieval and response.

How do I provide the documents and the previously generated Neo4j graph_store as input to the KnowledgeGraphIndex? Is there a way to automatically rehash the documents to the values in the graph_store?

Yeah, I tested that in colab and found that only after a more context or domain oriented prompt template was created, that the llm was able to generate at least 1 keyword that I actually wanted.

I'll try to compare the nodes and relationships retrieved from Neo4j with the question and then find the keywords and see if that works better.

The llm generates keywords. If no keywords match an triplets (like exactly match) then nothing gets returned. More than happy to have a pr if something is wrong, but if you stepped through the code in a debugger, you'd see what is happening

[kuguadawang12138]

New to llama_index. @gurveervirk Can I see how you tested it？Thank you so much.

[gurveervirk]

@kuguadawang12138 This is the colab link for my initial testing with an embed model.

[gurveervirk]

@dosu-bot How do I reinitialize a knowledgegraphindex using my pre-built neo4j graph_store and my documents? I want to convert the index to a query_engine and use the documents for querying also. And does the retrieverqueryengine use the nodes and relationships in any manner?

[dosubot[bot]]

To reinitialize a KnowledgeGraphIndex using your pre-built Neo4j graph_store and documents, and to integrate documents into the querying process with the KnowledgeGraphIndex, you'll need to follow a specific approach since the direct support for Neo4j or automatic document to graph_store value matching isn't explicitly provided in the base implementation.

1. Reinitializing KnowledgeGraphIndex with Neo4j graph_store: The initialization of KnowledgeGraphIndex doesn't directly reference Neo4j. It requires a GraphStore instance, which in your case, should be an implementation that connects to and interacts with your Neo4j database. You would need to create a custom class that extends GraphStore and implements the necessary methods to interact with Neo4j, fetching nodes and relationships based on the queries generated from your documents.

2. Matching Document Contents to Graph_Store Values: The process involves extracting keywords or entities from your documents and then querying your Neo4j graph_store to find matching nodes and relationships. This could be achieved by implementing a method that:

   • Extracts entities or keywords from the document.
   • Uses these entities or keywords to form a query against your Neo4j graph_store.
   • Retrieves matching nodes and relationships from Neo4j based on this query.

3. Utilizing Nodes and Relationships in the Querying Process: The KnowledgeGraphIndex and its retrieval process can be adapted to incorporate nodes and relationships from your Neo4j graph_store by:

   • Extending the retrieval process to include a step where the extracted keywords or entities from the documents are used to query the Neo4j graph_store.
   • Using the results (nodes and relationships) to enrich the querying process, potentially improving the relevance and accuracy of the retrieved information.

Here's a conceptual approach to integrating documents and Neo4j graph_store:

class CustomNeo4jGraphStore(GraphStore):
    def __init__(self, neo4j_connection_details):
        # Initialize connection to Neo4j
        pass

    def query_graph_store(self, entities):
        # Implement querying of Neo4j based on entities extracted from documents
        pass

# Usage
neo4j_graph_store = CustomNeo4jGraphStore(neo4j_connection_details)
knowledge_graph_index = KnowledgeGraphIndex(graph_store=neo4j_graph_store)

This approach requires a good understanding of your Neo4j schema and how you wish to match document contents with graph_store values. Implementing these steps will allow you to reinitialize the KnowledgeGraphIndex with your Neo4j graph_store and use documents for querying, potentially improving the quality of the query results by leveraging the rich relationships and nodes within your Neo4j database.

Sources

• llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py
• llama-index-legacy/llama_index/legacy/indices/knowledge_graph/base.py
• llama-index-legacy/llama_index/legacy/indices/knowledge_graph/retrievers.py

#### About Dosu

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.

[gurveervirk]

@dosu-bot I am able to create a KGI using the following which only uses my Neo4j graph store which was built using some documents:

graph_store = Neo4jGraphStore( username=username, password=password, url=url, database=database, ) storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex([], storage_context=storage_context, graph_store=graph_store)

However, I also want to pass the documents to it in place of [], without it recreating the graph. Meaning, I want the index to use my graph as well as the docs that were used to create the graph as context for the llm during querying, somewhat similar to how a KGI, just after being created using documents, can be converted to a query_engine using as_query_engine, but at a later time.

[dosubot[bot]]

To integrate your pre-built Neo4j graph_store with documents for enhanced querying context in the KnowledgeGraphIndex (KGI), you'll need to extend and adapt the existing classes to handle both graph data and document context. Here's a streamlined approach:

1. Extend StorageContext to Include a Document Store: Modify the StorageContext class to manage a document store alongside the graph store. This allows for storing and retrieving document data in addition to graph data.

2. Modify the KnowledgeGraphQueryEngine: Adapt the _retrieve method in the KnowledgeGraphQueryEngine to retrieve relevant documents based on the query, in addition to the graph data. This involves querying the document store for documents related to the nodes or edges found in the graph query response.

3. Incorporate Document Context in NodeWithScore: Include information from the retrieved documents in the TextNode metadata or text passed to NodeWithScore. This enables the language model to use context from both the graph and the original documents when generating responses.

4. Update the Prompt Template: Adjust the prompt template to include placeholders for document information, enriching the prompt sent to the language model with context from both the graph and the original documents.

Here's a conceptual code snippet for modifying the _retrieve method:

def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
    # Existing graph store query and retrieval
    graph_store_query = self.generate_query(query_bundle.query_str)
    graph_store_response = self.graph_store.query(query=graph_store_query)

    # Retrieve related documents
    related_documents = self._storage_context.document_store.retrieve_related_documents(graph_store_response)

    # Format retrieved graph context with document information
    retrieved_graph_context = self._graph_response_answer_prompt.format(
        query_str=query_bundle.query_str,
        kg_query_str=graph_store_query,
        kg_response_str=graph_store_response,
        document_context=related_documents,
    )

    node = NodeWithScore(
        node=TextNode(
            text=retrieved_graph_context,
            metadata={
                "related_documents": related_documents,
            },
        ),
        score=1.0,
    )
    return [node]

This method allows you to leverage the structured information from your Neo4j graph and the context from the original documents, enhancing the context available for querying without needing to rebuild the graph.

Remember, this is a high-level guide, and you may need to adjust the implementation details based on your specific requirements and the structure of your documents.

Sources

• llama-index-core/llama_index/core/query_engine/knowledge_graph_query_engine.py
• llama-index-legacy/llama_index/legacy/storage/storage_context.py
• llama-index-legacy/llama_index/legacy/indices/knowledge_graph/base.py

#### About Dosu

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.

[gurveervirk]

I was going through the github code for KGTableRetriever and have a doubt in this code:

def _retrieve(
        self,
        query_bundle: QueryBundle,
    ) -> List[NodeWithScore]:
        """Get nodes for response."""
        node_visited = set()
        keywords = self._get_keywords(query_bundle.query_str)
        if self._verbose:
            print_text(f"Extracted keywords: {keywords}\n", color="green")
        rel_texts = []
        cur_rel_map = {}
        chunk_indices_count: Dict[str, int] = defaultdict(int)
        if self._retriever_mode != KGRetrieverMode.EMBEDDING:
            for keyword in keywords:
                subjs = {keyword}
                node_ids = self._index_struct.search_node_by_keyword(keyword)

I believe this is where the keywords are being generated by the llm. Why is this being done? Aren't embeddings enough (assuming embeddings are being used)?

After testing with 2 different (domain) graphs, this keyword generation does little good, more often than not leads the model down the wrong sub-graph. It also wastes time while querying.

If embeddings are not being used and no documents and docstore is being used for KGI creation (like at a later time), then only this keyword generation is being used for responses (which is like shooting in the dark).

@dosu-bot, @logan-markewich Is there a way to create embeddings from the graph_store during initialization of KGI or KnowledgeGraphQueryEngine and use those for response generation in some way (not using load_index_from_storage)?

The llm generates keywords. If no keywords match an triplets (like exactly match) then nothing gets returned.

More than happy to have a pr if something is wrong, but if you stepped through the code in a debugger, you'd see what is happening

[dosubot[bot]]

To address your concerns about using keywords versus embeddings for query generation in the context of the KGTableRetriever and improving the query process with your Neo4j graph_store, let's dive into the specifics.

The current implementation relies on keyword extraction as a primary method for generating queries. This approach, while straightforward, may not always capture the nuanced relationships or the semantic context within your graph, especially when dealing with complex domains or when exact keyword matches are not present. This is why you've observed limitations in the effectiveness of this method for your use case.

Embeddings offer a more nuanced way to understand and match queries with graph data by capturing semantic similarities rather than relying on exact keyword matches. To integrate embeddings into your KnowledgeGraphIndex (KGI) or KnowledgeGraphQueryEngine, you would need to:

1. Generate Embeddings for Your Graph Data: Use a suitable model to create embeddings for nodes and relationships in your Neo4j graph. This could be done as a preprocessing step.

2. Store and Retrieve Embeddings: Modify your storage and retrieval mechanisms to handle embeddings. This means adjusting your GraphStore implementation to associate each node and relationship with its embedding and to enable efficient similarity searches based on these embeddings.

3. Adapt Query Processing: Instead of converting queries into keywords, convert them into embeddings using the same or a compatible model. Then, use these query embeddings to perform similarity searches against your graph embeddings to identify relevant nodes and relationships.

4. Enhance Response Generation: Use the graph elements identified through embedding similarity as context for generating responses. This could involve synthesizing responses directly or using them to inform a language model.

This approach requires significant modifications to the existing system but could lead to more accurate and contextually relevant query responses. It leverages the semantic understanding capabilities of modern NLP models to improve the interaction between your queries and the graph data.

Regarding your question about creating embeddings during the initialization of KGI or KnowledgeGraphQueryEngine, this would involve integrating an embedding generation step into the initialization process, ensuring that each node and relationship in your graph is associated with an embedding from the outset. This embedding data would then be used throughout the query and response generation process to enhance the relevance and accuracy of the system's outputs.

Implementing these changes would provide a more robust and semantically aware querying mechanism, potentially overcoming the limitations you've encountered with keyword-based querying.

Sources

• llama-index-core/llama_index/core/query_engine/knowledge_graph_query_engine.py
• llama-index-core/llama_index/core/indices/keyword_table/retrievers.py

#### About Dosu

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.

[gurveervirk]

@dosu-bot How do I test a branch of a fork of this repo for changes?

[dosubot[bot]]

To test a branch of a fork of this repository for changes, follow these steps:

1. Clone the Forked Repository: Use git clone with the URL of the forked repository to clone it to your local machine. For example:

   git clone https://github.com/username/repository-name.git

   Replace https://github.com/username/repository-name.git with the actual URL of the forked repository.

2. Switch to the Desired Branch: Navigate into the cloned repository's directory and switch to the branch you want to test using git checkout branch-name, replacing branch-name with the name of the branch.

3. Test the Changes Locally: Depending on the project, this could involve running the application, executing a test suite, or performing manual testing. Use appropriate commands based on the project's documentation to test the changes.

4. Evaluate the Modifications: After testing, review the changes based on your project's criteria to decide on their integration.

This process allows you to work with a forked repository and test its changes locally. If you encounter any issues or need further assistance, feel free to ask.

Sources

• llama-index-legacy/llama_index/legacy/readers/github_readers/github_api_client.py

#### About Dosu

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.