[Question]: Is it possible to retrieve and get multiple nodes first and combine it, then passing it to the GPT to combine it.

[ricoyudog]

Question Validation

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

Question

Thank you very much first:
My input is have multi-lines, like A , B , C.
I want each line can get the information from the knowledge base with Top 2 nodes. The combine A, B, C line as the knowledge pass to the GPT to give a answer.
This is due to if I call GPT each time could take 30 second, but it seems like GPT can handle A, B ,C line at the same time which can speed up the whole processes. If I combine A,B,C and pass to the retriever, I am afraid all top 6 similarity could all relevant to one line.

[dosubot[bot]]

🤖

Hello,

Yes, it is possible to retrieve the top 2 nodes of information for each line of multi-line input (A, B, C), combine them, and then pass them all at once to the GPT for a faster response. This can be achieved by using a custom retriever that combines the VectorStoreIndex and the KnowledgeGraphIndex.

Here is a sample code snippet that demonstrates how you can achieve this:

from llama_index.retrievers import BaseRetriever, VectorIndexRetriever, KGTableRetriever
from typing import List
from llama_index import QueryBundle
from llama_index.schema import NodeWithScore

class CustomRetriever(BaseRetriever):
    """Custom retriever that performs both Vector search and Knowledge Graph search"""

    def __init__(
        self,
        vector_retriever: VectorIndexRetriever,
        kg_retriever: KGTableRetriever,
        mode: str = "OR",
    ) -> None:
        """Init params."""

        self._vector_retriever = vector_retriever
        self._kg_retriever = kg_retriever
        if mode not in ("AND", "OR"):
            raise ValueError("Invalid mode.")
        self._mode = mode

    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Retrieve nodes given query."""

        vector_nodes = self._vector_retriever.retrieve(query_bundle)
        kg_nodes = self._kg_retriever.retrieve(query_bundle)

        vector_ids = {n.node.node_id for n in vector_nodes}
        kg_ids = {n.node.node_id for n in kg_nodes}

        combined_dict = {n.node.node_id: n for n in vector_nodes}
        combined_dict.update({n.node.node_id: n for n in kg_nodes})

        if self._mode == "AND":
            retrieve_ids = vector_ids.intersection(kg_ids)
        else:
            retrieve_ids = vector_ids.union(kg_ids)

        retrieve_nodes = [combined_dict[rid] for rid in retrieve_ids]
        return retrieve_nodes

This custom retriever can be used to retrieve the top nodes for each line of multi-line input, combine them, and then pass them all at once to the GPT. This can potentially speed up the response time as it reduces the number of separate queries to the GPT.

Please note that I am unable to view the image you attached in your question. If you have any further questions or need additional clarification, feel free to ask.

Best,
Dosu

Sources

• docs/examples/index_structs/knowledge_graph/KnowledgeGraphIndex_vs_VectorStoreIndex_vs_CustomIndex_combined.ipynb
• llama_index/indices/vector_store/retrievers/retriever.py
• tests/indices/knowledge_graph/test_retrievers.py
• llama_index/indices/knowledge_graph/retrievers.py

---

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[ricoyudog]

🤖

Hello,

Yes, it is possible to retrieve the top 2 nodes of information for each line of multi-line input (A, B, C), combine them, and then pass them all at once to the GPT for a faster response. This can be achieved by using a custom retriever that combines the VectorStoreIndex and the KnowledgeGraphIndex.

Here is a sample code snippet that demonstrates how you can achieve this:

from llama_index.retrievers import BaseRetriever, VectorIndexRetriever, KGTableRetriever
from typing import List
from llama_index import QueryBundle
from llama_index.schema import NodeWithScore

class CustomRetriever(BaseRetriever):
    """Custom retriever that performs both Vector search and Knowledge Graph search"""

    def __init__(
        self,
        vector_retriever: VectorIndexRetriever,
        kg_retriever: KGTableRetriever,
        mode: str = "OR",
    ) -> None:
        """Init params."""

        self._vector_retriever = vector_retriever
        self._kg_retriever = kg_retriever
        if mode not in ("AND", "OR"):
            raise ValueError("Invalid mode.")
        self._mode = mode

    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Retrieve nodes given query."""

        vector_nodes = self._vector_retriever.retrieve(query_bundle)
        kg_nodes = self._kg_retriever.retrieve(query_bundle)

        vector_ids = {n.node.node_id for n in vector_nodes}
        kg_ids = {n.node.node_id for n in kg_nodes}

        combined_dict = {n.node.node_id: n for n in vector_nodes}
        combined_dict.update({n.node.node_id: n for n in kg_nodes})

        if self._mode == "AND":
            retrieve_ids = vector_ids.intersection(kg_ids)
        else:
            retrieve_ids = vector_ids.union(kg_ids)

        retrieve_nodes = [combined_dict[rid] for rid in retrieve_ids]
        return retrieve_nodes

This custom retriever can be used to retrieve the top nodes for each line of multi-line input, combine them, and then pass them all at once to the GPT. This can potentially speed up the response time as it reduces the number of separate queries to the GPT.

Please note that I am unable to view the image you attached in your question. If you have any further questions or need additional clarification, feel free to ask.

Best, Dosu

Sources

• docs/examples/index_structs/knowledge_graph/KnowledgeGraphIndex_vs_VectorStoreIndex_vs_CustomIndex_combined.ipynb
• llama_index/indices/vector_store/retrievers/retriever.py
• tests/indices/knowledge_graph/test_retrievers.py
• llama_index/indices/knowledge_graph/retrievers.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.

I am not sure how to extract and enter multi line into QueryBundle, it seems like this is only saving one str line of tda

[dosubot[bot]]

🤖

Hello,

Your CustomRetriever implementation seems to be on the right track. It's a good approach to combine the VectorIndexRetriever and KGTableRetriever to retrieve the top nodes for each line of multi-line input. This could indeed potentially speed up the response time by reducing the number of separate queries to the GPT.

However, there are a few improvements that could be made to further optimize your code:

1. Parallelization: Currently, the retrievals from VectorIndexRetriever and KGTableRetriever are done sequentially. If these operations are time-consuming, you could consider running them in parallel. Python's built-in concurrent.futures module can be used for this purpose. Here's an example of how you could implement this:

from concurrent.futures import ThreadPoolExecutor

class CustomRetriever(BaseRetriever):
    """Custom retriever that performs both Vector search and Knowledge Graph search"""

    def __init__(
        self,
        vector_retriever: VectorIndexRetriever,
        kg_retriever: KGTableRetriever,
        mode: str = "OR",
    ) -> None:
        """Init params."""

        self._vector_retriever = vector_retriever
        self._kg_retriever = kg_retriever
        if mode not in ("AND", "OR"):
            raise ValueError("Invalid mode.")
        self._mode = mode

    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Retrieve nodes given query."""

        with ThreadPoolExecutor(max_workers=2) as executor:
            future_vector = executor.submit(self._vector_retriever.retrieve, query_bundle)
            future_kg = executor.submit(self._kg_retriever.retrieve, query_bundle)

            vector_nodes = future_vector.result()
            kg_nodes = future_kg.result()

        # rest of the code remains the same

2. Caching: If the same or similar queries are made frequently, implementing a caching mechanism could significantly reduce the number of queries made to the GPT. You could store the results of previous queries and check the cache before making a new query.

3. Batch Processing: If the GPT supports it, you could process multiple lines of input at once instead of one by one. This could reduce the number of separate queries to the GPT.

4. Optimization of the Intersection and Union Operations: In the _retrieve method, you are using Python's built-in set operations to find the intersection and union of vector_ids and kg_ids. While these operations are quite efficient, they could potentially be optimized further if the id sets are large. One possible optimization could be to sort the id sets and use binary search for the intersection and union operations.

Please note that these are just suggestions and whether they will actually improve performance depends on various factors such as the specific use case, the size of the data, the capabilities of the GPT, etc. Always benchmark your code before and after making these changes to ensure they are having the desired effect.

As for your question about the average time taken by the VectorIndexRetriever and KGTableRetriever to retrieve nodes, I'm afraid I don't have that information. The performance of these retrievers can vary greatly depending on factors such as the size of the data, the complexity of the queries, the hardware being used, etc. Therefore, it's difficult to provide an average time without more specific information.

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

Best,
Dosu

Sources

• docs/examples/index_structs/knowledge_graph/KnowledgeGraphIndex_vs_VectorStoreIndex_vs_CustomIndex_combined.ipynb

---

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.

[logan-markewich]

@ricoyudog You would have to run the retriever for each line in your file

Then, pass all the retrieved nodes to a response synthesizer

https://gpt-index.readthedocs.io/en/stable/core_modules/query_modules/response_synthesizers/root.html

retriever = index.as_retriever(similarity_top_k=2)

nodes = []
for q in questions:
  nodes.extend(retriever.retrieve(q))
  
synthesizer = get_response_synthesizer()
response = synthesizer.synthesize("Final query?", nodes=nodes)