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Updated 3 weeks ago

I am using a router query engine. is there a way to to use the .query method and just ret

ccmosguy
·
@kapa.ai I am using a router query engine. is there a way to to use the .query method and just retrieve the nodes instead of going into the llm for synthesized response? i just want the results of the retriever after the reranker
k
c
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25 comments
kkapa.ai
Please stand by @cmosguy as I search the knowledge base. I normally take about 15 seconds or so. :wave:
kkapa.ai
----------------------
Based on the knowledge sources provided, I can answer your question about using a router query engine in LlamaIndex to retrieve nodes without synthesizing a response.

Yes, there is a way to use the retriever functionality of a router query engine without going into the LLM for a synthesized response. You can achieve this by using the retrieve method instead of the query method. Here's how you can do it:

  1. First, set up your router retriever:
Plain Text
retriever = RouterRetriever(
    selector,
    retriever_tools,
    llm=llm
)


  1. Then, instead of using the query method, you can use the retrieve method directly on the retriever:
Plain Text
query_bundle = QueryBundle("Your query here")
retrieved_nodes = retriever.retrieve(query_bundle)


This will give you the retrieved nodes after the routing and any reranking that might be applied, without synthesizing a response using an LLM.

If you're using a RetrieverQueryEngine, you can access the underlying retriever and use its retrieve method:

Plain Text
query_engine = RetrieverQueryEngine(retriever=your_retriever)
retrieved_nodes = query_engine.retriever.retrieve(query_bundle)


The retrieved_nodes will be a list of NodeWithScore objects, which contain the retrieved nodes along with their relevance scores.

For more detailed information on the RouterRetriever and its usage, you can refer to the LlamaIndex API Reference for RouterRetriever.
kkapa.ai
Remember that the exact implementation might vary depending on your specific setup and the version of LlamaIndex you're using. Always refer to the most up-to-date documentation for the version you're working with.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
ccmosguy
@kapa.ai how do I take nodes that fetched after the post processing and build a new llm synthesized response with a custom prompt I have reranked nodes here:

nodes = index_by_flattened_namespace_section.router_retriever.retrieve(query)

print(nodes)

reranked_nodes = reranker.postprocess_nodes(nodes, query_str=query)
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
----------------------
To build a new LLM-synthesized response with a custom prompt using the reranked nodes, you can follow these steps:

  1. First, create a custom prompt template:
Plain Text
from llama_index.core.prompts import PromptTemplate

custom_prompt_template = PromptTemplate(
    "Answer the following question based on the given context:\n"
    "Context: {context_str}\n"
    "Question: {query_str}\n"
    "Answer: "
)


  1. Then, create a SummaryIndex using the reranked nodes:
Plain Text
from llama_index.core import SummaryIndex

summary_index = SummaryIndex([n.node for n in reranked_nodes])


  1. Create a query engine from the SummaryIndex with your custom prompt:
Plain Text
query_engine = summary_index.as_query_engine(
    text_qa_template=custom_prompt_template
)


  1. Finally, use the query engine to generate a response:
Plain Text
response = query_engine.query(query)


Here's the complete code snippet:

Plain Text
from llama_index.core import SummaryIndex
from llama_index.core.prompts import PromptTemplate

# Assuming you already have the reranked nodes
# nodes = index_by_flattened_namespace_section.router_retriever.retrieve(query)
# reranked_nodes = reranker.postprocess_nodes(nodes, query_str=query)

# Create a custom prompt template
custom_prompt_template = PromptTemplate(
    "Answer the following question based on the given context:\n"
    "Context: {context_str}\n"
    "Question: {query_str}\n"
    "Answer: "
)

# Create a SummaryIndex from the reranked nodes
summary_index = SummaryIndex([n.node for n in reranked_nodes])

# Create a query engine with the custom prompt
query_engine = summary_index.as_query_engine(
    text_qa_template=custom_prompt_template
)

# Generate a response
response = query_engine.query(query)

print(response)
kkapa.ai
This approach allows you to use your reranked nodes to generate a new LLM-synthesized response with a custom prompt. The SummaryIndex is used here as a convenient way to create a new index from the reranked nodes, which can then be queried with your custom prompt.

For more information on customizing prompts and query engines, you can refer to the LlamaIndex documentation on Query Engine.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
ccmosguy
@kapa.ai i do not what to use a summary index, I just want to feed all the resulting reranked nodes into a custom prompt and synthesize a response
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
----------------------
I understand you want to directly use the reranked nodes with a custom prompt to synthesize a response without using a SummaryIndex. We can achieve this using a ResponseSynthesizer with a custom prompt. Here's how you can do it:

  1. First, create your custom prompt:
Plain Text
from llama_index.core.prompts import PromptTemplate

custom_prompt = PromptTemplate(
    "Answer the following question based on the given context:\n"
    "Context: {context_str}\n"
    "Question: {query_str}\n"
    "Answer: "
)


  1. Then, create a ResponseSynthesizer with your custom prompt:
Plain Text
from llama_index.core.response_synthesizers import get_response_synthesizer

response_synthesizer = get_response_synthesizer(
    response_mode="compact",
    text_qa_template=custom_prompt
)


  1. Now, you can use this synthesizer to generate a response from your reranked nodes:
Plain Text
from llama_index.core import QueryBundle

# Assuming you already have the reranked nodes
# reranked_nodes = reranker.postprocess_nodes(nodes, query_str=query)

query_bundle = QueryBundle(query)
response = response_synthesizer.synthesize(
    query_bundle,
    nodes=reranked_nodes
)

print(response)


Here's the complete code snippet:

```python
from llama_index.core.prompts import PromptTemplate
from llama_index.core.response_synthesizers import get_response_synthesizer
from llama_index.core import QueryBundle

Create your custom prompt

custom_prompt = PromptTemplate(
"Answer the following question based on the given context:\n"
"Context: {context_str}\n"
"Question: {query_str}\n"
"Answer: "
)

Create a ResponseSynthesizer with your custom prompt

response_synthesizer = get_response_synthesizer(
response_mode="compact",
text_qa_template=custom_prompt
)
kkapa.ai

Assuming you already have the reranked nodes

reranked_nodes = reranker.postprocess_nodes(nodes, query_str=query)


Create a QueryBundle

query_bundle = QueryBundle(query)

Synthesize a response

response = response_synthesizer.synthesize(
query_bundle,
nodes=reranked_nodes
)

print(response)
```

This approach directly uses the reranked nodes with your custom prompt to synthesize a response, without creating a new index. The ResponseSynthesizer takes care of combining the information from the nodes and generating a response based on your custom prompt.

For more information on response synthesizers and customizing them, you can refer to the LlamaIndex documentation on Response Synthesizers.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
ccmosguy
@kapa.ai what is the possible response_modes and what do they do?
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
----------------------
Based on the knowledge sources provided, I can explain the different response modes available in LlamaIndex and what they do. The response modes determine how the query engine processes and synthesizes information from retrieved nodes to generate a response.

According to the LlamaIndex documentation on Response Modes, the following response modes are supported:

  1. refine: This mode creates and refines an answer by sequentially going through each retrieved text chunk. It makes a separate LLM call for each node/retrieved chunk. It's good for generating more detailed answers.
  1. compact (default): Similar to refine, but it compacts (concatenates) the chunks beforehand, resulting in fewer LLM calls. It's more efficient than refine while still providing detailed answers.
  1. tree_summarize: This mode queries the LLM using a summary template for all concatenated chunks, then recursively summarizes the results until there's only one final answer. It's particularly useful for summarization purposes.
  1. simple_summarize: This mode truncates all text chunks to fit into a single LLM prompt. It's good for quick summarization but may lose detail due to truncation.
  1. no_text: This mode only runs the retriever to fetch the nodes that would have been sent to the LLM, without actually sending them. The retrieved nodes can be inspected by checking response.source_nodes.
  1. accumulate: This mode applies the query to each text chunk separately while accumulating the responses into an array. It returns a concatenated string of all responses. It's useful when you need to run the same query against each text chunk individually.
kkapa.ai
  1. compact_accumulate: This is similar to accumulate, but it "compacts" each LLM prompt similar to the compact mode before running the query against each text chunk.
Each of these modes offers different trade-offs between response quality, processing time, and LLM usage. The choice of mode depends on your specific use case, such as whether you need detailed answers, quick summaries, or want to minimize LLM calls.

For more detailed information on how each mode works and when to use them, you can refer to the LlamaIndex documentation on Response Synthesizer.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
ccmosguy
@kapa.ai response_mode is not a viable argument, this s the function info:

def get_response_synthesizer(
llm: LLM | None = None,
prompt_helper: PromptHelper | None = None,
text_qa_template: BasePromptTemplate | None = None,
refine_template: BasePromptTemplate | None = None,
summary_template: BasePromptTemplate | None = None,
simple_template: BasePromptTemplate | None = None,
response_mode: ResponseMode = ResponseMode.COMPACT,
callback_manager: CallbackManager | None = None,
use_async: bool = False,
streaming: bool = False,
structured_answer_filtering: bool = False,
output_cls: type[BaseModel] | None = None,
program_factory: ((BasePromptTemplate) -> BasePydanticProgram[Unknown]) | None = None,
verbose: bool = False
) -> BaseSynthesizer
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
----------------------
I apologize for the confusion in my previous response. You are correct, and I thank you for providing the function signature. Let me clarify the usage of response_mode in the get_response_synthesizer function based on the information you've shared.

The response_mode parameter in the get_response_synthesizer function is indeed a valid argument, and it's of type ResponseMode. Here's a more accurate explanation of how to use it and what options are available:

Plain Text
from llama_index.core import get_response_synthesizer
from llama_index.core.response_synthesizers import ResponseMode

response_synthesizer = get_response_synthesizer(
    response_mode=ResponseMode.COMPACT
)


The ResponseMode is an enumeration that defines different modes for synthesizing responses. Based on the LlamaIndex documentation on Response Modes, the available options are:

  1. ResponseMode.REFINE
  2. ResponseMode.COMPACT (default)
  3. ResponseMode.TREE_SUMMARIZE
  4. ResponseMode.SIMPLE_SUMMARIZE
  5. ResponseMode.NO_TEXT
  6. ResponseMode.CONTEXT_ONLY
  7. ResponseMode.ACCUMULATE
  8. ResponseMode.COMPACT_ACCUMULATE
Each of these modes determines how the response synthesizer processes the retrieved nodes to generate a response. For example:

Plain Text
# Using TREE_SUMMARIZE mode
response_synthesizer = get_response_synthesizer(
    response_mode=ResponseMode.TREE_SUMMARIZE
)

# Using REFINE mode
response_synthesizer = get_response_synthesizer(
    response_mode=ResponseMode.REFINE
)


You can choose the appropriate ResponseMode based on your specific needs for response generation. The behavior of each mode is as described in my previous response.
kkapa.ai
For more detailed information on how to use get_response_synthesizer and the available options, you can refer to the LlamaIndex API Reference for get_response_synthesizer.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
ccmosguy
@kapa.ai what does the QueryBundle do? why is this needed to synthesize?
kkapa.ai
Let me check the knowledge base for you again. One moment! :books:
kkapa.ai
----------------------
To answer your question about QueryBundle in LlamaIndex, I'll refer to the relevant information from the knowledge sources provided.

The QueryBundle is an important component in LlamaIndex that encapsulates the query information. It's needed for synthesizing responses because it provides a structured way to pass query-related data to various components of the system, including the response synthesizer.

According to the LlamaIndex API Reference, QueryBundle is used in several methods, including the synthesize method:

Plain Text
def synthesize(
    self,
    query_bundle: QueryBundle,
    nodes: List[NodeWithScore],
    additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
) -> RESPONSE_TYPE:


The QueryBundle typically contains the following information:

  1. The query string (the actual question or prompt)
  2. Potentially, custom embedding strings for the query
  3. Any additional metadata or context related to the query
Here's an example of creating a QueryBundle:

Plain Text
from llama_index.core import QueryBundle

query_bundle = QueryBundle(
    query_str="What did the author do growing up?",
    custom_embedding_strs=["The author grew up painting."],
)


The QueryBundle is needed for synthesis because:

  1. It provides the core question that needs to be answered.
  2. It can include additional context or custom embeddings that help in retrieving and processing relevant information.
  3. It allows for a standardized way of passing query information throughout the LlamaIndex pipeline, ensuring that all components (retriever, synthesizer, etc.) have access to the same query data.
kkapa.ai
When you use the synthesize method of a response synthesizer, you pass the QueryBundle along with the retrieved nodes:

Plain Text
response = response_synthesizer.synthesize(
    query_bundle,
    nodes=reranked_nodes
)


This allows the synthesizer to generate a response that's tailored to the specific query, taking into account any additional context or custom embeddings provided in the QueryBundle.

For more detailed information on how QueryBundle is used in different components of LlamaIndex, you can refer to the LlamaIndex documentation.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
WWhiteFang_Jr
Does passing llm= None not work in the query engine?
WWhiteFang_Jr
It used to work this way in a very early version @cmosguy

Do give this a try
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