[Question]: How can I set output_parser locally?

[Hspix]

Question Validation

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Question

I use guardrails as output_parser like this:

from pydantic import BaseModel
import guardrails as gd
from llama_index.output_parsers.guardrails import GuardrailsOutputParser

class APydanticBaseModel(BaseModel):
        pass

guardrail_prompt_template="..."

guard = gd.Guard.from_pydantic(output_class=APydanticBaseModel, prompt=guardrail_prompt_template)
output_parser = GuardrailsOutputParser(guard)

Demonstrating by LLM args, output_parser serve for it. When using RetrieverQueryEngine, how can I set the parser locally to engine for extraction object predefined by APydanticBaseModel?

[dosubot[bot]]

🤖

Sure, I can guide you on how to set the parser locally to the RetrieverQueryEngine for an extraction object predefined by APydanticBaseModel in the LlamaIndex framework.

You should utilize the from_args class method of the RetrieverQueryEngine class. This method allows for the customization of the response_synthesizer, which is responsible for parsing and synthesizing the response based on the retrieved data and the query.

Assuming APydanticBaseModel is a subclass of BaseModel from llama_index.core.bridge.pydantic, and you want to use it as the output class for the response synthesizer, you would specify the output_cls parameter in the from_args method call. Here's how you can do it:

from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine
from your_application.models import APydanticBaseModel  # Assuming this is your defined Pydantic model
from llama_index.core.base.base_retriever import BaseRetriever  # Assuming you have a retriever implementation

# Initialize your retriever here (this is just a placeholder, replace with your actual retriever)
retriever = BaseRetriever()

# Create the RetrieverQueryEngine instance with APydanticBaseModel as the output class
retriever_query_engine = RetrieverQueryEngine.from_args(
    retriever=retriever,
    output_cls=APydanticBaseModel,  # This sets the parser locally to use APydanticBaseModel for parsing the response
)

# Now, retriever_query_engine is configured to use APydanticBaseModel for the response synthesis

This approach allows you to define how the data should be parsed and structured into a response object, leveraging the capabilities of Pydantic models for data validation and serialization. You can find more details in the source code of the RetrieverQueryEngine class.

The output_cls parameter in the from_args method of the RetrieverQueryEngine class is used to specify the class type for the output of the response synthesizer. This parameter allows for the customization of the output format by defining a Pydantic model (BaseModel) that the response synthesizer should use to structure its output. This is particularly useful for ensuring that the synthesized response adheres to a specific schema or format, facilitating easier integration and consistency across different parts of a system that utilizes the LlamaIndex framework.

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

-Dosu

Sources

• llama-index-core/llama_index/core/query_engine/retriever_query_engine.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.

[Hspix]

🤖

Sure, I can guide you on how to set the parser locally to the RetrieverQueryEngine for an extraction object predefined by APydanticBaseModel in the LlamaIndex framework.

You should utilize the from_args class method of the RetrieverQueryEngine class. This method allows for the customization of the response_synthesizer, which is responsible for parsing and synthesizing the response based on the retrieved data and the query.

Assuming APydanticBaseModel is a subclass of BaseModel from llama_index.core.bridge.pydantic, and you want to use it as the output class for the response synthesizer, you would specify the output_cls parameter in the from_args method call. Here's how you can do it:

from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine
from your_application.models import APydanticBaseModel  # Assuming this is your defined Pydantic model
from llama_index.core.base.base_retriever import BaseRetriever  # Assuming you have a retriever implementation

# Initialize your retriever here (this is just a placeholder, replace with your actual retriever)
retriever = BaseRetriever()

# Create the RetrieverQueryEngine instance with APydanticBaseModel as the output class
retriever_query_engine = RetrieverQueryEngine.from_args(
    retriever=retriever,
    output_cls=APydanticBaseModel,  # This sets the parser locally to use APydanticBaseModel for parsing the response
)

# Now, retriever_query_engine is configured to use APydanticBaseModel for the response synthesis

This approach allows you to define how the data should be parsed and structured into a response object, leveraging the capabilities of Pydantic models for data validation and serialization. You can find more details in the source code of the RetrieverQueryEngine class.

The output_cls parameter in the from_args method of the RetrieverQueryEngine class is used to specify the class type for the output of the response synthesizer. This parameter allows for the customization of the output format by defining a Pydantic model (BaseModel) that the response synthesizer should use to structure its output. This is particularly useful for ensuring that the synthesized response adheres to a specific schema or format, facilitating easier integration and consistency across different parts of a system that utilizes the LlamaIndex framework.

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

-Dosu

Sources

• llama-index-core/llama_index/core/query_engine/retriever_query_engine.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.

There is no need of GuardrailsOutputParser from your advice?

[dosubot[bot]]

🤖

Based on the context provided, it appears that you still need to use GuardrailsOutputParser when setting the parser locally to the RetrieverQueryEngine for an extraction object predefined by APydanticBaseModel in the LlamaIndex framework.

The GuardrailsOutputParser is a base class for parsing output from guardrails. It's explicitly exported from the llama_index.output_parsers.guardrails package, indicating its intended use as part of the framework's output parsing strategy. You can find more details in the source code.

Even though you're setting the output_cls parameter to APydanticBaseModel in the from_args method call, this doesn't replace the need for GuardrailsOutputParser. Instead, it sets the parser locally to use APydanticBaseModel for parsing the response. The GuardrailsOutputParser would still be used for its intended purpose within the framework.

If you have further questions or need more clarification, feel free to ask.

Sources

• llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/llama_index/output_parsers/guardrails/init.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.

[logan-markewich]

You can pass in the LLM as a kwarg

However, the code seems a little janky, its expecting the LLM object to have a __call__() function

Class JankyLLM:
  def __init__(self, llm):
    self.llm = llm

  def __call__(self, prompt):
    return str(self.llm.complete(prompt))

output_parser = GuardrailsOutputParser(guard, llm=JankyLLM(llm))

[Hspix]

🤖

Sure, I can guide you on how to set the parser locally to the RetrieverQueryEngine for an extraction object predefined by APydanticBaseModel in the LlamaIndex framework.

You should utilize the from_args class method of the RetrieverQueryEngine class. This method allows for the customization of the response_synthesizer, which is responsible for parsing and synthesizing the response based on the retrieved data and the query.

Assuming APydanticBaseModel is a subclass of BaseModel from llama_index.core.bridge.pydantic, and you want to use it as the output class for the response synthesizer, you would specify the output_cls parameter in the from_args method call. Here's how you can do it:

from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine
from your_application.models import APydanticBaseModel  # Assuming this is your defined Pydantic model
from llama_index.core.base.base_retriever import BaseRetriever  # Assuming you have a retriever implementation

# Initialize your retriever here (this is just a placeholder, replace with your actual retriever)
retriever = BaseRetriever()

# Create the RetrieverQueryEngine instance with APydanticBaseModel as the output class
retriever_query_engine = RetrieverQueryEngine.from_args(
    retriever=retriever,
    output_cls=APydanticBaseModel,  # This sets the parser locally to use APydanticBaseModel for parsing the response
)

# Now, retriever_query_engine is configured to use APydanticBaseModel for the response synthesis

This approach allows you to define how the data should be parsed and structured into a response object, leveraging the capabilities of Pydantic models for data validation and serialization. You can find more details in the source code of the RetrieverQueryEngine class.

The output_cls parameter in the from_args method of the RetrieverQueryEngine class is used to specify the class type for the output of the response synthesizer. This parameter allows for the customization of the output format by defining a Pydantic model (BaseModel) that the response synthesizer should use to structure its output. This is particularly useful for ensuring that the synthesized response adheres to a specific schema or format, facilitating easier integration and consistency across different parts of a system that utilizes the LlamaIndex framework.

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

-Dosu

Sources

• llama-index-core/llama_index/core/query_engine/retriever_query_engine.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.

The output_cls parameter is effective for all types response synthesizer in the retriever？

[dosubot[bot]]

Yes, the output_cls parameter in the from_args method of the RetrieverQueryEngine class applies to all types of response synthesizers in the retriever when using the LlamaIndex framework. This is because the output_cls parameter is passed to the get_response_synthesizer function, which is responsible for initializing the response synthesizer with the provided parameters, including output_cls.

Here is the relevant code snippet from the from_args method:

response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=llm,
            service_context=service_context,
            text_qa_template=text_qa_template,
            refine_template=refine_template,
            summary_template=summary_template,
            simple_template=simple_template,
            response_mode=response_mode,
            output_cls=output_cls,
            use_async=use_async,
            streaming=streaming,
        )

In this snippet, the output_cls parameter is used to define the class of the output response, allowing for customization of the response format across different types of response synthesizers.

The output_cls parameter in the from_args method of the RetrieverQueryEngine class in the LlamaIndex framework is used to specify the class type for the output of the response synthesizer. This parameter allows for the customization of the output format by defining a Pydantic model (BaseModel) that the response synthesizer should use to structure its output. This is particularly useful for ensuring that the synthesized response adheres to a specific schema or format, facilitating easier integration and consistency across different parts of a system that utilizes the LlamaIndex framework.

You can find more details in the llama-index-core/llama_index/core/query_engine/retriever_query_engine.py file in the LlamaIndex repository.

Sources

• llama-index-core/llama_index/core/query_engine/retriever_query_engine.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.

[Hspix]

Yes, the output_cls parameter in the from_args method of the RetrieverQueryEngine class applies to all types of response synthesizers in the retriever when using the LlamaIndex framework. This is because the output_cls parameter is passed to the get_response_synthesizer function, which is responsible for initializing the response synthesizer with the provided parameters, including output_cls.

Here is the relevant code snippet from the from_args method:

response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=llm,
            service_context=service_context,
            text_qa_template=text_qa_template,
            refine_template=refine_template,
            summary_template=summary_template,
            simple_template=simple_template,
            response_mode=response_mode,
            output_cls=output_cls,
            use_async=use_async,
            streaming=streaming,
        )

In this snippet, the output_cls parameter is used to define the class of the output response, allowing for customization of the response format across different types of response synthesizers.

The output_cls parameter in the from_args method of the RetrieverQueryEngine class in the LlamaIndex framework is used to specify the class type for the output of the response synthesizer. This parameter allows for the customization of the output format by defining a Pydantic model (BaseModel) that the response synthesizer should use to structure its output. This is particularly useful for ensuring that the synthesized response adheres to a specific schema or format, facilitating easier integration and consistency across different parts of a system that utilizes the LlamaIndex framework.

You can find more details in the llama-index-core/llama_index/core/query_engine/retriever_query_engine.py file in the LlamaIndex repository.

Sources

• llama-index-core/llama_index/core/query_engine/retriever_query_engine.py

About Dosu

However，it seems that only TreeSummarize synthesizer can accept output_cls. If pass it to the from_args method of the RetrieverQueryEngine class, it just get TreeSummarize synthesizer?

[dosubot[bot]]

Based on the provided context, the from_args method of the RetrieverQueryEngine class in the LlamaIndex framework does not restrict the output_cls parameter to only accept the TreeSummarize synthesizer. The method signature and implementation do not specify any restriction on the type of synthesizer that can be used with the output_cls parameter. The response_synthesizer parameter accepts an instance of Optional[BaseSynthesizer], and there is no code in the provided context that restricts it to only accept a TreeSummarize synthesizer. You can find this information in the source code.

The output_cls parameter in the from_args method is used to specify the class type for the output of the response synthesizer. This parameter allows for the customization of the response format by defining a Pydantic model that the response should conform to. It is passed to the get_response_synthesizer function, which likely uses it to structure the synthesized response according to the specified model, ensuring that the output is consistent with the expected schema. This is evident from the following line in the provided context:

response_synthesizer = response_synthesizer or get_response_synthesizer(
            llm=llm,
            service_context=service_context,
            text_qa_template=text_qa_template,
            refine_template=refine_template,
            summary_template=summary_template,
            simple_template=simple_template,
            response_mode=response_mode,
            output_cls=output_cls,
            use_async=use_async,
            streaming=streaming,
        )

Here, output_cls is one of the arguments passed to get_response_synthesizer, indicating its role in configuring the response synthesizer, which in turn affects the format and structure of the output generated by the RetrieverQueryEngine. You can find more details in the source code.

I hope this clarifies your question. If you have further questions or need more details, feel free to ask.

Source

Sources

• llama-index-legacy/llama_index/legacy/query_engine/retriever_query_engine.py
• llama-index-core/llama_index/core/query_engine/retriever_query_engine.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.