how to get output in this format using pydantic ?diameter namerequired: y/ntype: reques

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To get output in a specific format using Pydantic, you need to define a Pydantic model that matches the desired output structure. In your case, you might want to create a model that includes fields for "diameter name", "required", "type", and "description". Here's an example of how you might define such a model:



Once you've defined your model, you can use it to validate and parse data into that structure. For example, you might use it like this:



The message object will now be an instance of the DiameterMessage model, with all the data validated and parsed according to the model's definition. You can access the data in the object using dot notation, like message.diameter_name.

Please note that the above code is a general example of how to use Pydantic and may need to be adjusted to fit your specific needs.

For more information on using Pydantic with LlamaIndex, you can refer to these resources:

• Creating a guidance program to generate pydantic objects ()
• Using Fuyu-8B for Pydantic Structured Output ()
• Using MiniGPT-4 for Pydantic Structured Output ()

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@kapa.ai
erorr : Error: Chain must be a sequence of modules or module keys.. Please enter a valid number corresponding to the testcase.

llm= client

output_parser = PydanticOutputParser(DiameterMessage)
json_prompt_str = output_parser.format(prompt)
prompt_tmpl1 = PromptTemplate(json_prompt_str)
p = QueryPipeline(
chain=[prompt_tmpl1, llm ], verbose=True
)

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The error message you're seeing suggests that the chain argument you're passing to QueryPipeline is not a sequence of modules or module keys. In LlamaIndex, a module is a callable object that takes an input and produces an output. A module key is a string that identifies a module in a pipeline.

In your code, you're passing prompt_tmpl1 and llm to chain. prompt_tmpl1 is a PromptTemplate, which is a type of module in LlamaIndex. llm is assigned the value client, but without more context, it's not clear what client is. If client is not a module or a module key, this could be the source of your error.

Here's an example of how you might define a QueryPipeline with a chain of modules:



In this example, llm is an instance of OpenAI, which is a type of module in LlamaIndex. prompt_tmpl is a PromptTemplate, which is also a type of module. This code defines a QueryPipeline that first applies the PromptTemplate to the input, and then passes the result to the OpenAI module.

If you're still having trouble, I recommend checking the documentation for QueryPipeline and the modules you're using. You might also find it helpful to look at some examples of how to use QueryPipeline in the LlamaIndex documentation ().
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@kapa.ai
error: Error: Chain must be a sequence of modules or module keys.. Please enter a valid number corresponding to the testcase.

llm= OpenAI(api_key="sk-")

output_parser = PydanticOutputParser(DiameterMessage)
json_prompt_str = output_parser.format(prompt)
prompt_tmpl1 = PromptTemplate(json_prompt_str)
p = QueryPipeline(
chain=[prompt_tmpl1, llm ], verbose=True
)

output = p.run()
print(output)

Let me check the knowledge base for you. One moment! :books:

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The error message you're seeing suggests that the chain argument you're passing to QueryPipeline is not a sequence of modules or module keys. In LlamaIndex, a module is a callable object that takes an input and produces an output. A module key is a string that identifies a module in a pipeline.

In your code, you're passing prompt_tmpl1 and llm to chain. prompt_tmpl1 is a PromptTemplate, which is a type of module in LlamaIndex. llm is an instance of OpenAI, which is also a type of module. This should be a valid chain.

However, you're calling p.run() without passing any arguments. The run method of QueryPipeline requires at least one argument, which is the input to the pipeline. If you don't pass an argument to run, it might cause an error.

Here's an example of how you might call run:



In this example, query_str is the input to the pipeline. The exact arguments you need to pass to run will depend on the modules in your pipeline.

If you're still having trouble, I recommend checking the documentation for QueryPipeline and the modules you're using. You might also find it helpful to look at some examples of how to use QueryPipeline in the LlamaIndex documentation ().
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@kapa.ai No module named 'llama_index.agent.openai.utils'

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The error message "No module named 'llama_index.agent.openai.utils'" suggests that Python can't find a module with that name. This could be due to several reasons:

1. The module doesn't exist. It's possible that there's a mistake in the module name. In the knowledge sources provided, there's no mention of a module named 'llama_index.agent.openai.utils'. You might want to double-check the module name.

1. The module isn't installed. If the module is part of a package that you haven't installed, you'll need to install it. You can install packages using pip, like this: %pip install llama-index-llms-openai. However, please note that this command is just an example and may not install the specific module you're looking for.

1. The module isn't in your Python path. Python needs to know where to look for modules. If the module is in a directory that's not in your Python path, Python won't be able to find it. You can add directories to your Python path using the sys module, like this:

Replace "/path/to/directory" with the path to the directory that contains the module.

If you're still having trouble, I recommend checking the LlamaIndex documentation () or the source code on GitHub () to see if the module is mentioned there.
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