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Updated 10 months ago

how to add args_schema in Tools while using Query tool?

@kapa.ai how to add args_schema in Tools while using Query tool?
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Please stand by @andysingal as I search the knowledge base. I normally take about 20 seconds or so. :wave:
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Based on the provided extracts, there isn't specific information on how to add args_schema while using a Query tool in LlamaIndex. The extracts provide examples of setting up various tools, but none of them explicitly mention args_schema.

However, in general, args_schema is used to define the structure of the arguments that a tool accepts. It's typically a dictionary that describes the properties of each argument, including its type and whether it's required.

Here's an example of how it might look based on the information provided in the extracts:

Plain Text
from llama_index.tools.base import ToolMetadata, QueryEngineTool
from llama_index.query_engine.sql import SQLQueryEngine

sql_query_engine = SQLQueryEngine()

tool_metadata = ToolMetadata(
    name="sql_tool",
    description="Useful for translating a natural language query into a SQL query over a table containing: city_stats, containing the population/country of each city",
    args_schema={
        "type": "object",
        "properties": {
            "query": {"type": "string"},
        },
        "required": ["query"],
    },
)

sql_tool = QueryEngineTool(
    query_engine=sql_query_engine,
    metadata=tool_metadata,
)


In this example, args_schema is defined in the ToolMetadata. It specifies that the tool requires a single argument named query of type string.

Please note that this is a hypothetical example and may not work as-is with LlamaIndex. For accurate information, you should refer to the LlamaIndex documentation or source code.
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@kapa.ai is LLMCompilerAgentPack pack deprecated?
Let me check the knowledge base for you. One moment! :books:
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Based on the provided extracts, there is no information indicating that the LLMCompilerAgentPack is deprecated. The extracts show examples of how to use the LLMCompilerAgentPack, including how to download it and set up an LLMCompilerAgentWorker. However, they do not provide any information about the pack being deprecated.

For the most accurate and up-to-date information, you should refer to the official LlamaIndex documentation or the GitHub repository.
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@kapa.ai
Plain Text
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-19-e985ac5f8bf1> in <cell line: 4>()
      2 from llama_index.core.llama_pack import download_llama_pack
      3 
----> 4 download_llama_pack("LLMCompilerAgentPack", "./llm_compiler_agent_pack")
      5 from llama_index.core.agent import AgentRunner
      6 from llm_compiler_agent_pack.step import LLMCompilerAgentWorker

4 frames
/content/llm_compiler_agent_pack/llama_index/packs/agents_llm_compiler/base.py in <module>
     10 from llama_index.llms.openai import OpenAI
     11 
---> 12 from .step import LLMCompilerAgentWorker
     13 
     14 

ModuleNotFoundError: No module named 'llama_index.packs.step'

---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
Let me check the knowledge base for you. One moment! :books:
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Based on the provided error message, it seems like the module llama_index.packs.step cannot be found. This could be due to several reasons such as the module does not exist, it's not in the correct directory, or it's not installed.

However, the provided extracts do not contain any information or code related to llama_index.packs.step or the error you're encountering. Therefore, I'm unable to provide a specific solution to this issue based on the given extracts.

For accurate information and assistance, you should refer to the official LlamaIndex documentation or the GitHub repository. If the issue persists, consider reaching out to the maintainers of the project or the community around it for more specific help.
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@Logan M is this llamaIndex pack not working?
@kapa.ai any good way to reduce openai limit ?
Plain Text
Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}
Let me check the knowledge base for you. One moment! :books:
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Based on the provided extracts, there are a few strategies you can use to manage your OpenAI usage and potentially avoid hitting your quota:

  1. Use a smaller model: If you're using a large model like gpt-3.5-turbo, you might be able to switch to a smaller model to reduce the number of tokens processed per request. However, this could impact the quality of the results.
  1. Limit the chat history: In the chat engine configuration, you can set a smaller limit to the chat history. This can help reduce the number of tokens used in each request. Here's an example from the extracts:
Plain Text
    from llama_index.memory import ChatMemoryBuffer

    memory = ChatMemoryBuffer.from_defaults(token_limit=3900)

    chat_engine = index.as_chat_engine(
        chat_mode="condense_plus_context",
        memory=memory,
        ...
    )
    


  1. Use separate API keys for separate LLM instances: If you have access to multiple API keys, you can distribute your usage across them to avoid hitting the quota on a single key. Here's an example from the extracts:
Plain Text
    from llama_index.llms.openai import OpenAI

    llm = OpenAI(model="text-davinci-003", api_key="BAD_KEY")
    


Remember that these strategies can only help manage your usage within your existing quota. If you consistently need more capacity than your current quota allows, you might need to consider upgrading your OpenAI plan.

For more information, refer to the OpenAI documentation ().
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@kapa.ai is this correct
Plain Text
from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
Let me check the knowledge base for you. One moment! :books:
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Yes, the import statement you provided is correct and is found in the provided extracts. Here it is for reference:

Plain Text
from llama_index.llms.llama_utils import (
    messages_to_prompt,
    completion_to_prompt,
)


This statement is importing the messages_to_prompt and completion_to_prompt functions from the llama_utils module in the llms package of llama_index. These functions are typically used to format prompts and completions when working with Llama language models.
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@kapa.ai is this correct
Plain Text
query_engine_tool = QueryEngineTool(
                query_engine=query_engine,
                args_schema=DocumentInput,
                metadata=ToolMetadata(
                    name=f"{filename[:-4]}",  # Construct name without extension
                    description=(
                        f"Provides information about the U.S. government financial report {filename[:-4]}"
                    ),
                ),
            )
Let me check the knowledge base for you. One moment! :books:
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Based on the provided extracts, the structure of your QueryEngineTool instantiation seems correct. However, the args_schema parameter is not explicitly mentioned in the extracts.

Here's a similar example from the extracts:

Plain Text
from llama_index.tools import QueryEngineTool
from llama_index.agent import ToolMetadata

query_engine_tool = QueryEngineTool(
    query_engine=vector_query_engine,
    metadata=ToolMetadata(
        name="pg_essay",
        description="Paul Graham essay on What I Worked On",
    ),
)


In this example, QueryEngineTool is instantiated with a query_engine and metadata. The metadata is an instance of ToolMetadata and includes a name and description.

Your code follows a similar pattern, but includes an additional args_schema parameter. This parameter is typically used to define the structure of the arguments that a tool accepts. If DocumentInput is a valid schema for the arguments that your query_engine accepts, then your code should be correct.

However, without more specific information about the args_schema parameter and the DocumentInput schema, I can't confirm with 100% certainty. For the most accurate information, you should refer to the LlamaIndex documentation or source code.
----------------------
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@Logan M can i use args_schema within tool?
nah, thats for langchain
Assume i have a schema
Plain Text
class DocumentInput(BaseModel):
    question: str = Field()
how to use with LlamaIndex when working with Agents and Tools. Thanks
what are you trying to do with the schema? Get a structured output?
yes sir, client has a schema and want to use within the agent/tool for getting agent answer in the form of id, name, question...
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