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.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.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,
)args_schema is defined in the ToolMetadata. It specifies that the tool requires a single argument named query of type string.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.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.
---------------------------------------------------------------------------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.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.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'}}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. 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,
...
)
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="text-davinci-003", api_key="BAD_KEY")
from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
from llama_index.llms.llama_utils import (
messages_to_prompt,
completion_to_prompt,
)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.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]}"
),
),
)QueryEngineTool instantiation seems correct. However, the args_schema parameter is not explicitly mentioned in the extracts. 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",
),
)QueryEngineTool is instantiated with a query_engine and metadata. The metadata is an instance of ToolMetadata and includes a name and description.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.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.class DocumentInput(BaseModel):
question: str = Field()