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()