Probably the most boring PR to review ;)
Individual commits might be easier to digest
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Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- Description: pydantic's `ModelField.type_` only exposes the native
data type but not complex type hints like `List`. Thus, generating a
Tool with `from_function` through function signature produces incorrect
argument schemas (e.g., `str` instead of `List[str]`)
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @hinthornw
- Twitter handle: `mapped`
All the unittest (with an additional one in this PR) passed, though I
didn't try integration tests...
Fixes#5456
This PR removes the `callbacks` argument from a tool's schema when
creating a `Tool` or `StructuredTool` with the `from_function` method
and `infer_schema` is set to `True`. The `callbacks` argument is now
removed in the `create_schema_from_function` and `_get_filtered_args`
methods. As suggested by @vowelparrot, this fix provides a
straightforward solution that minimally affects the existing
implementation.
A test was added to verify that this change enables the expected use of
`Tool` and `StructuredTool` when using a `CallbackManager` and inferring
the tool's schema.
- @hwchase17
Throwing ToolException when incorrect arguments are passed to tools so
that that agent can course correct them.
# Incorrect argument count handling
I was facing an error where the agent passed incorrect arguments to
tools. As per the discussions going around, I started throwing
ToolException to allow the model to course correct.
## Before submitting
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## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
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Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
VectorStores / Retrievers / Memory
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Add ToolException that a tool can throw
This is an optional exception that tool throws when execution error
occurs.
When this exception is thrown, the agent will not stop working,but will
handle the exception according to the handle_tool_error variable of the
tool,and the processing result will be returned to the agent as
observation,and printed in pink on the console.It can be used like this:
```python
from langchain.schema import ToolException
from langchain import LLMMathChain, SerpAPIWrapper, OpenAI
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
class Error_tool:
def run(self, s: str):
raise ToolException('The current search tool is not available.')
def handle_tool_error(error) -> str:
return "The following errors occurred during tool execution:"+str(error)
search_tool1 = Error_tool()
search_tool2 = SerpAPIWrapper()
tools = [
Tool.from_function(
func=search_tool1.run,
name="Search_tool1",
description="useful for when you need to answer questions about current events.You should give priority to using it.",
handle_tool_error=handle_tool_error,
),
Tool.from_function(
func=search_tool2.run,
name="Search_tool2",
description="useful for when you need to answer questions about current events",
return_direct=True,
)
]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,
handle_tool_errors=handle_tool_error)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
```
![image](https://github.com/hwchase17/langchain/assets/32786500/51930410-b26e-4f85-a1e1-e6a6fb450ada)
## Who can review?
- @vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>