Langchain tools or custom tools not working for multiple paramaters. #1141
Replies: 6 comments
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So There is same issue which is in langchain repo here . The temporary resolve they found is using from /usr/local/lib/python3.10/dist-packages/crewai/agent.py in ImportError: cannot import name 'InstanceOf' from 'pydantic' (/usr/local/lib/python3.10/dist-packages/pydantic/init.cpython-310-x86_64-linux-gnu.so) NOTE: If your import is failing due to a missing package, you can To view examples of installing some common dependencies, click the
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For what its worth, I've been getting around it by having all my functions have a single parameter payload and that payload being a dictionary you can pass multiple arguments to. Here's how I updated the search_internet tool they provided: class SearchTools():
@tool("Search the internet")
def search_internet(payload):
"""Useful to search the internet about a given topic and return relevant results
:param payload: str, a string representation of dictionary containing the following keys:
query: str, the query to search for
image_count: int, the number of top results to return
example payload:
{
"query": "cat",
"result_count": 4
}
"""
url = "https://google.serper.dev/search"
query = json.loads(payload)['query']
top_result_to_return = json.loads(payload)['result_count']
search_payload = json.dumps({"q": query})
headers = {
'X-API-KEY': os.environ['SERPER_API_KEY'],
'content-type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=search_payload)
# check if there is an organic key
if 'organic' not in response.json():
return "Sorry, I couldn't find anything about that, there could be an error with you serper api key."
else:
results = response.json()['organic']
stirng = []
for result in results[:top_result_to_return]:
try:
stirng.append('\n'.join([
f"Title: {result['title']}", f"Link: {result['link']}",
f"Snippet: {result['snippet']}", "\n-----------------"
]))
except KeyError:
next
return '\n'.join(stirng)Example of agent using it: .Do I need to use a tool? Yes Title: Armstrong's Doping Downfall - Ethics Unwrapped Title: Lance Armstrong admits doping in Oprah Winfrey interview |
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Thank you so much.It is working for me. But will It work for inbuilt langchain tools? Because for langchain tools we cannot specify the payload right |
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hey folks, I def want to make sure multiple input tools work! So I'll do some digging into this one |
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Hi @joaomdmoura and @Haripritamreddy, I was also struggling with this, so did a little bit of investigation. Here's a few changes that allowed me to get it to work locally: https://github.com/ap-inflection/crewAI/pull/1/files It's very OpenAI-centric since that's what were using, but maybe a starting point for other models? Quite new to the repo (but really enjoying it!), happy to open a PR if there's interest |
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I found a solution that works for me, by wrapping a function and using # this is a general-purpose utility that you can call to wrap any function as a tool
# with a Pydantic schema
class CrewAIStructuredTool:
def from_function(
self,
func,
name,
description,
args_schema: Type[BaseModel] = None,
return_direct: bool = False,
):
"""Create a structured tool from a function."""
parser = PydanticOutputParser(pydantic_object=args_schema)
description_with_schema = f"""{description}
Input should be a string representation of a dictionary containing the following keys:
{parser.get_format_instructions()}
"""
def parse_input_and_delegate(input: str) -> Any:
"""Parse the input and delegate to the function."""
try:
parsed = parser.invoke(input)
except Exception as e:
return f"Could not parse input: {str(e)}"
return func(parsed)
tool = Tool.from_function(
parse_input_and_delegate,
name,
description_with_schema,
args_schema=None,
return_direct=return_direct,
)
return tool
class GetPostInput(BaseModel):
"""Input for reading a post"""
blog_id: int = Field(description="blog_id to look up")
post_id: int = Field(description="post_id to look up")
def get_post(input=GetPostInput) -> Any:
"""Gets content and metadata about blog posts."""
blog_id = input.blog_id
post_id = input.post_id
token = os.environ["MY_ACCESS_TOKEN"]
client = MyApiClient(access_token=token)
try:
return client.get_post(blog_id, post_id)
except Exception as e:
return f"Error fetching post: {str(e)}"
# now create the tool
get_post_tool = CrewAIStructuredTool().from_function(
get_post,
"get_post",
"Tool that gets content and metadata about blog posts",
args_schema=GetPostInput,
) |
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Langchain tool such as write_tool takes file_path and text to save a file in such cases it returns an error .
Working Agent: Senior Research Analyst
Starting Task: Create a file named example.txt and save text example in it.
Btw I am using gemini-pro model.I tried single query functions such as get_stock_price with single query they are working. Am I missing something?
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