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Update using langchain tools docs (#1664)

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* Update example of how to use LangChain tools with correct syntax

* Use .env

* Add  Code back

---------

Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
This commit is contained in:
Ola Hungerford
2024-12-03 08:13:06 -08:00
committed by GitHub
parent 308a8dc925
commit 9e9b945a46
1 changed files with 33 additions and 20 deletions
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53
docs/concepts/langchain-tools.mdx
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@@ -7,32 +7,45 @@ icon: link
## Using LangChain Tools
<Info>
CrewAI seamlessly integrates with LangChain’s comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.
CrewAI seamlessly integrates with LangChain's comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.
</Info>
```python Code
import os
from crewai import Agent
from langchain.agents import Tool
from langchain.utilities import GoogleSerperAPIWrapper
from dotenv import load_dotenv
from crewai import Agent, Task, Crew
from crewai.tools import BaseTool
from pydantic import Field
from langchain_community.utilities import GoogleSerperAPIWrapper
# Setup API keys
os.environ["SERPER_API_KEY"] = "Your Key"
# Set up your SERPER_API_KEY key in an .env file, eg:
# SERPER_API_KEY=<your api key>
load_dotenv()
search = GoogleSerperAPIWrapper()
# Create and assign the search tool to an agent
serper_tool = Tool(
name="Intermediate Answer",
func=search.run,
description="Useful for search-based queries",
)
class SearchTool(BaseTool):
name: str = "Search"
description: str = "Useful for search-based queries. Use this to find current information about markets, companies, and trends."
search: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper)
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[serper_tool]
def _run(self, query: str) -> str:
"""Execute the search query and return results"""
try:
return self.search.run(query)
except Exception as e:
return f"Error performing search: {str(e)}"
# Create Agents
researcher = Agent(
role='Research Analyst',
goal='Gather current market data and trends',
backstory="""You are an expert research analyst with years of experience in
gathering market intelligence. You're known for your ability to find
relevant and up-to-date market information and present it in a clear,
actionable format.""",
tools=[SearchTool()],
verbose=True
)
# rest of the code ...
@@ -40,6 +53,6 @@ agent = Agent(
## Conclusion
Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms,
and the flexibility of tool arguments to optimize your agents' performance and capabilities.
Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms,
and the flexibility of tool arguments to optimize your agents' performance and capabilities.