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Merge branch 'main' into bugfix/fix-description-on-file_read_tool

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This commit is contained in:
João Moura
2025-01-19 02:08:27 -03:00
committed by GitHub
parent fe2a5abf8d fe3f0bda81
commit fb4423c91d
45 changed files with 1089 additions and 312 deletions
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2
src/crewai_tools/__init__.py
View File

@@ -43,6 +43,8 @@ from .tools import (
SerplyScholarSearchTool,
SerplyWebpageToMarkdownTool,
SerplyWebSearchTool,
SnowflakeConfig,
SnowflakeSearchTool,
SpiderTool,
TXTSearchTool,
VisionTool,

5
src/crewai_tools/tools/__init__.py
View File

@@ -54,6 +54,11 @@ from .serply_api_tool.serply_news_search_tool import SerplyNewsSearchTool
from .serply_api_tool.serply_scholar_search_tool import SerplyScholarSearchTool
from .serply_api_tool.serply_web_search_tool import SerplyWebSearchTool
from .serply_api_tool.serply_webpage_to_markdown_tool import SerplyWebpageToMarkdownTool
from .snowflake_search_tool import (
SnowflakeConfig,
SnowflakeSearchTool,
SnowflakeSearchToolInput,
)
from .spider_tool.spider_tool import SpiderTool
from .txt_search_tool.txt_search_tool import TXTSearchTool
from .vision_tool.vision_tool import VisionTool

14
src/crewai_tools/tools/browserbase_load_tool/browserbase_load_tool.py
View File

@@ -1,8 +1,8 @@
import os
from typing import Any, Optional, Type
from pydantic import BaseModel, Field
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
class BrowserbaseLoadToolSchema(BaseModel):
@@ -11,12 +11,10 @@ class BrowserbaseLoadToolSchema(BaseModel):
class BrowserbaseLoadTool(BaseTool):
name: str = "Browserbase web load tool"
description: str = (
"Load webpages url in a headless browser using Browserbase and return the contents"
)
description: str = "Load webpages url in a headless browser using Browserbase and return the contents"
args_schema: Type[BaseModel] = BrowserbaseLoadToolSchema
api_key: Optional[str] = os.getenv('BROWSERBASE_API_KEY')
project_id: Optional[str] = os.getenv('BROWSERBASE_PROJECT_ID')
api_key: Optional[str] = os.getenv("BROWSERBASE_API_KEY")
project_id: Optional[str] = os.getenv("BROWSERBASE_PROJECT_ID")
text_content: Optional[bool] = False
session_id: Optional[str] = None
proxy: Optional[bool] = None
@@ -33,7 +31,9 @@ class BrowserbaseLoadTool(BaseTool):
):
super().__init__(**kwargs)
if not self.api_key:
raise EnvironmentError("BROWSERBASE_API_KEY environment variable is required for initialization")
raise EnvironmentError(
"BROWSERBASE_API_KEY environment variable is required for initialization"
)
try:
from browserbase import Browserbase # type: ignore
except ImportError:

18
src/crewai_tools/tools/code_interpreter_tool/code_interpreter_tool.py
View File

@@ -2,10 +2,10 @@ import importlib.util
import os
from typing import List, Optional, Type
from docker import from_env as docker_from_env
from docker.models.containers import Container
from docker.errors import ImageNotFound, NotFound
from crewai.tools import BaseTool
from docker import from_env as docker_from_env
from docker.errors import ImageNotFound, NotFound
from docker.models.containers import Container
from pydantic import BaseModel, Field
@@ -30,7 +30,7 @@ class CodeInterpreterTool(BaseTool):
default_image_tag: str = "code-interpreter:latest"
code: Optional[str] = None
user_dockerfile_path: Optional[str] = None
user_docker_base_url: Optional[str] = None
user_docker_base_url: Optional[str] = None
unsafe_mode: bool = False
@staticmethod
@@ -43,7 +43,11 @@ class CodeInterpreterTool(BaseTool):
Verify if the Docker image is available. Optionally use a user-provided Dockerfile.
"""
client = docker_from_env() if self.user_docker_base_url == None else docker.DockerClient(base_url=self.user_docker_base_url)
client = (
docker_from_env()
if self.user_docker_base_url == None
else docker.DockerClient(base_url=self.user_docker_base_url)
)
try:
client.images.get(self.default_image_tag)
@@ -76,9 +80,7 @@ class CodeInterpreterTool(BaseTool):
else:
return self.run_code_in_docker(code, libraries_used)
def _install_libraries(
self, container: Container, libraries: List[str]
) -> None:
def _install_libraries(self, container: Container, libraries: List[str]) -> None:
"""
Install missing libraries in the Docker container
"""

2
src/crewai_tools/tools/directory_read_tool/directory_read_tool.py
View File

@@ -8,8 +8,6 @@ from pydantic import BaseModel, Field
class FixedDirectoryReadToolSchema(BaseModel):
"""Input for DirectoryReadTool."""
pass
class DirectoryReadToolSchema(FixedDirectoryReadToolSchema):
"""Input for DirectoryReadTool."""

6
src/crewai_tools/tools/file_read_tool/file_read_tool.py
View File

@@ -32,6 +32,7 @@ class FileReadTool(BaseTool):
>>> content = tool.run() # Reads /path/to/file.txt
>>> content = tool.run(file_path="/path/to/other.txt") # Reads other.txt
"""
name: str = "Read a file's content"
description: str = "A tool that reads the content of a file. To use this tool, provide a 'file_path' parameter with the path to the file you want to read."
args_schema: Type[BaseModel] = FileReadToolSchema
@@ -58,7 +59,7 @@ class FileReadTool(BaseTool):
file_path = kwargs.get("file_path", self.file_path)
if file_path is None:
return "Error: No file path provided. Please provide a file path either in the constructor or as an argument."
try:
with open(file_path, "r") as file:
return file.read()
@@ -67,5 +68,4 @@ class FileReadTool(BaseTool):
except PermissionError:
return f"Error: Permission denied when trying to read file: {file_path}"
except Exception as e:
return f"Error: Failed to read file {file_path}. {str(e)}"
return f"Error: Failed to read file {file_path}. {str(e)}"

4
src/crewai_tools/tools/file_writer_tool/file_writer_tool.py
View File

@@ -15,9 +15,7 @@ class FileWriterToolInput(BaseModel):
class FileWriterTool(BaseTool):
name: str = "File Writer Tool"
description: str = (
"A tool to write content to a specified file. Accepts filename, content, and optionally a directory path and overwrite flag as input."
)
description: str = "A tool to write content to a specified file. Accepts filename, content, and optionally a directory path and overwrite flag as input."
args_schema: Type[BaseModel] = FileWriterToolInput
def _run(self, **kwargs: Any) -> str:

1
src/crewai_tools/tools/firecrawl_crawl_website_tool/firecrawl_crawl_website_tool.py
View File

@@ -72,4 +72,3 @@ except ImportError:
"""
When this tool is not used, then exception can be ignored.
"""
pass

1
src/crewai_tools/tools/firecrawl_scrape_website_tool/firecrawl_scrape_website_tool.py
View File

@@ -63,4 +63,3 @@ except ImportError:
"""
When this tool is not used, then exception can be ignored.
"""
pass

4
src/crewai_tools/tools/github_search_tool/github_search_tool.py
View File

@@ -27,9 +27,7 @@ class GithubSearchToolSchema(FixedGithubSearchToolSchema):
class GithubSearchTool(RagTool):
name: str = "Search a github repo's content"
description: str = (
"A tool that can be used to semantic search a query from a github repo's content. This is not the GitHub API, but instead a tool that can provide semantic search capabilities."
)
description: str = "A tool that can be used to semantic search a query from a github repo's content. This is not the GitHub API, but instead a tool that can provide semantic search capabilities."
summarize: bool = False
gh_token: str
args_schema: Type[BaseModel] = GithubSearchToolSchema

4
src/crewai_tools/tools/jina_scrape_website_tool/jina_scrape_website_tool.py
View File

@@ -13,9 +13,7 @@ class JinaScrapeWebsiteToolInput(BaseModel):
class JinaScrapeWebsiteTool(BaseTool):
name: str = "JinaScrapeWebsiteTool"
description: str = (
"A tool that can be used to read a website content using Jina.ai reader and return markdown content."
)
description: str = "A tool that can be used to read a website content using Jina.ai reader and return markdown content."
args_schema: Type[BaseModel] = JinaScrapeWebsiteToolInput
website_url: Optional[str] = None
api_key: Optional[str] = None

16
src/crewai_tools/tools/linkup/linkup_search_tool.py
View File

@@ -2,6 +2,7 @@ from typing import Any
try:
from linkup import LinkupClient
LINKUP_AVAILABLE = True
except ImportError:
LINKUP_AVAILABLE = False
@@ -9,10 +10,13 @@ except ImportError:
from pydantic import PrivateAttr
class LinkupSearchTool:
name: str = "Linkup Search Tool"
description: str = "Performs an API call to Linkup to retrieve contextual information."
_client: LinkupClient = PrivateAttr() # type: ignore
description: str = (
"Performs an API call to Linkup to retrieve contextual information."
)
_client: LinkupClient = PrivateAttr() # type: ignore
def __init__(self, api_key: str):
"""
@@ -25,7 +29,9 @@ class LinkupSearchTool:
)
self._client = LinkupClient(api_key=api_key)
def _run(self, query: str, depth: str = "standard", output_type: str = "searchResults") -> dict:
def _run(
self, query: str, depth: str = "standard", output_type: str = "searchResults"
) -> dict:
"""
Executes a search using the Linkup API.
@@ -36,9 +42,7 @@ class LinkupSearchTool:
"""
try:
response = self._client.search(
query=query,
depth=depth,
output_type=output_type
query=query, depth=depth, output_type=output_type
)
results = [
{"name": result.name, "url": result.url, "content": result.content}

4
src/crewai_tools/tools/mysql_search_tool/mysql_search_tool.py
View File

@@ -17,9 +17,7 @@ class MySQLSearchToolSchema(BaseModel):
class MySQLSearchTool(RagTool):
name: str = "Search a database's table content"
description: str = (
"A tool that can be used to semantic search a query from a database table's content."
)
description: str = "A tool that can be used to semantic search a query from a database table's content."
args_schema: Type[BaseModel] = MySQLSearchToolSchema
db_uri: str = Field(..., description="Mandatory database URI")

26
src/crewai_tools/tools/patronus_eval_tool/example.py
View File

@@ -1,30 +1,24 @@
from crewai import Agent, Crew, Task
from patronus_eval_tool import (
PatronusEvalTool,
)
from patronus_local_evaluator_tool import (
PatronusLocalEvaluatorTool,
)
from patronus_predefined_criteria_eval_tool import (
PatronusPredefinedCriteriaEvalTool,
)
from patronus import Client, EvaluationResult
import random
from crewai import Agent, Crew, Task
from patronus import Client, EvaluationResult
from patronus_local_evaluator_tool import PatronusLocalEvaluatorTool
# Test the PatronusLocalEvaluatorTool where agent uses the local evaluator
client = Client()
# Example of an evaluator that returns a random pass/fail result
@client.register_local_evaluator("random_evaluator")
def random_evaluator(**kwargs):
score = random.random()
return EvaluationResult(
score_raw=score,
pass_=score >= 0.5,
explanation="example explanation" # Optional justification for LLM judges
score_raw=score,
pass_=score >= 0.5,
explanation="example explanation", # Optional justification for LLM judges
)
# 1. Uses PatronusEvalTool: agent can pick the best evaluator and criteria
# patronus_eval_tool = PatronusEvalTool()
@@ -35,7 +29,9 @@ def random_evaluator(**kwargs):
# 3. Uses PatronusLocalEvaluatorTool: agent uses user defined evaluator
patronus_eval_tool = PatronusLocalEvaluatorTool(
patronus_client=client, evaluator="random_evaluator", evaluated_model_gold_answer="example label"
patronus_client=client,
evaluator="random_evaluator",
evaluated_model_gold_answer="example label",
)
# Create a new agent

14
src/crewai_tools/tools/patronus_eval_tool/patronus_eval_tool.py
View File

@@ -1,8 +1,9 @@
import os
import json
import requests
import os
import warnings
from typing import Any, List, Dict, Optional
from typing import Any, Dict, List, Optional
import requests
from crewai.tools import BaseTool
@@ -19,7 +20,9 @@ class PatronusEvalTool(BaseTool):
self.evaluators = temp_evaluators
self.criteria = temp_criteria
self.description = self._generate_description()
warnings.warn("You are allowing the agent to select the best evaluator and criteria when you use the `PatronusEvalTool`. If this is not intended then please use `PatronusPredefinedCriteriaEvalTool` instead.")
warnings.warn(
"You are allowing the agent to select the best evaluator and criteria when you use the `PatronusEvalTool`. If this is not intended then please use `PatronusPredefinedCriteriaEvalTool` instead."
)
def _init_run(self):
evaluators_set = json.loads(
@@ -104,7 +107,6 @@ class PatronusEvalTool(BaseTool):
evaluated_model_retrieved_context: Optional[str],
evaluators: List[Dict[str, str]],
) -> Any:
# Assert correct format of evaluators
evals = []
for ev in evaluators:
@@ -136,4 +138,4 @@ class PatronusEvalTool(BaseTool):
f"Failed to evaluate model input and output. Response status code: {response.status_code}. Reason: {response.text}"
)
return response.json()
return response.json()

17
src/crewai_tools/tools/patronus_eval_tool/patronus_local_evaluator_tool.py
View File

@@ -1,7 +1,8 @@
from typing import Any, Type
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
from patronus import Client
from pydantic import BaseModel, Field
class FixedLocalEvaluatorToolSchema(BaseModel):
@@ -24,16 +25,20 @@ class PatronusLocalEvaluatorTool(BaseTool):
name: str = "Patronus Local Evaluator Tool"
evaluator: str = "The registered local evaluator"
evaluated_model_gold_answer: str = "The agent's gold answer"
description: str = (
"This tool is used to evaluate the model input and output using custom function evaluators."
)
description: str = "This tool is used to evaluate the model input and output using custom function evaluators."
client: Any = None
args_schema: Type[BaseModel] = FixedLocalEvaluatorToolSchema
class Config:
arbitrary_types_allowed = True
def __init__(self, patronus_client: Client, evaluator: str, evaluated_model_gold_answer: str, **kwargs: Any):
def __init__(
self,
patronus_client: Client,
evaluator: str,
evaluated_model_gold_answer: str,
**kwargs: Any,
):
super().__init__(**kwargs)
self.client = patronus_client
if evaluator:
@@ -79,7 +84,7 @@ class PatronusLocalEvaluatorTool(BaseTool):
if isinstance(evaluated_model_gold_answer, str)
else evaluated_model_gold_answer.get("description")
),
tags={}, # Optional metadata, supports arbitrary kv pairs
tags={}, # Optional metadata, supports arbitrary kv pairs
)
output = f"Evaluation result: {result.pass_}, Explanation: {result.explanation}"
return output

12
src/crewai_tools/tools/patronus_eval_tool/patronus_predefined_criteria_eval_tool.py
View File

@@ -1,7 +1,8 @@
import os
import json
import os
from typing import Any, Dict, List, Type
import requests
from typing import Any, List, Dict, Type
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
@@ -33,9 +34,7 @@ class PatronusPredefinedCriteriaEvalTool(BaseTool):
"""
name: str = "Call Patronus API tool for evaluation of model inputs and outputs"
description: str = (
"""This tool calls the Patronus Evaluation API that takes the following arguments:"""
)
description: str = """This tool calls the Patronus Evaluation API that takes the following arguments:"""
evaluate_url: str = "https://api.patronus.ai/v1/evaluate"
args_schema: Type[BaseModel] = FixedBaseToolSchema
evaluators: List[Dict[str, str]] = []
@@ -52,7 +51,6 @@ class PatronusPredefinedCriteriaEvalTool(BaseTool):
self,
**kwargs: Any,
) -> Any:
evaluated_model_input = kwargs.get("evaluated_model_input")
evaluated_model_output = kwargs.get("evaluated_model_output")
evaluated_model_retrieved_context = kwargs.get(
@@ -103,4 +101,4 @@ class PatronusPredefinedCriteriaEvalTool(BaseTool):
f"Failed to evaluate model input and output. Status code: {response.status_code}. Reason: {response.text}"
)
return response.json()
return response.json()

8
src/crewai_tools/tools/pdf_text_writing_tool/pdf_text_writing_tool.py
View File

@@ -1,7 +1,9 @@
from typing import Optional, Type
from pydantic import BaseModel, Field
from pypdf import PdfReader, PdfWriter, PageObject, ContentStream, NameObject, Font
from pathlib import Path
from typing import Optional, Type
from pydantic import BaseModel, Field
from pypdf import ContentStream, Font, NameObject, PageObject, PdfReader, PdfWriter
from crewai_tools.tools.rag.rag_tool import RagTool

4
src/crewai_tools/tools/pg_seach_tool/pg_search_tool.py
View File

@@ -17,9 +17,7 @@ class PGSearchToolSchema(BaseModel):
class PGSearchTool(RagTool):
name: str = "Search a database's table content"
description: str = (
"A tool that can be used to semantic search a query from a database table's content."
)
description: str = "A tool that can be used to semantic search a query from a database table's content."
args_schema: Type[BaseModel] = PGSearchToolSchema
db_uri: str = Field(..., description="Mandatory database URI")

2
src/crewai_tools/tools/scrape_element_from_website/scrape_element_from_website.py
View File

@@ -10,8 +10,6 @@ from pydantic import BaseModel, Field
class FixedScrapeElementFromWebsiteToolSchema(BaseModel):
"""Input for ScrapeElementFromWebsiteTool."""
pass
class ScrapeElementFromWebsiteToolSchema(FixedScrapeElementFromWebsiteToolSchema):
"""Input for ScrapeElementFromWebsiteTool."""

2
src/crewai_tools/tools/scrape_website_tool/scrape_website_tool.py
View File

@@ -11,8 +11,6 @@ from pydantic import BaseModel, Field
class FixedScrapeWebsiteToolSchema(BaseModel):
"""Input for ScrapeWebsiteTool."""
pass
class ScrapeWebsiteToolSchema(FixedScrapeWebsiteToolSchema):
"""Input for ScrapeWebsiteTool."""

34
src/crewai_tools/tools/scrapegraph_scrape_tool/scrapegraph_scrape_tool.py
View File

@@ -10,17 +10,14 @@ from scrapegraph_py.logger import sgai_logger
class ScrapegraphError(Exception):
"""Base exception for Scrapegraph-related errors"""
pass
class RateLimitError(ScrapegraphError):
"""Raised when API rate limits are exceeded"""
pass
class FixedScrapegraphScrapeToolSchema(BaseModel):
"""Input for ScrapegraphScrapeTool when website_url is fixed."""
pass
class ScrapegraphScrapeToolSchema(FixedScrapegraphScrapeToolSchema):
@@ -32,7 +29,7 @@ class ScrapegraphScrapeToolSchema(FixedScrapegraphScrapeToolSchema):
description="Prompt to guide the extraction of content",
)
@validator('website_url')
@validator("website_url")
def validate_url(cls, v):
"""Validate URL format"""
try:
@@ -41,13 +38,15 @@ class ScrapegraphScrapeToolSchema(FixedScrapegraphScrapeToolSchema):
raise ValueError
return v
except Exception:
raise ValueError("Invalid URL format. URL must include scheme (http/https) and domain")
raise ValueError(
"Invalid URL format. URL must include scheme (http/https) and domain"
)
class ScrapegraphScrapeTool(BaseTool):
"""
A tool that uses Scrapegraph AI to intelligently scrape website content.
Raises:
ValueError: If API key is missing or URL format is invalid
RateLimitError: If API rate limits are exceeded
@@ -55,7 +54,9 @@ class ScrapegraphScrapeTool(BaseTool):
"""
name: str = "Scrapegraph website scraper"
description: str = "A tool that uses Scrapegraph AI to intelligently scrape website content."
description: str = (
"A tool that uses Scrapegraph AI to intelligently scrape website content."
)
args_schema: Type[BaseModel] = ScrapegraphScrapeToolSchema
website_url: Optional[str] = None
user_prompt: Optional[str] = None
@@ -70,7 +71,7 @@ class ScrapegraphScrapeTool(BaseTool):
):
super().__init__(**kwargs)
self.api_key = api_key or os.getenv("SCRAPEGRAPH_API_KEY")
if not self.api_key:
raise ValueError("Scrapegraph API key is required")
@@ -79,7 +80,7 @@ class ScrapegraphScrapeTool(BaseTool):
self.website_url = website_url
self.description = f"A tool that uses Scrapegraph AI to intelligently scrape {website_url}'s content."
self.args_schema = FixedScrapegraphScrapeToolSchema
if user_prompt is not None:
self.user_prompt = user_prompt
@@ -94,22 +95,24 @@ class ScrapegraphScrapeTool(BaseTool):
if not all([result.scheme, result.netloc]):
raise ValueError
except Exception:
raise ValueError("Invalid URL format. URL must include scheme (http/https) and domain")
raise ValueError(
"Invalid URL format. URL must include scheme (http/https) and domain"
)
def _handle_api_response(self, response: dict) -> str:
"""Handle and validate API response"""
if not response:
raise RuntimeError("Empty response from Scrapegraph API")
if "error" in response:
error_msg = response.get("error", {}).get("message", "Unknown error")
if "rate limit" in error_msg.lower():
raise RateLimitError(f"Rate limit exceeded: {error_msg}")
raise RuntimeError(f"API error: {error_msg}")
if "result" not in response:
raise RuntimeError("Invalid response format from Scrapegraph API")
return response["result"]
def _run(
@@ -117,7 +120,10 @@ class ScrapegraphScrapeTool(BaseTool):
**kwargs: Any,
) -> Any:
website_url = kwargs.get("website_url", self.website_url)
user_prompt = kwargs.get("user_prompt", self.user_prompt) or "Extract the main content of the webpage"
user_prompt = (
kwargs.get("user_prompt", self.user_prompt)
or "Extract the main content of the webpage"
)
if not website_url:
raise ValueError("website_url is required")

27
src/crewai_tools/tools/selenium_scraping_tool/selenium_scraping_tool.py
View File

@@ -17,33 +17,36 @@ class FixedSeleniumScrapingToolSchema(BaseModel):
class SeleniumScrapingToolSchema(FixedSeleniumScrapingToolSchema):
"""Input for SeleniumScrapingTool."""
website_url: str = Field(..., description="Mandatory website url to read the file. Must start with http:// or https://")
website_url: str = Field(
...,
description="Mandatory website url to read the file. Must start with http:// or https://",
)
css_element: str = Field(
...,
description="Mandatory css reference for element to scrape from the website",
)
@validator('website_url')
@validator("website_url")
def validate_website_url(cls, v):
if not v:
raise ValueError("Website URL cannot be empty")
if len(v) > 2048: # Common maximum URL length
raise ValueError("URL is too long (max 2048 characters)")
if not re.match(r'^https?://', v):
if not re.match(r"^https?://", v):
raise ValueError("URL must start with http:// or https://")
try:
result = urlparse(v)
if not all([result.scheme, result.netloc]):
raise ValueError("Invalid URL format")
except Exception as e:
raise ValueError(f"Invalid URL: {str(e)}")
if re.search(r'\s', v):
if re.search(r"\s", v):
raise ValueError("URL cannot contain whitespace")
return v
@@ -130,11 +133,11 @@ class SeleniumScrapingTool(BaseTool):
def _create_driver(self, url, cookie, wait_time):
if not url:
raise ValueError("URL cannot be empty")
# Validate URL format
if not re.match(r'^https?://', url):
if not re.match(r"^https?://", url):
raise ValueError("URL must start with http:// or https://")
options = Options()
options.add_argument("--headless")
driver = self.driver(options=options)

3
src/crewai_tools/tools/serpapi_tool/serpapi_base_tool.py
View File

@@ -1,9 +1,10 @@
import os
import re
from typing import Optional, Any, Union
from typing import Any, Optional, Union
from crewai.tools import BaseTool
class SerpApiBaseTool(BaseTool):
"""Base class for SerpApi functionality with shared capabilities."""

42
src/crewai_tools/tools/serpapi_tool/serpapi_google_search_tool.py
View File

@@ -1,14 +1,22 @@
from typing import Any, Type, Optional
from typing import Any, Optional, Type
import re
from pydantic import BaseModel, Field
from .serpapi_base_tool import SerpApiBaseTool
from serpapi import HTTPError
from urllib.error import HTTPError
from .serpapi_base_tool import SerpApiBaseTool
class SerpApiGoogleSearchToolSchema(BaseModel):
"""Input for Google Search."""
search_query: str = Field(..., description="Mandatory search query you want to use to Google search.")
location: Optional[str] = Field(None, description="Location you want the search to be performed in.")
search_query: str = Field(
..., description="Mandatory search query you want to use to Google search."
)
location: Optional[str] = Field(
None, description="Location you want the search to be performed in."
)
class SerpApiGoogleSearchTool(SerpApiBaseTool):
name: str = "Google Search"
@@ -22,19 +30,25 @@ class SerpApiGoogleSearchTool(SerpApiBaseTool):
**kwargs: Any,
) -> Any:
try:
results = self.client.search({
"q": kwargs.get("search_query"),
"location": kwargs.get("location"),
}).as_dict()
results = self.client.search(
{
"q": kwargs.get("search_query"),
"location": kwargs.get("location"),
}
).as_dict()
self._omit_fields(
results,
[r"search_metadata", r"search_parameters", r"serpapi_.+", r".+_token", r"displayed_link", r"pagination"]
results,
[
r"search_metadata",
r"search_parameters",
r"serpapi_.+",
r".+_token",
r"displayed_link",
r"pagination",
],
)
return results
except HTTPError as e:
return f"An error occurred: {str(e)}. Some parameters may be invalid."

42
src/crewai_tools/tools/serpapi_tool/serpapi_google_shopping_tool.py
View File

@@ -1,14 +1,21 @@
from typing import Any, Type, Optional
from typing import Any, Optional, Type
import re
from pydantic import BaseModel, Field
from .serpapi_base_tool import SerpApiBaseTool
from serpapi import HTTPError
from urllib.error import HTTPError
from .serpapi_base_tool import SerpApiBaseTool
class SerpApiGoogleShoppingToolSchema(BaseModel):
"""Input for Google Shopping."""
search_query: str = Field(..., description="Mandatory search query you want to use to Google shopping.")
location: Optional[str] = Field(None, description="Location you want the search to be performed in.")
search_query: str = Field(
..., description="Mandatory search query you want to use to Google shopping."
)
location: Optional[str] = Field(
None, description="Location you want the search to be performed in."
)
class SerpApiGoogleShoppingTool(SerpApiBaseTool):
@@ -23,20 +30,25 @@ class SerpApiGoogleShoppingTool(SerpApiBaseTool):
**kwargs: Any,
) -> Any:
try:
results = self.client.search({
"engine": "google_shopping",
"q": kwargs.get("search_query"),
"location": kwargs.get("location")
}).as_dict()
results = self.client.search(
{
"engine": "google_shopping",
"q": kwargs.get("search_query"),
"location": kwargs.get("location"),
}
).as_dict()
self._omit_fields(
results,
[r"search_metadata", r"search_parameters", r"serpapi_.+", r"filters", r"pagination"]
results,
[
r"search_metadata",
r"search_parameters",
r"serpapi_.+",
r"filters",
r"pagination",
],
)
return results
except HTTPError as e:
return f"An error occurred: {str(e)}. Some parameters may be invalid."

4
src/crewai_tools/tools/serper_dev_tool/serper_dev_tool.py
View File

@@ -1,19 +1,19 @@
import datetime
import json
import os
import logging
import os
from typing import Any, Type
import requests
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
def _save_results_to_file(content: str) -> None:
"""Saves the search results to a file."""
try:

4
src/crewai_tools/tools/serply_api_tool/serply_webpage_to_markdown_tool.py
View File

@@ -18,9 +18,7 @@ class SerplyWebpageToMarkdownToolSchema(BaseModel):
class SerplyWebpageToMarkdownTool(RagTool):
name: str = "Webpage to Markdown"
description: str = (
"A tool to perform convert a webpage to markdown to make it easier for LLMs to understand"
)
description: str = "A tool to perform convert a webpage to markdown to make it easier for LLMs to understand"
args_schema: Type[BaseModel] = SerplyWebpageToMarkdownToolSchema
request_url: str = "https://api.serply.io/v1/request"
proxy_location: Optional[str] = "US"

155
src/crewai_tools/tools/snowflake_search_tool/README.md Normal file
View File

@@ -0,0 +1,155 @@
# Snowflake Search Tool
A tool for executing queries on Snowflake data warehouse with built-in connection pooling, retry logic, and async execution support.
## Installation
```bash
uv sync --extra snowflake
OR
uv pip install snowflake-connector-python>=3.5.0 snowflake-sqlalchemy>=1.5.0 cryptography>=41.0.0
OR
pip install snowflake-connector-python>=3.5.0 snowflake-sqlalchemy>=1.5.0 cryptography>=41.0.0
```
## Quick Start
```python
import asyncio
from crewai_tools import SnowflakeSearchTool, SnowflakeConfig
# Create configuration
config = SnowflakeConfig(
account="your_account",
user="your_username",
password="your_password",
warehouse="COMPUTE_WH",
database="your_database",
snowflake_schema="your_schema" # Note: Uses snowflake_schema instead of schema
)
# Initialize tool
tool = SnowflakeSearchTool(
config=config,
pool_size=5,
max_retries=3,
enable_caching=True
)
# Execute query
async def main():
results = await tool._run(
query="SELECT * FROM your_table LIMIT 10",
timeout=300
)
print(f"Retrieved {len(results)} rows")
if __name__ == "__main__":
asyncio.run(main())
```
## Features
- ✨ Asynchronous query execution
- 🚀 Connection pooling for better performance
- 🔄 Automatic retries for transient failures
- 💾 Query result caching (optional)
- 🔒 Support for both password and key-pair authentication
- 📝 Comprehensive error handling and logging
## Configuration Options
### SnowflakeConfig Parameters
| Parameter | Required | Description |
|-----------|----------|-------------|
| account | Yes | Snowflake account identifier |
| user | Yes | Snowflake username |
| password | Yes* | Snowflake password |
| private_key_path | No* | Path to private key file (alternative to password) |
| warehouse | Yes | Snowflake warehouse name |
| database | Yes | Default database |
| snowflake_schema | Yes | Default schema |
| role | No | Snowflake role |
| session_parameters | No | Custom session parameters dict |
\* Either password or private_key_path must be provided
### Tool Parameters
| Parameter | Default | Description |
|-----------|---------|-------------|
| pool_size | 5 | Number of connections in the pool |
| max_retries | 3 | Maximum retry attempts for failed queries |
| retry_delay | 1.0 | Delay between retries in seconds |
| enable_caching | True | Enable/disable query result caching |
## Advanced Usage
### Using Key-Pair Authentication
```python
config = SnowflakeConfig(
account="your_account",
user="your_username",
private_key_path="/path/to/private_key.p8",
warehouse="your_warehouse",
database="your_database",
snowflake_schema="your_schema"
)
```
### Custom Session Parameters
```python
config = SnowflakeConfig(
# ... other config parameters ...
session_parameters={
"QUERY_TAG": "my_app",
"TIMEZONE": "America/Los_Angeles"
}
)
```
## Best Practices
1. **Error Handling**: Always wrap query execution in try-except blocks
2. **Logging**: Enable logging to track query execution and errors
3. **Connection Management**: Use appropriate pool sizes for your workload
4. **Timeouts**: Set reasonable query timeouts to prevent hanging
5. **Security**: Use key-pair auth in production and never hardcode credentials
## Example with Logging
```python
import logging
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
async def main():
try:
# ... tool initialization ...
results = await tool._run(query="SELECT * FROM table LIMIT 10")
logger.info(f"Query completed successfully. Retrieved {len(results)} rows")
except Exception as e:
logger.error(f"Query failed: {str(e)}")
raise
```
## Error Handling
The tool automatically handles common Snowflake errors:
- DatabaseError
- OperationalError
- ProgrammingError
- Network timeouts
- Connection issues
Errors are logged and retried based on your retry configuration.

11
src/crewai_tools/tools/snowflake_search_tool/__init__.py Normal file
View File

@@ -0,0 +1,11 @@
from .snowflake_search_tool import (
SnowflakeConfig,
SnowflakeSearchTool,
SnowflakeSearchToolInput,
)
__all__ = [
"SnowflakeSearchTool",
"SnowflakeSearchToolInput",
"SnowflakeConfig",
]

201
src/crewai_tools/tools/snowflake_search_tool/snowflake_search_tool.py Normal file
View File

@@ -0,0 +1,201 @@
import asyncio
import logging
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Dict, List, Optional, Type
import snowflake.connector
from crewai.tools.base_tool import BaseTool
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import serialization
from pydantic import BaseModel, ConfigDict, Field, SecretStr
from snowflake.connector.connection import SnowflakeConnection
from snowflake.connector.errors import DatabaseError, OperationalError
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Cache for query results
_query_cache = {}
class SnowflakeConfig(BaseModel):
"""Configuration for Snowflake connection."""
model_config = ConfigDict(protected_namespaces=())
account: str = Field(
..., description="Snowflake account identifier", pattern=r"^[a-zA-Z0-9\-_]+$"
)
user: str = Field(..., description="Snowflake username")
password: Optional[SecretStr] = Field(None, description="Snowflake password")
private_key_path: Optional[str] = Field(
None, description="Path to private key file"
)
warehouse: Optional[str] = Field(None, description="Snowflake warehouse")
database: Optional[str] = Field(None, description="Default database")
snowflake_schema: Optional[str] = Field(None, description="Default schema")
role: Optional[str] = Field(None, description="Snowflake role")
session_parameters: Optional[Dict[str, Any]] = Field(
default_factory=dict, description="Session parameters"
)
@property
def has_auth(self) -> bool:
return bool(self.password or self.private_key_path)
def model_post_init(self, *args, **kwargs):
if not self.has_auth:
raise ValueError("Either password or private_key_path must be provided")
class SnowflakeSearchToolInput(BaseModel):
"""Input schema for SnowflakeSearchTool."""
model_config = ConfigDict(protected_namespaces=())
query: str = Field(..., description="SQL query or semantic search query to execute")
database: Optional[str] = Field(None, description="Override default database")
snowflake_schema: Optional[str] = Field(None, description="Override default schema")
timeout: Optional[int] = Field(300, description="Query timeout in seconds")
class SnowflakeSearchTool(BaseTool):
"""Tool for executing queries and semantic search on Snowflake."""
name: str = "Snowflake Database Search"
description: str = (
"Execute SQL queries or semantic search on Snowflake data warehouse. "
"Supports both raw SQL and natural language queries."
)
args_schema: Type[BaseModel] = SnowflakeSearchToolInput
# Define Pydantic fields
config: SnowflakeConfig = Field(
..., description="Snowflake connection configuration"
)
pool_size: int = Field(default=5, description="Size of connection pool")
max_retries: int = Field(default=3, description="Maximum retry attempts")
retry_delay: float = Field(
default=1.0, description="Delay between retries in seconds"
)
enable_caching: bool = Field(
default=True, description="Enable query result caching"
)
model_config = ConfigDict(arbitrary_types_allowed=True)
def __init__(self, **data):
"""Initialize SnowflakeSearchTool."""
super().__init__(**data)
self._connection_pool: List[SnowflakeConnection] = []
self._pool_lock = asyncio.Lock()
self._thread_pool = ThreadPoolExecutor(max_workers=self.pool_size)
async def _get_connection(self) -> SnowflakeConnection:
"""Get a connection from the pool or create a new one."""
async with self._pool_lock:
if not self._connection_pool:
conn = self._create_connection()
self._connection_pool.append(conn)
return self._connection_pool.pop()
def _create_connection(self) -> SnowflakeConnection:
"""Create a new Snowflake connection."""
conn_params = {
"account": self.config.account,
"user": self.config.user,
"warehouse": self.config.warehouse,
"database": self.config.database,
"schema": self.config.snowflake_schema,
"role": self.config.role,
"session_parameters": self.config.session_parameters,
}
if self.config.password:
conn_params["password"] = self.config.password.get_secret_value()
elif self.config.private_key_path:
with open(self.config.private_key_path, "rb") as key_file:
p_key = serialization.load_pem_private_key(
key_file.read(), password=None, backend=default_backend()
)
conn_params["private_key"] = p_key
return snowflake.connector.connect(**conn_params)
def _get_cache_key(self, query: str, timeout: int) -> str:
"""Generate a cache key for the query."""
return f"{self.config.account}:{self.config.database}:{self.config.snowflake_schema}:{query}:{timeout}"
async def _execute_query(
self, query: str, timeout: int = 300
) -> List[Dict[str, Any]]:
"""Execute a query with retries and return results."""
if self.enable_caching:
cache_key = self._get_cache_key(query, timeout)
if cache_key in _query_cache:
logger.info("Returning cached result")
return _query_cache[cache_key]
for attempt in range(self.max_retries):
try:
conn = await self._get_connection()
try:
cursor = conn.cursor()
cursor.execute(query, timeout=timeout)
if not cursor.description:
return []
columns = [col[0] for col in cursor.description]
results = [dict(zip(columns, row)) for row in cursor.fetchall()]
if self.enable_caching:
_query_cache[self._get_cache_key(query, timeout)] = results
return results
finally:
cursor.close()
async with self._pool_lock:
self._connection_pool.append(conn)
except (DatabaseError, OperationalError) as e:
if attempt == self.max_retries - 1:
raise
await asyncio.sleep(self.retry_delay * (2**attempt))
logger.warning(f"Query failed, attempt {attempt + 1}: {str(e)}")
continue
async def _run(
self,
query: str,
database: Optional[str] = None,
snowflake_schema: Optional[str] = None,
timeout: int = 300,
**kwargs: Any,
) -> Any:
"""Execute the search query."""
try:
# Override database/schema if provided
if database:
await self._execute_query(f"USE DATABASE {database}")
if snowflake_schema:
await self._execute_query(f"USE SCHEMA {snowflake_schema}")
results = await self._execute_query(query, timeout)
return results
except Exception as e:
logger.error(f"Error executing query: {str(e)}")
raise
def __del__(self):
"""Cleanup connections on deletion."""
try:
for conn in getattr(self, "_connection_pool", []):
try:
conn.close()
except:
pass
if hasattr(self, "_thread_pool"):
self._thread_pool.shutdown()
except:
pass

154
src/crewai_tools/tools/stagehand_tool/stagehand_tool.py
View File

@@ -14,9 +14,8 @@ import os
from functools import lru_cache
from typing import Any, Dict, List, Optional, Type, Union
from pydantic import BaseModel, Field
from crewai.tools.base_tool import BaseTool
from pydantic import BaseModel, Field
# Set up logging
logger = logging.getLogger(__name__)
@@ -25,6 +24,7 @@ logger = logging.getLogger(__name__)
STAGEHAND_AVAILABLE = False
try:
import stagehand
STAGEHAND_AVAILABLE = True
except ImportError:
pass # Keep STAGEHAND_AVAILABLE as False
@@ -32,33 +32,45 @@ except ImportError:
class StagehandResult(BaseModel):
"""Result from a Stagehand operation.
Attributes:
success: Whether the operation completed successfully
data: The result data from the operation
error: Optional error message if the operation failed
"""
success: bool = Field(..., description="Whether the operation completed successfully")
data: Union[str, Dict, List] = Field(..., description="The result data from the operation")
error: Optional[str] = Field(None, description="Optional error message if the operation failed")
success: bool = Field(
..., description="Whether the operation completed successfully"
)
data: Union[str, Dict, List] = Field(
..., description="The result data from the operation"
)
error: Optional[str] = Field(
None, description="Optional error message if the operation failed"
)
class StagehandToolConfig(BaseModel):
"""Configuration for the StagehandTool.
Attributes:
api_key: OpenAI API key for Stagehand authentication
timeout: Maximum time in seconds to wait for operations (default: 30)
retry_attempts: Number of times to retry failed operations (default: 3)
"""
api_key: str = Field(..., description="OpenAI API key for Stagehand authentication")
timeout: int = Field(30, description="Maximum time in seconds to wait for operations")
retry_attempts: int = Field(3, description="Number of times to retry failed operations")
timeout: int = Field(
30, description="Maximum time in seconds to wait for operations"
)
retry_attempts: int = Field(
3, description="Number of times to retry failed operations"
)
class StagehandToolSchema(BaseModel):
"""Schema for the StagehandTool input parameters.
Examples:
```python
# Using the 'act' API to click a button
@@ -66,13 +78,13 @@ class StagehandToolSchema(BaseModel):
api_method="act",
instruction="Click the 'Sign In' button"
)
# Using the 'extract' API to get text
tool.run(
api_method="extract",
instruction="Get the text content of the main article"
)
# Using the 'observe' API to monitor changes
tool.run(
api_method="observe",
@@ -80,48 +92,49 @@ class StagehandToolSchema(BaseModel):
)
```
"""
api_method: str = Field(
...,
description="The Stagehand API to use: 'act' for interactions, 'extract' for getting content, or 'observe' for monitoring changes",
pattern="^(act|extract|observe)$"
pattern="^(act|extract|observe)$",
)
instruction: str = Field(
...,
description="An atomic instruction for Stagehand to execute. Instructions should be simple and specific to increase reliability.",
min_length=1,
max_length=500
max_length=500,
)
class StagehandTool(BaseTool):
"""A tool for using Stagehand's AI-powered web automation capabilities.
This tool provides access to Stagehand's three core APIs:
- act: Perform web interactions (e.g., clicking buttons, filling forms)
- extract: Extract information from web pages (e.g., getting text content)
- observe: Monitor web page changes (e.g., watching for updates)
Each function takes atomic instructions to increase reliability.
Required Environment Variables:
OPENAI_API_KEY: API key for OpenAI (required by Stagehand)
Examples:
```python
tool = StagehandTool()
# Perform a web interaction
result = tool.run(
api_method="act",
instruction="Click the 'Sign In' button"
)
# Extract content from a page
content = tool.run(
api_method="extract",
instruction="Get the text content of the main article"
)
# Monitor for changes
changes = tool.run(
api_method="observe",
@@ -129,7 +142,7 @@ class StagehandTool(BaseTool):
)
```
"""
name: str = "StagehandTool"
description: str = (
"A tool that uses Stagehand's AI-powered web automation to interact with websites. "
@@ -137,27 +150,29 @@ class StagehandTool(BaseTool):
"Each instruction should be atomic (simple and specific) to increase reliability."
)
args_schema: Type[BaseModel] = StagehandToolSchema
def __init__(self, config: StagehandToolConfig | None = None, **kwargs: Any) -> None:
def __init__(
self, config: StagehandToolConfig | None = None, **kwargs: Any
) -> None:
"""Initialize the StagehandTool.
Args:
config: Optional configuration for the tool. If not provided,
will attempt to use OPENAI_API_KEY from environment.
**kwargs: Additional keyword arguments passed to the base class.
Raises:
ImportError: If the stagehand package is not installed
ValueError: If no API key is provided via config or environment
"""
super().__init__(**kwargs)
if not STAGEHAND_AVAILABLE:
raise ImportError(
"The 'stagehand' package is required to use this tool. "
"Please install it with: pip install stagehand"
)
# Use config if provided, otherwise try environment variable
if config is not None:
self.config = config
@@ -168,24 +183,22 @@ class StagehandTool(BaseTool):
"Either provide config with api_key or set OPENAI_API_KEY environment variable"
)
self.config = StagehandToolConfig(
api_key=api_key,
timeout=30,
retry_attempts=3
api_key=api_key, timeout=30, retry_attempts=3
)
@lru_cache(maxsize=100)
def _cached_run(self, api_method: str, instruction: str) -> Any:
"""Execute a cached Stagehand command.
This method is cached to improve performance for repeated operations.
Args:
api_method: The Stagehand API to use ('act', 'extract', or 'observe')
instruction: An atomic instruction for Stagehand to execute
Returns:
The raw result from the Stagehand API call
Raises:
ValueError: If an invalid api_method is provided
Exception: If the Stagehand API call fails
@@ -193,23 +206,25 @@ class StagehandTool(BaseTool):
logger.debug(
"Cache operation - Method: %s, Instruction length: %d",
api_method,
len(instruction)
len(instruction),
)
# Initialize Stagehand with configuration
logger.info(
"Initializing Stagehand (timeout=%ds, retries=%d)",
self.config.timeout,
self.config.retry_attempts
self.config.retry_attempts,
)
st = stagehand.Stagehand(
api_key=self.config.api_key,
timeout=self.config.timeout,
retry_attempts=self.config.retry_attempts
retry_attempts=self.config.retry_attempts,
)
# Call the appropriate Stagehand API based on the method
logger.info("Executing %s operation with instruction: %s", api_method, instruction[:100])
logger.info(
"Executing %s operation with instruction: %s", api_method, instruction[:100]
)
try:
if api_method == "act":
result = st.act(instruction)
@@ -219,28 +234,27 @@ class StagehandTool(BaseTool):
result = st.observe(instruction)
else:
raise ValueError(f"Unknown api_method: {api_method}")
logger.info("Successfully executed %s operation", api_method)
return result
except Exception as e:
logger.warning(
"Operation failed (method=%s, error=%s), will be retried on next attempt",
api_method,
str(e)
str(e),
)
raise
def _run(self, api_method: str, instruction: str, **kwargs: Any) -> StagehandResult:
"""Execute a Stagehand command using the specified API method.
Args:
api_method: The Stagehand API to use ('act', 'extract', or 'observe')
instruction: An atomic instruction for Stagehand to execute
**kwargs: Additional keyword arguments passed to the Stagehand API
Returns:
Returns:
StagehandResult containing the operation result and status
"""
try:
@@ -249,56 +263,36 @@ class StagehandTool(BaseTool):
"Starting operation - Method: %s, Instruction length: %d, Args: %s",
api_method,
len(instruction),
kwargs
kwargs,
)
# Use cached execution
result = self._cached_run(api_method, instruction)
logger.info("Operation completed successfully")
return StagehandResult(success=True, data=result)
except stagehand.AuthenticationError as e:
logger.error(
"Authentication failed - Method: %s, Error: %s",
api_method,
str(e)
"Authentication failed - Method: %s, Error: %s", api_method, str(e)
)
return StagehandResult(
success=False,
data={},
error=f"Authentication failed: {str(e)}"
success=False, data={}, error=f"Authentication failed: {str(e)}"
)
except stagehand.APIError as e:
logger.error(
"API error - Method: %s, Error: %s",
api_method,
str(e)
)
return StagehandResult(
success=False,
data={},
error=f"API error: {str(e)}"
)
logger.error("API error - Method: %s, Error: %s", api_method, str(e))
return StagehandResult(success=False, data={}, error=f"API error: {str(e)}")
except stagehand.BrowserError as e:
logger.error(
"Browser error - Method: %s, Error: %s",
api_method,
str(e)
)
logger.error("Browser error - Method: %s, Error: %s", api_method, str(e))
return StagehandResult(
success=False,
data={},
error=f"Browser error: {str(e)}"
success=False, data={}, error=f"Browser error: {str(e)}"
)
except Exception as e:
logger.error(
"Unexpected error - Method: %s, Error type: %s, Message: %s",
api_method,
type(e).__name__,
str(e)
str(e),
)
return StagehandResult(
success=False,
data={},
error=f"Unexpected error: {str(e)}"
success=False, data={}, error=f"Unexpected error: {str(e)}"
)

24
src/crewai_tools/tools/vision_tool/vision_tool.py
View File

@@ -1,30 +1,36 @@
import base64
from typing import Type, Optional
from pathlib import Path
from typing import Optional, Type
from crewai.tools import BaseTool
from openai import OpenAI
from pydantic import BaseModel, validator
class ImagePromptSchema(BaseModel):
"""Input for Vision Tool."""
image_path_url: str = "The image path or URL."
@validator("image_path_url")
def validate_image_path_url(cls, v: str) -> str:
if v.startswith("http"):
return v
path = Path(v)
if not path.exists():
raise ValueError(f"Image file does not exist: {v}")
# Validate supported formats
valid_extensions = {".jpg", ".jpeg", ".png", ".gif", ".webp"}
if path.suffix.lower() not in valid_extensions:
raise ValueError(f"Unsupported image format. Supported formats: {valid_extensions}")
raise ValueError(
f"Unsupported image format. Supported formats: {valid_extensions}"
)
return v
class VisionTool(BaseTool):
name: str = "Vision Tool"
description: str = (
@@ -45,10 +51,10 @@ class VisionTool(BaseTool):
image_path_url = kwargs.get("image_path_url")
if not image_path_url:
return "Image Path or URL is required."
# Validate input using Pydantic
ImagePromptSchema(image_path_url=image_path_url)
if image_path_url.startswith("http"):
image_data = image_path_url
else:
@@ -68,12 +74,12 @@ class VisionTool(BaseTool):
{
"type": "image_url",
"image_url": {"url": image_data},
}
},
],
}
],
max_tokens=300,
)
)
return response.choices[0].message.content

3
src/crewai_tools/tools/weaviate_tool/vector_search.py
View File

@@ -15,9 +15,8 @@ except ImportError:
Vectorizers = Any
Auth = Any
from pydantic import BaseModel, Field
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
class WeaviateToolSchema(BaseModel):

4
src/crewai_tools/tools/website_search/website_search_tool.py
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@@ -25,9 +25,7 @@ class WebsiteSearchToolSchema(FixedWebsiteSearchToolSchema):
class WebsiteSearchTool(RagTool):
name: str = "Search in a specific website"
description: str = (
"A tool that can be used to semantic search a query from a specific URL content."
)
description: str = "A tool that can be used to semantic search a query from a specific URL content."
args_schema: Type[BaseModel] = WebsiteSearchToolSchema
def __init__(self, website: Optional[str] = None, **kwargs):

4
src/crewai_tools/tools/youtube_channel_search_tool/youtube_channel_search_tool.py
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@@ -25,9 +25,7 @@ class YoutubeChannelSearchToolSchema(FixedYoutubeChannelSearchToolSchema):
class YoutubeChannelSearchTool(RagTool):
name: str = "Search a Youtube Channels content"
description: str = (
"A tool that can be used to semantic search a query from a Youtube Channels content."
)
description: str = "A tool that can be used to semantic search a query from a Youtube Channels content."
args_schema: Type[BaseModel] = YoutubeChannelSearchToolSchema
def __init__(self, youtube_channel_handle: Optional[str] = None, **kwargs):

4
src/crewai_tools/tools/youtube_video_search_tool/youtube_video_search_tool.py
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@@ -25,9 +25,7 @@ class YoutubeVideoSearchToolSchema(FixedYoutubeVideoSearchToolSchema):
class YoutubeVideoSearchTool(RagTool):
name: str = "Search a Youtube Video content"
description: str = (
"A tool that can be used to semantic search a query from a Youtube Video content."
)
description: str = "A tool that can be used to semantic search a query from a Youtube Video content."
args_schema: Type[BaseModel] = YoutubeVideoSearchToolSchema
def __init__(self, youtube_video_url: Optional[str] = None, **kwargs):