CLEANUP

playground/agents/multion_agent.py

@@ -19,9 +19,7 @@ agent = Agent(
 # Task
 task = Task(
     agent=agent,
-    description=(
-        "Download https://www.coachcamel.com/"
-    ),
+    description="Download https://www.coachcamel.com/",
 )
 
 # Swarm
@@ -46,4 +44,4 @@ workflow.run()
 # Calculate execution time
 execution_time = timeit.default_timer() - start_time
 # logger.info(f"Execution time: {execution_time} seconds")
-print(f"Execution time: {execution_time} seconds")
+print(f"Execution time: {execution_time} seconds")

playground/memory/chroma_usage_example.py

@@ -14,4 +14,3 @@ chroma.add(text)
 
 # Search for similar text
 similar_text = chroma.query(text)
-

playground/structs/company_example.py

@@ -1,4 +1,3 @@
-
 import os
 
 from dotenv import load_dotenv

playground/tools/agent_with_tools_example.py

@@ -12,6 +12,7 @@ api_key = os.environ.get("OPENAI_API_KEY")
 
 llm = OpenAIChat(api_key=api_key)
 
+
 @tool
 def search_api(query: str) -> str:
     """Search API

swarms/agents/multion_agent.py

@@ -37,8 +37,6 @@ class MultiOnAgent(AbstractLLM):
         self.max_steps = max_steps
         self.starting_url = starting_url
 
-
-
     def run(self, task: str, *args, **kwargs):
         """
         Runs a browsing task.
@@ -57,7 +55,7 @@ class MultiOnAgent(AbstractLLM):
             *args,
             **kwargs,
         )
-        
+
         response = multion.browse(
             {
                 "cmd": task,

swarms/loaders/pdf_loader.py

@@ -1,77 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from pathlib import Path
-from typing import IO
-
-from pypdf import PdfReader
-
-from swarms.utils.hash import str_to_hash
-
-
-@dataclass
-class TextArtifact:
-    text: str
-
-
-@dataclass
-class PDFLoader:
-    """
-    A class for loading PDF files and extracting text artifacts.
-
-    Args:
-        tokenizer (str): The tokenizer to use for chunking the text.
-        max_tokens (int): The maximum number of tokens per chunk.
-
-    Methods:
-        load(source, password=None, *args, **kwargs):
-            Load a single PDF file and extract text artifacts.
-
-        load_collection(sources, password=None, *args, **kwargs):
-            Load a collection of PDF files and extract text artifacts.
-
-    Private Methods:
-        _load_pdf(stream, password=None):
-            Load a PDF file and extract text artifacts.
-
-    Attributes:
-        tokenizer (str): The tokenizer used for chunking the text.
-        max_tokens (int): The maximum number of tokens per chunk.
-    """
-
-    tokenizer: str
-    max_tokens: int
-
-    def __post_init__(self):
-        self.chunker = PdfChunker(
-            tokenizer=self.tokenizer, max_tokens=self.max_tokens
-        )
-
-    def load(
-        self,
-        source: str | IO | Path,
-        password: str | None = None,
-        *args,
-        **kwargs,
-    ) -> list[TextArtifact]:
-        return self._load_pdf(source, password)
-
-    def load_collection(
-        self,
-        sources: list[str | IO | Path],
-        password: str | None = None,
-        *args,
-        **kwargs,
-    ) -> dict[str, list[TextArtifact]]:
-        return {
-            str_to_hash(str(s)): self._load_pdf(s, password)
-            for s in sources
-        }
-
-    def _load_pdf(
-        self, stream: str | IO | Path, password: str | None
-    ) -> list[TextArtifact]:
-        reader = PdfReader(stream, strict=True, password=password)
-        return [
-            TextArtifact(text=p.extract_text()) for p in reader.pages
-        ]