[DEMO][Swarm Mechanic]

.gitignore

@@ -24,6 +24,7 @@ venv
 .DS_Store
 Cargo.lock
 .DS_STORE
+artifacts_logs
 Cargo.lock
 Medical Treatment Recommendation Agent_state.json
 swarms/agents/.DS_Store

playground/demos/swarm_mechanic/swarm_mechanic_example.py

@@ -0,0 +1,88 @@
+"""
+pip3 install -U swarms
+pip3 install -U chromadb
+
+
+
+task -> Understanding Agent [understands the problem better] -> Summarize of the conversation -> research agent that has access to internt perplexity -> final rag agent 
+
+
+# Todo
+- Use better llm -- gpt4, claude, gemini
+- Make better system prompt
+- Populate the vector database with q/a of past history
+"""
+
+from swarms import Agent, llama3Hosted, AgentRearrange
+from pydantic import BaseModel
+from playground.memory.chromadb_example import ChromaDB
+
+# Initialize the language model agent (e.g., GPT-3)
+llm = llama3Hosted(max_tokens=3000)
+
+
+# Initialize Memory
+memory = ChromaDB(output_dir="swarm_mechanic", n_results=2, verbose=True)
+
+
+# Perplexity Agent
+# def webbrowser(query: str):
+#     # put your logic here
+#     return query
+
+
+# Output
+class EvaluatorOuputSchema(BaseModel):
+    evaluation: str = None
+    question_for_user: str = None
+
+
+# Initialize agents for individual tasks
+agent1 = Agent(
+    agent_name="Summary ++ Hightlighter Agent",
+    system_prompt="Generate a simple, direct, and reliable summary of the input task alongside the highlights",
+    llm=llm,
+    max_loops=1,
+)
+
+# Point out that if their are details that can be added
+# What do you mean? What lights do you have turned on.
+agent2 = Agent(
+    agent_name="Evaluator",
+    system_prompt="Summarize and evaluate the summary and the users demand, always be interested in learning more about the situation with extreme precision.",
+    llm=llm,
+    max_loops=1,
+    list_base_models=[EvaluatorOuputSchema],
+)
+
+# research_agent = Agent(
+#     agent_name="Research Agent",
+#     system_prompt="Summarize and evaluate the summary and the users demand, always be interested in learning more about the situation with extreme precision.",
+#     llm=llm,
+#     max_loops=1,
+#     tool = [webbrowser]
+# )
+
+agent3 = Agent(
+    agent_name="Summarizer Agent",
+    system_prompt="Summarize the entire history of the interaction",
+    llm=llm,
+    max_loops=1,
+    long_term_memory=memory,
+)
+
+
+# Task
+task = "Car Model: S-Class, Car Year: 2020, Car Mileage: 10000, all my service lights are on, what should i do?"
+
+
+# Swarm
+swarm = AgentRearrange(
+    agents=[agent1, agent2, agent3],
+    flow=f"{agent1.agent_name} -> {agent2.agent_name} -> {agent3.agent_name}",
+    memory_system=memory,
+)
+
+# Task
+out = swarm.run(task)
+print(out)

playground/memory/chroma_usage_example.py

@@ -1,15 +0,0 @@
-from swarms.memory import ChromaDB
-
-# Initialize the memory
-chroma = ChromaDB(
-    metric="cosine",
-    limit_tokens=1000,
-    verbose=True,
-)
-
-# Add text
-text = "This is a test"
-chroma.add(text)
-
-# Search for similar text
-similar_text = chroma.query(text)

playground/memory/chromadb_example.py

@@ -1,15 +1,14 @@
 import logging
 import os
 import uuid
-from typing import Callable, List, Optional
+from typing import Callable, Optional
 
 import chromadb
-import numpy as np
 from dotenv import load_dotenv
 
+from swarms.memory.base_vectordb import BaseVectorDatabase
 from swarms.utils.data_to_text import data_to_text
 from swarms.utils.markdown_message import display_markdown_message
-from swarms.memory.base_vectordb import BaseVectorDatabase
 
 # Load environment variables
 load_dotenv()
@@ -46,7 +45,7 @@ class ChromaDB(BaseVectorDatabase):
         metric: str = "cosine",
         output_dir: str = "swarms",
         limit_tokens: Optional[int] = 1000,
-        n_results: int = 2,
+        n_results: int = 3,
         embedding_function: Callable = None,
         docs_folder: str = None,
         verbose: bool = False,
@@ -108,8 +107,6 @@ class ChromaDB(BaseVectorDatabase):
     def add(
         self,
         document: str,
-        images: List[np.ndarray] = None,
-        img_urls: List[str] = None,
         *args,
         **kwargs,
     ):
@@ -128,11 +125,12 @@ class ChromaDB(BaseVectorDatabase):
             self.collection.add(
                 ids=[doc_id],
                 documents=[document],
-                images=images,
-                uris=img_urls,
                 *args,
                 **kwargs,
             )
+            print("-----------------")
+            print("Document added successfully")
+            print("-----------------")
             return doc_id
         except Exception as e:
             raise Exception(f"Failed to add document: {str(e)}")
@@ -140,7 +138,6 @@ class ChromaDB(BaseVectorDatabase):
     def query(
         self,
         query_text: str,
-        query_images: List[np.ndarray],
         *args,
         **kwargs,
     ):
@@ -157,8 +154,7 @@ class ChromaDB(BaseVectorDatabase):
         try:
             docs = self.collection.query(
                 query_texts=[query_text],
-                query_images=query_images,
+                n_results=self.n_results,
-                n_results=self.n_docs,
                 *args,
                 **kwargs,
             )["documents"]
@@ -177,23 +173,12 @@ class ChromaDB(BaseVectorDatabase):
         """
         added_to_db = False
 
-        image_extensions = [
-            ".jpg",
-            ".jpeg",
-            ".png",
-        ]
-        images = []
         for root, dirs, files in os.walk(self.docs_folder):
             for file in files:
+                file = os.path.join(self.docs_folder, file)
                 _, ext = os.path.splitext(file)
-                if ext.lower() in image_extensions:
+                data = data_to_text(file)
-                    images.append(os.path.join(root, file))
+                added_to_db = self.add([data])
-                else:
+                print(f"{file} added to Database")
-                    data = data_to_text(file)
-                    added_to_db = self.add([data])
-                    print(f"{file} added to Database")
-        if images:
-            added_to_db = self.add(img_urls=[images])
-            print(f"{len(images)} images added to Database ")
 
         return added_to_db

playground/structs/load_balancer_example.py

@@ -11,7 +11,6 @@ agent1 = Agent(
     llm=llm,
     max_loops=1,
     dashboard=False,
-    tools=[],
 )
 agent2 = Agent(
     agent_name="Summarizer",
@@ -19,7 +18,6 @@ agent2 = Agent(
     llm=llm,
     max_loops=1,
     dashboard=False,
-    tools=[],
 )
 
 # Create the Sequential workflow

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "5.0.7"
+version = "5.0.8"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]

scripts/cleanup/json_log_cleanup.py

@@ -31,4 +31,4 @@ def cleanup_json_logs(name: str = None):
 
 
 # Call the function
-cleanup_json_logs("json_logs")
+cleanup_json_logs("artifacts_logs")

swarms/structs/hiearchical_swarm.py

@@ -1,7 +1,7 @@
 import json
 from typing import List
 
-from beartype import beartype
+# from beartype import beartype
 
 from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import BaseSwarm
@@ -214,7 +214,6 @@ class HiearchicalSwarm(BaseSwarm):
             logger.error("Invalid JSON data, try again.")
             raise json.JSONDecodeError
 
-    @beartype
     def select_agent_and_send_task(
         self, name: str = None, task: str = None, *args, **kwargs
     ):
@@ -246,7 +245,6 @@ class HiearchicalSwarm(BaseSwarm):
             logger.error(f"Error: {e}")
             raise e
 
-    @beartype
     def run(self, task: str = None, *args, **kwargs):
         """
         Run the hierarchical swarm.

swarms/utils/optimized_loop.py

@@ -0,0 +1,73 @@
+import timeit
+from typing import Callable, Iterable, List, Optional, TypeVar
+
+T = TypeVar("T")
+R = TypeVar("R")
+
+
+def optimized_loop(
+    data: Iterable[T],
+    operation: Callable[[T], R],
+    condition: Optional[Callable[[T], bool]] = None,
+) -> List[R]:
+    """
+    Perform an optimized loop over the input data, applying an operation to each element.
+    Optionally, filter elements based on a condition before applying the operation.
+
+    Args:
+        data (Iterable[T]): The input data to be processed. Can be any iterable type.
+        operation (Callable[[T], R]): The operation to be applied to each element.
+        condition (Optional[Callable[[T], bool]]): An optional condition to filter elements before applying the operation.
+
+    Returns:
+        List[R]: The result of applying the operation to the filtered elements.
+    """
+    if condition is not None:
+        return [operation(x) for x in data if condition(x)]
+    else:
+        return [operation(x) for x in data]
+
+
+# Sample data, operation, and condition for benchmarking
+data = list(range(1000000))
+operation = lambda x: x * x
+condition = lambda x: x % 2 == 0
+
+
+# Define a traditional loop for comparison
+def traditional_loop(data: Iterable[int]) -> List[int]:
+    result = []
+    for x in data:
+        if x % 2 == 0:
+            result.append(x * x)
+    return result
+
+
+# Define a benchmarking function
+def benchmark():
+    # Time the execution of the optimized loop
+    optimized_time = timeit.timeit(
+        stmt="optimized_loop(data, operation, condition)",
+        setup="from __main__ import optimized_loop, data, operation, condition",
+        globals=globals(),
+        number=10,
+    )
+
+    print(f"Optimized loop execution time: {optimized_time:.4f} seconds")
+
+    # Time the execution of the traditional loop for comparison
+    traditional_time = timeit.timeit(
+        stmt="traditional_loop(data)",
+        setup="from __main__ import traditional_loop, data",
+        globals=globals(),
+        number=10,
+    )
+
+    print(
+        f"Traditional loop execution time: {traditional_time:.4f} seconds"
+    )
+
+
+# Run the benchmark
+if __name__ == "__main__":
+    benchmark()