[CLEANUP][Feat][Swarms Memory]

README.md

@@ -118,179 +118,7 @@ agent.run("Generate a 10,000 word blog on health and wellness.")
 import os
 from dotenv import load_dotenv
 from swarms import Agent, OpenAIChat
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
-import logging
-import os
-import uuid
-from typing import Optional
-import chromadb
-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()
-
-
-# Results storage using local ChromaDB
-class ChromaDB(BaseVectorDatabase):
-    """
-
-    ChromaDB database
-
-    Args:
-        metric (str): The similarity metric to use.
-        output (str): The name of the collection to store the results in.
-        limit_tokens (int, optional): The maximum number of tokens to use for the query. Defaults to 1000.
-        n_results (int, optional): The number of results to retrieve. Defaults to 2.
-
-    Methods:
-        add: _description_
-        query: _description_
-
-    Examples:
-        >>> chromadb = ChromaDB(
-        >>>     metric="cosine",
-        >>>     output="results",
-        >>>     llm="gpt3",
-        >>>     openai_api_key=OPENAI_API_KEY,
-        >>> )
-        >>> chromadb.add(task, result, result_id)
-    """
-
-    def __init__(
-        self,
-        metric: str = "cosine",
-        output_dir: str = "swarms",
-        limit_tokens: Optional[int] = 1000,
-        n_results: int = 3,
-        docs_folder: str = None,
-        verbose: bool = False,
-        *args,
-        **kwargs,
-    ):
-        self.metric = metric
-        self.output_dir = output_dir
-        self.limit_tokens = limit_tokens
-        self.n_results = n_results
-        self.docs_folder = docs_folder
-        self.verbose = verbose
-
-        # Disable ChromaDB logging
-        if verbose:
-            logging.getLogger("chromadb").setLevel(logging.INFO)
-
-        # Create Chroma collection
-        chroma_persist_dir = "chroma"
-        chroma_client = chromadb.PersistentClient(
-            settings=chromadb.config.Settings(
-                persist_directory=chroma_persist_dir,
-            ),
-            *args,
-            **kwargs,
-        )
-
-        # Create ChromaDB client
-        self.client = chromadb.Client()
-
-        # Create Chroma collection
-        self.collection = chroma_client.get_or_create_collection(
-            name=output_dir,
-            metadata={"hnsw:space": metric},
-            *args,
-            **kwargs,
-        )
-        display_markdown_message(
-            "ChromaDB collection created:"
-            f" {self.collection.name} with metric: {self.metric} and"
-            f" output directory: {self.output_dir}"
-        )
-
-        # If docs
-        if docs_folder:
-            display_markdown_message(
-                f"Traversing directory: {docs_folder}"
-            )
-            self.traverse_directory()
-
-    def add(
-        self,
-        document: str,
-        *args,
-        **kwargs,
-    ):
-        """
-        Add a document to the ChromaDB collection.
-
-        Args:
-            document (str): The document to be added.
-            condition (bool, optional): The condition to check before adding the document. Defaults to True.
-
-        Returns:
-            str: The ID of the added document.
-        """
-        try:
-            doc_id = str(uuid.uuid4())
-            self.collection.add(
-                ids=[doc_id],
-                documents=[document],
-                *args,
-                **kwargs,
-            )
-            print("-----------------")
-            print("Document added successfully")
-            print("-----------------")
-            return doc_id
-        except Exception as e:
-            raise Exception(f"Failed to add document: {str(e)}")
-
-    def query(
-        self,
-        query_text: str,
-        *args,
-        **kwargs,
-    ):
-        """
-        Query documents from the ChromaDB collection.
-
-        Args:
-            query (str): The query string.
-            n_docs (int, optional): The number of documents to retrieve. Defaults to 1.
-
-        Returns:
-            dict: The retrieved documents.
-        """
-        try:
-            docs = self.collection.query(
-                query_texts=[query_text],
-                n_results=self.n_results,
-                *args,
-                **kwargs,
-            )["documents"]
-            return docs[0]
-        except Exception as e:
-            raise Exception(f"Failed to query documents: {str(e)}")
-
-    def traverse_directory(self):
-        """
-        Traverse through every file in the given directory and its subdirectories,
-        and return the paths of all files.
-        Parameters:
-        - directory_name (str): The name of the directory to traverse.
-        Returns:
-        - list: A list of paths to each file in the directory and its subdirectories.
-        """
-        added_to_db = False
-
-        for root, dirs, files in os.walk(self.docs_folder):
-            for file in files:
-                file_path = os.path.join(root, file)  # Change this line
-                _, ext = os.path.splitext(file_path)
-                data = data_to_text(file_path)
-                added_to_db = self.add(str(data))
-                print(f"{file_path} added to Database")
-
-        return added_to_db
 
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
@@ -334,218 +162,108 @@ An LLM equipped with long term memory and tools, a full stack agent capable of a
 
 ```python
 import logging
-import os
-import uuid
-from typing import Optional
-
-import chromadb
 from dotenv import load_dotenv
-
-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
 from swarms import Agent, OpenAIChat
+import subprocess
 
+# Making an instance of the ChromaDB class
+memory = ChromaDB(
+    metric="cosine",
+    n_results=3,
+    output_dir="results",
+    docs_folder="docs",
+)
 
-# Load environment variables
+# Tools
-load_dotenv()
+def terminal(
+    code: str,
+):
+    """
+    Run code in the terminal.
+
+    Args:
+        code (str): The code to run in the terminal.
+
+    Returns:
+        str: The output of the code.
+    """
+    out = subprocess.run(
+        code, shell=True, capture_output=True, text=True
+    ).stdout
+    return str(out)
 
+def browser(query: str):
+    """
+    Search the query in the browser with the `browser` tool.
 
+    Args:
+        query (str): The query to search in the browser.
 
-# Results storage using local ChromaDB
+    Returns:
-class ChromaDB(BaseVectorDatabase):
+        str: The search results.
     """
+    import webbrowser
+
+    url = f"https://www.google.com/search?q={query}"
+    webbrowser.open(url)
+    return f"Searching for {query} in the browser."
 
-    ChromaDB database
+def create_file(file_path: str, content: str):
+    """
+    Create a file using the file editor tool.
 
     Args:
-        metric (str): The similarity metric to use.
+        file_path (str): The path to the file.
-        output (str): The name of the collection to store the results in.
+        content (str): The content to write to the file.
-        limit_tokens (int, optional): The maximum number of tokens to use for the query. Defaults to 1000.
+
-        n_results (int, optional): The number of results to retrieve. Defaults to 2.
+    Returns:
-
+        str: The result of the file creation operation.
-    Methods:
-        add: _description_
-        query: _description_
-
-    Examples:
-        >>> chromadb = ChromaDB(
-        >>>     metric="cosine",
-        >>>     output="results",
-        >>>     llm="gpt3",
-        >>>     openai_api_key=OPENAI_API_KEY,
-        >>> )
-        >>> chromadb.add(task, result, result_id)
     """
+    with open(file_path, "w") as file:
+        file.write(content)
+    return f"File {file_path} created successfully."
 
-    def __init__(
+def file_editor(file_path: str, mode: str, content: str):
-        self,
+    """
-        metric: str = "cosine",
+    Edit a file using the file editor tool.
-        output_dir: str = "swarms",
-        limit_tokens: Optional[int] = 1000,
-        n_results: int = 3,
-        docs_folder: str = None,
-        verbose: bool = False,
-        *args,
-        **kwargs,
-    ):
-        self.metric = metric
-        self.output_dir = output_dir
-        self.limit_tokens = limit_tokens
-        self.n_results = n_results
-        self.docs_folder = docs_folder
-        self.verbose = verbose
-
-        # Disable ChromaDB logging
-        if verbose:
-            logging.getLogger("chromadb").setLevel(logging.INFO)
-
-        # Create Chroma collection
-        chroma_persist_dir = "chroma"
-        chroma_client = chromadb.PersistentClient(
-            settings=chromadb.config.Settings(
-                persist_directory=chroma_persist_dir,
-            ),
-            *args,
-            **kwargs,
-        )
-
-        # Create ChromaDB client
-        self.client = chromadb.Client()
-
-        # Create Chroma collection
-        self.collection = chroma_client.get_or_create_collection(
-            name=output_dir,
-            metadata={"hnsw:space": metric},
-            *args,
-            **kwargs,
-        )
-        display_markdown_message(
-            "ChromaDB collection created:"
-            f" {self.collection.name} with metric: {self.metric} and"
-            f" output directory: {self.output_dir}"
-        )
-
-        # If docs
-        if docs_folder:
-            display_markdown_message(
-                f"Traversing directory: {docs_folder}"
-            )
-            self.traverse_directory()
-
-    def add(
-        self,
-        document: str,
-        *args,
-        **kwargs,
-    ):
-        """
-        Add a document to the ChromaDB collection.
-
-        Args:
-            document (str): The document to be added.
-            condition (bool, optional): The condition to check before adding the document. Defaults to True.
-
-        Returns:
-            str: The ID of the added document.
-        """
-        try:
-            doc_id = str(uuid.uuid4())
-            self.collection.add(
-                ids=[doc_id],
-                documents=[document],
-                *args,
-                **kwargs,
-            )
-            print("-----------------")
-            print("Document added successfully")
-            print("-----------------")
-            return doc_id
-        except Exception as e:
-            raise Exception(f"Failed to add document: {str(e)}")
-
-    def query(
-        self,
-        query_text: str,
-        *args,
-        **kwargs,
-    ):
-        """
-        Query documents from the ChromaDB collection.
-
-        Args:
-            query (str): The query string.
-            n_docs (int, optional): The number of documents to retrieve. Defaults to 1.
-
-        Returns:
-            dict: The retrieved documents.
-        """
-        try:
-            docs = self.collection.query(
-                query_texts=[query_text],
-                n_results=self.n_results,
-                *args,
-                **kwargs,
-            )["documents"]
-            return docs[0]
-        except Exception as e:
-            raise Exception(f"Failed to query documents: {str(e)}")
-
-    def traverse_directory(self):
-        """
-        Traverse through every file in the given directory and its subdirectories,
-        and return the paths of all files.
-        Parameters:
-        - directory_name (str): The name of the directory to traverse.
-        Returns:
-        - list: A list of paths to each file in the directory and its subdirectories.
-        """
-        added_to_db = False
-
-        for root, dirs, files in os.walk(self.docs_folder):
-            for file in files:
-                file_path = os.path.join(root, file)  # Change this line
-                _, ext = os.path.splitext(file_path)
-                data = data_to_text(file_path)
-                added_to_db = self.add(str(data))
-                print(f"{file_path} added to Database")
-
-        return added_to_db
 
+    Args:
+        file_path (str): The path to the file.
+        mode (str): The mode to open the file in.
+        content (str): The content to write to the file.
 
-# Making an instance of the ChromaDB class
+    Returns:
-memory = ChromaDB(
+        str: The result of the file editing operation.
-    metric="cosine",
+    """
-    n_results=3,
+    with open(file_path, mode) as file:
-    output_dir="results",
+        file.write(content)
-    docs_folder="docs",
+    return f"File {file_path} edited successfully."
-)
 
-# Initialize a tool
-def search_api(query: str):
-    # Add your logic here
-    return query
 
-# Initializing the agent with the Gemini instance and other parameters
+# Agent
 agent = Agent(
-    agent_name="Covid-19-Chat",
+    agent_name="Devin",
-    agent_description=(
+    system_prompt=(
-        "This agent provides information about COVID-19 symptoms."
+        "Autonomous agent that can interact with humans and other"
+        " agents. Be Helpful and Kind. Use the tools provided to"
+        " assist the user. Return all code in markdown format."
     ),
-    llm=OpenAIChat(),
+    llm=llm,
     max_loops="auto",
     autosave=True,
+    dashboard=False,
+    streaming_on=True,
     verbose=True,
+    stopping_token="<DONE>",
+    interactive=True,
+    tools=[terminal, browser, file_editor, create_file],
+    code_interpreter=True,
+    # streaming=True,
     long_term_memory=memory,
-    stopping_condition="finish",
-    tools=[search_api],
 )
 
-# Defining the task and image path
+# Run the agent
-task = ("What are the symptoms of COVID-19?",)
+out = agent("Create a new file for a plan to take over the world.")
-
-# Running the agent with the specified task and image
-out = agent.run(task)
 print(out)
 
 ```

docs/swarms/structs/index.md

@@ -72,7 +72,7 @@ agent.run("Generate a 10,000 word blog on health and wellness.")
 
 ```python
 from swarms import Agent, OpenAIChat
-from playground.memory.chromadb_example import ChromaDB # Copy and paste the code and put it in your own local directory.
+from swarms_memory import ChromaDB # Copy and paste the code and put it in your own local directory.
 
 # Making an instance of the ChromaDB class
 memory = ChromaDB(

playground/agents/agent_with_long_term_memory.py

@@ -1,5 +1,5 @@
 from swarms import Agent, OpenAIChat
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 from swarms.models.tiktoken_wrapper import TikTokenizer
 
 # Initialize the agent

playground/agents/agent_with_longterm_memory.py

@@ -4,7 +4,7 @@ from dotenv import load_dotenv
 
 # Import the OpenAIChat model and the Agent struct
 from swarms import Agent, OpenAIChat
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 
 # Load the environment variables
 load_dotenv()

playground/agents/new_perplexity_agent.py

@@ -1,6 +1,6 @@
 from swarms import Agent
 from swarms.models.llama3_hosted import llama3Hosted
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 from swarms.tools.prebuilt.bing_api import fetch_web_articles_bing_api
 
 # Define the research system prompt

playground/agents/perplexity_agent.py

@@ -10,7 +10,7 @@ $ pip install swarms
 """
 
 from swarms import Agent, OpenAIChat
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 from swarms.tools.prebuilt.bing_api import fetch_web_articles_bing_api
 import os
 from dotenv import load_dotenv

playground/demos/patient_question_assist/main.py

@@ -1,6 +1,6 @@
 from swarms import Agent, OpenAIChat
 from typing import List
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 
 memory = ChromaDB(
     metric="cosine",

playground/demos/swarm_mechanic/swarm_mechanic_example.py

@@ -15,7 +15,7 @@ task -> Understanding Agent [understands the problem better] -> Summarize of the
 
 from swarms import Agent, llama3Hosted, AgentRearrange
 from pydantic import BaseModel
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 
 # Initialize the language model agent (e.g., GPT-3)
 llm = llama3Hosted(max_tokens=3000)

playground/demos/swarm_of_complaince/compliance_swarm.py

@@ -11,7 +11,7 @@ Todo [Improvements]
 
 from swarms import Agent
 from swarms.models.llama3_hosted import llama3Hosted
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 
 
 # Model

playground/structs/multi_agent_collaboration/mixture_of_agents/agent_ops_moa.py

@@ -1,6 +1,6 @@
 from swarms import Agent, OpenAIChat
 from swarms.structs.mixture_of_agents import MixtureOfAgents
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 
 
 SEC_DATA = """

playground/structs/multi_agent_collaboration/mixture_of_agents/moa_with_scp.py

@@ -1,6 +1,6 @@
 from swarms import Agent, OpenAIChat
 from swarms.structs.mixture_of_agents import MixtureOfAgents
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 
 
 SEC_DATA = """

playground/structs/multi_agent_collaboration/swarm_network_example.py

@@ -6,7 +6,7 @@ from swarms import (
     OpenAIChat,
     TogetherLLM,
 )
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 from dotenv import load_dotenv
 
 # load the environment variables

playground/swarms/movers_swarm.py

@@ -10,7 +10,7 @@ $ pip install swarms
 """
 
 from swarms import Agent, OpenAIChat
-from playground.memory.chromadb_example import ChromaDB
+from swarms_memory import ChromaDB
 from swarms.tools.prebuilt.bing_api import fetch_web_articles_bing_api
 import os
 from dotenv import load_dotenv

playground/swarms_example.ipynb

@@ -96,7 +96,7 @@
    "outputs": [],
    "source": [
     "from swarms import Agent, OpenAIChat\n",
-    "from playground.memory.chromadb_example import ChromaDB\n",
+    "from swarms_memory import ChromaDB\n",
     "\n",
     "# Making an instance of the ChromaDB class\n",
     "memory = ChromaDB(\n",

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "5.3.2"
+version = "5.3.3"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -55,6 +55,7 @@ openai = ">=1.30.1,<2.0"
 termcolor = "*"
 tiktoken = "*"
 networkx = "*"
+swarms-memory = "*"
 
 
 

requirements.txt

@@ -28,4 +28,5 @@ pytest>=8.1.1
 termcolor>=2.4.0
 pandas>=2.2.2
 fastapi>=0.110.1
-networkx
+networkx
+swarms-memory