Merge branch 'kyegomez:master' into master

.github/workflows/pr_labeler.yml

@@ -0,0 +1,15 @@
+name: "PR Labeler"
+
+on:
+  pull_request_target:
+    types: ["opened", "reopened", "ready_for_review"]
+
+jobs:
+  triage:
+    permissions:
+      contents: read
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/labeler@v4
+    if: ${{ github.event.pull_request.draft == false }}

.github/workflows/stale_isseue.yml

@@ -0,0 +1,49 @@
+# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
+#
+# You can adjust the behavior by modifying this file.
+# For more information, see:
+# https://github.com/actions/stale
+name: Mark stale issues and pull requests
+
+on:
+  schedule:
+  # Scheduled to run at 1.30 UTC everyday
+  - cron: '30 1 * * *'
+
+jobs:
+  stale:
+
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+
+    steps:
+    - uses: actions/stale@v5
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        days-before-issue-stale: 14
+        days-before-issue-close: 14
+        stale-issue-label: "status:stale"
+        close-issue-reason: not_planned
+        any-of-labels: "status:awaiting user response,status:more data needed"
+        stale-issue-message: >
+          Marking this issue as stale since it has been open for 14 days with no activity.
+          This issue will be closed if no further activity occurs.
+        close-issue-message: >
+          This issue was closed because it has been inactive for 28 days.
+          Please post a new issue if you need further assistance. Thanks!
+        days-before-pr-stale: 14
+        days-before-pr-close: 14
+        stale-pr-label: "status:stale"
+        stale-pr-message: >
+          Marking this pull request as stale since it has been open for 14 days with no activity.
+          This PR will be closed if no further activity occurs.
+        close-pr-message: >
+          This pull request was closed because it has been inactive for 28 days.
+          Please open a new pull request if you need further assistance. Thanks!
+        # Label that can be assigned to issues to exclude them from being marked as stale
+        exempt-issue-labels: 'override-stale'
+        # Label that can be assigned to PRs to exclude them from being marked as stale
+        exempt-pr-labels: "override-stale"
+        

.github/workflows/test_pr.yml

@@ -0,0 +1,81 @@
+# Notebook-related checks
+
+name: Presubmit checks
+
+on:
+  # Relevant PRs
+  pull_request:
+    paths:
+    - "swarms/**"
+    - "tests/**"
+  # Allow manual runs
+  workflow_dispatch:
+
+jobs:
+  test3_11:
+    name: Test Py3.11
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.11'
+    - name: Run tests
+      run: |
+        python --version
+        pip install .[dev]
+        python -m pytest
+  test3_10:
+    name: Test Py3.10
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Run tests
+      run: |
+        python --version
+        pip install -q .[dev]
+        python -m pytest
+  test3_9:
+    name: Test Py3.9
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.9'
+    - name: Run tests
+      run: |
+        python --version
+        pip install .[dev]
+        python -m pytest
+  pytype3_10:
+    name: pytype 3.10
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Run pytype
+      run: |
+        python --version
+        pip install .[dev]
+        pip install -q gspread ipython
+        pytype
+  format:
+    name: Check format with black
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.11'
+    - name: Check format
+      run: |
+        python --version
+        pip install -q .
+        pip install -q black
+        black . --check

docs/swarms/memory/pinecone.md

@@ -1,4 +1,4 @@
-# `PineconeVectorStoreStore` Documentation
+# `PineconDB` Documentation
 
 ## Table of Contents
 

docs/swarms/memory/short_term_memory.md

@@ -0,0 +1,248 @@
+# Short-Term Memory Module Documentation
+
+## Introduction
+The Short-Term Memory module is a component of the SWARMS framework designed for managing short-term and medium-term memory in a multi-agent system. This documentation provides a detailed explanation of the Short-Term Memory module, its purpose, functions, and usage.
+
+### Purpose
+The Short-Term Memory module serves the following purposes:
+1. To store and manage messages in short-term memory.
+2. To provide functions for retrieving, updating, and clearing memory.
+3. To facilitate searching for specific terms within the memory.
+4. To enable saving and loading memory data to/from a file.
+
+### Class Definition
+```python
+class ShortTermMemory(BaseStructure):
+    def __init__(
+        self,
+        return_str: bool = True,
+        autosave: bool = True,
+        *args,
+        **kwargs,
+    ):
+    ...
+```
+
+#### Parameters
+| Parameter           | Type     | Default Value | Description                                                                                                      |
+|---------------------|----------|---------------|------------------------------------------------------------------------------------------------------------------|
+| `return_str`        | bool     | True          | If True, returns memory as a string.                                                                            |
+| `autosave`          | bool     | True          | If True, enables automatic saving of memory data to a file.                                                     |
+| `*args`, `**kwargs` |          |               | Additional arguments and keyword arguments (not used in the constructor but allowed for flexibility).          |
+
+### Functions
+
+#### 1. `add`
+```python
+def add(self, role: str = None, message: str = None, *args, **kwargs):
+```
+
+- Adds a message to the short-term memory.
+- Parameters:
+  - `role` (str, optional): Role associated with the message.
+  - `message` (str, optional): The message to be added.
+- Returns: The added memory.
+
+##### Example 1: Adding a Message to Short-Term Memory
+```python
+memory.add(role="Agent 1", message="Received task assignment.")
+```
+
+##### Example 2: Adding Multiple Messages to Short-Term Memory
+```python
+messages = [("Agent 1", "Received task assignment."), ("Agent 2", "Task completed.")]
+for role, message in messages:
+    memory.add(role=role, message=message)
+```
+
+#### 2. `get_short_term`
+```python
+def get_short_term(self):
+```
+
+- Retrieves the short-term memory.
+- Returns: The contents of the short-term memory.
+
+##### Example: Retrieving Short-Term Memory
+```python
+short_term_memory = memory.get_short_term()
+for entry in short_term_memory:
+    print(entry["role"], ":", entry["message"])
+```
+
+#### 3. `get_medium_term`
+```python
+def get_medium_term(self):
+```
+
+- Retrieves the medium-term memory.
+- Returns: The contents of the medium-term memory.
+
+##### Example: Retrieving Medium-Term Memory
+```python
+medium_term_memory = memory.get_medium_term()
+for entry in medium_term_memory:
+    print(entry["role"], ":", entry["message"])
+```
+
+#### 4. `clear_medium_term`
+```python
+def clear_medium_term(self):
+```
+
+- Clears the medium-term memory.
+
+##### Example: Clearing Medium-Term Memory
+```python
+memory.clear_medium_term()
+```
+
+#### 5. `get_short_term_memory_str`
+```python
+def get_short_term_memory_str(self, *args, **kwargs):
+```
+
+- Retrieves the short-term memory as a string.
+- Returns: A string representation of the short-term memory.
+
+##### Example: Getting Short-Term Memory as a String
+```python
+short_term_memory_str = memory.get_short_term_memory_str()
+print(short_term_memory_str)
+```
+
+#### 6. `update_short_term`
+```python
+def update_short_term(self, index, role: str, message: str, *args, **kwargs):
+```
+
+- Updates a message in the short-term memory.
+- Parameters:
+  - `index` (int): The index of the message to update.
+  - `role` (str): New role for the message.
+  - `message` (str): New message content.
+- Returns: None.
+
+##### Example: Updating a Message in Short-Term Memory
+```python
+memory.update_short_term(index=0, role="Updated Role", message="Updated message content.")
+```
+
+#### 7. `clear`
+```python
+def clear(self):
+```
+
+- Clears the short-term memory.
+
+##### Example: Clearing Short-Term Memory
+```python
+memory.clear()
+```
+
+#### 8. `search_memory`
+```python
+def search_memory(self, term):
+```
+
+- Searches the memory for a specific term.
+- Parameters:
+  - `term` (str): The term to search for.
+- Returns: A dictionary containing search results for short-term and medium-term memory.
+
+##### Example: Searching Memory for a Term
+```python
+search_results = memory.search_memory("task")
+print("Short-Term Memory Results:", search_results["short_term"])
+print("Medium-Term Memory Results:", search_results["medium_term"])
+```
+
+#### 9. `return_shortmemory_as_str`
+```python
+def return_shortmemory_as_str(self):
+```
+
+- Returns the memory as a string.
+
+##### Example: Returning Short-Term Memory as a String
+```python
+short_term_memory_str = memory.return_shortmemory_as_str()
+print(short_term_memory_str)
+```
+
+#### 10. `move_to_medium_term`
+```python
+def move_to_medium_term(self, index):
+```
+
+- Moves a message from the short-term memory to the medium-term memory.
+- Parameters:
+  - `index` (int): The index of the message to move.
+
+##### Example: Moving a Message to Medium-Term Memory
+```python
+memory.move_to_medium_term(index=0)
+```
+
+#### 11. `return_medium_memory_as_str`
+```python
+def return_medium_memory_as_str(self):
+```
+
+- Returns the medium-term memory as a string.
+
+##### Example: Returning Medium-Term Memory as a String
+```python
+medium_term_memory_str = memory.return_medium_memory_as_str()
+print(medium_term_memory_str)
+```
+
+#### 12. `save_to_file`
+```python
+def save_to_file(self, filename: str):
+```
+
+- Saves the memory data to a file.
+- Parameters:
+  - `filename` (str): The name of the file to save the data to.
+
+##### Example: Saving Memory Data to a File
+```python
+memory.save_to_file("memory_data.json")
+```
+
+#### 13. `load_from_file`
+```python
+def load_from_file(self, filename: str, *args, **kwargs):
+```
+
+- Loads memory data from a file.
+- Parameters:
+  - `filename` (str): The name of the file to load data from.
+
+##### Example: Loading Memory Data from a File
+```python
+memory.load_from_file("memory_data.json")
+```
+
+### Additional Information and Tips
+
+- To use the Short-Term Memory module effectively, consider the following tips:
+  - Use the `add` function to store messages in short-term memory.
+  -
+
+ Retrieve memory contents using `get_short_term` and `get_medium_term` functions.
+  - Clear memory as needed using `clear` and `clear_medium_term` functions.
+  - Search for specific terms within the memory using the `search_memory` function.
+  - Save and load memory data to/from files using `save_to_file` and `load_from_file` functions.
+
+- Ensure proper exception handling when using memory functions to handle potential errors gracefully.
+
+- When using the `search_memory` function, iterate through the results dictionary to access search results for short-term and medium-term memory.
+
+### References and Resources
+
+- For more information on multi-agent systems and memory management, refer to the SWARMS framework documentation: [SWARMS Documentation](https://swarms.apac.ai/).
+
+- For advanced memory management and customization, explore the SWARMS framework source code.
+

mkdocs.yml

@@ -105,8 +105,9 @@ nav:
     - SequentialWorkflow: 'swarms/structs/sequential_workflow.md'
   - swarms.memory:
     - Weaviate: "swarms/memory/weaviate.md"
-    - PineconeVectorStoreStore: "swarms/memory/pinecone.md"
+    - PineconDB: "swarms/memory/pinecone.md"
     - PGVectorStore: "swarms/memory/pg.md"
+    - ShortTermMemory: "swarms/memory/short_term_memory.md"
   - swarms.utils:
     - phoenix_trace_decorator: "swarms/utils/phoenix_tracer.md"
 - Guides:

swarms/__init__.py

@@ -7,3 +7,7 @@ from swarms.swarms import *  # noqa: E402, F403
 from swarms.structs import *  # noqa: E402, F403
 from swarms.models import *  # noqa: E402, F403
 from swarms.telemetry import *  # noqa: E402, F403
+from swarms.utils import *  # noqa: E402, F403
+from swarms.prompts import *  # noqa: E402, F403
+
+# from swarms.cli import *  # noqa: E402, F403

swarms/memory/__init__.py

@@ -1,5 +1,7 @@
 from swarms.memory.base_vectordb import VectorDatabase
+from swarms.memory.short_term_memory import ShortTermMemory
 
 __all__ = [
     "VectorDatabase",
+    "ShortTermMemory"
 ]

swarms/memory/chroma_db.py

@@ -61,6 +61,8 @@ class ChromaDB:
         openai_api_key: str = OPENAI_API_KEY,
         top_results_num: int = 3,
         limit_tokens: Optional[int] = 1000,
+        *args,
+        **kwargs,
     ):
         self.metric = metric
         self.RESULTS_STORE_NAME = RESULTS_STORE_NAME
@@ -91,7 +93,9 @@ class ChromaDB:
             embedding_function=embedding_function,
         )
 
-    def add(self, task: Dict, result: str, result_id: str):
+    def add(
+        self, task: Dict, result: str, result_id: str, *args, **kwargs
+    ):
         """Adds a result to the ChromaDB collection
 
         Args:
@@ -137,16 +141,15 @@ class ChromaDB:
                         "task": task["task_name"],
                         "result": result,
                     },
+                    *args,
+                    **kwargs,
                 )
         except Exception as error:
             print(
                 colored(f"Error adding to ChromaDB: {error}", "red")
             )
 
-    def query(
+    def query(self, query: str, *args, **kwargs) -> List[dict]:
-        self,
-        query: str,
-    ) -> List[dict]:
         """Queries the ChromaDB collection with a query for the top results
 
         Args:
@@ -164,6 +167,8 @@ class ChromaDB:
                 query_texts=query,
                 n_results=min(self.top_results_num, count),
                 include=["metadatas"],
+                *args,
+                **kwargs,
             )
             out = [item["task"] for item in results["metadatas"][0]]
             out = limit_tokens_from_string(

swarms/memory/pinecone.py

@@ -1,14 +1,14 @@
 from typing import Optional
-from swarms.memory.base import BaseVectorStore
+from swarms.memory.base_vectordb import VectorDatabase
 import pinecone
 from attr import define, field
 from swarms.utils.hash import str_to_hash
 
 
 @define
-class PineconeVectorStoreStore(BaseVectorStore):
+class PineconDB(VectorDatabase):
     """
-    PineconeVectorStore is a vector storage driver that uses Pinecone as the underlying storage engine.
+    PineconDB is a vector storage driver that uses Pinecone as the underlying storage engine.
 
     Pinecone is a vector database that allows you to store, search, and retrieve high-dimensional vectors with
     blazing speed and low latency. It is a managed service that is easy to use and scales effortlessly, so you can
@@ -34,14 +34,14 @@ class PineconeVectorStoreStore(BaseVectorStore):
             Creates a new index.
 
     Usage:
-    >>> from swarms.memory.vector_stores.pinecone import PineconeVectorStore
+    >>> from swarms.memory.vector_stores.pinecone import PineconDB
     >>> from swarms.utils.embeddings import USEEmbedding
     >>> from swarms.utils.hash import str_to_hash
     >>> from swarms.utils.dataframe import dataframe_to_hash
     >>> import pandas as pd
     >>>
-    >>> # Create a new PineconeVectorStore instance:
+    >>> # Create a new PineconDB instance:
-    >>> pv = PineconeVectorStore(
+    >>> pv = PineconDB(
     >>>     api_key="your-api-key",
     >>>     index_name="your-index-name",
     >>>     environment="us-west1-gcp",
@@ -102,7 +102,7 @@ class PineconeVectorStoreStore(BaseVectorStore):
 
         self.index = pinecone.Index(self.index_name)
 
-    def upsert_vector(
+    def add(
         self,
         vector: list[float],
         vector_id: Optional[str] = None,
@@ -110,7 +110,17 @@ class PineconeVectorStoreStore(BaseVectorStore):
         meta: Optional[dict] = None,
         **kwargs,
     ) -> str:
-        """Upsert vector"""
+        """Add a vector to the index.
+
+        Args:
+            vector (list[float]): _description_
+            vector_id (Optional[str], optional): _description_. Defaults to None.
+            namespace (Optional[str], optional): _description_. Defaults to None.
+            meta (Optional[dict], optional): _description_. Defaults to None.
+
+        Returns:
+            str: _description_
+        """
         vector_id = (
             vector_id if vector_id else str_to_hash(str(vector))
         )
@@ -121,31 +131,15 @@ class PineconeVectorStoreStore(BaseVectorStore):
 
         return vector_id
 
-    def load_entry(
+    def load_entries(self, namespace: Optional[str] = None):
-        self, vector_id: str, namespace: Optional[str] = None
+        """Load all entries from the index.
-    ) -> Optional[BaseVectorStore.Entry]:
+
-        """Load entry"""
+        Args:
-        result = self.index.fetch(
+            namespace (Optional[str], optional): _description_. Defaults to None.
-            ids=[vector_id], namespace=namespace
-        ).to_dict()
-        vectors = list(result["vectors"].values())
-
-        if len(vectors) > 0:
-            vector = vectors[0]
-
-            return BaseVectorStore.Entry(
-                id=vector["id"],
-                meta=vector["metadata"],
-                vector=vector["values"],
-                namespace=result["namespace"],
-            )
-        else:
-            return None
 
-    def load_entries(
+        Returns:
-        self, namespace: Optional[str] = None
+            _type_: _description_
-    ) -> list[BaseVectorStore.Entry]:
+        """
-        """Load entries"""
         # This is a hacky way to query up to 10,000 values from Pinecone. Waiting on an official API for fetching
         # all values from a namespace:
         # https://community.pinecone.io/t/is-there-a-way-to-query-all-the-vectors-and-or-metadata-from-a-namespace/797/5
@@ -157,15 +151,14 @@ class PineconeVectorStoreStore(BaseVectorStore):
             namespace=namespace,
         )
 
-        return [
+        for result in results["matches"]:
-            BaseVectorStore.Entry(
+            entry = {
-                id=r["id"],
+                "id": result["id"],
-                vector=r["values"],
+                "vector": result["values"],
-                meta=r["metadata"],
+                "meta": result["metadata"],
-                namespace=results["namespace"],
+                "namespace": result["namespace"],
-            )
+            }
-            for r in results["matches"]
+            return entry
-        ]
 
     def query(
         self,
@@ -173,19 +166,26 @@ class PineconeVectorStoreStore(BaseVectorStore):
         count: Optional[int] = None,
         namespace: Optional[str] = None,
         include_vectors: bool = False,
-        # PineconeVectorStoreStorageDriver-specific params:
+        # PineconDBStorageDriver-specific params:
         include_metadata=True,
         **kwargs,
-    ) -> list[BaseVectorStore.QueryResult]:
+    ):
-        """Query vectors"""
+        """Query the index for vectors similar to the given query string.
+
+        Args:
+            query (str): _description_
+            count (Optional[int], optional): _description_. Defaults to None.
+            namespace (Optional[str], optional): _description_. Defaults to None.
+            include_vectors (bool, optional): _description_. Defaults to False.
+            include_metadata (bool, optional): _description_. Defaults to True.
+
+        Returns:
+            _type_: _description_
+        """
         vector = self.embedding_driver.embed_string(query)
 
         params = {
-            "top_k": (
+            "top_k": count,
-                count
-                if count
-                else BaseVectorStore.DEFAULT_QUERY_COUNT
-            ),
             "namespace": namespace,
             "include_values": include_vectors,
             "include_metadata": include_metadata,
@@ -193,19 +193,22 @@ class PineconeVectorStoreStore(BaseVectorStore):
 
         results = self.index.query(vector, **params)
 
-        return [
+        for r in results["matches"]:
-            BaseVectorStore.QueryResult(
+            entry = {
-                id=r["id"],
+                "id": results["id"],
-                vector=r["values"],
+                "vector": results["values"],
-                score=r["score"],
+                "score": results["scores"],
-                meta=r["metadata"],
+                "meta": results["metadata"],
-                namespace=results["namespace"],
+                "namespace": results["namespace"],
-            )
+            }
-            for r in results["matches"]
+            return entry
-        ]
 
     def create_index(self, name: str, **kwargs) -> None:
-        """Create index"""
+        """Create a new index.
+
+        Args:
+            name (str): _description_
+        """
         params = {
             "name": name,
             "dimension": self.embedding_driver.dimensions,

swarms/memory/qdrant.py

@@ -1,5 +1,6 @@
 import subprocess
 from typing import List
+
 from httpx import RequestError
 
 try:
@@ -15,8 +16,8 @@ try:
     from qdrant_client import QdrantClient
     from qdrant_client.http.models import (
         Distance,
-        VectorParams,
         PointStruct,
+        VectorParams,
     )
 except ImportError:
     print("Please install the qdrant-client package")
@@ -91,7 +92,7 @@ class Qdrant:
             )
             print(f"Collection '{self.collection_name}' created.")
 
-    def add_vectors(self, docs: List[dict]):
+    def add(self, docs: List[dict], *args, **kwargs):
         """
         Adds vector representations of documents to the Qdrant collection.
 
@@ -128,13 +129,15 @@ class Qdrant:
                 collection_name=self.collection_name,
                 wait=True,
                 points=points,
+                *args,
+                **kwargs,
             )
             return operation_info
         except Exception as e:
             print(f"Error adding vectors: {e}")
             return None
 
-    def search_vectors(self, query: str, limit: int = 3):
+    def query(self, query: str, limit: int = 3, *args, **kwargs):
         """
         Searches the collection for vectors similar to the query vector.
 
@@ -147,12 +150,14 @@ class Qdrant:
         """
         try:
             query_vector = self.model.encode(
-                query, normalize_embeddings=True
+                query, normalize_embeddings=True, *args, **kwargs
             )
             search_result = self.client.search(
                 collection_name=self.collection_name,
                 query_vector=query_vector,
                 limit=limit,
+                *args,
+                **kwargs,
             )
             return search_result
         except Exception as e:

swarms/memory/short_term_memory.py

@@ -0,0 +1,166 @@
+import logging
+from swarms.structs.base import BaseStructure
+import threading
+import json
+import os
+
+
+class ShortTermMemory(BaseStructure):
+    def __init__(
+        self,
+        return_str: bool = True,
+        autosave: bool = True,
+        *args,
+        **kwargs,
+    ):
+        self.return_str = return_str
+        self.autosave = autosave
+        self.short_term_memory = []
+        self.medium_term_memory = []
+        self.lock = threading.Lock()
+
+    def add(
+        self, role: str = None, message: str = None, *args, **kwargs
+    ):
+        """Add a message to the short term memory.
+
+        Args:
+            role (str, optional): _description_. Defaults to None.
+            message (str, optional): _description_. Defaults to None.
+
+        Returns:
+            _type_: _description_
+        """
+        try:
+            memory = self.short_term_memory.append(
+                {"role": role, "message": message}
+            )
+
+            return memory
+        except Exception as error:
+            print(f"Add to short term memory failed: {error}")
+            raise error
+
+    def get_short_term(self):
+        """Get the short term memory.
+
+        Returns:
+            _type_: _description_
+        """
+        return self.short_term_memory
+
+    def get_medium_term(self):
+        """Get the medium term memory.
+
+        Returns:
+            _type_: _description_
+        """
+        return self.medium_term_memory
+
+    def clear_medium_term(self):
+        """Clear the medium term memory."""
+        self.medium_term_memory = []
+
+    def get_short_term_memory_str(self, *args, **kwargs):
+        """Get the short term memory as a string."""
+        return str(self.short_term_memory)
+
+    def update_short_term(
+        self, index, role: str, message: str, *args, **kwargs
+    ):
+        self.short_term_memory[index] = {
+            "role": role,
+            "message": message,
+        }
+
+    def clear(self):
+        """Clear the short term memory."""
+        self.short_term_memory = []
+
+    def search_memory(self, term):
+        """Search the memory for a term.
+
+        Args:
+            term (_type_): _description_
+
+        Returns:
+            _type_: _description_
+        """
+        results = {"short_term": [], "medium_term": []}
+        for i, message in enumerate(self.short_term_memory):
+            if term in message["message"]:
+                results["short_term"].append((i, message))
+        for i, message in enumerate(self.medium_term_memory):
+            if term in message["message"]:
+                results["medium_term"].append((i, message))
+        return results
+
+    def return_shortmemory_as_str(self):
+        """Return the memory as a string.
+
+        Returns:
+            _type_: _description_
+        """
+        return str(self.short_term_memory)
+
+    def move_to_medium_term(self, index):
+        """Move a message from the short term memory to the medium term memory.
+
+        Args:
+            index (_type_): _description_
+        """
+        message = self.short_term_memory.pop(index)
+        self.medium_term_memory.append(message)
+
+    def return_medium_memory_as_str(self):
+        """Return the medium term memory as a string.
+
+        Returns:
+            _type_: _description_
+        """
+        return str(self.medium_term_memory)
+
+    def save_to_file(self, filename: str):
+        """Save the memory to a file.
+
+        Args:
+            filename (str): _description_
+        """
+        try:
+            with self.lock:
+                with open(filename, "w") as f:
+                    json.dump(
+                        {
+                            "short_term_memory": (
+                                self.short_term_memory
+                            ),
+                            "medium_term_memory": (
+                                self.medium_term_memory
+                            ),
+                        },
+                        f,
+                    )
+
+                    logging.info(f"Saved memory to {filename}")
+        except Exception as error:
+            print(f"Error saving memory to {filename}: {error}")
+
+    def load_from_file(self, filename: str, *args, **kwargs):
+        """Load the memory from a file.
+
+        Args:
+            filename (str): _description_
+        """
+        try:
+            with self.lock:
+                with open(filename, "r") as f:
+                    data = json.load(f)
+                self.short_term_memory = data.get(
+                    "short_term_memory", []
+                )
+                self.medium_term_memory = data.get(
+                    "medium_term_memory", []
+                )
+                logging.info(f"Loaded memory from {filename}")
+        except Exception as error:
+            print(f"Erorr loading memory from {filename}: {error}")

swarms/memory/vector_store_retrieveer.py

@@ -1,4 +0,0 @@
-"""
-Implement retreiever for vector store
-
-"""

swarms/models/__init__.py

@@ -31,7 +31,7 @@ from swarms.models.layoutlm_document_qa import (
 )  # noqa: E402
 from swarms.models.gpt4_vision_api import GPT4VisionAPI  # noqa: E402
 from swarms.models.openai_tts import OpenAITTS  # noqa: E402
-
+from swarms.models.gemini import Gemini # noqa: E402
 # from swarms.models.gpt4v import GPT4Vision
 # from swarms.models.dalle3 import Dalle3
 # from swarms.models.distilled_whisperx import DistilWhisperModel # noqa: E402
@@ -63,4 +63,5 @@ __all__ = [
     "GPT4VisionAPI",
     # "vLLM",
     "OpenAITTS",
+    "Gemini",
 ]

swarms/structs/agent.py

@@ -264,14 +264,6 @@ class Agent:
         if preset_stopping_token:
             self.stopping_token = "<DONE>"
 
-        # If memory then add the json to the memory vector database
-        if memory:
-            # Add all of the state to the memory
-            self.add_message_to_memory_db(
-                {"message": self.state_to_str()},
-                {"agent_id": self.id},
-            )
-
         # If tools exist then add the tool docs usage to the sop
         if self.tools:
             self.sop_list.append(

swarms/structs/base.py

@@ -79,7 +79,6 @@ class BaseStructure(ABC):
         self.save_metadata_path = save_metadata_path
         self.save_error_path = save_error_path
 
-    @abstractmethod
     def run(self, *args, **kwargs):
         """Run the structure."""
         pass

swarms/structs/sequential_workflow.py

@@ -6,74 +6,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 from termcolor import colored
 
 from swarms.structs.agent import Agent
-
+from swarms.structs.task import Task
-
-# Define a generic Task that can handle different types of callable objects
-@dataclass
-class Task:
-    """
-    Task class for running a task in a sequential workflow.
-
-
-    Args:
-        description (str): The description of the task.
-        agent (Union[Callable, Agent]): The model or agent to execute the task.
-        args (List[Any]): Additional arguments to pass to the task execution.
-        kwargs (Dict[str, Any]): Additional keyword arguments to pass to the task execution.
-        result (Any): The result of the task execution.
-        history (List[Any]): The history of the task execution.
-
-    Methods:
-        execute: Execute the task.
-
-
-    Examples:
-    >>> from swarms.structs import Task, Agent
-    >>> from swarms.models import OpenAIChat
-    >>> agent = Agent(llm=OpenAIChat(openai_api_key=""), max_loops=1, dashboard=False)
-    >>> task = Task(description="What's the weather in miami", agent=agent)
-    >>> task.execute()
-    >>> task.result
-
-    """
-
-    description: str
-    agent: Union[Callable, Agent]
-    args: List[Any] = field(default_factory=list)
-    kwargs: Dict[str, Any] = field(default_factory=dict)
-    result: Any = None
-    history: List[Any] = field(default_factory=list)
-    # logger = logging.getLogger(__name__)
-
-    def execute(self):
-        """
-        Execute the task.
-
-        Raises:
-            ValueError: If a Agent instance is used as a task and the 'task' argument is not provided.
-        """
-        if isinstance(self.agent, Agent):
-            # Add a prompt to notify the Agent of the sequential workflow
-            if "prompt" in self.kwargs:
-                self.kwargs["prompt"] += (
-                    f"\n\nPrevious output: {self.result}"
-                    if self.result
-                    else ""
-                )
-            else:
-                self.kwargs["prompt"] = (
-                    f"Main task: {self.description}"
-                    + (
-                        f"\n\nPrevious output: {self.result}"
-                        if self.result
-                        else ""
-                    )
-                )
-            self.result = self.agent.run(*self.args, **self.kwargs)
-        else:
-            self.result = self.agent(*self.args, **self.kwargs)
-
-        self.history.append(self.result)
 
 
 # SequentialWorkflow class definition using dataclasses
@@ -361,7 +294,10 @@ class SequentialWorkflow:
             )
 
     def workflow_bootup(self, **kwargs) -> None:
-        """Bootup the workflow."""
+        """
+        Workflow bootup.
+
+        """
         print(
             colored(
                 """

swarms/structs/task.py

@@ -1,132 +1,78 @@
-from dataclass import dataclass, field
+from dataclasses import dataclass, field
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Union,
+)
+
 from swarms.structs.agent import Agent
-from typing import Optional
-from typing import List, Dict, Any, Sequence
 
 
+# Define a generic Task that can handle different types of callable objects
 @dataclass
 class Task:
     """
-    Task is a unit of work that can be executed by a set of agents.
+    Task class for running a task in a sequential workflow.
 
-    A task is defined by a task name and a set of agents that can execute the task.
-    The task can also have a set of dependencies, which are the names of other tasks
-    that must be executed before this task can be executed.
 
     Args:
-        id (str): The name of the task.
+        description (str): The description of the task.
-        description (Optional[str]): A description of the task.
+        agent (Union[Callable, Agent]): The model or agent to execute the task.
-        task (str): The name of the task.
+        args (List[Any]): Additional arguments to pass to the task execution.
-        result (Any): The result of the task.
+        kwargs (Dict[str, Any]): Additional keyword arguments to pass to the task execution.
-        agents (Sequence[Agent]): A list of agents that can execute the task.
+        result (Any): The result of the task execution.
-        dependencies (List[str], optional): A list of task names that must be executed before this task can be executed. Defaults to [].
+        history (List[Any]): The history of the task execution.
-        args (List[Any], optional): A list of arguments to pass to the agents. Defaults to field(default_factory=list).
-        kwargs (List[Any], optional): A list of keyword arguments to pass to the agents. Defaults to field(default_factory=list).
 
     Methods:
-        execute: Executes the task by passing the results of the parent tasks to the agents.
+        execute: Execute the task.
-
-    Examples:
-    import os
-    from swarms.models import OpenAIChat
-    from swarms.structs import Agent
-    from swarms.structs.sequential_workflow import SequentialWorkflow
-    from dotenv import load_dotenv
-
-    load_dotenv()
-
-    # Load the environment variables
-    api_key = os.getenv("OPENAI_API_KEY")
-
-
-    # Initialize the language agent
-    llm = OpenAIChat(
-        openai_api_key=api_key,
-        temperature=0.5,
-        max_tokens=3000,
-    )
-
-
-    # Initialize the agent with the language agent
-    agent1 = Agent(llm=llm, max_loops=1)
-
-    # Create another agent for a different task
-    agent2 = Agent(llm=llm, max_loops=1)
 
-    # Create the workflow
-    workflow = SequentialWorkflow(max_loops=1)
 
-    # Add tasks to the workflow
+    Examples:
-    workflow.add(
+    >>> from swarms.structs import Task, Agent
-        agent1, "Generate a 10,000 word blog on health and wellness.",
+    >>> from swarms.models import OpenAIChat
-    )
+    >>> agent = Agent(llm=OpenAIChat(openai_api_key=""), max_loops=1, dashboard=False)
-
+    >>> task = Task(description="What's the weather in miami", agent=agent)
-    # Suppose the next task takes the output of the first task as input
+    >>> task.execute()
-    workflow.add(
+    >>> task.result
-        agent2, "Summarize the generated blog",
-    )
-
-    # Run the workflow
-    workflow.run()
-
-    # Output the results
-    for task in workflow.tasks:
-        print(f"Task: {task.description}, Result: {task.result}")
 
     """
 
-    def __init__(
+    description: str
-        self,
+    agent: Union[Callable, Agent]
-        id: str,
+    args: List[Any] = field(default_factory=list)
-        description: Optional[str],
+    kwargs: Dict[str, Any] = field(default_factory=dict)
-        task: str,
+    result: Any = None
-        result: Any,
+    history: List[Any] = field(default_factory=list)
-        agents: Sequence[Agent],
+    # logger = logging.getLogger(__name__)
-        dependencies: List[str] = [],
-        args: List[Any] = field(default_factory=list),
-        kwargs: List[Any] = field(default_factory=list),
-    ):
-        self.id = id
-        self.description = description
-        self.task = task
-        self.result = result
-        self.agents = agents
-        self.dependencies = dependencies
-        self.results = []
-        self.args = args
-        self.kwargs = kwargs
 
-    def execute(self, parent_results: Dict[str, Any]):
+    def execute(self):
-        """Executes the task by passing the results of the parent tasks to the agents.
+        """
-
+        Execute the task.
-        Args:
-            parent_results (Dict[str, Any]): A dictionary of task names and their results.
 
-        Examples:
+        Raises:
+            ValueError: If a Agent instance is used as a task and the 'task' argument is not provided.
         """
-        args = [parent_results[dep] for dep in self.dependencies]
+        if isinstance(self.agent, Agent):
-        for agent in self.agents:
+            # Add a prompt to notify the Agent of the sequential workflow
-            if isinstance(agent, Agent):
             if "prompt" in self.kwargs:
                 self.kwargs["prompt"] += (
-                        f"\n\nPrevious output: {self.results[-1]}"
+                    f"\n\nPrevious output: {self.result}"
-                        if self.results
+                    if self.result
                     else ""
                 )
             else:
                 self.kwargs["prompt"] = (
                     f"Main task: {self.description}"
                     + (
-                            f"\n\nPrevious output: {self.results[-1]}"
+                        f"\n\nPrevious output: {self.result}"
-                            if self.results
+                        if self.result
                         else ""
                     )
                 )
-                result = agent.run(
+            self.result = self.agent.run(*self.args, **self.kwargs)
-                    self.description, *args, **self.kwargs
-                )
         else:
-                result = agent(self.description, *args, **self.kwargs)
+            self.result = self.agent(*self.args, **self.kwargs)
-            self.results.append(result)
+
-            args = [result]
+        self.history.append(self.result)
-            self.history.append(result)

tests/memory/test_pinecone.py

@@ -1,6 +1,6 @@
 import os
 from unittest.mock import patch
-from swarms.memory.pinecone import PineconeVectorStore
+from swarms.memory.pinecone import PineconDB
 
 api_key = os.getenv("PINECONE_API_KEY") or ""
 
@@ -9,7 +9,7 @@ def test_init():
     with patch("pinecone.init") as MockInit, patch(
         "pinecone.Index"
     ) as MockIndex:
-        store = PineconeVectorStore(
+        store = PineconDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -21,7 +21,7 @@ def test_init():
 
 def test_upsert_vector():
     with patch("pinecone.init"), patch("pinecone.Index") as MockIndex:
-        store = PineconeVectorStore(
+        store = PineconDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -37,7 +37,7 @@ def test_upsert_vector():
 
 def test_load_entry():
     with patch("pinecone.init"), patch("pinecone.Index") as MockIndex:
-        store = PineconeVectorStore(
+        store = PineconDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -48,7 +48,7 @@ def test_load_entry():
 
 def test_load_entries():
     with patch("pinecone.init"), patch("pinecone.Index") as MockIndex:
-        store = PineconeVectorStore(
+        store = PineconDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -59,7 +59,7 @@ def test_load_entries():
 
 def test_query():
     with patch("pinecone.init"), patch("pinecone.Index") as MockIndex:
-        store = PineconeVectorStore(
+        store = PineconDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -72,7 +72,7 @@ def test_create_index():
     with patch("pinecone.init"), patch("pinecone.Index"), patch(
         "pinecone.create_index"
     ) as MockCreateIndex:
-        store = PineconeVectorStore(
+        store = PineconDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",

tests/memory/test_qdrant.py

@@ -6,7 +6,7 @@ from swarms.memory.qdrant import Qdrant
 
 @pytest.fixture
 def mock_qdrant_client():
-    with patch("your_module.QdrantClient") as MockQdrantClient:
+    with patch("swarms.memory.Qdrant") as MockQdrantClient:
         yield MockQdrantClient()
 
 

tests/memory/test_short_term_memory.py

@@ -0,0 +1,128 @@
+import pytest
+from swarms.memory.short_term_memory import ShortTermMemory
+import threading
+
+def test_init():
+    memory = ShortTermMemory()
+    assert memory.short_term_memory == []
+    assert memory.medium_term_memory == []
+
+
+def test_add():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    assert memory.short_term_memory == [
+        {"role": "user", "message": "Hello, world!"}
+    ]
+
+
+def test_get_short_term():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    assert memory.get_short_term() == [
+        {"role": "user", "message": "Hello, world!"}
+    ]
+
+
+def test_get_medium_term():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    memory.move_to_medium_term(0)
+    assert memory.get_medium_term() == [
+        {"role": "user", "message": "Hello, world!"}
+    ]
+
+
+def test_clear_medium_term():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    memory.move_to_medium_term(0)
+    memory.clear_medium_term()
+    assert memory.get_medium_term() == []
+
+
+def test_get_short_term_memory_str():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    assert (
+        memory.get_short_term_memory_str()
+        == "[{'role': 'user', 'message': 'Hello, world!'}]"
+    )
+
+
+def test_update_short_term():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    memory.update_short_term(0, "user", "Goodbye, world!")
+    assert memory.get_short_term() == [
+        {"role": "user", "message": "Goodbye, world!"}
+    ]
+
+
+def test_clear():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    memory.clear()
+    assert memory.get_short_term() == []
+
+
+def test_search_memory():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    assert memory.search_memory("Hello") == {
+        "short_term": [
+            (0, {"role": "user", "message": "Hello, world!"})
+        ],
+        "medium_term": [],
+    }
+
+
+def test_return_shortmemory_as_str():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    assert (
+        memory.return_shortmemory_as_str()
+        == "[{'role': 'user', 'message': 'Hello, world!'}]"
+    )
+
+
+def test_move_to_medium_term():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    memory.move_to_medium_term(0)
+    assert memory.get_medium_term() == [
+        {"role": "user", "message": "Hello, world!"}
+    ]
+    assert memory.get_short_term() == []
+
+
+def test_return_medium_memory_as_str():
+    memory = ShortTermMemory()
+    memory.add("user", "Hello, world!")
+    memory.move_to_medium_term(0)
+    assert (
+        memory.return_medium_memory_as_str()
+        == "[{'role': 'user', 'message': 'Hello, world!'}]"
+    )
+
+
+def test_thread_safety():
+    memory = ShortTermMemory()
+    def add_messages():
+        for _ in range(1000):
+            memory.add("user", "Hello, world!")
+    threads = [threading.Thread(target=add_messages) for _ in range(10)]
+    for thread in threads:
+        thread.start()
+    for thread in threads:
+        thread.join()
+    assert len(memory.get_short_term()) == 10000
+
+def test_save_and_load():
+    memory1 = ShortTermMemory()
+    memory1.add("user", "Hello, world!")
+    memory1.save_to_file("memory.json")
+    memory2 = ShortTermMemory()
+    memory2.load_from_file("memory.json")
+    assert memory1.get_short_term() == memory2.get_short_term()
+    assert memory1.get_medium_term() == memory2.get_medium_term()

tests/structs/test_task.py

@@ -108,3 +108,59 @@ def test_task_execute_with_mocked_agents(task, mocker):
     parent_results = {}
     task.execute(parent_results)
     assert len(task.results) == 5
+
+
+def test_task_creation():
+    agent = Agent()
+    task = Task(id="1", task="Task1", result=None, agents=[agent])
+    assert task.id == "1"
+    assert task.task == "Task1"
+    assert task.result is None
+    assert task.agents == [agent]
+
+
+def test_task_with_dependencies():
+    agent = Agent()
+    task = Task(
+        id="2",
+        task="Task2",
+        result=None,
+        agents=[agent],
+        dependencies=["Task1"],
+    )
+    assert task.dependencies == ["Task1"]
+
+
+def test_task_with_args():
+    agent = Agent()
+    task = Task(
+        id="3",
+        task="Task3",
+        result=None,
+        agents=[agent],
+        args=["arg1", "arg2"],
+    )
+    assert task.args == ["arg1", "arg2"]
+
+
+def test_task_with_kwargs():
+    agent = Agent()
+    task = Task(
+        id="4",
+        task="Task4",
+        result=None,
+        agents=[agent],
+        kwargs={"kwarg1": "value1"},
+    )
+    assert task.kwargs == {"kwarg1": "value1"}
+
+
+# ... continue creating tests for different scenarios
+
+
+# Test execute method
+def test_execute():
+    agent = Agent()
+    task = Task(id="5", task="Task5", result=None, agents=[agent])
+    # Assuming execute method returns True on successful execution
+    assert task.execute() == True