cleanup examples

.github/workflows/python-package-conda.yml

@@ -1,34 +0,0 @@
-name: Python Package using Conda
-
-on: [push]
-
-jobs:
-  build-linux:
-    runs-on: ubuntu-latest
-    strategy:
-      max-parallel: 5
-
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v5
-      with:
-        python-version: '3.10'
-    - name: Add conda to system path
-      run: |
-        # $CONDA is an environment variable pointing to the root of the miniconda directory
-        echo $CONDA/bin >> $GITHUB_PATH
-    - name: Install dependencies
-      run: |
-        conda env update --file environment.yml --name base
-    - name: Lint with flake8
-      run: |
-        conda install flake8
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with pytest
-      run: |
-        conda install pytest
-        pytest

example.py

@@ -1,4 +1,33 @@
 from swarms.structs.agent import Agent
+import pinecone
+import os
+from dotenv import load_dotenv
+from datetime import datetime
+from sentence_transformers import SentenceTransformer
+
+# Load environment variables
+load_dotenv()
+
+# Initialize Pinecone
+pinecone.init(
+    api_key=os.getenv("PINECONE_API_KEY"),
+    environment=os.getenv("PINECONE_ENVIRONMENT"),
+)
+
+# Initialize the embedding model
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Create or get the index
+index_name = "financial-agent-memory"
+if index_name not in pinecone.list_indexes():
+    pinecone.create_index(
+        name=index_name,
+        dimension=768,  # Dimension for all-MiniLM-L6-v2
+        metric="cosine",
+    )
+
+# Get the index
+pinecone_index = pinecone.Index(index_name)
 
 # Initialize the agent
 agent = Agent(
@@ -11,6 +40,45 @@ agent = Agent(
     output_type="all",
 )
 
-agent.run("Conduct an analysis of the best real undervalued ETFs")
+
+def run_agent(task):
+    # Run the agent and store the interaction
+    result = agent.run(task)
+
+    # Generate embedding for the document
+    doc_text = f"Task: {task}\nResult: {result}"
+    embedding = embedding_model.encode(doc_text).tolist()
+
+    # Store the interaction in Pinecone
+    pinecone_index.upsert(
+        vectors=[
+            {
+                "id": str(datetime.now().timestamp()),
+                "values": embedding,
+                "metadata": {
+                    "agent_name": agent.agent_name,
+                    "task_type": "financial_analysis",
+                    "timestamp": str(datetime.now()),
+                    "text": doc_text,
+                },
+            }
+        ]
+    )
+
+    return result
+
+
+def query_memory(query_text, top_k=5):
+    # Generate embedding for the query
+    query_embedding = embedding_model.encode(query_text).tolist()
+
+    # Query Pinecone
+    results = pinecone_index.query(
+        vector=query_embedding, top_k=top_k, include_metadata=True
+    )
+
+    return results
+
+
 # print(out)
 # print(type(out))

swarms/structs/conversation.py

@@ -115,13 +115,16 @@ class Conversation(BaseStructure):
         }
         self.cache_lock = threading.Lock()
         self.conversations_dir = conversations_dir
-        
+
         self.setup()
 
     def setup(self):
         # Set up conversations directory
-        self.conversations_dir = self.conversations_dir or os.path.join(
-            os.path.expanduser("~"), ".swarms", "conversations"
+        self.conversations_dir = (
+            self.conversations_dir
+            or os.path.join(
+                os.path.expanduser("~"), ".swarms", "conversations"
+            )
         )
         os.makedirs(self.conversations_dir, exist_ok=True)
 
@@ -150,7 +153,9 @@ class Conversation(BaseStructure):
                 self.add(self.user or "User", self.rules)
 
             if self.custom_rules_prompt is not None:
-                self.add(self.user or "User", self.custom_rules_prompt)
+                self.add(
+                    self.user or "User", self.custom_rules_prompt
+                )
 
             # If tokenizer then truncate
             if self.tokenizer is not None: