Main

scripts/ponelope_memory.py

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+import os
+import uuid
+import openai
+from pinecone import Pinecone
+
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY", "YOUR_API_KEY_HERE")
+PINECONE_ENVIRONMENT = os.getenv("PINECONE_ENVIRONMENT", "YOUR_ENVIRONMENT")
+INDEX_NAME = os.getenv("INDEX_NAME", "ponelope-memory")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_OPENAI_KEY_HERE")
+NAMESPACE = "ponelope"
+
+
+def _openai_client():
+    try:
+        return openai.OpenAI(api_key=OPENAI_API_KEY)
+    except AttributeError:
+        openai.api_key = OPENAI_API_KEY
+        return openai
+
+
+pc = Pinecone(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
+index = pc.Index(INDEX_NAME)
+client = _openai_client()
+
+
+def embed(text: str) -> list:
+    """Embed text using text-embedding-3-small."""
+    response = client.embeddings.create(model="text-embedding-3-small", input=[text])
+    return response.data[0].embedding
+
+
+def add_memory(text: str, metadata: dict | None = None) -> str:
+    """Store GPT output text in Pinecone."""
+    vector = embed(text)
+    vector_id = str(uuid.uuid4())
+    index.upsert(vectors=[{"id": vector_id, "values": vector, "metadata": metadata or {}}], namespace=NAMESPACE)
+    return vector_id
+
+
+def query_memory(query: str, top_k: int = 3):
+    """Query for the most relevant memories."""
+    vector = embed(query)
+    result = index.query(vector=vector, top_k=top_k, namespace=NAMESPACE, include_metadata=True)
+    return result.matches
+
+
+if __name__ == "__main__":
+    msg = "Example GPT-generated text to remember."
+    vid = add_memory(msg)
+    print(f"Stored vector ID: {vid}")
+    out = query_memory("Example GPT-generated query")
+    for match in out:
+        print(match)