Add support for vector lookup

Use indexing

.gitignore

@@ -5,6 +5,7 @@ __pycache__/
 
 # C extensions
 *.so
+.langgraph_api
 
 # Distribution / packaging
 .Python

langgraph.json

@@ -5,5 +5,11 @@
     },
     "env": ".env",
     "python_version": "3.11",
-    "dependencies": ["."]
+    "dependencies": ["."],
+    "store": {
+        "index": {
+            "dims": 1536,
+            "embed": "openai:text-embedding-3-small"
+        }
+    }
 }

pyproject.toml

@@ -13,7 +13,7 @@ dependencies = [
     # Optional (for selecting different models)
     "langchain-openai>=0.2.1",
     "langchain-anthropic>=0.2.1",
-    "langchain>=0.3.1",
+    "langchain>=0.3.8",
     "langchain-core>=0.3.8",
     "python-dotenv>=1.0.1",
     "langgraph-sdk>=0.1.32",

src/memory_agent/graph.py

@@ -24,11 +24,13 @@ async def call_model(state: State, config: RunnableConfig, *, store: BaseStore)
 
     # Retrieve the most recent memories for context
     memories = await store.asearch(
-        ("memories", configurable.user_id), limit=10
+        ("memories", configurable.user_id),
+        query=str([m.content for m in state.messages[-3:]]),
+        limit=10,
     )
 
     # Format memories for inclusion in the prompt
-    formatted = "\n".join(f"[{mem.key}]: {mem.value}" for mem in memories)
+    formatted = "\n".join(f"[{mem.key}]: {mem.value} (similarity: {mem.score})" for mem in memories)
     if formatted:
         formatted = f"""
 <memories>

src/memory_agent/tools.py

@@ -40,4 +40,4 @@ async def upsert_memory(
         key=str(mem_id),
         value={"content": content, "context": context},
     )
-    return f"Stored memory {memory_id}"
+    return f"Stored memory {mem_id}"