Initial commit: code-memory service

OpenAI-compatible FastAPI proxy with GraphRAG + persistent memory.
Includes 3-level Letta memory, CMA consolidation, AST-based code indexing.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ollama_client.py

@@ -0,0 +1,36 @@
+import httpx
+import os
+from typing import AsyncIterator
+
+OLLAMA = os.getenv("OLLAMA_URL", "http://192.168.20.47:11434")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "nomic-embed-text")
+CHAT_MODEL  = os.getenv("CHAT_MODEL",  "qwen2.5-coder:7b-instruct-q4_K_M")
+
+async def embed(text: str) -> list[float]:
+    async with httpx.AsyncClient(timeout=60) as c:
+        r = await c.post(f"{OLLAMA}/api/embeddings",
+                         json={"model": EMBED_MODEL, "prompt": text})
+        r.raise_for_status()
+        return r.json()["embedding"]
+
+async def chat(messages: list[dict], model: str = None, stream: bool = False):
+    async with httpx.AsyncClient(timeout=300) as c:
+        r = await c.post(f"{OLLAMA}/api/chat",
+                         json={"model": model or CHAT_MODEL,
+                               "messages": messages,
+                               "stream": False})
+        r.raise_for_status()
+        return r.json()["message"]["content"]
+
+async def chat_stream(messages: list[dict], model: str = None) -> AsyncIterator[str]:
+    async with httpx.AsyncClient(timeout=300) as c:
+        async with c.stream("POST", f"{OLLAMA}/api/chat",
+                            json={"model": model or CHAT_MODEL,
+                                  "messages": messages,
+                                  "stream": True}) as r:
+            async for line in r.aiter_lines():
+                if line:
+                    import json
+                    data = json.loads(line)
+                    if not data.get("done"):
+                        yield data.get("message", {}).get("content", "")