feat: TS SDK v3 port, graph store removal, client v3 API migration

TypeScript SDK v3 pipeline (full parity with Python):
- Single-pass additive extraction with ADDITIVE_EXTRACTION_PROMPT
- Hybrid search (semantic + BM25 + entity boost) with additive scoring
- 8-phase batch pipeline (batch embed, persist, entity linking)
- New utils: scoring.ts, lemmatization.ts (natural), entity_extraction.ts (compromise)
- keywordSearch() on 8 vector stores (3 full: PGVector, Memory, Azure AI Search)
- Message persistence in SQLiteManager (rolling window of 10)
- Entity store as second vector collection
- MAX_BATCH=100 chunking guard on OpenAI/Azure embedBatch
- Updated default LLM model to gpt-4.1-nano-2025-04-14
- compromise + natural added as peer dependencies

Graph store removal (Python + TypeScript):
- Removed Neo4j, Memgraph, Kuzu, Neptune, Apache AGE integrations
- Deleted 18 graph-related files across both SDKs
- Removed GraphStoreFactory, GraphStoreConfig, graph_store config field
- Removed "relations" key from all API responses
- Removed graph optional dependency group from pyproject.toml
- Removed neo4j-driver from TS peerDependencies
- Simplified add/search/delete/reset (no more parallel graph operations)

Client SDK v3 API migration:
- add() endpoint: /v1/memories/ -> /v3/memories/ (async response)
- search() endpoint: /v2/memories/search/ -> /v3/memories/search/
- Removed output_format injection and v1.1 unwrapping logic
- Applied to both Python (sync + async) and TypeScript clients

Review feedback fixes:
- Removed deprecated custom_update_memory_prompt from MemoryConfig
- Added MAX_BATCH=100 chunking to Python + TS embed_batch
- Moved all inline imports to top level in main.py
- Cleaned up GraphStoreError dead code from exceptions.py

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

server/main.py

@@ -40,14 +40,6 @@ POSTGRES_USER = os.environ.get("POSTGRES_USER", "postgres")
 POSTGRES_PASSWORD = os.environ.get("POSTGRES_PASSWORD", "postgres")
 POSTGRES_COLLECTION_NAME = os.environ.get("POSTGRES_COLLECTION_NAME", "memories")
 
-NEO4J_URI = os.environ.get("NEO4J_URI", "bolt://neo4j:7687")
-NEO4J_USERNAME = os.environ.get("NEO4J_USERNAME", "neo4j")
-NEO4J_PASSWORD = os.environ.get("NEO4J_PASSWORD", "mem0graph")
-
-MEMGRAPH_URI = os.environ.get("MEMGRAPH_URI", "bolt://localhost:7687")
-MEMGRAPH_USERNAME = os.environ.get("MEMGRAPH_USERNAME", "memgraph")
-MEMGRAPH_PASSWORD = os.environ.get("MEMGRAPH_PASSWORD", "mem0graph")
-
 OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
 HISTORY_DB_PATH = os.environ.get("HISTORY_DB_PATH", "/app/history/history.db")
 
@@ -64,10 +56,6 @@ DEFAULT_CONFIG = {
             "collection_name": POSTGRES_COLLECTION_NAME,
         },
     },
-    "graph_store": {
-        "provider": "neo4j",
-        "config": {"url": NEO4J_URI, "username": NEO4J_USERNAME, "password": NEO4J_PASSWORD},
-    },
     "llm": {"provider": "openai", "config": {"api_key": OPENAI_API_KEY, "temperature": 0.2, "model": "gpt-4.1-nano-2025-04-14"}},
     "embedder": {"provider": "openai", "config": {"api_key": OPENAI_API_KEY, "model": "text-embedding-3-small"}},
     "history_db_path": HISTORY_DB_PATH,