fix: address PR review feedback

- Remove deprecated custom_update_memory_prompt from MemoryConfig entirely
- Add MAX_BATCH=100 chunking guard to embed_batch in openai.py and azure_openai.py
- Move all inline imports (mem0.utils.*) to top-level in main.py
- Remove custom_update_memory_prompt from test fixture

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

mem0/configs/base.py

@@ -60,10 +60,6 @@ class MemoryConfig(BaseModel):
         description="Custom instructions for fact extraction",
         default=None,
     )
-    custom_update_memory_prompt: Optional[str] = Field(
-        description="Custom prompt for the update memory (deprecated: use custom_instructions)",
-        default=None,
-    )
 
 
 class AzureConfig(BaseModel):

mem0/embeddings/azure_openai.py

@@ -55,10 +55,18 @@ class AzureOpenAIEmbedding(EmbeddingBase):
         return self.client.embeddings.create(input=[text], model=self.config.model).data[0].embedding
 
     def embed_batch(self, texts, memory_action="add"):
-        """Embed multiple texts in a single Azure OpenAI API call."""
+        """Embed multiple texts in a single Azure OpenAI API call.
+
+        Automatically chunks into batches of 100 to stay within API limits.
+        """
+        MAX_BATCH = 100
         texts = [text.replace("\n", " ") for text in texts]
-        response = self.client.embeddings.create(
-            input=texts,
-            model=self.config.model,
-        )
-        return [item.embedding for item in sorted(response.data, key=lambda x: x.index)]
+        all_embeddings = []
+        for i in range(0, len(texts), MAX_BATCH):
+            chunk = texts[i : i + MAX_BATCH]
+            response = self.client.embeddings.create(
+                input=chunk,
+                model=self.config.model,
+            )
+            all_embeddings.extend(item.embedding for item in sorted(response.data, key=lambda x: x.index))
+        return all_embeddings

mem0/embeddings/openai.py

@@ -55,14 +55,22 @@ class OpenAIEmbedding(EmbeddingBase):
         return self.client.embeddings.create(**kwargs).data[0].embedding
 
     def embed_batch(self, texts, memory_action="add"):
-        """Embed multiple texts in a single OpenAI API call."""
+        """Embed multiple texts in a single OpenAI API call.
+
+        Automatically chunks into batches of 100 to stay within API limits.
+        """
+        MAX_BATCH = 100
         texts = [text.replace("\n", " ") for text in texts]
-        kwargs = {
-            "input": texts,
-            "model": self.config.model,
-            "encoding_format": "float",
-        }
-        if self._pass_dimensions_to_api:
-            kwargs["dimensions"] = self.config.embedding_dims
-        response = self.client.embeddings.create(**kwargs)
-        return [item.embedding for item in sorted(response.data, key=lambda x: x.index)]
+        all_embeddings = []
+        for i in range(0, len(texts), MAX_BATCH):
+            chunk = texts[i : i + MAX_BATCH]
+            kwargs = {
+                "input": chunk,
+                "model": self.config.model,
+                "encoding_format": "float",
+            }
+            if self._pass_dimensions_to_api:
+                kwargs["dimensions"] = self.config.embedding_dims
+            response = self.client.embeddings.create(**kwargs)
+            all_embeddings.extend(item.embedding for item in sorted(response.data, key=lambda x: x.index))
+        return all_embeddings

mem0/memory/main.py

@@ -24,7 +24,8 @@ from mem0.configs.prompts import (
     get_update_memory_messages,
 )
 from mem0.utils.lemmatization import lemmatize_for_bm25
-from mem0.utils.entity_extraction import extract_entities_batch
+from mem0.utils.entity_extraction import extract_entities, extract_entities_batch
+from mem0.utils.scoring import ENTITY_BOOST_WEIGHT, get_bm25_params, normalize_bm25, score_and_rank
 from mem0.exceptions import ValidationError as Mem0ValidationError
 from mem0.memory.base import MemoryBase
 from mem0.memory.setup import mem0_dir, setup_config
@@ -191,7 +192,6 @@ class Memory(MemoryBase):
     def __init__(self, config: MemoryConfig = MemoryConfig()):
         self.config = config
 
-        self.custom_update_memory_prompt = self.config.custom_update_memory_prompt
         self.embedding_model = EmbedderFactory.create(
             self.config.embedder.provider,
             self.config.embedder.config,
@@ -217,15 +217,6 @@ class Memory(MemoryBase):
         # Entity store is initialized lazily on first use
         self._entity_store = None
 
-        if self.config.custom_update_memory_prompt:
-            import warnings
-            warnings.warn(
-                "custom_update_memory_prompt is deprecated and has no effect in the v3 pipeline. "
-                "Use custom_instructions instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
         if self.config.graph_store.config:
             provider = self.config.graph_store.provider
             self.graph = GraphStoreFactory.create(provider, self.config)
@@ -1145,10 +1136,6 @@ class Memory(MemoryBase):
         return False
 
     def _search_vector_store(self, query, filters, limit, threshold=0.1):
-        from mem0.utils.lemmatization import lemmatize_for_bm25
-        from mem0.utils.entity_extraction import extract_entities
-        from mem0.utils.scoring import get_bm25_params, normalize_bm25, score_and_rank, ENTITY_BOOST_WEIGHT
-
         # Guard against None threshold (backward compat)
         if threshold is None:
             threshold = 0.1
@@ -1261,8 +1248,6 @@ class Memory(MemoryBase):
         Returns:
             Dict mapping memory_id (str) -> max entity boost [0, 0.5].
         """
-        from mem0.utils.scoring import ENTITY_BOOST_WEIGHT
-
         # Deduplicate entities (max 8)
         seen = set()
         deduped = []
@@ -2494,10 +2479,6 @@ class AsyncMemory(MemoryBase):
         return False
 
     async def _search_vector_store(self, query, filters, limit, threshold=0.1):
-        from mem0.utils.lemmatization import lemmatize_for_bm25
-        from mem0.utils.entity_extraction import extract_entities
-        from mem0.utils.scoring import get_bm25_params, normalize_bm25, score_and_rank, ENTITY_BOOST_WEIGHT
-
         if threshold is None:
             threshold = 0.1
 
@@ -2596,8 +2577,6 @@ class AsyncMemory(MemoryBase):
 
     async def _compute_entity_boosts_async(self, query_entities, filters):
         """Async version of entity boost computation."""
-        from mem0.utils.scoring import ENTITY_BOOST_WEIGHT
-
         seen = set()
         deduped = []
         for entity_type, entity_text in query_entities[:8]:

tests/test_main.py

@@ -61,7 +61,6 @@ def memory_custom_instance():
         config = MemoryConfig(
             version="v1.1",
             custom_instructions="custom prompt extracting memory in json format",
-            custom_update_memory_prompt="custom prompt determining memory update",
         )
         config.graph_store.config = {"some_config": "value"}
         return Memory(config)