Independent operator level cache + doc update

.github/tests/lm_tests.py

@@ -399,7 +399,7 @@ def test_custom_tokenizer():
 @pytest.mark.parametrize("model", get_enabled("gpt-4o-mini"))
 def test_cache(setup_models, model):
     lm = setup_models[model]
-    lotus.settings.configure(lm=lm, enable_message_cache=True)
+    lotus.settings.configure(lm=lm, enable_cache=True)
 
     # Check that "What is the capital of France?" becomes cached
     first_batch = [
@@ -428,19 +428,20 @@ def test_cache(setup_models, model):
 @pytest.mark.parametrize("model", get_enabled("gpt-4o-mini"))
 def test_disable_cache(setup_models, model):
     lm = setup_models[model]
-    lotus.settings.configure(lm=lm, enable_message_cache=False)
+    lotus.settings.configure(lm=lm, enable_cache=False)
 
     batch = [
         [{"role": "user", "content": "Hello, world!"}],
         [{"role": "user", "content": "What is the capital of France?"}],
     ]
+
     lm(batch)
     assert lm.stats.total_usage.cache_hits == 0
     lm(batch)
     assert lm.stats.total_usage.cache_hits == 0
 
     # Now enable cache. Note that the first batch is not cached.
-    lotus.settings.configure(enable_message_cache=True)
+    lotus.settings.configure(enable_cache=True)
     first_responses = lm(batch).outputs
     assert lm.stats.total_usage.cache_hits == 0
     second_responses = lm(batch).outputs
@@ -451,7 +452,7 @@ def test_disable_cache(setup_models, model):
 @pytest.mark.parametrize("model", get_enabled("gpt-4o-mini"))
 def test_reset_cache(setup_models, model):
     lm = setup_models[model]
-    lotus.settings.configure(lm=lm, enable_message_cache=True)
+    lotus.settings.configure(lm=lm, enable_cache=True)
 
     batch = [
         [{"role": "user", "content": "Hello, world!"}],
@@ -481,7 +482,7 @@ def test_operator_cache(setup_models, model):
     cache = CacheFactory.create_cache(cache_config)
 
     lm = LM(model="gpt-4o-mini", cache=cache)
-    lotus.settings.configure(lm=lm, enable_message_cache=True, enable_operator_cache=True)
+    lotus.settings.configure(lm=lm, enable_cache=True)
 
     data = {
         "Course Name": [
@@ -537,7 +538,7 @@ def test_disable_operator_cache(setup_models, model):
     cache = CacheFactory.create_cache(cache_config)
 
     lm = LM(model="gpt-4o-mini", cache=cache)
-    lotus.settings.configure(lm=lm, enable_message_cache=True, enable_operator_cache=False)
+    lotus.settings.configure(lm=lm, enable_cache=False)
 
     data = {
         "Course Name": [
@@ -569,15 +570,17 @@ def test_disable_operator_cache(setup_models, model):
     user_instruction = "What is a similar course to {Course Name}. Please just output the course name."
 
     first_response = df.sem_map(user_instruction)
+    first_response["_map"] = first_response["_map"].str.replace(r"[^a-zA-Z\s]", "", regex=True).str.lower()
     assert lm.stats.total_usage.operator_cache_hits == 0
 
     second_response = df.sem_map(user_instruction)
+    second_response["_map"] = second_response["_map"].str.replace(r"[^a-zA-Z\s]", "", regex=True).str.lower()
     assert lm.stats.total_usage.operator_cache_hits == 0
 
     pd.testing.assert_frame_equal(first_response, second_response)
 
     # Now enable operator cache.
-    lotus.settings.configure(enable_operator_cache=True)
+    lotus.settings.configure(enable_cache=True)
     first_responses = df.sem_map(user_instruction)
     first_responses["_map"] = first_responses["_map"].str.replace(r"[^a-zA-Z\s]", "", regex=True).str.lower()
     assert lm.stats.total_usage.operator_cache_hits == 0

docs/configurations.rst

@@ -24,9 +24,18 @@ Configurable Parameters
 1. enable_message_cache: 
     * Description: Enables or Disables cahcing mechanisms
     * Default: False
+    * Parameters: 
+        - cache_type: Type of caching (SQLITE or In_MEMORY)
+        - max_size: maximum size of cache
+        - cache_dir: Directory for where DB file is stored. Default: "~/.lotus/cache"
+    * Note: It is recommended to enable caching
 .. code-block:: python
 
-    lotus.settings.configure(enable_message_cache=True)
+    cache_config = CacheConfig(cache_type=CacheType.SQLITE, max_size=1000)
+    cache = CacheFactory.create_cache(cache_config)
+
+    lm = LM(model='gpt-4o-mini', cache=cache)
+    lotus.settings.configure(lm=lm, enable_cache=True)
 
 2. setting RM:
     * Description: Configures the retrieval model

lotus/cache.py

@@ -20,7 +20,7 @@ def require_cache_enabled(func: Callable) -> Callable:
 
     @wraps(func)
     def wrapper(self, *args, **kwargs):
-        if not lotus.settings.enable_message_cache:
+        if not lotus.settings.enable_cache:
             return None
         return func(self, *args, **kwargs)
 
@@ -33,21 +33,39 @@ def operator_cache(func: Callable) -> Callable:
     @wraps(func)
     def wrapper(self, *args, **kwargs):
         model = lotus.settings.lm
-        use_operator_cache = lotus.settings.enable_operator_cache
+        use_operator_cache = lotus.settings.enable_cache
 
         if use_operator_cache and model.cache:
 
-            def serialize(value):
-                if isinstance(value, pd.DataFrame):
-                    return value.to_json()
+            def serialize(value: Any) -> Any:
+                """
+                Serialize a value into a JSON-serializable format.
+                Supports basic types, pandas DataFrames, and objects with a `dict` or `__dict__` method.
+                """
+                if value is None or isinstance(value, (str, int, float, bool)):
+                    return value
+                elif isinstance(value, pd.DataFrame):
+                    return value.to_json(orient="split")
+                elif isinstance(value, (list, tuple)):
+                    return [serialize(item) for item in value]
+                elif isinstance(value, dict):
+                    return {key: serialize(val) for key, val in value.items()}
                 elif hasattr(value, "dict"):
                     return value.dict()
-                return value
-
+                elif hasattr(value, "__dict__"):
+                    return {key: serialize(val) for key, val in vars(value).items() if not key.startswith("_")}
+                else:
+                    # For unsupported types, convert to string (last resort)
+                    lotus.logger.warning(f"Unsupported type {type(value)} for serialization. Converting to string.")
+                    return str(value)
+
+            serialize_self = serialize(self)
             serialized_kwargs = {key: serialize(value) for key, value in kwargs.items()}
             serialized_args = [serialize(arg) for arg in args]
             cache_key = hashlib.sha256(
-                json.dumps({"args": serialized_args, "kwargs": serialized_kwargs}, sort_keys=True).encode()
+                json.dumps(
+                    {"self": serialize_self, "args": serialized_args, "kwargs": serialized_kwargs}, sort_keys=True
+                ).encode()
             ).hexdigest()
 
             cached_result = model.cache.get(cache_key)
@@ -134,7 +152,6 @@ def _create_table(self):
     def _get_time(self):
         return int(time.time())
 
-    @require_cache_enabled
     def get(self, key: str) -> Any | None:
         with self.conn:
             cursor = self.conn.execute("SELECT value FROM cache WHERE key = ?", (key,))
@@ -152,7 +169,6 @@ def get(self, key: str) -> Any | None:
                 return value
         return None
 
-    @require_cache_enabled
     def insert(self, key: str, value: Any):
         pickled_value = pickle.dumps(value)
         with self.conn:
@@ -196,14 +212,12 @@ def __init__(self, max_size: int):
         super().__init__(max_size)
         self.cache: OrderedDict[str, Any] = OrderedDict()
 
-    @require_cache_enabled
     def get(self, key: str) -> Any | None:
         if key in self.cache:
             lotus.logger.debug(f"Cache hit for {key}")
 
         return self.cache.get(key)
 
-    @require_cache_enabled
     def insert(self, key: str, value: Any):
         self.cache[key] = value
 

lotus/models/lm.py

@@ -55,25 +55,34 @@ def __call__(
         if all_kwargs.get("logprobs", False):
             all_kwargs.setdefault("top_logprobs", 10)
 
-        # Check cache and separate cached and uncached messages
-        hashed_messages = [self._hash_messages(msg, all_kwargs) for msg in messages]
-        cached_responses = [self.cache.get(hash) for hash in hashed_messages]
-        uncached_data = [
-            (msg, hash) for msg, hash, resp in zip(messages, hashed_messages, cached_responses) if resp is None
-        ]
+        if lotus.settings.enable_cache:
+            # Check cache and separate cached and uncached messages
+            hashed_messages = [self._hash_messages(msg, all_kwargs) for msg in messages]
+            cached_responses = [self.cache.get(hash) for hash in hashed_messages]
+
+        uncached_data = (
+            [(msg, hash) for msg, hash, resp in zip(messages, hashed_messages, cached_responses) if resp is None]
+            if lotus.settings.enable_cache
+            else [(msg, "no-cache") for msg in messages]
+        )
+
         self.stats.total_usage.cache_hits += len(messages) - len(uncached_data)
 
         # Process uncached messages in batches
         uncached_responses = self._process_uncached_messages(
             uncached_data, all_kwargs, show_progress_bar, progress_bar_desc
         )
-
-        # Add new responses to cache
-        for resp, (_, hash) in zip(uncached_responses, uncached_data):
-            self._cache_response(resp, hash)
+        if lotus.settings.enable_cache:
+            # Add new responses to cache
+            for resp, (_, hash) in zip(uncached_responses, uncached_data):
+                self._cache_response(resp, hash)
 
         # Merge all responses in original order and extract outputs
-        all_responses = self._merge_responses(cached_responses, uncached_responses)
+        all_responses = (
+            self._merge_responses(cached_responses, uncached_responses)
+            if lotus.settings.enable_cache
+            else uncached_responses
+        )
         outputs = [self._get_top_choice(resp) for resp in all_responses]
         logprobs = (
             [self._get_top_choice_logprobs(resp) for resp in all_responses] if all_kwargs.get("logprobs") else None

lotus/settings.py

@@ -12,8 +12,7 @@ class Settings:
     reranker: lotus.models.Reranker | None = None
 
     # Cache settings
-    enable_message_cache: bool = False
-    enable_operator_cache: bool = False
+    enable_cache: bool = False
 
     # Serialization setting
     serialization_format: SerializationFormat = SerializationFormat.DEFAULT