[Bug fix] Fix typos, static methods and other sanity improvements in the package (#1129)

embedchain/config/add_config.py

@@ -26,7 +26,7 @@ class ChunkerConfig(BaseConfig):
         if self.min_chunk_size >= self.chunk_size:
             raise ValueError(f"min_chunk_size {min_chunk_size} should be less than chunk_size {chunk_size}")
         if self.min_chunk_size < self.chunk_overlap:
-            logging.warn(
+            logging.warning(
                 f"min_chunk_size {min_chunk_size} should be greater than chunk_overlap {chunk_overlap}, otherwise it is redundant."  # noqa:E501
             )
 
@@ -35,7 +35,8 @@ class ChunkerConfig(BaseConfig):
         else:
             self.length_function = length_function if length_function else len
 
-    def load_func(self, dotpath: str):
+    @staticmethod
+    def load_func(dotpath: str):
         if "." not in dotpath:
             return getattr(builtins, dotpath)
         else:

embedchain/config/cache_config.py

@@ -10,12 +10,12 @@ class CacheSimilarityEvalConfig(BaseConfig):
     This is the evaluator to compare two embeddings according to their distance computed in embedding retrieval stage.
     In the retrieval stage, `search_result` is the distance used for approximate nearest neighbor search and have been
     put into `cache_dict`. `max_distance` is used to bound this distance to make it between [0-`max_distance`].
-    `positive` is used to indicate this distance is directly proportional to the similarity of two entites.
-    If `positive` is set `False`, `max_distance` will be used to substract this distance to get the final score.
+    `positive` is used to indicate this distance is directly proportional to the similarity of two entities.
+    If `positive` is set `False`, `max_distance` will be used to subtract this distance to get the final score.
 
     :param max_distance: the bound of maximum distance.
     :type max_distance: float
-    :param positive: if the larger distance indicates more similar of two entities, It is True. Otherwise it is False.
+    :param positive: if the larger distance indicates more similar of two entities, It is True. Otherwise, it is False.
     :type positive: bool
     """
 
@@ -29,6 +29,7 @@ class CacheSimilarityEvalConfig(BaseConfig):
         self.max_distance = max_distance
         self.positive = positive
 
+    @staticmethod
     def from_config(config: Optional[Dict[str, Any]]):
         if config is None:
             return CacheSimilarityEvalConfig()
@@ -63,6 +64,7 @@ class CacheInitConfig(BaseConfig):
         self.similarity_threshold = similarity_threshold
         self.auto_flush = auto_flush
 
+    @staticmethod
     def from_config(config: Optional[Dict[str, Any]]):
         if config is None:
             return CacheInitConfig()
@@ -83,6 +85,7 @@ class CacheConfig(BaseConfig):
         self.similarity_eval_config = similarity_eval_config
         self.init_config = init_config
 
+    @staticmethod
     def from_config(config: Optional[Dict[str, Any]]):
         if config is None:
             return CacheConfig()

embedchain/config/llm/base.py

@@ -155,24 +155,26 @@ class BaseLlmConfig(BaseConfig):
         self.stream = stream
         self.where = where
 
-    def validate_prompt(self, prompt: Template) -> bool:
+    @staticmethod
+    def validate_prompt(prompt: Template) -> Optional[re.Match[str]]:
         """
         validate the prompt
 
         :param prompt: the prompt to validate
         :type prompt: Template
         :return: valid (true) or invalid (false)
-        :rtype: bool
+        :rtype: Optional[re.Match[str]]
         """
         return re.search(query_re, prompt.template) and re.search(context_re, prompt.template)
 
-    def _validate_prompt_history(self, prompt: Template) -> bool:
+    @staticmethod
+    def _validate_prompt_history(prompt: Template) -> Optional[re.Match[str]]:
         """
         validate the prompt with history
 
         :param prompt: the prompt to validate
         :type prompt: Template
         :return: valid (true) or invalid (false)
-        :rtype: bool
+        :rtype: Optional[re.Match[str]]
         """
         return re.search(history_re, prompt.template)

embedchain/config/vectordb/qdrant.py

@@ -7,8 +7,8 @@ from embedchain.helpers.json_serializable import register_deserializable
 @register_deserializable
 class QdrantDBConfig(BaseVectorDbConfig):
     """
-    Config to initialize an qdrant client.
-    :param url. qdrant url or list of nodes url to be used for connection
+    Config to initialize a qdrant client.
+    :param: url. qdrant url or list of nodes url to be used for connection
     """
 
     def __init__(

embedchain/config/vectordb/zilliz.py

@@ -26,7 +26,7 @@ class ZillizDBConfig(BaseVectorDbConfig):
         :param uri: Cluster endpoint obtained from the Zilliz Console, defaults to None
         :type uri: Optional[str], optional
         :param token: API Key, if a Serverless Cluster, username:password, if a Dedicated Cluster, defaults to None
-        :type port: Optional[str], optional
+        :type token: Optional[str], optional
         """
         self.uri = uri or os.environ.get("ZILLIZ_CLOUD_URI")
         if not self.uri: