[chore]: Rename modules for better readability and maintainability (#587)

embedchain/embedder/base.py

@@ -0,0 +1,70 @@
+from typing import Any, Callable, Optional
+
+from embedchain.config.embedder.BaseEmbedderConfig import BaseEmbedderConfig
+
+try:
+    from chromadb.api.types import Documents, Embeddings
+except RuntimeError:
+    from embedchain.utils import use_pysqlite3
+
+    use_pysqlite3()
+    from chromadb.api.types import Documents, Embeddings
+
+
+class BaseEmbedder:
+    """
+    Class that manages everything regarding embeddings. Including embedding function, loaders and chunkers.
+
+    Embedding functions and vector dimensions are set based on the child class you choose.
+    To manually overwrite you can use this classes `set_...` methods.
+    """
+
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        """
+        Intialize the embedder class.
+
+        :param config: embedder configuration option class, defaults to None
+        :type config: Optional[BaseEmbedderConfig], optional
+        """
+        if config is None:
+            self.config = BaseEmbedderConfig()
+        else:
+            self.config = config
+        self.vector_dimension: int
+
+    def set_embedding_fn(self, embedding_fn: Callable[[list[str]], list[str]]):
+        """
+        Set or overwrite the embedding function to be used by the database to store and retrieve documents.
+
+        :param embedding_fn: Function to be used to generate embeddings.
+        :type embedding_fn: Callable[[list[str]], list[str]]
+        :raises ValueError: Embedding function is not callable.
+        """
+        if not hasattr(embedding_fn, "__call__"):
+            raise ValueError("Embedding function is not a function")
+        self.embedding_fn = embedding_fn
+
+    def set_vector_dimension(self, vector_dimension: int):
+        """
+        Set or overwrite the vector dimension size
+
+        :param vector_dimension: vector dimension size
+        :type vector_dimension: int
+        """
+        self.vector_dimension = vector_dimension
+
+    @staticmethod
+    def _langchain_default_concept(embeddings: Any):
+        """
+        Langchains default function layout for embeddings.
+
+        :param embeddings: Langchain embeddings
+        :type embeddings: Any
+        :return: embedding function
+        :rtype: Callable
+        """
+
+        def embed_function(texts: Documents) -> Embeddings:
+            return embeddings.embed_documents(texts)
+
+        return embed_function