feat: add support for Elastcisearch as vector data source (#402)

embedchain/config/apps/CustomAppConfig.py

@@ -3,7 +3,8 @@ from typing import Any
 from chromadb.api.types import Documents, Embeddings
 from dotenv import load_dotenv
 
-from embedchain.models import EmbeddingFunctions, Providers
+from embedchain.config.vectordbs import ElasticsearchDBConfig
+from embedchain.models import EmbeddingFunctions, Providers, VectorDatabases, VectorDimensions
 
 from .BaseAppConfig import BaseAppConfig
 
@@ -28,6 +29,8 @@ class CustomAppConfig(BaseAppConfig):
         provider: Providers = None,
         open_source_app_config=None,
         deployment_name=None,
+        db_type: VectorDatabases = None,
+        es_config: ElasticsearchDBConfig = None,
     ):
         """
         :param log_level: Optional. (String) Debug level
@@ -41,6 +44,8 @@ class CustomAppConfig(BaseAppConfig):
         :param collection_name: Optional. Collection name for the database.
         :param provider: Optional. (Providers): LLM Provider to use.
         :param open_source_app_config: Optional. Config instance needed for open source apps.
+        :param db_type: Optional. type of Vector database to use.
+        :param es_config: Optional. elasticsearch database config to be used for connection
         """
         if provider:
             self.provider = provider
@@ -59,6 +64,9 @@ class CustomAppConfig(BaseAppConfig):
             port=port,
             id=id,
             collection_name=collection_name,
+            db_type=db_type,
+            vector_dim=CustomAppConfig.get_vector_dimension(embedding_function=embedding_fn),
+            es_config=es_config,
         )
 
     @staticmethod
@@ -108,3 +116,20 @@ class CustomAppConfig(BaseAppConfig):
             from chromadb.utils import embedding_functions
 
             return embedding_functions.SentenceTransformerEmbeddingFunction(model_name=model)
+
+    @staticmethod
+    def get_vector_dimension(embedding_function: EmbeddingFunctions):
+        if not isinstance(embedding_function, EmbeddingFunctions):
+            raise ValueError(f"Invalid option: '{embedding_function}'.")
+
+        if embedding_function == EmbeddingFunctions.OPENAI:
+            return VectorDimensions.OPENAI.value
+
+        elif embedding_function == EmbeddingFunctions.HUGGING_FACE:
+            return VectorDimensions.HUGGING_FACE.value
+
+        elif embedding_function == EmbeddingFunctions.VERTEX_AI:
+            return VectorDimensions.VERTEX_AI.value
+
+        elif embedding_function == EmbeddingFunctions.GPT4ALL:
+            return VectorDimensions.GPT4ALL.value