feat: add new custom app (#313)

embedchain/config/apps/CustomAppConfig.py

@@ -1,4 +1,15 @@
+import logging
+from typing import Any
+
+from chromadb.api.types import Documents, Embeddings
+from dotenv import load_dotenv
+
+from embedchain.models import EmbeddingFunctions, Providers
+
 from .BaseAppConfig import BaseAppConfig
+from embedchain.models import Providers
+
+load_dotenv()
 
 
 class CustomAppConfig(BaseAppConfig):
@@ -6,14 +17,88 @@ class CustomAppConfig(BaseAppConfig):
     Config to initialize an embedchain custom `App` instance, with extra config options.
     """
 
-    def __init__(self, log_level=None, ef=None, db=None, host=None, port=None, id=None):
+    def __init__(
+        self,
+        log_level=None,
+        embedding_fn: EmbeddingFunctions = None,
+        embedding_fn_model=None,
+        db=None,
+        host=None,
+        port=None,
+        id=None,
+        provider: Providers = None,
+        model=None,
+        open_source_app_config=None,
+    ):
         """
         :param log_level: Optional. (String) Debug level
         ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'].
-        :param ef: Optional. Embedding function to use.
+        :param embedding_fn: Optional. Embedding function to use.
+        :param embedding_fn_model: Optional. Model name to use for embedding function.
         :param db: Optional. (Vector) database to use for embeddings.
         :param id: Optional. ID of the app. Document metadata will have this id.
         :param host: Optional. Hostname for the database server.
         :param port: Optional. Port for the database server.
+        :param provider: Optional. (Providers): LLM Provider to use.
+        :param open_source_app_config: Optional. Config instance needed for open source apps.
         """
-        super().__init__(log_level=log_level, db=db, host=host, port=port, id=id)
+        if provider:
+            self.provider = provider
+        else:
+            raise ValueError("CustomApp must have a provider assigned.")
+
+        self.open_source_app_config = open_source_app_config
+
+        super().__init__(
+            log_level=log_level,
+            embedding_fn=CustomAppConfig.embedding_function(embedding_function=embedding_fn, model=embedding_fn_model),
+            db=db,
+            host=host,
+            port=port,
+            id=id,
+        )
+
+    @staticmethod
+    def langchain_default_concept(embeddings: Any):
+        """
+        Langchains default function layout for embeddings.
+        """
+
+        def embed_function(texts: Documents) -> Embeddings:
+            return embeddings.embed_documents(texts)
+
+        return embed_function
+
+    @staticmethod
+    def embedding_function(embedding_function: EmbeddingFunctions, model: str = None):
+        if not isinstance(embedding_function, EmbeddingFunctions):
+            raise ValueError(
+                f"Invalid option: '{embedding_function}'. Expecting one of the following options: {list(map(lambda x: x.value, EmbeddingFunctions))}"  # noqa: E501
+            )
+
+        if embedding_function == EmbeddingFunctions.OPENAI:
+            from langchain.embeddings import OpenAIEmbeddings
+
+            if model:
+                embeddings = OpenAIEmbeddings(model=model)
+            else:
+                embeddings = OpenAIEmbeddings()
+            return CustomAppConfig.langchain_default_concept(embeddings)
+
+        elif embedding_function == EmbeddingFunctions.HUGGING_FACE:
+            from langchain.embeddings import HuggingFaceEmbeddings
+
+            embeddings = HuggingFaceEmbeddings(model_name=model)
+            return CustomAppConfig.langchain_default_concept(embeddings)
+
+        elif embedding_function == EmbeddingFunctions.VERTEX_AI:
+            from langchain.embeddings import VertexAIEmbeddings
+
+            embeddings = VertexAIEmbeddings(model_name=model)
+            return CustomAppConfig.langchain_default_concept(embeddings)
+
+        elif embedding_function == EmbeddingFunctions.GPT4ALL:
+            # Note: We could use langchains GPT4ALL embedding, but it's not available in all versions.
+            from chromadb.utils import embedding_functions
+
+            return embedding_functions.SentenceTransformerEmbeddingFunction(model_name=model)