diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 53c3d57f..2fcbbb07 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -23,9 +23,9 @@ jobs:
       - name: Install dependencies
         run: poetry install --all-extras
       - name: Lint with ruff
-        run: make ci_lint
+        run: make lint
       - name: Test with pytest
-        run: make ci_test
+        run: make test
       - name: Generate coverage report
         run: make coverage
       - name: Upload coverage reports to Codecov
diff --git a/Makefile b/Makefile
index e2bfad01..217c6bb1 100644
--- a/Makefile
+++ b/Makefile
@@ -20,7 +20,7 @@ install_opensearch:
 
 install_milvus:
 	poetry install --extras milvus
-	
+
 shell:
 	poetry shell
 
@@ -31,19 +31,13 @@ format:
 	$(PYTHON) -m black .
 	$(PYTHON) -m isort .
 
-lint:
-	$(PYTHON) -m ruff .
-
 clean:
 	rm -rf dist build *.egg-info
 
-test:
-	$(PYTHON) -m pytest
-
-ci_lint:
+lint:
 	poetry run ruff .
 
-ci_test:
+test:
 	poetry run pytest
 
 coverage:
diff --git a/embedchain/__init__.py b/embedchain/__init__.py
index b26a8f24..a0eb7de4 100644
--- a/embedchain/__init__.py
+++ b/embedchain/__init__.py
@@ -7,5 +7,5 @@ from embedchain.apps.custom_app import CustomApp  # noqa: F401
 from embedchain.apps.Llama2App import Llama2App  # noqa: F401
 from embedchain.apps.open_source_app import OpenSourceApp  # noqa: F401
 from embedchain.apps.person_app import (PersonApp,  # noqa: F401
-                                       PersonOpenSourceApp)
+                                        PersonOpenSourceApp)
 from embedchain.vectordb.chroma import ChromaDB  # noqa: F401
diff --git a/embedchain/apps/app.py b/embedchain/apps/app.py
index 9148fec8..18f03c8e 100644
--- a/embedchain/apps/app.py
+++ b/embedchain/apps/app.py
@@ -1,12 +1,13 @@
-import logging
 from typing import Optional
 
-from embedchain.config import (AppConfig, BaseEmbedderConfig, BaseLlmConfig,
-                               ChromaDbConfig)
+import yaml
+
+from embedchain.config import AppConfig, BaseEmbedderConfig, BaseLlmConfig
 from embedchain.config.vectordb.base import BaseVectorDbConfig
 from embedchain.embedchain import EmbedChain
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.embedder.openai import OpenAIEmbedder
+from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 from embedchain.llm.openai import OpenAILlm
@@ -35,7 +36,6 @@ class App(EmbedChain):
         db_config: Optional[BaseVectorDbConfig] = None,
         embedder: BaseEmbedder = None,
         embedder_config: Optional[BaseEmbedderConfig] = None,
-        chromadb_config: Optional[ChromaDbConfig] = None,
         system_prompt: Optional[str] = None,
     ):
         """
@@ -60,20 +60,10 @@ class App(EmbedChain):
         :param embedder_config: Allows you to configure the Embedder.
         example: `from embedchain.config import BaseEmbedderConfig`, defaults to None
         :type embedder_config: Optional[BaseEmbedderConfig], optional
-        :param chromadb_config: Deprecated alias of `db_config`, defaults to None
-        :type chromadb_config: Optional[ChromaDbConfig], optional
         :param system_prompt: System prompt that will be provided to the LLM as such, defaults to None
         :type system_prompt: Optional[str], optional
         :raises TypeError: LLM, database or embedder or their config is not a valid class instance.
         """
-        # Overwrite deprecated arguments
-        if chromadb_config:
-            logging.warning(
-                "DEPRECATION WARNING: Please use `db_config` argument instead of `chromadb_config`."
-                "`chromadb_config` will be removed in a future release."
-            )
-            db_config = chromadb_config
-
         # Type check configs
         if config and not isinstance(config, AppConfig):
             raise TypeError(
@@ -123,3 +113,33 @@ class App(EmbedChain):
                 "Please make sure the type is right and that you are passing an instance."
             )
         super().__init__(config, llm=llm, db=db, embedder=embedder, system_prompt=system_prompt)
+
+    @classmethod
+    def from_config(cls, yaml_path: str):
+        """
+        Instantiate an App object from a YAML configuration file.
+
+        :param yaml_path: Path to the YAML configuration file.
+        :type yaml_path: str
+        :return: An instance of the App class.
+        :rtype: App
+        """
+        with open(yaml_path, "r") as file:
+            config_data = yaml.safe_load(file)
+
+        app_config_data = config_data.get("app", {})
+        llm_config_data = config_data.get("llm", {})
+        db_config_data = config_data.get("vectordb", {})
+        embedder_config_data = config_data.get("embedder", {})
+
+        app_config = AppConfig(**app_config_data.get("config", {}))
+
+        llm_provider = llm_config_data.get("provider", "openai")
+        llm = LlmFactory.create(llm_provider, llm_config_data.get("config", {}))
+
+        db_provider = db_config_data.get("provider", "chroma")
+        db = VectorDBFactory.create(db_provider, db_config_data.get("config", {}))
+
+        embedder_provider = embedder_config_data.get("provider", "openai")
+        embedder = EmbedderFactory.create(embedder_provider, embedder_config_data.get("config", {}))
+        return cls(config=app_config, llm=llm, db=db, embedder=embedder)
diff --git a/embedchain/bots/base.py b/embedchain/bots/base.py
index 5be27121..03279857 100644
--- a/embedchain/bots/base.py
+++ b/embedchain/bots/base.py
@@ -3,7 +3,8 @@ from typing import Any
 from embedchain import App
 from embedchain.config import AddConfig, AppConfig, LlmConfig
 from embedchain.embedder.openai import OpenAIEmbedder
-from embedchain.helper.json_serializable import JSONSerializable, register_deserializable
+from embedchain.helper.json_serializable import (JSONSerializable,
+                                                 register_deserializable)
 from embedchain.llm.openai import OpenAILlm
 from embedchain.vectordb.chroma import ChromaDB
 
diff --git a/embedchain/config/vectordb/base.py b/embedchain/config/vectordb/base.py
index d0bdbd8a..3252880a 100644
--- a/embedchain/config/vectordb/base.py
+++ b/embedchain/config/vectordb/base.py
@@ -10,6 +10,7 @@ class BaseVectorDbConfig(BaseConfig):
         dir: str = "db",
         host: Optional[str] = None,
         port: Optional[str] = None,
+        **kwargs,
     ):
         """
         Initializes a configuration class instance for the vector database.
@@ -22,8 +23,14 @@ class BaseVectorDbConfig(BaseConfig):
         :type host: Optional[str], optional
         :param host: Database connection remote port. Use this if you run Embedchain as a client, defaults to None
         :type port: Optional[str], optional
+        :param kwargs: Additional keyword arguments
+        :type kwargs: dict
         """
         self.collection_name = collection_name or "embedchain_store"
         self.dir = dir
         self.host = host
         self.port = port
+        # Assign additional keyword arguments
+        if kwargs:
+            for key, value in kwargs.items():
+                setattr(self, key, value)
diff --git a/embedchain/factory.py b/embedchain/factory.py
new file mode 100644
index 00000000..900695b8
--- /dev/null
+++ b/embedchain/factory.py
@@ -0,0 +1,88 @@
+import importlib
+
+
+def load_class(class_type):
+    module_path, class_name = class_type.rsplit(".", 1)
+    module = importlib.import_module(module_path)
+    return getattr(module, class_name)
+
+
+class LlmFactory:
+    provider_to_class = {
+        "anthropic": "embedchain.llm.anthropic.AnthropicLlm",
+        "azure_openai": "embedchain.llm.azure_openai.AzureOpenAILlm",
+        "cohere": "embedchain.llm.cohere.CohereLlm",
+        "gpt4all": "embedchain.llm.gpt4all.GPT4ALLLlm",
+        "hugging_face_llm": "embedchain.llm.hugging_face_llm.HuggingFaceLlm",
+        "jina": "embedchain.llm.jina.JinaLlm",
+        "llama2": "embedchain.llm.llama2.Llama2Llm",
+        "openai": "embedchain.llm.openai.OpenAILlm",
+        "vertexai": "embedchain.llm.vertex_ai.VertexAILlm",
+    }
+    provider_to_config_class = {
+        "embedchain": "embedchain.config.llm.base_llm_config.BaseLlmConfig",
+        "openai": "embedchain.config.llm.base_llm_config.BaseLlmConfig",
+        "anthropic": "embedchain.config.llm.base_llm_config.BaseLlmConfig",
+    }
+
+    @classmethod
+    def create(cls, provider_name, config_data):
+        class_type = cls.provider_to_class.get(provider_name)
+        # Default to embedchain base config if the provider is not in the config map
+        config_name = "embedchain" if provider_name not in cls.provider_to_config_class else provider_name
+        config_class_type = cls.provider_to_config_class.get(config_name)
+        if class_type:
+            llm_class = load_class(class_type)
+            llm_config_class = load_class(config_class_type)
+            return llm_class(config=llm_config_class(**config_data))
+        else:
+            raise ValueError(f"Unsupported Llm provider: {provider_name}")
+
+
+class EmbedderFactory:
+    provider_to_class = {
+        "gpt4all": "embedchain.embedder.gpt4all.GPT4AllEmbedder",
+        "huggingface": "embedchain.embedder.huggingface.HuggingFaceEmbedder",
+        "vertexai": "embedchain.embedder.vertexai.VertexAiEmbedder",
+        "openai": "embedchain.embedder.openai.OpenAIEmbedder",
+    }
+    provider_to_config_class = {
+        "openai": "embedchain.config.embedder.base.BaseEmbedderConfig",
+    }
+
+    @classmethod
+    def create(cls, provider_name, config_data):
+        class_type = cls.provider_to_class.get(provider_name)
+        # Default to openai config if the provider is not in the config map
+        config_name = "openai" if provider_name not in cls.provider_to_config_class else provider_name
+        config_class_type = cls.provider_to_config_class.get(config_name)
+        if class_type:
+            embedder_class = load_class(class_type)
+            embedder_config_class = load_class(config_class_type)
+            return embedder_class(config=embedder_config_class(**config_data))
+        else:
+            raise ValueError(f"Unsupported Embedder provider: {provider_name}")
+
+
+class VectorDBFactory:
+    provider_to_class = {
+        "chroma": "embedchain.vectordb.chroma.ChromaDB",
+        "elasticsearch": "embedchain.vectordb.elasticsearch.ElasticsearchDB",
+        "opensearch": "embedchain.vectordb.opensearch.OpenSearchDB",
+    }
+    provider_to_config_class = {
+        "chroma": "embedchain.config.vectordb.chroma.ChromaDbConfig",
+        "elasticsearch": "embedchain.config.vectordb.elasticsearch.ElasticsearchDBConfig",
+        "opensearch": "embedchain.config.vectordb.opensearch.OpenSearchDBConfig",
+    }
+
+    @classmethod
+    def create(cls, provider_name, config_data):
+        class_type = cls.provider_to_class.get(provider_name)
+        config_class_type = cls.provider_to_config_class.get(provider_name)
+        if class_type:
+            embedder_class = load_class(class_type)
+            embedder_config_class = load_class(config_class_type)
+            return embedder_class(config=embedder_config_class(**config_data))
+        else:
+            raise ValueError(f"Unsupported Embedder provider: {provider_name}")
diff --git a/embedchain/llm/antrophic.py b/embedchain/llm/anthropic.py
similarity index 90%
rename from embedchain/llm/antrophic.py
rename to embedchain/llm/anthropic.py
index 08f653df..027da1ce 100644
--- a/embedchain/llm/antrophic.py
+++ b/embedchain/llm/anthropic.py
@@ -7,12 +7,12 @@ from embedchain.llm.base import BaseLlm
 
 
 @register_deserializable
-class AntrophicLlm(BaseLlm):
+class AnthropicLlm(BaseLlm):
     def __init__(self, config: Optional[BaseLlmConfig] = None):
         super().__init__(config=config)
 
     def get_llm_model_answer(self, prompt):
-        return AntrophicLlm._get_answer(prompt=prompt, config=self.config)
+        return AnthropicLlm._get_answer(prompt=prompt, config=self.config)
 
     @staticmethod
     def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
diff --git a/embedchain/llm/jina.py b/embedchain/llm/jina.py
index 2c906c7e..2af5a798 100644
--- a/embedchain/llm/jina.py
+++ b/embedchain/llm/jina.py
@@ -34,7 +34,8 @@ class JinaLlm(BaseLlm):
         if config.top_p:
             kwargs["model_kwargs"]["top_p"] = config.top_p
         if config.stream:
-            from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+            from langchain.callbacks.streaming_stdout import \
+                StreamingStdOutCallbackHandler
 
             chat = JinaChat(**kwargs, streaming=config.stream, callbacks=[StreamingStdOutCallbackHandler()])
         else:
diff --git a/embedchain/models/clip_processor.py b/embedchain/models/clip_processor.py
index e349f4ae..46a89c16 100644
--- a/embedchain/models/clip_processor.py
+++ b/embedchain/models/clip_processor.py
@@ -2,9 +2,7 @@ try:
     from PIL import Image, UnidentifiedImageError
     from sentence_transformers import SentenceTransformer
 except ImportError:
-    raise ImportError(
-        "Images requires extra dependencies. Install with `pip install 'embedchain[images]'"
-    ) from None
+    raise ImportError("Images requires extra dependencies. Install with `pip install 'embedchain[images]'") from None
 
 MODEL_NAME = "clip-ViT-B-32"
 
diff --git a/embedchain/vectordb/chroma.py b/embedchain/vectordb/chroma.py
index 1dfadd9b..be4e8604 100644
--- a/embedchain/vectordb/chroma.py
+++ b/embedchain/vectordb/chroma.py
@@ -37,7 +37,7 @@ class ChromaDB(BaseVectorDB):
             self.config = ChromaDbConfig()
 
         self.settings = Settings()
-        self.settings.allow_reset = self.config.allow_reset
+        self.settings.allow_reset = self.config.allow_reset if hasattr(self.config, "allow_reset") else False
         if self.config.chroma_settings:
             for key, value in self.config.chroma_settings.items():
                 if hasattr(self.settings, key):
@@ -72,6 +72,17 @@ class ChromaDB(BaseVectorDB):
         """Called during initialization"""
         return self.client
 
+    def _generate_where_clause(self, where: Dict[str, any]) -> str:
+        # If only one filter is supplied, return it as is
+        # (no need to wrap in $and based on chroma docs)
+        if len(where.keys()) == 1:
+            return where
+        where_filters = []
+        for k, v in where.items():
+            if isinstance(v, str):
+                where_filters.append({k: v})
+        return {"$and": where_filters}
+
     def _get_or_create_collection(self, name: str) -> Collection:
         """
         Get or create a named collection.
@@ -107,13 +118,14 @@ class ChromaDB(BaseVectorDB):
         if ids:
             args["ids"] = ids
         if where:
-            args["where"] = where
+            args["where"] = self._generate_where_clause(where)
         if limit:
             args["limit"] = limit
         return self.collection.get(**args)
 
     def get_advanced(self, where):
-        return self.collection.get(where=where, limit=1)
+        where_clause = self._generate_where_clause(where)
+        return self.collection.get(where=where_clause, limit=1)
 
     def add(
         self,
diff --git a/embedchain/vectordb/opensearch.py b/embedchain/vectordb/opensearch.py
index edfa69f8..3857300b 100644
--- a/embedchain/vectordb/opensearch.py
+++ b/embedchain/vectordb/opensearch.py
@@ -110,8 +110,13 @@ class OpenSearchDB(BaseVectorDB):
         return result
 
     def add(
-        self, embeddings: List[List[str]], documents: List[str], metadatas: List[object], ids: List[str],
-            skip_embedding: bool):
+        self,
+        embeddings: List[List[str]],
+        documents: List[str],
+        metadatas: List[object],
+        ids: List[str],
+        skip_embedding: bool,
+    ):
         """add data in vector database
 
         :param embeddings: list of embeddings to add
@@ -162,7 +167,8 @@ class OpenSearchDB(BaseVectorDB):
             embedding_function=embeddings,
             opensearch_url=f"{self.config.opensearch_url}",
             http_auth=self.config.http_auth,
-            use_ssl=True,
+            use_ssl=hasattr(self.config, "use_ssl") and self.config.use_ssl,
+            verify_certs=hasattr(self.config, "verify_certs") and self.config.verify_certs,
         )
 
         pre_filter = {"match_all": {}}  # default
diff --git a/embedchain/vectordb/zilliz.py b/embedchain/vectordb/zilliz.py
index dcd05cef..eb99ff2a 100644
--- a/embedchain/vectordb/zilliz.py
+++ b/embedchain/vectordb/zilliz.py
@@ -5,8 +5,8 @@ from embedchain.helper.json_serializable import register_deserializable
 from embedchain.vectordb.base import BaseVectorDB
 
 try:
-    from pymilvus import MilvusClient
-    from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility
+    from pymilvus import (Collection, CollectionSchema, DataType, FieldSchema,
+                          MilvusClient, connections, utility)
 except ImportError:
     raise ImportError(
         "Zilliz requires extra dependencies. Install with `pip install --upgrade embedchain[milvus]`"
diff --git a/embedchain/yaml/chroma.yaml b/embedchain/yaml/chroma.yaml
new file mode 100644
index 00000000..7b340b9e
--- /dev/null
+++ b/embedchain/yaml/chroma.yaml
@@ -0,0 +1,26 @@
+app:
+  config:
+    id: 'my-app'
+    collection_name: 'my-app'
+
+llm:
+  provider: openai
+  model: 'gpt-3.5-turbo'
+  config:
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'my-app'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
+    deployment_name: null
diff --git a/embedchain/yaml/opensearch.yaml b/embedchain/yaml/opensearch.yaml
new file mode 100644
index 00000000..07cf1c2d
--- /dev/null
+++ b/embedchain/yaml/opensearch.yaml
@@ -0,0 +1,33 @@
+app:
+  config:
+    id: 'my-app'
+    log_level: 'WARN'
+    collect_metrics: true
+    collection_name: 'my-app'
+
+llm:
+  provider: openai
+  model: 'gpt-3.5-turbo'
+  config:
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: opensearch
+  config:
+    opensearch_url: 'https://localhost:9200'
+    http_auth:
+      - admin
+      - admin
+    vector_dimension: 1536
+    collection_name: 'my-app'
+    use_ssl: false
+    verify_certs: false
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
+    deployment_name: null
diff --git a/embedchain/yaml/opensource.yaml b/embedchain/yaml/opensource.yaml
new file mode 100644
index 00000000..5e8e9c60
--- /dev/null
+++ b/embedchain/yaml/opensource.yaml
@@ -0,0 +1,27 @@
+app:
+  config:
+    id: 'open-source-app'
+    collection_name: 'open-source-app'
+    collect_metrics: false
+
+llm:
+  provider: gpt4all
+  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
+  config:
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'open-source-app'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: gpt4all
+  config:
+    model: 'all-MiniLM-L6-v2'
+    deployment_name: null
diff --git a/tests/apps/test_apps.py b/tests/apps/test_apps.py
index 0b2ad263..f046b560 100644
--- a/tests/apps/test_apps.py
+++ b/tests/apps/test_apps.py
@@ -1,8 +1,11 @@
 import os
 import unittest
 
+import yaml
+
 from embedchain import App, CustomApp, Llama2App, OpenSourceApp
-from embedchain.config import ChromaDbConfig, AppConfig, AddConfig, BaseLlmConfig, BaseEmbedderConfig
+from embedchain.config import (AddConfig, AppConfig, BaseEmbedderConfig,
+                               BaseLlmConfig, ChromaDbConfig)
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.llm.base import BaseLlm
 from embedchain.vectordb.base import BaseVectorDB, BaseVectorDbConfig
@@ -100,3 +103,74 @@ class TestConfigForAppComponents(unittest.TestCase):
             App(embedder_config=wrong_embedder_config)
 
         self.assertIsInstance(embedder_config, BaseEmbedderConfig)
+
+
+class TestAppFromConfig:
+    def load_config_data(self, yaml_path):
+        with open(yaml_path, "r") as file:
+            return yaml.safe_load(file)
+
+    def test_from_chroma_config(self):
+        yaml_path = "embedchain/yaml/chroma.yaml"
+        config_data = self.load_config_data(yaml_path)
+
+        app = App.from_config(yaml_path)
+
+        # Check if the App instance and its components were created correctly
+        assert isinstance(app, App)
+
+        # Validate the AppConfig values
+        assert app.config.id == config_data["app"]["config"]["id"]
+        assert app.config.collection_name == config_data["app"]["config"]["collection_name"]
+        # Even though not present in the config, the default value is used
+        assert app.config.collect_metrics is True
+
+        # Validate the LLM config values
+        llm_config = config_data["llm"]["config"]
+        assert app.llm.config.temperature == llm_config["temperature"]
+        assert app.llm.config.max_tokens == llm_config["max_tokens"]
+        assert app.llm.config.top_p == llm_config["top_p"]
+        assert app.llm.config.stream == llm_config["stream"]
+
+        # Validate the VectorDB config values
+        db_config = config_data["vectordb"]["config"]
+        assert app.db.config.collection_name == db_config["collection_name"]
+        assert app.db.config.dir == db_config["dir"]
+        assert app.db.config.allow_reset == db_config["allow_reset"]
+
+        # Validate the Embedder config values
+        embedder_config = config_data["embedder"]["config"]
+        assert app.embedder.config.model == embedder_config["model"]
+        assert app.embedder.config.deployment_name == embedder_config["deployment_name"]
+
+    def test_from_opensource_config(self):
+        yaml_path = "embedchain/yaml/opensource.yaml"
+        config_data = self.load_config_data(yaml_path)
+
+        app = App.from_config(yaml_path)
+
+        # Check if the App instance and its components were created correctly
+        assert isinstance(app, App)
+
+        # Validate the AppConfig values
+        assert app.config.id == config_data["app"]["config"]["id"]
+        assert app.config.collection_name == config_data["app"]["config"]["collection_name"]
+        assert app.config.collect_metrics == config_data["app"]["config"]["collect_metrics"]
+
+        # Validate the LLM config values
+        llm_config = config_data["llm"]["config"]
+        assert app.llm.config.temperature == llm_config["temperature"]
+        assert app.llm.config.max_tokens == llm_config["max_tokens"]
+        assert app.llm.config.top_p == llm_config["top_p"]
+        assert app.llm.config.stream == llm_config["stream"]
+
+        # Validate the VectorDB config values
+        db_config = config_data["vectordb"]["config"]
+        assert app.db.config.collection_name == db_config["collection_name"]
+        assert app.db.config.dir == db_config["dir"]
+        assert app.db.config.allow_reset == db_config["allow_reset"]
+
+        # Validate the Embedder config values
+        embedder_config = config_data["embedder"]["config"]
+        assert app.embedder.config.model == embedder_config["model"]
+        assert app.embedder.config.deployment_name == embedder_config["deployment_name"]
diff --git a/tests/bots/test_base.py b/tests/bots/test_base.py
index 050de6e6..d15f06cf 100644
--- a/tests/bots/test_base.py
+++ b/tests/bots/test_base.py
@@ -1,9 +1,11 @@
 import os
-import pytest
-from embedchain.config import AddConfig, BaseLlmConfig
-from embedchain.bots.base import BaseBot
 from unittest.mock import patch
 
+import pytest
+
+from embedchain.bots.base import BaseBot
+from embedchain.config import AddConfig, BaseLlmConfig
+
 
 @pytest.fixture
 def base_bot():
diff --git a/tests/chunkers/test_base_chunker.py b/tests/chunkers/test_base_chunker.py
index 2a9deffb..6f89cd03 100644
--- a/tests/chunkers/test_base_chunker.py
+++ b/tests/chunkers/test_base_chunker.py
@@ -1,6 +1,8 @@
 import hashlib
-import pytest
 from unittest.mock import MagicMock
+
+import pytest
+
 from embedchain.chunkers.base_chunker import BaseChunker
 from embedchain.models.data_type import DataType
 
diff --git a/tests/embedchain/test_embedchain.py b/tests/embedchain/test_embedchain.py
index 1bd97e31..3c5ffc7e 100644
--- a/tests/embedchain/test_embedchain.py
+++ b/tests/embedchain/test_embedchain.py
@@ -41,7 +41,8 @@ class TestChromaDbHostsLoglevel(unittest.TestCase):
         Test if the `App` instance is correctly reconstructed after a reset.
         """
         config = AppConfig(log_level="DEBUG", collect_metrics=False)
-        app = App(config=config, chromadb_config=ChromaDbConfig(chroma_settings={"allow_reset": True}))
+        chroma_config = {"allow_reset": True}
+        app = App(config=config, db_config=ChromaDbConfig(**chroma_config))
         app.reset()
 
         # Make sure the client is still healthy
diff --git a/tests/embedder/test_embedder.py b/tests/embedder/test_embedder.py
index e83f9770..bf03cab4 100644
--- a/tests/embedder/test_embedder.py
+++ b/tests/embedder/test_embedder.py
@@ -1,9 +1,11 @@
-import pytest
 from unittest.mock import MagicMock
-from embedchain.embedder.base import BaseEmbedder
-from embedchain.config.embedder.base import BaseEmbedderConfig
+
+import pytest
 from chromadb.api.types import Documents, Embeddings
 
+from embedchain.config.embedder.base import BaseEmbedderConfig
+from embedchain.embedder.base import BaseEmbedder
+
 
 @pytest.fixture
 def base_embedder():
diff --git a/tests/llm/test_antrophic.py b/tests/llm/test_antrophic.py
index d2489a05..2bb74d35 100644
--- a/tests/llm/test_antrophic.py
+++ b/tests/llm/test_antrophic.py
@@ -1,62 +1,63 @@
-import pytest
 from unittest.mock import MagicMock, patch
 
-from embedchain.llm.antrophic import AntrophicLlm
-from embedchain.config import BaseLlmConfig
+import pytest
 from langchain.schema import HumanMessage, SystemMessage
 
+from embedchain.config import BaseLlmConfig
+from embedchain.llm.anthropic import AnthropicLlm
+
 
 @pytest.fixture
-def antrophic_llm():
+def anthropic_llm():
     config = BaseLlmConfig(temperature=0.5, model="gpt2")
-    return AntrophicLlm(config)
+    return AnthropicLlm(config)
 
 
-def test_get_llm_model_answer(antrophic_llm):
-    with patch.object(AntrophicLlm, "_get_answer", return_value="Test Response") as mock_method:
+def test_get_llm_model_answer(anthropic_llm):
+    with patch.object(AnthropicLlm, "_get_answer", return_value="Test Response") as mock_method:
         prompt = "Test Prompt"
-        response = antrophic_llm.get_llm_model_answer(prompt)
+        response = anthropic_llm.get_llm_model_answer(prompt)
         assert response == "Test Response"
-        mock_method.assert_called_once_with(prompt=prompt, config=antrophic_llm.config)
+        mock_method.assert_called_once_with(prompt=prompt, config=anthropic_llm.config)
 
 
-def test_get_answer(antrophic_llm):
+def test_get_answer(anthropic_llm):
     with patch("langchain.chat_models.ChatAnthropic") as mock_chat:
         mock_chat_instance = mock_chat.return_value
         mock_chat_instance.return_value = MagicMock(content="Test Response")
 
         prompt = "Test Prompt"
-        response = antrophic_llm._get_answer(prompt, antrophic_llm.config)
+        response = anthropic_llm._get_answer(prompt, anthropic_llm.config)
 
         assert response == "Test Response"
         mock_chat.assert_called_once_with(
-            temperature=antrophic_llm.config.temperature, model=antrophic_llm.config.model
+            temperature=anthropic_llm.config.temperature, model=anthropic_llm.config.model
         )
         mock_chat_instance.assert_called_once_with(
-            antrophic_llm._get_messages(prompt, system_prompt=antrophic_llm.config.system_prompt)
+            anthropic_llm._get_messages(prompt, system_prompt=anthropic_llm.config.system_prompt)
         )
 
 
-def test_get_messages(antrophic_llm):
+def test_get_messages(anthropic_llm):
     prompt = "Test Prompt"
     system_prompt = "Test System Prompt"
-    messages = antrophic_llm._get_messages(prompt, system_prompt)
+    messages = anthropic_llm._get_messages(prompt, system_prompt)
     assert messages == [
         SystemMessage(content="Test System Prompt", additional_kwargs={}),
         HumanMessage(content="Test Prompt", additional_kwargs={}, example=False),
     ]
 
 
-def test_get_answer_max_tokens_is_provided(antrophic_llm, caplog):
+def test_get_answer_max_tokens_is_provided(anthropic_llm, caplog):
     with patch("langchain.chat_models.ChatAnthropic") as mock_chat:
         mock_chat_instance = mock_chat.return_value
         mock_chat_instance.return_value = MagicMock(content="Test Response")
 
         prompt = "Test Prompt"
-        config = antrophic_llm.config
+        config = anthropic_llm.config
         config.max_tokens = 500
 
-        response = antrophic_llm._get_answer(prompt, config)
+        response = anthropic_llm._get_answer(prompt, config)
 
         assert response == "Test Response"
         mock_chat.assert_called_once_with(temperature=config.temperature, model=config.model)
diff --git a/tests/llm/test_azure_openai.py b/tests/llm/test_azure_openai.py
index 7f2c7614..9ab3479d 100644
--- a/tests/llm/test_azure_openai.py
+++ b/tests/llm/test_azure_openai.py
@@ -1,9 +1,11 @@
-import pytest
 from unittest.mock import MagicMock, patch
-from embedchain.llm.azure_openai import AzureOpenAILlm
-from embedchain.config import BaseLlmConfig
+
+import pytest
 from langchain.schema import HumanMessage, SystemMessage
 
+from embedchain.config import BaseLlmConfig
+from embedchain.llm.azure_openai import AzureOpenAILlm
+
 
 @pytest.fixture
 def azure_openai_llm():
diff --git a/tests/llm/test_hugging_face_hub.py b/tests/llm/test_hugging_face_hub.py
index 63b1bfed..d4d9d64d 100644
--- a/tests/llm/test_hugging_face_hub.py
+++ b/tests/llm/test_hugging_face_hub.py
@@ -1,7 +1,7 @@
 import importlib
 import os
 import unittest
-from unittest.mock import patch, MagicMock
+from unittest.mock import MagicMock, patch
 
 from embedchain.config import BaseLlmConfig
 from embedchain.llm.hugging_face_hub import HuggingFaceHubLlm
diff --git a/tests/llm/test_vertex_ai.py b/tests/llm/test_vertex_ai.py
index 952d5c39..9941523d 100644
--- a/tests/llm/test_vertex_ai.py
+++ b/tests/llm/test_vertex_ai.py
@@ -1,9 +1,11 @@
-import pytest
 from unittest.mock import MagicMock, patch
-from embedchain.llm.vertex_ai import VertexAiLlm
-from embedchain.config import BaseLlmConfig
+
+import pytest
 from langchain.schema import HumanMessage, SystemMessage
 
+from embedchain.config import BaseLlmConfig
+from embedchain.llm.vertex_ai import VertexAiLlm
+
 
 @pytest.fixture
 def vertexai_llm():
diff --git a/tests/loaders/test_docs_site.py b/tests/loaders/test_docs_site.py
index e27bd1bf..31d03f67 100644
--- a/tests/loaders/test_docs_site.py
+++ b/tests/loaders/test_docs_site.py
@@ -1,7 +1,9 @@
 import hashlib
-import pytest
 from unittest.mock import Mock, patch
+
+import pytest
 from requests import Response
+
 from embedchain.loaders.docs_site_loader import DocsSiteLoader
 
 
diff --git a/tests/loaders/test_docx_file.py b/tests/loaders/test_docx_file.py
index 6b3bb193..b7deffcb 100644
--- a/tests/loaders/test_docx_file.py
+++ b/tests/loaders/test_docx_file.py
@@ -1,6 +1,8 @@
 import hashlib
-import pytest
 from unittest.mock import MagicMock, patch
+
+import pytest
+
 from embedchain.loaders.docx_file import DocxFileLoader
 
 
diff --git a/tests/loaders/test_local_qna_pair.py b/tests/loaders/test_local_qna_pair.py
index 29447d19..5bdfd2ca 100644
--- a/tests/loaders/test_local_qna_pair.py
+++ b/tests/loaders/test_local_qna_pair.py
@@ -1,5 +1,7 @@
 import hashlib
+
 import pytest
+
 from embedchain.loaders.local_qna_pair import LocalQnaPairLoader
 
 
diff --git a/tests/loaders/test_local_text.py b/tests/loaders/test_local_text.py
index 7d350ea5..58b6ec8f 100644
--- a/tests/loaders/test_local_text.py
+++ b/tests/loaders/test_local_text.py
@@ -1,5 +1,7 @@
 import hashlib
+
 import pytest
+
 from embedchain.loaders.local_text import LocalTextLoader
 
 
diff --git a/tests/loaders/test_mdx.py b/tests/loaders/test_mdx.py
index 960eb6e5..d4826209 100644
--- a/tests/loaders/test_mdx.py
+++ b/tests/loaders/test_mdx.py
@@ -1,6 +1,8 @@
 import hashlib
+from unittest.mock import mock_open, patch
+
 import pytest
-from unittest.mock import patch, mock_open
+
 from embedchain.loaders.mdx import MdxLoader
 
 
diff --git a/tests/loaders/test_notion.py b/tests/loaders/test_notion.py
index f4d30327..ca5f6d21 100644
--- a/tests/loaders/test_notion.py
+++ b/tests/loaders/test_notion.py
@@ -1,7 +1,9 @@
 import hashlib
 import os
-import pytest
 from unittest.mock import Mock, patch
+
+import pytest
+
 from embedchain.loaders.notion import NotionLoader
 
 
diff --git a/tests/loaders/test_web_page.py b/tests/loaders/test_web_page.py
index 61b9031b..cdaf0944 100644
--- a/tests/loaders/test_web_page.py
+++ b/tests/loaders/test_web_page.py
@@ -1,6 +1,8 @@
 import hashlib
-import pytest
 from unittest.mock import Mock, patch
+
+import pytest
+
 from embedchain.loaders.web_page import WebPageLoader
 
 
diff --git a/tests/loaders/test_youtube_video.py b/tests/loaders/test_youtube_video.py
index cc70d779..d4733161 100644
--- a/tests/loaders/test_youtube_video.py
+++ b/tests/loaders/test_youtube_video.py
@@ -1,6 +1,8 @@
 import hashlib
-import pytest
 from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+
 from embedchain.loaders.youtube_video import YoutubeVideoLoader
 
 
diff --git a/tests/test_factory.py b/tests/test_factory.py
new file mode 100644
index 00000000..5c6e81ed
--- /dev/null
+++ b/tests/test_factory.py
@@ -0,0 +1,61 @@
+import pytest
+
+import embedchain
+import embedchain.embedder.gpt4all
+import embedchain.embedder.huggingface
+import embedchain.embedder.openai
+import embedchain.embedder.vertexai
+import embedchain.llm.anthropic
+import embedchain.llm.openai
+import embedchain.vectordb.chroma
+import embedchain.vectordb.elasticsearch
+import embedchain.vectordb.opensearch
+from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
+
+
+class TestFactories:
+    @pytest.mark.parametrize(
+        "provider_name, config_data, expected_class",
+        [
+            ("openai", {}, embedchain.llm.openai.OpenAILlm),
+            ("anthropic", {}, embedchain.llm.anthropic.AnthropicLlm),
+        ],
+    )
+    def test_llm_factory_create(self, provider_name, config_data, expected_class):
+        llm_instance = LlmFactory.create(provider_name, config_data)
+        assert isinstance(llm_instance, expected_class)
+
+    @pytest.mark.parametrize(
+        "provider_name, config_data, expected_class",
+        [
+            ("gpt4all", {}, embedchain.embedder.gpt4all.GPT4AllEmbedder),
+            (
+                "huggingface",
+                {"model": "sentence-transformers/all-mpnet-base-v2"},
+                embedchain.embedder.huggingface.HuggingFaceEmbedder,
+            ),
+            ("vertexai", {"model": "textembedding-gecko"}, embedchain.embedder.vertexai.VertexAiEmbedder),
+            ("openai", {}, embedchain.embedder.openai.OpenAIEmbedder),
+        ],
+    )
+    def test_embedder_factory_create(self, mocker, provider_name, config_data, expected_class):
+        mocker.patch("embedchain.embedder.vertexai.VertexAiEmbedder", autospec=True)
+        embedder_instance = EmbedderFactory.create(provider_name, config_data)
+        assert isinstance(embedder_instance, expected_class)
+
+    @pytest.mark.parametrize(
+        "provider_name, config_data, expected_class",
+        [
+            ("chroma", {}, embedchain.vectordb.chroma.ChromaDB),
+            (
+                "opensearch",
+                {"opensearch_url": "http://localhost:9200", "http_auth": ("admin", "admin")},
+                embedchain.vectordb.opensearch.OpenSearchDB,
+            ),
+            ("elasticsearch", {"es_url": "http://localhost:9200"}, embedchain.vectordb.elasticsearch.ElasticsearchDB),
+        ],
+    )
+    def test_vectordb_factory_create(self, mocker, provider_name, config_data, expected_class):
+        mocker.patch("embedchain.vectordb.opensearch.OpenSearchDB", autospec=True)
+        vectordb_instance = VectorDBFactory.create(provider_name, config_data)
+        assert isinstance(vectordb_instance, expected_class)
diff --git a/tests/vectordb/test_chroma_db.py b/tests/vectordb/test_chroma_db.py
index 3be89d49..0472ec0f 100644
--- a/tests/vectordb/test_chroma_db.py
+++ b/tests/vectordb/test_chroma_db.py
@@ -28,19 +28,25 @@ class TestChromaDbHosts(unittest.TestCase):
         host = "test-host"
         port = "1234"
 
-        chroma_auth_settings = {
-            "chroma_client_auth_provider": "chromadb.auth.basic.BasicAuthClientProvider",
-            "chroma_client_auth_credentials": "admin:admin",
+        chroma_config = {
+            "host": host,
+            "port": port,
+            "chroma_settings": {
+                "chroma_client_auth_provider": "chromadb.auth.basic.BasicAuthClientProvider",
+                "chroma_client_auth_credentials": "admin:admin",
+            },
         }
 
-        config = ChromaDbConfig(host=host, port=port, chroma_settings=chroma_auth_settings)
+        config = ChromaDbConfig(**chroma_config)
         db = ChromaDB(config=config)
         settings = db.client.get_settings()
         self.assertEqual(settings.chroma_server_host, host)
         self.assertEqual(settings.chroma_server_http_port, port)
-        self.assertEqual(settings.chroma_client_auth_provider, chroma_auth_settings["chroma_client_auth_provider"])
         self.assertEqual(
-            settings.chroma_client_auth_credentials, chroma_auth_settings["chroma_client_auth_credentials"]
+            settings.chroma_client_auth_provider, chroma_config["chroma_settings"]["chroma_client_auth_provider"]
+        )
+        self.assertEqual(
+            settings.chroma_client_auth_credentials, chroma_config["chroma_settings"]["chroma_client_auth_credentials"]
         )
 
 
@@ -55,9 +61,9 @@ class TestChromaDbHostsInit(unittest.TestCase):
         port = "1234"
 
         config = AppConfig(collect_metrics=False)
-        chromadb_config = ChromaDbConfig(host=host, port=port)
+        db_config = ChromaDbConfig(host=host, port=port)
 
-        _app = App(config, chromadb_config=chromadb_config)
+        _app = App(config, db_config=db_config)
 
         called_settings: Settings = mock_client.call_args[0][0]
 
@@ -95,7 +101,7 @@ class TestChromaDbHostsLoglevel(unittest.TestCase):
 class TestChromaDbDuplicateHandling:
     chroma_config = ChromaDbConfig(allow_reset=True)
     app_config = AppConfig(collection_name=False, collect_metrics=False)
-    app_with_settings = App(config=app_config, chromadb_config=chroma_config)
+    app_with_settings = App(config=app_config, db_config=chroma_config)
 
     def test_duplicates_throw_warning(self, caplog):
         """
@@ -131,7 +137,7 @@ class TestChromaDbDuplicateHandling:
 class TestChromaDbCollection(unittest.TestCase):
     chroma_config = ChromaDbConfig(allow_reset=True)
     app_config = AppConfig(collection_name=False, collect_metrics=False)
-    app_with_settings = App(config=app_config, chromadb_config=chroma_config)
+    app_with_settings = App(config=app_config, db_config=chroma_config)
 
     def test_init_with_default_collection(self):
         """
@@ -296,7 +302,7 @@ class TestChromaDbCollection(unittest.TestCase):
 
         # Create four apps.
         # app1, which we are about to reset, shares an app with one, and an id with the other, none with the last.
-        app1 = App(AppConfig(id="new_app_id_1", collect_metrics=False), chromadb_config=self.chroma_config)
+        app1 = App(AppConfig(id="new_app_id_1", collect_metrics=False), db_config=self.chroma_config)
         app1.set_collection_name("one_collection")
         app2 = App(AppConfig(id="new_app_id_2", collect_metrics=False))
         app2.set_collection_name("one_collection")
diff --git a/tests/vectordb/test_zilliz_db.py b/tests/vectordb/test_zilliz_db.py
index b4149d42..6dca78f7 100644
--- a/tests/vectordb/test_zilliz_db.py
+++ b/tests/vectordb/test_zilliz_db.py
@@ -1,14 +1,15 @@
 # ruff: noqa: E501
 
 import os
+from unittest import mock
+from unittest.mock import Mock, patch
 
 import pytest
-from unittest import mock
-from unittest.mock import patch, Mock
 
 from embedchain.config import ZillizDBConfig
 from embedchain.vectordb.zilliz import ZillizVectorDB
 
+
 # to run tests, provide the URI and TOKEN in .env file
 class TestZillizVectorDBConfig:
     @mock.patch.dict(os.environ, {"ZILLIZ_CLOUD_URI": "mocked_uri", "ZILLIZ_CLOUD_TOKEN": "mocked_token"})
@@ -51,6 +52,7 @@ class TestZillizVectorDBConfig:
         with pytest.raises(AttributeError):
             ZillizDBConfig()
 
+
 class TestZillizVectorDB:
     @pytest.fixture
     @mock.patch.dict(os.environ, {"ZILLIZ_CLOUD_URI": "mocked_uri", "ZILLIZ_CLOUD_TOKEN": "mocked_token"})
@@ -147,7 +149,7 @@ class TestZillizDBCollection:
         zilliz_db = ZillizVectorDB(config=mock_config)
 
         # Add a 'embedder' attribute to the ZillizVectorDB instance for testing
-        zilliz_db.embedder = mock_embedder # Mock the 'collection' object
+        zilliz_db.embedder = mock_embedder  # Mock the 'collection' object
 
         # Add a 'collection' attribute to the ZillizVectorDB instance for testing
         zilliz_db.collection = Mock(is_empty=False)  # Mock the 'collection' object