refactor: app design concept (#305)

embedchain/Apps/PersonApp.py

@@ -0,0 +1,65 @@
+from string import Template
+
+from embedchain.apps.App import App
+from embedchain.apps.OpenSourceApp import OpenSourceApp
+from embedchain.config import ChatConfig, QueryConfig
+from embedchain.config.apps.BaseAppConfig import BaseAppConfig
+from embedchain.config.QueryConfig import (DEFAULT_PROMPT,
+                                           DEFAULT_PROMPT_WITH_HISTORY)
+
+
+class EmbedChainPersonApp:
+    """
+    Base class to create a person bot.
+    This bot behaves and speaks like a person.
+
+    :param person: name of the person, better if its a well known person.
+    :param config: BaseAppConfig instance to load as configuration.
+    """
+
+    def __init__(self, person, config: BaseAppConfig = None):
+        self.person = person
+        self.person_prompt = f"You are {person}. Whatever you say, you will always say in {person} style."  # noqa:E501
+        if config is None:
+            config = BaseAppConfig()
+        super().__init__(config)
+
+
+class PersonApp(EmbedChainPersonApp, App):
+    """
+    The Person app.
+    Extends functionality from EmbedChainPersonApp and App
+    """
+
+    def query(self, input_query, config: QueryConfig = None):
+        self.template = Template(self.person_prompt + " " + DEFAULT_PROMPT)
+        query_config = QueryConfig(
+            template=self.template,
+        )
+        return super().query(input_query, query_config)
+
+    def chat(self, input_query, config: ChatConfig = None):
+        self.template = Template(self.person_prompt + " " + DEFAULT_PROMPT_WITH_HISTORY)
+        chat_config = ChatConfig(
+            template=self.template,
+        )
+        return super().chat(input_query, chat_config)
+
+
+class PersonOpenSourceApp(EmbedChainPersonApp, OpenSourceApp):
+    """
+    The Person app.
+    Extends functionality from EmbedChainPersonApp and OpenSourceApp
+    """
+
+    def query(self, input_query, config: QueryConfig = None):
+        query_config = QueryConfig(
+            template=self.template,
+        )
+        return super().query(input_query, query_config)
+
+    def chat(self, input_query, config: ChatConfig = None):
+        chat_config = ChatConfig(
+            template=self.template,
+        )
+        return super().chat(input_query, chat_config)

embedchain/__init__.py

@@ -2,7 +2,7 @@ import importlib.metadata
 
 __version__ = importlib.metadata.version(__package__ or __name__)
 
-from .embedchain import App  # noqa: F401
+from embedchain.apps.App import App  # noqa: F401
-from .embedchain import OpenSourceApp  # noqa: F401
+from embedchain.apps.OpenSourceApp import OpenSourceApp  # noqa: F401
-from .embedchain import PersonApp  # noqa: F401
+from embedchain.apps.PersonApp import (PersonApp,  # noqa: F401
-from .embedchain import PersonOpenSourceApp  # noqa: F401
+                                       PersonOpenSourceApp)

embedchain/apps/App.py

@@ -0,0 +1,49 @@
+import openai
+
+from embedchain.config import AppConfig, ChatConfig
+from embedchain.embedchain import EmbedChain
+
+
+class App(EmbedChain):
+    """
+    The EmbedChain app.
+    Has two functions: add and query.
+
+    adds(data_type, url): adds the data from the given URL to the vector db.
+    query(query): finds answer to the given query using vector database and LLM.
+    dry_run(query): test your prompt without consuming tokens.
+    """
+
+    def __init__(self, config: AppConfig = None):
+        """
+        :param config: AppConfig instance to load as configuration. Optional.
+        """
+        if config is None:
+            config = AppConfig()
+
+        super().__init__(config)
+
+    def get_llm_model_answer(self, prompt, config: ChatConfig):
+        messages = []
+        messages.append({"role": "user", "content": prompt})
+        response = openai.ChatCompletion.create(
+            model=config.model,
+            messages=messages,
+            temperature=config.temperature,
+            max_tokens=config.max_tokens,
+            top_p=config.top_p,
+            stream=config.stream,
+        )
+
+        if config.stream:
+            return self._stream_llm_model_response(response)
+        else:
+            return response["choices"][0]["message"]["content"]
+
+    def _stream_llm_model_response(self, response):
+        """
+        This is a generator for streaming response from the OpenAI completions API
+        """
+        for line in response:
+            chunk = line["choices"][0].get("delta", {}).get("content", "")
+            yield chunk

embedchain/apps/OpenSourceApp.py

@@ -0,0 +1,39 @@
+import logging
+
+from embedchain.config import ChatConfig, OpenSourceAppConfig
+from embedchain.embedchain import EmbedChain
+
+gpt4all_model = None
+
+
+class OpenSourceApp(EmbedChain):
+    """
+    The OpenSource app.
+    Same as App, but uses an open source embedding model and LLM.
+
+    Has two function: add and query.
+
+    adds(data_type, url): adds the data from the given URL to the vector db.
+    query(query): finds answer to the given query using vector database and LLM.
+    """
+
+    def __init__(self, config: OpenSourceAppConfig = None):
+        """
+        :param config: InitConfig instance to load as configuration. Optional.
+        `ef` defaults to open source.
+        """
+        logging.info("Loading open source embedding model. This may take some time...")  # noqa:E501
+        if not config:
+            config = OpenSourceAppConfig()
+
+        logging.info("Successfully loaded open source embedding model.")
+        super().__init__(config)
+
+    def get_llm_model_answer(self, prompt, config: ChatConfig):
+        from gpt4all import GPT4All
+
+        global gpt4all_model
+        if gpt4all_model is None:
+            gpt4all_model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
+        response = gpt4all_model.generate(prompt=prompt, streaming=config.stream)
+        return response

embedchain/apps/PersonApp.py

@@ -0,0 +1,65 @@
+from string import Template
+
+from embedchain.apps.App import App
+from embedchain.apps.OpenSourceApp import OpenSourceApp
+from embedchain.config import ChatConfig, QueryConfig
+from embedchain.config.apps.BaseAppConfig import BaseAppConfig
+from embedchain.config.QueryConfig import (DEFAULT_PROMPT,
+                                           DEFAULT_PROMPT_WITH_HISTORY)
+
+
+class EmbedChainPersonApp:
+    """
+    Base class to create a person bot.
+    This bot behaves and speaks like a person.
+
+    :param person: name of the person, better if its a well known person.
+    :param config: BaseAppConfig instance to load as configuration.
+    """
+
+    def __init__(self, person, config: BaseAppConfig = None):
+        self.person = person
+        self.person_prompt = f"You are {person}. Whatever you say, you will always say in {person} style."  # noqa:E501
+        if config is None:
+            config = BaseAppConfig()
+        super().__init__(config)
+
+
+class PersonApp(EmbedChainPersonApp, App):
+    """
+    The Person app.
+    Extends functionality from EmbedChainPersonApp and App
+    """
+
+    def query(self, input_query, config: QueryConfig = None):
+        self.template = Template(self.person_prompt + " " + DEFAULT_PROMPT)
+        query_config = QueryConfig(
+            template=self.template,
+        )
+        return super().query(input_query, query_config)
+
+    def chat(self, input_query, config: ChatConfig = None):
+        self.template = Template(self.person_prompt + " " + DEFAULT_PROMPT_WITH_HISTORY)
+        chat_config = ChatConfig(
+            template=self.template,
+        )
+        return super().chat(input_query, chat_config)
+
+
+class PersonOpenSourceApp(EmbedChainPersonApp, OpenSourceApp):
+    """
+    The Person app.
+    Extends functionality from EmbedChainPersonApp and OpenSourceApp
+    """
+
+    def query(self, input_query, config: QueryConfig = None):
+        query_config = QueryConfig(
+            template=self.template,
+        )
+        return super().query(input_query, query_config)
+
+    def chat(self, input_query, config: ChatConfig = None):
+        chat_config = ChatConfig(
+            template=self.template,
+        )
+        return super().chat(input_query, chat_config)

embedchain/config/InitConfig.py

@@ -1,74 +0,0 @@
-import logging
-import os
-
-from chromadb.utils import embedding_functions
-
-from embedchain.config.BaseConfig import BaseConfig
-
-
-class InitConfig(BaseConfig):
-    """
-    Config to initialize an embedchain `App` instance.
-    """
-
-    def __init__(self, log_level=None, ef=None, db=None, host=None, port=None, id=None):
-        """
-        :param log_level: Optional. (String) Debug level
-        ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'].
-        :param ef: Optional. Embedding function to use.
-        :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.
-        """
-        self._setup_logging(log_level)
-        self.ef = ef
-        self.db = db
-        self.host = host
-        self.port = port
-        self.id = id
-        return
-
-    def _set_embedding_function(self, ef):
-        self.ef = ef
-        return
-
-    def _set_embedding_function_to_default(self):
-        """
-        Sets embedding function to default (`text-embedding-ada-002`).
-
-        :raises ValueError: If the template is not valid as template should contain
-        $context and $query
-        """
-        if os.getenv("OPENAI_API_KEY") is None and os.getenv("OPENAI_ORGANIZATION") is None:
-            raise ValueError("OPENAI_API_KEY or OPENAI_ORGANIZATION environment variables not provided")  # noqa:E501
-        self.ef = embedding_functions.OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            organization_id=os.getenv("OPENAI_ORGANIZATION"),
-            model_name="text-embedding-ada-002",
-        )
-        return
-
-    def _set_db(self, db):
-        if db:
-            self.db = db
-        return
-
-    def _set_db_to_default(self):
-        """
-        Sets database to default (`ChromaDb`).
-        """
-        from embedchain.vectordb.chroma_db import ChromaDB
-
-        self.db = ChromaDB(ef=self.ef, host=self.host, port=self.port)
-
-    def _setup_logging(self, debug_level):
-        level = logging.WARNING  # Default level
-        if debug_level is not None:
-            level = getattr(logging, debug_level.upper(), None)
-            if not isinstance(level, int):
-                raise ValueError(f"Invalid log level: {debug_level}")
-
-        logging.basicConfig(format="%(asctime)s [%(name)s] [%(levelname)s] %(message)s", level=level)
-        self.logger = logging.getLogger(__name__)
-        return

embedchain/config/__init__.py

@@ -1,5 +1,7 @@
 from .AddConfig import AddConfig, ChunkerConfig  # noqa: F401
+from .apps.AppConfig import AppConfig  # noqa: F401
+from .apps.CustomAppConfig import CustomAppConfig  # noqa: F401
+from .apps.OpenSourceAppConfig import OpenSourceAppConfig  # noqa: F401
 from .BaseConfig import BaseConfig  # noqa: F401
 from .ChatConfig import ChatConfig  # noqa: F401
-from .InitConfig import InitConfig  # noqa: F401
 from .QueryConfig import QueryConfig  # noqa: F401

embedchain/config/apps/AppConfig.py

@@ -0,0 +1,38 @@
+import os
+
+from chromadb.utils import embedding_functions
+
+from .BaseAppConfig import BaseAppConfig
+
+
+class AppConfig(BaseAppConfig):
+    """
+    Config to initialize an embedchain custom `App` instance, with extra config options.
+    """
+
+    def __init__(self, log_level=None, host=None, port=None, id=None):
+        """
+        :param log_level: Optional. (String) Debug level
+        ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'].
+        :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.
+        """
+        super().__init__(log_level=log_level, ef=AppConfig.default_embedding_function(), host=host, port=port, id=id)
+
+    @staticmethod
+    def default_embedding_function():
+        """
+        Sets embedding function to default (`text-embedding-ada-002`).
+
+        :raises ValueError: If the template is not valid as template should contain
+        $context and $query
+        :returns: The default embedding function for the app class.
+        """
+        if os.getenv("OPENAI_API_KEY") is None and os.getenv("OPENAI_ORGANIZATION") is None:
+            raise ValueError("OPENAI_API_KEY or OPENAI_ORGANIZATION environment variables not provided")  # noqa:E501
+        return embedding_functions.OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            organization_id=os.getenv("OPENAI_ORGANIZATION"),
+            model_name="text-embedding-ada-002",
+        )

embedchain/config/apps/BaseAppConfig.py

@@ -0,0 +1,53 @@
+import logging
+
+from embedchain.config.BaseConfig import BaseConfig
+
+
+class BaseAppConfig(BaseConfig):
+    """
+    Parent config to initialize an instance of `App`, `OpenSourceApp` or `CustomApp`.
+    """
+
+    def __init__(self, log_level=None, ef=None, db=None, host=None, port=None, id=None):
+        """
+        :param log_level: Optional. (String) Debug level
+        ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'].
+        :param ef: Embedding function to use.
+        :param db: Optional. (Vector) database instance 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.
+        """
+        self._setup_logging(log_level)
+
+        self.db = db if db else BaseAppConfig.default_db(ef=ef, host=host, port=port)
+        self.id = id
+        return
+
+    @staticmethod
+    def default_db(ef, host, port):
+        """
+        Sets database to default (`ChromaDb`).
+
+        :param ef: Embedding function to use in database.
+        :param host: Optional. Hostname for the database server.
+        :param port: Optional. Port for the database server.
+        :returns: Default database
+        :raises ValueError: BaseAppConfig knows no default embedding function.
+        """
+        if ef is None:
+            raise ValueError("ChromaDb cannot be instantiated without an embedding function")
+        from embedchain.vectordb.chroma_db import ChromaDB
+
+        return ChromaDB(ef=ef, host=host, port=port)
+
+    def _setup_logging(self, debug_level):
+        level = logging.WARNING  # Default level
+        if debug_level is not None:
+            level = getattr(logging, debug_level.upper(), None)
+            if not isinstance(level, int):
+                raise ValueError(f"Invalid log level: {debug_level}")
+
+        logging.basicConfig(format="%(asctime)s [%(name)s] [%(levelname)s] %(message)s", level=level)
+        self.logger = logging.getLogger(__name__)
+        return

embedchain/config/apps/CustomAppConfig.py

@@ -0,0 +1,19 @@
+from .BaseAppConfig import BaseAppConfig
+
+
+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):
+        """
+        :param log_level: Optional. (String) Debug level
+        ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'].
+        :param ef: Optional. Embedding function to use.
+        :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.
+        """
+        super().__init__(log_level=log_level, db=db, host=host, port=port, id=id)

embedchain/config/apps/OpenSourceAppConfig.py

@@ -0,0 +1,30 @@
+from chromadb.utils import embedding_functions
+
+from .BaseAppConfig import BaseAppConfig
+
+
+class OpenSourceAppConfig(BaseAppConfig):
+    """
+    Config to initialize an embedchain custom `OpenSourceApp` instance, with extra config options.
+    """
+
+    def __init__(self, log_level=None, host=None, port=None, id=None):
+        """
+        :param log_level: Optional. (String) Debug level
+        ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'].
+        :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.
+        """
+        super().__init__(
+            log_level=log_level, ef=OpenSourceAppConfig.default_embedding_function(), host=host, port=port, id=id
+        )
+
+    @staticmethod
+    def default_embedding_function():
+        """
+        Sets embedding function to default (`all-MiniLM-L6-v2`).
+
+        :returns: The default embedding function
+        """
+        return embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")

embedchain/embedchain.py

@@ -1,15 +1,13 @@
 import logging
 import os
-from string import Template
 
-import openai
-from chromadb.utils import embedding_functions
 from dotenv import load_dotenv
 from langchain.docstore.document import Document
 from langchain.memory import ConversationBufferMemory
 
-from embedchain.config import AddConfig, ChatConfig, InitConfig, QueryConfig
+from embedchain.config import AddConfig, ChatConfig, QueryConfig
-from embedchain.config.QueryConfig import DOCS_SITE_PROMPT_TEMPLATE, DEFAULT_PROMPT, DEFAULT_PROMPT_WITH_HISTORY
+from embedchain.config.apps.BaseAppConfig import BaseAppConfig
+from embedchain.config.QueryConfig import DOCS_SITE_PROMPT_TEMPLATE
 from embedchain.data_formatter import DataFormatter
 
 gpt4all_model = None
@@ -23,7 +21,7 @@ memory = ConversationBufferMemory()
 
 
 class EmbedChain:
-    def __init__(self, config: InitConfig):
+    def __init__(self, config: BaseAppConfig):
         """
         Initializes the EmbedChain instance, sets up a vector DB client and
         creates a collection.
@@ -139,7 +137,10 @@ class EmbedChain:
             )
         ]
 
-    def get_llm_model_answer(self, prompt):
+    def get_llm_model_answer(self):
+        """
+        Usually implemented by child class
+        """
         raise NotImplementedError
 
     def retrieve_from_database(self, input_query, config: QueryConfig):
@@ -329,152 +330,3 @@ class EmbedChain:
         `App` has to be reinitialized after using this method.
         """
         self.db_client.reset()
-
-
-class App(EmbedChain):
-    """
-    The EmbedChain app.
-    Has two functions: add and query.
-
-    adds(data_type, url): adds the data from the given URL to the vector db.
-    query(query): finds answer to the given query using vector database and LLM.
-    dry_run(query): test your prompt without consuming tokens.
-    """
-
-    def __init__(self, config: InitConfig = None):
-        """
-        :param config: InitConfig instance to load as configuration. Optional.
-        """
-        if config is None:
-            config = InitConfig()
-
-        if not config.ef:
-            config._set_embedding_function_to_default()
-
-        if not config.db:
-            config._set_db_to_default()
-
-        super().__init__(config)
-
-    def get_llm_model_answer(self, prompt, config: ChatConfig):
-        messages = []
-        messages.append({"role": "user", "content": prompt})
-        response = openai.ChatCompletion.create(
-            model=config.model,
-            messages=messages,
-            temperature=config.temperature,
-            max_tokens=config.max_tokens,
-            top_p=config.top_p,
-            stream=config.stream,
-        )
-
-        if config.stream:
-            return self._stream_llm_model_response(response)
-        else:
-            return response["choices"][0]["message"]["content"]
-
-    def _stream_llm_model_response(self, response):
-        """
-        This is a generator for streaming response from the OpenAI completions API
-        """
-        for line in response:
-            chunk = line["choices"][0].get("delta", {}).get("content", "")
-            yield chunk
-
-
-class OpenSourceApp(EmbedChain):
-    """
-    The OpenSource app.
-    Same as App, but uses an open source embedding model and LLM.
-
-    Has two function: add and query.
-
-    adds(data_type, url): adds the data from the given URL to the vector db.
-    query(query): finds answer to the given query using vector database and LLM.
-    """
-
-    def __init__(self, config: InitConfig = None):
-        """
-        :param config: InitConfig instance to load as configuration. Optional.
-        `ef` defaults to open source.
-        """
-        print("Loading open source embedding model. This may take some time...")  # noqa:E501
-        if not config:
-            config = InitConfig()
-
-        if not config.ef:
-            config._set_embedding_function(
-                embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
-            )
-
-        if not config.db:
-            config._set_db_to_default()
-
-        print("Successfully loaded open source embedding model.")
-        super().__init__(config)
-
-    def get_llm_model_answer(self, prompt, config: ChatConfig):
-        from gpt4all import GPT4All
-
-        global gpt4all_model
-        if gpt4all_model is None:
-            gpt4all_model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
-        response = gpt4all_model.generate(prompt=prompt, streaming=config.stream)
-        return response
-
-
-class EmbedChainPersonApp:
-    """
-    Base class to create a person bot.
-    This bot behaves and speaks like a person.
-
-    :param person: name of the person, better if its a well known person.
-    :param config: InitConfig instance to load as configuration.
-    """
-
-    def __init__(self, person, config: InitConfig = None):
-        self.person = person
-        self.person_prompt = f"You are {person}. Whatever you say, you will always say in {person} style."  # noqa:E501
-        if config is None:
-            config = InitConfig()
-        super().__init__(config)
-
-
-class PersonApp(EmbedChainPersonApp, App):
-    """
-    The Person app.
-    Extends functionality from EmbedChainPersonApp and App
-    """
-
-    def query(self, input_query, config: QueryConfig = None):
-        self.template = Template(self.person_prompt + " " + DEFAULT_PROMPT)
-        query_config = QueryConfig(
-            template=self.template,
-        )
-        return super().query(input_query, query_config)
-
-    def chat(self, input_query, config: ChatConfig = None):
-        self.template = Template(self.person_prompt + " " + DEFAULT_PROMPT_WITH_HISTORY)
-        chat_config = ChatConfig(
-            template=self.template,
-        )
-        return super().chat(input_query, chat_config)
-
-
-class PersonOpenSourceApp(EmbedChainPersonApp, OpenSourceApp):
-    """
-    The Person app.
-    Extends functionality from EmbedChainPersonApp and OpenSourceApp
-    """
-
-    def query(self, input_query, config: QueryConfig = None):
-        query_config = QueryConfig(
-            template=self.template,
-        )
-        return super().query(input_query, query_config)
-
-    def chat(self, input_query, config: ChatConfig = None):
-        chat_config = ChatConfig(
-            template=self.template,
-        )
-        return super().chat(input_query, chat_config)

tests/embedchain/test_embedchain.py

@@ -3,7 +3,7 @@ import unittest
 from unittest.mock import patch
 
 from embedchain import App
-from embedchain.config import InitConfig
+from embedchain.config import AppConfig
 
 
 class TestChromaDbHostsLoglevel(unittest.TestCase):
@@ -25,7 +25,7 @@ class TestChromaDbHostsLoglevel(unittest.TestCase):
         """
         Test if the `App` instance is initialized without a config that does not contain default hosts and ports.
         """
-        config = InitConfig(log_level="DEBUG")
+        config = AppConfig(log_level="DEBUG")
 
         app = App(config)
 

tests/vectordb/test_chroma_db.py

@@ -4,7 +4,7 @@ import unittest
 from unittest.mock import patch
 
 from embedchain import App
-from embedchain.config import InitConfig
+from embedchain.config import AppConfig
 from embedchain.vectordb.chroma_db import ChromaDB, chromadb
 
 
@@ -38,7 +38,7 @@ class TestChromaDbHostsInit(unittest.TestCase):
         host = "test-host"
         port = "1234"
 
-        config = InitConfig(host=host, port=port)
+        config = AppConfig(host=host, port=port)
 
         _app = App(config)
 
@@ -65,7 +65,7 @@ class TestChromaDbHostsLoglevel(unittest.TestCase):
         """
         Test if the `App` instance is initialized without a config that does not contain default hosts and ports.
         """
-        config = InitConfig(log_level="DEBUG")
+        config = AppConfig(log_level="DEBUG")
 
         _app = App(config)