refactor: app design concept (#305)

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)