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