Rename embedchain to mem0 and open sourcing code for long term memory (#1474)

Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>

embedchain/embedchain/llm/vertex_ai.py

@@ -0,0 +1,68 @@
+import importlib
+import logging
+from typing import Any, Optional
+
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain_google_vertexai import ChatVertexAI
+
+from embedchain.config import BaseLlmConfig
+from embedchain.helpers.json_serializable import register_deserializable
+from embedchain.llm.base import BaseLlm
+
+logger = logging.getLogger(__name__)
+
+
+@register_deserializable
+class VertexAILlm(BaseLlm):
+    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        try:
+            importlib.import_module("vertexai")
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for VertexAI are not installed."
+                'Please install with `pip install --upgrade "embedchain[vertexai]"`'
+            ) from None
+        super().__init__(config=config)
+
+    def get_llm_model_answer(self, prompt) -> tuple[str, Optional[dict[str, Any]]]:
+        if self.config.token_usage:
+            response, token_info = self._get_answer(prompt, self.config)
+            model_name = "vertexai/" + self.config.model
+            if model_name not in self.config.model_pricing_map:
+                raise ValueError(
+                    f"Model {model_name} not found in `model_prices_and_context_window.json`. \
+                    You can disable token usage by setting `token_usage` to False."
+                )
+            total_cost = (
+                self.config.model_pricing_map[model_name]["input_cost_per_token"] * token_info["prompt_token_count"]
+            ) + self.config.model_pricing_map[model_name]["output_cost_per_token"] * token_info[
+                "candidates_token_count"
+            ]
+            response_token_info = {
+                "prompt_tokens": token_info["prompt_token_count"],
+                "completion_tokens": token_info["candidates_token_count"],
+                "total_tokens": token_info["prompt_token_count"] + token_info["candidates_token_count"],
+                "total_cost": round(total_cost, 10),
+                "cost_currency": "USD",
+            }
+            return response, response_token_info
+        return self._get_answer(prompt, self.config)
+
+    @staticmethod
+    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
+        if config.top_p and config.top_p != 1:
+            logger.warning("Config option `top_p` is not supported by this model.")
+
+        if config.stream:
+            callbacks = config.callbacks if config.callbacks else [StreamingStdOutCallbackHandler()]
+            llm = ChatVertexAI(
+                temperature=config.temperature, model=config.model, callbacks=callbacks, streaming=config.stream
+            )
+        else:
+            llm = ChatVertexAI(temperature=config.temperature, model=config.model)
+
+        messages = VertexAILlm._get_messages(prompt)
+        chat_response = llm.invoke(messages)
+        if config.token_usage:
+            return chat_response.content, chat_response.response_metadata["usage_metadata"]
+        return chat_response.content