mem0/embedchain/apps/App.py

from typing import Optional

from embedchain.config import (AppConfig, BaseEmbedderConfig, BaseLlmConfig,
                               ChromaDbConfig)
from embedchain.embedchain import EmbedChain
from embedchain.embedder.openai_embedder import OpenAiEmbedder
from embedchain.helper_classes.json_serializable import register_deserializable
from embedchain.llm.openai_llm import OpenAiLlm
from embedchain.vectordb.chroma_db import ChromaDB


@register_deserializable
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,
        llm_config: BaseLlmConfig = None,
        chromadb_config: Optional[ChromaDbConfig] = None,
        system_prompt: Optional[str] = None,
    ):
        """
        :param config: AppConfig instance to load as configuration. Optional.
        :param system_prompt: System prompt string. Optional.
        """
        if config is None:
            config = AppConfig()

        llm = OpenAiLlm(config=llm_config)
        embedder = OpenAiEmbedder(config=BaseEmbedderConfig(model="text-embedding-ada-002"))
        database = ChromaDB(config=chromadb_config)

        super().__init__(config, llm, db=database, embedder=embedder, system_prompt=system_prompt)