Files
t6_mem0/embedchain/apps/app.py
2023-09-29 03:24:42 +05:30

55 lines
2.4 KiB
Python

from typing import Optional
from embedchain.config import (AppConfig, BaseEmbedderConfig, BaseLlmConfig,
ChromaDbConfig)
from embedchain.embedchain import EmbedChain
from embedchain.embedder.openai import OpenAIEmbedder
from embedchain.helper.json_serializable import register_deserializable
from embedchain.llm.openai import OpenAILlm
from embedchain.vectordb.chroma import ChromaDB
@register_deserializable
class App(EmbedChain):
"""
The EmbedChain app in it's simplest and most straightforward form.
An opinionated choice of LLM, vector database and embedding model.
Methods:
add(source, data_type): adds the data from the given URL to the vector db.
query(query): finds answer to the given query using vector database and LLM.
chat(query): finds answer to the given query using vector database and LLM, with conversation history.
"""
def __init__(
self,
config: AppConfig = None,
llm_config: BaseLlmConfig = None,
chromadb_config: Optional[ChromaDbConfig] = None,
system_prompt: Optional[str] = None,
):
"""
Initialize a new `CustomApp` instance. You only have a few choices to make.
:param config: Config for the app instance.
This is the most basic configuration, that does not fall into the LLM, database or embedder category,
defaults to None
:type config: AppConfig, optional
:param llm_config: Allows you to configure the LLM, e.g. how many documents to return,
example: `from embedchain.config import LlmConfig`, defaults to None
:type llm_config: BaseLlmConfig, optional
:param chromadb_config: Allows you to configure the vector database,
example: `from embedchain.config import ChromaDbConfig`, defaults to None
:type chromadb_config: Optional[ChromaDbConfig], optional
:param system_prompt: System prompt that will be provided to the LLM as such, defaults to None
:type system_prompt: Optional[str], 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)