84 lines
3.8 KiB
Python
84 lines
3.8 KiB
Python
from typing import Optional
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from embedchain.config import CustomAppConfig
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from embedchain.embedchain import EmbedChain
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from embedchain.embedder.base import BaseEmbedder
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from embedchain.helper.json_serializable import register_deserializable
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from embedchain.llm.base import BaseLlm
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from embedchain.vectordb.base import BaseVectorDB
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@register_deserializable
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class CustomApp(EmbedChain):
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"""
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Embedchain's custom app allows for most flexibility.
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You can craft your own mix of various LLMs, vector databases and embedding model/functions.
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Methods:
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add(source, data_type): adds the data from the given URL to the vector db.
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query(query): finds answer to the given query using vector database and LLM.
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chat(query): finds answer to the given query using vector database and LLM, with conversation history.
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"""
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def __init__(
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self,
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config: Optional[CustomAppConfig] = None,
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llm: BaseLlm = None,
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db: BaseVectorDB = None,
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embedder: BaseEmbedder = None,
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system_prompt: Optional[str] = None,
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):
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"""
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Initialize a new `CustomApp` instance. You have to choose a LLM, database and embedder.
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:param config: Config for the app instance. This is the most basic configuration,
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that does not fall into the LLM, database or embedder category, defaults to None
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:type config: Optional[CustomAppConfig], optional
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:param llm: LLM Class instance. example: `from embedchain.llm.openai import OpenAILlm`, defaults to None
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:type llm: BaseLlm
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:param db: The database to use for storing and retrieving embeddings,
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example: `from embedchain.vectordb.chroma_db import ChromaDb`, defaults to None
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:type db: BaseVectorDB
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:param embedder: The embedder (embedding model and function) use to calculate embeddings.
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example: `from embedchain.embedder.gpt4all_embedder import GPT4AllEmbedder`, defaults to None
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:type embedder: BaseEmbedder
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:param system_prompt: System prompt that will be provided to the LLM as such, defaults to None
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:type system_prompt: Optional[str], optional
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:raises ValueError: LLM, database or embedder has not been defined.
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:raises TypeError: LLM, database or embedder is not a valid class instance.
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"""
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# Config is not required, it has a default
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if config is None:
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config = CustomAppConfig()
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if llm is None:
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raise ValueError("LLM must be provided for custom app. Please import from `embedchain.llm`.")
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if db is None:
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raise ValueError("Database must be provided for custom app. Please import from `embedchain.vectordb`.")
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if embedder is None:
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raise ValueError("Embedder must be provided for custom app. Please import from `embedchain.embedder`.")
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if not isinstance(config, CustomAppConfig):
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raise TypeError(
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"Config is not a `CustomAppConfig` instance. "
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"Please make sure the type is right and that you are passing an instance."
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)
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if not isinstance(llm, BaseLlm):
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raise TypeError(
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"LLM is not a `BaseLlm` instance. "
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"Please make sure the type is right and that you are passing an instance."
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)
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if not isinstance(db, BaseVectorDB):
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raise TypeError(
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"Database is not a `BaseVectorDB` instance. "
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"Please make sure the type is right and that you are passing an instance."
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)
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if not isinstance(embedder, BaseEmbedder):
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raise TypeError(
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"Embedder is not a `BaseEmbedder` instance. "
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"Please make sure the type is right and that you are passing an instance."
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)
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super().__init__(config=config, llm=llm, db=db, embedder=embedder, system_prompt=system_prompt)
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