55 lines
2.4 KiB
Python
55 lines
2.4 KiB
Python
from typing import Optional
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from embedchain.config import (AppConfig, BaseEmbedderConfig, BaseLlmConfig,
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ChromaDbConfig)
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from embedchain.embedchain import EmbedChain
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from embedchain.embedder.openai import OpenAIEmbedder
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from embedchain.helper.json_serializable import register_deserializable
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from embedchain.llm.openai import OpenAILlm
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from embedchain.vectordb.chroma import ChromaDB
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@register_deserializable
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class App(EmbedChain):
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"""
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The EmbedChain app in it's simplest and most straightforward form.
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An opinionated choice of LLM, vector database and embedding model.
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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: AppConfig = None,
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llm_config: BaseLlmConfig = None,
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chromadb_config: Optional[ChromaDbConfig] = 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 only have a few choices to make.
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:param config: Config for the app instance.
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This is the most basic configuration, that does not fall into the LLM, database or embedder category,
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defaults to None
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:type config: AppConfig, optional
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:param llm_config: Allows you to configure the LLM, e.g. how many documents to return,
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example: `from embedchain.config import LlmConfig`, defaults to None
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:type llm_config: BaseLlmConfig, optional
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:param chromadb_config: Allows you to configure the vector database,
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example: `from embedchain.config import ChromaDbConfig`, defaults to None
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:type chromadb_config: Optional[ChromaDbConfig], optional
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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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"""
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if config is None:
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config = AppConfig()
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llm = OpenAILlm(config=llm_config)
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embedder = OpenAIEmbedder(config=BaseEmbedderConfig(model="text-embedding-ada-002"))
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database = ChromaDB(config=chromadb_config)
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super().__init__(config, llm, db=database, embedder=embedder, system_prompt=system_prompt)
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