docs: update docstrings (#565)
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@@ -12,12 +12,13 @@ from embedchain.vectordb.chroma_db 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.
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Has two functions: add and query.
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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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adds(data_type, url): adds the data from the given URL to the vector db.
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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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dry_run(query): test your prompt without consuming tokens.
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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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@@ -28,8 +29,20 @@ class App(EmbedChain):
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system_prompt: Optional[str] = None,
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):
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"""
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:param config: AppConfig instance to load as configuration. Optional.
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:param system_prompt: System prompt string. Optional.
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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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