58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
import logging
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from typing import Optional
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from embedchain.config import (BaseEmbedderConfig, BaseLlmConfig,
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ChromaDbConfig, OpenSourceAppConfig)
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from embedchain.embedchain import EmbedChain
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from embedchain.embedder.gpt4all_embedder import GPT4AllEmbedder
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from embedchain.helper_classes.json_serializable import register_deserializable
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from embedchain.llm.gpt4all_llm import GPT4ALLLlm
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from embedchain.vectordb.chroma_db import ChromaDB
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gpt4all_model = None
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@register_deserializable
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class OpenSourceApp(EmbedChain):
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"""
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The OpenSource app.
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Same as App, but uses an open source embedding model and LLM.
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Has two function: add and query.
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adds(data_type, url): 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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"""
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def __init__(
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self,
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config: OpenSourceAppConfig = 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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:param config: OpenSourceAppConfig instance to load as configuration. Optional.
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`ef` defaults to open source.
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:param system_prompt: System prompt string. Optional.
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"""
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logging.info("Loading open source embedding model. This may take some time...") # noqa:E501
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if not config:
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config = OpenSourceAppConfig()
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if not isinstance(config, OpenSourceAppConfig):
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raise ValueError(
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"OpenSourceApp needs a OpenSourceAppConfig passed to it. "
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"You can import it with `from embedchain.config import OpenSourceAppConfig`"
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)
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if not config.model:
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raise ValueError("OpenSourceApp needs a model to be instantiated. Maybe you passed the wrong config type?")
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logging.info("Successfully loaded open source embedding model.")
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llm = GPT4ALLLlm(config=BaseLlmConfig(model="orca-mini-3b.ggmlv3.q4_0.bin"))
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embedder = GPT4AllEmbedder(config=BaseEmbedderConfig(model="all-MiniLM-L6-v2"))
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database = ChromaDB(config=chromadb_config)
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super().__init__(config, llm=llm, db=database, embedder=embedder, system_prompt=system_prompt)
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