37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import os
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from langchain.llms import Replicate
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from embedchain.config import AppConfig
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from embedchain.embedchain import EmbedChain
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class Llama2App(EmbedChain):
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"""
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The EmbedChain Llama2App class.
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Has two functions: 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__(self, config: AppConfig = None):
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"""
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:param config: AppConfig instance to load as configuration. Optional.
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"""
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if "REPLICATE_API_TOKEN" not in os.environ:
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raise ValueError("Please set the REPLICATE_API_TOKEN environment variable.")
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if config is None:
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config = AppConfig()
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super().__init__(config)
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def get_llm_model_answer(self, prompt, config: AppConfig = None):
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# TODO: Move the model and other inputs into config
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llm = Replicate(
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model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
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input={"temperature": 0.75, "max_length": 500, "top_p": 1},
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)
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return llm(prompt)
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