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46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
from langchain.callbacks import CallbackManager
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from llama_index import ServiceContext, PromptHelper, LLMPredictor
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from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
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from core.embedding.openai_embedding import OpenAIEmbedding
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from core.llm.llm_builder import LLMBuilder
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class IndexBuilder:
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@classmethod
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def get_default_service_context(cls, tenant_id: str) -> ServiceContext:
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# set number of output tokens
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num_output = 512
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# only for verbose
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callback_manager = CallbackManager([DifyStdOutCallbackHandler()])
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llm = LLMBuilder.to_llm(
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tenant_id=tenant_id,
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model_name='text-davinci-003',
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temperature=0,
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max_tokens=num_output,
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callback_manager=callback_manager,
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)
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llm_predictor = LLMPredictor(llm=llm)
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# These parameters here will affect the logic of segmenting the final synthesized response.
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# The number of refinement iterations in the synthesis process depends
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# on whether the length of the segmented output exceeds the max_input_size.
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prompt_helper = PromptHelper(
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max_input_size=3500,
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num_output=num_output,
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max_chunk_overlap=20
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)
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model_credentials = LLMBuilder.get_model_credentials(
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tenant_id=tenant_id,
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model_name='text-embedding-ada-002'
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)
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return ServiceContext.from_defaults(
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llm_predictor=llm_predictor,
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prompt_helper=prompt_helper,
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embed_model=OpenAIEmbedding(**model_credentials),
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)
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