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adapter_insert.py
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57 lines (47 loc) · 2.13 KB
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# -*- coding: utf-8 -*-
import asyncio
from modelcache.utils.error import NotInitError
from modelcache.utils.time import time_cal
async def adapt_insert(*args, **kwargs):
chat_cache = kwargs.pop("cache_obj")
model = kwargs.pop("model", None)
require_object_store = kwargs.pop("require_object_store", False)
# Validate object store availability if required
if require_object_store:
assert chat_cache.data_manager.o, "Object store is required for adapter."
context = kwargs.pop("cache_context", {})
chat_info = kwargs.pop("chat_info", [])
# Initialize collections for parallel processing
pre_embedding_data_list = [] # Preprocessed data ready for embedding
embedding_futures_list = [] # Async embedding generation tasks
llm_data_list = [] # Extracted LLM response data
# Process each chat entry and prepare for parallel embedding generation
for row in chat_info:
# Preprocess chat data using configured preprocessing function
pre_embedding_data = chat_cache.insert_pre_embedding_func(
row,
extra_param=context.get("pre_embedding_func", None),
prompts=chat_cache.prompts,
)
pre_embedding_data_list.append(pre_embedding_data)
llm_data_list.append(row['answer']) # Extract answer text for storage
# Create async embedding generation task with performance monitoring
embedding_future = time_cal(
chat_cache.embedding_func,
func_name="embedding",
report_func=chat_cache.report.embedding,
cache_obj=chat_cache
)(pre_embedding_data)
embedding_futures_list.append(embedding_future)
# Wait for all embedding generation tasks to complete in parallel
embedding_data_list = await asyncio.gather(*embedding_futures_list)
# Save all processed data to the data manager asynchronously
await asyncio.to_thread(
chat_cache.data_manager.save,
pre_embedding_data_list,
llm_data_list,
embedding_data_list,
model=model,
extra_param=context.get("save_func", None)
)
return 'success'