96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This would makes sure Python is aware there is more than one sub-package within bigdl,
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# physically located elsewhere.
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# Otherwise there would be module not found error in non-pip's setting as Python would
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# only search the first bigdl package and end up finding only one sub-package.
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# Code is adapted from https://python.langchain.com/docs/modules/chains/additional/question_answering.html
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import argparse
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from langchain.vectorstores import Chroma
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from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
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QA_PROMPT)
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.chains.question_answering import load_qa_chain
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from langchain.callbacks.manager import CallbackManager
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from ipex_llm.langchain.llms import TransformersLLM
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from ipex_llm.langchain.embeddings import TransformersEmbeddings
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text_doc = '''
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BigDL seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries:
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LLM: Low-bit (INT3/INT4/INT5/INT8) large language model library for Intel CPU/GPU
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Orca: Distributed Big Data & AI (TF & PyTorch) Pipeline on Spark and Ray
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Nano: Transparent Acceleration of Tensorflow & PyTorch Programs on Intel CPU/GPU
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DLlib: “Equivalent of Spark MLlib” for Deep Learning
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Chronos: Scalable Time Series Analysis using AutoML
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Friesian: End-to-End Recommendation Systems
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PPML: Secure Big Data and AI (with SGX Hardware Security)
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'''
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def main(args):
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input_path = args.input_path
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model_path = args.model_path
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query = args.question
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# split texts of input doc
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if input_path is None:
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input_doc = text_doc
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else:
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with open(input_path) as f:
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input_doc = f.read()
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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texts = text_splitter.split_text(input_doc)
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# create embeddings and store into vectordb
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embeddings = TransformersEmbeddings.from_model_id(
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model_id=model_path,
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model_kwargs={"trust_remote_code": True}
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)
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docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
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#get relavant texts
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docs = docsearch.get_relevant_documents(query)
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bigdl_llm = TransformersLLM.from_model_id(
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model_id=model_path,
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model_kwargs={"temperature": 0, "max_length": 1024, "trust_remote_code": True},
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)
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doc_chain = load_qa_chain(
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bigdl_llm, chain_type="stuff", prompt=QA_PROMPT
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)
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output = doc_chain.run(input_documents=docs, question=query)
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print(output)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='TransformersLLM Langchain QA over Docs Example')
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parser.add_argument('-m','--model-path', type=str, required=True,
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help='the path to transformers model')
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parser.add_argument('-i', '--input-path', type=str,
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help='the path to the input doc.')
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parser.add_argument('-q', '--question', type=str, default='What is BigDL?',
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help='qustion you want to ask.')
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args = parser.parse_args()
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main(args)
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