90 lines
3.6 KiB
Python
90 lines
3.6 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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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 langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from bigdl.llm.langchain.llms import BigdlNativeLLM
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from bigdl.llm.langchain.embeddings import BigdlNativeEmbeddings
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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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model_family = args.model_family
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query = args.question
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n_ctx = args.n_ctx
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n_threads=args.thread_num
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callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
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# split texts of input doc
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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 = BigdlNativeEmbeddings(model_path=model_path, model_family=model_family, n_threads=n_threads, n_ctx=n_ctx)
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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 = BigdlNativeLLM(
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model_path=model_path, model_family=model_family, n_ctx=n_ctx, n_threads=n_threads, callback_manager=callback_manager
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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, callback_manager=callback_manager
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)
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doc_chain.run(input_documents=docs, question=query)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='BigDL-LLM Langchain Question Answering over Docs Example')
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parser.add_argument('-x','--model-family', type=str, required=True,
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choices=["llama", "bloom", "gptneox"],
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help='the model family')
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parser.add_argument('-m','--model-path', type=str, required=True,
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help='the path to the converted llm model')
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parser.add_argument('-i', '--input-path', type=str, required=True,
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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 AI?',
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help='qustion you want to ask.')
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parser.add_argument('-c','--n-ctx', type=int, default=2048,
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help='the maximum context size')
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parser.add_argument('-t','--thread-num', type=int, default=2,
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help='number of threads to use for inference')
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args = parser.parse_args()
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main(args)
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