d2926c7672
* deprecate BigDLNativeTransformers and add specific LMEmbedding method * deprecate and add LM methods for langchain llms * add native params to native langchain * new imple for embedding * move ut from bigdlnative to casual llm * rename embeddings api and examples update align with usage updating * docqa example hot-fix * add more api docs * add langchain ut for starcoder * support model_kwargs for transformer methods when calling causalLM and add ut * ut fix for transformers embedding * update for langchain causal supporting transformers * remove model_family in readme doc * add model_families params to support more models * update api docs and remove chatglm embeddings for now * remove chatglm embeddings in examples * new refactor for ut to add bloom and transformers llama ut * disable llama transformers embedding ut
140 lines
4.9 KiB
Python
140 lines
4.9 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 adapted from https://python.langchain.com/docs/use_cases/chatbots/voice_assistant
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from langchain import LLMChain, PromptTemplate
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from bigdl.llm.langchain.llms import *
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from langchain.memory import ConversationBufferWindowMemory
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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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import speech_recognition as sr
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import pyttsx3
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import argparse
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def prepare_chain(args):
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model_path = args.model_path
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n_threads = args.thread_num
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n_ctx = args.context_size
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# Use a easy prompt could bring good-enough result
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# You could tune the prompt based on your own model to perform better
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template = """
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{history}
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Q: {human_input}
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A:"""
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prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
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# We use our BigDLCausalLLM to subsititute OpenAI web-required API
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model_family_to_llm = {
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"llama": LlamaLLM,
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"gptneox": GptneoxLLM,
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"bloom": BloomLLM,
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"starcoder": StarcoderLLM,
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"chatglm": ChatGLMLLM
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}
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if model_family in model_family_to_llm:
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langchain_llm = model_family_to_llm[model_family]
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else:
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raise ValueError(f"Unknown model family: {model_family}")
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callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
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llm = langchain_llm(
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model_path=model_path,
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n_threads=n_threads,
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callback_manager=callback_manager,
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verbose=True,
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n_ctx=n_ctx,
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stop=['\n\n'] # You could tune the stop words based on your own model to perform better
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)
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# Following code are complete the same as the use-case
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voiceassitant_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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verbose=True,
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memory=ConversationBufferWindowMemory(k=2),
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)
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return voiceassitant_chain
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def listen(voiceassitant_chain):
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engine = pyttsx3.init()
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r = sr.Recognizer()
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with sr.Microphone() as source:
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print("Calibrating...")
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r.adjust_for_ambient_noise(source, duration=5)
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# optional parameters to adjust microphone sensitivity
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# r.energy_threshold = 200
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# r.pause_threshold=0.5
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print("Okay, go!")
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while 1:
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text = ""
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print("listening now...")
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try:
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audio = r.listen(source, timeout=5, phrase_time_limit=30)
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print("Recognizing...")
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# whisper model options are found here: https://github.com/openai/whisper#available-models-and-languages
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# other speech recognition models are also available.
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text = r.recognize_whisper(
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audio,
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model="medium.en",
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show_dict=True,
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)["text"]
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except Exception as e:
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unrecognized_speech_text = (
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f"Sorry, I didn't catch that. Exception was: {e}s"
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)
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text = unrecognized_speech_text
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print(text)
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response_text = voiceassitant_chain.predict(human_input=text)
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print(response_text)
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engine.say(response_text)
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engine.runAndWait()
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def main(args):
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chain = prepare_chain(args)
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listen(chain)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='BigDLCausalLM Langchain Voice Assistant Example')
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parser.add_argument('-x','--model-family', type=str, required=True,
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choices=["llama", "bloom", "gptneox", "chatglm", "starcoder"],
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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('-t','--thread-num', type=int, default=2,
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help='Number of threads to use for inference')
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parser.add_argument('-c','--context-size', type=int, default=512,
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help='Maximum context size')
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args = parser.parse_args()
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main(args)
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