ipex-llm/python/llm/example/CPU/LangChain

Langchain examples

The examples here show how to use langchain with bigdl-llm.

Install bigdl-llm

Follow the instructions in Install.

Install Required Dependencies for langchain examples.

pip install langchain==0.0.184
pip install -U chromadb==0.3.25
pip install -U pandas==2.0.3

Convert Models using bigdl-llm

Follow the instructions in Convert model.

Run the examples

1. Streaming Chat

python native_int4/streamchat.py -m CONVERTED_MODEL_PATH -x MODEL_FAMILY -q QUESTION -t THREAD_NUM

arguments info:

• -m CONVERTED_MODEL_PATH: required, path to the converted model
• -x MODEL_FAMILY: required, the model family of the model specified in -m, available options are llama, gptneox and bloom
• -q QUESTION: question to ask. Default is What is AI?.
• -t THREAD_NUM: specify the number of threads to use for inference. Default is 2.

2. Question Answering over Docs

python native_int4/docqa.py -m CONVERTED_MODEL_PATH -x MODEL_FAMILY -i DOC_PATH -q QUESTION -c CONTEXT_SIZE -t THREAD_NUM

arguments info:

• -m CONVERTED_MODEL_PATH: required, path to the converted model in above step
• -x MODEL_FAMILY: required, the model family of the model specified in -m, available options are llama, gptneox and bloom
• -i DOC_PATH: required, path to the input document
• -q QUESTION: question to ask. Default is What is AI?.
• -c CONTEXT_SIZE: specify the maximum context size. Default is 2048.
• -t THREAD_NUM: specify the number of threads to use for inference. Default is 2.

3. Voice Assistant

This example is adapted from https://python.langchain.com/docs/use_cases/chatbots/voice_assistant with only tiny code change.

Some extra dependencies are required to be installed for this example.

pip install SpeechRecognition
pip install pyttsx3
pip install PyAudio
pip install whisper.ai
pip install soundfile

python native_int4/voiceassistant.py -x MODEL_FAMILY -m CONVERTED_MODEL_PATH -t THREAD_NUM -c CONTEXT_SIZE

arguments info:

• -m CONVERTED_MODEL_PATH: required, path to the converted model
• -x MODEL_FAMILY: required, the model family of the model specified in -m, available options are llama, gptneox and bloom
• -t THREAD_NUM: specify the number of threads to use for inference. Default is 2.
• -c CONTEXT_SIZE: specify maximum context size. Default to be 512.

When you see output says

listening now...

Please say something through your microphone (e.g. What is AI). The program will automatically detect when you have completed your speech and recognize them.

Known Issues

The speech_recognition library may occasionally skip recording due to low volume. An alternative option is to save the recording in WAV format using PyAudio and read the file as an input. Here is an example using PyAudio:

import pyaudio
import speech_recognition as sr

CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1                # The desired number of input channels
RATE = 16000                # The desired rate (in Hz)
RECORD_SECONDS = 10         # Recording time (in second)
WAVE_OUTPUT_FILENAME = "/path/to/pyaudio_out.wav"
p = pyaudio.PyAudio()
                
stream = p.open(format=FORMAT,
                channels=CHANNELS,
                rate=RATE,
                input=True,
                frames_per_buffer=CHUNK)

print("*"*10, "Listening\n")
frames = []
data =0
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
  data = stream.read(CHUNK)  ## <class 'bytes'> ,exception_on_overflow = False
  frames.append(data)   ## <class 'list'>
print("*"*10, "Stop recording\n")

stream.stop_stream()
stream.close()
p.terminate()

wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
wf.close()

r = sr.Recognizer()
with sr.AudioFile(WAVE_OUTPUT_FILENAME) as source1:
    audio = r.record(source1)  # read the entire audio file   
frame_data = np.frombuffer(audio.frame_data, np.int16).flatten().astype(np.float32) / 32768.0

4. Math

This is an example using LLMMathChain. This example has been validated using phoenix-7b.

python transformers_int4/math.py -m MODEL_PATH -q QUESTION

arguments info:

• -m CONVERTED_MODEL_PATH: required, path to the transformers model
• -q QUESTION: question to ask. Default is What is 13 raised to the .3432 power?.