Langchain readme (#10348)
* update langchain readme * update readme * create new README * Update README_nativeint4.md
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Shengsheng Huang
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# Langchain examples
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## Langchain Examples
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The examples here show how to use langchain with `bigdl-llm`.
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This folder contains examples showcasing how to use `langchain` with `bigdl`.
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## Install bigdl-llm
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Follow the instructions in [Install](https://github.com/intel-analytics/BigDL/tree/main/python/llm#install).
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### Install BigDL
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Ensure `bigdl-llm` is installed by following the [BigDL-LLM Installation Guide](https://github.com/intel-analytics/BigDL/tree/main/python/llm#install).
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### Install Dependences Required by the Examples
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## Install Required Dependencies for langchain examples.
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```bash
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pip install langchain==0.0.184
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@ -14,115 +16,58 @@ pip install -U pandas==2.0.3
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```
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## Convert Models using bigdl-llm
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Follow the instructions in [Convert model](https://github.com/intel-analytics/BigDL/tree/main/python/llm#convert-model).
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### Example: Chat
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The chat example ([chat.py](./transformers_int4/chat.py)) shows how to use `LLMChain` to build a chat pipeline.
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## Run the examples
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### 1. Streaming Chat
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To run the example, execute the following command in the current directory:
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```bash
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python native_int4/streamchat.py -m CONVERTED_MODEL_PATH -x MODEL_FAMILY -q QUESTION -t THREAD_NUM
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python transformers_int4/chat.py -m <path_to_model> [-q <your_question>]
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```
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arguments info:
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- `-m CONVERTED_MODEL_PATH`: **required**, path to the converted model
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- `-x MODEL_FAMILY`: **required**, the model family of the model specified in `-m`, available options are `llama`, `gptneox` and `bloom`
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- `-q QUESTION`: question to ask. Default is `What is AI?`.
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- `-t THREAD_NUM`: specify the number of threads to use for inference. Default is `2`.
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> Note: if `-q` is not specified, it will use `What is AI` by default.
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### 2. Question Answering over Docs
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```bash
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python native_int4/docqa.py -m CONVERTED_MODEL_PATH -x MODEL_FAMILY -i DOC_PATH -q QUESTION -c CONTEXT_SIZE -t THREAD_NUM
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```
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arguments info:
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- `-m CONVERTED_MODEL_PATH`: **required**, path to the converted model in above step
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- `-x MODEL_FAMILY`: **required**, the model family of the model specified in `-m`, available options are `llama`, `gptneox` and `bloom`
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- `-i DOC_PATH`: **required**, path to the input document
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- `-q QUESTION`: question to ask. Default is `What is AI?`.
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- `-c CONTEXT_SIZE`: specify the maximum context size. Default is `2048`.
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- `-t THREAD_NUM`: specify the number of threads to use for inference. Default is `2`.
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### Example: RAG (Retrival Augmented Generation)
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### 3. Voice Assistant
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> This example is adapted from https://python.langchain.com/docs/use_cases/chatbots/voice_assistant with only tiny code change.
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The RAG example ([rag.py](./transformers_int4/docqa.py)) shows how to load the input text into vector database, and then use `load_qa_chain` to build a retrival pipeline.
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Some extra dependencies are required to be installed for this example.
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```bash
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pip install SpeechRecognition
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pip install pyttsx3
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pip install PyAudio
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pip install whisper.ai
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pip install soundfile
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```
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To run the example, execute the following command in the current directory:
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```bash
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python native_int4/voiceassistant.py -x MODEL_FAMILY -m CONVERTED_MODEL_PATH -t THREAD_NUM -c CONTEXT_SIZE
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python transformers_int4/rag.py -m <path_to_model> [-q <your_question>] [-i <path_to_input_txt>]
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```
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> Note: If `-i` is not specified, it will use a short introduction to Big-DL as input by default. if `-q` is not specified, `What is BigDL?` will be used by default.
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arguments info:
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- `-m CONVERTED_MODEL_PATH`: **required**, path to the converted model
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- `-x MODEL_FAMILY`: **required**, the model family of the model specified in `-m`, available options are `llama`, `gptneox` and `bloom`
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- `-t THREAD_NUM`: specify the number of threads to use for inference. Default is `2`.
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- `-c CONTEXT_SIZE`: specify maximum context size. Default to be 512.
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When you see output says
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> listening now...
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### Example: Math
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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.
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The math example ([math.py](./transformers_int4/llm_math.py)) shows how to build a chat pipeline specialized in solving math questions. For example, you can ask `What is 13 raised to the .3432 power?`
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#### Known Issues
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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:
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```python
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import pyaudio
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import speech_recognition as sr
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CHUNK = 1024
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FORMAT = pyaudio.paInt16
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CHANNELS = 1 # The desired number of input channels
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RATE = 16000 # The desired rate (in Hz)
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RECORD_SECONDS = 10 # Recording time (in second)
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WAVE_OUTPUT_FILENAME = "/path/to/pyaudio_out.wav"
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p = pyaudio.PyAudio()
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stream = p.open(format=FORMAT,
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channels=CHANNELS,
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rate=RATE,
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input=True,
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frames_per_buffer=CHUNK)
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print("*"*10, "Listening\n")
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frames = []
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data =0
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for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
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data = stream.read(CHUNK) ## <class 'bytes'> ,exception_on_overflow = False
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frames.append(data) ## <class 'list'>
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print("*"*10, "Stop recording\n")
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stream.stop_stream()
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stream.close()
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p.terminate()
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wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
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wf.setnchannels(CHANNELS)
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wf.setsampwidth(p.get_sample_size(FORMAT))
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wf.setframerate(RATE)
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wf.writeframes(b''.join(frames))
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wf.close()
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r = sr.Recognizer()
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with sr.AudioFile(WAVE_OUTPUT_FILENAME) as source1:
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audio = r.record(source1) # read the entire audio file
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frame_data = np.frombuffer(audio.frame_data, np.int16).flatten().astype(np.float32) / 32768.0
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```
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### 4. Math
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This is an example using `LLMMathChain`. This example has been validated using [phoenix-7b](https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b).
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To run the exmaple, execute the following command in the current directory:
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```bash
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python transformers_int4/math.py -m MODEL_PATH -q QUESTION
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python transformers_int4/llm_math.py -m <path_to_model> [-q <your_question>]
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```
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arguments info:
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- `-m CONVERTED_MODEL_PATH`: **required**, path to the transformers model
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- `-q QUESTION`: question to ask. Default is `What is 13 raised to the .3432 power?`.
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> Note: if `-q` is not specified, it will use `What is 13 raised to the .3432 power?` by default.
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### Example: Voice Assistant
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The voice assistant example ([voiceassistant.py](./transformers_int4/voiceassistant.py)) showcases how to use langchain to build a pipeline that takes in your speech as input in realtime, use an ASR model (e.g. [Whisper-Medium](https://huggingface.co/openai/whisper-medium)) to turn speech into text, and then feed the text into large language model to get response.
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To run the exmaple, execute the following command in the current directory:
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```bash
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python transformers_int4/voiceassistant.py -m <path_to_model> [-q <your_question>]
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```
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**Runtime Arguments Explained**:
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- `-m MODEL_PATH`: **Required**, the path to the
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- `-r RECOGNITION_MODEL_PATH`: **Required**, the path to the huggingface speech recognition model
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- `-x MAX_NEW_TOKENS`: the max new tokens of model tokens input
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- `-l LANGUAGE`: you can specify a language such as "english" or "chinese"
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- `-d True|False`: whether the model path specified in -m is saved low bit model.
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### Legacy (Native INT4 examples)
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BigDL also provides langchain integrations using native INT4 mode. Those examples can be foud in [native_int4](./native_int4/) folder. For detailed instructions of settting up and running `native_int4` examples, refer to [Native INT4 Examples README](./README_nativeint4.md).
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128
python/llm/example/CPU/LangChain/README_nativeint4.md
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python/llm/example/CPU/LangChain/README_nativeint4.md
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# Langchain Native INT4 examples
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The examples in [native_int4](./native_int4) folder show how to use langchain with `bigdl-llm` native INT4 mode.
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## Install bigdl-llm
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Follow the instructions in [Install](https://github.com/intel-analytics/BigDL/tree/main/python/llm#install).
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## Install Required Dependencies for langchain examples.
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```bash
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pip install langchain==0.0.184
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pip install -U chromadb==0.3.25
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pip install -U pandas==2.0.3
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```
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## Convert Models using bigdl-llm
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Follow the instructions in [Convert model](https://github.com/intel-analytics/BigDL/tree/main/python/llm#convert-model).
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## Run the examples
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### 1. Streaming Chat
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```bash
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python native_int4/streamchat.py -m CONVERTED_MODEL_PATH -x MODEL_FAMILY -q QUESTION -t THREAD_NUM
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```
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arguments info:
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- `-m CONVERTED_MODEL_PATH`: **required**, path to the converted model
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- `-x MODEL_FAMILY`: **required**, the model family of the model specified in `-m`, available options are `llama`, `gptneox` and `bloom`
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- `-q QUESTION`: question to ask. Default is `What is AI?`.
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- `-t THREAD_NUM`: specify the number of threads to use for inference. Default is `2`.
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### 2. Question Answering over Docs
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```bash
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python native_int4/docqa.py -m CONVERTED_MODEL_PATH -x MODEL_FAMILY -i DOC_PATH -q QUESTION -c CONTEXT_SIZE -t THREAD_NUM
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```
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arguments info:
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- `-m CONVERTED_MODEL_PATH`: **required**, path to the converted model in above step
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- `-x MODEL_FAMILY`: **required**, the model family of the model specified in `-m`, available options are `llama`, `gptneox` and `bloom`
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- `-i DOC_PATH`: **required**, path to the input document
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- `-q QUESTION`: question to ask. Default is `What is AI?`.
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- `-c CONTEXT_SIZE`: specify the maximum context size. Default is `2048`.
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- `-t THREAD_NUM`: specify the number of threads to use for inference. Default is `2`.
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### 3. Voice Assistant
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> This example is adapted from https://python.langchain.com/docs/use_cases/chatbots/voice_assistant with only tiny code change.
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Some extra dependencies are required to be installed for this example.
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```bash
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pip install SpeechRecognition
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pip install pyttsx3
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pip install PyAudio
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pip install whisper.ai
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pip install soundfile
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```
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```bash
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python native_int4/voiceassistant.py -x MODEL_FAMILY -m CONVERTED_MODEL_PATH -t THREAD_NUM -c CONTEXT_SIZE
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```
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arguments info:
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- `-m CONVERTED_MODEL_PATH`: **required**, path to the converted model
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- `-x MODEL_FAMILY`: **required**, the model family of the model specified in `-m`, available options are `llama`, `gptneox` and `bloom`
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- `-t THREAD_NUM`: specify the number of threads to use for inference. Default is `2`.
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- `-c CONTEXT_SIZE`: specify maximum context size. Default to be 512.
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When you see output says
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> listening now...
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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.
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#### Known Issues
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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:
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```python
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import pyaudio
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import speech_recognition as sr
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CHUNK = 1024
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FORMAT = pyaudio.paInt16
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CHANNELS = 1 # The desired number of input channels
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RATE = 16000 # The desired rate (in Hz)
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RECORD_SECONDS = 10 # Recording time (in second)
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WAVE_OUTPUT_FILENAME = "/path/to/pyaudio_out.wav"
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p = pyaudio.PyAudio()
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stream = p.open(format=FORMAT,
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channels=CHANNELS,
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rate=RATE,
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input=True,
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frames_per_buffer=CHUNK)
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print("*"*10, "Listening\n")
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frames = []
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data =0
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for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
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data = stream.read(CHUNK) ## <class 'bytes'> ,exception_on_overflow = False
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frames.append(data) ## <class 'list'>
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print("*"*10, "Stop recording\n")
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stream.stop_stream()
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stream.close()
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p.terminate()
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wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
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wf.setnchannels(CHANNELS)
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wf.setsampwidth(p.get_sample_size(FORMAT))
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wf.setframerate(RATE)
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wf.writeframes(b''.join(frames))
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wf.close()
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r = sr.Recognizer()
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with sr.AudioFile(WAVE_OUTPUT_FILENAME) as source1:
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audio = r.record(source1) # read the entire audio file
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frame_data = np.frombuffer(audio.frame_data, np.int16).flatten().astype(np.float32) / 32768.0
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```
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### 4. Math
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This is an example using `LLMMathChain`. This example has been validated using [phoenix-7b](https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b).
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```bash
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python transformers_int4/math.py -m MODEL_PATH -q QUESTION
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```
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arguments info:
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- `-m CONVERTED_MODEL_PATH`: **required**, path to the transformers model
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- `-q QUESTION`: question to ask. Default is `What is 13 raised to the .3432 power?`.
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