84 lines
3.3 KiB
Markdown
84 lines
3.3 KiB
Markdown
# Langchain examples
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The examples here shows how to use langchain with `bigdl-llm`.
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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 typing_extensions==4.5.0
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```
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Note that typing_extensions==4.5.0 is required, or you may encounter error `TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'` when running the examples.
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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
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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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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 programe will automatically detect when you have completed your speech and recogize them.
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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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