b827f534d5
* fix low-bit * fix * fix style --------- Co-authored-by: arda <arda@arda-arc12.sh.intel.com>
90 lines
3.6 KiB
Markdown
90 lines
3.6 KiB
Markdown
## Langchain Examples
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This folder contains examples showcasing how to use `langchain` with `ipex-llm`.
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### Install-IPEX LLM
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Ensure `ipex-llm` is installed by following the [IPEX-LLM Installation Guide](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm#install).
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### Install Dependences Required by the 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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### Example: Chat
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The chat example ([chat.py](./chat.py)) shows how to use `LLMChain` to build a chat pipeline.
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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 chat.py -m <path_to_model> [-q <your_question>]
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```
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> Note: if `-q` is not specified, it will use `What is AI` by default.
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### Example: RAG (Retrival Augmented Generation)
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The RAG example ([rag.py](./rag.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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To run the example, execute the following command in the current directory:
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```bash
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python 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 IPEX LLM?` will be used by default.
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### Example: Math
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The math example ([math.py](./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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To run the exmaple, execute the following command in the current directory:
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```bash
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python llm_math.py -m <path_to_model> [-q <your_question>]
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```
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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](./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 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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### Example: Low Bit
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The low_bit example ([low_bit.py](./low_bit.py)) showcases how to use use langchain with low_bit optimized model.
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By `save_low_bit` we save the weights of low_bit model into the target folder.
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> Note: `save_low_bit` only saves the weights of the model.
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> Users could copy the tokenizer model into the target folder or specify `tokenizer_id` during initialization.
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```bash
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python low_bit.py -m <path_to_model> -t <path_to_target> [-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 model
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- `-t TARGET_PATH`: **Required**, the path to save the low_bit model
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- `-q QUESTION`: the question
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### Legacy (Native INT4 examples)
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IPEX-LLM 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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