141 lines
4.8 KiB
Markdown
141 lines
4.8 KiB
Markdown
# LangChain Example
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The examples in this folder shows how to use [LangChain](https://www.langchain.com/) with `ipex-llm` on Intel CPU.
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> [!TIP]
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> For more information, please refer to the upstream LangChain LLM documentation with IPEX-LLM [here](https://python.langchain.com/docs/integrations/llms/ipex_llm), and upstream LangChain embedding model documentation with IPEX-LLM [here](https://python.langchain.com/docs/integrations/text_embedding/ipex_llm/).
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## 0. Requirements
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To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
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## 1. Install
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We suggest using conda to manage environment:
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On Linux:
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```bash
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conda create -n llm python=3.11
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conda activate llm
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# install ipex-llm with 'all' option
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pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
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```
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On Windows:
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```cmd
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onda create -n llm python=3.11
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conda activate llm
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pip install --pre --upgrade ipex-llm[all]
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```
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## 2. Run examples with LangChain
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### 2.1. Example: Streaming Chat
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Install LangChain dependencies:
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```bash
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pip install -U langchain langchain-community
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```
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In the current directory, run the example with command:
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```bash
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python chat.py -m MODEL_PATH -q QUESTION
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```
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**Additional Parameters for Configuration:**
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- `-m MODEL_PATH`: **required**, path to the model
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- `-q QUESTION`: question to ask. Default is `What is AI?`.
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### 2.2. Example: Retrival Augmented Generation (RAG)
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The RAG example ([rag.py](./rag.py)) shows how to load the input text into vector database, and then use LangChain to build a retrival pipeline.
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Install LangChain dependencies:
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```bash
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pip install -U langchain langchain-community langchain-chroma sentence-transformers==3.0.1
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```
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In the current directory, run the example with command:
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```bash
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python rag.py -m <path_to_llm_model> -e <path_to_embedding_model> [-q QUESTION] [-i INPUT_PATH]
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```
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**Additional Parameters for Configuration:**
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- `-m LLM_MODEL_PATH`: **required**, path to the model.
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- `-e EMBEDDING_MODEL_PATH`: **required**, path to the embedding model.
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- `-q QUESTION`: question to ask. Default is `What is IPEX-LLM?`.
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- `-i INPUT_PATH`: path to the input doc.
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### 2.3. 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]
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> `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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Install LangChain dependencies:
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```bash
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pip install -U langchain langchain-community
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```
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In the current directory, run the example with command:
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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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**Additional Parameters for Configuration:**
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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`: question to ask. Default is `What is AI?`.
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### 2.4. 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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Install LangChain dependencies:
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```bash
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pip install -U langchain langchain-community
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```
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In the current directory, run the example with command:
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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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**Additional Parameters for Configuration:**
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- `-m MODEL_PATH`: **Required**, the path to the 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]
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> If `-q` is not specified, it will use `What is 13 raised to the .3432 power?` by default.
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### 2.5. 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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Install LangChain dependencies:
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```bash
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pip install -U langchain langchain-community
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pip install transformers==4.36.2
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```
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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> -r <path_to_recognition_model> [-q <your_question>]
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```
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**Additional Parameters for Configuration:**
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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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