c2efa264d9
* Update LangChain examples to use upstream * Update README and fix links * Update LangChain CPU examples to use upstream * Update LangChain CPU voice_assistant example * Update CPU README * Update GPU README * Remove GPU Langchain vLLM example and fix comments * Change langchain -> LangChain * Add reference for both upstream llms and embeddings * Fix comments * Fix comments * Fix comments * Fix comments * Fix comment
169 lines
4.8 KiB
Markdown
169 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 GPU.
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> [!NOTE]
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> Please refer [here](https://python.langchain.com/docs/integrations/llms/ipex_llm) for upstream LangChain LLM documentation with ipex-llm and [here](https://python.langchain.com/docs/integrations/text_embedding/ipex_llm_gpu/) for upstream LangChain embedding documentation with ipex-llm.
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## 0. Requirements
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To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../README.md#requirements) for more information.
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## 1. Install
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### 1.1 Installation on Linux
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We suggest using conda to manage environment:
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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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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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```
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### 1.2 Installation on Windows
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.11 libuv
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conda activate llm
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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```
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## 2. Configures OneAPI environment variables for Linux
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> [!NOTE]
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> Skip this step if you are running on Windows.
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This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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## 3. Runtime Configurations
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For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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### 3.1 Configurations for Linux
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<details>
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<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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export ENABLE_SDP_FUSION=1
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```
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> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
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</details>
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<details>
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<summary>For Intel iGPU</summary>
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```bash
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export SYCL_CACHE_PERSISTENT=1
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export BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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### 3.2 Configurations for Windows
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<details>
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<summary>For Intel iGPU</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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set BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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<details>
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<summary>For Intel Arc™ A-Series Graphics</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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```
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</details>
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> [!NOTE]
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> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
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## 4. Run examples with LangChain
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### 4.1. 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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### 4.2. 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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### 4.3. 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.LangChain
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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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