9d8ba64c0d
* add tokenizer_id * fix * modify * add from_model_id and from_mode_id_low_bit * fix typo and add comment * fix python code style --------- Co-authored-by: pengyb2001 <284261055@qq.com>
176 lines
No EOL
6.9 KiB
Markdown
176 lines
No EOL
6.9 KiB
Markdown
# LlamaIndex Examples
|
|
|
|
|
|
This folder contains examples showcasing how to use [**LlamaIndex**](https://github.com/run-llama/llama_index) with `ipex-llm`.
|
|
> [**LlamaIndex**](https://github.com/run-llama/llama_index) is a data framework designed to improve large language models by providing tools for easier data ingestion, management, and application integration.
|
|
|
|
|
|
## Retrieval-Augmented Generation (RAG) Example
|
|
The RAG example ([rag.py](./rag.py)) is adapted from the [Official llama index RAG example](https://docs.llamaindex.ai/en/stable/examples/low_level/oss_ingestion_retrieval.html). This example builds a pipeline to ingest data (e.g. llama2 paper in pdf format) into a vector database (e.g. PostgreSQL), and then build a retrieval pipeline from that vector database.
|
|
|
|
|
|
|
|
### 1. Setting up Dependencies
|
|
|
|
* **Install LlamaIndex Packages**
|
|
```bash
|
|
pip install llama-index-readers-file llama-index-vector-stores-postgres llama-index-embeddings-huggingface
|
|
```
|
|
* **Install IPEX-LLM**
|
|
|
|
Follow the instructions in [GPU Install Guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install.html) to install ipex-llm.
|
|
|
|
* **Database Setup (using PostgreSQL)**:
|
|
* Linux
|
|
* Installation:
|
|
```bash
|
|
sudo apt-get install postgresql-client
|
|
sudo apt-get install postgresql
|
|
```
|
|
* Initialization:
|
|
|
|
Switch to the **postgres** user and launch **psql** console
|
|
```bash
|
|
sudo su - postgres
|
|
psql
|
|
```
|
|
|
|
Then, create a new user role:
|
|
```bash
|
|
CREATE ROLE <user> WITH LOGIN PASSWORD '<password>';
|
|
ALTER ROLE <user> SUPERUSER;
|
|
```
|
|
* Windows
|
|
* click `Download the installer` in [PostgreSQL](https://www.postgresql.org/download/windows/).
|
|
* Run the downloaded installation package as administrator, then click `next` continuously.
|
|
* Open PowerShell:
|
|
```bash
|
|
cd C:\Program Files\PostgreSQL\14\bin
|
|
```
|
|
The exact path will vary depending on your PostgreSQL location.
|
|
* Then in PowerShell:
|
|
```bash
|
|
.\psql -U postgres
|
|
```
|
|
Input the password you set in the previous installation. If PowerShell shows `postgres=#`, it indicates a successful connection.
|
|
* Create a new user role:
|
|
```bash
|
|
CREATE ROLE <user> WITH LOGIN PASSWORD '<password>';
|
|
ALTER ROLE <user> SUPERUSER;
|
|
```
|
|
* **Pgvector Installation**:
|
|
* Linux
|
|
* Follow installation instructions on [pgvector's GitHub](https://github.com/pgvector/pgvector) and refer to the [installation notes](https://github.com/pgvector/pgvector#installation-notes) for additional help.
|
|
* Windows
|
|
* It is recommended to use [pgvector for Windows](https://github.com/pgvector/pgvector?tab=readme-ov-file#windows) instead of others (such as conda-force) to avoid potential errors. Some steps may require running as administrator.
|
|
|
|
|
|
* **Data Preparation**: Download the Llama2 paper and save it as `data/llama2.pdf`, which serves as the default source file for retrieval.
|
|
```bash
|
|
mkdir data
|
|
wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"
|
|
```
|
|
|
|
### 2. Configures OneAPI environment variables
|
|
#### 2.1 Configurations for Linux
|
|
```bash
|
|
source /opt/intel/oneapi/setvars.sh
|
|
```
|
|
#### 2.2 Configurations for Windows
|
|
```cmd
|
|
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
|
|
```
|
|
> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
|
|
### 3. Runtime Configurations
|
|
For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
|
|
#### 3.1 Configurations for Linux
|
|
<details>
|
|
|
|
<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
|
|
|
|
```bash
|
|
export USE_XETLA=OFF
|
|
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
|
|
```
|
|
|
|
</details>
|
|
|
|
<details>
|
|
|
|
<summary>For Intel Data Center GPU Max Series</summary>
|
|
|
|
```bash
|
|
export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
|
|
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
|
|
export ENABLE_SDP_FUSION=1
|
|
```
|
|
> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
|
|
</details>
|
|
|
|
#### 3.2 Configurations for Windows
|
|
<details>
|
|
|
|
<summary>For Intel iGPU</summary>
|
|
|
|
```cmd
|
|
set SYCL_CACHE_PERSISTENT=1
|
|
set BIGDL_LLM_XMX_DISABLED=1
|
|
```
|
|
|
|
</details>
|
|
|
|
<details>
|
|
|
|
<summary>For Intel Arc™ A300-Series or Pro A60</summary>
|
|
|
|
```cmd
|
|
set SYCL_CACHE_PERSISTENT=1
|
|
```
|
|
|
|
</details>
|
|
|
|
<details>
|
|
|
|
<summary>For other Intel dGPU Series</summary>
|
|
|
|
There is no need to set further environment variables.
|
|
|
|
</details>
|
|
|
|
> Note: 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.
|
|
|
|
|
|
### 4. Running the RAG example
|
|
|
|
In the current directory, run the example with command:
|
|
|
|
```bash
|
|
python rag.py -m <path_to_model> -t <path_to_tokenizer>
|
|
```
|
|
**Additional Parameters for Configuration**:
|
|
- `-m MODEL_PATH`: **Required**, path to the LLM model
|
|
- `-e EMBEDDING_MODEL_PATH`: path to the embedding model
|
|
- `-u USERNAME`: username in the PostgreSQL database
|
|
- `-p PASSWORD`: password in the PostgreSQL database
|
|
- `-q QUESTION`: question you want to ask
|
|
- `-d DATA`: path to source data used for retrieval (in pdf format)
|
|
- `-n N_PREDICT`: max predict tokens
|
|
- `-t TOKENIZER_PATH`: **Required**, path to the tokenizer model
|
|
|
|
### 5. Example Output
|
|
|
|
A query such as **"How does Llama 2 compare to other open-source models?"** with the Llama2 paper as the data source, using the `Llama-2-7b-chat-hf` model, will produce the output like below:
|
|
|
|
```
|
|
The comparison between Llama 2 and other open-source models is complex and depends on various factors such as the specific benchmarks used, the model size, and the task at hand.
|
|
|
|
In terms of performance on the benchmarks provided in the table, Llama 2 outperforms other open-source models on most categories. For example, on the MMLU benchmark, Llama 2 achieves a score of 22.5, while the next best open-source model, Poplar Aggregated Benchmarks, scores 17.5. Similarly, on the BBH benchmark, Llama 2 scores 20.5, while the next best open-source model scores 16.5.
|
|
|
|
However, it's important to note that the performance of Llama 2 can vary depending on the specific task and dataset being used. For example, on the coding benchmarks, Llama 2 performs significantly worse than other open-source models, such as PaLM (540B) and GPT-4.
|
|
|
|
In conclusion, while Llama 2 performs well on most benchmarks compared to other open-source models, its performance
|
|
```
|
|
|
|
### 6. Trouble shooting
|
|
#### 6.1 Core dump
|
|
If you encounter a core dump error in your Python code, it is crucial to verify that the `import torch` statement is placed at the top of your Python file, just as what we did in `rag.py`. |