revise llamaindex readme (#10283)

python/llm/example/CPU/LlamaIndex/README.md

@@ -1,60 +1,74 @@
 # LlamaIndex Examples
 
-The examples here show how to use LlamaIndex with `bigdl-llm`.
-The RAG example is modified from the [demo](https://docs.llamaindex.ai/en/stable/examples/low_level/oss_ingestion_retrieval.html). 
 
-## Install bigdl-llm
+This folder contains examples showcasing how to use [**LlamaIndex**](https://github.com/run-llama/llama_index) with `bigdl-llm`.
-Follow the instructions in [Install](https://github.com/intel-analytics/BigDL/tree/main/python/llm#install).
+> [**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. 
 
-## Install Required Dependencies for llamaindex examples. 
+## Prerequisites
 
-### Install Site-packages
+Ensure `bigdl-llm` is installed by following the [BigDL-LLM Installation Guide](https://github.com/intel-analytics/BigDL/tree/main/python/llm#install) before proceeding with the examples provided here. 
-```bash
-pip install llama-index-readers-file
-pip install llama-index-vector-stores-postgres
-pip install llama-index-embeddings-huggingface
-```
 
-### Install Postgres
+
-> Note: There are plenty of open-source databases you can use. Here we provide an example using Postgres. 
+## Retrieval-Augmented Generation (RAG) Example
-* Download and install postgres by running the commands below. 
+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. 
+
+
+
+### Setting up Dependencies 
+
+* **Install LlamaIndex Packages**
+    ```bash
+    pip install llama-index-readers-file llama-index-vector-stores-postgres llama-index-embeddings-huggingface
+    ```
+
+* **Database Setup (using PostgreSQL)**:
+    * Installation: 
         ```bash
         sudo apt-get install postgresql-client
         sudo apt-get install postgresql
         ```
-* Initilize postgres. 
+    * Initialization:
+
+      Switch to the **postgres** user and launch **psql** console:
         ```bash
         sudo su - postgres
         psql
         ```
-    After running the commands in the shell, we reach the console of postgres. Then we can add a role like the following
+      Then, create a new user role:
         ```bash
         CREATE ROLE <user> WITH LOGIN PASSWORD '<password>';
         ALTER ROLE <user> SUPERUSER;    
         ```
-* Install pgvector according to the [page](https://github.com/pgvector/pgvector). If you encounter problem about the installation, please refer to the [notes](https://github.com/pgvector/pgvector#installation-notes) which may be helpful. 
+* **Pgvector Installation**:
-* Download the database.
+    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.
+
+
+* **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"
     ```
 
 
-## Run the examples
+### Running the RAG example
+
+In the current directory, run the example with command:
 
-### Retrieval-augmented Generation
 ```bash
-python rag.py -m MODEL_PATH -e EMBEDDING_MODEL_PATH -u USERNAME -p PASSWORD -q QUESTION -d DATA
+python rag.py -m <path_to_model>
 ```
-arguments info:
+**Additional Parameters for Configuration**:
-- `-m MODEL_PATH`: **required**, path to the llama model
+- `-m MODEL_PATH`: **Required**, path to the LLM model
 - `-e EMBEDDING_MODEL_PATH`: path to the embedding model
-- `-u USERNAME`: username in the postgres database
+- `-u USERNAME`: username in the PostgreSQL database
-- `-p PASSWORD`: password in the postgres database
+- `-p PASSWORD`: password in the PostgreSQL database
 - `-q QUESTION`: question you want to ask
-- `-d DATA`: path to data used during retrieval
+- `-d DATA`: path to source data used for retrieval (in pdf format)
+
+### 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:
 
-Here is the sample output when applying Llama-2-7b-chat-hf as the generatio model when we ask "How does Llama 2 perform compared to other open-source models?" and use llama.pdf as database. 
 ```
 Llama 2 performs better than most open-source models on the benchmarks we tested. Specifically, it outperforms all open-source models on MMLU and BBH, and is close to GPT-3.5 on these benchmarks. Additionally, Llama 2 is on par or better than PaLM-2-L on almost all benchmarks. The only exception is the coding benchmarks, where Llama 2 lags significantly behind GPT-4 and PaLM-2-L. Overall, Llama 2 demonstrates strong performance on a wide range of natural language processing tasks.
 ```