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https://github.com/lazauk/aoai-llamaindex-vectorstore
Sample code to demo the use of LlamaIndex with Azure OpenAI GPT-4 and Embedding models in RAG implementation.
https://github.com/lazauk/aoai-llamaindex-vectorstore
ai azure embedding gpt-4 llamaindex openai rag vectorstore
Last synced: 6 days ago
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Sample code to demo the use of LlamaIndex with Azure OpenAI GPT-4 and Embedding models in RAG implementation.
- Host: GitHub
- URL: https://github.com/lazauk/aoai-llamaindex-vectorstore
- Owner: LazaUK
- License: mit
- Created: 2024-03-13T22:16:45.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-03-14T00:43:06.000Z (10 months ago)
- Last Synced: 2024-11-13T00:19:51.838Z (2 months ago)
- Topics: ai, azure, embedding, gpt-4, llamaindex, openai, rag, vectorstore
- Language: Jupyter Notebook
- Homepage:
- Size: 108 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Retrieval-Augmented Generation (RAG) with LlamaIndex and Azure OpenAI
LlamaIndex is a popular open-source framework for building RAG solutions, thanks to its abstractions of data connectors, indexes and processing engines. You will find a Jupyter notebook in this repo, which utilises LlamaIndex and Azure OpenAI models (GPT-4 and Embedding) to answer queries with pre-indexed local content.
> **Note:** content file used in this demo was borrowed from [Microsoft's Azure OpenAI + Azure AI Search open-source solution](https://github.com/Azure-Samples/azure-search-openai-demo)
To build this demo, I used the latest version of LlamaIndex (**v0.10.19** at the time of writing). To upgrade your _llama-index_ Python package, please use the following pip command:
```
pip install --upgrade llama-index
```
## Table of contents:
- [Part 1: Configuring solution environment](https://github.com/LazaUK/AOAI-LlamaIndex-VectorStore#part-1-configuring-solution-environment)
- [Part 2: Indexing and retrieving content](https://github.com/LazaUK/AOAI-LlamaIndex-VectorStore#part-2-indexing-and-retrieving-content)
## Part 1: Configuring solution environment
1. To use Azure OpenAI backend, assign the API endpoint name, key and version, along with the Azure OpenAI deployment names of GPT and Embedding models to **OPENAI_API_BASE**, **OPENAI_API_KEY**, **OPENAI_API_VERSION**, **OPENAI_API_DEPLOY** (for GPT) and **OPENAI_API_DEPLOY_EMBED** (for Embedding) environment variables respectively.
2. Install the required Python packages, by using the **pip** command and the provided requirements.txt file.
```
pip install -r requirements.txt
```
## Part 2: Indexing and retrieving content
1. Instantiate AzureOpenAI class with details of your GPT model (I'm using **GPT-4 Turbo** deployment).
``` Python
llm = AzureOpenAI(
model = "gpt-4",
deployment_name = AOAI_DEPLOYMENT1,
api_key = AOAI_API_KEY,
azure_endpoint = AOAI_API_BASE,
api_version = AOAI_API_VERSION,
)
```
2. Instantiate AzureOpenAIEmbedding class with details of your Embedding model (I'm using **text-embedding-ada-002** deployment).
> **Note:** Assumptions are that both of your models are deployed in the same Azure OpenAI resource. If it's not the case, please adjust the values for Azure OpenAI endpoint and its API key accordingly.
``` Python
embed_model = AzureOpenAIEmbedding(
model = "text-embedding-ada-002",
deployment_name = AOAI_DEPLOYMENT2,
api_key = AOAI_API_KEY,
azure_endpoint = AOAI_API_BASE,
api_version = AOAI_API_VERSION,
)
```
3. Next step is to set the Azure OpenAI deployments as default LLM and Embedding models in LlamaIndex's configuration settings.
``` Python
Settings.llm = llm
Settings.embed_model = embed_model
```
4. We can now use the SimpleDirectoryReader class to create Document objects from all files in a given directory. In our case, the **data** directory contains single markdown file with description of a fictitious company, _Contoso Electronics_.
``` Python
documents = SimpleDirectoryReader(input_dir="data").load_data()
```
5. VectorStoreIndex class can help us to chunk our Document objects, generate vector embeddings and index them in a vector store.
``` Python
index = VectorStoreIndex.from_documents(documents)
```
6. We can use our vector store as a query engine to retrieve required content and feed it to the GPT-4 Turbo model for reasoning, e.g. to describe vacation perks available at Contoso.
``` Python
query_engine = index.as_query_engine()
answer = query_engine.query("What are the vacation perks at Contoso Electronics?")
```
7. If successful, you should get an output similar to this one:
``` JSON
Query: What are the vacation perks at Contoso Electronics?
-----------------
Answer: At Contoso Electronics, the vacation perks are structured into three tiers:
1. Standard Tier: Employees receive 2 weeks of vacation with a health and wellness stipend.
2. Senior Tier: Employees receive 4 weeks of vacation along with travel vouchers for a dream destination.
3. Executive Tier: Employees are granted 6 weeks of vacation and a luxury resort getaway with family.
```