Add RAG specific requirements

Author: pamelafox

README.md

@@ -13,10 +13,26 @@ Plus these scripts to demonstrate additional features:
 
 * [`chat_safety.py`](./chat_safety.py): The simple script with exception handling for Azure AI Content Safety filter errors.
 * [`chat_async.py`](./chat_async.py): Uses the async clients to make asynchronous calls, including an example of sending off multiple requests at once using `asyncio.gather`.
-* [`chat_langchain.py`](./chat_langchain.py): Uses the langchain SDK to generate chat completions. [Learn more from Langchain docs](https://python.langchain.com/docs/get_started/quickstart)
-* [`chat_llamaindex.py`](./chat_llamaindex.py): Uses the LlamaIndex SDK to generate chat completions. [Learn more from LlamaIndex docs](https://docs.llamaindex.ai/en/stable/)
 
-These scripts for RAG:
+## Using popular libraries
+
+These scripts use popular libraries to demonstrate how to use the OpenAI API with them:
+
+* [`chat_langchain.py`](./chat_langchain.py): Uses the Langchain package to generate chat completions. [Learn more from Langchain docs](https://python.langchain.com/docs/get_started/quickstart)
+* [`chat_llamaindex.py`](./chat_llamaindex.py): Uses the LlamaIndex package to generate chat completions. [Learn more from LlamaIndex docs](https://docs.llamaindex.ai/en/stable/)
+* [`chat_pydanticai.py`](./chat_pydanticai.py): Uses the PydanticAI package to generate chat completions. [Learn more from PydanticAI docs](https://ai.pydantic.dev/)
+
+## Retrieval-Augmented Generation (RAG)
+
+These scripts demonstrate how to use the OpenAI API for Retrieval-Augmented Generation (RAG) tasks, where the model retrieves relevant information from a source and uses it to generate a response.
+
+First install the RAG dependencies:
+
+```bash
+python -m pip install -r requirements-rag.txt
+```
+
+Then run the scripts (in order of increasing complexity):
 
 * [`rag_csv.py`](./rag.py): Retrieves matching results from a CSV file and uses them to answer user's question.
 * [`rag_multiturn.py`](./rag_multiturn.py): The same idea, but with a back-and-forth chat interface using `input()` which keeps track of past messages and sends them with each chat completion call.

requirements-rag.txt

@@ -0,0 +1,3 @@
+pymupdf4llm
+lunr
+sentence-transformers

requirements.txt

@@ -7,5 +7,3 @@ llama-index-llms-azure-openai
 llama-index-llms-openai
 llama-index-llms-openai-like
 pydantic-ai
-pymupdf4llm
-lunr