1. "Improving the Performance of Your RAG Pipeline"
2. "Setting Up Required Packages and API Keys"
3. "Implementing RAG Pipeline Using LlamaIndex in Python"
4. "Upgrading LlamaIndex Version"
5. "Storing Vector Embeddings in JSON Files"
6. "Using Weaviate for Metadata Storage and Hybrid Search Capabilities"
7. "Obtaining OpenAI API Key"
8. "Creating Local .env File for API Keys"
9. "Loading API Keys"
10. "Implementing Naive RAG Pipeline Using LlamaIndex"
11. "Defining the Embedding Model and LLM"
12. "Loading Data for Processing"
13. "Chunking Documents into Nodes"
14. "Building Index for External Knowledge"
15. "Setting Up Query Engine"
16. "Running a Naive RAG Query"
17. "Turning Naive RAG Pipeline into Advanced One"
18. "Adjustments for Sentence Window Retrieval Technique"
19. "Implementing Hybrid Search in LlamaIndex"
20. "Adding a Reranker to Advanced RAG Pipeline"

In response to the growing needs of developers and researchers, OpenAI has provided an advanced API that allows developers to programmatically interact with its powerful AI models. This article will demonstrate how to utilize this API to generate Python code.

To interact with the API, you need to use OpenAI’s Python client. The GPT-3.5-turbo model is the current recommended model for most use-cases. However, you can change this to use different models like "gpt-4" or "gpt-4-32k". For example, when giving API examples, you would indicate the following:

model: "gpt-3.5-turbo",
messages: [{ role: "user", content: inputQuestion }],
max_tokens: 4000, # Adjust the max tokens as needed
temperature: 0.7 # Adjust the temperature for more accuracy (e.g., 0.2 for more deterministic output)

To create a small program that generates Python code, you can use the OpenAI API as follows:

import openai

openai.api_key = 'your-api-key'

response = openai.Completion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a Python function to calculate the factorial of a number."},
    ],
)

print(response['choices'][0]['message']['content'])

The above program will generate Python code for a function that calculates the factorial of a number. The Completion.create function is used to send a prompt to the AI model and receive the generated text.

To use another model, replace "gpt-3.5-turbo" in the model parameter with the desired model. Similarly, you can adjust the max_tokens and temperature parameters to control the length and creativity of the output, respectively.

Before using the API, ensure you have the required packages installed and your API keys are correctly set up. The OpenAI API key can be obtained by creating an account on OpenAI’s platform and creating a new secret key.

The OpenAI API provides a powerful tool for generating Python code and other types of text. By adjusting the parameters and prompts, you can tailor the output to your specific needs.

For additional ideas on how to improve the performance of your RAG pipeline to make it production-ready, continue reading here.

Remember, the more specific and detailed your prompt is, the better the model can generate the desired output. Happy Coding!