Supercharge Your OpenAI Responses with Multi-Tool Orchestration and RAG

Harness the power of OpenAI's Responses API by creating dynamic workflows that intelligently route user queries to the right tools. This article dives deep into using a Retrieval-Augmented Generation (RAG) approach, enhancing your applications with context-aware and accurate responses. Learn how to integrate function calls, web searches, and document retrieval.

What is Multi-Tool Orchestration with RAG?

Multi-tool orchestration combines the strengths of various tools to answer complex user queries. RAG enhances this process by fetching relevant information from a knowledge base, ensuring the final answer benefits from detailed context. This fusion provides more accurate and comprehensive responses than relying on a single approach.

Setting Up Your Environment for RAG

Before building your system, you will need to install essential libraries and initialize API keys.

• Install the necessary Python packages: datasets, tqdm, pandas, pinecone, and openai.
• Import the OpenAI client and initialize it with your API key.
• Import necessary components from Pinecone, ensuring it's initialized with your API key.

Preparing Your Data: Medical Reasoning Dataset Example

Let’s use a medical reasoning dataset from Hugging Face for this example. The "Question" and "Response" columns are merged into a single text string. This combined text will be used to generate embeddings. These embeddings are then stored along with the original question and answer data in the Pinecone index.

#Dataset preparation code snippet
ds = load_dataset("FreedomIntelligence/medical-o1-reasoning-SFT", "en", split='train[:100]', trust_remote_code=True)
ds_dataframe = DataFrame(ds)

# Merge the Question and Response columns into a single string.
ds_dataframe['merged'] = ds_dataframe.apply(
lambda row: f"Question: {row['Question']} Answer: {row['Response']}", axis=1
)
print("Example merged text:", ds_dataframe['merged'].iloc[0])

Creating a Pinecone Index: Your Knowledge Vault

A Pinecone index acts as your knowledge repository. We'll use it to store and retrieve information for our RAG system.

• Determine the embedding dimensionality by computing the embedding from the merged data.
• Initialize Pinecone with your API key and define the serverless specification, including region.
• Create the index, connect to it, and review the index statistics to ensure proper setup.

Populate Pinecone with Your Data

Batch processing makes it easy to manage and upload your dataset into Pinecone. Generate embeddings, prepare metadata (including Question and Answer), and upsert each batch into the index. Consider updating metadata for specific entries if needed.

Querying The Knowledge Base for RAG

With Pinecone populated, it's time to query the index and retrieve relevant information.

defquery_pinecone_index(client, index, model, query_text):
# Generate an embedding for the query.
 query_embedding = client.embeddings.create(input=query_text, model=model).data[0].embedding

# Query the index and return top 5 matches.
 res = index.query(vector=[query_embedding], top_k=5, include_metadata=True)
print("Query Results:")
for match in res['matches']:
print(f"{match['score']:.2f}: {match['metadata'].get('Question', 'N/A')} - {match['metadata'].get('Answer', 'N/A')}")
return res

The function presented generates an embedding for the query then executes a similarity search. The code returns the top 5 matches.

Unleash the Power of OpenAI Responses API with RAG

By combining the Responses API with Pinecone vector database, you unlock a versatile solution. This method proves effective for various retrieval and generation tasks, allowing you to respond with precision and relevance. Use cases extend from customer service chatbots to expert medical reasoning systems!

Elevate Your Applications

Multi-tool orchestration with RAG empowers you to construct more sophisticated and intelligent applications. By integrating external knowledge through vector databases like Pinecone, your applications can deliver accurate and context-aware responses. Explore the possibilities and transform how your applications interact with data.