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Building a retrieval function

A key process in the Retrieval Augmented Generation (RAG) workflow is retrieving data from the database. In this exercise, you'll design a custom function called retrieve() that will perform this crucial process in the final exercise of the course.

Este exercício faz parte do curso

Vector Databases for Embeddings with Pinecone

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Instruções do exercício

  • Initialize the Pinecone client with your API key (the OpenAI client is available as client).
  • Define the function retrieve that takes four parameters: query, top_k, namespace, and emb_model.
  • Embed the input query using the emb_model argument.
  • Retrieve the top_k similar vectors to query_emb with metadata, specifying the namespace provided to the function as an argument.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Initialize the Pinecone client
pc = Pinecone(api_key="____")
index = pc.Index('pinecone-datacamp')

# Define a retrieve function that takes four arguments: query, top_k, namespace, and emb_model
def retrieve(query, top_k, namespace, emb_model):
    # Encode the input query using OpenAI
    query_response = ____(
        input=____,
        model=____
    )
    
    query_emb = query_response.data[0].embedding
    
    # Query the index using the query_emb
    docs = index.query(vector=____, top_k=____, namespace=____, include_metadata=True)
    
    retrieved_docs = []
    sources = []
    for doc in docs['matches']:
        retrieved_docs.append(doc['metadata']['text'])
        sources.append((doc['metadata']['title'], doc['metadata']['url']))
    
    return retrieved_docs, sources

documents, sources = retrieve(
  query="How to build next-level Q&A with OpenAI",
  top_k=3,
  namespace='youtube_rag_dataset',
  emb_model="text-embedding-3-small"
)
print(documents)
print(sources)
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