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RAG questions answering function

You're almost there! The final piece in the RAG workflow is to integrate the retrieved documents with a question-answering model.

A prompt_with_context_builder() function has already been defined and made available to you. This function takes the documents retrieved from the Pinecone index, and integrates them into a prompt that the question-answering model can ingest:

def prompt_with_context_builder(query, docs):
    delim = '\n\n---\n\n'
    prompt_start = 'Answer the question based on the context below.\n\nContext:\n'
    prompt_end = f'\n\nQuestion: {query}\nAnswer:'

    prompt = prompt_start + delim.join(docs) + prompt_end
    return prompt

You'll implement the question_answering() function, which will provide OpenAI's language model gpt-4o-mini with additional context and sources with which it can answer your questions.

Cet exercice fait partie du cours

Vector Databases for Embeddings with Pinecone

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Instructions

  • Initialize the Pinecone client with your API key (the OpenAI client is available as client).
  • Retrieve the three most similar documents to the query text from the 'youtube_rag_dataset' namespace.
  • Generate a response to the provided prompt and sys_prompt using OpenAI's 'gpt-4o-mini' model, specified using the chat_model function argument.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

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

query = "How to build next-level Q&A with OpenAI"

# Retrieve the top three most similar documents and their sources
documents, sources = retrieve(____, top_k=____, namespace='____', emb_model="text-embedding-3-small")

prompt_with_context = prompt_with_context_builder(query, documents)
print(prompt_with_context)

def question_answering(prompt, sources, chat_model):
    sys_prompt = "You are a helpful assistant that always answers questions."
    
    # Use OpenAI chat completions to generate a response
    res = ____(
        model=____,
        messages=[
            {"role": "system", "content": ____},
            {"role": "user", "content": ____}
        ],
        temperature=0
    )
    answer = res.choices[0].message.content.strip()
    answer += "\n\nSources:"
    for source in sources:
        answer += "\n" + source[0] + ": " + source[1]
    
    return answer

answer = question_answering(
  prompt=prompt_with_context,
  sources=sources,
  chat_model='gpt-4o-mini')
print(answer)
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