Multimodal RAG (Evidence-Grounded Generation)

from mixpeek import Mixpeek
from openai import OpenAI

mixpeek = Mixpeek(api_key="your-mixpeek-key")
openai = OpenAI(api_key="your-openai-key")

# Retrieve context with citations
context = mixpeek.retrievers.execute(
    retriever_id="rag-retriever",
    inputs={
        "query_text": "How did the product launch go?",
        "return_citations": True
    },
    limit=5
)

# Format context with sources
context_str = "\n".join([
    f"[{i+1}] {doc['text']} (Source: {doc['source_url']} @ {doc['timestamp']})"
    for i, doc in enumerate(context['documents'])
])

# Generate with LLM
response = openai.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": f"Context:\n{context_str}"},
        {"role": "user", "content": "Summarize the product launch feedback"}
    ]
)