Multimodal RAG

import requests
from openai import OpenAI

API_URL = "https://api.mixpeek.com"
headers = {"Authorization": "Bearer YOUR_API_KEY", "X-Namespace": "your-namespace"}
openai = OpenAI(api_key="your-openai-key")

# Retrieve multimodal context with citations
results = requests.post(
    f"{API_URL}/v1/retrievers/rag-retriever/execute",
    headers=headers,
    json={"query": {"text": "How did the product launch go?"}}
).json()

# Format context with source citations
context_str = "\n".join([
    f"[{i+1}] {doc['text']} (Source: {doc['root_object_id']} @ {doc['start_time']}s)"
    for i, doc in enumerate(results["documents"])
])

# Generate with your preferred LLM
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": f"Answer based on this context:\n{context_str}"},
        {"role": "user", "content": "Summarize the product launch feedback with citations"}
    ]
)

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