Mavera Surfaces & Patterns

response = client.responses.create(
    model="mavera-1",
    input=[{"role": "user", "content": "Rate this headline 1-10"}],
    extra_body={"persona_id": "YOUR_PERSONA_ID"},
)

response = client.responses.create(
    model="mavera-1",
    input=[{"role": "user", "content": "Score this email. Return JSON: score, issues[], clarity"}],
    extra_body={
        "persona_id": "YOUR_PERSONA_ID",
        "response_format": {
            "type": "json_schema",
            "json_schema": {
                "name": "email_score",
                "strict": True,
                "schema": {
                    "type": "object",
                    "properties": {
                        "score": {"type": "number"},
                        "issues": {"type": "array", "items": {"type": "string"}},
                        "clarity": {"type": "number"},
                    },
                    "required": ["score", "issues", "clarity"],
                },
            },
        },
    },
)
# Parse response.output[0].content[0].text or .parsed

# 1. Define tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_crm",
            "description": "Search CRM for lead/account data",
            "parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]},
        },
    },
    {
        "type": "function",
        "function": {
            "name": "score_messaging",
            "description": "Score messaging with persona",
            "parameters": {"type": "object", "properties": {"message": {"type": "string"}}, "required": ["message"]},
        },
    },
]

# 2. Call; if finish_reason == "tool_calls", execute tools and feed back
response = client.responses.create(
    model="mavera-1",
    input=[{"role": "user", "content": "Find our lead for Acme and score our last outreach"}],
    extra_body={"persona_id": "YOUR_PERSONA_ID", "tools": tools},
)
# Loop: execute tool_calls, append results, call again until done

# POST /focus-groups or use SDK
# Run Likert: "Rate 1-10: How clear is this feature name?"
# Run open-ended: "What does this name make you think of?"

# 1. Upload video via Files API
# 2. POST /video-analyses with asset_id, goal, brand
# 3. Poll until COMPLETED
# 4. Use results.chunks, results.full_video_metrics
# 5. Optional: POST /video-analyses/{id}/chat for Q&A about the analysis

# GET /news/stories/trending?topics=technology,ai
# POST /news/stories/{id}/analyze with persona_ids → sentiment, key takeaways
# POST /news/stories/{id}/chat for natural-language Q&A

# POST /mave/chat with message
# Returns: content, sources, thread_id for follow-up