Market Analyzer

def exa_market_analyzer_research(location: str) -> str:
    """Research the residential real estate market for a given location. Returns raw JSON string from Exa."""
    completion = client.chat.completions.create(
        model="exa-research",
        messages=[
            {
                "role": "user",
                "content": dedent(f"""
                Research the residential real estate market in {location}.

                Return findings as a JSON object with the fields below. Always include explicit numbers and ranges.
                Break down values into categories whenever possible.
                If no reliable data is found, include the field with value null(do not invent values).

                The JSON must include:

                - location: string(city, country)
                - average_price_sqm: string(overall average €/sqm, include ranges)
                - existing_avg_sqm: string(average €/sqm for existing properties)
                - new_build_avg_sqm: string(average €/sqm for new-build properties)
                - median_house_sqm: string(median €/sqm for houses, include ranges if possible)
                - recent_trends: string(summarize last 12–24 months, % changes, quarterly growth)
                - rents:
                    • per_sqm_month_avg: string(average monthly rent €/sqm)
                    • central_per_sqm_month_range: string(€/sqm/month in central districts)
                    • outer_per_sqm_month_range: string(€/sqm/month in outer districts)
                    • gross_yield_percent_range: string(% gross yield range)
                    • short_term_uplift_percent_range: string(% uplift range for short-term rentals)
                - market_drivers: array of strings(4–6 key factors: demand, demographics, supply, infrastructure, regulations)
                - risks: array of strings(2–4 key risks: oversupply, affordability, regulations, macro/geopolitical)
                - summary: string(3–5 sentence professional executive overview)
                - sources: array of strings(3–5 credible URLs)
                - report: string(full written market report, at least 3–5 paragraphs)

                Important:
                1. Always prefer sources that split data by type(existing vs new-build, central vs outer).
                2. Include explicit €/sqm values and percentages.
                3. Use local currency codes.
                4. Do not include financing or mortgage conditions.
                """),
            }
        ],
        stream=False,
    )
    full_content = ""
    for chunk in completion:
        if chunk.choices and chunk.choices[0].delta.content:
            full_content += chunk.choices[0].delta.content
    return full_content

def get_market_analyzer_last_data() -> str:
    """Read last market analyzer report from Documents/market_analyzer_last_data.json. Returns empty string if missing."""
    path = DOCUMENTS_DIR / "market_analyzer_last_data.json"
    if not path.exists():
        return ""
    return path.read_text(encoding="utf-8")

def save_market_analyzer_last_data(data: str | dict) -> str:
    """Save market analyzer result to Documents/market_analyzer_last_data.json. Accepts JSON string or dict."""
    obj = _parse_json_input(data)
    path = DOCUMENTS_DIR / "market_analyzer_last_data.json"
    path.write_text(json.dumps(obj, ensure_ascii=False, indent=2), encoding="utf-8")
    return f"Saved to {path}"