ringmaster_tools.py

# ============================================================================
# ringmaster_tools.py — Tools the LangGraph Ringmaster supervisor can call
# ============================================================================
#
# These tools exist ONLY for the LangGraph Ringmaster backend. They are NOT
# registered in the standard tools.py because the other 6 backends do not
# know about workbench state, data loading, or research-corpus inspection.
#
# COMPLIANCE
# ----------
# Every tool here is a thin wrapper. It:
#   - reads structured input
#   - calls a real domain function (workbench_grounded_theory.run,
#     workbench_thematic_analysis.run, or a simple string inspection)
#   - returns a plain-string summary the LLM can include in its reply
#
# Tools NEVER do control flow. They NEVER route. They NEVER decide what
# runs next. The supervisor decides, the tool executes, the supervisor
# sees the result string and decides again.
#
# DATA CONTRACT
# -------------
# Every tool receives `context` — a dict the ringmaster backend builds
# from the Gradio session state before invoking the supervisor. Fields:
#   context["loaded_context"]  -> str, newline-separated sentences (may be empty)
#   context["llm_provider"]    -> str, the LLM provider name
#   context["llm_key"]         -> str, the API key (may be empty)
#   context["cgt_result"]      -> dict or None, last CGT run result
#   context["cta_result"]      -> dict or None, last CTA run result
#
# Tools that produce new results MUTATE context["cgt_result"] or
# context["cta_result"] so subsequent tool calls in the same chat turn
# can see them (and so the chat handler can extract them afterward to
# update the workbench tabs).
# ============================================================================

from typing import Dict, Any, List


# ----------------------------------------------------------------
# TOOL 1 — check_data_status
# ----------------------------------------------------------------
def check_data_status(context: Dict[str, Any]) -> str:
    """Report whether research data is currently loaded, and if so how much."""
    loaded = (context.get("loaded_context") or "").strip()
    if not loaded:
        return (
            "NO DATA LOADED. The user has not uploaded a file, pasted text, "
            "or scraped a URL yet. Ask the user to go to the Inputs tab and "
            "load data before running any research workbench."
        )

    sentences = [s.strip() for s in loaded.split("\n") if s.strip()]
    n = len(sentences)
    preview = sentences[:3]

    if n == 0:
        return "NO DATA LOADED — loaded_context is whitespace only."

    return (
        f"DATA LOADED: {n} sentences available for analysis.\n"
        f"First 3 sentences for preview:\n"
        + "\n".join(f"  {i+1}. {s}" for i, s in enumerate(preview))
    )


# ----------------------------------------------------------------
# TOOL 2 — run_grounded_theory
# ----------------------------------------------------------------
def run_grounded_theory(

    context: Dict[str, Any],

    similarity_threshold: float = 0.60,

    min_cluster_size: int = 3,

    n_nearest: int = 3,

) -> str:
    """Run the Computational Grounded Theory supervisor on loaded data.



    Returns a short text summary. Mutates context["cgt_result"] with the

    full result dict so the chat handler can update the CGT tab afterward.

    """
    loaded = (context.get("loaded_context") or "").strip()
    if not loaded:
        return (
            "ERROR: cannot run grounded theory — no data loaded. "
            "Ask the user to load data via the Inputs tab first."
        )

    sentences = [s.strip() for s in loaded.split("\n") if s.strip()]
    true_labels = ["(unknown)"] * len(sentences)

    # Import here to keep the ringmaster_tools module import-light and to
    # avoid a circular import at app.py boot.
    import workbench_grounded_theory as wb_cgt

    result = wb_cgt.run(
        user_message="Run computational grounded theory.",
        sentences=sentences,
        true_labels=true_labels,
        data_source="uploaded",
        similarity_threshold=float(similarity_threshold),
        min_cluster_size=int(min_cluster_size),
        n_nearest=int(n_nearest),
        llm_provider=context.get("llm_provider", "Mistral"),
        llm_key=context.get("llm_key", ""),
    )

    context["cgt_result"] = result

    det = result.get("detection_result") or {}
    clusters = det.get("clusters") or []
    n_clusters = len(clusters)
    cluster_summary_lines = []
    for c in clusters:
        label = c.get("llm_label") or c.get("cluster_id") or "unknown"
        size = c.get("size") or 0
        cluster_summary_lines.append(f"  - Cluster {c.get('cluster_id')}: {label} ({size} sentences)")

    if not cluster_summary_lines:
        return (
            f"Ran grounded theory on {len(sentences)} sentences but no clusters were "
            f"found at similarity {similarity_threshold} / min size {min_cluster_size}. "
            f"Suggest the user lower similarity_threshold or min_cluster_size."
        )

    return (
        f"COMPLETED: grounded theory on {len(sentences)} sentences. "
        f"Found {n_clusters} cluster(s):\n"
        + "\n".join(cluster_summary_lines)
        + "\nThe full trace and per-sentence cluster table are now in the "
        "Researcher Workbench → Computational Grounded Theory tab."
    )


# ----------------------------------------------------------------
# TOOL 3 — run_thematic_analysis
# ----------------------------------------------------------------
def run_thematic_analysis(

    context: Dict[str, Any],

    max_sentences: int = 20,

) -> str:
    """Run the Computational Thematic Analysis supervisor on loaded data.



    Returns a short text summary. Mutates context["cta_result"].

    """
    loaded = (context.get("loaded_context") or "").strip()
    if not loaded:
        return (
            "ERROR: cannot run thematic analysis — no data loaded. "
            "Ask the user to load data via the Inputs tab first."
        )

    sentences = [s.strip() for s in loaded.split("\n") if s.strip()]
    true_labels = ["(unknown)"] * len(sentences)

    import workbench_thematic_analysis as wb_cta

    result = wb_cta.run(
        user_message="Run reflexive thematic analysis.",
        sentences=sentences,
        true_labels=true_labels,
        data_source="uploaded",
        max_sentences_to_code=int(max_sentences),
        llm_provider=context.get("llm_provider", "Mistral"),
        llm_key=context.get("llm_key", ""),
    )

    context["cta_result"] = result

    phase2 = result.get("phase2_initial_codes") or {}
    coded_rows = phase2.get("coded_rows") or []
    code_counts = phase2.get("code_frequency") or {}

    top_codes = sorted(code_counts.items(), key=lambda kv: -kv[1])[:5]
    top_codes_str = ", ".join(f"{code} ({count})" for code, count in top_codes) or "(none)"

    return (
        f"COMPLETED: thematic analysis on {len(coded_rows)} sentences "
        f"(out of {len(sentences)} loaded, capped at {max_sentences}). "
        f"Top 5 codes: {top_codes_str}. "
        f"The full trace and per-sentence code table are now in the "
        f"Researcher Workbench → Computational Thematic Analysis tab."
    )


# ----------------------------------------------------------------
# TOOL 4 — summarize_cgt_result
# ----------------------------------------------------------------
def summarize_cgt_result(context: Dict[str, Any]) -> str:
    """Return a text summary of the most recent grounded theory run."""
    result = context.get("cgt_result")
    if not result:
        return (
            "NO PRIOR GROUNDED THEORY RUN. The user has not yet run grounded "
            "theory in this session. Use run_grounded_theory first."
        )

    det = result.get("detection_result") or {}
    clusters = det.get("clusters") or []
    lines = ["Most recent Grounded Theory run:"]
    for c in clusters:
        lines.append(
            f"  - Cluster {c.get('cluster_id')}: {c.get('llm_label', 'unlabeled')} "
            f"({c.get('size', 0)} sentences)"
        )
    lines.append(f"Supervisor reply: {result.get('reply', '(empty)')}")
    return "\n".join(lines)


# ----------------------------------------------------------------
# TOOL 5 — summarize_cta_result
# ----------------------------------------------------------------
def summarize_cta_result(context: Dict[str, Any]) -> str:
    """Return a text summary of the most recent thematic analysis run."""
    result = context.get("cta_result")
    if not result:
        return (
            "NO PRIOR THEMATIC ANALYSIS RUN. The user has not yet run "
            "thematic analysis in this session. Use run_thematic_analysis first."
        )

    phase2 = result.get("phase2_initial_codes") or {}
    coded_rows = phase2.get("coded_rows") or []
    code_freq = phase2.get("code_frequency") or {}
    top_codes = sorted(code_freq.items(), key=lambda kv: -kv[1])[:5]

    lines = [f"Most recent Thematic Analysis run: {len(coded_rows)} sentences coded."]
    lines.append("Top 5 codes:")
    for code, count in top_codes:
        lines.append(f"  - {code}: {count}")
    lines.append(f"Supervisor reply: {result.get('reply', '(empty)')}")
    return "\n".join(lines)


# ============================================================================
# Tool registration — shape matches tools.py for consistency
# ============================================================================
RINGMASTER_TOOL_FUNCTIONS = {
    "check_data_status": check_data_status,
    "run_grounded_theory": run_grounded_theory,
    "run_thematic_analysis": run_thematic_analysis,
    "summarize_cgt_result": summarize_cgt_result,
    "summarize_cta_result": summarize_cta_result,
}


RINGMASTER_TOOL_SCHEMAS = [
    {
        "type": "function",
        "function": {
            "name": "check_data_status",
            "description": (
                "Check whether research data is currently loaded in the session. "
                "Returns the number of sentences and a short preview, or reports "
                "that no data is loaded. ALWAYS call this before run_grounded_theory "
                "or run_thematic_analysis so you know whether to ask the user to "
                "load data first."
            ),
            "parameters": {
                "type": "object",
                "properties": {},
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "run_grounded_theory",
            "description": (
                "Run Computational Grounded Theory (Nelson 2020) on the currently "
                "loaded research data. Only call this AFTER check_data_status "
                "confirmed data is loaded. The result is a short text summary of "
                "the clusters found; the full trace and sentence-level table will "
                "appear in the Researcher Workbench tab automatically."
            ),
            "parameters": {
                "type": "object",
                "properties": {
                    "similarity_threshold": {
                        "type": "number",
                        "description": "Cosine similarity threshold (0.4-0.9, default 0.60)",
                    },
                    "min_cluster_size": {
                        "type": "integer",
                        "description": "Minimum sentences per cluster (2-10, default 3)",
                    },
                    "n_nearest": {
                        "type": "integer",
                        "description": "Representatives per cluster for LLM labeling (1-10, default 3)",
                    },
                },
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "run_thematic_analysis",
            "description": (
                "Run Computational Thematic Analysis (Braun & Clarke 2006) on the "
                "currently loaded research data. Only call this AFTER "
                "check_data_status confirmed data is loaded. Phase 2 (generating "
                "initial codes) is the only real phase; the rest are placeholders. "
                "The result is a short text summary; the full per-sentence code "
                "table will appear in the Researcher Workbench tab automatically."
            ),
            "parameters": {
                "type": "object",
                "properties": {
                    "max_sentences": {
                        "type": "integer",
                        "description": "Cap on sentences to code (expensive — each is one LLM call, default 20)",
                    },
                },
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "summarize_cgt_result",
            "description": (
                "Return a text summary of the most recent Grounded Theory run so "
                "you can answer follow-up questions about it. Does not re-run the "
                "analysis."
            ),
            "parameters": {
                "type": "object",
                "properties": {},
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "summarize_cta_result",
            "description": (
                "Return a text summary of the most recent Thematic Analysis run "
                "so you can answer follow-up questions. Does not re-run."
            ),
            "parameters": {
                "type": "object",
                "properties": {},
            },
        },
    },
]