feat: LLM-friendly output types with pagination

Dockerfile

@@ -2,11 +2,15 @@ FROM python:3.11-slim
 
 WORKDIR /app
 
-# Install system dependencies
+# Install system dependencies + Chromium for nodriver scraper
 RUN apt-get update && apt-get install -y \
     curl git \
+    chromium \
     && rm -rf /var/lib/apt/lists/*
 
+# nodriver needs to find the Chromium binary
+ENV CHROME_PATH=/usr/bin/chromium
+
 # Copy requirements first for better Docker layer caching
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt

presets.py

@@ -0,0 +1,78 @@
+"""Load output presets and apply them before report generation.
+
+Loads presets from the volume-mounted config/output_presets.py.
+Before calling researcher.write_report(), call apply_preset() to
+set TOTAL_WORDS env var and get the custom_prompt for the output type.
+"""
+
+import os
+import logging
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_PRESETS = {
+    "summary": {"total_words": 200, "prompt": "Write a concise bullet-point summary of the key findings."},
+    "briefing": {"total_words": 600, "prompt": "Write an executive briefing with key findings and conclusions."},
+    "report": {"total_words": 1200, "prompt": None},
+    "deep_report": {"total_words": 2500, "prompt": None},
+    "raw_context": {"total_words": None, "prompt": None},
+}
+
+VALID_OUTPUT_TYPES = {"summary", "briefing", "report", "deep_report", "raw_context"}
+
+
+def _load_presets():
+    """Load presets from volume-mounted config file, fall back to defaults."""
+    config_path = os.environ.get("OUTPUT_PRESETS_PATH", "/app/config/output_presets.py")
+    if os.path.exists(config_path):
+        import importlib.util
+        spec = importlib.util.spec_from_file_location("output_presets", config_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        logger.info(f"Loaded output presets from {config_path}")
+        return module.PRESETS
+    else:
+        logger.warning(f"Presets config not found at {config_path}, using defaults")
+        return DEFAULT_PRESETS
+
+
+PRESETS = _load_presets()
+
+
+def validate_output_type(output_type: str) -> str:
+    """Validate and return normalized output type. Raises ValueError for unknown types."""
+    if output_type not in VALID_OUTPUT_TYPES:
+        raise ValueError(
+            f"Unknown output_type '{output_type}'. "
+            f"Valid types: {', '.join(sorted(VALID_OUTPUT_TYPES))}"
+        )
+    return output_type
+
+
+def apply_preset(output_type: str) -> str | None:
+    """Apply preset for the given output type.
+
+    Sets TOTAL_WORDS env var and returns custom_prompt (or None for default).
+    Call this BEFORE researcher.write_report().
+
+    Returns:
+        custom_prompt string, or None to use GPT-Researcher's default prompt.
+    """
+    validate_output_type(output_type)
+    preset = PRESETS.get(output_type, DEFAULT_PRESETS.get(output_type))
+
+    if preset["total_words"] is not None:
+        os.environ["TOTAL_WORDS"] = str(preset["total_words"])
+        logger.info(f"Set TOTAL_WORDS={preset['total_words']} for output_type={output_type}")
+
+    return preset.get("prompt")
+
+
+def is_paginated_type(output_type: str) -> bool:
+    """Return True if this output type should be paginated."""
+    return output_type in ("report", "deep_report", "raw_context")
+
+
+def is_raw_context(output_type: str) -> bool:
+    """Return True if this output type skips report generation."""
+    return output_type == "raw_context"

requirements.txt

@@ -8,6 +8,12 @@ fastapi>=0.103.1
 uvicorn>=0.23.2
 pydantic>=2.3.0
 
+# Scraper (NoDriverScraper imports zendriver)
+zendriver>=0.5
+
+# Additional retrievers (gpt-researcher imports ddgs)
+ddgs>=8.0
+
 # Utility dependencies
 loguru>=0.7.0