feat: add ragflow integration for #859

Merge PR #948: A2A auth — mutual TLS between fleet agents (#806)

docs/ragflow-integration.md

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+# RAGFlow integration
+
+This repo-side slice adds:
+
+- `tools/ragflow_tool.py`
+  - `ragflow_ingest(document_url, dataset)`
+  - `ragflow_query(query, dataset, limit=5)`
+- `scripts/ragflow_bootstrap.py`
+  - fetches the upstream RAGFlow Docker bundle
+  - runs `docker compose --profile cpu up -d` or `gpu`
+
+## Deployment
+
+Bootstrap the upstream CPU stack locally:
+
+```bash
+python3 scripts/ragflow_bootstrap.py --profile cpu
+```
+
+Dry-run only:
+
+```bash
+python3 scripts/ragflow_bootstrap.py --profile cpu --dry-run
+```
+
+Fetch files without launching Docker:
+
+```bash
+python3 scripts/ragflow_bootstrap.py --no-up
+```
+
+Default bundle target:
+
+- `~/.hermes/services/ragflow`
+
+## Runtime configuration
+
+Optional environment variables:
+
+- `RAGFLOW_API_URL` — defaults to `http://localhost:9380`
+- `RAGFLOW_API_KEY` — Bearer token for authenticated RAGFlow APIs
+
+## Supported document types
+
+RAGFlow ingest accepts:
+
+- PDF: `.pdf`
+- Word: `.doc`, `.docx`
+- Presentations: `.ppt`, `.pptx`
+- Images via OCR: `.png`, `.jpg`, `.jpeg`, `.webp`, `.bmp`, `.tif`, `.tiff`, `.gif`
+- Text and codebase documents: `.txt`, `.md`, `.rst`, `.html`, `.json`, `.yaml`, `.yml`, `.toml`, `.ini`, `.py`, `.js`, `.ts`, `.tsx`, `.jsx`, `.java`, `.go`, `.rs`, `.c`, `.cpp`, `.h`, `.hpp`, `.rb`, `.php`, `.sql`, `.sh`
+
+## Example tool usage
+
+```json
+{"document_url":"https://arxiv.org/pdf/1706.03762.pdf","dataset":"research-papers"}
+```
+
+```json
+{"query":"What does the paper say about attention heads?","dataset":"research-papers","limit":5}
+```
+
+## Use cases
+
+- research papers
+- technical documentation
+- OCR-heavy image workflows
+- ingested codebases and architecture docs

scripts/ragflow_bootstrap.py

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+#!/usr/bin/env python3
+"""Bootstrap an upstream RAGFlow Docker bundle for Hermes.
+
+This script fetches the upstream RAGFlow docker bundle into a local directory
+so operators can run `docker compose --profile cpu up -d` (or `gpu`) without
+manually assembling the required files.
+"""
+
+from __future__ import annotations
+
+import argparse
+import subprocess
+import urllib.request
+from pathlib import Path
+
+UPSTREAM_BASE = "https://raw.githubusercontent.com/infiniflow/ragflow/main/docker"
+UPSTREAM_FILES = {
+    "docker-compose.yml": f"{UPSTREAM_BASE}/docker-compose.yml",
+    "docker-compose-base.yml": f"{UPSTREAM_BASE}/docker-compose-base.yml",
+    ".env": f"{UPSTREAM_BASE}/.env",
+    "service_conf.yaml.template": f"{UPSTREAM_BASE}/service_conf.yaml.template",
+    "entrypoint.sh": f"{UPSTREAM_BASE}/entrypoint.sh",
+}
+
+
+def materialize_bundle(target_dir: str | Path, overwrite: bool = False) -> list[Path]:
+    target = Path(target_dir).expanduser()
+    target.mkdir(parents=True, exist_ok=True)
+    written: list[Path] = []
+    for name, url in UPSTREAM_FILES.items():
+        dest = target / name
+        if dest.exists() and not overwrite:
+            written.append(dest)
+            continue
+        with urllib.request.urlopen(url, timeout=60) as response:
+            dest.write_bytes(response.read())
+        if name == "entrypoint.sh":
+            dest.chmod(0o755)
+        written.append(dest)
+    return written
+
+
+def build_compose_command(target_dir: str | Path, profile: str = "cpu") -> list[str]:
+    return ["docker", "compose", "--profile", profile, "up", "-d"]
+
+
+def run_compose(target_dir: str | Path, profile: str = "cpu", dry_run: bool = False) -> dict:
+    target = Path(target_dir).expanduser()
+    command = build_compose_command(target, profile=profile)
+    if dry_run:
+        return {"target_dir": str(target), "command": command, "executed": False}
+    subprocess.run(command, cwd=target, check=True)
+    return {"target_dir": str(target), "command": command, "executed": True}
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description="Fetch and launch the upstream RAGFlow Docker bundle")
+    parser.add_argument("--target-dir", default=str(Path.home() / ".hermes" / "services" / "ragflow"))
+    parser.add_argument("--profile", choices=["cpu", "gpu"], default="cpu")
+    parser.add_argument("--overwrite", action="store_true")
+    parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument("--no-up", action="store_true", help="Only fetch bundle files; do not run docker compose")
+    args = parser.parse_args(argv)
+
+    written = materialize_bundle(args.target_dir, overwrite=args.overwrite)
+    print(f"Fetched {len(written)} RAGFlow docker files into {Path(args.target_dir).expanduser()}")
+    if args.no_up:
+        return 0
+    result = run_compose(args.target_dir, profile=args.profile, dry_run=args.dry_run)
+    print("Command:", " ".join(result["command"]))
+    if result["executed"]:
+        print("RAGFlow docker stack launch requested.")
+    else:
+        print("Dry run only; docker compose not executed.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

tests/test_ragflow_bootstrap.py

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+from __future__ import annotations
+
+import importlib.util
+import io
+from pathlib import Path
+from unittest.mock import patch
+
+
+ROOT = Path(__file__).resolve().parent.parent
+SCRIPT_PATH = ROOT / "scripts" / "ragflow_bootstrap.py"
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("ragflow_bootstrap", SCRIPT_PATH)
+    module = importlib.util.module_from_spec(spec)
+    assert spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+def test_materialize_bundle_downloads_required_upstream_artifacts(tmp_path):
+    module = _load_module()
+
+    def fake_urlopen(url, timeout=0):
+        name = url.rsplit("/", 1)[-1]
+        return io.BytesIO(f"# fetched {name}\n".encode())
+
+    with patch.object(module.urllib.request, "urlopen", side_effect=fake_urlopen):
+        written = module.materialize_bundle(tmp_path)
+
+    assert (tmp_path / "docker-compose.yml").exists()
+    assert (tmp_path / "docker-compose-base.yml").exists()
+    assert (tmp_path / ".env").exists()
+    assert any(path.name == "entrypoint.sh" for path in written)
+
+
+def test_build_compose_command_respects_profile_and_directory(tmp_path):
+    module = _load_module()
+
+    command = module.build_compose_command(tmp_path, profile="gpu")
+
+    assert command[:4] == ["docker", "compose", "--profile", "gpu"]
+    assert command[-2:] == ["up", "-d"]

tests/tools/test_ragflow_tool.py

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+from __future__ import annotations
+
+import importlib
+import json
+import sys
+from pathlib import Path
+from unittest.mock import patch
+
+from tools.registry import registry
+
+
+class _Response:
+    def __init__(self, payload: dict, status_code: int = 200):
+        self._payload = payload
+        self.status_code = status_code
+        self.text = json.dumps(payload)
+
+    def json(self):
+        return self._payload
+
+    def raise_for_status(self):
+        if self.status_code >= 400:
+            raise RuntimeError(f"HTTP {self.status_code}")
+
+
+def _reload_module():
+    registry.deregister("ragflow_ingest")
+    registry.deregister("ragflow_query")
+    sys.modules.pop("tools.ragflow_tool", None)
+    module = importlib.import_module("tools.ragflow_tool")
+    return importlib.reload(module)
+
+
+def test_ragflow_tools_register_and_support_document_formats():
+    module = _reload_module()
+
+    assert registry.get_entry("ragflow_ingest") is not None
+    assert registry.get_entry("ragflow_query") is not None
+    assert ".pdf" in module.SUPPORTED_EXTENSIONS
+    assert ".docx" in module.SUPPORTED_EXTENSIONS
+    assert ".png" in module.SUPPORTED_EXTENSIONS
+    assert ".md" in module.SUPPORTED_EXTENSIONS
+
+
+def test_ragflow_ingest_creates_dataset_uploads_and_starts_parse(tmp_path):
+    module = _reload_module()
+    document = tmp_path / "paper.pdf"
+    document.write_bytes(b"%PDF-1.7\n")
+    calls: list[tuple[str, str, dict | None, dict | None]] = []
+
+    def fake_request(method, url, *, headers=None, params=None, json=None, files=None, timeout=None):
+        calls.append((method, url, params, json))
+        if method == "GET" and url.endswith("/api/v1/datasets"):
+            return _Response({"code": 0, "data": []})
+        if method == "POST" and url.endswith("/api/v1/datasets"):
+            assert json["name"] == "research-papers"
+            assert json["chunk_method"] == "paper"
+            return _Response({"code": 0, "data": {"id": "ds-1", "name": "research-papers"}})
+        if method == "POST" and url.endswith("/api/v1/datasets/ds-1/documents"):
+            assert files and files[0][0] == "file"
+            return _Response({"code": 0, "data": [{"id": "doc-1", "name": "paper.pdf"}]})
+        if method == "POST" and url.endswith("/api/v1/datasets/ds-1/chunks"):
+            assert json == {"document_ids": ["doc-1"]}
+            return _Response({"code": 0})
+        raise AssertionError(f"Unexpected request: {method} {url}")
+
+    with patch("tools.ragflow_tool.requests.request", side_effect=fake_request):
+        result = json.loads(module.ragflow_ingest_tool(str(document), dataset="research-papers"))
+
+    assert result["dataset_id"] == "ds-1"
+    assert result["document_ids"] == ["doc-1"]
+    assert result["parse_started"] is True
+    assert result["chunk_method"] == "paper"
+    assert calls[0][0] == "GET"
+
+
+def test_ragflow_query_retrieves_chunks_for_named_dataset():
+    module = _reload_module()
+
+    def fake_request(method, url, *, headers=None, params=None, json=None, files=None, timeout=None):
+        if method == "GET" and url.endswith("/api/v1/datasets"):
+            assert params == {"name": "tech-docs"}
+            return _Response({"code": 0, "data": [{"id": "ds-9", "name": "tech-docs"}]})
+        if method == "POST" and url.endswith("/api/v1/retrieval"):
+            assert json["question"] == "How does parsing work?"
+            assert json["dataset_ids"] == ["ds-9"]
+            assert json["page_size"] == 2
+            return _Response(
+                {
+                    "code": 0,
+                    "data": {
+                        "chunks": [
+                            {
+                                "content": "Parsing starts by uploading documents.",
+                                "document_id": "doc-9",
+                                "document_keyword": "guide.md",
+                                "similarity": 0.98,
+                            }
+                        ],
+                        "total": 1,
+                    },
+                }
+            )
+        raise AssertionError(f"Unexpected request: {method} {url}")
+
+    with patch("tools.ragflow_tool.requests.request", side_effect=fake_request):
+        result = json.loads(module.ragflow_query_tool("How does parsing work?", "tech-docs", limit=2))
+
+    assert result["dataset_id"] == "ds-9"
+    assert result["total"] == 1
+    assert result["chunks"][0]["content"] == "Parsing starts by uploading documents."
+
+
+def test_ragflow_ingest_rejects_unsupported_document_types(tmp_path):
+    module = _reload_module()
+    document = tmp_path / "binary.exe"
+    document.write_bytes(b"MZ")
+
+    result = json.loads(module.ragflow_ingest_tool(str(document), dataset="ignored"))
+
+    assert "error" in result
+    assert "Unsupported document type" in result["error"]

tools/local_inference_tool.py

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+
+#!/usr/bin/env python3
+"""
+Local Inference Bridge — Fast-path for low-entropy LLM tasks.
+
+Detects local Ollama/llama-cpp instances and uses them for 'Auxiliary' tasks
+(summarization, extraction, simple verification) to reduce cloud dependency.
+"""
+
+import json
+import logging
+import os
+import requests
+from typing import Dict, List, Optional, Any
+from tools.registry import registry, tool_error, tool_result
+
+logger = logging.getLogger(__name__)
+
+LOCAL_INFERENCE_SCHEMA = {
+    "name": "local_inference",
+    "description": "Execute a task using a local inference engine (Ollama/llama-cpp) if available. Ideal for simple summarization, text cleanup, or data extraction where cloud-grade intelligence is overkill.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "prompt": {"type": "string", "description": "The task prompt."},
+            "system": {"type": "string", "description": "Optional system instruction."},
+            "engine": {"type": "string", "enum": ["auto", "ollama", "llama-cpp"], "default": "auto"}
+        },
+        "required": ["prompt"]
+    }
+}
+
+def detect_local_engine() -> Optional[Dict[str, str]]:
+    """Detect presence of local inference engines."""
+    # 1. Check Ollama (default port 11434)
+    try:
+        res = requests.get("http://localhost:11434/api/tags", timeout=1)
+        if res.status_code == 200:
+            return {"type": "ollama", "url": "http://localhost:11434"}
+    except:
+        pass
+
+    # 2. Check llama-cpp-python (commonly on 8000 or 8080)
+    for port in [8000, 8080]:
+        try:
+            res = requests.get(f"http://localhost:{port}/v1/models", timeout=1)
+            if res.status_code == 200:
+                return {"type": "llama-cpp", "url": f"http://localhost:{port}"}
+        except:
+            pass
+            
+    return None
+
+def run_local_task(prompt: str, system: str = None, engine: str = "auto"):
+    """Execute inference on a detected local engine."""
+    info = detect_local_engine()
+    if not info:
+        return tool_error("No local inference engine (Ollama or llama-cpp) detected on localhost.")
+
+    try:
+        if info["type"] == "ollama":
+            # Select first available model or default to gemma
+            models = requests.get(f"{info['url']}/api/tags").json().get("models", [])
+            model_name = models[0]["name"] if models else "gemma"
+            
+            payload = {
+                "model": model_name,
+                "prompt": prompt,
+                "stream": False
+            }
+            if system: payload["system"] = system
+            
+            res = requests.post(f"{info['url']}/api/generate", json=payload, timeout=60)
+            result = res.json().get("response", "")
+            return tool_result(engine="Ollama", model=model_name, response=result)
+
+        elif info["type"] == "llama-cpp":
+            payload = {
+                "model": "local-model",
+                "messages": [
+                    {"role": "system", "content": system or "You are a helpful assistant."},
+                    {"role": "user", "content": prompt}
+                ]
+            }
+            res = requests.post(f"{info['url']}/v1/chat/completions", json=payload, timeout=60)
+            result = res.json()["choices"][0]["message"]["content"]
+            return tool_result(engine="llama-cpp", response=result)
+
+    except Exception as e:
+        return tool_error(f"Local inference failed: {str(e)}")
+
+def _handle_local_inference(args, **kwargs):
+    return run_local_task(
+        prompt=args.get("prompt"),
+        system=args.get("system"),
+        engine=args.get("engine", "auto")
+    )
+
+registry.register(
+    name="local_inference",
+    toolset="inference",
+    schema=LOCAL_INFERENCE_SCHEMA,
+    handler=_handle_local_inference,
+    emoji="🏠"
+)
+    

tools/ragflow_tool.py

@@ -0,0 +1,344 @@
+#!/usr/bin/env python3
+"""RAGFlow tool integration for document understanding.
+
+Provides two tools:
+- ragflow_ingest(document_url, dataset): upload and parse a document into RAGFlow
+- ragflow_query(query, dataset): retrieve relevant chunks from a dataset
+
+Default deployment target is a local RAGFlow server on http://localhost:9380.
+"""
+
+from __future__ import annotations
+
+import json
+import mimetypes
+import os
+import tempfile
+from pathlib import Path
+from typing import Any
+from urllib.parse import urlparse
+
+import requests
+
+from tools.registry import registry, tool_error, tool_result
+
+RAGFLOW_INGEST_SCHEMA = {
+    "name": "ragflow_ingest",
+    "description": (
+        "Upload a document into a RAGFlow dataset, creating the dataset if needed, "
+        "then trigger parsing so Hermes can query the content later. Supports PDF, "
+        "Word, images via OCR, plus text and code documents."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "document_url": {
+                "type": "string",
+                "description": "HTTP(S) URL, file:// URL, or local filesystem path to the document.",
+            },
+            "dataset": {
+                "type": "string",
+                "description": "Dataset name or id to ingest into. Created automatically when absent.",
+            },
+        },
+        "required": ["document_url", "dataset"],
+    },
+}
+
+RAGFLOW_QUERY_SCHEMA = {
+    "name": "ragflow_query",
+    "description": (
+        "Query a RAGFlow dataset for relevant chunks. Useful for research papers, "
+        "technical docs, OCR-processed images, and ingested codebase documents."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": "Question or search query to run against RAGFlow.",
+            },
+            "dataset": {
+                "type": "string",
+                "description": "Dataset name or id to search.",
+            },
+            "limit": {
+                "type": "integer",
+                "description": "Maximum number of chunks to return.",
+                "default": 5,
+                "minimum": 1,
+                "maximum": 25,
+            },
+        },
+        "required": ["query", "dataset"],
+    },
+}
+
+SUPPORTED_EXTENSIONS = {
+    ".pdf": "paper",
+    ".doc": "paper",
+    ".docx": "paper",
+    ".ppt": "presentation",
+    ".pptx": "presentation",
+    ".png": "picture",
+    ".jpg": "picture",
+    ".jpeg": "picture",
+    ".webp": "picture",
+    ".bmp": "picture",
+    ".tif": "picture",
+    ".tiff": "picture",
+    ".gif": "picture",
+    ".txt": "naive",
+    ".md": "naive",
+    ".rst": "naive",
+    ".html": "naive",
+    ".htm": "naive",
+    ".csv": "table",
+    ".tsv": "table",
+    ".json": "naive",
+    ".yaml": "naive",
+    ".yml": "naive",
+    ".toml": "naive",
+    ".ini": "naive",
+    ".py": "naive",
+    ".js": "naive",
+    ".ts": "naive",
+    ".tsx": "naive",
+    ".jsx": "naive",
+    ".java": "naive",
+    ".go": "naive",
+    ".rs": "naive",
+    ".c": "naive",
+    ".cc": "naive",
+    ".cpp": "naive",
+    ".h": "naive",
+    ".hpp": "naive",
+    ".rb": "naive",
+    ".php": "naive",
+    ".sql": "naive",
+    ".sh": "naive",
+}
+
+
+def _ragflow_base_url() -> str:
+    return os.getenv("RAGFLOW_API_URL", "http://localhost:9380").rstrip("/")
+
+
+def _ragflow_headers(json_body: bool = True) -> dict[str, str]:
+    headers: dict[str, str] = {}
+    api_key = os.getenv("RAGFLOW_API_KEY", "").strip()
+    if api_key:
+        headers["Authorization"] = f"Bearer {api_key}"
+    if json_body:
+        headers["Content-Type"] = "application/json"
+    return headers
+
+
+def _ragflow_check_requirements() -> bool:
+    return True
+
+
+def _request_json(method: str, path: str, *, params=None, json_payload=None, files=None) -> dict[str, Any]:
+    response = requests.request(
+        method,
+        f"{_ragflow_base_url()}{path}",
+        headers=_ragflow_headers(json_body=files is None),
+        params=params,
+        json=json_payload,
+        files=files,
+        timeout=120,
+    )
+    response.raise_for_status()
+    payload = response.json()
+    if payload.get("code", 0) != 0:
+        message = payload.get("message") or payload.get("error") or "RAGFlow request failed"
+        raise RuntimeError(message)
+    return payload
+
+
+def _is_probable_dataset_id(dataset: str) -> bool:
+    compact = dataset.replace("-", "")
+    return len(compact) >= 16 and all(ch.isalnum() for ch in compact)
+
+
+def _resolve_dataset(dataset: str) -> tuple[str, str] | None:
+    dataset = dataset.strip()
+    if not dataset:
+        return None
+    params = {"id": dataset} if _is_probable_dataset_id(dataset) else {"name": dataset}
+    payload = _request_json("GET", "/api/v1/datasets", params=params)
+    data = payload.get("data") or []
+    if not data:
+        return None
+    match = data[0]
+    return match["id"], match.get("name", dataset)
+
+
+def _ensure_dataset(dataset: str, chunk_method: str) -> tuple[str, str]:
+    resolved = _resolve_dataset(dataset)
+    if resolved:
+        return resolved
+    payload = _request_json(
+        "POST",
+        "/api/v1/datasets",
+        json_payload={"name": dataset, "chunk_method": chunk_method},
+    )
+    data = payload.get("data") or {}
+    return data["id"], data.get("name", dataset)
+
+
+def _prepare_document(document_url: str) -> tuple[Path, bool]:
+    parsed = urlparse(document_url)
+    if parsed.scheme in {"http", "https"}:
+        response = requests.get(document_url, timeout=120)
+        response.raise_for_status()
+        suffix = Path(parsed.path).suffix or ".bin"
+        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
+        tmp.write(response.content)
+        tmp.flush()
+        tmp.close()
+        return Path(tmp.name), True
+    if parsed.scheme == "file":
+        return Path(parsed.path), False
+    return Path(document_url).expanduser(), False
+
+
+def _detect_chunk_method(path: Path) -> str:
+    extension = path.suffix.lower()
+    if extension not in SUPPORTED_EXTENSIONS:
+        supported = ", ".join(sorted(SUPPORTED_EXTENSIONS))
+        raise ValueError(f"Unsupported document type '{extension or path.name}'. Supported document types: {supported}")
+    return SUPPORTED_EXTENSIONS[extension]
+
+
+def _upload_document(dataset_id: str, path: Path) -> list[str]:
+    mime = mimetypes.guess_type(path.name)[0] or "application/octet-stream"
+    with path.open("rb") as handle:
+        payload = _request_json(
+            "POST",
+            f"/api/v1/datasets/{dataset_id}/documents",
+            files=[("file", (path.name, handle, mime))],
+        )
+    documents = payload.get("data") or []
+    ids = [item["id"] for item in documents if item.get("id")]
+    if not ids:
+        raise RuntimeError("RAGFlow upload did not return any document ids")
+    return ids
+
+
+def ragflow_ingest_tool(document_url: str, dataset: str) -> str:
+    local_path = None
+    should_cleanup = False
+    try:
+        local_path, should_cleanup = _prepare_document(document_url)
+        if not local_path.exists():
+            return tool_error(f"Document not found: {document_url}")
+        chunk_method = _detect_chunk_method(local_path)
+        dataset_id, dataset_name = _ensure_dataset(dataset, chunk_method)
+        document_ids = _upload_document(dataset_id, local_path)
+        _request_json(
+            "POST",
+            f"/api/v1/datasets/{dataset_id}/chunks",
+            json_payload={"document_ids": document_ids},
+        )
+        return tool_result(
+            success=True,
+            dataset_id=dataset_id,
+            dataset_name=dataset_name,
+            document_ids=document_ids,
+            parse_started=True,
+            chunk_method=chunk_method,
+            source=document_url,
+            filename=local_path.name,
+        )
+    except ValueError as exc:
+        return tool_error(str(exc))
+    except Exception as exc:
+        return tool_error(f"RAGFlow ingest failed: {exc}")
+    finally:
+        if should_cleanup and local_path is not None:
+            try:
+                local_path.unlink(missing_ok=True)
+            except Exception:
+                pass
+
+
+def _normalize_chunks(chunks: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    normalized = []
+    for chunk in chunks:
+        normalized.append(
+            {
+                "content": chunk.get("content", ""),
+                "document_id": chunk.get("document_id", ""),
+                "document_name": chunk.get("document_keyword", ""),
+                "similarity": chunk.get("similarity"),
+                "highlight": chunk.get("highlight", ""),
+            }
+        )
+    return normalized
+
+
+def ragflow_query_tool(query: str, dataset: str, limit: int = 5) -> str:
+    try:
+        resolved = _resolve_dataset(dataset)
+        if not resolved:
+            return tool_error(f"RAGFlow dataset not found: {dataset}")
+        dataset_id, dataset_name = resolved
+        payload = _request_json(
+            "POST",
+            "/api/v1/retrieval",
+            json_payload={
+                "question": query,
+                "dataset_ids": [dataset_id],
+                "page_size": max(1, min(int(limit), 25)),
+                "highlight": True,
+                "keyword": True,
+            },
+        )
+        data = payload.get("data") or {}
+        chunks = data.get("chunks") or []
+        return tool_result(
+            success=True,
+            dataset_id=dataset_id,
+            dataset_name=dataset_name,
+            total=data.get("total", len(chunks)),
+            chunks=_normalize_chunks(chunks),
+        )
+    except Exception as exc:
+        return tool_error(f"RAGFlow query failed: {exc}")
+
+
+def _handle_ragflow_ingest(args, **_kwargs):
+    return ragflow_ingest_tool(
+        document_url=args.get("document_url", ""),
+        dataset=args.get("dataset", ""),
+    )
+
+
+def _handle_ragflow_query(args, **_kwargs):
+    return ragflow_query_tool(
+        query=args.get("query", ""),
+        dataset=args.get("dataset", ""),
+        limit=args.get("limit", 5),
+    )
+
+
+registry.register(
+    name="ragflow_ingest",
+    toolset="web",
+    schema=RAGFLOW_INGEST_SCHEMA,
+    handler=_handle_ragflow_ingest,
+    check_fn=_ragflow_check_requirements,
+    requires_env=["RAGFLOW_API_URL", "RAGFLOW_API_KEY"],
+    emoji="📚",
+)
+
+registry.register(
+    name="ragflow_query",
+    toolset="web",
+    schema=RAGFLOW_QUERY_SCHEMA,
+    handler=_handle_ragflow_query,
+    check_fn=_ragflow_check_requirements,
+    requires_env=["RAGFLOW_API_URL", "RAGFLOW_API_KEY"],
+    emoji="🧠",
+)

tools/sovereign_scavenger.py

@@ -0,0 +1,86 @@
+
+#!/usr/bin/env python3
+"""
+Sovereign Scavenger — Autonomous Backlog Grooming.
+
+Scans the codebase for TODO/FIXME/DEBUG comments and converts them into
+actionable Gitea issues for the fleet to consume.
+"""
+
+import os
+import re
+import logging
+from typing import List, Dict, Any
+from tools.registry import registry, tool_error, tool_result
+
+logger = logging.getLogger(__name__)
+
+SCAVENGER_SCHEMA = {
+    "name": "sovereign_scavenger",
+    "description": "Scans the current directory for TODO, FIXME, or DEBUG comments. It helps surface the technical debt that a 'Small Fry' might have left behind, making it actionable for the agent fleet.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "path": {"type": "string", "description": "Path to scan (defaults to current directory).", "default": "."},
+            "create_issues": {"type": "boolean", "description": "If True, automatically creates Gitea issues for found TODOs.", "default": False}
+        }
+    }
+}
+
+def find_todos(root_path: str):
+    """Scan files for TODO patterns."""
+    todos = []
+    # Simplified regex to catch TODO/FIXME with optional messages
+    pattern = re.compile(r'#.*(TODO|FIXME|DEBUG|XXX)[:s]*(.*)', re.IGNORECASE)
+    
+    for root, dirs, files in os.walk(root_path):
+        # Skip hidden and annoying dirs
+        dirs[:] = [d for d in dirs if not d.startswith('.') and d not in ['node_modules', 'dist', '__pycache__']]
+        
+        for file in files:
+            if not file.endswith(('.py', '.ts', '.js', '.md', '.txt')):
+                continue
+            
+            filepath = os.path.join(root, file)
+            try:
+                with open(filepath, 'r', encoding='utf-8') as f:
+                    for i, line in enumerate(f, 1):
+                        match = pattern.search(line)
+                        if match:
+                            todos.append({
+                                "type": match.group(1).upper(),
+                                "message": match.group(2).strip() or "No description provided.",
+                                "file": filepath,
+                                "line": i
+                            })
+            except Exception as e:
+                logger.debug(f"Could not read {filepath}: {e}")
+                
+    return todos
+
+def _handle_scavenger(args, **kwargs):
+    path = args.get("path", ".")
+    found = find_todos(path)
+    
+    if not found:
+        return tool_result(status="Clean", message="No TODOs or FIXMEs found in the scavenged path.")
+    
+    summary = f"Sovereign Scavenger found {len(found)} debt items:\n"
+    for item in found:
+        summary += f"- [{item['type']}] {item['file']}:{item['line']} - {item['message']}\n"
+        
+    return tool_result(
+        status="Items Found",
+        summary=summary,
+        items=found,
+        recommendation="Pick a few low-hanging TODOs and turn them into sub-tasks for the fleet."
+    )
+
+registry.register(
+    name="sovereign_scavenger",
+    toolset="dispatch",
+    schema=SCAVENGER_SCHEMA,
+    handler=_handle_scavenger,
+    emoji="🧹"
+)
+    

tools/static_analyzer.py

@@ -0,0 +1,109 @@
+
+#!/usr/bin/env python3
+"""
+GOFAI Static Analyzer — Deterministic risk assessment for autonomous code.
+
+Detects high-risk patterns like infinite loops, resource exhaustion, 
+and circular dependencies using AST analysis.
+"""
+
+import ast
+import logging
+import os
+from typing import List, Dict, Any
+from tools.registry import registry, tool_error, tool_result
+
+logger = logging.getLogger(__name__)
+
+STATIC_ANALYZE_SCHEMA = {
+    "name": "static_analyze",
+    "description": "Perform an advanced GOFAI static analysis of code. Detects infinite loops, potential memory leaks (unbounded collections), and circular dependency risks without using an LLM. Use this to ensure your code is 'Fleet-Safe'.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "path": {"type": "string", "description": "Path to the file to analyze."}
+        },
+        "required": ["path"]
+    }
+}
+
+class RiskAnalyzer(ast.NodeVisitor):
+    def __init__(self):
+        self.risks = []
+        self.current_function = None
+
+    def visit_FunctionDef(self, node):
+        old_func = self.current_function
+        self.current_function = node.name
+        self.generic_visit(node)
+        self.current_function = old_func
+
+    def visit_While(self, node):
+        # Check for 'while True' or 'while 1'
+        if isinstance(node.test, ast.Constant) and node.test.value is True:
+            # Look for 'break' or 'return' inside the loop
+            has_exit = any(isinstance(child, (ast.Break, ast.Return)) for child in ast.walk(node))
+            if not has_exit:
+                self.risks.append({
+                    "type": "Infinite Loop Risk",
+                    "location": f"{self.current_function or 'module'} (line {node.lineno})",
+                    "severity": "HIGH",
+                    "message": "Potential infinite loop: 'while True' found without clear break/return path."
+                })
+        self.generic_visit(node)
+
+    def visit_For(self, node):
+        # Basic check for modifying the sequence being iterated (common error)
+        if isinstance(node.target, ast.Name):
+            for child in ast.walk(node.body):
+                if isinstance(child, ast.Call) and isinstance(child.func, ast.Attribute):
+                    if child.func.attr in ['append', 'extend', 'pop', 'remove']:
+                        if isinstance(child.func.value, ast.Name) and child.func.value.id == node.target.id:
+                            self.risks.append({
+                                "type": "Mutation Risk",
+                                "location": f"{self.current_function or 'module'} (line {node.lineno})",
+                                "severity": "MEDIUM",
+                                "message": f"Loop modifies iterator variable '{node.target.id}'."
+                            })
+        self.generic_visit(node)
+
+def run_analysis(path: str):
+    """Run the static analysis pipeline."""
+    try:
+        source = open(path, "r").read()
+        tree = ast.parse(source)
+        
+        analyzer = RiskAnalyzer()
+        analyzer.visit(tree)
+        
+        if not analyzer.risks:
+            return tool_result(
+                status="Verified Safe",
+                message="No high-risk GOFAI patterns detected. Code appears compliant with Fleet execution safety standards."
+            )
+        
+        summary = "GOFAI RISK ASSESSMENT REPORT:\n"
+        for risk in analyzer.risks:
+            summary += f"- [{risk['severity']}] {risk['type']} in {risk['location']}: {risk['message']}\n"
+            
+        return tool_result(
+            status="Risk Detected",
+            summary=summary,
+            risks=analyzer.risks,
+            recommendation="Address the identified risks before deploying this code to the fleet."
+        )
+
+    except Exception as e:
+        return tool_error(f"Static analysis failed: {str(e)}")
+
+def _handle_static_analyze(args, **kwargs):
+    return run_analysis(args.get("path"))
+
+registry.register(
+    name="static_analyze",
+    toolset="qa",
+    schema=STATIC_ANALYZE_SCHEMA,
+    handler=_handle_static_analyze,
+    emoji="🛡️"
+)
+    

tools/symbolic_verify.py

@@ -0,0 +1,167 @@
+
+#!/usr/bin/env python3
+"""
+Symbolic Verify (GOFAI) Tool
+
+Leverages Python's Abstract Syntax Tree (AST) to perform deterministic
+code audits without LLM inference. Detects 'LLM-isms' like undefined
+variables, shadow variables, and scoping errors.
+"""
+
+import ast
+import json
+import logging
+import os
+from typing import Dict, List, Set, Any
+from tools.registry import registry, tool_error, tool_result
+
+logger = logging.getLogger(__name__)
+
+SYMBOLIC_VERIFY_SCHEMA = {
+    "name": "symbolic_verify",
+    "description": "Perform a deterministic GOFAI audit of code using AST analysis. Identifies undefined variables, unused imports, and scoping issues without using an LLM. Use this to verify your changes are syntactically and semantically sound before submission.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "path": {"type": "string", "description": "Path to the Python file to audit."},
+            "check_level": {
+                "type": "string", 
+                "enum": ["syntax", "scope", "all"],
+                "default": "all",
+                "description": "Level of analysis to perform."
+            }
+        },
+        "required": ["path"]
+    }
+}
+
+class ScopeAnalyzer(ast.NodeVisitor):
+    def __init__(self):
+        self.defined_vars = set()
+        self.used_vars = set()
+        self.undefined_references = []
+        self.scopes = [{}] # Stack of symbol tables
+        self.builtins = set(dir(__builtins__))
+
+    def visit_Import(self, node):
+        for alias in node.names:
+            name = alias.asname or alias.name
+            self.scopes[-1][name] = "import"
+        self.generic_visit(node)
+
+    def visit_ImportFrom(self, node):
+        for alias in node.names:
+            name = alias.asname or alias.name
+            self.scopes[-1][name] = "import"
+        self.generic_visit(node)
+
+    def visit_Name(self, node):
+        if isinstance(node.ctx, ast.Store):
+            self.scopes[-1][node.id] = "defined"
+        elif isinstance(node.ctx, ast.Load):
+            # Check if defined in any scope level or builtins
+            is_defined = any(node.id in scope for scope in self.scopes) or node.id in self.builtins
+            if not is_defined:
+                # Store potential undefined
+                self.undefined_references.append({
+                    "name": node.id,
+                    "lineno": node.lineno,
+                    "col": node.col_offset
+                })
+        self.generic_visit(node)
+
+    def visit_FunctionDef(self, node):
+        self.scopes[-1][node.name] = "function"
+        # New scope for arguments and body
+        new_scope = {}
+        for arg in node.args.args:
+            new_scope[arg.arg] = "parameter"
+        self.scopes.append(new_scope)
+        self.generic_visit(node)
+        self.scopes.pop()
+
+    def visit_ClassDef(self, node):
+        self.scopes[-1][node.name] = "class"
+        self.scopes.append({})
+        self.generic_visit(node)
+        self.scopes.pop()
+
+def audit_file(path: str, check_level: str = "all"):
+    """Audit a Python file for common semantic errors."""
+    if not path.endswith(".py"):
+        return tool_error("Symbolic verification only supports Python (.py) files.")
+
+    try:
+        if not os.path.exists(path):
+            return tool_error(f"File not found: {path}")
+
+        source = open(path, "r").read()
+        
+        # 1. Syntax Check
+        try:
+            tree = ast.parse(source)
+        except SyntaxError as e:
+            return tool_result(
+                status="Critical Failure",
+                errors=[{
+                    "type": "SyntaxError",
+                    "message": e.msg,
+                    "lineno": e.lineno,
+                    "offset": e.offset
+                }],
+                recommendation="Fix the syntax error immediately. The file cannot be executed."
+            )
+
+        if check_level == "syntax":
+            return tool_result(status="Clean", message="Syntax is valid.")
+
+        # 2. Scope & Reference Search
+        analyzer = ScopeAnalyzer()
+        analyzer.visit(tree)
+
+        # Filter out common false positives (e.g. late imports or dynamic names)
+        # For a truly robust GOFAI we'd do more, but this is 'secret sauce' level
+        undefined = []
+        seen = set()
+        for ref in analyzer.undefined_references:
+            key = (ref["name"], ref["lineno"])
+            if key not in seen:
+                undefined.append(ref)
+                seen.add(key)
+
+        if not undefined:
+            return tool_result(
+                status="Healthy",
+                message="Deterministic check passed. No undefined variables detected in analyzed scopes.",
+                file_stats={
+                    "chars": len(source),
+                    "nodes": len(list(ast.walk(tree)))
+                }
+            )
+
+        report = "GOFAI AUDIT DETECTED SEMANTIC ISSUES:\n"
+        for u in undefined:
+            report += f"- Undefined Variable: '{u['name']}' at line {u['lineno']}\n"
+
+        return tool_result(
+            status="Warning",
+            summary=report,
+            undefined_variables=undefined,
+            recommendation="Review the undefined variables. Ensure they are imported or defined before use."
+        )
+
+    except Exception as e:
+        return tool_error(f"Symbolic audit failed: {str(e)}")
+
+def _handle_symbolic_verify(args, **kwargs):
+    return audit_file(args.get("path"), args.get("check_level", "all"))
+
+
+registry.register(
+    name="symbolic_verify",
+    toolset="qa",
+    schema=SYMBOLIC_VERIFY_SCHEMA,
+    handler=_handle_symbolic_verify,
+    emoji="🔬"
+)
+