feat: add ragflow integration for #859

docs/ragflow-integration.md

@@ -0,0 +1,68 @@
+# 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

pyproject.toml

@@ -38,7 +38,6 @@ dependencies = [
 
 [project.optional-dependencies]
 modal = ["modal>=1.0.0,<2"]
-rag = ["lightrag-hku>=1.4.0,<2", "aiohttp>=3.9.0,<4"]
 daytona = ["daytona>=0.148.0,<1"]
 dev = ["debugpy>=1.8.0,<2", "pytest>=9.0.2,<10", "pytest-asyncio>=1.3.0,<2", "pytest-xdist>=3.0,<4", "mcp>=1.2.0,<2"]
 messaging = ["python-telegram-bot[webhooks]>=22.6,<23", "discord.py[voice]>=2.7.1,<3", "aiohttp>=3.13.3,<4", "slack-bolt>=1.18.0,<2", "slack-sdk>=3.27.0,<4"]

scripts/ragflow_bootstrap.py

@@ -0,0 +1,79 @@
+#!/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

@@ -0,0 +1,43 @@
+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_lightrag_tool.py

@@ -1,176 +0,0 @@
-"""Tests for tools/lightrag_tool.py"""
-
-import json
-import sys
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-# LightRAG may not be installed in all test environments
-pytest.importorskip("lightrag", reason="lightrag-hku not installed")
-
-from tools.lightrag_tool import (
-    check_lightrag_requirements,
-    lightrag_index,
-    lightrag_query,
-    _collect_markdown_files,
-    _read_text_safe,
-    LIGHTRAG_DIR,
-)
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _parse_result(result: str) -> dict:
-    """Parse JSON tool result, falling back to error string detection."""
-    try:
-        return json.loads(result)
-    except json.JSONDecodeError:
-        return {"_error": result}
-
-
-# ---------------------------------------------------------------------------
-# Unit tests
-# ---------------------------------------------------------------------------
-
-class TestCollectMarkdownFiles:
-    def test_collects_md_files(self, tmp_path):
-        (tmp_path / "a.md").write_text("# A")
-        (tmp_path / "b.md").write_text("# B")
-        (tmp_path / "skip.txt").write_text("text")
-        found = _collect_markdown_files(tmp_path)
-        assert len(found) == 2
-        assert all(p.suffix == ".md" for p in found)
-
-    def test_skips_hidden_dirs(self, tmp_path):
-        (tmp_path / ".git").mkdir()
-        (tmp_path / ".git" / "readme.md").write_text("# git")
-        (tmp_path / "visible.md").write_text("# visible")
-        found = _collect_markdown_files(tmp_path)
-        names = [p.name for p in found]
-        assert "visible.md" in names
-        assert "readme.md" not in names
-
-    def test_returns_empty_for_missing_dir(self):
-        assert _collect_markdown_files(Path("/nonexistent")) == []
-
-
-class TestReadTextSafe:
-    def test_reads_small_file(self, tmp_path):
-        p = tmp_path / "test.md"
-        p.write_text("hello world")
-        assert _read_text_safe(p) == "hello world"
-
-    def test_truncates_large_file(self, tmp_path):
-        p = tmp_path / "big.md"
-        p.write_text("x" * 1_000_000)
-        text = _read_text_safe(p, limit=500_000)
-        assert len(text) == 500_000
-
-    def test_reads_binary_without_crashing(self, tmp_path):
-        p = tmp_path / "binary.md"
-        p.write_bytes(b"\x00\x01\x02")
-        result = _read_text_safe(p)
-        # Should not crash; control chars 0x00-0x7F are valid UTF-8
-        assert isinstance(result, str)
-
-
-class TestCheckRequirements:
-    @patch("tools.lightrag_tool._ollama_available", return_value=True)
-    def test_ok_when_ollama_up(self, mock_ollama):
-        assert check_lightrag_requirements() is True
-
-    @patch("tools.lightrag_tool._ollama_available", return_value=False)
-    def test_false_when_ollama_down(self, mock_ollama):
-        assert check_lightrag_requirements() is False
-
-    @patch.dict(sys.modules, {"lightrag": None}, clear=False)
-    def test_false_when_lightrag_missing(self):
-        with patch("tools.lightrag_tool._ollama_available", return_value=True):
-            # Force ImportError by removing lightrag from sys.modules
-            # and blocking import
-            assert check_lightrag_requirements() is False
-
-
-class TestLightragIndex:
-    @patch("tools.lightrag_tool._ollama_available", return_value=False)
-    def test_error_when_ollama_down(self, mock_ollama):
-        result = lightrag_index()
-        assert "Ollama is not running" in result
-
-    @patch("tools.lightrag_tool._ollama_available", return_value=True)
-    @patch("tools.lightrag_tool._has_ollama_model", return_value=False)
-    def test_error_when_model_missing(self, mock_model, mock_ollama):
-        result = lightrag_index()
-        assert "not found in Ollama" in result
-
-    @patch("tools.lightrag_tool._ollama_available", return_value=True)
-    @patch("tools.lightrag_tool._has_ollama_model", return_value=True)
-    @patch("tools.lightrag_tool._get_lightrag")
-    @patch("tools.lightrag_tool._collect_markdown_files", return_value=[])
-    def test_warning_when_no_files(self, mock_collect, mock_get_rag, mock_model, mock_ollama):
-        result = lightrag_index()
-        data = _parse_result(result)
-        assert data.get("status") == "warning"
-        assert "No markdown files found" in data.get("message", "")
-
-    @patch("tools.lightrag_tool._ollama_available", return_value=True)
-    @patch("tools.lightrag_tool._has_ollama_model", return_value=True)
-    @patch("tools.lightrag_tool._get_lightrag")
-    @patch("tools.lightrag_tool._collect_markdown_files")
-    @patch("tools.lightrag_tool._read_text_safe", return_value="# Skill doc\nContent.")
-    @patch("asyncio.run")
-    def test_indexes_files(self, mock_asyncio, mock_read, mock_collect, mock_get_rag, mock_model, mock_ollama):
-        mock_collect.return_value = [Path("/fake/skills/git.md"), Path("/fake/skills/docker.md")]
-        mock_rag = MagicMock()
-        mock_get_rag.return_value = mock_rag
-
-        result = lightrag_index()
-        data = _parse_result(result)
-        assert data.get("status") == "ok"
-        assert data.get("indexed_files") == 2
-        assert data.get("errors") == 0
-
-
-class TestLightragQuery:
-    @patch("tools.lightrag_tool._ollama_available", return_value=False)
-    def test_error_when_ollama_down(self, mock_ollama):
-        result = lightrag_query("test", mode="hybrid")
-        assert "Ollama is not running" in result
-
-    @patch("tools.lightrag_tool._ollama_available", return_value=True)
-    @patch("tools.lightrag_tool.LIGHTRAG_DIR")
-    def test_empty_index_message(self, mock_dir, mock_ollama):
-        mock_dir.exists.return_value = True
-        mock_dir.iterdir.return_value = iter([])
-        result = lightrag_query("test", mode="hybrid")
-        data = _parse_result(result)
-        assert data.get("status") == "empty"
-
-    @patch("tools.lightrag_tool._ollama_available", return_value=True)
-    @patch("tools.lightrag_tool.LIGHTRAG_DIR")
-    @patch("tools.lightrag_tool._get_lightrag")
-    @patch("asyncio.run", return_value="Use git clone for repos.")
-    def test_query_returns_answer(self, mock_asyncio, mock_get_rag, mock_dir, mock_ollama):
-        mock_dir.exists.return_value = True
-        mock_dir.iterdir.return_value = iter([Path("dummy")])
-        mock_rag = MagicMock()
-        mock_get_rag.return_value = mock_rag
-
-        result = lightrag_query("How do I clone a repo?", mode="hybrid")
-        data = _parse_result(result)
-        assert data.get("status") == "ok"
-        assert data.get("mode") == "hybrid"
-        assert "clone" in data.get("answer", "").lower()
-
-    @patch("tools.lightrag_tool._ollama_available", return_value=True)
-    def test_rejects_invalid_mode(self, mock_ollama):
-        result = lightrag_query("test", mode="invalid")
-        assert "mode must be one of" in result
-
-    def test_rejects_empty_query(self):
-        result = lightrag_query("", mode="hybrid")
-        assert "Query cannot be empty" in result

tests/tools/test_ragflow_tool.py

@@ -0,0 +1,122 @@
+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/lightrag_tool.py

@@ -1,405 +0,0 @@
-#!/usr/bin/env python3
-"""
-LightRAG Tool — Graph-based knowledge retrieval for skills and docs.
-
-Indexes markdown files under ~/.hermes/skills/ (and optional extra dirs)
-into a LightRAG knowledge graph stored at ~/.hermes/lightrag/.
-
-Requires:
-    - lightrag-hku  (pip install lightrag-hku)
-    - Ollama running locally with an embedding model (default: nomic-embed-text)
-    - Ollama running locally with a chat model (default: qwen2.5:7b)
-
-Usage:
-    lightrag_query("How do I dispatch the burn fleet?", mode="hybrid")
-    lightrag_index()                       # re-index skill files
-"""
-
-import asyncio
-import json
-import logging
-import os
-from pathlib import Path
-from typing import Dict, List, Optional
-
-import numpy as np
-
-from hermes_constants import get_hermes_home
-from tools.registry import registry, tool_error
-
-logger = logging.getLogger(__name__)
-
-# ---------------------------------------------------------------------------
-# Config
-# ---------------------------------------------------------------------------
-
-DEFAULT_EMBED_MODEL = os.environ.get("LIGHTRAG_EMBED_MODEL", "nomic-embed-text")
-DEFAULT_LLM_MODEL = os.environ.get("LIGHTRAG_LLM_MODEL", "qwen2.5:7b")
-DEFAULT_OLLAMA_HOST = os.environ.get("LIGHTRAG_OLLAMA_HOST", "http://localhost:11434")
-
-LIGHTRAG_DIR = get_hermes_home() / "lightrag"
-SKILLS_DIR = get_hermes_home() / "skills"
-
-# ---------------------------------------------------------------------------
-# Ollama helpers
-# ---------------------------------------------------------------------------
-
-
-def _ollama_available() -> bool:
-    """Check if Ollama server is reachable."""
-    try:
-        import urllib.request
-        req = urllib.request.Request(f"{DEFAULT_OLLAMA_HOST}/api/tags")
-        with urllib.request.urlopen(req, timeout=3) as resp:
-            return resp.status == 200
-    except Exception:
-        return False
-
-
-def _has_ollama_model(model_name: str) -> bool:
-    """Check if a specific model is pulled in Ollama."""
-    try:
-        import urllib.request
-        req = urllib.request.Request(f"{DEFAULT_OLLAMA_HOST}/api/tags")
-        with urllib.request.urlopen(req, timeout=3) as resp:
-            data = json.loads(resp.read())
-        models = [m["name"] for m in data.get("models", [])]
-        return any(model_name in m for m in models)
-    except Exception:
-        return False
-
-
-async def _ollama_embedding(texts: list, **kwargs) -> np.ndarray:
-    """Call Ollama embeddings API."""
-    import aiohttp
-
-    payload = {
-        "model": DEFAULT_EMBED_MODEL,
-        "input": texts,
-    }
-    async with aiohttp.ClientSession() as session:
-        async with session.post(
-            f"{DEFAULT_OLLAMA_HOST}/api/embed",
-            json=payload,
-            timeout=aiohttp.ClientTimeout(total=60),
-        ) as resp:
-            resp.raise_for_status()
-            data = await resp.json()
-            embeddings = data.get("embeddings", [])
-            if not embeddings:
-                raise RuntimeError("Ollama returned empty embeddings")
-            return np.array(embeddings, dtype=np.float32)
-
-
-async def _ollama_complete(
-    prompt, system_prompt=None, history_messages=None, **kwargs
-) -> str:
-    """Call Ollama generate API for LLM completion."""
-    import aiohttp
-
-    messages = []
-    if system_prompt:
-        messages.append({"role": "system", "content": system_prompt})
-    if history_messages:
-        for msg in history_messages:
-            role = "user" if msg.get("role") == "user" else "assistant"
-            messages.append({"role": role, "content": msg.get("content", "")})
-    messages.append({"role": "user", "content": prompt})
-
-    payload = {
-        "model": DEFAULT_LLM_MODEL,
-        "messages": messages,
-        "stream": False,
-        "options": {"temperature": 0.3, "num_predict": 2048},
-    }
-    async with aiohttp.ClientSession() as session:
-        async with session.post(
-            f"{DEFAULT_OLLAMA_HOST}/api/chat",
-            json=payload,
-            timeout=aiohttp.ClientTimeout(total=120),
-        ) as resp:
-            resp.raise_for_status()
-            data = await resp.json()
-            return data.get("message", {}).get("content", "")
-
-
-# ---------------------------------------------------------------------------
-# LightRAG setup
-# ---------------------------------------------------------------------------
-
-_lightrag_instance: Optional[object] = None
-
-
-def _get_lightrag() -> object:
-    """Lazy-initialize LightRAG with Ollama backends."""
-    global _lightrag_instance
-    if _lightrag_instance is not None:
-        return _lightrag_instance
-
-    try:
-        from lightrag import LightRAG, QueryParam
-        from lightrag.utils import EmbeddingFunc
-    except ImportError as e:
-        raise RuntimeError(
-            "lightrag is not installed. Run: pip install lightrag-hku"
-        ) from e
-
-    LIGHTRAG_DIR.mkdir(parents=True, exist_ok=True)
-
-    # Wrap Ollama embedding for LightRAG
-    embed_func = EmbeddingFunc(
-        embedding_dim=768,  # nomic-embed-text dimension
-        func=_ollama_embedding,
-        max_token_size=8192,
-        model_name=DEFAULT_EMBED_MODEL,
-    )
-
-    _lightrag_instance = LightRAG(
-        working_dir=str(LIGHTRAG_DIR),
-        embedding_func=embed_func,
-        llm_model_func=_ollama_complete,
-        llm_model_name=DEFAULT_LLM_MODEL,
-        chunk_token_size=1200,
-        chunk_overlap_token_size=100,
-    )
-    return _lightrag_instance
-
-
-# ---------------------------------------------------------------------------
-# Indexing
-# ---------------------------------------------------------------------------
-
-
-def _collect_markdown_files(root: Path) -> List[Path]:
-    """Collect all .md files under root, excluding node_modules and .git."""
-    files = []
-    if not root.exists():
-        return files
-    for path in root.rglob("*.md"):
-        if any(part.startswith(".") or part == "node_modules" for part in path.parts):
-            continue
-        files.append(path)
-    return sorted(files)
-
-
-def _read_text_safe(path: Path, limit: int = 500_000) -> str:
-    """Read file text with size limit."""
-    try:
-        stat = path.stat()
-        if stat.st_size > limit:
-            return path.read_text(encoding="utf-8", errors="ignore")[:limit]
-        return path.read_text(encoding="utf-8", errors="ignore")
-    except Exception as e:
-        logger.warning("Failed to read %s: %s", path, e)
-        return ""
-
-
-def lightrag_index(directories: Optional[List[str]] = None) -> str:
-    """Index markdown files into LightRAG knowledge graph.
-
-    Args:
-        directories: Extra directories to index (in addition to ~/.hermes/skills/).
-    """
-    if not _ollama_available():
-        return tool_error(
-            "Ollama is not running. Start it with: ollama serve"
-        )
-
-    if not _has_ollama_model(DEFAULT_EMBED_MODEL):
-        return tool_error(
-            f"Embedding model '{DEFAULT_EMBED_MODEL}' not found in Ollama. "
-            f"Pull it with: ollama pull {DEFAULT_EMBED_MODEL}"
-        )
-
-    if not _has_ollama_model(DEFAULT_LLM_MODEL):
-        return tool_error(
-            f"LLM model '{DEFAULT_LLM_MODEL}' not found in Ollama. "
-            f"Pull it with: ollama pull {DEFAULT_LLM_MODEL}"
-        )
-
-    rag = _get_lightrag()
-    dirs = [SKILLS_DIR]
-    if directories:
-        for d in directories:
-            p = Path(d).expanduser()
-            if p.exists():
-                dirs.append(p)
-
-    all_files = []
-    for d in dirs:
-        all_files.extend(_collect_markdown_files(d))
-
-    if not all_files:
-        return json.dumps({
-            "status": "warning",
-            "message": "No markdown files found to index.",
-            "directories": [str(d) for d in dirs],
-        })
-
-    # Read and insert files
-    inserted = 0
-    errors = 0
-    for path in all_files:
-        text = _read_text_safe(path)
-        if not text.strip():
-            continue
-        try:
-            # LightRAG insert is async; bridge it
-            asyncio.run(rag.atext(text))
-            inserted += 1
-        except Exception as e:
-            logger.warning("Failed to index %s: %s", path, e)
-            errors += 1
-
-    return json.dumps({
-        "status": "ok",
-        "indexed_files": inserted,
-        "errors": errors,
-        "total_files": len(all_files),
-        "storage_dir": str(LIGHTRAG_DIR),
-    })
-
-
-# ---------------------------------------------------------------------------
-# Query
-# ---------------------------------------------------------------------------
-
-
-def lightrag_query(query: str, mode: str = "hybrid") -> str:
-    """Query the LightRAG knowledge graph.
-
-    Args:
-        query: The question or search query.
-        mode: Search mode — "local" (nearby entities), "global" (graph-wide),
-              or "hybrid" (both).
-    """
-    if not query or not query.strip():
-        return tool_error("Query cannot be empty.")
-
-    if mode not in {"local", "global", "hybrid"}:
-        return tool_error("mode must be one of: local, global, hybrid")
-
-    if not _ollama_available():
-        return tool_error(
-            "Ollama is not running. Start it with: ollama serve"
-        )
-
-    rag = _get_lightrag()
-
-    # Check if any data has been indexed
-    if not LIGHTRAG_DIR.exists() or not any(LIGHTRAG_DIR.iterdir()):
-        return json.dumps({
-            "status": "empty",
-            "message": "LightRAG index is empty. Run lightrag_index() first.",
-        })
-
-    try:
-        from lightrag import QueryParam
-        param = QueryParam(mode=mode)
-        result = asyncio.run(rag.aquery(query, param=param))
-        return json.dumps({
-            "status": "ok",
-            "mode": mode,
-            "query": query,
-            "answer": result,
-        })
-    except Exception as e:
-        logger.exception("LightRAG query failed")
-        return tool_error(f"Query failed: {e}")
-
-
-# ---------------------------------------------------------------------------
-# Tool schemas
-# ---------------------------------------------------------------------------
-
-LIGHTRAG_QUERY_SCHEMA = {
-    "name": "lightrag_query",
-    "description": (
-        "Graph-based knowledge retrieval over indexed skills and documentation.\n\n"
-        "Use this when the user asks about: conventions, workflows, tool usage, "
-        "project-specific practices, or anything that might be documented in skills.\n\n"
-        "Modes:\n"
-        "- local: fast, searches nearby entities in the graph\n"
-        "- global: thorough, reasons across the entire knowledge graph\n"
-        "- hybrid: balanced, combines local and global (recommended)\n\n"
-        "If the index is empty, the tool will report that and you should "
-        "call lightrag_index() to populate it."
-    ),
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "query": {
-                "type": "string",
-                "description": "The question or search query.",
-            },
-            "mode": {
-                "type": "string",
-                "enum": ["local", "global", "hybrid"],
-                "description": "Search mode. hybrid is recommended.",
-            },
-        },
-        "required": ["query"],
-    },
-}
-
-LIGHTRAG_INDEX_SCHEMA = {
-    "name": "lightrag_index",
-    "description": (
-        "(Re-)build the LightRAG knowledge graph from skill files and docs.\n\n"
-        "By default indexes ~/.hermes/skills/. Pass extra directories if needed.\n"
-        "This is a one-time or occasional operation; queries work against the "
-        "existing index until you re-index."
-    ),
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "directories": {
-                "type": "array",
-                "items": {"type": "string"},
-                "description": "Optional extra directories to index (in addition to ~/.hermes/skills/).",
-            },
-        },
-    },
-}
-
-
-# ---------------------------------------------------------------------------
-# Availability check
-# ---------------------------------------------------------------------------
-
-
-def check_lightrag_requirements() -> bool:
-    """Return True if LightRAG and Ollama appear to be available."""
-    try:
-        import lightrag  # noqa: F401
-    except ImportError:
-        return False
-    return _ollama_available()
-
-
-# ---------------------------------------------------------------------------
-# Registry
-# ---------------------------------------------------------------------------
-
-registry.register(
-    name="lightrag_query",
-    toolset="rag",
-    schema=LIGHTRAG_QUERY_SCHEMA,
-    handler=lambda args, **kw: lightrag_query(
-        query=args.get("query", ""),
-        mode=args.get("mode", "hybrid"),
-    ),
-    check_fn=check_lightrag_requirements,
-    emoji="🔎",
-)
-
-registry.register(
-    name="lightrag_index",
-    toolset="rag",
-    schema=LIGHTRAG_INDEX_SCHEMA,
-    handler=lambda args, **kw: lightrag_index(
-        directories=args.get("directories"),
-    ),
-    check_fn=check_lightrag_requirements,
-    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="🧠",
+)

toolsets.py

@@ -167,12 +167,6 @@ TOOLSETS = {
         "tools": ["memory"],
         "includes": []
     },
-
-    "rag": {
-        "description": "Graph-based knowledge retrieval over indexed skills and docs (LightRAG)",
-        "tools": ["lightrag_query", "lightrag_index"],
-        "includes": []
-    },
     
     "session_search": {
         "description": "Search and recall past conversations with summarization",