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https://github.com/infiniflow/ragflow.git
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Feat: expand the capabilities of the MCP Server (#8707)
### What problem does this PR solve? Expand the capabilities of the MCP Server. #8644. Special thanks to @Drasek, this change is largely based on his original implementation, it is super neat and well-structured to me. I basically just integrated his code into the codebase with minimal modifications. My main contribution is implementing a proper cache layer for dataset and document metadata, using the LRU strategy with a 300s ± random 30s TTL. The original code did not actually perform caching. ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Co-authored-by: Caspar Armster <caspar@armster.de>
This commit is contained in:
Yongteng Lei
@ -16,6 +16,9 @@
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import json
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import logging
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import random
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import time
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from collections import OrderedDict
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from collections.abc import AsyncIterator
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from contextlib import asynccontextmanager
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from functools import wraps
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@ -53,6 +56,13 @@ JSON_RESPONSE = True
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class RAGFlowConnector:
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_MAX_DATASET_CACHE = 32
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_MAX_DOCUMENT_CACHE = 128
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_CACHE_TTL = 300
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_dataset_metadata_cache: OrderedDict[str, tuple[dict, float | int]] = OrderedDict() # "dataset_id" -> (metadata, expiry_ts)
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_document_metadata_cache: OrderedDict[str, tuple[list[tuple[str, dict]], float | int]] = OrderedDict() # "dataset_id" -> ([(document_id, doc_metadata)], expiry_ts)
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def __init__(self, base_url: str, version="v1"):
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self.base_url = base_url
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self.version = version
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@ -72,6 +82,43 @@ class RAGFlowConnector:
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res = requests.get(url=self.api_url + path, params=params, headers=self.authorization_header, json=json)
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return res
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def _is_cache_valid(self, ts):
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return time.time() < ts
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def _get_expiry_timestamp(self):
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offset = random.randint(-30, 30)
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return time.time() + self._CACHE_TTL + offset
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def _get_cached_dataset_metadata(self, dataset_id):
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entry = self._dataset_metadata_cache.get(dataset_id)
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if entry:
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data, ts = entry
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if self._is_cache_valid(ts):
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self._dataset_metadata_cache.move_to_end(dataset_id)
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return data
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return None
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def _set_cached_dataset_metadata(self, dataset_id, metadata):
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self._dataset_metadata_cache[dataset_id] = (metadata, self._get_expiry_timestamp())
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self._dataset_metadata_cache.move_to_end(dataset_id)
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if len(self._dataset_metadata_cache) > self._MAX_DATASET_CACHE:
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self._dataset_metadata_cache.popitem(last=False)
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def _get_cached_document_metadata_by_dataset(self, dataset_id):
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entry = self._document_metadata_cache.get(dataset_id)
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if entry:
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data_list, ts = entry
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if self._is_cache_valid(ts):
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self._document_metadata_cache.move_to_end(dataset_id)
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return {doc_id: doc_meta for doc_id, doc_meta in data_list}
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return None
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def _set_cached_document_metadata_by_dataset(self, dataset_id, doc_id_meta_list):
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self._document_metadata_cache[dataset_id] = (doc_id_meta_list, self._get_expiry_timestamp())
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self._document_metadata_cache.move_to_end(dataset_id)
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if len(self._document_metadata_cache) > self._MAX_DOCUMENT_CACHE:
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self._document_metadata_cache.popitem(last=False)
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def list_datasets(self, page: int = 1, page_size: int = 1000, orderby: str = "create_time", desc: bool = True, id: str | None = None, name: str | None = None):
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res = self._get("/datasets", {"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name})
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if not res:
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@ -87,10 +134,38 @@ class RAGFlowConnector:
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return ""
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def retrieval(
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self, dataset_ids, document_ids=None, question="", page=1, page_size=30, similarity_threshold=0.2, vector_similarity_weight=0.3, top_k=1024, rerank_id: str | None = None, keyword: bool = False
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self,
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dataset_ids,
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document_ids=None,
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question="",
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page=1,
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page_size=30,
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similarity_threshold=0.2,
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vector_similarity_weight=0.3,
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top_k=1024,
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rerank_id: str | None = None,
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keyword: bool = False,
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force_refresh: bool = False,
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):
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if document_ids is None:
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document_ids = []
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# If no dataset_ids provided or empty list, get all available dataset IDs
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if not dataset_ids:
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dataset_list_str = self.list_datasets()
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dataset_ids = []
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# Parse the dataset list to extract IDs
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if dataset_list_str:
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for line in dataset_list_str.strip().split('\n'):
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if line.strip():
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try:
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dataset_info = json.loads(line.strip())
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dataset_ids.append(dataset_info["id"])
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except (json.JSONDecodeError, KeyError):
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# Skip malformed lines
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continue
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data_json = {
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"page": page,
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"page_size": page_size,
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@ -110,12 +185,127 @@ class RAGFlowConnector:
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res = res.json()
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if res.get("code") == 0:
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data = res["data"]
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chunks = []
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for chunk_data in res["data"].get("chunks"):
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chunks.append(json.dumps(chunk_data, ensure_ascii=False))
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return [types.TextContent(type="text", text="\n".join(chunks))]
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# Cache document metadata and dataset information
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document_cache, dataset_cache = self._get_document_metadata_cache(dataset_ids, force_refresh=force_refresh)
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# Process chunks with enhanced field mapping including per-chunk metadata
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for chunk_data in data.get("chunks", []):
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enhanced_chunk = self._map_chunk_fields(chunk_data, dataset_cache, document_cache)
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chunks.append(enhanced_chunk)
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# Build structured response (no longer need response-level document_metadata)
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response = {
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"chunks": chunks,
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"pagination": {
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"page": data.get("page", page),
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"page_size": data.get("page_size", page_size),
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"total_chunks": data.get("total", len(chunks)),
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"total_pages": (data.get("total", len(chunks)) + page_size - 1) // page_size,
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},
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"query_info": {
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"question": question,
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"similarity_threshold": similarity_threshold,
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"vector_weight": vector_similarity_weight,
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"keyword_search": keyword,
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"dataset_count": len(dataset_ids),
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},
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}
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return [types.TextContent(type="text", text=json.dumps(response, ensure_ascii=False))]
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raise Exception([types.TextContent(type="text", text=res.get("message"))])
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def _get_document_metadata_cache(self, dataset_ids, force_refresh=False):
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"""Cache document metadata for all documents in the specified datasets"""
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document_cache = {}
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dataset_cache = {}
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try:
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for dataset_id in dataset_ids:
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dataset_meta = None if force_refresh else self._get_cached_dataset_metadata(dataset_id)
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if not dataset_meta:
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# First get dataset info for name
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dataset_res = self._get("/datasets", {"id": dataset_id, "page_size": 1})
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if dataset_res and dataset_res.status_code == 200:
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dataset_data = dataset_res.json()
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if dataset_data.get("code") == 0 and dataset_data.get("data"):
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dataset_info = dataset_data["data"][0]
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dataset_meta = {"name": dataset_info.get("name", "Unknown"), "description": dataset_info.get("description", "")}
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self._set_cached_dataset_metadata(dataset_id, dataset_meta)
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if dataset_meta:
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dataset_cache[dataset_id] = dataset_meta
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docs = None if force_refresh else self._get_cached_document_metadata_by_dataset(dataset_id)
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if docs is None:
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docs_res = self._get(f"/datasets/{dataset_id}/documents")
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docs_data = docs_res.json()
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if docs_data.get("code") == 0 and docs_data.get("data", {}).get("docs"):
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doc_id_meta_list = []
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docs = {}
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for doc in docs_data["data"]["docs"]:
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doc_id = doc.get("id")
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if not doc_id:
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continue
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doc_meta = {
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"document_id": doc_id,
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"name": doc.get("name", ""),
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"location": doc.get("location", ""),
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"type": doc.get("type", ""),
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"size": doc.get("size"),
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"chunk_count": doc.get("chunk_count"),
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# "chunk_method": doc.get("chunk_method", ""),
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"create_date": doc.get("create_date", ""),
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"update_date": doc.get("update_date", ""),
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# "process_begin_at": doc.get("process_begin_at", ""),
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# "process_duration": doc.get("process_duration"),
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# "progress": doc.get("progress"),
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# "progress_msg": doc.get("progress_msg", ""),
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# "status": doc.get("status", ""),
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# "run": doc.get("run", ""),
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"token_count": doc.get("token_count"),
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# "source_type": doc.get("source_type", ""),
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"thumbnail": doc.get("thumbnail", ""),
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"dataset_id": doc.get("dataset_id", dataset_id),
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"meta_fields": doc.get("meta_fields", {}),
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# "parser_config": doc.get("parser_config", {})
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}
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doc_id_meta_list.append((doc_id, doc_meta))
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docs[doc_id] = doc_meta
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self._set_cached_document_metadata_by_dataset(dataset_id, doc_id_meta_list)
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if docs:
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document_cache.update(docs)
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except Exception:
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# Gracefully handle metadata cache failures
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pass
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return document_cache, dataset_cache
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def _map_chunk_fields(self, chunk_data, dataset_cache, document_cache):
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"""Preserve all original API fields and add per-chunk document metadata"""
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# Start with ALL raw data from API (preserve everything like original version)
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mapped = dict(chunk_data)
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# Add dataset name enhancement
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dataset_id = chunk_data.get("dataset_id") or chunk_data.get("kb_id")
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if dataset_id and dataset_id in dataset_cache:
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mapped["dataset_name"] = dataset_cache[dataset_id]["name"]
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else:
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mapped["dataset_name"] = "Unknown"
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# Add document name convenience field
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mapped["document_name"] = chunk_data.get("document_keyword", "")
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# Add per-chunk document metadata
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document_id = chunk_data.get("document_id")
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if document_id and document_id in document_cache:
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mapped["document_metadata"] = document_cache[document_id]
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return mapped
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class RAGFlowCtx:
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def __init__(self, connector: RAGFlowConnector):
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@ -195,7 +385,58 @@ async def list_tools(*, connector) -> list[types.Tool]:
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"items": {"type": "string"},
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"description": "Optional array of document IDs to search within."
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},
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"question": {"type": "string", "description": "The question or query to search for."},
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"question": {
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"type": "string",
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"description": "The question or query to search for."
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},
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"page": {
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"type": "integer",
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"description": "Page number for pagination",
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"default": 1,
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"minimum": 1,
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},
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"page_size": {
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"type": "integer",
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"description": "Number of results to return per page (default: 10, max recommended: 50 to avoid token limits)",
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"default": 10,
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"minimum": 1,
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"maximum": 100,
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},
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"similarity_threshold": {
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"type": "number",
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"description": "Minimum similarity threshold for results",
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"default": 0.2,
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"minimum": 0.0,
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"maximum": 1.0,
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},
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"vector_similarity_weight": {
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"type": "number",
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"description": "Weight for vector similarity vs term similarity",
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"default": 0.3,
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"minimum": 0.0,
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"maximum": 1.0,
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},
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"keyword": {
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"type": "boolean",
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"description": "Enable keyword-based search",
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"default": False,
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},
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"top_k": {
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"type": "integer",
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"description": "Maximum results to consider before ranking",
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"default": 1024,
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"minimum": 1,
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"maximum": 1024,
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},
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"rerank_id": {
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"type": "string",
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"description": "Optional reranking model identifier",
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},
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"force_refresh": {
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"type": "boolean",
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"description": "Set to true only if fresh dataset and document metadata is explicitly required. Otherwise, cached metadata is used (default: false).",
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"default": False,
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},
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},
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"required": ["question"],
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},
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@ -209,6 +450,16 @@ async def call_tool(name: str, arguments: dict, *, connector) -> list[types.Text
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if name == "ragflow_retrieval":
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document_ids = arguments.get("document_ids", [])
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dataset_ids = arguments.get("dataset_ids", [])
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question = arguments.get("question", "")
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page = arguments.get("page", 1)
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page_size = arguments.get("page_size", 10)
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similarity_threshold = arguments.get("similarity_threshold", 0.2)
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vector_similarity_weight = arguments.get("vector_similarity_weight", 0.3)
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keyword = arguments.get("keyword", False)
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top_k = arguments.get("top_k", 1024)
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rerank_id = arguments.get("rerank_id")
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force_refresh = arguments.get("force_refresh", False)
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# If no dataset_ids provided or empty list, get all available dataset IDs
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if not dataset_ids:
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@ -229,7 +480,15 @@ async def call_tool(name: str, arguments: dict, *, connector) -> list[types.Text
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return connector.retrieval(
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dataset_ids=dataset_ids,
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document_ids=document_ids,
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question=arguments["question"],
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question=question,
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page=page,
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page_size=page_size,
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similarity_threshold=similarity_threshold,
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vector_similarity_weight=vector_similarity_weight,
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keyword=keyword,
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top_k=top_k,
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rerank_id=rerank_id,
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force_refresh=force_refresh,
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)
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raise ValueError(f"Tool not found: {name}")
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