mirror of
https://github.com/infiniflow/ragflow.git
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597ac1e900
### What problem does this PR solve? Fix: - Verify provider with empty llm list in llm_factories.json - Set search bot's chat_llm_name, use tenant default chat model as default ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
510 lines
22 KiB
Python
510 lines
22 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import copy
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import json
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import re
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import logging
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from quart import Response, request
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from agent.canvas import Canvas
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from api.apps import AUTH_BETA, login_required
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from api.db.services.api_service import API4ConversationService
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from api.db.services.canvas_service import UserCanvasService
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from api.db.services.canvas_service import completion as agent_completion
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from api.db.services.conversation_service import async_iframe_completion as iframe_completion
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from api.db.services.dialog_service import DialogService, async_ask, gen_mindmap
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from api.db.services.doc_metadata_service import DocMetadataService
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.llm_service import LLMBundle
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from api.db.services.user_service import TenantService
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from common.metadata_utils import apply_meta_data_filter
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from api.db.services.search_service import SearchService
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from api.db.services.user_service import UserTenantService
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from api.db.joint_services.tenant_model_service import get_tenant_default_model_by_type, get_model_config_from_provider_instance
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from common.misc_utils import thread_pool_exec
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from api.utils.api_utils import get_error_data_result, get_json_result, \
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add_tenant_id_to_kwargs, get_result, get_request_json, server_error_response, validate_request
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from rag.app.tag import label_question
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from rag.prompts.template import load_prompt
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from rag.prompts.generator import cross_languages, keyword_extraction
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from common.constants import RetCode, LLMType, StatusEnum
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from common import settings
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from api.utils.reference_metadata_utils import (
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enrich_chunks_with_document_metadata,
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resolve_reference_metadata_preferences,
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)
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logger = logging.getLogger(__name__)
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@manager.route("/chatbots/<dialog_id>/completions", methods=["POST"]) # noqa: F821
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@login_required(auth_types=AUTH_BETA)
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@add_tenant_id_to_kwargs
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async def chatbot_completions(dialog_id, tenant_id=None):
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req = await get_request_json()
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exists, dialog = DialogService.get_by_id(dialog_id)
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if (not exists
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or getattr(dialog, "tenant_id", None) != tenant_id
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or str(getattr(dialog, "status", "")) != StatusEnum.VALID.value):
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logger.warning(
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"Denied chatbot access: reason=%s tenant_id=%s dialog_id=%s user_id=%s session_id=%s",
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"no access to this chatbot",
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tenant_id,
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dialog_id,
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req.get("user_id"),
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req.get("session_id"),
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)
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return get_error_data_result(message="Authentication error: no access to this chatbot!")
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if "quote" not in req:
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req["quote"] = False
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def _validate_iframe_access():
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if req.get("session_id"):
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exists, conv = API4ConversationService.get_by_id(req.get("session_id"))
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if not exists:
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raise AssertionError("Session not found!")
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if conv.dialog_id != dialog_id:
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raise AssertionError("Session does not belong to this dialog")
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if tenant_id and conv.user_id and conv.user_id != tenant_id:
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raise AssertionError("Session does not belong to this tenant")
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if req.get("stream", True):
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try:
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_validate_iframe_access()
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except AssertionError:
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logger.warning(
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"Denied chatbot completion stream: reason=%s tenant_id=%s dialog_id=%s user_id=%s session_id=%s",
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"no access to this chatbot",
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tenant_id,
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dialog_id,
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req.get("user_id"),
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req.get("session_id"),
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)
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return get_error_data_result(message="Authentication error: no access to this chatbot!")
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resp = Response(iframe_completion(dialog_id, tenant_id=tenant_id, **req), mimetype="text/event-stream")
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resp.headers.add_header("Cache-control", "no-cache")
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resp.headers.add_header("Connection", "keep-alive")
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resp.headers.add_header("X-Accel-Buffering", "no")
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resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
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return resp
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try:
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_validate_iframe_access()
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async for answer in iframe_completion(dialog_id, tenant_id=tenant_id, **req):
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return get_result(data=answer)
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except AssertionError:
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logger.warning(
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"Denied chatbot completion: reason=%s tenant_id=%s dialog_id=%s user_id=%s session_id=%s",
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"no access to this chatbot",
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tenant_id,
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dialog_id,
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req.get("user_id"),
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req.get("session_id"),
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)
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return get_error_data_result(message="Authentication error: no access to this chatbot!")
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return None
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@manager.route("/chatbots/<dialog_id>/info", methods=["GET"]) # noqa: F821
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@login_required(auth_types=AUTH_BETA)
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@add_tenant_id_to_kwargs
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async def chatbots_inputs(dialog_id, tenant_id=None):
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exists, dialog = await thread_pool_exec(DialogService.get_by_id, dialog_id)
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if (not exists
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or getattr(dialog, "tenant_id", None) != tenant_id
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or str(getattr(dialog, "status", "")) != StatusEnum.VALID.value):
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request_args = getattr(request, "args", {}) or {}
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request_user_id = request_args.get("user_id") if hasattr(request_args, "get") else None
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request_session_id = request_args.get("session_id") if hasattr(request_args, "get") else None
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logger.warning(
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"Denied chatbot access: reason=%s tenant_id=%s dialog_id=%s user_id=%s session_id=%s",
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"no access to this chatbot",
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tenant_id,
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dialog_id,
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request_user_id,
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request_session_id,
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)
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return get_error_data_result(message="Authentication error: no access to this chatbot!")
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return get_result(
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data={
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"title": dialog.name,
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"avatar": dialog.icon,
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"prologue": dialog.prompt_config.get("prologue", ""),
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"has_tavily_key": bool(dialog.prompt_config.get("tavily_api_key", "").strip()),
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}
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)
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@manager.route("/agentbots/<agent_id>/completions", methods=["POST"]) # noqa: F821
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@login_required(auth_types=AUTH_BETA)
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@add_tenant_id_to_kwargs
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async def agent_bot_completions(agent_id, tenant_id=None):
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req = await get_request_json()
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if req.get("stream", True):
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async def stream():
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try:
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async for answer in agent_completion(tenant_id, agent_id, **req):
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yield answer
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except Exception as e:
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logging.exception(e)
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error_result = get_error_data_result(message=str(e) or "Unknown error")
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yield "data:" + json.dumps(
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{
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"event": "message",
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"data": {"content": f"Error {error_result['code']}: {error_result['message']}\n\n"},
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**error_result,
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},
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ensure_ascii=False,
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) + "\n\n"
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resp = Response(stream(), mimetype="text/event-stream")
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resp.headers.add_header("Cache-control", "no-cache")
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resp.headers.add_header("Connection", "keep-alive")
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resp.headers.add_header("X-Accel-Buffering", "no")
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resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
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return resp
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try:
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full_content = ""
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reference = {}
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structured_output = {}
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final_ans = {}
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async for answer in agent_completion(tenant_id, agent_id, **req):
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# agent_completion yields SSE-formatted strings. A single yielded
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# chunk can contain multiple "data:..." frames separated by "\n\n"
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# plus blank or comment lines, so parse line-by-line rather than
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# assuming one frame per chunk.
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if not isinstance(answer, str):
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continue
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for line in answer.splitlines():
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line = line.strip()
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if not line.startswith("data:"):
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continue
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payload = line[len("data:"):].strip()
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if not payload:
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continue
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try:
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ans = json.loads(payload)
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except Exception as e:
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logging.debug("agent_bot_completions: skipping malformed SSE frame: %s", e)
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continue
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event = ans.get("event")
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if event == "message":
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full_content += ans.get("data", {}).get("content", "") or ""
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if ans.get("data", {}).get("reference"):
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reference.update(ans["data"]["reference"])
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if event == "node_finished":
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data = ans.get("data", {})
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node_out = data.get("outputs") or {}
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component_id = data.get("component_id")
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if component_id is not None and "structured" in node_out:
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structured_output[component_id] = copy.deepcopy(node_out["structured"])
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final_ans = ans
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if not final_ans:
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return get_result(data={})
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if "data" not in final_ans or not isinstance(final_ans["data"], dict):
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final_ans["data"] = {}
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final_ans["data"]["content"] = full_content
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final_ans["data"]["reference"] = reference
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if structured_output:
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final_ans["data"]["structured"] = structured_output
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return get_result(data=final_ans)
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except Exception as e:
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logging.exception(e)
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return get_error_data_result(message=str(e) or "Unknown error")
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@manager.route("/agentbots/<agent_id>/inputs", methods=["GET"]) # noqa: F821
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@login_required(auth_types=AUTH_BETA)
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@add_tenant_id_to_kwargs
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async def begin_inputs(agent_id, tenant_id=None):
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e, cvs = await thread_pool_exec(UserCanvasService.get_by_id, agent_id)
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if not e:
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return get_error_data_result(f"Can't find agent by ID: {agent_id}")
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canvas = Canvas(json.dumps(cvs.dsl), tenant_id, canvas_id=cvs.id)
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return get_result(
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data={"title": cvs.title, "avatar": cvs.avatar, "inputs": canvas.get_component_input_form("begin"),
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"prologue": canvas.get_prologue(), "mode": canvas.get_mode()})
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@manager.route("/searchbots/ask", methods=["POST"]) # noqa: F821
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@login_required(auth_types=AUTH_BETA)
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@add_tenant_id_to_kwargs
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@validate_request("question", "kb_ids")
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async def ask_about_embedded(tenant_id=None):
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req = await get_request_json()
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uid = tenant_id
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search_id = req.get("search_id", "")
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search_config = {}
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if search_id:
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if search_app := await thread_pool_exec(SearchService.get_detail, search_id):
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search_config = search_app.get("search_config", {})
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chat_llm_name = ""
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if not search_config or not search_config.get("chat_id"):
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_, tenant_info = TenantService.get_by_id(uid)
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chat_llm_name = tenant_info.llm_id
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async def stream():
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nonlocal req, uid
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try:
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async for ans in async_ask(req["question"], req["kb_ids"], uid, chat_llm_name=chat_llm_name, search_config=search_config):
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yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
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except Exception as e:
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yield "data:" + json.dumps(
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{"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
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ensure_ascii=False) + "\n\n"
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yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
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resp = Response(stream(), mimetype="text/event-stream")
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resp.headers.add_header("Cache-control", "no-cache")
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resp.headers.add_header("Connection", "keep-alive")
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resp.headers.add_header("X-Accel-Buffering", "no")
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resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
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return resp
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@manager.route("/searchbots/retrieval_test", methods=["POST"]) # noqa: F821
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@login_required(auth_types=AUTH_BETA)
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@add_tenant_id_to_kwargs
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@validate_request("kb_id", "question")
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async def retrieval_test_embedded(tenant_id=None):
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req = await get_request_json()
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page = int(req.get("page", 1))
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size = int(req.get("size", 30))
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question = req["question"]
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kb_ids = req["kb_id"]
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if isinstance(kb_ids, str):
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kb_ids = [kb_ids]
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if not kb_ids:
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return get_json_result(data=False, message='Please specify dataset firstly.',
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code=RetCode.DATA_ERROR)
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doc_ids = req.get("doc_ids", [])
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similarity_threshold = float(req.get("similarity_threshold", 0.0))
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vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
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use_kg = req.get("use_kg", False)
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top = int(req.get("top_k", 1024))
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if top <= 0:
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return get_error_data_result("`top_k` must be greater than 0")
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langs = req.get("cross_languages", [])
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rerank_id = req.get("rerank_id", "")
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if not tenant_id:
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return get_error_data_result(message="permission denined.")
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search_config = {}
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async def _retrieval():
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nonlocal similarity_threshold, vector_similarity_weight, top, rerank_id
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local_doc_ids = list(doc_ids) if doc_ids else []
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tenant_ids = []
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_question = question
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meta_data_filter = {}
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chat_mdl = None
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if req.get("search_id", ""):
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nonlocal search_config
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detail = await thread_pool_exec(SearchService.get_detail, req.get("search_id", ""))
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if detail:
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search_config = detail.get("search_config", {})
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meta_data_filter = search_config.get("meta_data_filter", {})
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if meta_data_filter.get("method") in ["auto", "semi_auto"]:
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chat_id = search_config.get("chat_id", "")
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if chat_id:
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chat_model_config = await thread_pool_exec(get_model_config_from_provider_instance, tenant_id, LLMType.CHAT, chat_id)
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else:
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chat_model_config = await thread_pool_exec(get_tenant_default_model_by_type, tenant_id, LLMType.CHAT)
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chat_mdl = LLMBundle(tenant_id, chat_model_config)
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# Apply search_config settings if not explicitly provided in request
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if not req.get("similarity_threshold"):
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similarity_threshold = float(search_config.get("similarity_threshold", similarity_threshold))
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if not req.get("vector_similarity_weight"):
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vector_similarity_weight = float(search_config.get("vector_similarity_weight", vector_similarity_weight))
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if not req.get("top_k"):
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top = int(search_config.get("top_k", top))
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if not req.get("rerank_id"):
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rerank_id = search_config.get("rerank_id", "")
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else:
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meta_data_filter = req.get("meta_data_filter") or {}
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if meta_data_filter.get("method") in ["auto", "semi_auto"]:
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chat_model_config = await thread_pool_exec(get_tenant_default_model_by_type, tenant_id, LLMType.CHAT)
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chat_mdl = LLMBundle(tenant_id, chat_model_config)
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if meta_data_filter:
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local_doc_ids = await apply_meta_data_filter(
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meta_data_filter,
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None,
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_question,
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chat_mdl,
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local_doc_ids,
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kb_ids=kb_ids,
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metas_loader=lambda: DocMetadataService.get_flatted_meta_by_kbs(kb_ids),
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)
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tenants = await thread_pool_exec(UserTenantService.query, user_id=tenant_id)
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for kb_id in kb_ids:
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for tenant in tenants:
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if await thread_pool_exec(KnowledgebaseService.query, tenant_id=tenant.tenant_id, id=kb_id):
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tenant_ids.append(tenant.tenant_id)
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break
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else:
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return get_json_result(data=False, message="Only owner of dataset authorized for this operation.",
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code=RetCode.OPERATING_ERROR)
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e, kb = await thread_pool_exec(KnowledgebaseService.get_by_id, kb_ids[0])
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if not e:
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return get_error_data_result(message="Knowledgebase not found!")
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if langs:
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_question = await cross_languages(kb.tenant_id, None, _question, langs)
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embd_model_config = await thread_pool_exec(get_model_config_from_provider_instance, kb.tenant_id, LLMType.EMBEDDING, kb.embd_id)
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embd_mdl = LLMBundle(kb.tenant_id, embd_model_config)
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rerank_mdl = None
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if rerank_id:
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rerank_model_config = await thread_pool_exec(get_model_config_from_provider_instance, tenant_id, LLMType.RERANK, rerank_id)
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rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
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if req.get("keyword", False):
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default_chat_model = await thread_pool_exec(get_tenant_default_model_by_type, kb.tenant_id, LLMType.CHAT)
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chat_mdl = LLMBundle(kb.tenant_id, default_chat_model)
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_question += await keyword_extraction(chat_mdl, _question)
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labels = label_question(_question, [kb])
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ranks = await settings.retriever.retrieval(
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_question, embd_mdl, tenant_ids, kb_ids, page, size, similarity_threshold, vector_similarity_weight, top,
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local_doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"), rank_feature=labels
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)
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if use_kg:
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default_chat_model = await thread_pool_exec(get_tenant_default_model_by_type, kb.tenant_id, LLMType.CHAT)
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ck = await settings.kg_retriever.retrieval(_question, tenant_ids, kb_ids, embd_mdl,
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LLMBundle(kb.tenant_id, default_chat_model))
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if ck["content_with_weight"]:
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ranks["chunks"].insert(0, ck)
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for c in ranks["chunks"]:
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c.pop("vector", None)
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include_metadata, metadata_fields = _resolve_reference_metadata(req, search_config)
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if include_metadata:
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enrich_chunks_with_document_metadata(ranks["chunks"], metadata_fields)
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ranks["labels"] = labels
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return get_json_result(data=ranks)
|
|
|
|
try:
|
|
return await _retrieval()
|
|
except Exception as e:
|
|
if "not_found" in str(e):
|
|
return get_json_result(data=False, message="No chunk found! Check the chunk status please!",
|
|
code=RetCode.DATA_ERROR)
|
|
return server_error_response(e)
|
|
|
|
|
|
@manager.route("/searchbots/related_questions", methods=["POST"]) # noqa: F821
|
|
@login_required(auth_types=AUTH_BETA)
|
|
@add_tenant_id_to_kwargs
|
|
@validate_request("question")
|
|
async def related_questions_embedded(tenant_id=None):
|
|
req = await get_request_json()
|
|
if not tenant_id:
|
|
return get_error_data_result(message="permission denined.")
|
|
|
|
search_id = req.get("search_id", "")
|
|
search_config = {}
|
|
if search_id:
|
|
if search_app := await thread_pool_exec(SearchService.get_detail, search_id):
|
|
search_config = search_app.get("search_config", {})
|
|
|
|
question = req["question"]
|
|
|
|
chat_id = search_config.get("chat_id", "")
|
|
if chat_id:
|
|
chat_model_config = await thread_pool_exec(get_model_config_from_provider_instance, tenant_id, LLMType.CHAT, chat_id)
|
|
else:
|
|
chat_model_config = await thread_pool_exec(get_tenant_default_model_by_type, tenant_id, LLMType.CHAT)
|
|
chat_mdl = LLMBundle(tenant_id, chat_model_config)
|
|
|
|
gen_conf = search_config.get("llm_setting", {"temperature": 0.9})
|
|
prompt = load_prompt("related_question")
|
|
ans = await chat_mdl.async_chat(
|
|
prompt,
|
|
[
|
|
{
|
|
"role": "user",
|
|
"content": f"""
|
|
Keywords: {question}
|
|
Related search terms:
|
|
""",
|
|
}
|
|
],
|
|
gen_conf,
|
|
)
|
|
return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])
|
|
|
|
|
|
@manager.route("/searchbots/detail", methods=["GET"]) # noqa: F821
|
|
@login_required(auth_types=AUTH_BETA)
|
|
@add_tenant_id_to_kwargs
|
|
async def detail_share_embedded(tenant_id=None):
|
|
search_id = request.args["search_id"]
|
|
if not tenant_id:
|
|
return get_error_data_result(message="permission denined.")
|
|
try:
|
|
tenants = await thread_pool_exec(UserTenantService.query, user_id=tenant_id)
|
|
for tenant in tenants:
|
|
if await thread_pool_exec(SearchService.query, tenant_id=tenant.tenant_id, id=search_id):
|
|
break
|
|
else:
|
|
return get_json_result(data=False, message="Has no permission for this operation.",
|
|
code=RetCode.OPERATING_ERROR)
|
|
|
|
search = await thread_pool_exec(SearchService.get_detail, search_id)
|
|
if not search:
|
|
return get_error_data_result(message="Can't find this Search App!")
|
|
return get_json_result(data=search)
|
|
except Exception as e:
|
|
return server_error_response(e)
|
|
|
|
|
|
@manager.route("/searchbots/mindmap", methods=["POST"]) # noqa: F821
|
|
@login_required(auth_types=AUTH_BETA)
|
|
@add_tenant_id_to_kwargs
|
|
@validate_request("question", "kb_ids")
|
|
async def mindmap(tenant_id=None):
|
|
req = await get_request_json()
|
|
|
|
search_id = req.get("search_id", "")
|
|
search_app = await thread_pool_exec(SearchService.get_detail, search_id) if search_id else {}
|
|
|
|
mind_map =await gen_mindmap(req["question"], req["kb_ids"], tenant_id, search_app.get("search_config", {}))
|
|
if "error" in mind_map:
|
|
return server_error_response(Exception(mind_map["error"]))
|
|
return get_json_result(data=mind_map)
|
|
|
|
|
|
def _resolve_reference_metadata(req, search_config=None):
|
|
return resolve_reference_metadata_preferences(req, search_config)
|