Phase 5: 知识推理与问答增强
- 新增 knowledge_reasoner.py 推理引擎 - 支持因果/对比/时序/关联四种推理类型 - 智能项目总结 API (全面/高管/技术/风险) - 实体关联路径发现功能 - 前端推理面板 UI 和交互 - 更新 API 端点和健康检查 Refs: Phase 5 开发任务
This commit is contained in:
OpenClaw Bot
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,533 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
InsightFlow Knowledge Reasoning - Phase 5
|
||||
知识推理与问答增强模块
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import httpx
|
||||
from typing import List, Dict, Optional, Any
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
|
||||
KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
|
||||
|
||||
|
||||
class ReasoningType(Enum):
|
||||
"""推理类型"""
|
||||
CAUSAL = "causal" # 因果推理
|
||||
ASSOCIATIVE = "associative" # 关联推理
|
||||
TEMPORAL = "temporal" # 时序推理
|
||||
COMPARATIVE = "comparative" # 对比推理
|
||||
SUMMARY = "summary" # 总结推理
|
||||
|
||||
|
||||
@dataclass
|
||||
class ReasoningResult:
|
||||
"""推理结果"""
|
||||
answer: str
|
||||
reasoning_type: ReasoningType
|
||||
confidence: float
|
||||
evidence: List[Dict] # 支撑证据
|
||||
related_entities: List[str] # 相关实体
|
||||
gaps: List[str] # 知识缺口
|
||||
|
||||
|
||||
@dataclass
|
||||
class InferencePath:
|
||||
"""推理路径"""
|
||||
start_entity: str
|
||||
end_entity: str
|
||||
path: List[Dict] # 路径上的节点和关系
|
||||
strength: float # 路径强度
|
||||
|
||||
|
||||
class KnowledgeReasoner:
|
||||
"""知识推理引擎"""
|
||||
|
||||
def __init__(self, api_key: str = None, base_url: str = None):
|
||||
self.api_key = api_key or KIMI_API_KEY
|
||||
self.base_url = base_url or KIMI_BASE_URL
|
||||
self.headers = {
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
async def _call_llm(self, prompt: str, temperature: float = 0.3) -> str:
|
||||
"""调用 LLM"""
|
||||
if not self.api_key:
|
||||
raise ValueError("KIMI_API_KEY not set")
|
||||
|
||||
payload = {
|
||||
"model": "k2p5",
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": temperature
|
||||
}
|
||||
|
||||
async with httpx.AsyncClient() as client:
|
||||
response = await client.post(
|
||||
f"{self.base_url}/v1/chat/completions",
|
||||
headers=self.headers,
|
||||
json=payload,
|
||||
timeout=120.0
|
||||
)
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
return result["choices"][0]["message"]["content"]
|
||||
|
||||
async def enhanced_qa(
|
||||
self,
|
||||
query: str,
|
||||
project_context: Dict,
|
||||
graph_data: Dict,
|
||||
reasoning_depth: str = "medium"
|
||||
) -> ReasoningResult:
|
||||
"""
|
||||
增强问答 - 结合图谱推理的问答
|
||||
|
||||
Args:
|
||||
query: 用户问题
|
||||
project_context: 项目上下文
|
||||
graph_data: 知识图谱数据
|
||||
reasoning_depth: 推理深度 (shallow/medium/deep)
|
||||
"""
|
||||
# 1. 分析问题类型
|
||||
analysis = await self._analyze_question(query)
|
||||
|
||||
# 2. 根据问题类型选择推理策略
|
||||
if analysis["type"] == "causal":
|
||||
return await self._causal_reasoning(query, project_context, graph_data)
|
||||
elif analysis["type"] == "comparative":
|
||||
return await self._comparative_reasoning(query, project_context, graph_data)
|
||||
elif analysis["type"] == "temporal":
|
||||
return await self._temporal_reasoning(query, project_context, graph_data)
|
||||
else:
|
||||
return await self._associative_reasoning(query, project_context, graph_data)
|
||||
|
||||
async def _analyze_question(self, query: str) -> Dict:
|
||||
"""分析问题类型和意图"""
|
||||
prompt = f"""分析以下问题的类型和意图：
|
||||
|
||||
问题：{query}
|
||||
|
||||
请返回 JSON 格式：
|
||||
{{
|
||||
"type": "causal|comparative|temporal|factual|opinion",
|
||||
"entities": ["提到的实体"],
|
||||
"intent": "问题意图描述",
|
||||
"complexity": "simple|medium|complex"
|
||||
}}
|
||||
|
||||
类型说明：
|
||||
- causal: 因果类问题（为什么、导致、影响）
|
||||
- comparative: 对比类问题（区别、比较、优劣）
|
||||
- temporal: 时序类问题（什么时候、进度、变化）
|
||||
- factual: 事实类问题（是什么、有哪些）
|
||||
- opinion: 观点类问题（怎么看、态度、评价）"""
|
||||
|
||||
content = await self._call_llm(prompt, temperature=0.1)
|
||||
|
||||
import re
|
||||
json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
|
||||
if json_match:
|
||||
try:
|
||||
return json.loads(json_match.group())
|
||||
except:
|
||||
pass
|
||||
|
||||
return {"type": "factual", "entities": [], "intent": "general", "complexity": "simple"}
|
||||
|
||||
async def _causal_reasoning(
|
||||
self,
|
||||
query: str,
|
||||
project_context: Dict,
|
||||
graph_data: Dict
|
||||
) -> ReasoningResult:
|
||||
"""因果推理 - 分析原因和影响"""
|
||||
|
||||
# 构建因果分析提示
|
||||
entities_str = json.dumps(graph_data.get("entities", []), ensure_ascii=False, indent=2)
|
||||
relations_str = json.dumps(graph_data.get("relations", []), ensure_ascii=False, indent=2)
|
||||
|
||||
prompt = f"""基于以下知识图谱进行因果推理分析：
|
||||
|
||||
## 问题
|
||||
{query}
|
||||
|
||||
## 实体
|
||||
{entities_str[:2000]}
|
||||
|
||||
## 关系
|
||||
{relations_str[:2000]}
|
||||
|
||||
## 项目上下文
|
||||
{json.dumps(project_context, ensure_ascii=False, indent=2)[:1500]}
|
||||
|
||||
请进行因果分析，返回 JSON 格式：
|
||||
{{
|
||||
"answer": "详细回答",
|
||||
"reasoning_chain": ["推理步骤1", "推理步骤2"],
|
||||
"root_causes": ["根本原因1", "根本原因2"],
|
||||
"effects": ["影响1", "影响2"],
|
||||
"confidence": 0.85,
|
||||
"evidence": ["证据1", "证据2"],
|
||||
"knowledge_gaps": ["缺失信息1"]
|
||||
}}"""
|
||||
|
||||
content = await self._call_llm(prompt, temperature=0.3)
|
||||
|
||||
import re
|
||||
json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
|
||||
|
||||
if json_match:
|
||||
try:
|
||||
data = json.loads(json_match.group())
|
||||
return ReasoningResult(
|
||||
answer=data.get("answer", ""),
|
||||
reasoning_type=ReasoningType.CAUSAL,
|
||||
confidence=data.get("confidence", 0.7),
|
||||
evidence=[{"text": e} for e in data.get("evidence", [])],
|
||||
related_entities=[],
|
||||
gaps=data.get("knowledge_gaps", [])
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
return ReasoningResult(
|
||||
answer=content,
|
||||
reasoning_type=ReasoningType.CAUSAL,
|
||||
confidence=0.5,
|
||||
evidence=[],
|
||||
related_entities=[],
|
||||
gaps=["无法完成因果推理"]
|
||||
)
|
||||
|
||||
async def _comparative_reasoning(
|
||||
self,
|
||||
query: str,
|
||||
project_context: Dict,
|
||||
graph_data: Dict
|
||||
) -> ReasoningResult:
|
||||
"""对比推理 - 比较实体间的异同"""
|
||||
|
||||
prompt = f"""基于以下知识图谱进行对比分析：
|
||||
|
||||
## 问题
|
||||
{query}
|
||||
|
||||
## 实体
|
||||
{json.dumps(graph_data.get("entities", []), ensure_ascii=False, indent=2)[:2000]}
|
||||
|
||||
## 关系
|
||||
{json.dumps(graph_data.get("relations", []), ensure_ascii=False, indent=2)[:1500]}
|
||||
|
||||
请进行对比分析，返回 JSON 格式：
|
||||
{{
|
||||
"answer": "详细对比分析",
|
||||
"similarities": ["相似点1", "相似点2"],
|
||||
"differences": ["差异点1", "差异点2"],
|
||||
"comparison_table": {{"维度": ["实体A值", "实体B值"]}},
|
||||
"confidence": 0.85,
|
||||
"evidence": ["证据1"],
|
||||
"knowledge_gaps": []
|
||||
}}"""
|
||||
|
||||
content = await self._call_llm(prompt, temperature=0.3)
|
||||
|
||||
import re
|
||||
json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
|
||||
|
||||
if json_match:
|
||||
try:
|
||||
data = json.loads(json_match.group())
|
||||
return ReasoningResult(
|
||||
answer=data.get("answer", ""),
|
||||
reasoning_type=ReasoningType.COMPARATIVE,
|
||||
confidence=data.get("confidence", 0.7),
|
||||
evidence=[{"text": e} for e in data.get("evidence", [])],
|
||||
related_entities=[],
|
||||
gaps=data.get("knowledge_gaps", [])
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
return ReasoningResult(
|
||||
answer=content,
|
||||
reasoning_type=ReasoningType.COMPARATIVE,
|
||||
confidence=0.5,
|
||||
evidence=[],
|
||||
related_entities=[],
|
||||
gaps=[]
|
||||
)
|
||||
|
||||
async def _temporal_reasoning(
|
||||
self,
|
||||
query: str,
|
||||
project_context: Dict,
|
||||
graph_data: Dict
|
||||
) -> ReasoningResult:
|
||||
"""时序推理 - 分析时间线和演变"""
|
||||
|
||||
prompt = f"""基于以下知识图谱进行时序分析：
|
||||
|
||||
## 问题
|
||||
{query}
|
||||
|
||||
## 项目时间线
|
||||
{json.dumps(project_context.get("timeline", []), ensure_ascii=False, indent=2)[:2000]}
|
||||
|
||||
## 实体提及历史
|
||||
{json.dumps(graph_data.get("entities", []), ensure_ascii=False, indent=2)[:1500]}
|
||||
|
||||
请进行时序分析，返回 JSON 格式：
|
||||
{{
|
||||
"answer": "时序分析结果",
|
||||
"timeline": [{{"date": "时间", "event": "事件", "significance": "重要性"}}],
|
||||
"trends": ["趋势1", "趋势2"],
|
||||
"milestones": ["里程碑1"],
|
||||
"confidence": 0.85,
|
||||
"evidence": ["证据1"],
|
||||
"knowledge_gaps": []
|
||||
}}"""
|
||||
|
||||
content = await self._call_llm(prompt, temperature=0.3)
|
||||
|
||||
import re
|
||||
json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
|
||||
|
||||
if json_match:
|
||||
try:
|
||||
data = json.loads(json_match.group())
|
||||
return ReasoningResult(
|
||||
answer=data.get("answer", ""),
|
||||
reasoning_type=ReasoningType.TEMPORAL,
|
||||
confidence=data.get("confidence", 0.7),
|
||||
evidence=[{"text": e} for e in data.get("evidence", [])],
|
||||
related_entities=[],
|
||||
gaps=data.get("knowledge_gaps", [])
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
return ReasoningResult(
|
||||
answer=content,
|
||||
reasoning_type=ReasoningType.TEMPORAL,
|
||||
confidence=0.5,
|
||||
evidence=[],
|
||||
related_entities=[],
|
||||
gaps=[]
|
||||
)
|
||||
|
||||
async def _associative_reasoning(
|
||||
self,
|
||||
query: str,
|
||||
project_context: Dict,
|
||||
graph_data: Dict
|
||||
) -> ReasoningResult:
|
||||
"""关联推理 - 发现实体间的隐含关联"""
|
||||
|
||||
prompt = f"""基于以下知识图谱进行关联分析：
|
||||
|
||||
## 问题
|
||||
{query}
|
||||
|
||||
## 实体
|
||||
{json.dumps(graph_data.get("entities", [])[:20], ensure_ascii=False, indent=2)}
|
||||
|
||||
## 关系
|
||||
{json.dumps(graph_data.get("relations", [])[:30], ensure_ascii=False, indent=2)}
|
||||
|
||||
请进行关联推理，发现隐含联系，返回 JSON 格式：
|
||||
{{
|
||||
"answer": "关联分析结果",
|
||||
"direct_connections": ["直接关联1"],
|
||||
"indirect_connections": ["间接关联1"],
|
||||
"inferred_relations": [{{"source": "A", "target": "B", "relation": "可能关系", "confidence": 0.7}}],
|
||||
"confidence": 0.85,
|
||||
"evidence": ["证据1"],
|
||||
"knowledge_gaps": []
|
||||
}}"""
|
||||
|
||||
content = await self._call_llm(prompt, temperature=0.4)
|
||||
|
||||
import re
|
||||
json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
|
||||
|
||||
if json_match:
|
||||
try:
|
||||
data = json.loads(json_match.group())
|
||||
return ReasoningResult(
|
||||
answer=data.get("answer", ""),
|
||||
reasoning_type=ReasoningType.ASSOCIATIVE,
|
||||
confidence=data.get("confidence", 0.7),
|
||||
evidence=[{"text": e} for e in data.get("evidence", [])],
|
||||
related_entities=[],
|
||||
gaps=data.get("knowledge_gaps", [])
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
return ReasoningResult(
|
||||
answer=content,
|
||||
reasoning_type=ReasoningType.ASSOCIATIVE,
|
||||
confidence=0.5,
|
||||
evidence=[],
|
||||
related_entities=[],
|
||||
gaps=[]
|
||||
)
|
||||
|
||||
def find_inference_paths(
|
||||
self,
|
||||
start_entity: str,
|
||||
end_entity: str,
|
||||
graph_data: Dict,
|
||||
max_depth: int = 3
|
||||
) -> List[InferencePath]:
|
||||
"""
|
||||
发现两个实体之间的推理路径
|
||||
|
||||
使用 BFS 在关系图中搜索路径
|
||||
"""
|
||||
entities = {e["id"]: e for e in graph_data.get("entities", [])}
|
||||
relations = graph_data.get("relations", [])
|
||||
|
||||
# 构建邻接表
|
||||
adj = {}
|
||||
for r in relations:
|
||||
src = r.get("source_id") or r.get("source")
|
||||
tgt = r.get("target_id") or r.get("target")
|
||||
if src not in adj:
|
||||
adj[src] = []
|
||||
if tgt not in adj:
|
||||
adj[tgt] = []
|
||||
adj[src].append({"target": tgt, "relation": r.get("type", "related"), "data": r})
|
||||
# 无向图也添加反向
|
||||
adj[tgt].append({"target": src, "relation": r.get("type", "related"), "data": r, "reverse": True})
|
||||
|
||||
# BFS 搜索路径
|
||||
from collections import deque
|
||||
paths = []
|
||||
queue = deque([(start_entity, [{"entity": start_entity, "relation": None}])])
|
||||
visited = {start_entity}
|
||||
|
||||
while queue and len(paths) < 5:
|
||||
current, path = queue.popleft()
|
||||
|
||||
if current == end_entity and len(path) > 1:
|
||||
# 找到一条路径
|
||||
paths.append(InferencePath(
|
||||
start_entity=start_entity,
|
||||
end_entity=end_entity,
|
||||
path=path,
|
||||
strength=self._calculate_path_strength(path)
|
||||
))
|
||||
continue
|
||||
|
||||
if len(path) >= max_depth:
|
||||
continue
|
||||
|
||||
for neighbor in adj.get(current, []):
|
||||
next_entity = neighbor["target"]
|
||||
if next_entity not in [p["entity"] for p in path]: # 避免循环
|
||||
new_path = path + [{
|
||||
"entity": next_entity,
|
||||
"relation": neighbor["relation"],
|
||||
"relation_data": neighbor.get("data", {})
|
||||
}]
|
||||
queue.append((next_entity, new_path))
|
||||
|
||||
# 按强度排序
|
||||
paths.sort(key=lambda p: p.strength, reverse=True)
|
||||
return paths
|
||||
|
||||
def _calculate_path_strength(self, path: List[Dict]) -> float:
|
||||
"""计算路径强度"""
|
||||
if len(path) < 2:
|
||||
return 0.0
|
||||
|
||||
# 路径越短越强
|
||||
length_factor = 1.0 / len(path)
|
||||
|
||||
# 关系置信度
|
||||
confidence_sum = 0
|
||||
confidence_count = 0
|
||||
for node in path[1:]: # 跳过第一个节点
|
||||
rel_data = node.get("relation_data", {})
|
||||
if "confidence" in rel_data:
|
||||
confidence_sum += rel_data["confidence"]
|
||||
confidence_count += 1
|
||||
|
||||
confidence_factor = (confidence_sum / confidence_count) if confidence_count > 0 else 0.5
|
||||
|
||||
return length_factor * confidence_factor
|
||||
|
||||
async def summarize_project(
|
||||
self,
|
||||
project_context: Dict,
|
||||
graph_data: Dict,
|
||||
summary_type: str = "comprehensive"
|
||||
) -> Dict:
|
||||
"""
|
||||
项目智能总结
|
||||
|
||||
Args:
|
||||
summary_type: comprehensive/executive/technical/risk
|
||||
"""
|
||||
type_prompts = {
|
||||
"comprehensive": "全面总结项目的所有方面",
|
||||
"executive": "高管摘要，关注关键决策和风险",
|
||||
"technical": "技术总结，关注架构和技术栈",
|
||||
"risk": "风险分析，关注潜在问题和依赖"
|
||||
}
|
||||
|
||||
prompt = f"""请对以下项目进行{type_prompts.get(summary_type, "全面总结")}：
|
||||
|
||||
## 项目信息
|
||||
{json.dumps(project_context, ensure_ascii=False, indent=2)[:3000]}
|
||||
|
||||
## 知识图谱
|
||||
实体数: {len(graph_data.get("entities", []))}
|
||||
关系数: {len(graph_data.get("relations", []))}
|
||||
|
||||
请返回 JSON 格式：
|
||||
{{
|
||||
"overview": "项目概述",
|
||||
"key_points": ["要点1", "要点2"],
|
||||
"key_entities": ["关键实体1"],
|
||||
"risks": ["风险1"],
|
||||
"recommendations": ["建议1"],
|
||||
"confidence": 0.85
|
||||
}}"""
|
||||
|
||||
content = await self._call_llm(prompt, temperature=0.3)
|
||||
|
||||
import re
|
||||
json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
|
||||
|
||||
if json_match:
|
||||
try:
|
||||
return json.loads(json_match.group())
|
||||
except:
|
||||
pass
|
||||
|
||||
return {
|
||||
"overview": content,
|
||||
"key_points": [],
|
||||
"key_entities": [],
|
||||
"risks": [],
|
||||
"recommendations": [],
|
||||
"confidence": 0.5
|
||||
}
|
||||
|
||||
|
||||
# Singleton instance
|
||||
_reasoner = None
|
||||
|
||||
|
||||
def get_knowledge_reasoner() -> KnowledgeReasoner:
|
||||
global _reasoner
|
||||
if _reasoner is None:
|
||||
_reasoner = KnowledgeReasoner()
|
||||
return _reasoner
|
||||
+168
-3
@@ -61,6 +61,12 @@ try:
|
||||
except ImportError:
|
||||
LLM_CLIENT_AVAILABLE = False
|
||||
|
||||
try:
|
||||
from knowledge_reasoner import get_knowledge_reasoner, KnowledgeReasoner, ReasoningType
|
||||
REASONER_AVAILABLE = True
|
||||
except ImportError:
|
||||
REASONER_AVAILABLE = False
|
||||
|
||||
app = FastAPI(title="InsightFlow", version="0.3.0")
|
||||
|
||||
app.add_middleware(
|
||||
@@ -983,14 +989,15 @@ async def get_entity_mentions(entity_id: str):
|
||||
async def health_check():
|
||||
return {
|
||||
"status": "ok",
|
||||
"version": "0.5.0",
|
||||
"phase": "Phase 5 - Timeline View",
|
||||
"version": "0.6.0",
|
||||
"phase": "Phase 5 - Knowledge Reasoning",
|
||||
"oss_available": OSS_AVAILABLE,
|
||||
"tingwu_available": TINGWU_AVAILABLE,
|
||||
"db_available": DB_AVAILABLE,
|
||||
"doc_processor_available": DOC_PROCESSOR_AVAILABLE,
|
||||
"aligner_available": ALIGNER_AVAILABLE,
|
||||
"llm_client_available": LLM_CLIENT_AVAILABLE
|
||||
"llm_client_available": LLM_CLIENT_AVAILABLE,
|
||||
"reasoner_available": REASONER_AVAILABLE
|
||||
}
|
||||
|
||||
|
||||
@@ -1336,6 +1343,164 @@ async def get_entity_timeline(entity_id: str):
|
||||
}
|
||||
|
||||
|
||||
# ==================== Phase 5: 知识推理与问答增强 API ====================
|
||||
|
||||
class ReasoningQuery(BaseModel):
|
||||
query: str
|
||||
reasoning_depth: str = "medium" # shallow/medium/deep
|
||||
stream: bool = False
|
||||
|
||||
|
||||
@app.post("/api/v1/projects/{project_id}/reasoning/query")
|
||||
async def reasoning_query(project_id: str, query: ReasoningQuery):
|
||||
"""
|
||||
增强问答 - 基于知识推理的智能问答
|
||||
|
||||
支持多种推理类型：
|
||||
- 因果推理：分析原因和影响
|
||||
- 对比推理：比较实体间的异同
|
||||
- 时序推理：分析时间线和演变
|
||||
- 关联推理：发现隐含关联
|
||||
"""
|
||||
if not DB_AVAILABLE or not REASONER_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Knowledge reasoner not available")
|
||||
|
||||
db = get_db_manager()
|
||||
reasoner = get_knowledge_reasoner()
|
||||
|
||||
project = db.get_project(project_id)
|
||||
if not project:
|
||||
raise HTTPException(status_code=404, detail="Project not found")
|
||||
|
||||
# 获取项目上下文
|
||||
project_context = db.get_project_summary(project_id)
|
||||
|
||||
# 获取知识图谱数据
|
||||
entities = db.list_project_entities(project_id)
|
||||
relations = db.list_project_relations(project_id)
|
||||
|
||||
graph_data = {
|
||||
"entities": [{"id": e.id, "name": e.name, "type": e.type, "definition": e.definition} for e in entities],
|
||||
"relations": relations
|
||||
}
|
||||
|
||||
# 执行增强问答
|
||||
result = await reasoner.enhanced_qa(
|
||||
query=query.query,
|
||||
project_context=project_context,
|
||||
graph_data=graph_data,
|
||||
reasoning_depth=query.reasoning_depth
|
||||
)
|
||||
|
||||
return {
|
||||
"answer": result.answer,
|
||||
"reasoning_type": result.reasoning_type.value,
|
||||
"confidence": result.confidence,
|
||||
"evidence": result.evidence,
|
||||
"knowledge_gaps": result.gaps,
|
||||
"project_id": project_id
|
||||
}
|
||||
|
||||
|
||||
@app.post("/api/v1/projects/{project_id}/reasoning/inference-path")
|
||||
async def find_inference_path(
|
||||
project_id: str,
|
||||
start_entity: str,
|
||||
end_entity: str
|
||||
):
|
||||
"""
|
||||
发现两个实体之间的推理路径
|
||||
|
||||
在知识图谱中搜索从 start_entity 到 end_entity 的路径
|
||||
"""
|
||||
if not DB_AVAILABLE or not REASONER_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Knowledge reasoner not available")
|
||||
|
||||
db = get_db_manager()
|
||||
reasoner = get_knowledge_reasoner()
|
||||
|
||||
project = db.get_project(project_id)
|
||||
if not project:
|
||||
raise HTTPException(status_code=404, detail="Project not found")
|
||||
|
||||
# 获取知识图谱数据
|
||||
entities = db.list_project_entities(project_id)
|
||||
relations = db.list_project_relations(project_id)
|
||||
|
||||
graph_data = {
|
||||
"entities": [{"id": e.id, "name": e.name, "type": e.type} for e in entities],
|
||||
"relations": relations
|
||||
}
|
||||
|
||||
# 查找推理路径
|
||||
paths = reasoner.find_inference_paths(start_entity, end_entity, graph_data)
|
||||
|
||||
return {
|
||||
"start_entity": start_entity,
|
||||
"end_entity": end_entity,
|
||||
"paths": [
|
||||
{
|
||||
"path": path.path,
|
||||
"strength": path.strength,
|
||||
"path_description": " -> ".join([p["entity"] for p in path.path])
|
||||
}
|
||||
for path in paths[:5] # 最多返回5条路径
|
||||
],
|
||||
"total_paths": len(paths)
|
||||
}
|
||||
|
||||
|
||||
class SummaryRequest(BaseModel):
|
||||
summary_type: str = "comprehensive" # comprehensive/executive/technical/risk
|
||||
|
||||
|
||||
@app.post("/api/v1/projects/{project_id}/reasoning/summary")
|
||||
async def project_summary(project_id: str, req: SummaryRequest):
|
||||
"""
|
||||
项目智能总结
|
||||
|
||||
根据类型生成不同侧重点的总结：
|
||||
- comprehensive: 全面总结
|
||||
- executive: 高管摘要
|
||||
- technical: 技术总结
|
||||
- risk: 风险分析
|
||||
"""
|
||||
if not DB_AVAILABLE or not REASONER_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Knowledge reasoner not available")
|
||||
|
||||
db = get_db_manager()
|
||||
reasoner = get_knowledge_reasoner()
|
||||
|
||||
project = db.get_project(project_id)
|
||||
if not project:
|
||||
raise HTTPException(status_code=404, detail="Project not found")
|
||||
|
||||
# 获取项目上下文
|
||||
project_context = db.get_project_summary(project_id)
|
||||
|
||||
# 获取知识图谱数据
|
||||
entities = db.list_project_entities(project_id)
|
||||
relations = db.list_project_relations(project_id)
|
||||
|
||||
graph_data = {
|
||||
"entities": [{"id": e.id, "name": e.name, "type": e.type} for e in entities],
|
||||
"relations": relations
|
||||
}
|
||||
|
||||
# 生成总结
|
||||
summary = await reasoner.summarize_project(
|
||||
project_context=project_context,
|
||||
graph_data=graph_data,
|
||||
summary_type=req.summary_type
|
||||
)
|
||||
|
||||
return {
|
||||
"project_id": project_id,
|
||||
"summary_type": req.summary_type,
|
||||
**summary
|
||||
}
|
||||
|
||||
|
||||
# Serve frontend - MUST be last to not override API routes
|
||||
app.mount("/", StaticFiles(directory="frontend", html=True), name="frontend")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user