OpenViking 提供两种搜索方法:find 用于简单的语义搜索,search 用于带会话上下文的复杂检索。
| 方面 | find | search |
|---|---|---|
| 意图分析 | 否 | 是 |
| 会话上下文 | 否 | 是 |
| 查询扩展 | 否 | 是 |
| 默认限制数 | 10 | 10 |
| 使用场景 | 简单查询 | 对话式搜索 |
基本向量相似度搜索。
参数
| 参数 | 类型 | 必填 | 默认值 | 说明 |
|---|---|---|---|---|
| query | str | 是 | - | 搜索查询字符串 |
| target_uri | str | 否 | "" | 限制搜索范围到指定的 URI 前缀 |
| limit | int | 否 | 10 | 最大返回结果数 |
| score_threshold | float | 否 | None | 最低相关性分数阈值 |
| filter | Dict | 否 | None | 元数据过滤器 |
FindResult 结构
class FindResult:
memories: List[MatchedContext] # 记忆上下文
resources: List[MatchedContext] # 资源上下文
skills: List[MatchedContext] # 技能上下文
query_plan: Optional[QueryPlan] # 查询计划(仅 search)
query_results: Optional[List[QueryResult]] # 详细结果
total: int # 总数(自动计算)MatchedContext 结构
class MatchedContext:
uri: str # Viking URI
context_type: ContextType # "resource"、"memory" 或 "skill"
is_leaf: bool # 是否为叶子节点
abstract: str # L0 内容
category: str # 分类
score: float # 相关性分数 (0-1)
match_reason: str # 匹配原因
relations: List[RelatedContext] # 关联上下文Python SDK (Embedded / HTTP)
results = client.find("how to authenticate users")
for ctx in results.resources:
print(f"URI: {ctx.uri}")
print(f"Score: {ctx.score:.3f}")
print(f"Type: {ctx.context_type}")
print(f"Abstract: {ctx.abstract[:100]}...")
print("---")HTTP API
POST /api/v1/search/find
curl -X POST http://localhost:1933/api/v1/search/find \
-H "Content-Type: application/json" \
-H "X-API-Key: your-key" \
-d '{
"query": "how to authenticate users",
"limit": 10
}'CLI
openviking find "how to authenticate users" [--uri viking://resources/] [--limit 10]响应
{
"status": "ok",
"result": {
"memories": [],
"resources": [
{
"uri": "viking://resources/docs/auth/",
"context_type": "resource",
"is_leaf": false,
"abstract": "Authentication guide covering OAuth 2.0...",
"score": 0.92,
"match_reason": "Semantic match on authentication"
}
],
"skills": [],
"total": 1
},
"time": 0.1
}示例:使用 Target URI 搜索
Python SDK (Embedded / HTTP)
# 仅在资源中搜索
results = client.find(
"authentication",
target_uri="viking://resources/"
)
# 仅在用户记忆中搜索
results = client.find(
"preferences",
target_uri="viking://user/memories/"
)
# 仅在技能中搜索
results = client.find(
"web search",
target_uri="viking://skills/"
)
# 在特定项目中搜索
results = client.find(
"API endpoints",
target_uri="viking://resources/my-project/"
)HTTP API
# 仅在资源中搜索
curl -X POST http://localhost:1933/api/v1/search/find \
-H "Content-Type: application/json" \
-H "X-API-Key: your-key" \
-d '{
"query": "authentication",
"target_uri": "viking://resources/"
}'
# 使用分数阈值搜索
curl -X POST http://localhost:1933/api/v1/search/find \
-H "Content-Type: application/json" \
-H "X-API-Key: your-key" \
-d '{
"query": "API endpoints",
"target_uri": "viking://resources/my-project/",
"score_threshold": 0.5,
"limit": 5
}'带会话上下文和意图分析的搜索。
参数
| 参数 | 类型 | 必填 | 默认值 | 说明 |
|---|---|---|---|---|
| query | str | 是 | - | 搜索查询字符串 |
| target_uri | str | 否 | "" | 限制搜索范围到指定的 URI 前缀 |
| session | Session | 否 | None | 用于上下文感知搜索的会话(SDK) |
| session_id | str | 否 | None | 用于上下文感知搜索的会话 ID(HTTP) |
| limit | int | 否 | 10 | 最大返回结果数 |
| score_threshold | float | 否 | None | 最低相关性分数阈值 |
| filter | Dict | 否 | None | 元数据过滤器 |
Python SDK (Embedded / HTTP)
from openviking.message import TextPart
# 创建带对话上下文的会话
session = client.session()
session.add_message("user", [
TextPart(text="I'm building a login page with OAuth")
])
session.add_message("assistant", [
TextPart(text="I can help you with OAuth implementation.")
])
# 搜索能够理解对话上下文
results = client.search(
"best practices",
session=session
)
for ctx in results.resources:
print(f"Found: {ctx.uri}")
print(f"Abstract: {ctx.abstract[:200]}...")HTTP API
POST /api/v1/search/search
curl -X POST http://localhost:1933/api/v1/search/search \
-H "Content-Type: application/json" \
-H "X-API-Key: your-key" \
-d '{
"query": "best practices",
"session_id": "abc123",
"limit": 10
}'CLI
openviking search "best practices" [--session-id abc123] [--limit 10]响应
{
"status": "ok",
"result": {
"memories": [],
"resources": [
{
"uri": "viking://resources/docs/oauth-best-practices/",
"context_type": "resource",
"is_leaf": false,
"abstract": "OAuth 2.0 best practices for login pages...",
"score": 0.95,
"match_reason": "Context-aware match: OAuth login best practices"
}
],
"skills": [],
"query_plan": {
"expanded_queries": ["OAuth 2.0 best practices", "login page security"]
},
"total": 1
},
"time": 0.1
}示例:不使用会话的搜索
Python SDK (Embedded / HTTP)
# search 也可以在没有会话的情况下使用
# 它仍然会对查询进行意图分析
results = client.search(
"how to implement OAuth 2.0 authorization code flow",
)
for ctx in results.resources:
print(f"Found: {ctx.uri} (score: {ctx.score:.3f})")HTTP API
curl -X POST http://localhost:1933/api/v1/search/search \
-H "Content-Type: application/json" \
-H "X-API-Key: your-key" \
-d '{
"query": "how to implement OAuth 2.0 authorization code flow"
}'通过模式(正则表达式)搜索内容。
参数
| 参数 | 类型 | 必填 | 默认值 | 说明 |
|---|---|---|---|---|
| uri | str | 是 | - | 要搜索的 Viking URI |
| pattern | str | 是 | - | 搜索模式(正则表达式) |
| case_insensitive | bool | 否 | False | 忽略大小写 |
Python SDK (Embedded / HTTP)
results = client.grep(
"viking://resources/",
"authentication",
case_insensitive=True
)
print(f"Found {results['count']} matches")
for match in results['matches']:
print(f" {match['uri']}:{match['line']}")
print(f" {match['content']}")HTTP API
POST /api/v1/search/grep
curl -X POST http://localhost:1933/api/v1/search/grep \
-H "Content-Type: application/json" \
-H "X-API-Key: your-key" \
-d '{
"uri": "viking://resources/",
"pattern": "authentication",
"case_insensitive": true
}'CLI
openviking grep viking://resources/ "authentication" [--ignore-case]响应
{
"status": "ok",
"result": {
"matches": [
{
"uri": "viking://resources/docs/auth.md",
"line": 15,
"content": "User authentication is handled by..."
}
],
"count": 1
},
"time": 0.1
}通过 glob 模式匹配文件。
参数
| 参数 | 类型 | 必填 | 默认值 | 说明 |
|---|---|---|---|---|
| pattern | str | 是 | - | Glob 模式(例如 **/*.md) |
| uri | str | 否 | "viking://" | 起始 URI |
Python SDK (Embedded / HTTP)
# 查找所有 markdown 文件
results = client.glob("**/*.md", "viking://resources/")
print(f"Found {results['count']} markdown files:")
for uri in results['matches']:
print(f" {uri}")
# 查找所有 Python 文件
results = client.glob("**/*.py", "viking://resources/")
print(f"Found {results['count']} Python files")HTTP API
POST /api/v1/search/glob
curl -X POST http://localhost:1933/api/v1/search/glob \
-H "Content-Type: application/json" \
-H "X-API-Key: your-key" \
-d '{
"pattern": "**/*.md",
"uri": "viking://resources/"
}'CLI
openviking glob "**/*.md" [--uri viking://resources/]响应
{
"status": "ok",
"result": {
"matches": [
"viking://resources/docs/api.md",
"viking://resources/docs/guide.md"
],
"count": 2
},
"time": 0.1
}查询 -> 意图分析 -> 向量搜索 (L0) -> 重排序 (L1) -> 结果
- 意图分析(仅 search):理解查询意图,扩展查询
- 向量搜索:使用 Embedding 查找候选项
- 重排序:使用内容重新评分以提高准确性
- 结果:返回 top-k 上下文
Python SDK (Embedded / HTTP)
results = client.find("authentication")
for ctx in results.resources:
# 从 L0(摘要)开始 - 已包含在 ctx.abstract 中
print(f"Abstract: {ctx.abstract}")
if not ctx.is_leaf:
# 获取 L1(概览)
overview = client.overview(ctx.uri)
print(f"Overview: {overview[:500]}...")
else:
# 加载 L2(内容)
content = client.read(ctx.uri)
print(f"File content: {content}")HTTP API
# 步骤 1:搜索
curl -X POST http://localhost:1933/api/v1/search/find \
-H "Content-Type: application/json" \
-H "X-API-Key: your-key" \
-d '{"query": "authentication"}'
# 步骤 2:读取目录结果的概览
curl -X GET "http://localhost:1933/api/v1/content/overview?uri=viking://resources/docs/auth/" \
-H "X-API-Key: your-key"
# 步骤 3:读取文件结果的完整内容
curl -X GET "http://localhost:1933/api/v1/content/read?uri=viking://resources/docs/auth.md" \
-H "X-API-Key: your-key"Python SDK (Embedded / HTTP)
results = client.find("OAuth implementation")
for ctx in results.resources:
print(f"Found: {ctx.uri}")
# 获取关联资源
relations = client.relations(ctx.uri)
for rel in relations:
print(f" Related: {rel['uri']} - {rel['reason']}")HTTP API
# 获取资源的关联关系
curl -X GET "http://localhost:1933/api/v1/relations?uri=viking://resources/docs/auth/" \
-H "X-API-Key: your-key"# 好 - 具体的查询
results = client.find("OAuth 2.0 authorization code flow implementation")
# 效果较差 - 过于宽泛
results = client.find("auth")# 在相关范围内搜索以获得更好的结果
results = client.find(
"error handling",
target_uri="viking://resources/my-project/"
)# 对于对话式搜索,使用会话
from openviking.message import TextPart
session = client.session()
session.add_message("user", [
TextPart(text="I'm building a login page")
])
# 搜索能够理解上下文
results = client.search("best practices", session=session)