Retrieval

OpenViking provides multiple retrieval methods, including simple vector similarity search, intelligent retrieval with session context, regex pattern matching, and file pattern matching.

find vs search

Aspect	find	search
Intent Analysis	No	Yes
Session Context	No	Yes
Query Expansion	No	Yes
Default Limit	10	10
Use Case	Simple queries	Conversational search

Retrieval Pipeline

The core retrieval pipeline is as follows:

Query → Intent Analysis (search only) → Vector Search (L0) → Rerank (L1) → Results

Intent Analysis (search only): Understand query intent, expand queries
Vector Search: Find candidates using embeddings
Rerank: Re-score using content for better accuracy
Results: Return top-k contexts

API Reference

find()

Basic vector similarity search without session context.

1. API Implementation Introduction

The find() method performs pure vector similarity search for simple query scenarios. It uses hierarchical retrieval to search at the L0 summary level first, then matches in detail at L1/L2 levels.

Processing Pipeline:

Convert query text to vector
Perform global vector search within specified target URI
Use hierarchical retrieval strategy to recursively search relevant directories and files
Optional: Use rerank model to optimize result ordering
Return matched context list

Code Entry Points:

openviking_cli/client/sync_http.py:SyncHTTPClient.find() - Python SDK entry (HTTP)
openviking/retrieve/hierarchical_retriever.py:HierarchicalRetriever.retrieve() - Core retrieval implementation
openviking/server/routers/search.py:find() - HTTP router
crates/ov_cli/src/commands/search.rs:find() - Rust CLI command

2. Interface and Parameter Description

Parameters

Parameter	Type	Required	Default	Description
query	str	Yes	-	Search query string
target_uri	str \| List[str]	No	""	Limit search to specific URI prefix
limit	int	No	10	Maximum number of results
node_limit	int	No	None	Optional HTTP alias that overrides `limit` when provided
score_threshold	float	No	None	Minimum relevance score threshold
filter	Dict	No	None	Metadata filter
since	str	No	None	Lower time bound, accepts `2h` or ISO 8601 / `YYYY-MM-DD`. Timezone-less values are interpreted as UTC. CLI `--after` maps to this field
until	str	No	None	Upper time bound, accepts `30m` or ISO 8601 / `YYYY-MM-DD`. Timezone-less values are interpreted as UTC. CLI `--before` maps to this field
time_field	"updated_at" \| "created_at"	No	"updated_at"	Metadata time field used by `since` / `until`
include_provenance	bool	No	False	Include provenance/query-plan details in serialized result
telemetry	bool \| object	No	False	Attach telemetry data to response

FindResult Structure

python

class FindResult:
    memories: List[MatchedContext]   # Memory contexts
    resources: List[MatchedContext]  # Resource contexts
    skills: List[MatchedContext]     # Skill contexts
    query_plan: Optional[QueryPlan]  # Query plan (search only)
    query_results: Optional[List[QueryResult]]  # Detailed results
    total: int                       # Total count (auto-calculated)

MatchedContext Structure

python

class MatchedContext:
    uri: str                         # Viking URI
    context_type: ContextType        # "resource", "memory", or "skill"
    level: int                       # Tier (0=L0, 1=L1, 2=L2)
    abstract: str                    # L0 content
    overview: Optional[str]          # L1 overview (optional for non-leaf nodes)
    category: str                    # Category
    score: float                     # Relevance score (0-1)
    match_reason: str                # Why this matched
    relations: List[RelatedContext]  # Related contexts

3. Usage Examples

HTTP API

POST /api/v1/search/find

bash

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
    }'

Search with Target URI and Time Filter

bash

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",
        "since": "7d",
        "time_field": "created_at"
    }'

Python SDK

python

import openviking as ov

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

# Basic search
results = client.find("how to authenticate users")

# Search with filter and time range
recent_emails = client.find(
    "invoice",
    target_uri="viking://resources/email",
    since="7d",
    time_field="created_at",
)

# Iterate through results
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("---")

Search with Target URI Limitation

python

# Search only in resources
results = client.find(
    "authentication",
    target_uri="viking://resources"
)

# Search only in user memories
results = client.find(
    "preferences",
    target_uri="viking://user/memories"
)

# Search only in skills
results = client.find(
    "web search",
    target_uri="viking://agent/skills"
)

# Search in specific project
results = client.find(
    "API endpoints",
    target_uri="viking://resources/my-project"
)

CLI

bash

# Basic search
openviking find "how to authenticate users"

# Specify URI scope
openviking find "how to authenticate users" --uri "viking://resources"

# With time filter
openviking find "invoice" --after 7d

# With limit
openviking find "how to authenticate users" --limit 20

Response Example

json

{
    "status": "ok",
    "result": {
        "memories": [],
        "resources": [
            {
                "context_type": "resource",
                "uri": "viking://resources/01-overview/API_Overview/Documentation_Reading_P_2c6ae38b.md",
                "level": 2,
                "score": 0.12808319406977778,
                "category": "",
                "match_reason": "",
                "relations": [],
                "abstract": "This document is an API documentation reading plan that outlines the structure of subsequent API reference materials organized by functional module. Main sections or topics covered include resource management API, search API, file system operations, ses...",
                "overview": null
            },
            {
                "context_type": "resource",
                "uri": "viking://resources/01-overview/API_Overview/API_Endpoints/.abstract.md",
                "level": 0,
                "score": 0.12054087276495282,
                "category": "",
                "match_reason": "",
                "relations": [],
                "abstract": "This directory contains structured API reference documentation for the OpenViking platform, compiling detailed HTTP endpoint specifications for core and extended platform capabilities. It covers functional modules including system health checks, semanti...",
                "overview": null
            }
        ],
        "skills": [],
        "total": 2
    }
}

search()

Intelligent retrieval with session context and intent analysis.

1. API Implementation Introduction

The search() method adds session context understanding and intent analysis capability on top of find(). It better understands user query intent based on conversation history, performs query expansion, and provides more relevant search results.

Processing Pipeline:

Load session context (if session_id is provided)
Analyze query intent, understand actual needs combined with conversation history
Expand queries to improve recall rate
Execute same hierarchical retrieval pipeline as find()
Return search results with query plan

Code Entry Points:

openviking_cli/client/sync_http.py:SyncHTTPClient.search() - Python SDK entry (HTTP)
openviking/retrieve/hierarchical_retriever.py:HierarchicalRetriever.retrieve() - Core retrieval implementation
openviking/server/routers/search.py:search() - HTTP router
crates/ov_cli/src/commands/search.rs:search() - Rust CLI command

2. Interface and Parameter Description

Parameters

Parameter	Type	Required	Default	Description
query	str	Yes	-	Search query string
target_uri	str \| List[str]	No	""	Limit search to specific URI prefix
session	Session	No	None	Session for context-aware search (SDK)
session_id	str	No	None	Session ID for context-aware search (HTTP)
limit	int	No	10	Maximum number of results
node_limit	int	No	None	Optional HTTP alias that overrides `limit` when provided
score_threshold	float	No	None	Minimum relevance score threshold
filter	Dict	No	None	Metadata filter
since	str	No	None	Lower time bound, accepts `2h` or ISO 8601 / `YYYY-MM-DD`. Timezone-less values are interpreted as UTC. CLI `--after` maps to this field
until	str	No	None	Upper time bound, accepts `30m` or ISO 8601 / `YYYY-MM-DD`. Timezone-less values are interpreted as UTC. CLI `--before` maps to this field
time_field	"updated_at" \| "created_at"	No	"updated_at"	Metadata time field used by `since` / `until`
include_provenance	bool	No	False	Include provenance/query-plan details in serialized result
telemetry	bool \| object	No	False	Attach telemetry data to response

3. Usage Examples

HTTP API

POST /api/v1/search/search

bash

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",
        "since": "2h",
        "time_field": "updated_at",
        "limit": 10
    }'

Search without Session (Still Performs Intent Analysis)

bash

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"
    }'

Python SDK

python

import openviking as ov
from openviking.message import TextPart

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

# Create session with conversation context
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.")
])

# Search understands conversation context
results = client.search(
    "best practices",
    session=session,
    since="2h"
)

for ctx in results.resources:
    print(f"Found: {ctx.uri}")
    print(f"Abstract: {ctx.abstract[:200]}...")

Search without Session

python

# search can also be used without session
# It still performs intent analysis on the query
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})")

CLI

bash

# Search with session ID
openviking search "best practices" --session-id abc123

# Search with time filter
openviking search "watch vs scheduled" --after 2026-03-15 --before 2026-03-20

# Search without session (still performs intent analysis)
openviking search "how to implement OAuth 2.0 authorization code flow"

Response Example

json

{
    "status": "ok",
    "result": {
        "memories": [],
        "resources": [
            {
                "context_type": "resource",
                "uri": "viking://resources/docs/oauth-best-practices",
                "level": 1,
                "score": 0.95,
                "category": "",
                "match_reason": "Context-aware match: OAuth login best practices",
                "relations": [],
                "abstract": "OAuth 2.0 best practices for login pages...",
                "overview": "This guide covers OAuth 2.0 best practices including secure token handling, redirect URI validation, and state parameter usage..."
            }
        ],
        "skills": [],
        "query_plan": {
            "reasoning": "User is asking about OAuth implementation best practices, expanding to related security topics",
            "queries": [
                {
                    "query": "OAuth 2.0 best practices",
                    "context_type": "resource",
                    "intent": "Find OAuth 2.0 implementation guidelines",
                    "priority": 3
                },
                {
                    "query": "login page security",
                    "context_type": "resource",
                    "intent": "Find login page security recommendations",
                    "priority": 2
                }
            ]
        },
        "total": 1
    }
}

grep()

Search content by pattern (regex).

1. API Implementation Introduction

The grep() method performs regex pattern matching search in the file system, used to find files and content lines containing specific patterns. Unlike semantic search, grep is exact pattern matching.

Processing Pipeline:

Traverse file system starting from specified URI
Perform regex matching on each file content
Collect matching lines and position information
Return matching results list

Code Entry Points:

openviking_cli/client/sync_http.py:SyncHTTPClient.grep() - Python SDK entry (HTTP)
openviking/server/routers/search.py:grep() - HTTP router
crates/ov_cli/src/commands/search.rs:grep() - Rust CLI command

2. Interface and Parameter Description

Parameters

Parameter	Type	Required	Default	Description
uri	str	Yes	-	Viking URI to search in
pattern	str	Yes	-	Search pattern (regex)
case_insensitive	bool	No	False	Ignore case
exclude_uri	str	No	None	URI prefix to exclude from search
node_limit	int	No	None	Maximum number of nodes to search
level_limit	int	No	5	Maximum directory depth to traverse

3. Usage Examples

HTTP API

POST /api/v1/search/grep

bash

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
    }'

Python SDK

python

import openviking as ov

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

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']}")

CLI

bash

# Basic search
openviking grep viking://resources "authentication"

# Ignore case
openviking grep viking://resources "authentication" --ignore-case

# Specify depth limit
openviking grep viking://resources "TODO" --level-limit 3

Response Example

json

{
    "status": "ok",
    "result": {
        "matches": [
            {
                "uri": "viking://resources/docs/auth.md",
                "line": 15,
                "content": "User authentication is handled by..."
            }
        ],
        "count": 1
    },
    "time": 0.1
}

glob()

Match files by glob pattern.

1. API Implementation Introduction

The glob() method uses file wildcard pattern matching URIs, similar to Unix shell glob functionality. Used to find files and directories by name patterns.

Supported Pattern Syntax:

* matches any character (except path separator)
** recursively matches any directory
? matches single character
[] matches character range

Code Entry Points:

openviking_cli/client/sync_http.py:SyncHTTPClient.glob() - Python SDK entry (HTTP)
openviking/server/routers/search.py:glob() - HTTP router
crates/ov_cli/src/commands/search.rs:glob() - Rust CLI command

2. Interface and Parameter Description

Parameters

Parameter	Type	Required	Default	Description
pattern	str	Yes	-	Glob pattern (e.g., `*/.md`)
uri	str	No	"viking://"	Starting URI
node_limit	int	No	None	Maximum number of matches to return

3. Usage Examples

HTTP API

POST /api/v1/search/glob

bash

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"
    }'

Python SDK

python

import openviking as ov

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

# Find all markdown files
results = client.glob("**/*.md", "viking://resources")
print(f"Found {results['count']} markdown files:")
for uri in results['matches']:
    print(f"  {uri}")

# Find all Python files
results = client.glob("**/*.py", "viking://resources")
print(f"Found {results['count']} Python files")

CLI

bash

# Find all markdown files
openviking glob "**/*.md" --uri viking://resources

# Find all Python files
openviking glob "**/*.py"

Response Example

json

{
    "status": "ok",
    "result": {
        "matches": [
            "viking://resources/docs/api.md",
            "viking://resources/docs/guide.md"
        ],
        "count": 2
    },
    "time": 0.1
}

Working with Results

Read Content Progressively

Retrieval results usually only contain L0 summaries, you can progressively load more detailed content as needed.

Python SDK

python

import openviking as ov

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

results = client.find("authentication")

for ctx in results.resources:
    # Start with L0 (abstract) - already in ctx.abstract
    print(f"Abstract: {ctx.abstract}")

    if ctx.level < 2:
        # Get L1 (overview) for directories
        overview = client.overview(ctx.uri)
        print(f"Overview: {overview[:500]}...")
    else:
        # Load L2 (content) for files
        content = client.read(ctx.uri)
        print(f"File content: {content}")

HTTP API

bash

# Step 1: Search
curl -X POST http://localhost:1933/api/v1/search/find \
    -H "Content-Type: application/json" \
    -H "X-API-Key: your-key" \
    -d '{"query": "authentication"}'

# Step 2: Read overview for directory result
curl -X GET "http://localhost:1933/api/v1/content/overview?uri=viking://resources/docs/auth" \
    -H "X-API-Key: your-key"

# Step 3: Read full content for file result
curl -X GET "http://localhost:1933/api/v1/content/read?uri=viking://resources/docs/auth.md" \
    -H "X-API-Key: your-key"

Python SDK

python

import openviking as ov

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

results = client.find("OAuth implementation")

for ctx in results.resources:
    print(f"Found: {ctx.uri}")

    # Get related resources
    relations = client.relations(ctx.uri)
    for rel in relations:
        print(f"  Related: {rel['uri']} - {rel['reason']}")

HTTP API

bash

# Get relations for resource
curl -X GET "http://localhost:1933/api/v1/relations?uri=viking://resources/docs/auth" \
    -H "X-API-Key: your-key"

Best Practices

Use Specific Queries

python

import openviking as ov

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

# Good - specific query
results = client.find("OAuth 2.0 authorization code flow implementation")

# Less effective - too broad
results = client.find("auth")

Scope Your Searches

python

import openviking as ov

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

# Search in relevant scope for better results
results = client.find(
    "error handling",
    target_uri="viking://resources/my-project"
)

Use Session Context for Conversations

python

import openviking as ov
from openviking.message import TextPart

client = ov.SyncHTTPClient(url="http://localhost:1933", api_key="your-key")
client.initialize()

# For conversational search, use session
session = client.session()
session.add_message("user", [
    TextPart(text="I'm building a login page")
])

# Search understands context
results = client.search("best practices", session=session)

Resources - Resource management
Sessions - Session context
Context Layers - L0/L1/L2

Retrieval

Retrieval

find vs search

Retrieval Pipeline

API Reference

find()

1. API Implementation Introduction

2. Interface and Parameter Description

3. Usage Examples

search()

1. API Implementation Introduction

2. Interface and Parameter Description

3. Usage Examples

grep()

1. API Implementation Introduction

2. Interface and Parameter Description

3. Usage Examples

glob()

1. API Implementation Introduction

2. Interface and Parameter Description

3. Usage Examples

Working with Results

Read Content Progressively

Get Related Resources

Best Practices

Use Specific Queries

Scope Your Searches

Use Session Context for Conversations

Related Documentation