# Redis search tools

```json metadata
{
  "title": "Redis search tools",
  "description": "Vector, hybrid, text, and range search tools for Google ADK agents.",
  "categories": ["docs","integrate","oss","rs","rc"],
  "group": "ai",
  "tableOfContents": {"sections":[{"id":"overview","title":"Overview"},{"id":"vector-search","title":"Vector search"},{"id":"hybrid-search","title":"Hybrid search"},{"id":"text-search","title":"Text search"},{"id":"range-search","title":"Range search"},{"id":"multi-tool-agent","title":"Multi-tool agent"},{"id":"more-info","title":"More info"}]}

,
  "codeExamples": []
}
```
adk-redis provides four search tools that wrap [RedisVL]() query types into ADK-compatible tools. The LLM sees each tool as a callable function with a `query` parameter and gets back structured results.

## Overview

| Tool | Query type | Use case |
|------|-----------|----------|
| `RedisVectorSearchTool` | KNN vector similarity | Conceptual/semantic queries |
| `RedisHybridSearchTool` | Vector + BM25 | Queries with specific terms + concepts |
| `RedisTextSearchTool` | BM25 keyword | Exact terms, error messages, IDs |
| `RedisRangeSearchTool` | Distance threshold | Exhaustive retrieval within a radius |

## Vector search

Embeds the query using a vectorizer and performs K-nearest-neighbor search.

```python
from redisvl.index import SearchIndex
from redisvl.utils.vectorize import HFTextVectorizer
from adk_redis import RedisVectorSearchTool, RedisVectorQueryConfig

vectorizer = HFTextVectorizer(model="redis/langcache-embed-v2")
index = SearchIndex.from_existing("products", redis_url="redis://localhost:6379")

vector_tool = RedisVectorSearchTool(
    index=index,
    vectorizer=vectorizer,
    config=RedisVectorQueryConfig(num_results=5),
    return_fields=["name", "description", "price"],
    name="search_products",
    description="Find products by semantic similarity.",
)
```

## Hybrid search

Combines vector similarity with BM25 keyword matching. Auto-detects whether your Redis server supports native hybrid search (Redis 8.4+ with RedisVL 0.13+). Falls back to client-side aggregation if not.

```python
from adk_redis import RedisHybridSearchTool, RedisHybridQueryConfig

hybrid_tool = RedisHybridSearchTool(
    index=index,
    vectorizer=vectorizer,
    config=RedisHybridQueryConfig(
        text_field_name="content",
        combination_method="LINEAR",
        linear_alpha=0.7,
    ),
    name="search_docs",
    description="Search documents using semantic and keyword matching.",
)
```

## Text search

Pure BM25 keyword search. No embeddings or vectorizer needed.

```python
from adk_redis import RedisTextSearchTool, RedisTextQueryConfig

text_tool = RedisTextSearchTool(
    index=index,
    config=RedisTextQueryConfig(
        text_field_name="content",
        text_scorer="BM25STD",
    ),
    return_fields=["title", "content"],
    name="keyword_search",
    description="Search for exact terms and phrases.",
)
```

## Range search

Returns all documents within a distance threshold instead of top-K. Useful for exhaustive retrieval.

```python
from adk_redis import RedisRangeSearchTool, RedisRangeQueryConfig

range_tool = RedisRangeSearchTool(
    index=index,
    vectorizer=vectorizer,
    config=RedisRangeQueryConfig(distance_threshold=0.5),
    return_fields=["title", "content"],
    name="range_search",
    description="Find all documents within a semantic distance threshold.",
)
```

## Multi-tool agent

Wire multiple search tools into a single agent and let the LLM choose the right one:

```python
from google.adk import Agent

agent = Agent(
    model="gemini-2.5-flash",
    name="search_agent",
    instruction=(
        "You have three search tools. Use search_products for conceptual "
        "queries, keyword_search for exact terms, range_search for "
        "exhaustive retrieval."
    ),
    tools=[vector_tool, text_tool, range_tool],
)
```

The `name` and `description` on each tool matter: the LLM reads them to decide which tool to call and when. Specific descriptions like "Find products by semantic similarity" work better than generic ones like "search documents".

## More info

- [redis_search_tools example](https://github.com/redis-developer/adk-redis/tree/main/examples/redis_search_tools): All four search tools with a product catalog
- [RedisVL documentation]()