rembrembdocs
← redisoriginal source ↗

Redis search tools

Vector, hybrid, text, and range search tools for Google ADK agents.

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.

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.

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.

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.

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:

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

On this page