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

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.",
)

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.",
)

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

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