Add RedisVL Search Tools for Knowledge Base Retrieval

[nkanu17]

ADK has retrieval tools for Google Cloud (Vertex AI Search, Discovery Engine), but no self-hosted option for developers who need full control over their infrastructure. RAG is a fundamental pattern for building useful agents, these tools let developers add vector search, keyword search, and hybrid retrieval to their existing Redis infrastructure. RedisVL is the official Redis Vector Search library that's also well adopted.

Moreover, the repo has RedisSessionService for session persistence, but there are no tools for agents to search Redis-based knowledge bases without building it themselves.

Solution

Add redisvl as an optional dependency with four search tools that wrap RedisVL's query capabilities as ADK BaseTool implementations.

pyproject.toml change:

[project.optional-dependencies]
redis-vl = [
    "redisvl>=0.13.0",
    "nltk>=3.8.0",
    "sentence-transformers>=2.2.0",
]

Installation:

# Existing functionality unchanged
pip install google-adk-community

# Opt-in to vector search capabilities
pip install google-adk-community[redis-vl]

This aligns with the community repository's stated philosophy:

"This approach allows the core ADK to remain stable and lightweight, while giving the community the freedom to build and share powerful extensions."

Tools Proposed

Tool	Search Type	Use Case
RedisVectorSearchTool	KNN vector similarity	Semantic/conceptual queries
RedisTextSearchTool	BM25 full-text	Exact terms, acronyms, API names
RedisHybridSearchTool	Vector + BM25 combined	Best of both worlds
RedisRangeSearchTool	Distance threshold	Exhaustive retrieval, quality filtering

Developer Experience

from google.adk import Agent
from google.adk_community.tools.redis import RedisVectorSearchTool
from redisvl.index import SearchIndex
from redisvl.utils.vectorize import HFTextVectorizer

index = SearchIndex.from_yaml("schema.yaml")
index.connect("redis://localhost:6379")
vectorizer = HFTextVectorizer(model="redis/langcache-embed-v2")

tool = RedisVectorSearchTool(
    index=index,
    vectorizer=vectorizer,
    num_results=5,
    return_fields=["title", "content", "url"],
)

agent = Agent(model="gemini-2.0-flash", tools=[tool])

Common Features Across All Tools

• Filtering: Tag, numeric, and geo filters via filter_expression
• Field selection: Control returned fields via return_fields
• Async support: Works with both SearchIndex and AsyncSearchIndex
• Score normalization: Convert distances to 0-1 similarity via normalize_vector_distance=True
• Full parameter exposure: All RedisVL query parameters are configurable

Describe alternatives you've considered

1. Implement vector search with raw Redis commands — Would duplicate existing, maintained code in RedisVL. Users would get a degraded experience compared to using RedisVL directly.

2. Require users to install RedisVL separately — Creates friction and doesn't provide ADK-native abstractions like BaseTool wrappers with proper function declarations for LLMs.

Why RedisVL?

RedisVL is the official Redis vector library (~50MB footprint). It provides:

• Schema-driven index management
• Multiple query types (vector, text, hybrid, range)
• Built-in vectorizers
• Both sync and async APIs