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"""Use dynavec as a Strands Agents retrieval tool.

pip install "dynavec[sentence-transformers]" strands-agents

python examples/strands_retriever.py

"""

from strands import Agent, tool

from dynavec import Document, Dynavec, DynavecConfig

from dynavec.embeddings import SentenceTransformerEmbedder

from dynavec.integrations.tools import make_retriever_fn

embedder = SentenceTransformerEmbedder(model="all-MiniLM-L6-v2")

cfg = DynavecConfig(

vector_bucket="dynavec-demo",

index="strands-rag",

table="dynavec_strands_rag",

dimension=embedder.dimension,

region="us-east-1",

auto_provision=True,

)

db = Dynavec(cfg, embedder=embedder)

db.upsert(

[

Document(id="strands-1", text="Strands Agents builds agents from models and tools."),

Document(

id="strands-2", text="Retrieval-augmented generation grounds answers in documents."

),

Document(id="strands-3", text="dynavec keeps vector data inside your own AWS account."),

],

namespace="kb",

)

retrieve = make_retriever_fn(db, namespace="kb", top_k=2)

@tool

def search_knowledge_base(query: str) -> str:

"""Search the dynavec knowledge base for relevant passages.

Args:

query: Natural-language question to search for.

"""

return retrieve(query)

agent = Agent(tools=[search_knowledge_base])

response = agent("Where is dynavec vector data stored?")

print(response)