RAG Examples

Two library-mode walk-throughs of agentscope.rag — no FastAPI service, no manager, no message bus. Each script wires the building blocks (parser, chunker, embedding model, vector store, KnowledgeBase handle) by hand so the data flow is visible end-to-end.

Both examples use an in-memory Qdrant store (location=":memory:") and the DashScope text-embedding-v4 model, so no external services are required. The sections below show how to swap in Milvus Lite, MongoDB, or Elasticsearch instead; those backends need additional setup.

Install

# From PyPI
uv pip install "agentscope[rag]"

# Or from source (repo root)
uv pip install -e ".[rag]"

Milvus Lite (local persistence)

To use a local persistent Milvus Lite vector store instead of the in-memory Qdrant store, install the optional extra:

uv pip install "agentscope[vdb-milvus]"
# Or from source (repo root)
uv pip install -e ".[vdb-milvus]"

Then replace the vector store construction in index_and_search.py and/or integrate_with_agent.py:

from agentscope.rag import MilvusLiteStore

store = MilvusLiteStore(uri="./rag_demo.db")

MongoDB Vector Search

To use MongoDB as the vector backend instead of the in-memory Qdrant store — useful when your team already runs MongoDB as the primary data store and wants to avoid maintaining a separate vector database — install the optional extra:

uv pip install "agentscope[vdb-mongodb]"
# Or from source (repo root)
uv pip install -e ".[vdb-mongodb]"

Prerequisites

• A MongoDB deployment with Vector Search enabled:
  • MongoDB Atlas — create a cluster and enable Vector Search on the target database; or
  • Self-hosted — MongoDB 7.0+ replica set with Vector Search enabled.
• A connection URI available in the environment (do not hard-code credentials):

export MONGODB_URI="mongodb+srv://user:[email protected]/?retryWrites=true&w=majority"
# Self-hosted example:
# export MONGODB_URI="mongodb://localhost:27017"

Then replace the vector store construction in index_and_search.py and/or integrate_with_agent.py:

import os

from agentscope.rag import MongoDBStore

store = MongoDBStore(
    uri=os.environ["MONGODB_URI"],
    database="agentscope_rag",
    # Declare every field you plan to filter on in search().
    # Required for metadata_filter; defaults to ["document_id"] only.
    filter_fields=[
        "document_id",
        # "chunk.metadata.tenant_id",  # uncomment if you use metadata_filter
    ],
)

# MongoDBStore is also an async context manager — same as QdrantStore.
async with store:
    knowledge = KnowledgeBase(
        name="demo-kb",
        description="A toy corpus on cats and AgentScope.",
        embedding_model=embedding_model,
        vector_store=store,
        collection=COLLECTION,
    )
    ...

Notes

• The examples use DashScope text-embedding-v4 with dimensions=1024. MongoDBStore.create_collection is called automatically on the first index operation with that dimension — keep the embedding model and index dimensions aligned.
• Unlike Qdrant :memory: or Milvus Lite (local .db file), MongoDB is an external service; you must have a reachable cluster before running the scripts.
• If search(..., metadata_filter={...}) returns no results or errors, ensure each metadata key is listed in filter_fields as chunk.metadata.<key> when constructing MongoDBStore.
• For the full FastAPI RAG service, pass the same MongoDBStore instance to CollectionPerKbManager(storage=..., vector_store=...) in examples/agent_service/main.py (the default there uses in-memory Qdrant for zero-setup demos).

Elasticsearch

To use Elasticsearch as the vector backend, install the optional async client extra:

uv pip install "agentscope[vdb-elasticsearch]"
# Or from source (repo root)
uv pip install -e ".[vdb-elasticsearch]"

Prerequisites

• A reachable Elasticsearch 8.12+ deployment. The AgentScope extra uses the official Elasticsearch 8.x Python client, which is compatible with Elasticsearch 8.x and 9.x.
• An Elasticsearch URL available in the environment. Keep credentials and TLS settings outside source code.

For local development, the following starts a single-node Elasticsearch instance with security disabled:

docker run --rm --name agentscope-elasticsearch \
  -p 9200:9200 \
  -e discovery.type=single-node \
  -e xpack.security.enabled=false \
  docker.elastic.co/elasticsearch/elasticsearch:8.19.3

export ELASTICSEARCH_URL="http://localhost:9200"

Do not disable security in shared or production environments. For an authenticated HTTPS deployment, also export credentials and the CA certificate path, then pass them through client_kwargs as shown below.

Replace the vector store construction in index_and_search.py and/or integrate_with_agent.py:

import os

from agentscope.rag import ElasticsearchStore

client_kwargs = {}
if os.getenv("ELASTICSEARCH_API_KEY"):
    client_kwargs["api_key"] = os.environ["ELASTICSEARCH_API_KEY"]
if os.getenv("ELASTICSEARCH_CA_CERTS"):
    client_kwargs["ca_certs"] = os.environ["ELASTICSEARCH_CA_CERTS"]

store = ElasticsearchStore(
    hosts=os.environ["ELASTICSEARCH_URL"],
    # Number of HNSW candidates considered per shard. Higher values can
    # improve recall at the cost of additional search work.
    num_candidates=100,
    # Use False for higher write throughput during large imports when
    # immediate search visibility is not required.
    refresh="wait_for",
    client_kwargs=client_kwargs,
)

# ElasticsearchStore implements the same async context-manager and
# VectorStoreBase contracts as QdrantStore.
async with store:
    knowledge = KnowledgeBase(
        name="demo-kb",
        description="A toy corpus on cats and AgentScope.",
        embedding_model=embedding_model,
        vector_store=store,
        collection=COLLECTION,
    )
    ...

For username/password authentication, configure basic_auth instead of an API key:

store = ElasticsearchStore(
    hosts=os.environ["ELASTICSEARCH_URL"],
    client_kwargs={
        "basic_auth": (
            os.environ["ELASTICSEARCH_USERNAME"],
            os.environ["ELASTICSEARCH_PASSWORD"],
        ),
        "ca_certs": os.environ["ELASTICSEARCH_CA_CERTS"],
    },
)

Notes

• Each knowledge base collection maps to a separate Elasticsearch index.
• Collections use an indexed dense_vector field with cosine similarity. Keep the embedding model dimensions unchanged after an index is created.
• Chunk metadata is stored in an Elasticsearch flattened field. Any top-level metadata key can therefore be used with search(..., metadata_filter={"tenant_id": "bank-a"}); no filter-field declaration is required.
• Writes use a stable ID derived from document_id and chunk_index, so retrying the same indexing operation replaces existing chunks instead of creating duplicates.
• Writes default to refresh="wait_for", making indexed chunks searchable before the operation returns. For large imports, set refresh=False and allow Elasticsearch's normal refresh interval (or an explicit index refresh) to make changes visible with higher throughput.
• Elasticsearch transforms cosine similarity into a positive _score. ElasticsearchStore converts it back to raw cosine similarity so score thresholds behave consistently with the other vector backends.
• For service mode, construct CollectionPerKbManager(storage=storage, vector_store=store) and pass it to create_app(knowledge_base_manager=...).

Choosing a vector backend

integrate_with_agent.py additionally uses DashScopeChatModel, which is already in the base agentscope dependencies.

Run

export DASHSCOPE_API_KEY=sk-...

python examples/rag/index_and_search.py
python examples/rag/integrate_with_agent.py

When using MongoDB, also export MONGODB_URI before running. When using Elasticsearch, export ELASTICSEARCH_URL and any required authentication or TLS environment variables.

Service mode

The two scripts above are library-mode — you drive the pipeline yourself in a single process. For the full service-mode experience (FastAPI endpoints for knowledge base CRUD, document upload, indexing workers, and search), see examples/agent_service for the backend and examples/web_ui for the chat-style UI.