Milvus for DSH

Milvus for DSH lets a DSH Web agent inspect and search a Milvus deployment from chat. It supports Local Milvus and Zilliz Cloud, exact entity lookup, scalar queries, BM25 full-text search, and dense+BM25 hybrid retrieval. Natural-language dense search uses a DSH-managed embedding provider; BM25 runs entirely from the collection's Milvus Function schema.

Every Milvus operation exposed by this plugin is read-only. The plugin does not create collections, insert data, change indexes, or delete anything.

Requirements

• DSH Web 0.1.0-rc.7 or later
• Node.js 22.19 or later
• A Milvus HTTP(S) endpoint reachable from the DSH Web host
• Optional: an API key from one of the supported embedding providers for dense and hybrid search

Install

Install the package into the DSH Web profile:

dsh plugin --profile web add @zilliz/dsh-milvus
dsh web

If dsh is not installed globally:

npx --yes @deepseek-ai/[email protected] plugin --profile web add @zilliz/dsh-milvus
npx --yes @deepseek-ai/[email protected] web

After installing or updating the plugin, restart DSH Web and refresh the browser page.

Set up the plugin

Open Settings → Plugins → Milvus for DSH. The setup follows the same order as using Milvus: connect a deployment, choose a collection, then enable only the search capabilities you need.

Connect Milvus

Choose Local Milvus Standalone or Zilliz Cloud, then enter the endpoint and optional database. Local Milvus normally uses http://127.0.0.1:19530 and database default. Zilliz Cloud requires its HTTPS endpoint and token. For an authenticated local deployment, select Add optional authentication and enter its token.

After saving, use Test connection. The card collapses the form into a connection summary so the deployment details no longer compete with collection setup.

The endpoint is resolved from the machine running DSH Web. When Milvus runs in another container or on another host, use an address reachable from the DSH Web host—not a loopback address inside the Milvus container.

The active connection is bound when a new chat starts. Changing it affects new chats; it does not silently switch an existing chat to a different deployment.

Choose a collection

The Collection selector is populated from the connected Milvus database. Pick one collection and DSH inspects its fields, indexes, and Functions on the Host. You do not type collection or schema field names in the normal setup path.

The card then reports four capabilities:

• Scalar query is ready after a successful schema inspection.
• BM25 search is ready when the collection has one valid Milvus BM25 Function route. It does not need an external API key.
• Semantic search is ready after its embedding provider and discovered FloatVector field are mapped.
• Hybrid search becomes ready automatically when both BM25 and semantic search are ready.

Enable semantic search when needed

This step is required only for natural-language dense and hybrid search. BM25 text search does not use an external embedding provider.

1. Select Enable on the Semantic search capability.
2. Choose a provider and model that match the vectors already stored in the collection.
3. Enter the provider API key.
4. Choose one FloatVector field discovered from the selected collection.
5. Select Enable semantic search.

If a provider is already configured, reuse it instead of entering the key again. The field must contain document vectors created with that exact model and vector space. A matching dimension by itself does not prove compatibility; for example, gemini-embedding-001 and gemini-embedding-2 are not interchangeable.

Supported models in the settings UI:

| Provider | Models | Supported output dimensions | | --- | --- | --- | | OpenAI | text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002 | 1–1536; 1–3072; fixed 1536 | | Google Gemini | gemini-embedding-2, gemini-embedding-001 | 128–3072 | | Cohere | embed-v4.0, embed-english-v3.0, embed-english-light-v3.0, embed-multilingual-v3.0, embed-multilingual-light-v3.0 | v4: 256/512/1024/1536; v3 full: 1024; v3 light: 384 | | Voyage AI | voyage-4, voyage-4-large, voyage-4-lite, voyage-code-4, voyage-3.5, voyage-3.5-lite, voyage-code-3, voyage-finance-2, voyage-law-2 | 4/3.5/code-3: 256/512/1024/2048; finance/law: 1024 | | Mistral AI | mistral-embed, codestral-embed | 1024; 1–3072 | | Jina AI | jina-embeddings-v5-text-small, jina-embeddings-v5-text-nano, jina-embeddings-v5-omni-small, jina-embeddings-v5-omni-nano, jina-embeddings-v4, jina-embeddings-v3 | small/v3: 32/64/128/256/512/768/1024; nano: through 768; v4: 128/256/512/1024/2048 | | Together AI | intfloat/multilingual-e5-large-instruct | 1024 |

The first model shown for each provider is the recommended default. Older models appear only when the provider still serves them and they are useful for querying an existing collection created in that model's vector space. The catalog intentionally excludes deprecated models and models that require a dedicated endpoint.

The form disables vector fields whose dimensions the selected model cannot produce. Dimension compatibility is necessary but not sufficient: the stored document vectors must have been generated with that exact provider, model, task mode, and vector space.

Milvus tokens and embedding API keys are saved through write-only DSH Credentials. Their values are never stored in plugin settings, returned to the browser after saving, or added to chat history.

The chat tool accepts natural-language query text; it never asks the user or agent to supply a list of floats. DSH generates the query vector on the host, checks its dimension against the collection schema, and sends it directly to Milvus.

Use Advanced settings only when necessary

Advanced settings is collapsed by default. Open it only to remove a vector mapping, select among multiple schema-proven BM25 routes, or change hybrid ranking.

No collection policy is needed when the collection has one valid BM25 route and the default RRF with k=60 is suitable. When several routes exist, select one of the routes discovered from the schema; the UI does not accept arbitrary text or sparse field names. You can also choose another RRF k or configure named semantic/BM25 weights. A rerank parameter supplied in an individual chat request takes precedence over the saved default.

Collection requirements for search

The plugin inspects each collection before searching it and reports whether dense, BM25, and hybrid retrieval are ready.

Dense search requires a configured binding to a dimensional FloatVector field. BM25 search requires all of these collection facts:

• one analyzer-enabled VarChar or TEXT input field;
• a Milvus BM25 Function mapping that text field to a SparseFloatVector output field; and
• a BM25 index on that sparse field.

When exactly one valid BM25 route exists, the plugin selects it automatically. If a collection has several BM25 text fields, the user must identify the one to search in the request or save an exact collection policy; the agent does not guess. A plain SparseFloatVector field without a BM25 Function is not enough because the plugin cannot infer which external sparse encoder created it.

Hybrid search is ready only when one dense binding and one BM25 route are both ready. It never silently falls back to one route if the other route is missing or fails.

Use it from chat

Start a new chat after activating the desired Milvus profile. A useful first sequence is:

1. “List my Milvus collections.”
2. “Describe the documents collection.”
3. “Get IDs 10 and 11 from documents, returning id, title, and source.”
4. “Query documents where year >= 2025, returning id and title.”
5. “Search documents for documents about vector indexing, returning id, title, and source.”
6. “Use BM25 to search documents for the exact phrase HNSW efConstruction, returning id, title, and source.”
7. “Run hybrid search for how HNSW indexing works, returning id and title.”
8. “Run hybrid search with dense weight 0.7 and BM25 weight 0.3.”

The agent should discover and describe a collection before using its fields. When a collection, field, partition, or filter is ambiguous, it should ask rather than guess.

Available tools

| Tool | Purpose | | --- | --- | | milvus_list_collections | List collections visible to the chat's bound profile. | | milvus_describe_collection | Show schema, indexes, load state, and dense/BM25/hybrid readiness or blockers. | | milvus_get | Retrieve up to 50 entities by exact Int64 or VarChar primary key. | | milvus_query | Run a bounded scalar query with optional filter and partitions. | | milvus_search | Embed natural-language query text and run bounded dense search with optional filter and partitions. | | milvus_text_search | Run bounded natural-language BM25 search through a schema-proven Milvus BM25 Function. | | milvus_hybrid_search | Combine configured dense and BM25 routes, then fuse their rankings with RRF or Weighted rerank. |

Data-retrieval tools return only requested scalar fields. Stored dense/sparse vectors and generated query vectors are never returned to chat. The default result limit is 10 and the maximum is 50.

Dense-search results include the Milvus distance, vector field and metric, the embedding provider/model/dimension, and safe timing metadata. They do not include the API key, raw provider error body, or generated vector.

Hybrid rerank is part of milvus_hybrid_search, not a separate tool:

• no rerank parameter: RRF with k=60;
• explicit RRF: the user may provide another positive k;
• explicit Weighted: the user must provide both denseWeight and bm25Weight, each from 0 to 1 and not both zero.

Named weights prevent route-order mistakes. If the user asks only for “Weighted” without values, the agent asks for both weights instead of guessing. Search results state the effective rerank values and whether they came from the request, a collection policy, or the plugin default.

What happens without an embedding key

Collection listing, description, exact get, scalar query, and schema-compatible BM25 search continue to work. Dense and hybrid search are blocked, with a specific configuration result:

• no collection binding: retrieval_binding_absent;
• binding refers to a missing provider profile: embedding_profile_absent;
• API key is missing or unavailable: embedding_credential_unavailable.

The plugin does not fall back to the chat model, another provider, a guessed vector, or a scalar query.

Privacy and safety

• Dense-search query text is sent from the DSH host to the embedding provider selected in the binding.
• The generated vector remains in host memory and is sent only to Milvus.
• Milvus tokens and provider keys stay behind the DSH Credentials boundary.
• Output fields must exist in the inspected schema and must be scalar.
• Filters may reference only scalar fields discovered from that collection.
• Exact route fields prevent a saved BM25 plan from silently switching routes. A saved schema fingerprint, when present, additionally blocks the plan after any retrieval-schema change until it is reviewed.
• The plugin exposes no mutation, schema, index, database, user, role, or administrative operation.
• External sparse encoders, model/cross-encoder rerank, ingestion, custom embedding endpoints, and manual vector input are not currently supported.

Troubleshooting

The settings card is missing

Confirm that the package is installed in the web profile, restart DSH Web, and refresh the page:

dsh plugin --profile web why @zilliz/dsh-milvus

A tool says no Milvus profile is available

Create a Milvus profile, make it active for new chats, and start a new chat. Existing chats retain their original session binding.

The Milvus connection test fails

Check host-to-Milvus network reachability, endpoint protocol and port, database name, and token permissions. Local Milvus normally exposes HTTP on port 19530.

The embedding provider test fails

Check that the API key is configured and allowed to use the selected model. Also check provider rate limits and outbound network access from the DSH Web host.

Dense search reports a dimension mismatch

Describe the collection and compare the bound field's dimension with the model used during ingestion. Correct the binding or re-ingest with the intended model; do not choose a different model only because it can produce the same dimension.

BM25 or hybrid search is blocked

Describe the collection and read its retrieval capability section. Common blockers are bm25_route_absent, bm25_route_ambiguous, sparse_encoder_binding_absent, retrieval_plan_stale, and retrieval_binding_absent. Fix or re-save the collection policy, Function/index, or dense binding; hybrid search does not degrade to a single route.

Update or remove

Update the package, restart DSH Web, and refresh the browser:

dsh plugin --profile web update @zilliz/dsh-milvus
dsh web

Remove it from the Web profile with:

dsh plugin --profile web remove @zilliz/dsh-milvus

Removing the plugin does not change or delete Milvus data. Review stored DSH settings and credential records separately if they are no longer needed.

Development

Install dependencies and run the local checks:

npm ci
npm test
npm pack --dry-run

Load a source checkout into DSH Web from this repository and restart DSH Web:

dsh plugin --profile web add "$PWD"

Read-only integration probes run only when an endpoint is supplied:

MILVUS_TEST_ENDPOINT=http://127.0.0.1:19530 npm run test:integration
MILVUS_TEST_ENDPOINT=http://127.0.0.1:19530 npm run test:integration:connection

Provider API smoke tests are separately network-gated. Set any supported key in the environment, then opt in explicitly; providers without a key are skipped, and neither keys nor returned vectors are printed:

EMBEDDING_TEST_ALLOW_NETWORK=1 npm run test:integration:embeddings

The mutation integration test creates, searches, and removes a disposable fixture. Run it only against a non-production deployment after explicit opt-in:

MILVUS_TEST_ENDPOINT=http://127.0.0.1:19530 \
MILVUS_TEST_ALLOW_MUTATION=1 \
npm run test:integration:mutation

To verify the complete provider-to-Milvus path, also provide a Gemini API key. This test embeds a query, searches a disposable 128-dimensional collection through milvus_search, and removes the fixture:

MILVUS_TEST_ENDPOINT=http://127.0.0.1:19530 \
MILVUS_TEST_ALLOW_MUTATION=1 \
GEMINI_API_KEY=... \
npm run test:integration:retrieval

To verify BM25 and both hybrid rerank modes, use a non-production deployment. The test first searches the existing mfs_scale_2000 BM25 collection by default, then creates and removes one disposable hybrid collection. Override the existing collection name with MILVUS_TEST_BM25_COLLECTION when needed:

MILVUS_TEST_ENDPOINT=http://127.0.0.1:19530 \
MILVUS_TEST_ALLOW_MUTATION=1 \
npm run test:integration:hybrid

License

Apache-2.0. See LICENSE.