docling-pipelines

Milvus Vector Database Integration

Overview

The Milvus adapter provides vector database capabilities for the docpipe project, supporting Milvus Lite (embedded local .db file), standalone Milvus deployments, and IBM watsonx.data (wx.data) managed Milvus instances. This integration follows the hexagonal architecture pattern, implementing the VectorStorePort interface for seamless integration with the VectorDBOperator.

Features

• Milvus Lite (container-free): Run locally without Docker, Podman, or Kubernetes — data persists to a local .db file
• Dual Deployment Support: Works with standalone Milvus and wx.data in addition to Lite
• Multiple Index Types: HNSW, IVF_FLAT, IVF_SQ8, IVF_PQ, FLAT, DISKANN, AUTOINDEX, SPARSE_INVERTED_INDEX, SPARSE_WAND
• Flexible Similarity Metrics: L2, Inner Product (IP), COSINE, BM25 (only for sparse vectors)
• Dense and Sparse Vectors: Support for both dense embeddings and BM25 sparse vectors
• Batch Processing: Efficient bulk operations with size-aware batching
• Automatic Schema Management: Dynamic collection creation based on feature mappings
• Authentication Options: Token-based (wx.data), username/password (standalone), or local path (Lite)

Architecture

Components

MilvusAdapter (VectorStorePort)
├── MilvusClient - Connection management
├── MilvusIndexManager - Collection/schema operations
└── MilvusBatchProcessor - Bulk data operations

Key Classes

1. MilvusClient (adapters/outbound/milvus/client.py)
   • Manages connections to Milvus
   • Always constructs a URI internally from host/port (PyMilvusClient only accepts uri, not host/port directly)
   • Handles authentication via token="user:password"
2. MilvusIndexManager (adapters/outbound/milvus/index_manager.py)
   • Creates and manages collections
   • Configures index types and parameters
   • Maps PyArrow schema to Milvus schema
3. MilvusBatchProcessor (adapters/outbound/milvus/batch_processor.py)
   • Handles bulk insert, query, and delete operations
   • Implements size-aware batching
   • Prepares documents for indexing
4. MilvusAdapter (adapters/outbound/milvus/adapter.py)
   • Implements VectorStorePort interface
   • Registered with VectorStoreFactory
   • Coordinates between client, index manager, and batch processor

Configuration

Authentication Types

Milvus adapter supports five authentication types via the auth_type parameter:

1. lite: Milvus Lite — embedded local database, no external service required. uri must be a local .db file path. Only FLAT index type is supported.
2. standalone: Local Milvus with optional username/password
3. grpc: IBM wx.data with gRPC (requires username with ibmlhapikey_ prefix and API key as password)
4. uri: Pre-constructed URI with embedded API key
5. token: IAM token-based (constructs URI internally from host/port/username/token)

Milvus Lite (Container-Free Local)

No Docker, Podman, or Kubernetes required. Data is persisted to a local .db file.

Installation: milvus-lite is included as a core dependency — no separate installation needed.

Constraints:

• Only FLAT index type is supported (other index types are rejected at initialisation with an actionable error)
• Set enable_micro_batching: false in global_config — Milvus Lite holds a single-process file lock
• Not intended for production-scale workloads; use standalone or remote Milvus for those

{
  "type": "vectordb",
  "name": "store_locally",
  "config": {
    "provider": "milvus",
    "doc_id_column": "doc_id_hash",
    "create_index": true,
    "add_sparse_vector": false,
    "provider_config": {
      "collection_name": "documents",
      "auth_type": "lite",
      "uri": "./data/milvus/documents.db",
      "index_type": "FLAT",
      "metric_type": "COSINE",
      "batch_size": 100
    }
  },
  "depends_on": ["embed"]
}

Path resolution: relative paths (e.g. ./data/milvus/docs.db) are resolved from the working directory where docling-pipelines is invoked. The parent directory is created automatically if it does not exist. Use an absolute path to avoid ambiguity.

Persistence: data survives process restart — reopen the same .db file path to query or extend the collection.

Migration path: swap auth_type: "lite" for auth_type: "standalone" (and add host/port) to move from Milvus Lite to a standalone deployment without any other flow changes.

Standalone Milvus

{
  "id": "a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
  "name": "milvus_vector_store",
  "operator": "vectordb",
  "config": {
    "provider": "milvus",
    "embeddings_column": "embeddings",
    "vector_dimension": 384,
    "add_sparse_vector": false,
    "available_features": {
      "doc_id_hash": {
        "type": "keyword",
        "available_for_vector_db": true
      },
      "content": {
        "type": "text",
        "available_for_vector_db": true
      },
      "embeddings": {
        "type": "vector",
        "available_for_vector_db": true
      }
    },
    "feature_mappings": {
      "doc_id_hash": "pk",
      "content": "text",
      "embeddings": "vector"
    },
    "provider_config": {
      "collection_name": "my_collection",
      "auth_type": "standalone",
      "host": "localhost",
      "port": 19530,
      "username": "root",
      "password": "<your-milvus-password>",
      "database": "default",
      "index_type": "HNSW",
      "metric_type": "L2",
      "secure": false,
      "index_parameters": {
        "M": 16,
        "efConstruction": 200
      }
    }
  }
}

wx.data with gRPC (API Key)

{
  "id": "b2c3d4e5-f6a7-4b8c-9d0e-1f2a3b4c5d6e",
  "name": "wxdata_grpc_store",
  "operator": "vectordb",
  "config": {
    "provider": "milvus",
    "embeddings_column": "embeddings",
    "vector_dimension": 384,
    "add_sparse_vector": false,
    "provider_config": {
      "collection_name": "my_collection",
      "auth_type": "grpc",
      "host": "your-wxdata-instance.cloud.ibm.com",
      "port": 19530,
      "username": "ibmlhapikey_your_username",
      "password": "<your-api-key>",
      "database": "default",
      "index_type": "HNSW",
      "metric_type": "COSINE",
      "secure": true
    },
    "available_features": {
      "doc_id_hash": {
        "type": "keyword",
        "available_for_vector_db": true
      },
      "content": {
        "type": "text",
        "available_for_vector_db": true
      },
      "embeddings": {
        "type": "vector",
        "available_for_vector_db": true
      }
    },
    "feature_mappings": {
      "doc_id_hash": "pk",
      "content": "text",
      "embeddings": "vector"
    }
  }
}

wx.data with URI (API Key Embedded)

{
  "id": "c3d4e5f6-a7b8-4c9d-0e1f-2a3b4c5d6e7f",
  "name": "wxdata_uri_store",
  "operator": "vectordb",
  "config": {
    "provider": "milvus",
    "embeddings_column": "embeddings",
    "vector_dimension": 384,
    "add_sparse_vector": false,
    "available_features": {
      "doc_id_hash": {
        "type": "keyword",
        "available_for_vector_db": true
      },
      "content": {
        "type": "text",
        "available_for_vector_db": true
      },
      "embeddings": {
        "type": "vector",
        "available_for_vector_db": true
      }
    },
    "feature_mappings": {
      "doc_id_hash": "pk",
      "content": "text",
      "embeddings": "vector"
    },
    "provider_config": {
      "collection_name": "my_collection",
      "auth_type": "uri",
      "uri": "https://ibmlhapikey_your_username:<your-api-key>@your-wxdata-instance.cloud.ibm.com:19530",
      "database": "default",
      "index_type": "HNSW",
      "metric_type": "COSINE"
    }
  }
}

wx.data with IAM Token

{
  "id": "d4e5f6a7-b8c9-4d0e-1f2a-3b4c5d6e7f8a",
  "name": "wxdata_token_store",
  "operator": "vectordb",
  "config": {
    "provider": "milvus",
    "embeddings_column": "embeddings",
    "vector_dimension": 384,
    "add_sparse_vector": false,
    "available_features": {
      "doc_id_hash": {
        "type": "keyword",
        "available_for_vector_db": true
      },
      "content": {
        "type": "text",
        "available_for_vector_db": true
      },
      "embeddings": {
        "type": "vector",
        "available_for_vector_db": true
      }
    },
    "feature_mappings": {
      "doc_id_hash": "pk",
      "content": "text",
      "embeddings": "vector"
    },
    "provider_config": {
      "collection_name": "my_collection",
      "auth_type": "token",
      "host": "your-wxdata-instance.cloud.ibm.com",
      "port": 19530,
      "username": "your_username",
      "token": "your-iam-token",
      "database": "default",
      "index_type": "HNSW",
      "metric_type": "COSINE"
    }
  }
}

Configuration Parameters

Connection Parameters

Parameter	Type	Required	Default	Description
auth_type	string	Yes	-	Authentication type: lite, standalone, grpc, uri, or token
uri	string	Conditional*	-	Local .db file path (for lite) or full remote URI (for uri auth_type)
host	string	Conditional**	-	Milvus server host
port	integer	Conditional**	19530	Milvus server port
token	string	Conditional***	-	IAM token (for token auth_type)
username	string	Conditional**	-	Username for authentication
password	string	Conditional*****	-	Password/API key for authentication
database	string	No	“default”	Database name (not used for lite)
secure	bool	No	false	Enable TLS/SSL (not used for lite)

Auth Type Requirements

auth_type	Required Parameters	Description
lite	uri (local .db path)	Milvus Lite — embedded local database. No external service. Only FLAT index supported.
standalone	host, port	Local Milvus. Optional: username, password
grpc	host, port, username, password	IBM wx.data with gRPC. Username must have ibmlhapikey_ prefix, password is API key
uri	uri	Pre-constructed URI with embedded API key (format: https://ibmlhapikey_<username>:<api-key>@<host>:<port>)
token	host, port, username, token	IAM token-based. Constructs URI internally (format: https://ibmlhtoken_<username>:<token>@<host>:<port>)

Required for lite and uri auth types Required for standalone, grpc, and token auth types ***Required for token auth_type **Required for grpc and token auth types ****Required for grpc auth_type (should be API key)

Index Parameters

Parameter	Type	Required	Default	Description
index_type	string	No	“HNSW”	Index type (HNSW, IVF_FLAT, etc. Auto-set to SPARSE_INVERTED_INDEX in sparse mode)
metric_type	string	No	“L2”	Similarity metric (L2, IP, COSINE for dense; BM25 for sparse)
index_parameters	object	No	{}	Index-specific parameters

Operator Parameters

Parameter	Type	Required	Default	Description
provider	string	Yes	-	Must be “milvus”
provider_config.collection_name	string	Yes	-	Name of the Milvus collection
embeddings_column	string	No	“embeddings”	Column containing vectors
vector_dimension	integer	No	384	Vector dimension (dense mode only)
add_sparse_vector	boolean	No	false	Use BM25 sparse vectors instead of dense
batch_size	integer	No	100	Batch size for operations
primary_key_field	string	No	“pk”	Primary key field name
auto_id	boolean	No	false	Auto-generate IDs

Index Types

HNSW (Hierarchical Navigable Small World)

• Best for: High recall, fast search
• Parameters:
  • M: Number of connections (default: 16)
  • efConstruction: Build-time search depth (default: 200)

{
  "index_type": "HNSW",
  "index_parameters": {
    "M": 16,
    "efConstruction": 200
  }
}

IVF_FLAT (Inverted File with Flat Compression)

• Best for: Balance between speed and accuracy
• Parameters:
  • nlist: Number of clusters (default: 128)

{
  "index_type": "IVF_FLAT",
  "index_parameters": {
    "nlist": 128
  }
}

IVF_SQ8 (Inverted File with Scalar Quantization)

• Best for: Memory efficiency
• Parameters:
  • nlist: Number of clusters (default: 128)

IVF_PQ (Inverted File with Product Quantization)

• Best for: Maximum compression
• Parameters:
  • nlist: Number of clusters (default: 128)
  • m: Number of subquantizers (default: 8)
  • nbits: Bits per subquantizer (default: 8)

FLAT

• Best for: Exact search, small datasets
• Parameters: None

AUTOINDEX

• Best for: Automatic optimization
• Parameters: None (Milvus chooses optimal settings)

Similarity Metrics

Metric	Description	Use Case	Vector Type
L2	Euclidean distance	General purpose, normalized vectors	Dense only
IP	Inner product	Cosine similarity with normalized vectors	Dense only
COSINE	Cosine similarity	Text embeddings, semantic search	Dense only
BM25	BM25 text scoring	Sparse vectors, keyword search	Sparse only (required)

Deployment Scenarios

Docling Pipelines provides two example pipeline flows demonstrating different Milvus deployment scenarios:

1. Standalone Milvus (Local/Docker) - Sparse Vectors

Example Flow: sample_flows/vectordb/milvus_integration.json

Use Case: Local development, testing, or self-hosted Milvus

Key Configuration:

{
  "provider": "milvus",
  "doc_id_column": "doc_id_hash",
  "embeddings_column": "embeddings",
  "add_sparse_vector": true,
  "create_index": true,
  "provider_config": {
    "collection_name": "sample_documents_sparse",
    "auth_type": "standalone",
    "host": "localhost",
    "port": 19530,
    "secure": false,
    "username": "root",
    "password": "<YOUR_PASSWORD>",
    "metric_type": "BM25",
    "index_type": "SPARSE_INVERTED_INDEX"
  }
}

Features:

• No SSL/TLS required (secure parameter not needed)
• Simple username/password authentication
• Demonstrates sparse + dense vector storage with BM25

2. IBM watsonx.data Milvus (Cloud) - Dense Vectors

Example Flow: sample_flows/vectordb/milvus_integration.json

Use Case: Enterprise deployment with IBM watsonx.data managed Milvus

Key Configuration:

{
  "provider": "milvus",
  "doc_id_column": "doc_id_hash",
  "embeddings_column": "embeddings",
  "add_sparse_vector": false,
  "create_index": true,
  "provider_config": {
    "collection_name": "sample_documents_collection_dense",
    "auth_type": "grpc",
    "host": "<YOUR_WXDATA_HOST>",
    "port": "<YOUR_PORT>",
    "username": "<YOUR_USERNAME>",
    "password": "<YOUR_API_KEY>",
    "secure": true,
    "metric_type": "L2",
    "index_type": "HNSW"
  }
}

CRITICAL REQUIREMENTS:

⚠️ secure: true is MANDATORY for watsonx.data connections

IBM watsonx.data is a cloud-managed service that requires encrypted communication. The secure: true parameter enables SSL/TLS encryption for gRPC connections, which is essential for:

• Security: Encrypts data in transit between your application and watsonx.data
• Authentication: Required by watsonx.data’s gRPC endpoint to establish trusted connections
• Compliance: Meets enterprise security standards for cloud deployments

Without secure: true, the connection attempt will fail with HTTPConnectionError because watsonx.data’s gRPC endpoint rejects unencrypted connections.

Additional Requirements:

• macOS Users: Set GRPC_DNS_RESOLVER=native environment variable before running the pipeline. This resolves a known gRPC DNS resolution issue on macOS when connecting through VPNs or to cloud services.
• Password: For grpc authentication type use your IBM Cloud API key, not your account password

Vector Types

Dense Vectors (Default)

Dense vectors are traditional embeddings generated by models like Ollama, OpenAI, or HuggingFace. They require an upstream embeddings operator.

Configuration:

{
  "embeddings_column": "embeddings",
  "add_sparse_vector": false,
  "vector_dimension": 768,
  "feature_mappings": {
    "embeddings": "dense_embeddings"
  },
  "provider_config": {
    "index_type": "HNSW",
    "metric_type": "COSINE"
  }
}

Pipeline Requirements:

• Must include embeddings operator upstream
• Vector dimension must match embeddings model output
• Supports HNSW, IVF_FLAT, IVF_SQ8, IVF_PQ, FLAT indexes

Sparse Vectors (BM25)

Sparse vectors are automatically generated from text content using Milvus’s built-in BM25 function during insertion. The pipeline still requires an embeddings operator for dense embeddings, but the BM25 function generates additional sparse vectors from the text content.

Configuration:

{
  "embeddings_column": "embeddings",
  "add_sparse_vector": true,
  "feature_mappings": {
    "doc_id_hash": "pk",
    "content": "text",
    "embeddings": "vector",
    "sparse_embeddings": "sparse_vector"
  },
  "provider_config": {
    "auth_type": "standalone",
    "index_type": "SPARSE_INVERTED_INDEX",
    "metric_type": "BM25"
  }
}

Key Differences:

• Embeddings operator still required - Pipeline includes both dense and sparse vectors
• Content field required - Must map content column to Milvus text field for BM25 function
• Auto-set index type - Automatically set to SPARSE_INVERTED_INDEX in sparse mode
• Required metric - Must use BM25 (validated and enforced)
• No dimension parameter - Sparse vectors have variable dimensions
• Dual vector storage - Stores both dense embeddings and BM25-generated sparse vectors

How It Works:

1. Pipeline generates dense embeddings via embeddings operator
2. User provides text content in PyArrow table
3. Milvus BM25 function automatically converts text to sparse vectors during insertion
4. Both dense and sparse vectors stored in collection
5. Search can use either dense (semantic) or sparse (keyword) vectors

Usage Examples

Basic Pipeline with Milvus (Dense Vectors)

For a complete working example, see sample_flows/vectordb/milvus_integration.json.

Key Configuration:

{
  "type": "vectordb",
  "config": {
    "provider": "milvus",
    "embeddings_column": "embeddings",
    "vector_dimension": 384,
    "add_sparse_vector": false,
    "feature_mappings": {
      "doc_id_hash": "pk",
      "embeddings": "vector",
      "content": "text"
    },
    "provider_config": {
      "collection_name": "documents",
      "auth_type": "standalone",
      "host": "localhost",
      "port": 19530,
      "username": "root",
      "password": "<YOUR_PASSWORD>",
      "database": "default",
      "index_type": "HNSW",
      "metric_type": "L2",
      "index_parameters": {
        "M": 16,
        "efConstruction": 256
      }
    }
  }
}

Pipeline Structure:

1. Ingest documents
2. Extract text with Docling
3. Chunk documents
4. Generate embeddings (Ollama/HuggingFace/etc.)
5. Store in Milvus with dense vectors only

Sparse Vector Pipeline (BM25)

For a complete working example, see sample_flows/vectordb/milvus_integration.json.

Key Configuration:

{
  "type": "vectordb",
  "config": {
    "provider": "milvus",
    "embeddings_column": "embeddings",
    "add_sparse_vector": true,
    "feature_mappings": {
      "doc_id_hash": "pk",
      "embeddings": "vector",
      "sparse_embeddings": "sparse_vector",
      "content": "text"
    },
    "provider_config": {
      "collection_name": "documents_sparse",
      "auth_type": "standalone",
      "host": "localhost",
      "port": 19530,
      "username": "root",
      "password": "<YOUR_PASSWORD>",
      "database": "default",
      "index_type": "SPARSE_INVERTED_INDEX",
      "metric_type": "BM25"
    }
  }
}

Pipeline Structure:

1. Ingest documents
2. Extract text with Docling
3. Chunk documents
4. Generate embeddings (Ollama/HuggingFace/etc.)
5. Store in Milvus with both dense and sparse vectors

Note: Sparse vector pipeline INCLUDES embeddings operator - both dense embeddings and BM25 sparse vectors are stored together.

Python Integration Example

For a comprehensive Python example demonstrating Milvus operator usage, see examples/milvus_integration_example.py. This example includes:

1. Basic Document Indexing - HNSW index with metadata fields
2. IVF_FLAT Index - Optimized for large datasets
3. COSINE Similarity - Alternative metric type
4. Query and Delete Operations - Document retrieval and deletion
5. Large Batch Processing - Efficient bulk operations
6. Error Handling - Graceful handling of missing data

Run the example:

# Set up environment variables in .env file
export MILVUS_HOST=localhost
export MILVUS_PORT=19530
export MILVUS_USERNAME=root
export MILVUS_PASSWORD=your-milvus-password

# Run the example
python examples/milvus_integration_example.py

wx.data Configuration

{
  "id": "b8c9d0e1-f2a3-4b4c-5d6e-7f8a9b0c1d2e",
  "name": "wxdata_vector_store",
  "operator": "vectordb",
  "config": {
    "provider": "milvus",
    "embeddings_column": "embeddings",
    "vector_dimension": 768,
    "add_sparse_vector": false,
    "available_features": {
      "doc_id_hash": {
        "type": "keyword",
        "available_for_vector_db": true
      },
      "content": {
        "type": "text",
        "available_for_vector_db": true
      },
      "embeddings": {
        "type": "vector",
        "available_for_vector_db": true
      }
    },
    "feature_mappings": {
      "doc_id_hash": "pk",
      "content": "text",
      "embeddings": "vector"
    },
    "provider_config": {
      "collection_name": "enterprise_docs",
      "uri": "https://milvus.wxdata.ibm.com:19530",
      "token": "${WXDATA_TOKEN}",
      "db_name": "production",
      "secure": true,
      "index_type": "HNSW",
      "metric_type": "COSINE",
      "index_parameters": {
        "M": 32,
        "efConstruction": 400
      }
    }
  }
}

Feature Mappings

Feature mappings define which PyArrow table columns are stored in Milvus:

{
  "available_features": {
    "embeddings": {
      "type": "vector",
      "available_for_vector_db": true
    },
    "content": {
      "type": "text",
      "available_for_vector_db": true
    },
    "doc_id_hash": {
      "type": "keyword",
      "available_for_vector_db": true
    }
  },
  "feature_mappings": {
    "embeddings": "vector_field",
    "content": "text_content",
    "doc_id_hash": "document_id"
  }
}

Performance Tuning

Batch Size

• Small datasets (<10K): 100-500
• Medium datasets (10K-1M): 500-1000
• Large datasets (>1M): 1000-5000

Index Selection

• High accuracy required: HNSW or FLAT
• Memory constrained: IVF_SQ8 or IVF_PQ
• Balanced: IVF_FLAT
• Uncertain: AUTOINDEX

HNSW Parameters

• Higher M: Better recall, more memory
• Higher efConstruction: Better index quality, slower build
• Recommended: M=16-32, efConstruction=200-400

Troubleshooting

Connection Issues

Problem: Cannot connect to Milvus

MilvusDB Error: Failed to connect using standalone auth_type: Fail connecting to server on localhost:19530, illegal connection params or server unavailable

Solutions:

1. Verify Milvus is running: curl http://localhost:9091/healthz (should return OK)
2. Start with Docker: docker compose -f docker/docker-compose.milvus.yml up -d
3. Check port accessibility: telnet localhost 19530
4. Verify credentials match your Milvus setup (default: root / Milvus)
5. For wx.data, check token validity

macOS gRPC DNS Resolution Issue

Problem: Connection fails on macOS when accessing Milvus through VPN or watsonx.data

MilvusException: (code=2, message=Fail connecting to server on your-host-name:443,
illegal connection params or server unavailable)

Cause: macOS gRPC DNS resolver incompatibility with certain network configurations

Solution: Set the GRPC_DNS_RESOLVER environment variable before running your application:

export GRPC_DNS_RESOLVER=native

Or in Python code before importing Milvus libraries:

import os
os.environ["GRPC_DNS_RESOLVER"] = "native"

# Then import and use docpipe
from docpipe.core.operators.vectordb import VectorDBOperator

Verification Steps:

1. Check if the Milvus instance was deleted and recreated (host URL may have changed)
2. Verify the certificate is a gRPC certificate, not an HTTPS certificate
3. Apply the GRPC_DNS_RESOLVER=native environment variable
4. Retry the connection

Note: This issue primarily affects:

• macOS systems (not Linux or Windows)
• VPN connections
• IBM watsonx.data Milvus instances
• Non-VM macOS environments

Collection Creation Failures

Problem: Collection creation fails

Failed to create collection 'my_collection'

Solutions:

1. Check collection name (alphanumeric, underscores only)
2. Verify vector dimension matches embeddings
3. Ensure sufficient permissions
4. Check Milvus server logs

Index Build Errors

Problem: Index parameters invalid

Invalid index type 'HNSW'. Supported: [...]

Solutions:

1. Verify index type spelling (case-sensitive)
2. Check index parameters for selected type
3. Ensure metric type is compatible with index

Memory Issues

Problem: Out of memory during indexing

Memory allocation failed

Solutions:

1. Reduce batch_size
2. Use memory-efficient index (IVF_SQ8, IVF_PQ)
3. Increase Milvus memory limits
4. Process data in smaller chunks

Testing

Unit Tests

cd /path/to/docling-pipelines
export PYTHONPATH="$(pwd)/src:${PYTHONPATH}"
uv run pytest tests/unit/operators/vectordb/test_milvus_client.py -v

Integration Tests

Milvus Lite (no external service required)

The full end-to-end integration test (ingest → extract → chunk → embed → Milvus Lite) requires no running server:

source .venv/bin/activate
pytest tests/integration/test_ingest_extract_chunk_embed_milvus_lite.py -v

This test covers:

• test_full_pipeline_with_feature_mappings — E2E flow; verifies row count, schema fields, and content stored correctly
• test_incremental_reindex_no_duplicates — runs twice against the same .db; confirms row count is stable (no duplicates)
• test_unsupported_index_type_rejected — confirms non-FLAT index types are rejected at initialisation with a clear error

Standalone Milvus (requires running instance)

# Start Milvus (Docker)
docker run -d --name milvus \
  -p 19530:19530 \
  -p 9091:9091 \
  milvusdb/milvus:latest

# Run tests
uv run pytest tests/integration/milvus/ -v

Migration from OpenSearch

To migrate from OpenSearch to Milvus:

1. Update flow configuration:

   {
     "provider": "milvus",  // Changed from "opensearch"
     "provider_config": {
       "collection_name": "my_collection",
       "auth_type": "standalone",  // Required: standalone, grpc, uri, or token
       "host": "localhost",
       "port": 19530,  // Changed from 9200
       "username": "root",
       "password": "<YOUR_PASSWORD>",
       "index_type": "HNSW",  // Instead of "engine"
       "metric_type": "L2"  // Instead of "space_type"
     }
   }
   

2. Index type mapping:
   • OpenSearch faiss/hnsw → Milvus HNSW
   • OpenSearch faiss/ivf → Milvus IVF_FLAT
   • OpenSearch lucene/hnsw → Milvus HNSW
3. Metric type mapping:
   • OpenSearch l2 → Milvus L2
   • OpenSearch cosine → Milvus COSINE
   • OpenSearch inner_product → Milvus IP

References

• Milvus Documentation
• PyMilvus SDK
• IBM watsonx.data
• Docling Pipelines Architecture

Support

For issues or questions:

1. Check Milvus server logs
2. Review Docling Pipelines logs for detailed error messages
3. Consult Milvus documentation for index-specific issues
4. For wx.data issues, contact IBM support

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