Qdrant Vector Database Destination

Ingest unstructured documents (PDFs, text files, HTML) into Qdrant, a high-performance vector database for RAG and semantic search applications. The pipeline extracts text from the document, chunks it using a configurable strategy, generates vector embeddings, and upserts the chunks with metadata into a Qdrant collection.

Configuration

"source": {
    "fileAttributes": {
        "unstructuredAttributes": {
            "fileExtension": "pdf",
            "preserveFilename": true
        }
    }
},
"destination": {
    "qdrant": {
        "collectionName": "financial_documents",
        "chunking": {
            "strategy": "recursive",
            "chunkSize": 500,
            "chunkOverlap": 50
        },
        "metadata": {
            "company": "Apple Inc",
            "documentType": "10-Q",
            "filingDate": "2026-01-30"
        },
        "qdrantSecretName": "oss/qdrant"
    }
}

Field	Type	Default	Description
`collectionName`	string	(required)	Qdrant collection name. Auto-created if it doesn’t exist.
`chunking`	object	recursive, 500/50	Chunking strategy configuration (see below).
`metadata`	map	`{}`	Static key-value metadata stored on every chunk as Qdrant payload. Used for filtered search.
`embeddingSecretName`	string	server default	Optional override of the embedding Vault secret. Defaults to the server-level `ai.embedding.secretName` (`oss/embedding`), which is seeded automatically — set this only to point a single pipeline at a different embedding model.
`qdrantSecretName`	string	(required)	Vault secret name for Qdrant connection.

Supported File Types

Format	Description
PDF (`.pdf`)	Text extracted via Apache PDFBox
Word (`.doc`)	Text extracted via Apache POI (legacy format)
Word (`.docx`)	Text extracted via Apache POI (modern format)
PowerPoint (`.ppt`)	Text extracted via Apache POI (legacy format)
PowerPoint (`.pptx`)	Text extracted via Apache POI (modern format)
Excel (`.xls`, `.xlsx`)	Cell values extracted via Apache POI
HTML (`.html`, `.htm`)	Text extracted via JSoup (tags stripped)
RTF (`.rtf`)	Text extracted via javax.swing RTF parser
Email (`.msg`)	Subject, from, to, and body extracted via Apache POI
Email (`.eml`)	Subject, from, and body extracted via Jakarta Mail
EPUB (`.epub`)	XHTML content extracted and parsed via JSoup
Plain text (`.txt`, `.md`, `.csv`, `.json`, `.xml`)	Content used directly

For structured data (CSV, JSON, XML), use database destinations (PostgreSQL, MongoDB) instead.

Vault Secrets

Qdrant connection

vault kv put secret/oss/qdrant \
  host="host.docker.internal" \
  port="6334" \
  apiKey=""

Field	Description
`host`	Qdrant server hostname. Use `host.docker.internal` for local Qdrant.
`port`	gRPC port (default 6334).
`apiKey`	API key for Qdrant Cloud. Empty string for local instances.

Embedding API

The embedding secret is server-level (ai.embedding.secretName, default oss/embedding) and is seeded automatically by docker/vault-init.sh. Each Vault secret is self-describing — the resolver reads provider, endpoint, model, apiKey, and (optionally) version from inside. See AI Configuration for the full picture.

vault kv put secret/oss/embedding \
  provider="openai" \
  endpoint="https://api.openai.com/v1/embeddings" \
  model="text-embedding-3-small" \
  apiKey="sk-..."

Field	Description
`provider`	`openai` or `ollama`. (Anthropic has no embeddings API.)
`endpoint`	Embedding API URL. Must speak the OpenAI embeddings contract.
`model`	Embedding model name.
`apiKey`	API key. Omit for local Ollama.

Common embedding configurations:

Provider	Endpoint	Model	Dimensions
OpenAI	`https://api.openai.com/v1/embeddings`	`text-embedding-3-small`	1536
OpenAI	`https://api.openai.com/v1/embeddings`	`text-embedding-3-large`	3072
TEI (bundled sidecar)	`http://tei:80/v1/embeddings`	`BAAI/bge-m3`	1024

For Anthropic-only deployments, the bundled TEI sidecar serves bge-m3 and vault-init.sh seeds the embedding secret to point at it — no OpenAI key required.

Chunking Strategies

Documents are split into chunks before embedding. Each chunk becomes a separate Qdrant point with the document’s metadata plus chunk_index, filename, and source_pipeline fields.

"chunking": {
    "strategy": "recursive",
    "chunkSize": 500,
    "chunkOverlap": 50
}

Strategy	Description
`none`	No chunking — one embedding per document. Only for very short documents.
`fixed`	Split by character count. Fast but may cut mid-sentence.
`sentence`	Split on sentence boundaries (`.` `!` `?`). Preserves semantic units.
`paragraph`	Split on double newlines. Ideal for structured documents with clear sections.
`recursive`	Try `\n\n`, then `\n`, then `.`, then space — best general-purpose default.

chunkSize (default 500): maximum characters per chunk
chunkOverlap (default 50): characters of overlap between consecutive chunks

Metadata

Static metadata is attached to every chunk in the Qdrant payload, enabling filtered semantic search:

"metadata": {
    "company": "Apple Inc",
    "documentType": "10-Q",
    "filingDate": "2026-01-30"
}

This allows queries like “find chunks about revenue from Apple 10-Q filings” by combining vector similarity with metadata filters. In addition to static metadata, every chunk automatically includes:

text — the chunk text
chunk_index — position of the chunk in the document
filename — original uploaded filename
source_pipeline — pipeline name

How It Works

Upload — an unstructured file (PDF, text) is uploaded via POST /api/v1/pipeline/upload
Extract — text is extracted from the document (PDFBox for PDFs, UTF-8 for text files)
Chunk — text is split into chunks using the configured strategy
Embed — each chunk is sent to the embedding API to generate a vector
Upsert — vectors are upserted into the Qdrant collection with metadata payload
Notify — a pipeline notification is published on completion

The collection is auto-created on first upsert using cosine distance and the embedding dimension detected from the model.

Running Qdrant

Qdrant ships as a bundled opt-in Compose service. Enable it with one line in .env:

COMPOSE_PROFILES=qdrant

Then start the stack:

docker compose up -d

Combine profiles with commas (e.g., COMPOSE_PROFILES=qdrant,kafka). The install.sh installer can also enable Qdrant at install time — it writes the profile and seeds the oss/qdrant secret for you. For the bundled container, use the compose service name as the host in the secret (host="qdrant", gRPC port 6334). To use an existing external or Qdrant Cloud instance instead, skip the profile and point the oss/qdrant secret’s host/port/apiKey at it — the secret fields are the same either way. You can also run Qdrant standalone:

docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

Or install natively from qdrant.tech/documentation/quick-start.

Verifying

# List collections
curl http://localhost:6333/collections

# Get collection info
curl http://localhost:6333/collections/financial_documents

# Scroll points with payload
curl -X POST http://localhost:6333/collections/financial_documents/points/scroll \
  -H "Content-Type: application/json" \
  -d '{"limit": 5, "with_payload": true, "with_vector": false}'

# Search with metadata filter
curl -X POST http://localhost:6333/collections/financial_documents/points/scroll \
  -H "Content-Type: application/json" \
  -d '{"limit": 5, "with_payload": true, "filter": {"must": [{"key": "company", "match": {"value": "Apple Inc"}}]}}'

Getting Started

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Examples

Qdrant Vector Database Destination

Configuration

Supported File Types

Vault Secrets

Qdrant connection

Embedding API

Chunking Strategies

Metadata

How It Works

Running Qdrant

Verifying

​Configuration

​Supported File Types

​Vault Secrets

​Qdrant connection

​Embedding API

​Chunking Strategies

​Metadata

​How It Works

​Running Qdrant

​Verifying

Configuration

Supported File Types

Vault Secrets

Qdrant connection

Embedding API

Chunking Strategies

Metadata

How It Works

Running Qdrant

Verifying