FLUID by Example — From "Hello World" to Production

Examples 1–13 target fluidVersion: "0.7.5", the latest stable schema. Example 14 uses the 0.7.6 preview. Each example adds one block to the previous one. Read in order to learn the schema by accretion. Every complete contract on this page validates against the JSON Schema of the version it declares (Draft 2020-12) and with the reference implementation's fluid validate; examples that show only the new block validate when merged into the example before them. File names such as 01-minimal.fluid.yml are illustrative: FLUID does not prescribe a file name, and the reference implementation scaffolds contract.fluid.yaml. For the schema's mental model first, see Anatomy. For a one-row-per-field lookup, see Cheatsheet.

#	Example	What it adds
1	Minimal valid contract	the six required fields, nothing else
2	Add a schema	`exposes[].contract.schema`
3	Add data quality	`exposes[].contract.dq.rules`
4	Add a build	`build` — embedded SQL
5	Consume another product	`consumes` + `build.pattern: hybrid-reference` (dbt)
6	Restrict access	`accessPolicy`
7	Govern AI consumers	`agentPolicy`
8	Pin to a jurisdiction	`sovereignty`
9	Define business semantics	`exposes[].semantics`
10	Source-aligned acquisition	`build.pattern: acquisition`
11	Agent-consumable output port (MCP)	`exposes[].mcp` (⭐ 0.7.4)
12	Stream Kafka into Iceberg	`kafka-connect` Iceberg streaming sink + Iceberg catalog location (⭐ 0.7.5)
13	Vector output port (pgvector)	`platform: pgvector` + `vectorConfig` (⭐ 0.7.5)
14	Preview: packaging and consumers	`packaging`, `consumers` (🧪 0.7.6 preview)

1. Minimal valid contract

Goal: get a contract that validates against 0.7.5 with the fewest possible lines. New in this step: the six required top-level keys (fluidVersion, kind, id, name, metadata, exposes) and the four required fields inside each expose (exposeId, kind, contract, binding).

# 01-minimal.fluid.yml
fluidVersion: "0.7.5"
kind: DataProduct
id:   demo.bronze.hello_world
name: "Hello World"
metadata:
  owner: { team: data-platform }
exposes:
  - exposeId: hello
    kind: table
    contract:
      schema:
        - { name: id, type: STRING, required: true }
    binding:
      platform: local
      format:   parquet
      location: { path: "./hello.parquet" }

That's it. Everything else in this guide is opt-in.

💡 contract must define at least one of schema or openapiRef — an empty contract: {} fails validation. We use a one-column schema here.

2. Add a schema

Goal: describe the columns of the exposed table. New in this step: exposes[].contract.schema[].

# 02-with-schema.fluid.yml
fluidVersion: "0.7.5"
kind: DataProduct
id:   demo.bronze.payments
name: "Raw Payments"
metadata:
  owner: { team: data-platform, email: data-platform@company.com }
  layer: Bronze
exposes:
  - exposeId: payment_events
    kind: table
    contract:
      schema:
        - { name: payment_id, type: STRING,  required: true,  description: "Unique payment id" }
        - { name: amount,     type: NUMERIC, required: true,  description: "Payment amount" }
        - { name: currency,   type: STRING,  required: true,  description: "ISO 4217 currency code" }
        - { name: created_at, type: TIMESTAMP, required: true }
    binding:
      platform: gcp
      format:   bigquery_table
      location: { project: company-data, dataset: bronze_finance, table: payments }

Why bother: every consumer (humans, BI tools, AI agents) now has a machine-readable contract for the columns they'll see.

3. Add data quality

Goal: assert that certain quality conditions must hold for the data to be considered valid. New in this step: exposes[].contract.dq.rules[].

# 03-with-dq.fluid.yml
# ... (everything from example 2, plus:)
exposes:
  - exposeId: payment_events
    kind: table
    contract:
      schema:
        - { name: payment_id, type: STRING,  required: true }
        - { name: amount,     type: NUMERIC, required: true }
        - { name: currency,   type: STRING,  required: true }
      dq:
        rules:
          - id: positive_amount
            type: valid_values                          # type ∈ freshness | completeness | uniqueness | valid_values | accuracy | schema | anomaly_detection | drift_detection
            selector: "amount > 0"
            severity: error                             # severity ∈ info | warn | error | critical
          - id: known_currency
            type: valid_values
            selector: "currency IN ('USD','EUR','GBP','JPY')"
            severity: warn
          - id: freshness_15m
            type: freshness
            selector: created_at
            window: PT15M                               # ISO-8601 duration window
            severity: error
    binding:
      platform: gcp
      format:   bigquery_table
      location: { project: company-data, dataset: bronze_finance, table: payments }

DQ rules run as part of every build. severity: error fails the pipeline; warn annotates the run.

4. Add a build (embedded SQL)

Goal: describe how the data actually gets produced. New in this step: the build block with pattern: embedded-logic.

# 04-with-build.fluid.yml
# ... (everything from example 3, plus:)
build:
  pattern: embedded-logic
  engine:  sql
  properties:
    sql: |
      SELECT
        payment_id,
        amount,
        currency,
        created_at
      FROM raw_source.payments
      WHERE amount > 0
  execution:
    trigger:
      type: schedule                                # schedule | event | manual | dependency | dataset | schedule_and_dataset | timetable
      schedule: "0 * * * *"                         # cron expression (or named preset)
    retries:
      maxAttempts: 3
      backoffStrategy: exponential                  # fixed | exponential | linear
      initialDelay: PT60S                           # ISO-8601 duration

embedded-logic keeps the transformation in-file. For dbt-style references, use hybrid-reference (next example).

5. Consume another product

Goal: depend on another FLUID product upstream, and reference a dbt model for the build. New in this step: consumes[] + build.pattern: hybrid-reference.

# 05-customer-ltv.fluid.yml
fluidVersion: "0.7.5"
kind: DataProduct
id:   analytics.silver.customer_ltv
name: "Customer Lifetime Value"
metadata:
  owner: { team: analytics, email: analytics@company.com }
  layer: Silver

consumes:
  - productId: demo.bronze.payments        # from example 4
    exposeId:  payment_events
  - productId: crm.bronze.raw_customers
    exposeId:  customer_data
    versionConstraint: "^2.0.0"

exposes:
  - exposeId: customer_ltv
    kind: table
    contract:
      schema:
        - { name: customer_id,     type: STRING,  required: true, tags: [identifier] }
        - { name: total_spent,     type: NUMERIC, required: true }
        - { name: order_count,     type: INTEGER, required: true }
        - { name: avg_order_value, type: NUMERIC, required: true }
    binding:
      platform: gcp
      format:   bigquery_table
      location: { project: company-data, dataset: silver_analytics, table: customer_ltv, region: europe-west1 }

build:
  pattern: hybrid-reference
  engine:  dbt
  repository: "github.com/acme/analytics-dbt"
  properties:
    model: customer_ltv                    # path within the dbt repo

Now the orchestrator can auto-build the DAG: demo.bronze.payments → analytics.silver.customer_ltv.

6. Restrict access with `accessPolicy`

Goal: declare exactly who can read this product. The platform generates cloud IAM bindings from this block. New in this step: root-level accessPolicy.

# 06-with-access.fluid.yml
# ... (everything from example 5, plus:)
accessPolicy:
  grants:
    - principal: "group:analytics-team@company.com"
      permissions: [read, select, query]
      resources:   ["$.exposes[?(@.kind=='table')]"]
    - principal: "serviceAccount:airflow@acme-prod.iam.gserviceaccount.com"
      permissions: [read, select]
      conditions:
        ipRanges: ["10.0.0.0/8"]
    - principal: "user:alice@company.com"   # individual grant — labels go in commit msg / PR, not the contract
      permissions: [read, select, query]

The resources field uses JSONPath to target subsets of exposes — useful when some ports are public and others are restricted. The schema's accessPolicy.grants[] block accepts only principal, permissions, resources, and conditions — keep ticket/audit context in your commit message or PR, not the contract.

7. Govern AI consumers with `agentPolicy`

Goal: decide whether AI agents may read this product, which models, and for what purposes. New in this step: agentPolicy under exposes[].policy.agentPolicy. ⚠️ Important shape note: agentPolicy is per-expose, not top-level — it lives inside exposes[].policy.agentPolicy. (Some prior release notes show it at the root; the schema has never accepted it there.)

# 07-with-agent-policy.fluid.yml
# ... (everything from example 6, plus inside the relevant expose:)
exposes:
  - exposeId: customer_ltv
    kind: table
    # ...contract, binding as before...
    policy:                                       # ← expose-level policy block
      authn: iam                                  # oidc | oauth2 | api_key | none | custom | iam | jwt
      authz:
        readers: ["group:analytics-team@company.com"]
      privacy:
        masking:                                  # schema uses column+strategy, not a `piiColumns` shortcut
          - { column: email, strategy: tokenize }
      agentPolicy:
        allowedModels:   [gpt-4, gpt-4-turbo, claude-3-opus, claude-3-sonnet]
        deniedModels:    [gpt-3.5-turbo]          # legacy, not approved for PII
        maxTokensPerRequest: 8192
        maxTokensPerDay:     500000
        # allowedUseCases / deniedUseCases use a CONTROLLED VOCABULARY — not free strings.
        # Valid values: inference | reasoning | analysis | summarization | classification |
        #               embedding | search | qa | code_generation | fine_tuning | training | rag
        allowedUseCases: [inference, qa, rag, summarization, analysis]
        deniedUseCases:  [training, fine_tuning]
        canReason: true
        canStore:  false                          # ephemeral processing only
        retentionPolicy: { maxRetentionDays: 0, requireDeletion: true }
        auditRequired: true                       # log all AI consumption (required for SOC2)
        purposeLimitation: "Customer-LTV analytics and retention modeling only."

This block is enforced by FLUID-aware AI gateways. An LLM request that doesn't match allowedModels ∧ allowedUseCases ∧ token quotas is rejected before it ever sees the data.

💡 Custom business use-case names don't validate. The schema enforces a fixed twelve-value vocabulary. Use purposeLimitation (free text) when you need to express something more specific like "Customer support chatbot only."

8. Pin to a jurisdiction with `sovereignty`

Goal: enforce data residency — apply-time blocks any binding that would land data outside the allowed region. New in this step: root-level sovereignty.

# 08-with-sovereignty.fluid.yml
# ... (everything from example 7, plus:)
sovereignty:
  jurisdiction: EU
  allowedRegions: [europe-west1, europe-west3, europe-west4]
  deniedRegions:  [us-central1, us-east1, asia-southeast1]
  dataResidency:  true
  crossBorderTransfer: false
  regulatoryFramework: [GDPR]
  enforcementMode: strict                         # strict (block apply) | advisory (warn) | audit (log only)
  validationRequired: true                        # CI fails if binding location violates

With enforcementMode: strict and validationRequired: true, contract-apply diff-checks every binding.location against allowedRegions and refuses to deploy mismatches. No more "we accidentally provisioned a PII table in us-east1."

9. Define business semantics

Goal: give an LLM or BI tool the business meaning of the columns — so "What's our MRR?" returns the correct SQL, not an invented one. New in this step: exposes[].semantics.

# 09-with-semantics.fluid.yml
fluidVersion: "0.7.5"
kind: DataProduct
id:   analytics.gold.orders_revenue
name: "Orders Revenue Semantic Model"
metadata:
  owner: { team: finance-data, email: finance-data@company.com }
  layer: Gold

exposes:
  - exposeId: orders
    kind: table
    contract:
      schema:
        - { name: order_id,     type: STRING,    required: true }
        - { name: customer_id,  type: STRING,    required: true }
        - { name: amount,       type: NUMERIC,   required: true }
        - { name: status,       type: STRING,    required: true }
        - { name: discount,     type: NUMERIC }
        - { name: completed_at, type: TIMESTAMP, required: true }

    semantics:
      name: orders_revenue_model
      description: "Canonical semantic model for revenue analytics."
      defaultAggTimeDimension: order_date

      entities:
        - { name: order,    type: primary, expr: order_id }
        - { name: customer, type: foreign, expr: customer_id }

      measures:
        - { name: order_amount,  agg: sum,            expr: amount,   description: "Sum of order amounts" }
        - { name: order_count,   agg: count_distinct, expr: order_id, createMetric: true }
        - { name: total_discount, agg: sum,           expr: discount }

      dimensions:
        - { name: order_date, type: time,        expr: completed_at, typeParams: { timeGranularity: day } }
        - { name: region,     type: categorical }

      metrics:
        - name: gross_revenue
          description: "Total value of completed orders."
          type: simple
          measure: order_amount
          filter: "status = 'completed'"
        - name: net_revenue
          description: "GAAP-compliant net revenue — completed orders minus discounts."
          type: derived
          inputMetrics: [gross_revenue, total_discount]
          expr: "gross_revenue - total_discount"
          owner: "finance-data@company.com"

    binding:
      platform: snowflake
      format:   snowflake_table
      location: { database: ANALYTICS, schema: GOLD, table: ORDERS }

Now an agent asked "what is net revenue last quarter?" reads the net_revenue metric definition and produces the canonically-correct SQL instead of guessing.

The shape mirrors dbt MetricFlow and Snowflake Semantic Views — portable across engines.

10. Source-aligned acquisition

Goal: ingest an external system (Postgres CDC → Iceberg) as a first-class FLUID product, with delivery guarantees, schema-evolution semantics, DLP, and signed connector images. New in this step: build.pattern: acquisition.

# 10-acquisition.fluid.yml
fluidVersion: "0.7.5"
kind: DataProduct
id:   crm.bronze.customers_cdc
name: "Customers CDC from Production Postgres"
description: "Source-aligned ingestion of the customers table from prod Postgres into the bronze Iceberg lake."
metadata:
  owner: { team: data-platform, email: data-platform@company.com }
  layer: Bronze

exposes:
  - exposeId: customers
    kind: table
    contract:
      schema:
        - { name: customer_id,  type: STRING,    required: true,  tags: [primary-key] }
        - { name: email,        type: STRING,    required: true,  sensitivity: tokenized }
        - { name: country,      type: STRING,    required: true }
        - { name: created_at,   type: TIMESTAMP, required: true }
        - { name: updated_at,   type: TIMESTAMP, required: true }
        - { name: _ingested_at, type: TIMESTAMP, required: true, description: "FLUID-emitted ingestion timestamp" }
      dq:
        rules:
          - { id: not_null_pk,  type: completeness, selector: "customer_id IS NOT NULL",         severity: error }
          - { id: valid_email,  type: valid_values, selector: "email RLIKE '^[^@]+@[^@]+\\.[^@]+$'", severity: warn }
          - { id: unique_pk,    type: uniqueness,   selector: customer_id,                       severity: error }
    policy:                                          # expose-level (sibling of contract), not inside contract
      authn: iam
      authz:
        readers: ["group:customer-success@company.com"]
      privacy:
        masking:
          - { column: email, strategy: tokenize }
      agentPolicy:                                   # per-expose location for AI/LLM governance
        allowedModels: [claude-3-opus, gpt-4-turbo]
        allowedUseCases: [inference, qa, rag, summarization]   # controlled vocab (not free strings)
        deniedUseCases:  [training, fine_tuning]
        canStore: false
        auditRequired: true
        purposeLimitation: "Customer-support context and analytics only — never for marketing targeting or credit decisions."
    binding:
      platform: aws
      format:   iceberg
      location: { bucket: acme-bronze, path: "crm/customers/", region: eu-west-1 }
      icebergConfig:                                 # NB: icebergConfig.partitionSpec uses OBJECT form;
        writeVersion: 2                              #     acquisitionSink.partitionBy below uses STRING form
        fileFormat:   parquet
        partitionSpec: [ { sourceColumn: _ingested_at, transform: day } ]

build:
  pattern: acquisition                             # schema validates build.properties below as acquisitionPattern
  engine:  debezium                                # CDC engine
  capabilities: [cdc, schema_evolution, dlp_scan, at_least_once]

  properties:                                      # ← acquisitionPattern lives here
    source:
      kind: postgres
      mode: cdc
      cursor_field: updated_at
      connection:
        secretRef: "vault://pg-prod-readonly"      # a URI (<scheme>://…), e.g. vault://, env://
      streams: [public.customers]
      watermark: { strategy: lsn }                 # Postgres LSN-based

    sink:
      format: iceberg
      catalog: glue
      partitionBy: ["day(_ingested_at)"]           # acquisitionSink uses STRING array (function-form)

    delivery:
      guarantee: at_least_once
      idempotencyKey: "${stream}|${lsn}"
      dlq:
        enabled: true
        sink: { format: parquet, location: "s3://acme-dlq/customers/" }
        maxRecordsBeforeAbort: 10000
        alertOn: [pii_classification_failed, schema_violation, quality_gate_failed]

    schemaEvolution:
      policy: evolve_safe                          # additive allowed, removals warn, type changes fail
      onAddedColumn:   include                     # include | warn | fail
      onRemovedColumn: warn                        # drop    | warn | fail
      onTypeChange:    fail                        # cast    | warn | fail

    preLand: [dlp_scan, tokenize_pii, quality_gate, emit_lineage_input]

    cost:
      budget:
        monthly: { rows: 50000000, bytes: "200GB", computeMinutes: 600 }
        onExceed: warn                             # warn | abort
      chargeback: { team: data-platform, costCenter: ENG-1407 }

    catalog:
      register: [datahub]
      documentation: auto

    concurrency:
      lock: { scope: product }                     # single in-flight run per product

    debezium:
      connector_class: io.debezium.connector.postgresql.PostgresConnector
      deployment: { mode: managed }                # the platform provisions the connector
      image_signature:
        verifier: cosign                           # cosign is the only verifier today
        publicKey: "k8s://acme/cosign-pub"
        slsaProvenance: required                   # required | optional | disabled

orchestration:
  engine: airflow
  generateOnChange: true

# (agentPolicy is on the expose above, under exposes[].policy.agentPolicy)

sovereignty:
  jurisdiction: EU
  allowedRegions: [eu-west-1, eu-central-1]
  deniedRegions:  [us-east-1, us-west-2]
  regulatoryFramework: [GDPR]
  enforcementMode: strict                          # strict | advisory | audit
  validationRequired: true

accessPolicy:
  grants:
    - principal: "group:customer-success@company.com"
      permissions: [read, select]
    - principal: "serviceAccount:ml-platform@acme-prod.iam.gserviceaccount.com"
      permissions: [read, select]

retention:
  runState: P30D
  runLogs:  P90D
  lineage:  P365D
  dlq:      P180D

lifecycle:
  state: active

This single file expresses, end-to-end:

What is published (Iceberg table, schema, DQ rules, PII treatment)
How it's produced (Debezium CDC from prod Postgres, exactly-once intent, schema-evolution policy, DLP pre-land hooks, signed connector image)
Who may read it (group + service-account grants, agent policy for AI)
Where it may live (EU only, GDPR)
How long operational artifacts stick around

A FLUID-aware platform takes this file, provisions the connector, registers it in DataHub, wires the orchestrator, applies the IAM bindings, and starts emitting OpenLineage events — all from this one contract.

11. Agent-consumable output port (MCP)

Goal: publish an output port that an AI agent can describe, sample, and query directly — with governance enforced from the contract, not bolted on at the gateway. New in this step: exposes[].mcp (⭐ 0.7.4).

Adding an mcp block to an expose opts that output port into an MCP output-port gateway. The reference implementation's fluid mcp output-port serve then surfaces the port to Claude Code, Cursor, or any MCP client, exposing describe / sample / query operations against the contract.

The contract below is valid against 0.7.5 and runs without cloud credentials (DuckDB over a local CSV next to the contract):

fluidVersion: "0.7.5"
kind: DataProduct
id: silver.demo.customer_segments_v1
name: Customer Segments (MCP demo)
description: |
  Reference contract for `fluid mcp output-port serve`. Backed by a
  small DuckDB-loaded CSV so the demo runs without cloud credentials.
  Drop into Claude Code / Cursor as an MCP server to see describe /
  sample / query in action.
domain: demo
metadata:
  layer: Silver
  owner:
    team: data-platform
    email: platform@example.com
  businessContext:
    domain: Customer
exposes:
  - exposeId: customer_segments
    title: Customer Segments
    kind: table
    version: "1.0.0"
    contract:
      schema:
        - name: customer_id
          type: STRING
          required: true
          sensitivity: cleartext
        - name: email
          type: STRING
          businessName: Email Address
          # `sensitivity: pii` makes the gateway redact this column's
          # VALUES (→ "[REDACTED-PII]") on every sample / query result
          # while keeping the column visible. The agent learns the field
          # exists but never sees a real address. No flag, no proxy —
          # governance is enforced from the contract.
          sensitivity: pii
        - name: segment
          type: STRING
          required: true
          businessName: Customer Segment
          businessDefinition: enterprise / smb / consumer
        - name: signup_date
          type: DATE
        - name: lifetime_value_usd
          type: FLOAT64
          businessName: Lifetime Value (USD)
    binding:
      platform: local
      format: csv
      location:
        # Relative to the directory that contains this contract.
        # `fluid mcp output-port serve` resolves the path against
        # the contract's parent directory by default.
        path: ./customers.csv
        table: customer_segments
    semantics:
      name: customer_segments
      measures:
        - name: customer_count
          agg: count_distinct
          expr: customer_id
        - name: total_ltv_usd
          agg: sum
          expr: lifetime_value_usd
      dimensions:
        - name: segment
          type: categorical
        - name: signup_date
          type: time
      metrics:
        - name: active_customers
          type: simple
          measure: customer_count
        - name: ltv_total
          type: simple
          measure: total_ltv_usd
    mcp:
      sampling:
        maxRows: 50
      classification:
        dataClass: internal

What the `mcp` block does

mcp.sampling.maxRows (integer ≥ 1, default 100) — caps the number of rows any single sample / query response may return. Here it's lowered to 50, so an agent calling sample never pulls back more than 50 rows regardless of what it asks for.
mcp.classification.dataClass (public | internal | confidential | restricted) — declares the sensitivity tier of the port. The gateway uses it to decide which agents and use cases are allowed near the data and how responses are logged. internal means employees/approved agents, not the public.

How governance is enforced at the gateway

The mcp block opts the port in; the actual who/what/how rules come from the same blocks you've already seen:

policy.agentPolicy (Example 7) is enforced at the gateway on every read. allowedModels / deniedModels gate which LLM may call the port, and allowedUseCases / deniedUseCases gate why. A request that doesn't satisfy allowedModels ∧ allowedUseCases is rejected before any data is read.
Column-level sensitivity drives value redaction. A column marked sensitivity: pii (like email above) has its values replaced with a redaction marker (e.g. [REDACTED-PII]) in every sample / query result, while the column itself stays visible — the agent learns the field exists but never sees a real address. sensitivity: phi is treated the same way for protected health information.

The result: an agent connected through such a gateway can explore the port's shape and semantics, run governed queries, and stay inside the contract's model/use-case/redaction guardrails — without a single flag or proxy outside the contract.

See the MCP how-to for the end-to-end agentic-access story, and the 0.7.4 release notes for the full exposes[].mcp reference.

12. Stream Kafka into Iceberg

Goal: land Kafka topics continuously in an Iceberg table through Kafka Connect, with the table's catalog named in the contract. New in this step: the kafka-connect Iceberg streaming sink and the Iceberg catalog location fields (⭐ 0.7.5).

# 12-kafka-to-iceberg.fluid.yml
fluidVersion: "0.7.5"
kind: DataProduct
id:   sales.bronze.orders_stream
name: "Orders (streamed)"
metadata:
  owner: { team: sales-data }
  productType: SDP
builds:
  - id: ingest_orders
    pattern: acquisition
    engine: kafka-connect
    capabilities: [streaming]
    outputs: [orders]
    properties:
      source:
        kind: kafka
        mode: streaming
        connection: { secretRef: "env://KAFKA_BOOTSTRAP" }
        streams: [orders, payments]
      kafka-connect:
        iceberg_sink_enabled: true                 # derive the Iceberg sink connector
        sink_topics: [orders, payments]
        streamingSink: { commitIntervalMs: 60000, autoCreate: true, evolveSchema: true }
exposes:
  - exposeId: orders
    kind: table
    contract:
      schema:
        - { name: order_id, type: STRING,  required: true }
        - { name: amount,   type: NUMERIC }
    binding:
      platform: aws
      format: iceberg
      location:
        bucket: acme-lake
        database: sales
        table: orders
        region: eu-west-1
        catalog: glue                              # ⭐ 0.7.5 — Iceberg catalog kind
        warehouse: "s3://acme-lake/warehouse"      # ⭐ 0.7.5

The sink settings live under the build's properties, in the key named after the engine. For a managed alternative, the confluent platform (Confluent Cloud Tableflow) materialises a topic as an Iceberg table — see What's New in 0.7.5.

13. Vector output port (pgvector)

Goal: publish embeddings of a text column as a vector output port that a retrieval (RAG) application can query. New in this step: binding.platform: pgvector, format: pgvector_table and binding.vectorConfig (⭐ 0.7.5).

# 13-vector-port.fluid.yml
fluidVersion: "0.7.5"
kind: DataProduct
id:   support.gold.ticket_embeddings
name: "Support ticket embeddings"
metadata:
  owner: { team: support-ai }
exposes:
  - exposeId: tickets
    kind: vector
    contract:
      schema:
        - { name: ticket_id, type: STRING, required: true }
        - { name: body,      type: TEXT, labels: { ai-embeddable: "true" } }
    binding:
      platform: pgvector
      format: pgvector_table
      location: { database: support, schema: public, table: tickets }
      vectorConfig:
        dimensions: 1536                           # required, 1–16000
        embeddingModel: text-embedding-3-small
        distanceMetric: cosine                     # cosine | l2 | inner_product | l1
        indexType: hnsw                            # hnsw | ivfflat | none
        hnsw: { m: 16, efConstruction: 64 }
        sourceKeyColumn: ticket_id                 # ties each embedding back to its source row

Which columns are embedded is not part of the schema; the reference implementation embeds the columns labelled ai-embeddable: "true".

14. Preview: packaging and declared consumers (0.7.6)

🧪 0.7.6 is a preview. It is opt-in — declare fluidVersion: "0.7.6" — and can still change before it is promoted. See 0.7.6 (preview). New in this step: top-level packaging and consumers.

# 14-preview-packaging.fluid.yml
fluidVersion: "0.7.6"
kind: DataProduct
id:   finance.gold.revenue
name: "Revenue"
metadata:
  owner: { team: finance-analytics }
packaging:
  mode: shared                                     # write into platform-owned containers…
  pool: analytics-eu
  containers: { warehouse: isolated }              # …but own the warehouse
consumers:
  - name: weekly_revenue_dashboard
    type: dashboard                                # dashboard | notebook | analysis | ml | application
    label: "Weekly Revenue Dashboard"
    maturity: high
    exposeIds: [revenue]
exposes:
  - exposeId: revenue
    kind: table
    contract:
      schema:
        - { name: day,     type: DATE,    required: true }
        - { name: revenue, type: NUMERIC, required: true }
    binding:
      platform: snowflake
      format: snowflake_table
      location: { account: acme-eu, database: FINANCE, schema: GOLD, table: REVENUE }

The 0.7.6 release note covers the rest of the preview: cross-mesh pins on consumes[], encryption keys and principal mapping on bindings, and expiring data with lifecycle.expire.

Where to go from here

Anatomy — guided tour of every top-level block.
Cheatsheet — one-row-per-field lookup table.
What's New — release notes for each version.
Specification — validation semantics and the document structure.