ChromaDB Sink Connector for Confluent Cloud

The fully managed ChromaDB Sink connector for Confluent Cloud streams data from Apache Kafka® topics into ChromaDB collections, with optional built-in embedding generation for retrieval-augmented generation (RAG), semantic search, and recommendation use cases.

ChromaDB is an open-source vector database for artificial intelligence (AI) and machine learning (ML) workloads. The connector writes records to one or more ChromaDB collections and, when needed, generates the vector embeddings for you by calling an OpenAI-compatible embedding API.

Features

The ChromaDB Sink connector provides the following features:

Embedding generation: Optionally generates vector embeddings from document text by calling an OpenAI-compatible embedding API (for example, OpenAI, Azure OpenAI, or another compatible provider). When disabled, embeddings must already be present in each Kafka record.
Pre-computed embeddings: Supports records that already contain a computed embedding, so you can skip the embedding-generation step entirely.
Collection auto-creation: Automatically creates a ChromaDB collection if it does not already exist.
Input formats: Supports Struct, Map, and JSON String record formats.
Multi-tenancy: Supports configurable tenant, database, and collection settings for both Chroma Cloud and self-hosted ChromaDB deployments.
Token-based authentication: Authenticates to ChromaDB using an API token.
Multi-collection support: A single connector can write to up to 15 ChromaDB collections, mapping each Kafka topic to a different collection.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Limitations

For connector limitations, see ChromaDB Sink Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.

Quick Start

Select the connector and configure it to stream Kafka events to a ChromaDB collection.

Prerequisites

Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
The Confluent CLI installed and configured for the cluster. For more information, see Install the Confluent CLI.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
A Chroma target: either a Chroma Cloud account or a self-hosted ChromaDB endpoint reachable from Confluent Cloud, along with its tenant, database, and API token.
If you plan to use auto-generated embeddings, an OpenAI-compatible embedding endpoint, model, and API key.
Input records that match the connector’s expected fields: id, document, embedding, and metadata. If you disable auto-generated embeddings, each record must include a pre-computed embedding. If you enable auto-generated embeddings, each record must include the document text.
For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.

Kafka cluster credentials. The following lists the different ways you can provide credentials.
- Enter an existing service account resource ID.
- Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
- Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

To create and launch a Kafka cluster in Confluent Cloud, see Create a kafka cluster in Confluent Cloud.

Step 2: Add a connector

In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector

Click the ChromaDB Sink connector card.

Step 4: Enter the connector details

Before configuring the connector settings, ensure that you complete all prerequisites.

Note

An asterisk ( * ) designates a required entry.

At the Add ChromaDB Sink Connector screen, complete the following:

Select topic

If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Kafka credentials

Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
Note

Freight clusters support only service accounts for Kafka authentication.
Click Continue.

Authentication

Configure the authentication properties:

ChromaDB Connection
- ChromaDB Endpoint: The ChromaDB API endpoint URL. For example: https://api.trychroma.com:8000.
- ChromaDB API Key: API key for authenticating with ChromaDB Cloud.
- Tenant: The ChromaDB tenant ID. For self-hosted single-tenant deployments, use default_tenant. For ChromaDB Cloud, use the tenant ID from the ChromaDB Cloud console.
- Database: The ChromaDB database name. For self-hosted deployments, use default_database. For ChromaDB Cloud, use the database name from the ChromaDB Cloud console.
Click Continue.

Configuration

Note

Configuration properties that are not shown in the Confluent Cloud Console use the default values. For all property values and definitions, see Configuration Properties.

Input Kafka record value format: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, BYTES, or STRING. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON Schema, or Protobuf).

Embedding

Auto-Generate Embeddings: When enabled, the connector calls an external OpenAI-compatible embedding API to generate vector embeddings from the document text. When disabled, embeddings must be provided in each Kafka record. Each collection declares its own embedding endpoint, model, and API key under its own Collection configuration section. There is no shared global embedding configuration.

Collections

Number of collections: The number of ChromaDB collections to write to. Each collection maps to a set of Kafka topics.
Auto-Create Collections: Whether to automatically create collections if they do not exist.

Collection 1 configuration

Collection Name: The name of the ChromaDB collection to write to.
Topic: The Kafka topic to pull data from for this collection.
Embedding endpoint: The full URL of the OpenAI-compatible embedding API for this collection (for example, https://api.openai.com/v1/embeddings). Each collection declares its own embedding endpoint. There is no shared global fallback. Required when Auto-Generate Embeddings is enabled.
Embedding model: The embedding model for this collection (for example, text-embedding-3-small or nomic-embed-text). Each collection’s vectors are dimensioned by its model. ChromaDB rejects mixed dimensions within one collection. Required when Auto-Generate Embeddings is enabled.
Embedding API key: The API key for the embedding service (for example, OpenAI or Azure OpenAI).

Show advanced configurations

Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.

Additional Configs

Value Converter Schema ID Deserializer: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
Value Converter Schemas Enable: Includes schema within each of the serialized values. Input messages must contain schema and payload fields and must not contain additional fields. For plain JSON data, set this to false. Applies to the JSON converter.
Schema GUID For Key Converter: Sets the schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Value Converter Connect Meta Data: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
Value Converter Value Subject Name Strategy: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
Key Converter Key Subject Name Strategy: Determines how to construct the subject name for key schema registration.
Value Converter Replace Null With Default: Specifies whether to replace fields that have a default value and that are null to the default value. When set to true, the connector uses the default value; otherwise, it uses null. Applies to the JSON converter.
Value Converter Reference Subject Name Strategy: Sets the subject reference name strategy for values. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. You can use this strategy only with PROTOBUF format; the default strategy is DefaultReferenceSubjectNameStrategy.
Schema ID For Value Converter: Sets the schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Errors Tolerance: Use this property to configure the connector’s error handling behavior.

Warning

Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to all, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to none, the connector task fails on errant records.
Value Converter Ignore Default For Nullables: When set to true, this property ensures that the corresponding record in Kafka is null, instead of showing the default column value. Applies to the AVRO, PROTOBUF, and JSON_SR converters.
Key Converter Schema ID Deserializer: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers.
Value Converter Decimal Format: Specifies the JSON or JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals: BASE64 to serialize DECIMAL logical types as base64 encoded binary data, and NUMERIC to serialize DECIMAL logical type values in JSON or JSON_SR as a number representing the decimal value.
Schema GUID For Value Converter: Sets the schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Schema ID For Key Converter: Sets the schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Auto-restart policy

Enable Connector Auto-restart: Enables the auto-restart behavior of the connector and its task in the event of user-actionable errors. Defaults to true, enabling the connector to automatically restart in case of user-actionable errors. Set this property to false to disable auto-restart for failed connectors. If disabled, you must manually restart the connector.

Consumer configuration

Max poll interval(ms): Sets the maximum delay between subsequent consume requests to Kafka. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 300,000 milliseconds (5 minutes).
Max poll records: Sets the maximum number of records to consume from Kafka in a single request. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 500 records.

Collections

Distance Metric: The distance metric used when the connector creates a collection. Valid values are cosine (cosine similarity), ip (inner product), and l2 (squared L2 norm). This setting applies only to collections the connector creates while chromadb.auto.create.collection is true. A pre-existing collection keeps the space it was created with, and this setting is ignored for it. A single value is shared by every configured collection. Per-collection distance metrics are not supported.

Collection 1 configuration

Batch Size: Maximum number of records per upsert request for this collection. Chroma Cloud rejects writes larger than 300 records, so higher values fail with HTTP 422.
Behavior on null values: How to handle Kafka tombstone records (non-null key and null value). IGNORE skips the record, and FAIL stops the connector.
Embedding batch size: Number of texts to embed per API call for this collection. Higher values improve throughput but use more memory.

Behavior on error

Behavior On Errors: Error handling behavior for failed HTTP requests. IGNORE continues past failed batches. Failed and dropped records are still written to the error topic. Set FAIL while troubleshooting so the task surfaces a mapped error message and stops on the first unrecoverable batch.

Transforms

Single Message Transformations: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.

Processing position

Set offsets: Click Set offsets to define a specific offset for this connector to begin procession data from. For more information on managing offsets, see Manage offsets.

For all property values and definitions, see Configuration Properties.

Click Continue.

Sizing

Based on the number of topic partitions you select, the connector recommends a number of tasks.

To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
Click Continue.

Review and Launch

Verify the connection details.
Click Launch.

The status for the connector should go from Provisioning to Running.

Step 5: Check the ChromaDB collection

Verify that new records appear in your ChromaDB collection.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. For more information, see View Connector Dead Letter Queue Errors in Confluent Cloud.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

Create a JSON file that contains the connector configuration properties. The following example shows the required properties for a single collection using pre-computed embeddings (chromadb.auto.embed disabled):

{
  "name": "ChromaDBSink_0",
  "config": {
    "topics": "documents",
    "connector.class": "ChromaDBSink",
    "name": "ChromaDBSink_0",
    "input.data.format": "JSON",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "chromadb.endpoint": "https://api.trychroma.com:8000",
    "chromadb.api.key": "<my-chromadb-api-key>",
    "chromadb.tenant": "<my-chromadb-tenant>",
    "chromadb.database": "<my-chromadb-database>",
    "collection1.name": "my-embeddings",
    "collection1.topic": "documents",
    "tasks.max": "1"
  }
}

Note the following property definitions:

"name": Sets a name for your new connector.
"connector.class": Identifies the connector plugin name.
"topics": Identifies the topic name or a comma-separated list of topic names.
"input.data.format": Sets the input Kafka record value format.

"kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:
```
confluent iam service-account list
```
For example:
```
confluent iam service-account list

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2
```

"chromadb.endpoint": The ChromaDB API endpoint URL.
"chromadb.api.key": The API key for authenticating with Chroma Cloud.
"chromadb.tenant" and "chromadb.database": The ChromaDB tenant ID and database name.
"collection1.name": The name of the ChromaDB collection to write to.
"collection1.topic": The Kafka topic to pull data from for this collection.

The following are optional properties you can include in the configuration:

"collections.num": The number of ChromaDB collections to write to. Valid values are 1 to 15. Defaults to 1.
"chromadb.auto.create.collection": Whether to automatically create collections if they do not exist. Defaults to true.
"chromadb.distance.metric": The distance metric used when the connector creates a collection. Valid entries are cosine (cosine similarity), ip (inner product), or l2 (squared L2 norm). Defaults to cosine. ChromaDB’s own default space is l2, so a collection you create yourself can differ from this default.

This setting applies only to collections the connector creates while chromadb.auto.create.collection is true. A pre-existing collection keeps the space it was created with, and this setting is ignored for it. Every configured collection shares one value, so per-collection distance metrics are not supported.
"chromadb.auto.embed": Whether the connector generates embeddings by calling an OpenAI-compatible embedding API. Defaults to false. When set to true, set collection1.embedding.endpoint, collection1.embedding.model, and collection1.embedding.api.key for each collection.
"collection1.batch.size": The maximum number of records per upsert request for this collection. Valid values are 1 to 300. Defaults to 50. ChromaDB Cloud rejects writes larger than 300 records, so higher values fail with HTTP 422.
"collection1.behavior.on.null.values": How to handle Kafka tombstone records for this collection. Valid entries are IGNORE or FAIL. Defaults to IGNORE.
"behavior.on.error": Error handling behavior for failed HTTP requests. Valid entries are FAIL or IGNORE. Defaults to FAIL. IGNORE continues past failed batches. Failed and dropped records are still written to the error topic. Set FAIL while troubleshooting so the task surfaces a mapped error message and stops on the first unrecoverable batch.

Example: Auto-generated embeddings

The following example generates embeddings through an OpenAI-compatible API instead of requiring a pre-computed embedding in each record:

{
  "name": "ChromaDBSink_1",
  "config": {
    "topics": "documents",
    "connector.class": "ChromaDBSink",
    "name": "ChromaDBSink_1",
    "input.data.format": "JSON",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "chromadb.endpoint": "https://api.trychroma.com:8000",
    "chromadb.api.key": "<my-chromadb-api-key>",
    "chromadb.tenant": "<my-chromadb-tenant>",
    "chromadb.database": "<my-chromadb-database>",
    "chromadb.auto.embed": "true",
    "collection1.name": "my-embeddings",
    "collection1.topic": "documents",
    "collection1.embedding.endpoint": "https://api.openai.com/v1/embeddings",
    "collection1.embedding.model": "text-embedding-3-small",
    "collection1.embedding.api.key": "<my-openai-api-key>",
    "tasks.max": "1"
  }
}

SMTs: For details about adding SMTs using the Confluent CLI, see the Single Message Transformations documentation. For a list of SMTs that are not supported with this connector, see Unsupported transformations.

For all property values and definitions, see Configuration Properties.

Step 4: Load the configuration file and create the connector

Enter the following command to load the configuration and start the connector:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file chromadb-sink-config.json

Example output:

Created connector ChromaDBSink_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |        Name        | Status  | Type
+-----------+---------------------+---------+------+
lcc-ix4dl   | ChromaDBSink_0      | RUNNING | sink

Step 6: Check the ChromaDB collection

Verify that new records appear in your ChromaDB collection.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. For more information, see View Connector Dead Letter Queue Errors in Confluent Cloud.

Configuration Properties

Use the following configuration properties with the fully managed connector.

Which topics do you want to get data from?

topics.regex

A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.

Type: string
Importance: low

topics

Identifies the topic name or a comma-separated list of topic names.

Type: list
Importance: high

errors.deadletterqueue.topic.name

The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: dlq-${connector}
Importance: low

reporter.result.topic.name

The name of the topic to produce records to after successfully processing a sink record. Defaults to ‘success-${connector}’ if not set. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: success-${connector}
Importance: low

reporter.error.topic.name

The name of the topic to produce records to after each unsuccessful record sink attempt. Defaults to ‘error-${connector}’ if not set. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: error-${connector}
Importance: low

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

Type: string
Default: default
Importance: medium

Input messages

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, BYTES or STRING. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

Type: string
Default: JSON
Importance: high

How should we connect to your data?

name

Sets a name for your connector.

Type: string
Valid Values: A string at most 64 characters long
Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode, whenever possible.

Type: string
Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY
Importance: high

kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

Type: string
Importance: high

kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

Consumer configuration

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

Type: long
Default: 300000 (5 minutes)
Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
Importance: low

max.poll.records

The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.

Type: long
Default: 500
Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
Importance: low

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

Type: int
Valid Values: [1,…]
Importance: high

ChromaDB Connection

chromadb.endpoint

The ChromaDB API endpoint URL. For example: https://api.trychroma.com:8000.

Type: string
Importance: high

chromadb.api.key

API key for authenticating with ChromaDB Cloud.

Type: password
Importance: high

chromadb.tenant

The ChromaDB tenant ID. For self-hosted single-tenant deployments, use default_tenant. For ChromaDB Cloud, use the tenant ID from the Cloud console.

Type: string
Importance: high

chromadb.database

The ChromaDB database name. For self-hosted deployments, use default_database. For ChromaDB Cloud, use the database name from the Cloud console.

Type: string
Importance: high

Embedding

chromadb.auto.embed

When enabled, the connector calls an external OpenAI-compatible embedding API to generate vector embeddings from the document text. When disabled, embeddings must be provided in each Kafka record. Each collection declares its own embedding endpoint, model, and API key under its Collection {i} configuration section. There is no shared global embedding configuration.

Type: boolean
Default: false
Importance: high

Collections

collections.num

The number of ChromaDB collections to write to. Each collection maps to a set of Kafka topics. Valid values are 1 to 15. Defaults to 1.

Type: int
Default: 1
Valid Values: [1,…,15]
Importance: high

chromadb.auto.create.collection

Whether to automatically create collections if they do not exist.

Type: boolean
Default: true
Importance: medium

chromadb.distance.metric

The distance metric for newly created collections.

Type: string
Default: cosine
Valid Values: cosine, ip, l2
Importance: medium

Collection 1 configuration

collection1.name

The name of the ChromaDB collection to write to.

Type: string
Importance: high

collection1.topic

Kafka topic to pull data from for this collection.

Type: string
Default: “”
Importance: high

collection1.batch.size

Maximum number of records per upsert request for this collection.

Type: int
Default: 50
Valid Values: [1,…,500]
Importance: medium

collection1.behavior.on.null.values

How to handle Kafka tombstone records (non-null key and null value). IGNORE skips the record. FAIL stops the connector.

Type: string
Default: IGNORE
Valid Values: FAIL, IGNORE
Importance: low

collection1.embedding.endpoint

Full URL of the OpenAI-compatible embedding API for this collection (for example, https://api.openai.com/v1/embeddings). Each collection declares its own. There is no shared global fallback. This field is required when Auto-Generate Embeddings is enabled.

Type: string
Importance: high

collection1.embedding.model

Embedding model for this collection (for example, text-embedding-3-small, nomic-embed-text). Each collection’s vectors are dimensioned by its model. Mixing dimensions in one collection would be rejected by ChromaDB. This field is required when Auto-Generate Embeddings is enabled.

Type: string
Importance: high

collection1.embedding.api.key

API key for the embedding service (for example, OpenAI or Azure OpenAI).

Type: password
Importance: high

collection1.embedding.batch.size

Number of texts to embed per API call for this collection. Higher values improve throughput but use more memory.

Type: int
Default: 10
Valid Values: [1,…,100]
Importance: low

Behavior on error

behavior.on.error

Error handling behavior for failed HTTP requests. FAIL stops the connector on the first error after retries are exhausted. IGNORE reports the failed batch to the error topic and continues with the next batch. Both modes route batches to the configured error topic through the reporter. IGNORE does not silently drop records.

Type: string
Default: FAIL
Valid Values: FAIL, IGNORE
Importance: low

Additional Configs

consumer.override.auto.offset.reset

Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset

Type: string
Importance: low

consumer.override.isolation.level

Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to read_uncommitted (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level

Type: string
Importance: low

header.converter

The converter class for the headers. This is used to serialize and deserialize the headers of the messages.

Type: string
Importance: low

key.converter.use.schema.guid

The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: string
Importance: low

key.converter.use.schema.id

The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: int
Importance: low

value.converter.allow.optional.map.keys

Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.auto.register.schemas

Specify if the Serializer should attempt to register the Schema.

Type: boolean
Importance: low

value.converter.connect.meta.data

Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.enhanced.avro.schema.support

Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.enhanced.protobuf.schema.support

Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.flatten.unions

Whether to flatten unions (oneofs). Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.generate.index.for.unions

Whether to generate an index suffix for unions. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.generate.struct.for.nulls

Whether to generate a struct variable for null values. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.int.for.enums

Whether to represent enums as integers. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.latest.compatibility.strict

Verify latest subject version is backward compatible when use.latest.version is true.

Type: boolean
Importance: low

value.converter.object.additional.properties

Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.

Type: boolean
Importance: low

value.converter.optional.for.nullables

Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.optional.for.proto2

Whether proto2 optionals are supported. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.scrub.invalid.names

Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and Protobuf Converters.

Type: boolean
Importance: low

value.converter.use.latest.version

Use latest version of schema in subject for serialization when auto.register.schemas is false.

Type: boolean
Importance: low

value.converter.use.optional.for.nonrequired

Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.

Type: boolean
Importance: low

value.converter.use.schema.guid

The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: string
Importance: low

value.converter.use.schema.id

The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: int
Importance: low

value.converter.wrapper.for.nullables

Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.wrapper.for.raw.primitives

Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.

Type: boolean
Importance: low

errors.tolerance

Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.

Type: string
Default: all
Importance: low

key.converter.key.schema.id.deserializer

The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.

Type: string
Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
Importance: low

key.converter.key.subject.name.strategy

How to construct the subject name for key schema registration.

Type: string
Default: TopicNameStrategy
Importance: low

value.converter.decimal.format

Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

BASE64 to serialize DECIMAL logical types as base64 encoded binary data and

NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.

Type: string
Default: BASE64
Importance: low

value.converter.flatten.singleton.unions

Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.

Type: boolean
Default: false
Importance: low

value.converter.ignore.default.for.nullables

When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.

Type: boolean
Default: false
Importance: low

value.converter.reference.subject.name.strategy

Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.

Type: string
Default: DefaultReferenceSubjectNameStrategy
Importance: low

value.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.

Type: boolean
Default: true
Importance: low

value.converter.schemas.enable

Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.

Type: boolean
Default: false
Importance: low

value.converter.value.schema.id.deserializer

The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.

Type: string
Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
Importance: low

value.converter.value.subject.name.strategy

Determines how to construct the subject name under which the value schema is registered with Schema Registry.

Type: string
Default: TopicNameStrategy
Importance: low

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

Type: boolean
Default: true
Importance: medium

Frequently asked questions

Find answers to frequently asked questions about the ChromaDB Sink connector for Confluent Cloud.

Why does the connector fail with an HTTP 422 error?

An HTTP 422 response from ChromaDB usually means the upsert batch is too large or the records in the batch don’t have consistent embedding dimensions.

Common causes:

Batch size too large for ChromaDB Cloud: ChromaDB Cloud enforces a per-request upsert limit of approximately 300 records per request. If you increase collection1.batch.size above this limit, ChromaDB rejects the request.
Embedding dimension mismatch: All embeddings in a batch, and in the target collection, must share the same dimension. A record with a different-sized embedding than the rest of the collection triggers this error.

Resolution:

Set collection1.batch.size (and the equivalent property for any other configured collection) to 250 or lower when writing to ChromaDB Cloud.
Confirm that every record’s embedding field has the same number of dimensions as the target collection, and that the embedding model generating them hasn’t changed.

Why do I see errors about mixed batches or inconsistent embeddings?

This error occurs when chromadb.auto.embed is disabled and a batch contains some records with a populated embedding field and some without. ChromaDB requires that a single upsert either includes embeddings for every record or for none of them. The same applies to dimensions: every embedding in one upsert must be the same length.

What the connector does with such a batch depends on behavior.on.error:

FAIL (the default): the connector rejects the whole batch, writes it to the error topic, and stops the task.
IGNORE: the connector removes only the inconsistent records, writes those to the error topic with a reason of Record dropped before ChromaDB upsert, and upserts the rest.

In both modes the affected records are written to the error topic. They are never discarded without a record of the failure.

Resolution:

Ensure every record sent to the connector includes a pre-computed embedding field, or
Set chromadb.auto.embed to true so the connector generates embeddings for records that are missing one.

Why is the connector failing with an authentication error?

A 401 response can come from either ChromaDB itself or, in auto-embed mode, from the embedding provider.

Common causes:

Incorrect or rotated chromadb.api.key: the key configured on the connector no longer matches the key on your ChromaDB target.
Incorrect or expired embedding API key: when chromadb.auto.embed is true, an invalid collection1.embedding.api.key causes the embedding call itself to fail with a 401.

Resolution:

Verify chromadb.api.key matches the current key on your ChromaDB Cloud account or self-hosted deployment.
If you use auto-generated embeddings, verify collection1.embedding.api.key is valid for your embedding provider.
After updating credentials, restart the connector to apply the change.

Why do I see warnings that a ChromaDB collection wasn’t found?

When chromadb.auto.create.collection is set to true (the default), a brief “collection not found” warning is expected the first time the connector targets a new or recently deleted collection. The connector automatically recreates the collection and retries, typically within a few seconds, with no data loss.

This becomes a persistent issue only when:

chromadb.auto.create.collection is set to false and the target collection doesn’t exist, or
the configured chromadb.api.key doesn’t have permission to create collections in your ChromaDB tenant.

Resolution: Set chromadb.auto.create.collection to true, or manually create the collection and verify the API key’s permissions.

How can I improve the connector’s write throughput?

Throughput to a single ChromaDB collection scales with tasks.max up to a point, then can plateau or decline. This happens because ChromaDB serializes writes to a given collection on its side, not because of a connector limitation.

Resolution:

Increase tasks.max to scale throughput against a single collection, but avoid over-provisioning tasks beyond what one collection can absorb.
To scale further, distribute records across multiple collections using collections.num and multiple collection{n}.topic mappings, rather than continuing to raise tasks.max on one collection.
Deploy the connector in the same region as your ChromaDB target to avoid cross-region latency on every write.

Why is throughput lower when auto-generated embeddings are enabled?

With chromadb.auto.embed set to true, the connector calls an external embedding API for every batch. End-to-end throughput is then bound by that embedding provider’s latency and rate limits, not by the connector.

Resolution: For the highest sustained throughput, generate embeddings upstream, for example with a stream processing job, and supply them in the embedding field with chromadb.auto.embed set to false. This decouples sink throughput from embedding API latency.

The connector is running but no data appears. What should I check?

Common causes:

Empty source topic: verify the source topic actually has unconsumed records. A connector with nothing to read appears idle rather than failed.
Records dropped during preprocessing: with chromadb.auto.embed set to false and behavior.on.error set to IGNORE, records with a null or empty embedding field are removed from the upsert. They are written to the error topic rather than discarded, so check the error topic before assuming the records were lost.
behavior.on.error set to IGNORE: the connector continues past failed batches, so the task stays in a Running state while individual batches fail. Set behavior.on.error to FAIL while troubleshooting so the task surfaces a mapped error message and stops on the first unrecoverable batch.

Resolution:

Confirm new records are actually being produced to the source topic.
Check that every record includes a valid, non-empty embedding field, or enable chromadb.auto.embed.
Set behavior.on.error to FAIL and review the connector logs or dead letter queue for rejected records.

Next Steps

For an example that shows fully managed Confluent Cloud connectors in action with Confluent Cloud for Apache Flink, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.