INTEGRITY Cloudflare Docs

Workers binding

Workers provides a serverless execution environment that allows you to create new applications or augment existing ones. Use a Workers binding to search and chat with your AI Search instances from a Cloudflare Worker.

Configure the binding

To use AI Search with Workers, you must create an AI Search binding. You create bindings by updating your Wrangler configuration. AI Search provides two types of bindings:

Namespace binding

Access all instances within a namespace. You can get, create, list, and delete instances at runtime.

{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "compatibility_date": "2026-03-27",
  "ai_search_namespaces": [
    {
      "binding": "AI_SEARCH",
      "namespace": "my-namespace"
    }
  ]
}
compatibility_date = "2026-03-27"

[[ai_search_namespaces]]
binding = "AI_SEARCH"
namespace = "my-namespace"
Field Type Required Description
binding string Yes The variable name available on env. For example, "AI_SEARCH" makes it accessible as env.AI_SEARCH.
namespace string Yes The namespace to bind to. A default namespace is created automatically for every account. If the namespace does not exist, Wrangler creates it on deploy.
remote boolean No Set to true for local development with wrangler dev.

Instance binding

Bind directly to a single instance in the default namespace. Use this when you know which instance you need at deploy time.

{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "compatibility_date": "2026-03-27",
  "ai_search": [
    {
      "binding": "MY_SEARCH",
      "instance_name": "my-instance"
    }
  ]
}
compatibility_date = "2026-03-27"

[[ai_search]]
binding = "MY_SEARCH"
instance_name = "my-instance"
Field Type Required Description
binding string Yes The variable name available on env. For example, "MY_SEARCH" makes it accessible as env.MY_SEARCH.
instance_name string Yes The name of the AI Search instance. Must exist in the default namespace at deploy time.
remote boolean No Set to true for local development with wrangler dev.

Instance methods

The following methods are available on both the ai_search_namespaces and ai_search bindings. With the namespace binding, call methods on the handle returned by get(). With the instance binding, call methods directly on the binding (for example, env.MY_SEARCH.search()).

The examples below use the namespace binding.

Search for relevant content chunks from your indexed data source. Returns scored chunks with source references.

const instance = env.AI_SEARCH.get("my-instance");

const results = await instance.search({
	messages: [{ role: "user", content: "What is Cloudflare?" }],
});

Parameters

messages array required

An array of message objects representing the conversation. Each message has a role and content field.


query string optional

A simple text query string. Alternative to messages. Provide either query or messages, not both.


ai_search_options object optional

Configuration options for the search operation.

Response

The response contains the following fields:

Field Type Description
search_query string The query used for the search, which may be rewritten if query rewriting is enabled.
chunks array An array of matching content chunks.
chunks[].id string The unique identifier for the chunk.
chunks[].type string The type of content, typically text.
chunks[].score number The overall match score between 0 and 1.
chunks[].text string The text content of the chunk.
chunks[].item object Information about the source item.
chunks[].item.key string The file path or URL of the source document.
chunks[].item.timestamp number Unix timestamp of when the item was last modified.
chunks[].item.metadata object Custom metadata associated with the source item.
chunks[].scoring_details object Breakdown of how the chunk was scored.
chunks[].scoring_details.vector_score number The semantic similarity score (0 to 1).
chunks[].scoring_details.keyword_score number The keyword (BM25) match score. Present when using hybrid or keyword retrieval.
chunks[].scoring_details.keyword_rank number The keyword rank position.
chunks[].scoring_details.vector_rank number The vector rank position.
chunks[].scoring_details.reranking_score number The reranking score (0 to 1). Present when reranking is enabled.
chunks[].scoring_details.fusion_method string The fusion method used (rrf or max). Present when using hybrid retrieval.

chatCompletions()

Generate chat completions using your AI Search instance as context. This method retrieves relevant content and uses it to generate a response.

const instance = env.AI_SEARCH.get("my-instance");

const response = await instance.chatCompletions({
	messages: [
		{ role: "system", content: "You are a helpful documentation assistant." },
		{ role: "user", content: "What is Cloudflare?" },
	],
	model: "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
	ai_search_options: {
		retrieval: {
			max_num_results: 5,
		},
		query_rewrite: {
			enabled: true,
		},
	},
});

Stream responses

Set stream: true to receive responses as Server-Sent Events (SSE) as they are generated:

const instance = env.AI_SEARCH.get("my-instance");

const stream = await instance.chatCompletions({
	messages: [{ role: "user", content: "What is Cloudflare?" }],
	stream: true,
});

return new Response(stream, {
	headers: {
		"content-type": "text/event-stream",
		"cache-control": "no-cache",
	},
});

When stream is enabled, the method returns a ReadableStream of SSE events. Each event contains a JSON object with choices[0].delta.content for incremental text. The stream ends with a data: [DONE] event.

Parameters

messages array required

An array of message objects representing the conversation. Each message has a role and content field.


model string optional

The text-generation model used to generate responses. Defaults to the generation model configured in the AI Search instance settings. For a list of supported models, refer to Supported models.


stream boolean optional

Returns a stream of results as they are generated. When enabled, returns a Response object with a readable stream. Defaults to false.


ai_search_options object optional

Configuration options for the search and generation operation.

Response (non-streaming)

Field Type Description
id string Unique identifier for the completion.
object string Always chat.completion.
created number Unix timestamp of when the completion was created.
model string The model used to generate the response.
choices array Array of completion choices.
choices[].message.role string Always assistant.
choices[].message.content string The generated response text.
choices[].finish_reason string Why the model stopped generating. Typically stop.
usage.prompt_tokens number Number of tokens in the prompt.
usage.completion_tokens number Number of tokens in the generated response.
usage.total_tokens number Total tokens used.
chunks array The source chunks used as context. Same format as the search response.

Response (streaming)

When stream: true, the method returns a ReadableStream of Server-Sent Events. The retrieved chunks are sent first as a chunks event, followed by the streamed response.

event: chunks
data: [{"id":"chunk-001","type":"text","score":0.85,"text":"...","item":{"key":"about-cloudflare.md","timestamp":1775925540000},"scoring_details":{"vector_score":0.85}}]

data: {"id":"id-1776072781845","created":1776072781,"model":"@cf/meta/llama-3.3-70b-instruct-fp8-fast","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":" document"}}]}

data: {"id":"id-1776072781845","created":1776072781,"model":"@cf/meta/llama-3.3-70b-instruct-fp8-fast","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":" you provided doesn"}}]}

data: {"id":"id-1776072781845","created":1776072781,"model":"@cf/meta/llama-3.3-70b-instruct-fp8-fast","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"'t contain"}}]}

data: {"id":"id-1776072781845","created":1776072781,"model":"@cf/meta/llama-3.3-70b-instruct-fp8-fast","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":" information"}}]}

data: [DONE]

Namespace methods

The following methods are only available when using the ai_search_namespaces binding. Search and chat across multiple instances in a single call using the namespace handle directly (env.AI_SEARCH).

search()

Pass instance_ids in ai_search_options to specify which instances to query. Results are merged and ranked, and each chunk includes an instance_id field identifying which instance it came from.

const results = await env.AI_SEARCH.search({
	messages: [{ role: "user", content: "What is Cloudflare?" }],
	ai_search_options: {
		instance_ids: ["product-docs", "customer-abc123"],
	},
});

Parameters

Same as instance-level search, with one additional required field:

Parameter Type Required Description
ai_search_options object Yes Required for namespace-level search.
ai_search_options.instance_ids array Yes Instance IDs to search across. Minimum 1, maximum 10.

Response

Same as instance-level search, with additional fields:

Field Type Description
chunks[].instance_id string The instance this chunk came from.
errors array Per-instance errors if any instances failed. Each object has instance_id and message.

chatCompletions()

Generate chat completions using context retrieved from multiple instances.

const response = await env.AI_SEARCH.chatCompletions({
	messages: [{ role: "user", content: "What is Cloudflare?" }],
	ai_search_options: {
		instance_ids: ["product-docs", "customer-abc123"],
	},
});

Streaming is supported with stream: true.

Parameters

Same as instance-level chat completions, with one additional required field:

Parameter Type Required Description
ai_search_options object Yes Required for namespace-level chat completions.
ai_search_options.instance_ids array Yes Instance IDs to search across. Minimum 1, maximum 10.

Response

Same as instance-level chat completions, with additional fields on each chunk:

Field Type Description
chunks[].instance_id string The instance this chunk came from.
errors array Per-instance errors if any instances failed. Each object has instance_id and message.

Local development

Local development is supported by proxying requests to your deployed AI Search instance. Add remote: true to your binding configuration to enable local development with wrangler dev.

// wrangler.jsonc
{
	"ai_search": [
		{
			"binding": "MY_SEARCH",
			"instance_name": "my-instance",
			"remote": true,
		},
	],
}