feat(api): update via SDK Studio

.stats.yml

@@ -1,2 +1,2 @@
-configured_endpoints: 33
-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/contextual-ai%2Fsunrise-4ed32c3243ce7a772e55bb1ba204736fc3fb1d712d8ca0eb91bac0c7ac626938.yml
+configured_endpoints: 35
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/contextual-ai%2Fsunrise-c0b9cfd71efb976777313fb342d2e31ebeb44b1b3f9bb7ddea971e6b2bc5fd19.yml

api.md

@@ -156,3 +156,23 @@ Types:
 Methods:
 
 - <code title="post /lmunit">client.lmUnit.<a href="./src/resources/lmunit.ts">create</a>({ ...params }) -> LMUnitCreateResponse</code>
+
+# Rerank
+
+Types:
+
+- <code><a href="./src/resources/rerank.ts">RerankCreateResponse</a></code>
+
+Methods:
+
+- <code title="post /rerank">client.rerank.<a href="./src/resources/rerank.ts">create</a>({ ...params }) -> RerankCreateResponse</code>
+
+# Generate
+
+Types:
+
+- <code><a href="./src/resources/generate.ts">GenerateCreateResponse</a></code>
+
+Methods:
+
+- <code title="post /generate">client.generate.<a href="./src/resources/generate.ts">create</a>({ ...params }) -> GenerateCreateResponse</code>

src/index.ts

@@ -15,7 +15,9 @@ import {
 } from './pagination';
 import * as Uploads from './uploads';
 import * as API from './resources/index';
+import { Generate, GenerateCreateParams, GenerateCreateResponse } from './resources/generate';
 import { LMUnit, LMUnitCreateParams, LMUnitCreateResponse } from './resources/lmunit';
+import { Rerank, RerankCreateParams, RerankCreateResponse } from './resources/rerank';
 import {
   Agent as AgentsAPIAgent,
   AgentCreateParams,
@@ -157,6 +159,8 @@ export class ContextualAI extends Core.APIClient {
   datastores: API.Datastores = new API.Datastores(this);
   agents: API.Agents = new API.Agents(this);
   lmUnit: API.LMUnit = new API.LMUnit(this);
+  rerank: API.Rerank = new API.Rerank(this);
+  generate: API.Generate = new API.Generate(this);
 
   protected override defaultQuery(): Core.DefaultQuery | undefined {
     return this._options.defaultQuery;
@@ -203,6 +207,8 @@ ContextualAI.DatastoresDatastoresPage = DatastoresDatastoresPage;
 ContextualAI.Agents = Agents;
 ContextualAI.AgentsPage = AgentsPage;
 ContextualAI.LMUnit = LMUnit;
+ContextualAI.Rerank = Rerank;
+ContextualAI.Generate = Generate;
 export declare namespace ContextualAI {
   export type RequestOptions = Core.RequestOptions;
 
@@ -252,6 +258,18 @@ export declare namespace ContextualAI {
     type LMUnitCreateResponse as LMUnitCreateResponse,
     type LMUnitCreateParams as LMUnitCreateParams,
   };
+
+  export {
+    Rerank as Rerank,
+    type RerankCreateResponse as RerankCreateResponse,
+    type RerankCreateParams as RerankCreateParams,
+  };
+
+  export {
+    Generate as Generate,
+    type GenerateCreateResponse as GenerateCreateResponse,
+    type GenerateCreateParams as GenerateCreateParams,
+  };
 }
 
 export { toFile, fileFromPath } from './uploads';

src/resources/agents/datasets/evaluate.ts

@@ -9,20 +9,20 @@ import { type Response } from '../../../_shims/index';
 export class Evaluate extends APIResource {
   /**
    * Create a new evaluation `Dataset` for the specified `Agent` using the provided
-   * JSONL file. A `Dataset` is a versioned collection of samples conforming to a
-   * particular schema, and can be used to store `Evaluation` test-sets and retrieve
-   * `Evaluation` results.
+   * JSONL or CSV file. A `Dataset` is a versioned collection of samples conforming
+   * to a particular schema, and can be used to store `Evaluation` test-sets and
+   * retrieve `Evaluation` results.
    *
    * Each `Dataset` is versioned and validated against its schema during creation and
    * subsequent updates. The provided `Dataset` file must conform to the schema
    * defined for the `dataset_type`.
    *
-   * File schema for `dataset_type` `evaluation_set` is a JSONL or CSV file where
-   * each line is one JSON object with the following required keys:
+   * File schema for `dataset_type` `evaluation_set` is a CSV file or a JSONL file
+   * where each line is one JSON object. The following keys are required:
    *
-   * - `prompt` (required, `string`): Prompt or question
+   * - `prompt` (`string`): Prompt or question
    *
-   * - `reference` (required, `string`): Required reference or ground truth response
+   * - `reference` (`string`): Reference or ground truth response
    */
   create(
     agentId: string,
@@ -80,12 +80,12 @@ export class Evaluate extends APIResource {
    * Create a new version of the dataset by appending content to the `Dataset` and
    * validating against its schema.
    *
-   * File schema for `dataset_type` `evaluation_set` is a JSONL file where each line
-   * is one JSON object with the following required keys:
+   * File schema for `dataset_type` `evaluation_set` is a CSV file or a JSONL file
+   * where each line is one JSON object. The following keys are required:
    *
    * - `prompt` (`string`): Prompt or question
    *
-   * - `reference` (`string`): Required reference or ground truth response
+   * - `reference` (`string`): Reference or ground truth response
    */
   update(
     agentId: string,
@@ -184,7 +184,7 @@ export interface EvaluateCreateParams {
   dataset_type: 'evaluation_set';
 
   /**
-   * JSONL file containing the evaluation dataset
+   * JSONL or CSV file containing the evaluation dataset
    */
   file: Core.Uploadable;
 }
@@ -210,7 +210,7 @@ export interface EvaluateUpdateParams {
   dataset_type: 'evaluation_set';
 
   /**
-   * JSONL file containing the entries to append to the evaluation dataset
+   * JSONL or CSV file containing the entries to append to the evaluation dataset
    */
   file: Core.Uploadable;
 }

src/resources/agents/evaluate/evaluate.ts

@@ -72,7 +72,7 @@ export interface EvaluateCreateParams {
   /**
    * ID of the model to evaluate. Uses the default model if not specified.
    */
-  llm_model_id?: string | null;
+  llm_model_id?: string;
 }
 
 Evaluate.Jobs = Jobs;

src/resources/agents/query.ts

@@ -190,7 +190,7 @@ export namespace QueryResponse {
     content: string;
 
     /**
-     * Role of sender
+     * Role of the sender
      */
     role: 'user' | 'system' | 'assistant';
   }
@@ -301,7 +301,8 @@ export interface QueryCreateParams {
 
 export namespace QueryCreateParams {
   /**
-   * Message object for a message sent or received in a /query conversation
+   * Message object for a message sent or received in a /query and /generate
+   * conversation
    */
   export interface Message {
     /**
@@ -310,7 +311,7 @@ export namespace QueryCreateParams {
     content: string;
 
     /**
-     * Role of sender
+     * Role of the sender
      */
     role: 'user' | 'system' | 'assistant';
   }

src/resources/agents/tune/tune.ts

@@ -57,21 +57,21 @@ export interface TuneCreateParams {
    * JSON object represents a single training example. The four required fields are
    * `guideline`, `prompt`, `reference`, and `knowledge`.
    *
-   * - `knowledge` (`list[str]`): Knowledge or retrievals used to generate the
-   *   reference response, as a list of string text chunks
+   * - `knowledge` (`list[str]`): Retrieved knowledge used to generate the reference
+   *   answer. `knowledge` is a list of retrieved text chunks.
    *
-   * - `reference` field should be the model's response to the prompt.
+   * - `reference` (`str`): The gold-standard answer to the prompt.
    *
-   * - `guideline` (`str): Guidelines or criteria for model output
+   * - `guideline` (`str`): Guidelines for model output.
    *
-   * - `prompt` (required, `string`): Prompt or question model should respond to.
+   * - `prompt` (`str`): Question for the model to respond to.
    *
    * Example:
    *
    * ```json
    * [
    *   {
-   *     "guideline": "The response should be accurate.",
+   *     "guideline": "The answer should be accurate.",
    *     "prompt": "What was last quarter's revenue?",
    *     "reference": "According to recent reports, the Q3 revenue was $1.2 million, a 0.1 million increase from Q2.",
    *     "knowledge": [

src/resources/generate.ts

@@ -0,0 +1,86 @@
+// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+import { APIResource } from '../resource';
+import * as Core from '../core';
+
+export class Generate extends APIResource {
+  /**
+   * Generate a response using Contextual's Grounded Language Model (GLM), an LLM
+   * engineered specifically to prioritize faithfulness to in-context retrievals over
+   * parametric knowledge to reduce hallucinations in Retrieval-Augmented Generation.
+   *
+   * The total request cannot exceed 6,100 tokens.
+   */
+  create(body: GenerateCreateParams, options?: Core.RequestOptions): Core.APIPromise<GenerateCreateResponse> {
+    return this._client.post('/generate', { body, ...options });
+  }
+}
+
+/**
+ * /generate result object.
+ */
+export interface GenerateCreateResponse {
+  /**
+   * The model's response to the last user message.
+   */
+  response: string;
+}
+
+export interface GenerateCreateParams {
+  /**
+   * Extra parameters to be passed to Contextual's GLM
+   */
+  extra_body: GenerateCreateParams.ExtraBody;
+
+  /**
+   * List of messages in the conversation so far. The last message must be from the
+   * user.
+   */
+  messages: Array<GenerateCreateParams.Message>;
+
+  /**
+   * The version of the Contextual's GLM to use. Currently, we just have "v1".
+   */
+  model: string;
+}
+
+export namespace GenerateCreateParams {
+  /**
+   * Extra parameters to be passed to Contextual's GLM
+   */
+  export interface ExtraBody {
+    /**
+     * The knowledge sources the model can use when generating a response.
+     */
+    knowledge: Array<string>;
+
+    /**
+     * Instructions that the model follows when generating responses. Note that we do
+     * not guarantee that the model follows these instructions exactly.
+     */
+    system_prompt?: string;
+  }
+
+  /**
+   * Message object for a message sent or received in a /query and /generate
+   * conversation
+   */
+  export interface Message {
+    /**
+     * Content of the message
+     */
+    content: string;
+
+    /**
+     * Role of the sender
+     */
+    role: 'user' | 'system' | 'assistant';
+  }
+}
+
+export declare namespace Generate {
+  export {
+    type GenerateCreateResponse as GenerateCreateResponse,
+    type GenerateCreateParams as GenerateCreateParams,
+  };
+}

src/resources/index.ts

@@ -24,4 +24,6 @@ export {
   type DatastoreCreateParams,
   type DatastoreListParams,
 } from './datastores/datastores';
+export { Generate, type GenerateCreateResponse, type GenerateCreateParams } from './generate';
 export { LMUnit, type LMUnitCreateResponse, type LMUnitCreateParams } from './lmunit';
+export { Rerank, type RerankCreateResponse, type RerankCreateParams } from './rerank';

src/resources/rerank.ts

@@ -0,0 +1,75 @@
+// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+import { APIResource } from '../resource';
+import * as Core from '../core';
+
+export class Rerank extends APIResource {
+  /**
+   * Rank a list of documents according to their relevance to a query.
+   *
+   * The total request cannot exceed 400,000 tokens. The combined length of any
+   * document and the query must not exceed 4,000 tokens.
+   */
+  create(body: RerankCreateParams, options?: Core.RequestOptions): Core.APIPromise<RerankCreateResponse> {
+    return this._client.post('/rerank', { body, ...options });
+  }
+}
+
+/**
+ * Rerank output response.
+ */
+export interface RerankCreateResponse {
+  /**
+   * The ranked list of documents containing the index of the document and the
+   * relevance score, sorted by relevance score.
+   */
+  results: Array<RerankCreateResponse.Result>;
+}
+
+export namespace RerankCreateResponse {
+  /**
+   * Reranked result object.
+   */
+  export interface Result {
+    /**
+     * Index of the document in the input list, starting with 0
+     */
+    index: number;
+
+    /**
+     * Relevance scores assess how likely a document is to have information that is
+     * helpful to answer the query. Our model outputs the scores in a wide range, and
+     * we normalize scores to a 0-1 scale and truncate the response to 8 decimal
+     * places. Our reranker is designed for RAG, so its purpose is to check whether a
+     * document has information that is helpful to answer the query. A reranker that is
+     * designed for direct Q&A (Question & Answer) would behave differently.
+     */
+    relevance_score: number;
+  }
+}
+
+export interface RerankCreateParams {
+  /**
+   * The texts to be reranked according to their relevance to the query
+   */
+  documents: Array<string>;
+
+  /**
+   * The version of the reranker to use. Currently, we just have "v1".
+   */
+  model: string;
+
+  /**
+   * The string against which documents will be ranked for relevance
+   */
+  query: string;
+
+  /**
+   * The number of top-ranked results to return
+   */
+  top_n?: number;
+}
+
+export declare namespace Rerank {
+  export { type RerankCreateResponse as RerankCreateResponse, type RerankCreateParams as RerankCreateParams };
+}