import type {
  FlexibleSchema,
  InferSchema,
  ModelMessage,
  ProviderOptions,
} from "@ai-sdk/provider-utils";
import type { JSONValue } from "@ai-sdk/provider";
import type {
  EmbeddingModel,
  GenerateObjectResult,
  generateText,
  GenerateTextResult,
  LanguageModelRequestMetadata,
  LanguageModelResponseMetadata,
  LanguageModelUsage,
  LanguageModel,
  streamObject,
  streamText,
  StreamTextResult,
  ToolSet,
  CallSettings,
  generateObject,
} from "ai";

export interface Output<_T = any, _P = any, _E = any> {
  name: string;
  responseFormat: any;
  parseCompleteOutput: any;
  parsePartialOutput: any;
  createElementStreamTransform: any;
}
import type {
  GenericActionCtx,
  GenericDataModel,
  WithoutSystemFields,
} from "convex/server";
import type {
  MessageDoc,
  ProviderMetadata,
  StreamDelta,
  StreamMessage,
  ThreadDoc,
} from "../validators.js";
import type { StreamingOptions } from "./streaming.js";
import type { ComponentApi } from "../component/_generated/component.js";

/**
 * Type-level check that ensures models are from AI SDK v6.
 * If a v5 model (LanguageModelV2) is passed, TypeScript will show the error message string.
 */
type AssertAISDKv6<T> = T extends { specificationVersion: "v3" }
  ? T
  : "⚠️ @convex-dev/agent v0.6.0 requires AI SDK v6. Update your dependencies: npm install ai@^6.0.35 @ai-sdk/openai@^3.0.10 (or other provider). See: node_modules/@convex-dev/agent/MIGRATION.md";

export type AgentPrompt = {
  /**
   * System message to include in the prompt. Overwrites Agent instructions.
   */
  system?: string;
  /**
   * A prompt. It can be either a text prompt or a list of messages.
   * If used with `promptMessageId`, it will be used in place of that
   * prompt message and no input messages will be saved.
   * Otherwise, if used with the storageOptions "promptAndOutput" (default),
   * it will be the only message saved.
   * If a string is provided, it will be a user message.
   */
  prompt?: string | Array<ModelMessage> | undefined;
  /**
   * A list of messages to use as context before the prompt.
   * If used with `prompt`, these will precede the prompt.
   * If used with the storageOptions "promptAndOutput" (default),
   * none of these messages will be saved.
   */
  messages?: Array<ModelMessage> | undefined;
  /**
   * If provided, it uses this existing message to anchor the prompt:
   * - The specified message will be included, unless `prompt` is also
   *   provided, in which case that will be inserted in place of this
   *   specified message.
   * - Recent and search messages will not include messages after this
   *   message's order.
   * - If there are already responses on the same order,
   *   for example, tool calls and responses,
   *   those will be included automatically.
   *
   * Note: if this is provided, no input messages will be saved by default.
   */
  promptMessageId?: string | undefined;
  /**
   * The model to use for the LLM calls. This will override the languageModel
   * specified in the Agent config.
   */
  model?: LanguageModel;
};

export type Config = {
  /**
   * The LLM model to use for generating / streaming text and objects.
   * Requires AI SDK v6 (@ai-sdk/* packages v3.x).
   *
   * @example
   * import { openai } from "@ai-sdk/openai"
   * const myAgent = new Agent(components.agent, {
   *   languageModel: openai.chat("gpt-4o-mini"),
   * })
   */
  languageModel?: AssertAISDKv6<LanguageModel>;
  /**
   * @deprecated Use `embeddingModel` instead.
   */
  textEmbeddingModel?: EmbeddingModel;
  /**
   * The model to use for text embeddings. Optional.
   * If specified, it will use this for generating vector embeddings
   * of chats, and can opt-in to doing vector search for automatic context
   * on generateText, etc.
   * e.g.
   * import { openai } from "@ai-sdk/openai"
   * const myAgent = new Agent(components.agent, {
   *   ...
   *   embeddingModel: openai.embedding("text-embedding-3-small")
   */
  embeddingModel?: EmbeddingModel;
  /**
   * Options to determine what messages are included as context in message
   * generation. To disable any messages automatically being added, pass:
   * { recentMessages: 0 }
   */
  contextOptions?: ContextOptions;
  /**
   * Determines whether messages are automatically stored when passed as
   * arguments or generated.
   */
  storageOptions?: StorageOptions;
  /**
   * The usage handler to use for this agent.
   */
  usageHandler?: UsageHandler;
  /**
   * By default, messages are ordered with context in `fetchContextWithPrompt`,
   * but you can override this by providing a context handler. Here you can
   * filter, modify, or enrich the context messages. If provided, the default
   * ordering will not apply. This excludes the system message / instructions.
   */
  contextHandler?: ContextHandler;
  /**
   * Called for each LLM request/response, so you can do things like
   * log the raw request body or response headers to a table, or logs.
   */
  rawRequestResponseHandler?: RawRequestResponseHandler;
  /**
   * @deprecated Reach out if you use this. Otherwise will be removed soon.
   * Default provider options to pass for the LLM calls.
   * This can be overridden at each generate/stream callsite on a per-field
   * basis. To clear a default setting, you'll need to pass `undefined`.
   */
  providerOptions?: ProviderOptions;
  /**
   * The default settings to use for the LLM calls.
   * This can be overridden at each generate/stream callsite on a per-field
   * basis. To clear a default setting, you'll need to pass `undefined`.
   */
  callSettings?: CallSettings;
  /**
   * The maximum number of steps to allow for a single generation.
   *
   * For example, if an agent wants to call a tool, that call and tool response
   * will be one step. Generating a response based on the tool call & response
   * will be a second step.
   * If it runs out of steps, it will return the last step result, which may
   * not be an assistant message.

   * This becomes the default value when `stopWhen` is not specified in the
   * Agent or generation callsite.
   * AI SDK v5 removed the `maxSteps` argument, but this is kept here for
   * convenience and backwards compatibility.
   * Defaults to 1.
   */
  maxSteps?: number;
};

/**
 * Options to configure what messages are fetched as context,
 * automatically with thread.generateText, or directly via search.
 */
export type ContextOptions = {
  /**
   * Whether to include tool messages in the context.
   * By default, tool calls and results are not included.
   */
  excludeToolMessages?: boolean;
  /**
   * How many recent messages to include. These are added after the search
   * messages, and do not count against the search limit.
   * Default: 100
   */
  recentMessages?: number;
  /**
   * Options for searching messages.
   */
  searchOptions?: {
    /**
     * The maximum number of messages to fetch. Default is 10.
     */
    limit: number;
    /**
     * Whether to use text search to find messages. Default is false.
     */
    textSearch?: boolean;
    /**
     * Whether to use vector search to find messages. Default is false.
     * At least one of textSearch or vectorSearch must be true.
     */
    vectorSearch?: boolean;
    /**
     * The score threshold for vector search. Default is 0.0.
     */
    vectorScoreThreshold?: number;
    /**
     * What messages around the search results to include.
     * Default: { before: 2, after: 1 }
     * (two before, and one after each message found in the search)
     * Note, this is after the limit is applied.
     * By default this will quadruple the number of messages fetched.
     */
    messageRange?: { before: number; after: number };
  };
  /**
   * Whether to search across other threads for relevant messages.
   * By default, only the current thread is searched.
   */
  searchOtherThreads?: boolean;
};

/**
 * Options to configure the automatic saving of messages
 * when generating text / objects in a thread.
 */
export type StorageOptions = {
  /**
   * Whether to save messages to the thread history.
   * Pass "all" to save all input and output messages.
   * Pass "none" to not save any input or output messages.
   * Pass "promptAndOutput" to save the prompt and all output messages.
   * If you pass {messages} but no {prompt}, it will assume messages.at(-1) is
   * the prompt.
   * Defaults to "promptAndOutput".
   */
  saveMessages?: "all" | "none" | "promptAndOutput";
};

export type GenerationOutputMetadata = {
  /**
   * The ID of the prompt message for the generation.
   */
  promptMessageId?: string;
  /**
   * The order of the prompt message and responses for the generation.
   * Each order starts with a user message, then followed by agent responses.
   * If a promptMessageId is provided, that dictates the order.
   */
  order?: number;
  /**
   * The messages saved for the generation - both saved input and output.
   * If you passed promptMessageId, it will not include that message.
   */
  savedMessages?: MessageDoc[];
  /**
   * @deprecated Use promptMessageId instead.
   * The ID of the prompt message for the generation.
   */
  messageId?: string;
};

export type UsageHandler = (
  ctx: ActionCtx,
  args: {
    userId: string | undefined;
    threadId: string | undefined;
    agentName: string | undefined;
    usage: LanguageModelUsage;
    // Often has more information, like cached token usage in the case of openai.
    providerMetadata: ProviderMetadata | undefined;
    model: string;
    provider: string;
  },
) => void | Promise<void>;

/**
 * By default, messages are ordered with context in `fetchContextWithPrompt`,
 * but you can override this by providing a context handler. Here you can filter
 * out, add in, or reorder messages.
 */
export type ContextHandler = (
  ctx: ActionCtx,
  args: {
    /**
     * All messages in the default order.
     */
    allMessages: ModelMessage[];
    /**
     * The messages fetched from search.
     */
    search: ModelMessage[];
    /**
     * The recent messages already in the thread history,
     * excluding any messages that came after promptMessageId.
     */
    recent: ModelMessage[];
    /**
     * The messages passed as the `messages` argument to e.g. generateText.
     */
    inputMessages: ModelMessage[];
    /**
     * The message(s) passed as the `prompt` argument to e.g. generateText.
     * Otherwise, if `promptMessageId` was provided, the message at that id.
     * `prompt` will override the message at `promptMessageId`.
     */
    inputPrompt: ModelMessage[];
    /**
     * Any messages on the same `order` as the promptMessageId message after the
     * prompt message. These are presumably existing responses to the prompt
     * message.
     */
    existingResponses: ModelMessage[];
    /**
     * The user associated with the generation, if any.
     */
    userId: string | undefined;
    /**
     * The thread associated with the generation, if any.
     */
    threadId: string | undefined;
  },
) => ModelMessage[] | Promise<ModelMessage[]>;

export type RawRequestResponseHandler = (
  ctx: ActionCtx,
  args: {
    userId: string | undefined;
    threadId: string | undefined;
    agentName: string | undefined;
    request: LanguageModelRequestMetadata;
    response: LanguageModelResponseMetadata;
  },
) => void | Promise<void>;

export type AgentComponent = ComponentApi;

export type TextArgs<
  AgentTools extends ToolSet,
  TOOLS extends ToolSet | undefined = undefined,
  OUTPUT extends Output<any, any, any> = never,
> = Omit<
  Parameters<
    typeof generateText<TOOLS extends undefined ? AgentTools : TOOLS, OUTPUT>
  >[0],
  "model" | "prompt" | "messages"
> & {
  /**
   * The tools to use for the tool calls. This will override tools specified
   * in the Agent constructor or createThread / continueThread.
   */
  tools?: TOOLS;
} & AgentPrompt;

export type StreamingTextArgs<
  AgentTools extends ToolSet,
  TOOLS extends ToolSet | undefined = undefined,
  OUTPUT extends Output<any, any, any> = never,
> = Omit<
  Parameters<
    typeof streamText<TOOLS extends undefined ? AgentTools : TOOLS, OUTPUT>
  >[0],
  "model" | "prompt" | "messages"
> & {
  /**
   * The tools to use for the tool calls. This will override tools specified
   * in the Agent constructor or createThread / continueThread.
   */
  tools?: TOOLS;
} & AgentPrompt;

export type ObjectMode = "object" | "array" | "enum" | "no-schema";

export type GenerateObjectArgs<
  SCHEMA extends FlexibleSchema<unknown> = FlexibleSchema<JSONValue>,
  OUTPUT extends ObjectMode = InferSchema<SCHEMA> extends string
    ? "enum"
    : "object",
  RESULT = OUTPUT extends "array"
    ? Array<InferSchema<SCHEMA>>
    : InferSchema<SCHEMA>,
> = AgentPrompt &
  Omit<
    Parameters<typeof generateObject<SCHEMA, OUTPUT, RESULT>>[0],
    "model" | "prompt" | "messages"
  > & {
    schema?: SCHEMA;
    enum?: Array<RESULT>;
  };

export type StreamObjectArgs<
  SCHEMA extends FlexibleSchema<unknown> = FlexibleSchema<JSONValue>,
  OUTPUT extends ObjectMode = InferSchema<SCHEMA> extends string
    ? "enum"
    : "object",
  RESULT = OUTPUT extends "array"
    ? Array<InferSchema<SCHEMA>>
    : InferSchema<SCHEMA>,
> = AgentPrompt &
  Omit<
    Parameters<typeof streamObject<SCHEMA, OUTPUT, RESULT>>[0],
    "model" | "prompt" | "messages"
  > & {
    schema?: SCHEMA;
    enum?: Array<RESULT>;
  };

export type MaybeCustomCtx<
  CustomCtx,
  DataModel extends GenericDataModel,
  AgentTools extends ToolSet,
> =
  CustomCtx extends Record<string, unknown>
    ? {
        /**
         * If you have a custom ctx that you use with the Agent
         * (e.g. new Agent<{ orgId: string }>(...))
         * you need to provide this function to add any extra fields.
         * e.g.
         * ```ts
         * const myAgent = new Agent<{ orgId: string }>(...);
         * const myAction = myAgent.asTextAction({
         *   customCtx: (ctx: ActionCtx, target, llmArgs) => {
         *     const orgId = await lookupOrgId(ctx, target.threadId);
         *     return { orgId };
         *   },
         * });
         * ```
         * Then, in your tools, you can
         */
        customCtx: (
          ctx: GenericActionCtx<DataModel>,
          target: {
            userId?: string | undefined;
            threadId?: string | undefined;
          },
          llmArgs: TextArgs<AgentTools>,
        ) => CustomCtx;
      }
    : { customCtx?: never };

type ThreadOutputMetadata = Required<GenerationOutputMetadata>;

/**
 * The interface for a thread returned from {@link createThread} or {@link continueThread}.
 * This is contextual to a thread and/or user.
 */
export interface Thread<DefaultTools extends ToolSet> {
  /**
   * The target threadId, from the startThread or continueThread initializers.
   */
  threadId: string;
  /**
   * Get the metadata for the thread.
   */
  getMetadata: () => Promise<ThreadDoc>;
  /**
   * Update the metadata for the thread.
   */
  updateMetadata: (
    patch: Partial<WithoutSystemFields<ThreadDoc>>,
  ) => Promise<ThreadDoc>;
  /**
   * This behaves like {@link generateText} from the "ai" package except that
   * it add context based on the userId and threadId and saves the input and
   * resulting messages to the thread, if specified.
   * Use {@link continueThread} to get a version of this function already scoped
   * to a thread (and optionally userId).
   * @param args The arguments to the generateText function, along with extra controls
   * for the {@link ContextOptions} and {@link StorageOptions}.
   * @returns The result of the generateText function.
   */
  generateText<
    TOOLS extends ToolSet | undefined = undefined,
    OUTPUT extends Output<any, any, any> = never,
  >(
    generateTextArgs: AgentPrompt &
      TextArgs<TOOLS extends undefined ? DefaultTools : TOOLS, TOOLS, OUTPUT>,
    options?: Options,
  ): Promise<
    GenerateTextResult<TOOLS extends undefined ? DefaultTools : TOOLS, OUTPUT> &
      ThreadOutputMetadata
  >;

  /**
   * This behaves like {@link streamText} from the "ai" package except that
   * it add context based on the userId and threadId and saves the input and
   * resulting messages to the thread, if specified.
   * Use {@link continueThread} to get a version of this function already scoped
   * to a thread (and optionally userId).
   * @param args The arguments to the streamText function, along with extra controls
   * for the {@link ContextOptions} and {@link StorageOptions}.
   * @returns The result of the streamText function.
   */
  streamText<
    TOOLS extends ToolSet | undefined = undefined,
    OUTPUT extends Output<any, any, any> = never,
  >(
    streamTextArgs: AgentPrompt &
      StreamingTextArgs<
        TOOLS extends undefined ? DefaultTools : TOOLS,
        TOOLS,
        OUTPUT
      >,
    options?: Options & {
      /**
       * Whether to save incremental data (deltas) from streaming responses.
       * Defaults to false.
       * If false, it will not save any deltas to the database.
       * If true, it will save deltas with {@link DEFAULT_STREAMING_OPTIONS}.
       *
       * Regardless of this option, when streaming you are able to use this
       * `streamText` function as you would with the "ai" package's version:
       * iterating over the text, streaming it over HTTP, etc.
       */
      saveStreamDeltas?: boolean | StreamingOptions;
    },
  ): Promise<
    StreamTextResult<TOOLS extends undefined ? DefaultTools : TOOLS, OUTPUT> &
      ThreadOutputMetadata
  >;
  /**
   * This behaves like {@link generateObject} from the "ai" package except that
   * it add context based on the userId and threadId and saves the input and
   * resulting messages to the thread, if specified. This overload is for objects, arrays, and enums.
   * Use {@link continueThread} to get a version of this function already scoped
   * to a thread (and optionally userId).
   * @param args The arguments to the generateObject function, along with extra controls
   * for the {@link ContextOptions} and {@link StorageOptions}.
   * @returns The result of the generateObject function.
   */
  generateObject<
    SCHEMA extends FlexibleSchema<unknown> = FlexibleSchema<JSONValue>,
    OUTPUT extends ObjectMode = InferSchema<SCHEMA> extends string
      ? "enum"
      : "object",
    RESULT = OUTPUT extends "array"
      ? Array<InferSchema<SCHEMA>>
      : InferSchema<SCHEMA>,
  >(
    generateObjectArgs: AgentPrompt &
      GenerateObjectArgs<SCHEMA, OUTPUT, RESULT>,
    options?: Options,
  ): Promise<GenerateObjectResult<RESULT> & ThreadOutputMetadata>;
  /**
   * This behaves like {@link streamObject} from the "ai" package except that
   * it add context based on the userId and threadId and saves the input and
   * resulting messages to the thread, if specified.
   * Use {@link continueThread} to get a version of this function already scoped
   * to a thread (and optionally userId).
   * @param args The arguments to the streamObject function, along with extra controls
   * for the {@link ContextOptions} and {@link StorageOptions}.
   * @returns The result of the streamObject function.
   */
  streamObject<
    SCHEMA extends FlexibleSchema<unknown> = FlexibleSchema<JSONValue>,
    OUTPUT extends ObjectMode = InferSchema<SCHEMA> extends string
      ? "enum"
      : "object",
    RESULT = OUTPUT extends "array"
      ? Array<InferSchema<SCHEMA>>
      : InferSchema<SCHEMA>,
  >(
    /**
     * The same arguments you'd pass to "ai" sdk {@link streamObject}.
     */
    streamObjectArgs: AgentPrompt & StreamObjectArgs<SCHEMA, OUTPUT, RESULT>,
    options?: Options,
  ): Promise<
    ReturnType<typeof streamObject<SCHEMA, OUTPUT, RESULT>> &
      ThreadOutputMetadata
  >;
}

export type Options = {
  /**
   * The context options to use for passing in message history to the LLM.
   */
  contextOptions?: ContextOptions;
  /**
   * The storage options to use for saving the input and output messages to the thread.
   */
  storageOptions?: StorageOptions;
  /**
   * The usage handler to use for this thread. Overrides any handler
   * set in the agent constructor.
   */
  usageHandler?: UsageHandler;
  /**
   * By default, messages are ordered with context in `fetchContextWithPrompt`,
   * but you can override this by providing a context handler. Here you can
   * filter, modify, or enrich the context messages. If provided, the default
   * ordering will not apply. This excludes the system message / instructions.
   */
  contextHandler?: ContextHandler;
};

export type SyncStreamsReturnValue =
  | { kind: "list"; messages: StreamMessage[] }
  | { kind: "deltas"; deltas: StreamDelta[] }
  | undefined;

/* Type utils follow */
export type QueryCtx = Pick<GenericActionCtx<GenericDataModel>, "runQuery">;
export type MutationCtx = Pick<
  GenericActionCtx<GenericDataModel>,
  "runQuery" | "runMutation"
>;
export type ActionCtx = Pick<
  GenericActionCtx<GenericDataModel>,
  "runQuery" | "runMutation" | "runAction" | "storage" | "auth"
>;