ChatLunaLab · dependabot · Jan 20, 2025
diff --git a/package.json b/package.json
@@ -39,7 +39,7 @@
         "esbuild": "^0.23.1",
         "esbuild-register": "npm:@shigma/esbuild-register@^1.1.1",
         "eslint": "^8.57.1",
-        "eslint-config-prettier": "^9.1.0",
+        "eslint-config-prettier": "^10.0.1",
         "eslint-config-standard": "^17.1.0",
         "eslint-plugin-import": "^2.31.0",
         "eslint-plugin-n": "^17.13.2",

diff --git a/packages/core/src/llm-core/agent/agent.ts b/packages/core/src/llm-core/agent/agent.ts
 * planning agent actions with runnables.
 */
 export class RunnableSingleActionAgent extends BaseSingleActionAgent {
    lc_namespace = ['langchain', 'agents', 'runnable']

    runnable: Runnable<
        ChainValues & { steps: AgentStep[] },
 * planning agent actions with runnables.
 */
 export class RunnableMultiActionAgent extends BaseMultiActionAgent {
    lc_namespace = ['langchain', 'agents', 'runnable']

    // TODO: Rename input to "intermediate_steps"
    runnable: Runnable<
diff --git a/packages/core/src/llm-core/agent/executor.ts b/packages/core/src/llm-core/agent/executor.ts
    extends Serializable
    implements AgentExecutorIteratorInput
 {
    lc_namespace = ['langchain', 'agents', 'executor_iterator']

    agentExecutor: AgentExecutor

 * ```
 */
 export class AgentExecutor extends BaseChain<ChainValues, AgentExecutorOutput> {
    static lc_name() {
        return 'AgentExecutor'
    }

    get lc_namespace() {
        return ['langchain', 'agents', 'executor']
    }

diff --git a/packages/core/src/llm-core/chain/base.ts b/packages/core/src/llm-core/chain/base.ts
 {
    declare memory?: BaseMemory

    get lc_namespace(): string[] {
        return ['langchain', 'chains', this._chainType()]
    }

    ): Promise<RunOutput> {
        const config = ensureConfig(options)
        const fullValues = await this._formatValues(input)
        const callbackManager_ = CallbackManager.configure(
            config?.callbacks,
            this.callbacks,
            config?.tags,
diff --git a/packages/core/src/llm-core/memory/langchain/buffer_memory.ts b/packages/core/src/llm-core/memory/langchain/buffer_memory.ts
 *
 * // Create a prompt template for a friendly conversation between a human and an AI.
 * const prompt =
 *   PromptTemplate.fromTemplate(`The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
 *
 * Current conversation:
 * {chat_history}
diff --git a/packages/core/src/middlewares/list_room.ts b/packages/core/src/middlewares/list_room.ts
        .middleware('list_room', async (session, context) => {
            const {
                command,
                options: { page, limit, all_room }
            } = context

            if (command !== 'list_room') return ChainMiddlewareRunStatus.SKIPPED
diff --git a/packages/long-memory/src/similarity.ts b/packages/long-memory/src/similarity.ts
 import { cut } from 'jieba-wasm'
 import TinySegmenter from 'tiny-segmenter'
 import stopwords from 'stopwords-iso'

 const segmenter = new TinySegmenter()
	* planning agent actions with runnables.
	*/
	export class RunnableSingleActionAgent extends BaseSingleActionAgent {
	lc_namespace = ['langchain', 'agents', 'runnable']
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	runnable: Runnable<
	ChainValues & { steps: AgentStep[] },
	* planning agent actions with runnables.
	*/
	export class RunnableMultiActionAgent extends BaseMultiActionAgent {
	lc_namespace = ['langchain', 'agents', 'runnable']
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	// TODO: Rename input to "intermediate_steps"
	runnable: Runnable<
	extends Serializable
	implements AgentExecutorIteratorInput
	{
	lc_namespace = ['langchain', 'agents', 'executor_iterator']
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	agentExecutor: AgentExecutor

	* ```
	*/
	export class AgentExecutor extends BaseChain<ChainValues, AgentExecutorOutput> {
	static lc_name() {
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	return 'AgentExecutor'
	}

	get lc_namespace() {
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	return ['langchain', 'agents', 'executor']
	}
	{
	declare memory?: BaseMemory

	get lc_namespace(): string[] {
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	return ['langchain', 'chains', this._chainType()]
	}

	): Promise<RunOutput> {
	const config = ensureConfig(options)
	const fullValues = await this._formatValues(input)
	const callbackManager_ = CallbackManager.configure(
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	config?.callbacks,
	this.callbacks,
	config?.tags,
	*
	* // Create a prompt template for a friendly conversation between a human and an AI.
	* const prompt =
	* PromptTemplate.fromTemplate(`The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
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	*
	* Current conversation:
	* {chat_history}
	.middleware('list_room', async (session, context) => {
	const {
	command,
	options: { page, limit, all_room }
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	} = context

	if (command !== 'list_room') return ChainMiddlewareRunStatus.SKIPPED
	import { cut } from 'jieba-wasm'
	import TinySegmenter from 'tiny-segmenter'
Check warning on line 2 in packages/long-memory/src/similarity.ts GitHub Actions / lint Import name `TinySegmenter` must match one of the following formats: camelCase, UPPER_CASE Check warning on line 2 in packages/long-memory/src/similarity.ts GitHub Actions / lint Import name `TinySegmenter` must match one of the following formats: camelCase, UPPER_CASE
	import stopwords from 'stopwords-iso'

	const segmenter = new TinySegmenter()