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llamaindex

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<p align="center"> <img height="100" width="100" alt="LlamaIndex logo" src="https://ts.llamaindex.ai/square.svg" /> </p> <h1 align="center">LlamaIndex.TS</h1> <h3 align="center"> Data framework for your LLM application. </h3>

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import * as _llamaindex_core_agent from '@llamaindex/core/agent'; import { AgentWorker, TaskHandler, AgentRunner, AgentParamsBase } from '@llamaindex/core/agent'; export * from '@llamaindex/core/agent'; import * as _llamaindex_core_schema from '@llamaindex/core/schema'; import * as _llamaindex_core_chat_engine from '@llamaindex/core/chat-engine'; import * as _llamaindex_core_llms from '@llamaindex/core/llms'; import { LLM, ChatResponse } from '@llamaindex/core/llms'; import * as _llamaindex_core_retriever from '@llamaindex/core/retriever'; import { BaseRetriever } from '@llamaindex/core/retriever'; import { OpenAIAgentParams } from '@llamaindex/openai'; import { JSONValue, JSONObject } from '@llamaindex/core/global'; interface ContextAwareConfig { contextRetriever: BaseRetriever; } type AgentParams = OpenAIAgentParams; declare const OpenAIContextAwareAgent: { new (params: AgentParams & ContextAwareConfig): { readonly contextRetriever: _llamaindex_core_retriever.BaseRetriever; retrievedContext: string | null; retrieveContext(query: _llamaindex_core_llms.MessageContent): Promise<string>; injectContext(context: string): Promise<void>; chat(params: _llamaindex_core_chat_engine.NonStreamingChatEngineParams): Promise<_llamaindex_core_schema.EngineResponse>; chat(params: _llamaindex_core_chat_engine.StreamingChatEngineParams): Promise<ReadableStream<_llamaindex_core_schema.EngineResponse>>; createStore: typeof _llamaindex_core_agent.AgentRunner.defaultCreateStore; taskHandler: _llamaindex_core_agent.TaskHandler<_llamaindex_core_llms.LLM<object, object>>; "__#13@#private": any; readonly llm: _llamaindex_core_llms.LLM<object, object>; readonly chatHistory: _llamaindex_core_llms.ChatMessage<object>[]; readonly verbose: boolean; reset(): void; getTools(query: _llamaindex_core_llms.MessageContent): Promise<_llamaindex_core_llms.BaseToolWithCall[]> | _llamaindex_core_llms.BaseToolWithCall[]; createTask(message: _llamaindex_core_llms.MessageContent, stream?: boolean, verbose?: boolean | undefined, chatHistory?: _llamaindex_core_llms.ChatMessage<object>[] | undefined, additionalChatOptions?: object | undefined): ReadableStream<{ taskStep: _llamaindex_core_agent.TaskStep<_llamaindex_core_llms.LLM<object, object>, object, object, object>; output: _llamaindex_core_llms.ChatResponse<object> | ReadableStream<_llamaindex_core_llms.ChatResponseChunk<object>>; isLast: boolean; }>; }; defaultCreateStore(): object; defaultTaskHandler: _llamaindex_core_agent.TaskHandler<_llamaindex_core_llms.LLM>; shouldContinue<AI extends _llamaindex_core_llms.LLM, Store extends object = object, AdditionalMessageOptions extends object = AI extends _llamaindex_core_llms.LLM<object, infer AdditionalMessageOptions_1 extends object> ? AdditionalMessageOptions_1 : never>(task: Readonly<_llamaindex_core_agent.TaskStep<AI, Store, AdditionalMessageOptions>>): boolean; }; type ReACTAgentParams = AgentParamsBase<LLM>; type BaseReason = { type: unknown; }; type ObservationReason = BaseReason & { type: "observation"; observation: JSONValue; }; type ActionReason = BaseReason & { type: "action"; thought: string; action: string; input: JSONObject; }; type ResponseReason = BaseReason & { type: "response"; thought: string; response: ChatResponse; }; type Reason = ObservationReason | ActionReason | ResponseReason; type ReACTAgentStore = { reasons: Reason[]; }; declare class ReACTAgentWorker extends AgentWorker<LLM, ReACTAgentStore> { taskHandler: TaskHandler<LLM<object, object>, ReACTAgentStore>; } declare class ReActAgent extends AgentRunner<LLM, ReACTAgentStore> { constructor(params: ReACTAgentParams); createStore(): { reasons: never[]; }; static taskHandler: TaskHandler<LLM, ReACTAgentStore>; } export { OpenAIContextAwareAgent, type ReACTAgentParams, ReACTAgentWorker, ReActAgent };