fix(ai-bot): login token refresh and update actor flow with agent

Author: ascariandrea

packages/@liexp/shared/src/io/openai/prompts/actor.prompts.ts

@@ -1,23 +1,15 @@
 import { type PromptFn } from "./prompt.type.js";
 
-export const EMBED_ACTOR_PROMPT: PromptFn<{
-  text: string;
-  question: string;
-}> = ({ vars: { text, question } }) => `
+export const EMBED_ACTOR_PROMPT: PromptFn = () => `
 You are an expert in giving description about people.
 Your goal is to give a description of a given person in a text format, including the requested fields, without inventing details.
+Execute the tools available to retrieve the info you need.
 The text should be minimum 100 words long, but not exceeding 300 words long.
 
 The requested fields are:
   - the name of the person
   - the birth date of the person (in the format "YYYY-MM-DD")
   - if has passed away, the death date of the person (in the format "YYYY-MM-DD")
 
-Here's the text you should use to extract the information from:
-
----------------------------------------------------------------
-${text}
----------------------------------------------------------------
-
-There may be an additional question which answer should be included in the body of the text: ${question}
+If the user poses a specific question, try to include the answer in your description.
 `;

packages/@liexp/shared/src/utils/user.utils.ts

@@ -1,4 +1,9 @@
-import { AdminCreate, AdminDelete, AdminEdit } from "../io/http/User.js";
+import {
+  AdminCreate,
+  AdminDelete,
+  AdminEdit,
+  AdminRead,
+} from "../io/http/User.js";
 import { type User } from "../io/http/index.js";
 
 export const checkIsAdmin = (pp: readonly User.UserPermission[]): boolean => {
@@ -8,6 +13,7 @@ export const checkIsAdmin = (pp: readonly User.UserPermission[]): boolean => {
       AdminDelete.literals[0],
       AdminEdit.literals[0],
       AdminCreate.literals[0],
+      AdminRead.literals[0],
     ].some((p) => pp.includes(p))
   );
 };

services/ai-bot/AGENT.md

@@ -0,0 +1,51 @@
+# AI Agent Integration Guide
+
+This document describes how to integrate and configure AI agents to work with the lies.exposed platform using the Model Context Protocol (MCP) servers.
+
+## Overview
+
+The lies.exposed platform provides several MCP servers that expose domain-specific tools for working with various types of content. AI agents should proactively use these tools as their primary knowledge source and execution capability.
+
+When you identify a tool that can help with a task, you should:
+1. Execute the tool directly using the available methods
+2. Use the tool's response to complete the task
+3. Never output tool calls in your response - execute them instead
+
+When a tool call is needed, use it directly through the provided interface. Do not describe the tool call or show it in your response.
+
+### Available Tools
+
+The platform provides several domain-specific tools through MCP:
+
+#### Content Search Tools
+- `findActor` - Search for people and organizations
+- `findArea` - Search for geographical and thematic areas
+- `findEvents` - Search for events and timelines
+- `findGroup` - Search for groups and organizations
+- `findLink` - Search for web resources and references
+- `findMedia` - Search for images, videos and documents
+
+Each search tool supports:
+- Full-text search via the `query` parameter
+- Sorting options (typically by `createdAt` and resource-specific fields)
+- Order control (`ASC`/`DESC`)
+
+#### Content Parsing Tools
+- `blockNoteToText` - Convert BlockNote JSON documents to plain text
+
+### Tool Usage Guidelines
+
+1. **Proactive Tool Use**: 
+   - AI agents should actively use MCP tools without waiting for explicit user permission
+   - Tools should be used as the primary source of domain knowledge
+   - Multiple tools can be queried in parallel when needed
+
+2. **Search Strategy**:
+   - Start with broad searches using key terms
+   - Refine searches based on initial results
+   - Use multiple content types to build comprehensive context
+
+3. **Content Processing**:
+   - Parse BlockNote content when encountering formatted text
+   - Extract key entities (actors, areas, events) from search results
+   - Build relationships between different content types

services/ai-bot/config/ai-bot.config.json

@@ -6,7 +6,7 @@
     "models": {
       "summarize": "gpt-4o",
       "chat": "qwen3-4b",
-      "embeddings": "qwen3-embedding-4b"
+      "embeddings": "qwen3-4b"
     }
   }
 }

services/ai-bot/package.json

@@ -30,9 +30,10 @@
   "dependencies": {
     "@aws-sdk/client-sso-oidc": "^3.895.0",
     "@aws-sdk/client-sts": "^3.895.0",
-    "@langchain/community": "^0.3.56",
-    "@langchain/core": "^0.3.77",
+    "@langchain/community": "^0.3.57",
+    "@langchain/core": "^0.3.78",
     "@langchain/langgraph": "^0.4.9",
+    "@langchain/mcp-adapters": "^0.6.0",
     "@liexp/backend": "workspace:*",
     "@liexp/core": "workspace:*",
     "@liexp/shared": "workspace:*",
@@ -43,8 +44,8 @@
     "debug": "^4.4.0",
     "effect": "^3.17.13",
     "fp-ts": "^2.16.10",
-    "langchain": "^0.3.34",
-    "openai": "^5.20.2",
+    "langchain": "^0.3.35",
+    "openai": "^5.23.1",
     "pdfjs-dist": "^5.4.149",
     "prompts": "^2.4.2",
     "puppeteer-core": "^24.8.1",

services/ai-bot/src/cli/agent.command.ts

@@ -1,69 +1,38 @@
-import { MemorySaver } from "@langchain/langgraph";
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { type AIMessage, type ToolMessage } from "@langchain/core/messages";
+import { AIMessageLogger } from "@liexp/backend/lib/providers/ai/aiMessage.helper.js";
 import { fp } from "@liexp/core/lib/fp/index.js";
+import { uuid } from "@liexp/shared/lib/io/http/Common/UUID.js";
 import { throwTE } from "@liexp/shared/lib/utils/task.utils.js";
 import { pipe } from "fp-ts/lib/function.js";
 import prompts from "prompts";
 import { toAIBotError } from "../common/error/index.js";
 import { type CommandFlow } from "./CommandFlow.js";
 
-// const GraphState = Annotation.Root({
-//   messages: Annotation<BaseMessage[]>({
-//     reducer: (x, y) => x.concat(y),
-//     default: () => [],
-//   }),
-// });
-
-// export const ConfigurationSchema = Annotation.Root({
-//   /**
-//    * The system prompt to be used by the agent.
-//    */
-//   systemPromptTemplate: Annotation<string>,
-
-//   /**
-//    * The name of the language model to be used by the agent.
-//    */
-//   model: Annotation<string>,
-// });
-
-// Define the function that determines whether to continue or not
-// function routeModelOutput(state: typeof MessagesAnnotation.State): string {
-//   const messages = state.messages;
-//   const lastMessage = messages[messages.length - 1];
-//   // If the LLM is invoking tools, route there.
-//   if ((lastMessage as AIMessage)?.tool_calls?.length || 0 > 0) {
-//     return "tools";
-//   }
-//   // Otherwise end the graph.
-//   else {
-//     return "__end__";
-//   }
-// }
-
 export const agentCommand: CommandFlow = async (ctx, args) => {
-  // Initialize memory to persist state between graph runs
-
-  const agentCheckpointer = new MemorySaver();
-  const agent = createReactAgent({
-    llm: ctx.langchain.chat,
-    tools: [],
-    checkpointSaver: agentCheckpointer,
+  const threadId = uuid();
+  const agent = ctx.agent.agent.withConfig({
+    configurable: {
+      thread_id: threadId,
+    },
   });
 
   const ask = async (ag: typeof agent, message: string) => {
     const agentFinalState = await ag.stream(
       {
         messages: [message],
       },
-      { configurable: { thread_id: "42" } },
+      {},
     );
 
-    const messages = [];
+    const aiMessageLogger = AIMessageLogger(ctx.logger);
+
     for await (const chunk of agentFinalState) {
-      ctx.logger.debug.log(`Chunk %O`, chunk.agent.messages);
-      messages.push(chunk);
+      const messages = (chunk.agent?.messages ??
+        chunk.tools?.messages ??
+        []) as (ToolMessage | AIMessage)[];
+
+      messages.forEach(aiMessageLogger);
     }
-    return messages;
   };
 
   const chat = async (ag: typeof agent) => {
@@ -73,7 +42,7 @@ export const agentCommand: CommandFlow = async (ctx, args) => {
       name: "question",
     });
 
-    if (question.toLowerCase() === "exit") {
+    if (!question || question.toLowerCase() === "exit") {
       ctx.logger.info.log("Goodbye!");
       return;
     } else {

services/ai-bot/src/cli/index.ts

@@ -4,7 +4,7 @@ import { throwTE } from "@liexp/shared/lib/utils/task.utils.js";
 import D from "debug";
 import { pipe } from "fp-ts/lib/function.js";
 import { type ClientContext } from "../context.js";
-import { userLogin } from "../flows/userLogin.flow.js";
+import { currentToken, userLogin } from "../flows/userLogin.flow.js";
 import { loadContext } from "../load-context.js";
 import { type CommandFlow } from "./CommandFlow.js";
 import { agentCommand } from "./agent.command.js";
@@ -26,9 +26,13 @@ const run = async ([command, ...args]: string[]): Promise<void> => {
     process.exit(1);
   }
 
-  loadENV(process.cwd(), process.env.DOTENV_CONFIG_PATH ?? "../../.env");
+  loadENV(process.cwd(), process.env.DOTENV_CONFIG_PATH ?? ".env");
 
-  let token: string = "invalid-token";
+  if (process.env.NODE_ENV === "development") {
+    loadENV(process.cwd(), ".env.local", true);
+  }
+
+  let token: string = currentToken();
   let ctx: ClientContext | undefined = undefined;
   try {
     ctx = await pipe(

services/ai-bot/src/context.ts

@@ -4,36 +4,42 @@ import { type LangchainContext } from "@liexp/backend/lib/context/langchain.cont
 import { type LoggerContext } from "@liexp/backend/lib/context/logger.context.js";
 import { type PDFProviderContext } from "@liexp/backend/lib/context/pdf.context.js";
 import { type PuppeteerProviderContext } from "@liexp/backend/lib/context/puppeteer.context.js";
+import { type AgentProvider } from "@liexp/backend/lib/providers/ai/agent.provider.js";
 import { type Endpoints } from "@liexp/shared/lib/endpoints/index.js";
 import { type OpenAIProvider } from "@liexp/shared/lib/providers/openai/openai.provider.js";
 import { type API } from "@ts-endpoint/resource-client";
 import { type AIBotConfig } from "./config.js";
 import { type ENV } from "./env.js";
 import { type ConfigProvider } from "#common/config/config.reader.js";
 
-interface APIClientContext {
+export interface APIClientContext {
   api: API<Endpoints>;
 }
 
-interface ENVContext {
+export interface ENVContext {
   env: ENV;
 }
 
-interface ConfigContext {
+export interface AIBotConfigContext {
   config: ConfigProvider<AIBotConfig>;
 }
 
 interface OpenAIContext {
   openAI: OpenAIProvider;
 }
 
+interface AgentContext {
+  agent: AgentProvider;
+}
+
 export type ClientContext = ENVContext &
-  ConfigContext &
+  AIBotConfigContext &
   LangchainContext &
   OpenAIContext &
   HTTPProviderContext &
   PDFProviderContext &
   APIClientContext &
   FSClientContext &
   PuppeteerProviderContext &
+  AgentContext &
   LoggerContext;