Merge branch 'main' of github.com:Libertai/libertai-js

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@libertai/libertai-js",
-  "version": "0.0.4",
+  "version": "0.0.7",
   "description": "In-browser SDK for interacting with LibertAI Decentralized AI Network",
   "keywords": [],
   "type": "module",
@@ -49,22 +49,22 @@
     "prepare": "yarn build"
   },
   "devDependencies": {
-    "@types/node": "^20.12.2",
-    "@typescript-eslint/eslint-plugin": "^7.4.0",
-    "@typescript-eslint/parser": "^7.4.0",
-    "esbuild": "^0.20.2",
+    "@types/node": "^20.14.9",
+    "@typescript-eslint/eslint-plugin": "^7.15.0",
+    "@typescript-eslint/parser": "^7.15.0",
+    "esbuild": "^0.23.0",
     "eslint": "^8.57.0",
-    "prettier": "^3.0.0",
-    "rimraf": "^5.0.5",
-    "ts-node": "^10.2.1",
-    "typescript": "^5.4.3"
+    "prettier": "^3.3.2",
+    "rimraf": "^5.0.7",
+    "ts-node": "^10.9.2",
+    "typescript": "^5.5.3"
   },
   "dependencies": {
-    "axios": "^1.6.8",
-    "langchain": "^0.1.30",
+    "axios": "^1.7.2",
+    "langchain": "^0.1.37",
     "localforage": "^1.10.0",
     "ml-distance": "^4.0.1",
-    "uuid": "^9.0.1"
+    "uuid": "^10.0.0"
   },
   "resolutions": {
     "@langchain/core": "^0.1"

src/inference.ts

@@ -1,6 +1,6 @@
 import axios from 'axios';
 
-import { Persona, Model, Message } from './types.js';
+import { Message, Model, Persona } from './types.js';
 import { calculateTokenLength } from './utils.js';
 
 // Simple wrapper class around basic AI inference
@@ -69,9 +69,9 @@ export class LlamaCppApiEngine {
       }
 
       let fullResults = compoundedResult + lastResult;
-      for (let i = 0; i < stop_sequences.length; i++) {
-        fullResults = fullResults.split(`\n${stop_sequences[i]}`).join('|||||');
-        fullResults = fullResults.split(`${stop_sequences[i]}`).join('|||||');
+      for (const element of stop_sequences) {
+        fullResults = fullResults.split(`\n${element}`).join('|||||');
+        fullResults = fullResults.split(`${element}`).join('|||||');
       }
       const results = fullResults.split('|||||');
 
@@ -152,11 +152,11 @@ export class LlamaCppApiEngine {
       targetUser = messages[messages.length - 1].role;
     }
 
-    // Set {{char}} based on persona.name
+    // Set {{char}} based on persona.role
     // Set {{user}} based on targetUser
     // Set {{model}} based on model.name
     let description = persona.description;
-    description = description.replace(/\{\{char\}\}/g, persona.name);
+    description = description.replace(/\{\{char\}\}/g, persona.role);
     description = description.replace(/\{\{user\}\}/g, targetUser);
     description = description.replace(/\{\{model\}\}/g, model.name);
 
@@ -170,16 +170,13 @@ export class LlamaCppApiEngine {
     // Determine how many tokens we have left
     usedTokens = calculateTokenLength(systemPrompt);
 
-    // Iterate over messagse in reverse order
+    // Iterate over messages in reverse order
     // to generate the chat log
-    let chatLog = `${promptFormat.userPrepend}${persona.name.toLowerCase()}${promptFormat.userAppend}`;
+    let chatLog = `${promptFormat.userPrepend}${persona.role.toLowerCase()}${promptFormat.userAppend}`;
     for (let i = messages.length - 1; i >= 0; i--) {
       const message = messages[i];
-      const timestamp_string = message.timestamp
-        ? ` (at ${message.timestamp.toString()})`
-        : '';
       let messageLog = '';
-      messageLog += `${promptFormat.userPrepend}${message.role.toLowerCase()}${timestamp_string}${promptFormat.userAppend}`;
+      messageLog += `${promptFormat.userPrepend}${message.role.toLowerCase()}${promptFormat.userAppend}`;
       messageLog += `${message.content}`;
       messageLog += `${promptFormat.stopSequence}`;
       messageLog += `${promptFormat.lineSeparator}`;

src/knowledge-store.ts

@@ -3,12 +3,12 @@ import { distance } from 'ml-distance';
 import {
   Document,
   Embedding,
-  SearchResult,
   KnowledgeStoreConfig,
+  SearchResult,
 } from './types.js';
 import { defaultKnowledgeStoreConfig } from './config.js';
 import idb from './idb.js';
-import { chunkText, embed, createDocument, createEmbedding } from './utils.js';
+import { chunkText, createDocument, createEmbedding, embed } from './utils.js';
 
 export class KnowledgeStore {
   config: KnowledgeStoreConfig;
@@ -65,7 +65,7 @@ export class KnowledgeStore {
   async addDocument(
     title: string,
     content: string,
-    tags = []
+    tags: string[] = []
   ): Promise<Document> {
     // Create a new document object
     const doc = createDocument(title, tags);
@@ -105,7 +105,7 @@ export class KnowledgeStore {
 
   /**
    * Remove a document from the store
-   * @param documentIdd The ID of the document to remove
+   * @param documentId The ID of the document to remove
    * @returns The document that was removed
    * @throws An error if the document is not found
    */
@@ -149,7 +149,6 @@ export class KnowledgeStore {
   /**
    * Search the documents in the store for the given query for similarity by euclidean distance
    * @param query The query to search for
-   * @param callback A callback to be called with each result
    * @param k The number of results to return
    * @param max_distance The maximum distance between the query and a result
    * @param tags The tags to filter by. If empty, no filtering is done
@@ -159,7 +158,7 @@ export class KnowledgeStore {
     query: string,
     k = 5,
     max_distance = 15,
-    tags = []
+    tags: string[] = []
   ): Promise<SearchResult[]> {
     const query_vector = await embed(query, this.config.embeddingApiUrl);
     let matches: SearchResult[] | null = null;

src/types.ts

@@ -2,11 +2,11 @@
 
 // Document metadata
 export interface Document {
-  // Documents should have a uniqueid
+  // Documents should have a unique id
   id: string;
   // Title of the document
   title: string;
-  // Tags for the document. Can be used to place docments in a collection.
+  // Tags for the document. Can be used to place documents in a collection.
   tags: string[];
 }
 
@@ -16,7 +16,7 @@ export interface Embedding {
   id: string;
   // Embeddings refer back to the document they are from
   documentId: string;
-  // The actual emebedding content
+  // The actual embedding content
   content: string;
   // The embedding vector (float[768])
   vector: number[];
@@ -63,18 +63,15 @@ export interface PromptFormat {
 // and extensible persona format of our own
 //  https://github.com/TavernAI/TavernAI/tree/main
 export interface Persona {
-  // avatarUrl
-  avatarUrl: string;
-
-  // Persona name
-  name: string;
+  // Persona role
+  role: string;
 
   // Persona description
   description: string;
 }
 
 // Common model definition across different engine types
-// Defines resource, paramterization, persona, and prompt formatting
+// Defines resource, parameterization, persona, and prompt formatting
 // for the model
 export interface Model {
   /* Resource definition */
@@ -111,6 +108,6 @@ export type KnowledgeStoreConfig = {
   embeddingApiUrl: string;
   // Name of the local store to use
   storeName: string;
-  // Special key that identifies docuements metadata in the store
+  // Special key that identifies documents metadata in the store
   documentsKey: string;
 };

src/utils.ts

@@ -14,7 +14,7 @@ export function calculateTokenLength(input: string): number {
 /* Embedding utility functions */
 
 /*
- * Split a single Text Document into mutliple Documents of a given size
+ * Split a single Text Document into multiple Documents of a given size
  */
 export async function chunkText(
   title: string,