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adhityanadhityan
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Merge pull request #192 from llm-tools/confluence_updates
Updates
2 parents 519c1c2 + db1432e commit dcc1ede

40 files changed

Lines changed: 580 additions & 738 deletions

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‎core/embedjs-interfaces/package.json‎

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@@ -1,9 +1,9 @@
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{
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"name": "@llm-tools/embedjs-interfaces",
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"version": "0.1.25",
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"version": "0.1.26",
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"description": "Interfaces for extending the embedjs ecosystem",
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"dependencies": {
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"@langchain/core": "^0.3.25",
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"@langchain/core": "^0.3.26",
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"debug": "^4.4.0",
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"md5": "^2.3.0",
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"uuid": "^11.0.3"

‎core/embedjs-utils/package.json‎

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{
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"name": "@llm-tools/embedjs-utils",
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"version": "0.1.25",
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"version": "0.1.26",
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"description": "Useful util functions when extending the embedjs ecosystem",
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"dependencies": {
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"@llm-tools/embedjs-interfaces": "0.1.25"
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"@llm-tools/embedjs-interfaces": "0.1.26"
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},
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"type": "module",
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"main": "./src/index.js",

‎core/embedjs-utils/src/util/strings.ts‎

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@@ -41,10 +41,10 @@ export function toTitleCase(str: string) {
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});
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}
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export function isValidURL(url: string) {
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export function isValidURL(candidateUrl: string) {
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try {
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new URL(url);
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return true;
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const url = new URL(candidateUrl);
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return url.protocol === 'http:' || url.protocol === 'https:';
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} catch {
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return false;
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}

‎core/embedjs/package.json‎

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@@ -1,14 +1,14 @@
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{
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"type": "module",
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"name": "@llm-tools/embedjs",
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"version": "0.1.25",
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"version": "0.1.26",
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"description": "A NodeJS RAG framework to easily work with LLMs and custom datasets",
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"dependencies": {
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"@langchain/textsplitters": "^0.1.0",
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"@llm-tools/embedjs-interfaces": "0.1.25",
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"@llm-tools/embedjs-utils": "0.1.25",
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"@llm-tools/embedjs-interfaces": "0.1.26",
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"@llm-tools/embedjs-utils": "0.1.26",
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"debug": "^4.4.0",
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"langchain": "^0.3.7",
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"langchain": "^0.3.8",
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"md5": "^2.3.0",
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"mime": "^4.0.6",
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"stream-mime-type": "^2.0.0"

‎core/embedjs/src/core/rag-application.ts‎

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@@ -1,6 +1,5 @@
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import createDebugMessages from 'debug';
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3-
import { RAGEmbedding } from './rag-embedding.js';
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import { RAGApplicationBuilder } from './rag-application-builder.js';
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import {
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AddLoaderReturn,
@@ -14,6 +13,7 @@ import {
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QueryResponse,
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SIMPLE_MODELS,
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DEFAULT_INSERT_BATCH_SIZE,
16+
BaseEmbeddings,
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} from '@llm-tools/embedjs-interfaces';
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import { cleanString, getUnique } from '@llm-tools/embedjs-utils';
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@@ -24,12 +24,14 @@ export class RAGApplication {
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private readonly searchResultCount: number;
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private readonly systemMessage: string;
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private readonly vectorDatabase: BaseVectorDatabase;
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private readonly embeddingModel: BaseEmbeddings;
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private readonly store: BaseStore;
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private loaders: BaseLoader[];
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private model: BaseModel;
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3132
constructor(llmBuilder: RAGApplicationBuilder) {
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if (!llmBuilder.getEmbeddingModel()) throw new Error('Embedding model must be set!');
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this.embeddingModel = llmBuilder.getEmbeddingModel();
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this.storeConversationsToDefaultThread = llmBuilder.getParamStoreConversationsToDefaultThread();
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this.store = llmBuilder.getStore();
@@ -55,7 +57,7 @@ export class RAGApplication {
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* LLM based on the configuration provided
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*/
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public async init(llmBuilder: RAGApplicationBuilder) {
58-
await RAGEmbedding.init(llmBuilder.getEmbeddingModel());
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await this.embeddingModel.init();
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this.model = await this.getModel(llmBuilder.getModel());
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if (!this.model) this.debug('No base model set; query function unavailable!');
@@ -68,7 +70,7 @@ export class RAGApplication {
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this.debug('Initialized LLM class');
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}
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await this.vectorDatabase.init({ dimensions: await RAGEmbedding.getEmbedding().getDimensions() });
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await this.vectorDatabase.init({ dimensions: await this.embeddingModel.getDimensions() });
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this.debug('Initialized vector database');
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7476
if (this.store) {
@@ -117,7 +119,7 @@ export class RAGApplication {
117119
*/
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private async embedChunks(chunks: Pick<Chunk, 'pageContent'>[]) {
119121
const texts = chunks.map(({ pageContent }) => pageContent);
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return RAGEmbedding.getEmbedding().embedDocuments(texts);
122+
return this.embeddingModel.embedDocuments(texts);
121123
}
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123125
/**
@@ -352,7 +354,7 @@ export class RAGApplication {
352354
* only the number of results specified by the `searchResultCount` property.
353355
*/
354356
public async getEmbeddings(cleanQuery: string) {
355-
const queryEmbedded = await RAGEmbedding.getEmbedding().embedQuery(cleanQuery);
357+
const queryEmbedded = await this.embeddingModel.embedQuery(cleanQuery);
356358
const unfilteredResultSet = await this.vectorDatabase.similaritySearch(
357359
queryEmbedded,
358360
this.searchResultCount + 10,

‎core/embedjs/src/core/rag-embedding.ts‎

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This file was deleted.

‎databases/embedjs-astra/package.json‎

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{
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"name": "@llm-tools/embedjs-astradb",
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"version": "0.1.25",
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"version": "0.1.26",
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"description": "Add AstraDB support to embedjs",
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"dependencies": {
66
"@datastax/astra-db-ts": "^1.5.0",
7-
"@llm-tools/embedjs-interfaces": "0.1.25",
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"@llm-tools/embedjs-interfaces": "0.1.26",
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"debug": "^4.4.0"
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},
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"type": "module",

‎databases/embedjs-cosmos/package.json‎

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{
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"name": "@llm-tools/embedjs-cosmos",
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"version": "0.1.25",
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"version": "0.1.26",
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"description": "Add CosmosDB support to embedjs",
55
"dependencies": {
66
"@azure/cosmos": "^4.2.0",
7-
"@llm-tools/embedjs-interfaces": "0.1.25",
7+
"@llm-tools/embedjs-interfaces": "0.1.26",
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"debug": "^4.4.0"
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},
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"type": "module",

‎databases/embedjs-hnswlib/package.json‎

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{
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"name": "@llm-tools/embedjs-hnswlib",
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"version": "0.1.25",
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"version": "0.1.26",
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"description": "Add HNSWLib support to embedjs",
55
"dependencies": {
6-
"@llm-tools/embedjs-interfaces": "0.1.25",
6+
"@llm-tools/embedjs-interfaces": "0.1.26",
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"debug": "^4.4.0",
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"hnswlib-node": "^3.0.0"
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},

‎databases/embedjs-lancedb/package.json‎

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{
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"name": "@llm-tools/embedjs-lancedb",
3-
"version": "0.1.25",
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"version": "0.1.26",
44
"description": "Add LanceDb support to embedjs",
55
"dependencies": {
6-
"@lancedb/lancedb": "^0.14.0",
7-
"@llm-tools/embedjs-interfaces": "0.1.25",
6+
"@lancedb/lancedb": "^0.14.1",
7+
"@llm-tools/embedjs-interfaces": "0.1.26",
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"compute-cosine-similarity": "^1.1.0",
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"debug": "^4.4.0"
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},

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