starting to improve vectorstore

1 files changed, 0 insertions, 130 deletions

diff --git a/src/client/apis/vectorstore/VectorstoreUpload.ts b/src/client/apis/vectorstore/VectorstoreUpload.ts
deleted file mode 100644
index 6c60ad0c8..000000000
--- a/src/client/apis/vectorstore/VectorstoreUpload.ts
+++ /dev/null
@@ -1,130 +0,0 @@
-import { Pinecone, Index, IndexList, PineconeRecord } from '@pinecone-database/pinecone';
-import { CohereClient } from 'cohere-ai';
-import { EmbedResponse } from 'cohere-ai/api';
-import dotenv from 'dotenv';
-
-dotenv.config();
-
-interface ChunkMetadata {
-    text: string;
-    type: string;
-    original_document: string;
-    file_path: string;
-    location: string;
-    start_page: number;
-    end_page: number;
-    [key: string]: string | number; // Add this line
-}
-
-interface Chunk {
-    id: string;
-    values: number[];
-    metadata: ChunkMetadata;
-}
-
-interface Document {
-    purpose: string;
-    file_name: string;
-    num_pages: number;
-    summary: string;
-    chunks: Chunk[];
-    type: string;
-}
-
-class Vectorstore {
-    private pinecone: Pinecone;
-    private index: Index;
-    private cohere: CohereClient;
-    private indexName: string = 'pdf-chatbot';
-    private documents: Document[] = [];
-
-    constructor() {
-        this.pinecone = new Pinecone({
-            apiKey: process.env.PINECONE_API_KEY!,
-        });
-        this.cohere = new CohereClient({
-            token: process.env.COHERE_API_KEY!,
-        });
-        this.createIndex();
-    }
-
-    private async createIndex() {
-        const indexList: IndexList = await this.pinecone.listIndexes();
-
-        if (!indexList.indexes?.some(index => index.name === this.indexName)) {
-            await this.pinecone.createIndex({
-                name: this.indexName,
-                dimension: 1024,
-                metric: 'cosine',
-                spec: {
-                    serverless: {
-                        cloud: 'aws',
-                        region: 'us-east-1',
-                    },
-                },
-            });
-        }
-
-        this.index = this.pinecone.Index(this.indexName);
-    }
-
-    async addDocument(document: Document) {
-        this.documents.push(document);
-        await this.indexDocument(document);
-    }
-
-    private async indexDocument(document: Document) {
-        console.log('Uploading vectors to content namespace...');
-        const pineconeRecords: PineconeRecord[] = document.chunks.map(chunk => ({
-            id: chunk.id,
-            values: chunk.values,
-            metadata: chunk.metadata,
-        }));
-        await this.index.upsert(pineconeRecords);
-    }
-
-    async retrieve(query: string, topK: number = 10): Promise<Chunk[]> {
-        console.log(`Retrieving chunks for query: ${query}`);
-        try {
-            const queryEmbeddingResponse: EmbedResponse = await this.cohere.embed({
-                texts: [query],
-                model: 'embed-english-v3.0',
-                inputType: 'search_query',
-            });
-
-            let queryEmbedding: number[];
-
-            if (Array.isArray(queryEmbeddingResponse.embeddings)) {
-                queryEmbedding = queryEmbeddingResponse.embeddings[0];
-            } else if (queryEmbeddingResponse.embeddings && 'embeddings' in queryEmbeddingResponse.embeddings) {
-                queryEmbedding = (queryEmbeddingResponse.embeddings as { embeddings: number[][] }).embeddings[0];
-            } else {
-                throw new Error('Invalid embedding response format');
-            }
-
-            if (!Array.isArray(queryEmbedding)) {
-                throw new Error('Query embedding is not an array');
-            }
-
-            const queryResponse = await this.index.query({
-                vector: queryEmbedding,
-                topK,
-                includeValues: true,
-                includeMetadata: true,
-            });
-
-            return queryResponse.matches.map(match => ({
-                id: match.id,
-                values: match.values as number[],
-                metadata: match.metadata as ChunkMetadata,
-            }));
-        } catch (error) {
-            console.error(`Error retrieving chunks: ${error}`);
-            return [];
-        }
-    }
-
-    getSummaries(): string {
-        return this.documents.map((doc, index) => `${index + 1}. ${doc.summary}`).join('\n') + '\n';
-    }
-}