mirror of
https://github.com/run-llama/LlamaIndexTS.git
synced 2026-07-19 18:43:34 -04:00
fix: pinecone vector store (#1512)
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@@ -0,0 +1,5 @@
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---
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"llamaindex": patch
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---
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fix: pinecone vector store search
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@@ -1 +1,4 @@
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POSTGRES_USER=runner
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PINECONE_API_KEY=
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PINECONE_INDEX_NAME=
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PINECONE_NAMESPACE=
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@@ -0,0 +1,66 @@
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import { Document, MetadataMode } from "@llamaindex/core/schema";
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import { config } from "dotenv";
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import {
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OpenAIEmbedding,
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PineconeVectorStore,
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VectorStoreIndex,
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} from "llamaindex";
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import assert from "node:assert";
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import { test } from "node:test";
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config({ path: [".env.local", ".env", ".env.ci"] });
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await test("pinecone", async (t) => {
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if (
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!process.env.PINECONE_API_KEY ||
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!process.env.PINECONE_NAMESPACE ||
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!process.env.PINECONE_INDEX_NAME
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) {
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return t.skip(
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"PINECONE_API_KEY, PINECONE_NAMESPACE, and PINECONE_INDEX_NAME must be set to run this test",
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);
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}
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const openaiEmbedding = new OpenAIEmbedding({
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model: "text-embedding-3-large",
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});
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const vectorStore = new PineconeVectorStore({
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embeddingModel: openaiEmbedding,
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});
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t.after(async () => {
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await vectorStore.clearIndex();
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});
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const index = await VectorStoreIndex.fromVectorStore(vectorStore);
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const retriever = index.asRetriever({
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similarityTopK: 3,
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});
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const text = "We are open from 9am to 5pm";
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await vectorStore.add([
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new Document({
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text,
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embedding: await openaiEmbedding.getTextEmbedding(text),
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}),
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]);
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const results = await retriever.retrieve({
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query: "When are you open?",
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});
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results.every((result) => {
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assert.ok(result.node.embedding instanceof Array);
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result.node.embedding.every((embedding, idx) =>
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assert.ok(
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typeof embedding === "number",
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`Embedding at index ${idx} should be a number`,
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),
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);
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assert.ok(typeof result.score === "number", "Score should be a number");
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assert.ok(
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result.node.getContent(MetadataMode.NONE).length > 0,
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"Content should not be empty",
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);
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});
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});
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@@ -26,6 +26,7 @@ type PineconeParams = {
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chunkSize?: number;
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namespace?: string;
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textKey?: string;
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apiKey?: string;
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} & VectorStoreBaseParams;
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/**
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@@ -48,6 +49,8 @@ export class PineconeVectorStore extends BaseVectorStore {
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chunkSize: number;
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textKey: string;
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apiKey: string;
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constructor(params?: PineconeParams) {
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super(params);
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this.indexName =
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@@ -57,12 +60,19 @@ export class PineconeVectorStore extends BaseVectorStore {
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params?.chunkSize ??
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Number.parseInt(getEnv("PINECONE_CHUNK_SIZE") ?? "100");
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this.textKey = params?.textKey ?? "text";
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const apiKey = params?.apiKey ?? getEnv("PINECONE_API_KEY");
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if (!apiKey) {
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throw new Error("PINECONE_API_KEY is required");
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}
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this.apiKey = apiKey;
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}
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private async getDb(): Promise<Pinecone> {
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if (!this.db) {
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const { Pinecone } = await import("@pinecone-database/pinecone");
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this.db = await new Pinecone();
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this.db = new Pinecone({
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apiKey: this.apiKey,
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});
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}
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return Promise.resolve(this.db);
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@@ -100,7 +110,7 @@ export class PineconeVectorStore extends BaseVectorStore {
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*/
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async add(embeddingResults: BaseNode<Metadata>[]): Promise<string[]> {
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if (embeddingResults.length == 0) {
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return Promise.resolve([]);
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return [];
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}
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const idx: Index = await this.index();
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@@ -110,10 +120,10 @@ export class PineconeVectorStore extends BaseVectorStore {
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const chunk = nodes.slice(i, i + this.chunkSize);
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const result = await this.saveChunk(idx, chunk);
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if (!result) {
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return Promise.reject(new Error("Failed to save chunk"));
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throw new Error("Failed to save chunk");
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}
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}
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return Promise.resolve([]);
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return [];
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}
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protected async saveChunk(idx: Index, chunk: PineconeRecord[]) {
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@@ -157,13 +167,19 @@ export class PineconeVectorStore extends BaseVectorStore {
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topK: query.similarityTopK,
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includeValues: true,
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includeMetadata: true,
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filter: filter,
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};
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if (filter) {
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defaultOptions.filter = filter;
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}
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const idx = await this.index();
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const results = await idx.query(defaultOptions);
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const idList = results.matches.map((row) => row.id);
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if (idList.length == 0) {
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return { nodes: [], similarities: [], ids: [] };
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}
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const records: FetchResponse = await idx.fetch(idList);
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const rows = Object.values(records.records);
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@@ -179,13 +195,11 @@ export class PineconeVectorStore extends BaseVectorStore {
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return node;
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});
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const ret = {
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return {
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nodes: nodes,
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similarities: results.matches.map((row) => row.score || 999),
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ids: results.matches.map((row) => row.id),
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};
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return Promise.resolve(ret);
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}
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/**
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@@ -197,8 +211,8 @@ export class PineconeVectorStore extends BaseVectorStore {
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return Promise.resolve();
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}
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toPineconeFilter(stdFilters?: MetadataFilters): object {
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if (!stdFilters) return {};
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toPineconeFilter(stdFilters?: MetadataFilters): object | undefined {
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if (stdFilters == null) return undefined;
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const transformCondition = (
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condition: `${FilterCondition}` = "and",
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@@ -65,7 +65,19 @@ export function metadataDictToNode(
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}
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} else {
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nodeObj = JSON.parse(nodeContent);
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nodeObj.metadata = rest;
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nodeObj = {
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...rest,
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...options?.fallback,
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...nodeObj,
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};
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nodeObj.metadata = {
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...(options?.fallback &&
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"metadata" in options.fallback &&
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typeof options.fallback.metadata === "object"
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? options?.fallback.metadata
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: {}),
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...rest,
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};
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}
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// Note: we're using the name of the class stored in `_node_type`
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