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Code Issues 138 Packages Projects Releases 10 Wiki Activity

remove service context (#1618)

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Co-authored-by: thucpn <thucsh2@gmail.com>
This commit is contained in:
Marcus Schiesser
2025-02-12 15:10:11 +07:00
committed by GitHub
parent a87efb91a4
commit b49037612d
26 changed files with 94 additions and 315 deletions
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.changeset/tame-apples-help.md
+7
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@@ -0,0 +1,7 @@
---
"llamaindex": minor
"@llamaindex/cloudflare-hono": patch
"@llamaindex/examples": patch
---
Remove deprecated ServiceContext
e2e/examples/cloudflare-hono/src/index.ts
+4 -5
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@@ -20,9 +20,9 @@ app.post("/llm", async (c) => {
const {
extractText,
QueryEngineTool,
serviceContextFromDefaults,
VectorStoreIndex,
Settings,
SentenceSplitter,
} = await import("llamaindex");
const { OpenAIAgent, OpenAI, OpenAIEmbedding } = await import(
@@ -31,7 +31,7 @@ app.post("/llm", async (c) => {
const { PineconeVectorStore } = await import("@llamaindex/pinecone");
const llm = new OpenAI({
Settings.llm = new OpenAI({
model: "gpt-4o-mini",
apiKey: c.env.OPENAI_API_KEY,
});
@@ -41,8 +41,7 @@ app.post("/llm", async (c) => {
apiKey: c.env.OPENAI_API_KEY,
});
const serviceContext = serviceContextFromDefaults({
llm,
Settings.nodeParser = new SentenceSplitter({
chunkSize: 8191,
chunkOverlap: 0,
});
@@ -51,7 +50,7 @@ app.post("/llm", async (c) => {
namespace: "8xolsn4ulEQGdhnhP76yCzfLHdOZ",
});
const index = await VectorStoreIndex.fromVectorStore(store, serviceContext);
const index = await VectorStoreIndex.fromVectorStore(store);
const retriever = index.asRetriever({
similarityTopK: 3,
examples/astradb/query.ts
+2 -3
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@@ -1,5 +1,5 @@
import { AstraDBVectorStore } from "@llamaindex/astra";
import { VectorStoreIndex, serviceContextFromDefaults } from "llamaindex";
import { VectorStoreIndex } from "llamaindex";
const collectionName = "movie_reviews";
@@ -8,8 +8,7 @@ async function main() {
const astraVS = new AstraDBVectorStore({ contentKey: "reviewtext" });
await astraVS.connect(collectionName);
const ctx = serviceContextFromDefaults();
const index = await VectorStoreIndex.fromVectorStore(astraVS, ctx);
const index = await VectorStoreIndex.fromVectorStore(astraVS);
const retriever = await index.asRetriever({ similarityTopK: 20 });
examples/mongo.ts
+1 -2
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@@ -30,13 +30,12 @@ async function main() {
// Split text and create embeddings. Store them in a VectorStoreIndex
// var storageContext = await storageContextFromDefaults({});
// var serviceContext = serviceContextFromDefaults({});
// const docStore = storageContext.docStore;
// for (const doc of documents) {
// docStore.setDocumentHash(doc.id_, doc.hash);
// }
// const nodes = serviceContext.nodeParser.getNodesFromDocuments(documents);
// const nodes = Settings.nodeParser.getNodesFromDocuments(documents);
// console.log(nodes);
//
packages/llamaindex/src/ServiceContext.ts
-67
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@@ -1,67 +0,0 @@
import type { BaseEmbedding } from "@llamaindex/core/embeddings";
import { PromptHelper } from "@llamaindex/core/indices";
import type { LLM } from "@llamaindex/core/llms";
import {
type NodeParser,
SentenceSplitter,
} from "@llamaindex/core/node-parser";
import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
/**
* The ServiceContext is a collection of components that are used in different parts of the application.
*
* @deprecated This will no longer supported, please use `Settings` instead.
*/
export interface ServiceContext {
llm: LLM;
promptHelper: PromptHelper;
embedModel: BaseEmbedding;
nodeParser: NodeParser;
// llamaLogger: any;
}
export interface ServiceContextOptions {
llm?: LLM;
promptHelper?: PromptHelper;
embedModel?: BaseEmbedding;
nodeParser?: NodeParser;
// NodeParser arguments
chunkSize?: number;
chunkOverlap?: number;
}
export function serviceContextFromDefaults(options?: ServiceContextOptions) {
const serviceContext: ServiceContext = {
llm: options?.llm ?? new OpenAI(),
embedModel: options?.embedModel ?? new OpenAIEmbedding(),
nodeParser:
options?.nodeParser ??
new SentenceSplitter({
chunkSize: options?.chunkSize,
chunkOverlap: options?.chunkOverlap,
}),
promptHelper: options?.promptHelper ?? new PromptHelper(),
};
return serviceContext;
}
export function serviceContextFromServiceContext(
serviceContext: ServiceContext,
options: ServiceContextOptions,
) {
const newServiceContext = { ...serviceContext };
if (options.llm) {
newServiceContext.llm = options.llm;
}
if (options.promptHelper) {
newServiceContext.promptHelper = options.promptHelper;
}
if (options.embedModel) {
newServiceContext.embedModel = options.embedModel;
}
if (options.nodeParser) {
newServiceContext.nodeParser = options.nodeParser;
}
return newServiceContext;
}
packages/llamaindex/src/Settings.ts
-39
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@@ -12,7 +12,6 @@ import {
SentenceSplitter,
} from "@llamaindex/core/node-parser";
import { AsyncLocalStorage } from "@llamaindex/env";
import type { ServiceContext } from "./ServiceContext.js";
export type PromptConfig = {
llm?: string;
@@ -163,42 +162,4 @@ class GlobalSettings implements Config {
}
}
export const llmFromSettingsOrContext = (serviceContext?: ServiceContext) => {
if (serviceContext?.llm) {
return serviceContext.llm;
}
return Settings.llm;
};
export const nodeParserFromSettingsOrContext = (
serviceContext?: ServiceContext,
) => {
if (serviceContext?.nodeParser) {
return serviceContext.nodeParser;
}
return Settings.nodeParser;
};
export const embedModelFromSettingsOrContext = (
serviceContext?: ServiceContext,
) => {
if (serviceContext?.embedModel) {
return serviceContext.embedModel;
}
return Settings.embedModel;
};
export const promptHelperFromSettingsOrContext = (
serviceContext?: ServiceContext,
) => {
if (serviceContext?.promptHelper) {
return serviceContext.promptHelper;
}
return Settings.promptHelper;
};
export const Settings = new GlobalSettings();
packages/llamaindex/src/cloud/type.ts
-3
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@@ -1,5 +1,3 @@
import type { ServiceContext } from "../ServiceContext.js";
export type ClientParams = {
apiKey?: string | undefined;
baseUrl?: string | undefined;
@@ -9,5 +7,4 @@ export type CloudConstructorParams = {
name: string;
projectName: string;
organizationId?: string | undefined;
serviceContext?: ServiceContext | undefined;
} & ClientParams;
packages/llamaindex/src/engines/chat/CondenseQuestionChatEngine.ts
+2 -4
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@@ -18,8 +18,7 @@ import {
messagesToHistory,
streamReducer,
} from "@llamaindex/core/utils";
import type { ServiceContext } from "../../ServiceContext.js";
import { llmFromSettingsOrContext } from "../../Settings.js";
import { Settings } from "../../Settings.js";
/**
* CondenseQuestionChatEngine is used in conjunction with a Index (for example VectorStoreIndex).
@@ -44,7 +43,6 @@ export class CondenseQuestionChatEngine extends BaseChatEngine {
constructor(init: {
queryEngine: BaseQueryEngine;
chatHistory: ChatMessage[];
serviceContext?: ServiceContext;
condenseMessagePrompt?: CondenseQuestionPrompt;
}) {
super();
@@ -53,7 +51,7 @@ export class CondenseQuestionChatEngine extends BaseChatEngine {
this.memory = new ChatMemoryBuffer({
chatHistory: init?.chatHistory,
});
this.llm = llmFromSettingsOrContext(init?.serviceContext);
this.llm = Settings.llm;
this.condenseMessagePrompt =
init?.condenseMessagePrompt ?? defaultCondenseQuestionPrompt;
}
packages/llamaindex/src/engines/query/RouterQueryEngine.ts
+2 -8
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@@ -9,10 +9,9 @@ import {
} from "@llamaindex/core/response-synthesizers";
import { EngineResponse, type NodeWithScore } from "@llamaindex/core/schema";
import { extractText } from "@llamaindex/core/utils";
import type { ServiceContext } from "../../ServiceContext.js";
import { llmFromSettingsOrContext } from "../../Settings.js";
import type { BaseSelector } from "../../selectors/index.js";
import { LLMSingleSelector } from "../../selectors/index.js";
import { Settings } from "../../Settings.js";
type RouterQueryEngineTool = {
queryEngine: BaseQueryEngine;
@@ -60,7 +59,6 @@ export class RouterQueryEngine extends BaseQueryEngine {
constructor(init: {
selector: BaseSelector;
queryEngineTools: RouterQueryEngineTool[];
serviceContext?: ServiceContext | undefined;
summarizer?: BaseSynthesizer | undefined;
verbose?: boolean | undefined;
}) {
@@ -106,20 +104,16 @@ export class RouterQueryEngine extends BaseQueryEngine {
static fromDefaults(init: {
queryEngineTools: RouterQueryEngineTool[];
selector?: BaseSelector;
serviceContext?: ServiceContext;
summarizer?: BaseSynthesizer;
verbose?: boolean;
}) {
const serviceContext = init.serviceContext;
return new RouterQueryEngine({
selector:
init.selector ??
new LLMSingleSelector({
llm: llmFromSettingsOrContext(serviceContext),
llm: Settings.llm,
}),
queryEngineTools: init.queryEngineTools,
serviceContext,
summarizer: init.summarizer,
verbose: init.verbose,
});
packages/llamaindex/src/engines/query/SubQuestionQueryEngine.ts
-2
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@@ -2,7 +2,6 @@ import type { BaseSynthesizer } from "@llamaindex/core/response-synthesizers";
import { getResponseSynthesizer } from "@llamaindex/core/response-synthesizers";
import { TextNode, type NodeWithScore } from "@llamaindex/core/schema";
import { LLMQuestionGenerator } from "../../QuestionGenerator.js";
import type { ServiceContext } from "../../ServiceContext.js";
import type { BaseTool, ToolMetadata } from "@llamaindex/core/llms";
import type { PromptsRecord } from "@llamaindex/core/prompts";
@@ -93,7 +92,6 @@ export class SubQuestionQueryEngine extends BaseQueryEngine {
queryEngineTools: BaseTool[];
questionGen?: BaseQuestionGenerator;
responseSynthesizer?: BaseSynthesizer;
serviceContext?: ServiceContext;
}) {
const questionGen = init.questionGen ?? new LLMQuestionGenerator();
const responseSynthesizer =
packages/llamaindex/src/evaluation/Correctness.ts
+2 -4
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@@ -2,8 +2,7 @@ import type { ChatMessage, LLM } from "@llamaindex/core/llms";
import { PromptMixin } from "@llamaindex/core/prompts";
import { MetadataMode } from "@llamaindex/core/schema";
import { extractText } from "@llamaindex/core/utils";
import type { ServiceContext } from "../ServiceContext.js";
import { llmFromSettingsOrContext } from "../Settings.js";
import { Settings } from "../Settings.js";
import type { CorrectnessSystemPrompt } from "./prompts.js";
import {
defaultCorrectnessSystemPrompt,
@@ -18,7 +17,6 @@ import type {
import { defaultEvaluationParser } from "./utils.js";
type CorrectnessParams = {
serviceContext?: ServiceContext;
scoreThreshold?: number;
parserFunction?: (str: string) => [number, string];
};
@@ -35,7 +33,7 @@ export class CorrectnessEvaluator extends PromptMixin implements BaseEvaluator {
constructor(params?: CorrectnessParams) {
super();
this.llm = llmFromSettingsOrContext(params?.serviceContext);
this.llm = Settings.llm;
this.correctnessPrompt = defaultCorrectnessSystemPrompt;
this.scoreThreshold = params?.scoreThreshold ?? 4.0;
this.parserFunction = params?.parserFunction ?? defaultEvaluationParser;
packages/llamaindex/src/evaluation/Faithfulness.ts
+1 -7
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@@ -1,7 +1,6 @@
import { PromptMixin, type ModuleRecord } from "@llamaindex/core/prompts";
import { Document, MetadataMode } from "@llamaindex/core/schema";
import { extractText } from "@llamaindex/core/utils";
import type { ServiceContext } from "../ServiceContext.js";
import { SummaryIndex } from "../indices/summary/index.js";
import type {
FaithfulnessRefinePrompt,
@@ -22,19 +21,16 @@ export class FaithfulnessEvaluator
extends PromptMixin
implements BaseEvaluator
{
private serviceContext?: ServiceContext | undefined;
private raiseError: boolean;
private evalTemplate: FaithfulnessTextQAPrompt;
private refineTemplate: FaithfulnessRefinePrompt;
constructor(params?: {
serviceContext?: ServiceContext | undefined;
raiseError?: boolean | undefined;
faithfulnessSystemPrompt?: FaithfulnessTextQAPrompt | undefined;
faithFulnessRefinePrompt?: FaithfulnessRefinePrompt | undefined;
}) {
super();
this.serviceContext = params?.serviceContext;
this.raiseError = params?.raiseError ?? false;
this.evalTemplate =
@@ -92,9 +88,7 @@ export class FaithfulnessEvaluator
const docs = contexts?.map((context) => new Document({ text: context }));
const index = await SummaryIndex.fromDocuments(docs, {
serviceContext: this.serviceContext,
});
const index = await SummaryIndex.fromDocuments(docs, {});
const queryEngine = index.asQueryEngine();
packages/llamaindex/src/evaluation/Relevancy.ts
+1 -7
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@@ -1,7 +1,6 @@
import { PromptMixin, type ModuleRecord } from "@llamaindex/core/prompts";
import { Document, MetadataMode } from "@llamaindex/core/schema";
import { extractText } from "@llamaindex/core/utils";
import type { ServiceContext } from "../ServiceContext.js";
import { SummaryIndex } from "../indices/summary/index.js";
import type { RelevancyEvalPrompt, RelevancyRefinePrompt } from "./prompts.js";
import {
@@ -16,14 +15,12 @@ import type {
} from "./types.js";
type RelevancyParams = {
serviceContext?: ServiceContext | undefined;
raiseError?: boolean | undefined;
evalTemplate?: RelevancyEvalPrompt | undefined;
refineTemplate?: RelevancyRefinePrompt | undefined;
};
export class RelevancyEvaluator extends PromptMixin implements BaseEvaluator {
private serviceContext?: ServiceContext | undefined;
private raiseError: boolean;
private evalTemplate: RelevancyEvalPrompt;
@@ -32,7 +29,6 @@ export class RelevancyEvaluator extends PromptMixin implements BaseEvaluator {
constructor(params?: RelevancyParams) {
super();
this.serviceContext = params?.serviceContext;
this.raiseError = params?.raiseError ?? false;
this.evalTemplate = params?.evalTemplate ?? defaultRelevancyEvalPrompt;
this.refineTemplate =
@@ -78,9 +74,7 @@ export class RelevancyEvaluator extends PromptMixin implements BaseEvaluator {
const docs = contexts?.map((context) => new Document({ text: context }));
const index = await SummaryIndex.fromDocuments(docs, {
serviceContext: this.serviceContext,
});
const index = await SummaryIndex.fromDocuments(docs, {});
const queryResponse = `Question: ${extractText(query)}\nResponse: ${response}`;
packages/llamaindex/src/index.edge.ts
-1
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@@ -82,7 +82,6 @@ export * from "./OutputParser.js";
export * from "./postprocessors/index.js";
export * from "./QuestionGenerator.js";
export * from "./selectors/index.js";
export * from "./ServiceContext.js";
export * from "./storage/StorageContext.js";
export * from "./tools/index.js";
export * from "./types.js";
packages/llamaindex/src/indices/BaseIndex.ts
+2 -9
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@@ -8,13 +8,11 @@ import type { BaseRetriever } from "@llamaindex/core/retriever";
import type { BaseNode, Document } from "@llamaindex/core/schema";
import type { BaseDocumentStore } from "@llamaindex/core/storage/doc-store";
import type { BaseIndexStore } from "@llamaindex/core/storage/index-store";
import type { ServiceContext } from "../ServiceContext.js";
import { nodeParserFromSettingsOrContext } from "../Settings.js";
import { runTransformations } from "../ingestion/IngestionPipeline.js";
import { Settings } from "../Settings.js";
import type { StorageContext } from "../storage/StorageContext.js";
export interface BaseIndexInit<T> {
serviceContext?: ServiceContext | undefined;
storageContext: StorageContext;
docStore: BaseDocumentStore;
indexStore?: BaseIndexStore | undefined;
@@ -26,14 +24,12 @@ export interface BaseIndexInit<T> {
* they can be retrieved for our queries.
*/
export abstract class BaseIndex<T> {
serviceContext?: ServiceContext | undefined;
storageContext: StorageContext;
docStore: BaseDocumentStore;
indexStore?: BaseIndexStore | undefined;
indexStruct: T;
constructor(init: BaseIndexInit<T>) {
this.serviceContext = init.serviceContext;
this.storageContext = init.storageContext;
this.docStore = init.docStore;
this.indexStore = init.indexStore;
@@ -70,10 +66,7 @@ export abstract class BaseIndex<T> {
* @param document
*/
async insert(document: Document) {
const nodes = await runTransformations(
[document],
[nodeParserFromSettingsOrContext(this.serviceContext)],
);
const nodes = await runTransformations([document], [Settings.nodeParser]);
await this.insertNodes(nodes);
await this.docStore.setDocumentHash(document.id_, document.hash);
}
packages/llamaindex/src/indices/keyword/index.ts
+6 -23
View File
@@ -5,8 +5,6 @@ import type {
NodeWithScore,
} from "@llamaindex/core/schema";
import { MetadataMode } from "@llamaindex/core/schema";
import type { ServiceContext } from "../../ServiceContext.js";
import { serviceContextFromDefaults } from "../../ServiceContext.js";
import { RetrieverQueryEngine } from "../../engines/query/index.js";
import type { StorageContext } from "../../storage/StorageContext.js";
import { storageContextFromDefaults } from "../../storage/StorageContext.js";
@@ -34,7 +32,7 @@ import type {
import { BaseRetriever } from "@llamaindex/core/retriever";
import type { BaseDocumentStore } from "@llamaindex/core/storage/doc-store";
import { extractText } from "@llamaindex/core/utils";
import { llmFromSettingsOrContext } from "../../Settings.js";
import { Settings } from "../../Settings.js";
import {
ContextChatEngine,
type BaseChatEngine,
@@ -45,7 +43,6 @@ export interface KeywordIndexOptions {
nodes?: BaseNode[];
indexStruct?: KeywordTable;
indexId?: string;
serviceContext?: ServiceContext;
llm?: LLM;
storageContext?: StorageContext;
}
@@ -84,7 +81,7 @@ abstract class BaseKeywordTableRetriever extends BaseRetriever {
this.index = index;
this.indexStruct = index.indexStruct;
this.docstore = index.docStore;
this.llm = llmFromSettingsOrContext(index.serviceContext);
this.llm = Settings.llm;
this.maxKeywordsPerQuery = maxKeywordsPerQuery;
this.numChunksPerQuery = numChunksPerQuery;
@@ -172,7 +169,6 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
static async init(options: KeywordIndexOptions): Promise<KeywordTableIndex> {
const storageContext =
options.storageContext ?? (await storageContextFromDefaults({}));
const serviceContext = options.serviceContext;
const { docStore, indexStore } = storageContext;
// Setup IndexStruct from storage
@@ -219,7 +215,6 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
indexStruct = await KeywordTableIndex.buildIndexFromNodes(
options.nodes,
storageContext.docStore,
serviceContext,
);
await indexStore.addIndexStruct(indexStruct);
@@ -227,7 +222,6 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
return new KeywordTableIndex({
storageContext,
serviceContext,
docStore,
indexStore,
indexStruct,
@@ -268,11 +262,8 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
});
}
static async extractKeywords(
text: string,
serviceContext?: ServiceContext,
): Promise<Set<string>> {
const llm = llmFromSettingsOrContext(serviceContext);
static async extractKeywords(text: string): Promise<Set<string>> {
const llm = Settings.llm;
const response = await llm.complete({
prompt: defaultKeywordExtractPrompt.format({
@@ -288,19 +279,16 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
* @param documents
* @param args
* @param args.storageContext
* @param args.serviceContext
* @returns
*/
static async fromDocuments(
documents: Document[],
args: {
storageContext?: StorageContext;
serviceContext?: ServiceContext;
} = {},
): Promise<KeywordTableIndex> {
let { storageContext, serviceContext } = args;
let { storageContext } = args;
storageContext = storageContext ?? (await storageContextFromDefaults({}));
serviceContext = serviceContext ?? serviceContextFromDefaults({});
const docStore = storageContext.docStore;
await docStore.addDocuments(documents, true);
@@ -308,11 +296,10 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
await docStore.setDocumentHash(doc.id_, doc.hash);
}
const nodes = serviceContext.nodeParser.getNodesFromDocuments(documents);
const nodes = Settings.nodeParser.getNodesFromDocuments(documents);
const index = await KeywordTableIndex.init({
nodes,
storageContext,
serviceContext,
});
return index;
}
@@ -321,20 +308,17 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
* Get keywords for nodes and place them into the index.
* @param nodes
* @param docStore
* @param serviceContext
* @returns
*/
static async buildIndexFromNodes(
nodes: BaseNode[],
docStore: BaseDocumentStore,
serviceContext?: ServiceContext,
): Promise<KeywordTable> {
const indexStruct = new KeywordTable();
await docStore.addDocuments(nodes, true);
for (const node of nodes) {
const keywords = await KeywordTableIndex.extractKeywords(
node.getContent(MetadataMode.LLM),
serviceContext,
);
indexStruct.addNode([...keywords], node.id_);
}
@@ -345,7 +329,6 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
for (const node of nodes) {
const keywords = await KeywordTableIndex.extractKeywords(
node.getContent(MetadataMode.LLM),
this.serviceContext,
);
this.indexStruct.addNode([...keywords], node.id_);
}
packages/llamaindex/src/indices/summary/index.ts
+3 -19
View File
@@ -19,11 +19,7 @@ import type {
} from "@llamaindex/core/storage/doc-store";
import { extractText } from "@llamaindex/core/utils";
import _ from "lodash";
import type { ServiceContext } from "../../ServiceContext.js";
import {
llmFromSettingsOrContext,
nodeParserFromSettingsOrContext,
} from "../../Settings.js";
import { Settings } from "../../Settings.js";
import type {
BaseChatEngine,
ContextChatEngineOptions,
@@ -58,7 +54,6 @@ export interface SummaryIndexOptions {
nodes?: BaseNode[] | undefined;
indexStruct?: IndexList | undefined;
indexId?: string | undefined;
serviceContext?: ServiceContext | undefined;
storageContext?: StorageContext | undefined;
}
@@ -73,7 +68,6 @@ export class SummaryIndex extends BaseIndex<IndexList> {
static async init(options: SummaryIndexOptions): Promise<SummaryIndex> {
const storageContext =
options.storageContext ?? (await storageContextFromDefaults({}));
const serviceContext = options.serviceContext;
const { docStore, indexStore } = storageContext;
// Setup IndexStruct from storage
@@ -130,7 +124,6 @@ export class SummaryIndex extends BaseIndex<IndexList> {
return new SummaryIndex({
storageContext,
serviceContext,
docStore,
indexStore,
indexStruct,
@@ -141,11 +134,9 @@ export class SummaryIndex extends BaseIndex<IndexList> {
documents: Document[],
args: {
storageContext?: StorageContext | undefined;
serviceContext?: ServiceContext | undefined;
} = {},
): Promise<SummaryIndex> {
let { storageContext } = args;
const serviceContext = args.serviceContext;
storageContext = storageContext ?? (await storageContextFromDefaults({}));
const docStore = storageContext.docStore;
@@ -154,15 +145,11 @@ export class SummaryIndex extends BaseIndex<IndexList> {
await docStore.setDocumentHash(doc.id_, doc.hash);
}
const nodes =
nodeParserFromSettingsOrContext(serviceContext).getNodesFromDocuments(
documents,
);
const nodes = Settings.nodeParser.getNodesFromDocuments(documents);
const index = await SummaryIndex.init({
nodes,
storageContext,
serviceContext,
});
return index;
}
@@ -326,7 +313,6 @@ export class SummaryIndexLLMRetriever extends BaseRetriever {
choiceBatchSize: number;
formatNodeBatchFn: NodeFormatterFunction;
parseChoiceSelectAnswerFn: ChoiceSelectParserFunction;
serviceContext?: ServiceContext | undefined;
constructor(
index: SummaryIndex,
@@ -334,7 +320,6 @@ export class SummaryIndexLLMRetriever extends BaseRetriever {
choiceBatchSize: number = 10,
formatNodeBatchFn?: NodeFormatterFunction,
parseChoiceSelectAnswerFn?: ChoiceSelectParserFunction,
serviceContext?: ServiceContext,
) {
super();
this.index = index;
@@ -343,7 +328,6 @@ export class SummaryIndexLLMRetriever extends BaseRetriever {
this.formatNodeBatchFn = formatNodeBatchFn || defaultFormatNodeBatchFn;
this.parseChoiceSelectAnswerFn =
parseChoiceSelectAnswerFn || defaultParseChoiceSelectAnswerFn;
this.serviceContext = serviceContext || index.serviceContext;
}
async _retrieve(query: QueryBundle): Promise<NodeWithScore[]> {
@@ -357,7 +341,7 @@ export class SummaryIndexLLMRetriever extends BaseRetriever {
const fmtBatchStr = this.formatNodeBatchFn(nodesBatch);
const input = { context: fmtBatchStr, query: extractText(query) };
const llm = llmFromSettingsOrContext(this.serviceContext);
const llm = Settings.llm;
const rawResponse = (
await llm.complete({
packages/llamaindex/src/indices/vectorStore/index.ts
+8 -27
View File
@@ -24,16 +24,15 @@ import {
import type { BaseIndexStore } from "@llamaindex/core/storage/index-store";
import { extractText } from "@llamaindex/core/utils";
import { VectorStoreQueryMode } from "@llamaindex/core/vector-store";
import type { ServiceContext } from "../../ServiceContext.js";
import { nodeParserFromSettingsOrContext } from "../../Settings.js";
import { Settings } from "../../Settings.js";
import { RetrieverQueryEngine } from "../../engines/query/RetrieverQueryEngine.js";
import {
addNodesToVectorStores,
runTransformations,
} from "../../ingestion/IngestionPipeline.js";
import {
DocStoreStrategy,
createDocStoreStrategy,
DocStoreStrategy,
} from "../../ingestion/strategies/index.js";
import type { StorageContext } from "../../storage/StorageContext.js";
import { storageContextFromDefaults } from "../../storage/StorageContext.js";
@@ -52,7 +51,6 @@ interface IndexStructOptions {
}
export interface VectorIndexOptions extends IndexStructOptions {
nodes?: BaseNode[] | undefined;
serviceContext?: ServiceContext | undefined;
storageContext?: StorageContext | undefined;
vectorStores?: VectorStoreByType | undefined;
logProgress?: boolean | undefined;
@@ -81,7 +79,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
super(init);
this.indexStore = init.indexStore;
this.vectorStores = init.vectorStores ?? init.storageContext.vectorStores;
this.embedModel = init.serviceContext?.embedModel;
this.embedModel = Settings.embedModel;
}
/**
@@ -94,7 +92,6 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
): Promise<VectorStoreIndex> {
const storageContext =
options.storageContext ?? (await storageContextFromDefaults({}));
const serviceContext = options.serviceContext;
const indexStore = storageContext.indexStore;
const docStore = storageContext.docStore;
@@ -113,7 +110,6 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
const index = new this({
storageContext,
serviceContext,
docStore,
indexStruct,
indexStore,
@@ -214,10 +210,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
} = {},
): Promise<VectorStoreIndex> {
args.storageContext =
args.storageContext ??
(await storageContextFromDefaults({
serviceContext: args.serviceContext,
}));
args.storageContext ?? (await storageContextFromDefaults({}));
args.vectorStores = args.vectorStores ?? args.storageContext.vectorStores;
args.docStoreStrategy =
args.docStoreStrategy ??
@@ -240,7 +233,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
);
args.nodes = await runTransformations(
documents,
[nodeParserFromSettingsOrContext(args.serviceContext)],
[Settings.nodeParser],
{},
{ docStoreStrategy },
);
@@ -255,10 +248,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
}
}
static async fromVectorStores(
vectorStores: VectorStoreByType,
serviceContext?: ServiceContext,
) {
static async fromVectorStores(vectorStores: VectorStoreByType) {
if (!vectorStores[ModalityType.TEXT]?.storesText) {
throw new Error(
"Cannot initialize from a vector store that does not store text",
@@ -272,20 +262,13 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
const index = await this.init({
nodes: [],
storageContext,
serviceContext,
});
return index;
}
static async fromVectorStore(
vectorStore: BaseVectorStore,
serviceContext?: ServiceContext,
) {
return this.fromVectorStores(
{ [ModalityType.TEXT]: vectorStore },
serviceContext,
);
static async fromVectorStore(vectorStore: BaseVectorStore) {
return this.fromVectorStores({ [ModalityType.TEXT]: vectorStore });
}
asRetriever(
@@ -436,7 +419,6 @@ export class VectorIndexRetriever extends BaseRetriever {
index: VectorStoreIndex;
topK: TopKMap;
serviceContext?: ServiceContext | undefined;
filters?: MetadataFilters | undefined;
queryMode?: VectorStoreQueryMode | undefined;
@@ -444,7 +426,6 @@ export class VectorIndexRetriever extends BaseRetriever {
super();
this.index = options.index;
this.queryMode = options.mode ?? VectorStoreQueryMode.DEFAULT;
this.serviceContext = this.index.serviceContext;
if ("topK" in options && options.topK) {
this.topK = options.topK;
} else {
packages/llamaindex/src/selectors/utils.ts
+2 -4
View File
@@ -1,15 +1,13 @@
import type { ServiceContext } from "../ServiceContext.js";
import { llmFromSettingsOrContext } from "../Settings.js";
import { Settings } from "../Settings.js";
import type { BaseSelector } from "./base.js";
import { LLMMultiSelector, LLMSingleSelector } from "./llmSelectors.js";
export const getSelectorFromContext = (
serviceContext: ServiceContext,
isMulti: boolean = false,
): BaseSelector => {
let selector: BaseSelector | null = null;
const llm = llmFromSettingsOrContext(serviceContext);
const llm = Settings.llm;
if (isMulti) {
selector = new LLMMultiSelector({ llm });
packages/llamaindex/src/storage/StorageContext.ts
+2 -7
View File
@@ -9,7 +9,7 @@ import type {
BaseVectorStore,
VectorStoreByType,
} from "@llamaindex/core/vector-store";
import type { ServiceContext } from "../ServiceContext.js";
import { Settings } from "../Settings.js";
import { SimpleVectorStore } from "../vector-store/SimpleVectorStore.js";
import { SimpleDocumentStore } from "./docStore/SimpleDocumentStore.js";
@@ -25,10 +25,6 @@ type BuilderParams = {
vectorStore: BaseVectorStore;
vectorStores: VectorStoreByType;
persistDir: string;
/**
* @deprecated Please use `Settings` instead
*/
serviceContext?: ServiceContext | undefined;
};
export async function storageContextFromDefaults({
@@ -37,7 +33,6 @@ export async function storageContextFromDefaults({
vectorStore,
vectorStores,
persistDir,
serviceContext,
}: Partial<BuilderParams>): Promise<StorageContext> {
vectorStores = vectorStores ?? {};
if (!persistDir) {
@@ -47,7 +42,7 @@ export async function storageContextFromDefaults({
vectorStores[ModalityType.TEXT] = vectorStore ?? new SimpleVectorStore();
}
} else {
const embedModel = serviceContext?.embedModel;
const embedModel = Settings.embedModel;
docStore =
docStore ||
(await SimpleDocumentStore.fromPersistDir(persistDir, DEFAULT_NAMESPACE));
packages/llamaindex/tests/MetadataExtractors.test.ts
+8 -17
View File
@@ -1,7 +1,5 @@
import { Document } from "@llamaindex/core/schema";
import { Settings } from "llamaindex";
import type { ServiceContext } from "llamaindex/ServiceContext";
import { serviceContextFromDefaults } from "llamaindex/ServiceContext";
import { OpenAIEmbedding } from "llamaindex/embeddings/index";
import {
KeywordExtractor,
@@ -19,8 +17,6 @@ import {
} from "./utility/mockOpenAI.js";
describe("[MetadataExtractor]: Extractors should populate the metadata", () => {
let serviceContext: ServiceContext;
beforeAll(async () => {
const languageModel = new OpenAI({
model: "gpt-3.5-turbo",
@@ -33,11 +29,6 @@ describe("[MetadataExtractor]: Extractors should populate the metadata", () => {
const embedModel = new OpenAIEmbedding();
mockEmbeddingModel(embedModel);
serviceContext = serviceContextFromDefaults({
llm: languageModel,
embedModel,
});
});
afterAll(() => {
@@ -52,7 +43,7 @@ describe("[MetadataExtractor]: Extractors should populate the metadata", () => {
]);
const keywordExtractor = new KeywordExtractor({
llm: serviceContext.llm,
llm: Settings.llm,
keywords: 5,
});
@@ -71,7 +62,7 @@ describe("[MetadataExtractor]: Extractors should populate the metadata", () => {
]);
const titleExtractor = new TitleExtractor({
llm: serviceContext.llm,
llm: Settings.llm,
nodes: 5,
});
@@ -90,7 +81,7 @@ describe("[MetadataExtractor]: Extractors should populate the metadata", () => {
]);
const questionsAnsweredExtractor = new QuestionsAnsweredExtractor({
llm: serviceContext.llm,
llm: Settings.llm,
questions: 5,
});
@@ -109,10 +100,10 @@ describe("[MetadataExtractor]: Extractors should populate the metadata", () => {
new Document({ text: DEFAULT_LLM_TEXT_OUTPUT }),
]);
const llmCompleteSpy = vi.spyOn(serviceContext.llm, "complete");
const llmCompleteSpy = vi.spyOn(Settings.llm, "complete");
const questionsAnsweredExtractor = new QuestionsAnsweredExtractor({
llm: serviceContext.llm,
llm: Settings.llm,
questions: 5,
promptTemplate: `This is a custom prompt template for {context} with {numQuestions} questions`,
});
@@ -139,7 +130,7 @@ describe("[MetadataExtractor]: Extractors should populate the metadata", () => {
]);
const summaryExtractor = new SummaryExtractor({
llm: serviceContext.llm,
llm: Settings.llm,
});
const nodesWithKeywordMetadata = await summaryExtractor.processNodes(nodes);
@@ -156,10 +147,10 @@ describe("[MetadataExtractor]: Extractors should populate the metadata", () => {
new Document({ text: DEFAULT_LLM_TEXT_OUTPUT }),
]);
const llmCompleteSpy = vi.spyOn(serviceContext.llm, "complete");
const llmCompleteSpy = vi.spyOn(Settings.llm, "complete");
const keywordExtractor = new KeywordExtractor({
llm: serviceContext.llm,
llm: Settings.llm,
keywords: 5,
promptTemplate: `This is a custom prompt template for {context} with {maxKeywords} keywords`,
});
packages/llamaindex/tests/Selectors.test.ts
-3
View File
@@ -1,15 +1,12 @@
import { describe, expect, test } from "vitest";
// from unittest.mock import patch
import { serviceContextFromDefaults } from "llamaindex/ServiceContext";
import { OpenAI } from "llamaindex/llm/index";
import { LLMSingleSelector } from "llamaindex/selectors/index";
import { mocStructuredkLlmGeneration } from "./utility/mockOpenAI.js";
describe("LLMSelector", () => {
test("should be able to output a selection with a reason", async () => {
const serviceContext = serviceContextFromDefaults({});
const languageModel = new OpenAI({
model: "gpt-3.5-turbo",
});
packages/llamaindex/tests/indices/SummaryIndex.test.ts
+12 -8
View File
@@ -1,41 +1,45 @@
import {
Document,
OpenAIEmbedding,
Settings,
SummaryIndex,
VectorStoreIndex,
storageContextFromDefaults,
type ServiceContext,
type StorageContext,
} from "llamaindex";
import { rmSync } from "node:fs";
import { mkdtemp } from "node:fs/promises";
import { tmpdir } from "node:os";
import { join } from "node:path";
import { afterAll, beforeAll, describe, expect, it } from "vitest";
import { afterAll, beforeAll, describe, expect, it, vi } from "vitest";
import { mockEmbeddingModel } from "../utility/mockOpenAI.js";
const testDir = await mkdtemp(join(tmpdir(), "test-"));
import { mockServiceContext } from "../utility/mockServiceContext.js";
describe("SummaryIndex", () => {
let serviceContext: ServiceContext;
let storageContext: StorageContext;
beforeAll(async () => {
serviceContext = mockServiceContext();
storageContext = await storageContextFromDefaults({
persistDir: testDir,
});
const embedModel = new OpenAIEmbedding();
mockEmbeddingModel(embedModel);
Settings.embedModel = embedModel;
});
afterAll(() => {
vi.clearAllMocks();
});
it("SummaryIndex and VectorStoreIndex must be able to share the same storage context", async () => {
const documents = [new Document({ text: "lorem ipsem", id_: "1" })];
const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
serviceContext,
storageContext,
});
expect(vectorIndex).toBeDefined();
const summaryIndex = await SummaryIndex.fromDocuments(documents, {
serviceContext,
storageContext,
});
expect(summaryIndex).toBeDefined();
packages/llamaindex/tests/indices/VectorStoreIndex.test.ts
+17 -7
View File
@@ -1,18 +1,22 @@
import type { ServiceContext, StorageContext } from "llamaindex";
import { Document, VectorStoreIndex } from "llamaindex";
import type { StorageContext } from "llamaindex";
import {
Document,
OpenAIEmbedding,
Settings,
VectorStoreIndex,
} from "llamaindex";
import { DocStoreStrategy } from "llamaindex/ingestion/strategies/index";
import { mkdtemp, rm } from "node:fs/promises";
import { tmpdir } from "node:os";
import { join } from "node:path";
import { afterAll, beforeAll, describe, expect, test } from "vitest";
import { afterAll, beforeAll, describe, expect, test, vi } from "vitest";
const testDir = await mkdtemp(join(tmpdir(), "test-"));
import { mockServiceContext } from "../utility/mockServiceContext.js";
import { mockEmbeddingModel } from "../utility/mockOpenAI.js";
import { mockStorageContext } from "../utility/mockStorageContext.js";
describe("VectorStoreIndex", () => {
let serviceContext: ServiceContext;
let storageContext: StorageContext;
let testStrategy: (
strategy: DocStoreStrategy,
@@ -20,7 +24,6 @@ describe("VectorStoreIndex", () => {
) => Promise<Array<number>>;
beforeAll(async () => {
serviceContext = mockServiceContext();
storageContext = await mockStorageContext(testDir);
testStrategy = async (
strategy: DocStoreStrategy,
@@ -30,7 +33,6 @@ describe("VectorStoreIndex", () => {
const entries = [];
for (let i = 0; i < runs; i++) {
await VectorStoreIndex.fromDocuments(documents, {
serviceContext,
storageContext,
docStoreStrategy: strategy,
});
@@ -39,6 +41,14 @@ describe("VectorStoreIndex", () => {
}
return entries;
};
const embedModel = new OpenAIEmbedding();
mockEmbeddingModel(embedModel);
Settings.embedModel = embedModel;
});
afterAll(() => {
vi.clearAllMocks();
});
test("fromDocuments stores duplicates without a doc store strategy", async () => {
packages/llamaindex/tests/objects/ObjectIndex.test.ts
+12 -16
View File
@@ -1,19 +1,23 @@
import type { ServiceContext } from "llamaindex";
import {
FunctionTool,
ObjectIndex,
OpenAIEmbedding,
Settings,
SimpleToolNodeMapping,
VectorStoreIndex,
} from "llamaindex";
import { beforeAll, describe, expect, test } from "vitest";
import { mockServiceContext } from "../utility/mockServiceContext.js";
import { afterAll, beforeAll, describe, expect, test, vi } from "vitest";
import { mockEmbeddingModel } from "../utility/mockOpenAI.js";
describe("ObjectIndex", () => {
let serviceContext: ServiceContext;
beforeAll(async () => {
const embedModel = new OpenAIEmbedding();
mockEmbeddingModel(embedModel);
Settings.embedModel = embedModel;
});
beforeAll(() => {
serviceContext = mockServiceContext();
afterAll(() => {
vi.clearAllMocks();
});
test("test_object_with_tools", async () => {
@@ -51,14 +55,9 @@ describe("ObjectIndex", () => {
[tool1, tool2],
toolMapping,
VectorStoreIndex,
{
serviceContext,
},
);
const retriever = await objectRetriever.asRetriever({
serviceContext,
});
const retriever = await objectRetriever.asRetriever({});
expect(await retriever.retrieve("test")).toStrictEqual([tool1, tool2]);
});
@@ -98,9 +97,6 @@ describe("ObjectIndex", () => {
[tool1],
toolMapping,
VectorStoreIndex,
{
serviceContext,
},
);
let tools = objectRetriever.tools;
packages/llamaindex/tests/utility/mockServiceContext.ts
-23
View File
@@ -1,23 +0,0 @@
import {
OpenAI,
OpenAIEmbedding,
serviceContextFromDefaults,
} from "llamaindex";
import {
mockEmbeddingModel,
mockLlmGeneration,
} from "../utility/mockOpenAI.js";
export function mockServiceContext() {
const embeddingModel = new OpenAIEmbedding();
const llm = new OpenAI();
mockEmbeddingModel(embeddingModel);
mockLlmGeneration({ languageModel: llm });
return serviceContextFromDefaults({
embedModel: embeddingModel,
llm,
});
}