llamaindex - npm Package Compare versions

Comparing version 0.0.15 to 0.0.16

CHANGELOG.md

 # llamaindex
 
+## 0.0.16
+
+### Patch Changes
+
+- ec12633: Breaking: make vector store abstraction async (thank you @tyre for the PR)
+- 9214b06: Fix persistence bug (thanks @HenryHengZJ)
+- 3316c6b: Add Azure OpenAI support
+- 3316c6b: OpenAI Node v4-beta.8
+
 ## 0.0.15
@@ -4,0 +13,0 @@

CONTRIBUTING.md

@@ -15,3 +15,3 @@ # Contributing
 
-You can checkout how Turborepo works using the built in [README-turborepo.md](README-turborepo.md)
+You can checkout how Turborepo works using the default [README-turborepo.md](/README-turborepo.md)
 
@@ -45,3 +45,3 @@ ## Getting Started
 
-To write new test cases write them in packages/core/src/tests
+To write new test cases write them in [packages/core/src/tests](/packages/core/src/tests)
 
@@ -52,2 +52,4 @@ We use Jest https://jestjs.io/ to write our test cases. Jest comes with a bunch of built in assertions using the expect function: https://jestjs.io/docs/expect
 
+There is an existing ["simple"](/apps/simple/README.md) demos folder with mainly NodeJS scripts. Feel free to add additional demos to that folder. If you would like to try out your changes in the core package with a new demo, you need to run the build command in the README.
+
 You can create new demo applications in the apps folder. Just run pnpm init in the folder after you create it to create its own package.json
@@ -54,0 +56,0 @@

dist/index.d.ts

@@ -55,2 +55,7 @@ import OpenAI$1, { ClientOptions } from 'openai';
     asRelatedNodeInfo(): RelatedNodeInfo;
+    /**
+     * Used with built in JSON.stringify
+     * @returns
+     */
+    toJSON(): Record<string, any>;
 }
@@ -79,2 +84,3 @@ /**
     indexId: string;
+    constructor(init?: Partial<IndexNode>);
     getType(): ObjectType;
@@ -90,2 +96,3 @@ }
 }
+declare function jsonToNode(json: any): TextNode;
 /**
@@ -152,3 +159,5 @@ * A node with a similarity score
     openai: OpenAI$1;
-    constructor(options?: ClientOptions);
+    constructor(options?: ClientOptions & {
+        azure?: boolean;
+    });
 }
@@ -167,2 +176,9 @@
 
+interface AzureOpenAIConfig {
+    apiKey?: string;
+    endpoint?: string;
+    apiVersion?: string;
+    deploymentName?: string;
+}
+
 type MessageType = "user" | "assistant" | "system" | "generic" | "function";
@@ -240,3 +256,5 @@ interface ChatMessage {
     callbackManager?: CallbackManager;
-    constructor(init?: Partial<OpenAI>);
+    constructor(init?: Partial<OpenAI> & {
+        azure?: AzureOpenAIConfig;
+    });
     mapMessageType(messageType: MessageType): "user" | "assistant" | "system" | "function";
@@ -415,2 +433,3 @@ chat(messages: ChatMessage[], parentEvent?: Event): Promise<ChatResponse>;
 interface ExactMatchFilter {
+    filterType: "ExactMatch";
     key: string;
@@ -422,2 +441,16 @@ value: string | number;
 }
+interface VectorStoreQuerySpec {
+    query: string;
+    filters: ExactMatchFilter[];
+    topK?: number;
+}
+interface MetadataInfo {
+    name: string;
+    type: string;
+    description: string;
+}
+interface VectorStoreInfo {
+    metadataInfo: MetadataInfo[];
+    contentInfo: string;
+}
 interface VectorStoreQuery {
@@ -437,6 +470,6 @@ queryEmbedding?: number[];
     client(): any;
-    add(embeddingResults: NodeWithEmbedding[]): string[];
-    delete(refDocId: string, deleteKwargs?: any): void;
-    query(query: VectorStoreQuery, kwargs?: any): VectorStoreQueryResult;
-    persist(persistPath: string, fs?: GenericFileSystem): void;
+    add(embeddingResults: NodeWithEmbedding[]): Promise<string[]>;
+    delete(refDocId: string, deleteKwargs?: any): Promise<void>;
+    query(query: VectorStoreQuery, kwargs?: any): Promise<VectorStoreQueryResult>;
+    persist(persistPath: string, fs?: GenericFileSystem): Promise<void>;
 }
@@ -487,3 +520,5 @@
     session: OpenAISession;
-    constructor(init?: Partial<OpenAIEmbedding>);
+    constructor(init?: Partial<OpenAIEmbedding> & {
+        azure?: AzureOpenAIConfig;
+    });
     private getOpenAIEmbedding;
@@ -1191,3 +1226,3 @@ getTextEmbedding(text: string): Promise<number[]>;
 declare const DEFAULT_INDEX_STORE_PERSIST_FILENAME = "index_store.json";
-declare const DEFAULT_DOC_STORE_PERSIST_FILENAME = "docstore.json";
+declare const DEFAULT_DOC_STORE_PERSIST_FILENAME = "doc_store.json";
 declare const DEFAULT_VECTOR_STORE_PERSIST_FILENAME = "vector_store.json";
@@ -1197,2 +1232,2 @@ declare const DEFAULT_GRAPH_STORE_PERSIST_FILENAME = "graph_store.json";
 
-export { ALL_AVAILABLE_LLAMADEUCE_MODELS, ALL_AVAILABLE_OPENAI_MODELS, Anthropic, BaseEmbedding, BaseIndex, BaseIndexInit, BaseNode, BaseOutputParser, BaseQueryEngine, BaseQuestionGenerator, BaseReader, BaseRetriever, BaseTool, CallbackManager, ChatEngine, ChatMessage, ChatResponse, CompactAndRefine, CompleteFileSystem, CompletionResponse, CondenseQuestionChatEngine, ContextChatEngine, DEFAULT_CHUNK_OVERLAP, DEFAULT_CHUNK_OVERLAP_RATIO, DEFAULT_CHUNK_SIZE, DEFAULT_COLLECTION, DEFAULT_CONTEXT_WINDOW, DEFAULT_DOC_STORE_PERSIST_FILENAME, DEFAULT_EMBEDDING_DIM, DEFAULT_FS, DEFAULT_GRAPH_STORE_PERSIST_FILENAME, DEFAULT_INDEX_STORE_PERSIST_FILENAME, DEFAULT_NAMESPACE, DEFAULT_NUM_OUTPUTS, DEFAULT_PADDING, DEFAULT_PERSIST_DIR, DEFAULT_SIMILARITY_TOP_K, DEFAULT_VECTOR_STORE_PERSIST_FILENAME, DeuceChatStrategy, Document, Event, EventTag, EventType, GPT4_MODELS, GenericFileSystem, InMemoryFileSystem, IndexDict, IndexList, IndexNode, IndexStruct, IndexStructType, LLM, LLMQuestionGenerator, ListIndex, ListIndexLLMRetriever, ListIndexRetriever, ListRetrieverMode, LlamaDeuce, MessageType, MetadataMode, NodeParser, NodeRelationship, NodeWithEmbedding, NodeWithScore, ObjectType, OpenAI, OpenAIEmbedding, PDFReader, QueryEngineTool, Refine, RelatedNodeInfo, RelatedNodeType, Response, ResponseSynthesizer, RetrievalCallbackResponse, RetrieverQueryEngine, SentenceSplitter, ServiceContext, ServiceContextOptions, SimilarityType, SimpleChatEngine, SimpleDirectoryReader, SimpleDirectoryReaderLoadDataProps, SimpleNodeParser, SimplePrompt, SimpleResponseBuilder, StorageContext, StreamCallbackResponse, StreamToken, StructuredOutput, SubQuestion, SubQuestionOutputParser, SubQuestionQueryEngine, TURBO_MODELS, TextFileReader, TextNode, ToolMetadata, TreeSummarize, VectorIndexConstructorProps, VectorIndexOptions, VectorIndexRetriever, VectorStoreIndex, WalkableFileSystem, buildToolsText, contextSystemPrompt, defaultChoiceSelectPrompt, defaultCondenseQuestionPrompt, defaultRefinePrompt, defaultSubQuestionPrompt, defaultSummaryPrompt, defaultTextQaPrompt, exists, getNodeFS, getNodesFromDocument, getResponseBuilder, getTextSplitsFromDocument, getTopKEmbeddings, getTopKEmbeddingsLearner, getTopKMMREmbeddings, globalsHelper, jsonToIndexStruct, messagesToHistoryStr, serviceContextFromDefaults, serviceContextFromServiceContext, similarity, storageContextFromDefaults, walk };
+export { ALL_AVAILABLE_LLAMADEUCE_MODELS, ALL_AVAILABLE_OPENAI_MODELS, Anthropic, BaseEmbedding, BaseIndex, BaseIndexInit, BaseNode, BaseOutputParser, BaseQueryEngine, BaseQuestionGenerator, BaseReader, BaseRetriever, BaseTool, CallbackManager, ChatEngine, ChatMessage, ChatResponse, CompactAndRefine, CompleteFileSystem, CompletionResponse, CondenseQuestionChatEngine, ContextChatEngine, DEFAULT_CHUNK_OVERLAP, DEFAULT_CHUNK_OVERLAP_RATIO, DEFAULT_CHUNK_SIZE, DEFAULT_COLLECTION, DEFAULT_CONTEXT_WINDOW, DEFAULT_DOC_STORE_PERSIST_FILENAME, DEFAULT_EMBEDDING_DIM, DEFAULT_FS, DEFAULT_GRAPH_STORE_PERSIST_FILENAME, DEFAULT_INDEX_STORE_PERSIST_FILENAME, DEFAULT_NAMESPACE, DEFAULT_NUM_OUTPUTS, DEFAULT_PADDING, DEFAULT_PERSIST_DIR, DEFAULT_SIMILARITY_TOP_K, DEFAULT_VECTOR_STORE_PERSIST_FILENAME, DeuceChatStrategy, Document, Event, EventTag, EventType, ExactMatchFilter, GPT4_MODELS, GenericFileSystem, InMemoryFileSystem, IndexDict, IndexList, IndexNode, IndexStruct, IndexStructType, LLM, LLMQuestionGenerator, ListIndex, ListIndexLLMRetriever, ListIndexRetriever, ListRetrieverMode, LlamaDeuce, MessageType, MetadataFilters, MetadataInfo, MetadataMode, NodeParser, NodeRelationship, NodeWithEmbedding, NodeWithScore, ObjectType, OpenAI, OpenAIEmbedding, PDFReader, QueryEngineTool, Refine, RelatedNodeInfo, RelatedNodeType, Response, ResponseSynthesizer, RetrievalCallbackResponse, RetrieverQueryEngine, SentenceSplitter, ServiceContext, ServiceContextOptions, SimilarityType, SimpleChatEngine, SimpleDirectoryReader, SimpleDirectoryReaderLoadDataProps, SimpleNodeParser, SimplePrompt, SimpleResponseBuilder, StorageContext, StreamCallbackResponse, StreamToken, StructuredOutput, SubQuestion, SubQuestionOutputParser, SubQuestionQueryEngine, TURBO_MODELS, TextFileReader, TextNode, ToolMetadata, TreeSummarize, VectorIndexConstructorProps, VectorIndexOptions, VectorIndexRetriever, VectorStore, VectorStoreIndex, VectorStoreInfo, VectorStoreQuery, VectorStoreQueryMode, VectorStoreQueryResult, VectorStoreQuerySpec, WalkableFileSystem, buildToolsText, contextSystemPrompt, defaultChoiceSelectPrompt, defaultCondenseQuestionPrompt, defaultRefinePrompt, defaultSubQuestionPrompt, defaultSummaryPrompt, defaultTextQaPrompt, exists, getNodeFS, getNodesFromDocument, getResponseBuilder, getTextSplitsFromDocument, getTopKEmbeddings, getTopKEmbeddingsLearner, getTopKMMREmbeddings, globalsHelper, jsonToIndexStruct, jsonToNode, messagesToHistoryStr, serviceContextFromDefaults, serviceContextFromServiceContext, similarity, storageContextFromDefaults, walk };

package.json

 {
   "name": "llamaindex",
-  "version": "0.0.15",
+  "version": "0.0.16",
   "dependencies": {
-    "@anthropic-ai/sdk": "^0.5.8",
-    "js-tiktoken": "^1.0.7",
+    "@anthropic-ai/sdk": "^0.5.9",
     "lodash": "^4.17.21",
-    "openai": "4.0.0-beta.6",
+    "openai": "4.0.0-beta.8",
     "pdf-parse": "^1.1.1",
-    "replicate": "^0.12.3",
+    "replicate": "^0.14.1",
     "tiktoken-node": "^0.0.6",
@@ -12,0 +11,0 @@ "uuid": "^9.0.0",

README.md

 # LlamaIndex.TS
 
+LlamaIndex is a data framework for your LLM application.
+
 Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.
 
+Documentation: https://ts.llamaindex.ai/
+
 ## What is LlamaIndex.TS?
@@ -17,4 +21,7 @@
 export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
-npx tsc –-init # if needed
+pnpm init
+pnpm install typescript
+pnpm exec tsc –-init # if needed
 pnpm install llamaindex
+pnpm install @types/node
 ```
@@ -58,24 +65,34 @@
 ```bash
-npx ts-node example.ts
+pnpm dlx ts-node example.ts
 ```
 
+## Playground
+
+Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground
+
 ## Core concepts for getting started:
 
-- [Document](packages/core/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
+- [Document](/packages/core/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
 
-- [Node](packages/core/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
+- [Node](/packages/core/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
 
-- [Embedding](packages/core/src/Embedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that quesiton.
+- [Embedding](/packages/core/src/Embedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that quesiton.
 
-- [Indices](packages/core/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
+- [Indices](/packages/core/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
 
-- [QueryEngine](packages/core/src/QueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query.
+- [QueryEngine](/packages/core/src/QueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query.
 
-- [ChatEngine](packages/core/src/ChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices.
+- [ChatEngine](/packages/core/src/ChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices.
 
-- [SimplePrompt](packages/core/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
+- [SimplePrompt](/packages/core/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
 
+## Supported LLMs:
+
+- OpenAI GPT-3.5-turbo and GPT-4
+- Anthropic Claude Instant and Claude 2
+- Llama2 Chat LLMs (70B, 13B, and 7B parameters)
+
 ## Contributing:
 
-We are in the very early days of LlamaIndex.TS. If you’re interested in hacking on it with us check out our [contributing guide](CONTRIBUTING.md)
+We are in the very early days of LlamaIndex.TS. If you’re interested in hacking on it with us check out our [contributing guide](/CONTRIBUTING.md)
 
@@ -82,0 +99,0 @@ ## Bugs? Questions?

src/Embedding.ts

 import { DEFAULT_SIMILARITY_TOP_K } from "./constants";
+import {
+  AzureOpenAIConfig,
+  getAzureConfigFromEnv,
+  getAzureModel,
+  getAzureBaseUrl,
+  shouldUseAzure,
+} from "./llm/azure";
 import { OpenAISession, getOpenAISession } from "./llm/openai";
@@ -222,3 +229,3 @@ import { VectorStoreQueryMode } from "./storage/vectorStore/types";
 
-  constructor(init?: Partial<OpenAIEmbedding>) {
+  constructor(init?: Partial<OpenAIEmbedding> & { azure?: AzureOpenAIConfig }) {
     super();
@@ -228,10 +235,36 @@
 
-    this.apiKey = init?.apiKey ?? undefined;
     this.maxRetries = init?.maxRetries ?? 10;
     this.timeout = init?.timeout ?? undefined;
-    this.session = getOpenAISession({
-      apiKey: this.apiKey,
-      maxRetries: this.maxRetries,
-      timeout: this.timeout,
-    });
+
+    if (init?.azure || shouldUseAzure()) {
+      const azureConfig = getAzureConfigFromEnv({
+        ...init?.azure,
+        model: getAzureModel(this.model),
+      });
+
+      if (!azureConfig.apiKey) {
+        throw new Error(
+          "Azure API key is required for OpenAI Azure models. Please set the AZURE_OPENAI_KEY environment variable."
+        );
+      }
+
+      this.apiKey = azureConfig.apiKey;
+      this.session =
+        init?.session ??
+        getOpenAISession({
+          azure: true,
+          apiKey: this.apiKey,
+          baseURL: getAzureBaseUrl(azureConfig),
+          maxRetries: this.maxRetries,
+          timeout: this.timeout,
+          defaultQuery: { "api-version": azureConfig.apiVersion },
+        });
+    } else {
+      this.apiKey = init?.apiKey ?? undefined;
+      this.session = getOpenAISession({
+        apiKey: this.apiKey,
+        maxRetries: this.maxRetries,
+        timeout: this.timeout,
+      });
+    }
   }
@@ -238,0 +271,0 @@

src/index.ts

@@ -30,1 +30,2 @@ export * from "./ChatEngine";
 export * from "./storage/StorageContext";
+export * from "./storage/vectorStore/types";

src/indices/BaseIndex.ts

@@ -1,2 +0,2 @@
-import { Document, BaseNode } from "../Node";
+import { Document, BaseNode, jsonToNode } from "../Node";
 import { v4 as uuidv4 } from "uuid";
@@ -77,3 +77,8 @@ import { BaseRetriever } from "../Retriever";
     const indexDict = new IndexDict(json.indexId, json.summary);
-    indexDict.nodesDict = json.nodesDict;
+    indexDict.nodesDict = Object.entries(json.nodesDict).reduce<
+      Record<string, BaseNode>
+    >((acc, [key, value]) => {
+      acc[key] = jsonToNode(value);
+      return acc;
+    }, {});
     return indexDict;
@@ -80,0 +85,0 @@ } else {

src/indices/vectorStore/VectorIndexRetriever.ts

@@ -44,3 +44,3 @@ import { VectorStoreIndex } from "./VectorStoreIndex";
     };
-    const result = this.index.vectorStore.query(q);
+    const result = await this.index.vectorStore.query(q);
 
@@ -47,0 +47,0 @@ let nodesWithScores: NodeWithScore[] = [];

src/llm/LLM.ts

@@ -12,2 +12,9 @@ import { CallbackManager, Event } from "../callbacks/CallbackManager";
 } from "./anthropic";
+import {
+  AzureOpenAIConfig,
+  getAzureConfigFromEnv,
+  getAzureModel,
+  getAzureBaseUrl,
+  shouldUseAzure,
+} from "./azure";
 
@@ -88,3 +95,3 @@ export type MessageType =
 
-  constructor(init?: Partial<OpenAI>) {
+  constructor(init?: Partial<OpenAI> & { azure?: AzureOpenAIConfig }) {
     this.model = init?.model ?? "gpt-3.5-turbo";
@@ -95,13 +102,39 @@ this.temperature = init?.temperature ?? 0;
 
-    this.apiKey = init?.apiKey ?? undefined;
     this.maxRetries = init?.maxRetries ?? 10;
     this.timeout = init?.timeout ?? undefined; // Default is 60 seconds
-    this.session =
-      init?.session ??
-      getOpenAISession({
-        apiKey: this.apiKey,
-        maxRetries: this.maxRetries,
-        timeout: this.timeout,
+
+    if (init?.azure || shouldUseAzure()) {
+      const azureConfig = getAzureConfigFromEnv({
+        ...init?.azure,
+        model: getAzureModel(this.model),
       });
 
+      if (!azureConfig.apiKey) {
+        throw new Error(
+          "Azure API key is required for OpenAI Azure models. Please set the AZURE_OPENAI_KEY environment variable."
+        );
+      }
+
+      this.apiKey = azureConfig.apiKey;
+      this.session =
+        init?.session ??
+        getOpenAISession({
+          azure: true,
+          apiKey: this.apiKey,
+          baseURL: getAzureBaseUrl(azureConfig),
+          maxRetries: this.maxRetries,
+          timeout: this.timeout,
+          defaultQuery: { "api-version": azureConfig.apiVersion },
+        });
+    } else {
+      this.apiKey = init?.apiKey ?? undefined;
+      this.session =
+        init?.session ??
+        getOpenAISession({
+          apiKey: this.apiKey,
+          maxRetries: this.maxRetries,
+          timeout: this.timeout,
+        });
+    }
+
     this.callbackManager = init?.callbackManager;
@@ -157,5 +190,6 @@ }
       // Non-streaming
-      const response = await this.session.openai.chat.completions.create(
-        baseRequestParams
-      );
+      const response = await this.session.openai.chat.completions.create({
+        ...baseRequestParams,
+        stream: false,
+      });
 
@@ -162,0 +196,0 @@ const content = response.choices[0].message?.content ?? "";

src/llm/openai.ts

 import OpenAI, { ClientOptions } from "openai";
 import _ from "lodash";
 
+export class AzureOpenAI extends OpenAI {
+  protected override authHeaders() {
+    return { "api-key": this.apiKey };
+  }
+}
+
 export class OpenAISession {
   openai: OpenAI;
 
-  constructor(options: ClientOptions = {}) {
+  constructor(options: ClientOptions & { azure?: boolean } = {}) {
     if (!options.apiKey) {
@@ -18,3 +24,7 @@ if (typeof process !== undefined) {
 
-    this.openai = new OpenAI(options);
+    if (options.azure) {
+      this.openai = new AzureOpenAI(options);
+    } else {
+      this.openai = new OpenAI(options);
+    }
   }
@@ -35,3 +45,5 @@ }
  */
-export function getOpenAISession(options: ClientOptions = {}) {
+export function getOpenAISession(
+  options: ClientOptions & { azure?: boolean } = {}
+) {
   let session = defaultOpenAISession.find((session) => {
@@ -38,0 +50,0 @@ return _.isEqual(session.options, options);

src/Node.ts

@@ -131,2 +131,10 @@ import { v4 as uuidv4 } from "uuid";
   }
+
+  /**
+   * Used with built in JSON.stringify
+   * @returns
+   */
+  toJSON(): Record<string, any> {
+    return { ...this, type: this.getType() };
+  }
 }
@@ -208,2 +216,7 @@
 
+  constructor(init?: Partial<IndexNode>) {
+    super(init);
+    Object.assign(this, init);
+  }
+
   getType(): ObjectType {
@@ -232,2 +245,19 @@ return ObjectType.INDEX;
 
+export function jsonToNode(json: any) {
+  if (!json.type) {
+    throw new Error("Node type not found");
+  }
+
+  switch (json.type) {
+    case ObjectType.TEXT:
+      return new TextNode(json);
+    case ObjectType.INDEX:
+      return new IndexNode(json);
+    case ObjectType.DOCUMENT:
+      return new Document(json);
+    default:
+      throw new Error(`Invalid node type: ${json.type}`);
+  }
+}
+
 // export class ImageDocument extends Document {
@@ -234,0 +264,0 @@ //   image?: string;

src/storage/constants.ts

 export const DEFAULT_COLLECTION = "data";
 export const DEFAULT_PERSIST_DIR = "./storage";
 export const DEFAULT_INDEX_STORE_PERSIST_FILENAME = "index_store.json";
-export const DEFAULT_DOC_STORE_PERSIST_FILENAME = "docstore.json";
+export const DEFAULT_DOC_STORE_PERSIST_FILENAME = "doc_store.json";
 export const DEFAULT_VECTOR_STORE_PERSIST_FILENAME = "vector_store.json";
@@ -6,0 +6,0 @@ export const DEFAULT_GRAPH_STORE_PERSIST_FILENAME = "graph_store.json";

src/storage/vectorStore/SimpleVectorStore.ts

@@ -54,7 +54,7 @@ import * as path from "path";
 
-  get(textId: string): number[] {
+  async get(textId: string): Promise<number[]> {
     return this.data.embeddingDict[textId];
   }
 
-  add(embeddingResults: NodeWithEmbedding[]): string[] {
+  async add(embeddingResults: NodeWithEmbedding[]): Promise<string[]> {
     for (let result of embeddingResults) {
@@ -73,3 +73,3 @@ this.data.embeddingDict[result.node.id_] = result.embedding;
     if (this.persistPath) {
-      this.persist(this.persistPath, this.fs);
+      await this.persist(this.persistPath, this.fs);
     }
@@ -80,3 +80,3 @@
 
-  delete(refDocId: string): void {
+  async delete(refDocId: string): Promise<void> {
     let textIdsToDelete = Object.keys(this.data.textIdToRefDocId).filter(
@@ -89,5 +89,6 @@ (textId) => this.data.textIdToRefDocId[textId] === refDocId
     }
+    return Promise.resolve();
   }
 
-  query(query: VectorStoreQuery): VectorStoreQueryResult {
+  async query(query: VectorStoreQuery): Promise<VectorStoreQueryResult> {
     if (!_.isNil(query.filters)) {
@@ -144,6 +145,6 @@ throw new Error(
 
-    return {
+    return Promise.resolve({
       similarities: topSimilarities,
       ids: topIds,
-    };
+    });
   }
@@ -150,0 +151,0 @@

src/storage/vectorStore/types.ts

@@ -24,2 +24,3 @@ import { BaseNode } from "../../Node";
 export interface ExactMatchFilter {
+  filterType: "ExactMatch";
   key: string;
@@ -65,6 +66,6 @@ value: string | number;
   client(): any;
-  add(embeddingResults: NodeWithEmbedding[]): string[];
-  delete(refDocId: string, deleteKwargs?: any): void;
-  query(query: VectorStoreQuery, kwargs?: any): VectorStoreQueryResult;
-  persist(persistPath: string, fs?: GenericFileSystem): void;
+  add(embeddingResults: NodeWithEmbedding[]): Promise<string[]>;
+  delete(refDocId: string, deleteKwargs?: any): Promise<void>;
+  query(query: VectorStoreQuery, kwargs?: any): Promise<VectorStoreQueryResult>;
+  persist(persistPath: string, fs?: GenericFileSystem): Promise<void>;
 }

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increased by186.67%

Dependency changes

• + Addedopenai@4.0.0-beta.8(transitive)

• + Addedreplicate@0.14.1(transitive)

• - Removedjs-tiktoken@^1.0.7

• - Removedbase64-js@1.5.1(transitive)

• - Removedjs-tiktoken@1.0.15(transitive)

• - Removedopenai@4.0.0-beta.6(transitive)

• - Removedreplicate@0.12.3(transitive)

• Updated@anthropic-ai/sdk@^0.5.9

• Updatedopenai@4.0.0-beta.8

• Updatedreplicate@^0.14.1