initial stubs

.vscode/settings.json

+{
+  "editor.tabSize": 2,
+  "editor.formatOnSave": true,
+  "editor.defaultFormatter": "esbenp.prettier-vscode"
+}

packages/core/Document.ts

+export interface BaseDocument {
+  getText(): string;
+  getDocId(): string;
+  getDocHash(): string;
+  getEmbedding(): number[];
+}
+
+export class Document implements BaseDocument {
+  docId: string;
+  text: string;
+  embedding: number[];
+  docHash: string;
+
+  constructor(docId: string, text: string) {
+    this.docId = docId;
+    this.text = text;
+  }
+
+  getText() {
+    console.log("getText");
+    return "";
+  }
+
+  getDocId() {
+    console.log("getDocId");
+    return "";
+  }
+
+  getDocHash() {
+    console.log("getDocHash");
+    return "";
+  }
+
+  getEmbedding() {
+    console.log("getEmbedding");
+    return [];
+  }
+}

packages/core/Embedding.ts

+export enum SimilarityType {
+  DEFAULT = "cosine",
+  DOT_PRODUCT = "dot_product",
+  EUCLIDEAN = "euclidean",
+}
+
+export class BaseEmbedding {
+  getQueryEmbedding(query: string): number[] {
+    return [];
+  }
+
+  getTextEmbedding(text: string): number[] {
+    return [];
+  }
+
+  similarity(
+    embedding1: number[],
+    embedding2: number[],
+    mode: SimilarityType
+  ): number {
+    return 0;
+  }
+}

packages/core/Index.ts

+import { Document } from "./Document";
+import { Node } from "./Node";
+
+export class BaseIndex {
+  constructor(nodes?: Node[]) {}
+
+  fromDocuments(documents: Document[]) {
+    console.log("fromDocuments");
+  }
+
+  asQueryEngine() {
+    console.log("asQueryEngine");
+  }
+}
+
+export class VectorStoreIndex extends BaseIndex {}

packages/core/LLMPredictor.ts

+export interface BaseLLMPredictor {
+  getLlmMetadata(): Promise<any>;
+  predict(prompt: string, options: any): Promise<any>;
+  stream(prompt: string, options: any): Promise<any>;
+}
+
+export class LLMPredictor implements BaseLLMPredictor {
+  llm: string;
+  retryOnThrottling: boolean;
+
+  constructor(llm: string, retryOnThrottling: boolean = true) {
+    this.llm = llm;
+    this.retryOnThrottling = retryOnThrottling;
+  }
+
+  async getLlmMetadata() {
+    console.log("getLlmMetadata");
+  }
+
+  async predict(prompt: string, options: any) {
+    console.log("predict");
+  }
+
+  async stream(prompt: string, options: any) {
+    console.log("stream");
+  }
+}

packages/core/Node.ts

+import { BaseDocument } from "./Document";
+
+export enum DocumentRelationship {
+  SOURCE = "source",
+  PREVIOUS = "previous",
+  NEXT = "next",
+  PARENT = "parent",
+  CHILD = "child",
+}
+
+export enum NodeType {
+  TEXT,
+  IMAGE,
+  INDEX,
+}
+
+export class Node implements BaseDocument {
+  relationships: { [key in DocumentRelationship]: string | string[] };
+
+  getText(): string {
+    throw new Error("Method not implemented.");
+  }
+  getDocId(): string {
+    throw new Error("Method not implemented.");
+  }
+  getDocHash(): string {
+    throw new Error("Method not implemented.");
+  }
+  getEmbedding(): number[] {
+    throw new Error("Method not implemented.");
+  }
+
+  getNodeInfo(): { [key: string]: any } {
+    return {};
+  }
+
+  refDocId(): string | null {
+    return "";
+  }
+
+  prevNodeId(): string {
+    throw new Error("Node does not have previous node");
+  }
+
+  nextNodeId(): string {
+    throw new Error("Node does not have next node");
+  }
+
+  parentNodeId(): string {
+    throw new Error("Node does not have parent node");
+  }
+
+  childNodeIds(): string[] {
+    return [];
+  }
+}

packages/core/Prompt.ts

+/**
+ * A prompt is a function that takes a dictionary of inputs and returns a string.
+ * NOTE this is a different interface compared to LlamaIndex Python
+ */
+export type Prompt = (input: { [key: string]: string }) => string;
+
+/*
+DEFAULT_TEXT_QA_PROMPT_TMPL = (
+    "Context information is below. \n"
+    "---------------------\n"
+    "{context_str}"
+    "\n---------------------\n"
+    "Given the context information and not prior knowledge, "
+    "answer the question: {query_str}\n"
+)
+*/
+
+export const defaultTextQaPrompt: Prompt = (input: {
+  [key: string]: string;
+}) => {
+  const { context, query } = input;
+
+  return `Context information is below.
+---------------------
+${context}
+---------------------
+Given the context information and not prior knowledge, answer the question: ${query}
+`;
+};
+
+/*
+DEFAULT_SUMMARY_PROMPT_TMPL = (
+    "Write a summary of the following. Try to use only the "
+    "information provided. "
+    "Try to include as many key details as possible.\n"
+    "\n"
+    "\n"
+    "{context_str}\n"
+    "\n"
+    "\n"
+    'SUMMARY:"""\n'
+)
+*/
+
+export const defaultSummaryPrompt: Prompt = (input: {
+  [key: string]: string;
+}) => {
+  const { context } = input;
+
+  return `Write a summary of the following. Try to use only the information provided. Try to include as many key details as possible.
+
+
+${input.context}
+
+
+SUMMARY:"""
+`;
+};