add docs and unit test

fix: context not working in contextchatengine
2026-07-15 06:52:45 -04:00 · 2024-09-20 15:01:08 +07:00 · 2024-09-20 12:58:26 +07:00
77 changed files with 445 additions and 2565 deletions
@@ -0,0 +1,6 @@
+---
+"llamaindex": patch
+"@llamaindex/core": patch
+---
+
+Fix context not being sent using ContextChatEngine
@@ -167,13 +167,11 @@ export async function chatWithAgent(
      // ... adding your tools here
    ],
  });
-  const responseStream = await agent.chat(
-    {
-      message: question,
-      chatHistory: prevMessages,
-    },
-    true,
-  );
+  const responseStream = await agent.chat({
+    stream: true,
+    message: question,
+    chatHistory: prevMessages,
+  });
  const uiStream = createStreamableUI(<div>loading...</div>);
  responseStream
    .pipeTo(
@@ -1,35 +1,5 @@
 # docs

-## 0.0.77
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-
-## 0.0.76
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-
-## 0.0.75
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-
-## 0.0.74
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-
 ## 0.0.73

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "docs",
-  "version": "0.0.77",
+  "version": "0.0.73",
  "private": true,
  "scripts": {
    "docusaurus": "docusaurus",
@@ -18,7 +18,7 @@ import readline from "node:readline/promises";
  });
  const chatEngine = new SimpleChatEngine({
    llm,
-    memory: chatHistory,
+    chatHistory,
  });
  const rl = readline.createInterface({ input, output });

@@ -27,10 +27,12 @@ async function main() {

  // Query the index
  const queryEngine = index.asQueryEngine();
-  const stream = await queryEngine.query({
-    query: "What did the author do in college?",
-    stream: true,
-  });
+  const stream = await queryEngine.query(
+    {
+      query: "What did the author do in college?",
+    },
+    true,
+  );

  // Output response
  for await (const chunk of stream) {
@@ -37,10 +37,12 @@ async function main() {

  // Query the index
  const queryEngine = index.asQueryEngine();
-  const stream = await queryEngine.query({
-    query: "What did the author do in college?",
-    stream: true,
-  });
+  const stream = await queryEngine.query(
+    {
+      query: "What did the author do in college?",
+    },
+    true,
+  );

  // Output response
  for await (const chunk of stream) {
@@ -1,5 +1,4 @@
 // call pnpm tsx multimodal/load.ts first to init the storage
-import { extractText } from "@llamaindex/core/utils";
 import {
  ContextChatEngine,
  NodeWithScore,
@@ -26,9 +25,8 @@ Settings.callbackManager.on("retrieve-end", (event) => {
  const textNodes = nodes.filter(
    (node: NodeWithScore) => node.node.type === ObjectType.TEXT,
  );
-  const text = extractText(query);
  console.log(
-    `Retrieved ${textNodes.length} text nodes and ${imageNodes.length} image nodes for query: ${text}`,
+    `Retrieved ${textNodes.length} text nodes and ${imageNodes.length} image nodes for query: ${query}`,
  );
 });

@@ -1,4 +1,3 @@
-import { extractText } from "@llamaindex/core/utils";
 import {
  getResponseSynthesizer,
  OpenAI,
@@ -17,8 +16,7 @@ Settings.llm = new OpenAI({ model: "gpt-4-turbo", maxTokens: 512 });
 // Update callbackManager
 Settings.callbackManager.on("retrieve-end", (event) => {
  const { nodes, query } = event.detail;
-  const text = extractText(query);
-  console.log(`Retrieved ${nodes.length} nodes for query: ${text}`);
+  console.log(`Retrieved ${nodes.length} nodes for query: ${query}`);
 });

 async function main() {
@@ -32,10 +30,12 @@ async function main() {
    responseSynthesizer: getResponseSynthesizer("multi_modal"),
    retriever: index.asRetriever({ topK: { TEXT: 3, IMAGE: 1 } }),
  });
-  const stream = await queryEngine.query({
-    query: "Tell me more about Vincent van Gogh's famous paintings",
-    stream: true,
-  });
+  const stream = await queryEngine.query(
+    {
+      query: "Tell me more about Vincent van Gogh's famous paintings",
+    },
+    true,
+  );
  for await (const chunk of stream) {
    process.stdout.write(chunk.response);
  }
@@ -40,11 +40,7 @@ async function main(args: any) {
    const rdr = new SimpleDirectoryReader(callback);
    const docs = await rdr.loadData({ directoryPath: sourceDir });

-    const pgvs = new PGVectorStore({
-      clientConfig: {
-        connectionString: process.env.PG_CONNECTION_STRING,
-      },
-    });
+    const pgvs = new PGVectorStore();
    pgvs.setCollection(sourceDir);
    await pgvs.clearCollection();

@@ -7,11 +7,7 @@ async function main() {
  });

  try {
-    const pgvs = new PGVectorStore({
-      clientConfig: {
-        connectionString: process.env.PG_CONNECTION_STRING,
-      },
-    });
+    const pgvs = new PGVectorStore();
    // Optional - set your collection name, default is no filter on this field.
    // pgvs.setCollection();

@@ -1,35 +1,5 @@
 # @llamaindex/autotool

-## 3.0.8
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-
-## 3.0.7
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-
-## 3.0.6
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-
-## 3.0.5
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-
 ## 3.0.4

 ### Patch Changes
@@ -1,39 +1,5 @@
 # @llamaindex/autotool-01-node-example

-## 0.0.17
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-  - @llamaindex/autotool@3.0.8
-
-## 0.0.16
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-  - @llamaindex/autotool@3.0.7
-
-## 0.0.15
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-  - @llamaindex/autotool@3.0.6
-
-## 0.0.14
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-  - @llamaindex/autotool@3.0.5
-
 ## 0.0.13

 ### Patch Changes
@@ -13,5 +13,5 @@
  "scripts": {
    "start": "node --import tsx --import @llamaindex/autotool/node ./src/index.ts"
  },
-  "version": "0.0.17"
+  "version": "0.0.13"
 }
@@ -1,39 +1,5 @@
 # @llamaindex/autotool-02-next-example

-## 0.1.61
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-  - @llamaindex/autotool@3.0.8
-
-## 0.1.60
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-  - @llamaindex/autotool@3.0.7
-
-## 0.1.59
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-  - @llamaindex/autotool@3.0.6
-
-## 0.1.58
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-  - @llamaindex/autotool@3.0.5
-
 ## 0.1.57

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/autotool-02-next-example",
  "private": true,
-  "version": "0.1.61",
+  "version": "0.1.57",
  "scripts": {
    "dev": "next dev",
    "build": "next build",
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/autotool",
  "type": "module",
-  "version": "3.0.8",
+  "version": "3.0.4",
  "description": "auto transpile your JS function to LLM Agent compatible",
  "files": [
    "dist",
@@ -1,20 +1,5 @@
 # @llamaindex/community

-## 0.0.40
-
-### Patch Changes
-
- 50e6b57: feat: add Amazon Bedrock Retriever
- Updated dependencies [8b7fdba]
-  - @llamaindex/core@0.2.6
-
-## 0.0.39
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
-  - @llamaindex/core@0.2.5
-
 ## 0.0.38

 ### Patch Changes
@@ -7,7 +7,6 @@
 - Bedrock support for the Anthropic Claude Models [usage](https://ts.llamaindex.ai/modules/llms/available_llms/bedrock)
 - Bedrock support for the Meta LLama 2, 3 and 3.1 Models [usage](https://ts.llamaindex.ai/modules/llms/available_llms/bedrock)
 - Meta LLama3.1 405b tool call support
- Bedrock support for querying Knowledge Base

 ## LICENSE

@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/community",
  "description": "Community package for LlamaIndexTS",
-  "version": "0.0.40",
+  "version": "0.0.38",
  "type": "module",
  "types": "dist/type/index.d.ts",
  "main": "dist/cjs/index.js",
@@ -47,7 +47,6 @@
  },
  "dependencies": {
    "@aws-sdk/client-bedrock-runtime": "^3.642.0",
-    "@aws-sdk/client-bedrock-agent-runtime": "^3.642.0",
    "@llamaindex/core": "workspace:*",
    "@llamaindex/env": "workspace:*"
  }
@@ -3,4 +3,3 @@ export {
  BEDROCK_MODEL_MAX_TOKENS,
  Bedrock,
 } from "./llm/bedrock/index.js";
-export { AmazonKnowledgeBaseRetriever } from "./retrievers/bedrock.js";
@@ -1,165 +0,0 @@
-import type { KnowledgeBaseVectorSearchConfiguration } from "@aws-sdk/client-bedrock-agent-runtime";
-import {
-  BedrockAgentRuntimeClient,
-  type BedrockAgentRuntimeClientConfig,
-  type RetrievalFilter,
-  RetrieveCommand,
-  type SearchType,
-} from "@aws-sdk/client-bedrock-agent-runtime";
-import type { QueryBundle } from "@llamaindex/core/query-engine";
-import { BaseRetriever } from "@llamaindex/core/retriever";
-import { Document, type NodeWithScore } from "@llamaindex/core/schema";
-import { extractText } from "@llamaindex/core/utils";
-
-/**
- * Interface for the arguments required to initialize an
- * AmazonKnowledgeBaseRetriever instance.
- */
-export interface AmazonKnowledgeBaseRetrieverArgs {
-  knowledgeBaseId: string;
-  topK: number;
-  region: string;
-  clientOptions?: BedrockAgentRuntimeClientConfig;
-  filter?: RetrievalFilter;
-  overrideSearchType?: SearchType;
-}
-
-/**
- * Class for interacting with Amazon Bedrock Knowledge Bases, a RAG workflow oriented service
- * Extends the BaseRetriever class.
- * @example
- * ```typescript
- * const retriever = new AmazonKnowledgeBaseRetriever({
- *   topK: 10,
- *   knowledgeBaseId: "YOUR_KNOWLEDGE_BASE_ID",
- *   region: "us-east-2",
- *   clientOptions: {
- *     credentials: {
- *       accessKeyId: "YOUR_ACCESS_KEY_ID",
- *       secretAccessKey: "YOUR_SECRET_ACCESS_KEY",
- *     },
- *   },
- * });
- *
- * const docs = await retriever.retrieve({query: "How are clouds formed?"});
- * ```
- */
-export class AmazonKnowledgeBaseRetriever extends BaseRetriever {
-  static lc_name() {
-    return "AmazonKnowledgeBaseRetriever";
-  }
-
-  lc_namespace = ["llamaindex", "retrievers", "amazon_bedrock_knowledge_base"];
-
-  knowledgeBaseId: string;
-
-  topK: number;
-
-  bedrockAgentRuntimeClient: BedrockAgentRuntimeClient;
-
-  filter: RetrievalFilter | undefined;
-
-  overrideSearchType: SearchType | undefined;
-
-  constructor({
-    knowledgeBaseId,
-    topK = 10,
-    clientOptions,
-    region,
-    filter,
-    overrideSearchType,
-  }: AmazonKnowledgeBaseRetrieverArgs) {
-    super();
-
-    this.topK = topK;
-    this.filter = filter;
-    this.overrideSearchType = overrideSearchType;
-    this.bedrockAgentRuntimeClient = new BedrockAgentRuntimeClient({
-      region,
-      ...clientOptions,
-    });
-    this.knowledgeBaseId = knowledgeBaseId;
-  }
-
-  /**
-   * Cleans the result text by replacing sequences of whitespace with a
-   * single space and removing ellipses.
-   * @param resText The result text to clean.
-   * @returns The cleaned result text.
-   */
-  cleanResult(resText: string) {
-    const res = resText.replace(/\s+/g, " ").replace(/\.\.\./g, "");
-    return res;
-  }
-
-  async queryKnowledgeBase(
-    query: QueryBundle,
-    topK: number,
-    filter?: RetrievalFilter,
-    overrideSearchType?: SearchType,
-  ): Promise<NodeWithScore[]> {
-    const retrieveCommand = new RetrieveCommand({
-      knowledgeBaseId: this.knowledgeBaseId,
-      retrievalQuery: {
-        text: extractText(query),
-      },
-      retrievalConfiguration: {
-        vectorSearchConfiguration: {
-          numberOfResults: topK,
-          overrideSearchType,
-          filter,
-        } as KnowledgeBaseVectorSearchConfiguration,
-      },
-    });
-
-    const retrieveResponse =
-      await this.bedrockAgentRuntimeClient.send(retrieveCommand);
-
-    return (
-      retrieveResponse.retrievalResults?.map((result) => {
-        let source;
-        switch (result.location?.type) {
-          case "CONFLUENCE":
-            source = result.location?.confluenceLocation?.url;
-            break;
-          case "S3":
-            source = result.location?.s3Location?.uri;
-            break;
-          case "SALESFORCE":
-            source = result.location?.salesforceLocation?.url;
-            break;
-          case "SHAREPOINT":
-            source = result.location?.sharePointLocation?.url;
-            break;
-          case "WEB":
-            source = result.location?.webLocation?.url;
-            break;
-          default:
-            source = result.location?.s3Location?.uri;
-            break;
-        }
-
-        return {
-          node: new Document({
-            text: this.cleanResult(result.content?.text || ""),
-            metadata: {
-              source,
-              score: result.score,
-              ...result.metadata,
-            },
-          }),
-          score: result.score ?? 1.0,
-        };
-      }) ?? []
-    );
-  }
-
-  async _retrieve(query: QueryBundle): Promise<NodeWithScore[]> {
-    return await this.queryKnowledgeBase(
-      query,
-      this.topK,
-      this.filter,
-      this.overrideSearchType,
-    );
-  }
-}
@@ -1,20 +1,5 @@
 # @llamaindex/core

-## 0.2.6
-
-### Patch Changes
-
- 8b7fdba: refactor: move chat engine & retriever into core.
-
-  - `chatHistory` in BaseChatEngine now returns `ChatMessage[] | Promise<ChatMessage[]>`, instead of `BaseMemory`
-  - update `retrieve-end` type
-
-## 0.2.5
-
-### Patch Changes
-
- d902cc3: Fix context not being sent using ContextChatEngine
-
 ## 0.2.4

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/core",
  "type": "module",
-  "version": "0.2.6",
+  "version": "0.2.4",
  "description": "LlamaIndex Core Module",
  "exports": {
    "./node-parser": {
@@ -199,34 +199,6 @@
        "types": "./dist/response-synthesizers/index.d.ts",
        "default": "./dist/response-synthesizers/index.js"
      }
-    },
-    "./chat-engine": {
-      "require": {
-        "types": "./dist/chat-engine/index.d.cts",
-        "default": "./dist/chat-engine/index.cjs"
-      },
-      "import": {
-        "types": "./dist/chat-engine/index.d.ts",
-        "default": "./dist/chat-engine/index.js"
-      },
-      "default": {
-        "types": "./dist/chat-engine/index.d.ts",
-        "default": "./dist/chat-engine/index.js"
-      }
-    },
-    "./retriever": {
-      "require": {
-        "types": "./dist/retriever/index.d.cts",
-        "default": "./dist/retriever/index.cjs"
-      },
-      "import": {
-        "types": "./dist/retriever/index.d.ts",
-        "default": "./dist/retriever/index.js"
-      },
-      "default": {
-        "types": "./dist/retriever/index.d.ts",
-        "default": "./dist/retriever/index.js"
-      }
    }
  },
  "files": [
@@ -1,36 +0,0 @@
-import type { ChatMessage, MessageContent } from "../llms";
-import type { BaseMemory } from "../memory";
-import { EngineResponse } from "../schema";
-
-export interface BaseChatEngineParams<
-  AdditionalMessageOptions extends object = object,
-> {
-  message: MessageContent;
-  /**
-   * Optional chat history if you want to customize the chat history.
-   */
-  chatHistory?:
-    | ChatMessage<AdditionalMessageOptions>[]
-    | BaseMemory<AdditionalMessageOptions>;
-}
-
-export interface StreamingChatEngineParams<
-  AdditionalMessageOptions extends object = object,
-> extends BaseChatEngineParams<AdditionalMessageOptions> {
-  stream: true;
-}
-
-export interface NonStreamingChatEngineParams<
-  AdditionalMessageOptions extends object = object,
-> extends BaseChatEngineParams<AdditionalMessageOptions> {
-  stream?: false;
-}
-
-export abstract class BaseChatEngine {
-  abstract chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
-  abstract chat(
-    params: StreamingChatEngineParams,
-  ): Promise<AsyncIterable<EngineResponse>>;
-
-  abstract chatHistory: ChatMessage[] | Promise<ChatMessage[]>;
-}
@@ -11,7 +11,6 @@ import type {
  SynthesizeEndEvent,
  SynthesizeStartEvent,
 } from "../../response-synthesizers";
-import type { RetrieveEndEvent, RetrieveStartEvent } from "../../retriever";
 import { TextNode } from "../../schema";
 import { EventCaller, getEventCaller } from "../../utils";
 import type { UUID } from "../type";
@@ -70,8 +69,6 @@ export interface LlamaIndexEventMaps {
  "query-end": QueryEndEvent;
  "synthesize-start": SynthesizeStartEvent;
  "synthesize-end": SynthesizeEndEvent;
-  "retrieve-start": RetrieveStartEvent;
-  "retrieve-end": RetrieveEndEvent;
 }

 export class LlamaIndexCustomEvent<T = any> extends CustomEvent<T> {
@@ -2,7 +2,7 @@ import { randomUUID } from "@llamaindex/env";
 import { Settings } from "../global";
 import type { MessageContent } from "../llms";
 import { PromptMixin } from "../prompts";
-import { EngineResponse, type NodeWithScore } from "../schema";
+import { EngineResponse } from "../schema";
 import { wrapEventCaller } from "../utils";

 /**
@@ -18,18 +18,6 @@ export type QueryBundle = {

 export type QueryType = string | QueryBundle;

-export type BaseQueryParams = {
-  query: QueryType;
-};
-
-export interface StreamingQueryParams extends BaseQueryParams {
-  stream: true;
-}
-
-export interface NonStreamingQueryParams extends BaseQueryParams {
-  stream?: false;
-}
-
 export type QueryFn = (
  strOrQueryBundle: QueryType,
  stream?: boolean,
@@ -40,26 +28,23 @@ export abstract class BaseQueryEngine extends PromptMixin {
    super();
  }

-  async retrieve(params: QueryType): Promise<NodeWithScore[]> {
-    throw new Error(
-      "This query engine does not support retrieve, use query directly",
-    );
-  }
-
-  query(params: StreamingQueryParams): Promise<AsyncIterable<EngineResponse>>;
-  query(params: NonStreamingQueryParams): Promise<EngineResponse>;
+  query(
+    strOrQueryBundle: QueryType,
+    stream: true,
+  ): Promise<AsyncIterable<EngineResponse>>;
+  query(strOrQueryBundle: QueryType, stream?: false): Promise<EngineResponse>;
  @wrapEventCaller
  async query(
-    params: StreamingQueryParams | NonStreamingQueryParams,
+    strOrQueryBundle: QueryType,
+    stream = false,
  ): Promise<EngineResponse | AsyncIterable<EngineResponse>> {
-    const { stream, query } = params;
    const id = randomUUID();
    const callbackManager = Settings.callbackManager;
    callbackManager.dispatchEvent("query-start", {
      id,
-      query,
+      query: strOrQueryBundle,
    });
-    const response = await this._query(query, stream);
+    const response = await this._query(strOrQueryBundle, stream);
    callbackManager.dispatchEvent("query-end", {
      id,
      response,
@@ -1,112 +0,0 @@
-import { randomUUID } from "@llamaindex/env";
-import { Settings } from "../global";
-import type { MessageContent } from "../llms";
-import { PromptMixin } from "../prompts";
-import type { QueryBundle, QueryType } from "../query-engine";
-import { BaseNode, IndexNode, type NodeWithScore, ObjectType } from "../schema";
-
-export type RetrieveParams = {
-  query: MessageContent;
-  preFilters?: unknown;
-};
-
-export type RetrieveStartEvent = {
-  id: string;
-  query: QueryBundle;
-};
-
-export type RetrieveEndEvent = {
-  id: string;
-  query: QueryBundle;
-  nodes: NodeWithScore[];
-};
-
-export abstract class BaseRetriever extends PromptMixin {
-  objectMap: Map<string, unknown> = new Map();
-
-  protected _updatePrompts() {}
-  protected _getPrompts() {
-    return {};
-  }
-
-  protected _getPromptModules() {
-    return {};
-  }
-
-  protected constructor() {
-    super();
-  }
-
-  public async retrieve(params: QueryType): Promise<NodeWithScore[]> {
-    const cb = Settings.callbackManager;
-    const queryBundle = typeof params === "string" ? { query: params } : params;
-    const id = randomUUID();
-    cb.dispatchEvent("retrieve-start", { id, query: queryBundle });
-    let response = await this._retrieve(queryBundle);
-    response = await this._handleRecursiveRetrieval(queryBundle, response);
-    cb.dispatchEvent("retrieve-end", {
-      id,
-      query: queryBundle,
-      nodes: response,
-    });
-    return response;
-  }
-
-  abstract _retrieve(params: QueryBundle): Promise<NodeWithScore[]>;
-
-  async _handleRecursiveRetrieval(
-    params: QueryBundle,
-    nodes: NodeWithScore[],
-  ): Promise<NodeWithScore[]> {
-    const retrievedNodes = [];
-    for (const { node, score = 1.0 } of nodes) {
-      if (node.type === ObjectType.INDEX) {
-        const indexNode = node as IndexNode;
-        const object = this.objectMap.get(indexNode.indexId);
-        if (object !== undefined) {
-          retrievedNodes.push(
-            ...this._retrieveFromObject(object, params, score),
-          );
-        } else {
-          retrievedNodes.push({ node, score });
-        }
-      } else {
-        retrievedNodes.push({ node, score });
-      }
-    }
-    return nodes;
-  }
-
-  _retrieveFromObject(
-    object: unknown,
-    queryBundle: QueryBundle,
-    score: number,
-  ): NodeWithScore[] {
-    if (object == null) {
-      throw new TypeError("Object is not retrievable");
-    }
-    if (typeof object !== "object") {
-      throw new TypeError("Object is not retrievable");
-    }
-    if ("node" in object && object.node instanceof BaseNode) {
-      return [
-        {
-          node: object.node,
-          score:
-            "score" in object && typeof object.score === "number"
-              ? object.score
-              : score,
-        },
-      ];
-    }
-    if (object instanceof BaseNode) {
-      return [{ node: object, score }];
-    } else {
-      // todo: support other types
-      // BaseQueryEngine
-      // BaseRetriever
-      // QueryComponent
-      throw new TypeError("Object is not retrievable");
-    }
-  }
-}
@@ -1,35 +1,5 @@
 # @llamaindex/experimental

-## 0.0.86
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-
-## 0.0.85
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-
-## 0.0.84
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-
-## 0.0.83
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-
 ## 0.0.82

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/experimental",
  "description": "Experimental package for LlamaIndexTS",
-  "version": "0.0.86",
+  "version": "0.0.82",
  "type": "module",
  "types": "dist/type/index.d.ts",
  "main": "dist/cjs/index.js",
@@ -1,49 +1,5 @@
 # llamaindex

-## 0.6.8
-
-### Patch Changes
-
- 8b7fdba: refactor: move chat engine & retriever into core.
-
-  - `chatHistory` in BaseChatEngine now returns `ChatMessage[] | Promise<ChatMessage[]>`, instead of `BaseMemory`
-  - update `retrieve-end` type
-
- Updated dependencies [8b7fdba]
-  - @llamaindex/core@0.2.6
-  - @llamaindex/openai@0.1.8
-  - @llamaindex/groq@0.0.7
-
-## 0.6.7
-
-### Patch Changes
-
- 23bcc37: fix: add `serializer` in doc store
-
-  `PostgresDocumentStore` now will not use JSON.stringify for better performance
-
-## 0.6.6
-
-### Patch Changes
-
- d902cc3: Fix context not being sent using ContextChatEngine
- 025ffe6: fix: update `PostgresKVStore` constructor params
- a659574: Adds upstash vector store as a storage
- Updated dependencies [d902cc3]
-  - @llamaindex/core@0.2.5
-  - @llamaindex/openai@0.1.7
-  - @llamaindex/groq@0.0.6
-
-## 0.6.5
-
-### Patch Changes
-
- e9714db: feat: update `PGVectorStore`
-
-  - move constructor parameter `config.user` | `config.database` | `config.password` | `config.connectionString` into `config.clientConfig`
-  - if you pass `pg.Client` or `pg.Pool` instance to `PGVectorStore`, move it to `config.client`, setting `config.shouldConnect` to false if it's already connected
-  - default value of `PGVectorStore.collection` is now `"data"` instead of `""` (empty string)
-
 ## 0.6.4

 ### Patch Changes
@@ -1,35 +1,5 @@
 # @llamaindex/cloudflare-worker-agent-test

-## 0.0.70
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-
-## 0.0.69
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-
-## 0.0.68
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-
-## 0.0.67
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-
 ## 0.0.66

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.70",
+  "version": "0.0.66",
  "type": "module",
  "private": true,
  "scripts": {
@@ -1,35 +1,5 @@
 # @llamaindex/next-agent-test

-## 0.1.70
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-
-## 0.1.69
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-
-## 0.1.68
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-
-## 0.1.67
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-
 ## 0.1.66

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/next-agent-test",
-  "version": "0.1.70",
+  "version": "0.1.66",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,35 +1,5 @@
 # test-edge-runtime

-## 0.1.69
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-
-## 0.1.68
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-
-## 0.1.67
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-
-## 0.1.66
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-
 ## 0.1.65

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/nextjs-edge-runtime-test",
-  "version": "0.1.69",
+  "version": "0.1.65",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,35 +1,5 @@
 # @llamaindex/next-node-runtime

-## 0.0.51
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-
-## 0.0.50
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-
-## 0.0.49
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-
-## 0.0.48
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-
 ## 0.0.47

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/next-node-runtime-test",
-  "version": "0.0.51",
+  "version": "0.0.47",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,35 +1,5 @@
 # @llamaindex/waku-query-engine-test

-## 0.0.70
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - llamaindex@0.6.8
-
-## 0.0.69
-
-### Patch Changes
-
- Updated dependencies [23bcc37]
-  - llamaindex@0.6.7
-
-## 0.0.68
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
- Updated dependencies [025ffe6]
- Updated dependencies [a659574]
-  - llamaindex@0.6.6
-
-## 0.0.67
-
-### Patch Changes
-
- Updated dependencies [e9714db]
-  - llamaindex@0.6.5
-
 ## 0.0.66

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/waku-query-engine-test",
-  "version": "0.0.70",
+  "version": "0.0.66",
  "type": "module",
  "private": true,
  "scripts": {
@@ -9,54 +9,43 @@ import { registerTypes } from "pgvector/pg";

 config({ path: [".env.local", ".env", ".env.ci"] });

+let pgClient: pg.Client | pg.Pool;
+test.afterEach(async () => {
+  await pgClient.end();
+});
+
 const pgConfig = {
  user: process.env.POSTGRES_USER ?? "user",
  password: process.env.POSTGRES_PASSWORD ?? "password",
  database: "llamaindex_node_test",
 };

-await test("init with client", async (t) => {
-  const pgClient = new pg.Client(pgConfig);
+await test("init with client", async () => {
+  pgClient = new pg.Client(pgConfig);
  await pgClient.connect();
  await pgClient.query("CREATE EXTENSION IF NOT EXISTS vector");
  await registerTypes(pgClient);
-  t.after(async () => {
-    await pgClient.end();
-  });
-  const vectorStore = new PGVectorStore({
-    client: pgClient,
-    shouldConnect: false,
-  });
+  const vectorStore = new PGVectorStore(pgClient);
  assert.deepStrictEqual(await vectorStore.client(), pgClient);
 });

-await test("init with pool", async (t) => {
-  const pgClient = new pg.Pool(pgConfig);
+await test("init with pool", async () => {
+  pgClient = new pg.Pool(pgConfig);
  await pgClient.query("CREATE EXTENSION IF NOT EXISTS vector");
  const client = await pgClient.connect();
-  await client.query("CREATE EXTENSION IF NOT EXISTS vector");
  await registerTypes(client);
-  t.after(async () => {
-    client.release();
-    await pgClient.end();
-  });
-  const vectorStore = new PGVectorStore({
-    shouldConnect: false,
-    client,
-  });
+  const vectorStore = new PGVectorStore(client);
  assert.deepStrictEqual(await vectorStore.client(), client);
+  client.release();
 });

-await test("init without client", async (t) => {
-  const vectorStore = new PGVectorStore({ clientConfig: pgConfig });
-  const pgClient = (await vectorStore.client()) as pg.Client;
-  t.after(async () => {
-    await pgClient.end();
-  });
+await test("init without client", async () => {
+  const vectorStore = new PGVectorStore(pgConfig);
+  pgClient = (await vectorStore.client()) as pg.Client;
  assert.notDeepStrictEqual(pgClient, undefined);
 });

-await test("simple node", async (t) => {
+await test("simple node", async () => {
  const dimensions = 3;
  const schemaName =
    "llamaindex_vector_store_test_" + Math.random().toString(36).substring(7);
@@ -67,14 +56,10 @@ await test("simple node", async (t) => {
    embedding: [0.1, 0.2, 0.3],
  });
  const vectorStore = new PGVectorStore({
-    clientConfig: pgConfig,
+    ...pgConfig,
    dimensions,
    schemaName,
  });
-  const pgClient = (await vectorStore.client()) as pg.Client;
-  t.after(async () => {
-    await pgClient.end();
-  });

  await vectorStore.add([node]);

@@ -104,4 +89,6 @@ await test("simple node", async (t) => {
    });
    assert.deepStrictEqual(result.nodes, []);
  }
+
+  pgClient = (await vectorStore.client()) as pg.Client;
 });
@@ -1,6 +1,6 @@
 {
  "name": "llamaindex",
-  "version": "0.6.8",
+  "version": "0.6.4",
  "license": "MIT",
  "type": "module",
  "keywords": [
@@ -43,7 +43,6 @@
    "@types/node": "^22.5.1",
    "@types/papaparse": "^5.3.14",
    "@types/pg": "^8.11.8",
-    "@upstash/vector": "^1.1.5",
    "@zilliz/milvus2-sdk-node": "^2.4.6",
    "ajv": "^8.17.1",
    "assemblyai": "^4.7.0",
@@ -0,0 +1,20 @@
+import type { NodeWithScore } from "@llamaindex/core/schema";
+import type { ServiceContext } from "./ServiceContext.js";
+import type { MessageContent } from "./index.edge.js";
+
+export type RetrieveParams = {
+  query: MessageContent;
+  preFilters?: unknown;
+};
+
+/**
+ * Retrievers retrieve the nodes that most closely match our query in similarity.
+ */
+export interface BaseRetriever {
+  retrieve(params: RetrieveParams): Promise<NodeWithScore[]>;
+
+  /**
+   * @deprecated to be deprecated soon
+   */
+  serviceContext?: ServiceContext | undefined;
+}
@@ -1,9 +1,9 @@
-import type {
-  NonStreamingChatEngineParams,
-  StreamingChatEngineParams,
-} from "@llamaindex/core/chat-engine";
-import type { EngineResponse } from "@llamaindex/core/schema";
 import { Settings } from "../Settings.js";
+import type {
+  ChatEngineParamsNonStreaming,
+  ChatEngineParamsStreaming,
+  EngineResponse,
+} from "../index.edge.js";
 import { Anthropic } from "../llm/anthropic.js";
 import { LLMAgent, LLMAgentWorker, type LLMAgentParams } from "./llm.js";

@@ -24,13 +24,12 @@ export class AnthropicAgent extends LLMAgent {
    });
  }

-  async chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
-  async chat(params: StreamingChatEngineParams): Promise<never>;
+  async chat(params: ChatEngineParamsNonStreaming): Promise<EngineResponse>;
+  async chat(params: ChatEngineParamsStreaming): Promise<never>;
  override async chat(
-    params: NonStreamingChatEngineParams | StreamingChatEngineParams,
+    params: ChatEngineParamsNonStreaming | ChatEngineParamsStreaming,
  ) {
-    const { stream } = params;
-    if (stream) {
+    if (params.stream) {
      // Anthropic does support this, but looks like it's not supported in the LITS LLM
      throw new Error("Anthropic does not support streaming");
    }
@@ -1,8 +1,3 @@
-import {
-  BaseChatEngine,
-  type NonStreamingChatEngineParams,
-  type StreamingChatEngineParams,
-} from "@llamaindex/core/chat-engine";
 import type {
  BaseToolWithCall,
  ChatMessage,
@@ -15,6 +10,11 @@ import { EngineResponse } from "@llamaindex/core/schema";
 import { wrapEventCaller } from "@llamaindex/core/utils";
 import { randomUUID } from "@llamaindex/env";
 import { Settings } from "../Settings.js";
+import {
+  type ChatEngine,
+  type ChatEngineParamsNonStreaming,
+  type ChatEngineParamsStreaming,
+} from "../engines/chat/index.js";
 import { consoleLogger, emptyLogger } from "../internal/logger.js";
 import { isReadableStream } from "../internal/utils.js";
 import { ObjectRetriever } from "../objects/index.js";
@@ -207,7 +207,8 @@ export abstract class AgentRunner<
  >
    ? AdditionalMessageOptions
    : never,
-> extends BaseChatEngine {
+> implements ChatEngine
+{
  readonly #llm: AI;
  readonly #tools:
    | BaseToolWithCall[]
@@ -258,7 +259,6 @@ export abstract class AgentRunner<
  protected constructor(
    params: AgentRunnerParams<AI, Store, AdditionalMessageOptions>,
  ) {
-    super();
    const { llm, chatHistory, systemPrompt, runner, tools, verbose } = params;
    this.#llm = llm;
    this.#chatHistory = chatHistory;
@@ -345,13 +345,13 @@ export abstract class AgentRunner<
    });
  }

-  async chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
+  async chat(params: ChatEngineParamsNonStreaming): Promise<EngineResponse>;
  async chat(
-    params: StreamingChatEngineParams,
+    params: ChatEngineParamsStreaming,
  ): Promise<ReadableStream<EngineResponse>>;
  @wrapEventCaller
  async chat(
-    params: NonStreamingChatEngineParams | StreamingChatEngineParams,
+    params: ChatEngineParamsNonStreaming | ChatEngineParamsStreaming,
  ): Promise<EngineResponse | ReadableStream<EngineResponse>> {
    let chatHistory: ChatMessage<AdditionalMessageOptions>[] = [];

@@ -1,6 +1,7 @@
 import type { BaseQueryEngine } from "@llamaindex/core/query-engine";
 import type { BaseSynthesizer } from "@llamaindex/core/response-synthesizers";
 import type { Document, TransformComponent } from "@llamaindex/core/schema";
+import type { BaseRetriever } from "../Retriever.js";
 import { RetrieverQueryEngine } from "../engines/query/RetrieverQueryEngine.js";
 import type { BaseNodePostprocessor } from "../postprocessors/types.js";
 import type { CloudRetrieveParams } from "./LlamaCloudRetriever.js";
@@ -11,7 +12,6 @@ import { getAppBaseUrl, getProjectId, initService } from "./utils.js";

 import { PipelinesService, ProjectsService } from "@llamaindex/cloud/api";
 import { SentenceSplitter } from "@llamaindex/core/node-parser";
-import type { BaseRetriever } from "@llamaindex/core/retriever";
 import { getEnv } from "@llamaindex/env";
 import { OpenAIEmbedding } from "@llamaindex/openai";
 import { Settings } from "../Settings.js";
@@ -4,12 +4,11 @@ import {
  type RetrievalParams,
  type TextNodeWithScore,
 } from "@llamaindex/cloud/api";
-import { DEFAULT_PROJECT_NAME } from "@llamaindex/core/global";
-import type { QueryBundle } from "@llamaindex/core/query-engine";
-import { BaseRetriever } from "@llamaindex/core/retriever";
+import { DEFAULT_PROJECT_NAME, Settings } from "@llamaindex/core/global";
 import type { NodeWithScore } from "@llamaindex/core/schema";
 import { jsonToNode, ObjectType } from "@llamaindex/core/schema";
-import { extractText } from "@llamaindex/core/utils";
+import { extractText, wrapEventCaller } from "@llamaindex/core/utils";
+import type { BaseRetriever, RetrieveParams } from "../Retriever.js";
 import type { ClientParams, CloudConstructorParams } from "./type.js";
 import { getProjectId, initService } from "./utils.js";

@@ -18,7 +17,7 @@ export type CloudRetrieveParams = Omit<
  "query" | "search_filters" | "dense_similarity_top_k"
 > & { similarityTopK?: number; filters?: MetadataFilters };

-export class LlamaCloudRetriever extends BaseRetriever {
+export class LlamaCloudRetriever implements BaseRetriever {
  clientParams: ClientParams;
  retrieveParams: CloudRetrieveParams;
  organizationId?: string;
@@ -43,7 +42,6 @@ export class LlamaCloudRetriever extends BaseRetriever {
  }

  constructor(params: CloudConstructorParams & CloudRetrieveParams) {
-    super();
    this.clientParams = { apiKey: params.apiKey, baseUrl: params.baseUrl };
    initService(this.clientParams);
    this.retrieveParams = params;
@@ -56,7 +54,11 @@ export class LlamaCloudRetriever extends BaseRetriever {
    }
  }

-  async _retrieve(query: QueryBundle): Promise<NodeWithScore[]> {
+  @wrapEventCaller
+  async retrieve({
+    query,
+    preFilters,
+  }: RetrieveParams): Promise<NodeWithScore[]> {
    const { data: pipelines } =
      await PipelinesService.searchPipelinesApiV1PipelinesGet({
        query: {
@@ -95,11 +97,19 @@ export class LlamaCloudRetriever extends BaseRetriever {
        body: {
          ...this.retrieveParams,
          query: extractText(query),
-          search_filters: this.retrieveParams.filters as MetadataFilters,
+          search_filters:
+            this.retrieveParams.filters ?? (preFilters as MetadataFilters),
          dense_similarity_top_k: this.retrieveParams.similarityTopK!,
        },
      });

-    return this.resultNodesToNodeWithScore(results.retrieval_nodes);
+    const nodesWithScores = this.resultNodesToNodeWithScore(
+      results.retrieval_nodes,
+    );
+    Settings.callbackManager.dispatchEvent("retrieve-end", {
+      query,
+      nodes: nodesWithScores,
+    });
+    return nodesWithScores;
  }
 }
@@ -1,14 +1,10 @@
-import {
-  BaseChatEngine,
-  type NonStreamingChatEngineParams,
-  type StreamingChatEngineParams,
-} from "@llamaindex/core/chat-engine";
 import type { ChatMessage, LLM } from "@llamaindex/core/llms";
 import { BaseMemory, ChatMemoryBuffer } from "@llamaindex/core/memory";
 import {
  type CondenseQuestionPrompt,
  defaultCondenseQuestionPrompt,
  type ModuleRecord,
+  PromptMixin,
 } from "@llamaindex/core/prompts";
 import type { BaseQueryEngine } from "@llamaindex/core/query-engine";
 import type { EngineResponse } from "@llamaindex/core/schema";
@@ -20,6 +16,11 @@ import {
 } from "@llamaindex/core/utils";
 import type { ServiceContext } from "../../ServiceContext.js";
 import { llmFromSettingsOrContext } from "../../Settings.js";
+import type {
+  ChatEngine,
+  ChatEngineParamsNonStreaming,
+  ChatEngineParamsStreaming,
+} from "./types.js";

 /**
 * CondenseQuestionChatEngine is used in conjunction with a Index (for example VectorStoreIndex).
@@ -31,16 +32,16 @@ import { llmFromSettingsOrContext } from "../../Settings.js";
 * underlying data. It performs less well when the chat messages are not questions about the
 * data, or are very referential to previous context.
 */
-export class CondenseQuestionChatEngine extends BaseChatEngine {
+
+export class CondenseQuestionChatEngine
+  extends PromptMixin
+  implements ChatEngine
+{
  queryEngine: BaseQueryEngine;
-  memory: BaseMemory;
+  chatHistory: BaseMemory;
  llm: LLM;
  condenseMessagePrompt: CondenseQuestionPrompt;

-  get chatHistory() {
-    return this.memory.getMessages();
-  }
-
  constructor(init: {
    queryEngine: BaseQueryEngine;
    chatHistory: ChatMessage[];
@@ -50,7 +51,7 @@ export class CondenseQuestionChatEngine extends BaseChatEngine {
    super();

    this.queryEngine = init.queryEngine;
-    this.memory = new ChatMemoryBuffer({
+    this.chatHistory = new ChatMemoryBuffer({
      chatHistory: init?.chatHistory,
    });
    this.llm = llmFromSettingsOrContext(init?.serviceContext);
@@ -87,13 +88,13 @@ export class CondenseQuestionChatEngine extends BaseChatEngine {
    });
  }

-  chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
  chat(
-    params: StreamingChatEngineParams,
+    params: ChatEngineParamsStreaming,
  ): Promise<AsyncIterable<EngineResponse>>;
+  chat(params: ChatEngineParamsNonStreaming): Promise<EngineResponse>;
  @wrapEventCaller
  async chat(
-    params: NonStreamingChatEngineParams | StreamingChatEngineParams,
+    params: ChatEngineParamsStreaming | ChatEngineParamsNonStreaming,
  ): Promise<EngineResponse | AsyncIterable<EngineResponse>> {
    const { message, stream } = params;
    const chatHistory = params.chatHistory
@@ -103,7 +104,7 @@ export class CondenseQuestionChatEngine extends BaseChatEngine {
              ? await params.chatHistory.getMessages()
              : params.chatHistory,
        })
-      : this.memory;
+      : this.chatHistory;

    const condensedQuestion = (
      await this.condenseQuestion(chatHistory, extractText(message))
@@ -111,10 +112,12 @@ export class CondenseQuestionChatEngine extends BaseChatEngine {
    chatHistory.put({ content: message, role: "user" });

    if (stream) {
-      const stream = await this.queryEngine.query({
-        query: condensedQuestion,
-        stream: true,
-      });
+      const stream = await this.queryEngine.query(
+        {
+          query: condensedQuestion,
+        },
+        true,
+      );
      return streamReducer({
        stream,
        initialValue: "",
@@ -137,6 +140,6 @@ export class CondenseQuestionChatEngine extends BaseChatEngine {
  }

  reset() {
-    this.memory.reset();
+    this.chatHistory.reset();
  }
 }
@@ -1,8 +1,3 @@
-import type {
-  BaseChatEngine,
-  NonStreamingChatEngineParams,
-  StreamingChatEngineParams,
-} from "@llamaindex/core/chat-engine";
 import type {
  ChatMessage,
  LLM,
@@ -16,7 +11,6 @@ import {
  PromptMixin,
  type PromptsRecord,
 } from "@llamaindex/core/prompts";
-import type { BaseRetriever } from "@llamaindex/core/retriever";
 import { EngineResponse, MetadataMode } from "@llamaindex/core/schema";
 import {
  extractText,
@@ -24,25 +18,27 @@ import {
  streamReducer,
  wrapEventCaller,
 } from "@llamaindex/core/utils";
+import type { BaseRetriever } from "../../Retriever.js";
 import { Settings } from "../../Settings.js";
 import type { BaseNodePostprocessor } from "../../postprocessors/index.js";
 import { DefaultContextGenerator } from "./DefaultContextGenerator.js";
-import type { ContextGenerator } from "./types.js";
+import type {
+  ChatEngine,
+  ChatEngineParamsNonStreaming,
+  ChatEngineParamsStreaming,
+  ContextGenerator,
+} from "./types.js";

 /**
 * ContextChatEngine uses the Index to get the appropriate context for each query.
 * The context is stored in the system prompt, and the chat history is chunk: ChatResponseChunk, nodes?: NodeWithScore<import("/Users/marcus/code/llamaindex/LlamaIndexTS/packages/core/src/Node").Metadata>[], nodes?: NodeWithScore<import("/Users/marcus/code/llamaindex/LlamaIndexTS/packages/core/src/Node").Metadata>[]lowing the appropriate context to be surfaced for each query.
 */
-export class ContextChatEngine extends PromptMixin implements BaseChatEngine {
+export class ContextChatEngine extends PromptMixin implements ChatEngine {
  chatModel: LLM;
-  memory: BaseMemory;
+  chatHistory: BaseMemory;
  contextGenerator: ContextGenerator & PromptMixin;
  systemPrompt?: string | undefined;

-  get chatHistory() {
-    return this.memory.getMessages();
-  }
-
  constructor(init: {
    retriever: BaseRetriever;
    chatModel?: LLM | undefined;
@@ -54,7 +50,7 @@ export class ContextChatEngine extends PromptMixin implements BaseChatEngine {
  }) {
    super();
    this.chatModel = init.chatModel ?? Settings.llm;
-    this.memory = new ChatMemoryBuffer({ chatHistory: init?.chatHistory });
+    this.chatHistory = new ChatMemoryBuffer({ chatHistory: init?.chatHistory });
    this.contextGenerator = new DefaultContextGenerator({
      retriever: init.retriever,
      contextSystemPrompt: init?.contextSystemPrompt,
@@ -83,13 +79,13 @@ export class ContextChatEngine extends PromptMixin implements BaseChatEngine {
    };
  }

-  chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
  chat(
-    params: StreamingChatEngineParams,
+    params: ChatEngineParamsStreaming,
  ): Promise<AsyncIterable<EngineResponse>>;
+  chat(params: ChatEngineParamsNonStreaming): Promise<EngineResponse>;
  @wrapEventCaller
  async chat(
-    params: StreamingChatEngineParams | NonStreamingChatEngineParams,
+    params: ChatEngineParamsStreaming | ChatEngineParamsNonStreaming,
  ): Promise<EngineResponse | AsyncIterable<EngineResponse>> {
    const { message, stream } = params;
    const chatHistory = params.chatHistory
@@ -99,7 +95,7 @@ export class ContextChatEngine extends PromptMixin implements BaseChatEngine {
              ? await params.chatHistory.getMessages()
              : params.chatHistory,
        })
-      : this.memory;
+      : this.chatHistory;
    const requestMessages = await this.prepareRequestMessages(
      message,
      chatHistory,
@@ -129,7 +125,7 @@ export class ContextChatEngine extends PromptMixin implements BaseChatEngine {
  }

  reset() {
-    this.memory.reset();
+    this.chatHistory.reset();
  }

  private async prepareRequestMessages(
@@ -5,10 +5,10 @@ import {
  type ModuleRecord,
  PromptMixin,
 } from "@llamaindex/core/prompts";
-import type { BaseRetriever } from "@llamaindex/core/retriever";
 import { MetadataMode, type NodeWithScore } from "@llamaindex/core/schema";
 import { createMessageContent } from "@llamaindex/core/utils";
 import type { BaseNodePostprocessor } from "../../postprocessors/index.js";
+import type { BaseRetriever } from "../../Retriever.js";
 import type { Context, ContextGenerator } from "./types.js";

 export class DefaultContextGenerator
@@ -1,8 +1,3 @@
-import type {
-  BaseChatEngine,
-  NonStreamingChatEngineParams,
-  StreamingChatEngineParams,
-} from "@llamaindex/core/chat-engine";
 import type { LLM } from "@llamaindex/core/llms";
 import { BaseMemory, ChatMemoryBuffer } from "@llamaindex/core/memory";
 import { EngineResponse } from "@llamaindex/core/schema";
@@ -12,31 +7,32 @@ import {
  wrapEventCaller,
 } from "@llamaindex/core/utils";
 import { Settings } from "../../Settings.js";
+import type {
+  ChatEngine,
+  ChatEngineParamsNonStreaming,
+  ChatEngineParamsStreaming,
+} from "./types.js";

 /**
 * SimpleChatEngine is the simplest possible chat engine. Useful for using your own custom prompts.
 */

-export class SimpleChatEngine implements BaseChatEngine {
-  memory: BaseMemory;
+export class SimpleChatEngine implements ChatEngine {
+  chatHistory: BaseMemory;
  llm: LLM;

-  get chatHistory() {
-    return this.memory.getMessages();
-  }
-
  constructor(init?: Partial<SimpleChatEngine>) {
-    this.memory = init?.memory ?? new ChatMemoryBuffer();
+    this.chatHistory = init?.chatHistory ?? new ChatMemoryBuffer();
    this.llm = init?.llm ?? Settings.llm;
  }

-  chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
  chat(
-    params: StreamingChatEngineParams,
+    params: ChatEngineParamsStreaming,
  ): Promise<AsyncIterable<EngineResponse>>;
+  chat(params: ChatEngineParamsNonStreaming): Promise<EngineResponse>;
  @wrapEventCaller
  async chat(
-    params: NonStreamingChatEngineParams | StreamingChatEngineParams,
+    params: ChatEngineParamsStreaming | ChatEngineParamsNonStreaming,
  ): Promise<EngineResponse | AsyncIterable<EngineResponse>> {
    const { message, stream } = params;

@@ -47,7 +43,7 @@ export class SimpleChatEngine implements BaseChatEngine {
              ? await params.chatHistory.getMessages()
              : params.chatHistory,
        })
-      : this.memory;
+      : this.chatHistory;
    chatHistory.put({ content: message, role: "user" });

    if (stream) {
@@ -77,6 +73,6 @@ export class SimpleChatEngine implements BaseChatEngine {
  }

  reset() {
-    this.memory.reset();
+    this.chatHistory.reset();
  }
 }
@@ -1,10 +1,58 @@
-import type { ChatMessage } from "@llamaindex/core/llms";
-import type { NodeWithScore } from "@llamaindex/core/schema";
+import type { ChatMessage, MessageContent } from "@llamaindex/core/llms";
+import type { BaseMemory } from "@llamaindex/core/memory";
+import { EngineResponse, type NodeWithScore } from "@llamaindex/core/schema";
+
+/**
+ * Represents the base parameters for ChatEngine.
+ */
+export interface ChatEngineParamsBase {
+  message: MessageContent;
+  /**
+   * Optional chat history if you want to customize the chat history.
+   */
+  chatHistory?: ChatMessage[] | BaseMemory;
+  /**
+   * Optional flag to enable verbose mode.
+   * @default false
+   */
+  verbose?: boolean;
+}
+
+export interface ChatEngineParamsStreaming extends ChatEngineParamsBase {
+  stream: true;
+}
+
+export interface ChatEngineParamsNonStreaming extends ChatEngineParamsBase {
+  stream?: false | null;
+}
+
+/**
+ * A ChatEngine is used to handle back and forth chats between the application and the LLM.
+ */
+export interface ChatEngine<
+  // synchronous response
+  R = EngineResponse,
+  // asynchronous response
+  AR extends AsyncIterable<unknown> = AsyncIterable<R>,
+> {
+  /**
+   * Send message along with the class's current chat history to the LLM.
+   * @param params
+   */
+  chat(params: ChatEngineParamsStreaming): Promise<AR>;
+  chat(params: ChatEngineParamsNonStreaming): Promise<R>;
+
+  /**
+   * Resets the chat history so that it's empty.
+   */
+  reset(): void;
+}

 export interface Context {
  message: ChatMessage;
  nodes: NodeWithScore[];
 }
+
 /**
 * A ContextGenerator is used to generate a context based on a message's text content
 */
@@ -1,11 +1,10 @@
-import type { MessageContent } from "@llamaindex/core/llms";
-import { BaseQueryEngine, type QueryType } from "@llamaindex/core/query-engine";
+import { BaseQueryEngine } from "@llamaindex/core/query-engine";
 import type { BaseSynthesizer } from "@llamaindex/core/response-synthesizers";
 import { getResponseSynthesizer } from "@llamaindex/core/response-synthesizers";
-import { BaseRetriever } from "@llamaindex/core/retriever";
 import { type NodeWithScore } from "@llamaindex/core/schema";
 import { extractText } from "@llamaindex/core/utils";
 import type { BaseNodePostprocessor } from "../../postprocessors/index.js";
+import type { BaseRetriever } from "../../Retriever.js";

 /**
 * A query engine that uses a retriever to query an index and then synthesizes the response.
@@ -68,10 +67,7 @@ export class RetrieverQueryEngine extends BaseQueryEngine {
    };
  }

-  private async applyNodePostprocessors(
-    nodes: NodeWithScore[],
-    query: MessageContent,
-  ) {
+  private async applyNodePostprocessors(nodes: NodeWithScore[], query: string) {
    let nodesWithScore = nodes;

    for (const postprocessor of this.nodePostprocessors) {
@@ -84,10 +80,12 @@ export class RetrieverQueryEngine extends BaseQueryEngine {
    return nodesWithScore;
  }

-  override async retrieve(query: QueryType) {
-    const nodes = await this.retriever.retrieve(query);
+  private async retrieve(query: string) {
+    const nodes = await this.retriever.retrieve({
+      query,
+      preFilters: this.preFilters,
+    });

-    const messageContent = typeof query === "string" ? query : query.query;
-    return await this.applyNodePostprocessors(nodes, messageContent);
+    return await this.applyNodePostprocessors(nodes, query);
  }
 }
@@ -136,9 +136,7 @@ export class RouterQueryEngine extends BaseQueryEngine {
        }

        const selectedQueryEngine = this.queryEngines[engineInd.index]!;
-        responses.push(
-          await selectedQueryEngine.query({ query, stream: false }),
-        );
+        responses.push(await selectedQueryEngine.query(query));
      }

      if (responses.length > 1) {
@@ -103,8 +103,7 @@ export class FaithfulnessEvaluator
    });

    const responseObj = await queryEngine.query({
-      query: { query: response },
-      stream: false,
+      query: response,
    });

    const rawResponseTxt = responseObj.toString();
@@ -1,6 +1,6 @@
 import type { AgentEndEvent, AgentStartEvent } from "./agent/types.js";
+import type { RetrievalEndEvent, RetrievalStartEvent } from "./llm/types.js";

-export * from "@llamaindex/core/chat-engine";
 export {
  CallbackManager,
  DEFAULT_BASE_URL,
@@ -35,11 +35,12 @@ export * from "@llamaindex/core/llms";
 export * from "@llamaindex/core/prompts";
 export * from "@llamaindex/core/query-engine";
 export * from "@llamaindex/core/response-synthesizers";
-export * from "@llamaindex/core/retriever";
 export * from "@llamaindex/core/schema";

 declare module "@llamaindex/core/global" {
  export interface LlamaIndexEventMaps {
+    "retrieve-start": RetrievalStartEvent;
+    "retrieve-end": RetrievalEndEvent;
    // agent events
    "agent-start": AgentStartEvent;
    "agent-end": AgentEndEvent;
@@ -65,6 +66,7 @@ export * from "./objects/index.js";
 export * from "./OutputParser.js";
 export * from "./postprocessors/index.js";
 export * from "./QuestionGenerator.js";
+export * from "./Retriever.js";
 export * from "./selectors/index.js";
 export * from "./ServiceContext.js";
 export { Settings } from "./Settings.js";
@@ -1,7 +1,7 @@
 import type { BaseQueryEngine } from "@llamaindex/core/query-engine";
 import type { BaseSynthesizer } from "@llamaindex/core/response-synthesizers";
-import type { BaseRetriever } from "@llamaindex/core/retriever";
 import type { BaseNode, Document } from "@llamaindex/core/schema";
+import type { BaseRetriever } from "../Retriever.js";
 import type { ServiceContext } from "../ServiceContext.js";
 import { nodeParserFromSettingsOrContext } from "../Settings.js";
 import { runTransformations } from "../ingestion/IngestionPipeline.js";
@@ -5,6 +5,7 @@ import type {
  NodeWithScore,
 } from "@llamaindex/core/schema";
 import { MetadataMode } from "@llamaindex/core/schema";
+import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
 import { serviceContextFromDefaults } from "../../ServiceContext.js";
 import { RetrieverQueryEngine } from "../../engines/query/index.js";
@@ -28,11 +29,7 @@ import {
  type KeywordExtractPrompt,
  type QueryKeywordExtractPrompt,
 } from "@llamaindex/core/prompts";
-import type {
-  BaseQueryEngine,
-  QueryBundle,
-} from "@llamaindex/core/query-engine";
-import { BaseRetriever } from "@llamaindex/core/retriever";
+import type { BaseQueryEngine } from "@llamaindex/core/query-engine";
 import { extractText } from "@llamaindex/core/utils";
 import { llmFromSettingsOrContext } from "../../Settings.js";

@@ -51,7 +48,7 @@ export enum KeywordTableRetrieverMode {
 }

 // Base Keyword Table Retriever
-abstract class BaseKeywordTableRetriever extends BaseRetriever {
+abstract class BaseKeywordTableRetriever implements BaseRetriever {
  protected index: KeywordTableIndex;
  protected indexStruct: KeywordTable;
  protected docstore: BaseDocumentStore;
@@ -75,7 +72,6 @@ abstract class BaseKeywordTableRetriever extends BaseRetriever {
    maxKeywordsPerQuery: number;
    numChunksPerQuery: number;
  }) {
-    super();
    this.index = index;
    this.indexStruct = index.indexStruct;
    this.docstore = index.docStore;
@@ -91,7 +87,7 @@ abstract class BaseKeywordTableRetriever extends BaseRetriever {

  abstract getKeywords(query: string): Promise<string[]>;

-  async _retrieve(query: QueryBundle): Promise<NodeWithScore[]> {
+  async retrieve({ query }: RetrieveParams): Promise<NodeWithScore[]> {
    const keywords = await this.getKeywords(extractText(query));
    const chunkIndicesCount: { [key: string]: number } = {};
    const filteredKeywords = keywords.filter((keyword) =>
@@ -2,17 +2,16 @@ import {
  type ChoiceSelectPrompt,
  defaultChoiceSelectPrompt,
 } from "@llamaindex/core/prompts";
-import type { QueryBundle } from "@llamaindex/core/query-engine";
 import type { BaseSynthesizer } from "@llamaindex/core/response-synthesizers";
 import { getResponseSynthesizer } from "@llamaindex/core/response-synthesizers";
-import { BaseRetriever } from "@llamaindex/core/retriever";
 import type {
  BaseNode,
  Document,
  NodeWithScore,
 } from "@llamaindex/core/schema";
-import { extractText } from "@llamaindex/core/utils";
+import { extractText, wrapEventCaller } from "@llamaindex/core/utils";
 import _ from "lodash";
+import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
 import {
  llmFromSettingsOrContext,
@@ -280,15 +279,15 @@ export type ListRetrieverMode = SummaryRetrieverMode;
 /**
 * Simple retriever for SummaryIndex that returns all nodes
 */
-export class SummaryIndexRetriever extends BaseRetriever {
+export class SummaryIndexRetriever implements BaseRetriever {
  index: SummaryIndex;

  constructor(index: SummaryIndex) {
-    super();
    this.index = index;
  }

-  async _retrieve(queryBundle: QueryBundle): Promise<NodeWithScore[]> {
+  @wrapEventCaller
+  async retrieve({ query }: RetrieveParams): Promise<NodeWithScore[]> {
    const nodeIds = this.index.indexStruct.nodes;
    const nodes = await this.index.docStore.getNodes(nodeIds);
    return nodes.map((node) => ({
@@ -301,7 +300,7 @@ export class SummaryIndexRetriever extends BaseRetriever {
 /**
 * LLM retriever for SummaryIndex which lets you select the most relevant chunks.
 */
-export class SummaryIndexLLMRetriever extends BaseRetriever {
+export class SummaryIndexLLMRetriever implements BaseRetriever {
  index: SummaryIndex;
  choiceSelectPrompt: ChoiceSelectPrompt;
  choiceBatchSize: number;
@@ -318,7 +317,6 @@ export class SummaryIndexLLMRetriever extends BaseRetriever {
    parseChoiceSelectAnswerFn?: ChoiceSelectParserFunction,
    serviceContext?: ServiceContext,
  ) {
-    super();
    this.index = index;
    this.choiceSelectPrompt = choiceSelectPrompt || defaultChoiceSelectPrompt;
    this.choiceBatchSize = choiceBatchSize;
@@ -328,7 +326,7 @@ export class SummaryIndexLLMRetriever extends BaseRetriever {
    this.serviceContext = serviceContext || index.serviceContext;
  }

-  async _retrieve(query: QueryBundle): Promise<NodeWithScore[]> {
+  async retrieve({ query }: RetrieveParams): Promise<NodeWithScore[]> {
    const nodeIds = this.index.indexStruct.nodes;
    const results: NodeWithScore[] = [];

@@ -2,10 +2,9 @@ import {
  DEFAULT_SIMILARITY_TOP_K,
  type BaseEmbedding,
 } from "@llamaindex/core/embeddings";
+import { Settings } from "@llamaindex/core/global";
 import type { MessageContent } from "@llamaindex/core/llms";
-import type { QueryBundle } from "@llamaindex/core/query-engine";
 import type { BaseSynthesizer } from "@llamaindex/core/response-synthesizers";
-import { BaseRetriever } from "@llamaindex/core/retriever";
 import {
  ImageNode,
  ModalityType,
@@ -15,6 +14,8 @@ import {
  type Document,
  type NodeWithScore,
 } from "@llamaindex/core/schema";
+import { wrapEventCaller } from "@llamaindex/core/utils";
+import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
 import { nodeParserFromSettingsOrContext } from "../../Settings.js";
 import { RetrieverQueryEngine } from "../../engines/query/RetrieverQueryEngine.js";
@@ -387,7 +388,7 @@ export type VectorIndexRetrieverOptions = {
  filters?: MetadataFilters;
 };

-export class VectorIndexRetriever extends BaseRetriever {
+export class VectorIndexRetriever implements BaseRetriever {
  index: VectorStoreIndex;
  topK: TopKMap;

@@ -400,7 +401,6 @@ export class VectorIndexRetriever extends BaseRetriever {
    topK,
    filters,
  }: VectorIndexRetrieverOptions) {
-    super();
    this.index = index;
    this.serviceContext = this.index.serviceContext;
    this.topK = topK ?? {
@@ -417,17 +417,32 @@ export class VectorIndexRetriever extends BaseRetriever {
    this.topK[ModalityType.TEXT] = similarityTopK;
  }

-  async _retrieve(params: QueryBundle): Promise<NodeWithScore[]> {
-    const { query } = params;
+  @wrapEventCaller
+  async retrieve({
+    query,
+    preFilters,
+  }: RetrieveParams): Promise<NodeWithScore[]> {
+    Settings.callbackManager.dispatchEvent("retrieve-start", {
+      query,
+    });
    const vectorStores = this.index.vectorStores;
    let nodesWithScores: NodeWithScore[] = [];

    for (const type in vectorStores) {
      const vectorStore: VectorStore = vectorStores[type as ModalityType]!;
      nodesWithScores = nodesWithScores.concat(
-        await this.retrieveQuery(query, type as ModalityType, vectorStore),
+        await this.retrieveQuery(
+          query,
+          type as ModalityType,
+          vectorStore,
+          preFilters as MetadataFilters,
+        ),
      );
    }
+    Settings.callbackManager.dispatchEvent("retrieve-end", {
+      query,
+      nodes: nodesWithScores,
+    });
    return nodesWithScores;
  }

@@ -1,11 +1,7 @@
 import type { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 import { MetadataMode } from "@llamaindex/core/schema";
 import { createSHA256 } from "@llamaindex/env";
-import {
-  docToJson,
-  jsonSerializer,
-  jsonToDoc,
-} from "../storage/docStore/utils.js";
+import { docToJson, jsonToDoc } from "../storage/docStore/utils.js";
 import { SimpleKVStore } from "../storage/kvStore/SimpleKVStore.js";
 import type { BaseKVStore } from "../storage/kvStore/types.js";

@@ -57,7 +53,7 @@ export class IngestionCache {

  async put(hash: string, nodes: BaseNode[]) {
    const val = {
-      [this.nodesKey]: nodes.map((node) => docToJson(node, jsonSerializer)),
+      [this.nodesKey]: nodes.map((node) => docToJson(node)),
    };
    await this.cache.put(hash, val, this.collection);
  }
@@ -67,8 +63,6 @@ export class IngestionCache {
    if (!json || !json[this.nodesKey] || !Array.isArray(json[this.nodesKey])) {
      return undefined;
    }
-    return json[this.nodesKey].map((doc: any) =>
-      jsonToDoc(doc, jsonSerializer),
-    );
+    return json[this.nodesKey].map((doc: any) => jsonToDoc(doc));
  }
 }
@@ -0,0 +1,10 @@
+import type { MessageContent } from "@llamaindex/core/llms";
+import type { NodeWithScore } from "@llamaindex/core/schema";
+
+export type RetrievalStartEvent = {
+  query: MessageContent;
+};
+export type RetrievalEndEvent = {
+  query: MessageContent;
+  nodes: NodeWithScore[];
+};
@@ -1,8 +1,8 @@
 import type { BaseTool, MessageContent } from "@llamaindex/core/llms";
-import { BaseRetriever } from "@llamaindex/core/retriever";
 import type { BaseNode, Metadata } from "@llamaindex/core/schema";
 import { TextNode } from "@llamaindex/core/schema";
 import { extractText } from "@llamaindex/core/utils";
+import type { BaseRetriever } from "../Retriever.js";
 import type { VectorStoreIndex } from "../indices/vectorStore/index.js";

 // Assuming that necessary interfaces and classes (like OT, TextNode, BaseNode, etc.) are defined elsewhere
@@ -49,6 +49,9 @@ export abstract class BaseObjectNodeMapping {

 // You will need to implement specific subclasses of BaseObjectNodeMapping as per your project requirements.

+// todo: multimodal support
+type QueryType = MessageContent;
+
 export class ObjectRetriever<T = unknown> {
  _retriever: BaseRetriever;
  _objectNodeMapping: BaseObjectNodeMapping;
@@ -67,7 +70,7 @@ export class ObjectRetriever<T = unknown> {
  }

  // Translating the retrieve method
-  async retrieve(strOrQueryBundle: MessageContent): Promise<T[]> {
+  async retrieve(strOrQueryBundle: QueryType): Promise<T[]> {
    const nodes = await this.retriever.retrieve({
      query: extractText(strOrQueryBundle),
    });
@@ -29,7 +29,7 @@ export class KVDocumentStore extends BaseDocumentStore {
    for (const key in jsonDict) {
      const value = jsonDict[key];
      if (isValidDocJson(value)) {
-        docs[key] = jsonToDoc(value, this.serializer);
+        docs[key] = jsonToDoc(value);
      } else {
        console.warn(`Invalid JSON for docId ${key}`);
      }
@@ -52,7 +52,7 @@ export class KVDocumentStore extends BaseDocumentStore {
        );
      }
      const nodeKey = doc.id_;
-      const data = docToJson(doc, this.serializer);
+      const data = docToJson(doc);
      await this.kvstore.put(nodeKey, data, this.nodeCollection);
      const metadata: DocMetaData = { docHash: doc.hash };

@@ -94,7 +94,7 @@ export class KVDocumentStore extends BaseDocumentStore {
    if (!isValidDocJson(json)) {
      throw new Error(`Invalid JSON for docId ${docId}`);
    }
-    return jsonToDoc(json, this.serializer);
+    return jsonToDoc(json);
  }

  async getRefDocInfo(refDocId: string): Promise<RefDocInfo | undefined> {
@@ -1,32 +1,19 @@
 import { DEFAULT_NAMESPACE } from "@llamaindex/core/global";
-import {
-  PostgresKVStore,
-  type PostgresKVStoreConfig,
-} from "../kvStore/PostgresKVStore.js";
+import { PostgresKVStore } from "../kvStore/PostgresKVStore.js";
 import { KVDocumentStore } from "./KVDocumentStore.js";
-import { noneSerializer } from "./utils.js";

 const DEFAULT_TABLE_NAME = "llamaindex_doc_store";

-export type PostgresDocumentStoreConfig = PostgresKVStoreConfig & {
-  namespace?: string;
-};
-
 export class PostgresDocumentStore extends KVDocumentStore {
-  serializer = noneSerializer;
-
-  constructor(config?: PostgresDocumentStoreConfig) {
+  constructor(config?: {
+    schemaName?: string;
+    tableName?: string;
+    connectionString?: string;
+    namespace?: string;
+  }) {
    const kvStore = new PostgresKVStore({
      schemaName: config?.schemaName,
      tableName: config?.tableName || DEFAULT_TABLE_NAME,
-      ...(config && "clientConfig" in config
-        ? { clientConfig: config.clientConfig }
-        : config && "client" in config
-          ? {
-              client: config.client,
-              shouldConnect: config.shouldConnect ?? false,
-            }
-          : {}),
    });
    const namespace = config?.namespace || DEFAULT_NAMESPACE;
    super(kvStore, namespace);
@@ -3,7 +3,6 @@ import {
  DEFAULT_PERSIST_DIR,
 } from "@llamaindex/core/global";
 import { BaseNode } from "@llamaindex/core/schema";
-import { jsonSerializer, type Serializer } from "./utils.js";

 const defaultPersistPath = `${DEFAULT_PERSIST_DIR}/${DEFAULT_DOC_STORE_PERSIST_FILENAME}`;

@@ -13,8 +12,6 @@ export interface RefDocInfo {
 }

 export abstract class BaseDocumentStore {
-  serializer: Serializer<any> = jsonSerializer;
-
  // Save/load
  persist(persistPath: string = defaultPersistPath): void {
    // Persist the docstore to a file.
@@ -4,35 +4,12 @@ import { Document, ObjectType, TextNode } from "@llamaindex/core/schema";
 const TYPE_KEY = "__type__";
 const DATA_KEY = "__data__";

-export interface Serializer<T> {
-  toPersistence(data: Record<string, unknown>): T;
-  fromPersistence(data: T): Record<string, unknown>;
-}
-
-export const jsonSerializer: Serializer<string> = {
-  toPersistence(data) {
-    return JSON.stringify(data);
-  },
-  fromPersistence(data) {
-    return JSON.parse(data);
-  },
-};
-
-export const noneSerializer: Serializer<Record<string, unknown>> = {
-  toPersistence(data) {
-    return data;
-  },
-  fromPersistence(data) {
-    return data;
-  },
-};
-
-type DocJson<Data> = {
+type DocJson = {
  [TYPE_KEY]: ObjectType;
-  [DATA_KEY]: Data;
+  [DATA_KEY]: string;
 };

-export function isValidDocJson(docJson: any): docJson is DocJson<unknown> {
+export function isValidDocJson(docJson: any): docJson is DocJson {
  return (
    typeof docJson === "object" &&
    docJson !== null &&
@@ -41,22 +18,16 @@ export function isValidDocJson(docJson: any): docJson is DocJson<unknown> {
  );
 }

-export function docToJson(
-  doc: BaseNode,
-  serializer: Serializer<unknown>,
-): DocJson<unknown> {
+export function docToJson(doc: BaseNode): DocJson {
  return {
-    [DATA_KEY]: serializer.toPersistence(doc.toJSON()),
+    [DATA_KEY]: JSON.stringify(doc.toJSON()),
    [TYPE_KEY]: doc.type,
  };
 }

-export function jsonToDoc<Data>(
-  docDict: DocJson<Data>,
-  serializer: Serializer<Data>,
-): BaseNode {
+export function jsonToDoc(docDict: DocJson): BaseNode {
  const docType = docDict[TYPE_KEY];
-  const dataDict = serializer.fromPersistence(docDict[DATA_KEY]) as any;
+  const dataDict = JSON.parse(docDict[DATA_KEY]);
  let doc: BaseNode;

  if (docType === ObjectType.DOCUMENT) {
@@ -1,29 +1,19 @@
 import { DEFAULT_NAMESPACE } from "@llamaindex/core/global";
-import {
-  PostgresKVStore,
-  type PostgresKVStoreConfig,
-} from "../kvStore/PostgresKVStore.js";
+import { PostgresKVStore } from "../kvStore/PostgresKVStore.js";
 import { KVIndexStore } from "./KVIndexStore.js";

 const DEFAULT_TABLE_NAME = "llamaindex_index_store";

-export type PostgresIndexStoreConfig = PostgresKVStoreConfig & {
-  namespace?: string;
-};
-
 export class PostgresIndexStore extends KVIndexStore {
-  constructor(config?: PostgresIndexStoreConfig) {
+  constructor(config?: {
+    schemaName?: string;
+    tableName?: string;
+    connectionString?: string;
+    namespace?: string;
+  }) {
    const kvStore = new PostgresKVStore({
      schemaName: config?.schemaName,
      tableName: config?.tableName || DEFAULT_TABLE_NAME,
-      ...(config && "clientConfig" in config
-        ? { clientConfig: config.clientConfig }
-        : config && "client" in config
-          ? {
-              client: config.client,
-              shouldConnect: config.shouldConnect ?? false,
-            }
-          : {}),
    });
    const namespace = config?.namespace || DEFAULT_NAMESPACE;
    super(kvStore, namespace);
@@ -7,76 +7,41 @@ export type DataType = Record<string, Record<string, any>>;
 const DEFAULT_SCHEMA_NAME = "public";
 const DEFAULT_TABLE_NAME = "llamaindex_kv_store";

-export type PostgresKVStoreBaseConfig = {
-  schemaName?: string | undefined;
-  tableName?: string | undefined;
-};
-
-export type PostgresKVStoreClientConfig =
-  | {
-      /**
-       * Client configuration options for the pg client.
-       *
-       * {@link https://node-postgres.com/apis/client#new-client PostgresSQL Client API}
-       */
-      clientConfig?: pg.ClientConfig | undefined;
-    }
-  | {
-      /**
-       * A pg client or pool client instance.
-       * If provided, make sure it is not connected to the database yet, or it will throw an error.
-       */
-      shouldConnect?: boolean | undefined;
-      client?: pg.Client | pg.PoolClient;
-    };
-
-export type PostgresKVStoreConfig = PostgresKVStoreBaseConfig &
-  PostgresKVStoreClientConfig;
-
 export class PostgresKVStore extends BaseKVStore {
  private schemaName: string;
  private tableName: string;
+  private connectionString: string | undefined = undefined;
+  private db?: pg.Client;

-  private isDBConnected: boolean = false;
-  private clientConfig: pg.ClientConfig | undefined = undefined;
-  private db?: pg.ClientBase | undefined = undefined;
-
-  constructor(config?: PostgresKVStoreConfig) {
+  constructor(config?: {
+    schemaName?: string | undefined;
+    tableName?: string | undefined;
+    connectionString?: string | undefined;
+  }) {
    super();
    this.schemaName = config?.schemaName || DEFAULT_SCHEMA_NAME;
    this.tableName = config?.tableName || DEFAULT_TABLE_NAME;
-    if (config) {
-      if ("clientConfig" in config) {
-        this.clientConfig = config.clientConfig;
-      } else if ("client" in config) {
-        this.isDBConnected =
-          config?.shouldConnect !== undefined ? !config.shouldConnect : false;
-        this.db = config.client;
+    this.connectionString = config?.connectionString;
+  }
+
+  private async getDb(): Promise<pg.Client> {
+    if (!this.db) {
+      try {
+        const pg = await import("pg");
+        const { Client } = pg.default ? pg.default : pg;
+        const db = new Client({ connectionString: this.connectionString });
+        await db.connect();
+        await this.checkSchema(db);
+        this.db = db;
+      } catch (err) {
+        console.error(err);
+        return Promise.reject(err instanceof Error ? err : new Error(`${err}`));
      }
    }
+    return Promise.resolve(this.db);
  }

-  private async getDb(): Promise<pg.ClientBase> {
-    if (!this.db) {
-      const pg = await import("pg");
-      const { Client } = pg.default ? pg.default : pg;
-      const db = new Client({ ...this.clientConfig });
-      await db.connect();
-      this.isDBConnected = true;
-      this.db = db;
-    }
-    if (this.db && !this.isDBConnected) {
-      await this.db.connect();
-      this.isDBConnected = true;
-    }
-    this.db.on("end", () => {
-      this.isDBConnected = false;
-    });
-    await this.checkSchema(this.db);
-    return this.db;
-  }
-
-  private async checkSchema(db: pg.ClientBase) {
+  private async checkSchema(db: pg.Client) {
    await db.query(`CREATE SCHEMA IF NOT EXISTS ${this.schemaName}`);
    const tbl = `CREATE TABLE IF NOT EXISTS ${this.schemaName}.${this.tableName} (
      id uuid DEFAULT gen_random_uuid() PRIMARY KEY,
@@ -132,7 +97,7 @@ export class PostgresKVStore extends BaseKVStore {
      const sql = `SELECT * FROM ${this.schemaName}.${this.tableName} WHERE key = $1 AND collection = $2`;
      const result = await db.query(sql, [key, collection]);
      await db.query("COMMIT");
-      return result.rows[0]?.value;
+      return result.rows[0].value;
    } catch (error) {
      await db.query("ROLLBACK");
      throw error;
@@ -14,44 +14,25 @@ import {
 import { escapeLikeString } from "./utils.js";

 import type { BaseEmbedding } from "@llamaindex/core/embeddings";
-import { DEFAULT_COLLECTION } from "@llamaindex/core/global";
 import type { BaseNode, Metadata } from "@llamaindex/core/schema";
 import { Document, MetadataMode } from "@llamaindex/core/schema";

 export const PGVECTOR_SCHEMA = "public";
 export const PGVECTOR_TABLE = "llamaindex_embedding";
-export const DEFAULT_DIMENSIONS = 1536;

-type PGVectorStoreBaseConfig = {
+export type PGVectorStoreConfig = Pick<
+  pg.ClientConfig,
+  "user" | "database" | "password" | "connectionString"
+> & {
  schemaName?: string | undefined;
  tableName?: string | undefined;
  dimensions?: number | undefined;
  embedModel?: BaseEmbedding | undefined;
 };

-export type PGVectorStoreConfig = PGVectorStoreBaseConfig &
-  (
-    | {
-        /**
-         * Client configuration options for the pg client.
-         *
-         * {@link https://node-postgres.com/apis/client#new-client PostgresSQL Client API}
-         */
-        clientConfig: pg.ClientConfig;
-      }
-    | {
-        /**
-         * A pg client or pool client instance.
-         * If provided, make sure it is not connected to the database yet, or it will throw an error.
-         */
-        shouldConnect?: boolean | undefined;
-        client: pg.Client | pg.PoolClient;
-      }
-  );
-
 /**
 * Provides support for writing and querying vector data in Postgres.
- * Note: Can't be used with data created using the Python version of the vector store (https://docs.llamaindex.ai/en/stable/examples/vector_stores/postgres/)
+ * Note: Can't be used with data created using the Python version of the vector store (https://docs.llamaindex.ai/en/stable/examples/vector_stores/postgres.html)
 */
 export class PGVectorStore
  extends VectorStoreBase
@@ -59,26 +40,52 @@ export class PGVectorStore
 {
  storesText: boolean = true;

-  private collection: string = DEFAULT_COLLECTION;
-  private readonly schemaName: string = PGVECTOR_SCHEMA;
-  private readonly tableName: string = PGVECTOR_TABLE;
-  private readonly dimensions: number = DEFAULT_DIMENSIONS;
+  private collection: string = "";
+  private schemaName: string = PGVECTOR_SCHEMA;
+  private tableName: string = PGVECTOR_TABLE;

-  private isDBConnected: boolean = false;
-  private db: pg.ClientBase | null = null;
-  private readonly clientConfig: pg.ClientConfig | null = null;
+  private user: pg.ClientConfig["user"] | undefined = undefined;
+  private password: pg.ClientConfig["password"] | undefined = undefined;
+  private database: pg.ClientConfig["database"] | undefined = undefined;
+  private connectionString: pg.ClientConfig["connectionString"] | undefined =
+    undefined;

-  constructor(config: PGVectorStoreConfig) {
-    super(config?.embedModel);
-    this.schemaName = config?.schemaName ?? PGVECTOR_SCHEMA;
-    this.tableName = config?.tableName ?? PGVECTOR_TABLE;
-    this.dimensions = config?.dimensions ?? DEFAULT_DIMENSIONS;
-    if ("clientConfig" in config) {
-      this.clientConfig = config.clientConfig;
+  private dimensions: number = 1536;
+
+  private db?: pg.ClientBase;
+
+  /**
+   * Constructs a new instance of the PGVectorStore
+   *
+   * If the `connectionString` is not provided the following env variables are
+   * used to connect to the DB:
+   * PGHOST=your database host
+   * PGUSER=your database user
+   * PGPASSWORD=your database password
+   * PGDATABASE=your database name
+   * PGPORT=your database port
+   */
+  constructor(configOrClient?: PGVectorStoreConfig | pg.ClientBase) {
+    // We cannot import pg from top level, it might have side effects
+    //  so we only check if the config.connect function exists
+    if (
+      configOrClient &&
+      "connect" in configOrClient &&
+      typeof configOrClient.connect === "function"
+    ) {
+      const db = configOrClient as pg.ClientBase;
+      super();
+      this.db = db;
    } else {
-      this.isDBConnected =
-        config.shouldConnect !== undefined ? !config.shouldConnect : false;
-      this.db = config.client;
+      const config = configOrClient as PGVectorStoreConfig;
+      super(config?.embedModel);
+      this.schemaName = config?.schemaName ?? PGVECTOR_SCHEMA;
+      this.tableName = config?.tableName ?? PGVECTOR_TABLE;
+      this.user = config?.user;
+      this.password = config?.password;
+      this.database = config?.database;
+      this.connectionString = config?.connectionString;
+      this.dimensions = config?.dimensions ?? 1536;
    }
  }

@@ -106,41 +113,39 @@ export class PGVectorStore

  private async getDb(): Promise<pg.ClientBase> {
    if (!this.db) {
-      const pg = await import("pg");
-      const { Client } = pg.default ? pg.default : pg;
+      try {
+        const pg = await import("pg");
+        const { Client } = pg.default ? pg.default : pg;

-      const { registerTypes } = await import("pgvector/pg");
-      // Create DB connection
-      // Read connection params from env - see comment block above
-      const db = new Client({
-        ...this.clientConfig,
-      });
+        const { registerType } = await import("pgvector/pg");
+        // Create DB connection
+        // Read connection params from env - see comment block above
+        const db = new Client({
+          user: this.user,
+          password: this.password,
+          database: this.database,
+          connectionString: this.connectionString,
+        });
+        await db.connect();

-      await db.connect();
-      this.isDBConnected = true;
+        // Check vector extension
+        await db.query("CREATE EXTENSION IF NOT EXISTS vector");
+        await registerType(db);

-      // Check vector extension
-      await db.query("CREATE EXTENSION IF NOT EXISTS vector");
-      await registerTypes(db);
-
-      // All good?  Keep the connection reference
-      this.db = db;
+        // All good?  Keep the connection reference
+        this.db = db;
+      } catch (err) {
+        console.error(err);
+        return Promise.reject(err instanceof Error ? err : new Error(`${err}`));
+      }
    }

-    if (this.db && !this.isDBConnected) {
-      await this.db.connect();
-      this.isDBConnected = true;
-    }
-
-    this.db.on("end", () => {
-      // Connection closed
-      this.isDBConnected = false;
-    });
+    const db = this.db;

    // Check schema, table(s), index(es)
-    await this.checkSchema(this.db);
+    await this.checkSchema(db);

-    return this.db;
+    return Promise.resolve(this.db);
  }

  private async checkSchema(db: pg.ClientBase) {
@@ -1,237 +0,0 @@
-import {
-  VectorStoreBase,
-  type IEmbedModel,
-  type MetadataFilter,
-  type MetadataFilters,
-  type VectorStoreNoEmbedModel,
-  type VectorStoreQuery,
-  type VectorStoreQueryResult,
-} from "./types.js";
-
-import type { BaseNode, Metadata, TextNode } from "@llamaindex/core/schema";
-import { getEnv } from "@llamaindex/env";
-import { Index } from "@upstash/vector";
-import { metadataDictToNode, nodeToMetadata } from "./utils.js";
-
-type UpstashParams = {
-  namespace?: string;
-  token?: string;
-  endpoint?: string;
-  maxBatchSize?: number;
-} & IEmbedModel;
-
-/**
- * Provides support for writing and querying vector data in Upstash.
- */
-export class UpstashVectorStore
-  extends VectorStoreBase
-  implements VectorStoreNoEmbedModel
-{
-  storesText: boolean = true;
-
-  private db: Index;
-  private maxBatchSize: number;
-  namespace: string;
-
-  /**
-   * @param namespace namespace to use
-   * @param token upstash vector token. if not set, `process.env.UPSTASH_VECTOR_REST_TOKEN` is used.
-   * @param endpoint upstash vector endpoint. If not set, `process.env.UPSTASH_VECTOR_REST_URL` is used.
-   * @param maxBatchSize maximum number of vectors upserted at once. Default is 1000.
-   *
-   * @example
-   * ```ts
-   * const vectorStore = new UpstashVectorStore({ namespace: "my-namespace" })
-   * ```
-   */
-  constructor(params?: UpstashParams) {
-    super(params?.embedModel);
-    this.namespace = params?.namespace ?? "";
-    this.maxBatchSize = params?.maxBatchSize ?? 1000;
-    const token = params?.token ?? getEnv("UPSTASH_VECTOR_REST_TOKEN");
-    const endpoint = params?.endpoint ?? getEnv("UPSTASH_VECTOR_REST_URL");
-
-    if (!token) {
-      throw new Error(
-        "Must specify UPSTASH_VECTOR_REST_TOKEN via env variable.",
-      );
-    }
-    if (!endpoint) {
-      throw new Error("Must specify UPSTASH_VECTOR_REST_URL via env variable.");
-    }
-    this.db = new Index({ token, url: endpoint });
-  }
-
-  private async getDb(): Promise<Index> {
-    if (!this.db) {
-      const { Index } = await import("@upstash/vector");
-      this.db = new Index();
-    }
-
-    return this.db;
-  }
-
-  /**
-   * Connects to the database specified in environment vars.
-   * @returns A connection to the database, or the error encountered while connecting/setting up.
-   */
-  client(): Promise<Index> {
-    return this.getDb();
-  }
-
-  /**
-   * Adds vector record(s) to the table.
-   * @param embeddingResults The Nodes to be inserted, optionally including metadata tuples.
-   * @returns ids of the embeddings (infered from the id_ field of embeddingResults objects)
-   */
-  async add(embeddingResults: BaseNode<Metadata>[]): Promise<string[]> {
-    if (embeddingResults.length == 0) {
-      return [];
-    }
-
-    const nodes = embeddingResults.map(this.nodeToRecord);
-    const result = await this.upsertInBatches(nodes);
-    if (result != "OK") {
-      throw new Error("Failed to save chunk");
-    }
-    return nodes.map((node) => node.id);
-  }
-
-  /**
-   * Adds plain text record(s) to the table. Upstash take cares of embedding conversion.
-   * @param text The Nodes to be inserted, optionally including metadata tuples.
-   * @returns ids of the embeddings (infered from the id_ field of embeddingResults objects)
-   */
-  async addPlainText(text: TextNode<Metadata>[]): Promise<string[]> {
-    if (text.length == 0) {
-      return [];
-    }
-
-    const nodes = text.map(this.textNodeToRecord);
-    const result = await this.upsertInBatches(nodes);
-    if (result != "OK") {
-      throw new Error("Failed to save chunk");
-    }
-    return nodes.map((node) => node.id);
-  }
-
-  private async upsertInBatches(
-    nodes:
-      | ReturnType<UpstashVectorStore["textNodeToRecord"]>[]
-      | ReturnType<UpstashVectorStore["nodeToRecord"]>[],
-  ) {
-    const promises: Promise<string>[] = [];
-    for (let i = 0; i < nodes.length; i += this.maxBatchSize) {
-      const batch = nodes.slice(i, i + this.maxBatchSize);
-      promises.push(this.db.upsert(batch, { namespace: this.namespace }));
-    }
-    const results = await Promise.all(promises);
-    return results.every((result) => result === "OK") ? "OK" : "NOT-OK";
-  }
-
-  /**
-   * Deletes a single record from the database by id.
-   * NOTE: Uses the collection property controlled by setCollection/getCollection.
-   * @param refDocId Unique identifier for the record to delete.
-   * @returns Promise that resolves if the delete query did not throw an error.
-   */
-  async delete(refDocId: string): Promise<void> {
-    await this.db.namespace(this.namespace).delete(refDocId);
-  }
-
-  /**
-   * Deletes a single record from the database by id.
-   * NOTE: Uses the collection property controlled by setCollection/getCollection.
-   * @param refDocId Unique identifier for the record to delete.
-   * @param deleteKwargs Required by VectorStore interface.  Currently ignored.
-   * @returns Promise that resolves if the delete query did not throw an error.
-   */
-  async deleteMany(refDocId: string[]): Promise<void> {
-    await this.db.namespace(this.namespace).delete(refDocId);
-  }
-
-  /**
-   * Query the vector store for the closest matching data to the query embeddings
-   * @param query The VectorStoreQuery to be used
-   * @param options Required by VectorStore interface.  Currently ignored.
-   * @returns Zero or more Document instances with data from the vector store.
-   */
-  async query(
-    query: VectorStoreQuery,
-    _options?: any,
-  ): Promise<VectorStoreQueryResult> {
-    const filter = this.toUpstashFilter(query.filters);
-
-    const defaultOptions: any = {
-      vector: query.queryEmbedding,
-      topK: query.similarityTopK,
-      includeVectors: true,
-      includeMetadata: true,
-      filter,
-    };
-
-    const db = this.db;
-    const results = await db.query(defaultOptions, {
-      namespace: this.namespace,
-    });
-
-    const nodes = results.map((result) => {
-      const node = metadataDictToNode(result.metadata as Record<string, any>, {
-        fallback: {
-          id: result.id,
-          metadata: result.metadata,
-          embedding: result.vector,
-        },
-      });
-      return node;
-    });
-
-    const ret = {
-      nodes: nodes,
-      similarities: results.map((row) => row.score || 999),
-      ids: results.map((row) => String(row.id)),
-    };
-
-    return ret;
-  }
-
-  toFilterString(filter: MetadataFilter) {
-    return `${filter.key} ${filter.operator} ${filter.value}`;
-  }
-
-  toUpstashFilter(stdFilters?: MetadataFilters) {
-    if (!stdFilters?.filters) return;
-
-    for (const item of stdFilters.filters) {
-      if (item.operator === "==") {
-        //@ts-expect-error Upstash equal operator uses only one equal sign, so we have to replace it.
-        item.operator = "=";
-      }
-    }
-
-    const filterStrings = stdFilters.filters.map(this.toFilterString);
-
-    if (filterStrings.length === 1) {
-      return filterStrings[0];
-    }
-    return filterStrings.join(` ${stdFilters.condition ?? "and"} `);
-  }
-
-  nodeToRecord(node: BaseNode<Metadata>) {
-    const id: any = node.id_.length ? node.id_ : null;
-    return {
-      id: id,
-      vector: node.getEmbedding(),
-      metadata: nodeToMetadata(node),
-    };
-  }
-
-  textNodeToRecord(node: TextNode<Metadata>) {
-    const id: any = node.id_.length ? node.id_ : null;
-    return {
-      id,
-      data: node.text,
-      metadata: nodeToMetadata(node),
-    };
-  }
-}
@@ -1,17 +1,5 @@
 # @llamaindex/groq

-## 0.0.7
-
-### Patch Changes
-
- @llamaindex/openai@0.1.8
-
-## 0.0.6
-
-### Patch Changes
-
- @llamaindex/openai@0.1.7
-
 ## 0.0.5

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/groq",
  "description": "Groq Adapter for LlamaIndex",
-  "version": "0.0.7",
+  "version": "0.0.5",
  "type": "module",
  "main": "./dist/index.cjs",
  "module": "./dist/index.js",
@@ -1,19 +1,5 @@
 # @llamaindex/openai

-## 0.1.8
-
-### Patch Changes
-
- Updated dependencies [8b7fdba]
-  - @llamaindex/core@0.2.6
-
-## 0.1.7
-
-### Patch Changes
-
- Updated dependencies [d902cc3]
-  - @llamaindex/core@0.2.5
-
 ## 0.1.6

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/openai",
  "description": "OpenAI Adapter for LlamaIndex",
-  "version": "0.1.8",
+  "version": "0.1.6",
  "type": "module",
  "main": "./dist/index.cjs",
  "module": "./dist/index.js",
Author	SHA1	Message	Date
Marcus Schiesser	b622f49845	add docs and unit test	2024-09-20 15:01:08 +07:00
Marcus Schiesser	f02ecbff14	fix: context not working in contextchatengine	2024-09-20 12:58:26 +07:00