chore: remove console.log

.changeset/clever-doors-invite.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-feat: experimental package + json query engine

.changeset/cuddly-cars-doubt.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Add LlamaParse option when selecting a pdf file or a folder

.changeset/eight-dogs-burn.md

@@ -1,12 +0,0 @@
----
-"llamaindex": patch
-"@llamaindex/core-test": patch
----
-
-- Add missing exports:
-  - `IndexStructType`,
-  - `IndexDict`,
-  - `jsonToIndexStruct`,
-  - `IndexList`,
-  - `IndexStruct`
-- Fix `IndexDict.toJson()` method

.changeset/khaki-rivers-unite.md

@@ -1,5 +0,0 @@
----
-"llamaindex": patch
----
-
-Add streaming to agents

.changeset/strange-waves-sit.md

@@ -1,5 +0,0 @@
----
-"llamaindex": minor
----
-
-Use parameter object for retrieve function of Retriever (to align usage with query function of QueryEngine)

.eslintrc.js

@@ -11,13 +11,5 @@ module.exports = {
     "max-params": ["error", 4],
     "prefer-const": "error",
   },
-  overrides: [
-    {
-      files: ["examples/**/*.ts"],
-      rules: {
-        "turbo/no-undeclared-env-vars": "off",
-      },
-    },
-  ],
   ignorePatterns: ["dist/", "lib/"],
 };

.github/workflows/publish.yml

@@ -1,28 +0,0 @@
-name: Publish
-on:
-  push:
-    branches:
-      - main
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-
-    permissions:
-      contents: read
-      id-token: write
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Publish @llamaindex/env
-        run: npx jsr publish
-        working-directory: packages/env
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Publish @llamaindex/core
-        run: npx jsr publish --allow-slow-types
-        working-directory: packages/core
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -45,7 +45,6 @@ playwright-report/
 blob-report/
 playwright/.cache/
 .tsbuildinfo
-packages/create-llama/e2e/cache
 
 # intellij
 **/.idea

.prettierignore

@@ -4,4 +4,3 @@ pnpm-lock.yaml
 lib/
 dist/
 .docusaurus/
-packages/create-llama/e2e/cache/

apps/docs/docs/modules/embeddings/available_embeddings/huggingface.md

@@ -23,15 +23,3 @@ const results = await queryEngine.query({
   query,
 });
 ```
-
-Per default, `HuggingFaceEmbedding` is using the `Xenova/all-MiniLM-L6-v2` model. You can change the model by passing the `modelType` parameter to the constructor.
-If you're not using a quantized model, set the `quantized` parameter to `false`.
-
-For example, to use the not quantized `BAAI/bge-small-en-v1.5` model, you can use the following code:
-
-```
-const embedModel = new HuggingFaceEmbedding({
-  modelType: "BAAI/bge-small-en-v1.5",
-  quantized: false,
-});
-```

apps/docs/docs/modules/ingestion_pipeline/transformations.md

@@ -1,6 +1,6 @@
 # Transformations
 
-A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the Transformation class has both a `transform` definition responsible for transforming the nodes.
+A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the Transformatio class has both a `transform` definition responsible for transforming the nodes
 
 Currently, the following components are Transformation objects:
 

apps/docs/docs/modules/node_postprocessors/index.md

@@ -100,7 +100,7 @@ const response = await queryEngine.query("<user_query>");
 ```ts
 import { SimilarityPostprocessor } from "llamaindex";
 
-nodes = await index.asRetriever().retrieve({ query: "test query str" });
+nodes = await index.asRetriever().retrieve("test query str");
 
 const processor = new SimilarityPostprocessor({
   similarityCutoff: 0.7,

apps/docs/docs/modules/retriever.md

@@ -11,7 +11,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Fetch nodes!
-const nodesWithScore = await retriever.retrieve({ query: "query string" });
+const nodesWithScore = await retriever.retrieve("query string");
 ```
 
 ## API Reference

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // جلب العقد!
-const nodesWithScore = await retriever.retrieve({ query: "سلسلة الاستعلام" });
+const nodesWithScore = await retriever.retrieve("سلسلة الاستعلام");
 ```
 
 ## مرجع الواجهة البرمجية (API Reference)

apps/docs/i18n/bg/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Извличане на върхове!
-const nodesWithScore = await retriever.retrieve({ query: "query string" });
+const nodesWithScore = await retriever.retrieve("query string");
 ```
 
 ## API Reference (API справка)

apps/docs/i18n/cat/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const recuperador = vector_index.asRetriever();
 recuperador.similarityTopK = 3;
 
 // Obteniu els nodes!
-const nodesAmbPuntuació = await recuperador.retrieve({ query: "cadena de consulta" });
+const nodesAmbPuntuació = await recuperador.retrieve("cadena de consulta");
 ```
 
 ## Referència de l'API

apps/docs/i18n/cs/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Získání uzlů!
-const nodesWithScore = await retriever.retrieve({ query: "dotazovací řetězec" });
+const nodesWithScore = await retriever.retrieve("dotazovací řetězec");
 ```
 
 ## API Reference (Odkazy na rozhraní)

apps/docs/i18n/da/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Hent noder!
-const nodesWithScore = await retriever.retrieve({ query: "forespørgselsstreng" });
+const nodesWithScore = await retriever.retrieve("forespørgselsstreng");
 ```
 
 ## API Reference

apps/docs/i18n/de/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Knoten abrufen!
-const nodesWithScore = await retriever.retrieve({ query: "Abfragezeichenfolge" });
+const nodesWithScore = await retriever.retrieve("Abfragezeichenfolge");
 ```
 
 ## API-Referenz

apps/docs/i18n/el/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Ανάκτηση κόμβων!
-const nodesWithScore = await retriever.retrieve({ query: "συμβολοσειρά ερωτήματος" });
+const nodesWithScore = await retriever.retrieve("συμβολοσειρά ερωτήματος");
 ```
 
 ## Αναφορά API

apps/docs/i18n/es/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const recuperador = vector_index.asRetriever();
 recuperador.similarityTopK = 3;
 
 // ¡Obtener nodos!
-const nodosConPuntuación = await recuperador.retrieve({ query: "cadena de consulta" });
+const nodosConPuntuación = await recuperador.retrieve("cadena de consulta");
 ```
 
 ## Referencia de la API

apps/docs/i18n/et/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Too sõlmed!
-const nodesWithScore = await retriever.retrieve({ query: "päringu string" });
+const nodesWithScore = await retriever.retrieve("päringu string");
 ```
 
 ## API viide

apps/docs/i18n/fa/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // بازیابی گره ها!
-const nodesWithScore = await retriever.retrieve({ query: "رشته پرس و جو" });
+const nodesWithScore = await retriever.retrieve("رشته پرس و جو");
 ```
 
 ## مرجع API

apps/docs/i18n/fi/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Hae solmut!
-const nodesWithScore = await retriever.retrieve({ query: "kyselymerkkijono" });
+const nodesWithScore = await retriever.retrieve("kyselymerkkijono");
 ```
 
 ## API-viite

apps/docs/i18n/fr/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -11,7 +11,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Récupérer les nœuds !
-const nodesWithScore = await retriever.retrieve({ query: "chaîne de requête" });
+const nodesWithScore = await retriever.retrieve("chaîne de requête");
 ```
 
 ## Référence de l'API

apps/docs/i18n/he/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // אחזור צמתים!
-const nodesWithScore = await retriever.retrieve({ query: "מחרוזת שאילתה" });
+const nodesWithScore = await retriever.retrieve("מחרוזת שאילתה");
 ```
 
 ## מדריך לממשק API

apps/docs/i18n/hi/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // नोड्स प्राप्त करें!
-const nodesWithScore = await retriever.retrieve({ query: "क्वेरी स्ट्रिंग" });
+const nodesWithScore = await retriever.retrieve("क्वेरी स्ट्रिंग");
 ```
 
 ## एपीआई संदर्भ (API Reference)

apps/docs/i18n/hr/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const dohvatnik = vector_index.asRetriever();
 dohvatnik.similarityTopK = 3;
 
 // Dohvati čvorove!
-const čvoroviSaRezultatom = await dohvatnik.retrieve({ query: "upitni niz" });
+const čvoroviSaRezultatom = await dohvatnik.retrieve("upitni niz");
 ```
 
 ## API Referenca

apps/docs/i18n/hu/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Node-ok lekérése!
-const nodesWithScore = await retriever.retrieve({ query: "lekérdezési karakterlánc" });
+const nodesWithScore = await retriever.retrieve("lekérdezési karakterlánc");
 ```
 
 ## API Referencia

apps/docs/i18n/in/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Mengambil node!
-const nodesWithScore = await retriever.retrieve({ query: "string query" });
+const nodesWithScore = await retriever.retrieve("string query");
 ```
 
 ## Referensi API

apps/docs/i18n/it/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Recupera i nodi!
-const nodesWithScore = await retriever.retrieve({ query: "stringa di query" });
+const nodesWithScore = await retriever.retrieve("stringa di query");
 ```
 
 ## Riferimento API

apps/docs/i18n/ja/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // ノードを取得します！
-const nodesWithScore = await retriever.retrieve({ query: "クエリ文字列" });
+const nodesWithScore = await retriever.retrieve("クエリ文字列");
 ```
 
 ## API リファレンス

apps/docs/i18n/ko/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // 노드를 가져옵니다!
-const nodesWithScore = await retriever.retrieve({ query: "쿼리 문자열" });
+const nodesWithScore = await retriever.retrieve("쿼리 문자열");
 ```
 
 ## API 참조

apps/docs/i18n/lt/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const gavėjas = vector_index.asRetriever();
 gavėjas.similarityTopK = 3;
 
 // Išgaunami mazgai!
-const mazgaiSuRezultatu = await gavėjas.retrieve({ query: "užklausos eilutė" });
+const mazgaiSuRezultatu = await gavėjas.retrieve("užklausos eilutė");
 ```
 
 ## API nuorodos (API Reference)

apps/docs/i18n/nl/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Haal knooppunten op!
-const nodesWithScore = await retriever.retrieve({ query: "zoekopdracht" });
+const nodesWithScore = await retriever.retrieve("zoekopdracht");
 ```
 
 ## API Referentie

apps/docs/i18n/no/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Hent noder!
-const nodesWithScore = await retriever.retrieve({ query: "spørringsstreng" });
+const nodesWithScore = await retriever.retrieve("spørringsstreng");
 ```
 
 ## API-referanse

apps/docs/i18n/pl/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Pobierz węzły!
-const nodesWithScore = await retriever.retrieve({ query: "ciąg zapytania" });
+const nodesWithScore = await retriever.retrieve("ciąg zapytania");
 ```
 
 ## Dokumentacja interfejsu API

apps/docs/i18n/pt/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const recuperador = vector_index.asRetriever();
 recuperador.similarityTopK = 3;
 
 // Buscar nós!
-const nósComPontuação = await recuperador.retrieve({ query: "string de consulta" });
+const nósComPontuação = await recuperador.retrieve("string de consulta");
 ```
 
 ## Referência da API

apps/docs/i18n/ro/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const recuperator = vector_index.asRetriever();
 recuperator.similarityTopK = 3;
 
 // Preia nodurile!
-const noduriCuScor = await recuperator.retrieve({ query: "șir de interogare" });
+const noduriCuScor = await recuperator.retrieve("șir de interogare");
 ```
 
 ## Referință API

apps/docs/i18n/ru/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Получение узлов!
-const nodesWithScore = await retriever.retrieve({ query: "строка запроса" });
+const nodesWithScore = await retriever.retrieve("строка запроса");
 ```
 
 ## Справочник по API

apps/docs/i18n/se/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Dohvati čvorove!
-const nodesWithScore = await retriever.retrieve({ query: "upitni niz" });
+const nodesWithScore = await retriever.retrieve("upitni niz");
 ```
 
 ## API Referenca

apps/docs/i18n/sk/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const pridobitelj = vector_index.asRetriever();
 pridobitelj.similarityTopK = 3;
 
 // Pridobivanje vozlišč!
-const vozliščaZRezultatom = await pridobitelj.retrieve({ query: "poizvedbeni niz" });
+const vozliščaZRezultatom = await pridobitelj.retrieve("poizvedbeni niz");
 ```
 
 ## API Sklic

apps/docs/i18n/sl/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Získajte uzly!
-const nodesWithScore = await retriever.retrieve({ query: "reťazec dotazu" });
+const nodesWithScore = await retriever.retrieve("reťazec dotazu");
 ```
 
 ## API Referencia

apps/docs/i18n/sv/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Hämta noder!
-const nodesWithScore = await retriever.retrieve({ query: "frågesträng" });
+const nodesWithScore = await retriever.retrieve("frågesträng");
 ```
 
 ## API-referens

apps/docs/i18n/th/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // เรียกคืนโหนด!
-const nodesWithScore = await retriever.retrieve({ query: "query string" });
+const nodesWithScore = await retriever.retrieve("query string");
 ```
 
 ## API Reference (การอ้างอิง API)

apps/docs/i18n/tr/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Düğümleri getir!
-const nodesWithScore = await retriever.retrieve({ query: "sorgu dizesi" });
+const nodesWithScore = await retriever.retrieve("sorgu dizesi");
 ```
 
 ## API Referansı

apps/docs/i18n/uk/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Отримати вузли!
-const nodesWithScore = await retriever.retrieve({ query: "рядок запиту" });
+const nodesWithScore = await retriever.retrieve("рядок запиту");
 ```
 
 ## Довідник API

apps/docs/i18n/vi/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // Lấy các node!
-const nodesWithScore = await retriever.retrieve({ query: "chuỗi truy vấn" });
+const nodesWithScore = await retriever.retrieve("chuỗi truy vấn");
 ```
 
 ## Tài liệu tham khảo API

apps/docs/i18n/zh-Hans/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -11,7 +11,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // 获取节点！
-const nodesWithScore = await retriever.retrieve({ query: "查询字符串" });
+const nodesWithScore = await retriever.retrieve("查询字符串");
 ```
 
 ## API 参考

apps/docs/i18n/zh_tw/docusaurus-plugin-content-docs/current/modules/low_level/retriever.md

@@ -13,7 +13,7 @@ const retriever = vector_index.asRetriever();
 retriever.similarityTopK = 3;
 
 // 提取節點！
-const nodesWithScore = await retriever.retrieve({ query: "查詢字串" });
+const nodesWithScore = await retriever.retrieve("查詢字串");
 ```
 
 ## API 參考

examples/agent/stream_openai_agent.ts

@@ -1,77 +0,0 @@
-import { FunctionTool, OpenAIAgent } from "llamaindex";
-
-// Define a function to sum two numbers
-function sumNumbers({ a, b }: { a: number; b: number }): number {
-  return a + b;
-}
-
-// Define a function to divide two numbers
-function divideNumbers({ a, b }: { a: number; b: number }): number {
-  return a / b;
-}
-
-// Define the parameters of the sum function as a JSON schema
-const sumJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The first number",
-    },
-    b: {
-      type: "number",
-      description: "The second number",
-    },
-  },
-  required: ["a", "b"],
-};
-
-const divideJSON = {
-  type: "object",
-  properties: {
-    a: {
-      type: "number",
-      description: "The dividend",
-    },
-    b: {
-      type: "number",
-      description: "The divisor",
-    },
-  },
-  required: ["a", "b"],
-};
-
-async function main() {
-  // Create a function tool from the sum function
-  const functionTool = new FunctionTool(sumNumbers, {
-    name: "sumNumbers",
-    description: "Use this function to sum two numbers",
-    parameters: sumJSON,
-  });
-
-  // Create a function tool from the divide function
-  const functionTool2 = new FunctionTool(divideNumbers, {
-    name: "divideNumbers",
-    description: "Use this function to divide two numbers",
-    parameters: divideJSON,
-  });
-
-  // Create an OpenAIAgent with the function tools
-  const agent = new OpenAIAgent({
-    tools: [functionTool, functionTool2],
-    verbose: false,
-  });
-
-  const stream = await agent.chat({
-    message: "Divide 16 by 2 then add 20",
-    stream: true,
-  });
-
-  for await (const chunk of stream.response) {
-    process.stdout.write(chunk.response);
-  }
-}
-
-main().then(() => {
-  console.log("\nDone");
-});

examples/anthropic.ts

@@ -3,7 +3,6 @@ import { Anthropic } from "llamaindex";
 (async () => {
   const anthropic = new Anthropic({
     apiKey: process.env.ANTHROPIC_API_KEY,
-    model: "claude-3-opus",
   });
   const result = await anthropic.chat({
     messages: [

examples/anthropic/chat_interactive.ts

@@ -1,34 +0,0 @@
-import { Anthropic, SimpleChatEngine, SimpleChatHistory } from "llamaindex";
-import { stdin as input, stdout as output } from "node:process";
-import readline from "node:readline/promises";
-
-(async () => {
-  const llm = new Anthropic({
-    apiKey: process.env.ANTHROPIC_API_KEY,
-    model: "claude-3-opus",
-  });
-  // chatHistory will store all the messages in the conversation
-  const chatHistory = new SimpleChatHistory({
-    messages: [
-      {
-        content: "You want to talk in rhymes.",
-        role: "system",
-      },
-    ],
-  });
-  const chatEngine = new SimpleChatEngine({
-    llm,
-    chatHistory,
-  });
-  const rl = readline.createInterface({ input, output });
-
-  while (true) {
-    const query = await rl.question("User: ");
-    process.stdout.write("Assistant: ");
-    const stream = await chatEngine.chat({ message: query, stream: true });
-    for await (const chunk of stream) {
-      process.stdout.write(chunk.response);
-    }
-    process.stdout.write("\n");
-  }
-})();

examples/anthropic/stream.ts

@@ -1,23 +0,0 @@
-import { Anthropic } from "llamaindex";
-
-(async () => {
-  const anthropic = new Anthropic({
-    apiKey: process.env.ANTHROPIC_API_KEY,
-    model: "claude-instant-1.2",
-  });
-  const stream = await anthropic.chat({
-    messages: [
-      { content: "You want to talk in rhymes.", role: "system" },
-      {
-        content:
-          "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
-        role: "user",
-      },
-    ],
-    stream: true,
-  });
-
-  for await (const chunk of stream) {
-    process.stdout.write(chunk.delta);
-  }
-})();

examples/multimodal/retrieve.ts

@@ -27,9 +27,9 @@ async function main() {
   // retrieve documents using the index
   const index = await createIndex();
   const retriever = index.asRetriever({ similarityTopK: 3 });
-  const results = await retriever.retrieve({
-    query: "what are Vincent van Gogh's famous paintings",
-  });
+  const results = await retriever.retrieve(
+    "what are Vincent van Gogh's famous paintings",
+  );
   for (const result of results) {
     const node = result.node;
     if (!node) {

examples/nlsqlTableQueryEngine.ts

@@ -0,0 +1,81 @@
+import knex from "knex";
+import {
+  NLSQLQueryEngine,
+  OpenAI,
+  SQLDatabase,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  const llm = new OpenAI({
+    model: "gpt-4",
+  });
+
+  const engine = knex({
+    client: "sqlite3", // or 'better-sqlite3'
+    connection: {
+      filename: ":memory:",
+    },
+  });
+
+  const db = new SQLDatabase({
+    engine,
+    schema: undefined,
+    metadata: {},
+    ignoreTables: undefined,
+    includeTables: ["test_table_1"],
+    sampleRowsInTableInfo: 3,
+    indexesInTableInfo: true,
+    customTableInfo: undefined,
+    maxStringLength: 100,
+  });
+
+  const tableName = "test_table_1";
+
+  await engine.schema.createTable(tableName, async (table) => {
+    table.increments("id");
+    table.string("comment");
+    table.string("author");
+
+    await db.insertIntoTable(tableName, {
+      comment: "this is a test1",
+      author: "emanuel",
+    });
+    await db.insertIntoTable(tableName, {
+      comment: "this is a test2",
+      author: "alex",
+    });
+    await db.insertIntoTable(tableName, {
+      comment: "this is a test3",
+      author: "yi",
+    });
+    await db.insertIntoTable(tableName, {
+      comment: "this is a test4",
+      author: "alex",
+    });
+
+    const ctx = serviceContextFromDefaults({
+      llm,
+    });
+
+    const engine = new NLSQLQueryEngine({
+      sqlDatabase: db,
+      tables: ["test_table_1"],
+      verbose: true,
+      serviceContext: ctx,
+      synthesizeResponse: true,
+    });
+
+    const response = await engine.query({
+      query: "What's the comment from author yi and emanuel?",
+    });
+
+    console.log({ response });
+
+    process.exit(0);
+  });
+}
+
+main().then(() => [
+  // process.exit(0)
+]);

examples/package.json

@@ -9,8 +9,10 @@
     "chromadb": "^1.8.1",
     "commander": "^11.1.0",
     "dotenv": "^16.4.1",
+    "knex": "^3.1.0",
     "llamaindex": "latest",
-    "mongodb": "^6.2.0"
+    "mongodb": "^6.2.0",
+    "sqlite3": "^5.1.7"
   },
   "devDependencies": {
     "@types/node": "^18.19.10",

examples/test.ts

@@ -0,0 +1,46 @@
+import knex from "knex";
+import { SQLDatabase } from "llamaindex";
+
+async function main() {
+  const engine = knex({
+    client: "sqlite3", // or 'better-sqlite3'
+    connection: {
+      filename: ":memory:",
+    },
+  });
+
+  const db = new SQLDatabase({
+    engine,
+    schema: undefined,
+    metadata: {},
+    ignoreTables: undefined,
+    includeTables: ["test_table"],
+    sampleRowsInTableInfo: 3,
+    indexesInTableInfo: true,
+    customTableInfo: undefined,
+    maxStringLength: 100,
+  });
+
+  const tableName = "test_table";
+
+  await engine.schema.createTable(tableName, () => {});
+
+  await db.insertIntoTable(tableName, {
+    name: "test1",
+    comment: "this is a test1",
+  });
+  await db.insertIntoTable(tableName, {
+    name: "test2",
+    comment: "this is a test2",
+  });
+  await db.insertIntoTable(tableName, {
+    name: "test3",
+    comment: "this is a test3",
+  });
+  await db.insertIntoTable(tableName, {
+    name: "test4",
+    comment: "this is a test4",
+  });
+}
+
+main();

package.json

@@ -13,8 +13,8 @@
     "type-check": "tsc -b --diagnostics",
     "release": "pnpm run build:release && changeset publish",
     "new-llamaindex": "pnpm run build:release && changeset version --ignore create-llama",
-    "new-create-llama": "pnpm run build:release && changeset version --ignore llamaindex --ignore @llamaindex/core-test",
-    "new-experimental": "pnpm run build:release && changeset version --ignore create-llama"
+    "new-create-llama": "pnpm run build:release && changeset version --ignore llamaindex",
+    "new-snapshots": "pnpm run build:release && changeset version --snapshot"
   },
   "devDependencies": {
     "@changesets/cli": "^2.27.1",

packages/core/CHANGELOG.md

@@ -1,12 +1,5 @@
 # llamaindex
 
-## 0.1.21
-
-### Patch Changes
-
-- 552a61a: Add quantized parameter to HuggingFaceEmbedding
-- d824876: Add support for Claude 3
-
 ## 0.1.20
 
 ### Patch Changes

packages/core/jsr.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/core",
-  "version": "0.1.21",
+  "version": "0.1.20",
   "exports": "./src/index.ts",
   "imports": {
     "@llamaindex/env": "jsr:@llamaindex/env@0.0.5"

packages/core/package.json

@@ -1,10 +1,10 @@
 {
   "name": "llamaindex",
-  "version": "0.1.21",
+  "version": "0.1.20",
   "license": "MIT",
   "type": "module",
   "dependencies": {
-    "@anthropic-ai/sdk": "^0.15.0",
+    "@anthropic-ai/sdk": "^0.13.0",
     "@aws-crypto/sha256-js": "^5.2.0",
     "@datastax/astra-db-ts": "^0.1.4",
     "@llamaindex/cloud": "0.0.4",
@@ -23,6 +23,7 @@
     "cohere-ai": "^7.7.5",
     "file-type": "^18.7.0",
     "js-tiktoken": "^1.0.10",
+    "knex": "^3.1.0",
     "lodash": "^4.17.21",
     "mammoth": "^1.6.0",
     "md-utils-ts": "^2.0.0",
@@ -92,9 +93,9 @@
   "scripts": {
     "lint": "eslint .",
     "build": "rm -rf ./dist && pnpm run build:esm && pnpm run build:cjs && pnpm run build:type",
-    "build:esm": "swc src -d dist --strip-leading-paths --config-file ../../.swcrc",
-    "build:cjs": "swc src -d dist/cjs --strip-leading-paths --config-file ../../.cjs.swcrc",
-    "build:type": "tsc -p tsconfig.json",
+    "build:esm": "swc src -d dist --strip-leading-paths --config-file .swcrc",
+    "build:cjs": "swc src -d dist/cjs --strip-leading-paths --config-file .cjs.swcrc",
+    "build:type": "rm -f .tsbuildinfo && tsc -b --diagnostics",
     "copy": "cp -r ../../README.md ../../LICENSE .",
     "postbuild": "pnpm run copy && node -e \"require('fs').writeFileSync('./dist/cjs/package.json', JSON.stringify({ type: 'commonjs' }))\"",
     "circular-check": "madge -c ./src/index.ts",

packages/core/src/Retriever.ts

@@ -2,16 +2,14 @@ import type { Event } from "./callbacks/CallbackManager.js";
 import type { NodeWithScore } from "./Node.js";
 import type { ServiceContext } from "./ServiceContext.js";
 
-export type RetrieveParams = {
-  query: string;
-  parentEvent?: Event;
-  preFilters?: unknown;
-};
-
 /**
  * Retrievers retrieve the nodes that most closely match our query in similarity.
  */
 export interface BaseRetriever {
-  retrieve(params: RetrieveParams): Promise<NodeWithScore[]>;
+  retrieve(
+    query: string,
+    parentEvent?: Event,
+    preFilters?: unknown,
+  ): Promise<NodeWithScore[]>;
   getServiceContext(): ServiceContext;
 }

packages/core/src/agent/openai/worker.ts

@@ -1,10 +1,10 @@
+// Assuming that the necessary interfaces and classes (like BaseTool, OpenAI, ChatMessage, CallbackManager, etc.) are defined elsewhere
+
 import { randomUUID } from "@llamaindex/env";
-import { Response } from "../../Response.js";
 import type { CallbackManager } from "../../callbacks/CallbackManager.js";
 import {
   AgentChatResponse,
   ChatResponseMode,
-  StreamingAgentChatResponse,
 } from "../../engines/chat/types.js";
 import type {
   ChatMessage,
@@ -12,7 +12,6 @@ import type {
   ChatResponseChunk,
 } from "../../llm/index.js";
 import { OpenAI } from "../../llm/index.js";
-import { streamConverter, streamReducer } from "../../llm/utils.js";
 import { ChatMemoryBuffer } from "../../memory/ChatMemoryBuffer.js";
 import type { ObjectRetriever } from "../../objects/base.js";
 import type { ToolOutput } from "../../tools/types.js";
@@ -193,40 +192,13 @@ export class OpenAIAgentWorker implements AgentWorker {
   private _processMessage(
     task: Task,
     chatResponse: ChatResponse,
-  ): AgentChatResponse {
+  ): AgentChatResponse | AsyncIterable<ChatResponseChunk> {
     const aiMessage = chatResponse.message;
     task.extraState.newMemory.put(aiMessage);
 
     return new AgentChatResponse(aiMessage.content, task.extraState.sources);
   }
 
-  private async _getStreamAiResponse(
-    task: Task,
-    llmChatKwargs: any,
-  ): Promise<StreamingAgentChatResponse> {
-    const stream = await this.llm.chat({
-      stream: true,
-      ...llmChatKwargs,
-    });
-
-    const iterator = streamConverter(
-      streamReducer({
-        stream,
-        initialValue: "",
-        reducer: (accumulator, part) => (accumulator += part.delta),
-        finished: (accumulator) => {
-          task.extraState.newMemory.put({
-            content: accumulator,
-            role: "assistant",
-          });
-        },
-      }),
-      (r: ChatResponseChunk) => new Response(r.delta),
-    );
-
-    return new StreamingAgentChatResponse(iterator, task.extraState.sources);
-  }
-
   /**
    * Get agent response.
    * @param task: task
@@ -238,7 +210,7 @@ export class OpenAIAgentWorker implements AgentWorker {
     task: Task,
     mode: ChatResponseMode,
     llmChatKwargs: any,
-  ): Promise<AgentChatResponse | StreamingAgentChatResponse> {
+  ): Promise<AgentChatResponse> {
     if (mode === ChatResponseMode.WAIT) {
       const chatResponse = (await this.llm.chat({
         stream: false,
@@ -246,11 +218,9 @@ export class OpenAIAgentWorker implements AgentWorker {
       })) as unknown as ChatResponse;
 
       return this._processMessage(task, chatResponse) as AgentChatResponse;
-    } else if (mode === ChatResponseMode.STREAM) {
-      return this._getStreamAiResponse(task, llmChatKwargs);
+    } else {
+      throw new Error("Not implemented");
     }
-
-    throw new Error("Invalid mode");
   }
 
   /**

packages/core/src/agent/runner/base.ts

@@ -4,7 +4,6 @@ import type { ChatEngineAgentParams } from "../../engines/chat/index.js";
 import {
   AgentChatResponse,
   ChatResponseMode,
-  StreamingAgentChatResponse,
 } from "../../engines/chat/index.js";
 import type { ChatMessage, LLM } from "../../llm/index.js";
 import { ChatMemoryBuffer } from "../../memory/ChatMemoryBuffer.js";
@@ -232,26 +231,23 @@ export class AgentRunner extends BaseAgentRunner {
     taskId: string,
     stepOutput: TaskStepOutput,
     kwargs?: any,
-  ): Promise<AgentChatResponse | StreamingAgentChatResponse> {
+  ): Promise<AgentChatResponse> {
     if (!stepOutput) {
       stepOutput =
         this.getCompletedSteps(taskId)[
           this.getCompletedSteps(taskId).length - 1
         ];
     }
-
     if (!stepOutput.isLast) {
       throw new Error(
         "finalizeResponse can only be called on the last step output",
       );
     }
 
-    if (!(stepOutput.output instanceof StreamingAgentChatResponse)) {
-      if (!(stepOutput.output instanceof AgentChatResponse)) {
-        throw new Error(
-          `When \`isLast\` is True, cur_step_output.output must be AGENT_CHAT_RESPONSE_TYPE: ${stepOutput.output}`,
-        );
-      }
+    if (!(stepOutput.output instanceof AgentChatResponse)) {
+      throw new Error(
+        `When \`isLast\` is True, cur_step_output.output must be AGENT_CHAT_RESPONSE_TYPE: ${stepOutput.output}`,
+      );
     }
 
     this.agentWorker.finalizeTask(this.getTask(taskId), kwargs);
@@ -266,32 +262,20 @@ export class AgentRunner extends BaseAgentRunner {
   protected async _chat({
     message,
     toolChoice,
-    stream,
-  }: ChatEngineAgentParams): Promise<AgentChatResponse>;
-  protected async _chat({
-    message,
-    toolChoice,
-    stream,
-  }: ChatEngineAgentParams & {
-    stream: true;
-  }): Promise<StreamingAgentChatResponse>;
-  protected async _chat({
-    message,
-    toolChoice,
-    stream,
-  }: ChatEngineAgentParams): Promise<
-    AgentChatResponse | StreamingAgentChatResponse
-  > {
+  }: ChatEngineAgentParams & { mode: ChatResponseMode }) {
     const task = this.createTask(message as string);
 
     let resultOutput;
 
-    const mode = stream ? ChatResponseMode.STREAM : ChatResponseMode.WAIT;
-
     while (true) {
-      const curStepOutput = await this._runStep(task.taskId, undefined, mode, {
-        toolChoice,
-      });
+      const curStepOutput = await this._runStep(
+        task.taskId,
+        undefined,
+        ChatResponseMode.WAIT,
+        {
+          toolChoice,
+        },
+      );
 
       if (curStepOutput.isLast) {
         resultOutput = curStepOutput;
@@ -315,26 +299,7 @@ export class AgentRunner extends BaseAgentRunner {
     message,
     chatHistory,
     toolChoice,
-    stream,
-  }: ChatEngineAgentParams & {
-    stream?: false;
-  }): Promise<AgentChatResponse>;
-  public async chat({
-    message,
-    chatHistory,
-    toolChoice,
-    stream,
-  }: ChatEngineAgentParams & {
-    stream: true;
-  }): Promise<StreamingAgentChatResponse>;
-  public async chat({
-    message,
-    chatHistory,
-    toolChoice,
-    stream,
-  }: ChatEngineAgentParams): Promise<
-    AgentChatResponse | StreamingAgentChatResponse
-  > {
+  }: ChatEngineAgentParams): Promise<AgentChatResponse> {
     if (!toolChoice) {
       toolChoice = this.defaultToolChoice;
     }
@@ -343,7 +308,7 @@ export class AgentRunner extends BaseAgentRunner {
       message,
       chatHistory,
       toolChoice,
-      stream,
+      mode: ChatResponseMode.WAIT,
     });
 
     return chatResponse;

packages/core/src/agent/runner/types.ts

@@ -1,7 +1,4 @@
-import type {
-  AgentChatResponse,
-  StreamingAgentChatResponse,
-} from "../../engines/chat/index.js";
+import type { AgentChatResponse } from "../../engines/chat/index.js";
 import type { Task, TaskStep, TaskStepOutput } from "../types.js";
 import { BaseAgent } from "../types.js";
 
@@ -60,7 +57,7 @@ export abstract class BaseAgentRunner extends BaseAgent {
     taskId: string,
     stepOutput: TaskStepOutput,
     kwargs?: any,
-  ): Promise<AgentChatResponse | StreamingAgentChatResponse>;
+  ): Promise<AgentChatResponse>;
 
   abstract undoStep(taskId: string): void;
 }

packages/core/src/agent/types.ts

@@ -1,9 +1,7 @@
 import type {
   AgentChatResponse,
   ChatEngineAgentParams,
-  StreamingAgentChatResponse,
 } from "../engines/chat/index.js";
-
 import type { QueryEngineParamsNonStreaming } from "../types.js";
 
 export interface AgentWorker {
@@ -14,15 +12,11 @@ export interface AgentWorker {
 }
 
 interface BaseChatEngine {
-  chat(
-    params: ChatEngineAgentParams,
-  ): Promise<AgentChatResponse | StreamingAgentChatResponse>;
+  chat(params: ChatEngineAgentParams): Promise<AgentChatResponse>;
 }
 
 interface BaseQueryEngine {
-  query(
-    params: QueryEngineParamsNonStreaming,
-  ): Promise<AgentChatResponse | StreamingAgentChatResponse>;
+  query(params: QueryEngineParamsNonStreaming): Promise<AgentChatResponse>;
 }
 
 /**
@@ -37,10 +31,7 @@ export abstract class BaseAgent implements BaseChatEngine, BaseQueryEngine {
     return [];
   }
 
-  abstract chat(
-    params: ChatEngineAgentParams,
-  ): Promise<AgentChatResponse | StreamingAgentChatResponse>;
-
+  abstract chat(params: ChatEngineAgentParams): Promise<AgentChatResponse>;
   abstract reset(): void;
 
   /**
@@ -50,7 +41,7 @@ export abstract class BaseAgent implements BaseChatEngine, BaseQueryEngine {
    */
   async query(
     params: QueryEngineParamsNonStreaming,
-  ): Promise<AgentChatResponse | StreamingAgentChatResponse> {
+  ): Promise<AgentChatResponse> {
     // Handle non-streaming query
     const agentResponse = await this.chat({
       message: params.query,

packages/core/src/cloud/LlamaCloudIndex.ts

@@ -3,7 +3,7 @@ import { RetrieverQueryEngine } from "../engines/query/RetrieverQueryEngine.js";
 import type { BaseNodePostprocessor } from "../postprocessors/types.js";
 import type { BaseSynthesizer } from "../synthesizers/types.js";
 import type { BaseQueryEngine } from "../types.js";
-import type { CloudRetrieveParams } from "./LlamaCloudRetriever.js";
+import type { RetrieveParams } from "./LlamaCloudRetriever.js";
 import { LlamaCloudRetriever } from "./LlamaCloudRetriever.js";
 import type { CloudConstructorParams } from "./types.js";
 
@@ -14,7 +14,7 @@ export class LlamaCloudIndex {
     this.params = params;
   }
 
-  asRetriever(params: CloudRetrieveParams = {}): BaseRetriever {
+  asRetriever(params: RetrieveParams = {}): BaseRetriever {
     return new LlamaCloudRetriever({ ...this.params, ...params });
   }
 
@@ -23,7 +23,7 @@ export class LlamaCloudIndex {
       responseSynthesizer?: BaseSynthesizer;
       preFilters?: unknown;
       nodePostprocessors?: BaseNodePostprocessor[];
-    } & CloudRetrieveParams,
+    } & RetrieveParams,
   ): BaseQueryEngine {
     const retriever = new LlamaCloudRetriever({
       ...this.params,

packages/core/src/cloud/LlamaCloudRetriever.ts

@@ -2,14 +2,15 @@ import type { PlatformApi, PlatformApiClient } from "@llamaindex/cloud";
 import { globalsHelper } from "../GlobalsHelper.js";
 import type { NodeWithScore } from "../Node.js";
 import { ObjectType, jsonToNode } from "../Node.js";
-import type { BaseRetriever, RetrieveParams } from "../Retriever.js";
+import type { BaseRetriever } from "../Retriever.js";
 import type { ServiceContext } from "../ServiceContext.js";
 import { serviceContextFromDefaults } from "../ServiceContext.js";
+import type { Event } from "../callbacks/CallbackManager.js";
 import type { ClientParams, CloudConstructorParams } from "./types.js";
 import { DEFAULT_PROJECT_NAME } from "./types.js";
 import { getClient } from "./utils.js";
 
-export type CloudRetrieveParams = Omit<
+export type RetrieveParams = Omit<
   PlatformApi.RetrievalParams,
   "query" | "searchFilters" | "pipelineId" | "className"
 > & { similarityTopK?: number };
@@ -17,7 +18,7 @@ export type CloudRetrieveParams = Omit<
 export class LlamaCloudRetriever implements BaseRetriever {
   client?: PlatformApiClient;
   clientParams: ClientParams;
-  retrieveParams: CloudRetrieveParams;
+  retrieveParams: RetrieveParams;
   projectName: string = DEFAULT_PROJECT_NAME;
   pipelineName: string;
   serviceContext: ServiceContext;
@@ -34,7 +35,7 @@ export class LlamaCloudRetriever implements BaseRetriever {
     });
   }
 
-  constructor(params: CloudConstructorParams & CloudRetrieveParams) {
+  constructor(params: CloudConstructorParams & RetrieveParams) {
     this.clientParams = { apiKey: params.apiKey, baseUrl: params.baseUrl };
     if (params.similarityTopK) {
       params.denseSimilarityTopK = params.similarityTopK;
@@ -54,11 +55,11 @@ export class LlamaCloudRetriever implements BaseRetriever {
     return this.client;
   }
 
-  async retrieve({
-    query,
-    parentEvent,
-    preFilters,
-  }: RetrieveParams): Promise<NodeWithScore[]> {
+  async retrieve(
+    query: string,
+    parentEvent?: Event | undefined,
+    preFilters?: unknown,
+  ): Promise<NodeWithScore[]> {
     const pipelines = await (
       await this.getClient()
     ).pipeline.searchPipelines({

packages/core/src/embeddings/HuggingFaceEmbedding.ts

@@ -20,7 +20,6 @@ export enum HuggingFaceEmbeddingModelType {
  */
 export class HuggingFaceEmbedding extends BaseEmbedding {
   modelType: string = HuggingFaceEmbeddingModelType.XENOVA_ALL_MINILM_L6_V2;
-  quantized: boolean = true;
 
   private extractor: any;
 
@@ -32,9 +31,7 @@ export class HuggingFaceEmbedding extends BaseEmbedding {
   async getExtractor() {
     if (!this.extractor) {
       const { pipeline } = await import("@xenova/transformers");
-      this.extractor = await pipeline("feature-extraction", this.modelType, {
-        quantized: this.quantized,
-      });
+      this.extractor = await pipeline("feature-extraction", this.modelType);
     }
     return this.extractor;
   }

packages/core/src/engines/chat/DefaultContextGenerator.ts

@@ -64,10 +64,10 @@ export class DefaultContextGenerator
         tags: ["final"],
       };
     }
-    const sourceNodesWithScore = await this.retriever.retrieve({
-      query: message,
+    const sourceNodesWithScore = await this.retriever.retrieve(
+      message,
       parentEvent,
-    });
+    );
 
     const nodes = await this.applyNodePostprocessors(
       sourceNodesWithScore,

packages/core/src/engines/chat/types.ts

@@ -27,7 +27,6 @@ export interface ChatEngineParamsNonStreaming extends ChatEngineParamsBase {
 
 export interface ChatEngineAgentParams extends ChatEngineParamsBase {
   toolChoice?: string | Record<string, any>;
-  stream?: boolean;
 }
 
 /**
@@ -87,20 +86,3 @@ export class AgentChatResponse {
     return this.response ?? "";
   }
 }
-
-export class StreamingAgentChatResponse {
-  response: AsyncIterable<Response>;
-
-  sources: ToolOutput[];
-  sourceNodes?: BaseNode[];
-
-  constructor(
-    response: AsyncIterable<Response>,
-    sources?: ToolOutput[],
-    sourceNodes?: BaseNode[],
-  ) {
-    this.response = response;
-    this.sources = sources ?? [];
-    this.sourceNodes = sourceNodes ?? [];
-  }
-}

packages/core/src/engines/query/RetrieverQueryEngine.ts

@@ -63,11 +63,11 @@ export class RetrieverQueryEngine
   }
 
   private async retrieve(query: string, parentEvent: Event) {
-    const nodes = await this.retriever.retrieve({
+    const nodes = await this.retriever.retrieve(
       query,
       parentEvent,
-      preFilters: this.preFilters,
-    });
+      this.preFilters,
+    );
 
     return await this.applyNodePostprocessors(nodes, query);
   }

packages/core/src/engines/query/index.ts

@@ -1,3 +1,4 @@
 export * from "./RetrieverQueryEngine.js";
 export * from "./RouterQueryEngine.js";
 export * from "./SubQuestionQueryEngine.js";
+export * from "./sql/index.js";

packages/core/src/engines/query/sql/NLSQLQueryEngine.ts

@@ -0,0 +1,59 @@
+import {
+  NLSQLRetriever,
+  type SQLDatabase,
+  type ServiceContext,
+} from "../../../index.js";
+import type { TextToSQLPrompt } from "../../../retriever/sql/prompts.js";
+import { BaseSQLTableQueryEngine } from "./types.js";
+
+type NLSQLQueryEngineParams = {
+  sqlDatabase: SQLDatabase;
+  textToSQLPrompt?: TextToSQLPrompt;
+  contextQueryKwargs?: any | null;
+  synthesizeResponse?: boolean;
+  responseSynthesisPrompt?: any | null;
+  tables?: any[] | string[] | undefined;
+  serviceContext?: ServiceContext | undefined;
+  contextStrPrefix?: string | undefined;
+  sqlOnly?: boolean;
+  verbose?: boolean;
+};
+
+export class NLSQLQueryEngine extends BaseSQLTableQueryEngine {
+  _sqlRetriever: NLSQLRetriever;
+
+  constructor({
+    sqlDatabase,
+    textToSQLPrompt,
+    contextQueryKwargs = null,
+    synthesizeResponse = true,
+    responseSynthesisPrompt = null,
+    tables,
+    serviceContext,
+    contextStrPrefix,
+    sqlOnly = false,
+    verbose = false,
+  }: NLSQLQueryEngineParams) {
+    super({
+      synthesizeResponse,
+      responseSynthesisPrompt,
+      serviceContext,
+      verbose,
+    });
+
+    this._sqlRetriever = new NLSQLRetriever({
+      sqlDatabase,
+      textToSQLPrompt,
+      contextQueryKwargs,
+      tables,
+      contextStrPrefix,
+      serviceContext,
+      sqlOnly,
+      verbose,
+    });
+  }
+
+  get sqlRetriever(): NLSQLRetriever {
+    return this._sqlRetriever;
+  }
+}

packages/core/src/engines/query/sql/index.ts

@@ -0,0 +1 @@
+export * from "./NLSQLQueryEngine.js";

packages/core/src/engines/query/sql/prompts.ts

@@ -0,0 +1,17 @@
+export const defaultResponseSynthesisPrompt = ({
+  query,
+  context,
+  sqlQuery,
+}: {
+  query?: string;
+  context?: string;
+  sqlQuery: string;
+}) => `
+Given an input question, synthesize a response from the query results.
+Query: ${query}
+SQL: ${sqlQuery}
+SQL Response: ${context}
+Response: 
+`;
+
+export type ResponseSynthesisPrompt = typeof defaultResponseSynthesisPrompt;

packages/core/src/engines/query/sql/types.ts

@@ -0,0 +1,117 @@
+import { Response } from "../../../Response.js";
+import {
+  serviceContextFromDefaults,
+  type ServiceContext,
+} from "../../../ServiceContext.js";
+import {
+  CompactAndRefine,
+  MetadataMode,
+  ResponseSynthesizer,
+} from "../../../index.js";
+import type { SQLRetriever } from "../../../retriever/sql/types.js";
+import type {
+  BaseQueryEngine,
+  QueryEngineParamsNonStreaming,
+  QueryEngineParamsStreaming,
+} from "../../../types.js";
+import {
+  defaultResponseSynthesisPrompt,
+  type ResponseSynthesisPrompt,
+} from "./prompts.js";
+
+export abstract class BaseSQLTableQueryEngine implements BaseQueryEngine {
+  synthesizeResponse: boolean;
+  responseSynthesisPrompt: ResponseSynthesisPrompt;
+  serviceContext: ServiceContext;
+  verbose: boolean;
+
+  constructor(init: {
+    synthesizeResponse?: boolean;
+    responseSynthesisPrompt?: ResponseSynthesisPrompt;
+    serviceContext?: ServiceContext;
+    verbose?: boolean;
+  }) {
+    this.synthesizeResponse = init.synthesizeResponse ?? true;
+    this.responseSynthesisPrompt =
+      init.responseSynthesisPrompt || defaultResponseSynthesisPrompt;
+    this.serviceContext = init.serviceContext || serviceContextFromDefaults({});
+    this.verbose = init.verbose || false;
+  }
+
+  getPrompts(): {
+    responseSynthesisPrompt: ResponseSynthesisPrompt;
+  } {
+    return { responseSynthesisPrompt: this.responseSynthesisPrompt };
+  }
+
+  updatePrompts(prompts: {
+    responseSynthesisPrompt: ResponseSynthesisPrompt;
+  }): void {
+    if ("responseSynthesisPrompt" in prompts) {
+      this.responseSynthesisPrompt = prompts.responseSynthesisPrompt;
+    }
+  }
+
+  getPromptModules(): {
+    sqlRetriever: SQLRetriever;
+  } {
+    return { sqlRetriever: this.sqlRetriever };
+  }
+
+  abstract get sqlRetriever(): SQLRetriever;
+
+  query(params: QueryEngineParamsStreaming): Promise<AsyncIterable<Response>>;
+  query(params: QueryEngineParamsNonStreaming): Promise<Response>;
+  async query(
+    params: QueryEngineParamsStreaming | QueryEngineParamsNonStreaming,
+  ): Promise<Response | AsyncIterable<Response>> {
+    const { query, stream } = params;
+
+    if (stream) {
+      throw new Error("Streaming is not supported");
+    }
+
+    const [retrievedNodes, metadata] =
+      await this.sqlRetriever.retrieveWithMetadata({
+        queryStr: query,
+      });
+
+    const sqlQueryStr = metadata.sqlQuery;
+
+    console.log(`> SQL query: ${sqlQueryStr}`); // TODO: Remove
+
+    console.log(`> Sythesize Response ${this.synthesizeResponse}`);
+
+    if (this.synthesizeResponse) {
+      const responseBuilder = new CompactAndRefine(
+        this.serviceContext,
+        ({ query, context }) =>
+          this.responseSynthesisPrompt({
+            query,
+            context,
+            sqlQuery: sqlQueryStr,
+          }),
+      );
+
+      const responseSynthesizer = new ResponseSynthesizer({
+        serviceContext: this.serviceContext,
+        responseBuilder,
+      });
+
+      const response = await responseSynthesizer.synthesize({
+        query,
+        nodesWithScore: retrievedNodes,
+      });
+
+      response.metadata.sqlQuery = sqlQueryStr;
+
+      return response;
+    }
+
+    const responseStr = retrievedNodes
+      .map((node) => node.node.getContent(MetadataMode.ALL))
+      .join("\n");
+
+    return new Response(responseStr, []);
+  }
+}

packages/core/src/index.ts

@@ -26,8 +26,9 @@ export * from "./objects/index.js";
 export * from "./postprocessors/index.js";
 export * from "./prompts/index.js";
 export * from "./readers/index.js";
+export * from "./retriever/index.js";
 export * from "./selectors/index.js";
 export * from "./storage/index.js";
 export * from "./synthesizers/index.js";
 export * from "./tools/index.js";
-export * from "./types.js";
+export * from "./utilities/index.js";

packages/core/src/indices/index.ts

@@ -1,6 +1,4 @@
 export * from "./BaseIndex.js";
-export * from "./IndexStruct.js";
-export * from "./json-to-index-struct.js";
 export * from "./keyword/index.js";
 export * from "./summary/index.js";
 export * from "./vectorStore/index.js";

packages/core/src/indices/json-to-index-struct.ts

@@ -24,15 +24,9 @@ export class IndexDict extends IndexStruct {
   }
 
   toJson(): Record<string, unknown> {
-    const nodesDict: Record<string, unknown> = {};
-
-    for (const [key, node] of Object.entries(this.nodesDict)) {
-      nodesDict[key] = node.toJSON();
-    }
-
     return {
       ...super.toJson(),
-      nodesDict,
+      nodesDict: this.nodesDict,
       type: this.type,
     };
   }

packages/core/src/indices/keyword/index.ts

@@ -8,7 +8,7 @@ import {
   defaultKeywordExtractPrompt,
   defaultQueryKeywordExtractPrompt,
 } from "../../Prompt.js";
-import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
+import type { BaseRetriever } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
 import { serviceContextFromDefaults } from "../../ServiceContext.js";
 import { RetrieverQueryEngine } from "../../engines/query/index.js";
@@ -79,7 +79,7 @@ abstract class BaseKeywordTableRetriever implements BaseRetriever {
 
   abstract getKeywords(query: string): Promise<string[]>;
 
-  async retrieve({ query }: RetrieveParams): Promise<NodeWithScore[]> {
+  async retrieve(query: string): Promise<NodeWithScore[]> {
     const keywords = await this.getKeywords(query);
     const chunkIndicesCount: { [key: string]: number } = {};
     const filteredKeywords = keywords.filter((keyword) =>

packages/core/src/indices/summary/index.ts

@@ -3,9 +3,10 @@ import { globalsHelper } from "../../GlobalsHelper.js";
 import type { BaseNode, Document, NodeWithScore } from "../../Node.js";
 import type { ChoiceSelectPrompt } from "../../Prompt.js";
 import { defaultChoiceSelectPrompt } from "../../Prompt.js";
-import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
+import type { BaseRetriever } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
 import { serviceContextFromDefaults } from "../../ServiceContext.js";
+import type { Event } from "../../callbacks/CallbackManager.js";
 import { RetrieverQueryEngine } from "../../engines/query/index.js";
 import type { BaseNodePostprocessor } from "../../postprocessors/index.js";
 import type {
@@ -280,10 +281,7 @@ export class SummaryIndexRetriever implements BaseRetriever {
     this.index = index;
   }
 
-  async retrieve({
-    query,
-    parentEvent,
-  }: RetrieveParams): Promise<NodeWithScore[]> {
+  async retrieve(query: string, parentEvent?: Event): Promise<NodeWithScore[]> {
     const nodeIds = this.index.indexStruct.nodes;
     const nodes = await this.index.docStore.getNodes(nodeIds);
     const result = nodes.map((node) => ({
@@ -339,10 +337,7 @@ export class SummaryIndexLLMRetriever implements BaseRetriever {
     this.serviceContext = serviceContext || index.serviceContext;
   }
 
-  async retrieve({
-    query,
-    parentEvent,
-  }: RetrieveParams): Promise<NodeWithScore[]> {
+  async retrieve(query: string, parentEvent?: Event): Promise<NodeWithScore[]> {
     const nodeIds = this.index.indexStruct.nodes;
     const results: NodeWithScore[] = [];
 

packages/core/src/indices/vectorStore/index.ts

@@ -11,7 +11,7 @@ import {
   ObjectType,
   splitNodesByType,
 } from "../../Node.js";
-import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
+import type { BaseRetriever } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
 import { serviceContextFromDefaults } from "../../ServiceContext.js";
 import type { Event } from "../../callbacks/CallbackManager.js";
@@ -426,17 +426,14 @@ export class VectorIndexRetriever implements BaseRetriever {
     this.imageSimilarityTopK = imageSimilarityTopK ?? DEFAULT_SIMILARITY_TOP_K;
   }
 
-  async retrieve({
-    query,
-    parentEvent,
-    preFilters,
-  }: RetrieveParams): Promise<NodeWithScore[]> {
-    let nodesWithScores = await this.textRetrieve(
-      query,
-      preFilters as MetadataFilters,
-    );
+  async retrieve(
+    query: string,
+    parentEvent?: Event,
+    preFilters?: MetadataFilters,
+  ): Promise<NodeWithScore[]> {
+    let nodesWithScores = await this.textRetrieve(query, preFilters);
     nodesWithScores = nodesWithScores.concat(
-      await this.textToImageRetrieve(query, preFilters as MetadataFilters),
+      await this.textToImageRetrieve(query, preFilters),
     );
     this.sendEvent(query, nodesWithScores, parentEvent);
     return nodesWithScores;

packages/core/src/llm/LLM.ts

@@ -1,6 +1,7 @@
 import type OpenAILLM from "openai";
 import type { ClientOptions as OpenAIClientOptions } from "openai";
 import type {
+  AnthropicStreamToken,
   CallbackManager,
   Event,
   EventType,
@@ -12,7 +13,11 @@ import type { ChatCompletionMessageParam } from "openai/resources/index.js";
 import type { LLMOptions } from "portkey-ai";
 import { Tokenizers, globalsHelper } from "../GlobalsHelper.js";
 import type { AnthropicSession } from "./anthropic.js";
-import { getAnthropicSession } from "./anthropic.js";
+import {
+  ANTHROPIC_AI_PROMPT,
+  ANTHROPIC_HUMAN_PROMPT,
+  getAnthropicSession,
+} from "./anthropic.js";
 import type { AzureOpenAIConfig } from "./azure.js";
 import {
   getAzureBaseUrl,
@@ -608,30 +613,12 @@ If a question does not make any sense, or is not factually coherent, explain why
   }
 }
 
-export const ALL_AVAILABLE_ANTHROPIC_LEGACY_MODELS = {
-  "claude-2.1": {
-    contextWindow: 200000,
-  },
-  "claude-instant-1.2": {
-    contextWindow: 100000,
-  },
-};
-
-export const ALL_AVAILABLE_V3_MODELS = {
-  "claude-3-opus": { contextWindow: 200000 },
-  "claude-3-sonnet": { contextWindow: 200000 },
-};
-
 export const ALL_AVAILABLE_ANTHROPIC_MODELS = {
-  ...ALL_AVAILABLE_ANTHROPIC_LEGACY_MODELS,
-  ...ALL_AVAILABLE_V3_MODELS,
+  // both models have 100k context window, see https://docs.anthropic.com/claude/reference/selecting-a-model
+  "claude-2": { contextWindow: 200000 },
+  "claude-instant-1": { contextWindow: 100000 },
 };
 
-const AVAILABLE_ANTHROPIC_MODELS_WITHOUT_DATE: { [key: string]: string } = {
-  "claude-3-opus": "claude-3-opus-20240229",
-  "claude-3-sonnet": "claude-3-sonnet-20240229",
-} as { [key in keyof typeof ALL_AVAILABLE_ANTHROPIC_MODELS]: string };
-
 /**
  * Anthropic LLM implementation
  */
@@ -653,7 +640,7 @@ export class Anthropic extends BaseLLM {
 
   constructor(init?: Partial<Anthropic>) {
     super();
-    this.model = init?.model ?? "claude-3-opus";
+    this.model = init?.model ?? "claude-2";
     this.temperature = init?.temperature ?? 0.1;
     this.topP = init?.topP ?? 0.999; // Per Ben Mann
     this.maxTokens = init?.maxTokens ?? undefined;
@@ -687,24 +674,21 @@ export class Anthropic extends BaseLLM {
     };
   }
 
-  getModelName = (model: string): string => {
-    if (Object.keys(AVAILABLE_ANTHROPIC_MODELS_WITHOUT_DATE).includes(model)) {
-      return AVAILABLE_ANTHROPIC_MODELS_WITHOUT_DATE[model];
-    }
-    return model;
-  };
-
-  formatMessages(messages: ChatMessage[]) {
-    return messages.map((message) => {
-      if (message.role !== "user" && message.role !== "assistant") {
-        throw new Error("Unsupported Anthropic role");
-      }
-
-      return {
-        content: message.content,
-        role: message.role,
-      };
-    });
+  mapMessagesToPrompt(messages: ChatMessage[]) {
+    return (
+      messages.reduce((acc, message) => {
+        return (
+          acc +
+          `${
+            message.role === "system"
+              ? ""
+              : message.role === "assistant"
+                ? ANTHROPIC_AI_PROMPT + " "
+                : ANTHROPIC_HUMAN_PROMPT + " "
+          }${message.content.trim()}`
+        );
+      }, "") + ANTHROPIC_AI_PROMPT
+    );
   }
 
   chat(
@@ -714,67 +698,49 @@ export class Anthropic extends BaseLLM {
   async chat(
     params: LLMChatParamsNonStreaming | LLMChatParamsStreaming,
   ): Promise<ChatResponse | AsyncIterable<ChatResponseChunk>> {
-    let { messages } = params;
-
-    const { parentEvent, stream } = params;
-
-    let systemPrompt: string | null = null;
-
-    const systemMessages = messages.filter(
-      (message) => message.role === "system",
-    );
-
-    if (systemMessages.length > 0) {
-      systemPrompt = systemMessages
-        .map((message) => message.content)
-        .join("\n");
-      messages = messages.filter((message) => message.role !== "system");
-    }
-
+    const { messages, parentEvent, stream } = params;
     //Streaming
     if (stream) {
-      return this.streamChat(messages, parentEvent, systemPrompt);
+      return this.streamChat(messages, parentEvent);
     }
 
     //Non-streaming
-    const response = await this.session.anthropic.messages.create({
-      model: this.getModelName(this.model),
-      messages: this.formatMessages(messages),
-      max_tokens: this.maxTokens ?? 4096,
+    const response = await this.session.anthropic.completions.create({
+      model: this.model,
+      prompt: this.mapMessagesToPrompt(messages),
+      max_tokens_to_sample: this.maxTokens ?? 100000,
       temperature: this.temperature,
       top_p: this.topP,
-      ...(systemPrompt && { system: systemPrompt }),
     });
 
     return {
-      message: { content: response.content[0].text, role: "assistant" },
+      message: { content: response.completion.trimStart(), role: "assistant" },
+      //^ We're trimming the start because Anthropic often starts with a space in the response
+      // That space will be re-added when we generate the next prompt.
     };
   }
 
   protected async *streamChat(
     messages: ChatMessage[],
     parentEvent?: Event | undefined,
-    systemPrompt?: string | null,
   ): AsyncIterable<ChatResponseChunk> {
-    const stream = await this.session.anthropic.messages.create({
-      model: this.getModelName(this.model),
-      messages: this.formatMessages(messages),
-      max_tokens: this.maxTokens ?? 4096,
-      temperature: this.temperature,
-      top_p: this.topP,
-      stream: true,
-      ...(systemPrompt && { system: systemPrompt }),
-    });
+    // AsyncIterable<AnthropicStreamToken>
+    const stream: AsyncIterable<AnthropicStreamToken> =
+      await this.session.anthropic.completions.create({
+        model: this.model,
+        prompt: this.mapMessagesToPrompt(messages),
+        max_tokens_to_sample: this.maxTokens ?? 100000,
+        temperature: this.temperature,
+        top_p: this.topP,
+        stream: true,
+      });
 
     let idx_counter: number = 0;
     for await (const part of stream) {
-      const content =
-        part.type === "content_block_delta" ? part.delta.text : null;
-
-      if (typeof content !== "string") continue;
+      //TODO: LLM Stream Callback, pending re-work.
 
       idx_counter++;
-      yield { delta: content };
+      yield { delta: part.completion };
     }
     return;
   }

packages/core/src/objects/base.ts

@@ -69,7 +69,7 @@ export class ObjectRetriever {
 
   // Translating the retrieve method
   async retrieve(strOrQueryBundle: QueryType): Promise<any> {
-    const nodes = await this.retriever.retrieve({ query: strOrQueryBundle });
+    const nodes = await this.retriever.retrieve(strOrQueryBundle);
     const objs = nodes.map((n) => this._objectNodeMapping.fromNode(n.node));
     return objs;
   }

packages/core/src/retriever/index.ts

@@ -0,0 +1 @@
+export * from "./sql/index.js";

packages/core/src/retriever/sql/NLSQLRetriever.ts

@@ -0,0 +1,259 @@
+import { serviceContextFromDefaults } from "../../ServiceContext.js";
+import {
+  TextNode,
+  type BaseRetriever,
+  type CallbackManager,
+  type LLM,
+  type NodeWithScore,
+  type ObjectRetriever,
+  type SQLDatabase,
+  type ServiceContext,
+} from "../../index.js";
+import { QueryBundle } from "../../types.js";
+import { defaultTextToSQLPrompt, type TextToSQLPrompt } from "./prompts.js";
+import {
+  DefaultSQLParser,
+  SQLParserMode,
+  SQLRetriever,
+  type SQLTableSchema,
+} from "./types.js";
+
+export class NLSQLRetriever extends SQLRetriever implements BaseRetriever {
+  sqlDatabase: SQLDatabase;
+  sqlRetriever: SQLRetriever;
+  sqlParser: DefaultSQLParser;
+  textToSQLPrompt: TextToSQLPrompt;
+  contextQueryKwargs: Record<string, any> | undefined;
+  tables: any[] | string[] | undefined;
+  tableRetriever: ObjectRetriever | undefined;
+  contextStrPrefix: string | undefined;
+  sqlParserMode: SQLParserMode;
+  llm: LLM;
+  serviceContext: ServiceContext;
+  returnRaw: boolean;
+  handleSQLErrors: boolean;
+  sqlOnly: boolean;
+  callbackManager: CallbackManager | undefined;
+  verbose: boolean;
+  getTables: any;
+
+  constructor({
+    sqlDatabase,
+    textToSQLPrompt,
+    contextQueryKwargs,
+    tables,
+    tableRetriever,
+    contextStrPrefix,
+    sqlParserMode,
+    llm,
+    serviceContext,
+    returnRaw,
+    handleSQLErrors,
+    sqlOnly,
+    callbackManager,
+    verbose,
+  }: {
+    sqlDatabase: SQLDatabase;
+    textToSQLPrompt?: TextToSQLPrompt;
+    contextQueryKwargs?: Record<string, any>;
+    tables?: any[] | string[];
+    tableRetriever?: ObjectRetriever;
+    contextStrPrefix?: string;
+    sqlParserMode?: SQLParserMode;
+    llm?: LLM;
+    serviceContext?: ServiceContext;
+    returnRaw?: boolean;
+    handleSQLErrors?: boolean;
+    sqlOnly?: boolean;
+    callbackManager?: CallbackManager;
+    verbose?: boolean;
+  }) {
+    super(sqlDatabase, returnRaw, callbackManager);
+
+    this.sqlRetriever = new SQLRetriever(sqlDatabase, returnRaw);
+    this.sqlDatabase = sqlDatabase;
+    this.getTables = this.loadGetTablesFn(
+      sqlDatabase,
+      tables,
+      contextQueryKwargs,
+      tableRetriever,
+    );
+    this.contextStrPrefix = contextStrPrefix;
+    this.serviceContext = serviceContext ?? serviceContextFromDefaults();
+    this.textToSQLPrompt = textToSQLPrompt ?? defaultTextToSQLPrompt;
+    this.sqlParserMode = sqlParserMode ?? SQLParserMode.DEFAULT;
+    this.sqlParser = this.loadSQLParser(
+      this.sqlParserMode,
+      this.serviceContext,
+    );
+    this.handleSQLErrors = handleSQLErrors ?? true;
+    this.sqlOnly = sqlOnly ?? false;
+    this.verbose = verbose ?? false;
+    this.returnRaw = returnRaw ?? false;
+    this.llm = llm ?? this.serviceContext.llm;
+  }
+
+  _getPrompts() {
+    return {
+      textToSQLPrompt: this.textToSQLPrompt,
+    };
+  }
+
+  _updatePrompts(prompts: Record<string, any>) {
+    if ("textToSQLPrompt" in prompts) {
+      this.textToSQLPrompt = prompts.textToSQLPrompt;
+    }
+  }
+
+  _getPromptModules() {
+    return {};
+  }
+
+  getServiceContext(): ServiceContext {
+    return this.serviceContext;
+  }
+
+  loadSQLParser(sqlParserMode: SQLParserMode, serviceContext: ServiceContext) {
+    if (sqlParserMode === SQLParserMode.DEFAULT) {
+      return new DefaultSQLParser();
+    } else {
+      throw new Error(`Unknown SQL parser mode: ${sqlParserMode}`);
+    }
+  }
+
+  loadGetTablesFn(
+    sqlDatabase: SQLDatabase,
+    tables: any[] | string[] | undefined,
+    contextQueryKwargs: Record<string, any> | undefined,
+    tableRetriever: ObjectRetriever | undefined,
+  ) {
+    contextQueryKwargs = contextQueryKwargs || {};
+
+    if (tableRetriever) {
+      return async (queryStr: string) =>
+        await tableRetriever.retrieve(queryStr);
+    } else {
+      let tableNames: SQLTableSchema[] | string[];
+
+      if (tables) {
+        tableNames = tables.map((t) => t);
+      } else {
+        tableNames = Array.from(sqlDatabase.usableTableNames);
+      }
+
+      const contextStrs: string[] = [];
+
+      const tableSchemas = tableNames.map((t, i) => {
+        if (typeof t === "string") {
+          return {
+            tableName: t,
+            ...(contextQueryKwargs
+              ? { contextStr: contextQueryKwargs[t] }
+              : {}),
+          };
+        }
+
+        return {
+          tableName: t.tableName,
+          ...(contextQueryKwargs
+            ? { contextStr: contextQueryKwargs[t.tableName] }
+            : {}),
+        };
+      });
+
+      return () => tableSchemas;
+    }
+  }
+
+  async retrieveWithMetadata(strOrQueryBundle: string | QueryBundle): Promise<
+    [
+      NodeWithScore[],
+      {
+        sqlQuery: string;
+      },
+    ]
+  > {
+    const queryBundle =
+      typeof strOrQueryBundle === "string"
+        ? { queryStr: strOrQueryBundle }
+        : strOrQueryBundle;
+
+    const tableDescStr = await this.getTableContext(queryBundle);
+
+    if (this.verbose) {
+      console.log(`> Table desc str: ${tableDescStr}`);
+    }
+
+    const response = await this.serviceContext?.llm?.complete({
+      prompt: this.textToSQLPrompt({
+        dialect: "sql",
+        schema: tableDescStr,
+        queryStr: queryBundle.queryStr,
+      }),
+    });
+
+    if (!response) {
+      throw new Error("No response from LLM");
+    }
+
+    const sqlQueryStr = this.sqlParser.parseResponseToSQL(
+      response?.text,
+      queryBundle,
+    );
+
+    if (this.verbose) {
+      console.log(`> Predicted SQL query: ${sqlQueryStr}`);
+    }
+
+    let retrievedNodes: NodeWithScore[];
+    let metadata: Record<string, unknown> = {};
+
+    if (this.sqlOnly) {
+      const sqlOnlyNode = new TextNode({ text: sqlQueryStr });
+      retrievedNodes = [{ node: sqlOnlyNode }];
+      metadata = {};
+    } else {
+      try {
+        const retrieverResponse = await this.sqlRetriever.retrieveWithMetadata({
+          queryStr: sqlQueryStr,
+        });
+
+        retrievedNodes = retrieverResponse[0];
+        metadata = retrieverResponse[1];
+      } catch (e) {
+        if (this.handleSQLErrors) {
+          const errNode = new TextNode({ text: `Error: ${e}` });
+          retrievedNodes = [{ node: errNode }];
+          metadata = {};
+        } else {
+          throw e;
+        }
+      }
+    }
+    return [retrievedNodes, { sqlQuery: sqlQueryStr, ...metadata }];
+  }
+
+  async retrieve(query: string): Promise<NodeWithScore[]> {
+    const [retrievedNodes] = await this.retrieveWithMetadata(query);
+    return retrievedNodes;
+  }
+
+  async getTableContext(queryBundle: QueryBundle) {
+    const tableSchemaObjs = this.getTables(queryBundle.queryStr);
+    const contextStrs = [];
+    if (this.contextStrPrefix) {
+      contextStrs.push(this.contextStrPrefix);
+    }
+    for (const tableSchemaObj of tableSchemaObjs) {
+      let tableInfo = await this.sqlDatabase.getSingleTableInfo(
+        tableSchemaObj.tableName,
+      );
+      if (tableSchemaObj.contextStr) {
+        const tableOptContext = `The table description is: ${tableSchemaObj.contextStr}`;
+        tableInfo += tableOptContext;
+      }
+      contextStrs.push(tableInfo);
+    }
+    return contextStrs.join("\n\n");
+  }
+}

packages/core/src/retriever/sql/index.ts

@@ -0,0 +1 @@
+export * from "./NLSQLRetriever.js";

packages/core/src/retriever/sql/prompts.ts

@@ -0,0 +1,31 @@
+export const defaultTextToSQLPrompt = ({
+  dialect,
+  schema,
+  queryStr,
+}: {
+  dialect: string;
+  schema: string;
+  queryStr: string;
+}) => `Given an input question, first create a syntactically correct ${dialect}
+query to run, then look at the results of the query and return the answer.
+You can order the results by a relevant column to return the most
+interesting examples in the database.
+Never query for all the columns from a specific table, only ask for a
+few relevant columns given the question.
+Pay attention to use only the column names that you can see in the schema
+description.
+Be careful to not query for columns that do not exist.
+Pay attention to which column is in which table.
+Also, qualify column names with the table name when needed.
+You are required to use the following format, each taking one line:
+Question: Question here
+SQLQuery: SQL Query to run
+SQLResult: Result of the SQLQuery
+Answer: Final answer here
+Only use tables listed below.
+${schema}
+Question: ${queryStr}
+SQLQuery:
+`;
+
+export type TextToSQLPrompt = typeof defaultTextToSQLPrompt;

packages/core/src/retriever/sql/types.ts

@@ -0,0 +1,105 @@
+import type { BaseRetriever } from "../../Retriever.js";
+import {
+  TextNode,
+  type CallbackManager,
+  type Event,
+  type NodeWithScore,
+  type SQLDatabase,
+  type ServiceContext,
+} from "../../index.js";
+import type { QueryBundle } from "../../types.js";
+
+export interface SQLTableSchema {
+  tableName: string;
+  contextStr: string;
+}
+
+export enum SQLParserMode {
+  DEFAULT = "default",
+  PGVECTOR = "pgvector",
+}
+
+// export type SQLParserMode = "default" | "pgvector";
+
+export interface BaseSQLParser {
+  parseResponseToSQL(response: string, queryBundle: QueryBundle): string;
+}
+
+export class DefaultSQLParser implements BaseSQLParser {
+  parseResponseToSQL(response: string, queryBundle: QueryBundle): string {
+    const sqlQueryStart = response.indexOf("SQLQuery:");
+    if (sqlQueryStart !== -1) {
+      response = response.slice(sqlQueryStart);
+      if (response.startsWith("SQLQuery:")) {
+        response = response.slice("SQLQuery:".length);
+      }
+    }
+    const sqlResultStart = response.indexOf("SQLResult:");
+    if (sqlResultStart !== -1) {
+      response = response.slice(0, sqlResultStart);
+    }
+    return response.trim().replace("```", "").trim();
+  }
+}
+
+export class SQLRetriever implements BaseRetriever {
+  sqlDatabase: SQLDatabase;
+  returnRaw: boolean;
+
+  constructor(
+    sqlDatabase: SQLDatabase,
+    returnRaw: boolean = true,
+    callbackManager: CallbackManager | null = null,
+    kwargs: any = {},
+  ) {
+    this.sqlDatabase = sqlDatabase;
+    this.returnRaw = returnRaw;
+  }
+
+  getServiceContext(): ServiceContext {
+    throw new Error("Method not implemented.");
+  }
+
+  _formatNodeResults(results: any[][], colKeys: string[]): NodeWithScore[] {
+    const nodes: NodeWithScore[] = [];
+    for (const result of results) {
+      const metadata = Object.fromEntries(
+        colKeys.map((key, i) => [key, result[i]]),
+      );
+      const textNode = new TextNode({
+        text: "",
+        metadata,
+      });
+      nodes.push({ node: textNode });
+    }
+    return nodes;
+  }
+
+  async retrieveWithMetadata(
+    strOrQueryBundle: QueryBundle,
+  ): Promise<[NodeWithScore[], any]> {
+    const [rawResponseStr, metadata] = await this.sqlDatabase.runSQL(
+      strOrQueryBundle.queryStr,
+    );
+
+    if (this.returnRaw) {
+      return [[{ node: new TextNode({ text: rawResponseStr }) }], metadata];
+    } else {
+      const results = metadata.result;
+      const colKeys = metadata.colKeys;
+      return [this._formatNodeResults(results, colKeys), metadata];
+    }
+  }
+
+  async retrieve(
+    query: string,
+    parentEvent: Event | undefined,
+    preFilters: unknown,
+  ): Promise<NodeWithScore[]> {
+    const retrievedNodes = await this.retrieveWithMetadata({
+      queryStr: query,
+    });
+
+    return retrievedNodes;
+  }
+}

packages/core/src/utilities/SQLWrapper.ts

@@ -0,0 +1,126 @@
+import knex from "knex";
+
+type SQLDatabaseParams = {
+  engine: knex.Knex;
+  schema: string | undefined;
+  metadata: any;
+  ignoreTables: string[] | undefined;
+  includeTables: string[] | undefined;
+  sampleRowsInTableInfo: number;
+  indexesInTableInfo: boolean;
+  customTableInfo: Record<string, any> | undefined;
+  maxStringLength: number;
+};
+
+export class SQLDatabase {
+  engine: knex.Knex;
+  schema: string | undefined;
+  metadata: any;
+  inspector: knex.Knex;
+  allTables: Set<string>;
+  includeTables: Set<string>;
+  ignoreTables: Set<string>;
+  usableTables: Set<string>;
+  sampleRowsInTableInfo: number;
+  indexesInTableInfo: boolean;
+  customTableInfo: Record<string, any> | undefined;
+  maxStringLength: number;
+
+  constructor({
+    engine,
+    schema,
+    metadata,
+    ignoreTables,
+    includeTables,
+    sampleRowsInTableInfo,
+    indexesInTableInfo,
+    customTableInfo,
+    maxStringLength,
+  }: SQLDatabaseParams) {
+    this.engine = engine;
+    this.schema = schema;
+    this.metadata = metadata;
+    this.inspector = engine;
+    this.allTables = new Set(["test_table_1"]);
+    this.includeTables = new Set(includeTables || []);
+    this.ignoreTables = new Set(ignoreTables || []);
+    this.usableTables = new Set();
+    this.sampleRowsInTableInfo = sampleRowsInTableInfo;
+    this.indexesInTableInfo = indexesInTableInfo;
+    this.customTableInfo = customTableInfo;
+    this.maxStringLength = maxStringLength;
+  }
+
+  get usableTableNames(): string[] {
+    if (this.includeTables.size > 0) {
+      return Array.from(this.includeTables);
+    }
+    return Array.from(this.allTables);
+  }
+
+  async getTableColumns(tableName: string) {
+    return await this.inspector(tableName).columnInfo();
+  }
+
+  async getSingleTableInfo(tableName: string) {
+    const columns = await this.getTableColumns(tableName);
+
+    const columnStr = Object.keys(columns)
+      .map((column) => {
+        return `${column} (${columns[column].type})`;
+      })
+      .join(", ");
+
+    return `Table '${tableName}' has columns: ${columnStr}.`;
+  }
+
+  insertIntoTable(tableName: string, data: Record<string, any>): Promise<void> {
+    return this.engine(tableName).insert(data);
+  }
+
+  truncateWord(content: any, length: number, suffix = "..."): string {
+    if (typeof content !== "string" || length <= 0) {
+      return content;
+    }
+
+    if (content.length <= length) {
+      return content;
+    }
+
+    return (
+      content
+        .slice(0, length - suffix.length - 1)
+        .split(" ")
+        .slice(0, -1)
+        .join(" ") + suffix
+    );
+  }
+
+  async runSQL(
+    command: string,
+  ): Promise<[string, { result: any[]; colKeys: string[] }]> {
+    return this.engine.raw(command).then((result: any) => {
+      if (result.length > 0) {
+        const truncatedResults = result.map((row: any) =>
+          this.truncateWord(row, this.maxStringLength),
+        );
+        return [
+          JSON.stringify(truncatedResults),
+          { result: truncatedResults, colKeys: Object.keys(result[0]) },
+        ];
+      }
+      return ["", { result: [], colKeys: [] }];
+    });
+  }
+
+  async getTableInfo(tableName: string): Promise<string> {
+    const columns = await this.getTableColumns(tableName);
+    const columnStr = Object.keys(columns)
+      .map((column: any) => {
+        const comment = column.COMMENT ? `'${column.COMMENT}'` : "";
+        return `${column.COLUMN_NAME} (${column.DATA_TYPE}): ${comment}`;
+      })
+      .join(", ");
+    return `Table '${tableName}' has columns: ${columnStr}.`;
+  }
+}

packages/core/src/utilities/index.ts

@@ -0,0 +1 @@
+export * from "./SQLWrapper.js";

packages/core/tests/indices/json-to-index-struct.test.ts

@@ -1,71 +0,0 @@
-import {
-  IndexDict,
-  IndexList,
-  IndexStruct,
-  IndexStructType,
-  MetadataMode,
-  TextNode,
-  jsonToIndexStruct,
-} from "llamaindex";
-import { describe, expect, it } from "vitest";
-
-describe("jsonToIndexStruct", () => {
-  it("transforms json to IndexDict", () => {
-    function isIndexDict(some: IndexStruct): some is IndexDict {
-      return "type" in some && some.type === IndexStructType.SIMPLE_DICT;
-    }
-
-    const node = new TextNode({ text: "text", id_: "nodeId" });
-    const expected = new IndexDict();
-    expected.addNode(node);
-
-    console.log("expected.toJson()", expected.toJson());
-    const actual = jsonToIndexStruct(expected.toJson());
-
-    expect(isIndexDict(actual)).toBe(true);
-    expect(
-      (actual as IndexDict).nodesDict.nodeId.getContent(MetadataMode.NONE),
-    ).toEqual("text");
-  });
-  it("transforms json to IndexList", () => {
-    function isIndexList(some: IndexStruct): some is IndexList {
-      return "type" in some && some.type === IndexStructType.LIST;
-    }
-
-    const node = new TextNode({ text: "text", id_: "nodeId" });
-    const expected = new IndexList();
-    expected.addNode(node);
-
-    const actual = jsonToIndexStruct(expected.toJson());
-
-    expect(isIndexList(actual)).toBe(true);
-    expect((actual as IndexList).nodes[0]).toEqual("nodeId");
-  });
-  it("fails for unknown index type", () => {
-    expect(() => {
-      const json = {
-        indexId: "dd120b16-8dce-4ce3-9bb6-15ca87fe4a1d",
-        summary: undefined,
-        nodesDict: {},
-        type: "FOO",
-      };
-      return jsonToIndexStruct(json);
-    }).toThrowError("Unknown index struct type: FOO");
-  });
-  it("fails for unknown node type", () => {
-    expect(() => {
-      const json = {
-        indexId: "dd120b16-8dce-4ce3-9bb6-15ca87fe4a1d",
-        summary: undefined,
-        nodesDict: {
-          nodeId: {
-            ...new TextNode({ text: "text", id_: "nodeId" }).toJSON(),
-            type: "BAR",
-          },
-        },
-        type: IndexStructType.SIMPLE_DICT,
-      };
-      return jsonToIndexStruct(json);
-    }).toThrowError("Invalid node type: BAR");
-  });
-});

packages/core/tsconfig.json

@@ -3,7 +3,7 @@
   "compilerOptions": {
     "rootDir": "./src",
     "outDir": "./dist/type",
-    "tsBuildInfoFile": "./dist/.tsbuildinfo",
+    "tsBuildInfoFile": ".tsbuildinfo",
     "emitDeclarationOnly": true,
     "module": "node16",
     "moduleResolution": "node16",

packages/create-llama/.eslintrc

@@ -1,20 +0,0 @@
-{
-  "root": false,
-  "rules": {
-    "turbo/no-undeclared-env-vars": [
-      "error",
-      {
-        "allowList": [
-          "OPENAI_API_KEY",
-          "LLAMA_CLOUD_API_KEY",
-          "npm_config_user_agent",
-          "http_proxy",
-          "https_proxy",
-          "MODEL",
-          "NEXT_PUBLIC_CHAT_API",
-          "NEXT_PUBLIC_MODEL"
-        ]
-      }
-    ]
-  }
-}

packages/create-llama/CHANGELOG.md

@@ -1,20 +1,5 @@
 # create-llama
 
-## 0.0.28
-
-### Patch Changes
-
-- 89a49f4: Add more config variables to .env file
-- fdf48dd: Add "Start in VSCode" option to postInstallAction
-- fdf48dd: Add devcontainers to generated code
-
-## 0.0.27
-
-### Patch Changes
-
-- 2d29350: Add LlamaParse option when selecting a pdf file or a folder (FastAPI only)
-- b354f23: Add embedding model option to create-llama (FastAPI only)
-
 ## 0.0.26
 
 ### Patch Changes

packages/create-llama/create-app.ts

@@ -11,7 +11,6 @@ import fs from "fs";
 import terminalLink from "terminal-link";
 import type { InstallTemplateArgs } from "./helpers";
 import { installTemplate } from "./helpers";
-import { writeDevcontainer } from "./helpers/devcontainer";
 import { templatesDir } from "./helpers/dir";
 import { toolsRequireConfig } from "./helpers/tools";
 
@@ -35,7 +34,6 @@ export async function createApp({
   openAiKey,
   llamaCloudKey,
   model,
-  embeddingModel,
   communityProjectPath,
   llamapack,
   vectorDb,
@@ -82,7 +80,6 @@ export async function createApp({
     openAiKey,
     llamaCloudKey,
     model,
-    embeddingModel,
     communityProjectPath,
     llamapack,
     vectorDb,
@@ -113,7 +110,7 @@ export async function createApp({
       path.join(root, "README.md"),
     );
   } else {
-    await installTemplate({ ...args, backend: true });
+    await installTemplate({ ...args, backend: true, forBackend: framework });
   }
 
   process.chdir(root);
@@ -122,8 +119,6 @@ export async function createApp({
     console.log();
   }
 
-  await writeDevcontainer(root, templatesDir, framework, frontend);
-
   if (toolsRequireConfig(tools)) {
     console.log(
       yellow(