Release 0.7.1 (#1342)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: himself65 <himself65@users.noreply.github.com>

.github/workflows/preview.yml

@@ -25,4 +25,4 @@ jobs:
         run: pnpm run build
 
       - name: Pre Release
-        run: pnpx pkg-pr-new publish ./packages/*
+        run: pnpx pkg-pr-new publish ./packages/* ./packages/providers/*

.github/workflows/test.yml

@@ -136,27 +136,26 @@ jobs:
         run: pnpm run build
       - name: Copy examples
         run: rsync -rv --exclude=node_modules ./examples ${{ runner.temp }}
-      - name: Pack @llamaindex/cloud
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/cloud
-      - name: Pack @llamaindex/openai
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llm/openai
-      - name: Pack @llamaindex/groq
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llm/groq
-      - name: Pack @llamaindex/ollama
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llm/ollama
-      - name: Pack @llamaindex/core
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/core
-      - name: Pack @llamaindex/env
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/env
-      - name: Pack llamaindex
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llamaindex
+      - name: Pack packages
+        run: |
+          for dir in packages/*; do
+            if [ -d "$dir" ] && [ -f "$dir/package.json" ] && [[ ! "$dir" =~ autotool ]]; then
+              echo "Packing $dir"
+              pnpm pack --pack-destination ${{ runner.temp }} -C $dir
+            else
+              echo "Skipping $dir, no package.json found"
+            fi
+          done
+      - name: Pack provider packages
+        run: |
+          for dir in packages/providers/*; do
+            if [ -d "$dir" ] && [ -f "$dir/package.json" ]; then
+              echo "Packing $dir"
+              pnpm pack --pack-destination ${{ runner.temp }} -C $dir
+            else
+              echo "Skipping $dir, no package.json found"
+            fi
+          done
       - name: Install
         run: npm add ${{ runner.temp }}/*.tgz
         working-directory: ${{ runner.temp }}/examples

apps/docs/CHANGELOG.md

@@ -1,5 +1,72 @@
 # docs
 
+## 0.0.93
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.0.92
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/examples@0.0.9
+
+## 0.0.91
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.0.90
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.0.89
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.0.88
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.0.87
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.0.86
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
 ## 0.0.85
 
 ### Patch Changes

apps/docs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.85",
+  "version": "0.0.93",
   "private": true,
   "scripts": {
     "docusaurus": "docusaurus",

examples/CHANGELOG.md

@@ -1,5 +1,16 @@
 # examples
 
+## 0.0.9
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [96fc69c]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/core@0.3.0
+
 ## 0.0.8
 
 ### Patch Changes

examples/astradb/example.ts

@@ -1,6 +1,7 @@
 import {
   AstraDBVectorStore,
   Document,
+  MetadataFilters,
   storageContextFromDefaults,
   VectorStoreIndex,
 } from "llamaindex";
@@ -42,8 +43,10 @@ async function main() {
     const index = await VectorStoreIndex.fromDocuments(docs, {
       storageContext: ctx,
     });
-
-    const queryEngine = index.asQueryEngine();
+    const preFilters: MetadataFilters = {
+      filters: [{ key: "id", operator: "in", value: [123, 789] }],
+    }; // try changing the filters to see the different results
+    const queryEngine = index.asQueryEngine({ preFilters });
     const response = await queryEngine.query({
       query: "Describe AstraDB.",
     });

examples/chromadb/preFilters.ts

@@ -1,57 +1,83 @@
 import {
   ChromaVectorStore,
   Document,
+  MetadataFilters,
   VectorStoreIndex,
   storageContextFromDefaults,
 } from "llamaindex";
 
-const collectionName = "dog_colors";
+const collectionName = "dogs_with_color";
 
 async function main() {
   try {
-    const docs = [
-      new Document({
-        text: "The dog is brown",
-        metadata: {
-          dogId: "1",
-        },
-      }),
-      new Document({
-        text: "The dog is red",
-        metadata: {
-          dogId: "2",
-        },
-      }),
-    ];
-
-    console.log("Creating ChromaDB vector store");
     const chromaVS = new ChromaVectorStore({ collectionName });
-    const ctx = await storageContextFromDefaults({ vectorStore: chromaVS });
+    const index = await VectorStoreIndex.fromVectorStore(chromaVS);
 
-    console.log("Embedding documents and adding to index");
-    const index = await VectorStoreIndex.fromDocuments(docs, {
-      storageContext: ctx,
-    });
+    const queryFn = async (filters?: MetadataFilters) => {
+      console.log("\nQuerying dogs by filters: ", JSON.stringify(filters));
+      const query = "List all colors of dogs";
+      const queryEngine = index.asQueryEngine({
+        preFilters: filters,
+        similarityTopK: 3,
+      });
+      const response = await queryEngine.query({ query });
+      console.log(response.toString());
+    };
 
-    console.log("Querying index");
-    const queryEngine = index.asQueryEngine({
-      preFilters: {
-        filters: [
-          {
-            key: "dogId",
-            value: "2",
-            operator: "==",
-          },
-        ],
-      },
-    });
-    const response = await queryEngine.query({
-      query: "What is the color of the dog?",
-    });
-    console.log(response.toString());
+    await queryFn(); // red, brown, yellow
+    await queryFn({ filters: [{ key: "dogId", value: "1", operator: "==" }] }); // brown
+    await queryFn({ filters: [{ key: "dogId", value: "1", operator: "!=" }] }); // red, yellow
+    await queryFn({
+      filters: [
+        { key: "dogId", value: "1", operator: "==" },
+        { key: "dogId", value: "3", operator: "==" },
+      ],
+      condition: "or",
+    }); // brown, yellow
+    await queryFn({
+      filters: [{ key: "dogId", value: ["1", "2"], operator: "in" }],
+    }); // red, brown
   } catch (e) {
     console.error(e);
   }
 }
 
-void main();
+async function generate() {
+  const docs = [
+    new Document({
+      id_: "doc1",
+      text: "The dog is brown",
+      metadata: {
+        dogId: "1",
+      },
+    }),
+    new Document({
+      id_: "doc2",
+      text: "The dog is red",
+      metadata: {
+        dogId: "2",
+      },
+    }),
+    new Document({
+      id_: "doc3",
+      text: "The dog is yellow",
+      metadata: {
+        dogId: "3",
+      },
+    }),
+  ];
+
+  console.log("Creating ChromaDB vector store");
+  const chromaVS = new ChromaVectorStore({ collectionName });
+  const ctx = await storageContextFromDefaults({ vectorStore: chromaVS });
+
+  console.log("Embedding documents and adding to index");
+  await VectorStoreIndex.fromDocuments(docs, {
+    storageContext: ctx,
+  });
+}
+
+(async () => {
+  await generate();
+  await main();
+})();

examples/package.json

@@ -1,12 +1,12 @@
 {
   "name": "@llamaindex/examples",
   "private": true,
-  "version": "0.0.8",
+  "version": "0.0.9",
   "dependencies": {
     "@aws-crypto/sha256-js": "^5.2.0",
     "@azure/identity": "^4.4.1",
     "@datastax/astra-db-ts": "^1.4.1",
-    "@llamaindex/core": "^0.2.0",
+    "@llamaindex/core": "^0.3.0",
     "@notionhq/client": "^2.2.15",
     "@pinecone-database/pinecone": "^3.0.2",
     "@vercel/postgres": "^0.10.0",
@@ -15,7 +15,7 @@
     "commander": "^12.1.0",
     "dotenv": "^16.4.5",
     "js-tiktoken": "^1.0.14",
-    "llamaindex": "^0.6.0",
+    "llamaindex": "^0.7.0",
     "mongodb": "^6.7.0",
     "pathe": "^1.1.2",
     "postgres": "^3.4.4"

examples/vectorIndexFromVectorStore.ts

@@ -1,4 +1,5 @@
 import {
+  BaseVectorStore,
   getResponseSynthesizer,
   OpenAI,
   OpenAIEmbedding,
@@ -6,7 +7,6 @@ import {
   Settings,
   TextNode,
   VectorIndexRetriever,
-  VectorStore,
   VectorStoreIndex,
   VectorStoreQuery,
   VectorStoreQueryResult,
@@ -24,7 +24,7 @@ Settings.llm = new OpenAI({
  * Please do not use this class in production; it's only for demonstration purposes.
  */
 class PineconeVectorStore<T extends RecordMetadata = RecordMetadata>
-  implements VectorStore
+  implements BaseVectorStore
 {
   storesText = true;
   isEmbeddingQuery = false;

packages/autotool/CHANGELOG.md

@@ -1,5 +1,72 @@
 # @llamaindex/autotool
 
+## 4.0.1
+
+### Patch Changes
+
+- a75af83: refactor: move some llm and embedding to single package
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 4.0.0
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+
+## 3.0.22
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 3.0.21
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 3.0.20
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 3.0.19
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 3.0.18
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 3.0.17
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
 ## 3.0.16
 
 ### Patch Changes

packages/autotool/examples/01_node/CHANGELOG.md

@@ -1,5 +1,79 @@
 # @llamaindex/autotool-01-node-example
 
+## 0.0.33
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+  - @llamaindex/autotool@4.0.1
+
+## 0.0.32
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/autotool@4.0.0
+
+## 0.0.31
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+  - @llamaindex/autotool@3.0.22
+
+## 0.0.30
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+  - @llamaindex/autotool@3.0.21
+
+## 0.0.29
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+  - @llamaindex/autotool@3.0.20
+
+## 0.0.28
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+  - @llamaindex/autotool@3.0.19
+
+## 0.0.27
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+  - @llamaindex/autotool@3.0.18
+
+## 0.0.26
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+  - @llamaindex/autotool@3.0.17
+
 ## 0.0.25
 
 ### Patch Changes

packages/autotool/examples/01_node/package.json

@@ -13,5 +13,5 @@
   "scripts": {
     "start": "node --import tsx --import @llamaindex/autotool/node ./src/index.ts"
   },
-  "version": "0.0.25"
+  "version": "0.0.33"
 }

packages/autotool/examples/02_nextjs/CHANGELOG.md

@@ -1,5 +1,79 @@
 # @llamaindex/autotool-02-next-example
 
+## 0.1.77
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+  - @llamaindex/autotool@4.0.1
+
+## 0.1.76
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/autotool@4.0.0
+
+## 0.1.75
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+  - @llamaindex/autotool@3.0.22
+
+## 0.1.74
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+  - @llamaindex/autotool@3.0.21
+
+## 0.1.73
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+  - @llamaindex/autotool@3.0.20
+
+## 0.1.72
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+  - @llamaindex/autotool@3.0.19
+
+## 0.1.71
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+  - @llamaindex/autotool@3.0.18
+
+## 0.1.70
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+  - @llamaindex/autotool@3.0.17
+
 ## 0.1.69
 
 ### Patch Changes

packages/autotool/examples/02_nextjs/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/autotool-02-next-example",
   "private": true,
-  "version": "0.1.69",
+  "version": "0.1.77",
   "scripts": {
     "dev": "next dev",
     "build": "next build",

packages/autotool/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/autotool",
   "type": "module",
-  "version": "3.0.16",
+  "version": "4.0.1",
   "description": "auto transpile your JS function to LLM Agent compatible",
   "files": [
     "dist",
@@ -70,7 +70,7 @@
     "@swc/types": "^0.1.12",
     "@types/json-schema": "^7.0.15",
     "@types/node": "^22.5.1",
-    "bunchee": "5.3.2",
+    "bunchee": "5.5.1",
     "llamaindex": "workspace:*",
     "next": "14.2.11",
     "rollup": "^4.21.2",

packages/cloud/CHANGELOG.md

@@ -1,5 +1,39 @@
 # @llamaindex/cloud
 
+## 1.0.1
+
+### Patch Changes
+
+- 4c38c1b: fix(cloud): do not detect file type in llama parse
+- 24d065f: Log Parse Job Errors when verbose is enabled
+- a75af83: refactor: move some llm and embedding to single package
+- Updated dependencies [ae49ff4]
+- Updated dependencies [a75af83]
+  - @llamaindex/env@0.1.14
+  - @llamaindex/core@0.3.1
+
+## 1.0.0
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [96fc69c]
+  - @llamaindex/core@0.3.0
+
+## 0.2.14
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+  - @llamaindex/core@0.2.12
+
+## 0.2.13
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - @llamaindex/core@0.2.11
+
 ## 0.2.12
 
 ### Patch Changes

packages/cloud/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/cloud",
-  "version": "0.2.12",
+  "version": "1.0.1",
   "type": "module",
   "license": "MIT",
   "scripts": {
@@ -53,13 +53,10 @@
     "@hey-api/openapi-ts": "^0.53.0",
     "@llamaindex/core": "workspace:*",
     "@llamaindex/env": "workspace:*",
-    "bunchee": "5.3.2"
+    "bunchee": "5.5.1"
   },
   "peerDependencies": {
     "@llamaindex/core": "workspace:*",
     "@llamaindex/env": "workspace:*"
-  },
-  "dependencies": {
-    "magic-bytes.js": "^1.10.0"
   }
 }

packages/cloud/src/reader.ts

@@ -1,7 +1,6 @@
 import { type Client, createClient, createConfig } from "@hey-api/client-fetch";
 import { Document, FileReader } from "@llamaindex/core/schema";
 import { fs, getEnv, path } from "@llamaindex/env";
-import { filetypeinfo } from "magic-bytes.js";
 import {
   type Body_upload_file_api_v1_parsing_upload_post,
   type ParserLanguages,
@@ -13,99 +12,6 @@ export type Language = ParserLanguages;
 
 export type ResultType = "text" | "markdown" | "json";
 
-const SUPPORT_FILE_EXT: string[] = [
-  ".pdf",
-  // document and presentations
-  ".602",
-  ".abw",
-  ".cgm",
-  ".cwk",
-  ".doc",
-  ".docx",
-  ".docm",
-  ".dot",
-  ".dotm",
-  ".hwp",
-  ".key",
-  ".lwp",
-  ".mw",
-  ".mcw",
-  ".pages",
-  ".pbd",
-  ".ppt",
-  ".pptm",
-  ".pptx",
-  ".pot",
-  ".potm",
-  ".potx",
-  ".rtf",
-  ".sda",
-  ".sdd",
-  ".sdp",
-  ".sdw",
-  ".sgl",
-  ".sti",
-  ".sxi",
-  ".sxw",
-  ".stw",
-  ".sxg",
-  ".txt",
-  ".uof",
-  ".uop",
-  ".uot",
-  ".vor",
-  ".wpd",
-  ".wps",
-  ".xml",
-  ".zabw",
-  ".epub",
-  // images
-  ".jpg",
-  ".jpeg",
-  ".png",
-  ".gif",
-  ".bmp",
-  ".svg",
-  ".tiff",
-  ".webp",
-  // web
-  ".htm",
-  ".html",
-  // spreadsheets
-  ".xlsx",
-  ".xls",
-  ".xlsm",
-  ".xlsb",
-  ".xlw",
-  ".csv",
-  ".dif",
-  ".sylk",
-  ".slk",
-  ".prn",
-  ".numbers",
-  ".et",
-  ".ods",
-  ".fods",
-  ".uos1",
-  ".uos2",
-  ".dbf",
-  ".wk1",
-  ".wk2",
-  ".wk3",
-  ".wk4",
-  ".wks",
-  ".123",
-  ".wq1",
-  ".wq2",
-  ".wb1",
-  ".wb2",
-  ".wb3",
-  ".qpw",
-  ".xlr",
-  ".eth",
-  ".tsv",
-];
-
 //todo: should move into @llamaindex/env
 type WriteStream = {
   write: (text: string) => void;
@@ -239,17 +145,12 @@ export class LlamaParseReader extends FileReader {
 
   // Create a job for the LlamaParse API
   private async createJob(data: Uint8Array): Promise<string> {
-    // Load data, set the mime type
-    const { mime } = await LlamaParseReader.getMimeType(data);
-
     if (this.verbose) {
       console.log("Started uploading the file");
     }
 
     const body = {
-      file: new Blob([data], {
-        type: mime,
-      }),
+      file: new Blob([data]),
       language: this.language,
       parsing_instruction: this.parsingInstruction,
       skip_diagonal_text: this.skipDiagonalText,
@@ -368,6 +269,11 @@ export class LlamaParseReader extends FileReader {
         }
         tries++;
       } else {
+        if (this.verbose) {
+          console.error(
+            `Recieved Error response ${status} for job ${jobId}.  Got Error Code: ${data.error_code} and Error Message: ${data.error_message}`,
+          );
+        }
         throw new Error(
           `Failed to parse the file: ${jobId}, status: ${status}`,
         );
@@ -564,24 +470,4 @@ export class LlamaParseReader extends FileReader {
         }),
     );
   }
-
-  static async getMimeType(
-    data: Uint8Array,
-  ): Promise<{ mime: string; extension: string }> {
-    const typeinfos = filetypeinfo(data);
-    // find the first type info that matches the supported MIME types
-    // It could be happened that docx file is recognized as zip file, so we need to check the mime type
-    const info = typeinfos.find((info) => {
-      if (info.extension && SUPPORT_FILE_EXT.includes(`.${info.extension}`)) {
-        return info;
-      }
-    });
-    if (!info || !info.mime || !info.extension) {
-      const ext = SUPPORT_FILE_EXT.join(", ");
-      throw new Error(
-        `File has type which does not match supported MIME Types. Supported formats include: ${ext}`,
-      );
-    }
-    return { mime: info.mime, extension: info.extension };
-  }
 }

packages/community/CHANGELOG.md

@@ -1,5 +1,37 @@
 # @llamaindex/community
 
+## 0.0.49
+
+### Patch Changes
+
+- a75af83: refactor: move some llm and embedding to single package
+- Updated dependencies [ae49ff4]
+- Updated dependencies [a75af83]
+  - @llamaindex/env@0.1.14
+  - @llamaindex/core@0.3.1
+
+## 0.0.48
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [96fc69c]
+  - @llamaindex/core@0.3.0
+
+## 0.0.47
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+  - @llamaindex/core@0.2.12
+
+## 0.0.46
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - @llamaindex/core@0.2.11
+
 ## 0.0.45
 
 ### Patch Changes

packages/community/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/community",
   "description": "Community package for LlamaIndexTS",
-  "version": "0.0.45",
+  "version": "0.0.49",
   "type": "module",
   "types": "dist/type/index.d.ts",
   "main": "dist/cjs/index.js",
@@ -43,11 +43,11 @@
   },
   "devDependencies": {
     "@types/node": "^22.5.1",
-    "bunchee": "5.3.2"
+    "bunchee": "5.5.1"
   },
   "dependencies": {
-    "@aws-sdk/client-bedrock-runtime": "^3.642.0",
     "@aws-sdk/client-bedrock-agent-runtime": "^3.642.0",
+    "@aws-sdk/client-bedrock-runtime": "^3.642.0",
     "@llamaindex/core": "workspace:*",
     "@llamaindex/env": "workspace:*"
   }

packages/core/CHANGELOG.md

@@ -1,5 +1,33 @@
 # @llamaindex/core
 
+## 0.3.1
+
+### Patch Changes
+
+- a75af83: refactor: move some llm and embedding to single package
+- Updated dependencies [ae49ff4]
+- Updated dependencies [a75af83]
+  - @llamaindex/env@0.1.14
+
+## 0.3.0
+
+### Minor Changes
+
+- 1364e8e: update metadata extractors to use PromptTemplate
+- 96fc69c: add defaultQuestionExtractPrompt
+
+## 0.2.12
+
+### Patch Changes
+
+- 5f67820: Fix that node parsers generate nodes with UUIDs
+
+## 0.2.11
+
+### Patch Changes
+
+- ee697fb: fix: generate uuid when inserting to Qdrant
+
 ## 0.2.10
 
 ### Patch Changes

packages/core/data-structs/package.json

@@ -0,0 +1,8 @@
+{
+  "type": "module",
+  "main": "./dist/index.cjs",
+  "module": "./dist/index.js",
+  "types": "./dist/index.d.ts",
+  "exports": "./dist/index.js",
+  "private": true
+}

packages/core/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/core",
   "type": "module",
-  "version": "0.2.10",
+  "version": "0.3.1",
   "description": "LlamaIndex Core Module",
   "exports": {
     "./agent": {
@@ -258,16 +258,44 @@
     },
     "./vector-store": {
       "require": {
-        "types": "./dist/vector-store/index.d.cts",
-        "default": "./dist/vector-store/index.cjs"
+        "types": "./vector-store/dist/index.d.cts",
+        "default": "./vector-store/dist/index.cjs"
       },
       "import": {
-        "types": "./dist/vector-store/index.d.ts",
-        "default": "./dist/vector-store/index.js"
+        "types": "./vector-store/dist/index.d.ts",
+        "default": "./vector-store/dist/index.js"
       },
       "default": {
-        "types": "./dist/vector-store/index.d.ts",
-        "default": "./dist/vector-store/index.js"
+        "types": "./vector-store/dist/index.d.ts",
+        "default": "./vector-store/dist/index.js"
+      }
+    },
+    "./tools": {
+      "require": {
+        "types": "./tools/dist/index.d.cts",
+        "default": "./tools/dist/index.cjs"
+      },
+      "import": {
+        "types": "./tools/dist/index.d.ts",
+        "default": "./tools/dist/index.js"
+      },
+      "default": {
+        "types": "./tools/dist/index.d.ts",
+        "default": "./tools/dist/index.js"
+      }
+    },
+    "./data-structs": {
+      "require": {
+        "types": "./data-structs/dist/index.d.cts",
+        "default": "./data-structs/dist/index.cjs"
+      },
+      "import": {
+        "types": "./data-structs/dist/index.d.ts",
+        "default": "./data-structs/dist/index.js"
+      },
+      "default": {
+        "types": "./data-structs/dist/index.d.ts",
+        "default": "./data-structs/dist/index.js"
       }
     }
   },
@@ -289,7 +317,10 @@
     "./storage",
     "./response-synthesizers",
     "./chat-engine",
-    "./retriever"
+    "./retriever",
+    "./vector-store",
+    "./tools",
+    "./data-structs"
   ],
   "scripts": {
     "dev": "bunchee --watch",
@@ -303,7 +334,7 @@
   "devDependencies": {
     "@edge-runtime/vm": "^4.0.3",
     "ajv": "^8.17.1",
-    "bunchee": "5.3.2",
+    "bunchee": "5.5.1",
     "happy-dom": "^15.7.4",
     "natural": "^8.0.1",
     "python-format-js": "^1.4.3"
@@ -312,6 +343,7 @@
     "@llamaindex/env": "workspace:*",
     "@types/node": "^22.5.1",
     "magic-bytes.js": "^1.10.0",
-    "zod": "^3.23.8"
+    "zod": "^3.23.8",
+    "zod-to-json-schema": "^3.23.3"
   }
 }

packages/core/src/data-structs/data-structs.ts

@@ -0,0 +1,67 @@
+import { randomUUID } from "@llamaindex/env";
+import type { UUID } from "../global";
+import { IndexStructType } from "./struct-type";
+
+export abstract class IndexStruct {
+  indexId: string;
+  summary: string | undefined;
+
+  constructor(
+    indexId: UUID = randomUUID(),
+    summary: string | undefined = undefined,
+  ) {
+    this.indexId = indexId;
+    this.summary = summary;
+  }
+
+  toJson(): Record<string, unknown> {
+    return {
+      indexId: this.indexId,
+      summary: this.summary,
+    };
+  }
+
+  getSummary(): string {
+    if (this.summary === undefined) {
+      throw new Error("summary field of the index struct is not set");
+    }
+    return this.summary;
+  }
+}
+
+// A table of keywords mapping keywords to text chunks.
+export class KeywordTable extends IndexStruct {
+  table: Map<string, Set<string>> = new Map();
+  type: IndexStructType = IndexStructType.KEYWORD_TABLE;
+
+  addNode(keywords: string[], nodeId: string): void {
+    keywords.forEach((keyword) => {
+      if (!this.table.has(keyword)) {
+        this.table.set(keyword, new Set());
+      }
+      this.table.get(keyword)!.add(nodeId);
+    });
+  }
+
+  deleteNode(keywords: string[], nodeId: string) {
+    keywords.forEach((keyword) => {
+      if (this.table.has(keyword)) {
+        this.table.get(keyword)!.delete(nodeId);
+      }
+    });
+  }
+
+  toJson(): Record<string, unknown> {
+    return {
+      ...super.toJson(),
+      table: Array.from(this.table.entries()).reduce(
+        (acc, [keyword, nodeIds]) => {
+          acc[keyword] = Array.from(nodeIds);
+          return acc;
+        },
+        {} as Record<string, string[]>,
+      ),
+      type: this.type,
+    };
+  }
+}

packages/core/src/data-structs/index.ts

@@ -0,0 +1,2 @@
+export { IndexStruct, KeywordTable } from "./data-structs";
+export { IndexStructType } from "./struct-type";

packages/core/src/data-structs/struct-type.ts

@@ -0,0 +1,39 @@
+export const IndexStructType = {
+  NODE: "node",
+  TREE: "tree",
+  LIST: "list",
+  KEYWORD_TABLE: "keyword_table",
+  DICT: "dict",
+  SIMPLE_DICT: "simple_dict",
+  WEAVIATE: "weaviate",
+  PINECONE: "pinecone",
+  QDRANT: "qdrant",
+  LANCEDB: "lancedb",
+  MILVUS: "milvus",
+  CHROMA: "chroma",
+  MYSCALE: "myscale",
+  CLICKHOUSE: "clickhouse",
+  VECTOR_STORE: "vector_store",
+  OPENSEARCH: "opensearch",
+  DASHVECTOR: "dashvector",
+  CHATGPT_RETRIEVAL_PLUGIN: "chatgpt_retrieval_plugin",
+  DEEPLAKE: "deeplake",
+  EPSILLA: "epsilla",
+  MULTIMODAL_VECTOR_STORE: "multimodal",
+  SQL: "sql",
+  KG: "kg",
+  SIMPLE_KG: "simple_kg",
+  SIMPLE_LPG: "simple_lpg",
+  NEBULAGRAPH: "nebulagraph",
+  FALKORDB: "falkordb",
+  EMPTY: "empty",
+  COMPOSITE: "composite",
+  PANDAS: "pandas",
+  DOCUMENT_SUMMARY: "document_summary",
+  VECTARA: "vectara",
+  ZILLIZ_CLOUD_PIPELINE: "zilliz_cloud_pipeline",
+  POSTGRESML: "postgresml",
+} as const;
+
+export type IndexStructType =
+  (typeof IndexStructType)[keyof typeof IndexStructType];

packages/core/src/prompts/index.ts

@@ -12,11 +12,15 @@ export {
   defaultCondenseQuestionPrompt,
   defaultContextSystemPrompt,
   defaultKeywordExtractPrompt,
+  defaultNodeTextTemplate,
   defaultQueryKeywordExtractPrompt,
+  defaultQuestionExtractPrompt,
   defaultRefinePrompt,
   defaultSubQuestionPrompt,
   defaultSummaryPrompt,
   defaultTextQAPrompt,
+  defaultTitleCombinePromptTemplate,
+  defaultTitleExtractorPromptTemplate,
   defaultTreeSummarizePrompt,
 } from "./prompt";
 export type {
@@ -25,9 +29,12 @@ export type {
   ContextSystemPrompt,
   KeywordExtractPrompt,
   QueryKeywordExtractPrompt,
+  QuestionExtractPrompt,
   RefinePrompt,
   SubQuestionPrompt,
   SummaryPrompt,
   TextQAPrompt,
+  TitleCombinePrompt,
+  TitleExtractorPrompt,
   TreeSummarizePrompt,
 } from "./prompt";

packages/core/src/prompts/prompt.ts

@@ -13,8 +13,12 @@ export type CondenseQuestionPrompt = PromptTemplate<
   ["chatHistory", "question"]
 >;
 export type ContextSystemPrompt = PromptTemplate<["context"]>;
-export type KeywordExtractPrompt = PromptTemplate<["context"]>;
+export type KeywordExtractPrompt = PromptTemplate<["context", "maxKeywords"]>;
 export type QueryKeywordExtractPrompt = PromptTemplate<["question"]>;
+export type QuestionExtractPrompt = PromptTemplate<["context", "numQuestions"]>;
+export type TitleExtractorPrompt = PromptTemplate<["context"]>;
+export type TitleCombinePrompt = PromptTemplate<["context"]>;
+export type KeywordExtractorPrompt = PromptTemplate<["context", "numKeywords"]>;
 
 export const defaultTextQAPrompt: TextQAPrompt = new PromptTemplate({
   templateVars: ["context", "query"],
@@ -253,3 +257,55 @@ export const defaultQueryKeywordExtractPrompt = new PromptTemplate({
 }).partialFormat({
   maxKeywords: "10",
 });
+
+export const defaultQuestionExtractPrompt = new PromptTemplate({
+  templateVars: ["numQuestions", "context"],
+  template: `(
+  "Given the contextual informations below, generate {numQuestions} questions this context can provides specific answers to which are unlikely to be found else where. Higher-level summaries of surrounding context may be provided as well. "
+  "Try using these summaries to generate better questions that this context can answer."
+  "---------------------"
+  "{context}"
+  "---------------------"
+  "Provide questions in the following format: 'QUESTIONS: <questions>'"
+)`,
+}).partialFormat({
+  numQuestions: "5",
+});
+
+export const defaultTitleExtractorPromptTemplate = new PromptTemplate({
+  templateVars: ["context"],
+  template: `{context}
+Give a title that summarizes all of the unique entities, titles or themes found in the context. 
+Title: `,
+});
+
+export const defaultTitleCombinePromptTemplate = new PromptTemplate({
+  templateVars: ["context"],
+  template: `{context} 
+Based on the above candidate titles and contents, what is the comprehensive title for this document? 
+Title: `,
+});
+
+export const defaultKeywordExtractorPromptTemplate = new PromptTemplate({
+  templateVars: ["context", "numKeywords"],
+  template: `{context}
+Give {numKeywords} unique keywords for this document. 
+Format as comma separated. 
+Keywords: `,
+}).partialFormat({
+  keywordCount: "5",
+});
+
+export const defaultNodeTextTemplate = new PromptTemplate({
+  templateVars: ["metadataStr", "content"],
+  template: `[Excerpt from document]
+{metadataStr}
+Excerpt:
+-----
+{content}
+-----
+`,
+}).partialFormat({
+  metadataStr: "",
+  content: "",
+});

packages/core/src/response-synthesizers/factory.ts

@@ -403,27 +403,27 @@ class MultiModal extends BaseSynthesizer {
   }
 }
 
-export function getResponseSynthesizer(
-  mode: ResponseMode,
+const modeToSynthesizer = {
+  compact: CompactAndRefine,
+  refine: Refine,
+  tree_summarize: TreeSummarize,
+  multi_modal: MultiModal,
+} as const;
+
+export function getResponseSynthesizer<Mode extends ResponseMode>(
+  mode: Mode,
   options: BaseSynthesizerOptions & {
     textQATemplate?: TextQAPrompt;
     refineTemplate?: RefinePrompt;
     summaryTemplate?: TreeSummarizePrompt;
     metadataMode?: MetadataMode;
   } = {},
-) {
-  switch (mode) {
-    case "compact": {
-      return new CompactAndRefine(options);
-    }
-    case "refine": {
-      return new Refine(options);
-    }
-    case "tree_summarize": {
-      return new TreeSummarize(options);
-    }
-    case "multi_modal": {
-      return new MultiModal(options);
-    }
+): InstanceType<(typeof modeToSynthesizer)[Mode]> {
+  const Synthesizer: (typeof modeToSynthesizer)[Mode] = modeToSynthesizer[mode];
+  if (!Synthesizer) {
+    throw new Error(`Invalid response mode: ${mode}`);
   }
+  return new Synthesizer(options) as InstanceType<
+    (typeof modeToSynthesizer)[Mode]
+  >;
 }

packages/core/src/schema/node.ts

@@ -479,7 +479,7 @@ export function buildNodeFromSplits(
     ) {
       const imageDoc = doc as ImageNode;
       const imageNode = new ImageNode({
-        id_: imageDoc.id_ ?? idGenerator(i, imageDoc),
+        id_: idGenerator(i, imageDoc),
         text: textChunk,
         image: imageDoc.image,
         embedding: imageDoc.embedding,
@@ -496,7 +496,7 @@ export function buildNodeFromSplits(
     ) {
       const textDoc = doc as TextNode;
       const node = new TextNode({
-        id_: textDoc.id_ ?? idGenerator(i, textDoc),
+        id_: idGenerator(i, textDoc),
         text: textChunk,
         embedding: textDoc.embedding,
         excludedEmbedMetadataKeys: [...textDoc.excludedEmbedMetadataKeys],

packages/core/src/tools/function-tool.ts

@@ -0,0 +1,62 @@
+import type { JSONSchemaType } from "ajv";
+import { z } from "zod";
+import { zodToJsonSchema } from "zod-to-json-schema";
+import type { JSONValue } from "../global";
+import type { BaseTool, ToolMetadata } from "../llms";
+
+const kOriginalFn = Symbol("originalFn");
+
+export class FunctionTool<T, R extends JSONValue | Promise<JSONValue>>
+  implements BaseTool<T>
+{
+  [kOriginalFn]?: (input: T) => R;
+
+  #fn: (input: T) => R;
+  #metadata: ToolMetadata<JSONSchemaType<T>>;
+  // todo: for the future, we can use zod to validate the input parameters
+  #zodType: z.ZodType<T> | null = null;
+  constructor(
+    fn: (input: T) => R,
+    metadata: ToolMetadata<JSONSchemaType<T>>,
+    zodType?: z.ZodType<T>,
+  ) {
+    this.#fn = fn;
+    this.#metadata = metadata;
+    if (zodType) {
+      this.#zodType = zodType;
+    }
+  }
+
+  static from<T>(
+    fn: (input: T) => JSONValue | Promise<JSONValue>,
+    schema: ToolMetadata<JSONSchemaType<T>>,
+  ): FunctionTool<T, JSONValue | Promise<JSONValue>>;
+  static from<T, R extends z.ZodType<T>>(
+    fn: (input: T) => JSONValue | Promise<JSONValue>,
+    schema: Omit<ToolMetadata, "parameters"> & {
+      parameters: R;
+    },
+  ): FunctionTool<T, JSONValue>;
+  static from(fn: any, schema: any): any {
+    if (schema.parameter instanceof z.ZodSchema) {
+      const jsonSchema = zodToJsonSchema(schema.parameter);
+      return new FunctionTool(
+        fn,
+        {
+          ...schema,
+          parameters: jsonSchema,
+        },
+        schema.parameter,
+      );
+    }
+    return new FunctionTool(fn, schema);
+  }
+
+  get metadata(): BaseTool<T>["metadata"] {
+    return this.#metadata as BaseTool<T>["metadata"];
+  }
+
+  call(input: T) {
+    return this.#fn.call(null, input);
+  }
+}

packages/core/src/tools/index.ts

@@ -0,0 +1 @@
+export { FunctionTool } from "./function-tool";

packages/core/tests/node-parser/text-splitter.test.ts

@@ -2,6 +2,7 @@ import {
   SentenceSplitter,
   splitBySentenceTokenizer,
 } from "@llamaindex/core/node-parser";
+import { Document } from "@llamaindex/core/schema";
 import { describe, expect, test } from "vitest";
 
 describe("sentence splitter", () => {
@@ -115,4 +116,26 @@ describe("sentence splitter", () => {
     const split = splitBySentenceTokenizer();
     expect(split(text)).toEqual([text]);
   });
+
+  test("split nodes with UUID IDs and correct relationships", () => {
+    const UUID_REGEX =
+      /^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$/i;
+    const sentenceSplitter = new SentenceSplitter();
+    const docId = "test-doc-id";
+    const doc = new Document({
+      id_: docId,
+      text: "This is a test sentence. This is another test sentence.",
+    });
+    const nodes = sentenceSplitter.getNodesFromDocuments([doc]);
+    nodes.forEach((node) => {
+      // test node id should match uuid regex
+      expect(node.id_).toMatch(UUID_REGEX);
+
+      // test source reference to the doc ID
+      const source = node.relationships?.SOURCE;
+      expect(source).toBeDefined();
+      expect(source).toHaveProperty("nodeId");
+      expect((source as { nodeId: string }).nodeId).toEqual(docId);
+    });
+  });
 });

packages/core/tests/tools.test.ts

@@ -0,0 +1,35 @@
+import { FunctionTool } from "@llamaindex/core/tools";
+import { describe, test } from "vitest";
+import { z } from "zod";
+
+describe("FunctionTool", () => {
+  test("type system", () => {
+    FunctionTool.from((input: string) => input, {
+      name: "test",
+      description: "test",
+    });
+    FunctionTool.from(({ input }: { input: string }) => input, {
+      name: "test",
+      description: "test",
+      parameters: {
+        type: "object",
+        properties: {
+          input: {
+            type: "string",
+          },
+        },
+        required: ["input"],
+      },
+    });
+    const inputSchema = z
+      .object({
+        input: z.string(),
+      })
+      .required();
+    FunctionTool.from(({ input }: { input: string }) => input, {
+      name: "test",
+      description: "test",
+      parameters: inputSchema,
+    });
+  });
+});

packages/core/tools/package.json

@@ -0,0 +1,8 @@
+{
+  "type": "module",
+  "main": "./dist/index.cjs",
+  "module": "./dist/index.js",
+  "types": "./dist/index.d.ts",
+  "exports": "./dist/index.js",
+  "private": true
+}

packages/core/vector-store/package.json

@@ -0,0 +1,8 @@
+{
+  "type": "module",
+  "main": "./dist/index.cjs",
+  "module": "./dist/index.js",
+  "types": "./dist/index.d.ts",
+  "exports": "./dist/index.js",
+  "private": true
+}

packages/env/CHANGELOG.md

@@ -1,5 +1,12 @@
 # @llamaindex/env
 
+## 0.1.14
+
+### Patch Changes
+
+- ae49ff4: feat: use `gpt-tokenizer`
+- a75af83: refactor: move some llm and embedding to single package
+
 ## 0.1.13
 
 ### Patch Changes

packages/env/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/env",
   "description": "environment wrapper, supports all JS environment including node, deno, bun, edge runtime, and cloudflare worker",
-  "version": "0.1.13",
+  "version": "0.1.14",
   "type": "module",
   "types": "dist/type/index.d.ts",
   "main": "dist/cjs/index.js",
@@ -75,8 +75,8 @@
     "@swc/core": "^1.7.22",
     "@xenova/transformers": "^2.17.2",
     "concurrently": "^8.2.2",
+    "gpt-tokenizer": "^2.5.0",
     "pathe": "^1.1.2",
-    "tiktoken": "^1.0.16",
     "vitest": "^2.0.5"
   },
   "dependencies": {
@@ -85,9 +85,9 @@
   "peerDependencies": {
     "@aws-crypto/sha256-js": "^5.2.0",
     "@xenova/transformers": "^2.17.2",
+    "gpt-tokenizer": "^2.5.0",
     "js-tiktoken": "^1.0.12",
-    "pathe": "^1.1.2",
-    "tiktoken": "^1.0.15"
+    "pathe": "^1.1.2"
   },
   "peerDependenciesMeta": {
     "@aws-crypto/sha256-js": {

packages/env/src/index.browser.ts

@@ -14,7 +14,7 @@ export {
   type OnLoad,
 } from "./multi-model/index.browser.js";
 export { Tokenizers, tokenizers, type Tokenizer } from "./tokenizers/js.js";
-
+export { NotSupportCurrentRuntimeClass } from "./utils/shared.js";
 // @ts-expect-error
 if (typeof window === "undefined") {
   console.warn(

packages/env/src/index.edge-light.ts

@@ -14,3 +14,4 @@ export {
   type OnLoad,
 } from "./multi-model/index.non-nodejs.js";
 export { Tokenizers, tokenizers, type Tokenizer } from "./tokenizers/js.js";
+export { NotSupportCurrentRuntimeClass } from "./utils/shared.js";

packages/env/src/index.ts

@@ -47,6 +47,7 @@ export {
   getEnv,
   setEnvs,
 } from "./utils/index.js";
+export { NotSupportCurrentRuntimeClass } from "./utils/shared.js";
 export {
   createWriteStream,
   EOL,

packages/env/src/index.workerd.ts

@@ -7,6 +7,8 @@
  */
 import { INTERNAL_ENV } from "./utils/index.js";
 
+export { NotSupportCurrentRuntimeClass } from "./utils/shared.js";
+
 export * from "./node-polyfill.js";
 
 export function getEnv(name: string): string | undefined {

packages/env/src/tokenizers/node.ts

@@ -2,21 +2,18 @@
 import type { Tokenizer } from "./types.js";
 import { Tokenizers } from "./types.js";
 
-import { get_encoding } from "tiktoken";
+import cl100kBase from "gpt-tokenizer";
 
 class TokenizerSingleton {
-  private defaultTokenizer: Tokenizer;
+  #defaultTokenizer: Tokenizer;
 
   constructor() {
-    const encoding = get_encoding("cl100k_base");
-
-    this.defaultTokenizer = {
-      encode: (text: string) => {
-        return encoding.encode(text);
+    this.#defaultTokenizer = {
+      encode: (text: string): Uint32Array => {
+        return new Uint32Array(cl100kBase.encode(text));
       },
       decode: (tokens: Uint32Array) => {
-        const text = encoding.decode(tokens);
-        return new TextDecoder().decode(text);
+        return cl100kBase.decode(tokens);
       },
     };
   }
@@ -26,7 +23,7 @@ class TokenizerSingleton {
       throw new Error(`Tokenizer encoding ${encoding} not yet supported`);
     }
 
-    return this.defaultTokenizer;
+    return this.#defaultTokenizer;
   }
 }
 

packages/env/src/utils/shared.ts

@@ -0,0 +1,13 @@
+export class NotSupportCurrentRuntimeClass {
+  constructor(runtime: string) {
+    throw new Error(`Current environment ${runtime} is not supported`);
+  }
+
+  static bind(runtime: string) {
+    return class extends NotSupportCurrentRuntimeClass {
+      constructor(...args: any[]) {
+        super(runtime);
+      }
+    } as any;
+  }
+}

packages/env/tests/tokenizer.test.ts

@@ -0,0 +1,11 @@
+import { describe, expect, it } from "vitest";
+import { tokenizers } from "../src/tokenizers/node.js";
+
+describe("tokenizer", () => {
+  it("should tokenize text", () => {
+    const tokenizer = tokenizers.tokenizer();
+    expect(tokenizer.decode(tokenizer.encode("hello world"))).toBe(
+      "hello world",
+    );
+  });
+});

packages/experimental/CHANGELOG.md

@@ -1,5 +1,72 @@
 # @llamaindex/experimental
 
+## 0.0.102
+
+### Patch Changes
+
+- a75af83: refactor: move some llm and embedding to single package
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.0.101
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+
+## 0.0.100
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.0.99
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.0.98
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.0.97
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.0.96
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.0.95
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
 ## 0.0.94
 
 ### Patch Changes

packages/experimental/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/experimental",
   "description": "Experimental package for LlamaIndexTS",
-  "version": "0.0.94",
+  "version": "0.0.102",
   "type": "module",
   "types": "dist/type/index.d.ts",
   "main": "dist/cjs/index.js",

packages/llamaindex/CHANGELOG.md

@@ -1,5 +1,104 @@
 # llamaindex
 
+## 0.7.1
+
+### Patch Changes
+
+- ae49ff4: feat: use `gpt-tokenizer`
+- 4c38c1b: fix(cloud): do not detect file type in llama parse
+- a75af83: feat: allow passing perform setup in pg vector store
+- a75af83: refactor: move some llm and embedding to single package
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [24d065f]
+- Updated dependencies [a75af83]
+  - @llamaindex/env@0.1.14
+  - @llamaindex/cloud@1.0.1
+  - @llamaindex/huggingface@0.0.2
+  - @llamaindex/portkey-ai@0.0.2
+  - @llamaindex/anthropic@0.0.2
+  - @llamaindex/deepinfra@0.0.2
+  - @llamaindex/replicate@0.0.2
+  - @llamaindex/ollama@0.0.9
+  - @llamaindex/openai@0.1.18
+  - @llamaindex/clip@0.0.2
+  - @llamaindex/groq@0.0.17
+  - @llamaindex/core@0.3.1
+
+## 0.7.0
+
+### Minor Changes
+
+- 1364e8e: update metadata extractors to use PromptTemplate
+- 96fc69c: Correct initialization of QuestionsAnsweredExtractor so that it uses the promptTemplate arg when passed in
+
+### Patch Changes
+
+- 3b7736f: feat: added gemini 002 support
+- Updated dependencies [1364e8e]
+- Updated dependencies [96fc69c]
+  - @llamaindex/core@0.3.0
+  - @llamaindex/cloud@1.0.0
+  - @llamaindex/ollama@0.0.8
+  - @llamaindex/openai@0.1.17
+  - @llamaindex/groq@0.0.16
+
+## 0.6.22
+
+### Patch Changes
+
+- 5729bd9: Fix LlamaCloud API calls for ensuring an index and for file uploads
+
+## 0.6.21
+
+### Patch Changes
+
+- 6f75306: feat: support metadata filters for AstraDB
+- 94cb4ad: feat: Add metadata filters to ChromaDb and update to 1.9.2
+
+## 0.6.20
+
+### Patch Changes
+
+- 6a9a7b1: fix: take init api key into account
+- Updated dependencies [6a9a7b1]
+  - @llamaindex/openai@0.1.16
+  - @llamaindex/groq@0.0.15
+
+## 0.6.19
+
+### Patch Changes
+
+- 62cba52: Add ensureIndex function to LlamaCloudIndex
+- d265e96: fix: ignore resolving unpdf for nextjs
+- d30bbf7: Convert undefined values to null in LlamaCloud filters
+- 53fd00a: Fix getPipelineId in LlamaCloudIndex
+
+## 0.6.18
+
+### Patch Changes
+
+- 5f67820: Fix that node parsers generate nodes with UUIDs
+- fe08d04: Fix LlamaCloud retrieval with multiple pipelines
+- Updated dependencies [5f67820]
+  - @llamaindex/core@0.2.12
+  - @llamaindex/cloud@0.2.14
+  - @llamaindex/ollama@0.0.7
+  - @llamaindex/openai@0.1.15
+  - @llamaindex/groq@0.0.14
+
+## 0.6.17
+
+### Patch Changes
+
+- ee697fb: fix: generate uuid when inserting to Qdrant
+- Updated dependencies [ee697fb]
+  - @llamaindex/core@0.2.11
+  - @llamaindex/cloud@0.2.13
+  - @llamaindex/ollama@0.0.6
+  - @llamaindex/openai@0.1.14
+  - @llamaindex/groq@0.0.13
+
 ## 0.6.16
 
 ### Patch Changes

packages/llamaindex/e2e/examples/cloudflare-worker-agent/CHANGELOG.md

@@ -1,5 +1,71 @@
 # @llamaindex/cloudflare-worker-agent-test
 
+## 0.0.86
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.0.85
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+
+## 0.0.84
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.0.83
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.0.82
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.0.81
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.0.80
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.0.79
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
 ## 0.0.78
 
 ### Patch Changes

packages/llamaindex/e2e/examples/cloudflare-worker-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.78",
+  "version": "0.0.86",
   "type": "module",
   "private": true,
   "scripts": {

packages/llamaindex/e2e/examples/llama-parse-browser/CHANGELOG.md

@@ -1,5 +1,32 @@
 # @llamaindex/llama-parse-browser-test
 
+## 0.0.12
+
+### Patch Changes
+
+- Updated dependencies [4c38c1b]
+- Updated dependencies [24d065f]
+- Updated dependencies [a75af83]
+  - @llamaindex/cloud@1.0.1
+
+## 0.0.11
+
+### Patch Changes
+
+- @llamaindex/cloud@1.0.0
+
+## 0.0.10
+
+### Patch Changes
+
+- @llamaindex/cloud@0.2.14
+
+## 0.0.9
+
+### Patch Changes
+
+- @llamaindex/cloud@0.2.13
+
 ## 0.0.8
 
 ### Patch Changes

packages/llamaindex/e2e/examples/llama-parse-browser/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/llama-parse-browser-test",
   "private": true,
-  "version": "0.0.8",
+  "version": "0.0.12",
   "type": "module",
   "scripts": {
     "dev": "vite",

packages/llamaindex/e2e/examples/nextjs-agent/CHANGELOG.md

@@ -1,5 +1,71 @@
 # @llamaindex/next-agent-test
 
+## 0.1.86
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.1.85
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+
+## 0.1.84
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.1.83
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.1.82
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.1.81
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.1.80
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.1.79
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
 ## 0.1.78
 
 ### Patch Changes

packages/llamaindex/e2e/examples/nextjs-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-agent-test",
-  "version": "0.1.78",
+  "version": "0.1.86",
   "private": true,
   "scripts": {
     "dev": "next dev",

packages/llamaindex/e2e/examples/nextjs-edge-runtime/CHANGELOG.md

@@ -1,5 +1,71 @@
 # test-edge-runtime
 
+## 0.1.85
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.1.84
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+
+## 0.1.83
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.1.82
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.1.81
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.1.80
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.1.79
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.1.78
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
 ## 0.1.77
 
 ### Patch Changes

packages/llamaindex/e2e/examples/nextjs-edge-runtime/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/nextjs-edge-runtime-test",
-  "version": "0.1.77",
+  "version": "0.1.85",
   "private": true,
   "scripts": {
     "dev": "next dev",

packages/llamaindex/e2e/examples/nextjs-edge-runtime/src/app/page.tsx

@@ -1,19 +1,12 @@
-import { tokenizerResultPromise } from "@/utils/llm";
-import { use } from "react";
+import "@/utils/llm";
 
 export const runtime = "edge";
 
 export default function Home() {
-  const result = use(tokenizerResultPromise);
   return (
     <main>
       <div>
         <h1>Next.js Edge Runtime</h1>
-        <div>
-          {result.map((value, index) => (
-            <span key={index}>{value}</span>
-          ))}
-        </div>
       </div>
     </main>
   );

packages/llamaindex/e2e/examples/nextjs-edge-runtime/src/utils/llm.ts

@@ -1,23 +1,8 @@
 // test runtime
 import "llamaindex";
-import { ClipEmbedding } from "llamaindex";
 import "llamaindex/readers/SimpleDirectoryReader";
 
 // @ts-expect-error
 if (typeof EdgeRuntime !== "string") {
   throw new Error("Expected run in EdgeRuntime");
 }
-
-export const tokenizerResultPromise = new Promise<number[]>(
-  (resolve, reject) => {
-    const embedding = new ClipEmbedding();
-    //#region make sure @xenova/transformers is working in edge runtime
-    embedding
-      .getTokenizer()
-      .then((tokenizer) => {
-        resolve(tokenizer.encode("hello world"));
-      })
-      .catch(reject);
-    //#endregion
-  },
-);

packages/llamaindex/e2e/examples/nextjs-node-runtime/CHANGELOG.md

@@ -1,5 +1,71 @@
 # @llamaindex/next-node-runtime
 
+## 0.0.67
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.0.66
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+
+## 0.0.65
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.0.64
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.0.63
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.0.62
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.0.61
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.0.60
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
 ## 0.0.59
 
 ### Patch Changes

packages/llamaindex/e2e/examples/nextjs-node-runtime/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-node-runtime-test",
-  "version": "0.0.59",
+  "version": "0.0.67",
   "private": true,
   "scripts": {
     "dev": "next dev",

packages/llamaindex/e2e/examples/waku-query-engine/CHANGELOG.md

@@ -1,5 +1,71 @@
 # @llamaindex/waku-query-engine-test
 
+## 0.0.86
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.0.85
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+
+## 0.0.84
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.0.83
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.0.82
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.0.81
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.0.80
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.0.79
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
 ## 0.0.78
 
 ### Patch Changes

packages/llamaindex/e2e/examples/waku-query-engine/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/waku-query-engine-test",
-  "version": "0.0.78",
+  "version": "0.0.86",
   "type": "module",
   "private": true,
   "scripts": {
@@ -10,17 +10,16 @@
   },
   "dependencies": {
     "llamaindex": "workspace:*",
-    "react": "19.0.0-rc-7771d3a7-20240827",
-    "react-dom": "19.0.0-rc-7771d3a7-20240827",
-    "react-server-dom-webpack": "19.0.0-rc-7771d3a7-20240827",
-    "waku": "0.21.1"
+    "react": "19.0.0-rc-bf7e210c-20241017",
+    "react-dom": "19.0.0-rc-bf7e210c-20241017",
+    "react-server-dom-webpack": "19.0.0-rc-bf7e210c-20241017",
+    "waku": "0.21.4"
   },
   "devDependencies": {
-    "@types/react": "18.3.5",
-    "@types/react-dom": "18.3.0",
-    "autoprefixer": "10.4.20",
-    "tailwindcss": "3.4.10",
-    "typescript": "5.6.2",
-    "vite-plugin-wasm": "^3.3.0"
+    "@types/react": "18.3.11",
+    "@types/react-dom": "18.3.1",
+    "autoprefixer": "^10.4.20",
+    "tailwindcss": "^3.4.14",
+    "typescript": "5.6.2"
   }
 }

packages/llamaindex/e2e/examples/waku-query-engine/vite.config.ts

@@ -1,8 +0,0 @@
-import wasm from "vite-plugin-wasm";
-
-export default {
-  plugins: [wasm()],
-  ssr: {
-    external: ["tiktoken"],
-  },
-};

packages/llamaindex/e2e/node/vector-store/pg-vector-store.e2e.ts

@@ -105,3 +105,22 @@ await test("simple node", async (t) => {
     assert.deepStrictEqual(result.nodes, []);
   }
 });
+
+await test("no setup", async (t) => {
+  // @ts-expect-error private method
+  assert.ok(PGVectorStore.prototype.checkSchema);
+  // @ts-expect-error private method
+  const Mock = class extends PGVectorStore {
+    private override async checkSchema(): Promise<any> {
+      throw new Error("should not be called");
+    }
+  };
+  const vectorStore = new Mock({
+    clientConfig: pgConfig,
+    performSetup: false,
+  });
+  const db = await vectorStore.client();
+  t.after(async () => {
+    await db.close();
+  });
+});

packages/llamaindex/package.json

@@ -1,6 +1,6 @@
 {
   "name": "llamaindex",
-  "version": "0.6.16",
+  "version": "0.7.1",
   "license": "MIT",
   "type": "module",
   "keywords": [
@@ -29,13 +29,18 @@
     "@google-cloud/vertexai": "1.2.0",
     "@google/generative-ai": "0.12.0",
     "@grpc/grpc-js": "^1.11.1",
-    "@huggingface/inference": "^2.8.0",
+    "@llamaindex/anthropic": "workspace:*",
+    "@llamaindex/clip": "workspace:*",
     "@llamaindex/cloud": "workspace:*",
     "@llamaindex/core": "workspace:*",
+    "@llamaindex/deepinfra": "workspace:*",
     "@llamaindex/env": "workspace:*",
     "@llamaindex/groq": "workspace:*",
+    "@llamaindex/huggingface": "workspace:*",
     "@llamaindex/ollama": "workspace:*",
     "@llamaindex/openai": "workspace:*",
+    "@llamaindex/portkey-ai": "workspace:*",
+    "@llamaindex/replicate": "workspace:^0.0.2",
     "@mistralai/mistralai": "^1.0.4",
     "@mixedbread-ai/sdk": "^2.2.11",
     "@pinecone-database/pinecone": "^3.0.2",
@@ -48,9 +53,10 @@
     "@zilliz/milvus2-sdk-node": "^2.4.6",
     "ajv": "^8.17.1",
     "assemblyai": "^4.7.0",
-    "chromadb": "1.8.1",
+    "chromadb": "1.9.2",
     "cohere-ai": "7.13.0",
     "discord-api-types": "^0.37.98",
+    "gpt-tokenizer": "^2.5.0",
     "groq-sdk": "^0.6.1",
     "js-tiktoken": "^1.0.14",
     "lodash": "^4.17.21",
@@ -62,10 +68,8 @@
     "openai": "^4.60.0",
     "papaparse": "^5.4.1",
     "pathe": "^1.1.2",
-    "portkey-ai": "0.1.16",
     "rake-modified": "^1.0.8",
     "string-strip-html": "^13.4.8",
-    "tiktoken": "^1.0.15",
     "unpdf": "^0.11.0",
     "weaviate-client": "^3.1.4",
     "wikipedia": "^2.1.2",

packages/llamaindex/src/Settings.ts

@@ -11,7 +11,6 @@ import {
   type NodeParser,
   SentenceSplitter,
 } from "@llamaindex/core/node-parser";
-import type { LoadTransformerEvent } from "@llamaindex/env";
 import { AsyncLocalStorage } from "@llamaindex/env";
 import type { ServiceContext } from "./ServiceContext.js";
 import {
@@ -20,12 +19,6 @@ import {
   withEmbeddedModel,
 } from "./internal/settings/EmbedModel.js";
 
-declare module "@llamaindex/core/global" {
-  interface LlamaIndexEventMaps {
-    "load-transformers": LoadTransformerEvent;
-  }
-}
-
 export type PromptConfig = {
   llm?: string;
   lang?: string;

packages/llamaindex/src/agent/anthropic.ts

@@ -1,43 +1 @@
-import {
-  LLMAgent,
-  LLMAgentWorker,
-  type LLMAgentParams,
-} from "@llamaindex/core/agent";
-import type {
-  NonStreamingChatEngineParams,
-  StreamingChatEngineParams,
-} from "@llamaindex/core/chat-engine";
-import type { EngineResponse } from "@llamaindex/core/schema";
-import { Settings } from "../Settings.js";
-import { Anthropic } from "../llm/anthropic.js";
-
-export type AnthropicAgentParams = LLMAgentParams;
-
-export class AnthropicAgentWorker extends LLMAgentWorker {}
-
-export class AnthropicAgent extends LLMAgent {
-  constructor(params: AnthropicAgentParams) {
-    const llm =
-      params.llm ??
-      (Settings.llm instanceof Anthropic
-        ? (Settings.llm as Anthropic)
-        : new Anthropic());
-    super({
-      ...params,
-      llm,
-    });
-  }
-
-  async chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
-  async chat(params: StreamingChatEngineParams): Promise<never>;
-  override async chat(
-    params: NonStreamingChatEngineParams | StreamingChatEngineParams,
-  ) {
-    const { stream } = params;
-    if (stream) {
-      // Anthropic does support this, but looks like it's not supported in the LITS LLM
-      throw new Error("Anthropic does not support streaming");
-    }
-    return super.chat(params);
-  }
-}
+export * from "@llamaindex/anthropic";

packages/llamaindex/src/cloud/LLamaCloudFileService.ts

@@ -41,7 +41,7 @@ export class LLamaCloudFileService {
   ) {
     initService();
     const { data: file } = await FilesService.uploadFileApiV1FilesPost({
-      path: { project_id: projectId },
+      query: { project_id: projectId },
       body: {
         upload_file: uploadFile,
       },
@@ -85,7 +85,7 @@ export class LLamaCloudFileService {
       await new Promise((resolve) => setTimeout(resolve, 100)); // Sleep for 100ms
     }
     throw new Error(
-      `File processing did not complete after ${maxAttempts} attempts.`,
+      `File processing did not complete after ${maxAttempts} attempts. Check your LlamaCloud index at https://cloud.llamaindex.ai/project/${projectId}/deploy/${pipelineId} for more details.`,
     );
   }
 

packages/llamaindex/src/cloud/LlamaCloudIndex.ts

@@ -1,19 +1,21 @@
 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 { Document } from "@llamaindex/core/schema";
 import { RetrieverQueryEngine } from "../engines/query/RetrieverQueryEngine.js";
 import type { BaseNodePostprocessor } from "../postprocessors/types.js";
 import type { CloudRetrieveParams } from "./LlamaCloudRetriever.js";
 import { LlamaCloudRetriever } from "./LlamaCloudRetriever.js";
-import { getPipelineCreate } from "./config.js";
 import type { CloudConstructorParams } from "./type.js";
-import { getAppBaseUrl, getProjectId, initService } from "./utils.js";
+import {
+  getAppBaseUrl,
+  getPipelineId,
+  getProjectId,
+  initService,
+} from "./utils.js";
 
-import { PipelinesService, ProjectsService } from "@llamaindex/cloud/api";
-import { SentenceSplitter } from "@llamaindex/core/node-parser";
+import { PipelinesService, type PipelineCreate } from "@llamaindex/cloud/api";
 import type { BaseRetriever } from "@llamaindex/core/retriever";
 import { getEnv } from "@llamaindex/env";
-import { OpenAIEmbedding } from "@llamaindex/openai";
 import { Settings } from "../Settings.js";
 
 export class LlamaCloudIndex {
@@ -28,10 +30,7 @@ export class LlamaCloudIndex {
     verbose = Settings.debug,
     raiseOnError = false,
   ): Promise<void> {
-    const pipelineId = await this.getPipelineId(
-      this.params.name,
-      this.params.projectName,
-    );
+    const pipelineId = await this.getPipelineId();
 
     if (verbose) {
       console.log("Waiting for pipeline ingestion: ");
@@ -78,10 +77,7 @@ export class LlamaCloudIndex {
     verbose = Settings.debug,
     raiseOnError = false,
   ): Promise<void> {
-    const pipelineId = await this.getPipelineId(
-      this.params.name,
-      this.params.projectName,
-    );
+    const pipelineId = await this.getPipelineId();
 
     if (verbose) {
       console.log("Loading data: ");
@@ -143,17 +139,13 @@ export class LlamaCloudIndex {
   public async getPipelineId(
     name?: string,
     projectName?: string,
+    organizationId?: string,
   ): Promise<string> {
-    const { data: pipelines } =
-      await PipelinesService.searchPipelinesApiV1PipelinesGet({
-        path: {
-          project_id: await this.getProjectId(projectName),
-          project_name: name ?? this.params.name,
-        },
-        throwOnError: true,
-      });
-
-    return pipelines[0]!.id;
+    return await getPipelineId(
+      name ?? this.params.name,
+      projectName ?? this.params.projectName,
+      organizationId ?? this.params.organizationId,
+    );
   }
 
   public async getProjectId(
@@ -166,75 +158,42 @@ export class LlamaCloudIndex {
     );
   }
 
+  /**
+   * Adds documents to the given index parameters. If the index does not exist, it will be created.
+   *
+   * @param params - An object containing the following properties:
+   *   - documents: An array of Document objects to be added to the index.
+   *   - verbose: Optional boolean to enable verbose logging.
+   *   - Additional properties from CloudConstructorParams.
+   * @returns A Promise that resolves to a new LlamaCloudIndex instance.
+   */
   static async fromDocuments(
     params: {
       documents: Document[];
-      transformations?: TransformComponent[];
       verbose?: boolean;
     } & CloudConstructorParams,
+    config?: {
+      embedding: PipelineCreate["embedding_config"];
+      transform: PipelineCreate["transform_config"];
+    },
   ): Promise<LlamaCloudIndex> {
-    initService(params);
-    const defaultTransformations: TransformComponent[] = [
-      new SentenceSplitter(),
-      new OpenAIEmbedding({
-        apiKey: getEnv("OPENAI_API_KEY"),
-      }),
-    ];
+    const index = new LlamaCloudIndex({ ...params });
+    await index.ensureIndex({ ...config, verbose: params.verbose ?? false });
+    await index.addDocuments(params.documents, params.verbose);
+    return index;
+  }
+
+  async addDocuments(documents: Document[], verbose?: boolean): Promise<void> {
     const apiUrl = getAppBaseUrl();
-
-    const pipelineCreateParams = await getPipelineCreate({
-      pipelineName: params.name,
-      pipelineType: "MANAGED",
-      inputNodes: params.documents,
-      transformations: params.transformations ?? defaultTransformations,
-    });
-
-    const { data: project } =
-      await ProjectsService.upsertProjectApiV1ProjectsPut({
-        path: {
-          organization_id: params.organizationId,
-        },
-        body: {
-          name: params.projectName ?? "default",
-        },
-        throwOnError: true,
-      });
-
-    if (!project.id) {
-      throw new Error("Project ID should be defined");
-    }
-
-    const { data: pipeline } =
-      await PipelinesService.upsertPipelineApiV1PipelinesPut({
-        path: {
-          project_id: project.id,
-        },
-        body: pipelineCreateParams.configured_transformations
-          ? {
-              name: params.name,
-              configured_transformations:
-                pipelineCreateParams.configured_transformations,
-            }
-          : {
-              name: params.name,
-            },
-        throwOnError: true,
-      });
-
-    if (!pipeline.id) {
-      throw new Error("Pipeline ID must be defined");
-    }
-
-    if (params.verbose) {
-      console.log(`Created pipeline ${pipeline.id} with name ${params.name}`);
-    }
+    const projectId = await this.getProjectId();
+    const pipelineId = await this.getPipelineId();
 
     await PipelinesService.upsertBatchPipelineDocumentsApiV1PipelinesPipelineIdDocumentsPut(
       {
         path: {
-          pipeline_id: pipeline.id,
+          pipeline_id: pipelineId,
         },
-        body: params.documents.map((doc) => ({
+        body: documents.map((doc) => ({
           metadata: doc.metadata,
           text: doc.text,
           excluded_embed_metadata_keys: doc.excludedEmbedMetadataKeys,
@@ -248,7 +207,7 @@ export class LlamaCloudIndex {
       const { data: pipelineStatus } =
         await PipelinesService.getPipelineStatusApiV1PipelinesPipelineIdStatusGet(
           {
-            path: { pipeline_id: pipeline.id },
+            path: { pipeline_id: pipelineId },
             throwOnError: true,
           },
         );
@@ -262,32 +221,30 @@ export class LlamaCloudIndex {
 
       if (pipelineStatus.status === "ERROR") {
         console.error(
-          `Some documents failed to ingest, check your pipeline logs at ${apiUrl}/project/${project.id}/deploy/${pipeline.id}`,
+          `Some documents failed to ingest, check your pipeline logs at ${apiUrl}/project/${projectId}/deploy/${pipelineId}`,
         );
         throw new Error("Some documents failed to ingest");
       }
 
       if (pipelineStatus.status === "PARTIAL_SUCCESS") {
         console.info(
-          `Documents ingestion partially succeeded, to check a more complete status check your pipeline at ${apiUrl}/project/${project.id}/deploy/${pipeline.id}`,
+          `Documents ingestion partially succeeded, to check a more complete status check your pipeline at ${apiUrl}/project/${projectId}/deploy/${pipelineId}`,
         );
         break;
       }
 
-      if (params.verbose) {
+      if (verbose) {
         process.stdout.write(".");
       }
 
       await new Promise((resolve) => setTimeout(resolve, 1000));
     }
 
-    if (params.verbose) {
+    if (verbose) {
       console.info(
-        `Ingestion completed, find your index at ${apiUrl}/project/${project.id}/deploy/${pipeline.id}`,
+        `Ingestion completed, find your index at ${apiUrl}/project/${projectId}/deploy/${pipelineId}`,
       );
     }
-
-    return new LlamaCloudIndex({ ...params });
   }
 
   asRetriever(params: CloudRetrieveParams = {}): BaseRetriever {
@@ -313,14 +270,7 @@ export class LlamaCloudIndex {
   }
 
   async insert(document: Document) {
-    const pipelineId = await this.getPipelineId(
-      this.params.name,
-      this.params.projectName,
-    );
-
-    if (!pipelineId) {
-      throw new Error("We couldn't find the pipeline ID for the given name");
-    }
+    const pipelineId = await this.getPipelineId();
 
     await PipelinesService.createBatchPipelineDocumentsApiV1PipelinesPipelineIdDocumentsPost(
       {
@@ -343,14 +293,7 @@ export class LlamaCloudIndex {
   }
 
   async delete(document: Document) {
-    const pipelineId = await this.getPipelineId(
-      this.params.name,
-      this.params.projectName,
-    );
-
-    if (!pipelineId) {
-      throw new Error("We couldn't find the pipeline ID for the given name");
-    }
+    const pipelineId = await this.getPipelineId();
 
     await PipelinesService.deletePipelineDocumentApiV1PipelinesPipelineIdDocumentsDocumentIdDelete(
       {
@@ -365,14 +308,7 @@ export class LlamaCloudIndex {
   }
 
   async refreshDoc(document: Document) {
-    const pipelineId = await this.getPipelineId(
-      this.params.name,
-      this.params.projectName,
-    );
-
-    if (!pipelineId) {
-      throw new Error("We couldn't find the pipeline ID for the given name");
-    }
+    const pipelineId = await this.getPipelineId();
 
     await PipelinesService.upsertBatchPipelineDocumentsApiV1PipelinesPipelineIdDocumentsPut(
       {
@@ -393,4 +329,71 @@ export class LlamaCloudIndex {
 
     await this.waitForDocumentIngestion([document.id_]);
   }
+
+  public async ensureIndex(config?: {
+    embedding?: PipelineCreate["embedding_config"];
+    transform?: PipelineCreate["transform_config"];
+    verbose?: boolean;
+  }): Promise<void> {
+    const projectId = await this.getProjectId();
+
+    const { data: pipelines } =
+      await PipelinesService.searchPipelinesApiV1PipelinesGet({
+        query: {
+          project_id: projectId,
+          pipeline_name: this.params.name,
+        },
+        throwOnError: true,
+      });
+
+    if (pipelines.length === 0) {
+      // no pipeline found, create a new one
+      let embeddingConfig = config?.embedding;
+      if (!embeddingConfig) {
+        // no embedding config provided, use OpenAI as default
+        const openAIApiKey = getEnv("OPENAI_API_KEY");
+        const embeddingModel = getEnv("EMBEDDING_MODEL");
+        if (!openAIApiKey || !embeddingModel) {
+          throw new Error(
+            "No embedding configuration provided. Fallback to OpenAI embedding model. OPENAI_API_KEY and EMBEDDING_MODEL environment variables must be set.",
+          );
+        }
+        embeddingConfig = {
+          type: "OPENAI_EMBEDDING",
+          component: {
+            api_key: openAIApiKey,
+            model_name: embeddingModel,
+          },
+        };
+      }
+
+      let transformConfig = config?.transform;
+      if (!transformConfig) {
+        transformConfig = {
+          mode: "auto",
+          chunk_size: 1024,
+          chunk_overlap: 200,
+        };
+      }
+
+      const { data: pipeline } =
+        await PipelinesService.upsertPipelineApiV1PipelinesPut({
+          query: {
+            project_id: projectId,
+          },
+          body: {
+            name: this.params.name,
+            embedding_config: embeddingConfig,
+            transform_config: transformConfig,
+          },
+          throwOnError: true,
+        });
+
+      if (config?.verbose) {
+        console.log(
+          `Created pipeline ${pipeline.id} with name ${pipeline.name}`,
+        );
+      }
+    }
+  }
 }

packages/llamaindex/src/cloud/LlamaCloudRetriever.ts

@@ -1,4 +1,5 @@
 import {
+  type MetadataFilter,
   type MetadataFilters,
   PipelinesService,
   type RetrievalParams,
@@ -11,7 +12,7 @@ import type { NodeWithScore } from "@llamaindex/core/schema";
 import { jsonToNode, ObjectType } from "@llamaindex/core/schema";
 import { extractText } from "@llamaindex/core/utils";
 import type { ClientParams, CloudConstructorParams } from "./type.js";
-import { getProjectId, initService } from "./utils.js";
+import { getPipelineId, initService } from "./utils.js";
 
 export type CloudRetrieveParams = Omit<
   RetrievalParams,
@@ -42,6 +43,24 @@ export class LlamaCloudRetriever extends BaseRetriever {
     });
   }
 
+  // LlamaCloud expects null values for filters, but LlamaIndexTS uses undefined for empty values
+  // This function converts the undefined values to null
+  private convertFilter(filters?: MetadataFilters): MetadataFilters | null {
+    if (!filters) return null;
+
+    const processFilter = (
+      filter: MetadataFilter | MetadataFilters,
+    ): MetadataFilter | MetadataFilters => {
+      if ("filters" in filter) {
+        // type MetadataFilters
+        return { ...filter, filters: filter.filters.map(processFilter) };
+      }
+      return { ...filter, value: filter.value ?? null };
+    };
+
+    return { ...filters, filters: filters.filters.map(processFilter) };
+  }
+
   constructor(params: CloudConstructorParams & CloudRetrieveParams) {
     super();
     this.clientParams = { apiKey: params.apiKey, baseUrl: params.baseUrl };
@@ -57,45 +76,24 @@ export class LlamaCloudRetriever extends BaseRetriever {
   }
 
   async _retrieve(query: QueryBundle): Promise<NodeWithScore[]> {
-    const { data: pipelines } =
-      await PipelinesService.searchPipelinesApiV1PipelinesGet({
-        query: {
-          project_id: await getProjectId(this.projectName, this.organizationId),
-          project_name: this.pipelineName,
-        },
-        throwOnError: true,
-      });
+    const pipelineId = await getPipelineId(
+      this.pipelineName,
+      this.projectName,
+      this.organizationId,
+    );
 
-    if (pipelines.length === 0 || !pipelines[0]!.id) {
-      throw new Error(
-        `No pipeline found with name ${this.pipelineName} in project ${this.projectName}`,
-      );
-    }
-
-    const { data: pipeline } =
-      await PipelinesService.getPipelineApiV1PipelinesPipelineIdGet({
-        path: {
-          pipeline_id: pipelines[0]!.id,
-        },
-        throwOnError: true,
-      });
-
-    if (!pipeline) {
-      throw new Error(
-        `No pipeline found with name ${this.pipelineName} in project ${this.projectName}`,
-      );
-    }
+    const filters = this.convertFilter(this.retrieveParams.filters);
 
     const { data: results } =
       await PipelinesService.runSearchApiV1PipelinesPipelineIdRetrievePost({
         throwOnError: true,
         path: {
-          pipeline_id: pipeline.id,
+          pipeline_id: pipelineId,
         },
         body: {
           ...this.retrieveParams,
           query: extractText(query),
-          search_filters: this.retrieveParams.filters as MetadataFilters,
+          search_filters: filters,
           dense_similarity_top_k: this.retrieveParams.similarityTopK!,
         },
       });

packages/llamaindex/src/cloud/config.ts

@@ -1,55 +0,0 @@
-import type {
-  ConfiguredTransformationItem,
-  PipelineCreate,
-  PipelineType,
-} from "@llamaindex/cloud/api";
-import { SentenceSplitter } from "@llamaindex/core/node-parser";
-import { BaseNode, type TransformComponent } from "@llamaindex/core/schema";
-import { OpenAIEmbedding } from "@llamaindex/openai";
-
-export type GetPipelineCreateParams = {
-  pipelineName: string;
-  pipelineType: PipelineType;
-  transformations?: TransformComponent[];
-  inputNodes?: BaseNode[];
-};
-
-function getTransformationConfig(
-  transformation: TransformComponent,
-): ConfiguredTransformationItem {
-  if (transformation instanceof SentenceSplitter) {
-    return {
-      configurable_transformation_type: "SENTENCE_AWARE_NODE_PARSER",
-      component: {
-        chunk_size: transformation.chunkSize, // TODO: set to public in SentenceSplitter
-        chunk_overlap: transformation.chunkOverlap, // TODO: set to public in SentenceSplitter
-        include_metadata: transformation.includeMetadata,
-        include_prev_next_rel: transformation.includePrevNextRel,
-      },
-    };
-  }
-  if (transformation instanceof OpenAIEmbedding) {
-    return {
-      configurable_transformation_type: "OPENAI_EMBEDDING",
-      component: {
-        model: transformation.model,
-        api_key: transformation.apiKey,
-        embed_batch_size: transformation.embedBatchSize,
-        dimensions: transformation.dimensions,
-      },
-    };
-  }
-  throw new Error(`Unsupported transformation: ${typeof transformation}`);
-}
-
-export async function getPipelineCreate(
-  params: GetPipelineCreateParams,
-): Promise<PipelineCreate> {
-  const { pipelineName, pipelineType, transformations = [] } = params;
-
-  return {
-    name: pipelineName,
-    configured_transformations: transformations.map(getTransformationConfig),
-    pipeline_type: pipelineType,
-  };
-}

packages/llamaindex/src/cloud/utils.ts

@@ -1,4 +1,8 @@
-import { client, ProjectsService } from "@llamaindex/cloud/api";
+import {
+  client,
+  PipelinesService,
+  ProjectsService,
+} from "@llamaindex/cloud/api";
 import { DEFAULT_BASE_URL } from "@llamaindex/core/global";
 import { getEnv } from "@llamaindex/env";
 import type { ClientParams } from "./type.js";
@@ -40,9 +44,9 @@ export async function getProjectId(
 ): Promise<string> {
   const { data: projects } = await ProjectsService.listProjectsApiV1ProjectsGet(
     {
-      path: {
+      query: {
         project_name: projectName,
-        organization_id: organizationId,
+        organization_id: organizationId ?? null,
       },
       throwOnError: true,
     },
@@ -66,3 +70,26 @@ export async function getProjectId(
 
   return project.id;
 }
+
+export async function getPipelineId(
+  name: string,
+  projectName: string,
+  organizationId?: string,
+): Promise<string> {
+  const { data: pipelines } =
+    await PipelinesService.searchPipelinesApiV1PipelinesGet({
+      query: {
+        project_id: await getProjectId(projectName, organizationId),
+        pipeline_name: name,
+      },
+      throwOnError: true,
+    });
+
+  if (pipelines.length === 0 || !pipelines[0]!.id) {
+    throw new Error(
+      `No pipeline found with name ${name} in project ${projectName}`,
+    );
+  }
+
+  return pipelines[0]!.id;
+}

packages/llamaindex/src/embeddings/ClipEmbedding.ts

@@ -1,139 +1 @@
-import { MultiModalEmbedding } from "@llamaindex/core/embeddings";
-import type { ImageType } from "@llamaindex/core/schema";
-import _ from "lodash";
-// only import type, to avoid bundling error
-import { loadTransformers } from "@llamaindex/env";
-import type {
-  CLIPTextModelWithProjection,
-  CLIPVisionModelWithProjection,
-  PreTrainedTokenizer,
-  Processor,
-} from "@xenova/transformers";
-import { Settings } from "../Settings.js";
-
-async function readImage(input: ImageType) {
-  const { RawImage } = await loadTransformers((transformer) => {
-    Settings.callbackManager.dispatchEvent(
-      "load-transformers",
-      {
-        transformer,
-      },
-      true,
-    );
-  });
-  if (input instanceof Blob) {
-    return await RawImage.fromBlob(input);
-  } else if (_.isString(input) || input instanceof URL) {
-    return await RawImage.fromURL(input);
-  } else {
-    throw new Error(`Unsupported input type: ${typeof input}`);
-  }
-}
-
-export enum ClipEmbeddingModelType {
-  XENOVA_CLIP_VIT_BASE_PATCH32 = "Xenova/clip-vit-base-patch32",
-  XENOVA_CLIP_VIT_BASE_PATCH16 = "Xenova/clip-vit-base-patch16",
-}
-
-export class ClipEmbedding extends MultiModalEmbedding {
-  modelType: ClipEmbeddingModelType =
-    ClipEmbeddingModelType.XENOVA_CLIP_VIT_BASE_PATCH16;
-
-  private tokenizer: PreTrainedTokenizer | null = null;
-  private processor: Processor | null = null;
-  private visionModel: CLIPVisionModelWithProjection | null = null;
-  private textModel: CLIPTextModelWithProjection | null = null;
-
-  constructor() {
-    super();
-  }
-
-  async getTokenizer() {
-    const { AutoTokenizer } = await loadTransformers((transformer) => {
-      Settings.callbackManager.dispatchEvent(
-        "load-transformers",
-        {
-          transformer,
-        },
-        true,
-      );
-    });
-    if (!this.tokenizer) {
-      this.tokenizer = await AutoTokenizer.from_pretrained(this.modelType);
-    }
-    return this.tokenizer;
-  }
-
-  async getProcessor() {
-    const { AutoProcessor } = await loadTransformers((transformer) => {
-      Settings.callbackManager.dispatchEvent(
-        "load-transformers",
-        {
-          transformer,
-        },
-        true,
-      );
-    });
-    if (!this.processor) {
-      this.processor = await AutoProcessor.from_pretrained(this.modelType);
-    }
-    return this.processor;
-  }
-
-  async getVisionModel() {
-    const { CLIPVisionModelWithProjection } = await loadTransformers(
-      (transformer) => {
-        Settings.callbackManager.dispatchEvent(
-          "load-transformers",
-          {
-            transformer,
-          },
-          true,
-        );
-      },
-    );
-    if (!this.visionModel) {
-      this.visionModel = await CLIPVisionModelWithProjection.from_pretrained(
-        this.modelType,
-      );
-    }
-
-    return this.visionModel;
-  }
-
-  async getTextModel() {
-    const { CLIPTextModelWithProjection } = await loadTransformers(
-      (transformer) => {
-        Settings.callbackManager.dispatchEvent(
-          "load-transformers",
-          {
-            transformer,
-          },
-          true,
-        );
-      },
-    );
-    if (!this.textModel) {
-      this.textModel = await CLIPTextModelWithProjection.from_pretrained(
-        this.modelType,
-      );
-    }
-
-    return this.textModel;
-  }
-
-  async getImageEmbedding(image: ImageType): Promise<number[]> {
-    const loadedImage = await readImage(image);
-    const imageInputs = await (await this.getProcessor())(loadedImage);
-    const { image_embeds } = await (await this.getVisionModel())(imageInputs);
-    return Array.from(image_embeds.data);
-  }
-
-  async getTextEmbedding(text: string): Promise<number[]> {
-    const textInputs = await (
-      await this.getTokenizer()
-    )([text], { padding: true, truncation: true });
-    const { text_embeds } = await (await this.getTextModel())(textInputs);
-    return text_embeds.data;
-  }
-}
+export * from "@llamaindex/clip";

packages/llamaindex/src/embeddings/CloudflareWorkerEmbedding.ts

@@ -1,17 +0,0 @@
-import { MultiModalEmbedding } from "@llamaindex/core/embeddings";
-import type { ImageType } from "@llamaindex/core/schema";
-
-/**
- * Cloudflare worker doesn't support image embeddings for now
- */
-export class CloudflareWorkerMultiModalEmbedding extends MultiModalEmbedding {
-  constructor() {
-    super();
-  }
-  getImageEmbedding(images: ImageType): Promise<number[]> {
-    throw new Error("Method not implemented.");
-  }
-  getTextEmbedding(text: string): Promise<number[]> {
-    throw new Error("Method not implemented.");
-  }
-}

packages/llamaindex/src/embeddings/DeepInfraEmbedding.ts

@@ -1,152 +1 @@
-import { BaseEmbedding } from "@llamaindex/core/embeddings";
-import type { MessageContentDetail } from "@llamaindex/core/llms";
-import { extractSingleText } from "@llamaindex/core/utils";
-import { getEnv } from "@llamaindex/env";
-
-const DEFAULT_MODEL = "sentence-transformers/clip-ViT-B-32";
-
-const API_TOKEN_ENV_VARIABLE_NAME = "DEEPINFRA_API_TOKEN";
-
-const API_ROOT = "https://api.deepinfra.com/v1/inference";
-
-const DEFAULT_TIMEOUT = 60 * 1000;
-
-const DEFAULT_MAX_RETRIES = 5;
-
-export interface DeepInfraEmbeddingResponse {
-  embeddings: number[][];
-  request_id: string;
-  inference_status: InferenceStatus;
-}
-
-export interface InferenceStatus {
-  status: string;
-  runtime_ms: number;
-  cost: number;
-  tokens_input: number;
-}
-
-const mapPrefixWithInputs = (prefix: string, inputs: string[]): string[] => {
-  return inputs.map((input) => (prefix ? `${prefix} ${input}` : input));
-};
-
-/**
- * DeepInfraEmbedding is an alias for DeepInfra that implements the BaseEmbedding interface.
- */
-export class DeepInfraEmbedding extends BaseEmbedding {
-  /**
-   * DeepInfra model to use
-   * @default "sentence-transformers/clip-ViT-B-32"
-   * @see https://deepinfra.com/models/embeddings
-   */
-  model: string;
-
-  /**
-   * DeepInfra API token
-   * @see https://deepinfra.com/dash/api_keys
-   * If not provided, it will try to get the token from the environment variable `DEEPINFRA_API_TOKEN`
-   *
-   */
-  apiToken: string;
-
-  /**
-   * Prefix to add to the query
-   * @default ""
-   */
-  queryPrefix: string;
-
-  /**
-   * Prefix to add to the text
-   * @default ""
-   */
-  textPrefix: string;
-
-  /**
-   *
-   * @default 5
-   */
-  maxRetries: number;
-
-  /**
-   *
-   * @default 60 * 1000
-   */
-  timeout: number;
-
-  constructor(init?: Partial<DeepInfraEmbedding>) {
-    super();
-
-    this.model = init?.model ?? DEFAULT_MODEL;
-    this.apiToken = init?.apiToken ?? getEnv(API_TOKEN_ENV_VARIABLE_NAME) ?? "";
-    this.queryPrefix = init?.queryPrefix ?? "";
-    this.textPrefix = init?.textPrefix ?? "";
-    this.maxRetries = init?.maxRetries ?? DEFAULT_MAX_RETRIES;
-    this.timeout = init?.timeout ?? DEFAULT_TIMEOUT;
-  }
-
-  async getTextEmbedding(text: string): Promise<number[]> {
-    const texts = mapPrefixWithInputs(this.textPrefix, [text]);
-    const embeddings = await this.getDeepInfraEmbedding(texts);
-    return embeddings[0]!;
-  }
-
-  async getQueryEmbedding(
-    query: MessageContentDetail,
-  ): Promise<number[] | null> {
-    const text = extractSingleText(query);
-    if (text) {
-      const queries = mapPrefixWithInputs(this.queryPrefix, [text]);
-      const embeddings = await this.getDeepInfraEmbedding(queries);
-      return embeddings[0]!;
-    } else {
-      return null;
-    }
-  }
-
-  getTextEmbeddings = async (texts: string[]): Promise<number[][]> => {
-    const textsWithPrefix = mapPrefixWithInputs(this.textPrefix, texts);
-    return this.getDeepInfraEmbedding(textsWithPrefix);
-  };
-
-  async getQueryEmbeddings(queries: string[]): Promise<number[][]> {
-    const queriesWithPrefix = mapPrefixWithInputs(this.queryPrefix, queries);
-    return await this.getDeepInfraEmbedding(queriesWithPrefix);
-  }
-
-  private async getDeepInfraEmbedding(inputs: string[]): Promise<number[][]> {
-    const url = this.getUrl(this.model);
-
-    for (let attempt = 0; attempt < this.maxRetries; attempt++) {
-      const controller = new AbortController();
-      const id = setTimeout(() => controller.abort(), this.timeout);
-
-      try {
-        const response = await fetch(url, {
-          method: "POST",
-          headers: {
-            "Content-Type": "application/json",
-            Authorization: `Bearer ${this.apiToken}`,
-          },
-          body: JSON.stringify({ inputs }),
-          signal: controller.signal,
-        });
-        if (!response.ok) {
-          throw new Error(`Request failed with status ${response.status}`);
-        }
-
-        const responseJson: DeepInfraEmbeddingResponse = await response.json();
-        return responseJson.embeddings;
-      } catch (error) {
-        console.error(`Attempt ${attempt + 1} failed: ${error}`);
-      } finally {
-        clearTimeout(id);
-      }
-    }
-
-    throw new Error("Exceeded maximum retries");
-  }
-
-  private getUrl(model: string): string {
-    return `${API_ROOT}/${model}`;
-  }
-}
+export * from "@llamaindex/deepinfra";

packages/llamaindex/src/embeddings/HuggingFaceEmbedding.ts

@@ -1,110 +1 @@
-import { HfInference } from "@huggingface/inference";
-import { BaseEmbedding } from "@llamaindex/core/embeddings";
-import { loadTransformers } from "@llamaindex/env";
-import { Settings } from "../Settings.js";
-
-export enum HuggingFaceEmbeddingModelType {
-  XENOVA_ALL_MINILM_L6_V2 = "Xenova/all-MiniLM-L6-v2",
-  XENOVA_ALL_MPNET_BASE_V2 = "Xenova/all-mpnet-base-v2",
-}
-
-/**
- * Uses feature extraction from '@xenova/transformers' to generate embeddings.
- * Per default the model [XENOVA_ALL_MINILM_L6_V2](https://huggingface.co/Xenova/all-MiniLM-L6-v2) is used.
- *
- * Can be changed by setting the `modelType` parameter in the constructor, e.g.:
- * ```
- * new HuggingFaceEmbedding({
- *     modelType: HuggingFaceEmbeddingModelType.XENOVA_ALL_MPNET_BASE_V2,
- * });
- * ```
- *
- * @extends BaseEmbedding
- */
-export class HuggingFaceEmbedding extends BaseEmbedding {
-  modelType: string = HuggingFaceEmbeddingModelType.XENOVA_ALL_MINILM_L6_V2;
-  quantized: boolean = true;
-
-  private extractor: any;
-
-  constructor(init?: Partial<HuggingFaceEmbedding>) {
-    super();
-    Object.assign(this, init);
-  }
-
-  async getExtractor() {
-    if (!this.extractor) {
-      const { pipeline } = await loadTransformers((transformer) => {
-        Settings.callbackManager.dispatchEvent(
-          "load-transformers",
-          {
-            transformer,
-          },
-          true,
-        );
-      });
-      this.extractor = await pipeline("feature-extraction", this.modelType, {
-        quantized: this.quantized,
-      });
-    }
-    return this.extractor;
-  }
-
-  override async getTextEmbedding(text: string): Promise<number[]> {
-    const extractor = await this.getExtractor();
-    const output = await extractor(text, { pooling: "mean", normalize: true });
-    return Array.from(output.data);
-  }
-}
-
-// Workaround to get the Options type from @huggingface/inference@2.7.0
-type HfInferenceOptions = ConstructorParameters<typeof HfInference>[1];
-
-export type HFConfig = HfInferenceOptions & {
-  model: string;
-  accessToken: string;
-  endpoint?: string;
-};
-
-/**
- * Uses feature extraction from Hugging Face's Inference API to generate embeddings.
- *
- * Set the `model` and `accessToken` parameter in the constructor, e.g.:
- * ```
- * new HuggingFaceInferenceAPIEmbedding({
- *     model: HuggingFaceEmbeddingModelType.XENOVA_ALL_MPNET_BASE_V2,
- *     accessToken: "<your-access-token>"
- * });
- * ```
- *
- * @extends BaseEmbedding
- */
-export class HuggingFaceInferenceAPIEmbedding extends BaseEmbedding {
-  model: string;
-  hf: HfInference;
-
-  constructor(init: HFConfig) {
-    super();
-    const { model, accessToken, endpoint, ...hfInferenceOpts } = init;
-
-    this.hf = new HfInference(accessToken, hfInferenceOpts);
-    this.model = model;
-    if (endpoint) this.hf.endpoint(endpoint);
-  }
-
-  async getTextEmbedding(text: string): Promise<number[]> {
-    const res = await this.hf.featureExtraction({
-      model: this.model,
-      inputs: text,
-    });
-    return res as number[];
-  }
-
-  getTextEmbeddings = async (texts: string[]): Promise<Array<number[]>> => {
-    const res = await this.hf.featureExtraction({
-      model: this.model,
-      inputs: texts,
-    });
-    return res as number[][];
-  };
-}
+export * from "@llamaindex/huggingface";

packages/llamaindex/src/embeddings/index.ts

@@ -1,4 +1,5 @@
 export * from "@llamaindex/core/embeddings";
+export { ClipEmbedding, ClipEmbeddingModelType } from "./ClipEmbedding.js";
 export { DeepInfraEmbedding } from "./DeepInfraEmbedding.js";
 export { FireworksEmbedding } from "./fireworks.js";
 export * from "./GeminiEmbedding.js";
@@ -9,5 +10,3 @@ export * from "./MixedbreadAIEmbeddings.js";
 export { OllamaEmbedding } from "./OllamaEmbedding.js";
 export * from "./OpenAIEmbedding.js";
 export { TogetherEmbedding } from "./together.js";
-// ClipEmbedding might not work in non-node.js runtime, but it doesn't have side effects
-export { ClipEmbedding, ClipEmbeddingModelType } from "./ClipEmbedding.js";

packages/llamaindex/src/extractors/MetadataExtractors.ts

@@ -1,14 +1,20 @@
 import type { LLM } from "@llamaindex/core/llms";
+import {
+  PromptTemplate,
+  defaultKeywordExtractPrompt,
+  defaultQuestionExtractPrompt,
+  defaultSummaryPrompt,
+  defaultTitleCombinePromptTemplate,
+  defaultTitleExtractorPromptTemplate,
+  type KeywordExtractPrompt,
+  type QuestionExtractPrompt,
+  type SummaryPrompt,
+  type TitleCombinePrompt,
+  type TitleExtractorPrompt,
+} from "@llamaindex/core/prompts";
 import type { BaseNode } from "@llamaindex/core/schema";
 import { MetadataMode, TextNode } from "@llamaindex/core/schema";
 import { OpenAI } from "@llamaindex/openai";
-import {
-  defaultKeywordExtractorPromptTemplate,
-  defaultQuestionAnswerPromptTemplate,
-  defaultSummaryExtractorPromptTemplate,
-  defaultTitleCombinePromptTemplate,
-  defaultTitleExtractorPromptTemplate,
-} from "./prompts.js";
 import { BaseExtractor } from "./types.js";
 
 const STRIP_REGEX = /(\r\n|\n|\r)/gm;
@@ -16,6 +22,7 @@ const STRIP_REGEX = /(\r\n|\n|\r)/gm;
 type KeywordExtractArgs = {
   llm?: LLM;
   keywords?: number;
+  promptTemplate?: KeywordExtractPrompt["template"];
 };
 
 type ExtractKeyword = {
@@ -39,6 +46,12 @@ export class KeywordExtractor extends BaseExtractor {
    */
   keywords: number = 5;
 
+  /**
+   * The prompt template to use for the question extractor.
+   * @type {string}
+   */
+  promptTemplate: KeywordExtractPrompt;
+
   /**
    * Constructor for the KeywordExtractor class.
    * @param {LLM} llm LLM instance.
@@ -53,6 +66,12 @@ export class KeywordExtractor extends BaseExtractor {
 
     this.llm = options?.llm ?? new OpenAI();
     this.keywords = options?.keywords ?? 5;
+    this.promptTemplate = options?.promptTemplate
+      ? new PromptTemplate({
+          templateVars: ["context", "maxKeywords"],
+          template: options.promptTemplate,
+        })
+      : defaultKeywordExtractPrompt;
   }
 
   /**
@@ -66,9 +85,9 @@ export class KeywordExtractor extends BaseExtractor {
     }
 
     const completion = await this.llm.complete({
-      prompt: defaultKeywordExtractorPromptTemplate({
-        contextStr: node.getContent(MetadataMode.ALL),
-        keywords: this.keywords,
+      prompt: this.promptTemplate.format({
+        context: node.getContent(MetadataMode.ALL),
+        maxKeywords: this.keywords.toString(),
       }),
     });
 
@@ -93,8 +112,8 @@ export class KeywordExtractor extends BaseExtractor {
 type TitleExtractorsArgs = {
   llm?: LLM;
   nodes?: number;
-  nodeTemplate?: string;
-  combineTemplate?: string;
+  nodeTemplate?: TitleExtractorPrompt["template"];
+  combineTemplate?: TitleCombinePrompt["template"];
 };
 
 type ExtractTitle = {
@@ -129,19 +148,19 @@ export class TitleExtractor extends BaseExtractor {
    * The prompt template to use for the title extractor.
    * @type {string}
    */
-  nodeTemplate: string;
+  nodeTemplate: TitleExtractorPrompt;
 
   /**
    * The prompt template to merge title with..
    * @type {string}
    */
-  combineTemplate: string;
+  combineTemplate: TitleCombinePrompt;
 
   /**
    * Constructor for the TitleExtractor class.
    * @param {LLM} llm LLM instance.
    * @param {number} nodes Number of nodes to extract titles from.
-   * @param {string} nodeTemplate The prompt template to use for the title extractor.
+   * @param {TitleExtractorPrompt} nodeTemplate The prompt template to use for the title extractor.
    * @param {string} combineTemplate The prompt template to merge title with..
    */
   constructor(options?: TitleExtractorsArgs) {
@@ -150,10 +169,19 @@ export class TitleExtractor extends BaseExtractor {
     this.llm = options?.llm ?? new OpenAI();
     this.nodes = options?.nodes ?? 5;
 
-    this.nodeTemplate =
-      options?.nodeTemplate ?? defaultTitleExtractorPromptTemplate();
-    this.combineTemplate =
-      options?.combineTemplate ?? defaultTitleCombinePromptTemplate();
+    this.nodeTemplate = options?.nodeTemplate
+      ? new PromptTemplate({
+          templateVars: ["context"],
+          template: options.nodeTemplate,
+        })
+      : defaultTitleExtractorPromptTemplate;
+
+    this.combineTemplate = options?.combineTemplate
+      ? new PromptTemplate({
+          templateVars: ["context"],
+          template: options.combineTemplate,
+        })
+      : defaultTitleCombinePromptTemplate;
   }
 
   /**
@@ -218,8 +246,8 @@ export class TitleExtractor extends BaseExtractor {
       const titleCandidates = await this.getTitlesCandidates(nodes);
       const combinedTitles = titleCandidates.join(", ");
       const completion = await this.llm.complete({
-        prompt: defaultTitleCombinePromptTemplate({
-          contextStr: combinedTitles,
+        prompt: this.combineTemplate.format({
+          context: combinedTitles,
         }),
       });
 
@@ -232,8 +260,8 @@ export class TitleExtractor extends BaseExtractor {
   private async getTitlesCandidates(nodes: BaseNode[]): Promise<string[]> {
     const titleJobs = nodes.map(async (node) => {
       const completion = await this.llm.complete({
-        prompt: defaultTitleExtractorPromptTemplate({
-          contextStr: node.getContent(MetadataMode.ALL),
+        prompt: this.nodeTemplate.format({
+          context: node.getContent(MetadataMode.ALL),
         }),
       });
 
@@ -247,7 +275,7 @@ export class TitleExtractor extends BaseExtractor {
 type QuestionAnswerExtractArgs = {
   llm?: LLM;
   questions?: number;
-  promptTemplate?: string;
+  promptTemplate?: QuestionExtractPrompt["template"];
   embeddingOnly?: boolean;
 };
 
@@ -276,7 +304,7 @@ export class QuestionsAnsweredExtractor extends BaseExtractor {
    * The prompt template to use for the question extractor.
    * @type {string}
    */
-  promptTemplate: string;
+  promptTemplate: QuestionExtractPrompt;
 
   /**
    * Wheter to use metadata for embeddings only
@@ -289,7 +317,7 @@ export class QuestionsAnsweredExtractor extends BaseExtractor {
    * Constructor for the QuestionsAnsweredExtractor class.
    * @param {LLM} llm LLM instance.
    * @param {number} questions Number of questions to generate.
-   * @param {string} promptTemplate The prompt template to use for the question extractor.
+   * @param {TextQAPrompt} promptTemplate The prompt template to use for the question extractor.
    * @param {boolean} embeddingOnly Wheter to use metadata for embeddings only.
    */
   constructor(options?: QuestionAnswerExtractArgs) {
@@ -300,12 +328,14 @@ export class QuestionsAnsweredExtractor extends BaseExtractor {
 
     this.llm = options?.llm ?? new OpenAI();
     this.questions = options?.questions ?? 5;
-    this.promptTemplate =
-      options?.promptTemplate ??
-      defaultQuestionAnswerPromptTemplate({
-        numQuestions: this.questions,
-        contextStr: "",
-      });
+    this.promptTemplate = options?.promptTemplate
+      ? new PromptTemplate({
+          templateVars: ["numQuestions", "context"],
+          template: options.promptTemplate,
+        }).partialFormat({
+          numQuestions: "5",
+        })
+      : defaultQuestionExtractPrompt;
     this.embeddingOnly = options?.embeddingOnly ?? false;
   }
 
@@ -323,9 +353,9 @@ export class QuestionsAnsweredExtractor extends BaseExtractor {
 
     const contextStr = node.getContent(this.metadataMode);
 
-    const prompt = defaultQuestionAnswerPromptTemplate({
-      contextStr,
-      numQuestions: this.questions,
+    const prompt = this.promptTemplate.format({
+      context: contextStr,
+      numQuestions: this.questions.toString(),
     });
 
     const questions = await this.llm.complete({
@@ -356,7 +386,7 @@ export class QuestionsAnsweredExtractor extends BaseExtractor {
 type SummaryExtractArgs = {
   llm?: LLM;
   summaries?: string[];
-  promptTemplate?: string;
+  promptTemplate?: SummaryPrompt["template"];
 };
 
 type ExtractSummary = {
@@ -385,7 +415,7 @@ export class SummaryExtractor extends BaseExtractor {
    * The prompt template to use for the summary extractor.
    * @type {string}
    */
-  promptTemplate: string;
+  promptTemplate: SummaryPrompt;
 
   private selfSummary: boolean;
   private prevSummary: boolean;
@@ -404,8 +434,12 @@ export class SummaryExtractor extends BaseExtractor {
 
     this.llm = options?.llm ?? new OpenAI();
     this.summaries = summaries;
-    this.promptTemplate =
-      options?.promptTemplate ?? defaultSummaryExtractorPromptTemplate();
+    this.promptTemplate = options?.promptTemplate
+      ? new PromptTemplate({
+          templateVars: ["context"],
+          template: options.promptTemplate,
+        })
+      : defaultSummaryPrompt;
 
     this.selfSummary = summaries?.includes("self") ?? false;
     this.prevSummary = summaries?.includes("prev") ?? false;
@@ -422,10 +456,10 @@ export class SummaryExtractor extends BaseExtractor {
       return "";
     }
 
-    const contextStr = node.getContent(this.metadataMode);
+    const context = node.getContent(this.metadataMode);
 
-    const prompt = defaultSummaryExtractorPromptTemplate({
-      contextStr,
+    const prompt = this.promptTemplate.format({
+      context,
     });
 
     const summary = await this.llm.complete({

packages/llamaindex/src/extractors/prompts.ts

@@ -1,74 +0,0 @@
-export interface DefaultPromptTemplate {
-  contextStr: string;
-}
-
-export interface DefaultKeywordExtractorPromptTemplate
-  extends DefaultPromptTemplate {
-  keywords: number;
-}
-
-export interface DefaultQuestionAnswerPromptTemplate
-  extends DefaultPromptTemplate {
-  numQuestions: number;
-}
-
-export interface DefaultNodeTextTemplate {
-  metadataStr: string;
-  content: string;
-}
-
-export const defaultKeywordExtractorPromptTemplate = ({
-  contextStr = "",
-  keywords = 5,
-}: DefaultKeywordExtractorPromptTemplate) => `${contextStr} 
-Give ${keywords} unique keywords for this document. 
-Format as comma separated. 
-Keywords: `;
-
-export const defaultTitleExtractorPromptTemplate = (
-  { contextStr = "" }: DefaultPromptTemplate = {
-    contextStr: "",
-  },
-) => `${contextStr}
-Give a title that summarizes all of the unique entities, titles or themes found in the context. 
-Title: `;
-
-export const defaultTitleCombinePromptTemplate = (
-  { contextStr = "" }: DefaultPromptTemplate = {
-    contextStr: "",
-  },
-) => `${contextStr} 
-Based on the above candidate titles and contents, what is the comprehensive title for this document? 
-Title: `;
-
-export const defaultQuestionAnswerPromptTemplate = (
-  { contextStr = "", numQuestions = 5 }: DefaultQuestionAnswerPromptTemplate = {
-    contextStr: "",
-    numQuestions: 5,
-  },
-) => `${contextStr}
-Given the contextual informations, generate ${numQuestions} questions this context can provides specific answers to which are unlikely to be found else where. Higher-level summaries of surrounding context may be provideds as well. 
-Try using these summaries to generate better questions that this context can answer.
-`;
-
-export const defaultSummaryExtractorPromptTemplate = (
-  { contextStr = "" }: DefaultPromptTemplate = {
-    contextStr: "",
-  },
-) => `${contextStr}
-Summarize the key topics and entities of the sections.
-Summary: `;
-
-export const defaultNodeTextTemplate = ({
-  metadataStr = "",
-  content = "",
-}: {
-  metadataStr?: string;
-  content?: string;
-} = {}) => `[Excerpt from document]
-${metadataStr}
-Excerpt:
------
-${content}
------
-`;

packages/llamaindex/src/extractors/types.ts

@@ -1,10 +1,10 @@
+import { defaultNodeTextTemplate } from "@llamaindex/core/prompts";
 import {
   BaseNode,
   MetadataMode,
   TextNode,
   TransformComponent,
 } from "@llamaindex/core/schema";
-import { defaultNodeTextTemplate } from "./prompts.js";
 
 /*
  * Abstract class for all extractors.
@@ -71,7 +71,7 @@ export abstract class BaseExtractor extends TransformComponent {
         if (newNodes[idx] instanceof TextNode) {
           newNodes[idx] = new TextNode({
             ...newNodes[idx],
-            textTemplate: defaultNodeTextTemplate(),
+            textTemplate: defaultNodeTextTemplate.format(),
           });
         }
       }

packages/llamaindex/src/indices/BaseIndex.ts

@@ -8,39 +8,6 @@ import { runTransformations } from "../ingestion/IngestionPipeline.js";
 import type { StorageContext } from "../storage/StorageContext.js";
 import type { BaseDocumentStore } from "../storage/docStore/types.js";
 import type { BaseIndexStore } from "../storage/indexStore/types.js";
-import { IndexStruct } from "./IndexStruct.js";
-import { IndexStructType } from "./json-to-index-struct.js";
-
-// A table of keywords mapping keywords to text chunks.
-export class KeywordTable extends IndexStruct {
-  table: Map<string, Set<string>> = new Map();
-  type: IndexStructType = IndexStructType.KEYWORD_TABLE;
-
-  addNode(keywords: string[], nodeId: string): void {
-    keywords.forEach((keyword) => {
-      if (!this.table.has(keyword)) {
-        this.table.set(keyword, new Set());
-      }
-      this.table.get(keyword)!.add(nodeId);
-    });
-  }
-
-  deleteNode(keywords: string[], nodeId: string) {
-    keywords.forEach((keyword) => {
-      if (this.table.has(keyword)) {
-        this.table.get(keyword)!.delete(nodeId);
-      }
-    });
-  }
-
-  toJson(): Record<string, unknown> {
-    return {
-      ...super.toJson(),
-      table: this.table,
-      type: this.type,
-    };
-  }
-}
 
 export interface BaseIndexInit<T> {
   serviceContext?: ServiceContext | undefined;

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

@@ -13,7 +13,7 @@ import type { StorageContext } from "../../storage/StorageContext.js";
 import { storageContextFromDefaults } from "../../storage/StorageContext.js";
 import type { BaseDocumentStore } from "../../storage/docStore/types.js";
 import type { BaseIndexInit } from "../BaseIndex.js";
-import { BaseIndex, KeywordTable } from "../BaseIndex.js";
+import { BaseIndex } from "../BaseIndex.js";
 import { IndexStructType } from "../json-to-index-struct.js";
 import {
   extractKeywordsGivenResponse,
@@ -21,6 +21,7 @@ import {
   simpleExtractKeywords,
 } from "./utils.js";
 
+import { KeywordTable } from "@llamaindex/core/data-structs";
 import type { LLM } from "@llamaindex/core/llms";
 import {
   defaultKeywordExtractPrompt,

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

@@ -31,8 +31,8 @@ import type { StorageContext } from "../../storage/StorageContext.js";
 import { storageContextFromDefaults } from "../../storage/StorageContext.js";
 import type { BaseIndexStore } from "../../storage/indexStore/types.js";
 import type {
+  BaseVectorStore,
   MetadataFilters,
-  VectorStore,
   VectorStoreByType,
   VectorStoreQueryResult,
 } from "../../vector-store/index.js";
@@ -264,7 +264,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
   }
 
   static async fromVectorStore(
-    vectorStore: VectorStore,
+    vectorStore: BaseVectorStore,
     serviceContext?: ServiceContext,
   ) {
     return this.fromVectorStores(
@@ -307,7 +307,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
   protected async insertNodesToStore(
     newIds: string[],
     nodes: BaseNode[],
-    vectorStore: VectorStore,
+    vectorStore: BaseVectorStore,
   ): Promise<void> {
     // NOTE: if the vector store doesn't store text,
     // we need to add the nodes to the index struct and document store
@@ -357,7 +357,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
   }
 
   protected async deleteRefDocFromStore(
-    vectorStore: VectorStore,
+    vectorStore: BaseVectorStore,
     refDocId: string,
   ): Promise<void> {
     await vectorStore.delete(refDocId);
@@ -425,7 +425,7 @@ export class VectorIndexRetriever extends BaseRetriever {
     let nodesWithScores: NodeWithScore[] = [];
 
     for (const type in vectorStores) {
-      const vectorStore: VectorStore = vectorStores[type as ModalityType]!;
+      const vectorStore: BaseVectorStore = vectorStores[type as ModalityType]!;
       nodesWithScores = nodesWithScores.concat(
         await this.retrieveQuery(query, type as ModalityType, vectorStore),
       );
@@ -436,7 +436,7 @@ export class VectorIndexRetriever extends BaseRetriever {
   protected async retrieveQuery(
     query: MessageContent,
     type: ModalityType,
-    vectorStore: VectorStore,
+    vectorStore: BaseVectorStore,
     filters?: MetadataFilters,
   ): Promise<NodeWithScore[]> {
     // convert string message to multi-modal format

packages/llamaindex/src/ingestion/IngestionPipeline.ts

@@ -7,7 +7,10 @@ import {
   type Metadata,
 } from "@llamaindex/core/schema";
 import type { BaseDocumentStore } from "../storage/docStore/types.js";
-import type { VectorStore, VectorStoreByType } from "../vector-store/types.js";
+import type {
+  BaseVectorStore,
+  VectorStoreByType,
+} from "../vector-store/types.js";
 import { IngestionCache, getTransformationHash } from "./IngestionCache.js";
 import {
   DocStoreStrategy,
@@ -59,7 +62,7 @@ export class IngestionPipeline {
   transformations: TransformComponent[] = [];
   documents?: Document[] | undefined;
   reader?: BaseReader | undefined;
-  vectorStore?: VectorStore | undefined;
+  vectorStore?: BaseVectorStore | undefined;
   vectorStores?: VectorStoreByType | undefined;
   docStore?: BaseDocumentStore;
   docStoreStrategy: DocStoreStrategy = DocStoreStrategy.UPSERTS;
@@ -133,7 +136,7 @@ export async function addNodesToVectorStores(
   nodesAdded?: (
     newIds: string[],
     nodes: BaseNode<Metadata>[],
-    vectorStore: VectorStore,
+    vectorStore: BaseVectorStore,
   ) => Promise<void>,
 ) {
   const nodeMap = splitNodesByType(nodes);

packages/llamaindex/src/ingestion/strategies/UpsertsAndDeleteStrategy.ts

@@ -1,6 +1,6 @@
 import { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 import type { BaseDocumentStore } from "../../storage/docStore/types.js";
-import type { VectorStore } from "../../vector-store/types.js";
+import type { BaseVectorStore } from "../../vector-store/types.js";
 import { classify } from "./classify.js";
 
 /**
@@ -9,9 +9,9 @@ import { classify } from "./classify.js";
  */
 export class UpsertsAndDeleteStrategy extends TransformComponent {
   protected docStore: BaseDocumentStore;
-  protected vectorStores: VectorStore[] | undefined;
+  protected vectorStores: BaseVectorStore[] | undefined;
 
-  constructor(docStore: BaseDocumentStore, vectorStores?: VectorStore[]) {
+  constructor(docStore: BaseDocumentStore, vectorStores?: BaseVectorStore[]) {
     super(async (nodes: BaseNode[]): Promise<BaseNode[]> => {
       const { dedupedNodes, missingDocs, unusedDocs } = await classify(
         this.docStore,

packages/llamaindex/src/ingestion/strategies/UpsertsStrategy.ts

@@ -1,6 +1,6 @@
 import { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 import type { BaseDocumentStore } from "../../storage/docStore/types.js";
-import type { VectorStore } from "../../vector-store/types.js";
+import type { BaseVectorStore } from "../../vector-store/types.js";
 import { classify } from "./classify.js";
 
 /**
@@ -8,9 +8,9 @@ import { classify } from "./classify.js";
  */
 export class UpsertsStrategy extends TransformComponent {
   protected docStore: BaseDocumentStore;
-  protected vectorStores: VectorStore[] | undefined;
+  protected vectorStores: BaseVectorStore[] | undefined;
 
-  constructor(docStore: BaseDocumentStore, vectorStores?: VectorStore[]) {
+  constructor(docStore: BaseDocumentStore, vectorStores?: BaseVectorStore[]) {
     super(async (nodes: BaseNode[]): Promise<BaseNode[]> => {
       const { dedupedNodes, unusedDocs } = await classify(this.docStore, nodes);
       // remove unused docs

packages/llamaindex/src/ingestion/strategies/index.ts

@@ -1,6 +1,6 @@
 import { TransformComponent } from "@llamaindex/core/schema";
 import type { BaseDocumentStore } from "../../storage/docStore/types.js";
-import type { VectorStore } from "../../vector-store/types.js";
+import type { BaseVectorStore } from "../../vector-store/types.js";
 import { DuplicatesStrategy } from "./DuplicatesStrategy.js";
 import { UpsertsAndDeleteStrategy } from "./UpsertsAndDeleteStrategy.js";
 import { UpsertsStrategy } from "./UpsertsStrategy.js";
@@ -28,7 +28,7 @@ class NoOpStrategy extends TransformComponent {
 export function createDocStoreStrategy(
   docStoreStrategy: DocStoreStrategy,
   docStore?: BaseDocumentStore,
-  vectorStores: VectorStore[] = [],
+  vectorStores: BaseVectorStore[] = [],
 ): TransformComponent {
   if (docStoreStrategy === DocStoreStrategy.NONE) {
     return new NoOpStrategy();

packages/llamaindex/src/internal/deps/replicate.d.ts

@@ -1,305 +0,0 @@
-type Status = "starting" | "processing" | "succeeded" | "failed" | "canceled";
-type Visibility = "public" | "private";
-type WebhookEventType = "start" | "output" | "logs" | "completed";
-
-export interface ApiError extends Error {
-  request: Request;
-  response: Response;
-}
-
-export interface Account {
-  type: "user" | "organization";
-  username: string;
-  name: string;
-  github_url?: string;
-}
-
-export interface Collection {
-  name: string;
-  slug: string;
-  description: string;
-  models?: Model[];
-}
-
-export interface Deployment {
-  owner: string;
-  name: string;
-  current_release: {
-    number: number;
-    model: string;
-    version: string;
-    created_at: string;
-    created_by: Account;
-    configuration: {
-      hardware: string;
-      min_instances: number;
-      max_instances: number;
-    };
-  };
-}
-
-export interface Hardware {
-  sku: string;
-  name: string;
-}
-
-export interface Model {
-  url: string;
-  owner: string;
-  name: string;
-  description?: string;
-  visibility: "public" | "private";
-  github_url?: string;
-  paper_url?: string;
-  license_url?: string;
-  run_count: number;
-  cover_image_url?: string;
-  default_example?: Prediction;
-  latest_version?: ModelVersion;
-}
-
-export interface ModelVersion {
-  id: string;
-  created_at: string;
-  cog_version: string;
-  openapi_schema: object;
-}
-
-export interface Prediction {
-  id: string;
-  status: Status;
-  model: string;
-  version: string;
-  input: object;
-  output?: any;
-  source: "api" | "web";
-  error?: any;
-  logs?: string;
-  metrics?: {
-    predict_time?: number;
-  };
-  webhook?: string;
-  webhook_events_filter?: WebhookEventType[];
-  created_at: string;
-  started_at?: string;
-  completed_at?: string;
-  urls: {
-    get: string;
-    cancel: string;
-    stream?: string;
-  };
-}
-
-export type Training = Prediction;
-
-export interface Page<T> {
-  previous?: string;
-  next?: string;
-  results: T[];
-}
-
-export interface ServerSentEvent {
-  event: string;
-  data: string;
-  id?: string;
-  retry?: number;
-}
-
-export interface WebhookSecret {
-  key: string;
-}
-
-export default class Replicate {
-  constructor(options?: {
-    auth?: string;
-    userAgent?: string;
-    baseUrl?: string;
-    fetch?: (input: Request | string, init?: RequestInit) => Promise<Response>;
-  });
-
-  auth: string;
-  userAgent?: string;
-  baseUrl?: string;
-  fetch: (input: Request | string, init?: RequestInit) => Promise<Response>;
-
-  run(
-    identifier: `${string}/${string}` | `${string}/${string}:${string}`,
-    options: {
-      input: object;
-      wait?: { interval?: number };
-      webhook?: string;
-      webhook_events_filter?: WebhookEventType[];
-      signal?: AbortSignal;
-    },
-    progress?: (prediction: Prediction) => void,
-  ): Promise<object>;
-
-  stream(
-    identifier: `${string}/${string}` | `${string}/${string}:${string}`,
-    options: {
-      input: object;
-      webhook?: string;
-      webhook_events_filter?: WebhookEventType[];
-      signal?: AbortSignal;
-    },
-  ): AsyncGenerator<ServerSentEvent>;
-
-  request(
-    route: string | URL,
-    options: {
-      method?: string;
-      headers?: object | Headers;
-      params?: object;
-      data?: object;
-    },
-  ): Promise<Response>;
-
-  paginate<T>(endpoint: () => Promise<Page<T>>): AsyncGenerator<[T]>;
-
-  wait(
-    prediction: Prediction,
-    options?: {
-      interval?: number;
-    },
-    stop?: (prediction: Prediction) => Promise<boolean>,
-  ): Promise<Prediction>;
-
-  accounts: {
-    current(): Promise<Account>;
-  };
-
-  collections: {
-    list(): Promise<Page<Collection>>;
-    get(collection_slug: string): Promise<Collection>;
-  };
-
-  deployments: {
-    predictions: {
-      create(
-        deployment_owner: string,
-        deployment_name: string,
-        options: {
-          input: object;
-          stream?: boolean;
-          webhook?: string;
-          webhook_events_filter?: WebhookEventType[];
-        },
-      ): Promise<Prediction>;
-    };
-    get(deployment_owner: string, deployment_name: string): Promise<Deployment>;
-    create(deployment_config: {
-      name: string;
-      model: string;
-      version: string;
-      hardware: string;
-      min_instances: number;
-      max_instances: number;
-    }): Promise<Deployment>;
-    update(
-      deployment_owner: string,
-      deployment_name: string,
-      deployment_config: {
-        version?: string;
-        hardware?: string;
-        min_instances?: number;
-        max_instances?: number;
-      } & (
-        | { version: string }
-        | { hardware: string }
-        | { min_instances: number }
-        | { max_instances: number }
-      ),
-    ): Promise<Deployment>;
-    list(): Promise<Page<Deployment>>;
-  };
-
-  hardware: {
-    list(): Promise<Hardware[]>;
-  };
-
-  models: {
-    get(model_owner: string, model_name: string): Promise<Model>;
-    list(): Promise<Page<Model>>;
-    create(
-      model_owner: string,
-      model_name: string,
-      options: {
-        visibility: Visibility;
-        hardware: string;
-        description?: string;
-        github_url?: string;
-        paper_url?: string;
-        license_url?: string;
-        cover_image_url?: string;
-      },
-    ): Promise<Model>;
-    versions: {
-      list(model_owner: string, model_name: string): Promise<ModelVersion[]>;
-      get(
-        model_owner: string,
-        model_name: string,
-        version_id: string,
-      ): Promise<ModelVersion>;
-    };
-  };
-
-  predictions: {
-    create(
-      options: {
-        model?: string;
-        version?: string;
-        input: object;
-        stream?: boolean;
-        webhook?: string;
-        webhook_events_filter?: WebhookEventType[];
-      } & ({ version: string } | { model: string }),
-    ): Promise<Prediction>;
-    get(prediction_id: string): Promise<Prediction>;
-    cancel(prediction_id: string): Promise<Prediction>;
-    list(): Promise<Page<Prediction>>;
-  };
-
-  trainings: {
-    create(
-      model_owner: string,
-      model_name: string,
-      version_id: string,
-      options: {
-        destination: `${string}/${string}`;
-        input: object;
-        webhook?: string;
-        webhook_events_filter?: WebhookEventType[];
-      },
-    ): Promise<Training>;
-    get(training_id: string): Promise<Training>;
-    cancel(training_id: string): Promise<Training>;
-    list(): Promise<Page<Training>>;
-  };
-
-  webhooks: {
-    default: {
-      secret: {
-        get(): Promise<WebhookSecret>;
-      };
-    };
-  };
-}
-
-export function validateWebhook(
-  requestData:
-    | Request
-    | {
-        id?: string;
-        timestamp?: string;
-        body: string;
-        secret?: string;
-        signature?: string;
-      },
-  secret: string,
-): Promise<boolean>;
-
-export function parseProgressFromLogs(logs: Prediction | string): {
-  percentage: number;
-  current: number;
-  total: number;
-} | null;

packages/llamaindex/src/internal/deps/replicate.license

@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.

packages/llamaindex/src/internal/settings/image-embed-model.ts

@@ -1,8 +0,0 @@
-export async function getImageEmbedModel() {
-  if (globalThis.navigator?.userAgent === "Cloudflare-Workers") {
-    return (await import("../../embeddings/CloudflareWorkerEmbedding.js"))
-      .CloudflareWorkerMultiModalEmbedding;
-  } else {
-    return (await import("../../embeddings/ClipEmbedding.js")).ClipEmbedding;
-  }
-}

packages/llamaindex/src/llm/anthropic.ts

@@ -1,462 +1 @@
-import type { ClientOptions } from "@anthropic-ai/sdk";
-import { Anthropic as SDKAnthropic } from "@anthropic-ai/sdk";
-import type {
-  TextBlock,
-  TextBlockParam,
-} from "@anthropic-ai/sdk/resources/index";
-import type {
-  ImageBlockParam,
-  MessageCreateParamsNonStreaming,
-  MessageParam,
-  Tool,
-  ToolResultBlockParam,
-  ToolUseBlock,
-  ToolUseBlockParam,
-} from "@anthropic-ai/sdk/resources/messages";
-import { wrapLLMEvent } from "@llamaindex/core/decorator";
-import type {
-  BaseTool,
-  ChatMessage,
-  ChatResponse,
-  ChatResponseChunk,
-  LLMChatParamsNonStreaming,
-  LLMChatParamsStreaming,
-  ToolCallLLMMessageOptions,
-} from "@llamaindex/core/llms";
-import { ToolCallLLM } from "@llamaindex/core/llms";
-import { extractText } from "@llamaindex/core/utils";
-import { getEnv } from "@llamaindex/env";
-import _ from "lodash";
-
-export class AnthropicSession {
-  anthropic: SDKAnthropic;
-
-  constructor(options: ClientOptions = {}) {
-    if (!options.apiKey) {
-      options.apiKey = getEnv("ANTHROPIC_API_KEY");
-    }
-
-    if (!options.apiKey) {
-      throw new Error("Set Anthropic Key in ANTHROPIC_API_KEY env variable");
-    }
-
-    this.anthropic = new SDKAnthropic(options);
-  }
-}
-
-// I'm not 100% sure this is necessary vs. just starting a new session
-// every time we make a call. They say they try to reuse connections
-// so in theory this is more efficient, but we should test it in the future.
-const defaultAnthropicSession: {
-  session: AnthropicSession;
-  options: ClientOptions;
-}[] = [];
-
-/**
- * Get a session for the Anthropic API. If one already exists with the same options,
- * it will be returned. Otherwise, a new session will be created.
- * @param options
- * @returns
- */
-export function getAnthropicSession(options: ClientOptions = {}) {
-  let session = defaultAnthropicSession.find((session) => {
-    return _.isEqual(session.options, options);
-  })?.session;
-
-  if (!session) {
-    session = new AnthropicSession(options);
-    defaultAnthropicSession.push({ session, options });
-  }
-
-  return session;
-}
-
-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 },
-  "claude-3-haiku": { contextWindow: 200000 },
-};
-
-export const ALL_AVAILABLE_V3_5_MODELS = {
-  "claude-3-5-sonnet": { contextWindow: 200000 },
-};
-
-export const ALL_AVAILABLE_ANTHROPIC_MODELS = {
-  ...ALL_AVAILABLE_ANTHROPIC_LEGACY_MODELS,
-  ...ALL_AVAILABLE_V3_MODELS,
-  ...ALL_AVAILABLE_V3_5_MODELS,
-};
-
-const AVAILABLE_ANTHROPIC_MODELS_WITHOUT_DATE: { [key: string]: string } = {
-  "claude-3-opus": "claude-3-opus-20240229",
-  "claude-3-sonnet": "claude-3-sonnet-20240229",
-  "claude-3-haiku": "claude-3-haiku-20240307",
-  "claude-3-5-sonnet": "claude-3-5-sonnet-20240620",
-} as { [key in keyof typeof ALL_AVAILABLE_ANTHROPIC_MODELS]: string };
-
-export type AnthropicAdditionalChatOptions = {};
-
-export class Anthropic extends ToolCallLLM<AnthropicAdditionalChatOptions> {
-  // Per completion Anthropic params
-  model: keyof typeof ALL_AVAILABLE_ANTHROPIC_MODELS;
-  temperature: number;
-  topP: number;
-  maxTokens?: number | undefined;
-
-  // Anthropic session params
-  apiKey?: string | undefined;
-  maxRetries: number;
-  timeout?: number;
-  session: AnthropicSession;
-
-  constructor(init?: Partial<Anthropic>) {
-    super();
-    this.model = init?.model ?? "claude-3-opus";
-    this.temperature = init?.temperature ?? 0.1;
-    this.topP = init?.topP ?? 0.999; // Per Ben Mann
-    this.maxTokens = init?.maxTokens ?? undefined;
-
-    this.apiKey = init?.apiKey ?? undefined;
-    this.maxRetries = init?.maxRetries ?? 10;
-    this.timeout = init?.timeout ?? 60 * 1000; // Default is 60 seconds
-    this.session =
-      init?.session ??
-      getAnthropicSession({
-        apiKey: this.apiKey,
-        maxRetries: this.maxRetries,
-        timeout: this.timeout,
-      });
-  }
-
-  get supportToolCall() {
-    return this.model.startsWith("claude-3");
-  }
-
-  get metadata() {
-    return {
-      model: this.model,
-      temperature: this.temperature,
-      topP: this.topP,
-      maxTokens: this.maxTokens,
-      contextWindow: ALL_AVAILABLE_ANTHROPIC_MODELS[this.model].contextWindow,
-      tokenizer: undefined,
-    };
-  }
-
-  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<ToolCallLLMMessageOptions>[],
-  ): MessageParam[] {
-    const result: MessageParam[] = messages
-      .filter(
-        (message) => message.role === "user" || message.role === "assistant",
-      )
-      .map((message) => {
-        const options = message.options ?? {};
-        if ("toolResult" in options) {
-          const { id, isError } = options.toolResult;
-          return {
-            role: "user",
-            content: [
-              {
-                type: "tool_result",
-                is_error: isError,
-                content: [
-                  {
-                    type: "text",
-                    text: extractText(message.content),
-                  },
-                ],
-                tool_use_id: id,
-              },
-            ] satisfies ToolResultBlockParam[],
-          } satisfies MessageParam;
-        } else if ("toolCall" in options) {
-          const aiThinkingText = extractText(message.content);
-          return {
-            role: "assistant",
-            content: [
-              // this could be empty when you call two tools in one query
-              ...(aiThinkingText.trim()
-                ? [
-                    {
-                      type: "text",
-                      text: aiThinkingText,
-                    } satisfies TextBlockParam,
-                  ]
-                : []),
-              ...options.toolCall.map(
-                (toolCall) =>
-                  ({
-                    type: "tool_use",
-                    id: toolCall.id,
-                    name: toolCall.name,
-                    input: toolCall.input,
-                  }) satisfies ToolUseBlockParam,
-              ),
-            ],
-          } satisfies MessageParam;
-        }
-
-        return {
-          content:
-            typeof message.content === "string"
-              ? message.content
-              : message.content.map(
-                  (content): TextBlockParam | ImageBlockParam =>
-                    content.type === "text"
-                      ? {
-                          type: "text",
-                          text: content.text,
-                        }
-                      : {
-                          type: "image",
-                          source: {
-                            data: content.image_url.url.substring(
-                              content.image_url.url.indexOf(",") + 1,
-                            ),
-                            media_type:
-                              `image/${content.image_url.url.substring("data:image/".length, content.image_url.url.indexOf(";base64"))}` as
-                                | "image/jpeg"
-                                | "image/png"
-                                | "image/gif"
-                                | "image/webp",
-                            type: "base64",
-                          },
-                        },
-                ),
-          role: message.role as "user" | "assistant",
-        } satisfies MessageParam;
-      });
-    // merge messages with the same role
-    // in case of 'messages: roles must alternate between "user" and "assistant", but found multiple "user" roles in a row'
-    const realResult: MessageParam[] = [];
-    for (let i = 0; i < result.length; i++) {
-      if (i === 0) {
-        realResult.push(result[i]!);
-        continue;
-      }
-      const current = result[i]!;
-      const previous = result[i - 1]!;
-      if (current.role === previous.role) {
-        // merge two messages with the same role
-        if (Array.isArray(previous.content)) {
-          if (Array.isArray(current.content)) {
-            previous.content.push(...current.content);
-          } else {
-            previous.content.push({
-              type: "text",
-              text: current.content,
-            });
-          }
-        } else {
-          if (Array.isArray(current.content)) {
-            previous.content = [
-              {
-                type: "text",
-                text: previous.content,
-              },
-              ...current.content,
-            ];
-          } else {
-            previous.content += `\n${current.content}`;
-          }
-        }
-        // no need to push the message
-      }
-      // if the roles are different, just push the message
-      else {
-        realResult.push(current);
-      }
-    }
-
-    return realResult;
-  }
-
-  chat(
-    params: LLMChatParamsStreaming<
-      AnthropicAdditionalChatOptions,
-      ToolCallLLMMessageOptions
-    >,
-  ): Promise<AsyncIterable<ChatResponseChunk<ToolCallLLMMessageOptions>>>;
-  chat(
-    params: LLMChatParamsNonStreaming<
-      AnthropicAdditionalChatOptions,
-      ToolCallLLMMessageOptions
-    >,
-  ): Promise<ChatResponse<ToolCallLLMMessageOptions>>;
-  @wrapLLMEvent
-  async chat(
-    params:
-      | LLMChatParamsNonStreaming<
-          AnthropicAdditionalChatOptions,
-          ToolCallLLMMessageOptions
-        >
-      | LLMChatParamsStreaming<
-          AnthropicAdditionalChatOptions,
-          ToolCallLLMMessageOptions
-        >,
-  ): Promise<
-    | ChatResponse<ToolCallLLMMessageOptions>
-    | AsyncIterable<ChatResponseChunk<ToolCallLLMMessageOptions>>
-  > {
-    let { messages } = params;
-
-    const { stream, tools } = 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");
-    }
-
-    // case: Streaming
-    if (stream) {
-      if (tools) {
-        console.error("Tools are not supported in streaming mode");
-      }
-      return this.streamChat(messages, systemPrompt);
-    }
-    // case: Non-streaming
-    const anthropic = this.session.anthropic;
-
-    if (tools) {
-      const params: MessageCreateParamsNonStreaming = {
-        messages: this.formatMessages(messages),
-        tools: tools.map(Anthropic.toTool),
-        model: this.getModelName(this.model),
-        temperature: this.temperature,
-        max_tokens: this.maxTokens ?? 4096,
-        top_p: this.topP,
-        ...(systemPrompt && { system: systemPrompt }),
-      };
-      // Remove tools if there are none, as it will cause an error
-      if (tools.length === 0) {
-        delete params.tools;
-      }
-      const response = await anthropic.messages.create(params);
-
-      const toolUseBlock = response.content.filter(
-        (content): content is ToolUseBlock => content.type === "tool_use",
-      );
-
-      return {
-        raw: response,
-        message: {
-          content: response.content
-            .filter((content): content is TextBlock => content.type === "text")
-            .map((content) => ({
-              type: "text",
-              text: content.text,
-            })),
-          role: "assistant",
-          options:
-            toolUseBlock.length > 0
-              ? {
-                  toolCall: toolUseBlock.map((block) => ({
-                    id: block.id,
-                    name: block.name,
-                    input: block.input,
-                  })),
-                }
-              : {},
-        },
-      };
-    } else {
-      const response = await anthropic.messages.create({
-        model: this.getModelName(this.model),
-        messages: this.formatMessages(messages),
-        max_tokens: this.maxTokens ?? 4096,
-        temperature: this.temperature,
-        top_p: this.topP,
-        ...(systemPrompt && { system: systemPrompt }),
-      });
-
-      return {
-        raw: response,
-        message: {
-          content: response.content
-            .filter((content): content is TextBlock => content.type === "text")
-            .map((content) => ({
-              type: "text",
-              text: content.text,
-            })),
-          role: "assistant",
-          options: {},
-        },
-      };
-    }
-  }
-
-  protected async *streamChat(
-    messages: ChatMessage<ToolCallLLMMessageOptions>[],
-    systemPrompt?: string | null,
-  ): AsyncIterable<ChatResponseChunk<ToolCallLLMMessageOptions>> {
-    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 }),
-    });
-
-    let idx_counter: number = 0;
-    for await (const part of stream) {
-      const content =
-        part.type === "content_block_delta"
-          ? part.delta.type === "text_delta"
-            ? part.delta.text
-            : part.delta
-          : undefined;
-
-      if (typeof content !== "string") continue;
-
-      idx_counter++;
-      yield {
-        raw: part,
-        delta: content,
-        options: {},
-      };
-    }
-    return;
-  }
-
-  static toTool(tool: BaseTool): Tool {
-    if (tool.metadata.parameters?.type !== "object") {
-      throw new TypeError("Tool parameters must be an object");
-    }
-    return {
-      input_schema: {
-        type: "object",
-        properties: tool.metadata.parameters.properties,
-        required: tool.metadata.parameters.required,
-      },
-      name: tool.metadata.name,
-      description: tool.metadata.description,
-    };
-  }
-}
+export * from "@llamaindex/anthropic";

packages/llamaindex/src/llm/deepinfra.ts

@@ -1,33 +1 @@
-import { getEnv } from "@llamaindex/env";
-import { OpenAI } from "@llamaindex/openai";
-
-const ENV_VARIABLE_NAME = "DEEPINFRA_API_TOKEN";
-const DEFAULT_MODEL = "mistralai/Mixtral-8x22B-Instruct-v0.1";
-const BASE_URL = "https://api.deepinfra.com/v1/openai";
-
-export class DeepInfra extends OpenAI {
-  constructor(init?: Omit<Partial<OpenAI>, "session">) {
-    const {
-      apiKey = getEnv(ENV_VARIABLE_NAME),
-      additionalSessionOptions = {},
-      model = DEFAULT_MODEL,
-      ...rest
-    } = init ?? {};
-
-    if (!apiKey) {
-      throw new Error(
-        `Set DeepInfra API key in ${ENV_VARIABLE_NAME} env variable`,
-      );
-    }
-
-    additionalSessionOptions.baseURL =
-      additionalSessionOptions.baseURL ?? BASE_URL;
-
-    super({
-      apiKey,
-      additionalSessionOptions,
-      model,
-      ...rest,
-    });
-  }
-}
+export * from "@llamaindex/deepinfra";

packages/llamaindex/src/llm/gemini/base.ts

@@ -44,12 +44,15 @@ import {
 export const GEMINI_MODEL_INFO_MAP: Record<GEMINI_MODEL, GeminiModelInfo> = {
   [GEMINI_MODEL.GEMINI_PRO]: { contextWindow: 30720 },
   [GEMINI_MODEL.GEMINI_PRO_VISION]: { contextWindow: 12288 },
-  [GEMINI_MODEL.GEMINI_PRO_LATEST]: { contextWindow: 10 ** 6 },
   // multi-modal/multi turn
+  [GEMINI_MODEL.GEMINI_PRO_LATEST]: { contextWindow: 10 ** 6 },
+  [GEMINI_MODEL.GEMINI_PRO_FLASH_LATEST]: { contextWindow: 10 ** 6 },
   [GEMINI_MODEL.GEMINI_PRO_1_5_PRO_PREVIEW]: { contextWindow: 10 ** 6 },
   [GEMINI_MODEL.GEMINI_PRO_1_5_FLASH_PREVIEW]: { contextWindow: 10 ** 6 },
   [GEMINI_MODEL.GEMINI_PRO_1_5]: { contextWindow: 2 * 10 ** 6 },
   [GEMINI_MODEL.GEMINI_PRO_1_5_FLASH]: { contextWindow: 10 ** 6 },
+  [GEMINI_MODEL.GEMINI_PRO_1_5_LATEST]: { contextWindow: 2 * 10 ** 6 },
+  [GEMINI_MODEL.GEMINI_PRO_1_5_FLASH_LATEST]: { contextWindow: 10 ** 6 },
 };
 
 const SUPPORT_TOOL_CALL_MODELS: GEMINI_MODEL[] = [
@@ -59,6 +62,10 @@ const SUPPORT_TOOL_CALL_MODELS: GEMINI_MODEL[] = [
   GEMINI_MODEL.GEMINI_PRO_1_5_FLASH_PREVIEW,
   GEMINI_MODEL.GEMINI_PRO_1_5,
   GEMINI_MODEL.GEMINI_PRO_1_5_FLASH,
+  GEMINI_MODEL.GEMINI_PRO_LATEST,
+  GEMINI_MODEL.GEMINI_PRO_FLASH_LATEST,
+  GEMINI_MODEL.GEMINI_PRO_1_5_LATEST,
+  GEMINI_MODEL.GEMINI_PRO_1_5_FLASH_LATEST,
 ];
 
 const DEFAULT_GEMINI_PARAMS = {

packages/llamaindex/src/llm/gemini/types.ts

@@ -56,10 +56,14 @@ export enum GEMINI_MODEL {
   GEMINI_PRO = "gemini-pro",
   GEMINI_PRO_VISION = "gemini-pro-vision",
   GEMINI_PRO_LATEST = "gemini-1.5-pro-latest",
+  GEMINI_PRO_FLASH_LATEST = "gemini-1.5-flash-latest",
   GEMINI_PRO_1_5_PRO_PREVIEW = "gemini-1.5-pro-preview-0514",
   GEMINI_PRO_1_5_FLASH_PREVIEW = "gemini-1.5-flash-preview-0514",
   GEMINI_PRO_1_5 = "gemini-1.5-pro-001",
   GEMINI_PRO_1_5_FLASH = "gemini-1.5-flash-001",
+  // Note: should be switched to -latest suffix when google supports it
+  GEMINI_PRO_1_5_LATEST = "gemini-1.5-pro-002",
+  GEMINI_PRO_1_5_FLASH_LATEST = "gemini-1.5-flash-002",
 }
 
 export interface GeminiModelInfo {

packages/llamaindex/src/llm/huggingface.ts

@@ -1,313 +1 @@
-import { HfInference } from "@huggingface/inference";
-import { wrapLLMEvent } from "@llamaindex/core/decorator";
-import "@llamaindex/core/llms";
-import {
-  BaseLLM,
-  type ChatMessage,
-  type ChatResponse,
-  type ChatResponseChunk,
-  type LLMChatParamsNonStreaming,
-  type LLMChatParamsStreaming,
-  type LLMMetadata,
-  type ToolCallLLMMessageOptions,
-} from "@llamaindex/core/llms";
-import { streamConverter } from "@llamaindex/core/utils";
-import { loadTransformers } from "@llamaindex/env";
-import type {
-  PreTrainedModel,
-  PreTrainedTokenizer,
-  Tensor,
-} from "@xenova/transformers";
-import { Settings } from "../Settings.js";
-
-// TODO workaround issue with @huggingface/inference@2.7.0
-interface HfInferenceOptions {
-  /**
-   * (Default: true) Boolean. If a request 503s and wait_for_model is set to false, the request will be retried with the same parameters but with wait_for_model set to true.
-   */
-  retry_on_error?: boolean;
-  /**
-   * (Default: true). Boolean. There is a cache layer on Inference API (serverless) to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.
-   */
-  use_cache?: boolean;
-  /**
-   * (Default: false). Boolean. Do not load the model if it's not already available.
-   */
-  dont_load_model?: boolean;
-  /**
-   * (Default: false). Boolean to use GPU instead of CPU for inference (requires Startup plan at least).
-   */
-  use_gpu?: boolean;
-  /**
-   * (Default: false) Boolean. If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.
-   */
-  wait_for_model?: boolean;
-  /**
-   * Custom fetch function to use instead of the default one, for example to use a proxy or edit headers.
-   */
-  fetch?: typeof fetch;
-  /**
-   * Abort Controller signal to use for request interruption.
-   */
-  signal?: AbortSignal;
-  /**
-   * (Default: "same-origin"). String | Boolean. Credentials to use for the request. If this is a string, it will be passed straight on. If it's a boolean, true will be "include" and false will not send credentials at all.
-   */
-  includeCredentials?: string | boolean;
-}
-
-const DEFAULT_PARAMS = {
-  temperature: 0.1,
-  topP: 1,
-  maxTokens: undefined,
-  contextWindow: 3900,
-};
-
-export type HFConfig = Partial<typeof DEFAULT_PARAMS> &
-  HfInferenceOptions & {
-    model: string;
-    accessToken: string;
-    endpoint?: string;
-  };
-
-/**
-    Wrapper on the Hugging Face's Inference API.
-    API Docs: https://huggingface.co/docs/huggingface.js/inference/README
-    List of tasks with models: huggingface.co/api/tasks 
-    
-    Note that Conversational API is not yet supported by the Inference API.
-    They recommend using the text generation API instead.
-    See: https://github.com/huggingface/huggingface.js/issues/586#issuecomment-2024059308
- */
-export class HuggingFaceInferenceAPI extends BaseLLM {
-  model: string;
-  temperature: number;
-  topP: number;
-  maxTokens?: number | undefined;
-  contextWindow: number;
-  hf: HfInference;
-
-  constructor(init: HFConfig) {
-    super();
-    const {
-      model,
-      temperature,
-      topP,
-      maxTokens,
-      contextWindow,
-      accessToken,
-      endpoint,
-      ...hfInferenceOpts
-    } = init;
-    this.hf = new HfInference(accessToken, hfInferenceOpts);
-    this.model = model;
-    this.temperature = temperature ?? DEFAULT_PARAMS.temperature;
-    this.topP = topP ?? DEFAULT_PARAMS.topP;
-    this.maxTokens = maxTokens ?? DEFAULT_PARAMS.maxTokens;
-    this.contextWindow = contextWindow ?? DEFAULT_PARAMS.contextWindow;
-    if (endpoint) this.hf.endpoint(endpoint);
-  }
-
-  get metadata(): LLMMetadata {
-    return {
-      model: this.model,
-      temperature: this.temperature,
-      topP: this.topP,
-      maxTokens: this.maxTokens,
-      contextWindow: this.contextWindow,
-      tokenizer: undefined,
-    };
-  }
-
-  chat(
-    params: LLMChatParamsStreaming,
-  ): Promise<AsyncIterable<ChatResponseChunk>>;
-  chat(params: LLMChatParamsNonStreaming): Promise<ChatResponse>;
-  @wrapLLMEvent
-  async chat(
-    params: LLMChatParamsStreaming | LLMChatParamsNonStreaming,
-  ): Promise<AsyncIterable<ChatResponseChunk> | ChatResponse<object>> {
-    if (params.stream) return this.streamChat(params);
-    return this.nonStreamChat(params);
-  }
-
-  private messagesToPrompt(messages: ChatMessage<ToolCallLLMMessageOptions>[]) {
-    let prompt = "";
-    for (const message of messages) {
-      if (message.role === "system") {
-        prompt += `<|system|>\n${message.content}</s>\n`;
-      } else if (message.role === "user") {
-        prompt += `<|user|>\n${message.content}</s>\n`;
-      } else if (message.role === "assistant") {
-        prompt += `<|assistant|>\n${message.content}</s>\n`;
-      }
-    }
-    // ensure we start with a system prompt, insert blank if needed
-    if (!prompt.startsWith("<|system|>\n")) {
-      prompt = "<|system|>\n</s>\n" + prompt;
-    }
-    // add final assistant prompt
-    prompt = prompt + "<|assistant|>\n";
-    return prompt;
-  }
-
-  protected async nonStreamChat(
-    params: LLMChatParamsNonStreaming,
-  ): Promise<ChatResponse> {
-    const res = await this.hf.textGeneration({
-      model: this.model,
-      inputs: this.messagesToPrompt(params.messages),
-      parameters: this.metadata,
-    });
-    return {
-      raw: res,
-      message: {
-        content: res.generated_text,
-        role: "assistant",
-      },
-    };
-  }
-
-  protected async *streamChat(
-    params: LLMChatParamsStreaming,
-  ): AsyncIterable<ChatResponseChunk> {
-    const stream = this.hf.textGenerationStream({
-      model: this.model,
-      inputs: this.messagesToPrompt(params.messages),
-      parameters: this.metadata,
-    });
-    yield* streamConverter(stream, (chunk: any) => ({
-      delta: chunk.token.text,
-      raw: chunk,
-    }));
-  }
-}
-
-const DEFAULT_HUGGINGFACE_MODEL = "stabilityai/stablelm-tuned-alpha-3b";
-
-export interface HFLLMConfig {
-  modelName?: string;
-  tokenizerName?: string;
-  temperature?: number;
-  topP?: number;
-  maxTokens?: number;
-  contextWindow?: number;
-}
-
-export class HuggingFaceLLM extends BaseLLM {
-  modelName: string;
-  tokenizerName: string;
-  temperature: number;
-  topP: number;
-  maxTokens?: number | undefined;
-  contextWindow: number;
-
-  private tokenizer: PreTrainedTokenizer | null = null;
-  private model: PreTrainedModel | null = null;
-
-  constructor(init?: HFLLMConfig) {
-    super();
-    this.modelName = init?.modelName ?? DEFAULT_HUGGINGFACE_MODEL;
-    this.tokenizerName = init?.tokenizerName ?? DEFAULT_HUGGINGFACE_MODEL;
-    this.temperature = init?.temperature ?? DEFAULT_PARAMS.temperature;
-    this.topP = init?.topP ?? DEFAULT_PARAMS.topP;
-    this.maxTokens = init?.maxTokens ?? DEFAULT_PARAMS.maxTokens;
-    this.contextWindow = init?.contextWindow ?? DEFAULT_PARAMS.contextWindow;
-  }
-
-  get metadata(): LLMMetadata {
-    return {
-      model: this.modelName,
-      temperature: this.temperature,
-      topP: this.topP,
-      maxTokens: this.maxTokens,
-      contextWindow: this.contextWindow,
-      tokenizer: undefined,
-    };
-  }
-
-  async getTokenizer() {
-    const { AutoTokenizer } = await loadTransformers((transformer) => {
-      Settings.callbackManager.dispatchEvent(
-        "load-transformers",
-        {
-          transformer,
-        },
-        true,
-      );
-    });
-    if (!this.tokenizer) {
-      this.tokenizer = await AutoTokenizer.from_pretrained(this.tokenizerName);
-    }
-    return this.tokenizer;
-  }
-
-  async getModel() {
-    const { AutoModelForCausalLM } = await loadTransformers((transformer) => {
-      Settings.callbackManager.dispatchEvent(
-        "load-transformers",
-        {
-          transformer,
-        },
-        true,
-      );
-    });
-    if (!this.model) {
-      this.model = await AutoModelForCausalLM.from_pretrained(this.modelName);
-    }
-    return this.model;
-  }
-
-  chat(
-    params: LLMChatParamsStreaming,
-  ): Promise<AsyncIterable<ChatResponseChunk>>;
-  chat(params: LLMChatParamsNonStreaming): Promise<ChatResponse>;
-  @wrapLLMEvent
-  async chat(
-    params: LLMChatParamsStreaming | LLMChatParamsNonStreaming,
-  ): Promise<AsyncIterable<ChatResponseChunk> | ChatResponse<object>> {
-    if (params.stream) return this.streamChat(params);
-    return this.nonStreamChat(params);
-  }
-
-  protected async nonStreamChat(
-    params: LLMChatParamsNonStreaming,
-  ): Promise<ChatResponse> {
-    const tokenizer = await this.getTokenizer();
-    const model = await this.getModel();
-
-    const messageInputs = params.messages.map((msg) => ({
-      role: msg.role,
-      content: msg.content as string,
-    }));
-    const inputs = tokenizer.apply_chat_template(messageInputs, {
-      add_generation_prompt: true,
-      ...this.metadata,
-    }) as Tensor;
-
-    // TODO: the input for model.generate should be updated when using @xenova/transformers v3
-    // We should add `stopping_criteria` also when it's supported in v3
-    // See: https://github.com/xenova/transformers.js/blob/3260640b192b3e06a10a1f4dc004b1254fdf1b80/src/models.js#L1248C9-L1248C27
-    const outputs = await model.generate(inputs, this.metadata);
-    const outputText = tokenizer.batch_decode(outputs, {
-      skip_special_tokens: false,
-    });
-
-    return {
-      raw: outputs,
-      message: {
-        content: outputText.join(""),
-        role: "assistant",
-      },
-    };
-  }
-
-  protected async *streamChat(
-    params: LLMChatParamsStreaming,
-  ): AsyncIterable<ChatResponseChunk> {
-    // @xenova/transformers v2 doesn't support streaming generation yet
-    // they are working on it in v3
-    // See: https://github.com/xenova/transformers.js/blob/3260640b192b3e06a10a1f4dc004b1254fdf1b80/src/models.js#L1249
-    throw new Error("Method not implemented.");
-  }
-}
+export * from "@llamaindex/huggingface";

packages/llamaindex/src/llm/portkey.ts

@@ -1,135 +1 @@
-import { wrapLLMEvent } from "@llamaindex/core/decorator";
-import {
-  BaseLLM,
-  type ChatMessage,
-  type ChatResponse,
-  type ChatResponseChunk,
-  type LLMChatParamsNonStreaming,
-  type LLMChatParamsStreaming,
-  type LLMMetadata,
-  type MessageType,
-} from "@llamaindex/core/llms";
-import { extractText } from "@llamaindex/core/utils";
-import { getEnv } from "@llamaindex/env";
-import _ from "lodash";
-import { Portkey as OrigPortKey } from "portkey-ai";
-
-type PortkeyOptions = ConstructorParameters<typeof OrigPortKey>[0];
-
-export class PortkeySession {
-  portkey: OrigPortKey;
-
-  constructor(options: PortkeyOptions = {}) {
-    if (!options.apiKey) {
-      options.apiKey = getEnv("PORTKEY_API_KEY")!;
-    }
-
-    if (!options.baseURL) {
-      options.baseURL = getEnv("PORTKEY_BASE_URL") ?? "https://api.portkey.ai";
-    }
-
-    this.portkey = new OrigPortKey({});
-    if (!options.apiKey) {
-      throw new Error("Set Portkey ApiKey in PORTKEY_API_KEY env variable");
-    }
-
-    this.portkey = new OrigPortKey(options);
-  }
-}
-
-const defaultPortkeySession: {
-  session: PortkeySession;
-  options: PortkeyOptions;
-}[] = [];
-
-/**
- * Get a session for the Portkey API. If one already exists with the same options,
- * it will be returned. Otherwise, a new session will be created.
- * @param options
- * @returns
- */
-export function getPortkeySession(options: PortkeyOptions = {}) {
-  let session = defaultPortkeySession.find((session) => {
-    return _.isEqual(session.options, options);
-  })?.session;
-
-  if (!session) {
-    session = new PortkeySession(options);
-    defaultPortkeySession.push({ session, options });
-  }
-  return session;
-}
-
-export class Portkey extends BaseLLM {
-  apiKey?: string | undefined = undefined;
-  baseURL?: string | undefined = undefined;
-  session: PortkeySession;
-
-  constructor(init?: Partial<Portkey> & PortkeyOptions) {
-    super();
-    const { apiKey, baseURL, ...rest } = init || {};
-    this.apiKey = apiKey;
-    this.baseURL = baseURL;
-    this.session = getPortkeySession({
-      ...rest,
-      apiKey: this.apiKey ?? null,
-      baseURL: this.baseURL ?? null,
-    });
-  }
-
-  get metadata(): LLMMetadata {
-    throw new Error("metadata not implemented for Portkey");
-  }
-
-  chat(
-    params: LLMChatParamsStreaming,
-  ): Promise<AsyncIterable<ChatResponseChunk>>;
-  chat(params: LLMChatParamsNonStreaming): Promise<ChatResponse>;
-  @wrapLLMEvent
-  async chat(
-    params: LLMChatParamsNonStreaming | LLMChatParamsStreaming,
-  ): Promise<ChatResponse | AsyncIterable<ChatResponseChunk>> {
-    const { messages, stream, additionalChatOptions } = params;
-    if (stream) {
-      return this.streamChat(messages, additionalChatOptions);
-    } else {
-      const bodyParams = additionalChatOptions || {};
-      const response = await this.session.portkey.chatCompletions.create({
-        messages: messages.map((message) => ({
-          content: extractText(message.content),
-          role: message.role,
-        })),
-        ...bodyParams,
-      });
-
-      const content = response.choices[0]!.message?.content ?? "";
-      const role = response.choices[0]!.message?.role || "assistant";
-      return { raw: response, message: { content, role: role as MessageType } };
-    }
-  }
-
-  async *streamChat(
-    messages: ChatMessage[],
-    params?: Record<string, any>,
-  ): AsyncIterable<ChatResponseChunk> {
-    const chunkStream = await this.session.portkey.chatCompletions.create({
-      messages: messages.map((message) => ({
-        content: extractText(message.content),
-        role: message.role,
-      })),
-      ...params,
-      stream: true,
-    });
-
-    //Indices
-    let idx_counter: number = 0;
-    for await (const part of chunkStream) {
-      part.choices[0]!.index = idx_counter;
-
-      idx_counter++;
-
-      yield { raw: part, delta: part.choices[0]!.delta?.content ?? "" };
-    }
-    return;
-  }
-}
+export * from "@llamaindex/portkey-ai";

packages/llamaindex/src/llm/replicate_ai.ts

@@ -1,379 +1 @@
-import { wrapLLMEvent } from "@llamaindex/core/decorator";
-import {
-  BaseLLM,
-  type ChatMessage,
-  type ChatResponse,
-  type ChatResponseChunk,
-  type LLMChatParamsNonStreaming,
-  type LLMChatParamsStreaming,
-  type MessageType,
-} from "@llamaindex/core/llms";
-import {
-  extractText,
-  streamCallbacks,
-  streamConverter,
-} from "@llamaindex/core/utils";
-import { getEnv } from "@llamaindex/env";
-import Replicate from "../internal/deps/replicate.js";
-
-export class ReplicateSession {
-  replicateKey: string | null = null;
-  replicate: Replicate;
-
-  constructor(replicateKey: string | null = null) {
-    if (replicateKey) {
-      this.replicateKey = replicateKey;
-    } else if (getEnv("REPLICATE_API_TOKEN")) {
-      this.replicateKey = getEnv("REPLICATE_API_TOKEN") as string;
-    } else {
-      throw new Error(
-        "Set Replicate token in REPLICATE_API_TOKEN env variable",
-      );
-    }
-
-    this.replicate = new Replicate({ auth: this.replicateKey });
-  }
-}
-
-export const ALL_AVAILABLE_REPLICATE_MODELS = {
-  // TODO: add more models from replicate
-  "Llama-2-70b-chat-old": {
-    contextWindow: 4096,
-    replicateApi:
-      "replicate/llama70b-v2-chat:e951f18578850b652510200860fc4ea62b3b16fac280f83ff32282f87bbd2e48",
-    //^ Previous 70b model. This is also actually 4 bit, although not exllama.
-  },
-  "Llama-2-70b-chat-4bit": {
-    contextWindow: 4096,
-    replicateApi:
-      "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
-    //^ Model is based off of exllama 4bit.
-  },
-  "Llama-2-13b-chat-old": {
-    contextWindow: 4096,
-    replicateApi:
-      "a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
-  },
-  //^ Last known good 13b non-quantized model. In future versions they add the SYS and INST tags themselves
-  "Llama-2-13b-chat-4bit": {
-    contextWindow: 4096,
-    replicateApi:
-      "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d",
-  },
-  "Llama-2-7b-chat-old": {
-    contextWindow: 4096,
-    replicateApi:
-      "a16z-infra/llama7b-v2-chat:4f0a4744c7295c024a1de15e1a63c880d3da035fa1f49bfd344fe076074c8eea",
-    //^ Last (somewhat) known good 7b non-quantized model. In future versions they add the SYS and INST
-    // tags themselves
-    // https://github.com/replicate/cog-llama-template/commit/fa5ce83912cf82fc2b9c01a4e9dc9bff6f2ef137
-    // Problem is that they fix the max_new_tokens issue in the same commit. :-(
-  },
-  "Llama-2-7b-chat-4bit": {
-    contextWindow: 4096,
-    replicateApi:
-      "meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0",
-  },
-
-  "llama-3-70b-instruct": {
-    contextWindow: 8192,
-    replicateApi: "meta/meta-llama-3-70b-instruct",
-  },
-
-  "llama-3-8b-instruct": {
-    contextWindow: 8192,
-    replicateApi: "meta/meta-llama-3-8b-instruct",
-  },
-};
-
-export enum ReplicateChatStrategy {
-  A16Z = "a16z",
-  META = "meta",
-  METAWBOS = "metawbos",
-  //^ This is not exactly right because SentencePiece puts the BOS and EOS token IDs in after tokenization
-  // Unfortunately any string only API won't support these properly.
-  REPLICATE4BIT = "replicate4bit",
-  //^ To satisfy Replicate's 4 bit models' requirements where they also insert some INST tags
-  REPLICATE4BITWNEWLINES = "replicate4bitwnewlines",
-  //^ Replicate's documentation recommends using newlines: https://replicate.com/blog/how-to-prompt-llama
-  LLAMA3 = "llama3",
-}
-
-export const DeuceChatStrategy = ReplicateChatStrategy;
-
-/**
- * Replicate LLM implementation used
- */
-export class ReplicateLLM extends BaseLLM {
-  model: keyof typeof ALL_AVAILABLE_REPLICATE_MODELS;
-  chatStrategy: ReplicateChatStrategy;
-  temperature: number;
-  topP: number;
-  maxTokens?: number;
-  replicateSession: ReplicateSession;
-
-  constructor(init?: Partial<ReplicateLLM> & { noWarn?: boolean }) {
-    super();
-
-    if (!init?.model && !init?.noWarn) {
-      console.warn(
-        "The default model has been changed to llama-3-70b-instruct. Set noWarn to true to suppress this warning.",
-      );
-    }
-
-    this.model = init?.model ?? "llama-3-70b-instruct";
-    this.chatStrategy =
-      init?.chatStrategy ??
-      (this.model.startsWith("llama-3")
-        ? ReplicateChatStrategy.LLAMA3
-        : this.model.endsWith("4bit")
-          ? ReplicateChatStrategy.REPLICATE4BITWNEWLINES // With the newer Replicate models they do the system message themselves.
-          : ReplicateChatStrategy.METAWBOS); // With BOS and EOS seems to work best, although they all have problems past a certain point
-    this.temperature = init?.temperature ?? 0.1; // minimum temperature is 0.01 for Replicate endpoint
-    this.topP = init?.topP ?? (this.model.startsWith("llama-3") ? 0.9 : 1); // llama-3 defaults to 0.9 top P
-    this.maxTokens =
-      init?.maxTokens ??
-      ALL_AVAILABLE_REPLICATE_MODELS[this.model].contextWindow; // For Replicate, the default is 500 tokens which is too low.
-    this.replicateSession = init?.replicateSession ?? new ReplicateSession();
-  }
-
-  get metadata() {
-    return {
-      model: this.model,
-      temperature: this.temperature,
-      topP: this.topP,
-      maxTokens: this.maxTokens,
-      contextWindow: ALL_AVAILABLE_REPLICATE_MODELS[this.model].contextWindow,
-      tokenizer: undefined,
-    };
-  }
-
-  mapMessagesToPrompt(messages: ChatMessage[]) {
-    if (this.chatStrategy === ReplicateChatStrategy.LLAMA3) {
-      return this.mapMessagesToPromptLlama3(messages);
-    } else if (this.chatStrategy === ReplicateChatStrategy.A16Z) {
-      return this.mapMessagesToPromptA16Z(messages);
-    } else if (this.chatStrategy === ReplicateChatStrategy.META) {
-      return this.mapMessagesToPromptMeta(messages);
-    } else if (this.chatStrategy === ReplicateChatStrategy.METAWBOS) {
-      return this.mapMessagesToPromptMeta(messages, { withBos: true });
-    } else if (this.chatStrategy === ReplicateChatStrategy.REPLICATE4BIT) {
-      return this.mapMessagesToPromptMeta(messages, {
-        replicate4Bit: true,
-        withNewlines: true,
-      });
-    } else if (
-      this.chatStrategy === ReplicateChatStrategy.REPLICATE4BITWNEWLINES
-    ) {
-      return this.mapMessagesToPromptMeta(messages, {
-        replicate4Bit: true,
-        withNewlines: true,
-      });
-    } else {
-      return this.mapMessagesToPromptMeta(messages);
-    }
-  }
-
-  mapMessagesToPromptLlama3(messages: ChatMessage[]) {
-    return {
-      prompt:
-        "<|begin_of_text|>" +
-        messages.reduce((acc, message) => {
-          let content = "";
-          if (typeof message.content === "string") {
-            content = message.content;
-          } else {
-            if (message.content[0]!.type === "text") {
-              content = message.content[0]!.text;
-            } else {
-              content = "";
-            }
-          }
-
-          return (
-            acc +
-            `<|start_header_id|>${message.role}<|end_header_id|>\n\n${content}<|eot_id|>`
-          );
-        }, "") +
-        "<|start_header_id|>assistant<|end_header_id|>\n\n",
-      systemPrompt: undefined,
-    };
-  }
-
-  mapMessagesToPromptA16Z(messages: ChatMessage[]) {
-    return {
-      prompt:
-        messages.reduce((acc, message) => {
-          return (
-            (acc && `${acc}\n\n`) +
-            `${this.mapMessageTypeA16Z(message.role)}${message.content}`
-          );
-        }, "") + "\n\nAssistant:",
-      //^ Here we're differing from A16Z by omitting the space. Generally spaces at the end of prompts decrease performance due to tokenization
-      systemPrompt: undefined,
-    };
-  }
-
-  mapMessageTypeA16Z(messageType: MessageType): string {
-    switch (messageType) {
-      case "user":
-        return "User: ";
-      case "assistant":
-        return "Assistant: ";
-      case "system":
-        return "";
-      default:
-        throw new Error("Unsupported ReplicateLLM message type");
-    }
-  }
-
-  mapMessagesToPromptMeta(
-    messages: ChatMessage[],
-    opts?: {
-      withBos?: boolean;
-      replicate4Bit?: boolean;
-      withNewlines?: boolean;
-    },
-  ) {
-    const {
-      withBos = false,
-      replicate4Bit = false,
-      withNewlines = false,
-    } = opts ?? {};
-    const DEFAULT_SYSTEM_PROMPT = `You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.`;
-
-    const B_SYS = "<<SYS>>\n";
-    const E_SYS = "\n<</SYS>>\n\n";
-    const B_INST = "[INST]";
-    const E_INST = "[/INST]";
-    const BOS = "<s>";
-    const EOS = "</s>";
-
-    if (messages.length === 0) {
-      return { prompt: "", systemPrompt: undefined };
-    }
-
-    messages = [...messages]; // so we can use shift without mutating the original array
-
-    let systemPrompt = undefined;
-    if (messages[0]!.role === "system") {
-      const systemMessage = messages.shift()!;
-
-      if (replicate4Bit) {
-        systemPrompt = systemMessage.content;
-      } else {
-        const systemStr = `${B_SYS}${systemMessage.content}${E_SYS}`;
-
-        // TS Bug: https://github.com/microsoft/TypeScript/issues/9998
-        // @ts-ignore
-        if (messages[0].role !== "user") {
-          throw new Error(
-            "ReplicateLLM: if there is a system message, the second message must be a user message.",
-          );
-        }
-
-        const userContent = messages[0]!.content;
-
-        messages[0]!.content = `${systemStr}${userContent}`;
-      }
-    } else {
-      if (!replicate4Bit) {
-        messages[0]!.content = `${B_SYS}${DEFAULT_SYSTEM_PROMPT}${E_SYS}${messages[0]!.content}`;
-      }
-    }
-
-    return {
-      prompt: messages.reduce((acc, message, index) => {
-        const content = extractText(message.content);
-        if (index % 2 === 0) {
-          return (
-            `${acc}${withBos ? BOS : ""}${B_INST} ${content.trim()} ${E_INST}` +
-            (withNewlines ? "\n" : "")
-          );
-        } else {
-          return (
-            `${acc} ${content.trim()}` +
-            (withNewlines ? "\n" : " ") +
-            (withBos ? EOS : "")
-          ); // Yes, the EOS comes after the space. This is not a mistake.
-        }
-      }, ""),
-      systemPrompt,
-    };
-  }
-
-  chat(
-    params: LLMChatParamsStreaming,
-  ): Promise<AsyncIterable<ChatResponseChunk>>;
-  chat(params: LLMChatParamsNonStreaming): Promise<ChatResponse>;
-  @wrapLLMEvent
-  async chat(
-    params: LLMChatParamsNonStreaming | LLMChatParamsStreaming,
-  ): Promise<ChatResponse | AsyncIterable<ChatResponseChunk>> {
-    const { messages, stream } = params;
-    const api = ALL_AVAILABLE_REPLICATE_MODELS[this.model]
-      .replicateApi as `${string}/${string}:${string}`;
-
-    const { prompt, systemPrompt } = this.mapMessagesToPrompt(messages);
-
-    const replicateOptions: any = {
-      input: {
-        prompt,
-        system_prompt: systemPrompt,
-        temperature: this.temperature,
-        max_tokens: this.maxTokens,
-        top_p: this.topP,
-      },
-    };
-
-    if (this.model.endsWith("4bit")) {
-      replicateOptions.input.max_new_tokens = this.maxTokens;
-    } else {
-      replicateOptions.input.max_length = this.maxTokens;
-    }
-
-    if (this.model.startsWith("llama-3")) {
-      replicateOptions.input.prompt_template = "{prompt}";
-    }
-
-    if (stream) {
-      const controller = new AbortController();
-      const stream = this.replicateSession.replicate.stream(api, {
-        ...replicateOptions,
-        signal: controller.signal,
-      });
-      // replicate.stream is not closing if used as AsyncIterable, force closing after consumption with the AbortController
-      return streamCallbacks(
-        streamConverter(stream, (chunk) => {
-          if (chunk.event === "done") {
-            return null;
-          }
-          return {
-            raw: chunk,
-            delta: chunk.data,
-          };
-        }),
-        { finished: () => controller.abort() },
-      );
-    }
-
-    //Non-streaming
-    const response = await this.replicateSession.replicate.run(
-      api,
-      replicateOptions,
-    );
-
-    return {
-      raw: response,
-      message: {
-        content: (response as Array<string>).join("").trimStart(),
-        //^ We need to do this because Replicate returns a list of strings (for streaming functionality which is not exposed by the run function)
-        role: "assistant",
-      },
-    };
-  }
-}
-
-export const LlamaDeuce = ReplicateLLM;
+export * from "@llamaindex/replicate";

packages/llamaindex/src/next.ts

@@ -17,12 +17,6 @@
  */
 export default function withLlamaIndex(config: any) {
   config.experimental = config.experimental ?? {};
-  // copy tiktoken WASM files to the NextJS build
-  config.experimental.outputFileTracingIncludes =
-    config.experimental.outputFileTracingIncludes ?? {};
-  config.experimental.outputFileTracingIncludes["/**/*"] = [
-    "./node_modules/tiktoken/*.wasm",
-  ];
   // needed for transformers, see https://huggingface.co/docs/transformers.js/en/tutorials/next#step-2-install-and-configure-transformersjs
   config.experimental.serverComponentsExternalPackages =
     config.experimental.serverComponentsExternalPackages ?? [];
@@ -37,12 +31,14 @@ export default function withLlamaIndex(config: any) {
     webpackConfig.resolve.alias = {
       ...webpackConfig.resolve.alias,
       "@google-cloud/vertexai": false,
+      unpdf: false,
     };
     // Following lines will fix issues with onnxruntime-node when using pnpm
     // See: https://github.com/vercel/next.js/issues/43433
     webpackConfig.externals.push({
       "onnxruntime-node": "commonjs onnxruntime-node",
       sharp: "commonjs sharp",
+      chromadb: "commonjs chromadb",
     });
     return webpackConfig;
   };

packages/llamaindex/src/storage/StorageContext.ts

@@ -1,13 +1,16 @@
+import { ClipEmbedding } from "@llamaindex/clip";
 import {
   DEFAULT_IMAGE_VECTOR_NAMESPACE,
   DEFAULT_NAMESPACE,
 } from "@llamaindex/core/global";
 import { ModalityType, ObjectType } from "@llamaindex/core/schema";
 import { path } from "@llamaindex/env";
-import { getImageEmbedModel } from "../internal/settings/image-embed-model.js";
 import type { ServiceContext } from "../ServiceContext.js";
 import { SimpleVectorStore } from "../vector-store/SimpleVectorStore.js";
-import type { VectorStore, VectorStoreByType } from "../vector-store/types.js";
+import type {
+  BaseVectorStore,
+  VectorStoreByType,
+} from "../vector-store/types.js";
 import { SimpleDocumentStore } from "./docStore/SimpleDocumentStore.js";
 import type { BaseDocumentStore } from "./docStore/types.js";
 import { SimpleIndexStore } from "./indexStore/SimpleIndexStore.js";
@@ -22,7 +25,7 @@ export interface StorageContext {
 type BuilderParams = {
   docStore: BaseDocumentStore;
   indexStore: BaseIndexStore;
-  vectorStore: VectorStore;
+  vectorStore: BaseVectorStore;
   vectorStores: VectorStoreByType;
   storeImages: boolean;
   persistDir: string;
@@ -50,7 +53,7 @@ export async function storageContextFromDefaults({
     }
     if (storeImages && !(ModalityType.IMAGE in vectorStores)) {
       vectorStores[ModalityType.IMAGE] = new SimpleVectorStore({
-        embedModel: new (await getImageEmbedModel())(),
+        embeddingModel: new ClipEmbedding(),
       });
     }
   } else {
@@ -68,7 +71,7 @@ export async function storageContextFromDefaults({
     if (storeImages && !(ObjectType.IMAGE in vectorStores)) {
       vectorStores[ModalityType.IMAGE] = await SimpleVectorStore.fromPersistDir(
         path.join(persistDir, DEFAULT_IMAGE_VECTOR_NAMESPACE),
-        new (await getImageEmbedModel())(),
+        new ClipEmbedding(),
       );
     }
   }