diff --git a/.changeset/lovely-papayas-exist.md b/.changeset/lovely-papayas-exist.md
new file mode 100644
index 0000000000000000000000000000000000000000..ce01fa9bd1b6f8835b6b22b3412aee0cfdfe61a0
--- /dev/null
+++ b/.changeset/lovely-papayas-exist.md
@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Support Anthropic and Gemini as model providers
diff --git a/.changeset/ninety-clocks-ring.md b/.changeset/ninety-clocks-ring.md
new file mode 100644
index 0000000000000000000000000000000000000000..cac6cb886f0a41fabc50fffd5bd5f6a2cd1840a3
--- /dev/null
+++ b/.changeset/ninety-clocks-ring.md
@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Support new agents from LITS 0.3
diff --git a/helpers/env-variables.ts b/helpers/env-variables.ts
index 5451d435880aa5d5bf8863d56271461f98c6d253..5a91da6b582fd925c4a15dea8db1057f3cbf4616 100644
--- a/helpers/env-variables.ts
+++ b/helpers/env-variables.ts
@@ -173,6 +173,24 @@ const getModelEnvs = (modelConfig: ModelConfig): EnvVar[] => {
},
]
: []),
+ ...(modelConfig.provider === "anthropic"
+ ? [
+ {
+ name: "ANTHROPIC_API_KEY",
+ description: "The Anthropic API key to use.",
+ value: modelConfig.apiKey,
+ },
+ ]
+ : []),
+ ...(modelConfig.provider === "gemini"
+ ? [
+ {
+ name: "GOOGLE_API_KEY",
+ description: "The Google API key to use.",
+ value: modelConfig.apiKey,
+ },
+ ]
+ : []),
];
};
diff --git a/helpers/index.ts b/helpers/index.ts
index b7b3991df77413b4755c58cdc349cfae469792e5..571182f186a73396faccf1dc59dbcb1cffa730cb 100644
--- a/helpers/index.ts
+++ b/helpers/index.ts
@@ -9,7 +9,6 @@ import { createBackendEnvFile, createFrontendEnvFile } from "./env-variables";
import { PackageManager } from "./get-pkg-manager";
import { installLlamapackProject } from "./llama-pack";
import { isHavingPoetryLockFile, tryPoetryRun } from "./poetry";
-import { isModelConfigured } from "./providers";
import { installPythonTemplate } from "./python";
import { downloadAndExtractRepo } from "./repo";
import { ConfigFileType, writeToolsConfig } from "./tools";
@@ -38,7 +37,7 @@ async function generateContextData(
? "poetry run generate"
: `${packageManager} run generate`,
)}`;
- const modelConfigured = isModelConfigured(modelConfig);
+ const modelConfigured = modelConfig.isConfigured();
const llamaCloudKeyConfigured = useLlamaParse
? llamaCloudKey || process.env["LLAMA_CLOUD_API_KEY"]
: true;
diff --git a/helpers/providers/anthropic.ts b/helpers/providers/anthropic.ts
new file mode 100644
index 0000000000000000000000000000000000000000..1239a0c4954975a077b136949e8f4c4371202fc2
--- /dev/null
+++ b/helpers/providers/anthropic.ts
@@ -0,0 +1,106 @@
+import ciInfo from "ci-info";
+import prompts from "prompts";
+import { ModelConfigParams } from ".";
+import { questionHandlers, toChoice } from "../../questions";
+
+const MODELS = [
+ "claude-3-opus",
+ "claude-3-sonnet",
+ "claude-3-haiku",
+ "claude-2.1",
+ "claude-instant-1.2",
+];
+const DEFAULT_MODEL = MODELS[0];
+
+// TODO: get embedding vector dimensions from the anthropic sdk (currently not supported)
+// Use huggingface embedding models for now
+enum HuggingFaceEmbeddingModelType {
+ XENOVA_ALL_MINILM_L6_V2 = "all-MiniLM-L6-v2",
+ XENOVA_ALL_MPNET_BASE_V2 = "all-mpnet-base-v2",
+}
+type ModelData = {
+ dimensions: number;
+};
+const EMBEDDING_MODELS: Record<HuggingFaceEmbeddingModelType, ModelData> = {
+ [HuggingFaceEmbeddingModelType.XENOVA_ALL_MINILM_L6_V2]: {
+ dimensions: 384,
+ },
+ [HuggingFaceEmbeddingModelType.XENOVA_ALL_MPNET_BASE_V2]: {
+ dimensions: 768,
+ },
+};
+const DEFAULT_EMBEDDING_MODEL = Object.keys(EMBEDDING_MODELS)[0];
+const DEFAULT_DIMENSIONS = Object.values(EMBEDDING_MODELS)[0].dimensions;
+
+type AnthropicQuestionsParams = {
+ apiKey?: string;
+ askModels: boolean;
+};
+
+export async function askAnthropicQuestions({
+ askModels,
+ apiKey,
+}: AnthropicQuestionsParams): Promise<ModelConfigParams> {
+ const config: ModelConfigParams = {
+ apiKey,
+ model: DEFAULT_MODEL,
+ embeddingModel: DEFAULT_EMBEDDING_MODEL,
+ dimensions: DEFAULT_DIMENSIONS,
+ isConfigured(): boolean {
+ if (config.apiKey) {
+ return true;
+ }
+ if (process.env["ANTHROPIC_API_KEY"]) {
+ return true;
+ }
+ return false;
+ },
+ };
+
+ if (!config.apiKey) {
+ const { key } = await prompts(
+ {
+ type: "text",
+ name: "key",
+ message:
+ "Please provide your Anthropic API key (or leave blank to use ANTHROPIC_API_KEY env variable):",
+ },
+ questionHandlers,
+ );
+ config.apiKey = key || process.env.ANTHROPIC_API_KEY;
+ }
+
+ // use default model values in CI or if user should not be asked
+ const useDefaults = ciInfo.isCI || !askModels;
+ if (!useDefaults) {
+ const { model } = await prompts(
+ {
+ type: "select",
+ name: "model",
+ message: "Which LLM model would you like to use?",
+ choices: MODELS.map(toChoice),
+ initial: 0,
+ },
+ questionHandlers,
+ );
+ config.model = model;
+
+ const { embeddingModel } = await prompts(
+ {
+ type: "select",
+ name: "embeddingModel",
+ message: "Which embedding model would you like to use?",
+ choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
+ initial: 0,
+ },
+ questionHandlers,
+ );
+ config.embeddingModel = embeddingModel;
+ config.dimensions =
+ EMBEDDING_MODELS[
+ embeddingModel as HuggingFaceEmbeddingModelType
+ ].dimensions;
+ }
+
+ return config;
+}
diff --git a/helpers/providers/gemini.ts b/helpers/providers/gemini.ts
new file mode 100644
index 0000000000000000000000000000000000000000..b0f6733f69d296b767af3f496a7686fb15e8a4de
--- /dev/null
+++ b/helpers/providers/gemini.ts
@@ -0,0 +1,87 @@
+import ciInfo from "ci-info";
+import prompts from "prompts";
+import { ModelConfigParams } from ".";
+import { questionHandlers, toChoice } from "../../questions";
+
+const MODELS = ["gemini-1.5-pro-latest", "gemini-pro", "gemini-pro-vision"];
+type ModelData = {
+ dimensions: number;
+};
+const EMBEDDING_MODELS: Record<string, ModelData> = {
+ "embedding-001": { dimensions: 768 },
+ "text-embedding-004": { dimensions: 768 },
+};
+
+const DEFAULT_MODEL = MODELS[0];
+const DEFAULT_EMBEDDING_MODEL = Object.keys(EMBEDDING_MODELS)[0];
+const DEFAULT_DIMENSIONS = Object.values(EMBEDDING_MODELS)[0].dimensions;
+
+type GeminiQuestionsParams = {
+ apiKey?: string;
+ askModels: boolean;
+};
+
+export async function askGeminiQuestions({
+ askModels,
+ apiKey,
+}: GeminiQuestionsParams): Promise<ModelConfigParams> {
+ const config: ModelConfigParams = {
+ apiKey,
+ model: DEFAULT_MODEL,
+ embeddingModel: DEFAULT_EMBEDDING_MODEL,
+ dimensions: DEFAULT_DIMENSIONS,
+ isConfigured(): boolean {
+ if (config.apiKey) {
+ return true;
+ }
+ if (process.env["GOOGLE_API_KEY"]) {
+ return true;
+ }
+ return false;
+ },
+ };
+
+ if (!config.apiKey) {
+ const { key } = await prompts(
+ {
+ type: "text",
+ name: "key",
+ message:
+ "Please provide your Google API key (or leave blank to use GOOGLE_API_KEY env variable):",
+ },
+ questionHandlers,
+ );
+ config.apiKey = key || process.env.GOOGLE_API_KEY;
+ }
+
+ // use default model values in CI or if user should not be asked
+ const useDefaults = ciInfo.isCI || !askModels;
+ if (!useDefaults) {
+ const { model } = await prompts(
+ {
+ type: "select",
+ name: "model",
+ message: "Which LLM model would you like to use?",
+ choices: MODELS.map(toChoice),
+ initial: 0,
+ },
+ questionHandlers,
+ );
+ config.model = model;
+
+ const { embeddingModel } = await prompts(
+ {
+ type: "select",
+ name: "embeddingModel",
+ message: "Which embedding model would you like to use?",
+ choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
+ initial: 0,
+ },
+ questionHandlers,
+ );
+ config.embeddingModel = embeddingModel;
+ config.dimensions = EMBEDDING_MODELS[embeddingModel].dimensions;
+ }
+
+ return config;
+}
diff --git a/helpers/providers/index.ts b/helpers/providers/index.ts
index bb793c090675ebcda172e3ebed1c19ff35a70c11..62202c0f6f9f4aed96de17668f9760ddc260cf15 100644
--- a/helpers/providers/index.ts
+++ b/helpers/providers/index.ts
@@ -2,8 +2,10 @@ import ciInfo from "ci-info";
import prompts from "prompts";
import { questionHandlers } from "../../questions";
import { ModelConfig, ModelProvider } from "../types";
+import { askAnthropicQuestions } from "./anthropic";
+import { askGeminiQuestions } from "./gemini";
import { askOllamaQuestions } from "./ollama";
-import { askOpenAIQuestions, isOpenAIConfigured } from "./openai";
+import { askOpenAIQuestions } from "./openai";
const DEFAULT_MODEL_PROVIDER = "openai";
@@ -31,6 +33,8 @@ export async function askModelConfig({
value: "openai",
},
{ title: "Ollama", value: "ollama" },
+ { title: "Anthropic", value: "anthropic" },
+ { title: "Gemini", value: "gemini" },
],
initial: 0,
},
@@ -44,6 +48,12 @@ export async function askModelConfig({
case "ollama":
modelConfig = await askOllamaQuestions({ askModels });
break;
+ case "anthropic":
+ modelConfig = await askAnthropicQuestions({ askModels });
+ break;
+ case "gemini":
+ modelConfig = await askGeminiQuestions({ askModels });
+ break;
default:
modelConfig = await askOpenAIQuestions({
openAiKey,
@@ -55,12 +65,3 @@ export async function askModelConfig({
provider: modelProvider,
};
}
-
-export function isModelConfigured(modelConfig: ModelConfig): boolean {
- switch (modelConfig.provider) {
- case "openai":
- return isOpenAIConfigured(modelConfig);
- default:
- return true;
- }
-}
diff --git a/helpers/providers/ollama.ts b/helpers/providers/ollama.ts
index a32d9d309034a60587f54791856c7998745d53a6..e70b25f06336bb45911e4878d1328cd00dfc08eb 100644
--- a/helpers/providers/ollama.ts
+++ b/helpers/providers/ollama.ts
@@ -29,6 +29,9 @@ export async function askOllamaQuestions({
model: DEFAULT_MODEL,
embeddingModel: DEFAULT_EMBEDDING_MODEL,
dimensions: EMBEDDING_MODELS[DEFAULT_EMBEDDING_MODEL].dimensions,
+ isConfigured(): boolean {
+ return true;
+ },
};
// use default model values in CI or if user should not be asked
diff --git a/helpers/providers/openai.ts b/helpers/providers/openai.ts
index 2e13d99f0b27e4d4b8d9ff55058a3ef2532a18ba..c02fee36cdbb0afa5e90ad26d7bdf28894f45e6b 100644
--- a/helpers/providers/openai.ts
+++ b/helpers/providers/openai.ts
@@ -20,6 +20,15 @@ export async function askOpenAIQuestions({
model: DEFAULT_MODEL,
embeddingModel: DEFAULT_EMBEDDING_MODEL,
dimensions: getDimensions(DEFAULT_EMBEDDING_MODEL),
+ isConfigured(): boolean {
+ if (config.apiKey) {
+ return true;
+ }
+ if (process.env["OPENAI_API_KEY"]) {
+ return true;
+ }
+ return false;
+ },
};
if (!config.apiKey) {
@@ -31,7 +40,6 @@ export async function askOpenAIQuestions({
? "Please provide your OpenAI API key (or leave blank to use OPENAI_API_KEY env variable):"
: "Please provide your OpenAI API key (leave blank to skip):",
validate: (value: string) => {
- console.log(value);
if (askModels && !value) {
if (process.env.OPENAI_API_KEY) {
return true;
@@ -78,16 +86,6 @@ export async function askOpenAIQuestions({
return config;
}
-export function isOpenAIConfigured(params: ModelConfigParams): boolean {
- if (params.apiKey) {
- return true;
- }
- if (process.env["OPENAI_API_KEY"]) {
- return true;
- }
- return false;
-}
-
async function getAvailableModelChoices(
selectEmbedding: boolean,
apiKey?: string,
diff --git a/helpers/python.ts b/helpers/python.ts
index 316fe52d51b3b821f20e2992b7d80a2e869f529c..8c2d7bce6b6a9494df9bffc832d56bbd038826e9 100644
--- a/helpers/python.ts
+++ b/helpers/python.ts
@@ -127,6 +127,26 @@ const getAdditionalDependencies = (
version: "0.2.2",
});
break;
+ case "anthropic":
+ dependencies.push({
+ name: "llama-index-llms-anthropic",
+ version: "0.1.10",
+ });
+ dependencies.push({
+ name: "llama-index-embeddings-huggingface",
+ version: "0.2.0",
+ });
+ break;
+ case "gemini":
+ dependencies.push({
+ name: "llama-index-llms-gemini",
+ version: "0.1.7",
+ });
+ dependencies.push({
+ name: "llama-index-embeddings-gemini",
+ version: "0.1.6",
+ });
+ break;
}
return dependencies;
diff --git a/helpers/types.ts b/helpers/types.ts
index 42b571e7f16f587dd6fdf7348d24bd7277f04b5b..b70f586aac35ac7ac2206cbd2a0a593927b60b6c 100644
--- a/helpers/types.ts
+++ b/helpers/types.ts
@@ -1,13 +1,14 @@
import { PackageManager } from "../helpers/get-pkg-manager";
import { Tool } from "./tools";
-export type ModelProvider = "openai" | "ollama";
+export type ModelProvider = "openai" | "ollama" | "anthropic" | "gemini";
export type ModelConfig = {
provider: ModelProvider;
apiKey?: string;
model: string;
embeddingModel: string;
dimensions: number;
+ isConfigured(): boolean;
};
export type TemplateType = "streaming" | "community" | "llamapack";
export type TemplateFramework = "nextjs" | "express" | "fastapi";
diff --git a/questions.ts b/questions.ts
index 31eed224772df4ccb6180ba80db824a5523d1e4f..3e0460847242bcf08a9d3066612eb69c1ec92d02 100644
--- a/questions.ts
+++ b/questions.ts
@@ -14,7 +14,7 @@ import { COMMUNITY_OWNER, COMMUNITY_REPO } from "./helpers/constant";
import { EXAMPLE_FILE } from "./helpers/datasources";
import { templatesDir } from "./helpers/dir";
import { getAvailableLlamapackOptions } from "./helpers/llama-pack";
-import { askModelConfig, isModelConfigured } from "./helpers/providers";
+import { askModelConfig } from "./helpers/providers";
import { getProjectOptions } from "./helpers/repo";
import { supportedTools, toolsRequireConfig } from "./helpers/tools";
@@ -257,7 +257,8 @@ export const askQuestions = async (
},
];
- const modelConfigured = isModelConfigured(program.modelConfig);
+ const modelConfigured =
+ !program.llamapack && program.modelConfig.isConfigured();
// If using LlamaParse, require LlamaCloud API key
const llamaCloudKeyConfigured = program.useLlamaParse
? program.llamaCloudKey || process.env["LLAMA_CLOUD_API_KEY"]
@@ -268,8 +269,7 @@ export const askQuestions = async (
!hasVectorDb &&
modelConfigured &&
llamaCloudKeyConfigured &&
- !toolsRequireConfig(program.tools) &&
- !program.llamapack
+ !toolsRequireConfig(program.tools)
) {
actionChoices.push({
title:
@@ -398,7 +398,6 @@ export const askQuestions = async (
if (program.framework === "express" || program.framework === "fastapi") {
// if a backend-only framework is selected, ask whether we should create a frontend
- // (only for streaming backends)
if (program.frontend === undefined) {
if (ciInfo.isCI) {
program.frontend = getPrefOrDefault("frontend");
diff --git a/templates/components/engines/typescript/agent/chat.ts b/templates/components/engines/typescript/agent/chat.ts
index 8fdbe272c7616f09b9bb054ed2367a91cf716b58..41d7118d915ee331138ea663ecd2f98d6a590931 100644
--- a/templates/components/engines/typescript/agent/chat.ts
+++ b/templates/components/engines/typescript/agent/chat.ts
@@ -1,4 +1,4 @@
-import { BaseTool, OpenAIAgent, QueryEngineTool } from "llamaindex";
+import { BaseToolWithCall, OpenAIAgent, QueryEngineTool } from "llamaindex";
import { ToolsFactory } from "llamaindex/tools/ToolsFactory";
import fs from "node:fs/promises";
import path from "node:path";
@@ -6,7 +6,7 @@ import { getDataSource } from "./index";
import { STORAGE_CACHE_DIR } from "./shared";
export async function createChatEngine() {
- let tools: BaseTool[] = [];
+ let tools: BaseToolWithCall[] = [];
// Add a query engine tool if we have a data source
// Delete this code if you don't have a data source
diff --git a/templates/components/vectordbs/typescript/none/generate.ts b/templates/components/vectordbs/typescript/none/generate.ts
index 732ba21129c8aab1dc7de78df2e03df54092a791..8c162805b132f056106f52e20b567af1a7985aa5 100644
--- a/templates/components/vectordbs/typescript/none/generate.ts
+++ b/templates/components/vectordbs/typescript/none/generate.ts
@@ -1,4 +1,5 @@
-import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { storageContextFromDefaults } from "llamaindex/storage/StorageContext";
import * as dotenv from "dotenv";
diff --git a/templates/components/vectordbs/typescript/none/index.ts b/templates/components/vectordbs/typescript/none/index.ts
index 919ba3a615b454dbcc9230f00dcff57e69fd33ec..64b289750cd978e88b4b17d8194bb468026e8910 100644
--- a/templates/components/vectordbs/typescript/none/index.ts
+++ b/templates/components/vectordbs/typescript/none/index.ts
@@ -1,8 +1,5 @@
-import {
- SimpleDocumentStore,
- storageContextFromDefaults,
- VectorStoreIndex,
-} from "llamaindex";
+import { SimpleDocumentStore, VectorStoreIndex } from "llamaindex";
+import { storageContextFromDefaults } from "llamaindex/storage/StorageContext";
import { STORAGE_CACHE_DIR } from "./shared";
export async function getDataSource() {
diff --git a/templates/types/streaming/express/package.json b/templates/types/streaming/express/package.json
index 612076e5415ec558a8af7f1c05135d1265060da3..d59f18721f8a7e5cef5ff8fdaa1dfc1bab43f77e 100644
--- a/templates/types/streaming/express/package.json
+++ b/templates/types/streaming/express/package.json
@@ -14,7 +14,8 @@
"cors": "^2.8.5",
"dotenv": "^16.3.1",
"express": "^4.18.2",
- "llamaindex": "0.2.10"
+ "llamaindex": "0.3.3",
+ "pdf2json": "3.0.5"
},
"devDependencies": {
"@types/cors": "^2.8.16",
diff --git a/templates/types/streaming/express/src/controllers/chat.controller.ts b/templates/types/streaming/express/src/controllers/chat.controller.ts
index bb77c6e5c46733bf4936c13e3e24f7b83aa3ba55..d9ba57dcab0ffcee1ed80a33ca9ecaa95e2c50c9 100644
--- a/templates/types/streaming/express/src/controllers/chat.controller.ts
+++ b/templates/types/streaming/express/src/controllers/chat.controller.ts
@@ -1,6 +1,11 @@
import { Message, StreamData, streamToResponse } from "ai";
import { Request, Response } from "express";
-import { ChatMessage, MessageContent, Settings } from "llamaindex";
+import {
+ CallbackManager,
+ ChatMessage,
+ MessageContent,
+ Settings,
+} from "llamaindex";
import { createChatEngine } from "./engine/chat";
import { LlamaIndexStream } from "./llamaindex-stream";
import { appendEventData } from "./stream-helper";
@@ -45,14 +50,15 @@ export const chat = async (req: Request, res: Response) => {
// Init Vercel AI StreamData
const vercelStreamData = new StreamData();
- appendEventData(
- vercelStreamData,
- `Retrieving context for query: '${userMessage.content}'`,
- );
- // Setup callback for streaming data before chatting
- Settings.callbackManager.on("retrieve", (data) => {
+ // Setup callbacks
+ const callbackManager = new CallbackManager();
+ callbackManager.on("retrieve", (data) => {
const { nodes } = data.detail;
+ appendEventData(
+ vercelStreamData,
+ `Retrieving context for query: '${userMessage.content}'`,
+ );
appendEventData(
vercelStreamData,
`Retrieved ${nodes.length} sources to use as context for the query`,
@@ -60,31 +66,23 @@ export const chat = async (req: Request, res: Response) => {
});
// Calling LlamaIndex's ChatEngine to get a streamed response
- const response = await chatEngine.chat({
- message: userMessageContent,
- chatHistory: messages as ChatMessage[],
- stream: true,
+ const response = await Settings.withCallbackManager(callbackManager, () => {
+ return chatEngine.chat({
+ message: userMessageContent,
+ chatHistory: messages as ChatMessage[],
+ stream: true,
+ });
});
// Return a stream, which can be consumed by the Vercel/AI client
- const { stream } = LlamaIndexStream(response, vercelStreamData, {
+ const stream = LlamaIndexStream(response, vercelStreamData, {
parserOptions: {
image_url: data?.imageUrl,
},
});
-
- // Pipe LlamaIndexStream to response
const processedStream = stream.pipeThrough(vercelStreamData.stream);
- return streamToResponse(processedStream, res, {
- headers: {
- // response MUST have the `X-Experimental-Stream-Data: 'true'` header
- // so that the client uses the correct parsing logic, see
- // https://sdk.vercel.ai/docs/api-reference/stream-data#on-the-server
- "X-Experimental-Stream-Data": "true",
- "Content-Type": "text/plain; charset=utf-8",
- "Access-Control-Expose-Headers": "X-Experimental-Stream-Data",
- },
- });
+
+ return streamToResponse(processedStream, res);
} catch (error) {
console.error("[LlamaIndex]", error);
return res.status(500).json({
diff --git a/templates/types/streaming/express/src/controllers/engine/settings.ts b/templates/types/streaming/express/src/controllers/engine/settings.ts
index 258043993aa14f618c62a4a196d00645047ada63..6e980afbb6137f65d7e3c6cf6b2720a11897024c 100644
--- a/templates/types/streaming/express/src/controllers/engine/settings.ts
+++ b/templates/types/streaming/express/src/controllers/engine/settings.ts
@@ -1,10 +1,17 @@
import {
- Ollama,
- OllamaEmbedding,
+ Anthropic,
+ GEMINI_EMBEDDING_MODEL,
+ GEMINI_MODEL,
+ Gemini,
+ GeminiEmbedding,
OpenAI,
OpenAIEmbedding,
Settings,
} from "llamaindex";
+import { HuggingFaceEmbedding } from "llamaindex/embeddings/HuggingFaceEmbedding";
+import { OllamaEmbedding } from "llamaindex/embeddings/OllamaEmbedding";
+import { ALL_AVAILABLE_ANTHROPIC_MODELS } from "llamaindex/llm/anthropic";
+import { Ollama } from "llamaindex/llm/ollama";
const CHUNK_SIZE = 512;
const CHUNK_OVERLAP = 20;
@@ -12,10 +19,21 @@ const CHUNK_OVERLAP = 20;
export const initSettings = async () => {
// HINT: you can delete the initialization code for unused model providers
console.log(`Using '${process.env.MODEL_PROVIDER}' model provider`);
+
+ if (!process.env.MODEL || !process.env.EMBEDDING_MODEL) {
+ throw new Error("'MODEL' and 'EMBEDDING_MODEL' env variables must be set.");
+ }
+
switch (process.env.MODEL_PROVIDER) {
case "ollama":
initOllama();
break;
+ case "anthropic":
+ initAnthropic();
+ break;
+ case "gemini":
+ initGemini();
+ break;
default:
initOpenAI();
break;
@@ -38,11 +56,6 @@ function initOpenAI() {
}
function initOllama() {
- if (!process.env.MODEL || !process.env.EMBEDDING_MODEL) {
- throw new Error(
- "Using Ollama as model provider, 'MODEL' and 'EMBEDDING_MODEL' env variables must be set.",
- );
- }
Settings.llm = new Ollama({
model: process.env.MODEL ?? "",
});
@@ -50,3 +63,25 @@ function initOllama() {
model: process.env.EMBEDDING_MODEL ?? "",
});
}
+
+function initAnthropic() {
+ const embedModelMap: Record<string, string> = {
+ "all-MiniLM-L6-v2": "Xenova/all-MiniLM-L6-v2",
+ "all-mpnet-base-v2": "Xenova/all-mpnet-base-v2",
+ };
+ Settings.llm = new Anthropic({
+ model: process.env.MODEL as keyof typeof ALL_AVAILABLE_ANTHROPIC_MODELS,
+ });
+ Settings.embedModel = new HuggingFaceEmbedding({
+ modelType: embedModelMap[process.env.EMBEDDING_MODEL!],
+ });
+}
+
+function initGemini() {
+ Settings.llm = new Gemini({
+ model: process.env.MODEL as GEMINI_MODEL,
+ });
+ Settings.embedModel = new GeminiEmbedding({
+ model: process.env.EMBEDDING_MODEL as GEMINI_EMBEDDING_MODEL,
+ });
+}
diff --git a/templates/types/streaming/express/src/controllers/llamaindex-stream.ts b/templates/types/streaming/express/src/controllers/llamaindex-stream.ts
index b689905727767fb1f17f099298d13e72f0234df7..df2c17ffc34744d044ecdcc375955dbc7ea5ba70 100644
--- a/templates/types/streaming/express/src/controllers/llamaindex-stream.ts
+++ b/templates/types/streaming/express/src/controllers/llamaindex-stream.ts
@@ -9,16 +9,22 @@ import {
Metadata,
NodeWithScore,
Response,
- StreamingAgentChatResponse,
+ ToolCallLLMMessageOptions,
} from "llamaindex";
+
+import { AgentStreamChatResponse } from "llamaindex/agent/base";
import { appendImageData, appendSourceData } from "./stream-helper";
+type LlamaIndexResponse =
+ | AgentStreamChatResponse<ToolCallLLMMessageOptions>
+ | Response;
+
type ParserOptions = {
image_url?: string;
};
function createParser(
- res: AsyncIterable<Response>,
+ res: AsyncIterable<LlamaIndexResponse>,
data: StreamData,
opts?: ParserOptions,
) {
@@ -33,17 +39,27 @@ function createParser(
async pull(controller): Promise<void> {
const { value, done } = await it.next();
if (done) {
- appendSourceData(data, sourceNodes);
+ if (sourceNodes) {
+ appendSourceData(data, sourceNodes);
+ }
controller.close();
data.close();
return;
}
- if (!sourceNodes) {
- // get source nodes from the first response
- sourceNodes = value.sourceNodes;
+ let delta;
+ if (value instanceof Response) {
+ // handle Response type
+ if (value.sourceNodes) {
+ // get source nodes from the first response
+ sourceNodes = value.sourceNodes;
+ }
+ delta = value.response ?? "";
+ } else {
+ // handle other types
+ delta = value.response.delta;
}
- const text = trimStartOfStream(value.response ?? "");
+ const text = trimStartOfStream(delta ?? "");
if (text) {
controller.enqueue(text);
}
@@ -52,21 +68,14 @@ function createParser(
}
export function LlamaIndexStream(
- response: StreamingAgentChatResponse | AsyncIterable<Response>,
+ response: AsyncIterable<LlamaIndexResponse>,
data: StreamData,
opts?: {
callbacks?: AIStreamCallbacksAndOptions;
parserOptions?: ParserOptions;
},
-): { stream: ReadableStream; data: StreamData } {
- const res =
- response instanceof StreamingAgentChatResponse
- ? response.response
- : response;
- return {
- stream: createParser(res, data, opts?.parserOptions)
- .pipeThrough(createCallbacksTransformer(opts?.callbacks))
- .pipeThrough(createStreamDataTransformer()),
- data,
- };
+): ReadableStream<string> {
+ return createParser(response, data, opts?.parserOptions)
+ .pipeThrough(createCallbacksTransformer(opts?.callbacks))
+ .pipeThrough(createStreamDataTransformer());
}
diff --git a/templates/types/streaming/fastapi/app/settings.py b/templates/types/streaming/fastapi/app/settings.py
index be272d548c8bcbe2e960b3b476e97c836ee7d931..41ab539584d0201444f609477d620696e793b998 100644
--- a/templates/types/streaming/fastapi/app/settings.py
+++ b/templates/types/streaming/fastapi/app/settings.py
@@ -9,6 +9,10 @@ def init_settings():
init_openai()
elif model_provider == "ollama":
init_ollama()
+ elif model_provider == "anthropic":
+ init_anthropic()
+ elif model_provider == "gemini":
+ init_gemini()
else:
raise ValueError(f"Invalid model provider: {model_provider}")
Settings.chunk_size = int(os.getenv("CHUNK_SIZE", "1024"))
@@ -42,3 +46,47 @@ def init_openai():
"dimension": int(dimension) if dimension is not None else None,
}
Settings.embed_model = OpenAIEmbedding(**config)
+
+
+def init_anthropic():
+ from llama_index.llms.anthropic import Anthropic
+ from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+ model_map: Dict[str, str] = {
+ "claude-3-opus": "claude-3-opus-20240229",
+ "claude-3-sonnet": "claude-3-sonnet-20240229",
+ "claude-3-haiku": "claude-3-haiku-20240307",
+ "claude-2.1": "claude-2.1",
+ "claude-instant-1.2": "claude-instant-1.2",
+ }
+
+ embed_model_map: Dict[str, str] = {
+ "all-MiniLM-L6-v2": "sentence-transformers/all-MiniLM-L6-v2",
+ "all-mpnet-base-v2": "sentence-transformers/all-mpnet-base-v2",
+ }
+
+ Settings.llm = Anthropic(model=model_map[os.getenv("MODEL")])
+ Settings.embed_model = HuggingFaceEmbedding(
+ model_name=embed_model_map[os.getenv("EMBEDDING_MODEL")]
+ )
+
+
+def init_gemini():
+ from llama_index.llms.gemini import Gemini
+ from llama_index.embeddings.gemini import GeminiEmbedding
+
+ model_map: Dict[str, str] = {
+ "gemini-1.5-pro-latest": "models/gemini-1.5-pro-latest",
+ "gemini-pro": "models/gemini-pro",
+ "gemini-pro-vision": "models/gemini-pro-vision",
+ }
+
+ embed_model_map: Dict[str, str] = {
+ "embedding-001": "models/embedding-001",
+ "text-embedding-004": "models/text-embedding-004",
+ }
+
+ Settings.llm = Gemini(model=model_map[os.getenv("MODEL")])
+ Settings.embed_model = GeminiEmbedding(
+ model_name=embed_model_map[os.getenv("EMBEDDING_MODEL")]
+ )
diff --git a/templates/types/streaming/nextjs/app/api/chat/engine/settings.ts b/templates/types/streaming/nextjs/app/api/chat/engine/settings.ts
index 258043993aa14f618c62a4a196d00645047ada63..6e980afbb6137f65d7e3c6cf6b2720a11897024c 100644
--- a/templates/types/streaming/nextjs/app/api/chat/engine/settings.ts
+++ b/templates/types/streaming/nextjs/app/api/chat/engine/settings.ts
@@ -1,10 +1,17 @@
import {
- Ollama,
- OllamaEmbedding,
+ Anthropic,
+ GEMINI_EMBEDDING_MODEL,
+ GEMINI_MODEL,
+ Gemini,
+ GeminiEmbedding,
OpenAI,
OpenAIEmbedding,
Settings,
} from "llamaindex";
+import { HuggingFaceEmbedding } from "llamaindex/embeddings/HuggingFaceEmbedding";
+import { OllamaEmbedding } from "llamaindex/embeddings/OllamaEmbedding";
+import { ALL_AVAILABLE_ANTHROPIC_MODELS } from "llamaindex/llm/anthropic";
+import { Ollama } from "llamaindex/llm/ollama";
const CHUNK_SIZE = 512;
const CHUNK_OVERLAP = 20;
@@ -12,10 +19,21 @@ const CHUNK_OVERLAP = 20;
export const initSettings = async () => {
// HINT: you can delete the initialization code for unused model providers
console.log(`Using '${process.env.MODEL_PROVIDER}' model provider`);
+
+ if (!process.env.MODEL || !process.env.EMBEDDING_MODEL) {
+ throw new Error("'MODEL' and 'EMBEDDING_MODEL' env variables must be set.");
+ }
+
switch (process.env.MODEL_PROVIDER) {
case "ollama":
initOllama();
break;
+ case "anthropic":
+ initAnthropic();
+ break;
+ case "gemini":
+ initGemini();
+ break;
default:
initOpenAI();
break;
@@ -38,11 +56,6 @@ function initOpenAI() {
}
function initOllama() {
- if (!process.env.MODEL || !process.env.EMBEDDING_MODEL) {
- throw new Error(
- "Using Ollama as model provider, 'MODEL' and 'EMBEDDING_MODEL' env variables must be set.",
- );
- }
Settings.llm = new Ollama({
model: process.env.MODEL ?? "",
});
@@ -50,3 +63,25 @@ function initOllama() {
model: process.env.EMBEDDING_MODEL ?? "",
});
}
+
+function initAnthropic() {
+ const embedModelMap: Record<string, string> = {
+ "all-MiniLM-L6-v2": "Xenova/all-MiniLM-L6-v2",
+ "all-mpnet-base-v2": "Xenova/all-mpnet-base-v2",
+ };
+ Settings.llm = new Anthropic({
+ model: process.env.MODEL as keyof typeof ALL_AVAILABLE_ANTHROPIC_MODELS,
+ });
+ Settings.embedModel = new HuggingFaceEmbedding({
+ modelType: embedModelMap[process.env.EMBEDDING_MODEL!],
+ });
+}
+
+function initGemini() {
+ Settings.llm = new Gemini({
+ model: process.env.MODEL as GEMINI_MODEL,
+ });
+ Settings.embedModel = new GeminiEmbedding({
+ model: process.env.EMBEDDING_MODEL as GEMINI_EMBEDDING_MODEL,
+ });
+}
diff --git a/templates/types/streaming/nextjs/app/api/chat/llamaindex-stream.ts b/templates/types/streaming/nextjs/app/api/chat/llamaindex-stream.ts
index b689905727767fb1f17f099298d13e72f0234df7..df2c17ffc34744d044ecdcc375955dbc7ea5ba70 100644
--- a/templates/types/streaming/nextjs/app/api/chat/llamaindex-stream.ts
+++ b/templates/types/streaming/nextjs/app/api/chat/llamaindex-stream.ts
@@ -9,16 +9,22 @@ import {
Metadata,
NodeWithScore,
Response,
- StreamingAgentChatResponse,
+ ToolCallLLMMessageOptions,
} from "llamaindex";
+
+import { AgentStreamChatResponse } from "llamaindex/agent/base";
import { appendImageData, appendSourceData } from "./stream-helper";
+type LlamaIndexResponse =
+ | AgentStreamChatResponse<ToolCallLLMMessageOptions>
+ | Response;
+
type ParserOptions = {
image_url?: string;
};
function createParser(
- res: AsyncIterable<Response>,
+ res: AsyncIterable<LlamaIndexResponse>,
data: StreamData,
opts?: ParserOptions,
) {
@@ -33,17 +39,27 @@ function createParser(
async pull(controller): Promise<void> {
const { value, done } = await it.next();
if (done) {
- appendSourceData(data, sourceNodes);
+ if (sourceNodes) {
+ appendSourceData(data, sourceNodes);
+ }
controller.close();
data.close();
return;
}
- if (!sourceNodes) {
- // get source nodes from the first response
- sourceNodes = value.sourceNodes;
+ let delta;
+ if (value instanceof Response) {
+ // handle Response type
+ if (value.sourceNodes) {
+ // get source nodes from the first response
+ sourceNodes = value.sourceNodes;
+ }
+ delta = value.response ?? "";
+ } else {
+ // handle other types
+ delta = value.response.delta;
}
- const text = trimStartOfStream(value.response ?? "");
+ const text = trimStartOfStream(delta ?? "");
if (text) {
controller.enqueue(text);
}
@@ -52,21 +68,14 @@ function createParser(
}
export function LlamaIndexStream(
- response: StreamingAgentChatResponse | AsyncIterable<Response>,
+ response: AsyncIterable<LlamaIndexResponse>,
data: StreamData,
opts?: {
callbacks?: AIStreamCallbacksAndOptions;
parserOptions?: ParserOptions;
},
-): { stream: ReadableStream; data: StreamData } {
- const res =
- response instanceof StreamingAgentChatResponse
- ? response.response
- : response;
- return {
- stream: createParser(res, data, opts?.parserOptions)
- .pipeThrough(createCallbacksTransformer(opts?.callbacks))
- .pipeThrough(createStreamDataTransformer()),
- data,
- };
+): ReadableStream<string> {
+ return createParser(response, data, opts?.parserOptions)
+ .pipeThrough(createCallbacksTransformer(opts?.callbacks))
+ .pipeThrough(createStreamDataTransformer());
}
diff --git a/templates/types/streaming/nextjs/app/api/chat/route.ts b/templates/types/streaming/nextjs/app/api/chat/route.ts
index f7afa08399357fff5eb85a87c19090387a0e85ed..117848d37c291a3b8dece5236c29c1c926d7ba00 100644
--- a/templates/types/streaming/nextjs/app/api/chat/route.ts
+++ b/templates/types/streaming/nextjs/app/api/chat/route.ts
@@ -1,6 +1,11 @@
import { initObservability } from "@/app/observability";
import { Message, StreamData, StreamingTextResponse } from "ai";
-import { ChatMessage, MessageContent, Settings } from "llamaindex";
+import {
+ CallbackManager,
+ ChatMessage,
+ MessageContent,
+ Settings,
+} from "llamaindex";
import { NextRequest, NextResponse } from "next/server";
import { createChatEngine } from "./engine/chat";
import { initSettings } from "./engine/settings";
@@ -57,14 +62,15 @@ export async function POST(request: NextRequest) {
// Init Vercel AI StreamData
const vercelStreamData = new StreamData();
- appendEventData(
- vercelStreamData,
- `Retrieving context for query: '${userMessage.content}'`,
- );
- // Setup callback for streaming data before chatting
- Settings.callbackManager.on("retrieve", (data) => {
+ // Setup callbacks
+ const callbackManager = new CallbackManager();
+ callbackManager.on("retrieve", (data) => {
const { nodes } = data.detail;
+ appendEventData(
+ vercelStreamData,
+ `Retrieving context for query: '${userMessage.content}'`,
+ );
appendEventData(
vercelStreamData,
`Retrieved ${nodes.length} sources to use as context for the query`,
@@ -72,14 +78,16 @@ export async function POST(request: NextRequest) {
});
// Calling LlamaIndex's ChatEngine to get a streamed response
- const response = await chatEngine.chat({
- message: userMessageContent,
- chatHistory: messages as ChatMessage[],
- stream: true,
+ const response = await Settings.withCallbackManager(callbackManager, () => {
+ return chatEngine.chat({
+ message: userMessageContent,
+ chatHistory: messages as ChatMessage[],
+ stream: true,
+ });
});
// Transform LlamaIndex stream to Vercel/AI format
- const { stream } = LlamaIndexStream(response, vercelStreamData, {
+ const stream = LlamaIndexStream(response, vercelStreamData, {
parserOptions: {
image_url: data?.imageUrl,
},
diff --git a/templates/types/streaming/nextjs/package.json b/templates/types/streaming/nextjs/package.json
index 707bd689f9aab5688e1dd77adc06570047f68149..3d043a14d2fb748c7cf1e12613a791c70225e180 100644
--- a/templates/types/streaming/nextjs/package.json
+++ b/templates/types/streaming/nextjs/package.json
@@ -17,9 +17,10 @@
"class-variance-authority": "^0.7.0",
"clsx": "^1.2.1",
"dotenv": "^16.3.1",
- "llamaindex": "0.2.10",
+ "llamaindex": "0.3.3",
"lucide-react": "^0.294.0",
"next": "^14.0.3",
+ "pdf2json": "3.0.5",
"react": "^18.2.0",
"react-dom": "^18.2.0",
"react-markdown": "^8.0.7",