Release 0.1.2 (#79 )

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
feat: add weather widget and weather tool (#72 )
2026-07-03 08:24:39 -04:00 · 2024-05-10 14:12:31 +07:00 · 2024-05-10 14:00:16 +07:00 · 2024-05-09 15:48:25 +07:00 · 2024-05-08 10:14:34 +07:00 · 2024-05-03 10:38:01 +07:00
76 changed files with 1685 additions and 459 deletions
@@ -1,5 +0,0 @@
---
-"create-llama": patch
---
-
-Use ingestion pipeline for Python
@@ -1,5 +0,0 @@
---
-"create-llama": patch
---
-
-Display events (e.g. retrieving nodes) per chat message
@@ -1,5 +1,19 @@
 # create-llama

+## 0.1.2
+
+### Patch Changes
+
+- 056e376: Add support for displaying tool outputs (including weather widget as example)
+
+## 0.1.1
+
+### Patch Changes
+
+- 7bd3ed5: Support Anthropic and Gemini as model providers
+- 7bd3ed5: Support new agents from LITS 0.3
+- cfb5257: Display events (e.g. retrieving nodes) per chat message
+
 ## 0.1.0

 ### Minor Changes
@@ -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,
+          },
+        ]
+      : []),
  ];
 };

@@ -199,13 +217,7 @@ const getFrameworkEnvs = (
      name: "SYSTEM_PROMPT",
      description: `Custom system prompt.
 Example:
-SYSTEM_PROMPT="
-We have provided context information below.
---------------------
-{context_str}
---------------------
-Given this information, please answer the question: {query_str}
-"`,
+SYSTEM_PROMPT="You are a helpful assistant who helps users with their questions."`,
    },
  ];
 };
@@ -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;
@@ -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;
+}
@@ -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;
+}
@@ -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;
-  }
-}
@@ -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
@@ -8,7 +8,7 @@ import { questionHandlers } from "../../questions";

 const OPENAI_API_URL = "https://api.openai.com/v1";

-const DEFAULT_MODEL = "gpt-4-turbo";
+const DEFAULT_MODEL = "gpt-3.5-turbo";
 const DEFAULT_EMBEDDING_MODEL = "text-embedding-3-large";

 export async function askOpenAIQuestions({
@@ -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,
@@ -24,7 +24,7 @@ interface Dependency {
 const getAdditionalDependencies = (
  modelConfig: ModelConfig,
  vectorDb?: TemplateVectorDB,
-  dataSource?: TemplateDataSource,
+  dataSources?: TemplateDataSource[],
  tools?: Tool[],
 ) => {
  const dependencies: Dependency[] = [];
@@ -43,6 +43,7 @@ const getAdditionalDependencies = (
        name: "llama-index-vector-stores-postgres",
        version: "^0.1.1",
      });
+      break;
    }
    case "pinecone": {
      dependencies.push({
@@ -72,38 +73,43 @@ const getAdditionalDependencies = (
  }

  // Add data source dependencies
-  const dataSourceType = dataSource?.type;
-  switch (dataSourceType) {
-    case "file":
-      dependencies.push({
-        name: "docx2txt",
-        version: "^0.8",
-      });
-      break;
-    case "web":
-      dependencies.push({
-        name: "llama-index-readers-web",
-        version: "^0.1.6",
-      });
-      break;
-    case "db":
-      dependencies.push({
-        name: "llama-index-readers-database",
-        version: "^0.1.3",
-      });
-      dependencies.push({
-        name: "pymysql",
-        version: "^1.1.0",
-        extras: ["rsa"],
-      });
-      dependencies.push({
-        name: "psycopg2",
-        version: "^2.9.9",
-      });
-      break;
+  if (dataSources) {
+    for (const ds of dataSources) {
+      const dsType = ds?.type;
+      switch (dsType) {
+        case "file":
+          dependencies.push({
+            name: "docx2txt",
+            version: "^0.8",
+          });
+          break;
+        case "web":
+          dependencies.push({
+            name: "llama-index-readers-web",
+            version: "^0.1.6",
+          });
+          break;
+        case "db":
+          dependencies.push({
+            name: "llama-index-readers-database",
+            version: "^0.1.3",
+          });
+          dependencies.push({
+            name: "pymysql",
+            version: "^1.1.0",
+            extras: ["rsa"],
+          });
+          dependencies.push({
+            name: "psycopg2",
+            version: "^2.9.9",
+          });
+          break;
+      }
+    }
  }

  // Add tools dependencies
+  console.log("Adding tools dependencies");
  tools?.forEach((tool) => {
    tool.dependencies?.forEach((dep) => {
      dependencies.push(dep);
@@ -127,6 +133,26 @@ 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;
@@ -278,9 +304,14 @@ export const installPythonTemplate = async ({
    cwd: path.join(compPath, "engines", "python", engine),
  });

-  const addOnDependencies = dataSources
-    .map((ds) => getAdditionalDependencies(modelConfig, vectorDb, ds, tools))
-    .flat();
+  console.log("Adding additional dependencies");
+
+  const addOnDependencies = getAdditionalDependencies(
+    modelConfig,
+    vectorDb,
+    dataSources,
+    tools,
+  );

  if (observability === "opentelemetry") {
    addOnDependencies.push({
@@ -5,12 +5,18 @@ import yaml from "yaml";
 import { makeDir } from "./make-dir";
 import { TemplateFramework } from "./types";

+export enum ToolType {
+  LLAMAHUB = "llamahub",
+  LOCAL = "local",
+}
+
 export type Tool = {
  display: string;
  name: string;
  config?: Record<string, any>;
  dependencies?: ToolDependencies[];
  supportedFrameworks?: Array<TemplateFramework>;
+  type: ToolType;
 };

 export type ToolDependencies = {
@@ -35,6 +41,7 @@ export const supportedTools: Tool[] = [
      },
    ],
    supportedFrameworks: ["fastapi"],
+    type: ToolType.LLAMAHUB,
  },
  {
    display: "Wikipedia",
@@ -46,6 +53,14 @@ export const supportedTools: Tool[] = [
      },
    ],
    supportedFrameworks: ["fastapi", "express", "nextjs"],
+    type: ToolType.LLAMAHUB,
+  },
+  {
+    display: "Weather",
+    name: "weather",
+    dependencies: [],
+    supportedFrameworks: ["fastapi", "express", "nextjs"],
+    type: ToolType.LOCAL,
  },
 ];

@@ -90,9 +105,19 @@ export const writeToolsConfig = async (
  type: ConfigFileType = ConfigFileType.YAML,
 ) => {
  if (tools.length === 0) return; // no tools selected, no config need
-  const configContent: Record<string, any> = {};
+  const configContent: {
+    [key in ToolType]: Record<string, any>;
+  } = {
+    local: {},
+    llamahub: {},
+  };
  tools.forEach((tool) => {
-    configContent[tool.name] = tool.config ?? {};
+    if (tool.type === ToolType.LLAMAHUB) {
+      configContent.llamahub[tool.name] = tool.config ?? {};
+    }
+    if (tool.type === ToolType.LOCAL) {
+      configContent.local[tool.name] = tool.config ?? {};
+    }
  });
  const configPath = path.join(root, "config");
  await makeDir(configPath);
@@ -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";
@@ -105,7 +105,7 @@ export const installTSTemplate = async ({
  const enginePath = path.join(root, relativeEngineDestPath, "engine");

  // copy vector db component
-  console.log("\nUsing vector DB:", vectorDb, "\n");
+  console.log("\nUsing vector DB:", vectorDb ?? "none", "\n");
  await copy("**", enginePath, {
    parents: true,
    cwd: path.join(compPath, "vectordbs", "typescript", vectorDb ?? "none"),
@@ -1,6 +1,6 @@
 {
  "name": "create-llama",
-  "version": "0.1.0",
+  "version": "0.1.2",
  "description": "Create LlamaIndex-powered apps with one command",
  "keywords": [
    "rag",
@@ -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");
@@ -1,35 +0,0 @@
-import os
-import yaml
-import importlib
-
-from llama_index.core.tools.tool_spec.base import BaseToolSpec
-from llama_index.core.tools.function_tool import FunctionTool
-
-
-class ToolFactory:
-
-    @staticmethod
-    def create_tool(tool_name: str, **kwargs) -> list[FunctionTool]:
-        try:
-            tool_package, tool_cls_name = tool_name.split(".")
-            module_name = f"llama_index.tools.{tool_package}"
-            module = importlib.import_module(module_name)
-            tool_class = getattr(module, tool_cls_name)
-            tool_spec: BaseToolSpec = tool_class(**kwargs)
-            return tool_spec.to_tool_list()
-        except (ImportError, AttributeError) as e:
-            raise ValueError(f"Unsupported tool: {tool_name}") from e
-        except TypeError as e:
-            raise ValueError(
-                f"Could not create tool: {tool_name}. With config: {kwargs}"
-            ) from e
-
-    @staticmethod
-    def from_env() -> list[FunctionTool]:
-        tools = []
-        if os.path.exists("config/tools.yaml"):
-            with open("config/tools.yaml", "r") as f:
-                tool_configs = yaml.safe_load(f)
-                for name, config in tool_configs.items():
-                    tools += ToolFactory.create_tool(name, **config)
-        return tools
@@ -0,0 +1,56 @@
+import os
+import yaml
+import importlib
+
+from llama_index.core.tools.tool_spec.base import BaseToolSpec
+from llama_index.core.tools.function_tool import FunctionTool
+
+
+class ToolType:
+    LLAMAHUB = "llamahub"
+    LOCAL = "local"
+
+
+class ToolFactory:
+
+    TOOL_SOURCE_PACKAGE_MAP = {
+        ToolType.LLAMAHUB: "llama_index.tools",
+        ToolType.LOCAL: "app.engine.tools",
+    }
+
+    @staticmethod
+    def load_tools(tool_type: str, tool_name: str, config: dict) -> list[FunctionTool]:
+        source_package = ToolFactory.TOOL_SOURCE_PACKAGE_MAP[tool_type]
+        try:
+            if "ToolSpec" in tool_name:
+                tool_package, tool_cls_name = tool_name.split(".")
+                module_name = f"{source_package}.{tool_package}"
+                module = importlib.import_module(module_name)
+                tool_class = getattr(module, tool_cls_name)
+                tool_spec: BaseToolSpec = tool_class(**config)
+                return tool_spec.to_tool_list()
+            else:
+                module = importlib.import_module(f"{source_package}.{tool_name}")
+                tools = getattr(module, "tools")
+                if not all(isinstance(tool, FunctionTool) for tool in tools):
+                    raise ValueError(
+                        f"The module {module} does not contain valid tools"
+                    )
+                return tools
+        except ImportError as e:
+            raise ValueError(f"Failed to import tool {tool_name}: {e}")
+        except AttributeError as e:
+            raise ValueError(f"Failed to load tool {tool_name}: {e}")
+
+    @staticmethod
+    def from_env() -> list[FunctionTool]:
+        tools = []
+        if os.path.exists("config/tools.yaml"):
+            with open("config/tools.yaml", "r") as f:
+                tool_configs = yaml.safe_load(f)
+                for tool_type, config_entries in tool_configs.items():
+                    for tool_name, config in config_entries.items():
+                        tools.extend(
+                            ToolFactory.load_tools(tool_type, tool_name, config)
+                        )
+        return tools
@@ -0,0 +1,72 @@
+"""Open Meteo weather map tool spec."""
+
+import logging
+import requests
+import pytz
+from llama_index.core.tools import FunctionTool
+
+logger = logging.getLogger(__name__)
+
+
+class OpenMeteoWeather:
+    geo_api = "https://geocoding-api.open-meteo.com/v1"
+    weather_api = "https://api.open-meteo.com/v1"
+
+    @classmethod
+    def _get_geo_location(cls, location: str) -> dict:
+        """Get geo location from location name."""
+        params = {"name": location, "count": 10, "language": "en", "format": "json"}
+        response = requests.get(f"{cls.geo_api}/search", params=params)
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch geo location: {response.status_code}")
+        else:
+            data = response.json()
+            result = data["results"][0]
+            geo_location = {
+                "id": result["id"],
+                "name": result["name"],
+                "latitude": result["latitude"],
+                "longitude": result["longitude"],
+            }
+            return geo_location
+
+    @classmethod
+    def get_weather_information(cls, location: str) -> dict:
+        """Use this function to get the weather of any given location.
+        Note that the weather code should follow WMO Weather interpretation codes (WW):
+        0: Clear sky
+        1, 2, 3: Mainly clear, partly cloudy, and overcast
+        45, 48: Fog and depositing rime fog
+        51, 53, 55: Drizzle: Light, moderate, and dense intensity
+        56, 57: Freezing Drizzle: Light and dense intensity
+        61, 63, 65: Rain: Slight, moderate and heavy intensity
+        66, 67: Freezing Rain: Light and heavy intensity
+        71, 73, 75: Snow fall: Slight, moderate, and heavy intensity
+        77: Snow grains
+        80, 81, 82: Rain showers: Slight, moderate, and violent
+        85, 86: Snow showers slight and heavy
+        95: Thunderstorm: Slight or moderate
+        96, 99: Thunderstorm with slight and heavy hail
+        """
+        logger.info(
+            f"Calling open-meteo api to get weather information of location: {location}"
+        )
+        geo_location = cls._get_geo_location(location)
+        timezone = pytz.timezone("UTC").zone
+        params = {
+            "latitude": geo_location["latitude"],
+            "longitude": geo_location["longitude"],
+            "current": "temperature_2m,weather_code",
+            "hourly": "temperature_2m,weather_code",
+            "daily": "weather_code",
+            "timezone": timezone,
+        }
+        response = requests.get(f"{cls.weather_api}/forecast", params=params)
+        if response.status_code != 200:
+            raise Exception(
+                f"Failed to fetch weather information: {response.status_code}"
+            )
+        return response.json()
+
+
+tools = [FunctionTool.from_defaults(OpenMeteoWeather.get_weather_information)]
@@ -1,12 +1,13 @@
-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";
 import { getDataSource } from "./index";
 import { STORAGE_CACHE_DIR } from "./shared";
+import { createLocalTools } from "./tools";

 export async function createChatEngine() {
-  let tools: BaseTool[] = [];
+  const tools: BaseToolWithCall[] = [];

  // Add a query engine tool if we have a data source
  // Delete this code if you don't have a data source
@@ -28,7 +29,14 @@ export async function createChatEngine() {
    const config = JSON.parse(
      await fs.readFile(path.join("config", "tools.json"), "utf8"),
    );
-    tools = tools.concat(await ToolsFactory.createTools(config));
+
+    // add local tools from the 'tools' folder (if configured)
+    const localTools = createLocalTools(config.local);
+    tools.push(...localTools);
+
+    // add tools from LlamaIndexTS (if configured)
+    const llamaTools = await ToolsFactory.createTools(config.llamahub);
+    tools.push(...llamaTools);
  } catch {}

  return new OpenAIAgent({
@@ -0,0 +1,26 @@
+import { BaseToolWithCall } from "llamaindex";
+import { WeatherTool, WeatherToolParams } from "./weather";
+
+type ToolCreator = (config: unknown) => BaseToolWithCall;
+
+const toolFactory: Record<string, ToolCreator> = {
+  weather: (config: unknown) => {
+    return new WeatherTool(config as WeatherToolParams);
+  },
+};
+
+export function createLocalTools(
+  localConfig: Record<string, unknown>,
+): BaseToolWithCall[] {
+  const tools: BaseToolWithCall[] = [];
+
+  Object.keys(localConfig).forEach((key) => {
+    if (key in toolFactory) {
+      const toolConfig = localConfig[key];
+      const tool = toolFactory[key](toolConfig);
+      tools.push(tool);
+    }
+  });
+
+  return tools;
+}
@@ -0,0 +1,81 @@
+import type { JSONSchemaType } from "ajv";
+import { BaseTool, ToolMetadata } from "llamaindex";
+
+interface GeoLocation {
+  id: string;
+  name: string;
+  latitude: number;
+  longitude: number;
+}
+
+export type WeatherParameter = {
+  location: string;
+};
+
+export type WeatherToolParams = {
+  metadata?: ToolMetadata<JSONSchemaType<WeatherParameter>>;
+};
+
+const DEFAULT_META_DATA: ToolMetadata<JSONSchemaType<WeatherParameter>> = {
+  name: "get_weather_information",
+  description: `
+    Use this function to get the weather of any given location.
+    Note that the weather code should follow WMO Weather interpretation codes (WW):
+    0: Clear sky
+    1, 2, 3: Mainly clear, partly cloudy, and overcast
+    45, 48: Fog and depositing rime fog
+    51, 53, 55: Drizzle: Light, moderate, and dense intensity
+    56, 57: Freezing Drizzle: Light and dense intensity
+    61, 63, 65: Rain: Slight, moderate and heavy intensity
+    66, 67: Freezing Rain: Light and heavy intensity
+    71, 73, 75: Snow fall: Slight, moderate, and heavy intensity
+    77: Snow grains
+    80, 81, 82: Rain showers: Slight, moderate, and violent
+    85, 86: Snow showers slight and heavy
+    95: Thunderstorm: Slight or moderate
+    96, 99: Thunderstorm with slight and heavy hail
+  `,
+  parameters: {
+    type: "object",
+    properties: {
+      location: {
+        type: "string",
+        description: "The location to get the weather information",
+      },
+    },
+    required: ["location"],
+  },
+};
+
+export class WeatherTool implements BaseTool<WeatherParameter> {
+  metadata: ToolMetadata<JSONSchemaType<WeatherParameter>>;
+
+  private getGeoLocation = async (location: string): Promise<GeoLocation> => {
+    const apiUrl = `https://geocoding-api.open-meteo.com/v1/search?name=${location}&count=10&language=en&format=json`;
+    const response = await fetch(apiUrl);
+    const data = await response.json();
+    const { id, name, latitude, longitude } = data.results[0];
+    return { id, name, latitude, longitude };
+  };
+
+  private getWeatherByLocation = async (location: string) => {
+    console.log(
+      "Calling open-meteo api to get weather information of location:",
+      location,
+    );
+    const { latitude, longitude } = await this.getGeoLocation(location);
+    const timezone = Intl.DateTimeFormat().resolvedOptions().timeZone;
+    const apiUrl = `https://api.open-meteo.com/v1/forecast?latitude=${latitude}&longitude=${longitude}&current=temperature_2m,weather_code&hourly=temperature_2m,weather_code&daily=weather_code&timezone=${timezone}`;
+    const response = await fetch(apiUrl);
+    const data = await response.json();
+    return data;
+  };
+
+  constructor(params?: WeatherToolParams) {
+    this.metadata = params?.metadata || DEFAULT_META_DATA;
+  }
+
+  async call(input: WeatherParameter) {
+    return await this.getWeatherByLocation(input.location);
+  }
+}
@@ -27,7 +27,10 @@ def llama_parse_parser():
 def get_file_documents(config: FileLoaderConfig):
    from llama_index.core.readers import SimpleDirectoryReader

-    reader = SimpleDirectoryReader(config.data_dir, recursive=True, filename_as_id=True)
+    reader = SimpleDirectoryReader(
+        config.data_dir,
+        recursive=True,
+    )
    if config.use_llama_parse:
        parser = llama_parse_parser()
        reader.file_extractor = {".pdf": parser}
@@ -0,0 +1,37 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import os
+import logging
+from llama_index.core.storage import StorageContext
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.vector_stores.astra_db import AstraDBVectorStore
+from app.settings import init_settings
+from app.engine.loaders import get_documents
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+
+def generate_datasource():
+    init_settings()
+    logger.info("Creating new index")
+    documents = get_documents()
+    store = AstraDBVectorStore(
+        token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
+        api_endpoint=os.environ["ASTRA_DB_ENDPOINT"],
+        collection_name=os.environ["ASTRA_DB_COLLECTION"],
+        embedding_dimension=int(os.environ["EMBEDDING_DIM"]),
+    )
+    storage_context = StorageContext.from_defaults(vector_store=store)
+    VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        show_progress=True,  # this will show you a progress bar as the embeddings are created
+    )
+    logger.info(f"Successfully created embeddings in the AstraDB")
+
+
+if __name__ == "__main__":
+    generate_datasource()
@@ -1,12 +1,21 @@
+import logging
 import os
+
+from llama_index.core.indices import VectorStoreIndex
 from llama_index.vector_stores.astra_db import AstraDBVectorStore


-def get_vector_store():
+logger = logging.getLogger("uvicorn")
+
+
+def get_index():
+    logger.info("Connecting to index from AstraDB...")
    store = AstraDBVectorStore(
        token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
        api_endpoint=os.environ["ASTRA_DB_ENDPOINT"],
        collection_name=os.environ["ASTRA_DB_COLLECTION"],
        embedding_dimension=int(os.environ["EMBEDDING_DIM"]),
    )
-    return store
+    index = VectorStoreIndex.from_vector_store(store)
+    logger.info("Finished connecting to index from AstraDB.")
+    return index
@@ -0,0 +1,39 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import os
+import logging
+from llama_index.core.storage import StorageContext
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.vector_stores.milvus import MilvusVectorStore
+from app.settings import init_settings
+from app.engine.loaders import get_documents
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+
+def generate_datasource():
+    init_settings()
+    logger.info("Creating new index")
+    # load the documents and create the index
+    documents = get_documents()
+    store = MilvusVectorStore(
+        uri=os.environ["MILVUS_ADDRESS"],
+        user=os.getenv("MILVUS_USERNAME"),
+        password=os.getenv("MILVUS_PASSWORD"),
+        collection_name=os.getenv("MILVUS_COLLECTION"),
+        dim=int(os.getenv("EMBEDDING_DIM")),
+    )
+    storage_context = StorageContext.from_defaults(vector_store=store)
+    VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        show_progress=True,  # this will show you a progress bar as the embeddings are created
+    )
+    logger.info(f"Successfully created embeddings in the Milvus")
+
+
+if __name__ == "__main__":
+    generate_datasource()
@@ -0,0 +1,22 @@
+import logging
+import os
+
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.vector_stores.milvus import MilvusVectorStore
+
+
+logger = logging.getLogger("uvicorn")
+
+
+def get_index():
+    logger.info("Connecting to index from Milvus...")
+    store = MilvusVectorStore(
+        uri=os.getenv("MILVUS_ADDRESS"),
+        user=os.getenv("MILVUS_USERNAME"),
+        password=os.getenv("MILVUS_PASSWORD"),
+        collection_name=os.getenv("MILVUS_COLLECTION"),
+        dim=int(os.getenv("EMBEDDING_DIM")),
+    )
+    index = VectorStoreIndex.from_vector_store(store)
+    logger.info("Finished connecting to index from Milvus.")
+    return index
@@ -1,13 +0,0 @@
-import os
-from llama_index.vector_stores.milvus import MilvusVectorStore
-
-
-def get_vector_store():
-    store = MilvusVectorStore(
-        uri=os.environ["MILVUS_ADDRESS"],
-        user=os.getenv("MILVUS_USERNAME"),
-        password=os.getenv("MILVUS_PASSWORD"),
-        collection_name=os.getenv("MILVUS_COLLECTION"),
-        dim=int(os.getenv("EMBEDDING_DIM")),
-    )
-    return store
@@ -0,0 +1,43 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import os
+import logging
+from llama_index.core.storage import StorageContext
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
+from app.settings import init_settings
+from app.engine.loaders import get_documents
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+
+def generate_datasource():
+    init_settings()
+    logger.info("Creating new index")
+    # load the documents and create the index
+    documents = get_documents()
+    store = MongoDBAtlasVectorSearch(
+        db_name=os.environ["MONGODB_DATABASE"],
+        collection_name=os.environ["MONGODB_VECTORS"],
+        index_name=os.environ["MONGODB_VECTOR_INDEX"],
+    )
+    storage_context = StorageContext.from_defaults(vector_store=store)
+    VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        show_progress=True,  # this will show you a progress bar as the embeddings are created
+    )
+    logger.info(
+        f"Successfully created embeddings in the MongoDB collection {os.environ['MONGODB_VECTORS']}"
+    )
+    logger.info(
+        """IMPORTANT: You can't query your index yet because you need to create a vector search index in MongoDB's UI now.
+See https://github.com/run-llama/mongodb-demo/tree/main?tab=readme-ov-file#create-a-vector-search-index"""
+    )
+
+
+if __name__ == "__main__":
+    generate_datasource()
@@ -0,0 +1,20 @@
+import logging
+import os
+
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
+
+
+logger = logging.getLogger("uvicorn")
+
+
+def get_index():
+    logger.info("Connecting to index from MongoDB...")
+    store = MongoDBAtlasVectorSearch(
+        db_name=os.environ["MONGODB_DATABASE"],
+        collection_name=os.environ["MONGODB_VECTORS"],
+        index_name=os.environ["MONGODB_VECTOR_INDEX"],
+    )
+    index = VectorStoreIndex.from_vector_store(store)
+    logger.info("Finished connecting to index from MongoDB.")
+    return index
@@ -1,11 +0,0 @@
-import os
-from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
-
-
-def get_vector_store():
-    store = MongoDBAtlasVectorSearch(
-        db_name=os.environ["MONGODB_DATABASE"],
-        collection_name=os.environ["MONGODB_VECTORS"],
-        index_name=os.environ["MONGODB_VECTOR_INDEX"],
-    )
-    return store
@@ -0,0 +1 @@
+STORAGE_DIR = "storage"  # directory to cache the generated index
@@ -0,0 +1,32 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import logging
+from llama_index.core.indices import (
+    VectorStoreIndex,
+)
+from app.engine.constants import STORAGE_DIR
+from app.engine.loaders import get_documents
+from app.settings import init_settings
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+
+def generate_datasource():
+    init_settings()
+    logger.info("Creating new index")
+    # load the documents and create the index
+    documents = get_documents()
+    index = VectorStoreIndex.from_documents(
+        documents,
+    )
+    # store it for later
+    index.storage_context.persist(STORAGE_DIR)
+    logger.info(f"Finished creating new index. Stored in {STORAGE_DIR}")
+
+
+if __name__ == "__main__":
+    generate_datasource()
@@ -0,0 +1,20 @@
+import logging
+import os
+
+from app.engine.constants import STORAGE_DIR
+from llama_index.core.storage import StorageContext
+from llama_index.core.indices import load_index_from_storage
+
+logger = logging.getLogger("uvicorn")
+
+
+def get_index():
+    # check if storage already exists
+    if not os.path.exists(STORAGE_DIR):
+        return None
+    # load the existing index
+    logger.info(f"Loading index from {STORAGE_DIR}...")
+    storage_context = StorageContext.from_defaults(persist_dir=STORAGE_DIR)
+    index = load_index_from_storage(storage_context)
+    logger.info(f"Finished loading index from {STORAGE_DIR}")
+    return index
@@ -1,16 +0,0 @@
-import os
-
-from llama_index.core.vector_stores import SimpleVectorStore
-from app.constants import STORAGE_DIR
-
-
-def get_vector_store():
-    if not os.path.exists(STORAGE_DIR):
-        # Vector store hasn't been persisted before, create a new one
-        vector_store = SimpleVectorStore()
-    else:
-        # Vector store has already been persisted before at STORAGE_DIR - load it
-        vector_store = SimpleVectorStore.from_persist_dir(
-            STORAGE_DIR, namespace="default"
-        )
-    return vector_store
@@ -0,0 +1,2 @@
+PGVECTOR_SCHEMA = "public"
+PGVECTOR_TABLE = "llamaindex_embedding"
@@ -0,0 +1,35 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import logging
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.core.storage import StorageContext
+
+from app.engine.loaders import get_documents
+from app.settings import init_settings
+from app.engine.utils import init_pg_vector_store_from_env
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+
+def generate_datasource():
+    init_settings()
+    logger.info("Creating new index")
+    # load the documents and create the index
+    documents = get_documents()
+    store = init_pg_vector_store_from_env()
+    storage_context = StorageContext.from_defaults(vector_store=store)
+    VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        show_progress=True,  # this will show you a progress bar as the embeddings are created
+    )
+    logger.info(
+        f"Successfully created embeddings in the PG vector store, schema={store.schema_name} table={store.table_name}"
+    )
+
+
+if __name__ == "__main__":
+    generate_datasource()
@@ -0,0 +1,13 @@
+import logging
+from llama_index.core.indices.vector_store import VectorStoreIndex
+from app.engine.utils import init_pg_vector_store_from_env
+
+logger = logging.getLogger("uvicorn")
+
+
+def get_index():
+    logger.info("Connecting to index from PGVector...")
+    store = init_pg_vector_store_from_env()
+    index = VectorStoreIndex.from_vector_store(store)
+    logger.info("Finished connecting to index from PGVector.")
+    return index
@@ -1,13 +1,10 @@
 import os
 from llama_index.vector_stores.postgres import PGVectorStore
 from urllib.parse import urlparse
-
-STORAGE_DIR = "storage"
-PGVECTOR_SCHEMA = "public"
-PGVECTOR_TABLE = "llamaindex_embedding"
+from app.engine.constants import PGVECTOR_SCHEMA, PGVECTOR_TABLE


-def get_vector_store():
+def init_pg_vector_store_from_env():
    original_conn_string = os.environ.get("PG_CONNECTION_STRING")
    if original_conn_string is None or original_conn_string == "":
        raise ValueError("PG_CONNECTION_STRING environment variable is not set.")
@@ -27,5 +24,4 @@ def get_vector_store():
        async_connection_string=async_conn_string,
        schema_name=PGVECTOR_SCHEMA,
        table_name=PGVECTOR_TABLE,
-        embed_dim=int(os.environ.get("EMBEDDING_DIM", 768)),
    )
@@ -0,0 +1,39 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import os
+import logging
+from llama_index.core.storage import StorageContext
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.vector_stores.pinecone import PineconeVectorStore
+from app.settings import init_settings
+from app.engine.loaders import get_documents
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+
+def generate_datasource():
+    init_settings()
+    logger.info("Creating new index")
+    # load the documents and create the index
+    documents = get_documents()
+    store = PineconeVectorStore(
+        api_key=os.environ["PINECONE_API_KEY"],
+        index_name=os.environ["PINECONE_INDEX_NAME"],
+        environment=os.environ["PINECONE_ENVIRONMENT"],
+    )
+    storage_context = StorageContext.from_defaults(vector_store=store)
+    VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        show_progress=True,  # this will show you a progress bar as the embeddings are created
+    )
+    logger.info(
+        f"Successfully created embeddings and save to your Pinecone index {os.environ['PINECONE_INDEX_NAME']}"
+    )
+
+
+if __name__ == "__main__":
+    generate_datasource()
@@ -0,0 +1,20 @@
+import logging
+import os
+
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.vector_stores.pinecone import PineconeVectorStore
+
+
+logger = logging.getLogger("uvicorn")
+
+
+def get_index():
+    logger.info("Connecting to index from Pinecone...")
+    store = PineconeVectorStore(
+        api_key=os.environ["PINECONE_API_KEY"],
+        index_name=os.environ["PINECONE_INDEX_NAME"],
+        environment=os.environ["PINECONE_ENVIRONMENT"],
+    )
+    index = VectorStoreIndex.from_vector_store(store)
+    logger.info("Finished connecting to index from Pinecone.")
+    return index
@@ -1,11 +0,0 @@
-import os
-from llama_index.vector_stores.pinecone import PineconeVectorStore
-
-
-def get_vector_store():
-    store = PineconeVectorStore(
-        api_key=os.environ["PINECONE_API_KEY"],
-        index_name=os.environ["PINECONE_INDEX_NAME"],
-        environment=os.environ["PINECONE_ENVIRONMENT"],
-    )
-    return store
@@ -0,0 +1,37 @@
+import logging
+import os
+from app.engine.loaders import get_documents
+from app.settings import init_settings
+from dotenv import load_dotenv
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.core.storage import StorageContext
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+load_dotenv()
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+
+def generate_datasource():
+    init_settings()
+    logger.info("Creating new index with Qdrant")
+    # load the documents and create the index
+    documents = get_documents()
+    store = QdrantVectorStore(
+        collection_name=os.getenv("QDRANT_COLLECTION"),
+        url=os.getenv("QDRANT_URL"),
+        api_key=os.getenv("QDRANT_API_KEY"),
+    )
+    storage_context = StorageContext.from_defaults(vector_store=store)
+    VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        show_progress=True,  # this will show you a progress bar as the embeddings are created
+    )
+    logger.info(
+        f"Successfully uploaded documents to the {os.getenv('QDRANT_COLLECTION')} collection."
+    )
+
+
+if __name__ == "__main__":
+    generate_datasource()
@@ -0,0 +1,20 @@
+import logging
+import os
+
+from llama_index.core.indices import VectorStoreIndex
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+
+
+logger = logging.getLogger("uvicorn")
+
+
+def get_index():
+    logger.info("Connecting to Qdrant collection..")
+    store = QdrantVectorStore(
+        collection_name=os.getenv("QDRANT_COLLECTION"),
+        url=os.getenv("QDRANT_URL"),
+        api_key=os.getenv("QDRANT_API_KEY"),
+    )
+    index = VectorStoreIndex.from_vector_store(store)
+    logger.info("Finished connecting to Qdrant collection.")
+    return index
@@ -1,11 +0,0 @@
-import os
-from llama_index.vector_stores.qdrant import QdrantVectorStore
-
-
-def get_vector_store():
-    store = QdrantVectorStore(
-        collection_name=os.getenv("QDRANT_COLLECTION"),
-        url=os.getenv("QDRANT_URL"),
-        api_key=os.getenv("QDRANT_API_KEY"),
-    )
-    return store
@@ -1,4 +1,5 @@
-import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { storageContextFromDefaults } from "llamaindex/storage/StorageContext";

 import * as dotenv from "dotenv";

@@ -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() {
@@ -14,7 +14,9 @@
    "cors": "^2.8.5",
    "dotenv": "^16.3.1",
    "express": "^4.18.2",
-    "llamaindex": "0.2.10"
+    "llamaindex": "0.3.7",
+    "pdf2json": "3.0.5",
+    "ajv": "^8.12.0"
  },
  "devDependencies": {
    "@types/cors": "^2.8.16",
@@ -3,7 +3,7 @@ import { Request, Response } from "express";
 import { ChatMessage, MessageContent, Settings } from "llamaindex";
 import { createChatEngine } from "./engine/chat";
 import { LlamaIndexStream } from "./llamaindex-stream";
-import { appendEventData } from "./stream-helper";
+import { createCallbackManager } from "./stream-helper";

 const convertMessageContent = (
  textMessage: string,
@@ -45,46 +45,28 @@ 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) => {
-      const { nodes } = data.detail;
-      appendEventData(
-        vercelStreamData,
-        `Retrieved ${nodes.length} sources to use as context for the query`,
-      );
-    });
+    // Setup callbacks
+    const callbackManager = createCallbackManager(vercelStreamData);

    // 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({
@@ -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,
+  });
+}
@@ -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<Uint8Array> {
+  return createParser(response, data, opts?.parserOptions)
+    .pipeThrough(createCallbacksTransformer(opts?.callbacks))
+    .pipeThrough(createStreamDataTransformer());
 }
@@ -1,5 +1,11 @@
 import { StreamData } from "ai";
-import { Metadata, NodeWithScore } from "llamaindex";
+import {
+  CallbackManager,
+  Metadata,
+  NodeWithScore,
+  ToolCall,
+  ToolOutput,
+} from "llamaindex";

 export function appendImageData(data: StreamData, imageUrl?: string) {
  if (!imageUrl) return;
@@ -37,3 +43,55 @@ export function appendEventData(data: StreamData, title?: string) {
    },
  });
 }
+
+export function appendToolData(
+  data: StreamData,
+  toolCall: ToolCall,
+  toolOutput: ToolOutput,
+) {
+  data.appendMessageAnnotation({
+    type: "tools",
+    data: {
+      toolCall: {
+        id: toolCall.id,
+        name: toolCall.name,
+        input: toolCall.input,
+      },
+      toolOutput: {
+        output: toolOutput.output,
+        isError: toolOutput.isError,
+      },
+    },
+  });
+}
+
+export function createCallbackManager(stream: StreamData) {
+  const callbackManager = new CallbackManager();
+
+  callbackManager.on("retrieve", (data) => {
+    const { nodes, query } = data.detail;
+    appendEventData(stream, `Retrieving context for query: '${query}'`);
+    appendEventData(
+      stream,
+      `Retrieved ${nodes.length} sources to use as context for the query`,
+    );
+  });
+
+  callbackManager.on("llm-tool-call", (event) => {
+    const { name, input } = event.detail.payload.toolCall;
+    const inputString = Object.entries(input)
+      .map(([key, value]) => `${key}: ${value}`)
+      .join(", ");
+    appendEventData(
+      stream,
+      `Using tool: '${name}' with inputs: '${inputString}'`,
+    );
+  });
+
+  callbackManager.on("llm-tool-result", (event) => {
+    const { toolCall, toolResult } = event.detail.payload;
+    appendToolData(stream, toolCall, toolResult);
+  });
+
+  return callbackManager;
+}
@@ -1,10 +1,7 @@
 from pydantic import BaseModel
 from typing import List, Any, Optional, Dict, Tuple
 from fastapi import APIRouter, Depends, HTTPException, Request, status
-from llama_index.core.chat_engine.types import (
-    BaseChatEngine,
-    StreamingAgentChatResponse,
-)
+from llama_index.core.chat_engine.types import BaseChatEngine
 from llama_index.core.schema import NodeWithScore
 from llama_index.core.llms import ChatMessage, MessageRole
 from app.engine import get_chat_engine
@@ -109,12 +106,9 @@ async def chat(
        # Yield the events from the event handler
        async def _event_generator():
            async for event in event_handler.async_event_gen():
-                yield VercelStreamResponse.convert_data(
-                    {
-                        "type": "events",
-                        "data": {"title": event.get_title()},
-                    }
-                )
+                event_response = event.to_response()
+                if event_response is not None:
+                    yield VercelStreamResponse.convert_data(event_response)

        combine = stream.merge(_text_generator(), _event_generator())
        async with combine.stream() as streamer:
@@ -1,8 +1,9 @@
+import json
 import asyncio
 from typing import AsyncGenerator, Dict, Any, List, Optional
-
 from llama_index.core.callbacks.base import BaseCallbackHandler
 from llama_index.core.callbacks.schema import CBEventType
+from llama_index.core.tools.types import ToolOutput
 from pydantic import BaseModel


@@ -11,19 +12,73 @@ class CallbackEvent(BaseModel):
    payload: Optional[Dict[str, Any]] = None
    event_id: str = ""

-    def get_title(self) -> str | None:
-        # Return as None for the unhandled event types
-        # to avoid showing them in the UI
+    def get_retrieval_message(self) -> dict | None:
+        if self.payload:
+            nodes = self.payload.get("nodes")
+            if nodes:
+                msg = f"Retrieved {len(nodes)} sources to use as context for the query"
+            else:
+                msg = f"Retrieving context for query: '{self.payload.get('query_str')}'"
+            return {
+                "type": "events",
+                "data": {"title": msg},
+            }
+        else:
+            return None
+
+    def get_tool_message(self) -> dict | None:
+        func_call_args = self.payload.get("function_call")
+        if func_call_args is not None and "tool" in self.payload:
+            tool = self.payload.get("tool")
+            return {
+                "type": "events",
+                "data": {
+                    "title": f"Calling tool: {tool.name} with inputs: {func_call_args}",
+                },
+            }
+
+    def _is_output_serializable(self, output: Any) -> bool:
+        try:
+            json.dumps(output)
+            return True
+        except TypeError:
+            return False
+
+    def get_agent_tool_response(self) -> dict | None:
+        response = self.payload.get("response")
+        if response is not None:
+            sources = response.sources
+            for source in sources:
+                # Return the tool response here to include the toolCall information
+                if isinstance(source, ToolOutput):
+                    if self._is_output_serializable(source.raw_output):
+                        output = source.raw_output
+                    else:
+                        output = source.content
+
+                    return {
+                        "type": "tools",
+                        "data": {
+                            "toolOutput": {
+                                "output": output,
+                                "isError": source.is_error,
+                            },
+                            "toolCall": {
+                                "id": None,  # There is no tool id in the ToolOutput
+                                "name": source.tool_name,
+                                "input": source.raw_input,
+                            },
+                        },
+                    }
+
+    def to_response(self):
        match self.event_type:
            case "retrieve":
-                if self.payload:
-                    nodes = self.payload.get("nodes")
-                    if nodes:
-                        return f"Retrieved {len(nodes)} sources to use as context for the query"
-                    else:
-                        return f"Retrieving context for query: '{self.payload.get('query_str')}'"
-                else:
-                    return None
+                return self.get_retrieval_message()
+            case "function_call":
+                return self.get_tool_message()
+            case "agent_step":
+                return self.get_agent_tool_response()
            case _:
                return None

@@ -54,7 +109,7 @@ class EventCallbackHandler(BaseCallbackHandler):
        **kwargs: Any,
    ) -> str:
        event = CallbackEvent(event_id=event_id, event_type=event_type, payload=payload)
-        if event.get_title() is not None:
+        if event.to_response() is not None:
            self._aqueue.put_nowait(event)

    def on_event_end(
@@ -65,7 +120,7 @@ class EventCallbackHandler(BaseCallbackHandler):
        **kwargs: Any,
    ) -> None:
        event = CallbackEvent(event_id=event_id, event_type=event_type, payload=payload)
-        if event.get_title() is not None:
+        if event.to_response() is not None:
            self._aqueue.put_nowait(event)

    def start_trace(self, trace_id: Optional[str] = None) -> None:
@@ -1 +0,0 @@
-STORAGE_DIR = "storage"  # directory to save the stores to (document store and if used, the `SimpleVectorStore`)
@@ -1,96 +0,0 @@
-from dotenv import load_dotenv
-
-load_dotenv()
-
-import os
-import logging
-from llama_index.core.settings import Settings
-from llama_index.core.ingestion import IngestionPipeline
-from llama_index.core.node_parser import SentenceSplitter
-from llama_index.core.vector_stores import SimpleVectorStore
-from llama_index.core.storage.docstore import SimpleDocumentStore
-from llama_index.core.storage import StorageContext
-from llama_index.core import VectorStoreIndex
-from app.constants import STORAGE_DIR
-from app.settings import init_settings
-from app.engine.loaders import get_documents
-from app.engine.vectordb import get_vector_store
-
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-
-def get_doc_store():
-    if not os.path.exists(STORAGE_DIR):
-        docstore = SimpleDocumentStore()
-        return docstore
-    else:
-        return SimpleDocumentStore.from_persist_dir(STORAGE_DIR)
-
-
-def run_ingestion_pipeline(docstore, vector_store, documents):
-    # Create ingestion pipeline
-    ingestion_pipeline = IngestionPipeline(
-        transformations=[
-            SentenceSplitter(
-                chunk_size=Settings.chunk_size,
-                chunk_overlap=Settings.chunk_overlap,
-            ),
-            Settings.embed_model,
-        ],
-        docstore=docstore,
-        docstore_strategy="upserts_and_delete",
-    )
-
-    # llama_index having an typing issue when passing vector_store to IngestionPipeline
-    # so we need to set it manually after initialization
-    ingestion_pipeline.vector_store = vector_store
-
-    # Run the ingestion pipeline and store the results
-    nodes = ingestion_pipeline.run(show_progress=True, documents=documents)
-
-    return nodes
-
-
-def persist_storage(docstore, vector_store, nodes):
-    storage_context = StorageContext.from_defaults(
-        docstore=docstore,
-        vector_store=vector_store,
-    )
-    # SimpleVectorStore does not include index by default
-    # so we need to create the index manually
-    # can be removed if using other vector store
-    if isinstance(vector_store, SimpleVectorStore):
-        VectorStoreIndex(
-            nodes=nodes,
-            storage_context=storage_context,
-            store_nodes_override=True,  # Need enable this to store the nodes and index's id
-        )
-    storage_context.persist(STORAGE_DIR)
-
-
-def generate_datasource():
-    init_settings()
-    logger.info("Generate index for the provided data")
-
-    # Get the stores and documents or create new ones
-    documents = get_documents()
-    docstore = get_doc_store()
-    vector_store = get_vector_store()
-
-    # Run the ingestion pipeline
-    nodes = run_ingestion_pipeline(
-        docstore=docstore,
-        vector_store=vector_store,
-        documents=documents,
-    )
-
-    # Build the index and persist storage
-    persist_storage(docstore, vector_store, nodes)
-
-    logger.info("Finished generating the index")
-
-
-if __name__ == "__main__":
-    generate_datasource()
@@ -1,27 +0,0 @@
-import logging
-from llama_index.core import load_index_from_storage
-from llama_index.core.storage import StorageContext
-from llama_index.core.indices.vector_store import VectorStoreIndex
-from llama_index.core.vector_stores.simple import SimpleVectorStore
-from app.constants import STORAGE_DIR
-from app.engine.vectordb import get_vector_store
-
-logger = logging.getLogger("uvicorn")
-
-
-def get_index():
-    logger.info("Loading the index...")
-    store = get_vector_store()
-    # If the store is a SimpleVectorStore, we need to load the index from the storage
-    if isinstance(store, SimpleVectorStore):
-        index = load_index_from_storage(
-            StorageContext.from_defaults(
-                vector_store=store,
-                persist_dir=STORAGE_DIR,
-            )
-        )
-    else:
-        index = VectorStoreIndex.from_vector_store(store)
-
-    logger.info("Loaded index successfully.")
-    return index
@@ -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():
        "dimensions": int(dimensions) if dimensions 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")]
+    )
@@ -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,
+  });
+}
@@ -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<Uint8Array> {
+  return createParser(response, data, opts?.parserOptions)
+    .pipeThrough(createCallbacksTransformer(opts?.callbacks))
+    .pipeThrough(createStreamDataTransformer());
 }
@@ -5,7 +5,7 @@ import { NextRequest, NextResponse } from "next/server";
 import { createChatEngine } from "./engine/chat";
 import { initSettings } from "./engine/settings";
 import { LlamaIndexStream } from "./llamaindex-stream";
-import { appendEventData } from "./stream-helper";
+import { createCallbackManager } from "./stream-helper";

 initObservability();
 initSettings();
@@ -57,29 +57,21 @@ 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) => {
-      const { nodes } = data.detail;
-      appendEventData(
-        vercelStreamData,
-        `Retrieved ${nodes.length} sources to use as context for the query`,
-      );
-    });
+    // Setup callbacks
+    const callbackManager = createCallbackManager(vercelStreamData);

    // 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,
      },
@@ -1,5 +1,11 @@
 import { StreamData } from "ai";
-import { Metadata, NodeWithScore } from "llamaindex";
+import {
+  CallbackManager,
+  Metadata,
+  NodeWithScore,
+  ToolCall,
+  ToolOutput,
+} from "llamaindex";

 export function appendImageData(data: StreamData, imageUrl?: string) {
  if (!imageUrl) return;
@@ -37,3 +43,55 @@ export function appendEventData(data: StreamData, title?: string) {
    },
  });
 }
+
+export function appendToolData(
+  data: StreamData,
+  toolCall: ToolCall,
+  toolOutput: ToolOutput,
+) {
+  data.appendMessageAnnotation({
+    type: "tools",
+    data: {
+      toolCall: {
+        id: toolCall.id,
+        name: toolCall.name,
+        input: toolCall.input,
+      },
+      toolOutput: {
+        output: toolOutput.output,
+        isError: toolOutput.isError,
+      },
+    },
+  });
+}
+
+export function createCallbackManager(stream: StreamData) {
+  const callbackManager = new CallbackManager();
+
+  callbackManager.on("retrieve", (data) => {
+    const { nodes, query } = data.detail;
+    appendEventData(stream, `Retrieving context for query: '${query}'`);
+    appendEventData(
+      stream,
+      `Retrieved ${nodes.length} sources to use as context for the query`,
+    );
+  });
+
+  callbackManager.on("llm-tool-call", (event) => {
+    const { name, input } = event.detail.payload.toolCall;
+    const inputString = Object.entries(input)
+      .map(([key, value]) => `${key}: ${value}`)
+      .join(", ");
+    appendEventData(
+      stream,
+      `Using tool: '${name}' with inputs: '${inputString}'`,
+    );
+  });
+
+  callbackManager.on("llm-tool-result", (event) => {
+    const { toolCall, toolResult } = event.detail.payload;
+    appendToolData(stream, toolCall, toolResult);
+  });
+
+  return callbackManager;
+}
@@ -17,7 +17,8 @@ export default function ChatSection() {
    headers: {
      "Content-Type": "application/json", // using JSON because of vercel/ai 2.2.26
    },
-    onError: (error) => {
+    onError: (error: unknown) => {
+      if (!(error instanceof Error)) throw error;
      const message = JSON.parse(error.message);
      alert(message.detail);
    },
@@ -7,6 +7,7 @@ import ChatAvatar from "./chat-avatar";
 import { ChatEvents } from "./chat-events";
 import { ChatImage } from "./chat-image";
 import { ChatSources } from "./chat-sources";
+import ChatTools from "./chat-tools";
 import {
  AnnotationData,
  EventData,
@@ -14,6 +15,7 @@ import {
  MessageAnnotation,
  MessageAnnotationType,
  SourceData,
+  ToolData,
 } from "./index";
 import Markdown from "./markdown";
 import { useCopyToClipboard } from "./use-copy-to-clipboard";
@@ -52,19 +54,27 @@ function ChatMessageContent({
    annotations,
    MessageAnnotationType.SOURCES,
  );
+  const toolData = getAnnotationData<ToolData>(
+    annotations,
+    MessageAnnotationType.TOOLS,
+  );

  const contents: ContentDisplayConfig[] = [
    {
-      order: -2,
+      order: -3,
      component: imageData[0] ? <ChatImage data={imageData[0]} /> : null,
    },
    {
-      order: -1,
+      order: -2,
      component:
        eventData.length > 0 ? (
          <ChatEvents isLoading={isLoading} data={eventData} />
        ) : null,
    },
+    {
+      order: -1,
+      component: toolData[0] ? <ChatTools data={toolData[0]} /> : null,
+    },
    {
      order: 0,
      component: <Markdown content={message.content} />,
@@ -40,9 +40,16 @@ export default function ChatMessages(
        className="flex h-[50vh] flex-col gap-5 divide-y overflow-y-auto pb-4"
        ref={scrollableChatContainerRef}
      >
-        {props.messages.map((m) => (
-          <ChatMessage key={m.id} chatMessage={m} isLoading={props.isLoading} />
-        ))}
+        {props.messages.map((m, i) => {
+          const isLoadingMessage = i === messageLength - 1 && props.isLoading;
+          return (
+            <ChatMessage
+              key={m.id}
+              chatMessage={m}
+              isLoading={isLoadingMessage}
+            />
+          );
+        })}
        {isPending && (
          <div className="flex justify-center items-center pt-10">
            <Loader2 className="h-4 w-4 animate-spin" />
@@ -0,0 +1,26 @@
+import { ToolData } from "./index";
+import { WeatherCard, WeatherData } from "./widgets/WeatherCard";
+
+// TODO: If needed, add displaying more tool outputs here
+export default function ChatTools({ data }: { data: ToolData }) {
+  if (!data) return null;
+  const { toolCall, toolOutput } = data;
+
+  if (toolOutput.isError) {
+    return (
+      <div className="border-l-2 border-red-400 pl-2">
+        There was an error when calling the tool {toolCall.name} with input:{" "}
+        <br />
+        {JSON.stringify(toolCall.input)}
+      </div>
+    );
+  }
+
+  switch (toolCall.name) {
+    case "get_weather_information":
+      const weatherData = toolOutput.output as unknown as WeatherData;
+      return <WeatherCard data={weatherData} />;
+    default:
+      return null;
+  }
+}
@@ -1,3 +1,4 @@
+import { JSONValue } from "ai";
 import ChatInput from "./chat-input";
 import ChatMessages from "./chat-messages";

@@ -8,6 +9,7 @@ export enum MessageAnnotationType {
  IMAGE = "image",
  SOURCES = "sources",
  EVENTS = "events",
+  TOOLS = "tools",
 }

 export type ImageData = {
@@ -30,7 +32,21 @@ export type EventData = {
  isCollapsed: boolean;
 };

-export type AnnotationData = ImageData | SourceData | EventData;
+export type ToolData = {
+  toolCall: {
+    id: string;
+    name: string;
+    input: {
+      [key: string]: JSONValue;
+    };
+  };
+  toolOutput: {
+    output: JSONValue;
+    isError: boolean;
+  };
+};
+
+export type AnnotationData = ImageData | SourceData | EventData | ToolData;

 export type MessageAnnotation = {
  type: MessageAnnotationType;
@@ -0,0 +1,213 @@
+export interface WeatherData {
+  latitude: number;
+  longitude: number;
+  generationtime_ms: number;
+  utc_offset_seconds: number;
+  timezone: string;
+  timezone_abbreviation: string;
+  elevation: number;
+  current_units: {
+    time: string;
+    interval: string;
+    temperature_2m: string;
+    weather_code: string;
+  };
+  current: {
+    time: string;
+    interval: number;
+    temperature_2m: number;
+    weather_code: number;
+  };
+  hourly_units: {
+    time: string;
+    temperature_2m: string;
+    weather_code: string;
+  };
+  hourly: {
+    time: string[];
+    temperature_2m: number[];
+    weather_code: number[];
+  };
+  daily_units: {
+    time: string;
+    weather_code: string;
+  };
+  daily: {
+    time: string[];
+    weather_code: number[];
+  };
+}
+
+// Follow WMO Weather interpretation codes (WW)
+const weatherCodeDisplayMap: Record<
+  string,
+  {
+    icon: JSX.Element;
+    status: string;
+  }
+> = {
+  "0": {
+    icon: <span>☀️</span>,
+    status: "Clear sky",
+  },
+  "1": {
+    icon: <span>🌤️</span>,
+    status: "Mainly clear",
+  },
+  "2": {
+    icon: <span>☁️</span>,
+    status: "Partly cloudy",
+  },
+  "3": {
+    icon: <span>☁️</span>,
+    status: "Overcast",
+  },
+  "45": {
+    icon: <span>🌫️</span>,
+    status: "Fog",
+  },
+  "48": {
+    icon: <span>🌫️</span>,
+    status: "Depositing rime fog",
+  },
+  "51": {
+    icon: <span>🌧️</span>,
+    status: "Drizzle",
+  },
+  "53": {
+    icon: <span>🌧️</span>,
+    status: "Drizzle",
+  },
+  "55": {
+    icon: <span>🌧️</span>,
+    status: "Drizzle",
+  },
+  "56": {
+    icon: <span>🌧️</span>,
+    status: "Freezing Drizzle",
+  },
+  "57": {
+    icon: <span>🌧️</span>,
+    status: "Freezing Drizzle",
+  },
+  "61": {
+    icon: <span>🌧️</span>,
+    status: "Rain",
+  },
+  "63": {
+    icon: <span>🌧️</span>,
+    status: "Rain",
+  },
+  "65": {
+    icon: <span>🌧️</span>,
+    status: "Rain",
+  },
+  "66": {
+    icon: <span>🌧️</span>,
+    status: "Freezing Rain",
+  },
+  "67": {
+    icon: <span>🌧️</span>,
+    status: "Freezing Rain",
+  },
+  "71": {
+    icon: <span>❄️</span>,
+    status: "Snow fall",
+  },
+  "73": {
+    icon: <span>❄️</span>,
+    status: "Snow fall",
+  },
+  "75": {
+    icon: <span>❄️</span>,
+    status: "Snow fall",
+  },
+  "77": {
+    icon: <span>❄️</span>,
+    status: "Snow grains",
+  },
+  "80": {
+    icon: <span>🌧️</span>,
+    status: "Rain showers",
+  },
+  "81": {
+    icon: <span>🌧️</span>,
+    status: "Rain showers",
+  },
+  "82": {
+    icon: <span>🌧️</span>,
+    status: "Rain showers",
+  },
+  "85": {
+    icon: <span>❄️</span>,
+    status: "Snow showers",
+  },
+  "86": {
+    icon: <span>❄️</span>,
+    status: "Snow showers",
+  },
+  "95": {
+    icon: <span>⛈️</span>,
+    status: "Thunderstorm",
+  },
+  "96": {
+    icon: <span>⛈️</span>,
+    status: "Thunderstorm",
+  },
+  "99": {
+    icon: <span>⛈️</span>,
+    status: "Thunderstorm",
+  },
+};
+
+const displayDay = (time: string) => {
+  return new Date(time).toLocaleDateString("en-US", {
+    weekday: "long",
+  });
+};
+
+export function WeatherCard({ data }: { data: WeatherData }) {
+  const currentDayString = new Date(data.current.time).toLocaleDateString(
+    "en-US",
+    {
+      weekday: "long",
+      month: "long",
+      day: "numeric",
+    },
+  );
+
+  return (
+    <div className="bg-[#61B9F2] rounded-2xl shadow-xl p-5 space-y-4 text-white w-fit">
+      <div className="flex justify-between">
+        <div className="space-y-2">
+          <div className="text-xl">{currentDayString}</div>
+          <div className="text-5xl font-semibold flex gap-4">
+            <span>
+              {data.current.temperature_2m} {data.current_units.temperature_2m}
+            </span>
+            {weatherCodeDisplayMap[data.current.weather_code].icon}
+          </div>
+        </div>
+        <span className="text-xl">
+          {weatherCodeDisplayMap[data.current.weather_code].status}
+        </span>
+      </div>
+      <div className="gap-2 grid grid-cols-6">
+        {data.daily.time.map((time, index) => {
+          if (index === 0) return null; // skip the current day
+          return (
+            <div key={time} className="flex flex-col items-center gap-4">
+              <span>{displayDay(time)}</span>
+              <div className="text-4xl">
+                {weatherCodeDisplayMap[data.daily.weather_code[index]].icon}
+              </div>
+              <span className="text-sm">
+                {weatherCodeDisplayMap[data.daily.weather_code[index]].status}
+              </span>
+            </div>
+          );
+        })}
+      </div>
+    </div>
+  );
+}
@@ -14,12 +14,14 @@
    "@radix-ui/react-hover-card": "^1.0.7",
    "@radix-ui/react-slot": "^1.0.2",
    "ai": "^3.0.21",
+    "ajv": "^8.12.0",
    "class-variance-authority": "^0.7.0",
-    "clsx": "^1.2.1",
+    "clsx": "^2.1.1",
    "dotenv": "^16.3.1",
-    "llamaindex": "0.2.10",
+    "llamaindex": "0.3.9",
    "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",
Author	SHA1	Message	Date
github-actions[bot]	988bfc2a60	Release 0.1.2 (#79 ) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2024-05-10 14:12:31 +07:00
Thuc Pham	056e376ee0	feat: add weather widget and weather tool (#72 ) --------- Co-authored-by: leehuwuj <leehuwuj@gmail.com> Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2024-05-10 14:00:16 +07:00
Thuc Pham	819cccb11a	feat: use 3.5 as default model (#77 )	2024-05-09 15:48:25 +07:00
Huu Le (Lee)	8a5ece10c2	chores: update wrong example system prompt and fix missing switch breaking (#75 )	2024-05-08 10:14:34 +07:00
github-actions[bot]	63bb0505d6	Release 0.1.1 (#60 ) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2024-05-03 10:38:01 +07:00
Huu Le (Lee)	2e80ef47ee	Fix typo in settings.py (#73 )	2024-05-03 10:36:12 +07:00
Marcus Schiesser	a1feb524e9	Revert "Use ingestion pipeline in Python code (#61 )" This reverts commit `c094b0c6bf`.	2024-05-03 11:06:02 +08:00
Marcus Schiesser	06823da849	fix: stream type	2024-05-02 17:25:49 +08:00
Thuc Pham	7bd3ed551f	feat: support anthropic and gemini model providers and update to LITS 0.3.3 (#63 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2024-05-02 16:13:31 +07:00
				`@@ -0,0 +1 @@`
				`STORAGE_DIR = "storage" # directory to cache the generated index`
				`@@ -1 +0,0 @@`
				STORAGE_DIR = "storage" # directory to save the stores to (document store and if used, the `SimpleVectorStore`)