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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
27 changed files with 809 additions and 157 deletions
+6
View File
@@ -1,5 +1,11 @@
# create-llama
## 0.1.2
### Patch Changes
- 056e376: Add support for displaying tool outputs (including weather widget as example)
## 0.1.1
### Patch Changes
+1 -7
View File
@@ -217,13 +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."`,
},
];
};
+1 -1
View File
@@ -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({
+44 -33
View File
@@ -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);
@@ -298,9 +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({
+27 -2
View File
@@ -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 -1
View File
@@ -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 -1
View File
@@ -1,6 +1,6 @@
{
"name": "create-llama",
"version": "0.1.1",
"version": "0.1.2",
"description": "Create LlamaIndex-powered apps with one command",
"keywords": [
"rag",
@@ -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)]
@@ -4,9 +4,10 @@ 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: BaseToolWithCall[] = [];
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);
}
}
@@ -14,8 +14,9 @@
"cors": "^2.8.5",
"dotenv": "^16.3.1",
"express": "^4.18.2",
"llamaindex": "0.3.3",
"pdf2json": "3.0.5"
"llamaindex": "0.3.7",
"pdf2json": "3.0.5",
"ajv": "^8.12.0"
},
"devDependencies": {
"@types/cors": "^2.8.16",
@@ -1,14 +1,9 @@
import { Message, StreamData, streamToResponse } from "ai";
import { Request, Response } from "express";
import {
CallbackManager,
ChatMessage,
MessageContent,
Settings,
} from "llamaindex";
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,
@@ -52,18 +47,7 @@ export const chat = async (req: Request, res: Response) => {
const vercelStreamData = new StreamData();
// 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 ${nodes.length} sources to use as context for the query`,
);
});
const callbackManager = createCallbackManager(vercelStreamData);
// Calling LlamaIndex's ChatEngine to get a streamed response
const response = await Settings.withCallbackManager(callbackManager, () => {
@@ -1,5 +1,11 @@
import { StreamData } from "ai";
import { Metadata, NodeWithScore } from "llamaindex";
import {
CallbackManager,
Metadata,
NodeWithScore,
ToolCall,
ToolOutput,
} from "llamaindex";
export function appendImageData(data: StreamData, imageUrl?: string) {
if (!imageUrl) return;
@@ -37,3 +43,55 @@ export function appendEventData(data: StreamData, title?: string) {
},
});
}
export function appendToolData(
data: StreamData,
toolCall: ToolCall,
toolOutput: ToolOutput,
) {
data.appendMessageAnnotation({
type: "tools",
data: {
toolCall: {
id: toolCall.id,
name: toolCall.name,
input: toolCall.input,
},
toolOutput: {
output: toolOutput.output,
isError: toolOutput.isError,
},
},
});
}
export function createCallbackManager(stream: StreamData) {
const callbackManager = new CallbackManager();
callbackManager.on("retrieve", (data) => {
const { nodes, query } = data.detail;
appendEventData(stream, `Retrieving context for query: '${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:
@@ -1,16 +1,11 @@
import { initObservability } from "@/app/observability";
import { Message, StreamData, StreamingTextResponse } from "ai";
import {
CallbackManager,
ChatMessage,
MessageContent,
Settings,
} from "llamaindex";
import { ChatMessage, MessageContent, Settings } from "llamaindex";
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();
@@ -64,18 +59,7 @@ export async function POST(request: NextRequest) {
const vercelStreamData = new StreamData();
// 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 ${nodes.length} sources to use as context for the query`,
);
});
const callbackManager = createCallbackManager(vercelStreamData);
// Calling LlamaIndex's ChatEngine to get a streamed response
const response = await Settings.withCallbackManager(callbackManager, () => {
@@ -1,5 +1,11 @@
import { StreamData } from "ai";
import { Metadata, NodeWithScore } from "llamaindex";
import {
CallbackManager,
Metadata,
NodeWithScore,
ToolCall,
ToolOutput,
} from "llamaindex";
export function appendImageData(data: StreamData, imageUrl?: string) {
if (!imageUrl) return;
@@ -37,3 +43,55 @@ export function appendEventData(data: StreamData, title?: string) {
},
});
}
export function appendToolData(
data: StreamData,
toolCall: ToolCall,
toolOutput: ToolOutput,
) {
data.appendMessageAnnotation({
type: "tools",
data: {
toolCall: {
id: toolCall.id,
name: toolCall.name,
input: toolCall.input,
},
toolOutput: {
output: toolOutput.output,
isError: toolOutput.isError,
},
},
});
}
export function createCallbackManager(stream: StreamData) {
const callbackManager = new CallbackManager();
callbackManager.on("retrieve", (data) => {
const { nodes, query } = data.detail;
appendEventData(stream, `Retrieving context for query: '${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>
);
}
@@ -14,10 +14,11 @@
"@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": "0.3.3",
"llamaindex": "0.3.9",
"lucide-react": "^0.294.0",
"next": "^14.0.3",
"pdf2json": "3.0.5",