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168 lines
5.5 KiB
Go
168 lines
5.5 KiB
Go
/*
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* Copyright 2025 CloudWeGo Authors
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package main
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import (
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"context"
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"os"
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"strconv"
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"time"
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"github.com/cloudwego/eino/adk"
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"github.com/cloudwego/eino/adk/prebuilt/deep"
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"github.com/cloudwego/eino/components/model"
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"github.com/cloudwego/eino/components/tool"
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"github.com/cloudwego/eino/compose"
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"github.com/cloudwego/eino/schema"
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commonModel "github.com/cloudwego/eino-examples/adk/common/model"
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tool2 "github.com/cloudwego/eino-examples/adk/common/tool"
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"github.com/cloudwego/eino-examples/components/tool/middlewares/errorremover"
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)
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type rateLimitedModel struct {
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m model.ToolCallingChatModel
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delay time.Duration
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}
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func (r *rateLimitedModel) WithTools(tools []*schema.ToolInfo) (model.ToolCallingChatModel, error) {
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newM, err := r.m.WithTools(tools)
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if err != nil {
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return nil, err
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}
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return &rateLimitedModel{newM, r.delay}, nil
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}
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func (r *rateLimitedModel) Generate(ctx context.Context, input []*schema.Message, opts ...model.Option) (*schema.Message, error) {
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time.Sleep(r.delay)
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return r.m.Generate(ctx, input, opts...)
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}
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func (r *rateLimitedModel) Stream(ctx context.Context, input []*schema.Message, opts ...model.Option) (*schema.StreamReader[*schema.Message], error) {
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time.Sleep(r.delay)
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return r.m.Stream(ctx, input, opts...)
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}
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func getRateLimitDelay() time.Duration {
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delayMs := os.Getenv("RATE_LIMIT_DELAY_MS")
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if delayMs == "" {
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return 0
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}
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ms, err := strconv.Atoi(delayMs)
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if err != nil {
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return 0
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}
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return time.Duration(ms) * time.Millisecond
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}
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func newRateLimitedModel() model.ToolCallingChatModel {
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delay := getRateLimitDelay()
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if delay == 0 {
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return commonModel.NewChatModel()
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}
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return &rateLimitedModel{
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m: commonModel.NewChatModel(),
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delay: delay,
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}
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}
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func buildResearchAgent(ctx context.Context, m model.ToolCallingChatModel) (adk.Agent, error) {
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searchTool, err := NewSearchTool(ctx)
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if err != nil {
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return nil, err
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}
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return adk.NewChatModelAgent(ctx, &adk.ChatModelAgentConfig{
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Name: "ResearchAgent",
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Description: "A research agent that can search for information and gather data on various topics.",
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Instruction: `You are a research agent specialized in gathering information.
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Use the search tool to find relevant information for the given task.
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Provide comprehensive and accurate results.`,
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Model: m,
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ToolsConfig: adk.ToolsConfig{
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ToolsNodeConfig: compose.ToolsNodeConfig{
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Tools: []tool.BaseTool{searchTool},
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},
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},
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MaxIterations: 10,
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})
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}
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func buildAnalysisAgent(ctx context.Context, m model.ToolCallingChatModel) (adk.Agent, error) {
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analyzeTool, err := NewAnalyzeTool(ctx)
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if err != nil {
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return nil, err
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}
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return adk.NewChatModelAgent(ctx, &adk.ChatModelAgentConfig{
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Name: "AnalysisAgent",
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Description: "An analysis agent that processes data and generates insights.",
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Instruction: `You are an analysis agent specialized in processing data and generating insights.
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Use the analyze tool to process data and provide meaningful analysis.
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Present your findings clearly and concisely.`,
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Model: m,
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ToolsConfig: adk.ToolsConfig{
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ToolsNodeConfig: compose.ToolsNodeConfig{
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Tools: []tool.BaseTool{analyzeTool},
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},
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},
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MaxIterations: 10,
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})
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}
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func NewDataAnalysisDeepAgent(ctx context.Context, m model.ToolCallingChatModel) (adk.Agent, error) {
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researchAgent, err := buildResearchAgent(ctx, m)
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if err != nil {
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return nil, err
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}
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analysisAgent, err := buildAnalysisAgent(ctx, m)
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if err != nil {
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return nil, err
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}
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followUpTool := tool2.GetFollowUpTool()
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return deep.New(ctx, &deep.Config{
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Name: "DataAnalysisAgent",
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Description: "A deep agent for comprehensive data analysis tasks that may require clarification from users.",
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Instruction: `You are a data analysis agent that helps users analyze market data and provide insights.
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IMPORTANT: Before starting any analysis, you MUST first use the FollowUpTool to ask the user clarifying questions to understand:
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1. What specific market sectors or industries they are interested in (e.g., technology, finance, healthcare)
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2. What time period they want to analyze (e.g., last quarter, year-to-date, specific dates)
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3. What type of analysis they need (e.g., trend analysis, comparison, statistical analysis)
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4. Their risk tolerance for investment recommendations (e.g., conservative, moderate, aggressive)
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Only after receiving answers from the user should you proceed with the analysis using the ResearchAgent and AnalysisAgent.
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Available tools:
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- FollowUpTool: Use this FIRST to ask clarifying questions before any analysis
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- ResearchAgent: Use to search for market data and information
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- AnalysisAgent: Use to analyze data and generate insights`,
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ChatModel: m,
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SubAgents: []adk.Agent{researchAgent, analysisAgent},
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ToolsConfig: adk.ToolsConfig{
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ToolsNodeConfig: compose.ToolsNodeConfig{
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Tools: []tool.BaseTool{followUpTool},
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ToolCallMiddlewares: []compose.ToolMiddleware{errorremover.Middleware()}, // Inject the remove_error middleware.
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},
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},
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MaxIteration: 50,
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})
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}
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