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update:修正英文版翻译遗漏的chatHistory.前缀
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@@ -183,8 +183,9 @@ This project provides two deployment methods. Please choose based on your specif
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| Deployment Method | Features | Applicable Scenarios | Deployment Docs | Configuration Requirements | Video Tutorials |
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|---------|------|---------|---------|---------|---------|
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| **Simplified Installation** | Intelligent dialogue, single agent management | Low-configuration environments, data stored in config files, no database required | [①Docker Version](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%B8%80docker%E5%8F%AA%E8%BF%90%E8%A1%8Cserver) / [②Source Code Deployment](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E5%8F%AA%E8%BF%90%E8%A1%8Cserver)| 2 cores 4GB if using `FunASR`, 2 cores 2GB if all APIs | - |
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| **Full Module Installation** | Intelligent dialogue, multi-user management, multi-agent management, intelligent console interface operation | Complete functionality experience, data stored in database |[①Docker Version](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%B8%80docker%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [②Source Code Deployment](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [③Source Code Deployment Auto-Update Tutorial](./docs/dev-ops-integration.md) | 4 cores 8GB if using `FunASR`, 2 cores 4GB if all APIs| [Local Source Code Startup Video Tutorial](https://www.bilibili.com/video/BV1wBJhz4Ewe) |
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| **Full Module Installation** | Intelligent dialogue, multi-user management, multi-agent management, intelligent console interface operation | Complete functionality experience, data stored in database |[①Docker Version](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%B8%80docker%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [②Source Code Deployment](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [③Source Code Deployment Auto-Update Tutorial](./docs/dev-ops-integration.md) | 4 cores 8GB if using `FunASR`, 2 cores 4GB if all APIs| [Local Source Code Startup Video Tutorial](https://www.bilibili.com/video/BV1wBJhz4Ewe) |
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For frequently asked questions and related tutorials, please refer to [this link](./docs/FAQ.md)
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> 💡 Note: Below is a test platform deployed with the latest code. You can burn and test if needed. Concurrent users: 6, data will be cleared daily.
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@@ -216,14 +217,15 @@ Websocket Interface Address: wss://2662r3426b.vicp.fun/xiaozhi/v1/
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| Intent(Intent Recognition) | function_call(Function calling) | function_call(Function calling) |
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| Memory(Memory function) | mem_local_short(Local short-term memory) | mem_local_short(Local short-term memory) |
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If you are concerned about the latency of each component, please refer to the [Xiaozhi Component Performance Test Report](https://github.com/xinnan-tech/xiaozhi-performance-research), and test in your own environment following the test methods in the report.
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#### 🔧 Testing Tools
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This project provides the following testing tools to help you verify the system and choose suitable models:
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| Tool Name | Location | Usage Method | Function Description |
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|:---:|:---|:---:|:---:|
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| Audio Interaction Test Tool | main》xiaozhi-server》test》test_page.html | Open directly with Google Chrome | Tests audio playback and reception functions, verifies if Python-side audio processing is normal |
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| Model Response Test Tool 1 | main》xiaozhi-server》performance_tester.py | Execute `python performance_tester.py` | Tests response speed of three core modules: ASR(speech recognition), LLM(large model), TTS(speech synthesis) |
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| Model Response Test Tool 2 | main》xiaozhi-server》performance_tester_vllm.py | Execute `python performance_tester_vllm.py` | Tests VLLM(vision model) response speed |
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| Model Response Test Tool | main》xiaozhi-server》performance_tester.py | Execute `python performance_tester.py` | Tests response speed of three core modules: ASR(speech recognition), LLM(large model), VLLM(vision model), TTS(speech synthesis) |
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> 💡 Note: When testing model speed, only models with configured keys will be tested.
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@@ -241,8 +243,8 @@ This project provides the following testing tools to help you verify the system
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| Intent Recognition | Supports LLM intent recognition, Function Call function calling, provides plugin-based intent processing mechanism |
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| Memory System | Supports local short-term memory, mem0ai interface memory, PowerMem intelligent memory, with memory summarization functionality |
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| Knowledge Base | Supports RAGFlow knowledge base, enabling LLM to judge whether to schedule the knowledge base after receiving the user's question, and then answer the question |
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| Command Delivery | Supports MCP command delivery to ESP32 devices via MQTT protocol from Smart Console |
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| Tool Calling | Supports client IOT protocol, client MCP protocol, server MCP protocol, MCP endpoint protocol, custom tool functions |
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| Command Delivery | Supports MCP command delivery to ESP32 devices via MQTT protocol from Smart Console |
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| Management Backend | Provides Web management interface, supports user management, system configuration and device management; Supports Simplified Chinese, Traditional Chinese and English display |
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| Testing Tools | Provides performance testing tools, vision model testing tools, and audio interaction testing tools |
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| Deployment Support | Supports Docker deployment and local deployment, provides complete configuration file management |
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@@ -250,20 +252,14 @@ This project provides the following testing tools to help you verify the system
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### Under Development 🚧
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To learn about specific development plan progress, [click here](https://github.com/users/xinnan-tech/projects/3)
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To learn about specific development plan progress, [click here](https://github.com/users/xinnan-tech/projects/3). For frequently asked questions and related tutorials, please refer to [this link](./docs/FAQ.md)
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If you are a software developer, here is an [Open Letter to Developers](docs/contributor_open_letter.md). Welcome to join!
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---
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## Product Ecosystem 👬
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Xiaozhi is an ecosystem. When using this product, you can also check out other [excellent projects](https://github.com/78/xiaozhi-esp32?tab=readme-ov-file#related-open-source-projects) in this ecosystem
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| Project Name | Project Address | Project Description |
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|:---------------------|:--------|:--------|
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| Xiaozhi Android Client | [xiaozhi-android-client](https://github.com/TOM88812/xiaozhi-android-client) | An Android and iOS voice dialogue application based on xiaozhi-server, supporting real-time voice interaction and text dialogue.<br/>Currently a Flutter version, connecting iOS and Android platforms. |
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| Xiaozhi Desktop Client | [py-xiaozhi](https://github.com/Huang-junsen/py-xiaozhi) | This project provides a Python-based AI client for beginners, allowing users to experience Xiaozhi AI functionality through code even without physical hardware conditions. |
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| Xiaozhi Java Server | [xiaozhi-esp32-server-java](https://github.com/joey-zhou/xiaozhi-esp32-server-java) | Xiaozhi open-source backend service Java version is a Java-based open-source project.<br/>It includes frontend and backend services, aiming to provide users with a complete backend service solution. |
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Xiaozhi is an ecosystem. When using this product, you can also check out other [excellent projects](https://github.com/78/xiaozhi-esp32/blob/main/README_zh.md#%E7%9B%B8%E5%85%B3%E5%BC%80%E6%BA%90%E9%A1%B9%E7%9B%AE) in this ecosystem
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---
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@@ -277,8 +273,10 @@ Xiaozhi is an ecosystem. When using this product, you can also check out other [
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| Dify interface calls | Dify | - |
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| FastGPT interface calls | FastGPT | - |
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| Coze interface calls | Coze | - |
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| Xinference interface calls | Xinference | - |
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| HomeAssistant interface calls | HomeAssistant | - |
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In fact, any LLM that supports OpenAI interface calls can be integrated and used, including Xinference and HomeAssistant interfaces.
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In fact, any LLM that supports OpenAI interface calls can be integrated and used.
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---
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@@ -297,7 +295,7 @@ In fact, any VLLM that supports OpenAI interface calls can be integrated and use
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| Usage Method | Supported Platforms | Free Platforms |
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|:---:|:---:|:---:|
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| Interface calls | EdgeTTS, iFLYTEK, Volcano Engine, Tencent Cloud, Alibaba Cloud and Bailian, CosyVoiceSiliconflow, TTS302AI, CozeCnTTS, GizwitsTTS, ACGNTTS, OpenAITTS, Lingxi Streaming TTS, MinimaxTTS | Lingxi Streaming TTS, EdgeTTS, CosyVoiceSiliconflow(partial) |
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| Local services | FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3, MinimaxTTS | FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3, MinimaxTTS |
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| Local services | FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3, Index-TTS, PaddleSpeech | Index-TTS, PaddleSpeech, FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3 |
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---
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@@ -343,6 +341,7 @@ In fact, any VLLM that supports OpenAI interface calls can be integrated and use
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|:------:|:-------------:|:----:|:-------:|:---------------------:|
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| Intent | intent_llm | Interface calls | Based on LLM pricing | Recognizes intent through large models, strong generalization |
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| Intent | function_call | Interface calls | Based on LLM pricing | Completes intent through large model function calling, fast speed, good effect |
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| Intent | nointent | No intent mode | Free | Does not perform intent recognition, directly returns dialogue result |
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---
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