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Use LLMProcess implement Agent.OnMessage

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Liu Zhicong
2023-12-09 18:39:42 -08:00
parent 0708daf2ec
commit ddee31c6ab
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doc/LLMProcess.md
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# LLMProcess
设计目的是理解到 提示词=>LLM=>LLM Result 的过程是系统的核心复杂度。Agent的粒度太大了，需要更合适的设计来封装这个复杂度，并给予这个过程更大的灵活性和可组合型。并更易于构建测试
比如
1. 可以很容易的组合两个已知的LLM Process（上一个的输出是下一个的输入），这个设计有一点类似LangChain (我们在正式系统中，肯定允许整个Agent都用LangChain来构建)
2. 可以用用一个LLM Process来构建另一个LLM Process的Prompt
3. 继承一个复杂的LLM Process，进行简单配置，就可以得到一个新的LLM Process。这个新的LLM Process可以享受到复杂LLM Process持续迭代的好处
4. 有一些常用的，系统内置的LLM Process可以从配置文件中加载。
```python
def agent.on_process_message():
llm_process = self.on_message_llm_process.clone()
llm_result = llm_process.do()
```
doc/agent & workflow.drawio
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# Proposal for Adjusting the Goals for Version 0.5.2
Dear Team,
Given the recent launch of OpenAI's new version in early November 2023, many of us may have felt a profound shift in the industry. As the world changes, I believe we should adapt accordingly. Here are some of my thoughts:
1. **Affirmation of Our Path**: OpenAI's latest release, particularly the functionalities of the so-called GPTs Agent platform, is largely similar to our 0.5.1 version released on September 28th. This strongly affirms the correctness of our direction. OpenAI has done a great job educating the market about the Agent, so we no longer need to emphasize the correct use of LLM based on the Agent through version releases. This part of user education product design can be simplified.
2. **Innovation in Version 0.5.2**: For our new version (0.5.2), besides maintaining the combinational advantages brought by private deployment of LLM, I believe we need to implement some of the innovative ideas we've discussed about the Agent. This is crucial to maintaining our leading position and avoiding the impression that OpenDAN is merely a follower of GPTs.
3. **Integration of OpenAI's New Capabilities**: We should fully integrate the new capabilities brought by OpenAI's latest release, especially the longer Token Windows, GPT-V, and Code-interpreter. I believe these new features can effectively solve some known issues.
Therefore, I propose to adjust the goals and plans for version 0.5.2. Here are the core objectives:
- Aim to release version 0.5.2 by the end of November, focusing on:
- Launching a new Agent with Autonomous capabilities and multi-Agent collaboration based on Workspace.
- The integrated product of 0.5.2 will be a private deployment email analysis Agent for small and medium-sized enterprises. This will allow any company to better support its CEO and other management positions through LLM while ensuring privacy and security.
- (Optional) By combining LLM and AIGC, build an Agent-based personalized AIGC application, such as a "children's audio picture book" generator that includes both text-to-image and text-to-sound.
- (Optional) Through multi-Agent collaboration, fully utilize the capabilities of GPT4-Turbo, and attempt to let AI and engineers collaborate on research and development tasks based on Git.
The detailed version plan is as follows:
## MVP plan adjustment
In order to keep the list below too long, the system distributed version is 0.5.3, I think we will open another ISSUE discussion and record, this list does not include.
The modules that are not specially explained are components completed in the 0.5.2 plan
- [x] AIOS Kernel
- [x] Basic Agent,@waterflier, A2
- [x] Python Agent Extend, @wugren, S2
- [x] Basic Workflow,@waterflier, A2
- [ ] Workflow Refactor,@waterflier, A2
- [x] AI Functions,@waterflier,A2
- [ ] Upgrade to GPT4 tools API,S2
- [x] AI Environments,@waterflier, A2
- [x] Celender Environment,@waterflier, S2
- [x] Contanct Manage Support,@waterflier, S2
- [x] AI Shell Enviroment,@waterflier, S1
- [x] Upgrade Agent Working Cycle
- [x] Process Message,@waterflier,A2
- [x] Process Group Message,@waterflier,A2
- [ ] Process Event,(0.5.3)
- [ ] Completion of self-drive,@waterflier,A4
- [ ] Self-learn,@weaterflier,A2
- [ ] Introspection,@waterflier,A2
- [ ] Workspace Environment
- [ ] Task/TODO Manager,@waterflier, A2
- [x] Local File System,@waterflier, S1
- [ ] Web Search, A4
- [ ] Code Interpeter (The first implementation can be based on Openai), A4
- [ ] Query SQL DB, S1
- [x] AI BUS,@waterflier, A2
- [ ] Agent Message MIME Support (Image,Video,Audio), A3
- [x] Connect to Human, @waterflier,A2
- [x] Chatsession,@waterflier, S2
- [ ] Compress Chatsession By Text Summary, @waterflier, A2
- [ ] Knowlege Base,@lurenpluto ,@photosssa
- [x] Knowledge Base Frame,@photosssa,A3
- [x] Knowledge Base Object Store,@lurenpluto ,A4
- [x] Knowledge Base Basic Pipline,@photosssa ,A3
- [ ] Customize pipeline,@photosss,A4
- [ ] Support Local Text Search,A4
- [x] Text Summary Pipline,@waterflier, A2
- [ ] Text Parser Support
- [x] PDF Parser,@waterflier,S2
- [ ] doc Parser,S4
- [x] MD Parser,@waterflier,S1
- [ ] Source Code Parser,S4
- [ ] Image Parser (Base on GPT-V),(S2)
- [ ] Video Parser (Base on GPT-V), (S4)
- [ ] Personal AIGC Models
- [ ] Stable Diffusion Controler Agent (Optional),A6
- [x] AI Compute System,@waterflier, A2
- [x] Scheduler,@streetycat, A2
- [x] LLM Kernel
- [x] GPT4 (Cloud),@waterflier, S1
- [x] LLaMa2,@streetycat, A2
- [ ] Claude2, S2
- [ ] Falcon2, S2
- [ ] MPT-7B, S2
- [ ] Vicuna, S2
- [x] Embeding, @lurenpluto , A4
- [x] Txt2img,@glen0125,A4
- [ ] Support DALL-e, @glen0125, S2
- [x] Img2txt,based on GPT4-V,@alexsunxl S2
- [x] Txt2voice,@wugren A3
- [ ] Txt2Voice,base on OpenAI, @wugren A2
- [ ] Voice2txt, base on OpenAI, A2
- [ ] Language Translate (Pending)
- [ ] Build-in Service
- [x] Spider,@alexsunxl, A2
- [x] E-mail Spider,@alexsunxl, S4
- [x] Agent Message Tunnel Frame,@waterflier, A2
- [x] E-mail Tunnel,@waterflier,A2
- [x] Telegram Tunnel,@waterflier,S2
- [ ] Discord Tunnel,S2
- [ ] Home IoT Environment (0.5.3), A4
- [ ] Compatible Home Assistant (0.5.3), A4
- [ ] Build-in Agents/Apps
- [x] Agent Mia: Personal Information Assistant,@photosssa,@lurenpluto , A2
- [x] Agent Jarvis: Peersonal Bulter & Assistant ,@waterflier, A2
- [ ] A Agent Can Create Other Agent,@wugren, A3
- [ ] App: Personal Station (0.5.3),A4+S4
- [ ] UI
- [x] CLI UI (aios_shell),@waterflier,S2
- [ ] Web UI ,@alexsunxl,S4
- [ ] OpenDAN Desktop Installer,@alexsunxl+@waterflier,S4
- [x] 0.5.1 Integration Test
- [x] Workflow -> AI Agent -> AI Agent,@waterflier,S1
- [x] Spider -> Pipline -> Knowledge Base,@photosssa,S2
- [x] AI Agent <- Functions <- Knowledge Base,@lurenpluto,S2
- [x] 0.5.2 Integration Test
- [ ] Email Agent/CEO assistant,S4
- [ ] My AIGC Assistant (optional), S8
- [ ] My Software Company(Advance Workflow demo) (optional), S8
- [ ] SDK
- [x] Workflow SDK,@waterflier, A2
- [ ] Agent SDK,@waterflier, A2
- [ ] AI Environments SDK (0.5.2), A2
- [ ] Compute Kernel SDK (0.5.3), A2
- [ ] Document (>0.5.2)
- [ ] System design document, including the design document of each subsystem
- [ ] Installation/use document for end users
- [ ] SDK document for developers
## Some Explanation
### Upgrade Agent Working Cycle
The goal is to transform the Agent from a passive message-handling Assistant to an actively acting Agent based on roles. The concept of the relevant modules mainly involves the Agent's behavior patterns (4 types), the Agent's capabilities, and the Agent's memory management (learning and introspection).
For a detailed introduction, refer here: https://github.com/fiatrete/OpenDAN-Personal-AI-OS/issues/91
### Workspace Environment
The Workspace supports the implementation of the Agent Working Cycle design. Its core abstraction is defined as: saving the shared state needed for Agent collaboration and providing the basic capabilities for Agents to complete their work. I carefully referenced AutoGPT in the design. The difference between Workspace and AutoGPT is the emphasis on collaboration (Agent with Agent, Agent with humans). After contemplation, the Workspace primarily consists of the following components:
1. Task/Todo manager, representing the unfinished tasks in the Workspace.
2. Saving work logs.
3. Saving learning outcomes and records of known documents.
4. Ability to access the Knowledge Base (RAG support).
5. Virtual file system for saving any work outcomes.
6. A set of SQL-based databases to save any structured data.
7. Real-time internet search capability.
8. Ability to use existing internet services.
9. Ability to use major blockchain systems (Web3).
10. Ability to write/improve code (based on git), run code, and publish services.
11. Communication capabilities with the outside world.
12. Ability to use social networks.
Each Agent has its own private Workspace, not shared with others. I hope to achieve diversity through the combination of "Agent and Workflow Role". Each user "trains" different Agents through their usage habits, and then these Agents collaborate to complete complex tasks defined in the Workflow. The final results of these complex tasks can reflect the user's inherent personality and preferences.
This component design also reflects my thoughts on the key question, "What capabilities should we endow an Agent with, and how do we control the security boundaries when it transitions from a consultant to a steward?" It's not a simple question, so I anticipate this component will continue to iterate in the future.
### Agent Message MIME Support
Agent Message MIME Support means that Agents can handle multiple types of messages, including images, videos, audio, files, etc. For most Agents, this requires adding a customizable standard step of parsing messages in the message handling process. The input of this step is the message's MIME type, and the output is the text content of the message. This step can be implemented by calling the text_parser module.
Another core requirement of MIME support is to use a unified method to save these non-text content data.
### Text base Knowledge Base
In 0.5.1, we mainly implemented RAG based on the popular Embedding + vector database solution. Through practice, we found that this solution did not fully utilize the potential of LLM, so I want to introduce two new modes to further enhance RAG:
1. Build a local text search engine that LLM can use for proper local searches when needed.
2. Assuming LLM will become cheaper in the future, let LLM learn all the documents once and organize the learning results by directory structure (Text Summary). LLM can use browsing methods to find the information it needs.
#### Text Parser Support
Both MIME Support and Text-based Knowledge Base require the system to support converting various document formats into text that can express semantics as much as possible. This component, known as TextParser, should be implemented as an open and extensible framework, given the vast amount of digital content that exists in different formats.
#### Local Text Search
Using traditional inverted index technology to save all document content locally and provide rapid local search capabilities. The implementation of this component can refer to ElasticSearch.
#### Text Summary
Using the capabilities of LLM to learn all the documents and then save the learning results locally. This behavior can be considered "Self-Learn". Users can let Agents responsible for organizing materials use different prompts according to the purpose of organizing the materials to obtain more targeted results.
### Stable Diffusion Controler Agent
Practice the concept of "Agent as a new era method of using computing", replacing the complex Stable Diffusion WebUI with an easy-to-use Agent. Help users complete complex AIGC tasks and build a paradigm. This paradigm can cover the entire process of AIGC: LORA training, use, model downloading, plugin downloading, generation of prompt words, selection of AIGC results.
### Email Agent/CEO assistant
The integrated test product of 0.5.2, aimed at private deployment for small and medium-sized enterprises, is a CEO Assistant that can read all company emails and materials. I am writing a detailed product document, which is not elaborated here.
----------------------------------------
I look forward to hearing your thoughts on these proposed adjustments.
doc/promps/Introspection.md
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doc/promps/Review Task.md
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目的是结合已知信息（重点是已经进行操作的记录），对失败的，完成的不好的任务进行思考，尝试给出更好的解决方案
1. 管理学方法：更换负责人
2. 管理学方法：拆分
3. 给出建议（该建议可以在下次一次DO-Check）循环中被使用
3. 给出建议（该建议可以在下次一次DO-Check）循环中被使用
## Quick Review
有一些简单的Task是永远不会结束的（比如定时提醒）。此时通过Quick Review来调整这些Task的状态，让其在正确的时间进入Review和DO
doc/promps/Self-improve.md
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# Self Improve Prompt
这是一个改进Prompt的Prompt,其设计目标是利用LLM来改进LLM.(输入是一个LLM Process)
注意理解Self Improve和Self Thinking的区别: Self Improve有可能改进Agent的某个LLM Process的提示词，而Self Thinkg只会更新Agent的Memory
提示词：
行为模式：Input形式， Goal（目的）
理想结果：Input， 结果
当前情况：当前Prompt,实际结果
输出：
新的Prompt
## 当前版本
```
你是LLM的专家，尤其擅长编写Prompt，你会帮助我改进Prompt。
我会给你一个已有的Prompt,并说明该Prompt的设计目标，期望的结果和实际的结果。你会step-by-step的进行分析，说明改进思路，并给出改进后的Prompt。
```
##
doc/promps/Self-thinking.md
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# Self-Thinking (Introspection)
基于自己的角色定位，结合已有的历史记录（聊天记录，工作记录），进行思考和总结，进而更好的改进未来的工作
自省从某个角度看，就是对Agent Memory的自我总结
## 当前版本
```
You are the best deep thinking in the world, and you will think about the information I give you, sometimes some chat records.Then you will generate a briefing or summary of no more than 400 words based on this information.
You mainly use the following methods to generate summary:
1. Try to understand the theme of each sentence, and call the relevant operation to record the relationship between the dialogue and the theme
2. Try to analyze the personality of different people involved in information
3. Try to summarize important events in the information and record it
4. Try to understand the attitude of different people on different topics or events
5. For the key information or TODO in the information, such as the time, place, amount and other information of the certainty, it must be stored in the summary.
Just give me a summary without any other word.
```
doc/promps/process message.md
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# Process Message
处理消息的首要是目的是分析消息的意图，并给予回复
处理消息的首要是目的是分析消息的意图，并给予正确的回复。
## 提示词的构成
1. Agent的身份说明，处理信息的基本原则（目的
2. 处理信息的通用套路。
要产生一个合适的回复。
通过actions来设置话题状态、创建task等
3. 当前的常规Context，包括现在的时间、对话发生的地点（通常来自AgentMsg里的Context），地点所在的天气等信息
4. 已知信息（组合而来）
和信息发送者的近期的交流记录
Agent和信息发送者近期未完成话题的标题和简介
关于信息发送者，和相关人物的更多资料
查阅更多信息的方法 ： 搜索法和浏览法。注意区分外部资料和内部记忆
其它相关信息（比如RAG根据 输入消息
## 当前版本
## 想法：LLM生成提示词
根据 Agent的身份说明，处理信息的基本原则（目的），当前信息，通过另一个LLM来生成提示词里的某些部分的内容