mirror of
https://github.com/FoundationAgents/MetaGPT.git
synced 2026-06-02 14:45:17 +02:00
Merge branch 'geekan:main' into feat_st_game
This commit is contained in:
better629
GitHub
commit
64350d2c6d
303 changed files with 9155 additions and 3587 deletions
5
.gitattributes
vendored
5
.gitattributes
vendored
|
|
@ -12,6 +12,11 @@
|
|||
*.jpg binary
|
||||
*.gif binary
|
||||
*.ico binary
|
||||
*.jpeg binary
|
||||
*.mp3 binary
|
||||
*.zip binary
|
||||
*.bin binary
|
||||
|
||||
|
||||
# Preserve original line endings for specific document files
|
||||
*.doc text eol=crlf
|
||||
|
|
|
|||
1
.github/workflows/fulltest.yaml
vendored
1
.github/workflows/fulltest.yaml
vendored
|
|
@ -54,7 +54,6 @@ jobs:
|
|||
export ALLOW_OPENAI_API_CALL=0
|
||||
echo "${{ secrets.METAGPT_KEY_YAML }}" | base64 -d > config/key.yaml
|
||||
mkdir -p ~/.metagpt && echo "${{ secrets.METAGPT_CONFIG2_YAML }}" | base64 -d > ~/.metagpt/config2.yaml
|
||||
echo "${{ secrets.SPARK_YAML }}" | base64 -d > ~/.metagpt/spark.yaml
|
||||
pytest tests/ --doctest-modules --cov=./metagpt/ --cov-report=xml:cov.xml --cov-report=html:htmlcov --durations=20 | tee unittest.txt
|
||||
- name: Show coverage report
|
||||
run: |
|
||||
|
|
|
|||
2
.github/workflows/unittest.yaml
vendored
2
.github/workflows/unittest.yaml
vendored
|
|
@ -31,7 +31,7 @@ jobs:
|
|||
- name: Test with pytest
|
||||
run: |
|
||||
export ALLOW_OPENAI_API_CALL=0
|
||||
mkdir -p ~/.metagpt && cp tests/config2.yaml ~/.metagpt/config2.yaml && cp tests/spark.yaml ~/.metagpt/spark.yaml
|
||||
mkdir -p ~/.metagpt && cp tests/config2.yaml ~/.metagpt/config2.yaml
|
||||
pytest tests/ --doctest-modules --cov=./metagpt/ --cov-report=xml:cov.xml --cov-report=html:htmlcov --durations=20 | tee unittest.txt
|
||||
- name: Show coverage report
|
||||
run: |
|
||||
|
|
|
|||
15
.gitignore
vendored
15
.gitignore
vendored
|
|
@ -1,7 +1,7 @@
|
|||
### Python template
|
||||
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
__pycache__
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
|
|
@ -27,6 +27,8 @@ share/python-wheels/
|
|||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
metagpt/tools/schemas/
|
||||
examples/data/search_kb/*.json
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python scripts from a template
|
||||
|
|
@ -151,9 +153,14 @@ allure-results
|
|||
.vscode
|
||||
|
||||
key.yaml
|
||||
data
|
||||
/data/
|
||||
data.ms
|
||||
examples/nb/
|
||||
examples/default__vector_store.json
|
||||
examples/docstore.json
|
||||
examples/graph_store.json
|
||||
examples/image__vector_store.json
|
||||
examples/index_store.json
|
||||
.chroma
|
||||
*~$*
|
||||
workspace/*
|
||||
|
|
@ -168,6 +175,7 @@ output
|
|||
tmp.png
|
||||
.dependencies.json
|
||||
tests/metagpt/utils/file_repo_git
|
||||
tests/data/rsp_cache_new.json
|
||||
*.tmp
|
||||
*.png
|
||||
htmlcov
|
||||
|
|
@ -178,4 +186,5 @@ cov.xml
|
|||
*.faiss
|
||||
*-structure.csv
|
||||
*-structure.json
|
||||
metagpt/tools/schemas
|
||||
*.dot
|
||||
.python-version
|
||||
|
|
|
|||
83
README.md
83
README.md
|
|
@ -26,6 +26,10 @@ # MetaGPT: The Multi-Agent Framework
|
|||
</p>
|
||||
|
||||
## News
|
||||
🚀 Mar. 14, 2024: Our **Data Interpreter** paper is on [arxiv](https://arxiv.org/abs/2402.18679). Check the [example](https://docs.deepwisdom.ai/main/en/DataInterpreter/) and [code](https://github.com/geekan/MetaGPT/tree/main/examples/di)!
|
||||
|
||||
🚀 Feb. 08, 2024: [v0.7.0](https://github.com/geekan/MetaGPT/releases/tag/v0.7.0) released, supporting assigning different LLMs to different Roles. We also introduced [Data Interpreter](https://github.com/geekan/MetaGPT/blob/main/examples/di/README.md), a powerful agent capable of solving a wide range of real-world problems.
|
||||
|
||||
🚀 Jan. 16, 2024: Our paper [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
|
||||
](https://arxiv.org/abs/2308.00352) accepted for oral presentation **(top 1.2%)** at ICLR 2024, **ranking #1** in the LLM-based Agent category.
|
||||
|
||||
|
|
@ -51,20 +55,49 @@ ## Software Company as Multi-Agent System
|
|||
|
||||
<p align="center">Software Company Multi-Agent Schematic (Gradually Implementing)</p>
|
||||
|
||||
## Install
|
||||
## Get Started
|
||||
|
||||
### Pip installation
|
||||
### Installation
|
||||
|
||||
> Ensure that Python 3.9+ is installed on your system. You can check this by using: `python --version`.
|
||||
> You can use conda like this: `conda create -n metagpt python=3.9 && conda activate metagpt`
|
||||
|
||||
```bash
|
||||
pip install metagpt
|
||||
metagpt --init-config # create ~/.metagpt/config2.yaml, modify it to your own config
|
||||
pip install --upgrade metagpt
|
||||
# or `pip install --upgrade git+https://github.com/geekan/MetaGPT.git`
|
||||
# or `git clone https://github.com/geekan/MetaGPT && cd MetaGPT && pip install --upgrade -e .`
|
||||
```
|
||||
|
||||
For detailed installation guidance, please refer to [cli_install](https://docs.deepwisdom.ai/main/en/guide/get_started/installation.html#install-stable-version)
|
||||
or [docker_install](https://docs.deepwisdom.ai/main/en/guide/get_started/installation.html#install-with-docker)
|
||||
|
||||
### Configuration
|
||||
|
||||
You can init the config of MetaGPT by running the following command, or manually create `~/.metagpt/config2.yaml` file:
|
||||
```bash
|
||||
# Check https://docs.deepwisdom.ai/main/en/guide/get_started/configuration.html for more details
|
||||
metagpt --init-config # it will create ~/.metagpt/config2.yaml, just modify it to your needs
|
||||
```
|
||||
|
||||
You can configure `~/.metagpt/config2.yaml` according to the [example](https://github.com/geekan/MetaGPT/blob/main/config/config2.example.yaml) and [doc](https://docs.deepwisdom.ai/main/en/guide/get_started/configuration.html):
|
||||
|
||||
```yaml
|
||||
llm:
|
||||
api_type: "openai" # or azure / ollama / open_llm etc. Check LLMType for more options
|
||||
model: "gpt-4-turbo-preview" # or gpt-3.5-turbo-1106 / gpt-4-1106-preview
|
||||
base_url: "https://api.openai.com/v1" # or forward url / other llm url
|
||||
api_key: "YOUR_API_KEY"
|
||||
```
|
||||
|
||||
### Usage
|
||||
|
||||
After installation, you can use MetaGPT at CLI
|
||||
|
||||
```bash
|
||||
metagpt "Create a 2048 game" # this will create a repo in ./workspace
|
||||
```
|
||||
|
||||
or you can use it as library
|
||||
or use it as library
|
||||
|
||||
```python
|
||||
from metagpt.software_company import generate_repo, ProjectRepo
|
||||
|
|
@ -72,28 +105,19 @@ ### Pip installation
|
|||
print(repo) # it will print the repo structure with files
|
||||
```
|
||||
|
||||
detail installation please refer to [cli_install](https://docs.deepwisdom.ai/main/en/guide/get_started/installation.html#install-stable-version)
|
||||
You can also use its [Data Interpreter](https://github.com/geekan/MetaGPT/tree/main/examples/di)
|
||||
|
||||
### Docker installation
|
||||
> Note: In the Windows, you need to replace "/opt/metagpt" with a directory that Docker has permission to create, such as "D:\Users\x\metagpt"
|
||||
```python
|
||||
import asyncio
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
```bash
|
||||
# Step 1: Download metagpt official image and prepare config2.yaml
|
||||
docker pull metagpt/metagpt:latest
|
||||
mkdir -p /opt/metagpt/{config,workspace}
|
||||
docker run --rm metagpt/metagpt:latest cat /app/metagpt/config/config2.yaml > /opt/metagpt/config/config2.yaml
|
||||
vim /opt/metagpt/config/config2.yaml # Change the config
|
||||
async def main():
|
||||
di = DataInterpreter()
|
||||
await di.run("Run data analysis on sklearn Iris dataset, include a plot")
|
||||
|
||||
# Step 2: Run metagpt demo with container
|
||||
docker run --rm \
|
||||
--privileged \
|
||||
-v /opt/metagpt/config/config2.yaml:/app/metagpt/config/config2.yaml \
|
||||
-v /opt/metagpt/workspace:/app/metagpt/workspace \
|
||||
metagpt/metagpt:latest \
|
||||
metagpt "Create a 2048 game"
|
||||
asyncio.run(main()) # or await main() in a jupyter notebook setting
|
||||
```
|
||||
|
||||
detail installation please refer to [docker_install](https://docs.deepwisdom.ai/main/en/guide/get_started/installation.html#install-with-docker)
|
||||
|
||||
### QuickStart & Demo Video
|
||||
- Try it on [MetaGPT Huggingface Space](https://huggingface.co/spaces/deepwisdom/MetaGPT)
|
||||
|
|
@ -113,6 +137,7 @@ ## Tutorial
|
|||
- 🧑💻 Contribution
|
||||
- [Develop Roadmap](docs/ROADMAP.md)
|
||||
- 🔖 Use Cases
|
||||
- [Data Interpreter](https://docs.deepwisdom.ai/main/en/guide/use_cases/agent/interpreter/intro.html)
|
||||
- [Debate](https://docs.deepwisdom.ai/main/en/guide/use_cases/multi_agent/debate.html)
|
||||
- [Researcher](https://docs.deepwisdom.ai/main/en/guide/use_cases/agent/researcher.html)
|
||||
- [Recepit Assistant](https://docs.deepwisdom.ai/main/en/guide/use_cases/agent/receipt_assistant.html)
|
||||
|
|
@ -136,7 +161,9 @@ ### Contact Information
|
|||
|
||||
## Citation
|
||||
|
||||
For now, cite the [arXiv paper](https://arxiv.org/abs/2308.00352):
|
||||
To stay updated with the latest research and development, follow [@MetaGPT_](https://twitter.com/MetaGPT_) on Twitter.
|
||||
|
||||
To cite [MetaGPT](https://arxiv.org/abs/2308.00352) or [Data Interpreter](https://arxiv.org/abs/2402.18679) in publications, please use the following BibTeX entries.
|
||||
|
||||
```bibtex
|
||||
@misc{hong2023metagpt,
|
||||
|
|
@ -147,4 +174,14 @@ ## Citation
|
|||
archivePrefix={arXiv},
|
||||
primaryClass={cs.AI}
|
||||
}
|
||||
@misc{hong2024data,
|
||||
title={Data Interpreter: An LLM Agent For Data Science},
|
||||
author={Sirui Hong and Yizhang Lin and Bang Liu and Bangbang Liu and Binhao Wu and Danyang Li and Jiaqi Chen and Jiayi Zhang and Jinlin Wang and Li Zhang and Lingyao Zhang and Min Yang and Mingchen Zhuge and Taicheng Guo and Tuo Zhou and Wei Tao and Wenyi Wang and Xiangru Tang and Xiangtao Lu and Xiawu Zheng and Xinbing Liang and Yaying Fei and Yuheng Cheng and Zongze Xu and Chenglin Wu},
|
||||
year={2024},
|
||||
eprint={2402.18679},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.AI}
|
||||
}
|
||||
|
||||
```
|
||||
|
||||
|
|
|
|||
14
SECURITY.md
Normal file
14
SECURITY.md
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
# Security Policy
|
||||
|
||||
## Supported Versions
|
||||
|
||||
| Version | Supported |
|
||||
|---------|--------------------|
|
||||
| 0.7.x | :x: |
|
||||
| 0.6.x | :x: |
|
||||
| < 0.6.x | :x: |
|
||||
|
||||
|
||||
## Reporting a Vulnerability
|
||||
|
||||
If you have any vulnerability reports, please contact alexanderwu@deepwisdom.ai .
|
||||
|
|
@ -1,10 +1,21 @@
|
|||
llm:
|
||||
api_type: "openai"
|
||||
api_type: "openai" # or azure / ollama / open_llm etc. Check LLMType for more options
|
||||
base_url: "YOUR_BASE_URL"
|
||||
api_key: "YOUR_API_KEY"
|
||||
model: "gpt-4-turbo-preview" # or gpt-3.5-turbo-1106 / gpt-4-1106-preview
|
||||
proxy: "YOUR_PROXY" # for LLM API requests
|
||||
# timeout: 600 # Optional. If set to 0, default value is 300.
|
||||
pricing_plan: "" # Optional. If invalid, it will be automatically filled in with the value of the `model`.
|
||||
# Azure-exclusive pricing plan mappings：
|
||||
# - gpt-3.5-turbo 4k: "gpt-3.5-turbo-1106"
|
||||
# - gpt-4-turbo: "gpt-4-turbo-preview"
|
||||
# - gpt-4-turbo-vision: "gpt-4-vision-preview"
|
||||
# - gpt-4 8k: "gpt-4"
|
||||
# See for more: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
|
||||
|
||||
proxy: "YOUR_PROXY"
|
||||
repair_llm_output: true # when the output is not a valid json, try to repair it
|
||||
|
||||
proxy: "YOUR_PROXY" # for tools like requests, playwright, selenium, etc.
|
||||
|
||||
search:
|
||||
api_type: "google"
|
||||
|
|
@ -41,5 +52,3 @@ iflytek_api_key: "YOUR_API_KEY"
|
|||
iflytek_api_secret: "YOUR_API_SECRET"
|
||||
|
||||
metagpt_tti_url: "YOUR_MODEL_URL"
|
||||
|
||||
repair_llm_output: true
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Full Example: https://github.com/geekan/MetaGPT/blob/main/config/config2.example.yaml
|
||||
# Reflected Code: https://github.com/geekan/MetaGPT/blob/main/metagpt/config2.py
|
||||
llm:
|
||||
api_key: "YOUR_API_KEY"
|
||||
model: "gpt-4-turbo-preview" # or gpt-3.5-turbo-1106 / gpt-4-1106-preview
|
||||
api_type: "openai" # or azure / ollama / open_llm etc. Check LLMType for more options
|
||||
model: "gpt-4-turbo-preview" # or gpt-3.5-turbo-1106 / gpt-4-1106-preview
|
||||
base_url: "https://api.openai.com/v1" # or forward url / other llm url
|
||||
api_key: "YOUR_API_KEY"
|
||||
|
|
@ -116,7 +116,7 @@ ### 联系信息
|
|||
|
||||
## 引用
|
||||
|
||||
引用 [arXiv paper](https://arxiv.org/abs/2308.00352):
|
||||
如果您在研究论文中使用 MetaGPT 或 Data Interpreter，请引用我们的工作：
|
||||
|
||||
```bibtex
|
||||
@misc{hong2023metagpt,
|
||||
|
|
@ -127,4 +127,12 @@ ## 引用
|
|||
archivePrefix={arXiv},
|
||||
primaryClass={cs.AI}
|
||||
}
|
||||
@misc{hong2024data,
|
||||
title={Data Interpreter: An LLM Agent For Data Science},
|
||||
author={Sirui Hong and Yizhang Lin and Bang Liu and Bangbang Liu and Binhao Wu and Danyang Li and Jiaqi Chen and Jiayi Zhang and Jinlin Wang and Li Zhang and Lingyao Zhang and Min Yang and Mingchen Zhuge and Taicheng Guo and Tuo Zhou and Wei Tao and Wenyi Wang and Xiangru Tang and Xiangtao Lu and Xiawu Zheng and Xinbing Liang and Yaying Fei and Yuheng Cheng and Zongze Xu and Chenglin Wu},
|
||||
year={2024},
|
||||
eprint={2402.18679},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.AI}
|
||||
}
|
||||
```
|
||||
|
|
|
|||
|
|
@ -295,7 +295,7 @@ ## クイックスタート
|
|||
|
||||
## 引用
|
||||
|
||||
現時点では、[arXiv 論文](https://arxiv.org/abs/2308.00352)を引用してください:
|
||||
研究論文でMetaGPTやData Interpreterを使用する場合は、以下のように当社の作業を引用してください：
|
||||
|
||||
```bibtex
|
||||
@misc{hong2023metagpt,
|
||||
|
|
@ -306,6 +306,14 @@ ## 引用
|
|||
archivePrefix={arXiv},
|
||||
primaryClass={cs.AI}
|
||||
}
|
||||
@misc{hong2024data,
|
||||
title={Data Interpreter: An LLM Agent For Data Science},
|
||||
author={Sirui Hong and Yizhang Lin and Bang Liu and Bangbang Liu and Binhao Wu and Danyang Li and Jiaqi Chen and Jiayi Zhang and Jinlin Wang and Li Zhang and Lingyao Zhang and Min Yang and Mingchen Zhuge and Taicheng Guo and Tuo Zhou and Wei Tao and Wenyi Wang and Xiangru Tang and Xiangtao Lu and Xiawu Zheng and Xinbing Liang and Yaying Fei and Yuheng Cheng and Zongze Xu and Chenglin Wu},
|
||||
year={2024},
|
||||
eprint={2402.18679},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.AI}
|
||||
}
|
||||
```
|
||||
|
||||
## お問い合わせ先
|
||||
|
|
|
|||
|
|
@ -35,14 +35,14 @@ ### Tasks
|
|||
3. Strategies
|
||||
1. Support ReAct strategy (experimentation done with game agents)
|
||||
2. Support CoT strategy (experimentation done with game agents)
|
||||
3. Support ToT strategy
|
||||
3. ~~Support ToT strategy~~ (v0.6.0)
|
||||
4. Support Reflection strategy (experimentation done with game agents)
|
||||
5. Support planning
|
||||
5. ~~Support planning~~ (v0.7.0)
|
||||
4. Actions
|
||||
1. ~~Implementation: Search~~ (v0.2.1)
|
||||
2. Implementation: Knowledge search, supporting 10+ data formats
|
||||
3. Implementation: Data EDA (expected v0.7.0)
|
||||
4. Implementation: Review & Revise (expected v0.7.0)
|
||||
3. ~~Implementation: Data EDA~~ (v0.7.0)
|
||||
4. ~~Implementation: Review & Revise~~ (v0.7.0)
|
||||
5. ~~Implementation: Add Document~~ (v0.5.0)
|
||||
6. ~~Implementation: Delete Document~~ (v0.5.0)
|
||||
7. Implementation: Self-training
|
||||
|
|
@ -50,7 +50,7 @@ ### Tasks
|
|||
9. Implementation: Generate reliable unit tests based on YAPI
|
||||
10. Implementation: Self-evaluation
|
||||
11. Implementation: AI Invocation
|
||||
12. Implementation: Learning and using third-party standard libraries
|
||||
12. ~~Implementation: Learning and using third-party standard libraries~~ (v0.7.0)
|
||||
13. Implementation: Data collection
|
||||
14. Implementation: AI training
|
||||
15. ~~Implementation: Run code~~ (v0.2.1)
|
||||
|
|
@ -63,14 +63,14 @@ ### Tasks
|
|||
7. Roles
|
||||
1. Perfect the action pool/skill pool for each role
|
||||
2. E-commerce seller
|
||||
3. Data analyst (expected v0.7.0)
|
||||
3. ~~Data analyst~~ (v0.7.0)
|
||||
4. News observer
|
||||
5. ~~Institutional researcher~~ (v0.2.1)
|
||||
8. Evaluation
|
||||
1. Support an evaluation on a game dataset (experimentation done with game agents)
|
||||
2. Reproduce papers, implement full skill acquisition for a single game role, achieving SOTA results (experimentation done with game agents)
|
||||
3. Support an evaluation on a math dataset (expected v0.7.0)
|
||||
4. Reproduce papers, achieving SOTA results for current mathematical problem solving process
|
||||
3. Support an evaluation on a math dataset (expected v0.8.0)
|
||||
4. Reproduce papers, achieving SOTA results for current mathematical problem solving process (expected v0.8.0)
|
||||
9. LLM
|
||||
1. Support Claude underlying API
|
||||
2. ~~Support Azure asynchronous API~~
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
coverage run --source ./metagpt -m pytest --durations=0 --timeout=100 && coverage report -m && coverage html && open htmlcov/index.html
|
||||
coverage run --source ./metagpt -m pytest -n 8 --durations=0 --timeout=100 && coverage report -m && coverage html && open htmlcov/index.html
|
||||
|
|
|
|||
|
|
@ -1,22 +0,0 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
"""
|
||||
@Date : 2024/01/24 15:11:27
|
||||
@Author : orange-crow
|
||||
@File : crawl_webpage.py
|
||||
"""
|
||||
|
||||
from metagpt.roles.ci.code_interpreter import CodeInterpreter
|
||||
|
||||
|
||||
async def main():
|
||||
prompt = """Get data from `paperlist` table in https://papercopilot.com/statistics/iclr-statistics/iclr-2024-statistics/,
|
||||
and save it to a csv file. paper title must include `multiagent` or `large language model`. *notice: print key variables*"""
|
||||
ci = CodeInterpreter(goal=prompt, use_tools=True)
|
||||
|
||||
await ci.run(prompt)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
1
examples/data/rag/travel.txt
Normal file
1
examples/data/rag/travel.txt
Normal file
|
|
@ -0,0 +1 @@
|
|||
Bob likes traveling.
|
||||
109
examples/data/rag/writer.txt
Normal file
109
examples/data/rag/writer.txt
Normal file
|
|
@ -0,0 +1,109 @@
|
|||
Productivity
|
||||
I think I am at least somewhat more productive than average, and people sometimes ask me for productivity tips. So I decided to just write them all down in one place.
|
||||
|
||||
Compound growth gets discussed as a financial concept, but it works in careers as well, and it is magic. A small productivity gain, compounded over 50 years, is worth a lot. So it’s worth figuring out how to optimize productivity. If you get 10% more done and 1% better every day compared to someone else, the compounded difference is massive.
|
||||
|
||||
What you work on
|
||||
|
||||
Famous writers have some essential qualities, creativity and discipline
|
||||
|
||||
It doesn’t matter how fast you move if it’s in a worthless direction. Picking the right thing to work on is the most important element of productivity and usually almost ignored. So think about it more! Independent thought is hard but it’s something you can get better at with practice.
|
||||
|
||||
The most impressive people I know have strong beliefs about the world, which is rare in the general population. If you find yourself always agreeing with whomever you last spoke with, that’s bad. You will of course be wrong sometimes, but develop the confidence to stick with your convictions. It will let you be courageous when you’re right about something important that most people don’t see.
|
||||
|
||||
I make sure to leave enough time in my schedule to think about what to work on. The best ways for me to do this are reading books, hanging out with interesting people, and spending time in nature.
|
||||
|
||||
I’ve learned that I can’t be very productive working on things I don’t care about or don’t like. So I just try not to put myself in a position where I have to do them (by delegating, avoiding, or something else). Stuff that you don’t like is a painful drag on morale and momentum.
|
||||
|
||||
By the way, here is an important lesson about delegation: remember that everyone else is also most productive when they’re doing what they like, and do what you’d want other people to do for you—try to figure out who likes (and is good at) doing what, and delegate that way.
|
||||
|
||||
If you find yourself not liking what you’re doing for a long period of time, seriously consider a major job change. Short-term burnout happens, but if it isn’t resolved with some time off, maybe it’s time to do something you’re more interested in.
|
||||
|
||||
I’ve been very fortunate to find work I like so much I’d do it for free, which makes it easy to be really productive.
|
||||
|
||||
It’s important to learn that you can learn anything you want, and that you can get better quickly. This feels like an unlikely miracle the first few times it happens, but eventually you learn to trust that you can do it.
|
||||
|
||||
Doing great work usually requires colleagues of some sort. Try to be around smart, productive, happy, and positive people that don’t belittle your ambitions. I love being around people who push me and inspire me to be better. To the degree you able to, avoid the opposite kind of people—the cost of letting them take up your mental cycles is horrific.
|
||||
|
||||
You have to both pick the right problem and do the work. There aren’t many shortcuts. If you’re going to do something really important, you are very likely going to work both smart and hard. The biggest prizes are heavily competed for. This isn’t true in every field (there are great mathematicians who never spend that many hours a week working) but it is in most.
|
||||
|
||||
Prioritization
|
||||
|
||||
Writers have to work hard to be successful
|
||||
|
||||
My system has three key pillars: “Make sure to get the important shit done”, “Don’t waste time on stupid shit”, and “make a lot of lists”.
|
||||
|
||||
I highly recommend using lists. I make lists of what I want to accomplish each year, each month, and each day. Lists are very focusing, and they help me with multitasking because I don’t have to keep as much in my head. If I’m not in the mood for some particular task, I can always find something else I’m excited to do.
|
||||
|
||||
I prefer lists written down on paper. It’s easy to add and remove tasks. I can access them during meetings without feeling rude. I re-transcribe lists frequently, which forces me to think about everything on the list and gives me an opportunity to add and remove items.
|
||||
|
||||
I don’t bother with categorization or trying to size tasks or anything like that (the most I do is put a star next to really important items).
|
||||
|
||||
I try to prioritize in a way that generates momentum. The more I get done, the better I feel, and then the more I get done. I like to start and end each day with something I can really make progress on.
|
||||
|
||||
I am relentless about getting my most important projects done—I’ve found that if I really want something to happen and I push hard enough, it usually happens.
|
||||
|
||||
I try to be ruthless about saying no to stuff, and doing non-critical things in the quickest way possible. I probably take this too far—for example, I am almost sure I am terse to the point of rudeness when replying to emails.
|
||||
|
||||
Passion and adaptability are key qualities to writers
|
||||
|
||||
I generally try to avoid meetings and conferences as I find the time cost to be huge—I get the most value out of time in my office. However, it is critical that you keep enough space in your schedule to allow for chance encounters and exposure to new people and ideas. Having an open network is valuable; though probably 90% of the random meetings I take are a waste of time, the other 10% really make up for it.
|
||||
|
||||
I find most meetings are best scheduled for 15-20 minutes, or 2 hours. The default of 1 hour is usually wrong, and leads to a lot of wasted time.
|
||||
|
||||
I have different times of day I try to use for different kinds of work. The first few hours of the morning are definitely my most productive time of the day, so I don’t let anyone schedule anything then. I try to do meetings in the afternoon. I take a break, or switch tasks, whenever I feel my attention starting to fade.
|
||||
|
||||
I don’t think most people value their time enough—I am surprised by the number of people I know who make $100 an hour and yet will spend a couple of hours doing something they don’t want to do to save $20.
|
||||
|
||||
Also, don’t fall into the trap of productivity porn—chasing productivity for its own sake isn’t helpful. Many people spend too much time thinking about how to perfectly optimize their system, and not nearly enough asking if they’re working on the right problems. It doesn’t matter what system you use or if you squeeze out every second if you’re working on the wrong thing.
|
||||
|
||||
The right goal is to allocate your year optimally, not your day.
|
||||
|
||||
Physical factors
|
||||
|
||||
Very likely what is optimal for me won’t be optimal for you. You’ll have to experiment to find out what works best for your body. It’s definitely worth doing—it helps in all aspects of life, and you’ll feel a lot better and happier overall.
|
||||
|
||||
It probably took a little bit of my time every week for a few years to arrive at what works best for me, but my sense is if I do a good job at all the below I’m at least 1.5x more productive than if not.
|
||||
|
||||
Sleep seems to be the most important physical factor in productivity for me. Some sort of sleep tracker to figure out how to sleep best is helpful. I’ve found the only thing I’m consistent with are in the set-it-and-forget-it category, and I really like the Emfit QS+Active.
|
||||
|
||||
I like a cold, dark, quiet room, and a great mattress (I resisted spending a bunch of money on a great mattress for years, which was stupid—it makes a huge difference to my sleep quality. I love this one). Not eating a lot in the few hours before sleep helps. Not drinking alcohol helps a lot, though I’m not willing to do that all the time.
|
||||
|
||||
I use a Chili Pad to be cold while I sleep if I can’t get the room cold enough, which is great but loud (I set it up to have the cooler unit outside my room).
|
||||
|
||||
When traveling, I use an eye mask and ear plugs.
|
||||
|
||||
Writers usually have empathy to write good books.
|
||||
|
||||
This is likely to be controversial, but I take a low dose of sleeping pills (like a third of a normal dose) or a very low dose of cannabis whenever I can’t sleep. I am a bad sleeper in general, and a particularly bad sleeper when I travel. It likely has tradeoffs, but so does not sleeping well. If you can already sleep well, I wouldn’t recommend this.
|
||||
|
||||
I use a full spectrum LED light most mornings for about 10-15 minutes while I catch up on email. It’s great—if you try nothing else in here, this is the thing I’d try. It’s a ridiculous gain for me. I like this one, and it’s easy to travel with.
|
||||
|
||||
Exercise is probably the second most important physical factor. I tried a number of different exercise programs for a few months each and the one that seemed best was lifting heavy weights 3x a week for an hour, and high intensity interval training occasionally. In addition to productivity gains, this is also the exercise program that makes me feel the best overall.
|
||||
|
||||
The third area is nutrition. I very rarely eat breakfast, so I get about 15 hours of fasting most days (except an espresso when I wake up). I know this is contrary to most advice, and I suspect it’s not optimal for most people, but it definitely works well for me.
|
||||
|
||||
Eating lots of sugar is the thing that makes me feel the worst and that I try hardest to avoid. I also try to avoid foods that aggravate my digestion or spike up inflammation (for example, very spicy foods). I don’t have much willpower when it comes to sweet things, so I mostly just try to keep junk food out of the house.
|
||||
|
||||
I have one big shot of espresso immediately when I wake up and one after lunch. I assume this is about 200mg total of caffeine per day. I tried a few other configurations; this was the one that worked by far the best. I otherwise aggressively avoid stimulants, but I will have more coffee if I’m super tired and really need to get something done.
|
||||
|
||||
If a writer want to be super, then should include innovative thinking.
|
||||
|
||||
I’m vegetarian and have been since I was a kid, and I supplement methyl B-12, Omega-3, Iron, and Vitamin D-3. I got to this list with a year or so of quarterly blood tests; it’s worked for me ever since (I re-test maybe every year and a half or so). There are many doctors who will happily work with you on a super comprehensive blood test (and services like WellnessFX). I also go out of my way to drink a lot of protein shakes, which I hate and I wouldn’t do if I weren’t vegetarian.
|
||||
|
||||
Other stuff
|
||||
|
||||
Here’s what I like in a workspace: natural light, quiet, knowing that I won’t be interrupted if I don’t want to be, long blocks of time, and being comfortable and relaxed (I’ve got a beautiful desk with a couple of 4k monitors on it in my office, but I spend almost all my time on my couch with my laptop).
|
||||
|
||||
I wrote custom software for the annoying things I have to do frequently, which is great. I also made an effort to learn to type really fast and the keyboard shortcuts that help with my workflow.
|
||||
|
||||
Like most people, I sometimes go through periods of a week or two where I just have no motivation to do anything (I suspect it may have something to do with nutrition). This sucks and always seems to happen at inconvenient times. I have not figured out what to do about it besides wait for the fog to lift, and to trust that eventually it always does. And I generally try to avoid people and situations that put me in bad moods, which is good advice whether you care about productivity or not.
|
||||
|
||||
In general, I think it’s good to overcommit a little bit. I find that I generally get done what I take on, and if I have a little bit too much to do it makes me more efficient at everything, which is a way to train to avoid distractions (a great habit to build!). However, overcommitting a lot is disastrous.
|
||||
|
||||
Don’t neglect your family and friends for the sake of productivity—that’s a very stupid tradeoff (and very likely a net productivity loss, because you’ll be less happy). Don’t neglect doing things you love or that clear your head either.
|
||||
|
||||
Finally, to repeat one more time: productivity in the wrong direction isn’t worth anything at all. Think more about what to work on.
|
||||
|
||||
Open-Mindedness and curiosity are essential to writers
|
||||
|
||||
|
|
@ -5,6 +5,7 @@ Author: garylin2099
|
|||
@Modified By: mashenquan, 2023-11-1. In accordance with Chapter 2.1.3 of RFC 116, modify the data type of the `send_to`
|
||||
value of the `Message` object; modify the argument type of `get_by_actions`.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import platform
|
||||
from typing import Any
|
||||
|
|
@ -105,4 +106,4 @@ def main(idea: str, investment: float = 3.0, n_round: int = 10):
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(main)
|
||||
fire.Fire(main) # run as python debate.py --idea="TOPIC" --investment=3.0 --n_round=5
|
||||
|
|
|
|||
|
|
@ -8,14 +8,17 @@
|
|||
import asyncio
|
||||
|
||||
from metagpt.actions import Action
|
||||
from metagpt.config2 import Config
|
||||
from metagpt.environment import Environment
|
||||
from metagpt.roles import Role
|
||||
from metagpt.team import Team
|
||||
|
||||
action1 = Action(name="AlexSay", instruction="Express your opinion with emotion and don't repeat it")
|
||||
action1.llm.model = "gpt-4-1106-preview"
|
||||
action2 = Action(name="BobSay", instruction="Express your opinion with emotion and don't repeat it")
|
||||
action2.llm.model = "gpt-3.5-turbo-1106"
|
||||
gpt35 = Config.default()
|
||||
gpt35.llm.model = "gpt-3.5-turbo-1106"
|
||||
gpt4 = Config.default()
|
||||
gpt4.llm.model = "gpt-4-1106-preview"
|
||||
action1 = Action(config=gpt4, name="AlexSay", instruction="Express your opinion with emotion and don't repeat it")
|
||||
action2 = Action(config=gpt35, name="BobSay", instruction="Express your opinion with emotion and don't repeat it")
|
||||
alex = Role(name="Alex", profile="Democratic candidate", goal="Win the election", actions=[action1], watch=[action2])
|
||||
bob = Role(name="Bob", profile="Republican candidate", goal="Win the election", actions=[action2], watch=[action1])
|
||||
env = Environment(desc="US election live broadcast")
|
||||
|
|
|
|||
20
examples/di/README.md
Normal file
20
examples/di/README.md
Normal file
|
|
@ -0,0 +1,20 @@
|
|||
# Data Interpreter (DI)
|
||||
|
||||
## What is Data Interpreter
|
||||
Data Interpreter is an agent who solves data-related problems through codes. It understands user requirements, makes plans, writes codes for execution, and uses tools if necessary. These capabilities enable it to tackle a wide range of scenarios, please check out the examples below. For overall design and technical details, please see our [paper](https://arxiv.org/abs/2402.18679).
|
||||
|
||||
## Example List
|
||||
- Data visualization
|
||||
- Machine learning modeling
|
||||
- Image background removal
|
||||
- Solve math problems
|
||||
- Receipt OCR
|
||||
- Tool usage: web page imitation
|
||||
- Tool usage: web crawling
|
||||
- Tool usage: text2image
|
||||
- Tool usage: email summarization and response\
|
||||
- More on the way!
|
||||
|
||||
Please see the [docs](https://docs.deepwisdom.ai/main/en/guide/use_cases/agent/interpreter/intro.html) for more explanation.
|
||||
|
||||
We are continuously releasing codes, stay tuned!
|
||||
21
examples/di/arxiv_reader.py
Normal file
21
examples/di/arxiv_reader.py
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
|
||||
async def main():
|
||||
template = "https://arxiv.org/list/{tag}/pastweek?skip=0&show=300"
|
||||
tags = ["cs.ai", "cs.cl", "cs.lg", "cs.se"]
|
||||
urls = [template.format(tag=tag) for tag in tags]
|
||||
prompt = f"""This is a collection of arxiv urls: '{urls}' .
|
||||
Record each article, remove duplicates by title (they may have multiple tags), filter out papers related to
|
||||
large language model / agent / llm, print top 100 and visualize the word count of the titles"""
|
||||
di = DataInterpreter(react_mode="react", tools=["scrape_web_playwright"])
|
||||
|
||||
await di.run(prompt)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
40
examples/di/crawl_webpage.py
Normal file
40
examples/di/crawl_webpage.py
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
"""
|
||||
@Date : 2024/01/24 15:11:27
|
||||
@Author : orange-crow
|
||||
@File : crawl_webpage.py
|
||||
"""
|
||||
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
PAPER_LIST_REQ = """"
|
||||
Get data from `paperlist` table in https://papercopilot.com/statistics/iclr-statistics/iclr-2024-statistics/,
|
||||
and save it to a csv file. paper title must include `multiagent` or `large language model`. *notice: print key variables*
|
||||
"""
|
||||
|
||||
ECOMMERCE_REQ = """
|
||||
Get products data from website https://scrapeme.live/shop/ and save it as a csv file.
|
||||
**Notice: Firstly parse the web page encoding and the text HTML structure;
|
||||
The first page product name, price, product URL, and image URL must be saved in the csv;**
|
||||
"""
|
||||
|
||||
NEWS_36KR_REQ = """从36kr创投平台https://pitchhub.36kr.com/financing-flash 所有初创企业融资的信息, **注意: 这是一个中文网站**;
|
||||
下面是一个大致流程, 你会根据每一步的运行结果对当前计划中的任务做出适当调整:
|
||||
1. 爬取并本地保存html结构;
|
||||
2. 直接打印第7个*`快讯`*关键词后2000个字符的html内容, 作为*快讯的html内容示例*;
|
||||
3. 反思*快讯的html内容示例*中的规律, 设计正则匹配表达式来获取*`快讯`*的标题、链接、时间;
|
||||
4. 筛选最近3天的初创企业融资*`快讯`*, 以list[dict]形式打印前5个。
|
||||
5. 将全部结果存在本地csv中
|
||||
"""
|
||||
|
||||
|
||||
async def main():
|
||||
di = DataInterpreter(tools=["scrape_web_playwright"])
|
||||
|
||||
await di.run(ECOMMERCE_REQ)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
36
examples/di/custom_tool.py
Normal file
36
examples/di/custom_tool.py
Normal file
|
|
@ -0,0 +1,36 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@Time : 2024/3/22 10:54
|
||||
@Author : alexanderwu
|
||||
@File : custom_tool.py
|
||||
"""
|
||||
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
from metagpt.tools.tool_registry import register_tool
|
||||
|
||||
|
||||
@register_tool()
|
||||
def magic_function(arg1: str, arg2: int) -> dict:
|
||||
"""
|
||||
The magic function that does something.
|
||||
|
||||
Args:
|
||||
arg1 (str): ...
|
||||
arg2 (int): ...
|
||||
|
||||
Returns:
|
||||
dict: ...
|
||||
"""
|
||||
return {"arg1": arg1 * 3, "arg2": arg2 * 5}
|
||||
|
||||
|
||||
async def main():
|
||||
di = DataInterpreter(tools=["magic_function"])
|
||||
await di.run("Just call the magic function with arg1 'A' and arg2 2. Tell me the result.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
17
examples/di/data_visualization.py
Normal file
17
examples/di/data_visualization.py
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
import asyncio
|
||||
|
||||
from metagpt.logs import logger
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
from metagpt.utils.recovery_util import save_history
|
||||
|
||||
|
||||
async def main(requirement: str = ""):
|
||||
di = DataInterpreter()
|
||||
rsp = await di.run(requirement)
|
||||
logger.info(rsp)
|
||||
save_history(role=di)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
requirement = "Run data analysis on sklearn Iris dataset, include a plot"
|
||||
asyncio.run(main(requirement))
|
||||
33
examples/di/email_summary.py
Normal file
33
examples/di/email_summary.py
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
"""
|
||||
@Date : 2024/02/07
|
||||
@Author : Tuo Zhou
|
||||
@File : email_summary.py
|
||||
"""
|
||||
import os
|
||||
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
|
||||
async def main():
|
||||
email_account = "your_email_account"
|
||||
# your password will stay only on your device and not go to LLM api
|
||||
os.environ["email_password"] = "your_email_password"
|
||||
|
||||
### Prompt for automatic email reply, uncomment to try this too ###
|
||||
# prompt = f"""I will give you your Outlook email account ({email_account}) and password (email_password item in the environment variable). You need to find the latest email in my inbox with the sender's suffix @gmail.com and reply "Thank you! I have received your email~"""""
|
||||
|
||||
### Prompt for automatic email summary ###
|
||||
prompt = f"""I will give you your Outlook email account ({email_account}) and password (email_password item in the environment variable).
|
||||
Firstly, Please help me fetch the latest 5 senders and full letter contents.
|
||||
Then, summarize each of the 5 emails into one sentence (you can do this by yourself, no need to import other models to do this) and output them in a markdown format."""
|
||||
|
||||
di = DataInterpreter()
|
||||
|
||||
await di.run(prompt)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
|
|
@ -5,19 +5,18 @@
|
|||
@Author : mannaandpoem
|
||||
@File : imitate_webpage.py
|
||||
"""
|
||||
from metagpt.roles.ci.code_interpreter import CodeInterpreter
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
|
||||
async def main():
|
||||
web_url = "https://pytorch.org/"
|
||||
prompt = f"""This is a URL of webpage: '{web_url}' .
|
||||
Firstly, utilize Selenium and WebDriver for rendering.
|
||||
Secondly, convert image to a webpage including HTML, CSS and JS in one go.
|
||||
Finally, save webpage in a text file.
|
||||
Secondly, convert image to a webpage including HTML, CSS and JS in one go.
|
||||
Note: All required dependencies and environments have been fully installed and configured."""
|
||||
ci = CodeInterpreter(goal=prompt, use_tools=True)
|
||||
di = DataInterpreter(tools=["GPTvGenerator"])
|
||||
|
||||
await ci.run(prompt)
|
||||
await di.run(prompt)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
23
examples/di/machine_learning.py
Normal file
23
examples/di/machine_learning.py
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
import fire
|
||||
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
WINE_REQ = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy."
|
||||
|
||||
DATA_DIR = "path/to/your/data"
|
||||
# sales_forecast data from https://www.kaggle.com/datasets/aslanahmedov/walmart-sales-forecast/data
|
||||
SALES_FORECAST_REQ = f"""Train a model to predict sales for each department in every store (split the last 40 weeks records as validation dataset, the others is train dataset), include plot total sales trends, print metric and plot scatter plots of
|
||||
groud truth and predictions on validation data. Dataset is {DATA_DIR}/train.csv, the metric is weighted mean absolute error (WMAE) for test data. Notice: *print* key variables to get more information for next task step.
|
||||
"""
|
||||
|
||||
REQUIREMENTS = {"wine": WINE_REQ, "sales_forecast": SALES_FORECAST_REQ}
|
||||
|
||||
|
||||
async def main(use_case: str = "wine"):
|
||||
mi = DataInterpreter()
|
||||
requirement = REQUIREMENTS[use_case]
|
||||
await mi.run(requirement)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(main)
|
||||
16
examples/di/machine_learning_with_tools.py
Normal file
16
examples/di/machine_learning_with_tools.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
import asyncio
|
||||
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
|
||||
async def main(requirement: str):
|
||||
role = DataInterpreter(use_reflection=True, tools=["<all>"])
|
||||
await role.run(requirement)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
data_path = "your/path/to/titanic"
|
||||
train_path = f"{data_path}/split_train.csv"
|
||||
eval_path = f"{data_path}/split_eval.csv"
|
||||
requirement = f"This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: '{train_path}', eval data path: '{eval_path}'."
|
||||
asyncio.run(main(requirement))
|
||||
21
examples/di/ocr_receipt.py
Normal file
21
examples/di/ocr_receipt.py
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
|
||||
async def main():
|
||||
# Notice: pip install metagpt[ocr] before using this example
|
||||
image_path = "image.jpg"
|
||||
language = "English"
|
||||
requirement = f"""This is a {language} receipt image.
|
||||
Your goal is to perform OCR on images using PaddleOCR, output text content from the OCR results and discard
|
||||
coordinates and confidence levels, then recognize the total amount from ocr text content, and finally save as table.
|
||||
Image path: {image_path}.
|
||||
NOTE: The environments for Paddle and PaddleOCR are all ready and has been fully installed."""
|
||||
di = DataInterpreter()
|
||||
|
||||
await di.run(requirement)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
15
examples/di/rm_image_background.py
Normal file
15
examples/di/rm_image_background.py
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
import asyncio
|
||||
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
|
||||
async def main(requirement: str = ""):
|
||||
di = DataInterpreter()
|
||||
await di.run(requirement)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
image_path = "/your/path/to/the/image.jpeg"
|
||||
save_path = "/your/intended/save/path/for/image_rm_bg.png"
|
||||
requirement = f"This is a image, you need to use python toolkit rembg to remove the background of the image and save the result. image path:{image_path}; save path:{save_path}."
|
||||
asyncio.run(main(requirement))
|
||||
|
|
@ -4,12 +4,12 @@
|
|||
# @Desc :
|
||||
import asyncio
|
||||
|
||||
from metagpt.roles.ci.code_interpreter import CodeInterpreter
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
|
||||
async def main(requirement: str = ""):
|
||||
code_interpreter = CodeInterpreter(use_tools=True, goal=requirement)
|
||||
await code_interpreter.run(requirement)
|
||||
di = DataInterpreter(tools=["SDEngine"])
|
||||
await di.run(requirement)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
14
examples/di/solve_math_problems.py
Normal file
14
examples/di/solve_math_problems.py
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
import asyncio
|
||||
|
||||
from metagpt.roles.di.data_interpreter import DataInterpreter
|
||||
|
||||
|
||||
async def main(requirement: str = ""):
|
||||
di = DataInterpreter()
|
||||
await di.run(requirement)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
requirement = "Solve this math problem: The greatest common divisor of positive integers m and n is 6. The least common multiple of m and n is 126. What is the least possible value of m + n?"
|
||||
# answer: 60 (m = 18, n = 42)
|
||||
asyncio.run(main(requirement))
|
||||
|
|
@ -6,16 +6,25 @@
|
|||
@File : llm_hello_world.py
|
||||
"""
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
|
||||
from metagpt.llm import LLM
|
||||
from metagpt.logs import logger
|
||||
from metagpt.utils.common import encode_image
|
||||
|
||||
|
||||
async def main():
|
||||
llm = LLM()
|
||||
logger.info(await llm.aask("hello world"))
|
||||
# llm type check
|
||||
question = "what's your name"
|
||||
logger.info(f"{question}: ")
|
||||
logger.info(await llm.aask(question))
|
||||
logger.info("\n\n")
|
||||
|
||||
logger.info(
|
||||
await llm.aask(
|
||||
"who are you", system_msgs=["act as a robot, just answer 'I'am robot' if the question is 'who are you'"]
|
||||
)
|
||||
)
|
||||
|
||||
logger.info(await llm.aask_batch(["hi", "write python hello world."]))
|
||||
|
||||
hello_msg = [{"role": "user", "content": "count from 1 to 10. split by newline."}]
|
||||
|
|
@ -29,12 +38,6 @@ async def main():
|
|||
if hasattr(llm, "completion"):
|
||||
logger.info(llm.completion(hello_msg))
|
||||
|
||||
# check if the configured llm supports llm-vision capacity. If not, it will throw a error
|
||||
invoice_path = Path(__file__).parent.joinpath("..", "tests", "data", "invoices", "invoice-2.png")
|
||||
img_base64 = encode_image(invoice_path)
|
||||
res = await llm.aask(msg="if this is a invoice, just return True else return False", images=[img_base64])
|
||||
assert "true" in res.lower()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
|
|||
23
examples/llm_vision.py
Normal file
23
examples/llm_vision.py
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : example to run the ability of LLM vision
|
||||
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
|
||||
from metagpt.llm import LLM
|
||||
from metagpt.utils.common import encode_image
|
||||
|
||||
|
||||
async def main():
|
||||
llm = LLM()
|
||||
|
||||
# check if the configured llm supports llm-vision capacity. If not, it will throw a error
|
||||
invoice_path = Path(__file__).parent.joinpath("..", "tests", "data", "invoices", "invoice-2.png")
|
||||
img_base64 = encode_image(invoice_path)
|
||||
res = await llm.aask(msg="if this is a invoice, just return True else return False", images=[img_base64])
|
||||
assert "true" in res.lower()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
211
examples/rag_pipeline.py
Normal file
211
examples/rag_pipeline.py
Normal file
|
|
@ -0,0 +1,211 @@
|
|||
"""RAG pipeline"""
|
||||
|
||||
import asyncio
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from metagpt.const import DATA_PATH, EXAMPLE_DATA_PATH
|
||||
from metagpt.logs import logger
|
||||
from metagpt.rag.engines import SimpleEngine
|
||||
from metagpt.rag.schema import (
|
||||
BM25RetrieverConfig,
|
||||
ChromaIndexConfig,
|
||||
ChromaRetrieverConfig,
|
||||
FAISSRetrieverConfig,
|
||||
LLMRankerConfig,
|
||||
)
|
||||
|
||||
DOC_PATH = EXAMPLE_DATA_PATH / "rag/writer.txt"
|
||||
QUESTION = "What are key qualities to be a good writer?"
|
||||
|
||||
TRAVEL_DOC_PATH = EXAMPLE_DATA_PATH / "rag/travel.txt"
|
||||
TRAVEL_QUESTION = "What does Bob like?"
|
||||
|
||||
LLM_TIP = "If you not sure, just answer I don't know."
|
||||
|
||||
|
||||
class Player(BaseModel):
|
||||
"""To demonstrate rag add objs."""
|
||||
|
||||
name: str = ""
|
||||
goal: str = "Win The 100-meter Sprint."
|
||||
tool: str = "Red Bull Energy Drink."
|
||||
|
||||
def rag_key(self) -> str:
|
||||
"""For search"""
|
||||
return self.goal
|
||||
|
||||
|
||||
class RAGExample:
|
||||
"""Show how to use RAG."""
|
||||
|
||||
def __init__(self):
|
||||
self.engine = SimpleEngine.from_docs(
|
||||
input_files=[DOC_PATH],
|
||||
retriever_configs=[FAISSRetrieverConfig(), BM25RetrieverConfig()],
|
||||
ranker_configs=[LLMRankerConfig()],
|
||||
)
|
||||
|
||||
async def run_pipeline(self, question=QUESTION, print_title=True):
|
||||
"""This example run rag pipeline, use faiss&bm25 retriever and llm ranker, will print something like:
|
||||
|
||||
Retrieve Result:
|
||||
0. Productivi..., 10.0
|
||||
1. I wrote cu..., 7.0
|
||||
2. I highly r..., 5.0
|
||||
|
||||
Query Result:
|
||||
Passion, adaptability, open-mindedness, creativity, discipline, and empathy are key qualities to be a good writer.
|
||||
"""
|
||||
if print_title:
|
||||
self._print_title("Run Pipeline")
|
||||
|
||||
nodes = await self.engine.aretrieve(question)
|
||||
self._print_retrieve_result(nodes)
|
||||
|
||||
answer = await self.engine.aquery(question)
|
||||
self._print_query_result(answer)
|
||||
|
||||
async def add_docs(self):
|
||||
"""This example show how to add docs.
|
||||
|
||||
Before add docs llm anwser I don't know.
|
||||
After add docs llm give the correct answer, will print something like:
|
||||
|
||||
[Before add docs]
|
||||
Retrieve Result:
|
||||
|
||||
Query Result:
|
||||
Empty Response
|
||||
|
||||
[After add docs]
|
||||
Retrieve Result:
|
||||
0. Bob like..., 10.0
|
||||
|
||||
Query Result:
|
||||
Bob likes traveling.
|
||||
"""
|
||||
self._print_title("Add Docs")
|
||||
|
||||
travel_question = f"{TRAVEL_QUESTION}{LLM_TIP}"
|
||||
travel_filepath = TRAVEL_DOC_PATH
|
||||
|
||||
logger.info("[Before add docs]")
|
||||
await self.run_pipeline(question=travel_question, print_title=False)
|
||||
|
||||
logger.info("[After add docs]")
|
||||
self.engine.add_docs([travel_filepath])
|
||||
await self.run_pipeline(question=travel_question, print_title=False)
|
||||
|
||||
async def add_objects(self, print_title=True):
|
||||
"""This example show how to add objects.
|
||||
|
||||
Before add docs, engine retrieve nothing.
|
||||
After add objects, engine give the correct answer, will print something like:
|
||||
|
||||
[Before add objs]
|
||||
Retrieve Result:
|
||||
|
||||
[After add objs]
|
||||
Retrieve Result:
|
||||
0. 100m Sprin..., 10.0
|
||||
|
||||
[Object Detail]
|
||||
{'name': 'Mike', 'goal': 'Win The 100-meter Sprint', 'tool': 'Red Bull Energy Drink'}
|
||||
"""
|
||||
if print_title:
|
||||
self._print_title("Add Objects")
|
||||
|
||||
player = Player(name="Mike")
|
||||
question = f"{player.rag_key()}"
|
||||
|
||||
logger.info("[Before add objs]")
|
||||
await self._retrieve_and_print(question)
|
||||
|
||||
logger.info("[After add objs]")
|
||||
self.engine.add_objs([player])
|
||||
|
||||
try:
|
||||
nodes = await self._retrieve_and_print(question)
|
||||
|
||||
logger.info("[Object Detail]")
|
||||
player: Player = nodes[0].metadata["obj"]
|
||||
logger.info(player.name)
|
||||
except Exception as e:
|
||||
logger.error(f"nodes is empty, llm don't answer correctly, exception: {e}")
|
||||
|
||||
async def init_objects(self):
|
||||
"""This example show how to from objs, will print something like:
|
||||
|
||||
Same as add_objects.
|
||||
"""
|
||||
self._print_title("Init Objects")
|
||||
|
||||
pre_engine = self.engine
|
||||
self.engine = SimpleEngine.from_objs(retriever_configs=[FAISSRetrieverConfig()])
|
||||
await self.add_objects(print_title=False)
|
||||
self.engine = pre_engine
|
||||
|
||||
async def init_and_query_chromadb(self):
|
||||
"""This example show how to use chromadb. how to save and load index. will print something like:
|
||||
|
||||
Query Result:
|
||||
Bob likes traveling.
|
||||
"""
|
||||
self._print_title("Init And Query ChromaDB")
|
||||
|
||||
# save index
|
||||
output_dir = DATA_PATH / "rag"
|
||||
SimpleEngine.from_docs(
|
||||
input_files=[TRAVEL_DOC_PATH],
|
||||
retriever_configs=[ChromaRetrieverConfig(persist_path=output_dir)],
|
||||
)
|
||||
|
||||
# load index
|
||||
engine = SimpleEngine.from_index(
|
||||
index_config=ChromaIndexConfig(persist_path=output_dir),
|
||||
)
|
||||
|
||||
# query
|
||||
answer = engine.query(TRAVEL_QUESTION)
|
||||
self._print_query_result(answer)
|
||||
|
||||
@staticmethod
|
||||
def _print_title(title):
|
||||
logger.info(f"{'#'*30} {title} {'#'*30}")
|
||||
|
||||
@staticmethod
|
||||
def _print_retrieve_result(result):
|
||||
"""Print retrieve result."""
|
||||
logger.info("Retrieve Result:")
|
||||
|
||||
for i, node in enumerate(result):
|
||||
logger.info(f"{i}. {node.text[:10]}..., {node.score}")
|
||||
|
||||
logger.info("")
|
||||
|
||||
@staticmethod
|
||||
def _print_query_result(result):
|
||||
"""Print query result."""
|
||||
logger.info("Query Result:")
|
||||
|
||||
logger.info(f"{result}\n")
|
||||
|
||||
async def _retrieve_and_print(self, question):
|
||||
nodes = await self.engine.aretrieve(question)
|
||||
self._print_retrieve_result(nodes)
|
||||
return nodes
|
||||
|
||||
|
||||
async def main():
|
||||
"""RAG pipeline"""
|
||||
e = RAGExample()
|
||||
await e.run_pipeline()
|
||||
await e.add_docs()
|
||||
await e.add_objects()
|
||||
await e.init_objects()
|
||||
await e.init_and_query_chromadb()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
21
examples/rag_search.py
Normal file
21
examples/rag_search.py
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
"""Agent with RAG search."""
|
||||
|
||||
import asyncio
|
||||
|
||||
from examples.rag_pipeline import DOC_PATH, QUESTION
|
||||
from metagpt.logs import logger
|
||||
from metagpt.rag.engines import SimpleEngine
|
||||
from metagpt.roles import Sales
|
||||
|
||||
|
||||
async def search():
|
||||
"""Agent with RAG search."""
|
||||
|
||||
store = SimpleEngine.from_docs(input_files=[DOC_PATH])
|
||||
role = Sales(profile="Sales", store=store)
|
||||
result = await role.run(QUESTION)
|
||||
logger.info(result)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(search())
|
||||
72
examples/reverse_engineering.py
Normal file
72
examples/reverse_engineering.py
Normal file
|
|
@ -0,0 +1,72 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import asyncio
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import typer
|
||||
|
||||
from metagpt.actions.rebuild_class_view import RebuildClassView
|
||||
from metagpt.actions.rebuild_sequence_view import RebuildSequenceView
|
||||
from metagpt.context import Context
|
||||
from metagpt.llm import LLM
|
||||
from metagpt.logs import logger
|
||||
from metagpt.utils.git_repository import GitRepository
|
||||
from metagpt.utils.project_repo import ProjectRepo
|
||||
|
||||
app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False)
|
||||
|
||||
|
||||
@app.command("", help="Python project reverse engineering.")
|
||||
def startup(
|
||||
project_root: str = typer.Argument(
|
||||
default="",
|
||||
help="Specify the root directory of the existing project for reverse engineering.",
|
||||
),
|
||||
output_dir: str = typer.Option(default="", help="Specify the output directory path for reverse engineering."),
|
||||
):
|
||||
package_root = Path(project_root)
|
||||
if not package_root.exists():
|
||||
raise FileNotFoundError(f"{project_root} not exists")
|
||||
if not _is_python_package_root(package_root):
|
||||
raise FileNotFoundError(f'There are no "*.py" files under "{project_root}".')
|
||||
init_file = package_root / "__init__.py" # used by pyreverse
|
||||
init_file_exists = init_file.exists()
|
||||
if not init_file_exists:
|
||||
init_file.touch()
|
||||
|
||||
if not output_dir:
|
||||
output_dir = package_root / "../reverse_engineering_output"
|
||||
logger.info(f"output dir:{output_dir}")
|
||||
try:
|
||||
asyncio.run(reverse_engineering(package_root, Path(output_dir)))
|
||||
finally:
|
||||
if not init_file_exists:
|
||||
init_file.unlink(missing_ok=True)
|
||||
tmp_dir = package_root / "__dot__"
|
||||
if tmp_dir.exists():
|
||||
shutil.rmtree(tmp_dir, ignore_errors=True)
|
||||
|
||||
|
||||
def _is_python_package_root(package_root: Path) -> bool:
|
||||
for file_path in package_root.iterdir():
|
||||
if file_path.is_file():
|
||||
if file_path.suffix == ".py":
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
async def reverse_engineering(package_root: Path, output_dir: Path):
|
||||
ctx = Context()
|
||||
ctx.git_repo = GitRepository(output_dir)
|
||||
ctx.repo = ProjectRepo(ctx.git_repo)
|
||||
action = RebuildClassView(name="ReverseEngineering", i_context=str(package_root), llm=LLM(), context=ctx)
|
||||
await action.run()
|
||||
|
||||
action = RebuildSequenceView(name="ReverseEngineering", llm=LLM(), context=ctx)
|
||||
await action.run()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app()
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@File : search_kb.py
|
||||
@Modified By: mashenquan, 2023-12-22. Delete useless codes.
|
||||
"""
|
||||
import asyncio
|
||||
|
||||
from langchain.embeddings import OpenAIEmbeddings
|
||||
|
||||
from metagpt.config2 import config
|
||||
from metagpt.const import DATA_PATH, EXAMPLE_PATH
|
||||
from metagpt.document_store import FaissStore
|
||||
from metagpt.logs import logger
|
||||
from metagpt.roles import Sales
|
||||
|
||||
|
||||
def get_store():
|
||||
llm = config.get_openai_llm()
|
||||
embedding = OpenAIEmbeddings(openai_api_key=llm.api_key, openai_api_base=llm.base_url)
|
||||
return FaissStore(DATA_PATH / "example.json", embedding=embedding)
|
||||
|
||||
|
||||
async def search():
|
||||
store = FaissStore(EXAMPLE_PATH / "example.json")
|
||||
role = Sales(profile="Sales", store=store)
|
||||
query = "Which facial cleanser is good for oily skin?"
|
||||
result = await role.run(query)
|
||||
logger.info(result)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(search())
|
||||
|
|
@ -4,21 +4,17 @@
|
|||
"""
|
||||
import asyncio
|
||||
|
||||
from metagpt.config2 import Config
|
||||
from metagpt.roles import Searcher
|
||||
from metagpt.tools.search_engine import SearchEngine, SearchEngineType
|
||||
from metagpt.tools.search_engine import SearchEngine
|
||||
|
||||
|
||||
async def main():
|
||||
question = "What are the most interesting human facts?"
|
||||
kwargs = {"api_key": "", "cse_id": "", "proxy": None}
|
||||
# Serper API
|
||||
# await Searcher(search_engine=SearchEngine(engine=SearchEngineType.SERPER_GOOGLE, **kwargs)).run(question)
|
||||
# SerpAPI
|
||||
# await Searcher(search_engine=SearchEngine(engine=SearchEngineType.SERPAPI_GOOGLE, **kwargs)).run(question)
|
||||
# Google API
|
||||
# await Searcher(search_engine=SearchEngine(engine=SearchEngineType.DIRECT_GOOGLE, **kwargs)).run(question)
|
||||
# DDG API
|
||||
await Searcher(search_engine=SearchEngine(engine=SearchEngineType.DUCK_DUCK_GO, **kwargs)).run(question)
|
||||
|
||||
search = Config.default().search
|
||||
kwargs = search.model_dump()
|
||||
await Searcher(search_engine=SearchEngine(engine=search.api_type, **kwargs)).run(question)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -14,6 +14,22 @@ from metagpt.actions.action_node import ActionNode
|
|||
from metagpt.llm import LLM
|
||||
|
||||
|
||||
class Chapter(BaseModel):
|
||||
name: str = Field(default="Chapter 1", description="The name of the chapter.")
|
||||
content: str = Field(default="...", description="The content of the chapter. No more than 1000 words.")
|
||||
|
||||
|
||||
class Chapters(BaseModel):
|
||||
chapters: List[Chapter] = Field(
|
||||
default=[
|
||||
{"name": "Chapter 1", "content": "..."},
|
||||
{"name": "Chapter 2", "content": "..."},
|
||||
{"name": "Chapter 3", "content": "..."},
|
||||
],
|
||||
description="The chapters of the novel.",
|
||||
)
|
||||
|
||||
|
||||
class Novel(BaseModel):
|
||||
name: str = Field(default="The Lord of the Rings", description="The name of the novel.")
|
||||
user_group: str = Field(default="...", description="The user group of the novel.")
|
||||
|
|
@ -28,22 +44,17 @@ class Novel(BaseModel):
|
|||
ending: str = Field(default="...", description="The ending of the novel.")
|
||||
|
||||
|
||||
class Chapter(BaseModel):
|
||||
name: str = Field(default="Chapter 1", description="The name of the chapter.")
|
||||
content: str = Field(default="...", description="The content of the chapter. No more than 1000 words.")
|
||||
|
||||
|
||||
async def generate_novel():
|
||||
instruction = (
|
||||
"Write a novel named 'Harry Potter in The Lord of the Rings'. "
|
||||
"Write a novel named 'Reborn in Skyrim'. "
|
||||
"Fill the empty nodes with your own ideas. Be creative! Use your own words!"
|
||||
"I will tip you $100,000 if you write a good novel."
|
||||
)
|
||||
novel_node = await ActionNode.from_pydantic(Novel).fill(context=instruction, llm=LLM())
|
||||
chap_node = await ActionNode.from_pydantic(Chapter).fill(
|
||||
chap_node = await ActionNode.from_pydantic(Chapters).fill(
|
||||
context=f"### instruction\n{instruction}\n### novel\n{novel_node.content}", llm=LLM()
|
||||
)
|
||||
print(chap_node.content)
|
||||
print(chap_node.instruct_content)
|
||||
|
||||
|
||||
asyncio.run(generate_novel())
|
||||
|
|
|
|||
|
|
@ -22,9 +22,9 @@ from metagpt.actions.write_code_review import WriteCodeReview
|
|||
from metagpt.actions.write_prd import WritePRD
|
||||
from metagpt.actions.write_prd_review import WritePRDReview
|
||||
from metagpt.actions.write_test import WriteTest
|
||||
from metagpt.actions.ci.execute_nb_code import ExecuteNbCode
|
||||
from metagpt.actions.ci.write_analysis_code import WriteCodeWithoutTools, WriteCodeWithTools
|
||||
from metagpt.actions.ci.write_plan import WritePlan
|
||||
from metagpt.actions.di.execute_nb_code import ExecuteNbCode
|
||||
from metagpt.actions.di.write_analysis_code import WriteAnalysisCode
|
||||
from metagpt.actions.di.write_plan import WritePlan
|
||||
|
||||
|
||||
class ActionType(Enum):
|
||||
|
|
@ -46,8 +46,7 @@ class ActionType(Enum):
|
|||
WEB_BROWSE_AND_SUMMARIZE = WebBrowseAndSummarize
|
||||
CONDUCT_RESEARCH = ConductResearch
|
||||
EXECUTE_NB_CODE = ExecuteNbCode
|
||||
WRITE_CODE_WITHOUT_TOOLS = WriteCodeWithoutTools
|
||||
WRITE_CODE_WITH_TOOLS = WriteCodeWithTools
|
||||
WRITE_ANALYSIS_CODE = WriteAnalysisCode
|
||||
WRITE_PLAN = WritePlan
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ from pydantic import BaseModel, Field, create_model, model_validator
|
|||
from tenacity import retry, stop_after_attempt, wait_random_exponential
|
||||
|
||||
from metagpt.actions.action_outcls_registry import register_action_outcls
|
||||
from metagpt.const import USE_CONFIG_TIMEOUT
|
||||
from metagpt.llm import BaseLLM
|
||||
from metagpt.logs import logger
|
||||
from metagpt.provider.postprocess.llm_output_postprocess import llm_output_postprocess
|
||||
|
|
@ -330,7 +331,7 @@ class ActionNode:
|
|||
|
||||
def compile_to(self, i: Dict, schema, kv_sep) -> str:
|
||||
if schema == "json":
|
||||
return json.dumps(i, indent=4)
|
||||
return json.dumps(i, indent=4, ensure_ascii=False)
|
||||
elif schema == "markdown":
|
||||
return dict_to_markdown(i, kv_sep=kv_sep)
|
||||
else:
|
||||
|
|
@ -416,7 +417,7 @@ class ActionNode:
|
|||
images: Optional[Union[str, list[str]]] = None,
|
||||
system_msgs: Optional[list[str]] = None,
|
||||
schema="markdown", # compatible to original format
|
||||
timeout=3,
|
||||
timeout=USE_CONFIG_TIMEOUT,
|
||||
) -> (str, BaseModel):
|
||||
"""Use ActionOutput to wrap the output of aask"""
|
||||
content = await self.llm.aask(prompt, system_msgs, images=images, timeout=timeout)
|
||||
|
|
@ -448,7 +449,9 @@ class ActionNode:
|
|||
def set_context(self, context):
|
||||
self.set_recursive("context", context)
|
||||
|
||||
async def simple_fill(self, schema, mode, images: Optional[Union[str, list[str]]] = None, timeout=3, exclude=None):
|
||||
async def simple_fill(
|
||||
self, schema, mode, images: Optional[Union[str, list[str]]] = None, timeout=USE_CONFIG_TIMEOUT, exclude=None
|
||||
):
|
||||
prompt = self.compile(context=self.context, schema=schema, mode=mode, exclude=exclude)
|
||||
|
||||
if schema != "raw":
|
||||
|
|
@ -473,7 +476,7 @@ class ActionNode:
|
|||
mode="auto",
|
||||
strgy="simple",
|
||||
images: Optional[Union[str, list[str]]] = None,
|
||||
timeout=3,
|
||||
timeout=USE_CONFIG_TIMEOUT,
|
||||
exclude=[],
|
||||
):
|
||||
"""Fill the node(s) with mode.
|
||||
|
|
|
|||
|
|
@ -1,109 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from metagpt.actions.ci.write_analysis_code import BaseWriteAnalysisCode
|
||||
from metagpt.logs import logger
|
||||
from metagpt.schema import Message
|
||||
from metagpt.utils.common import create_func_call_config
|
||||
|
||||
DEBUG_REFLECTION_EXAMPLE = '''
|
||||
Example 1:
|
||||
[previous impl]:
|
||||
```python
|
||||
def add(a: int, b: int) -> int:
|
||||
"""
|
||||
Given integers a and b, return the total value of a and b.
|
||||
"""
|
||||
return a - b
|
||||
```
|
||||
|
||||
[runtime Error]:
|
||||
Tested passed:
|
||||
|
||||
Tests failed:
|
||||
assert add(1, 2) == 3 # output: -1
|
||||
assert add(1, 2) == 4 # output: -1
|
||||
|
||||
[reflection on previous impl]:
|
||||
The implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.
|
||||
|
||||
[improved impl]:
|
||||
```python
|
||||
def add(a: int, b: int) -> int:
|
||||
"""
|
||||
Given integers a and b, return the total value of a and b.
|
||||
"""
|
||||
return a + b
|
||||
```
|
||||
'''
|
||||
|
||||
REFLECTION_PROMPT = """
|
||||
Here is an example for you.
|
||||
{debug_example}
|
||||
[context]
|
||||
{context}
|
||||
|
||||
[previous impl]
|
||||
{code}
|
||||
[runtime Error]
|
||||
{runtime_result}
|
||||
|
||||
Analysis the error step by step, provide me improve method and code. Remember to follow [context] requirement. Don't forget write code for steps behind the error step.
|
||||
[reflection on previous impl]:
|
||||
xxx
|
||||
"""
|
||||
|
||||
CODE_REFLECTION = {
|
||||
"name": "execute_reflection_code",
|
||||
"description": "Execute reflection code.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"reflection": {
|
||||
"type": "string",
|
||||
"description": "Reflection on previous impl.",
|
||||
},
|
||||
"improved_impl": {
|
||||
"type": "string",
|
||||
"description": "Refined code after reflection.",
|
||||
},
|
||||
},
|
||||
"required": ["reflection", "improved_impl"],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class DebugCode(BaseWriteAnalysisCode):
|
||||
async def run(
|
||||
self,
|
||||
context: list[Message] = None,
|
||||
code: str = "",
|
||||
runtime_result: str = "",
|
||||
) -> str:
|
||||
"""
|
||||
Execute the debugging process based on the provided context, code, and runtime_result.
|
||||
|
||||
Args:
|
||||
context (list[Message]): A list of Message objects representing the context.
|
||||
code (str): The code to be debugged.
|
||||
runtime_result (str): The result of the code execution.
|
||||
|
||||
Returns:
|
||||
str: The improved implementation based on the debugging process.
|
||||
"""
|
||||
|
||||
info = []
|
||||
reflection_prompt = REFLECTION_PROMPT.format(
|
||||
debug_example=DEBUG_REFLECTION_EXAMPLE,
|
||||
context=context,
|
||||
code=code,
|
||||
runtime_result=runtime_result,
|
||||
)
|
||||
system_prompt = "You are an AI Python assistant. You will be given your previous implementation code of a task, runtime error results, and a hint to change the implementation appropriately. Write your full implementation "
|
||||
info.append(Message(role="system", content=system_prompt))
|
||||
info.append(Message(role="user", content=reflection_prompt))
|
||||
|
||||
tool_config = create_func_call_config(CODE_REFLECTION)
|
||||
reflection = await self.llm.aask_code(messages=info, **tool_config)
|
||||
logger.info(f"reflection is {reflection}")
|
||||
|
||||
return {"code": reflection["improved_impl"]}
|
||||
|
|
@ -1,70 +0,0 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import Tuple
|
||||
|
||||
from metagpt.actions import Action
|
||||
from metagpt.actions.ci.write_analysis_code import WriteCodeWithTools
|
||||
from metagpt.prompts.ci.ml_action import (
|
||||
ML_GENERATE_CODE_PROMPT,
|
||||
ML_TOOL_USAGE_PROMPT,
|
||||
PRINT_DATA_COLUMNS,
|
||||
UPDATE_DATA_COLUMNS,
|
||||
)
|
||||
from metagpt.prompts.ci.write_analysis_code import CODE_GENERATOR_WITH_TOOLS
|
||||
from metagpt.schema import Message, Plan
|
||||
from metagpt.utils.common import create_func_call_config, remove_comments
|
||||
|
||||
|
||||
class WriteCodeWithToolsML(WriteCodeWithTools):
|
||||
async def run(
|
||||
self,
|
||||
context: list[Message],
|
||||
plan: Plan = None,
|
||||
column_info: str = "",
|
||||
**kwargs,
|
||||
) -> Tuple[list[Message], str]:
|
||||
# prepare tool schemas and tool-type-specific instruction
|
||||
tool_schemas, tool_type_usage_prompt = await self._prepare_tools(plan=plan)
|
||||
|
||||
# ML-specific variables to be used in prompt
|
||||
finished_tasks = plan.get_finished_tasks()
|
||||
code_context = [remove_comments(task.code) for task in finished_tasks]
|
||||
code_context = "\n\n".join(code_context)
|
||||
|
||||
# prepare prompt depending on tool availability & LLM call
|
||||
if tool_schemas:
|
||||
prompt = ML_TOOL_USAGE_PROMPT.format(
|
||||
user_requirement=plan.goal,
|
||||
history_code=code_context,
|
||||
current_task=plan.current_task.instruction,
|
||||
column_info=column_info,
|
||||
tool_type_usage_prompt=tool_type_usage_prompt,
|
||||
tool_schemas=tool_schemas,
|
||||
)
|
||||
|
||||
else:
|
||||
prompt = ML_GENERATE_CODE_PROMPT.format(
|
||||
user_requirement=plan.goal,
|
||||
history_code=code_context,
|
||||
current_task=plan.current_task.instruction,
|
||||
column_info=column_info,
|
||||
tool_type_usage_prompt=tool_type_usage_prompt,
|
||||
)
|
||||
tool_config = create_func_call_config(CODE_GENERATOR_WITH_TOOLS)
|
||||
rsp = await self.llm.aask_code(prompt, **tool_config)
|
||||
|
||||
# Extra output to be used for potential debugging
|
||||
context = [Message(content=prompt, role="user")]
|
||||
|
||||
return context, rsp
|
||||
|
||||
|
||||
class UpdateDataColumns(Action):
|
||||
async def run(self, plan: Plan = None) -> dict:
|
||||
finished_tasks = plan.get_finished_tasks()
|
||||
code_context = [remove_comments(task.code) for task in finished_tasks]
|
||||
code_context = "\n\n".join(code_context)
|
||||
prompt = UPDATE_DATA_COLUMNS.format(history_code=code_context)
|
||||
tool_config = create_func_call_config(PRINT_DATA_COLUMNS)
|
||||
rsp = await self.llm.aask_code(prompt, **tool_config)
|
||||
return rsp
|
||||
|
|
@ -1,155 +0,0 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
"""
|
||||
@Date : 2023/11/20 13:19:39
|
||||
@Author : orange-crow
|
||||
@File : write_analysis_code.py
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Tuple
|
||||
|
||||
from metagpt.actions import Action
|
||||
from metagpt.logs import logger
|
||||
from metagpt.prompts.ci.write_analysis_code import (
|
||||
CODE_GENERATOR_WITH_TOOLS,
|
||||
SELECT_FUNCTION_TOOLS,
|
||||
TOOL_RECOMMENDATION_PROMPT,
|
||||
TOOL_USAGE_PROMPT,
|
||||
)
|
||||
from metagpt.schema import Message, Plan, SystemMessage
|
||||
from metagpt.tools import TOOL_REGISTRY
|
||||
from metagpt.tools.tool_registry import validate_tool_names
|
||||
from metagpt.utils.common import create_func_call_config
|
||||
|
||||
|
||||
class BaseWriteAnalysisCode(Action):
|
||||
DEFAULT_SYSTEM_MSG: str = """You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**""" # prompt reference: https://github.com/KillianLucas/open-interpreter/blob/v0.1.4/interpreter/system_message.txt
|
||||
# REUSE_CODE_INSTRUCTION = """ATTENTION: DONT include codes from previous tasks in your current code block, include new codes only, DONT repeat codes!"""
|
||||
|
||||
def insert_system_message(self, context: list[Message], system_msg: str = None):
|
||||
system_msg = system_msg or self.DEFAULT_SYSTEM_MSG
|
||||
context.insert(0, SystemMessage(content=system_msg)) if context[0].role != "system" else None
|
||||
return context
|
||||
|
||||
async def run(self, context: list[Message], plan: Plan = None) -> dict:
|
||||
"""Run of a code writing action, used in data analysis or modeling
|
||||
|
||||
Args:
|
||||
context (list[Message]): Action output history, source action denoted by Message.cause_by
|
||||
plan (Plan, optional): Overall plan. Defaults to None.
|
||||
|
||||
Returns:
|
||||
dict: code result in the format of {"code": "print('hello world')", "language": "python"}
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class WriteCodeWithoutTools(BaseWriteAnalysisCode):
|
||||
"""Ask LLM to generate codes purely by itself without local user-defined tools"""
|
||||
|
||||
async def run(self, context: list[Message], plan: Plan = None, system_msg: str = None, **kwargs) -> dict:
|
||||
messages = self.insert_system_message(context, system_msg)
|
||||
rsp = await self.llm.aask_code(messages, **kwargs)
|
||||
return rsp
|
||||
|
||||
|
||||
class WriteCodeWithTools(BaseWriteAnalysisCode):
|
||||
"""Write code with help of local available tools. Choose tools first, then generate code to use the tools"""
|
||||
|
||||
# selected tools to choose from, listed by their names. An empty list means selection from all tools.
|
||||
selected_tools: list[str] = []
|
||||
|
||||
def _get_tools_by_type(self, tool_type: str) -> dict:
|
||||
"""
|
||||
Retreive tools by tool type from registry, but filtered by pre-selected tool list
|
||||
|
||||
Args:
|
||||
tool_type (str): Tool type to retrieve from the registry
|
||||
|
||||
Returns:
|
||||
dict: A dict of tool name to Tool object, representing available tools under the type
|
||||
"""
|
||||
candidate_tools = TOOL_REGISTRY.get_tools_by_type(tool_type)
|
||||
if self.selected_tools:
|
||||
candidate_tool_names = set(self.selected_tools) & candidate_tools.keys()
|
||||
candidate_tools = {tool_name: candidate_tools[tool_name] for tool_name in candidate_tool_names}
|
||||
return candidate_tools
|
||||
|
||||
async def _recommend_tool(
|
||||
self,
|
||||
task: str,
|
||||
available_tools: dict,
|
||||
) -> dict:
|
||||
"""
|
||||
Recommend tools for the specified task.
|
||||
|
||||
Args:
|
||||
task (str): the task to recommend tools for
|
||||
available_tools (dict): the available tools description
|
||||
|
||||
Returns:
|
||||
dict: schemas of recommended tools for the specified task
|
||||
"""
|
||||
prompt = TOOL_RECOMMENDATION_PROMPT.format(
|
||||
current_task=task,
|
||||
available_tools=available_tools,
|
||||
)
|
||||
tool_config = create_func_call_config(SELECT_FUNCTION_TOOLS)
|
||||
rsp = await self.llm.aask_code(prompt, **tool_config)
|
||||
recommend_tools = rsp["recommend_tools"]
|
||||
logger.info(f"Recommended tools: \n{recommend_tools}")
|
||||
|
||||
# Parses and validates the recommended tools, for LLM might hallucinate and recommend non-existing tools
|
||||
valid_tools = validate_tool_names(recommend_tools, return_tool_object=True)
|
||||
|
||||
tool_schemas = {tool.name: tool.schemas for tool in valid_tools}
|
||||
|
||||
return tool_schemas
|
||||
|
||||
async def _prepare_tools(self, plan: Plan) -> Tuple[dict, str]:
|
||||
"""Prepare tool schemas and usage instructions according to current task
|
||||
|
||||
Args:
|
||||
plan (Plan): The overall plan containing task information.
|
||||
|
||||
Returns:
|
||||
Tuple[dict, str]: A tool schemas ({tool_name: tool_schema_dict}) and a usage prompt for the type of tools selected
|
||||
"""
|
||||
# find tool type from task type through exact match, can extend to retrieval in the future
|
||||
tool_type = plan.current_task.task_type
|
||||
|
||||
# prepare tool-type-specific instruction
|
||||
tool_type_usage_prompt = (
|
||||
TOOL_REGISTRY.get_tool_type(tool_type).usage_prompt if TOOL_REGISTRY.has_tool_type(tool_type) else ""
|
||||
)
|
||||
|
||||
# prepare schemas of available tools
|
||||
tool_schemas = {}
|
||||
available_tools = self._get_tools_by_type(tool_type)
|
||||
if available_tools:
|
||||
available_tools = {tool_name: tool.schemas["description"] for tool_name, tool in available_tools.items()}
|
||||
tool_schemas = await self._recommend_tool(plan.current_task.instruction, available_tools)
|
||||
|
||||
return tool_schemas, tool_type_usage_prompt
|
||||
|
||||
async def run(
|
||||
self,
|
||||
context: list[Message],
|
||||
plan: Plan,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
# prepare tool schemas and tool-type-specific instruction
|
||||
tool_schemas, tool_type_usage_prompt = await self._prepare_tools(plan=plan)
|
||||
|
||||
# form a complete tool usage instruction and include it as a message in context
|
||||
tools_instruction = TOOL_USAGE_PROMPT.format(
|
||||
tool_schemas=tool_schemas, tool_type_usage_prompt=tool_type_usage_prompt
|
||||
)
|
||||
context.append(Message(content=tools_instruction, role="user"))
|
||||
|
||||
# prepare prompt & LLM call
|
||||
prompt = self.insert_system_message(context)
|
||||
tool_config = create_func_call_config(CODE_GENERATOR_WITH_TOOLS)
|
||||
rsp = await self.llm.aask_code(prompt, **tool_config)
|
||||
|
||||
return rsp
|
||||
|
|
@ -8,7 +8,6 @@
|
|||
from typing import List
|
||||
|
||||
from metagpt.actions.action_node import ActionNode
|
||||
from metagpt.logs import logger
|
||||
from metagpt.utils.mermaid import MMC1, MMC2
|
||||
|
||||
IMPLEMENTATION_APPROACH = ActionNode(
|
||||
|
|
@ -109,14 +108,3 @@ REFINED_NODES = [
|
|||
|
||||
DESIGN_API_NODE = ActionNode.from_children("DesignAPI", NODES)
|
||||
REFINED_DESIGN_NODE = ActionNode.from_children("RefinedDesignAPI", REFINED_NODES)
|
||||
|
||||
|
||||
def main():
|
||||
prompt = DESIGN_API_NODE.compile(context="")
|
||||
logger.info(prompt)
|
||||
prompt = REFINED_DESIGN_NODE.compile(context="")
|
||||
logger.info(prompt)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
|
|||
0
metagpt/actions/di/__init__.py
Normal file
0
metagpt/actions/di/__init__.py
Normal file
|
|
@ -9,7 +9,6 @@ from __future__ import annotations
|
|||
import asyncio
|
||||
import base64
|
||||
import re
|
||||
import traceback
|
||||
from typing import Literal, Tuple
|
||||
|
||||
import nbformat
|
||||
|
|
@ -58,7 +57,23 @@ class ExecuteNbCode(Action):
|
|||
|
||||
async def terminate(self):
|
||||
"""kill NotebookClient"""
|
||||
await self.nb_client._async_cleanup_kernel()
|
||||
if self.nb_client.km is not None and await self.nb_client.km.is_alive():
|
||||
await self.nb_client.km.shutdown_kernel(now=True)
|
||||
await self.nb_client.km.cleanup_resources()
|
||||
|
||||
channels = [
|
||||
self.nb_client.kc.stdin_channel, # The channel for handling standard input to the kernel.
|
||||
self.nb_client.kc.hb_channel, # The channel for heartbeat communication between the kernel and client.
|
||||
self.nb_client.kc.control_channel, # The channel for controlling the kernel.
|
||||
]
|
||||
|
||||
# Stops all the running channels for this kernel
|
||||
for channel in channels:
|
||||
if channel.is_alive():
|
||||
channel.stop()
|
||||
|
||||
self.nb_client.kc = None
|
||||
self.nb_client.km = None
|
||||
|
||||
async def reset(self):
|
||||
"""reset NotebookClient"""
|
||||
|
|
@ -91,17 +106,17 @@ class ExecuteNbCode(Action):
|
|||
else:
|
||||
cell["outputs"].append(new_output(output_type="stream", name="stdout", text=str(output)))
|
||||
|
||||
def parse_outputs(self, outputs: list[str]) -> str:
|
||||
def parse_outputs(self, outputs: list[str], keep_len: int = 2000) -> Tuple[bool, str]:
|
||||
"""Parses the outputs received from notebook execution."""
|
||||
assert isinstance(outputs, list)
|
||||
parsed_output = ""
|
||||
|
||||
parsed_output, is_success = [], True
|
||||
for i, output in enumerate(outputs):
|
||||
output_text = ""
|
||||
if output["output_type"] == "stream" and not any(
|
||||
tag in output["text"]
|
||||
for tag in ["| INFO | metagpt", "| ERROR | metagpt", "| WARNING | metagpt"]
|
||||
for tag in ["| INFO | metagpt", "| ERROR | metagpt", "| WARNING | metagpt", "DEBUG"]
|
||||
):
|
||||
parsed_output += output["text"]
|
||||
output_text = output["text"]
|
||||
elif output["output_type"] == "display_data":
|
||||
if "image/png" in output["data"]:
|
||||
self.show_bytes_figure(output["data"]["image/png"], self.interaction)
|
||||
|
|
@ -110,8 +125,22 @@ class ExecuteNbCode(Action):
|
|||
f"{i}th output['data'] from nbclient outputs dont have image/png, continue next output ..."
|
||||
)
|
||||
elif output["output_type"] == "execute_result":
|
||||
parsed_output += output["data"]["text/plain"]
|
||||
return parsed_output
|
||||
output_text = output["data"]["text/plain"]
|
||||
elif output["output_type"] == "error":
|
||||
output_text, is_success = "\n".join(output["traceback"]), False
|
||||
|
||||
# handle coroutines that are not executed asynchronously
|
||||
if output_text.strip().startswith("<coroutine object"):
|
||||
output_text = "Executed code failed, you need use key word 'await' to run a async code."
|
||||
is_success = False
|
||||
|
||||
output_text = remove_escape_and_color_codes(output_text)
|
||||
# The useful information of the exception is at the end,
|
||||
# the useful information of normal output is at the begining.
|
||||
output_text = output_text[:keep_len] if is_success else output_text[-keep_len:]
|
||||
|
||||
parsed_output.append(output_text)
|
||||
return is_success, ",".join(parsed_output)
|
||||
|
||||
def show_bytes_figure(self, image_base64: str, interaction_type: Literal["ipython", None]):
|
||||
image_bytes = base64.b64decode(image_base64)
|
||||
|
|
@ -145,7 +174,7 @@ class ExecuteNbCode(Action):
|
|||
"""
|
||||
try:
|
||||
await self.nb_client.async_execute_cell(cell, cell_index)
|
||||
return True, ""
|
||||
return self.parse_outputs(self.nb.cells[-1].outputs)
|
||||
except CellTimeoutError:
|
||||
assert self.nb_client.km is not None
|
||||
await self.nb_client.km.interrupt_kernel()
|
||||
|
|
@ -156,7 +185,7 @@ class ExecuteNbCode(Action):
|
|||
await self.reset()
|
||||
return False, "DeadKernelError"
|
||||
except Exception:
|
||||
return False, f"{traceback.format_exc()}"
|
||||
return self.parse_outputs(self.nb.cells[-1].outputs)
|
||||
|
||||
async def run(self, code: str, language: Literal["python", "markdown"] = "python") -> Tuple[str, bool]:
|
||||
"""
|
||||
|
|
@ -173,16 +202,9 @@ class ExecuteNbCode(Action):
|
|||
|
||||
# run code
|
||||
cell_index = len(self.nb.cells) - 1
|
||||
success, error_message = await self.run_cell(self.nb.cells[-1], cell_index)
|
||||
success, outputs = await self.run_cell(self.nb.cells[-1], cell_index)
|
||||
|
||||
if not success:
|
||||
return truncate(remove_escape_and_color_codes(error_message), is_success=success)
|
||||
|
||||
# code success
|
||||
outputs = self.parse_outputs(self.nb.cells[-1].outputs)
|
||||
outputs, success = truncate(remove_escape_and_color_codes(outputs), is_success=success)
|
||||
|
||||
if "!pip" in outputs:
|
||||
if "!pip" in code:
|
||||
success = False
|
||||
|
||||
return outputs, success
|
||||
|
|
@ -196,54 +218,39 @@ class ExecuteNbCode(Action):
|
|||
raise ValueError(f"Only support for language: python, markdown, but got {language}, ")
|
||||
|
||||
|
||||
def truncate(result: str, keep_len: int = 2000, is_success: bool = True):
|
||||
"""对于超出keep_len个字符的result: 执行失败的代码, 展示result后keep_len个字符; 执行成功的代码, 展示result前keep_len个字符。"""
|
||||
if is_success:
|
||||
desc = f"Executed code successfully. Truncated to show only first {keep_len} characters\n"
|
||||
else:
|
||||
desc = f"Executed code failed, please reflect the cause of bug and then debug. Truncated to show only last {keep_len} characters\n"
|
||||
|
||||
if result.strip().startswith("<coroutine object"):
|
||||
result = "Executed code failed, you need use key word 'await' to run a async code."
|
||||
return result, False
|
||||
|
||||
if len(result) > keep_len:
|
||||
result = result[-keep_len:] if not is_success else result[:keep_len]
|
||||
return desc + result, is_success
|
||||
|
||||
return result, is_success
|
||||
|
||||
|
||||
def remove_escape_and_color_codes(input_str: str):
|
||||
# 使用正则表达式去除转义字符和颜色代码
|
||||
# 使用正则表达式去除jupyter notebook输出结果中的转义字符和颜色代码
|
||||
# Use regular expressions to get rid of escape characters and color codes in jupyter notebook output.
|
||||
pattern = re.compile(r"\x1b\[[0-9;]*[mK]")
|
||||
result = pattern.sub("", input_str)
|
||||
return result
|
||||
|
||||
|
||||
def display_markdown(content: str):
|
||||
# 使用正则表达式逐个匹配代码块
|
||||
# Use regular expressions to match blocks of code one by one.
|
||||
matches = re.finditer(r"```(.+?)```", content, re.DOTALL)
|
||||
start_index = 0
|
||||
content_panels = []
|
||||
# 逐个打印匹配到的文本和代码
|
||||
# Set the text background color and text color.
|
||||
style = "black on white"
|
||||
# Print the matching text and code one by one.
|
||||
for match in matches:
|
||||
text_content = content[start_index : match.start()].strip()
|
||||
code_content = match.group(0).strip()[3:-3] # Remove triple backticks
|
||||
|
||||
if text_content:
|
||||
content_panels.append(Panel(Markdown(text_content), box=MINIMAL))
|
||||
content_panels.append(Panel(Markdown(text_content), style=style, box=MINIMAL))
|
||||
|
||||
if code_content:
|
||||
content_panels.append(Panel(Markdown(f"```{code_content}"), box=MINIMAL))
|
||||
content_panels.append(Panel(Markdown(f"```{code_content}"), style=style, box=MINIMAL))
|
||||
start_index = match.end()
|
||||
|
||||
# 打印剩余文本（如果有）
|
||||
# Print remaining text (if any).
|
||||
remaining_text = content[start_index:].strip()
|
||||
if remaining_text:
|
||||
content_panels.append(Panel(Markdown(remaining_text), box=MINIMAL))
|
||||
content_panels.append(Panel(Markdown(remaining_text), style=style, box=MINIMAL))
|
||||
|
||||
# 在Live模式中显示所有Panel
|
||||
# Display all panels in Live mode.
|
||||
with Live(auto_refresh=False, console=Console(), vertical_overflow="visible") as live:
|
||||
live.update(Group(*content_panels))
|
||||
live.refresh()
|
||||
73
metagpt/actions/di/write_analysis_code.py
Normal file
73
metagpt/actions/di/write_analysis_code.py
Normal file
|
|
@ -0,0 +1,73 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
"""
|
||||
@Date : 2023/11/20 13:19:39
|
||||
@Author : orange-crow
|
||||
@File : write_analysis_code.py
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
|
||||
from metagpt.actions import Action
|
||||
from metagpt.prompts.di.write_analysis_code import (
|
||||
CHECK_DATA_PROMPT,
|
||||
DEBUG_REFLECTION_EXAMPLE,
|
||||
INTERPRETER_SYSTEM_MSG,
|
||||
REFLECTION_PROMPT,
|
||||
REFLECTION_SYSTEM_MSG,
|
||||
STRUCTUAL_PROMPT,
|
||||
)
|
||||
from metagpt.schema import Message, Plan
|
||||
from metagpt.utils.common import CodeParser, remove_comments
|
||||
|
||||
|
||||
class WriteAnalysisCode(Action):
|
||||
async def _debug_with_reflection(self, context: list[Message], working_memory: list[Message]):
|
||||
reflection_prompt = REFLECTION_PROMPT.format(
|
||||
debug_example=DEBUG_REFLECTION_EXAMPLE,
|
||||
context=context,
|
||||
previous_impl=working_memory,
|
||||
)
|
||||
|
||||
rsp = await self._aask(reflection_prompt, system_msgs=[REFLECTION_SYSTEM_MSG])
|
||||
reflection = json.loads(CodeParser.parse_code(block=None, text=rsp))
|
||||
|
||||
return reflection["improved_impl"]
|
||||
|
||||
async def run(
|
||||
self,
|
||||
user_requirement: str,
|
||||
plan_status: str = "",
|
||||
tool_info: str = "",
|
||||
working_memory: list[Message] = None,
|
||||
use_reflection: bool = False,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
structual_prompt = STRUCTUAL_PROMPT.format(
|
||||
user_requirement=user_requirement,
|
||||
plan_status=plan_status,
|
||||
tool_info=tool_info,
|
||||
)
|
||||
|
||||
working_memory = working_memory or []
|
||||
context = self.llm.format_msg([Message(content=structual_prompt, role="user")] + working_memory)
|
||||
|
||||
# LLM call
|
||||
if use_reflection:
|
||||
code = await self._debug_with_reflection(context=context, working_memory=working_memory)
|
||||
else:
|
||||
rsp = await self.llm.aask(context, system_msgs=[INTERPRETER_SYSTEM_MSG], **kwargs)
|
||||
code = CodeParser.parse_code(block=None, text=rsp)
|
||||
|
||||
return code
|
||||
|
||||
|
||||
class CheckData(Action):
|
||||
async def run(self, plan: Plan) -> dict:
|
||||
finished_tasks = plan.get_finished_tasks()
|
||||
code_written = [remove_comments(task.code) for task in finished_tasks]
|
||||
code_written = "\n\n".join(code_written)
|
||||
prompt = CHECK_DATA_PROMPT.format(code_written=code_written)
|
||||
rsp = await self._aask(prompt)
|
||||
code = CodeParser.parse_code(block=None, text=rsp)
|
||||
return code
|
||||
|
|
@ -12,81 +12,49 @@ from typing import Tuple
|
|||
|
||||
from metagpt.actions import Action
|
||||
from metagpt.logs import logger
|
||||
from metagpt.prompts.ci.write_analysis_code import (
|
||||
ASSIGN_TASK_TYPE_CONFIG,
|
||||
ASSIGN_TASK_TYPE_PROMPT,
|
||||
)
|
||||
from metagpt.schema import Message, Plan, Task
|
||||
from metagpt.tools import TOOL_REGISTRY
|
||||
from metagpt.utils.common import CodeParser, create_func_call_config
|
||||
from metagpt.strategy.task_type import TaskType
|
||||
from metagpt.utils.common import CodeParser
|
||||
|
||||
|
||||
class WritePlan(Action):
|
||||
PROMPT_TEMPLATE: str = """
|
||||
# Context:
|
||||
__context__
|
||||
{context}
|
||||
# Available Task Types:
|
||||
{task_type_desc}
|
||||
# Task:
|
||||
Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to __max_tasks__ tasks.
|
||||
Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to {max_tasks} tasks.
|
||||
If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.
|
||||
If you encounter errors on the current task, revise and output the current single task only.
|
||||
Output a list of jsons following the format:
|
||||
```json
|
||||
[
|
||||
{
|
||||
{{
|
||||
"task_id": str = "unique identifier for a task in plan, can be an ordinal",
|
||||
"dependent_task_ids": list[str] = "ids of tasks prerequisite to this task",
|
||||
"instruction": "what you should do in this task, one short phrase or sentence",
|
||||
},
|
||||
"task_type": "type of this task, should be one of Available Task Types",
|
||||
}},
|
||||
...
|
||||
]
|
||||
```
|
||||
"""
|
||||
|
||||
async def assign_task_type(self, tasks: list[dict]) -> str:
|
||||
"""Assign task type to each task in tasks
|
||||
|
||||
Args:
|
||||
tasks (list[dict]): tasks to be assigned task type
|
||||
|
||||
Returns:
|
||||
str: tasks with task type assigned in a json string
|
||||
"""
|
||||
task_info = "\n".join([f"Task {task['task_id']}: {task['instruction']}" for task in tasks])
|
||||
task_type_desc = "\n".join(
|
||||
[f"- **{tool_type.name}**: {tool_type.desc}" for tool_type in TOOL_REGISTRY.get_tool_types().values()]
|
||||
) # task type are binded with tool type now, should be improved in the future
|
||||
prompt = ASSIGN_TASK_TYPE_PROMPT.format(
|
||||
task_info=task_info, task_type_desc=task_type_desc
|
||||
) # task types are set to be the same as tool types, for now
|
||||
tool_config = create_func_call_config(ASSIGN_TASK_TYPE_CONFIG)
|
||||
rsp = await self.llm.aask_code(prompt, **tool_config)
|
||||
task_type_list = rsp["task_type"]
|
||||
logger.info(f"assigned task types: {task_type_list}")
|
||||
for task, task_type in zip(tasks, task_type_list):
|
||||
task["task_type"] = task_type
|
||||
return json.dumps(tasks)
|
||||
|
||||
async def run(self, context: list[Message], max_tasks: int = 5, use_tools: bool = False) -> str:
|
||||
prompt = (
|
||||
self.PROMPT_TEMPLATE.replace("__context__", "\n".join([str(ct) for ct in context]))
|
||||
# .replace("__current_plan__", current_plan)
|
||||
.replace("__max_tasks__", str(max_tasks))
|
||||
async def run(self, context: list[Message], max_tasks: int = 5) -> str:
|
||||
task_type_desc = "\n".join([f"- **{tt.type_name}**: {tt.value.desc}" for tt in TaskType])
|
||||
prompt = self.PROMPT_TEMPLATE.format(
|
||||
context="\n".join([str(ct) for ct in context]), max_tasks=max_tasks, task_type_desc=task_type_desc
|
||||
)
|
||||
rsp = await self._aask(prompt)
|
||||
rsp = CodeParser.parse_code(block=None, text=rsp)
|
||||
if use_tools:
|
||||
rsp = await self.assign_task_type(json.loads(rsp))
|
||||
return rsp
|
||||
|
||||
|
||||
def rsp_to_tasks(rsp: str) -> list[Task]:
|
||||
def update_plan_from_rsp(rsp: str, current_plan: Plan):
|
||||
rsp = json.loads(rsp)
|
||||
tasks = [Task(**task_config) for task_config in rsp]
|
||||
return tasks
|
||||
|
||||
|
||||
def update_plan_from_rsp(rsp: str, current_plan: Plan):
|
||||
tasks = rsp_to_tasks(rsp)
|
||||
if len(tasks) == 1 or tasks[0].dependent_task_ids:
|
||||
if tasks[0].dependent_task_ids and len(tasks) > 1:
|
||||
# tasks[0].dependent_task_ids means the generated tasks are not a complete plan
|
||||
|
|
@ -8,7 +8,6 @@
|
|||
from typing import List
|
||||
|
||||
from metagpt.actions.action_node import ActionNode
|
||||
from metagpt.logs import logger
|
||||
|
||||
REQUIRED_PYTHON_PACKAGES = ActionNode(
|
||||
key="Required Python packages",
|
||||
|
|
@ -119,14 +118,3 @@ REFINED_NODES = [
|
|||
|
||||
PM_NODE = ActionNode.from_children("PM_NODE", NODES)
|
||||
REFINED_PM_NODE = ActionNode.from_children("REFINED_PM_NODE", REFINED_NODES)
|
||||
|
||||
|
||||
def main():
|
||||
prompt = PM_NODE.compile(context="")
|
||||
logger.info(prompt)
|
||||
prompt = REFINED_PM_NODE.compile(context="")
|
||||
logger.info(prompt)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
|
|||
|
|
@ -4,10 +4,12 @@
|
|||
@Time : 2023/12/19
|
||||
@Author : mashenquan
|
||||
@File : rebuild_class_view.py
|
||||
@Desc : Rebuild class view info
|
||||
@Desc : Reconstructs class diagram from a source code project.
|
||||
Implement RFC197, https://deepwisdom.feishu.cn/wiki/VyK0wfq56ivuvjklMKJcmHQknGt
|
||||
"""
|
||||
import re
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Optional, Set, Tuple
|
||||
|
||||
import aiofiles
|
||||
|
||||
|
|
@ -21,86 +23,144 @@ from metagpt.const import (
|
|||
GRAPH_REPO_FILE_REPO,
|
||||
)
|
||||
from metagpt.logs import logger
|
||||
from metagpt.repo_parser import RepoParser
|
||||
from metagpt.schema import ClassAttribute, ClassMethod, ClassView
|
||||
from metagpt.utils.common import split_namespace
|
||||
from metagpt.repo_parser import DotClassInfo, RepoParser
|
||||
from metagpt.schema import UMLClassView
|
||||
from metagpt.utils.common import concat_namespace, split_namespace
|
||||
from metagpt.utils.di_graph_repository import DiGraphRepository
|
||||
from metagpt.utils.graph_repository import GraphKeyword, GraphRepository
|
||||
|
||||
|
||||
class RebuildClassView(Action):
|
||||
"""
|
||||
Reconstructs a graph repository about class diagram from a source code project.
|
||||
|
||||
Attributes:
|
||||
graph_db (Optional[GraphRepository]): The optional graph repository.
|
||||
"""
|
||||
|
||||
graph_db: Optional[GraphRepository] = None
|
||||
|
||||
async def run(self, with_messages=None, format=config.prompt_schema):
|
||||
"""
|
||||
Implementation of `Action`'s `run` method.
|
||||
|
||||
Args:
|
||||
with_messages (Optional[Type]): An optional argument specifying messages to react to.
|
||||
format (str): The format for the prompt schema.
|
||||
"""
|
||||
graph_repo_pathname = self.context.git_repo.workdir / GRAPH_REPO_FILE_REPO / self.context.git_repo.workdir.name
|
||||
graph_db = await DiGraphRepository.load_from(str(graph_repo_pathname.with_suffix(".json")))
|
||||
self.graph_db = await DiGraphRepository.load_from(str(graph_repo_pathname.with_suffix(".json")))
|
||||
repo_parser = RepoParser(base_directory=Path(self.i_context))
|
||||
# use pylint
|
||||
class_views, relationship_views, package_root = await repo_parser.rebuild_class_views(path=Path(self.i_context))
|
||||
await GraphRepository.update_graph_db_with_class_views(graph_db, class_views)
|
||||
await GraphRepository.update_graph_db_with_class_relationship_views(graph_db, relationship_views)
|
||||
await GraphRepository.update_graph_db_with_class_views(self.graph_db, class_views)
|
||||
await GraphRepository.update_graph_db_with_class_relationship_views(self.graph_db, relationship_views)
|
||||
await GraphRepository.rebuild_composition_relationship(self.graph_db)
|
||||
# use ast
|
||||
direction, diff_path = self._diff_path(path_root=Path(self.i_context).resolve(), package_root=package_root)
|
||||
symbols = repo_parser.generate_symbols()
|
||||
for file_info in symbols:
|
||||
# Align to the same root directory in accordance with `class_views`.
|
||||
file_info.file = self._align_root(file_info.file, direction, diff_path)
|
||||
await GraphRepository.update_graph_db_with_file_info(graph_db, file_info)
|
||||
await self._create_mermaid_class_views(graph_db=graph_db)
|
||||
await graph_db.save()
|
||||
await GraphRepository.update_graph_db_with_file_info(self.graph_db, file_info)
|
||||
await self._create_mermaid_class_views()
|
||||
await self.graph_db.save()
|
||||
|
||||
async def _create_mermaid_class_views(self, graph_db):
|
||||
path = Path(self.context.git_repo.workdir) / DATA_API_DESIGN_FILE_REPO
|
||||
async def _create_mermaid_class_views(self) -> str:
|
||||
"""Creates a Mermaid class diagram using data from the `graph_db` graph repository.
|
||||
|
||||
This method utilizes information stored in the graph repository to generate a Mermaid class diagram.
|
||||
Returns:
|
||||
mermaid class diagram file name.
|
||||
"""
|
||||
path = self.context.git_repo.workdir / DATA_API_DESIGN_FILE_REPO
|
||||
path.mkdir(parents=True, exist_ok=True)
|
||||
pathname = path / self.context.git_repo.workdir.name
|
||||
async with aiofiles.open(str(pathname.with_suffix(".mmd")), mode="w", encoding="utf-8") as writer:
|
||||
filename = str(pathname.with_suffix(".class_diagram.mmd"))
|
||||
async with aiofiles.open(filename, mode="w", encoding="utf-8") as writer:
|
||||
content = "classDiagram\n"
|
||||
logger.debug(content)
|
||||
await writer.write(content)
|
||||
# class names
|
||||
rows = await graph_db.select(predicate=GraphKeyword.IS, object_=GraphKeyword.CLASS)
|
||||
rows = await self.graph_db.select(predicate=GraphKeyword.IS, object_=GraphKeyword.CLASS)
|
||||
class_distinct = set()
|
||||
relationship_distinct = set()
|
||||
for r in rows:
|
||||
await RebuildClassView._create_mermaid_class(r.subject, graph_db, writer, class_distinct)
|
||||
content = await self._create_mermaid_class(r.subject)
|
||||
if content:
|
||||
await writer.write(content)
|
||||
class_distinct.add(r.subject)
|
||||
for r in rows:
|
||||
await RebuildClassView._create_mermaid_relationship(r.subject, graph_db, writer, relationship_distinct)
|
||||
content, distinct = await self._create_mermaid_relationship(r.subject)
|
||||
if content:
|
||||
logger.debug(content)
|
||||
await writer.write(content)
|
||||
relationship_distinct.update(distinct)
|
||||
logger.info(f"classes: {len(class_distinct)}, relationship: {len(relationship_distinct)}")
|
||||
|
||||
@staticmethod
|
||||
async def _create_mermaid_class(ns_class_name, graph_db, file_writer, distinct):
|
||||
if self.i_context:
|
||||
r_filename = Path(filename).relative_to(self.context.git_repo.workdir)
|
||||
await self.graph_db.insert(
|
||||
subject=self.i_context, predicate="hasMermaidClassDiagramFile", object_=str(r_filename)
|
||||
)
|
||||
logger.info(f"{self.i_context} hasMermaidClassDiagramFile {filename}")
|
||||
return filename
|
||||
|
||||
async def _create_mermaid_class(self, ns_class_name) -> str:
|
||||
"""Generates a Mermaid class diagram for a specific class using data from the `graph_db` graph repository.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed name of the class for which the Mermaid class diagram is to be created.
|
||||
|
||||
Returns:
|
||||
str: A Mermaid code block object in markdown representing the class diagram.
|
||||
"""
|
||||
fields = split_namespace(ns_class_name)
|
||||
if len(fields) > 2:
|
||||
# Ignore sub-class
|
||||
return
|
||||
return ""
|
||||
|
||||
class_view = ClassView(name=fields[1])
|
||||
rows = await graph_db.select(subject=ns_class_name)
|
||||
for r in rows:
|
||||
name = split_namespace(r.object_)[-1]
|
||||
name, visibility, abstraction = RebuildClassView._parse_name(name=name, language="python")
|
||||
if r.predicate == GraphKeyword.HAS_CLASS_PROPERTY:
|
||||
var_type = await RebuildClassView._parse_variable_type(r.object_, graph_db)
|
||||
attribute = ClassAttribute(
|
||||
name=name, visibility=visibility, abstraction=bool(abstraction), value_type=var_type
|
||||
)
|
||||
class_view.attributes.append(attribute)
|
||||
elif r.predicate == GraphKeyword.HAS_CLASS_FUNCTION:
|
||||
method = ClassMethod(name=name, visibility=visibility, abstraction=bool(abstraction))
|
||||
await RebuildClassView._parse_function_args(method, r.object_, graph_db)
|
||||
class_view.methods.append(method)
|
||||
rows = await self.graph_db.select(subject=ns_class_name, predicate=GraphKeyword.HAS_DETAIL)
|
||||
if not rows:
|
||||
return ""
|
||||
dot_class_info = DotClassInfo.model_validate_json(rows[0].object_)
|
||||
class_view = UMLClassView.load_dot_class_info(dot_class_info)
|
||||
|
||||
# update graph db
|
||||
await graph_db.insert(ns_class_name, GraphKeyword.HAS_CLASS_VIEW, class_view.model_dump_json())
|
||||
# update uml view
|
||||
await self.graph_db.insert(ns_class_name, GraphKeyword.HAS_CLASS_VIEW, class_view.model_dump_json())
|
||||
# update uml isCompositeOf
|
||||
for c in dot_class_info.compositions:
|
||||
await self.graph_db.insert(
|
||||
subject=ns_class_name,
|
||||
predicate=GraphKeyword.IS + COMPOSITION + GraphKeyword.OF,
|
||||
object_=concat_namespace("?", c),
|
||||
)
|
||||
|
||||
# update uml isAggregateOf
|
||||
for a in dot_class_info.aggregations:
|
||||
await self.graph_db.insert(
|
||||
subject=ns_class_name,
|
||||
predicate=GraphKeyword.IS + AGGREGATION + GraphKeyword.OF,
|
||||
object_=concat_namespace("?", a),
|
||||
)
|
||||
|
||||
content = class_view.get_mermaid(align=1)
|
||||
logger.debug(content)
|
||||
await file_writer.write(content)
|
||||
distinct.add(ns_class_name)
|
||||
return content
|
||||
|
||||
@staticmethod
|
||||
async def _create_mermaid_relationship(ns_class_name, graph_db, file_writer, distinct):
|
||||
async def _create_mermaid_relationship(self, ns_class_name: str) -> Tuple[Optional[str], Optional[Set]]:
|
||||
"""Generates a Mermaid class relationship diagram for a specific class using data from the `graph_db` graph repository.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed class name for which the Mermaid relationship diagram is to be created.
|
||||
|
||||
Returns:
|
||||
Tuple[str, Set]: A tuple containing the relationship diagram as a string and a set of deduplication.
|
||||
"""
|
||||
s_fields = split_namespace(ns_class_name)
|
||||
if len(s_fields) > 2:
|
||||
# Ignore sub-class
|
||||
return
|
||||
return None, None
|
||||
|
||||
predicates = {GraphKeyword.IS + v + GraphKeyword.OF: v for v in [GENERALIZATION, COMPOSITION, AGGREGATION]}
|
||||
mappings = {
|
||||
|
|
@ -109,8 +169,9 @@ class RebuildClassView(Action):
|
|||
AGGREGATION: " o-- ",
|
||||
}
|
||||
content = ""
|
||||
distinct = set()
|
||||
for p, v in predicates.items():
|
||||
rows = await graph_db.select(subject=ns_class_name, predicate=p)
|
||||
rows = await self.graph_db.select(subject=ns_class_name, predicate=p)
|
||||
for r in rows:
|
||||
o_fields = split_namespace(r.object_)
|
||||
if len(o_fields) > 2:
|
||||
|
|
@ -121,86 +182,26 @@ class RebuildClassView(Action):
|
|||
distinct.add(link)
|
||||
content += f"\t{link}\n"
|
||||
|
||||
if content:
|
||||
logger.debug(content)
|
||||
await file_writer.write(content)
|
||||
|
||||
@staticmethod
|
||||
def _parse_name(name: str, language="python"):
|
||||
pattern = re.compile(r"<I>(.*?)<\/I>")
|
||||
result = re.search(pattern, name)
|
||||
|
||||
abstraction = ""
|
||||
if result:
|
||||
name = result.group(1)
|
||||
abstraction = "*"
|
||||
if name.startswith("__"):
|
||||
visibility = "-"
|
||||
elif name.startswith("_"):
|
||||
visibility = "#"
|
||||
else:
|
||||
visibility = "+"
|
||||
return name, visibility, abstraction
|
||||
|
||||
@staticmethod
|
||||
async def _parse_variable_type(ns_name, graph_db) -> str:
|
||||
rows = await graph_db.select(subject=ns_name, predicate=GraphKeyword.HAS_TYPE_DESC)
|
||||
if not rows:
|
||||
return ""
|
||||
vals = rows[0].object_.replace("'", "").split(":")
|
||||
if len(vals) == 1:
|
||||
return ""
|
||||
val = vals[-1].strip()
|
||||
return "" if val == "NoneType" else val + " "
|
||||
|
||||
@staticmethod
|
||||
async def _parse_function_args(method: ClassMethod, ns_name: str, graph_db: GraphRepository):
|
||||
rows = await graph_db.select(subject=ns_name, predicate=GraphKeyword.HAS_ARGS_DESC)
|
||||
if not rows:
|
||||
return
|
||||
info = rows[0].object_.replace("'", "")
|
||||
|
||||
fs_tag = "("
|
||||
ix = info.find(fs_tag)
|
||||
fe_tag = "):"
|
||||
eix = info.rfind(fe_tag)
|
||||
if eix < 0:
|
||||
fe_tag = ")"
|
||||
eix = info.rfind(fe_tag)
|
||||
args_info = info[ix + len(fs_tag) : eix].strip()
|
||||
method.return_type = info[eix + len(fe_tag) :].strip()
|
||||
if method.return_type == "None":
|
||||
method.return_type = ""
|
||||
if "(" in method.return_type:
|
||||
method.return_type = method.return_type.replace("(", "Tuple[").replace(")", "]")
|
||||
|
||||
# parse args
|
||||
if not args_info:
|
||||
return
|
||||
splitter_ixs = []
|
||||
cost = 0
|
||||
for i in range(len(args_info)):
|
||||
if args_info[i] == "[":
|
||||
cost += 1
|
||||
elif args_info[i] == "]":
|
||||
cost -= 1
|
||||
if args_info[i] == "," and cost == 0:
|
||||
splitter_ixs.append(i)
|
||||
splitter_ixs.append(len(args_info))
|
||||
args = []
|
||||
ix = 0
|
||||
for eix in splitter_ixs:
|
||||
args.append(args_info[ix:eix])
|
||||
ix = eix + 1
|
||||
for arg in args:
|
||||
parts = arg.strip().split(":")
|
||||
if len(parts) == 1:
|
||||
method.args.append(ClassAttribute(name=parts[0].strip()))
|
||||
continue
|
||||
method.args.append(ClassAttribute(name=parts[0].strip(), value_type=parts[-1].strip()))
|
||||
return content, distinct
|
||||
|
||||
@staticmethod
|
||||
def _diff_path(path_root: Path, package_root: Path) -> (str, str):
|
||||
"""Returns the difference between the root path and the path information represented in the package name.
|
||||
|
||||
Args:
|
||||
path_root (Path): The root path.
|
||||
package_root (Path): The package root path.
|
||||
|
||||
Returns:
|
||||
Tuple[str, str]: A tuple containing the representation of the difference ("+", "-", "=") and the path detail of the differing part.
|
||||
|
||||
Example:
|
||||
>>> _diff_path(path_root=Path("/Users/x/github/MetaGPT"), package_root=Path("/Users/x/github/MetaGPT/metagpt"))
|
||||
"-", "metagpt"
|
||||
|
||||
>>> _diff_path(path_root=Path("/Users/x/github/MetaGPT/metagpt"), package_root=Path("/Users/x/github/MetaGPT/metagpt"))
|
||||
"=", "."
|
||||
"""
|
||||
if len(str(path_root)) > len(str(package_root)):
|
||||
return "+", str(path_root.relative_to(package_root))
|
||||
if len(str(path_root)) < len(str(package_root)):
|
||||
|
|
@ -208,7 +209,24 @@ class RebuildClassView(Action):
|
|||
return "=", "."
|
||||
|
||||
@staticmethod
|
||||
def _align_root(path: str, direction: str, diff_path: str):
|
||||
def _align_root(path: str, direction: str, diff_path: str) -> str:
|
||||
"""Aligns the path to the same root represented by `diff_path`.
|
||||
|
||||
Args:
|
||||
path (str): The path to be aligned.
|
||||
direction (str): The direction of alignment ('+', '-', '=').
|
||||
diff_path (str): The path representing the difference.
|
||||
|
||||
Returns:
|
||||
str: The aligned path.
|
||||
|
||||
Example:
|
||||
>>> _align_root(path="metagpt/software_company.py", direction="+", diff_path="MetaGPT")
|
||||
"MetaGPT/metagpt/software_company.py"
|
||||
|
||||
>>> _align_root(path="metagpt/software_company.py", direction="-", diff_path="metagpt")
|
||||
"software_company.py"
|
||||
"""
|
||||
if direction == "=":
|
||||
return path
|
||||
if direction == "+":
|
||||
|
|
|
|||
|
|
@ -4,34 +4,214 @@
|
|||
@Time : 2024/1/4
|
||||
@Author : mashenquan
|
||||
@File : rebuild_sequence_view.py
|
||||
@Desc : Rebuild sequence view info
|
||||
@Desc : Reconstruct sequence view information through reverse engineering.
|
||||
Implement RFC197, https://deepwisdom.feishu.cn/wiki/VyK0wfq56ivuvjklMKJcmHQknGt
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import List
|
||||
from typing import List, Optional, Set
|
||||
|
||||
from pydantic import BaseModel
|
||||
from tenacity import retry, stop_after_attempt, wait_random_exponential
|
||||
|
||||
from metagpt.actions import Action
|
||||
from metagpt.config2 import config
|
||||
from metagpt.const import GRAPH_REPO_FILE_REPO
|
||||
from metagpt.logs import logger
|
||||
from metagpt.utils.common import aread, list_files
|
||||
from metagpt.repo_parser import CodeBlockInfo, DotClassInfo
|
||||
from metagpt.schema import UMLClassView
|
||||
from metagpt.utils.common import (
|
||||
add_affix,
|
||||
aread,
|
||||
auto_namespace,
|
||||
concat_namespace,
|
||||
general_after_log,
|
||||
list_files,
|
||||
parse_json_code_block,
|
||||
read_file_block,
|
||||
split_namespace,
|
||||
)
|
||||
from metagpt.utils.di_graph_repository import DiGraphRepository
|
||||
from metagpt.utils.graph_repository import GraphKeyword
|
||||
from metagpt.utils.graph_repository import SPO, GraphKeyword, GraphRepository
|
||||
|
||||
|
||||
class ReverseUseCase(BaseModel):
|
||||
"""
|
||||
Represents a reverse engineered use case.
|
||||
|
||||
Attributes:
|
||||
description (str): A description of the reverse use case.
|
||||
inputs (List[str]): List of inputs for the reverse use case.
|
||||
outputs (List[str]): List of outputs for the reverse use case.
|
||||
actors (List[str]): List of actors involved in the reverse use case.
|
||||
steps (List[str]): List of steps for the reverse use case.
|
||||
reason (str): The reason behind the reverse use case.
|
||||
"""
|
||||
|
||||
description: str
|
||||
inputs: List[str]
|
||||
outputs: List[str]
|
||||
actors: List[str]
|
||||
steps: List[str]
|
||||
reason: str
|
||||
|
||||
|
||||
class ReverseUseCaseDetails(BaseModel):
|
||||
"""
|
||||
Represents details of a reverse engineered use case.
|
||||
|
||||
Attributes:
|
||||
description (str): A description of the reverse use case details.
|
||||
use_cases (List[ReverseUseCase]): List of reverse use cases.
|
||||
relationship (List[str]): List of relationships associated with the reverse use case details.
|
||||
"""
|
||||
|
||||
description: str
|
||||
use_cases: List[ReverseUseCase]
|
||||
relationship: List[str]
|
||||
|
||||
|
||||
class RebuildSequenceView(Action):
|
||||
async def run(self, with_messages=None, format=config.prompt_schema):
|
||||
graph_repo_pathname = self.context.git_repo.workdir / GRAPH_REPO_FILE_REPO / self.context.git_repo.workdir.name
|
||||
graph_db = await DiGraphRepository.load_from(str(graph_repo_pathname.with_suffix(".json")))
|
||||
entries = await RebuildSequenceView._search_main_entry(graph_db)
|
||||
for entry in entries:
|
||||
await self._rebuild_sequence_view(entry, graph_db)
|
||||
await graph_db.save()
|
||||
"""
|
||||
Represents an action to reconstruct sequence view through reverse engineering.
|
||||
|
||||
@staticmethod
|
||||
async def _search_main_entry(graph_db) -> List:
|
||||
rows = await graph_db.select(predicate=GraphKeyword.HAS_PAGE_INFO)
|
||||
Attributes:
|
||||
graph_db (Optional[GraphRepository]): An optional instance of GraphRepository for graph database operations.
|
||||
"""
|
||||
|
||||
graph_db: Optional[GraphRepository] = None
|
||||
|
||||
async def run(self, with_messages=None, format=config.prompt_schema):
|
||||
"""
|
||||
Implementation of `Action`'s `run` method.
|
||||
|
||||
Args:
|
||||
with_messages (Optional[Type]): An optional argument specifying messages to react to.
|
||||
format (str): The format for the prompt schema.
|
||||
"""
|
||||
graph_repo_pathname = self.context.git_repo.workdir / GRAPH_REPO_FILE_REPO / self.context.git_repo.workdir.name
|
||||
self.graph_db = await DiGraphRepository.load_from(str(graph_repo_pathname.with_suffix(".json")))
|
||||
if not self.i_context:
|
||||
entries = await self._search_main_entry()
|
||||
else:
|
||||
entries = [SPO(subject=self.i_context, predicate="", object_="")]
|
||||
for entry in entries:
|
||||
await self._rebuild_main_sequence_view(entry)
|
||||
while await self._merge_sequence_view(entry):
|
||||
pass
|
||||
await self.graph_db.save()
|
||||
|
||||
@retry(
|
||||
wait=wait_random_exponential(min=1, max=20),
|
||||
stop=stop_after_attempt(6),
|
||||
after=general_after_log(logger),
|
||||
)
|
||||
async def _rebuild_main_sequence_view(self, entry: SPO):
|
||||
"""
|
||||
Reconstruct the sequence diagram for the __main__ entry of the source code through reverse engineering.
|
||||
|
||||
Args:
|
||||
entry (SPO): The SPO (Subject, Predicate, Object) object in the graph database that is related to the
|
||||
subject `__name__:__main__`.
|
||||
"""
|
||||
filename = entry.subject.split(":", 1)[0]
|
||||
rows = await self.graph_db.select(predicate=GraphKeyword.IS, object_=GraphKeyword.CLASS)
|
||||
classes = []
|
||||
prefix = filename + ":"
|
||||
for r in rows:
|
||||
if prefix in r.subject:
|
||||
classes.append(r)
|
||||
await self._rebuild_use_case(r.subject)
|
||||
participants = await self._search_participants(split_namespace(entry.subject)[0])
|
||||
class_details = []
|
||||
class_views = []
|
||||
for c in classes:
|
||||
detail = await self._get_class_detail(c.subject)
|
||||
if not detail:
|
||||
continue
|
||||
class_details.append(detail)
|
||||
view = await self._get_uml_class_view(c.subject)
|
||||
if view:
|
||||
class_views.append(view)
|
||||
|
||||
actors = await self._get_participants(c.subject)
|
||||
participants.update(set(actors))
|
||||
|
||||
use_case_blocks = []
|
||||
for c in classes:
|
||||
use_cases = await self._get_class_use_cases(c.subject)
|
||||
use_case_blocks.append(use_cases)
|
||||
prompt_blocks = ["## Use Cases\n" + "\n".join(use_case_blocks)]
|
||||
block = "## Participants\n"
|
||||
for p in participants:
|
||||
block += f"- {p}\n"
|
||||
prompt_blocks.append(block)
|
||||
block = "## Mermaid Class Views\n```mermaid\n"
|
||||
block += "\n\n".join([c.get_mermaid() for c in class_views])
|
||||
block += "\n```\n"
|
||||
prompt_blocks.append(block)
|
||||
block = "## Source Code\n```python\n"
|
||||
block += await self._get_source_code(filename)
|
||||
block += "\n```\n"
|
||||
prompt_blocks.append(block)
|
||||
prompt = "\n---\n".join(prompt_blocks)
|
||||
|
||||
rsp = await self.llm.aask(
|
||||
msg=prompt,
|
||||
system_msgs=[
|
||||
"You are a python code to Mermaid Sequence Diagram translator in function detail.",
|
||||
"Translate the given markdown text to a Mermaid Sequence Diagram.",
|
||||
"Return the merged Mermaid sequence diagram in a markdown code block format.",
|
||||
],
|
||||
stream=False,
|
||||
)
|
||||
sequence_view = rsp.removeprefix("```mermaid").removesuffix("```")
|
||||
rows = await self.graph_db.select(subject=entry.subject, predicate=GraphKeyword.HAS_SEQUENCE_VIEW)
|
||||
for r in rows:
|
||||
if r.predicate == GraphKeyword.HAS_SEQUENCE_VIEW:
|
||||
await self.graph_db.delete(subject=r.subject, predicate=r.predicate, object_=r.object_)
|
||||
await self.graph_db.insert(
|
||||
subject=entry.subject, predicate=GraphKeyword.HAS_SEQUENCE_VIEW, object_=sequence_view
|
||||
)
|
||||
await self.graph_db.insert(
|
||||
subject=entry.subject,
|
||||
predicate=GraphKeyword.HAS_SEQUENCE_VIEW_VER,
|
||||
object_=concat_namespace(datetime.now().strftime("%Y%m%d%H%M%S%f")[:-3], add_affix(sequence_view)),
|
||||
)
|
||||
for c in classes:
|
||||
await self.graph_db.insert(
|
||||
subject=entry.subject, predicate=GraphKeyword.HAS_PARTICIPANT, object_=auto_namespace(c.subject)
|
||||
)
|
||||
await self._save_sequence_view(subject=entry.subject, content=sequence_view)
|
||||
|
||||
async def _merge_sequence_view(self, entry: SPO) -> bool:
|
||||
"""
|
||||
Augments additional information to the provided SPO (Subject, Predicate, Object) entry in the sequence diagram.
|
||||
|
||||
Args:
|
||||
entry (SPO): The SPO object representing the relationship in the graph database.
|
||||
|
||||
Returns:
|
||||
bool: True if additional information has been augmented, otherwise False.
|
||||
"""
|
||||
new_participant = await self._search_new_participant(entry)
|
||||
if not new_participant:
|
||||
return False
|
||||
|
||||
await self._merge_participant(entry, new_participant)
|
||||
return True
|
||||
|
||||
async def _search_main_entry(self) -> List:
|
||||
"""
|
||||
Asynchronously searches for the SPO object that is related to `__name__:__main__`.
|
||||
|
||||
Returns:
|
||||
List: A list containing information about the main entry in the sequence diagram.
|
||||
"""
|
||||
rows = await self.graph_db.select(predicate=GraphKeyword.HAS_PAGE_INFO)
|
||||
tag = "__name__:__main__"
|
||||
entries = []
|
||||
for r in rows:
|
||||
|
|
@ -39,24 +219,395 @@ class RebuildSequenceView(Action):
|
|||
entries.append(r)
|
||||
return entries
|
||||
|
||||
async def _rebuild_sequence_view(self, entry, graph_db):
|
||||
filename = entry.subject.split(":", 1)[0]
|
||||
src_filename = RebuildSequenceView._get_full_filename(root=self.i_context, pathname=filename)
|
||||
if not src_filename:
|
||||
@retry(
|
||||
wait=wait_random_exponential(min=1, max=20),
|
||||
stop=stop_after_attempt(6),
|
||||
after=general_after_log(logger),
|
||||
)
|
||||
async def _rebuild_use_case(self, ns_class_name: str):
|
||||
"""
|
||||
Asynchronously reconstructs the use case for the provided namespace-prefixed class name.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed class name for which the use case is to be reconstructed.
|
||||
"""
|
||||
rows = await self.graph_db.select(subject=ns_class_name, predicate=GraphKeyword.HAS_CLASS_USE_CASE)
|
||||
if rows:
|
||||
return
|
||||
content = await aread(filename=src_filename, encoding="utf-8")
|
||||
content = f"```python\n{content}\n```\n\n---\nTranslate the code above into Mermaid Sequence Diagram."
|
||||
data = await self.llm.aask(
|
||||
msg=content, system_msgs=["You are a python code to Mermaid Sequence Diagram translator in function detail"]
|
||||
|
||||
detail = await self._get_class_detail(ns_class_name)
|
||||
if not detail:
|
||||
return
|
||||
participants = set()
|
||||
participants.update(set(detail.compositions))
|
||||
participants.update(set(detail.aggregations))
|
||||
class_view = await self._get_uml_class_view(ns_class_name)
|
||||
source_code = await self._get_source_code(ns_class_name)
|
||||
|
||||
# prompt_blocks = [
|
||||
# "## Instruction\n"
|
||||
# "You are a python code to UML 2.0 Use Case translator.\n"
|
||||
# 'The generated UML 2.0 Use Case must include the roles or entities listed in "Participants".\n'
|
||||
# "The functional descriptions of Actors and Use Cases in the generated UML 2.0 Use Case must not "
|
||||
# 'conflict with the information in "Mermaid Class Views".\n'
|
||||
# 'The section under `if __name__ == "__main__":` of "Source Code" contains information about external '
|
||||
# "system interactions with the internal system.\n"
|
||||
# ]
|
||||
prompt_blocks = []
|
||||
block = "## Participants\n"
|
||||
for p in participants:
|
||||
block += f"- {p}\n"
|
||||
prompt_blocks.append(block)
|
||||
block = "## Mermaid Class Views\n```mermaid\n"
|
||||
block += class_view.get_mermaid()
|
||||
block += "\n```\n"
|
||||
prompt_blocks.append(block)
|
||||
block = "## Source Code\n```python\n"
|
||||
block += source_code
|
||||
block += "\n```\n"
|
||||
prompt_blocks.append(block)
|
||||
prompt = "\n---\n".join(prompt_blocks)
|
||||
|
||||
rsp = await self.llm.aask(
|
||||
msg=prompt,
|
||||
system_msgs=[
|
||||
"You are a python code to UML 2.0 Use Case translator.",
|
||||
'The generated UML 2.0 Use Case must include the roles or entities listed in "Participants".',
|
||||
"The functional descriptions of Actors and Use Cases in the generated UML 2.0 Use Case must not "
|
||||
'conflict with the information in "Mermaid Class Views".',
|
||||
'The section under `if __name__ == "__main__":` of "Source Code" contains information about external '
|
||||
"system interactions with the internal system.",
|
||||
"Return a markdown JSON object with:\n"
|
||||
'- a "description" key to explain what the whole source code want to do;\n'
|
||||
'- a "use_cases" key list all use cases, each use case in the list should including a `description` '
|
||||
"key describes about what the use case to do, a `inputs` key lists the input names of the use case "
|
||||
"from external sources, a `outputs` key lists the output names of the use case to external sources, "
|
||||
"a `actors` key lists the participant actors of the use case, a `steps` key lists the steps about how "
|
||||
"the use case works step by step, a `reason` key explaining under what circumstances would the "
|
||||
"external system execute this use case.\n"
|
||||
'- a "relationship" key lists all the descriptions of relationship among these use cases.\n',
|
||||
],
|
||||
stream=False,
|
||||
)
|
||||
|
||||
code_blocks = parse_json_code_block(rsp)
|
||||
for block in code_blocks:
|
||||
detail = ReverseUseCaseDetails.model_validate_json(block)
|
||||
await self.graph_db.insert(
|
||||
subject=ns_class_name, predicate=GraphKeyword.HAS_CLASS_USE_CASE, object_=detail.model_dump_json()
|
||||
)
|
||||
|
||||
@retry(
|
||||
wait=wait_random_exponential(min=1, max=20),
|
||||
stop=stop_after_attempt(6),
|
||||
after=general_after_log(logger),
|
||||
)
|
||||
async def _rebuild_sequence_view(self, ns_class_name: str):
|
||||
"""
|
||||
Asynchronously reconstructs the sequence diagram for the provided namespace-prefixed class name.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed class name for which the sequence diagram is to be reconstructed.
|
||||
"""
|
||||
await self._rebuild_use_case(ns_class_name)
|
||||
|
||||
prompts_blocks = []
|
||||
use_case_markdown = await self._get_class_use_cases(ns_class_name)
|
||||
if not use_case_markdown: # external class
|
||||
await self.graph_db.insert(subject=ns_class_name, predicate=GraphKeyword.HAS_SEQUENCE_VIEW, object_="")
|
||||
return
|
||||
block = f"## Use Cases\n{use_case_markdown}"
|
||||
prompts_blocks.append(block)
|
||||
|
||||
participants = await self._get_participants(ns_class_name)
|
||||
block = "## Participants\n" + "\n".join([f"- {s}" for s in participants])
|
||||
prompts_blocks.append(block)
|
||||
|
||||
view = await self._get_uml_class_view(ns_class_name)
|
||||
block = "## Mermaid Class Views\n```mermaid\n"
|
||||
block += view.get_mermaid()
|
||||
block += "\n```\n"
|
||||
prompts_blocks.append(block)
|
||||
|
||||
block = "## Source Code\n```python\n"
|
||||
block += await self._get_source_code(ns_class_name)
|
||||
block += "\n```\n"
|
||||
prompts_blocks.append(block)
|
||||
prompt = "\n---\n".join(prompts_blocks)
|
||||
|
||||
rsp = await self.llm.aask(
|
||||
prompt,
|
||||
system_msgs=[
|
||||
"You are a Mermaid Sequence Diagram translator in function detail.",
|
||||
"Translate the markdown text to a Mermaid Sequence Diagram.",
|
||||
"Return a markdown mermaid code block.",
|
||||
],
|
||||
stream=False,
|
||||
)
|
||||
|
||||
sequence_view = rsp.removeprefix("```mermaid").removesuffix("```")
|
||||
await self.graph_db.insert(
|
||||
subject=ns_class_name, predicate=GraphKeyword.HAS_SEQUENCE_VIEW, object_=sequence_view
|
||||
)
|
||||
|
||||
async def _get_participants(self, ns_class_name: str) -> List[str]:
|
||||
"""
|
||||
Asynchronously returns the participants list of the sequence diagram for the provided namespace-prefixed SPO
|
||||
object.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed class name for which to retrieve the participants list.
|
||||
|
||||
Returns:
|
||||
List[str]: A list of participants in the sequence diagram.
|
||||
"""
|
||||
participants = set()
|
||||
detail = await self._get_class_detail(ns_class_name)
|
||||
if not detail:
|
||||
return []
|
||||
participants.update(set(detail.compositions))
|
||||
participants.update(set(detail.aggregations))
|
||||
return list(participants)
|
||||
|
||||
async def _get_class_use_cases(self, ns_class_name: str) -> str:
|
||||
"""
|
||||
Asynchronously assembles the context about the use case information of the namespace-prefixed SPO object.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed class name for which to retrieve use case information.
|
||||
|
||||
Returns:
|
||||
str: A string containing the assembled context about the use case information.
|
||||
"""
|
||||
block = ""
|
||||
rows = await self.graph_db.select(subject=ns_class_name, predicate=GraphKeyword.HAS_CLASS_USE_CASE)
|
||||
for i, r in enumerate(rows):
|
||||
detail = ReverseUseCaseDetails.model_validate_json(r.object_)
|
||||
block += f"\n### {i + 1}. {detail.description}"
|
||||
for j, use_case in enumerate(detail.use_cases):
|
||||
block += f"\n#### {i + 1}.{j + 1}. {use_case.description}\n"
|
||||
block += "\n##### Inputs\n" + "\n".join([f"- {s}" for s in use_case.inputs])
|
||||
block += "\n##### Outputs\n" + "\n".join([f"- {s}" for s in use_case.outputs])
|
||||
block += "\n##### Actors\n" + "\n".join([f"- {s}" for s in use_case.actors])
|
||||
block += "\n##### Steps\n" + "\n".join([f"- {s}" for s in use_case.steps])
|
||||
block += "\n#### Use Case Relationship\n" + "\n".join([f"- {s}" for s in detail.relationship])
|
||||
return block + "\n"
|
||||
|
||||
async def _get_class_detail(self, ns_class_name: str) -> DotClassInfo | None:
|
||||
"""
|
||||
Asynchronously retrieves the dot format class details of the namespace-prefixed SPO object.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed class name for which to retrieve class details.
|
||||
|
||||
Returns:
|
||||
Union[DotClassInfo, None]: A DotClassInfo object representing the dot format class details,
|
||||
or None if the details are not available.
|
||||
"""
|
||||
rows = await self.graph_db.select(subject=ns_class_name, predicate=GraphKeyword.HAS_DETAIL)
|
||||
if not rows:
|
||||
return None
|
||||
dot_class_info = DotClassInfo.model_validate_json(rows[0].object_)
|
||||
return dot_class_info
|
||||
|
||||
async def _get_uml_class_view(self, ns_class_name: str) -> UMLClassView | None:
|
||||
"""
|
||||
Asynchronously retrieves the UML 2.0 format class details of the namespace-prefixed SPO object.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed class name for which to retrieve UML class details.
|
||||
|
||||
Returns:
|
||||
Union[UMLClassView, None]: A UMLClassView object representing the UML 2.0 format class details,
|
||||
or None if the details are not available.
|
||||
"""
|
||||
rows = await self.graph_db.select(subject=ns_class_name, predicate=GraphKeyword.HAS_CLASS_VIEW)
|
||||
if not rows:
|
||||
return None
|
||||
class_view = UMLClassView.model_validate_json(rows[0].object_)
|
||||
return class_view
|
||||
|
||||
async def _get_source_code(self, ns_class_name: str) -> str:
|
||||
"""
|
||||
Asynchronously retrieves the source code of the namespace-prefixed SPO object.
|
||||
|
||||
Args:
|
||||
ns_class_name (str): The namespace-prefixed class name for which to retrieve the source code.
|
||||
|
||||
Returns:
|
||||
str: A string containing the source code of the specified namespace-prefixed class.
|
||||
"""
|
||||
rows = await self.graph_db.select(subject=ns_class_name, predicate=GraphKeyword.HAS_PAGE_INFO)
|
||||
filename = split_namespace(ns_class_name=ns_class_name)[0]
|
||||
if not rows:
|
||||
src_filename = RebuildSequenceView._get_full_filename(root=self.i_context, pathname=filename)
|
||||
if not src_filename:
|
||||
return ""
|
||||
return await aread(filename=src_filename, encoding="utf-8")
|
||||
code_block_info = CodeBlockInfo.model_validate_json(rows[0].object_)
|
||||
return await read_file_block(
|
||||
filename=filename, lineno=code_block_info.lineno, end_lineno=code_block_info.end_lineno
|
||||
)
|
||||
await graph_db.insert(subject=filename, predicate=GraphKeyword.HAS_SEQUENCE_VIEW, object_=data)
|
||||
logger.info(data)
|
||||
|
||||
@staticmethod
|
||||
def _get_full_filename(root: str | Path, pathname: str | Path) -> Path | None:
|
||||
"""
|
||||
Convert package name to the full path of the module.
|
||||
|
||||
Args:
|
||||
root (Union[str, Path]): The root path or string representing the package.
|
||||
pathname (Union[str, Path]): The pathname or string representing the module.
|
||||
|
||||
Returns:
|
||||
Union[Path, None]: The full path of the module, or None if the path cannot be determined.
|
||||
|
||||
Examples:
|
||||
If `root`(workdir) is "/User/xxx/github/MetaGPT/metagpt", and the `pathname` is
|
||||
"metagpt/management/skill_manager.py", then the returned value will be
|
||||
"/User/xxx/github/MetaGPT/metagpt/management/skill_manager.py"
|
||||
"""
|
||||
if re.match(r"^/.+", pathname):
|
||||
return pathname
|
||||
files = list_files(root=root)
|
||||
postfix = "/" + str(pathname)
|
||||
for i in files:
|
||||
if str(i).endswith(postfix):
|
||||
return i
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def parse_participant(mermaid_sequence_diagram: str) -> List[str]:
|
||||
"""
|
||||
Parses the provided Mermaid sequence diagram and returns the list of participants.
|
||||
|
||||
Args:
|
||||
mermaid_sequence_diagram (str): The Mermaid sequence diagram string to be parsed.
|
||||
|
||||
Returns:
|
||||
List[str]: A list of participants extracted from the sequence diagram.
|
||||
"""
|
||||
pattern = r"participant ([a-zA-Z\.0-9_]+)"
|
||||
matches = re.findall(pattern, mermaid_sequence_diagram)
|
||||
matches = [re.sub(r"[\\/'\"]+", "", i) for i in matches]
|
||||
return matches
|
||||
|
||||
async def _search_new_participant(self, entry: SPO) -> str | None:
|
||||
"""
|
||||
Asynchronously retrieves a participant whose sequence diagram has not been augmented.
|
||||
|
||||
Args:
|
||||
entry (SPO): The SPO object representing the relationship in the graph database.
|
||||
|
||||
Returns:
|
||||
Union[str, None]: A participant whose sequence diagram has not been augmented, or None if not found.
|
||||
"""
|
||||
rows = await self.graph_db.select(subject=entry.subject, predicate=GraphKeyword.HAS_SEQUENCE_VIEW)
|
||||
if not rows:
|
||||
return None
|
||||
sequence_view = rows[0].object_
|
||||
rows = await self.graph_db.select(subject=entry.subject, predicate=GraphKeyword.HAS_PARTICIPANT)
|
||||
merged_participants = []
|
||||
for r in rows:
|
||||
name = split_namespace(r.object_)[-1]
|
||||
merged_participants.append(name)
|
||||
participants = self.parse_participant(sequence_view)
|
||||
for p in participants:
|
||||
if p in merged_participants:
|
||||
continue
|
||||
return p
|
||||
return None
|
||||
|
||||
@retry(
|
||||
wait=wait_random_exponential(min=1, max=20),
|
||||
stop=stop_after_attempt(6),
|
||||
after=general_after_log(logger),
|
||||
)
|
||||
async def _merge_participant(self, entry: SPO, class_name: str):
|
||||
"""
|
||||
Augments the sequence diagram of `class_name` to the sequence diagram of `entry`.
|
||||
|
||||
Args:
|
||||
entry (SPO): The SPO object representing the base sequence diagram.
|
||||
class_name (str): The class name whose sequence diagram is to be augmented.
|
||||
"""
|
||||
rows = await self.graph_db.select(predicate=GraphKeyword.IS, object_=GraphKeyword.CLASS)
|
||||
participants = []
|
||||
for r in rows:
|
||||
name = split_namespace(r.subject)[-1]
|
||||
if name == class_name:
|
||||
participants.append(r)
|
||||
if len(participants) == 0: # external participants
|
||||
await self.graph_db.insert(
|
||||
subject=entry.subject, predicate=GraphKeyword.HAS_PARTICIPANT, object_=concat_namespace("?", class_name)
|
||||
)
|
||||
return
|
||||
if len(participants) > 1:
|
||||
for r in participants:
|
||||
await self.graph_db.insert(
|
||||
subject=entry.subject, predicate=GraphKeyword.HAS_PARTICIPANT, object_=auto_namespace(r.subject)
|
||||
)
|
||||
return
|
||||
|
||||
participant = participants[0]
|
||||
await self._rebuild_sequence_view(participant.subject)
|
||||
sequence_views = await self.graph_db.select(
|
||||
subject=participant.subject, predicate=GraphKeyword.HAS_SEQUENCE_VIEW
|
||||
)
|
||||
if not sequence_views: # external class
|
||||
return
|
||||
rows = await self.graph_db.select(subject=entry.subject, predicate=GraphKeyword.HAS_SEQUENCE_VIEW)
|
||||
prompt = f"```mermaid\n{sequence_views[0].object_}\n```\n---\n```mermaid\n{rows[0].object_}\n```"
|
||||
|
||||
rsp = await self.llm.aask(
|
||||
prompt,
|
||||
system_msgs=[
|
||||
"You are a tool to merge sequence diagrams into one.",
|
||||
"Participants with the same name are considered identical.",
|
||||
"Return the merged Mermaid sequence diagram in a markdown code block format.",
|
||||
],
|
||||
stream=False,
|
||||
)
|
||||
|
||||
sequence_view = rsp.removeprefix("```mermaid").removesuffix("```")
|
||||
rows = await self.graph_db.select(subject=entry.subject, predicate=GraphKeyword.HAS_SEQUENCE_VIEW)
|
||||
for r in rows:
|
||||
await self.graph_db.delete(subject=r.subject, predicate=r.predicate, object_=r.object_)
|
||||
await self.graph_db.insert(
|
||||
subject=entry.subject, predicate=GraphKeyword.HAS_SEQUENCE_VIEW, object_=sequence_view
|
||||
)
|
||||
await self.graph_db.insert(
|
||||
subject=entry.subject,
|
||||
predicate=GraphKeyword.HAS_SEQUENCE_VIEW_VER,
|
||||
object_=concat_namespace(datetime.now().strftime("%Y%m%d%H%M%S%f")[:-3], add_affix(sequence_view)),
|
||||
)
|
||||
await self.graph_db.insert(
|
||||
subject=entry.subject, predicate=GraphKeyword.HAS_PARTICIPANT, object_=auto_namespace(participant.subject)
|
||||
)
|
||||
await self._save_sequence_view(subject=entry.subject, content=sequence_view)
|
||||
|
||||
async def _save_sequence_view(self, subject: str, content: str):
|
||||
pattern = re.compile(r"[^a-zA-Z0-9]")
|
||||
name = re.sub(pattern, "_", subject)
|
||||
filename = Path(name).with_suffix(".sequence_diagram.mmd")
|
||||
await self.context.repo.resources.data_api_design.save(filename=str(filename), content=content)
|
||||
|
||||
async def _search_participants(self, filename: str) -> Set:
|
||||
content = await self._get_source_code(filename)
|
||||
|
||||
rsp = await self.llm.aask(
|
||||
msg=content,
|
||||
system_msgs=[
|
||||
"You are a tool for listing all class names used in a source file.",
|
||||
"Return a markdown JSON object with: "
|
||||
'- a "class_names" key containing the list of class names used in the file; '
|
||||
'- a "reasons" key lists all reason objects, each object containing a "class_name" key for class name, a "reference" key explaining the line where the class has been used.',
|
||||
],
|
||||
)
|
||||
|
||||
class _Data(BaseModel):
|
||||
class_names: List[str]
|
||||
reasons: List
|
||||
|
||||
json_blocks = parse_json_code_block(rsp)
|
||||
data = _Data.model_validate_json(json_blocks[0])
|
||||
return set(data.class_names)
|
||||
|
|
|
|||
|
|
@ -1,16 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@Time : 2024/1/4
|
||||
@Author : mashenquan
|
||||
@File : rebuild_sequence_view_an.py
|
||||
"""
|
||||
from metagpt.actions.action_node import ActionNode
|
||||
from metagpt.utils.mermaid import MMC2
|
||||
|
||||
CODE_2_MERMAID_SEQUENCE_DIAGRAM = ActionNode(
|
||||
key="Program call flow",
|
||||
expected_type=str,
|
||||
instruction='Translate the "context" content into "format example" format.',
|
||||
example=MMC2,
|
||||
)
|
||||
|
|
@ -133,8 +133,8 @@ class CollectLinks(Action):
|
|||
if len(remove) == 0:
|
||||
break
|
||||
|
||||
model_name = config.get_openai_llm().model
|
||||
prompt = reduce_message_length(gen_msg(), model_name, system_text, 4096)
|
||||
model_name = config.llm.model
|
||||
prompt = reduce_message_length(gen_msg(), model_name, system_text, config.llm.max_token)
|
||||
logger.debug(prompt)
|
||||
queries = await self._aask(prompt, [system_text])
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -50,6 +50,7 @@ class ArgumentsParingAction(Action):
|
|||
rsp = await self.llm.aask(
|
||||
msg=prompt,
|
||||
system_msgs=["You are a function parser.", "You can convert spoken words into function parameters."],
|
||||
stream=False,
|
||||
)
|
||||
logger.debug(f"SKILL:{prompt}\n, RESULT:{rsp}")
|
||||
self.args = ArgumentsParingAction.parse_arguments(skill_name=self.skill.name, txt=rsp)
|
||||
|
|
|
|||
|
|
@ -92,7 +92,7 @@ class TalkAction(Action):
|
|||
|
||||
async def run(self, with_message=None, **kwargs) -> Message:
|
||||
msg, format_msgs, system_msgs = self.aask_args
|
||||
rsp = await self.llm.aask(msg=msg, format_msgs=format_msgs, system_msgs=system_msgs)
|
||||
rsp = await self.llm.aask(msg=msg, format_msgs=format_msgs, system_msgs=system_msgs, stream=False)
|
||||
self.rsp = Message(content=rsp, role="assistant", cause_by=self)
|
||||
return self.rsp
|
||||
|
||||
|
|
|
|||
|
|
@ -6,30 +6,44 @@
|
|||
@File : write_code_plan_and_change_an.py
|
||||
"""
|
||||
import os
|
||||
from typing import List
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from metagpt.actions.action import Action
|
||||
from metagpt.actions.action_node import ActionNode
|
||||
from metagpt.logs import logger
|
||||
from metagpt.schema import CodePlanAndChangeContext
|
||||
|
||||
CODE_PLAN_AND_CHANGE = ActionNode(
|
||||
key="Code Plan And Change",
|
||||
expected_type=str,
|
||||
instruction="Developing comprehensive and step-by-step incremental development plan, and write Incremental "
|
||||
"Change by making a code draft that how to implement incremental development including detailed steps based on the "
|
||||
"context. Note: Track incremental changes using mark of '+' or '-' for add/modify/delete code, and conforms to the "
|
||||
"output format of git diff",
|
||||
example="""
|
||||
1. Plan for calculator.py: Enhance the functionality of `calculator.py` by extending it to incorporate methods for subtraction, multiplication, and division. Additionally, implement robust error handling for the division operation to mitigate potential issues related to division by zero.
|
||||
```python
|
||||
DEVELOPMENT_PLAN = ActionNode(
|
||||
key="Development Plan",
|
||||
expected_type=List[str],
|
||||
instruction="Develop a comprehensive and step-by-step incremental development plan, providing the detail "
|
||||
"changes to be implemented at each step based on the order of 'Task List'",
|
||||
example=[
|
||||
"Enhance the functionality of `calculator.py` by extending it to incorporate methods for subtraction, ...",
|
||||
"Update the existing codebase in main.py to incorporate new API endpoints for subtraction, ...",
|
||||
],
|
||||
)
|
||||
|
||||
INCREMENTAL_CHANGE = ActionNode(
|
||||
key="Incremental Change",
|
||||
expected_type=List[str],
|
||||
instruction="Write Incremental Change by making a code draft that how to implement incremental development "
|
||||
"including detailed steps based on the context. Note: Track incremental changes using the marks `+` and `-` to "
|
||||
"indicate additions and deletions, and ensure compliance with the output format of `git diff`",
|
||||
example=[
|
||||
'''```diff
|
||||
--- Old/calculator.py
|
||||
+++ New/calculator.py
|
||||
|
||||
class Calculator:
|
||||
self.result = number1 + number2
|
||||
return self.result
|
||||
|
||||
- def sub(self, number1, number2) -> float:
|
||||
+ def subtract(self, number1: float, number2: float) -> float:
|
||||
+ '''
|
||||
+ """
|
||||
+ Subtracts the second number from the first and returns the result.
|
||||
+
|
||||
+ Args:
|
||||
|
|
@ -38,13 +52,13 @@ class Calculator:
|
|||
+
|
||||
+ Returns:
|
||||
+ float: The difference of number1 and number2.
|
||||
+ '''
|
||||
+ """
|
||||
+ self.result = number1 - number2
|
||||
+ return self.result
|
||||
+
|
||||
def multiply(self, number1: float, number2: float) -> float:
|
||||
- pass
|
||||
+ '''
|
||||
+ """
|
||||
+ Multiplies two numbers and returns the result.
|
||||
+
|
||||
+ Args:
|
||||
|
|
@ -53,15 +67,15 @@ class Calculator:
|
|||
+
|
||||
+ Returns:
|
||||
+ float: The product of number1 and number2.
|
||||
+ '''
|
||||
+ """
|
||||
+ self.result = number1 * number2
|
||||
+ return self.result
|
||||
+
|
||||
def divide(self, number1: float, number2: float) -> float:
|
||||
- pass
|
||||
+ '''
|
||||
+ """
|
||||
+ ValueError: If the second number is zero.
|
||||
+ '''
|
||||
+ """
|
||||
+ if number2 == 0:
|
||||
+ raise ValueError('Cannot divide by zero')
|
||||
+ self.result = number1 / number2
|
||||
|
|
@ -75,10 +89,11 @@ class Calculator:
|
|||
+ print("Result is already zero, no need to clear.")
|
||||
+
|
||||
self.result = 0.0
|
||||
```
|
||||
```''',
|
||||
"""```diff
|
||||
--- Old/main.py
|
||||
+++ New/main.py
|
||||
|
||||
2. Plan for main.py: Integrate new API endpoints for subtraction, multiplication, and division into the existing codebase of `main.py`. Then, ensure seamless integration with the overall application architecture and maintain consistency with coding standards.
|
||||
```python
|
||||
def add_numbers():
|
||||
result = calculator.add_numbers(num1, num2)
|
||||
return jsonify({'result': result}), 200
|
||||
|
|
@ -106,6 +121,7 @@ def add_numbers():
|
|||
if __name__ == '__main__':
|
||||
app.run()
|
||||
```""",
|
||||
],
|
||||
)
|
||||
|
||||
CODE_PLAN_AND_CHANGE_CONTEXT = """
|
||||
|
|
@ -172,14 +188,16 @@ Role: You are a professional engineer; The main goal is to complete incremental
|
|||
2. COMPLETE CODE: Your code will be part of the entire project, so please implement complete, reliable, reusable code snippets.
|
||||
3. Set default value: If there is any setting, ALWAYS SET A DEFAULT VALUE, ALWAYS USE STRONG TYPE AND EXPLICIT VARIABLE. AVOID circular import.
|
||||
4. Follow design: YOU MUST FOLLOW "Data structures and interfaces". DONT CHANGE ANY DESIGN. Do not use public member functions that do not exist in your design.
|
||||
5. Follow Code Plan And Change: If there is any Incremental Change that is marked by the git diff format using '+' and '-' for add/modify/delete code, or Legacy Code files contain "{filename} to be rewritten", you must merge it into the code file according to the plan.
|
||||
5. Follow Code Plan And Change: If there is any "Incremental Change" that is marked by the git diff format with '+' and '-' symbols, or Legacy Code files contain "{filename} to be rewritten", you must merge it into the code file according to the "Development Plan".
|
||||
6. CAREFULLY CHECK THAT YOU DONT MISS ANY NECESSARY CLASS/FUNCTION IN THIS FILE.
|
||||
7. Before using a external variable/module, make sure you import it first.
|
||||
8. Write out EVERY CODE DETAIL, DON'T LEAVE TODO.
|
||||
9. Attention: Retain details that are not related to incremental development but are important for maintaining the consistency and clarity of the old code.
|
||||
"""
|
||||
|
||||
WRITE_CODE_PLAN_AND_CHANGE_NODE = ActionNode.from_children("WriteCodePlanAndChange", [CODE_PLAN_AND_CHANGE])
|
||||
CODE_PLAN_AND_CHANGE = [DEVELOPMENT_PLAN, INCREMENTAL_CHANGE]
|
||||
|
||||
WRITE_CODE_PLAN_AND_CHANGE_NODE = ActionNode.from_children("WriteCodePlanAndChange", CODE_PLAN_AND_CHANGE)
|
||||
|
||||
|
||||
class WriteCodePlanAndChange(Action):
|
||||
|
|
@ -192,14 +210,14 @@ class WriteCodePlanAndChange(Action):
|
|||
prd_doc = await self.repo.docs.prd.get(filename=self.i_context.prd_filename)
|
||||
design_doc = await self.repo.docs.system_design.get(filename=self.i_context.design_filename)
|
||||
task_doc = await self.repo.docs.task.get(filename=self.i_context.task_filename)
|
||||
code_text = await self.get_old_codes()
|
||||
context = CODE_PLAN_AND_CHANGE_CONTEXT.format(
|
||||
requirement=self.i_context.requirement,
|
||||
prd=prd_doc.content,
|
||||
design=design_doc.content,
|
||||
task=task_doc.content,
|
||||
code=code_text,
|
||||
code=await self.get_old_codes(),
|
||||
)
|
||||
logger.info("Writing code plan and change..")
|
||||
return await WRITE_CODE_PLAN_AND_CHANGE_NODE.fill(context=context, llm=self.llm, schema="json")
|
||||
|
||||
async def get_old_codes(self) -> str:
|
||||
|
|
|
|||
|
|
@ -56,7 +56,7 @@ REFINED_PRODUCT_GOALS = ActionNode(
|
|||
key="Refined Product Goals",
|
||||
expected_type=List[str],
|
||||
instruction="Update and expand the original product goals to reflect the evolving needs due to incremental "
|
||||
"development.Ensure that the refined goals align with the current project direction and contribute to its success.",
|
||||
"development. Ensure that the refined goals align with the current project direction and contribute to its success.",
|
||||
example=[
|
||||
"Enhance user engagement through new features",
|
||||
"Optimize performance for scalability",
|
||||
|
|
|
|||
|
|
@ -92,7 +92,7 @@ class Config(CLIParams, YamlModel):
|
|||
"""
|
||||
default_config_paths: List[Path] = [
|
||||
METAGPT_ROOT / "config/config2.yaml",
|
||||
Path.home() / ".metagpt/config2.yaml",
|
||||
CONFIG_ROOT / "config2.yaml",
|
||||
]
|
||||
|
||||
dicts = [dict(os.environ)]
|
||||
|
|
@ -100,6 +100,20 @@ class Config(CLIParams, YamlModel):
|
|||
final = merge_dict(dicts)
|
||||
return Config(**final)
|
||||
|
||||
@classmethod
|
||||
def from_llm_config(cls, llm_config: dict):
|
||||
"""user config llm
|
||||
example:
|
||||
llm_config = {"api_type": "xxx", "api_key": "xxx", "model": "xxx"}
|
||||
gpt4 = Config.from_llm_config(llm_config)
|
||||
A = Role(name="A", profile="Democratic candidate", goal="Win the election", actions=[a1], watch=[a2], config=gpt4)
|
||||
"""
|
||||
llm_config = LLMConfig.model_validate(llm_config)
|
||||
dicts = [dict(os.environ)]
|
||||
dicts += [{"llm": llm_config}]
|
||||
final = merge_dict(dicts)
|
||||
return Config(**final)
|
||||
|
||||
def update_via_cli(self, project_path, project_name, inc, reqa_file, max_auto_summarize_code):
|
||||
"""update config via cli"""
|
||||
|
||||
|
|
|
|||
|
|
@ -10,12 +10,14 @@ from typing import Optional
|
|||
|
||||
from pydantic import field_validator
|
||||
|
||||
from metagpt.const import LLM_API_TIMEOUT
|
||||
from metagpt.utils.yaml_model import YamlModel
|
||||
|
||||
|
||||
class LLMType(Enum):
|
||||
OPENAI = "openai"
|
||||
ANTHROPIC = "anthropic"
|
||||
CLAUDE = "claude" # alias name of anthropic
|
||||
SPARK = "spark"
|
||||
ZHIPUAI = "zhipuai"
|
||||
FIREWORKS = "fireworks"
|
||||
|
|
@ -24,6 +26,11 @@ class LLMType(Enum):
|
|||
METAGPT = "metagpt"
|
||||
AZURE = "azure"
|
||||
OLLAMA = "ollama"
|
||||
QIANFAN = "qianfan" # Baidu BCE
|
||||
DASHSCOPE = "dashscope" # Aliyun LingJi DashScope
|
||||
MOONSHOT = "moonshot"
|
||||
MISTRAL = "mistral"
|
||||
YI = "yi" # lingyiwanwu
|
||||
|
||||
def __missing__(self, key):
|
||||
return self.OPENAI
|
||||
|
|
@ -36,12 +43,18 @@ class LLMConfig(YamlModel):
|
|||
Optional Fields in pydantic: https://docs.pydantic.dev/latest/migration/#required-optional-and-nullable-fields
|
||||
"""
|
||||
|
||||
api_key: str
|
||||
api_key: str = "sk-"
|
||||
api_type: LLMType = LLMType.OPENAI
|
||||
base_url: str = "https://api.openai.com/v1"
|
||||
api_version: Optional[str] = None
|
||||
|
||||
model: Optional[str] = None # also stands for DEPLOYMENT_NAME
|
||||
pricing_plan: Optional[str] = None # Cost Settlement Plan Parameters.
|
||||
|
||||
# For Cloud Service Provider like Baidu/ Alibaba
|
||||
access_key: Optional[str] = None
|
||||
secret_key: Optional[str] = None
|
||||
endpoint: Optional[str] = None # for self-deployed model on the cloud
|
||||
|
||||
# For Spark(Xunfei), maybe remove later
|
||||
app_id: Optional[str] = None
|
||||
|
|
@ -62,7 +75,7 @@ class LLMConfig(YamlModel):
|
|||
stream: bool = False
|
||||
logprobs: Optional[bool] = None # https://cookbook.openai.com/examples/using_logprobs
|
||||
top_logprobs: Optional[int] = None
|
||||
timeout: int = 60
|
||||
timeout: int = 600
|
||||
|
||||
# For Network
|
||||
proxy: Optional[str] = None
|
||||
|
|
@ -76,3 +89,8 @@ class LLMConfig(YamlModel):
|
|||
if v in ["", None, "YOUR_API_KEY"]:
|
||||
raise ValueError("Please set your API key in config2.yaml")
|
||||
return v
|
||||
|
||||
@field_validator("timeout")
|
||||
@classmethod
|
||||
def check_timeout(cls, v):
|
||||
return v or LLM_API_TIMEOUT
|
||||
|
|
|
|||
|
|
@ -7,6 +7,8 @@
|
|||
"""
|
||||
from typing import Callable, Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from metagpt.tools import SearchEngineType
|
||||
from metagpt.utils.yaml_model import YamlModel
|
||||
|
||||
|
|
@ -18,3 +20,11 @@ class SearchConfig(YamlModel):
|
|||
api_key: str = ""
|
||||
cse_id: str = "" # for google
|
||||
search_func: Optional[Callable] = None
|
||||
params: dict = Field(
|
||||
default_factory=lambda: {
|
||||
"engine": "google",
|
||||
"google_domain": "google.com",
|
||||
"gl": "us",
|
||||
"hl": "en",
|
||||
}
|
||||
)
|
||||
|
|
|
|||
|
|
@ -49,6 +49,7 @@ METAGPT_ROOT = get_metagpt_root() # Dependent on METAGPT_PROJECT_ROOT
|
|||
DEFAULT_WORKSPACE_ROOT = METAGPT_ROOT / "workspace"
|
||||
|
||||
EXAMPLE_PATH = METAGPT_ROOT / "examples"
|
||||
EXAMPLE_DATA_PATH = EXAMPLE_PATH / "data"
|
||||
DATA_PATH = METAGPT_ROOT / "data"
|
||||
TEST_DATA_PATH = METAGPT_ROOT / "tests/data"
|
||||
RESEARCH_PATH = DATA_PATH / "research"
|
||||
|
|
@ -104,6 +105,7 @@ CODE_SUMMARIES_PDF_FILE_REPO = "resources/code_summary"
|
|||
RESOURCES_FILE_REPO = "resources"
|
||||
SD_OUTPUT_FILE_REPO = "resources/sd_output"
|
||||
GRAPH_REPO_FILE_REPO = "docs/graph_repo"
|
||||
VISUAL_GRAPH_REPO_FILE_REPO = "resources/graph_db"
|
||||
CLASS_VIEW_FILE_REPO = "docs/class_view"
|
||||
|
||||
YAPI_URL = "http://yapi.deepwisdomai.com/"
|
||||
|
|
@ -121,7 +123,6 @@ BASE64_FORMAT = "base64"
|
|||
|
||||
# REDIS
|
||||
REDIS_KEY = "REDIS_KEY"
|
||||
LLM_API_TIMEOUT = 300
|
||||
|
||||
# Message id
|
||||
IGNORED_MESSAGE_ID = "0"
|
||||
|
|
@ -130,3 +131,7 @@ IGNORED_MESSAGE_ID = "0"
|
|||
GENERALIZATION = "Generalize"
|
||||
COMPOSITION = "Composite"
|
||||
AGGREGATION = "Aggregate"
|
||||
|
||||
# Timeout
|
||||
USE_CONFIG_TIMEOUT = 0 # Using llm.timeout configuration.
|
||||
LLM_API_TIMEOUT = 300
|
||||
|
|
|
|||
|
|
@ -12,10 +12,14 @@ from typing import Any, Optional
|
|||
from pydantic import BaseModel, ConfigDict
|
||||
|
||||
from metagpt.config2 import Config
|
||||
from metagpt.configs.llm_config import LLMConfig
|
||||
from metagpt.configs.llm_config import LLMConfig, LLMType
|
||||
from metagpt.provider.base_llm import BaseLLM
|
||||
from metagpt.provider.llm_provider_registry import create_llm_instance
|
||||
from metagpt.utils.cost_manager import CostManager
|
||||
from metagpt.utils.cost_manager import (
|
||||
CostManager,
|
||||
FireworksCostManager,
|
||||
TokenCostManager,
|
||||
)
|
||||
from metagpt.utils.git_repository import GitRepository
|
||||
from metagpt.utils.project_repo import ProjectRepo
|
||||
|
||||
|
|
@ -80,12 +84,21 @@ class Context(BaseModel):
|
|||
# self._llm = None
|
||||
# return self._llm
|
||||
|
||||
def _select_costmanager(self, llm_config: LLMConfig) -> CostManager:
|
||||
"""Return a CostManager instance"""
|
||||
if llm_config.api_type == LLMType.FIREWORKS:
|
||||
return FireworksCostManager()
|
||||
elif llm_config.api_type == LLMType.OPEN_LLM:
|
||||
return TokenCostManager()
|
||||
else:
|
||||
return self.cost_manager
|
||||
|
||||
def llm(self) -> BaseLLM:
|
||||
"""Return a LLM instance, fixme: support cache"""
|
||||
# if self._llm is None:
|
||||
self._llm = create_llm_instance(self.config.llm)
|
||||
if self._llm.cost_manager is None:
|
||||
self._llm.cost_manager = self.cost_manager
|
||||
self._llm.cost_manager = self._select_costmanager(self.config.llm)
|
||||
return self._llm
|
||||
|
||||
def llm_with_cost_manager_from_llm_config(self, llm_config: LLMConfig) -> BaseLLM:
|
||||
|
|
@ -93,5 +106,5 @@ class Context(BaseModel):
|
|||
# if self._llm is None:
|
||||
llm = create_llm_instance(llm_config)
|
||||
if llm.cost_manager is None:
|
||||
llm.cost_manager = self.cost_manager
|
||||
llm.cost_manager = self._select_costmanager(llm_config)
|
||||
return llm
|
||||
|
|
|
|||
|
|
@ -11,15 +11,13 @@ from pathlib import Path
|
|||
from typing import Optional, Union
|
||||
|
||||
import pandas as pd
|
||||
from langchain.document_loaders import (
|
||||
TextLoader,
|
||||
UnstructuredPDFLoader,
|
||||
UnstructuredWordDocumentLoader,
|
||||
)
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
from llama_index.core import Document, SimpleDirectoryReader
|
||||
from llama_index.core.node_parser import SimpleNodeParser
|
||||
from llama_index.readers.file import PDFReader
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
from tqdm import tqdm
|
||||
|
||||
from metagpt.logs import logger
|
||||
from metagpt.repo_parser import RepoParser
|
||||
|
||||
|
||||
|
|
@ -28,7 +26,7 @@ def validate_cols(content_col: str, df: pd.DataFrame):
|
|||
raise ValueError("Content column not found in DataFrame.")
|
||||
|
||||
|
||||
def read_data(data_path: Path):
|
||||
def read_data(data_path: Path) -> Union[pd.DataFrame, list[Document]]:
|
||||
suffix = data_path.suffix
|
||||
if ".xlsx" == suffix:
|
||||
data = pd.read_excel(data_path)
|
||||
|
|
@ -37,14 +35,13 @@ def read_data(data_path: Path):
|
|||
elif ".json" == suffix:
|
||||
data = pd.read_json(data_path)
|
||||
elif suffix in (".docx", ".doc"):
|
||||
data = UnstructuredWordDocumentLoader(str(data_path), mode="elements").load()
|
||||
data = SimpleDirectoryReader(input_files=[str(data_path)]).load_data()
|
||||
elif ".txt" == suffix:
|
||||
data = TextLoader(str(data_path)).load()
|
||||
text_splitter = CharacterTextSplitter(separator="\n", chunk_size=256, chunk_overlap=0)
|
||||
texts = text_splitter.split_documents(data)
|
||||
data = texts
|
||||
data = SimpleDirectoryReader(input_files=[str(data_path)]).load_data()
|
||||
node_parser = SimpleNodeParser.from_defaults(separator="\n", chunk_size=256, chunk_overlap=0)
|
||||
data = node_parser.get_nodes_from_documents(data)
|
||||
elif ".pdf" == suffix:
|
||||
data = UnstructuredPDFLoader(str(data_path), mode="elements").load()
|
||||
data = PDFReader.load_data(str(data_path))
|
||||
else:
|
||||
raise NotImplementedError("File format not supported.")
|
||||
return data
|
||||
|
|
@ -130,9 +127,12 @@ class IndexableDocument(Document):
|
|||
if isinstance(data, pd.DataFrame):
|
||||
validate_cols(content_col, data)
|
||||
return cls(data=data, content=str(data), content_col=content_col, meta_col=meta_col)
|
||||
else:
|
||||
try:
|
||||
content = data_path.read_text()
|
||||
return cls(data=data, content=content, content_col=content_col, meta_col=meta_col)
|
||||
except Exception as e:
|
||||
logger.debug(f"Load {str(data_path)} error: {e}")
|
||||
content = ""
|
||||
return cls(data=data, content=content, content_col=content_col, meta_col=meta_col)
|
||||
|
||||
def _get_docs_and_metadatas_by_df(self) -> (list, list):
|
||||
df = self.data
|
||||
|
|
@ -146,9 +146,9 @@ class IndexableDocument(Document):
|
|||
metadatas.append({})
|
||||
return docs, metadatas
|
||||
|
||||
def _get_docs_and_metadatas_by_langchain(self) -> (list, list):
|
||||
def _get_docs_and_metadatas_by_llamaindex(self) -> (list, list):
|
||||
data = self.data
|
||||
docs = [i.page_content for i in data]
|
||||
docs = [i.text for i in data]
|
||||
metadatas = [i.metadata for i in data]
|
||||
return docs, metadatas
|
||||
|
||||
|
|
@ -156,7 +156,7 @@ class IndexableDocument(Document):
|
|||
if isinstance(self.data, pd.DataFrame):
|
||||
return self._get_docs_and_metadatas_by_df()
|
||||
elif isinstance(self.data, list):
|
||||
return self._get_docs_and_metadatas_by_langchain()
|
||||
return self._get_docs_and_metadatas_by_llamaindex()
|
||||
else:
|
||||
raise NotImplementedError("Data type not supported for metadata extraction.")
|
||||
|
||||
|
|
|
|||
|
|
@ -38,9 +38,9 @@ class LocalStore(BaseStore, ABC):
|
|||
if not self.store:
|
||||
self.store = self.write()
|
||||
|
||||
def _get_index_and_store_fname(self, index_ext=".index", pkl_ext=".pkl"):
|
||||
index_file = self.cache_dir / f"{self.fname}{index_ext}"
|
||||
store_file = self.cache_dir / f"{self.fname}{pkl_ext}"
|
||||
def _get_index_and_store_fname(self, index_ext=".json", docstore_ext=".json"):
|
||||
index_file = self.cache_dir / "default__vector_store" / index_ext
|
||||
store_file = self.cache_dir / "docstore" / docstore_ext
|
||||
return index_file, store_file
|
||||
|
||||
@abstractmethod
|
||||
|
|
|
|||
|
|
@ -11,9 +11,9 @@ import chromadb
|
|||
class ChromaStore:
|
||||
"""If inherited from BaseStore, or importing other modules from metagpt, a Python exception occurs, which is strange."""
|
||||
|
||||
def __init__(self, name):
|
||||
def __init__(self, name: str, get_or_create: bool = False):
|
||||
client = chromadb.Client()
|
||||
collection = client.create_collection(name)
|
||||
collection = client.create_collection(name, get_or_create=get_or_create)
|
||||
self.client = client
|
||||
self.collection = collection
|
||||
|
||||
|
|
|
|||
|
|
@ -7,10 +7,14 @@
|
|||
"""
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
from typing import Any, Optional
|
||||
|
||||
from langchain.vectorstores import FAISS
|
||||
from langchain_core.embeddings import Embeddings
|
||||
import faiss
|
||||
from llama_index.core import VectorStoreIndex, load_index_from_storage
|
||||
from llama_index.core.embeddings import BaseEmbedding
|
||||
from llama_index.core.schema import Document, QueryBundle, TextNode
|
||||
from llama_index.core.storage import StorageContext
|
||||
from llama_index.vector_stores.faiss import FaissVectorStore
|
||||
|
||||
from metagpt.document import IndexableDocument
|
||||
from metagpt.document_store.base_store import LocalStore
|
||||
|
|
@ -20,36 +24,50 @@ from metagpt.utils.embedding import get_embedding
|
|||
|
||||
class FaissStore(LocalStore):
|
||||
def __init__(
|
||||
self, raw_data: Path, cache_dir=None, meta_col="source", content_col="output", embedding: Embeddings = None
|
||||
self, raw_data: Path, cache_dir=None, meta_col="source", content_col="output", embedding: BaseEmbedding = None
|
||||
):
|
||||
self.meta_col = meta_col
|
||||
self.content_col = content_col
|
||||
self.embedding = embedding or get_embedding()
|
||||
self.store: VectorStoreIndex
|
||||
super().__init__(raw_data, cache_dir)
|
||||
|
||||
def _load(self) -> Optional["FaissStore"]:
|
||||
index_file, store_file = self._get_index_and_store_fname(index_ext=".faiss") # langchain FAISS using .faiss
|
||||
def _load(self) -> Optional["VectorStoreIndex"]:
|
||||
index_file, store_file = self._get_index_and_store_fname()
|
||||
|
||||
if not (index_file.exists() and store_file.exists()):
|
||||
logger.info("Missing at least one of index_file/store_file, load failed and return None")
|
||||
return None
|
||||
vector_store = FaissVectorStore.from_persist_dir(persist_dir=self.cache_dir)
|
||||
storage_context = StorageContext.from_defaults(persist_dir=self.cache_dir, vector_store=vector_store)
|
||||
index = load_index_from_storage(storage_context, embed_model=self.embedding)
|
||||
|
||||
return FAISS.load_local(self.raw_data_path.parent, self.embedding, self.fname)
|
||||
return index
|
||||
|
||||
def _write(self, docs, metadatas):
|
||||
store = FAISS.from_texts(docs, self.embedding, metadatas=metadatas)
|
||||
return store
|
||||
def _write(self, docs: list[str], metadatas: list[dict[str, Any]]) -> VectorStoreIndex:
|
||||
assert len(docs) == len(metadatas)
|
||||
documents = [Document(text=doc, metadata=metadatas[idx]) for idx, doc in enumerate(docs)]
|
||||
|
||||
vector_store = FaissVectorStore(faiss_index=faiss.IndexFlatL2(1536))
|
||||
storage_context = StorageContext.from_defaults(vector_store=vector_store)
|
||||
index = VectorStoreIndex.from_documents(
|
||||
documents=documents, storage_context=storage_context, embed_model=self.embedding
|
||||
)
|
||||
|
||||
return index
|
||||
|
||||
def persist(self):
|
||||
self.store.save_local(self.raw_data_path.parent, self.fname)
|
||||
self.store.storage_context.persist(self.cache_dir)
|
||||
|
||||
def search(self, query: str, expand_cols=False, sep="\n", *args, k=5, **kwargs):
|
||||
retriever = self.store.as_retriever(similarity_top_k=k)
|
||||
rsp = retriever.retrieve(QueryBundle(query_str=query, embedding=self.embedding.get_text_embedding(query)))
|
||||
|
||||
def search(self, query, expand_cols=False, sep="\n", *args, k=5, **kwargs):
|
||||
rsp = self.store.similarity_search(query, k=k, **kwargs)
|
||||
logger.debug(rsp)
|
||||
if expand_cols:
|
||||
return str(sep.join([f"{x.page_content}: {x.metadata}" for x in rsp]))
|
||||
return str(sep.join([f"{x.node.text}: {x.node.metadata}" for x in rsp]))
|
||||
else:
|
||||
return str(sep.join([f"{x.page_content}" for x in rsp]))
|
||||
return str(sep.join([f"{x.node.text}" for x in rsp]))
|
||||
|
||||
async def asearch(self, *args, **kwargs):
|
||||
return await asyncio.to_thread(self.search, *args, **kwargs)
|
||||
|
|
@ -67,8 +85,12 @@ class FaissStore(LocalStore):
|
|||
|
||||
def add(self, texts: list[str], *args, **kwargs) -> list[str]:
|
||||
"""FIXME: Currently, the store is not updated after adding."""
|
||||
return self.store.add_texts(texts)
|
||||
texts_embeds = self.embedding.get_text_embedding_batch(texts)
|
||||
nodes = [TextNode(text=texts[idx], embedding=embed) for idx, embed in enumerate(texts_embeds)]
|
||||
self.store.insert_nodes(nodes)
|
||||
|
||||
return []
|
||||
|
||||
def delete(self, *args, **kwargs):
|
||||
"""Currently, langchain does not provide a delete interface."""
|
||||
"""Currently, faiss does not provide a delete interface."""
|
||||
raise NotImplementedError
|
||||
|
|
|
|||
|
|
@ -34,5 +34,5 @@ # do a `tap` action on the screen
|
|||
## TODO
|
||||
- add android app operation assistant under `examples/android_assistant`
|
||||
- migrate roles/actions of werewolf game from old version into current version
|
||||
- migrate roles/actions of mincraft game from old version into current version
|
||||
- migrate roles/actions of minecraft game from old version into current version
|
||||
- migrate roles/actions of stanford_town game from old version into current version
|
||||
|
|
|
|||
|
|
@ -4,10 +4,9 @@
|
|||
|
||||
from metagpt.environment.base_env import Environment
|
||||
from metagpt.environment.android_env.android_env import AndroidEnv
|
||||
from metagpt.environment.mincraft_env.mincraft_env import MincraftExtEnv
|
||||
from metagpt.environment.werewolf_env.werewolf_env import WerewolfEnv
|
||||
from metagpt.environment.stanford_town_env.stanford_town_env import StanfordTownEnv
|
||||
from metagpt.environment.software_env.software_env import SoftwareEnv
|
||||
|
||||
|
||||
__all__ = ["AndroidEnv", "MincraftExtEnv", "WerewolfEnv", "StanfordTownEnv", "SoftwareEnv", "Environment"]
|
||||
__all__ = ["AndroidEnv", "WerewolfEnv", "StanfordTownEnv", "SoftwareEnv", "Environment"]
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ class EnvType(Enum):
|
|||
ANDROID = "Android"
|
||||
GYM = "Gym"
|
||||
WEREWOLF = "Werewolf"
|
||||
MINCRAFT = "Mincraft"
|
||||
MINECRAFT = "Minecraft"
|
||||
STANFORDTOWN = "StanfordTown"
|
||||
|
||||
|
||||
|
|
@ -47,7 +47,7 @@ def mark_as_writeable(func):
|
|||
|
||||
|
||||
class ExtEnv(BaseModel):
|
||||
"""External Env to intergate actual game environment"""
|
||||
"""External Env to integrate actual game environment"""
|
||||
|
||||
def _check_api_exist(self, rw_api: Optional[str] = None):
|
||||
if not rw_api:
|
||||
|
|
@ -129,8 +129,8 @@ class Environment(ExtEnv):
|
|||
self.roles[role.profile] = role
|
||||
|
||||
for role in roles: # setup system message with roles
|
||||
role.set_env(self)
|
||||
role.context = self.context
|
||||
role.set_env(self)
|
||||
|
||||
def publish_message(self, message: Message, peekable: bool = True) -> bool:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@
|
|||
|
||||
from metagpt.const import METAGPT_ROOT
|
||||
|
||||
# For Mincraft Game Agent
|
||||
MC_CKPT_DIR = METAGPT_ROOT / "data/mincraft/ckpt"
|
||||
# For Minecraft Game Agent
|
||||
MC_CKPT_DIR = METAGPT_ROOT / "data/minecraft/ckpt"
|
||||
MC_LOG_DIR = METAGPT_ROOT / "logs"
|
||||
MC_DEFAULT_WARMUP = {
|
||||
"context": 15,
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : MG Mincraft Env
|
||||
# @Desc : MG Minecraft Env
|
||||
# refs to `voyager voyager.py`
|
||||
|
||||
import json
|
||||
|
|
@ -8,20 +8,19 @@ import re
|
|||
import time
|
||||
from typing import Any, Iterable
|
||||
|
||||
from langchain.embeddings.openai import OpenAIEmbeddings
|
||||
from langchain.vectorstores import Chroma
|
||||
from pydantic import ConfigDict, Field
|
||||
|
||||
from metagpt.config2 import config as CONFIG
|
||||
from metagpt.environment.base_env import Environment
|
||||
from metagpt.environment.mincraft_env.const import MC_CKPT_DIR
|
||||
from metagpt.environment.mincraft_env.mincraft_ext_env import MincraftExtEnv
|
||||
from metagpt.environment.minecraft_env.const import MC_CKPT_DIR
|
||||
from metagpt.environment.minecraft_env.minecraft_ext_env import MinecraftExtEnv
|
||||
from metagpt.logs import logger
|
||||
from metagpt.rag.vector_stores.chroma import ChromaVectorStore
|
||||
from metagpt.utils.common import load_mc_skills_code, read_json_file, write_json_file
|
||||
|
||||
|
||||
class MincraftEnv(Environment, MincraftExtEnv):
|
||||
"""MincraftEnv, including shared memory of cache and infomation between roles"""
|
||||
class MinecraftEnv(Environment, MinecraftExtEnv):
|
||||
"""MinecraftEnv, including shared memory of cache and information between roles"""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
|
|
@ -48,9 +47,9 @@ class MincraftEnv(Environment, MincraftExtEnv):
|
|||
|
||||
runtime_status: bool = False # equal to action execution status: success or failed
|
||||
|
||||
vectordb: Chroma = Field(default_factory=Chroma)
|
||||
vectordb: ChromaVectorStore = Field(default_factory=ChromaVectorStore)
|
||||
|
||||
qa_cache_questions_vectordb: Chroma = Field(default_factory=Chroma)
|
||||
qa_cache_questions_vectordb: ChromaVectorStore = Field(default_factory=ChromaVectorStore)
|
||||
|
||||
@property
|
||||
def progress(self):
|
||||
|
|
@ -73,16 +72,14 @@ class MincraftEnv(Environment, MincraftExtEnv):
|
|||
self.set_mc_resume()
|
||||
|
||||
def set_mc_resume(self):
|
||||
self.qa_cache_questions_vectordb = Chroma(
|
||||
self.qa_cache_questions_vectordb = ChromaVectorStore(
|
||||
collection_name="qa_cache_questions_vectordb",
|
||||
embedding_function=OpenAIEmbeddings(),
|
||||
persist_directory=f"{MC_CKPT_DIR}/curriculum/vectordb",
|
||||
persist_dir=f"{MC_CKPT_DIR}/curriculum/vectordb",
|
||||
)
|
||||
|
||||
self.vectordb = Chroma(
|
||||
self.vectordb = ChromaVectorStore(
|
||||
collection_name="skill_vectordb",
|
||||
embedding_function=OpenAIEmbeddings(),
|
||||
persist_directory=f"{MC_CKPT_DIR}/skill/vectordb",
|
||||
persist_dir=f"{MC_CKPT_DIR}/skill/vectordb",
|
||||
)
|
||||
|
||||
if CONFIG.resume:
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : The Mincraft external environment to integrate with Mincraft game
|
||||
# @Desc : The Minecraft external environment to integrate with Minecraft game
|
||||
# refs to `voyager bridge.py`
|
||||
|
||||
import json
|
||||
|
|
@ -11,18 +11,18 @@ import requests
|
|||
from pydantic import ConfigDict, Field, model_validator
|
||||
|
||||
from metagpt.environment.base_env import ExtEnv, mark_as_writeable
|
||||
from metagpt.environment.mincraft_env.const import (
|
||||
from metagpt.environment.minecraft_env.const import (
|
||||
MC_CKPT_DIR,
|
||||
MC_CORE_INVENTORY_ITEMS,
|
||||
MC_CURRICULUM_OB,
|
||||
MC_DEFAULT_WARMUP,
|
||||
METAGPT_ROOT,
|
||||
)
|
||||
from metagpt.environment.mincraft_env.process_monitor import SubprocessMonitor
|
||||
from metagpt.environment.minecraft_env.process_monitor import SubprocessMonitor
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class MincraftExtEnv(ExtEnv):
|
||||
class MinecraftExtEnv(ExtEnv):
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
mc_port: Optional[int] = Field(default=None)
|
||||
|
|
@ -48,7 +48,7 @@ class MincraftExtEnv(ExtEnv):
|
|||
self.mineflayer = SubprocessMonitor(
|
||||
commands=[
|
||||
"node",
|
||||
METAGPT_ROOT.joinpath("metagpt", "environment", "mincraft_env", "mineflayer", "index.js"),
|
||||
METAGPT_ROOT.joinpath("metagpt", "environment", "minecraft_env", "mineflayer", "index.js"),
|
||||
str(self.server_port),
|
||||
],
|
||||
name="mineflayer",
|
||||
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Reference in a new issue