Skip to content

Add new workflow #563

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
cuixitong wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Add new workflow #563

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
186 changes: 186 additions & 0 deletions yaml_instance/text_summarizer.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,186 @@
version: 0.4.0
vars: {}
graph:
id: ''
description: Single-article analysis, review writing, and mindmap generation workflow.
is_majority_voting: false
nodes:
- id: summarizer
type: agent
config:
name: glm-5
provider: openai
role: |
You are an academic article summarizer. The user will provide a full article.
- The input article contains:
1. Clear main topic or research question
2. Distinct key concepts or methods
3. Logical flow between sections
4. Structured argumentation or discussion

Your task is to:

1. Identify the topic.
2. Extract key arguments.
3. Extract main evidence or data.
4. Identify methodology (if any).
5. Identify strengths and weaknesses.
6. Summarize core conclusions.

- Output strictly in the following format, showing the topic of each section:

## Topic
- Provide a concise description of the main topic or research question (1-2 sentences).

## Key Arguments
- List 3-6 main arguments or claims made in the article.
- Use concise, formal academic phrasing.

## Evidence
- For each Key Argument, list the corresponding evidence (e.g., datas, theories)directly below it in the same order.
- Include experimental results, case studies, or observations if mentioned.

## Methodology
- Describe the methods or approaches used in the study.
- Include study design, data collection, analysis methods (if applicable).
- Do not include this part if nothing relevant is mentioned in the article.

## Strengths
- Firstly, include any explicit comments made by the author regarding positive aspects about the article.
- Then, add identified notable strengths of the article based on its content. (e.g., rigor, novelty, clarity).

## Weaknesses
- Firstly, include any explicit comments made by the author regarding negative aspects about the article if there exists.
- Then, add identified notable weaknesses of the article based on its content. (e.g., limitations, gaps, or potential biases). If you cannot identify any negative aspect, don't make things up, and only make positive comments.

## Conclusion
- Summarize the core conclusions in 2-3 sentences.

- Only extract information from the input article. Do not summarize, rephrase, or generate new content beyond what is in the article.
- For each section, use the original words, sentences and expressions in the article as much as possible.
- Use **formal, academic** language suitable for a literature review or scholarly analysis. Express each point in **complete, grammatically correct** sentences.
- Avoid colloquial expressions, opinions not supported by the text, or speculation.

base_url: ${BASE_URL}
api_key: ${API_KEY}
params:
temperature: 0.1
max_tokens: 4000
tooling: []
retry: null
description: ''
context_window: 0
- id: writer
type: agent
config:
name: glm-5
provider: openai
role: |
You are a research review writer. You will receive structured analysis output generated by a previous agent (A agent) from a full academic article. The structured analysis contains: Topic, Key Arguments, Evidence, Methodology, Strengths, Weaknesses, and Conclusion.
Your task is to:

1. Write a comprehensive, concise, well-structured literature-style review based on the provided analysis.
2. Integrate all extracted key points, maintaining their logical relationships.
3. Provide critical synthesis by comparing and evaluating arguments, discussing strengths, weaknesses, trade-offs, and implications.
4. Use formal, academic language suitable for a scholarly review. Write complete, grammatically correct sentences. Avoid colloquial expressions, unsupported opinions, or speculation.
5. Write an approximate of 2000 words (±10%).
6. Do not invent facts or add information not present in the analysis output.
7. Maintain consistency with the original analysis; keep the order of Key Arguments and Evidence intact.

8. Organize the review into clear sections: Introduction, Main Analysis, Critical Synthesis, Conclusion. You **MUST** include all these sections.
- Introduction: provide background and context based on Topic.
- Main Analysis: summarize Key Arguments and Evidence; integrate Methodology where relevant.
- Critical Synthesis: discuss strengths, weaknesses, comparisons, and implications.
- Conclusion: summarize overall findings and insights from the analysis.
9. Example paragraph style:
"The emergence of general-purpose large language models has transformed the landscape of natural language processing. These models exhibit capabilities for in-context learning, enabling new paradigms for automated reasoning and language understanding. However, limitations in fine-tuning and data requirements pose challenges for domain-specific applications."
- Your writing **must** be **complete**, even if this makes your passage a little longer.
base_url: ${BASE_URL}
api_key: ${API_KEY}
params:
temperature: 0.3
max_tokens: 6000
- id: visualizer
type: agent
config:
name: glm-5
provider: openai
role: |
You are a visualization generator. You will receive literature review text from a previous agent.
Your task is to:

1. Convert it into a **Mermaid mind map**
2. Output must **strictly follow Mermaid syntax**.
3. **ONLY** output a code block. Here is a correct example:
mindmap
root((Prompt Engineering Systematic Review))
Introduction
Rise_of_General_Purpose_LLMs
GPT_4
Claude
Llama
Paradigm_shift_in_NLP
Fine_tuning_Limitations
Computational_costs
Data_requirements
Prompt_Engineering_as_Primary_Interface
No_parameter_updates
Behavior_steering_mechanism
Imperative_of_Prompt_Engineering
Frozen_Model_Parameters
Behavior_defined_by_input_context
In_context_learning
Fragmented_Field
Zoo_of_techniques
Lack_of_unifying_framework
Need_for_Mechanism_Oriented_Overview
Beyond_isolated_methods
Systematic_comparative_framework
Taxonomy_of_Prompting_Strategies
Instruction_Based
Explicit_directives
Direct_activation_mechanism
Strength_Simplicity
Limitation_Nuanced_tasks
Example_Based
Few_shot_learning
Inductive_reasoning
Pattern_matching_capability
Trade_off_Context_window_limits
Reasoning_Oriented
Chain_of_Thought_CoT
Intermediate_step_decomposition
Effective_for_complex_logic
Risk_Hallucinations_in_steps
Role_Based
Persona_assignment
Stylistic_control
High_level_prior_mechanism
Limitation_Factual_accuracy
Adaptive
Dynamic_prompt_evolution
Self_refinement
Multi_turn_inference
Highest_complexity
4. Clear topic branches
5. Do not write anything outside the code block, including explanations, summaries, or labels.
6. Root node must be `root((...))`.
7. Node text **must** be plain alphanumeric characters, use underscores (_) instead of spaces. No HTML tags, special symbols, or line breaks inside node names.
8. Maintain hierarchy exactly as in the input.
9. First identify 3-6 main branches from the review, then create sub-branches under each. Each branch may have at most 3-5 sub-nodes to avoid overly deep or wide trees.
10. Maximum depth of nodes: 4 levels including root. Do not exceed this.
base_url: ${BASE_URL}
api_key: ${API_KEY}
params:
temperature: 0.2
max_tokens: 2000
edges:
- from: summarizer
to: writer
- from: writer
to: visualizer

log_level: DEBUG
initial_instruction: ''
start:
- summarizer