Merge pull request #262 from SylphAI-Inc/li_doc_github

[readme] Add quick start

.github/ISSUE_TEMPLATE/1_bug_report.yaml

@@ -1,6 +1,6 @@
 name: Report a bug
 description: Any errors that you encounter.
-labels: ['needs triage', 'bug']
+labels: ['bug']
 body:
   - type: markdown
     attributes:
@@ -71,10 +71,7 @@ body:
         Please provide details about your environment, including the following:
         - OS (e.g., Linux, Windows, macOS)
       value: |
-        <details>
-          <summary>Current environment</summary>
-
-        </details>
+        - OS: [e.g., Linux, Windows, macOS]
     validations:
       required: false
 

.github/ISSUE_TEMPLATE/2_suggest_improvement.yaml

@@ -1,6 +1,6 @@
 name: Improvement suggestion
 description: Suggest an improvement, a code refactor, or deprecation
-labels: ['needs triage', 'refactor']
+labels: ['[adalflow] improvement']
 body:
   - type: textarea
     attributes:

.github/ISSUE_TEMPLATE/3_feature_request.yaml

@@ -1,6 +1,6 @@
 name: Feature request
 description: Propose a feature for this project
-labels: ["needs triage", "feature"]
+labels: ["[adalflow] new feature request"]
 body:
   - type: textarea
     attributes:

.github/ISSUE_TEMPLATE/4_documenting.yaml

@@ -1,6 +1,6 @@
 name: Typos and doc fixes
 description: Tell us about how we can improve our documentation and Google colab/ipynb notebooks.
-labels: ["needs triage", "docs"]
+labels: ["documentation"]
 body:
   - type: textarea
     attributes:

.github/ISSUE_TEMPLATE/5_suggest_integration.yaml

@@ -1,6 +1,6 @@
-name: Feature request
+name: New integration proposal
 description: Propose a new integration for this project, either db, retriever, model_client. We highly recommend you to find a POC from the provider team to work together on this.
-labels: ['needs triage', 'feature']
+labels: ['[adalflow] integration']
 body:
   - type: textarea
     attributes:

.github/ISSUE_TEMPLATE/6_suggest_usecases_benchmarks.yaml

@@ -0,0 +1,32 @@
+name: Suggest use cases and benchmarks
+description: Propose new use cases that AdalFlow should support or benchmarks that we should compare against
+labels: ["new use cases/benchmarks"]
+body:
+  - type: textarea
+    attributes:
+      label: Description & Motivation
+      description: A clear and concise description of the new use case or benchmark proposal
+      placeholder: |
+        Please outline the motivation for the proposal.
+
+
+  - type: textarea
+    attributes:
+      label: Pitch
+      description: A clear and concise description of what you want to happen.
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Alternatives
+      description: A clear and concise description of any alternative solutions or features you've considered, if any.
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Additional context
+      description: Add any other context or screenshots about the feature request here.
+    validations:
+      required: false

README.md

@@ -111,6 +111,8 @@ Here is an optimization demonstration on a text classification task:
 
 Among all libraries, AdalFlow achieved the highest accuracy with manual prompting (starting at 82%) and the highest accuracy after optimization.
 
+
+
 Further reading: [Optimize Classification](https://adalflow.sylph.ai/use_cases/classification.html)
 
 ## Light, Modular, and Model-Agnostic Task Pipeline
@@ -192,7 +194,8 @@ Just define it as a ``Parameter`` and pass it to AdalFlow's ``Generator``.
 
 </p>
 
-# Quick Install
+# Quick Start
+
 
 Install AdalFlow with pip:
 
@@ -203,6 +206,9 @@ pip install adalflow
 Please refer to the [full installation guide](https://adalflow.sylph.ai/get_started/installation.html) for more details.
 
 
+* Try the [Building Quickstart](https://colab.research.google.com/drive/1TKw_JHE42Z_AWo8UuRYZCO2iuMgyslTZ?usp=sharing) in Colab to see how AdalFlow can build the task pipeline, including Chatbot, RAG, agent, and structured output.
+* Try the [Optimization Quickstart](https://colab.research.google.com/github/SylphAI-Inc/AdalFlow/blob/main/notebooks/qas/adalflow_object_count_auto_optimization.ipynb) to see how AdalFlow can optimize the task pipeline.
+
 
 
 # Documentation