add faq doc

docs/_toc.yml

@@ -12,6 +12,11 @@ parts:
     - file: quickstart/quick_start_2
     - file: quickstart/virtualhome
 
+  - caption: FAQ
+    numbered: false
+    chapters:
+      - file: faq/faq
+
   - caption: 📚Tutorials
     chapters:
     - file: tutorials/basic_tutorial

docs/faq/faq.md

@@ -0,0 +1,47 @@
+# FAQ
+
+### Difference to Libraries like TextGrad
+
+TextGrad is both a library and an optimizer algorithm. Currently, we support three optimizers:
+
+- OPRO: [Large Language Models as Optimizers](https://arxiv.org/abs/2309.03409)
+- TextGrad: [TextGrad: Automatic "Differentiation" via Text](https://arxiv.org/abs/2406.07496)
+- OptoPrime: [Our proposed algorithm](https://arxiv.org/abs/2406.16218) -- using the entire computational graph to perform parameter update. It is 2-3x
+  faster than TextGrad.
+
+Using our framework, you can seamlessly switch between different optimizers:
+
+```python
+optimizer1 = OptoPrime(strange_sort_list.parameters())
+optimizer2 = OPRO(strange_sort_list.parameters())
+optimizer3 = TextGrad(strange_sort_list.parameters())
+```
+
+Here is a summary of the optimizers:
+
+|                                   | Computation Graph | Code as Functions | Library Support | Supported Optimizers      | Speed       | Large Graph |
+|-----------------------------------|-------------------|-------------------|------------------|---------------------------|-------------|-------------|
+| OPRO                              | ❌                 | ❌                 | ❌            | OPRO                      | ⚡️          | ✅      |
+| TextGrad                          | ✅                 | ❌                 | ✅            | TextGrad                  | 🐌          | ✅      |
+| Trace  | ✅                 | ✅                 | ✅            | OPRO, OptoPrime, TextGrad | ⚡  | ✅      |
+
+The table evaluates the frameworks in the following aspects:
+
+- Computation Graph: Whether the optimizer leverages the computation graph of the workflow.
+- Code as Functions: Whether the framework allows users to write actual executable Python functions and not require
+  users to wrap them in strings.
+- Library Support: Whether the framework has a library to support the optimizer.
+- Speed: TextGrad is about 2-3x slower than OptoPrime (Trace). OPRO has no concept of computational graph, therefore is very fast.
+- Large Graph: OptoPrime (Trace) represents the entire computation graph in context, therefore, might have issue with graphs that have more than hundreds of operations. TextGrad does not have the context-length issue, however, might be very slow on large graphs.
+
+We provide a comparison to validate our implementation of TextGrad in Trace:
+
+<p align="center">
+    <img src="https://github.com/microsoft/Trace/blob/main/docs/images/compare_to_textgrad3.png" alt="drawing" width="100%"/>
+</p>
+
+To produce this table, we ran the TextGrad pip-installed repo on 2024-10-30, and we also include the numbers reported in the TextGrad paper.
+The LLM APIs are called around the same time to ensure a fair comparison. TextGrad paper's result was reported in 2024-06.
+
+### Difference to Libraries like AutoGen, AG2, OpenAI Swarm, Llama Stack
+