Example: Simple MoA Chatbot with langgraph, ollama and mesop

Mesop_Langgraph/.gitignore

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Mesop_Langgraph/.vscode/settings.json

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+{
+    "python.analysis.packageIndexDepths": [
+        {
+            "name": "",
+            "depth": 10
+        }
+    ]
+}

Mesop_Langgraph/README.md

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+# Mesop Langgraph Chatbot
+## Goal
+- Implement MoA with langgraph
+- Demonstrate MoA Chatbot with Mesop
+
+## Installation
+### Python
+1. Install [poetry](https://python-poetry.org/docs/#installation)
+2. Run `poetry install` in this directory.
+3. Run `poetry shell` to use venv
+### Ollama
+1. [Install ollama](https://ollama.com/download)
+2. Install below models(proposers, aggregator)
+
+## Run demo
+1. `mesop src/mesop_langgraph/main.py`
+2. `Open /chat`
+3. Chat!
+
+
+## Design
+### MoA Architecture
+```mermaid
+flowchart LR
+    question --> proposer1_1 & proposer1_2 & proposer1_3 --> aggregate_and_synthesize1 --> proposer2_1 & proposer2_2 & proposer2_3 --> aggregate_and_synthesize2 --> aggregator --> answer
+```
+- Proposer Layer: 2
+- Aggregator Layer: 1
+### LLMs
+#### Proposers
+- [gemma:2b-instruct](https://ollama.com/library/gemma:2b-instruct)
+    - temperature: 0.2
+- [qwen2:1.5b-instruct-fp16](https://ollama.com/library/qwen2:1.5b-instruct-fp16)
+    - temperature: 0.2
+- [phi3:3.8b-instruct](https://ollama.com/library/phi3:3.8b-instruct)
+    - temperature: 0.2
+#### Aggregator
+- [qwen2:7b-instruct](https://ollama.com/library/qwen2:7b-instruct)
+    - temperature: 0.2
+### Prompts
+Aggregate-and-Synthesize Prompt
+```plaintext
+<<SYSTEM>>
+You have been provided with a set of responses from various open-source models to the latest user query. Your
+task is to synthesize these responses into a single, high-quality response. It is crucial to critically evaluate the
+information provided in these responses, recognizing that some of it may be biased or incorrect. Your response
+should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply
+to the instruction. Ensure your response is well-structured, coherent, and adheres to the highest standards of
+accuracy and reliability.
+Responses from models:
+1. [Model Response from Ai,1]
+2. [Model Response from Ai,2]
+...
+n. [Model Response from Ai,n]
+
+Human: [x_i]
+```
+
+## References
+- [Langgraph](https://langchain-ai.github.io/langgraph/)
+- [Mesop](https://google.github.io/mesop/)