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Lance Martin
2024-12-11 14:19:53 -08:00
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# Reply gAI
# reply gAI
Reply gAI is an AI-powered personal assistant for Twitter/X users that creates interactive chatbot personas. It automatically collects a user's Tweets, stores them in long-term memory, and uses Retrieval-Augmented Generation (RAG) to generate responses that match their unique writing style and viewpoints.
![reply_gai](https://github.com/user-attachments/assets/91e5bf27-04c0-4584-817f-16e43296cd34)
## 🚀 Quickstart
One option for accessing Twitter/X data is the [Arcade API](https://docs.arcade-ai.com/integrations/toolkits/x) toolkit.
@@ -29,11 +31,11 @@ You should see the following output and Studio will open in your browser:
In the `configuration` tab, add the Twitter/X handle of any user:
![Screenshot 2024-12-06 at 4 15 39 PM](https://github.com/user-attachments/assets/c9a12f72-4f45-42a9-b8a6-e30cc15128c4)
![Screenshot 2024-12-11 at 1 30 51 PM](https://github.com/user-attachments/assets/10cc592b-9b1d-4132-87e1-db3e65257fc9)
You can interact with a chatbot persona for that user:
Then, just interact with a chatbot persona for that user:
![Screenshot 2024-12-06 at 4 15 14 PM](https://github.com/user-attachments/assets/bd39a211-34c3-4d72-87ef-79efb382e334)
![Screenshot 2024-12-11 at 1 30 30 PM](https://github.com/user-attachments/assets/6bbfbd5a-40a2-46c5-b329-c66e1c1952d8)
## How it works
@@ -69,7 +71,11 @@ In the quickstart, we use a [locally running LangGraph server](https://langchain
This uses the `langraph dev` command, which [launches the server in development mode](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#dev).
Tweets are saved to the LangGraph store, which uses Postgres as is saved in the `.langgraph_api/` directory.
Tweets are saved to the [LangGraph store](https://langchain-ai.github.io/langgraph/concepts/persistence/#memory-store), which uses Postgres for persistence and is saved in the `.langgraph_api/` folder in this directory.
You can visualize Tweets saved per each user in the Store directly with LangGraph Studio.
![Screenshot 2024-12-11 at 1 31 09 PM](https://github.com/user-attachments/assets/41a06245-0659-4309-b7e5-e78a2f108c2b)
## Deployment
@@ -77,6 +83,10 @@ If you want to want to launch the server in a mode suitable for production, you
* Add `LANGSMITH_API_KEY` to your `.env` file.
* Ensure [Docker](https://docs.docker.com/engine/install/) is running on your machine.
* [Run with `langgraph up`](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#up): `luvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph up`
* [Run with `langgraph up`](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#up)
See [Module 6](https://github.com/langchain-ai/langchain-academy/tree/main/module-6) of LangChain Academy for a detailed walkthrough of deployment options with LangGraph.
```bash
luvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph up
```
See [Module 6](https://github.com/langchain-ai/langchain-academy/tree/main/module-6) of LangChain Academy for a detailed walkthrough of deployment options with LangGraph.