Lark ×（GPT-4 + DALL·E + Whisper）

🚀 Lark OpenAI 🚀

www.connectai-e.com

🗣Voice Communication: Private Direct Says with Robots

💬Multi-topic dialogue: support private and group chat multi-topic discussion, efficient and coherent

🖼Text graph: supports text graph and graph search

🛖Scene preset: built-in rich scene list, one-click switch AI role

🎭Role play: Support scene mode, add fun and creative discussion

🤖AI mode: Built-in 4 AI modes, feel the wisdom and creativity of AI

🔄Context preservation: reply dialog to continue the same topic discussion

⏰Automatic end: timeout automatically end the dialogue, support to clear the discussion history

📝Rich text card: support rich text card reply, more colorful information

👍Interactive Feedback: Instant access to robot processing results

🎰Balance query: obtain token consumption in real time

🔙History Back to File: Easily Back to File History Dialogue and Continue Topic Discussion🚧

🔒Administrator mode: built-in administrator mode, use more secure and reliable🚧

🌐Multi-token load balancing: Optimizing high-frequency call scenarios at the production level

↩️ Support reverse proxy: provide faster and more stable access experience for users in different regions

📚Interact with Flying Book Documents: Become a Super Assistant for Enterprise Employees🚧

🎥Topic Content Seconds to PPT: Make Your Report Simpler from Now on🚧

📊Table Analysis: Easily import flying book tables to improve data analysis efficiency🚧

🍊Private data training: use the company's product information for GPT secondary training to better meet the individual needs of customers.🚧

• 🍏 The dialogue is based on OpenAI-GPT4 and Lark
• 🥒 support Serverless、local、Docker、binary package

Run On Replit

The fastest way to deploy the lark-openai to repl.it is to click the button below.

Remember switch to secrets tab then edit System environment variables.You can also edit raw json:

{
  "BOT_NAME": "Lark-OpenAI",
  "APP_ID": "",
  "APP_SECRET": "",
  "APP_ENCRYPT_KEY": "",
  "APP_VERIFICATION_TOKEN": "",
  "OPENAI_KEY": "sk-xx",
  "OPENAI_MODEL": "gpt-3.5-turbo",
  "APP_LANG": "en"
}

Final callback addresses are

https://YOUR_ADDRESS.repl.co/webhook/event
https://YOUR_ADDRESS.repl.co/webhook/card

git clone git@github.com:ConnectAI-E/lark-openai.git
cd Lark-OpenAI/code

# Configure config.yaml
mv config.example.yaml config.yaml

// Testing deployment.
go run ./
cpolar http 9000

//Production deployment
nohup cpolar http 9000 -log=stdout &

//Check server status
https://dashboard.cpolar.com/status

// Take down the service
ps -ef | grep cpolar
kill -9 PID

git clone git@github.com:ConnectAI/lark-openai.git
cd Lark-OpenAI/code

# Configure config.yaml
mv config.example.yaml config.yaml
# install severless cli
npm install @serverless-devs/s -g

edition: 1.0.0
name: lark-openai
access: "aliyun" #  Modify the custom key name.

vars: # Global variables
region: "cn-hongkong" # Modify the region where the cloud function wants to be deployed.

cd ..
s deploy

set GO111MODULE=on
set GOARCH=amd64
set GOOS=linux
set CGO_ENABLED=0

go env -w GOOS=linux

edition: 1.0.0
name: lark-openai
access: "aliyun" #  Modify the custom key alias

vars: # Global variables
  region: "ap-southeast-1" # Modify the desired deployment region for the cloud functions

  pre-deploy:
        - run: go mod tidy
          path: ./code
        - run: go build -o
            target/main main.go  # del GO111MODULE=on GOOS=linux GOARCH=amd64 CGO_ENABLED=0
          path: ./code

cd ..
s deploy

Railway Deployment

Just configure environment variables on the platform. The process of deploying this project is as follows:

1. Generate the Railway project

Click the button below to create a corresponding Railway project, which will automatically fork this project to your GitHub account.

2. Generate the Railway project

In the opened page, configure the environment variables. The description of each variable is shown in the figure below:

3. deployment project

After filling in the environment variables, click Deploy to complete the deployment of the project. After the deployment is complete, you need to obtain the corresponding domain name for the Feishu robot to access, as shown in the following figure:

Uncertainty about success or failure of self-determination，can be passed through the above mentioned area name (https://xxxxxxxx.railway.app/ping)

，The result returned pong，The description department succeeded.。

docker build -t lark-openai:latest .
docker run -d --name lark-openai -p 9000:9000 \
--env APP_LANG=en \
--env APP_ID=xxx \
--env APP_SECRET=xxx \
--env APP_ENCRYPT_KEY=xxx \
--env APP_VERIFICATION_TOKEN=xxx \
--env BOT_NAME=chatGpt \
--env OPENAI_KEY="sk-xxx1,sk-xxx2,sk-xxx3" \
--env API_URL="https://api.openai.com" \
--env HTTP_PROXY="" \
feishu-chatgpt:latest

docker build -t lark-openai:latest .
docker run -d --name lark-openai -p 9000:9000 \
--env APP_LANG=en \
--env APP_ID=xxx \
--env APP_SECRET=xxx \
--env APP_ENCRYPT_KEY=xxx \
--env APP_VERIFICATION_TOKEN=xxx \
--env BOT_NAME=chatGpt \
--env AZURE_ON=true \
--env AZURE_API_VERSION=xxx \
--env AZURE_RESOURCE_NAME=xxx \
--env AZURE_DEPLOYMENT_NAME=xxx \
--env AZURE_OPENAI_TOKEN=xxx \
feishu-chatgpt:latest

# Build the image
docker compose build

# Start the service
docker compose up -d

# Stop the service
docker compose down

• Get OpenAI KEY( 🙉 Below are free keys available for everyone to test deployment )

• Create lark Bot

  1. Go Lark Open Platform creat app , get APPID and Secret
  2. Go Features-Bot , creat bot
  3. Obtain the public address from cpolar, serverless, or Railway, and fill it in the "Event Subscription" section of the Lark bot backend. For example,
  • http://xxxx.r6.cpolar.top is the public address exposed by cpolar.
  • /webhook/event is the unified application route.
  • The final callback address is http://xxxx.r6.cpolar.top/webhook/event.
  4. In the "Bot" section of the Lark bot backend, fill in the request URL for message cards. For example,
  • http://xxxx.r6.cpolar.top is the public address exposed by cpolar.
  • /webhook/card is the unified application route.
  • The final request URL for message cards is http://xxxx.r6.cpolar.top/webhook/card.
  5. In the "Event Subscription" section, search for the three terms: "Bot Join Group," "Receive Messages," and "Messages Read." Check all the permissions behind them. Go to the permission management interface, search for "Image," and check "Get and upload image or file resources." Finally, the following callback events will be added.
     • im:resource(Read and upload images or other files)
     • im:message
     • im:message.group_at_msg(Read group chat messages mentioning the bot)
     • im:message.group_at_msg:readonly(Obtain group messages mentioning the bot)
     • im:message.p2p_msg(Read private messages sent to the bot)
     • im:message.p2p_msg:readonly(Obtain private messages sent to the bot)
     • im:message:send_as_bot(Send messages as an app)
     • im:chat:readonly(Obtain group information)
     • im:chat(Obtain and update group information)

5. Publish the version and wait for the approval of the enterprise administrator

If you encounter problems, you can join the Lark group to communicate~

AI	SDK	Application
🎒OpenAI	Go-OpenAI	🏅Feishu-OpenAI, 🎖Lark-OpenAI, Feishu-EX-ChatGPT, 🎖Feishu-OpenAI-Stream-Chatbot, Feishu-TLDR,Feishu-OpenAI-Amazing, Feishu-Oral-Friend, Feishu-OpenAI-Base-Helper, Feishu-Vector-Knowledge-Management, Feishu-OpenAI-PDF-Helper, 🏅Dingtalk-OpenAI, Wework-OpenAI, WeWork-OpenAI-Node, llmplugin
🤖 AutoGPT	------	🏅AutoGPT-Next-Web
🎭 Stablediffusion	------	🎖Feishu-Stablediffusion
🍎 Midjourney	Go-Midjourney	🏅Feishu-Midjourney, 🔥MidJourney-Web, Dingtalk-Midjourney
🍍 文心一言	Go-Wenxin	Feishu-Wenxin, Dingtalk-Wenxin, Wework-Wenxin
💸 Minimax	Go-Minimax	Feishu-Minimax, Dingtalk-Minimax, Wework-Minimax
⛳️ CLAUDE	Go-Claude	Feishu-Claude, DingTalk-Claude, Wework-Claude
🥁 PaLM	Go-PaLM	Feishu-PaLM,DingTalk-PaLM,Wework-PaLM
🎡 Prompt	------	📖 Prompt-Engineering-Tutior
🍋 ChatGLM	------	Feishu-ChatGLM
⛓ LangChain	------	📖 LangChain-Tutior
🪄 One-click	------	🎖Awesome-One-Click-Deployment