Skip to content

Latest commit

 

History

History
 
 

webrtc

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
dockerfiles
dockerfiles
 
 
README.md
README.md
 
 
docker-compose.yml
docker-compose.yml
 
 
quickstart_server.py
quickstart_server.py
 
 
requirements.txt
requirements.txt
 
 

README.md

Bolna With Daily

Seamlessly merge Bolna with Daily for websocket connection. This is docker compose by which you can host bolna server Daily together in cloud just by clone this repo and follow these simple steps to deploy, but before that you have to make sure that you have docker and docker compose and make a .env file refer to .env-sample.

in .env file kindly save DAILY_API_KEY = YOUR_DAILY_API_KEY

Start Serices

docker compose up -d

note: make sure that your all service were runing

let assume your server IP is 192.168.1.10

Creating Agent

for creating agent you have to execute following command mention below

curl --location 'http://192.168.1.10:5001/agent' \
--header 'Content-Type: application/json' \
--data '{
  "agent_config": {
    "agent_name": "Alfred",
    "agent_type": "other",
    "agent_welcome_message": "Welcome",
    "tasks": [
      {
        "task_type": "conversation",
        "toolchain": {
          "execution": "parallel",
          "pipelines": [
            [
              "transcriber",
              "llm",
              "synthesizer"
            ]
          ]
        },
        "tools_config": {
          "input": {
            "format": "wav",
            "provider": "daily"
          },
          "output": {
            "format": "wav",
            "provider": "daily"
          },
          "llm_agent": {
            "max_tokens": 100,
            "presence_penalty": 0,
            "top_p": 0.9,
            "agent_flow_type": "streaming",
            "request_json": false,
            "min_p": 0.1,
            "frequency_penalty": 0,
            "provider": "openai",
            "top_k": 0,
            "temperature": 0.2,
            "model": "gpt-3.5-turbo",
            "family": "openai"
          },
          "synthesizer": {
            "audio_format": "wav",
            "buffer_size": 150,
            "caching": true,
            "provider": "polly",
            "provider_config": {
              "engine": "neural",
              "language": "en-US",
              "voice": "Danielle"
            },
            "stream": true
          },
          "transcriber": {
            "encoding": "linear16",
            "endpointing": 100,
            "keywords": "",
            "language": "en",
            "model": "nova-2",
            "provider": "deepgram",
            "sampling_rate": 16000,
            "stream": true,
            "task": "transcribe"
          }
        },
        "task_config": {
          "hangup_after_silence": 30
        }
      }
    ]
  },
  "agent_prompts": {
    "task_1": {
      "system_prompt": "You are assistant at Dr. Sharma clinic you have to book an appointment"
    }
  }
}
'

below given is the response

{"agent_id":"dcfe02de-bOdf-4589-b15b-64c76f0077d0", "state" : "created" }

save / copy the agent_id we have to use in next step

Connect frontend to the websocket call

Example frontent - https://codefile.io/f/rWIsJ0GztJ

  1. Connect your frontend with the (’/chat/v1/{agent_id}’) websocket and use the agent_id you saved in above step. It's that easy!
  2. Our daily web-socket call will be up and running, so you can start chatting with the agent through your frontend!

Stop Services

docker compose down

Conservation DEMO

This is demo using below prompt to the LLM

"task_1": {
      "system_prompt": "You are assistant at Dr. Sharma clinic you have to book an appointment"
}