YoMo is an open-source LLM Function Calling Framework for building Geo-distributed AI applications. Built atop QUIC Transport Protocol and Stateful Serverless architecture, makes your AI application low-latency, reliable, secure, and easy.
π We care about: Customer Experience in the Age of AI
Features | |
---|---|
β‘οΈ | Low-latency Guaranteed by implementing atop QUIC QUIC |
π | Security TLS v1.3 on every data packet by design |
πΈ | Stateful Serverless Make your GPU serverless 10x faster |
π | Geo-Distributed Architecture Brings AI inference closer to end users |
π | Y3 a faster than real-time codec |
Let's implement a function calling with sfn-currency-converter
:
curl -fsSL https://get.yomo.run | sh
Verify if the CLI was installed successfully
yomo version
Prepare the configuration as my-agent.yaml
name: ai-zipper
host: 0.0.0.0
port: 9000
auth:
type: token
token: SECRET_TOKEN
bridge:
ai:
server:
addr: 0.0.0.0:8000 ## Restful API endpoint
provider: azopenai ## LLM API Service we will use
providers:
azopenai:
api_key: <YOUR_AZURE_OPENAI_API_KEY>
api_endpoint: <YOUR_AZURE_OPENAI_ENDPOINT>
openai:
api_key: <OPENAI_API_KEY>
model: <OPENAI_MODEL>
gemini:
api_key: <GEMINI_API_KEY>
huggingface:
model:
Start the server:
YOMO_LOG_LEVEL=debug yomo serve -c my-agent.yaml
First, let's define what this function do and how's the parameters required, these will be combined to prompt when invoking LLM.
func Description() string {
return "Get the current exchange rates"
}
type Parameter struct {
SourceCurrency string `json:"source" jsonschema:"description=The source currency to be queried in 3-letter ISO 4217 format"`
TargetCurrency string `json:"target" jsonschema:"description=The target currency to be queried in 3-letter ISO 4217 format"`
Amount float64 `json:"amount" jsonschema:"description=The amount of the USD currency to be converted to the target currency"`
}
func InputSchema() any {
return &Parameter{}
}
Retrieve the real-time exchange rate by calling the openexchangerates.org API.:
type Rates struct {
Rates map[string]float64 `json:"rates"`
}
func fetchRate(sourceCurrency string, targetCurrency string, amount float64) (float64, error) {
resp, _ := http.Get(fmt.Sprintf("https://openexchangerates.org/api/latest.json?app_id=%s&base=%s&symbols=%s", os.Getenv("API_KEY"), sourceCurrency, targetCurrency))
defer resp.Body.Close()
body, _ := io.ReadAll(resp.Body)
var rt *Rates
_ = json.Unmarshal(body, &rt)
return rates.Rates[targetCurrency], nil
}
Wrap to a Stateful Serverless Function:
func handler(ctx serverless.Context) {
fcCtx, _ := ai.ParseFunctionCallContext(ctx)
var msg Parameter
fcCtx.UnmarshalArguments(&msg)
rate, _ := fetchRate(msg.SourceCurrency, msg.TargetCurrency, msg.Amount)
result = fmt.Sprintf("%f", msg.Amount*rate)
fcCtx.SetRetrievalResult(fmt.Sprintf("based on today's exchange rate: %f, %f %s is equivalent to approximately %f %s",rate, msg.Amount, msg.SourceCurrency, msg.Amount*rate, msg.TargetCurrency))
fcCtx.Write(result)
}
Finally, let's run it
$ API_KEY=<get_from_openexchangerates.org> go run main.go
time=2024-02-26T17:29:52.868+08:00 level=INFO msg="connected to zipper" component=StreamFunction sfn_id=GqfKopi2ECx7GIlzw6ZL3 sfn_name=fn-exchange-rates zipper_addr=localhost:9000
time=2024-02-26T17:29:52.869+08:00 level=INFO msg="register ai function success" component=StreamFunction sfn_id=GqfKopi2ECx7GIlzw6ZL3 sfn_name=fn-exchange-rates zipper_addr=localhost:9000 name=fn-exchange-rates tag=16
$ curl -i -X POST -H "Content-Type: application/json" -d '{"prompt":"How much is 100 dollar in Korea and UK currency"}' http://127.0.0.1:8000/invoke
HTTP/1.1 200 OK
Transfer-Encoding: chunked
Connection: keep-alive
Content-Type: text/event-stream
Date: Mon, 26 Feb 2024 09:30:35 GMT
Keep-Alive: timeout=4
Proxy-Connection: keep-alive
event:result
data: {"req_id":"7YU0SY","result":"78.920600","retrieval_result":"based on today's exchange rate: 0.789206, 100.000000 USD is equivalent to approximately 78.920600 GBP","tool_call_id":"call_mgGM9fqGHTtUueokUa7uwYHT","function_name":"fn-exchange-rates","arguments":"{\"amount\": 100, \"source\": \"USD\", \"target\": \"GBP\"}"}
event:result
data: {"req_id":"7YU0SY","result":"133139.226800","retrieval_result":"based on today's exchange rate: 1331.392268, 100.000000 USD is equivalent to approximately 133139.226800 KRW","tool_call_id":"call_1IFlbtKNC5CEN13tBSM0Nson","function_name":"fn-exchange-rates","arguments":"{\"amount\": 100, \"source\": \"USD\", \"target\": \"KRW\"}"}
Full LLM Function Calling Codes
Read more about YoMo at yomo.run/docs.
YoMo β€οΈ Vercel, our documentation website is
Itβs no secret that todayβs users want instant AI inference, every AI
application is more powerful when it response quickly. But, currently, when we
talk about distribution
, it represents distribution in data center. The AI model is
far away from their users from all over the world.
If an application can be deployed anywhere close to their end users, solve the problem, this is Geo-distributed System Architecture:
First off, thank you for considering making contributions. It's people like you that make YoMo better. There are many ways in which you can participate in the project, for example:
- File a bug report. Be sure to include information like what version of YoMo you are using, what your operating system is, and steps to recreate the bug.
- Suggest a new feature.
- Read our contributing guidelines to learn about what types of contributions we are looking for.
- We have also adopted a code of conduct that we expect project participants to adhere to.