example(functions): Add OpenAI functions example (#767)

renovate/github.com-tmc-langchaingo-digest
Ettore Di Giacinto 1 year ago committed by GitHub
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  1. 8
      examples/README.md
  2. 9
      examples/functions/.env
  3. 5
      examples/functions/Dockerfile
  4. 18
      examples/functions/README.md
  5. 23
      examples/functions/docker-compose.yaml
  6. 76
      examples/functions/functions-openai.py
  7. 2
      examples/functions/requirements.txt

@ -64,6 +64,14 @@ A ready to use example to show e2e how to integrate LocalAI with langchain
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/langchain-python/)
### LocalAI functions
_by [@mudler](https://github.com/mudler)_
A ready to use example to show how to use OpenAI functions with LocalAI
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/functions/)
### LocalAI WebUI
_by [@dhruvgera](https://github.com/dhruvgera)_

@ -0,0 +1,9 @@
OPENAI_API_KEY=sk---anystringhere
OPENAI_API_BASE=http://api:8080/v1
# Models to preload at start
# Here we configure gpt4all as gpt-3.5-turbo and bert as embeddings
PRELOAD_MODELS=[{"url": "github:go-skynet/model-gallery/openllama-7b-open-instruct.yaml", "name": "gpt-3.5-turbo"}]
## Change the default number of threads
#THREADS=14

@ -0,0 +1,5 @@
FROM python:3.10-bullseye
COPY . /app
WORKDIR /app
RUN pip install --no-cache-dir -r requirements.txt
ENTRYPOINT [ "python", "./functions-openai.py" ];

@ -0,0 +1,18 @@
# LocalAI functions
Example of using LocalAI functions, see the [OpenAI](https://openai.com/blog/function-calling-and-other-api-updates) blog post.
## Run
```bash
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/functions
docker-compose run --rm functions
```
Note: The example automatically downloads the `openllama` model as it is under a permissive license.
See the `.env` configuration file to set a different model with the [model-gallery](https://github.com/go-skynet/model-gallery) by editing `PRELOAD_MODELS`.

@ -0,0 +1,23 @@
version: "3.9"
services:
api:
image: quay.io/go-skynet/local-ai:master
ports:
- 8080:8080
env_file:
- .env
environment:
- DEBUG=true
- MODELS_PATH=/models
volumes:
- ./models:/models:cached
command: ["/usr/bin/local-ai" ]
functions:
build:
context: .
dockerfile: Dockerfile
depends_on:
api:
condition: service_healthy
env_file:
- .env

@ -0,0 +1,76 @@
import openai
import json
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit="fahrenheit"):
"""Get the current weather in a given location"""
weather_info = {
"location": location,
"temperature": "72",
"unit": unit,
"forecast": ["sunny", "windy"],
}
return json.dumps(weather_info)
def run_conversation():
# Step 1: send the conversation and available functions to GPT
messages = [{"role": "user", "content": "What's the weather like in Boston?"}]
functions = [
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
}
]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages,
functions=functions,
function_call="auto", # auto is default, but we'll be explicit
)
response_message = response["choices"][0]["message"]
# Step 2: check if GPT wanted to call a function
if response_message.get("function_call"):
# Step 3: call the function
# Note: the JSON response may not always be valid; be sure to handle errors
available_functions = {
"get_current_weather": get_current_weather,
} # only one function in this example, but you can have multiple
function_name = response_message["function_call"]["name"]
fuction_to_call = available_functions[function_name]
function_args = json.loads(response_message["function_call"]["arguments"])
function_response = fuction_to_call(
location=function_args.get("location"),
unit=function_args.get("unit"),
)
# Step 4: send the info on the function call and function response to GPT
messages.append(response_message) # extend conversation with assistant's reply
messages.append(
{
"role": "function",
"name": function_name,
"content": function_response,
}
) # extend conversation with function response
second_response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages,
) # get a new response from GPT where it can see the function response
return second_response
print(run_conversation())

@ -0,0 +1,2 @@
langchain==0.0.234
openai==0.27.8
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