https://github.com/uezo/gpt3-contextual
Contextual chat with GPT-3 model of OpenAI API
https://github.com/uezo/gpt3-contextual
Last synced: 28 days ago
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Contextual chat with GPT-3 model of OpenAI API
- Host: GitHub
- URL: https://github.com/uezo/gpt3-contextual
- Owner: uezo
- License: mit
- Created: 2023-02-11T14:27:20.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-03-14T02:05:19.000Z (about 2 years ago)
- Last Synced: 2025-03-14T17:08:06.081Z (about 1 month ago)
- Language: Python
- Size: 57.6 KB
- Stars: 25
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - gpt3-contextual - Contextual chat with GPT-3 model of OpenAI API (NLP)
README
# gpt3-contextual
Contextual chat with ChatGPT / GPT-3 of OpenAI API.
# ✨ ChatGPT is now available!
NOTE:
- This feature is an experimental version.
- Parameters for GPT-3 Completion API will be ignored.
- We may change the interface in the (near) future.
# 🚀 Quick start
Install.
```bash
$ pip install gpt3-contextual
```
Make script as console.py.
```python
import asyncio
from gpt3contextual import ContextualChat, ContextManager
async def main():
cm = ContextManager()
cc = ContextualChat("YOUR_OPENAI_APIKEY", context_manager=cm)
while True:
text = input("Human> ")
resp, prompt, completion = await cc.chat("user1234567890", text)
print(f"AI> {resp}")
asyncio.run(main())
```
Run console.py and chat with GPT-3.
```
human> hello
AI> Hi, how can I help you?
human> I'm hungry
AI> What would you like to eat?
human> sandwitches
AI> What kind of sandwich would you like?
human> ham&egg
AI> Would you like that on a white or wheat bread?
human> wheet
AI> Would you like anything else with your ham and egg sandwich on wheat bread?
human> Everything is fine, thank you.
AI> Great! Your order has been placed. Enjoy your meal!
```
If you want to use ChatGPT, use `ContextualChatGTP` instead of `ContextualChat` like below:
```python
import asyncio
from gpt3contextual import ContextualChatGPT, ContextManager
async def main():
cm = ContextManager()
cc = ContextualChatGPT("YOUR_OPENAI_APIKEY", context_manager=cm)
while True:
text = input("Human> ")
resp, prompt, completion = await cc.chat("user1234567890", text)
print(f"AI> {resp}")
asyncio.run(main())
```
# 🧸 Usage
You can set parameters to customize conversation senario when you make the instance of `ContextManager`.
- `timeout`: int : Stored context will be ignored when timeout(sec) passed after last updated.
- `username`: str : Name of roll of user. Default=`Human`.
- `agentname`: str = Name or role of agent(bot). Default=`AI`.
- `chat_description`: str : Some conditions to be considered in the senario of conversation.
- `history_count`: int: History count to use in prompt.
If you want to change these values for specific user(context) at runtime, call `ContextManager#reset`.
```python
context_manager.reset("user1234567890", username="兄", agentname="妹", chat_description="仲のいい兄と妹の会話です。丁寧語は使いません。")
```
And, you can customize OpenAI specs to pass the parameters to `ContextualChat`.
See also https://platform.openai.com/docs/api-reference/completions to understand more.
- `api_key`: str : API key for OpenAI API.
- `connection_str`: str : SQLAlchemy connection string for database. Default=`sqlite:///gpt3contextual.db`.
- `context_manager`: ContextManager : ContextManager you build.
- `model`: str : The AI model to use. Default=`text-davinci-003`.
- `temperature`: float : What sampling temperature to use, between 0 and 2. Default=`0.5`.
- `max_tokens`: int : The maximum number of tokens to generate in the completion. Default=`2000`.
- `**completion_params`: Other parameters for completions if you want to set.
Especially, to use other RDBMS instead of SQLite, set `connection_str` like bellow:
```python
cc = ContextualChat(
"YOUR_OPENAI_APIKEY",
connection_str="postgresql://user:password@host:port/dbname",
context_manager=cm
)
```
# 💡 Tips
GPT-3 has capability of various kinds of task such as chat, research, translation, calculation, games and so on. You can switch the "mode" by setting `username`, `agentname` and `chat_description` like below.
```python
# Translation
cm = ContextManager(username="English", agentname="Japanese", chat_description="Translate from English to Japanese.")
# Calculation
cm = ContextManager(username="Question", agentname="Answer", chat_description="Calculate.")
# Customizing end of sentence (Japanese)
cm = ContextManager(username="兄", agentname="妹", chat_description="これは兄と妹との会話です。妹は語尾に「ニャ」をつけて話します。")
```
# 🔥 Host your own API server
Set your OpenAI API key and secret access key for configuration endpoints to `example/api.py`
```python
# Settings
openai_apikey = "SET_YOUR_OPENAI_API_KEY"
config_access_key = "CHANGE_THIS_VALUE_AS_YOU_LIKE"
```
Install dependencies.
```bash
$ pip install uvicorn fastapi
```
Run.
```bash
$ uvicorn run:app
```
See API document and try APIs. 👉 http://127.0.0.1:8000/docs
If you want to change IP address or port, start uvicorn like this:
```bash
$ uvicorn run:app --host 0.0.0.0 --port 80
```
And, if you want to run this API in multi-process, use database for context management.
See `gpt3contextual.context.SQLiteContextManager` and customize it for the RDBMS you want to use.
Enjoy your own contextual GPT-3 API👍
# 🥪 How it works
**This chapter is based on `text-davinci-003`**
As you know, `Completion` endpoint doesn't provide the features for keep and use context.
So, simply, this library send the previous conversation histories with the request text like below.
Prompt on the 1st turn:
```
human:hello
AI:
```
Chatbot returns `Hi, how can I help you?`.
Prompt on the 2nd turn:
```
human:hello
AI:Hi, how can I help you?
human:I'm hungry
AI:
```
Chatbot returns `What would you like to eat?`
:
Prompt on the 6th turn:
```
human:sandwitches
AI:What kind of sandwich would you like?
human:ham&egg
AI:Would you like that on a white or wheat bread?
human:wheet
AI:Would you like anything else with your ham and egg sandwich on wheat bread?
human:Everything is fine, thank you.
AI:
```
Chatbot returns `Great! Your order has been placed. Enjoy your meal!`
If you change the `username` and `agentname` the senario of conversation changes.
And, setting `chat_description` also make effects on the situations.
Here is an example to simulate the conversation by brother and sister in Japanese.
```python
cm = ContextManager(
username="兄",
agentname="妹",
chat_description="これは兄と親しい妹との会話です。仲良しなので丁寧語を使わずに話してください。"
)
cc = ContextualChat("YOUR_OPENAI_APIKEY", context_manager=cm)
```
Prompt to be sent to OpenAI API is like bellow. `chat_description` is always set to the first line, no matter how many turns of conversation proceed.
```
これは兄と親しい妹との会話です。仲良しなので丁寧語を使わずに話してください。
兄:おはよー
妹:おはよー！今日の予定は？
兄:いつも通り、特にないよ
妹:じゃあ、今日は一緒に遊ぼうよ！
兄:何かしたいことある？
妹:うん、今日は映画を見よう！
兄:いいね。 どんなのがいい？
妹:思いついた！サスペンス映画がいいかな！
````