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https://github.com/canxin121/async-bing-client
async bing client for bing.com
https://github.com/canxin121/async-bing-client
Last synced: about 2 months ago
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async bing client for bing.com
- Host: GitHub
- URL: https://github.com/canxin121/async-bing-client
- Owner: canxin121
- Created: 2023-08-21T06:19:48.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-16T16:07:03.000Z (about 1 year ago)
- Last Synced: 2024-08-17T19:09:03.649Z (5 months ago)
- Language: Python
- Size: 229 KB
- Stars: 9
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[中文文档](README_zh_cn.md)
Features
- Use API similar to the Newbing website
- Support image recognition by providing images as input
- Enable human language-based drawing
- Facilitate persistent storage of session data
- Organize WebSocket communication (WSS) information into custom classes, supporting output categorization for easy developer encapsulation
- High concurrency processing capability
- Rapid retrieval of chat data
- Bulk conversation deletion functionality
- No need for Tun mode proxy
- Support HTTP and SOCKS proxies
# Usage Guide
## Table of Contents
[Step 1: Create bing client](#step-1-create-bing-client)
[Step 2: Use client](#step-2-use-client)
- [1]. [Create new conversation](#1-create-new-conversation)
- [2]. [Get all cached chat lists](#2-get-all-cached-chat-lists)
- [3]. [Reload cached chat lists](#3-reload-cached-chat-lists)
- [4]. [Delete specified number of chats (up to 200)](#4-delete-specified-number-of-chats-up-to-200)
- [5]. [Delete specified chat](#5-delete-specified-chat)
- [5]. [Chat with bing](#5-chat-with-bing)
- [6]. [AI drawing (provided by openai's dall-e)](#6-ai-drawing-provided-by-openais-dall-e)
You can click the table of contents to jump to the corresponding content.
## Step 1: Create bing client
1. Bing_Client:
Parameters:
- (1).`cookie`: bing.com cookie, need to use browser extension `cookie-editor`
Export cookie to json in bing chat interface, then pass file path of str or Path, can also directly pass
list[dict] formatted cookie
- (2).`proxy`: LAN proxy, need to fill in when there is no global proxy or split tunneling on local machine, support
http, httpx, socks5
```python
import asyncio
from async_bing_client import Bing_Client
client = Bing_Client(cookie="cookie.json", proxy="socks5://127.0.0.1:7890")
if __name__ == '__main__':
async def main():
await client.init()
asyncio.run(main())
```
## Step 2: Use client
### [1]. Create new conversation
```python
# The new conversation created here can be directly used for ask_stream
chat = await client.create_chat()
```
### [2]. Get all cached chat lists
Contains chat history of all latest 200 conversations
```python
chat_list = client.chat_list
```
### [3]. Reload cached chat lists
```python
# Just reinitialize
await client.init()
```
### [4]. Delete specified number of chats (up to 200)
Parameters:
- (1). `count: int = 20`: Number to delete
- (2). `del_all: bool = False`: Whether to delete all (up to 200, because bing can only load latest 200 conversations)
```python
await client.delete_conversation_by_count()
```
### [5]. Delete specified chat
Parameters:
- (1). `conversation_id:str`: conversation_id of chat to delete (chat key is conversation_id)
```python
await client.delete_conversation('conversation_id')
```
### [5]. Chat with bing
Can pass pictures for image recognition, can also return Image type or md image format including new ai drawings
generated by bing
1. Return different generator functions: ask_stream_raw
Parameters:
- (1). `question: str`: Your question
- (2). `image: str | Path | bytes = None`: Image you want to recognize (str here can be link of web image, can also be
str of path)
- (3). `chat: dict = None`: Chat window to send to, will create new one if not passed, and yield a NewChat class
containing the new chat
- (4). `conversation_style: ConversationStyle = ConversationStyle.Creative`: Conversation style to use
- (5). `personality=None`: Preset personality to load (just needs to be normal text, will auto obfuscate), content must
be within bing's moral and legal limits, otherwise will keep returning Apology
- (6). `locale:str =guess_locale()`: Locale to use (usually don't need to specify yourself)
Return types:
`AsyncGenerator[NewChat | Apology | Notice | SearchResult | Text | SourceAttribution | SuggestRely | Limit | Response]`
- `Text`: bing's text reply. Can access content attribute or use str method to get content. Addition of Text will still
return Text
- `Image`: bing's image reply. url attribute is image link, base64 (Optional) attribute is image base64 content, name is
image name
- `NewChat`: Newly generated chat, can access chat attribute of NewChat to obtain.
- `Apology`: bing's refusal or error message. Can access content attribute or use str method to obtain content
- `Notice`: bing's notification message. Can access content attribute or use str method to obtain content
- `SearchResult`: bing's search results. content attribute stores list[dict] or str. Can use str method to convert to
suitable str format
- `SourceAttribution`: bing's resource link reply, display_name attribute is displayed link name, see_more_url attribute
is link, image (optional) attribute is resource preview image
- `SuggestRely`: bing's suggested reply. Can access content attribute or use str method to obtain content
- `Limit`: bing's conversation limit content, will return Apology after exceeding limit. num_user_messages attribute is
number of messages user has sent, max_num_user_messages is max number of consecutive messages user can send
- `Response`: Summary of bing's messages in this conversation, content stores dict of all data generated in this
conversation.
ask_stream function wraps ask_stream_raw example
```python
chat = client.create_chat()
sources = []
suggest_reply = []
images = []
limit = None
async for data in client.ask_stream_raw('question', 'image', chat=chat):
if isinstance(data, Text):
yield data.content
elif isinstance(data, SuggestRely):
suggest_reply.append(data)
elif isinstance(data, SourceAttribution):
sources.append(data)
elif isinstance(data, Apology):
yield '\n' + data.content
elif isinstance(data, Image):
images.append(data)
elif isinstance(data, Limit):
limit = data
elif isinstance(data, SearchResult) and yield_search:
yield str(data)
for index, source in enumerate(sources):
if index == 0:
yield "\nSee more: \n"
yield f"({index + 1}):{source} \n"
for index, image in enumerate(images):
if index == 0:
yield "\nDrew images: \n"
yield f"{index + 1}:{image} \n"
for index, reply in enumerate(suggest_reply):
if index == 0:
yield "\nSuggest Replys: \n"
yield f"{index + 1}:{reply.content} \n"
if limit:
yield f"\n\nLimit:{limit.num_user_messages} of {limit.max_num_user_messages} "
```
2. Generator function outputting pure str ask_stream:
```python
async for text in client.ask_stream("hello", chat=chat,
yield_search=False,
conversation_style=ConversationStyle.Balanced):
print(text, end="")
```
### [6]. AI drawing (provided by openai's dall-e)
(This function will be called automatically in ask_stream, can directly use human language to let bing generate images)
Return value: `List[Image]`
`Image`: bing's image reply. url attribute is image link, name is image name
```python
images = await client.draw("drawing prompt")
```