stray_cat
StrayCat
Session object containing user data, conversation state and many utility pointers. The framework creates an instance for every http request and websocket connection, making it available for plugins.
You will be interacting with an instance of this class directly from within your plugins:
- in
@hook
,@tool
and@endpoint
decorated functions will be passed as argumentcat
orstray
- in
@form
decorated classes you can access it viaself.cat
Parameters:
Name | Type | Description | Default |
---|---|---|---|
user_data
|
AuthUserInfo
|
User data object containing user information. |
required |
Source code in cat/looking_glass/stray_cat.py
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|
cache
property
Gives access to internal cache.
embedder
property
mad_hatter
property
Gives access to the MadHatter
plugin manager.
Returns:
Name | Type | Description |
---|---|---|
mad_hatter |
MadHatter
|
Module to manage plugins. |
Examples:
Obtain the path in which your plugin is located
Obtain plugin settings
main_agent
property
Gives access to the default main agent.
memory
property
rabbit_hole
property
user_data
property
AuthUserInfo
object containing user data.
Returns:
Name | Type | Description |
---|---|---|
user_data |
AuthUserInfo
|
Current user's data. |
user_id
property
The user's id.
Returns:
Name | Type | Description |
---|---|---|
user_id |
str
|
Current user's id. |
white_rabbit
property
Gives access to WhiteRabbit
, to schedule repeatable tasks.
Returns:
Name | Type | Description |
---|---|---|
white_rabbit |
WhiteRabbit
|
Module to manage cron tasks via |
Examples:
Send a websocket message after 30 seconds
working_memory
instance-attribute
State machine containing the conversation state, acting as a simple dictionary / object. Can be used in plugins to store and retrieve data to drive the conversation or do anything else.
Examples:
Store a value in the working memory during conversation
Retrieve a value in later conversation turns
__call__(message_dict)
Run the conversation turn.
This method is called on the user's message received from the client.
It is the main pipeline of the Cat, it is called automatically.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
message_dict
|
dict
|
Dictionary received from the client via http or websocket. |
required |
Returns:
Name | Type | Description |
---|---|---|
final_output |
CatMessage
|
CatMessage object, the Cat's answer to be sent back to the client. |
Source code in cat/looking_glass/stray_cat.py
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|
classify(sentence, labels)
Classify a sentence.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sentence
|
str
|
Sentence to be classified. |
required |
labels
|
List[str] or Dict[str, List[str]]
|
Possible output categories and optional examples. |
required |
Returns:
Name | Type | Description |
---|---|---|
label |
str
|
Sentence category. |
Examples:
Or giving examples for each category:
>>> example_labels = {
... "positive": ["I feel nice", "happy today"],
... "negative": ["I feel bad", "not my best day"],
... }
... cat.classify("it is a bad day", labels=example_labels)
"negative"
Source code in cat/looking_glass/stray_cat.py
langchainfy_chat_history(latest_n=20)
Redirects to WorkingMemory.langchainfy_chat_history. Will be removed from this class in v2.
llm(prompt, stream=False)
Generate a response using the Large Language Model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
prompt
|
str
|
The prompt for generating the response. |
required |
stream
|
bool
|
Whether to stream the tokens via websocket or not. |
False
|
Returns:
Type | Description |
---|---|
str
|
The generated LLM response. |
Examples:
Detect profanity in a message
>>> message = cat.working_memory.user_message_json.text
... cat.llm(f"Does this message contain profanity: '{message}'? Reply with 'yes' or 'no'.")
"no"
Run the LLM and stream the tokens via websocket
Source code in cat/looking_glass/stray_cat.py
load_working_memory_from_cache()
recall_relevant_memories_to_working_memory(query=None)
Retrieve context from memory.
The method retrieves the relevant memories from the vector collections that are given as context to the LLM. Recalled memories are stored in the working memory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query
|
str
|
The query used to make a similarity search in the Cat's vector memories. |
None
|
Examples:
Recall memories from custom query
Notes
The user's message is used as a query to make a similarity search in the Cat's vector memories. Five hooks allow to customize the recall pipeline before and after it is done.
See Also
cat_recall_query before_cat_recalls_memories before_cat_recalls_episodic_memories before_cat_recalls_declarative_memories before_cat_recalls_procedural_memories after_cat_recalls_memories
Source code in cat/looking_glass/stray_cat.py
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|
send_chat_message(message, save=False)
Sends a chat message to the user using the active WebSocket connection.
In case there is no connection the message is skipped and a warning is logged
Parameters:
Name | Type | Description | Default |
---|---|---|---|
message
|
str | CatMessage
|
Message to send |
required |
save
|
Save the message in the conversation history. Defaults to False. |
False
|
Examples:
Send a chat message during conversation from a hook, tool or form
Using a CatMessage
object
>>> message = CatMessage(text="Hello, dear!", user_id=cat.user_id)
... cat.send_chat_message(message)
Source code in cat/looking_glass/stray_cat.py
send_error(error)
Sends an error message to the user using the active WebSocket connection.
In case there is no connection the message is skipped and a warning is logged
Parameters:
Name | Type | Description | Default |
---|---|---|---|
error
|
Union[str, Exception]
|
Message to send |
required |
Examples:
Send an error message to the user
>>> cat.send_error("Something went wrong!")
or
>>> cat.send_error(CustomException("Something went wrong!"))
Source code in cat/looking_glass/stray_cat.py
send_notification(content)
Sends a notification message to the user using the active WebSocket connection.
In case there is no connection the message is skipped and a warning is logged
Parameters:
Name | Type | Description | Default |
---|---|---|---|
content
|
str
|
Message to send |
required |
Examples:
Send a notification to the user
Source code in cat/looking_glass/stray_cat.py
send_ws_message(content, msg_type='notification')
Send a message via websocket.
This method is useful for sending a message via websocket directly without passing through the LLM.
In case there is no connection the message is skipped and a warning is logged.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
content
|
str
|
The content of the message. |
required |
msg_type
|
str
|
The type of the message. Should be either |
'notification'
|
Examples:
Send a notification via websocket
Send a chat message via websocket
Send an error message via websocket
Send custom data
Source code in cat/looking_glass/stray_cat.py
stringify_chat_history(latest_n=20)
Redirects to WorkingMemory.stringify_chat_history. Will be removed from this class in v2.
update_working_memory_cache()
Update the working memory in the cache.