LLMMessage#

class council.llm.LLMMessage(role: LLMMessageRole, content: str, name: str | None = None, data: Sequence[LLMMessageData] | None = None)[source]#

Bases: object

Represents chat messages. Used in the payload

Parameters:

role (LLMMessageRole) – the role/persona the message is coming from. Could be either user, system or assistant
content (str) – the message content
name (str) – name of the author of this message
data (Sequence[LLMMessageData]) – the data associated with this message

__init__(role: LLMMessageRole, content: str, name: str | None = None, data: Sequence[LLMMessageData] | None = None) → None[source]#: Initialize a new instance of LLMMessage

add_content(*, path: str | None = None, url: str | None = None) → None[source]#: Add content to the message.

add_data(data: LLMMessageData) → None[source]#: Add data to the message.

static assistant_message(content: str, name: str | None = None) → LLMMessage[source]#

Create a new assistant message instance

Parameters:

content (str) – the message content
name (str) – name of the author of this message

property content: str#: Retrieve the content of this instance

property data: Sequence[LLMMessageData]#: Get the list of data associated with this message

format(role_prefix: str = '#') → str[source]#: Format message to string, including role and LLMMessageData if any

static from_chat_message(chat_message: ChatMessage) → LLMMessage | None[source]#: Convert ChatMessage into LLMMessage

property has_data: bool#: Check if this message has data associated with it

is_of_role(role: LLMMessageRole) → bool[source]#: Check the role of this instance

property name: str | None#: Retrieve the name authoring the content of this instance

property role: LLMMessageRole#: Retrieve the role of this instance

static system_message(content: str, name: str | None = None, data: Sequence[LLMMessageData] | None = None) → LLMMessage[source]#

Create a new system message instance

Parameters:

content (str) – the message content
name (str) – name of the author of this message
data (Sequence[LLMMessageData]) – list of data associated with this message

static user_message(content: str, name: str | None = None, data: Sequence[LLMMessageData] | None = None) → LLMMessage[source]#

Create a new user message instance

Parameters:

content (str) – the message content
name (str) – name of the author of this message
data (Sequence[LLMMessageData]) – list of data associated with this message

LLMMessageData#

class council.llm.llm_message.LLMMessageData(content: str, mime_type: str)[source]#

Bases: object

Represents the data of a message.

classmethod from_file(path: str) → LLMMessageData[source]#: Add data from file to the message.

classmethod from_uri(uri: str) → LLMMessageData[source]#: Add an uri to the message.

LLMCacheControlData#

class council.llm.llm_message.LLMCacheControlData(content: str)[source]#

Bases: LLMMessageData

Data class to hold cache control information for Anthropic prompt caching.

static ephemeral() → LLMCacheControlData[source]#: Returns ephemeral cache type

Here’s how you can use Anthropic prompt caching with council.

import os

# !pip install council-ai==0.0.24

from council.llm import AnthropicLLM
from council.llm.llm_message import LLMMessage, LLMCacheControlData
from council.contexts import LLMContext

os.environ["ANTHROPIC_API_KEY"] = "sk-YOUR-KEY-HERE"
os.environ["ANTHROPIC_LLM_MODEL"] = "claude-3-haiku-20240307"

# Ensure that the number of tokens in a cacheable message exceeding
# the minimum cacheable token count, which is 2048 for Haiku;
# otherwise, the message will not be cached.
HUGE_STATIC_SYSTEM_PROMPT = ""

# Create a system message with ephemeral caching
system_message_with_cache = LLMMessage.system_message(
    HUGE_STATIC_SYSTEM_PROMPT,
    data=[LLMCacheControlData.ephemeral()]
)

# Initialize the messages list with cachable system message
messages = [
    system_message_with_cache,
    LLMMessage.user_message("What are benefits of using caching?")
]

llm = AnthropicLLM.from_env()

result = llm.post_chat_request(LLMContext.empty(), messages)
print(result.first_choice)
print(result.raw_response["usage"])