BaseModelResponseParser

class council.llm.llm_response_parser.BaseModelResponseParser

Bases: BaseModel

Base class for parsing LLM responses into structured data models

classmethod from_response(response: LLMResponse) → T

Parse an LLM response into a structured data model. Must be implemented by subclasses to define specific parsing logic.

validator() → None

Implement custom validation logic for the parsed data. Can be overridden by subclasses to add specific validation rules. Raise LLMParsingException to trigger local correction. Alternatively, use pydantic validation.

CodeBlocksResponseParser

class council.llm.llm_response_parser.CodeBlocksResponseParser

Bases: BaseModelResponseParser

classmethod from_response(response: LLMResponse) → T

LLMFunction ResponseParser for response containing multiple named code blocks

Here’s how you can simplify council.llm.LLMFunction example for a sample SQL generation task.

import os

# !pip install council-ai==0.0.24

from council import OpenAILLM
from council.llm import LLMParsingException
from council.llm.llm_function import LLMFunction
from council.llm.llm_response_parser import CodeBlocksResponseParser

SYSTEM_PROMPT = "same system prompt as in LLMFunction example"


# CodeBlocksResponseParser will provide from_response() automatically for you
class SQLResultFromCodeBlocks(CodeBlocksResponseParser):
    solved: bool
    sql: str

    def validator(self) -> None:
        if "limit" not in self.sql.lower():
            raise LLMParsingException("Generated SQL query should contain a LIMIT clause")


os.environ["OPENAI_API_KEY"] = "sk-YOUR-KEY-HERE"
os.environ["OPENAI_LLM_MODEL"] = "gpt-4o-mini-2024-07-18"
llm = OpenAILLM.from_env()

# All the remaining code stays the same
llm_function: LLMFunction[SQLResultFromCodeBlocks] = LLMFunction(
    llm, SQLResultFromCodeBlocks.from_response, SYSTEM_PROMPT
)

response = llm_function.execute(
    user_message="Show me first 5 rows of the dataset ordered by price"
)
print(type(response))
print(response.sql)

YAMLBlockResponseParser

class council.llm.llm_response_parser.YAMLBlockResponseParser

Bases: BaseModelResponseParser

classmethod from_response(response: LLMResponse) → T

LLMFunction ResponseParser for response containing a single YAML code block

Usage example:

import os
from typing import Literal

# !pip install council-ai==0.0.24

from council import OpenAILLM
from council.llm.llm_function import LLMFunction
from council.llm.llm_response_parser import YAMLBlockResponseParser
from pydantic import Field

SYSTEM_PROMPT = """
Output RPG character info in the following YAML block:

```yaml
character_class: # character's class (Warrior, Mage, Rogue, Bard or Tech Support)
name: # character's name
description: # character's tragic backstory, 50 chars minimum
health: # character's health, integer, from 1 to 100 points
```
"""


class RPGCharacterFromYAMLBlock(YAMLBlockResponseParser):
    name: str
    character_class: Literal["Warrior", "Mage", "Rogue", "Bard", "Tech Support"]
    description: str = Field(..., min_length=50)
    health: int = Field(..., ge=1, le=100)


os.environ["OPENAI_API_KEY"] = "sk-YOUR-KEY-HERE"
os.environ["OPENAI_LLM_MODEL"] = "gpt-4o-mini-2024-07-18"
llm = OpenAILLM.from_env()

llm_function: LLMFunction[RPGCharacterFromYAMLBlock] = LLMFunction(
    llm, RPGCharacterFromYAMLBlock.from_response, SYSTEM_PROMPT
)

character = llm_function.execute(user_message="Create some wise mage")
print(type(character))
print(f"{character.name}, {character.character_class} ({character.health}/100 hp)")
print(character.description)

YAMLResponseParser

class council.llm.llm_response_parser.YAMLResponseParser

Bases: BaseModelResponseParser

classmethod from_response(response: LLMResponse) → T

LLMFunction ResponseParser for response containing raw YAML content

JSONBlockResponseParser

class council.llm.llm_response_parser.JSONBlockResponseParser

Bases: BaseModelResponseParser

classmethod from_response(response: LLMResponse) → T

LLMFunction ResponseParser for response containing a single JSON code block

JSONResponseParser

class council.llm.llm_response_parser.JSONResponseParser

Bases: BaseModelResponseParser

classmethod from_response(response: LLMResponse) → T

LLMFunction ResponseParser for response containing raw JSON content

Usage example with OpenAI json mode:

import os
from typing import Literal

# !pip install council-ai==0.0.24

from council import OpenAILLM
from council.llm.llm_function import LLMFunction
from council.llm.llm_response_parser import JSONResponseParser
from pydantic import Field

SYSTEM_PROMPT = """
Output RPG character info in the following JSON format:

{
character_class: # character's class (Warrior, Mage, Rogue, Bard or Tech Support)
name: # character's name
description: # character's tragic backstory, 50 chars minimum
health: # character's health, integer, from 1 to 100 points
}
"""


class RPGCharacterFromJSON(JSONResponseParser):
    name: str
    character_class: Literal["Warrior", "Mage", "Rogue", "Bard", "Tech Support"]
    description: str = Field(..., min_length=50)
    health: int = Field(..., ge=1, le=100)


os.environ["OPENAI_API_KEY"] = "sk-YOUR-KEY-HERE"
os.environ["OPENAI_LLM_MODEL"] = "gpt-4o-mini-2024-07-18"
llm = OpenAILLM.from_env()

llm_function: LLMFunction[RPGCharacterFromJSON] = LLMFunction(
    llm, RPGCharacterFromJSON.from_response, SYSTEM_PROMPT
)

character = llm_function.execute(
    user_message="Create some wise mage",
    response_format={"type": "json_object"}  # using OpenAI's json mode
)
print(type(character))
print(f"{character.name}, {character.character_class} ({character.health}/100 hp)")
print(character.description)