Copyright	(c) 2025 Tushar Adhatrao
License	MIT
Maintainer	Tushar Adhatrao <tusharadhatrao@gmail.com>
Stability	experimental
Safe Haskell	Safe-Inferred
Language	Haskell2010

Langchain.LLM.Core

Contents

LLM Typeclass
Parameters
Default Values

Description

This module provides the core types and typeclasses for the Langchain library in Haskell, which is designed to facilitate interaction with language models (LLMs). It defines a standardized interface that allows different LLM implementations to be used interchangeably, promoting code reuse and modularity.

The main components include:

The LLM typeclass, which defines the interface for language models.
Data types such as Params for configuring model invocations, Message for conversation messages, and StreamHandler for handling streaming responses.
Default values like defaultParams and defaultMessageData for convenience.

This module is intended to be used as the foundation for building applications that interact with LLMs, providing a consistent API across different model implementations.

Synopsis

class LLM m where
- generate :: m -> Text -> Maybe Params -> IO (Either String Text)
- chat :: m -> ChatMessage -> Maybe Params -> IO (Either String Text)
- stream :: m -> ChatMessage -> StreamHandler -> Maybe Params -> IO (Either String ())
data Message = Message {
- role :: Role
- content :: Text
- messageData :: MessageData
}
data Role
- = System
- | User
- | Assistant
- | Tool
type ChatMessage = NonEmpty Message
data MessageData = MessageData {
- name :: Maybe Text
- toolCalls :: Maybe [Text]
}
data Params = Params {
- temperature :: Maybe Double
- maxTokens :: Maybe Integer
- topP :: Maybe Double
- n :: Maybe Int
- stop :: Maybe [Text]
}
data StreamHandler = StreamHandler {
- onToken :: Text -> IO ()
- onComplete :: IO ()
}
defaultParams :: Params
defaultMessageData :: MessageData

LLM Typeclass

class LLM m where Source #

Typeclass defining the interface for language models. This provides methods for invoking the model, chatting with it, and streaming responses.

data TestLLM = TestLLM
  { responseText :: Text
  , shouldSucceed :: Bool
  }

instance LLM TestLLM where
  generate m _ _ = pure $ if shouldSucceed m
    then Right (responseText m)
    else Left "Test error"

ollamaLLM = Ollama "llama3.2:latest" [stdOutCallback]
response <- generate ollamaLLM "What is Haskell?" Nothing

Methods

generate Source #

Arguments

:: m	The type of the language model instance.
-> Text	The prompt to send to the model.
-> Maybe Params	Optional configuration parameters.
-> IO (Either String Text)

Invoke the language model with a single prompt. Suitable for simple queries; returns either an error or generated text.

chat Source #

Arguments

:: m	The type of the language model instance.
-> ChatMessage	A non-empty list of messages to send to the model.
-> Maybe Params	Optional configuration parameters.
-> IO (Either String Text)	The result of the chat, either an error or the response text.

Chat with the language model using a sequence of messages. Suitable for multi-turn conversations; returns either an error or the response.

stream :: m -> ChatMessage -> StreamHandler -> Maybe Params -> IO (Either String ()) Source #

Stream responses from the language model for a sequence of messages. Uses callbacks to process tokens in real-time; returns either an error or unit.

Instances

Instances details

LLM Ollama Source #	Ollama implementation of the LLM typeclass Note: Params argument is currently ignored (see TODOs). Example instance usage: -- Generate text with error handling case generate ollamaLLM Hello Nothing of Left err -> putStrLn $ "Error: " ++ err Right res -> putStrLn res
Instance details Defined in Langchain.LLM.Ollama Methods generate :: Ollama -> Text -> Maybe Params -> IO (Either String Text) Source # chat :: Ollama -> ChatMessage -> Maybe Params -> IO (Either String Text) Source # stream :: Ollama -> ChatMessage -> StreamHandler -> Maybe Params -> IO (Either String ()) Source #
LLM OpenAI Source #
Instance details Defined in Langchain.LLM.OpenAI Methods generate :: OpenAI -> Text -> Maybe Params -> IO (Either String Text) Source # chat :: OpenAI -> ChatMessage -> Maybe Params -> IO (Either String Text) Source # stream :: OpenAI -> ChatMessage -> StreamHandler -> Maybe Params -> IO (Either String ()) Source #

Parameters

data Message Source #

Represents a message in a conversation, including the sender's role, content, and additional metadata. https:/python.langchain.comdocsconceptsmessages/

userMsg :: Message
userMsg = Message
  { role = User
  , content = "Explain functional programming"
  , messageData = defaultMessageData
  }

Constructors

Message
Fields role :: Role The role of the message sender content :: Text The content of the message messageData :: MessageData Additional data associated with the message

Instances

Instances details

Show Message Source #
Instance details Defined in Langchain.LLM.Core Methods showsPrec :: Int -> Message -> ShowS # show :: Message -> String # showList :: [Message] -> ShowS #
Eq Message Source #
Instance details Defined in Langchain.LLM.Core Methods (==) :: Message -> Message -> Bool # (/=) :: Message -> Message -> Bool #

data Role Source #

Enumeration of possible roles in a conversation.

Constructors

System	System role, typically for instructions or context
User	User role, for user inputs
Assistant	Assistant role, for model responses
Tool	Tool role, for tool outputs or interactions

Instances

Instances details

FromJSON Role Source #
Instance details Defined in Langchain.LLM.Core Methods parseJSON :: Value -> Parser Role # parseJSONList :: Value -> Parser [Role] # omittedField :: Maybe Role #
ToJSON Role Source #
Instance details Defined in Langchain.LLM.Core Methods toJSON :: Role -> Value # toEncoding :: Role -> Encoding # toJSONList :: [Role] -> Value # toEncodingList :: [Role] -> Encoding # omitField :: Role -> Bool #
Generic Role Source #
Instance details Defined in Langchain.LLM.Core Associated Types type Rep Role :: Type -> Type # Methods from :: Role -> Rep Role x # to :: Rep Role x -> Role #
Show Role Source #
Instance details Defined in Langchain.LLM.Core Methods showsPrec :: Int -> Role -> ShowS # show :: Role -> String # showList :: [Role] -> ShowS #
Eq Role Source #
Instance details Defined in Langchain.LLM.Core Methods (==) :: Role -> Role -> Bool # (/=) :: Role -> Role -> Bool #
type Rep Role Source #
Instance details Defined in Langchain.LLM.Core type Rep Role = D1 ('MetaData "Role" "Langchain.LLM.Core" "langchain-hs-0.0.1.0-inplace" 'False) ((C1 ('MetaCons "System" 'PrefixI 'False) (U1 :: Type -> Type) :+: C1 ('MetaCons "User" 'PrefixI 'False) (U1 :: Type -> Type)) :+: (C1 ('MetaCons "Assistant" 'PrefixI 'False) (U1 :: Type -> Type) :+: C1 ('MetaCons "Tool" 'PrefixI 'False) (U1 :: Type -> Type)))

type ChatMessage = NonEmpty Message Source #

Type alias for NonEmpty Message

data MessageData Source #

Additional data for a message, such as a name or tool calls. This type is designed for extensibility, allowing new fields to be added without breaking changes. Use defaultMessageData for typical usage.

Constructors

MessageData
Fields name :: Maybe Text Optional name associated with the message toolCalls :: Maybe [Text] Optional list of tool calls invoked by the message

Instances

Instances details

FromJSON MessageData Source #	JSON deserialization for MessageData.
Instance details Defined in Langchain.LLM.Core Methods parseJSON :: Value -> Parser MessageData # parseJSONList :: Value -> Parser [MessageData] # omittedField :: Maybe MessageData #
ToJSON MessageData Source #	JSON serialization for MessageData.
Instance details Defined in Langchain.LLM.Core Methods toJSON :: MessageData -> Value # toEncoding :: MessageData -> Encoding # toJSONList :: [MessageData] -> Value # toEncodingList :: [MessageData] -> Encoding # omitField :: MessageData -> Bool #
Show MessageData Source #
Instance details Defined in Langchain.LLM.Core Methods showsPrec :: Int -> MessageData -> ShowS # show :: MessageData -> String # showList :: [MessageData] -> ShowS #
Eq MessageData Source #
Instance details Defined in Langchain.LLM.Core Methods (==) :: MessageData -> MessageData -> Bool # (/=) :: MessageData -> MessageData -> Bool #

data Params Source #

Parameters for configuring language model invocations. These parameters control aspects such as randomness, length, and stopping conditions of generated output. This type corresponds to standard parameters in Python Langchain: https:/python.langchain.comdocsconceptschat_models/#standard-parameters

Example usage:

myParams :: Params
myParams = defaultParams
  { temperature = Just 0.7
  , maxTokens = Just 100
  }

Constructors

Params

Fields

temperature :: Maybe Double
Sampling temperature. Higher values increase randomness (creativity), while lower values make output more focused.
maxTokens :: Maybe Integer
topP :: Maybe Double
Nucleus sampling parameter. Considers tokens whose cumulative probability mass is at least topP.
n :: Maybe Int
Number of responses to generate (e.g., for sampling multiple outputs).
stop :: Maybe [Text]
Sequences where generation should stop (e.g., ["n"] stops at newlines).

Instances

Instances details

Show Params Source #
Instance details Defined in Langchain.LLM.Core Methods showsPrec :: Int -> Params -> ShowS # show :: Params -> String # showList :: [Params] -> ShowS #
Eq Params Source #
Instance details Defined in Langchain.LLM.Core Methods (==) :: Params -> Params -> Bool # (/=) :: Params -> Params -> Bool #

data StreamHandler Source #

Callbacks for handling streaming responses from a language model. This allows real-time processing of tokens as they are generated and an action upon completion.

printHandler :: StreamHandler
printHandler = StreamHandler
  { onToken = putStrLn . ("Token: " ++)
  , onComplete = putStrLn "Streaming complete"
  }

Constructors

StreamHandler
Fields onToken :: Text -> IO () Action to perform for each token received onComplete :: IO () Action to perform when streaming is complete

Default Values

defaultParams :: Params Source #

Default parameters with all fields set to Nothing. Use this when no specific configuration is needed for the language model.

>>> generate myLLM "Hello" (Just defaultParams)

defaultMessageData :: MessageData Source #

Default message data with all fields set to Nothing. Use this for standard messages without additional metadata