ollama-haskell 0.1.0.0 → 0.1.0.1

raw patch · 4 files changed

+144/−140 lines, 4 filesPVP: major bump suggested

API removals or changes: PVP suggests a major version bump

API changes (from Hackage documentation)

Files

• CHANGELOG.md +4/−0
• ollama-haskell.cabal +2/−2
• src/Lib.hs +0/−138
• src/OllamaExamples.hs +138/−0

@@ -1,5 +1,9 @@ # Revision history for ollama-haskell +## 0.1.0.1 -- 2024-10-18++* Renaming Lib.hs to OllamaExamples.hs as it was conflicting `Lib.hs` name+ ## 0.1.0.0 -- YYYY-mm-dd  * First version. Released on an unsuspecting world.

@@ -1,6 +1,6 @@ cabal-version:      3.4 name:               ollama-haskell-version:            0.1.0.0+version:            0.1.0.1 synopsis:           Ollama Haskell library -- description: license:            MIT@@ -40,7 +40,7 @@                     , Data.Ollama.Pull                     , Data.Ollama.Push                     , Data.Ollama.Show-                    , Lib+                    , OllamaExamples     -- other-modules:     -- other-extensions:     build-depends:    base ^>=4.18

@@ -1,138 +0,0 @@-{-# LANGUAGE OverloadedStrings #-}-{-# LANGUAGE RecordWildCards #-}--module Lib where--import Control.Monad (void)-import Data.List.NonEmpty (NonEmpty((:|)))-import Data.Maybe (fromMaybe)-import Data.Ollama.Chat qualified as Chat-import Data.Text.IO qualified as T-import Ollama (GenerateOps(..), Role(..), chat, defaultChatOps, defaultGenerateOps, generate)-import Ollama qualified--main :: IO ()-main = do-  -- Example 1: Streamed Text Generation-  -- This example demonstrates how to generate text using a model and stream the output directly to the console.-  -- The `stream` option enables processing of each chunk of the response as it arrives.-  void $-    generate-      defaultGenerateOps-        { modelName = "llama3.2"-        , prompt = "what is functional programming?"-        , stream = Just (T.putStr . Ollama.response_, pure ())-        }--  -- Example 2: Non-streamed Text Generation-  -- This example shows how to generate text and handle the complete response.-  -- The result is either an error message or the generated text.-  eRes <--    generate-      defaultGenerateOps-        { modelName = "llama3.2"-        , prompt = "What is 2+2?"-        }-  case eRes of-    Left e -> putStrLn e-    Right Ollama.GenerateResponse {..} -> T.putStrLn response_--  -- Example 3: Chat with Streaming-  -- This example demonstrates setting up a chat session with streaming enabled.-  -- As messages are received, they are printed to the console.-  let msg = Ollama.Message User "What is functional programming?" Nothing-      defaultMsg = Ollama.Message User "" Nothing-  void $-    chat-      defaultChatOps-        { Chat.chatModelName = "llama3.2"-        , Chat.messages = msg :| []-        , Chat.stream =-            Just (T.putStr . Chat.content . fromMaybe defaultMsg . Chat.message, pure ())-        }--  -- Example 4: Non-streamed Chat-  -- Here, we handle a complete chat response, checking for potential errors.-  eRes1 <--    chat-      defaultChatOps-        { Chat.chatModelName = "llama3.2"-        , Chat.messages = msg :| []-        }-  case eRes1 of-    Left e -> putStrLn e-    Right r -> do-      let mMessage = Ollama.message r-      case mMessage of-        Nothing -> putStrLn "Something went wrong"-        Just res -> T.putStrLn $ Ollama.content res--  -- Example 5: Check Model Status (ps)-  -- This example checks the status of models using the `ps` function.-  -- It outputs the status or details of the available models.-  res <- Ollama.ps-  print res--  -- Example 6: Simple Embedding-  -- This demonstrates how to request embeddings for a given text using a specific model.-  void $ Ollama.embedding "llama3.1" "What is 5+2?"--  -- Example 7: Embedding with Options-  -- This example uses the `embeddingOps` function, allowing for additional configuration like options and streaming.-  void $ Ollama.embeddingOps "llama3.1" "What is 5+2?" Nothing Nothing--{--Scotty example:-{-# LANGUAGE OverloadedStrings #-}--module Main where--import Web.Scotty-import Control.Monad.IO.Class (liftIO)-import Data.Aeson (FromJSON, ToJSON)-import Data.Text (Text)-import Data.Text qualified as T-import Database.SQLite.Simple-import Ollama (GenerateOps(..), defaultGenerateOps, generate)-import Data.Maybe (fromRight)--data PromptInput = PromptInput-  { conversation_id :: Int-  , prompt :: Text-  } deriving (Show, Generic)--instance FromJSON PromptInput-instance ToJSON PromptInput--main :: IO ()-main = do-  conn <- open "chat.db"-  execute_ conn "CREATE TABLE IF NOT EXISTS conversation (convo_id INTEGER PRIMARY KEY, convo_title TEXT)"-  execute_ conn "CREATE TABLE IF NOT EXISTS chats (chat_id INTEGER PRIMARY KEY, convo_id INTEGER, role TEXT, message TEXT, FOREIGN KEY(convo_id) REFERENCES conversation(convo_id))"-  -  scotty 3000 $ do-    post "/chat" $ do-      p <- jsonData :: ActionM PromptInput-      let cId = conversation_id p-      let trimmedP = T.dropEnd 3 $ T.drop 3 $ prompt p-      newConvoId <- case cId of-        -1 -> do-          liftIO $ execute conn "INSERT INTO conversation (convo_title) VALUES (?)" (Only ("latest title" :: String))-          [Only convoId] <- liftIO $ query_ conn "SELECT last_insert_rowid()" :: ActionM [Only Int]-          pure convoId-        _ -> pure cId--      liftIO $ execute conn "INSERT INTO chats (convo_id, role, message) VALUES (?, 'user', ?)" (newConvoId, trimmedP)-      -      stream $ \sendChunk flush -> do-        eRes <- generate defaultGenerateOps-                { modelName = "llama3.2"-                , prompt = prompt p-                , stream = Just (sendChunk . T.pack, flush)-                }-        case eRes of-            Left e -> return ()-            Right r -> do-                let res = response_ r-                liftIO $ execute conn "INSERT INTO chats (convo_id, role, message) VALUES (?, 'ai', ?)" (newConvoId, res)--}

@@ -0,0 +1,138 @@+{-# LANGUAGE OverloadedStrings #-}+{-# LANGUAGE RecordWildCards #-}++module OllamaExamples where++import Control.Monad (void)+import Data.List.NonEmpty (NonEmpty((:|)))+import Data.Maybe (fromMaybe)+import Data.Ollama.Chat qualified as Chat+import Data.Text.IO qualified as T+import Ollama (GenerateOps(..), Role(..), chat, defaultChatOps, defaultGenerateOps, generate)+import Ollama qualified++main :: IO ()+main = do+  -- Example 1: Streamed Text Generation+  -- This example demonstrates how to generate text using a model and stream the output directly to the console.+  -- The `stream` option enables processing of each chunk of the response as it arrives.+  void $+    generate+      defaultGenerateOps+        { modelName = "llama3.2"+        , prompt = "what is functional programming?"+        , stream = Just (T.putStr . Ollama.response_, pure ())+        }++  -- Example 2: Non-streamed Text Generation+  -- This example shows how to generate text and handle the complete response.+  -- The result is either an error message or the generated text.+  eRes <-+    generate+      defaultGenerateOps+        { modelName = "llama3.2"+        , prompt = "What is 2+2?"+        }+  case eRes of+    Left e -> putStrLn e+    Right Ollama.GenerateResponse {..} -> T.putStrLn response_++  -- Example 3: Chat with Streaming+  -- This example demonstrates setting up a chat session with streaming enabled.+  -- As messages are received, they are printed to the console.+  let msg = Ollama.Message User "What is functional programming?" Nothing+      defaultMsg = Ollama.Message User "" Nothing+  void $+    chat+      defaultChatOps+        { Chat.chatModelName = "llama3.2"+        , Chat.messages = msg :| []+        , Chat.stream =+            Just (T.putStr . Chat.content . fromMaybe defaultMsg . Chat.message, pure ())+        }++  -- Example 4: Non-streamed Chat+  -- Here, we handle a complete chat response, checking for potential errors.+  eRes1 <-+    chat+      defaultChatOps+        { Chat.chatModelName = "llama3.2"+        , Chat.messages = msg :| []+        }+  case eRes1 of+    Left e -> putStrLn e+    Right r -> do+      let mMessage = Ollama.message r+      case mMessage of+        Nothing -> putStrLn "Something went wrong"+        Just res -> T.putStrLn $ Ollama.content res++  -- Example 5: Check Model Status (ps)+  -- This example checks the status of models using the `ps` function.+  -- It outputs the status or details of the available models.+  res <- Ollama.ps+  print res++  -- Example 6: Simple Embedding+  -- This demonstrates how to request embeddings for a given text using a specific model.+  void $ Ollama.embedding "llama3.1" "What is 5+2?"++  -- Example 7: Embedding with Options+  -- This example uses the `embeddingOps` function, allowing for additional configuration like options and streaming.+  void $ Ollama.embeddingOps "llama3.1" "What is 5+2?" Nothing Nothing++{-+Scotty example:+{-# LANGUAGE OverloadedStrings #-}++module Main where++import Web.Scotty+import Control.Monad.IO.Class (liftIO)+import Data.Aeson (FromJSON, ToJSON)+import Data.Text (Text)+import Data.Text qualified as T+import Database.SQLite.Simple+import Ollama (GenerateOps(..), defaultGenerateOps, generate)+import Data.Maybe (fromRight)++data PromptInput = PromptInput+  { conversation_id :: Int+  , prompt :: Text+  } deriving (Show, Generic)++instance FromJSON PromptInput+instance ToJSON PromptInput++main :: IO ()+main = do+  conn <- open "chat.db"+  execute_ conn "CREATE TABLE IF NOT EXISTS conversation (convo_id INTEGER PRIMARY KEY, convo_title TEXT)"+  execute_ conn "CREATE TABLE IF NOT EXISTS chats (chat_id INTEGER PRIMARY KEY, convo_id INTEGER, role TEXT, message TEXT, FOREIGN KEY(convo_id) REFERENCES conversation(convo_id))"+  +  scotty 3000 $ do+    post "/chat" $ do+      p <- jsonData :: ActionM PromptInput+      let cId = conversation_id p+      let trimmedP = T.dropEnd 3 $ T.drop 3 $ prompt p+      newConvoId <- case cId of+        -1 -> do+          liftIO $ execute conn "INSERT INTO conversation (convo_title) VALUES (?)" (Only ("latest title" :: String))+          [Only convoId] <- liftIO $ query_ conn "SELECT last_insert_rowid()" :: ActionM [Only Int]+          pure convoId+        _ -> pure cId++      liftIO $ execute conn "INSERT INTO chats (convo_id, role, message) VALUES (?, 'user', ?)" (newConvoId, trimmedP)+      +      stream $ \sendChunk flush -> do+        eRes <- generate defaultGenerateOps+                { modelName = "llama3.2"+                , prompt = prompt p+                , stream = Just (sendChunk . T.pack, flush)+                }+        case eRes of+            Left e -> return ()+            Right r -> do+                let res = response_ r+                liftIO $ execute conn "INSERT INTO chats (convo_id, role, message) VALUES (?, 'ai', ?)" (newConvoId, res)+-}