Copyright	(c) 2025 Tushar Adhatrao
License	MIT
Maintainer	Tushar Adhatrao <[email protected]>
Stability	experimental
Safe Haskell	None
Language	Haskell2010

Langchain.Embeddings.Core

Contents

Embedding Interface

Description

Haskell implementation of LangChain's embedding model abstraction, providing:

Document vectorization for semantic search
Query embedding for similarity comparisons
Integration with document loading pipelines

Example usage:

  let oEmbed = defaultOpenAIEmbeddings { apiKey = "api-key" }
  let p = PdfLoader "homeuserDocumentsTSlangchainSOP.pdf"
  eDocs <- load p
  case eDocs of
    Left err -> error err
    Right docs -> do
      eRes <- embedQuery oEmbed Hello
      print eRes

Synopsis

class Embeddings m where
- embedDocuments :: m -> [Document] -> IO (Either String [[Float]])
- embedQuery :: m -> Text -> IO (Either String [Float])

Embedding Interface

class Embeddings m where Source #

Typeclass for embedding models following LangChain's pattern. Converts text/documents into numerical vectors for machine learning tasks.

Implementations should handle:

Text preprocessing
API calls to embedding services
Error handling for failed requests
Consistent vector dimensionality

Example instance for a test model:

data TestEmbeddings = TestEmbeddings

instance Embeddings TestEmbeddings where
  embedDocuments _ _ = return $ Right [[0.1, 0.2, 0.3]]
  embedQuery _ _ = return $ Right [0.4, 0.5, 0.6]

Methods

embedDocuments :: m -> [Document] -> IO (Either String [[Float]]) Source #

Convert documents to embedding vectors

Example:

>>> let doc = Document "Hello world" mempty
>>> embedDocuments TestEmbeddings [doc]
Right [[0.1, 0.2, 0.3]]

embedQuery :: m -> Text -> IO (Either String [Float]) Source #

Convert query text to embedding vector

Example:

>>> embedQuery TestEmbeddings "Search query"
Right [0.4, 0.5, 0.6]

Instances

Instances details

Embeddings OllamaEmbeddings Source #
Instance details Defined in Langchain.Embeddings.Ollama Methods embedDocuments :: OllamaEmbeddings -> [Document] -> IO (Either String [[Float]]) Source # embedQuery :: OllamaEmbeddings -> Text -> IO (Either String [Float]) Source #
Embeddings OpenAIEmbeddings Source #
Instance details Defined in Langchain.Embeddings.OpenAI Methods embedDocuments :: OpenAIEmbeddings -> [Document] -> IO (Either String [[Float]]) Source # embedQuery :: OpenAIEmbeddings -> Text -> IO (Either String [Float]) Source #