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

Langchain.VectorStore.Core

Description

Haskell implementation of LangChain's vector store interface, providing:

Document storage with vector embeddings
Similarity-based search capabilities
Integration with Runnable workflows

Example usage with hypothetical FAISS store:

-- Create vector store instance
faissStore :: FAISSStore
faissStore = emptyFAISSStore

-- Add documents with embeddings
docs = [Document "Haskell is functional" mempty, ...]
updatedStore <- addDocuments faissStore docs

-- Perform similarity search
results <- similaritySearch updatedStore "functional programming" 5
-- Returns top 5 relevant documents

Synopsis

class VectorStore m where
- addDocuments :: m -> [Document] -> IO (Either String m)
- delete :: m -> [Int64] -> IO (Either String m)
- similaritySearch :: m -> Text -> Int -> IO (Either String [Document])
- similaritySearchByVector :: m -> [Float] -> Int -> IO (Either String [Document])

Documentation

class VectorStore m where Source #

Vector store abstraction following LangChain's design patterns Implementations should handle document storage, vectorization, and similarity search.

Example instance for an in-memory store:

data InMemoryStore = InMemoryStore
  { documents :: [Document]
  , embeddings :: [[Float]]
  }

instance VectorStore InMemoryStore where
  addDocuments store docs = ...
  similaritySearch store query k = ...

Methods

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

Add documents to the vector store

Example:

>>> addDocuments myStore [Document "Test content" mempty]
Right (updatedStoreWithNewDocs)

delete :: m -> [Int64] -> IO (Either String m) Source #

Requires document ID tracking to be implemented in store instances.

Example usage (when implemented):

>>> delete myStore [123]
Right (storeWithoutDoc123)

similaritySearch :: m -> Text -> Int -> IO (Either String [Document]) Source #

Find documents similar to query text Uses embedded vector representations for semantic search.

Example:

>>> similaritySearch store "Haskell monads" 3
Right [Document "Monads in FP...", ...]

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

Find documents similar to vector representation For direct vector comparisons without text conversion.

Example:

>>> similaritySearchByVector store [0.1, 0.3, ...] 5
Right [mostSimilarDoc1, ...]

Instances

Instances details

Embeddings m => VectorStore (InMemory m) Source #

Instance details

Defined in Langchain.VectorStore.InMemory

Methods

addDocuments :: InMemory m -> [Document] -> IO (Either String (InMemory m)) Source #

delete :: InMemory m -> [Int64] -> IO (Either String (InMemory m)) Source #

similaritySearch :: InMemory m -> Text -> Int -> IO (Either String [Document]) Source #