langchain-teradata

Teradata Package for LangChain

langchain-teradata is a Teradata package for Langchain that provides users with access to Teradata's Vector Store capabilities.

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For Teradata customer support, please visit Teradata Support.

Copyright 2025, Teradata. All Rights Reserved.

Table of Contents

• Documentation
• Release Notes
• Installation and Requirements
• Usage Examples
• License

Documentation

General product information, including installation instructions, is available in the Teradata Documentation website.

Release Notes

Version 20.00.00.00

• langchain-teradata 20.00.00.00 marks the first release of the package.

• Compatible with August Lake drop (Tahoe-1.2.1) .

• Added methods for managing and creating vector stores:

• from_documents(name, documents, embedding=None, **kwargs): Creates a new vector store, either 'file-based' or 'content-based', depending on the type of input documents. If the input is PDF file(s) or file path(s), a file-based vector store is created. If the input is LangChain Document object(s), a content-based vector store is created. If the store already exists, raises an error.

• from_texts(name, texts, embedding=None, **kwargs): Creates a content-based vector store from raw text or a list of texts. Supports embedding models and chat completion models. If the store already exists, raises an error.

• from_datasets(name, data, embedding=None, **kwargs): Creates a content-based vector store from tables or DataFrames, specifying data columns and optional key columns, with embedding model support. If the store already exists, raises an error.

• from_embeddings(name, data, **kwargs): Creates an embedding-based vector store from pre-embedded tables or DataFrames, specifying the embedding columns. If the store already exists, raises an error.

• add_documents(documents, **kwargs): Adds documents (PDFs, directories, wildcards or Langchain Documents) to an existing vector store. Automatically creates the store if it does not exist.

• add_datasets(data, **kwargs): Adds tables or DataFrames to a content-based vector store. Creates the store if needed.

• add_embeddings(data, **kwargs): Adds embedding data to an embedding-based vector store.

• add_texts(texts, **kwargs): Adds raw text or list of texts to a content-based vector store.

• delete_documents(documents, **kwargs): Removes specified documents from a file-based vector store.

• delete_datasets(data, **kwargs): Removes specified datasets from a content-based vector store.

• delete_embeddings(data, **kwargs): Removes embedding data from an embedding-based vector store.

• update() : Updates the search parameters of an existing vector store.

• as_retriever(): Creates a TeradataVectorStoreRetriever instance that can be used to retrieve relevant documents.

Installation and Requirements

Package Requirements:

• Python 3.9 or later

Note: 32-bit Python is not supported.

Minimum System Requirements:

• Windows 7 (64Bit) or later
• macOS 10.9 (64Bit) or later
• Red Hat 7 or later versions
• Ubuntu 16.04 or later versions
• CentOS 7 or later versions
• SLES 12 or later versions

Minimum Database Requirements

• Teradata Vantage with database release 20.00.25.XX or later
• Vector Store (Data insights) service is enabled.

Installation

Use pip to install the Teradata Package for Langchain

Platform	Command
macOS/Linux	pip install langchain-teradata
Windows	python -m pip install langchain-teradata

License

Use of the Teradata package for LangChain is governed by the Teradata License Agreement. After installation, the LICENSE.pdf and LICENSE-3RD-PARTY.pdf files are located in the langchain-teradata directory.