langchain-teradata 20.0.0.0

Teradata package for langchain

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.

For community support, please visit the Teradata Community.

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.