Release version 20.00.00.00

Author: lcl-teradata

LICENSE-3RD-PARTY.pdf

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+
+# 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](https://support.teradata.com/community?id=community_forum&sys_id=14fe131e1bf7f304682ca8233a4bcb1d).
+
+For Teradata customer support, please visit [Teradata Support](https://support.teradata.com/csm).
+
+Copyright 2025, Teradata. All Rights Reserved.
+
+## Table of Contents
+* [Documentation](#documentation)
+* [Release Notes](#release-notes)
+* [Installation and Requirements](#installation-and-requirements)
+* [Usage Examples](#usage-examples)
+* [License](#license)
+
+## Documentation
+General product information, including installation instructions, is available in the [Teradata Documentation website](https://docs.teradata.com/search/documents?query=Python+package+for+Generative-AI&sort=last_update&virtual-field=title_only&content-lang=en-US).
+
+## 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.