Merge pull request #1 from Teradata/20.00.00.01

notebooks readme for 20.00.00.01

Author: lcl-teradata

README.md

@@ -13,16 +13,57 @@ Copyright 2025, Teradata. All Rights Reserved.
 * [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 20.00.00.01
+  * New features introduced in this release require Database version 20.00.28.XX
+  * Added a method `rename_metadata_keys` to rename restricted metadata keys in Langchain Document objects.
+  * Added support for `search_type` "mmr" and filters to TeradataVectorStoreRetriever.
+  * Added support for metadata for input Langchain Document objects.
+  * Added support for embedding-based Vector Store to TeradataVectorStoreRetriever.
+  * Added support to delete specific chunks from a file in the vector store using `delete_by_ids`.
+  * Added new parameters to pass model url parameters and ingest parameters to from_* and add_* methods during vector store creation:
+    * `model_urls` - Specifies the urls and model information to be used during Vector Store creation.
+    * `ingest_params` - Specifies the parameters to be used for document ingestion for NIM. Applicable only for file-based vector stores.
+
+  * Added new parameters for similarity_search:
+    * `column`: Specifies the column name which contains the question in text format.
+    * `data`: Specifies the table name/DataFrame which contains the question in text format.
+
+  * Added new parameters for ask:
+    * `batch_vector_column`: Specifies the column that contains the questions in embedded form.
+    * `question_vector`: Specifies the question in vector/embedded form.
+    * `data`: Specifies table name or corresponding teradataml DataFrame where the question is stored (only one question/row should be present).
+    * `column`: Specifies the column name which contains the question in text format.
+    * `vector_column`: Specifies the column name which contains the question in embedded format.
+
+  * Added new common parameters for similarity_search, similarity_search_by_vector and ask:
+    * `top_k`: Specifies the number of top similarity matches to be generated.
+    * `search_threshold`: Specifies the threshold value to consider matching tables/views while searching.
+    * `search_numcluster`: Specifies the number of clusters or fraction of train_numcluster to be considered while searching. 
+    * `ef_search`: Specifies the number of neighbors to consider during search in HNSW graph.  
+    * `filter`: Specifies the filter to be used for filtering the results.
+    * `filter_style`: Specifies whether to apply filtering before or after the similarity_search.
+    * `maximal_marginal_relevance`: Specifies whether to use Maximal Marginal Relevance (MMR) for retrieving documents.
+    * `lambda_multiplier`: Specifies Lambda multiplier to control the trade-off between relevance and diversity when selecting documents.
+  
+  * Exposed the following classes:
+      * `ModelUrlParams` class to configure model and URL-related parameters for vector store creation using from_* and add_* methods on AI-Factory.
+      * `IngestParams` class to configure ingestor-related parameters for file-based vector store creation using from_* and add_* methods on AI-Factory.
+      * Note: Users can still pass these parameters directly while creating the vector store.
+  
+  * Added the following methods to set the search parameters based on the "search_algorithm":
+    * `set_kmeans_search_params()` method to configure KMEANS search parameters for the vector store.
+    * `set_hnsw_search_params()` method to configure HNSW search parameters for the vector store. 
+    * `set_vectordistance_search_params()` method to configure VECTORDISTANCE search algorithm parameters for the vector store.
+
 ### 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) .
+  * Features introduced in this release require Database version 20.00.27.XX
   * 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.
@@ -36,10 +77,13 @@ General product information, including installation instructions, is available i
   * `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.
+  * `similarity_search(question, **kwargs)`: Performs similarity search in the Vector Store for the input question.
+  * `similarity_search_by_vector(**kwargs)`: Performs similarity search in the Vector Store for the input question vector or embedded question stored in the input table. 
+  * `prepare_response(similarity_results, question, prompt, **kwargs)`: Prepare a natural language response to the user using the input question and similarity_results provided by similarity_search() method using interactive/batch mode.
+  * `ask(question, prompt, **kwargs)`: Performs similarity search in the vector store for the input question followed by preparing a natural language response to the user using interactive/batch mode.
   * `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
@@ -55,7 +99,7 @@ Note: 32-bit Python is not supported.
 * SLES 12 or later versions
 
 ### Minimum Database Requirements
-* Teradata Vantage with database release 20.00.25.XX or later 
+* Teradata Vantage with database release 20.00.27.XX or later 
 * Vector Store (Data insights) service is enabled.