3 Commits (c010ec8b71771dc3f54dc148475c90070b6a7c0b)

Author	SHA1	Message	Date
ccurme	c010ec8b71	patch: deprecate (a)get_relevant_documents (#20477) ... - `.get_relevant_documents(query)` -> `.invoke(query)` - `.get_relevant_documents(query=query)` -> `.invoke(query)` - `.get_relevant_documents(query, callbacks=callbacks)` -> `.invoke(query, config={"callbacks": callbacks})` - `.get_relevant_documents(query, **kwargs)` -> `.invoke(query, **kwargs)` --------- Co-authored-by: Erick Friis <erick@langchain.dev>	1 month ago
Ian	7f504c1f81	docs: Improve the tidb vector store notebook (#18773) ... Remove redundant useless content, and fix some minor oversight	3 months ago
Ian	390ef6abe3	community[minor]: Add Initial Support for TiDB Vector Store (#15796) ... This pull request introduces initial support for the TiDB vector store. The current version is basic, laying the foundation for the vector store integration. While this implementation provides the essential features, we plan to expand and improve the TiDB vector store support with additional enhancements in future updates. Upcoming Enhancements: * Support for Vector Index Creation: To enhance the efficiency and performance of the vector store. * Support for max marginal relevance search. * Customized Table Structure Support: Recognizing the need for flexibility, we plan for more tailored and efficient data store solutions. Simple use case exmaple ```python from typing import List, Tuple from langchain.docstore.document import Document from langchain_community.vectorstores import TiDBVectorStore from langchain_openai import OpenAIEmbeddings db = TiDBVectorStore.from_texts( embedding=embeddings, texts=['Andrew like eating oranges', 'Alexandra is from England', 'Ketanji Brown Jackson is a judge'], table_name="tidb_vector_langchain", connection_string=tidb_connection_url, distance_strategy="cosine", ) query = "Can you tell me about Alexandra?" docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query) for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80) ```	3 months ago