Neo4j-genai

https://github.com/neo4j/neo4j-genai-python/blob/main/CHANGELOG.md#050

Minor Changes

- **Experimental Knowledge Graph Constructor**: Introduces a PDF-to-knowledge-graph constructor in experimental mode. This constructor uses a flexible component/pipeline architecture and includes components such as text splitters, a schema builder, an entity-relation extractor, and a Neo4j database writer.
- **Hybrid Retriever Fix**: Fixes the hybrid retriever query to ensure proper normalization of scores returned by both vector and full-text indexes.

https://github.com/neo4j/neo4j-genai-python/blob/main/CHANGELOG.md#040

Minor Changes

- Introduced an optional custom_prompt argument to the Text2CypherRetriever class for enhanced customization capabilities.
- Renamed the first parameter of GraphRAG.search from query to query_text for consistency, while ensuring backward compatibility with deprecation warnings for the query parameter.

https://github.com/neo4j/neo4j-genai-python/blob/main/CHANGELOG.md#031

Patch Changes
- **OpenAIEmbedding fixes**: Fixed embedding model initialization and removed unused import to prevent ImportError when sentence_transformers is not installed.

https://github.com/neo4j/neo4j-genai-python/blob/main/CHANGELOG.md#030

Minor Changes

- **GraphRAG Integration**: Introducing the GraphRAG object to enable a complete Retrieval-Augmented Generation pipeline. This major feature supports context retrieval, sophisticated prompt formatting, and dynamic answer generation, streamlining the use of RAG techniques within Neo4j.
- **Enhanced Customization Options**: Added `PromptTemplate` and `RagTemplate` for customizable prompt generation. Also, a new `result_formatter` argument across all retrievers allows for tailored formatting of retrieval results, offering greater flexibility in how results are presented and utilized.
- **VectorRetriever Updates**: Enhanced to return metadata such as `nodeLabels` and `id` without direct embedding output, simplifying data handling.
- **Developer Experience**: Enhanced developer interactions with improved documentation, including a new User Guide that provides comprehensive instructions and tips for getting started and effectively using the `neo4j-genai-python` package.