Neo4j GraphRAG Package for Python 1.6.0

New in 1.6.0

https://github.com/neo4j/neo4j-graphrag-python/blob/main/CHANGELOG.md#160

Hybrid retrievers

• Add linear hybrid search ranker to give more weight to either the full text or the vector score.
• Raise SearchQueryParseError when HybridRetriever and HybridCypherRetriever encounters invalid Lucene string

KG Builder pipeline

• Add optional schema enforcement for KG builder as a validation layer after entity and relation extraction. If strict mode is enabled, properties, nodes and relationships not declared in the schema will be dropped from the LLM output.

Changed in 1.6.0

Dependencies

• Update dependencies in pyproject.toml (pypdf, anthropic, cohere)

Fixed in 1.6.0

• Config loading after module reload (for use in Jupyter notebooks)
• Fallback to Qdrant point id if external_id_property is not found in the point's payload in QdrantNeo4jRetriever

New Contributors

• @AlbertDoesProgramming made their first contribution in #298
• @ClemDoum made their first contribution in #288

Neo4j GraphRAG Package for Python 1.5.0

https://github.com/neo4j/neo4j-graphrag-python/blob/main/CHANGELOG.md#150

What's New in 1.5.0

• Added utility functions to retrieve metadata about existing vector and full-text indexes.
• Added support for the effective_search_ratio parameter in vector and hybrid searches, allowing finer control over query accuracy by adjusting the candidate pool size in similarity searches.
• Introduced the upsert_vectors utility function for batch upserting embeddings into vector indexes on both nodes and relationships.
• Introduced the extract_cypher function to improve the extraction of LLM-generated Cypher queries in Text2CypherRetriever.
• Added Neo4jMessageHistory for saving LLM chat message history to a Neo4j database.
• Added InMemoryMessageHistory for storing LLM chat message history in memory.
• Added example scripts and documentation for using the new message history classes.
• Updated LLM and GraphRAG classes to support message history functionality.

Changed in 1.5.0

• Added deprecation warnings to upsert_vector and upsert_vector_on_relationship, recommending migration to upsert_vectors.
• Added deprecation warnings to async_upsert_vector and async_upsert_vector_on_relationship, notifying developers of their planned removal in a future release.
• Added support for database, timeout, and sanitize arguments in schema retrieval functions. The sanitize option allows for the removal of large properties such as embeddings.

Fixed in 1.5.0

• Fixed an issue where a node alias was incorrectly hardcoded in the _handle_field_filter function.

Neo4j GraphRAG Package for Python 1.4.3

https://github.com/neo4j/neo4j-graphrag-python/blob/main/CHANGELOG.md#143

What's New in 1.4.3

• Added the ability to add event listener to get notifications about Pipeline progress.
• Added py.typed so that mypy knows to use type annotations from the neo4j-graphrag package.

Changed in 1.4.3

• Changed the default behaviour of FixedSizeSplitter to avoid words cut-off in the chunks whenever it is possible. Back to exact size splitter is possible using approximate=False.
• Updates tests to work with new Neo4j calendar versioning
• neo4j_schema is now used in custom prompt formatting in Text2CypherRetriever (if provided).

Fixed in 1.4.3

• Fixed a bug in the AnthropicLLM class preventing it from being used in GraphRAG pipeline.
• Fixed a bug where the extras section of a config file was not resolved properly.
• Fixed a bug where the LLM producing a valid JSON array was causing the LLMEntityRelationExtractor to fail.
• Removed the uuid package from dependencies (not needed with Python 3).

New Contributors

• @NathalieCharbel made their first contribution in #242
• @koyonkym made their first contribution in #256

Neo4j GraphRAG Package for Python 1.4.2

Neo4j GraphRAG Package for Python 1.4.2

Fixed in 1.4.2

Ollama Embedding

Fixed a bug where OllamaEmbedding.embed_query method was returning a list[list[float]] instead of list[float].

Neo4j GraphRAG Package for Python 1.4.1

Neo4j GraphRAG Package for Python 1.4.1

Fixed in 1.4.1

Dependencies

• PyYAML dependency was missing and has been added.
• Weaviate was unintentionally added as a mandatory dependency in previous version, this behavior has been reverted.
• PyPDF and fsspec are not optional anymore so that SimpleKGPipeline examples can run out of the box.

Neo4j GraphRAG Package for Python 1.4.0

What's New in 1.4.0

https://github.com/neo4j/neo4j-graphrag-python/blob/main/CHANGELOG.md#140

LLM Interface and GraphRAG

• Added ability to pass system_instructions to the LLM
• Added ability to pass message_history to the LLM and GraphRAG
• Enhanced PromptTemplate to add a system_instruction parameter.

Changed in 1.4.0

KG Construction Pipeline

• The id_prefix of LexicalGraphConfig is not used anymore and will be removed in a future version.

Fixed in 1.4.0

KG Construction Pipeline

• Fixed a bug where the chunk IDs were not unique and too many relationships were created in the lexical graph (between chunks and between entities and chunks). Now chunk IDs are UUIDs, truly unique, even for multiple runs on the same document or running the pipeline on multiple documents.

Neo4j GraphRAG Package for Python 1.3.0

https://github.com/neo4j/neo4j-graphrag-python/blob/main/CHANGELOG.md#130

What's New in 1.3.0

Entity and Relation Extraction Improvements

• Updated LLM prompt for Entity and Relation extraction to include stricter instructions for generating valid JSON.
• Integrated json-repair package to handle and repair invalid JSON generated by LLMs.
• Introduced InvalidJSONError exception for handling cases where JSON repair fails.

SimpleKGPipeline from config files

• Added the ability to create a Pipeline or SimpleKGPipeline from a config file. See the example.

Ollama support

• Added OllamaLLM and OllamaEmbeddings classes to make Ollama support more explicit.
  • Implementations using the OpenAILLM and OpenAIEmbeddings classes will still work.

Changed in 1.3.0

• The default prompt in the ERExtractionTemplate prompt template has been updated to include more instructions about the expected return format.

Fixed in 1.3.0

Documentation

• Added schema functions to the documentation (get_structured_schema and get_schema)
• Improved documentation around the Text2CypherTemplate:
  • Class added to the API doc
  • New example showcasing how to use a custom prompt

Neo4j GraphRAG Package for Python 1.2.1

https://github.com/neo4j/neo4j-graphrag-python/blob/main/CHANGELOG.md#121

What's New in 1.2.1

SimpleKGPipeline improvements

• Ability to provide description and list of properties for entities and relations in the schema
• Optional lexical graph config parameter, enhancing flexibility in customizing node labels and relationship types in the lexical graph.

Neo4j database configuration

New optional neo4j_database parameter for SimpleKGPipeline, Neo4jChunkReader and Text2CypherRetriever.

Changed in 1.2.1

Query routing for Neo4j clusters

• All READ queries are now routed to a reader replica (for clusters). This impacts all retrievers, the Neo4jChunkReader and SinglePropertyExactMatchResolver components.

Fixed in 1.2.1

Use of Neo4j database

• neo4j_database parameter is now used for all queries in the Neo4jWriter.

Updated examples

• Updated all examples to use neo4j_database parameter instead of an undocumented neo4j driver constructor.

Neo4j GraphRAG Package for Python 1.2.0

https://github.com/neo4j/neo4j-graphrag-python/blob/main/CHANGELOG.md#120

What's New in 1.2.0

Enhanced Schema Building:

• Optional Relations and Schema: Made relations and potential_schema optional in the SchemaBuilder, providing greater flexibility in knowledge graph construction.

Deprecation Safeguards:

• Cypher Syntax Check: Added a mechanism to prevent the use of deprecated Cypher syntax for Neo4j versions 5.23.0 and above, ensuring compatibility with future Neo4j releases.

New Components:

• LexicalGraphBuilder: Enables the import of the lexical graph (documents and chunks) without performing entity and relation extraction.
• Neo4jChunkReader: Facilitates reading chunk text directly from the Neo4j database.

Changed in 1.2.0

Retriever Enhancements:

• Embedding Property Filtering: HybridRetriever now excludes the embedding property index specified in self.vector_index_name from the retriever results by default, streamlining the output.

Pipeline Adjustments:

• AsyncDriver Removal: Removed support for neo4j.AsyncDriver in the knowledge graph creation pipeline, affecting Neo4jWriter and related components. Updated examples and unit tests to reflect this change.

Fixed in 1.2.0

Inheritance Correction:

• AzureOpenAIEmbeddings Fix: Resolved an issue where AzureOpenAIEmbeddings incorrectly inherited from OpenAIEmbeddings. It now correctly inherits from BaseOpenAIEmbeddings, ensuring proper functionality and integration.

Retriever Correction:

• Extended Return Properties: Vector and Hybrid retrievers using return_properties now also return the node labels (nodeLabels) and the node's element ID (id), enriching the retriever results with additional metadata.

Neo4j GraphRAG Package for Python 1.1.0

https://github.com/neo4j/neo4j-graphrag-python/blob/main/CHANGELOG.md#110

Added

• Added a QdrantNeo4jRetriever external retriever for when vectors and saved in the Qdrant vector database
• Introduced a fail_if_exist option to index creation functions to control behavior when an index already exists.

Changed

• Comprehensive rewrite of the README to improve clarity and provide detailed usage examples.
• Documentation improvements