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@neo4j/neo4j-graphrag-python

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Added

  • Support for conversations with message history, including a new message_history parameter for LLM interactions.
  • Ability to include system instructions and override them for specific invocations.
  • Summarization of chat history to enhance query embedding and context handling.

Changed

  • Updated LLM implementations to handle message history consistently across providers.

1.3.0

Added

  • Integrated json-repair package to handle and repair invalid JSON generated by LLMs.
  • Introduced InvalidJSONError exception for handling cases where JSON repair fails.
  • Ability to create a Pipeline or SimpleKGPipeline from a config file. See the example.
  • Added OllamaLLM and OllamaEmbeddings classes to make Ollama support more explicit. Implementations using the OpenAILLM and OpenAIEmbeddings classes will still work.

Changed

  • Updated LLM prompt for Entity and Relation extraction to include stricter instructions for generating valid JSON.

Fixed

  • Added schema functions to the documentation.

1.2.1

Added

  • Introduced optional lexical graph configuration for SimpleKGPipeline, enhancing flexibility in customizing node labels and relationship types in the lexical graph.
  • Introduced optional neo4j_database parameter for SimpleKGPipeline, Neo4jChunkReaderand Text2CypherRetriever.
  • Ability to provide description and list of properties for entities and relations in the SimpleKGPipeline constructor.

Fixed

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

Changed

  • Updated all examples to use neo4j_database parameter instead of an undocumented neo4j driver constructor.
  • All READ queries are now routed to a reader replica (for clusters). This impacts all retrievers, the Neo4jChunkReader and SinglePropertyExactMatchResolver components.

1.2.0

Added

  • Made relations and potential_schema optional in SchemaBuilder.
  • Added a check to prevent the use of deprecated Cypher syntax for Neo4j versions 5.23.0 and above.
  • Added a LexicalGraphBuilder component to enable the import of the lexical graph (document, chunks) without performing entity and relation extraction.
  • Added a Neo4jChunkReader component to be able to read chunk text from the database.

Changed

  • Vector and Hybrid retrievers used with return_properties now also return the node labels (nodeLabels) and the node's element ID (id).
  • HybridRetriever now filters out the embedding property index in self.vector_index_name from the retriever result by default.
  • Removed support for neo4j.AsyncDriver in the KG creation pipeline, affecting Neo4jWriter and related components.
  • Updated examples and unit tests to reflect the removal of async driver support.

Fixed

  • Resolved issue with AzureOpenAIEmbeddings incorrectly inheriting from OpenAIEmbeddings, now inherits from BaseOpenAIEmbeddings.

1.1.0

Added

  • Introduced a fail_if_exist option to index creation functions to control behavior when an index already exists.
  • Added Qdrant retriever in neo4j_graphrag.retrievers.

Changed

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

1.0.0

Fixed

  • Fix a bug where openai Python client and numpy were required to import any embedder or LLM.

Changed

  • The value associated to the enum field OnError.IGNORE has been changed from "CONTINUE" to "IGNORE" to stick to the convention and match the field name.

Added

  • Added SinglePropertyExactMatchResolver component allowing to merge entities with exact same property (e.g. name)
  • Added the SimpleKGPipeline class, a simplified abstraction layer to streamline knowledge graph building processes from text documents.

1.0.0a1

1.0.0a0

Added

  • Added SinglePropertyExactMatchResolver component allowing to merge entities with exact same property (e.g. name)

0.7.0

Added

  • Added AzureOpenAILLM and AzureOpenAIEmbeddings to support Azure served OpenAI models
  • Added template validation in PromptTemplate class upon construction.
  • Examples demonstrating the use of Mistral embeddings and LLM in RAG pipelines.
  • Added feature to include kwargs in Text2CypherRetriever.search() that will be injected into a custom prompt, if provided.
  • Added validation to custom_prompt parameter of Text2CypherRetriever to ensure that query_text placeholder exists in prompt.
  • Introduced a fixed size text splitter component for splitting text into specified fixed size chunks with overlap. Updated examples and tests to utilize this new component.
  • Introduced Vertex AI LLM class for integrating Vertex AI models.
  • Added unit tests for the Vertex AI LLM class.
  • Added support for Cohere LLM and embeddings - added optional dependency to cohere.
  • Added support for Anthropic LLM - added optional dependency to anthropic.
  • Added support for MistralAI LLM - added optional dependency to mistralai.
  • Added support for Qdrant - added optional dependency to qdrant-client.

Fixed

  • Resolved import issue with the Vertex AI Embeddings class.
  • Fixed bug in Text2CypherRetriever using custom_prompt arg where the search method would not inject the query_text content.
  • custom_prompt arg is now converted to Text2CypherTemplate class within the Text2CypherRetriever.get_search_results method.
  • Text2CypherTemplate and RAGTemplate prompt templates now require query_text arg and will error if it is not present. Previous query_text aliases may be used, but will warn of deprecation.
  • Resolved issue where Neo4jWriter component would raise an error if the start or end node ID was not defined properly in the input.
  • Resolved issue where relationship types was not escaped in the insert Cypher query.
  • Improved query performance in Neo4jWriter: created nodes now have a generic __KGBuilder__ label and an index is created on the __KGBuilder__.id property. Moreover, insertion queries are now batched. Batch size can be controlled using the batch_size parameter in the Neo4jWriter component.

Changed

  • Moved the Embedder class to the neo4j_graphrag.embeddings directory for better organization alongside other custom embedders.
  • Removed query argument from the GraphRAG class' .search method; users must now use query_text.
  • Neo4jWriter component now runs a single query to merge node and set its embeddings if any.
  • Nodes created by the Neo4jWriter now have an extra __KGBuilder__ label. Nodes from the entity graph also have an __Entity__ label.
  • Dropped support for Python 3.8 (end of life).

0.6.3

Changed

  • Updated documentation links in README.
  • Renamed deprecated package references in documentation.

Added

  • Introduction page to the documentation content tree.
  • Introduced a new Vertex AI embeddings class for generating text embeddings using Vertex AI.
  • Updated documentation to include OpenAI and Vertex AI embeddings classes.
  • Added google-cloud-aiplatform as an optional dependency for Vertex AI embeddings.

Fixed

  • Make pygraphviz an optional dependency - it is now only required when calling pipeline.draw.

0.6.2

Fixed

  • Moved pygraphviz to optional dependencies under [tool.poetry.extras] in pyproject.toml to resolve an issue where pip install neo4j-graphrag incorrectly required pygraphviz as a mandatory dependency.

0.6.1

Changed

0.6.0

IMPORTANT NOTICE

  • The neo4j-genai package is now deprecated. Users are advised to switch to the new package neo4j-graphrag.

Added

  • Ability to visualise pipeline with my_pipeline.draw("pipeline.png").
  • LexicalGraphBuilder component to create the lexical graph without entity-relation extraction.

Fixed

  • Pipelines now return correct results when the same pipeline is run in parallel.

Changed

  • Pipeline run method now return a PipelineResult object.
  • Improved parameter validation for pipelines (#124). Pipeline now raise an error before a run starts if:
    • the same parameter is mapped twice
    • or a parameter is defined in the mapping but is not a valid component input

0.5.0

Added

  • PDF-to-graph pipeline for knowledge graph construction in experimental mode
  • Introduced support for Component/Pipeline flexible architecture.
  • Added new components for knowledge graph construction, including text splitters, schema builders, entity-relation extractors, and Neo4j writers.
  • Implemented end-to-end tests for the new knowledge graph builder pipeline.

Changed

  • When saving the lexical graph in a KG creation pipeline, the document is also saved as a specific node, together with relationships between each chunk and the document they were created from.

Fixed

  • Corrected the hybrid retriever query to ensure proper normalization of scores in vector search results.

0.4.0

Added

  • Add optional custom_prompt arg to the Text2CypherRetriever class.

Changed

  • GraphRAG.search method first parameter has been renamed query_text (was query) for consistency with the retrievers interface.
  • Made GraphRAG.search method backwards compatible with the query parameter, raising warnings to encourage using query_text instead.

0.3.1

Fixed

  • Corrected initialization to allow specifying the embedding model name.
  • Removed sentence_transformers from embeddings/init.py to avoid ImportError when the package is not installed.

0.3.0

Added

  • Stopped embeddings from being returned when searching with VectorRetriever. Added nodeLabels and id to the metadata of VectorRetriever results.
  • Added upsert_vector utility function for attaching vectors to node properties.
  • Introduced Neo4jInsertionError for handling insertion failures in Neo4j.
  • Included Pinecone and Weaviate retrievers in neo4j_graphrag.retrievers.
  • Introduced the GraphRAG object, enabling a full RAG (Retrieval-Augmented Generation) pipeline with context retrieval, prompt formatting, and answer generation.
  • Added PromptTemplate and RagTemplate for customizable prompt generation.
  • Added LLMInterface with implementation for OpenAI LLM.
  • Updated project configuration to support multiple Python versions (3.8 to 3.12) in CI workflows.
  • Improved developer experience by copying the docstring from the Retriever.get_search_results method to the Retriever.search method
  • Support for specifying database names in index handling methods and retrievers.
  • User Guide in documentation.
  • Introduced result_formatter argument to all retrievers, allowing custom formatting of retriever results.

Changed

  • Refactored import paths for retrievers to neo4j_graphrag.retrievers.
  • Implemented exception chaining for all re-raised exceptions to improve stack trace readability.
  • Made error messages in index.py more consistent.
  • Renamed Retriever._get_search_results to Retriever.get_search_results
  • Updated retrievers and index handling methods to accept optional database names.

0.2.0

Fixed

  • Removed Pinecone and Weaviate retrievers from init.py to prevent ImportError when optional dependencies are not installed.
  • Moved few-shot examples in Text2CypherRetriever to the constructor for better initialization and usage. Updated unit tests and example script accordingly.
  • Fixed regex warnings in E2E tests for Weaviate and Pinecone retrievers.
  • Corrected HuggingFaceEmbeddings import in E2E tests.

0.2.0a5

0.2.0a3

Added

  • Introduced custom exceptions for improved error handling, including RetrieverInitializationError, SearchValidationError, FilterValidationError, EmbeddingRequiredError, RecordCreationError, Neo4jIndexError, and Neo4jVersionError.
  • Retrievers that integrates with a Weaviate vector database: WeaviateNeo4jRetriever.
  • New return types that help with getting retriever results: RetrieverResult and RetrieverResultItem.
  • Supported wrapper embedder object for sentence-transformers embeddings: SentenceTransformerEmbeddings.
  • Text2CypherRetriever object which allows for the retrieval of records from a Neo4j database using natural language.

Changed

  • Replaced ValueError with custom exceptions across various modules for clearer and more specific error messages.

Fixed

  • Updated documentation to include new custom exceptions.
  • Improved the use of Pydantic for input data validation for retriever objects.