Neo4j-graphrag

Added
- Support for conversations with message history, including a new `message_history` parameter for LLM interactions.
- Ability to include system instructions in LLM invoke method.
- Summarization of chat history to enhance query embedding and context handling in GraphRAG.

Changed
- Updated LLM implementations to handle message history consistently across providers.
- The `id_prefix` parameter in the `LexicalGraphConfig` is deprecated.

Fixed
- IDs for the Document and Chunk nodes in the lexical graph are now randomly generated and unique across multiple runs, fixing issues in the lexical graph where relationships were created between chunks that were created by different pipeline runs.
- Improve logging for a better debugging experience: long lists and strings are now truncated. The max length can be controlled using the `LOGGING__MAX_LIST_LENGTH` and `LOGGING__MAX_STRING_LENGTH` env variables.

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](examples/build_graph/from_config_files/simple_kg_pipeline_from_config_file.py).
- 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.

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`, `Neo4jChunkReader`and `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.

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`.

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.

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.