156 lines
7.2 KiB
Markdown
156 lines
7.2 KiB
Markdown
# Examples Index
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This folder contains examples usage for the different features
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supported by the `neo4j-graphrag` package:
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- [Automatic Schema Extraction](#schema-extraction) from PDF or text
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- [Build Knowledge Graph](#build-knowledge-graph) from PDF or text
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- [Retrieve](#retrieve) information from the graph
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- [Question Answering](#answer-graphrag) (Q&A)
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Each of these steps have many customization options which
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are listed in [the last section of this file](#customize).
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## Build Knowledge Graph
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- [End to end PDF to graph simple pipeline](build_graph/simple_kg_builder_from_pdf.py)
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- [End to end text to graph simple pipeline](build_graph/simple_kg_builder_from_text.py)
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- [Build KG pipeline from config file](build_graph/from_config_files/simple_kg_pipeline_from_config_file.py)
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- [Build KG pipeline with PDF URL](build_graph/from_config_files/simple_kg_pipeline_from_config_file_with_url.py)
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## Retrieve
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- [Retriever from an embedding vector](retrieve/similarity_search_for_vector.py)
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- [Retriever from a text](retrieve/similarity_search_for_text.py)
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- [Graph-based retrieval with VectorCypherRetriever](retrieve/vector_cypher_retriever.py)
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- [Hybrid retriever](./retrieve/hybrid_retriever.py)
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- [Hybrid Cypher retriever](./retrieve/hybrid_cypher_retriever.py)
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- [Text2Cypher retriever](./retrieve/text2cypher_search.py)
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- [Cypher Template retriever](./retrieve/tools/cypher_template_to_tool_example.py)
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### External Retrievers
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#### Weaviate
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- [Vector search](customize/retrievers/external/weaviate/weaviate_vector_search.py)
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- [Text search with local embeder](customize/retrievers/external/weaviate/weaviate_text_search_local_embedder.py)
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- [Text search with remote embeder](customize/retrievers/external/weaviate/weaviate_text_search_remote_embedder.py)
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#### Pinecone
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- [Vector search](./customize/retrievers/external/pinecone/pinecone_vector_search.py)
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- [Text search](./customize/retrievers/external/pinecone/pinecone_text_search.py)
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### Qdrant
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- [Vector search](./customize/retrievers/external/qdrant/qdrant_vector_search.py)
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- [Text search](./customize/retrievers/external/qdrant/qdrant_text_search.py)
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## Answer: GraphRAG
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- [End to end GraphRAG](./answer/graphrag.py)
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- [GraphRAG with message history](./question_answering/graphrag_with_message_history.py)
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- [GraphRAG with Neo4j message history](./question_answering/graphrag_with_neo4j_message_history.py)
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## Customize
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### Retriever
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- [Control result format for VectorRetriever](customize/retrievers/result_formatter_vector_retriever.py)
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- [Control result format for VectorCypherRetriever](customize/retrievers/result_formatter_vector_cypher_retriever.py)
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- [Use pre-filters](customize/retrievers/use_pre_filters.py)
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- [Text2Cypher: use a custom prompt](customize/retrievers/text2cypher_custom_prompt.py)
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### LLMs
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- [OpenAI (GPT)](./customize/llms/openai_llm.py)
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- [Azure OpenAI]()
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- [VertexAI (Gemini)](./customize/llms/vertexai_llm.py)
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- [MistralAI](./customize/llms/mistalai_llm.py)
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- [Cohere](./customize/llms/cohere_llm.py)
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- [Anthropic (Claude)](./customize/llms/anthropic_llm.py)
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- [Ollama](./customize/llms/ollama_llm.py)
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- [Custom LLM](./customize/llms/custom_llm.py)
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- [Message history](./customize/llms/llm_with_message_history.py)
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- [Message history with Neo4j](./customize/llms/llm_with_neo4j_message_history.py)
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- [System Instruction](./customize/llms/llm_with_system_instructions.py)
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- [Tool Calling with OpenAI](./customize/llms/openai_tool_calls.py)
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- [Tool Calling with VertexAI](./customize/llms/vertexai_tool_calls.py)
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- [Tool Calling with Ollama](./customize/llms/ollama_tool_calls.py)
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### Prompts
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- [Using a custom prompt for RAG](customize/answer/custom_prompt.py)
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### Embedders
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- [OpenAI](./customize/embeddings/openai_embeddings.py)
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- [Azure OpenAI](./customize/embeddings/azure_openai_embeddings.py)
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- [VertexAI](./customize/embeddings/vertexai_embeddings.py)
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- [MistralAI](./customize/embeddings/mistalai_embeddings.py)
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- [Cohere](./customize/embeddings/cohere_embeddings.py)
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- [Ollama](./customize/embeddings/ollama_embeddings.py)
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- [Custom LLM](./customize/embeddings/custom_embeddings.py)
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### KG Construction - Pipeline
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- [End to end example with explicit components and text input](./customize/build_graph/pipeline/kg_builder_from_text.py)
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- [End to end example with explicit components and PDF input](./customize/build_graph/pipeline/kg_builder_from_pdf.py)
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- [Process multiple documents](./customize/build_graph/pipeline/kg_builder_two_documents_entity_resolution.py)
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- [Export lexical graph creation into another pipeline](./customize/build_graph/pipeline/text_to_lexical_graph_to_entity_graph_two_pipelines.py)
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- [Build pipeline from config file](customize/build_graph/pipeline/from_config_files/pipeline_from_config_file.py)
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- [Add event listener to get notification about Pipeline progress](./customize/build_graph/pipeline/pipeline_with_notifications.py)
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- [Use component context to send notifications about Component progress](./customize/build_graph/pipeline/pipeline_with_component_notifications.py)
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#### Components
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- Loaders:
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- [Load PDF file](./customize/build_graph/components/loaders/pdf_loader.py)
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- [Custom](./customize/build_graph/components/loaders/custom_loader.py)
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- Text Splitter:
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- [Fixed size splitter](./customize/build_graph/components/splitters/fixed_size_splitter.py)
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- [Splitter from LangChain](./customize/build_graph/components/splitters/langhchain_splitter.py)
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- [Splitter from LLamaIndex](./customize/build_graph/components/splitters/llamaindex_splitter.py)
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- [Custom](./customize/build_graph/components/splitters/custom_splitter.py)
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- [Chunk embedder]()
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- Schema Builder:
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- [User-defined](./customize/build_graph/components/schema_builders/schema.py)
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- [Automatic schema extraction](./automatic_schema_extraction/schema_from_text.py)
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- Entity Relation Extractor:
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- [LLM-based](./customize/build_graph/components/extractors/llm_entity_relation_extractor.py)
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- [LLM-based with custom prompt](./customize/build_graph/components/extractors/llm_entity_relation_extractor_with_custom_prompt.py)
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- [Custom](./customize/build_graph/components/extractors/custom_extractor.py)
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- [Graph Pruner](./customize/build_graph/components/pruners/graph_pruner.py)
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- Knowledge Graph Writer:
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- [Neo4j writer](./customize/build_graph/components/writers/neo4j_writer.py)
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- [Custom](./customize/build_graph/components/writers/custom_writer.py)
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- Entity Resolver:
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- [FuzzyMatchResolver](./customize/build_graph/components/resolvers/fuzzy_match_entity_resolver_pre_filter.py)
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- [SinglePropertyExactMatchResolver with pre-filter](./customize/build_graph/components/resolvers/simple_entity_resolver_pre_filter.py)
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- [SpaCySemanticMatchResolver with pre-filter](./customize/build_graph/components/resolvers/spacy_entity_resolver_pre_filter.py)
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- [Custom resolver](./customize/build_graph/components/resolvers/custom_resolver.py)
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- [Custom component](./customize/build_graph/components/custom_component.py)
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### Answer: GraphRAG
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- [LangChain compatibility](./customize/answer/langchain_compatiblity.py)
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- [Use a custom prompt](./customize/answer/custom_prompt.py)
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## Database Operations
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- [Create vector index](database_operations/create_vector_index.py)
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- [Create full text index](create_fulltext_index.py)
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- [Populate vector index](populate_vector_index.py)
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