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docs/source/user_guide_kg_builder.rst

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@@ -20,12 +20,12 @@ Pipeline structure
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A Knowledge Graph (KG) construction pipeline requires a few components:
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- A document **parser**: extract text from files (PDFs, ...)
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- A document **chunker**: split the text into smaller pieces of text, manageable by the LLM context window (token limit).
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- A chunk **embeder** (optional): compute and store the chunk embeddings
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- A **schema builder**: provide a schema to ground the LLM extracted entities and relations and obtain an easily navigable KG.
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- An **entity and relation extractor**: extract relevant entities and relations from the text.
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- A **Knowledge Graph writer**: write the identified entities and relations to a Neo4j database.
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- Document **parser**: extract text from files (PDFs, ...)
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- Document **chunker**: split the text into smaller pieces of text, manageable by the LLM context window (token limit).
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- Chunk **embedder** (optional): compute and store the chunk embeddings
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- **Schema builder**: provide a schema to ground the LLM extracted entities and relations and obtain an easily navigable KG.
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- **Entity and relation extractor**: extract relevant entities and relations from the text.
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- **Knowledge Graph writer**: write the identified entities and relations to a Neo4j database.
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.. image:: images/kg_builder_pipeline.png
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:alt: KG Builder pipeline
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.. code:: python
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import asyncio
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# replace `PdfLoader` by a real component name
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from neo4j_genai.experimental.components.pdf_loader import PdfLoader
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my_component = PdfLoader()
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asyncio.run(my_component.run("my_file.pdf"))
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.. code:: python
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from neo4j_genai.experimental.pipeline import Pipeline
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# replace `PdfLoader` by a real component name
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from neo4j_genai.experimental.components.pdf_loader import PdfLoader
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pipeline = Pipeline()
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my_component = PdfLoader()

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