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Apr 26, 2025
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simplify neo4j example #399

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82 changes: 33 additions & 49 deletions examples/docs_to_knowledge_graph/main.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
"""
This example shows how to extract relationships from Markdown documents and build a knowledge graph.
This example shows how to extract relationships from documents and build a knowledge graph.
"""
import dataclasses
from dotenv import load_dotenv
Expand All @@ -13,17 +13,17 @@ class DocumentSummary:

@dataclasses.dataclass
class Relationship:
"""Describe a relationship between two nodes."""
"""Describe a relationship between two entities."""
subject: str
predicate: str
object: str

@cocoindex.flow_def(name="DocsToKG")
def docs_to_kg_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
"""
Define an example flow that extracts triples from files and build knowledge graph.
Define an example flow that extracts relationship from files and build knowledge graph.
"""

# configure neo4j connection
conn_spec = cocoindex.add_auth_entry(
"Neo4jConnection",
cocoindex.storages.Neo4jConnection(
Expand All @@ -41,78 +41,66 @@ def docs_to_kg_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.D
entity_mention = data_scope.add_collector()

with data_scope["documents"].row() as doc:
doc["chunks"] = doc["content"].transform(
cocoindex.functions.SplitRecursively(),
language="markdown", chunk_size=10000)

# extract summary from document
doc["summary"] = doc["content"].transform(
cocoindex.functions.ExtractByLlm(
llm_spec=cocoindex.LlmSpec(
# Supported LLM: https://cocoindex.io/docs/ai/llm
api_type=cocoindex.LlmApiType.OPENAI, model="gpt-4o"),
output_type=DocumentSummary,
instruction="Please summarize the content of the document."))
document_node.collect(
filename=doc["filename"], title=doc["summary"]["title"],
summary=doc["summary"]["summary"])

with doc["chunks"].row() as chunk:
chunk["relationships"] = chunk["text"].transform(
cocoindex.functions.ExtractByLlm(
llm_spec=cocoindex.LlmSpec(
api_type=cocoindex.LlmApiType.OPENAI, model="gpt-4o"),
# Replace by this spec below, to use Ollama API instead of OpenAI
# llm_spec=cocoindex.LlmSpec(
# api_type=cocoindex.LlmApiType.OLLAMA, model="llama3.2"),
# extract relationships from document
doc["relationships"] = doc["content"].transform(
cocoindex.functions.ExtractByLlm(
llm_spec=cocoindex.LlmSpec(
# Supported LLM: https://cocoindex.io/docs/ai/llm
api_type=cocoindex.LlmApiType.OPENAI, model="gpt-4o"),
output_type=list[Relationship],
instruction=(
"Please extract relationships from CocoIndex documents. "
"Focus on concepts and ingnore specific examples. "
"Each relationship should be a tuple of (subject, predicate, object).")))

with chunk["relationships"].row() as relationship:
relationship["subject_embedding"] = relationship["subject"].transform(
cocoindex.functions.SentenceTransformerEmbed(
model="sentence-transformers/all-MiniLM-L6-v2"))
relationship["object_embedding"] = relationship["object"].transform(
cocoindex.functions.SentenceTransformerEmbed(
model="sentence-transformers/all-MiniLM-L6-v2"))
entity_relationship.collect(
id=cocoindex.GeneratedField.UUID,
subject=relationship["subject"],
subject_embedding=relationship["subject_embedding"],
object=relationship["object"],
object_embedding=relationship["object_embedding"],
predicate=relationship["predicate"],
)
entity_mention.collect(
id=cocoindex.GeneratedField.UUID, entity=relationship["subject"],
filename=doc["filename"], location=chunk["location"],
)
entity_mention.collect(
id=cocoindex.GeneratedField.UUID, entity=relationship["object"],
filename=doc["filename"], location=chunk["location"],
)
with doc["relationships"].row() as relationship:
# relationship between two entities
entity_relationship.collect(
id=cocoindex.GeneratedField.UUID,
subject=relationship["subject"],
object=relationship["object"],
predicate=relationship["predicate"],
)
# mention of an entity in a document, for subject
entity_mention.collect(
id=cocoindex.GeneratedField.UUID, entity=relationship["subject"],
filename=doc["filename"],
)
# mention of an entity in a document, for object
entity_mention.collect(
id=cocoindex.GeneratedField.UUID, entity=relationship["object"],
filename=doc["filename"],
)


# export to neo4j
document_node.export(
"document_node",
cocoindex.storages.Neo4j(
connection=conn_spec,
mapping=cocoindex.storages.NodeMapping(label="Document")),
primary_key_fields=["filename"],
)
# Declare reference Node to reference entity node in a relationship
flow_builder.declare(
cocoindex.storages.Neo4jDeclarations(
connection=conn_spec,
referenced_nodes=[
cocoindex.storages.ReferencedNode(
label="Entity",
primary_key_fields=["value"],
vector_indexes=[
cocoindex.VectorIndexDef(
field_name="embedding",
metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY,
),
],
)
]
)
Expand All @@ -128,17 +116,13 @@ def docs_to_kg_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.D
fields=[
cocoindex.storages.TargetFieldMapping(
source="subject", target="value"),
cocoindex.storages.TargetFieldMapping(
source="subject_embedding", target="embedding"),
]
),
target=cocoindex.storages.NodeReferenceMapping(
label="Entity",
fields=[
cocoindex.storages.TargetFieldMapping(
source="object", target="value"),
cocoindex.storages.TargetFieldMapping(
source="object_embedding", target="embedding"),
]
),
),
Expand Down