diff --git a/.github/workflows/pr-e2e-tests.yaml b/.github/workflows/pr-e2e-tests.yaml index 14ae884e..31952fc7 100644 --- a/.github/workflows/pr-e2e-tests.yaml +++ b/.github/workflows/pr-e2e-tests.yaml @@ -13,6 +13,8 @@ concurrency: jobs: e2e-tests: runs-on: ubuntu-latest + strategy: + max-parallel: 1 strategy: matrix: python-version: ['3.8', '3.12'] diff --git a/CHANGELOG.md b/CHANGELOG.md index cf4cccd9..e1b040f4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,9 @@ ### Added - Add optional custom_prompt arg to the Text2CypherRetriever class. +- 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 - `GraphRAG.search` method first parameter has been renamed `query_text` (was `query`) for consistency with the retrievers interface. diff --git a/docs/source/api.rst b/docs/source/api.rst index 16aba124..0e309b7a 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -3,6 +3,66 @@ API Documentation ################# +.. _components-section: + +********** +Components +********** + +KGWriter +======== + +.. autoclass:: neo4j_genai.components.kg_writer.KGWriter + :members: run + +Neo4jWriter +=========== + +.. autoclass:: neo4j_genai.components.kg_writer.Neo4jWriter + :members: run + +TextSplitter +============ + +.. autoclass:: neo4j_genai.components.text_splitters.base.TextSplitter + :members: run + +LangChainTextSplitterAdapter +============================ + +.. autoclass:: neo4j_genai.components.text_splitters.langchain.LangChainTextSplitterAdapter + :members: run + +LlamaIndexTextSplitterAdapter +============================= + +.. autoclass:: neo4j_genai.components.text_splitters.llamaindex.LlamaIndexTextSplitterAdapter + :members: run + +TextChunkEmbedder +================= + +.. autoclass:: neo4j_genai.components.embedder.TextChunkEmbedder + :members: run + +SchemaBuilder +============= + +.. autoclass:: neo4j_genai.components.schema.SchemaBuilder + :members: run + +EntityRelationExtractor +======================= + +.. autoclass:: neo4j_genai.components.entity_relation_extractor.EntityRelationExtractor + :members: run + +LLMEntityRelationExtractor +========================== + +.. autoclass:: neo4j_genai.components.entity_relation_extractor.LLMEntityRelationExtractor + :members: run + .. _retrievers-section: ********** @@ -10,7 +70,7 @@ Retrievers ********** RetrieverInterface -=================== +================== .. autoclass:: neo4j_genai.retrievers.base.Retriever :members: @@ -70,9 +130,9 @@ PineconeNeo4jRetriever :members: search -********** +******** Embedder -********** +******** .. autoclass:: neo4j_genai.embedder.Embedder :members: @@ -80,7 +140,7 @@ Embedder SentenceTransformerEmbeddings ================================ -.. autoclass:: neo4j_genai.embeddings.SentenceTransformerEmbeddings +.. autoclass:: neo4j_genai.embeddings.sentence_transformers.SentenceTransformerEmbeddings :members: ********** @@ -88,21 +148,21 @@ Generation ********** LLMInterface -====================== +============ .. autoclass:: neo4j_genai.llm.LLMInterface :members: OpenAILLM -====================== +========= .. autoclass:: neo4j_genai.llm.OpenAILLM :members: PromptTemplate -====================== +============== .. autoclass:: neo4j_genai.generation.prompts.PromptTemplate :members: @@ -125,6 +185,8 @@ Database Interaction .. autofunction:: neo4j_genai.indexes.upsert_vector +.. autofunction:: neo4j_genai.indexes.upsert_vector_on_relationship + ****** Errors @@ -157,6 +219,12 @@ Errors * :class:`neo4j_genai.exceptions.LLMGenerationError` + * :class:`neo4j_genai.pipeline.exceptions.PipelineDefinitionError` + + * :class:`neo4j_genai.pipeline.exceptions.PipelineMissingDependencyError` + + * :class:`neo4j_genai.pipeline.exceptions.PipelineStatusUpdateError` + Neo4jGenAiError =============== @@ -222,7 +290,7 @@ Neo4jVersionError Text2CypherRetrievalError -========================== +========================= .. autoclass:: neo4j_genai.exceptions.Text2CypherRetrievalError :show-inheritance: @@ -236,21 +304,42 @@ SchemaFetchError RagInitializationError -========================== +====================== .. autoclass:: neo4j_genai.exceptions.RagInitializationError :show-inheritance: PromptMissingInputError -========================== +======================= .. autoclass:: neo4j_genai.exceptions.PromptMissingInputError :show-inheritance: LLMGenerationError -========================== +================== .. autoclass:: neo4j_genai.exceptions.LLMGenerationError :show-inheritance: + + +PipelineDefinitionError +======================= + +.. autoclass:: neo4j_genai.pipeline.exceptions.PipelineDefinitionError + :show-inheritance: + + +PipelineMissingDependencyError +============================== + +.. autoclass:: neo4j_genai.pipeline.exceptions.PipelineMissingDependencyError + :show-inheritance: + + +PipelineStatusUpdateError +========================= + +.. autoclass:: neo4j_genai.pipeline.exceptions.PipelineStatusUpdateError + :show-inheritance: diff --git a/docs/source/index.rst b/docs/source/index.rst index a677b027..fd2f066c 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -30,7 +30,8 @@ Python versions supported: Topics ****** -+ :ref:`user-guide` ++ :ref:`user-guide-rag` ++ :ref:`user-guide-pipeline` + :ref:`api-documentation` + :ref:`types-documentation` @@ -39,7 +40,8 @@ Topics :caption: Contents: :hidden: - user_guide.rst + user_guide_rag.rst + user_guide_pipeline.rst api.rst types.rst diff --git a/docs/source/types.rst b/docs/source/types.rst index 46e2652e..ec763a6e 100644 --- a/docs/source/types.rst +++ b/docs/source/types.rst @@ -5,30 +5,80 @@ Types ***** RawSearchResult -================== +=============== .. autoclass:: neo4j_genai.types.RawSearchResult RetrieverResult -================== +=============== .. autoclass:: neo4j_genai.types.RetrieverResult RetrieverResultItem -==================== +=================== .. autoclass:: neo4j_genai.types.RetrieverResultItem LLMResponse -==================== +=========== .. autoclass:: neo4j_genai.llm.types.LLMResponse RagResultModel -==================== +============== .. autoclass:: neo4j_genai.generation.types.RagResultModel + +TextChunk +========= + +.. autoclass:: neo4j_genai.components.types.TextChunk + +TextChunks +========== + +.. autoclass:: neo4j_genai.components.types.TextChunks + +Neo4jNode +========= + +.. autoclass:: neo4j_genai.components.types.Neo4jNode + +Neo4jRelationship +================= + +.. autoclass:: neo4j_genai.components.types.Neo4jRelationship + +Neo4jGraph +========== + +.. autoclass:: neo4j_genai.components.types.Neo4jGraph + +KGWriterModel +============= + +.. autoclass:: neo4j_genai.components.kg_writer.KGWriterModel + +SchemaProperty +============== + +.. autoclass:: neo4j_genai.components.schema.SchemaProperty + +SchemaEntity +============ + +.. autoclass:: neo4j_genai.components.schema.SchemaEntity + +SchemaRelation +============== + +.. autoclass:: neo4j_genai.components.schema.SchemaEntity + +SchemaConfig +============ + +.. autoclass:: neo4j_genai.components.schema.SchemaConfig diff --git a/docs/source/user_guide_pipeline.rst b/docs/source/user_guide_pipeline.rst new file mode 100644 index 00000000..e6745f1a --- /dev/null +++ b/docs/source/user_guide_pipeline.rst @@ -0,0 +1,83 @@ +.. _user-guide-pipeline: + +User Guide: Pipeline +#################### + +This page provides information about how to create a pipeline. + + +.. note:: + + Pipelines run asynchronously, see examples below. + +****************************** +Creating Components +****************************** + +Components are asynchronous units of work that perform simple tasks, +such as chunking documents or saving results to Neo4j. +This package includes a few default components, but developers can create +their own by following these steps: + +1. Create a subclass of the Pydantic `neo4j_genai.pipeline.DataModel` to represent the data being returned by the component +2. Create a subclass of `neo4j_genai.pipeline.Component` +3. Create a run method in this new class and specify the required inputs and output model using the just created `DataModel` +4. Implement the run method: it's an `async` method, allowing tasks to be parallelized and awaited within this method. + +An example is given below, where a `ComponentAdd` is created to add two numbers together and return +the resulting sum: + +.. code:: python + + from neo4j_genai.pipeline import Component, DataModel + + class IntResultModel(DataModel): + result: int + + class ComponentAdd(Component): + async def run(self, number1: int, number2: int = 1) -> IntResultModel: + return IntResultModel(result = number1 + number2) + +Read more about :ref:`components-section` in the API Documentation. + +*************************************** +Connecting Components within a Pipeline +*************************************** + +The ultimate aim of creating components is to assemble them into a complex pipeline +for a specific purpose, such as building a Knowledge Graph from text data. + +Here's how to create a simple pipeline and propagate results from one component to another +(detailed explanations follow): + +.. code:: python + + import asyncio + from neo4j_genai.pipeline import Pipeline + + pipe = Pipeline() + pipe.add_component("a", ComponentAdd()) + pipe.add_component("b", ComponentAdd()) + + pipe.connect("a", "b", {"number2": "a.result"}) + asyncio.run(pipe.run({"a": {"number1": 10, "number2": 1}, "b": {"number1": 4})) + # result: 10+1+4 = 15 + +1. First, a pipeline is created, and two components named "a" and "b" are added to it. +2. Next, the two components are connected so that "b" runs after "a", with the "number2" parameter for component "b" being the result of component "a". +3. Finally, the pipeline is run with 10 and 1 as input parameters for "a". Component "b" will receive 11 (10 + 1, the result of "a") as "number1" and 4 as "number2" (as specified in the pipeline.run parameters). + +The data flow is illustrated in the diagram below: + +.. code-block:: + + 10 ---\ + Component "a" -> 11 + 1 ----/ \ + \ + Component "b" -> 15 + 4 -------------------------/ + +.. warning:: + + Cycles are not allowed in a Pipeline. diff --git a/docs/source/user_guide.rst b/docs/source/user_guide_rag.rst similarity index 99% rename from docs/source/user_guide.rst rename to docs/source/user_guide_rag.rst index cdc3c8d9..94379553 100644 --- a/docs/source/user_guide.rst +++ b/docs/source/user_guide_rag.rst @@ -1,6 +1,6 @@ -.. _user-guide: +.. _user-guide-rag: -User Guide +User Guide: RAG ################# This guide provides a starting point for using the Neo4j GenAI Python package diff --git a/examples/pipeline/__init__.py b/examples/pipeline/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/pipeline/kg_builder.py b/examples/pipeline/kg_builder.py new file mode 100644 index 00000000..754d3a0f --- /dev/null +++ b/examples/pipeline/kg_builder.py @@ -0,0 +1,167 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import asyncio +import logging.config +from typing import Any + +import neo4j +from langchain_text_splitters import CharacterTextSplitter +from neo4j_genai.components.embedder import TextChunkEmbedder +from neo4j_genai.components.entity_relation_extractor import ( + LLMEntityRelationExtractor, + OnError, +) +from neo4j_genai.components.kg_writer import Neo4jWriter +from neo4j_genai.components.schema import ( + SchemaBuilder, + SchemaEntity, + SchemaProperty, + SchemaRelation, +) +from neo4j_genai.components.text_splitters.langchain import LangChainTextSplitterAdapter +from neo4j_genai.embeddings.openai import OpenAIEmbeddings +from neo4j_genai.llm import OpenAILLM +from neo4j_genai.pipeline import Pipeline + +# set log level to DEBUG for all neo4j_genai.* loggers +logging.config.dictConfig( + { + "version": 1, + "handlers": { + "console": { + "class": "logging.StreamHandler", + } + }, + "loggers": { + "root": { + "handlers": ["console"], + }, + "neo4j_genai": { + "level": "DEBUG", + }, + }, + } +) + + +async def main(neo4j_driver: neo4j.Driver) -> dict[str, Any]: + """This is where we define and run the KG builder pipeline, instantiating a few + components: + - Text Splitter: in this example we use a text splitter from the LangChain package + - Schema Builder: this component takes a list of entities, relationships and + possible triplets as inputs, validate them and return a schema ready to use + for the rest of the pipeline + - LLM Entity Relation Extractor is an LLM-based entity and relation extractor: + based on the provided schema, the LLM will do its best to identity these + entities and their relations within the provided text + - KG writer: once entities and relations are extracted, they can be writen + to a Neo4j database + """ + pipe = Pipeline() + # define the components + pipe.add_component( + "splitter", + LangChainTextSplitterAdapter( + # chunk_size=50 for the sake of this demo + CharacterTextSplitter(chunk_size=50, chunk_overlap=10, separator=".") + ), + ) + pipe.add_component("chunk_embedder", TextChunkEmbedder(embedder=OpenAIEmbeddings())) + pipe.add_component("schema", SchemaBuilder()) + pipe.add_component( + "extractor", + LLMEntityRelationExtractor( + llm=OpenAILLM( + model_name="gpt-4o", + model_params={ + "max_tokens": 1000, + "response_format": {"type": "json_object"}, + }, + ), + on_error=OnError.RAISE, + ), + ) + pipe.add_component("writer", Neo4jWriter(neo4j_driver)) + # define the execution order of component + # and how the output of previous components must be used + pipe.connect("splitter", "chunk_embedder", input_config={"text_chunks": "splitter"}) + pipe.connect("schema", "extractor", input_config={"schema": "schema"}) + pipe.connect( + "chunk_embedder", "extractor", input_config={"chunks": "chunk_embedder"} + ) + pipe.connect( + "extractor", + "writer", + input_config={"graph": "extractor"}, + ) + # user input: + # the initial text + # and the list of entities and relations we are looking for + pipe_inputs = { + "splitter": { + "text": """Albert Einstein was a German physicist born in 1879 who + wrote many groundbreaking papers especially about general relativity + and quantum mechanics. He worked for many different institutions, including + the University of Bern in Switzerland and the University of Oxford.""" + }, + "schema": { + "entities": [ + SchemaEntity( + label="Person", + properties=[ + SchemaProperty(name="name", type="STRING"), + SchemaProperty(name="place_of_birth", type="STRING"), + SchemaProperty(name="date_of_birth", type="DATE"), + ], + ), + SchemaEntity( + label="Organization", + properties=[ + SchemaProperty(name="name", type="STRING"), + SchemaProperty(name="country", type="STRING"), + ], + ), + SchemaEntity( + label="Field", + properties=[ + SchemaProperty(name="name", type="STRING"), + ], + ), + ], + "relations": [ + SchemaRelation( + label="WORKED_ON", + ), + SchemaRelation( + label="WORKED_FOR", + ), + ], + "potential_schema": [ + ("Person", "WORKED_ON", "Field"), + ("Person", "WORKED_FOR", "Organization"), + ], + }, + } + # run the pipeline + return await pipe.run(pipe_inputs) + + +if __name__ == "__main__": + with neo4j.GraphDatabase.driver( + "bolt://localhost:7687", auth=("neo4j", "password") + ) as driver: + print(asyncio.run(main(driver))) diff --git a/examples/pipeline/kg_builder_with_schema.py b/examples/pipeline/kg_builder_with_schema.py new file mode 100644 index 00000000..b2470d01 --- /dev/null +++ b/examples/pipeline/kg_builder_with_schema.py @@ -0,0 +1,146 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import asyncio +import logging +from typing import Any + +from neo4j_genai.components.schema import ( + SchemaBuilder, + SchemaEntity, + SchemaRelation, +) +from neo4j_genai.pipeline import Component, DataModel +from pydantic import BaseModel, validate_call + +logging.basicConfig(level=logging.DEBUG) + + +class DocumentChunkModel(DataModel): + chunks: list[str] + + +class DocumentChunker(Component): + async def run(self, text: str) -> DocumentChunkModel: + chunks = [t.strip() for t in text.split(".") if t.strip()] + return DocumentChunkModel(chunks=chunks) + + +class EntityModel(BaseModel): + label: str + properties: dict[str, str] + + +class Neo4jGraph(DataModel): + entities: list[dict[str, Any]] + relations: list[dict[str, Any]] + + +class ERExtractor(Component): + async def _process_chunk(self, chunk: str, schema: str) -> dict[str, Any]: + return { + "entities": [{"label": "Person", "properties": {"name": "John Doe"}}], + "relations": [], + } + + async def run(self, chunks: list[str], schema: str) -> Neo4jGraph: + tasks = [self._process_chunk(chunk, schema) for chunk in chunks] + result = await asyncio.gather(*tasks) + merged_result: dict[str, Any] = {"entities": [], "relations": []} + for res in result: + merged_result["entities"] += res["entities"] + merged_result["relations"] += res["relations"] + return Neo4jGraph( + entities=merged_result["entities"], relations=merged_result["relations"] + ) + + +class WriterModel(DataModel): + status: str + entities: list[EntityModel] + relations: list[EntityModel] + + +class Writer(Component): + @validate_call + async def run(self, graph: Neo4jGraph) -> WriterModel: + entities = graph.entities + relations = graph.relations + return WriterModel( + status="OK", + entities=[EntityModel(**e) for e in entities], + relations=[EntityModel(**r) for r in relations], + ) + + +if __name__ == "__main__": + from neo4j_genai.pipeline import Pipeline + + # Instantiate Entity and Relation objects + entities = [ + SchemaEntity(label="PERSON", description="An individual human being."), + SchemaEntity( + label="ORGANIZATION", + description="A structured group of people with a common purpose.", + ), + SchemaEntity( + label="AGE", + ), + ] + relations = [ + SchemaRelation( + label="EMPLOYED_BY", description="Indicates employment relationship." + ), + SchemaRelation( + label="ORGANIZED_BY", + description="Indicates organization responsible for an event.", + ), + SchemaRelation( + label="ATTENDED_BY", description="Indicates attendance at an event." + ), + ] + potential_schema = [ + ("PERSON", "EMPLOYED_BY", "ORGANIZATION"), + ("ORGANIZATION", "ATTENDED_BY", "PERSON"), + ] + + # Set up the pipeline + pipe = Pipeline() + pipe.add_component("chunker", DocumentChunker()) + pipe.add_component("schema", SchemaBuilder()) + pipe.add_component("extractor", ERExtractor()) + pipe.add_component("writer", Writer()) + pipe.connect("chunker", "extractor", input_config={"chunks": "chunker.chunks"}) + pipe.connect("schema", "extractor", input_config={"schema": "schema"}) + pipe.connect( + "extractor", + "writer", + input_config={"graph": "extractor"}, + ) + + pipe_inputs = { + "chunker": { + "text": """Graphs are everywhere. + GraphRAG is the future of Artificial Intelligence. + Robots are already running the world.""" + }, + "schema": { + "entities": entities, + "relations": relations, + "potential_schema": potential_schema, + }, + } + print(asyncio.run(pipe.run(pipe_inputs))) diff --git a/examples/pipeline/rag.py b/examples/pipeline/rag.py new file mode 100644 index 00000000..f645f45c --- /dev/null +++ b/examples/pipeline/rag.py @@ -0,0 +1,112 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""This example illustrates how to use a Pipeline with +the existing Retriever and LLM interfaces. It consists +in creating a Component wrapper around the required +objects. +""" + +from __future__ import annotations + +import asyncio + +import neo4j +from neo4j_genai.embeddings.openai import OpenAIEmbeddings +from neo4j_genai.generation import PromptTemplate, RagTemplate +from neo4j_genai.llm import LLMInterface, OpenAILLM +from neo4j_genai.pipeline import Component, Pipeline +from neo4j_genai.pipeline.component import DataModel +from neo4j_genai.pipeline.types import ComponentConfig, ConnectionConfig, PipelineConfig +from neo4j_genai.retrievers import VectorRetriever +from neo4j_genai.retrievers.base import Retriever + + +class StringDataModel(DataModel): + result: str + + +class RetrieverComponent(Component): + def __init__(self, retriever: Retriever) -> None: + self.retriever = retriever + + async def run(self, query: str) -> StringDataModel: + res = self.retriever.search(query_text=query) + return StringDataModel(result="\n".join(c.content for c in res.items)) + + +class PromptTemplateComponent(Component): + def __init__(self, prompt: PromptTemplate) -> None: + self.prompt = prompt + + async def run(self, query: str, context: list[str]) -> StringDataModel: + prompt = self.prompt.format(query, context, examples="") + return StringDataModel(result=prompt) + + +class LLMComponent(Component): + def __init__(self, llm: LLMInterface) -> None: + self.llm = llm + + async def run(self, prompt: str) -> StringDataModel: + llm_response = self.llm.invoke(prompt) + return StringDataModel(result=llm_response.content) + + +if __name__ == "__main__": + driver = neo4j.GraphDatabase.driver( + "bolt://localhost:7687", + auth=("neo4j", "password"), + database="neo4j", + ) + embedder = OpenAIEmbeddings() + retriever = VectorRetriever( + driver, index_name="moviePlotsEmbedding", embedder=embedder + ) + prompt_template = RagTemplate() + llm = OpenAILLM(model_name="gpt-4o") + + pipe = Pipeline.from_template( + PipelineConfig( + components=[ + ComponentConfig( + name="retrieve", component=RetrieverComponent(retriever) + ), + ComponentConfig( + name="augment", component=PromptTemplateComponent(prompt_template) + ), + ComponentConfig(name="generate", component=LLMComponent(llm)), + ], + connections=[ + ConnectionConfig( + start="retrieve", + end="augment", + input_config={"context": "retrieve.result"}, + ), + ConnectionConfig( + start="augment", + end="generate", + input_config={"prompt": "augment.result"}, + ), + ], + ) + ) + + query = "A movie about the US presidency" + result = asyncio.run( + pipe.run({"retrieve": {"query": query}, "augment": {"query": query}}) + ) + print(result["generate"]["result"]) + + driver.close() diff --git a/poetry.lock b/poetry.lock index a240f3ed..c3a9be62 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,91 +1,103 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] 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external_clients = ["weaviate-client", "pinecone-client"] diff --git a/src/neo4j_genai/components/__init__.py b/src/neo4j_genai/components/__init__.py new file mode 100644 index 00000000..c0199c14 --- /dev/null +++ b/src/neo4j_genai/components/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/src/neo4j_genai/components/embedder.py b/src/neo4j_genai/components/embedder.py new file mode 100644 index 00000000..cf38a8bd --- /dev/null +++ b/src/neo4j_genai/components/embedder.py @@ -0,0 +1,73 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from pydantic import validate_call + +from neo4j_genai.components.types import TextChunk, TextChunks +from neo4j_genai.embedder import Embedder +from neo4j_genai.pipeline.component import Component + + +class TextChunkEmbedder(Component): + """Component for creating embeddings from text chunks. + + Args: + embedder (Embedder): The embedder to use to create the embeddings. + + Example: + + .. code-block:: python + + from neo4j_genai.components.embedder import TextChunkEmbedder + from neo4j_genai.embeddings.openai import OpenAIEmbeddings + from neo4j_genai.pipeline import Pipeline + + embedder = OpenAIEmbeddings(model="text-embedding-3-large") + chunk_embedder = TextChunkEmbedder(embedder) + pipeline = Pipeline() + pipeline.add_component("chunk_embedder", chunk_embedder) + + """ + + def __init__(self, embedder: Embedder): + self._embedder = embedder + + def _embed_chunk(self, text_chunk: TextChunk) -> TextChunk: + """Embed a single text chunk. + + Args: + text_chunk (TextChunk): The text chunk to embed. + + Returns: + TextChunk: The text chunk with an added "embedding" key in its + metadata containing the embeddings of the text chunk's text. + """ + embedding = self._embedder.embed_query(text_chunk.text) + metadata = text_chunk.metadata if text_chunk.metadata else {} + metadata["embedding"] = embedding + return TextChunk(text=text_chunk.text, metadata=metadata) + + @validate_call + async def run(self, text_chunks: TextChunks) -> TextChunks: + """Embed a list of text chunks. + + Args: + text_chunks (TextChunks): The text chunks to embed. + + Returns: + TextChunks: The input text chunks with each one having an added embedding. + """ + return TextChunks( + chunks=[self._embed_chunk(text_chunk) for text_chunk in text_chunks.chunks] + ) diff --git a/src/neo4j_genai/components/entity_relation_extractor.py b/src/neo4j_genai/components/entity_relation_extractor.py new file mode 100644 index 00000000..19be51bf --- /dev/null +++ b/src/neo4j_genai/components/entity_relation_extractor.py @@ -0,0 +1,273 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import abc +import asyncio +import enum +import json +import logging +from datetime import datetime +from typing import Any, Dict, List, Union + +from pydantic import ValidationError, validate_call + +from neo4j_genai.components.schema import SchemaConfig +from neo4j_genai.components.types import ( + Neo4jGraph, + Neo4jNode, + Neo4jRelationship, + TextChunk, + TextChunks, +) +from neo4j_genai.exceptions import LLMGenerationError +from neo4j_genai.generation.prompts import ERExtractionTemplate, PromptTemplate +from neo4j_genai.llm import LLMInterface +from neo4j_genai.pipeline.component import Component + +logger = logging.getLogger(__name__) + + +class OnError(enum.Enum): + RAISE = "RAISE" + IGNORE = "CONTINUE" + + +CHUNK_NODE_LABEL = "Chunk" +NEXT_CHUNK_RELATIONSHIP_TYPE = "NEXT_CHUNK" +NODE_TO_CHUNK_RELATIONSHIP_TYPE = "FROM_CHUNK" + + +class EntityRelationExtractor(Component, abc.ABC): + """Abstract class for entity relation extraction components. + + Args: + on_error (OnError): What to do when an error occurs during extraction. Defaults to raising an error. + create_lexical_graph (bool): Whether to include the text chunks in the graph in addition to the extracted entities and relations. Defaults to True. + """ + + def __init__( + self, + *args: Any, + on_error: OnError = OnError.IGNORE, + create_lexical_graph: bool = True, + **kwargs: Any, + ) -> None: + self.create_lexical_graph = create_lexical_graph + self.on_error = on_error + self._id_prefix = "" + + @abc.abstractmethod + async def run(self, chunks: TextChunks, **kwargs: Any) -> Neo4jGraph: + pass + + def update_ids(self, graph: Neo4jGraph, chunk_index: int) -> Neo4jGraph: + """Make node IDs unique across chunks and pipeline runs by + prefixing them with a custom prefix (set in the run method) + and chunk index.""" + for node in graph.nodes: + node.id = f"{self._id_prefix}:{chunk_index}:{node.id}" + if node.properties is None: + node.properties = {} + node.properties.update({"chunk_index": chunk_index}) + for rel in graph.relationships: + rel.start_node_id = f"{self._id_prefix}:{chunk_index}:{rel.start_node_id}" + rel.end_node_id = f"{self._id_prefix}:{chunk_index}:{rel.end_node_id}" + return graph + + def create_next_chunk_relationship( + self, previous_chunk_id: str, chunk_id: str + ) -> Neo4jRelationship: + """Create relationship between a chunk and the next one""" + return Neo4jRelationship( + type=NEXT_CHUNK_RELATIONSHIP_TYPE, + start_node_id=previous_chunk_id, + end_node_id=chunk_id, + ) + + def create_chunk_node(self, chunk: TextChunk, chunk_id: str) -> Neo4jNode: + """Create chunk node with properties 'text' and any 'metadata' added during + the process. Special case for the potential chunk embedding property that + gets added as an embedding_property""" + chunk_properties: Dict[str, Any] = { + "text": chunk.text, + } + embedding_properties = {} + if chunk.metadata: + if "embedding" in chunk.metadata: + embedding_properties["embedding"] = chunk.metadata.pop("embedding") + chunk_properties.update(chunk.metadata) + return Neo4jNode( + id=chunk_id, + label=CHUNK_NODE_LABEL, + properties=chunk_properties, + embedding_properties=embedding_properties, + ) + + def create_node_to_chunk_rel( + self, node: Neo4jNode, chunk_id: str + ) -> Neo4jRelationship: + """Create relationship between a chunk and entities found in that chunk""" + return Neo4jRelationship( + start_node_id=node.id, + end_node_id=chunk_id, + type=NODE_TO_CHUNK_RELATIONSHIP_TYPE, + ) + + def build_lexical_graph( + self, chunk_graph: Neo4jGraph, chunk_index: int, chunk: TextChunk + ) -> Neo4jGraph: + """Add chunks and relationships between them (NEXT_CHUNK) and between + chunks and extracted entities from that chunk. + """ + chunk_id = f"{self._id_prefix}:{chunk_index}" + chunk_node = self.create_chunk_node(chunk, chunk_id) + chunk_graph.nodes.append(chunk_node) + if chunk_index > 0: + previous_chunk_id = f"{self._id_prefix}:{chunk_index - 1}" + next_chunk_rel = self.create_next_chunk_relationship( + previous_chunk_id, chunk_id + ) + chunk_graph.relationships.append(next_chunk_rel) + for node in chunk_graph.nodes: + if node.label == CHUNK_NODE_LABEL: + continue + node_to_chunk_rel = self.create_node_to_chunk_rel(node, chunk_id) + chunk_graph.relationships.append(node_to_chunk_rel) + return chunk_graph + + +class LLMEntityRelationExtractor(EntityRelationExtractor): + """ + Extracts a knowledge graph from a series of text chunks using a large language model. + + Args: + llm (LLMInterface): The language model to use for extraction. + prompt_template (ERExtractionTemplate | str): A custom prompt template to use for extraction. + create_lexical_graph (bool): Whether to include the text chunks in the graph in addition to the extracted entities and relations. Defaults to True. + on_error (OnError): What to do when an error occurs during extraction. Defaults to raising an error. + + Example: + + .. code-block:: python + + from neo4j_genai.components.entity_relation_extractor import LLMEntityRelationExtractor + from neo4j_genai.llm import OpenAILLM + from neo4j_genai.pipeline import Pipeline + + llm = OpenAILLM(model_name="gpt-4o", model_params={"temperature": 0, "response_format": {"type": "object"}}) + + extractor = LLMEntityRelationExtractor(llm=llm) + pipe = Pipeline() + pipe.add_component("extractor", extractor) + + """ + + def __init__( + self, + llm: LLMInterface, + prompt_template: ERExtractionTemplate | str = ERExtractionTemplate(), + create_lexical_graph: bool = True, + on_error: OnError = OnError.RAISE, + ) -> None: + super().__init__(on_error=on_error, create_lexical_graph=create_lexical_graph) + self.llm = llm # with response_format={ "type": "json_object" }, + if isinstance(prompt_template, str): + template = PromptTemplate(prompt_template, expected_inputs=[]) + else: + template = prompt_template + self.prompt_template = template + + async def extract_for_chunk( + self, schema: SchemaConfig, examples: str, chunk_index: int, chunk: TextChunk + ) -> Neo4jGraph: + """Run entity extraction for a given text chunk.""" + prompt = self.prompt_template.format( + text=chunk.text, schema=schema.model_dump(), examples=examples + ) + llm_result = self.llm.invoke(prompt) + try: + result = json.loads(llm_result.content) + except json.JSONDecodeError as e: + if self.on_error == OnError.RAISE: + raise LLMGenerationError( + f"LLM response is not valid JSON {llm_result.content}: {e}" + ) + else: + logger.error( + f"LLM response is not valid JSON {llm_result.content} for chunk_index={chunk_index}" + ) + result = {"nodes": [], "relationships": []} + try: + chunk_graph = Neo4jGraph(**result) + except ValidationError as e: + if self.on_error == OnError.RAISE: + raise LLMGenerationError( + f"LLM response has improper format {result}: {e}" + ) + else: + logger.error( + f"LLM response has improper format {result} for chunk_index={chunk_index}" + ) + chunk_graph = Neo4jGraph() + return chunk_graph + + async def post_process_chunk( + self, chunk_graph: Neo4jGraph, chunk_index: int, chunk: TextChunk + ) -> None: + """Perform post-processing after entity and relation extraction: + - Update node IDs to make them unique across chunks + - Build the lexical graph if requested + """ + self.update_ids(chunk_graph, chunk_index) + if self.create_lexical_graph: + self.build_lexical_graph(chunk_graph, chunk_index, chunk) + + def combine_chunk_graphs(self, chunk_graphs: List[Neo4jGraph]) -> Neo4jGraph: + """Combine sub-graphs obtained for each chunk into a single Neo4jGraph object""" + graph = Neo4jGraph() + for chunk_graph in chunk_graphs: + graph.nodes.extend(chunk_graph.nodes) + graph.relationships.extend(chunk_graph.relationships) + return graph + + async def run_for_chunk( + self, schema: SchemaConfig, examples: str, chunk_index: int, chunk: TextChunk + ) -> Neo4jGraph: + """Run extraction and post processing for a single chunk""" + chunk_graph = await self.extract_for_chunk(schema, examples, chunk_index, chunk) + await self.post_process_chunk(chunk_graph, chunk_index, chunk) + return chunk_graph + + @validate_call + async def run( + self, + chunks: TextChunks, + schema: Union[SchemaConfig, None] = None, + examples: str = "", + **kwargs: Any, + ) -> Neo4jGraph: + """Perform entity and relation extraction for all chunks in a list.""" + schema = schema or SchemaConfig(entities={}, relations={}, potential_schema=[]) + examples = examples or "" + self._id_prefix = str(datetime.now().timestamp()) + tasks = [ + self.run_for_chunk(schema, examples, chunk_index, chunk) + for chunk_index, chunk in enumerate(chunks.chunks) + ] + chunk_graphs = await asyncio.gather(*tasks) + graph = self.combine_chunk_graphs(chunk_graphs) + logger.debug(f"{self.__class__.__name__}: {graph}") + return graph diff --git a/src/neo4j_genai/components/kg_writer.py b/src/neo4j_genai/components/kg_writer.py new file mode 100644 index 00000000..d32b90ab --- /dev/null +++ b/src/neo4j_genai/components/kg_writer.py @@ -0,0 +1,171 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import logging +from abc import abstractmethod +from typing import Literal, Optional + +import neo4j +from pydantic import validate_call + +from neo4j_genai.components.types import Neo4jGraph, Neo4jNode, Neo4jRelationship +from neo4j_genai.indexes import upsert_vector, upsert_vector_on_relationship +from neo4j_genai.neo4j_queries import UPSERT_NODE_QUERY, UPSERT_RELATIONSHIP_QUERY +from neo4j_genai.pipeline.component import Component, DataModel + +logger = logging.getLogger(__name__) + + +class KGWriterModel(DataModel): + """Data model for the output of the Knowledge Graph writer. + + Attributes: + status (Literal["SUCCESS", "FAILURE"]): Whether or not the write operation was successful. + """ + + status: Literal["SUCCESS", "FAILURE"] + + +class KGWriter(Component): + """Abstract class used to write a knowledge graph to a data store.""" + + @abstractmethod + @validate_call + async def run(self, graph: Neo4jGraph) -> KGWriterModel: + """ + Writes the graph to a data store. + + Args: + graph (Neo4jGraph): The knowledge graph to write to the data store. + """ + pass + + +class Neo4jWriter(KGWriter): + """Writes a knowledge graph to a Neo4j database. + + Args: + driver (neo4j.driver): The Neo4j driver to connect to the database. + neo4j_database (Optional[str]): The name of the Neo4j database to write to. Defaults to 'neo4j' if not provided. + + Example: + + .. code-block:: python + + from neo4j import GraphDatabase + from neo4j_genai.components.kg_writer import Neo4jWriter + from neo4j_genai.pipeline import Pipeline + + URI = "neo4j://localhost:7687" + AUTH = ("neo4j", "password") + DATABASE = "neo4j" + + driver = GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) + writer = Neo4jWriter(driver=driver, neo4j_database=DATABASE) + + pipeline = Pipeline() + pipeline.add_component("writer", writer) + + """ + + def __init__( + self, + driver: neo4j.driver, + neo4j_database: Optional[str] = None, + ): + self.driver = driver + self.neo4j_database = neo4j_database + + def _upsert_node(self, node: Neo4jNode) -> None: + """Upserts a single node into the Neo4j database." + + Args: + node (Neo4jNode): The node to upsert into the database. + """ + # Create the initial node + parameters = {"id": node.id} + if node.properties: + parameters.update(node.properties) + properties = ( + "{" + ", ".join(f"{key}: ${key}" for key in parameters.keys()) + "}" + ) + query = UPSERT_NODE_QUERY.format(label=node.label, properties=properties) + result = self.driver.execute_query(query, parameters_=parameters) + node_id = result.records[0]["elementID(n)"] + # Add the embedding properties to the node + if node.embedding_properties: + for prop, vector in node.embedding_properties.items(): + upsert_vector( + driver=self.driver, + node_id=node_id, + embedding_property=prop, + vector=vector, + neo4j_database=self.neo4j_database, + ) + + def _upsert_relationship(self, rel: Neo4jRelationship) -> None: + """Upserts a single relationship into the Neo4j database. + + Args: + rel (Neo4jRelationship): The relationship to upsert into the database. + """ + # Create the initial relationship + parameters = { + "start_node_id": rel.start_node_id, + "end_node_id": rel.end_node_id, + } + if rel.properties: + properties = ( + "{" + ", ".join(f"{key}: ${key}" for key in rel.properties.keys()) + "}" + ) + parameters.update(rel.properties) + else: + properties = "{}" + query = UPSERT_RELATIONSHIP_QUERY.format( + type=rel.type, + properties=properties, + ) + result = self.driver.execute_query(query, parameters_=parameters) + rel_id = result.records[0]["elementID(r)"] + # Add the embedding properties to the relationship + if rel.embedding_properties: + for prop, vector in rel.embedding_properties.items(): + upsert_vector_on_relationship( + driver=self.driver, + rel_id=rel_id, + embedding_property=prop, + vector=vector, + neo4j_database=self.neo4j_database, + ) + + @validate_call + async def run(self, graph: Neo4jGraph) -> KGWriterModel: + """Upserts a knowledge graph into a Neo4j database. + + Args: + graph (Neo4jGraph): The knowledge graph to upsert into the database. + """ + try: + for node in graph.nodes: + self._upsert_node(node) + + for rel in graph.relationships: + self._upsert_relationship(rel) + + return KGWriterModel(status="SUCCESS") + except neo4j.exceptions.ClientError as e: + logger.exception(e) + return KGWriterModel(status="FAILURE") diff --git a/src/neo4j_genai/components/schema.py b/src/neo4j_genai/components/schema.py new file mode 100644 index 00000000..72c96828 --- /dev/null +++ b/src/neo4j_genai/components/schema.py @@ -0,0 +1,208 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from typing import Any, Dict, List, Literal, Tuple + +from pydantic import BaseModel, ValidationError, model_validator, validate_call + +from neo4j_genai.exceptions import SchemaValidationError +from neo4j_genai.pipeline import Component, DataModel + + +class SchemaProperty(BaseModel): + """ + Represents a property on a node or relationship in the graph. + """ + + name: str + # See https://neo4j.com/docs/cypher-manual/current/values-and-types/property-structural-constructed/#property-types + type: Literal[ + "BOOLEAN", + "DATE", + "DURATION", + "FLOAT", + "INTEGER", + "LIST", + "LOCAL_DATETIME", + "LOCAL_TIME", + "POINT", + "STRING", + "ZONED_DATETIME", + "ZONED_TIME", + ] + description: str = "" + + +class SchemaEntity(BaseModel): + """ + Represents a possible node in the graph. + """ + + label: str + description: str = "" + properties: List[SchemaProperty] = [] + + +class SchemaRelation(BaseModel): + """ + Represents a possible relationship between nodes in the graph. + """ + + label: str + description: str = "" + properties: List[SchemaProperty] = [] + + +class SchemaConfig(DataModel): + """ + Represents possible relationships between entities and relations in the graph. + """ + + entities: Dict[str, Dict[str, Any]] + relations: Dict[str, Dict[str, Any]] + potential_schema: List[Tuple[str, str, str]] + + @model_validator(mode="before") + def check_schema(cls, data: Dict[str, Any]) -> Dict[str, Any]: + entities = data.get("entities", {}).keys() + relations = data.get("relations", {}).keys() + potential_schema = data.get("potential_schema", []) + + for entity1, relation, entity2 in potential_schema: + if entity1 not in entities: + raise SchemaValidationError( + f"Entity '{entity1}' is not defined in the provided entities." + ) + if relation not in relations: + raise SchemaValidationError( + f"Relation '{relation}' is not defined in the provided relations." + ) + if entity2 not in entities: + raise SchemaValidationError( + f"Entity '{entity1}' is not defined in the provided entities." + ) + + return data + + +class SchemaBuilder(Component): + """ + A builder class for constructing SchemaConfig objects from given entities, + relations, and their interrelationships defined in a potential schema. + + Example: + + .. code-block:: python + + from neo4j_genai.components.schema import ( + SchemaBuilder, + SchemaEntity, + SchemaProperty, + SchemaRelation, + ) + from neo4j_genai.pipeline import Pipeline + + entities = [ + SchemaEntity( + label="PERSON", + description="An individual human being.", + properties=[ + SchemaProperty( + name="name", type="STRING", description="The name of the person" + ) + ], + ), + SchemaEntity( + label="ORGANIZATION", + description="A structured group of people with a common purpose.", + properties=[ + SchemaProperty( + name="name", type="STRING", description="The name of the organization" + ) + ], + ), + ] + relations = [ + SchemaRelation( + label="EMPLOYED_BY", description="Indicates employment relationship." + ), + ] + potential_schema = [ + ("PERSON", "EMPLOYED_BY", "ORGANIZATION"), + ] + pipe = Pipeline() + schema_builder = SchemaBuilder() + pipe.add_component("schema_builder", schema_builder) + pipe_inputs = { + "schema": { + "entities": entities, + "relations": relations, + "potential_schema": potential_schema, + }, + ... + } + pipe.run(pipe_inputs) + """ + + @staticmethod + def create_schema_model( + entities: List[SchemaEntity], + relations: List[SchemaRelation], + potential_schema: List[Tuple[str, str, str]], + ) -> SchemaConfig: + """ + Creates a SchemaConfig object from Lists of Entity and Relation objects + and a Dictionary defining potential relationships. + + Args: + entities (List[SchemaEntity]): List of Entity objects. + relations (List[SchemaRelation]): List of Relation objects. + potential_schema (Dict[str, List[str]]): Dictionary mapping entity names to Lists of relation names. + + Returns: + SchemaConfig: A configured schema object. + """ + entity_dict = {entity.label: entity.dict() for entity in entities} + relation_dict = {relation.label: relation.dict() for relation in relations} + + try: + return SchemaConfig( + entities=entity_dict, + relations=relation_dict, + potential_schema=potential_schema, + ) + except (ValidationError, SchemaValidationError) as e: + raise SchemaValidationError(e) + + @validate_call + async def run( + self, + entities: List[SchemaEntity], + relations: List[SchemaRelation], + potential_schema: List[Tuple[str, str, str]], + ) -> SchemaConfig: + """ + Asynchronously constructs and returns a SchemaConfig object. + + Args: + entities (List[SchemaEntity]): List of Entity objects. + relations (List[SchemaRelation]): List of Relation objects. + potential_schema (Dict[str, List[str]]): Dictionary mapping entity names to Lists of relation names. + + Returns: + SchemaConfig: A configured schema object, constructed asynchronously. + """ + return self.create_schema_model(entities, relations, potential_schema) diff --git a/src/neo4j_genai/components/text_splitters/__init__.py b/src/neo4j_genai/components/text_splitters/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/neo4j_genai/components/text_splitters/base.py b/src/neo4j_genai/components/text_splitters/base.py new file mode 100644 index 00000000..f0bc9fa5 --- /dev/null +++ b/src/neo4j_genai/components/text_splitters/base.py @@ -0,0 +1,28 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from abc import abstractmethod + +from neo4j_genai.components.types import TextChunks +from neo4j_genai.pipeline.component import Component + + +class TextSplitter(Component): + """Interface for a text splitter.""" + + @abstractmethod + async def run(self, text: str) -> TextChunks: + pass diff --git a/src/neo4j_genai/components/text_splitters/langchain.py b/src/neo4j_genai/components/text_splitters/langchain.py new file mode 100644 index 00000000..6ef28823 --- /dev/null +++ b/src/neo4j_genai/components/text_splitters/langchain.py @@ -0,0 +1,61 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from langchain_text_splitters import TextSplitter as LangChainTextSplitter + +from neo4j_genai.components.text_splitters.base import TextSplitter +from neo4j_genai.components.types import TextChunk, TextChunks + + +class LangChainTextSplitterAdapter(TextSplitter): + """Adapter for LangChain TextSplitters. + Allows instances of this class to be used in the knowledge graph builder pipeline. + + Args: + text_splitter (LangChainTextSplitter): An instance of LangChain's TextSplitter class. + + Example: + + .. code-block:: python + + from langchain_text_splitters import RecursiveCharacterTextSplitter + from neo4j_genai.components.text_splitters.langchain import LangChainTextSplitterAdapter + from neo4j_genai.pipeline import Pipeline + + pipeline = Pipeline() + text_splitter = LangChainTextSplitterAdapter(RecursiveCharacterTextSplitter()) + pipeline.add_component("text_splitter", text_splitter) + + """ + + def __init__(self, text_splitter: LangChainTextSplitter) -> None: + self.text_splitter = text_splitter + + async def run(self, text: str) -> TextChunks: + """ + Splits text into chunks. + + Args: + text (str): The text to split. + + Returns: + TextChunks: The text split into chunks. + """ + return TextChunks( + chunks=[ + TextChunk(text=chunk) for chunk in self.text_splitter.split_text(text) + ] + ) diff --git a/src/neo4j_genai/components/text_splitters/llamaindex.py b/src/neo4j_genai/components/text_splitters/llamaindex.py new file mode 100644 index 00000000..9b489d86 --- /dev/null +++ b/src/neo4j_genai/components/text_splitters/llamaindex.py @@ -0,0 +1,61 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from llama_index.core.node_parser import TextSplitter as LlamaIndexTextSplitter + +from neo4j_genai.components.text_splitters.base import TextSplitter +from neo4j_genai.components.types import TextChunk, TextChunks + + +class LlamaIndexTextSplitterAdapter(TextSplitter): + """Adapter for LlamaIndex TextSplitters. + Allows instances of this class to be used in the knowledge graph builder pipeline. + + Args: + text_splitter (LlamaIndexTextSplitter): An instance of LlamaIndex's TextSplitter class. + + Example: + + .. code-block:: python + + from llama_index.core.node_parser.text.sentence import SentenceSplitter + from neo4j_genai.components.text_splitters.langchain import LangChainTextSplitterAdapter + from neo4j_genai.pipeline import Pipeline + + pipeline = Pipeline() + text_splitter = LlamaIndexTextSplitterAdapter(SentenceSplitter()) + pipeline.add_component("text_splitter", text_splitter) + + """ + + def __init__(self, text_splitter: LlamaIndexTextSplitter) -> None: + self.text_splitter = text_splitter + + async def run(self, text: str) -> TextChunks: + """ + Splits text into chunks. + + Args: + text (str): The text to split. + + Returns: + TextChunks: The text split into chunks. + """ + return TextChunks( + chunks=[ + TextChunk(text=chunk) for chunk in self.text_splitter.split_text(text) + ] + ) diff --git a/src/neo4j_genai/components/types.py b/src/neo4j_genai/components/types.py new file mode 100644 index 00000000..ff7fdee5 --- /dev/null +++ b/src/neo4j_genai/components/types.py @@ -0,0 +1,98 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from typing import Any, Optional + +from pydantic import BaseModel, field_validator + +from neo4j_genai.pipeline.component import DataModel + + +class TextChunk(BaseModel): + """A chunk of text split from a document by a text splitter. + + Attributes: + text (str): The raw chunk text. + metadata (Optional[dict[str, Any]]): Metadata associated with this chunk such as the id of the next chunk in the original document. + """ + + text: str + metadata: Optional[dict[str, Any]] = None + + +class TextChunks(DataModel): + """A collection of text chunks returned from a text splitter. + + Attributes: + chunks (list[TextChunk]): A list of text chunks. + """ + + chunks: list[TextChunk] + + +class Neo4jNode(BaseModel): + """Represents a Neo4j node. + + Attributes: + id (str): The ID of the node. + label (str): The label of the node. + properties (Optional[dict[str, Any]]): A dictionary of properties attached to the node. + embedding_properties (Optional[dict[str, list[float]]]): A list of embedding properties attached to the node. + """ + + id: str + label: str + properties: Optional[dict[str, Any]] = None + embedding_properties: Optional[dict[str, list[float]]] = None + + @field_validator("properties", "embedding_properties") + @classmethod + def check_for_id_properties( + cls, v: Optional[dict[str, Any]] + ) -> Optional[dict[str, Any]]: + if v and "id" in v.keys(): + raise TypeError("'id' as a property name is not allowed") + return v + + +class Neo4jRelationship(BaseModel): + """Represents a Neo4j relationship. + + Attributes: + start_node_id (str): The ID of the start node. + end_node_id (str): The ID of the end node. + type (str): The relationship type. + properties (Optional[dict[str, Any]]): A dictionary of properties attached to the relationship. + embedding_properties (Optional[dict[str, list[float]]]): A list of embedding properties attached to the relationship. + """ + + start_node_id: str + end_node_id: str + type: str + properties: Optional[dict[str, Any]] = None + embedding_properties: Optional[dict[str, list[float]]] = None + + +class Neo4jGraph(DataModel): + """Represents a Neo4j graph. + + Attributes: + nodes (list[Neo4jNode]): A list of nodes in the graph. + relationships (list[Neo4jRelationship]): A list of relationships in the graph. + """ + + nodes: list[Neo4jNode] = [] + relationships: list[Neo4jRelationship] = [] diff --git a/src/neo4j_genai/exceptions.py b/src/neo4j_genai/exceptions.py index b1025f54..0788b4e4 100644 --- a/src/neo4j_genai/exceptions.py +++ b/src/neo4j_genai/exceptions.py @@ -104,3 +104,9 @@ class SchemaFetchError(Neo4jGenAiError): """Exception raised when a Neo4jSchema cannot be fetched.""" pass + + +class SchemaValidationError(Exception): + """Custom exception for errors in schema configuration.""" + + pass diff --git a/src/neo4j_genai/generation/prompts.py b/src/neo4j_genai/generation/prompts.py index 4a7f3b15..132dda6e 100644 --- a/src/neo4j_genai/generation/prompts.py +++ b/src/neo4j_genai/generation/prompts.py @@ -119,3 +119,35 @@ class Text2CypherTemplate(PromptTemplate): def format(self, query: str, schema: str, examples: str) -> str: return super().format(query=query, schema=schema, examples=examples) + + +class ERExtractionTemplate(PromptTemplate): + DEFAULT_TEMPLATE = """ +You are a top-tier algorithm designed for extracting +information in structured formats to build a knowledge graph. + +Extract the entities (nodes) and specify their type from the following text. +Also extract the relationships between these nodes. + +Return result as JSON using the following format: +{{"nodes": [ {{"id": "0", "label": "Person", "properties": {{"name": "John"}} }}], +"relationships": [{{"type": "KNOWS", "start_node_id": "0", "end_node_id": "1", "properties": {{"since": "2024-08-01"}} }}] }} + +Use only fhe following nodes and relationships (if provided): +{schema} + +Assign a unique ID (string) to each node, and reuse it to define relationships. +Do respect the source and target node types for relationship and +the relationship direction. + +Examples: +{examples} + +Input text: + +{text} +""" + EXPECTED_INPUTS = ["text"] + + def format(self, text: str, schema: dict[str, Any], examples: str) -> str: + return super().format(text=text, schema=schema, examples=examples) diff --git a/src/neo4j_genai/indexes.py b/src/neo4j_genai/indexes.py index 1ae01854..54f90170 100644 --- a/src/neo4j_genai/indexes.py +++ b/src/neo4j_genai/indexes.py @@ -278,13 +278,13 @@ def upsert_vector( Neo4jInsertionError: If upserting of the vector fails. """ try: - query = """ - MATCH (n) - WHERE elementId(n) = $id - WITH n - CALL db.create.setNodeVectorProperty(n, $embedding_property, $vector) - RETURN n - """ + query = ( + "MATCH (n) " + "WHERE elementId(n) = $id " + "WITH n " + "CALL db.create.setNodeVectorProperty(n, $embedding_property, $vector) " + "RETURN n" + ) parameters = { "id": node_id, "embedding_property": embedding_property, @@ -295,3 +295,64 @@ def upsert_vector( raise Neo4jInsertionError( f"Upserting vector to Neo4j failed: {e.message}" ) from e + + +def upsert_vector_on_relationship( + driver: neo4j.Driver, + rel_id: int, + embedding_property: str, + vector: list[float], + neo4j_database: Optional[str] = None, +) -> None: + """ + This method constructs a Cypher query and executes it to upsert (insert or update) a vector property on a specific relationship. + + Example: + + .. code-block:: python + + from neo4j import GraphDatabase + from neo4j_genai.indexes import upsert_vector_on_relationship + + URI = "neo4j://localhost:7687" + AUTH = ("neo4j", "password") + + # Connect to Neo4j database + driver = GraphDatabase.driver(URI, auth=AUTH) + + # Upsert the vector data + upsert_vector_on_relationship( + driver, + node_id="nodeId", + embedding_property="vectorProperty", + vector=..., + ) + + Args: + driver (neo4j.Driver): Neo4j Python driver instance. + rel_id (int): The id of the relationship. + embedding_property (str): The name of the property to store the vector in. + vector (list[float]): The vector to store. + neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to "neo4j" in the database (`see reference to documentation `_). + + Raises: + Neo4jInsertionError: If upserting of the vector fails. + """ + try: + query = ( + "MATCH ()-[r]->() " + "WHERE elementId(r) = $id " + "WITH r " + "CALL db.create.setRelationshipVectorProperty(r, $embedding_property, $vector) " + "RETURN r" + ) + parameters = { + "id": rel_id, + "embedding_property": embedding_property, + "vector": vector, + } + driver.execute_query(query, parameters, database_=neo4j_database) + except neo4j.exceptions.ClientError as e: + raise Neo4jInsertionError( + f"Upserting vector to Neo4j failed: {e.message}" + ) from e diff --git a/src/neo4j_genai/neo4j_queries.py b/src/neo4j_genai/neo4j_queries.py index 27cfad98..105a5e28 100644 --- a/src/neo4j_genai/neo4j_queries.py +++ b/src/neo4j_genai/neo4j_queries.py @@ -41,6 +41,14 @@ "YIELD node, score" ) +UPSERT_NODE_QUERY = "MERGE (n:`{label}` {properties}) RETURN elementID(n)" + +UPSERT_RELATIONSHIP_QUERY = ( + "MATCH (start {{ id: $start_node_id }}), (end {{ id: $end_node_id }}) " + "MERGE (start)-[r:{type} {properties}]->(end) " + "RETURN elementID(r)" +) + def _get_hybrid_query() -> str: return ( diff --git a/src/neo4j_genai/pipeline/__init__.py b/src/neo4j_genai/pipeline/__init__.py new file mode 100644 index 00000000..4cb0c941 --- /dev/null +++ b/src/neo4j_genai/pipeline/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .component import Component, DataModel +from .pipeline import Pipeline +from .stores import InMemoryStore, Store + +__all__ = [ + "Store", + "InMemoryStore", + "Component", + "DataModel", + "Pipeline", +] diff --git a/src/neo4j_genai/pipeline/component.py b/src/neo4j_genai/pipeline/component.py new file mode 100644 index 00000000..4446d251 --- /dev/null +++ b/src/neo4j_genai/pipeline/component.py @@ -0,0 +1,81 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import abc +import inspect +from typing import Any, get_type_hints + +from pydantic import BaseModel + +from neo4j_genai.pipeline.exceptions import PipelineDefinitionError + + +class DataModel(BaseModel): + """Input or Output data model for Components""" + + pass + + +class ComponentMeta(abc.ABCMeta): + def __new__( + meta, name: str, bases: tuple[type, ...], attrs: dict[str, Any] + ) -> type: + # extract required inputs and outputs from the run method signature + run_method = attrs.get("run") + if run_method is not None: + sig = inspect.signature(run_method) + attrs["component_inputs"] = { + param.name: { + "has_default": param.default != inspect.Parameter.empty, + "annotation": param.annotation, + } + for param in sig.parameters.values() + if param.name not in ("self", "kwargs") + } + # extract returned fields from the run method return type hint + return_model = get_type_hints(run_method).get("return") + if return_model is None: + raise PipelineDefinitionError( + f"The run method return type must be annotated in {name}" + ) + # the type hint must be a subclass of DataModel + if not issubclass(return_model, DataModel): + raise PipelineDefinitionError( + f"The run method must return a subclass of DataModel in {name}" + ) + attrs["component_outputs"] = { + f: { + "has_default": field.is_required(), + "annotation": field.annotation, + } + for f, field in return_model.model_fields.items() + } + return type.__new__(meta, name, bases, attrs) + + +class Component(abc.ABC, metaclass=ComponentMeta): + """Interface that needs to be implemented + by all components. + """ + + # these variables are filled by the metaclass + # added here for the type checker + component_inputs: dict[str, dict[str, str | bool]] + component_outputs: dict[str, dict[str, str | bool]] + + @abc.abstractmethod + async def run(self, *args: Any, **kwargs: Any) -> DataModel: + pass diff --git a/src/neo4j_genai/pipeline/exceptions.py b/src/neo4j_genai/pipeline/exceptions.py new file mode 100644 index 00000000..2a35857d --- /dev/null +++ b/src/neo4j_genai/pipeline/exceptions.py @@ -0,0 +1,33 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from neo4j_genai.exceptions import Neo4jGenAiError + + +class PipelineDefinitionError(Neo4jGenAiError): + """Raised when the pipeline graph is invalid""" + + pass + + +class PipelineMissingDependencyError(Neo4jGenAiError): + """Raised when a task is scheduled but its dependencies are not yet done""" + + pass + + +class PipelineStatusUpdateError(Neo4jGenAiError): + """Raises when trying an invalid change of state (e.g. DONE => DOING)""" + + pass diff --git a/src/neo4j_genai/pipeline/pipeline.py b/src/neo4j_genai/pipeline/pipeline.py new file mode 100644 index 00000000..7c8e35d5 --- /dev/null +++ b/src/neo4j_genai/pipeline/pipeline.py @@ -0,0 +1,470 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import asyncio +import enum +import logging +from datetime import datetime +from typing import Any, AsyncGenerator, Awaitable, Callable, Optional + +from pydantic import BaseModel, Field + +from neo4j_genai.pipeline.component import Component, DataModel +from neo4j_genai.pipeline.exceptions import ( + PipelineDefinitionError, + PipelineMissingDependencyError, + PipelineStatusUpdateError, +) +from neo4j_genai.pipeline.pipeline_graph import ( + PipelineEdge, + PipelineGraph, + PipelineNode, +) +from neo4j_genai.pipeline.stores import InMemoryStore, Store +from neo4j_genai.pipeline.types import ComponentConfig, ConnectionConfig, PipelineConfig + +logger = logging.getLogger(__name__) + + +class RunStatus(enum.Enum): + UNKNOWN = "UNKNOWN" + SCHEDULED = "SCHEDULED" + WAITING = "WAITING" + RUNNING = "RUNNING" + SKIP = "SKIP" + DONE = "DONE" + + +class RunResult(BaseModel): + status: RunStatus + result: Optional[DataModel] = None + timestamp: datetime = Field(default_factory=datetime.utcnow) + + +class TaskPipelineNode(PipelineNode): + """Runnable node. It must have: + - a name (unique within the pipeline) + - a component instance + - a reference to the pipeline it belongs to + (to find dependent tasks) + """ + + def __init__(self, name: str, component: Component, pipeline: "Pipeline"): + """TaskNode is a graph node with a run method. + + Args: + name (str): node's name + component (Component): component instance + pipeline (Pipeline): pipeline the task belongs to + """ + super().__init__(name, {}) + self.component = component + self.status = RunStatus.UNKNOWN + self._pipeline = pipeline + self._lock = asyncio.Lock() + """This lock is used to make sure we're not trying + to update the status in //. This should prevent the task to + be executed multiple times because the status was not known + by the orchestrator. + """ + + async def set_status(self, status: RunStatus) -> None: + """Set a new status + + Args: + status (RunStatus): new status + + Raises: + StatusUpdateError if the new status is not + compatible with the current one. + """ + async with self._lock: + if status == self.status: + raise PipelineStatusUpdateError() + if status == RunStatus.RUNNING and self.status == RunStatus.DONE: + # can't go back to RUNNING from DONE + raise PipelineStatusUpdateError() + self.status = status + + async def read_status(self) -> RunStatus: + async with self._lock: + return self.status + + async def execute(self, **kwargs: Any) -> RunResult | None: + """Execute the task: + 1. Set status to RUNNING + 2. Calls the component.run method + 3. Set status to DONE + + Returns: + RunResult | None: RunResult with status and result dict + if the task run successfully, None if the status update + was unsuccessful. + """ + logger.debug(f"Running component {self.name} with {kwargs}") + try: + await self.set_status(RunStatus.RUNNING) + except PipelineStatusUpdateError: + logger.info(f"Component {self.name} already running or done {self.status}") + return None + component_result = await self.component.run(**kwargs) + await self.set_status(RunStatus.DONE) + run_result = RunResult( + status=self.status, + result=component_result, + ) + return run_result + + def validate_inputs_config(self, input_data: dict[str, Any]) -> None: + """Make sure the parameter defined in the input config + matches a parameter in the previous component output model. + """ + component = self.component + expected_mandatory_inputs = [ + param_name + for param_name, config in component.component_inputs.items() + if config["has_default"] is False + ] + # start building the actual input list, starting + # from the inputs provided in the pipeline.run method + actual_inputs = list(input_data.get(self.name, {}).keys()) + prev_edges = self._pipeline.previous_edges(self.name) + # then, iterate over all parents to find the parameter propagation + for edge in prev_edges: + edge_data = edge.data or {} + edge_inputs = edge_data.get("input_config") or {} + # check that the previous component is actually returning + # the mapped parameter + for param, path in edge_inputs.items(): + try: + source_component_name, param_name = path.split(".") + except ValueError: + # no specific output mapped + # the full source component result will be + # passed to the next component, so no need + # for further check + continue + source_node = self._pipeline.get_node_by_name(source_component_name) + source_component = source_node.component + source_component_outputs = source_component.component_outputs + if param_name and param_name not in source_component_outputs: + raise PipelineDefinitionError( + f"Parameter {param_name} is not valid output for " + f"{source_component_name} (must be one of " + f"{list(source_component_outputs.keys())})" + ) + + actual_inputs.extend(list(edge_inputs.keys())) + if set(expected_mandatory_inputs) - set(actual_inputs): + raise PipelineDefinitionError( + f"Missing input parameters for {self.name}: " + f"Expected parameters: {expected_mandatory_inputs}. " + f"Got: {actual_inputs}" + ) + + async def get_input_config_from_parents(self) -> dict[str, str]: + """Build input definition for this component. For this, + the method needs the input defs defined in the edges + between this task and its parents. + + Returns: + dict: a dict of + {input_parameter: path_to_value_in_the_pipeline_results_dict} + + Raises: + MissingDependencyError if a parent dependency is not done yet + """ + input_config: dict[str, Any] = {} + # make sure dependencies are satisfied + # and save the inputs defs that needs to be propagated from parent components + for prev_edge in self._pipeline.previous_edges(self.name): + prev_node = self._pipeline.get_node_by_name(prev_edge.start) + prev_status = await prev_node.read_status() + if prev_status != RunStatus.DONE: + logger.critical(f"Missing dependency {prev_edge.start}") + raise PipelineMissingDependencyError(f"{prev_edge.start} not ready") + if prev_edge.data: + prev_edge_data = prev_edge.data.get("input_config") or {} + input_config.update(**prev_edge_data) + return input_config + + def reinitialize(self) -> None: + self.status = RunStatus.SCHEDULED + + async def run( + self, data: dict[str, Any], callback: Callable[[Any, Any, Any], Awaitable[Any]] + ) -> None: + """Main method to execute the task. + 1. Get the input defs (path to all required inputs) + 2. Build the input dict from the previous input defs + 3. Call the execute method + 4. Call the pipeline callback method to deal with child dependencies + """ + logger.debug(f"TASK START {self.name=} {data=}") + # prepare the inputs defs that needs to be + # propagated from parent components + input_config = await self.get_input_config_from_parents() + # create component's input based on initial input data, + # already done component's outputs and inputs definition mapping + inputs = self._pipeline.get_component_inputs(self.name, input_config, data) + # execute component task + res = await self.execute(**inputs) + if res is None: + return + logger.debug(f"TASK RESULT {self.name=} {res=}") + # save its results + await callback(self, data, res) + + +class Orchestrator: + """Orchestrate a pipeline + Once a TaskNode is done, it calls the `on_task_complete` callback + that will save the results, find the next tasks to be executed + (checking that all dependencies are met), and run them. + """ + + def __init__(self, pipeline: "Pipeline"): + self.pipeline = pipeline + + async def run_task(self, task: TaskPipelineNode, data: dict[str, Any]) -> None: + await task.run(data, callback=self.on_task_complete) + + async def on_task_complete( + self, task: TaskPipelineNode, data: dict[str, Any], res: RunResult + ) -> None: + """When a given task is complete, it will call this method + to find the next tasks to run. + """ + # first call the method for the pipeline + # this is where the results can be saved + self.pipeline.on_task_complete(task, res) + # then get the next tasks to be executed + # and run them in // + await asyncio.gather(*[self.run_task(n, data) async for n in self.next(task)]) + + async def check_dependencies_complete(self, task: TaskPipelineNode) -> None: + """Check that all parent tasks are complete. + + Raises: + MissingDependencyError if a parent task's status is different from DONE. + """ + dependencies = self.pipeline.previous_edges(task.name) + for d in dependencies: + start_node = self.pipeline.get_node_by_name(d.start) + d_status = await start_node.read_status() + if d_status != RunStatus.DONE: + logger.warning( + f"Missing dependency {d.start} for {task.name} (status: {d_status})" + ) + raise PipelineMissingDependencyError() + + async def next( + self, task: TaskPipelineNode + ) -> AsyncGenerator[TaskPipelineNode, None]: + """Find the next tasks to be excuted after `task` is complete. + + 1. Find the task children + 2. Check each child's status: + - if it's already running or done, do not need to run it again + - otherwise, check that all its dependencies are met, if yes + add this task to the list of next tasks to be executed + """ + possible_nexts = self.pipeline.next_edges(task.name) + for next_edge in possible_nexts: + next_node = self.pipeline.get_node_by_name(next_edge.end) + # check status + next_node_status = await next_node.read_status() + if next_node_status in [RunStatus.RUNNING, RunStatus.DONE]: + # already running + continue + # check deps + try: + await self.check_dependencies_complete(next_node) + except PipelineMissingDependencyError: + continue + yield next_node + return + + async def run(self, data: dict[str, Any]) -> None: + """Run the pipline, starting from the root nodes + (node without any parent). Then the callback on_task_complete + will handle the task dependencies. + """ + logger.debug(f"PIPELINE START {data=}") + tasks = [ + root.run(data, callback=self.on_task_complete) + for root in self.pipeline.roots() + ] + await asyncio.gather(*tasks) + + +class Pipeline(PipelineGraph[TaskPipelineNode, PipelineEdge]): + """This is our pipeline, when we configure components + and their execution order.""" + + def __init__(self, store: Optional[Store] = None) -> None: + super().__init__() + self._store = store or InMemoryStore() + self._final_results = InMemoryStore() + + @classmethod + def from_template( + cls, pipeline_template: PipelineConfig, store: Optional[Store] = None + ) -> Pipeline: + """Create a Pipeline from a pydantic model defining the components and their connections""" + pipeline = Pipeline(store=store) + for component in pipeline_template.components: + pipeline.add_component(component.name, component.component) + for edge in pipeline_template.connections: + pipeline_edge = PipelineEdge( + edge.start, edge.end, data={"input_config": edge.input_config} + ) + pipeline.add_edge(pipeline_edge) + return pipeline + + def show_as_dict(self) -> dict[str, Any]: + component_config = [] + for name, task in self._nodes.items(): + component_config.append( + ComponentConfig(name=name, component=task.component) + ) + connection_config = [] + for edge in self._edges: + connection_config.append( + ConnectionConfig( + start=edge.start, + end=edge.end, + input_config=edge.data["input_config"] if edge.data else {}, + ) + ) + pipeline_config = PipelineConfig( + components=component_config, connections=connection_config + ) + return pipeline_config.model_dump() + + def add_component(self, name: str, component: Component) -> None: + task = TaskPipelineNode(name, component, self) + self.add_node(task) + + def set_component(self, name: str, component: Component) -> None: + task = TaskPipelineNode(name, component, self) + self.set_node(task) + + def connect( + self, + start_component_name: str, + end_component_name: str, + input_config: Optional[dict[str, str]] = None, + ) -> None: + """Connect one component to another. + + Args: + start_component_name (str): name of the component as defined in + the add_component method + end_component_name (str): name of the component as defined in + the add_component method + input_config (Optional[dict[str, str]]): end component input configuration: + propagate previous components outputs. + + Raises: + PipelineDefinitionError: if the provided component are not in the Pipeline + or if the created graph is cyclic + """ + edge = PipelineEdge( + start_component_name, + end_component_name, + data={"input_config": input_config}, + ) + try: + self.add_edge(edge) + except KeyError: + raise PipelineDefinitionError( + f"{start_component_name} or {end_component_name} is not in the Pipeline" + ) + if self.is_cyclic(): + raise PipelineDefinitionError("Cyclic graph are not allowed") + + def on_task_complete(self, node: TaskPipelineNode, result: RunResult) -> None: + res_to_save = None + if result.result: + res_to_save = result.result.model_dump() + self.add_result_for_component(node.name, res_to_save, is_final=node.is_leaf()) + + def add_result_for_component( + self, name: str, result: dict[str, Any] | None, is_final: bool = False + ) -> None: + self._store.add(name, result) + if is_final: + # The pipeline only returns the results + # of the leaf nodes + # TODO: make this configurable in the future. + self._final_results.add(name, result) + + def get_results_for_component(self, name: str) -> Any: + return self._store.get(name) + + def get_component_inputs( + self, + component_name: str, + input_config: dict[str, Any], + input_data: dict[str, Any], + ) -> dict[str, Any]: + """Find the component inputs from: + - data: the user input data + - input_config: the mapping between components results and inputs + """ + component_inputs: dict[str, Any] = input_data.get(component_name, {}) + if input_config: + for parameter, mapping in input_config.items(): + try: + component, output_param = mapping.split(".") + except ValueError: + # we will use the full output of + # component as input + component = mapping + output_param = None + component_result = self._store.get(component) + if output_param is not None: + value = component_result.get(output_param) + else: + value = component_result + component_inputs[parameter] = value + return component_inputs + + def reinitialize(self) -> None: + """Reinitialize the result stores and component status + if we want to rerun the same pipeline again + (maybe with inputs)""" + self._store.empty() + self._final_results.empty() + for task in self._nodes.values(): + task.reinitialize() + + def validate_inputs_config(self, data: dict[str, Any]) -> None: + """Go through the graph and make sure each component will not miss any input + + Args: + data (dict[str, Any]): the user provided data in the pipeline.run method. + """ + for task in self._nodes.values(): + task.validate_inputs_config(data) + + async def run(self, data: dict[str, Any]) -> dict[str, Any]: + self.validate_inputs_config(data) + self.reinitialize() + orchestrator = Orchestrator(self) + await orchestrator.run(data) + return self._final_results.all() diff --git a/src/neo4j_genai/pipeline/pipeline_graph.py b/src/neo4j_genai/pipeline/pipeline_graph.py new file mode 100644 index 00000000..ffbddf49 --- /dev/null +++ b/src/neo4j_genai/pipeline/pipeline_graph.py @@ -0,0 +1,148 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Basic graph structure for Pipeline. +""" + +from __future__ import annotations + +from typing import Any, Generic, Optional, TypeVar + + +class PipelineNode: + def __init__(self, name: str, data: dict[str, Any]) -> None: + self.name = name + self.data = data + self.parents: list[str] = [] + self.children: list[str] = [] + + def is_root(self) -> bool: + return len(self.parents) == 0 + + def is_leaf(self) -> bool: + return len(self.children) == 0 + + +class PipelineEdge: + def __init__( + self, + start: str, + end: str, + data: Optional[dict[str, Any]] = None, + ): + self.start = start + self.end = end + self.data = data + + +GenericNodeType = TypeVar("GenericNodeType", bound=PipelineNode) +GenericEdgeType = TypeVar("GenericEdgeType", bound=PipelineEdge) + + +class PipelineGraph(Generic[GenericNodeType, GenericEdgeType]): + """When defining a pipeline, user must define + the node and edge types. + The node type must inherit from PipelineNode. + The edge type must inherit from PipelineEdge. + + This allows users to add more features to the node/edges, + while preserving type checker compatibility. + """ + + def __init__(self) -> None: + self._nodes: dict[str, GenericNodeType] = {} + self._edges: list[GenericEdgeType] = [] + + def add_node(self, node: GenericNodeType) -> None: + if node in self: + raise ValueError(f"Node {node.name} already exists") + self._nodes[node.name] = node + + def set_node(self, node: GenericNodeType) -> None: + """Replace an existing node with a new one based on node name.""" + if node not in self: + raise ValueError(f"Node {node.name} does not exist") + # propagate the graph info to the new node: + old_node = self._nodes[node.name] + node.parents = old_node.parents + node.children = old_node.children + self._nodes[node.name] = node + + def _validate_edge(self, start: str, end: str) -> None: + if start not in self: + raise KeyError(f"Node {start} does not exist") + if end not in self: + raise KeyError(f"Node {end} does not exist") + for edge in self._edges: + if edge.start == start and edge.end == end: + raise ValueError(f"{start} and {end} are already connected") + + def add_edge(self, edge: GenericEdgeType) -> None: + self._validate_edge(edge.start, edge.end) + self._edges.append(edge) + self._nodes[edge.end].parents.append(edge.start) + self._nodes[edge.start].children.append(edge.end) + + def get_node_by_name(self, name: str) -> GenericNodeType: + node = self._nodes[name] + return node + + def roots(self) -> list[GenericNodeType]: + root = [] + for node in self._nodes.values(): + if node.is_root(): + root.append(node) + return root + + def next_edges(self, node: str) -> list[GenericEdgeType]: + res = [] + for edge in self._edges: + if edge.start == node: + res.append(edge) + return res + + def previous_edges(self, node: str) -> list[GenericEdgeType]: + res = [] + for edge in self._edges: + if edge.end == node: + res.append(edge) + return res + + def __contains__(self, node: GenericNodeType | str) -> bool: + if isinstance(node, str): + return node in self._nodes + return node.name in self._nodes + + def dfs(self, visited: set[str], node: str) -> bool: + if node in visited: + return True + else: + for edge in self.next_edges(node): + if self.dfs(visited | {node}, edge.end): + return True + return False + + def is_cyclic(self) -> bool: + """Returns True if at least one cycle is + found in the graph, False if no cycle is + detected. + + Traverse the graph from each node. + If the same node is encountered again, + the graph is cyclic.""" + for node in self._nodes: + if self.dfs(set(), node): + return True + return False diff --git a/src/neo4j_genai/pipeline/stores.py b/src/neo4j_genai/pipeline/stores.py new file mode 100644 index 00000000..cfd27d87 --- /dev/null +++ b/src/neo4j_genai/pipeline/stores.py @@ -0,0 +1,82 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Result store interface +and in-memory store implementation. +""" + +from __future__ import annotations + +import abc +from typing import Any + + +class Store(abc.ABC): + """An interface to save component outputs""" + + @abc.abstractmethod + def add(self, key: str, value: Any, overwrite: bool = True) -> None: + """ + Args: + key (str): The key to access the data. + value (Any): The value to store in the data. + overwrite (bool): Whether to overwrite existing data. + If overwrite is False and the key already exists + in the store, an exception is raised. + + Raises: + KeyError: If the key already exists in the store and overwrite is False. + """ + pass + + @abc.abstractmethod + def get(self, key: str) -> Any: + """Retrieve value for `key`. + If key not found, returns None. + """ + pass + + def all(self) -> dict[str, Any]: + """Return all stored data + Might not be relevant to implement + in all subclasses, that's why it is + not marked as abstract. + """ + raise NotImplementedError() + + def empty(self) -> None: + """Remove everything from store""" + raise NotImplementedError() + + +class InMemoryStore(Store): + """Simple in-memory store. + Saves each component's results in a _data dict.""" + + def __init__(self) -> None: + self._data: dict[str, Any] = {} + + def add(self, key: str, value: Any, overwrite: bool = True) -> None: + if (not overwrite) and key in self._data: + raise KeyError(f"{key} already exists") + self._data[key] = value + + def get(self, key: str) -> Any: + return self._data.get(key) + + def all(self) -> dict[str, Any]: + return self._data + + def empty(self) -> None: + self._data = {} diff --git a/src/neo4j_genai/pipeline/types.py b/src/neo4j_genai/pipeline/types.py new file mode 100644 index 00000000..89ad169d --- /dev/null +++ b/src/neo4j_genai/pipeline/types.py @@ -0,0 +1,37 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict + +from neo4j_genai.pipeline.component import Component + + +class ComponentConfig(BaseModel): + name: str + component: Component + + model_config = ConfigDict(arbitrary_types_allowed=True) + + +class ConnectionConfig(BaseModel): + start: str + end: str + input_config: dict[str, str] + + +class PipelineConfig(BaseModel): + components: list[ComponentConfig] + connections: list[ConnectionConfig] diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py index 3ab4546f..e1092313 100644 --- a/tests/e2e/conftest.py +++ b/tests/e2e/conftest.py @@ -155,3 +155,23 @@ def setup_neo4j_for_schema_query_with_excluded_labels(driver: Driver) -> None: driver.execute_query( "CREATE (:_Bloom_Scene_{property_a: 'a'})-[:_Bloom_HAS_SCENE_{property_b: 'b'}]->(:_Bloom_Perspective_)" ) + + +@pytest.fixture(scope="module") +def setup_neo4j_for_kg_construction(driver: Driver) -> None: + # Delete all nodes and indexes in the graph + driver.execute_query("MATCH (n) DETACH DELETE n") + vector_index_name = "vector-index-name" + fulltext_index_name = "fulltext-index-name" + drop_index_if_exists(driver, vector_index_name) + drop_index_if_exists(driver, fulltext_index_name) + + # Create a vector index with the dimensions used by the Hugging Face all-MiniLM-L6-v2 model + create_vector_index( + driver, + vector_index_name, + label="Document", + embedding_property="vectorProperty", + dimensions=3, + similarity_fn="euclidean", + ) diff --git a/tests/e2e/data/harry_potter.txt b/tests/e2e/data/harry_potter.txt new file mode 100644 index 00000000..dbc30035 --- /dev/null +++ b/tests/e2e/data/harry_potter.txt @@ -0,0 +1,15 @@ +At Malfoy Manor, Snape tells Voldemort the date that Harry’s friends are planning to +move him from the house on Privet Drive to a new safe location, so that Voldemort +can capture Harry en route. + +As Harry packs to leave Privet Drive, he reads two obituaries for Dumbledore, both +of which make him think that he didn’t know Dumbledore as well as he should have. +Downstairs, he bids good-bye to the Dursleys for the final time, as the threat of +Voldemort forces them to go into hiding themselves. + +The Order of the Phoenix, led by Alastor “Mad-Eye” Moody, arrives to take Harry to +his new home at the Weasleys’ house, the Burrow. Six of Harry’s friends take +Polyjuice Potion to disguise themselves as Harry and act as decoys, and they all fly +off in different directions. The Death Eaters, alerted to their departure by Snape, +attack Harry and his friends. Voldemort chases Harry down, but Harry’s wand fends +Voldemort off, seemingly without Harry’s help. diff --git a/tests/e2e/pinecone_e2e/populate_dbs.py b/tests/e2e/pinecone_e2e/populate_dbs.py index d1caf515..22b8c0db 100644 --- a/tests/e2e/pinecone_e2e/populate_dbs.py +++ b/tests/e2e/pinecone_e2e/populate_dbs.py @@ -14,7 +14,6 @@ # limitations under the License. from __future__ import annotations -import os.path from typing import Any import neo4j @@ -23,8 +22,6 @@ from ..utils import build_data_objects, populate_neo4j -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) - def populate_dbs( neo4j_driver: neo4j.Driver, pc_client: Pinecone, index_name: str = "jeopardy" diff --git a/tests/e2e/test_kb_builder_pipeline_e2e.py b/tests/e2e/test_kb_builder_pipeline_e2e.py new file mode 100644 index 00000000..28d8ebcd --- /dev/null +++ b/tests/e2e/test_kb_builder_pipeline_e2e.py @@ -0,0 +1,465 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import os +from collections import Counter +from unittest.mock import MagicMock + +import neo4j +import pytest +from langchain_text_splitters import CharacterTextSplitter +from neo4j_genai.components.embedder import TextChunkEmbedder +from neo4j_genai.components.entity_relation_extractor import ( + LLMEntityRelationExtractor, + OnError, +) +from neo4j_genai.components.kg_writer import Neo4jWriter +from neo4j_genai.components.schema import ( + SchemaBuilder, + SchemaEntity, + SchemaProperty, + SchemaRelation, +) +from neo4j_genai.components.text_splitters.langchain import LangChainTextSplitterAdapter +from neo4j_genai.embedder import Embedder +from neo4j_genai.exceptions import LLMGenerationError +from neo4j_genai.llm import LLMInterface, LLMResponse +from neo4j_genai.pipeline import Pipeline + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) + + +@pytest.fixture +def llm() -> LLMInterface: + llm = MagicMock(spec=LLMInterface) + return llm + + +@pytest.fixture +def embedder() -> Embedder: + embedder = MagicMock(spec=Embedder) + return embedder + + +@pytest.fixture +def schema_builder() -> SchemaBuilder: + return SchemaBuilder() + + +@pytest.fixture +def text_splitter() -> LangChainTextSplitterAdapter: + return LangChainTextSplitterAdapter( + CharacterTextSplitter(chunk_size=50, chunk_overlap=10, separator="\n\n") + ) + + +@pytest.fixture +def chunk_embedder(embedder: Embedder) -> TextChunkEmbedder: + return TextChunkEmbedder(embedder=embedder) + + +@pytest.fixture +def entity_relation_extractor(llm: LLMInterface) -> LLMEntityRelationExtractor: + return LLMEntityRelationExtractor( + llm=llm, + on_error=OnError.RAISE, + ) + + +@pytest.fixture +def kg_writer(driver: neo4j.Driver) -> Neo4jWriter: + return Neo4jWriter(driver) + + +@pytest.fixture +def kg_builder_pipeline( + text_splitter: LangChainTextSplitterAdapter, + chunk_embedder: TextChunkEmbedder, + schema_builder: SchemaBuilder, + entity_relation_extractor: LLMEntityRelationExtractor, + kg_writer: Neo4jWriter, +) -> Pipeline: + pipe = Pipeline() + # define the components + pipe.add_component( + "splitter", + text_splitter, + ) + pipe.add_component("embedder", chunk_embedder) + pipe.add_component("schema", schema_builder) + pipe.add_component( + "extractor", + entity_relation_extractor, + ) + pipe.add_component("writer", kg_writer) + # define the execution order of component + # and how the output of previous components must be used + pipe.connect("splitter", "embedder", input_config={"text_chunks": "splitter"}) + # pipe.connect("splitter", "extractor", input_config={"chunks": "splitter"}) + pipe.connect("schema", "extractor", input_config={"schema": "schema"}) + pipe.connect("embedder", "extractor", input_config={"chunks": "embedder"}) + pipe.connect( + "extractor", + "writer", + input_config={"graph": "extractor"}, + ) + return pipe + + +@pytest.fixture +def harry_potter_text() -> str: + with open(os.path.join(BASE_DIR, "data/harry_potter.txt"), "r") as f: + text = f.read() + return text + + +@pytest.mark.asyncio +@pytest.mark.usefixtures("setup_neo4j_for_kg_construction") +async def test_pipeline_builder_happy_path( + harry_potter_text: str, + llm: MagicMock, + embedder: MagicMock, + driver: neo4j.Driver, + kg_builder_pipeline: Pipeline, +) -> None: + """When everything works as expected, extracted entities, relations and text + chunks must be in the DB + """ + driver.execute_query("MATCH (n) DETACH DELETE n") + embedder.embed_query.return_value = [1, 2, 3] + llm.invoke.side_effect = [ + LLMResponse( + content="""{ + "nodes": [ + { + "id": "0", + "label": "Person", + "properties": { + "name": "Harry Potter" + } + }, + { + "id": "1", + "label": "Person", + "properties": { + "name": "Alastor Mad-Eye Moody" + } + }, + { + "id": "2", + "label": "Organization", + "properties": { + "name": "The Order of the Phoenix" + } + } + ], + "relationships": [ + { + "type": "KNOWS", + "start_node_id": "0", + "end_node_id": "1" + }, + { + "type": "LED_BY", + "start_node_id": "2", + "end_node_id": "1" + } + ] + }""" + ), + LLMResponse(content='{"nodes": [], "relationships": []}'), + LLMResponse(content='{"nodes": [], "relationships": []}'), + ] + + # user input: + # the initial text + # and the list of entities and relations we are looking for + pipe_inputs = { + "splitter": {"text": harry_potter_text}, + "schema": { + "entities": [ + SchemaEntity( + label="Person", + properties=[ + SchemaProperty(name="name", type="STRING"), + SchemaProperty(name="place_of_birth", type="STRING"), + SchemaProperty(name="date_of_birth", type="DATE"), + ], + ), + SchemaEntity( + label="Organization", + properties=[ + SchemaProperty(name="name", type="STRING"), + ], + ), + SchemaEntity( + label="Potion", + properties=[ + SchemaProperty(name="name", type="STRING"), + ], + ), + SchemaEntity( + label="Location", + properties=[ + SchemaProperty(name="address", type="STRING"), + ], + ), + ], + "relations": [ + SchemaRelation( + label="KNOWS", + ), + SchemaRelation( + label="PART_OF", + ), + SchemaRelation( + label="LED_BY", + ), + SchemaRelation( + label="DRINKS", + ), + ], + "potential_schema": [ + ("Person", "KNOWS", "Person"), + ("Person", "DRINKS", "Potion"), + ("Person", "PART_OF", "Organization"), + ("Organization", "LED_BY", "Person"), + ], + }, + } + res = await kg_builder_pipeline.run(pipe_inputs) + # llm must have been called for each chunk + assert llm.invoke.call_count == 3 + # result must be success + assert res == {"writer": {"status": "SUCCESS"}} + # check component's results + chunks = kg_builder_pipeline.get_results_for_component("splitter") + assert len(chunks["chunks"]) == 3 + graph = kg_builder_pipeline.get_results_for_component("extractor") + # 3 entities + 3 chunks + nodes = graph["nodes"] + assert len(nodes) == 6 + label_counts = dict(Counter([n["label"] for n in nodes])) + assert label_counts == { + "Chunk": 3, + "Person": 2, + "Organization": 1, + } + # 2 relationships between entities + # + 3 rels between entities and their chunk + # + 2 "NEXT_CHUNK" rels + relationships = graph["relationships"] + assert len(relationships) == 7 + type_counts = dict(Counter([r["type"] for r in relationships])) + assert type_counts == {"FROM_CHUNK": 3, "KNOWS": 1, "LED_BY": 1, "NEXT_CHUNK": 2} + # then check content of neo4j db + created_nodes = driver.execute_query("MATCH (n) RETURN n") + assert len(created_nodes.records) == 6 + created_rels = driver.execute_query("MATCH ()-[r]->() RETURN r") + assert len(created_rels.records) == 7 + + created_chunks = driver.execute_query("MATCH (n:Chunk) RETURN n").records + assert len(created_chunks) == 3 + for c in created_chunks: + node = c.get("n") + assert node.get("embedding") == [1, 2, 3] + assert node.get("text") is not None + + +@pytest.mark.asyncio +@pytest.mark.usefixtures("setup_neo4j_for_kg_construction") +async def test_pipeline_builder_failing_chunk_raise( + harry_potter_text: str, + embedder: MagicMock, + llm: MagicMock, + driver: neo4j.Driver, + kg_builder_pipeline: Pipeline, +) -> None: + """If on_error is set to "RAISE", any issue with the entity/relation + extractor should stop the process with an exception. Nothing should be + added to the DB + """ + driver.execute_query("MATCH (n) DETACH DELETE n") + embedder.embed_query.return_value = [1, 2, 3] + llm.invoke.side_effect = [ + LLMResponse( + content="""{ + "nodes": [ + { + "id": "0", + "label": "Person", + "properties": { + "name": "Harry Potter" + } + }, + { + "id": "1", + "label": "Person", + "properties": { + "name": "Alastor Mad-Eye Moody" + } + }, + { + "id": "2", + "label": "Organization", + "properties": { + "name": "The Order of the Phoenix" + } + } + ], + "relationships": [ + { + "type": "KNOWS", + "start_node_id": "0", + "end_node_id": "1" + }, + { + "type": "LED_BY", + "start_node_id": "2", + "end_node_id": "1" + } + ] + }""" + ), + LLMResponse(content="invalid json"), + LLMResponse(content='{"nodes": [], "relationships": []}'), + ] + + # user input: + # the initial text + # and the list of entities and relations we are looking for + pipe_inputs = { + "splitter": {"text": harry_potter_text}, + # note: schema not used in this test because + # we are mocking the LLM + "schema": { + "entities": [], + "relations": [], + "potential_schema": [], + }, + } + with pytest.raises(LLMGenerationError): + await kg_builder_pipeline.run(pipe_inputs) + created_nodes = driver.execute_query("MATCH (n) RETURN n") + assert len(created_nodes.records) == 0 + created_rels = driver.execute_query("MATCH ()-[r]->() RETURN r") + assert len(created_rels.records) == 0 + + +@pytest.mark.asyncio +@pytest.mark.usefixtures("setup_neo4j_for_kg_construction") +async def test_pipeline_builder_failing_chunk_do_not_raise( + harry_potter_text: str, + llm: MagicMock, + embedder: MagicMock, + driver: neo4j.Driver, + kg_builder_pipeline: Pipeline, +) -> None: + """If on_error is set to "IGNORE", process must continue + and nodes/relationships created for the chunks that succeeded + """ + driver.execute_query("MATCH (n) DETACH DELETE n") + embedder.embed_query.return_value = [1, 2, 3] + llm.invoke.side_effect = [ + LLMResponse(content="invalid json"), + LLMResponse( + content="""{ + "nodes": [ + { + "id": "0", + "label": "Person", + "properties": { + "name": "Harry Potter" + } + }, + { + "id": "1", + "label": "Person", + "properties": { + "name": "Alastor Mad-Eye Moody" + } + }, + { + "id": "2", + "label": "Organization", + "properties": { + "name": "The Order of the Phoenix" + } + } + ], + "relationships": [ + { + "type": "KNOWS", + "start_node_id": "0", + "end_node_id": "1" + }, + { + "type": "LED_BY", + "start_node_id": "2", + "end_node_id": "1" + } + ] + }""" + ), + LLMResponse(content='{"nodes": [], "relationships": []}'), + ] + + # user input: + # the initial text + # and the list of entities and relations we are looking for + pipe_inputs = { + "splitter": {"text": harry_potter_text}, + # note: schema not used in this test because + # we are mocking the LLM + "schema": { + "entities": [], + "relations": [], + "potential_schema": [], + }, + } + kg_builder_pipeline.get_node_by_name( + "extractor" + ).component.on_error = OnError.IGNORE # type: ignore[attr-defined, unused-ignore] + res = await kg_builder_pipeline.run(pipe_inputs) + # llm must have been called for each chunk + assert llm.invoke.call_count == 3 + # result must be success + assert res == {"writer": {"status": "SUCCESS"}} + # check component's results + chunks = kg_builder_pipeline.get_results_for_component("splitter") + assert len(chunks["chunks"]) == 3 + graph = kg_builder_pipeline.get_results_for_component("extractor") + # 3 entities + 3 chunks + nodes = graph["nodes"] + assert len(nodes) == 6 + label_counts = dict(Counter([n["label"] for n in nodes])) + assert label_counts == { + "Chunk": 3, + "Person": 2, + "Organization": 1, + } + # 2 relationships between entities + # + 3 rels between entities and their chunk + # + 2 "NEXT_CHUNK" rels + relationships = graph["relationships"] + assert len(relationships) == 7 + type_counts = dict(Counter([r["type"] for r in relationships])) + assert type_counts == {"FROM_CHUNK": 3, "KNOWS": 1, "LED_BY": 1, "NEXT_CHUNK": 2} + # then check content of neo4j db + created_nodes = driver.execute_query("MATCH (n) RETURN n") + assert len(created_nodes.records) == 6 + created_rels = driver.execute_query("MATCH ()-[r]->() RETURN r") + assert len(created_rels.records) == 7 diff --git a/tests/e2e/test_kg_construction_e2e.py b/tests/e2e/test_kg_construction_e2e.py new file mode 100644 index 00000000..548ba7cd --- /dev/null +++ b/tests/e2e/test_kg_construction_e2e.py @@ -0,0 +1,84 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import neo4j +import pytest +from neo4j_genai.components.kg_writer import Neo4jWriter +from neo4j_genai.components.types import ( + Neo4jGraph, + Neo4jNode, + Neo4jRelationship, +) + + +@pytest.mark.asyncio +@pytest.mark.usefixtures("setup_neo4j_for_kg_construction") +async def test_kg_writer(driver: neo4j.Driver) -> None: + start_node = Neo4jNode( + id="1", + label="Document", + properties={"chunk": 1}, + embedding_properties={"vectorProperty": [1.0, 2.0, 3.0]}, + ) + end_node = Neo4jNode( + id="2", + label="Document", + properties={"chunk": 2}, + embedding_properties={"vectorProperty": [1.0, 2.0, 3.0]}, + ) + relationship = Neo4jRelationship( + start_node_id="1", end_node_id="2", type="NEXT_CHUNK" + ) + graph = Neo4jGraph(nodes=[start_node, end_node], relationships=[relationship]) + + neo4j_writer = Neo4jWriter(driver=driver) + await neo4j_writer.run(graph=graph) + + query = """ + MATCH (a:Document {id: '1'})-[r:NEXT_CHUNK]->(b:Document {id: '2'}) + RETURN a, r, b + """ + record = driver.execute_query(query).records[0] + assert "a" and "b" and "r" in record.keys() + + node_a = record["a"] + assert start_node.label == list(node_a.labels)[0] + assert start_node.id == str(node_a.get("id")) + if start_node.properties: + for key, val in start_node.properties.items(): + assert key in node_a.keys() + assert val == node_a.get(key) + if start_node.embedding_properties: + for key, val in start_node.embedding_properties.items(): + assert key in node_a.keys() + assert node_a.get(key) == [1.0, 2.0, 3.0] + + node_b = record["b"] + assert end_node.label == list(node_b.labels)[0] + assert end_node.id == str(node_b.get("id")) + if end_node.properties: + for key, val in end_node.properties.items(): + assert key in node_b.keys() + assert val == node_b.get(key) + if end_node.embedding_properties: + for key, val in end_node.embedding_properties.items(): + assert key in node_b.keys() + assert node_b.get(key) == [1.0, 2.0, 3.0] + + rel = record["r"] + assert rel.type == relationship.type + assert relationship.start_node_id and relationship.end_node_id in [ + str(node.get("id")) for node in rel.nodes + ] diff --git a/tests/e2e/test_schema_e2e.py b/tests/e2e/test_schema_e2e.py index 3db74d53..7024388a 100644 --- a/tests/e2e/test_schema_e2e.py +++ b/tests/e2e/test_schema_e2e.py @@ -12,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations import pytest from neo4j import Driver diff --git a/tests/e2e/weaviate_e2e/populate_dbs.py b/tests/e2e/weaviate_e2e/populate_dbs.py index a1bd2a18..9f8e2c6f 100644 --- a/tests/e2e/weaviate_e2e/populate_dbs.py +++ b/tests/e2e/weaviate_e2e/populate_dbs.py @@ -14,8 +14,6 @@ # limitations under the License. from __future__ import annotations -import os.path - import weaviate.classes as wvc from neo4j import Driver, GraphDatabase from weaviate.client import WeaviateClient @@ -24,8 +22,6 @@ from ..utils import build_data_objects, populate_neo4j -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) - def populate_dbs( neo4j_driver: Driver, w_client: WeaviateClient, collection_name: str = "Jeopardy" diff --git a/tests/unit/components/__init__.py b/tests/unit/components/__init__.py new file mode 100644 index 00000000..c0199c14 --- /dev/null +++ b/tests/unit/components/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/unit/components/test_embedder.py b/tests/unit/components/test_embedder.py new file mode 100644 index 00000000..ff87f8e1 --- /dev/null +++ b/tests/unit/components/test_embedder.py @@ -0,0 +1,36 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from unittest.mock import MagicMock + +import pytest +from neo4j_genai.components.embedder import TextChunkEmbedder +from neo4j_genai.components.types import TextChunk, TextChunks + + +@pytest.mark.asyncio +async def test_text_chunk_embedder_run(embedder: MagicMock) -> None: + embedder.embed_query.return_value = [1.0, 2.0, 3.0] + text_chunk_embedder = TextChunkEmbedder(embedder=embedder) + text_chunks = TextChunks(chunks=[TextChunk(text="may thy knife chip and shatter")]) + embedded_chunks = await text_chunk_embedder.run(text_chunks) + embedder.embed_query.assert_called_once_with("may thy knife chip and shatter") + assert isinstance(embedded_chunks, TextChunks) + for chunk in embedded_chunks.chunks: + assert isinstance(chunk, TextChunk) + assert chunk.metadata is not None + assert "embedding" in chunk.metadata.keys() + assert isinstance(chunk.metadata["embedding"], list) + for i in chunk.metadata["embedding"]: + assert isinstance(i, float) diff --git a/tests/unit/components/test_entity_relation_extractor.py b/tests/unit/components/test_entity_relation_extractor.py new file mode 100644 index 00000000..8e1e6200 --- /dev/null +++ b/tests/unit/components/test_entity_relation_extractor.py @@ -0,0 +1,211 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from unittest.mock import MagicMock + +import pytest +from neo4j_genai.components.entity_relation_extractor import ( + EntityRelationExtractor, + LLMEntityRelationExtractor, + OnError, +) +from neo4j_genai.components.types import Neo4jGraph, Neo4jNode, TextChunk, TextChunks +from neo4j_genai.exceptions import LLMGenerationError +from neo4j_genai.llm import LLMInterface, LLMResponse + + +def test_create_chunk_node_no_metadata() -> None: + # instantiating an abstract class to test common methods + extractor = EntityRelationExtractor() # type: ignore + node = extractor.create_chunk_node( + chunk=TextChunk(text="text chunk"), chunk_id="10" + ) + assert isinstance(node, Neo4jNode) + assert node.id == "10" + assert node.properties == {"text": "text chunk"} + assert node.embedding_properties == {} + + +def test_create_chunk_node_metadata_no_embedding() -> None: + # instantiating an abstract class to test common methods + extractor = EntityRelationExtractor() # type: ignore + node = extractor.create_chunk_node( + chunk=TextChunk(text="text chunk", metadata={"status": "ok"}), chunk_id="10" + ) + assert isinstance(node, Neo4jNode) + assert node.id == "10" + assert node.properties == {"text": "text chunk", "status": "ok"} + assert node.embedding_properties == {} + + +def test_create_chunk_node_metadata_embedding() -> None: + # instantiating an abstract class to test common methods + extractor = EntityRelationExtractor() # type: ignore + node = extractor.create_chunk_node( + chunk=TextChunk( + text="text chunk", metadata={"status": "ok", "embedding": [1, 2, 3]} + ), + chunk_id="10", + ) + assert isinstance(node, Neo4jNode) + assert node.id == "10" + assert node.properties == {"text": "text chunk", "status": "ok"} + assert node.embedding_properties == {"embedding": [1, 2, 3]} + + +@pytest.mark.asyncio +async def test_extractor_happy_path_no_entities() -> None: + llm = MagicMock(spec=LLMInterface) + llm.invoke.return_value = LLMResponse(content='{"nodes": [], "relationships": []}') + + extractor = LLMEntityRelationExtractor( + llm=llm, + ) + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + result = await extractor.run(chunks=chunks) + assert isinstance(result, Neo4jGraph) + # only one Chunk node + assert len(result.nodes) == 1 + assert result.nodes[0].label == "Chunk" + assert result.relationships == [] + + +@pytest.mark.asyncio +async def test_extractor_happy_path_no_entities_no_lexical_graph() -> None: + llm = MagicMock(spec=LLMInterface) + llm.invoke.return_value = LLMResponse(content='{"nodes": [], "relationships": []}') + + extractor = LLMEntityRelationExtractor( + llm=llm, + create_lexical_graph=False, + ) + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + result = await extractor.run(chunks=chunks) + assert result.nodes == [] + assert result.relationships == [] + + +@pytest.mark.asyncio +async def test_extractor_happy_path_non_empty_result() -> None: + llm = MagicMock(spec=LLMInterface) + llm.invoke.return_value = LLMResponse( + content='{"nodes": [{"id": "0", "label": "Person", "properties": {}}], "relationships": []}' + ) + + extractor = LLMEntityRelationExtractor( + llm=llm, + ) + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + result = await extractor.run(chunks=chunks) + assert isinstance(result, Neo4jGraph) + assert len(result.nodes) == 2 + entity = result.nodes[0] + assert entity.id.endswith("0:0") + assert entity.label == "Person" + assert entity.properties == {"chunk_index": 0} + chunk_entity = result.nodes[1] + assert chunk_entity.label == "Chunk" + assert len(result.relationships) == 1 + assert result.relationships[0].type == "FROM_CHUNK" + + +@pytest.mark.asyncio +async def test_extractor_missing_entity_id() -> None: + llm = MagicMock(spec=LLMInterface) + llm.invoke.return_value = LLMResponse( + content='{"nodes": [{"label": "Person", "properties": {}}], "relationships": []}' + ) + extractor = LLMEntityRelationExtractor( + llm=llm, + ) + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + with pytest.raises(LLMGenerationError): + await extractor.run(chunks=chunks) + + +@pytest.mark.asyncio +async def test_extractor_llm_invoke_failed() -> None: + llm = MagicMock(spec=LLMInterface) + llm.invoke.side_effect = LLMGenerationError() + + extractor = LLMEntityRelationExtractor( + llm=llm, + ) + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + with pytest.raises(LLMGenerationError): + await extractor.run(chunks=chunks) + + +@pytest.mark.asyncio +async def test_extractor_llm_badly_formatted_json() -> None: + llm = MagicMock(spec=LLMInterface) + llm.invoke.return_value = LLMResponse( + content='{"nodes": [{"id": "0", "label": "Person", "properties": {}}], "relationships": [}' + ) + + extractor = LLMEntityRelationExtractor( + llm=llm, + ) + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + with pytest.raises(LLMGenerationError): + await extractor.run(chunks=chunks) + + +@pytest.mark.asyncio +async def test_extractor_llm_invalid_json() -> None: + """Test what happens when the returned JSON is valid JSON but + does not match the expected Pydantic model""" + llm = MagicMock(spec=LLMInterface) + llm.invoke.return_value = LLMResponse( + # missing "label" for entity + content='{"nodes": [{"id": 0, "entity_type": "Person", "properties": {}}], "relationships": []}' + ) + + extractor = LLMEntityRelationExtractor( + llm=llm, + ) + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + with pytest.raises(LLMGenerationError): + await extractor.run(chunks=chunks) + + +@pytest.mark.asyncio +async def test_extractor_llm_badly_formatted_json_do_not_raise() -> None: + llm = MagicMock(spec=LLMInterface) + llm.invoke.return_value = LLMResponse( + content='{"nodes": [{"id": "0", "label": "Person", "properties": {}}], "relationships": [}' + ) + + extractor = LLMEntityRelationExtractor( + llm=llm, + on_error=OnError.IGNORE, + create_lexical_graph=False, + ) + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + res = await extractor.run(chunks=chunks) + assert res.nodes == [] + assert res.relationships == [] + + +@pytest.mark.asyncio +async def test_extractor_custom_prompt() -> None: + llm = MagicMock(spec=LLMInterface) + llm.invoke.return_value = LLMResponse(content='{"nodes": [], "relationships": []}') + + extractor = LLMEntityRelationExtractor(llm=llm, prompt_template="this is my prompt") + chunks = TextChunks(chunks=[TextChunk(text="some text")]) + await extractor.run(chunks=chunks) + llm.invoke.assert_called_once_with("this is my prompt") diff --git a/tests/unit/components/test_kg_writer.py b/tests/unit/components/test_kg_writer.py new file mode 100644 index 00000000..f2b90ba0 --- /dev/null +++ b/tests/unit/components/test_kg_writer.py @@ -0,0 +1,141 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from unittest.mock import MagicMock + +import pytest +from neo4j_genai.components.kg_writer import Neo4jWriter +from neo4j_genai.components.types import ( + Neo4jGraph, + Neo4jNode, + Neo4jRelationship, +) +from neo4j_genai.neo4j_queries import UPSERT_NODE_QUERY, UPSERT_RELATIONSHIP_QUERY + + +def test_upsert_node(driver: MagicMock) -> None: + neo4j_writer = Neo4jWriter(driver=driver) + node = Neo4jNode(id="1", label="Label", properties={"key": "value"}) + neo4j_writer._upsert_node(node=node) + parameters = {"id": "1", "key": "value"} + driver.execute_query.assert_called_once_with( + UPSERT_NODE_QUERY.format(label="Label", properties="{id: $id, key: $key}"), + parameters_=parameters, + ) + + +def test_upsert_node_with_embedding( + driver: MagicMock, +) -> None: + neo4j_writer = Neo4jWriter(driver=driver) + node = Neo4jNode( + id="1", + label="Label", + properties={"key": "value"}, + embedding_properties={"embeddingProp": [1.0, 2.0, 3.0]}, + ) + driver.execute_query.return_value.records = [{"elementID(n)": 1}] + neo4j_writer._upsert_node(node=node) + parameters = {"id": "1", "key": "value"} + driver.execute_query.assert_any_call( + UPSERT_NODE_QUERY.format(label="Label", properties="{id: $id, key: $key}"), + parameters_=parameters, + ) + query = ( + "MATCH (n) " + "WHERE elementId(n) = $id " + "WITH n " + "CALL db.create.setNodeVectorProperty(n, $embedding_property, $vector) " + "RETURN n" + ) + parameters_ = { + "id": 1, + "embedding_property": "embeddingProp", + "vector": [1.0, 2.0, 3.0], + } + driver.execute_query.assert_any_call(query, parameters_, database_=None) + + +def test_upsert_relationship(driver: MagicMock) -> None: + neo4j_writer = Neo4jWriter(driver=driver) + rel = Neo4jRelationship( + start_node_id="1", + end_node_id="2", + type="RELATIONSHIP", + properties={"key": "value"}, + ) + neo4j_writer._upsert_relationship(rel=rel) + parameters = {"start_node_id": "1", "end_node_id": "2", "key": "value"} + driver.execute_query.assert_called_once_with( + UPSERT_RELATIONSHIP_QUERY.format( + type="RELATIONSHIP", + properties="{key: $key}", + ), + parameters_=parameters, + ) + + +def test_upsert_relationship_with_embedding(driver: MagicMock) -> None: + neo4j_writer = Neo4jWriter(driver=driver) + rel = Neo4jRelationship( + start_node_id="1", + end_node_id="2", + type="RELATIONSHIP", + properties={"key": "value"}, + embedding_properties={"embeddingProp": [1.0, 2.0, 3.0]}, + ) + driver.execute_query.return_value.records = [{"elementID(r)": "rel_elem_id"}] + neo4j_writer._upsert_relationship(rel=rel) + parameters = {"start_node_id": "1", "end_node_id": "2", "key": "value"} + driver.execute_query.assert_any_call( + UPSERT_RELATIONSHIP_QUERY.format( + type="RELATIONSHIP", + properties="{key: $key}", + ), + parameters_=parameters, + ) + query = ( + "MATCH ()-[r]->() " + "WHERE elementId(r) = $id " + "WITH r " + "CALL db.create.setRelationshipVectorProperty(r, $embedding_property, $vector) " + "RETURN r" + ) + parameters_ = { + "id": "rel_elem_id", + "embedding_property": "embeddingProp", + "vector": [1.0, 2.0, 3.0], + } + driver.execute_query.assert_any_call(query, parameters_, database_=None) + + +@pytest.mark.asyncio +async def test_run(driver: MagicMock) -> None: + neo4j_writer = Neo4jWriter(driver=driver) + node = Neo4jNode(id="1", label="Label") + rel = Neo4jRelationship(start_node_id="1", end_node_id="2", type="RELATIONSHIP") + graph = Neo4jGraph(nodes=[node], relationships=[rel]) + await neo4j_writer.run(graph=graph) + parameters = {"id": "1"} + driver.execute_query.assert_any_call( + UPSERT_NODE_QUERY.format(label="Label", properties="{id: $id}"), + parameters_=parameters, + ) + parameters_ = {"start_node_id": "1", "end_node_id": "2"} + driver.execute_query.assert_any_call( + UPSERT_RELATIONSHIP_QUERY.format(type="RELATIONSHIP", properties="{}"), + parameters_=parameters_, + ) diff --git a/tests/unit/components/test_schema.py b/tests/unit/components/test_schema.py new file mode 100644 index 00000000..c4e9074f --- /dev/null +++ b/tests/unit/components/test_schema.py @@ -0,0 +1,338 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import pytest +from neo4j_genai.components.schema import ( + SchemaBuilder, + SchemaEntity, + SchemaProperty, + SchemaRelation, +) +from neo4j_genai.exceptions import SchemaValidationError +from pydantic import ValidationError + + +@pytest.fixture +def valid_entities() -> list[SchemaEntity]: + return [ + SchemaEntity( + label="PERSON", + description="An individual human being.", + properties=[ + SchemaProperty(name="birth date", type="ZONED_DATETIME"), + SchemaProperty(name="name", type="STRING"), + ], + ), + SchemaEntity( + label="ORGANIZATION", + description="A structured group of people with a common purpose.", + ), + SchemaEntity(label="AGE", description="Age of a person in years."), + ] + + +@pytest.fixture +def valid_relations() -> list[SchemaRelation]: + return [ + SchemaRelation( + label="EMPLOYED_BY", + description="Indicates employment relationship.", + properties=[ + SchemaProperty(name="start_time", type="LOCAL_DATETIME"), + SchemaProperty(name="end_time", type="LOCAL_DATETIME"), + ], + ), + SchemaRelation( + label="ORGANIZED_BY", + description="Indicates organization responsible for an event.", + ), + SchemaRelation( + label="ATTENDED_BY", description="Indicates attendance at an event." + ), + ] + + +@pytest.fixture +def potential_schema() -> list[tuple[str, str, str]]: + return [ + ("PERSON", "EMPLOYED_BY", "ORGANIZATION"), + ("ORGANIZATION", "ATTENDED_BY", "PERSON"), + ] + + +@pytest.fixture +def potential_schema_with_invalid_entity() -> list[tuple[str, str, str]]: + return [ + ("PERSON", "EMPLOYED_BY", "ORGANIZATION"), + ("NON_EXISTENT_ENTITY", "ATTENDED_BY", "PERSON"), + ] + + +@pytest.fixture +def potential_schema_with_invalid_relation() -> list[tuple[str, str, str]]: + return [ + ("PERSON", "NON_EXISTENT_RELATION", "ORGANIZATION"), + ] + + +@pytest.fixture +def schema_builder() -> SchemaBuilder: + return SchemaBuilder() + + +def test_create_schema_model_valid_data( + schema_builder: SchemaBuilder, + valid_entities: list[SchemaEntity], + valid_relations: list[SchemaRelation], + potential_schema: list[tuple[str, str, str]], +) -> None: + schema_instance = schema_builder.create_schema_model( + valid_entities, valid_relations, potential_schema + ) + + assert ( + schema_instance.entities["PERSON"]["description"] + == "An individual human being." + ) + assert schema_instance.entities["PERSON"]["properties"] == [ + {"description": "", "name": "birth date", "type": "ZONED_DATETIME"}, + {"description": "", "name": "name", "type": "STRING"}, + ] + assert ( + schema_instance.entities["ORGANIZATION"]["description"] + == "A structured group of people with a common purpose." + ) + assert schema_instance.entities["AGE"]["description"] == "Age of a person in years." + + assert ( + schema_instance.relations["EMPLOYED_BY"]["description"] + == "Indicates employment relationship." + ) + assert ( + schema_instance.relations["ORGANIZED_BY"]["description"] + == "Indicates organization responsible for an event." + ) + assert ( + schema_instance.relations["ATTENDED_BY"]["description"] + == "Indicates attendance at an event." + ) + assert schema_instance.relations["EMPLOYED_BY"]["properties"] == [ + {"description": "", "name": "start_time", "type": "LOCAL_DATETIME"}, + {"description": "", "name": "end_time", "type": "LOCAL_DATETIME"}, + ] + + assert schema_instance.potential_schema == potential_schema + + +def test_create_schema_model_missing_description( + schema_builder: SchemaBuilder, potential_schema: list[tuple[str, str, str]] +) -> None: + entities = [ + SchemaEntity(label="PERSON", description="An individual human being."), + SchemaEntity(label="ORGANIZATION", description=""), + SchemaEntity(label="AGE", description=""), + ] + relations = [ + SchemaRelation( + label="EMPLOYED_BY", description="Indicates employment relationship." + ), + SchemaRelation(label="ORGANIZED_BY", description=""), + SchemaRelation(label="ATTENDED_BY", description=""), + ] + + schema_instance = schema_builder.create_schema_model( + entities, relations, potential_schema + ) + + assert schema_instance.entities["ORGANIZATION"]["description"] == "" + assert schema_instance.entities["AGE"]["description"] == "" + assert schema_instance.relations["ORGANIZED_BY"]["description"] == "" + assert schema_instance.relations["ATTENDED_BY"]["description"] == "" + + +def test_create_schema_model_empty_lists(schema_builder: SchemaBuilder) -> None: + schema_instance = schema_builder.create_schema_model([], [], []) + + assert schema_instance.entities == {} + assert schema_instance.relations == {} + assert schema_instance.potential_schema == [] + + +def test_create_schema_model_invalid_data_types( + schema_builder: SchemaBuilder, potential_schema: list[tuple[str, str, str]] +) -> None: + with pytest.raises(ValidationError): + entities = [ + SchemaEntity(label="PERSON", description="An individual human being."), + SchemaEntity( + label="ORGANIZATION", + description="A structured group of people with a common purpose.", + ), + ] + relations = [ + SchemaRelation( + label="EMPLOYED_BY", description="Indicates employment relationship." + ), + SchemaRelation( + label=456, # type: ignore + description="Indicates organization responsible for an event.", + ), + ] + + schema_builder.create_schema_model(entities, relations, potential_schema) + + +def test_create_schema_model_invalid_properties_types( + schema_builder: SchemaBuilder, + potential_schema: list[tuple[str, str, str]], +) -> None: + with pytest.raises(ValidationError): + entities = [ + SchemaEntity( + label="PERSON", + description="An individual human being.", + properties=[42, 1337], # type: ignore + ), + SchemaEntity( + label="ORGANIZATION", + description="A structured group of people with a common purpose.", + ), + ] + relations = [ + SchemaRelation( + label="EMPLOYED_BY", + description="Indicates employment relationship.", + properties=[42, 1337], # type: ignore + ), + SchemaRelation( + label="ORGANIZED_BY", + description="Indicates organization responsible for an event.", + ), + ] + + schema_builder.create_schema_model(entities, relations, potential_schema) + + +@pytest.mark.asyncio +async def test_run_method( + schema_builder: SchemaBuilder, + valid_entities: list[SchemaEntity], + valid_relations: list[SchemaRelation], + potential_schema: list[tuple[str, str, str]], +) -> None: + schema = await schema_builder.run(valid_entities, valid_relations, potential_schema) + + assert schema.entities["PERSON"]["description"] == "An individual human being." + assert ( + schema.entities["ORGANIZATION"]["description"] + == "A structured group of people with a common purpose." + ) + assert schema.entities["AGE"]["description"] == "Age of a person in years." + + assert ( + schema.relations["EMPLOYED_BY"]["description"] + == "Indicates employment relationship." + ) + assert ( + schema.relations["ORGANIZED_BY"]["description"] + == "Indicates organization responsible for an event." + ) + assert ( + schema.relations["ATTENDED_BY"]["description"] + == "Indicates attendance at an event." + ) + + assert schema.potential_schema == potential_schema + + +def test_create_schema_model_invalid_entity( + schema_builder: SchemaBuilder, + valid_entities: list[SchemaEntity], + valid_relations: list[SchemaRelation], + potential_schema_with_invalid_entity: list[tuple[str, str, str]], +) -> None: + with pytest.raises(SchemaValidationError) as exc_info: + schema_builder.create_schema_model( + valid_entities, valid_relations, potential_schema_with_invalid_entity + ) + assert "Entity 'NON_EXISTENT_ENTITY' is not defined" in str( + exc_info.value + ), "Should fail due to non-existent entity" + + +def test_create_schema_model_invalid_relation( + schema_builder: SchemaBuilder, + valid_entities: list[SchemaEntity], + valid_relations: list[SchemaRelation], + potential_schema_with_invalid_relation: list[tuple[str, str, str]], +) -> None: + with pytest.raises(SchemaValidationError) as exc_info: + schema_builder.create_schema_model( + valid_entities, valid_relations, potential_schema_with_invalid_relation + ) + assert "Relation 'NON_EXISTENT_RELATION' is not defined" in str( + exc_info.value + ), "Should fail due to non-existent relation" + + +def test_create_schema_model_missing_properties( + schema_builder: SchemaBuilder, potential_schema: list[tuple[str, str, str]] +) -> None: + entities = [ + SchemaEntity(label="PERSON", description="An individual human being."), + SchemaEntity( + label="ORGANIZATION", + description="A structured group of people with a common purpose.", + ), + SchemaEntity(label="AGE", description="Age of a person in years."), + ] + + relations = [ + SchemaRelation( + label="EMPLOYED_BY", description="Indicates employment relationship." + ), + SchemaRelation( + label="ORGANIZED_BY", + description="Indicates organization responsible for an event.", + ), + SchemaRelation( + label="ATTENDED_BY", description="Indicates attendance at an event." + ), + ] + + schema_instance = schema_builder.create_schema_model( + entities, relations, potential_schema + ) + + assert ( + schema_instance.entities["PERSON"]["properties"] == [] + ), "Expected empty properties for PERSON" + assert ( + schema_instance.entities["ORGANIZATION"]["properties"] == [] + ), "Expected empty properties for ORGANIZATION" + assert ( + schema_instance.entities["AGE"]["properties"] == [] + ), "Expected empty properties for AGE" + + assert ( + schema_instance.relations["EMPLOYED_BY"]["properties"] == [] + ), "Expected empty properties for EMPLOYED_BY" + assert ( + schema_instance.relations["ORGANIZED_BY"]["properties"] == [] + ), "Expected empty properties for ORGANIZED_BY" + assert ( + schema_instance.relations["ATTENDED_BY"]["properties"] == [] + ), "Expected empty properties for ATTENDED_BY" diff --git a/tests/unit/components/test_types.py b/tests/unit/components/test_types.py new file mode 100644 index 00000000..840fda8c --- /dev/null +++ b/tests/unit/components/test_types.py @@ -0,0 +1,28 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pytest +from neo4j_genai.components.types import Neo4jNode + + +def test_neo4j_node_invalid_property() -> None: + with pytest.raises(TypeError) as excinfo: + Neo4jNode(id="0", label="Label", properties={"id": "1"}) + assert "'id' as a property name is not allowed" in str(excinfo) + + +def test_neo4j_node_invalid_embedding_property() -> None: + with pytest.raises(TypeError) as excinfo: + Neo4jNode(id="0", label="Label", embedding_properties={"id": [1.0, 2.0, 3.0]}) + assert "'id' as a property name is not allowed" in str(excinfo) diff --git a/tests/unit/components/text_splitters/test_langchain.py b/tests/unit/components/text_splitters/test_langchain.py new file mode 100644 index 00000000..6b523547 --- /dev/null +++ b/tests/unit/components/text_splitters/test_langchain.py @@ -0,0 +1,38 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pytest +from langchain_text_splitters import RecursiveCharacterTextSplitter +from neo4j_genai.components.text_splitters.langchain import LangChainTextSplitterAdapter +from neo4j_genai.components.types import TextChunk, TextChunks + +text = """ +Lorem ipsum dolor sit amet, consectetur adipiscing elit. +In cursus erat quis ornare condimentum. Ut sollicitudin libero nec quam vestibulum, non tristique augue tempor. +Nulla fringilla, augue ac fermentum ultricies, mauris tellus tempor orci, at tincidunt purus arcu vitae nisl. +Nunc suscipit neque vitae ipsum viverra, eu interdum tortor iaculis. +Suspendisse sit amet quam non ipsum molestie euismod finibus eu nisi. Quisque sit amet aliquet leo, vel auctor dolor. +Sed auctor enim at tempus eleifend. Suspendisse potenti. Suspendisse congue tellus id justo bibendum, at commodo sapien porta. +Nam sagittis nisl vitae nibh pellentesque, et convallis turpis ultrices. +""" + + +@pytest.mark.asyncio +async def test_langchain_adapter() -> None: + text_splitter = LangChainTextSplitterAdapter(RecursiveCharacterTextSplitter()) + text_chunks = await text_splitter.run(text) + assert isinstance(text_chunks, TextChunks) + for text_chunk in text_chunks.chunks: + assert isinstance(text_chunk, TextChunk) + assert text_chunk.text in text diff --git a/tests/unit/components/text_splitters/test_llamaindex.py b/tests/unit/components/text_splitters/test_llamaindex.py new file mode 100644 index 00000000..4e9b784b --- /dev/null +++ b/tests/unit/components/text_splitters/test_llamaindex.py @@ -0,0 +1,40 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pytest +from llama_index.core.node_parser.text.sentence import SentenceSplitter +from neo4j_genai.components.text_splitters.llamaindex import ( + LlamaIndexTextSplitterAdapter, +) +from neo4j_genai.components.types import TextChunk, TextChunks + +text = """ +Lorem ipsum dolor sit amet, consectetur adipiscing elit. +In cursus erat quis ornare condimentum. Ut sollicitudin libero nec quam vestibulum, non tristique augue tempor. +Nulla fringilla, augue ac fermentum ultricies, mauris tellus tempor orci, at tincidunt purus arcu vitae nisl. +Nunc suscipit neque vitae ipsum viverra, eu interdum tortor iaculis. +Suspendisse sit amet quam non ipsum molestie euismod finibus eu nisi. Quisque sit amet aliquet leo, vel auctor dolor. +Sed auctor enim at tempus eleifend. Suspendisse potenti. Suspendisse congue tellus id justo bibendum, at commodo sapien porta. +Nam sagittis nisl vitae nibh pellentesque, et convallis turpis ultrices. +""" + + +@pytest.mark.asyncio +async def test_llamaindex_adapter() -> None: + text_splitter = LlamaIndexTextSplitterAdapter(SentenceSplitter()) + text_chunks = await text_splitter.run(text) + assert isinstance(text_chunks, TextChunks) + for text_chunk in text_chunks.chunks: + assert isinstance(text_chunk, TextChunk) + assert text_chunk.text in text diff --git a/tests/unit/pipeline/__init__.py b/tests/unit/pipeline/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/unit/pipeline/components.py b/tests/unit/pipeline/components.py new file mode 100644 index 00000000..25fac635 --- /dev/null +++ b/tests/unit/pipeline/components.py @@ -0,0 +1,43 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from neo4j_genai.pipeline import Component, DataModel + + +class StringResultModel(DataModel): + result: str + + +class IntResultModel(DataModel): + result: int + + +class ComponentNoParam(Component): + async def run(self) -> StringResultModel: + return StringResultModel(result="") + + +class ComponentPassThrough(Component): + async def run(self, value: str) -> StringResultModel: + return StringResultModel(result=f"value is: {value}") + + +class ComponentAdd(Component): + async def run(self, number1: int, number2: int) -> IntResultModel: + return IntResultModel(result=number1 + number2) + + +class ComponentMultiply(Component): + async def run(self, number1: int, number2: int = 2) -> IntResultModel: + return IntResultModel(result=number1 * number2) diff --git a/tests/unit/pipeline/test_component.py b/tests/unit/pipeline/test_component.py new file mode 100644 index 00000000..701554d7 --- /dev/null +++ b/tests/unit/pipeline/test_component.py @@ -0,0 +1,28 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .components import ComponentMultiply + + +def test_component_inputs() -> None: + inputs = ComponentMultiply.component_inputs + assert "number1" in inputs + assert inputs["number1"]["has_default"] is False + assert "number2" in inputs + assert inputs["number2"]["has_default"] is True + + +def test_component_outputs() -> None: + outputs = ComponentMultiply.component_outputs + assert "result" in outputs diff --git a/tests/unit/pipeline/test_orchestrator.py b/tests/unit/pipeline/test_orchestrator.py new file mode 100644 index 00000000..e311549a --- /dev/null +++ b/tests/unit/pipeline/test_orchestrator.py @@ -0,0 +1,72 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pytest +from neo4j_genai.pipeline import Component +from neo4j_genai.pipeline.pipeline import Orchestrator, Pipeline, RunStatus + + +@pytest.fixture(scope="function") +def pipeline_branch() -> Pipeline: + pipe = Pipeline() + pipe.add_component("a", Component()) # type: ignore + pipe.add_component("b", Component()) # type: ignore + pipe.add_component("c", Component()) # type: ignore + pipe.connect("a", "b") + pipe.connect("a", "c") + return pipe + + +@pytest.fixture(scope="function") +def pipeline_aggregation() -> Pipeline: + pipe = Pipeline() + pipe.add_component("a", Component()) # type: ignore + pipe.add_component("b", Component()) # type: ignore + pipe.add_component("c", Component()) # type: ignore + pipe.connect("a", "b") + pipe.connect("a", "c") + return pipe + + +@pytest.mark.asyncio +async def test_orchestrator_branch(pipeline_branch: Pipeline) -> None: + orchestrator = Orchestrator(pipeline=pipeline_branch) + node_a = pipeline_branch.get_node_by_name("a") + node_a.status = RunStatus.DONE + next_tasks = [n async for n in orchestrator.next(node_a)] + next_task_names = [n.name for n in next_tasks] + assert next_task_names == ["b", "c"] + + +@pytest.mark.asyncio +async def test_orchestrator_aggregation(pipeline_aggregation: Pipeline) -> None: + orchestrator = Orchestrator(pipeline=pipeline_aggregation) + node_a = pipeline_aggregation.get_node_by_name("a") + node_a.status = RunStatus.DONE + node_b = pipeline_aggregation.get_node_by_name("b") + node_b.status = RunStatus.DONE + next_tasks = [n async for n in orchestrator.next(node_a)] + next_task_names = [n.name for n in next_tasks] + assert next_task_names == ["c"] + + +@pytest.mark.asyncio +async def test_orchestrator_aggregation_waiting(pipeline_aggregation: Pipeline) -> None: + orchestrator = Orchestrator(pipeline=pipeline_aggregation) + node_a = pipeline_aggregation.get_node_by_name("a") + node_a.status = RunStatus.DONE + node_b = pipeline_aggregation.get_node_by_name("a") + node_b.status = RunStatus.UNKNOWN + next_tasks = [n async for n in orchestrator.next(node_a)] + assert next_tasks == [] diff --git a/tests/unit/pipeline/test_pipeline.py b/tests/unit/pipeline/test_pipeline.py new file mode 100644 index 00000000..1839874b --- /dev/null +++ b/tests/unit/pipeline/test_pipeline.py @@ -0,0 +1,188 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from unittest import mock +from unittest.mock import AsyncMock + +import pytest +from neo4j_genai.pipeline import Component, Pipeline +from neo4j_genai.pipeline.exceptions import PipelineDefinitionError + +from .components import ( + ComponentAdd, + ComponentMultiply, + ComponentNoParam, + ComponentPassThrough, + StringResultModel, +) + + +@pytest.mark.asyncio +async def test_simple_pipeline_two_components() -> None: + pipe = Pipeline() + component_a = ComponentNoParam() + component_b = ComponentNoParam() + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + pipe.connect("a", "b", {}) + with mock.patch( + "tests.unit.pipeline.test_pipeline.ComponentNoParam.run" + ) as mock_run: + mock_run.side_effect = [ + StringResultModel(result=""), + StringResultModel(result=""), + ] + res = await pipe.run({}) + mock_run.assert_awaited_with(**{}) + mock_run.assert_awaited_with(**{}) + assert "b" in res + assert res["b"] == {"result": ""} + + +@pytest.mark.asyncio +async def test_pipeline_parameter_propagation() -> None: + pipe = Pipeline() + component_a = ComponentPassThrough() + component_b = ComponentPassThrough() + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + # first component output product goes to second component input number1 + pipe.connect( + "a", + "b", + { + "value": "a.result", + }, + ) + with mock.patch( + "tests.unit.pipeline.test_pipeline.ComponentPassThrough.run" + ) as mock_run: + mock_run.side_effect = [ + StringResultModel(result="text"), + StringResultModel(result="text"), + ] + res = await pipe.run({"a": {"value": "text"}}) + mock_run.assert_awaited_with(**{"value": "text"}) + mock_run.assert_awaited_with(**{"value": "text"}) + assert res == {"b": {"result": "text"}} + + +@pytest.mark.asyncio +async def test_pipeline_branches() -> None: + pipe = Pipeline() + component_a = AsyncMock(spec=Component) + component_a.run = AsyncMock(return_value={}) + component_b = AsyncMock(spec=Component) + component_b.run = AsyncMock(return_value={}) + component_c = AsyncMock(spec=Component) + component_c.run = AsyncMock(return_value={}) + + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + pipe.add_component("c", component_c) + pipe.connect("a", "b") + pipe.connect("a", "c") + res = await pipe.run({}) + assert "b" in res + assert "c" in res + + +@pytest.mark.asyncio +async def test_pipeline_aggregation() -> None: + pipe = Pipeline() + component_a = AsyncMock(spec=Component) + component_a.run = AsyncMock(return_value={}) + component_b = AsyncMock(spec=Component) + component_b.run = AsyncMock(return_value={}) + component_c = AsyncMock(spec=Component) + component_c.run = AsyncMock(return_value={}) + + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + pipe.add_component("c", component_c) + pipe.connect("a", "c") + pipe.connect("b", "c") + res = await pipe.run({}) + assert "c" in res + + +@pytest.mark.asyncio +async def test_pipeline_missing_param_on_init() -> None: + pipe = Pipeline() + component_a = ComponentAdd() + component_b = ComponentAdd() + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + pipe.connect("a", "b", {"number1": "a.result"}) + with pytest.raises(PipelineDefinitionError) as excinfo: + await pipe.run({"a": {"number1": 1}}) + assert ( + "Missing input parameters for a: Expected parameters: ['number1', 'number2']. Got: ['number1']" + in str(excinfo.value) + ) + + +@pytest.mark.asyncio +async def test_pipeline_missing_param_on_connect() -> None: + pipe = Pipeline() + component_a = ComponentAdd() + component_b = ComponentAdd() + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + pipe.connect("a", "b", {"number1": "a.result"}) + with pytest.raises(PipelineDefinitionError) as excinfo: + await pipe.run({"a": {"number1": 1, "number2": 2}}) + assert ( + "Missing input parameters for b: Expected parameters: ['number1', 'number2']. Got: ['number1']" + in str(excinfo.value) + ) + + +@pytest.mark.asyncio +async def test_pipeline_with_default_params() -> None: + pipe = Pipeline() + component_a = ComponentAdd() + component_b = ComponentMultiply() + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + pipe.connect("a", "b", {"number1": "a.result"}) + res = await pipe.run({"a": {"number1": 1, "number2": 2}}) + assert res == {"b": {"result": 6}} # (1+2)*2 + + +@pytest.mark.asyncio +async def test_pipeline_cycle() -> None: + pipe = Pipeline() + component_a = ComponentNoParam() + component_b = ComponentNoParam() + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + pipe.connect("a", "b", {}) + with pytest.raises(PipelineDefinitionError) as excinfo: + pipe.connect("b", "a", {}) + assert "Cycles are not allowed" in str(excinfo.value) + + +@pytest.mark.asyncio +async def test_pipeline_wrong_component_name() -> None: + pipe = Pipeline() + component_a = ComponentNoParam() + component_b = ComponentNoParam() + pipe.add_component("a", component_a) + pipe.add_component("b", component_b) + with pytest.raises(PipelineDefinitionError) as excinfo: + pipe.connect("a", "c", {}) + assert "a or c not in the Pipeline" in str(excinfo.value) diff --git a/tests/unit/pipeline/test_pipeline_graph.py b/tests/unit/pipeline/test_pipeline_graph.py new file mode 100644 index 00000000..8a2b1576 --- /dev/null +++ b/tests/unit/pipeline/test_pipeline_graph.py @@ -0,0 +1,113 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pytest +from neo4j_genai.pipeline.pipeline_graph import ( + PipelineEdge, + PipelineGraph, + PipelineNode, +) + + +def test_node_alone() -> None: + n = PipelineNode(name="node", data={}) + assert n.is_root() is True + assert n.is_leaf() is True + + +def test_node_not_root() -> None: + n = PipelineNode(name="node", data={}) + n.parents = ["other_node"] + assert n.is_root() is False + assert n.is_leaf() is True + + +def test_node_not_leaf() -> None: + n = PipelineNode(name="node", data={}) + n.children = ["other_node"] + assert n.is_root() is True + assert n.is_leaf() is False + + +def test_graph_add_nodes() -> None: + g: PipelineGraph[PipelineNode, PipelineEdge] = PipelineGraph() + n1 = PipelineNode("n1", {}) + n2 = PipelineNode("n2", {}) + g.add_node(n1) + g.add_node(n2) + assert len(g._nodes) == 2 + edge = PipelineEdge(n1.name, n2.name, {"key": "value"}) + g.add_edge(edge) + assert len(g._edges) == 1 + + assert n1.children == [n2.name] + assert n2.parents == [n1.name] + + +@pytest.fixture(scope="function") +def graph() -> PipelineGraph[PipelineNode, PipelineEdge]: + g: PipelineGraph[PipelineNode, PipelineEdge] = PipelineGraph() + n1 = PipelineNode("n1", {}) + n2 = PipelineNode("n2", {}) + g.add_node(n1) + g.add_node(n2) + edge = PipelineEdge(n1.name, n2.name, {"key": "value"}) + g.add_edge(edge) + return g + + +def test_graph_roots(graph: PipelineGraph[PipelineNode, PipelineEdge]) -> None: + roots = graph.roots() + assert len(roots) == 1 + assert roots[0].name == "n1" + + +def test_graph_next_edge(graph: PipelineGraph[PipelineNode, PipelineEdge]) -> None: + start = graph._nodes["n1"] + next_edges = graph.next_edges(start.name) + assert len(next_edges) == 1 + next_edge = next_edges[0] + assert isinstance(next_edge, PipelineEdge) + assert next_edge.start == "n1" + assert next_edge.end == "n2" + + +def test_graph_prev_edge(graph: PipelineGraph[PipelineNode, PipelineEdge]) -> None: + start = graph._nodes["n2"] + next_edges = graph.previous_edges(start.name) + assert len(next_edges) == 1 + next_edge = next_edges[0] + assert isinstance(next_edge, PipelineEdge) + assert next_edge.start == "n1" + assert next_edge.end == "n2" + + +def test_graph_contains(graph: PipelineGraph[PipelineNode, PipelineEdge]) -> None: + start = graph._nodes["n2"] + assert start in graph + + +def test_graph_is_cyclic(graph: PipelineGraph[PipelineNode, PipelineEdge]) -> None: + g: PipelineGraph[PipelineNode, PipelineEdge] = PipelineGraph() + n1 = PipelineNode("n1", {}) + n2 = PipelineNode("n2", {}) + g.add_node(n1) + g.add_node(n2) + edge = PipelineEdge(n1.name, n2.name, {}) + g.add_edge(edge) + assert g.is_cyclic() is False + + edge = PipelineEdge(n2.name, n1.name, {}) + g.add_edge(edge) + assert g.is_cyclic() is True diff --git a/tests/unit/pipeline/test_store.py b/tests/unit/pipeline/test_store.py new file mode 100644 index 00000000..6864e3f8 --- /dev/null +++ b/tests/unit/pipeline/test_store.py @@ -0,0 +1,13 @@ +import pytest +from neo4j_genai.pipeline.stores import InMemoryStore + + +def test_memory_store() -> None: + store = InMemoryStore() + store.add("key", "value") + assert store.get("key") == "value" + + with pytest.raises(KeyError): + store.add("key", "value", overwrite=False) + + assert store.all() == {"key": "value"} diff --git a/tests/unit/test_indexes.py b/tests/unit/test_indexes.py index c8c10d3e..d0c6dbc0 100644 --- a/tests/unit/test_indexes.py +++ b/tests/unit/test_indexes.py @@ -24,6 +24,7 @@ create_vector_index, drop_index_if_exists, upsert_vector, + upsert_vector_on_relationship, ) @@ -179,13 +180,35 @@ def test_upsert_vector_happy_path(driver: MagicMock) -> None: upsert_vector(driver, id, embedding_property, vector) - upsert_query = """ - MATCH (n) - WHERE elementId(n) = $id - WITH n - CALL db.create.setNodeVectorProperty(n, $embedding_property, $vector) - RETURN n - """ + upsert_query = ( + "MATCH (n) " + "WHERE elementId(n) = $id " + "WITH n " + "CALL db.create.setNodeVectorProperty(n, $embedding_property, $vector) " + "RETURN n" + ) + + driver.execute_query.assert_called_once_with( + upsert_query, + {"id": id, "embedding_property": embedding_property, "vector": vector}, + database_=None, + ) + + +def test_upsert_vector_on_relationship_happy_path(driver: MagicMock) -> None: + id = 1 + embedding_property = "embedding" + vector = [1.0, 2.0, 3.0] + + upsert_vector_on_relationship(driver, id, embedding_property, vector) + + upsert_query = ( + "MATCH ()-[r]->() " + "WHERE elementId(r) = $id " + "WITH r " + "CALL db.create.setRelationshipVectorProperty(r, $embedding_property, $vector) " + "RETURN r" + ) driver.execute_query.assert_called_once_with( upsert_query, @@ -194,7 +217,20 @@ def test_upsert_vector_happy_path(driver: MagicMock) -> None: ) -def test_upsert_vector_raises_error_with_neo4j_insertion_error( +def test_upsert_vector_on_relationship_raises_neo4j_insertion_error( + driver: MagicMock, +) -> None: + id = 1 + embedding_property = "embedding" + vector = [1.0, 2.0, 3.0] + driver.execute_query.side_effect = neo4j.exceptions.ClientError + + with pytest.raises(Neo4jInsertionError) as excinfo: + upsert_vector_on_relationship(driver, id, embedding_property, vector) + assert "Upserting vector to Neo4j failed" in str(excinfo) + + +def test_upsert_vector_raises_neo4j_insertion_error( driver: MagicMock, ) -> None: id = 1