Make pipeline work with parallel runs (#119)

* Add failing test

* Define a "run_id" in Orchestrator - save results per run_id

* Make unit test work

* Make intermediate results accessible from outside pipeline for investigation

* Remove unused imports

* Update examples and CHANGELOG

* Cleaning: remove deprecated code

* Fix ruff

* Fix examples

* Fix examples again

* PR reviews

* Removing useless status assignment

CHANGELOG.md

@@ -2,6 +2,9 @@
 
 ## Next
 
+## Fixed
+- Pipelines now return correct results when the same pipeline is run in parallel.
+
 ## 0.5.0
 
 ### Added

examples/pipeline/kg_builder_from_pdf.py

@@ -35,10 +35,11 @@
     LangChainTextSplitterAdapter,
 )
 from neo4j_genai.experimental.pipeline import Component, DataModel
+from neo4j_genai.experimental.pipeline.pipeline import PipelineResult
 from neo4j_genai.llm import OpenAILLM
 from pydantic import BaseModel, validate_call
 
-logging.basicConfig(level=logging.DEBUG)
+logging.basicConfig(level=logging.INFO)
 
 
 class DocumentChunkModel(DataModel):
@@ -98,7 +99,7 @@ async def run(self, graph: Neo4jGraph) -> WriterModel:
         )
 
 
-async def main(neo4j_driver: neo4j.Driver) -> dict[str, Any]:
+async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
     from neo4j_genai.experimental.pipeline import Pipeline
 
     # Instantiate Entity and Relation objects

examples/pipeline/kg_builder_from_text.py

@@ -16,10 +16,11 @@
 
 import asyncio
 import logging.config
-from typing import Any
 
 import neo4j
 from langchain_text_splitters import CharacterTextSplitter
+from neo4j_genai.embeddings.openai import OpenAIEmbeddings
+from neo4j_genai.experimental.components.embedder import TextChunkEmbedder
 from neo4j_genai.experimental.components.entity_relation_extractor import (
     LLMEntityRelationExtractor,
     OnError,
@@ -35,6 +36,7 @@
     LangChainTextSplitterAdapter,
 )
 from neo4j_genai.experimental.pipeline import Pipeline
+from neo4j_genai.experimental.pipeline.pipeline import PipelineResult
 from neo4j_genai.llm import OpenAILLM
 
 # set log level to DEBUG for all neo4j_genai.* loggers
@@ -58,7 +60,7 @@
 )
 
 
-async def main(neo4j_driver: neo4j.Driver) -> dict[str, Any]:
+async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
     """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
@@ -80,6 +82,7 @@ async def main(neo4j_driver: neo4j.Driver) -> dict[str, Any]:
         ),
         "splitter",
     )
+    pipe.add_component(TextChunkEmbedder(embedder=OpenAIEmbeddings()), "chunk_embedder")
     pipe.add_component(SchemaBuilder(), "schema")
     pipe.add_component(
         LLMEntityRelationExtractor(

examples/pipeline/rag.py

@@ -21,11 +21,13 @@
 from __future__ import annotations
 
 import asyncio
+from typing import List
 
 import neo4j
 from neo4j_genai.embeddings.openai import OpenAIEmbeddings
 from neo4j_genai.experimental.pipeline import Component, Pipeline
 from neo4j_genai.experimental.pipeline.component import DataModel
+from neo4j_genai.experimental.pipeline.pipeline import PipelineResult
 from neo4j_genai.experimental.pipeline.types import (
     ComponentConfig,
     ConnectionConfig,
@@ -37,35 +39,37 @@
 from neo4j_genai.retrievers.base import Retriever
 
 
-class StringDataModel(DataModel):
-    result: str
+class ComponentResultDataModel(DataModel):
+    """A simple DataModel with a single text field"""
+
+    text: str
 
 
 class RetrieverComponent(Component):
     def __init__(self, retriever: Retriever) -> None:
         self.retriever = retriever
 
-    async def run(self, query: str) -> StringDataModel:
+    async def run(self, query: str) -> ComponentResultDataModel:
         res = self.retriever.search(query_text=query)
-        return StringDataModel(result="\n".join(c.content for c in res.items))
+        return ComponentResultDataModel(text="\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:
+    async def run(self, query: str, context: List[str]) -> ComponentResultDataModel:
         prompt = self.prompt.format(query, context, examples="")
-        return StringDataModel(result=prompt)
+        return ComponentResultDataModel(text=prompt)
 
 
 class LLMComponent(Component):
     def __init__(self, llm: LLMInterface) -> None:
         self.llm = llm
 
-    async def run(self, prompt: str) -> StringDataModel:
+    async def run(self, prompt: str) -> ComponentResultDataModel:
         llm_response = self.llm.invoke(prompt)
-        return StringDataModel(result=llm_response.content)
+        return ComponentResultDataModel(text=llm_response.content)
 
 
 if __name__ == "__main__":
@@ -96,21 +100,21 @@ async def run(self, prompt: str) -> StringDataModel:
                 ConnectionConfig(
                     start="retrieve",
                     end="augment",
-                    input_config={"context": "retrieve.result"},
+                    input_config={"context": "retrieve.text"},
                 ),
                 ConnectionConfig(
                     start="augment",
                     end="generate",
-                    input_config={"prompt": "augment.result"},
+                    input_config={"prompt": "augment.text"},
                 ),
             ],
         )
     )
 
     query = "A movie about the US presidency"
-    result = asyncio.run(
+    pipe_output: PipelineResult = asyncio.run(
         pipe.run({"retrieve": {"query": query}, "augment": {"query": query}})
     )
-    print(result["generate"]["result"])
+    print(pipe_output.result["generate"]["text"])
 
     driver.close()