Rag pipeline POC

examples/question_answering/graphrag_pipeline.py

@@ -0,0 +1,70 @@
+import asyncio
+
+import neo4j
+from neo4j_graphrag.embeddings import OpenAIEmbeddings
+from neo4j_graphrag.experimental.components.rag.generate import Generator
+from neo4j_graphrag.experimental.components.rag.prompt_builder import PromptBuilder
+from neo4j_graphrag.experimental.components.rag.retrievers import RetrieverWrapper
+from neo4j_graphrag.experimental.pipeline import Pipeline
+from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult
+from neo4j_graphrag.generation import RagTemplate
+from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.retrievers import VectorRetriever
+
+URI = "neo4j+s://demo.neo4jlabs.com"
+AUTH = ("recommendations", "recommendations")
+DATABASE = "recommendations"
+INDEX_NAME = "moviePlotsEmbedding"
+
+
+async def main() -> PipelineResult:
+    pipeline = Pipeline()
+    driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)
+    llm = OpenAILLM(model_name="gpt-4o")
+    embedder = OpenAIEmbeddings()
+    retriever = VectorRetriever(
+        driver,
+        index_name=INDEX_NAME,
+        neo4j_database=DATABASE,
+        embedder=embedder,
+    )
+    pipeline.add_component(RetrieverWrapper(retriever), "retriever")
+    pipeline.add_component(PromptBuilder(RagTemplate()), "prompt")
+    pipeline.add_component(Generator(llm), "generate")
+
+    pipeline.connect(
+        "retriever",
+        "prompt",
+        {
+            "context": "retriever.result",
+        },
+    )
+    pipeline.connect(
+        "prompt",
+        "generate",
+        {
+            "prompt": "prompt.prompt",
+        },
+    )
+
+    query = "show me a movie with cats"
+    res = await pipeline.run(
+        {
+            "retriever": {"query_text": query},
+            "prompt": {"query_text": query, "examples": ""},
+        }
+    )
+
+    driver.close()
+    await llm.async_client.close()
+
+    # context_result = await pipeline.store.get_result_for_component(
+    #     res.run_id, "retriever"
+    # )
+    # context = context_result.get("result")
+    # res.result["context"] = context
+    return res
+
+
+if __name__ == "__main__":
+    print(asyncio.run(main()))

examples/question_answering/graphrag_simple_pipeline.py

@@ -0,0 +1,26 @@
+from neo4j_graphrag.experimental.pipeline.config.runner import PipelineRunner
+
+if __name__ == "__main__":
+    import asyncio
+    import os
+
+    os.environ["NEO4J_URI"] = "neo4j+s://demo.neo4jlabs.com"
+    os.environ["NEO4J_USER"] = "recommendations"
+    os.environ["NEO4J_PASSWORD"] = "recommendations"
+
+    runner = PipelineRunner.from_config_file(
+        "examples/question_answering/simple_rag_pipeline_config.json"
+    )
+    print(
+        asyncio.run(
+            runner.run(
+                dict(
+                    query_text="show me a movie about cats",
+                    retriever_config={
+                        "top_k": 2,
+                    },
+                    # return_context=True,
+                )
+            )
+        )
+    )

examples/question_answering/simple_rag_pipeline_config.json

@@ -0,0 +1,57 @@
+{
+    "version_": "1",
+    "template_": "SimpleRAGPipeline",
+    "neo4j_config": {
+        "params_": {
+            "uri": {
+                "resolver_": "ENV",
+                "var_": "NEO4J_URI"
+            },
+            "user": {
+                "resolver_": "ENV",
+                "var_": "NEO4J_USER"
+            },
+            "password": {
+                "resolver_": "ENV",
+                "var_": "NEO4J_PASSWORD"
+            }
+        }
+    },
+    "llm_config": {
+        "class_": "OpenAILLM",
+        "params_": {
+            "api_key": {
+                "resolver_": "ENV",
+                "var_": "OPENAI_API_KEY"
+            },
+            "model_name": "gpt-4o",
+            "model_params": {
+                "temperature": 0,
+                "max_tokens": 2000
+            }
+        }
+    },
+    "embedder_config": {
+        "class_": "OpenAIEmbeddings",
+        "params_": {
+            "api_key": {
+                "resolver_": "ENV",
+                "var_": "OPENAI_API_KEY"
+            }
+        }
+    },
+    "retriever": {
+        "class_": "VectorRetriever",
+        "params_": {
+            "driver": {
+                "resolver_": "CONFIG_KEY",
+                "key_": "neo4j_config.default"
+            },
+            "index_name": "moviePlotsEmbedding",
+            "embedder": {
+                "resolver_": "CONFIG_KEY",
+                "key_": "embedder_config.default"
+            }
+        }
+    }
+}

src/neo4j_graphrag/experimental/components/rag/__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.

src/neo4j_graphrag/experimental/components/rag/generate.py

@@ -0,0 +1,31 @@
+#  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_graphrag.experimental.pipeline import Component, DataModel
+from neo4j_graphrag.llm import LLMInterface
+
+
+class GenerationResult(DataModel):
+    content: str
+
+
+class Generator(Component):
+    def __init__(self, llm: LLMInterface) -> None:
+        self.llm = llm
+
+    async def run(self, prompt: str) -> GenerationResult:
+        llm_response = await self.llm.ainvoke(prompt)
+        return GenerationResult(
+            content=llm_response.content,
+        )

src/neo4j_graphrag/experimental/components/rag/prompt_builder.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 typing import Any
+
+from neo4j_graphrag.experimental.pipeline import Component, DataModel
+from neo4j_graphrag.generation import PromptTemplate
+
+# class PromptData(DataModel):
+#     inputs: dict[str, Any]
+
+
+class PromptResult(DataModel):
+    prompt: str
+
+
+class PromptBuilder(Component):
+    def __init__(self, template: PromptTemplate):
+        self.template = template
+
+    async def run(self, **kwargs: Any) -> PromptResult:
+        return PromptResult(prompt=self.template.format(**kwargs))

src/neo4j_graphrag/experimental/components/rag/retrievers.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 typing import Any
+
+from neo4j_graphrag.experimental.pipeline import Component, DataModel
+from neo4j_graphrag.retrievers.base import Retriever
+from neo4j_graphrag.types import RetrieverResult
+
+
+class RetrieverWrapperResult(DataModel):
+    result: RetrieverResult
+
+
+class RetrieverWrapper(Component):
+    def __init__(self, retriever: Retriever):
+        self.retriever = retriever
+
+    async def run(self, **kwargs: Any) -> RetrieverWrapperResult:
+        return RetrieverWrapperResult(
+            result=self.retriever.search(**kwargs),
+        )

src/neo4j_graphrag/experimental/pipeline/component.py

@@ -45,6 +45,9 @@ def __new__(
                 for param in sig.parameters.values()
                 if param.name not in ("self", "kwargs")
             }
+            attrs["anonymous_input_allowed"] = any(
+                param.name == "kwargs" for param in sig.parameters.values()
+            )
             # extract returned fields from the run method return type hint
             return_model = get_type_hints(run_method).get("return")
             if return_model is None:
@@ -76,6 +79,7 @@ class Component(abc.ABC, metaclass=ComponentMeta):
     # DO NOT CHANGE
     component_inputs: dict[str, dict[str, str | bool]]
     component_outputs: dict[str, dict[str, str | bool]]
+    anonymous_input_allowed: bool
 
     @abc.abstractmethod
     async def run(self, *args: Any, **kwargs: Any) -> DataModel:

src/neo4j_graphrag/experimental/pipeline/config/runner.py

@@ -45,9 +45,13 @@
 from neo4j_graphrag.experimental.pipeline.config.template_pipeline.simple_kg_builder import (
     SimpleKGPipelineConfig,
 )
+from neo4j_graphrag.experimental.pipeline.config.template_pipeline.simple_rag_pipeline import (
+    SimpleRAGPipelineConfig,
+)
 from neo4j_graphrag.experimental.pipeline.config.types import PipelineType
 from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult
 from neo4j_graphrag.experimental.pipeline.types import PipelineDefinition
+from neo4j_graphrag.generation.types import RagResultModel
 from neo4j_graphrag.utils.logging import prettify
 
 logger = logging.getLogger(__name__)
@@ -68,6 +72,7 @@ class PipelineConfigWrapper(BaseModel):
     config: Union[
         Annotated[PipelineConfig, Tag(PipelineType.NONE)],
         Annotated[SimpleKGPipelineConfig, Tag(PipelineType.SIMPLE_KG_PIPELINE)],
+        Annotated[SimpleRAGPipelineConfig, Tag(PipelineType.SIMPLE_RAG_PIPELINE)],
     ] = Field(discriminator=Discriminator(_get_discriminator_value))
 
     def parse(self, resolved_data: dict[str, Any] | None = None) -> PipelineDefinition:
@@ -136,3 +141,18 @@ async def close(self) -> None:
         logger.debug("PIPELINE_RUNNER: cleaning up (closing instantiated drivers...)")
         if self.config:
             await self.config.close()
+
+
+class RagPipelineRunner(PipelineRunner):
+    async def search(self, **kwargs: Any) -> RagResultModel:
+        result = await self.run(kwargs)
+        context = None
+        if kwargs.get("return_context"):
+            context = await self.pipeline.store.get_result_for_component(
+                result.run_id, "retriever"
+            )
+            context = context.get("result")
+        return RagResultModel(
+            answer=result.result["generator"]["content"],
+            retriever_result=context,
+        )