two version of R2R are here HEAD master

1 files changed, 140 insertions, 0 deletions

diff --git a/R2R/r2r/pipelines/search_pipeline.py b/R2R/r2r/pipelines/search_pipeline.py
new file mode 100755
index 00000000..25e0c7bb
--- /dev/null
+++ b/R2R/r2r/pipelines/search_pipeline.py
@@ -0,0 +1,140 @@
+import asyncio
+import logging
+from asyncio import Queue
+from typing import Any, Optional
+
+from ..base.abstractions.search import (
+    AggregateSearchResult,
+    KGSearchSettings,
+    VectorSearchSettings,
+)
+from ..base.logging.kv_logger import KVLoggingSingleton
+from ..base.logging.run_manager import RunManager, manage_run
+from ..base.pipeline.base_pipeline import AsyncPipeline, dequeue_requests
+from ..base.pipes.base_pipe import AsyncPipe, AsyncState
+
+logger = logging.getLogger(__name__)
+
+
+class SearchPipeline(AsyncPipeline):
+    """A pipeline for search."""
+
+    pipeline_type: str = "search"
+
+    def __init__(
+        self,
+        pipe_logger: Optional[KVLoggingSingleton] = None,
+        run_manager: Optional[RunManager] = None,
+    ):
+        super().__init__(pipe_logger, run_manager)
+        self._parsing_pipe = None
+        self._vector_search_pipeline = None
+        self._kg_search_pipeline = None
+
+    async def run(
+        self,
+        input: Any,
+        state: Optional[AsyncState] = None,
+        stream: bool = False,
+        run_manager: Optional[RunManager] = None,
+        log_run_info: bool = True,
+        vector_search_settings: VectorSearchSettings = VectorSearchSettings(),
+        kg_search_settings: KGSearchSettings = KGSearchSettings(),
+        *args: Any,
+        **kwargs: Any,
+    ):
+        self.state = state or AsyncState()
+        do_vector_search = (
+            self._vector_search_pipeline is not None
+            and vector_search_settings.use_vector_search
+        )
+        do_kg = (
+            self._kg_search_pipeline is not None
+            and kg_search_settings.use_kg_search
+        )
+        async with manage_run(run_manager, self.pipeline_type):
+            if log_run_info:
+                await run_manager.log_run_info(
+                    key="pipeline_type",
+                    value=self.pipeline_type,
+                    is_info_log=True,
+                )
+
+            vector_search_queue = Queue()
+            kg_queue = Queue()
+
+            async def enqueue_requests():
+                async for message in input:
+                    if do_vector_search:
+                        await vector_search_queue.put(message)
+                    if do_kg:
+                        await kg_queue.put(message)
+
+                await vector_search_queue.put(None)
+                await kg_queue.put(None)
+
+            # Start the document enqueuing process
+            enqueue_task = asyncio.create_task(enqueue_requests())
+
+            # Start the embedding and KG pipelines in parallel
+            if do_vector_search:
+                vector_search_task = asyncio.create_task(
+                    self._vector_search_pipeline.run(
+                        dequeue_requests(vector_search_queue),
+                        state,
+                        stream,
+                        run_manager,
+                        log_run_info=False,
+                        vector_search_settings=vector_search_settings,
+                    )
+                )
+
+            if do_kg:
+                kg_task = asyncio.create_task(
+                    self._kg_search_pipeline.run(
+                        dequeue_requests(kg_queue),
+                        state,
+                        stream,
+                        run_manager,
+                        log_run_info=False,
+                        kg_search_settings=kg_search_settings,
+                    )
+                )
+
+        await enqueue_task
+
+        vector_search_results = (
+            await vector_search_task if do_vector_search else None
+        )
+        kg_results = await kg_task if do_kg else None
+
+        return AggregateSearchResult(
+            vector_search_results=vector_search_results,
+            kg_search_results=kg_results,
+        )
+
+    def add_pipe(
+        self,
+        pipe: AsyncPipe,
+        add_upstream_outputs: Optional[list[dict[str, str]]] = None,
+        kg_pipe: bool = False,
+        vector_search_pipe: bool = False,
+        *args,
+        **kwargs,
+    ) -> None:
+        logger.debug(f"Adding pipe {pipe.config.name} to the SearchPipeline")
+
+        if kg_pipe:
+            if not self._kg_search_pipeline:
+                self._kg_search_pipeline = AsyncPipeline()
+            self._kg_search_pipeline.add_pipe(
+                pipe, add_upstream_outputs, *args, **kwargs
+            )
+        elif vector_search_pipe:
+            if not self._vector_search_pipeline:
+                self._vector_search_pipeline = AsyncPipeline()
+            self._vector_search_pipeline.add_pipe(
+                pipe, add_upstream_outputs, *args, **kwargs
+            )
+        else:
+            raise ValueError("Pipe must be a vector search or KG pipe")