two version of R2R are here HEAD master

1 files changed, 130 insertions, 0 deletions

diff --git a/R2R/r2r/pipes/retrieval/search_rag_pipe.py b/R2R/r2r/pipes/retrieval/search_rag_pipe.py
new file mode 100755
index 00000000..4d01d2df
--- /dev/null
+++ b/R2R/r2r/pipes/retrieval/search_rag_pipe.py
@@ -0,0 +1,130 @@
+import logging
+import uuid
+from typing import Any, AsyncGenerator, Optional, Tuple
+
+from r2r.base import (
+    AggregateSearchResult,
+    AsyncPipe,
+    AsyncState,
+    LLMProvider,
+    PipeType,
+    PromptProvider,
+)
+from r2r.base.abstractions.llm import GenerationConfig, RAGCompletion
+
+from ..abstractions.generator_pipe import GeneratorPipe
+
+logger = logging.getLogger(__name__)
+
+
+class SearchRAGPipe(GeneratorPipe):
+    class Input(AsyncPipe.Input):
+        message: AsyncGenerator[Tuple[str, AggregateSearchResult], None]
+
+    def __init__(
+        self,
+        llm_provider: LLMProvider,
+        prompt_provider: PromptProvider,
+        type: PipeType = PipeType.GENERATOR,
+        config: Optional[GeneratorPipe] = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(
+            llm_provider=llm_provider,
+            prompt_provider=prompt_provider,
+            type=type,
+            config=config
+            or GeneratorPipe.Config(
+                name="default_rag_pipe", task_prompt="default_rag"
+            ),
+            *args,
+            **kwargs,
+        )
+
+    async def _run_logic(
+        self,
+        input: Input,
+        state: AsyncState,
+        run_id: uuid.UUID,
+        rag_generation_config: GenerationConfig,
+        *args: Any,
+        **kwargs: Any,
+    ) -> AsyncGenerator[RAGCompletion, None]:
+        context = ""
+        search_iteration = 1
+        total_results = 0
+        # must select a query if there are multiple
+        sel_query = None
+        async for query, search_results in input.message:
+            if search_iteration == 1:
+                sel_query = query
+            context_piece, total_results = await self._collect_context(
+                query, search_results, search_iteration, total_results
+            )
+            context += context_piece
+            search_iteration += 1
+
+        messages = self._get_message_payload(sel_query, context)
+
+        response = await self.llm_provider.aget_completion(
+            messages=messages, generation_config=rag_generation_config
+        )
+        yield RAGCompletion(completion=response, search_results=search_results)
+
+        await self.enqueue_log(
+            run_id=run_id,
+            key="llm_response",
+            value=response.choices[0].message.content,
+        )
+
+    def _get_message_payload(self, query: str, context: str) -> dict:
+        return [
+            {
+                "role": "system",
+                "content": self.prompt_provider.get_prompt(
+                    self.config.system_prompt,
+                ),
+            },
+            {
+                "role": "user",
+                "content": self.prompt_provider.get_prompt(
+                    self.config.task_prompt,
+                    inputs={
+                        "query": query,
+                        "context": context,
+                    },
+                ),
+            },
+        ]
+
+    async def _collect_context(
+        self,
+        query: str,
+        results: AggregateSearchResult,
+        iteration: int,
+        total_results: int,
+    ) -> Tuple[str, int]:
+        context = f"Query:\n{query}\n\n"
+        if results.vector_search_results:
+            context += f"Vector Search Results({iteration}):\n"
+            it = total_results + 1
+            for result in results.vector_search_results:
+                context += f"[{it}]: {result.metadata['text']}\n\n"
+                it += 1
+            total_results = (
+                it - 1
+            )  # Update total_results based on the last index used
+        if results.kg_search_results:
+            context += f"Knowledge Graph ({iteration}):\n"
+            it = total_results + 1
+            for query, search_results in results.kg_search_results:  # [1]:
+                context += f"Query: {query}\n\n"
+                context += f"Results:\n"
+                for search_result in search_results:
+                    context += f"[{it}]: {search_result}\n\n"
+                    it += 1
+            total_results = (
+                it - 1
+            )  # Update total_results based on the last index used
+        return context, total_results