two version of R2R are here HEAD master

1 files changed, 84 insertions, 0 deletions

diff --git a/R2R/r2r/base/abstractions/search.py b/R2R/r2r/base/abstractions/search.py
new file mode 100755
index 00000000..b13cc5aa
--- /dev/null
+++ b/R2R/r2r/base/abstractions/search.py
@@ -0,0 +1,84 @@
+"""Abstractions for search functionality."""
+
+import uuid
+from typing import Any, Dict, List, Optional, Tuple
+
+from pydantic import BaseModel, Field
+
+from .llm import GenerationConfig
+
+
+class VectorSearchRequest(BaseModel):
+    """Request for a search operation."""
+
+    query: str
+    limit: int
+    filters: Optional[dict[str, Any]] = None
+
+
+class VectorSearchResult(BaseModel):
+    """Result of a search operation."""
+
+    id: uuid.UUID
+    score: float
+    metadata: dict[str, Any]
+
+    def __str__(self) -> str:
+        return f"VectorSearchResult(id={self.id}, score={self.score}, metadata={self.metadata})"
+
+    def __repr__(self) -> str:
+        return f"VectorSearchResult(id={self.id}, score={self.score}, metadata={self.metadata})"
+
+    def dict(self) -> dict:
+        return {
+            "id": self.id,
+            "score": self.score,
+            "metadata": self.metadata,
+        }
+
+
+class KGSearchRequest(BaseModel):
+    """Request for a knowledge graph search operation."""
+
+    query: str
+
+
+# [query, ...]
+KGSearchResult = List[Tuple[str, List[Dict[str, Any]]]]
+
+
+class AggregateSearchResult(BaseModel):
+    """Result of an aggregate search operation."""
+
+    vector_search_results: Optional[List[VectorSearchResult]]
+    kg_search_results: Optional[KGSearchResult] = None
+
+    def __str__(self) -> str:
+        return f"AggregateSearchResult(vector_search_results={self.vector_search_results}, kg_search_results={self.kg_search_results})"
+
+    def __repr__(self) -> str:
+        return f"AggregateSearchResult(vector_search_results={self.vector_search_results}, kg_search_results={self.kg_search_results})"
+
+    def dict(self) -> dict:
+        return {
+            "vector_search_results": (
+                [result.dict() for result in self.vector_search_results]
+                if self.vector_search_results
+                else []
+            ),
+            "kg_search_results": self.kg_search_results or [],
+        }
+
+
+class VectorSearchSettings(BaseModel):
+    use_vector_search: bool = True
+    search_filters: dict[str, Any] = Field(default_factory=dict)
+    search_limit: int = 10
+    do_hybrid_search: bool = False
+
+
+class KGSearchSettings(BaseModel):
+    use_kg_search: bool = False
+    agent_generation_config: Optional[GenerationConfig] = Field(
+        default_factory=GenerationConfig
+    )