Shreyas/kgsearchresult model

js/sdk/src/models.tsx

@@ -60,6 +60,26 @@ export interface KGSearchSettings {
   max_llm_queries_for_global_search?: number;
   local_search_limits?: Record<string, number>;
 }
+
+export interface KGLocalSearchResult {
+  query: string;
+  entities: Record<string, any>;
+  relationships: Record<string, any>;
+  communities: Record<string, any>;
+}
+
+export interface KGGlobalSearchResult {
+  query: string;
+  search_result: string[];
+}
+
+export interface KGSearchResult {
+  local_result?: KGLocalSearchResult;
+  global_result?: KGGlobalSearchResult;
+}
+
+
+
 export interface Message {
   role: string;
   content: string;

py/cli/commands/server.py

@@ -218,6 +218,7 @@ def serve(
             ).replace(":", "")
 
     if docker:
+
         run_docker_serve(
             client,
             host,
@@ -227,6 +228,8 @@ def serve(
             exclude_postgres,
             project_name,
             image,
+            config_name,
+            config_path,
         )
         if (
             "pytest" in sys.modules

py/cli/utils/docker_utils.py

@@ -110,19 +110,20 @@ def run_docker_serve(
     exclude_postgres: bool,
     project_name: str,
     image: str,
+    config_name: Optional[str] = None,
     config_path: Optional[str] = None,
 ):
     check_set_docker_env_vars(exclude_neo4j, exclude_postgres)
     set_ollama_api_base(exclude_ollama)
 
+    if config_path and config_name:
+        raise ValueError("Cannot specify both config_path and config_name")
+
     if config_path:
         config = R2RConfig.from_toml(config_path)
     else:
-        if hasattr(client, "config_name"):
-            config_name = client.config_name
-        else:
+        if not config_name:
             config_name = "default"
-
         config = R2RConfig.from_toml(R2RBuilder.CONFIG_OPTIONS[config_name])
 
     if config.parsing.provider == "unstructured" and not image:

py/core/__init__.py

@@ -73,6 +73,8 @@
     "Prompt",
     # Search abstractions
     "AggregateSearchResult",
+    "KGLocalSearchResult",
+    "KGGlobalSearchResult",
     "KGSearchResult",
     "KGSearchSettings",
     "VectorSearchResult",

py/core/base/__init__.py

@@ -43,6 +43,8 @@
     "Prompt",
     # Search abstractions
     "AggregateSearchResult",
+    "KGLocalSearchResult",
+    "KGGlobalSearchResult",
     "KGSearchResult",
     "KGSearchSettings",
     "VectorSearchResult",

py/core/base/abstractions/__init__.py

@@ -31,6 +31,8 @@
 from .restructure import KGEnrichmentSettings
 from .search import (
     AggregateSearchResult,
+    KGLocalSearchResult,
+    KGGlobalSearchResult,
     KGSearchResult,
     KGSearchSettings,
     VectorSearchResult,
@@ -78,6 +80,8 @@
     "Prompt",
     # Search abstractions
     "AggregateSearchResult",
+    "KGLocalSearchResult",
+    "KGGlobalSearchResult",
     "KGSearchResult",
     "KGSearchSettings",
     "VectorSearchResult",

py/core/base/abstractions/search.py

@@ -54,14 +54,12 @@ class Config:
             },
         }
 
-
 class KGLocalSearchResult(BaseModel):
     """Result of a local knowledge graph search operation."""
-
     query: str
-    entities: list[dict[str, Any]]
-    relationships: list[dict[str, Any]]
-    communities: list[dict[str, Any]]
+    entities: dict[str, Any]
+    relationships: dict[str, Any]
+    communities: dict[str, Any]
 
     def __str__(self) -> str:
         return f"LocalSearchResult(query={self.query}, search_result={self.search_result})"
@@ -72,35 +70,44 @@ def __repr__(self) -> str:
 
 class KGGlobalSearchResult(BaseModel):
     """Result of a global knowledge graph search operation."""
-
     query: str
-    search_result: list[Dict[str, Any]]
+    search_result: list[str]
 
     def __str__(self) -> str:
-        return f"GlobalSearchResult(query={self.query}, search_result={self.search_result})"
+        return f"KGGlobalSearchResult(query={self.query}, search_result={self.search_result})"
 
     def __repr__(self) -> str:
         return self.__str__()
 
+    def dict(self) -> dict:
+        return {
+            "query": self.query,
+            "search_result": self.search_result
+        }
 
 class KGSearchResult(BaseModel):
     """Result of a knowledge graph search operation."""
-
     local_result: Optional[KGLocalSearchResult] = None
     global_result: Optional[KGGlobalSearchResult] = None
-
+    
     def __str__(self) -> str:
         return f"KGSearchResult(local_result={self.local_result}, global_result={self.global_result})"
 
     def __repr__(self) -> str:
         return self.__str__()
+
+    def dict(self) -> dict:
+        return {
+            "local_result": self.local_result.dict() if self.local_result else None,
+            "global_result": self.global_result.dict() if self.global_result else None
+        }
 
 
 class AggregateSearchResult(BaseModel):
     """Result of an aggregate search operation."""
 
     vector_search_results: Optional[list[VectorSearchResult]]
-    kg_search_results: Optional[KGSearchResult] = None
+    kg_search_results: Optional[list[KGSearchResult]] = None
 
     def __str__(self) -> str:
         return f"AggregateSearchResult(vector_search_results={self.vector_search_results}, kg_search_results={self.kg_search_results})"

py/core/base/api/models/retrieval/responses.py

@@ -11,7 +11,7 @@ class SearchResponse(BaseModel):
         ...,
         description="List of vector search results",
     )
-    kg_search_results: Optional[KGSearchResult] = Field(
+    kg_search_results: Optional[list[KGSearchResult]] = Field(
         None,
         description="Knowledge graph search results, if applicable",
     )

py/core/configs/neo4j_kg.toml

@@ -28,7 +28,7 @@ kg_extraction_prompt = "graphrag_triplet_extraction_zero_shot"
   [kg.kg_extraction_config]
     model = "gpt-4o-mini"
 
-  [kg.kg_enrichment_config]
+  [kg.kg_enrichment_settings]
     max_knowledge_triples = 100
     generation_config = { model = "gpt-4o-mini" } # and other params
     leiden_params = { max_cluster_size = 1000 } # more params in graspologic/partition/leiden.py

py/core/pipes/retrieval/kg_search_search_pipe.py

@@ -13,6 +13,7 @@
     PipeType,
     PromptProvider,
     RunLoggingSingleton,
+    R2RException
 )
 from core.base.abstractions.search import (
     KGGlobalSearchResult,
@@ -24,7 +25,6 @@
 
 logger = logging.getLogger(__name__)
 
-
 class KGSearchSearchPipe(GeneratorPipe):
     """
     Embeds and stores documents using a specified embedding model and database.
@@ -132,12 +132,11 @@ async def local_search(
                 )
                 all_search_results.append(search_result)
 
-            yield KGLocalSearchResult(
-                query=message,
-                entities=all_search_results[0],
-                relationships=all_search_results[1],
-                communities=all_search_results[2],
-            )
+
+            if len(all_search_results[0])==0:
+                raise R2RException("No search results found. Please make sure you have run the KG enrichment step before running the search: r2r enrich-graph", 400)
+
+            yield KGLocalSearchResult(query=message, entities=all_search_results[0], relationships=all_search_results[1], communities=all_search_results[2])
 
     async def global_search(
         self,
@@ -217,11 +216,9 @@ async def process_community(merged_report):
                 generation_config=kg_search_settings.kg_search_generation_config,
             )
 
-            output = output.choices[0].message.content
+            output = [output.choices[0].message.content]
 
-            yield KGGlobalSearchResult(
-                query=message, search_result=output, citations=None
-            )
+            yield KGGlobalSearchResult(query=message, search_result=output)
 
     async def _run_logic(
         self,

py/core/pipes/retrieval/vector_search_pipe.py

@@ -91,6 +91,7 @@ async def search(
             result.metadata["associatedQuery"] = message
             results.append(result)
             yield result
+
         await self.enqueue_log(
             run_id=run_id,
             key="search_results",

py/sdk/models.py

@@ -187,27 +187,79 @@ class Config:
             },
         }
 
+class KGLocalSearchResult(BaseModel):
+    query: str
+    entities: list[dict[str, Any]]
+    relationships: list[dict[str, Any]]
+    communities: list[dict[str, Any]]
 
-class KGSearchResult(BaseModel):
+    def __str__(self) -> str:
+        return f"KGLocalSearchResult(query={self.query}, entities={self.entities}, relationships={self.relationships}, communities={self.communities})"
+
+    def dict(self) -> dict:
+        return {
+            "query": self.query,
+            "entities": self.entities,
+            "relationships": self.relationships,
+            "communities": self.communities
+        }
+
+class KGGlobalSearchResult(BaseModel):
     query: str
-    results: list[Dict[str, Any]]
+    search_result: list[str]
+
+    def __str__(self) -> str:
+        return f"KGGlobalSearchResult(query={self.query}, search_result={self.search_result})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+    def dict(self) -> dict:
+        return {
+            "query": self.query,
+            "search_result": self.search_result
+        }
+
+
+class KGSearchResult(BaseModel):
+    local_result: Optional[KGLocalSearchResult] = None
+    global_result: Optional[KGGlobalSearchResult] = None
+
+    def __str__(self) -> str:
+        return f"KGSearchResult(local_result={self.local_result}, global_result={self.global_result})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+    def dict(self) -> dict:
+        return {
+            "local_result": self.local_result.dict() if self.local_result else None,
+            "global_result": self.global_result.dict() if self.global_result else None
+        }
 
     class Config:
         json_schema_extra = {
             "example": {
-                "query": "What is the capital of France?",
-                "results": [
-                    {
+                "local_result": {
+                    "query": "What is the capital of France?",
+                    "entities": {
                         "Paris": {
                             "name": "Paris",
-                            "description": "Paris is the capital of France.",
+                            "description": "Paris is the capital of France."
                         }
-                    }
-                ],
+                    },
+                    "relationships": {},
+                    "communities": {},
+                },
+                "global_result": {
+                    "query": "What is the capital of France?",
+                    "search_result": [
+                        "Paris is the capital and most populous city of France."
+                    ]
+                }
             }
         }
 
-
 class R2RException(Exception):
     def __init__(
         self, message: str, status_code: int, detail: Optional[Any] = None
@@ -419,7 +471,7 @@ class SearchResponse(BaseModel):
         ...,
         description="List of vector search results",
     )
-    kg_search_results: Optional[KGSearchResult] = Field(
+    kg_search_results: Optional[list[KGSearchResult]] = Field(
         None,
         description="Knowledge graph search results, if applicable",
     )