Remove test_cli.py

go/cli/rag_operations.go

@@ -48,7 +48,7 @@ var ragCmd = &cobra.Command{
 			json.Unmarshal([]byte(filtersStr), &filters)
 		}
 		searchLimit, _ := cmd.Flags().GetInt("search-limit")
-		doHybridSearch, _ := cmd.Flags().GetBool("do-hybrid-search")
+		doHybridSearch, _ := cmd.Flags().GetBool("use-hybrid-search")
 		useKgSearch, _ := cmd.Flags().GetBool("use-kg-search")
 		kgSearchModel, _ := cmd.Flags().GetString("kg-search-model")
 		stream, _ := cmd.Flags().GetBool("stream")
@@ -108,7 +108,7 @@ var searchCmd = &cobra.Command{
 			json.Unmarshal([]byte(filtersStr), &filters)
 		}
 		searchLimit, _ := cmd.Flags().GetInt("search-limit")
-		doHybridSearch, _ := cmd.Flags().GetBool("do-hybrid-search")
+		doHybridSearch, _ := cmd.Flags().GetBool("use-hybrid-search")
 		useKgSearch, _ := cmd.Flags().GetBool("use-kg-search")
 		kgSearchModel, _ := cmd.Flags().GetString("kg-search-model")
 		kgSearchType, _ := cmd.Flags().GetString("kg-search-type")
@@ -161,7 +161,7 @@ func init() {
 	ragCmd.Flags().Bool("use-vector-search", true, "Use vector search")
 	ragCmd.Flags().String("filters", "", "Search filters as JSON")
 	ragCmd.Flags().Int("search-limit", 10, "Number of search results to return")
-	ragCmd.Flags().Bool("do-hybrid-search", false, "Perform hybrid search")
+	ragCmd.Flags().Bool("use-hybrid-search", false, "Perform hybrid search")
 	ragCmd.Flags().Bool("use-kg-search", false, "Use knowledge graph search")
 	ragCmd.Flags().String("kg-search-model", "", "Model for KG agent")
 	ragCmd.Flags().Bool("stream", false, "Stream the RAG response")
@@ -173,7 +173,7 @@ func init() {
 	searchCmd.Flags().Bool("use-vector-search", true, "Use vector search")
 	searchCmd.Flags().String("filters", "", "Search filters as JSON")
 	searchCmd.Flags().Int("search-limit", 10, "Number of search results to return")
-	searchCmd.Flags().Bool("do-hybrid-search", false, "Perform hybrid search")
+	searchCmd.Flags().Bool("use-hybrid-search", false, "Perform hybrid search")
 	searchCmd.Flags().Bool("use-kg-search", false, "Use knowledge graph search")
 	searchCmd.Flags().String("kg-search-model", "", "Model for KG agent")
 	searchCmd.Flags().String("kg-search-type", "global", "Local or Global")

go/sdk/pkg/sdk/retrieval.go

@@ -39,7 +39,7 @@ type VectorSearchSettings struct {
 	UseVectorSearch  bool                   `json:"use_vector_search"`
 	Filters          map[string]interface{} `json:"filters"`
 	SearchLimit      int                    `json:"search_limit"`
-	DoHybridSearch   bool                   `json:"do_hybrid_search"`
+	DoHybridSearch   bool                   `json:"use_hybrid_search"`
 	SelectedGroupIDs []string               `json:"selected_group_ids"`
 }
 

js/sdk/README.md

@@ -95,7 +95,7 @@ const ragResult = await client.rag({
   use_vector_search: true,
   search_filters: {},
   search_limit: 10,
-  do_hybrid_search: false,
+  use_hybrid_search: false,
   use_kg_search: false,
   kg_generation_config: {},
   rag_generation_config: {

js/sdk/src/models.tsx

@@ -37,7 +37,7 @@ export interface VectorSearchSettings {
   use_vector_search?: boolean;
   filters?: Record<string, any>;
   search_limit?: number;
-  do_hybrid_search?: boolean;
+  use_hybrid_search?: boolean;
 }
 
 export interface KGSearchSettings {

py/cli/commands/retrieval.py

@@ -23,7 +23,7 @@
 @click.option(
     "--search-limit", default=None, help="Number of search results to return"
 )
-@click.option("--do-hybrid-search", is_flag=True, help="Perform hybrid search")
+@click.option("--use-hybrid-search", is_flag=True, help="Perform hybrid search")
 @click.option(
     "--selected-group-ids", type=JSON, help="Group IDs to search for as a JSON"
 )
@@ -64,7 +64,7 @@ def search(client, query, **kwargs):
             "use_vector_search",
             "filters",
             "search_limit",
-            "do_hybrid_search",
+            "use_hybrid_search",
             "selected_group_ids",
         ]
         and v is not None
@@ -122,7 +122,7 @@ def search(client, query, **kwargs):
 @click.option(
     "--search-limit", default=10, help="Number of search results to return"
 )
-@click.option("--do-hybrid-search", is_flag=True, help="Perform hybrid search")
+@click.option("--use-hybrid-search", is_flag=True, help="Perform hybrid search")
 @click.option(
     "--selected-group-ids", type=JSON, help="Group IDs to search for as a JSON"
 )
@@ -169,7 +169,7 @@ def rag(client, query, **kwargs):
             "use_vector_search",
             "filters",
             "search_limit",
-            "do_hybrid_search",
+            "use_hybrid_search",
             "selected_group_ids",
         ]
         and v is not None

py/core/examples/hello_r2r.py

@@ -1,25 +1,23 @@
-from core import R2R, Document, GenerationConfig
+from r2r import R2RClient
 
-app = R2R()  # You may pass a custom configuration to `R2R` with config=...
+client = R2RClient("http://localhost:8000")
 
-app.ingest_documents(
-    [
-        Document(
-            type="txt",
-            data="John is a person that works at Google.",
-            metadata={},
-        )
-    ]
-)
+with open("test.txt", "w") as file:
+    file.write("John is a person that works at Google.")
+
+client.ingest_files(file_paths=["test.txt"])
 
-rag_results = app.rag(
-    "Who is john", GenerationConfig(model="gpt-3.5-turbo", temperature=0.0)
+# Call RAG directly on an R2R object
+rag_response = client.rag(
+    query="Who is john",
+    rag_generation_config={"model": "gpt-3.5-turbo", "temperature": 0.0},
 )
-print(f"Search Results:\n{rag_results.search_results}")
-print(f"Completion:\n{rag_results.completion}")
+results = rag_response['results']
+print(f"Search Results:\n{results['search_results']}")
+print(f"Completion:\n{results['completion']}")
 
 # RAG Results:
 # Search Results:
 # AggregateSearchResult(vector_search_results=[VectorSearchResult(id=2d71e689-0a0e-5491-a50b-4ecb9494c832, score=0.6848798582029441, metadata={'text': 'John is a person that works at Google.', 'version': 'v0', 'chunk_order': 0, 'document_id': 'ed76b6ee-dd80-5172-9263-919d493b439a', 'extraction_id': '1ba494d7-cb2f-5f0e-9f64-76c31da11381', 'associatedQuery': 'Who is john'})], kg_search_results=None)
 # Completion:
-# ChatCompletion(id='chatcmpl-9g0HnjGjyWDLADe7E2EvLWa35cMkB', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='John is a person that works at Google [1].', role='agent', function_call=None, tool_calls=None))], created=1719797903, model='gpt-3.5-turbo-0125', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=11, prompt_tokens=145, total_tokens=156))
+# ChatCompletion(id='chatcmpl-9g0HnjGjyWDLADe7E2EvLWa35cMkB', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='John is a person that works at Google [1].', role='assistant', function_call=None, tool_calls=None))], created=1719797903, model='gpt-3.5-turbo-0125', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=11, prompt_tokens=145, total_tokens=156))

py/core/main/api/routes/ingestion/base.py

@@ -168,7 +168,6 @@ async def update_files_app(
                 chunking_provider = (
                     R2RProviderFactory.create_chunking_provider(config)
                 )
-            print("input metadatas = ", metadatas)
 
             return await self.engine.aupdate_files(
                 files=files,

py/core/main/services/ingestion_service.py

@@ -104,7 +104,7 @@ async def update_files(
         self,
         files: list[UploadFile],
         user: UserResponse,
-        document_ids: list[UUID],
+        document_ids: Optional[list[UUID]],
         metadatas: Optional[list[dict]] = None,
         chunking_provider: Optional[ChunkingProvider] = None,
         *args: Any,
@@ -120,12 +120,16 @@ async def update_files(
                 message="Database provider is not available for updating documents.",
             )
         try:
-            if len(document_ids) != len(files):
-                raise R2RException(
-                    status_code=400,
-                    message="Number of ids does not match number of files.",
-                )
-
+            if document_ids:
+                if len(document_ids) != len(files):
+                    raise R2RException(
+                        status_code=400,
+                        message="Number of ids does not match number of files.",
+                    )
+            else:
+                document_ids = [generate_user_document_id(file.filename, user.id) for file in files]
+            print('user_id = ', user.id)
+            print('document_ids = ', document_ids)
             # Only superusers can modify arbitrary document ids, which this gate guarantees in conjuction with the check that follows
             documents_overview = (
                 (
@@ -171,7 +175,6 @@ async def update_files(
                 document = self._file_to_document(
                     file, user, doc_id, updated_metadata
                 )
-                print("document = ", document)
                 documents.append(document)
 
             ingestion_results = await self.ingest_documents(

py/sdk/ingestion.py

@@ -23,14 +23,31 @@ async def ingest_files(
 
         Args:
             file_paths (List[str]): List of file paths to ingest.
-            metadatas (Optional[List[dict]]): List of metadata dictionaries for each file.
             document_ids (Optional[List[str]]): List of document IDs.
+            metadatas (Optional[List[dict]]): List of metadata dictionaries for each file.
             versions (Optional[List[str]]): List of version strings for each file.
             chunking_settings (Optional[Union[dict, ChunkingConfig]]): Custom chunking configuration.
 
         Returns:
             dict: Ingestion results containing processed, failed, and skipped documents.
         """
+        if document_ids is not None and len(file_paths) != len(document_ids):
+            raise ValueError(
+                "Number of file paths must match number of document IDs."
+            )
+        if metadatas is not None and len(file_paths) != len(metadatas):
+            raise ValueError(
+                "Number of metadatas must match number of document IDs."
+            )
+        if versions is not None and len(file_paths) != len(versions):
+            raise ValueError(
+                "Number of versions must match number of document IDs."
+            )
+        if chunking_settings is not None and chunking_settings is not ChunkingConfig:
+            # check if the provided dict maps to a ChunkingConfig
+            ChunkingConfig(**chunking_settings)
+
+
         all_file_paths = []
         for path in file_paths:
             if os.path.isdir(path):
@@ -80,7 +97,7 @@ async def ingest_files(
     async def update_files(
         client,
         file_paths: list[str],
-        document_ids: Optional[list[str]],
+        document_ids: Optional[list[str]] = None,
         metadatas: Optional[list[dict]] = None,
         chunking_settings: Optional[Union[dict, ChunkingConfig]] = None,
     ) -> dict:
@@ -96,10 +113,15 @@ async def update_files(
         Returns:
             dict: Update results containing processed, failed, and skipped documents.
         """
-        if len(file_paths) != len(document_ids):
+        if document_ids is not None and len(file_paths) != len(document_ids):
+            raise ValueError(
+                "Number of file paths must match number of document IDs."
+            )
+        if metadatas is not None and len(file_paths) != len(metadatas):
             raise ValueError(
                 "Number of file paths must match number of document IDs."
             )
+
 
         with ExitStack() as stack:
             files = [

py/sdk/models.py

@@ -219,7 +219,7 @@ class VectorSearchSettings(BaseModel):
         ge=1,
         le=100,
     )
-    do_hybrid_search: bool = Field(
+    use_hybrid_search: bool = Field(
         default=False,
         description="Whether to perform a hybrid search (combining vector and keyword search)",
     )