Fixed linting

.gitignore

@@ -14,4 +14,4 @@ ignore_this.txt
 .ruff_cache/
 gui/
 *.log
-.vscode
+.vscode

api/.gitignore

@@ -1,2 +1,2 @@
 inputs
-rag_storage
+rag_storage

api/README_OPENAI.md

@@ -169,4 +169,3 @@ This project is licensed under the MIT License - see the LICENSE file for detail
 - Built with [FastAPI](https://fastapi.tiangolo.com/)
 - Uses [LightRAG](https://github.com/HKUDS/LightRAG) for document processing
 - Powered by [OpenAI](https://openai.com/) for language model inference
-

api/ollama_lightrag_server.py

@@ -1,8 +1,5 @@
 from fastapi import FastAPI, HTTPException, File, UploadFile, Form
-from fastapi.responses import JSONResponse
 from pydantic import BaseModel
-import asyncio
-import os
 import logging
 import argparse
 from lightrag import LightRAG, QueryParam
@@ -13,7 +10,8 @@ from enum import Enum
 from pathlib import Path
 import shutil
 import aiofiles
-from ascii_colors import ASCIIColors, trace_exception
+from ascii_colors import trace_exception
+
 
 def parse_args():
     parser = argparse.ArgumentParser(
@@ -21,45 +19,84 @@ def parse_args():
     )
 
     # Server configuration
-    parser.add_argument('--host', default='0.0.0.0', help='Server host (default: 0.0.0.0)')
-    parser.add_argument('--port', type=int, default=9621, help='Server port (default: 9621)')
-    
+    parser.add_argument(
+        "--host", default="0.0.0.0", help="Server host (default: 0.0.0.0)"
+    )
+    parser.add_argument(
+        "--port", type=int, default=9621, help="Server port (default: 9621)"
+    )
+
     # Directory configuration
-    parser.add_argument('--working-dir', default='./rag_storage', 
-                       help='Working directory for RAG storage (default: ./rag_storage)')
-    parser.add_argument('--input-dir', default='./inputs',
-                       help='Directory containing input documents (default: ./inputs)')
-    
+    parser.add_argument(
+        "--working-dir",
+        default="./rag_storage",
+        help="Working directory for RAG storage (default: ./rag_storage)",
+    )
+    parser.add_argument(
+        "--input-dir",
+        default="./inputs",
+        help="Directory containing input documents (default: ./inputs)",
+    )
+
     # Model configuration
-    parser.add_argument('--model', default='mistral-nemo:latest', help='LLM model name (default: mistral-nemo:latest)')
-    parser.add_argument('--embedding-model', default='bge-m3:latest', 
-                       help='Embedding model name (default: bge-m3:latest)')
-    parser.add_argument('--ollama-host', default='http://localhost:11434', 
-                       help='Ollama host URL (default: http://localhost:11434)')
-    
+    parser.add_argument(
+        "--model",
+        default="mistral-nemo:latest",
+        help="LLM model name (default: mistral-nemo:latest)",
+    )
+    parser.add_argument(
+        "--embedding-model",
+        default="bge-m3:latest",
+        help="Embedding model name (default: bge-m3:latest)",
+    )
+    parser.add_argument(
+        "--ollama-host",
+        default="http://localhost:11434",
+        help="Ollama host URL (default: http://localhost:11434)",
+    )
+
     # RAG configuration
-    parser.add_argument('--max-async', type=int, default=4, help='Maximum async operations (default: 4)')
-    parser.add_argument('--max-tokens', type=int, default=32768, help='Maximum token size (default: 32768)')
-    parser.add_argument('--embedding-dim', type=int, default=1024, 
-                       help='Embedding dimensions (default: 1024)')
-    parser.add_argument('--max-embed-tokens', type=int, default=8192, 
-                       help='Maximum embedding token size (default: 8192)')
-    
+    parser.add_argument(
+        "--max-async", type=int, default=4, help="Maximum async operations (default: 4)"
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=32768,
+        help="Maximum token size (default: 32768)",
+    )
+    parser.add_argument(
+        "--embedding-dim",
+        type=int,
+        default=1024,
+        help="Embedding dimensions (default: 1024)",
+    )
+    parser.add_argument(
+        "--max-embed-tokens",
+        type=int,
+        default=8192,
+        help="Maximum embedding token size (default: 8192)",
+    )
+
     # Logging configuration
-    parser.add_argument('--log-level', default='INFO', 
-                       choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
-                       help='Logging level (default: INFO)')
-    
+    parser.add_argument(
+        "--log-level",
+        default="INFO",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+        help="Logging level (default: INFO)",
+    )
+
     return parser.parse_args()
 
+
 class DocumentManager:
     """Handles document operations and tracking"""
-    
-    def __init__(self, input_dir: str, supported_extensions: tuple = ('.txt', '.md')):
+
+    def __init__(self, input_dir: str, supported_extensions: tuple = (".txt", ".md")):
         self.input_dir = Path(input_dir)
         self.supported_extensions = supported_extensions
         self.indexed_files = set()
-        
+
         # Create input directory if it doesn't exist
         self.input_dir.mkdir(parents=True, exist_ok=True)
 
@@ -67,7 +104,7 @@ class DocumentManager:
         """Scan input directory for new files"""
         new_files = []
         for ext in self.supported_extensions:
-            for file_path in self.input_dir.rglob(f'*{ext}'):
+            for file_path in self.input_dir.rglob(f"*{ext}"):
                 if file_path not in self.indexed_files:
                     new_files.append(file_path)
         return new_files
@@ -80,6 +117,7 @@ class DocumentManager:
         """Check if file type is supported"""
         return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
 
+
 # Pydantic models
 class SearchMode(str, Enum):
     naive = "naive"
@@ -87,31 +125,38 @@ class SearchMode(str, Enum):
     global_ = "global"
     hybrid = "hybrid"
 
+
 class QueryRequest(BaseModel):
     query: str
     mode: SearchMode = SearchMode.hybrid
     stream: bool = False
 
+
 class QueryResponse(BaseModel):
     response: str
 
+
 class InsertTextRequest(BaseModel):
     text: str
     description: Optional[str] = None
 
+
 class InsertResponse(BaseModel):
     status: str
     message: str
     document_count: int
 
+
 def create_app(args):
     # Setup logging
-    logging.basicConfig(format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level))
+    logging.basicConfig(
+        format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level)
+    )
 
     # Initialize FastAPI app
     app = FastAPI(
         title="LightRAG API",
-        description="API for querying text using LightRAG with separate storage and input directories"
+        description="API for querying text using LightRAG with separate storage and input directories",
     )
 
     # Create working directory if it doesn't exist
@@ -127,7 +172,10 @@ def create_app(args):
         llm_model_name=args.model,
         llm_model_max_async=args.max_async,
         llm_model_max_token_size=args.max_tokens,
-        llm_model_kwargs={"host": args.ollama_host, "options": {"num_ctx": args.max_tokens}},
+        llm_model_kwargs={
+            "host": args.ollama_host,
+            "options": {"num_ctx": args.max_tokens},
+        },
         embedding_func=EmbeddingFunc(
             embedding_dim=args.embedding_dim,
             max_token_size=args.max_embed_tokens,
@@ -136,6 +184,7 @@ def create_app(args):
             ),
         ),
     )
+
     @app.on_event("startup")
     async def startup_event():
         """Index all files in input directory during startup"""
@@ -144,7 +193,7 @@ def create_app(args):
             for file_path in new_files:
                 try:
                     # Use async file reading
-                    async with aiofiles.open(file_path, 'r', encoding='utf-8') as f:
+                    async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
                         content = await f.read()
                         # Use the async version of insert directly
                         await rag.ainsert(content)
@@ -153,9 +202,9 @@ def create_app(args):
                 except Exception as e:
                     trace_exception(e)
                     logging.error(f"Error indexing file {file_path}: {str(e)}")
-            
+
             logging.info(f"Indexed {len(new_files)} documents from {args.input_dir}")
-            
+
         except Exception as e:
             logging.error(f"Error during startup indexing: {str(e)}")
 
@@ -165,21 +214,21 @@ def create_app(args):
         try:
             new_files = doc_manager.scan_directory()
             indexed_count = 0
-            
+
             for file_path in new_files:
                 try:
-                    with open(file_path, 'r', encoding='utf-8') as f:
+                    with open(file_path, "r", encoding="utf-8") as f:
                         content = f.read()
                         rag.insert(content)
                         doc_manager.mark_as_indexed(file_path)
                         indexed_count += 1
                 except Exception as e:
                     logging.error(f"Error indexing file {file_path}: {str(e)}")
-            
+
             return {
                 "status": "success",
                 "indexed_count": indexed_count,
-                "total_documents": len(doc_manager.indexed_files)
+                "total_documents": len(doc_manager.indexed_files),
             }
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -191,23 +240,23 @@ def create_app(args):
             if not doc_manager.is_supported_file(file.filename):
                 raise HTTPException(
                     status_code=400,
-                    detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}"
+                    detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
                 )
-            
+
             file_path = doc_manager.input_dir / file.filename
             with open(file_path, "wb") as buffer:
                 shutil.copyfileobj(file.file, buffer)
-            
+
             # Immediately index the uploaded file
             with open(file_path, "r", encoding="utf-8") as f:
                 content = f.read()
                 rag.insert(content)
                 doc_manager.mark_as_indexed(file_path)
-            
+
             return {
                 "status": "success",
                 "message": f"File uploaded and indexed: {file.filename}",
-                "total_documents": len(doc_manager.indexed_files)
+                "total_documents": len(doc_manager.indexed_files),
             }
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -217,9 +266,9 @@ def create_app(args):
         try:
             response = await rag.aquery(
                 request.query,
-                param=QueryParam(mode=request.mode, stream=request.stream)
+                param=QueryParam(mode=request.mode, stream=request.stream),
             )
-            
+
             if request.stream:
                 result = ""
                 async for chunk in response:
@@ -234,14 +283,13 @@ def create_app(args):
     async def query_text_stream(request: QueryRequest):
         try:
             response = rag.query(
-                request.query,
-                param=QueryParam(mode=request.mode, stream=True)
+                request.query, param=QueryParam(mode=request.mode, stream=True)
             )
-            
+
             async def stream_generator():
                 async for chunk in response:
                     yield chunk
-            
+
             return stream_generator()
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -253,32 +301,29 @@ def create_app(args):
             return InsertResponse(
                 status="success",
                 message="Text successfully inserted",
-                document_count=len(rag)
+                document_count=len(rag),
             )
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
 
     @app.post("/documents/file", response_model=InsertResponse)
-    async def insert_file(
-        file: UploadFile = File(...),
-        description: str = Form(None)
-    ):
+    async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
         try:
             content = await file.read()
-            
-            if file.filename.endswith(('.txt', '.md')):
-                text = content.decode('utf-8')
+
+            if file.filename.endswith((".txt", ".md")):
+                text = content.decode("utf-8")
                 rag.insert(text)
             else:
                 raise HTTPException(
                     status_code=400,
-                    detail="Unsupported file type. Only .txt and .md files are supported"
+                    detail="Unsupported file type. Only .txt and .md files are supported",
                 )
-            
+
             return InsertResponse(
                 status="success",
                 message=f"File '{file.filename}' successfully inserted",
-                document_count=len(rag)
+                document_count=len(rag),
             )
         except UnicodeDecodeError:
             raise HTTPException(status_code=400, detail="File encoding not supported")
@@ -290,27 +335,27 @@ def create_app(args):
         try:
             inserted_count = 0
             failed_files = []
-            
+
             for file in files:
                 try:
                     content = await file.read()
-                    if file.filename.endswith(('.txt', '.md')):
-                        text = content.decode('utf-8')
+                    if file.filename.endswith((".txt", ".md")):
+                        text = content.decode("utf-8")
                         rag.insert(text)
                         inserted_count += 1
                     else:
                         failed_files.append(f"{file.filename} (unsupported type)")
                 except Exception as e:
                     failed_files.append(f"{file.filename} ({str(e)})")
-            
+
             status_message = f"Successfully inserted {inserted_count} documents"
             if failed_files:
                 status_message += f". Failed files: {', '.join(failed_files)}"
-            
+
             return InsertResponse(
                 status="success" if inserted_count > 0 else "partial_success",
                 message=status_message,
-                document_count=len(rag)
+                document_count=len(rag),
             )
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -324,12 +369,11 @@ def create_app(args):
             return InsertResponse(
                 status="success",
                 message="All documents cleared successfully",
-                document_count=0
+                document_count=0,
             )
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
 
-
     @app.get("/health")
     async def get_status():
         """Get current system status"""
@@ -342,14 +386,16 @@ def create_app(args):
                 "model": args.model,
                 "embedding_model": args.embedding_model,
                 "max_tokens": args.max_tokens,
-                "ollama_host": args.ollama_host
-            }
+                "ollama_host": args.ollama_host,
+            },
         }
 
     return app
 
+
 if __name__ == "__main__":
     args = parse_args()
     import uvicorn
+
     app = create_app(args)
     uvicorn.run(app, host=args.host, port=args.port)

api/openai_lightrag_server.py

@@ -1,8 +1,6 @@
 from fastapi import FastAPI, HTTPException, File, UploadFile, Form
-from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 import asyncio
-import os
 import logging
 import argparse
 from lightrag import LightRAG, QueryParam
@@ -13,53 +11,81 @@ from enum import Enum
 from pathlib import Path
 import shutil
 import aiofiles
-from ascii_colors import ASCIIColors, trace_exception
-import numpy as np
+from ascii_colors import trace_exception
 import nest_asyncio
 
 # Apply nest_asyncio to solve event loop issues
 nest_asyncio.apply()
 
+
 def parse_args():
     parser = argparse.ArgumentParser(
         description="LightRAG FastAPI Server with OpenAI integration"
     )
 
     # Server configuration
-    parser.add_argument('--host', default='0.0.0.0', help='Server host (default: 0.0.0.0)')
-    parser.add_argument('--port', type=int, default=9621, help='Server port (default: 9621)')
-    
+    parser.add_argument(
+        "--host", default="0.0.0.0", help="Server host (default: 0.0.0.0)"
+    )
+    parser.add_argument(
+        "--port", type=int, default=9621, help="Server port (default: 9621)"
+    )
+
     # Directory configuration
-    parser.add_argument('--working-dir', default='./rag_storage', 
-                       help='Working directory for RAG storage (default: ./rag_storage)')
-    parser.add_argument('--input-dir', default='./inputs',
-                       help='Directory containing input documents (default: ./inputs)')
-    
+    parser.add_argument(
+        "--working-dir",
+        default="./rag_storage",
+        help="Working directory for RAG storage (default: ./rag_storage)",
+    )
+    parser.add_argument(
+        "--input-dir",
+        default="./inputs",
+        help="Directory containing input documents (default: ./inputs)",
+    )
+
     # Model configuration
-    parser.add_argument('--model', default='gpt-4', help='OpenAI model name (default: gpt-4)')
-    parser.add_argument('--embedding-model', default='text-embedding-3-large', 
-                       help='OpenAI embedding model (default: text-embedding-3-large)')
-    
+    parser.add_argument(
+        "--model", default="gpt-4", help="OpenAI model name (default: gpt-4)"
+    )
+    parser.add_argument(
+        "--embedding-model",
+        default="text-embedding-3-large",
+        help="OpenAI embedding model (default: text-embedding-3-large)",
+    )
+
     # RAG configuration
-    parser.add_argument('--max-tokens', type=int, default=32768, help='Maximum token size (default: 32768)')
-    parser.add_argument('--max-embed-tokens', type=int, default=8192, 
-                       help='Maximum embedding token size (default: 8192)')
-    
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=32768,
+        help="Maximum token size (default: 32768)",
+    )
+    parser.add_argument(
+        "--max-embed-tokens",
+        type=int,
+        default=8192,
+        help="Maximum embedding token size (default: 8192)",
+    )
+
     # Logging configuration
-    parser.add_argument('--log-level', default='INFO', 
-                       choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
-                       help='Logging level (default: INFO)')
-    
+    parser.add_argument(
+        "--log-level",
+        default="INFO",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+        help="Logging level (default: INFO)",
+    )
+
     return parser.parse_args()
 
+
 class DocumentManager:
     """Handles document operations and tracking"""
-    
-    def __init__(self, input_dir: str, supported_extensions: tuple = ('.txt', '.md')):
+
+    def __init__(self, input_dir: str, supported_extensions: tuple = (".txt", ".md")):
         self.input_dir = Path(input_dir)
         self.supported_extensions = supported_extensions
         self.indexed_files = set()
-        
+
         # Create input directory if it doesn't exist
         self.input_dir.mkdir(parents=True, exist_ok=True)
 
@@ -67,7 +93,7 @@ class DocumentManager:
         """Scan input directory for new files"""
         new_files = []
         for ext in self.supported_extensions:
-            for file_path in self.input_dir.rglob(f'*{ext}'):
+            for file_path in self.input_dir.rglob(f"*{ext}"):
                 if file_path not in self.indexed_files:
                     new_files.append(file_path)
         return new_files
@@ -80,6 +106,7 @@ class DocumentManager:
         """Check if file type is supported"""
         return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
 
+
 # Pydantic models
 class SearchMode(str, Enum):
     naive = "naive"
@@ -87,37 +114,45 @@ class SearchMode(str, Enum):
     global_ = "global"
     hybrid = "hybrid"
 
+
 class QueryRequest(BaseModel):
     query: str
     mode: SearchMode = SearchMode.hybrid
     stream: bool = False
 
+
 class QueryResponse(BaseModel):
     response: str
 
+
 class InsertTextRequest(BaseModel):
     text: str
     description: Optional[str] = None
 
+
 class InsertResponse(BaseModel):
     status: str
     message: str
     document_count: int
 
+
 async def get_embedding_dim(embedding_model: str) -> int:
     """Get embedding dimensions for the specified model"""
     test_text = ["This is a test sentence."]
     embedding = await openai_embedding(test_text, model=embedding_model)
     return embedding.shape[1]
 
+
 def create_app(args):
     # Setup logging
-    logging.basicConfig(format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level))
+    logging.basicConfig(
+        format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level)
+    )
 
     # Initialize FastAPI app
     app = FastAPI(
         title="LightRAG API",
-        description="API for querying text using LightRAG with OpenAI integration"
+        description="API for querying text using LightRAG with OpenAI integration",
     )
 
     # Create working directory if it doesn't exist
@@ -129,6 +164,18 @@ def create_app(args):
     # Get embedding dimensions
     embedding_dim = asyncio.run(get_embedding_dim(args.embedding_model))
 
+    async def async_openai_complete(
+        prompt, system_prompt=None, history_messages=[], **kwargs
+    ):
+        """Async wrapper for OpenAI completion"""
+        return await openai_complete_if_cache(
+            args.model,
+            prompt,
+            system_prompt=system_prompt,
+            history_messages=history_messages,
+            **kwargs,
+        )
+
     # Initialize RAG with OpenAI configuration
     rag = LightRAG(
         working_dir=args.working_dir,
@@ -142,15 +189,6 @@ def create_app(args):
         ),
     )
 
-    async def async_openai_complete(prompt, system_prompt=None, history_messages=[], **kwargs):
-        """Async wrapper for OpenAI completion"""
-        return await openai_complete_if_cache(
-            args.model,
-            prompt,
-            system_prompt=system_prompt,
-            history_messages=history_messages,
-            **kwargs
-        )
     @app.on_event("startup")
     async def startup_event():
         """Index all files in input directory during startup"""
@@ -159,7 +197,7 @@ def create_app(args):
             for file_path in new_files:
                 try:
                     # Use async file reading
-                    async with aiofiles.open(file_path, 'r', encoding='utf-8') as f:
+                    async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
                         content = await f.read()
                         # Use the async version of insert directly
                         await rag.ainsert(content)
@@ -168,9 +206,9 @@ def create_app(args):
                 except Exception as e:
                     trace_exception(e)
                     logging.error(f"Error indexing file {file_path}: {str(e)}")
-            
+
             logging.info(f"Indexed {len(new_files)} documents from {args.input_dir}")
-            
+
         except Exception as e:
             logging.error(f"Error during startup indexing: {str(e)}")
 
@@ -180,21 +218,21 @@ def create_app(args):
         try:
             new_files = doc_manager.scan_directory()
             indexed_count = 0
-            
+
             for file_path in new_files:
                 try:
-                    with open(file_path, 'r', encoding='utf-8') as f:
+                    with open(file_path, "r", encoding="utf-8") as f:
                         content = f.read()
                         rag.insert(content)
                         doc_manager.mark_as_indexed(file_path)
                         indexed_count += 1
                 except Exception as e:
                     logging.error(f"Error indexing file {file_path}: {str(e)}")
-            
+
             return {
                 "status": "success",
                 "indexed_count": indexed_count,
-                "total_documents": len(doc_manager.indexed_files)
+                "total_documents": len(doc_manager.indexed_files),
             }
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -206,23 +244,23 @@ def create_app(args):
             if not doc_manager.is_supported_file(file.filename):
                 raise HTTPException(
                     status_code=400,
-                    detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}"
+                    detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
                 )
-            
+
             file_path = doc_manager.input_dir / file.filename
             with open(file_path, "wb") as buffer:
                 shutil.copyfileobj(file.file, buffer)
-            
+
             # Immediately index the uploaded file
             with open(file_path, "r", encoding="utf-8") as f:
                 content = f.read()
                 rag.insert(content)
                 doc_manager.mark_as_indexed(file_path)
-            
+
             return {
                 "status": "success",
                 "message": f"File uploaded and indexed: {file.filename}",
-                "total_documents": len(doc_manager.indexed_files)
+                "total_documents": len(doc_manager.indexed_files),
             }
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -232,9 +270,9 @@ def create_app(args):
         try:
             response = await rag.aquery(
                 request.query,
-                param=QueryParam(mode=request.mode, stream=request.stream)
+                param=QueryParam(mode=request.mode, stream=request.stream),
             )
-            
+
             if request.stream:
                 result = ""
                 async for chunk in response:
@@ -249,14 +287,13 @@ def create_app(args):
     async def query_text_stream(request: QueryRequest):
         try:
             response = rag.query(
-                request.query,
-                param=QueryParam(mode=request.mode, stream=True)
+                request.query, param=QueryParam(mode=request.mode, stream=True)
             )
-            
+
             async def stream_generator():
                 async for chunk in response:
                     yield chunk
-            
+
             return stream_generator()
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -268,32 +305,29 @@ def create_app(args):
             return InsertResponse(
                 status="success",
                 message="Text successfully inserted",
-                document_count=len(rag)
+                document_count=len(rag),
             )
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
 
     @app.post("/documents/file", response_model=InsertResponse)
-    async def insert_file(
-        file: UploadFile = File(...),
-        description: str = Form(None)
-    ):
+    async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
         try:
             content = await file.read()
-            
-            if file.filename.endswith(('.txt', '.md')):
-                text = content.decode('utf-8')
+
+            if file.filename.endswith((".txt", ".md")):
+                text = content.decode("utf-8")
                 rag.insert(text)
             else:
                 raise HTTPException(
                     status_code=400,
-                    detail="Unsupported file type. Only .txt and .md files are supported"
+                    detail="Unsupported file type. Only .txt and .md files are supported",
                 )
-            
+
             return InsertResponse(
                 status="success",
                 message=f"File '{file.filename}' successfully inserted",
-                document_count=len(rag)
+                document_count=len(rag),
             )
         except UnicodeDecodeError:
             raise HTTPException(status_code=400, detail="File encoding not supported")
@@ -305,27 +339,27 @@ def create_app(args):
         try:
             inserted_count = 0
             failed_files = []
-            
+
             for file in files:
                 try:
                     content = await file.read()
-                    if file.filename.endswith(('.txt', '.md')):
-                        text = content.decode('utf-8')
+                    if file.filename.endswith((".txt", ".md")):
+                        text = content.decode("utf-8")
                         rag.insert(text)
                         inserted_count += 1
                     else:
                         failed_files.append(f"{file.filename} (unsupported type)")
                 except Exception as e:
                     failed_files.append(f"{file.filename} ({str(e)})")
-            
+
             status_message = f"Successfully inserted {inserted_count} documents"
             if failed_files:
                 status_message += f". Failed files: {', '.join(failed_files)}"
-            
+
             return InsertResponse(
                 status="success" if inserted_count > 0 else "partial_success",
                 message=status_message,
-                document_count=len(rag)
+                document_count=len(rag),
             )
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -339,7 +373,7 @@ def create_app(args):
             return InsertResponse(
                 status="success",
                 message="All documents cleared successfully",
-                document_count=0
+                document_count=0,
             )
         except Exception as e:
             raise HTTPException(status_code=500, detail=str(e))
@@ -356,14 +390,16 @@ def create_app(args):
                 "model": args.model,
                 "embedding_model": args.embedding_model,
                 "max_tokens": args.max_tokens,
-                "embedding_dim": embedding_dim
-            }
+                "embedding_dim": embedding_dim,
+            },
         }
 
     return app
 
+
 if __name__ == "__main__":
     args = parse_args()
     import uvicorn
+
     app = create_app(args)
     uvicorn.run(app, host=args.host, port=args.port)

api/requirements.txt

@@ -1,4 +1,4 @@
+ascii_colors
 fastapi
-uvicorn
 python-multipart
-ascii_colors
+uvicorn