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LightRAG/lightrag/api/lightrag_server.py
yangdx 89c01c686f Fix casting dateime it to str in needed 
- Added datetime formatting utility and standardized timestamp handling in DocStatusResponse
2025-02-18 17:46:28 +08:00

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from fastapi import (
FastAPI,
HTTPException,
File,
UploadFile,
BackgroundTasks,
)
import asyncio
import threading
import os
import json
import re
from fastapi.staticfiles import StaticFiles
import logging
import argparse
from typing import List, Any, Literal, Optional, Dict
from pydantic import BaseModel, Field, field_validator
from lightrag import LightRAG, QueryParam
from lightrag.base import DocProcessingStatus, DocStatus
from lightrag.types import GPTKeywordExtractionFormat
from lightrag.api import __api_version__
from lightrag.utils import EmbeddingFunc
from pathlib import Path
import shutil
import aiofiles
from ascii_colors import trace_exception, ASCIIColors
import sys
from fastapi import Depends, Security
from fastapi.security import APIKeyHeader
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
from starlette.status import HTTP_403_FORBIDDEN
import pipmaster as pm
from dotenv import load_dotenv
import configparser
import traceback
from datetime import datetime
from lightrag.utils import logger
from .ollama_api import (
OllamaAPI,
)
from .ollama_api import ollama_server_infos
def get_db_type_from_storage_class(class_name: str) -> str | None:
"""Determine database type based on storage class name"""
if class_name.startswith("PG"):
return "postgres"
elif class_name.startswith("Oracle"):
return "oracle"
elif class_name.startswith("TiDB"):
return "tidb"
return None
def import_db_module(db_type: str):
"""Dynamically import database module"""
if db_type == "postgres":
from ..kg.postgres_impl import PostgreSQLDB
return PostgreSQLDB
elif db_type == "oracle":
from ..kg.oracle_impl import OracleDB
return OracleDB
elif db_type == "tidb":
from ..kg.tidb_impl import TiDB
return TiDB
return None
# Load environment variables
try:
load_dotenv(override=True)
except Exception as e:
logger.warning(f"Failed to load .env file: {e}")
# Initialize config parser
config = configparser.ConfigParser()
config.read("config.ini")
class DefaultRAGStorageConfig:
KV_STORAGE = "JsonKVStorage"
VECTOR_STORAGE = "NanoVectorDBStorage"
GRAPH_STORAGE = "NetworkXStorage"
DOC_STATUS_STORAGE = "JsonDocStatusStorage"
# Global progress tracker
scan_progress: Dict = {
"is_scanning": False,
"current_file": "",
"indexed_count": 0,
"total_files": 0,
"progress": 0,
}
# Lock for thread-safe operations
progress_lock = threading.Lock()
def estimate_tokens(text: str) -> int:
"""Estimate the number of tokens in text
Chinese characters: approximately 1.5 tokens per character
English characters: approximately 0.25 tokens per character
"""
# Use regex to match Chinese and non-Chinese characters separately
chinese_chars = len(re.findall(r"[\u4e00-\u9fff]", text))
non_chinese_chars = len(re.findall(r"[^\u4e00-\u9fff]", text))
# Calculate estimated token count
tokens = chinese_chars * 1.5 + non_chinese_chars * 0.25
return int(tokens)
def get_default_host(binding_type: str) -> str:
default_hosts = {
"ollama": os.getenv("LLM_BINDING_HOST", "http://localhost:11434"),
"lollms": os.getenv("LLM_BINDING_HOST", "http://localhost:9600"),
"azure_openai": os.getenv("AZURE_OPENAI_ENDPOINT", "https://api.openai.com/v1"),
"openai": os.getenv("LLM_BINDING_HOST", "https://api.openai.com/v1"),
}
return default_hosts.get(
binding_type, os.getenv("LLM_BINDING_HOST", "http://localhost:11434")
) # fallback to ollama if unknown
def get_env_value(env_key: str, default: Any, value_type: type = str) -> Any:
"""
Get value from environment variable with type conversion
Args:
env_key (str): Environment variable key
default (Any): Default value if env variable is not set
value_type (type): Type to convert the value to
Returns:
Any: Converted value from environment or default
"""
value = os.getenv(env_key)
if value is None:
return default
if value_type is bool:
return value.lower() in ("true", "1", "yes", "t", "on")
try:
return value_type(value)
except ValueError:
return default
def display_splash_screen(args: argparse.Namespace) -> None:
"""
Display a colorful splash screen showing LightRAG server configuration
Args:
args: Parsed command line arguments
"""
# Banner
ASCIIColors.cyan(f"""
╔══════════════════════════════════════════════════════════════╗
║ 🚀 LightRAG Server v{__api_version__} ║
║ Fast, Lightweight RAG Server Implementation ║
╚══════════════════════════════════════════════════════════════╝
""")
# Server Configuration
ASCIIColors.magenta("\n📡 Server Configuration:")
ASCIIColors.white(" ├─ Host: ", end="")
ASCIIColors.yellow(f"{args.host}")
ASCIIColors.white(" ├─ Port: ", end="")
ASCIIColors.yellow(f"{args.port}")
ASCIIColors.white(" ├─ CORS Origins: ", end="")
ASCIIColors.yellow(f"{os.getenv('CORS_ORIGINS', '*')}")
ASCIIColors.white(" ├─ SSL Enabled: ", end="")
ASCIIColors.yellow(f"{args.ssl}")
ASCIIColors.white(" └─ API Key: ", end="")
ASCIIColors.yellow("Set" if args.key else "Not Set")
if args.ssl:
ASCIIColors.white(" ├─ SSL Cert: ", end="")
ASCIIColors.yellow(f"{args.ssl_certfile}")
ASCIIColors.white(" └─ SSL Key: ", end="")
ASCIIColors.yellow(f"{args.ssl_keyfile}")
# Directory Configuration
ASCIIColors.magenta("\n📂 Directory Configuration:")
ASCIIColors.white(" ├─ Working Directory: ", end="")
ASCIIColors.yellow(f"{args.working_dir}")
ASCIIColors.white(" └─ Input Directory: ", end="")
ASCIIColors.yellow(f"{args.input_dir}")
# LLM Configuration
ASCIIColors.magenta("\n🤖 LLM Configuration:")
ASCIIColors.white(" ├─ Binding: ", end="")
ASCIIColors.yellow(f"{args.llm_binding}")
ASCIIColors.white(" ├─ Host: ", end="")
ASCIIColors.yellow(f"{args.llm_binding_host}")
ASCIIColors.white(" └─ Model: ", end="")
ASCIIColors.yellow(f"{args.llm_model}")
# Embedding Configuration
ASCIIColors.magenta("\n📊 Embedding Configuration:")
ASCIIColors.white(" ├─ Binding: ", end="")
ASCIIColors.yellow(f"{args.embedding_binding}")
ASCIIColors.white(" ├─ Host: ", end="")
ASCIIColors.yellow(f"{args.embedding_binding_host}")
ASCIIColors.white(" ├─ Model: ", end="")
ASCIIColors.yellow(f"{args.embedding_model}")
ASCIIColors.white(" └─ Dimensions: ", end="")
ASCIIColors.yellow(f"{args.embedding_dim}")
# RAG Configuration
ASCIIColors.magenta("\n⚙️ RAG Configuration:")
ASCIIColors.white(" ├─ Max Async Operations: ", end="")
ASCIIColors.yellow(f"{args.max_async}")
ASCIIColors.white(" ├─ Max Tokens: ", end="")
ASCIIColors.yellow(f"{args.max_tokens}")
ASCIIColors.white(" ├─ Max Embed Tokens: ", end="")
ASCIIColors.yellow(f"{args.max_embed_tokens}")
ASCIIColors.white(" ├─ Chunk Size: ", end="")
ASCIIColors.yellow(f"{args.chunk_size}")
ASCIIColors.white(" ├─ Chunk Overlap Size: ", end="")
ASCIIColors.yellow(f"{args.chunk_overlap_size}")
ASCIIColors.white(" ├─ History Turns: ", end="")
ASCIIColors.yellow(f"{args.history_turns}")
ASCIIColors.white(" ├─ Cosine Threshold: ", end="")
ASCIIColors.yellow(f"{args.cosine_threshold}")
ASCIIColors.white(" └─ Top-K: ", end="")
ASCIIColors.yellow(f"{args.top_k}")
# System Configuration
ASCIIColors.magenta("\n💾 Storage Configuration:")
ASCIIColors.white(" ├─ KV Storage: ", end="")
ASCIIColors.yellow(f"{args.kv_storage}")
ASCIIColors.white(" ├─ Vector Storage: ", end="")
ASCIIColors.yellow(f"{args.vector_storage}")
ASCIIColors.white(" ├─ Graph Storage: ", end="")
ASCIIColors.yellow(f"{args.graph_storage}")
ASCIIColors.white(" └─ Document Status Storage: ", end="")
ASCIIColors.yellow(f"{args.doc_status_storage}")
ASCIIColors.magenta("\n🛠️ System Configuration:")
ASCIIColors.white(" ├─ Ollama Emulating Model: ", end="")
ASCIIColors.yellow(f"{ollama_server_infos.LIGHTRAG_MODEL}")
ASCIIColors.white(" ├─ Log Level: ", end="")
ASCIIColors.yellow(f"{args.log_level}")
ASCIIColors.white(" ├─ Verbose Debug: ", end="")
ASCIIColors.yellow(f"{args.verbose}")
ASCIIColors.white(" └─ Timeout: ", end="")
ASCIIColors.yellow(f"{args.timeout if args.timeout else 'None (infinite)'}")
# Server Status
ASCIIColors.green("\n✨ Server starting up...\n")
# Server Access Information
protocol = "https" if args.ssl else "http"
if args.host == "0.0.0.0":
ASCIIColors.magenta("\n🌐 Server Access Information:")
ASCIIColors.white(" ├─ Local Access: ", end="")
ASCIIColors.yellow(f"{protocol}://localhost:{args.port}")
ASCIIColors.white(" ├─ Remote Access: ", end="")
ASCIIColors.yellow(f"{protocol}://<your-ip-address>:{args.port}")
ASCIIColors.white(" ├─ API Documentation (local): ", end="")
ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/docs")
ASCIIColors.white(" ├─ Alternative Documentation (local): ", end="")
ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/redoc")
ASCIIColors.white(" └─ WebUI (local): ", end="")
ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/webui")
ASCIIColors.yellow("\n📝 Note:")
ASCIIColors.white(""" Since the server is running on 0.0.0.0:
- Use 'localhost' or '127.0.0.1' for local access
- Use your machine's IP address for remote access
- To find your IP address:
• Windows: Run 'ipconfig' in terminal
• Linux/Mac: Run 'ifconfig' or 'ip addr' in terminal
""")
else:
base_url = f"{protocol}://{args.host}:{args.port}"
ASCIIColors.magenta("\n🌐 Server Access Information:")
ASCIIColors.white(" ├─ Base URL: ", end="")
ASCIIColors.yellow(f"{base_url}")
ASCIIColors.white(" ├─ API Documentation: ", end="")
ASCIIColors.yellow(f"{base_url}/docs")
ASCIIColors.white(" └─ Alternative Documentation: ", end="")
ASCIIColors.yellow(f"{base_url}/redoc")
# Usage Examples
ASCIIColors.magenta("\n📚 Quick Start Guide:")
ASCIIColors.cyan("""
1. Access the Swagger UI:
Open your browser and navigate to the API documentation URL above
2. API Authentication:""")
if args.key:
ASCIIColors.cyan(""" Add the following header to your requests:
X-API-Key: <your-api-key>
""")
else:
ASCIIColors.cyan(" No authentication required\n")
ASCIIColors.cyan(""" 3. Basic Operations:
- POST /upload_document: Upload new documents to RAG
- POST /query: Query your document collection
- GET /collections: List available collections
4. Monitor the server:
- Check server logs for detailed operation information
- Use healthcheck endpoint: GET /health
""")
# Security Notice
if args.key:
ASCIIColors.yellow("\n⚠️ Security Notice:")
ASCIIColors.white(""" API Key authentication is enabled.
Make sure to include the X-API-Key header in all your requests.
""")
ASCIIColors.green("Server is ready to accept connections! 🚀\n")
# Ensure splash output flush to system log
sys.stdout.flush()
def parse_args() -> argparse.Namespace:
"""
Parse command line arguments with environment variable fallback
Returns:
argparse.Namespace: Parsed arguments
"""
parser = argparse.ArgumentParser(
description="LightRAG FastAPI Server with separate working and input directories"
)
parser.add_argument(
"--kv-storage",
default=get_env_value(
"LIGHTRAG_KV_STORAGE", DefaultRAGStorageConfig.KV_STORAGE
),
help=f"KV storage implementation (default: {DefaultRAGStorageConfig.KV_STORAGE})",
)
parser.add_argument(
"--doc-status-storage",
default=get_env_value(
"LIGHTRAG_DOC_STATUS_STORAGE", DefaultRAGStorageConfig.DOC_STATUS_STORAGE
),
help=f"Document status storage implementation (default: {DefaultRAGStorageConfig.DOC_STATUS_STORAGE})",
)
parser.add_argument(
"--graph-storage",
default=get_env_value(
"LIGHTRAG_GRAPH_STORAGE", DefaultRAGStorageConfig.GRAPH_STORAGE
),
help=f"Graph storage implementation (default: {DefaultRAGStorageConfig.GRAPH_STORAGE})",
)
parser.add_argument(
"--vector-storage",
default=get_env_value(
"LIGHTRAG_VECTOR_STORAGE", DefaultRAGStorageConfig.VECTOR_STORAGE
),
help=f"Vector storage implementation (default: {DefaultRAGStorageConfig.VECTOR_STORAGE})",
)
# Bindings configuration
parser.add_argument(
"--llm-binding",
default=get_env_value("LLM_BINDING", "ollama"),
help="LLM binding to be used. Supported: lollms, ollama, openai (default: from env or ollama)",
)
parser.add_argument(
"--embedding-binding",
default=get_env_value("EMBEDDING_BINDING", "ollama"),
help="Embedding binding to be used. Supported: lollms, ollama, openai (default: from env or ollama)",
)
# Server configuration
parser.add_argument(
"--host",
default=get_env_value("HOST", "0.0.0.0"),
help="Server host (default: from env or 0.0.0.0)",
)
parser.add_argument(
"--port",
type=int,
default=get_env_value("PORT", 9621, int),
help="Server port (default: from env or 9621)",
)
# Directory configuration
parser.add_argument(
"--working-dir",
default=get_env_value("WORKING_DIR", "./rag_storage"),
help="Working directory for RAG storage (default: from env or ./rag_storage)",
)
parser.add_argument(
"--input-dir",
default=get_env_value("INPUT_DIR", "./inputs"),
help="Directory containing input documents (default: from env or ./inputs)",
)
# LLM Model configuration
parser.add_argument(
"--llm-binding-host",
default=get_env_value("LLM_BINDING_HOST", None),
help="LLM server host URL. If not provided, defaults based on llm-binding:\n"
+ "- ollama: http://localhost:11434\n"
+ "- lollms: http://localhost:9600\n"
+ "- openai: https://api.openai.com/v1",
)
default_llm_api_key = get_env_value("LLM_BINDING_API_KEY", None)
parser.add_argument(
"--llm-binding-api-key",
default=default_llm_api_key,
help="llm server API key (default: from env or empty string)",
)
parser.add_argument(
"--llm-model",
default=get_env_value("LLM_MODEL", "mistral-nemo:latest"),
help="LLM model name (default: from env or mistral-nemo:latest)",
)
# Embedding model configuration
parser.add_argument(
"--embedding-binding-host",
default=get_env_value("EMBEDDING_BINDING_HOST", None),
help="Embedding server host URL. If not provided, defaults based on embedding-binding:\n"
+ "- ollama: http://localhost:11434\n"
+ "- lollms: http://localhost:9600\n"
+ "- openai: https://api.openai.com/v1",
)
default_embedding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "")
parser.add_argument(
"--embedding-binding-api-key",
default=default_embedding_api_key,
help="embedding server API key (default: from env or empty string)",
)
parser.add_argument(
"--embedding-model",
default=get_env_value("EMBEDDING_MODEL", "bge-m3:latest"),
help="Embedding model name (default: from env or bge-m3:latest)",
)
parser.add_argument(
"--chunk_size",
default=get_env_value("CHUNK_SIZE", 1200),
help="chunk chunk size default 1200",
)
parser.add_argument(
"--chunk_overlap_size",
default=get_env_value("CHUNK_OVERLAP_SIZE", 100),
help="chunk overlap size default 100",
)
def timeout_type(value):
if value is None or value == "None":
return None
return int(value)
parser.add_argument(
"--timeout",
default=get_env_value("TIMEOUT", None, timeout_type),
type=timeout_type,
help="Timeout in seconds (useful when using slow AI). Use None for infinite timeout",
)
# RAG configuration
parser.add_argument(
"--max-async",
type=int,
default=get_env_value("MAX_ASYNC", 4, int),
help="Maximum async operations (default: from env or 4)",
)
parser.add_argument(
"--max-tokens",
type=int,
default=get_env_value("MAX_TOKENS", 32768, int),
help="Maximum token size (default: from env or 32768)",
)
parser.add_argument(
"--embedding-dim",
type=int,
default=get_env_value("EMBEDDING_DIM", 1024, int),
help="Embedding dimensions (default: from env or 1024)",
)
parser.add_argument(
"--max-embed-tokens",
type=int,
default=get_env_value("MAX_EMBED_TOKENS", 8192, int),
help="Maximum embedding token size (default: from env or 8192)",
)
# Logging configuration
parser.add_argument(
"--log-level",
default=get_env_value("LOG_LEVEL", "INFO"),
choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
help="Logging level (default: from env or INFO)",
)
parser.add_argument(
"--key",
type=str,
default=get_env_value("LIGHTRAG_API_KEY", None),
help="API key for authentication. This protects lightrag server against unauthorized access",
)
# Optional https parameters
parser.add_argument(
"--ssl",
action="store_true",
default=get_env_value("SSL", False, bool),
help="Enable HTTPS (default: from env or False)",
)
parser.add_argument(
"--ssl-certfile",
default=get_env_value("SSL_CERTFILE", None),
help="Path to SSL certificate file (required if --ssl is enabled)",
)
parser.add_argument(
"--ssl-keyfile",
default=get_env_value("SSL_KEYFILE", None),
help="Path to SSL private key file (required if --ssl is enabled)",
)
parser.add_argument(
"--auto-scan-at-startup",
action="store_true",
default=False,
help="Enable automatic scanning when the program starts",
)
parser.add_argument(
"--history-turns",
type=int,
default=get_env_value("HISTORY_TURNS", 3, int),
help="Number of conversation history turns to include (default: from env or 3)",
)
# Search parameters
parser.add_argument(
"--top-k",
type=int,
default=get_env_value("TOP_K", 60, int),
help="Number of most similar results to return (default: from env or 60)",
)
parser.add_argument(
"--cosine-threshold",
type=float,
default=get_env_value("COSINE_THRESHOLD", 0.2, float),
help="Cosine similarity threshold (default: from env or 0.4)",
)
# Ollama model name
parser.add_argument(
"--simulated-model-name",
type=str,
default=get_env_value(
"SIMULATED_MODEL_NAME", ollama_server_infos.LIGHTRAG_MODEL
),
help="Number of conversation history turns to include (default: from env or 3)",
)
# Namespace
parser.add_argument(
"--namespace-prefix",
type=str,
default=get_env_value("NAMESPACE_PREFIX", ""),
help="Prefix of the namespace",
)
parser.add_argument(
"--verbose",
type=bool,
default=get_env_value("VERBOSE", False, bool),
help="Verbose debug output(default: from env or false)",
)
args = parser.parse_args()
# conver relative path to absolute path
args.working_dir = os.path.abspath(args.working_dir)
args.input_dir = os.path.abspath(args.input_dir)
ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name
return args
class DocumentManager:
"""Handles document operations and tracking"""
def __init__(
self,
input_dir: str,
supported_extensions: tuple = (
".txt",
".md",
".pdf",
".docx",
".pptx",
".xlsx",
),
):
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)
def scan_directory_for_new_files(self) -> List[Path]:
"""Scan input directory for new files"""
new_files = []
for ext in self.supported_extensions:
logger.info(f"Scanning for {ext} files in {self.input_dir}")
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
def scan_directory(self) -> List[Path]:
"""Scan input directory for new files"""
new_files = []
for ext in self.supported_extensions:
for file_path in self.input_dir.rglob(f"*{ext}"):
new_files.append(file_path)
return new_files
def mark_as_indexed(self, file_path: Path):
"""Mark a file as indexed"""
self.indexed_files.add(file_path)
def is_supported_file(self, filename: str) -> bool:
"""Check if file type is supported"""
return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
class QueryRequest(BaseModel):
query: str = Field(
min_length=1,
description="The query text",
)
mode: Literal["local", "global", "hybrid", "naive", "mix"] = Field(
default="hybrid",
description="Query mode",
)
only_need_context: Optional[bool] = Field(
default=None,
description="If True, only returns the retrieved context without generating a response.",
)
only_need_prompt: Optional[bool] = Field(
default=None,
description="If True, only returns the generated prompt without producing a response.",
)
response_type: Optional[str] = Field(
min_length=1,
default=None,
description="Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'.",
)
top_k: Optional[int] = Field(
ge=1,
default=None,
description="Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode.",
)
max_token_for_text_unit: Optional[int] = Field(
gt=1,
default=None,
description="Maximum number of tokens allowed for each retrieved text chunk.",
)
max_token_for_global_context: Optional[int] = Field(
gt=1,
default=None,
description="Maximum number of tokens allocated for relationship descriptions in global retrieval.",
)
max_token_for_local_context: Optional[int] = Field(
gt=1,
default=None,
description="Maximum number of tokens allocated for entity descriptions in local retrieval.",
)
hl_keywords: Optional[List[str]] = Field(
default=None,
description="List of high-level keywords to prioritize in retrieval.",
)
ll_keywords: Optional[List[str]] = Field(
default=None,
description="List of low-level keywords to refine retrieval focus.",
)
conversation_history: Optional[List[dict[str, Any]]] = Field(
default=None,
description="Stores past conversation history to maintain context. Format: [{'role': 'user/assistant', 'content': 'message'}].",
)
history_turns: Optional[int] = Field(
ge=0,
default=None,
description="Number of complete conversation turns (user-assistant pairs) to consider in the response context.",
)
@field_validator("query", mode="after")
@classmethod
def query_strip_after(cls, query: str) -> str:
return query.strip()
@field_validator("hl_keywords", mode="after")
@classmethod
def hl_keywords_strip_after(cls, hl_keywords: List[str] | None) -> List[str] | None:
if hl_keywords is None:
return None
return [keyword.strip() for keyword in hl_keywords]
@field_validator("ll_keywords", mode="after")
@classmethod
def ll_keywords_strip_after(cls, ll_keywords: List[str] | None) -> List[str] | None:
if ll_keywords is None:
return None
return [keyword.strip() for keyword in ll_keywords]
@field_validator("conversation_history", mode="after")
@classmethod
def conversation_history_role_check(
cls, conversation_history: List[dict[str, Any]] | None
) -> List[dict[str, Any]] | None:
if conversation_history is None:
return None
for msg in conversation_history:
if "role" not in msg or msg["role"] not in {"user", "assistant"}:
raise ValueError(
"Each message must have a 'role' key with value 'user' or 'assistant'."
)
return conversation_history
def to_query_params(self, is_stream: bool) -> QueryParam:
"""Converts a QueryRequest instance into a QueryParam instance."""
# Use Pydantic's `.model_dump(exclude_none=True)` to remove None values automatically
request_data = self.model_dump(exclude_none=True, exclude={"query"})
# Ensure `mode` and `stream` are set explicitly
param = QueryParam(**request_data)
param.stream = is_stream
return param
class QueryResponse(BaseModel):
response: str = Field(
description="The generated response",
)
class InsertTextRequest(BaseModel):
text: str = Field(
min_length=1,
description="The text to insert",
)
@field_validator("text", mode="after")
@classmethod
def strip_after(cls, text: str) -> str:
return text.strip()
class InsertTextsRequest(BaseModel):
texts: list[str] = Field(
min_length=1,
description="The texts to insert",
)
@field_validator("texts", mode="after")
@classmethod
def strip_after(cls, texts: list[str]) -> list[str]:
return [text.strip() for text in texts]
class InsertResponse(BaseModel):
status: str = Field(description="Status of the operation")
message: str = Field(description="Message describing the operation result")
class DocStatusResponse(BaseModel):
@staticmethod
def format_datetime(dt: Any) -> Optional[str]:
"""Format datetime to ISO string
Args:
dt: Datetime object or string
Returns:
Formatted datetime string or None
"""
if dt is None:
return None
if isinstance(dt, str):
return dt
return dt.isoformat()
"""Response model for document status
Attributes:
id: Document identifier
content_summary: Summary of document content
content_length: Length of document content
status: Current processing status
created_at: Creation timestamp (ISO format string)
updated_at: Last update timestamp (ISO format string)
chunks_count: Number of chunks (optional)
error: Error message if any (optional)
metadata: Additional metadata (optional)
"""
id: str
content_summary: str
content_length: int
status: DocStatus
created_at: str
updated_at: str
chunks_count: Optional[int] = None
error: Optional[str] = None
metadata: Optional[dict[str, Any]] = None
class DocsStatusesResponse(BaseModel):
statuses: Dict[DocStatus, List[DocStatusResponse]] = {}
def get_api_key_dependency(api_key: Optional[str]):
if not api_key:
# If no API key is configured, return a dummy dependency that always succeeds
async def no_auth():
return None
return no_auth
# If API key is configured, use proper authentication
api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
async def api_key_auth(
api_key_header_value: Optional[str] = Security(api_key_header),
):
if not api_key_header_value:
raise HTTPException(
status_code=HTTP_403_FORBIDDEN, detail="API Key required"
)
if api_key_header_value != api_key:
raise HTTPException(
status_code=HTTP_403_FORBIDDEN, detail="Invalid API Key"
)
return api_key_header_value
return api_key_auth
# Global configuration
global_top_k = 60 # default value
temp_prefix = "__tmp_" # prefix for temporary files
def create_app(args):
# Initialize verbose debug setting
from lightrag.utils import set_verbose_debug
set_verbose_debug(args.verbose)
global global_top_k
global_top_k = args.top_k # save top_k from args
# Verify that bindings are correctly setup
if args.llm_binding not in [
"lollms",
"ollama",
"openai",
"openai-ollama",
"azure_openai",
]:
raise Exception("llm binding not supported")
if args.embedding_binding not in ["lollms", "ollama", "openai", "azure_openai"]:
raise Exception("embedding binding not supported")
# Set default hosts if not provided
if args.llm_binding_host is None:
args.llm_binding_host = get_default_host(args.llm_binding)
if args.embedding_binding_host is None:
args.embedding_binding_host = get_default_host(args.embedding_binding)
# Add SSL validation
if args.ssl:
if not args.ssl_certfile or not args.ssl_keyfile:
raise Exception(
"SSL certificate and key files must be provided when SSL is enabled"
)
if not os.path.exists(args.ssl_certfile):
raise Exception(f"SSL certificate file not found: {args.ssl_certfile}")
if not os.path.exists(args.ssl_keyfile):
raise Exception(f"SSL key file not found: {args.ssl_keyfile}")
# Setup logging
logging.basicConfig(
format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level)
)
# Check if API key is provided either through env var or args
api_key = os.getenv("LIGHTRAG_API_KEY") or args.key
# Initialize document manager
doc_manager = DocumentManager(args.input_dir)
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Lifespan context manager for startup and shutdown events"""
# Initialize database connections
db_instances = {}
# Store background tasks
app.state.background_tasks = set()
try:
# Check which database types are used
db_types = set()
for storage_name, storage_instance in storage_instances:
db_type = get_db_type_from_storage_class(
storage_instance.__class__.__name__
)
if db_type:
db_types.add(db_type)
# Import and initialize databases as needed
for db_type in db_types:
if db_type == "postgres":
DB = import_db_module("postgres")
db = DB(_get_postgres_config())
await db.initdb()
await db.check_tables()
db_instances["postgres"] = db
elif db_type == "oracle":
DB = import_db_module("oracle")
db = DB(_get_oracle_config())
await db.check_tables()
db_instances["oracle"] = db
elif db_type == "tidb":
DB = import_db_module("tidb")
db = DB(_get_tidb_config())
await db.check_tables()
db_instances["tidb"] = db
# Inject database instances into storage classes
for storage_name, storage_instance in storage_instances:
db_type = get_db_type_from_storage_class(
storage_instance.__class__.__name__
)
if db_type:
if db_type not in db_instances:
error_msg = f"Database type '{db_type}' is required by {storage_name} but not initialized"
logger.error(error_msg)
raise RuntimeError(error_msg)
storage_instance.db = db_instances[db_type]
logger.info(f"Injected {db_type} db to {storage_name}")
# Auto scan documents if enabled
if args.auto_scan_at_startup:
# Start scanning in background
with progress_lock:
if not scan_progress["is_scanning"]:
scan_progress["is_scanning"] = True
scan_progress["indexed_count"] = 0
scan_progress["progress"] = 0
# Create background task
task = asyncio.create_task(run_scanning_process())
app.state.background_tasks.add(task)
task.add_done_callback(app.state.background_tasks.discard)
ASCIIColors.info(
f"Started background scanning of documents from {args.input_dir}"
)
else:
ASCIIColors.info(
"Skip document scanning(anohter scanning is active)"
)
yield
finally:
# Clean up database connections
for db_type, db in db_instances.items():
if hasattr(db, "pool"):
await db.pool.close()
# Use more accurate database name display
db_names = {
"postgres": "PostgreSQL",
"oracle": "Oracle",
"tidb": "TiDB",
}
db_name = db_names.get(db_type, db_type)
logger.info(f"Closed {db_name} database connection pool")
# Initialize FastAPI
app = FastAPI(
title="LightRAG API",
description="API for querying text using LightRAG with separate storage and input directories"
+ "(With authentication)"
if api_key
else "",
version=__api_version__,
openapi_tags=[{"name": "api"}],
lifespan=lifespan,
)
def get_cors_origins():
"""Get allowed origins from environment variable
Returns a list of allowed origins, defaults to ["*"] if not set
"""
origins_str = os.getenv("CORS_ORIGINS", "*")
if origins_str == "*":
return ["*"]
return [origin.strip() for origin in origins_str.split(",")]
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=get_cors_origins(),
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Database configuration functions
def _get_postgres_config():
return {
"host": os.environ.get(
"POSTGRES_HOST",
config.get("postgres", "host", fallback="localhost"),
),
"port": os.environ.get(
"POSTGRES_PORT", config.get("postgres", "port", fallback=5432)
),
"user": os.environ.get(
"POSTGRES_USER", config.get("postgres", "user", fallback=None)
),
"password": os.environ.get(
"POSTGRES_PASSWORD",
config.get("postgres", "password", fallback=None),
),
"database": os.environ.get(
"POSTGRES_DATABASE",
config.get("postgres", "database", fallback=None),
),
"workspace": os.environ.get(
"POSTGRES_WORKSPACE",
config.get("postgres", "workspace", fallback="default"),
),
}
def _get_oracle_config():
return {
"user": os.environ.get(
"ORACLE_USER",
config.get("oracle", "user", fallback=None),
),
"password": os.environ.get(
"ORACLE_PASSWORD",
config.get("oracle", "password", fallback=None),
),
"dsn": os.environ.get(
"ORACLE_DSN",
config.get("oracle", "dsn", fallback=None),
),
"config_dir": os.environ.get(
"ORACLE_CONFIG_DIR",
config.get("oracle", "config_dir", fallback=None),
),
"wallet_location": os.environ.get(
"ORACLE_WALLET_LOCATION",
config.get("oracle", "wallet_location", fallback=None),
),
"wallet_password": os.environ.get(
"ORACLE_WALLET_PASSWORD",
config.get("oracle", "wallet_password", fallback=None),
),
"workspace": os.environ.get(
"ORACLE_WORKSPACE",
config.get("oracle", "workspace", fallback="default"),
),
}
def _get_tidb_config():
return {
"host": os.environ.get(
"TIDB_HOST",
config.get("tidb", "host", fallback="localhost"),
),
"port": os.environ.get(
"TIDB_PORT", config.get("tidb", "port", fallback=4000)
),
"user": os.environ.get(
"TIDB_USER",
config.get("tidb", "user", fallback=None),
),
"password": os.environ.get(
"TIDB_PASSWORD",
config.get("tidb", "password", fallback=None),
),
"database": os.environ.get(
"TIDB_DATABASE",
config.get("tidb", "database", fallback=None),
),
"workspace": os.environ.get(
"TIDB_WORKSPACE",
config.get("tidb", "workspace", fallback="default"),
),
}
# Create the optional API key dependency
optional_api_key = get_api_key_dependency(api_key)
# Create working directory if it doesn't exist
Path(args.working_dir).mkdir(parents=True, exist_ok=True)
if args.llm_binding == "lollms" or args.embedding_binding == "lollms":
from lightrag.llm.lollms import lollms_model_complete, lollms_embed
if args.llm_binding == "ollama" or args.embedding_binding == "ollama":
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
if args.llm_binding == "openai" or args.embedding_binding == "openai":
from lightrag.llm.openai import openai_complete_if_cache, openai_embed
if args.llm_binding == "azure_openai" or args.embedding_binding == "azure_openai":
from lightrag.llm.azure_openai import (
azure_openai_complete_if_cache,
azure_openai_embed,
)
if args.llm_binding_host == "openai-ollama" or args.embedding_binding == "ollama":
from lightrag.llm.openai import openai_complete_if_cache
from lightrag.llm.ollama import ollama_embed
async def openai_alike_model_complete(
prompt,
system_prompt=None,
history_messages=None,
keyword_extraction=False,
**kwargs,
) -> str:
keyword_extraction = kwargs.pop("keyword_extraction", None)
if keyword_extraction:
kwargs["response_format"] = GPTKeywordExtractionFormat
if history_messages is None:
history_messages = []
return await openai_complete_if_cache(
args.llm_model,
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
base_url=args.llm_binding_host,
api_key=args.llm_binding_api_key,
**kwargs,
)
async def azure_openai_model_complete(
prompt,
system_prompt=None,
history_messages=None,
keyword_extraction=False,
**kwargs,
) -> str:
keyword_extraction = kwargs.pop("keyword_extraction", None)
if keyword_extraction:
kwargs["response_format"] = GPTKeywordExtractionFormat
if history_messages is None:
history_messages = []
return await azure_openai_complete_if_cache(
args.llm_model,
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
base_url=args.llm_binding_host,
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-08-01-preview"),
**kwargs,
)
embedding_func = EmbeddingFunc(
embedding_dim=args.embedding_dim,
max_token_size=args.max_embed_tokens,
func=lambda texts: lollms_embed(
texts,
embed_model=args.embedding_model,
host=args.embedding_binding_host,
api_key=args.embedding_binding_api_key,
)
if args.embedding_binding == "lollms"
else ollama_embed(
texts,
embed_model=args.embedding_model,
host=args.embedding_binding_host,
api_key=args.embedding_binding_api_key,
)
if args.embedding_binding == "ollama"
else azure_openai_embed(
texts,
model=args.embedding_model, # no host is used for openai,
api_key=args.embedding_binding_api_key,
)
if args.embedding_binding == "azure_openai"
else openai_embed(
texts,
model=args.embedding_model,
base_url=args.embedding_binding_host,
api_key=args.embedding_binding_api_key,
),
)
# Initialize RAG
if args.llm_binding in ["lollms", "ollama", "openai-ollama"]:
rag = LightRAG(
working_dir=args.working_dir,
llm_model_func=lollms_model_complete
if args.llm_binding == "lollms"
else ollama_model_complete
if args.llm_binding == "ollama"
else openai_alike_model_complete,
llm_model_name=args.llm_model,
llm_model_max_async=args.max_async,
llm_model_max_token_size=args.max_tokens,
chunk_token_size=int(args.chunk_size),
chunk_overlap_token_size=int(args.chunk_overlap_size),
llm_model_kwargs={
"host": args.llm_binding_host,
"timeout": args.timeout,
"options": {"num_ctx": args.max_tokens},
"api_key": args.llm_binding_api_key,
}
if args.llm_binding == "lollms" or args.llm_binding == "ollama"
else {},
embedding_func=embedding_func,
kv_storage=args.kv_storage,
graph_storage=args.graph_storage,
vector_storage=args.vector_storage,
doc_status_storage=args.doc_status_storage,
vector_db_storage_cls_kwargs={
"cosine_better_than_threshold": args.cosine_threshold
},
enable_llm_cache_for_entity_extract=False, # set to True for debuging to reduce llm fee
embedding_cache_config={
"enabled": True,
"similarity_threshold": 0.95,
"use_llm_check": False,
},
log_level=args.log_level,
namespace_prefix=args.namespace_prefix,
)
else:
rag = LightRAG(
working_dir=args.working_dir,
llm_model_func=azure_openai_model_complete
if args.llm_binding == "azure_openai"
else openai_alike_model_complete,
chunk_token_size=int(args.chunk_size),
chunk_overlap_token_size=int(args.chunk_overlap_size),
llm_model_kwargs={
"timeout": args.timeout,
},
llm_model_name=args.llm_model,
llm_model_max_async=args.max_async,
llm_model_max_token_size=args.max_tokens,
embedding_func=embedding_func,
kv_storage=args.kv_storage,
graph_storage=args.graph_storage,
vector_storage=args.vector_storage,
doc_status_storage=args.doc_status_storage,
vector_db_storage_cls_kwargs={
"cosine_better_than_threshold": args.cosine_threshold
},
enable_llm_cache_for_entity_extract=False, # set to True for debuging to reduce llm fee
embedding_cache_config={
"enabled": True,
"similarity_threshold": 0.95,
"use_llm_check": False,
},
log_level=args.log_level,
namespace_prefix=args.namespace_prefix,
)
# Collect all storage instances
storage_instances = [
("full_docs", rag.full_docs),
("text_chunks", rag.text_chunks),
("chunk_entity_relation_graph", rag.chunk_entity_relation_graph),
("entities_vdb", rag.entities_vdb),
("relationships_vdb", rag.relationships_vdb),
("chunks_vdb", rag.chunks_vdb),
("doc_status", rag.doc_status),
("llm_response_cache", rag.llm_response_cache),
]
async def pipeline_enqueue_file(file_path: Path) -> bool:
"""Add a file to the queue for processing
Args:
file_path: Path to the saved file
Returns:
bool: True if the file was successfully enqueued, False otherwise
"""
try:
content = ""
ext = file_path.suffix.lower()
file = None
async with aiofiles.open(file_path, "rb") as f:
file = await f.read()
# Process based on file type
match ext:
case ".txt" | ".md":
content = file.decode("utf-8")
case ".pdf":
if not pm.is_installed("pypdf2"):
pm.install("pypdf2")
from PyPDF2 import PdfReader # type: ignore
from io import BytesIO
pdf_file = BytesIO(file)
reader = PdfReader(pdf_file)
for page in reader.pages:
content += page.extract_text() + "\n"
case ".docx":
if not pm.is_installed("docx"):
pm.install("docx")
from docx import Document
from io import BytesIO
docx_content = await file.read()
docx_file = BytesIO(docx_content)
doc = Document(docx_file)
content = "\n".join(
[paragraph.text for paragraph in doc.paragraphs]
)
case ".pptx":
if not pm.is_installed("pptx"):
pm.install("pptx")
from pptx import Presentation # type: ignore
from io import BytesIO
pptx_content = await file.read()
pptx_file = BytesIO(pptx_content)
prs = Presentation(pptx_file)
for slide in prs.slides:
for shape in slide.shapes:
if hasattr(shape, "text"):
content += shape.text + "\n"
case _:
logging.error(
f"Unsupported file type: {file_path.name} (extension {ext})"
)
return False
# Insert into the RAG queue
if content:
await rag.apipeline_enqueue_documents(content)
logging.info(
f"Successfully processed and enqueued file: {file_path.name}"
)
return True
else:
logging.error(
f"No content could be extracted from file: {file_path.name}"
)
except Exception as e:
logging.error(
f"Error processing or enqueueing file {file_path.name}: {str(e)}"
)
logging.error(traceback.format_exc())
finally:
if file_path.name.startswith(temp_prefix):
# Clean up the temporary file after indexing
try:
file_path.unlink()
except Exception as e:
logging.error(f"Error deleting file {file_path}: {str(e)}")
return False
async def pipeline_index_file(file_path: Path):
"""Index a file
Args:
file_path: Path to the saved file
"""
try:
if await pipeline_enqueue_file(file_path):
await rag.apipeline_process_enqueue_documents()
except Exception as e:
logging.error(f"Error indexing file {file_path.name}: {str(e)}")
logging.error(traceback.format_exc())
async def pipeline_index_files(file_paths: List[Path]):
"""Index multiple files concurrently
Args:
file_paths: Paths to the files to index
"""
if not file_paths:
return
try:
enqueued = False
if len(file_paths) == 1:
enqueued = await pipeline_enqueue_file(file_paths[0])
else:
tasks = [pipeline_enqueue_file(path) for path in file_paths]
enqueued = any(await asyncio.gather(*tasks))
if enqueued:
await rag.apipeline_process_enqueue_documents()
except Exception as e:
logging.error(f"Error indexing files: {str(e)}")
logging.error(traceback.format_exc())
async def pipeline_index_texts(texts: List[str]):
"""Index a list of texts
Args:
texts: The texts to index
"""
if not texts:
return
await rag.apipeline_enqueue_documents(texts)
await rag.apipeline_process_enqueue_documents()
async def save_temp_file(file: UploadFile = File(...)) -> Path:
"""Save the uploaded file to a temporary location
Args:
file: The uploaded file
Returns:
Path: The path to the saved file
"""
# Generate unique filename to avoid conflicts
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
unique_filename = f"{temp_prefix}{timestamp}_{file.filename}"
# Create a temporary file to save the uploaded content
temp_path = doc_manager.input_dir / "temp" / unique_filename
temp_path.parent.mkdir(exist_ok=True)
# Save the file
with open(temp_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
return temp_path
async def run_scanning_process():
"""Background task to scan and index documents"""
global scan_progress
try:
new_files = doc_manager.scan_directory_for_new_files()
scan_progress["total_files"] = len(new_files)
logger.info(f"Found {len(new_files)} new files to index.")
for file_path in new_files:
try:
with progress_lock:
scan_progress["current_file"] = os.path.basename(file_path)
await pipeline_index_file(file_path)
with progress_lock:
scan_progress["indexed_count"] += 1
scan_progress["progress"] = (
scan_progress["indexed_count"]
/ scan_progress["total_files"]
) * 100
except Exception as e:
logging.error(f"Error indexing file {file_path}: {str(e)}")
except Exception as e:
logging.error(f"Error during scanning process: {str(e)}")
finally:
with progress_lock:
scan_progress["is_scanning"] = False
@app.post("/documents/scan", dependencies=[Depends(optional_api_key)])
async def scan_for_new_documents(background_tasks: BackgroundTasks):
"""Trigger the scanning process"""
global scan_progress
with progress_lock:
if scan_progress["is_scanning"]:
return {"status": "already_scanning"}
scan_progress["is_scanning"] = True
scan_progress["indexed_count"] = 0
scan_progress["progress"] = 0
# Start the scanning process in the background
background_tasks.add_task(run_scanning_process)
return {"status": "scanning_started"}
@app.get("/documents/scan-progress")
async def get_scan_progress():
"""Get the current scanning progress"""
with progress_lock:
return scan_progress
@app.post("/documents/upload", dependencies=[Depends(optional_api_key)])
async def upload_to_input_dir(
background_tasks: BackgroundTasks, file: UploadFile = File(...)
):
"""
Endpoint for uploading a file to the input directory and indexing it.
This API endpoint accepts a file through an HTTP POST request, checks if the
uploaded file is of a supported type, saves it in the specified input directory,
indexes it for retrieval, and returns a success status with relevant details.
Parameters:
background_tasks: FastAPI BackgroundTasks for async processing
file (UploadFile): The file to be uploaded. It must have an allowed extension as per
`doc_manager.supported_extensions`.
Returns:
dict: A dictionary containing the upload status ("success"),
a message detailing the operation result, and
the total number of indexed documents.
Raises:
HTTPException: If the file type is not supported, it raises a 400 Bad Request error.
If any other exception occurs during the file handling or indexing,
it raises a 500 Internal Server Error with details about the exception.
"""
try:
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}",
)
file_path = doc_manager.input_dir / file.filename
with open(file_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
# Add to background tasks
background_tasks.add_task(pipeline_index_file, file_path)
return InsertResponse(
status="success",
message=f"File '{file.filename}' uploaded successfully. Processing will continue in background.",
)
except Exception as e:
logging.error(f"Error /documents/upload: {file.filename}: {str(e)}")
logging.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@app.post(
"/documents/text",
response_model=InsertResponse,
dependencies=[Depends(optional_api_key)],
)
async def insert_text(
request: InsertTextRequest, background_tasks: BackgroundTasks
):
"""
Insert text into the Retrieval-Augmented Generation (RAG) system.
This endpoint allows you to insert text data into the RAG system for later retrieval and use in generating responses.
Args:
request (InsertTextRequest): The request body containing the text to be inserted.
background_tasks: FastAPI BackgroundTasks for async processing
Returns:
InsertResponse: A response object containing the status of the operation, a message, and the number of documents inserted.
"""
try:
background_tasks.add_task(pipeline_index_texts, [request.text])
return InsertResponse(
status="success",
message="Text successfully received. Processing will continue in background.",
)
except Exception as e:
logging.error(f"Error /documents/text: {str(e)}")
logging.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@app.post(
"/documents/texts",
response_model=InsertResponse,
dependencies=[Depends(optional_api_key)],
)
async def insert_texts(
request: InsertTextsRequest, background_tasks: BackgroundTasks
):
"""
Insert texts into the Retrieval-Augmented Generation (RAG) system.
This endpoint allows you to insert text data into the RAG system for later retrieval and use in generating responses.
Args:
request (InsertTextsRequest): The request body containing the text to be inserted.
background_tasks: FastAPI BackgroundTasks for async processing
Returns:
InsertResponse: A response object containing the status of the operation, a message, and the number of documents inserted.
"""
try:
background_tasks.add_task(pipeline_index_texts, request.texts)
return InsertResponse(
status="success",
message="Text successfully received. Processing will continue in background.",
)
except Exception as e:
logging.error(f"Error /documents/text: {str(e)}")
logging.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@app.post(
"/documents/file",
response_model=InsertResponse,
dependencies=[Depends(optional_api_key)],
)
async def insert_file(
background_tasks: BackgroundTasks, file: UploadFile = File(...)
):
"""Insert a file directly into the RAG system
Args:
background_tasks: FastAPI BackgroundTasks for async processing
file: Uploaded file
Returns:
InsertResponse: Status of the insertion operation
Raises:
HTTPException: For unsupported file types or processing errors
"""
try:
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}",
)
# Create a temporary file to save the uploaded content
temp_path = save_temp_file(file)
# Add to background tasks
background_tasks.add_task(pipeline_index_file, temp_path)
return InsertResponse(
status="success",
message=f"File '{file.filename}' saved successfully. Processing will continue in background.",
)
except Exception as e:
logging.error(f"Error /documents/file: {str(e)}")
logging.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@app.post(
"/documents/file_batch",
response_model=InsertResponse,
dependencies=[Depends(optional_api_key)],
)
async def insert_batch(
background_tasks: BackgroundTasks, files: List[UploadFile] = File(...)
):
"""Process multiple files in batch mode
Args:
background_tasks: FastAPI BackgroundTasks for async processing
files: List of files to process
Returns:
InsertResponse: Status of the batch insertion operation
Raises:
HTTPException: For processing errors
"""
try:
inserted_count = 0
failed_files = []
temp_files = []
for file in files:
if doc_manager.is_supported_file(file.filename):
# Create a temporary file to save the uploaded content
temp_files.append(save_temp_file(file))
inserted_count += 1
else:
failed_files.append(f"{file.filename} (unsupported type)")
if temp_files:
background_tasks.add_task(pipeline_index_files, temp_files)
# Prepare status message
if inserted_count == len(files):
status = "success"
status_message = f"Successfully inserted all {inserted_count} documents"
elif inserted_count > 0:
status = "partial_success"
status_message = f"Successfully inserted {inserted_count} out of {len(files)} documents"
if failed_files:
status_message += f". Failed files: {', '.join(failed_files)}"
else:
status = "failure"
status_message = "No documents were successfully inserted"
if failed_files:
status_message += f". Failed files: {', '.join(failed_files)}"
return InsertResponse(status=status, message=status_message)
except Exception as e:
logging.error(f"Error /documents/batch: {file.filename}: {str(e)}")
logging.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@app.delete(
"/documents",
response_model=InsertResponse,
dependencies=[Depends(optional_api_key)],
)
async def clear_documents():
"""
Clear all documents from the LightRAG system.
This endpoint deletes all text chunks, entities vector database, and relationships vector database,
effectively clearing all documents from the LightRAG system.
Returns:
InsertResponse: A response object containing the status, message, and the new document count (0 in this case).
"""
try:
rag.text_chunks = []
rag.entities_vdb = None
rag.relationships_vdb = None
return InsertResponse(
status="success", message="All documents cleared successfully"
)
except Exception as e:
logging.error(f"Error DELETE /documents: {str(e)}")
logging.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@app.post(
"/query", response_model=QueryResponse, dependencies=[Depends(optional_api_key)]
)
async def query_text(request: QueryRequest):
"""
Handle a POST request at the /query endpoint to process user queries using RAG capabilities.
Parameters:
request (QueryRequest): The request object containing the query parameters.
Returns:
QueryResponse: A Pydantic model containing the result of the query processing.
If a string is returned (e.g., cache hit), it's directly returned.
Otherwise, an async generator may be used to build the response.
Raises:
HTTPException: Raised when an error occurs during the request handling process,
with status code 500 and detail containing the exception message.
"""
try:
response = await rag.aquery(
request.query, param=request.to_query_params(False)
)
# If response is a string (e.g. cache hit), return directly
if isinstance(response, str):
return QueryResponse(response=response)
if isinstance(response, dict):
result = json.dumps(response, indent=2)
return QueryResponse(response=result)
else:
return QueryResponse(response=str(response))
except Exception as e:
trace_exception(e)
raise HTTPException(status_code=500, detail=str(e))
@app.post("/query/stream", dependencies=[Depends(optional_api_key)])
async def query_text_stream(request: QueryRequest):
"""
This endpoint performs a retrieval-augmented generation (RAG) query and streams the response.
Args:
request (QueryRequest): The request object containing the query parameters.
optional_api_key (Optional[str], optional): An optional API key for authentication. Defaults to None.
Returns:
StreamingResponse: A streaming response containing the RAG query results.
"""
try:
response = await rag.aquery(
request.query, param=request.to_query_params(True)
)
from fastapi.responses import StreamingResponse
async def stream_generator():
if isinstance(response, str):
# If it's a string, send it all at once
yield f"{json.dumps({'response': response})}\n"
else:
# If it's an async generator, send chunks one by one
try:
async for chunk in response:
if chunk: # Only send non-empty content
yield f"{json.dumps({'response': chunk})}\n"
except Exception as e:
logging.error(f"Streaming error: {str(e)}")
yield f"{json.dumps({'error': str(e)})}\n"
return StreamingResponse(
stream_generator(),
media_type="application/x-ndjson",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"Content-Type": "application/x-ndjson",
"X-Accel-Buffering": "no", # Ensure proper handling of streaming response when proxied by Nginx
},
)
except Exception as e:
trace_exception(e)
raise HTTPException(status_code=500, detail=str(e))
# query all graph labels
@app.get("/graph/label/list")
async def get_graph_labels():
return await rag.get_graph_labels()
# query all graph
@app.get("/graphs")
async def get_knowledge_graph(label: str):
return await rag.get_knowledge_graph(nodel_label=label, max_depth=100)
# Add Ollama API routes
ollama_api = OllamaAPI(rag, top_k=args.top_k)
app.include_router(ollama_api.router, prefix="/api")
@app.get("/documents", dependencies=[Depends(optional_api_key)])
async def documents() -> DocsStatusesResponse:
"""
Get documents statuses
Returns:
DocsStatusesResponse: A response object containing a dictionary where keys are DocStatus
and values are lists of DocStatusResponse objects representing documents in each status category.
"""
try:
statuses = (
DocStatus.PENDING,
DocStatus.PROCESSING,
DocStatus.PROCESSED,
DocStatus.FAILED,
)
tasks = [rag.get_docs_by_status(status) for status in statuses]
results: List[Dict[str, DocProcessingStatus]] = await asyncio.gather(*tasks)
response = DocsStatusesResponse()
for idx, result in enumerate(results):
status = statuses[idx]
for doc_id, doc_status in result.items():
if status not in response.statuses:
response.statuses[status] = []
response.statuses[status].append(
DocStatusResponse(
id=doc_id,
content_summary=doc_status.content_summary,
content_length=doc_status.content_length,
status=doc_status.status,
created_at=DocStatusResponse.format_datetime(
doc_status.created_at
),
updated_at=DocStatusResponse.format_datetime(
doc_status.updated_at
),
chunks_count=doc_status.chunks_count,
error=doc_status.error,
metadata=doc_status.metadata,
)
)
return response
except Exception as e:
logging.error(f"Error GET /documents: {str(e)}")
logging.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health", dependencies=[Depends(optional_api_key)])
async def get_status():
"""Get current system status"""
return {
"status": "healthy",
"working_directory": str(args.working_dir),
"input_directory": str(args.input_dir),
"configuration": {
# LLM configuration binding/host address (if applicable)/model (if applicable)
"llm_binding": args.llm_binding,
"llm_binding_host": args.llm_binding_host,
"llm_model": args.llm_model,
# embedding model configuration binding/host address (if applicable)/model (if applicable)
"embedding_binding": args.embedding_binding,
"embedding_binding_host": args.embedding_binding_host,
"embedding_model": args.embedding_model,
"max_tokens": args.max_tokens,
"kv_storage": args.kv_storage,
"doc_status_storage": args.doc_status_storage,
"graph_storage": args.graph_storage,
"vector_storage": args.vector_storage,
},
}
# Webui mount webui/index.html
static_dir = Path(__file__).parent / "webui"
static_dir.mkdir(exist_ok=True)
app.mount("/webui", StaticFiles(directory=static_dir, html=True), name="webui")
return app
def main():
args = parse_args()
import uvicorn
app = create_app(args)
display_splash_screen(args)
uvicorn_config = {
"app": app,
"host": args.host,
"port": args.port,
}
if args.ssl:
uvicorn_config.update(
{
"ssl_certfile": args.ssl_certfile,
"ssl_keyfile": args.ssl_keyfile,
}
)
uvicorn.run(**uvicorn_config)
if __name__ == "__main__":
main()
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