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cherry-pick 48b67d30 (resolved)

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Raphaël MANSUY 2025-12-04 19:17:51 +08:00
parent 142fcf9592
commit b9af4ce724
1 changed files with 349 additions and 62 deletions
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411
lightrag/api/lightrag_server.py
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@ -15,7 +15,6 @@ import logging.config
import sys
import uvicorn
import pipmaster as pm
import inspect
from fastapi.staticfiles import StaticFiles
from fastapi.responses import RedirectResponse
from pathlib import Path
@ -57,7 +56,8 @@ from lightrag.api.routers.ollama_api import OllamaAPI
from lightrag.utils import logger, set_verbose_debug
from lightrag.kg.shared_storage import (
get_namespace_data,
initialize_pipeline_status,
get_default_workspace,
# set_default_workspace,
cleanup_keyed_lock,
finalize_share_data,
)
@ -90,6 +90,7 @@ class LLMConfigCache:
# Initialize configurations based on binding conditions
self.openai_llm_options = None
self.gemini_llm_options = None
self.gemini_embedding_options = None
self.ollama_llm_options = None
self.ollama_embedding_options = None
@ -136,24 +137,44 @@ class LLMConfigCache:
)
self.ollama_embedding_options = {}
# Only initialize and log Gemini Embedding options when using Gemini Embedding binding
if args.embedding_binding == "gemini":
try:
from lightrag.llm.binding_options import GeminiEmbeddingOptions
self.gemini_embedding_options = GeminiEmbeddingOptions.options_dict(
args
)
logger.info(
f"Gemini Embedding Options: {self.gemini_embedding_options}"
)
except ImportError:
logger.warning(
"GeminiEmbeddingOptions not available, using default configuration"
)
self.gemini_embedding_options = {}
def check_frontend_build():
"""Check if frontend is built and optionally check if source is up-to-date
Returns:
bool: True if frontend is outdated, False if up-to-date or production environment
tuple: (assets_exist: bool, is_outdated: bool)
- assets_exist: True if WebUI build files exist
- is_outdated: True if source is newer than build (only in dev environment)
"""
webui_dir = Path(__file__).parent / "webui"
index_html = webui_dir / "index.html"
# 1. Check if build files exist (required)
# 1. Check if build files exist
if not index_html.exists():
ASCIIColors.red("\n" + "=" * 80)
ASCIIColors.red("ERROR: Frontend Not Built")
ASCIIColors.red("=" * 80)
ASCIIColors.yellow("\n" + "=" * 80)
ASCIIColors.yellow("WARNING: Frontend Not Built")
ASCIIColors.yellow("=" * 80)
ASCIIColors.yellow("The WebUI frontend has not been built yet.")
ASCIIColors.yellow("The API server will start without the WebUI interface.")
ASCIIColors.yellow(
"Please build the frontend code first using the following commands:\n"
"\nTo enable WebUI, build the frontend using these commands:\n"
)
ASCIIColors.cyan(" cd lightrag_webui")
ASCIIColors.cyan(" bun install --frozen-lockfile")
@ -163,8 +184,8 @@ def check_frontend_build():
ASCIIColors.cyan(
"Note: Make sure you have Bun installed. Visit https://bun.sh for installation."
)
ASCIIColors.red("=" * 80 + "\n")
sys.exit(1) # Exit immediately
ASCIIColors.yellow("=" * 80 + "\n")
return (False, False) # Assets don't exist, not outdated
# 2. Check if this is a development environment (source directory exists)
try:
@ -177,7 +198,7 @@ def check_frontend_build():
logger.debug(
"Production environment detected, skipping source freshness check"
)
return False
return (True, False) # Assets exist, not outdated (prod environment)
# Development environment, perform source code timestamp check
logger.debug("Development environment detected, checking source freshness")
@ -252,20 +273,20 @@ def check_frontend_build():
ASCIIColors.cyan(" cd ..")
ASCIIColors.yellow("\nThe server will continue with the current build.")
ASCIIColors.yellow("=" * 80 + "\n")
return True # Frontend is outdated
return (True, True) # Assets exist, outdated
else:
logger.info("Frontend build is up-to-date")
return False # Frontend is up-to-date
return (True, False) # Assets exist, up-to-date
except Exception as e:
# If check fails, log warning but don't affect startup
logger.warning(f"Failed to check frontend source freshness: {e}")
return False # Assume up-to-date on error
return (True, False) # Assume assets exist and up-to-date on error
def create_app(args):
# Check frontend build first and get outdated status
is_frontend_outdated = check_frontend_build()
# Check frontend build first and get status
webui_assets_exist, is_frontend_outdated = check_frontend_build()
# Create unified API version display with warning symbol if frontend is outdated
api_version_display = (
@ -297,6 +318,7 @@ def create_app(args):
"azure_openai",
"aws_bedrock",
"jina",
"gemini",
]:
raise Exception("embedding binding not supported")
@ -332,8 +354,8 @@ def create_app(args):
try:
# Initialize database connections
# Note: initialize_storages() now auto-initializes pipeline_status for rag.workspace
await rag.initialize_storages()
await initialize_pipeline_status()
# Data migration regardless of storage implementation
await rag.check_and_migrate_data()
@ -434,6 +456,28 @@ def create_app(args):
# Create combined auth dependency for all endpoints
combined_auth = get_combined_auth_dependency(api_key)
def get_workspace_from_request(request: Request) -> str | None:
"""
Extract workspace from HTTP request header or use default.
This enables multi-workspace API support by checking the custom
'LIGHTRAG-WORKSPACE' header. If not present, falls back to the
server's default workspace configuration.
Args:
request: FastAPI Request object
Returns:
Workspace identifier (may be empty string for global namespace)
"""
# Check custom header first
workspace = request.headers.get("LIGHTRAG-WORKSPACE", "").strip()
if not workspace:
workspace = None
return workspace
# Create working directory if it doesn't exist
Path(args.working_dir).mkdir(parents=True, exist_ok=True)
@ -599,34 +643,109 @@ def create_app(args):
return {}
def create_optimized_embedding_function(
config_cache: LLMConfigCache, binding, model, host, api_key, dimensions, args
):
config_cache: LLMConfigCache, binding, model, host, api_key, args
) -> EmbeddingFunc:
"""
Create optimized embedding function with pre-processed configuration for applicable bindings.
Uses lazy imports for all bindings and avoids repeated configuration parsing.
Create optimized embedding function and return an EmbeddingFunc instance
with proper max_token_size inheritance from provider defaults.
This function:
1. Imports the provider embedding function
2. Extracts max_token_size and embedding_dim from provider if it's an EmbeddingFunc
3. Creates an optimized wrapper that calls the underlying function directly (avoiding double-wrapping)
4. Returns a properly configured EmbeddingFunc instance
"""
async def optimized_embedding_function(texts):
# Step 1: Import provider function and extract default attributes
provider_func = None
provider_max_token_size = None
provider_embedding_dim = None
try:
if binding == "openai":
from lightrag.llm.openai import openai_embed
provider_func = openai_embed
elif binding == "ollama":
from lightrag.llm.ollama import ollama_embed
provider_func = ollama_embed
elif binding == "gemini":
from lightrag.llm.gemini import gemini_embed
provider_func = gemini_embed
elif binding == "jina":
from lightrag.llm.jina import jina_embed
provider_func = jina_embed
elif binding == "azure_openai":
from lightrag.llm.azure_openai import azure_openai_embed
provider_func = azure_openai_embed
elif binding == "aws_bedrock":
from lightrag.llm.bedrock import bedrock_embed
provider_func = bedrock_embed
elif binding == "lollms":
from lightrag.llm.lollms import lollms_embed
provider_func = lollms_embed
# Extract attributes if provider is an EmbeddingFunc
if provider_func and isinstance(provider_func, EmbeddingFunc):
provider_max_token_size = provider_func.max_token_size
provider_embedding_dim = provider_func.embedding_dim
logger.debug(
f"Extracted from {binding} provider: "
f"max_token_size={provider_max_token_size}, "
f"embedding_dim={provider_embedding_dim}"
)
except ImportError as e:
logger.warning(f"Could not import provider function for {binding}: {e}")
# Step 2: Apply priority (user config > provider default)
# For max_token_size: explicit env var > provider default > None
final_max_token_size = args.embedding_token_limit or provider_max_token_size
# For embedding_dim: user config (always has value) takes priority
# Only use provider default if user config is explicitly None (which shouldn't happen)
final_embedding_dim = (
args.embedding_dim if args.embedding_dim else provider_embedding_dim
)
# Step 3: Create optimized embedding function (calls underlying function directly)
async def optimized_embedding_function(texts, embedding_dim=None):
try:
if binding == "lollms":
from lightrag.llm.lollms import lollms_embed
return await lollms_embed(
# Get real function, skip EmbeddingFunc wrapper if present
actual_func = (
lollms_embed.func
if isinstance(lollms_embed, EmbeddingFunc)
else lollms_embed
)
return await actual_func(
texts, embed_model=model, host=host, api_key=api_key
)
elif binding == "ollama":
from lightrag.llm.ollama import ollama_embed
# Use pre-processed configuration if available, otherwise fallback to dynamic parsing
# Get real function, skip EmbeddingFunc wrapper if present
actual_func = (
ollama_embed.func
if isinstance(ollama_embed, EmbeddingFunc)
else ollama_embed
)
# Use pre-processed configuration if available
if config_cache.ollama_embedding_options is not None:
ollama_options = config_cache.ollama_embedding_options
else:
# Fallback for cases where config cache wasn't initialized properly
from lightrag.llm.binding_options import OllamaEmbeddingOptions
ollama_options = OllamaEmbeddingOptions.options_dict(args)
return await ollama_embed(
return await actual_func(
texts,
embed_model=model,
host=host,
@ -636,27 +755,93 @@ def create_app(args):
elif binding == "azure_openai":
from lightrag.llm.azure_openai import azure_openai_embed
return await azure_openai_embed(texts, model=model, api_key=api_key)
actual_func = (
azure_openai_embed.func
if isinstance(azure_openai_embed, EmbeddingFunc)
else azure_openai_embed
)
return await actual_func(texts, model=model, api_key=api_key)
elif binding == "aws_bedrock":
from lightrag.llm.bedrock import bedrock_embed
return await bedrock_embed(texts, model=model)
actual_func = (
bedrock_embed.func
if isinstance(bedrock_embed, EmbeddingFunc)
else bedrock_embed
)
return await actual_func(texts, model=model)
elif binding == "jina":
from lightrag.llm.jina import jina_embed
return await jina_embed(
texts, dimensions=dimensions, base_url=host, api_key=api_key
actual_func = (
jina_embed.func
if isinstance(jina_embed, EmbeddingFunc)
else jina_embed
)
return await actual_func(
texts,
embedding_dim=embedding_dim,
base_url=host,
api_key=api_key,
)
elif binding == "gemini":
from lightrag.llm.gemini import gemini_embed
actual_func = (
gemini_embed.func
if isinstance(gemini_embed, EmbeddingFunc)
else gemini_embed
)
# Use pre-processed configuration if available
if config_cache.gemini_embedding_options is not None:
gemini_options = config_cache.gemini_embedding_options
else:
from lightrag.llm.binding_options import GeminiEmbeddingOptions
gemini_options = GeminiEmbeddingOptions.options_dict(args)
return await actual_func(
texts,
model=model,
base_url=host,
api_key=api_key,
embedding_dim=embedding_dim,
task_type=gemini_options.get("task_type", "RETRIEVAL_DOCUMENT"),
)
else: # openai and compatible
from lightrag.llm.openai import openai_embed
return await openai_embed(
texts, model=model, base_url=host, api_key=api_key
actual_func = (
openai_embed.func
if isinstance(openai_embed, EmbeddingFunc)
else openai_embed
)
return await actual_func(
texts,
model=model,
base_url=host,
api_key=api_key,
embedding_dim=embedding_dim,
)
except ImportError as e:
raise Exception(f"Failed to import {binding} embedding: {e}")
return optimized_embedding_function
# Step 4: Wrap in EmbeddingFunc and return
embedding_func_instance = EmbeddingFunc(
embedding_dim=final_embedding_dim,
func=optimized_embedding_function,
max_token_size=final_max_token_size,
send_dimensions=False, # Will be set later based on binding requirements
)
# Log final embedding configuration
logger.info(
f"Embedding config: binding={binding} model={model} "
f"embedding_dim={final_embedding_dim} max_token_size={final_max_token_size}"
)
return embedding_func_instance
llm_timeout = get_env_value("LLM_TIMEOUT", DEFAULT_LLM_TIMEOUT, int)
embedding_timeout = get_env_value(
@ -690,20 +875,63 @@ def create_app(args):
**kwargs,
)
# Create embedding function with optimized configuration
embedding_func = EmbeddingFunc(
embedding_dim=args.embedding_dim,
func=create_optimized_embedding_function(
config_cache=config_cache,
binding=args.embedding_binding,
model=args.embedding_model,
host=args.embedding_binding_host,
api_key=args.embedding_binding_api_key,
dimensions=args.embedding_dim,
args=args, # Pass args object for fallback option generation
),
# Create embedding function with optimized configuration and max_token_size inheritance
import inspect
# Create the EmbeddingFunc instance (now returns complete EmbeddingFunc with max_token_size)
embedding_func = create_optimized_embedding_function(
config_cache=config_cache,
binding=args.embedding_binding,
model=args.embedding_model,
host=args.embedding_binding_host,
api_key=args.embedding_binding_api_key,
args=args,
)
# Get embedding_send_dim from centralized configuration
embedding_send_dim = args.embedding_send_dim
# Check if the underlying function signature has embedding_dim parameter
sig = inspect.signature(embedding_func.func)
has_embedding_dim_param = "embedding_dim" in sig.parameters
# Determine send_dimensions value based on binding type
# Jina and Gemini REQUIRE dimension parameter (forced to True)
# OpenAI and others: controlled by EMBEDDING_SEND_DIM environment variable
if args.embedding_binding in ["jina", "gemini"]:
# Jina and Gemini APIs require dimension parameter - always send it
send_dimensions = has_embedding_dim_param
dimension_control = f"forced by {args.embedding_binding.title()} API"
else:
# For OpenAI and other bindings, respect EMBEDDING_SEND_DIM setting
send_dimensions = embedding_send_dim and has_embedding_dim_param
if send_dimensions or not embedding_send_dim:
dimension_control = "by env var"
else:
dimension_control = "by not hasparam"
# Set send_dimensions on the EmbeddingFunc instance
embedding_func.send_dimensions = send_dimensions
logger.info(
f"Send embedding dimension: {send_dimensions} {dimension_control} "
f"(dimensions={embedding_func.embedding_dim}, has_param={has_embedding_dim_param}, "
f"binding={args.embedding_binding})"
)
# Log max_token_size source
if embedding_func.max_token_size:
source = (
"env variable"
if args.embedding_token_limit
else f"{args.embedding_binding} provider default"
)
logger.info(
f"Embedding max_token_size: {embedding_func.max_token_size} (from {source})"
)
else:
logger.info("Embedding max_token_size: not set (90% token warning disabled)")
# Configure rerank function based on args.rerank_bindingparameter
rerank_model_func = None
if args.rerank_binding != "null":
@ -842,8 +1070,11 @@ def create_app(args):
@app.get("/")
async def redirect_to_webui():
"""Redirect root path to /webui"""
return RedirectResponse(url="/webui")
"""Redirect root path based on WebUI availability"""
if webui_assets_exist:
return RedirectResponse(url="/webui")
else:
return RedirectResponse(url="/docs")
@app.get("/auth-status")
async def get_auth_status():
@ -910,11 +1141,49 @@ def create_app(args):
"webui_description": webui_description,
}
@app.get("/health", dependencies=[Depends(combined_auth)])
async def get_status():
"""Get current system status"""
@app.get(
"/health",
dependencies=[Depends(combined_auth)],
summary="Get system health and configuration status",
description="Returns comprehensive system status including WebUI availability, configuration, and operational metrics",
response_description="System health status with configuration details",
responses={
200: {
"description": "Successful response with system status",
"content": {
"application/json": {
"example": {
"status": "healthy",
"webui_available": True,
"working_directory": "/path/to/working/dir",
"input_directory": "/path/to/input/dir",
"configuration": {
"llm_binding": "openai",
"llm_model": "gpt-4",
"embedding_binding": "openai",
"embedding_model": "text-embedding-ada-002",
"workspace": "default",
},
"auth_mode": "enabled",
"pipeline_busy": False,
"core_version": "0.0.1",
"api_version": "0.0.1",
}
}
},
}
},
)
async def get_status(request: Request):
"""Get current system status including WebUI availability"""
try:
pipeline_status = await get_namespace_data("pipeline_status")
workspace = get_workspace_from_request(request)
default_workspace = get_default_workspace()
if workspace is None:
workspace = default_workspace
pipeline_status = await get_namespace_data(
"pipeline_status", workspace=workspace
)
if not auth_configured:
auth_mode = "disabled"
@ -926,6 +1195,7 @@ def create_app(args):
return {
"status": "healthy",
"webui_available": webui_assets_exist,
"working_directory": str(args.working_dir),
"input_directory": str(args.input_dir),
"configuration": {
@ -945,7 +1215,7 @@ def create_app(args):
"vector_storage": args.vector_storage,
"enable_llm_cache_for_extract": args.enable_llm_cache_for_extract,
"enable_llm_cache": args.enable_llm_cache,
"workspace": args.workspace,
"workspace": default_workspace,
"max_graph_nodes": args.max_graph_nodes,
# Rerank configuration
"enable_rerank": rerank_model_func is not None,
@ -1015,16 +1285,27 @@ def create_app(args):
name="swagger-ui-static",
)
# Webui mount webui/index.html
static_dir = Path(__file__).parent / "webui"
static_dir.mkdir(exist_ok=True)
app.mount(
"/webui",
SmartStaticFiles(
directory=static_dir, html=True, check_dir=True
), # Use SmartStaticFiles
name="webui",
)
# Conditionally mount WebUI only if assets exist
if webui_assets_exist:
static_dir = Path(__file__).parent / "webui"
static_dir.mkdir(exist_ok=True)
app.mount(
"/webui",
SmartStaticFiles(
directory=static_dir, html=True, check_dir=True
), # Use SmartStaticFiles
name="webui",
)
logger.info("WebUI assets mounted at /webui")
else:
logger.info("WebUI assets not available, /webui route not mounted")
# Add redirect for /webui when assets are not available
@app.get("/webui")
@app.get("/webui/")
async def webui_redirect_to_docs():
"""Redirect /webui to /docs when WebUI is not available"""
return RedirectResponse(url="/docs")
return app
@ -1134,6 +1415,12 @@ def check_and_install_dependencies():
def main():
# Explicitly initialize configuration for clarity
# (The proxy will auto-initialize anyway, but this makes intent clear)
from .config import initialize_config
initialize_config()
# Check if running under Gunicorn
if "GUNICORN_CMD_ARGS" in os.environ:
# If started with Gunicorn, return directly as Gunicorn will call get_application