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fix: lightrag combatibility

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This commit is contained in:
laansdole
2025-09-06 15:45:36 +07:00
parent 2a3d222140
commit 8d8805c66f
2 changed files with 17 additions and 60 deletions
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1
env.example
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@@ -117,7 +117,6 @@ LLM_BINDING_API_KEY=your_api_key
# LMSTUDIO_API_HOST=http://localhost:1234/v1
# LMSTUDIO_API_KEY=lm-studio
# MODEL_CHOICE=your-model-name
# VISION_MODEL_CHOICE=your-vision-model-name
# EMBEDDING_MODEL_CHOICE=text-embedding-nomic-embed-text-v1.5
### LM Studio working directory (separate from main RAG storage)
# LMSTUDIO_WORKING_DIR=./rag_storage_lmstudio

76
examples/lmstudio_integration_example.py
View File

@@ -2,7 +2,7 @@
LM Studio Integration Example with RAG-Anything
This example demonstrates how to integrate LM Studio with RAG-Anything for local
multimodal document processing and querying.
text document processing and querying.
Requirements:
- LM Studio running locally with server enabled
@@ -14,13 +14,13 @@ Create a .env file with:
LMSTUDIO_API_HOST=http://localhost:1234/v1
LMSTUDIO_API_KEY=lm-studio
MODEL_CHOICE=your-model-name
VISION_MODEL_CHOICE=your-vision-model-name # Optional for vision tasks
EMBEDDING_MODEL_CHOICE=text-embedding-nomic-embed-text-v1.5 # Default LM Studio embedding model
"""
import os
import uuid
import asyncio
from typing import List, Dict, Any, Optional
from typing import List, Dict, Optional
from dotenv import load_dotenv
from openai import AsyncOpenAI
@@ -35,7 +35,6 @@ from lightrag.llm.openai import openai_complete_if_cache
LM_BASE_URL = os.getenv('LMSTUDIO_API_HOST', 'http://localhost:1234/v1')
LM_API_KEY = os.getenv('LMSTUDIO_API_KEY', 'lm-studio')
LM_MODEL_NAME = os.getenv('MODEL_CHOICE', 'openai/gpt-oss-20b')
LM_VISION_MODEL_NAME = os.getenv('VISION_MODEL_CHOICE', LM_MODEL_NAME)
LM_EMBED_MODEL = os.getenv('EMBEDDING_MODEL_CHOICE', 'text-embedding-nomic-embed-text-v1.5')
async def lmstudio_llm_model_func(prompt: str, system_prompt: Optional[str] = None,
@@ -51,49 +50,6 @@ async def lmstudio_llm_model_func(prompt: str, system_prompt: Optional[str] = No
**kwargs,
)
async def lmstudio_vision_model_func(prompt: str, system_prompt: Optional[str] = None,
history_messages: List[Dict] = None, image_data: Optional[str] = None,
messages: Optional[List[Dict]] = None, **kwargs) -> str:
"""Top-level Vision/Multimodal function for LightRAG (pickle-safe)."""
try:
if messages:
return await openai_complete_if_cache(
model=LM_VISION_MODEL_NAME,
prompt="",
system_prompt=None,
history_messages=[],
messages=messages,
base_url=LM_BASE_URL,
api_key=LM_API_KEY,
**kwargs,
)
elif image_data:
vision_messages = []
if system_prompt:
vision_messages.append({"role": "system", "content": system_prompt})
if history_messages:
vision_messages.extend(history_messages)
vision_messages.append({
"role": "user",
"content": [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}},
],
})
return await openai_complete_if_cache(
model=LM_VISION_MODEL_NAME,
prompt="",
system_prompt=None,
history_messages=[],
messages=vision_messages,
base_url=LM_BASE_URL,
api_key=LM_API_KEY,
**kwargs,
)
else:
return await lmstudio_llm_model_func(prompt, system_prompt, history_messages, **kwargs)
except Exception:
return await lmstudio_llm_model_func(prompt, system_prompt, history_messages, **kwargs)
async def lmstudio_embedding_async(texts: List[str]) -> List[List[float]]:
"""Top-level embedding function for LightRAG (pickle-safe)."""
@@ -114,19 +70,20 @@ class LMStudioRAGIntegration:
self.base_url = os.getenv('LMSTUDIO_API_HOST', 'http://localhost:1234/v1')
self.api_key = os.getenv('LMSTUDIO_API_KEY', 'lm-studio')
self.model_name = os.getenv('MODEL_CHOICE', 'openai/gpt-oss-20b')
self.vision_model = os.getenv('VISION_MODEL_CHOICE', self.model_name)
self.embedding_model = os.getenv('EMBEDDING_MODEL_CHOICE', 'text-embedding-nomic-embed-text-v1.5')
# RAG-Anything configuration
# Use a fresh working directory each run to avoid legacy doc_status schema conflicts
self.config = RAGAnythingConfig(
working_dir="./rag_storage_lmstudio",
working_dir=f"./rag_storage_lmstudio_demo/{uuid.uuid4()}",
parser="mineru",
parse_method="auto",
enable_image_processing=True,
enable_image_processing=False,
enable_table_processing=True,
enable_equation_processing=True,
)
print(f"📁 Using working_dir: {self.config.working_dir}")
self.rag = None
@@ -190,13 +147,7 @@ class LMStudioRAGIntegration:
except Exception:
pass
def llm_model_func_factory(self):
"""Deprecated: keep for backward compatibility; returns top-level function."""
return lmstudio_llm_model_func
def vision_model_func_factory(self):
"""Deprecated: keep for backward compatibility; returns top-level function."""
return lmstudio_vision_model_func
# Deprecated factory helpers removed to reduce redundancy
def embedding_func_factory(self):
"""Create a completely serializable embedding function."""
@@ -214,9 +165,15 @@ class LMStudioRAGIntegration:
self.rag = RAGAnything(
config=self.config,
llm_model_func=lmstudio_llm_model_func,
vision_model_func=lmstudio_vision_model_func,
embedding_func=self.embedding_func_factory(),
)
# Compatibility: avoid writing unknown field 'multimodal_processed' to LightRAG doc_status
# Older LightRAG versions may not accept this extra field in DocProcessingStatus
async def _noop_mark_multimodal(doc_id: str):
return None
self.rag._mark_multimodal_processing_complete = _noop_mark_multimodal
print("✅ RAG-Anything initialized successfully!")
return True
except Exception as e:
@@ -297,7 +254,8 @@ Key benefits include:
await self.rag.insert_content_list(
content_list=content_list,
file_path="lmstudio_integration_demo.txt",
doc_id="demo-content-001",
# Use a unique doc_id to avoid collisions and doc_status reuse across runs
doc_id=f"demo-content-{uuid.uuid4()}",
display_stats=True
)
print("✅ Sample content added to knowledge base")