mirror of
https://github.com/HKUDS/RAG-Anything.git
synced 2026-07-07 22:54:45 +08:00
335 lines
11 KiB
Python
335 lines
11 KiB
Python
"""
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LM Studio Integration Example with RAG-Anything
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This example demonstrates how to integrate LM Studio with RAG-Anything for local
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text document processing and querying.
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Requirements:
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- LM Studio running locally with server enabled
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- OpenAI Python package: pip install openai
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- RAG-Anything installed: pip install raganything
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Environment Setup:
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Create a .env file with:
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LLM_BINDING=lmstudio
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LLM_MODEL=openai/gpt-oss-20b
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LLM_BINDING_HOST=http://localhost:1234/v1
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LLM_BINDING_API_KEY=lm-studio
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EMBEDDING_BINDING=lmstudio
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EMBEDDING_MODEL=text-embedding-nomic-embed-text-v1.5
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EMBEDDING_BINDING_HOST=http://localhost:1234/v1
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EMBEDDING_BINDING_API_KEY=lm-studio
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"""
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import os
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import uuid
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import asyncio
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from typing import List, Dict, Optional
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from dotenv import load_dotenv
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from openai import AsyncOpenAI
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# Load environment variables
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load_dotenv()
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# RAG-Anything imports
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from raganything import RAGAnything, RAGAnythingConfig
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from lightrag.utils import EmbeddingFunc
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from lightrag.llm.openai import openai_complete_if_cache
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LM_BASE_URL = os.getenv("LLM_BINDING_HOST", "http://localhost:1234/v1")
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LM_API_KEY = os.getenv("LLM_BINDING_API_KEY", "lm-studio")
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LM_MODEL_NAME = os.getenv("LLM_MODEL", "openai/gpt-oss-20b")
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LM_EMBED_MODEL = os.getenv("EMBEDDING_MODEL", "text-embedding-nomic-embed-text-v1.5")
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async def lmstudio_llm_model_func(
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prompt: str,
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system_prompt: Optional[str] = None,
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history_messages: List[Dict] = None,
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**kwargs,
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) -> str:
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"""Top-level LLM function for LightRAG (pickle-safe)."""
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return await openai_complete_if_cache(
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model=LM_MODEL_NAME,
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prompt=prompt,
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system_prompt=system_prompt,
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history_messages=history_messages or [],
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base_url=LM_BASE_URL,
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api_key=LM_API_KEY,
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**kwargs,
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)
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async def lmstudio_embedding_async(texts: List[str]) -> List[List[float]]:
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"""Top-level embedding function for LightRAG (pickle-safe)."""
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from lightrag.llm.openai import openai_embed
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embeddings = await openai_embed(
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texts=texts,
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model=LM_EMBED_MODEL,
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base_url=LM_BASE_URL,
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api_key=LM_API_KEY,
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)
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return embeddings.tolist()
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class LMStudioRAGIntegration:
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"""Integration class for LM Studio with RAG-Anything."""
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def __init__(self):
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# LM Studio configuration using standard LLM_BINDING variables
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self.base_url = os.getenv("LLM_BINDING_HOST", "http://localhost:1234/v1")
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self.api_key = os.getenv("LLM_BINDING_API_KEY", "lm-studio")
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self.model_name = os.getenv("LLM_MODEL", "openai/gpt-oss-20b")
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self.embedding_model = os.getenv(
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"EMBEDDING_MODEL", "text-embedding-nomic-embed-text-v1.5"
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)
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# RAG-Anything configuration
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# Use a fresh working directory each run to avoid legacy doc_status schema conflicts
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self.config = RAGAnythingConfig(
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working_dir=f"./rag_storage_lmstudio/{uuid.uuid4()}",
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parser="mineru",
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parse_method="auto",
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enable_image_processing=False,
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enable_table_processing=True,
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enable_equation_processing=True,
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)
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print(f"📁 Using working_dir: {self.config.working_dir}")
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self.rag = None
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async def test_connection(self) -> bool:
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"""Test LM Studio connection."""
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try:
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print(f"🔌 Testing LM Studio connection at: {self.base_url}")
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client = AsyncOpenAI(base_url=self.base_url, api_key=self.api_key)
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models = await client.models.list()
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print(f"✅ Connected successfully! Found {len(models.data)} models")
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# Show available models
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print("📊 Available models:")
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for i, model in enumerate(models.data[:5]):
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marker = "🎯" if model.id == self.model_name else " "
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print(f"{marker} {i+1}. {model.id}")
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if len(models.data) > 5:
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print(f" ... and {len(models.data) - 5} more models")
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return True
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except Exception as e:
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print(f"❌ Connection failed: {str(e)}")
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print("\n💡 Troubleshooting tips:")
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print("1. Ensure LM Studio is running")
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print("2. Start the local server in LM Studio")
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print("3. Load a model or enable just-in-time loading")
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print(f"4. Verify server address: {self.base_url}")
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return False
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finally:
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try:
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await client.close()
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except Exception:
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pass
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async def test_chat_completion(self) -> bool:
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"""Test basic chat functionality."""
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try:
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print(f"💬 Testing chat with model: {self.model_name}")
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client = AsyncOpenAI(base_url=self.base_url, api_key=self.api_key)
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response = await client.chat.completions.create(
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model=self.model_name,
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant."},
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{
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"role": "user",
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"content": "Hello! Please confirm you're working and tell me your capabilities.",
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},
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],
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max_tokens=100,
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temperature=0.7,
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)
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result = response.choices[0].message.content.strip()
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print("✅ Chat test successful!")
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print(f"Response: {result}")
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return True
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except Exception as e:
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print(f"❌ Chat test failed: {str(e)}")
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return False
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finally:
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try:
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await client.close()
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except Exception:
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pass
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# Deprecated factory helpers removed to reduce redundancy
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def embedding_func_factory(self):
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"""Create a completely serializable embedding function."""
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return EmbeddingFunc(
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embedding_dim=768, # nomic-embed-text-v1.5 default dimension
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max_token_size=8192, # nomic-embed-text-v1.5 context length
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func=lmstudio_embedding_async,
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)
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async def initialize_rag(self):
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"""Initialize RAG-Anything with LM Studio functions."""
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print("Initializing RAG-Anything with LM Studio...")
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try:
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self.rag = RAGAnything(
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config=self.config,
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llm_model_func=lmstudio_llm_model_func,
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embedding_func=self.embedding_func_factory(),
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)
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# Compatibility: avoid writing unknown field 'multimodal_processed' to LightRAG doc_status
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# Older LightRAG versions may not accept this extra field in DocProcessingStatus
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async def _noop_mark_multimodal(doc_id: str):
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return None
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self.rag._mark_multimodal_processing_complete = _noop_mark_multimodal
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print("✅ RAG-Anything initialized successfully!")
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return True
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except Exception as e:
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print(f"❌ RAG initialization failed: {str(e)}")
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return False
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async def process_document_example(self, file_path: str):
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"""Example: Process a document with LM Studio backend."""
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if not self.rag:
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print("❌ RAG not initialized. Call initialize_rag() first.")
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return
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try:
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print(f"📄 Processing document: {file_path}")
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await self.rag.process_document_complete(
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file_path=file_path,
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output_dir="./output_lmstudio",
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parse_method="auto",
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display_stats=True,
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)
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print("✅ Document processing completed!")
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except Exception as e:
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print(f"❌ Document processing failed: {str(e)}")
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async def query_examples(self):
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"""Example queries with different modes."""
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if not self.rag:
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print("❌ RAG not initialized. Call initialize_rag() first.")
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return
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# Example queries
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queries = [
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("What are the main topics in the processed documents?", "hybrid"),
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("Summarize any tables or data found in the documents", "local"),
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("What images or figures are mentioned?", "global"),
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]
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print("\n🔍 Running example queries...")
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for query, mode in queries:
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try:
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print(f"\nQuery ({mode}): {query}")
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result = await self.rag.aquery(query, mode=mode)
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print(f"Answer: {result[:200]}...")
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except Exception as e:
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print(f"❌ Query failed: {str(e)}")
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async def simple_query_example(self):
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"""Example basic text query with sample content."""
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if not self.rag:
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print("❌ RAG not initialized")
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return
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try:
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print("\nAdding sample content for testing...")
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# Create content list in the format expected by RAGAnything
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content_list = [
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{
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"type": "text",
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"text": """LM Studio Integration with RAG-Anything
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This integration demonstrates how to connect LM Studio's local AI models with RAG-Anything's document processing capabilities. The system uses:
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- LM Studio for local LLM inference
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- nomic-embed-text-v1.5 for embeddings (768 dimensions)
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- RAG-Anything for document processing and retrieval
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Key benefits include:
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- Privacy: All processing happens locally
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- Performance: Direct API access to local models
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- Flexibility: Support for various document formats
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- Cost-effective: No external API usage""",
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"page_idx": 0,
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}
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]
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# Insert the content list using the correct method
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await self.rag.insert_content_list(
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content_list=content_list,
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file_path="lmstudio_integration_demo.txt",
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# Use a unique doc_id to avoid collisions and doc_status reuse across runs
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doc_id=f"demo-content-{uuid.uuid4()}",
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display_stats=True,
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)
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print("✅ Sample content added to knowledge base")
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print("\nTesting basic text query...")
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# Simple text query example
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result = await self.rag.aquery(
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"What are the key benefits of this LM Studio integration?",
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mode="hybrid",
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)
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print(f"✅ Query result: {result[:300]}...")
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except Exception as e:
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print(f"❌ Query failed: {str(e)}")
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async def main():
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"""Main example function."""
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print("=" * 70)
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print("LM Studio + RAG-Anything Integration Example")
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print("=" * 70)
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# Initialize integration
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integration = LMStudioRAGIntegration()
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# Test connection
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if not await integration.test_connection():
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return False
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print()
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if not await integration.test_chat_completion():
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return False
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# Initialize RAG
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print("\n" + "─" * 50)
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if not await integration.initialize_rag():
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return False
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# Example document processing (uncomment and provide a real file path)
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# await integration.process_document_example("path/to/your/document.pdf")
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# Example queries (uncomment after processing documents)
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# await integration.query_examples()
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# Example basic query
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await integration.simple_query_example()
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print("\n" + "=" * 70)
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print("Integration example completed successfully!")
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print("=" * 70)
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return True
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if __name__ == "__main__":
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print("🚀 Starting LM Studio integration example...")
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success = asyncio.run(main())
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exit(0 if success else 1)
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