LightRAG 实战：基于 Ollama 搭建带知识图谱的可控 RAG 系统-开发者社区

LightRAG 是个开源的 RAG 框架，专门用来快速搭建模块化的检索增强生成管道。这个项目在 GitHub 上热度不低，我们今天来看看他到底怎么用

基础安装与环境配置

LightRAG 的安装过程很简单，几行命令就能搞定：

pip install "lightrag-hku[api]" cp env.example .env # --->这个有很多参数 非常丰富 lightrag-server

官方提供的 UI 界面做得还算不错，不过测试时基本没用上，因为更关注的是代码层面的实现。

环境搭好之后，可以先跑一下官方提供的示例代码(摘自 readme)：

import os import asyncio from lightrag import LightRAG, QueryParam from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed from lightrag.kg.shared_storage import initialize_pipeline_status from lightrag.utils import setup_logger setup_logger("lightrag", level="INFO") WORKING_DIR = "./rag_storage" if not os.path.exists(WORKING_DIR): os.mkdir(WORKING_DIR) async def initialize_rag(): rag = LightRAG( working_dir=WORKING_DIR, embedding_func=openai_embed, llm_model_func=gpt_4o_mini_complete, ) # IMPORTANT: Both initialization calls are required! await rag.initialize_storages() # Initialize storage backends await initialize_pipeline_status() # Initialize processing pipeline return rag async def main(): try: # Initialize RAG instance rag = await initialize_rag() await rag.ainsert("Your text") # Perform hybrid search mode = "hybrid" print( await rag.aquery( "What are the top themes in this story?", param=QueryParam(mode=mode) ) ) except Exception as e: print(f"An error occurred: {e}") finally: if rag: await rag.finalize_storages() if __name__ == "__main__": asyncio.run(main())

官方示例里还有个基于 Gemini 的版本，看着比较简单就选了这个来测试：

# pip install -q -U google-genai to use gemini as a client import os import numpy as np from google import genai from google.genai import types from dotenv import load_dotenv from lightrag.utils import EmbeddingFunc from lightrag import LightRAG, QueryParam from sentence_transformers import SentenceTransformer from lightrag.kg.shared_storage import initialize_pipeline_status import asyncio import nest_asyncio # Apply nest_asyncio to solve event loop issues nest_asyncio.apply() load_dotenv() gemini_api_key = os.getenv("GEMINI_API_KEY") WORKING_DIR = "./dickens" if os.path.exists(WORKING_DIR): import shutil shutil.rmtree(WORKING_DIR) os.mkdir(WORKING_DIR) async def llm_model_func( prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs ) -> str: # 1. Initialize the GenAI Client with your Gemini API Key client = genai.Client(api_key=gemini_api_key) # 2. Combine prompts: system prompt, history, and user prompt if history_messages is None: history_messages = [] combined_prompt = "" if system_prompt: combined_prompt += f"{system_prompt}\n" for msg in history_messages: # Each msg is expected to be a dict: {"role": "...", "content": "..."} combined_prompt += f"{msg['role']}: {msg['content']}\n" # Finally, add the new user prompt combined_prompt += f"user: {prompt}" # 3. Call the Gemini model response = client.models.generate_content( model="gemini-1.5-flash", contents=[combined_prompt], config=types.GenerateContentConfig(max_output_tokens=500, temperature=0.1), ) # 4. Return the response text return response.text async def embedding_func(texts: list[str]) -> np.ndarray: model = SentenceTransformer("all-MiniLM-L6-v2") embeddings = model.encode(texts, convert_to_numpy=True) return embeddings async def initialize_rag(): rag = LightRAG( working_dir=WORKING_DIR, llm_model_func=llm_model_func, embedding_func=EmbeddingFunc( embedding_dim=384, max_token_size=8192, func=embedding_func, ), ) await rag.initialize_storages() await initialize_pipeline_status() return rag def main(): # Initialize RAG instance rag = asyncio.run(initialize_rag()) file_path = "story.txt" with open(file_path, "r") as file: text = file.read() rag.insert(text) response = rag.query( query="What is the main theme of the story?", param=QueryParam(mode="hybrid", top_k=5, response_type="single line"), ) print(response) if __name__ == "__main__": main()

.env

配置文件的参数非常丰富，需要根据实际使用的工具链做适配。为了调用本地模型，我这里用Ollama 做了相应调整。

### This is sample file of .env ########################### ### Server Configuration ########################### HOST=0.0.0.0 PORT=9621 WEBUI_TITLE='My Graph KB' WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System" # WORKERS=2 ### gunicorn worker timeout(as default LLM request timeout if LLM_TIMEOUT is not set) # TIMEOUT=150 # CORS_ORIGINS=http://localhost:3000,http://localhost:8080 ### Optional SSL Configuration # SSL=true # SSL_CERTFILE=/path/to/cert.pem # SSL_KEYFILE=/path/to/key.pem ### Directory Configuration (defaults to current working directory) ### Default value is ./inputs and ./rag_storage # INPUT_DIR=<absolute_path_for_doc_input_dir> # WORKING_DIR=<absolute_path_for_working_dir> ### Tiktoken cache directory (Store cached files in this folder for offline deployment) # TIKTOKEN_CACHE_DIR=/app/data/tiktoken ### Ollama Emulating Model and Tag # OLLAMA_EMULATING_MODEL_NAME=lightrag OLLAMA_EMULATING_MODEL_TAG=latest ### Max nodes return from graph retrieval in webui # MAX_GRAPH_NODES=1000 ### Logging level # LOG_LEVEL=INFO # VERBOSE=False # LOG_MAX_BYTES=10485760 # LOG_BACKUP_COUNT=5 ### Logfile location (defaults to current working directory) # LOG_DIR=/path/to/log/directory ##################################### ### Login and API-Key Configuration ##################################### # AUTH_ACCOUNTS='admin:admin123,user1:pass456' # TOKEN_SECRET=Your-Key-For-LightRAG-API-Server # TOKEN_EXPIRE_HOURS=48 # GUEST_TOKEN_EXPIRE_HOURS=24 # JWT_ALGORITHM=HS256 ### API-Key to access LightRAG Server API # LIGHTRAG_API_KEY=your-secure-api-key-here # WHITELIST_PATHS=/health,/api/* ###################################################################################### ### Query Configuration ### ### How to control the context length sent to LLM: ### MAX_ENTITY_TOKENS + MAX_RELATION_TOKENS < MAX_TOTAL_TOKENS ### Chunk_Tokens = MAX_TOTAL_TOKENS - Actual_Entity_Tokens - Actual_Relation_Tokens ###################################################################################### # LLM response cache for query (Not valid for streaming response) ENABLE_LLM_CACHE=true # COSINE_THRESHOLD=0.2 ### Number of entities or relations retrieved from KG # TOP_K=40 ### Maximum number or chunks for naive vector search # CHUNK_TOP_K=20 ### control the actual entities send to LLM # MAX_ENTITY_TOKENS=6000 ### control the actual relations send to LLM # MAX_RELATION_TOKENS=8000 ### control the maximum tokens send to LLM (include entities, relations and chunks) # MAX_TOTAL_TOKENS=30000 ### chunk selection strategies ### VECTOR: Pick KG chunks by vector similarity, delivered chunks to the LLM aligning more closely with naive retrieval ### WEIGHT: Pick KG chunks by entity and chunk weight, delivered more solely KG related chunks to the LLM ### If reranking is enabled, the impact of chunk selection strategies will be diminished. # KG_CHUNK_PICK_METHOD=VECTOR ######################################################### ### Reranking configuration ### RERANK_BINDING type: null, cohere, jina, aliyun ### For rerank model deployed by vLLM use cohere binding ######################################################### RERANK_BINDING=null ### Enable rerank by default in query params when RERANK_BINDING is not null # RERANK_BY_DEFAULT=True ### rerank score chunk filter(set to 0.0 to keep all chunks, 0.6 or above if LLM is not strong enough) # MIN_RERANK_SCORE=0.0 ### For local deployment with vLLM # RERANK_MODEL=BAAI/bge-reranker-v2-m3 # RERANK_BINDING_HOST=http://localhost:8000/v1/rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here ### Default value for Cohere AI # RERANK_MODEL=rerank-v3.5 # RERANK_BINDING_HOST=https://api.cohere.com/v2/rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here ### Default value for Jina AI # RERANK_MODEL=jina-reranker-v2-base-multilingual # RERANK_BINDING_HOST=https://api.jina.ai/v1/rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here ### Default value for Aliyun # RERANK_MODEL=gte-rerank-v2 # RERANK_BINDING_HOST=https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here ######################################## ### Document processing configuration ######################################## ENABLE_LLM_CACHE_FOR_EXTRACT=true ### Document processing output language: English, Chinese, French, German ... SUMMARY_LANGUAGE=English ### Entity types that the LLM will attempt to recognize # ENTITY_TYPES='["Person", "Creature", "Organization", "Location", "Event", "Concept", "Method", "Content", "Data", "Artifact", "NaturalObject"]' ### Chunk size for document splitting, 500~1500 is recommended # CHUNK_SIZE=1200 # CHUNK_OVERLAP_SIZE=100 ### Number of summary segments or tokens to trigger LLM summary on entity/relation merge (at least 3 is recommended) # FORCE_LLM_SUMMARY_ON_MERGE=8 ### Max description token size to trigger LLM summary # SUMMARY_MAX_TOKENS = 1200 ### Recommended LLM summary output length in tokens # SUMMARY_LENGTH_RECOMMENDED_=600 ### Maximum context size sent to LLM for description summary # SUMMARY_CONTEXT_SIZE=12000 ### control the maximum chunk_ids stored in vector and graph db # MAX_SOURCE_IDS_PER_ENTITY=300 # MAX_SOURCE_IDS_PER_RELATION=300 ### control chunk_ids limitation method: FIFO, KEEP ### FIFO: First in first out ### KEEP: Keep oldest (less merge action and faster) # SOURCE_IDS_LIMIT_METHOD=FIFO # Maximum number of file paths stored in entity/relation file_path field (For displayed only, does not affect query performance) # MAX_FILE_PATHS=100 ### maximum number of related chunks per source entity or relation ### The chunk picker uses this value to determine the total number of chunks selected from KG(knowledge graph) ### Higher values increase re-ranking time # RELATED_CHUNK_NUMBER=5 ############################### ### Concurrency Configuration ############################### ### Max concurrency requests of LLM (for both query and document processing) MAX_ASYNC=4 ### Number of parallel processing documents(between 2~10, MAX_ASYNC/3 is recommended) MAX_PARALLEL_INSERT=2 ### Max concurrency requests for Embedding # EMBEDDING_FUNC_MAX_ASYNC=8 ### Num of chunks send to Embedding in single request # EMBEDDING_BATCH_NUM=10 ########################################################### ### LLM Configuration ### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock ########################################################### ### LLM request timeout setting for all llm (0 means no timeout for Ollma) # LLM_TIMEOUT=180 LLM_BINDING=ollama LLM_MODEL=granite4:latest LLM_BINDING_HOST=http://localhost:11434 [#LLM](#LLM)_BINDING_API_KEY=your_api_key ### Optional for Azure # AZURE_OPENAI_API_VERSION=2024-08-01-preview # AZURE_OPENAI_DEPLOYMENT=gpt-4o ### Openrouter example # LLM_MODEL=google/gemini-2.5-flash # LLM_BINDING_HOST=https://openrouter.ai/api/v1 # LLM_BINDING_API_KEY=your_api_key # LLM_BINDING=openai ### OpenAI Compatible API Specific Parameters ### Increased temperature values may mitigate infinite inference loops in certain LLM, such as Qwen3-30B. # OPENAI_LLM_TEMPERATURE=0.9 ### Set the max_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s) ### Typically, max_tokens does not include prompt content, though some models, such as Gemini Models, are exceptions ### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider # OPENAI_LLM_MAX_TOKENS=9000 ### For OpenAI o1-mini or newer modles [#OPENAI](#OPENAI)_LLM_MAX_COMPLETION_TOKENS=9000 #### OpenAI's new API utilizes max_completion_tokens instead of max_tokens # OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 ### use the following command to see all support options for OpenAI, azure_openai or OpenRouter ### lightrag-server --llm-binding openai --help ### OpenAI Specific Parameters # OPENAI_LLM_REASONING_EFFORT=minimal ### OpenRouter Specific Parameters # OPENAI_LLM_EXTRA_BODY='{"reasoning": {"enabled": false}}' ### Qwen3 Specific Parameters deploy by vLLM # OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}' ### use the following command to see all support options for Ollama LLM ### If LightRAG deployed in Docker uses host.docker.internal instead of localhost in LLM_BINDING_HOST ### lightrag-server --llm-binding ollama --help ### Ollama Server Specific Parameters ### OLLAMA_LLM_NUM_CTX must be provided, and should at least larger than MAX_TOTAL_TOKENS + 2000 OLLAMA_LLM_NUM_CTX=32768 ### Set the max_output_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s) # OLLAMA_LLM_NUM_PREDICT=9000 ### Stop sequences for Ollama LLM # OLLAMA_LLM_STOP='["</s>", "<|EOT|>"]' ### Bedrock Specific Parameters # BEDROCK_LLM_TEMPERATURE=1.0 #################################################################################### ### Embedding Configuration (Should not be changed after the first file processed) ### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, aws_bedrock #################################################################################### # EMBEDDING_TIMEOUT=30 EMBEDDING_BINDING=ollama EMBEDDING_MODEL=granite-embedding:latest EMBEDDING_DIM=1024 EMBEDDING_BINDING_API_KEY=your_api_key # If LightRAG deployed in Docker uses host.docker.internal instead of localhost EMBEDDING_BINDING_HOST=http://localhost:11434 ### OpenAI compatible (VoyageAI embedding openai compatible) # EMBEDDING_BINDING=openai # EMBEDDING_MODEL=text-embedding-3-large # EMBEDDING_DIM=3072 # EMBEDDING_BINDING_HOST=https://api.openai.com/v1 # EMBEDDING_BINDING_API_KEY=your_api_key ### Optional for Azure # AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large # AZURE_EMBEDDING_API_VERSION=2023-05-15 # AZURE_EMBEDDING_ENDPOINT=your_endpoint # AZURE_EMBEDDING_API_KEY=your_api_key ### Jina AI Embedding # EMBEDDING_BINDING=jina # EMBEDDING_BINDING_HOST=https://api.jina.ai/v1/embeddings # EMBEDDING_MODEL=jina-embeddings-v4 # EMBEDDING_DIM=2048 # EMBEDDING_BINDING_API_KEY=your_api_key ### Optional for Ollama embedding OLLAMA_EMBEDDING_NUM_CTX=8192 ### use the following command to see all support options for Ollama embedding ### lightrag-server --embedding-binding ollama --help #################################################################### ### WORKSPACE sets workspace name for all storage types ### for the purpose of isolating data from LightRAG instances. ### Valid workspace name constraints: a-z, A-Z, 0-9, and _ #################################################################### # WORKSPACE=space1 ############################ ### Data storage selection ############################ ### Default storage (Recommended for small scale deployment) # LIGHTRAG_KV_STORAGE=JsonKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=NetworkXStorage # LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage ### Redis Storage (Recommended for production deployment) # LIGHTRAG_KV_STORAGE=RedisKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=RedisDocStatusStorage ### Vector Storage (Recommended for production deployment) # LIGHTRAG_VECTOR_STORAGE=MilvusVectorDBStorage # LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage # LIGHTRAG_VECTOR_STORAGE=FaissVectorDBStorage ### Graph Storage (Recommended for production deployment) # LIGHTRAG_GRAPH_STORAGE=Neo4JStorage # LIGHTRAG_GRAPH_STORAGE=MemgraphStorage ### PostgreSQL # LIGHTRAG_KV_STORAGE=PGKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=PGGraphStorage # LIGHTRAG_VECTOR_STORAGE=PGVectorStorage ### MongoDB (Vector storage only available on Atlas Cloud) # LIGHTRAG_KV_STORAGE=MongoKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=MongoDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=MongoGraphStorage # LIGHTRAG_VECTOR_STORAGE=MongoVectorDBStorage ### PostgreSQL Configuration POSTGRES_HOST=localhost POSTGRES_PORT=5432 POSTGRES_USER=your_username POSTGRES_PASSWORD='your_password' POSTGRES_DATABASE=your_database POSTGRES_MAX_CONNECTIONS=12 # POSTGRES_WORKSPACE=forced_workspace_name ### PostgreSQL Vector Storage Configuration ### Vector storage type: HNSW, IVFFlat POSTGRES_VECTOR_INDEX_TYPE=HNSW POSTGRES_HNSW_M=16 POSTGRES_HNSW_EF=200 POSTGRES_IVFFLAT_LISTS=100 ### PostgreSQL Connection Retry Configuration (Network Robustness) ### Number of retry attempts (1-10, default: 3) ### Initial retry backoff in seconds (0.1-5.0, default: 0.5) ### Maximum retry backoff in seconds (backoff-60.0, default: 5.0) ### Connection pool close timeout in seconds (1.0-30.0, default: 5.0) # POSTGRES_CONNECTION_RETRIES=3 # POSTGRES_CONNECTION_RETRY_BACKOFF=0.5 # POSTGRES_CONNECTION_RETRY_BACKOFF_MAX=5.0 # POSTGRES_POOL_CLOSE_TIMEOUT=5.0 ### PostgreSQL SSL Configuration (Optional) # POSTGRES_SSL_MODE=require # POSTGRES_SSL_CERT=/path/to/client-cert.pem # POSTGRES_SSL_KEY=/path/to/client-key.pem # POSTGRES_SSL_ROOT_CERT=/path/to/ca-cert.pem # POSTGRES_SSL_CRL=/path/to/crl.pem ### PostgreSQL Server Settings (for Supabase Supavisor) # Use this to pass extra options to the PostgreSQL connection string. # For Supabase, you might need to set it like this: # POSTGRES_SERVER_SETTINGS="options=reference%3D[project-ref]" # Default is 100 set to 0 to disable # POSTGRES_STATEMENT_CACHE_SIZE=100 ### Neo4j Configuration NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io NEO4J_USERNAME=neo4j NEO4J_PASSWORD='your_password' NEO4J_DATABASE=neo4j NEO4J_MAX_CONNECTION_POOL_SIZE=100 NEO4J_CONNECTION_TIMEOUT=30 NEO4J_CONNECTION_ACQUISITION_TIMEOUT=30 NEO4J_MAX_TRANSACTION_RETRY_TIME=30 NEO4J_MAX_CONNECTION_LIFETIME=300 NEO4J_LIVENESS_CHECK_TIMEOUT=30 NEO4J_KEEP_ALIVE=true # NEO4J_WORKSPACE=forced_workspace_name ### MongoDB Configuration MONGO_URI=mongodb://root:root@localhost:27017/ [#MONGO](#MONGO)_URI=mongodb+srv://xxxx MONGO_DATABASE=LightRAG # MONGODB_WORKSPACE=forced_workspace_name ### Milvus Configuration MILVUS_URI=http://localhost:19530 MILVUS_DB_NAME=lightrag # MILVUS_USER=root # MILVUS_PASSWORD=your_password # MILVUS_TOKEN=your_token # MILVUS_WORKSPACE=forced_workspace_name ### Qdrant QDRANT_URL=http://localhost:6333 # QDRANT_API_KEY=your-api-key # QDRANT_WORKSPACE=forced_workspace_name ### Redis REDIS_URI=redis://localhost:6379 REDIS_SOCKET_TIMEOUT=30 REDIS_CONNECT_TIMEOUT=10 REDIS_MAX_CONNECTIONS=100 REDIS_RETRY_ATTEMPTS=3 # REDIS_WORKSPACE=forced_workspace_name ### Memgraph Configuration MEMGRAPH_URI=bolt://localhost:7687 MEMGRAPH_USERNAME= MEMGRAPH_PASSWORD= MEMGRAPH_DATABASE=memgraph # MEMGRAPH_WORKSPACE=forced_workspace_name

参考前面的 Gemini 示例，写了下面的代码包含了一些硬编码文本的测试代码：

# 准备环境 python3 -m venv venv source venv/bin/activate pip install --upgrade pip pip install "lightrag-hku[api]" pip install ollama

import os import asyncio from functools import partial from datetime import datetime from lightrag import LightRAG, QueryParam try: from ollama import AsyncClient except ImportError: print("Warning: The 'ollama' Python package is required. Please run: pip install ollama") class AsyncClient: def __init__(self, host): pass async def chat(self, **kwargs): raise NotImplementedError("ollama package not installed.") from lightrag.llm.ollama import ollama_embed from lightrag.utils import setup_logger, EmbeddingFunc from lightrag.kg.shared_storage import initialize_pipeline_status OLLAMA_BASE_URL = "http://localhost:11434" LLM_MODEL = "granite4:latest" EMBEDDING_MODEL = "granite-embedding:latest" WORKING_DIR = "./rag_storage_ollama" EMBEDDING_DIMENSION = 384 OUTPUT_DIR = "./output" setup_logger("lightrag", level="INFO") if not os.path.exists(WORKING_DIR): os.mkdir(WORKING_DIR) if not os.path.exists(OUTPUT_DIR): os.mkdir(OUTPUT_DIR) async def custom_ollama_llm_complete(prompt: str, system_prompt: str = None, **kwargs): """ A custom function that handles the Ollama client initialization and model/base_url parameters that are injected via functools.partial, while robustly filtering out unwanted internal keywords. """ model = kwargs.pop('model') base_url = kwargs.pop('base_url') client = AsyncClient(host=base_url) messages = [] if system_prompt: messages.append({"role": "system", "content": system_prompt}) messages.append({"role": "user", "content": prompt}) keys_to_filter = { 'host', 'hashing_kv', 'llm_model_name', 'history_messages', 'keyword_extraction', 'enable_cot', 'is_system_prompt_only', 'prompt_config' } cleaned_kwargs = {k: v for k, v in kwargs.items() if k not in keys_to_filter} try: response = await client.chat( model=model, messages=messages, **cleaned_kwargs ) return response['message']['content'] except Exception as e: raise e async def initialize_rag(): """Initializes the LightRAG instance using standard Ollama configuration.""" configured_ollama_complete = partial( custom_ollama_llm_complete, model=LLM_MODEL, base_url=OLLAMA_BASE_URL, ) configured_ollama_embed = partial( ollama_embed, embed_model=EMBEDDING_MODEL, base_url=OLLAMA_BASE_URL ) wrapped_embedding_func = EmbeddingFunc( embedding_dim=EMBEDDING_DIMENSION, func=configured_ollama_embed, ) rag = LightRAG( working_dir=WORKING_DIR, llm_model_func=configured_ollama_complete, embedding_func=wrapped_embedding_func, ) await rag.initialize_storages() await initialize_pipeline_status() return rag async def main(): rag = None query = "How does RAG solve the problem of LLM hallucination and what are its main use cases?" try: print("Checking if required Ollama models are pulled...") # the knowledge source sample_text = """ The concept of Retrieval-Augmented Generation (RAG) is a critical development in the field of large language models (LLMs). It addresses the 'hallucination' problem by grounding LLM responses in external, verified knowledge sources. Instead of relying solely on the LLM's static training data, RAG first retrieves relevant documents from a knowledge base (often a vector store) and then feeds these documents, alongside the user's query, to the LLM for generation. This two-step process significantly improves the accuracy, relevance, and transparency of the generated output. Popular applications include enterprise search, customer support, and domain-specific QA systems. """ print(f"--- 1. Initializing RAG with Ollama Models ---") rag = await initialize_rag() print(f"\n--- 2. Inserting Sample Text ({len(sample_text.split())} words) ---") await rag.ainsert(sample_text) print("Insertion complete. Data is ready for retrieval.") mode = "hybrid" print(f"\n--- 3. Querying the RAG System (Mode: {mode}) ---") print(f"Query: '{query}'") rag_result = await rag.aquery( query, param=QueryParam(mode=mode) ) response_text = None if hasattr(rag_result, 'get_response_text'): response_text = rag_result.get_response_text() elif isinstance(rag_result, str): response_text = rag_result print("\n" + "="*50) print("FINAL RAG RESPONSE") print("="*50) output_content = "" # Prepare string for file output if response_text and not str(response_text).strip().startswith("Error:"): print(response_text) output_content += f"# RAG Query Result\n\n" output_content += f"## Query\n\n> {query}\n\n" output_content += f"## LLM/Cache Response\n\n{response_text}\n\n" print("\n" + "="*50) print("\n--- Context Retrieved (Sources) ---") output_content += f"## Retrieved Context (Sources)\n\n" if not isinstance(rag_result, str) and rag_result.retriever_output and rag_result.retriever_output.docs: for i, doc in enumerate(rag_result.retriever_output.docs): source_text = doc.text print(f"Source {i+1}: {source_text[:100]}...") output_content += f"### Source {i+1}\n\n" output_content += f"```text\n{source_text}\n```\n" else: print("No context documents were retrieved (or result was a cache hit string).") output_content += "No context documents were retrieved (or result was a cache hit string).\n" else: error_message = "LLM failed to generate a response (Check Ollama logs for details)." print(error_message) output_content += f"# RAG Query Result\n\n## Error\n\n{error_message}\n\n" if response_text: print(f"\nError String from LightRAG: {response_text}") output_content += f"**Error Detail:** {response_text}\n" timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"rag_query_output_{timestamp}.md" output_filepath = os.path.join(OUTPUT_DIR, filename) with open(output_filepath, 'w', encoding='utf-8') as f: f.write(output_content) print(f"\n--- Output Written to File ---") print(f"Successfully wrote output to: {output_filepath}") except Exception as e: if "'str' object has no attribute 'retriever_output'" in str(e): print("\n--- ERROR BYPASS: Detected Cache Hit String Result ---") print("The response was successfully retrieved from the cache and written to the output file.") else: # For all other (real) exceptions, print the detailed error block print("\n" + "="*50) print("AN ERROR OCCURRED DURING RAG PROCESS") print("="*50) print(f"Error: {e}") print(f"Please ensure Ollama is running and accessible at {OLLAMA_BASE_URL}, and the models '{LLM_MODEL}' and '{EMBEDDING_MODEL}' are pulled locally.") print(f"To pull: 'ollama pull {LLM_MODEL}' and 'ollama pull {EMBEDDING_MODEL}'") print("="*50 + "\n") finally: if rag: print("\n--- Finalizing storages ---") await rag.finalize_storages() if __name__ == "__main__": asyncio.run(main())

虽然

.env

文件里有参数可以配置 input 文件夹路径，但测试时直接在代码里写死了路径。

运行后的输出包括控制台日志和 markdown 格式的结果文件，结果太长我就不贴了。

接下来测试了更实际的场景：准备了几份 markdown 格式的文档（其他格式应该也支持，但没测），用这些文档构建了自己的 RAG 系统，继续用 Ollama 和 Granite 模型来验证效果，这次的代码就没那么硬编码了。

.env
文件提供了 input 文件夹的配置选项，不过这里还是用的硬编码方式

import os import asyncio from functools import partial from datetime import datetime from lightrag import LightRAG, QueryParam import glob try: from ollama import AsyncClient except ImportError: print("Warning: The 'ollama' Python package is required. Please run: pip install ollama") class AsyncClient: def __init__(self, host): pass async def chat(self, **kwargs): raise NotImplementedError("ollama package not installed.") from lightrag.llm.ollama import ollama_embed from lightrag.utils import setup_logger, EmbeddingFunc from lightrag.kg.shared_storage import initialize_pipeline_status OLLAMA_BASE_URL = "http://localhost:11434" LLM_MODEL = "granite4:latest" EMBEDDING_MODEL = "granite-embedding:latest" WORKING_DIR = "./rag_storage_ollama" EMBEDDING_DIMENSION = 384 DOCUMENTS_DIR = "./documents" # Directory to read source files from OUTPUT_DIR = "./output" # Directory to write RAG results to setup_logger("lightrag", level="INFO") if not os.path.exists(WORKING_DIR): os.mkdir(WORKING_DIR) print(f"Created working directory: {WORKING_DIR}") if not os.path.exists(OUTPUT_DIR): os.mkdir(OUTPUT_DIR) print(f"Created output directory: {OUTPUT_DIR}") if not os.path.exists(DOCUMENTS_DIR): os.mkdir(DOCUMENTS_DIR) print(f"Created documents directory: {DOCUMENTS_DIR}") async def custom_ollama_llm_complete(prompt: str, system_prompt: str = None, **kwargs): """ A custom function that handles the Ollama client initialization and model/base_url parameters that are injected via functools.partial, while robustly filtering out unwanted internal keywords. """ model = kwargs.pop('model') base_url = kwargs.pop('base_url') client = AsyncClient(host=base_url) messages = [] if system_prompt: messages.append({"role": "system", "content": system_prompt}) messages.append({"role": "user", "content": prompt}) keys_to_filter = { 'host', 'hashing_kv', 'llm_model_name', 'history_messages', 'keyword_extraction', 'enable_cot', 'is_system_prompt_only', 'prompt_config' } cleaned_kwargs = {k: v for k, v in kwargs.items() if k not in keys_to_filter} try: response = await client.chat( model=model, messages=messages, **cleaned_kwargs ) return response['message']['content'] except Exception as e: raise e async def initialize_rag(): """Initializes the LightRAG instance using standard Ollama configuration.""" configured_ollama_complete = partial( custom_ollama_llm_complete, model=LLM_MODEL, base_url=OLLAMA_BASE_URL, ) configured_ollama_embed = partial( ollama_embed, embed_model=EMBEDDING_MODEL, base_url=OLLAMA_BASE_URL ) wrapped_embedding_func = EmbeddingFunc( embedding_dim=EMBEDDING_DIMENSION, func=configured_ollama_embed, ) rag = LightRAG( working_dir=WORKING_DIR, llm_model_func=configured_ollama_complete, embedding_func=wrapped_embedding_func, ) await rag.initialize_storages() await initialize_pipeline_status() return rag async def load_and_insert_documents(rag: LightRAG): """ Reads files from the DOCUMENTS_DIR and inserts their content into the RAG system. Fixed to use a more compatible method for document insertion. """ file_paths = glob.glob(os.path.join(DOCUMENTS_DIR, '*.[mM][dD]')) + \ glob.glob(os.path.join(DOCUMENTS_DIR, '*.[tT][xX][tT]')) if not file_paths: print("\n--- WARNING: No documents found in './documents' directory. ---") print("Please add some Markdown (.md) or Text (.txt) files to populate the knowledge base.") return False print(f"\n--- 2. Inserting Documents ({len(file_paths)} file(s) found) ---") insertion_succeeded = 0 for file_path in file_paths: filename = os.path.basename(file_path) try: with open(file_path, 'r', encoding='utf-8') as f: content = f.read() await rag.ainsert(content, doc_meta={'doc_id': filename}) print(f" > Successfully inserted: {filename} ({len(content.split())} words)") insertion_succeeded += 1 except TypeError as te: if "'doc_id'" in str(te) or "'doc_meta'" in str(te): print(f" > FAILED (Type Error): {filename}. Attempting insertion without metadata to check compatibility.") try: await rag.ainsert(content) print(f" > Successfully inserted (no metadata): {filename}") insertion_succeeded += 1 except Exception as e: print(f" > FAILED (General Error): {filename} - {e}") else: print(f" > FAILED to read or insert {filename} (Type Error): {te}") except Exception as e: print(f" > FAILED to read or insert {filename} (General Error): {e}") if insertion_succeeded == 0: print("Insertion complete, but no documents were successfully inserted. Please check LightRAG documentation for the correct argument name for source IDs.") return False print("Insertion complete. Data is ready for retrieval.") return True async def main(): rag = None query = "Describe Quantum-Safe cryptography?" try: print("Checking if required Ollama models are pulled...") print(f"--- 1. Initializing RAG with Ollama Models ---") rag = await initialize_rag() documents_inserted = await load_and_insert_documents(rag) if not documents_inserted: return mode = "hybrid" print(f"\n--- 3. Querying the RAG System (Mode: {mode}) ---") print(f"Query: '{query}'") rag_result = await rag.aquery( query, param=QueryParam(mode=mode) ) response_text = None if hasattr(rag_result, 'get_response_text'): response_text = rag_result.get_response_text() elif isinstance(rag_result, str): response_text = rag_result print("\n" + "="*50) print("FINAL RAG RESPONSE") print("="*50) output_content = "" # Prepare string for file output if response_text and not str(response_text).strip().startswith("Error:"): print(response_text) output_content += f"# RAG Query Result\n\n" output_content += f"## Query\n\n> {query}\n\n" output_content += f"## LLM/Cache Response\n\n{response_text}\n\n" print("\n" + "="*50) print("\n--- Context Retrieved (Sources) ---") output_content += f"## Retrieved Context (Sources)\n\n" if not isinstance(rag_result, str) and rag_result.retriever_output and rag_result.retriever_output.docs: unique_sources = set() for i, doc in enumerate(rag_result.retriever_output.docs): source_text = doc.text source_id = doc.doc_id if hasattr(doc, 'doc_id') and doc.doc_id else ( doc.doc_meta.get('doc_id') if hasattr(doc, 'doc_meta') and isinstance(doc.doc_meta, dict) else 'Unknown Source' ) unique_sources.add(source_id) print(f"Source {i+1} (File: {source_id}): {source_text[:100]}...") output_content += f"### Source {i+1} (File: `{source_id}`)\n\n" output_content += f"```text\n{source_text}\n```\n" print(f"\nAnswer Grounded in: {', '.join(sorted(list(unique_sources)))}") else: print("No context documents were retrieved (or result was a cache hit string).") output_content += "No context documents were retrieved (or result was a cache hit string).\n" else: error_message = "LLM failed to generate a response (Check Ollama logs for details)." print(error_message) output_content += f"# RAG Query Result\n\n## Error\n\n{error_message}\n\n" if response_text: print(f"\nError String from LightRAG: {response_text}") output_content += f"**Error Detail:** {response_text}\n" timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"rag_query_output_{timestamp}.md" output_filepath = os.path.join(OUTPUT_DIR, filename) with open(output_filepath, 'w', encoding='utf-8') as f: f.write(output_content) print(f"\n--- Output Written to File ---") print(f"Successfully wrote output to: {output_filepath}") except Exception as e: if "'str' object has no attribute 'retriever_output'" in str(e): print("\n--- ERROR BYPASS: Detected Cache Hit String Result ---") print("The response was successfully retrieved from the cache and written to the output file.") else: print("\n" + "="*50) print("AN ERROR OCCURRED DURING RAG PROCESS") print("="*50) print(f"Error: {e}") print(f"Please ensure Ollama is running and accessible at {OLLAMA_BASE_URL}, and the models '{LLM_MODEL}' and '{EMBEDDING_MODEL}' are pulled locally.") print(f"To pull: 'ollama pull {LLM_MODEL}' and 'ollama pull {EMBEDDING_MODEL}'") print("="*50 + "\n") finally: if rag: print("\n--- Finalizing storages ---") await rag.finalize_storages() if __name__ == "__main__": asyncio.run(main())

实际的查询输出示例：

RAG Query Result ## Query > Describe Quantum-Safe cryptography? ## LLM/Cache Response ### What is Quantum-Safe Cryptography? Quantum-safe cryptography, also known as post‑quantum cryptography (PQC), refers to cryptographic algorithms and protocols designed to remain secure even against attacks by a sufficiently powerful quantum computer. Traditional public-key cryptosystems like RSA, ECC, Diffie‑Hellman, and elliptic curve variants are vulnerable to Shor’s algorithm, which could efficiently factor large integers and compute discrete logarithms—tasks that form the basis of these cryptographic schemes.

知识图谱生成

LightRAG 自带知识图谱生成功能，这点比较实用。

为了把生成的图谱文件展示出来，我还用用 Streamlit 写了个简单的可视化应用。代码比较粗糙，后续可以继续优化。

import streamlit as st import networkx as nx import matplotlib.pyplot as plt import io import itertools from io import StringIO def visualize_graph(G: nx.Graph, layout_name: str, layout_params: dict): """ Generates a Matplotlib plot of the NetworkX graph with custom styling. """ node_labels = nx.get_node_attributes(G, 'label') if not node_labels: node_labels = nx.get_node_attributes(G, 'text') edge_labels = nx.get_edge_attributes(G, 'text') node_types = nx.get_node_attributes(G, 'type') type_color_map = { 'Entity': '[#1f78b4](#1f78b4)', # Blue 'Chunk': '[#b2df8a](#b2df8a)', # Light Green 'Relation': '[#33a02c](#33a02c)', # Dark Green 'Unknown': '[#a6cee3](#a6cee3)' # Light Blue } node_colors = [type_color_map.get(node_types.get(node, 'Unknown'), type_color_map['Unknown']) for node in G.nodes()] if layout_name == 'Spring Layout': pos = nx.spring_layout(G, **layout_params) elif layout_name == 'Circular Layout': pos = nx.circular_layout(G) elif layout_name == 'Spectral Layout': pos = nx.spectral_layout(G) elif layout_name == 'Kamada-Kawai Layout': pos = nx.kamada_kawai_layout(G) else: pos = nx.spring_layout(G, **layout_params) fig, ax = plt.subplots(figsize=(16, 10)) nx.draw_networkx_nodes( G, pos, node_size=2500, node_color=node_colors, alpha=0.9 ) # Draw edges nx.draw_networkx_edges( G, pos, ax=ax, edge_color='gray', style='dashed', arrowstyle='->', arrowsize=25 ) nx.draw_networkx_labels( G, pos, labels=node_labels, font_size=11, font_color='black', font_weight='bold', ) nx.draw_networkx_edge_labels( G, pos, edge_labels=edge_labels, font_color='red', font_size=9, bbox={"boxstyle": "round,pad=0.4", "fc": "white", "alpha": 0.7, "ec": "none"} ) ax.set_title(f"Visualized Graph: {G.number_of_nodes()} Nodes, {G.number_of_edges()} Edges", fontsize=16) plt.axis('off') plt.tight_layout() st.pyplot(fig) def app(): st.set_page_config(layout="wide", page_title="GraphML Viewer") st.title("GraphML Visualization App") st.markdown("A tool to visualize GraphML (e.g., LightRAG) outputs using NetworkX and Streamlit.") st.sidebar.header("Data Upload & Layout Controls") uploaded_file = st.sidebar.file_uploader( "Upload your .graphml file", type=["graphml"] ) graph_data = None if uploaded_file is not None: try: graph_data = uploaded_file.read().decode("utf-8") st.sidebar.success("File uploaded successfully! Graph loading...") except Exception as e: st.sidebar.error(f"Error reading file: {e}") graph_data = None else: st.info("Please upload a GraphML file in the sidebar to visualize your knowledge graph.") st.sidebar.subheader("Layout Algorithm") layout_name = st.sidebar.selectbox( "Choose Graph Layout:", ('Spring Layout', 'Kamada-Kawai Layout', 'Circular Layout', 'Spectral Layout') ) layout_params = {} if layout_name == 'Spring Layout': st.sidebar.caption("Fine-tune the Spring Layout forces:") k_val = st.sidebar.slider("k (Node Spacing)", 0.01, 1.0, 0.15, 0.01) iters = st.sidebar.slider("Iterations", 10, 100, 50, 10) layout_params = {'k': k_val, 'iterations': iters} if graph_data: try: G = nx.read_graphml(StringIO(graph_data)) st.header("Knowledge Graph Visualization") st.write(f"Graph loaded: {G.number_of_nodes()} Nodes, {G.number_of_edges()} Edges") visualize_graph(G, layout_name, layout_params) except Exception as e: st.error(f"An error occurred while processing the graph: {e}") st.code(f"Error details: {e}") st.warning("Please check if the GraphML file is correctly formatted and contains valid data.") if __name__ == '__main__': app()

运行以后你就能看到上面的结果