LightRAG 实战： 基于 Ollama 搭建带知识图谱的可控 RAG 系统

  • threebb0

    2025-11-14 19:58:01
  • 机器学习
  • 原创

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 issuesnest_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=# 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=lightragOLLAMA_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=180LLM_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 + 2000OLLAMA_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='["", "<|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=30EMBEDDING_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 localhostEMBEDDING_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 embeddingOLLAMA_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 ConfigurationPOSTGRES_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, IVFFlatPOSTGRES_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 ConfigurationNEO4J_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 ConfigurationMONGO_URI=mongodb://root:root@localhost:27017/[#MONGO](#MONGO)_URI=mongodb+srv://xxxxMONGO_DATABASE=LightRAG# MONGODB_WORKSPACE=forced_workspace_name### Milvus ConfigurationMILVUS_URI=http://localhost:19530
MILVUS_DB_NAME=lightrag# MILVUS_USER=root# MILVUS_PASSWORD=your_password# MILVUS_TOKEN=your_token# MILVUS_WORKSPACE=forced_workspace_name### QdrantQDRANT_URL=http://localhost:6333# QDRANT_API_KEY=your-api-key# QDRANT_WORKSPACE=forced_workspace_name### RedisREDIS_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 ConfigurationMEMGRAPH_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 osimport asynciofrom functools import partialfrom datetime import datetimefrom lightrag import LightRAG, QueryParamimport glob 
try:    from ollama import AsyncClientexcept 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, EmbeddingFuncfrom 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

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