LightRAG
1.0.0
Este repositorio aloja el código de Lightrag. La estructura de este código se basa en Nano-Gragrag.
textract . Figura 1: diagrama de flujo de indexación de Lightrag Figura 2: Recuperación de Lightrag y diagrama de flujo de consulta
cd LightRAG
pip install -e .pip install lightrag-hkuexamples .export OPENAI_API_KEY="sk-...".curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txtUse el fragmento de Python (en un script) a continuación para inicializar Lightrag y realizar consultas:
import os
from lightrag import LightRAG , QueryParam
from lightrag . llm import gpt_4o_mini_complete , gpt_4o_complete
#########
# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
# import nest_asyncio
# nest_asyncio.apply()
#########
WORKING_DIR = "./dickens"
if not os . path . exists ( WORKING_DIR ):
os . mkdir ( WORKING_DIR )
rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = gpt_4o_mini_complete # Use gpt_4o_mini_complete LLM model
# llm_model_func=gpt_4o_complete # Optionally, use a stronger model
)
with open ( "./book.txt" ) as f :
rag . insert ( f . read ())
# Perform naive search
print ( rag . query ( "What are the top themes in this story?" , param = QueryParam ( mode = "naive" )))
# Perform local search
print ( rag . query ( "What are the top themes in this story?" , param = QueryParam ( mode = "local" )))
# Perform global search
print ( rag . query ( "What are the top themes in this story?" , param = QueryParam ( mode = "global" )))
# Perform hybrid search
print ( rag . query ( "What are the top themes in this story?" , param = QueryParam ( mode = "hybrid" ))) async def llm_model_func (
prompt , system_prompt = None , history_messages = [], ** kwargs
) -> str :
return await openai_complete_if_cache (
"solar-mini" ,
prompt ,
system_prompt = system_prompt ,
history_messages = history_messages ,
api_key = os . getenv ( "UPSTAGE_API_KEY" ),
base_url = "https://api.upstage.ai/v1/solar" ,
** kwargs
)
async def embedding_func ( texts : list [ str ]) -> np . ndarray :
return await openai_embedding (
texts ,
model = "solar-embedding-1-large-query" ,
api_key = os . getenv ( "UPSTAGE_API_KEY" ),
base_url = "https://api.upstage.ai/v1/solar"
)
rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = llm_model_func ,
embedding_func = EmbeddingFunc (
embedding_dim = 4096 ,
max_token_size = 8192 ,
func = embedding_func
)
) from lightrag . llm import hf_model_complete , hf_embedding
from transformers import AutoModel , AutoTokenizer
from lightrag . utils import EmbeddingFunc
# Initialize LightRAG with Hugging Face model
rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = hf_model_complete , # Use Hugging Face model for text generation
llm_model_name = 'meta-llama/Llama-3.1-8B-Instruct' , # Model name from Hugging Face
# Use Hugging Face embedding function
embedding_func = EmbeddingFunc (
embedding_dim = 384 ,
max_token_size = 5000 ,
func = lambda texts : hf_embedding (
texts ,
tokenizer = AutoTokenizer . from_pretrained ( "sentence-transformers/all-MiniLM-L6-v2" ),
embed_model = AutoModel . from_pretrained ( "sentence-transformers/all-MiniLM-L6-v2" )
)
),
) Si desea utilizar modelos Ollama, debe extraer el modelo que planea usar e incrustar el modelo, por ejemplo nomic-embed-text .
Entonces solo necesitas establecer Lightrag de la siguiente manera:
from lightrag . llm import ollama_model_complete , ollama_embedding
from lightrag . utils import EmbeddingFunc
# Initialize LightRAG with Ollama model
rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = ollama_model_complete , # Use Ollama model for text generation
llm_model_name = 'your_model_name' , # Your model name
# Use Ollama embedding function
embedding_func = EmbeddingFunc (
embedding_dim = 768 ,
max_token_size = 8192 ,
func = lambda texts : ollama_embedding (
texts ,
embed_model = "nomic-embed-text"
)
),
) export NEO4J_URI = "neo4j://localhost:7687"
export NEO4J_USERNAME = "neo4j"
export NEO4J_PASSWORD = "password"
When you launch the project be sure to override the default KG : NetworkS
by specifying kg = "Neo4JStorage" .
# Note: Default settings use NetworkX
#Initialize LightRAG with Neo4J implementation.
WORKING_DIR = "./local_neo4jWorkDir"
rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = gpt_4o_mini_complete , # Use gpt_4o_mini_complete LLM model
kg = "Neo4JStorage" , #<-----------override KG default
log_level = "DEBUG" #<-----------override log_level default
)Consulte test_neo4j.py para obtener un ejemplo de trabajo.
Para que el contexto de Lightrag debe ser de al menos 32k tokens. Por defecto, los modelos Ollama tienen un tamaño de contexto de 8k. Puede lograr esto utilizando una de dos maneras:
num_ctx en Modelfile.ollama pull qwen2ollama show --modelfile qwen2 > ModelfilePARAMETER num_ctx 32768ollama create -f Modelfile qwen2mnum_ctx a través de la API Ollama. TIY puede usar llm_model_kwargs Param para configurar Ollama:
rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = ollama_model_complete , # Use Ollama model for text generation
llm_model_name = 'your_model_name' , # Your model name
llm_model_kwargs = { "options" : { "num_ctx" : 32768 }},
# Use Ollama embedding function
embedding_func = EmbeddingFunc (
embedding_dim = 768 ,
max_token_size = 8192 ,
func = lambda texts : ollama_embedding (
texts ,
embed_model = "nomic-embed-text"
)
),
) Hay examples/lightrag_ollama_demo.py que utiliza el modelo gemma2:2b , ejecuta solo 4 solicitudes en paralelo y establece un tamaño de contexto en 32k.
Para ejecutar este experimento en GPU de baja RAM, debe seleccionar una pequeña ventana de contexto de modelo y ajuste (aumentar el contexto aumenta el consumo de memoria). Por ejemplo, ejecutar este ejemplo de Ollama en GPU minero reutilizado con 6 GB de RAM requerido para establecer el tamaño de contexto en 26k mientras usa gemma2:2b . Pudo encontrar 197 entidades y 19 relaciones en book.txt .
class QueryParam :
mode : Literal [ "local" , "global" , "hybrid" , "naive" ] = "global"
only_need_context : bool = False
response_type : str = "Multiple Paragraphs"
# Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode.
top_k : int = 60
# Number of tokens for the original chunks.
max_token_for_text_unit : int = 4000
# Number of tokens for the relationship descriptions
max_token_for_global_context : int = 4000
# Number of tokens for the entity descriptions
max_token_for_local_context : int = 4000 # Batch Insert: Insert multiple texts at once
rag . insert ([ "TEXT1" , "TEXT2" ,...]) # Incremental Insert: Insert new documents into an existing LightRAG instance
rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = llm_model_func ,
embedding_func = EmbeddingFunc (
embedding_dim = embedding_dimension ,
max_token_size = 8192 ,
func = embedding_func ,
),
)
with open ( "./newText.txt" ) as f :
rag . insert ( f . read ()) rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = llm_model_func ,
embedding_func = EmbeddingFunc (
embedding_dim = embedding_dimension ,
max_token_size = 8192 ,
func = embedding_func ,
),
)
custom_kg = {
"entities" : [
{
"entity_name" : "CompanyA" ,
"entity_type" : "Organization" ,
"description" : "A major technology company" ,
"source_id" : "Source1"
},
{
"entity_name" : "ProductX" ,
"entity_type" : "Product" ,
"description" : "A popular product developed by CompanyA" ,
"source_id" : "Source1"
}
],
"relationships" : [
{
"src_id" : "CompanyA" ,
"tgt_id" : "ProductX" ,
"description" : "CompanyA develops ProductX" ,
"keywords" : "develop, produce" ,
"weight" : 1.0 ,
"source_id" : "Source1"
}
]
}
rag . insert_custom_kg ( custom_kg ) # Delete Entity: Deleting entities by their names
rag = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = llm_model_func ,
embedding_func = EmbeddingFunc (
embedding_dim = embedding_dimension ,
max_token_size = 8192 ,
func = embedding_func ,
),
)
rag . delete_by_entity ( "Project Gutenberg" ) textract admite tipos de archivos de lectura como TXT, DOCX, PPTX, CSV y PDF.
import textract
file_path = 'TEXT.pdf'
text_content = textract . process ( file_path )
rag . insert ( text_content . decode ( 'utf-8' ))examples/graph_visual_with_html.py import networkx as nx
from pyvis . network import Network
# Load the GraphML file
G = nx . read_graphml ( './dickens/graph_chunk_entity_relation.graphml' )
# Create a Pyvis network
net = Network ( notebook = True )
# Convert NetworkX graph to Pyvis network
net . from_nx ( G )
# Save and display the network
net . show ( 'knowledge_graph.html' )examples/graph_visual_with_neo4j.py import os
import json
from lightrag . utils import xml_to_json
from neo4j import GraphDatabase
# Constants
WORKING_DIR = "./dickens"
BATCH_SIZE_NODES = 500
BATCH_SIZE_EDGES = 100
# Neo4j connection credentials
NEO4J_URI = "bolt://localhost:7687"
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "your_password"
def convert_xml_to_json ( xml_path , output_path ):
"""Converts XML file to JSON and saves the output."""
if not os . path . exists ( xml_path ):
print ( f"Error: File not found - { xml_path } " )
return None
json_data = xml_to_json ( xml_path )
if json_data :
with open ( output_path , 'w' , encoding = 'utf-8' ) as f :
json . dump ( json_data , f , ensure_ascii = False , indent = 2 )
print ( f"JSON file created: { output_path } " )
return json_data
else :
print ( "Failed to create JSON data" )
return None
def process_in_batches ( tx , query , data , batch_size ):
"""Process data in batches and execute the given query."""
for i in range ( 0 , len ( data ), batch_size ):
batch = data [ i : i + batch_size ]
tx . run ( query , { "nodes" : batch } if "nodes" in query else { "edges" : batch })
def main ():
# Paths
xml_file = os . path . join ( WORKING_DIR , 'graph_chunk_entity_relation.graphml' )
json_file = os . path . join ( WORKING_DIR , 'graph_data.json' )
# Convert XML to JSON
json_data = convert_xml_to_json ( xml_file , json_file )
if json_data is None :
return
# Load nodes and edges
nodes = json_data . get ( 'nodes' , [])
edges = json_data . get ( 'edges' , [])
# Neo4j queries
create_nodes_query = """
UNWIND $nodes AS node
MERGE (e:Entity {id: node.id})
SET e.entity_type = node.entity_type,
e.description = node.description,
e.source_id = node.source_id,
e.displayName = node.id
REMOVE e:Entity
WITH e, node
CALL apoc.create.addLabels(e, [node.entity_type]) YIELD node AS labeledNode
RETURN count(*)
"""
create_edges_query = """
UNWIND $edges AS edge
MATCH (source {id: edge.source})
MATCH (target {id: edge.target})
WITH source, target, edge,
CASE
WHEN edge.keywords CONTAINS 'lead' THEN 'lead'
WHEN edge.keywords CONTAINS 'participate' THEN 'participate'
WHEN edge.keywords CONTAINS 'uses' THEN 'uses'
WHEN edge.keywords CONTAINS 'located' THEN 'located'
WHEN edge.keywords CONTAINS 'occurs' THEN 'occurs'
ELSE REPLACE(SPLIT(edge.keywords, ',')[0], ' " ', '')
END AS relType
CALL apoc.create.relationship(source, relType, {
weight: edge.weight,
description: edge.description,
keywords: edge.keywords,
source_id: edge.source_id
}, target) YIELD rel
RETURN count(*)
"""
set_displayname_and_labels_query = """
MATCH (n)
SET n.displayName = n.id
WITH n
CALL apoc.create.setLabels(n, [n.entity_type]) YIELD node
RETURN count(*)
"""
# Create a Neo4j driver
driver = GraphDatabase . driver ( NEO4J_URI , auth = ( NEO4J_USERNAME , NEO4J_PASSWORD ))
try :
# Execute queries in batches
with driver . session () as session :
# Insert nodes in batches
session . execute_write ( process_in_batches , create_nodes_query , nodes , BATCH_SIZE_NODES )
# Insert edges in batches
session . execute_write ( process_in_batches , create_edges_query , edges , BATCH_SIZE_EDGES )
# Set displayName and labels
session . run ( set_displayname_and_labels_query )
except Exception as e :
print ( f"Error occurred: { e } " )
finally :
driver . close ()
if __name__ == "__main__" :
main ()| Parámetro | Tipo | Explicación | Por defecto |
|---|---|---|---|
| Working_dir | str | Directorio donde se almacenará el caché | lightrag_cache+timestamp |
| KV_Storaz | str | Tipo de almacenamiento para documentos y trozos de texto. Tipos compatibles: JsonKVStorage , OracleKVStorage | JsonKVStorage |
| vector_storay | str | Tipo de almacenamiento para incrustar vectores. Tipos compatibles: NanoVectorDBStorage , OracleVectorDBStorage | NanoVectorDBStorage |
| Graph_storage | str | Tipo de almacenamiento para bordes y nodos gráficos. Tipos compatibles: NetworkXStorage , Neo4JStorage , OracleGraphStorage | NetworkXStorage |
| LOG_LEVEL | Nivel de registro para el tiempo de ejecución de la aplicación | logging.DEBUG | |
| chunk_token_size | int | Tamaño máximo de token por fragmento al dividir documentos | 1200 |
| chunk_overlap_token_size | int | Superponga el tamaño del token entre dos trozos al dividir documentos | 100 |
| tiktoken_model_name | str | Nombre del modelo para el codificador tiktoken utilizado para calcular los números de token | gpt-4o-mini |
| entity_extract_max_gleaning | int | Número de bucles en el proceso de extracción de entidad, agregando mensajes de historial | 1 |
| entity_summary_to_max_tokens | int | Tamaño máximo de token para cada resumen de entidad | 500 |
| node_embedding_algorithm | str | Algoritmo para la incrustación de nodos (actualmente no se usa) | node2vec |
| nodo2vec_params | dict | Parámetros para la incrustación de nodos | {"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,} |
| incrustación_func | EmbeddingFunc | Función para generar vectores de incrustación del texto | openai_embedding |
| incrustar_batch_num | int | Tamaño de lote máximo para procesos de incrustación (múltiples textos enviados por lote) | 32 |
| incrustar_func_max_async | int | Número máximo de procesos de incrustación asincrónicos concurrentes | 16 |
| llm_model_func | callable | Función para la generación de LLM | gpt_4o_mini_complete |
| LLM_MODEL_NAME | str | Nombre del modelo LLM para la generación | meta-llama/Llama-3.2-1B-Instruct |
| llm_model_max_token_size | int | Tamaño máximo de token para la generación de LLM (afecta los resúmenes de la relación de entidad) | 32768 |
| LLM_MODEL_MAX_ASYNC | int | Número máximo de procesos LLM asincrónicos concurrentes | 16 |
| llm_model_kwargs | dict | Parámetros adicionales para la generación de LLM | |
| vector_db_storage_cls_kwargs | dict | Parámetros adicionales para la base de datos vectorial (actualmente no se usa) | |
| enable_llm_cache | bool | Si TRUE , las tiendas LLM resulta en caché; Respuestas de devolución de indicaciones repetidas | TRUE |
| addon_params | dict | Parámetros adicionales, por ejemplo, {"example_number": 1, "language": "Simplified Chinese"} : Establece el límite de límite y salida de ejemplo de ejemplo | example_number: all examples, language: English |
| convert_response_to_json_func | callable | No utilizado | convert_response_to_json |
Lightrag también proporciona una implementación del servidor basada en Fastapi para el acceso a API RESTFUL a las operaciones de RAG. Esto le permite ejecutar Lightrag como un servicio e interactuar con él a través de solicitudes HTTP.
pip install fastapi uvicorn pydantic export RAG_DIR= " your_index_directory " # Optional: Defaults to "index_default"
export OPENAI_BASE_URL= " Your OpenAI API base URL " # Optional: Defaults to "https://api.openai.com/v1"
export OPENAI_API_KEY= " Your OpenAI API key " # Required
export LLM_MODEL= " Your LLM model " # Optional: Defaults to "gpt-4o-mini"
export EMBEDDING_MODEL= " Your embedding model " # Optional: Defaults to "text-embedding-3-large"python examples/lightrag_api_openai_compatible_demo.py El servidor comenzará en http://0.0.0.0:8020 .
El servidor API proporciona los siguientes puntos finales:
/query{
"query" : " Your question here " ,
"mode" : " hybrid " , // Can be "naive", "local", "global", or "hybrid"
"only_need_context" : true // Optional: Defaults to false, if true, only the referenced context will be returned, otherwise the llm answer will be returned
}curl -X POST " http://127.0.0.1:8020/query "
-H " Content-Type: application/json "
-d ' {"query": "What are the main themes?", "mode": "hybrid"} ' /insert{
"text" : " Your text content here "
}curl -X POST " http://127.0.0.1:8020/insert "
-H " Content-Type: application/json "
-d ' {"text": "Content to be inserted into RAG"} ' /insert_file{
"file_path" : " path/to/your/file.txt "
}curl -X POST " http://127.0.0.1:8020/insert_file "
-H " Content-Type: application/json "
-d ' {"file_path": "./book.txt"} ' /healthcurl -X GET " http://127.0.0.1:8020/health "El servidor API se puede configurar utilizando variables de entorno:
RAG_DIR : directorio para almacenar el índice RAG (predeterminado: "index_default")La API incluye el manejo integral de errores:
El conjunto de datos utilizado en Lightrag se puede descargar desde Tommychien/Ultradominio.
Lightrag utiliza el siguiente mensaje para generar consultas de alto nivel, con el código correspondiente en example/generate_query.py .
Given the following description of a dataset :
{ description }
Please identify 5 potential users who would engage with this dataset . For each user , list 5 tasks they would perform with this dataset . Then , for each ( user , task ) combination , generate 5 questions that require a high - level understanding of the entire dataset .
Output the results in the following structure :
- User 1 : [ user description ]
- Task 1 : [ task description ]
- Question 1 :
- Question 2 :
- Question 3 :
- Question 4 :
- Question 5 :
- Task 2 : [ task description ]
...
- Task 5 : [ task description ]
- User 2 : [ user description ]
...
- User 5 : [ user description ]
... Para evaluar el rendimiento de dos sistemas RAG en consultas de alto nivel, Lightrag utiliza el siguiente mensaje, con el código específico disponible en example/batch_eval.py .
- - - Role - - -
You are an expert tasked with evaluating two answers to the same question based on three criteria : ** Comprehensiveness ** , ** Diversity ** , and ** Empowerment ** .
- - - Goal - - -
You will evaluate two answers to the same question based on three criteria : ** Comprehensiveness ** , ** Diversity ** , and ** Empowerment ** .
- ** Comprehensiveness ** : How much detail does the answer provide to cover all aspects and details of the question ?
- ** Diversity ** : How varied and rich is the answer in providing different perspectives and insights on the question ?
- ** Empowerment ** : How well does the answer help the reader understand and make informed judgments about the topic ?
For each criterion , choose the better answer ( either Answer 1 or Answer 2 ) and explain why . Then , select an overall winner based on these three categories .
Here is the question :
{ query }
Here are the two answers :
** Answer 1 : **
{ answer1 }
** Answer 2 : **
{ answer2 }
Evaluate both answers using the three criteria listed above and provide detailed explanations for each criterion .
Output your evaluation in the following JSON format :
{{
"Comprehensiveness" : {{
"Winner" : "[Answer 1 or Answer 2]" ,
"Explanation" : "[Provide explanation here]"
}},
"Empowerment" : {{
"Winner" : "[Answer 1 or Answer 2]" ,
"Explanation" : "[Provide explanation here]"
}},
"Overall Winner" : {{
"Winner" : "[Answer 1 or Answer 2]" ,
"Explanation" : "[Summarize why this answer is the overall winner based on the three criteria]"
}}
}}| Agricultura | CS | Legal | Mezcla | |||||
|---|---|---|---|---|---|---|---|---|
| Naiverag | Flojo | Naiverag | Flojo | Naiverag | Flojo | Naiverag | Flojo | |
| Amplitud | 32.4% | 67.6% | 38.4% | 61.6% | 16.4% | 83.6% | 38.8% | 61.2% |
| Diversidad | 23.6% | 76.4% | 38.0% | 62.0% | 13.6% | 86.4% | 32.4% | 67.6% |
| Empoderamiento | 32.4% | 67.6% | 38.8% | 61.2% | 16.4% | 83.6% | 42.8% | 57.2% |
| En general | 32.4% | 67.6% | 38.8% | 61.2% | 15.2% | 84.8% | 40.0% | 60.0% |
| RQ-RAG | Flojo | RQ-RAG | Flojo | RQ-RAG | Flojo | RQ-RAG | Flojo | |
| Amplitud | 31.6% | 68.4% | 38.8% | 61.2% | 15.2% | 84.8% | 39.2% | 60.8% |
| Diversidad | 29.2% | 70.8% | 39.2% | 60.8% | 11.6% | 88.4% | 30.8% | 69.2% |
| Empoderamiento | 31.6% | 68.4% | 36.4% | 63.6% | 15.2% | 84.8% | 42.4% | 57.6% |
| En general | 32.4% | 67.6% | 38.0% | 62.0% | 14.4% | 85.6% | 40.0% | 60.0% |
| Hyde | Flojo | Hyde | Flojo | Hyde | Flojo | Hyde | Flojo | |
| Amplitud | 26.0% | 74.0% | 41.6% | 58.4% | 26.8% | 73.2% | 40.4% | 59.6% |
| Diversidad | 24.0% | 76.0% | 38.8% | 61.2% | 20.0% | 80.0% | 32.4% | 67.6% |
| Empoderamiento | 25.2% | 74.8% | 40.8% | 59.2% | 26.0% | 74.0% | 46.0% | 54.0% |
| En general | 24.8% | 75.2% | 41.6% | 58.4% | 26.4% | 73.6% | 42.4% | 57.6% |
| Gráfica | Flojo | Gráfica | Flojo | Gráfica | Flojo | Gráfica | Flojo | |
| Amplitud | 45.6% | 54.4% | 48.4% | 51.6% | 48.4% | 51.6% | 50.4% | 49.6% |
| Diversidad | 22.8% | 77.2% | 40.8% | 59.2% | 26.4% | 73.6% | 36.0% | 64.0% |
| Empoderamiento | 41.2% | 58.8% | 45.2% | 54.8% | 43.6% | 56.4% | 50.8% | 49.2% |
| En general | 45.2% | 54.8% | 48.0% | 52.0% | 47.2% | 52.8% | 50.4% | 49.6% |
Todo el código se puede encontrar en el directorio ./reproduce .
Primero, necesitamos extraer contextos únicos en los conjuntos de datos.
def extract_unique_contexts ( input_directory , output_directory ):
os . makedirs ( output_directory , exist_ok = True )
jsonl_files = glob . glob ( os . path . join ( input_directory , '*.jsonl' ))
print ( f"Found { len ( jsonl_files ) } JSONL files." )
for file_path in jsonl_files :
filename = os . path . basename ( file_path )
name , ext = os . path . splitext ( filename )
output_filename = f" { name } _unique_contexts.json"
output_path = os . path . join ( output_directory , output_filename )
unique_contexts_dict = {}
print ( f"Processing file: { filename } " )
try :
with open ( file_path , 'r' , encoding = 'utf-8' ) as infile :
for line_number , line in enumerate ( infile , start = 1 ):
line = line . strip ()
if not line :
continue
try :
json_obj = json . loads ( line )
context = json_obj . get ( 'context' )
if context and context not in unique_contexts_dict :
unique_contexts_dict [ context ] = None
except json . JSONDecodeError as e :
print ( f"JSON decoding error in file { filename } at line { line_number } : { e } " )
except FileNotFoundError :
print ( f"File not found: { filename } " )
continue
except Exception as e :
print ( f"An error occurred while processing file { filename } : { e } " )
continue
unique_contexts_list = list ( unique_contexts_dict . keys ())
print ( f"There are { len ( unique_contexts_list ) } unique `context` entries in the file { filename } ." )
try :
with open ( output_path , 'w' , encoding = 'utf-8' ) as outfile :
json . dump ( unique_contexts_list , outfile , ensure_ascii = False , indent = 4 )
print ( f"Unique `context` entries have been saved to: { output_filename } " )
except Exception as e :
print ( f"An error occurred while saving to the file { output_filename } : { e } " )
print ( "All files have been processed." )Para los contextos extraídos, los insertamos en el sistema Lightrag.
def insert_text ( rag , file_path ):
with open ( file_path , mode = 'r' ) as f :
unique_contexts = json . load ( f )
retries = 0
max_retries = 3
while retries < max_retries :
try :
rag . insert ( unique_contexts )
break
except Exception as e :
retries += 1
print ( f"Insertion failed, retrying ( { retries } / { max_retries } ), error: { e } " )
time . sleep ( 10 )
if retries == max_retries :
print ( "Insertion failed after exceeding the maximum number of retries" )Extraemos tokens de la primera y la segunda mitad de cada contexto en el conjunto de datos, luego las combinamos como descripciones del conjunto de datos para generar consultas.
tokenizer = GPT2Tokenizer . from_pretrained ( 'gpt2' )
def get_summary ( context , tot_tokens = 2000 ):
tokens = tokenizer . tokenize ( context )
half_tokens = tot_tokens // 2
start_tokens = tokens [ 1000 : 1000 + half_tokens ]
end_tokens = tokens [ - ( 1000 + half_tokens ): 1000 ]
summary_tokens = start_tokens + end_tokens
summary = tokenizer . convert_tokens_to_string ( summary_tokens )
return summaryPara las consultas generadas en el Paso 2, las extraeremos y consultamos Lightrag.
def extract_queries ( file_path ):
with open ( file_path , 'r' ) as f :
data = f . read ()
data = data . replace ( '**' , '' )
queries = re . findall ( r'- Question d+: (.+)' , data )
return queries .
├── examples
│ ├── batch_eval . py
│ ├── generate_query . py
│ ├── graph_visual_with_html . py
│ ├── graph_visual_with_neo4j . py
│ ├── lightrag_api_openai_compatible_demo . py
│ ├── lightrag_azure_openai_demo . py
│ ├── lightrag_bedrock_demo . py
│ ├── lightrag_hf_demo . py
│ ├── lightrag_lmdeploy_demo . py
│ ├── lightrag_ollama_demo . py
│ ├── lightrag_openai_compatible_demo . py
│ ├── lightrag_openai_demo . py
│ ├── lightrag_siliconcloud_demo . py
│ └── vram_management_demo . py
├── lightrag
│ ├── kg
│ │ ├── __init__ . py
│ │ └── neo4j_impl . py
│ ├── __init__ . py
│ ├── base . py
│ ├── lightrag . py
│ ├── llm . py
│ ├── operate . py
│ ├── prompt . py
│ ├── storage . py
│ └── utils . py
├── reproduce
│ ├── Step_0 . py
│ ├── Step_1_openai_compatible . py
│ ├── Step_1 . py
│ ├── Step_2 . py
│ ├── Step_3_openai_compatible . py
│ └── Step_3 . py
├── . gitignore
├── . pre - commit - config . yaml
├── Dockerfile
├── get_all_edges_nx . py
├── LICENSE
├── README . md
├── requirements . txt
├── setup . py
├── test_neo4j . py
└── test . py ¡Gracias a todos nuestros contribuyentes!
@ article { guo2024lightrag ,
title = { LightRAG : Simple and Fast Retrieval - Augmented Generation },
author = { Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang },
year = { 2024 },
eprint = { 2410.05779 },
archivePrefix = { arXiv },
primaryClass = { cs . IR }
}¡Gracias por su interés en nuestro trabajo!
