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Remove deprecated demo code

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This commit is contained in:
yangdx
2025-05-14 00:36:38 +08:00
parent ab75027b22
commit aa36894d6e
3 changed files with 0 additions and 349 deletions
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122
examples/lightrag_ollama_gremlin_demo.py
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##############################################
# Gremlin storage implementation is deprecated
##############################################
import asyncio
import inspect
import os
# Uncomment these lines below to filter out somewhat verbose INFO level
# logging prints (the default loglevel is INFO).
# This has to go before the lightrag imports to work,
# which triggers linting errors, so we keep it commented out:
# import logging
# logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.WARN)
from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_embed, ollama_model_complete
from lightrag.utils import EmbeddingFunc
from lightrag.kg.shared_storage import initialize_pipeline_status
WORKING_DIR = "./dickens_gremlin"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
# Gremlin
os.environ["GREMLIN_HOST"] = "localhost"
os.environ["GREMLIN_PORT"] = "8182"
os.environ["GREMLIN_GRAPH"] = "dickens"
# Creating a non-default source requires manual
# configuration and a restart on the server: use the dafault "g"
os.environ["GREMLIN_TRAVERSE_SOURCE"] = "g"
# No authorization by default on docker tinkerpop/gremlin-server
os.environ["GREMLIN_USER"] = ""
os.environ["GREMLIN_PASSWORD"] = ""
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=ollama_model_complete,
llm_model_name="llama3.1:8b",
llm_model_max_async=4,
llm_model_max_token_size=32768,
llm_model_kwargs={
"host": "http://localhost:11434",
"options": {"num_ctx": 32768},
},
embedding_func=EmbeddingFunc(
embedding_dim=768,
max_token_size=8192,
func=lambda texts: ollama_embed(
texts, embed_model="nomic-embed-text", host="http://localhost:11434"
),
),
graph_storage="GremlinStorage",
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
async def print_stream(stream):
async for chunk in stream:
print(chunk, end="", flush=True)
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
# Insert example text
with open("./book.txt", "r", encoding="utf-8") as f:
rag.insert(f.read())
# Test different query modes
print("\nNaive Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="naive")
)
)
print("\nLocal Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="local")
)
)
print("\nGlobal Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="global")
)
)
print("\nHybrid Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="hybrid")
)
)
# stream response
resp = rag.query(
"What are the top themes in this story?",
param=QueryParam(mode="hybrid", stream=True),
)
if inspect.isasyncgen(resp):
asyncio.run(print_stream(resp))
else:
print(resp)
if __name__ == "__main__":
main()

104
examples/lightrag_ollama_neo4j_milvus_mongo_demo.py
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import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
from lightrag.utils import EmbeddingFunc
import asyncio
import nest_asyncio
nest_asyncio.apply()
from lightrag.kg.shared_storage import initialize_pipeline_status
# WorkingDir
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
WORKING_DIR = os.path.join(ROOT_DIR, "myKG")
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
print(f"WorkingDir: {WORKING_DIR}")
# mongo
os.environ["MONGO_URI"] = "mongodb://root:root@localhost:27017/"
os.environ["MONGO_DATABASE"] = "LightRAG"
# neo4j
BATCH_SIZE_NODES = 500
BATCH_SIZE_EDGES = 100
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "neo4j"
# milvus
os.environ["MILVUS_URI"] = "http://localhost:19530"
os.environ["MILVUS_USER"] = "root"
os.environ["MILVUS_PASSWORD"] = "root"
os.environ["MILVUS_DB_NAME"] = "lightrag"
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=ollama_model_complete,
llm_model_name="qwen2.5:14b",
llm_model_max_async=4,
llm_model_max_token_size=32768,
llm_model_kwargs={
"host": "http://127.0.0.1:11434",
"options": {"num_ctx": 32768},
},
embedding_func=EmbeddingFunc(
embedding_dim=1024,
max_token_size=8192,
func=lambda texts: ollama_embed(
texts=texts, embed_model="bge-m3:latest", host="http://127.0.0.1:11434"
),
),
kv_storage="MongoKVStorage",
graph_storage="Neo4JStorage",
vector_storage="MilvusVectorDBStorage",
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
# Insert example text
with open("./book.txt", "r", encoding="utf-8") as f:
rag.insert(f.read())
# Test different query modes
print("\nNaive Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="naive")
)
)
print("\nLocal Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="local")
)
)
print("\nGlobal Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="global")
)
)
print("\nHybrid Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="hybrid")
)
)
if __name__ == "__main__":
main()

123
examples/lightrag_openai_compatible_demo_embedding_cache.py
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import os
import asyncio
from lightrag import LightRAG, QueryParam
from lightrag.llm.openai import openai_complete_if_cache, openai_embed
from lightrag.utils import EmbeddingFunc
import numpy as np
from lightrag.kg.shared_storage import initialize_pipeline_status
WORKING_DIR = "./dickens"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **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_embed(
texts,
model="solar-embedding-1-large-query",
api_key=os.getenv("UPSTAGE_API_KEY"),
base_url="https://api.upstage.ai/v1/solar",
)
async def get_embedding_dim():
test_text = ["This is a test sentence."]
embedding = await embedding_func(test_text)
embedding_dim = embedding.shape[1]
return embedding_dim
# function test
async def test_funcs():
result = await llm_model_func("How are you?")
print("llm_model_func: ", result)
result = await embedding_func(["How are you?"])
print("embedding_func: ", result)
# asyncio.run(test_funcs())
async def initialize_rag():
embedding_dimension = await get_embedding_dim()
print(f"Detected embedding dimension: {embedding_dimension}")
rag = LightRAG(
working_dir=WORKING_DIR,
embedding_cache_config={
"enabled": True,
"similarity_threshold": 0.90,
},
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=8192,
func=embedding_func,
),
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
async def main():
try:
# Initialize RAG instance
rag = await initialize_rag()
with open("./book.txt", "r", encoding="utf-8") as f:
await rag.ainsert(f.read())
# Perform naive search
print(
await rag.aquery(
"What are the top themes in this story?", param=QueryParam(mode="naive")
)
)
# Perform local search
print(
await rag.aquery(
"What are the top themes in this story?", param=QueryParam(mode="local")
)
)
# Perform global search
print(
await rag.aquery(
"What are the top themes in this story?",
param=QueryParam(mode="global"),
)
)
# Perform hybrid search
print(
await rag.aquery(
"What are the top themes in this story?",
param=QueryParam(mode="hybrid"),
)
)
except Exception as e:
print(f"An error occurred: {e}")
if __name__ == "__main__":
asyncio.run(main())