Remove deprecated demo code
This commit is contained in:
@@ -1,113 +0,0 @@
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import asyncio
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import nest_asyncio
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import inspect
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import logging
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import os
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.ollama import ollama_embed, ollama_model_complete
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from lightrag.utils import EmbeddingFunc
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from lightrag.kg.shared_storage import initialize_pipeline_status
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nest_asyncio.apply()
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WORKING_DIR = "./dickens_age"
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logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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# AGE
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os.environ["AGE_POSTGRES_DB"] = "postgresDB"
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os.environ["AGE_POSTGRES_USER"] = "postgresUser"
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os.environ["AGE_POSTGRES_PASSWORD"] = "postgresPW"
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os.environ["AGE_POSTGRES_HOST"] = "localhost"
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os.environ["AGE_POSTGRES_PORT"] = "5455"
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os.environ["AGE_GRAPH_NAME"] = "dickens"
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async def initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=ollama_model_complete,
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llm_model_name="llama3.1:8b",
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llm_model_max_async=4,
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llm_model_max_token_size=32768,
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llm_model_kwargs={
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"host": "http://localhost:11434",
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"options": {"num_ctx": 32768},
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},
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embedding_func=EmbeddingFunc(
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embedding_dim=768,
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max_token_size=8192,
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func=lambda texts: ollama_embed(
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texts, embed_model="nomic-embed-text", host="http://localhost:11434"
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),
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),
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graph_storage="AGEStorage",
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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async def print_stream(stream):
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async for chunk in stream:
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print(chunk, end="", flush=True)
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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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# Insert example text
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with open("./book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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# Test different query modes
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print("\nNaive Search:")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="naive")
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)
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)
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print("\nLocal Search:")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="local")
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)
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)
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print("\nGlobal Search:")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="global")
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)
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)
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print("\nHybrid Search:")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="hybrid")
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)
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)
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# stream response
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resp = rag.query(
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"What are the top themes in this story?",
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param=QueryParam(mode="hybrid", stream=True),
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)
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if inspect.isasyncgen(resp):
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asyncio.run(print_stream(resp))
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else:
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print(resp)
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if __name__ == "__main__":
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main()
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@@ -1,103 +0,0 @@
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import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import openai_complete_if_cache
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from lightrag.llm.siliconcloud import siliconcloud_embedding
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from lightrag.utils import EmbeddingFunc
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import numpy as np
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from lightrag.kg.shared_storage import initialize_pipeline_status
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WORKING_DIR = "./dickens"
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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return await openai_complete_if_cache(
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"Qwen/Qwen2.5-7B-Instruct",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key=os.getenv("SILICONFLOW_API_KEY"),
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base_url="https://api.siliconflow.cn/v1/",
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**kwargs,
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)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await siliconcloud_embedding(
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texts,
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model="netease-youdao/bce-embedding-base_v1",
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api_key=os.getenv("SILICONFLOW_API_KEY"),
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max_token_size=512,
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)
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# function test
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async def test_funcs():
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result = await llm_model_func("How are you?")
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print("llm_model_func: ", result)
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result = await embedding_func(["How are you?"])
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print("embedding_func: ", result)
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asyncio.run(test_funcs())
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async def initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=768, max_token_size=512, func=embedding_func
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),
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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with open("./book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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# Perform naive search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="naive")
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)
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)
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# Perform local search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="local")
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)
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)
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# Perform global search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="global")
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)
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)
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# Perform hybrid search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="hybrid")
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)
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)
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if __name__ == "__main__":
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main()
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@@ -1,110 +0,0 @@
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import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import openai_complete_if_cache
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from lightrag.llm.siliconcloud import siliconcloud_embedding
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from lightrag.utils import EmbeddingFunc
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from lightrag.utils import TokenTracker
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import numpy as np
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from dotenv import load_dotenv
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load_dotenv()
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token_tracker = TokenTracker()
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WORKING_DIR = "./dickens"
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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return await openai_complete_if_cache(
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"Qwen/Qwen2.5-7B-Instruct",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key=os.getenv("SILICONFLOW_API_KEY"),
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base_url="https://api.siliconflow.cn/v1/",
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token_tracker=token_tracker,
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**kwargs,
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)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await siliconcloud_embedding(
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texts,
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model="BAAI/bge-m3",
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api_key=os.getenv("SILICONFLOW_API_KEY"),
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max_token_size=512,
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)
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# function test
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async def test_funcs():
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# Context Manager Method
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with token_tracker:
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result = await llm_model_func("How are you?")
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print("llm_model_func: ", result)
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asyncio.run(test_funcs())
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async def initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=1024, max_token_size=512, func=embedding_func
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),
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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# Reset tracker before processing queries
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token_tracker.reset()
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with open("./book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="naive")
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)
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)
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="local")
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)
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)
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="global")
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)
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)
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="hybrid")
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)
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)
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# Display final token usage after main query
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print("Token usage:", token_tracker.get_usage())
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if __name__ == "__main__":
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main()
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@@ -1,116 +0,0 @@
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###########################################
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# TiDB storage implementation is deprecated
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###########################################
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import asyncio
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import os
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import numpy as np
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import siliconcloud_embedding, openai_complete_if_cache
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from lightrag.utils import EmbeddingFunc
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from lightrag.kg.shared_storage import initialize_pipeline_status
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WORKING_DIR = "./dickens"
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# We use SiliconCloud API to call LLM on Oracle Cloud
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# More docs here https://docs.siliconflow.cn/introduction
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BASE_URL = "https://api.siliconflow.cn/v1/"
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APIKEY = ""
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CHATMODEL = ""
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EMBEDMODEL = ""
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os.environ["TIDB_HOST"] = ""
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os.environ["TIDB_PORT"] = ""
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os.environ["TIDB_USER"] = ""
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os.environ["TIDB_PASSWORD"] = ""
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os.environ["TIDB_DATABASE"] = "lightrag"
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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return await openai_complete_if_cache(
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CHATMODEL,
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key=APIKEY,
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base_url=BASE_URL,
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**kwargs,
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)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await siliconcloud_embedding(
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texts,
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# model=EMBEDMODEL,
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api_key=APIKEY,
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)
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async def get_embedding_dim():
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test_text = ["This is a test sentence."]
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embedding = await embedding_func(test_text)
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embedding_dim = embedding.shape[1]
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return embedding_dim
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async def initialize_rag():
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# Detect embedding dimension
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embedding_dimension = await get_embedding_dim()
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print(f"Detected embedding dimension: {embedding_dimension}")
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# Initialize LightRAG
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# We use TiDB DB as the KV/vector
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rag = LightRAG(
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enable_llm_cache=False,
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working_dir=WORKING_DIR,
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chunk_token_size=512,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=embedding_dimension,
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max_token_size=512,
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func=embedding_func,
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),
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kv_storage="TiDBKVStorage",
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vector_storage="TiDBVectorDBStorage",
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graph_storage="TiDBGraphStorage",
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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async def main():
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try:
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# Initialize RAG instance
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rag = await initialize_rag()
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with open("./book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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# Perform search in different modes
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modes = ["naive", "local", "global", "hybrid"]
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for mode in modes:
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print("=" * 20, mode, "=" * 20)
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print(
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await rag.aquery(
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"What are the top themes in this story?",
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param=QueryParam(mode=mode),
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)
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)
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print("-" * 100, "\n")
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except Exception as e:
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print(f"An error occurred: {e}")
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if __name__ == "__main__":
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asyncio.run(main())
|
@@ -1,136 +0,0 @@
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import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.utils import EmbeddingFunc
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import numpy as np
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from dotenv import load_dotenv
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import logging
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from openai import OpenAI
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from lightrag.kg.shared_storage import initialize_pipeline_status
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logging.basicConfig(level=logging.INFO)
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load_dotenv()
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LLM_MODEL = os.environ.get("LLM_MODEL", "qwen-turbo-latest")
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LLM_BINDING_HOST = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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LLM_BINDING_API_KEY = os.getenv("LLM_BINDING_API_KEY")
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EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-v3")
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EMBEDDING_BINDING_HOST = os.getenv("EMBEDDING_BINDING_HOST", LLM_BINDING_HOST)
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EMBEDDING_BINDING_API_KEY = os.getenv("EMBEDDING_BINDING_API_KEY", LLM_BINDING_API_KEY)
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EMBEDDING_DIM = int(os.environ.get("EMBEDDING_DIM", 1024))
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EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
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EMBEDDING_MAX_BATCH_SIZE = int(os.environ.get("EMBEDDING_MAX_BATCH_SIZE", 10))
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print(f"LLM_MODEL: {LLM_MODEL}")
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print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
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WORKING_DIR = "./dickens"
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if os.path.exists(WORKING_DIR):
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import shutil
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shutil.rmtree(WORKING_DIR)
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os.mkdir(WORKING_DIR)
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|
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
|
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) -> str:
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client = OpenAI(
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api_key=LLM_BINDING_API_KEY,
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base_url=LLM_BINDING_HOST,
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)
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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if history_messages:
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messages.extend(history_messages)
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messages.append({"role": "user", "content": prompt})
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chat_completion = client.chat.completions.create(
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model=LLM_MODEL,
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messages=messages,
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temperature=kwargs.get("temperature", 0),
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top_p=kwargs.get("top_p", 1),
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n=kwargs.get("n", 1),
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extra_body={"enable_thinking": False},
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)
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return chat_completion.choices[0].message.content
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async def embedding_func(texts: list[str]) -> np.ndarray:
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client = OpenAI(
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api_key=EMBEDDING_BINDING_API_KEY,
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base_url=EMBEDDING_BINDING_HOST,
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)
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print("##### embedding: texts: %d #####" % len(texts))
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max_batch_size = EMBEDDING_MAX_BATCH_SIZE
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embeddings = []
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for i in range(0, len(texts), max_batch_size):
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batch = texts[i : i + max_batch_size]
|
||||
embedding = client.embeddings.create(model=EMBEDDING_MODEL, input=batch)
|
||||
embeddings += [item.embedding for item in embedding.data]
|
||||
|
||||
return np.array(embeddings)
|
||||
|
||||
|
||||
async def test_funcs():
|
||||
result = await llm_model_func("How are you?")
|
||||
print("Resposta do llm_model_func: ", result)
|
||||
|
||||
result = await embedding_func(["How are you?"])
|
||||
print("Resultado do embedding_func: ", result.shape)
|
||||
print("Dimensão da embedding: ", result.shape[1])
|
||||
|
||||
|
||||
asyncio.run(test_funcs())
|
||||
|
||||
|
||||
async def initialize_rag():
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=llm_model_func,
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=EMBEDDING_DIM,
|
||||
max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
|
||||
func=embedding_func,
|
||||
),
|
||||
)
|
||||
|
||||
await rag.initialize_storages()
|
||||
await initialize_pipeline_status()
|
||||
|
||||
return rag
|
||||
|
||||
|
||||
def main():
|
||||
rag = asyncio.run(initialize_rag())
|
||||
|
||||
with open("./book.txt", "r", encoding="utf-8") as f:
|
||||
rag.insert(f.read())
|
||||
|
||||
query_text = "What are the main themes?"
|
||||
|
||||
print("Result (Naive):")
|
||||
print(rag.query(query_text, param=QueryParam(mode="naive")))
|
||||
|
||||
print("\nResult (Local):")
|
||||
print(rag.query(query_text, param=QueryParam(mode="local")))
|
||||
|
||||
print("\nResult (Global):")
|
||||
print(rag.query(query_text, param=QueryParam(mode="global")))
|
||||
|
||||
print("\nResult (Hybrid):")
|
||||
print(rag.query(query_text, param=QueryParam(mode="hybrid")))
|
||||
|
||||
print("\nResult (mix):")
|
||||
print(rag.query(query_text, param=QueryParam(mode="mix")))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
@@ -1,80 +0,0 @@
|
||||
import os
|
||||
import logging
|
||||
import asyncio
|
||||
|
||||
|
||||
from lightrag import LightRAG, QueryParam
|
||||
from lightrag.llm.zhipu import zhipu_complete, zhipu_embedding
|
||||
from lightrag.utils import EmbeddingFunc
|
||||
from lightrag.kg.shared_storage import initialize_pipeline_status
|
||||
|
||||
WORKING_DIR = "./dickens"
|
||||
|
||||
logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
|
||||
|
||||
if not os.path.exists(WORKING_DIR):
|
||||
os.mkdir(WORKING_DIR)
|
||||
|
||||
api_key = os.environ.get("ZHIPUAI_API_KEY")
|
||||
if api_key is None:
|
||||
raise Exception("Please set ZHIPU_API_KEY in your environment")
|
||||
|
||||
|
||||
async def initialize_rag():
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=zhipu_complete,
|
||||
llm_model_name="glm-4-flashx", # Using the most cost/performance balance model, but you can change it here.
|
||||
llm_model_max_async=4,
|
||||
llm_model_max_token_size=32768,
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=2048, # Zhipu embedding-3 dimension
|
||||
max_token_size=8192,
|
||||
func=lambda texts: zhipu_embedding(texts),
|
||||
),
|
||||
)
|
||||
|
||||
await rag.initialize_storages()
|
||||
await initialize_pipeline_status()
|
||||
|
||||
return rag
|
||||
|
||||
|
||||
def main():
|
||||
# Initialize RAG instance
|
||||
rag = asyncio.run(initialize_rag())
|
||||
|
||||
with open("./book.txt", "r", encoding="utf-8") 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")
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
@@ -1,109 +0,0 @@
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from lightrag import LightRAG, QueryParam
|
||||
from lightrag.llm.zhipu import zhipu_complete
|
||||
from lightrag.llm.ollama import ollama_embedding
|
||||
from lightrag.utils import EmbeddingFunc
|
||||
from lightrag.kg.shared_storage import initialize_pipeline_status
|
||||
|
||||
load_dotenv()
|
||||
ROOT_DIR = os.environ.get("ROOT_DIR")
|
||||
WORKING_DIR = f"{ROOT_DIR}/dickens-pg"
|
||||
|
||||
logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
|
||||
|
||||
if not os.path.exists(WORKING_DIR):
|
||||
os.mkdir(WORKING_DIR)
|
||||
|
||||
# AGE
|
||||
os.environ["AGE_GRAPH_NAME"] = "dickens"
|
||||
|
||||
os.environ["POSTGRES_HOST"] = "localhost"
|
||||
os.environ["POSTGRES_PORT"] = "15432"
|
||||
os.environ["POSTGRES_USER"] = "rag"
|
||||
os.environ["POSTGRES_PASSWORD"] = "rag"
|
||||
os.environ["POSTGRES_DATABASE"] = "rag"
|
||||
|
||||
|
||||
async def initialize_rag():
|
||||
rag = LightRAG(
|
||||
working_dir=WORKING_DIR,
|
||||
llm_model_func=zhipu_complete,
|
||||
llm_model_name="glm-4-flashx",
|
||||
llm_model_max_async=4,
|
||||
llm_model_max_token_size=32768,
|
||||
enable_llm_cache_for_entity_extract=True,
|
||||
embedding_func=EmbeddingFunc(
|
||||
embedding_dim=1024,
|
||||
max_token_size=8192,
|
||||
func=lambda texts: ollama_embedding(
|
||||
texts, embed_model="bge-m3", host="http://localhost:11434"
|
||||
),
|
||||
),
|
||||
kv_storage="PGKVStorage",
|
||||
doc_status_storage="PGDocStatusStorage",
|
||||
graph_storage="PGGraphStorage",
|
||||
vector_storage="PGVectorStorage",
|
||||
auto_manage_storages_states=False,
|
||||
)
|
||||
|
||||
await rag.initialize_storages()
|
||||
await initialize_pipeline_status()
|
||||
|
||||
return rag
|
||||
|
||||
|
||||
async def main():
|
||||
# Initialize RAG instance
|
||||
rag = await initialize_rag()
|
||||
|
||||
# add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c
|
||||
rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func
|
||||
|
||||
with open(f"{ROOT_DIR}/book.txt", "r", encoding="utf-8") as f:
|
||||
await rag.ainsert(f.read())
|
||||
|
||||
print("==== Trying to test the rag queries ====")
|
||||
print("**** Start Naive Query ****")
|
||||
start_time = time.time()
|
||||
# Perform naive search
|
||||
print(
|
||||
await rag.aquery(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="naive")
|
||||
)
|
||||
)
|
||||
print(f"Naive Query Time: {time.time() - start_time} seconds")
|
||||
# Perform local search
|
||||
print("**** Start Local Query ****")
|
||||
start_time = time.time()
|
||||
print(
|
||||
await rag.aquery(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="local")
|
||||
)
|
||||
)
|
||||
print(f"Local Query Time: {time.time() - start_time} seconds")
|
||||
# Perform global search
|
||||
print("**** Start Global Query ****")
|
||||
start_time = time.time()
|
||||
print(
|
||||
await rag.aquery(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="global")
|
||||
)
|
||||
)
|
||||
print(f"Global Query Time: {time.time() - start_time}")
|
||||
# Perform hybrid search
|
||||
print("**** Start Hybrid Query ****")
|
||||
print(
|
||||
await rag.aquery(
|
||||
"What are the top themes in this story?", param=QueryParam(mode="hybrid")
|
||||
)
|
||||
)
|
||||
print(f"Hybrid Query Time: {time.time() - start_time} seconds")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
Reference in New Issue
Block a user