Remove deprecated demo code

examples/lightrag_ollama_age_demo.py

@@ -1,113 +0,0 @@
-import asyncio
-import nest_asyncio
-
-import inspect
-import logging
-import os
-
-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
-
-nest_asyncio.apply()
-
-WORKING_DIR = "./dickens_age"
-
-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_POSTGRES_DB"] = "postgresDB"
-os.environ["AGE_POSTGRES_USER"] = "postgresUser"
-os.environ["AGE_POSTGRES_PASSWORD"] = "postgresPW"
-os.environ["AGE_POSTGRES_HOST"] = "localhost"
-os.environ["AGE_POSTGRES_PORT"] = "5455"
-os.environ["AGE_GRAPH_NAME"] = "dickens"
-
-
-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="AGEStorage",
-    )
-
-    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()

examples/lightrag_siliconcloud_demo.py

@@ -1,103 +0,0 @@
-import os
-import asyncio
-from lightrag import LightRAG, QueryParam
-from lightrag.llm.openai import openai_complete_if_cache
-from lightrag.llm.siliconcloud import siliconcloud_embedding
-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(
-        "Qwen/Qwen2.5-7B-Instruct",
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        api_key=os.getenv("SILICONFLOW_API_KEY"),
-        base_url="https://api.siliconflow.cn/v1/",
-        **kwargs,
-    )
-
-
-async def embedding_func(texts: list[str]) -> np.ndarray:
-    return await siliconcloud_embedding(
-        texts,
-        model="netease-youdao/bce-embedding-base_v1",
-        api_key=os.getenv("SILICONFLOW_API_KEY"),
-        max_token_size=512,
-    )
-
-
-# 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():
-    rag = LightRAG(
-        working_dir=WORKING_DIR,
-        llm_model_func=llm_model_func,
-        embedding_func=EmbeddingFunc(
-            embedding_dim=768, max_token_size=512, func=embedding_func
-        ),
-    )
-
-    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()

examples/lightrag_siliconcloud_track_token_demo.py

@@ -1,110 +0,0 @@
-import os
-import asyncio
-from lightrag import LightRAG, QueryParam
-from lightrag.llm.openai import openai_complete_if_cache
-from lightrag.llm.siliconcloud import siliconcloud_embedding
-from lightrag.utils import EmbeddingFunc
-from lightrag.utils import TokenTracker
-import numpy as np
-from lightrag.kg.shared_storage import initialize_pipeline_status
-from dotenv import load_dotenv
-
-load_dotenv()
-
-token_tracker = TokenTracker()
-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(
-        "Qwen/Qwen2.5-7B-Instruct",
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        api_key=os.getenv("SILICONFLOW_API_KEY"),
-        base_url="https://api.siliconflow.cn/v1/",
-        token_tracker=token_tracker,
-        **kwargs,
-    )
-
-
-async def embedding_func(texts: list[str]) -> np.ndarray:
-    return await siliconcloud_embedding(
-        texts,
-        model="BAAI/bge-m3",
-        api_key=os.getenv("SILICONFLOW_API_KEY"),
-        max_token_size=512,
-    )
-
-
-# function test
-async def test_funcs():
-    # Context Manager Method
-    with token_tracker:
-        result = await llm_model_func("How are you?")
-        print("llm_model_func: ", result)
-
-
-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=1024, max_token_size=512, func=embedding_func
-        ),
-    )
-
-    await rag.initialize_storages()
-    await initialize_pipeline_status()
-
-    return rag
-
-
-def main():
-    # Initialize RAG instance
-    rag = asyncio.run(initialize_rag())
-
-    # Reset tracker before processing queries
-    token_tracker.reset()
-
-    with open("./book.txt", "r", encoding="utf-8") as f:
-        rag.insert(f.read())
-
-    print(
-        rag.query(
-            "What are the top themes in this story?", param=QueryParam(mode="naive")
-        )
-    )
-
-    print(
-        rag.query(
-            "What are the top themes in this story?", param=QueryParam(mode="local")
-        )
-    )
-
-    print(
-        rag.query(
-            "What are the top themes in this story?", param=QueryParam(mode="global")
-        )
-    )
-
-    print(
-        rag.query(
-            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
-        )
-    )
-
-    # Display final token usage after main query
-    print("Token usage:", token_tracker.get_usage())
-
-
-if __name__ == "__main__":
-    main()

examples/lightrag_tidb_demo.py

@@ -1,116 +0,0 @@
-###########################################
-# TiDB storage implementation is deprecated
-###########################################
-
-import asyncio
-import os
-
-import numpy as np
-
-from lightrag import LightRAG, QueryParam
-from lightrag.llm import siliconcloud_embedding, openai_complete_if_cache
-from lightrag.utils import EmbeddingFunc
-from lightrag.kg.shared_storage import initialize_pipeline_status
-
-WORKING_DIR = "./dickens"
-
-# We use SiliconCloud API to call LLM on Oracle Cloud
-# More docs here https://docs.siliconflow.cn/introduction
-BASE_URL = "https://api.siliconflow.cn/v1/"
-APIKEY = ""
-CHATMODEL = ""
-EMBEDMODEL = ""
-
-os.environ["TIDB_HOST"] = ""
-os.environ["TIDB_PORT"] = ""
-os.environ["TIDB_USER"] = ""
-os.environ["TIDB_PASSWORD"] = ""
-os.environ["TIDB_DATABASE"] = "lightrag"
-
-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(
-        CHATMODEL,
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        api_key=APIKEY,
-        base_url=BASE_URL,
-        **kwargs,
-    )
-
-
-async def embedding_func(texts: list[str]) -> np.ndarray:
-    return await siliconcloud_embedding(
-        texts,
-        # model=EMBEDMODEL,
-        api_key=APIKEY,
-    )
-
-
-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
-
-
-async def initialize_rag():
-    # Detect embedding dimension
-    embedding_dimension = await get_embedding_dim()
-    print(f"Detected embedding dimension: {embedding_dimension}")
-
-    # Initialize LightRAG
-    # We use TiDB DB as the KV/vector
-    rag = LightRAG(
-        enable_llm_cache=False,
-        working_dir=WORKING_DIR,
-        chunk_token_size=512,
-        llm_model_func=llm_model_func,
-        embedding_func=EmbeddingFunc(
-            embedding_dim=embedding_dimension,
-            max_token_size=512,
-            func=embedding_func,
-        ),
-        kv_storage="TiDBKVStorage",
-        vector_storage="TiDBVectorDBStorage",
-        graph_storage="TiDBGraphStorage",
-    )
-
-    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:
-            rag.insert(f.read())
-
-        # Perform search in different modes
-        modes = ["naive", "local", "global", "hybrid"]
-        for mode in modes:
-            print("=" * 20, mode, "=" * 20)
-            print(
-                await rag.aquery(
-                    "What are the top themes in this story?",
-                    param=QueryParam(mode=mode),
-                )
-            )
-            print("-" * 100, "\n")
-
-    except Exception as e:
-        print(f"An error occurred: {e}")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/lightrag_tongyi_openai_demo.py

@@ -1,136 +0,0 @@
-import os
-import asyncio
-from lightrag import LightRAG, QueryParam
-from lightrag.utils import EmbeddingFunc
-import numpy as np
-from dotenv import load_dotenv
-import logging
-from openai import OpenAI
-from lightrag.kg.shared_storage import initialize_pipeline_status
-
-logging.basicConfig(level=logging.INFO)
-
-load_dotenv()
-
-LLM_MODEL = os.environ.get("LLM_MODEL", "qwen-turbo-latest")
-LLM_BINDING_HOST = "https://dashscope.aliyuncs.com/compatible-mode/v1"
-LLM_BINDING_API_KEY = os.getenv("LLM_BINDING_API_KEY")
-
-EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-v3")
-EMBEDDING_BINDING_HOST = os.getenv("EMBEDDING_BINDING_HOST", LLM_BINDING_HOST)
-EMBEDDING_BINDING_API_KEY = os.getenv("EMBEDDING_BINDING_API_KEY", LLM_BINDING_API_KEY)
-EMBEDDING_DIM = int(os.environ.get("EMBEDDING_DIM", 1024))
-EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
-EMBEDDING_MAX_BATCH_SIZE = int(os.environ.get("EMBEDDING_MAX_BATCH_SIZE", 10))
-
-print(f"LLM_MODEL: {LLM_MODEL}")
-print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
-
-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:
-    client = OpenAI(
-        api_key=LLM_BINDING_API_KEY,
-        base_url=LLM_BINDING_HOST,
-    )
-
-    messages = []
-    if system_prompt:
-        messages.append({"role": "system", "content": system_prompt})
-    if history_messages:
-        messages.extend(history_messages)
-    messages.append({"role": "user", "content": prompt})
-
-    chat_completion = client.chat.completions.create(
-        model=LLM_MODEL,
-        messages=messages,
-        temperature=kwargs.get("temperature", 0),
-        top_p=kwargs.get("top_p", 1),
-        n=kwargs.get("n", 1),
-        extra_body={"enable_thinking": False},
-    )
-    return chat_completion.choices[0].message.content
-
-
-async def embedding_func(texts: list[str]) -> np.ndarray:
-    client = OpenAI(
-        api_key=EMBEDDING_BINDING_API_KEY,
-        base_url=EMBEDDING_BINDING_HOST,
-    )
-
-    print("##### embedding: texts: %d #####" % len(texts))
-    max_batch_size = EMBEDDING_MAX_BATCH_SIZE
-    embeddings = []
-    for i in range(0, len(texts), max_batch_size):
-        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()

examples/lightrag_zhipu_demo.py

@@ -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()

examples/lightrag_zhipu_postgres_demo.py

@@ -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())