Remove deprecated demo code

examples/lightrag_ollama_gremlin_demo.py

@@ -1,122 +0,0 @@
-##############################################
-# 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()

examples/lightrag_ollama_neo4j_milvus_mongo_demo.py

@@ -1,104 +0,0 @@
-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()

examples/lightrag_openai_compatible_demo_embedding_cache.py

@@ -1,123 +0,0 @@
-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())