Remove deprecated demo code

examples/query_keyword_separation_example.py

@@ -1,126 +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 AzureOpenAI
-from lightrag.kg.shared_storage import initialize_pipeline_status
-
-logging.basicConfig(level=logging.INFO)
-
-load_dotenv()
-
-AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION")
-AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT")
-AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
-AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
-
-AZURE_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_EMBEDDING_DEPLOYMENT")
-AZURE_EMBEDDING_API_VERSION = os.getenv("AZURE_EMBEDDING_API_VERSION")
-
-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 = AzureOpenAI(
-        api_key=AZURE_OPENAI_API_KEY,
-        api_version=AZURE_OPENAI_API_VERSION,
-        azure_endpoint=AZURE_OPENAI_ENDPOINT,
-    )
-
-    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=AZURE_OPENAI_DEPLOYMENT,  # model = "deployment_name".
-        messages=messages,
-        temperature=kwargs.get("temperature", 0),
-        top_p=kwargs.get("top_p", 1),
-        n=kwargs.get("n", 1),
-    )
-    return chat_completion.choices[0].message.content
-
-
-async def embedding_func(texts: list[str]) -> np.ndarray:
-    client = AzureOpenAI(
-        api_key=AZURE_OPENAI_API_KEY,
-        api_version=AZURE_EMBEDDING_API_VERSION,
-        azure_endpoint=AZURE_OPENAI_ENDPOINT,
-    )
-    embedding = client.embeddings.create(model=AZURE_EMBEDDING_DEPLOYMENT, input=texts)
-
-    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())
-
-embedding_dimension = 3072
-
-
-async def initialize_rag():
-    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,
-        ),
-    )
-
-    await rag.initialize_storages()
-    await initialize_pipeline_status()
-
-    return rag
-
-
-# Example function demonstrating the new query_with_separate_keyword_extraction usage
-async def run_example():
-    # Initialize RAG instance
-    rag = await initialize_rag()
-
-    book1 = open("./book_1.txt", encoding="utf-8")
-    book2 = open("./book_2.txt", encoding="utf-8")
-
-    rag.insert([book1.read(), book2.read()])
-    query = "What are the top themes in this story?"
-    prompt = "Please simplify the response for a young audience."
-
-    # Using the new method to ensure the keyword extraction is only applied to the query
-    response = rag.query_with_separate_keyword_extraction(
-        query=query,
-        prompt=prompt,
-        param=QueryParam(mode="hybrid"),  # Adjust QueryParam mode as necessary
-    )
-
-    print("Extracted Response:", response)
-
-
-# Run the example asynchronously
-if __name__ == "__main__":
-    asyncio.run(run_example())