Add example usage for separate keyword extraction of user's query

examples/query_keyword_separation_example.py

@@ -0,0 +1,114 @@
+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
+
+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
+
+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,
+    ),
+)
+
+book1 = open("./book_1.txt", encoding="utf-8")
+book2 = open("./book_2.txt", encoding="utf-8")
+
+rag.insert([book1.read(), book2.read()])
+
+# Example function demonstrating the new query_with_separate_keyword_extraction usage
+async def run_example():
+    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())