Create lightrag_azure_openai_demo.py

Author: lightrag

examples/lightrag_azure_openai_demo.py

@@ -0,0 +1,125 @@
+import os
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+from dotenv import load_dotenv
+import aiohttp
+import logging
+
+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=[], **kwargs
+) -> str:
+    headers = {
+        "Content-Type": "application/json",
+        "api-key": AZURE_OPENAI_API_KEY,
+    }
+    endpoint = f"{AZURE_OPENAI_ENDPOINT}openai/deployments/{AZURE_OPENAI_DEPLOYMENT}/chat/completions?api-version={AZURE_OPENAI_API_VERSION}"
+
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+    if history_messages:
+        messages.extend(history_messages)
+    messages.append({"role": "user", "content": prompt})
+
+    payload = {
+        "messages": messages,
+        "temperature": kwargs.get("temperature", 0),
+        "top_p": kwargs.get("top_p", 1),
+        "n": kwargs.get("n", 1),
+    }
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(endpoint, headers=headers, json=payload) as response:
+            if response.status != 200:
+                raise ValueError(
+                    f"Request failed with status {response.status}: {await response.text()}"
+                )
+            result = await response.json()
+            return result["choices"][0]["message"]["content"]
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    headers = {
+        "Content-Type": "application/json",
+        "api-key": AZURE_OPENAI_API_KEY,
+    }
+    endpoint = f"{AZURE_OPENAI_ENDPOINT}openai/deployments/{AZURE_EMBEDDING_DEPLOYMENT}/embeddings?api-version={AZURE_EMBEDDING_API_VERSION}"
+
+    payload = {"input": texts}
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(endpoint, headers=headers, json=payload) as response:
+            if response.status != 200:
+                raise ValueError(
+                    f"Request failed with status {response.status}: {await response.text()}"
+                )
+            result = await response.json()
+            embeddings = [item["embedding"] for item in result["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()])
+
+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")))