Merge pull request #750 from gurjot-05/main

examples/lightrag_gemini_demo.py

@@ -0,0 +1,84 @@
+# pip install -q -U google-genai to use gemini as a client
+
+import os
+import numpy as np
+from google import genai
+from google.genai import types
+from dotenv import load_dotenv
+from lightrag.utils import EmbeddingFunc
+from lightrag import LightRAG, QueryParam
+from sentence_transformers import SentenceTransformer
+
+load_dotenv()
+gemini_api_key = os.getenv("GEMINI_API_KEY")
+
+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:
+    # 1. Initialize the GenAI Client with your Gemini API Key
+    client = genai.Client(api_key=gemini_api_key)
+
+    # 2. Combine prompts: system prompt, history, and user prompt
+    if history_messages is None:
+        history_messages = []
+
+    combined_prompt = ""
+    if system_prompt:
+        combined_prompt += f"{system_prompt}\n"
+
+    for msg in history_messages:
+        # Each msg is expected to be a dict: {"role": "...", "content": "..."}
+        combined_prompt += f"{msg['role']}: {msg['content']}\n"
+
+    # Finally, add the new user prompt
+    combined_prompt += f"user: {prompt}"
+
+    # 3. Call the Gemini model
+    response = client.models.generate_content(
+        model="gemini-1.5-flash",
+        contents=[combined_prompt],
+        config=types.GenerateContentConfig(max_output_tokens=500, temperature=0.1),
+    )
+
+    # 4. Return the response text
+    return response.text
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    model = SentenceTransformer("all-MiniLM-L6-v2")
+    embeddings = model.encode(texts, convert_to_numpy=True)
+    return embeddings
+
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=llm_model_func,
+    embedding_func=EmbeddingFunc(
+        embedding_dim=384,
+        max_token_size=8192,
+        func=embedding_func,
+    ),
+)
+
+file_path = "story.txt"
+with open(file_path, "r") as file:
+    text = file.read()
+
+rag.insert(text)
+
+response = rag.query(
+    query="What is the main theme of the story?",
+    param=QueryParam(mode="hybrid", top_k=5, response_type="single line"),
+)
+
+print(response)