update Step_3.py and openai compatible script

reproduce/Step_1_openai_compatible.py

@@ -0,0 +1,66 @@
+import os
+import json
+import time
+import numpy as np
+
+from lightrag import LightRAG
+from lightrag.utils import EmbeddingFunc
+from lightrag.llm import openai_complete_if_cache, openai_embedding
+
+## For Upstage API
+# please check if embedding_dim=4096 in lightrag.py and llm.py in lightrag direcotry
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], **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_embedding(
+        texts,
+        model="solar-embedding-1-large-query",
+        api_key=os.getenv("UPSTAGE_API_KEY"),
+        base_url="https://api.upstage.ai/v1/solar"
+    )
+## /For Upstage API
+
+def insert_text(rag, file_path):
+    with open(file_path, mode='r') as f:
+        unique_contexts = json.load(f)
+    
+    retries = 0
+    max_retries = 3
+    while retries < max_retries:
+        try:
+            rag.insert(unique_contexts)
+            break
+        except Exception as e:
+            retries += 1
+            print(f"Insertion failed, retrying ({retries}/{max_retries}), error: {e}")
+            time.sleep(10)
+    if retries == max_retries:
+        print("Insertion failed after exceeding the maximum number of retries")
+
+cls = "mix"
+WORKING_DIR = f"../{cls}"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(working_dir=WORKING_DIR,
+            llm_model_func=llm_model_func,
+            embedding_func=EmbeddingFunc(
+                embedding_dim=4096,
+                max_token_size=8192,
+                func=embedding_func
+            )
+        )
+
+insert_text(rag, f"../datasets/unique_contexts/{cls}_unique_contexts.json")

reproduce/Step_3.py

@@ -53,10 +53,10 @@ def run_queries_and_save_to_json(queries, rag_instance, query_param, output_file
 if __name__ == "__main__":
     cls = "agriculture"
     mode = "hybrid"
-    WORKING_DIR = "../{cls}"
+    WORKING_DIR = f"../{cls}"
 
     rag = LightRAG(working_dir=WORKING_DIR)
     query_param = QueryParam(mode=mode)
 
     queries = extract_queries(f"../datasets/questions/{cls}_questions.txt")
-    run_queries_and_save_to_json(queries, rag, query_param, "result.json", "errors.json")
+    run_queries_and_save_to_json(queries, rag, query_param, f"{cls}_result.json", f"{cls}_errors.json")

reproduce/Step_3_openai_compatible.py

@@ -0,0 +1,99 @@
+import os
+import re
+import json
+import asyncio
+from lightrag import LightRAG, QueryParam
+from tqdm import tqdm
+from lightrag.llm import openai_complete_if_cache, openai_embedding
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+
+## For Upstage API
+# please check if embedding_dim=4096 in lightrag.py and llm.py in lightrag direcotry
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], **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_embedding(
+        texts,
+        model="solar-embedding-1-large-query",
+        api_key=os.getenv("UPSTAGE_API_KEY"),
+        base_url="https://api.upstage.ai/v1/solar"
+    )
+## /For Upstage API
+
+def extract_queries(file_path):
+    with open(file_path, 'r') as f:
+        data = f.read()
+    
+    data = data.replace('**', '')
+
+    queries = re.findall(r'- Question \d+: (.+)', data)
+
+    return queries
+
+async def process_query(query_text, rag_instance, query_param):
+    try:
+        result, context = await rag_instance.aquery(query_text, param=query_param)
+        return {"query": query_text, "result": result, "context": context}, None
+    except Exception as e:
+        return None, {"query": query_text, "error": str(e)}
+
+def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
+    try:
+        loop = asyncio.get_event_loop()
+    except RuntimeError:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+    return loop
+
+def run_queries_and_save_to_json(queries, rag_instance, query_param, output_file, error_file):
+    loop = always_get_an_event_loop()
+
+    with open(output_file, 'a', encoding='utf-8') as result_file, open(error_file, 'a', encoding='utf-8') as err_file:
+        result_file.write("[\n")
+        first_entry = True
+
+        for query_text in tqdm(queries, desc="Processing queries", unit="query"):
+            result, error = loop.run_until_complete(process_query(query_text, rag_instance, query_param))
+
+            if result:
+                if not first_entry:
+                    result_file.write(",\n")
+                json.dump(result, result_file, ensure_ascii=False, indent=4)
+                first_entry = False
+            elif error:
+                json.dump(error, err_file, ensure_ascii=False, indent=4)
+                err_file.write("\n")
+
+        result_file.write("\n]")
+
+if __name__ == "__main__":
+    cls = "mix"
+    mode = "hybrid"
+    WORKING_DIR = f"../{cls}"
+
+    rag = LightRAG(working_dir=WORKING_DIR)
+    rag = LightRAG(working_dir=WORKING_DIR,
+                llm_model_func=llm_model_func,
+                embedding_func=EmbeddingFunc(
+                    embedding_dim=4096,
+                    max_token_size=8192,
+                    func=embedding_func
+                )
+            )
+    query_param = QueryParam(mode=mode)
+
+    base_dir='../datasets/questions'
+    queries = extract_queries(f"{base_dir}/{cls}_questions.txt")
+    run_queries_and_save_to_json(queries, rag, query_param, f"{base_dir}/result.json", f"{base_dir}/errors.json")