The project now runs the example script on ollama, but sure to have the

latest version as it's needed by one of the models

videorag/_llm.py

@@ -34,7 +34,7 @@ def get_azure_openai_async_client_instance():
 def get_ollama_async_client_instance():
     global global_ollama_client
     if global_ollama_client is None:
-        #global_ollama_client = AsyncClient(host="http://localhost:11434")  # Adjust base URL if necessary
+        # set OLLAMA_HOST
         global_ollama_client = AsyncClient(host="http://10.0.1.12:11434")  # Adjust base URL if necessary        
     return global_ollama_client
 
@@ -208,35 +208,37 @@ async def ollama_complete_if_cache(
     # Send the request to Ollama
     response = await ollama_client.chat(
         model=model,
-        messages=messages,
-        **kwargs
+        messages=messages
     )
+    # print(messages)
+    # print(response['message']['content'])
 
+    
     if hashing_kv is not None:
         await hashing_kv.upsert(
-            {args_hash: {"return": response.response, "model": model}}
+            {args_hash: {"return": response['message']['content'], "model": model}}
         )
         await hashing_kv.index_done_callback()
 
-    return response.response
+    return response['message']['content']
 
 
 async def ollama_complete(prompt, system_prompt=None, history_messages=[], **kwargs) -> str:
     return await ollama_complete_if_cache(
-        "deepseek-r1:32b",  # For now select your model
+        #"deepseek-r1:32b",  # For now select your model
+        "gemma2:latest",
         prompt,
         system_prompt=system_prompt,
-        history_messages=history_messages,
-        **kwargs,
+        history_messages=history_messages
     )
 
 async def ollama_mini_complete(prompt, system_prompt=None, history_messages=[], **kwargs) -> str:
     return await ollama_complete_if_cache(
-        "deepseek-r1:latest",  # For now select your model
+        # "deepseek-r1:latest",  # For now select your model
+        "olmo2",
         prompt,
         system_prompt=system_prompt,
-        history_messages=history_messages,
-        **kwargs,
+        history_messages=history_messages
     )
 
 @wrap_embedding_func_with_attrs(embedding_dim=768, max_token_size=8192)

videorag/_op.py

@@ -411,9 +411,12 @@ async def extract_entities(
         maybe_edges = defaultdict(list)
         for record in records:
             record = re.search(r"\((.*)\)", record)
+            print('+++++++')
+            print(record)
             if record is None:
                 continue
             record = record.group(1)
+            print(record)            
             record_attributes = split_string_by_multi_markers(
                 record, [context_base["tuple_delimiter"]]
             )