Update Readme

Cleanup _llm.py

README.md

@@ -244,6 +244,43 @@ python batch_winrate_quant_download.py
 python batch_winrate_quant_calculate.py
 ```
 
+### Ollama Support
+
+This project also supports ollama.  To use, edit the ollama_config in _llm.py.
+Adjust the paramters of the models being used
+
+```
+ollama_config = LLMConfig(
+    embedding_func_raw = ollama_embedding,
+    embedding_model_name = "nomic-embed-text",
+    embedding_dim = 768,
+    embedding_max_token_size=8192,
+    embedding_batch_num = 1,
+    embedding_func_max_async = 1,
+    query_better_than_threshold = 0.2,
+    best_model_func_raw = ollama_complete ,
+    best_model_name = "gemma2:latest", # need to be a solid instruct model
+    best_model_max_token_size = 32768,
+    best_model_max_async  = 1,
+    cheap_model_func_raw = ollama_mini_complete,
+    cheap_model_name = "olmo2",
+    cheap_model_max_token_size = 32768,
+    cheap_model_max_async = 1)
+```
+And specify the config when creating your VideoRag instance
+
+### Jupyter Notebook
+
+To  test the solution on a single video, just load the notebook in the notebook folder and
+update the paramters to fit your situation.
+
+YouTube video for example can be downloaded as
+
+```
+yt-dlp -o "%(id)s.%(ext)s" -S "res:720" https://www.youtube.com/live/DPa2iRgzadM?si=8cf8WbYtqiglrwtN  -P .
+```
+
+
 ## Citation
 If you find this work is helpful to your research, please consider citing our paper:
 ```bibtex

videorag/_llm.py

@@ -36,8 +36,8 @@ def get_azure_openai_async_client_instance():
 def get_ollama_async_client_instance():
     global global_ollama_client
     if global_ollama_client is None:
-        # set OLLAMA_HOST
-        global_ollama_client = AsyncClient(host="http://10.0.1.12:11434")  # Adjust base URL if necessary        
+        # set OLLAMA_HOST or pass in host="http://127.0.0.1:11434"
+        global_ollama_client = AsyncClient()  # Adjust base URL if necessary        
     return global_ollama_client
 
 @retry(