Add ability to passadditional parameters to ollama library like host and timeout

.gitignore

@@ -0,0 +1,121 @@
+# Byte-compiled / optimized / DLL files
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+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
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+venv.bak/
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+.cache
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+*.cover
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+.hypothesis/
+
+# Translations
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+
+# Django stuff:
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+# Cython debug symbols
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+
+# Example files
+book.txt
+dickens/

examples/lightrag_ollama_demo.py

@@ -1,4 +1,7 @@
 import os
+import logging
+
+logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.DEBUG)
 
 from lightrag import LightRAG, QueryParam
 from lightrag.llm import ollama_model_complete, ollama_embedding
@@ -11,15 +14,17 @@ if not os.path.exists(WORKING_DIR):
 
 rag = LightRAG(
     working_dir=WORKING_DIR,
-    llm_model_func=ollama_model_complete,  
-    llm_model_name='your_model_name',
+    tiktoken_model_name="mistral:7b",
+    llm_model_func=ollama_model_complete,
+    llm_model_name="mistral:7b",
+    llm_model_max_async=2,
+    llm_model_kwargs={"host": "http://localhost:11434"},
     embedding_func=EmbeddingFunc(
         embedding_dim=768,
         max_token_size=8192,
         func=lambda texts: ollama_embedding(
-            texts, 
-            embed_model="nomic-embed-text"
-        )
+            texts, embed_model="nomic-embed-text", host="http://localhost:11434"
+        ),
     ),
 )
 
@@ -28,13 +33,21 @@ with open("./book.txt") as f:
     rag.insert(f.read())
 
 # Perform naive search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+)
 
 # Perform local search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
+)
 
 # Perform global search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
+)
 
 # Perform hybrid search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+)

lightrag/lightrag.py

@@ -86,6 +86,7 @@ class LightRAG:
     llm_model_name: str = 'meta-llama/Llama-3.2-1B-Instruct'#'meta-llama/Llama-3.2-1B'#'google/gemma-2-2b-it'
     llm_model_max_token_size: int = 32768
     llm_model_max_async: int = 16
+    llm_model_kwargs: dict = field(default_factory=dict)
 
     # storage
     key_string_value_json_storage_cls: Type[BaseKVStorage] = JsonKVStorage
@@ -158,7 +159,7 @@ class LightRAG:
         )
         
         self.llm_model_func = limit_async_func_call(self.llm_model_max_async)(
-            partial(self.llm_model_func, hashing_kv=self.llm_response_cache)
+            partial(self.llm_model_func, hashing_kv=self.llm_response_cache, **self.llm_model_kwargs)
         )
 
     def insert(self, string_or_strings):

lightrag/llm.py

@@ -98,8 +98,10 @@ async def ollama_model_if_cache(
 ) -> str:
     kwargs.pop("max_tokens", None)
     kwargs.pop("response_format", None)
+    host = kwargs.pop("host", None)
+    timeout = kwargs.pop("timeout", None)
 
-    ollama_client = ollama.AsyncClient()
+    ollama_client = ollama.AsyncClient(host=host, timeout=timeout)
     messages = []
     if system_prompt:
         messages.append({"role": "system", "content": system_prompt})
@@ -193,10 +195,11 @@ async def hf_embedding(texts: list[str], tokenizer, embed_model) -> np.ndarray:
         embeddings = outputs.last_hidden_state.mean(dim=1)
     return embeddings.detach().numpy()
 
-async def ollama_embedding(texts: list[str], embed_model) -> np.ndarray:
+async def ollama_embedding(texts: list[str], embed_model, **kwargs) -> np.ndarray:
     embed_text = []
+    ollama_client = ollama.Client(**kwargs)
     for text in texts:
-        data = ollama.embeddings(model=embed_model, prompt=text)
+        data = ollama_client.embeddings(model=embed_model, prompt=text)
         embed_text.append(data["embedding"])
 
     return embed_text