--- a
+++ b/aitrika/llm/ollama.py
@@ -0,0 +1,59 @@
+from llama_index.llms.ollama import Ollama
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core.node_parser import SimpleNodeParser
+from llama_index.core import (
+    VectorStoreIndex,
+    Settings,
+    StorageContext,
+    load_index_from_storage,
+    Document,
+)
+from llama_index.vector_stores.lancedb import LanceDBVectorStore
+import os
+from aitrika.llm.base_llm import BaseLLM
+from aitrika.config.config import LLMConfig
+
+
+class OllamaLLM(BaseLLM):
+    config = LLMConfig()
+
+    def __init__(self, documents: Document):
+        self.documents = documents
+
+    def _build_index(self):
+        llm = Ollama(model=self.model_name, request_timeout=120.0)
+        embed_model = HuggingFaceEmbedding(
+            model_name=self.config.DEFAULT_EMBEDDINGS,
+            cache_folder=f"aitrika/rag/embeddings/{self.config.DEFAULT_EMBEDDINGS.replace('/','_')}",
+        )
+        Settings.llm = llm
+        Settings.embed_model = embed_model
+        Settings.chunk_size = self.config.CHUNK_SIZE
+        Settings.chunk_overlap = self.config.CHUNK_OVERLAP
+        Settings.context_window = self.config.CONTEXT_WINDOW
+        Settings.num_output = self.config.NUM_OUTPUT
+
+        if os.path.exists("aitrika/rag/vectorstores/ollama"):
+            vector_store = LanceDBVectorStore(uri="aitrika/rag/vectorstores/ollama")
+            storage_context = StorageContext.from_defaults(
+                vector_store=vector_store, persist_dir="aitrika/rag/vectorstores/ollama"
+            )
+            index = load_index_from_storage(storage_context=storage_context)
+            parser = SimpleNodeParser()
+            new_nodes = parser.get_nodes_from_documents(self.documents)
+            index.insert_nodes(new_nodes)
+            index = load_index_from_storage(storage_context=storage_context)
+        else:
+            vector_store = LanceDBVectorStore(uri="aitrika/rag/vectorstores/ollama")
+            storage_context = StorageContext.from_defaults(vector_store=vector_store)
+            index = VectorStoreIndex(
+                nodes=self.documents, storage_context=storage_context
+            )
+            index.storage_context.persist(persist_dir="aitrika/rag/vectorstores/ollama")
+        self.index = index
+
+    def query(self, query: str):
+        self._build_index()
+        query_engine = self.index.as_query_engine()
+        response = query_engine.query(query)
+        return str(response).strip()