diff --git a/app.py b/app.py
index 6ba2143..0101459 100644
--- a/app.py
+++ b/app.py
@@ -60,7 +60,10 @@ if pdf_name:
         )
 
         # Cargamos el modelo LLM desde LangChain
-        llm = langChainTools.load_llm_open_source()
+        # llm = langChainTools.load_llm_open_source()
+
+        # Cargamos el modelo LLm de Ollama
+        llm = langChainTools.load_llm_ollama()
 
         # Creamos la cadena que integra Vectorstroe, el LLM para hacer consultas.
         # Para este caso la cadena tene el parametro de memoria.
diff --git a/chats/__pycache__/streamlit_tools.cpython-311.pyc b/chats/__pycache__/streamlit_tools.cpython-311.pyc
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diff --git a/embeddings/socialbigdatasociologiaycienciassocialescomputacio/eaf7d8a5-f2d4-4b12-a6d3-94efe772d2fd/data_level0.bin b/embeddings/socialbigdatasociologiaycienciassocialescomputacio/eaf7d8a5-f2d4-4b12-a6d3-94efe772d2fd/data_level0.bin
index 7775772..ea3192e 100644
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diff --git a/embeddings/socialbigdatasociologiaycienciassocialescomputacio/eaf7d8a5-f2d4-4b12-a6d3-94efe772d2fd/length.bin b/embeddings/socialbigdatasociologiaycienciassocialescomputacio/eaf7d8a5-f2d4-4b12-a6d3-94efe772d2fd/length.bin
index a20ee2c..1dc89f8 100644
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diff --git a/langchain_tools/__pycache__/lc_tools.cpython-311.pyc b/langchain_tools/__pycache__/lc_tools.cpython-311.pyc
index f2454a1..3dcd76d 100644
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diff --git a/langchain_tools/lc_tools.py b/langchain_tools/lc_tools.py
index 929e737..a75ab81 100644
--- a/langchain_tools/lc_tools.py
+++ b/langchain_tools/lc_tools.py
@@ -10,6 +10,7 @@ from langchain.chains import RetrievalQAWithSourcesChain, ConversationalRetrieva
 from langchain_community.llms import HuggingFaceEndpoint
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from streamlit.runtime.state import session_state
+from langchain_community.llms import Ollama
 
 
 class LangChainTools:
@@ -150,6 +151,26 @@ class LangChainTools:
 
         return llm
 
+    @st.cache_resource
+    def load_llm_ollama(_self):
+        """Esta funcion carga un modelo de LLM OpenSource desde Ollama
+
+        Returns:
+            _type_: Retorno a un objetito de tipo LLM de OpenAI
+        """
+
+        # Elegimos el modelo de Ollama que utilizaremos
+
+        model: str = "gemma:2b"
+
+        llm = Ollama(
+            model=model,
+            temperature=0.1,
+            num_ctx=1000,
+        )
+
+        return llm
+
     def define_retrieval_qa(self, _llm, _vectordb, _file_name, _embedding_model):
         """Esta función integra un LLM y una base de datos vectorial en una
         chain de LangChain para hacer requerimientos. Este modelo no integra memoria.