@Alberto_UmanaBelow I add all the log:

File <command-3498969875739223>, line 12
     10 print(UC_MODEL_NAME)
     11 print(uc_registered_model_info.version)
---> 12 deployment_info = agents.deploy("emoysis_catalog.cookbook.sop_chatbot", 13)
     14 # Wait for the Review App to be ready
     15 print("\nWaiting for endpoint to deploy.  This can take 15 - 20 minutes.", end="")
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/databricks/agents/deployments.py:479, in deploy(model_name, model_version, scale_to_zero, environment_vars, instance_profile_arn, tags, workload_size, endpoint_name, **kwargs)
    451 def deploy(
    452     model_name: str,
    453     model_version: int,
   (...)
    460     **kwargs,
    461 ) -> Deployment:
    462     """
    463     Deploy new version of the agent.
    464 
   (...)
    477         Chain deployment metadata.
    478     """
--> 479     _check_model_is_rag_compatible(model_name, model_version)
    480     _check_model_name(model_name)
    481     if endpoint_name is None:
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/databricks/agents/utils/mlflow_utils.py:96, in _check_model_is_rag_compatible(model_name, version)
     95 def _check_model_is_rag_compatible(model_name: str, version: int):
---> 96     input_schema, output_schema = _load_model_schema(model_name, version)
     97     return _check_model_is_rag_compatible_helper(input_schema, output_schema)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/databricks/agents/utils/mlflow_utils.py:90, in _load_model_schema(model_name, version)
     88 mlflow.set_registry_uri(get_databricks_uc_registry_uri())
     89 model_uri = f"models:/{model_name}/{str(version)}"
---> 90 model_info = mlflow.models.get_model_info(model_uri)
     91 signature = model_info.signature
     92 return signature.inputs, signature.outputs
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/models/model.py:984, in get_model_info(model_uri)
    981 from mlflow.pyfunc import _download_artifact_from_uri
    983 meta_file_uri = model_uri.rstrip("/") + "/" + MLMODEL_FILE_NAME
--> 984 meta_local_path = _download_artifact_from_uri(artifact_uri=meta_file_uri)
    985 model_meta = Model.load(meta_local_path)
    986 return ModelInfo(
    987     artifact_path=model_meta.artifact_path,
    988     flavors=model_meta.flavors,
   (...)
    997     metadata=model_meta.metadata,
    998 )
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/tracking/artifact_utils.py:111, in _download_artifact_from_uri(artifact_uri, output_path, lineage_header_info)
    108 repo = get_artifact_repository(artifact_uri=root_uri)
    110 if isinstance(repo, ModelsArtifactRepository):
--> 111     return repo.download_artifacts(
    112         artifact_path=artifact_path,
    113         dst_path=output_path,
    114         lineage_header_info=lineage_header_info,
    115     )
    116 return repo.download_artifacts(artifact_path=artifact_path, dst_path=output_path)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/store/artifact/models_artifact_repo.py:201, in ModelsArtifactRepository.download_artifacts(self, artifact_path, dst_path, lineage_header_info)
    199 # Pass lineage header info if model is registered in UC
    200 if isinstance(self.repo, UnityCatalogModelsArtifactRepository):
--> 201     model_path = self.repo.download_artifacts(
    202         artifact_path, dst_path, lineage_header_info=lineage_header_info
    203     )
    204 else:
    205     model_path = self.repo.download_artifacts(artifact_path, dst_path)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/store/artifact/unity_catalog_models_artifact_repo.py:157, in UnityCatalogModelsArtifactRepository.download_artifacts(self, artifact_path, dst_path, lineage_header_info)
    156 def download_artifacts(self, artifact_path, dst_path=None, lineage_header_info=None):
--> 157     return self._get_artifact_repo(lineage_header_info=lineage_header_info).download_artifacts(
    158         artifact_path, dst_path
    159     )
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/store/artifact/artifact_repo.py:268, in ArtifactRepository.download_artifacts(self, artifact_path, dst_path)
    266 # Submit download tasks
    267 futures = {}
--> 268 if self._is_directory(artifact_path):
    269     for file_info in self._iter_artifacts_recursive(artifact_path):
    270         if file_info.is_dir:  # Empty directory
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/store/artifact/artifact_repo.py:167, in ArtifactRepository._is_directory(self, artifact_path)
    166 def _is_directory(self, artifact_path):
--> 167     listing = self.list_artifacts(artifact_path)
    168     return len(listing) > 0
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/store/artifact/presigned_url_artifact_repo.py:104, in PresignedUrlArtifactRepository.list_artifacts(self, path)
    101 req_body = json.dumps({"page_token": page_token}) if page_token else None
    103 response_proto = ListDirectoryResponse()
--> 104 resp = call_endpoint(
    105     host_creds=self.db_creds,
    106     endpoint=endpoint,
    107     method="GET",
    108     json_body=req_body,
    109     response_proto=response_proto,
    110 )
    111 for dir_entry in resp.contents:
    112     rel_path = posixpath.relpath(dir_entry.path, self.artifact_uri)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/utils/rest_utils.py:370, in call_endpoint(host_creds, endpoint, method, json_body, response_proto, extra_headers)
    367     call_kwargs["json"] = json_body
    368     response = http_request(**call_kwargs)
--> 370 response = verify_rest_response(response, endpoint)
    371 response_to_parse = response.text
    372 js_dict = json.loads(response_to_parse)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0ea80acf-754f-4b74-9414-f078ba584e3e/lib/python3.10/site-packages/mlflow/utils/rest_utils.py:240, in verify_rest_response(response, endpoint)
    238 if response.status_code != 200:
    239     if _can_parse_as_json_object(response.text):
--> 240         raise RestException(json.loads(response.text))
    241     else:
    242         base_msg = (
    243             f"API request to endpoint {endpoint} "
    244             f"failed with error code {response.status_code} != 200"
    245         )