diff --git a/ci/Jenkinsfile b/ci/Jenkinsfile
index d816f96..11c7f3a 100644
--- a/ci/Jenkinsfile
+++ b/ci/Jenkinsfile
@@ -25,6 +25,7 @@ pipeline {
         stage('Create venv'){
             steps {
                 sh 'python3 -m venv .venv'
+                sh 'exit 1'
             }
         }
 
@@ -80,7 +81,6 @@ pipeline {
                      withChecks('Static Type Checks'){
                          junit 'mypy-report.xml'
                      }
-
                 }
             }
         }
@@ -102,18 +102,32 @@ pipeline {
                      withChecks('Integration Tests'){
                         junit 'pytest-report.xml'
                      }
-
-                     cleanWs(
-                        cleanWhenNotBuilt: false,
-                        deleteDirs: true,
-                        disableDeferredWipeout: true,
-                        notFailBuild: true,
-                        patterns: [[pattern: '.gitignore', type: 'INCLUDE'],
-                                   [pattern: '.propsfile', type: 'EXCLUDE']]
-                             )
                 }
             }
         }
-
+    }
+    post {
+        failure{
+            sh 'python3 ci/modify_test_status.py --fail'
+            sh 'git add README.md'
+            sh 'git commit -m "test status updated"'
+            sh 'git push'
+        }
+        success{
+            sh 'python3 ci/modify_test_status.py'
+            sh 'git add README.md'
+            sh 'git commit -m "test status updated"'
+            sh 'git push'
+        }
+        cleanup{
+            cleanWs(
+                cleanWhenNotBuilt: false,
+                deleteDirs: true,
+                disableDeferredWipeout: true,
+                notFailBuild: true,
+                patterns: [[pattern: '.gitignore', type: 'INCLUDE'],
+                           [pattern: '.propsfile', type: 'EXCLUDE']]
+                    )
+        }
     }
 }
diff --git a/ci/modify_test_status.py b/ci/modify_test_status.py
new file mode 100644
index 0000000..aafed35
--- /dev/null
+++ b/ci/modify_test_status.py
@@ -0,0 +1,22 @@
+import argparse
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-f', '--fail', action='store_true')
+    args = parser.parse_args()
+    readme_path = 'README.md'  # Path to your README file
+
+    with open(readme_path, 'r') as file:
+        lines = file.readlines()
+
+    for i, line in enumerate(lines):
+        if 'img.shields.io' in line and ('passing' in line or 'failing' in line):
+            if args.f:
+                lines[i] = line.replace('passing', 'failing').replace('darggreen', 'red')
+            else:
+                lines[i] = line.replace('failing', 'passing').replace('red', 'darggreen')
+            print('README file has been updated.')
+
+    with open(readme_path, 'w') as file:
+        file.writelines(lines)
diff --git a/cookbook/Basic-RAG/BasicRAG_ingest.py b/cookbook/Basic-RAG/BasicRAG_ingest.py
index 00e8f0b..7f42b5e 100644
--- a/cookbook/Basic-RAG/BasicRAG_ingest.py
+++ b/cookbook/Basic-RAG/BasicRAG_ingest.py
@@ -1,5 +1,6 @@
 """A cookbook demonstrating how to ingest pdf files for use with Basic RAG."""
 
+import asyncio
 from pathlib import Path
 
 from grag.components.multivec_retriever import Retriever
@@ -7,9 +8,16 @@
 
 # from grag.components.vectordb.chroma_client import ChromaClient
 
+SYNC = True  # Run synchronously (slow)
+ASYNC = True  # Run asynchronously 
+
 client = DeepLakeClient(collection_name="ci_test")
 # client = ChromaClient(collection_name="ci_test")
 retriever = Retriever(vectordb=client)
 
 dir_path = Path(__file__).parents[2] / "data/test/pdfs/new_papers"
-retriever.ingest(dir_path)
+
+if SYNC:
+    retriever.ingest(dir_path)
+elif ASYNC:
+    asyncio.run(retriever.aingest(dir_path))
diff --git a/src/grag/components/llm.py b/src/grag/components/llm.py
index bf0665d..3f7a3f0 100644
--- a/src/grag/components/llm.py
+++ b/src/grag/components/llm.py
@@ -2,6 +2,7 @@
 
 import os
 from pathlib import Path
+from typing import Optional, Union
 
 import torch
 from langchain.callbacks.manager import CallbackManager
@@ -38,18 +39,18 @@ class LLM:
 
     def __init__(
         self,
-        model_name=llm_conf["model_name"],
-        device_map=llm_conf["device_map"],
-        task=llm_conf["task"],
-        max_new_tokens=llm_conf["max_new_tokens"],
-        temperature=llm_conf["temperature"],
-        n_batch=llm_conf["n_batch_gpu_cpp"],
-        n_ctx=llm_conf["n_ctx_cpp"],
-        n_gpu_layers=llm_conf["n_gpu_layers_cpp"],
-        std_out=llm_conf["std_out"],
-        base_dir=llm_conf["base_dir"],
-        quantization=llm_conf["quantization"],
-        pipeline=llm_conf["pipeline"],
+        model_name: str = llm_conf["model_name"],
+        device_map: str = llm_conf["device_map"],
+        task: str = llm_conf["task"],
+        max_new_tokens: str = llm_conf["max_new_tokens"],
+        temperature: str = llm_conf["temperature"],
+        n_batch: str = llm_conf["n_batch_gpu_cpp"],
+        n_ctx: str = llm_conf["n_ctx_cpp"],
+        n_gpu_layers: str = llm_conf["n_gpu_layers_cpp"],
+        std_out: Union[bool, str] = llm_conf["std_out"],
+        base_dir: str = llm_conf["base_dir"],
+        quantization: str = llm_conf["quantization"],
+        pipeline: str = llm_conf["pipeline"],
     ):
         """Initialize the LLM class using the given parameters."""
         self.base_dir = Path(base_dir)
@@ -66,7 +67,7 @@ def __init__(
         if std_out:
             self.callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
         else:
-            self.callback_manager = None
+            self.callback_manager = None  # type: ignore
 
     @property
     def model_name(self):
@@ -85,7 +86,7 @@ def model_path(self):
             self.base_dir / self.model_name / f"ggml-model-{self.quantization}.gguf"
         )
 
-    def hf_pipeline(self, is_local=False):
+    def hf_pipeline(self, is_local: Optional[bool] = False):
         """Loads the model using Hugging Face transformers.
 
         Args:
@@ -161,7 +162,8 @@ def llama_cpp(self):
         return llm
 
     def load_model(
-        self, model_name=None, pipeline=None, quantization=None, is_local=None
+        self, model_name: Optional[str] = None, pipeline: Optional[str] = None, quantization: Optional[str] = None,
+        is_local: Optional[bool] = None
     ):
         """Loads the model based on the specified pipeline and model name.