diff --git a/ingest.py b/ingest.py
index 5819e0f8..bad8cd1c 100644
--- a/ingest.py
+++ b/ingest.py
@@ -153,17 +153,36 @@ def main(device_type):
     logging.info(f"Loaded {len(documents)} documents from {SOURCE_DIRECTORY}")
     logging.info(f"Split into {len(texts)} chunks of text")
 
-    # Create embeddings
-    embeddings = HuggingFaceInstructEmbeddings(
-        model_name=EMBEDDING_MODEL_NAME,
-        model_kwargs={"device": device_type},
-    )
-    # change the embedding type here if you are running into issues.
-    # These are much smaller embeddings and will work for most appications
-    # If you use HuggingFaceEmbeddings, make sure to also use the same in the
-    # run_localGPT.py file.
-
-    # embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
+    """
+    (1) Chooses an appropriate langchain library based on the enbedding model name.  Matching code is contained within fun_localGPT.py.
+    
+    (2) Provides additional arguments for instructor and BGE models to improve results, pursuant to the instructions contained on
+    their respective huggingface repository, project page or github repository.
+    """
+    
+    if "instructor" in EMBEDDING_MODEL_NAME:
+        return HuggingFaceInstructEmbeddings(
+            model_name=EMBEDDING_MODEL_NAME,
+            model_kwargs={"device": compute_device},
+            embed_instruction='Represent the document for retrieval:',
+            query_instruction='Represent the question for retrieving supporting documents:'
+        )
+
+    elif "bge" in EMBEDDING_MODEL_NAME:
+        query_instruction = 'Represent this sentence for searching relevant passages:'
+
+        return HuggingFaceBgeEmbeddings(
+            model_name=EMBEDDING_MODEL_NAME,
+            model_kwargs={"device": compute_device},
+            query_instruction='Represent this sentence for searching relevant passages:'
+        )
+
+    else:
+        
+        return HuggingFaceEmbeddings(
+            model_name=EMBEDDING_MODEL_NAME,
+            model_kwargs={"device": compute_device},
+        )
 
     db = Chroma.from_documents(
         texts,
@@ -171,8 +190,6 @@ def main(device_type):
         persist_directory=PERSIST_DIRECTORY,
         client_settings=CHROMA_SETTINGS,
     )
-   
-
 
 if __name__ == "__main__":
     logging.basicConfig(
diff --git a/run_localGPT.py b/run_localGPT.py
index c9f0a2fa..eedb3c35 100644
--- a/run_localGPT.py
+++ b/run_localGPT.py
@@ -119,9 +119,33 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"):
     - The QA system retrieves relevant documents using the retriever and then answers questions based on those documents.
     """
 
-    embeddings = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": device_type})
-    # uncomment the following line if you used HuggingFaceEmbeddings in the ingest.py
-    # embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
+    """
+    (1) Chooses an appropriate langchain library based on the enbedding model name.  Matching code is contained within ingest.py.
+    
+    (2) Provides additional arguments for instructor and BGE models to improve results, pursuant to the instructions contained on
+    their respective huggingface repository, project page or github repository.
+    """
+    
+    if "instructor" in EMBEDDING_MODEL_NAME:
+        return HuggingFaceInstructEmbeddings(
+            model_name=EMBEDDING_MODEL_NAME,
+            model_kwargs={"device": compute_device},
+            embed_instruction='Represent the document for retrieval:',
+            query_instruction='Represent the question for retrieving supporting documents:'
+        )
+
+    elif "bge" in EMBEDDING_MODEL_NAME:
+        return HuggingFaceBgeEmbeddings(
+            model_name=EMBEDDING_MODEL_NAME,
+            model_kwargs={"device": compute_device},
+            query_instruction='Represent this sentence for searching relevant passages:'
+        )
+
+    else:
+        return HuggingFaceEmbeddings(
+            model_name=EMBEDDING_MODEL_NAME,
+            model_kwargs={"device": compute_device},
+        )
 
     # load the vectorstore
     db = Chroma(