fix: fix the deprecation warning and typos

docs/RAG.md

@@ -67,5 +67,5 @@ the python script.
 ### Run Scripts
 * Run `rag.py` with a parameter providing the path to the directory of a sentence transformer model
   - `python rag.py ./all-MiniLM-L6-v2/`
-  - The last two responses in the exectution result shows the language model's output
+  - The last two responses in the execution result shows the language model's output
   with and without the use of RAG.

docs/ReflectChatHistory.md

@@ -73,5 +73,5 @@ The converted sentences are more accurate and appropriate compared to those gene
 ### Run Scripts
 * Run `chat_history_with_summary.py` or `chat_history_with_prompt.py`
   - `python chat_history_with_summary.py` or `python chat_history_with_prompt.py`
-  - The last two responses in the exectution result shows the language model's output
+  - The last two responses in the execution result shows the language model's output
   with and without the contextual information.

jobs/RAG/chat_history_with_prompt.py

@@ -10,9 +10,6 @@
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 
-# from langchain_core.globals import set_debug
-# set_debug(True)
-
 # Define the Ollama model to use
 model = "llama3"
 

jobs/RAG/chat_history_with_summary.py

@@ -10,9 +10,6 @@
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 
-# from langchain_core.globals import set_debug
-# set_debug(True)
-
 # Define the Ollama model to use
 model = "llama3"
 

jobs/RAG/rag.py

@@ -11,7 +11,7 @@
 from langchain_community.document_loaders import TextLoader
 from langchain_text_splitters import CharacterTextSplitter
 from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.chat_models import ChatOllama
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
@@ -36,11 +36,9 @@ def printUsageThenExit():
 
 loader = TextLoader(user_doc)
 documents = loader.load()
-# print(f"Loaded documents (first 2 rows):\n{documents[:2]}\n\n")
 
 text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=0)
 splitted_docs = text_splitter.split_documents(documents)
-# print(f"Splitted documents (first 2 rows):\n{splitted_docs[:2]}\n\n")
 
 # Instantiate the embedding class
 embedding_func = HuggingFaceEmbeddings(model_name=sentence_transformer_dir)

jobs/RAG/requirements.txt

@@ -1,5 +1,6 @@
 langchain_community
 langchain_core
 langchain_text_splitters
+langchain-huggingface
 sentence_transformers
 faiss-cpu