Update/faiss

local_app.py

@@ -9,7 +9,7 @@
 from parameters import load_config
 global DATA_PATH
 load_config('test')
-from parameters import CHROMA_ROOT_PATH, EMBEDDING_MODEL, LLM_MODEL, PROMPT_TEMPLATE, DATA_PATH, REPHRASING_PROMPT, STANDALONE_PROMPT, ROUTER_DECISION_PROMPT
+from parameters import DATABASE_ROOT_PATH, EMBEDDING_MODEL, LLM_MODEL, PROMPT_TEMPLATE, DATA_PATH, REPHRASING_PROMPT, STANDALONE_PROMPT, ROUTER_DECISION_PROMPT
 from get_llm_function import get_llm_function
 from get_rag_chain import get_rag_chain
 from ConversationalRagChain import ConversationalRagChain
@@ -82,7 +82,6 @@ def get_Chat_response(query):
     "chat_history": []
     }
     res = rag_conv._call(inputs)
-    print(res['metadatas'])
     output = jsonify({
         'response': res['result'],
         'context': res['context'],

python_script/config.json

@@ -1,31 +1,31 @@
 {
     "default": {
         "data_path": "data/documents/default",
-        "chroma_root_path": "data/chroma/default",
+        "database_root_path": "data/database/default",
         "embedding_model": "voyage-law-2",
         "llm_model": "gpt-3.5-turbo"
     },
     "seus": {
       "data_path": "data/documents/ship_data",
-      "chroma_root_path": "data/chroma/ship_chroma",
+      "database_root_path": "data/database/ship",
       "embedding_model": "openai",
       "llm_model": "gpt-3.5-turbo"
     },
     "arch-en": {
       "data_path": "data/documents/arch_data-en",
-      "chroma_root_path": "data/chroma/arch_data-en_chroma",
+      "database_root_path": "data/database/arch_data-en",
       "embedding_model": "openai",
       "llm_model": "gpt-3.5-turbo"
     },
     "arch-ru": {
       "data_path": "data/documents/arch_data-ru",
-      "chroma_root_path": "data/chroma/arch_data-ru_chroma",
+      "database_root_path": "data/database/arch_data-ru",
       "embedding_model": "openai",
       "llm_model": "gpt-3.5-turbo"
     },
     "test": {
       "data_path": "data/documents/test_data",
-      "chroma_root_path": "data/chroma/test_chroma",
+      "database_root_path": "data/database/test",
       "embedding_model": "openai",
       "llm_model": "gpt-3.5-turbo"
     }

python_script/get_rag_chain.py

@@ -1,14 +1,14 @@
-from parameters import CHROMA_ROOT_PATH, EMBEDDING_MODEL, LLM_MODEL
+from parameters import DATABASE_ROOT_PATH, EMBEDDING_MODEL, LLM_MODEL
 
 from get_embedding_function import get_embedding_function
 from get_llm_function import get_llm_function
-from populate_database import find_chroma_path
+from populate_database import find_database_path
 
-from langchain.vectorstores.chroma import Chroma
 from langchain.chains import create_history_aware_retriever
 from langchain.chains import create_retrieval_chain
 from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_community.vectorstores import FAISS
 
 def get_rag_chain(params = None):
     """
@@ -21,7 +21,7 @@ def get_rag_chain(params = None):
 
     Parameters:
         params (dict, optional): A dictionary of configuration parameters.
-            - chroma_root_path (str): The root path for Chroma data storage.
+            - database_root_path (str): The root path for data storage.
             - embedding_model (str): The model name for the embedding function.
             - llm_model (str): The model name for the language model.
             - search_type (str): The type of search to perform. Options are:
@@ -41,7 +41,7 @@ def get_rag_chain(params = None):
     """
 
     default_params = {
-        "chroma_root_path": CHROMA_ROOT_PATH,
+        "database_root_path": DATABASE_ROOT_PATH,
         "embedding_model": EMBEDDING_MODEL,
         "llm_model": LLM_MODEL,
         "search_type": "similarity",
@@ -59,25 +59,34 @@ def get_rag_chain(params = None):
         params = {**default_params, **params}
 
     try:
-        required_keys = ["chroma_root_path", "embedding_model", "llm_model"]
+        required_keys = ["database_root_path", "embedding_model", "llm_model"]
         for key in required_keys:
             if key not in params:
                 raise NameError(f"Required setting '{key}' not defined.")
 
         embedding_model = get_embedding_function(model_name=params["embedding_model"])
         llm = get_llm_function(model_name=params["llm_model"])
-        db = Chroma(persist_directory=find_chroma_path(model_name=params["embedding_model"], base_path=params["chroma_root_path"]), embedding_function=embedding_model)
-
+
+        # Load the FAISS index from disk
+        vector_store = FAISS.load_local(find_database_path(EMBEDDING_MODEL,DATABASE_ROOT_PATH)
+                                        , embedding_model, allow_dangerous_deserialization=True)
+
         search_type = params["search_type"]
         if search_type == "similarity":
-            retriever = db.as_retriever(search_type=search_type, search_kwargs={"k": params["similarity_doc_nb"]})
+            retriever = vector_store.as_retriever(search_type=search_type,
+                                                  search_kwargs={"k": params["similarity_doc_nb"]})
         elif search_type == "similarity_score_threshold":
-            retriever = db.as_retriever(search_type=search_type, search_kwargs={"k": params["max_chunk_return"],"score_threshold": params["score_threshold"]})
+            retriever = vector_store.as_retriever(search_type=search_type, 
+                                                  search_kwargs={"k": params["max_chunk_return"],
+                                                                 "score_threshold": params["score_threshold"]})
         elif search_type == "mmr":
-            retriever = db.as_retriever(search_type=search_type, search_kwargs={"k": params["mmr_doc_nb"], "fetch_k": params["considered_chunk"], "lambda_mult": params["lambda_mult"]})
+            retriever = vector_store.as_retriever(search_type=search_type, 
+                                                  search_kwargs={"k": params["mmr_doc_nb"], 
+                                                                 "fetch_k": params["considered_chunk"], 
+                                                                 "lambda_mult": params["lambda_mult"]})
         else:
             raise ValueError("Invalid 'search_type' setting")
-        
+
     except NameError as e:
         variable_name = str(e).split("'")[1]
         raise NameError(f"{variable_name} isn't defined")
@@ -118,5 +127,4 @@ def get_rag_chain(params = None):
     question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
 
     rag_chain = create_retrieval_chain(retriever, question_answer_chain)
-
     return rag_chain

python_script/parameters.py

@@ -4,7 +4,7 @@
 from langchain.prompts import PromptTemplate
 
 DATA_PATH = None
-CHROMA_ROOT_PATH = None
+DATABASE_ROOT_PATH = None
 EMBEDDING_MODEL = None
 LLM_MODEL = None
 PROMPT_TEMPLATE = None
@@ -19,7 +19,7 @@ def load_api_keys():
     load_dotenv()
     os.environ["HF_API_TOKEN"] = os.getenv("HF_API_TOKEN")
     os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
-    os.environ["VOYAGE_API_KEY"] = os.getenv("VOYAGE_API_KEY")
+    #os.environ["VOYAGE_API_KEY"] = os.getenv("VOYAGE_API_KEY")
 
 
 def load_config(config_name = 'default', show_config = False):
@@ -31,7 +31,7 @@ def load_config(config_name = 'default', show_config = False):
     {
       "config_name": {
         "data_path": "",        # Path to the data folder
-        "chroma_root_path": "", # Path to the folder where the Chroma database will be stored
+        "database_root_path": "", # Path to the folder where the database will be stored
         "embedding_model": "",  # Model to use for embeddings (e.g., 'sentence-transformers/all-mpnet-base-v2', 'openai', 'voyage-law-2')
         "llm_model": "",        # Model to use for the language model (e.g., 'gpt-3.5-turbo', 'mistralai/Mistral-7B-Instruct-v0.1', 'nvidia/Llama3-ChatQA-1.5-8B')
       }
@@ -48,7 +48,7 @@ def load_config(config_name = 'default', show_config = False):
                     - "mistralai/Mixtral-8x7B-Instruct-v0.1"
                     - "nvidia/Llama3-ChatQA-1.5-8B"                        
     """
-    global DATA_PATH, CHROMA_ROOT_PATH, EMBEDDING_MODEL, LLM_MODEL
+    global DATA_PATH, DATABASE_ROOT_PATH, EMBEDDING_MODEL, LLM_MODEL
     try:
         with open('config.json', 'r') as file:
             config = json.load(file)
@@ -60,11 +60,10 @@ def load_config(config_name = 'default', show_config = False):
             raise FileNotFoundError("The configuration file cannot be found in the specified paths.")
     except json.JSONDecodeError:
         raise ValueError("The configuration file is present but contains a JSON format error.")
-
     selected_config = config[config_name]
 
     DATA_PATH = selected_config['data_path']
-    CHROMA_ROOT_PATH = selected_config['chroma_root_path']
+    DATABASE_ROOT_PATH = selected_config['database_root_path']
     EMBEDDING_MODEL = selected_config['embedding_model']
     LLM_MODEL = selected_config['llm_model']
 
@@ -79,11 +78,11 @@ def print_config():
     Print the current configuration settings.
     This function prints the values of the global configuration parameters.
     """
-    global DATA_PATH, CHROMA_ROOT_PATH, EMBEDDING_MODEL, LLM_MODEL
+    global DATA_PATH, DATABASE_ROOT_PATH, EMBEDDING_MODEL, LLM_MODEL
 
     print("\nCurrent Configuration Settings:\n")
     print(f"Data Path: {DATA_PATH}")
-    print(f"Chroma Root Path: {CHROMA_ROOT_PATH}")
+    print(f"Database Root Path: {DATABASE_ROOT_PATH}")
     print(f"Embedding Model: {EMBEDDING_MODEL}")
     print(f"Language Model: {LLM_MODEL}\n")