Merge branch 'main' of https://github.com/maozdemir/privateGPT into maozdemir-main

ingest.py

@@ -27,6 +27,16 @@ from langchain.docstore.document import Document
 from constants import CHROMA_SETTINGS
 
 
+load_dotenv()
+
+
+# Load environment variables
+persist_directory = os.environ.get('PERSIST_DIRECTORY')
+source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
+embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME')
+chunk_size = 500
+chunk_overlap = 50
+
 class MyElmLoader(UnstructuredEmailLoader):
     """Wrapper to fallback to text/plain when default does not work"""
 
@@ -71,7 +81,6 @@ LOADER_MAPPING = {
 
 load_dotenv()
 
-
 def load_single_document(file_path: str) -> Document:
     ext = "." + file_path.rsplit(".", 1)[-1]
     if ext in LOADER_MAPPING:
@@ -81,44 +90,69 @@ def load_single_document(file_path: str) -> Document:
 
     raise ValueError(f"Unsupported file extension '{ext}'")
 
-def load_documents(source_dir: str) -> List[Document]:
-    # Loads all documents from source documents directory
+
+def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
+    """
+    Loads all documents from the source documents directory, ignoring specified files
+    """
     all_files = []
     for ext in LOADER_MAPPING:
         all_files.extend(
             glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
         )
+    filtered_files = [file_path for file_path in all_files if file_path not in ignored_files]
 
     with Pool(processes=os.cpu_count()) as pool:
         results = []
-        with tqdm(total=len(all_files), desc='Loading documents', ncols=80) as pbar:
-            for i, doc in enumerate(pool.imap_unordered(load_single_document, all_files)):
+        with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
+            for i, doc in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
                 results.append(doc)
                 pbar.update()
 
     return results
 
-
-def main():
-    # Load environment variables
-    persist_directory = os.environ.get('PERSIST_DIRECTORY')
-    source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
-    embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME')
-
-    # Load documents and split in chunks
+def process_documents(ignored_files: List[str] = []) -> List[Document]:
+    """
+    Load documents and split in chunks
+    """
     print(f"Loading documents from {source_directory}")
-    chunk_size = 500
-    chunk_overlap = 50
-    documents = load_documents(source_directory)
+    documents = load_documents(source_directory, ignored_files)
+    if not documents:
+        print("No new documents to load")
+        exit(0)
+    print(f"Loaded {len(documents)} new documents from {source_directory}")
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
     texts = text_splitter.split_documents(documents)
-    print(f"Loaded {len(documents)} documents from {source_directory}")
-    print(f"Split into {len(texts)} chunks of text (max. {chunk_size} characters each)")
+    print(f"Split into {len(texts)} chunks of text (max. 500 tokens each)")
+    return texts
 
+def does_vectorstore_exist(persist_directory: str) -> bool:
+    """
+    Checks if vectorstore exists
+    """
+    if os.path.exists(os.path.join(persist_directory, 'index')):
+         if os.path.exists(os.path.join(persist_directory, 'chroma-collections.parquet')) and os.path.exists(os.path.join(persist_directory, 'chroma-embeddings.parquet')):
+            list_index_files = glob.glob(os.path.join(persist_directory, 'index/*.bin'))
+            list_index_files += glob.glob(os.path.join(persist_directory, 'index/*.pkl'))
+            if len(list_index_files) == 4:
+                return True
+    return False
+
+def main():
     # Create embeddings
     embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
 
+    if does_vectorstore_exist(persist_directory):
+        # Update and store locally vectorstore
+        print(f"Appending to existing vectorstore at {persist_directory}")
+        db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+        collection = db.get()
+        texts = process_documents([metadata['source'] for metadata in collection['metadatas']])
+        db.add_documents(texts)
+    else:
         # Create and store locally vectorstore
+        print("Creating new vectorstore")
+        texts = process_documents()
         db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
     db.persist()
     db = None