Update ingest.py

ingest.py

@@ -1,64 +1,70 @@
-import os
-import logging
-from langchain.document_loaders import PDFMinerLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import Chroma
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def create_vector_store():
-    documents = []
-    docs_dir = "docs"
-    if not os.path.exists(docs_dir):
-        logger.error(f"The directory '{docs_dir}' does not exist.")
-        return
-    
-    for root, dirs, files in os.walk(docs_dir):
-        for file in files:
-            if file.endswith(".pdf"):
-                file_path = os.path.join(root, file)
-                logger.info(f"Loading document: {file_path}")
-                try:
-                    loader = PDFMinerLoader(file_path)
-                    loaded_docs = loader.load()
-                    if loaded_docs:
-                        logger.info(f"Loaded {len(loaded_docs)} documents from {file_path}")
-                        documents.extend(loaded_docs)
-                    else:
-                        logger.warning(f"No documents loaded from {file_path}")
-                except Exception as e:
-                    logger.error(f"Error loading {file_path}: {e}")
-
-    if not documents:
-        logger.error("No documents were loaded. Check the 'docs' directory and file paths.")
-        return
-
-    logger.info(f"Loaded {len(documents)} documents.")
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    texts = text_splitter.split_documents(documents)
-    logger.info(f"Created {len(texts)} text chunks.")
-
-    if not texts:
-        logger.error("No text chunks created. Check the text splitting process.")
-        return
-
-    try:
-        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-        logger.info("Embeddings initialized successfully.")
-    except Exception as e:
-        logger.error(f"Failed to initialize embeddings: {e}")
-        return
-
-    try:
-        vector_store = Chroma.from_documents(texts, embeddings, persist_directory="./chroma_db")
-        vector_store.persist()
-        logger.info(f"Created Chroma vector store with {len(texts)} vectors.")
-    except Exception as e:
-        logger.error(f"Failed to create Chroma vector store: {e}")
-
-if __name__ == "__main__":
-    create_vector_store()
+import os
+import logging
+from langchain.document_loaders import PDFMinerLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def create_vector_store():
+    # Ensure the directory exists
+    persist_directory = "./chroma_db"
+    if not os.path.exists(persist_directory):
+        os.makedirs(persist_directory)
+        logger.info(f"Created directory '{persist_directory}'")
+
+    documents = []
+    docs_dir = "docs"
+    if not os.path.exists(docs_dir):
+        logger.error(f"The directory '{docs_dir}' does not exist.")
+        return
+    
+    for root, dirs, files in os.walk(docs_dir):
+        for file in files:
+            if file.endswith(".pdf"):
+                file_path = os.path.join(root, file)
+                logger.info(f"Loading document: {file_path}")
+                try:
+                    loader = PDFMinerLoader(file_path)
+                    loaded_docs = loader.load()
+                    if loaded_docs:
+                        logger.info(f"Loaded {len(loaded_docs)} documents from {file_path}")
+                        documents.extend(loaded_docs)
+                    else:
+                        logger.warning(f"No documents loaded from {file_path}")
+                except Exception as e:
+                    logger.error(f"Error loading {file_path}: {e}")
+
+    if not documents:
+        logger.error("No documents were loaded. Check the 'docs' directory and file paths.")
+        return
+
+    logger.info(f"Loaded {len(documents)} documents.")
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    texts = text_splitter.split_documents(documents)
+    logger.info(f"Created {len(texts)} text chunks.")
+
+    if not texts:
+        logger.error("No text chunks created. Check the text splitting process.")
+        return
+
+    try:
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        logger.info("Embeddings initialized successfully.")
+    except Exception as e:
+        logger.error(f"Failed to initialize embeddings: {e}")
+        return
+
+    try:
+        vector_store = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
+        vector_store.persist()
+        logger.info(f"Created Chroma vector store with {len(texts)} vectors.")
+    except Exception as e:
+        logger.error(f"Failed to create Chroma vector store: {e}")
+
+if __name__ == "__main__":
+    create_vector_store()