Update ingest.py

ingest.py

@@ -1,73 +1,39 @@
-import os
-import logging
-import streamlit as st
-from langchain_community.document_loaders import PDFMinerLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def create_faiss_index():
-    documents = []
-    docs_dir = "docs"
-
-    if not os.path.exists(docs_dir):
-        st.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)
-                st.info(f"Loading document: {file_path}")
-                try:
-                    loader = PDFMinerLoader(file_path)
-                    documents.extend(loader.load())
-                except Exception as e:
-                    st.error(f"Error loading {file_path}: {e}")
-
-    if not documents:
-        st.error("No documents were loaded. Check the 'docs' directory and file paths.")
-        return
-
-    st.info(f"Loaded {len(documents)} documents.")
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    texts = text_splitter.split_documents(documents)
-
-    if not texts:
-        st.error("No text chunks were created. Check the text splitting process.")
-        return
-
-    st.info(f"Created {len(texts)} text chunks.")
-
-    try:
-        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-    except Exception as e:
-        st.error(f"Failed to initialize embeddings: {e}")
-        return
-
-    try:
-        db = FAISS.from_documents(texts, embeddings)
-        st.info(f"Created FAISS index with {len(texts)} vectors")
-    except Exception as e:
-        st.error(f"Failed to create FAISS index: {e}")
-        return
-
-    index_dir = "faiss_index"
-    if not os.path.exists(index_dir):
-        os.makedirs(index_dir)
-
-    try:
-        db.save_local(index_dir)
-        st.success(f"FAISS index successfully saved to {index_dir}")
-        index_path = os.path.join(index_dir, "index.faiss")
-        st.info(f"Index file size: {os.path.getsize(index_path)} bytes")
-        st.info(f"Index file permissions: {oct(os.stat(index_path).st_mode)[-3:]}")
-    except Exception as e:
-        st.error(f"Failed to save FAISS index: {e}")
-
-if __name__ == "__main__":
-    create_faiss_index()
+import os
+from langchain.document_loaders import PyPDFLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+
+def create_faiss_index():
+    try:
+        # Ensure the 'docs' directory exists and contains files
+        docs_directory = 'docs'
+        if not os.path.exists(docs_directory) or not os.listdir(docs_directory):
+            raise ValueError(f"Directory '{docs_directory}' is empty or does not exist.")
+
+        # Load all documents from the 'docs' directory
+        documents = []
+        for file in os.listdir(docs_directory):
+            if file.endswith('.pdf'):
+                loader = PyPDFLoader(os.path.join(docs_directory, file))
+                documents.extend(loader.load())
+        
+        if not documents:
+            raise ValueError("No valid documents found in the 'docs' directory.")
+
+        # Create embeddings using HuggingFace's 'sentence-transformers/all-MiniLM-L6-v2' model
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        
+        # Create the FAISS vector store index
+        faiss_index = FAISS.from_documents(documents, embeddings)
+
+        # Save the FAISS index locally
+        index_path = "faiss_index"
+        os.makedirs(index_path, exist_ok=True)
+        faiss_index.save_local(index_path)
+        
+        print("FAISS index created and saved successfully.")
+    except Exception as e:
+        print(f"An error occurred during FAISS index creation: {e}")
+
+if __name__ == "__main__":
+    create_faiss_index()