Create ingest.py

ingest.py

@@ -0,0 +1,32 @@
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader 
+from langchain.text_splitter import RecursiveCharacterTextSplitter 
+from langchain.embeddings import SentenceTransformerEmbeddings 
+from langchain.vectorstores import Chroma 
+import os 
+from constants import CHROMA_SETTINGS
+
+persist_directory = "db"
+
+def main():
+    for root, dirs, files in os.walk("docs"):
+        for file in files:
+            if file.endswith(".pdf"):
+                print(file)
+                loader = PyPDFLoader(os.path.join(root, file))
+    documents = loader.load()
+    print("splitting into chunks")
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    texts = text_splitter.split_documents(documents)
+    #create embeddings here
+    print("Loading sentence transformers model")
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    #create vector store here
+    print(f"Creating embeddings. May take some minutes...")
+    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+    db.persist()
+    db=None 
+
+    print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
+
+if __name__ == "__main__":
+    main()