Loading embedder.py +8 −0 Original line number Diff line number Diff line Loading @@ -388,6 +388,14 @@ class Embedder(Consts): fig.show() def collection_exists(self, collection_name): """ Checks if a collection exists. :param collection_name: The name of the collection to check. :return: True if the collection exists, False otherwise. """ return True if self._get_collection(collection_name) else False embedder = Embedder() # embedder.load_docs(directory="aiani dedomena/*", chunking_type=Embedder.ByChar) Loading generator.py +7 −2 Original line number Diff line number Diff line Loading @@ -139,12 +139,17 @@ class Generator: def get_conversation(self): pass embedder = Embedder() embedder.load_docs(directory="aiani dedomena/*", chunking_type=Embedder.ByChar) if not embedder.collection_exists("Mycollection"): embedder.add_data("Mycollection") gen = Generator(embedder=embedder, collection_name="Mycollection", n_results=5) while True: gen.generate_answer(input("Ask a question: "), model="gpt-4o-mini") print("\n") # embedder.visualize("Mycollection", dimensions=["2d", "3d"]) Loading
embedder.py +8 −0 Original line number Diff line number Diff line Loading @@ -388,6 +388,14 @@ class Embedder(Consts): fig.show() def collection_exists(self, collection_name): """ Checks if a collection exists. :param collection_name: The name of the collection to check. :return: True if the collection exists, False otherwise. """ return True if self._get_collection(collection_name) else False embedder = Embedder() # embedder.load_docs(directory="aiani dedomena/*", chunking_type=Embedder.ByChar) Loading
generator.py +7 −2 Original line number Diff line number Diff line Loading @@ -139,12 +139,17 @@ class Generator: def get_conversation(self): pass embedder = Embedder() embedder.load_docs(directory="aiani dedomena/*", chunking_type=Embedder.ByChar) if not embedder.collection_exists("Mycollection"): embedder.add_data("Mycollection") gen = Generator(embedder=embedder, collection_name="Mycollection", n_results=5) while True: gen.generate_answer(input("Ask a question: "), model="gpt-4o-mini") print("\n") # embedder.visualize("Mycollection", dimensions=["2d", "3d"])