![]() ![]() Jul 12, 2021Adaptive HDR open tour Tour Description: This tour is an example of an "Adaptive HDR" panorama. This is a small demo to show the possibilities of Virtual Staging in 3DVista VT Pro. Sep 28, 2021Virtual Staging (by swapping elements) open tour Tour Description: Choose the finish you like best for each item and see how it combines to create the "perfect" kitchen. But even after that, the virtual showroom. Jan 19, 2022Roth Werke Showroom open tour Tour Description: The goal of this project was to create an informative and promotionally effective alternative to on-site physical trade shows that are currently not possible due to the Pandemic. For large organizations or multilateral development banks it is a challenge to explain what they do and finance and, at the same time, make it. ![]() Jan 19, 2022EIB 360 / Pathways to the Future open tour Tour Description: The best way to understand something is to experience it. Jan 27, 2022Virtual Tour – Heroes and heroines of Peru open tour Tour Description: The virtual tour "Heroes and heroines of Peru" was commissioned by the state oil company "Petroperú" to commemorate the bicentennial of the country's independence. Virtual Tour – Heroes and heroines of Peru May 25, 2022Gorpcore / A VRS™ by Sarradet open tour Tour Description: This virtual store known internally at Sarradet, as a VRS™ (virtual recreation space) was developed to explore and showcase the possibilities of how we might rebuild the retail industry following its downfall. To see how exactly we have created this demo, watch. For this, we took a classic car and added various customization options. Jan 11, 20233D Model demo – Orbital mode View Tour Description: This demo aims to show how you can present a product using a configurable 3D model. message.Jan 12, 20233D model of a Site – Fly Over Mode View Tour Description: In this demo, we're connecting a 3d model of the historical complex of Uplistsikhe (Georgia) with a series of panoramic photos taken in the same place, allowing the user to navigate seamlessly between. When question asked, get nearest information as context and ask ChatGPT def ask(question:str,embeddings,sources): ordered_candidates = order_document_sections_by_query_similarity(question,embeddings) ctx = "" for candi in ordered_candidates: next = ctx " " sources] if len(next)>CONTEXT_TOKEN_LIMIT: break ctx = next if len(ctx) = 0: return "" prompt = "".join() completion = (model="gpt-3.5-turbo", messages=) return. """ query_embedding = get_embedding(query) document_similarities = sorted(, reverse=True, key=lambda x: x) return document_similarities Return the list of document sections, sorted by relevance in descending order. Sort by distance from user’s question def order_document_sections_by_query_similarity(query: str, embeddings) -> list: #pprint.pprint("embeddings") #pprint.pprint(embeddings) """ Find the query embedding for the supplied query, and compare it against all of the pre-calculated document embeddings to find the most relevant sections. """ return np.dot(np.array(x), np.array(y)) Because OpenAI Embeddings are normalized to length 1, the cosine similarity is the same as the dot product. Get embedding from string import openai def get_embedding(text: str, model: str=EMBEDDING_MODEL) -> list: result = ( model=model, input=text ) return resultīreak full content into pieces, and get embedding of each piece for source in content.split('\n'): if source.strip() = '': continue embeddings.append(get_embedding(source)) sources.append(source)Ĭalculate distance between two strings def vector_similarity(x: list, y: list) -> float: """ Returns the similarity between two vectors. Process flow of ChatPDF (I guess) The Coding Part ![]()
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