feat: phase 1 more clearly
Автор
aaronmwanjala
aaronmwanjalaКоммитерaaronmwanjala
год назад aaronmwanjala Файлов изменено: 1
+4
–59
5f4a589
src/shoppingassistantservice/shoppingassistantservice.py
+4
–59
| @@ -46,78 +46,23 @@ | ||
| @app.route("/", methods=['POST']) | ||
| def talkToGemini(): | ||
| print("Beginning RAG call") | ||
| prompt = request.json['message'] | ||
| prompt = unquote(prompt) | ||
| llm = ChatGoogleGenerativeAI(model="gemini-pro-vision") | ||
| #Decsription prompt: | ||
| #Send in the image, ask for a description of the room, search for relevant products | ||
| llm_vision = ChatGoogleGenerativeAI(model="gemini-pro-vision") | ||
| llm = ChatGoogleGenerativeAI(model="gemini-pro") | ||
| message = HumanMessage( | ||
| content=[ | ||
| { | ||
| "type": "text", | ||
| "text": "You are a professional interior designer, give me a detailed decsription of the style of the room in this image", | ||
| "text": prompt, | ||
| }, | ||
| {"type": "image_url", "image_url": request.json['image']}, | ||
| ] | ||
| ) | ||
| response = llm_vision.invoke([message]) | ||
| print("Description step:") | ||
| print(response) | ||
| description_response = response.content | ||
| vector_search_prompt = f""" | ||
| This is the user's request: {prompt} | ||
| Find the most relevant items for that prompt, while matching style | ||
| of the room described here: {description_response} | ||
| """ | ||
| print(vector_search_prompt) | ||
| docs = vectorstore.similarity_search(vector_search_prompt) | ||
| print(f"Vector search: {description_response}") | ||
| print(f"Retrieved documents: {len(docs)}") | ||
| relevant_docs = "" | ||
| for doc in docs: | ||
| doc_details = doc.to_json() | ||
| print(f"Adding relevant document to prompt context: {doc_details}") | ||
| relevant_docs += str(doc_details) + ", " | ||
| print("Relevant docs:") | ||
| print(relevant_docs) | ||
| design_prompt = f""" | ||
| You are an interior designer that works for Online Boutique. You are | ||
| tasked with providing recommendations to a customer on what they | ||
| should add to a given room from our catalog. | ||
| This is the description of the room: {description_response} | ||
| Here are a list of products that are relevant to it: {relevant_docs} | ||
| Specifically, this is what the customer has asked for, see if you | ||
| can accommodate it: {prompt} | ||
| Start by repeating a brief description of the room's design to the | ||
| customer, then provide your recommendations. | ||
| Do your best to pick the most relevant item out of the list of | ||
| products provided, but if none of them seem relevant, then say that | ||
| instead of inventing a new product. | ||
| At the end of the response, add a list of the IDs of the relevant | ||
| products in the following format for the top 3 results: | ||
| [<first product ID>], [<second product ID>], [<third product ID>] | ||
| """ | ||
| print("Final design prompt: ") | ||
| print(design_prompt) | ||
| design_response = llm.invoke( | ||
| design_prompt | ||
| ) | ||
| response = llm.invoke([message]) | ||
| data = {} | ||
| data['content'] = design_response.content | ||
| data['content'] = response.content | ||
| return data | ||
| return app |
Cherry-pick
Команда cherry-pick позволяет выбрать отдельные коммиты из одной ветки и применить их к другой.
