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Runtime error
Runtime error
| from fastapi import FastAPI | |
| # from transformers import pipeline | |
| from txtai.embeddings import Embeddings | |
| from txtai.pipeline import Extractor | |
| # NOTE - we configure docs_url to serve the interactive Docs at the root path | |
| # of the app. This way, we can use the docs as a landing page for the app on Spaces. | |
| app = FastAPI(docs_url="/") | |
| # Create embeddings model with content support | |
| embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2", "content": True}) | |
| embeddings.load('index') | |
| # Create extractor instance | |
| extractor = Extractor(embeddings, "google/flan-t5-base") | |
| pipe = pipeline("text2text-generation", model="google/flan-t5-large") | |
| def generate(text: str): | |
| """ | |
| Using the text2text-generation pipeline from `transformers`, generate text | |
| from the given input text. The model used is `google/flan-t5-small`, which | |
| can be found [here](https://huggingface.co/google/flan-t5-small). | |
| """ | |
| output = pipe(text) | |
| return {"output": output[0]["generated_text"]} | |
| def prompt(question): | |
| return f"""Answer the following question using only the context below. Say 'no answer' when the question can't be answered. | |
| Question: {question} | |
| Context: """ | |
| def search(query, question=None): | |
| # Default question to query if empty | |
| if not question: | |
| question = query | |
| return extractor([("answer", query, prompt(question), False)])[0][1] | |
| def rag(question: str): | |
| # question = "what is the document about?" | |
| answer = search(question) | |
| # print(question, answer) | |
| return {answer} | |