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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-small")
# @app.get("/generate")
# 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]
@app.get("/rag")
def rag(question: str):
# question = "what is the document about?"
answer = search(question)
# print(question, answer)
return {answer}
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