#from transformers import pipeline | |
from fastapi import FastAPI | |
app = FastAPI() | |
#generator = pipeline('text-generation',model='Open-Orca/Mistral-7B-OpenOrca') | |
from haystack.document_stores import InMemoryDocumentStore | |
from haystack.utils import build_pipeline, add_example_data, print_answers | |
# We are model agnostic :) Here, you can choose from: "anthropic", "cohere", "huggingface", and "openai". | |
provider = "openai" | |
API_KEY = "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" # ADD YOUR KEY HERE | |
# We support many different databases. Here we load a simple and lightweight in-memory database. | |
document_store = InMemoryDocumentStore(use_bm25=True) | |
# Download and add Game of Thrones TXT articles to Haystack DocumentStore. | |
# You can also provide a folder with your local documents. | |
#add_example_data(document_store, "data/GoT_getting_started") | |
add_example_data(document_store, "/content/Books") | |
# Build a pipeline with a Retriever to get relevant documents to the query and a PromptNode interacting with LLMs using a custom prompt. | |
pipeline = build_pipeline(provider, API_KEY, document_store) | |
# Ask a question on the data you just added. | |
result = pipeline.run(query="What is job yoga?") | |
# For details, like which documents were used to generate the answer, look into the <result> object | |
#print_answers(result, details="medium") | |
async def root(): | |
#return {"message": "Hello World"} | |
#return generator('What is love',max_length=100, num_return_sequences=1) | |
return print_answers(result, details="medium") | |
async def root(text): | |
#return {"message": "Hello World"} | |
return generator(text,max_length=100, num_return_sequences=1) |