Update main.py

main.py

@@ -1,21 +1,38 @@
-#pip install fastapi
-#uvicorn main:app --reload
-#import gradio as gr
-
-from transformers import pipeline
+#from transformers import pipeline
 from fastapi import FastAPI
-
 app = FastAPI()
 
-#generator = pipeline('text-generation',model='gpt2')
-#generator = pipeline('text-generation',model='Open-Orca/Mistral-7B-OpenOrca')
 #generator = pipeline("text-generation", model="lmsys/vicuna-7b-v1.5")
 
+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")
 
 @app.get("/")
 async def root():
-    return {"message": "Hello World"}
+    #return {"message": "Hello World"}
     #return generator('What is love',max_length=100, num_return_sequences=1)
+    return print_answers(result, details="medium")
 
 @app.post("/predict")
 async def root(text):