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* add rag pipeline * Update examples/getting_started/rag.py Co-authored-by: Massimiliano Pippi <mpippi@gmail.com> --------- Co-authored-by: Vladimir Blagojevic <dovlex@gmail.com> Co-authored-by: Massimiliano Pippi <mpippi@gmail.com>
23 lines
909 B
Python
23 lines
909 B
Python
import os
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from haystack import Document
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from haystack.document_stores import InMemoryDocumentStore
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from haystack.pipeline_utils import build_rag_pipeline
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API_KEY = "SET YOUR OPENAI API KEY HERE"
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# We support many different databases. Here we load a simple and lightweight in-memory document store.
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document_store = InMemoryDocumentStore()
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# Create some example documents and add them to the document store.
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documents = [
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Document(content="My name is Jean and I live in Paris."),
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Document(content="My name is Mark and I live in Berlin."),
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Document(content="My name is Giorgio and I live in Rome."),
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]
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document_store.write_documents(documents)
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# Let's now build a simple RAG pipeline that uses a generative model to answer questions.
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rag_pipeline = build_rag_pipeline(llm_api_key=API_KEY, document_store=document_store)
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answers = rag_pipeline.run(query="Who lives in Rome?")
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print(answers.data)
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