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https://github.com/deepset-ai/haystack.git
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72b637ae6d
* dpr training and inference code refactored with FARM modules * dpr test cases modified * docstring and default arguments updated * dpr training docstring updated * bugfix in dense retriever inference, DPR tutorials modified * Bump FARM to 0.5.0 * update README for DPR * dpr training and inference code refactored with FARM modules * dpr test cases modified * docstring and default arguments updated * dpr training docstring updated * bugfix in dense retriever inference, DPR tutorials modified * Bump FARM to 0.5.0 * update README for DPR * mypy errors fix * DPR instantiation bugfix * Fix DPR init in RAG Tutorial Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
from typing import List
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import requests
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import pandas as pd
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from haystack import Document
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from haystack.document_store.faiss import FAISSDocumentStore
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from haystack.generator.transformers import RAGenerator
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from haystack.retriever.dense import DensePassageRetriever
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# Add documents from which you want generate answers
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# Download a csv containing some sample documents data
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# Here some sample documents data
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temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
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open('small_generator_dataset.csv', 'wb').write(temp.content)
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# Get dataframe with columns "title", and "text"
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df = pd.read_csv("small_generator_dataset.csv", sep=',')
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# Minimal cleaning
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df.fillna(value="", inplace=True)
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print(df.head())
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titles = list(df["title"].values)
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texts = list(df["text"].values)
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# Create to haystack document format
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documents: List[Document] = []
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for title, text in zip(titles, texts):
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documents.append(
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Document(
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text=text,
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meta={
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"name": title or ""
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}
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)
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)
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# Initialize FAISS document store to documents and corresponding index for embeddings
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# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
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document_store = FAISSDocumentStore(
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faiss_index_factory_str="Flat",
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return_embedding=True
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)
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# Initialize DPR Retriever to encode documents, encode question and query documents
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retriever = DensePassageRetriever(
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document_store=document_store,
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query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
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passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
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use_gpu=False,
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embed_title=True,
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)
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# Initialize RAG Generator
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generator = RAGenerator(
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model_name_or_path="facebook/rag-token-nq",
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use_gpu=False,
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top_k_answers=1,
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max_length=200,
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min_length=2,
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embed_title=True,
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num_beams=2,
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)
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# Delete existing documents in documents store
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document_store.delete_all_documents()
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# Write documents to document store
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document_store.write_documents(documents)
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# Add documents embeddings to index
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document_store.update_embeddings(
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retriever=retriever
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)
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# Now ask your questions
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# We have some sample questions
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QUESTIONS = [
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"who got the first nobel prize in physics",
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"when is the next deadpool movie being released",
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"which mode is used for short wave broadcast service",
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"who is the owner of reading football club",
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"when is the next scandal episode coming out",
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"when is the last time the philadelphia won the superbowl",
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"what is the most current adobe flash player version",
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"how many episodes are there in dragon ball z",
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"what is the first step in the evolution of the eye",
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"where is gall bladder situated in human body",
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"what is the main mineral in lithium batteries",
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"who is the president of usa right now",
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"where do the greasers live in the outsiders",
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"panda is a national animal of which country",
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"what is the name of manchester united stadium",
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]
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# Now generate answer for question
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for question in QUESTIONS:
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# Retrieve related documents from retriever
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retriever_results = retriever.retrieve(
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query=question
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)
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# Now generate answer from question and retrieved documents
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predicted_result = generator.predict(
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question=question,
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documents=retriever_results,
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top_k=1
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)
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# Print you answer
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answers = predicted_result["answers"]
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print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
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