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* move logging config from haystack lib to application * Update Documentation & Code Style * config logging before importing haystack * Update Documentation & Code Style * add logging config to all tutorials * Update Documentation & Code Style Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
121 lines
4.8 KiB
Python
121 lines
4.8 KiB
Python
import logging
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# We configure how logging messages should be displayed and which log level should be used before importing Haystack.
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# Example log message:
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# INFO - haystack.utils.preprocessing - Converting data/tutorial1/218_Olenna_Tyrell.txt
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# Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
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logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING)
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logging.getLogger("haystack").setLevel(logging.INFO)
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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_stores import FAISSDocumentStore
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from haystack.nodes import RAGenerator, DensePassageRetriever
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from haystack.utils import print_answers, fetch_archive_from_http
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def tutorial7_rag_generator():
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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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doc_dir = "data/tutorial7/"
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s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/small_generator_dataset.csv.zip"
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fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
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# Get dataframe with columns "title", and "text"
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df = pd.read_csv(f"{doc_dir}/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(Document(content=text, meta={"name": title or ""}))
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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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# Don't forget to install FAISS dependencies with `pip install farm-haystack[faiss]`
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document_store = FAISSDocumentStore(faiss_index_factory_str="Flat", return_embedding=True)
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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=True,
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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=True,
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top_k=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_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(retriever=retriever)
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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(query=question)
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# Now generate answer from question and retrieved documents
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predicted_result = generator.predict(query=question, documents=retriever_results, top_k=1)
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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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# Or alternatively use the Pipeline class
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from haystack.pipelines import GenerativeQAPipeline
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pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
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for question in QUESTIONS:
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res = pipe.run(query=question, params={"Generator": {"top_k": 1}, "Retriever": {"top_k": 5}})
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print_answers(res, details="minimum")
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if __name__ == "__main__":
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tutorial7_rag_generator()
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# This Haystack script was made with love by deepset in Berlin, Germany
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# Haystack: https://github.com/deepset-ai/haystack
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# deepset: https://deepset.ai/
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