mirror of
https://github.com/deepset-ai/haystack.git
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196 lines
6.0 KiB
Plaintext
196 lines
6.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"metadata": {
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"id": "iDyfhfyp7Sjh"
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},
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"source": [
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"!pip install git+https://github.com/deepset-ai/haystack.git\n",
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"!pip install urllib3==1.25.4\n",
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"!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "ICZanGLa7khF"
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},
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"source": [
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"from typing import List\n",
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"import requests\n",
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"import pandas as pd\n",
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"from haystack import Document\n",
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"from haystack.document_store.faiss import FAISSDocumentStore\n",
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"from haystack.generator.transformers import RAGenerator\n",
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"from haystack.retriever.dense import DensePassageRetriever"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "D3f-CQ4c7lEN"
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},
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"source": [
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"# Add documents from which you want generate answers\n",
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"# Download a csv containing some sample documents data\n",
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"# Here some sample documents data\n",
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"temp = requests.get(\"https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv\")\n",
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"open('small_generator_dataset.csv', 'wb').write(temp.content)\n",
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"\n",
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"# Get dataframe with columns \"title\", and \"text\"\n",
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"df = pd.read_csv(\"small_generator_dataset.csv\", sep=',')\n",
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"# Minimal cleaning\n",
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"df.fillna(value=\"\", inplace=True)\n",
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"\n",
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"print(df.head())\n",
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"\n",
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"# Create to haystack document format\n",
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"titles = list(df[\"title\"].values)\n",
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"texts = list(df[\"text\"].values)\n",
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"\n",
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"documents: List[Document] = []\n",
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"for title, text in zip(titles, texts):\n",
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" documents.append(\n",
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" Document(\n",
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" text=text,\n",
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" meta={\n",
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" \"name\": title or \"\"\n",
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" }\n",
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" )\n",
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" )"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "upRu3ebX7nr_"
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},
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"source": [
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"# Initialize FAISS document store to documents and corresponding index for embeddings\n",
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"# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding\n",
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"document_store = FAISSDocumentStore(\n",
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" faiss_index_factory_str=\"Flat\",\n",
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" return_embedding=True\n",
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")\n",
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"\n",
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"# Initialize DPR Retriever to encode documents, encode question and query documents\n",
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"retriever = DensePassageRetriever(\n",
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" document_store=document_store,\n",
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" query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
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" passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
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" use_gpu=False,\n",
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" embed_title=True,\n",
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")\n",
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"\n",
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"# Initialize RAG Generator\n",
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"generator = RAGenerator(\n",
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" model_name_or_path=\"facebook/rag-token-nq\",\n",
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" use_gpu=False,\n",
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" top_k_answers=1,\n",
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" max_length=200,\n",
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" min_length=2,\n",
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" embed_title=True,\n",
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" num_beams=2,\n",
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")"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "as8j7hkW7rOW"
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},
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"source": [
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"# Delete existing documents in documents store\n",
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"document_store.delete_all_documents()\n",
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"# Write documents to document store\n",
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"document_store.write_documents(documents)\n",
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"# Add documents embeddings to index\n",
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"document_store.update_embeddings(\n",
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" retriever=retriever\n",
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")"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "j8It45R872vb",
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"cellView": "form"
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},
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"source": [
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"#@title\n",
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"# Now ask your questions\n",
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"# We have some sample questions\n",
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"QUESTIONS = [\n",
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" \"who got the first nobel prize in physics\",\n",
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" \"when is the next deadpool movie being released\",\n",
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" \"which mode is used for short wave broadcast service\",\n",
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" \"who is the owner of reading football club\",\n",
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" \"when is the next scandal episode coming out\",\n",
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" \"when is the last time the philadelphia won the superbowl\",\n",
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" \"what is the most current adobe flash player version\",\n",
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" \"how many episodes are there in dragon ball z\",\n",
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" \"what is the first step in the evolution of the eye\",\n",
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" \"where is gall bladder situated in human body\",\n",
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" \"what is the main mineral in lithium batteries\",\n",
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" \"who is the president of usa right now\",\n",
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" \"where do the greasers live in the outsiders\",\n",
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" \"panda is a national animal of which country\",\n",
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" \"what is the name of manchester united stadium\",\n",
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"]"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "xPUHRuTP742h"
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},
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"source": [
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"# Now generate answer for question\n",
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"for question in QUESTIONS:\n",
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" # Retrieve related documents from retriever\n",
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" retriever_results = retriever.retrieve(\n",
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" query=question\n",
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" )\n",
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"\n",
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" # Now generate answer from question and retrieved documents\n",
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" predicted_result = generator.predict(\n",
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" question=question,\n",
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" documents=retriever_results,\n",
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" top_k=1\n",
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" )\n",
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"\n",
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" # Print you answer\n",
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" answers = predicted_result[\"answers\"]\n",
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" print(f'Generated answer is \\'{answers[0][\"answer\"]}\\' for the question = \\'{question}\\'')"
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],
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"execution_count": null,
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"outputs": []
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}
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],
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"metadata": {
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"colab": {
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"name": "Tutorial7_RAG_Generator.ipynb",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |