At the heart of MeTA lies its indexes. Every piece of data that MeTA’s algorithms operate on must first reside in an index. In this tutorial, we’ll walk you through how to create your own inverted_index and how to use your inverted_index to create a search engine.

Initially setting up the config file

By default, there are several fields at the top of your config file. These are general settings that deal with paths:

prefix = "../data/"
libsvm-modules = "../deps/libsvm-modules/"
punctuation = "../data/sentence-boundaries/sentence-punctuation.txt"
stop-words = "../data/lemur-stopwords.txt"
start-exceptions = "../data/sentence-boundaries/sentence-start-exceptions.txt"
end-exceptions = "../data/sentence-boundaries/sentence-end-exceptions.txt"
function-words = "../data/function-words.txt"

This assumes you put the 20 newsgroups dataset in your meta/data/ directory. You can leave the other paths as they are; they’ll work by default.

Next, we have a section for the corpus (20newsgroups) and its analyzer.

corpus = "line.toml"  # corpus-specific config, described in overview tutorial
dataset = "20newsgroups"
index = "20newsgroups-idx"

method = "ngram-word"
ngram = 1
filter = "default-unigram-chain"  # ignores <s> tags for retrieval

If the executable needs to search the index, we can add the following to say which ranker to use:

method = "bm25"

For all the premade ranking functions, you can additionally specify any of the ranking function parameters:

method = "bm25"
k1 = 1.2
b = 0.75
k3 = 500

For a list of MeTA’s ranking functions, see the index::ranker documentation.

Using the example apps

Now that we know how to set up our config file, let’s experiment with some indexes. It’s possible to index and search a corpus without writing any code! In the root directory of MeTA, there are several example apps that deal with creating indexes for search engines or information retrieval tasks:

  • index.cpp: indexes a corpus
  • interactive_search.cpp: searches for documents based on user input
  • query_runner.cpp: runs a list of queries specified by a file

Let’s go through these one by one:


./index config.toml

You can then see it being indexed. After it’s done, some stats are printed out, such as number of documents, average document length, and number of unique terms. If you run it again, it will just print out the stats since it detects that the index has already been created.

./interactive-search config.toml

Running this will first create an index if one does not exist. Then, it will prompt the user to type a query. The current analyzer is run on the text query to turn it into a document, and then that document is used to search. The top results are returned.

Query Runner

For this app, we need to add a path to the query file.

query-path = "./path-to-queries.txt"

The query file contains one query per line, and they are run on the current index. You can quickly make your own query file in the current directory if you don’t have one.

./query-runner config.toml

The top 10 documents for each query are then displayed along with the time taken to run all the queries.

Relevance judgements

Check out the index::ir_eval class if you have relevance judgements for your queries.

The format for the relevance judgements follow the format

queryid docid relevance

For two queries with a few judgements, you could have the following file:

0 6 2
0 7 1
0 45 2
0 21 1
0 99 1
1 6 1
1 4 1
1 89 2

This corresponds to query id 0 having docids 6, 7, 45, 21, and 99 being relevant at levels 2, 1, 2, 1, and 1. For query id 1, the docids 6, 4, and 89 are relevant at levels 1, 1, and 2. These lines can appear in any order. Docids are assigned sequentially when indexing, or you can find docids by running search yourself and creating manual relevance judgements. The different relevance levels are used for NDCG scoring. If using something like precision or recall, any nonzero scores are counted as relevant.

Note that it’s not necessary to store irrelevant documents (relevance zero) since any document not in the relevance judgements file is assumed to be nonrelevant.

The path to the relevance judgements file is set in the config file as

query-judgements = "path/to/judgements.txt"

If a relevance file is found when running ./query-runner, then its evaluation results are displayed for each query.

Writing code

Now we’ll look at some examples of how to create and search an index. All this code can be found in the demo apps as well.

Creating an index

This is the simplest way to make an inverted index:

auto config = cpptoml::parse_file(argv[1]);
auto idx = index::make_index<index::inverted_index>(*config);

This assumes that argv[1] is the path to the TOML config file, which is standard MeTA practice.

Warning: this index does not have a cache! A cache is used when running queries on the index. The cache saves frequently used postings_data objects so we don’t need to do a disk seek for every term while scoring.

Here are two different ways to specify a cache:

// Create an inverted index using a DBLRU cache. The arguments forwarded to
//  make_index are the config file for the index and any parameters for the
//  cache. In this case, we set the maximum hash table size for the
//  dblru_cache to be 10000.
auto idx = index::make_index<index::dblru_inverted_index>(*config, 10000);

// Create an inverted index using a splay cache. The arguments forwarded
//  to make_index are the config file for the index and any parameters
//  for the cache. In this case, we set the maximum number of nodes in
//  the splay_cache to be 10000.
auto idx = index::make_index<index::splay_inverted_index>(*config, 10000);

Searching an index

Now that we have an index, let’s search it! First, we need to create a ranker:

auto config = cpptoml::parse_file(argv[1]);
auto group = config.get_table("ranker");
if (!group)
    throw std::runtime_error{"\"ranker\" group needed in config file!"};
auto ranker = index::make_ranker(*group);

We can also manually specify our ranker instead of reading from the config file:

index::okapi_bm25 ranker{1.2, 0.75, 500.0};

We don’t have to specify the arguments for the ranking parameters, but we did anyway.

Finally, we are able to search our index with our ranker. Let’s assume we used make_ranker to create the ranker, so the ranker object is a std::unique_ptr to the actual object.

corpus::document query{doc_id{0}};
query.content("my query text");

auto ranking = ranker->score(*idx, query);

ranking is a sorted vector of pairs of (doc_id,double). By default, the top 10 documents are returned. To return a different number, just add a third parameter:

auto ranking = ranker->score(*idx, query, 25);

Iterate through ranking and display your results!

Writing your own ranker

Perhaps you’d like to experiment and write your own ranking function. Any rankers you write should subclass index::ranker and implement the pure virtual function score_one(const score_data& sd). score_data is a struct that contains useful information to ranking functions. Read up on it here.

If you would like to be able to create your ranker by specifying it in a configuration file, you will need to provide a public static string id member that specifies the text that identifies your ranker class, and register it with the toolkit somewhere in main() like this:


If you need to read parameters from the config group, you should specialize the make_ranker() function as follows:

namespace meta
namespace index
template <>
std::unique_ptr<ranker> make_ranker<my_ranker>(const cpptoml::table& config)
    // read config file
    // set default params if config options not found (for example)