Xapian索引-文档检索过程分析

本文是Xapian检索过程的分析，本文内容中源码比较多。检索过程，总的来说就是拉取倒排链，取得合法doc，然后做打分排序的过程。

1 理论分析

1.1  检索语法

面对不同的检索业务，我们会有多种检索需求，譬如：要求A term和B term都在Doc中出现；要求A term或者B term任意在Doc中出现；要求A term或者B term任意在Doc出现，并且C term不出现…...，用符号表示：

A & B

A || B

(A || B) & ~C

( A & ( B || C ) ) || D

…

以上的种种检索需求，复杂繁多，每一个检索需求都单独实现一份代码，是不现实的，需要有一种简单、高效、可扩展的检索语法来支持他们。

1.2 检索过程

首先是根据业务需求，组装检索语句，然后调用检索内核提供的API获取检索结果。

检索内核的实现，以xapian为例：首先根据用户组装的检索语句形成query-tree（query检索树），然后将query-tree转换为postlist-tree（倒排链树），最后获取postlist-tree运算后的结果。在获取postlist-tree和最后的计算过程中，穿插着相关性公式（如：BM25）的运算。

1.3 相关性

计算query跟doc相关性方式有好几种，

（1） 布尔模型（Boolean Model）
判断用户的term在不在文档中出现，如果出现了则认为文档跟用户需求相关，否则认为不相关。
优点：简单；
缺点：结果是二元的，只有YES 或者 NO， 多条结果之间没有先后顺序；

（2）向量空间模型（Vector Space Model）
将query和doc都向量化，计算query跟doc的余弦值，这个值就是query跟doc的相似性打分。这里将查询跟文档的内容相似性替换相关性。
这个模型对长文本比较抑制。
consine公式：向量点积 / 向量长度相乘。
那么，怎么向量化？每一纬的值，给多少合适？
词频因子（TF）：某个单词在文档中出现的次数；一般或取log做平滑，避免直接使用词频导致出现1次和出现10次的term权重差异过大。 常见公式: Wtf = 1 + log(TF). 常量1是为了避免TF=1时，log(TF) = 0，导致W变成0。
变体公式： Wtf = a + (1 - a) * TF/Max(TF)，其中a是调节因子，取值0.4或者0.5，TF表示词频，Max(TF)表示文档中出现次数最多的单词对应的词频数目。这个变种有利于抑制长文本，使得不同长度文档的词频因子具有可比性。
逆文档频率因子（IDF）：包含有某个词的文档数量的倒数。如果一个词在所有文档中都出现，那么这个词对文档的区分度贡献不高，不是那么重要，反之，则说明这个词很重要。
公式： IDFk = log(N/nk)， N代表文档集合总共有多少个文档；nk代表词在多少个文档中出现过。

TF*IDF框架：
Weight = TF * IDF

（3）概率检索模型
BIM模型的公式，由四个部分组成，这四个部分可以理解为：1、某个term在相关集合中出现的次数，正面因素；2、某个term在不相关集合中出现的次数，负面因素；3、某个term在不相关集合中出现的次数，负面因素；4、某个term在不相关集合中不出现的次数，正面因素。

BM25公式，三部分：1、BIM模型，等价于IDF；2、term在文档中的权重（doc-tf）；3、term在query中的权重（query-tf）；

N，表示索引中总的文档数，
Ni，表示索引中包含有term的文档数，也就是df，
fi，表示term在文档中出现的次数，
qfi，表示term在query中出现的次数，
dl，表示文档长度，
avdl，表示平均文档长度

BM25F
考虑到不同的域，对第二部分的平均长度、调节因子，需要根据不同的域设置不同的值，并且需要一个跟域相关联的权重值。

相关性部分资料参考：《这就是搜索引擎》

2 源码分析

2.1 主要类

下面以xapian为例，介绍一般检索过程，因涉及源码众多，部分枝节策略不一一细说。首先，这里列出，涉及到的主要类，从这里也可以一窥xapian在检索上的设计思路。

绿色背景的块是用户看到的，蓝色背景是其底层涉及到的。

Enquire::Internal，Enquire的内部实现，Xapian的设计风格都是包一层壳，功能实际的实现放在Internal中；

BM25Weight，Xapian默认使用的相关性打分类；

Weight::Internal，打分需要用到的基础信息，譬如：索引库文档量、索引库总的term长度、query里的 term的tf、df数据…；

MultiMatch，检索的实现类；

LocalSubMatch，本地子索引库操作的封装。 xapian支持远程索引库，也支持一个索引库拆分成多个子索引库；

QueryOptimiser ，从Query-Tree构建PostList-Tree时的帮助类，主要记录了一些子索引库相关的信息，譬如：LocalSubMatch的引用、索引库DataBase的引用…；

QueryOr、QueryBranch、QueryTerm ，这系列是Query Tree上的一个个类；

PostList、LeafPostList，PostList-Tree上的一个个类；

InMemoryPostList，内存索引库的PostList封装；

OrContext，记录在Query-Tree转PostList-Tree过程中的PostList上下文信息，包括：QueryOptimiser对象指针、临时存放的PostList指针；

2.2 检索过程

2.2.1 用户demo代码

Xapian::Query term_one = Xapian::Query("T世界");
Xapian::Query term_two = Xapian::Query("T比赛");
Xapian::Query query = Xapian::Query(Xapian::Query::OP_OR, term_one, term_two); // query组装

std::cout << "query=" << query.get_description() << std::endl;

Xapian::Enquire enquire(db);
enquire.set_query(query);
Xapian::MSet result = enquire.get_mset(0, 10); // 执行检索，获取结果
std::cout << "find results count=" << result.get_matches_estimated() << std::endl;

for (auto it = result.begin(); it != result.end(); ++it) {
    Xapian::Document doc = it.get_document();
    std::string data = doc.get_data();
    double doc_score_weight = it.get_weight();
    int doc_score_percent = it.get_percent();
    std::cout << "doc=" << data << ",weight=" << doc_score_weight << ",percent=" << doc_score_percent << std::endl;
}

2.2.2 query组装的实现

只有一个类——Query，通过构造函数重载，提供了一切需要的功能。

eg：

Query::Query(const string & term, Xapian::termcount wqf, Xapian::termpos pos)
    : internal(new Xapian::Internal::QueryTerm(term, wqf, pos)) {
    LOGCALL_CTOR(API, "Query", term | wqf | pos);
}

Query(op op_, const Xapian::Query & a, const Xapian::Query & b) {
    init(op_, 2);
    bool positional = (op_ == OP_NEAR || op_ == OP_PHRASE);
    add_subquery(positional, a);
    add_subquery(positional, b);
    done();
}

/* 根据OP，生成对应的Query派生类，譬如：or的生成 QueryOr类，含有两个子query，这个QueryOr类对象作为Query的internal成员存在；
在组合多个query时，直接添加到vector中；
如果最后发现vector是空的则将internal设置为NULL，或者=1，则将internal设置为子query的internal，这样子可以避免不必要的vector嵌套，如：[xxquery]，单个元素没必要放在vector中。*/

...

检索树的组织没有做特别的设计，譬如：用vector来存储OR的元素。

2.2.3 检索的实现

（1）检索函数入口：

MSet Enquire::Internal::get_mset(Xapian::doccount first, Xapian::doccount maxitems, Xapian::doccount check_at_least, const RSet *rset, const MatchDecider *mdecider) const {
    LOGCALL(MATCH, MSet, "Enquire::Internal::get_mset", first | maxitems | check_at_least | rset | mdecider);

    if (percent_cutoff && (sort_by == VAL || sort_by == VAL_REL)) {
        throw Xapian::UnimplementedError("Use of a percentage cutoff while sorting primary by value isn‘t currently supported");
    }

    if (weight == 0) {
        weight = new BM25Weight;  // 如果外界没有指定打分策略，采用BM25Weight
    }

    Xapian::doccount first_orig = first;
    {
        Xapian::doccount docs = db.get_doccount();
        first = min(first, docs);
        maxitems = min(maxitems, docs - first);
        check_at_least = min(check_at_least, docs);
        check_at_least = max(check_at_least, first + maxitems);
    }

    AutoPtr<Xapian::Weight::Internal> stats(new Xapian::Weight::Internal);  // 用于记录打分用的全局信息    // MultiMatch对象的初始化，会执行检索的初始化工作，譬如：填充stats对象，    ::MultiMatch match(db, query, qlen, rset,
               collapse_max, collapse_key,
               percent_cutoff, weight_cutoff,
               order, sort_key, sort_by, sort_value_forward,
               time_limit, *(stats.get()), weight, spies,
               (sorter.get() != NULL),
               (mdecider != NULL));
    // Run query and put results into supplied Xapian::MSet object.
    MSet retval;
    match.get_mset(first, maxitems, check_at_least, retval, *(stats.get()), mdecider, sorter.get());  // 检索
    if (first_orig != first && retval.internal.get()) {
        retval.internal->firstitem = first_orig;
    }

    Assert(weight->name() != "bool" || retval.get_max_possible() == 0);

    // The Xapian::MSet needs to have a pointer to ourselves, so that it can
    // retrieve the documents.  This is set here explicitly to avoid having
    // to pass it into the matcher, which gets messy particularly in the
    // networked case.
    retval.internal->enquire = this;

    if (!retval.internal->stats) {
        retval.internal->stats = stats.release();
    }

    RETURN(retval);
}

（2）检索之前的准备工作，在 MultiMatch 对象构造的时候做，prepare_sub_matches：

static void prepare_sub_matches(vector<intrusive_ptr<SubMatch> > & leaves, Xapian::Weight::Internal & stats) {
    LOGCALL_STATIC_VOID(MATCH, "prepare_sub_matches", leaves | stats);
    // We use a vector<bool> to track which SubMatches we‘re already prepared.
    vector<bool> prepared;
    prepared.resize(leaves.size(), false);
    size_t unprepared = leaves.size();
    bool nowait = true;
    while (unprepared) {
        for (size_t leaf = 0; leaf < leaves.size(); ++leaf) {
            if (prepared[leaf]) continue;
            SubMatch * submatch = leaves[leaf].get();
            if (!submatch || submatch->prepare_match(nowait, stats)) {
                prepared[leaf] = true;
                --unprepared;
            }
        }
        // Use blocking IO on subsequent passes, so that we don‘t go into
        // a tight loop.
        nowait = false;
    }
}

bool LocalSubMatch::prepare_match(bool nowait, Xapian::Weight::Internal & total_stats) {
    LOGCALL(MATCH, bool, "LocalSubMatch::prepare_match", nowait | total_stats);
    (void)nowait;
    Assert(db);
    total_stats.accumulate_stats(*db, rset);
    RETURN(true);
}

void Weight::Internal::accumulate_stats(const Xapian::Database::Internal &subdb, const Xapian::RSet &rset) {
#ifdef XAPIAN_ASSERTIONS
    Assert(!finalised);
    ++subdbs;
#endif
    total_length += subdb.get_total_length();
    collection_size += subdb.get_doccount();
    rset_size += rset.size();

    total_term_count += subdb.get_doccount() * subdb.get_total_length();
    Xapian::TermIterator t;
    for (t = query.get_unique_terms_begin(); t != Xapian::TermIterator(); ++t) {
        const string & term = *t;

        Xapian::doccount sub_tf;
        Xapian::termcount sub_cf;
        subdb.get_freqs(term, &sub_tf, &sub_cf);
        TermFreqs & tf = termfreqs[term];
        tf.termfreq += sub_tf;
        tf.collfreq += sub_cf;
    }

    const set<Xapian::docid> & items(rset.internal->get_items());
    set<Xapian::docid>::const_iterator d;
    for (d = items.begin(); d != items.end(); ++d) {
        Xapian::docid did = *d;
        Assert(did);
        // The query is likely to contain far fewer terms than the documents,
        // and we can skip the document‘s termlist, so look for each query term
        // in the document.
        AutoPtr<TermList> tl(subdb.open_term_list(did));
        map<string, TermFreqs>::iterator i;
        for (i = termfreqs.begin(); i != termfreqs.end(); ++i) {
            const string & term = i->first;
            TermList * ret = tl->skip_to(term);
            Assert(ret == NULL);
            (void)ret;
            if (tl->at_end()) {
                break;
            }
            if (term == tl->get_termname()) {
                ++i->second.reltermfreq;
            }
        }
    }
}

prepare_sub_matches()： BM25计算之前的准备工作
Weight::Internal::accumulate_stats:
total_length：db的总文档长度加和；
collection_size：db的总文档数量；
total_term_count： 存疑，变量名是term计数，实际上是总文档长度加和 * 总文档数量；
termfreqs： term的tf信息（term在多少个doc中出现）和cf信息（term在索引集合中出现的次数）；
query中涉及到的所有term，都获取到它们的TF、IDF信息；
极致的压缩：VectorTermList，把几个string存储的term压缩存储到一个块内存中。如果使用vector来存储，则会增加30Byte每一个term。

（3）打开倒排链，构造postlist-tree：

打开倒排链和检索放在一个800行的超大函数里面：

void MultiMatch::get_mset(Xapian::doccount first, Xapian::doccount maxitems,
             Xapian::doccount check_at_least,
             Xapian::MSet & mset,
             Xapian::Weight::Internal & stats,
             const Xapian::MatchDecider *mdecider,
             const Xapian::KeyMaker *sorter) {
........
}

打开倒排链的过程，函数多层嵌套非常深入，这也是检索树解析-->重建过程：

PostList * LocalSubMatch::get_postlist(MultiMatch * matcher, Xapian::termcount * total_subqs_ptr) {
    LOGCALL(MATCH, PostList *, "LocalSubMatch::get_postlist", matcher | total_subqs_ptr);

    if (query.empty()) {
        RETURN(new EmptyPostList); // MatchNothing
    }

    // Build the postlist tree for the query.  This calls
    // LocalSubMatch::open_post_list() for each term in the query.
    PostList * pl;
    {
        QueryOptimiser opt(*db, *this, matcher);
        pl = query.internal->postlist(&opt, 1.0);
        *total_subqs_ptr = opt.get_total_subqs();
    }

    AutoPtr<Xapian::Weight> extra_wt(wt_factory->clone());
    // Only uses term-independent stats.
    extra_wt->init_(*stats, qlen);
    if (extra_wt->get_maxextra() != 0.0) {
        // There‘s a term-independent weight contribution, so we combine the
        // postlist tree with an ExtraWeightPostList which adds in this
        // contribution.
        pl = new ExtraWeightPostList(pl, extra_wt.release(), matcher);
    }

    RETURN(pl);
}

PostingIterator::Internal * QueryOr::postlist(QueryOptimiser * qopt, double factor) const {
    LOGCALL(QUERY, PostingIterator::Internal *, "QueryOr::postlist", qopt | factor);
    OrContext ctx(qopt, subqueries.size());
    do_or_like(ctx, qopt, factor);
    RETURN(ctx.postlist());
}

void QueryBranch::do_or_like(OrContext& ctx, QueryOptimiser * qopt, double factor, Xapian::termcount elite_set_size, size_t first) const {
    LOGCALL_VOID(MATCH, "QueryBranch::do_or_like", ctx | qopt | factor | elite_set_size);

    // FIXME: we could optimise by merging OP_ELITE_SET and OP_OR like we do
    // for AND-like operations.

    // OP_SYNONYM with a single subquery is only simplified by
    // QuerySynonym::done() if the single subquery is a term or MatchAll.
    Assert(subqueries.size() >= 2 || get_op() == Query::OP_SYNONYM);

    vector<PostList *> postlists;
    postlists.reserve(subqueries.size() - first);

    QueryVector::const_iterator q;
    for (q = subqueries.begin() + first; q != subqueries.end(); ++q) {
        // MatchNothing subqueries should have been removed by done().
        Assert((*q).internal.get());
        (*q).internal->postlist_sub_or_like(ctx, qopt, factor);
    }

    if (elite_set_size && elite_set_size < subqueries.size()) {
        ctx.select_elite_set(elite_set_size, subqueries.size());
        // FIXME: not right!
    }
}

...

LeafPostList * LocalSubMatch::open_post_list(const string& term,
                  Xapian::termcount wqf,
                  double factor,
                  bool need_positions,
                  bool in_synonym,
                  QueryOptimiser * qopt,
                  bool lazy_weight) {
    LOGCALL(MATCH, LeafPostList *, "LocalSubMatch::open_post_list", term | wqf | factor | need_positions | qopt | lazy_weight);

    bool weighted = (factor != 0.0 && !term.empty());

    LeafPostList * pl = NULL;
    if (!term.empty() && !need_positions) {
        if ((!weighted && !in_synonym) ||
            !wt_factory->get_sumpart_needs_wdf_()) {
            Xapian::doccount sub_tf;
            db->get_freqs(term, &sub_tf, NULL);
            if (sub_tf == db->get_doccount()) {
                // If we‘re not going to use the wdf or term positions, and the
                // term indexes all documents, we can replace it with the
                // MatchAll postlist, which is especially efficient if there
                // are no gaps in the docids.
                pl = db->open_post_list(string());
                // Set the term name so the postlist looks up the correct term
                // frequencies - this is necessary if the weighting scheme
                // needs collection frequency or reltermfreq (termfreq would be
                // correct anyway since it‘s just the collection size in this
                // case).
                pl->set_term(term);
            }
        }
    }

    if (!pl) {
        const LeafPostList * hint = qopt->get_hint_postlist();
        if (hint)
            pl = hint->open_nearby_postlist(term);
        if (!pl)
            pl = db->open_post_list(term);
        qopt->set_hint_postlist(pl);
    }

    if (lazy_weight) {
        // Term came from a wildcard, but we may already have that term in the
        // query anyway, so check before accumulating its TermFreqs.
        map<string, TermFreqs>::iterator i = stats->termfreqs.find(term);
        if (i == stats->termfreqs.end()) {
            Xapian::doccount sub_tf;
            Xapian::termcount sub_cf;
            db->get_freqs(term, &sub_tf, &sub_cf);
            stats->termfreqs.insert(make_pair(term, TermFreqs(sub_tf, 0, sub_cf)));
        }
    }

    if (weighted) {
        Xapian::Weight * wt = wt_factory->clone();
        if (!lazy_weight) {
            wt->init_(*stats, qlen, term, wqf, factor);  // BM25Weight::init()计算不涉及query跟doc相关性部分的打分（只跟term和query相关）
            stats->set_max_part(term, wt->get_maxpart());
        } else {
            // Delay initialising the actual weight object, so that we can
            // gather stats for the terms lazily expanded from a wildcard
            // (needed for the remote database case).
            wt = new LazyWeight(pl, wt, stats, qlen, wqf, factor);
        }
        pl->set_termweight(wt);
    }
    RETURN(pl);
}

weight的init：

void BM25Weight::init(double factor) {
    Xapian::doccount tf = get_termfreq();

    double tw = 0;
    if (get_rset_size() != 0) {
        Xapian::doccount reltermfreq = get_reltermfreq();

        // There can‘t be more relevant documents indexed by a term than there
        // are documents indexed by that term.
        AssertRel(reltermfreq,<=,tf);

        // There can‘t be more relevant documents indexed by a term than there
        // are relevant documents.
        AssertRel(reltermfreq,<=,get_rset_size());

        Xapian::doccount reldocs_not_indexed = get_rset_size() - reltermfreq;

        // There can‘t be more relevant documents not indexed by a term than
        // there are documents not indexed by that term.
        AssertRel(reldocs_not_indexed,<=,get_collection_size() - tf);

        Xapian::doccount Q = get_collection_size() - reldocs_not_indexed;

        Xapian::doccount nonreldocs_indexed = tf - reltermfreq;
        double numerator = (reltermfreq + 0.5) * (Q - tf + 0.5);
        double denom = (reldocs_not_indexed + 0.5) * (nonreldocs_indexed + 0.5);
        tw = numerator / denom;
    } else {
        tw = (get_collection_size() - tf + 0.5) / (tf + 0.5);
    }

    AssertRel(tw,>,0);

    // The "official" formula can give a negative termweight in unusual cases
    // (without an RSet, when a term indexes more than half the documents in
    // the database).  These negative weights aren‘t actually helpful, and it
    // is common for implementations to replace them with a small positive
    // weight or similar.
    //
    // Truncating to zero doesn‘t seem a great approach in practice as it
    // means that some terms in the query can have no effect at all on the
    // ranking, and that some results can have zero weight, both of which
    // are seem surprising.
    //
    // Xapian 1.0.x and earlier adjusted the termweight for any term indexing
    // more than a third of documents, which seems rather "intrusive".  That‘s
    // what the code currently enabled does, but perhaps it would be better to
    // do something else. (FIXME)
#if 0
    if (rare(tw <= 1.0)) {
        termweight = 0;
    } else {
        termweight = log(tw) * factor;
        if (param_k3 != 0) {
            double wqf_double = get_wqf();
            termweight *= (param_k3 + 1) * wqf_double / (param_k3 + wqf_double);
        }
    }
#else
    if (tw < 2) tw = tw * 0.5 + 1;
    termweight = log(tw) * factor;
    if (param_k3 != 0) {
        double wqf_double = get_wqf();
        termweight *= (param_k3 + 1) * wqf_double / (param_k3 + wqf_double);
    }
#endif
    termweight *= (param_k1 + 1);

    LOGVALUE(WTCALC, termweight);

    if (param_k2 == 0 && (param_b == 0 || param_k1 == 0)) {
        // If k2 is 0, and either param_b or param_k1 is 0 then the document
        // length doesn‘t affect the weight.
        len_factor = 0;
    } else {
        len_factor = get_average_length();
        // len_factor can be zero if all documents are empty (or the database
        // is empty!)
        if (len_factor != 0) len_factor = 1 / len_factor;
    }

    LOGVALUE(WTCALC, len_factor);
}

总的来说，这一阶段：

stats设置给LocalSubMatch对象；
获取倒排列表，根据query-tree构建postlist-tree；同时，clone一个Weight对象，计算BM25所需要的计算因子；平均文档长度，文档的最短长度，term最大的wdf（term在某doc中出现的次数）；
计算BM25公式的idf部分：tw = (get_collection_size() - tf + 0.5) / (tf + 0.5); termweight = log(tw) * factor；
计算BM25公式的term在query中的权重部分：double wqf_double = get_wqf(); termweight *= (param_k3 + 1) * wqf_double / (param_k3 + wqf_double);
计算BM25公式的term跟doc相关程度的一部分参数： termweight *= (param_k1 + 1);
计算BM25公式的平均长度分之一：len_factor = 1 / len_factor;
计算maxpart() ，BM25算法，没有地方用这个值；

这就把BM25公式中，不跟具体doc相关的第一和第三部分计算完成。

构建postlist-tree，如果是And的语法，则使用PostList * AndContext::postlist() 生成postlist，然后把子postlist-tree销毁掉；

（4）最终召回排序

循环从postlist-tree拉取docid，然后计算BM25打分，
倒排与链求交过程：
PostList * MultiAndPostList::find_next_match(double w_min)

两个有序链表求交：
0、第一个链表pos往前走一步；
1、取出第一个链表的元素；
2、find_next_match() --> check_helper() 将第二链表的pos往前走，保证第二链表当前位置大于等于第一链表；
3、取出来第二链表的当前元素，跟第一链表原始做比较；
4、如果不匹配则让第一链表往前走；

PostList * MultiAndPostList::find_next_match(double w_min) {
advanced_plist0:
    if (plist[0]->at_end()) {
        did = 0;
        return NULL;
    }
    did = plist[0]->get_docid();
    for (size_t i = 1; i < n_kids; ++i) {
        bool valid;
        check_helper(i, did, w_min, valid);
        if (!valid) {
            next_helper(0, w_min);
            goto advanced_plist0;
        }
        if (plist[i]->at_end()) {
            did = 0;
            return NULL;
        }
        Xapian::docid new_did = plist[i]->get_docid();
        if (new_did != did) {
            skip_to_helper(0, new_did, w_min);
            goto advanced_plist0;
        }
    }
    return NULL;
}

获取BM25打分：

double LeafPostList::get_weight() const {
    if (!weight) return 0;
    Xapian::termcount doclen = 0, unique_terms = 0;
    // Fetching the document length and number of unique terms is work we can
    // avoid if the weighting scheme doesn‘t use them.
    if (need_doclength)
        doclen = get_doclength();
    if (need_unique_terms)
        unique_terms = get_unique_terms();
    double sumpart = weight->get_sumpart(get_wdf(), doclen, unique_terms);
    AssertRel(sumpart, <=, weight->get_maxpart());
    return sumpart;
}

两个有序链表求并：
PostList * OrPostList::next(double w_min)
两个链表都取，每次取最小did；

percent是怎么计算的？
    percent_scale = greatest_wt_subqs_matched / double(total_subqs);
    percent_scale /= greatest_wt;
首先跟命中词个数占总搜索term个数有关系，然后，跟最大的匹配得分有关系，percent_scale会作为percent的因子：

double v = wt * percent_factor + 100.0 * DBL_EPSILON;  // percent_scale就是percent_factor，v就是percent

从BM25打分的执行过程，可以想到，有部分BM25打分因子（第一部分idf因子、第二部分term-doc相关性因子）是不需要在线计算的，只需要离线计算后并存储在倒排中即可。

当前默认使用的BM25Weight打分策略，没有使用get_maxextra函数和get_sumextra函数。

原文地址：https://www.cnblogs.com/cswuyg/p/10508114.html