From 618b2ec27715cfbaba988cb0392b3010eb257f4b Mon Sep 17 00:00:00 2001 From: mrjleo Date: Sun, 15 Dec 2024 12:17:31 +0000 Subject: [PATCH] deploy: de010fc04a4f9b81a39700cd695b313f87b41a4f --- docs/index.html | 2 +- docs/main/fast_forward/util/index.html | 4 +- docs/main/fast_forward/util/pyterrier.html | 212 ++++++++++----------- docs/main/search.js | 2 +- 4 files changed, 107 insertions(+), 113 deletions(-) diff --git a/docs/index.html b/docs/index.html index 60260b1..febde85 100644 --- a/docs/index.html +++ b/docs/index.html @@ -37,7 +37,7 @@ diff --git a/docs/main/fast_forward/util/index.html b/docs/main/fast_forward/util/index.html index 21a0788..c8b9f31 100644 --- a/docs/main/fast_forward/util/index.html +++ b/docs/main/fast_forward/util/index.html @@ -21,13 +21,13 @@ -../--pyterrier.html89.09 KB2024-12-15 11:50:12 +../--pyterrier.html89.95 KB2024-12-15 12:17:30 diff --git a/docs/main/fast_forward/util/pyterrier.html b/docs/main/fast_forward/util/pyterrier.html index 1bb14ad..7b5cd7b 100644 --- a/docs/main/fast_forward/util/pyterrier.html +++ b/docs/main/fast_forward/util/pyterrier.html @@ -143,65 +143,63 @@

23 self._index = index 24 super().__init__() 25 -26 def transform(self, topics_or_res: "pd.DataFrame") -> "pd.DataFrame": +26 def transform(self, inp: "pd.DataFrame") -> "pd.DataFrame": 27 """Compute the scores for all query-document pairs in the data frame. 28 29 The previous scores are moved to the "score_0" column. 30 -31 :param topics_or_res: The PyTerrier data frame. +31 :param inp: The PyTerrier data frame. 32 :return: A data frame with the computed scores. 33 """ 34 ff_scores = self._index( 35 Ranking( -36 topics_or_res.rename(columns={"qid": "q_id", "docno": "id"}), +36 inp.rename(columns={"qid": "q_id", "docno": "id"}), 37 copy=False, 38 is_sorted=True, 39 ) 40 )._df.rename(columns={"q_id": "qid", "id": "docno"}) 41 -42 return topics_or_res[["qid", "docno", "score"]].merge( +42 result = inp[["qid", "docno", "score"]].merge( 43 ff_scores[["qid", "docno", "score", "query"]], 44 on=["qid", "docno"], 45 suffixes=("_0", None), 46 ) -47 -48 def __repr__(self) -> str: -49 """Return a string representation. -50 -51 The representation is unique w.r.t. the index and its query encoder. -52 -53 :return: The representation. -54 """ -55 cls_name = self.__class__.__name__ -56 index_id = id(self._index) -57 encoder_id = id(self._index._query_encoder) -58 return f"{cls_name}({index_id}, {encoder_id})" -59 +47 return pt.model.add_ranks(result, single_query=False) +48 +49 def __repr__(self) -> str: +50 """Return a string representation. +51 +52 The representation is unique w.r.t. the index and its query encoder. +53 +54 :return: The representation. +55 """ +56 cls_name = self.__class__.__name__ +57 index_id = id(self._index) +58 encoder_id = id(self._index._query_encoder) +59 return f"{cls_name}({index_id}, {encoder_id})" 60 -61class FFInterpolate(pt.Transformer): -62 """PyTerrier transformer that interpolates scores computed by `FFScore`.""" -63 -64 def __init__(self, alpha: float) -> None: -65 """Create an FFInterpolate transformer. -66 -67 :param alpha: The interpolation parameter. -68 """ -69 # attribute name needs to be exactly this for pyterrier.GridScan to work -70 self.alpha = alpha -71 super().__init__() -72 -73 def transform(self, topics_or_res: "pd.DataFrame") -> "pd.DataFrame": -74 """Interpolate the scores as `alpha * score_0 + (1 - alpha) * score`. -75 -76 :param topics_or_res: The PyTerrier data frame. -77 :return: A data frame with the interpolated scores. -78 """ -79 new_df = topics_or_res[["qid", "docno", "query"]].copy() -80 new_df["score"] = ( -81 self.alpha * topics_or_res["score_0"] -82 + (1 - self.alpha) * topics_or_res["score"] -83 ) -84 return new_df +61 +62class FFInterpolate(pt.Transformer): +63 """PyTerrier transformer that interpolates scores computed by `FFScore`.""" +64 +65 def __init__(self, alpha: float) -> None: +66 """Create an FFInterpolate transformer. +67 +68 :param alpha: The interpolation parameter. +69 """ +70 # attribute name needs to be exactly this for pyterrier.GridScan to work +71 self.alpha = alpha +72 super().__init__() +73 +74 def transform(self, inp: "pd.DataFrame") -> "pd.DataFrame": +75 """Interpolate the scores as `alpha * score_0 + (1 - alpha) * score`. +76 +77 :param inp: The PyTerrier data frame. +78 :return: A data frame with the interpolated scores. +79 """ +80 new_df = inp[["qid", "docno", "query"]].copy() +81 new_df["score"] = self.alpha * inp["score_0"] + (1 - self.alpha) * inp["score"] +82 return pt.model.add_ranks(new_df, single_query=False) @@ -228,39 +226,40 @@

24 self._index = index 25 super().__init__() 26 -27 def transform(self, topics_or_res: "pd.DataFrame") -> "pd.DataFrame": +27 def transform(self, inp: "pd.DataFrame") -> "pd.DataFrame": 28 """Compute the scores for all query-document pairs in the data frame. 29 30 The previous scores are moved to the "score_0" column. 31 -32 :param topics_or_res: The PyTerrier data frame. +32 :param inp: The PyTerrier data frame. 33 :return: A data frame with the computed scores. 34 """ 35 ff_scores = self._index( 36 Ranking( -37 topics_or_res.rename(columns={"qid": "q_id", "docno": "id"}), +37 inp.rename(columns={"qid": "q_id", "docno": "id"}), 38 copy=False, 39 is_sorted=True, 40 ) 41 )._df.rename(columns={"q_id": "qid", "id": "docno"}) 42 -43 return topics_or_res[["qid", "docno", "score"]].merge( +43 result = inp[["qid", "docno", "score"]].merge( 44 ff_scores[["qid", "docno", "score", "query"]], 45 on=["qid", "docno"], 46 suffixes=("_0", None), 47 ) -48 -49 def __repr__(self) -> str: -50 """Return a string representation. -51 -52 The representation is unique w.r.t. the index and its query encoder. -53 -54 :return: The representation. -55 """ -56 cls_name = self.__class__.__name__ -57 index_id = id(self._index) -58 encoder_id = id(self._index._query_encoder) -59 return f"{cls_name}({index_id}, {encoder_id})" +48 return pt.model.add_ranks(result, single_query=False) +49 +50 def __repr__(self) -> str: +51 """Return a string representation. +52 +53 The representation is unique w.r.t. the index and its query encoder. +54 +55 :return: The representation. +56 """ +57 cls_name = self.__class__.__name__ +58 index_id = id(self._index) +59 encoder_id = id(self._index._query_encoder) +60 return f"{cls_name}({index_id}, {encoder_id})" @@ -304,33 +303,34 @@

Parameters
def - transform( self, topics_or_res: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame: + transform(self, inp: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:
- 
27    def transform(self, topics_or_res: "pd.DataFrame") -> "pd.DataFrame":
+            
27    def transform(self, inp: "pd.DataFrame") -> "pd.DataFrame":
 28        """Compute the scores for all query-document pairs in the data frame.
 29
 30        The previous scores are moved to the "score_0" column.
 31
-32        :param topics_or_res: The PyTerrier data frame.
+32        :param inp: The PyTerrier data frame.
 33        :return: A data frame with the computed scores.
 34        """
 35        ff_scores = self._index(
 36            Ranking(
-37                topics_or_res.rename(columns={"qid": "q_id", "docno": "id"}),
+37                inp.rename(columns={"qid": "q_id", "docno": "id"}),
 38                copy=False,
 39                is_sorted=True,
 40            )
 41        )._df.rename(columns={"q_id": "qid", "id": "docno"})
 42
-43        return topics_or_res[["qid", "docno", "score"]].merge(
+43        result = inp[["qid", "docno", "score"]].merge(
 44            ff_scores[["qid", "docno", "score", "query"]],
 45            on=["qid", "docno"],
 46            suffixes=("_0", None),
 47        )
+48        return pt.model.add_ranks(result, single_query=False)
 

 
 
@@ -341,7 +341,7 @@ 
Parameters

 
Parameters

 
 
    
-
  • topics_or_res:  The PyTerrier data frame.
    • 
+
  • inp:  The PyTerrier data frame.
    • 
 

 
 
Returns

@@ -365,30 +365,27 @@ 
Returns
- 
62class FFInterpolate(pt.Transformer):
-63    """PyTerrier transformer that interpolates scores computed by `FFScore`."""
-64
-65    def __init__(self, alpha: float) -> None:
-66        """Create an FFInterpolate transformer.
-67
-68        :param alpha: The interpolation parameter.
-69        """
-70        # attribute name needs to be exactly this for pyterrier.GridScan to work
-71        self.alpha = alpha
-72        super().__init__()
-73
-74    def transform(self, topics_or_res: "pd.DataFrame") -> "pd.DataFrame":
-75        """Interpolate the scores as `alpha * score_0 + (1 - alpha) * score`.
-76
-77        :param topics_or_res: The PyTerrier data frame.
-78        :return: A data frame with the interpolated scores.
-79        """
-80        new_df = topics_or_res[["qid", "docno", "query"]].copy()
-81        new_df["score"] = (
-82            self.alpha * topics_or_res["score_0"]
-83            + (1 - self.alpha) * topics_or_res["score"]
-84        )
-85        return new_df
+            
63class FFInterpolate(pt.Transformer):
+64    """PyTerrier transformer that interpolates scores computed by `FFScore`."""
+65
+66    def __init__(self, alpha: float) -> None:
+67        """Create an FFInterpolate transformer.
+68
+69        :param alpha: The interpolation parameter.
+70        """
+71        # attribute name needs to be exactly this for pyterrier.GridScan to work
+72        self.alpha = alpha
+73        super().__init__()
+74
+75    def transform(self, inp: "pd.DataFrame") -> "pd.DataFrame":
+76        """Interpolate the scores as `alpha * score_0 + (1 - alpha) * score`.
+77
+78        :param inp: The PyTerrier data frame.
+79        :return: A data frame with the interpolated scores.
+80        """
+81        new_df = inp[["qid", "docno", "query"]].copy()
+82        new_df["score"] = self.alpha * inp["score_0"] + (1 - self.alpha) * inp["score"]
+83        return pt.model.add_ranks(new_df, single_query=False)
 

 
 
@@ -406,14 +403,14 @@ 
Returns
- 
65    def __init__(self, alpha: float) -> None:
-66        """Create an FFInterpolate transformer.
-67
-68        :param alpha: The interpolation parameter.
-69        """
-70        # attribute name needs to be exactly this for pyterrier.GridScan to work
-71        self.alpha = alpha
-72        super().__init__()
+            
66    def __init__(self, alpha: float) -> None:
+67        """Create an FFInterpolate transformer.
+68
+69        :param alpha: The interpolation parameter.
+70        """
+71        # attribute name needs to be exactly this for pyterrier.GridScan to work
+72        self.alpha = alpha
+73        super().__init__()
 

 
 
@@ -444,24 +441,21 @@ 
Parameters

 

             
         def
-        transform(	self,	topics_or_res: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:
+        transform(self, inp: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:
 
                 
 
     

     
-            
74    def transform(self, topics_or_res: "pd.DataFrame") -> "pd.DataFrame":
-75        """Interpolate the scores as `alpha * score_0 + (1 - alpha) * score`.
-76
-77        :param topics_or_res: The PyTerrier data frame.
-78        :return: A data frame with the interpolated scores.
-79        """
-80        new_df = topics_or_res[["qid", "docno", "query"]].copy()
-81        new_df["score"] = (
-82            self.alpha * topics_or_res["score_0"]
-83            + (1 - self.alpha) * topics_or_res["score"]
-84        )
-85        return new_df
+            
75    def transform(self, inp: "pd.DataFrame") -> "pd.DataFrame":
+76        """Interpolate the scores as `alpha * score_0 + (1 - alpha) * score`.
+77
+78        :param inp: The PyTerrier data frame.
+79        :return: A data frame with the interpolated scores.
+80        """
+81        new_df = inp[["qid", "docno", "query"]].copy()
+82        new_df["score"] = self.alpha * inp["score_0"] + (1 - self.alpha) * inp["score"]
+83        return pt.model.add_ranks(new_df, single_query=False)
 

 
 
@@ -470,7 +464,7 @@ 
Parameters

 
Parameters

 
 
    
-
  • topics_or_res:  The PyTerrier data frame.
    • 
+
  • inp:  The PyTerrier data frame.
    • 
 

 
 
Returns

diff --git a/docs/main/search.js b/docs/main/search.js
index 304f4dc..44ffccb 100644
--- a/docs/main/search.js
+++ b/docs/main/search.js
@@ -1,6 +1,6 @@
 window.pdocSearch = (function(){
 /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oThis is the implementation of Fast-Forward indexes.
\n\n
Important: As this library is still in its early stages, the API is subject to change!
\n\n
Features
\n\n
    \n
  • Efficient look-up-based computation of semantic ranking scores
    • \n
  • Interpolation of lexical (retrieval) and semantic (re-ranking) scores
    • \n
  • Passage- and document-level ranking, including MaxP, FirstP, and AverageP
    • \n
  • Early stopping for limiting index look-ups
    • \n
  • Index compression via quantization and sequential coalescing
    • \n
\n\n
How to...
\n\n\n\n
Installation
\n\n
Install the package via pip:
\n\n
\n
pip install fast-forward-indexes\n
\n
\n\n
Alternatively, the package can be installed from source:
\n\n
\n
git clone https://github.com/mrjleo/fast-forward-indexes.git\ncd fast-forward-indexes\npython -m pip install .\n
\n
\n\n
After installing the package, simply import the library:
\n\n
\n
import fast_forward\n
\n
\n\n
Getting started
\n\n
Using a Fast-Forward index is as simple as providing a TREC run with sparse scores:
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from pathlib import Path\n\nfrom fast_forward import Ranking\nfrom fast_forward.index import OnDiskIndex, Mode\nfrom fast_forward.encoder import TCTColBERTQueryEncoder\n\n# choose a pre-trained query encoder\nencoder = TCTColBERTQueryEncoder("castorini/tct_colbert-msmarco")\n\n# load an index on disk\nff_index = OnDiskIndex.load(Path("/path/to/index.h5"), encoder, mode=Mode.MAXP)\n\n# load a run (TREC format) and attach all required queries\nfirst_stage_ranking = (\n    Ranking.from_file(Path("/path/to/input/run.tsv"))\n    .attach_queries(\n        {\n            "q1": "query 1",\n            "q2": "query 2",\n            # ...\n            "qn": "query n",\n        }\n    )\n    .cut(5000)\n)\n\n# compute the corresponding semantic scores\nout = ff_index(first_stage_ranking)\n\n# interpolate scores and create a new TREC runfile\nfirst_stage_ranking.interpolate(out, 0.1).save(Path("/path/to/output/run.tsv"))\n
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Rankings
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Rankings (or runs) are represented using the Ranking class. Each ranking contains query IDs, document/passage IDs, and corresponding scores. Rankings may be instantiated from TREC runfiles using Ranking.from_file:
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r = Ranking.from_file(Path("/path/to/TREC/run.tsv"))\n
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Alternatively, rankings can be created from nested dictionaries using Ranking.from_run:
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run = {\n    "q1": {"d0": 1, "d1": 2, "d2": 3},\n    "q2": {"d0": 4, "d1": 5, "d2": 6},\n}\nr = Ranking.from_run(run)\n
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Optionally, rankings can have queries attached using Ranking.attach_queries:
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r = r.attach_queries(\n    {\n        "q1": "query 1",\n        "q2": "query 2",\n        # ...\n        "qn": "query n",\n    }\n)\n
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Rankings implement addition and multiplication operators, for example:
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ranking_3 = 0.1 * ranking_1 + ranking_2\n
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Ranking.interpolate allows to interpolate the scores of two rankings (i.e., retrieval and re-ranking):
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first_stage_ranking = Ranking.from_file(Path("/path/to/TREC/run.tsv"))\nsemantic_scores = my_index(first_stage_ranking)\ninterpolated_ranking = first_stage_ranking.interpolate(semantic_scores, 0.1)\n# equivalent to (but slightly more efficient than):\ninterpolated_ranking = first_stage_ranking * 0.1 + semantic_scores * 0.9\n
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Additionally, Ranking.rr_scores recomputes a ranking's scores based on the reciprocal rank. This allows, for example, to perform reciprocal rank fusion (RRF) as follows:
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rrf_ranking = first_stage_ranking.rr_scores() + semantic_scores.rr_scores()\n
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Further information about rank fusion can be found here.
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Finally, a ranking can have a name set and be saved as a TREC runfile:
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interpolated_ranking.name = "Fast-Forward, alpha=0.1"\ninterpolated_ranking.save(Path("/path/to/output/run.tsv"))\n
\n
\n"}, "fast_forward.Ranking": {"fullname": "fast_forward.Ranking", "modulename": "fast_forward", "qualname": "Ranking", "kind": "class", "doc": "
Represents rankings of documents/passages w.r.t. queries.
\n"}, "fast_forward.Ranking.__init__": {"fullname": "fast_forward.Ranking.__init__", "modulename": "fast_forward", "qualname": "Ranking.__init__", "kind": "function", "doc": "
Create a ranking from an existing data frame. Removes rows with NaN scores.
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Parameters
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    \n
  • df:  Data frame containing IDs and scores.
    • \n
  • name:  Method name.
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  • queries:  Query IDs mapped to queries.
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  • dtype:  How the scores should be represented in the data frame.
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  • copy:  Whether to copy the data frame.
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  • is_sorted:  Whether the data frame is already sorted (by score).
    • \n
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Raises
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    \n
  • ValueError:  When the queries are incomplete.
    • \n
\n", "signature": "(\tdf: pandas.core.frame.DataFrame,\tname: str | None = None,\tqueries: Mapping[str, str] | None = None,\tdtype: numpy.dtype = dtype('float32'),\tcopy: bool = True,\tis_sorted: bool = False)"}, "fast_forward.Ranking.name": {"fullname": "fast_forward.Ranking.name", "modulename": "fast_forward", "qualname": "Ranking.name", "kind": "variable", "doc": "
\n"}, "fast_forward.Ranking.has_queries": {"fullname": "fast_forward.Ranking.has_queries", "modulename": "fast_forward", "qualname": "Ranking.has_queries", "kind": "variable", "doc": "
Whether the ranking has queries attached.
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Returns
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Whether queries exist.
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\n", "annotation": ": bool"}, "fast_forward.Ranking.q_ids": {"fullname": "fast_forward.Ranking.q_ids", "modulename": "fast_forward", "qualname": "Ranking.q_ids", "kind": "variable", "doc": "
The set of (unique) query IDs in this ranking.
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Only queries with at least one scored document are considered.
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Returns
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\n  
The query IDs.
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\n", "annotation": ": set[str]"}, "fast_forward.Ranking.attach_queries": {"fullname": "fast_forward.Ranking.attach_queries", "modulename": "fast_forward", "qualname": "Ranking.attach_queries", "kind": "function", "doc": "
Attach queries to the ranking.
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Parameters
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    \n
  • queries:  Query IDs mapped to queries.
    • \n
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Raises
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    \n
  • ValueError:  When the queries are incomplete.
    • \n
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Returns
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\n  
The ranking with queries attached.
\n
\n", "signature": "(self, queries: Mapping[str, str]) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.normalize": {"fullname": "fast_forward.Ranking.normalize", "modulename": "fast_forward", "qualname": "Ranking.normalize", "kind": "function", "doc": "
Normalize the scores (min-max) to be in [0, 1].
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If all scores are equal, they are set to 0.
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Returns
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\n  
A ranking with normalized scores.
\n
\n", "signature": "(self) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.cut": {"fullname": "fast_forward.Ranking.cut", "modulename": "fast_forward", "qualname": "Ranking.cut", "kind": "function", "doc": "
For each query, remove all but the top-k scoring documents/passages.
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Parameters
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    \n
  • cutoff:  Number of best scores per query to keep (k).
    • \n
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Returns
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\n  
The resulting ranking.
\n
\n", "signature": "(self, cutoff: int) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.interpolate": {"fullname": "fast_forward.Ranking.interpolate", "modulename": "fast_forward", "qualname": "Ranking.interpolate", "kind": "function", "doc": "
Interpolate as score = self.score * alpha + other.score * (1 - alpha).
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Missing scores in either ranking are treated as zero.
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Parameters
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    \n
  • other:  Ranking to interpolate with.
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  • alpha:  Interpolation parameter.
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  • normalize:  Perform min-max normalization.
    • \n
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Returns
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\n  
The resulting ranking.
\n
\n", "signature": "(\tself,\tother: fast_forward.ranking.Ranking,\talpha: float,\tnormalize: bool = False) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.rr_scores": {"fullname": "fast_forward.Ranking.rr_scores", "modulename": "fast_forward", "qualname": "Ranking.rr_scores", "kind": "function", "doc": "
Re-score documents/passages using reciprocal rank.
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A score is computed as 1 / (rank + k).
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Used by RRF.
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Parameters
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    \n
  • k:  RR scoring parameter.
    • \n
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Returns
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\n  
A ranking with RR scores.
\n
\n", "signature": "(self, k: int = 60) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.save": {"fullname": "fast_forward.Ranking.save", "modulename": "fast_forward", "qualname": "Ranking.save", "kind": "function", "doc": "
Save the ranking in a TREC runfile.
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Parameters
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    \n
  • target:  Output file.
    • \n
\n", "signature": "(self, target: pathlib.Path) -> None:", "funcdef": "def"}, "fast_forward.Ranking.from_run": {"fullname": "fast_forward.Ranking.from_run", "modulename": "fast_forward", "qualname": "Ranking.from_run", "kind": "function", "doc": "
Create a Ranking object from a TREC run.
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Parameters
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    \n
  • run:  TREC run.
    • \n
  • name:  Method name.
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  • queries:  Query IDs mapped to queries.
    • \n
  • dtype:  How the score should be represented in the data frame.
    • \n
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Returns
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\n  
The resulting ranking.
\n
\n", "signature": "(\tcls,\trun: Mapping[str, Mapping[str, float]],\tname: str | None = None,\tqueries: Mapping[str, str] | None = None,\tdtype: numpy.dtype = dtype('float32')) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.from_file": {"fullname": "fast_forward.Ranking.from_file", "modulename": "fast_forward", "qualname": "Ranking.from_file", "kind": "function", "doc": "
Create a Ranking object from a runfile in TREC format.
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Parameters
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    \n
  • f:  TREC runfile to read.
    • \n
  • queries:  Query IDs mapped to queries.
    • \n
  • dtype:  How the score should be represented in the data frame.
    • \n
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Returns
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\n  
The resulting ranking.
\n
\n", "signature": "(\tcls,\tf: pathlib.Path,\tqueries: Mapping[str, str] | None = None,\tdtype: numpy.dtype = dtype('float32')) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.encoder": {"fullname": "fast_forward.encoder", "modulename": "fast_forward.encoder", "kind": "module", "doc": "
Custom encoders
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Custom encoders can easily be implemented. The preferred way to do this is by subclassing fast_forward.encoder.base.Encoder and overriding the _encode method. This allows the new encoder to make use of batch encoding.
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Alternatively, one can use the LambdaEncoder class, which wraps a function that encodes a single piece of text. The following example shows how to do this with a Pyserini query encoder:
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pyserini_encoder = pyserini.encode.AnceQueryEncoder("castorini/ance-msmarco-passage")\nance_encoder = LambdaEncoder(pyserini_encoder.encode)\n
\n
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Note that this method is usually less efficient, as the texts are encoded one by one rather than in batches.
\n"}, "fast_forward.encoder.Encoder": {"fullname": "fast_forward.encoder.Encoder", "modulename": "fast_forward.encoder", "qualname": "Encoder", "kind": "class", "doc": "
Base class for encoders.
\n", "bases": "abc.ABC"}, "fast_forward.encoder.LambdaEncoder": {"fullname": "fast_forward.encoder.LambdaEncoder", "modulename": "fast_forward.encoder", "qualname": "LambdaEncoder", "kind": "class", "doc": "
Encoder adapter class for arbitrary encoding functions.
\n", "bases": "fast_forward.encoder.base.Encoder"}, "fast_forward.encoder.LambdaEncoder.__init__": {"fullname": "fast_forward.encoder.LambdaEncoder.__init__", "modulename": "fast_forward.encoder", "qualname": "LambdaEncoder.__init__", "kind": "function", "doc": "
Create a lambda encoder.
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Parameters
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    \n
  • f:  Function to encode a single piece of text.
    • \n
\n", "signature": "(f: Callable[[str], numpy.ndarray])"}, "fast_forward.encoder.TransformerEncoder": {"fullname": "fast_forward.encoder.TransformerEncoder", "modulename": "fast_forward.encoder", "qualname": "TransformerEncoder", "kind": "class", "doc": "
Uses a pre-trained transformer model for encoding. Returns the pooler output.
\n", "bases": "fast_forward.encoder.base.Encoder"}, "fast_forward.encoder.TransformerEncoder.__init__": {"fullname": "fast_forward.encoder.TransformerEncoder.__init__", "modulename": "fast_forward.encoder", "qualname": "TransformerEncoder.__init__", "kind": "function", "doc": "
Create a transformer encoder.
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Parameters
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    \n
  • model:  Pre-trained transformer model (name or path).
    • \n
  • device:  PyTorch device.
    • \n
  • **tokenizer_args:  Additional tokenizer arguments.
    • \n
\n", "signature": "(\tmodel: str | pathlib.Path,\tdevice: str = 'cpu',\t**tokenizer_args: Any)"}, "fast_forward.encoder.TCTColBERTQueryEncoder": {"fullname": "fast_forward.encoder.TCTColBERTQueryEncoder", "modulename": "fast_forward.encoder", "qualname": "TCTColBERTQueryEncoder", "kind": "class", "doc": "
Query encoder for pre-trained TCT-ColBERT models.
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Adapted from Pyserini:\nhttps://github.com/castorini/pyserini/blob/310c828211bb3b9528cfd59695184c80825684a2/pyserini/encode/_tct_colbert.py#L72
\n", "bases": "fast_forward.encoder.transformer.TransformerEncoder"}, "fast_forward.encoder.TCTColBERTDocumentEncoder": {"fullname": "fast_forward.encoder.TCTColBERTDocumentEncoder", "modulename": "fast_forward.encoder", "qualname": "TCTColBERTDocumentEncoder", "kind": "class", "doc": "
Document encoder for pre-trained TCT-ColBERT models.
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Adapted from Pyserini:\nhttps://github.com/castorini/pyserini/blob/310c828211bb3b9528cfd59695184c80825684a2/pyserini/encode/_tct_colbert.py#L27
\n", "bases": "fast_forward.encoder.transformer.TransformerEncoder"}, "fast_forward.index": {"fullname": "fast_forward.index", "modulename": "fast_forward.index", "kind": "module", "doc": "
Indexes
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Indexes are the core of the Fast-Forward library. In the following, you'll find some snippets how to create and use indexes.
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Index types
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Currently, two types of indexes are available:
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    \n
  • InMemoryIndex: Everything is held in memory entirely.
    • \n
  • OnDiskIndex: Vectors are stored on disk and accessed on demand.
    • \n
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OnDiskIndexes can be loaded into memory using OnDiskIndex.to_memory.
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Creating an index
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The following snippet illustrates how to create an OnDiskIndex object (given a fast_forward.encoder.Encoder, my_query_encoder) and add some vector representations to it:
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my_index = OnDiskIndex(Path("my_index.h5"), my_query_encoder)\nmy_index.add(\n    my_vectors,  # shape (3, 768)\n    doc_ids=["d1", "d1", "d2"],\n    psg_ids=["d1_p1", "d1_p2", "d2_p1"]\n)\n
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Here, my_vectors is a Numpy array of shape (3, 768), 768 being the dimensionality of the vector representations. The first two vectors correspond to two passages of the document d1, the third vector corresponds to d2, which has only a single passage. It is also possible to provide either only document IDs or only passage IDs.
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The index can then be subsequently loaded back using OnDiskIndex.load.
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Using an index
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An index can be used to compute semantic re-ranking scores by calling them directly. It requires a fast_forward.Ranking (typically, this comes from a sparse retriever) with the corresponding queries:
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ranking = Ranking.from_file(Path("/path/to/sparse/run.tsv"), queries)\nresult = my_index(ranking)\n
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Here, queries is a simple dictionary mapping query IDs to actual queries to be encoded. The resulting ranking, result, has the semantic scores for the query-document (or query-passage) pairs. Afterwards, retrieval and re-ranking scores may be combined (see Rankings).
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If the input ranking has a large number of queries, one can use the batch_size parameter. The following example processes the ranking in batches of 16 queries each:
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result = my_index(ranking, batch_size=16)\n
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Ranking mode
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Each index has a ranking Mode. The active mode determines the way scores are computed. For example, consider the example index from earlier. Setting the mode to Mode.PASSAGE instructs the index to compute scores on the passage level (and expect passage IDs in the input ranking):
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my_index.mode = Mode.PASSAGE\n
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Similarly, the index can return document IDs, where the score of a document computes as
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    \n
  • the highest score of its passages (Mode.MAXP),
    • \n
  • the score of the its first passage (Mode.FIRSTP) or
    • \n
  • the average score of all its passages (Mode.AVEP).
    • \n
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Early stopping
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Early stopping is a technique to limit the number of index look-ups. This can be beneficial for OnDiskIndexes, especially when the disk is slow. For early stopping, a relatively small cut-off depth (e.g., 10) is required, and it is mostly helpful when a large number of candidates are to be re-ranked. More information can be found in the paper. Note that the implementation here differs slightly from the algorithm in the paper, as the early stopping criterion is only computed at depths that are specified via the early_stopping_depths parameter for performance reasons.
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The following snippet demonstrates early stopping with
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    \n
  • a cut-off depth of 5,
    • \n
  • interpolation parameter 0.2,
    • \n
  • depths 400 and 5000.
    • \n
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result = my_index(\n    ranking,\n    early_stopping=5,\n    early_stopping_alpha=0.2,\n    early_stopping_depths=(400, 5000),\n)\n
\n
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Specifically, in the first step, the top-400 documents/passages for each query are scored. Afterwards, the top-5000 documents/passages are scored only for those queries that do not meet the early stopping criterion yet.
\n"}, "fast_forward.index.Index": {"fullname": "fast_forward.index.Index", "modulename": "fast_forward.index", "qualname": "Index", "kind": "class", "doc": "
Abstract base class for Fast-Forward indexes.
\n", "bases": "abc.ABC"}, "fast_forward.index.Index.__init__": {"fullname": "fast_forward.index.Index.__init__", "modulename": "fast_forward.index", "qualname": "Index.__init__", "kind": "function", "doc": "
Create an index.
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Parameters
\n\n
    \n
  • query_encoder:  The query encoder to use.
    • \n
  • quantizer:  The quantizer to use.
    • \n
  • mode:  The ranking mode.
    • \n
  • encoder_batch_size:  The encoder batch size.
    • \n
\n", "signature": "(\tquery_encoder: fast_forward.encoder.base.Encoder | None = None,\tquantizer: fast_forward.quantizer.base.Quantizer | None = None,\tmode: fast_forward.index.base.Mode = <Mode.MAXP: 2>,\tencoder_batch_size: int = 32)"}, "fast_forward.index.Index.mode": {"fullname": "fast_forward.index.Index.mode", "modulename": "fast_forward.index", "qualname": "Index.mode", "kind": "variable", "doc": "
Return the ranking mode.
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Returns
\n\n
\n  
The ranking mode.
\n
\n", "annotation": ": fast_forward.index.base.Mode"}, "fast_forward.index.Index.encode_queries": {"fullname": "fast_forward.index.Index.encode_queries", "modulename": "fast_forward.index", "qualname": "Index.encode_queries", "kind": "function", "doc": "
Encode queries.
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Parameters
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    \n
  • queries:  The queries to encode.
    • \n
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Raises
\n\n
    \n
  • RuntimeError:  When no query encoder exists.
    • \n
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Returns
\n\n
\n  
The query representations.
\n
\n", "signature": "(self, queries: Sequence[str]) -> numpy.ndarray:", "funcdef": "def"}, "fast_forward.index.Index.query_encoder": {"fullname": "fast_forward.index.Index.query_encoder", "modulename": "fast_forward.index", "qualname": "Index.query_encoder", "kind": "variable", "doc": "
Return the query encoder if it exists.
\n\n
Returns
\n\n
\n  
The query encoder (if any).
\n
\n", "annotation": ": fast_forward.encoder.base.Encoder | None"}, "fast_forward.index.Index.quantizer": {"fullname": "fast_forward.index.Index.quantizer", "modulename": "fast_forward.index", "qualname": "Index.quantizer", "kind": "variable", "doc": "
Return the quantizer if it exists.
\n\n
Returns
\n\n
\n  
The quantizer (if any).
\n
\n", "annotation": ": fast_forward.quantizer.base.Quantizer | None"}, "fast_forward.index.Index.dim": {"fullname": "fast_forward.index.Index.dim", "modulename": "fast_forward.index", "qualname": "Index.dim", "kind": "variable", "doc": "
Return the dimensionality of the vector index.
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If no vectors exist, return None.\nIf a quantizer is used, return the dimension of the codes.
\n\n
Returns
\n\n
\n  
The dimensionality (if any).
\n
\n", "annotation": ": int | None"}, "fast_forward.index.Index.doc_ids": {"fullname": "fast_forward.index.Index.doc_ids", "modulename": "fast_forward.index", "qualname": "Index.doc_ids", "kind": "variable", "doc": "
Return all unique document IDs.
\n\n
Returns
\n\n
\n  
The document IDs.
\n
\n", "annotation": ": set[str]"}, "fast_forward.index.Index.psg_ids": {"fullname": "fast_forward.index.Index.psg_ids", "modulename": "fast_forward.index", "qualname": "Index.psg_ids", "kind": "variable", "doc": "
Return all unique passage IDs.
\n\n
Returns
\n\n
\n  
The passage IDs.
\n
\n", "annotation": ": set[str]"}, "fast_forward.index.Index.add": {"fullname": "fast_forward.index.Index.add", "modulename": "fast_forward.index", "qualname": "Index.add", "kind": "function", "doc": "
Add vector representations and corresponding IDs to the index.
\n\n
Only one of doc_ids and psg_ids may be None.\nIndividual IDs in the sequence may also be None, but each vector must have at\nleast one associated ID.\nDocument IDs may have duplicates, passage IDs must be unique.
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Parameters
\n\n
    \n
  • vectors:  The representations, shape (num_vectors, dim).
    • \n
  • doc_ids:  The corresponding document IDs (may be duplicate).
    • \n
  • psg_ids:  The corresponding passage IDs (must be unique).
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When the number of IDs does not match the number of vectors.
    • \n
  • ValueError: \nWhen the input vector and index dimensionalities do not match.
    • \n
  • ValueError:  When a vector has neither a document nor a passage ID.
    • \n
  • RuntimeError:  When items can't be added to the index for any reason.
    • \n
\n", "signature": "(\tself,\tvectors: numpy.ndarray,\tdoc_ids: Sequence[str | None] | None = None,\tpsg_ids: Sequence[str | None] | None = None) -> None:", "funcdef": "def"}, "fast_forward.index.Index.batch_iter": {"fullname": "fast_forward.index.Index.batch_iter", "modulename": "fast_forward.index", "qualname": "Index.batch_iter", "kind": "function", "doc": "
Iterate over all vectors, document IDs, and passage IDs in batches.
\n\n
IDs may be either strings or None.
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Parameters
\n\n
    \n
  • batch_size:  Batch size.\n:yield: Batches of vectors, document IDs (if any), passage IDs (if any).
    • \n
\n", "signature": "(\tself,\tbatch_size: int) -> Iterator[tuple[numpy.ndarray, Sequence[str | None], Sequence[str | None]]]:", "funcdef": "def"}, "fast_forward.index.Mode": {"fullname": "fast_forward.index.Mode", "modulename": "fast_forward.index", "qualname": "Mode", "kind": "class", "doc": "
Enum used to set the ranking mode of an index.
\n", "bases": "enum.Enum"}, "fast_forward.index.Mode.PASSAGE": {"fullname": "fast_forward.index.Mode.PASSAGE", "modulename": "fast_forward.index", "qualname": "Mode.PASSAGE", "kind": "variable", "doc": "
\n", "default_value": "<Mode.PASSAGE: 1>"}, "fast_forward.index.Mode.MAXP": {"fullname": "fast_forward.index.Mode.MAXP", "modulename": "fast_forward.index", "qualname": "Mode.MAXP", "kind": "variable", "doc": "
\n", "default_value": "<Mode.MAXP: 2>"}, "fast_forward.index.Mode.FIRSTP": {"fullname": "fast_forward.index.Mode.FIRSTP", "modulename": "fast_forward.index", "qualname": "Mode.FIRSTP", "kind": "variable", "doc": "
\n", "default_value": "<Mode.FIRSTP: 3>"}, "fast_forward.index.Mode.AVEP": {"fullname": "fast_forward.index.Mode.AVEP", "modulename": "fast_forward.index", "qualname": "Mode.AVEP", "kind": "variable", "doc": "
\n", "default_value": "<Mode.AVEP: 4>"}, "fast_forward.index.OnDiskIndex": {"fullname": "fast_forward.index.OnDiskIndex", "modulename": "fast_forward.index", "qualname": "OnDiskIndex", "kind": "class", "doc": "
Fast-Forward index that is read on-demand from disk.
\n\n
Uses HDF5 via h5py under the hood. The max_indexing_size argument works around a\nh5py limitation.
\n", "bases": "fast_forward.index.base.Index"}, "fast_forward.index.OnDiskIndex.__init__": {"fullname": "fast_forward.index.OnDiskIndex.__init__", "modulename": "fast_forward.index", "qualname": "OnDiskIndex.__init__", "kind": "function", "doc": "
Create an index on disk.
\n\n
Parameters
\n\n
    \n
  • index_file:  The index file to create (or overwrite).
    • \n
  • query_encoder:  The query encoder.
    • \n
  • quantizer:  The quantizer to use.
    • \n
  • mode:  The ranking mode.
    • \n
  • encoder_batch_size:  Batch size for the query encoder.
    • \n
  • init_size:  Initial size to allocate (number of vectors).
    • \n
  • resize_min_val:  Minimum number of vectors to increase index size by.
    • \n
  • hdf5_chunk_size:  Override chunk size used by HDF5.
    • \n
  • max_id_length: \nMaximum length of document and passage IDs (number of characters).
    • \n
  • overwrite:  Overwrite index file if it exists.
    • \n
  • max_indexing_size: \nMaximum number of vectors to retrieve from the HDF5 dataset at once.
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When the file exists and overwrite=False.
    • \n
\n", "signature": "(\tindex_file: pathlib.Path,\tquery_encoder: fast_forward.encoder.base.Encoder | None = None,\tquantizer: fast_forward.quantizer.base.Quantizer | None = None,\tmode: fast_forward.index.base.Mode = <Mode.MAXP: 2>,\tencoder_batch_size: int = 32,\tinit_size: int = 16384,\tresize_min_val: int = 1024,\thdf5_chunk_size: int | None = None,\tmax_id_length: int = 8,\toverwrite: bool = False,\tmax_indexing_size: int = 1024)"}, "fast_forward.index.OnDiskIndex.to_memory": {"fullname": "fast_forward.index.OnDiskIndex.to_memory", "modulename": "fast_forward.index", "qualname": "OnDiskIndex.to_memory", "kind": "function", "doc": "
Load the index entirely into memory.
\n\n
Parameters
\n\n
    \n
  • batch_size:  Use batches instead of adding all vectors at once.
    • \n
\n\n
Returns
\n\n
\n  
The loaded index.
\n
\n", "signature": "(\tself,\tbatch_size: int | None = None) -> fast_forward.index.memory.InMemoryIndex:", "funcdef": "def"}, "fast_forward.index.OnDiskIndex.load": {"fullname": "fast_forward.index.OnDiskIndex.load", "modulename": "fast_forward.index", "qualname": "OnDiskIndex.load", "kind": "function", "doc": "
Open an existing index on disk.
\n\n
Parameters
\n\n
    \n
  • index_file:  The index file to open.
    • \n
  • query_encoder:  The query encoder.
    • \n
  • mode:  The ranking mode.
    • \n
  • encoder_batch_size:  Batch size for the query encoder.
    • \n
  • resize_min_val:  Minimum value to increase index size by.
    • \n
  • max_indexing_size: \nMaximum number of vectors to retrieve from the HDF5 dataset at once.
    • \n
\n\n
Returns
\n\n
\n  
The index.
\n
\n", "signature": "(\tcls,\tindex_file: pathlib.Path,\tquery_encoder: fast_forward.encoder.base.Encoder | None = None,\tmode: fast_forward.index.base.Mode = <Mode.MAXP: 2>,\tencoder_batch_size: int = 32,\tresize_min_val: int = 1024,\tmax_indexing_size: int = 1024) -> fast_forward.index.disk.OnDiskIndex:", "funcdef": "def"}, "fast_forward.index.InMemoryIndex": {"fullname": "fast_forward.index.InMemoryIndex", "modulename": "fast_forward.index", "qualname": "InMemoryIndex", "kind": "class", "doc": "
Fast-Forward index that is held entirely in memory.
\n", "bases": "fast_forward.index.base.Index"}, "fast_forward.index.InMemoryIndex.__init__": {"fullname": "fast_forward.index.InMemoryIndex.__init__", "modulename": "fast_forward.index", "qualname": "InMemoryIndex.__init__", "kind": "function", "doc": "
Create an index in memory.
\n\n
Parameters
\n\n
    \n
  • query_encoder:  The query encoder to use.
    • \n
  • quantizer:  The quantizer to use.
    • \n
  • mode:  The ranking mode.
    • \n
  • encoder_batch_size:  Batch size for the query encoder.
    • \n
  • init_size:  Initial index size (number of vectors).
    • \n
  • alloc_size:  Shard size (number of vectors) allocated when index is full.
    • \n
\n", "signature": "(\tquery_encoder: fast_forward.encoder.base.Encoder | None = None,\tquantizer: fast_forward.quantizer.base.Quantizer | None = None,\tmode: fast_forward.index.base.Mode = <Mode.MAXP: 2>,\tencoder_batch_size: int = 32,\tinit_size: int = 16384,\talloc_size: int = 16384)"}, "fast_forward.index.InMemoryIndex.consolidate": {"fullname": "fast_forward.index.InMemoryIndex.consolidate", "modulename": "fast_forward.index", "qualname": "InMemoryIndex.consolidate", "kind": "function", "doc": "
Combine all shards of the index in one contiguous section in the memory.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "fast_forward.quantizer": {"fullname": "fast_forward.quantizer", "modulename": "fast_forward.quantizer", "kind": "module", "doc": "
Quantization
\n\n
Fast-Forward indexes support (product) quantization as a means of compressing an index. The Quantizer class defines the interface for quantizers to implement. Currently, the following quantizers are available:
\n\n\n\n
Quantizers must be trained before they are used with the corresponding Fast-Forward index. The typical workflow is as follows:
\n\n
\n
from pathlib import Path\n\nimport numpy as np\n\nfrom fast_forward.index import OnDiskIndex\nfrom fast_forward.quantizer import NanoPQ\n\n# in practice, a subset of the encoded corpus should be used as training vectors\ntraining_vectors = np.random.normal(size=(2**10, 768)).astype(np.float32)\n\nquantizer = NanoPQ(M=8, Ks=256)\nquantizer.fit(training_vectors)\n\nindex = OnDiskIndex(Path("my_index.h5"), quantizer=quantizer)\n
\n
\n"}, "fast_forward.quantizer.Quantizer": {"fullname": "fast_forward.quantizer.Quantizer", "modulename": "fast_forward.quantizer", "qualname": "Quantizer", "kind": "class", "doc": "
Base class for quantizers.
\n", "bases": "abc.ABC"}, "fast_forward.quantizer.Quantizer.set_attached": {"fullname": "fast_forward.quantizer.Quantizer.set_attached", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.set_attached", "kind": "function", "doc": "
Set the quantizer as attached, preventing calls to Quantizer.fit.
\n\n
Raises
\n\n
    \n
  • RuntimeError:  When the quantizer has not been fit.
    • \n
\n", "signature": "(self) -> None:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.fit": {"fullname": "fast_forward.quantizer.Quantizer.fit", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.fit", "kind": "function", "doc": "
Fit (train) the quantizer.
\n\n
Quantizers can only be trained before being attached to an index in order to\navoid inconsistencies.
\n\n
Parameters
\n\n
    \n
  • vectors:  The training vectors.
    • \n
  • **kwargs:  Arguments specific to the quantizer.
    • \n
\n\n
Raises
\n\n
    \n
  • RuntimeError:  When the quantizer is aready attached to an index.
    • \n
\n", "signature": "(self, vectors: numpy.ndarray, **kwargs: Any) -> None:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.dtype": {"fullname": "fast_forward.quantizer.Quantizer.dtype", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.dtype", "kind": "variable", "doc": "
The data type of the codes produced by this quantizer.
\n\n
Returns
\n\n
\n  
The data type of the codes.
\n
\n", "annotation": ": numpy.dtype"}, "fast_forward.quantizer.Quantizer.dims": {"fullname": "fast_forward.quantizer.Quantizer.dims", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.dims", "kind": "variable", "doc": "
The dimensions before and after quantization.
\n\n
May return None values before the quantizer is trained.
\n\n
Returns
\n\n
\n  
Dimension of the original vectors and dimension of the codes.
\n
\n", "annotation": ": tuple[int | None, int | None]"}, "fast_forward.quantizer.Quantizer.encode": {"fullname": "fast_forward.quantizer.Quantizer.encode", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.encode", "kind": "function", "doc": "
Encode a batch of vectors.
\n\n
Parameters
\n\n
    \n
  • vectors:  The vectors to be encoded.
    • \n
\n\n
Returns
\n\n
\n  
The codes corresponding to the vectors.
\n
\n", "signature": "(self, vectors: numpy.ndarray) -> numpy.ndarray:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.decode": {"fullname": "fast_forward.quantizer.Quantizer.decode", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.decode", "kind": "function", "doc": "
Decode a batch of codes to obtain approximate vector representations.
\n\n
Parameters
\n\n
    \n
  • codes:  The codes to be decoded.
    • \n
\n\n
Raises
\n\n
    \n
  • RuntimeError:  When the quantizer has not been fit.
    • \n
\n\n
Returns
\n\n
\n  
The approximated vectors.
\n
\n", "signature": "(self, codes: numpy.ndarray) -> numpy.ndarray:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.serialize": {"fullname": "fast_forward.quantizer.Quantizer.serialize", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.serialize", "kind": "function", "doc": "
Return a serialized representation of the quantizer.
\n\n
This representations is used to be stored in the index.
\n\n
Returns
\n\n
\n  
The serialized quantizer.
\n
\n", "signature": "(\tself) -> tuple[Mapping[str, str | bool | float], Mapping[str, str | bool | float], Mapping[str, numpy.ndarray]]:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.deserialize": {"fullname": "fast_forward.quantizer.Quantizer.deserialize", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.deserialize", "kind": "function", "doc": "
Reconstruct a serialized quantizer.
\n\n
Parameters
\n\n
    \n
  • meta:  The quantizer metadata.
    • \n
  • attributes:  The quantizer attributes.
    • \n
  • data:  The quantizer data.
    • \n
\n\n
Returns
\n\n
\n  
The loaded quantizer.
\n
\n", "signature": "(\tcls,\tmeta: Mapping[str, str | bool | float],\tattributes: Mapping[str, str | bool | float],\tdata: Mapping[str, numpy.ndarray]) -> fast_forward.quantizer.base.Quantizer:", "funcdef": "def"}, "fast_forward.quantizer.NanoPQ": {"fullname": "fast_forward.quantizer.NanoPQ", "modulename": "fast_forward.quantizer", "qualname": "NanoPQ", "kind": "class", "doc": "
Product quantizer that uses the nanopq library.
\n\n
More information is available\nhere.
\n", "bases": "fast_forward.quantizer.base.Quantizer"}, "fast_forward.quantizer.NanoPQ.__init__": {"fullname": "fast_forward.quantizer.NanoPQ.__init__", "modulename": "fast_forward.quantizer", "qualname": "NanoPQ.__init__", "kind": "function", "doc": "
Instantiate a nanopq product quantizer.
\n\n
Parameters
\n\n
    \n
  • M:  The number of subspaces.
    • \n
  • Ks:  The number of codewords per subspace.
    • \n
  • metric:  The metric to use.
    • \n
  • verbose:  Enable verbosity.
    • \n
\n", "signature": "(M: int, Ks: int, metric: str = 'dot', verbose: bool = False)"}, "fast_forward.quantizer.NanoOPQ": {"fullname": "fast_forward.quantizer.NanoOPQ", "modulename": "fast_forward.quantizer", "qualname": "NanoOPQ", "kind": "class", "doc": "
Optimized product quantizer that uses the nanopq library.
\n\n
More information is available\nhere.
\n", "bases": "fast_forward.quantizer.base.Quantizer"}, "fast_forward.quantizer.NanoOPQ.__init__": {"fullname": "fast_forward.quantizer.NanoOPQ.__init__", "modulename": "fast_forward.quantizer", "qualname": "NanoOPQ.__init__", "kind": "function", "doc": "
Instantiate a nanopq optimized product quantizer.
\n\n
Parameters
\n\n
    \n
  • M:  The number of subspaces.
    • \n
  • Ks:  The number of codewords per subspace.
    • \n
  • metric:  The metric to use.
    • \n
  • verbose:  Enable verbosity.
    • \n
\n", "signature": "(M: int, Ks: int, metric: str = 'dot', verbose: bool = False)"}, "fast_forward.util": {"fullname": "fast_forward.util", "modulename": "fast_forward.util", "kind": "module", "doc": "
Indexing
\n\n
The Indexer class is a utility for indexing collections or adding pre-computed vectors to an index.
\n\n
If the size of the collection is known in advance, it can be specified when the index is created in order to avoid subsequent resizing operations:
\n\n
\n
my_index = OnDiskIndex(Path("my_index.h5"), init_size=1000000)\n
\n
\n\n
In order to index a corpus, a document/passage encoder is required, for example:
\n\n
\n
doc_encoder = TCTColBERTDocumentEncoder(\n    "castorini/tct_colbert-msmarco",\n    device="cuda:0" if torch.cuda.is_available() else "cpu",\n)\n
\n
\n\n
The indexer can be created as follows:
\n\n
\n
indexer = Indexer(my_index, doc_encoder, encoder_batch_size=8)\n
\n
\n\n
Indexer.from_dicts consumes an iterator that yields dictionaries:
\n\n
\n
def docs_iter():\n    for doc in my_corpus:\n        yield {"doc_id": doc.get_doc_id(), "text": doc.get_text()}\n\nindexer.from_dicts(docs_iter())\n
\n
\n\n
Additionally, indexers can be used to automatically fit and attach a quantizer during indexing. In this example, a quantized version (target_index) of an existing index (source_index) is created:
\n\n
\n
from fast_forward.quantizer import NanoPQ\n\nIndexer(\n    target_index,\n    quantizer=NanoPQ(8, 256),\n    batch_size=2**16,\n    quantizer_fit_batches=2,\n).from_index(source_index)\n
\n
\n\n
Here, the first two batches (of size $2^{16}$) are buffered and used to fit the quantizer.
\n\n
Sequential coalescing
\n\n
The sequential coalescing algorithm is a compression technique for indexes with multiple representations per document. More information can be found in the paper. It is implemented as fast_forward.util.create_coalesced_index. Example usage:
\n\n
\n
my_index = OnDiskIndex.load(Path("/path/to/index.h5"))\ncoalesced_index = InMemoryIndex(mode=Mode.MAXP)\ncreate_coalesced_index(my_index, coalesced_index, 0.3)\n
\n
\n"}, "fast_forward.util.Indexer": {"fullname": "fast_forward.util.Indexer", "modulename": "fast_forward.util", "qualname": "Indexer", "kind": "class", "doc": "
Utility class for indexing collections.
\n"}, "fast_forward.util.Indexer.__init__": {"fullname": "fast_forward.util.Indexer.__init__", "modulename": "fast_forward.util", "qualname": "Indexer.__init__", "kind": "function", "doc": "
Create an indexer.
\n\n
Optionally, a quantizer can automatically be fit on the first batch(es) to be\nindexed. This requires the index to be empty.\nIf a quantizer is provided, the first batch(es) will be buffered and used to fit\nthe quantizer.
\n\n
Parameters
\n\n
    \n
  • index:  The target index.
    • \n
  • encoder:  Document/passage encoder.
    • \n
  • encoder_batch_size:  Encoder batch size.
    • \n
  • batch_size:  How many vectors to add to the index at once.
    • \n
  • quantizer:  A quantizer to be fit and attached to the index.
    • \n
  • quantizer_fit_batches:  How many batches to use to fit the quantizer.
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When a quantizer is provided that has already been fit.
    • \n
  • ValueError:  When a quantizer is provided and the index is not empty.
    • \n
\n", "signature": "(\tindex: fast_forward.index.base.Index,\tencoder: fast_forward.encoder.base.Encoder | None = None,\tencoder_batch_size: int = 128,\tbatch_size: int = 65536,\tquantizer: fast_forward.quantizer.base.Quantizer | None = None,\tquantizer_fit_batches: int = 1)"}, "fast_forward.util.Indexer.from_dicts": {"fullname": "fast_forward.util.Indexer.from_dicts", "modulename": "fast_forward.util", "qualname": "Indexer.from_dicts", "kind": "function", "doc": "
Index data from dictionaries.
\n\n
Parameters
\n\n
    \n
  • data:  An iterable of the dictionaries.
    • \n
\n", "signature": "(self, data: Iterable[fast_forward.util.indexer.IndexingDict]) -> None:", "funcdef": "def"}, "fast_forward.util.Indexer.from_index": {"fullname": "fast_forward.util.Indexer.from_index", "modulename": "fast_forward.util", "qualname": "Indexer.from_index", "kind": "function", "doc": "
Transfer vectors and IDs from another index.
\n\n
If the source index uses quantized representations, the vectors are\nreconstructed first.
\n\n
Parameters
\n\n
    \n
  • index:  The source index.
    • \n
\n", "signature": "(self, index: fast_forward.index.base.Index) -> None:", "funcdef": "def"}, "fast_forward.util.IndexingDict": {"fullname": "fast_forward.util.IndexingDict", "modulename": "fast_forward.util", "qualname": "IndexingDict", "kind": "class", "doc": "
Dictionary that represents a document/passage.
\n\n
Consumed by Indexer.from_dicts.
\n", "bases": "typing.TypedDict"}, "fast_forward.util.IndexingDict.text": {"fullname": "fast_forward.util.IndexingDict.text", "modulename": "fast_forward.util", "qualname": "IndexingDict.text", "kind": "variable", "doc": "
\n", "annotation": ": str"}, "fast_forward.util.IndexingDict.doc_id": {"fullname": "fast_forward.util.IndexingDict.doc_id", "modulename": "fast_forward.util", "qualname": "IndexingDict.doc_id", "kind": "variable", "doc": "
\n", "annotation": ": str | None"}, "fast_forward.util.IndexingDict.psg_id": {"fullname": "fast_forward.util.IndexingDict.psg_id", "modulename": "fast_forward.util", "qualname": "IndexingDict.psg_id", "kind": "variable", "doc": "
\n", "annotation": ": str | None"}, "fast_forward.util.to_ir_measures": {"fullname": "fast_forward.util.to_ir_measures", "modulename": "fast_forward.util", "qualname": "to_ir_measures", "kind": "function", "doc": "
Return a ranking as a data frame suitable for the ir-measures library.
\n\n
Parameters
\n\n
    \n
  • ranking:  The input ranking.
    • \n
\n\n
Returns
\n\n
\n  
The data frame.
\n
\n", "signature": "(ranking: fast_forward.ranking.Ranking) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "fast_forward.util.cos_dist": {"fullname": "fast_forward.util.cos_dist", "modulename": "fast_forward.util", "qualname": "cos_dist", "kind": "function", "doc": "
Cosine distance of two vectors.
\n\n
Parameters
\n\n
    \n
  • a:  First vector.
    • \n
  • b:  Second vector.
    • \n
\n\n
Returns
\n\n
\n  
Cosine distance.
\n
\n", "signature": "(a: numpy.ndarray, b: numpy.ndarray) -> float:", "funcdef": "def"}, "fast_forward.util.create_coalesced_index": {"fullname": "fast_forward.util.create_coalesced_index", "modulename": "fast_forward.util", "qualname": "create_coalesced_index", "kind": "function", "doc": "
Create a compressed index using sequential coalescing.
\n\n
Parameters
\n\n
    \n
  • source_index:  Source index (containing multiple vectors for each document).
    • \n
  • target_index:  Target index (must be empty).
    • \n
  • delta:  The coalescing threshold.
    • \n
  • distance_function:  The distance function.
    • \n
  • batch_size:  Use batches instead of adding all vectors at the end.
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When the target index is not empty.
    • \n
\n", "signature": "(\tsource_index: fast_forward.index.base.Index,\ttarget_index: fast_forward.index.base.Index,\tdelta: float,\tdistance_function: Callable[[numpy.ndarray, numpy.ndarray], float] = <function cos_dist>,\tbatch_size: int | None = None) -> None:", "funcdef": "def"}, "fast_forward.util.pyterrier": {"fullname": "fast_forward.util.pyterrier", "modulename": "fast_forward.util.pyterrier", "kind": "module", "doc": "
PyTerrier transformers
\n\n
Fast-Forward indexes can seamlessly be integrated into PyTerrier pipelines using the transformers provided in fast_forward.util.pyterrier. Specifically, a re-ranking pipeline might look like this, given that my_index is a Fast-Forward index of the MS MARCO passage corpus:
\n\n
\n
bm25 = pt.BatchRetrieve.from_dataset(\n    "msmarco_passage", variant="terrier_stemmed", wmodel="BM25"\n)\n\nff_pl = bm25 % 5000 >> FFScore(my_index) >> FFInterpolate(0.2)\n
\n
\n"}, "fast_forward.util.pyterrier.FFScore": {"fullname": "fast_forward.util.pyterrier.FFScore", "modulename": "fast_forward.util.pyterrier", "qualname": "FFScore", "kind": "class", "doc": "
PyTerrier transformer that computes scores using a Fast-Forward index.
\n", "bases": "pyterrier.transformer.Transformer"}, "fast_forward.util.pyterrier.FFScore.__init__": {"fullname": "fast_forward.util.pyterrier.FFScore.__init__", "modulename": "fast_forward.util.pyterrier", "qualname": "FFScore.__init__", "kind": "function", "doc": "
Create an FFScore transformer.
\n\n
Parameters
\n\n
    \n
  • index:  The Fast-Forward index.
    • \n
\n", "signature": "(index: fast_forward.index.base.Index)"}, "fast_forward.util.pyterrier.FFScore.transform": {"fullname": "fast_forward.util.pyterrier.FFScore.transform", "modulename": "fast_forward.util.pyterrier", "qualname": "FFScore.transform", "kind": "function", "doc": "
Compute the scores for all query-document pairs in the data frame.
\n\n
The previous scores are moved to the \"score_0\" column.
\n\n
Parameters
\n\n
    \n
  • topics_or_res:  The PyTerrier data frame.
    • \n
\n\n
Returns
\n\n
\n  
A data frame with the computed scores.
\n
\n", "signature": "(\tself,\ttopics_or_res: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "fast_forward.util.pyterrier.FFInterpolate": {"fullname": "fast_forward.util.pyterrier.FFInterpolate", "modulename": "fast_forward.util.pyterrier", "qualname": "FFInterpolate", "kind": "class", "doc": "
PyTerrier transformer that interpolates scores computed by FFScore.
\n", "bases": "pyterrier.transformer.Transformer"}, "fast_forward.util.pyterrier.FFInterpolate.__init__": {"fullname": "fast_forward.util.pyterrier.FFInterpolate.__init__", "modulename": "fast_forward.util.pyterrier", "qualname": "FFInterpolate.__init__", "kind": "function", "doc": "
Create an FFInterpolate transformer.
\n\n
Parameters
\n\n
    \n
  • alpha:  The interpolation parameter.
    • \n
\n", "signature": "(alpha: float)"}, "fast_forward.util.pyterrier.FFInterpolate.alpha": {"fullname": "fast_forward.util.pyterrier.FFInterpolate.alpha", "modulename": "fast_forward.util.pyterrier", "qualname": "FFInterpolate.alpha", "kind": "variable", "doc": "
\n"}, "fast_forward.util.pyterrier.FFInterpolate.transform": {"fullname": "fast_forward.util.pyterrier.FFInterpolate.transform", "modulename": "fast_forward.util.pyterrier", "qualname": "FFInterpolate.transform", "kind": "function", "doc": "
Interpolate the scores as alpha * score_0 + (1 - alpha) * score.
\n\n
Parameters
\n\n
    \n
  • topics_or_res:  The PyTerrier data frame.
    • \n
\n\n
Returns
\n\n
\n  
A data frame with the interpolated scores.
\n
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+    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"fast_forward": {"fullname": "fast_forward", "modulename": "fast_forward", "kind": "module", "doc": "
This is the implementation of Fast-Forward indexes.
\n\n
Important: As this library is still in its early stages, the API is subject to change!
\n\n
Features
\n\n
    \n
  • Efficient look-up-based computation of semantic ranking scores
    • \n
  • Interpolation of lexical (retrieval) and semantic (re-ranking) scores
    • \n
  • Passage- and document-level ranking, including MaxP, FirstP, and AverageP
    • \n
  • Early stopping for limiting index look-ups
    • \n
  • Index compression via quantization and sequential coalescing
    • \n
\n\n
How to...
\n\n\n\n
Installation
\n\n
Install the package via pip:
\n\n
\n
pip install fast-forward-indexes\n
\n
\n\n
Alternatively, the package can be installed from source:
\n\n
\n
git clone https://github.com/mrjleo/fast-forward-indexes.git\ncd fast-forward-indexes\npython -m pip install .\n
\n
\n\n
After installing the package, simply import the library:
\n\n
\n
import fast_forward\n
\n
\n\n
Getting started
\n\n
Using a Fast-Forward index is as simple as providing a TREC run with sparse scores:
\n\n
\n
from pathlib import Path\n\nfrom fast_forward import Ranking\nfrom fast_forward.index import OnDiskIndex, Mode\nfrom fast_forward.encoder import TCTColBERTQueryEncoder\n\n# choose a pre-trained query encoder\nencoder = TCTColBERTQueryEncoder("castorini/tct_colbert-msmarco")\n\n# load an index on disk\nff_index = OnDiskIndex.load(Path("/path/to/index.h5"), encoder, mode=Mode.MAXP)\n\n# load a run (TREC format) and attach all required queries\nfirst_stage_ranking = (\n    Ranking.from_file(Path("/path/to/input/run.tsv"))\n    .attach_queries(\n        {\n            "q1": "query 1",\n            "q2": "query 2",\n            # ...\n            "qn": "query n",\n        }\n    )\n    .cut(5000)\n)\n\n# compute the corresponding semantic scores\nout = ff_index(first_stage_ranking)\n\n# interpolate scores and create a new TREC runfile\nfirst_stage_ranking.interpolate(out, 0.1).save(Path("/path/to/output/run.tsv"))\n
\n
\n\n
Rankings
\n\n
Rankings (or runs) are represented using the Ranking class. Each ranking contains query IDs, document/passage IDs, and corresponding scores. Rankings may be instantiated from TREC runfiles using Ranking.from_file:
\n\n
\n
r = Ranking.from_file(Path("/path/to/TREC/run.tsv"))\n
\n
\n\n
Alternatively, rankings can be created from nested dictionaries using Ranking.from_run:
\n\n
\n
run = {\n    "q1": {"d0": 1, "d1": 2, "d2": 3},\n    "q2": {"d0": 4, "d1": 5, "d2": 6},\n}\nr = Ranking.from_run(run)\n
\n
\n\n
Optionally, rankings can have queries attached using Ranking.attach_queries:
\n\n
\n
r = r.attach_queries(\n    {\n        "q1": "query 1",\n        "q2": "query 2",\n        # ...\n        "qn": "query n",\n    }\n)\n
\n
\n\n
Rankings implement addition and multiplication operators, for example:
\n\n
\n
ranking_3 = 0.1 * ranking_1 + ranking_2\n
\n
\n\n
Ranking.interpolate allows to interpolate the scores of two rankings (i.e., retrieval and re-ranking):
\n\n
\n
first_stage_ranking = Ranking.from_file(Path("/path/to/TREC/run.tsv"))\nsemantic_scores = my_index(first_stage_ranking)\ninterpolated_ranking = first_stage_ranking.interpolate(semantic_scores, 0.1)\n# equivalent to (but slightly more efficient than):\ninterpolated_ranking = first_stage_ranking * 0.1 + semantic_scores * 0.9\n
\n
\n\n
Additionally, Ranking.rr_scores recomputes a ranking's scores based on the reciprocal rank. This allows, for example, to perform reciprocal rank fusion (RRF) as follows:
\n\n
\n
rrf_ranking = first_stage_ranking.rr_scores() + semantic_scores.rr_scores()\n
\n
\n\n
Further information about rank fusion can be found here.
\n\n
Finally, a ranking can have a name set and be saved as a TREC runfile:
\n\n
\n
interpolated_ranking.name = "Fast-Forward, alpha=0.1"\ninterpolated_ranking.save(Path("/path/to/output/run.tsv"))\n
\n
\n"}, "fast_forward.Ranking": {"fullname": "fast_forward.Ranking", "modulename": "fast_forward", "qualname": "Ranking", "kind": "class", "doc": "
Represents rankings of documents/passages w.r.t. queries.
\n"}, "fast_forward.Ranking.__init__": {"fullname": "fast_forward.Ranking.__init__", "modulename": "fast_forward", "qualname": "Ranking.__init__", "kind": "function", "doc": "
Create a ranking from an existing data frame. Removes rows with NaN scores.
\n\n
Parameters
\n\n
    \n
  • df:  Data frame containing IDs and scores.
    • \n
  • name:  Method name.
    • \n
  • queries:  Query IDs mapped to queries.
    • \n
  • dtype:  How the scores should be represented in the data frame.
    • \n
  • copy:  Whether to copy the data frame.
    • \n
  • is_sorted:  Whether the data frame is already sorted (by score).
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When the queries are incomplete.
    • \n
\n", "signature": "(\tdf: pandas.core.frame.DataFrame,\tname: str | None = None,\tqueries: Mapping[str, str] | None = None,\tdtype: numpy.dtype = dtype('float32'),\tcopy: bool = True,\tis_sorted: bool = False)"}, "fast_forward.Ranking.name": {"fullname": "fast_forward.Ranking.name", "modulename": "fast_forward", "qualname": "Ranking.name", "kind": "variable", "doc": "
\n"}, "fast_forward.Ranking.has_queries": {"fullname": "fast_forward.Ranking.has_queries", "modulename": "fast_forward", "qualname": "Ranking.has_queries", "kind": "variable", "doc": "
Whether the ranking has queries attached.
\n\n
Returns
\n\n
\n  
Whether queries exist.
\n
\n", "annotation": ": bool"}, "fast_forward.Ranking.q_ids": {"fullname": "fast_forward.Ranking.q_ids", "modulename": "fast_forward", "qualname": "Ranking.q_ids", "kind": "variable", "doc": "
The set of (unique) query IDs in this ranking.
\n\n
Only queries with at least one scored document are considered.
\n\n
Returns
\n\n
\n  
The query IDs.
\n
\n", "annotation": ": set[str]"}, "fast_forward.Ranking.attach_queries": {"fullname": "fast_forward.Ranking.attach_queries", "modulename": "fast_forward", "qualname": "Ranking.attach_queries", "kind": "function", "doc": "
Attach queries to the ranking.
\n\n
Parameters
\n\n
    \n
  • queries:  Query IDs mapped to queries.
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When the queries are incomplete.
    • \n
\n\n
Returns
\n\n
\n  
The ranking with queries attached.
\n
\n", "signature": "(self, queries: Mapping[str, str]) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.normalize": {"fullname": "fast_forward.Ranking.normalize", "modulename": "fast_forward", "qualname": "Ranking.normalize", "kind": "function", "doc": "
Normalize the scores (min-max) to be in [0, 1].
\n\n
If all scores are equal, they are set to 0.
\n\n
Returns
\n\n
\n  
A ranking with normalized scores.
\n
\n", "signature": "(self) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.cut": {"fullname": "fast_forward.Ranking.cut", "modulename": "fast_forward", "qualname": "Ranking.cut", "kind": "function", "doc": "
For each query, remove all but the top-k scoring documents/passages.
\n\n
Parameters
\n\n
    \n
  • cutoff:  Number of best scores per query to keep (k).
    • \n
\n\n
Returns
\n\n
\n  
The resulting ranking.
\n
\n", "signature": "(self, cutoff: int) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.interpolate": {"fullname": "fast_forward.Ranking.interpolate", "modulename": "fast_forward", "qualname": "Ranking.interpolate", "kind": "function", "doc": "
Interpolate as score = self.score * alpha + other.score * (1 - alpha).
\n\n
Missing scores in either ranking are treated as zero.
\n\n
Parameters
\n\n
    \n
  • other:  Ranking to interpolate with.
    • \n
  • alpha:  Interpolation parameter.
    • \n
  • normalize:  Perform min-max normalization.
    • \n
\n\n
Returns
\n\n
\n  
The resulting ranking.
\n
\n", "signature": "(\tself,\tother: fast_forward.ranking.Ranking,\talpha: float,\tnormalize: bool = False) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.rr_scores": {"fullname": "fast_forward.Ranking.rr_scores", "modulename": "fast_forward", "qualname": "Ranking.rr_scores", "kind": "function", "doc": "
Re-score documents/passages using reciprocal rank.
\n\n
A score is computed as 1 / (rank + k).
\n\n
Used by RRF.
\n\n
Parameters
\n\n
    \n
  • k:  RR scoring parameter.
    • \n
\n\n
Returns
\n\n
\n  
A ranking with RR scores.
\n
\n", "signature": "(self, k: int = 60) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.save": {"fullname": "fast_forward.Ranking.save", "modulename": "fast_forward", "qualname": "Ranking.save", "kind": "function", "doc": "
Save the ranking in a TREC runfile.
\n\n
Parameters
\n\n
    \n
  • target:  Output file.
    • \n
\n", "signature": "(self, target: pathlib.Path) -> None:", "funcdef": "def"}, "fast_forward.Ranking.from_run": {"fullname": "fast_forward.Ranking.from_run", "modulename": "fast_forward", "qualname": "Ranking.from_run", "kind": "function", "doc": "
Create a Ranking object from a TREC run.
\n\n
Parameters
\n\n
    \n
  • run:  TREC run.
    • \n
  • name:  Method name.
    • \n
  • queries:  Query IDs mapped to queries.
    • \n
  • dtype:  How the score should be represented in the data frame.
    • \n
\n\n
Returns
\n\n
\n  
The resulting ranking.
\n
\n", "signature": "(\tcls,\trun: Mapping[str, Mapping[str, float]],\tname: str | None = None,\tqueries: Mapping[str, str] | None = None,\tdtype: numpy.dtype = dtype('float32')) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.Ranking.from_file": {"fullname": "fast_forward.Ranking.from_file", "modulename": "fast_forward", "qualname": "Ranking.from_file", "kind": "function", "doc": "
Create a Ranking object from a runfile in TREC format.
\n\n
Parameters
\n\n
    \n
  • f:  TREC runfile to read.
    • \n
  • queries:  Query IDs mapped to queries.
    • \n
  • dtype:  How the score should be represented in the data frame.
    • \n
\n\n
Returns
\n\n
\n  
The resulting ranking.
\n
\n", "signature": "(\tcls,\tf: pathlib.Path,\tqueries: Mapping[str, str] | None = None,\tdtype: numpy.dtype = dtype('float32')) -> fast_forward.ranking.Ranking:", "funcdef": "def"}, "fast_forward.encoder": {"fullname": "fast_forward.encoder", "modulename": "fast_forward.encoder", "kind": "module", "doc": "
Custom encoders
\n\n
Custom encoders can easily be implemented. The preferred way to do this is by subclassing fast_forward.encoder.base.Encoder and overriding the _encode method. This allows the new encoder to make use of batch encoding.
\n\n
Alternatively, one can use the LambdaEncoder class, which wraps a function that encodes a single piece of text. The following example shows how to do this with a Pyserini query encoder:
\n\n
\n
pyserini_encoder = pyserini.encode.AnceQueryEncoder("castorini/ance-msmarco-passage")\nance_encoder = LambdaEncoder(pyserini_encoder.encode)\n
\n
\n\n
Note that this method is usually less efficient, as the texts are encoded one by one rather than in batches.
\n"}, "fast_forward.encoder.Encoder": {"fullname": "fast_forward.encoder.Encoder", "modulename": "fast_forward.encoder", "qualname": "Encoder", "kind": "class", "doc": "
Base class for encoders.
\n", "bases": "abc.ABC"}, "fast_forward.encoder.LambdaEncoder": {"fullname": "fast_forward.encoder.LambdaEncoder", "modulename": "fast_forward.encoder", "qualname": "LambdaEncoder", "kind": "class", "doc": "
Encoder adapter class for arbitrary encoding functions.
\n", "bases": "fast_forward.encoder.base.Encoder"}, "fast_forward.encoder.LambdaEncoder.__init__": {"fullname": "fast_forward.encoder.LambdaEncoder.__init__", "modulename": "fast_forward.encoder", "qualname": "LambdaEncoder.__init__", "kind": "function", "doc": "
Create a lambda encoder.
\n\n
Parameters
\n\n
    \n
  • f:  Function to encode a single piece of text.
    • \n
\n", "signature": "(f: Callable[[str], numpy.ndarray])"}, "fast_forward.encoder.TransformerEncoder": {"fullname": "fast_forward.encoder.TransformerEncoder", "modulename": "fast_forward.encoder", "qualname": "TransformerEncoder", "kind": "class", "doc": "
Uses a pre-trained transformer model for encoding. Returns the pooler output.
\n", "bases": "fast_forward.encoder.base.Encoder"}, "fast_forward.encoder.TransformerEncoder.__init__": {"fullname": "fast_forward.encoder.TransformerEncoder.__init__", "modulename": "fast_forward.encoder", "qualname": "TransformerEncoder.__init__", "kind": "function", "doc": "
Create a transformer encoder.
\n\n
Parameters
\n\n
    \n
  • model:  Pre-trained transformer model (name or path).
    • \n
  • device:  PyTorch device.
    • \n
  • **tokenizer_args:  Additional tokenizer arguments.
    • \n
\n", "signature": "(\tmodel: str | pathlib.Path,\tdevice: str = 'cpu',\t**tokenizer_args: Any)"}, "fast_forward.encoder.TCTColBERTQueryEncoder": {"fullname": "fast_forward.encoder.TCTColBERTQueryEncoder", "modulename": "fast_forward.encoder", "qualname": "TCTColBERTQueryEncoder", "kind": "class", "doc": "
Query encoder for pre-trained TCT-ColBERT models.
\n\n
Adapted from Pyserini:\nhttps://github.com/castorini/pyserini/blob/310c828211bb3b9528cfd59695184c80825684a2/pyserini/encode/_tct_colbert.py#L72
\n", "bases": "fast_forward.encoder.transformer.TransformerEncoder"}, "fast_forward.encoder.TCTColBERTDocumentEncoder": {"fullname": "fast_forward.encoder.TCTColBERTDocumentEncoder", "modulename": "fast_forward.encoder", "qualname": "TCTColBERTDocumentEncoder", "kind": "class", "doc": "
Document encoder for pre-trained TCT-ColBERT models.
\n\n
Adapted from Pyserini:\nhttps://github.com/castorini/pyserini/blob/310c828211bb3b9528cfd59695184c80825684a2/pyserini/encode/_tct_colbert.py#L27
\n", "bases": "fast_forward.encoder.transformer.TransformerEncoder"}, "fast_forward.index": {"fullname": "fast_forward.index", "modulename": "fast_forward.index", "kind": "module", "doc": "
Indexes
\n\n
Indexes are the core of the Fast-Forward library. In the following, you'll find some snippets how to create and use indexes.
\n\n
Index types
\n\n
Currently, two types of indexes are available:
\n\n
    \n
  • InMemoryIndex: Everything is held in memory entirely.
    • \n
  • OnDiskIndex: Vectors are stored on disk and accessed on demand.
    • \n
\n\n
OnDiskIndexes can be loaded into memory using OnDiskIndex.to_memory.
\n\n
Creating an index
\n\n
The following snippet illustrates how to create an OnDiskIndex object (given a fast_forward.encoder.Encoder, my_query_encoder) and add some vector representations to it:
\n\n
\n
my_index = OnDiskIndex(Path("my_index.h5"), my_query_encoder)\nmy_index.add(\n    my_vectors,  # shape (3, 768)\n    doc_ids=["d1", "d1", "d2"],\n    psg_ids=["d1_p1", "d1_p2", "d2_p1"]\n)\n
\n
\n\n
Here, my_vectors is a Numpy array of shape (3, 768), 768 being the dimensionality of the vector representations. The first two vectors correspond to two passages of the document d1, the third vector corresponds to d2, which has only a single passage. It is also possible to provide either only document IDs or only passage IDs.
\n\n
The index can then be subsequently loaded back using OnDiskIndex.load.
\n\n
Using an index
\n\n
An index can be used to compute semantic re-ranking scores by calling them directly. It requires a fast_forward.Ranking (typically, this comes from a sparse retriever) with the corresponding queries:
\n\n
\n
ranking = Ranking.from_file(Path("/path/to/sparse/run.tsv"), queries)\nresult = my_index(ranking)\n
\n
\n\n
Here, queries is a simple dictionary mapping query IDs to actual queries to be encoded. The resulting ranking, result, has the semantic scores for the query-document (or query-passage) pairs. Afterwards, retrieval and re-ranking scores may be combined (see Rankings).
\n\n
If the input ranking has a large number of queries, one can use the batch_size parameter. The following example processes the ranking in batches of 16 queries each:
\n\n
\n
result = my_index(ranking, batch_size=16)\n
\n
\n\n
Ranking mode
\n\n
Each index has a ranking Mode. The active mode determines the way scores are computed. For example, consider the example index from earlier. Setting the mode to Mode.PASSAGE instructs the index to compute scores on the passage level (and expect passage IDs in the input ranking):
\n\n
\n
my_index.mode = Mode.PASSAGE\n
\n
\n\n
Similarly, the index can return document IDs, where the score of a document computes as
\n\n
    \n
  • the highest score of its passages (Mode.MAXP),
    • \n
  • the score of the its first passage (Mode.FIRSTP) or
    • \n
  • the average score of all its passages (Mode.AVEP).
    • \n
\n\n
Early stopping
\n\n
Early stopping is a technique to limit the number of index look-ups. This can be beneficial for OnDiskIndexes, especially when the disk is slow. For early stopping, a relatively small cut-off depth (e.g., 10) is required, and it is mostly helpful when a large number of candidates are to be re-ranked. More information can be found in the paper. Note that the implementation here differs slightly from the algorithm in the paper, as the early stopping criterion is only computed at depths that are specified via the early_stopping_depths parameter for performance reasons.
\n\n
The following snippet demonstrates early stopping with
\n\n
    \n
  • a cut-off depth of 5,
    • \n
  • interpolation parameter 0.2,
    • \n
  • depths 400 and 5000.
    • \n
\n\n
\n
result = my_index(\n    ranking,\n    early_stopping=5,\n    early_stopping_alpha=0.2,\n    early_stopping_depths=(400, 5000),\n)\n
\n
\n\n
Specifically, in the first step, the top-400 documents/passages for each query are scored. Afterwards, the top-5000 documents/passages are scored only for those queries that do not meet the early stopping criterion yet.
\n"}, "fast_forward.index.Index": {"fullname": "fast_forward.index.Index", "modulename": "fast_forward.index", "qualname": "Index", "kind": "class", "doc": "
Abstract base class for Fast-Forward indexes.
\n", "bases": "abc.ABC"}, "fast_forward.index.Index.__init__": {"fullname": "fast_forward.index.Index.__init__", "modulename": "fast_forward.index", "qualname": "Index.__init__", "kind": "function", "doc": "
Create an index.
\n\n
Parameters
\n\n
    \n
  • query_encoder:  The query encoder to use.
    • \n
  • quantizer:  The quantizer to use.
    • \n
  • mode:  The ranking mode.
    • \n
  • encoder_batch_size:  The encoder batch size.
    • \n
\n", "signature": "(\tquery_encoder: fast_forward.encoder.base.Encoder | None = None,\tquantizer: fast_forward.quantizer.base.Quantizer | None = None,\tmode: fast_forward.index.base.Mode = <Mode.MAXP: 2>,\tencoder_batch_size: int = 32)"}, "fast_forward.index.Index.mode": {"fullname": "fast_forward.index.Index.mode", "modulename": "fast_forward.index", "qualname": "Index.mode", "kind": "variable", "doc": "
Return the ranking mode.
\n\n
Returns
\n\n
\n  
The ranking mode.
\n
\n", "annotation": ": fast_forward.index.base.Mode"}, "fast_forward.index.Index.encode_queries": {"fullname": "fast_forward.index.Index.encode_queries", "modulename": "fast_forward.index", "qualname": "Index.encode_queries", "kind": "function", "doc": "
Encode queries.
\n\n
Parameters
\n\n
    \n
  • queries:  The queries to encode.
    • \n
\n\n
Raises
\n\n
    \n
  • RuntimeError:  When no query encoder exists.
    • \n
\n\n
Returns
\n\n
\n  
The query representations.
\n
\n", "signature": "(self, queries: Sequence[str]) -> numpy.ndarray:", "funcdef": "def"}, "fast_forward.index.Index.query_encoder": {"fullname": "fast_forward.index.Index.query_encoder", "modulename": "fast_forward.index", "qualname": "Index.query_encoder", "kind": "variable", "doc": "
Return the query encoder if it exists.
\n\n
Returns
\n\n
\n  
The query encoder (if any).
\n
\n", "annotation": ": fast_forward.encoder.base.Encoder | None"}, "fast_forward.index.Index.quantizer": {"fullname": "fast_forward.index.Index.quantizer", "modulename": "fast_forward.index", "qualname": "Index.quantizer", "kind": "variable", "doc": "
Return the quantizer if it exists.
\n\n
Returns
\n\n
\n  
The quantizer (if any).
\n
\n", "annotation": ": fast_forward.quantizer.base.Quantizer | None"}, "fast_forward.index.Index.dim": {"fullname": "fast_forward.index.Index.dim", "modulename": "fast_forward.index", "qualname": "Index.dim", "kind": "variable", "doc": "
Return the dimensionality of the vector index.
\n\n
If no vectors exist, return None.\nIf a quantizer is used, return the dimension of the codes.
\n\n
Returns
\n\n
\n  
The dimensionality (if any).
\n
\n", "annotation": ": int | None"}, "fast_forward.index.Index.doc_ids": {"fullname": "fast_forward.index.Index.doc_ids", "modulename": "fast_forward.index", "qualname": "Index.doc_ids", "kind": "variable", "doc": "
Return all unique document IDs.
\n\n
Returns
\n\n
\n  
The document IDs.
\n
\n", "annotation": ": set[str]"}, "fast_forward.index.Index.psg_ids": {"fullname": "fast_forward.index.Index.psg_ids", "modulename": "fast_forward.index", "qualname": "Index.psg_ids", "kind": "variable", "doc": "
Return all unique passage IDs.
\n\n
Returns
\n\n
\n  
The passage IDs.
\n
\n", "annotation": ": set[str]"}, "fast_forward.index.Index.add": {"fullname": "fast_forward.index.Index.add", "modulename": "fast_forward.index", "qualname": "Index.add", "kind": "function", "doc": "
Add vector representations and corresponding IDs to the index.
\n\n
Only one of doc_ids and psg_ids may be None.\nIndividual IDs in the sequence may also be None, but each vector must have at\nleast one associated ID.\nDocument IDs may have duplicates, passage IDs must be unique.
\n\n
Parameters
\n\n
    \n
  • vectors:  The representations, shape (num_vectors, dim).
    • \n
  • doc_ids:  The corresponding document IDs (may be duplicate).
    • \n
  • psg_ids:  The corresponding passage IDs (must be unique).
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When the number of IDs does not match the number of vectors.
    • \n
  • ValueError: \nWhen the input vector and index dimensionalities do not match.
    • \n
  • ValueError:  When a vector has neither a document nor a passage ID.
    • \n
  • RuntimeError:  When items can't be added to the index for any reason.
    • \n
\n", "signature": "(\tself,\tvectors: numpy.ndarray,\tdoc_ids: Sequence[str | None] | None = None,\tpsg_ids: Sequence[str | None] | None = None) -> None:", "funcdef": "def"}, "fast_forward.index.Index.batch_iter": {"fullname": "fast_forward.index.Index.batch_iter", "modulename": "fast_forward.index", "qualname": "Index.batch_iter", "kind": "function", "doc": "
Iterate over all vectors, document IDs, and passage IDs in batches.
\n\n
IDs may be either strings or None.
\n\n
Parameters
\n\n
    \n
  • batch_size:  Batch size.\n:yield: Batches of vectors, document IDs (if any), passage IDs (if any).
    • \n
\n", "signature": "(\tself,\tbatch_size: int) -> Iterator[tuple[numpy.ndarray, Sequence[str | None], Sequence[str | None]]]:", "funcdef": "def"}, "fast_forward.index.Mode": {"fullname": "fast_forward.index.Mode", "modulename": "fast_forward.index", "qualname": "Mode", "kind": "class", "doc": "
Enum used to set the ranking mode of an index.
\n", "bases": "enum.Enum"}, "fast_forward.index.Mode.PASSAGE": {"fullname": "fast_forward.index.Mode.PASSAGE", "modulename": "fast_forward.index", "qualname": "Mode.PASSAGE", "kind": "variable", "doc": "
\n", "default_value": "<Mode.PASSAGE: 1>"}, "fast_forward.index.Mode.MAXP": {"fullname": "fast_forward.index.Mode.MAXP", "modulename": "fast_forward.index", "qualname": "Mode.MAXP", "kind": "variable", "doc": "
\n", "default_value": "<Mode.MAXP: 2>"}, "fast_forward.index.Mode.FIRSTP": {"fullname": "fast_forward.index.Mode.FIRSTP", "modulename": "fast_forward.index", "qualname": "Mode.FIRSTP", "kind": "variable", "doc": "
\n", "default_value": "<Mode.FIRSTP: 3>"}, "fast_forward.index.Mode.AVEP": {"fullname": "fast_forward.index.Mode.AVEP", "modulename": "fast_forward.index", "qualname": "Mode.AVEP", "kind": "variable", "doc": "
\n", "default_value": "<Mode.AVEP: 4>"}, "fast_forward.index.OnDiskIndex": {"fullname": "fast_forward.index.OnDiskIndex", "modulename": "fast_forward.index", "qualname": "OnDiskIndex", "kind": "class", "doc": "
Fast-Forward index that is read on-demand from disk.
\n\n
Uses HDF5 via h5py under the hood. The max_indexing_size argument works around a\nh5py limitation.
\n", "bases": "fast_forward.index.base.Index"}, "fast_forward.index.OnDiskIndex.__init__": {"fullname": "fast_forward.index.OnDiskIndex.__init__", "modulename": "fast_forward.index", "qualname": "OnDiskIndex.__init__", "kind": "function", "doc": "
Create an index on disk.
\n\n
Parameters
\n\n
    \n
  • index_file:  The index file to create (or overwrite).
    • \n
  • query_encoder:  The query encoder.
    • \n
  • quantizer:  The quantizer to use.
    • \n
  • mode:  The ranking mode.
    • \n
  • encoder_batch_size:  Batch size for the query encoder.
    • \n
  • init_size:  Initial size to allocate (number of vectors).
    • \n
  • resize_min_val:  Minimum number of vectors to increase index size by.
    • \n
  • hdf5_chunk_size:  Override chunk size used by HDF5.
    • \n
  • max_id_length: \nMaximum length of document and passage IDs (number of characters).
    • \n
  • overwrite:  Overwrite index file if it exists.
    • \n
  • max_indexing_size: \nMaximum number of vectors to retrieve from the HDF5 dataset at once.
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When the file exists and overwrite=False.
    • \n
\n", "signature": "(\tindex_file: pathlib.Path,\tquery_encoder: fast_forward.encoder.base.Encoder | None = None,\tquantizer: fast_forward.quantizer.base.Quantizer | None = None,\tmode: fast_forward.index.base.Mode = <Mode.MAXP: 2>,\tencoder_batch_size: int = 32,\tinit_size: int = 16384,\tresize_min_val: int = 1024,\thdf5_chunk_size: int | None = None,\tmax_id_length: int = 8,\toverwrite: bool = False,\tmax_indexing_size: int = 1024)"}, "fast_forward.index.OnDiskIndex.to_memory": {"fullname": "fast_forward.index.OnDiskIndex.to_memory", "modulename": "fast_forward.index", "qualname": "OnDiskIndex.to_memory", "kind": "function", "doc": "
Load the index entirely into memory.
\n\n
Parameters
\n\n
    \n
  • batch_size:  Use batches instead of adding all vectors at once.
    • \n
\n\n
Returns
\n\n
\n  
The loaded index.
\n
\n", "signature": "(\tself,\tbatch_size: int | None = None) -> fast_forward.index.memory.InMemoryIndex:", "funcdef": "def"}, "fast_forward.index.OnDiskIndex.load": {"fullname": "fast_forward.index.OnDiskIndex.load", "modulename": "fast_forward.index", "qualname": "OnDiskIndex.load", "kind": "function", "doc": "
Open an existing index on disk.
\n\n
Parameters
\n\n
    \n
  • index_file:  The index file to open.
    • \n
  • query_encoder:  The query encoder.
    • \n
  • mode:  The ranking mode.
    • \n
  • encoder_batch_size:  Batch size for the query encoder.
    • \n
  • resize_min_val:  Minimum value to increase index size by.
    • \n
  • max_indexing_size: \nMaximum number of vectors to retrieve from the HDF5 dataset at once.
    • \n
\n\n
Returns
\n\n
\n  
The index.
\n
\n", "signature": "(\tcls,\tindex_file: pathlib.Path,\tquery_encoder: fast_forward.encoder.base.Encoder | None = None,\tmode: fast_forward.index.base.Mode = <Mode.MAXP: 2>,\tencoder_batch_size: int = 32,\tresize_min_val: int = 1024,\tmax_indexing_size: int = 1024) -> fast_forward.index.disk.OnDiskIndex:", "funcdef": "def"}, "fast_forward.index.InMemoryIndex": {"fullname": "fast_forward.index.InMemoryIndex", "modulename": "fast_forward.index", "qualname": "InMemoryIndex", "kind": "class", "doc": "
Fast-Forward index that is held entirely in memory.
\n", "bases": "fast_forward.index.base.Index"}, "fast_forward.index.InMemoryIndex.__init__": {"fullname": "fast_forward.index.InMemoryIndex.__init__", "modulename": "fast_forward.index", "qualname": "InMemoryIndex.__init__", "kind": "function", "doc": "
Create an index in memory.
\n\n
Parameters
\n\n
    \n
  • query_encoder:  The query encoder to use.
    • \n
  • quantizer:  The quantizer to use.
    • \n
  • mode:  The ranking mode.
    • \n
  • encoder_batch_size:  Batch size for the query encoder.
    • \n
  • init_size:  Initial index size (number of vectors).
    • \n
  • alloc_size:  Shard size (number of vectors) allocated when index is full.
    • \n
\n", "signature": "(\tquery_encoder: fast_forward.encoder.base.Encoder | None = None,\tquantizer: fast_forward.quantizer.base.Quantizer | None = None,\tmode: fast_forward.index.base.Mode = <Mode.MAXP: 2>,\tencoder_batch_size: int = 32,\tinit_size: int = 16384,\talloc_size: int = 16384)"}, "fast_forward.index.InMemoryIndex.consolidate": {"fullname": "fast_forward.index.InMemoryIndex.consolidate", "modulename": "fast_forward.index", "qualname": "InMemoryIndex.consolidate", "kind": "function", "doc": "
Combine all shards of the index in one contiguous section in the memory.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "fast_forward.quantizer": {"fullname": "fast_forward.quantizer", "modulename": "fast_forward.quantizer", "kind": "module", "doc": "
Quantization
\n\n
Fast-Forward indexes support (product) quantization as a means of compressing an index. The Quantizer class defines the interface for quantizers to implement. Currently, the following quantizers are available:
\n\n\n\n
Quantizers must be trained before they are used with the corresponding Fast-Forward index. The typical workflow is as follows:
\n\n
\n
from pathlib import Path\n\nimport numpy as np\n\nfrom fast_forward.index import OnDiskIndex\nfrom fast_forward.quantizer import NanoPQ\n\n# in practice, a subset of the encoded corpus should be used as training vectors\ntraining_vectors = np.random.normal(size=(2**10, 768)).astype(np.float32)\n\nquantizer = NanoPQ(M=8, Ks=256)\nquantizer.fit(training_vectors)\n\nindex = OnDiskIndex(Path("my_index.h5"), quantizer=quantizer)\n
\n
\n"}, "fast_forward.quantizer.Quantizer": {"fullname": "fast_forward.quantizer.Quantizer", "modulename": "fast_forward.quantizer", "qualname": "Quantizer", "kind": "class", "doc": "
Base class for quantizers.
\n", "bases": "abc.ABC"}, "fast_forward.quantizer.Quantizer.set_attached": {"fullname": "fast_forward.quantizer.Quantizer.set_attached", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.set_attached", "kind": "function", "doc": "
Set the quantizer as attached, preventing calls to Quantizer.fit.
\n\n
Raises
\n\n
    \n
  • RuntimeError:  When the quantizer has not been fit.
    • \n
\n", "signature": "(self) -> None:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.fit": {"fullname": "fast_forward.quantizer.Quantizer.fit", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.fit", "kind": "function", "doc": "
Fit (train) the quantizer.
\n\n
Quantizers can only be trained before being attached to an index in order to\navoid inconsistencies.
\n\n
Parameters
\n\n
    \n
  • vectors:  The training vectors.
    • \n
  • **kwargs:  Arguments specific to the quantizer.
    • \n
\n\n
Raises
\n\n
    \n
  • RuntimeError:  When the quantizer is aready attached to an index.
    • \n
\n", "signature": "(self, vectors: numpy.ndarray, **kwargs: Any) -> None:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.dtype": {"fullname": "fast_forward.quantizer.Quantizer.dtype", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.dtype", "kind": "variable", "doc": "
The data type of the codes produced by this quantizer.
\n\n
Returns
\n\n
\n  
The data type of the codes.
\n
\n", "annotation": ": numpy.dtype"}, "fast_forward.quantizer.Quantizer.dims": {"fullname": "fast_forward.quantizer.Quantizer.dims", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.dims", "kind": "variable", "doc": "
The dimensions before and after quantization.
\n\n
May return None values before the quantizer is trained.
\n\n
Returns
\n\n
\n  
Dimension of the original vectors and dimension of the codes.
\n
\n", "annotation": ": tuple[int | None, int | None]"}, "fast_forward.quantizer.Quantizer.encode": {"fullname": "fast_forward.quantizer.Quantizer.encode", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.encode", "kind": "function", "doc": "
Encode a batch of vectors.
\n\n
Parameters
\n\n
    \n
  • vectors:  The vectors to be encoded.
    • \n
\n\n
Returns
\n\n
\n  
The codes corresponding to the vectors.
\n
\n", "signature": "(self, vectors: numpy.ndarray) -> numpy.ndarray:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.decode": {"fullname": "fast_forward.quantizer.Quantizer.decode", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.decode", "kind": "function", "doc": "
Decode a batch of codes to obtain approximate vector representations.
\n\n
Parameters
\n\n
    \n
  • codes:  The codes to be decoded.
    • \n
\n\n
Raises
\n\n
    \n
  • RuntimeError:  When the quantizer has not been fit.
    • \n
\n\n
Returns
\n\n
\n  
The approximated vectors.
\n
\n", "signature": "(self, codes: numpy.ndarray) -> numpy.ndarray:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.serialize": {"fullname": "fast_forward.quantizer.Quantizer.serialize", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.serialize", "kind": "function", "doc": "
Return a serialized representation of the quantizer.
\n\n
This representations is used to be stored in the index.
\n\n
Returns
\n\n
\n  
The serialized quantizer.
\n
\n", "signature": "(\tself) -> tuple[Mapping[str, str | bool | float], Mapping[str, str | bool | float], Mapping[str, numpy.ndarray]]:", "funcdef": "def"}, "fast_forward.quantizer.Quantizer.deserialize": {"fullname": "fast_forward.quantizer.Quantizer.deserialize", "modulename": "fast_forward.quantizer", "qualname": "Quantizer.deserialize", "kind": "function", "doc": "
Reconstruct a serialized quantizer.
\n\n
Parameters
\n\n
    \n
  • meta:  The quantizer metadata.
    • \n
  • attributes:  The quantizer attributes.
    • \n
  • data:  The quantizer data.
    • \n
\n\n
Returns
\n\n
\n  
The loaded quantizer.
\n
\n", "signature": "(\tcls,\tmeta: Mapping[str, str | bool | float],\tattributes: Mapping[str, str | bool | float],\tdata: Mapping[str, numpy.ndarray]) -> fast_forward.quantizer.base.Quantizer:", "funcdef": "def"}, "fast_forward.quantizer.NanoPQ": {"fullname": "fast_forward.quantizer.NanoPQ", "modulename": "fast_forward.quantizer", "qualname": "NanoPQ", "kind": "class", "doc": "
Product quantizer that uses the nanopq library.
\n\n
More information is available\nhere.
\n", "bases": "fast_forward.quantizer.base.Quantizer"}, "fast_forward.quantizer.NanoPQ.__init__": {"fullname": "fast_forward.quantizer.NanoPQ.__init__", "modulename": "fast_forward.quantizer", "qualname": "NanoPQ.__init__", "kind": "function", "doc": "
Instantiate a nanopq product quantizer.
\n\n
Parameters
\n\n
    \n
  • M:  The number of subspaces.
    • \n
  • Ks:  The number of codewords per subspace.
    • \n
  • metric:  The metric to use.
    • \n
  • verbose:  Enable verbosity.
    • \n
\n", "signature": "(M: int, Ks: int, metric: str = 'dot', verbose: bool = False)"}, "fast_forward.quantizer.NanoOPQ": {"fullname": "fast_forward.quantizer.NanoOPQ", "modulename": "fast_forward.quantizer", "qualname": "NanoOPQ", "kind": "class", "doc": "
Optimized product quantizer that uses the nanopq library.
\n\n
More information is available\nhere.
\n", "bases": "fast_forward.quantizer.base.Quantizer"}, "fast_forward.quantizer.NanoOPQ.__init__": {"fullname": "fast_forward.quantizer.NanoOPQ.__init__", "modulename": "fast_forward.quantizer", "qualname": "NanoOPQ.__init__", "kind": "function", "doc": "
Instantiate a nanopq optimized product quantizer.
\n\n
Parameters
\n\n
    \n
  • M:  The number of subspaces.
    • \n
  • Ks:  The number of codewords per subspace.
    • \n
  • metric:  The metric to use.
    • \n
  • verbose:  Enable verbosity.
    • \n
\n", "signature": "(M: int, Ks: int, metric: str = 'dot', verbose: bool = False)"}, "fast_forward.util": {"fullname": "fast_forward.util", "modulename": "fast_forward.util", "kind": "module", "doc": "
Indexing
\n\n
The Indexer class is a utility for indexing collections or adding pre-computed vectors to an index.
\n\n
If the size of the collection is known in advance, it can be specified when the index is created in order to avoid subsequent resizing operations:
\n\n
\n
my_index = OnDiskIndex(Path("my_index.h5"), init_size=1000000)\n
\n
\n\n
In order to index a corpus, a document/passage encoder is required, for example:
\n\n
\n
doc_encoder = TCTColBERTDocumentEncoder(\n    "castorini/tct_colbert-msmarco",\n    device="cuda:0" if torch.cuda.is_available() else "cpu",\n)\n
\n
\n\n
The indexer can be created as follows:
\n\n
\n
indexer = Indexer(my_index, doc_encoder, encoder_batch_size=8)\n
\n
\n\n
Indexer.from_dicts consumes an iterator that yields dictionaries:
\n\n
\n
def docs_iter():\n    for doc in my_corpus:\n        yield {"doc_id": doc.get_doc_id(), "text": doc.get_text()}\n\nindexer.from_dicts(docs_iter())\n
\n
\n\n
Additionally, indexers can be used to automatically fit and attach a quantizer during indexing. In this example, a quantized version (target_index) of an existing index (source_index) is created:
\n\n
\n
from fast_forward.quantizer import NanoPQ\n\nIndexer(\n    target_index,\n    quantizer=NanoPQ(8, 256),\n    batch_size=2**16,\n    quantizer_fit_batches=2,\n).from_index(source_index)\n
\n
\n\n
Here, the first two batches (of size $2^{16}$) are buffered and used to fit the quantizer.
\n\n
Sequential coalescing
\n\n
The sequential coalescing algorithm is a compression technique for indexes with multiple representations per document. More information can be found in the paper. It is implemented as fast_forward.util.create_coalesced_index. Example usage:
\n\n
\n
my_index = OnDiskIndex.load(Path("/path/to/index.h5"))\ncoalesced_index = InMemoryIndex(mode=Mode.MAXP)\ncreate_coalesced_index(my_index, coalesced_index, 0.3)\n
\n
\n"}, "fast_forward.util.Indexer": {"fullname": "fast_forward.util.Indexer", "modulename": "fast_forward.util", "qualname": "Indexer", "kind": "class", "doc": "
Utility class for indexing collections.
\n"}, "fast_forward.util.Indexer.__init__": {"fullname": "fast_forward.util.Indexer.__init__", "modulename": "fast_forward.util", "qualname": "Indexer.__init__", "kind": "function", "doc": "
Create an indexer.
\n\n
Optionally, a quantizer can automatically be fit on the first batch(es) to be\nindexed. This requires the index to be empty.\nIf a quantizer is provided, the first batch(es) will be buffered and used to fit\nthe quantizer.
\n\n
Parameters
\n\n
    \n
  • index:  The target index.
    • \n
  • encoder:  Document/passage encoder.
    • \n
  • encoder_batch_size:  Encoder batch size.
    • \n
  • batch_size:  How many vectors to add to the index at once.
    • \n
  • quantizer:  A quantizer to be fit and attached to the index.
    • \n
  • quantizer_fit_batches:  How many batches to use to fit the quantizer.
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When a quantizer is provided that has already been fit.
    • \n
  • ValueError:  When a quantizer is provided and the index is not empty.
    • \n
\n", "signature": "(\tindex: fast_forward.index.base.Index,\tencoder: fast_forward.encoder.base.Encoder | None = None,\tencoder_batch_size: int = 128,\tbatch_size: int = 65536,\tquantizer: fast_forward.quantizer.base.Quantizer | None = None,\tquantizer_fit_batches: int = 1)"}, "fast_forward.util.Indexer.from_dicts": {"fullname": "fast_forward.util.Indexer.from_dicts", "modulename": "fast_forward.util", "qualname": "Indexer.from_dicts", "kind": "function", "doc": "
Index data from dictionaries.
\n\n
Parameters
\n\n
    \n
  • data:  An iterable of the dictionaries.
    • \n
\n", "signature": "(self, data: Iterable[fast_forward.util.indexer.IndexingDict]) -> None:", "funcdef": "def"}, "fast_forward.util.Indexer.from_index": {"fullname": "fast_forward.util.Indexer.from_index", "modulename": "fast_forward.util", "qualname": "Indexer.from_index", "kind": "function", "doc": "
Transfer vectors and IDs from another index.
\n\n
If the source index uses quantized representations, the vectors are\nreconstructed first.
\n\n
Parameters
\n\n
    \n
  • index:  The source index.
    • \n
\n", "signature": "(self, index: fast_forward.index.base.Index) -> None:", "funcdef": "def"}, "fast_forward.util.IndexingDict": {"fullname": "fast_forward.util.IndexingDict", "modulename": "fast_forward.util", "qualname": "IndexingDict", "kind": "class", "doc": "
Dictionary that represents a document/passage.
\n\n
Consumed by Indexer.from_dicts.
\n", "bases": "typing.TypedDict"}, "fast_forward.util.IndexingDict.text": {"fullname": "fast_forward.util.IndexingDict.text", "modulename": "fast_forward.util", "qualname": "IndexingDict.text", "kind": "variable", "doc": "
\n", "annotation": ": str"}, "fast_forward.util.IndexingDict.doc_id": {"fullname": "fast_forward.util.IndexingDict.doc_id", "modulename": "fast_forward.util", "qualname": "IndexingDict.doc_id", "kind": "variable", "doc": "
\n", "annotation": ": str | None"}, "fast_forward.util.IndexingDict.psg_id": {"fullname": "fast_forward.util.IndexingDict.psg_id", "modulename": "fast_forward.util", "qualname": "IndexingDict.psg_id", "kind": "variable", "doc": "
\n", "annotation": ": str | None"}, "fast_forward.util.to_ir_measures": {"fullname": "fast_forward.util.to_ir_measures", "modulename": "fast_forward.util", "qualname": "to_ir_measures", "kind": "function", "doc": "
Return a ranking as a data frame suitable for the ir-measures library.
\n\n
Parameters
\n\n
    \n
  • ranking:  The input ranking.
    • \n
\n\n
Returns
\n\n
\n  
The data frame.
\n
\n", "signature": "(ranking: fast_forward.ranking.Ranking) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "fast_forward.util.cos_dist": {"fullname": "fast_forward.util.cos_dist", "modulename": "fast_forward.util", "qualname": "cos_dist", "kind": "function", "doc": "
Cosine distance of two vectors.
\n\n
Parameters
\n\n
    \n
  • a:  First vector.
    • \n
  • b:  Second vector.
    • \n
\n\n
Returns
\n\n
\n  
Cosine distance.
\n
\n", "signature": "(a: numpy.ndarray, b: numpy.ndarray) -> float:", "funcdef": "def"}, "fast_forward.util.create_coalesced_index": {"fullname": "fast_forward.util.create_coalesced_index", "modulename": "fast_forward.util", "qualname": "create_coalesced_index", "kind": "function", "doc": "
Create a compressed index using sequential coalescing.
\n\n
Parameters
\n\n
    \n
  • source_index:  Source index (containing multiple vectors for each document).
    • \n
  • target_index:  Target index (must be empty).
    • \n
  • delta:  The coalescing threshold.
    • \n
  • distance_function:  The distance function.
    • \n
  • batch_size:  Use batches instead of adding all vectors at the end.
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError:  When the target index is not empty.
    • \n
\n", "signature": "(\tsource_index: fast_forward.index.base.Index,\ttarget_index: fast_forward.index.base.Index,\tdelta: float,\tdistance_function: Callable[[numpy.ndarray, numpy.ndarray], float] = <function cos_dist>,\tbatch_size: int | None = None) -> None:", "funcdef": "def"}, "fast_forward.util.pyterrier": {"fullname": "fast_forward.util.pyterrier", "modulename": "fast_forward.util.pyterrier", "kind": "module", "doc": "
PyTerrier transformers
\n\n
Fast-Forward indexes can seamlessly be integrated into PyTerrier pipelines using the transformers provided in fast_forward.util.pyterrier. Specifically, a re-ranking pipeline might look like this, given that my_index is a Fast-Forward index of the MS MARCO passage corpus:
\n\n
\n
bm25 = pt.BatchRetrieve.from_dataset(\n    "msmarco_passage", variant="terrier_stemmed", wmodel="BM25"\n)\n\nff_pl = bm25 % 5000 >> FFScore(my_index) >> FFInterpolate(0.2)\n
\n
\n"}, "fast_forward.util.pyterrier.FFScore": {"fullname": "fast_forward.util.pyterrier.FFScore", "modulename": "fast_forward.util.pyterrier", "qualname": "FFScore", "kind": "class", "doc": "
PyTerrier transformer that computes scores using a Fast-Forward index.
\n", "bases": "pyterrier.transformer.Transformer"}, "fast_forward.util.pyterrier.FFScore.__init__": {"fullname": "fast_forward.util.pyterrier.FFScore.__init__", "modulename": "fast_forward.util.pyterrier", "qualname": "FFScore.__init__", "kind": "function", "doc": "
Create an FFScore transformer.
\n\n
Parameters
\n\n
    \n
  • index:  The Fast-Forward index.
    • \n
\n", "signature": "(index: fast_forward.index.base.Index)"}, "fast_forward.util.pyterrier.FFScore.transform": {"fullname": "fast_forward.util.pyterrier.FFScore.transform", "modulename": "fast_forward.util.pyterrier", "qualname": "FFScore.transform", "kind": "function", "doc": "
Compute the scores for all query-document pairs in the data frame.
\n\n
The previous scores are moved to the \"score_0\" column.
\n\n
Parameters
\n\n
    \n
  • inp:  The PyTerrier data frame.
    • \n
\n\n
Returns
\n\n
\n  
A data frame with the computed scores.
\n
\n", "signature": "(self, inp: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "fast_forward.util.pyterrier.FFInterpolate": {"fullname": "fast_forward.util.pyterrier.FFInterpolate", "modulename": "fast_forward.util.pyterrier", "qualname": "FFInterpolate", "kind": "class", "doc": "
PyTerrier transformer that interpolates scores computed by FFScore.
\n", "bases": "pyterrier.transformer.Transformer"}, "fast_forward.util.pyterrier.FFInterpolate.__init__": {"fullname": "fast_forward.util.pyterrier.FFInterpolate.__init__", "modulename": "fast_forward.util.pyterrier", "qualname": "FFInterpolate.__init__", "kind": "function", "doc": "
Create an FFInterpolate transformer.
\n\n
Parameters
\n\n
    \n
  • alpha:  The interpolation parameter.
    • \n
\n", "signature": "(alpha: float)"}, "fast_forward.util.pyterrier.FFInterpolate.alpha": {"fullname": "fast_forward.util.pyterrier.FFInterpolate.alpha", "modulename": "fast_forward.util.pyterrier", "qualname": "FFInterpolate.alpha", "kind": "variable", "doc": "
\n"}, "fast_forward.util.pyterrier.FFInterpolate.transform": {"fullname": "fast_forward.util.pyterrier.FFInterpolate.transform", "modulename": "fast_forward.util.pyterrier", "qualname": "FFInterpolate.transform", "kind": "function", "doc": "
Interpolate the scores as alpha * score_0 + (1 - alpha) * score.
\n\n
Parameters
\n\n
    \n
  • inp:  The PyTerrier data frame.
    • \n
\n\n
Returns
\n\n
\n  
A data frame with the interpolated scores.
\n
\n", "signature": "(self, inp: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}}, "docInfo": {"fast_forward": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 1479}, "fast_forward.Ranking": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "fast_forward.Ranking.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 162, "bases": 0, "doc": 116}, "fast_forward.Ranking.name": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "fast_forward.Ranking.has_queries": {"qualname": 3, "fullname": 5, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 22}, "fast_forward.Ranking.q_ids": {"qualname": 3, "fullname": 5, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 38}, "fast_forward.Ranking.attach_queries": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 47, "bases": 0, "doc": 59}, "fast_forward.Ranking.normalize": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 45}, "fast_forward.Ranking.cut": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 35, "bases": 0, "doc": 53}, "fast_forward.Ranking.interpolate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 77, "bases": 0, "doc": 79}, "fast_forward.Ranking.rr_scores": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 42, "bases": 0, "doc": 64}, "fast_forward.Ranking.save": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 25}, "fast_forward.Ranking.from_run": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 154, "bases": 0, "doc": 74}, "fast_forward.Ranking.from_file": {"qualname": 3, 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     // mirrored in build-search-index.js (part 1)
     // Also split on html tags. this is a cheap heuristic, but good enough.