Package redisearch Documentation

Overview

redisearch-py is a python search engine library that utilizes the RediSearch Redis Module API.

It is the "official" client of redisearch, and should be regarded as its canonical client implementation.

The source code can be found at http://github.com/RedisLabs/redisearch-py

Example: Using the Python Client

from redisearch import Client, TextField, NumericField, Query

# Creating a client with a given index name
client = Client('myIndex')

# Creating the index definition and schema
client.create_index([TextField('title', weight=5.0), TextField('body')])

# Indexing a document
client.add_document('doc1', title = 'RediSearch', body = 'Redisearch impements a search engine on top of redis')

# Simple search
res = client.search("search engine")

# the result has the total number of results, and a list of documents
print res.total # "1"
print res.docs[0].title 

# Searching with snippets
res = client.search("search engine", snippet_sizes = {'body': 50})

# Searching with complext parameters:
q = Query("search engine").verbatim().no_content().paging(0,5)
res = client.search(q)

Example: Using the Auto Completer Client:

# Using the auto-completer
ac = AutoCompleter('ac')

# Adding some terms
ac.add_suggestions(Suggestion('foo', 5.0), Suggestion('bar', 1.0))

# Getting suggestions
suggs = ac.get_suggestions('goo') # returns nothing

suggs = ac.get_suggestions('goo', fuzzy = True) # returns ['foo']

Installing

  1. Install redis 4.0 RC2 or above

  2. Install RediSearch

  3. Install the python client

$ pip install redisearch

Class AutoCompleter

A client to RediSearch's AutoCompleter API

It provides prefix searches with optionally fuzzy matching of prefixes

__init__

def __init__(self, key, host='localhost', port=6379, conn=None)

Create a new AutoCompleter client for the given key, and optional host and port

If conn is not None, we employ an already existing redis connection

add_suggestions

def add_suggestions(self, *suggestions, **kwargs)

Add suggestion terms to the AutoCompleter engine. Each suggestion has a score and string.

If kwargs['increment'] is true and the terms are already in the server's dictionary, we increment their scores

delete

def delete(self, string)

Delete a string from the AutoCompleter index. Returns 1 if the string was found and deleted, 0 otherwise

get_suggestions

def get_suggestions(self, prefix, fuzzy=False, num=10, with_scores=False, with_payloads=False)

Get a list of suggestions from the AutoCompleter, for a given prefix

Parameters:

  • prefix: the prefix we are searching. Must be valid ascii or utf-8
  • fuzzy: If set to true, the prefix search is done in fuzzy mode. NOTE: Running fuzzy searches on short (<3 letters) prefixes can be very slow, and even scan the entire index.
  • with_scores: if set to true, we also return the (refactored) score of each suggestion. This is normally not needed, and is NOT the original score inserted into the index
  • with_payloads: Return suggestion payloads
  • num: The maximum number of results we return. Note that we might return less. The algorithm trims irrelevant suggestions.

Returns a list of Suggestion objects. If with_scores was False, the score of all suggestions is 1.

len

def len(self)

Return the number of entries in the AutoCompleter index

Class Client

A client for the RediSearch module. It abstracts the API of the module and lets you just use the engine

__init__

def __init__(self, index_name, host='localhost', port=6379, conn=None)

Create a new Client for the given index_name, and optional host and port

If conn is not None, we employ an already existing redis connection

add_document

def add_document(self, doc_id, nosave=False, score=1.0, payload=None, replace=False, partial=False, **fields)

Add a single document to the index.

Parameters

  • doc_id: the id of the saved document.
  • nosave: if set to true, we just index the document, and don't save a copy of it. This means that searches will just return ids.
  • score: the document ranking, between 0.0 and 1.0
  • payload: optional inner-index payload we can save for fast access in scoring functions
  • replace: if True, and the document already is in the index, we perform an update and reindex the document
  • partial: if True, the fields specified will be added to the existing document. This has the added benefit that any fields specified with no_index will not be reindexed again. Implies replace
  • fields kwargs dictionary of the document fields to be saved and/or indexed. NOTE: Geo points shoule be encoded as strings of "lon,lat"

batch_indexer

def batch_indexer(self, chunk_size=100)

Create a new batch indexer from the client with a given chunk size

create_index

def create_index(self, fields, no_term_offsets=False, no_field_flags=False, stopwords=None)

Create the search index. Creating an existing index juts updates its properties

Parameters:

  • fields: a list of TextField or NumericField objects
  • no_term_offsets: If true, we will not save term offsets in the index
  • no_field_flags: If true, we will not save field flags that allow searching in specific fields
  • stopwords: If not None, we create the index with this custom stopword list. The list can be empty

delete_document

def delete_document(self, doc_id, conn=None)

Delete a document from index Returns 1 if the document was deleted, 0 if not

drop_index

def drop_index(self)

Drop the index if it exists

explain

def explain(self, query)

info

def info(self)

Get info an stats about the the current index, including the number of documents, memory consumption, etc

load_document

def load_document(self, id)

Load a single document by id

def search(self, query)

Search the index for a given query, and return a result of documents

Parameters

  • query: the search query. Either a text for simple queries with default parameters, or a Query object for complex queries. See RediSearch's documentation on query format
  • snippet_sizes: A dictionary of {field: snippet_size} used to trim and format the result. e.g.e {'body': 500}

Class BatchIndexer

A batch indexer allows you to automatically batch document indexeing in pipelines, flushing it every N documents.

__init__

def __init__(self, client, chunk_size=1000)

add_document

def add_document(self, doc_id, nosave=False, score=1.0, payload=None, replace=False, partial=False, **fields)

Add a document to the batch query

commit

def commit(self)

Manually commit and flush the batch indexing query

Class Document

Represents a single document in a result set

__init__

def __init__(self, id, payload=None, **fields)

Class GeoField

GeoField is used to define a geo-indexing field in a schema defintion

__init__

def __init__(self, name)

redis_args

def redis_args(self)

Class GeoFilter

None

__init__

def __init__(self, field, lon, lat, radius, unit='km')

Class NumericField

NumericField is used to define a numeric field in a schema defintion

__init__

def __init__(self, name, sortable=False, no_index=False)

redis_args

def redis_args(self)

Class NumericFilter

None

__init__

def __init__(self, field, minval, maxval, minExclusive=False, maxExclusive=False)

Class Query

Query is used to build complex queries that have more parameters than just the query string. The query string is set in the constructor, and other options have setter functions.

The setter functions return the query object, so they can be chained, i.e. Query("foo").verbatim().filter(...) etc.

__init__

def __init__(self, query_string)

Create a new query object. The query string is set in the constructor, and other options have setter functions.

add_filter

def add_filter(self, flt)

Add a numeric or geo filter to the query. Currently only one of each filter is supported by the engine

  • flt: A NumericFilter or GeoFilter object, used on a corresponding field

get_args

def get_args(self)

Format the redis arguments for this query and return them

highlight

def highlight(self, fields=None, tags=None)

Apply specified markup to matched term(s) within the returned field(s)

  • fields If specified then only those mentioned fields are highlighted, otherwise all fields are highlighted
  • tags A list of two strings to surround the match.

in_order

def in_order(self)

Match only documents where the query terms appear in the same order in the document. i.e. for the query 'hello world', we do not match 'world hello'

limit_fields

def limit_fields(self, *fields)

Limit the search to specific TEXT fields only

  • fields: A list of strings, case sensitive field names from the defined schema

limit_ids

def limit_ids(self, *ids)

Limit the results to a specific set of pre-known document ids of any length

no_content

def no_content(self)

Set the query to only return ids and not the document content

no_stopwords

def no_stopwords(self)

Prevent the query from being filtered for stopwords. Only useful in very big queries that you are certain contain no stopwords.

paging

def paging(self, offset, num)

Set the paging for the query (defaults to 0..10).

  • offset: Paging offset for the results. Defaults to 0
  • num: How many results do we want

query_string

def query_string(self)

Return the query string of this query only

return_fields

def return_fields(self, *fields)

Only return values from these fields

slop

def slop(self, slop)

Allow a masimum of N intervening non matched terms between phrase terms (0 means exact phrase)

sort_by

def sort_by(self, field, asc=True)

Add a sortby field to the query

  • field - the name of the field to sort by
  • asc - when True, sorting will be done in asceding order

summarize

def summarize(self, fields=None, context_len=None, num_frags=None, sep=None)

Return an abridged format of the field, containing only the segments of the field which contain the matching term(s).

If fields is specified, then only the mentioned fields are summarized; otherwise all results are summarized.

Server side defaults are used for each option (except fields) if not specified

  • fields List of fields to summarize. All fields are summarized if not specified
  • context_len Amount of context to include with each fragment
  • num_frags Number of fragments per document
  • sep Separator string to separate fragments

verbatim

def verbatim(self)

Set the query to be verbatim, i.e. use no query expansion or stemming

with_payloads

def with_payloads(self)

Ask the engine to return document payloads

Class Result

Represents the result of a search query, and has an array of Document objects

__init__

def __init__(self, res, hascontent, duration=0, has_payload=False)
  • snippets: An optional dictionary of the form {field: snippet_size} for snippet formatting

Class SortbyField

None

__init__

def __init__(self, field, asc=True)

Class Suggestion

Represents a single suggestion being sent or returned from the auto complete server

__init__

def __init__(self, string, score=1.0, payload=None)

Class TagField

TagField is a tag-indexing field with simpler compression and tokenization. See http://redisearch.io/Tags/

__init__

def __init__(self, name, separator=',', no_index=False)

redis_args

def redis_args(self)

Class TextField

TextField is used to define a text field in a schema definition

__init__

def __init__(self, name, weight=1.0, sortable=False, no_stem=False, no_index=False)

redis_args

def redis_args(self)