\n"}, "tantivy.Order": {"fullname": "tantivy.Order", "modulename": "tantivy", "qualname": "Order", "kind": "class", "doc": "Enum representing the direction in which something should be sorted.
\n"}, "tantivy.Order.Asc": {"fullname": "tantivy.Order.Asc", "modulename": "tantivy", "qualname": "Order.Asc", "kind": "variable", "doc": "\n", "default_value": "Order.Asc"}, "tantivy.Order.Desc": {"fullname": "tantivy.Order.Desc", "modulename": "tantivy", "qualname": "Order.Desc", "kind": "variable", "doc": "\n", "default_value": "Order.Desc"}, "tantivy.Schema": {"fullname": "tantivy.Schema", "modulename": "tantivy", "qualname": "Schema", "kind": "class", "doc": "Tantivy schema.
\n\nThe schema is very strict. To build the schema the SchemaBuilder class is\nprovided.
\n"}, "tantivy.SearchResult": {"fullname": "tantivy.SearchResult", "modulename": "tantivy", "qualname": "SearchResult", "kind": "class", "doc": "Object holding a results successful search.
\n"}, "tantivy.SearchResult.count": {"fullname": "tantivy.SearchResult.count", "modulename": "tantivy", "qualname": "SearchResult.count", "kind": "variable", "doc": "How many documents matched the query. Only available if count was set\nto true during the search.
\n"}, "tantivy.SearchResult.hits": {"fullname": "tantivy.SearchResult.hits", "modulename": "tantivy", "qualname": "SearchResult.hits", "kind": "variable", "doc": "The list of tuples that contains the scores and DocAddress of the\nsearch results.
\n"}, "tantivy.Document": {"fullname": "tantivy.Document", "modulename": "tantivy", "qualname": "Document", "kind": "class", "doc": "Tantivy's Document is the object that can be indexed and then searched for.
\n\nDocuments are fundamentally a collection of unordered tuples\n(field_name, value). In this list, one field may appear more than once.
\n\nExample:
\n\n>>> doc = tantivy.Document()\n>>> doc.add_text(\"title\", \"The Old Man and the Sea\")\n>>> doc.add_text(\"body\", (\"He was an old man who fished alone in a \"\n... \"skiff in the Gulf Stream and he had gone \"\n... \"eighty-four days now without taking a fish.\"))\n>>> doc\nDocument(body=[He was an ],title=[The Old Ma])\n
\n\nFor simplicity, it is also possible to build a Document by passing the field\nvalues directly as constructor arguments.
\n\nExample:
\n\n>>> doc = tantivy.Document(title=[\"The Old Man and the Sea\"], body=[\"...\"])\n
\n\nAs syntactic sugar, tantivy also allows the user to pass a single values\nif there is only one. In other words, the following is also legal.
\n\nExample:
\n\n>>> doc = tantivy.Document(title=\"The Old Man and the Sea\", body=\"...\")\n
\n\nFor numeric fields, the [Document] constructor does not have any\ninformation about the type and will try to guess the type.\nTherefore, it is recommended to use the [Document::from_dict()],\n[Document::extract()], or Document::add_*() functions to provide\nexplicit type information.
\n\nExample:
\n\n>>> schema = (\n... SchemaBuilder()\n... .add_unsigned_field(\"unsigned\")\n... .add_integer_field(\"signed\")\n... .add_float_field(\"float\")\n... .build()\n... )\n>>> doc = tantivy.Document.from_dict(\n... {\"unsigned\": 1000, \"signed\": -5, \"float\": 0.4},\n... schema,\n... )\n
\n"}, "tantivy.Document.extend": {"fullname": "tantivy.Document.extend", "modulename": "tantivy", "qualname": "Document.extend", "kind": "function", "doc": "\n", "signature": "(self, /, py_dict, schema=None):", "funcdef": "def"}, "tantivy.Document.from_dict": {"fullname": "tantivy.Document.from_dict", "modulename": "tantivy", "qualname": "Document.from_dict", "kind": "function", "doc": "\n", "signature": "(py_dict, schema=None):", "funcdef": "def"}, "tantivy.Document.to_dict": {"fullname": "tantivy.Document.to_dict", "modulename": "tantivy", "qualname": "Document.to_dict", "kind": "function", "doc": "Returns a dictionary with the different\nfield values.
\n\nIn tantivy, Document can be hold multiple\nvalues for a single field.
\n\nFor this reason, the dictionary, will associate\na list of value for every field.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.Document.add_text": {"fullname": "tantivy.Document.add_text", "modulename": "tantivy", "qualname": "Document.add_text", "kind": "function", "doc": "Add a text value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the text.\n text (str): The text that will be added to the document.
\n", "signature": "(self, /, field_name, text):", "funcdef": "def"}, "tantivy.Document.add_unsigned": {"fullname": "tantivy.Document.add_unsigned", "modulename": "tantivy", "qualname": "Document.add_unsigned", "kind": "function", "doc": "Add an unsigned integer value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the unsigned integer.\n value (int): The integer that will be added to the document.
\n", "signature": "(self, /, field_name, value):", "funcdef": "def"}, "tantivy.Document.add_integer": {"fullname": "tantivy.Document.add_integer", "modulename": "tantivy", "qualname": "Document.add_integer", "kind": "function", "doc": "Add a signed integer value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the integer.\n value (int): The integer that will be added to the document.
\n", "signature": "(self, /, field_name, value):", "funcdef": "def"}, "tantivy.Document.add_float": {"fullname": "tantivy.Document.add_float", "modulename": "tantivy", "qualname": "Document.add_float", "kind": "function", "doc": "Add a float value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the value.\n value (f64): The float that will be added to the document.
\n", "signature": "(self, /, field_name, value):", "funcdef": "def"}, "tantivy.Document.add_boolean": {"fullname": "tantivy.Document.add_boolean", "modulename": "tantivy", "qualname": "Document.add_boolean", "kind": "function", "doc": "Add a boolean value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the value.\n value (bool): The boolean that will be added to the document.
\n", "signature": "(self, /, field_name, value):", "funcdef": "def"}, "tantivy.Document.add_date": {"fullname": "tantivy.Document.add_date", "modulename": "tantivy", "qualname": "Document.add_date", "kind": "function", "doc": "Add a date value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the date.\n value (datetime): The date that will be added to the document.
\n", "signature": "(self, /, field_name, value):", "funcdef": "def"}, "tantivy.Document.add_facet": {"fullname": "tantivy.Document.add_facet", "modulename": "tantivy", "qualname": "Document.add_facet", "kind": "function", "doc": "Add a facet value to the document.\nArgs:\n field_name (str): The field name for which we are adding the facet.\n value (Facet): The Facet that will be added to the document.
\n", "signature": "(self, /, field_name, facet):", "funcdef": "def"}, "tantivy.Document.add_bytes": {"fullname": "tantivy.Document.add_bytes", "modulename": "tantivy", "qualname": "Document.add_bytes", "kind": "function", "doc": "Add a bytes value to the document.
\n\nArgs:\n field_name (str): The field for which we are adding the bytes.\n value (bytes): The bytes that will be added to the document.
\n", "signature": "(self, /, field_name, bytes):", "funcdef": "def"}, "tantivy.Document.add_json": {"fullname": "tantivy.Document.add_json", "modulename": "tantivy", "qualname": "Document.add_json", "kind": "function", "doc": "Add a JSON value to the document.
\n\nArgs:\n field_name (str): The field for which we are adding the bytes.\n value (str | Dict[str, Any]): The JSON object that will be added\n to the document.
\n\nRaises a ValueError if the JSON is invalid.
\n", "signature": "(self, /, field_name, value):", "funcdef": "def"}, "tantivy.Document.get_first": {"fullname": "tantivy.Document.get_first", "modulename": "tantivy", "qualname": "Document.get_first", "kind": "function", "doc": "Get the first value associated with the given field.
\n\nArgs:\n field (Field): The field for which we would like to get the value.
\n\nReturns the value if one is found, otherwise None.\nThe type of the value depends on the field.
\n", "signature": "(self, /, fieldname):", "funcdef": "def"}, "tantivy.Document.get_all": {"fullname": "tantivy.Document.get_all", "modulename": "tantivy", "qualname": "Document.get_all", "kind": "function", "doc": "Get the all values associated with the given field.
\n\nArgs:\n field (Field): The field for which we would like to get the values.
\n\nReturns a list of values.\nThe type of the value depends on the field.
\n", "signature": "(self, /, field_name):", "funcdef": "def"}, "tantivy.Document.num_fields": {"fullname": "tantivy.Document.num_fields", "modulename": "tantivy", "qualname": "Document.num_fields", "kind": "variable", "doc": "Returns the number of added fields that have been added to the document
\n"}, "tantivy.Document.is_empty": {"fullname": "tantivy.Document.is_empty", "modulename": "tantivy", "qualname": "Document.is_empty", "kind": "variable", "doc": "True if the document is empty, False otherwise.
\n"}, "tantivy.DocAddress": {"fullname": "tantivy.DocAddress", "modulename": "tantivy", "qualname": "DocAddress", "kind": "class", "doc": "DocAddress contains all the necessary information to identify a document\ngiven a Searcher object.
\n\nIt consists in an id identifying its segment, and its segment-local DocId.\nThe id used for the segment is actually an ordinal in the list of segment\nhold by a Searcher.
\n"}, "tantivy.DocAddress.segment_ord": {"fullname": "tantivy.DocAddress.segment_ord", "modulename": "tantivy", "qualname": "DocAddress.segment_ord", "kind": "variable", "doc": "The segment ordinal is an id identifying the segment hosting the\ndocument. It is only meaningful, in the context of a searcher.
\n"}, "tantivy.DocAddress.doc": {"fullname": "tantivy.DocAddress.doc", "modulename": "tantivy", "qualname": "DocAddress.doc", "kind": "variable", "doc": "The segment local DocId
\n"}, "tantivy.Facet": {"fullname": "tantivy.Facet", "modulename": "tantivy", "qualname": "Facet", "kind": "class", "doc": "A Facet represent a point in a given hierarchy.
\n\nThey are typically represented similarly to a filepath. For instance, an\ne-commerce website could have a Facet for /electronics/tv_and_video/led_tv.
\n\nA document can be associated to any number of facets. The hierarchy\nimplicitely imply that a document belonging to a facet also belongs to the\nancestor of its facet. In the example above, /electronics/tv_and_video/\nand /electronics.
\n"}, "tantivy.Facet.from_encoded": {"fullname": "tantivy.Facet.from_encoded", "modulename": "tantivy", "qualname": "Facet.from_encoded", "kind": "function", "doc": "Creates a Facet from its binary representation.
\n", "signature": "(encoded_bytes):", "funcdef": "def"}, "tantivy.Facet.root": {"fullname": "tantivy.Facet.root", "modulename": "tantivy", "qualname": "Facet.root", "kind": "function", "doc": "Create a new instance of the \"root facet\" Equivalent to /.
\n", "signature": "(cls, /):", "funcdef": "def"}, "tantivy.Facet.is_prefix_of": {"fullname": "tantivy.Facet.is_prefix_of", "modulename": "tantivy", "qualname": "Facet.is_prefix_of", "kind": "function", "doc": "Returns true if another Facet is a subfacet of this facet.\nArgs:\n other (Facet): The Facet that we should check if this facet is a\n subset of.
\n", "signature": "(self, /, other):", "funcdef": "def"}, "tantivy.Facet.from_string": {"fullname": "tantivy.Facet.from_string", "modulename": "tantivy", "qualname": "Facet.from_string", "kind": "function", "doc": "Create a Facet object from a string.\nArgs:\n facet_string (str): The string that contains a facet.
\n\nReturns the created Facet.
\n", "signature": "(cls, /, facet_string):", "funcdef": "def"}, "tantivy.Facet.to_path": {"fullname": "tantivy.Facet.to_path", "modulename": "tantivy", "qualname": "Facet.to_path", "kind": "function", "doc": "Returns the list of segments that forms a facet path.
\n\nFor instance //europe/france becomes [\"europe\", \"france\"].
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.Facet.to_path_str": {"fullname": "tantivy.Facet.to_path_str", "modulename": "tantivy", "qualname": "Facet.to_path_str", "kind": "function", "doc": "Returns the facet string representation.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.Facet.is_root": {"fullname": "tantivy.Facet.is_root", "modulename": "tantivy", "qualname": "Facet.is_root", "kind": "variable", "doc": "Returns true if the facet is the root facet /.
\n"}, "tantivy.tantivy": {"fullname": "tantivy.tantivy", "modulename": "tantivy.tantivy", "kind": "module", "doc": "Python bindings for the search engine library Tantivy.
\n\nTantivy is a full text search engine library written in rust.
\n\nIt is closer to Apache Lucene than to Elasticsearch and Apache Solr in\nthe sense it is not an off-the-shelf search engine server, but rather\na library that can be used to build such a search engine.\nTantivy is, in fact, strongly inspired by Lucene's design.
\n\nExample:
\n\n\n \n \n import json\n import tantivy
\n\n>>> builder = tantivy.SchemaBuilder()\n\n>>> title = builder.add_text_field(\"title\", stored=True)\n>>> body = builder.add_text_field(\"body\")\n\n>>> schema = builder.build()\n>>> index = tantivy.Index(schema)\n>>> doc = tantivy.Document()\n>>> doc.add_text(title, \"The Old Man and the Sea\")\n>>> doc.add_text(body, (\"He was an old man who fished alone in a \"\n \"skiff in the Gulf Stream and he had gone \"\n \"eighty-four days now without taking a fish.\"))\n\n>>> writer.add_document(doc)\n\n>>> doc = schema.parse_document(json.dumps({\n \"title\": [\"Frankenstein\", \"The Modern Prometheus\"],\n \"body\": (\"You will rejoice to hear that no disaster has \"\n \"accompanied the commencement of an enterprise which \"\n \"you have regarded with such evil forebodings. \"\n \"I arrived here yesterday, and my first task is to \"\n \"assure my dear sister of my welfare and increasing \"\n \"confidence in the success of my undertaking.\")\n}))\n\n>>> writer.add_document(doc)\n>>> writer.commit()\n\n>>> reader = index.reader()\n>>> searcher = reader.searcher()\n\n>>> query = index.parse_query(\"sea whale\", [title, body])\n\n>>> result = searcher.search(query, 10)\n\n>>> assert len(result) == 1\n
\n
\n
\n
\n"}, "tantivy.tantivy.Order": {"fullname": "tantivy.tantivy.Order", "modulename": "tantivy.tantivy", "qualname": "Order", "kind": "class", "doc": "An enumeration.
\n"}, "tantivy.tantivy.Order.Asc": {"fullname": "tantivy.tantivy.Order.Asc", "modulename": "tantivy.tantivy", "qualname": "Order.Asc", "kind": "variable", "doc": "\n", "default_value": "Order.Asc"}, "tantivy.tantivy.Order.Desc": {"fullname": "tantivy.tantivy.Order.Desc", "modulename": "tantivy.tantivy", "qualname": "Order.Desc", "kind": "variable", "doc": "\n", "default_value": "Order.Desc"}, "tantivy.tantivy.Schema": {"fullname": "tantivy.tantivy.Schema", "modulename": "tantivy.tantivy", "qualname": "Schema", "kind": "class", "doc": "Tantivy schema.
\n\nThe schema is very strict. To build the schema the SchemaBuilder class is\nprovided.
\n"}, "tantivy.tantivy.SchemaBuilder": {"fullname": "tantivy.tantivy.SchemaBuilder", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder", "kind": "class", "doc": "Tantivy has a very strict schema.\nYou need to specify in advance whether a field is indexed or not,\nstored or not.
\n\nThis is done by creating a schema object, and\nsetting up the fields one by one.
\n\nExamples:
\n\n>>> builder = tantivy.SchemaBuilder()\n\n>>> title = builder.add_text_field(\"title\", stored=True)\n>>> body = builder.add_text_field(\"body\")\n\n>>> schema = builder.build()\n
\n"}, "tantivy.tantivy.SchemaBuilder.is_valid_field_name": {"fullname": "tantivy.tantivy.SchemaBuilder.is_valid_field_name", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.is_valid_field_name", "kind": "function", "doc": "\n", "signature": "(name: str) -> bool:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_text_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_text_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_text_field", "kind": "function", "doc": "Add a new text field to the schema.
\n\nArgs:\n name (str): The name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n fast (bool, optional): Set the text options as a fast field. A\n fast field is a column-oriented fashion storage for tantivy.\n Text fast fields will have the term ids stored in the fast\n field. The fast field will be a multivalued fast field.\n It is recommended to use the \"raw\" tokenizer, since it will\n store the original text unchanged. The \"default\" tokenizer will\n store the terms as lower case and this will be reflected in the\n dictionary.\n tokenizer_name (str, optional): The name of the tokenizer that\n should be used to process the field. Defaults to 'default'\n index_option (str, optional): Sets which information should be\n indexed with the tokens. Can be one of 'position', 'freq' or\n 'basic'. Defaults to 'position'. The 'basic' index_option\n records only the document ID, the 'freq' option records the\n document id and the term frequency, while the 'position' option\n records the document id, term frequency and the positions of\n the term occurrences in the document.
\n\nReturns the associated field handle.\nRaises a ValueError if there was an error with the field creation.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\ttokenizer_name: str = 'default',\tindex_option: str = 'position') -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_integer_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_integer_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_integer_field", "kind": "function", "doc": "Add a new signed integer field to the schema.
\n\nArgs:\n name (str): The name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n indexed (bool, optional): If true sets the field to be indexed.\n fast (bool, optional): Set the numeric options as a fast field. A\n fast field is a column-oriented fashion storage for tantivy.\n It is designed for the fast random access of some document\n fields given a document id.
\n\nReturns the associated field handle.\nRaises a ValueError if there was an error with the field creation.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\tindexed: bool = False,\tfast: bool = False) -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_float_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_float_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_float_field", "kind": "function", "doc": "Add a new float field to the schema.
\n\nArgs:\n name (str): The name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n indexed (bool, optional): If true sets the field to be indexed.\n fast (bool, optional): Set the numeric options as a fast field. A\n fast field is a column-oriented fashion storage for tantivy.\n It is designed for the fast random access of some document\n fields given a document id.
\n\nReturns the associated field handle.\nRaises a ValueError if there was an error with the field creation.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\tindexed: bool = False,\tfast: bool = False) -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_unsigned_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_unsigned_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_unsigned_field", "kind": "function", "doc": "Add a new unsigned integer field to the schema.
\n\nArgs:\n name (str): The name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n indexed (bool, optional): If true sets the field to be indexed.\n fast (bool, optional): Set the numeric options as a fast field. A\n fast field is a column-oriented fashion storage for tantivy.\n It is designed for the fast random access of some document\n fields given a document id.
\n\nReturns the associated field handle.\nRaises a ValueError if there was an error with the field creation.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\tindexed: bool = False,\tfast: bool = False) -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_boolean_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_boolean_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_boolean_field", "kind": "function", "doc": "Add a new boolean field to the schema.
\n\nArgs:\n name (str): The name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n indexed (bool, optional): If true sets the field to be indexed.\n fast (bool, optional): Set the numeric options as a fast field. A\n fast field is a column-oriented fashion storage for tantivy.\n It is designed for the fast random access of some document\n fields given a document id.
\n\nReturns the associated field handle.\nRaises a ValueError if there was an error with the field creation.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\tindexed: bool = False,\tfast: bool = False) -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_date_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_date_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_date_field", "kind": "function", "doc": "Add a new date field to the schema.
\n\nArgs:\n name (str): The name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n indexed (bool, optional): If true sets the field to be indexed.\n fast (bool, optional): Set the date options as a fast field. A fast\n field is a column-oriented fashion storage for tantivy. It is\n designed for the fast random access of some document fields\n given a document id.
\n\nReturns the associated field handle.\nRaises a ValueError if there was an error with the field creation.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\tindexed: bool = False,\tfast: bool = False) -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_json_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_json_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_json_field", "kind": "function", "doc": "Add a new json field to the schema.
\n\nArgs:\n name (str): the name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n fast (bool, optional): Set the text options as a fast field. A\n fast field is a column-oriented fashion storage for tantivy.\n Text fast fields will have the term ids stored in the fast\n field. The fast field will be a multivalued fast field.\n It is recommended to use the \"raw\" tokenizer, since it will\n store the original text unchanged. The \"default\" tokenizer will\n store the terms as lower case and this will be reflected in the\n dictionary.\n tokenizer_name (str, optional): The name of the tokenizer that\n should be used to process the field. Defaults to 'default'\n index_option (str, optional): Sets which information should be\n indexed with the tokens. Can be one of 'position', 'freq' or\n 'basic'. Defaults to 'position'. The 'basic' index_option\n records only the document ID, the 'freq' option records the\n document id and the term frequency, while the 'position' option\n records the document id, term frequency and the positions of\n the term occurrences in the document.
\n\nReturns the associated field handle.\nRaises a ValueError if there was an error with the field creation.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\ttokenizer_name: str = 'default',\tindex_option: str = 'position') -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_facet_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_facet_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_facet_field", "kind": "function", "doc": "Add a Facet field to the schema.\nArgs:\n name (str): The name of the field.
\n", "signature": "(self, name: str) -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_bytes_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_bytes_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_bytes_field", "kind": "function", "doc": "Add a fast bytes field to the schema.
\n\nArgs:\n name (str): The name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n indexed (bool, optional): If true sets the field to be indexed.\n fast (bool, optional): Set the bytes options as a fast field. A fast\n field is a column-oriented fashion storage for tantivy. It is\n designed for the fast random access of some document fields\n given a document id.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\tindexed: bool = False,\tfast: bool = False,\tindex_option: str = 'position') -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.add_ip_addr_field": {"fullname": "tantivy.tantivy.SchemaBuilder.add_ip_addr_field", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.add_ip_addr_field", "kind": "function", "doc": "Add an IP address field to the schema.
\n\nArgs:\n name (str): The name of the field.\n stored (bool, optional): If true sets the field as stored, the\n content of the field can be later restored from a Searcher.\n Defaults to False.\n indexed (bool, optional): If true sets the field to be indexed.\n fast (bool, optional): Set the IP address options as a fast field. A\n fast field is a column-oriented fashion storage for tantivy. It\n is designed for the fast random access of some document fields\n given a document id.
\n", "signature": "(\tself,\tname: str,\tstored: bool = False,\tindexed: bool = False,\tfast: bool = False) -> tantivy.tantivy.SchemaBuilder:", "funcdef": "def"}, "tantivy.tantivy.SchemaBuilder.build": {"fullname": "tantivy.tantivy.SchemaBuilder.build", "modulename": "tantivy.tantivy", "qualname": "SchemaBuilder.build", "kind": "function", "doc": "Finalize the creation of a Schema.
\n\nReturns a Schema object. After this is called the SchemaBuilder cannot\nbe used anymore.
\n", "signature": "(self) -> tantivy.tantivy.Schema:", "funcdef": "def"}, "tantivy.tantivy.Searcher": {"fullname": "tantivy.tantivy.Searcher", "modulename": "tantivy.tantivy", "qualname": "Searcher", "kind": "class", "doc": "Tantivy's Searcher class
\n\nA Searcher is used to search the index given a prepared Query.
\n"}, "tantivy.tantivy.Searcher.search": {"fullname": "tantivy.tantivy.Searcher.search", "modulename": "tantivy.tantivy", "qualname": "Searcher.search", "kind": "function", "doc": "Search the index with the given query and collect results.
\n\nArgs:\n query (Query): The query that will be used for the search.\n limit (int, optional): The maximum number of search results to\n return. Defaults to 10.\n count (bool, optional): Should the number of documents that match\n the query be returned as well. Defaults to true.\n order_by_field (Field, optional): A schema field that the results\n should be ordered by. The field must be declared as a fast field\n when building the schema. Note, this only works for unsigned\n fields.\n offset (Field, optional): The offset from which the results have\n to be returned.\n order (Order, optional): The order in which the results\n should be sorted. If not specified, defaults to descending.
\n\nReturns SearchResult object.
\n\nRaises a ValueError if there was an error with the search.
\n", "signature": "(\tself,\tquery: tantivy.tantivy.Query,\tlimit: int = 10,\tcount: bool = True,\torder_by_field: Optional[str] = None,\toffset: int = 0,\torder: tantivy.tantivy.Order = <Order.Desc: 2>) -> tantivy.tantivy.SearchResult:", "funcdef": "def"}, "tantivy.tantivy.Searcher.doc": {"fullname": "tantivy.tantivy.Searcher.doc", "modulename": "tantivy.tantivy", "qualname": "Searcher.doc", "kind": "function", "doc": "Fetches a document from Tantivy's store given a DocAddress.
\n\nArgs:\n doc_address (DocAddress): The DocAddress that is associated with\n the document that we wish to fetch.
\n\nReturns the Document, raises ValueError if the document can't be found.
\n", "signature": "(\tself,\tdoc_address: tantivy.tantivy.DocAddress) -> tantivy.tantivy.Document:", "funcdef": "def"}, "tantivy.tantivy.Searcher.num_segments": {"fullname": "tantivy.tantivy.Searcher.num_segments", "modulename": "tantivy.tantivy", "qualname": "Searcher.num_segments", "kind": "variable", "doc": "Returns the number of segments in the index.
\n", "annotation": ": int"}, "tantivy.tantivy.Searcher.num_docs": {"fullname": "tantivy.tantivy.Searcher.num_docs", "modulename": "tantivy.tantivy", "qualname": "Searcher.num_docs", "kind": "variable", "doc": "Returns the overall number of documents in the index.
\n", "annotation": ": int"}, "tantivy.tantivy.SearchResult": {"fullname": "tantivy.tantivy.SearchResult", "modulename": "tantivy.tantivy", "qualname": "SearchResult", "kind": "class", "doc": "Object holding a results successful search.
\n"}, "tantivy.tantivy.SearchResult.count": {"fullname": "tantivy.tantivy.SearchResult.count", "modulename": "tantivy.tantivy", "qualname": "SearchResult.count", "kind": "variable", "doc": "How many documents matched the query. Only available if count was set\nto true during the search.
\n"}, "tantivy.tantivy.SearchResult.hits": {"fullname": "tantivy.tantivy.SearchResult.hits", "modulename": "tantivy.tantivy", "qualname": "SearchResult.hits", "kind": "variable", "doc": "The list of tuples that contains the scores and DocAddress of the\nsearch results.
\n", "annotation": ": list[tuple[typing.Any, tantivy.tantivy.DocAddress]]"}, "tantivy.tantivy.Document": {"fullname": "tantivy.tantivy.Document", "modulename": "tantivy.tantivy", "qualname": "Document", "kind": "class", "doc": "Tantivy's Document is the object that can be indexed and then searched for.
\n\nDocuments are fundamentally a collection of unordered tuples\n(field_name, value). In this list, one field may appear more than once.
\n\nExample:
\n\n>>> doc = tantivy.Document()\n>>> doc.add_text(\"title\", \"The Old Man and the Sea\")\n>>> doc.add_text(\"body\", (\"He was an old man who fished alone in a \"\n... \"skiff in the Gulf Stream and he had gone \"\n... \"eighty-four days now without taking a fish.\"))\n>>> doc\nDocument(body=[He was an ],title=[The Old Ma])\n
\n\nFor simplicity, it is also possible to build a Document by passing the field\nvalues directly as constructor arguments.
\n\nExample:
\n\n>>> doc = tantivy.Document(title=[\"The Old Man and the Sea\"], body=[\"...\"])\n
\n\nAs syntactic sugar, tantivy also allows the user to pass a single values\nif there is only one. In other words, the following is also legal.
\n\nExample:
\n\n>>> doc = tantivy.Document(title=\"The Old Man and the Sea\", body=\"...\")\n
\n\nFor numeric fields, the [Document] constructor does not have any\ninformation about the type and will try to guess the type.\nTherefore, it is recommended to use the [Document::from_dict()],\n[Document::extract()], or Document::add_*() functions to provide\nexplicit type information.
\n\nExample:
\n\n>>> schema = (\n... SchemaBuilder()\n... .add_unsigned_field(\"unsigned\")\n... .add_integer_field(\"signed\")\n... .add_float_field(\"float\")\n... .build()\n... )\n>>> doc = tantivy.Document.from_dict(\n... {\"unsigned\": 1000, \"signed\": -5, \"float\": 0.4},\n... schema,\n... )\n
\n"}, "tantivy.tantivy.Document.extend": {"fullname": "tantivy.tantivy.Document.extend", "modulename": "tantivy.tantivy", "qualname": "Document.extend", "kind": "function", "doc": "\n", "signature": "(self, py_dict: dict, schema: Optional[tantivy.tantivy.Schema]) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.from_dict": {"fullname": "tantivy.tantivy.Document.from_dict", "modulename": "tantivy.tantivy", "qualname": "Document.from_dict", "kind": "function", "doc": "\n", "signature": "(\tpy_dict: dict,\tschema: Optional[tantivy.tantivy.Schema]) -> tantivy.tantivy.Document:", "funcdef": "def"}, "tantivy.tantivy.Document.to_dict": {"fullname": "tantivy.tantivy.Document.to_dict", "modulename": "tantivy.tantivy", "qualname": "Document.to_dict", "kind": "function", "doc": "Returns a dictionary with the different\nfield values.
\n\nIn tantivy, Document can be hold multiple\nvalues for a single field.
\n\nFor this reason, the dictionary, will associate\na list of value for every field.
\n", "signature": "(self) -> Any:", "funcdef": "def"}, "tantivy.tantivy.Document.add_text": {"fullname": "tantivy.tantivy.Document.add_text", "modulename": "tantivy.tantivy", "qualname": "Document.add_text", "kind": "function", "doc": "Add a text value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the text.\n text (str): The text that will be added to the document.
\n", "signature": "(self, field_name: str, text: str) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.add_unsigned": {"fullname": "tantivy.tantivy.Document.add_unsigned", "modulename": "tantivy.tantivy", "qualname": "Document.add_unsigned", "kind": "function", "doc": "Add an unsigned integer value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the unsigned integer.\n value (int): The integer that will be added to the document.
\n", "signature": "(self, field_name: str, value: int) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.add_integer": {"fullname": "tantivy.tantivy.Document.add_integer", "modulename": "tantivy.tantivy", "qualname": "Document.add_integer", "kind": "function", "doc": "Add a signed integer value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the integer.\n value (int): The integer that will be added to the document.
\n", "signature": "(self, field_name: str, value: int) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.add_float": {"fullname": "tantivy.tantivy.Document.add_float", "modulename": "tantivy.tantivy", "qualname": "Document.add_float", "kind": "function", "doc": "Add a float value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the value.\n value (f64): The float that will be added to the document.
\n", "signature": "(self, field_name: str, value: float) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.add_boolean": {"fullname": "tantivy.tantivy.Document.add_boolean", "modulename": "tantivy.tantivy", "qualname": "Document.add_boolean", "kind": "function", "doc": "Add a boolean value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the value.\n value (bool): The boolean that will be added to the document.
\n", "signature": "(self, field_name: str, value: bool) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.add_date": {"fullname": "tantivy.tantivy.Document.add_date", "modulename": "tantivy.tantivy", "qualname": "Document.add_date", "kind": "function", "doc": "Add a date value to the document.
\n\nArgs:\n field_name (str): The field name for which we are adding the date.\n value (datetime): The date that will be added to the document.
\n", "signature": "(self, field_name: str, value: datetime.datetime) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.add_facet": {"fullname": "tantivy.tantivy.Document.add_facet", "modulename": "tantivy.tantivy", "qualname": "Document.add_facet", "kind": "function", "doc": "Add a facet value to the document.\nArgs:\n field_name (str): The field name for which we are adding the facet.\n value (Facet): The Facet that will be added to the document.
\n", "signature": "(self, field_name: str, facet: tantivy.tantivy.Facet) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.add_bytes": {"fullname": "tantivy.tantivy.Document.add_bytes", "modulename": "tantivy.tantivy", "qualname": "Document.add_bytes", "kind": "function", "doc": "Add a bytes value to the document.
\n\nArgs:\n field_name (str): The field for which we are adding the bytes.\n value (bytes): The bytes that will be added to the document.
\n", "signature": "(self, field_name: str, bytes: bytes) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.add_json": {"fullname": "tantivy.tantivy.Document.add_json", "modulename": "tantivy.tantivy", "qualname": "Document.add_json", "kind": "function", "doc": "Add a JSON value to the document.
\n\nArgs:\n field_name (str): The field for which we are adding the bytes.\n value (str | Dict[str, Any]): The JSON object that will be added\n to the document.
\n\nRaises a ValueError if the JSON is invalid.
\n", "signature": "(self, field_name: str, value: Any) -> None:", "funcdef": "def"}, "tantivy.tantivy.Document.get_first": {"fullname": "tantivy.tantivy.Document.get_first", "modulename": "tantivy.tantivy", "qualname": "Document.get_first", "kind": "function", "doc": "Get the first value associated with the given field.
\n\nArgs:\n field (Field): The field for which we would like to get the value.
\n\nReturns the value if one is found, otherwise None.\nThe type of the value depends on the field.
\n", "signature": "(self, field_name: str) -> Optional[Any]:", "funcdef": "def"}, "tantivy.tantivy.Document.get_all": {"fullname": "tantivy.tantivy.Document.get_all", "modulename": "tantivy.tantivy", "qualname": "Document.get_all", "kind": "function", "doc": "Get the all values associated with the given field.
\n\nArgs:\n field (Field): The field for which we would like to get the values.
\n\nReturns a list of values.\nThe type of the value depends on the field.
\n", "signature": "(self, field_name: str) -> list[typing.Any]:", "funcdef": "def"}, "tantivy.tantivy.Document.num_fields": {"fullname": "tantivy.tantivy.Document.num_fields", "modulename": "tantivy.tantivy", "qualname": "Document.num_fields", "kind": "variable", "doc": "Returns the number of added fields that have been added to the document
\n", "annotation": ": int"}, "tantivy.tantivy.Document.is_empty": {"fullname": "tantivy.tantivy.Document.is_empty", "modulename": "tantivy.tantivy", "qualname": "Document.is_empty", "kind": "variable", "doc": "True if the document is empty, False otherwise.
\n", "annotation": ": bool"}, "tantivy.tantivy.Index": {"fullname": "tantivy.tantivy.Index", "modulename": "tantivy.tantivy", "qualname": "Index", "kind": "class", "doc": "Create a new index object.
\n\nArgs:\n schema (Schema): The schema of the index.\n path (str, optional): The path where the index should be stored. If\n no path is provided, the index will be stored in memory.\n reuse (bool, optional): Should we open an existing index if one exists\n or always create a new one.
\n\nIf an index already exists it will be opened and reused. Raises OSError\nif there was a problem during the opening or creation of the index.
\n"}, "tantivy.tantivy.Index.open": {"fullname": "tantivy.tantivy.Index.open", "modulename": "tantivy.tantivy", "qualname": "Index.open", "kind": "function", "doc": "\n", "signature": "(path: str) -> tantivy.tantivy.Index:", "funcdef": "def"}, "tantivy.tantivy.Index.writer": {"fullname": "tantivy.tantivy.Index.writer", "modulename": "tantivy.tantivy", "qualname": "Index.writer", "kind": "function", "doc": "Create a IndexWriter for the index.
\n\nThe writer will be multithreaded and the provided heap size will be\nsplit between the given number of threads.
\n\nArgs:\n overall_heap_size (int, optional): The total target heap memory usage of\n the writer. Tantivy requires that this can't be less\n than 3000000 per thread. Lower values will result in more\n frequent internal commits when adding documents (slowing down\n write progress), and larger values will results in fewer\n commits but greater memory usage. The best value will depend\n on your specific use case.\n num_threads (int, optional): The number of threads that the writer\n should use. If this value is 0, tantivy will choose\n automatically the number of threads.
\n\nRaises ValueError if there was an error while creating the writer.
\n", "signature": "(\tself,\theap_size: int = 128000000,\tnum_threads: int = 0) -> tantivy.tantivy.IndexWriter:", "funcdef": "def"}, "tantivy.tantivy.Index.config_reader": {"fullname": "tantivy.tantivy.Index.config_reader", "modulename": "tantivy.tantivy", "qualname": "Index.config_reader", "kind": "function", "doc": "Configure the index reader.
\n\nArgs:\n reload_policy (str, optional): The reload policy that the\n IndexReader should use. Can be Manual or OnCommit.\n num_warmers (int, optional): The number of searchers that the\n reader should create.
\n", "signature": "(self, reload_policy: str = 'commit', num_warmers: int = 0) -> None:", "funcdef": "def"}, "tantivy.tantivy.Index.searcher": {"fullname": "tantivy.tantivy.Index.searcher", "modulename": "tantivy.tantivy", "qualname": "Index.searcher", "kind": "function", "doc": "Returns a searcher
\n\nThis method should be called every single time a search query is performed.\nThe same searcher must be used for a given query, as it ensures the use of a consistent segment set.
\n", "signature": "(self) -> tantivy.tantivy.Searcher:", "funcdef": "def"}, "tantivy.tantivy.Index.exists": {"fullname": "tantivy.tantivy.Index.exists", "modulename": "tantivy.tantivy", "qualname": "Index.exists", "kind": "function", "doc": "Check if the given path contains an existing index.\nArgs:\n path: The path where tantivy will search for an index.
\n\nReturns True if an index exists at the given path, False otherwise.
\n\nRaises OSError if the directory cannot be opened.
\n", "signature": "(path: str) -> bool:", "funcdef": "def"}, "tantivy.tantivy.Index.reload": {"fullname": "tantivy.tantivy.Index.reload", "modulename": "tantivy.tantivy", "qualname": "Index.reload", "kind": "function", "doc": "Update searchers so that they reflect the state of the last .commit().
\n\nIf you set up the the reload policy to be on 'commit' (which is the\ndefault) every commit should be rapidly reflected on your IndexReader\nand you should not need to call reload() at all.
\n", "signature": "(self) -> None:", "funcdef": "def"}, "tantivy.tantivy.Index.parse_query": {"fullname": "tantivy.tantivy.Index.parse_query", "modulename": "tantivy.tantivy", "qualname": "Index.parse_query", "kind": "function", "doc": "Parse a query
\n\nArgs:\n query: the query, following the tantivy query language.\n default_fields_names (List[Field]): A list of fields used to search if no\n field is specified in the query.
\n", "signature": "(\tself,\tquery: str,\tdefault_field_names: Optional[list[str]] = None) -> tantivy.tantivy.Query:", "funcdef": "def"}, "tantivy.tantivy.Index.parse_query_lenient": {"fullname": "tantivy.tantivy.Index.parse_query_lenient", "modulename": "tantivy.tantivy", "qualname": "Index.parse_query_lenient", "kind": "function", "doc": "Parse a query leniently.
\n\nThis variant parses invalid query on a best effort basis. If some part of the query can't\nreasonably be executed (range query without field, searching on a non existing field,\nsearching without precising field when no default field is provided...), they may get turned\ninto a \"match-nothing\" subquery.
\n\nArgs:\n query: the query, following the tantivy query language.\n default_fields_names (List[Field]): A list of fields used to search if no\n field is specified in the query.
\n\nReturns a tuple containing the parsed query and a list of errors.
\n\nRaises ValueError if a field in default_field_names is not defined or marked as indexed.
\n", "signature": "(\tself,\tquery: str,\tdefault_field_names: Optional[list[str]] = None) -> tantivy.tantivy.Query:", "funcdef": "def"}, "tantivy.tantivy.Index.schema": {"fullname": "tantivy.tantivy.Index.schema", "modulename": "tantivy.tantivy", "qualname": "Index.schema", "kind": "variable", "doc": "The schema of the current index.
\n", "annotation": ": tantivy.tantivy.Schema"}, "tantivy.tantivy.DocAddress": {"fullname": "tantivy.tantivy.DocAddress", "modulename": "tantivy.tantivy", "qualname": "DocAddress", "kind": "class", "doc": "DocAddress contains all the necessary information to identify a document\ngiven a Searcher object.
\n\nIt consists in an id identifying its segment, and its segment-local DocId.\nThe id used for the segment is actually an ordinal in the list of segment\nhold by a Searcher.
\n"}, "tantivy.tantivy.DocAddress.segment_ord": {"fullname": "tantivy.tantivy.DocAddress.segment_ord", "modulename": "tantivy.tantivy", "qualname": "DocAddress.segment_ord", "kind": "variable", "doc": "The segment ordinal is an id identifying the segment hosting the\ndocument. It is only meaningful, in the context of a searcher.
\n", "annotation": ": int"}, "tantivy.tantivy.DocAddress.doc": {"fullname": "tantivy.tantivy.DocAddress.doc", "modulename": "tantivy.tantivy", "qualname": "DocAddress.doc", "kind": "variable", "doc": "The segment local DocId
\n", "annotation": ": int"}, "tantivy.tantivy.Facet": {"fullname": "tantivy.tantivy.Facet", "modulename": "tantivy.tantivy", "qualname": "Facet", "kind": "class", "doc": "A Facet represent a point in a given hierarchy.
\n\nThey are typically represented similarly to a filepath. For instance, an\ne-commerce website could have a Facet for /electronics/tv_and_video/led_tv.
\n\nA document can be associated to any number of facets. The hierarchy\nimplicitely imply that a document belonging to a facet also belongs to the\nancestor of its facet. In the example above, /electronics/tv_and_video/\nand /electronics.
\n"}, "tantivy.tantivy.Facet.from_encoded": {"fullname": "tantivy.tantivy.Facet.from_encoded", "modulename": "tantivy.tantivy", "qualname": "Facet.from_encoded", "kind": "function", "doc": "Creates a Facet from its binary representation.
\n", "signature": "(encoded_bytes: bytes) -> tantivy.tantivy.Facet:", "funcdef": "def"}, "tantivy.tantivy.Facet.root": {"fullname": "tantivy.tantivy.Facet.root", "modulename": "tantivy.tantivy", "qualname": "Facet.root", "kind": "function", "doc": "Create a new instance of the \"root facet\" Equivalent to /.
\n", "signature": "(cls) -> tantivy.tantivy.Facet:", "funcdef": "def"}, "tantivy.tantivy.Facet.is_prefix_of": {"fullname": "tantivy.tantivy.Facet.is_prefix_of", "modulename": "tantivy.tantivy", "qualname": "Facet.is_prefix_of", "kind": "function", "doc": "Returns true if another Facet is a subfacet of this facet.\nArgs:\n other (Facet): The Facet that we should check if this facet is a\n subset of.
\n", "signature": "(self, other: tantivy.tantivy.Facet) -> bool:", "funcdef": "def"}, "tantivy.tantivy.Facet.from_string": {"fullname": "tantivy.tantivy.Facet.from_string", "modulename": "tantivy.tantivy", "qualname": "Facet.from_string", "kind": "function", "doc": "Create a Facet object from a string.\nArgs:\n facet_string (str): The string that contains a facet.
\n\nReturns the created Facet.
\n", "signature": "(cls, facet_string: str) -> tantivy.tantivy.Facet:", "funcdef": "def"}, "tantivy.tantivy.Facet.to_path": {"fullname": "tantivy.tantivy.Facet.to_path", "modulename": "tantivy.tantivy", "qualname": "Facet.to_path", "kind": "function", "doc": "Returns the list of segments that forms a facet path.
\n\nFor instance //europe/france becomes [\"europe\", \"france\"].
\n", "signature": "(self) -> list[str]:", "funcdef": "def"}, "tantivy.tantivy.Facet.to_path_str": {"fullname": "tantivy.tantivy.Facet.to_path_str", "modulename": "tantivy.tantivy", "qualname": "Facet.to_path_str", "kind": "function", "doc": "Returns the facet string representation.
\n", "signature": "(self) -> str:", "funcdef": "def"}, "tantivy.tantivy.Facet.is_root": {"fullname": "tantivy.tantivy.Facet.is_root", "modulename": "tantivy.tantivy", "qualname": "Facet.is_root", "kind": "variable", "doc": "Returns true if the facet is the root facet /.
\n", "annotation": ": bool"}, "tantivy.tantivy.Query": {"fullname": "tantivy.tantivy.Query", "modulename": "tantivy.tantivy", "qualname": "Query", "kind": "class", "doc": "Tantivy's Query
\n"}, "tantivy.tantivy.Query.term_query": {"fullname": "tantivy.tantivy.Query.term_query", "modulename": "tantivy.tantivy", "qualname": "Query.term_query", "kind": "function", "doc": "Construct a Tantivy's TermQuery
\n", "signature": "(\tschema: tantivy.tantivy.Schema,\tfield_name: str,\tfield_value: Any,\tindex_option: str = 'position') -> tantivy.tantivy.Query:", "funcdef": "def"}, "tantivy.tantivy.Snippet": {"fullname": "tantivy.tantivy.Snippet", "modulename": "tantivy.tantivy", "qualname": "Snippet", "kind": "class", "doc": "Tantivy schema.
\n\nThe schema is very strict. To build the schema the SchemaBuilder class is\nprovided.
\n"}, "tantivy.tantivy.Snippet.to_html": {"fullname": "tantivy.tantivy.Snippet.to_html", "modulename": "tantivy.tantivy", "qualname": "Snippet.to_html", "kind": "function", "doc": "\n", "signature": "(self) -> str:", "funcdef": "def"}, "tantivy.tantivy.Snippet.highlighted": {"fullname": "tantivy.tantivy.Snippet.highlighted", "modulename": "tantivy.tantivy", "qualname": "Snippet.highlighted", "kind": "function", "doc": "\n", "signature": "(self) -> list[tantivy.tantivy.Range]:", "funcdef": "def"}, "tantivy.tantivy.SnippetGenerator": {"fullname": "tantivy.tantivy.SnippetGenerator", "modulename": "tantivy.tantivy", "qualname": "SnippetGenerator", "kind": "class", "doc": "\n"}, "tantivy.tantivy.SnippetGenerator.create": {"fullname": "tantivy.tantivy.SnippetGenerator.create", "modulename": "tantivy.tantivy", "qualname": "SnippetGenerator.create", "kind": "function", "doc": "\n", "signature": "(\tsearcher: tantivy.tantivy.Searcher,\tquery: tantivy.tantivy.Query,\tschema: tantivy.tantivy.Schema,\tfield_name: str) -> tantivy.tantivy.SnippetGenerator:", "funcdef": "def"}, "tantivy.tantivy.SnippetGenerator.snippet_from_doc": {"fullname": "tantivy.tantivy.SnippetGenerator.snippet_from_doc", "modulename": "tantivy.tantivy", "qualname": "SnippetGenerator.snippet_from_doc", "kind": "function", "doc": "\n", "signature": "(self, doc: tantivy.tantivy.Document) -> tantivy.tantivy.Snippet:", "funcdef": "def"}, "tantivy.query_parser_error": {"fullname": "tantivy.query_parser_error", "modulename": "tantivy.query_parser_error", "kind": "module", "doc": "Submodule containing all the possible errors that can be raised during\nquery parsing.
\n\nExample:
\n\n\n \n \n import tantivy\n from tantivy import query_parser_error
\n\n>>> builder = tantivy.SchemaBuilder()\n\n>>> title = builder.add_text_field(\"title\", stored=True)\n>>> body = builder.add_text_field(\"body\")\n>>> id = builder.add_unsigned_field(\"id\")\n>>> rating = builder.add_float_field(\"rating\")\n\n>>> schema = builder.build()\n>>> index = tantivy.Index(schema)\n\n>>> query, errors = index.parse_query_lenient(\n \"bod:'world' AND id:<3.5 AND rating:5.0\"\n )\n\n>>> assert len(errors) == 2\n>>> assert isinstance(errors[0], query_parser_error.FieldDoesNotExistError)\n>>> assert isinstance(errors[1], query_parser_error.ExpectedIntError)\n
\n
\n
\n
\n"}, "tantivy.query_parser_error.SyntaxError": {"fullname": "tantivy.query_parser_error.SyntaxError", "modulename": "tantivy.query_parser_error", "qualname": "SyntaxError", "kind": "class", "doc": "Error in the query syntax.
\n"}, "tantivy.query_parser_error.SyntaxError.inner_message": {"fullname": "tantivy.query_parser_error.SyntaxError.inner_message", "modulename": "tantivy.query_parser_error", "qualname": "SyntaxError.inner_message", "kind": "variable", "doc": "\n"}, "tantivy.query_parser_error.UnsupportedQueryError": {"fullname": "tantivy.query_parser_error.UnsupportedQueryError", "modulename": "tantivy.query_parser_error", "qualname": "UnsupportedQueryError", "kind": "class", "doc": "This query is unsupported.
\n"}, "tantivy.query_parser_error.UnsupportedQueryError.inner_message": {"fullname": "tantivy.query_parser_error.UnsupportedQueryError.inner_message", "modulename": "tantivy.query_parser_error", "qualname": "UnsupportedQueryError.inner_message", "kind": "variable", "doc": "\n"}, "tantivy.query_parser_error.FieldDoesNotExistError": {"fullname": "tantivy.query_parser_error.FieldDoesNotExistError", "modulename": "tantivy.query_parser_error", "qualname": "FieldDoesNotExistError", "kind": "class", "doc": "The query references a field that is not in the schema.
\n"}, "tantivy.query_parser_error.FieldDoesNotExistError.field": {"fullname": "tantivy.query_parser_error.FieldDoesNotExistError.field", "modulename": "tantivy.query_parser_error", "qualname": "FieldDoesNotExistError.field", "kind": "variable", "doc": "The name of the field causing the error.
\n"}, "tantivy.query_parser_error.ExpectedIntError": {"fullname": "tantivy.query_parser_error.ExpectedIntError", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedIntError", "kind": "class", "doc": "The query contains a term for a u64 or i64-field, but the value is neither.
\n"}, "tantivy.query_parser_error.ExpectedIntError.caused_by_empty": {"fullname": "tantivy.query_parser_error.ExpectedIntError.caused_by_empty", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedIntError.caused_by_empty", "kind": "function", "doc": "If true, the value being parsed was empty.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedIntError.caused_by_invalid_digit": {"fullname": "tantivy.query_parser_error.ExpectedIntError.caused_by_invalid_digit", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedIntError.caused_by_invalid_digit", "kind": "function", "doc": "If true, an invalid digit was found.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedIntError.caused_by_pos_overflow": {"fullname": "tantivy.query_parser_error.ExpectedIntError.caused_by_pos_overflow", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedIntError.caused_by_pos_overflow", "kind": "function", "doc": "If true, the value being parsed was too large.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedIntError.caused_by_neg_overflow": {"fullname": "tantivy.query_parser_error.ExpectedIntError.caused_by_neg_overflow", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedIntError.caused_by_neg_overflow", "kind": "function", "doc": "If true, the value being parsed was too small.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedBase64Error": {"fullname": "tantivy.query_parser_error.ExpectedBase64Error", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedBase64Error", "kind": "class", "doc": "The query contains a term for a bytes field, but the value is not valid base64.
\n"}, "tantivy.query_parser_error.ExpectedBase64Error.caused_by_invalid_byte": {"fullname": "tantivy.query_parser_error.ExpectedBase64Error.caused_by_invalid_byte", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedBase64Error.caused_by_invalid_byte", "kind": "function", "doc": "If true, an invalid byte was found in the query. Padding characters (=) interspersed in\nthe encoded form will be treated as invalid bytes.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedBase64Error.invalid_byte_info": {"fullname": "tantivy.query_parser_error.ExpectedBase64Error.invalid_byte_info", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedBase64Error.invalid_byte_info", "kind": "function", "doc": "If the error was caused by an invalid byte, returns the offset and offending byte.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedBase64Error.caused_by_invalid_length": {"fullname": "tantivy.query_parser_error.ExpectedBase64Error.caused_by_invalid_length", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedBase64Error.caused_by_invalid_length", "kind": "function", "doc": "If true, the length of the base64 string was invalid.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedBase64Error.caused_by_invalid_last_symbol": {"fullname": "tantivy.query_parser_error.ExpectedBase64Error.caused_by_invalid_last_symbol", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedBase64Error.caused_by_invalid_last_symbol", "kind": "function", "doc": "The last non-padding input symbol's encoded 6 bits have nonzero bits that will be discarded.\nIf true, this is indicative of corrupted or truncated Base64.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedBase64Error.invalid_last_symbol_info": {"fullname": "tantivy.query_parser_error.ExpectedBase64Error.invalid_last_symbol_info", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedBase64Error.invalid_last_symbol_info", "kind": "function", "doc": "If the error was caused by an invalid last symbol, returns the offset and offending byte.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedBase64Error.caused_by_invalid_padding": {"fullname": "tantivy.query_parser_error.ExpectedBase64Error.caused_by_invalid_padding", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedBase64Error.caused_by_invalid_padding", "kind": "function", "doc": "The nature of the padding was not as configured: absent or incorrect when it must be\ncanonical, or present when it must be absent, etc.
\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.ExpectedFloatError": {"fullname": "tantivy.query_parser_error.ExpectedFloatError", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedFloatError", "kind": "class", "doc": "The query contains a term for a f64-field, but the value is not a f64.
\n"}, "tantivy.query_parser_error.ExpectedBoolError": {"fullname": "tantivy.query_parser_error.ExpectedBoolError", "modulename": "tantivy.query_parser_error", "qualname": "ExpectedBoolError", "kind": "class", "doc": "The query contains a term for a bool-field, but the value is not a bool.
\n"}, "tantivy.query_parser_error.AllButQueryForbiddenError": {"fullname": "tantivy.query_parser_error.AllButQueryForbiddenError", "modulename": "tantivy.query_parser_error", "qualname": "AllButQueryForbiddenError", "kind": "class", "doc": "It is forbidden queries that are only \"excluding\". (e.g. -title:pop)
\n"}, "tantivy.query_parser_error.NoDefaultFieldDeclaredError": {"fullname": "tantivy.query_parser_error.NoDefaultFieldDeclaredError", "modulename": "tantivy.query_parser_error", "qualname": "NoDefaultFieldDeclaredError", "kind": "class", "doc": "If no default field is declared, running a query without any field specified is forbbidden.
\n"}, "tantivy.query_parser_error.FieldNotIndexedError": {"fullname": "tantivy.query_parser_error.FieldNotIndexedError", "modulename": "tantivy.query_parser_error", "qualname": "FieldNotIndexedError", "kind": "class", "doc": "The field searched for is not declared as indexed in the schema.
\n"}, "tantivy.query_parser_error.FieldNotIndexedError.field": {"fullname": "tantivy.query_parser_error.FieldNotIndexedError.field", "modulename": "tantivy.query_parser_error", "qualname": "FieldNotIndexedError.field", "kind": "function", "doc": "\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.FieldDoesNotHavePositionsIndexedError": {"fullname": "tantivy.query_parser_error.FieldDoesNotHavePositionsIndexedError", "modulename": "tantivy.query_parser_error", "qualname": "FieldDoesNotHavePositionsIndexedError", "kind": "class", "doc": "A phrase query was requested for a field that does not have any positions indexed.
\n"}, "tantivy.query_parser_error.FieldDoesNotHavePositionsIndexedError.field": {"fullname": "tantivy.query_parser_error.FieldDoesNotHavePositionsIndexedError.field", "modulename": "tantivy.query_parser_error", "qualname": "FieldDoesNotHavePositionsIndexedError.field", "kind": "function", "doc": "\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.PhrasePrefixRequiresAtLeastTwoTermsError": {"fullname": "tantivy.query_parser_error.PhrasePrefixRequiresAtLeastTwoTermsError", "modulename": "tantivy.query_parser_error", "qualname": "PhrasePrefixRequiresAtLeastTwoTermsError", "kind": "class", "doc": "A phrase-prefix query requires at least two terms
\n"}, "tantivy.query_parser_error.PhrasePrefixRequiresAtLeastTwoTermsError.phrase": {"fullname": "tantivy.query_parser_error.PhrasePrefixRequiresAtLeastTwoTermsError.phrase", "modulename": "tantivy.query_parser_error", "qualname": "PhrasePrefixRequiresAtLeastTwoTermsError.phrase", "kind": "function", "doc": "\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.PhrasePrefixRequiresAtLeastTwoTermsError.tokenizer": {"fullname": "tantivy.query_parser_error.PhrasePrefixRequiresAtLeastTwoTermsError.tokenizer", "modulename": "tantivy.query_parser_error", "qualname": "PhrasePrefixRequiresAtLeastTwoTermsError.tokenizer", "kind": "function", "doc": "\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.UnknownTokenizerError": {"fullname": "tantivy.query_parser_error.UnknownTokenizerError", "modulename": "tantivy.query_parser_error", "qualname": "UnknownTokenizerError", "kind": "class", "doc": "The tokenizer for the given field is unknown.
\n"}, "tantivy.query_parser_error.UnknownTokenizerError.tokenizer": {"fullname": "tantivy.query_parser_error.UnknownTokenizerError.tokenizer", "modulename": "tantivy.query_parser_error", "qualname": "UnknownTokenizerError.tokenizer", "kind": "function", "doc": "\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.UnknownTokenizerError.field": {"fullname": "tantivy.query_parser_error.UnknownTokenizerError.field", "modulename": "tantivy.query_parser_error", "qualname": "UnknownTokenizerError.field", "kind": "function", "doc": "\n", "signature": "(self, /):", "funcdef": "def"}, "tantivy.query_parser_error.RangeMustNotHavePhraseError": {"fullname": "tantivy.query_parser_error.RangeMustNotHavePhraseError", "modulename": "tantivy.query_parser_error", "qualname": "RangeMustNotHavePhraseError", "kind": "class", "doc": "The query contains a range query with a phrase as one of the bounds. Only terms can be used as\nbounds.
\n"}, "tantivy.query_parser_error.DateFormatError": {"fullname": "tantivy.query_parser_error.DateFormatError", "modulename": "tantivy.query_parser_error", "qualname": "DateFormatError", "kind": "class", "doc": "The format for the date field is not RFC 3339 compliant.
\n"}, "tantivy.query_parser_error.FacetFormatError": {"fullname": "tantivy.query_parser_error.FacetFormatError", "modulename": "tantivy.query_parser_error", "qualname": "FacetFormatError", "kind": "class", "doc": "The format for the facet field is invalid.
\n"}, "tantivy.query_parser_error.IpFormatError": {"fullname": "tantivy.query_parser_error.IpFormatError", "modulename": "tantivy.query_parser_error", "qualname": "IpFormatError", "kind": "class", "doc": "The format for the ip field is invalid.
\n"}}, "docInfo": {"tantivy": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "tantivy.Order": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 13}, "tantivy.Order.Asc": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "tantivy.Order.Desc": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "tantivy.Schema": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 24}, "tantivy.SearchResult": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "tantivy.SearchResult.count": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 22}, "tantivy.SearchResult.hits": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 17}, "tantivy.Document": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 321}, "tantivy.Document.extend": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 3}, "tantivy.Document.from_dict": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 22, "bases": 0, "doc": 3}, "tantivy.Document.to_dict": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 45}, "tantivy.Document.add_text": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 40}, "tantivy.Document.add_unsigned": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 42}, "tantivy.Document.add_integer": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 41}, "tantivy.Document.add_float": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 40}, "tantivy.Document.add_boolean": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 40}, "tantivy.Document.add_date": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 40}, "tantivy.Document.add_facet": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 37}, "tantivy.Document.add_bytes": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 39}, "tantivy.Document.add_json": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 54}, "tantivy.Document.get_first": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 51}, "tantivy.Document.get_all": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 22, "bases": 0, "doc": 47}, "tantivy.Document.num_fields": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 15}, "tantivy.Document.is_empty": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "tantivy.DocAddress": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 52}, "tantivy.DocAddress.segment_ord": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 25}, "tantivy.DocAddress.doc": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 6}, "tantivy.Facet": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 79}, "tantivy.Facet.from_encoded": {"qualname": 3, "fullname": 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"tantivy.Document.get_all": {"tf": 1.4142135623730951}, "tantivy.tantivy.Document.get_first": {"tf": 1.4142135623730951}, "tantivy.tantivy.Document.get_all": {"tf": 1.4142135623730951}, "tantivy.tantivy.Index.parse_query_lenient": {"tf": 1}}, "df": 5}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"tantivy.Document.get_first": {"tf": 1}, "tantivy.Document.get_all": {"tf": 1}, "tantivy.DocAddress": {"tf": 1}, "tantivy.Facet": {"tf": 1}, "tantivy.tantivy.SchemaBuilder.add_integer_field": {"tf": 1}, "tantivy.tantivy.SchemaBuilder.add_float_field": {"tf": 1}, "tantivy.tantivy.SchemaBuilder.add_unsigned_field": {"tf": 1}, "tantivy.tantivy.SchemaBuilder.add_boolean_field": {"tf": 1}, "tantivy.tantivy.SchemaBuilder.add_date_field": {"tf": 1}, "tantivy.tantivy.SchemaBuilder.add_bytes_field": {"tf": 1}, "tantivy.tantivy.SchemaBuilder.add_ip_addr_field": {"tf": 1}, "tantivy.tantivy.Searcher": {"tf": 1}, "tantivy.tantivy.Searcher.search": {"tf": 1}, "tantivy.tantivy.Searcher.doc": {"tf": 1}, "tantivy.tantivy.Document.get_first": {"tf": 1}, "tantivy.tantivy.Document.get_all": {"tf": 1}, "tantivy.tantivy.Index.writer": {"tf": 1}, "tantivy.tantivy.Index.searcher": {"tf": 1}, "tantivy.tantivy.Index.exists": {"tf": 1.4142135623730951}, "tantivy.tantivy.DocAddress": {"tf": 1}, "tantivy.tantivy.Facet": {"tf": 1}, "tantivy.query_parser_error.UnknownTokenizerError": {"tf": 1}}, "df": 22}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"tantivy.tantivy.Index.writer": {"tf": 1}}, "df": 1}}}}}}}, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"tantivy.Document.add_json": {"tf": 1.7320508075688772}, "tantivy.tantivy": {"tf": 1.4142135623730951}, "tantivy.tantivy.SchemaBuilder.add_json_field": {"tf": 1}, "tantivy.tantivy.Document.add_json": {"tf": 1.7320508075688772}}, "df": 4}}}}, "y": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {"tantivy.tantivy": {"tf": 1.4142135623730951}, "tantivy.tantivy.SchemaBuilder": {"tf": 1}, "tantivy.tantivy.Index.reload": {"tf": 1.4142135623730951}}, "df": 3, "r": {"docs": {"tantivy.tantivy.Index.writer": {"tf": 1}, "tantivy.tantivy.Index.reload": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"tantivy.tantivy": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};
+
+ // mirrored in build-search-index.js (part 1)
+ // Also split on html tags. this is a cheap heuristic, but good enough.
+ elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/);
+
+ let searchIndex;
+ if (docs._isPrebuiltIndex) {
+ console.info("using precompiled search index");
+ searchIndex = elasticlunr.Index.load(docs);
+ } else {
+ console.time("building search index");
+ // mirrored in build-search-index.js (part 2)
+ searchIndex = elasticlunr(function () {
+ this.pipeline.remove(elasticlunr.stemmer);
+ this.pipeline.remove(elasticlunr.stopWordFilter);
+ this.addField("qualname");
+ this.addField("fullname");
+ this.addField("annotation");
+ this.addField("default_value");
+ this.addField("signature");
+ this.addField("bases");
+ this.addField("doc");
+ this.setRef("fullname");
+ });
+ for (let doc of docs) {
+ searchIndex.addDoc(doc);
+ }
+ console.timeEnd("building search index");
+ }
+
+ return (term) => searchIndex.search(term, {
+ fields: {
+ qualname: {boost: 4},
+ fullname: {boost: 2},
+ annotation: {boost: 2},
+ default_value: {boost: 2},
+ signature: {boost: 2},
+ bases: {boost: 2},
+ doc: {boost: 1},
+ },
+ expand: true
+ });
+})();
\ No newline at end of file
diff --git a/docs/api/tantivy.md b/docs/api/tantivy.md
new file mode 100644
index 00000000..f8b113df
--- /dev/null
+++ b/docs/api/tantivy.md
@@ -0,0 +1,844 @@
+---
+title: tantivy
+---
+
+
+
+
+
+
+
+ class
+ Order:
+
+
+
+
+
+ Enum representing the direction in which something should be sorted.
+
+
+ class
+ Schema:
+
+
+
+
+
+ Tantivy schema.
+
+
The schema is very strict. To build the schema the SchemaBuilder class is
+provided.
+
+
+ class
+ SearchResult:
+
+
+
+
+
+ Object holding a results successful search.
+
+
+ count
+
+
+
+
+
+
How many documents matched the query. Only available if count was set
+to true during the search.
+
+
+
+
+
+ hits
+
+
+
+
+
+
The list of tuples that contains the scores and DocAddress of the
+search results.
+
+
+
+
+
+ class
+ Document:
+
+
+
+
+
+ Tantivy's Document is the object that can be indexed and then searched for.
+
+
Documents are fundamentally a collection of unordered tuples
+(field_name, value). In this list, one field may appear more than once.
+
+
Example:
+
+
>>> doc = Document()
+>>> doc.add_text("title", "The Old Man and the Sea")
+>>> doc.add_text("body", ("He was an old man who fished alone in a "
+... "skiff in the Gulf Stream and he had gone "
+... "eighty-four days now without taking a fish."))
+>>> doc
+Document(body=[He was an ],title=[The Old Ma])
+
+
+
For simplicity, it is also possible to build a Document by passing the field
+values directly as constructor arguments.
+
+
Example:
+
+
>>> doc = Document(title=["The Old Man and the Sea"], body=["..."])
+
+
+
As syntactic sugar, tantivy also allows the user to pass a single values
+if there is only one. In other words, the following is also legal.
+
+
Example:
+
+
>>> doc = Document(title="The Old Man and the Sea", body="...")
+
+
+
For numeric fields, the [Document] constructor does not have any
+information about the type and will try to guess the type.
+Therefore, it is recommended to use the [Document::from_dict()],
+[Document::extract()], or Document::add_*() functions to provide
+explicit type information.
+
+
Example:
+
+
>>> schema = (
+... SchemaBuilder()
+... .add_unsigned_field("unsigned")
+... .add_integer_field("signed")
+... .add_float_field("float")
+... .build()
+... )
+>>> doc = Document.from_dict(
+... {"unsigned": 1000, "signed": -5, "float": 0.4},
+... schema,
+... )
+
+
+
+
+ def
+ extend(self, /, py_dict, schema=None):
+
+
+
+
+
+
+
+
+
+
+ def
+ from_dict(py_dict, schema=None):
+
+
+
+
+
+
+
+
+
+
+ def
+ to_dict(self, /):
+
+
+
+
+
+
Returns a dictionary with the different
+field values.
+
+
In tantivy, Document can be hold multiple
+values for a single field.
+
+
For this reason, the dictionary, will associate
+a list of value for every field.
+
+
+
+
+
+
+ def
+ add_text(self, /, field_name, text):
+
+
+
+
+
+
Add a text value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the text.
+ text (str): The text that will be added to the document.
+
+
+
+
+
+
+ def
+ add_unsigned(self, /, field_name, value):
+
+
+
+
+
+
Add an unsigned integer value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the unsigned integer.
+ value (int): The integer that will be added to the document.
+
+
+
+
+
+
+ def
+ add_integer(self, /, field_name, value):
+
+
+
+
+
+
Add a signed integer value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the integer.
+ value (int): The integer that will be added to the document.
+
+
+
+
+
+
+ def
+ add_float(self, /, field_name, value):
+
+
+
+
+
+
Add a float value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the value.
+ value (f64): The float that will be added to the document.
+
+
+
+
+
+
+ def
+ add_boolean(self, /, field_name, value):
+
+
+
+
+
+
Add a boolean value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the value.
+ value (bool): The boolean that will be added to the document.
+
+
+
+
+
+
+ def
+ add_date(self, /, field_name, value):
+
+
+
+
+
+
Add a date value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the date.
+ value (datetime): The date that will be added to the document.
+
+
+
+
+
+
+ def
+ add_facet(self, /, field_name, facet):
+
+
+
+
+
+
Add a facet value to the document.
+Args:
+ field_name (str): The field name for which we are adding the facet.
+ value (Facet): The Facet that will be added to the document.
+
+
+
+
+
+
+ def
+ add_bytes(self, /, field_name, bytes):
+
+
+
+
+
+
Add a bytes value to the document.
+
+
Args:
+ field_name (str): The field for which we are adding the bytes.
+ value (bytes): The bytes that will be added to the document.
+
+
+
+
+
+
+ def
+ add_json(self, /, field_name, value):
+
+
+
+
+
+
Add a JSON value to the document.
+
+
Args:
+ field_name (str): The field for which we are adding the bytes.
+ value (str | Dict[str, Any]): The JSON object that will be added
+ to the document.
+
+
Raises a ValueError if the JSON is invalid.
+
+
+
+
+
+
+ def
+ get_first(self, /, fieldname):
+
+
+
+
+
+
Get the first value associated with the given field.
+
+
Args:
+ field (Field): The field for which we would like to get the value.
+
+
Returns the value if one is found, otherwise None.
+The type of the value depends on the field.
+
+
+
+
+
+
+ def
+ get_all(self, /, field_name):
+
+
+
+
+
+
Get the all values associated with the given field.
+
+
Args:
+ field (Field): The field for which we would like to get the values.
+
+
Returns a list of values.
+The type of the value depends on the field.
+
+
+
+
+
+ num_fields
+
+
+
+
+
+
Returns the number of added fields that have been added to the document
+
+
+
+
+
+ is_empty
+
+
+
+
+
+
True if the document is empty, False otherwise.
+
+
+
+
+
+ class
+ DocAddress:
+
+
+
+
+
+ DocAddress contains all the necessary information to identify a document
+given a Searcher object.
+
+
It consists in an id identifying its segment, and its segment-local DocId.
+The id used for the segment is actually an ordinal in the list of segment
+hold by a Searcher.
+
+
+ segment_ord
+
+
+
+
+
+
The segment ordinal is an id identifying the segment hosting the
+document. It is only meaningful, in the context of a searcher.
+
+
+
+
+
+ doc
+
+
+
+
+
+
The segment local DocId
+
+
+
+
+
+ class
+ Facet:
+
+
+
+
+
+ A Facet represent a point in a given hierarchy.
+
+
They are typically represented similarly to a filepath. For instance, an
+e-commerce website could have a Facet for /electronics/tv_and_video/led_tv.
+
+
A document can be associated to any number of facets. The hierarchy
+implicitely imply that a document belonging to a facet also belongs to the
+ancestor of its facet. In the example above, /electronics/tv_and_video/
+and /electronics.
+
+
+
+ def
+ from_encoded(encoded_bytes):
+
+
+
+
+
+
Creates a Facet from its binary representation.
+
+
+
+
+
+
+ def
+ root(cls, /):
+
+
+
+
+
+
Create a new instance of the "root facet" Equivalent to /.
+
+
+
+
+
+
+ def
+ is_prefix_of(self, /, other):
+
+
+
+
+
+
Returns true if another Facet is a subfacet of this facet.
+Args:
+ other (Facet): The Facet that we should check if this facet is a
+ subset of.
+
+
+
+
+
+
+ def
+ from_string(cls, /, facet_string):
+
+
+
+
+
+
Create a Facet object from a string.
+Args:
+ facet_string (str): The string that contains a facet.
+
+
Returns the created Facet.
+
+
+
+
+
+
+ def
+ to_path(self, /):
+
+
+
+
+
+
Returns the list of segments that forms a facet path.
+
+
For instance //europe/france becomes ["europe", "france"].
+
+
+
+
+
+
+ def
+ to_path_str(self, /):
+
+
+
+
+
+
Returns the facet string representation.
+
+
+
+
+
+ is_root
+
+
+
+
+
+
Returns true if the facet is the root facet /.
+
+
+
+
+
+
+
+
+
+
+
+
+tantivy.query_parser_error
+
+ Submodule containing all the possible errors that can be raised during
+query parsing.
+
+
Example:
+
+
+
+
+ import tantivy
+ from tantivy import query_parser_error
+
+>>> builder = tantivy.SchemaBuilder()
+
+>>> title = builder.add_text_field("title", stored=True)
+>>> body = builder.add_text_field("body")
+>>> id = builder.add_unsigned_field("id")
+>>> rating = builder.add_float_field("rating")
+
+>>> schema = builder.build()
+>>> index = tantivy.Index(schema)
+
+>>> query, errors = index.parse_query_lenient(
+ "bod:'world' AND id:<3.5 AND rating:5.0"
+ )
+
+>>> assert len(errors) == 2
+>>> assert isinstance(errors[0], query_parser_error.FieldDoesNotExistError)
+>>> assert isinstance(errors[1], query_parser_error.ExpectedIntError)
+
+
+
+
+
+
+ class
+ SyntaxError:
+
+
+
+
+
+ Error in the query syntax.
+
+
+ inner_message
+
+
+
+
+
+
+
+
+
+ class
+ UnsupportedQueryError:
+
+
+
+
+
+ This query is unsupported.
+
+
+ inner_message
+
+
+
+
+
+
+
+
+
+ class
+ FieldDoesNotExistError:
+
+
+
+
+
+ The query references a field that is not in the schema.
+
+
+ field
+
+
+
+
+
+
The name of the field causing the error.
+
+
+
+
+
+ class
+ ExpectedIntError:
+
+
+
+
+
+ The query contains a term for a u64 or i64-field, but the value is neither.
+
+
+
+ def
+ caused_by_empty(self, /):
+
+
+
+
+
+
If true, the value being parsed was empty.
+
+
+
+
+
+
+ def
+ caused_by_invalid_digit(self, /):
+
+
+
+
+
+
If true, an invalid digit was found.
+
+
+
+
+
+
+ def
+ caused_by_pos_overflow(self, /):
+
+
+
+
+
+
If true, the value being parsed was too large.
+
+
+
+
+
+
+ def
+ caused_by_neg_overflow(self, /):
+
+
+
+
+
+
If true, the value being parsed was too small.
+
+
+
+
+
+ class
+ ExpectedBase64Error:
+
+
+
+
+
+ The query contains a term for a bytes field, but the value is not valid base64.
+
+
+
+ def
+ caused_by_invalid_byte(self, /):
+
+
+
+
+
+
If true, an invalid byte was found in the query. Padding characters (=) interspersed in
+the encoded form will be treated as invalid bytes.
+
+
+
+
+
+
+ def
+ invalid_byte_info(self, /):
+
+
+
+
+
+
If the error was caused by an invalid byte, returns the offset and offending byte.
+
+
+
+
+
+
+ def
+ caused_by_invalid_length(self, /):
+
+
+
+
+
+
If true, the length of the base64 string was invalid.
+
+
+
+
+
+
+ def
+ caused_by_invalid_last_symbol(self, /):
+
+
+
+
+
+
The last non-padding input symbol's encoded 6 bits have nonzero bits that will be discarded.
+If true, this is indicative of corrupted or truncated Base64.
+
+
+
+
+
+
+ def
+ invalid_last_symbol_info(self, /):
+
+
+
+
+
+
If the error was caused by an invalid last symbol, returns the offset and offending byte.
+
+
+
+
+
+
+ def
+ caused_by_invalid_padding(self, /):
+
+
+
+
+
+
The nature of the padding was not as configured: absent or incorrect when it must be
+canonical, or present when it must be absent, etc.
+
+
+
+
+
+ class
+ ExpectedFloatError:
+
+
+
+
+
+ The query contains a term for a f64-field, but the value is not a f64.
+
+
+ class
+ ExpectedBoolError:
+
+
+
+
+
+ The query contains a term for a bool-field, but the value is not a bool.
+
+
+ class
+ AllButQueryForbiddenError:
+
+
+
+
+
+ It is forbidden queries that are only "excluding". (e.g. -title:pop)
+
+
+ class
+ NoDefaultFieldDeclaredError:
+
+
+
+
+
+ If no default field is declared, running a query without any field specified is forbbidden.
+
+
+ class
+ FieldNotIndexedError:
+
+
+
+
+
+ The field searched for is not declared as indexed in the schema.
+
+
+
+ def
+ field(self, /):
+
+
+
+
+
+
+
+
+
+ class
+ FieldDoesNotHavePositionsIndexedError:
+
+
+
+
+
+ A phrase query was requested for a field that does not have any positions indexed.
+
+
+
+ def
+ field(self, /):
+
+
+
+
+
+
+
+
+
+ class
+ PhrasePrefixRequiresAtLeastTwoTermsError:
+
+
+
+
+
+ A phrase-prefix query requires at least two terms
+
+
+
+ def
+ phrase(self, /):
+
+
+
+
+
+
+
+
+
+
+ def
+ tokenizer(self, /):
+
+
+
+
+
+
+
+
+
+ class
+ UnknownTokenizerError:
+
+
+
+
+
+ The tokenizer for the given field is unknown.
+
+
+
+ def
+ tokenizer(self, /):
+
+
+
+
+
+
+
+
+
+
+ def
+ field(self, /):
+
+
+
+
+
+
+
+
+
+ class
+ RangeMustNotHavePhraseError:
+
+
+
+
+
+ The query contains a range query with a phrase as one of the bounds. Only terms can be used as
+bounds.
+
+
+
+
+
+
+
+
+
+
+ Python bindings for the search engine library Tantivy.
+
+
Tantivy is a full text search engine library written in rust.
+
+
It is closer to Apache Lucene than to Elasticsearch and Apache Solr in
+the sense it is not an off-the-shelf search engine server, but rather
+a library that can be used to build such a search engine.
+Tantivy is, in fact, strongly inspired by Lucene's design.
+
+
Example:
+
+
+
+
+ import json
+ import tantivy
+
+>>> builder = tantivy.SchemaBuilder()
+
+>>> title = builder.add_text_field("title", stored=True)
+>>> body = builder.add_text_field("body")
+
+>>> schema = builder.build()
+>>> index = tantivy.Index(schema)
+>>> doc = Document()
+>>> doc.add_text(title, "The Old Man and the Sea")
+>>> doc.add_text(body, ("He was an old man who fished alone in a "
+ "skiff in the Gulf Stream and he had gone "
+ "eighty-four days now without taking a fish."))
+
+>>> writer.add_document(doc)
+
+>>> doc = schema.parse_document(json.dumps({
+ "title": ["Frankenstein", "The Modern Prometheus"],
+ "body": ("You will rejoice to hear that no disaster has "
+ "accompanied the commencement of an enterprise which "
+ "you have regarded with such evil forebodings. "
+ "I arrived here yesterday, and my first task is to "
+ "assure my dear sister of my welfare and increasing "
+ "confidence in the success of my undertaking.")
+}))
+
+>>> writer.add_document(doc)
+>>> writer.commit()
+
+>>> reader = index.reader()
+>>> searcher = reader.searcher()
+
+>>> query = index.parse_query("sea whale", [title, body])
+
+>>> result = searcher.search(query, 10)
+
+>>> assert len(result) == 1
+
+
+
+
+
+
+ class
+ Order:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ class
+ Schema:
+
+
+
+
+
+ Tantivy schema.
+
+
The schema is very strict. To build the schema the SchemaBuilder class is
+provided.
+
+
+ class
+ SchemaBuilder:
+
+
+
+
+
+ Tantivy has a very strict schema.
+You need to specify in advance whether a field is indexed or not,
+stored or not.
+
+
This is done by creating a schema object, and
+setting up the fields one by one.
+
+
Examples:
+
+
>>> builder = tantivy.SchemaBuilder()
+
+>>> title = builder.add_text_field("title", stored=True)
+>>> body = builder.add_text_field("body")
+
+>>> schema = builder.build()
+
+
+
+
+ def
+ is_valid_field_name(name: str) -> bool:
+
+
+
+
+
+
+
+
+
+
+
def
+
add_text_field( self, name: str, stored: bool = False, tokenizer_name: str = 'default', index_option: str = 'position') -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add a new text field to the schema.
+
+
Args:
+ name (str): The name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ fast (bool, optional): Set the text options as a fast field. A
+ fast field is a column-oriented fashion storage for tantivy.
+ Text fast fields will have the term ids stored in the fast
+ field. The fast field will be a multivalued fast field.
+ It is recommended to use the "raw" tokenizer, since it will
+ store the original text unchanged. The "default" tokenizer will
+ store the terms as lower case and this will be reflected in the
+ dictionary.
+ tokenizer_name (str, optional): The name of the tokenizer that
+ should be used to process the field. Defaults to 'default'
+ index_option (str, optional): Sets which information should be
+ indexed with the tokens. Can be one of 'position', 'freq' or
+ 'basic'. Defaults to 'position'. The 'basic' index_option
+ records only the document ID, the 'freq' option records the
+ document id and the term frequency, while the 'position' option
+ records the document id, term frequency and the positions of
+ the term occurrences in the document.
+
+
Returns the associated field handle.
+Raises a ValueError if there was an error with the field creation.
+
+
+
+
+
+
+
def
+
add_integer_field( self, name: str, stored: bool = False, indexed: bool = False, fast: bool = False) -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add a new signed integer field to the schema.
+
+
Args:
+ name (str): The name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ indexed (bool, optional): If true sets the field to be indexed.
+ fast (bool, optional): Set the numeric options as a fast field. A
+ fast field is a column-oriented fashion storage for tantivy.
+ It is designed for the fast random access of some document
+ fields given a document id.
+
+
Returns the associated field handle.
+Raises a ValueError if there was an error with the field creation.
+
+
+
+
+
+
+
def
+
add_float_field( self, name: str, stored: bool = False, indexed: bool = False, fast: bool = False) -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add a new float field to the schema.
+
+
Args:
+ name (str): The name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ indexed (bool, optional): If true sets the field to be indexed.
+ fast (bool, optional): Set the numeric options as a fast field. A
+ fast field is a column-oriented fashion storage for tantivy.
+ It is designed for the fast random access of some document
+ fields given a document id.
+
+
Returns the associated field handle.
+Raises a ValueError if there was an error with the field creation.
+
+
+
+
+
+
+
def
+
add_unsigned_field( self, name: str, stored: bool = False, indexed: bool = False, fast: bool = False) -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add a new unsigned integer field to the schema.
+
+
Args:
+ name (str): The name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ indexed (bool, optional): If true sets the field to be indexed.
+ fast (bool, optional): Set the numeric options as a fast field. A
+ fast field is a column-oriented fashion storage for tantivy.
+ It is designed for the fast random access of some document
+ fields given a document id.
+
+
Returns the associated field handle.
+Raises a ValueError if there was an error with the field creation.
+
+
+
+
+
+
+
def
+
add_boolean_field( self, name: str, stored: bool = False, indexed: bool = False, fast: bool = False) -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add a new boolean field to the schema.
+
+
Args:
+ name (str): The name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ indexed (bool, optional): If true sets the field to be indexed.
+ fast (bool, optional): Set the numeric options as a fast field. A
+ fast field is a column-oriented fashion storage for tantivy.
+ It is designed for the fast random access of some document
+ fields given a document id.
+
+
Returns the associated field handle.
+Raises a ValueError if there was an error with the field creation.
+
+
+
+
+
+
+
def
+
add_date_field( self, name: str, stored: bool = False, indexed: bool = False, fast: bool = False) -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add a new date field to the schema.
+
+
Args:
+ name (str): The name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ indexed (bool, optional): If true sets the field to be indexed.
+ fast (bool, optional): Set the date options as a fast field. A fast
+ field is a column-oriented fashion storage for tantivy. It is
+ designed for the fast random access of some document fields
+ given a document id.
+
+
Returns the associated field handle.
+Raises a ValueError if there was an error with the field creation.
+
+
+
+
+
+
+
def
+
add_json_field( self, name: str, stored: bool = False, tokenizer_name: str = 'default', index_option: str = 'position') -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add a new json field to the schema.
+
+
Args:
+ name (str): the name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ fast (bool, optional): Set the text options as a fast field. A
+ fast field is a column-oriented fashion storage for tantivy.
+ Text fast fields will have the term ids stored in the fast
+ field. The fast field will be a multivalued fast field.
+ It is recommended to use the "raw" tokenizer, since it will
+ store the original text unchanged. The "default" tokenizer will
+ store the terms as lower case and this will be reflected in the
+ dictionary.
+ tokenizer_name (str, optional): The name of the tokenizer that
+ should be used to process the field. Defaults to 'default'
+ index_option (str, optional): Sets which information should be
+ indexed with the tokens. Can be one of 'position', 'freq' or
+ 'basic'. Defaults to 'position'. The 'basic' index_option
+ records only the document ID, the 'freq' option records the
+ document id and the term frequency, while the 'position' option
+ records the document id, term frequency and the positions of
+ the term occurrences in the document.
+
+
Returns the associated field handle.
+Raises a ValueError if there was an error with the field creation.
+
+
+
+
+
+
+
+
Add a Facet field to the schema.
+Args:
+ name (str): The name of the field.
+
+
+
+
+
+
+
def
+
add_bytes_field( self, name: str, stored: bool = False, indexed: bool = False, fast: bool = False, index_option: str = 'position') -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add a fast bytes field to the schema.
+
+
Args:
+ name (str): The name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ indexed (bool, optional): If true sets the field to be indexed.
+ fast (bool, optional): Set the bytes options as a fast field. A fast
+ field is a column-oriented fashion storage for tantivy. It is
+ designed for the fast random access of some document fields
+ given a document id.
+
+
+
+
+
+
+
def
+
add_ip_addr_field( self, name: str, stored: bool = False, indexed: bool = False, fast: bool = False) -> tantivy.tantivy.SchemaBuilder:
+
+
+
+
+
+
Add an IP address field to the schema.
+
+
Args:
+ name (str): The name of the field.
+ stored (bool, optional): If true sets the field as stored, the
+ content of the field can be later restored from a Searcher.
+ Defaults to False.
+ indexed (bool, optional): If true sets the field to be indexed.
+ fast (bool, optional): Set the IP address options as a fast field. A
+ fast field is a column-oriented fashion storage for tantivy. It
+ is designed for the fast random access of some document fields
+ given a document id.
+
+
+
+
+
+
+
+
Finalize the creation of a Schema.
+
+
Returns a Schema object. After this is called the SchemaBuilder cannot
+be used anymore.
+
+
+
+
+
+ class
+ Searcher:
+
+
+
+
+
+ Tantivy's Searcher class
+
+
A Searcher is used to search the index given a prepared Query.
+
+
+
+
+
Search the index with the given query and collect results.
+
+
Args:
+ query (Query): The query that will be used for the search.
+ limit (int, optional): The maximum number of search results to
+ return. Defaults to 10.
+ count (bool, optional): Should the number of documents that match
+ the query be returned as well. Defaults to true.
+ order_by_field (Field, optional): A schema field that the results
+ should be ordered by. The field must be declared as a fast field
+ when building the schema. Note, this only works for unsigned
+ fields.
+ offset (Field, optional): The offset from which the results have
+ to be returned.
+ order (Order, optional): The order in which the results
+ should be sorted. If not specified, defaults to descending.
+
+
Returns SearchResult object.
+
+
Raises a ValueError if there was an error with the search.
+
+
+
+
+
+
+
+
Fetches a document from Tantivy's store given a DocAddress.
+
+
Args:
+ doc_address (DocAddress): The DocAddress that is associated with
+ the document that we wish to fetch.
+
+
Returns the Document, raises ValueError if the document can't be found.
+
+
+
+
+
+ num_segments: int
+
+
+
+
+
+
Returns the number of segments in the index.
+
+
+
+
+
+ num_docs: int
+
+
+
+
+
+
Returns the overall number of documents in the index.
+
+
+
+
+
+ class
+ SearchResult:
+
+
+
+
+
+ Object holding a results successful search.
+
+
+ count
+
+
+
+
+
+
How many documents matched the query. Only available if count was set
+to true during the search.
+
+
+
+
+
+
+
+
The list of tuples that contains the scores and DocAddress of the
+search results.
+
+
+
+
+
+ class
+ Document:
+
+
+
+
+
+ Tantivy's Document is the object that can be indexed and then searched for.
+
+
Documents are fundamentally a collection of unordered tuples
+(field_name, value). In this list, one field may appear more than once.
+
+
Example:
+
+
>>> doc = Document()
+>>> doc.add_text("title", "The Old Man and the Sea")
+>>> doc.add_text("body", ("He was an old man who fished alone in a "
+... "skiff in the Gulf Stream and he had gone "
+... "eighty-four days now without taking a fish."))
+>>> doc
+Document(body=[He was an ],title=[The Old Ma])
+
+
+
For simplicity, it is also possible to build a Document by passing the field
+values directly as constructor arguments.
+
+
Example:
+
+
>>> doc = Document(title=["The Old Man and the Sea"], body=["..."])
+
+
+
As syntactic sugar, tantivy also allows the user to pass a single values
+if there is only one. In other words, the following is also legal.
+
+
Example:
+
+
>>> doc = Document(title="The Old Man and the Sea", body="...")
+
+
+
For numeric fields, the [Document] constructor does not have any
+information about the type and will try to guess the type.
+Therefore, it is recommended to use the [Document::from_dict()],
+[Document::extract()], or Document::add_*() functions to provide
+explicit type information.
+
+
Example:
+
+
>>> schema = (
+... SchemaBuilder()
+... .add_unsigned_field("unsigned")
+... .add_integer_field("signed")
+... .add_float_field("float")
+... .build()
+... )
+>>> doc = Document.from_dict(
+... {"unsigned": 1000, "signed": -5, "float": 0.4},
+... schema,
+... )
+
+
+
+
+ def
+ to_dict(self) -> Any:
+
+
+
+
+
+
Returns a dictionary with the different
+field values.
+
+
In tantivy, Document can be hold multiple
+values for a single field.
+
+
For this reason, the dictionary, will associate
+a list of value for every field.
+
+
+
+
+
+
+ def
+ add_text(self, field_name: str, text: str) -> None:
+
+
+
+
+
+
Add a text value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the text.
+ text (str): The text that will be added to the document.
+
+
+
+
+
+
+ def
+ add_unsigned(self, field_name: str, value: int) -> None:
+
+
+
+
+
+
Add an unsigned integer value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the unsigned integer.
+ value (int): The integer that will be added to the document.
+
+
+
+
+
+
+ def
+ add_integer(self, field_name: str, value: int) -> None:
+
+
+
+
+
+
Add a signed integer value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the integer.
+ value (int): The integer that will be added to the document.
+
+
+
+
+
+
+ def
+ add_float(self, field_name: str, value: float) -> None:
+
+
+
+
+
+
Add a float value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the value.
+ value (f64): The float that will be added to the document.
+
+
+
+
+
+
+ def
+ add_boolean(self, field_name: str, value: bool) -> None:
+
+
+
+
+
+
Add a boolean value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the value.
+ value (bool): The boolean that will be added to the document.
+
+
+
+
+
+
+ def
+ add_date(self, field_name: str, value: datetime.datetime) -> None:
+
+
+
+
+
+
Add a date value to the document.
+
+
Args:
+ field_name (str): The field name for which we are adding the date.
+ value (datetime): The date that will be added to the document.
+
+
+
+
+
+
+
+
Add a facet value to the document.
+Args:
+ field_name (str): The field name for which we are adding the facet.
+ value (Facet): The Facet that will be added to the document.
+
+
+
+
+
+
+ def
+ add_bytes(self, field_name: str, bytes: bytes) -> None:
+
+
+
+
+
+
Add a bytes value to the document.
+
+
Args:
+ field_name (str): The field for which we are adding the bytes.
+ value (bytes): The bytes that will be added to the document.
+
+
+
+
+
+
+ def
+ add_json(self, field_name: str, value: Any) -> None:
+
+
+
+
+
+
Add a JSON value to the document.
+
+
Args:
+ field_name (str): The field for which we are adding the bytes.
+ value (str | Dict[str, Any]): The JSON object that will be added
+ to the document.
+
+
Raises a ValueError if the JSON is invalid.
+
+
+
+
+
+
+ def
+ get_first(self, field_name: str) -> Optional[Any]:
+
+
+
+
+
+
Get the first value associated with the given field.
+
+
Args:
+ field (Field): The field for which we would like to get the value.
+
+
Returns the value if one is found, otherwise None.
+The type of the value depends on the field.
+
+
+
+
+
+
+ def
+ get_all(self, field_name: str) -> list[typing.Any]:
+
+
+
+
+
+
Get the all values associated with the given field.
+
+
Args:
+ field (Field): The field for which we would like to get the values.
+
+
Returns a list of values.
+The type of the value depends on the field.
+
+
+
+
+
+ num_fields: int
+
+
+
+
+
+
Returns the number of added fields that have been added to the document
+
+
+
+
+
+ is_empty: bool
+
+
+
+
+
+
True if the document is empty, False otherwise.
+
+
+
+
+
+ class
+ Index:
+
+
+
+
+
+ Create a new index object.
+
+
Args:
+ schema (Schema): The schema of the index.
+ path (str, optional): The path where the index should be stored. If
+ no path is provided, the index will be stored in memory.
+ reuse (bool, optional): Should we open an existing index if one exists
+ or always create a new one.
+
+
If an index already exists it will be opened and reused. Raises OSError
+if there was a problem during the opening or creation of the index.
+
+
+
+ def
+ writer( self, heap_size: int = 128000000, num_threads: int = 0) -> tantivy.tantivy.IndexWriter:
+
+
+
+
+
+
Create a IndexWriter for the index.
+
+
The writer will be multithreaded and the provided heap size will be
+split between the given number of threads.
+
+
Args:
+ overall_heap_size (int, optional): The total target heap memory usage of
+ the writer. Tantivy requires that this can't be less
+ than 3000000 per thread. Lower values will result in more
+ frequent internal commits when adding documents (slowing down
+ write progress), and larger values will results in fewer
+ commits but greater memory usage. The best value will depend
+ on your specific use case.
+ num_threads (int, optional): The number of threads that the writer
+ should use. If this value is 0, tantivy will choose
+ automatically the number of threads.
+
+
Raises ValueError if there was an error while creating the writer.
+
+
+
+
+
+
+ def
+ config_reader(self, reload_policy: str = 'commit', num_warmers: int = 0) -> None:
+
+
+
+
+
+
Configure the index reader.
+
+
Args:
+ reload_policy (str, optional): The reload policy that the
+ IndexReader should use. Can be Manual or OnCommit.
+ num_warmers (int, optional): The number of searchers that the
+ reader should create.
+
+
+
+
+
+
+
+
Returns a searcher
+
+
This method should be called every single time a search query is performed.
+The same searcher must be used for a given query, as it ensures the use of a consistent segment set.
+
+
+
+
+
+
+ def
+ exists(path: str) -> bool:
+
+
+
+
+
+
Check if the given path contains an existing index.
+Args:
+ path: The path where tantivy will search for an index.
+
+
Returns True if an index exists at the given path, False otherwise.
+
+
Raises OSError if the directory cannot be opened.
+
+
+
+
+
+
+ def
+ reload(self) -> None:
+
+
+
+
+
+
Update searchers so that they reflect the state of the last .commit().
+
+
If you set up the the reload policy to be on 'commit' (which is the
+default) every commit should be rapidly reflected on your IndexReader
+and you should not need to call reload() at all.
+
+
+
+
+
+
+
def
+
parse_query( self, query: str, default_field_names: Optional[list[str]] = None) -> tantivy.tantivy.Query:
+
+
+
+
+
+
Parse a query
+
+
Args:
+ query: the query, following the tantivy query language.
+ default_fields_names (List[Field]): A list of fields used to search if no
+ field is specified in the query.
+
+
+
+
+
+
+
def
+
parse_query_lenient( self, query: str, default_field_names: Optional[list[str]] = None) -> tantivy.tantivy.Query:
+
+
+
+
+
+
Parse a query leniently.
+
+
This variant parses invalid query on a best effort basis. If some part of the query can't
+reasonably be executed (range query without field, searching on a non existing field,
+searching without precising field when no default field is provided...), they may get turned
+into a "match-nothing" subquery.
+
+
Args:
+ query: the query, following the tantivy query language.
+ default_fields_names (List[Field]): A list of fields used to search if no
+ field is specified in the query.
+
+
Returns a tuple containing the parsed query and a list of errors.
+
+
Raises ValueError if a field in default_field_names is not defined or marked as indexed.
+
+
+
+
+
+
+
+
The schema of the current index.
+
+
+
+
+
+ class
+ DocAddress:
+
+
+
+
+
+ DocAddress contains all the necessary information to identify a document
+given a Searcher object.
+
+
It consists in an id identifying its segment, and its segment-local DocId.
+The id used for the segment is actually an ordinal in the list of segment
+hold by a Searcher.
+
+
+ segment_ord: int
+
+
+
+
+
+
The segment ordinal is an id identifying the segment hosting the
+document. It is only meaningful, in the context of a searcher.
+
+
+
+
+
+ doc: int
+
+
+
+
+
+
The segment local DocId
+
+
+
+
+
+ class
+ Facet:
+
+
+
+
+
+ A Facet represent a point in a given hierarchy.
+
+
They are typically represented similarly to a filepath. For instance, an
+e-commerce website could have a Facet for /electronics/tv_and_video/led_tv.
+
+
A document can be associated to any number of facets. The hierarchy
+implicitely imply that a document belonging to a facet also belongs to the
+ancestor of its facet. In the example above, /electronics/tv_and_video/
+and /electronics.
+
+
+
+
+
Creates a Facet from its binary representation.
+
+
+
+
+
+
+
+
Create a new instance of the "root facet" Equivalent to /.
+
+
+
+
+
+
+
+
Returns true if another Facet is a subfacet of this facet.
+Args:
+ other (Facet): The Facet that we should check if this facet is a
+ subset of.
+
+
+
+
+
+
+
+
Create a Facet object from a string.
+Args:
+ facet_string (str): The string that contains a facet.
+
+
Returns the created Facet.
+
+
+
+
+
+
+ def
+ to_path(self) -> list[str]:
+
+
+
+
+
+
Returns the list of segments that forms a facet path.
+
+
For instance //europe/france becomes ["europe", "france"].
+
+
+
+
+
+
+ def
+ to_path_str(self) -> str:
+
+
+
+
+
+
Returns the facet string representation.
+
+
+
+
+
+ is_root: bool
+
+
+
+
+
+
Returns true if the facet is the root facet /.
+
+
+
+
+
+ class
+ Query:
+
+
+
+
+
+
+
+
+
+
+
+
+
Construct a Tantivy's TermQuery
+
+
+
+
+
+ class
+ Snippet:
+
+
+
+
+
+ Tantivy schema.
+
+
The schema is very strict. To build the schema the SchemaBuilder class is
+provided.
+
+
+
+ def
+ to_html(self) -> str:
+
+
+
+
+
+
+
+
+
+
+ def
+ highlighted(self) -> list[tantivy.tantivy.Range]:
+
+
+
+
+
+
+
+
+
+ class
+ SnippetGenerator:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ {% block content %}{% endblock %}
+
+ {% filter minify_css %}
+ {% block style %}
+ {#
+ The important part is that we leave out layout.css here.
+ Ideally we would still include Bootstrap Reboot, but that breaks mkdocs' theme.
+
+ #}
+
+
+
+ {% endblock %}
+ {% endfilter %}
+
diff --git a/docs/pdoc-template/pdoc_hook.py b/docs/pdoc-template/pdoc_hook.py
new file mode 100644
index 00000000..e1fa100e
--- /dev/null
+++ b/docs/pdoc-template/pdoc_hook.py
@@ -0,0 +1,20 @@
+def on_pre_build(config):
+ # From here: https://github.com/mitmproxy/pdoc/blob/main/examples/mkdocs/make.py
+ from pathlib import Path
+ import shutil
+
+ from pdoc import pdoc
+ from pdoc import render
+
+ here = Path(__file__).parent.parent
+ out = here / "api"
+ if out.exists():
+ shutil.rmtree(out)
+
+ # Render parts of pdoc's documentation into docs/api...
+ render.configure(template_directory=here / "pdoc-template")
+ pdoc("tantivy", output_directory=out)
+
+ # ...and rename the .html files to .md so that mkdocs picks them up!
+ for f in out.glob("**/*.html"):
+ f.rename(f.with_suffix(".md"))
diff --git a/docs/reference.md b/docs/reference.md
index 8ca4294b..a5245455 100644
--- a/docs/reference.md
+++ b/docs/reference.md
@@ -1,6 +1,4 @@
-# Reference
-
-## Valid Query Formats
+# Valid Query Formats
tantivy-py supports the [query language](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html#method.parse_query) used in tantivy.
Below a few basic query formats are shown:
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 1a16205e..3905abc1 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1 +1,2 @@
mkdocs==1.4.3
+pdoc==14.3.0
diff --git a/mkdocs.yml b/mkdocs.yml
index 2640f635..34601c47 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -5,7 +5,9 @@ nav:
- Tutorials: tutorials.md
- How-to Guides: howto.md
- Explanation: explanation.md
- - Reference: reference.md
+ - Reference:
+ - Query Language: reference.md
+ - API: api/tantivy.md
- About: about.md
theme: readthedocs
@@ -13,3 +15,10 @@ theme: readthedocs
# - 'User Guide':
# - 'Writing your docs': 'writing-your-docs.md'
# - 'Styling your docs': 'styling-your-docs.md'
+#
+plugins:
+- search
+
+# Hook to run pdoc
+hooks:
+- docs/pdoc-template/pdoc_hook.py
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 20faf861..4b7fd65f 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,2 +1,3 @@
maturin
+pdoc
pytest>=4.0
diff --git a/src/document.rs b/src/document.rs
index 8768463d..d3c1ea6b 100644
--- a/src/document.rs
+++ b/src/document.rs
@@ -425,6 +425,7 @@ impl<'a> From<&'a Value> for BorrowedSerdeValue<'a> {
/// (field_name, value). In this list, one field may appear more than once.
///
/// Example:
+///
/// >>> doc = tantivy.Document()
/// >>> doc.add_text("title", "The Old Man and the Sea")
/// >>> doc.add_text("body", ("He was an old man who fished alone in a "
@@ -437,12 +438,14 @@ impl<'a> From<&'a Value> for BorrowedSerdeValue<'a> {
/// values directly as constructor arguments.
///
/// Example:
+///
/// >>> doc = tantivy.Document(title=["The Old Man and the Sea"], body=["..."])
///
/// As syntactic sugar, tantivy also allows the user to pass a single values
/// if there is only one. In other words, the following is also legal.
///
/// Example:
+///
/// >>> doc = tantivy.Document(title="The Old Man and the Sea", body="...")
///
/// For numeric fields, the [`Document`] constructor does not have any
@@ -452,6 +455,7 @@ impl<'a> From<&'a Value> for BorrowedSerdeValue<'a> {
/// explicit type information.
///
/// Example:
+///
/// >>> schema = (
/// ... SchemaBuilder()
/// ... .add_unsigned_field("unsigned")