Vector Store Retrievers#

Base vector store index query.

class llama_index.indices.vector_store.retrievers.retriever.VectorIndexRetriever(index: VectorStoreIndex, similarity_top_k: int = 2, vector_store_query_mode: VectorStoreQueryMode = VectorStoreQueryMode.DEFAULT, filters: Optional[MetadataFilters] = None, alpha: Optional[float] = None, node_ids: Optional[List[str]] = None, doc_ids: Optional[List[str]] = None, sparse_top_k: Optional[int] = None, callback_manager: Optional[CallbackManager] = None, object_map: Optional[dict] = None, verbose: bool = False, **kwargs: Any)#

Vector index retriever.

Parameters

index (VectorStoreIndex) – vector store index.
similarity_top_k (int) – number of top k results to return.
vector_store_query_mode (str) – vector store query mode See reference for VectorStoreQueryMode for full list of supported modes.
filters (Optional[MetadataFilters]) – metadata filters, defaults to None
alpha (float) – weight for sparse/dense retrieval, only used for hybrid query mode.
doc_ids (Optional[List[str]]) – list of documents to constrain search.
vector_store_kwargs (dict) – Additional vector store specific kwargs to pass through to the vector store at query time.

as_query_component(partial: Optional[Dict[str, Any]] = None, **kwargs: Any) → QueryComponent#: Get query component.

get_prompts() → Dict[str, BasePromptTemplate]#: Get a prompt.

get_service_context() → Optional[ServiceContext]#: Attempts to resolve a service context. Short-circuits at self.service_context, self._service_context, or self._index.service_context.

retrieve(str_or_query_bundle: Union[str, QueryBundle]) → List[NodeWithScore]#

Retrieve nodes given query.

Parameters: str_or_query_bundle (QueryType) – Either a query string or a QueryBundle object.

property similarity_top_k: int#: Return similarity top k.

update_prompts(prompts_dict: Dict[str, BasePromptTemplate]) → None#

Update prompts.

Other prompts will remain in place.

class llama_index.indices.vector_store.retrievers.auto_retriever.auto_retriever.VectorIndexAutoRetriever(index: VectorStoreIndex, vector_store_info: VectorStoreInfo, prompt_template_str: Optional[str] = None, service_context: Optional[ServiceContext] = None, max_top_k: int = 10, similarity_top_k: int = 2, empty_query_top_k: Optional[int] = 10, vector_store_query_mode: VectorStoreQueryMode = VectorStoreQueryMode.DEFAULT, default_empty_query_vector: Optional[List[float]] = None, callback_manager: Optional[CallbackManager] = None, verbose: bool = False, extra_filters: Optional[MetadataFilters] = None, object_map: Optional[dict] = None, objects: Optional[List[IndexNode]] = None, **kwargs: Any)#

Vector store auto retriever.

A retriever for vector store index that uses an LLM to automatically set vector store query parameters.

Parameters

index (VectorStoreIndex) – vector store index
vector_store_info (VectorStoreInfo) – additional information about vector store content and supported metadata filters. The natural language description is used by an LLM to automatically set vector store query parameters.
prompt_template_str – custom prompt template string for LLM. Uses default template string if None.
service_context – service context containing reference to an LLM. Uses service context from index be default if None.
similarity_top_k (int) – number of top k results to return.
empty_query_top_k (Optional[int]) – number of top k results to return if the inferred query string is blank (uses metadata filters only). Can be set to None, which would use the similarity_top_k instead. By default, set to 10.
max_top_k (int) – the maximum top_k allowed. The top_k set by LLM or similarity_top_k will be clamped to this value.
vector_store_query_mode (str) – vector store query mode See reference for VectorStoreQueryMode for full list of supported modes.
default_empty_query_vector (Optional[List[float]]) – default empty query vector. Defaults to None. If not None, then this vector will be used as the query vector if the query is empty.
callback_manager (Optional[CallbackManager]) – callback manager
verbose (bool) – verbose mode

async agenerate_retrieval_spec(query_bundle: QueryBundle, **kwargs: Any) → BaseModel#: Generate retrieval spec asynchronously.

as_query_component(partial: Optional[Dict[str, Any]] = None, **kwargs: Any) → QueryComponent#: Get query component.

generate_retrieval_spec(query_bundle: QueryBundle, **kwargs: Any) → BaseModel#: Generate retrieval spec synchronously.

get_prompts() → Dict[str, BasePromptTemplate]#: Get a prompt.

get_service_context() → Optional[ServiceContext]#: Attempts to resolve a service context. Short-circuits at self.service_context, self._service_context, or self._index.service_context.

retrieve(str_or_query_bundle: Union[str, QueryBundle]) → List[NodeWithScore]#

Retrieve nodes given query.

Parameters: str_or_query_bundle (QueryType) – Either a query string or a QueryBundle object.

update_prompts(prompts_dict: Dict[str, BasePromptTemplate]) → None#

Update prompts.

Other prompts will remain in place.

Vector store index types.

pydantic model llama_index.vector_stores.types.BasePydanticVectorStore#

Abstract vector store protocol.

Show JSON schema

{
   "title": "BasePydanticVectorStore",
   "description": "Abstract vector store protocol.",
   "type": "object",
   "properties": {
      "stores_text": {
         "title": "Stores Text",
         "type": "boolean"
      },
      "is_embedding_query": {
         "title": "Is Embedding Query",
         "default": true,
         "type": "boolean"
      },
      "class_name": {
         "title": "Class Name",
         "type": "string",
         "default": "base_component"
      }
   },
   "required": [
      "stores_text"
   ]
}

Config

schema_extra: function = <function BaseComponent.Config.schema_extra at 0x7ff1e41e53a0>

Fields

is_embedding_query (bool)
stores_text (bool)

field is_embedding_query: bool = True#

field stores_text: bool [Required]#

abstract add(nodes: List[BaseNode]) → List[str]#: Add nodes to vector store.

async adelete(ref_doc_id: str, **delete_kwargs: Any) → None#: Delete nodes using with ref_doc_id. NOTE: this is not implemented for all vector stores. If not implemented, it will just call delete synchronously.

async aquery(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult#: Asynchronously query vector store. NOTE: this is not implemented for all vector stores. If not implemented, it will just call query synchronously.

async async_add(nodes: List[BaseNode], **kwargs: Any) → List[str]#: Asynchronously add nodes to vector store. NOTE: this is not implemented for all vector stores. If not implemented, it will just call add synchronously.

classmethod class_name() → str#

Get the class name, used as a unique ID in serialization.

This provides a key that makes serialization robust against actual class name changes.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

abstract delete(ref_doc_id: str, **delete_kwargs: Any) → None#: Delete nodes using with ref_doc_id.

dict(**kwargs: Any) → Dict[str, Any]#: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_dict(data: Dict[str, Any], **kwargs: Any) → Self#

classmethod from_json(data_str: str, **kwargs: Any) → Self#

classmethod from_orm(obj: Any) → Model#

json(**kwargs: Any) → str#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod parse_obj(obj: Any) → Model#

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

persist(persist_path: str, fs: Optional[AbstractFileSystem] = None) → None#

abstract query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult#: Query vector store.

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#

to_dict(**kwargs: Any) → Dict[str, Any]#

to_json(**kwargs: Any) → str#

classmethod update_forward_refs(**localns: Any) → None#: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#

abstract property client: Any#: Get client.

llama_index.vector_stores.types.ExactMatchFilter#: alias of MetadataFilter

class llama_index.vector_stores.types.FilterCondition(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)#

Vector store filter conditions to combine different filters.

capitalize()#

Return a capitalized version of the string.

More specifically, make the first character have upper case and the rest lower case.

casefold()#: Return a version of the string suitable for caseless comparisons.

center(width, fillchar=' ', /)#

Return a centered string of length width.

Padding is done using the specified fill character (default is a space).

count(sub[, start[, end]]) → int#: Return the number of non-overlapping occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation.

encode(encoding='utf-8', errors='strict')#

Encode the string using the codec registered for encoding.

encoding: The encoding in which to encode the string.
errors: The error handling scheme to use for encoding errors. The default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors.

endswith(suffix[, start[, end]]) → bool#: Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to try.

expandtabs(tabsize=8)#

Return a copy where all tab characters are expanded using spaces.

If tabsize is not given, a tab size of 8 characters is assumed.

find(sub[, start[, end]]) → int#

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

format(*args, **kwargs) → str#: Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{‘ and ‘}’).

format_map(mapping) → str#: Return a formatted version of S, using substitutions from mapping. The substitutions are identified by braces (‘{‘ and ‘}’).

index(sub[, start[, end]]) → int#

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

isalnum()#

Return True if the string is an alpha-numeric string, False otherwise.

A string is alpha-numeric if all characters in the string are alpha-numeric and there is at least one character in the string.

isalpha()#

Return True if the string is an alphabetic string, False otherwise.

A string is alphabetic if all characters in the string are alphabetic and there is at least one character in the string.

isascii()#

Return True if all characters in the string are ASCII, False otherwise.

ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too.

isdecimal()#

Return True if the string is a decimal string, False otherwise.

A string is a decimal string if all characters in the string are decimal and there is at least one character in the string.

isdigit()#

Return True if the string is a digit string, False otherwise.

A string is a digit string if all characters in the string are digits and there is at least one character in the string.

isidentifier()#

Return True if the string is a valid Python identifier, False otherwise.

Call keyword.iskeyword(s) to test whether string s is a reserved identifier, such as “def” or “class”.

islower()#

Return True if the string is a lowercase string, False otherwise.

A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string.

isnumeric()#

Return True if the string is a numeric string, False otherwise.

A string is numeric if all characters in the string are numeric and there is at least one character in the string.

isprintable()#

Return True if the string is printable, False otherwise.

A string is printable if all of its characters are considered printable in repr() or if it is empty.

isspace()#

Return True if the string is a whitespace string, False otherwise.

A string is whitespace if all characters in the string are whitespace and there is at least one character in the string.

istitle()#

Return True if the string is a title-cased string, False otherwise.

In a title-cased string, upper- and title-case characters may only follow uncased characters and lowercase characters only cased ones.

isupper()#

Return True if the string is an uppercase string, False otherwise.

A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string.

join(iterable, /)#

Concatenate any number of strings.

The string whose method is called is inserted in between each given string. The result is returned as a new string.

Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’

ljust(width, fillchar=' ', /)#

Return a left-justified string of length width.

Padding is done using the specified fill character (default is a space).

lower()#: Return a copy of the string converted to lowercase.

lstrip(chars=None, /)#

Return a copy of the string with leading whitespace removed.

If chars is given and not None, remove characters in chars instead.

static maketrans()#

Return a translation table usable for str.translate().

If there is only one argument, it must be a dictionary mapping Unicode ordinals (integers) or characters to Unicode ordinals, strings or None. Character keys will be then converted to ordinals. If there are two arguments, they must be strings of equal length, and in the resulting dictionary, each character in x will be mapped to the character at the same position in y. If there is a third argument, it must be a string, whose characters will be mapped to None in the result.

partition(sep, /)#

Partition the string into three parts using the given separator.

This will search for the separator in the string. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing the original string and two empty strings.

removeprefix(prefix, /)#

Return a str with the given prefix string removed if present.

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string.

removesuffix(suffix, /)#

Return a str with the given suffix string removed if present.

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string.

replace(old, new, count=-1, /)#

Return a copy with all occurrences of substring old replaced by new.

count
Maximum number of occurrences to replace. -1 (the default value) means replace all occurrences.

If the optional argument count is given, only the first count occurrences are replaced.

rfind(sub[, start[, end]]) → int#

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

rindex(sub[, start[, end]]) → int#

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

rjust(width, fillchar=' ', /)#

Return a right-justified string of length width.

Padding is done using the specified fill character (default is a space).

rpartition(sep, /)#

Partition the string into three parts using the given separator.

This will search for the separator in the string, starting at the end. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing two empty strings and the original string.

rsplit(sep=None, maxsplit=-1)#

Return a list of the substrings in the string, using sep as the separator string.

sep
The separator used to split the string.

When set to None (the default value), will split on any whitespace character (including n r t f and spaces) and will discard empty strings from the result.

maxsplit
Maximum number of splits (starting from the left). -1 (the default value) means no limit.

Splitting starts at the end of the string and works to the front.

rstrip(chars=None, /)#

Return a copy of the string with trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

split(sep=None, maxsplit=-1)#

Return a list of the substrings in the string, using sep as the separator string.

sep
The separator used to split the string.

When set to None (the default value), will split on any whitespace character (including n r t f and spaces) and will discard empty strings from the result.

maxsplit
Maximum number of splits (starting from the left). -1 (the default value) means no limit.

Note, str.split() is mainly useful for data that has been intentionally delimited. With natural text that includes punctuation, consider using the regular expression module.

splitlines(keepends=False)#

Return a list of the lines in the string, breaking at line boundaries.

Line breaks are not included in the resulting list unless keepends is given and true.

startswith(prefix[, start[, end]]) → bool#: Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to try.

strip(chars=None, /)#

Return a copy of the string with leading and trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

swapcase()#: Convert uppercase characters to lowercase and lowercase characters to uppercase.

title()#

Return a version of the string where each word is titlecased.

More specifically, words start with uppercased characters and all remaining cased characters have lower case.

translate(table, /)#

Replace each character in the string using the given translation table.

table
Translation table, which must be a mapping of Unicode ordinals to Unicode ordinals, strings, or None.

The table must implement lookup/indexing via __getitem__, for instance a dictionary or list. If this operation raises LookupError, the character is left untouched. Characters mapped to None are deleted.

upper()#: Return a copy of the string converted to uppercase.

zfill(width, /)#

Pad a numeric string with zeros on the left, to fill a field of the given width.

The string is never truncated.

class llama_index.vector_stores.types.FilterOperator(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)#

Vector store filter operator.

capitalize()#

Return a capitalized version of the string.

More specifically, make the first character have upper case and the rest lower case.

casefold()#: Return a version of the string suitable for caseless comparisons.

center(width, fillchar=' ', /)#

Return a centered string of length width.

Padding is done using the specified fill character (default is a space).

count(sub[, start[, end]]) → int#: Return the number of non-overlapping occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation.

encode(encoding='utf-8', errors='strict')#

Encode the string using the codec registered for encoding.

encoding: The encoding in which to encode the string.
errors: The error handling scheme to use for encoding errors. The default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors.

endswith(suffix[, start[, end]]) → bool#: Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to try.

expandtabs(tabsize=8)#

Return a copy where all tab characters are expanded using spaces.

If tabsize is not given, a tab size of 8 characters is assumed.

find(sub[, start[, end]]) → int#

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

format(*args, **kwargs) → str#: Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{‘ and ‘}’).

format_map(mapping) → str#: Return a formatted version of S, using substitutions from mapping. The substitutions are identified by braces (‘{‘ and ‘}’).

index(sub[, start[, end]]) → int#

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

isalnum()#

Return True if the string is an alpha-numeric string, False otherwise.

A string is alpha-numeric if all characters in the string are alpha-numeric and there is at least one character in the string.

isalpha()#

Return True if the string is an alphabetic string, False otherwise.

A string is alphabetic if all characters in the string are alphabetic and there is at least one character in the string.

isascii()#

Return True if all characters in the string are ASCII, False otherwise.

ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too.

isdecimal()#

Return True if the string is a decimal string, False otherwise.

A string is a decimal string if all characters in the string are decimal and there is at least one character in the string.

isdigit()#

Return True if the string is a digit string, False otherwise.

A string is a digit string if all characters in the string are digits and there is at least one character in the string.

isidentifier()#

Return True if the string is a valid Python identifier, False otherwise.

Call keyword.iskeyword(s) to test whether string s is a reserved identifier, such as “def” or “class”.

islower()#

Return True if the string is a lowercase string, False otherwise.

A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string.

isnumeric()#

Return True if the string is a numeric string, False otherwise.

A string is numeric if all characters in the string are numeric and there is at least one character in the string.

isprintable()#

Return True if the string is printable, False otherwise.

A string is printable if all of its characters are considered printable in repr() or if it is empty.

isspace()#

Return True if the string is a whitespace string, False otherwise.

A string is whitespace if all characters in the string are whitespace and there is at least one character in the string.

istitle()#

Return True if the string is a title-cased string, False otherwise.

In a title-cased string, upper- and title-case characters may only follow uncased characters and lowercase characters only cased ones.

isupper()#

Return True if the string is an uppercase string, False otherwise.

A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string.

join(iterable, /)#

Concatenate any number of strings.

The string whose method is called is inserted in between each given string. The result is returned as a new string.

Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’

ljust(width, fillchar=' ', /)#

Return a left-justified string of length width.

Padding is done using the specified fill character (default is a space).

lower()#: Return a copy of the string converted to lowercase.

lstrip(chars=None, /)#

Return a copy of the string with leading whitespace removed.

If chars is given and not None, remove characters in chars instead.

static maketrans()#

Return a translation table usable for str.translate().

partition(sep, /)#

Partition the string into three parts using the given separator.

This will search for the separator in the string. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing the original string and two empty strings.

removeprefix(prefix, /)#

Return a str with the given prefix string removed if present.

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string.

removesuffix(suffix, /)#

Return a str with the given suffix string removed if present.

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string.

replace(old, new, count=-1, /)#

Return a copy with all occurrences of substring old replaced by new.

count
Maximum number of occurrences to replace. -1 (the default value) means replace all occurrences.

If the optional argument count is given, only the first count occurrences are replaced.

rfind(sub[, start[, end]]) → int#

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

rindex(sub[, start[, end]]) → int#

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

rjust(width, fillchar=' ', /)#

Return a right-justified string of length width.

Padding is done using the specified fill character (default is a space).

rpartition(sep, /)#

Partition the string into three parts using the given separator.

If the separator is not found, returns a 3-tuple containing two empty strings and the original string.

rsplit(sep=None, maxsplit=-1)#

Return a list of the substrings in the string, using sep as the separator string.

sep
The separator used to split the string.

When set to None (the default value), will split on any whitespace character (including n r t f and spaces) and will discard empty strings from the result.

maxsplit
Maximum number of splits (starting from the left). -1 (the default value) means no limit.

Splitting starts at the end of the string and works to the front.

rstrip(chars=None, /)#

Return a copy of the string with trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

split(sep=None, maxsplit=-1)#

Return a list of the substrings in the string, using sep as the separator string.

sep
The separator used to split the string.

When set to None (the default value), will split on any whitespace character (including n r t f and spaces) and will discard empty strings from the result.

maxsplit
Maximum number of splits (starting from the left). -1 (the default value) means no limit.

Note, str.split() is mainly useful for data that has been intentionally delimited. With natural text that includes punctuation, consider using the regular expression module.

splitlines(keepends=False)#

Return a list of the lines in the string, breaking at line boundaries.

Line breaks are not included in the resulting list unless keepends is given and true.

startswith(prefix[, start[, end]]) → bool#: Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to try.

strip(chars=None, /)#

Return a copy of the string with leading and trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

swapcase()#: Convert uppercase characters to lowercase and lowercase characters to uppercase.

title()#

Return a version of the string where each word is titlecased.

More specifically, words start with uppercased characters and all remaining cased characters have lower case.

translate(table, /)#

Replace each character in the string using the given translation table.

table
Translation table, which must be a mapping of Unicode ordinals to Unicode ordinals, strings, or None.

upper()#: Return a copy of the string converted to uppercase.

zfill(width, /)#

Pad a numeric string with zeros on the left, to fill a field of the given width.

The string is never truncated.

pydantic model llama_index.vector_stores.types.MetadataFilter#

Comprehensive metadata filter for vector stores to support more operators.

Value uses Strict* types, as int, float and str are compatible types and were all converted to string before.

See: https://docs.pydantic.dev/latest/usage/types/#strict-types

Show JSON schema

{
   "title": "MetadataFilter",
   "description": "Comprehensive metadata filter for vector stores to support more operators.\n\nValue uses Strict* types, as int, float and str are compatible types and were all\nconverted to string before.\n\nSee: https://docs.pydantic.dev/latest/usage/types/#strict-types",
   "type": "object",
   "properties": {
      "key": {
         "title": "Key",
         "type": "string"
      },
      "value": {
         "title": "Value",
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "number"
            },
            {
               "type": "string"
            }
         ]
      },
      "operator": {
         "default": "==",
         "allOf": [
            {
               "$ref": "#/definitions/FilterOperator"
            }
         ]
      }
   },
   "required": [
      "key",
      "value"
   ],
   "definitions": {
      "FilterOperator": {
         "title": "FilterOperator",
         "description": "Vector store filter operator.",
         "enum": [
            "==",
            ">",
            "<",
            "!=",
            ">=",
            "<=",
            "in",
            "nin",
            "text_match"
         ],
         "type": "string"
      }
   }
}

Fields

key (str)
operator (llama_index.vector_stores.types.FilterOperator)
value (Union[pydantic.types.StrictInt, pydantic.types.StrictFloat, pydantic.types.StrictStr])

field key: str [Required]#

field operator: FilterOperator = FilterOperator.EQ#

field value: Union[StrictInt, StrictFloat, StrictStr] [Required]#

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_dict(filter_dict: Dict) → MetadataFilter#

Create MetadataFilter from dictionary.

Parameters: filter_dict – Dict with key, value and operator.

classmethod from_orm(obj: Any) → Model#

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod parse_obj(obj: Any) → Model#

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#

classmethod update_forward_refs(**localns: Any) → None#: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#

pydantic model llama_index.vector_stores.types.MetadataFilters#

Metadata filters for vector stores.

Currently only supports exact match filters. TODO: support more advanced expressions.

Show JSON schema

{
   "title": "MetadataFilters",
   "description": "Metadata filters for vector stores.\n\nCurrently only supports exact match filters.\nTODO: support more advanced expressions.",
   "type": "object",
   "properties": {
      "filters": {
         "title": "Filters",
         "type": "array",
         "items": {
            "$ref": "#/definitions/MetadataFilter"
         }
      },
      "condition": {
         "default": "and",
         "allOf": [
            {
               "$ref": "#/definitions/FilterCondition"
            }
         ]
      }
   },
   "required": [
      "filters"
   ],
   "definitions": {
      "FilterOperator": {
         "title": "FilterOperator",
         "description": "Vector store filter operator.",
         "enum": [
            "==",
            ">",
            "<",
            "!=",
            ">=",
            "<=",
            "in",
            "nin",
            "text_match"
         ],
         "type": "string"
      },
      "MetadataFilter": {
         "title": "MetadataFilter",
         "description": "Comprehensive metadata filter for vector stores to support more operators.\n\nValue uses Strict* types, as int, float and str are compatible types and were all\nconverted to string before.\n\nSee: https://docs.pydantic.dev/latest/usage/types/#strict-types",
         "type": "object",
         "properties": {
            "key": {
               "title": "Key",
               "type": "string"
            },
            "value": {
               "title": "Value",
               "anyOf": [
                  {
                     "type": "integer"
                  },
                  {
                     "type": "number"
                  },
                  {
                     "type": "string"
                  }
               ]
            },
            "operator": {
               "default": "==",
               "allOf": [
                  {
                     "$ref": "#/definitions/FilterOperator"
                  }
               ]
            }
         },
         "required": [
            "key",
            "value"
         ]
      },
      "FilterCondition": {
         "title": "FilterCondition",
         "description": "Vector store filter conditions to combine different filters.",
         "enum": [
            "and",
            "or"
         ],
         "type": "string"
      }
   }
}

Fields

condition (Optional[llama_index.vector_stores.types.FilterCondition])
filters (List[llama_index.vector_stores.types.MetadataFilter])

field condition: Optional[FilterCondition] = FilterCondition.AND#

field filters: List[MetadataFilter] [Required]#

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_dict(filter_dict: Dict) → MetadataFilters#: Create MetadataFilters from json.

classmethod from_dicts(filter_dicts: List[Dict], condition: Optional[FilterCondition] = FilterCondition.AND) → MetadataFilters#

Create MetadataFilters from dicts.

This takes in a list of individual MetadataFilter objects, along with the condition.

Parameters

filter_dicts – List of dicts, each dict is a MetadataFilter.
condition – FilterCondition to combine different filters.

classmethod from_orm(obj: Any) → Model#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

legacy_filters() → List[MetadataFilter]#: Convert MetadataFilters to legacy ExactMatchFilters.

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod parse_obj(obj: Any) → Model#

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#

classmethod update_forward_refs(**localns: Any) → None#: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#

pydantic model llama_index.vector_stores.types.MetadataInfo#

Information about a metadata filter supported by a vector store.

Currently only used by VectorIndexAutoRetriever.

Show JSON schema

{
   "title": "MetadataInfo",
   "description": "Information about a metadata filter supported by a vector store.\n\nCurrently only used by VectorIndexAutoRetriever.",
   "type": "object",
   "properties": {
      "name": {
         "title": "Name",
         "type": "string"
      },
      "type": {
         "title": "Type",
         "type": "string"
      },
      "description": {
         "title": "Description",
         "type": "string"
      }
   },
   "required": [
      "name",
      "type",
      "description"
   ]
}

Fields

description (str)
name (str)
type (str)

field description: str [Required]#

field name: str [Required]#

field type: str [Required]#

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod parse_obj(obj: Any) → Model#

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#

classmethod update_forward_refs(**localns: Any) → None#: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#

pydantic model llama_index.vector_stores.types.VectorStoreInfo#

Information about a vector store (content and supported metadata filters).

Currently only used by VectorIndexAutoRetriever.

Show JSON schema

{
   "title": "VectorStoreInfo",
   "description": "Information about a vector store (content and supported metadata filters).\n\nCurrently only used by VectorIndexAutoRetriever.",
   "type": "object",
   "properties": {
      "metadata_info": {
         "title": "Metadata Info",
         "type": "array",
         "items": {
            "$ref": "#/definitions/MetadataInfo"
         }
      },
      "content_info": {
         "title": "Content Info",
         "type": "string"
      }
   },
   "required": [
      "metadata_info",
      "content_info"
   ],
   "definitions": {
      "MetadataInfo": {
         "title": "MetadataInfo",
         "description": "Information about a metadata filter supported by a vector store.\n\nCurrently only used by VectorIndexAutoRetriever.",
         "type": "object",
         "properties": {
            "name": {
               "title": "Name",
               "type": "string"
            },
            "type": {
               "title": "Type",
               "type": "string"
            },
            "description": {
               "title": "Description",
               "type": "string"
            }
         },
         "required": [
            "name",
            "type",
            "description"
         ]
      }
   }
}

Fields

content_info (str)
metadata_info (List[llama_index.vector_stores.types.MetadataInfo])

field content_info: str [Required]#

field metadata_info: List[MetadataInfo] [Required]#

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod parse_obj(obj: Any) → Model#

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#

classmethod update_forward_refs(**localns: Any) → None#: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#

class llama_index.vector_stores.types.VectorStoreQueryMode(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)#

Vector store query mode.

capitalize()#

Return a capitalized version of the string.

More specifically, make the first character have upper case and the rest lower case.

casefold()#: Return a version of the string suitable for caseless comparisons.

center(width, fillchar=' ', /)#

Return a centered string of length width.

Padding is done using the specified fill character (default is a space).

count(sub[, start[, end]]) → int#: Return the number of non-overlapping occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation.

encode(encoding='utf-8', errors='strict')#

Encode the string using the codec registered for encoding.

encoding: The encoding in which to encode the string.
errors: The error handling scheme to use for encoding errors. The default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors.

endswith(suffix[, start[, end]]) → bool#: Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to try.

expandtabs(tabsize=8)#

Return a copy where all tab characters are expanded using spaces.

If tabsize is not given, a tab size of 8 characters is assumed.

find(sub[, start[, end]]) → int#

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

format(*args, **kwargs) → str#: Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{‘ and ‘}’).

format_map(mapping) → str#: Return a formatted version of S, using substitutions from mapping. The substitutions are identified by braces (‘{‘ and ‘}’).

index(sub[, start[, end]]) → int#

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

isalnum()#

Return True if the string is an alpha-numeric string, False otherwise.

A string is alpha-numeric if all characters in the string are alpha-numeric and there is at least one character in the string.

isalpha()#

Return True if the string is an alphabetic string, False otherwise.

A string is alphabetic if all characters in the string are alphabetic and there is at least one character in the string.

isascii()#

Return True if all characters in the string are ASCII, False otherwise.

ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too.

isdecimal()#

Return True if the string is a decimal string, False otherwise.

A string is a decimal string if all characters in the string are decimal and there is at least one character in the string.

isdigit()#

Return True if the string is a digit string, False otherwise.

A string is a digit string if all characters in the string are digits and there is at least one character in the string.

isidentifier()#

Return True if the string is a valid Python identifier, False otherwise.

Call keyword.iskeyword(s) to test whether string s is a reserved identifier, such as “def” or “class”.

islower()#

Return True if the string is a lowercase string, False otherwise.

A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string.

isnumeric()#

Return True if the string is a numeric string, False otherwise.

A string is numeric if all characters in the string are numeric and there is at least one character in the string.

isprintable()#

Return True if the string is printable, False otherwise.

A string is printable if all of its characters are considered printable in repr() or if it is empty.

isspace()#

Return True if the string is a whitespace string, False otherwise.

A string is whitespace if all characters in the string are whitespace and there is at least one character in the string.

istitle()#

Return True if the string is a title-cased string, False otherwise.

In a title-cased string, upper- and title-case characters may only follow uncased characters and lowercase characters only cased ones.

isupper()#

Return True if the string is an uppercase string, False otherwise.

A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string.

join(iterable, /)#

Concatenate any number of strings.

The string whose method is called is inserted in between each given string. The result is returned as a new string.

Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’

ljust(width, fillchar=' ', /)#

Return a left-justified string of length width.

Padding is done using the specified fill character (default is a space).

lower()#: Return a copy of the string converted to lowercase.

lstrip(chars=None, /)#

Return a copy of the string with leading whitespace removed.

If chars is given and not None, remove characters in chars instead.

static maketrans()#

Return a translation table usable for str.translate().

partition(sep, /)#

Partition the string into three parts using the given separator.

This will search for the separator in the string. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing the original string and two empty strings.

removeprefix(prefix, /)#

Return a str with the given prefix string removed if present.

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string.

removesuffix(suffix, /)#

Return a str with the given suffix string removed if present.

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string.

replace(old, new, count=-1, /)#

Return a copy with all occurrences of substring old replaced by new.

count
Maximum number of occurrences to replace. -1 (the default value) means replace all occurrences.

If the optional argument count is given, only the first count occurrences are replaced.

rfind(sub[, start[, end]]) → int#

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

rindex(sub[, start[, end]]) → int#

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

rjust(width, fillchar=' ', /)#

Return a right-justified string of length width.

Padding is done using the specified fill character (default is a space).

rpartition(sep, /)#

Partition the string into three parts using the given separator.

If the separator is not found, returns a 3-tuple containing two empty strings and the original string.

rsplit(sep=None, maxsplit=-1)#

Return a list of the substrings in the string, using sep as the separator string.

sep
The separator used to split the string.

When set to None (the default value), will split on any whitespace character (including n r t f and spaces) and will discard empty strings from the result.

maxsplit
Maximum number of splits (starting from the left). -1 (the default value) means no limit.

Splitting starts at the end of the string and works to the front.

rstrip(chars=None, /)#

Return a copy of the string with trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

split(sep=None, maxsplit=-1)#

Return a list of the substrings in the string, using sep as the separator string.

sep
The separator used to split the string.

When set to None (the default value), will split on any whitespace character (including n r t f and spaces) and will discard empty strings from the result.

maxsplit
Maximum number of splits (starting from the left). -1 (the default value) means no limit.

Note, str.split() is mainly useful for data that has been intentionally delimited. With natural text that includes punctuation, consider using the regular expression module.

splitlines(keepends=False)#

Return a list of the lines in the string, breaking at line boundaries.

Line breaks are not included in the resulting list unless keepends is given and true.

startswith(prefix[, start[, end]]) → bool#: Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to try.

strip(chars=None, /)#

Return a copy of the string with leading and trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

swapcase()#: Convert uppercase characters to lowercase and lowercase characters to uppercase.

title()#

Return a version of the string where each word is titlecased.

More specifically, words start with uppercased characters and all remaining cased characters have lower case.

translate(table, /)#

Replace each character in the string using the given translation table.

table
Translation table, which must be a mapping of Unicode ordinals to Unicode ordinals, strings, or None.

upper()#: Return a copy of the string converted to uppercase.

zfill(width, /)#

Pad a numeric string with zeros on the left, to fill a field of the given width.

The string is never truncated.