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3395111140a3-0 | langchain.schema.Generation¶
class langchain.schema.Generation(*, text: str, generation_info: Optional[Dict[str, Any]] = None)[source]¶
Bases: Serializable
Output of a single generation.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param generation_info: Optional[Dict[str, Any]] = None¶
Raw generation info response from the provider
param text: str [Required]¶
Generated text output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
This class is LangChain serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/schema/langchain.schema.Generation.html |
fa3089f62534-0 | langchain.schema.messages_from_dict¶
langchain.schema.messages_from_dict(messages: List[dict]) → List[BaseMessage][source]¶
Convert messages from dict.
Parameters
messages – List of messages (dicts) to convert.
Returns
List of messages (BaseMessages). | [
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0be82bcf9b94-0 | langchain.schema.HumanMessage¶
class langchain.schema.HumanMessage(*, content: str, additional_kwargs: dict = None, example: bool = False)[source]¶
Bases: BaseMessage
Type of message that is spoken by the human.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
param content: str [Required]¶
param example: bool = False¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
This class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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92059a5bc117-0 | langchain.schema.BaseLLMOutputParser¶
class langchain.schema.BaseLLMOutputParser[source]¶
Bases: Serializable, ABC, Generic[T]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
abstract parse_result(result: List[Generation]) → T[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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0af433734dff-0 | langchain.schema.ChatGeneration¶
class langchain.schema.ChatGeneration(*, text: str = '', generation_info: Optional[Dict[str, Any]] = None, message: BaseMessage)[source]¶
Bases: Generation
Output of a single generation.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param generation_info: Optional[Dict[str, Any]] = None¶
Raw generation info response from the provider
param message: langchain.schema.BaseMessage [Required]¶
param text: str = ''¶
Generated text output.
validator set_text » all fields[source]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
This class is LangChain serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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8408ca9e4b2b-0 | langchain.schema.ChatResult¶
class langchain.schema.ChatResult(*, generations: List[ChatGeneration], llm_output: Optional[dict] = None)[source]¶
Bases: BaseModel
Class that contains all relevant information for a Chat Result.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param generations: List[langchain.schema.ChatGeneration] [Required]¶
List of the things generated.
param llm_output: Optional[dict] = None¶
For arbitrary LLM provider specific output. | [
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ea58fbe25881-0 | langchain.schema.SystemMessage¶
class langchain.schema.SystemMessage(*, content: str, additional_kwargs: dict = None)[source]¶
Bases: BaseMessage
Type of message that is a system message.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
param content: str [Required]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
This class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/schema/langchain.schema.SystemMessage.html |
357dacf64473-0 | langchain.schema.BaseOutputParser¶
class langchain.schema.BaseOutputParser[source]¶
Bases: BaseLLMOutputParser, ABC, Generic[T]
Class to parse the output of an LLM call.
Output parsers help structure language model responses.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
dict(**kwargs: Any) → Dict[source]¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
abstract parse(text: str) → T[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T[source]¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any[source]¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”] | [
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] | https://langchain.readthedocs.io/en/latest/schema/langchain.schema.BaseOutputParser.html |
357dacf64473-1 | eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/schema/langchain.schema.BaseOutputParser.html |
19d9f3ce2720-0 | langchain.schema.ChatMessage¶
class langchain.schema.ChatMessage(*, content: str, additional_kwargs: dict = None, role: str)[source]¶
Bases: BaseMessage
Type of message with arbitrary speaker.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
param content: str [Required]¶
param role: str [Required]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
This class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/schema/langchain.schema.ChatMessage.html |
f1cbe1ca5358-0 | langchain.schema.BaseDocumentTransformer¶
class langchain.schema.BaseDocumentTransformer[source]¶
Bases: ABC
Base interface for transforming documents.
Methods
__init__()
atransform_documents(documents, **kwargs)
Asynchronously transform a list of documents.
transform_documents(documents, **kwargs)
Transform a list of documents.
abstract async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Asynchronously transform a list of documents.
abstract transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Transform a list of documents. | [
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b32c18e3b0a9-0 | langchain.schema.get_buffer_string¶
langchain.schema.get_buffer_string(messages: List[BaseMessage], human_prefix: str = 'Human', ai_prefix: str = 'AI') → str[source]¶
Get buffer string of messages. | [
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33ef0fcf77dc-0 | langchain.schema.NoOpOutputParser¶
class langchain.schema.NoOpOutputParser[source]¶
Bases: BaseOutputParser[str]
Output parser that just returns the text as is.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
parse(text: str) → str[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids. | [
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] | https://langchain.readthedocs.io/en/latest/schema/langchain.schema.NoOpOutputParser.html |
33ef0fcf77dc-1 | Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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bb5c412a276c-0 | langchain.schema.BaseChatMessageHistory¶
class langchain.schema.BaseChatMessageHistory[source]¶
Bases: ABC
Base interface for chat message history
See ChatMessageHistory for default implementation.
Methods
__init__()
add_ai_message(message)
Add an AI message to the store
add_message(message)
Add a self-created message to the store
add_user_message(message)
Add a user message to the store
clear()
Remove all messages from the store
Attributes
messages
add_ai_message(message: str) → None[source]¶
Add an AI message to the store
add_message(message: BaseMessage) → None[source]¶
Add a self-created message to the store
add_user_message(message: str) → None[source]¶
Add a user message to the store
abstract clear() → None[source]¶
Remove all messages from the store
messages: List[langchain.schema.BaseMessage]¶ | [
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] | https://langchain.readthedocs.io/en/latest/schema/langchain.schema.BaseChatMessageHistory.html |
db419cb01799-0 | langchain.schema.RunInfo¶
class langchain.schema.RunInfo(*, run_id: UUID)[source]¶
Bases: BaseModel
Class that contains all relevant metadata for a Run.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param run_id: uuid.UUID [Required]¶ | [
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41a4ec87d601-0 | langchain.base_language.BaseLanguageModel¶
class langchain.base_language.BaseLanguageModel[source]¶
Bases: Serializable, ABC
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
abstract async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult[source]¶
Take in a list of prompt values and return an LLMResult.
classmethod all_required_field_names() → Set[source]¶
abstract async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str[source]¶
Predict text from text.
abstract async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage[source]¶
Predict message from messages.
abstract generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult[source]¶
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int[source]¶
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[BaseMessage]) → int[source]¶
Get the number of tokens in the message.
get_token_ids(text: str) → List[int][source]¶
Get the token present in the text.
abstract predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str[source]¶
Predict text from text. | [
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] | https://langchain.readthedocs.io/en/latest/base_language/langchain.base_language.BaseLanguageModel.html |
41a4ec87d601-1 | Predict text from text.
abstract predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage[source]¶
Predict message from messages.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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8a09e1dc2c2f-0 | langchain.load.load.loads¶
langchain.load.load.loads(text: str, *, secrets_map: Optional[Dict[str, str]] = None) → Any[source]¶ | [
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444d68d14635-0 | langchain.load.serializable.SerializedSecret¶
class langchain.load.serializable.SerializedSecret[source]¶
Bases: dict
Serialized secret.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
type
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶ | [
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] | https://langchain.readthedocs.io/en/latest/load/langchain.load.serializable.SerializedSecret.html |
444d68d14635-1 | pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k]
values() → an object providing a view on D's values¶
id: List[str]¶
lc: int¶
type: Literal['secret']¶ | [
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] | https://langchain.readthedocs.io/en/latest/load/langchain.load.serializable.SerializedSecret.html |
97d7269d78ab-0 | langchain.load.dump.dumpd¶
langchain.load.dump.dumpd(obj: Any) → Dict[str, Any][source]¶
Return a json dict representation of an object. | [
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6179edd97003-0 | langchain.load.serializable.SerializedConstructor¶
class langchain.load.serializable.SerializedConstructor[source]¶
Bases: dict
Serialized constructor.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
type
kwargs
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶ | [
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] | https://langchain.readthedocs.io/en/latest/load/langchain.load.serializable.SerializedConstructor.html |
6179edd97003-1 | pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k]
values() → an object providing a view on D's values¶
id: List[str]¶
kwargs: Dict[str, Any]¶
lc: int¶
type: Literal['constructor']¶ | [
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] | https://langchain.readthedocs.io/en/latest/load/langchain.load.serializable.SerializedConstructor.html |
f47a1f39ff32-0 | langchain.load.serializable.SerializedNotImplemented¶
class langchain.load.serializable.SerializedNotImplemented[source]¶
Bases: dict
Serialized not implemented.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
type
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶ | [
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] | https://langchain.readthedocs.io/en/latest/load/langchain.load.serializable.SerializedNotImplemented.html |
f47a1f39ff32-1 | pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k]
values() → an object providing a view on D's values¶
id: List[str]¶
lc: int¶
type: Literal['not_implemented']¶ | [
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] | https://langchain.readthedocs.io/en/latest/load/langchain.load.serializable.SerializedNotImplemented.html |
c352feebe22a-0 | langchain.load.serializable.to_json_not_implemented¶
langchain.load.serializable.to_json_not_implemented(obj: object) → SerializedNotImplemented[source]¶
Serialize a “not implemented” object.
Parameters
obj – object to serialize
Returns
SerializedNotImplemented | [
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97b10cbdd40a-0 | langchain.load.dump.dumps¶
langchain.load.dump.dumps(obj: Any, *, pretty: bool = False) → str[source]¶
Return a json string representation of an object. | [
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59cfd0820b79-0 | langchain.load.serializable.Serializable¶
class langchain.load.serializable.Serializable[source]¶
Bases: BaseModel, ABC
Serializable base class.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
to_json() → Union[SerializedConstructor, SerializedNotImplemented][source]¶
to_json_not_implemented() → SerializedNotImplemented[source]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config[source]¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/load/langchain.load.serializable.Serializable.html |
e6ecff77f334-0 | langchain.load.dump.default¶
langchain.load.dump.default(obj: Any) → Any[source]¶
Return a default value for a Serializable object or
a SerializedNotImplemented object. | [
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f5e18a23b021-0 | langchain.load.serializable.BaseSerialized¶
class langchain.load.serializable.BaseSerialized[source]¶
Bases: TypedDict
Base class for serialized objects.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
lc
id
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶ | [
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f5e18a23b021-1 | pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k]
values() → an object providing a view on D's values¶
id: List[str]¶
lc: int¶ | [
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] | https://langchain.readthedocs.io/en/latest/load/langchain.load.serializable.BaseSerialized.html |
00a9e29b2e3d-0 | langchain.output_parsers.json.parse_and_check_json_markdown¶
langchain.output_parsers.json.parse_and_check_json_markdown(text: str, expected_keys: List[str]) → dict[source]¶
Parse a JSON string from a Markdown string and check that it
contains the expected keys.
Parameters
text – The Markdown string.
expected_keys – The expected keys in the JSON string.
Returns
The parsed JSON object as a Python dictionary. | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.json.parse_and_check_json_markdown.html |
701ac510672e-0 | langchain.output_parsers.regex.RegexParser¶
class langchain.output_parsers.regex.RegexParser(*, regex: str, output_keys: List[str], default_output_key: Optional[str] = None)[source]¶
Bases: BaseOutputParser
Class to parse the output into a dictionary.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param default_output_key: Optional[str] = None¶
param output_keys: List[str] [Required]¶
param regex: str [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Dict[str, str][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”] | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
701ac510672e-1 | eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
dd7444cc1882-0 | langchain.output_parsers.retry.RetryWithErrorOutputParser¶
class langchain.output_parsers.retry.RetryWithErrorOutputParser(*, parser: BaseOutputParser[T], retry_chain: LLMChain)[source]¶
Bases: BaseOutputParser[T]
Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt, the completion, AND the error
that was raised to another language model and telling it that the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error that was raised back to the
LLM, which in theory should give it more information on how to fix it.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T] [Required]¶
param retry_chain: langchain.chains.llm.LLMChain [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_llm(llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = PromptTemplate(input_variables=['completion', 'error', 'prompt'], output_parser=None, partial_variables={}, template='Prompt:\n{prompt}\nCompletion:\n{completion}\n\nAbove, the Completion did not satisfy the constraints given in the Prompt.\nDetails: {error}\nPlease try again:', template_format='f-string', validate_template=True)) → RetryWithErrorOutputParser[T][source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(completion: str) → T[source]¶
Parse the output of an LLM call. | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html |
dd7444cc1882-1 | Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt_value: PromptValue) → T[source]¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html |
47fca95fc3f9-0 | langchain.output_parsers.combining.CombiningOutputParser¶
class langchain.output_parsers.combining.CombiningOutputParser(*, parsers: List[BaseOutputParser])[source]¶
Bases: BaseOutputParser
Class to combine multiple output parsers into one.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param parsers: List[langchain.schema.BaseOutputParser] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Dict[str, Any][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_parsers » all fields[source]¶
Validate the parsers.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”] | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html |
47fca95fc3f9-1 | eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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d9d7751c50e4-0 | langchain.output_parsers.openai_functions.OutputFunctionsParser¶
class langchain.output_parsers.openai_functions.OutputFunctionsParser(*, args_only: bool = True)[source]¶
Bases: BaseLLMOutputParser[Any]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.openai_functions.OutputFunctionsParser.html |
a446ff36f52c-0 | langchain.output_parsers.structured.StructuredOutputParser¶
class langchain.output_parsers.structured.StructuredOutputParser(*, response_schemas: List[ResponseSchema])[source]¶
Bases: BaseOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param response_schemas: List[langchain.output_parsers.structured.ResponseSchema] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_response_schemas(response_schemas: List[ResponseSchema]) → StructuredOutputParser[source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Any[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html |
a446ff36f52c-1 | constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html |
d7465871506b-0 | langchain.output_parsers.list.CommaSeparatedListOutputParser¶
class langchain.output_parsers.list.CommaSeparatedListOutputParser[source]¶
Bases: ListOutputParser
Parse out comma separated lists.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → List[str][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶ | [
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d7465871506b-1 | property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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074b16e958bb-0 | langchain.output_parsers.fix.OutputFixingParser¶
class langchain.output_parsers.fix.OutputFixingParser(*, parser: BaseOutputParser[T], retry_chain: LLMChain)[source]¶
Bases: BaseOutputParser[T]
Wraps a parser and tries to fix parsing errors.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param parser: langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T] [Required]¶
param retry_chain: langchain.chains.llm.LLMChain [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_llm(llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = PromptTemplate(input_variables=['completion', 'error', 'instructions'], output_parser=None, partial_variables={}, template='Instructions:\n--------------\n{instructions}\n--------------\nCompletion:\n--------------\n{completion}\n--------------\n\nAbove, the Completion did not satisfy the constraints given in the Instructions.\nError:\n--------------\n{error}\n--------------\n\nPlease try again. Please only respond with an answer that satisfies the constraints laid out in the Instructions:', template_format='f-string', validate_template=True)) → OutputFixingParser[T][source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(completion: str) → T[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result. | [
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074b16e958bb-1 | parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html |
6a3dd9373615-0 | langchain.output_parsers.json.parse_json_markdown¶
langchain.output_parsers.json.parse_json_markdown(json_string: str) → dict[source]¶
Parse a JSON string from a Markdown string.
Parameters
json_string – The Markdown string.
Returns
The parsed JSON object as a Python dictionary. | [
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b3e2a14a33ed-0 | langchain.output_parsers.boolean.BooleanOutputParser¶
class langchain.output_parsers.boolean.BooleanOutputParser(*, true_val: str = 'YES', false_val: str = 'NO')[source]¶
Bases: BaseOutputParser[bool]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param false_val: str = 'NO'¶
param true_val: str = 'YES'¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
parse(text: str) → bool[source]¶
Parse the output of an LLM call to a boolean.
Parameters
text – output of language model
Returns
boolean
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.boolean.BooleanOutputParser.html |
b3e2a14a33ed-1 | property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.boolean.BooleanOutputParser.html |
bf34a456224e-0 | langchain.output_parsers.openai_functions.JsonKeyOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.JsonKeyOutputFunctionsParser(*, args_only: bool = True, key_name: str)[source]¶
Bases: JsonOutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
param key_name: str [Required]¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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14cd8d5f6a96-0 | langchain.output_parsers.rail_parser.GuardrailsOutputParser¶
class langchain.output_parsers.rail_parser.GuardrailsOutputParser(*, guard: Any = None, api: Optional[Callable] = None, args: Any = None, kwargs: Any = None)[source]¶
Bases: BaseOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api: Optional[Callable] = None¶
param args: Any = None¶
param guard: Any = None¶
param kwargs: Any = None¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_pydantic(output_class: Any, num_reasks: int = 1, api: Optional[Callable] = None, *args: Any, **kwargs: Any) → GuardrailsOutputParser[source]¶
classmethod from_rail(rail_file: str, num_reasks: int = 1, api: Optional[Callable] = None, *args: Any, **kwargs: Any) → GuardrailsOutputParser[source]¶
classmethod from_rail_string(rail_str: str, num_reasks: int = 1, api: Optional[Callable] = None, *args: Any, **kwargs: Any) → GuardrailsOutputParser[source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Dict[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result. | [
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14cd8d5f6a96-1 | parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.rail_parser.GuardrailsOutputParser.html |
ee3e38a97cfd-0 | langchain.output_parsers.enum.EnumOutputParser¶
class langchain.output_parsers.enum.EnumOutputParser(*, enum: Type[Enum])[source]¶
Bases: BaseOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param enum: Type[enum.Enum] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(response: str) → Any[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
validator raise_deprecation » all fields[source]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object. | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html |
ee3e38a97cfd-1 | property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html |
c8e5ddc1c5ee-0 | langchain.output_parsers.openai_functions.JsonOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.JsonOutputFunctionsParser(*, args_only: bool = True)[source]¶
Bases: OutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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7bfb587c0c2a-0 | langchain.output_parsers.loading.load_output_parser¶
langchain.output_parsers.loading.load_output_parser(config: dict) → dict[source]¶
Load output parser. | [
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980dad920e9f-0 | langchain.output_parsers.datetime.DatetimeOutputParser¶
class langchain.output_parsers.datetime.DatetimeOutputParser(*, format: str = '%Y-%m-%dT%H:%M:%S.%fZ')[source]¶
Bases: BaseOutputParser[datetime]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param format: str = '%Y-%m-%dT%H:%M:%S.%fZ'¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(response: str) → datetime[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object. | [
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980dad920e9f-1 | property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html |
8b90fc235465-0 | langchain.output_parsers.pydantic.PydanticOutputParser¶
class langchain.output_parsers.pydantic.PydanticOutputParser(*, pydantic_object: Type[T])[source]¶
Bases: BaseOutputParser[T]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param pydantic_object: Type[langchain.output_parsers.pydantic.T] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → T[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object. | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html |
8b90fc235465-1 | property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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24a756c3511f-0 | langchain.output_parsers.regex_dict.RegexDictParser¶
class langchain.output_parsers.regex_dict.RegexDictParser(*, regex_pattern: str = "{}:\\s?([^.'\\n']*)\\.?", output_key_to_format: Dict[str, str], no_update_value: Optional[str] = None)[source]¶
Bases: BaseOutputParser
Class to parse the output into a dictionary.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param no_update_value: Optional[str] = None¶
param output_key_to_format: Dict[str, str] [Required]¶
param regex_pattern: str = "{}:\\s?([^.'\\n']*)\\.?"¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Dict[str, str][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor. | [
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24a756c3511f-1 | serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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3c912129f729-0 | langchain.output_parsers.list.ListOutputParser¶
class langchain.output_parsers.list.ListOutputParser[source]¶
Bases: BaseOutputParser
Class to parse the output of an LLM call to a list.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
abstract parse(text: str) → List[str][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.list.ListOutputParser.html |
3c912129f729-1 | property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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47e06b90bb11-0 | langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser(*, args_only: bool = True, pydantic_schema: Any = None)[source]¶
Bases: OutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
param pydantic_schema: Any = None¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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3027294ac240-0 | langchain.output_parsers.structured.ResponseSchema¶
class langchain.output_parsers.structured.ResponseSchema(*, name: str, description: str, type: str = 'string')[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param description: str [Required]¶
param name: str [Required]¶
param type: str = 'string'¶ | [
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09ff59925e03-0 | langchain.output_parsers.retry.RetryOutputParser¶
class langchain.output_parsers.retry.RetryOutputParser(*, parser: BaseOutputParser[T], retry_chain: LLMChain)[source]¶
Bases: BaseOutputParser[T]
Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt and the completion to another
LLM, and telling it the completion did not satisfy criteria in the prompt.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T] [Required]¶
param retry_chain: langchain.chains.llm.LLMChain [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_llm(llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = PromptTemplate(input_variables=['completion', 'prompt'], output_parser=None, partial_variables={}, template='Prompt:\n{prompt}\nCompletion:\n{completion}\n\nAbove, the Completion did not satisfy the constraints given in the Prompt.\nPlease try again:', template_format='f-string', validate_template=True)) → RetryOutputParser[T][source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(completion: str) → T[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result. | [
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09ff59925e03-1 | parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt_value: PromptValue) → T[source]¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html |
324b9381f84c-0 | langchain.output_parsers.openai_functions.PydanticAttrOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.PydanticAttrOutputFunctionsParser(*, args_only: bool = True, pydantic_schema: Any = None, attr_name: str)[source]¶
Bases: PydanticOutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
param attr_name: str [Required]¶
param pydantic_schema: Any = None¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticAttrOutputFunctionsParser.html |
cdea2b262092-0 | langchain.document_transformers.get_stateful_documents¶
langchain.document_transformers.get_stateful_documents(documents: Sequence[Document]) → Sequence[_DocumentWithState][source]¶
Convert a list of documents to a list of documents with state.
Parameters
documents – The documents to convert.
Returns
A list of documents with state. | [
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d877b45a02e9-0 | langchain.document_transformers.EmbeddingsRedundantFilter¶
class langchain.document_transformers.EmbeddingsRedundantFilter(*, embeddings: ~langchain.embeddings.base.Embeddings, similarity_fn: ~typing.Callable = <function cosine_similarity>, similarity_threshold: float = 0.95)[source]¶
Bases: BaseDocumentTransformer, BaseModel
Filter that drops redundant documents by comparing their embeddings.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param embeddings: langchain.embeddings.base.Embeddings [Required]¶
Embeddings to use for embedding document contents.
param similarity_fn: Callable = <function cosine_similarity>¶
Similarity function for comparing documents. Function expected to take as input
two matrices (List[List[float]]) and return a matrix of scores where higher values
indicate greater similarity.
param similarity_threshold: float = 0.95¶
Threshold for determining when two documents are similar enough
to be considered redundant.
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Asynchronously transform a list of documents.
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Filter down documents.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_transformers/langchain.document_transformers.EmbeddingsRedundantFilter.html |
b1b4841ca157-0 | langchain.formatting.StrictFormatter¶
class langchain.formatting.StrictFormatter[source]¶
Bases: Formatter
A subclass of formatter that checks for extra keys.
Methods
__init__()
check_unused_args(used_args, args, kwargs)
Check to see if extra parameters are passed.
convert_field(value, conversion)
format(format_string, /, *args, **kwargs)
format_field(value, format_spec)
get_field(field_name, args, kwargs)
get_value(key, args, kwargs)
parse(format_string)
validate_input_variables(format_string, ...)
vformat(format_string, args, kwargs)
Check that no arguments are provided.
check_unused_args(used_args: Sequence[Union[int, str]], args: Sequence, kwargs: Mapping[str, Any]) → None[source]¶
Check to see if extra parameters are passed.
convert_field(value, conversion)¶
format(format_string, /, *args, **kwargs)¶
format_field(value, format_spec)¶
get_field(field_name, args, kwargs)¶
get_value(key, args, kwargs)¶
parse(format_string)¶
validate_input_variables(format_string: str, input_variables: List[str]) → None[source]¶
vformat(format_string: str, args: Sequence, kwargs: Mapping[str, Any]) → str[source]¶
Check that no arguments are provided. | [
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003a80ee2121-0 | langchain.docstore.base.AddableMixin¶
class langchain.docstore.base.AddableMixin[source]¶
Bases: ABC
Mixin class that supports adding texts.
Methods
__init__()
add(texts)
Add more documents.
abstract add(texts: Dict[str, Document]) → None[source]¶
Add more documents. | [
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3d4937c88983-0 | langchain.docstore.wikipedia.Wikipedia¶
class langchain.docstore.wikipedia.Wikipedia[source]¶
Bases: Docstore
Wrapper around wikipedia API.
Check that wikipedia package is installed.
Methods
__init__()
Check that wikipedia package is installed.
search(search)
Try to search for wiki page.
search(search: str) → Union[str, Document][source]¶
Try to search for wiki page.
If page exists, return the page summary, and a PageWithLookups object.
If page does not exist, return similar entries. | [
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] | https://langchain.readthedocs.io/en/latest/docstore/langchain.docstore.wikipedia.Wikipedia.html |
e89b064c8a08-0 | langchain.docstore.in_memory.InMemoryDocstore¶
class langchain.docstore.in_memory.InMemoryDocstore(_dict: Dict[str, Document])[source]¶
Bases: Docstore, AddableMixin
Simple in memory docstore in the form of a dict.
Initialize with dict.
Methods
__init__(_dict)
Initialize with dict.
add(texts)
Add texts to in memory dictionary.
search(search)
Search via direct lookup.
add(texts: Dict[str, Document]) → None[source]¶
Add texts to in memory dictionary.
search(search: str) → Union[str, Document][source]¶
Search via direct lookup. | [
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1248aa6faf48-0 | langchain.docstore.arbitrary_fn.DocstoreFn¶
class langchain.docstore.arbitrary_fn.DocstoreFn(lookup_fn: Callable[[str], Union[Document, str]])[source]¶
Bases: Docstore
Langchain Docstore via arbitrary lookup function.
This is useful when:
it’s expensive to construct an InMemoryDocstore/dict
you retrieve documents from remote sources
you just want to reuse existing objects
Methods
__init__(lookup_fn)
search(search)
Search for document.
search(search: str) → Document[source]¶
Search for document.
If page exists, return the page summary, and a Document object.
If page does not exist, return similar entries. | [
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497f3a9d0652-0 | langchain.docstore.base.Docstore¶
class langchain.docstore.base.Docstore[source]¶
Bases: ABC
Interface to access to place that stores documents.
Methods
__init__()
search(search)
Search for document.
abstract search(search: str) → Union[str, Document][source]¶
Search for document.
If page exists, return the page summary, and a Document object.
If page does not exist, return similar entries. | [
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c9b7cc0664da-0 | langchain.prompts.chat.BaseStringMessagePromptTemplate¶
class langchain.prompts.chat.BaseStringMessagePromptTemplate(*, prompt: StringPromptTemplate, additional_kwargs: dict = None)[source]¶
Bases: BaseMessagePromptTemplate, ABC
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
param prompt: langchain.prompts.base.StringPromptTemplate [Required]¶
abstract format(**kwargs: Any) → BaseMessage[source]¶
To a BaseMessage.
format_messages(**kwargs: Any) → List[BaseMessage][source]¶
To messages.
classmethod from_template(template: str, template_format: str = 'f-string', **kwargs: Any) → MessagePromptTemplateT[source]¶
classmethod from_template_file(template_file: Union[str, Path], input_variables: List[str], **kwargs: Any) → MessagePromptTemplateT[source]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property input_variables: List[str]¶
Input variables for this prompt template.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object | [
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c9b7cc0664da-1 | Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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86039ecbd8bf-0 | langchain.prompts.base.jinja2_formatter¶
langchain.prompts.base.jinja2_formatter(template: str, **kwargs: Any) → str[source]¶
Format a template using jinja2. | [
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cf11d9156d4b-0 | langchain.prompts.example_selector.ngram_overlap.NGramOverlapExampleSelector¶
class langchain.prompts.example_selector.ngram_overlap.NGramOverlapExampleSelector(*, examples: List[dict], example_prompt: PromptTemplate, threshold: float = - 1.0)[source]¶
Bases: BaseExampleSelector, BaseModel
Select and order examples based on ngram overlap score (sentence_bleu score).
https://www.nltk.org/_modules/nltk/translate/bleu_score.html
https://aclanthology.org/P02-1040.pdf
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param example_prompt: langchain.prompts.prompt.PromptTemplate [Required]¶
Prompt template used to format the examples.
param examples: List[dict] [Required]¶
A list of the examples that the prompt template expects.
param threshold: float = -1.0¶
Threshold at which algorithm stops. Set to -1.0 by default.
For negative threshold:
select_examples sorts examples by ngram_overlap_score, but excludes none.
For threshold greater than 1.0:
select_examples excludes all examples, and returns an empty list.
For threshold equal to 0.0:
select_examples sorts examples by ngram_overlap_score,
and excludes examples with no ngram overlap with input.
add_example(example: Dict[str, str]) → None[source]¶
Add new example to list.
validator check_dependencies » all fields[source]¶
Check that valid dependencies exist.
select_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Return list of examples sorted by ngram_overlap_score with input.
Descending order.
Excludes any examples with ngram_overlap_score less than or equal to threshold. | [
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436db5cbd940-0 | langchain.prompts.chat.ChatPromptValue¶
class langchain.prompts.chat.ChatPromptValue(*, messages: List[BaseMessage])[source]¶
Bases: PromptValue
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param messages: List[langchain.schema.BaseMessage] [Required]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
to_messages() → List[BaseMessage][source]¶
Return prompt as messages.
to_string() → str[source]¶
Return prompt as string.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.chat.ChatPromptValue.html |
22f8f179bfcd-0 | langchain.prompts.chat.BaseMessagePromptTemplate¶
class langchain.prompts.chat.BaseMessagePromptTemplate[source]¶
Bases: Serializable, ABC
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
abstract format_messages(**kwargs: Any) → List[BaseMessage][source]¶
To messages.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
abstract property input_variables: List[str]¶
Input variables for this prompt template.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.chat.BaseMessagePromptTemplate.html |
221d2b29308d-0 | langchain.prompts.example_selector.length_based.LengthBasedExampleSelector¶
class langchain.prompts.example_selector.length_based.LengthBasedExampleSelector(*, examples: ~typing.List[dict], example_prompt: ~langchain.prompts.prompt.PromptTemplate, get_text_length: ~typing.Callable[[str], int] = <function _get_length_based>, max_length: int = 2048, example_text_lengths: ~typing.List[int] = [])[source]¶
Bases: BaseExampleSelector, BaseModel
Select examples based on length.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param example_prompt: langchain.prompts.prompt.PromptTemplate [Required]¶
Prompt template used to format the examples.
param examples: List[dict] [Required]¶
A list of the examples that the prompt template expects.
param get_text_length: Callable[[str], int] = <function _get_length_based>¶
Function to measure prompt length. Defaults to word count.
param max_length: int = 2048¶
Max length for the prompt, beyond which examples are cut.
add_example(example: Dict[str, str]) → None[source]¶
Add new example to list.
validator calculate_example_text_lengths » example_text_lengths[source]¶
Calculate text lengths if they don’t exist.
select_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Select which examples to use based on the input lengths. | [
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9027056dff4a-0 | langchain.prompts.loading.load_prompt¶
langchain.prompts.loading.load_prompt(path: Union[str, Path]) → BasePromptTemplate[source]¶
Unified method for loading a prompt from LangChainHub or local fs. | [
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e460fcc9125b-0 | langchain.prompts.example_selector.ngram_overlap.ngram_overlap_score¶
langchain.prompts.example_selector.ngram_overlap.ngram_overlap_score(source: List[str], example: List[str]) → float[source]¶
Compute ngram overlap score of source and example as sentence_bleu score.
Use sentence_bleu with method1 smoothing function and auto reweighting.
Return float value between 0.0 and 1.0 inclusive.
https://www.nltk.org/_modules/nltk/translate/bleu_score.html
https://aclanthology.org/P02-1040.pdf | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.example_selector.ngram_overlap.ngram_overlap_score.html |
e0d00275d662-0 | langchain.prompts.chat.BaseChatPromptTemplate¶
class langchain.prompts.chat.BaseChatPromptTemplate(*, input_variables: List[str], output_parser: Optional[BaseOutputParser] = None, partial_variables: Mapping[str, Union[str, Callable[[], str]]] = None)[source]¶
Bases: BasePromptTemplate, ABC
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param input_variables: List[str] [Required]¶
A list of the names of the variables the prompt template expects.
param output_parser: Optional[BaseOutputParser] = None¶
How to parse the output of calling an LLM on this formatted prompt.
param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of prompt.
format(**kwargs: Any) → str[source]¶
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
abstract format_messages(**kwargs: Any) → List[BaseMessage][source]¶
Format kwargs into a list of messages.
format_prompt(**kwargs: Any) → PromptValue[source]¶
Create Chat Messages.
partial(**kwargs: Union[str, Callable[[], str]]) → BasePromptTemplate¶
Return a partial of the prompt template.
save(file_path: Union[Path, str]) → None¶
Save the prompt.
Parameters
file_path – Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.chat.BaseChatPromptTemplate.html |
e0d00275d662-1 | to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_variable_names » all fields¶
Validate variable names do not include restricted names.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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a3e01013c534-0 | langchain.prompts.base.StringPromptValue¶
class langchain.prompts.base.StringPromptValue(*, text: str)[source]¶
Bases: PromptValue
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param text: str [Required]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
to_messages() → List[BaseMessage][source]¶
Return prompt as messages.
to_string() → str[source]¶
Return prompt as string.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.base.StringPromptValue.html |
f5e862d4a376-0 | langchain.prompts.few_shot_with_templates.FewShotPromptWithTemplates¶
class langchain.prompts.few_shot_with_templates.FewShotPromptWithTemplates(*, input_variables: List[str], output_parser: Optional[BaseOutputParser] = None, partial_variables: Mapping[str, Union[str, Callable[[], str]]] = None, examples: Optional[List[dict]] = None, example_selector: Optional[BaseExampleSelector] = None, example_prompt: PromptTemplate, suffix: StringPromptTemplate, example_separator: str = '\n\n', prefix: Optional[StringPromptTemplate] = None, template_format: str = 'f-string', validate_template: bool = True)[source]¶
Bases: StringPromptTemplate
Prompt template that contains few shot examples.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param example_prompt: langchain.prompts.prompt.PromptTemplate [Required]¶
PromptTemplate used to format an individual example.
param example_selector: Optional[langchain.prompts.example_selector.base.BaseExampleSelector] = None¶
ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided.
param example_separator: str = '\n\n'¶
String separator used to join the prefix, the examples, and suffix.
param examples: Optional[List[dict]] = None¶
Examples to format into the prompt.
Either this or example_selector should be provided.
param input_variables: List[str] [Required]¶
A list of the names of the variables the prompt template expects.
param output_parser: Optional[BaseOutputParser] = None¶
How to parse the output of calling an LLM on this formatted prompt.
param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.few_shot_with_templates.FewShotPromptWithTemplates.html |
f5e862d4a376-1 | param prefix: Optional[langchain.prompts.base.StringPromptTemplate] = None¶
A PromptTemplate to put before the examples.
param suffix: langchain.prompts.base.StringPromptTemplate [Required]¶
A PromptTemplate to put after the examples.
param template_format: str = 'f-string'¶
The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
param validate_template: bool = True¶
Whether or not to try validating the template.
validator check_examples_and_selector » all fields[source]¶
Check that one and only one of examples/example_selector are provided.
dict(**kwargs: Any) → Dict[source]¶
Return a dictionary of the prompt.
format(**kwargs: Any) → str[source]¶
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
format_prompt(**kwargs: Any) → PromptValue¶
Create Chat Messages.
partial(**kwargs: Union[str, Callable[[], str]]) → BasePromptTemplate¶
Return a partial of the prompt template.
save(file_path: Union[Path, str]) → None¶
Save the prompt.
Parameters
file_path – Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
validator template_is_valid » all fields[source]¶
Check that prefix, suffix and input variables are consistent.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_variable_names » all fields¶
Validate variable names do not include restricted names.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.few_shot_with_templates.FewShotPromptWithTemplates.html |
f5e862d4a376-2 | property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.few_shot_with_templates.FewShotPromptWithTemplates.html |
7bbe400ebcc9-0 | langchain.prompts.few_shot.FewShotPromptTemplate¶
class langchain.prompts.few_shot.FewShotPromptTemplate(*, input_variables: List[str], output_parser: Optional[BaseOutputParser] = None, partial_variables: Mapping[str, Union[str, Callable[[], str]]] = None, examples: Optional[List[dict]] = None, example_selector: Optional[BaseExampleSelector] = None, example_prompt: PromptTemplate, suffix: str, example_separator: str = '\n\n', prefix: str = '', template_format: str = 'f-string', validate_template: bool = True)[source]¶
Bases: StringPromptTemplate
Prompt template that contains few shot examples.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param example_prompt: langchain.prompts.prompt.PromptTemplate [Required]¶
PromptTemplate used to format an individual example.
param example_selector: Optional[langchain.prompts.example_selector.base.BaseExampleSelector] = None¶
ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided.
param example_separator: str = '\n\n'¶
String separator used to join the prefix, the examples, and suffix.
param examples: Optional[List[dict]] = None¶
Examples to format into the prompt.
Either this or example_selector should be provided.
param input_variables: List[str] [Required]¶
A list of the names of the variables the prompt template expects.
param output_parser: Optional[BaseOutputParser] = None¶
How to parse the output of calling an LLM on this formatted prompt.
param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]¶
param prefix: str = ''¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.few_shot.FewShotPromptTemplate.html |
7bbe400ebcc9-1 | param prefix: str = ''¶
A prompt template string to put before the examples.
param suffix: str [Required]¶
A prompt template string to put after the examples.
param template_format: str = 'f-string'¶
The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
param validate_template: bool = True¶
Whether or not to try validating the template.
validator check_examples_and_selector » all fields[source]¶
Check that one and only one of examples/example_selector are provided.
dict(**kwargs: Any) → Dict[source]¶
Return a dictionary of the prompt.
format(**kwargs: Any) → str[source]¶
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
format_prompt(**kwargs: Any) → PromptValue¶
Create Chat Messages.
partial(**kwargs: Union[str, Callable[[], str]]) → BasePromptTemplate¶
Return a partial of the prompt template.
save(file_path: Union[Path, str]) → None¶
Save the prompt.
Parameters
file_path – Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
validator template_is_valid » all fields[source]¶
Check that prefix, suffix and input variables are consistent.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_variable_names » all fields¶
Validate variable names do not include restricted names.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor. | [
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7bbe400ebcc9-2 | serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.few_shot.FewShotPromptTemplate.html |
7f211b82a8e4-0 | langchain.prompts.loading.load_prompt_from_config¶
langchain.prompts.loading.load_prompt_from_config(config: dict) → BasePromptTemplate[source]¶
Load prompt from Config Dict. | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.loading.load_prompt_from_config.html |
5c6902124345-0 | langchain.prompts.base.check_valid_template¶
langchain.prompts.base.check_valid_template(template: str, template_format: str, input_variables: List[str]) → None[source]¶
Check that template string is valid. | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.base.check_valid_template.html |
9938a988c250-0 | langchain.prompts.pipeline.PipelinePromptTemplate¶
class langchain.prompts.pipeline.PipelinePromptTemplate(*, input_variables: List[str], output_parser: Optional[BaseOutputParser] = None, partial_variables: Mapping[str, Union[str, Callable[[], str]]] = None, final_prompt: BasePromptTemplate, pipeline_prompts: List[Tuple[str, BasePromptTemplate]])[source]¶
Bases: BasePromptTemplate
A prompt template for composing multiple prompts together.
This can be useful when you want to reuse parts of prompts.
A PipelinePrompt consists of two main parts:
final_prompt: This is the final prompt that is returned
pipeline_prompts: This is a list of tuples, consistingof a string (name) and a Prompt Template.
Each PromptTemplate will be formatted and then passed
to future prompt templates as a variable with
the same name as name
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param final_prompt: langchain.prompts.base.BasePromptTemplate [Required]¶
param input_variables: List[str] [Required]¶
A list of the names of the variables the prompt template expects.
param output_parser: Optional[BaseOutputParser] = None¶
How to parse the output of calling an LLM on this formatted prompt.
param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]¶
param pipeline_prompts: List[Tuple[str, langchain.prompts.base.BasePromptTemplate]] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of prompt.
format(**kwargs: Any) → str[source]¶
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example: | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.pipeline.PipelinePromptTemplate.html |
9938a988c250-1 | Returns
A formatted string.
Example:
prompt.format(variable1="foo")
format_prompt(**kwargs: Any) → PromptValue[source]¶
Create Chat Messages.
validator get_input_variables » all fields[source]¶
Get input variables.
partial(**kwargs: Union[str, Callable[[], str]]) → BasePromptTemplate¶
Return a partial of the prompt template.
save(file_path: Union[Path, str]) → None¶
Save the prompt.
Parameters
file_path – Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_variable_names » all fields¶
Validate variable names do not include restricted names.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.pipeline.PipelinePromptTemplate.html |
fae94db8a7ae-0 | langchain.prompts.chat.SystemMessagePromptTemplate¶
class langchain.prompts.chat.SystemMessagePromptTemplate(*, prompt: StringPromptTemplate, additional_kwargs: dict = None)[source]¶
Bases: BaseStringMessagePromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
param prompt: langchain.prompts.base.StringPromptTemplate [Required]¶
format(**kwargs: Any) → BaseMessage[source]¶
To a BaseMessage.
format_messages(**kwargs: Any) → List[BaseMessage]¶
To messages.
classmethod from_template(template: str, template_format: str = 'f-string', **kwargs: Any) → MessagePromptTemplateT¶
classmethod from_template_file(template_file: Union[str, Path], input_variables: List[str], **kwargs: Any) → MessagePromptTemplateT¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property input_variables: List[str]¶
Input variables for this prompt template.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | [
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] | https://langchain.readthedocs.io/en/latest/prompts/langchain.prompts.chat.SystemMessagePromptTemplate.html |
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