viruPVS/langchain-docs huggingface dataset:
The unique identifier is a list of strings that describes the path
to the object.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
Examples using AIMessage¶
Twitter (via Apify)
Zep
Zep Memory
SQL Chat Message History
Anthropic
🚅 LiteLLM
Konko
OpenAI
JinaChat
Set env var OPENAI_API_KEY or load from a .env file:
CAMEL Role-Playing Autonomous Cooperative Agents
Multi-agent decentralized speaker selection
Multi-agent authoritarian speaker selection
langchain_core.messages.human.HumanMessage¶
class langchain_core.messages.human.HumanMessage[source]¶
Bases: BaseMessage
A Message from a 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]¶
Any additional information.
param content: Union[str, List[Union[str, Dict]]] [Required]¶
The string contents of the message.
param example: bool = False¶
Whether this Message is being passed in to the model as part of an example
conversation.
param type: Literal['human'] = 'human'¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
The unique identifier is a list of strings that describes the path
to the object.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
Examples using HumanMessage¶
Zep
Zep Memory
SQL Chat Message History
AzureML Chat Online Endpoint
Anthropic
🚅 LiteLLM
Konko
OpenAI
Google Cloud Platform Vertex AI PaLM 
Bedrock Chat
JinaChat
Ollama
Azure
Baidu Qianfan
ERNIE-Bot Chat
PromptLayer ChatOpenAI
Anyscale
ERNIE-Bot Chat
PromptLayer ChatOpenAI
Anyscale
Anthropic Functions
LLMonitor
Context
Label Studio
PromptLayer
Log10
MLflow AI Gateway
Flyte
Arthur
Set env var OPENAI_API_KEY or load from a .env file:
Structure answers with OpenAI functions
CAMEL Role-Playing Autonomous Cooperative Agents
Multi-Agent Simulated Environment: Petting Zoo
Multi-agent decentralized speaker selection
Multi-agent authoritarian speaker selection
Two-Player Dungeons & Dragons
Multi-Player Dungeons & Dragons
Simulated Environment: Gymnasium
Agent Debates with Tools
Custom callback handlers
Async callbacks
Tools as OpenAI Functions
Prompt pipelining
Using OpenAI functions
langchain_core.load.load.load¶
langchain_core.load.load.load(obj: Any, *, secrets_map: Optional[Dict[str, str]] = None, valid_namespaces: Optional[List[str]] = None) → Any[source]¶
Revive a LangChain class from a JSON object. Use this if you already
have a parsed JSON object, eg. from json.load or orjson.loads.
Parameters
obj – The object to load.
secrets_map – A map of secrets to load.
valid_namespaces – A list of additional namespaces (modules)
to allow to be deserialized.
Returns
Revived LangChain objects.
langchain_core.load.load.loads¶
langchain_core.load.load.loads(text: str, *, secrets_map: Optional[Dict[str, str]] = None, valid_namespaces: Optional[List[str]] = None) → Any[source]¶
Revive a LangChain class from a JSON string.
Equivalent to load(json.loads(text)).
Parameters
text – The string to load.
secrets_map – A map of secrets to load.
valid_namespaces – A list of additional namespaces (modules)
to allow to be deserialized.
Returns
Revived LangChain objects.
langchain_core.load.load.Reviver¶
class langchain_core.load.load.Reviver(secrets_map: Optional[Dict[str, str]] = None, valid_namespaces: Optional[List[str]] = None)[source]¶
Reviver for JSON objects.
Methods
__init__([secrets_map, valid_namespaces])
__init__(secrets_map: Optional[Dict[str, str]] = None, valid_namespaces: Optional[List[str]] = None) → None[source]¶
langchain_core.load.serializable.Serializable¶
class langchain_core.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.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
classmethod get_lc_namespace() → List[str][source]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
classmethod is_lc_serializable() → bool[source]¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod lc_id() → List[str][source]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented][source]¶
to_json_not_implemented() → SerializedNotImplemented[source]¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
langchain.indexes.vectorstore.VectorStoreIndexWrapper¶
class langchain.indexes.vectorstore.VectorStoreIndexWrapper[source]¶
Bases: BaseModel
Wrapper around a vectorstore for easy access.
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 vectorstore: langchain_core.vectorstores.VectorStore [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
query(question: str, llm: Optional[BaseLanguageModel] = None, retriever_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Any) → str[source]¶
Query the vectorstore.
query_with_sources(question: str, llm: Optional[BaseLanguageModel] = None, retriever_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Any) → dict[source]¶
Query the vectorstore and get back sources.
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
langchain.indexes.base.RecordManager¶
class langchain.indexes.base.RecordManager(namespace: str)[source]¶
An abstract base class representing the interface for a record manager.
Initialize the record manager.
Parameters
namespace (str) – The namespace for the record manager.
Methods
__init__(namespace)
Initialize the record manager.
acreate_schema()
Create the database schema for the record manager.
adelete_keys(keys)
Delete specified records from the database.
aexists(keys)
Check if the provided keys exist in the database.
aget_time()
Get the current server time as a high resolution timestamp!
alist_keys(*[, before, after, group_ids, limit])
List records in the database based on the provided filters.
aupdate(keys, *[, group_ids, time_at_least])
Upsert records into the database.
create_schema()
Create the database schema for the record manager.
delete_keys(keys)
Delete specified records from the database.
exists(keys)
Check if the provided keys exist in the database.
get_time()
Get the current server time as a high resolution timestamp!
list_keys(*[, before, after, group_ids, limit])
List records in the database based on the provided filters.
update(keys, *[, group_ids, time_at_least])
Upsert records into the database.
__init__(namespace: str) → None[source]¶
Initialize the record manager.
Parameters
namespace (str) – The namespace for the record manager.
abstract async acreate_schema() → None[source]¶
Create the database schema for the record manager.
abstract async adelete_keys(keys: Sequence[str]) → None[source]¶
Delete specified records from the database.
Parameters
keys – A list of keys to delete.
Delete specified records from the database.
Parameters
keys – A list of keys to delete.
abstract async aexists(keys: Sequence[str]) → List[bool][source]¶
Check if the provided keys exist in the database.
Parameters
keys – A list of keys to check.
Returns
A list of boolean values indicating the existence of each key.
abstract async aget_time() → float[source]¶
Get the current server time as a high resolution timestamp!
It’s important to get this from the server to ensure a monotonic clock,
otherwise there may be data loss when cleaning up old documents!
Returns
The current server time as a float timestamp.
abstract async alist_keys(*, before: Optional[float] = None, after: Optional[float] = None, group_ids: Optional[Sequence[str]] = None, limit: Optional[int] = None) → List[str][source]¶
List records in the database based on the provided filters.
Parameters
before – Filter to list records updated before this time.
after – Filter to list records updated after this time.
group_ids – Filter to list records with specific group IDs.
limit – optional limit on the number of records to return.
Returns
A list of keys for the matching records.
abstract async aupdate(keys: Sequence[str], *, group_ids: Optional[Sequence[Optional[str]]] = None, time_at_least: Optional[float] = None) → None[source]¶
Upsert records into the database.
Parameters
keys – A list of record keys to upsert.
group_ids – A list of group IDs corresponding to the keys.
time_at_least – if provided, updates should only happen if the
updated_at field is at least this time.
Raises
ValueError – If the length of keys doesn’t match the length of group_ids.
abstract create_schema() → None[source]¶
abstract create_schema() → None[source]¶
Create the database schema for the record manager.
abstract delete_keys(keys: Sequence[str]) → None[source]¶
Delete specified records from the database.
Parameters
keys – A list of keys to delete.
abstract exists(keys: Sequence[str]) → List[bool][source]¶
Check if the provided keys exist in the database.
Parameters
keys – A list of keys to check.
Returns
A list of boolean values indicating the existence of each key.
abstract get_time() → float[source]¶
Get the current server time as a high resolution timestamp!
It’s important to get this from the server to ensure a monotonic clock,
otherwise there may be data loss when cleaning up old documents!
Returns
The current server time as a float timestamp.
abstract list_keys(*, before: Optional[float] = None, after: Optional[float] = None, group_ids: Optional[Sequence[str]] = None, limit: Optional[int] = None) → List[str][source]¶
List records in the database based on the provided filters.
Parameters
before – Filter to list records updated before this time.
after – Filter to list records updated after this time.
group_ids – Filter to list records with specific group IDs.
limit – optional limit on the number of records to return.
Returns
A list of keys for the matching records.
abstract update(keys: Sequence[str], *, group_ids: Optional[Sequence[Optional[str]]] = None, time_at_least: Optional[float] = None) → None[source]¶
Upsert records into the database.
Parameters
keys – A list of record keys to upsert.
group_ids – A list of group IDs corresponding to the keys.
time_at_least – if provided, updates should only happen if the
updated_at field is at least this time.
Raises
langchain_community.indexes.base.RecordManager¶
class langchain_community.indexes.base.RecordManager(namespace: str)[source]¶
An abstract base class representing the interface for a record manager.
Initialize the record manager.
Parameters
namespace (str) – The namespace for the record manager.
Methods
__init__(namespace)
Initialize the record manager.
acreate_schema()
Create the database schema for the record manager.
adelete_keys(keys)
Delete specified records from the database.
aexists(keys)
Check if the provided keys exist in the database.
aget_time()
Get the current server time as a high resolution timestamp!
alist_keys(*[, before, after, group_ids, limit])
List records in the database based on the provided filters.
aupdate(keys, *[, group_ids, time_at_least])
Upsert records into the database.
create_schema()
Create the database schema for the record manager.
delete_keys(keys)
Delete specified records from the database.
exists(keys)
Check if the provided keys exist in the database.
get_time()
Get the current server time as a high resolution timestamp!
list_keys(*[, before, after, group_ids, limit])
List records in the database based on the provided filters.
update(keys, *[, group_ids, time_at_least])
Upsert records into the database.
__init__(namespace: str) → None[source]¶
Initialize the record manager.
Parameters
namespace (str) – The namespace for the record manager.
abstract async acreate_schema() → None[source]¶
Create the database schema for the record manager.
abstract async adelete_keys(keys: Sequence[str]) → None[source]¶
Delete specified records from the database.
Parameters
keys – A list of keys to delete.
langchain.indexes.graph.GraphIndexCreator¶
class langchain.indexes.graph.GraphIndexCreator[source]¶
Bases: BaseModel
Functionality to create graph index.
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 graph_type: Type[langchain_community.graphs.networkx_graph.NetworkxEntityGraph] = <class 'langchain_community.graphs.networkx_graph.NetworkxEntityGraph'>¶
param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶
param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶
async afrom_text(text: str, prompt: BasePromptTemplate = PromptTemplate(input_variables=['text'], template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrating them with your knowledge stored within your weights as well as that stored in a knowledge graph. Extract all of the knowledge triples from the text. A knowledge triple is a clause that contains a subject, a predicate, and an object. The subject is the entity being described, the predicate is the property of the subject that is being described, and the object is the value of the property.\n\nEXAMPLE\nIt's a state in the US. It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nI'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nOh huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nEXAMPLE\n{text}Output:")) → NetworkxEntityGraph[source]¶
Create graph index from text asynchronously.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
classmethod from_orm(obj: Any) → Model¶
from_text(text: str, prompt: BasePromptTemplate = PromptTemplate(input_variables=['text'], template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrating them with your knowledge stored within your weights as well as that stored in a knowledge graph. Extract all of the knowledge triples from the text. A knowledge triple is a clause that contains a subject, a predicate, and an object. The subject is the entity being described, the predicate is the property of the subject that is being described, and the object is the value of the property.\n\nEXAMPLE\nIt's a state in the US. It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nI'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nOh huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nEXAMPLE\n{text}Output:")) → NetworkxEntityGraph[source]¶
Create graph index from text.