viruPVS/langchain-docs huggingface dataset:
es_user – (str, optional): Elasticsearch username.
es_password – (str, optional): Elasticsearch password.
Example
from langchain_community.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
# Optional, only if different from 'text_field'
input_field = "your_input_field"
# Credentials can be passed in two ways. Either set the env vars
# ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically
# pulled in, or pass them in directly as kwargs.
embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id,
    input_field=input_field,
    # es_cloud_id="foo",
    # es_user="bar",
    # es_password="baz",
)
documents = [
    "This is an example document.",
    "Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
classmethod from_es_connection(model_id: str, es_connection: Elasticsearch, input_field: str = 'text_field') → ElasticsearchEmbeddings[source]¶
Instantiate embeddings from an existing Elasticsearch connection.
This method provides a way to create an instance of the ElasticsearchEmbeddings
class using an existing Elasticsearch connection. The connection object is used
to create an MlClient, which is then used to initialize the
ElasticsearchEmbeddings instance.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch cluster.
es_connection (elasticsearch.Elasticsearch): An existing Elasticsearch
connection object. input_field (str, optional): The name of the key for the
input text field in the document. Defaults to ‘text_field’.
Returns:
ElasticsearchEmbeddings: An instance of the ElasticsearchEmbeddings class.
Example
from elasticsearch import Elasticsearch
Example
from elasticsearch import Elasticsearch
from langchain_community.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
# Optional, only if different from 'text_field'
input_field = "your_input_field"
# Create Elasticsearch connection
es_connection = Elasticsearch(
    hosts=["localhost:9200"], http_auth=("user", "password")
)
# Instantiate ElasticsearchEmbeddings using the existing connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
    model_id,
    es_connection,
    input_field=input_field,
)
documents = [
    "This is an example document.",
    "Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
Examples using ElasticsearchEmbeddings¶
Elasticsearch
langchain_community.embeddings.huggingface_hub.HuggingFaceHubEmbeddings¶
class langchain_community.embeddings.huggingface_hub.HuggingFaceHubEmbeddings[source]¶
Bases: BaseModel, Embeddings
HuggingFaceHub embedding models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain_community.embeddings import HuggingFaceHubEmbeddings
model = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceHubEmbeddings(
    model=model,
    task="feature-extraction",
    huggingfacehub_api_token="my-api-key",
)
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 huggingfacehub_api_token: Optional[str] = None¶
param model: Optional[str] = None¶
Model name to use.
param model_kwargs: Optional[dict] = None¶
Keyword arguments to pass to the model.
param repo_id: Optional[str] = None¶
Huggingfacehub repository id, for backward compatibility.
param task: Optional[str] = 'feature-extraction'¶
Task to call the model with.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
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.
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.
embed_documents(texts: List[str]) → List[List[float]][source]¶
Call out to HuggingFaceHub’s embedding endpoint for embedding search docs.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]¶
Call out to HuggingFaceHub’s embedding endpoint for embedding query text.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
Examples using HuggingFaceHubEmbeddings¶
Hugging Face
langchain_community.embeddings.fastembed.FastEmbedEmbeddings¶
class langchain_community.embeddings.fastembed.FastEmbedEmbeddings[source]¶
Bases: BaseModel, Embeddings
Qdrant FastEmbedding models.
FastEmbed is a lightweight, fast, Python library built for embedding generation.
See more documentation at:
* https://github.com/qdrant/fastembed/
* https://qdrant.github.io/fastembed/
To use this class, you must install the fastembed Python package.
pip install fastembed
.. rubric:: Example
from langchain_community.embeddings import FastEmbedEmbeddings
fastembed = FastEmbedEmbeddings()
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 cache_dir: Optional[str] = None¶
The path to the cache directory.
Defaults to local_cache in the parent directory
param doc_embed_type: Literal['default', 'passage'] = 'default'¶
Type of embedding to use for documents
“default”: Uses FastEmbed’s default embedding method
“passage”: Prefixes the text with “passage” before embedding.
param max_length: int = 512¶
The maximum number of tokens. Defaults to 512.
Unknown behavior for values > 512.
param model_name: str = 'BAAI/bge-small-en-v1.5'¶
Name of the FastEmbedding model to use
Defaults to “BAAI/bge-small-en-v1.5”
Find the list of supported models at
https://qdrant.github.io/fastembed/examples/Supported_Models/
param threads: Optional[int] = None¶
The number of threads single onnxruntime session can use.
Defaults to None
async aembed_documents(texts: List[str]) → List[List[float]]¶
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
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.
embed_documents(texts: List[str]) → List[List[float]][source]¶
embed_documents(texts: List[str]) → List[List[float]][source]¶
Generate embeddings for documents using FastEmbed.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]¶
Generate query embeddings using FastEmbed.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
langchain_core.caches.BaseCache¶
class langchain_core.caches.BaseCache[source]¶
Base interface for cache.
Methods
__init__()
clear(**kwargs)
Clear cache that can take additional keyword arguments.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
__init__()¶
abstract clear(**kwargs: Any) → None[source]¶
Clear cache that can take additional keyword arguments.
abstract lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
Look up based on prompt and llm_string.
abstract update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]¶
Update cache based on prompt and llm_string.
langchain.memory.token_buffer.ConversationTokenBufferMemory¶
class langchain.memory.token_buffer.ConversationTokenBufferMemory[source]¶
Bases: BaseChatMemory
Conversation chat memory with token limit.
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 ai_prefix: str = 'AI'¶
param chat_memory: BaseChatMessageHistory [Optional]¶
param human_prefix: str = 'Human'¶
param input_key: Optional[str] = None¶
param llm: langchain_core.language_models.base.BaseLanguageModel [Required]¶
param max_token_limit: int = 2000¶
param memory_key: str = 'history'¶
param output_key: Optional[str] = None¶
param return_messages: bool = False¶
clear() → None¶
Clear memory contents.
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
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]¶
Return history buffer.
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¶
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this conversation to buffer. Pruned.
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 buffer: Any¶
String buffer of memory.
property buffer_as_messages: List[langchain_core.messages.base.BaseMessage]¶
Exposes the buffer as a list of messages in case return_messages is True.
property buffer_as_str: str¶
Exposes the buffer as a string in case return_messages is False.
property lc_attributes: Dict¶
langchain.memory.buffer_window.ConversationBufferWindowMemory¶
class langchain.memory.buffer_window.ConversationBufferWindowMemory[source]¶
Bases: BaseChatMemory
Buffer for storing conversation memory inside a limited size window.
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 ai_prefix: str = 'AI'¶
param chat_memory: BaseChatMessageHistory [Optional]¶
param human_prefix: str = 'Human'¶
param input_key: Optional[str] = None¶
param k: int = 5¶
Number of messages to store in buffer.
param output_key: Optional[str] = None¶
param return_messages: bool = False¶
clear() → None¶
Clear memory contents.
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
The unique identifier is a list of strings that describes the path
to the object.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]¶
Return history buffer.
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¶
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None¶
Save context from this conversation to buffer.
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 buffer: Union[str, List[langchain_core.messages.base.BaseMessage]]¶
String buffer of memory.
property buffer_as_messages: List[langchain_core.messages.base.BaseMessage]¶
Exposes the buffer as a list of messages in case return_messages is False.
property buffer_as_str: str¶
Exposes the buffer as a string in case return_messages is True.
property lc_attributes: Dict¶
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 ConversationBufferWindowMemory¶
Figma
OpaquePrompts
Set env var OPENAI_API_KEY or load from a .env file:
Meta-Prompt
Create ChatGPT clone
langchain.memory.kg.ConversationKGMemory¶
class langchain.memory.kg.ConversationKGMemory[source]¶
Bases: BaseChatMemory
Knowledge graph conversation memory.
Integrates with external knowledge graph to store and retrieve
information about knowledge triples in the conversation.
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 ai_prefix: str = 'AI'¶
param chat_memory: BaseChatMessageHistory [Optional]¶
param entity_extraction_prompt: langchain_core.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various
i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:')¶
param knowledge_extraction_prompt: langchain_core.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], 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 last line of conversation. 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\nConversation history:\nPerson #1: Did you hear aliens landed in Area 51?\nAI: No, I didn't hear that. What do you know about Area 51?\nPerson #1: It's a secret military base in Nevada.\nAI: What do you know about Nevada?\nLast line of conversation:\nPerson #1: It'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\nConversation history:\nPerson #1: Hello.\nAI: Hi! How are you?\nPerson #1: I'm good. How are you?\nAI: I'm good too.\nLast line of conversation:\nPerson #1: I'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: What do you know about Descartes?\nAI: Descartes was a French philosopher, mathematician, and scientist who lived in the 17th century.\nPerson #1: The