id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
9b6f65715ed0-5 | 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(**kw... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.minimax.MiniMaxChat.html |
9b6f65715ed0-6 | first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
An LLMResult, which co... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.minimax.MiniMaxChat.html |
9b6f65715ed0-7 | classmethod is_lc_serializable() → bool¶
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_defa... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.minimax.MiniMaxChat.html |
9b6f65715ed0-8 | Pass a single string input to the model and return a string prediction.
Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages.
Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
fir... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.minimax.MiniMaxChat.html |
9b6f65715ed0-9 | Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Defau... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.minimax.MiniMaxChat.html |
9b6f65715ed0-10 | 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”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.minimax.MiniMaxChat.html |
da1fcdda4829-0 | langchain.chat_models.openai.ChatOpenAI¶
class langchain.chat_models.openai.ChatOpenAI[source]¶
Bases: BaseChatModel
OpenAI Chat large language models API.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be ... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-1 | Base URL path for API requests,
leave blank if not using a proxy or service emulator.
param openai_organization: Optional[str] = None¶
param openai_proxy: Optional[str] = None¶
param request_timeout: Optional[Union[float, Tuple[float, float]]] = None¶
Timeout for requests to OpenAI completion API. Default is 600 second... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-2 | Call self as a function.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can ... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-3 | callbacks – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for ea... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-4 | first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-5 | input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can ba... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-6 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Return a dictionary of the LLM.
classmethod from_orm(obj: Any) → Model¶
generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallba... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-7 | to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then ... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-8 | Return whether this model can be serialized by Langchain.
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 ... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-9 | Pass a single string input to the model and return a string prediction.
Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages.
Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
fir... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-10 | Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Defau... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-11 | 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”}
property output_schema: Type[pydantic.main.BaseModel]¶
... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
da1fcdda4829-12 | Structure answers with OpenAI functions
QA using Activeloop’s DeepLake
Neptune Open Cypher QA Chain
NebulaGraphQAChain
Memgraph QA chain
KuzuQAChain
HugeGraph QA Chain
GraphSparqlQAChain
Diffbot Graph Transformer
ArangoDB QA chain
Neo4j DB QA chain
FalkorDBQAChain
Agents
AutoGPT
!pip install bs4
Wikibase Agent
SalesGPT... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.openai.ChatOpenAI.html |
8284294a02fd-0 | langchain.chat_models.google_palm.ChatGooglePalmError¶
class langchain.chat_models.google_palm.ChatGooglePalmError[source]¶
Error with the Google PaLM API. | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.google_palm.ChatGooglePalmError.html |
4a054991c90a-0 | langchain.chat_models.mlflow_ai_gateway.ChatParams¶
class langchain.chat_models.mlflow_ai_gateway.ChatParams[source]¶
Bases: BaseModel
Parameters for the MLflow AI Gateway LLM.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to fo... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.mlflow_ai_gateway.ChatParams.html |
4a054991c90a-1 | 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, ex... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.mlflow_ai_gateway.ChatParams.html |
4a054991c90a-2 | 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... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.mlflow_ai_gateway.ChatParams.html |
c9ddddd24f8b-0 | langchain.chat_models.anyscale.ChatAnyscale¶
class langchain.chat_models.anyscale.ChatAnyscale[source]¶
Bases: ChatOpenAI
Anyscale Chat large language models.
To use, you should have the openai python package installed, and the
environment variable ANYSCALE_API_KEY set with your API key.
Alternatively, you can use the ... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-1 | param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param model_name: str = 'meta-llama/Llama-2-7b-chat-hf' (alias 'model')¶
Model name to use.
param n: int = 1¶
Numbe... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-2 | when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(messages: List[BaseMessage], sto... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-3 | need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptValue is an object that can be
converted to match the format of any languag... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-4 | to the model provider API call.
Returns
Top model prediction as a string.
async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
Asynchronously pass messages to the model and return a message prediction.
Use this method when calling chat models and on... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-5 | jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default im... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-6 | 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... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-7 | API.
Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptVal... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-8 | Calculate num tokens with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
get_token_ids(text: str) → List[int]¶
Get the tokens present in the text with tiktoken package.
invoke(input: Union[PromptValue, str, List[BaseMe... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-9 | by calling invoke() with each input.
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 = No... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-10 | to the model provider API call.
Returns
Top model prediction as a message.
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¶
stream(in... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
c9ddddd24f8b-11 | Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) →... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
b68d3b5dbbed-0 | langchain.chat_models.human.HumanInputChatModel¶
class langchain.chat_models.human.HumanInputChatModel[source]¶
Bases: BaseChatModel
ChatModel which returns user input as the response.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be pars... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-1 | Subclasses should override this method if they can batch more efficiently.
async agenerate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = Non... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-2 | async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → BaseMessageChunk¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-3 | Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-4 | Bind arguments to a Runnable, returning a new Runnable.
call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
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.
Defaul... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-5 | Top Level call
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls f... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-6 | Parameters
text – The string input to tokenize.
Returns
The integer number of tokens in the text.
get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶
Get the number of tokens in the messages.
Useful for checking if an input will fit in a model’s context window.
Parameters
messages – The message inputs to t... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-7 | The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encod... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-8 | stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
classmethod schema(by_alias: bool = True, ref_templ... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
b68d3b5dbbed-9 | Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) →... | https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.human.HumanInputChatModel.html |
2819661d7b3c-0 | langchain.vectorstores.redis.filters.RedisText¶
class langchain.vectorstores.redis.filters.RedisText(field: str)[source]¶
A RedisText is a RedisFilterField representing a text field in a Redis index.
Attributes
OPERATORS
OPERATOR_MAP
escaper
Methods
__init__(field)
equals(other)
__init__(field: str)¶
equals(other: Redi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.filters.RedisText.html |
57051c2c6793-0 | langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings¶
class langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings(endpoint: str, instance_id: str, username: str, password: str, datasource_name: str, embedding_index_name: str, field_name_mapping: Dict[str, str])[source]¶
... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings.html |
57051c2c6793-1 | Methods
__init__(endpoint, instance_id, username, ...)
__init__(endpoint: str, instance_id: str, username: str, password: str, datasource_name: str, embedding_index_name: str, field_name_mapping: Dict[str, str]) → None[source]¶
Examples using AlibabaCloudOpenSearchSettings¶
Alibaba Cloud OpenSearch | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings.html |
3a980d96fe69-0 | langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch¶
class langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch(opensearch_url: str, index_name: str, embedding_function: Embeddings, **kwargs: Any)[source]¶
Amazon OpenSearch Vector Engine vector store.
Example
from langchain.vectorst... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-1 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilar... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-2 | Initialize with necessary components.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[s... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-3 | to “text”.
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, bulk_size: int = 500, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Op... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-4 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“s... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-5 | )
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwa... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-6 | Return VectorStore initialized from documents and embeddings.
classmethod from_embeddings(embeddings: List[List[float]], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, bulk_size: int = 500, ids: Optional[List[str]] = None, **kwargs: Any) → OpenSearchVectorSearch[source]¶
Construct Open... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-7 | Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 100; default: 16
Keyword Args for Script Scoring or Painless Scripting:is_appx_search: False
classmethod from_texts(texts: Li... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-8 | lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-9 | k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-10 | k-NN search with pre-filtering or an approximate search with modified
post-filtering. (deprecated, use efficient_filter)
efficient_filter: the Lucene Engine or Faiss Engine decides whether to
perform an exact k-NN search with pre-filtering or an approximate search
with modified post-filtering.
Optional Args for Script ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
3a980d96fe69-11 | query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_wit... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
7bd9c0fae5f6-0 | langchain.vectorstores.neo4j_vector.SearchType¶
class langchain.vectorstores.neo4j_vector.SearchType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enumerator of the Distance strategies.
VECTOR = 'vector'¶
HYBRID = 'hybrid'¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.neo4j_vector.SearchType.html |
7f0547e3c5bc-0 | langchain.vectorstores.redis.schema.read_schema¶
langchain.vectorstores.redis.schema.read_schema(index_schema: Optional[Union[Dict[str, str], str, PathLike]]) → Dict[str, Any][source]¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.read_schema.html |
4fb7688e5c72-0 | langchain.vectorstores.azuresearch.AzureSearch¶
class langchain.vectorstores.azuresearch.AzureSearch(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Callable, search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, semantic_query_language: str = 'en-us', f... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-1 | asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs mo... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-2 | similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
vector_search(query[, k])
Returns the most similar indexed documents to the query text.
vector_search_with_score(query[, k, filters])
Return docs most similar to query.
__init__(azure_search_endpoint: str, azure_search_key: str, index_na... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-3 | Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Add texts data to an existing index.
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-4 | score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for Vec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-5 | Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-6 | Return type
List[Document]
hybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with an hybrid query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
Li... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-7 | fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-8 | Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs and relevance... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
4fb7688e5c72-9 | Returns
List of Documents most similar to the query and score for each
Examples using AzureSearch¶
Azure Cognitive Search | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
ae4e0ac44948-0 | langchain.vectorstores.neo4j_vector.check_if_not_null¶
langchain.vectorstores.neo4j_vector.check_if_not_null(props: List[str], values: List[Any]) → None[source]¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.neo4j_vector.check_if_not_null.html |
bd1aba7ca8bc-0 | langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch¶
class langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch(embedding: Embeddings, config: AlibabaCloudOpenSearchSettings, **kwargs: Any)[source]¶
Alibaba Cloud OpenSearch vector store.
Attributes
embeddings
Access the query embeddin... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
bd1aba7ca8bc-1 | Return docs most similar to query.
create_results(json_result)
create_results_with_score(json_result)
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding[, ids, ...])
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, config])
Retu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
bd1aba7ca8bc-2 | Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added text... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
bd1aba7ca8bc-3 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“s... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
bd1aba7ca8bc-4 | )
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwa... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
bd1aba7ca8bc-5 | False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, ids: Optional[List[str]] = None, config: Optional[AlibabaCloudOpenSearchSettings] = None, **kwargs: Any) → AlibabaCloudOpenSearch[source]¶
Return VectorStore initialized from... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
bd1aba7ca8bc-6 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevan... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
bd1aba7ca8bc-7 | Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, search_filter: Optional[dict] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
qu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
44c4aac9afd4-0 | langchain.vectorstores.elastic_vector_search.ElasticKnnSearch¶
class langchain.vectorstores.elastic_vector_search.ElasticKnnSearch(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-1 | that contains the original text data.
Type
str, optional
Usage:>>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
>>> es_search.add_texts(['Hello world!', 'Another text'])
>>> results = es_search.knn_search('Hello')
[(Document(page_cont... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-2 | Type
str, optional
Usage:>>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
>>> es_search.add_texts(['Hello world!', 'Another text'])
>>> results = es_search.knn_search('Hello')
[(Document(page_content='Hello world!', metadata={}), 0.9)... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-3 | asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
create_knn_index(mapping)
Create a new k-NN index in Elasticsearch.
delete([ids]... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-4 | similarity_search_with_score(query[, k])
Pass through to knn_search including score
__init__(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, vector_query_field: Optional[str] = '... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-5 | to associate with the texts.
model_id (str, optional) – The ID of the model to use for transforming the
texts into vectors.
refresh_indices (bool, optional) – Whether to refresh the Elasticsearch
indices after adding the texts.
**kwargs – Arbitrary keyword arguments.
Returns
A list of IDs for the added texts.
async cla... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-6 | k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by doc... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-7 | Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-8 | to associate with the texts.
**kwargs – Arbitrary keyword arguments.
Returns
A new ElasticKnnSearch instance.
knn_hybrid_search(query: Optional[str] = None, k: Optional[int] = 10, query_vector: Optional[List[float]] = None, model_id: Optional[str] = None, size: Optional[int] = 10, source: Optional[bool] = True, knn_boo... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-9 | Returns
A list of tuples, where each tuple contains a Document object and a score.
knn_search(query: Optional[str] = None, k: Optional[int] = 10, query_vector: Optional[List[float]] = None, model_id: Optional[str] = None, size: Optional[int] = 10, source: Optional[bool] = True, fields: Optional[Union[List[Mapping[str, ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-10 | among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to max... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
44c4aac9afd4-11 | Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Docume... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
ee0e31c7667f-0 | langchain.vectorstores.timescalevector.TimescaleVector¶
class langchain.vectorstores.timescalevector.TimescaleVector(service_url: str, embedding: Embeddings, collection_name: str = 'langchain_store', num_dimensions: int = 1536, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool =... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.timescalevector.TimescaleVector.html |
ee0e31c7667f-1 | Attributes
DEFAULT_INDEX_TYPE
embeddings
Access the query embedding object if available.
Methods
__init__(service_url, embedding[, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_embeddings(texts, embeddings[, ...])
Add embeddings to the vectorstore.
... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.timescalevector.TimescaleVector.html |
ee0e31c7667f-2 | asimilarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
asimilarity_search_with_score(query[, k, ...])
Return docs most similar to query.
asimilarity_search_with_score_by_vector(...)
create_index([... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.timescalevector.TimescaleVector.html |
ee0e31c7667f-3 | Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
__init__(service_url: str, embedding: Embe... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.timescalevector.TimescaleVector.html |
ee0e31c7667f-4 | Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
add_documents(doc... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.timescalevector.TimescaleVector.html |
ee0e31c7667f-5 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain_store', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Op... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.timescalevector.TimescaleVector.html |
ee0e31c7667f-6 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStore... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.timescalevector.TimescaleVector.html |
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