id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
8ab9cc7dfede-24 | callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
class langchain.agents.agent_toolkits.JsonToolkit(*, spec)[source]ο
Bases: langch... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-25 | Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with SQL databases.
Parameters
db (langchain.sql_database.SQLDatabase) β
llm (langchain.base_language.BaseLanguageModel) β
Return type
None
attribute db: langchain.sql_database.SQLDatabase [Required]ο
attribute llm: langchain.base_languag... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-26 | llm (langchain.base_language.BaseLanguageModel) β
Return type
None
attribute db: langchain.utilities.spark_sql.SparkSQL [Required]ο
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.age... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-27 | List of API Endpoint Tools.
classmethod from_llm_and_ai_plugin(llm, ai_plugin, requests=None, verbose=False, **kwargs)[source]ο
Instantiate the toolkit from an OpenAPI Spec URL
Parameters
llm (langchain.base_language.BaseLanguageModel) β
ai_plugin (langchain.tools.plugin.AIPlugin) β
requests (Optional[langchain.reque... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-28 | verbose (bool) β
kwargs (Any) β
Return type
langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit
classmethod from_llm_and_spec(llm, spec, requests=None, verbose=False, **kwargs)[source]ο
Instantiate the toolkit by creating tools for each operation.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
spec ... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-29 | open_api_url (str) β
requests (Optional[langchain.requests.Requests]) β
verbose (bool) β
kwargs (Any) β
Return type
langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit
get_tools()[source]ο
Get the tools for all the API operations.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolk... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-30 | max_iterations (int) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
Return type
None
attribute callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = Noneο
attribute examples: Optional[str] = Noneο
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-31 | Parameters
json_agent (langchain.agents.agent.AgentExecutor) β
requests_wrapper (langchain.requests.TextRequestsWrapper) β
Return type
None
attribute json_agent: langchain.agents.agent.AgentExecutor [Required]ο
attribute requests_wrapper: langchain.requests.TextRequestsWrapper [Required]ο
classmethod from_llm(llm, js... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-32 | List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.VectorStoreToolkit(*, vectorstore_info, llm=None)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with a vector store.
Parameters
vectorstore_info (langchain.agents.agent_toolkits.vectorstore.toolkit.Vecto... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-33 | List[langchain.tools.base.BaseTool]
langchain.agents.agent_toolkits.create_vectorstore_router_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to answer questions.\nYou have access to tools for interacting with different sources, and the inputs to the tools are questions.\nYour main task is ... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-34 | prefix (str) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
class langchain.agents.agent_toolkits.VectorStoreInfo(*, vectorstore, name, description)[source]ο
Bases: pydantic.main.BaseModel
Information about a vectorsto... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-35 | Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for routing between vector stores.
Parameters
vectorstores (List[langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo]) β
llm (langchain.base_language.BaseLanguageModel) β
Return type
None
attribute llm: langchain.base_language.BaseLanguag... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-36 | List[langchain.tools.base.BaseTool]
langchain.agents.agent_toolkits.create_pandas_dataframe_agent(llm, df, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager=None, prefix=None, suffix=None, input_variables=None, verbose=False, return_intermediate_steps=False, max_iterations=15, max_execution_time=None, ... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-37 | verbose (bool) β
return_intermediate_steps (bool) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
include_df_in_prompt (Optional[bool]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentE... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-38 | langchain.agents.agent.AgentExecutor
langchain.agents.agent_toolkits.create_spark_dataframe_agent(llm, df, callback_manager=None, prefix='\nYou are working with a spark dataframe in Python. The name of the dataframe is `df`.\nYou should use the tools below to answer the question posed of you:', suffix='\nThis is the re... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-39 | prefix (str) β
suffix (str) β
input_variables (Optional[List[str]]) β
verbose (bool) β
return_intermediate_steps (bool) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return ty... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-40 | langchain.agents.agent_toolkits.create_spark_sql_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to interact with Spark SQL.\nGiven an input question, create a syntactically correct Spark SQL query to run, then look at the results of the query and return the answer.\nUnless the user specifi... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-41 | while executing a query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix='Begin!\n\nQuestion: {input}\nThought: I should look at the tab... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-42 | answer\nFinal Answer: the final answer to the original input question', input_variables=None, top_k=10, max_iterations=15, max_execution_time=None, early_stopping_method='force', verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-43 | Construct a sql agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
format_instructions (str)... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-44 | langchain.agents.agent.AgentExecutor
langchain.agents.agent_toolkits.create_csv_agent(llm, path, pandas_kwargs=None, **kwargs)[source]ο
Create csv agent by loading to a dataframe and using pandas agent.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
path (Union[str, List[str]]) β
pandas_kwargs (Optional... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-45 | classmethod from_zapier_nla_wrapper(zapier_nla_wrapper)[source]ο
Create a toolkit from a ZapierNLAWrapper.
Parameters
zapier_nla_wrapper (langchain.utilities.zapier.ZapierNLAWrapper) β
Return type
langchain.agents.agent_toolkits.zapier.toolkit.ZapierToolkit
get_tools()[source]ο
Get the tools in the toolkit.
Return typ... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-46 | Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.JiraToolkit(*, tools=[])[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Jira Toolkit.
Parameters
tools (List[langchain.tools.base.BaseTool]) β
Return type
None
attribute tools: List[langchain.tools.base.BaseTool] = ... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-47 | Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with a Local Files.
Parameters
root_dir (Optional[str]) β
selected_tools (Optional[List[str]]) β
Return type
None
attribute root_dir: Optional[str] = Noneο
If specified, all file operations are made relative to root_dir.
attribute selecte... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
8ab9cc7dfede-48 | async_browser (Optional['AsyncBrowser']) β
Return type
None
attribute async_browser: Optional['AsyncBrowser'] = Noneο
attribute sync_browser: Optional['SyncBrowser'] = Noneο
classmethod from_browser(sync_browser=None, async_browser=None)[source]ο
Instantiate the toolkit.
Parameters
sync_browser (Optional[SyncBrowser])... | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
fd62e9172960-0 | Retrieversο
class langchain.retrievers.AmazonKendraRetriever(index_id, region_name=None, credentials_profile_name=None, top_k=3, attribute_filter=None, client=None)[source]ο
Bases: langchain.schema.BaseRetriever
Retriever class to query documents from Amazon Kendra Index.
Parameters
index_id (str) β Kendra index id
reg... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-1 | attribute_filter (Optional[Dict]) β Additional filtering of results based on metadata
See: https://docs.aws.amazon.com/kendra/latest/APIReference
client (Optional[Any]) β boto3 client for Kendra
Example
retriever = AmazonKendraRetriever(
index_id="c0806df7-e76b-4bce-9b5c-d5582f6b1a03"
)
get_relevant_documents(query... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-2 | Returns
List of relevant documents
Return type
List[langchain.schema.Document]
class langchain.retrievers.ArxivRetriever(*, arxiv_search=None, arxiv_exceptions=None, top_k_results=3, load_max_docs=100, load_all_available_meta=False, doc_content_chars_max=4000, ARXIV_MAX_QUERY_LENGTH=300)[source]ο
Bases: langchain.schem... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-3 | ARXIV_MAX_QUERY_LENGTH (int) β
Return type
None
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
get_relevant_documents(query)[source]ο
Get docu... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-4 | Wrapper around Azure Cognitive Search.
Parameters
service_name (str) β
index_name (str) β
api_key (str) β
api_version (str) β
aiosession (Optional[aiohttp.client.ClientSession]) β
content_key (str) β
Return type
None
attribute aiosession: Optional[aiohttp.client.ClientSession] = Noneο
ClientSession, in case we wa... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-5 | attribute index_name: str = ''ο
Name of Index inside Azure Cognitive Search service
attribute service_name: str = ''ο
Name of Azure Cognitive Search service
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of re... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-6 | Parameters
url (str) β
bearer_token (str) β
top_k (int) β
filter (Optional[dict]) β
aiosession (Optional[aiohttp.client.ClientSession]) β
Return type
None
attribute aiosession: Optional[aiohttp.client.ClientSession] = Noneο
attribute bearer_token: str [Required]ο
attribute filter: Optional[dict] = Noneο
attribute ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-7 | Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
class langchain.retrievers.ContextualCompressionRetriever(*, base_compressor, base_retriever)[source]ο
Bases: langchain.schema.BaseRetriever, ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-8 | Base Retriever to use for getting relevant documents.
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
get_relevant_documents(query)[source]ο
Get... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-9 | api_key (Optional[str]) β
datastore_url: strο
api_key: Optional[str]ο
top_k: Optional[int]ο
get_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
async aget... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-10 | including Elastic Cloud, use the Elasticsearch URL format
https://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearch constructor as the named parameter
elasticsearch_url.
You can obtain yo... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-11 | The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Parameters
client (Any) β
index_name (str) β
classmethod create(elasticsearch_url, index_name, k1=2.0, b=0.75)[source]ο
Parameters
elasticsearch_url (str) β
index_name (str) β
k1 (float) β
b (float) β
Return ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-12 | Returns
List of ids from adding the texts into the retriever.
Return type
List[str]
get_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
async aget_relevant... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-13 | KNN Retriever.
Parameters
embeddings (langchain.embeddings.base.Embeddings) β
index (Any) β
texts (List[str]) β
k (int) β
relevancy_threshold (Optional[float]) β
Return type
None
attribute embeddings: langchain.embeddings.base.Embeddings [Required]ο
attribute index: Any = Noneο
attribute k: int = 4ο
attribute rele... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-14 | Parameters
texts (List[str]) β
embeddings (langchain.embeddings.base.Embeddings) β
kwargs (Any) β
Return type
langchain.retrievers.knn.KNNRetriever
get_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant docume... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-15 | attribute graph: Any = Noneο
attribute query_configs: List[Dict] [Optional]ο
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
get_relevant_docume... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-16 | Return type
None
attribute index: Any = Noneο
attribute query_kwargs: Dict [Optional]ο
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
get_relev... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-17 | Get the relevant documents for a given query.
Parameters
query (str) β The query to search for.
Returns
A list of relevant documents.
Return type
List[langchain.schema.Document]
async aget_relevant_documents(query)[source]ο
Asynchronously get the relevant documents for a given query.
Parameters
query (str) β The query ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-18 | Returns
A list of merged documents.
Return type
List[langchain.schema.Document]
class langchain.retrievers.MetalRetriever(client, params=None)[source]ο
Bases: langchain.schema.BaseRetriever
Retriever that uses the Metal API.
Parameters
client (Any) β
params (Optional[dict]) β
get_relevant_documents(query)[source]ο
Ge... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-19 | List[langchain.schema.Document]
class langchain.retrievers.MilvusRetriever(embedding_function, collection_name='LangChainCollection', connection_args=None, consistency_level='Session', search_params=None)[source]ο
Bases: langchain.schema.BaseRetriever
Retriever that uses the Milvus API.
Parameters
embedding_function (l... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-20 | Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents f... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-21 | alpha (float) β
Return type
None
attribute alpha: float = 0.5ο
attribute embeddings: langchain.embeddings.base.Embeddings [Required]ο
attribute index: Any = Noneο
attribute sparse_encoder: Any = Noneο
attribute top_k: int = 4ο
add_texts(texts, ids=None, metadatas=None)[source]ο
Parameters
texts (List[str]) β
ids (Opt... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-22 | Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
class langchain.retrievers.PubMedRetriever(*, top_k_results=3, load_max_docs=25, doc_content_chars_max=2000, load_all_available_meta=False, em... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-23 | It wraps load() to get_relevant_documents().
It uses all PubMedAPIWrapper arguments without any change.
Parameters
top_k_results (int) β
load_max_docs (int) β
doc_content_chars_max (int) β
load_all_available_meta (bool) β
email (str) β
base_url_esearch (str) β
base_url_efetch (str) β
max_retry (int) β
sleep_tim... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-24 | Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
class langchain.retrievers.RemoteLangChainRetriever(*, url, headers=None, input_key='message', response_key='response', page_content_key='page... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-25 | attribute page_content_key: str = 'page_content'ο
attribute response_key: str = 'response'ο
attribute url: str [Required]ο
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-26 | SVM Retriever.
Parameters
embeddings (langchain.embeddings.base.Embeddings) β
index (Any) β
texts (List[str]) β
k (int) β
relevancy_threshold (Optional[float]) β
Return type
None
attribute embeddings: langchain.embeddings.base.Embeddings [Required]ο
attribute index: Any = Noneο
attribute k: int = 4ο
attribute rele... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-27 | Parameters
texts (List[str]) β
embeddings (langchain.embeddings.base.Embeddings) β
kwargs (Any) β
Return type
langchain.retrievers.svm.SVMRetriever
get_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant docume... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-28 | the vector store queries.
Parameters
vectorstore (langchain.vectorstores.base.VectorStore) β
llm_chain (langchain.chains.llm.LLMChain) β
search_type (str) β
search_kwargs (dict) β
structured_query_translator (langchain.chains.query_constructor.ir.Visitor) β
verbose (bool) β
use_original_query (bool) β
Return typ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-29 | Translator for turning internal query language into vectorstore search params.
attribute use_original_query: bool = Falseο
attribute vectorstore: langchain.vectorstores.base.VectorStore [Required]ο
The underlying vector store from which documents will be retrieved.
attribute verbose: bool = Falseο
Use original query in... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-30 | vectorstore (langchain.vectorstores.base.VectorStore) β
document_contents (str) β
metadata_field_info (List[langchain.chains.query_constructor.schema.AttributeInfo]) β
structured_query_translator (Optional[langchain.chains.query_constructor.ir.Visitor]) β
chain_kwargs (Optional[Dict]) β
enable_limit (bool) β
use_... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-31 | List[langchain.schema.Document]
class langchain.retrievers.TFIDFRetriever(*, vectorizer=None, docs, tfidf_array=None, k=4)[source]ο
Bases: langchain.schema.BaseRetriever, pydantic.main.BaseModel
Parameters
vectorizer (Any) β
docs (List[langchain.schema.Document]) β
tfidf_array (Any) β
k (int) β
Return type
None
att... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-32 | Returns
List of relevant documents
Return type
List[langchain.schema.Document]
classmethod from_documents(documents, *, tfidf_params=None, **kwargs)[source]ο
Parameters
documents (Iterable[langchain.schema.Document]) β
tfidf_params (Optional[Dict[str, Any]]) β
kwargs (Any) β
Return type
langchain.retrievers.tfidf.TF... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-33 | Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
class langchain.retrievers.TimeWeightedVectorStoreRetriever(*, vectorstore, search_kwargs=None, memory_stream=None, decay_rate=0.01, k=4, othe... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-34 | Return type
None
attribute decay_rate: float = 0.01ο
The exponential decay factor used as (1.0-decay_rate)**(hrs_passed).
attribute default_salience: Optional[float] = Noneο
The salience to assign memories not retrieved from the vector store.
None assigns no salience to documents not fetched from the vector store.
attr... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-35 | attribute vectorstore: langchain.vectorstores.base.VectorStore [Required]ο
The vectorstore to store documents and determine salience.
async aadd_documents(documents, **kwargs)[source]ο
Add documents to vectorstore.
Parameters
documents (List[langchain.schema.Document]) β
kwargs (Any) β
Return type
List[str]
add_docum... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-36 | Parameters
query (str) β
Return type
List[langchain.schema.Document]
get_salient_docs(query)[source]ο
Return documents that are salient to the query.
Parameters
query (str) β
Return type
Dict[int, Tuple[langchain.schema.Document, float]]
class langchain.retrievers.VespaRetriever(app, body, content_field, metadata_fie... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-37 | Returns
List of relevant documents
Return type
List[langchain.schema.Document]
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
get_relevant_docu... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-38 | k (Optional[int]) β Number of Documents to return. Defaults to None.
metadata_fields (Sequence[str] or "*") β Fields in results to include in
document metadata. Defaults to empty tuple ().
sources (Sequence[str] or "*" or None) β Sources to retrieve
from. Defaults to None.
_filter (Optional[str]) β Document filter cond... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-39 | Bases: langchain.schema.BaseRetriever
Parameters
client (Any) β
index_name (str) β
text_key (str) β
alpha (float) β
k (int) β
attributes (Optional[List[str]]) β
create_schema_if_missing (bool) β
class Config[source]ο
Bases: object
Configuration for this pydantic object.
extra = 'forbid'ο
arbitrary_types_allowed ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-40 | Parameters
query (str) β
where_filter (Optional[Dict[str, object]]) β
Return type
List[langchain.schema.Document]
async aget_relevant_documents(query, where_filter=None)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
where_filter (Optional[Dict[str, object]... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-41 | It uses all WikipediaAPIWrapper arguments without any change.
Parameters
wiki_client (Any) β
top_k_results (int) β
lang (str) β
load_all_available_meta (bool) β
doc_content_chars_max (int) β
Return type
None
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-42 | Bases: langchain.schema.BaseRetriever
A Retriever implementation for the Zep long-term memory store. Search your
userβs long-term chat history with Zep.
Note: You will need to provide the userβs session_id to use this retriever.
More on Zep:
Zep provides long-term conversation storage for LLM apps. The server stores,
s... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-43 | metadata (Optional[Dict]) β
Returns
List of relevant documents
Return type
List[langchain.schema.Document]
async aget_relevant_documents(query, metadata=None)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
metadata (Optional[Dict]) β
Returns
List of relevan... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-44 | consistency_level (str) β
search_params (Optional[dict]) β
add_texts(texts, metadatas=None)[source]ο
Add text to the Zilliz store
Parameters
texts (List[str]) β The text
metadatas (List[dict]) β Metadata dicts, must line up with existing store
Return type
None
get_relevant_documents(query)[source]ο
Get documents rele... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-45 | List[langchain.schema.Document]
class langchain.retrievers.DocArrayRetriever(*, index=None, embeddings, search_field, content_field, search_type=SearchType.similarity, top_k=1, filters=None)[source]ο
Bases: langchain.schema.BaseRetriever, pydantic.main.BaseModel
Retriever class for DocArray Document Indices.
Currently,... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-46 | Return type
None
indexο
One of the above-mentioned index instances
embeddingsο
Embedding model to represent text as vectors
search_fieldο
Field to consider for searching in the documents.
Should be an embedding/vector/tensor.
content_fieldο
Field that represents the main content in your document schema.
Will be used as... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-47 | attribute search_type: langchain.retrievers.docarray.SearchType = SearchType.similarityο
attribute top_k: int = 1ο
async aget_relevant_documents(query)[source]ο
Get documents relevant for a query.
Parameters
query (str) β string to find relevant documents for
Returns
List of relevant documents
Return type
List[langchai... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-48 | Parameters
transformers (List[Union[langchain.schema.BaseDocumentTransformer, langchain.retrievers.document_compressors.base.BaseDocumentCompressor]]) β
Return type
None
attribute transformers: List[Union[langchain.schema.BaseDocumentTransformer, langchain.retrievers.document_compressors.base.BaseDocumentCompressor]] ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-49 | Sequence[langchain.schema.Document]
class langchain.retrievers.document_compressors.EmbeddingsFilter(*, embeddings, similarity_fn=<function cosine_similarity>, k=20, similarity_threshold=None)[source]ο
Bases: langchain.retrievers.document_compressors.base.BaseDocumentCompressor
Parameters
embeddings (langchain.embeddin... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-50 | Similarity function for comparing documents. Function expected to take as input
two matrices (List[List[float]]) and return a matrix of scores where higher values
indicate greater similarity.
attribute similarity_threshold: Optional[float] = Noneο
Threshold for determining when two documents are similar enough
to be co... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-51 | Sequence[langchain.schema.Document]
class langchain.retrievers.document_compressors.LLMChainExtractor(*, llm_chain, get_input=<function default_get_input>)[source]ο
Bases: langchain.retrievers.document_compressors.base.BaseDocumentCompressor
Parameters
llm_chain (langchain.chains.llm.LLMChain) β
get_input (Callable[[s... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-52 | Parameters
documents (Sequence[langchain.schema.Document]) β
query (str) β
Return type
Sequence[langchain.schema.Document]
compress_documents(documents, query)[source]ο
Compress page content of raw documents.
Parameters
documents (Sequence[langchain.schema.Document]) β
query (str) β
Return type
Sequence[langchain.s... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-53 | class langchain.retrievers.document_compressors.LLMChainFilter(*, llm_chain, get_input=<function default_get_input>)[source]ο
Bases: langchain.retrievers.document_compressors.base.BaseDocumentCompressor
Filter that drops documents that arenβt relevant to the query.
Parameters
llm_chain (langchain.chains.llm.LLMChain) β... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-54 | Filter down documents.
Parameters
documents (Sequence[langchain.schema.Document]) β
query (str) β
Return type
Sequence[langchain.schema.Document]
compress_documents(documents, query)[source]ο
Filter down documents based on their relevance to the query.
Parameters
documents (Sequence[langchain.schema.Document]) β
que... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-55 | class langchain.retrievers.document_compressors.CohereRerank(*, client, top_n=3, model='rerank-english-v2.0')[source]ο
Bases: langchain.retrievers.document_compressors.base.BaseDocumentCompressor
Parameters
client (Client) β
top_n (int) β
model (str) β
Return type
None
attribute client: Client [Required]ο
attribute ... | https://api.python.langchain.com/en/latest/modules/retrievers.html |
fd62e9172960-56 | Compress retrieved documents given the query context.
Parameters
documents (Sequence[langchain.schema.Document]) β
query (str) β
Return type
Sequence[langchain.schema.Document] | https://api.python.langchain.com/en/latest/modules/retrievers.html |
af1f52c14822-0 | Chat Modelsο
class langchain.chat_models.ChatOpenAI(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, model='gpt-3.5-turbo', temperature=0.7, model_kwargs=None, openai_api_key=None, openai_api_base=None, openai_organization=None, openai_proxy=None, request_timeout=None, max_ret... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-1 | Example
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
Parameters
cache (Optional[bool]) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchai... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-2 | max_retries (int) β
streaming (bool) β
n (int) β
max_tokens (Optional[int]) β
tiktoken_model_name (Optional[str]) β
Return type
None
attribute max_retries: int = 6ο
Maximum number of retries to make when generating.
attribute max_tokens: Optional[int] = Noneο
Maximum number of tokens to generate.
attribute model_k... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-3 | attribute openai_api_key: Optional[str] = Noneο
Base URL path for API requests,
leave blank if not using a proxy or service emulator.
attribute openai_organization: Optional[str] = Noneο
attribute openai_proxy: Optional[str] = Noneο
attribute request_timeout: Optional[Union[float, Tuple[float, float]]] = Noneο
Timeout ... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-4 | be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-5 | Parameters
messages (List[langchain.schema.BaseMessage]) β
Return type
int
get_token_ids(text)[source]ο
Get the tokens present in the text with tiktoken package.
Parameters
text (str) β
Return type
List[int]
property lc_secrets: Dict[str, str]ο
Return a map of constructor argument names to secret ids.
eg. {βopenai_ap... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-6 | class langchain.chat_models.AzureChatOpenAI(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, model='gpt-3.5-turbo', temperature=0.7, model_kwargs=None, openai_api_key='', openai_api_base='', openai_organization='', openai_proxy='', request_timeout=None, max_retries=6, streamin... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-7 | following environment variables set or passed in constructor in lower case:
- OPENAI_API_TYPE (default: azure)
- OPENAI_API_KEY
- OPENAI_API_BASE
- OPENAI_API_VERSION
- OPENAI_PROXY
For exmaple, if you have gpt-35-turbo deployed, with the deployment name
35-turbo-dev, the constructor should look like:
AzureChatOpenAI(
... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-8 | callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
tags (Optional[List[str]]) β
client (Any) β
model (str) β
temperature (float) β
model_kwargs (Dict[str, Any]) β
openai_api_key (str) β
openai_api_base (str) β
openai_organization (str) β
openai_proxy (str) β
request_timeout (Optional[... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-9 | attribute deployment_name: str = ''ο
attribute openai_api_base: str = ''ο
attribute openai_api_key: str = ''ο
Base URL path for API requests,
leave blank if not using a proxy or service emulator.
attribute openai_api_type: str = 'azure'ο
attribute openai_api_version: str = ''ο
attribute openai_organization: str = ''ο
a... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-10 | verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
tags (Optional[List[str]]) β
responses (List) β
i (int) β
Return type
None
attribute i: int =... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-11 | attribute responses: List [Required]ο
class langchain.chat_models.PromptLayerChatOpenAI(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, model='gpt-3.5-turbo', temperature=0.7, model_kwargs=None, openai_api_key=None, openai_api_base=None, openai_organization=None, openai_proxy... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-12 | promptlayer key respectively.
All parameters that can be passed to the OpenAI LLM can also
be passed here. The PromptLayerChatOpenAI adds to optional
Parameters
pl_tags (Optional[List[str]]) β List of strings to tag the request with.
return_pl_id (Optional[bool]) β If True, the PromptLayer request ID will be
returned i... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-13 | openai_api_base (Optional[str]) β
openai_organization (Optional[str]) β
openai_proxy (Optional[str]) β
request_timeout (Optional[Union[float, Tuple[float, float]]]) β
max_retries (int) β
streaming (bool) β
n (int) β
max_tokens (Optional[int]) β
tiktoken_model_name (Optional[str]) β
Return type
None
Example
fro... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-14 | class langchain.chat_models.ChatAnthropic(*, client=None, model='claude-v1', max_tokens_to_sample=256, temperature=None, top_k=None, top_p=None, streaming=False, default_request_timeout=None, anthropic_api_url=None, anthropic_api_key=None, HUMAN_PROMPT=None, AI_PROMPT=None, count_tokens=None, cache=None, verbose=None, ... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-15 | from langchain.llms import Anthropic
model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
Parameters
client (Any) β
model (str) β
max_tokens_to_sample (int) β
temperature (Optional[float]) β
top_k (Optional[int]) β
top_p (Optional[float]) β
streaming (bool) β
default_request_timeout (Optio... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-16 | verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
tags (Optional[List[str]]) β
Return type
None
get_num_tokens(text)[source]ο
Calculate number of... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
af1f52c14822-17 | Wrapper around Googleβs PaLM Chat API.
To use you must have the google.generativeai Python package installed and
either:
The GOOGLE_API_KEY` environment varaible set with your API key, or
Pass your API key using the google_api_key kwarg to the ChatGoogle
constructor.
Example
from langchain.chat_models import ChatGoogle... | https://api.python.langchain.com/en/latest/modules/chat_models.html |
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