langchain-cohere: 0.3.2#

chains #

Functions

`chains.summarize.summarize_chain.create_summarize_prompt`([...])	Create prompt for this agent. :param system_message: Message to use as the system message that will be the first in the prompt. :param extra_prompt_messages: Prompt messages that will be placed between the system message and the new human input.
`chains.summarize.summarize_chain.load_summarize_chain`(llm)

chat_models #

Classes

chat_models.ChatCohere

Implements the BaseChatModel (and BaseLanguageModel) interface with Cohere's large language models.

Functions

`chat_models.get_cohere_chat_request`(messages, *)	Get the request for the Cohere chat API.
`chat_models.get_role`(message)	Get the role of the message.

common #

Classes

common.CohereCitation(start, end, text, ...)

Cohere has fine-grained citations that specify the exact part of text.

csv_agent #

Functions

`csv_agent.agent.count_words_in_file`(file_path)
`csv_agent.agent.create_csv_agent`(llm, path)	Create csv agent with the specified language model.
`csv_agent.agent.create_prompt`([...])	Create prompt for this agent.
`csv_agent.tools.get_file_peek_tool`()
`csv_agent.tools.get_file_read_tool`()
`csv_agent.tools.get_python_tool`()	Returns a tool that will execute python code and return the output.

embeddings #

Classes

embeddings.CohereEmbeddings

Implements the Embeddings interface with Cohere's text representation language models.

llms #

Classes

`llms.BaseCohere`	Base class for Cohere models.
`llms.Cohere`	Cohere large language models.

Functions

`llms.acompletion_with_retry`(llm, **kwargs)	Use tenacity to retry the completion call.
`llms.completion_with_retry`(llm, **kwargs)	Use tenacity to retry the completion call.
`llms.enforce_stop_tokens`(text, stop)	Cut off the text as soon as any stop words occur.

rag_retrievers #

Classes

rag_retrievers.CohereRagRetriever

Cohere Chat API with RAG.

react_multi_hop #

Classes

react_multi_hop.parsing.CohereToolsReactAgentOutputParser

Parses a message into agent actions/finish.

Functions

`react_multi_hop.agent.create_cohere_react_agent`(...)	Create an agent that enables multiple tools to be used in sequence to complete a task.
`react_multi_hop.parsing.parse_actions`(generation)	Parse action selections from model output.
`react_multi_hop.parsing.parse_answer_with_prefixes`(...)	parses string into key-value pairs,
`react_multi_hop.parsing.parse_citations`(...)	Parses a grounded_generation (from parse_actions) and documents (from convert_to_documents) into a (generation, CohereCitation list) tuple.
`react_multi_hop.parsing.parse_jsonified_tool_use_generation`(...)	Parses model-generated jsonified actions.
`react_multi_hop.prompt.convert_to_documents`(...)	Converts observations into a 'document' dict
`react_multi_hop.prompt.create_directly_answer_tool`()	directly_answer is a special tool that's always presented to the model as an available tool.
`react_multi_hop.prompt.multi_hop_prompt`(...)	The returned function produces a BasePromptTemplate suitable for multi-hop.
`react_multi_hop.prompt.render_intermediate_steps`(...)	Renders an agent's intermediate steps into prompt content.
`react_multi_hop.prompt.render_messages`(messages)	Renders one or more BaseMessage implementations into prompt content.
`react_multi_hop.prompt.render_observations`(...)	Renders the 'output' part of an Agent's intermediate step into prompt content.
`react_multi_hop.prompt.render_role`(message)	Renders the role of a message into prompt content.
`react_multi_hop.prompt.render_structured_preamble`([...])	Renders the structured preamble part of the prompt content.
`react_multi_hop.prompt.render_tool`([tool, ...])	Renders a tool into prompt content. Either a BaseTool instance, or, a JSON

rerank #

Classes

rerank.CohereRerank

Document compressor that uses Cohere Rerank API.

sql_agent #

Functions

sql_agent.agent.create_sql_agent(llm[, ...])

Construct a SQL agent from an LLM and toolkit or database.