langchain-cohere: 0.2.4

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.