Documentation Index
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
Use this file to discover all available pages before exploring further.
This notebook covers how to get started with Cohere chat models.
Head to the API reference for detailed documentation of all attributes and methods.
Setup
The integration lives in the langchain-cohere package. We can install these with:
pip install -U langchain-cohere
COHERE_API_KEY environment variable:
import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass()
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
Usage
ChatCohere supports all ChatModel functionality:
from langchain_cohere import ChatCohere
from langchain.messages import HumanMessage
messages = [HumanMessage(content="1"), HumanMessage(content="2 3")]
chat.invoke(messages)
AIMessage(content='4 && 5 \n6 || 7 \n\nWould you like to play a game of odds and evens?', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, id='run-3475e0c8-c89b-4937-9300-e07d652455e1-0')
await chat.ainvoke(messages)
AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-1635e63e-2994-4e7f-986e-152ddfc95777-0')
for chunk in chat.stream(messages):
print(chunk.content, end="", flush=True)
[AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-8d6fade2-1b39-4e31-ab23-4be622dd0027-0')]
from langchain.messages import (
HumanMessage,
ToolMessage,
)
from langchain.tools import tool
@tool
def magic_function(number: int) -> int:
"""Applies a magic operation to an integer
Args:
number: Number to have magic operation performed on
"""
return number + 10
def invoke_tools(tool_calls, messages):
for tool_call in tool_calls:
selected_tool = {"magic_function": magic_function}[tool_call["name"].lower()]
tool_output = selected_tool.invoke(tool_call["args"])
messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))
return messages
tools = [magic_function]
llm_with_tools = chat.bind_tools(tools=tools)
messages = [HumanMessage(content="What is the value of magic_function(2)?")]
res = llm_with_tools.invoke(messages)
while res.tool_calls:
messages.append(res)
messages = invoke_tools(res.tool_calls, messages)
res = llm_with_tools.invoke(messages)
res
AIMessage(content='The value of magic_function(2) is 12.', additional_kwargs={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, response_metadata={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, id='run-f318a9cf-55c8-44f4-91d1-27cf46c6a465-0')
