mirror of https://github.com/hwchase17/langchain
Add cohere /chat integration (#11389)
Add cohere /chat integration and an iPython notebook to demonstrate the addition. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>pull/11443/head
billytrend-cohere
10 months ago
committed by
GitHub
parent
ca346011b7
commit
2ff91a46c0
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bf733a38-db84-4363-89e2-de6735c37230",
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"metadata": {},
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"source": [
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"# Cohere\n",
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"\n",
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"This notebook covers how to get started with Cohere chat models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatCohere\n",
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"from langchain.schema import AIMessage, HumanMessage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat = ChatCohere()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"Who's there?\")"
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]
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},
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"execution_count": 56,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" HumanMessage(\n",
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" content=\"knock knock\"\n",
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" )\n",
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"]\n",
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"chat(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
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"metadata": {},
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"source": [
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"## `ChatCohere` also supports async and streaming functionality:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.callbacks.manager import CallbackManager\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Who's there?"
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]
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},
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{
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"data": {
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"text/plain": [
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"LLMResult(generations=[[ChatGenerationChunk(text=\"Who's there?\", message=AIMessageChunk(content=\"Who's there?\"))]], llm_output={}, run=[RunInfo(run_id=UUID('1e9eaefc-9c99-4fa9-8297-ef9975d4751e'))])"
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]
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},
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"execution_count": 64,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"await chat.agenerate([messages])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Who's there?"
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]
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},
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{
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"data": {
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"text/plain": [
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"AIMessageChunk(content=\"Who's there?\")"
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]
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},
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"execution_count": 63,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chat = ChatCohere(\n",
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" streaming=True,\n",
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" verbose=True,\n",
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" callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
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")\n",
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"chat(messages)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,162 @@
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.chat_models.base import (
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BaseChatModel,
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_agenerate_from_stream,
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_generate_from_stream,
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)
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from langchain.llms.cohere import BaseCohere
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from langchain.schema.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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ChatMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain.schema.output import ChatGeneration, ChatGenerationChunk, ChatResult
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def get_role(message: BaseMessage) -> str:
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if isinstance(message, ChatMessage) or isinstance(message, HumanMessage):
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return "User"
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elif isinstance(message, AIMessage):
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return "Chatbot"
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elif isinstance(message, SystemMessage):
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return "System"
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else:
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raise ValueError(f"Got unknown type {message}")
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class ChatCohere(BaseChatModel, BaseCohere):
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"""`Cohere` chat large language models.
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To use, you should have the ``cohere`` python package installed, and the
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environment variable ``COHERE_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain.chat_models import ChatCohere
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from langchain.schema import HumanMessage
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chat = ChatCohere(model="foo")
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result = chat([HumanMessage(content="Hello")])
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print(result.content)
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"""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "cohere-chat"
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Cohere API."""
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return {
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"temperature": self.temperature,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model": self.model}, **self._default_params}
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def get_cohere_chat_request(
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self, messages: List[BaseMessage], **kwargs: Any
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) -> Dict[str, Any]:
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return {
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"message": messages[0].content,
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"chat_history": [
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{"role": get_role(x), "message": x.content} for x in messages[1:]
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],
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**self._default_params,
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**kwargs,
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}
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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request = self.get_cohere_chat_request(messages, **kwargs)
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stream = self.client.chat(**request, stream=True)
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for data in stream:
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if data.event_type == "text-generation":
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delta = data.text
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yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
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if run_manager:
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run_manager.on_llm_new_token(delta)
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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request = self.get_cohere_chat_request(messages, **kwargs)
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stream = await self.async_client.chat(**request, stream=True)
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async for data in stream:
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if data.event_type == "text-generation":
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delta = data.text
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yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
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if run_manager:
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await run_manager.on_llm_new_token(delta)
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return _generate_from_stream(stream_iter)
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request = self.get_cohere_chat_request(messages, **kwargs)
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response = self.client.chat(**request)
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message = AIMessage(content=response.text)
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return ChatResult(generations=[ChatGeneration(message=message)])
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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stream_iter = self._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return await _agenerate_from_stream(stream_iter)
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request = self.get_cohere_chat_request(messages, **kwargs)
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response = self.client.chat(**request, stream=False)
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message = AIMessage(content=response.text)
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return ChatResult(generations=[ChatGeneration(message=message)])
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def get_num_tokens(self, text: str) -> int:
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"""Calculate number of tokens."""
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return len(self.client.tokenize(text).tokens)
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