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4da3d4b18e
- **Description:** Update the Cohere's provider and RagRetriever
documentations with latest updates.
- **Twitter handle:** Anirudh1810
353 lines
11 KiB
Plaintext
353 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"id": "53fbf15f",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Cohere\n",
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"---"
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]
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},
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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](https://cohere.com/chat).\n",
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"\n",
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"Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
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]
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},
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{
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"cell_type": "markdown",
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"id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"The integration lives in the `langchain-cohere` package. We can install these with:\n",
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"\n",
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"```bash\n",
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"pip install -U langchain-cohere\n",
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"```\n",
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"\n",
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"We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
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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": 11,
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"id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cf690fbb",
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"metadata": {},
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"source": [
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"It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
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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": 12,
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"id": "7f11de02",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4c26754b-b3c9-4d93-8f36-43049bd943bf",
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"metadata": {},
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"source": [
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"## Usage\n",
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"\n",
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"ChatCohere supports all [ChatModel](/docs/how_to#chat-models) 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": 5,
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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_cohere import ChatCohere\n",
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"from langchain_core.messages import 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": 13,
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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": 15,
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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='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')"
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]
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},
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"execution_count": 15,
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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 = [HumanMessage(content=\"1\"), HumanMessage(content=\"2 3\")]\n",
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"chat.invoke(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": 16,
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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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"data": {
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"text/plain": [
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"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')"
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]
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},
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"execution_count": 16,
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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.ainvoke(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": 17,
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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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"4 && 5"
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]
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}
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],
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"source": [
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"for chunk in chat.stream(messages):\n",
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" print(chunk.content, end=\"\", flush=True)"
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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": 18,
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"id": "064288e4-f184-4496-9427-bcf148fa055e",
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"metadata": {},
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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='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')]"
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]
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},
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"execution_count": 18,
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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.batch([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": "f1c56460",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"\n",
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"You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language-lcel)"
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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": 19,
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"id": "0851b103",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
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"chain = prompt | chat"
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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": 20,
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"id": "ae950c0f-1691-47f1-b609-273033cae707",
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"metadata": {},
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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='What color socks do bears wear?\\n\\nThey don’t wear socks, they have bear feet. \\n\\nHope you laughed! If not, maybe this will help: laughter is the best medicine, and a good sense of humor is infectious!', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, id='run-ef7f9789-0d4d-43bf-a4f7-f2a0e27a5320-0')"
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]
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},
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"execution_count": 20,
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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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"chain.invoke({\"topic\": \"bears\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "12db8d69",
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"metadata": {},
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"source": [
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"## Tool calling\n",
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"\n",
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"Cohere supports tool calling functionalities!"
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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": 7,
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"id": "337e24af",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.messages import (\n",
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" HumanMessage,\n",
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" ToolMessage,\n",
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")\n",
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"from langchain_core.tools import tool"
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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": 8,
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"id": "74d292e7",
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"metadata": {},
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"outputs": [],
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"source": [
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"@tool\n",
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"def magic_function(number: int) -> int:\n",
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" \"\"\"Applies a magic operation to an integer\n",
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" Args:\n",
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" number: Number to have magic operation performed on\n",
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" \"\"\"\n",
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" return number + 10\n",
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"\n",
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"\n",
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"def invoke_tools(tool_calls, messages):\n",
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" for tool_call in tool_calls:\n",
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" selected_tool = {\"magic_function\": magic_function}[tool_call[\"name\"].lower()]\n",
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" tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
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" messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
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" return messages\n",
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"\n",
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"\n",
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"tools = [magic_function]"
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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": 9,
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"id": "ecafcbc6",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_with_tools = chat.bind_tools(tools=tools)\n",
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"messages = [HumanMessage(content=\"What is the value of magic_function(2)?\")]"
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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": 11,
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"id": "aa34fc39",
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"metadata": {},
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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='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')"
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]
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},
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"execution_count": 11,
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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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"res = llm_with_tools.invoke(messages)\n",
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"while res.tool_calls:\n",
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" messages.append(res)\n",
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" messages = invoke_tools(res.tool_calls, messages)\n",
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" res = llm_with_tools.invoke(messages)\n",
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"\n",
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"res"
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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": "3.9.6"
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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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