mirror of
https://github.com/hwchase17/langchain.git
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290 lines
7.3 KiB
Plaintext
290 lines
7.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9597802c",
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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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":::caution\n",
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"You are currently on a page documenting the use of Cohere models as [text completion models](/docs/concepts/text_llms). Many popular Cohere models are [chat completion models](/docs/concepts/chat_models).\n",
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"\n",
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"You may be looking for [this page instead](/docs/integrations/chat/cohere/).\n",
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":::\n",
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"\n",
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">[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.\n",
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"\n",
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"Head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html) for detailed documentation of all attributes and methods.\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/cohere/) | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
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"| [Cohere](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | beta | ✅ |  |  |\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "873eb81e-6049-4a68-b219-baa421d7cba8",
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"metadata": {
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"tags": []
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},
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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-community` package. We also need to install the `cohere` package itself. We can install these with:\n",
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"\n",
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"### Credentials\n",
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"\n",
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"We'll 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": null,
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"id": "3f5dc9d7-65e3-4b5b-9086-3327d016cfe0",
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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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"import getpass\n",
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"import os\n",
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"\n",
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"if \"COHERE_API_KEY\" not in os.environ:\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": "ff211537",
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"metadata": {},
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"source": [
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"### Installation"
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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": null,
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"id": "318454f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install -U langchain-community langchain-cohere"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c07a576d-e39d-4ca2-8f16-41df284d136c",
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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": null,
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"id": "5af022d3-d24a-49fa-b660-ec76f1bce9a9",
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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": "0b4e02bf-5beb-48af-a2a2-52cbcd8ebed6",
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"metadata": {},
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"source": [
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"## Invocation\n",
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"\n",
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"Cohere supports all [LLM](/docs/how_to#llms) 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": 1,
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"id": "6fb585dd",
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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 Cohere\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": 2,
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"id": "be042d9f-c625-4316-b5e5-272b5ce8904f",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = Cohere(max_tokens=256, temperature=0.75)"
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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": 6,
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"id": "8cbfc906-4278-4bc9-8756-1681bb647752",
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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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"\" Who's there?\""
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]
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},
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"execution_count": 6,
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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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"message = \"Knock knock\"\n",
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"model.invoke(message)"
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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": "a9a9ffcf-5a74-4875-ad3e-d66d3b871f66",
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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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"\" Who's there?\""
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]
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},
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"execution_count": 8,
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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 model.ainvoke(message)"
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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": "ab3550b5-4271-4333-a75c-e4bce58c0452",
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"metadata": {},
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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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"source": [
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"for chunk in model.stream(message):\n",
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" print(chunk, 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": 10,
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"id": "587c850d-76bd-4f74-bcf7-50cdacec538e",
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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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"[\" Who's there?\"]"
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]
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},
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"execution_count": 10,
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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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"model.batch([message])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "39198f7d-6fc8-4662-954a-37ad38c4bec4",
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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/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": 12,
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"id": "7cbe3136-eff2-4e6a-807c-81cbf2a488a6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import PromptTemplate\n",
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"\n",
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"prompt = PromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
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"chain = prompt | model"
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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": "d08eb676-dc24-41ae-ba32-19a95e22d3bb",
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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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"' Why did the teddy bear cross the road?\\nBecause he had bear crossings.\\n\\nWould you like to hear another joke? '"
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]
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},
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"execution_count": 13,
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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": "ac5fcbed",
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"metadata": {},
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all `Cohere` llm features and configurations head to the API reference: https://python.langchain.com/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html"
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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": "3.11.7"
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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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