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Update API keys to use GROQ instead of OpenAI #46

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Update API keys to use GROQ instead of OpenAI #46

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8 changes: 4 additions & 4 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -59,9 +59,9 @@ $ export API_ENV_VAR="your-api-key-here"
PS> $env:API_ENV_VAR = "your-api-key-here"
```

### Set OpenAI API key
* If you don't have an OpenAI API key, you can sign up [here](https://openai.com/index/openai-api/).
* Set `OPENAI_API_KEY` in your environment
### Set GROQ API key
* If you don't have an GROQ API key, you can sign up [here](https://console.groq.com/keys).
* Set `GROQ_API_KEY` in your environment

### Sign up and Set LangSmith API
* Sign up for LangSmith [here](https://smith.langchain.com/), find out more about LangSmith
Expand Down Expand Up @@ -91,7 +91,7 @@ Graphs for LangGraph Studio are in the `module-x/studio/` folders.
```
$ for i in {1..4}; do
cp module-$i/studio/.env.example module-$i/studio/.env
echo "OPENAI_API_KEY=\"$OPENAI_API_KEY\"" > module-$i/studio/.env
echo "GROQ_API_KEY=\"$GROQ_API_KEY\"" > module-$i/studio/.env
done
$ echo "TAVILY_API_KEY=\"$TAVILY_API_KEY\"" >> module-4/studio/.env
```
83 changes: 30 additions & 53 deletions module-0/basics.ipynb
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Expand Up @@ -14,7 +14,6 @@
"metadata": {},
"source": [
"# LangChain Academy\n",
"\n",
"Welcome to LangChain Academy! \n",
"\n",
"## Context\n",
Expand All @@ -35,25 +34,25 @@
"\n",
"## Chat models\n",
"\n",
"In this course, we'll be using [Chat Models](https://python.langchain.com/v0.2/docs/concepts/#chat-models), which do a few things take a sequence of messages as inputs and return chat messages as outputs. LangChain does not host any Chat Models, rather we rely on third party integrations. [Here](https://python.langchain.com/v0.2/docs/integrations/chat/) is a list of 3rd party chat model integrations within LangChain! By default, the course will use [ChatOpenAI](https://python.langchain.com/v0.2/docs/integrations/chat/openai/) because it is both popular and performant. As noted, please ensure that you have an `OPENAI_API_KEY`.\n",
"In this course, we'll be using [Chat Models](https://python.langchain.com/v0.2/docs/concepts/#chat-models), which do a few things take a sequence of messages as inputs and return chat messages as outputs. LangChain does not host any Chat Models, rather we rely on third party integrations. [Here](https://python.langchain.com/docs/integrations/chat/) is a list of 3rd party chat model integrations within LangChain! By default, the course will use [ChatQroq](https://python.langchain.com/docs/integrations/chat/groq/) because it is both popular and performant and provides free tier for new users.\n",
"\n",
"Let's check that your `OPENAI_API_KEY` is set and, if not, you will be asked to enter it."
"Let's check that your `QROQ_API_KEY` is set and, if not, you will be asked to enter it."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "0f9a52c8",
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install --quiet -U langchain_openai langchain_core langchain_community tavily-python"
"%pip install --quiet -U langchain_groq langchain_core langchain_community tavily-python"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 19,
"id": "c2a15227",
"metadata": {},
"outputs": [],
Expand All @@ -64,15 +63,15 @@
" if not os.environ.get(var):\n",
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
"\n",
"_set_env(\"OPENAI_API_KEY\")"
"_set_env(\"GROQ_API_KEY\")"
]
},
{
"cell_type": "markdown",
"id": "a326f35b",
"metadata": {},
"source": [
"[Here](https://python.langchain.com/v0.2/docs/how_to/#chat-models) is a useful how-do for all the things that you can do with chat models, but we'll show a few highlights below. If you've run `pip install -r requirements.txt` as noted in the README, then you've installed the `langchain-openai` package. With this, we can instantiate our `ChatOpenAI` model object. If you are signing up for the API for the first time, you should receive [free credits](https://community.openai.com/t/understanding-api-limits-and-free-tier/498517) that can be applied to any of the models. You can see pricing for various models [here](https://openai.com/api/pricing/). The notebooks will default to `gpt-4o` because it's a good balance of quality, price, and speed [see more here](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4-gpt-4-turbo-gpt-4o-and-gpt-4o-mini), but you can also opt for the lower priced `gpt-3.5` series models. \n",
"[Here](https://python.langchain.com/v0.2/docs/how_to/#chat-models) is a useful how-do for all the things that you can do with chat models, but we'll show a few highlights below. If you've run `pip install -r requirements.txt` as noted in the README, then you've installed the `langchain-groq` package. With this, we can instantiate our `ChatGroq` model object. If you are signing up for the API for the first time, you should receive [free credits](https://console.groq.com/) that can be applied to any of the models. You can see pricing for various models [here](https://groq.com/pricing/). The notebooks will default to `llama3-8b-8192` because it's a good balance of quality, price, and speed.\n",
"\n",
"There are [a few standard parameters](https://python.langchain.com/v0.2/docs/concepts/#chat-models) that we can set with chat models. Two of the most common are:\n",
"\n",
Expand All @@ -84,14 +83,13 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 31,
"id": "e19a54d3",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"gpt4o_chat = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
"gpt35_chat = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
"from langchain_groq import ChatGroq\n",
"llama_chat = ChatGroq(model=\"llama3-8b-8192\", temperature=0)"
]
},
{
Expand All @@ -109,17 +107,17 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 32,
"id": "b1280e1b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 11, 'total_tokens': 20}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-d3c4bc85-ef14-49f6-ba7e-91bf455cffee-0', usage_metadata={'input_tokens': 11, 'output_tokens': 9, 'total_tokens': 20})"
"AIMessage(content=\"Hello world! It's nice to meet you. Is there something I can help you with, or would you like to chat?\", additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 12, 'total_tokens': 39, 'completion_time': 0.0225, 'prompt_time': 0.001680411, 'queue_time': 0.011593389, 'total_time': 0.024180411}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_6a6771ae9c', 'finish_reason': 'stop', 'logprobs': None}, id='run-f28ddcdf-72fc-4a47-a418-c4f555b48380-0', usage_metadata={'input_tokens': 12, 'output_tokens': 27, 'total_tokens': 39})"
]
},
"execution_count": 3,
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -134,7 +132,7 @@
"messages = [msg]\n",
"\n",
"# Invoke the model with a list of messages \n",
"gpt4o_chat.invoke(messages)"
"llama_chat.invoke(messages)"
]
},
{
Expand All @@ -147,44 +145,23 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 22,
"id": "f27c6c9a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 9, 'total_tokens': 18}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-d6f6b682-e29a-44de-b45e-79fad1e405e5-0', usage_metadata={'input_tokens': 9, 'output_tokens': 9, 'total_tokens': 18})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gpt4o_chat.invoke(\"hello world\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fdc2f0ca",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 9, 'total_tokens': 18}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-c75d3f0f-2d71-47be-b14c-42b8dd2b4b08-0', usage_metadata={'input_tokens': 9, 'output_tokens': 9, 'total_tokens': 18})"
"AIMessage(content=\"Hello world! It's nice to meet you. Is there something I can help you with, or would you like to chat?\", additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 12, 'total_tokens': 39, 'completion_time': 0.0225, 'prompt_time': 0.001662041, 'queue_time': 0.011706418, 'total_time': 0.024162041}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_a97cfe35ae', 'finish_reason': 'stop', 'logprobs': None}, id='run-fad3d1ad-5eaf-4126-b1e2-033f8ce4c70e-0', usage_metadata={'input_tokens': 12, 'output_tokens': 27, 'total_tokens': 39})"
]
},
"execution_count": 5,
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gpt35_chat.invoke(\"hello world\")"
"llama_chat.invoke(\"hello world\")"
]
},
{
Expand All @@ -209,7 +186,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 23,
"id": "091dff13",
"metadata": {},
"outputs": [],
Expand All @@ -219,7 +196,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 24,
"id": "52d69da9",
"metadata": {},
"outputs": [],
Expand All @@ -231,22 +208,22 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 25,
"id": "d06f87e6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'url': 'https://www.datacamp.com/tutorial/langgraph-tutorial',\n",
" 'content': 'LangGraph is a library within the LangChain ecosystem designed to tackle these challenges head-on. LangGraph provides a framework for defining, coordinating, and executing multiple LLM agents (or chains) in a structured manner.'},\n",
" {'url': 'https://langchain-ai.github.io/langgraph/',\n",
" 'content': 'Overview LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a ...'},\n",
" {'url': 'https://www.youtube.com/watch?v=nmDFSVRnr4Q',\n",
" 'content': 'LangGraph is an extension of LangChain enabling Multi-Agent conversation and cyclic chains. This video explains the basics of LangGraph and codesLangChain in...'}]"
"[{'url': 'https://www.langchain.com/langgraph',\n",
" 'content': 'LangGraph is a stateful, orchestration framework that brings added control to agent workflows. LangGraph Cloud is a service for deploying and scaling LangGraph applications, with a built-in Studio for prototyping, debugging, and sharing LangGraph applications.'},\n",
" {'url': 'https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141',\n",
" 'content': 'LangGraph is a library built on top of LangChain, designed to add cyclic computational capabilities to your LLM applications.'},\n",
" {'url': 'https://towardsdatascience.com/from-basics-to-advanced-exploring-langgraph-e8c1cf4db787',\n",
" 'content': 'LangGraph (as you might guess from the name) models all interactions as cyclical graphs. These graphs enable the development of advanced workflows and interactions with multiple loops and if-statements, making it a handy tool for creating both agent and multi-agent workflows.'}]"
]
},
"execution_count": 7,
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -266,7 +243,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -280,7 +257,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.1"
"version": "3.11.5"
}
},
"nbformat": 4,
Expand Down
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