mirror of https://github.com/hwchase17/langchain
partner: Add groq partner integration and chat model (#17856)
Description: Add a Groq chat model issue: TODO Dependencies: groq Twitter handle: N/Apull/17932/head
Graden Rea
3 months ago
committed by
GitHub
parent
da957a22cc
commit
e5e38e89ce
@ -0,0 +1,181 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Groq\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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"metadata": {},
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"source": [
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"# Groq\n",
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"\n",
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"Install the langchain-groq package if not already installed:\n",
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"\n",
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"```bash\n",
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"pip install langchain-groq\n",
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"```\n",
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"\n",
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"Request an [API key](https://wow.groq.com) and set it as an environment variable:\n",
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"\n",
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"```bash\n",
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"export GROQ_API_KEY=<YOUR API KEY>\n",
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"```\n",
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"\n",
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"Alternatively, you may configure the API key when you initialize ChatGroq."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Import the ChatGroq class and initialize it with a 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": 27,
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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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"from langchain_groq import ChatGroq"
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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": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"chat = ChatGroq(temperature=0, model_name=\"mixtral-8x7b-32768\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can view the available models [here](https://console.groq.com/docs/models).\n",
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"\n",
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"If you do not want to set your API key in the environment, you can pass it directly to the client:\n",
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"```python\n",
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"chat = ChatGroq(temperature=0, groq_api_key=\"YOUR_API_KEY\", model_name=\"mixtral-8x7b-32768\")\n",
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"\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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"metadata": {},
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"source": [
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"Write a prompt and invoke ChatGroq to create completions:"
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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": 29,
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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='Low Latency Large Language Models (LLMs) are a type of artificial intelligence model that can understand and generate human-like text. The term \"low latency\" refers to the model\\'s ability to process and respond to inputs quickly, with minimal delay.\\n\\nThe importance of low latency in LLMs can be explained through the following points:\\n\\n1. Improved user experience: In real-time applications such as chatbots, virtual assistants, and interactive games, users expect quick and responsive interactions. Low latency LLMs can provide instant feedback and responses, creating a more seamless and engaging user experience.\\n\\n2. Better decision-making: In time-sensitive scenarios, such as financial trading or autonomous vehicles, low latency LLMs can quickly process and analyze vast amounts of data, enabling faster and more informed decision-making.\\n\\n3. Enhanced accessibility: For individuals with disabilities, low latency LLMs can help create more responsive and inclusive interfaces, such as voice-controlled assistants or real-time captioning systems.\\n\\n4. Competitive advantage: In industries where real-time data analysis and decision-making are crucial, low latency LLMs can provide a competitive edge by enabling businesses to react more quickly to market changes, customer needs, or emerging opportunities.\\n\\n5. Scalability: Low latency LLMs can efficiently handle a higher volume of requests and interactions, making them more suitable for large-scale applications and services.\\n\\nIn summary, low latency is an essential aspect of LLMs, as it significantly impacts user experience, decision-making, accessibility, competitiveness, and scalability. By minimizing delays and response times, low latency LLMs can unlock new possibilities and applications for artificial intelligence in various industries and scenarios.')"
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]
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},
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"execution_count": 29,
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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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"system = \"You are a helpful assistant.\"\n",
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"human = \"{text}\"\n",
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"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
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"\n",
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"chain = prompt | chat\n",
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"chain.invoke({\n",
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" \"text\": \"Explain the importance of low latency LLMs.\"\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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"metadata": {},
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"source": [
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"## `ChatGroq` 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": 32,
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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=\"There's a star that shines up in the sky,\\nThe Sun, that makes the day bright and spry.\\nIt rises and sets,\\nIn a daily, predictable bet,\\nGiving life to the world, oh my!\")"
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]
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},
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"execution_count": 32,
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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 = ChatGroq(temperature=0, model_name=\"mixtral-8x7b-32768\")\n",
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"prompt = ChatPromptTemplate.from_messages([(\"human\", \"Write a Limerick about {topic}\")])\n",
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"chain = prompt | chat\n",
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"await chain.ainvoke({\"topic\": \"The Sun\"})"
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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": 33,
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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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"The moon's gentle glow\n",
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"Illuminates the night sky\n",
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"Peaceful and serene"
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]
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}
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],
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"source": [
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"chat = ChatGroq(temperature=0, model_name=\"llama2-70b-4096\")\n",
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"prompt = ChatPromptTemplate.from_messages([(\"human\", \"Write a haiku about {topic}\")])\n",
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"chain = prompt | chat\n",
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"for chunk in chain.stream({\"topic\": \"The Moon\"}):\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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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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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": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,28 @@
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# Groq
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Welcome to Groq! 🚀 At Groq, we've developed the world's first Language Processing Unit™, or LPU. The Groq LPU has a deterministic, single core streaming architecture that sets the standard for GenAI inference speed with predictable and repeatable performance for any given workload.
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Beyond the architecture, our software is designed to empower developers like you with the tools you need to create innovative, powerful AI applications. With Groq as your engine, you can:
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* Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥
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* Know the exact performance and compute time for any given workload 🔮
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* Take advantage of our cutting-edge technology to stay ahead of the competition 💪
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Want more Groq? Check out our [website](https://groq.com) for more resources and join our [Discord community](https://discord.gg/JvNsBDKeCG) to connect with our developers!
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## Installation and Setup
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Install the integration package:
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```bash
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pip install langchain-groq
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```
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Request an [API key](https://wow.groq.com) and set it as an environment variable:
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```bash
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export GROQ_API_KEY=gsk_...
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```
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## Chat Model
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See a [usage example](/docs/integrations/chat/groq).
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@ -0,0 +1 @@
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__pycache__
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@ -0,0 +1,21 @@
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
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copies of the Software, and to permit persons to whom the Software is
|
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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@ -0,0 +1,57 @@
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.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
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# Default target executed when no arguments are given to make.
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all: help
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# Define a variable for the test file path.
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TEST_FILE ?= tests/unit_tests/
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integration_test integration_tests: TEST_FILE=tests/integration_tests/
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test tests integration_test integration_tests:
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poetry run pytest $(TEST_FILE)
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######################
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# LINTING AND FORMATTING
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######################
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# Define a variable for Python and notebook files.
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PYTHON_FILES=.
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MYPY_CACHE=.mypy_cache
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lint format: PYTHON_FILES=.
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lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/groq --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
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lint_package: PYTHON_FILES=langchain_groq
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lint_tests: PYTHON_FILES=tests
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lint_tests: MYPY_CACHE=.mypy_cache_test
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lint lint_diff lint_package lint_tests:
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poetry run ruff .
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poetry run ruff format $(PYTHON_FILES) --diff
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poetry run ruff --select I $(PYTHON_FILES)
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mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
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format format_diff:
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poetry run ruff format $(PYTHON_FILES)
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poetry run ruff --select I --fix $(PYTHON_FILES)
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spell_check:
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poetry run codespell --toml pyproject.toml
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spell_fix:
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poetry run codespell --toml pyproject.toml -w
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check_imports: $(shell find langchain_groq -name '*.py')
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poetry run python ./scripts/check_imports.py $^
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######################
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# HELP
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######################
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help:
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@echo '----'
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@echo 'check_imports - check imports'
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@echo 'format - run code formatters'
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@echo 'lint - run linters'
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@echo 'test - run unit tests'
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@echo 'tests - run unit tests'
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@echo 'test TEST_FILE=<test_file> - run all tests in file'
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@ -0,0 +1,70 @@
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# langchain-groq
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## Welcome to Groq! 🚀
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At Groq, we've developed the world's first Language Processing Unit™, or LPU. The Groq LPU has a deterministic, single core streaming architecture that sets the standard for GenAI inference speed with predictable and repeatable performance for any given workload.
|
||||
|
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Beyond the architecture, our software is designed to empower developers like you with the tools you need to create innovative, powerful AI applications. With Groq as your engine, you can:
|
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|
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* Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥
|
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* Know the exact performance and compute time for any given workload 🔮
|
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* Take advantage of our cutting-edge technology to stay ahead of the competition 💪
|
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|
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Want more Groq? Check out our [website](https://groq.com) for more resources and join our [Discord community](https://discord.gg/JvNsBDKeCG) to connect with our developers!
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|
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|
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## Installation and Setup
|
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Install the integration package:
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|
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```bash
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pip install langchain-groq
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```
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Request an [API key](https://wow.groq.com) and set it as an environment variable
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```bash
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export GROQ_API_KEY=gsk_...
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```
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## Chat Model
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See a [usage example](https://python.langchain.com/docs/integrations/chat/groq).
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## Development
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To develop the `langchain-groq` package, you'll need to follow these instructions:
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### Install dev dependencies
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```bash
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poetry install --with test,test_integration,lint,codespell
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```
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### Build the package
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```bash
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poetry build
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```
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### Run unit tests
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Unit tests live in `tests/unit_tests` and SHOULD NOT require an internet connection or a valid API KEY. Run unit tests with
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```bash
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make tests
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```
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### Run integration tests
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Integration tests live in `tests/integration_tests` and require a connection to the Groq API and a valid API KEY.
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```bash
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make integration_tests
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```
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### Lint & Format
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Run additional tests and linters to ensure your code is up to standard.
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```bash
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make lint spell_check check_imports
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```
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@ -0,0 +1,3 @@
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from langchain_groq.chat_models import ChatGroq
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__all__ = ["ChatGroq"]
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"""Groq Chat wrapper."""
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from __future__ import annotations
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import os
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import warnings
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from typing import (
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Any,
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AsyncIterator,
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Dict,
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Iterator,
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List,
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Mapping,
|
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Optional,
|
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Tuple,
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Type,
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Union,
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cast,
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)
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models 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_core.messages import (
|
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
|
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ChatMessage,
|
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ChatMessageChunk,
|
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FunctionMessage,
|
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FunctionMessageChunk,
|
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HumanMessage,
|
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HumanMessageChunk,
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SystemMessage,
|
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SystemMessageChunk,
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ToolMessage,
|
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ToolMessageChunk,
|
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
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from langchain_core.utils import (
|
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convert_to_secret_str,
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get_from_dict_or_env,
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get_pydantic_field_names,
|
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)
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||||
|
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|
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class ChatGroq(BaseChatModel):
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"""`Groq` Chat large language models API.
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||||
|
||||
To use, you should have the
|
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environment variable ``GROQ_API_KEY`` set with your API key.
|
||||
|
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Any parameters that are valid to be passed to the groq.create call can be passed
|
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in, even if not explicitly saved on this class.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.chat_models import ChatGroq
|
||||
groq = ChatGroq(model_name="mixtral-8x7b-32768")
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"""
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||||
|
||||
client: Any = Field(default=None, exclude=True) #: :meta private:
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||||
async_client: Any = Field(default=None, exclude=True) #: :meta private:
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model_name: str = Field(default="mixtral-8x7b-32768", alias="model")
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"""Model name to use."""
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||||
temperature: float = 0.7
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"""What sampling temperature to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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groq_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `groq_API_KEY` if not provided."""
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groq_api_base: Optional[str] = Field(default=None, alias="base_url")
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"""Base URL path for API requests, leave blank if not using a proxy or service
|
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emulator."""
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||||
# to support explicit proxy for Groq
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groq_proxy: Optional[str] = None
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||||
request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
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default=None, alias="timeout"
|
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)
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"""Timeout for requests to Groq completion API. Can be float, httpx.Timeout or
|
||||
None."""
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max_retries: int = 2
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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n: int = 1
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"""Number of chat completions to generate for each prompt."""
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max_tokens: Optional[int] = None
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"""Maximum number of tokens to generate."""
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default_headers: Union[Mapping[str, str], None] = None
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default_query: Union[Mapping[str, object], None] = None
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# Configure a custom httpx client. See the
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||||
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
|
||||
http_client: Union[Any, None] = None
|
||||
"""Optional httpx.Client."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
allow_population_by_field_name = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = get_pydantic_field_names(cls)
|
||||
extra = values.get("model_kwargs", {})
|
||||
for field_name in list(values):
|
||||
if field_name in extra:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
if field_name not in all_required_field_names:
|
||||
warnings.warn(
|
||||
f"""WARNING! {field_name} is not default parameter.
|
||||
{field_name} was transferred to model_kwargs.
|
||||
Please confirm that {field_name} is what you intended."""
|
||||
)
|
||||
extra[field_name] = values.pop(field_name)
|
||||
|
||||
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
|
||||
if invalid_model_kwargs:
|
||||
raise ValueError(
|
||||
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
||||
f"Instead they were passed in as part of `model_kwargs` parameter."
|
||||
)
|
||||
|
||||
values["model_kwargs"] = extra
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
if values["n"] < 1:
|
||||
raise ValueError("n must be at least 1.")
|
||||
if values["n"] > 1 and values["streaming"]:
|
||||
raise ValueError("n must be 1 when streaming.")
|
||||
|
||||
if values["temperature"] == 0:
|
||||
values["temperature"] = 1e-8
|
||||
|
||||
values["groq_api_key"] = convert_to_secret_str(
|
||||
get_from_dict_or_env(values, "groq_api_key", "GROQ_API_KEY")
|
||||
)
|
||||
values["groq_api_base"] = values["groq_api_base"] or os.getenv("GROQ_API_BASE")
|
||||
values["groq_proxy"] = values["groq_proxy"] = os.getenv("GROQ_PROXY")
|
||||
|
||||
client_params = {
|
||||
"api_key": values["groq_api_key"].get_secret_value(),
|
||||
"base_url": values["groq_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
|
||||
try:
|
||||
import groq
|
||||
|
||||
if not values.get("client"):
|
||||
values["client"] = groq.Groq(**client_params).chat.completions
|
||||
if not values.get("async_client"):
|
||||
values["async_client"] = groq.AsyncGroq(
|
||||
**client_params
|
||||
).chat.completions
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import groq python package. "
|
||||
"Please install it with `pip install groq`."
|
||||
)
|
||||
return values
|
||||
|
||||
#
|
||||
# Serializable class method overrides
|
||||
#
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"groq_api_key": "GROQ_API_KEY"}
|
||||
|
||||
@classmethod
|
||||
def is_lc_serializable(cls) -> bool:
|
||||
"""Return whether this model can be serialized by Langchain."""
|
||||
return True
|
||||
|
||||
#
|
||||
# BaseChatModel method overrides
|
||||
#
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of model."""
|
||||
return "groq-chat"
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._stream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return generate_from_stream(stream_iter)
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {
|
||||
**params,
|
||||
**({"stream": stream} if stream is not None else {}),
|
||||
**kwargs,
|
||||
}
|
||||
response = self.client.create(messages=message_dicts, **params)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._astream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return await agenerate_from_stream(stream_iter)
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {
|
||||
**params,
|
||||
**({"stream": stream} if stream is not None else {}),
|
||||
**kwargs,
|
||||
}
|
||||
response = await self.async_client.create(messages=message_dicts, **params)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
for chunk in self.client.create(messages=message_dicts, **params):
|
||||
if not isinstance(chunk, dict):
|
||||
chunk = chunk.dict()
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
choice = chunk["choices"][0]
|
||||
chunk = _convert_delta_to_message_chunk(
|
||||
choice["delta"], default_chunk_class
|
||||
)
|
||||
generation_info = {}
|
||||
if finish_reason := choice.get("finish_reason"):
|
||||
generation_info["finish_reason"] = finish_reason
|
||||
logprobs = choice.get("logprobs")
|
||||
if logprobs:
|
||||
generation_info["logprobs"] = logprobs
|
||||
default_chunk_class = chunk.__class__
|
||||
chunk = ChatGenerationChunk(
|
||||
message=chunk, generation_info=generation_info or None
|
||||
)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
async for chunk in await self.async_client.create(
|
||||
messages=message_dicts, **params
|
||||
):
|
||||
if not isinstance(chunk, dict):
|
||||
chunk = chunk.dict()
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
choice = chunk["choices"][0]
|
||||
chunk = _convert_delta_to_message_chunk(
|
||||
choice["delta"], default_chunk_class
|
||||
)
|
||||
generation_info = {}
|
||||
if finish_reason := choice.get("finish_reason"):
|
||||
generation_info["finish_reason"] = finish_reason
|
||||
logprobs = choice.get("logprobs")
|
||||
if logprobs:
|
||||
generation_info["logprobs"] = logprobs
|
||||
default_chunk_class = chunk.__class__
|
||||
chunk = ChatGenerationChunk(
|
||||
message=chunk, generation_info=generation_info or None
|
||||
)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
token=chunk.text, chunk=chunk, logprobs=logprobs
|
||||
)
|
||||
|
||||
#
|
||||
# Internal methods
|
||||
#
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling Groq API."""
|
||||
params = {
|
||||
"model": self.model_name,
|
||||
"stream": self.streaming,
|
||||
"n": self.n,
|
||||
"temperature": self.temperature,
|
||||
**self.model_kwargs,
|
||||
}
|
||||
if self.max_tokens is not None:
|
||||
params["max_tokens"] = self.max_tokens
|
||||
return params
|
||||
|
||||
def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
|
||||
generations = []
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
for res in response["choices"]:
|
||||
message = _convert_dict_to_message(res["message"])
|
||||
generation_info = dict(finish_reason=res.get("finish_reason"))
|
||||
if "logprobs" in res:
|
||||
generation_info["logprobs"] = res["logprobs"]
|
||||
gen = ChatGeneration(
|
||||
message=message,
|
||||
generation_info=generation_info,
|
||||
)
|
||||
generations.append(gen)
|
||||
token_usage = response.get("usage", {})
|
||||
llm_output = {
|
||||
"token_usage": token_usage,
|
||||
"model_name": self.model_name,
|
||||
"system_fingerprint": response.get("system_fingerprint", ""),
|
||||
}
|
||||
return ChatResult(generations=generations, llm_output=llm_output)
|
||||
|
||||
def _create_message_dicts(
|
||||
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
||||
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
||||
params = self._default_params
|
||||
if stop is not None:
|
||||
if "stop" in params:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
params["stop"] = stop
|
||||
message_dicts = [_convert_message_to_dict(m) for m in messages]
|
||||
return message_dicts, params
|
||||
|
||||
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
||||
overall_token_usage: dict = {}
|
||||
system_fingerprint = None
|
||||
for output in llm_outputs:
|
||||
if output is None:
|
||||
# Happens in streaming
|
||||
continue
|
||||
token_usage = output["token_usage"]
|
||||
if token_usage is not None:
|
||||
for k, v in token_usage.items():
|
||||
if k in overall_token_usage:
|
||||
overall_token_usage[k] += v
|
||||
else:
|
||||
overall_token_usage[k] = v
|
||||
if system_fingerprint is None:
|
||||
system_fingerprint = output.get("system_fingerprint")
|
||||
combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
|
||||
if system_fingerprint:
|
||||
combined["system_fingerprint"] = system_fingerprint
|
||||
return combined
|
||||
|
||||
|
||||
#
|
||||
# Type conversion helpers
|
||||
#
|
||||
def _convert_message_to_dict(message: BaseMessage) -> dict:
|
||||
"""Convert a LangChain message to a dictionary.
|
||||
|
||||
Args:
|
||||
message: The LangChain message.
|
||||
|
||||
Returns:
|
||||
The dictionary.
|
||||
"""
|
||||
message_dict: Dict[str, Any]
|
||||
if isinstance(message, ChatMessage):
|
||||
message_dict = {"role": message.role, "content": message.content}
|
||||
elif isinstance(message, HumanMessage):
|
||||
message_dict = {"role": "user", "content": message.content}
|
||||
elif isinstance(message, AIMessage):
|
||||
message_dict = {"role": "assistant", "content": message.content}
|
||||
if "function_call" in message.additional_kwargs:
|
||||
message_dict["function_call"] = message.additional_kwargs["function_call"]
|
||||
# If function call only, content is None not empty string
|
||||
if message_dict["content"] == "":
|
||||
message_dict["content"] = None
|
||||
if "tool_calls" in message.additional_kwargs:
|
||||
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
|
||||
# If tool calls only, content is None not empty string
|
||||
if message_dict["content"] == "":
|
||||
message_dict["content"] = None
|
||||
elif isinstance(message, SystemMessage):
|
||||
message_dict = {"role": "system", "content": message.content}
|
||||
elif isinstance(message, FunctionMessage):
|
||||
message_dict = {
|
||||
"role": "function",
|
||||
"content": message.content,
|
||||
"name": message.name,
|
||||
}
|
||||
elif isinstance(message, ToolMessage):
|
||||
message_dict = {
|
||||
"role": "tool",
|
||||
"content": message.content,
|
||||
"tool_call_id": message.tool_call_id,
|
||||
}
|
||||
else:
|
||||
raise TypeError(f"Got unknown type {message}")
|
||||
if "name" in message.additional_kwargs:
|
||||
message_dict["name"] = message.additional_kwargs["name"]
|
||||
return message_dict
|
||||
|
||||
|
||||
def _convert_delta_to_message_chunk(
|
||||
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
|
||||
) -> BaseMessageChunk:
|
||||
role = cast(str, _dict.get("role"))
|
||||
content = cast(str, _dict.get("content") or "")
|
||||
additional_kwargs: Dict = {}
|
||||
if _dict.get("function_call"):
|
||||
function_call = dict(_dict["function_call"])
|
||||
if "name" in function_call and function_call["name"] is None:
|
||||
function_call["name"] = ""
|
||||
additional_kwargs["function_call"] = function_call
|
||||
if _dict.get("tool_calls"):
|
||||
additional_kwargs["tool_calls"] = _dict["tool_calls"]
|
||||
|
||||
if role == "user" or default_class == HumanMessageChunk:
|
||||
return HumanMessageChunk(content=content)
|
||||
elif role == "assistant" or default_class == AIMessageChunk:
|
||||
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
|
||||
elif role == "system" or default_class == SystemMessageChunk:
|
||||
return SystemMessageChunk(content=content)
|
||||
elif role == "function" or default_class == FunctionMessageChunk:
|
||||
return FunctionMessageChunk(content=content, name=_dict["name"])
|
||||
elif role == "tool" or default_class == ToolMessageChunk:
|
||||
return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
|
||||
elif role or default_class == ChatMessageChunk:
|
||||
return ChatMessageChunk(content=content, role=role)
|
||||
else:
|
||||
return default_class(content=content) # type: ignore
|
||||
|
||||
|
||||
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
|
||||
"""Convert a dictionary to a LangChain message.
|
||||
|
||||
Args:
|
||||
_dict: The dictionary.
|
||||
|
||||
Returns:
|
||||
The LangChain message.
|
||||
"""
|
||||
role = _dict.get("role")
|
||||
if role == "user":
|
||||
return HumanMessage(content=_dict.get("content", ""))
|
||||
elif role == "assistant":
|
||||
content = _dict.get("content", "")
|
||||
additional_kwargs: Dict = {}
|
||||
if function_call := _dict.get("function_call"):
|
||||
additional_kwargs["function_call"] = dict(function_call)
|
||||
if tool_calls := _dict.get("tool_calls"):
|
||||
additional_kwargs["tool_calls"] = tool_calls
|
||||
return AIMessage(content=content, additional_kwargs=additional_kwargs)
|
||||
elif role == "system":
|
||||
return SystemMessage(content=_dict.get("content", ""))
|
||||
elif role == "function":
|
||||
return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name"))
|
||||
elif role == "tool":
|
||||
additional_kwargs = {}
|
||||
if "name" in _dict:
|
||||
additional_kwargs["name"] = _dict["name"]
|
||||
return ToolMessage(
|
||||
content=_dict.get("content", ""),
|
||||
tool_call_id=_dict.get("tool_call_id"),
|
||||
additional_kwargs=additional_kwargs,
|
||||
)
|
||||
else:
|
||||
return ChatMessage(content=_dict.get("content", ""), role=role)
|
||||
@ -0,0 +1,871 @@
|
||||
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.6.0"
|
||||
description = "Reusable constraint types to use with typing.Annotated"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
|
||||
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.2.0"
|
||||
description = "High level compatibility layer for multiple asynchronous event loop implementations"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
|
||||
{file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
|
||||
idna = ">=2.8"
|
||||
sniffio = ">=1.1"
|
||||
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
|
||||
|
||||
[package.extras]
|
||||
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
|
||||
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
|
||||
trio = ["trio (>=0.23)"]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2023.11.17"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
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|
||||
[package.extras]
|
||||
watchmedo = ["PyYAML (>=3.10)"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "672d4fc5b14a6506a6c37ebe445fbd88bb3ee5f0c2276b183e2b93c21d04c641"
|
||||
@ -0,0 +1,95 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-groq"
|
||||
version = "0.0.1"
|
||||
description = "An integration package connecting Groq and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
license = "MIT"
|
||||
|
||||
[tool.poetry.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/groq"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.8.1,<4.0"
|
||||
langchain-core = "^0.1"
|
||||
groq = ">=0.4.1,<1"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.3.0"
|
||||
pytest-mock = "^3.10.0"
|
||||
pytest-watcher = "^0.3.4"
|
||||
pytest-asyncio = "^0.21.1"
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.codespell]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.codespell.dependencies]
|
||||
codespell = "^2.2.0"
|
||||
|
||||
[tool.poetry.group.lint]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.1.5"
|
||||
|
||||
[tool.poetry.group.typing.dependencies]
|
||||
mypy = "^0.991"
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.test_integration]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test_integration.dependencies]
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.ruff]
|
||||
select = [
|
||||
"E", # pycodestyle
|
||||
"F", # pyflakes
|
||||
"I", # isort
|
||||
"W", # Warnings
|
||||
]
|
||||
|
||||
[tool.mypy]
|
||||
disallow_untyped_defs = "True"
|
||||
|
||||
[tool.coverage.run]
|
||||
omit = [
|
||||
"tests/*",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
# --strict-markers will raise errors on unknown marks.
|
||||
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
|
||||
#
|
||||
# https://docs.pytest.org/en/7.1.x/reference/reference.html
|
||||
# --strict-config any warnings encountered while parsing the `pytest`
|
||||
# section of the configuration file raise errors.
|
||||
addopts = "--strict-markers --strict-config --durations=5"
|
||||
# Registering custom markers.
|
||||
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
|
||||
markers = [
|
||||
"compile: mark placeholder test used to compile integration tests without running them",
|
||||
"scheduled: mark tests to run in scheduled testing",
|
||||
]
|
||||
filterwarnings = [
|
||||
"error",
|
||||
# Maintain support for pydantic 1.X
|
||||
'default:The `dict` method is deprecated; use `model_dump` instead.*:DeprecationWarning',
|
||||
]
|
||||
asyncio_mode = "auto"
|
||||
@ -0,0 +1,17 @@
|
||||
import sys
|
||||
import traceback
|
||||
from importlib.machinery import SourceFileLoader
|
||||
|
||||
if __name__ == "__main__":
|
||||
files = sys.argv[1:]
|
||||
has_failure = False
|
||||
for file in files:
|
||||
try:
|
||||
SourceFileLoader("x", file).load_module()
|
||||
except Exception:
|
||||
has_faillure = True
|
||||
print(file)
|
||||
traceback.print_exc()
|
||||
print()
|
||||
|
||||
sys.exit(1 if has_failure else 0)
|
||||
@ -0,0 +1,27 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# This script searches for lines starting with "import pydantic" or "from pydantic"
|
||||
# in tracked files within a Git repository.
|
||||
#
|
||||
# Usage: ./scripts/check_pydantic.sh /path/to/repository
|
||||
|
||||
# Check if a path argument is provided
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: $0 /path/to/repository"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
repository_path="$1"
|
||||
|
||||
# Search for lines matching the pattern within the specified repository
|
||||
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
|
||||
|
||||
# Check if any matching lines were found
|
||||
if [ -n "$result" ]; then
|
||||
echo "ERROR: The following lines need to be updated:"
|
||||
echo "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
||||
@ -0,0 +1,17 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -eu
|
||||
|
||||
# Initialize a variable to keep track of errors
|
||||
errors=0
|
||||
|
||||
# make sure not importing from langchain or langchain_experimental
|
||||
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
|
||||
|
||||
# Decide on an exit status based on the errors
|
||||
if [ "$errors" -gt 0 ]; then
|
||||
exit 1
|
||||
else
|
||||
exit 0
|
||||
fi
|
||||
@ -0,0 +1,233 @@
|
||||
"""Test ChatGroq chat model."""
|
||||
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from langchain_core.messages import (
|
||||
BaseMessage,
|
||||
BaseMessageChunk,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
from langchain_core.outputs import ChatGeneration, LLMResult
|
||||
|
||||
from langchain_groq import ChatGroq
|
||||
from tests.unit_tests.fake.callbacks import (
|
||||
FakeCallbackHandler,
|
||||
FakeCallbackHandlerWithChatStart,
|
||||
)
|
||||
|
||||
|
||||
#
|
||||
# Smoke test Runnable interface
|
||||
#
|
||||
@pytest.mark.scheduled
|
||||
def test_invoke() -> None:
|
||||
"""Test Chat wrapper."""
|
||||
chat = ChatGroq(
|
||||
temperature=0.7,
|
||||
base_url=None,
|
||||
groq_proxy=None,
|
||||
timeout=10.0,
|
||||
max_retries=3,
|
||||
http_client=None,
|
||||
n=1,
|
||||
max_tokens=10,
|
||||
default_headers=None,
|
||||
default_query=None,
|
||||
)
|
||||
message = HumanMessage(content="Welcome to the Groqetship")
|
||||
response = chat.invoke([message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_ainvoke() -> None:
|
||||
"""Test ainvoke tokens from ChatGroq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
result = await llm.ainvoke("Welcome to the Groqetship!", config={"tags": ["foo"]})
|
||||
assert isinstance(result, BaseMessage)
|
||||
assert isinstance(result.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_batch() -> None:
|
||||
"""Test batch tokens from ChatGroq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
result = llm.batch(["Hello!", "Welcome to the Groqetship!"])
|
||||
for token in result:
|
||||
assert isinstance(token, BaseMessage)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_abatch() -> None:
|
||||
"""Test abatch tokens from ChatGroq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
result = await llm.abatch(["Hello!", "Welcome to the Groqetship!"])
|
||||
for token in result:
|
||||
assert isinstance(token, BaseMessage)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_stream() -> None:
|
||||
"""Test streaming tokens from Groq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
for token in llm.stream("Welcome to the Groqetship!"):
|
||||
assert isinstance(token, BaseMessageChunk)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_astream() -> None:
|
||||
"""Test streaming tokens from Groq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
async for token in llm.astream("Welcome to the Groqetship!"):
|
||||
assert isinstance(token, BaseMessageChunk)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
#
|
||||
# Test Legacy generate methods
|
||||
#
|
||||
@pytest.mark.scheduled
|
||||
def test_generate() -> None:
|
||||
"""Test sync generate."""
|
||||
n = 1
|
||||
chat = ChatGroq(max_tokens=10)
|
||||
message = HumanMessage(content="Hello", n=1)
|
||||
response = chat.generate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
assert response.llm_output
|
||||
assert response.llm_output["model_name"] == chat.model_name
|
||||
for generations in response.generations:
|
||||
assert len(generations) == n
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_agenerate() -> None:
|
||||
"""Test async generation."""
|
||||
n = 1
|
||||
chat = ChatGroq(max_tokens=10, n=1)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
assert response.llm_output
|
||||
assert response.llm_output["model_name"] == chat.model_name
|
||||
for generations in response.generations:
|
||||
assert len(generations) == n
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
#
|
||||
# Test streaming flags in invoke and generate
|
||||
#
|
||||
@pytest.mark.scheduled
|
||||
def test_invoke_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
chat = ChatGroq(
|
||||
max_tokens=2,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callbacks=[callback_handler],
|
||||
)
|
||||
message = HumanMessage(content="Welcome to the Groqetship")
|
||||
response = chat.invoke([message])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, BaseMessage)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_agenerate_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandlerWithChatStart()
|
||||
chat = ChatGroq(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callbacks=[callback_handler],
|
||||
)
|
||||
message = HumanMessage(content="Welcome to the Groqetship")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
assert response.llm_output is not None
|
||||
assert response.llm_output["model_name"] == chat.model_name
|
||||
for generations in response.generations:
|
||||
assert len(generations) == 1
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
#
|
||||
# Misc tests
|
||||
#
|
||||
def test_streaming_generation_info() -> None:
|
||||
"""Test that generation info is preserved when streaming."""
|
||||
|
||||
class _FakeCallback(FakeCallbackHandler):
|
||||
saved_things: dict = {}
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
# Save the generation
|
||||
self.saved_things["generation"] = args[0]
|
||||
|
||||
callback = _FakeCallback()
|
||||
chat = ChatGroq(
|
||||
max_tokens=2,
|
||||
temperature=0,
|
||||
callbacks=[callback],
|
||||
)
|
||||
list(chat.stream("Respond with the single word Hello"))
|
||||
generation = callback.saved_things["generation"]
|
||||
# `Hello!` is two tokens, assert that that is what is returned
|
||||
assert isinstance(generation, LLMResult)
|
||||
assert generation.generations[0][0].text == "Hello"
|
||||
|
||||
|
||||
def test_system_message() -> None:
|
||||
"""Test ChatGroq wrapper with system message."""
|
||||
chat = ChatGroq(max_tokens=10)
|
||||
system_message = SystemMessage(content="You are to chat with the user.")
|
||||
human_message = HumanMessage(content="Hello")
|
||||
response = chat.invoke([system_message, human_message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
# Groq does not currently support N > 1
|
||||
# @pytest.mark.scheduled
|
||||
# def test_chat_multiple_completions() -> None:
|
||||
# """Test ChatGroq wrapper with multiple completions."""
|
||||
# chat = ChatGroq(max_tokens=10, n=5)
|
||||
# message = HumanMessage(content="Hello")
|
||||
# response = chat._generate([message])
|
||||
# assert isinstance(response, ChatResult)
|
||||
# assert len(response.generations) == 5
|
||||
# for generation in response.generations:
|
||||
# assert isinstance(generation.message, BaseMessage)
|
||||
# assert isinstance(generation.message.content, str)
|
||||
@ -0,0 +1,7 @@
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.mark.compile
|
||||
def test_placeholder() -> None:
|
||||
"""Used for compiling integration tests without running any real tests."""
|
||||
pass
|
||||
@ -0,0 +1,393 @@
|
||||
"""A fake callback handler for testing purposes."""
|
||||
from itertools import chain
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
from uuid import UUID
|
||||
|
||||
from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
|
||||
class BaseFakeCallbackHandler(BaseModel):
|
||||
"""Base fake callback handler for testing."""
|
||||
|
||||
starts: int = 0
|
||||
ends: int = 0
|
||||
errors: int = 0
|
||||
errors_args: List[Any] = []
|
||||
text: int = 0
|
||||
ignore_llm_: bool = False
|
||||
ignore_chain_: bool = False
|
||||
ignore_agent_: bool = False
|
||||
ignore_retriever_: bool = False
|
||||
ignore_chat_model_: bool = False
|
||||
|
||||
# to allow for similar callback handlers that are not technically equal
|
||||
fake_id: Union[str, None] = None
|
||||
|
||||
# add finer-grained counters for easier debugging of failing tests
|
||||
chain_starts: int = 0
|
||||
chain_ends: int = 0
|
||||
llm_starts: int = 0
|
||||
llm_ends: int = 0
|
||||
llm_streams: int = 0
|
||||
tool_starts: int = 0
|
||||
tool_ends: int = 0
|
||||
agent_actions: int = 0
|
||||
agent_ends: int = 0
|
||||
chat_model_starts: int = 0
|
||||
retriever_starts: int = 0
|
||||
retriever_ends: int = 0
|
||||
retriever_errors: int = 0
|
||||
retries: int = 0
|
||||
|
||||
|
||||
class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
|
||||
"""Base fake callback handler mixin for testing."""
|
||||
|
||||
def on_llm_start_common(self) -> None:
|
||||
self.llm_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_llm_end_common(self) -> None:
|
||||
self.llm_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_llm_error_common(self, *args: Any, **kwargs: Any) -> None:
|
||||
self.errors += 1
|
||||
self.errors_args.append({"args": args, "kwargs": kwargs})
|
||||
|
||||
def on_llm_new_token_common(self) -> None:
|
||||
self.llm_streams += 1
|
||||
|
||||
def on_retry_common(self) -> None:
|
||||
self.retries += 1
|
||||
|
||||
def on_chain_start_common(self) -> None:
|
||||
self.chain_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_chain_end_common(self) -> None:
|
||||
self.chain_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chain_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_tool_start_common(self) -> None:
|
||||
self.tool_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_tool_end_common(self) -> None:
|
||||
self.tool_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_tool_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_agent_action_common(self) -> None:
|
||||
self.agent_actions += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_agent_finish_common(self) -> None:
|
||||
self.agent_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chat_model_start_common(self) -> None:
|
||||
self.chat_model_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_text_common(self) -> None:
|
||||
self.text += 1
|
||||
|
||||
def on_retriever_start_common(self) -> None:
|
||||
self.starts += 1
|
||||
self.retriever_starts += 1
|
||||
|
||||
def on_retriever_end_common(self) -> None:
|
||||
self.ends += 1
|
||||
self.retriever_ends += 1
|
||||
|
||||
def on_retriever_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
self.retriever_errors += 1
|
||||
|
||||
|
||||
class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
|
||||
"""Fake callback handler for testing."""
|
||||
|
||||
@property
|
||||
def ignore_llm(self) -> bool:
|
||||
"""Whether to ignore LLM callbacks."""
|
||||
return self.ignore_llm_
|
||||
|
||||
@property
|
||||
def ignore_chain(self) -> bool:
|
||||
"""Whether to ignore chain callbacks."""
|
||||
return self.ignore_chain_
|
||||
|
||||
@property
|
||||
def ignore_agent(self) -> bool:
|
||||
"""Whether to ignore agent callbacks."""
|
||||
return self.ignore_agent_
|
||||
|
||||
@property
|
||||
def ignore_retriever(self) -> bool:
|
||||
"""Whether to ignore retriever callbacks."""
|
||||
return self.ignore_retriever_
|
||||
|
||||
def on_llm_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_start_common()
|
||||
|
||||
def on_llm_new_token(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_new_token_common()
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_end_common()
|
||||
|
||||
def on_llm_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_error_common(*args, **kwargs)
|
||||
|
||||
def on_retry(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retry_common()
|
||||
|
||||
def on_chain_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_start_common()
|
||||
|
||||
def on_chain_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_end_common()
|
||||
|
||||
def on_chain_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_error_common()
|
||||
|
||||
def on_tool_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_start_common()
|
||||
|
||||
def on_tool_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_end_common()
|
||||
|
||||
def on_tool_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_error_common()
|
||||
|
||||
def on_agent_action(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_action_common()
|
||||
|
||||
def on_agent_finish(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_finish_common()
|
||||
|
||||
def on_text(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_text_common()
|
||||
|
||||
def on_retriever_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_start_common()
|
||||
|
||||
def on_retriever_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_end_common()
|
||||
|
||||
def on_retriever_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_error_common()
|
||||
|
||||
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":
|
||||
return self
|
||||
|
||||
|
||||
class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
|
||||
def on_chat_model_start(
|
||||
self,
|
||||
serialized: Dict[str, Any],
|
||||
messages: List[List[BaseMessage]],
|
||||
*,
|
||||
run_id: UUID,
|
||||
parent_run_id: Optional[UUID] = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
|
||||
self.on_chat_model_start_common()
|
||||
|
||||
|
||||
class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixin):
|
||||
"""Fake async callback handler for testing."""
|
||||
|
||||
@property
|
||||
def ignore_llm(self) -> bool:
|
||||
"""Whether to ignore LLM callbacks."""
|
||||
return self.ignore_llm_
|
||||
|
||||
@property
|
||||
def ignore_chain(self) -> bool:
|
||||
"""Whether to ignore chain callbacks."""
|
||||
return self.ignore_chain_
|
||||
|
||||
@property
|
||||
def ignore_agent(self) -> bool:
|
||||
"""Whether to ignore agent callbacks."""
|
||||
return self.ignore_agent_
|
||||
|
||||
async def on_retry(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retry_common()
|
||||
|
||||
async def on_llm_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_start_common()
|
||||
|
||||
async def on_llm_new_token(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_new_token_common()
|
||||
|
||||
async def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_end_common()
|
||||
|
||||
async def on_llm_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_error_common(*args, **kwargs)
|
||||
|
||||
async def on_chain_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_start_common()
|
||||
|
||||
async def on_chain_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_end_common()
|
||||
|
||||
async def on_chain_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_error_common()
|
||||
|
||||
async def on_tool_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_start_common()
|
||||
|
||||
async def on_tool_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_end_common()
|
||||
|
||||
async def on_tool_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_error_common()
|
||||
|
||||
async def on_agent_action(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_agent_action_common()
|
||||
|
||||
async def on_agent_finish(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_agent_finish_common()
|
||||
|
||||
async def on_text(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_text_common()
|
||||
|
||||
def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler":
|
||||
return self
|
||||
@ -0,0 +1,207 @@
|
||||
"""Test Groq Chat API wrapper."""
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Any
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import langchain_core.load as lc_load
|
||||
import pytest
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
FunctionMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
|
||||
from langchain_groq.chat_models import ChatGroq, _convert_dict_to_message
|
||||
|
||||
os.environ["GROQ_API_KEY"] = "fake-key"
|
||||
|
||||
|
||||
def test_groq_model_param() -> None:
|
||||
llm = ChatGroq(model="foo")
|
||||
assert llm.model_name == "foo"
|
||||
llm = ChatGroq(model_name="foo")
|
||||
assert llm.model_name == "foo"
|
||||
|
||||
|
||||
def test_function_message_dict_to_function_message() -> None:
|
||||
content = json.dumps({"result": "Example #1"})
|
||||
name = "test_function"
|
||||
result = _convert_dict_to_message(
|
||||
{
|
||||
"role": "function",
|
||||
"name": name,
|
||||
"content": content,
|
||||
}
|
||||
)
|
||||
assert isinstance(result, FunctionMessage)
|
||||
assert result.name == name
|
||||
assert result.content == content
|
||||
|
||||
|
||||
def test__convert_dict_to_message_human() -> None:
|
||||
message = {"role": "user", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = HumanMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_dict_to_message_ai() -> None:
|
||||
message = {"role": "assistant", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = AIMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_dict_to_message_system() -> None:
|
||||
message = {"role": "system", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = SystemMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_completion() -> dict:
|
||||
return {
|
||||
"id": "chatcmpl-7fcZavknQda3SQ",
|
||||
"object": "chat.completion",
|
||||
"created": 1689989000,
|
||||
"model": "test-model",
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "Bar Baz",
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def test_groq_invoke(mock_completion: dict) -> None:
|
||||
llm = ChatGroq()
|
||||
mock_client = MagicMock()
|
||||
completed = False
|
||||
|
||||
def mock_create(*args: Any, **kwargs: Any) -> Any:
|
||||
nonlocal completed
|
||||
completed = True
|
||||
return mock_completion
|
||||
|
||||
mock_client.create = mock_create
|
||||
with patch.object(
|
||||
llm,
|
||||
"client",
|
||||
mock_client,
|
||||
):
|
||||
res = llm.invoke("bar")
|
||||
assert res.content == "Bar Baz"
|
||||
assert type(res) == AIMessage
|
||||
assert completed
|
||||
|
||||
|
||||
async def test_groq_ainvoke(mock_completion: dict) -> None:
|
||||
llm = ChatGroq()
|
||||
mock_client = AsyncMock()
|
||||
completed = False
|
||||
|
||||
async def mock_create(*args: Any, **kwargs: Any) -> Any:
|
||||
nonlocal completed
|
||||
completed = True
|
||||
return mock_completion
|
||||
|
||||
mock_client.create = mock_create
|
||||
with patch.object(
|
||||
llm,
|
||||
"async_client",
|
||||
mock_client,
|
||||
):
|
||||
res = await llm.ainvoke("bar")
|
||||
assert res.content == "Bar Baz"
|
||||
assert type(res) == AIMessage
|
||||
assert completed
|
||||
|
||||
|
||||
def test_chat_groq_extra_kwargs() -> None:
|
||||
"""Test extra kwargs to chat groq."""
|
||||
# Check that foo is saved in extra_kwargs.
|
||||
with pytest.warns(UserWarning) as record:
|
||||
llm = ChatGroq(foo=3, max_tokens=10)
|
||||
assert llm.max_tokens == 10
|
||||
assert llm.model_kwargs == {"foo": 3}
|
||||
assert len(record) == 1
|
||||
assert type(record[0].message) is UserWarning
|
||||
assert "foo is not default parameter" in record[0].message.args[0]
|
||||
|
||||
# Test that if extra_kwargs are provided, they are added to it.
|
||||
with pytest.warns(UserWarning) as record:
|
||||
llm = ChatGroq(foo=3, model_kwargs={"bar": 2})
|
||||
assert llm.model_kwargs == {"foo": 3, "bar": 2}
|
||||
assert len(record) == 1
|
||||
assert type(record[0].message) is UserWarning
|
||||
assert "foo is not default parameter" in record[0].message.args[0]
|
||||
|
||||
# Test that if provided twice it errors
|
||||
with pytest.raises(ValueError):
|
||||
ChatGroq(foo=3, model_kwargs={"foo": 2})
|
||||
|
||||
# Test that if explicit param is specified in kwargs it errors
|
||||
with pytest.raises(ValueError):
|
||||
ChatGroq(model_kwargs={"temperature": 0.2})
|
||||
|
||||
# Test that "model" cannot be specified in kwargs
|
||||
with pytest.raises(ValueError):
|
||||
ChatGroq(model_kwargs={"model": "test-model"})
|
||||
|
||||
|
||||
def test_chat_groq_invalid_streaming_params() -> None:
|
||||
"""Test that an error is raised if streaming is invoked with n>1."""
|
||||
with pytest.raises(ValueError):
|
||||
ChatGroq(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
n=5,
|
||||
)
|
||||
|
||||
|
||||
def test_chat_groq_secret() -> None:
|
||||
"""Test that secret is not printed"""
|
||||
secret = "secretKey"
|
||||
not_secret = "safe"
|
||||
llm = ChatGroq(api_key=secret, model_kwargs={"not_secret": not_secret})
|
||||
stringified = str(llm)
|
||||
assert not_secret in stringified
|
||||
assert secret not in stringified
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings("ignore:The function `loads` is in beta")
|
||||
def test_groq_serialization() -> None:
|
||||
"""Test that ChatGroq can be successfully serialized and deserialized"""
|
||||
api_key1 = "top secret"
|
||||
api_key2 = "topest secret"
|
||||
llm = ChatGroq(api_key=api_key1, temperature=0.5)
|
||||
dump = lc_load.dumps(llm)
|
||||
llm2 = lc_load.loads(
|
||||
dump,
|
||||
valid_namespaces=["langchain_groq"],
|
||||
secrets_map={"GROQ_API_KEY": api_key2},
|
||||
)
|
||||
|
||||
assert type(llm2) is ChatGroq
|
||||
|
||||
# Ensure api key wasn't dumped and instead was read from secret map.
|
||||
assert llm.groq_api_key is not None
|
||||
assert llm.groq_api_key.get_secret_value() not in dump
|
||||
assert llm2.groq_api_key is not None
|
||||
assert llm2.groq_api_key.get_secret_value() == api_key2
|
||||
|
||||
# Ensure a non-secret field was preserved
|
||||
assert llm.temperature == llm2.temperature
|
||||
|
||||
# Ensure a None was preserved
|
||||
assert llm.groq_api_base == llm2.groq_api_base
|
||||
@ -0,0 +1,7 @@
|
||||
from langchain_groq import __all__
|
||||
|
||||
EXPECTED_ALL = ["ChatGroq"]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
Loading…
Reference in New Issue