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community: add langchain-abso documentation (#29739)
Add the documentation for the community package `langchain-abso`. It provides a new Chat Model class, that uses https://abso.ai --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Hugues Chocart
GitHub
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docs/docs/integrations/chat/abso.ipynb
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206
docs/docs/integrations/chat/abso.ipynb
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Abso\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e49f1e0d",
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"metadata": {},
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"source": [
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"# ChatAbso\n",
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"\n",
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"This will help you getting started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n",
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"\n",
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"- You can find the full documentation for the Abso router [here] (https://abso.ai)\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/abso) | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
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"| [ChatAbso](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html) | [langchain-abso](https://python.langchain.com/api_reference/en/latest/abso_api_reference.html) | ❌ | ❌ | ❌ |  |  |\n",
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"\n",
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"## Setup\n",
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"To access ChatAbso models you'll need to create an OpenAI account, get an API key, and install the `langchain-abso` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"- TODO: Update with relevant info.\n",
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"\n",
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"Head to (TODO: link) to sign up to ChatAbso and generate an API key. Once you've done this set the ABSO_API_KEY environment variable:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"if not os.getenv(\"OPENAI_API_KEY\"):\n",
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" os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0730d6a1-c893-4840-9817-5e5251676d5d",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain ChatAbso integration lives in the `langchain-abso` package:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "652d6238-1f87-422a-b135-f5abbb8652fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-abso"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a38cde65-254d-4219-a441-068766c0d4b5",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate chat 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": null,
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"id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_abso import ChatAbso\n",
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"\n",
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"llm = ChatAbso(fast_model=\"gpt-4o\", slow_model=\"o3-mini\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b4f3e15",
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"metadata": {},
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"source": [
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"## Invocation\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62e0dbc3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"messages = [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
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" ),\n",
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" (\"human\", \"I love programming.\"),\n",
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"]\n",
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"ai_msg = llm.invoke(messages)\n",
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"ai_msg"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(ai_msg.content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"\n",
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"We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
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" ),\n",
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" (\"human\", \"{input}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"chain = prompt | llm\n",
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"chain.invoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"German\",\n",
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" \"input\": \"I love programming.\",\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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"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
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"metadata": {},
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all ChatAbso features and configurations head to the API reference: https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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14
docs/docs/integrations/providers/abso.md
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14
docs/docs/integrations/providers/abso.md
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# Abso
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[Abso](https://abso.ai/#router) is an open-source LLM proxy that automatically routes requests between fast and slow models based on prompt complexity. It uses various heuristics to chose the proper model. It's very fast and has low latency.
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## Installation and setup
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```bash
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pip install langchain-abso
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```
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## Chat Model
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See usage details [here](/docs/integrations/chat/abso)
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@ -390,3 +390,7 @@ packages:
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repo: cvs-health/langfair
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path: .
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downloads: 0
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- name: langchain-abso
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repo: lunary-ai/langchain-abso
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path: .
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downloads: 0
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