mirror of https://github.com/hwchase17/langchain
integrate JinaChat (#6927)
Integration with https://chat.jina.ai/api. It is OpenAI compatible API. - Twitter handle: [https://twitter.com/JinaAI_](https://twitter.com/JinaAI_) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>pull/7392/head
Delgermurun
1 year ago
committed by
GitHub
parent
4ba7396f96
commit
a1603fccfb
@ -0,0 +1,162 @@
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{
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"cells": [
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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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"# JinaChat\n",
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"\n",
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"This notebook covers how to get started with JinaChat chat models."
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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": 4,
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"id": "522686de",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import JinaChat\n",
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"from langchain.prompts.chat import (\n",
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" ChatPromptTemplate,\n",
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" SystemMessagePromptTemplate,\n",
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" AIMessagePromptTemplate,\n",
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" HumanMessagePromptTemplate,\n",
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")\n",
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"from langchain.schema import AIMessage, HumanMessage, SystemMessage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "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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"chat = JinaChat(temperature=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" SystemMessage(\n",
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" content=\"You are a helpful assistant that translates English to French.\"\n",
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" ),\n",
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" HumanMessage(\n",
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" content=\"Translate this sentence from English to French. I love programming.\"\n",
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" ),\n",
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"]\n",
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"chat(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
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"metadata": {},
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"source": [
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"You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
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"\n",
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"For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "180c5cc8",
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"metadata": {},
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"outputs": [],
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"source": [
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"template = (\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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"system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
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"human_template = \"{text}\"\n",
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"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "fbb043e6",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 9,
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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_prompt = ChatPromptTemplate.from_messages(\n",
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" [system_message_prompt, human_message_prompt]\n",
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")\n",
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"\n",
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"# get a chat completion from the formatted messages\n",
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"chat(\n",
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" chat_prompt.format_prompt(\n",
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" input_language=\"English\", output_language=\"French\", text=\"I love programming.\"\n",
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" ).to_messages()\n",
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")"
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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": "c095285d",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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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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@ -0,0 +1,357 @@
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"""JinaChat wrapper."""
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from __future__ import annotations
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import logging
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from typing import (
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Any,
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Callable,
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Dict,
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List,
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Mapping,
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Optional,
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Tuple,
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Union,
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)
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from pydantic import Field, root_validator
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from tenacity import (
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.chat_models.base import BaseChatModel
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from langchain.schema import (
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AIMessage,
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BaseMessage,
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ChatGeneration,
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ChatMessage,
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ChatResult,
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HumanMessage,
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SystemMessage,
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)
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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def _create_retry_decorator(llm: JinaChat) -> Callable[[Any], Any]:
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import openai
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min_seconds = 1
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max_seconds = 60
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# Wait 2^x * 1 second between each retry starting with
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# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
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return retry(
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reraise=True,
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stop=stop_after_attempt(llm.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(
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retry_if_exception_type(openai.error.Timeout)
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| retry_if_exception_type(openai.error.APIError)
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| retry_if_exception_type(openai.error.APIConnectionError)
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| retry_if_exception_type(openai.error.RateLimitError)
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| retry_if_exception_type(openai.error.ServiceUnavailableError)
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),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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async def acompletion_with_retry(llm: JinaChat, **kwargs: Any) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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# Use OpenAI's async api https://github.com/openai/openai-python#async-api
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return await llm.client.acreate(**kwargs)
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return await _completion_with_retry(**kwargs)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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content = _dict["content"] or ""
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return AIMessage(content=content)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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else:
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raise ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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class JinaChat(BaseChatModel):
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"""Wrapper around JinaChat API.
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To use, you should have the ``openai`` python package installed, and the
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environment variable ``JINACHAT_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain.chat_models import JinaChat
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chat = JinaChat()
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"""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"jinachat_api_key": "JINACHAT_API_KEY"}
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@property
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def lc_serializable(self) -> bool:
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return True
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client: Any #: :meta private:
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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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jinachat_api_key: Optional[str] = None
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"""Base URL path for API requests,
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leave blank if not using a proxy or service emulator."""
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request_timeout: Optional[Union[float, Tuple[float, float]]] = None
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"""Timeout for requests to JinaChat completion API. Default is 600 seconds."""
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max_retries: int = 6
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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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max_tokens: Optional[int] = None
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"""Maximum number of tokens to generate."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = cls.all_required_field_names()
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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logger.warning(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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values["model_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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values["jinachat_api_key"] = get_from_dict_or_env(
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values, "jinachat_api_key", "JINACHAT_API_KEY"
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)
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try:
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import openai
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except ImportError:
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raise ValueError(
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"Could not import openai python package. "
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"Please install it with `pip install openai`."
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)
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try:
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values["client"] = openai.ChatCompletion
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except AttributeError:
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raise ValueError(
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"`openai` has no `ChatCompletion` attribute, this is likely "
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"due to an old version of the openai package. Try upgrading it "
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"with `pip install --upgrade openai`."
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling JinaChat API."""
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return {
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"request_timeout": self.request_timeout,
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"max_tokens": self.max_tokens,
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"stream": self.streaming,
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"temperature": self.temperature,
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**self.model_kwargs,
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}
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def _create_retry_decorator(self) -> Callable[[Any], Any]:
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import openai
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min_seconds = 1
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max_seconds = 60
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# Wait 2^x * 1 second between each retry starting with
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# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
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return retry(
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reraise=True,
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stop=stop_after_attempt(self.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(
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retry_if_exception_type(openai.error.Timeout)
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| retry_if_exception_type(openai.error.APIError)
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| retry_if_exception_type(openai.error.APIConnectionError)
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| retry_if_exception_type(openai.error.RateLimitError)
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| retry_if_exception_type(openai.error.ServiceUnavailableError)
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),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def completion_with_retry(self, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = self._create_retry_decorator()
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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return self.client.create(**kwargs)
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return _completion_with_retry(**kwargs)
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def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
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overall_token_usage: dict = {}
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for output in llm_outputs:
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if output is None:
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# Happens in streaming
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continue
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token_usage = output["token_usage"]
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for k, v in token_usage.items():
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if k in overall_token_usage:
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overall_token_usage[k] += v
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else:
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overall_token_usage[k] = v
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return {"token_usage": overall_token_usage}
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs}
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if self.streaming:
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inner_completion = ""
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role = "assistant"
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params["stream"] = True
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for stream_resp in self.completion_with_retry(
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messages=message_dicts, **params
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):
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role = stream_resp["choices"][0]["delta"].get("role", role)
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token = stream_resp["choices"][0]["delta"].get("content") or ""
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inner_completion += token
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if run_manager:
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run_manager.on_llm_new_token(token)
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message = _convert_dict_to_message(
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{
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"content": inner_completion,
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"role": role,
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}
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)
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return ChatResult(generations=[ChatGeneration(message=message)])
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response = self.completion_with_retry(messages=message_dicts, **params)
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return self._create_chat_result(response)
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = dict(self._invocation_params)
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [_convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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generations = []
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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gen = ChatGeneration(message=message)
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generations.append(gen)
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llm_output = {"token_usage": response["usage"]}
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return ChatResult(generations=generations, llm_output=llm_output)
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs}
|
||||
if self.streaming:
|
||||
inner_completion = ""
|
||||
role = "assistant"
|
||||
params["stream"] = True
|
||||
async for stream_resp in await acompletion_with_retry(
|
||||
self, messages=message_dicts, **params
|
||||
):
|
||||
role = stream_resp["choices"][0]["delta"].get("role", role)
|
||||
token = stream_resp["choices"][0]["delta"].get("content", "")
|
||||
inner_completion += token or ""
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(token)
|
||||
message = _convert_dict_to_message(
|
||||
{
|
||||
"content": inner_completion,
|
||||
"role": role,
|
||||
}
|
||||
)
|
||||
return ChatResult(generations=[ChatGeneration(message=message)])
|
||||
else:
|
||||
response = await acompletion_with_retry(
|
||||
self, messages=message_dicts, **params
|
||||
)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
@property
|
||||
def _invocation_params(self) -> Mapping[str, Any]:
|
||||
"""Get the parameters used to invoke the model."""
|
||||
jinachat_creds: Dict[str, Any] = {
|
||||
"api_key": self.jinachat_api_key,
|
||||
"api_base": "https://api.chat.jina.ai/v1",
|
||||
"model": "jinachat",
|
||||
}
|
||||
return {**jinachat_creds, **self._default_params}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of chat model."""
|
||||
return "jinachat"
|
||||
@ -0,0 +1,127 @@
|
||||
"""Test JinaChat wrapper."""
|
||||
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain.callbacks.manager import CallbackManager
|
||||
from langchain.chat_models.jinachat import JinaChat
|
||||
from langchain.schema import (
|
||||
BaseMessage,
|
||||
ChatGeneration,
|
||||
HumanMessage,
|
||||
LLMResult,
|
||||
SystemMessage,
|
||||
)
|
||||
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
|
||||
|
||||
|
||||
def test_jinachat() -> None:
|
||||
"""Test JinaChat wrapper."""
|
||||
chat = JinaChat(max_tokens=10)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_jinachat_system_message() -> None:
|
||||
"""Test JinaChat wrapper with system message."""
|
||||
chat = JinaChat(max_tokens=10)
|
||||
system_message = SystemMessage(content="You are to chat with the user.")
|
||||
human_message = HumanMessage(content="Hello")
|
||||
response = chat([system_message, human_message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_jinachat_generate() -> None:
|
||||
"""Test JinaChat wrapper with generate."""
|
||||
chat = JinaChat(max_tokens=10)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat.generate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
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
|
||||
|
||||
|
||||
def test_jinachat_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = JinaChat(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, BaseMessage)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_jinachat() -> None:
|
||||
"""Test async generation."""
|
||||
chat = JinaChat(max_tokens=102)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
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
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_jinachat_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = JinaChat(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
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
|
||||
|
||||
|
||||
def test_jinachat_extra_kwargs() -> None:
|
||||
"""Test extra kwargs to chat openai."""
|
||||
# Check that foo is saved in extra_kwargs.
|
||||
llm = JinaChat(foo=3, max_tokens=10)
|
||||
assert llm.max_tokens == 10
|
||||
assert llm.model_kwargs == {"foo": 3}
|
||||
|
||||
# Test that if extra_kwargs are provided, they are added to it.
|
||||
llm = JinaChat(foo=3, model_kwargs={"bar": 2})
|
||||
assert llm.model_kwargs == {"foo": 3, "bar": 2}
|
||||
|
||||
# Test that if provided twice it errors
|
||||
with pytest.raises(ValueError):
|
||||
JinaChat(foo=3, model_kwargs={"foo": 2})
|
||||
|
||||
# Test that if explicit param is specified in kwargs it errors
|
||||
with pytest.raises(ValueError):
|
||||
JinaChat(model_kwargs={"temperature": 0.2})
|
||||
Loading…
Reference in New Issue