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Harrison/official pre release (#8106)
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Harrison Chase
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parent
95bcf68802
commit
aa0e69bc98
10
MIGRATE.md
10
MIGRATE.md
@ -1,6 +1,6 @@
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# Migrating to `langchain.experimental`
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We are moving any experimental components of langchain, or components with vulnerability issues, into `langchain.experimental`.
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We are moving any experimental components of langchain, or components with vulnerability issues, into `langchain_experimental`.
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This guide covers how to migrate.
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## Installation
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@ -11,7 +11,7 @@ Previously:
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Now:
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`pip install -U langchain langchain.experimental`
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`pip install -U langchain langchain_experimental`
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## PALChain
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@ -21,7 +21,7 @@ Previously:
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Now:
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`from langchain.experimental.pal_chain import PALChain`
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`from langchain_experimental.pal_chain import PALChain`
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## SQLDatabaseChain
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@ -31,7 +31,7 @@ Previously:
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Now:
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`from langchain.experimental.sql import SQLDatabaseChain`
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`from langchain_experimental.sql import SQLDatabaseChain`
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## `load_prompt` for Python files
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@ -44,4 +44,4 @@ Previously:
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Now:
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`from langchain.experimental.prompts import load_prompt`
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`from langchain_experimental.prompts import load_prompt`
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@ -1,168 +0,0 @@
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{
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"cells": [
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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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"# Apify\n",
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"\n",
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"This notebook shows how to use the [Apify integration](/docs/ecosystem/integrations/apify.html) for LangChain.\n",
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"\n",
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"[Apify](https://apify.com) is a cloud platform for web scraping and data extraction,\n",
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"which provides an [ecosystem](https://apify.com/store) of more than a thousand\n",
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"ready-made apps called *Actors* for various web scraping, crawling, and data extraction use cases.\n",
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"For example, you can use it to extract Google Search results, Instagram and Facebook profiles, products from Amazon or Shopify, Google Maps reviews, etc. etc.\n",
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"\n",
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"In this example, we'll use the [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor,\n",
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"which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs,\n",
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"and extract text content from the web pages. Then we feed the documents into a vector index and answer questions from it.\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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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install apify-client openai langchain chromadb tiktoken"
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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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"First, import `ApifyWrapper` into your source code:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders.base import Document\n",
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"from langchain.indexes import VectorstoreIndexCreator\n",
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"from langchain.utilities import ApifyWrapper"
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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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"Initialize it using your [Apify API token](https://console.apify.com/account/integrations) and for the purpose of this example, also with your OpenAI API key:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"Your OpenAI API key\"\n",
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"os.environ[\"APIFY_API_TOKEN\"] = \"Your Apify API token\"\n",
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"\n",
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"apify = ApifyWrapper()"
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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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"Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.\n",
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"\n",
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"Note that if you already have some results in an Apify dataset, you can load them directly using `ApifyDatasetLoader`, as shown in [this notebook](/docs/modules/data_connection/document_loaders/integrations/apify_dataset.html). In that notebook, you'll also find the explanation of the `dataset_mapping_function`, which is used to map fields from the Apify dataset records to LangChain `Document` fields."
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = apify.call_actor(\n",
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" actor_id=\"apify/website-content-crawler\",\n",
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" run_input={\"startUrls\": [{\"url\": \"https://python.langchain.com/en/latest/\"}]},\n",
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" dataset_mapping_function=lambda item: Document(\n",
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" page_content=item[\"text\"] or \"\", metadata={\"source\": item[\"url\"]}\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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"Initialize the vector index from the crawled documents:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"index = VectorstoreIndexCreator().from_loaders([loader])"
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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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"And finally, query the vector index:"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What is LangChain?\"\n",
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"result = index.query_with_sources(query)"
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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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"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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" LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules that can be used to build language model applications, and it also provides chains and agents with memory capabilities.\n",
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"\n",
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"https://python.langchain.com/en/latest/modules/models/llms.html, https://python.langchain.com/en/latest/getting_started/getting_started.html\n"
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]
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}
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],
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"source": [
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"print(result[\"answer\"])\n",
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"print(result[\"sources\"])"
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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": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@ -1,19 +0,0 @@
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from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT
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from langchain.experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
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from langchain.experimental.generative_agents.generative_agent import GenerativeAgent
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from langchain.experimental.generative_agents.memory import GenerativeAgentMemory
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from langchain.experimental.plan_and_execute import (
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PlanAndExecute,
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load_agent_executor,
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load_chat_planner,
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)
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__all__ = [
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"BabyAGI",
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"AutoGPT",
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"GenerativeAgent",
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"GenerativeAgentMemory",
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"PlanAndExecute",
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"load_agent_executor",
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"load_chat_planner",
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]
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@ -1,4 +0,0 @@
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from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT
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from langchain.experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
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__all__ = ["BabyAGI", "AutoGPT"]
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@ -1,6 +0,0 @@
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"""Experimental LLM wrappers."""
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from langchain.experimental.llms.jsonformer_decoder import JsonFormer
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from langchain.experimental.llms.rellm_decoder import RELLM
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__all__ = ["RELLM", "JsonFormer"]
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@ -1,9 +0,0 @@
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from langchain.experimental.plan_and_execute.agent_executor import PlanAndExecute
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from langchain.experimental.plan_and_execute.executors.agent_executor import (
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load_agent_executor,
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)
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from langchain.experimental.plan_and_execute.planners.chat_planner import (
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load_chat_planner,
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)
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__all__ = ["PlanAndExecute", "load_agent_executor", "load_chat_planner"]
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@ -1,3 +0,0 @@
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from langchain.experimental.prompts.load import load_prompt
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__all__ = ["load_prompt"]
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19
libs/experimental/langchain_experimental/__init__.py
Normal file
19
libs/experimental/langchain_experimental/__init__.py
Normal file
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from langchain_experimental.autonomous_agents.autogpt.agent import AutoGPT
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from langchain_experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
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from langchain_experimental.generative_agents.generative_agent import GenerativeAgent
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from langchain_experimental.generative_agents.memory import GenerativeAgentMemory
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from langchain_experimental.plan_and_execute import (
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PlanAndExecute,
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load_agent_executor,
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load_chat_planner,
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)
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__all__ = [
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"BabyAGI",
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"AutoGPT",
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"GenerativeAgent",
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"GenerativeAgentMemory",
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"PlanAndExecute",
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"load_agent_executor",
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"load_chat_planner",
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]
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@ -0,0 +1,4 @@
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from langchain_experimental.autonomous_agents.autogpt.agent import AutoGPT
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from langchain_experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
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__all__ = ["BabyAGI", "AutoGPT"]
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@ -2,18 +2,8 @@ from __future__ import annotations
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from typing import List, Optional
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from pydantic import ValidationError
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from langchain.chains.llm import LLMChain
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from langchain.chat_models.base import BaseChatModel
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from langchain.experimental.autonomous_agents.autogpt.output_parser import (
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AutoGPTOutputParser,
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BaseAutoGPTOutputParser,
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)
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from langchain.experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt
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from langchain.experimental.autonomous_agents.autogpt.prompt_generator import (
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FINISH_NAME,
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)
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from langchain.memory import ChatMessageHistory
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from langchain.schema import (
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BaseChatMessageHistory,
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@ -23,6 +13,16 @@ from langchain.schema.messages import AIMessage, HumanMessage, SystemMessage
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from langchain.tools.base import BaseTool
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from langchain.tools.human.tool import HumanInputRun
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from langchain.vectorstores.base import VectorStoreRetriever
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from pydantic import ValidationError
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from langchain_experimental.autonomous_agents.autogpt.output_parser import (
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AutoGPTOutputParser,
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BaseAutoGPTOutputParser,
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)
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from langchain_experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt
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from langchain_experimental.autonomous_agents.autogpt.prompt_generator import (
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FINISH_NAME,
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)
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class AutoGPT:
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@ -1,9 +1,8 @@
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from typing import Any, Dict, List
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from pydantic import Field
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from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key
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from langchain.vectorstores.base import VectorStoreRetriever
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from pydantic import Field
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class AutoGPTMemory(BaseChatMemory):
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@ -1,15 +1,15 @@
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import time
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from typing import Any, Callable, List
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from pydantic import BaseModel
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from langchain.experimental.autonomous_agents.autogpt.prompt_generator import get_prompt
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from langchain.prompts.chat import (
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BaseChatPromptTemplate,
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)
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from langchain.schema.messages import BaseMessage, HumanMessage, SystemMessage
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from langchain.tools.base import BaseTool
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from langchain.vectorstores.base import VectorStoreRetriever
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from pydantic import BaseModel
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from langchain_experimental.autonomous_agents.autogpt.prompt_generator import get_prompt
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class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):
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@ -1,11 +1,11 @@
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from langchain.experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
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from langchain.experimental.autonomous_agents.baby_agi.task_creation import (
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from langchain_experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
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from langchain_experimental.autonomous_agents.baby_agi.task_creation import (
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TaskCreationChain,
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)
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from langchain.experimental.autonomous_agents.baby_agi.task_execution import (
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from langchain_experimental.autonomous_agents.baby_agi.task_execution import (
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TaskExecutionChain,
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)
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from langchain.experimental.autonomous_agents.baby_agi.task_prioritization import (
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from langchain_experimental.autonomous_agents.baby_agi.task_prioritization import (
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TaskPrioritizationChain,
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)
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@ -2,21 +2,21 @@
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from collections import deque
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Field
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from langchain.experimental.autonomous_agents.baby_agi.task_creation import (
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TaskCreationChain,
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)
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from langchain.experimental.autonomous_agents.baby_agi.task_execution import (
|
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TaskExecutionChain,
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)
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from langchain.experimental.autonomous_agents.baby_agi.task_prioritization import (
|
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TaskPrioritizationChain,
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)
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from langchain.schema.language_model import BaseLanguageModel
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from langchain.vectorstores.base import VectorStore
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from pydantic import BaseModel, Field
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from langchain_experimental.autonomous_agents.baby_agi.task_creation import (
|
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TaskCreationChain,
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)
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from langchain_experimental.autonomous_agents.baby_agi.task_execution import (
|
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TaskExecutionChain,
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)
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from langchain_experimental.autonomous_agents.baby_agi.task_prioritization import (
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TaskPrioritizationChain,
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)
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class BabyAGI(Chain, BaseModel):
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@ -7,33 +7,33 @@ import json
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from typing import Any, ClassVar, Dict, List, Optional, Type
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import pydantic
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.experimental.cpal.constants import Constant
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from langchain.experimental.cpal.models import (
|
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from langchain.output_parsers import PydanticOutputParser
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||||
from langchain.prompts.prompt import PromptTemplate
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from langchain_experimental.cpal.constants import Constant
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from langchain_experimental.cpal.models import (
|
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CausalModel,
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InterventionModel,
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NarrativeModel,
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QueryModel,
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StoryModel,
|
||||
)
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from langchain.experimental.cpal.templates.univariate.causal import (
|
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from langchain_experimental.cpal.templates.univariate.causal import (
|
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template as causal_template,
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)
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from langchain.experimental.cpal.templates.univariate.intervention import (
|
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from langchain_experimental.cpal.templates.univariate.intervention import (
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||||
template as intervention_template,
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)
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from langchain.experimental.cpal.templates.univariate.narrative import (
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||||
from langchain_experimental.cpal.templates.univariate.narrative import (
|
||||
template as narrative_template,
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)
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from langchain.experimental.cpal.templates.univariate.query import (
|
||||
from langchain_experimental.cpal.templates.univariate.query import (
|
||||
template as query_template,
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)
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from langchain.output_parsers import PydanticOutputParser
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||||
from langchain.prompts.prompt import PromptTemplate
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class _BaseStoryElementChain(Chain):
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@ -5,10 +5,10 @@ from typing import Any, Optional, Union
|
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import duckdb
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import pandas as pd
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from langchain.graphs.networkx_graph import NetworkxEntityGraph
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from pydantic import BaseModel, Field, PrivateAttr, root_validator, validator
|
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|
||||
from langchain.experimental.cpal.constants import Constant
|
||||
from langchain.graphs.networkx_graph import NetworkxEntityGraph
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||||
from langchain_experimental.cpal.constants import Constant
|
||||
|
||||
|
||||
class NarrativeModel(BaseModel):
|
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@ -1,5 +1,5 @@
|
||||
"""Generative Agents primitives."""
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||||
from langchain.experimental.generative_agents.generative_agent import GenerativeAgent
|
||||
from langchain.experimental.generative_agents.memory import GenerativeAgentMemory
|
||||
from langchain_experimental.generative_agents.generative_agent import GenerativeAgent
|
||||
from langchain_experimental.generative_agents.memory import GenerativeAgentMemory
|
||||
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__all__ = ["GenerativeAgent", "GenerativeAgentMemory"]
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||||
@ -2,12 +2,12 @@ import re
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from datetime import datetime
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from typing import Any, Dict, List, Optional, Tuple
|
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|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from langchain.chains import LLMChain
|
||||
from langchain.experimental.generative_agents.memory import GenerativeAgentMemory
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from langchain_experimental.generative_agents.memory import GenerativeAgentMemory
|
||||
|
||||
|
||||
class GenerativeAgent(BaseModel):
|
||||
@ -0,0 +1,6 @@
|
||||
"""Experimental LLM wrappers."""
|
||||
|
||||
from langchain_experimental.llms.jsonformer_decoder import JsonFormer
|
||||
from langchain_experimental.llms.rellm_decoder import RELLM
|
||||
|
||||
__all__ = ["RELLM", "JsonFormer"]
|
||||
@ -4,10 +4,9 @@ from __future__ import annotations
|
||||
import json
|
||||
from typing import TYPE_CHECKING, Any, List, Optional, cast
|
||||
|
||||
from pydantic import Field, root_validator
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForLLMRun
|
||||
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
|
||||
from pydantic import Field, root_validator
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import jsonformer
|
||||
@ -3,11 +3,10 @@ from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Any, List, Optional, cast
|
||||
|
||||
from pydantic import Field, root_validator
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForLLMRun
|
||||
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
from pydantic import Field, root_validator
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import rellm
|
||||
@ -5,6 +5,6 @@ As in https://arxiv.org/pdf/2211.10435.pdf.
|
||||
This is vulnerable to arbitrary code execution:
|
||||
https://github.com/hwchase17/langchain/issues/5872
|
||||
"""
|
||||
from langchain.experimental.pal_chain.base import PALChain
|
||||
from langchain_experimental.pal_chain.base import PALChain
|
||||
|
||||
__all__ = ["PALChain"]
|
||||
@ -11,16 +11,16 @@ import ast
|
||||
import warnings
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import Extra, Field, root_validator
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForChainRun
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
|
||||
from langchain.chains.pal.math_prompt import MATH_PROMPT
|
||||
from langchain.schema import BasePromptTemplate
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
from langchain.utilities import PythonREPL
|
||||
from pydantic import Extra, Field, root_validator
|
||||
|
||||
from langchain_experimental.pal_chain.colored_object_prompt import COLORED_OBJECT_PROMPT
|
||||
from langchain_experimental.pal_chain.math_prompt import MATH_PROMPT
|
||||
|
||||
COMMAND_EXECUTION_FUNCTIONS = ["system", "exec", "execfile", "eval"]
|
||||
|
||||
@ -0,0 +1,9 @@
|
||||
from langchain_experimental.plan_and_execute.agent_executor import PlanAndExecute
|
||||
from langchain_experimental.plan_and_execute.executors.agent_executor import (
|
||||
load_agent_executor,
|
||||
)
|
||||
from langchain_experimental.plan_and_execute.planners.chat_planner import (
|
||||
load_chat_planner,
|
||||
)
|
||||
|
||||
__all__ = ["PlanAndExecute", "load_agent_executor", "load_chat_planner"]
|
||||
@ -1,12 +1,12 @@
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForChainRun
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.experimental.plan_and_execute.executors.base import BaseExecutor
|
||||
from langchain.experimental.plan_and_execute.planners.base import BasePlanner
|
||||
from langchain.experimental.plan_and_execute.schema import (
|
||||
from pydantic import Field
|
||||
|
||||
from langchain_experimental.plan_and_execute.executors.base import BaseExecutor
|
||||
from langchain_experimental.plan_and_execute.planners.base import BasePlanner
|
||||
from langchain_experimental.plan_and_execute.schema import (
|
||||
BaseStepContainer,
|
||||
ListStepContainer,
|
||||
)
|
||||
@ -2,10 +2,11 @@ from typing import List
|
||||
|
||||
from langchain.agents.agent import AgentExecutor
|
||||
from langchain.agents.structured_chat.base import StructuredChatAgent
|
||||
from langchain.experimental.plan_and_execute.executors.base import ChainExecutor
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
from langchain.tools import BaseTool
|
||||
|
||||
from langchain_experimental.plan_and_execute.executors.base import ChainExecutor
|
||||
|
||||
HUMAN_MESSAGE_TEMPLATE = """Previous steps: {previous_steps}
|
||||
|
||||
Current objective: {current_step}
|
||||
@ -1,11 +1,11 @@
|
||||
from abc import abstractmethod
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.callbacks.manager import Callbacks
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.experimental.plan_and_execute.schema import StepResponse
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain_experimental.plan_and_execute.schema import StepResponse
|
||||
|
||||
|
||||
class BaseExecutor(BaseModel):
|
||||
@ -1,11 +1,11 @@
|
||||
from abc import abstractmethod
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.callbacks.manager import Callbacks
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.experimental.plan_and_execute.schema import Plan, PlanOutputParser
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain_experimental.plan_and_execute.schema import Plan, PlanOutputParser
|
||||
|
||||
|
||||
class BasePlanner(BaseModel):
|
||||
@ -1,15 +1,16 @@
|
||||
import re
|
||||
|
||||
from langchain.chains import LLMChain
|
||||
from langchain.experimental.plan_and_execute.planners.base import LLMPlanner
|
||||
from langchain.experimental.plan_and_execute.schema import (
|
||||
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
from langchain.schema.messages import SystemMessage
|
||||
|
||||
from langchain_experimental.plan_and_execute.planners.base import LLMPlanner
|
||||
from langchain_experimental.plan_and_execute.schema import (
|
||||
Plan,
|
||||
PlanOutputParser,
|
||||
Step,
|
||||
)
|
||||
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
from langchain.schema.messages import SystemMessage
|
||||
|
||||
SYSTEM_PROMPT = (
|
||||
"Let's first understand the problem and devise a plan to solve the problem."
|
||||
@ -1,9 +1,8 @@
|
||||
from abc import abstractmethod
|
||||
from typing import List, Tuple
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from langchain.schema import BaseOutputParser
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class Step(BaseModel):
|
||||
@ -0,0 +1,3 @@
|
||||
from langchain_experimental.prompts.load import load_prompt
|
||||
|
||||
__all__ = ["load_prompt"]
|
||||
@ -5,7 +5,6 @@ from pathlib import Path
|
||||
from typing import Union
|
||||
|
||||
import yaml
|
||||
|
||||
from langchain.prompts.loading import load_prompt_from_config, try_load_from_hub
|
||||
from langchain.schema.prompts import BasePromptTemplate
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
"""Chain for interacting with SQL Database."""
|
||||
from langchain.experimental.sql.base import SQLDatabaseChain
|
||||
from langchain_experimental.sql.base import SQLDatabaseChain
|
||||
|
||||
__all__ = ["SQLDatabaseChain"]
|
||||
@ -4,17 +4,17 @@ from __future__ import annotations
|
||||
import warnings
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import Extra, Field, root_validator
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForChainRun
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.chains.sql_database.prompt import DECIDER_PROMPT, PROMPT, SQL_PROMPTS
|
||||
from langchain.prompts.prompt import PromptTemplate
|
||||
from langchain.schema import BasePromptTemplate
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
from langchain.tools.sql_database.prompt import QUERY_CHECKER
|
||||
from langchain.utilities.sql_database import SQLDatabase
|
||||
from pydantic import Extra, Field, root_validator
|
||||
|
||||
from langchain_experimental.sql.prompt import DECIDER_PROMPT, PROMPT, SQL_PROMPTS
|
||||
|
||||
INTERMEDIATE_STEPS_KEY = "intermediate_steps"
|
||||
|
||||
@ -25,7 +25,7 @@ class SQLDatabaseChain(Chain):
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.experimental.sql import SQLDatabaseChain
|
||||
from langchain_experimental.sql import SQLDatabaseChain
|
||||
from langchain import OpenAI, SQLDatabase
|
||||
db = SQLDatabase(...)
|
||||
db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)
|
||||
@ -1,14 +1,11 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-experimental"
|
||||
version = "0.0.1rc0"
|
||||
version = "0.0.1rc4"
|
||||
description = "Building applications with LLMs through composability"
|
||||
authors = []
|
||||
license = "MIT"
|
||||
readme = "README.md"
|
||||
repository = "https://www.github.com/hwchase17/langchain"
|
||||
packages = [
|
||||
{include = "langchain"}
|
||||
]
|
||||
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
|
||||
@ -1,8 +1,17 @@
|
||||
"""GitHub Toolkit."""
|
||||
from typing import List
|
||||
from typing import Dict, List
|
||||
|
||||
from langchain.agents.agent_toolkits.base import BaseToolkit
|
||||
from langchain.tools import BaseTool
|
||||
from langchain.tools.github.prompt import (
|
||||
COMMENT_ON_ISSUE_PROMPT,
|
||||
CREATE_FILE_PROMPT,
|
||||
DELETE_FILE_PROMPT,
|
||||
GET_ISSUE_PROMPT,
|
||||
GET_ISSUES_PROMPT,
|
||||
READ_FILE_PROMPT,
|
||||
UPDATE_FILE_PROMPT,
|
||||
)
|
||||
from langchain.tools.github.tool import GitHubAction
|
||||
from langchain.utilities.github import GitHubAPIWrapper
|
||||
|
||||
@ -16,7 +25,43 @@ class GitHubToolkit(BaseToolkit):
|
||||
def from_github_api_wrapper(
|
||||
cls, github_api_wrapper: GitHubAPIWrapper
|
||||
) -> "GitHubToolkit":
|
||||
actions = github_api_wrapper.list()
|
||||
operations: List[Dict] = [
|
||||
{
|
||||
"mode": "get_issues",
|
||||
"name": "Get Issues",
|
||||
"description": GET_ISSUES_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "get_issue",
|
||||
"name": "Get Issue",
|
||||
"description": GET_ISSUE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "comment_on_issue",
|
||||
"name": "Comment on Issue",
|
||||
"description": COMMENT_ON_ISSUE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "create_file",
|
||||
"name": "Create File",
|
||||
"description": CREATE_FILE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "read_file",
|
||||
"name": "Read File",
|
||||
"description": READ_FILE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "update_file",
|
||||
"name": "Update File",
|
||||
"description": UPDATE_FILE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "delete_file",
|
||||
"name": "Delete File",
|
||||
"description": DELETE_FILE_PROMPT,
|
||||
},
|
||||
]
|
||||
tools = [
|
||||
GitHubAction(
|
||||
name=action["name"],
|
||||
@ -24,7 +69,7 @@ class GitHubToolkit(BaseToolkit):
|
||||
mode=action["mode"],
|
||||
api_wrapper=github_api_wrapper,
|
||||
)
|
||||
for action in actions
|
||||
for action in operations
|
||||
]
|
||||
return cls(tools=tools)
|
||||
|
||||
|
||||
@ -1,7 +1,14 @@
|
||||
from typing import List
|
||||
from typing import Dict, List
|
||||
|
||||
from langchain.agents.agent_toolkits.base import BaseToolkit
|
||||
from langchain.tools import BaseTool
|
||||
from langchain.tools.jira.prompt import (
|
||||
JIRA_CATCH_ALL_PROMPT,
|
||||
JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
|
||||
JIRA_GET_ALL_PROJECTS_PROMPT,
|
||||
JIRA_ISSUE_CREATE_PROMPT,
|
||||
JIRA_JQL_PROMPT,
|
||||
)
|
||||
from langchain.tools.jira.tool import JiraAction
|
||||
from langchain.utilities.jira import JiraAPIWrapper
|
||||
|
||||
@ -13,7 +20,33 @@ class JiraToolkit(BaseToolkit):
|
||||
|
||||
@classmethod
|
||||
def from_jira_api_wrapper(cls, jira_api_wrapper: JiraAPIWrapper) -> "JiraToolkit":
|
||||
actions = jira_api_wrapper.list()
|
||||
operations: List[Dict] = [
|
||||
{
|
||||
"mode": "jql",
|
||||
"name": "JQL Query",
|
||||
"description": JIRA_JQL_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "get_projects",
|
||||
"name": "Get Projects",
|
||||
"description": JIRA_GET_ALL_PROJECTS_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "create_issue",
|
||||
"name": "Create Issue",
|
||||
"description": JIRA_ISSUE_CREATE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "other",
|
||||
"name": "Catch all Jira API call",
|
||||
"description": JIRA_CATCH_ALL_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "create_page",
|
||||
"name": "Create confluence page",
|
||||
"description": JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
|
||||
},
|
||||
]
|
||||
tools = [
|
||||
JiraAction(
|
||||
name=action["name"],
|
||||
@ -21,7 +54,7 @@ class JiraToolkit(BaseToolkit):
|
||||
mode=action["mode"],
|
||||
api_wrapper=jira_api_wrapper,
|
||||
)
|
||||
for action in actions
|
||||
for action in operations
|
||||
]
|
||||
return cls(tools=tools)
|
||||
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
"""General utilities."""
|
||||
from langchain.requests import TextRequestsWrapper
|
||||
from langchain.utilities.apify import ApifyWrapper
|
||||
from langchain.utilities.arxiv import ArxivAPIWrapper
|
||||
from langchain.utilities.awslambda import LambdaWrapper
|
||||
from langchain.utilities.bash import BashProcess
|
||||
@ -32,7 +31,6 @@ from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
|
||||
from langchain.utilities.zapier import ZapierNLAWrapper
|
||||
|
||||
__all__ = [
|
||||
"ApifyWrapper",
|
||||
"ArxivAPIWrapper",
|
||||
"GoldenQueryAPIWrapper",
|
||||
"BashProcess",
|
||||
|
||||
@ -1,205 +0,0 @@
|
||||
from typing import Any, Callable, Dict, Optional
|
||||
|
||||
from pydantic import BaseModel, root_validator
|
||||
|
||||
from langchain.document_loaders import ApifyDatasetLoader
|
||||
from langchain.document_loaders.base import Document
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
|
||||
|
||||
class ApifyWrapper(BaseModel):
|
||||
"""Wrapper around Apify.
|
||||
|
||||
To use, you should have the ``apify-client`` python package installed,
|
||||
and the environment variable ``APIFY_API_TOKEN`` set with your API key, or pass
|
||||
`apify_api_token` as a named parameter to the constructor.
|
||||
"""
|
||||
|
||||
apify_client: Any
|
||||
apify_client_async: Any
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate environment.
|
||||
|
||||
Validate that an Apify API token is set and the apify-client
|
||||
Python package exists in the current environment.
|
||||
"""
|
||||
apify_api_token = get_from_dict_or_env(
|
||||
values, "apify_api_token", "APIFY_API_TOKEN"
|
||||
)
|
||||
|
||||
try:
|
||||
from apify_client import ApifyClient, ApifyClientAsync
|
||||
|
||||
values["apify_client"] = ApifyClient(apify_api_token)
|
||||
values["apify_client_async"] = ApifyClientAsync(apify_api_token)
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import apify-client Python package. "
|
||||
"Please install it with `pip install apify-client`."
|
||||
)
|
||||
|
||||
return values
|
||||
|
||||
def call_actor(
|
||||
self,
|
||||
actor_id: str,
|
||||
run_input: Dict,
|
||||
dataset_mapping_function: Callable[[Dict], Document],
|
||||
*,
|
||||
build: Optional[str] = None,
|
||||
memory_mbytes: Optional[int] = None,
|
||||
timeout_secs: Optional[int] = None,
|
||||
) -> ApifyDatasetLoader:
|
||||
"""Run an Actor on the Apify platform and wait for results to be ready.
|
||||
|
||||
Args:
|
||||
actor_id (str): The ID or name of the Actor on the Apify platform.
|
||||
run_input (Dict): The input object of the Actor that you're trying to run.
|
||||
dataset_mapping_function (Callable): A function that takes a single
|
||||
dictionary (an Apify dataset item) and converts it to an
|
||||
instance of the Document class.
|
||||
build (str, optional): Optionally specifies the actor build to run.
|
||||
It can be either a build tag or build number.
|
||||
memory_mbytes (int, optional): Optional memory limit for the run,
|
||||
in megabytes.
|
||||
timeout_secs (int, optional): Optional timeout for the run, in seconds.
|
||||
|
||||
Returns:
|
||||
ApifyDatasetLoader: A loader that will fetch the records from the
|
||||
Actor run's default dataset.
|
||||
"""
|
||||
actor_call = self.apify_client.actor(actor_id).call(
|
||||
run_input=run_input,
|
||||
build=build,
|
||||
memory_mbytes=memory_mbytes,
|
||||
timeout_secs=timeout_secs,
|
||||
)
|
||||
|
||||
return ApifyDatasetLoader(
|
||||
dataset_id=actor_call["defaultDatasetId"],
|
||||
dataset_mapping_function=dataset_mapping_function,
|
||||
)
|
||||
|
||||
async def acall_actor(
|
||||
self,
|
||||
actor_id: str,
|
||||
run_input: Dict,
|
||||
dataset_mapping_function: Callable[[Dict], Document],
|
||||
*,
|
||||
build: Optional[str] = None,
|
||||
memory_mbytes: Optional[int] = None,
|
||||
timeout_secs: Optional[int] = None,
|
||||
) -> ApifyDatasetLoader:
|
||||
"""Run an Actor on the Apify platform and wait for results to be ready.
|
||||
|
||||
Args:
|
||||
actor_id (str): The ID or name of the Actor on the Apify platform.
|
||||
run_input (Dict): The input object of the Actor that you're trying to run.
|
||||
dataset_mapping_function (Callable): A function that takes a single
|
||||
dictionary (an Apify dataset item) and converts it to
|
||||
an instance of the Document class.
|
||||
build (str, optional): Optionally specifies the actor build to run.
|
||||
It can be either a build tag or build number.
|
||||
memory_mbytes (int, optional): Optional memory limit for the run,
|
||||
in megabytes.
|
||||
timeout_secs (int, optional): Optional timeout for the run, in seconds.
|
||||
|
||||
Returns:
|
||||
ApifyDatasetLoader: A loader that will fetch the records from the
|
||||
Actor run's default dataset.
|
||||
"""
|
||||
actor_call = await self.apify_client_async.actor(actor_id).call(
|
||||
run_input=run_input,
|
||||
build=build,
|
||||
memory_mbytes=memory_mbytes,
|
||||
timeout_secs=timeout_secs,
|
||||
)
|
||||
|
||||
return ApifyDatasetLoader(
|
||||
dataset_id=actor_call["defaultDatasetId"],
|
||||
dataset_mapping_function=dataset_mapping_function,
|
||||
)
|
||||
|
||||
def call_actor_task(
|
||||
self,
|
||||
task_id: str,
|
||||
task_input: Dict,
|
||||
dataset_mapping_function: Callable[[Dict], Document],
|
||||
*,
|
||||
build: Optional[str] = None,
|
||||
memory_mbytes: Optional[int] = None,
|
||||
timeout_secs: Optional[int] = None,
|
||||
) -> ApifyDatasetLoader:
|
||||
"""Run a saved Actor task on Apify and wait for results to be ready.
|
||||
|
||||
Args:
|
||||
task_id (str): The ID or name of the task on the Apify platform.
|
||||
task_input (Dict): The input object of the task that you're trying to run.
|
||||
Overrides the task's saved input.
|
||||
dataset_mapping_function (Callable): A function that takes a single
|
||||
dictionary (an Apify dataset item) and converts it to an
|
||||
instance of the Document class.
|
||||
build (str, optional): Optionally specifies the actor build to run.
|
||||
It can be either a build tag or build number.
|
||||
memory_mbytes (int, optional): Optional memory limit for the run,
|
||||
in megabytes.
|
||||
timeout_secs (int, optional): Optional timeout for the run, in seconds.
|
||||
|
||||
Returns:
|
||||
ApifyDatasetLoader: A loader that will fetch the records from the
|
||||
task run's default dataset.
|
||||
"""
|
||||
task_call = self.apify_client.task(task_id).call(
|
||||
task_input=task_input,
|
||||
build=build,
|
||||
memory_mbytes=memory_mbytes,
|
||||
timeout_secs=timeout_secs,
|
||||
)
|
||||
|
||||
return ApifyDatasetLoader(
|
||||
dataset_id=task_call["defaultDatasetId"],
|
||||
dataset_mapping_function=dataset_mapping_function,
|
||||
)
|
||||
|
||||
async def acall_actor_task(
|
||||
self,
|
||||
task_id: str,
|
||||
task_input: Dict,
|
||||
dataset_mapping_function: Callable[[Dict], Document],
|
||||
*,
|
||||
build: Optional[str] = None,
|
||||
memory_mbytes: Optional[int] = None,
|
||||
timeout_secs: Optional[int] = None,
|
||||
) -> ApifyDatasetLoader:
|
||||
"""Run a saved Actor task on Apify and wait for results to be ready.
|
||||
|
||||
Args:
|
||||
task_id (str): The ID or name of the task on the Apify platform.
|
||||
task_input (Dict): The input object of the task that you're trying to run.
|
||||
Overrides the task's saved input.
|
||||
dataset_mapping_function (Callable): A function that takes a single
|
||||
dictionary (an Apify dataset item) and converts it to an
|
||||
instance of the Document class.
|
||||
build (str, optional): Optionally specifies the actor build to run.
|
||||
It can be either a build tag or build number.
|
||||
memory_mbytes (int, optional): Optional memory limit for the run,
|
||||
in megabytes.
|
||||
timeout_secs (int, optional): Optional timeout for the run, in seconds.
|
||||
|
||||
Returns:
|
||||
ApifyDatasetLoader: A loader that will fetch the records from the
|
||||
task run's default dataset.
|
||||
"""
|
||||
task_call = await self.apify_client_async.task(task_id).call(
|
||||
task_input=task_input,
|
||||
build=build,
|
||||
memory_mbytes=memory_mbytes,
|
||||
timeout_secs=timeout_secs,
|
||||
)
|
||||
|
||||
return ApifyDatasetLoader(
|
||||
dataset_id=task_call["defaultDatasetId"],
|
||||
dataset_mapping_function=dataset_mapping_function,
|
||||
)
|
||||
@ -4,15 +4,6 @@ from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import BaseModel, Extra, root_validator
|
||||
|
||||
from langchain.tools.github.prompt import (
|
||||
COMMENT_ON_ISSUE_PROMPT,
|
||||
CREATE_FILE_PROMPT,
|
||||
DELETE_FILE_PROMPT,
|
||||
GET_ISSUE_PROMPT,
|
||||
GET_ISSUES_PROMPT,
|
||||
READ_FILE_PROMPT,
|
||||
UPDATE_FILE_PROMPT,
|
||||
)
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
|
||||
|
||||
@ -26,52 +17,11 @@ class GitHubAPIWrapper(BaseModel):
|
||||
github_app_private_key: Optional[str] = None
|
||||
github_branch: Optional[str] = None
|
||||
|
||||
operations: List[Dict] = [
|
||||
{
|
||||
"mode": "get_issues",
|
||||
"name": "Get Issues",
|
||||
"description": GET_ISSUES_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "get_issue",
|
||||
"name": "Get Issue",
|
||||
"description": GET_ISSUE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "comment_on_issue",
|
||||
"name": "Comment on Issue",
|
||||
"description": COMMENT_ON_ISSUE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "create_file",
|
||||
"name": "Create File",
|
||||
"description": CREATE_FILE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "read_file",
|
||||
"name": "Read File",
|
||||
"description": READ_FILE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "update_file",
|
||||
"name": "Update File",
|
||||
"description": UPDATE_FILE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "delete_file",
|
||||
"name": "Delete File",
|
||||
"description": DELETE_FILE_PROMPT,
|
||||
},
|
||||
]
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def list(self) -> List[Dict]:
|
||||
return self.operations
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
|
||||
@ -3,13 +3,6 @@ from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import BaseModel, Extra, root_validator
|
||||
|
||||
from langchain.tools.jira.prompt import (
|
||||
JIRA_CATCH_ALL_PROMPT,
|
||||
JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
|
||||
JIRA_GET_ALL_PROJECTS_PROMPT,
|
||||
JIRA_ISSUE_CREATE_PROMPT,
|
||||
JIRA_JQL_PROMPT,
|
||||
)
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
|
||||
|
||||
@ -23,42 +16,11 @@ class JiraAPIWrapper(BaseModel):
|
||||
jira_api_token: Optional[str] = None
|
||||
jira_instance_url: Optional[str] = None
|
||||
|
||||
operations: List[Dict] = [
|
||||
{
|
||||
"mode": "jql",
|
||||
"name": "JQL Query",
|
||||
"description": JIRA_JQL_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "get_projects",
|
||||
"name": "Get Projects",
|
||||
"description": JIRA_GET_ALL_PROJECTS_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "create_issue",
|
||||
"name": "Create Issue",
|
||||
"description": JIRA_ISSUE_CREATE_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "other",
|
||||
"name": "Catch all Jira API call",
|
||||
"description": JIRA_CATCH_ALL_PROMPT,
|
||||
},
|
||||
{
|
||||
"mode": "create_page",
|
||||
"name": "Create confluence page",
|
||||
"description": JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
|
||||
},
|
||||
]
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def list(self) -> List[Dict]:
|
||||
return self.operations
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
|
||||
@ -1,9 +1,8 @@
|
||||
"""Util that calls OpenWeatherMap using PyOWM."""
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from pydantic import Extra, root_validator
|
||||
from pydantic import BaseModel, Extra, root_validator
|
||||
|
||||
from langchain.tools.base import BaseModel
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "langchain"
|
||||
version = "0.0.240rc0"
|
||||
version = "0.0.240rc4"
|
||||
description = "Building applications with LLMs through composability"
|
||||
authors = []
|
||||
license = "MIT"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user