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lc_public_repos/langchain-extract/docker-compose.yml
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name: langchain-extract
services:
postgres:
# Careful if bumping postgres version.
# Make sure to keep in sync with CI
# version if being tested on CI.
image: postgres:16
expose:
- "5432"
ports:
- "5432:5432"
environment:
POSTGRES_DB: langchain
POSTGRES_USER: langchain
POSTGRES_PASSWORD: langchain
healthcheck:
test: ["CMD-SHELL", "pg_isready -U langchain -d langchain -W langchain"]
interval: 10s
timeout: 5s
retries: 5
volumes:
- postgres_data:/var/lib/postgresql/data
backend:
build:
context: .
dockerfile: ./backend/Dockerfile
target: development
env_file:
- .local.env
environment:
- PG_HOST=postgres
# Define CORS origins for dev work on UI
- CORS_ORIGINS=http://localhost:3000
ports:
- "8000:8000" # Backend is accessible on localhost:8100
depends_on:
- postgres
volumes:
- ./backend:/backend
frontend:
build:
context: ./frontend
dockerfile: ./Dockerfile
target: development
ports:
- "3000:3000"
environment:
- NODE_ENV=development
volumes:
- ./frontend:/app
- /app/node_modules
depends_on:
- backend
volumes:
postgres_data:
|
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lc_public_repos
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lc_public_repos/langchain-extract/Dockerfile
|
# All directory paths for COPY commands are relative to the build context
# Ensure this python version stays in sync with CI
FROM python:3.11-slim as base
WORKDIR /backend
# set environment variables
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
ENV POETRY_HOME="/opt/poetry"
ENV MYPYPATH="/app/src/stubs"
# Use bash as the shell for the build
# https://github.com/docker/for-linux/issues/408#issuecomment-414748815
SHELL ["/bin/bash", "-o", "pipefail", "-c"]
RUN set -eux && \
apt-get update && \
apt-get install -y \
build-essential \
curl \
libpq-dev \
python3-dev \
libmagic1
# https://python-poetry.org/docs
RUN pip install poetry
# install deps before copying project files so the cache is only invalidated
# when the deps change
COPY ./backend/pyproject.toml ./backend/poetry.lock ./
RUN poetry config virtualenvs.create false
RUN poetry install --no-root --only main
COPY ./backend .
EXPOSE 8080
###
# development image
###
FROM base as development
ENTRYPOINT ["bash", "./scripts/prod_entry_point.sh"]
|
0
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lc_public_repos
|
lc_public_repos/langchain-extract/LICENSE
|
MIT License
Copyright (c) 2024-Present Langchain AI
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0
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lc_public_repos/langchain-extract/README.md
|
π§ Under Active Development π§
This repo is under active developments. Do not use code from `main`. Instead please checkout code from [releases](https://github.com/langchain-ai/langchain-extract/releases)
This repository is not a library, but a jumping point for your own application -- so do not be surprised to find breaking changes between releases!
Checkout the demo service deployed at [extract.langchain.com/](https://extract.langchain.com/).
# π¦βοΈ LangChain Extract
https://github.com/langchain-ai/langchain-extract/assets/26529506/6657280e-d05f-4c0f-9c47-07a0ef7c559d
[](https://github.com/langchain-ai/langchain-extract/actions/workflows/ci.yml)
[](https://opensource.org/licenses/MIT)
[](https://twitter.com/langchainai)
[](https://discord.gg/6adMQxSpJS)
[](https://github.com/langchain-ai/langchain-extract/issues)
`langchain-extract` is a simple web server that allows you to extract information from text and files using LLMs. It is build using [FastAPI](https://fastapi.tiangolo.com/), [LangChain](https://python.langchain.com/) and [Postgresql](https://www.postgresql.org/).
The backend closely follows the [extraction use-case documentation](https://python.langchain.com/docs/use_cases/extraction) and provides
a reference implementation of an app that helps to do extraction over data using LLMs.
This repository is meant to be a starting point for building your own extraction application which
may have slightly different requirements or use cases.
## Functionality
- π FastAPI webserver with a REST API
- π OpenAPI Documentation
- π Use [JSON Schema](https://json-schema.org/) to define what to extract
- π Use examples to improve the quality of extracted results
- π¦ Create and save extractors and examples in a database
- π Extract information from text and/or binary files
- π¦οΈπ [LangServe](https://github.com/langchain-ai/langserve) endpoint to integrate with LangChain `RemoteRunnnable`
## Releases:
0.0.1: https://github.com/langchain-ai/langchain-extract/releases/tag/0.0.1
0.0.2: https://github.com/langchain-ai/langchain-extract/releases/tag/0.0.2
## π Documentation
See the example notebooks in the [documentation](https://github.com/langchain-ai/langchain-extract/tree/main/docs/source/notebooks)
to see how to create examples to improve extraction results, upload files (e.g., HTML, PDF) and more.
Documentation and server code are both under development!
## π― Example API
Below are two sample `curl` requests to demonstrate how to use the API.
These only provide minimal examples of how to use the API,
see the [documentation](https://github.com/langchain-ai/langchain-extract/tree/main/docs/source/notebooks) for more information
about the API and the [extraction use-case documentation](https://python.langchain.com/docs/use_cases/extraction) for more information about how to extract
information using LangChain.
First we generate a user ID for ourselves. **The application does not properly manage users or include legitimate authentication**. Access to extractors, few-shot examples, and other artifacts is controlled via this ID. Consider it secret.
```sh
USER_ID=$(uuidgen)
export USER_ID
```
### Create an extractor
```sh
curl -X 'POST' \
'http://localhost:8000/extractors' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-H "x-key: ${USER_ID}" \
-d '{
"name": "Personal Information",
"description": "Use to extract personal information",
"schema": {
"type": "object",
"title": "Person",
"required": [
"name",
"age"
],
"properties": {
"age": {
"type": "integer",
"title": "Age"
},
"name": {
"type": "string",
"title": "Name"
}
}
},
"instruction": "Use information about the person from the given user input."
}'
```
Response:
```json
{
"uuid": "e07f389f-3577-4e94-bd88-6b201d1b10b9"
}
```
Use the extract endpoint to extract information from the text (or a file)
using an existing pre-defined extractor.
```sh
curl -s -X 'POST' \
'http://localhost:8000/extract' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-H "x-key: ${USER_ID}" \
-F 'extractor_id=e07f389f-3577-4e94-bd88-6b201d1b10b9' \
-F 'text=my name is chester and i am 20 years old. My name is eugene and I am 1 year older than chester.' \
-F 'mode=entire_document' \
-F 'file=' | jq .
```
Response:
```json
{
"data": [
{
"name": "chester",
"age": 20
},
{
"name": "eugene",
"age": 21
}
]
}
```
Add a few shot example:
```sh
curl -X POST "http://localhost:8000/examples" \
-H "Content-Type: application/json" \
-H "x-key: ${USER_ID}" \
-d '{
"extractor_id": "e07f389f-3577-4e94-bd88-6b201d1b10b9",
"content": "marcos is 10.",
"output": [
{
"name": "MARCOS",
"age": 10
}
]
}' | jq .
```
The response will contain a UUID for the example. Examples can be deleted with a DELETE request. This example is now persisted and associated with our extractor, and subsequent extraction runs will incorporate it.
## β
Running locally
The easiest way to get started is to use `docker-compose` to run the server.
**Configure the environment**
Add `.local.env` file to the root directory with the following content:
```sh
OPENAI_API_KEY=... # Your OpenAI API key
```
Adding `FIREWORKS_API_KEY` or `TOGETHER_API_KEY` to this file would enable additional models. You can access available models for the server and other information via a `GET` request to the `configuration` endpoint.
Build the images:
```sh
docker compose build
```
Run the services:
```sh
docker compose up
```
This will launch both the extraction server and the postgres instance.
Verify that the server is running:
```sh
curl -X 'GET' 'http://localhost:8000/ready'
```
This should return `ok`.
The UI will be available at [http://localhost:3000](http://localhost:3000).
## Contributions
Feel free to develop in this project for your own needs!
For now, we are not accepting pull requests, but would love to hear [questions, ideas or issues](https://github.com/langchain-ai/langchain-extract/discussions).
## Development
To set up for development, you will need to install [Poetry](https://python-poetry.org/).
The backend code is located in the `backend` directory.
```sh
cd backend
```
Set up the environment using poetry:
```sh
poetry install --with lint,dev,test
```
Run the following script to create a database and schema:
```sh
python -m scripts.run_migrations create
```
From `/backend`:
```sh
OPENAI_API_KEY=[YOUR API KEY] python -m server.main
```
### Testing
Create a test database. The test database is used for running tests and is
separate from the main database. It will have the same schema as the main
database.
```sh
python -m scripts.run_migrations create-test-db
```
Run the tests
```sh
make test
```
### Linting and format
Testing and formatting is done using a Makefile inside `[root]/backend`
```sh
make format
```
|
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|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/Dockerfile
|
FROM node:18-alpine AS base
FROM base AS base-deps
WORKDIR /app
COPY --link ./yarn.lock ./package.json ./.yarnrc.yml ./
FROM base AS installer
WORKDIR /app
COPY --link --from=base-deps /app/package.json ./package.json
COPY --link --from=base-deps /app/yarn.lock ./yarn.lock
COPY --link .yarnrc.yml .
RUN yarn install
FROM base AS builder
WORKDIR /app
COPY --link --from=installer /app .
COPY --link tsconfig.json tsconfig.json
RUN yarn build
FROM base AS development
WORKDIR /app
COPY --link --from=installer /app .
ENV NODE_ENV=development
CMD ["yarn", "dev"]
|
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lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/tsconfig.json
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"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "node",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "preserve",
"incremental": true,
"baseUrl": "./",
"plugins": [
{
"name": "next"
}
],
"paths": {
"@/*": ["./*"]
}
},
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
"exclude": ["node_modules"]
}
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lc_public_repos/langchain-extract
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0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/.eslintrc.json
|
{
"plugins": ["react", "@typescript-eslint", "eslint-plugin-import"],
"env": {
"es2021": true,
"commonjs": true,
"es6": true,
"node": true,
"browser": true
},
"globals": {
"window": true,
"process": true
},
"extends": [
"plugin:react/recommended",
"plugin:react/jsx-runtime",
"plugin:@typescript-eslint/eslint-recommended",
"plugin:@typescript-eslint/recommended",
"plugin:@typescript-eslint/recommended"
],
"parserOptions": {
"ecmaFeatures": {
"jsx": true
},
"ecmaVersion": 12,
"sourceType": "module"
},
"rules": {
"import/no-extraneous-dependencies": "error",
"no-underscore-dangle": [
"error",
{
"allow": ["__typename"]
}
],
"react/display-name": "off",
"react/prop-types": "off",
"@typescript-eslint/class-methods-use-this": [
"error",
{
"ignoreOverrideMethods": true
}
],
"@typescript-eslint/consistent-type-assertions": [
"error",
{
"assertionStyle": "never"
}
],
"@typescript-eslint/default-param-last": "error",
"@typescript-eslint/no-empty-function": "error",
"@typescript-eslint/no-explicit-any": "error",
"@typescript-eslint/no-shadow": "error",
"@typescript-eslint/no-unused-vars": [
"error",
{
"argsIgnorePattern": "^_",
"ignoreRestSiblings": true
}
],
"@typescript-eslint/no-useless-constructor": "error",
"camelcase": "off",
"class-methods-use-this": "off",
"default-case": "off",
"default-param-last": "off",
"import/extensions": "off",
"import/prefer-default-export": "off",
"import/order": "error",
"linebreak-style": ["error", "unix"],
"max-len": [
"warn",
{
"code": 80,
"tabWidth": 2,
"comments": 80,
"ignoreComments": false,
"ignoreTrailingComments": true,
"ignoreUrls": true,
"ignoreStrings": true,
"ignoreTemplateLiterals": true,
"ignoreRegExpLiterals": true
}
],
"no-console": ["warn", { "allow": ["warn", "error", "debug"] }],
"no-empty-function": "off",
"no-plusplus": [
"error",
{
"allowForLoopAfterthoughts": true
}
]
},
"settings": {
"import/resolver": {
"typescript": {}
}
}
}
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/.yarnrc.yml
|
nodeLinker: node-modules
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/.env.example
|
# Only set for non development builds.
# Development builds default to `http://localhost:8000`
NEXT_PUBLIC_BASE_API_URL=https://example.com
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/package.json
|
{
"name": "langchain-extract-frontend",
"version": "0.1.0",
"private": true,
"packageManager": "yarn@1.22.19",
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"format": "prettier --write .",
"format:check": "prettier --config .prettierrc --check \"app\"",
"lint": "eslint --cache --ext .tsx,.jsx --config .eslintrc.json . --ignore-path .gitignore",
"lint:fix": "yarn lint --fix",
"fix": "prettier --write . && yarn lint --fix"
},
"dependencies": {
"@chakra-ui/icons": "^2.1.0",
"@chakra-ui/react": "^2.8.1",
"@codemirror/lang-json": "^6.0.1",
"@emotion/react": "^11.11.1",
"@emotion/styled": "^11.11.0",
"@heroicons/react": "^2.1.1",
"@langchain/community": "^0.0.27",
"@langchain/core": "^0.1.26",
"@langchain/openai": "^0.0.14",
"@langchain/weaviate": "^0.0.1",
"@rjsf/chakra-ui": "^5.17.1",
"@rjsf/core": "^5.17.1",
"@rjsf/utils": "^5.17.1",
"@rjsf/validator-ajv8": "^5.17.1",
"@tanstack/react-query": "^5.28.4",
"@types/dompurify": "^3.0.5",
"@types/marked": "^5.0.1",
"@types/node": "20.4.9",
"@types/react": "18.2.20",
"@types/react-dom": "18.2.7",
"@uiw/react-codemirror": "^4.21.24",
"ajv": "^8.12.0",
"ajv-formats": "^2.1.1",
"autoprefixer": "10.4.14",
"axios": "^1.6.8",
"chakra-react-select": "^4.7.6",
"framer-motion": "^11.0.14",
"highlight.js": "^11.8.0",
"marked": "^7.0.2",
"next": "13.5.4",
"postcss": "8.4.31",
"react": "18.2.0",
"react-dom": "18.2.0",
"react-syntax-highlighter": "^15.5.0",
"react-textarea-autosize": "^8.5.3",
"tailwindcss": "3.3.3",
"uuid": "^9.0.1"
},
"devDependencies": {
"@types/react-syntax-highlighter": "^15.5.11",
"@typescript-eslint/eslint-plugin": "^7.3.0",
"@typescript-eslint/parser": "^7.3.0",
"eslint": "^8.33.0",
"eslint-config-airbnb-base": "^15.0.0",
"eslint-config-prettier": "^8.6.0",
"eslint-import-resolver-typescript": "^3.6.1",
"eslint-plugin-import": "^2.27.5",
"eslint-plugin-jest": "^27.6.0",
"eslint-plugin-no-instanceof": "^1.0.1",
"eslint-plugin-prettier": "^4.2.1",
"eslint-plugin-react": "^7.34.1",
"prettier": "^3.0.3",
"typescript": "5.1.6"
}
}
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/tailwind.config.ts
|
import type { Config } from "tailwindcss";
const config: Config = {
content: [
"./pages/**/*.{js,ts,jsx,tsx,mdx}",
"./components/**/*.{js,ts,jsx,tsx,mdx}",
"./app/**/*.{js,ts,jsx,tsx,mdx}",
],
theme: {
extend: {
backgroundImage: {
"gradient-radial": "radial-gradient(var(--tw-gradient-stops))",
"gradient-conic":
"conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))",
},
},
},
plugins: [],
};
export default config;
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/postcss.config.js
|
module.exports = {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
};
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/.prettierrc
|
{
"endOfLine": "lf"
}
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/frontend/next.config.js
|
/** @type {import('next').NextConfig} */
const nextConfig = {};
module.exports = nextConfig;
|
0
|
lc_public_repos/langchain-extract/frontend
|
lc_public_repos/langchain-extract/frontend/app/globals.css
|
@tailwind base;
@tailwind components;
@tailwind utilities;
|
0
|
lc_public_repos/langchain-extract/frontend
|
lc_public_repos/langchain-extract/frontend/app/layout.tsx
|
import "./globals.css";
import { ReactNode } from "react";
import type { Metadata } from "next";
import { Inter } from "next/font/google";
import { Sidebar } from "./components/Sidebar";
import { Providers } from "./providers";
const inter = Inter({ subsets: ["latin"] });
export const metadata: Metadata = {
title: "LangChain Extract",
icons:
"data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>π¦</text></svg>",
viewport: {
width: "device-width",
initialScale: 1.0,
},
};
export default function RootLayout({ children }: { children: ReactNode }) {
return (
<html lang="en" className="h-full">
<body className={`${inter.className} h-full`}>
<Providers>
<div className="flex flex-col w-100% h-full">
<div className="flex justify-between bg-slate-200 mb-4 p-3 items-center gap-2">
<div className="font-semibold">π¦βοΈ LangChain Extract</div>
<div className="text-s text-rose-800">
<strong>Research Preview</strong>: this app is unauthenticated
and all data can be found. Do not use with sensitive data.
</div>
</div>
<div className="flex gap-3 ml-5 mr-5">
<div className="w-1/6">
<Sidebar />
</div>
<main className="m-auto w-5/6">{children}</main>
</div>
</div>
</Providers>
</body>
</html>
);
}
|
0
|
lc_public_repos/langchain-extract/frontend
|
lc_public_repos/langchain-extract/frontend/app/page.tsx
|
"use client";
import CreateExtractor from "./components/CreateExtractor";
export default CreateExtractor;
|
0
|
lc_public_repos/langchain-extract/frontend
|
lc_public_repos/langchain-extract/frontend/app/providers.tsx
|
"use client";
import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
import { ChakraProvider } from "@chakra-ui/react";
const queryClient = new QueryClient();
export function Providers({ children }: { children: React.ReactNode }) {
return (
<QueryClientProvider client={queryClient}>
<ChakraProvider>{children}</ChakraProvider>
</QueryClientProvider>
);
}
|
0
|
lc_public_repos/langchain-extract/frontend/app/e
|
lc_public_repos/langchain-extract/frontend/app/e/[extractorId]/page.tsx
|
"use client";
import { Playground } from "../../components/Playground";
interface ExtractorPageProps {
params: {
extractorId: string;
};
}
export default function Page({ params }: ExtractorPageProps) {
return <Playground extractorId={params.extractorId} isShared={false} />;
}
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/utils/api.tsx
|
"use client";
/* Expose API hooks for use in components */
import axios from "axios";
import {
useQuery,
useQueryClient,
useMutation,
MutationFunction,
QueryFunctionContext,
} from "@tanstack/react-query";
import { v4 as uuidv4 } from "uuid";
import { getBaseApiUrl } from "./api_url";
type ExtractorData = {
uuid: string;
name: string;
description: string;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
schema: any;
};
const getApiKey = (): string => {
if (typeof window === "undefined") {
return uuidv4();
}
const key = localStorage.getItem("lc-extract-key");
if (!key) {
// Generate key
const newKey = uuidv4();
localStorage.setItem("lc-extract-key", newKey);
return newKey;
}
return key;
};
// Create an instance with custom headers
export const axiosClient = axios.create({
headers: {
"x-key": getApiKey(),
},
});
type GetExtractorQueryKey = [string, string, boolean]; // [queryKey, uuid, isShared]
type OnSuccessFn = (data: { uuid: string }) => void;
const getExtractor = async ({
queryKey,
}: QueryFunctionContext<GetExtractorQueryKey>): Promise<ExtractorData> => {
const [, uuid, isShared] = queryKey;
const baseUrl = getBaseApiUrl();
if (isShared) {
const response = await axiosClient.get(
`${baseUrl}/shared/extractors/${uuid}`,
);
return response.data;
} else {
const response = await axiosClient.get(`${baseUrl}/extractors/${uuid}`);
return response.data;
}
};
const listExtractors = async () => {
const baseUrl = getBaseApiUrl();
const response = await axiosClient.get(`${baseUrl}/extractors`);
return response.data;
};
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const createExtractor: MutationFunction<any, any> = async (extractor) => {
const baseUrl = getBaseApiUrl();
const response = await axiosClient.post(`${baseUrl}/extractors`, extractor);
return response.data;
};
export type Model = {
name: string;
description: string;
};
export type ServerConfiguration = {
max_file_size_mb: number;
accepted_mimetypes: string[];
models: Model[];
};
const getConfiguration = async (): Promise<ServerConfiguration> => {
const baseUrl = getBaseApiUrl();
const response = await axiosClient.get(`${baseUrl}/configuration`);
return response.data;
};
export const useConfiguration = () => {
return useQuery({
queryKey: ["getConfiguration"],
queryFn: getConfiguration,
});
};
export const suggestExtractor = async ({
description,
jsonSchema,
}: {
description: string;
jsonSchema: string;
}) => {
if (description === "") {
return {};
}
const baseUrl = getBaseApiUrl();
const response = await axiosClient.post(`${baseUrl}/suggest`, {
description,
jsonSchema,
});
return response.data;
};
type ExtractionRequest = {
extractor_id: string;
text?: string;
file?: File;
};
export type ExtractionResponse = {
data: unknown[];
content_too_long?: boolean;
};
export const runExtraction: MutationFunction<
ExtractionResponse,
[ExtractionRequest, boolean]
> = async ([extractionRequest, isShared]) => {
const endpoint = isShared ? "extract/shared" : "extract";
const baseUrl = getBaseApiUrl();
const response = await axiosClient.postForm(
`${baseUrl}/${endpoint}`,
extractionRequest,
);
return response.data;
};
export const useRunExtraction = () => {
return useMutation({ mutationFn: runExtraction });
};
export const useGetExtractor = (uuid: string, isShared: boolean) => {
return useQuery({
queryKey: ["getExtractor", uuid, isShared],
queryFn: getExtractor,
});
};
export const useGetExtractors = () => {
return useQuery({ queryKey: ["getExtractors"], queryFn: listExtractors });
};
export const useDeleteExtractor = () => {
const baseUrl = getBaseApiUrl();
const queryClient = useQueryClient();
return useMutation({
mutationFn: (uuid: string) =>
axiosClient.delete(`${baseUrl}/extractors/${uuid}`),
onSuccess: () => {
queryClient.invalidateQueries({ queryKey: ["getExtractors"] });
},
});
};
export const useCreateExtractor = ({
onSuccess,
}: {
onSuccess: OnSuccessFn;
}) => {
const queryClient = useQueryClient();
return useMutation({
mutationFn: createExtractor,
onSuccess: (data) => {
queryClient.invalidateQueries({ queryKey: ["getExtractors"] });
onSuccess(data);
},
});
};
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/utils/api_url.ts
|
export const getBaseApiUrl = () => {
if (process.env.NODE_ENV === "development") {
return "http://localhost:8000";
}
if (!process.env.NEXT_PUBLIC_BASE_API_URL) {
throw new Error(
"NEXT_PUBLIC_BASE_API_URL must be set if not in development.",
);
}
return process.env.NEXT_PUBLIC_BASE_API_URL;
};
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/components/CreateExtractor.tsx
|
"use client";
import {
AbsoluteCenter,
Accordion,
AccordionButton,
AccordionIcon,
AccordionItem,
AccordionPanel,
Badge,
Box,
Button,
Card,
CardBody,
CircularProgress,
Divider,
FormControl,
Heading,
Icon,
IconButton,
Input,
Text,
} from "@chakra-ui/react";
import { json } from "@codemirror/lang-json";
import Form from "@rjsf/chakra-ui";
import validator from "@rjsf/validator-ajv8";
import CodeMirror from "@uiw/react-codemirror";
import Ajv from "ajv";
import addFormats from "ajv-formats";
import { useRouter } from "next/navigation";
import React from "react";
import { ChatBubbleBottomCenterTextIcon } from "@heroicons/react/24/outline";
import { useMutation } from "@tanstack/react-query";
import { suggestExtractor, useCreateExtractor } from "../utils/api";
const ajv = new Ajv();
// Adds support for parsing format types like "date-time"
// and "email" in JSON Schema.
// A lot of the JSON Schema generated by LLMS will
// be generated with these formats out of the box.
addFormats(ajv);
/**
* Component to create a new extractor with fields
* for name, description, schema, and examples
*/
const CreateExtractor = ({}) => {
const startSchema = "{}";
// You might use a mutation hook here if you're
// using something like React Query for state management
const [schema, setSchema] = React.useState(startSchema);
const [creatable, setCreatable] = React.useState(false);
const [lastValidSchema, setLastValidSchema] = React.useState(
JSON.parse(startSchema),
);
const [currentSchemaValid, setCurrentSchemaValid] = React.useState(true);
const [userInput, setUserInput] = React.useState("");
const suggestMutation = useMutation({
mutationFn: suggestExtractor,
onSuccess: (data) => {
let prettySchema = data.json_schema;
try {
prettySchema = JSON.stringify(JSON.parse(data.json_schema), null, 2);
} catch (e) {}
setSchema(prettySchema);
},
});
const { push } = useRouter();
const { mutate } = useCreateExtractor({
onSuccess: (data) => {
push(`/e/${data.uuid}`);
},
});
React.useMemo(() => {
try {
const parsedSchema = JSON.parse(schema);
ajv.compile(parsedSchema);
setCurrentSchemaValid(true);
setLastValidSchema(parsedSchema);
// OK to create if schema is parseable and not empty
// and contains an object at the top level
setCreatable(parsedSchema.type === "object");
} catch (e) {
setCurrentSchemaValid(false);
setCreatable(false);
}
}, [schema]);
const handleSubmit = (event: React.FormEvent<HTMLFormElement>) => {
event.preventDefault();
const instruction = "";
const objectSchema = JSON.parse(schema);
// Extract information from schema like name, and description
const name = objectSchema.title || "Unnamed";
const description = objectSchema.description || "";
// backend uses varchar(100) for description
const shortDescription =
description.length > 100
? description.substring(0, 95) + "..."
: description;
mutate({
name,
description: shortDescription,
schema: objectSchema,
instruction,
});
};
const handleSuggest = (event: React.FormEvent<HTMLFormElement>) => {
event.preventDefault();
const description = event.currentTarget.userInput.value;
if (description === "") {
return;
}
suggestMutation.mutate({ description, jsonSchema: schema });
setUserInput("");
};
return (
<div className="w-4/5 m-auto">
<Heading size={"md"} className="m-auto w-4/5" textAlign={"center"}>
What would you like to extract today?
</Heading>
<form className="m-auto flex gap-2 mt-5" onSubmit={handleSuggest}>
<FormControl id="userInput">
<Input
htmlSize={4}
width="100%"
autoFocus
height="auto"
placeholder="Describe your extraction task..."
value={userInput}
onChange={(event) => setUserInput(event.target.value)}
/>
</FormControl>
{suggestMutation.isPending ? (
<CircularProgress isIndeterminate />
) : (
<IconButton
type="submit"
icon={<Icon as={ChatBubbleBottomCenterTextIcon} />}
aria-label="OK"
colorScheme={userInput === "" ? "gray" : "blue"}
disabled={userInput === ""}
/>
)}
</form>
<form
className="m-auto flex flex-col content-between gap-5 mt-10"
onSubmit={handleSubmit}
>
<Box position="relative" padding="10">
<Divider />
<AbsoluteCenter bg="white" px="10">
OR
</AbsoluteCenter>
</Box>
<Accordion allowToggle={true}>
<AccordionItem>
<AccordionButton>
Edit JSON Schema
<div className="ml-auto">
{currentSchemaValid ? (
<Badge colorScheme="green">OK</Badge>
) : (
<Badge colorScheme="red">Errors!</Badge>
)}
<AccordionIcon />
</div>
</AccordionButton>
<AccordionPanel>
<FormControl isInvalid={!currentSchemaValid}>
<CodeMirror
id="schema"
value={schema}
aria-label="JSON Schema Editor"
onChange={(value) => setSchema(value)}
basicSetup={{ autocompletion: true }}
extensions={[json()]}
minHeight="300px"
className="border-4 border-slate-300 border-double"
/>
</FormControl>
</AccordionPanel>
</AccordionItem>
</Accordion>
{Object.keys(lastValidSchema).length !== 0 && (
<>
<Heading size="md">Preview</Heading>
{!currentSchemaValid && (
<Text color="red.500">
JSON Schema has errors. Showing previous valid JSON Schema.
</Text>
)}
<Card>
<CardBody>
<Form
schema={lastValidSchema}
validator={validator}
disabled={!currentSchemaValid || suggestMutation.isPending}
>
{true} {/* Disables the submit button */}
</Form>
</CardBody>
</Card>
</>
)}
<Button className="btn" type="submit" size="lg" isDisabled={!creatable}>
Create
</Button>
</form>
</div>
);
};
export default CreateExtractor;
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/components/Sidebar.tsx
|
"use client";
import {
Button,
Link as ChakraLink,
Divider,
Flex,
Icon,
IconButton,
Menu,
MenuButton,
MenuItem,
MenuList,
Spacer,
Text,
VStack,
useDisclosure,
} from "@chakra-ui/react";
import React from "react";
import {
ArrowTopRightOnSquareIcon,
EllipsisVerticalIcon,
PencilSquareIcon,
TrashIcon,
} from "@heroicons/react/24/outline";
import { useRouter, useParams } from "next/navigation";
import { useMutation } from "@tanstack/react-query";
import {
useDeleteExtractor,
useGetExtractors,
axiosClient,
} from "../utils/api";
import { getBaseApiUrl } from "../utils/api_url";
import { ShareModal } from "./ShareModal";
export function Sidebar() {
const [shareUUID, setShareUUID] = React.useState("");
const params = useParams();
const currentExtractorId = params.extractorId;
const { isOpen, onClose, onOpen } = useDisclosure();
const { push } = useRouter();
const { data } = useGetExtractors();
const deleteExtractor = useDeleteExtractor();
const baseUrl = getBaseApiUrl();
const mutateShare = useMutation({
mutationFn: async (uuid: string) =>
axiosClient.post(`${baseUrl}/extractors/${uuid}/share`),
onSuccess: (onSuccessData) => {
setShareUUID(onSuccessData.data.share_uuid);
onOpen();
},
});
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const buttons = data?.map((extractor: any) => {
const selectedProps =
extractor.uuid === currentExtractorId
? {
borderLeft: "4px solid gray",
borderRadius: "4px",
padding: "4px",
}
: {};
return (
<Flex
flexDirection="column"
key={extractor.uuid}
w="100%"
{...selectedProps}
>
<Flex alignItems="center">
<ChakraLink
p={1}
onClick={() => push(`/e/${extractor.uuid}`)}
cursor="pointer"
>
<Text noOfLines={1}>
<strong>{extractor.name}</strong>
</Text>
</ChakraLink>
<Spacer />
<Menu>
<MenuButton
as={IconButton}
aria-label="Options"
icon={<Icon as={EllipsisVerticalIcon} />}
variant="outline"
/>
<MenuList>
<MenuItem
icon={<Icon as={ArrowTopRightOnSquareIcon} />}
onClick={() => {
mutateShare.mutate(extractor.uuid);
}}
>
Share
{isOpen && (
<ShareModal
shareUUID={shareUUID}
isOpen={isOpen}
onClose={onClose}
/>
)}
</MenuItem>
<MenuItem
icon={<Icon as={TrashIcon} />}
onClick={() => deleteExtractor.mutate(extractor.uuid)}
>
Delete
</MenuItem>
</MenuList>
</Menu>
</Flex>
<Text p={1} noOfLines={1} color={"gray"}>
{extractor.description}
</Text>
</Flex>
);
});
return (
<div>
<VStack>
<Button
rightIcon={<Icon as={PencilSquareIcon} />}
w="80%"
onClick={() => push("/new")}
>
New
</Button>
<Divider />
{buttons}
</VStack>
</div>
);
}
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/components/Playground.tsx
|
"use client";
import {
Button,
Heading,
Tab,
Box,
Divider,
AbsoluteCenter,
TabList,
TabPanel,
TabPanels,
Tabs,
Text,
Textarea,
FormControl,
FormLabel,
HStack,
Radio,
RadioGroup,
} from "@chakra-ui/react";
import { useMutation } from "@tanstack/react-query";
import React from "react";
import SyntaxHighlighter from "react-syntax-highlighter";
import { docco } from "react-syntax-highlighter/dist/esm/styles/hljs";
import { runExtraction, useConfiguration } from "../utils/api";
import { Extractor } from "./Extractor";
import { ResultsTable } from "./ResultsTable";
interface PlaygroundProps {
/**
* The playground currently support viewing
* both shared and non-shared extractors
*/
extractorId: string;
isShared: boolean;
}
/**
* Playground to work with an existing extractor.
*/
export const Playground = (props: PlaygroundProps) => {
const { extractorId, isShared } = props;
const { data, isPending, mutate } = useMutation({
mutationFn: runExtraction,
});
const requestServerConfig = useConfiguration();
const [isDisabled, setIsDisabled] = React.useState(true);
const handleSubmit = (event: React.FormEvent<HTMLFormElement>) => {
event.preventDefault();
const request = {
extractor_id: extractorId,
model_name: event.currentTarget.modelId.value,
};
if (event.currentTarget.text.value) {
Object.assign(request, { text: event.currentTarget.text.value });
} else {
Object.assign(request, { file: event.currentTarget.file.files[0] });
}
mutate([request, isShared]);
};
const handleKeyDown = (event: React.KeyboardEvent<HTMLTextAreaElement>) => {
if (event.key === "Enter" && !event.shiftKey) {
event.preventDefault(); // Prevent the default Enter action
if (isDisabled) {
return;
}
event.currentTarget.form?.dispatchEvent(
new Event("submit", { cancelable: true, bubbles: true }),
);
}
};
const handleChange = (event: React.FormEvent<HTMLFormElement>) => {
if (
event.currentTarget.text.value === "" &&
event.currentTarget.file.files.length === 0
) {
setIsDisabled(true);
return;
}
// Also disable if both are present
if (
event.currentTarget.text.value !== "" &&
event.currentTarget.file.files.length !== 0
) {
setIsDisabled(true);
return;
}
setIsDisabled(false);
};
return (
<div className="w-full flex-col justify-between">
<div className="m-auto">
{isShared && <Heading>Using a shared exractor</Heading>}
<div>
<Extractor extractorId={extractorId} isShared={isShared} />
</div>
<Heading>Extract</Heading>
<form
className="m-auto flex flex-col content-between gap-5 mt-10 mb-10"
onSubmit={handleSubmit}
onChange={handleChange}
>
{requestServerConfig.isFetched && (
<FormControl as="fieldset">
<FormLabel as="legend">Extraction Model</FormLabel>
<RadioGroup
name="modelId"
defaultValue={requestServerConfig.data?.models[0].name}
>
<HStack spacing="24px">
{requestServerConfig.data?.models.map((model) => (
<Radio value={model.name} key={model.name}>
{model.description}
</Radio>
))}
</HStack>
</RadioGroup>
</FormControl>
)}
{requestServerConfig.isFetched && (
<>
<input
type="file"
name="file"
accept={requestServerConfig.data?.accepted_mimetypes.join(", ")}
color="blue"
className="border-2 border-dashed border-gray-300 rounded-md p-4 w-full file:mr-4"
/>
<Text fontSize="xs">
Max file size is: {requestServerConfig.data?.max_file_size_mb}MB
</Text>
<Text fontSize="xs">
Supported mimetypes:{" "}
{requestServerConfig.data?.accepted_mimetypes.join(", ")}
</Text>
</>
)}
<Box position="relative" padding="10">
<Divider />
<AbsoluteCenter bg="white" px="4">
OR
</AbsoluteCenter>
</Box>
<Textarea
placeholder="Enter text to extract information from..."
name="text"
className="textarea textarea-bordered h-3/4"
autoFocus
onKeyDown={handleKeyDown}
/>
<Button type="submit" isDisabled={isDisabled}>
Run
</Button>
</form>
</div>
<div className="m-auto">
{data?.content_too_long && (
<Text color={"red"} margin={5}>
The content was too long to be processed. Extraction was run on a
truncated version of the content.
</Text>
)}
<Tabs variant={"enclosed"} colorScheme="blue" size="sm">
<TabList>
<Tab>Table</Tab>
<Tab>JSON</Tab>
</TabList>
<TabPanels>
<TabPanel>
<ResultsTable data={data} isPending={isPending} />
</TabPanel>
<TabPanel>
<Text className="mt-1 mb-5">
Shows the output from the extractor in JSON format.
</Text>
<SyntaxHighlighter language="json" style={docco}>
{JSON.stringify(data, null, 2)}
</SyntaxHighlighter>
</TabPanel>
</TabPanels>
</Tabs>
</div>
</div>
);
};
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/components/ShareModal.tsx
|
import {
Modal,
ModalContent,
ModalHeader,
ModalFooter,
ModalBody,
ModalOverlay,
Text,
Input,
ModalCloseButton,
useClipboard,
Flex,
Button,
} from "@chakra-ui/react";
interface ShareModalProps {
shareUUID: string;
isOpen: boolean;
onClose: () => void;
}
export function ShareModal(props: ShareModalProps) {
const { shareUUID, isOpen, onClose } = props;
const url = `${window.origin}/s/${shareUUID}`;
const { onCopy, hasCopied } = useClipboard(url);
return (
<Modal isOpen={isOpen} onClose={onClose}>
<ModalOverlay />
<ModalContent>
<ModalHeader>Share Link</ModalHeader>
<ModalCloseButton />
<ModalBody>
<Text>
You can share this link with others to give them access to your
extractor:
</Text>
<Flex mb={2}>
<Input value={url} isReadOnly={true} />
<Button onClick={onCopy}>{hasCopied ? "Copied!" : "Copy"}</Button>
</Flex>
</ModalBody>
<ModalFooter>
<Button colorScheme="blue" mr={3} onClick={onClose}>
Close
</Button>
</ModalFooter>
</ModalContent>
</Modal>
);
}
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/components/ResultsTable.tsx
|
import {
Spinner,
Table,
TableCaption,
TableContainer,
Tbody,
Td,
Th,
Thead,
Tr,
} from "@chakra-ui/react";
import { ExtractionResponse } from "../utils/api";
function isRecord(value: unknown): value is Record<string, unknown> {
return typeof value === "object" && value !== null;
}
function getColumns(records: unknown[]): Array<string> {
// Create a set to store unique keys
const uniqueKeys = new Set<string>();
// Iterate over each record in the list
records.forEach((record) => {
// For each key in the current record, add it to the set
if (!isRecord(record)) {
return;
}
Object.keys(record).forEach((key) => {
if (typeof key === "string") {
uniqueKeys.add(key);
}
});
});
// Convert the set back into an array and return it
return Array.from(uniqueKeys);
}
/*
* This function takes a value and returns a string representation of it.
* If the value is an array, it will join the elements with a comma and space.
* If the value is an object, it will create an array of strings representing
* each key-value pair, then join them with a comma and space.
* Otherwise, it will return the string representation of the value.
* @param value - The value to display
* @returns The string representation of the value
*/
function getDisplayValue(value: unknown): string {
if (Array.isArray(value)) {
return value.map(getDisplayValue).join(", ");
}
if (isRecord(value)) {
// Creating an array of strings representing each key-value pair,
// then joining them with a comma and space.
return Object.entries(value)
.map(([key, val]) => `${key}: ${getDisplayValue(val)}`)
.join(", ");
}
return String(value);
}
export const ResultsTable = ({
data,
isPending,
}: {
data: ExtractionResponse | undefined;
isPending: boolean;
}) => {
// scan all the results to determine the columns
// then display the results in a table
if (isPending) {
return (
<Spinner
thickness="4px"
speed="0.65s"
emptyColor="gray.200"
color="blue.500"
size="xl"
/>
);
}
const actualData = data?.data;
const columns = actualData ? getColumns(actualData) : [];
return (
<div>
<TableContainer>
<Table>
<TableCaption>Extraction Results</TableCaption>
<Thead>
<Tr>
{columns.map((column, idx) => (
<Th key={`table-header-${idx}`}>{column}</Th>
))}
</Tr>
</Thead>
<Tbody>
{actualData?.map((row, index) => {
return (
<Tr key={index}>
{columns.map((column, idx) => (
// Check if the row has the column,
// if not, display an empty cell
<Td key={`table-cell-${idx}`}>
{isRecord(row) && column in row
? getDisplayValue(row[column])
: ""}
</Td>
))}
</Tr>
);
})}
</Tbody>
</Table>
</TableContainer>
</div>
);
};
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/components/Extractor.tsx
|
"use client";
import {
Tab,
TabList,
TabPanel,
TabPanels,
Tabs,
Text,
} from "@chakra-ui/react";
import Form from "@rjsf/chakra-ui";
import validator from "@rjsf/validator-ajv8";
import { docco } from "react-syntax-highlighter/dist/esm/styles/hljs";
import SyntaxHighlighter from "react-syntax-highlighter";
import { useGetExtractor } from "../utils/api";
type ExtractorProps = {
extractorId: string;
isShared: boolean;
};
export const Extractor = ({ extractorId, isShared }: ExtractorProps) => {
const { data, isLoading, isError } = useGetExtractor(extractorId, isShared);
if (isLoading) {
return <div>Loading...</div>;
}
if (isError) {
return <div>Unable to load extractor with ID: {extractorId}</div>;
}
if (data === undefined) {
throw new Error("Data is undefined");
}
return (
<div className="mr-auto">
<Tabs className="mt-5" variant={"enclosed"} colorScheme="blue" size="sm">
<TabList>
<Tab>Form</Tab>
<Tab>Code</Tab>
</TabList>
<TabPanels>
<TabPanel>
<Form schema={data.schema} validator={validator}>
{true} {/* Disables the submit button */}
</Form>
</TabPanel>
<TabPanel>
<Text className="mt-1 mb-5">
This shows the raw JSON Schema that describes what information the
extractor will be extracting from the content.
</Text>
<SyntaxHighlighter language="json" style={docco}>
{JSON.stringify(data.schema, null, 2)}
</SyntaxHighlighter>
</TabPanel>
</TabPanels>
</Tabs>
</div>
);
};
|
0
|
lc_public_repos/langchain-extract/frontend/app/s
|
lc_public_repos/langchain-extract/frontend/app/s/[sharedExtractorId]/page.tsx
|
"use client";
import { Playground } from "../../components/Playground";
interface ExtractorPageProps {
params: {
sharedExtractorId: string;
};
}
export default function Page({ params }: ExtractorPageProps) {
return <Playground extractorId={params.sharedExtractorId} isShared={true} />;
}
|
0
|
lc_public_repos/langchain-extract/frontend/app
|
lc_public_repos/langchain-extract/frontend/app/new/page.tsx
|
import CreateExtractor from "../components/CreateExtractor";
export default CreateExtractor;
|
0
|
lc_public_repos/langchain-extract/frontend/public
|
lc_public_repos/langchain-extract/frontend/public/images/github-mark.svg
|
<svg width="98" height="96" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M48.854 0C21.839 0 0 22 0 49.217c0 21.756 13.993 40.172 33.405 46.69 2.427.49 3.316-1.059 3.316-2.362 0-1.141-.08-5.052-.08-9.127-13.59 2.934-16.42-5.867-16.42-5.867-2.184-5.704-5.42-7.17-5.42-7.17-4.448-3.015.324-3.015.324-3.015 4.934.326 7.523 5.052 7.523 5.052 4.367 7.496 11.404 5.378 14.235 4.074.404-3.178 1.699-5.378 3.074-6.6-10.839-1.141-22.243-5.378-22.243-24.283 0-5.378 1.94-9.778 5.014-13.2-.485-1.222-2.184-6.275.486-13.038 0 0 4.125-1.304 13.426 5.052a46.97 46.97 0 0 1 12.214-1.63c4.125 0 8.33.571 12.213 1.63 9.302-6.356 13.427-5.052 13.427-5.052 2.67 6.763.97 11.816.485 13.038 3.155 3.422 5.015 7.822 5.015 13.2 0 18.905-11.404 23.06-22.324 24.283 1.78 1.548 3.316 4.481 3.316 9.126 0 6.6-.08 11.897-.08 13.526 0 1.304.89 2.853 3.316 2.364 19.412-6.52 33.405-24.935 33.405-46.691C97.707 22 75.788 0 48.854 0z" fill="#ffffff"/></svg>
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lc_public_repos/langchain-extract
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lc_public_repos/langchain-extract/docs/Makefile
|
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = source
BUILDDIR = build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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0
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lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/docs/make.bat
|
@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=source
set BUILDDIR=build
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.https://www.sphinx-doc.org/
exit /b 1
)
if "%1" == "" goto help
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd
|
0
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lc_public_repos/langchain-extract/docs
|
lc_public_repos/langchain-extract/docs/source/conf.py
|
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
import pathlib
import sys
from typing import List
import toml
ROOT_FOLDER = str(pathlib.Path(__file__).parent.parent.parent)
# Add the project root to the path
sys.path.insert(0, ROOT_FOLDER)
with open("../../pyproject.toml") as f:
data = toml.load(f)
project = "LangChain Extract"
copyright = "2024, Langchain AI"
author = "Langchain AI"
version = data["tool"]["poetry"]["version"]
release = version
html_title = project + " " + version
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.autodoc.typehints",
"sphinx.ext.autosummary",
"sphinx.ext.napoleon",
"sphinx.ext.viewcode",
"myst_nb",
"sphinx_copybutton",
"IPython.sphinxext.ipython_console_highlighting",
]
source_suffix = [".ipynb", ".html", ".md", ".rst"]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns: List[str] = []
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "sphinx_book_theme"
html_theme_options = {
"path_to_docs": "docs/source",
"repository_url": "https://github.com/langchain-ai/langchain-extract",
"home_page_in_toc": True,
"show_navbar_depth": 2,
"use_sidenotes": True,
"use_repository_button": True,
"use_issues_button": True,
"use_source_button": True,
"use_fullscreen_button": True,
"repository_branch": "main",
"launch_buttons": {
"notebook_interface": "jupyterlab",
"colab_url": "https://colab.research.google.com",
},
}
html_context = {
"display_github": True, # Integrate GitHub
"github_user": "langchain-ai", # Username
"github_repo": "langchain-extract", # Repo name
"github_version": "main", # Version
"conf_py_path": "/docs/", # Path in the checkout to the docs root
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# These paths are either relative to html_static_path
# or fully qualified paths (eg. https://...)
html_css_files = [
"css/custom.css",
]
nb_execution_mode = "off"
autosummary_generate = True
|
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lc_public_repos/langchain-extract/docs
|
lc_public_repos/langchain-extract/docs/source/toc.segment
|
```{toctree}
:maxdepth: 2
:caption: Introduction
./notebooks/getting_started
```
|
0
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lc_public_repos/langchain-extract/docs/source
|
lc_public_repos/langchain-extract/docs/source/notebooks/quick_start.ipynb
|
from langserve import RemoteRunnablefrom typing import Optional, List
from pydantic import BaseModel, Field
class Person(BaseModel):
age: Optional[int] = Field(None, description="The age of the person in years.")
name: Optional[str] = Field(None, description="The name of the person.")runnable = RemoteRunnable("http://localhost:8000/extract_text/")text = """
My name is Chester. I am 42 years old. My friend Jane is a year older than me.
"""response = runnable.invoke({"text": text, "schema": Person.schema()})
responseinstructions = "Redact all names using the characters `######`"
response = runnable.invoke(
{
"text": text,
"schema": Person.schema(),
"instructions": instructions,
}
)
responseinstructions = "Redact all names using the characters `######`"
examples = [
{
"text": "My name is Grung. I am 100.",
"output": [ {"age": 100, "name": "######", "hello": "meow"}] ,
}
]
response = runnable.invoke(
{
"text": text,
"schema": Person.schema(),
"instructions": instructions,
"examples": examples,
}
)
responseimport requests
from uuid import uuid4url = "http://localhost:8000"
user_id = str(uuid4()) # indicates owner for extractor
headers = {"x-key": user_id}data = {
"name": "people_extractor",
"description": "Extract references to people, having properties name and age.",
"schema": Person.schema(),
"instruction": "Redact all names using the characters `######`",
}
response = requests.post(f"{url}/extractors", json=data, headers=headers)
responseuuid = response.json()['uuid']
print(uuid)import json
examples = [
{
"text": "My name is Grung. I am 100.",
"output": [Person(age=100, name="######").dict()],
}
]
responses = []
for example in examples:
create_request = {
"extractor_id": uuid,
"content": example["text"],
"output": example['output'],
}
response = requests.post(f"{url}/examples", json=create_request, headers=headers)
responses.append(response)response = requests.get(f"{url}/examples?extractor_id={uuid}", headers=headers)
responseresponse.json()request_data = {"extractor_id": uuid, "text": text}
print(request_data)response = requests.post(f"{url}/extract", data=request_data, headers=headers)
responseresponse.text
|
0
|
lc_public_repos/langchain-extract/docs/source
|
lc_public_repos/langchain-extract/docs/source/notebooks/earnings_call_example.ipynb
|
import requests
url = "http://localhost:8000"# Uber transcripts from earnings calls and other events at https://investor.uber.com/news-events/default.aspx
pdf_url = "https://s23.q4cdn.com/407969754/files/doc_earnings/2023/q4/transcript/Uber-Q4-23-Prepared-Remarks.pdf"# Get PDF bytes
pdf_response = requests.get(pdf_url)
assert(pdf_response.status_code == 200)
pdf_bytes = pdf_response.contentfrom uuid import uuid4
from pydantic import BaseModel, Field
class FinancialData(BaseModel):
name: str = Field(..., description="Name of the financial figure, such as revenue.")
value: int = Field(..., description="Nominal earnings in local currency.")
scale: str = Field(..., description="Scale of figure, such as MM, B, or percent.")
period_start: str = Field(..., description="The start of the time period in ISO format.")
period_duration: int = Field(..., description="Duration of period, in months")
evidence: str = Field(..., description="Verbatim sentence of text where figure was found.")
user_id = str(uuid4())
headers = {"x-key": user_id}
data = {
"user_id": user_id,
"description": "Financial revenues and other figures.",
"schema": FinancialData.schema(),
"instruction": (
"Extract standard financial figures, specifically earnings and "
"revenue figures."
)
}
response = requests.post(f"{url}/extractors", json=data, headers=headers)
responseextractor = response.json()
print(extractor)result = requests.post(
f"{url}/extract",
data={"extractor_id": extractor["uuid"]},
files={"file": pdf_bytes},
headers=headers,
)
resultresult.json()examples = [
{
"text": "In 2022, Revenue was $1 million and EBIT was $2M.",
"output": [
FinancialData(
name="revenue",
value=1,
scale="MM",
period_start="2022-01-01",
period_duration=12,
evidence="In 2022, Revenue was $1 million and EBIT was $2M.",
).dict(),
FinancialData(
name="ebit",
value=2,
scale="MM",
period_start="2022-01-01",
period_duration=12,
evidence="In 2022, Revenue was $1 million and EBIT was $2M.",
).dict()
],
},
]
responses = []
for example in examples:
create_request = {
"extractor_id": extractor["uuid"],
"content": example["text"],
"output": example['output'],
}
response = requests.post(f"{url}/examples", json=create_request, headers=headers)
responses.append(response)result = requests.post(
f"{url}/extract",
data={"extractor_id": extractor["uuid"]},
files={"file": pdf_bytes},
headers=headers,
)
resultresult.json()
|
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|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/backend/Dockerfile
|
# All directory paths for COPY commands are relative to the build context
# Ensure this python version stays in sync with CI
FROM python:3.11-slim as base
WORKDIR /backend
# set environment variables
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
ENV POETRY_HOME="/opt/poetry"
ENV MYPYPATH="/app/src/stubs"
# Use bash as the shell for the build
# https://github.com/docker/for-linux/issues/408#issuecomment-414748815
SHELL ["/bin/bash", "-o", "pipefail", "-c"]
RUN set -eux && \
apt-get update && \
apt-get install -y \
build-essential \
curl \
libpq-dev \
python3-dev \
libmagic1
# https://python-poetry.org/docs
RUN pip install poetry
# install deps before copying project files so the cache is only invalidated
# when the deps change
COPY ./backend/pyproject.toml ./backend/poetry.lock .
RUN poetry config virtualenvs.create false
RUN poetry install --no-root --only main
COPY ./backend .
EXPOSE 8000
###
# development image
###
FROM base as development
ENTRYPOINT ["bash", "./scripts/local_entry_point.sh"]
|
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|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/backend/Makefile
|
.PHONY: all lint format test help
# Default target executed when no arguments are given to make.
all: help
######################
# TESTING AND COVERAGE
######################
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
test:
poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
test_watch:
poetry run ptw . -- $(TEST_FILE)
openapi:
OPENAI_API_KEY=placeholder python -c "from server import main; import json; print(json.dumps(main.app.openapi()))" > openapi.json
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=. --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint lint_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
# [ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
######################
# HELP
######################
help:
@echo '===================='
@echo '-- LINTING --'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'spell_check - run codespell on the project'
@echo 'spell_fix - run codespell on the project and fix the errors'
@echo '-- TESTS --'
@echo 'coverage - run unit tests and generate coverage report'
@echo 'test - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
@echo '-- DOCUMENTATION tasks are from the top-level Makefile --'
|
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lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/backend/poetry.lock
|
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
[[package]]
name = "accessible-pygments"
version = "0.0.4"
description = "A collection of accessible pygments styles"
optional = false
python-versions = "*"
files = [
{file = "accessible-pygments-0.0.4.tar.gz", hash = "sha256:e7b57a9b15958e9601c7e9eb07a440c813283545a20973f2574a5f453d0e953e"},
{file = "accessible_pygments-0.0.4-py2.py3-none-any.whl", hash = "sha256:416c6d8c1ea1c5ad8701903a20fcedf953c6e720d64f33dc47bfb2d3f2fa4e8d"},
]
[package.dependencies]
pygments = ">=1.5"
[[package]]
name = "aiofiles"
version = "22.1.0"
description = "File support for asyncio."
optional = false
python-versions = ">=3.7,<4.0"
files = [
{file = "aiofiles-22.1.0-py3-none-any.whl", hash = "sha256:1142fa8e80dbae46bb6339573ad4c8c0841358f79c6eb50a493dceca14621bad"},
{file = "aiofiles-22.1.0.tar.gz", hash = "sha256:9107f1ca0b2a5553987a94a3c9959fe5b491fdf731389aa5b7b1bd0733e32de6"},
]
[[package]]
name = "aiohttp"
version = "3.9.5"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
files = [
{file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"},
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{file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[[package]]
name = "ypy-websocket"
version = "0.8.4"
description = "WebSocket connector for Ypy"
optional = false
python-versions = ">=3.7"
files = [
{file = "ypy_websocket-0.8.4-py3-none-any.whl", hash = "sha256:b1ba0dfcc9762f0ca168d2378062d3ca1299d39076b0f145d961359121042be5"},
{file = "ypy_websocket-0.8.4.tar.gz", hash = "sha256:43a001473f5c8abcf182f603049cf305cbc855ad8deaa9dfa0f3b5a7cea9d0ff"},
]
[package.dependencies]
aiofiles = ">=22.1.0,<23"
aiosqlite = ">=0.17.0,<1"
y-py = ">=0.6.0,<0.7.0"
[package.extras]
test = ["mypy", "pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)"]
[[package]]
name = "zipp"
version = "3.18.1"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
{file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[metadata]
lock-version = "2.0"
python-versions = "^3.8.1"
content-hash = "8c6e7fdf785b70e487af7d06270f801ca787da3c3d5f5d4433d306d3c7c6752d"
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/backend/README.md
|
See readme at repo root.
|
0
|
lc_public_repos/langchain-extract
|
lc_public_repos/langchain-extract/backend/pyproject.toml
|
[tool.poetry]
name = "langchain-extract"
version = "0.0.1"
description = "Sample extraction backend."
authors = ["LangChain AI"]
license = "MIT"
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.8.1"
langchain = "~0.1"
langsmith = ">=0.0.66"
fastapi = "^0.109.2"
langserve = "^0.0.45"
uvicorn = "^0.27.1"
pydantic = "^1.10"
langchain-openai = "^0.1.3"
jsonschema = "^4.21.1"
sse-starlette = "^2.0.0"
alembic = "^1.13.1"
psycopg2 = "^2.9.9"
python-magic = "^0.4.27"
pdfminer-six = "^20231228"
beautifulsoup4 = "^4.12.3"
lxml = "^5.1.0"
faiss-cpu = "^1.7.4"
python-multipart = "^0.0.9"
langchain-fireworks = "^0.1.1"
langchain-anthropic = "^0.1.11"
langchain-groq = "^0.1.3"
[tool.poetry.group.dev.dependencies]
jupyterlab = "^3.6.1"
[tool.poetry.group.typing.dependencies]
mypy = "^1.7.0"
[tool.poetry.group.lint.dependencies]
ruff = "^0.1.5"
[tool.poetry.group.docs.dependencies]
nbsphinx = ">=0.8.9"
sphinx = ">=5.2.0"
sphinx-autobuild = "^2021.3.14"
sphinx_book_theme = "^1.0.0"
myst-nb = { version = "^1.0.0", python = "^3.9" }
toml = "^0.10.2"
sphinx-copybutton = ">=0.5.1"
[tool.poetry.group.test.dependencies]
pytest = "^7.2.1"
pytest-cov = "^4.0.0"
pytest-asyncio = "^0.21.1"
pytest-mock = "^3.11.1"
pytest-socket = "^0.6.0"
pytest-watch = "^4.2.0"
pytest-timeout = "^2.2.0"
[tool.ruff]
select = [
"E", # pycodestyle
"F", # pyflakes
"I", # isort
]
extend-include = ["*.ipynb"]
# Same as Black.
line-length = 88
[tool.mypy]
disallow_untyped_defs = "True"
ignore_missing_imports = "True"
[tool.coverage.run]
omit = [
"tests/*",
]
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.pytest.ini_options]
# --strict-markers will raise errors on unknown marks.
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
#
# https://docs.pytest.org/en/7.1.x/reference/reference.html
# --strict-config any warnings encountered while parsing the `pytest`
# section of the configuration file raise errors.
addopts = "--strict-markers --strict-config --durations=5 -vv"
# Global timeout for all tests. There shuold be a good reason for a test to
# take more than 5 second
timeout = 5
# Registering custom markers.
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
markers = [
"asyncio: mark tests as requiring asyncio",
]
asyncio_mode = "auto"
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/db/models.py
|
import uuid
from datetime import datetime
from typing import Generator
from sqlalchemy import (
Column,
DateTime,
ForeignKey,
String,
Text,
UniqueConstraint,
create_engine,
)
from sqlalchemy.dialects.postgresql import JSONB, UUID
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import Session, relationship, sessionmaker
from server.settings import get_postgres_url
ENGINE = create_engine(get_postgres_url())
SessionClass = sessionmaker(bind=ENGINE)
Base = declarative_base()
# TODO(Eugene): Convert to async code
def get_session() -> Generator[Session, None, None]:
"""Create a new session."""
session = SessionClass()
try:
yield session
except:
session.rollback()
raise
finally:
session.close()
class TimestampedModel(Base):
"""An abstract base model that includes the timestamp fields."""
__abstract__ = True
created_at = Column(
DateTime,
default=datetime.utcnow,
comment="The time the record was created (UTC).",
)
updated_at = Column(
DateTime,
default=datetime.utcnow,
onupdate=datetime.utcnow,
doc="The time the record was last updated (UTC).",
)
# This is our own uuid assigned to the artifact.
# By construction guaranteed to be unique no matter what.
uuid = Column(
UUID(as_uuid=True),
primary_key=True,
default=lambda: str(uuid.uuid4()),
doc="Unique identifier for this model.",
)
class Example(TimestampedModel):
"""A representation of an example.
Examples consist of content together with the expected output.
The output is a JSON object that is expected to be extracted from the content.
The JSON object should be valid according to the schema of the associated extractor.
The JSON object is defined by the schema of the associated extractor, so
it's perfectly fine for a given example to represent the extraction
of multiple instances of some object from the content since
the JSON schema can represent a list of objects.
"""
__tablename__ = "examples"
content = Column(
Text,
nullable=False,
comment="The input portion of the example.",
)
output = Column(
JSONB,
comment="The output associated with the example.",
)
extractor_id = Column(
UUID(as_uuid=True),
ForeignKey("extractors.uuid", ondelete="CASCADE"),
nullable=False,
comment="Foreign key referencing the associated extractor.",
)
def __repr__(self) -> str:
return f"<Example(uuid={self.uuid}, content={self.content[:20]}>"
class SharedExtractors(TimestampedModel):
"""A table for managing sharing of extractors."""
__tablename__ = "shared_extractors"
extractor_id = Column(
UUID(as_uuid=True),
ForeignKey("extractors.uuid", ondelete="CASCADE"),
index=True,
nullable=False,
comment="The extractor that is being shared.",
)
share_token = Column(
UUID(as_uuid=True),
index=True,
nullable=False,
unique=True,
comment="The token that is used to access the shared extractor.",
)
# Add unique constraint for (extractor_id, share_token)
__table_args__ = (
UniqueConstraint("extractor_id", "share_token", name="unique_share_token"),
)
def __repr__(self) -> str:
"""Return a string representation of the object."""
return f"<SharedExtractor(id={self.id}, run_id={self.run_id})>"
class Extractor(TimestampedModel):
__tablename__ = "extractors"
name = Column(
String(100),
nullable=False,
server_default="",
comment="The name of the extractor.",
)
owner_id = Column(
UUID(as_uuid=True),
nullable=False,
comment="Owner uuid.",
)
schema = Column(
JSONB,
nullable=False,
comment="JSON Schema that describes what content will be "
"extracted from the document",
)
description = Column(
String(100),
nullable=False,
server_default="",
comment="Surfaced via UI to the users.",
)
instruction = Column(
Text, nullable=False, comment="The prompt to the language model."
) # TODO: This will need to evolve
examples = relationship("Example", backref="extractor")
# Used for sharing the extractor with others.
share_uuid = Column(
UUID(as_uuid=True),
nullable=True,
comment="The uuid of the shareable link.",
)
def __repr__(self) -> str:
return f"<Extractor(id={self.uuid}, description={self.description})>"
def validate_extractor_owner(
session: Session, extractor_id: UUID, user_id: UUID
) -> Extractor:
"""Validate the extractor id."""
extractor = (
session.query(Extractor).filter_by(uuid=extractor_id, owner_id=user_id).first()
)
return extractor is not None
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/extraction/parsing.py
|
"""Convert binary input to blobs and parse them using the appropriate parser."""
from __future__ import annotations
from typing import BinaryIO, List
from fastapi import HTTPException
from langchain.document_loaders.parsers import BS4HTMLParser, PDFMinerParser
from langchain.document_loaders.parsers.generic import MimeTypeBasedParser
from langchain.document_loaders.parsers.txt import TextParser
from langchain_community.document_loaders import Blob
from langchain_core.documents import Document
HANDLERS = {
"application/pdf": PDFMinerParser(),
"text/plain": TextParser(),
"text/html": BS4HTMLParser(),
# Disable for now as they rely on unstructured and there's some install
# issue with unstructured.
# from langchain.document_loaders.parsers.msword import MsWordParser
# "application/msword": MsWordParser(),
# "application/vnd.openxmlformats-officedocument.wordprocessingml.document": (
# MsWordParser()
# ),
}
SUPPORTED_MIMETYPES = sorted(HANDLERS.keys())
MAX_FILE_SIZE_MB = 10 # in MB
def _guess_mimetype(file_bytes: bytes) -> str:
"""Guess the mime-type of a file."""
try:
import magic
except ImportError as e:
raise ImportError(
"magic package not found, please install it with `pip install python-magic`"
) from e
mime = magic.Magic(mime=True)
mime_type = mime.from_buffer(file_bytes)
return mime_type
def _get_file_size_in_mb(data: BinaryIO) -> float:
"""Get file size in MB."""
data.seek(0, 2) # Move the cursor to the end of the file
file_size = data.tell()
file_size_in_mb = file_size / (1024 * 1024)
data.seek(0)
return file_size_in_mb
# PUBLIC API
MIMETYPE_BASED_PARSER = MimeTypeBasedParser(
handlers=HANDLERS,
fallback_parser=None,
)
def convert_binary_input_to_blob(data: BinaryIO) -> Blob:
"""Convert ingestion input to blob."""
file_size_in_mb = _get_file_size_in_mb(data)
if file_size_in_mb > MAX_FILE_SIZE_MB:
raise HTTPException(
status_code=413,
detail=f"File size exceeds the maximum limit of {MAX_FILE_SIZE_MB} MB.",
)
file_data = data.read()
mimetype = _guess_mimetype(file_data)
file_name = data.name
return Blob.from_data(
data=file_data,
path=file_name,
mime_type=mimetype,
)
def parse_binary_input(data: BinaryIO) -> List[Document]:
"""Parse binary input."""
blob = convert_binary_input_to_blob(data)
return MIMETYPE_BASED_PARSER.parse(blob)
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/extraction/utils.py
|
"""Adapters to convert between different formats."""
from __future__ import annotations
from langchain_core.utils.json_schema import dereference_refs
def _rm_titles(kv: dict) -> dict:
"""Remove titles from a dictionary."""
new_kv = {}
for k, v in kv.items():
if k == "title":
continue
elif isinstance(v, dict):
new_kv[k] = _rm_titles(v)
else:
new_kv[k] = v
return new_kv
# PUBLIC API
def update_json_schema(
schema: dict,
*,
multi: bool = True,
) -> dict:
"""Add missing fields to JSON schema and add support for multiple records."""
if multi:
# Wrap the schema in an object called "Root" with a property called: "data"
# which will be a json array of the original schema.
schema_ = {
"type": "object",
"properties": {
"data": {
"type": "array",
"items": dereference_refs(schema),
},
},
"required": ["data"],
}
else:
raise NotImplementedError("Only multi is supported for now.")
schema_["title"] = "extractor"
schema_["description"] = "Extract information matching the given schema."
return schema_
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/tests/db.py
|
"""Utility code that sets up a test database and client for tests."""
from contextlib import asynccontextmanager
from typing import Generator
from httpx import AsyncClient
from sqlalchemy import URL, create_engine
from sqlalchemy.orm import sessionmaker
from db.models import Base, get_session
from server.main import app
url = URL.create(
drivername="postgresql",
username="langchain",
password="langchain",
host="localhost",
database="langchain_test",
port=5432,
)
engine = create_engine(url)
TestingSession = sessionmaker(bind=engine)
def override_get_session() -> Generator[TestingSession, None, None]:
"""Override the get_session dependency with a test session.
This fixture also re-creats the database before each test and drops it after to
ensure a clean slate for each test.
"""
try:
session = TestingSession()
yield session
finally:
session.close()
app.dependency_overrides[get_session] = override_get_session
@asynccontextmanager
async def get_async_client() -> AsyncClient:
"""Get an async client."""
# Clear the database before each test
Base.metadata.drop_all(engine)
Base.metadata.create_all(engine)
async_client = AsyncClient(app=app, base_url="http://test")
try:
yield async_client
finally:
await async_client.aclose()
|
0
|
lc_public_repos/langchain-extract/backend/tests
|
lc_public_repos/langchain-extract/backend/tests/integration_tests/test_extraction.py
|
"""Makes it easy to run an integration tests using a real chat model."""
from contextlib import asynccontextmanager
from typing import Optional
import httpx
from fastapi import FastAPI
from httpx import AsyncClient
from langchain_core.pydantic_v1 import BaseModel
from server.main import app
@asynccontextmanager
async def get_async_test_client(
server: FastAPI, *, path: Optional[str] = None, raise_app_exceptions: bool = True
) -> AsyncClient:
"""Get an async client."""
url = "http://localhost:9999/"
if path:
url += path
transport = httpx.ASGITransport(
app=server,
raise_app_exceptions=raise_app_exceptions,
)
async_client = AsyncClient(app=server, base_url=url, transport=transport)
try:
yield async_client
finally:
await async_client.aclose()
async def test_extraction_api() -> None:
"""Test the extraction API endpoint."""
class Person(BaseModel):
age: Optional[int]
name: Optional[str]
alias: Optional[str]
async with get_async_test_client(app) as client:
text = """
My name is Chester. I am young. I love cats. I have two cats. My age
is the number of cats I have to the power of 5. (Approximately.)
I also have a friend. His name is Neo. He is older than me. He is
also a cat lover. He has 3 cats. He is 25 years old.
"""
result = await client.post(
"/extract_text/invoke",
json={"input": {"text": text, "schema": Person.schema()}},
)
assert result.status_code == 200, result.text
response_data = result.json()
assert isinstance(response_data["output"]["data"], list)
# Test with instructions
result = await client.post(
"/extract_text/invoke",
json={
"input": {
"text": text,
"schema": Person.schema(),
"instructions": "Very important: Chester's alias is Neo.",
}
},
)
response_data = result.json()
assert result.status_code == 200, result.text
# Test with few shot examples
examples = [
{
"text": "My name is Grung. I am 100.",
"output": [Person(age=100, name="######").dict()],
},
]
result = await client.post(
"/extract_text/invoke",
json={
"input": {
"text": text,
"schema": Person.schema(),
"instructions": "Redact all names using the characters `######`",
"examples": examples,
}
},
)
assert result.status_code == 200, result.text
|
0
|
lc_public_repos/langchain-extract/backend/tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/test_validators.py
|
import pytest
from server.validators import validate_json_schema
def test_validate_json_schema() -> None:
"""Test validate_json_schema."""
# TODO: Validate more extensively to make sure that it actually validates
# the schema as expected.
with pytest.raises(Exception):
validate_json_schema({"type": "meow"})
with pytest.raises(Exception):
validate_json_schema({"type": "str"})
validate_json_schema({"type": "string"})
|
0
|
lc_public_repos/langchain-extract/backend/tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/test_parsing.py
|
"""Test parsing logic."""
import mimetypes
from langchain.document_loaders import Blob
from extraction.parsing import (
MIMETYPE_BASED_PARSER,
SUPPORTED_MIMETYPES,
)
from tests.unit_tests.fixtures import get_sample_paths
def test_list_of_accepted_mimetypes() -> None:
"""This list should generally grow! Protecting against typos in mimetypes."""
assert SUPPORTED_MIMETYPES == [
# Two MS Word mimetypes are disabled for now
# Need to install unstructured to enable them
# "application/msword",
"application/pdf",
# "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"text/html",
"text/plain",
]
def test_attempt_to_parse_each_fixture() -> None:
"""Attempt to parse supported fixtures."""
seen_mimetypes = set()
for path in get_sample_paths():
type_, _ = mimetypes.guess_type(path)
if type_ not in SUPPORTED_MIMETYPES:
continue
seen_mimetypes.add(type_)
blob = Blob.from_path(path)
documents = MIMETYPE_BASED_PARSER.parse(blob)
try:
assert len(documents) == 1
doc = documents[0]
assert "source" in doc.metadata
assert doc.metadata["source"] == str(path)
assert "π¦" in doc.page_content
except Exception as e:
raise AssertionError(f"Failed to parse {path}") from e
known_missing = {"application/msword"}
assert set(SUPPORTED_MIMETYPES) - known_missing == seen_mimetypes
|
0
|
lc_public_repos/langchain-extract/backend/tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/test_deduplication.py
|
from server.extraction_runnable import ExtractResponse, deduplicate
async def test_deduplication_different_results() -> None:
"""Test deduplication of extraction results."""
result = deduplicate(
[
{"data": [{"name": "Chester", "age": 42}]},
{"data": [{"name": "Jane", "age": 42}]},
]
)
expected = ExtractResponse(
data=[
{"name": "Chester", "age": 42},
{"name": "Jane", "age": 42},
]
)
assert expected == result
result = deduplicate(
[
{
"data": [
{"field_1": 1, "field_2": "a"},
{"field_1": 2, "field_2": "b"},
]
},
{
"data": [
{"field_1": 1, "field_2": "a"},
{"field_1": 2, "field_2": "c"},
]
},
]
)
expected = ExtractResponse(
data=[
{"field_1": 1, "field_2": "a"},
{"field_1": 2, "field_2": "b"},
{"field_1": 2, "field_2": "c"},
]
)
assert expected == result
# Test with data being a list of strings
result = deduplicate([{"data": ["1", "2"]}, {"data": ["1", "3"]}])
expected = ExtractResponse(data=["1", "2", "3"])
assert expected == result
# Test with data being a mix of integer and string
result = deduplicate([{"data": [1, "2"]}, {"data": ["1", "3"]}])
expected = ExtractResponse(data=[1, "2", "1", "3"])
assert expected == result
|
0
|
lc_public_repos/langchain-extract/backend/tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/utils.py
|
from contextlib import asynccontextmanager
from typing import Optional
import httpx
from fastapi import FastAPI
from httpx import AsyncClient
@asynccontextmanager
async def get_async_test_client(
server: FastAPI, *, path: Optional[str] = None, raise_app_exceptions: bool = True
) -> AsyncClient:
"""Get an async client."""
url = "http://localhost:9999/"
if path:
url += path
transport = httpx.ASGITransport(
app=server,
raise_app_exceptions=raise_app_exceptions,
)
async_client = AsyncClient(app=server, base_url=url, transport=transport)
try:
yield async_client
finally:
await async_client.aclose()
|
0
|
lc_public_repos/langchain-extract/backend/tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/conftest.py
|
import os
os.environ["OPENAI_API_KEY"] = "placeholder"
os.environ["FIREWORKS_API_KEY"] = "placeholder"
os.environ["TOGETHER_API_KEY"] = "placeholder"
|
0
|
lc_public_repos/langchain-extract/backend/tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/test_upload.py
|
from extraction.parsing import _guess_mimetype
from tests.unit_tests.fixtures import get_sample_paths
async def test_mimetype_guessing() -> None:
"""Verify mimetype guessing for all fixtures."""
name_to_mime = {}
for file in sorted(get_sample_paths()):
data = file.read_bytes()
name_to_mime[file.name] = _guess_mimetype(data)
assert {
"sample.docx": (
"application/vnd.openxmlformats-officedocument.wordprocessingml.document"
),
"sample.epub": "application/epub+zip",
"sample.html": "text/html",
"sample.odt": "application/vnd.oasis.opendocument.text",
"sample.pdf": "application/pdf",
"sample.rtf": "text/rtf",
"sample.txt": "text/plain",
} == name_to_mime
|
0
|
lc_public_repos/langchain-extract/backend/tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/test_utils.py
|
from langchain.pydantic_v1 import BaseModel, Field
from langchain_core.messages import AIMessage
from extraction.utils import update_json_schema
from server.extraction_runnable import ExtractionExample, _make_prompt_template
def test_update_json_schema() -> None:
"""Test updating JSON schema."""
class Person(BaseModel):
name: str = Field(..., description="The name of the person.")
age: int = Field(..., description="The age of the person.")
schema = Person.schema()
assert schema == {
"properties": {
"age": {
"description": "The age of the person.",
"title": "Age",
"type": "integer",
},
"name": {
"description": "The name of the person.",
"title": "Name",
"type": "string",
},
},
"required": ["name", "age"],
"title": "Person",
"type": "object",
}
updated_schema = update_json_schema(schema)
assert updated_schema == {
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"title": "Person",
"type": "object",
"properties": {
"name": {
"title": "Name",
"description": "The name of the person.",
"type": "string",
},
"age": {
"title": "Age",
"description": "The age of the person.",
"type": "integer",
},
},
"required": ["name", "age"],
},
}
},
"required": ["data"],
"title": "extractor",
"description": "Extract information matching the given schema.",
}
def test_make_prompt_template() -> None:
"""Test making a system message from instructions and examples."""
instructions = "Test instructions."
examples = [
ExtractionExample(
text="Test text.",
output=[
{"name": "Test Name", "age": 0},
{"name": "Test Name 2", "age": 1},
],
)
]
prefix = (
"You are a top-tier algorithm for extracting information from text. "
"Only extract information that is relevant to the provided text. "
"If no information is relevant, use the schema and output "
"an empty list where appropriate."
)
prompt = _make_prompt_template(instructions, examples, "name")
messages = prompt.messages
assert 5 == len(messages)
system = messages[0].prompt.template
assert system.startswith(prefix)
assert system.endswith(instructions)
example_input = messages[1]
assert example_input.content == "Test text."
example_output = messages[2]
assert isinstance(example_output, AIMessage)
assert example_output.tool_calls
assert len(example_output.tool_calls) == 1
assert example_output.tool_calls[0]["name"] == "name"
prompt = _make_prompt_template(instructions, None, "name")
assert 2 == len(prompt.messages)
prompt = _make_prompt_template(None, examples, "name")
assert 5 == len(prompt.messages)
|
0
|
lc_public_repos/langchain-extract/backend/tests/unit_tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/api/test_api_extract.py
|
"""Code to test API endpoints."""
import tempfile
from unittest.mock import patch
from uuid import UUID, uuid4
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import FakeEmbeddings
from langchain_core.runnables import RunnableLambda
from tests.db import get_async_client
def mock_extraction_runnable(*args, **kwargs):
"""Mock the extraction_runnable function."""
extract_request = args[0]
return {
"data": [
extract_request.text[:10],
]
}
def mock_text_splitter(*args, **kwargs):
return CharacterTextSplitter()
def mock_embeddings(*args, **kwargs):
return FakeEmbeddings(size=10)
@patch(
"server.extraction_runnable.extraction_runnable",
new=RunnableLambda(mock_extraction_runnable),
)
@patch(
"server.retrieval.extraction_runnable",
new=RunnableLambda(mock_extraction_runnable),
)
@patch("server.extraction_runnable.TokenTextSplitter", mock_text_splitter)
@patch("server.retrieval.OpenAIEmbeddings", mock_embeddings)
async def test_extract_from_file() -> None:
"""Test extract from file API."""
async with get_async_client() as client:
user_id = str(uuid4())
headers = {"x-key": user_id}
# Test with invalid extractor
extractor_id = UUID(int=1027) # 1027 is a good number.
response = await client.post(
"/extract",
data={
"extractor_id": str(extractor_id),
"text": "Test Content",
},
headers=headers,
)
assert response.status_code == 404, response.text
# First create an extractor
create_request = {
"name": "Test Name",
"description": "Test Description",
"schema": {"type": "object"},
"instruction": "Test Instruction",
}
response = await client.post(
"/extractors",
json=create_request,
headers=headers,
)
assert response.status_code == 200, response.text
# Get the extractor id
extractor_id = response.json()["uuid"]
# Run an extraction.
# We'll use multi-form data here.
response = await client.post(
"/extract",
data={
"extractor_id": extractor_id,
"text": "Test Content",
"mode": "entire_document",
},
headers=headers,
)
assert response.status_code == 200
assert response.json() == {
"data": ["Test Conte"],
"content_too_long": False,
}
# Vary chat model
response = await client.post(
"/extract",
data={
"extractor_id": extractor_id,
"text": "Test Content",
"mode": "entire_document",
"model_name": "gpt-3.5-turbo",
},
headers=headers,
)
assert response.status_code == 200
assert response.json() == {
"data": ["Test Conte"],
"content_too_long": False,
}
# Test retrieval
response = await client.post(
"/extract",
data={
"extractor_id": extractor_id,
"text": "Test Content",
"mode": "retrieval",
},
headers=headers,
)
assert response.status_code == 200
assert response.json() == {
"data": ["Test Conte"],
}
# We'll use multi-form data here.
# Create a named temporary file
with tempfile.NamedTemporaryFile(mode="w+t", delete=True) as f:
f.write("This is a named temporary file.")
f.seek(0)
f.flush()
response = await client.post(
"/extract",
data={
"extractor_id": extractor_id,
"mode": "entire_document",
},
files={"file": f},
headers=headers,
)
assert response.status_code == 200, response.text
assert response.json() == {"data": ["This is a "], "content_too_long": False}
@patch(
"server.extraction_runnable.extraction_runnable",
new=RunnableLambda(mock_extraction_runnable),
)
@patch("server.extraction_runnable.TokenTextSplitter", mock_text_splitter)
async def test_extract_from_large_file() -> None:
user_id = str(uuid4())
headers = {"x-key": user_id}
async with get_async_client() as client:
# First create an extractor
create_request = {
"name": "Test Name",
"description": "Test Description",
"schema": {"type": "object"},
"instruction": "Test Instruction",
}
response = await client.post(
"/extractors", json=create_request, headers=headers
)
assert response.status_code == 200, response.text
# Get the extractor id
extractor_id = response.json()["uuid"]
# Test file size constraint
with tempfile.NamedTemporaryFile(mode="w+t", delete=True) as f:
f.write("This is a named temporary file.")
f.seek(0)
f.flush()
with patch("extraction.parsing._get_file_size_in_mb", return_value=20):
response = await client.post(
"/extract",
data={
"extractor_id": extractor_id,
"mode": "entire_document",
},
files={"file": f},
headers=headers,
)
assert response.status_code == 413
# Test chunk count constraint
with tempfile.NamedTemporaryFile(mode="w+t", delete=True) as f:
f.write("This is a named temporary file.")
f.seek(0)
f.flush()
with patch("server.extraction_runnable.settings.MAX_CHUNKS", 1):
with patch.object(
CharacterTextSplitter, "split_text", return_value=["a", "b"]
):
response = await client.post(
"/extract",
data={
"extractor_id": extractor_id,
"mode": "entire_document",
},
files={"file": f},
headers=headers,
)
assert response.status_code == 200
assert response.json() == {
"data": ["a"],
"content_too_long": True,
}
|
0
|
lc_public_repos/langchain-extract/backend/tests/unit_tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/api/test_api_configuration.py
|
from tests.db import get_async_client
async def test_configuration_api() -> None:
"""Test the configuration API."""
async with get_async_client() as client:
response = await client.get("/configuration")
assert response.status_code == 200
result = response.json()
assert isinstance(result, dict)
assert sorted(result) == [
"accepted_mimetypes",
"available_models",
"max_chunks",
"max_concurrency",
"max_file_size_mb",
"models",
]
models = result["available_models"]
assert all(isinstance(model_name, str) for model_name in models)
assert "gpt-3.5-turbo" in models
assert len(models) >= 2
|
0
|
lc_public_repos/langchain-extract/backend/tests/unit_tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/api/test_api_defining_extractors.py
|
"""Code to test API endpoints."""
import uuid
from tests.db import get_async_client
async def test_extractors_api() -> None:
"""This will test a few of the extractors API endpoints."""
# First verify that the database is empty
async with get_async_client() as client:
user_id = str(uuid.uuid4())
headers = {"x-key": user_id}
response = await client.get("/extractors", headers=headers)
assert response.status_code == 200
assert response.json() == []
# Verify that we can create an extractor
create_request = {
"description": "Test Description",
"schema": {"type": "object"},
"instruction": "Test Instruction",
}
response = await client.post(
"/extractors", json=create_request, headers=headers
)
assert response.status_code == 200
# Verify that the extractor was created
response = await client.get("/extractors", headers=headers)
assert response.status_code == 200
get_response = response.json()
assert len(get_response) == 1
# Check headers
bad_headers = {"x-key": str(uuid.uuid4())}
bad_response = await client.get("/extractors", headers=bad_headers)
assert bad_response.status_code == 200
assert len(bad_response.json()) == 0
# Check we need cookie to delete
uuid_str = get_response[0]["uuid"]
_ = uuid.UUID(uuid_str) # assert valid uuid
await client.delete(f"/extractors/{uuid_str}", headers=bad_headers)
# Check extractor was not deleted
response = await client.get("/extractors", headers=headers)
assert len(response.json()) == 1
# Verify that we can delete an extractor
_ = uuid.UUID(uuid_str) # assert valid uuid
response = await client.delete(f"/extractors/{uuid_str}", headers=headers)
assert response.status_code == 200
get_response = await client.get("/extractors", headers=headers)
assert get_response.status_code == 200
assert get_response.json() == []
# Verify that we can create an extractor
create_request = {
"description": "Test Description",
"schema": {"type": "object"},
"instruction": "Test Instruction",
}
response = await client.post(
"/extractors", json=create_request, headers=headers
)
assert response.status_code == 200
# Verify that the extractor was created
response = await client.get("/extractors", headers=headers)
assert response.status_code == 200
assert len(response.json()) == 1
# Verify that we can delete an extractor
get_response = response.json()
uuid_str = get_response[0]["uuid"]
_ = uuid.UUID(uuid_str) # assert valid uuid
response = await client.delete(f"/extractors/{uuid_str}", headers=headers)
assert response.status_code == 200
get_response = await client.get("/extractors", headers=headers)
assert get_response.status_code == 200
assert get_response.json() == []
# Verify that we can create an extractor, including other properties
user_id = str(uuid.uuid4())
create_request = {
"name": "my extractor",
"description": "Test Description",
"schema": {"type": "object"},
"instruction": "Test Instruction",
}
response = await client.post(
"/extractors", json=create_request, headers=headers
)
extractor_uuid = response.json()["uuid"]
assert response.status_code == 200
response = await client.get(f"/extractors/{extractor_uuid}", headers=headers)
response_data = response.json()
assert extractor_uuid == response_data["uuid"]
assert "my extractor" == response_data["name"]
assert "user_id" not in response_data
async def test_sharing_extractor() -> None:
"""Test sharing an extractor."""
async with get_async_client() as client:
user_id = str(uuid.uuid4())
headers = {"x-key": user_id}
response = await client.get("/extractors", headers=headers)
assert response.status_code == 200
assert response.json() == []
# Verify that we can create an extractor
create_request = {
"name": "Test Name",
"description": "Test Description",
"schema": {"type": "object"},
"instruction": "Test Instruction",
}
response = await client.post(
"/extractors", json=create_request, headers=headers
)
assert response.status_code == 200
uuid_str = response.json()["uuid"]
# Generate a share uuid
response = await client.post(f"/extractors/{uuid_str}/share", headers=headers)
assert response.status_code == 200
assert "share_uuid" in response.json()
share_uuid = response.json()["share_uuid"]
# Test idempotency
response = await client.post(f"/extractors/{uuid_str}/share", headers=headers)
assert response.status_code == 200
assert "share_uuid" in response.json()
assert response.json()["share_uuid"] == share_uuid
# Check headers
bad_headers = {"x-key": str(uuid.uuid4())}
response = await client.post(
f"/extractors/{uuid_str}/share", headers=bad_headers
)
assert response.status_code == 404
# Check that we can retrieve the shared extractor
response = await client.get(f"/shared/extractors/{share_uuid}")
assert response.status_code == 200
keys = sorted(response.json())
assert keys == ["description", "instruction", "name", "schema"]
assert response.json() == {
"description": "Test Description",
"instruction": "Test Instruction",
"name": "Test Name",
"schema": {"type": "object"},
}
|
0
|
lc_public_repos/langchain-extract/backend/tests/unit_tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/api/test_api_examples.py
|
"""Code to test API endpoints."""
import uuid
from tests.db import get_async_client
async def _list_extractors() -> list:
async with get_async_client() as client:
response = await client.get("/extractors")
assert response.status_code == 200
return response.json()
async def test_examples_api() -> None:
"""Runs through a set of API calls to test the examples API."""
async with get_async_client() as client:
# First create an extractor
user_id = str(uuid.uuid4())
headers = {"x-key": user_id}
create_request = {
"description": "Test Description",
"name": "Test Name",
"schema": {"type": "object"},
"instruction": "Test Instruction",
}
response = await client.post(
"/extractors", json=create_request, headers=headers
)
assert response.status_code == 200
# Get the extractor id
extractor_id = response.json()["uuid"]
# Let's verify that there are no examples
response = await client.get(
"/examples?extractor_id=" + extractor_id, headers=headers
)
assert response.status_code == 200
assert response.json() == []
# Now let's create an example
create_request = {
"extractor_id": extractor_id,
"content": "Test Content",
"output": [
{
"age": 100,
"name": "Grung",
}
],
}
response = await client.post("/examples", json=create_request, headers=headers)
assert response.status_code == 200
example_id = response.json()["uuid"]
# Check headers
bad_headers = {"x-key": str(uuid.uuid4())}
response = await client.post(
"/examples", json=create_request, headers=bad_headers
)
assert response.status_code == 404
# Verify that the example was created
response = await client.get(
"/examples?extractor_id=" + extractor_id, headers=headers
)
assert response.status_code == 200
assert len(response.json()) == 1
keys = ["content", "extractor_id", "output", "uuid"]
projected_response = {
key: record[key] for key in keys for record in response.json()
}
assert projected_response == {
"content": "Test Content",
"extractor_id": extractor_id,
"output": [
{
"age": 100,
"name": "Grung",
}
],
"uuid": example_id,
}
# Check headers
response = await client.get(
"/examples?extractor_id=" + extractor_id, headers=bad_headers
)
assert response.status_code == 404
# Check we need cookie to delete
response = await client.delete(f"/examples/{example_id}", headers=bad_headers)
assert response.status_code == 404
# Verify that we can delete an example
response = await client.delete(f"/examples/{example_id}", headers=headers)
assert response.status_code == 200
# Verify that the example was deleted
response = await client.get(
"/examples?extractor_id=" + extractor_id, headers=headers
)
assert response.status_code == 200
assert response.json() == []
|
0
|
lc_public_repos/langchain-extract/backend/tests/unit_tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/fake/chat_model.py
|
"""Fake Chat Model wrapper for testing purposes."""
from typing import Any, Iterator, List, Optional
from langchain_core.callbacks.manager import (
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
BaseMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult
class GenericFakeChatModel(BaseChatModel):
"""A generic fake chat model that can be used to test the chat model interface."""
messages: Iterator[AIMessage]
"""Get an iterator over messages.
This can be expanded to accept other types like Callables / dicts / strings
to make the interface more generic if needed.
Note: if you want to pass a list, you can use `iter` to convert it to an iterator.
Please note that streaming is not implemented yet. We should try to implement it
in the future by delegating to invoke and then breaking the resulting output
into message chunks.
"""
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Top Level call"""
message = next(self.messages)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
@property
def _llm_type(self) -> str:
return "generic-fake-chat-model"
|
0
|
lc_public_repos/langchain-extract/backend/tests/unit_tests
|
lc_public_repos/langchain-extract/backend/tests/unit_tests/fake/test_fake_chat_model.py
|
"""Tests for verifying that testing utility code works as expected."""
from itertools import cycle
from langchain_core.messages import AIMessage
from tests.unit_tests.fake.chat_model import GenericFakeChatModel
class AnyStr(str):
def __init__(self) -> None:
super().__init__()
def __eq__(self, other: object) -> bool:
return isinstance(other, str)
def test_generic_fake_chat_model_invoke() -> None:
# Will alternate between responding with hello and goodbye
infinite_cycle = cycle([AIMessage(content="hello"), AIMessage(content="goodbye")])
model = GenericFakeChatModel(messages=infinite_cycle)
response = model.invoke("meow")
assert response == AIMessage(content="hello", id=AnyStr())
response = model.invoke("kitty")
assert response == AIMessage(content="goodbye", id=AnyStr())
response = model.invoke("meow")
assert response == AIMessage(content="hello", id=AnyStr())
async def test_generic_fake_chat_model_ainvoke() -> None:
# Will alternate between responding with hello and goodbye
infinite_cycle = cycle([AIMessage(content="hello"), AIMessage(content="goodbye")])
model = GenericFakeChatModel(messages=infinite_cycle)
response = await model.ainvoke("meow")
assert response == AIMessage(content="hello", id=AnyStr())
response = await model.ainvoke("kitty")
assert response == AIMessage(content="goodbye", id=AnyStr())
response = await model.ainvoke("meow")
assert response == AIMessage(content="hello", id=AnyStr())
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lc_public_repos/langchain-extract/backend/tests/unit_tests
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lc_public_repos/langchain-extract/backend/tests/unit_tests/fixtures/sample.html
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class="c13 doc-content"><p class="c1"><span class="c3">π¦οΈ LangChain</span></p><p class="c1 c9"><span class="c3"></span></p><p class="c1 c9"><span class="c3"></span></p><p class="c1"><span class="c0">Underline</span></p><p class="c1 c9"><span class="c0"></span></p><p class="c1"><span class="c12">Bold</span></p><p class="c1 c9"><span class="c12"></span></p><p class="c1"><span class="c11">Italics</span></p><p class="c1 c9"><span class="c11"></span></p><p class="c1 c9"><span class="c11"></span></p><a id="t.e89270b97fc18eabe5c666cba79cd82cff5b5c3d"></a><a id="t.0"></a><table class="c14"><tbody><tr class="c2"><td class="c6" colspan="1" rowspan="1"><p class="c4"><span class="c12"></span></p></td><td class="c6" colspan="1" rowspan="1"><p class="c7"><span class="c12">Col 1</span></p></td><td class="c6" colspan="1" rowspan="1"><p class="c7"><span class="c12">Col 2</span></p></td></tr><tr class="c2"><td class="c6" colspan="1" rowspan="1"><p class="c7"><span class="c12">Row 1</span></p></td><td class="c6" colspan="1" rowspan="1"><p class="c7"><span class="c3">1</span></p></td><td class="c6" colspan="1" rowspan="1"><p class="c7"><span class="c3">2</span></p></td></tr><tr class="c2"><td class="c6" colspan="1" rowspan="1"><p class="c7"><span class="c12">Row 2</span></p></td><td class="c6" colspan="1" rowspan="1"><p class="c7"><span class="c3">3</span></p></td><td class="c6" colspan="1" rowspan="1"><p class="c7"><span class="c3">4</span></p></td></tr></tbody></table><p class="c1 c9"><span class="c3"></span></p><p class="c1 c9"><span class="c3"></span></p><p class="c1"><span>Link: </span><span class="c8"><a class="c5" href="https://www.google.com/url?q=https://www.langchain.com/&sa=D&source=editors&ust=1699572948600868&usg=AOvVaw2T4jvAmPuMvcyed6PrEjq1">https://www.langchain.com/</a></span></p><p class="c1 c9"><span class="c3"></span></p><p class="c1 c9"><span class="c3"></span></p><ul class="c15 lst-kix_n6n0tzfwn8i8-0 start"><li class="c1 c10 li-bullet-0"><span class="c3">Item 1</span></li><li class="c1 c10 li-bullet-0"><span class="c3">Item 2</span></li><li class="c1 c10 li-bullet-0"><span class="c3">Item 3</span></li><li class="c1 c10 li-bullet-0"><span class="c3">We also love cats π±</span></li></ul><p class="c1 c9"><span class="c3"></span></p><p class="c1"><span class="c3">Image</span></p><p class="c1 c9"><span class="c3"></span></p><p class="c1"><span style="overflow: hidden; display: inline-block; margin: 0.00px 0.00px; border: 0.00px solid #000000; transform: rotate(0.00rad) translateZ(0px); -webkit-transform: rotate(0.00rad) translateZ(0px); width: 624.00px; height: 132.00px;"><img alt="" src="sample_files/image1.png" style="width: 624.00px; height: 132.00px; margin-left: 0.00px; margin-top: 0.00px; transform: rotate(0.00rad) translateZ(0px); -webkit-transform: rotate(0.00rad) translateZ(0px);" title=""></span></p><p class="c1 c9"><span class="c3"></span></p><p class="c1 c9"><span class="c3"></span></p><p class="c1 c9"><span class="c3"></span></p><p class="c1 c9"><span class="c3"></span></p></body></html>
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lc_public_repos/langchain-extract/backend/tests/unit_tests
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lc_public_repos/langchain-extract/backend/tests/unit_tests/fixtures/__init__.py
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from pathlib import Path
from typing import List
HERE = Path(__file__).parent
# PUBLIC API
def get_sample_paths() -> List[Path]:
"""List all fixtures."""
return list(HERE.glob("sample.*"))
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lc_public_repos/langchain-extract/backend/tests/unit_tests
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lc_public_repos/langchain-extract/backend/tests/unit_tests/fixtures/sample.txt
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ο»Ώπ¦οΈ LangChain
Underline
Bold
Italics
Col 1
Col 2
Row 1
1
2
Row 2
3
4
Link: https://www.langchain.com/
* Item 1
* Item 2
* Item 3
* We also love cats π±
Image
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lc_public_repos/langchain-extract/backend/tests/unit_tests
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lc_public_repos/langchain-extract/backend/tests/unit_tests/fixtures/sample.rtf
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{\rtlch\ab0\ai\af2\afs22\ltrch\b0\i\fs22\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\par}\pard\plain\itap0\s0\ilvl0\fi0\sb0\sa0\aspalpha\aspnum\adjustright\brdrt\brdrl\brdrb\brdrr\brdrbtw\brdrbar\widctlpar\ltrpar\li0\lin0\ri0\rin0\ql
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\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 Col}{\rtlch\ab\ai0\af2\alang1025\afs22\ltrch\b\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 1}{\rtlch\ab\ai0\af2\alang1025\afs22\ltrch\b\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033
\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\cell}\pard\plain\intbl\itap1\s0\ilvl0\fi0\sb0\sa0\aspalpha\aspnum\adjustright\brdrt\brdrl\brdrb\brdrr\brdrbtw\brdrbar\widctlpar\ltrpar\li0\lin0\ri0\rin0\ql\faauto\sl240\slmult1\rtlch\ab0\ai0\af2\afs22
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\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 2}{\rtlch\ab\ai0\af2\alang1025\afs22\ltrch\b\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\cell}{\trowd\irow0\irowband0\ltrrow\trql\trgaph100\trpaddl100\trpaddfl3\trbrdrt\brdrs\brdrw10\brdrcf0\brsp0\trbrdrl\brdrs\brdrw10\brdrcf0\brsp0\trbrdrb\brdrs\brdrw10\brdrcf0\brsp0\trbrdrr\brdrs\brdrw10\brdrcf0\brsp0\trbrdrh\brdrs\brdrw10\brdrcf0\brsp0\trbrdrv
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\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1{\rtlch\ab\ai0\af2\alang1025\afs22\ltrch\b\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 Row}{\rtlch\ab\ai0\af2\alang1025\afs22\ltrch\b\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033
\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 1}{\rtlch\ab\ai0\af2\alang1025\afs22\ltrch\b\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\cell}\pard\plain\intbl\itap1\s0\ilvl0\fi0\sb0\sa0
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\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 1}{\rtlch\ab0\ai0\af2\alang1025\afs22\ltrch\b0\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\cell}\pard\plain\intbl\itap1\s0\ilvl0\fi0\sb0\sa0
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\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 2}{\rtlch\ab0\ai0\af2\alang1025\afs22\ltrch\b0\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\cell}{\trowd\irow1\irowband1\ltrrow\trql\trgaph100\trpaddl100\trpaddfl3\trbrdrt\brdrs\brdrw10\brdrcf0\brsp0\trbrdrl\brdrs\brdrw10\brdrcf0\brsp0\trbrdrb\brdrs\brdrw10\brdrcf0\brsp0\trbrdrr\brdrs\brdrw10\brdrcf0\brsp0\trbrdrh\brdrs\brdrw10\brdrcf0\brsp0\trbrdrv
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\hich\f2\strike0\ulnone\cf1 love}{\rtlch\ab0\ai0\af2\alang1025\afs22\ltrch\b0\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 }{\rtlch\ab0\ai0\af2\alang1025\afs22\ltrch\b0\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033
\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 cats}{\rtlch\ab0\ai0\af2\alang1025\afs22\ltrch\b0\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 \u-10179 \u-9167 }{\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22
\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\par}\pard\plain\itap0\s0\ilvl0\fi0\sb0\sa0\aspalpha\aspnum\adjustright\brdrt\brdrl\brdrb\brdrr\brdrbtw\brdrbar\widctlpar\ltrpar\li0\lin0\ri0\rin0\ql\faauto\sl276\slmult1\rtlch\ab0\ai0\af2\afs22
\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1{\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\par}\pard\plain\itap0\s0\ilvl0\fi0\sb0\sa0\aspalpha\aspnum\adjustright\brdrt\brdrl\brdrb
\brdrr\brdrbtw\brdrbar\widctlpar\ltrpar\li0\lin0\ri0\rin0\ql\faauto\sl276\slmult1\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1{\rtlch\ab0\ai0\af2\alang1025\afs22\ltrch\b0\i0\fs22\lang1033\langnp1033\langfe1033\langfenp1033
\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 Image}{\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\par}\pard\plain\itap0\s0\ilvl0\fi0\sb0\sa0\aspalpha\aspnum\adjustright\brdrt\brdrl\brdrb\brdrr\brdrbtw
\brdrbar\widctlpar\ltrpar\li0\lin0\ri0\rin0\ql\faauto\sl276\slmult1\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1{\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\insrsid10976062\strike0\ulnone\cf1\par}
\pard\plain\itap0\s0\ilvl0\fi0\sb0\sa0\aspalpha\aspnum\adjustright\brdrt\brdrl\brdrb\brdrr\brdrbtw\brdrbar\widctlpar\ltrpar\li0\lin0\ri0\rin0\ql\faauto\sl276\slmult1\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1{\field{
\*\fldinst{\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1 INCLUDEPICTURE "Image_0" \\* MERGEFORMAT }}{\fldrslt{\rtlch\ab0\ai0\af2\afs22\ltrch\b0\i0\fs22\loch\af2\dbch\af2\hich\f2\strike0\ulnone\cf1{\*\shppict{\pict{
\*\picprop\shplid1025{\sp{\sn fLayoutInCell}{\sv 1}}{\sp{\sn fLockAspectRatio}{\sv 0}}{\sp{\sn cropFromTop}{\sv 0}}{\sp{\sn cropFromBottom}{\sv 0}}{\sp{\sn cropFromLeft}{\sv 0}}{\sp{\sn cropFromRight}{\sv 0}}{\sp{\sn dxWrapDistLeft}{\sv 114300}}{\sp{\sn dyWrapDistTop}
{\sv 114300}}{\sp{\sn dxWrapDistRight}{\sv 114300}}{\sp{\sn dyWrapDistBottom}{\sv 114300}}{\sp{\sn shapeType}{\sv 75}}{\sp{\sn pibName}{\sv Image_0}}{\sp{\sn pibFlags}{\sv 10}}}\pngblip\picw25770\pich5450\picwgoal14610\pichgoal3090\picscalex64\picscaley64\piccropl0\piccropr0\piccropt0\piccropb0\bliptag1760923360
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|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/server/models.py
|
import os
from typing import Optional
from langchain_anthropic import ChatAnthropic
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_fireworks import ChatFireworks
from langchain_groq import ChatGroq
from langchain_openai import ChatOpenAI
def get_supported_models():
"""Get models according to environment secrets."""
models = {}
if "OPENAI_API_KEY" in os.environ:
models["gpt-3.5-turbo"] = {
"chat_model": ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
"description": "GPT-3.5 Turbo",
}
if os.environ.get("DISABLE_GPT4", "").lower() != "true":
models["gpt-4-0125-preview"] = {
"chat_model": ChatOpenAI(model="gpt-4-0125-preview", temperature=0),
"description": "GPT-4 0125 Preview",
}
if "FIREWORKS_API_KEY" in os.environ:
models["fireworks"] = {
"chat_model": ChatFireworks(
model="accounts/fireworks/models/firefunction-v1",
temperature=0,
),
"description": "Fireworks Firefunction-v1",
}
if "TOGETHER_API_KEY" in os.environ:
models["together-ai-mistral-8x7b-instruct-v0.1"] = {
"chat_model": ChatOpenAI(
base_url="https://api.together.xyz/v1",
api_key=os.environ["TOGETHER_API_KEY"],
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
temperature=0,
),
"description": "Mixtral 8x7B Instruct v0.1 (Together AI)",
}
if "ANTHROPIC_API_KEY" in os.environ:
models["claude-3-sonnet-20240229"] = {
"chat_model": ChatAnthropic(
model="claude-3-sonnet-20240229", temperature=0
),
"description": "Claude 3 Sonnet",
}
if "GROQ_API_KEY" in os.environ:
models["groq-llama3-8b-8192"] = {
"chat_model": ChatGroq(
model="llama3-8b-8192",
temperature=0,
),
"description": "GROQ Llama 3 8B",
}
return models
SUPPORTED_MODELS = get_supported_models()
DEFAULT_MODEL = "gpt-3.5-turbo"
CHUNK_SIZES = { # in tokens, defaults to int(4_096 * 0.8). Override here.
"gpt-4-0125-preview": int(128_000 * 0.8),
}
def get_chunk_size(model_name: str) -> int:
"""Get the chunk size."""
return CHUNK_SIZES.get(model_name, int(4_096 * 0.8))
def get_model(model_name: Optional[str] = None) -> BaseChatModel:
"""Get the model."""
if model_name is None:
return SUPPORTED_MODELS[DEFAULT_MODEL]["chat_model"]
else:
supported_model_names = list(SUPPORTED_MODELS.keys())
if model_name not in supported_model_names:
raise ValueError(
f"Model {model_name} not found. "
f"Supported models: {supported_model_names}"
)
else:
return SUPPORTED_MODELS[model_name]["chat_model"]
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/server/main.py
|
"""Entry point into the server."""
import logging
import os
from pathlib import Path
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from langserve import add_routes
from server.api import configurables, examples, extract, extractors, shared, suggest
from server.extraction_runnable import (
ExtractRequest,
ExtractResponse,
extraction_runnable,
)
logger = logging.getLogger(__name__)
app = FastAPI(
title="Extraction Powered by LangChain",
description="An extraction service powered by LangChain.",
version="0.0.1",
openapi_tags=[
{
"name": "extraction",
"description": "Operations related to extracting content from text.",
}
],
)
ROOT = Path(__file__).parent.parent
ORIGINS = os.environ.get("CORS_ORIGINS", "").split(",")
if ORIGINS:
app.add_middleware(
CORSMiddleware,
allow_origins=ORIGINS,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/ready")
def ready() -> str:
return "ok"
# Include API endpoints for extractor definitions
app.include_router(extractors.router)
app.include_router(examples.router)
app.include_router(extract.router)
app.include_router(suggest.router)
app.include_router(shared.router)
app.include_router(configurables.router)
add_routes(
app,
extraction_runnable.with_types(
input_type=ExtractRequest, output_type=ExtractResponse
),
path="/extract_text",
enabled_endpoints=["invoke", "batch"],
)
# Serve the frontend
UI_DIR = str(ROOT / "ui")
if os.path.exists(UI_DIR):
app.mount("/", StaticFiles(directory=UI_DIR, html=True), name="ui")
else:
logger.warning("No UI directory found, serving API only.")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="localhost", port=8000)
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/server/validators.py
|
from typing import Any, Dict
from fastapi import HTTPException
from jsonschema import exceptions
from jsonschema.validators import Draft202012Validator
def validate_json_schema(schema: Dict[str, Any]) -> None:
"""Validate a JSON schema."""
try:
Draft202012Validator.check_schema(schema)
except exceptions.ValidationError as e:
raise HTTPException(
status_code=422, detail=f"Not a valid JSON schema: {e.message}"
)
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/server/settings.py
|
from __future__ import annotations
import os
from sqlalchemy.engine import URL
def get_postgres_url() -> URL:
if "INSTANCE_UNIX_SOCKET" in os.environ:
return URL.create(
drivername="postgresql+psycopg2",
username=os.environ.get("PG_USER", "langchain"),
password=os.environ.get("PG_PASSWORD", "langchain"),
database=os.environ.get("PG_DATABASE", "langchain"),
query={
"host": os.environ["INSTANCE_UNIX_SOCKET"],
},
)
url = URL.create(
drivername="postgresql+psycopg2",
username=os.environ.get("PG_USER", "langchain"),
password=os.environ.get("PG_PASSWORD", "langchain"),
host=os.environ.get("PG_HOST", "localhost"),
database=os.environ.get("PG_DATABASE", "langchain"),
port=5432,
)
return url
# Max concurrency used for extracting content from documents.
# A long document is broken into smaller chunks this controls
# how many chunks are processed concurrently.
MAX_CONCURRENCY = int(os.environ.get("MAX_CONCURRENCY", 1))
# Max number of chunks to process per documents
# When a long document is split into chunks, this controls
# how many of those chunks will be processed.
# Set to 0 or negative to disable the max chunks limit.
MAX_CHUNKS = int(os.environ.get("MAX_CHUNKS", -1))
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/server/extraction_runnable.py
|
from __future__ import annotations
import json
import uuid
from typing import Any, Dict, List, Optional, Sequence
from fastapi import HTTPException
from jsonschema import Draft202012Validator, exceptions
from langchain.text_splitter import TokenTextSplitter
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import chain
from langserve import CustomUserType
from pydantic import BaseModel, Field, validator
from typing_extensions import TypedDict
from db.models import Example, Extractor
from extraction.utils import update_json_schema
from server import settings
from server.models import DEFAULT_MODEL, get_chunk_size, get_model
from server.validators import validate_json_schema
class ExtractionExample(BaseModel):
"""An example extraction.
This example consists of a text and the expected output of the extraction.
"""
text: str = Field(..., description="The input text")
output: List[Dict[str, Any]] = Field(
..., description="The expected output of the example. A list of objects."
)
class ExtractRequest(CustomUserType):
"""Request body for the extract endpoint."""
text: str = Field(..., description="The text to extract from.")
json_schema: Dict[str, Any] = Field(
...,
description="JSON schema that describes what content should be extracted "
"from the text.",
alias="schema",
)
instructions: Optional[str] = Field(
None, description="Supplemental system instructions."
)
examples: Optional[List[ExtractionExample]] = Field(
None, description="Examples of extractions."
)
model_name: Optional[str] = Field("gpt-3.5-turbo", description="Chat model to use.")
@validator("json_schema")
def validate_schema(cls, v: Any) -> Dict[str, Any]:
"""Validate the schema."""
validate_json_schema(v)
return v
class ExtractResponse(TypedDict, total=False):
"""Response body for the extract endpoint."""
data: List[Any]
# content to long will be set to true if the content is too long
# and had to be truncated
content_too_long: Optional[bool]
def _cast_example_to_dict(example: Example) -> Dict[str, Any]:
"""Cast example record to dictionary."""
return {
"text": example.content,
"output": example.output,
}
def _make_prompt_template(
instructions: Optional[str],
examples: Optional[Sequence[ExtractionExample]],
function_name: str,
) -> ChatPromptTemplate:
"""Make a system message from instructions and examples."""
prefix = (
"You are a top-tier algorithm for extracting information from text. "
"Only extract information that is relevant to the provided text. "
"If no information is relevant, use the schema and output "
"an empty list where appropriate."
)
if instructions:
system_message = ("system", f"{prefix}\n\n{instructions}")
else:
system_message = ("system", prefix)
prompt_components = [system_message]
if examples is not None:
few_shot_prompt = []
for example in examples:
# TODO: We'll need to refactor this at some point to
# support other encoding strategies. The function calling logic here
# has some hard-coded assumptions (e.g., name of parameters like `data`).
_id = uuid.uuid4().hex[:]
tool_call = {
"args": {"data": example.output},
"name": function_name,
"id": _id,
}
few_shot_prompt.extend(
[
HumanMessage(content=example.text),
AIMessage(content="", tool_calls=[tool_call]),
ToolMessage(
content="You have correctly called this tool.", tool_call_id=_id
),
]
)
prompt_components.extend(few_shot_prompt)
prompt_components.append(
(
"human",
"I need to extract information from "
"the following text: ```\n{text}\n```\n",
),
)
return ChatPromptTemplate.from_messages(prompt_components)
# PUBLIC API
def deduplicate(
extract_responses: Sequence[ExtractResponse],
) -> ExtractResponse:
"""Deduplicate the results.
The deduplication is done by comparing the serialized JSON of each of the results
and only keeping the unique ones.
"""
unique_extracted = []
seen = set()
for response in extract_responses:
for data_item in response["data"]:
# Serialize the data item for comparison purposes
serialized = json.dumps(data_item, sort_keys=True)
if serialized not in seen:
seen.add(serialized)
unique_extracted.append(data_item)
return {
"data": unique_extracted,
}
def get_examples_from_extractor(extractor: Extractor) -> List[Dict[str, Any]]:
"""Get examples from an extractor."""
return [_cast_example_to_dict(example) for example in extractor.examples]
@chain
async def extraction_runnable(extraction_request: ExtractRequest) -> ExtractResponse:
"""An end point to extract content from a given text object."""
# TODO: Add validation for model context window size
schema = update_json_schema(extraction_request.json_schema)
try:
Draft202012Validator.check_schema(schema)
except exceptions.ValidationError as e:
raise HTTPException(status_code=422, detail=f"Invalid schema: {e.message}")
prompt = _make_prompt_template(
extraction_request.instructions,
extraction_request.examples,
schema["title"],
)
model = get_model(extraction_request.model_name)
runnable = (prompt | model.with_structured_output(schema=schema)).with_config(
{"run_name": "extraction"}
)
return await runnable.ainvoke({"text": extraction_request.text})
async def extract_entire_document(
content: str,
extractor: Extractor,
model_name: str,
) -> ExtractResponse:
"""Extract from entire document."""
json_schema = extractor.schema
examples = get_examples_from_extractor(extractor)
text_splitter = TokenTextSplitter(
chunk_size=get_chunk_size(model_name),
chunk_overlap=20,
model_name=DEFAULT_MODEL,
)
texts = text_splitter.split_text(content)
extraction_requests = [
ExtractRequest(
text=text,
schema=json_schema,
instructions=extractor.instruction, # TODO: consistent naming
examples=examples,
model_name=model_name,
)
for text in texts
]
# Limit the number of chunks to process
if len(extraction_requests) > settings.MAX_CHUNKS and settings.MAX_CHUNKS > 0:
content_too_long = True
extraction_requests = extraction_requests[: settings.MAX_CHUNKS]
else:
content_too_long = False
# Run extractions which may potentially yield duplicate results
extract_responses: List[ExtractResponse] = await extraction_runnable.abatch(
extraction_requests, {"max_concurrency": settings.MAX_CONCURRENCY}
)
# Deduplicate the results
return {
"data": deduplicate(extract_responses)["data"],
"content_too_long": content_too_long,
}
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/server/retrieval.py
|
from operator import itemgetter
from typing import Any, Dict, List, Optional
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_core.runnables import RunnableLambda
from langchain_openai import OpenAIEmbeddings
from db.models import Extractor
from server.extraction_runnable import (
ExtractRequest,
ExtractResponse,
deduplicate,
extraction_runnable,
get_examples_from_extractor,
)
def _make_extract_requests(input_dict: Dict[str, Any]) -> List[ExtractRequest]:
docs = input_dict.pop("text")
return [ExtractRequest(text=doc.page_content, **input_dict) for doc in docs]
async def extract_from_content(
content: str,
extractor: Extractor,
model_name: str,
*,
text_splitter_kwargs: Optional[Dict[str, Any]] = None,
) -> ExtractResponse:
"""Extract from potentially long-form content."""
if text_splitter_kwargs is None:
text_splitter_kwargs = {
"separator": "\n\n",
"chunk_size": 1000,
"chunk_overlap": 50,
}
text_splitter = CharacterTextSplitter(**text_splitter_kwargs)
docs = text_splitter.create_documents([content])
doc_contents = [doc.page_content for doc in docs]
vectorstore = FAISS.from_texts(doc_contents, embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
runnable = (
{
"text": itemgetter("query") | retriever,
"schema": itemgetter("schema"),
"instructions": lambda x: x.get("instructions"),
"examples": lambda x: x.get("examples"),
"model_name": lambda x: x.get("model_name"),
}
| RunnableLambda(_make_extract_requests)
| extraction_runnable.abatch
)
schema = extractor.schema
examples = get_examples_from_extractor(extractor)
description = extractor.description # TODO: improve this
result = await runnable.ainvoke(
{
"query": description,
"schema": schema,
"examples": examples,
"instructions": extractor.instruction,
"model_name": model_name,
}
)
return deduplicate(result)
|
0
|
lc_public_repos/langchain-extract/backend/server
|
lc_public_repos/langchain-extract/backend/server/api/suggest.py
|
"""Module to handle the suggest API endpoint.
This is logic that leverages LLMs to suggest an extractor for a given task.
"""
from typing import Optional
from fastapi import APIRouter
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field
from server.models import get_model
router = APIRouter(
prefix="/suggest",
tags=["Suggest an extractor"],
responses={404: {"description": "Not found"}},
)
model = get_model()
class SuggestExtractor(BaseModel):
"""A request to create an extractor."""
description: str = Field(
default="",
description=(
"Short description of what information the extractor is extracting."
),
)
jsonSchema: Optional[str] = Field(
default=None,
description=(
"Existing JSON Schema that describes the entity / "
"information that should be extracted."
),
)
class ExtractorDefinition(BaseModel):
"""Define an information extractor to be used in an information extraction system.""" # noqa: E501
json_schema: str = Field(
...,
description=(
"JSON Schema that describes the entity / "
"information that should be extracted. "
"This schema is specified in JSON Schema format. "
),
)
SUGGEST_PROMPT = ChatPromptTemplate.from_messages(
[
(
"system",
"You are are an expert ontologist and have been asked to help a user "
"define an information extractor.The user will describe an entity, "
"a topic or a piece of information that they would like to extract from "
"text. Based on the user input, you are to provide a schema and "
"description for the extractor. The schema should be a JSON Schema that "
"describes the entity or information to be extracted. information to be "
"extracted. Make sure to include title and description for all the "
"attributes in the schema.The JSON Schema should describe a top level "
"object. The object MUST have a title and description.Unless otherwise "
"stated all entity properties in the schema should be considered optional.",
),
("human", "{input}"),
]
)
suggestion_chain = SUGGEST_PROMPT | model.with_structured_output(
schema=ExtractorDefinition
).with_config({"run_name": "suggest"})
UPDATE_PROMPT = ChatPromptTemplate.from_messages(
[
(
"system",
"You are are an expert ontologist and have been asked to help a user "
"define an information extractor.gThe existing extractor schema is "
"provided.\ng```\n{json_schema}\n```\nThe user will describe a desired "
"modification to the schema (e.g., adding a new field, changing a field "
"type, etc.).Your goal is to provide a new schema that incorporates the "
"user's desired modification.The user may also request a completely new "
"schema, in which case you should provide a new schema based on the "
"user's input, and ignore the existing schema.The JSON Schema should "
"describe a top level object. The object MUST have a title and "
"description.Unless otherwise stated all entity properties in the schema "
"should be considered optional.",
),
("human", "{input}"),
]
)
UPDATE_CHAIN = (
UPDATE_PROMPT | model.with_structured_output(schema=ExtractorDefinition)
).with_config({"run_name": "suggest_update"})
# PUBLIC API
@router.post("")
async def suggest(request: SuggestExtractor) -> ExtractorDefinition:
"""Endpoint to create an extractor."""
if len(request.jsonSchema) > 10:
return await UPDATE_CHAIN.ainvoke(
{"input": request.description, "json_schema": request.jsonSchema}
)
return await suggestion_chain.ainvoke({"input": request.description})
|
0
|
lc_public_repos/langchain-extract/backend/server
|
lc_public_repos/langchain-extract/backend/server/api/extract.py
|
from typing import Literal, Optional
from uuid import UUID
from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile
from sqlalchemy.orm import Session
from typing_extensions import Annotated
from db.models import Extractor, SharedExtractors, get_session
from extraction.parsing import parse_binary_input
from server.api.api_key import UserToken
from server.extraction_runnable import ExtractResponse, extract_entire_document
from server.models import DEFAULT_MODEL
from server.retrieval import extract_from_content
router = APIRouter(
prefix="/extract",
tags=["extract"],
responses={404: {"description": "Not found"}},
)
@router.post("", response_model=ExtractResponse)
async def extract_using_existing_extractor(
*,
extractor_id: Annotated[UUID, Form()],
text: Optional[str] = Form(None),
mode: Literal["entire_document", "retrieval"] = Form("entire_document"),
file: Optional[UploadFile] = File(None),
model_name: Optional[str] = Form(DEFAULT_MODEL),
session: Session = Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> ExtractResponse:
"""Endpoint that is used with an existing extractor.
This endpoint will be expanded to support upload of binary files as well as
text files.
"""
if text is None and file is None:
raise HTTPException(status_code=422, detail="No text or file provided.")
extractor = (
session.query(Extractor).filter_by(uuid=extractor_id, owner_id=user_id).scalar()
)
if extractor is None:
raise HTTPException(status_code=404, detail="Extractor not found for owner.")
if text:
text_ = text
else:
documents = parse_binary_input(file.file)
# TODO: Add metadata like location from original file where
# the text was extracted from
text_ = "\n".join([document.page_content for document in documents])
if mode == "entire_document":
return await extract_entire_document(text_, extractor, model_name)
elif mode == "retrieval":
return await extract_from_content(text_, extractor, model_name)
else:
raise ValueError(
f"Invalid mode {mode}. Expected one of 'entire_document', 'retrieval'."
)
@router.post("/shared", response_model=ExtractResponse)
async def extract_using_shared_extractor(
*,
extractor_id: Annotated[UUID, Form()],
text: Optional[str] = Form(None),
mode: Literal["entire_document", "retrieval"] = Form("entire_document"),
file: Optional[UploadFile] = File(None),
model_name: Optional[str] = Form("default"),
session: Session = Depends(get_session),
) -> ExtractResponse:
"""Endpoint that is used with an existing extractor.
Args:
extractor_id: The UUID of the shared extractor.
This is the UUID that is used to share the extractor, not
the UUID of the extractor itself.
text: The text to extract from.
mode: The mode to use for extraction.
file: The file to extract from.
model_name: The model to use for extraction.
session: The database session.
"""
if text is None and file is None:
raise HTTPException(status_code=422, detail="No text or file provided.")
extractor = (
session.query(Extractor)
.join(SharedExtractors, Extractor.uuid == SharedExtractors.extractor_id)
.filter(SharedExtractors.share_token == extractor_id)
.scalar()
)
if not extractor:
raise HTTPException(status_code=404, detail="Extractor not found.")
if text:
text_ = text
else:
documents = parse_binary_input(file.file)
# TODO: Add metadata like location from original file where
# the text was extracted from
text_ = "\n".join([document.page_content for document in documents])
if mode == "entire_document":
return await extract_entire_document(text_, extractor, model_name)
elif mode == "retrieval":
return await extract_from_content(text_, extractor, model_name)
else:
raise ValueError(
f"Invalid mode {mode}. Expected one of 'entire_document', 'retrieval'."
)
|
0
|
lc_public_repos/langchain-extract/backend/server
|
lc_public_repos/langchain-extract/backend/server/api/shared.py
|
"""Endpoints for working with shared resources."""
from typing import Any, Dict
from uuid import UUID
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel, Field
from sqlalchemy.orm import Session
from db.models import Extractor, SharedExtractors, get_session
router = APIRouter(
prefix="/shared/extractors",
tags=["extractor definitions"],
responses={404: {"description": "Not found"}},
)
class SharedExtractorResponse(BaseModel):
"""Response for sharing an extractor."""
# UUID should not be included in the response since it is not a public identifier!
name: str
description: str
# schema is a reserved keyword by pydantic
schema_: Dict[str, Any] = Field(..., alias="schema")
instruction: str
@router.get("/{uuid}")
def get(
uuid: UUID,
*,
session: Session = Depends(get_session),
) -> SharedExtractorResponse:
"""Get a shared extractor."""
extractor = (
session.query(Extractor)
.join(SharedExtractors, Extractor.uuid == SharedExtractors.extractor_id)
.filter(SharedExtractors.share_token == uuid)
.first()
)
if not extractor:
raise HTTPException(status_code=404, detail="Extractor not found.")
return SharedExtractorResponse(
name=extractor.name,
description=extractor.description,
schema=extractor.schema,
instruction=extractor.instruction,
)
|
0
|
lc_public_repos/langchain-extract/backend/server
|
lc_public_repos/langchain-extract/backend/server/api/configurables.py
|
"""Endpoint for listing available chat models for extraction."""
from typing import List
from fastapi import APIRouter
from typing_extensions import TypedDict
from extraction.parsing import MAX_FILE_SIZE_MB, SUPPORTED_MIMETYPES
from server.models import SUPPORTED_MODELS
from server.settings import MAX_CHUNKS, MAX_CONCURRENCY
router = APIRouter(
prefix="/configuration",
tags=["Configuration"],
responses={404: {"description": "Not found"}},
)
class ConfigurationResponse(TypedDict):
"""Response for configuration."""
available_models: List[str]
accepted_mimetypes: List[str]
max_file_size_mb: int
max_concurrency: int
max_chunks: int
models: List[dict]
@router.get("")
def get() -> ConfigurationResponse:
"""Endpoint to show server configuration."""
return {
"available_models": sorted(SUPPORTED_MODELS), # Deprecate
"models": [
{
"name": model,
"description": data["description"],
}
for model, data in SUPPORTED_MODELS.items()
],
"accepted_mimetypes": SUPPORTED_MIMETYPES,
"max_file_size_mb": MAX_FILE_SIZE_MB,
"max_concurrency": MAX_CONCURRENCY,
"max_chunks": MAX_CHUNKS,
}
|
0
|
lc_public_repos/langchain-extract/backend/server
|
lc_public_repos/langchain-extract/backend/server/api/extractors.py
|
"""Endpoints for managing definition of extractors."""
from typing import Any, Dict, List
from uuid import UUID, uuid4
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel, Field, validator
from sqlalchemy.exc import IntegrityError
from sqlalchemy.orm import Session
from db.models import Extractor, SharedExtractors, get_session, validate_extractor_owner
from server.api.api_key import UserToken
from server.validators import validate_json_schema
router = APIRouter(
prefix="/extractors",
tags=["extractor definitions"],
responses={404: {"description": "Not found"}},
)
class CreateExtractor(BaseModel):
"""A request to create an extractor."""
name: str = Field(default="", description="The name of the extractor.")
description: str = Field(
default="", description="Short description of the extractor."
)
json_schema: Dict[str, Any] = Field(
..., description="The schema to use for extraction.", alias="schema"
)
instruction: str = Field(..., description="The instruction to use for extraction.")
@validator("json_schema")
def validate_schema(cls, v: Any) -> Dict[str, Any]:
"""Validate the schema."""
validate_json_schema(v)
return v
class CreateExtractorResponse(BaseModel):
"""Response for creating an extractor."""
uuid: UUID = Field(..., description="The UUID of the created extractor.")
class ShareExtractorRequest(BaseModel):
"""Response for sharing an extractor."""
uuid: UUID = Field(..., description="The UUID of the extractor to share.")
class ShareExtractorResponse(BaseModel):
"""Response for sharing an extractor."""
share_uuid: UUID = Field(..., description="The UUID for the shared extractor.")
@router.post("/{uuid}/share", response_model=ShareExtractorResponse)
def share(
uuid: UUID,
*,
session: Session = Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> ShareExtractorResponse:
"""Endpoint to share an extractor.
Look up a shared extractor by UUID and return the share UUID if it exists.
If not shared, create a new shared extractor entry and return the new share UUID.
Args:
uuid: The UUID of the extractor to share.
session: The database session.
Returns:
The UUID for the shared extractor.
"""
if not validate_extractor_owner(session, uuid, user_id):
raise HTTPException(status_code=404, detail="Extractor not found for owner.")
# Check if the extractor is already shared
shared_extractor = (
session.query(SharedExtractors)
.filter(SharedExtractors.extractor_id == uuid)
.scalar()
)
if shared_extractor:
# The extractor is already shared, return the existing share_uuid
return ShareExtractorResponse(share_uuid=shared_extractor.share_token)
# If not shared, create a new shared extractor entry
new_shared_extractor = SharedExtractors(
extractor_id=uuid,
# This will automatically generate a new UUID for share_token
share_token=uuid4(),
)
session.add(new_shared_extractor)
try:
session.commit()
except IntegrityError:
session.rollback()
raise HTTPException(status_code=400, detail="Failed to share the extractor.")
# Return the new share_uuid
return ShareExtractorResponse(share_uuid=new_shared_extractor.share_token)
@router.post("")
def create(
create_request: CreateExtractor,
*,
session: Session = Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> CreateExtractorResponse:
"""Endpoint to create an extractor."""
instance = Extractor(
name=create_request.name,
owner_id=user_id,
schema=create_request.json_schema,
description=create_request.description,
instruction=create_request.instruction,
)
session.add(instance)
session.commit()
return CreateExtractorResponse(uuid=instance.uuid)
@router.get("/{uuid}")
def get(
uuid: UUID,
*,
session: Session = Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> Dict[str, Any]:
"""Endpoint to get an extractor."""
extractor = (
session.query(Extractor).filter_by(uuid=str(uuid), owner_id=user_id).scalar()
)
if extractor is None:
raise HTTPException(status_code=404, detail="Extractor not found for owner.")
return {
"uuid": extractor.uuid,
"name": extractor.name,
"description": extractor.description,
"schema": extractor.schema,
"instruction": extractor.instruction,
}
@router.get("")
def list(
*,
limit: int = 10,
offset: int = 0,
session=Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> List[Any]:
"""Endpoint to get all extractors."""
return (
session.query(Extractor)
.filter_by(owner_id=user_id)
.limit(limit)
.offset(offset)
.all()
)
@router.delete("/{uuid}")
def delete(
uuid: UUID,
*,
session: Session = Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> None:
"""Endpoint to delete an extractor."""
session.query(Extractor).filter_by(uuid=str(uuid), owner_id=user_id).delete()
session.commit()
|
0
|
lc_public_repos/langchain-extract/backend/server
|
lc_public_repos/langchain-extract/backend/server/api/examples.py
|
"""Endpoints for managing definition of examples.."""
from typing import Any, List
from uuid import UUID
from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.orm import Session
from typing_extensions import Annotated, TypedDict
from db.models import Example, get_session, validate_extractor_owner
from server.api.api_key import UserToken
router = APIRouter(
prefix="/examples",
tags=["example definitions"],
responses={404: {"description": "Not found"}},
)
class CreateExample(TypedDict):
"""A request to create an example."""
extractor_id: Annotated[UUID, "The extractor ID that this is an example for."]
content: Annotated[str, "The input portion of the example."]
output: Annotated[
List[Any], "JSON object that is expected to be extracted from the content."
]
class CreateExampleResponse(TypedDict):
"""Response for creating an example."""
uuid: UUID
@router.post("")
def create(
create_request: CreateExample,
*,
session: Session = Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> CreateExampleResponse:
"""Endpoint to create an example."""
if not validate_extractor_owner(session, create_request["extractor_id"], user_id):
raise HTTPException(status_code=404, detail="Extractor not found for owner.")
instance = Example(
extractor_id=create_request["extractor_id"],
content=create_request["content"],
output=create_request["output"],
)
session.add(instance)
session.commit()
return {"uuid": instance.uuid}
@router.get("")
def list(
extractor_id: UUID,
*,
limit: int = 10,
offset: int = 0,
session=Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> List[Any]:
"""Endpoint to get all examples."""
if not validate_extractor_owner(session, extractor_id, user_id):
raise HTTPException(status_code=404, detail="Extractor not found for owner.")
return (
session.query(Example)
.filter(Example.extractor_id == extractor_id)
.order_by(Example.uuid)
.limit(limit)
.offset(offset)
.all()
)
@router.delete("/{uuid}")
def delete(
uuid: UUID,
*,
session: Session = Depends(get_session),
user_id: UUID = Depends(UserToken),
) -> None:
"""Endpoint to delete an example."""
example = session.query(Example).filter_by(uuid=str(uuid)).first()
if example is None:
raise HTTPException(status_code=404, detail="Example not found.")
extractor_id = example.extractor_id
if not validate_extractor_owner(session, extractor_id, user_id):
raise HTTPException(status_code=404, detail="Extractor not found for owner.")
session.query(Example).filter_by(uuid=str(uuid)).delete()
session.commit()
|
0
|
lc_public_repos/langchain-extract/backend/server
|
lc_public_repos/langchain-extract/backend/server/api/api_key.py
|
from fastapi.security import APIKeyHeader
# For actual auth, you'd need to check the key against a database or some other
# data store. Here, we don't need actual auth, just a key that matches
# a UUID
UserToken = APIKeyHeader(name="x-key")
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/scripts/run_migrations.py
|
#!/usr/bin/env python
"""Run migrations."""
import click
from db.models import ENGINE, Base
@click.group()
def cli():
"""Database migration commands."""
pass
@cli.command()
def create():
"""Create all tables."""
Base.metadata.create_all(ENGINE)
click.echo("All tables created successfully.")
@cli.command()
@click.confirmation_option(prompt="Are you sure you want to drop all tables?")
def drop():
"""Drop all tables."""
Base.metadata.drop_all(ENGINE)
click.echo("All tables dropped successfully.")
@cli.command()
def create_test_db():
"""Create a test database called langchain_test used for testing purposes."""
import psycopg2
from psycopg2.errors import DuplicateDatabase
# establishing the connection
conn = psycopg2.connect(
database="langchain",
user="langchain",
password="langchain",
host="localhost",
port="5432",
)
conn.autocommit = True
# Creating a cursor object using the cursor() method
with conn.cursor() as cursor:
# Preparing query to create a database
sql = "CREATE DATABASE langchain_test;"
# Creating a database
try:
cursor.execute(sql)
print("Database created successfully.")
except DuplicateDatabase:
print("Database already exists")
if __name__ == "__main__":
cli()
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/scripts/local_entry_point.sh
|
# -e: fail on any nonzero exit status
# -u: fail if any referenced variables are not set
# -x: print commands before running them
# -o pipefail: fail if a command in a pipe has a nonzero exit code
set -euxo pipefail
# For now just create the db if it doesn't exist
python -m scripts.run_migrations create
uvicorn server.main:app --host 0.0.0.0 --port 8000 --reload
|
0
|
lc_public_repos/langchain-extract/backend
|
lc_public_repos/langchain-extract/backend/scripts/prod_entry_point.sh
|
# -e: fail on any nonzero exit status
# -u: fail if any referenced variables are not set
# -x: print commands before running them
# -o pipefail: fail if a command in a pipe has a nonzero exit code
set -euxo pipefail
# For now just create the db if it doesn't exist
python -m scripts.run_migrations create
uvicorn server.main:app --host 0.0.0.0 --port 8080 --reload
|
0
|
lc_public_repos
|
lc_public_repos/langgraph-studio/README.md
|
# LangGraph Studio (Beta)
LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications
With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with [LangSmith](https://smith.langchain.com/) so you can collaborate with teammates to debug failure modes.
While in Beta, LangGraph Studio is available for free to all LangSmith users on any plan tier. [Sign up for LangSmith here](http://smith.langchain.com/).
## Download
Download the latest `.dmg` file of LangGraph Studio by clicking [here](https://langgraph-studio.vercel.app/api/mac/latest) or by visiting the [releases page](https://github.com/langchain-ai/langgraph-studio/releases).
The desktop application only supports macOS. Other users can [run a local LangGraph server and use the web studio](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/#langgraph-studio-web-ui). We also depend on Docker Engine to be running, currently we only support the following runtimes:
- [Docker Desktop](https://docs.docker.com/engine/install/)
- [Orbstack](https://orbstack.dev/)
## Setup
LangGraph Studio requires docker-compose version 2.22.0+ or higher. Please make sure you have [Docker Desktop](https://docs.docker.com/engine/install/) or [Orbstack](https://orbstack.dev/) installed and running before continuing.
To use LangGraph Studio, make sure you have a [project with a LangGraph app](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/) set up.
For this example, we will use this example repository [here](https://github.com/langchain-ai/langgraph-example) which uses a `requirements.txt` file for dependencies:
```shell
git clone https://github.com/langchain-ai/langgraph-example.git
```
If you would like to use a `pyproject.toml` file instead for managing dependencies, you can use [this](https://github.com/langchain-ai/langgraph-example-pyproject) example repository.
```shell
git clone https://github.com/langchain-ai/langgraph-example-pyproject.git
```
You will then want to create a `.env` file with the relevant environment variables:
```shell
cp .env.example .env
```
You should then open up the `.env` file and fill in with relevant OpenAI, Anthropic, and [Tavily](https://app.tavily.com/sign-in) API keys.
If you already have them set in your environment, you can save them to this .env file with the following commands:
```shell
echo "OPENAI_API_KEY=\"$OPENAI_API_KEY\"" > .env
echo "ANTHROPIC_API_KEY=\"$ANTHROPIC_API_KEY\"" >> .env
echo "TAVILY_API_KEY=\"$TAVILY_API_KEY\"" >> .env
```
**Note: do NOT add a LANGSMITH_API_KEY to the .env file.** We will do this automatically for you when you authenticate, and manually setting this may cause errors.
Once you've set up the project, you can use it in LangGraph Studio. Let's dive in!
## Open a project
When you open LangGraph Studio desktop app for the first time, you need to login via LangSmith.
Once you have successfully authenticated, you can choose the LangGraph application folder to use β you can either drag and drop or manually select it in the file picker. If you are using the example project, the folder would be `langgraph-example`.
> [!IMPORTANT]
> The application directory you select needs to contain correctly configured `langgraph.json` file. See more information on how to configure it [here](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#configuration-file) and how to set up a LangGraph app [here](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/).
Once you select a valid project, LangGraph Studio will start a LangGraph API server and you should see a UI with your graph rendered.
## Invoke graph
Now we can run the graph! LangGraph Studio lets you run your graph with different inputs and configurations.
### Start a new run
To start a new run:
1. In the dropdown menu (top-left corner of the left-hand pane), select a graph. In our example the graph is called `agent`. The list of graphs corresponds to the `graphs` keys in your `langgraph.json` configuration.
1. In the bottom of the left-hand pane, edit the `Input` section.
1. Click `Submit` to invoke the selected graph.
1. View output of the invocation in the right-hand pane.
The following video shows how to start a new run:
https://github.com/user-attachments/assets/e0e7487e-17e2-4194-a4ad-85b346c2f1c4
### Configure graph run
To change configuration for a given graph run, press `Configurable` button in the `Input` section. Then click `Submit` to invoke the graph.
> [!IMPORTANT]
> In order for the `Configurable` menu to be visible, make sure to specify config schema when creating `StateGraph`. You can read more about how to add config schema to your graph [here](https://langchain-ai.github.io/langgraph/how-tos/configuration/#configure-the-graph).
The following video shows how to edit configuration and start a new run:
https://github.com/user-attachments/assets/8495b476-7e33-42d4-85cb-2f9269bea20c
## Create and edit threads
### Create a thread
When you open LangGraph Studio, you will automatically be in a new thread window. If you have an existing thread open, follow these steps to create a new thread:
1. In the top-right corner of the right-hand pane, press `+` to open a new thread menu.
The following video shows how to create a thread:
https://github.com/user-attachments/assets/78d4a692-2042-48e2-a7e2-5a7ca3d5a611
### Select a thread
To select a thread:
1. Click on `New Thread` / `Thread <thread-id>` label at the top of the right-hand pane to open a thread list dropdown.
1. Select a thread that you wish to view / edit.
The following video shows how to select a thread:
https://github.com/user-attachments/assets/5f0dbd63-fa59-4496-8d8e-4fb8d0eab893
### Edit thread state
LangGraph Studio allows you to edit the thread state and fork the threads to create alternative graph execution with the updated state. To do it:
1. Select a thread you wish to edit.
1. In the right-hand pane hover over the step you wish to edit and click on "pencil" icon to edit.
1. Make your edits.
1. Click `Fork` to update the state and create a new graph execution with the updated state.
The following video shows how to edit a thread in the studio:
https://github.com/user-attachments/assets/47f887e7-2e3f-46ce-977c-f474c3cd797e
## How to add interrupts to your graph
You might want to execute your graph step by step, or stop graph execution before/after a specific node executes. You can do so by adding interrupts. Interrupts can be set for all nodes (i.e. walk through the agent execution step by step) or for specific nodes. An interrupt in LangGraph Studio means that the graph execution will be interrupted both before and after a given node runs.
### Add interrupts to a list of nodes
To walk through the agent execution step by step, you can add interrupts to a all or a subset of nodes in the graph:
1. In the dropdown menu (top-right corner of the left-hand pane), click `Interrupt`.
2. Select a subset of nodes to interrupt on, or click `Interrupt on all`.
The following video shows how to add interrupts to all nodes:
https://github.com/user-attachments/assets/db44ebda-4d6e-482d-9ac8-ea8f5f0148ea
### Add interrupt to a specific node
1. Navigate to the left-hand pane with the graph visualization.
1. Hover over a node you want to add an interrupt to. You should see a `+` button show up on the left side of the node.
1. Click `+` to invoke the selected graph.
1. Run the graph by adding `Input` / configuration and clicking `Submit`
The following video shows how to add interrupts to a specific node:
https://github.com/user-attachments/assets/13429609-18fc-4f21-9cb9-4e0daeea62c4
To remove the interrupt, simply follow the same step and press `x` button on the left side of the node.
## Human-in-the-loop
In addition to interrupting on a node and editing the graph state, you might want to support human-in-the-loop workflows with the ability to manually update state. Here is a modified version of `agent.py` with `agent` and `human` nodes, where the graph execution will be interrupted on `human` node. This will let you send input as part of the `human` node. This can be useful when you want the agent to get user input. This essentially replaces how you might use `input()` if you were running this from the command line.
```python
from typing import TypedDict, Annotated, Sequence, Literal
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_anthropic import ChatAnthropic
from langgraph.graph import StateGraph, END, add_messages
class AgentState(TypedDict):
messages: Annotated[Sequence[BaseMessage], add_messages]
model = ChatAnthropic(temperature=0, model_name="claude-3-sonnet-20240229")
def call_model(state: AgentState) -> AgentState:
messages = state["messages"]
response = model.invoke(messages)
return {"messages": [response]}
# no-op node that should be interrupted on
def human_feedback(state: AgentState) -> AgentState:
pass
def should_continue(state: AgentState) -> Literal["agent", "end"]:
messages = state['messages']
last_message = messages[-1]
if isinstance(last_message, HumanMessage):
return "agent"
return "end"
workflow = StateGraph(AgentState)
workflow.set_entry_point("agent")
workflow.add_node("agent", call_model)
workflow.add_node("human", human_feedback)
workflow.add_edge("agent", "human")
workflow.add_conditional_edges(
"human",
should_continue,
{
"agent": "agent",
"end": END,
},
)
graph = workflow.compile(interrupt_before=["human"])
```
The following video shows how to manually send state updates (i.e. messages in our example) when interrupted:
https://github.com/user-attachments/assets/f6d4fd18-df4d-45b7-8b1b-ad8506d08abd
## Edit project config
LangGraph Studio allows you to modify your project config (`langgraph.json`) interactively.
To modify the config from the studio, follow these steps:
1. Click `Configure` on the bottom right. This will open an interactive config menu with the values that correspond to the existing `langgraph.json`.
1. Make your edits.
1. Click `Save and Restart` to reload the LangGraph API server with the updated config.
The following video shows how to edit project config from the studio:
https://github.com/user-attachments/assets/86d7d1f7-800c-4739-80bc-8122b4728817
## Edit graph code
With LangGraph Studio you can modify your graph code and sync the changes live to the interactive graph.
To modify your graph from the studio, follow these steps:
1. Click `Open in VS Code` on the bottom right. This will open the project that is currently opened in LangGraph studio.
1. Make changes to the `.py` files where the compiled graph is defined or associated dependencies.
1. LangGraph studio will automatically reload once the changes are saved in the project directory.
The following video shows how to open code editor from the studio:
https://github.com/user-attachments/assets/8ac0443d-460b-438e-a379-182ec9f68ff5
After you modify the underlying code you can also replay a node in the graph. For example, if an agent responds poorly, you can update the agent node implementation in your code editor and rerun it. This can make it much easier to iterate on long-running agents.
https://github.com/user-attachments/assets/9ec1b8ed-c6f8-433d-8bef-0dbda58a1075
## Troubleshooting
### How do I access local services and models such as Ollama, Chroma, etc?
LangGraph Studio relies on Docker Compose to run the API, Redis and Postgres, which in turn creates its own network. Thus, to access local services you need to use `host.docker.internal` as the hostname instead of `localhost`. See [#112](https://github.com/langchain-ai/langgraph-studio/issues/112) for more details.
### Failing to install native dependencies during build
By default, we try to make the image as small as possible, thus some dependencies such as `gcc` or `build-essentials` are missing from the base image. If you need to install additional dependencies, you can do so by adding additional Dockerfile instructions in the `dockerfile_lines` section of your `langgraph.json` file:
```
{
"dockerfile_lines": [
"RUN apt-get update && apt-get install -y gcc"
]
}
```
See [How to customize Dockerfile](https://langchain-ai.github.io/langgraph/cloud/deployment/custom_docker) for more details.
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|
0
|
lc_public_repos
|
lc_public_repos/langchainjs/.vercelignore
|
node_modules/
**/node_modules/
.next/
**/.next/
|
0
|
lc_public_repos
|
lc_public_repos/langchainjs/release_workspace.js
|
const { execSync } = require("child_process");
const { Command } = require("commander");
const fs = require("fs");
const path = require("path");
const { spawn } = require("child_process");
const readline = require("readline");
const semver = require("semver");
const RELEASE_BRANCH = "release";
const MAIN_BRANCH = "main";
/**
* Handles execSync errors and logs them in a readable format.
* @param {string} command
* @param {{ doNotExit?: boolean }} [options] - Optional configuration
* @param {boolean} [options.doNotExit] - Whether or not to exit the process on error
*/
function execSyncWithErrorHandling(command, options = {}) {
try {
execSync(
command,
{ stdio: "inherit" } // This will stream output in real-time
);
} catch (error) {
console.error(error.message);
if (!options.doNotExit) {
process.exit(1);
}
}
}
/**
* Get the version of a workspace inside a directory.
*
* @param {string} workspaceDirectory
* @returns {string} The version of the workspace in the input directory.
*/
function getWorkspaceVersion(workspaceDirectory) {
const pkgJsonFile = fs.readFileSync(
path.join(process.cwd(), workspaceDirectory, "package.json")
);
const parsedJSONFile = JSON.parse(pkgJsonFile);
return parsedJSONFile.version;
}
/**
* Finds all workspaces in the monorepo and returns an array of objects.
* Each object in the return value contains the relative path to the workspace
* directory, along with the full package.json file contents.
*
* @returns {Array<{ dir: string, packageJSON: Record<string, any>}>}
*/
function getAllWorkspaces() {
const possibleWorkspaceDirectories = [
"./libs/*",
"./langchain",
"./langchain-core",
];
const allWorkspaces = possibleWorkspaceDirectories.flatMap(
(workspaceDirectory) => {
if (workspaceDirectory.endsWith("*")) {
// List all folders inside directory, require, and return the package.json.
const allDirs = fs.readdirSync(
path.join(process.cwd(), workspaceDirectory.replace("*", ""))
);
const subDirs = allDirs.map((dir) => {
return {
dir: `${workspaceDirectory.replace("*", "")}${dir}`,
packageJSON: require(path.join(
process.cwd(),
`${workspaceDirectory.replace("*", "")}${dir}`,
"package.json"
)),
};
});
return subDirs;
}
const packageJSON = require(path.join(
process.cwd(),
workspaceDirectory,
"package.json"
));
return {
dir: workspaceDirectory,
packageJSON,
};
}
);
return allWorkspaces;
}
/**
* Writes the JSON file with the updated dependency version. Accounts
* for version prefixes, eg ~, ^, >, <, >=, <=, ||, *. Also skips
* versions which are "latest" or "workspace:*".
*
* @param {Array<string>} workspaces
* @param {"dependencies" | "devDependencies" | "peerDependencies"} dependencyType
* @param {string} workspaceName
* @param {string} newVersion
*/
function updateDependencies(
workspaces,
dependencyType,
workspaceName,
newVersion
) {
const versionPrefixes = ["~", "^", ">", "<", ">=", "<=", "||", "*"];
const skipVersions = ["latest", "workspace:*"];
workspaces.forEach((workspace) => {
const currentVersion =
workspace.packageJSON[dependencyType]?.[workspaceName];
if (currentVersion) {
const prefix = versionPrefixes.find((p) => currentVersion.startsWith(p));
const shouldSkip = skipVersions.some((v) => currentVersion === v);
if (!shouldSkip) {
const versionToUpdate = prefix ? `${prefix}${newVersion}` : newVersion;
workspace.packageJSON[dependencyType][workspaceName] = versionToUpdate;
fs.writeFileSync(
path.join(workspace.dir, "package.json"),
JSON.stringify(workspace.packageJSON, null, 2) + "\n"
);
}
}
});
}
/**
* Runs `release-it` with args in the input package directory,
* passing the new version as an argument, along with other
* release-it args.
*
* @param {string} packageDirectory The directory to run yarn release in.
* @param {string} npm2FACode The 2FA code for NPM.
* @param {string | undefined} tag An optional tag to publish to.
* @returns {Promise<void>}
*/
async function runYarnRelease(packageDirectory, npm2FACode, tag) {
return new Promise((resolve, reject) => {
const workingDirectory = path.join(process.cwd(), packageDirectory);
const tagArg = tag ? `--npm.tag=${tag}` : "";
const args = [
"release-it",
`--npm.otp=${npm2FACode}`,
tagArg,
"--config",
".release-it.json",
];
console.log(`Running command: "yarn ${args.join(" ")}"`);
// Use 'inherit' for stdio to allow direct CLI interaction
const yarnReleaseProcess = spawn("yarn", args, {
stdio: "inherit",
cwd: workingDirectory,
});
yarnReleaseProcess.on("close", (code) => {
if (code === 0) {
resolve();
} else {
reject(`Process exited with code ${code}`);
}
});
yarnReleaseProcess.on("error", (err) => {
reject(`Failed to start process: ${err.message}`);
});
});
}
/**
* Finds all `package.json`'s which contain the input workspace as a dependency.
* Then, updates the dependency to the new version, runs yarn install and
* commits the changes.
*
* @param {string} workspaceName The name of the workspace to bump dependencies for.
* @param {string} workspaceDirectory The path to the workspace directory.
* @param {Array<{ dir: string, packageJSON: Record<string, any>}>} allWorkspaces
* @param {string | undefined} tag An optional tag to publish to.
* @param {string} preReleaseVersion The version of the workspace before it was released.
* @returns {void}
*/
function bumpDeps(
workspaceName,
workspaceDirectory,
allWorkspaces,
tag,
preReleaseVersion
) {
// Read workspace file, get version (edited by release-it), and bump pkgs to that version.
let updatedWorkspaceVersion = getWorkspaceVersion(workspaceDirectory);
if (!semver.valid(updatedWorkspaceVersion)) {
console.error("Invalid workspace version: ", updatedWorkspaceVersion);
process.exit(1);
}
// If the updated version is not greater than the pre-release version,
// the branch is out of sync. Pull from github and check again.
if (!semver.gt(updatedWorkspaceVersion, preReleaseVersion)) {
console.log(
"Updated version is not greater than the pre-release version. Pulling from github and checking again."
);
execSyncWithErrorHandling(`git pull origin ${RELEASE_BRANCH}`);
updatedWorkspaceVersion = getWorkspaceVersion(workspaceDirectory);
if (!semver.gt(updatedWorkspaceVersion, preReleaseVersion)) {
console.warn(
`Workspace version has not changed in repo. Version in repo: ${updatedWorkspaceVersion}. Exiting.`
);
process.exit(0);
}
}
console.log(`Bumping other packages which depend on ${workspaceName}.`);
console.log(`Checking out ${MAIN_BRANCH} branch.`);
// Separate variable for the branch name, incase it includes a tag.
let versionString = updatedWorkspaceVersion;
if (tag) {
versionString = `${updatedWorkspaceVersion}-${tag}`;
}
execSyncWithErrorHandling(`git checkout ${MAIN_BRANCH}`);
const newBranchName = `bump-${workspaceName}-to-${versionString}`;
console.log(`Checking out new branch: ${newBranchName}`);
execSyncWithErrorHandling(`git checkout -b ${newBranchName}`);
const allWorkspacesWhichDependOn = allWorkspaces.filter(({ packageJSON }) =>
Object.keys(packageJSON.dependencies ?? {}).includes(workspaceName)
);
const allWorkspacesWhichDevDependOn = allWorkspaces.filter(
({ packageJSON }) =>
Object.keys(packageJSON.devDependencies ?? {}).includes(workspaceName)
);
const allWorkspacesWhichPeerDependOn = allWorkspaces.filter(
({ packageJSON }) =>
Object.keys(packageJSON.peerDependencies ?? {}).includes(workspaceName)
);
// For console log, get all workspaces which depend and filter out duplicates.
const allWhichDependOn = new Set(
[
...allWorkspacesWhichDependOn,
...allWorkspacesWhichDevDependOn,
...allWorkspacesWhichPeerDependOn,
].map(({ packageJSON }) => packageJSON.name)
);
if (allWhichDependOn.size !== 0) {
console.log(`Found ${
[...allWhichDependOn].length
} workspaces which depend on ${workspaceName}.
Workspaces:
- ${[...allWhichDependOn].map((name) => name).join("\n- ")}
`);
// Update packages which depend on the input workspace.
updateDependencies(
allWorkspacesWhichDependOn,
"dependencies",
workspaceName,
updatedWorkspaceVersion
);
updateDependencies(
allWorkspacesWhichDevDependOn,
"devDependencies",
workspaceName,
updatedWorkspaceVersion
);
updateDependencies(
allWorkspacesWhichPeerDependOn,
"peerDependencies",
workspaceName,
updatedWorkspaceVersion
);
console.log("Updated package.json's! Running yarn install.");
try {
execSyncWithErrorHandling(`yarn install`);
} catch (_) {
console.log(
"Yarn install failed. Likely because NPM has not finished publishing the new version. Continuing."
);
}
// Add all current changes, commit, push and log branch URL.
console.log("Adding and committing all changes.");
execSyncWithErrorHandling(`git add -A`);
execSyncWithErrorHandling(
`git commit -m "all[minor]: bump deps on ${workspaceName} to ${versionString}"`
);
console.log("Pushing changes.");
execSyncWithErrorHandling(`git push -u origin ${newBranchName}`);
console.log(
"π Open %s and merge the bump-deps PR.",
`\x1b[34mhttps://github.com/langchain-ai/langchainjs/compare/${newBranchName}?expand=1\x1b[0m`
);
} else {
console.log(`No workspaces depend on ${workspaceName}.`);
}
}
/**
* Create a commit message for the input workspace and version.
*
* @param {string} workspaceName
* @param {string} version
*/
function createCommitMessage(workspaceName, version) {
const cleanedWorkspaceName = workspaceName.replace("@langchain/", "");
return `release(${cleanedWorkspaceName}): ${version}`;
}
/**
* Commits all changes and pushes to the current branch.
*
* @param {string} workspaceName The name of the workspace being released
* @param {string} version The new version being released
* @param {boolean} onlyPush Whether or not to only push the changes, and not commit
* @returns {void}
*/
function commitAndPushChanges(workspaceName, version, onlyPush) {
if (!onlyPush) {
console.log("Committing changes...");
const commitMsg = createCommitMessage(workspaceName, version);
try {
execSyncWithErrorHandling("git add -A", { doNotExit: true });
execSyncWithErrorHandling(`git commit -m "${commitMsg}"`, {
doNotExit: true,
});
} catch (_) {
// No-op. Likely erroring because there are no unstaged changes.
}
}
console.log("Pushing changes...");
// Pushes to the current branch
execSyncWithErrorHandling(
"git push -u origin $(git rev-parse --abbrev-ref HEAD)"
);
console.log("Successfully committed and pushed changes.");
}
/**
* Verifies the current branch is main, then checks out a new release branch
* and pushes an empty commit.
*
* @returns {void}
* @throws {Error} If the current branch is not main.
*/
function checkoutReleaseBranch() {
const currentBranch = execSync("git branch --show-current").toString().trim();
if (currentBranch === MAIN_BRANCH || currentBranch === RELEASE_BRANCH) {
console.log(`Checking out '${RELEASE_BRANCH}' branch.`);
execSyncWithErrorHandling(`git checkout -B ${RELEASE_BRANCH}`);
execSyncWithErrorHandling(`git push -u origin ${RELEASE_BRANCH}`);
} else {
throw new Error(
`Current branch is not ${MAIN_BRANCH} or ${RELEASE_BRANCH}. Current branch: ${currentBranch}`
);
}
}
/**
* Prompts the user for input and returns the input. This is used
* for requesting an OTP from the user for NPM 2FA.
*
* @param {string} question The question to log to the users terminal.
* @returns {Promise<string>} The user input.
*/
async function getUserInput(question) {
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout,
});
return new Promise((resolve) => {
rl.question(`\x1b[30m\x1b[47m${question}\x1b[0m`, (input) => {
rl.close();
resolve(input);
});
});
}
/**
* Checks if there are any uncommitted changes in the git repository.
*
* @returns {boolean} True if there are uncommitted changes, false otherwise
*/
function hasUncommittedChanges() {
try {
// Check for uncommitted changes (both staged and unstaged)
const uncommittedOutput = execSync("git status --porcelain").toString();
return uncommittedOutput.length > 0;
} catch (error) {
console.error("Error checking git status:", error);
// If we can't check, better to assume there are changes
return true;
}
}
/**
* Checks if there are any staged commits in the git repository.
*
* @returns {boolean} True if there are staged changes, false otherwise
*/
function hasStagedChanges() {
try {
// Check for staged but unpushed changes
const unPushedOutput = execSync("git log '@{u}..'").toString();
return unPushedOutput.length > 0;
} catch (error) {
console.error("Error checking git status:", error);
// If we can't check, better to assume there are changes
return true;
}
}
async function main() {
const program = new Command();
program
.description("Release a new workspace version to NPM.")
.option("--workspace <workspace>", "Workspace name, eg @langchain/core")
.option(
"--bump-deps",
"Whether or not to bump other workspaces that depend on this one."
)
.option("--tag <tag>", "Optionally specify a tag to publish to.");
program.parse();
/**
* @type {{ workspace: string, bumpDeps?: boolean, tag?: string }}
*/
const options = program.opts();
if (!options.workspace) {
throw new Error("--workspace is a required flag.");
}
if (hasUncommittedChanges()) {
console.warn(
"[WARNING]: You have uncommitted changes. These will be included in the release commit."
);
}
// Find the workspace package.json's.
const allWorkspaces = getAllWorkspaces();
const matchingWorkspace = allWorkspaces.find(
({ packageJSON }) => packageJSON.name === options.workspace
);
if (!matchingWorkspace) {
throw new Error(`Could not find workspace ${options.workspace}`);
}
// Checkout new "release" branch & push
checkoutReleaseBranch();
// Run build, lint, tests
console.log("Running build, lint, and tests.");
execSyncWithErrorHandling(
`yarn turbo:command run --filter ${options.workspace} build lint test --concurrency 1`
);
console.log("Successfully ran build, lint, and tests.");
const npm2FACode = await getUserInput(
"Please enter your NPM 2FA authentication code:"
);
const preReleaseVersion = getWorkspaceVersion(matchingWorkspace.dir);
// Run `release-it` on workspace
await runYarnRelease(matchingWorkspace.dir, npm2FACode, options.tag);
const hasStaged = hasStagedChanges();
const hasUnCommitted = hasUncommittedChanges();
if (hasStaged || hasUnCommitted) {
const updatedVersion = getWorkspaceVersion(matchingWorkspace.dir);
// Only push and do not commit if there are staged changes and no uncommitted changes
const onlyPush = hasStaged && !hasUnCommitted;
commitAndPushChanges(options.workspace, updatedVersion, onlyPush);
}
// Log release branch URL
console.log(
"π Open %s and merge the release PR.",
`\x1b[34mhttps://github.com/langchain-ai/langchainjs/compare/release?expand=1\x1b[0m`
);
// If `bump-deps` flag is set, find all workspaces which depend on the input workspace.
// Then, update their package.json to use the new version of the input workspace.
// This will create a new branch, commit and push the changes and log the branch URL.
if (options.bumpDeps) {
bumpDeps(
options.workspace,
matchingWorkspace.dir,
allWorkspaces,
options.tag,
preReleaseVersion
);
}
}
main().catch((error) => {
console.error(error);
process.exit(1);
});
|
0
|
lc_public_repos
|
lc_public_repos/langchainjs/deno.json
|
{
"imports": {
"langchain/": "npm:/langchain@0.3.2/",
"@langchain/anthropic": "npm:@langchain/anthropic@0.3.0",
"@langchain/cloudflare": "npm:@langchain/cloudflare@0.1.0",
"@langchain/community/": "npm:/@langchain/community@0.3.0/",
"@langchain/openai": "npm:@langchain/openai@0.3.0",
"@langchain/cohere": "npm:@langchain/cohere@0.3.0",
"@langchain/textsplitters": "npm:@langchain/textsplitters@0.1.0",
"@langchain/google-vertexai-web": "npm:@langchain/google-vertexai-web@0.1.0",
"@langchain/mistralai": "npm:@langchain/mistralai@0.1.0",
"@langchain/core/": "npm:/@langchain/core@0.3.1/",
"@langchain/pinecone": "npm:@langchain/pinecone@0.1.0",
"@langchain/google-common": "npm:@langchain/google-common@0.1.0",
"@langchain/langgraph": "npm:/@langchain/langgraph@0.2.3",
"@langchain/langgraph/": "npm:/@langchain/langgraph@0.2.3/",
"@faker-js/faker": "npm:@faker-js/faker",
"@microsoft/fetch-event-source": "npm:@microsoft/fetch-event-source",
"@pinecone-database/pinecone": "npm:@pinecone-database/pinecone",
"cheerio": "npm:cheerio",
"chromadb": "npm:/chromadb",
"dotenv/": "npm:/dotenv/",
"zod": "npm:/zod",
"zod-to-json-schema": "npm:/zod-to-json-schema",
"node-llama-cpp": "npm:/node-llama-cpp",
"pdf-parse": "npm:/pdf-parse",
"peggy": "npm:/peggy",
"readline": "https://deno.land/x/readline@v1.1.0/mod.ts",
"uuid": "npm:/uuid",
"youtubei.js": "npm:/youtubei.js",
"youtube-transcript": "npm:/youtube-transcript",
"neo4j-driver": "npm:/neo4j-driver",
"axios": "npm:/axios",
"@mendable/firecrawl-js": "npm:/@mendable/firecrawl-js",
"@aws-crypto/sha256-js": "npm:/@aws-crypto/sha256-js",
"@aws-sdk/credential-provider-node": "npm:/@aws-sdk/credential-provider-node",
"@smithy/protocol-http": "npm:/@smithy/protocol-http",
"@smithy/signature-v4": "npm:/@smithy/signature-v4",
"@smithy/eventstream-codec": "npm:/@smithy/eventstream-codec",
"@smithy/util-utf8": "npm:/@smithy/util-utf8",
"@aws-sdk/types": "npm:/@aws-sdk/types"
}
}
|
0
|
lc_public_repos
|
lc_public_repos/langchainjs/.codecov.yml
|
coverage:
status:
project:
default:
informational: true
patch:
default:
informational: true
# When modifying this file, please validate using
# curl -X POST --data-binary @codecov.yml https://codecov.io/validate
|
0
|
lc_public_repos
|
lc_public_repos/langchainjs/tsconfig.json
|
{
"extends": "@tsconfig/recommended",
"compilerOptions": {
"target": "ES2021",
"lib": [
"ES2021",
"ES2022.Object",
"DOM"
],
"module": "ES2020",
"moduleResolution": "nodenext",
"esModuleInterop": true,
"declaration": true,
"noImplicitReturns": true,
"noFallthroughCasesInSwitch": true,
"noUnusedLocals": true,
"noUnusedParameters": true,
"useDefineForClassFields": true,
"strictPropertyInitialization": false,
"allowJs": true,
"strict": true
},
"include": [
"langchain/src/**/*",
"langchain-core/src/**/*",
"libs/*/src/**/*",
],
"exclude": [
"node_modules",
"**/dist/",
"docs",
"langchain/dist/",
"langchain-core/dist/",
"libs/*/dist/",
]
}
|
0
|
lc_public_repos
|
lc_public_repos/langchainjs/LICENSE
|
The MIT License
Copyright (c) Harrison Chase
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
|
0
|
lc_public_repos
|
lc_public_repos/langchainjs/.editorconfig
|
# top-most EditorConfig file
root = true
# Unix-style newlines with a newline ending every file
[*]
end_of_line = lf
|
0
|
lc_public_repos
|
lc_public_repos/langchainjs/yarn.lock
|
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# Contributing to LangChain
π Hi there! Thank you for being interested in contributing to LangChain.
As an open source project in a rapidly developing field, we are extremely open
to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow. Please do not try to push directly to this repo unless you are a maintainer.
## Quick Links
### Not sure what to work on?
If you are not sure what to work on, we have a few suggestions:
- Look at the issues with the [help wanted](https://github.com/langchain-ai/langchainjs/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) label. These are issues that we think are good targets for contributors. If you are interested in working on one of these, please comment on the issue so that we can assign it to you. And if you have any questions let us know, we're happy to guide you!
- At the moment our main focus is reaching parity with the Python version for features and base functionality. If you are interested in working on a specific integration or feature, please let us know and we can help you get started.
### New abstractions
We aim to keep the same core APIs between the Python and JS versions of LangChain, where possible. As such we ask that if you have an idea for a new abstraction, please open an issue first to discuss it. This will help us make sure that the API is consistent across both versions. If you're not sure what to work on, we recommend looking at the links above first.
### Want to add a specific integration?
LangChain supports several different types of integrations with third-party providers and frameworks, including LLM providers (e.g. [OpenAI](https://github.com/langchain-ai/langchainjs/blob/main/langchain/src/llms/openai.ts)), vector stores (e.g. [FAISS](https://github.com/ewfian/langchainjs/blob/main/langchain/src/vectorstores/faiss.ts)), document loaders (e.g. [Apify](https://github.com/langchain-ai/langchainjs/blob/main/langchain/src/document_loaders/web/apify_dataset.ts)) persistent message history stores (e.g. [Redis](https://github.com/langchain-ai/langchainjs/blob/main/langchain/src/stores/message/redis.ts)), and more.
We welcome such contributions, but ask that you read our dedicated [integration contribution guide](https://github.com/langchain-ai/langchainjs/blob/main/.github/contributing/INTEGRATIONS.md) for specific details and patterns to consider before opening a pull request.
You can also check out the [guides on extending LangChain.js](https://js.langchain.com/docs/how_to/#custom) in our docs.
#### Integration packages
Integrations should generally reside in the `libs/langchain-community` workspace and be imported as `@langchain/community/module/name`. More in-depth integrations or suites of integrations may also reside in separate packages that depend on and extend `@langchain/core`. See [`@langchain/google-genai`](https://github.com/langchain-ai/langchainjs/blob/main/libs/langchain-google-genai) for an example.
To make creating packages like this easier, we offer the [`create-langchain-integration`](https://github.com/langchain-ai/langchainjs/blob/main/libs/create-langchain-integration/) utility that will automatically scaffold a repo with support for both ESM + CJS entrypoints. You can run it like this:
```bash
$ npx create-langchain-integration
```
After creating the new integration package, you should add it to the [`unit-tests-integrations.yml`](./.github/workflows/unit-tests-integrations.yml) GitHub action workflow so that it is tested in CI. To do this,simply update the `env` section of the `prepare-matrix` job with your package name inside the `PACKAGES` variable:
```yaml
prepare-matrix:
needs: get-changed-files
runs-on: ubuntu-latest
env:
PACKAGES: "anthropic,azure-openai,cloudflare,<your-package>"
...
```
### Want to add a feature that's already in Python?
If you're interested in contributing a feature that's already in the [LangChain Python repo](https://github.com/langchain-ai/langchain) and you'd like some help getting started, you can try pasting code snippets and classes into the [LangChain Python to JS translator](https://langchain-translator.vercel.app/).
It's a chat interface wrapping a fine-tuned `gpt-3.5-turbo` instance trained on prior ported features. This allows the model to innately take into account LangChain-specific code style and imports.
It's an ongoing project, and feedback on runs will be used to improve the [LangSmith dataset](https://smith.langchain.com) for further fine-tuning! Try it out below:
https://langchain-translator.vercel.app/
## πΊοΈ Contributing Guidelines
### π© GitHub Issues
Our [issues](https://github.com/langchain-ai/langchainjs/issues) page contains
with bugs, improvements, and feature requests.
If you start working on an issue, please assign it to yourself.
If you are adding an issue, please try to keep it focused on a single modular bug/improvement/feature.
If the two issues are related, or blocking, please link them rather than keep them as one single one.
We will try to keep these issues as up to date as possible, though
with the rapid rate of development in this field some may get out of date.
If you notice this happening, please just let us know.
### π Getting Help
Although we try to have a developer setup to make it as easy as possible for others to contribute (see below)
it is possible that some pain point may arise around environment setup, linting, documentation, or other.
Should that occur, please contact a maintainer! Not only do we want to help get you unblocked,
but we also want to make sure that the process is smooth for future contributors.
In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
If you are finding these difficult (or even just annoying) to work with,
feel free to contact a maintainer for help - we do not want these to get in the way of getting
good code into the codebase.
### π Release process
As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
a developer and published to [npm](https://www.npmjs.com/package/langchain).
LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
#### Integration releases
The release script can be executed only while on a fresh `main` branch, with no un-committed changes, from the package root. If working from a fork of the repository, make sure to sync the forked `main` branch with the upstream `main` branch first.
You can invoke the script by calling `yarn release`. If new dependencies have been added to the integration package, install them first (i.e. run `yarn`, then `yarn release`).
There are three parameters which can be passed to this script, one required and two optional.
- **Required**: `<workspace name>`. eg: `@langchain/core` The name of the package to release. Can be found in the `name` value of the package's `package.json`
- **Optional**: `--bump-deps` eg `--bump-deps` Will find all packages in the repo which depend on this workspace and checkout a new branch, update the dep version, run yarn install, commit & push to new branch. Generally, this is not necessary.
- **Optional**: `--tag <tag>` eg `--tag beta` Add a tag to the NPM release. Useful if you want to push a release candidate.
This script automatically bumps the package version, creates a new release branch with the changes, pushes the branch to GitHub, uses `release-it` to automatically release to NPM, and more depending on the flags passed.
Halfway through this script, you'll be prompted to enter an NPM OTP (typically from an authenticator app). This value is not stored anywhere and is only used to authenticate the NPM release.
> **Note** Unless releasing `langchain`, `no` should be answered to all prompts following `Publish @langchain/<package> to npm?`. Then, the change should be manually committed with the following commit message: `<package>[patch]: Release <new version>`. E.g.: `groq[patch]: Release 0.0.1`.
Docker must be running if releasing one of `langchain`, `@langchain/core` or `@langchain/community`. These packages run LangChain's export tests, which run inside docker containers.
Full example: `yarn release @langchain/core`.
### π οΈ Tooling
This project uses the following tools, which are worth getting familiar
with if you plan to contribute:
- **[yarn](https://yarnpkg.com/) (v3.4.1)** - dependency management
- **[eslint](https://eslint.org/)** - enforcing standard lint rules
- **[prettier](https://prettier.io/)** - enforcing standard code formatting
- **[jest](https://jestjs.io/)** - testing code
- **[TypeDoc](https://typedoc.org/)** - reference doc generation from
comments
- **[Docusaurus](https://docusaurus.io/)** - static site generation for documentation
## π Quick Start
Clone this repo, then cd into it:
```bash
cd langchainjs
```
Next, try running the following common tasks:
## β
Common Tasks
Our goal is to make it as easy as possible for you to contribute to this project.
All of the below commands should be run from within a workspace directory (e.g. `langchain`, `libs/langchain-community`) unless otherwise noted.
```bash
cd langchain
```
Or, if you are working on a community integration:
```bash
cd libs/langchain-community
```
### Setup
**Prerequisite**: Node version 18+ is required. Please check node version `node -v` and update it if required.
To get started, you will need to install the dependencies for the project. To do so, run:
```bash
yarn
```
Then, you will need to switch directories into `langchain-core` and build core by running:
```bash
cd ../../langchain-core
yarn
yarn build
```
### Linting
We use [eslint](https://eslint.org/) to enforce standard lint rules.
To run the linter, run:
```bash
yarn lint
```
### Formatting
We use [prettier](https://prettier.io) to enforce code formatting style.
To run the formatter, run:
```bash
yarn format
```
To just check for formatting differences, without fixing them, run:
```bash
yarn format:check
```
### Testing
In general, tests should be added within a `tests/` folder alongside the modules they
are testing.
**Unit tests** cover modular logic that does not require calls to outside APIs.
If you add new logic, please add a unit test.
Unit tests should be called `*.test.ts`.
To run only unit tests, run:
```bash
yarn test
```
#### Running a single test
To run a single test, run the following from within a workspace:
```bash
yarn test:single /path/to/yourtest.test.ts
```
This is useful for developing individual features.
**Integration tests** cover logic that requires making calls to outside APIs (often integration with other services).
If you add support for a new external API, please add a new integration test.
Integration tests should be called `*.int.test.ts`.
Note that most integration tests require credentials or other setup. You will likely need to set up a `langchain/.env` or `libs/langchain-community/.env` file
like the example [here](https://github.com/langchain-ai/langchainjs/blob/main/langchain/.env.example).
We generally recommend only running integration tests with `yarn test:single`, but if you want to run all integration tests, run:
```bash
yarn test:integration
```
### Building
To build the project, run:
```bash
yarn build
```
### Adding an Entrypoint
LangChain exposes multiple subpaths the user can import from, e.g.
```typescript
import { OpenAI } from "langchain/llms/openai";
```
We call these subpaths "entrypoints". In general, you should create a new entrypoint if you are adding a new integration with a 3rd party library. If you're adding self-contained functionality without any external dependencies, you can add it to an existing entrypoint.
In order to declare a new entrypoint that users can import from, you
should edit the `langchain/langchain.config.js` or `libs/langchain-community/langchain.config.js` file. To add an
entrypoint `tools` that imports from `tools/index.ts` you'd add
the following to the `entrypoints` key inside the `config` variable:
```typescript
// ...
entrypoints: {
// ...
tools: "tools/index",
},
// ...
```
If you're adding a new integration which requires installing a third party dependency, you must add the entrypoint to the `requiresOptionalDependency` array, also located inside `langchain/langchain.config.js` or `libs/langchain-community/langchain.config.js`.
```typescript
// ...
requiresOptionalDependency: [
// ...
"tools/index",
],
// ...
```
This will make sure the entrypoint is included in the published package,
and in generated documentation.
## Documentation
### Contribute Documentation
#### Install dependencies
##### Note: you only need to follow these steps if you are building the docs site locally.
1. [Quarto](https://quarto.org/) - package that converts Jupyter notebooks (`.ipynb` files) into `.mdx` files for serving in Docusaurus.
2. `yarn build --filter=core_docs` - It's as simple as that! (or you can simply run `yarn build` from `docs/core_docs/`)
All notebooks are converted to `.md` files and automatically gitignored. If you would like to create a non notebook doc, it must be a `.mdx` file.
### Writing Notebooks
When adding new dependencies inside the notebook you must update the import map inside `deno.json` in the root of the LangChain repo.
This is required because the notebooks use the Deno runtime, and Deno formats imports differently than Node.js.
Example:
```typescript
// Import in Node:
import { z } from "zod";
// Import in Deno:
import { z } from "npm:/zod";
```
See examples inside `deno.json` for more details.
Docs are largely autogenerated by [TypeDoc](https://typedoc.org/) from the code.
For that reason, we ask that you add good documentation to all classes and methods.
Similar to linting, we recognize documentation can be annoying. If you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
Documentation and the skeleton lives under the `docs/` folder. Example code is imported from under the `examples/` folder.
**If you are contributing an integration, please copy and use the appropriate template from here:**
https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-scripts/src/cli/docs/templates
### Running examples
If you add a new major piece of functionality, it is helpful to add an
example to showcase how to use it. Most of our users find examples to be the
most helpful kind of documentation.
Examples can be added in the `examples/src` directory, e.g.
`examples/src/path/to/example`. This
example can then be invoked with `yarn example path/to/example` at the top
level of the repo.
To run examples that require an environment variable, you'll need to add a `.env` file under `examples/.env`.
### Build Documentation Locally
To generate and view the documentation locally, change to the project root and run `yarn` to ensure dependencies get installed
in both the `docs/` and `examples/` workspaces:
```bash
cd ..
yarn
```
Then run:
```bash
yarn docs
```
## Advanced
**Environment tests** test whether LangChain works across different JS environments, including Node.js (both ESM and CJS), Edge environments (eg. Cloudflare Workers), and browsers (using Webpack).
To run the environment tests with Docker, run the following command from the project root:
```bash
yarn test:exports:docker
```
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# π¦οΈπ LangChain.js
β‘ Building applications with LLMs through composability β‘
[](https://github.com/langchain-ai/langchainjs/actions/workflows/ci.yml)  [](https://opensource.org/licenses/MIT) [](https://twitter.com/langchainai) [](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchainjs)
[<img src="https://github.com/codespaces/badge.svg" title="Open in Github Codespace" width="150" height="20">](https://codespaces.new/langchain-ai/langchainjs)
Looking for the Python version? Check out [LangChain](https://github.com/langchain-ai/langchain).
To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
## β‘οΈ Quick Install
You can use npm, yarn, or pnpm to install LangChain.js
`npm install -S langchain` or `yarn add langchain` or `pnpm add langchain`
## π Supported Environments
LangChain is written in TypeScript and can be used in:
- Node.js (ESM and CommonJS) - 18.x, 19.x, 20.x, 22.x
- Cloudflare Workers
- Vercel / Next.js (Browser, Serverless and Edge functions)
- Supabase Edge Functions
- Browser
- Deno
## π€ What is LangChain?
**LangChain** is a framework for developing applications powered by language models. It enables applications that:
- **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
- **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
This framework consists of several parts.
- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://js.langchain.com/docs/concepts/lcel), [components](https://js.langchain.com/docs/concepts), and [third-party integrations](https://js.langchain.com/docs/integrations/platforms/).
Use [LangGraph.js](https://js.langchain.com/docs/concepts/#langgraphjs) to build stateful agents with first-class streaming and human-in-the-loop support.
- **Productionization**: Use [LangSmith](https://docs.smith.langchain.com/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
The LangChain libraries themselves are made up of several different packages.
- **[`@langchain/core`](https://github.com/langchain-ai/langchainjs/blob/main/langchain-core)**: Base abstractions and LangChain Expression Language.
- **[`@langchain/community`](https://github.com/langchain-ai/langchainjs/blob/main/libs/langchain-community)**: Third party integrations.
- **[`langchain`](https://github.com/langchain-ai/langchainjs/blob/main/langchain)**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
- **[LangGraph.js](https://langchain-ai.github.io/langgraphjs/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
Integrations may also be split into their own compatible packages.
This library aims to assist in the development of those types of applications. Common examples of these applications include:
**βQuestion Answering over specific documents**
- [Documentation](https://js.langchain.com/docs/tutorials/rag/)
- End-to-end Example: [Doc-Chatbot](https://github.com/dissorial/doc-chatbot)
**π¬ Chatbots**
- [Documentation](https://js.langchain.com/docs/tutorials/chatbot)
- End-to-end Example: [Chat-LangChain](https://github.com/langchain-ai/chat-langchain)
## π How does LangChain help?
The main value props of the LangChain libraries are:
1. **Components**: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
Components fall into the following **modules**:
**π Model I/O:**
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
**π Retrieval:**
Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
**π€ Agents:**
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://js.langchain.com/docs/concepts/#agents), along with [LangGraph.js](https://github.com/langchain-ai/langgraphjs/) for building custom agents.
## π Documentation
Please see [here](https://js.langchain.com) for full documentation, which includes:
- [Getting started](https://js.langchain.com/docs/introduction): installation, setting up the environment, simple examples
- Overview of the [interfaces](https://js.langchain.com/docs/how_to/lcel_cheatsheet/), [modules](https://js.langchain.com/docs/concepts) and [integrations](https://js.langchain.com/docs/integrations/platforms/)
- [Tutorial](https://js.langchain.com/docs/tutorials/) walkthroughs
- [Reference](https://api.js.langchain.com): full API docs
## π Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see [here](https://github.com/langchain-ai/langchainjs/blob/main/CONTRIBUTING.md).
Please report any security issues or concerns following our [security guidelines](https://github.com/langchain-ai/langchainjs/blob/main/SECURITY.md).
## ποΈ Relationship with Python LangChain
This is built to integrate as seamlessly as possible with the [LangChain Python package](https://github.com/langchain-ai/langchain). Specifically, this means all objects (prompts, LLMs, chains, etc) are designed in a way where they can be serialized and shared between languages.
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nodeLinker: node-modules
plugins:
- path: .yarn/plugins/@yarnpkg/plugin-typescript.cjs
spec: "@yarnpkg/plugin-typescript"
- path: .yarn/plugins/@yarnpkg/plugin-workspace-tools.cjs
spec: "@yarnpkg/plugin-workspace-tools"
supportedArchitectures:
cpu:
- x64
- arm64
libc:
- glibc
- musl
os:
- darwin
- linux
yarnPath: .yarn/releases/yarn-3.5.1.cjs
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{
"ignore_dirs": [
"langchain/dist",
"langchain/dist-cjs",
"docs/build",
"node_modules",
"langchain/.turbo",
"docs/.turbo",
"test-exports/.turbo",
"test-exports-cjs/.turbo"
]
}
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# Security Policy
## Reporting a Vulnerability
Please report security vulnerabilities by email to `security@langchain.dev`.
This email is an alias to a subset of our maintainers, and will ensure the issue is promptly triaged and acted upon as needed.
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babel.config.js
jest.config.js
.eslintrc.js
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lc_public_repos/langchainjs/test-int-deps-docker-compose.yml
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version: '3.8'
services:
redis:
image: redis:6.2-alpine
restart: always
ports:
- '6379:6379'
command: redis-server --save 20 1 --loglevel warning
redis-vectorsearch:
image: redis/redis-stack-server:latest
restart: always
ports:
- '6378:6379'
command: redis-stack-server
weaviate:
image: semitechnologies/weaviate:1.19.7
ports:
- 8080:8080
restart: on-failure:0
environment:
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'none'
CLUSTER_HOSTNAME: 'node1'
db:
image: pgvector/pgvector:pg16
ports:
- 5433:5432
volumes:
- ./tmp/integration_tests/pgvector:/var/lib/postgresql/data
environment:
- POSTGRES_PASSWORD=ChangeMe
- POSTGRES_USER=myuser
- POSTGRES_DB=api
qdrant:
image: qdrant/qdrant:v1.9.1
ports:
- 6333:6333
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{
"name": "langchainjs",
"private": true,
"engines": {
"node": ">=18"
},
"homepage": "https://github.com/langchain-ai/langchainjs/tree/main/",
"workspaces": [
"langchain",
"langchain-core",
"libs/*",
"examples",
"docs/*"
],
"repository": {
"type": "git",
"url": "https://github.com/langchain-ai/langchainjs.git"
},
"packageManager": "yarn@3.5.1",
"scripts": {
"build": "turbo build --filter=\"!test-exports-*\"",
"turbo:command": "turbo",
"clean": "turbo clean",
"format": "turbo format --concurrency=50",
"format:check": "turbo format:check --concurrency=50",
"lint": "turbo lint --concurrency=50",
"lint:fix": "turbo lint:fix --concurrency=50",
"test": "yarn test:unit && yarn test:exports:docker",
"test:unit": "turbo test --filter=\"!test-exports-*\" --filter=!examples --filter=!api_refs --filter=!core_docs --filter=!create-langchain-integration",
"test:unit:ci": "turbo test:ci",
"test:int": "yarn run test:int:deps && turbo test:integration ; yarn run test:int:deps:down",
"test:int:deps": "docker compose -f test-int-deps-docker-compose.yml up -d",
"test:int:deps:down": "docker compose -f test-int-deps-docker-compose.yml down",
"test:ranges:docker": "docker compose -f dependency_range_tests/docker-compose.yml up --force-recreate",
"test:exports:docker": "docker compose -f environment_tests/docker-compose.yml up --force-recreate",
"test:standard:unit": "turbo test:standard:unit",
"test:standard:int": "turbo test:standard:int",
"test:standard": "yarn test:standard:unit && yarn test:standard:int",
"example": "yarn workspace examples start",
"precommit": "turbo precommit",
"docs": "yarn workspace core_docs start",
"docs:api_refs": "yarn workspace api_refs start",
"release": "node release_workspace.js --workspace"
},
"author": "LangChain",
"license": "MIT",
"devDependencies": {
"@tsconfig/recommended": "^1.0.2",
"@types/jest": "^29.5.3",
"@types/semver": "^7",
"commander": "^11.1.0",
"dotenv": "^16.0.3",
"lint-staged": "^13.1.1",
"prettier": "^2.8.3",
"semver": "^7.5.4",
"turbo": "^1.13.3",
"typescript": "~5.1.6"
},
"resolutions": {
"dpdm@^3.12.0": "patch:dpdm@npm%3A3.12.0#./.yarn/patches/dpdm-npm-3.12.0-0dfdd8e3b8.patch",
"typedoc-plugin-markdown@next": "patch:typedoc-plugin-markdown@npm%3A4.0.0-next.6#./.yarn/patches/typedoc-plugin-markdown-npm-4.0.0-next.6-96b4b47746.patch",
"voy-search@0.6.2": "patch:voy-search@npm%3A0.6.2#./.yarn/patches/voy-search-npm-0.6.2-d4aca30a0e.patch",
"protobufjs": "^7.2.5",
"zod": "3.23.8"
},
"lint-staged": {
"**/*.{ts,tsx}": [
"prettier --config .prettierrc --write --ignore-unknown",
"eslint --cache --fix"
],
"*.md": "prettier --config .prettierrc --write"
}
}
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{
"$schema": "https://turbo.build/schema.json",
"globalDependencies": ["**/.env"],
"pipeline": {
"build": {
"dependsOn": ["^build"],
"outputs": ["dist/**", ".next/**", "!.next/cache/**"]
},
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"lint:fix": {
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"clean": {
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"build:internal": {
"dependsOn": ["^build:internal"]
}
}
}
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