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Repository: adithya-s-k/RAG-SaaS
Branch: main
Commit: 751ab3a4ad79
Files: 191
Total size: 505.7 KB
Directory structure:
gitextract_kge9g_mh/
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug_report.md
│ │ └── feature_request.md
│ └── PULL_REQUEST_TEMPLATE/
│ └── pull_request_template.md
├── .gitignore
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── LICENSING.md
├── README.md
├── backend/
│ ├── .gitignore
│ ├── Dockerfile
│ ├── README.md
│ ├── app/
│ │ ├── __init__.py
│ │ ├── api/
│ │ │ ├── __init__.py
│ │ │ ├── admin/
│ │ │ │ ├── __init__.py
│ │ │ │ └── route.py
│ │ │ ├── auth/
│ │ │ │ ├── __init__.py
│ │ │ │ └── route.py
│ │ │ ├── chat/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── chat_config.py
│ │ │ │ ├── engine/
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── engine.py
│ │ │ │ │ ├── generate.py
│ │ │ │ │ ├── index.py
│ │ │ │ │ ├── loaders/
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── db.py
│ │ │ │ │ │ ├── file.py
│ │ │ │ │ │ └── web.py
│ │ │ │ │ ├── node_postprocessors.py
│ │ │ │ │ ├── query_filter.py
│ │ │ │ │ └── vectordb.py
│ │ │ │ ├── events.py
│ │ │ │ ├── models.py
│ │ │ │ ├── route.py
│ │ │ │ ├── services/
│ │ │ │ │ ├── file.py
│ │ │ │ │ └── suggestion.py
│ │ │ │ ├── summary.py
│ │ │ │ ├── upload.py
│ │ │ │ └── vercel_response.py
│ │ │ └── conversation/
│ │ │ ├── __init__.py
│ │ │ └── route.py
│ │ ├── config.py
│ │ ├── core/
│ │ │ ├── config.py
│ │ │ ├── security.py
│ │ │ └── user.py
│ │ ├── db.py
│ │ ├── llmhub.py
│ │ ├── models/
│ │ │ └── user_model.py
│ │ ├── observability.py
│ │ ├── schemas/
│ │ │ ├── admin_schema.py
│ │ │ ├── auth_schema.py
│ │ │ └── user_schema.py
│ │ ├── services/
│ │ │ ├── __init__.py
│ │ │ ├── admin_service.py
│ │ │ ├── config_service.py
│ │ │ ├── conversation_service.py
│ │ │ └── user_service.py
│ │ └── settings.py
│ ├── config/
│ │ ├── loaders.yaml
│ │ └── tools.yaml
│ ├── main.py
│ ├── pyproject.toml
│ └── tests/
│ └── __init__.py
├── docker-compose.yaml
└── frontend/
├── .eslintrc.json
├── .gitignore
├── Dockerfile
├── README.md
├── app/
│ ├── (auth)/
│ │ ├── signin/
│ │ │ └── page.tsx
│ │ └── signup/
│ │ └── page.tsx
│ ├── ConversationContext.tsx
│ ├── admin/
│ │ ├── AdminAuthProvider.tsx
│ │ ├── DataIngestion.tsx
│ │ ├── RAGConfiguration.tsx
│ │ ├── UsersManagement.tsx
│ │ ├── layout.tsx
│ │ └── page.tsx
│ ├── authProvider.tsx
│ ├── chat/
│ │ └── page.tsx
│ ├── features/
│ │ └── page.tsx
│ ├── globals.css
│ ├── layout.tsx
│ ├── markdown.css
│ ├── page.tsx
│ └── share/
│ └── page.tsx
├── components/
│ ├── analytics.tsx
│ ├── banner-card.tsx
│ ├── header.tsx
│ ├── history.tsx
│ ├── loading.tsx
│ ├── magicui/
│ │ ├── animated-gradient-text.tsx
│ │ ├── border-beam.tsx
│ │ ├── box-reveal.tsx
│ │ ├── gradual-spacing.tsx
│ │ ├── hyper-text.tsx
│ │ ├── meteors.tsx
│ │ ├── neon-gradient-card.tsx
│ │ ├── number-ticker.tsx
│ │ ├── ripple.tsx
│ │ ├── shimmer-button.tsx
│ │ ├── shine-border.tsx
│ │ ├── shiny-button.tsx
│ │ ├── sparkles-text.tsx
│ │ └── typing-animation.tsx
│ ├── sidebar.tsx
│ ├── theme-provider.tsx
│ ├── theme-toggle.tsx
│ └── ui/
│ ├── accordion.tsx
│ ├── alert-dialog.tsx
│ ├── alert.tsx
│ ├── aspect-ratio.tsx
│ ├── avatar.tsx
│ ├── badge.tsx
│ ├── breadcrumb.tsx
│ ├── button.tsx
│ ├── card-hover-effect.tsx
│ ├── card.tsx
│ ├── chat/
│ │ ├── chat-actions.tsx
│ │ ├── chat-input.tsx
│ │ ├── chat-message/
│ │ │ ├── chat-avatar.tsx
│ │ │ ├── chat-events.tsx
│ │ │ ├── chat-files.tsx
│ │ │ ├── chat-image.tsx
│ │ │ ├── chat-sources.tsx
│ │ │ ├── chat-suggestedQuestions.tsx
│ │ │ ├── chat-tools.tsx
│ │ │ ├── codeblock.tsx
│ │ │ ├── index.tsx
│ │ │ └── markdown.tsx
│ │ ├── chat-messages.tsx
│ │ ├── chat.interface.ts
│ │ ├── hooks/
│ │ │ ├── use-config.ts
│ │ │ ├── use-copy-to-clipboard.tsx
│ │ │ └── use-file.ts
│ │ ├── index.ts
│ │ └── widgets/
│ │ ├── PdfDialog.tsx
│ │ └── WeatherCard.tsx
│ ├── checkbox.tsx
│ ├── collapsible.tsx
│ ├── command.tsx
│ ├── context-menu.tsx
│ ├── dialog.tsx
│ ├── document-preview.tsx
│ ├── drawer.tsx
│ ├── dropdown-menu.tsx
│ ├── file-upload.tsx
│ ├── file-uploader.tsx
│ ├── form.tsx
│ ├── hover-card.tsx
│ ├── input-otp.tsx
│ ├── input.tsx
│ ├── label.tsx
│ ├── menubar.tsx
│ ├── navigation-menu.tsx
│ ├── pagination.tsx
│ ├── placeholders-and-vanish-input.tsx
│ ├── popover.tsx
│ ├── progress.tsx
│ ├── radio-group.tsx
│ ├── resizable.tsx
│ ├── scroll-area.tsx
│ ├── select.tsx
│ ├── separator.tsx
│ ├── sheet.tsx
│ ├── skeleton.tsx
│ ├── slider.tsx
│ ├── sonner.tsx
│ ├── switch.tsx
│ ├── table.tsx
│ ├── tabs.tsx
│ ├── textarea.tsx
│ ├── toast.tsx
│ ├── toaster.tsx
│ ├── toggle-group.tsx
│ ├── toggle.tsx
│ ├── tooltip.tsx
│ ├── upload-image-preview.tsx
│ └── use-toast.ts
├── components.json
├── config/
│ ├── features.ts
│ ├── site.ts
│ └── tools.json
├── hooks/
│ └── useGitHubStars.tsx
├── lib/
│ └── utils.ts
├── next.config.mjs
├── package.json
├── postcss.config.mjs
├── public/
│ └── site.webmanifest
├── tailwind.config.ts
├── tsconfig.json
└── types/
└── index.d.ts
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/ISSUE_TEMPLATE/bug_report.md
================================================
---
name: Bug report
about: Create a report to help us improve
title: ''
labels: ''
assignees: ''
---
**Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Steps to reproduce the behavior:
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error
**Expected behavior**
A clear and concise description of what you expected to happen.
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Desktop (please complete the following information):**
- OS: [e.g. iOS]
- Browser [e.g. chrome, safari]
- Version [e.g. 22]
**Smartphone (please complete the following information):**
- Device: [e.g. iPhone6]
- OS: [e.g. iOS8.1]
- Browser [e.g. stock browser, safari]
- Version [e.g. 22]
**Additional context**
Add any other context about the problem here.
================================================
FILE: .github/ISSUE_TEMPLATE/feature_request.md
================================================
---
name: Feature request
about: Suggest an idea for this project
title: ''
labels: ''
assignees: ''
---
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.
================================================
FILE: .github/PULL_REQUEST_TEMPLATE/pull_request_template.md
================================================
# Pull Request Template
## Description
Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
Fixes # (issue)
## Type of change
Please delete options that are not relevant.
- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
- [ ] This change requires a documentation update
## How Has This Been Tested?
Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration
- [ ] Test A
- [ ] Test B
## Checklist:
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my own code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged and published in downstream modules
## Additional context
Add any other context or screenshots about the pull request here.
================================================
FILE: .gitignore
================================================
/rag-venv
/ragsaas-venvs
backend/poetry.lock
backend/rag_config.json
backend/data/**
================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
.
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to RAG-SaaS
First off, thank you for considering contributing to RAG-SaaS! It's people like you that make RAG-SaaS such a great tool. We welcome contributions from everyone, whether it's a bug report, feature request, documentation improvement, or a code contribution.
## Table of Contents
1. [Code of Conduct](#code-of-conduct)
2. [Getting Started](#getting-started)
3. [How Can I Contribute?](#how-can-i-contribute)
- [Reporting Bugs](#reporting-bugs)
- [Suggesting Enhancements](#suggesting-enhancements)
- [Your First Code Contribution](#your-first-code-contribution)
- [Pull Requests](#pull-requests)
4. [Style Guidelines](#style-guidelines)
- [Git Commit Messages](#git-commit-messages)
- [Python Style Guide](#python-style-guide)
- [JavaScript Style Guide](#javascript-style-guide)
5. [Additional Notes](#additional-notes)
## Code of Conduct
This project and everyone participating in it is governed by the [RAG-SaaS Code of Conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. Please report unacceptable behavior to [adithyaskolavi@gmail.com](mailto:adithyaskolavi@gmail.com).
## Getting Started
1. Fork the repository on GitHub
2. Clone your fork locally
3. Set up your environment (see README.md for details)
4. Create a branch for your changes
5. Make your changes
6. Run tests and ensure they pass
7. Commit your changes
8. Push your changes to your fork
9. Open a pull request
## How Can I Contribute?
### Reporting Bugs
- Before submitting a bug report, please check the existing issues to see if someone has already reported the problem.
- When you are creating a bug report, please include as many details as possible. Fill out the required template, the information it asks for helps us resolve issues faster.
### Suggesting Enhancements
- Before creating enhancement suggestions, please check the existing issues to see if the enhancement has already been suggested.
- When you are creating an enhancement suggestion, please include as many details as possible. Fill in the template, including the steps that you imagine you would take if the feature you're requesting existed.
### Your First Code Contribution
Unsure where to begin contributing to RAG-SaaS? You can start by looking through these `beginner` and `help-wanted` issues:
- [Beginner issues](https://github.com/adithya-s-k/RAG-SaaS/labels/beginner) - issues which should only require a few lines of code, and a test or two.
- [Help wanted issues](https://github.com/adithya-s-k/RAG-SaaS/labels/help%20wanted) - issues which should be a bit more involved than `beginner` issues.
### Pull Requests
- Fill in the required template
- Do not include issue numbers in the PR title
- Include screenshots and animated GIFs in your pull request whenever possible
- Follow the [Python](#python-style-guide) and [JavaScript](#javascript-style-guide) style guides
- Document new code based on the Documentation Style Guide
- End all files with a newline
## Style Guidelines
### Git Commit Messages
- Use the present tense ("Add feature" not "Added feature")
- Use the imperative mood ("Move cursor to..." not "Moves cursor to...")
- Limit the first line to 72 characters or less
- Reference issues and pull requests liberally after the first line
### Python Style Guide
- Follow [PEP 8](https://www.python.org/dev/peps/pep-0008/)
- Use 4 spaces for indentation (not tabs)
- Use docstrings for functions and classes
- Use type hints where possible
### JavaScript Style Guide
- Use 2 spaces for indentation (not tabs)
- Use semicolons at the end of each statement
- Use single quotes for strings
- Use camelCase for variable and function names
- Use PascalCase for class names
## Additional Notes
### Issue and Pull Request Labels
This section lists the labels we use to help us track and manage issues and pull requests.
- `bug` - Issues for bugs in the code
- `enhancement` - Issues for new features or improvements
- `documentation` - Issues related to documentation
- `good first issue` - Good for newcomers
- `help wanted` - Extra attention is needed
- `question` - Further information is requested
Thank you for contributing to RAG-SaaS!
================================================
FILE: LICENSE
================================================
RAG-SaaS Dual License
This project is licensed under two licenses:
1. Apache License, Version 2.0 (for students, developers, and individuals)
2. GNU General Public License v3.0 (for companies and commercial use)
The full text of both licenses is included below. Please see LICENSING.md for details on which license applies to your use case.
---
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result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [2024] [Adithya S Kolavi]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
---
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
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stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
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avoid the special danger that patents applied to a free program could
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patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
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Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
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and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
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trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
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it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) 2024 Adithya S Kolavi
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) 2024 Adithya S Kolavi
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
FILE: LICENSING.md
================================================
# Licensing for RAG-SaaS
RAG-SaaS is released under a dual licensing model designed to accommodate both open-source and commercial use.
## For Students, Developers, Individuals, and Educational Institutions
If you are a student, individual developer, educational institution, or using this software for non-profit educational purposes (including commercial projects for educational purposes), you may use, modify, and distribute this software under the terms of the **Apache License 2.0**.
The Apache License 2.0 allows you to:
- Use the software for any purpose, including commercial purposes
- Modify the software and distribute your modifications
- Distribute the software, in source or binary forms
- Sublicense the software
For the full terms of the Apache License 2.0, please see the [LICENSE](LICENSE) file in this repository.
## For Companies and Commercial Use (Non-Educational)
If you are a company or using this software for commercial purposes outside of educational contexts, you must use this software under the terms of the **GNU General Public License v3.0 (GPL-3.0)**.
The GPL-3.0 requires that:
- Any modifications to the software must also be released under the GPL-3.0
- If you distribute the software, you must make the source code available
- Any software that incorporates GPL-3.0 licensed code must also be released under the GPL-3.0
For the full terms of the GNU General Public License v3.0, please see the [LICENSE](LICENSE) file in this repository.
## Choosing the Appropriate License
1. If you are a student, individual developer, or educational institution (including for commercial educational projects), you may choose to use the software under the Apache License 2.0.
2. If you are a company or using the software for non-educational commercial purposes, you must use the software under the GNU General Public License v3.0.
## Additional Notes
- This dual licensing model is designed to promote open-source use and development while also ensuring that commercial entities contribute back to the open-source community.
- If you are unsure which license applies to your use case, please contact us for clarification.
- Contributions to this project will be accepted under the terms of both licenses.
## Contact
If you have any questions about licensing, please open an issue in the GitHub repository or contact the project maintainers at [your contact email].
By using this software, you agree to abide by the terms of the applicable license.
================================================
FILE: README.md
================================================
<h2 align="center">RAG SaaS</h2>
<p align="center">
Ship RAG solutions quickly⚡
</p>
<p align="center">
<img alt="RAG-SaaS Logo" src="./assets/banner.png" width="80%">
</p>
<p align="center">
<strong>A end to end SaaS Solution for Retrieval-Augmented Generation (RAG) <br> and Agentic based applications.</strong><br>
</p>
<p align="center">
<a href="#-features">Features</a> ·
<a href="#%EF%B8%8F-tech-stack">Tech Stack</a> ·
<a href="#-getting-started">Getting Started</a> ·
<a href="#-docker-compose-deployment">Deployment</a> ·
<a href="#roadmap">Roadmap</a>
</p>
<p align="center">
<a href="https://github.com/adithya-s-k/RAG-SaaS/stargazers">
<img src="https://img.shields.io/github/stars/adithya-s-k/RAG-SaaS?style=social" alt="GitHub Stars">
</a>
<a href="https://github.com/adithya-s-k/RAG-SaaS/network/members">
<img src="https://img.shields.io/github/forks/adithya-s-k/RAG-SaaS?style=social" alt="GitHub Forks">
</a>
<a href="https://github.com/adithya-s-k/RAG-SaaS/issues">
<img src="https://img.shields.io/github/issues/adithya-s-k/RAG-SaaS" alt="GitHub Issues">
</a>
<a href="https://github.com/adithya-s-k/RAG-SaaS/pulls">
<img src="https://img.shields.io/github/issues-pr/adithya-s-k/RAG-SaaS" alt="GitHub Pull Requests">
</a>
</p>
<table>
<tr>
<th>Features</th>
<th>Demo Video</th>
</tr>
<tr>
<td>
<ul>
<li>🔐 Basic Authentication</li>
<li>💬 Chat History Tracking</li>
<li>🧠 Multiple RAG Variations
<ul>
<li>Basic RAG</li>
<li>Two additional configurations</li>
</ul>
</li>
<li>👨💼 Admin Dashboard
<ul>
<li>📥 Data Ingestion</li>
<li>📊 Monitoring</li>
<li>👁️ Observability</li>
<li>🔄 RAG Configuration Switching</li>
</ul>
</li>
<li>🗄️ S3 Integration for PDF uploads</li>
<li>🐳 Easy Deployment with Docker / Docker Compose</li>
</ul>
</td>
<td>
<div align="center">
</div>
</td>
</tr>
</table>
<div align="center">
<video width="100%" src="https://github.com/user-attachments/assets/f6de3ae5-bcc9-480c-bc46-dee69ec22795" alt="Demo Video RAG SAAS">
</div>
## Tech Stack
- 🦙 LlamaIndex: For building and orchestrating RAG pipelines
- 📦 MongoDB: Used as both a normal database and a vector database
- ⚡ FastAPI: Backend API framework
- ⚛️ Next.js: Frontend framework
- 🔍 Qdrant: Vector database for efficient similarity search
- 👁️ Arize Phoenix: Observability Platform to monitor/evaluate your RAG system
## 🌟 Why RAG-SaaS?
Setting up reliable RAG systems can be time-consuming and complex. RAG-SaaS allows developers to focus on fine-tuning and developing their RAG pipeline rather than worrying about packaging it into a usable application. Built on top of [create-llama](https://www.llamaindex.ai/blog/create-llama-a-command-line-tool-to-generate-llamaindex-apps-8f7683021191) by LlamaIndex, RAG-SaaS provides a solid foundation for your RAG-based projects.
## 🚀 Getting Started
1. Clone the repository:
```bash
git clone https://github.com/adithya-s-k/RAG-SaaS.git
cd RAG-SaaS
```
## 🐳 Docker Compose Deployment
### Environment Variables
<details>
<summary>🔑 How to Set up .env</summary>
### Environment Variables
To properly configure and run RAG-SaaS, you need to set up several environment variables. These are divided into three main sections: Frontend, Backend, and Docker Compose. Here's a detailed explanation of each:
#### Frontend Environment (./frontend/.env.local)
- `NEXT_PUBLIC_SERVER_URL`: (Compulsory) The endpoint URL of your FastAPI server.
- `NEXT_PUBLIC_CHAT_API`: (Compulsory) Derived from NEXT_SERVER_URL, typically set to `${NEXT_PUBLIC_SERVER_URL}/api/chat`.
#### Backend Environment (./backend/.env)
1. Model Configuration:
- `MODEL_PROVIDER`: (Compulsory) The AI model provider (e.g., 'openai').
- `MODEL`: (Compulsory) The name of the LLM model to use.
- `EMBEDDING_MODEL`: (Compulsory) The name of the embedding model.
- `EMBEDDING_DIM`: (Compulsory) The dimensionality of the embedding model.
2. OpenAI Configuration:
- `OPENAI_API_KEY`: (Compulsory) Your OpenAI API key.
3. Application Settings:
- `CONVERSATION_STARTERS`: (Compulsory) A list of starter questions for users.
- `SYSTEM_PROMPT`: (Compulsory) The system prompt for the AI model.
- `SYSTEM_CITATION_PROMPT`: (Optional) Additional prompt for citation.
- `APP_HOST`: (Compulsory) The host address for the backend (default: '0.0.0.0').
- `APP_PORT`: (Compulsory) The port for the backend (default: 8000).
4. Database Configuration:
- `MONGODB_URI`: (Compulsory) The MongoDB connection URI.
- `MONGODB_NAME`: (Compulsory) The MongoDB database name (default: 'RAGSAAS').
- `QDRANT_URL`: (Compulsory) The URL for the Qdrant server.
- `QDRANT_COLLECTION`: (Compulsory) The Qdrant collection name.
- `QDRANT_API_KEY`: (Optional) API key for Qdrant authentication.
5. Authentication:
- `JWT_SECRET_KEY`: (Compulsory) Secret key for signing JWT tokens.
- `JWT_REFRESH_SECRET_KEY`: (Compulsory) Secret key for signing JWT refresh tokens.
- `ADMIN_EMAIL`: (Compulsory) Administrator email for application login.
- `ADMIN_PASSWORD`: (Compulsory) Administrator password for application login.
6. AWS S3 Configuration (Optional):
- `AWS_ACCESS_KEY_ID`: AWS Access Key ID.
- `AWS_SECRET_ACCESS_KEY`: AWS Secret Access Key.
- `AWS_REGION`: AWS Region for your services.
- `BUCKET_NAME`: The name of the S3 bucket to use.
7. Observability:
- `ARIZE_PHOENIX_ENDPOINT`: (Optional) Endpoint for Arize Phoenix observability.
#### S3 Integration
To enable S3 integration for PDF uploads/Ingestion:
1. Set the following environment variables in your `.env` file:
```
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=bucket_region
BUCKET_NAME=your_bucket_name
```
### Docker Compose Env (./env)
```
backend:
build:
context: ./backend
dockerfile: Dockerfile
image: ragsaas/backend:latest
container_name: backend
ports:
- '8000:8000'
environment:
# MongoDB Configuration
MONGODB_NAME: RAGSAAS
MONGODB_URI: mongodb://admin:password@mongodb:27017/
# Qdrant Configuration
QDRANT_COLLECTION: default
QDRANT_URL: http://qdrant:6333
# QDRANT_API_KEY:
# OPENAI_API_KEY is compulsory
OPENAI_API_KEY:
# Backend Application Configuration
MODEL_PROVIDER: openai
MODEL: gpt-4o-mini
EMBEDDING_MODEL: text-embedding-3-small
EMBEDDING_DIM: 1536
FILESERVER_URL_PREFIX: http://backend:8000/api/files
SYSTEM_PROMPT: 'You are a helpful assistant who helps users with their questions.'
APP_HOST: 0.0.0.0
APP_PORT: 8000
JWT_SECRET_KEY:
JWT_REFRESH_SECRET_KEY:
ARIZE_PHOENIX_ENDPOINT: http://arizephoenix:4317
```
</details>
For Docker Compose deployment, use:
```bash
docker compose up -d
```
Pull down the containers
```bash
docker compose down
```
### Development Mode
To run the project in development mode, follow these steps:
1. **Start the Next.js Frontend:**
Navigate to the `frontend` directory and install the required dependencies. Then, run the development server:
```bash
cd frontend
npm install
npm run dev
```
2. **Set Up the Vector Database (Qdrant), Database (MongoDB), and Observability Platform (Arize Phoenix):**
You can either self-host these services using Docker or use hosted solutions.
**Self-Hosted Options:**
- Qdrant:
```bash
docker pull qdrant/qdrant
```
- MongoDB:
```bash
docker pull mongo
```
- Arize Phoenix:
```bash
docker pull arizephoenix/phoenix
```
**Hosted Options:**
- Qdrant Cloud: [Qdrant Cloud](https://cloud.qdrant.io/)
- MongoDB Atlas: [MongoDB Atlas](https://www.mongodb.com/cloud/atlas)
- Arize Phoenix: [Arize Phoenix](https://app.phoenix.arize.com/)
3. **Start the FastAPI Server:**
Navigate to the `backend` directory and set up the Python environment. You can use either Conda or Python's built-in `venv`:
```bash
cd backend
```
**Using Conda:**
```bash
conda create -n ragsaas-venv python=3.11
conda activate ragsaas-venv
```
**Using Python's `venv`:**
```bash
python -m venv ragsaas-venv
\ragsaas-venv\Scripts\activate # On Windows
source ragsaas-venv/bin/activate # On macOS/Linux
```
Install the required dependencies and run the server:
```bash
pip install -e .
python main.py
```
---
## Roadmap
- [x] add support to store ingested data in AWS S3
- [x] Add Docker compose for each set up
- [x] Implement Observability
- [ ] Improve authentication system
- [ ] Integrate OmniParse API for efficient Data ingestion
- [ ] Provide more control to Admin over RAG configuration
- [ ] Implement Advanced and Agentic RAG
## 👥 Contributing
We welcome contributions to RAG-SaaS! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) for more details on how to get started.
## 📄 Licensing
This project is available under a dual license:
- Apache License 2.0 for students, developers, and individuals
- GNU General Public License v3.0 for companies and commercial use
See the [LICENSING.md](LICENSING.md) file for more details.
## 🙏 Acknowledgements
This project is built on the following frameworks, technologies and tools:
- [LlamaIndex](https://www.llamaindex.ai/) for the create-llama tool and RAG orchestration
- [FastAPI](https://fastapi.tiangolo.com/)
- [Next.js](https://nextjs.org/)
- [MongoDB](https://www.mongodb.com/)
- [Qdrant](https://qdrant.tech/)
- [Arize Phoenix](https://docs.arize.com/phoenix)
## Contact & Support
### Bug Reports
If you encounter any issues or bugs, please report them in the [Issues](https://github.com/adithya-s-k/RAG-SaaS/issues) tab of our GitHub repository.
### Commercial Use & Custom Solutions
For inquiries regarding:
- Commercial licensing
- Custom modifications
- Managed deployment
- Specialized integrations
Please contact: adithyaskolavi@gmail.com
We're here to help tailor RAG-SaaS to your specific needs and ensure you get the most out of our solution.
## Star History
<p align="center">
<a href="https://cognitivelab.in">
<img src="https://api.star-history.com/svg?repos=adithya-s-k/RAG-SaaS&type=Date" alt="Star History Chart">
</a>
</p>
================================================
FILE: backend/.gitignore
================================================
__pycache__
storage
.env
output
================================================
FILE: backend/Dockerfile
================================================
FROM python:3.11 as build
WORKDIR /app
ENV PYTHONPATH=/app
# Install Poetry
RUN curl -sSL https://install.python-poetry.org | POETRY_HOME=/opt/poetry python && \
cd /usr/local/bin && \
ln -s /opt/poetry/bin/poetry && \
poetry config virtualenvs.create false
# Install Chromium for web loader
# Can disable this if you don't use the web loader to reduce the image size
RUN apt update && apt install -y chromium chromium-driver
# Install dependencies
COPY ./pyproject.toml ./poetry.lock* /app/
RUN poetry install --no-root --no-cache --only main
# ====================================
FROM build as release
COPY . .
CMD ["python", "main.py"]
================================================
FILE: backend/README.md
================================================
This is a [LlamaIndex](https://www.llamaindex.ai/) project using [FastAPI](https://fastapi.tiangolo.com/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
## Getting Started
First, setup the environment with poetry:
> **_Note:_** This step is not needed if you are using the dev-container.
```
poetry install
poetry shell
```
Then check the parameters that have been pre-configured in the `.env` file in this directory. (E.g. you might need to configure an `OPENAI_API_KEY` if you're using OpenAI as model provider).
If you are using any tools or data sources, you can update their config files in the `config` folder.
Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
```
poetry run generate
```
Third, run the development server:
```
python main.py
```
The example provides two different API endpoints:
1. `/api/chat` - a streaming chat endpoint
2. `/api/chat/request` - a non-streaming chat endpoint
You can test the streaming endpoint with the following curl request:
```
curl --location 'localhost:8000/api/chat' \
--header 'Content-Type: application/json' \
--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
```
And for the non-streaming endpoint run:
```
curl --location 'localhost:8000/api/chat/request' \
--header 'Content-Type: application/json' \
--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
```
You can start editing the API endpoints by modifying `app/api/chat/chat.py`. The endpoints auto-update as you save the file. You can delete the endpoint you're not using.
Open [http://localhost:8000/docs](http://localhost:8000/docs) with your browser to see the Swagger UI of the API.
The API allows CORS for all origins to simplify development. You can change this behavior by setting the `ENVIRONMENT` environment variable to `prod`:
```
ENVIRONMENT=prod python main.py
```
## Using Docker
1. Build an image for the FastAPI app:
```
docker build -t <your_backend_image_name> .
```
2. Generate embeddings:
Parse the data and generate the vector embeddings if the `./data` folder exists - otherwise, skip this step:
```
docker run \
--rm \
-v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-v $(pwd)/config:/app/config \
-v $(pwd)/data:/app/data \ # Use your local folder to read the data
-v $(pwd)/storage:/app/storage \ # Use your file system to store the vector database
<your_backend_image_name> \
poetry run generate
```
3. Start the API:
```
docker run \
-v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-v $(pwd)/config:/app/config \
-v $(pwd)/storage:/app/storage \ # Use your file system to store gea vector database
-p 8000:8000 \
<your_backend_image_name>
```
## Learn More
To learn more about LlamaIndex, take a look at the following resources:
- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex.
You can check out [the LlamaIndex GitHub repository](https://github.com/run-llama/llama_index) - your feedback and contributions are welcome!
================================================
FILE: backend/app/__init__.py
================================================
================================================
FILE: backend/app/api/__init__.py
================================================
================================================
FILE: backend/app/api/admin/__init__.py
================================================
from .route import admin_router
__all__ = ["admin_router"]
================================================
FILE: backend/app/api/admin/route.py
================================================
import os
from grpc import Status
import boto3
import tempfile
import shutil
from dotenv import load_dotenv
from fastapi import APIRouter, File, UploadFile, Depends, HTTPException, status
from fastapi.responses import JSONResponse
from typing import List, Dict, Any, Optional
from pydantic import BaseModel, EmailStr, Field
from app.core.user import get_current_user
from app.models.user_model import User
from app.services.admin_service import admin_service
from app.schemas.admin_schema import UserOut, UsersOut, MessageOut, ErrorOut
from app.services.config_service import config_service
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.settings import Settings
from app.api.chat.engine.vectordb import get_vector_store
from llama_index.core import SimpleDirectoryReader
from llama_index.core.schema import Document
from phoenix.trace import using_project
admin_router = APIRouter()
load_dotenv()
# AWS configuration
AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
AWS_REGION = os.getenv("AWS_REGION")
AWS_S3_BUCKET_NAME = os.getenv("AWS_S3_BUCKET_NAME")
# Local storage configuration
DATA_DIR = os.getenv("DATA_DIR", "data")
PRIVATE_STORE_PATH = os.path.join(DATA_DIR)
def get_documents(
directory: str, recursive: bool = True, filename_as_id: bool = True
) -> List[Document]:
"""
Load documents from a specified directory using SimpleDirectoryReader.
Args:
directory (str): The path to the directory containing the documents.
recursive (bool, optional): Whether to recursively search for documents in subdirectories. Defaults to True.
filename_as_id (bool, optional): Whether to use the filename as the document ID. Defaults to True.
Returns:
List[Document]: A list of loaded documents.
"""
reader = SimpleDirectoryReader(
directory, recursive=recursive, filename_as_id=filename_as_id
)
documents = reader.load_data(show_progress=True)
return documents
class UserUpdate(BaseModel):
first_name: Optional[str] = None
last_name: Optional[str] = None
email: Optional[EmailStr] = None
role: Optional[str] = None
disabled: Optional[bool] = None
def verify_admin(current_user: User = Depends(get_current_user)):
if current_user.role != "admin":
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="You don't have permission to access this resource",
)
return current_user
@admin_router.get(
"/users", response_model=UsersOut, responses={403: {"model": ErrorOut}}
)
async def get_all_users(admin: User = Depends(verify_admin)) -> UsersOut:
users = await admin_service.get_all_users()
return UsersOut(users=[UserOut(**user) for user in users])
@admin_router.get(
"/users/{user_id}",
response_model=UserOut,
responses={403: {"model": ErrorOut}, 404: {"model": ErrorOut}},
)
async def get_user(user_id: str, admin: User = Depends(verify_admin)) -> UserOut:
user = await admin_service.get_user_by_id(user_id)
if not user:
raise HTTPException(status_code=404, detail="User not found")
return UserOut(**user)
@admin_router.put(
"/users/{user_id}",
response_model=MessageOut,
responses={403: {"model": ErrorOut}, 404: {"model": ErrorOut}},
)
async def edit_user(
user_id: str, user_update: UserUpdate, admin: User = Depends(verify_admin)
) -> MessageOut:
success = await admin_service.edit_user(
user_id, user_update.model_dump(exclude_unset=True)
)
if not success:
raise HTTPException(status_code=404, detail="User not found")
return MessageOut(message="User updated successfully")
@admin_router.delete(
"/users/{user_id}",
response_model=MessageOut,
responses={
403: {"model": ErrorOut},
404: {"model": ErrorOut},
400: {"model": ErrorOut},
},
)
async def delete_user(user_id: str, admin: User = Depends(verify_admin)) -> MessageOut:
user = await admin_service.get_user_by_id(user_id)
if not user:
raise HTTPException(status_code=404, detail="User not found")
if user["role"] == "admin":
raise HTTPException(status_code=400, detail="Cannot delete an admin user")
success = await admin_service.delete_user(user_id)
if not success:
raise HTTPException(status_code=500, detail="Failed to delete user")
return MessageOut(message="User deleted successfully")
@admin_router.get("/system-prompt")
async def get_system_prompt(admin: User = Depends(verify_admin)):
system_prompt = await config_service.get_system_prompt()
return {"system_prompt": system_prompt}
class SystemPromptUpdate(BaseModel):
new_prompt: str
@admin_router.put("/system-prompt")
async def update_system_prompt(
prompt_update: SystemPromptUpdate, admin: User = Depends(verify_admin)
):
success = await config_service.update_system_prompt(prompt_update.new_prompt)
if not success:
raise HTTPException(status_code=500, detail="Failed to update system prompt")
return {"message": "System prompt updated successfully"}
class ConversationStartersUpdate(BaseModel):
new_starters: List[str] = Field(
...,
description="A list of new conversation starter questions",
min_items=1,
example=[
"What is RAG?",
"How does chunking work?",
"Explain vector embeddings",
],
)
@admin_router.put(
"/conversation-starters",
response_model=MessageOut,
responses={
200: {
"model": MessageOut,
"description": "Conversation starters updated successfully",
},
500: {
"model": ErrorOut,
"description": "Failed to update conversation starters",
},
},
summary="Update conversation starters",
description="Update the list of conversation starter questions for the chat interface",
)
async def update_conversation_starters(
starters_update: ConversationStartersUpdate, admin: User = Depends(verify_admin)
):
success = await config_service.update_conversation_starters(
starters_update.new_starters
)
if not success:
raise HTTPException(
status_code=500, detail="Failed to update conversation starters"
)
return MessageOut(message="Conversation starters updated successfully")
@admin_router.post("/upload_data")
async def upload_and_ingest_data(
file: UploadFile = File(...), admin: User = Depends(verify_admin)
):
with using_project("RAGSAAS-Data-Ingest"):
try:
# Check if AWS credentials are available
use_aws = all(
[
AWS_ACCESS_KEY_ID,
AWS_SECRET_ACCESS_KEY,
AWS_REGION,
AWS_S3_BUCKET_NAME,
]
)
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
shutil.copyfileobj(file.file, tmp_file)
tmp_file_path = tmp_file.name
if use_aws:
# Initialize S3 client
s3_client = boto3.client(
"s3",
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
region_name=AWS_REGION,
)
# Upload file to S3
folder_name = "admin_uploads/"
file_key = f"{folder_name}{file.filename}"
s3_client.upload_file(tmp_file_path, AWS_S3_BUCKET_NAME, file_key)
# Generate the file URL
file_url = f"https://{AWS_S3_BUCKET_NAME}.s3.{AWS_REGION}.amazonaws.com/{file_key}"
else:
# Store file locally
os.makedirs(PRIVATE_STORE_PATH, exist_ok=True)
local_file_path = os.path.join(PRIVATE_STORE_PATH, file.filename)
shutil.move(tmp_file_path, local_file_path)
file_url = f"/api/files/data/{file.filename}"
# Ingest file into vector database
with tempfile.TemporaryDirectory() as temp_dir:
file_path = os.path.join(temp_dir, file.filename)
shutil.copy(
local_file_path if not use_aws else tmp_file_path, file_path
)
documents = get_documents(temp_dir)
# Set metadata for all documents
for doc in documents:
doc.metadata["private"] = "false"
doc.metadata["file_id"] = file_key if use_aws else file.filename
doc.metadata["user_id"] = "admin"
doc.metadata["url"] = file_url
doc.metadata["file_path"] = file_url
doc.metadata["document_id"] = file_url
doc.metadata["document_id"] = file_url
vector_store = get_vector_store()
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(
chunk_size=Settings.chunk_size,
chunk_overlap=Settings.chunk_overlap,
),
Settings.embed_model,
],
vector_store=vector_store,
)
nodes = pipeline.run(documents=documents, show_progress=True)
return JSONResponse(
status_code=200,
content={
"file_url": file_url,
"message": f"Ingestion complete. {len(nodes)} nodes inserted.",
"storage": "S3" if use_aws else "Local",
},
)
except Exception as e:
return JSONResponse(status_code=500, content={"error": str(e)})
================================================
FILE: backend/app/api/auth/__init__.py
================================================
from .route import auth_router
__all__ = ["auth_router"]
================================================
FILE: backend/app/api/auth/route.py
================================================
from fastapi import APIRouter, Depends, HTTPException, Response, status, Body
from fastapi.security import OAuth2PasswordRequestForm
from typing import Any
from app.services import user_service
from app.core.security import create_access_token, create_refresh_token
from app.schemas.auth_schema import TokenSchema
from app.schemas.user_schema import UserOut
from app.models.user_model import User
from app.core.user import get_current_user
from app.core.config import settings
from app.schemas.auth_schema import TokenPayload, AuthErrorOut, RefreshTokenRequest
from app.schemas.user_schema import UserAuth, UserUpdate
from pydantic import ValidationError
from jose import JWTError, jwt
import pymongo
auth_router = APIRouter()
@auth_router.post("/signup", summary="Create new user", response_model=dict)
async def create_user(data: UserAuth):
try:
user = await user_service.create_user(data)
return {
"status": "success",
"user": UserOut(
# user_id=user.user_id,
email=user.email,
first_name=user.first_name,
last_name=user.last_name,
role=user.role,
),
}
except ValueError as e:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e))
except pymongo.errors.DuplicateKeyError:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="User with this email already exists",
)
@auth_router.post(
"/login",
summary="Create access and refresh tokens for user",
response_model=TokenSchema,
)
@auth_router.post(
"/login",
summary="Create access and refresh tokens for user",
response_model=TokenSchema,
)
async def login(form_data: OAuth2PasswordRequestForm = Depends()) -> Any:
user = await user_service.authenticate(
email=form_data.username, password=form_data.password
)
if not user:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Incorrect email or password",
)
if user.disabled:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="User account is disabled",
)
return {
"access_token": create_access_token(user.user_id),
"refresh_token": create_refresh_token(user.user_id),
}
# @auth_router.post("/refresh", summary="Refresh token", response_model=TokenSchema)
# async def refresh_token(refresh_token: str = Body(...)):
# try:
# payload = jwt.decode(
# refresh_token,
# settings.JWT_REFRESH_SECRET_KEY,
# algorithms=[settings.ALGORITHM],
# )
# token_data = TokenPayload(**payload)
# except (jwt.JWTError, ValidationError):
# raise HTTPException(
# status_code=status.HTTP_403_FORBIDDEN,
# detail="Invalid token",
# headers={"WWW-Authenticate": "Bearer"},
# )
# user = await user_service.get_user_by_id(token_data.sub)
# if not user:
# raise HTTPException(
# status_code=status.HTTP_404_NOT_FOUND,
# detail="Invalid token for user",
# )
# return {
# "access_token": create_access_token(user.user_id),
# "refresh_token": create_refresh_token(user.user_id),
# }
@auth_router.post(
"/refresh",
response_model=TokenSchema,
summary="Refresh token",
responses={403: {"model": AuthErrorOut}, 404: {"model": AuthErrorOut}},
)
async def refresh_token(response: Response, refresh_request: RefreshTokenRequest):
try:
payload = jwt.decode(
refresh_request.refresh_token,
settings.JWT_REFRESH_SECRET_KEY,
algorithms=[settings.ALGORITHM],
)
token_data = TokenPayload(**payload)
except (JWTError, ValidationError):
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Invalid token",
headers={"WWW-Authenticate": "Bearer"},
)
user = await user_service.get_user_by_id(token_data.sub)
if not user:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="Invalid token for user",
)
if user.disabled:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="User account is disabled",
)
new_access_token = create_access_token(user.user_id)
new_refresh_token = create_refresh_token(user.user_id)
# Set cookies
response.set_cookie(
key="accessToken",
value=new_access_token,
httponly=True,
max_age=settings.ACCESS_TOKEN_EXPIRE_MINUTES * 60,
expires=settings.ACCESS_TOKEN_EXPIRE_MINUTES * 60,
samesite="lax",
secure=settings.COOKIE_SECURE, # True in production
)
response.set_cookie(
key="refreshToken",
value=new_refresh_token,
httponly=True,
max_age=settings.REFRESH_TOKEN_EXPIRE_MINUTES * 60,
expires=settings.REFRESH_TOKEN_EXPIRE_MINUTES * 60,
samesite="lax",
secure=settings.COOKIE_SECURE, # True in production
)
return {
"access_token": new_access_token,
"refresh_token": new_refresh_token,
}
@auth_router.get(
"/me", summary="Get details of currently logged in user", response_model=UserOut
)
async def get_me(user: User = Depends(get_current_user)):
return user
@auth_router.get("/verify-admin", response_model=dict)
async def verify_admin(current_user: User = Depends(get_current_user)):
if not current_user.role or current_user.role != "admin":
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="User is not an admin",
)
return {"isAdmin": True}
@auth_router.post("/update", summary="Update User", response_model=UserOut)
async def update_user(data: UserUpdate, user: User = Depends(get_current_user)):
try:
return await user_service.update_user(user.user_id, data)
except pymongo.errors.OperationFailure:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST, detail="User does not exist"
)
@auth_router.post(
"/test-token", summary="Test if the access token is valid", response_model=UserOut
)
async def test_token(user: User = Depends(get_current_user)):
return user
================================================
FILE: backend/app/api/chat/__init__.py
================================================
from .route import chat_router
from .chat_config import config_router
from .upload import file_upload_router
__all__ = ["chat_router", "config_router", "file_upload_router"]
================================================
FILE: backend/app/api/chat/chat_config.py
================================================
import logging
import os
from fastapi import APIRouter
from app.api.chat.models import ChatConfig
from app.services.config_service import config_service
config_router = r = APIRouter()
logger = logging.getLogger("uvicorn")
@r.get("")
async def chat_config() -> ChatConfig:
return await config_service.get_chat_config()
================================================
FILE: backend/app/api/chat/engine/__init__.py
================================================
from .engine import get_chat_engine as get_chat_engine
================================================
FILE: backend/app/api/chat/engine/engine.py
================================================
import os
from app.api.chat.engine.index import get_index
from app.api.chat.engine.node_postprocessors import NodeCitationProcessor
from fastapi import HTTPException
from llama_index.core.chat_engine import CondensePlusContextChatEngine
from app.db import sync_mongodb
from motor.motor_asyncio import AsyncIOMotorClient
def get_system_prompt_from_db():
config_collection = sync_mongodb.db.config
config = config_collection.find_one({"_id": "app_config"})
if config and "SYSTEM_PROMPT" in config:
return config["SYSTEM_PROMPT"]
return None
def get_chat_engine(filters=None, params=None):
system_prompt = get_system_prompt_from_db()
if system_prompt is None:
system_prompt = os.getenv("SYSTEM_PROMPT", "")
citation_prompt = os.getenv("SYSTEM_CITATION_PROMPT", None)
top_k = int(os.getenv("TOP_K", 0))
# node_postprocessors = []
# if citation_prompt:
# node_postprocessors = [NodeCitationProcessor()]
# system_prompt = f"{system_prompt}\n{citation_prompt}"
index = get_index(params)
if index is None:
raise HTTPException(
status_code=500,
detail=str(
"StorageContext is empty - call 'poetry run generate' to generate the storage first"
),
)
retriever = index.as_retriever(
filters=filters, **({"similarity_top_k": top_k} if top_k != 0 else {})
)
return CondensePlusContextChatEngine.from_defaults(
system_prompt=system_prompt,
retriever=retriever,
# node_postprocessors=node_postprocessors,
)
================================================
FILE: backend/app/api/chat/engine/generate.py
================================================
# flake8: noqa: E402
from dotenv import load_dotenv
load_dotenv()
import logging
import os
from app.api.chat.engine.loaders import get_documents
from app.api.chat.engine.vectordb import get_vector_store
from app.settings import init_settings
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.settings import Settings
from llama_index.core.storage import StorageContext
from llama_index.core.storage.docstore import SimpleDocumentStore
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
STORAGE_DIR = os.getenv("STORAGE_DIR", "storage")
def get_doc_store():
# If the storage directory is there, load the document store from it.
# If not, set up an in-memory document store since we can't load from a directory that doesn't exist.
if os.path.exists(STORAGE_DIR):
return SimpleDocumentStore.from_persist_dir(STORAGE_DIR)
else:
return SimpleDocumentStore()
def run_pipeline(docstore, vector_store, documents):
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(
chunk_size=Settings.chunk_size,
chunk_overlap=Settings.chunk_overlap,
),
Settings.embed_model,
],
docstore=docstore,
docstore_strategy="upserts_and_delete",
vector_store=vector_store,
)
# Run the ingestion pipeline and store the results
nodes = pipeline.run(show_progress=True, documents=documents)
return nodes
def persist_storage(docstore, vector_store):
storage_context = StorageContext.from_defaults(
docstore=docstore,
vector_store=vector_store,
)
storage_context.persist(STORAGE_DIR)
def generate_datasource():
init_settings()
logger.info("Generate index for the provided data")
# Get the stores and documents or create new ones
documents = get_documents()
# Set private=false to mark the document as public (required for filtering)
for doc in documents:
doc.metadata["private"] = "false"
docstore = get_doc_store()
vector_store = get_vector_store()
# Run the ingestion pipeline
_ = run_pipeline(docstore, vector_store, documents)
# Build the index and persist storage
persist_storage(docstore, vector_store)
logger.info("Finished generating the index")
if __name__ == "__main__":
generate_datasource()
================================================
FILE: backend/app/api/chat/engine/index.py
================================================
import logging
from llama_index.core.indices import VectorStoreIndex
from app.api.chat.engine.vectordb import get_vector_store
logger = logging.getLogger("uvicorn")
def get_index(params=None):
logger.info("Connecting vector store...")
store = get_vector_store()
# Load the index from the vector store
# If you are using a vector store that doesn't store text,
# you must load the index from both the vector store and the document store
index = VectorStoreIndex.from_vector_store(store)
logger.info("Finished load index from vector store.")
return index
================================================
FILE: backend/app/api/chat/engine/loaders/__init__.py
================================================
import logging
import yaml
from app.api.chat.engine.loaders.db import DBLoaderConfig, get_db_documents
from app.api.chat.engine.loaders.file import FileLoaderConfig, get_file_documents
from app.api.chat.engine.loaders.web import WebLoaderConfig, get_web_documents
logger = logging.getLogger(__name__)
def load_configs():
with open("config/loaders.yaml") as f:
configs = yaml.safe_load(f)
return configs
def get_documents():
documents = []
config = load_configs()
for loader_type, loader_config in config.items():
logger.info(
f"Loading documents from loader: {loader_type}, config: {loader_config}"
)
match loader_type:
case "file":
document = get_file_documents(FileLoaderConfig(**loader_config))
case "web":
document = get_web_documents(WebLoaderConfig(**loader_config))
case "db":
document = get_db_documents(
configs=[DBLoaderConfig(**cfg) for cfg in loader_config]
)
case _:
raise ValueError(f"Invalid loader type: {loader_type}")
documents.extend(document)
return documents
================================================
FILE: backend/app/api/chat/engine/loaders/db.py
================================================
import logging
from typing import List
from pydantic import BaseModel
logger = logging.getLogger(__name__)
class DBLoaderConfig(BaseModel):
uri: str
queries: List[str]
def get_db_documents(configs: list[DBLoaderConfig]):
from llama_index.readers.database import DatabaseReader
docs = []
for entry in configs:
loader = DatabaseReader(uri=entry.uri)
for query in entry.queries:
logger.info(f"Loading data from database with query: {query}")
documents = loader.load_data(query=query)
docs.extend(documents)
return documents
================================================
FILE: backend/app/api/chat/engine/loaders/file.py
================================================
import os
import logging
from typing import Dict
from llama_parse import LlamaParse
from pydantic import BaseModel
from app.config import DATA_DIR
logger = logging.getLogger(__name__)
class FileLoaderConfig(BaseModel):
use_llama_parse: bool = False
def llama_parse_parser():
if os.getenv("LLAMA_CLOUD_API_KEY") is None:
raise ValueError(
"LLAMA_CLOUD_API_KEY environment variable is not set. "
"Please set it in .env file or in your shell environment then run again!"
)
parser = LlamaParse(
result_type="markdown",
verbose=True,
language="en",
ignore_errors=False,
)
return parser
def llama_parse_extractor() -> Dict[str, LlamaParse]:
from llama_parse.utils import SUPPORTED_FILE_TYPES
parser = llama_parse_parser()
return {file_type: parser for file_type in SUPPORTED_FILE_TYPES}
def get_file_documents(config: FileLoaderConfig):
from llama_index.core.readers import SimpleDirectoryReader
try:
file_extractor = None
if config.use_llama_parse:
# LlamaParse is async first,
# so we need to use nest_asyncio to run it in sync mode
import nest_asyncio
nest_asyncio.apply()
file_extractor = llama_parse_extractor()
reader = SimpleDirectoryReader(
DATA_DIR,
recursive=True,
filename_as_id=True,
raise_on_error=True,
file_extractor=file_extractor,
)
return reader.load_data()
except Exception as e:
import sys
import traceback
# Catch the error if the data dir is empty
# and return as empty document list
_, _, exc_traceback = sys.exc_info()
function_name = traceback.extract_tb(exc_traceback)[-1].name
if function_name == "_add_files":
logger.warning(
f"Failed to load file documents, error message: {e} . Return as empty document list."
)
return []
else:
# Raise the error if it is not the case of empty data dir
raise e
================================================
FILE: backend/app/api/chat/engine/loaders/web.py
================================================
from pydantic import BaseModel, Field
class CrawlUrl(BaseModel):
base_url: str
prefix: str
max_depth: int = Field(default=1, ge=0)
class WebLoaderConfig(BaseModel):
driver_arguments: list[str] = Field(default=None)
urls: list[CrawlUrl]
def get_web_documents(config: WebLoaderConfig):
from llama_index.readers.web import WholeSiteReader
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
options = Options()
driver_arguments = config.driver_arguments or []
for arg in driver_arguments:
options.add_argument(arg)
docs = []
for url in config.urls:
scraper = WholeSiteReader(
prefix=url.prefix,
max_depth=url.max_depth,
driver=webdriver.Chrome(options=options),
)
docs.extend(scraper.load_data(url.base_url))
return docs
================================================
FILE: backend/app/api/chat/engine/node_postprocessors.py
================================================
from typing import List, Optional
from llama_index.core import QueryBundle
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import NodeWithScore
class NodeCitationProcessor(BaseNodePostprocessor):
"""
Append node_id into metadata for citation purpose.
Config SYSTEM_CITATION_PROMPT in your runtime environment variable to enable this feature.
"""
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
for node_score in nodes:
node_score.node.metadata["node_id"] = node_score.node.node_id
return nodes
================================================
FILE: backend/app/api/chat/engine/query_filter.py
================================================
from llama_index.core.vector_stores.types import MetadataFilter, MetadataFilters
def generate_filters(doc_ids):
"""
Generate public/private document filters based on the doc_ids and the vector store.
"""
public_doc_filter = MetadataFilter(
key="private",
value="true",
operator="!=", # type: ignore
)
selected_doc_filter = MetadataFilter(
key="doc_id",
value=doc_ids,
operator="in", # type: ignore
)
if len(doc_ids) > 0:
# If doc_ids are provided, we will select both public and selected documents
filters = MetadataFilters(
filters=[
public_doc_filter,
selected_doc_filter,
],
condition="or", # type: ignore
)
else:
filters = MetadataFilters(
filters=[
public_doc_filter,
]
)
return filters
================================================
FILE: backend/app/api/chat/engine/vectordb.py
================================================
import os
import qdrant_client
from llama_index.vector_stores.qdrant import QdrantVectorStore
def get_vector_store():
collection_name = os.getenv("QDRANT_COLLECTION", "ragsaas")
QDRANT_URL = os.getenv("QDRANT_URL")
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
if not collection_name or not QDRANT_URL:
raise ValueError(
"Please set QDRANT_COLLECTION, QDRANT_URL"
" to your environment variables or config them in the .env file"
)
if QDRANT_API_KEY:
# creating a qdrant client instance
client = qdrant_client.QdrantClient(
url=QDRANT_URL,
api_key=QDRANT_API_KEY,
)
aclient = qdrant_client.AsyncQdrantClient(
url=QDRANT_URL,
api_key=QDRANT_API_KEY,
)
store = QdrantVectorStore(
client=client, aclient=aclient, collection_name=collection_name
)
else:
client = qdrant_client.QdrantClient(
url=QDRANT_URL,
)
aclient = qdrant_client.AsyncQdrantClient(
url=QDRANT_URL,
)
store = QdrantVectorStore(
client=client, aclient=aclient, collection_name=collection_name
)
return store
================================================
FILE: backend/app/api/chat/events.py
================================================
import json
import asyncio
import logging
from typing import AsyncGenerator, Dict, Any, List, Optional
from llama_index.core.callbacks.base import BaseCallbackHandler
from llama_index.core.callbacks.schema import CBEventType
from llama_index.core.tools.types import ToolOutput
from pydantic import BaseModel
logger = logging.getLogger(__name__)
class CallbackEvent(BaseModel):
event_type: CBEventType
payload: Optional[Dict[str, Any]] = None
event_id: str = ""
def get_retrieval_message(self) -> dict | None:
if self.payload:
nodes = self.payload.get("nodes")
if nodes:
msg = f"Retrieved {len(nodes)} sources to use as context for the query"
else:
msg = f"Retrieving context for query: '{self.payload.get('query_str')}'"
return {
"type": "events",
"data": {"title": msg},
}
else:
return None
def get_tool_message(self) -> dict | None:
func_call_args = self.payload.get("function_call")
if func_call_args is not None and "tool" in self.payload:
tool = self.payload.get("tool")
return {
"type": "events",
"data": {
"title": f"Calling tool: {tool.name} with inputs: {func_call_args}",
},
}
def _is_output_serializable(self, output: Any) -> bool:
try:
json.dumps(output)
return True
except TypeError:
return False
def get_agent_tool_response(self) -> dict | None:
response = self.payload.get("response")
if response is not None:
sources = response.sources
for source in sources:
# Return the tool response here to include the toolCall information
if isinstance(source, ToolOutput):
if self._is_output_serializable(source.raw_output):
output = source.raw_output
else:
output = source.content
return {
"type": "tools",
"data": {
"toolOutput": {
"output": output,
"isError": source.is_error,
},
"toolCall": {
"id": None, # There is no tool id in the ToolOutput
"name": source.tool_name,
"input": source.raw_input,
},
},
}
def to_response(self):
try:
match self.event_type:
case "retrieve":
return self.get_retrieval_message()
case "function_call":
return self.get_tool_message()
case "agent_step":
return self.get_agent_tool_response()
case _:
return None
except Exception as e:
logger.error(f"Error in converting event to response: {e}")
return None
class EventCallbackHandler(BaseCallbackHandler):
_aqueue: asyncio.Queue
is_done: bool = False
def __init__(
self,
):
"""Initialize the base callback handler."""
ignored_events = [
CBEventType.CHUNKING,
CBEventType.NODE_PARSING,
CBEventType.EMBEDDING,
CBEventType.LLM,
CBEventType.TEMPLATING,
]
super().__init__(ignored_events, ignored_events)
self._aqueue = asyncio.Queue()
def on_event_start(
self,
event_type: CBEventType,
payload: Optional[Dict[str, Any]] = None,
event_id: str = "",
**kwargs: Any,
) -> str:
event = CallbackEvent(event_id=event_id, event_type=event_type, payload=payload)
if event.to_response() is not None:
self._aqueue.put_nowait(event)
def on_event_end(
self,
event_type: CBEventType,
payload: Optional[Dict[str, Any]] = None,
event_id: str = "",
**kwargs: Any,
) -> None:
event = CallbackEvent(event_id=event_id, event_type=event_type, payload=payload)
if event.to_response() is not None:
self._aqueue.put_nowait(event)
def start_trace(self, trace_id: Optional[str] = None) -> None:
"""No-op."""
def end_trace(
self,
trace_id: Optional[str] = None,
trace_map: Optional[Dict[str, List[str]]] = None,
) -> None:
"""No-op."""
async def async_event_gen(self) -> AsyncGenerator[CallbackEvent, None]:
while not self._aqueue.empty() or not self.is_done:
try:
yield await asyncio.wait_for(self._aqueue.get(), timeout=0.1)
except asyncio.TimeoutError:
pass
================================================
FILE: backend/app/api/chat/models.py
================================================
import logging
import os
from typing import Any, Dict, List, Literal, Optional
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.core.schema import NodeWithScore
from pydantic import BaseModel, Field, validator
from pydantic.alias_generators import to_camel
from app.config import DATA_DIR
logger = logging.getLogger("uvicorn")
class FileContent(BaseModel):
type: Literal["text", "ref"]
# If the file is pure text then the value is be a string
# otherwise, it's a list of document IDs
value: str | List[str]
class File(BaseModel):
id: str
content: FileContent
filename: str
filesize: int
filetype: str
class AnnotationFileData(BaseModel):
files: List[File] = Field(
default=[],
description="List of files",
)
class Config:
json_schema_extra = {
"example": {
"csvFiles": [
{
"content": "Name, Age\nAlice, 25\nBob, 30",
"filename": "example.csv",
"filesize": 123,
"id": "123",
"type": "text/csv",
}
]
}
}
alias_generator = to_camel
class Annotation(BaseModel):
type: str
data: AnnotationFileData | List[str]
def to_content(self) -> str | None:
if self.type == "document_file":
# We only support generating context content for CSV files for now
csv_files = [file for file in self.data.files if file.filetype == "csv"]
if len(csv_files) > 0:
return "Use data from following CSV raw content\n" + "\n".join(
[f"```csv\n{csv_file.content.value}\n```" for csv_file in csv_files]
)
else:
logger.warning(
f"The annotation {self.type} is not supported for generating context content"
)
return None
class Message(BaseModel):
role: MessageRole
content: str
annotations: List[Annotation] | None = None
class ChatData(BaseModel):
messages: List[Message]
data: Any = None
class Config:
json_schema_extra = {
"example": {
"messages": [
{
"role": "user",
"content": "What standards for letters exist?",
}
]
}
}
@validator("messages")
def messages_must_not_be_empty(cls, v):
if len(v) == 0:
raise ValueError("Messages must not be empty")
return v
def get_last_message_content(self) -> str:
"""
Get the content of the last message along with the data content if available.
Fallback to use data content from previous messages
"""
if len(self.messages) == 0:
raise ValueError("There is not any message in the chat")
last_message = self.messages[-1]
message_content = last_message.content
for message in reversed(self.messages):
if message.role == MessageRole.USER and message.annotations is not None:
annotation_contents = filter(
None,
[annotation.to_content() for annotation in message.annotations],
)
if not annotation_contents:
continue
annotation_text = "\n".join(annotation_contents)
message_content = f"{message_content}\n{annotation_text}"
break
return message_content
def get_history_messages(self) -> List[ChatMessage]:
"""
Get the history messages
"""
return [
ChatMessage(role=message.role, content=message.content)
for message in self.messages[:-1]
]
def is_last_message_from_user(self) -> bool:
return self.messages[-1].role == MessageRole.USER
def get_chat_document_ids(self) -> List[str]:
"""
Get the document IDs from the chat messages
"""
document_ids: List[str] = []
for message in self.messages:
if message.role == MessageRole.USER and message.annotations is not None:
for annotation in message.annotations:
if (
annotation.type == "document_file"
and annotation.data.files is not None
):
for fi in annotation.data.files:
if fi.content.type == "ref":
document_ids += fi.content.value
return list(set(document_ids))
class SourceNodes(BaseModel):
id: str
metadata: Dict[str, Any]
score: Optional[float]
text: str
url: Optional[str]
@classmethod
def from_source_node(cls, source_node: NodeWithScore):
metadata = source_node.node.metadata
url = cls.get_url_from_metadata(metadata)
return cls(
id=source_node.node.node_id,
metadata=metadata,
score=source_node.score,
text=source_node.node.text, # type: ignore
url=url,
)
@classmethod
def get_url_from_metadata(cls, metadata: Dict[str, Any]) -> str:
url_prefix = os.getenv("FILESERVER_URL_PREFIX")
if not url_prefix:
logger.warning(
"Warning: FILESERVER_URL_PREFIX not set in environment variables. Can't use file server"
)
file_name = metadata.get("file_name")
if file_name and url_prefix:
# file_name exists and file server is configured
pipeline_id = metadata.get("pipeline_id")
if pipeline_id:
# file is from LlamaCloud
file_name = f"{pipeline_id}${file_name}"
return f"{url_prefix}/output/llamacloud/{file_name}"
is_private = metadata.get("private", "false") == "true"
if is_private:
# file is a private upload
return f"{url_prefix}/output/uploaded/{file_name}"
# file is from calling the 'generate' script
# Get the relative path of file_path to data_dir
file_path = metadata.get("file_path")
data_dir = os.path.abspath(DATA_DIR)
if file_path and data_dir:
relative_path = os.path.relpath(file_path, data_dir)
return f"{url_prefix}/data/{relative_path}"
# fallback to URL in metadata (e.g. for websites)
return metadata.get("URL")
@classmethod
def from_source_nodes(cls, source_nodes: List[NodeWithScore]):
return [cls.from_source_node(node) for node in source_nodes]
class Result(BaseModel):
result: Message
nodes: List[SourceNodes]
class ChatConfig(BaseModel):
starter_questions: Optional[List[str]] = Field(
default=None,
description="List of starter questions",
serialization_alias="starterQuestions",
)
class Config:
json_schema_extra = {
"example": {
"starterQuestions": [
"What standards for letters exist?",
"What are the requirements for a letter to be considered a letter?",
],
}
}
================================================
FILE: backend/app/api/chat/route.py
================================================
import json
import logging
from typing import List, Dict, Any, Optional
from fastapi import (
APIRouter,
Depends,
HTTPException,
Request,
status,
Query,
)
from llama_index.core.chat_engine.types import BaseChatEngine, NodeWithScore
from llama_index.core.llms import MessageRole
from app.api.chat.events import EventCallbackHandler
from app.api.chat.models import (
ChatData,
Message,
Result,
SourceNodes,
)
from app.api.chat.vercel_response import VercelStreamResponse
from app.api.chat.engine import get_chat_engine
from app.api.chat.engine.query_filter import generate_filters
from app.models.user_model import User
from app.core.user import get_current_user
from app.api.chat.summary import summary_generator
from app.services import conversation_service
from phoenix.trace import using_project
chat_router = r = APIRouter()
logger = logging.getLogger("uvicorn")
# streaming endpoint - delete if not needed
@r.post("")
async def chat(
request: Request,
data: ChatData,
conversation_id: Optional[str] = Query(None),
current_user: User = Depends(get_current_user),
):
with using_project("RAGSAAS-Chat"):
if not conversation_id:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Conversation ID is required for authenticated requests.",
)
try:
USER_ID = current_user.email
conversation = await conversation_service.get_or_create_conversation(
conversation_id
)
if conversation:
stored_messages = conversation.get("messages", [])
incoming_messages = data.messages
if len(incoming_messages) < len(stored_messages):
await conversation_service.truncate_conversation(
conversation_id, len(incoming_messages), USER_ID
)
if conversation.get("summary") == "New Chat":
if len(data.messages) <= 2:
summary = await summary_generator(data.messages)
else:
try:
summary = conversation.get("summary")
except Exception as e:
summary = "New Chat"
last_message_content = data.get_last_message_content()
messages = data.get_history_messages()
await conversation_service.update_conversation(
conversation_id,
{"role": MessageRole.USER, "content": last_message_content},
)
doc_ids = data.get_chat_document_ids()
filters = generate_filters(doc_ids)
params = data.data or {}
logger.info(
f"Creating chat engine with filters: {str(filters)}",
)
chat_engine = get_chat_engine(filters=filters, params=params)
event_handler = EventCallbackHandler()
chat_engine.callback_manager.handlers.append(event_handler) # type: ignore
response = await chat_engine.astream_chat(last_message_content, messages)
# process_response_nodes(response.source_nodes, background_tasks)
final_response = ""
suggested_questions = []
source_nodes = []
event = []
tools = []
async def enhanced_content_generator():
nonlocal final_response, suggested_questions, source_nodes, event, tools
async for chunk in VercelStreamResponse.content_generator(
request, event_handler, response, data
):
# print(chunk, end="", flush=True) # Print each chunk in the backend
yield chunk
if chunk.startswith(VercelStreamResponse.TEXT_PREFIX):
final_response += json.loads(chunk[2:].strip())
elif chunk.startswith(VercelStreamResponse.DATA_PREFIX):
data_chunk = json.loads(chunk[2:].strip())[0]
if data_chunk["type"] == "suggested_questions":
suggested_questions = data_chunk["data"]
elif data_chunk["type"] == "sources":
try:
source_nodes = data_chunk[
"data"
] # might have chidlen key value pair
except Exception:
source_nodes = []
elif data_chunk["type"] == "events":
try:
event = data_chunk[
"data"
] # might have chidlen key value pair
except Exception:
event = [] # might have chidlen key value pair
elif data_chunk["type"] == "tools":
try:
tools = data_chunk["data"]
except Exception:
tools = []
await conversation_service.update_conversation(
conversation_id,
{
"role": MessageRole.ASSISTANT,
"content": final_response,
"annotations": [
{"type": "sources", "data": source_nodes},
{
"type": "suggested_questions",
"data": suggested_questions,
},
{"type": "events", "data": event},
{"type": "tools", "data": tools},
],
},
summary=summary,
user_id=USER_ID,
)
return VercelStreamResponse(
request,
event_handler,
response,
data,
content=enhanced_content_generator(),
)
except Exception as e:
logger.exception("Error in chat engine", exc_info=True)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Error in chat engine: {e}",
) from e
# non-streaming endpoint - delete if not needed
# @r.post("/request")
# async def chat_request(
# data: ChatData,
# chat_engine: BaseChatEngine = Depends(get_chat_engine),
# ) -> Result:
# last_message_content = data.get_last_message_content()
# messages = data.get_history_messages()
# response = await chat_engine.achat(last_message_content, messages)
# return Result(
# result=Message(role=MessageRole.ASSISTANT, content=response.response),
# nodes=SourceNodes.from_source_nodes(response.source_nodes),
# )
# def process_response_nodes(
# nodes: List[NodeWithScore],
# background_tasks: BackgroundTasks,
# ):
# try:
# # Start background tasks to download documents from LlamaCloud if needed
# from app.api.chat.engine.service import LLamaCloudFileService
# LLamaCloudFileService.download_files_from_nodes(nodes, background_tasks)
# except ImportError:
# logger.debug("LlamaCloud is not configured. Skipping post processing of nodes")
# pass
================================================
FILE: backend/app/api/chat/services/file.py
================================================
import base64
import mimetypes
import os
from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple
from app.api.chat.engine.index import get_index
from llama_index.core import VectorStoreIndex
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.readers.file.base import (
_try_loading_included_file_formats as get_file_loaders_map,
)
from llama_index.core.schema import Document
from llama_index.indices.managed.llama_cloud.base import LlamaCloudIndex
from llama_index.readers.file import FlatReader
def get_llamaparse_parser():
from app.api.chat.engine.loaders import load_configs
from app.api.chat.engine.loaders.file import FileLoaderConfig, llama_parse_parser
config = load_configs()
file_loader_config = FileLoaderConfig(**config["file"])
if file_loader_config.use_llama_parse:
return llama_parse_parser()
else:
return None
def default_file_loaders_map():
default_loaders = get_file_loaders_map()
default_loaders[".txt"] = FlatReader
return default_loaders
class PrivateFileService:
PRIVATE_STORE_PATH = "output/uploaded"
@staticmethod
def preprocess_base64_file(base64_content: str) -> Tuple[bytes, str | None]:
header, data = base64_content.split(",", 1)
mime_type = header.split(";")[0].split(":", 1)[1]
extension = mimetypes.guess_extension(mime_type)
# File data as bytes
return base64.b64decode(data), extension
@staticmethod
def store_and_parse_file(file_name, file_data, extension) -> List[Document]:
# Store file to the private directory
os.makedirs(PrivateFileService.PRIVATE_STORE_PATH, exist_ok=True)
file_path = Path(os.path.join(PrivateFileService.PRIVATE_STORE_PATH, file_name))
# write file
with open(file_path, "wb") as f:
f.write(file_data)
# Load file to documents
# If LlamaParse is enabled, use it to parse the file
# Otherwise, use the default file loaders
reader = get_llamaparse_parser()
if reader is None:
reader_cls = default_file_loaders_map().get(extension)
if reader_cls is None:
raise ValueError(f"File extension {extension} is not supported")
reader = reader_cls()
documents = reader.load_data(file_path)
# Add custom metadata
for doc in documents:
doc.metadata["file_name"] = file_name
doc.metadata["private"] = "true"
return documents
@staticmethod
def process_file(file_name: str, base64_content: str, params: Any) -> List[str]:
file_data, extension = PrivateFileService.preprocess_base64_file(base64_content)
# Add the nodes to the index and persist it
current_index = get_index(params)
# Insert the documents into the index
if isinstance(current_index, LlamaCloudIndex):
from app.api.chat.engine.service import LLamaCloudFileService
project_id = current_index._get_project_id()
pipeline_id = current_index._get_pipeline_id()
# LlamaCloudIndex is a managed index so we can directly use the files
upload_file = (file_name, BytesIO(file_data))
return [
LLamaCloudFileService.add_file_to_pipeline(
project_id,
pipeline_id,
upload_file,
custom_metadata={
# Set private=true to mark the document as private user docs (required for filtering)
"private": "true",
},
)
]
else:
# First process documents into nodes
documents = PrivateFileService.store_and_parse_file(
file_name, file_data, extension
)
pipeline = IngestionPipeline()
nodes = pipeline.run(documents=documents)
# Add the nodes to the index and persist it
if current_index is None:
current_index = VectorStoreIndex(nodes=nodes)
else:
current_index.insert_nodes(nodes=nodes)
current_index.storage_context.persist(
persist_dir=os.environ.get("STORAGE_DIR", "storage")
)
# Return the document ids
return [doc.doc_id for doc in documents]
================================================
FILE: backend/app/api/chat/services/suggestion.py
================================================
import logging
from typing import List
from app.api.chat.models import Message
from llama_index.core.prompts import PromptTemplate
from llama_index.core.settings import Settings
from pydantic import BaseModel
NEXT_QUESTIONS_SUGGESTION_PROMPT = PromptTemplate(
"You're a helpful assistant! Your task is to suggest the next question that user might ask. "
"\nHere is the conversation history"
"\n---------------------\n{conversation}\n---------------------"
"Given the conversation history, please give me {number_of_questions} questions that you might ask next!"
)
N_QUESTION_TO_GENERATE = 3
logger = logging.getLogger("uvicorn")
class NextQuestions(BaseModel):
"""A list of questions that user might ask next"""
questions: List[str]
class NextQuestionSuggestion:
@staticmethod
async def suggest_next_questions(
messages: List[Message],
number_of_questions: int = N_QUESTION_TO_GENERATE,
) -> List[str]:
"""
Suggest the next questions that user might ask based on the conversation history
Return as empty list if there is an error
"""
try:
# Reduce the cost by only using the last two messages
last_user_message = None
last_assistant_message = None
for message in reversed(messages):
if message.role == "user":
last_user_message = f"User: {message.content}"
elif message.role == "assistant":
last_assistant_message = f"Assistant: {message.content}"
if last_user_message and last_assistant_message:
break
conversation: str = f"{last_user_message}\n{last_assistant_message}"
output: NextQuestions = await Settings.llm.astructured_predict(
NextQuestions,
prompt=NEXT_QUESTIONS_SUGGESTION_PROMPT,
conversation=conversation,
number_of_questions=number_of_questions,
)
return output.questions
except Exception as e:
logger.error(f"Error when generating next question: {e}")
return []
================================================
FILE: backend/app/api/chat/summary.py
================================================
from typing import List
from app.api.chat.models import Message
from llama_index.core.settings import Settings
async def summary_generator(
messages: List[Message],
) -> str:
# Reduce the cost by only using the last two messages
last_user_message = None
last_assistant_message = None
for message in reversed(messages):
if message.role == "user":
last_user_message = f"User: {message.content}"
elif message.role == "assistant":
last_assistant_message = f"Assistant: {message.content}"
if last_user_message and last_assistant_message:
break
conversation: str = f"{last_user_message}\n{last_assistant_message}"
response = await Settings.llm.acomplete(
prompt=f"""
Here is the conversation history
\n---------------------\n{messages}\n---------------------\n
Given the a conversation between a user and an Medical AI assistant
give me one line summary of the conversation so that is instantly recognizable
make sure its really short don't mention user or assistant in the summary
dont start with conversation , discussion , inquiry it should always start with a keyboard of the conversation
the summary should be short less then 5 to 10 words, straight to the point and distinct
""",
formatted=False,
)
# print(response)
return str(response)
================================================
FILE: backend/app/api/chat/upload.py
================================================
import logging
from typing import List, Any
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from app.api.chat.services.file import PrivateFileService
file_upload_router = r = APIRouter()
logger = logging.getLogger("uvicorn")
class FileUploadRequest(BaseModel):
base64: str
filename: str
params: Any = None
# @r.post("")
# def upload_file(request: FileUploadRequest) -> List[str]:
# try:
# logger.info("Processing file")
# return PrivateFileService.process_file(
# request.filename, request.base64, request.params
# )
# except Exception as e:
# logger.error(f"Error processing file: {e}", exc_info=True)
# raise HTTPException(status_code=500, detail="Error processing file")
================================================
FILE: backend/app/api/chat/vercel_response.py
================================================
import json
from aiostream import stream
from fastapi import Request
from fastapi.responses import StreamingResponse
from llama_index.core.chat_engine.types import StreamingAgentChatResponse
from app.api.chat.events import EventCallbackHandler
from app.api.chat.models import ChatData, Message, SourceNodes
from app.api.chat.services.suggestion import NextQuestionSuggestion
class VercelStreamResponse(StreamingResponse):
"""
Class to convert the response from the chat engine to the streaming format expected by Vercel
"""
TEXT_PREFIX = "0:"
DATA_PREFIX = "8:"
@classmethod
def convert_text(cls, token: str):
# Escape newlines and double quotes to avoid breaking the stream
token = json.dumps(token)
return f"{cls.TEXT_PREFIX}{token}\n"
@classmethod
def convert_data(cls, data: dict):
data_str = json.dumps(data)
return f"{cls.DATA_PREFIX}[{data_str}]\n"
def __init__(
self,
request: Request,
event_handler: EventCallbackHandler,
response: StreamingAgentChatResponse,
chat_data: ChatData,
content=None,
):
if content is None:
content = VercelStreamResponse.content_generator(
request, event_handler, response, chat_data
)
super().__init__(content=content)
@classmethod
async def content_generator(
cls,
request: Request,
event_handler: EventCallbackHandler,
response: StreamingAgentChatResponse,
chat_data: ChatData,
):
# Yield the text response
async def _chat_response_generator():
final_response = ""
async for token in response.async_response_gen():
final_response += token
yield VercelStreamResponse.convert_text(token)
# Generate questions that user might interested to
conversation = chat_data.messages + [
Message(role="assistant", content=final_response)
]
questions = await NextQuestionSuggestion.suggest_next_questions(
conversation
)
if len(questions) > 0:
yield VercelStreamResponse.convert_data(
{
"type": "suggested_questions",
"data": questions,
}
)
# the text_generator is the leading stream, once it's finished, also finish the event stream
event_handler.is_done = True
# Yield the source nodes
yield cls.convert_data(
{
"type": "sources",
"data": {
"nodes": [
SourceNodes.from_source_node(node).model_dump()
for node in response.source_nodes
]
},
}
)
# Yield the events from the event handler
async def _event_generator():
async for event in event_handler.async_event_gen():
event_response = event.to_response()
if event_response is not None:
yield VercelStreamResponse.convert_data(event_response)
combine = stream.merge(_chat_response_generator(), _event_generator())
is_stream_started = False
async with combine.stream() as streamer:
async for output in streamer:
if not is_stream_started:
is_stream_started = True
# Stream a blank message to start the stream
yield VercelStreamResponse.convert_text("")
yield output
if await request.is_disconnected():
break
================================================
FILE: backend/app/api/conversation/__init__.py
================================================
from .route import conversation_router
__all__ = ["conversation_router"]
================================================
FILE: backend/app/api/conversation/route.py
================================================
import logging
from pydantic import BaseModel
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
from bson import ObjectId
from fastapi import APIRouter, HTTPException, status, Depends
from fastapi.responses import JSONResponse
from dotenv import load_dotenv
from app.core.user import get_current_user
from app.services import conversation_service
from app.models.user_model import User
conversation_router = APIRouter(tags=["Conversation"])
logger = logging.getLogger("uvicorn")
load_dotenv()
@conversation_router.get("/")
async def get_new_conversation(
current_user: User = Depends(get_current_user),
):
try:
new_conversation_id = ObjectId()
# user_email = current_user.get("email") if current_user else None
user_email = current_user.email
await conversation_service.get_or_create_conversation(
str(new_conversation_id), user_email
)
return {"conversation_id": str(new_conversation_id)}
except Exception as e:
logger.exception("Error creating new conversation", exc_info=True)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Error creating new conversation: {e}",
) from e
@conversation_router.get("/list")
async def get_conversation_history(
current_user: Dict[str, Any] = Depends(get_current_user),
):
try:
conversations_by_date = (
await conversation_service.get_all_conversations_for_user(
current_user.email
)
)
return {"conversations": conversations_by_date}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@conversation_router.get("/{conversation_id}")
async def get_conversation(
conversation_id: str, current_user: Dict[str, Any] = Depends(get_current_user)
):
conversation = await conversation_service.get_or_create_conversation(
conversation_id, current_user.email
)
conversation["_id"] = str(conversation["_id"])
return {"messages": conversation.get("messages", [])}
@conversation_router.get("/sharable/{conversation_id}")
async def get_sharable_conversation(conversation_id: str):
conversation = await conversation_service.get_sharable_conversation(
conversation_id,
)
if conversation is None:
raise HTTPException(status_code=404, detail="Sharable conversation not found")
return {"messages": conversation.get("messages", [])}
@conversation_router.delete("/{conversation_id}")
async def delete_conversation(
conversation_id: str, current_user: Dict[str, Any] = Depends(get_current_user)
):
try:
deleted_count = await conversation_service.delete_conversation(
conversation_id, current_user.email
)
if deleted_count == 1:
return JSONResponse(
status_code=status.HTTP_200_OK,
content={
"message": f"Conversation {conversation_id} deleted successfully."
},
)
else:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Conversation {conversation_id} not found for the current user.",
)
except Exception as e:
logger.exception("Error deleting conversation", exc_info=True)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Error deleting conversation: {e}",
) from e
class ConversationSummaryUpdate(BaseModel):
summary: str
@conversation_router.patch("/{conversation_id}/share")
async def edit_conversation_sharable(
conversation_id: str,
current_user: User = Depends(get_current_user),
):
try:
success = await conversation_service.make_conversation_sharable(
conversation_id, current_user.email
)
if success:
return JSONResponse(
status_code=status.HTTP_200_OK,
content={
"message": f"Conversation {conversation_id} is now shareable."
},
)
else:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Conversation {conversation_id} not found for the current user.",
)
except HTTPException as he:
raise he
except Exception as e:
logger.exception("Error updating conversation shareable status", exc_info=True)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Error updating conversation shareable status: {str(e)}",
) from e
@conversation_router.patch("/{conversation_id}/summary")
async def edit_conversation_summary(
conversation_id: str,
summary_update: ConversationSummaryUpdate,
current_user: User = Depends(get_current_user),
):
try:
matched_count = await conversation_service.edit_conversation_summary(
conversation_id, current_user.email, summary_update.summary
)
if matched_count == 1:
return JSONResponse(
status_code=status.HTTP_200_OK,
content={
"message": f"Summary for conversation {conversation_id} updated successfully."
},
)
else:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Conversation {conversation_id} not found for the current user.",
)
except Exception as e:
logger.exception("Error updating conversation summary", exc_info=True)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Error updating conversation summary: {e}",
) from e
================================================
FILE: backend/app/config.py
================================================
DATA_DIR = "data"
================================================
FILE: backend/app/core/config.py
================================================
from typing import List, ClassVar
from decouple import config
from pydantic import AnyHttpUrl
from pydantic_settings import BaseSettings
class Settings(BaseSettings):
API_V1_STR: str = "/api"
JWT_SECRET_KEY: str = config("JWT_SECRET_KEY", cast=str)
JWT_REFRESH_SECRET_KEY: str = config("JWT_REFRESH_SECRET_KEY", cast=str)
ALGORITHM: ClassVar[str] = "HS256"
ACCESS_TOKEN_EXPIRE_MINUTES: int = 60 * 24
REFRESH_TOKEN_EXPIRE_MINUTES: int = 60 * 24 * 7 # 7 days
BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = ["http://localhost:3000"]
PROJECT_NAME: str = "RAGSAAS"
COOKIE_SECURE: bool = False
# Database
MONGO_CONNECTION_STRING: str = config("MONGODB_URI", cast=str)
class Config:
case_sensitive = True
settings = Settings()
================================================
FILE: backend/app/core/security.py
================================================
from datetime import datetime, timedelta
from passlib.context import CryptContext
from typing import Union, Any
from app.core.config import settings
from jose import jwt
password_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
def create_access_token(subject: Union[str, Any], expires_delta: int = None) -> str:
if expires_delta is not None:
expires_delta = datetime.utcnow() + expires_delta
else:
expires_delta = datetime.utcnow() + timedelta(minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES)
to_encode = {"exp": expires_delta, "sub": str(subject)}
encoded_jwt = jwt.encode(to_encode, settings.JWT_SECRET_KEY, settings.ALGORITHM)
return encoded_jwt
def create_refresh_token(subject: Union[str, Any], expires_delta: int = None) -> str:
if expires_delta is not None:
expires_delta = datetime.utcnow() + expires_delta
else:
expires_delta = datetime.utcnow() + timedelta(minutes=settings.REFRESH_TOKEN_EXPIRE_MINUTES)
to_encode = {"exp": expires_delta, "sub": str(subject)}
encoded_jwt = jwt.encode(to_encode, settings.JWT_REFRESH_SECRET_KEY, settings.ALGORITHM)
return encoded_jwt
def get_password(password: str) -> str:
return password_context.hash(password)
def verify_password(password: str, hashed_pass: str) -> bool:
return password_context.verify(password, hashed_pass)
================================================
FILE: backend/app/core/user.py
================================================
from datetime import datetime
from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from app.core.config import settings
from app.models.user_model import User
from jose import jwt
from pydantic import ValidationError
from app.services.user_service import user_service
from app.schemas.auth_schema import TokenPayload
reuseable_oauth = OAuth2PasswordBearer(
tokenUrl=f"{settings.API_V1_STR}/auth/login", scheme_name="JWT"
)
async def get_current_user(token: str = Depends(reuseable_oauth)) -> User:
try:
payload = jwt.decode(
token, settings.JWT_SECRET_KEY, algorithms=[settings.ALGORITHM]
)
token_data = TokenPayload(**payload)
if datetime.fromtimestamp(token_data.exp) < datetime.now():
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Token expired",
headers={"WWW-Authenticate": "Bearer"},
)
except (jwt.JWTError, ValidationError):
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Could not validate credentials",
headers={"WWW-Authenticate": "Bearer"},
)
user = await user_service.get_user_by_id(token_data.sub)
if not user:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="Could not find user",
)
return user
================================================
FILE: backend/app/db.py
================================================
import asyncio
import re
import os
import json
import time
from motor.motor_asyncio import AsyncIOMotorClient
from pymongo import MongoClient
from dotenv import load_dotenv
from pymongo.errors import ServerSelectionTimeoutError
from app.models.user_model import User
from app.core.security import get_password
load_dotenv()
MONGODB_URI = os.getenv("MONGODB_URI")
MONGODB_NAME = os.getenv("MONGODB_NAME", "RAGSAAS")
ADMIN_EMAIL = os.getenv("ADMIN_EMAIL")
ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD")
ADMIN_USERNAME = os.getenv("ADMIN_USERNAME", "admin")
CONFIG_FILE = "rag_config.json"
MAX_RETRIES = 5
RETRY_DELAY = 5
class AsyncMongoDB:
client: AsyncIOMotorClient = None
db = None
async def connect_to_database(self):
for attempt in range(MAX_RETRIES):
try:
print(
f"Attempting to connect to MongoDB (Async) - Attempt {attempt + 1}"
)
self.client = AsyncIOMotorClient(MONGODB_URI)
await (
self.client.server_info()
) # This will raise an exception if it can't connect
self.db = self.client[MONGODB_NAME]
print("Connected to MongoDB (Async)")
return
except ServerSelectionTimeoutError as e:
print(f"Connection attempt {attempt + 1} failed: {str(e)}")
if attempt < MAX_RETRIES - 1:
print(f"Retrying in {RETRY_DELAY} seconds...")
await asyncio.sleep(RETRY_DELAY)
else:
print("Max retries reached. Unable to connect to MongoDB.")
raise
async def close_database_connection(self):
if self.client:
self.client.close()
print("Closed MongoDB connection (Async)")
async def database_init(self):
users_collection = self.db.users
config_collection = self.db.config
# Create admin user if not exists
existing_user = await users_collection.find_one({"email": ADMIN_EMAIL})
if not existing_user:
admin_user = User(
first_name=ADMIN_USERNAME,
last_name=ADMIN_USERNAME,
email=ADMIN_EMAIL,
hashed_password=get_password(ADMIN_PASSWORD),
role="admin",
)
await users_collection.insert_one(admin_user.to_mongo())
print(f"Admin user created with email: {ADMIN_EMAIL}")
# Check if config already exists
existing_config = await config_collection.find_one({"_id": "app_config"})
if existing_config:
print("Configuration already exists. Skipping initialization.")
return
# Initialize system prompt and conversation starters
system_prompt = os.getenv("SYSTEM_PROMPT", "")
conversation_starters_raw = os.getenv("CONVERSATION_STARTERS", "")
conversation_starters = re.split(r"[,\n]+", conversation_starters_raw)
conversation_starters = [
starter.strip() for starter in conversation_starters if starter.strip()
]
initial_config = {
"SYSTEM_PROMPT": system_prompt,
"CONVERSATION_STARTERS": conversation_starters,
}
await config_collection.insert_one({"_id": "app_config", **initial_config})
with open(CONFIG_FILE, "w") as f:
json.dump(initial_config, f, indent=2)
print("System prompt and conversation starters initialized")
print(f"Conversation starters: {conversation_starters}")
class SyncMongoDB:
client: MongoClient = None
db = None
def connect_to_database(self):
for attempt in range(MAX_RETRIES):
try:
print(
f"Attempting to connect to MongoDB (Sync) - Attempt {attempt + 1}"
)
self.client = MongoClient(MONGODB_URI)
self.client.server_info() # This will raise an exception if it can't connect
self.db = self.client[MONGODB_NAME]
print("Connected to MongoDB (Sync)")
return
except ServerSelectionTimeoutError as e:
print(f"Connection attempt {attempt + 1} failed: {str(e)}")
if attempt < MAX_RETRIES - 1:
print(f"Retrying in {RETRY_DELAY} seconds...")
time.sleep(RETRY_DELAY)
else:
print("Max retries reached. Unable to connect to MongoDB.")
raise
def close_database_connection(self):
if self.client:
self.client.close()
print("Closed MongoDB connection (Sync)")
def database_init(self):
users_collection = self.db.users
config_collection = self.db.config
# Create admin user if not exists
existing_user = users_collection.find_one({"email": ADMIN_EMAIL})
if not existing_user:
admin_user = User(
first_name=ADMIN_USERNAME,
last_name=ADMIN_USERNAME,
email=ADMIN_EMAIL,
hashed_password=get_password(ADMIN_PASSWORD),
role="admin",
)
users_collection.insert_one(admin_user.to_mongo())
print(f"Admin user created with email: {ADMIN_EMAIL}")
# Check if config already exists
existing_config = config_collection.find_one({"_id": "app_config"})
if existing_config:
print("Configuration already exists. Skipping initialization.")
return
# Initialize system prompt and conversation starters
system_prompt = os.getenv("SYSTEM_PROMPT", "")
conversation_starters_raw = os.getenv("CONVERSATION_STARTERS", "")
conversation_starters = re.split(r"[,\n]+", conversation_starters_raw)
conversation_starters = [
starter.strip() for starter in conversation_starters if starter.strip()
]
initial_config = {
"SYSTEM_PROMPT": system_prompt,
"CONVERSATION_STARTERS": conversation_starters,
}
config_collection.insert_one({"_id": "app_config", **initial_config})
with open(CONFIG_FILE, "w") as f:
json.dump(initial_config, f, indent=2)
print("System prompt and conversation starters initialized")
print(f"Conversation starters: {conversation_starters}")
async_mongodb = AsyncMongoDB()
sync_mongodb = SyncMongoDB()
================================================
FILE: backend/app/llmhub.py
================================================
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.settings import Settings
from typing import Dict
import os
DEFAULT_MODEL = "gpt-3.5-turbo"
DEFAULT_EMBEDDING_MODEL = "text-embedding-3-large"
class TSIEmbedding(OpenAIEmbedding):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._query_engine = self._text_engine = self.model_name
def llm_config_from_env() -> Dict:
from llama_index.core.constants import DEFAULT_TEMPERATURE
model = os.getenv("MODEL", DEFAULT_MODEL)
temperature = os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)
max_tokens = os.getenv("LLM_MAX_TOKENS")
api_key = os.getenv("T_SYSTEMS_LLMHUB_API_KEY")
api_base = os.getenv("T_SYSTEMS_LLMHUB_BASE_URL")
config = {
"model": model,
"api_key": api_key,
"api_base": api_base,
"temperature": float(temperature),
"max_tokens": int(max_tokens) if max_tokens is not None else None,
}
return config
def embedding_config_from_env() -> Dict:
from llama_index.core.constants import DEFAULT_EMBEDDING_DIM
model = os.getenv("EMBEDDING_MODEL", DEFAULT_EMBEDDING_MODEL)
dimension = os.getenv("EMBEDDING_DIM", DEFAULT_EMBEDDING_DIM)
api_key = os.getenv("T_SYSTEMS_LLMHUB_API_KEY")
api_base = os.getenv("T_SYSTEMS_LLMHUB_BASE_URL")
config = {
"model_name": model,
"dimension": int(dimension) if dimension is not None else None,
"api_key": api_key,
"api_base": api_base,
}
return config
def init_llmhub():
from llama_index.llms.openai_like import OpenAILike
llm_configs = llm_config_from_env()
embedding_configs = embedding_config_from_env()
Settings.embed_model = TSIEmbedding(**embedding_configs)
Settings.llm = OpenAILike(
**llm_configs,
is_chat_model=True,
is_function_calling_model=False,
context_window=4096,
)
================================================
FILE: backend/app/models/user_model.py
================================================
from typing import Optional
from datetime import datetime
from uuid import UUID, uuid4
from pydantic import BaseModel, Field, EmailStr
from bson import Binary
class User(BaseModel):
user_id: UUID = Field(default_factory=uuid4)
email: EmailStr
hashed_password: str
first_name: Optional[str] = None
last_name: Optional[str] = None
disabled: Optional[bool] = None
role: str = "user"
created_at: datetime = Field(default_factory=datetime.utcnow)
def __repr__(self) -> str:
return f"<User {self.email}>"
def __str__(self) -> str:
return self.email
def __hash__(self) -> int:
return hash(self.email)
def __eq__(self, other: object) -> bool:
if isinstance(other, User):
return self.email == other.email
return False
@property
def create(self) -> datetime:
return self.created_at
class Config:
allow_population_by_field_name = True
arbitrary_types_allowed = True
json_encoders = {UUID: str}
def to_mongo(self):
# Convert the model to a dictionary
data = self.model_dump()
# Convert UUID to Binary for MongoDB storage
data["user_id"] = Binary.from_uuid(data["user_id"])
return data
@classmethod
def from_mongo(cls, data):
# Convert Binary back to UUID
if data.get("user_id"):
data["user_id"] = data["user_id"].as_uuid()
return cls(**data)
================================================
FILE: backend/app/observability.py
================================================
import os
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from dotenv import load_dotenv
load_dotenv()
def init_observability():
ARIZE_PHOENIX_ENDPOINT = os.getenv("ARIZE_PHOENIX_ENDPOINT")
if ARIZE_PHOENIX_ENDPOINT:
try:
if ARIZE_PHOENIX_ENDPOINT and "/v1/traces" in ARIZE_PHOENIX_ENDPOINT:
PHOENIX_ENDPOINT_TRACE_ENDPOINT = ARIZE_PHOENIX_ENDPOINT
else:
PHOENIX_ENDPOINT_TRACE_ENDPOINT = f"{ARIZE_PHOENIX_ENDPOINT}/v1/traces"
resource = Resource(attributes={})
tracer_provider = trace_sdk.TracerProvider(resource=resource)
span_exporter = OTLPSpanExporter(endpoint=PHOENIX_ENDPOINT_TRACE_ENDPOINT)
span_processor = SimpleSpanProcessor(span_exporter=span_exporter)
tracer_provider.add_span_processor(span_processor=span_processor)
trace_api.set_tracer_provider(tracer_provider=tracer_provider)
LlamaIndexInstrumentor().instrument()
print("🔭 ARIZE PHOENIX - Set up complete")
except Exception as e:
print("Wasnt able to set up Arize Phoenix", e)
else:
print("Arize Phoenix API Endpoint Not provided")
================================================
FILE: backend/app/schemas/admin_schema.py
================================================
from pydantic import BaseModel, EmailStr
from typing import List, Optional, Any
from datetime import datetime
from uuid import UUID
class UserOut(BaseModel):
user_id: UUID
email: EmailStr
first_name: Optional[str] = None
last_name: Optional[str] = None
role: str
disabled: Optional[bool] = None
created_at: datetime
class UsersOut(BaseModel):
users: List[UserOut]
class MessageOut(BaseModel):
message: str
class ErrorOut(BaseModel):
detail: str
status_code: int
class AuthErrorOut(ErrorOut):
error_description: Optional[str] = None
error_code: Optional[str] = None
================================================
FILE: backend/app/schemas/auth_schema.py
================================================
from uuid import UUID
from pydantic import BaseModel
from typing_extensions import Optional
class TokenSchema(BaseModel):
access_token: str
refresh_token: str
class TokenPayload(BaseModel):
sub: UUID = None
exp: int = None
class ErrorOut(BaseModel):
detail: str
status_code: int
class AuthErrorOut(ErrorOut):
error_description: Optional[str] = None
error_code: Optional[str] = None
class RefreshTokenRequest(BaseModel):
refresh_token: str
================================================
FILE: backend/app/schemas/user_schema.py
================================================
from typing import Optional
from pydantic import BaseModel, EmailStr, Field
class UserAuth(BaseModel):
email: EmailStr = Field(..., description="user email")
first_name: str = Field(..., description="first name")
last_name: str = Field(..., description="last name")
password: str = Field(..., min_length=5, max_length=24, description="user password")
class UserOut(BaseModel):
# user_id: UUID
email: EmailStr
first_name: Optional[str]
last_name: Optional[str]
role: Optional[str]
class UserUpdate(BaseModel):
first_name: Optional[str] = None
last_name: Optional[str] = None
================================================
FILE: backend/app/services/__init__.py
================================================
from .user_service import user_service
from .conversation_service import conversation_service
from .admin_service import admin_service
from .config_service import config_service
__all__ = ["user_service", "conversation_service", "admin_service", "config_service"]
================================================
FILE: backend/app/services/admin_service.py
================================================
from typing import List, Dict, Any, Optional
from bson import ObjectId, Binary
from datetime import datetime
from app.db import async_mongodb
from app.models.user_model import User
from uuid import UUID
class AdminService:
@property
def user_collection(self):
return async_mongodb.db.users
async def get_all_users(self) -> List[Dict[str, Any]]:
users = await self.user_collection.find().to_list(length=None)
return [self._serialize_user(User.from_mongo(user)) for user in users]
async def get_user_by_id(self, user_id: str) -> Optional[Dict[str, Any]]:
user = await self.user_collection.find_one(
{"user_id": Binary.from_uuid(UUID(user_id))}
)
return self._serialize_user(User.from_mongo(user)) if user else None
async def edit_user(self, user_id: str, updated_data: Dict[str, Any]) -> bool:
result = await self.user_collection.update_one(
{"user_id": Binary.from_uuid(UUID(user_id))}, {"$set": updated_data}
)
return result.modified_count > 0
async def delete_user(self, user_id: str) -> bool:
result = await self.user_collection.delete_one(
{"user_id": Binary.from_uuid(UUID(user_id))}
)
return result.deleted_count > 0
def _serialize_user(self, user: User) -> Dict[str, Any]:
return {
"user_id": str(user.user_id),
"email": user.email,
"first_name": user.first_name,
"last_name": user.last_name,
"role": user.role,
"disabled": user.disabled,
"created_at": user.created_at.isoformat() if user.created_at else None,
}
admin_service = AdminService()
================================================
FILE: backend/app/services/config_service.py
================================================
from typing import List, Dict, Any, Optional
from app.db import async_mongodb
from app.api.chat.models import ChatConfig
class ConfigService:
@property
def config_collection(self):
return async_mongodb.db.config
async def get_chat_config(self) -> ChatConfig:
config = await self.config_collection.find_one({"_id": "app_config"})
if config:
return ChatConfig(
system_prompt=config.get("SYSTEM_PROMPT", ""),
starter_questions=config.get("CONVERSATION_STARTERS", []),
)
return ChatConfig(system_prompt="", starter_questions=[])
async def update_chat_config(self, updated_data: Dict[str, Any]) -> bool:
result = await self.config_collection.update_one(
{"_id": "app_config"}, {"$set": updated_data}, upsert=True
)
return result.modified_count > 0 or result.upserted_id is not None
async def get_system_prompt(self) -> str:
config = await self.config_collection.find_one({"_id": "app_config"})
return config.get("SYSTEM_PROMPT", "") if config else ""
async def update_system_prompt(self, new_prompt: str) -> bool:
return await self.update_chat_config({"SYSTEM_PROMPT": new_prompt})
async def update_conversation_starters(self, new_starters: List[str]) -> bool:
return await self.update_chat_config({"CONVERSATION_STARTERS": new_starters})
config_service = ConfigService()
================================================
FILE: backend/app/services/conversation_service.py
================================================
import logging
from pydantic import BaseModel
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
from bson import ObjectId
from dotenv import load_dotenv
from app.db import async_mongodb
logger = logging.getLogger("uvicorn")
load_dotenv()
class ConversationService:
@property
def conversation_collection(self):
return async_mongodb.db.conversation
async def get_or_create_conversation(
self, conversation_id: str, user_id: Optional[str] = None
) -> Dict[str, Any]:
conversation = await self.conversation_collection.find_one(
{"_id": ObjectId(conversation_id)}
)
if not conversation:
conversation = {
"_id": ObjectId(conversation_id),
"messages": [],
"created_at": datetime.utcnow(),
"updated_at": datetime.utcnow(),
"summary": "New Chat",
}
if user_id:
conversation["user_id"] = user_id
await self.conversation_collection.insert_one(conversation)
return conversation
async def update_conversation(
self,
conversation_id: str,
new_message: Dict[str, Any],
summary: Optional[str] = None,
user_id: Optional[str] = None,
) -> None:
update_fields = {
"$push": {"messages": new_message},
"$set": {"updated_at": datetime.utcnow()},
}
if summary:
update_fields["$set"] = update_fields.get("$set", {})
update_fields["$set"]["summary"] = summary
if user_id:
update_fields["$set"] = update_fields.get("$set", {})
update_fields["$set"]["user_id"] = user_id
await self.conversation_collection.update_one(
{"_id": ObjectId(conversation_id)}, update_fields
)
async def truncate_conversation(
self, conversation_id: str, index: int, user_id: str
) -> None:
conversation = await self.get_or_create_conversation(conversation_id)
if conversation and conversation.get("user_id") == user_id:
stored_messages = conversation.get("messages", [])
truncated_messages = stored_messages[: index - 1]
await self.conversation_collection.update_one(
{"_id": ObjectId(conversation_id)},
{
"$set": {
"messages": truncated_messages,
"updated_at": datetime.utcnow(),
}
},
)
else:
raise PermissionError("User ID does not match or conversation not found.")
async def get_all_conversations_for_user(self, user_id: str):
projection = {"summary": 1, "created_at": 1, "updated_at": 1}
conversations_cursor = self.conversation_collection.find(
{"user_id": user_id}, projection
).sort("updated_at", -1)
conversations = await conversations_cursor.to_list(length=None)
now = datetime.utcnow()
today = now.replace(hour=0, minute=0, second=0, microsecond=0)
yesterday = today - timedelta(days=1)
last_week = today - timedelta(days=7)
categorized_conversations = {
"today": [],
"yesterday": [],
"last_7_days": [],
"before_that": [],
}
for conversation in conversations:
conversation["_id"] = str(conversation["_id"])
updated_at = conversation["updated_at"]
if updated_at >= today:
categorized_conversations["today"].append(conversation)
elif updated_at >= yesterday:
categorized_conversations["yesterday"].append(conversation)
elif updated_at >= last_week:
categorized_conversations["last_7_days"].append(conversation)
else:
categorized_conversations["before_that"].append(conversation)
for category in categorized_conversations:
categorized_conversations[category].sort(
key=lambda x: x["updated_at"], reverse=True
)
return categorized_conversations
async def delete_conversation(self, conversation_id: str, user_id: str) -> int:
result = await self.conversation_collection.delete_one(
{"_id": ObjectId(conversation_id), "user_id": user_id}
)
return result.deleted_count
async def edit_conversation_summary(
self, conversation_id: str, user_id: str, new_summary: str
) -> int:
result = await self.conversation_collection.update_one(
{"_id": ObjectId(conversation_id), "user_id": user_id},
{
"$set": {
"summary": new_summary,
"updated_at": datetime.utcnow(),
}
},
)
return result.matched_count
async def get_sharable_conversation(
self,
conversation_id: str,
) -> Dict[str, Any]:
conversation = await self.conversation_collection.find_one(
{"_id": ObjectId(conversation_id), "sharable": True}
)
if conversation is None:
return None
conversation["_id"] = str(conversation["_id"])
return conversation
async def make_conversation_sharable(
self, conversation_id: str, user_id: str
) -> bool:
conversation = await self.conversation_collection.find_one(
{"_id": ObjectId(conversation_id), "user_id": user_id}
)
if not conversation:
return False
if conversation.get("sharable", False): # Changed True to False here
return True
result = await self.conversation_collection.update_one(
{"_id": ObjectId(conversation_id), "user_id": user_id},
{
"$set": {
"sharable": True,
"updated_at": datetime.utcnow(),
}
},
)
return result.modified_count == 1
conversation_service = ConversationService()
================================================
FILE: backend/app/services/user_service.py
================================================
from typing import Optional
from uuid import UUID
from app.schemas.user_schema import UserAuth, UserUpdate
from app.models.user_model import User
from app.core.security import get_password, verify_password
from pymongo.errors import DuplicateKeyError
from bson import Binary
from dotenv import load_dotenv
from app.db import async_mongodb
# Load environment variables
load_dotenv()
class UserService:
@property
def users_collection(self):
return async_mongodb.db.users
async def create_user(self, user: UserAuth) -> User:
user_obj = User(
email=user.email,
first_name=user.first_name,
last_name=user.last_name,
hashed_password=get_password(user.password),
role="user", # Set default role to "user"
)
user_dict = user_obj.to_mongo()
try:
result = await self.users_collection.insert_one(user_dict)
user_dict["_id"] = result.inserted_id
return User.from_mongo(user_dict)
except DuplicateKeyError:
raise ValueError("Username or email already exists")
async def authenticate(self, email: str, password: str) -> Optional[User]:
user = await self.get_user_by_email(email=email)
if not user:
return None
if not verify_password(password=password, hashed_pass=user.hashed_password):
return None
return user
async def get_user_by_email(self, email: str) -> Optional[User]:
user_dict = await self.users_collection.find_one({"email": email})
return User.from_mongo(user_dict) if user_dict else None
async def get_user_by_id(self, id: UUID) -> Optional[User]:
user_dict = await self.users_collection.find_one(
{"user_id": Binary.from_uuid(id)}
)
return User.from_mongo(user_dict) if user_dict else None
async def update_user(self, id: UUID, data: UserUpdate) -> User:
update_data = data.model_dump(exclude_unset=True)
result = await self.users_collection.update_one(
{"user_id": Binary.from_uuid(id)}, {"$set": update_data}
)
if result.modified_count == 0:
raise ValueError("User not found")
updated_user = await self.get_user_by_id(id)
return updated_user
user_service = UserService()
================================================
FILE: backend/app/settings.py
================================================
import os
from typing import Dict
from llama_index.core.settings import Settings
def init_settings():
model_provider = os.getenv("MODEL_PROVIDER")
match model_provider:
case "openai":
init_openai()
case "groq":
init_groq()
case "ollama":
init_ollama()
case "anthropic":
init_anthropic()
case "gemini":
init_gemini()
case "mistral":
init_mistral()
case "azure-openai":
init_azure_openai()
case "t-systems":
from .llmhub import init_llmhub
init_llmhub()
case _:
raise ValueError(f"Invalid model provider: {model_provider}")
Settings.chunk_size = int(os.getenv("CHUNK_SIZE", "1024"))
Settings.chunk_overlap = int(os.getenv("CHUNK_OVERLAP", "20"))
def init_ollama():
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.llms.ollama.base import DEFAULT_REQUEST_TIMEOUT, Ollama
base_url = os.getenv("OLLAMA_BASE_URL") or "http://127.0.0.1:11434"
request_timeout = float(
os.getenv("OLLAMA_REQUEST_TIMEOUT", DEFAULT_REQUEST_TIMEOUT)
)
Settings.embed_model = OllamaEmbedding(
base_url=base_url,
model_name=os.getenv("EMBEDDING_MODEL"),
)
Settings.llm = Ollama(
base_url=base_url, model=os.getenv("MODEL"), request_timeout=request_timeout
)
def init_openai():
from llama_index.core.constants import DEFAULT_TEMPERATURE
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
max_tokens = os.getenv("LLM_MAX_TOKENS")
config = {
"model": os.getenv("MODEL"),
"temperature": float(os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)),
"max_tokens": int(max_tokens) if max_tokens is not None else None,
}
Settings.llm = OpenAI(**config)
dimensions = os.getenv("EMBEDDING_DIM")
config = {
"model": os.getenv("EMBEDDING_MODEL"),
"dimensions": int(dimensions) if dimensions is not None else None,
}
Settings.embed_model = OpenAIEmbedding(**config)
def init_azure_openai():
from llama_index.core.constants import DEFAULT_TEMPERATURE
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from llama_index.llms.azure_openai import AzureOpenAI
llm_deployment = os.environ["AZURE_OPENAI_LLM_DEPLOYMENT"]
embedding_deployment = os.environ["AZURE_OPENAI_EMBEDDING_DEPLOYMENT"]
max_tokens = os.getenv("LLM_MAX_TOKENS")
temperature = os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)
dimensions = os.getenv("EMBEDDING_DIM")
azure_config = {
"api_key": os.environ["AZURE_OPENAI_API_KEY"],
"azure_endpoint": os.environ["AZURE_OPENAI_ENDPOINT"],
"api_version": os.getenv("AZURE_OPENAI_API_VERSION")
or os.getenv("OPENAI_API_VERSION"),
}
Settings.llm = AzureOpenAI(
model=os.getenv("MODEL"),
max_tokens=int(max_tokens) if max_tokens is not None else None,
temperature=float(temperature),
deployment_name=llm_deployment,
**azure_config,
)
Settings.embed_model = AzureOpenAIEmbedding(
model=os.getenv("EMBEDDING_MODEL"),
dimensions=int(dimensions) if dimensions is not None else None,
deployment_name=embedding_deployment,
**azure_config,
)
def init_fastembed():
"""
Use Qdrant Fastembed as the local embedding provider.
"""
from llama_index.embeddings.fastembed import FastEmbedEmbedding
embed_model_map: Dict[str, str] = {
# Small and multilingual
"all-MiniLM-L6-v2": "sentence-transformers/all-MiniLM-L6-v2",
# Large and multilingual
"paraphrase-multilingual-mpnet-base-v2": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2", # noqa: E501
}
# This will download the model automatically if it is not already downloaded
Settings.embed_model = FastEmbedEmbedding(
model_name=embed_model_map[os.getenv("EMBEDDING_MODEL")]
)
def init_groq():
from llama_index.llms.groq import Groq
model_map: Dict[str, str] = {
"llama3-8b": "llama3-8b-8192",
"llama3-70b": "llama3-70b-8192",
"mixtral-8x7b": "mixtral-8x7b-32768",
}
Settings.llm = Groq(model=model_map[os.getenv("MODEL")])
# Groq does not provide embeddings, so we use FastEmbed instead
init_fastembed()
def init_anthropic():
from llama_index.llms.anthropic import Anthropic
model_map: Dict[str, str] = {
"claude-3-opus": "claude-3-opus-20240229",
"claude-3-sonnet": "claude-3-sonnet-20240229",
"claude-3-haiku": "claude-3-haiku-20240307",
"claude-2.1": "claude-2.1",
"claude-instant-1.2": "claude-instant-1.2",
}
Settings.llm = Anthropic(model=model_map[os.getenv("MODEL")])
# Anthropic does not provide embeddings, so we use FastEmbed instead
init_fastembed()
def init_gemini():
from llama_index.embeddings.gemini import GeminiEmbedding
from llama_index.llms.gemini import Gemini
model_name = f"models/{os.getenv('MODEL')}"
embed_model_name = f"models/{os.getenv('EMBEDDING_MODEL')}"
Settings.llm = Gemini(model=model_name)
Settings.embed_model = GeminiEmbedding(model_name=embed_model_name)
def init_mistral():
from llama_index.embeddings.mistralai import MistralAIEmbedding
from llama_index.llms.mistralai import MistralAI
Settings.llm = MistralAI(model=os.getenv("MODEL"))
Settings.embed_model = MistralAIEmbedding(model_name=os.getenv("EMBEDDING_MODEL"))
================================================
FILE: backend/config/loaders.yaml
================================================
file:
# use_llama_parse: Use LlamaParse if `true`. Needs a `LLAMA_CLOUD_API_KEY` from https://cloud.llamaindex.ai set as environment variable
use_llama_parse: false
================================================
FILE: backend/config/tools.yaml
================================================
local: {}
llamahub: {}
================================================
FILE: backend/main.py
================================================
# flake8: noqa: E402
from dotenv import load_dotenv
from app.config import DATA_DIR
load_dotenv()
import logging
import os
import json
import uvicorn
from app.api.chat import chat_router
from app.api.chat import config_router
from app.api.chat import file_upload_router
from app.api.auth import auth_router
from app.api.conversation import conversation_router
from app.api.admin import admin_router
from app.observability import init_observability
from app.settings import init_settings
from app.db import async_mongodb, sync_mongodb
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse
from fastapi.staticfiles import StaticFiles
from contextlib import asynccontextmanager
@asynccontextmanager
async def lifespan(app: FastAPI):
await async_mongodb.connect_to_database()
sync_mongodb.connect_to_database()
await async_mongodb.database_init()
yield
# Shutdown: Close the database connection
sync_mongodb.close_database_connection()
await async_mongodb.close_database_connection()
app = FastAPI(lifespan=lifespan)
init_settings()
init_observability()
environment = os.getenv("ENVIRONMENT", "dev") # Default to 'development' if not set
logger = logging.getLogger("uvicorn")
# if environment == "dev":
# logger.warning("Running in development mode - allowing CORS for all origins")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Redirect to documentation page when accessing base URL
@app.get("/")
async def redirect_to_docs():
return RedirectResponse(url="/docs")
def mount_static_files(directory, path):
if os.path.exists(directory):
logger.info(f"Mounting static files '{directory}' at '{path}'")
app.mount(
path,
StaticFiles(directory=directory, check_dir=False),
name=f"{directory}-static",
)
# Mount the data files to serve the file viewer
mount_static_files(DATA_DIR, "/api/files/data")
# Mount the output files from tools
mount_static_files("output", "/api/files/output")
app.include_router(auth_router, prefix="/api/auth", tags=["Authentication"])
app.include_router(chat_router, prefix="/api/chat", tags=["Chat"])
app.include_router(config_router, prefix="/api/chat/config", tags=["Chat"])
app.include_router(file_upload_router, prefix="/api/chat/upload", tags=["Chat"])
app.include_router(
conversation_router, prefix="/api/conversation", tags=["Conversation"]
)
app.include_router(admin_router, prefix="/api/admin", tags=["Admin"])
if __name__ == "__main__":
app_host = os.getenv("APP_HOST", "0.0.0.0")
app_port = int(os.getenv("APP_PORT", "8000"))
reload = True if environment == "dev" else False
uvicorn.run(app="main:app", host=app_host, port=app_port, reload=reload)
================================================
FILE: backend/pyproject.toml
================================================
[tool]
[tool.poetry]
name = "app"
version = "0.1.0"
description = "⚡ Ship RAG Solutions Quickly"
authors = [ "Adithya S K <Adithyaskolavi@gmail.com>" ]
readme = "README.md"
[tool.poetry.scripts]
generate = "app.api.chat.engine.generate:generate_datasource"
[tool.poetry.dependencies]
python = ">=3.11,<3.12"
fastapi = {extras = ["all"], version = "^0.112.2"}
python-dotenv = "^1.0.0"
aiostream = "^0.5.2"
llama-index = "0.10.58"
cachetools = "^5.3.3"
pymongo = "^4.8.0"
motor = "^3.5.1"
python-jose = "^3.3.0"
passlib = {extras = ["bcrypt"], version = "^1.7.4"}
python-multipart = "^0.0.9"
pydantic = {extras = ["email"], version = "^2.8.2"}
pydantic-settings = "^2.4.0"
fastapi-mail = "^1.4.1"
beanie = "^1.26.0"
python-decouple = "^3.8"
boto3 = "^1.35.6"
openinference-instrumentation = "^0.1.12"
openinference-instrumentation-llama-index = "^2.2.1"
openinference-instrumentation-openai = "^0.1.12"
openinference-semantic-conventions = "^0.1.9"
opentelemetry-api = "^1.26.0"
opentelemetry-exporter-otlp = "^1.26.0"
arize-phoenix = {extras = ["evals"], version = "^4.19.0"}
[tool.poetry.dependencies.uvicorn]
extras = [ "standard" ]
version = "^0.23.2"
[tool.poetry.dependencies.llama-index-vector-stores-qdrant]
version = "^0.2.8"
[tool.poetry.dependencies.docx2txt]
version = "^0.8"
[tool.poetry.dependencies.llama-index-agent-openai]
version = "0.2.6"
[build-system]
requires = [ "poetry-core" ]
build-backend = "poetry.core.masonry.api"
================================================
FILE: backend/tests/__init__.py
================================================
================================================
FILE: docker-compose.yaml
================================================
version: '3.8'
services:
qdrant:
image: qdrant/qdrant:latest
container_name: qdrant
ports:
- 6333:6333
- 6334:6334
networks:
- ragsaas-network
mongodb:
image: mongo:latest
container_name: mongodb
ports:
- 27017:27017
environment:
MONGO_INITDB_ROOT_USERNAME: admin
MONGO_INITDB_ROOT_PASSWORD: password
networks:
- ragsaas-network
volumes:
- mongodb_data:/data/db
arizephoenix:
image: arizephoenix/phoenix:latest
container_name: arizephoenix
ports:
- '6006:6006'
- '4317:4317'
networks:
- ragsaas-network
backend:
build:
context: ./backend
dockerfile: Dockerfile
image: ragsaas/backend:latest
container_name: backend
ports:
- '8000:8000'
environment:
# MongoDB Configuration
MONGODB_NAME: RAGSAAS
MONGODB_URI: mongodb://admin:password@mongodb:27017/
# Qdrant Configuration
QDRANT_COLLECTION: default
QDRANT_URL: http://qdrant:6333
# QDRANT_API_KEY:
OPENAI_API_KEY:
# Backend Application Configuration
MODEL_PROVIDER: openai
MODEL: gpt-4o-mini
EMBEDDING_MODEL: text-embedding-3-small
EMBEDDING_DIM: 1536
FILESERVER_URL_PREFIX: http://backend:8000/api/files
SYSTEM_PROMPT: 'You are a helpful assistant who helps users with their questions.'
APP_HOST: 0.0.0.0
APP_PORT: 8000
ADMIN_EMAIL: admin@ragsaas.com
ADMIN_PASSWORD: ragsaas
JWT_SECRET_KEY:
JWT_REFRESH_SECRET_KEY:
ARIZE_PHOENIX_ENDPOINT: http://arizephoenix:6006
depends_on:
- qdrant
- mongodb
- arizephoenix
networks:
- ragsaas-network
frontend:
build:
context: ./frontend
dockerfile: Dockerfile
image: ragsaas/frontend:latest
container_name: frontend
ports:
- '3000:3000'
environment:
NEXT_PUBLIC_SERVER_URL: http://backend:8000
NEXT_PUBLIC_CHAT_API: http://backend:8000/api/chat
depends_on:
- backend
networks:
- ragsaas-network
networks:
ragsaas-network:
name: ragsaas-network
driver: bridge
volumes:
mongodb_data:
================================================
FILE: frontend/.eslintrc.json
================================================
{
"extends": "next/core-web-vitals"
}
================================================
FILE: frontend/.gitignore
================================================
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.js
.yarn/install-state.gz
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
# local env files
.env*.local
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts
================================================
FILE: frontend/Dockerfile
================================================
FROM node:18-alpine AS base
FROM base AS deps
RUN apk add --no-cache libc6-compat
WORKDIR /app
COPY package.json ./
RUN npm update && npm install
# If you want yarn update and install uncomment the bellow
# RUN yarn install && yarn upgrade
FROM base AS builder
WORKDIR /app
COPY --from=deps /app/node_modules ./node_modules
COPY . .
RUN npm run build
FROM base AS runner
WORKDIR /app
ENV NODE_ENV production
RUN addgroup --system --gid 1001 nodejs
RUN adduser --system --uid 1001 nextjs
COPY --from=builder /app/public ./public
RUN mkdir .next
RUN chown nextjs:nodejs .next
COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
USER nextjs
EXPOSE 3000
ENV PORT 3000
CMD ["node", "server.js"]
================================================
FILE: frontend/README.md
================================================
This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app).
## Getting Started
First, run the development server:
```bash
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
```
Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
## Learn More
To learn more about Next.js, take a look at the following resources:
- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
## Deploy on Vercel
The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.
================================================
FILE: frontend/app/(auth)/signin/page.tsx
================================================
'use client';
import Image from 'next/image';
import Link from 'next/link';
import { Button } from '@/components/ui/button';
import { Input } from '@/components/ui/input';
import { Label } from '@/components/ui/label';
import BannerCard from '@/components/banner-card';
import { ArrowLeft, Eye, EyeOff, Loader2 } from 'lucide-react';
import { useState, FormEvent, Suspense } from 'react';
import { useRouter } from 'next/navigation';
import { useAuth } from '@/app/authProvider';
import axios, { AxiosError } from 'axios';
import { toast } from 'sonner';
import Loading from '@/components/loading';
interface LoginResponse {
access_token: string;
refresh_token: string;
}
interface UserResponse {
id: string;
email: string;
first_name: string;
last_name: string;
role: string;
}
function SignInContent() {
const [email, setEmail] = useState<string>('');
const [password, setPassword] = useState<string>('');
const [showPassword, setShowPassword] = useState<boolean>(false);
const [error, setError] = useState<string>('');
const [isLoading, setIsLoading] = useState<boolean>(false);
const router = useRouter();
const { login } = useAuth();
const togglePasswordVisibility = () => {
setShowPassword(!showPassword);
};
const handleSubmit = async (e: FormEvent<HTMLFormElement>) => {
e.preventDefault();
setError('');
setIsLoading(true);
const formData = new FormData();
formData.append('username', email);
formData.append('password', password);
try {
const response = await axios.post<LoginResponse>(
`${process.env.NEXT_PUBLIC_SERVER_URL}/api/auth/login`,
formData,
{
headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
}
);
const { access_token, refresh_token } = response.data;
// Get user details
const userResponse = await axios.get<UserResponse>(
`${process.env.NEXT_PUBLIC_SERVER_URL}/api/auth/me`,
{
headers: { Authorization: `Bearer ${access_token}` },
}
);
const {
email: userEmail,
first_name,
last_name,
role,
} = userResponse.data;
login(access_token, refresh_token, userEmail, first_name, last_name);
toast.success('Sign In successful');
router.push('/chat');
} catch (error) {
if (axios.isAxiosError(error)) {
const axiosError = error as AxiosError<{ detail: string }>;
setError(
axiosError.response?.data?.detail ||
'An error occurred during sign in'
);
} else {
setError('An unexpected error occurred');
}
toast.error('Sign In failed. Please try again.');
} finally {
setIsLoading(false);
}
};
return (
<div className="flex h-full w-full flex-col lg:flex-row">
<div className="flex flex-1 items-center jus
gitextract_kge9g_mh/
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug_report.md
│ │ └── feature_request.md
│ └── PULL_REQUEST_TEMPLATE/
│ └── pull_request_template.md
├── .gitignore
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── LICENSING.md
├── README.md
├── backend/
│ ├── .gitignore
│ ├── Dockerfile
│ ├── README.md
│ ├── app/
│ │ ├── __init__.py
│ │ ├── api/
│ │ │ ├── __init__.py
│ │ │ ├── admin/
│ │ │ │ ├── __init__.py
│ │ │ │ └── route.py
│ │ │ ├── auth/
│ │ │ │ ├── __init__.py
│ │ │ │ └── route.py
│ │ │ ├── chat/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── chat_config.py
│ │ │ │ ├── engine/
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── engine.py
│ │ │ │ │ ├── generate.py
│ │ │ │ │ ├── index.py
│ │ │ │ │ ├── loaders/
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── db.py
│ │ │ │ │ │ ├── file.py
│ │ │ │ │ │ └── web.py
│ │ │ │ │ ├── node_postprocessors.py
│ │ │ │ │ ├── query_filter.py
│ │ │ │ │ └── vectordb.py
│ │ │ │ ├── events.py
│ │ │ │ ├── models.py
│ │ │ │ ├── route.py
│ │ │ │ ├── services/
│ │ │ │ │ ├── file.py
│ │ │ │ │ └── suggestion.py
│ │ │ │ ├── summary.py
│ │ │ │ ├── upload.py
│ │ │ │ └── vercel_response.py
│ │ │ └── conversation/
│ │ │ ├── __init__.py
│ │ │ └── route.py
│ │ ├── config.py
│ │ ├── core/
│ │ │ ├── config.py
│ │ │ ├── security.py
│ │ │ └── user.py
│ │ ├── db.py
│ │ ├── llmhub.py
│ │ ├── models/
│ │ │ └── user_model.py
│ │ ├── observability.py
│ │ ├── schemas/
│ │ │ ├── admin_schema.py
│ │ │ ├── auth_schema.py
│ │ │ └── user_schema.py
│ │ ├── services/
│ │ │ ├── __init__.py
│ │ │ ├── admin_service.py
│ │ │ ├── config_service.py
│ │ │ ├── conversation_service.py
│ │ │ └── user_service.py
│ │ └── settings.py
│ ├── config/
│ │ ├── loaders.yaml
│ │ └── tools.yaml
│ ├── main.py
│ ├── pyproject.toml
│ └── tests/
│ └── __init__.py
├── docker-compose.yaml
└── frontend/
├── .eslintrc.json
├── .gitignore
├── Dockerfile
├── README.md
├── app/
│ ├── (auth)/
│ │ ├── signin/
│ │ │ └── page.tsx
│ │ └── signup/
│ │ └── page.tsx
│ ├── ConversationContext.tsx
│ ├── admin/
│ │ ├── AdminAuthProvider.tsx
│ │ ├── DataIngestion.tsx
│ │ ├── RAGConfiguration.tsx
│ │ ├── UsersManagement.tsx
│ │ ├── layout.tsx
│ │ └── page.tsx
│ ├── authProvider.tsx
│ ├── chat/
│ │ └── page.tsx
│ ├── features/
│ │ └── page.tsx
│ ├── globals.css
│ ├── layout.tsx
│ ├── markdown.css
│ ├── page.tsx
│ └── share/
│ └── page.tsx
├── components/
│ ├── analytics.tsx
│ ├── banner-card.tsx
│ ├── header.tsx
│ ├── history.tsx
│ ├── loading.tsx
│ ├── magicui/
│ │ ├── animated-gradient-text.tsx
│ │ ├── border-beam.tsx
│ │ ├── box-reveal.tsx
│ │ ├── gradual-spacing.tsx
│ │ ├── hyper-text.tsx
│ │ ├── meteors.tsx
│ │ ├── neon-gradient-card.tsx
│ │ ├── number-ticker.tsx
│ │ ├── ripple.tsx
│ │ ├── shimmer-button.tsx
│ │ ├── shine-border.tsx
│ │ ├── shiny-button.tsx
│ │ ├── sparkles-text.tsx
│ │ └── typing-animation.tsx
│ ├── sidebar.tsx
│ ├── theme-provider.tsx
│ ├── theme-toggle.tsx
│ └── ui/
│ ├── accordion.tsx
│ ├── alert-dialog.tsx
│ ├── alert.tsx
│ ├── aspect-ratio.tsx
│ ├── avatar.tsx
│ ├── badge.tsx
│ ├── breadcrumb.tsx
│ ├── button.tsx
│ ├── card-hover-effect.tsx
│ ├── card.tsx
│ ├── chat/
│ │ ├── chat-actions.tsx
│ │ ├── chat-input.tsx
│ │ ├── chat-message/
│ │ │ ├── chat-avatar.tsx
│ │ │ ├── chat-events.tsx
│ │ │ ├── chat-files.tsx
│ │ │ ├── chat-image.tsx
│ │ │ ├── chat-sources.tsx
│ │ │ ├── chat-suggestedQuestions.tsx
│ │ │ ├── chat-tools.tsx
│ │ │ ├── codeblock.tsx
│ │ │ ├── index.tsx
│ │ │ └── markdown.tsx
│ │ ├── chat-messages.tsx
│ │ ├── chat.interface.ts
│ │ ├── hooks/
│ │ │ ├── use-config.ts
│ │ │ ├── use-copy-to-clipboard.tsx
│ │ │ └── use-file.ts
│ │ ├── index.ts
│ │ └── widgets/
│ │ ├── PdfDialog.tsx
│ │ └── WeatherCard.tsx
│ ├── checkbox.tsx
│ ├── collapsible.tsx
│ ├── command.tsx
│ ├── context-menu.tsx
│ ├── dialog.tsx
│ ├── document-preview.tsx
│ ├── drawer.tsx
│ ├── dropdown-menu.tsx
│ ├── file-upload.tsx
│ ├── file-uploader.tsx
│ ├── form.tsx
│ ├── hover-card.tsx
│ ├── input-otp.tsx
│ ├── input.tsx
│ ├── label.tsx
│ ├── menubar.tsx
│ ├── navigation-menu.tsx
│ ├── pagination.tsx
│ ├── placeholders-and-vanish-input.tsx
│ ├── popover.tsx
│ ├── progress.tsx
│ ├── radio-group.tsx
│ ├── resizable.tsx
│ ├── scroll-area.tsx
│ ├── select.tsx
│ ├── separator.tsx
│ ├── sheet.tsx
│ ├── skeleton.tsx
│ ├── slider.tsx
│ ├── sonner.tsx
│ ├── switch.tsx
│ ├── table.tsx
│ ├── tabs.tsx
│ ├── textarea.tsx
│ ├── toast.tsx
│ ├── toaster.tsx
│ ├── toggle-group.tsx
│ ├── toggle.tsx
│ ├── tooltip.tsx
│ ├── upload-image-preview.tsx
│ └── use-toast.ts
├── components.json
├── config/
│ ├── features.ts
│ ├── site.ts
│ └── tools.json
├── hooks/
│ └── useGitHubStars.tsx
├── lib/
│ └── utils.ts
├── next.config.mjs
├── package.json
├── postcss.config.mjs
├── public/
│ └── site.webmanifest
├── tailwind.config.ts
├── tsconfig.json
└── types/
└── index.d.ts
SYMBOL INDEX (338 symbols across 112 files)
FILE: backend/app/api/admin/route.py
function get_documents (line 40) | def get_documents(
class UserUpdate (line 61) | class UserUpdate(BaseModel):
function verify_admin (line 69) | def verify_admin(current_user: User = Depends(get_current_user)):
function get_all_users (line 81) | async def get_all_users(admin: User = Depends(verify_admin)) -> UsersOut:
function get_user (line 91) | async def get_user(user_id: str, admin: User = Depends(verify_admin)) ->...
function edit_user (line 103) | async def edit_user(
function delete_user (line 123) | async def delete_user(user_id: str, admin: User = Depends(verify_admin))...
function get_system_prompt (line 138) | async def get_system_prompt(admin: User = Depends(verify_admin)):
class SystemPromptUpdate (line 143) | class SystemPromptUpdate(BaseModel):
function update_system_prompt (line 148) | async def update_system_prompt(
class ConversationStartersUpdate (line 157) | class ConversationStartersUpdate(BaseModel):
function update_conversation_starters (line 186) | async def update_conversation_starters(
function upload_and_ingest_data (line 200) | async def upload_and_ingest_data(
FILE: backend/app/api/auth/route.py
function create_user (line 21) | async def create_user(data: UserAuth):
function login (line 53) | async def login(form_data: OAuth2PasswordRequestForm = Depends()) -> Any:
function refresh_token (line 108) | async def refresh_token(response: Response, refresh_request: RefreshToke...
function get_me (line 168) | async def get_me(user: User = Depends(get_current_user)):
function verify_admin (line 173) | async def verify_admin(current_user: User = Depends(get_current_user)):
function update_user (line 183) | async def update_user(data: UserUpdate, user: User = Depends(get_current...
function test_token (line 195) | async def test_token(user: User = Depends(get_current_user)):
FILE: backend/app/api/chat/chat_config.py
function chat_config (line 15) | async def chat_config() -> ChatConfig:
FILE: backend/app/api/chat/engine/engine.py
function get_system_prompt_from_db (line 12) | def get_system_prompt_from_db():
function get_chat_engine (line 20) | def get_chat_engine(filters=None, params=None):
FILE: backend/app/api/chat/engine/generate.py
function get_doc_store (line 24) | def get_doc_store():
function run_pipeline (line 33) | def run_pipeline(docstore, vector_store, documents):
function persist_storage (line 53) | def persist_storage(docstore, vector_store):
function generate_datasource (line 61) | def generate_datasource():
FILE: backend/app/api/chat/engine/index.py
function get_index (line 9) | def get_index(params=None):
FILE: backend/app/api/chat/engine/loaders/__init__.py
function load_configs (line 11) | def load_configs():
function get_documents (line 17) | def get_documents():
FILE: backend/app/api/chat/engine/loaders/db.py
class DBLoaderConfig (line 8) | class DBLoaderConfig(BaseModel):
function get_db_documents (line 13) | def get_db_documents(configs: list[DBLoaderConfig]):
FILE: backend/app/api/chat/engine/loaders/file.py
class FileLoaderConfig (line 12) | class FileLoaderConfig(BaseModel):
function llama_parse_parser (line 16) | def llama_parse_parser():
function llama_parse_extractor (line 31) | def llama_parse_extractor() -> Dict[str, LlamaParse]:
function get_file_documents (line 38) | def get_file_documents(config: FileLoaderConfig):
FILE: backend/app/api/chat/engine/loaders/web.py
class CrawlUrl (line 4) | class CrawlUrl(BaseModel):
class WebLoaderConfig (line 10) | class WebLoaderConfig(BaseModel):
function get_web_documents (line 15) | def get_web_documents(config: WebLoaderConfig):
FILE: backend/app/api/chat/engine/node_postprocessors.py
class NodeCitationProcessor (line 8) | class NodeCitationProcessor(BaseNodePostprocessor):
method _postprocess_nodes (line 14) | def _postprocess_nodes(
FILE: backend/app/api/chat/engine/query_filter.py
function generate_filters (line 4) | def generate_filters(doc_ids):
FILE: backend/app/api/chat/engine/vectordb.py
function get_vector_store (line 6) | def get_vector_store():
FILE: backend/app/api/chat/events.py
class CallbackEvent (line 14) | class CallbackEvent(BaseModel):
method get_retrieval_message (line 19) | def get_retrieval_message(self) -> dict | None:
method get_tool_message (line 33) | def get_tool_message(self) -> dict | None:
method _is_output_serializable (line 44) | def _is_output_serializable(self, output: Any) -> bool:
method get_agent_tool_response (line 51) | def get_agent_tool_response(self) -> dict | None:
method to_response (line 78) | def to_response(self):
class EventCallbackHandler (line 94) | class EventCallbackHandler(BaseCallbackHandler):
method __init__ (line 98) | def __init__(
method on_event_start (line 112) | def on_event_start(
method on_event_end (line 123) | def on_event_end(
method start_trace (line 134) | def start_trace(self, trace_id: Optional[str] = None) -> None:
method end_trace (line 137) | def end_trace(
method async_event_gen (line 144) | async def async_event_gen(self) -> AsyncGenerator[CallbackEvent, None]:
FILE: backend/app/api/chat/models.py
class FileContent (line 15) | class FileContent(BaseModel):
class File (line 22) | class File(BaseModel):
class AnnotationFileData (line 30) | class AnnotationFileData(BaseModel):
class Config (line 36) | class Config:
class Annotation (line 53) | class Annotation(BaseModel):
method to_content (line 57) | def to_content(self) -> str | None:
class Message (line 72) | class Message(BaseModel):
class ChatData (line 78) | class ChatData(BaseModel):
class Config (line 82) | class Config:
method messages_must_not_be_empty (line 95) | def messages_must_not_be_empty(cls, v):
method get_last_message_content (line 100) | def get_last_message_content(self) -> str:
method get_history_messages (line 122) | def get_history_messages(self) -> List[ChatMessage]:
method is_last_message_from_user (line 131) | def is_last_message_from_user(self) -> bool:
method get_chat_document_ids (line 134) | def get_chat_document_ids(self) -> List[str]:
class SourceNodes (line 152) | class SourceNodes(BaseModel):
method from_source_node (line 160) | def from_source_node(cls, source_node: NodeWithScore):
method get_url_from_metadata (line 173) | def get_url_from_metadata(cls, metadata: Dict[str, Any]) -> str:
method from_source_nodes (line 203) | def from_source_nodes(cls, source_nodes: List[NodeWithScore]):
class Result (line 207) | class Result(BaseModel):
class ChatConfig (line 212) | class ChatConfig(BaseModel):
class Config (line 219) | class Config:
FILE: backend/app/api/chat/route.py
function chat (line 38) | async def chat(
FILE: backend/app/api/chat/services/file.py
function get_llamaparse_parser (line 20) | def get_llamaparse_parser():
function default_file_loaders_map (line 32) | def default_file_loaders_map():
class PrivateFileService (line 38) | class PrivateFileService:
method preprocess_base64_file (line 42) | def preprocess_base64_file(base64_content: str) -> Tuple[bytes, str | ...
method store_and_parse_file (line 50) | def store_and_parse_file(file_name, file_data, extension) -> List[Docu...
method process_file (line 76) | def process_file(file_name: str, base64_content: str, params: Any) -> ...
FILE: backend/app/api/chat/services/suggestion.py
class NextQuestions (line 21) | class NextQuestions(BaseModel):
class NextQuestionSuggestion (line 27) | class NextQuestionSuggestion:
method suggest_next_questions (line 29) | async def suggest_next_questions(
FILE: backend/app/api/chat/summary.py
function summary_generator (line 6) | async def summary_generator(
FILE: backend/app/api/chat/upload.py
class FileUploadRequest (line 14) | class FileUploadRequest(BaseModel):
FILE: backend/app/api/chat/vercel_response.py
class VercelStreamResponse (line 13) | class VercelStreamResponse(StreamingResponse):
method convert_text (line 22) | def convert_text(cls, token: str):
method convert_data (line 28) | def convert_data(cls, data: dict):
method __init__ (line 32) | def __init__(
method content_generator (line 47) | async def content_generator(
FILE: backend/app/api/conversation/route.py
function get_new_conversation (line 21) | async def get_new_conversation(
function get_conversation_history (line 42) | async def get_conversation_history(
function get_conversation (line 57) | async def get_conversation(
function get_sharable_conversation (line 68) | async def get_sharable_conversation(conversation_id: str):
function delete_conversation (line 78) | async def delete_conversation(
class ConversationSummaryUpdate (line 105) | class ConversationSummaryUpdate(BaseModel):
function edit_conversation_sharable (line 110) | async def edit_conversation_sharable(
function edit_conversation_summary (line 141) | async def edit_conversation_summary(
FILE: backend/app/core/config.py
class Settings (line 8) | class Settings(BaseSettings):
class Config (line 22) | class Config:
FILE: backend/app/core/security.py
function create_access_token (line 10) | def create_access_token(subject: Union[str, Any], expires_delta: int = N...
function create_refresh_token (line 20) | def create_refresh_token(subject: Union[str, Any], expires_delta: int = ...
function get_password (line 31) | def get_password(password: str) -> str:
function verify_password (line 35) | def verify_password(password: str, hashed_pass: str) -> bool:
FILE: backend/app/core/user.py
function get_current_user (line 16) | async def get_current_user(token: str = Depends(reuseable_oauth)) -> User:
FILE: backend/app/db.py
class AsyncMongoDB (line 28) | class AsyncMongoDB:
method connect_to_database (line 32) | async def connect_to_database(self):
method close_database_connection (line 54) | async def close_database_connection(self):
method database_init (line 59) | async def database_init(self):
class SyncMongoDB (line 105) | class SyncMongoDB:
method connect_to_database (line 109) | def connect_to_database(self):
method close_database_connection (line 129) | def close_database_connection(self):
method database_init (line 134) | def database_init(self):
FILE: backend/app/llmhub.py
class TSIEmbedding (line 10) | class TSIEmbedding(OpenAIEmbedding):
method __init__ (line 11) | def __init__(self, **kwargs):
function llm_config_from_env (line 16) | def llm_config_from_env() -> Dict:
function embedding_config_from_env (line 35) | def embedding_config_from_env() -> Dict:
function init_llmhub (line 52) | def init_llmhub():
FILE: backend/app/models/user_model.py
class User (line 8) | class User(BaseModel):
method __repr__ (line 18) | def __repr__(self) -> str:
method __str__ (line 21) | def __str__(self) -> str:
method __hash__ (line 24) | def __hash__(self) -> int:
method __eq__ (line 27) | def __eq__(self, other: object) -> bool:
method create (line 33) | def create(self) -> datetime:
class Config (line 36) | class Config:
method to_mongo (line 41) | def to_mongo(self):
method from_mongo (line 49) | def from_mongo(cls, data):
FILE: backend/app/observability.py
function init_observability (line 13) | def init_observability():
FILE: backend/app/schemas/admin_schema.py
class UserOut (line 7) | class UserOut(BaseModel):
class UsersOut (line 17) | class UsersOut(BaseModel):
class MessageOut (line 21) | class MessageOut(BaseModel):
class ErrorOut (line 25) | class ErrorOut(BaseModel):
class AuthErrorOut (line 30) | class AuthErrorOut(ErrorOut):
FILE: backend/app/schemas/auth_schema.py
class TokenSchema (line 6) | class TokenSchema(BaseModel):
class TokenPayload (line 11) | class TokenPayload(BaseModel):
class ErrorOut (line 16) | class ErrorOut(BaseModel):
class AuthErrorOut (line 21) | class AuthErrorOut(ErrorOut):
class RefreshTokenRequest (line 26) | class RefreshTokenRequest(BaseModel):
FILE: backend/app/schemas/user_schema.py
class UserAuth (line 5) | class UserAuth(BaseModel):
class UserOut (line 12) | class UserOut(BaseModel):
class UserUpdate (line 20) | class UserUpdate(BaseModel):
FILE: backend/app/services/admin_service.py
class AdminService (line 9) | class AdminService:
method user_collection (line 11) | def user_collection(self):
method get_all_users (line 14) | async def get_all_users(self) -> List[Dict[str, Any]]:
method get_user_by_id (line 18) | async def get_user_by_id(self, user_id: str) -> Optional[Dict[str, Any]]:
method edit_user (line 24) | async def edit_user(self, user_id: str, updated_data: Dict[str, Any]) ...
method delete_user (line 30) | async def delete_user(self, user_id: str) -> bool:
method _serialize_user (line 36) | def _serialize_user(self, user: User) -> Dict[str, Any]:
FILE: backend/app/services/config_service.py
class ConfigService (line 6) | class ConfigService:
method config_collection (line 8) | def config_collection(self):
method get_chat_config (line 11) | async def get_chat_config(self) -> ChatConfig:
method update_chat_config (line 20) | async def update_chat_config(self, updated_data: Dict[str, Any]) -> bool:
method get_system_prompt (line 26) | async def get_system_prompt(self) -> str:
method update_system_prompt (line 30) | async def update_system_prompt(self, new_prompt: str) -> bool:
method update_conversation_starters (line 33) | async def update_conversation_starters(self, new_starters: List[str]) ...
FILE: backend/app/services/conversation_service.py
class ConversationService (line 15) | class ConversationService:
method conversation_collection (line 17) | def conversation_collection(self):
method get_or_create_conversation (line 20) | async def get_or_create_conversation(
method update_conversation (line 39) | async def update_conversation(
method truncate_conversation (line 63) | async def truncate_conversation(
method get_all_conversations_for_user (line 82) | async def get_all_conversations_for_user(self, user_id: str):
method delete_conversation (line 121) | async def delete_conversation(self, conversation_id: str, user_id: str...
method edit_conversation_summary (line 127) | async def edit_conversation_summary(
method get_sharable_conversation (line 141) | async def get_sharable_conversation(
method make_conversation_sharable (line 155) | async def make_conversation_sharable(
FILE: backend/app/services/user_service.py
class UserService (line 15) | class UserService:
method users_collection (line 17) | def users_collection(self):
method create_user (line 20) | async def create_user(self, user: UserAuth) -> User:
method authenticate (line 37) | async def authenticate(self, email: str, password: str) -> Optional[Us...
method get_user_by_email (line 45) | async def get_user_by_email(self, email: str) -> Optional[User]:
method get_user_by_id (line 49) | async def get_user_by_id(self, id: UUID) -> Optional[User]:
method update_user (line 55) | async def update_user(self, id: UUID, data: UserUpdate) -> User:
FILE: backend/app/settings.py
function init_settings (line 7) | def init_settings():
function init_ollama (line 35) | def init_ollama():
function init_openai (line 52) | def init_openai():
function init_azure_openai (line 73) | def init_azure_openai():
function init_fastembed (line 107) | def init_fastembed():
function init_groq (line 126) | def init_groq():
function init_anthropic (line 140) | def init_anthropic():
function init_gemini (line 156) | def init_gemini():
function init_mistral (line 167) | def init_mistral():
FILE: backend/main.py
function lifespan (line 33) | async def lifespan(app: FastAPI):
function redirect_to_docs (line 64) | async def redirect_to_docs():
function mount_static_files (line 68) | def mount_static_files(directory, path):
FILE: frontend/app/(auth)/signin/page.tsx
type LoginResponse (line 17) | interface LoginResponse {
type UserResponse (line 22) | interface UserResponse {
function SignInContent (line 29) | function SignInContent() {
function SignUp (line 178) | function SignUp() {
FILE: frontend/app/(auth)/signup/page.tsx
function SignUpContent (line 19) | function SignUpContent() {
function SignUp (line 218) | function SignUp() {
FILE: frontend/app/ConversationContext.tsx
type Conversation (line 5) | interface Conversation {
type ConversationsByDate (line 12) | interface ConversationsByDate {
type ConversationContextProps (line 19) | interface ConversationContextProps {
type ConversationProviderProps (line 42) | interface ConversationProviderProps {
FILE: frontend/app/admin/AdminAuthProvider.tsx
type AdminAuthContextType (line 8) | interface AdminAuthContextType {
function AdminAuthProvider (line 22) | function AdminAuthProvider({ children }: { children: React.ReactNode }) {
FILE: frontend/app/admin/DataIngestion.tsx
function DataIngestion (line 24) | function DataIngestion() {
FILE: frontend/app/admin/RAGConfiguration.tsx
function RAGConfiguration (line 18) | function RAGConfiguration() {
FILE: frontend/app/admin/UsersManagement.tsx
type User (line 31) | interface User {
type UsersManagementProps (line 40) | interface UsersManagementProps {
function UsersManagement (line 44) | function UsersManagement({ adminEmail }: UsersManagementProps) {
FILE: frontend/app/admin/layout.tsx
function AdminLayout (line 7) | function AdminLayout({
FILE: frontend/app/admin/page.tsx
function AdminPage (line 20) | function AdminPage() {
FILE: frontend/app/authProvider.tsx
type AuthContextType (line 9) | interface AuthContextType {
function AuthProvider (line 32) | function AuthProvider({ children }: { children: React.ReactNode }) {
FILE: frontend/app/chat/page.tsx
function ChatContent (line 15) | function ChatContent() {
function Chat (line 198) | function Chat() {
FILE: frontend/app/features/page.tsx
function IndexPage (line 156) | function IndexPage() {
FILE: frontend/app/layout.tsx
function RootLayout (line 44) | function RootLayout({
FILE: frontend/app/page.tsx
function HomeContent (line 197) | function HomeContent() {
FILE: frontend/app/share/page.tsx
function ChatContent (line 16) | function ChatContent() {
function Chat (line 105) | function Chat() {
FILE: frontend/components/analytics.tsx
function Analytics (line 5) | function Analytics() {
FILE: frontend/components/header.tsx
type MobileNavigationProps (line 17) | interface MobileNavigationProps {
type AuthContextType (line 22) | interface AuthContextType {
function MobileNavigation (line 27) | function MobileNavigation({
function Header (line 81) | function Header() {
FILE: frontend/components/history.tsx
type Conversation (line 41) | interface Conversation {
constant MAX_SUMMARY_LENGTH (line 48) | const MAX_SUMMARY_LENGTH = 27;
function HistoryComponent (line 50) | function HistoryComponent() {
function History (line 426) | function History() {
FILE: frontend/components/magicui/animated-gradient-text.tsx
function AnimatedGradientText (line 5) | function AnimatedGradientText({
FILE: frontend/components/magicui/border-beam.tsx
type BorderBeamProps (line 3) | interface BorderBeamProps {
FILE: frontend/components/magicui/box-reveal.tsx
type BoxRevealProps (line 6) | interface BoxRevealProps {
FILE: frontend/components/magicui/gradual-spacing.tsx
type GradualSpacingProps (line 7) | interface GradualSpacingProps {
function GradualSpacing (line 15) | function GradualSpacing({
FILE: frontend/components/magicui/hyper-text.tsx
type HyperTextProps (line 8) | interface HyperTextProps {
function HyperText (line 20) | function HyperText({
FILE: frontend/components/magicui/meteors.tsx
type MeteorsProps (line 7) | interface MeteorsProps {
FILE: frontend/components/magicui/neon-gradient-card.tsx
type NeonColorsProps (line 14) | interface NeonColorsProps {
type NeonGradientCardProps (line 19) | interface NeonGradientCardProps {
FILE: frontend/components/magicui/number-ticker.tsx
function NumberTicker (line 8) | function NumberTicker({
FILE: frontend/components/magicui/ripple.tsx
type RippleProps (line 3) | interface RippleProps {
FILE: frontend/components/magicui/shimmer-button.tsx
type ShimmerButtonProps (line 5) | interface ShimmerButtonProps
FILE: frontend/components/magicui/shine-border.tsx
type TColorProp (line 5) | type TColorProp = string | string[];
type ShineBorderProps (line 7) | interface ShineBorderProps {
function ShineBorder (line 26) | function ShineBorder({
FILE: frontend/components/magicui/shiny-button.tsx
type ShinyButtonProps (line 27) | interface ShinyButtonProps {
FILE: frontend/components/magicui/sparkles-text.tsx
type Sparkle (line 8) | interface Sparkle {
type SparklesTextProps (line 18) | interface SparklesTextProps {
FILE: frontend/components/magicui/typing-animation.tsx
type TypingAnimationProps (line 7) | interface TypingAnimationProps {
function TypingAnimation (line 13) | function TypingAnimation({
FILE: frontend/components/theme-provider.tsx
function ThemeProvider (line 7) | function ThemeProvider({ children, ...props }: ThemeProviderProps) {
FILE: frontend/components/theme-toggle.tsx
function ThemeToggle (line 9) | function ThemeToggle() {
FILE: frontend/components/ui/badge.tsx
type BadgeProps (line 26) | interface BadgeProps
function Badge (line 30) | function Badge({ className, variant, ...props }: BadgeProps) {
FILE: frontend/components/ui/button.tsx
type ButtonProps (line 36) | interface ButtonProps
FILE: frontend/components/ui/chat/chat-actions.tsx
function ChatActions (line 6) | function ChatActions(
FILE: frontend/components/ui/chat/chat-input.tsx
constant ALLOWED_EXTENSIONS (line 13) | const ALLOWED_EXTENSIONS = ['pdf', 'txt', 'docx'];
function ChatInput (line 15) | function ChatInput(
FILE: frontend/components/ui/chat/chat-message/chat-avatar.tsx
function ChatAvatar (line 5) | function ChatAvatar({ role }: { role: string }) {
FILE: frontend/components/ui/chat/chat-message/chat-events.tsx
function ChatEvents (line 11) | function ChatEvents({
FILE: frontend/components/ui/chat/chat-message/chat-files.tsx
function ChatFiles (line 4) | function ChatFiles({ data }: { data: DocumentFileData }) {
FILE: frontend/components/ui/chat/chat-message/chat-image.tsx
function ChatImage (line 4) | function ChatImage({ data }: { data: ImageData }) {
FILE: frontend/components/ui/chat/chat-message/chat-sources.tsx
constant SCORE_THRESHOLD (line 14) | const SCORE_THRESHOLD = 0.3;
type SourceNode (line 16) | interface SourceNode {
type SourceData (line 26) | interface SourceData {
type NodeInfo (line 30) | interface NodeInfo {
type SourceNumberButtonProps (line 39) | interface SourceNumberButtonProps {
function SourceNumberButton (line 43) | function SourceNumberButton({ index }: SourceNumberButtonProps) {
function prefixUrl (line 51) | function prefixUrl(url: string): string {
type NodeInfoProps (line 58) | interface NodeInfoProps {
function NodeInfo (line 62) | function NodeInfo({ nodeInfo }: NodeInfoProps) {
type ChatSourcesProps (line 102) | interface ChatSourcesProps {
function ChatSources (line 106) | function ChatSources({ data }: ChatSourcesProps) {
FILE: frontend/components/ui/chat/chat-message/chat-suggestedQuestions.tsx
function SuggestedQuestions (line 4) | function SuggestedQuestions({
FILE: frontend/components/ui/chat/chat-message/chat-tools.tsx
function ChatTools (line 5) | function ChatTools({ data }: { data: ToolData }) {
FILE: frontend/components/ui/chat/chat-message/codeblock.tsx
type Props (line 14) | interface Props {
type languageMap (line 19) | interface languageMap {
FILE: frontend/components/ui/chat/chat-message/index.tsx
type ContentDisplayConfig (line 36) | type ContentDisplayConfig = {
function ChatMessageContent (line 41) | function ChatMessageContent({
function ChatMessage (line 130) | function ChatMessage({
FILE: frontend/components/ui/chat/chat-message/markdown.tsx
function Markdown (line 153) | function Markdown({ content }: { content: string }) {
FILE: frontend/components/ui/chat/chat-messages.tsx
function ChatMessages (line 14) | function ChatMessages(
FILE: frontend/components/ui/chat/chat.interface.ts
type ChatHandler (line 3) | interface ChatHandler {
FILE: frontend/components/ui/chat/hooks/use-config.ts
type ChatConfig (line 39) | interface ChatConfig {
function useClientConfig (line 44) | function useClientConfig(): ChatConfig {
FILE: frontend/components/ui/chat/hooks/use-copy-to-clipboard.tsx
type useCopyToClipboardProps (line 5) | interface useCopyToClipboardProps {
function useCopyToClipboard (line 9) | function useCopyToClipboard({
FILE: frontend/components/ui/chat/hooks/use-file.ts
function useFile (line 23) | function useFile() {
FILE: frontend/components/ui/chat/index.ts
type MessageAnnotationType (line 8) | enum MessageAnnotationType {
type ImageData (line 17) | type ImageData = {
type DocumentFileType (line 21) | type DocumentFileType = "csv" | "pdf" | "txt" | "docx";
type DocumentFileContent (line 23) | type DocumentFileContent = {
type DocumentFile (line 28) | type DocumentFile = {
type DocumentFileData (line 36) | type DocumentFileData = {
type SourceNode (line 40) | type SourceNode = {
type SourceData (line 48) | type SourceData = {
type EventData (line 52) | type EventData = {
type ToolData (line 57) | type ToolData = {
type SuggestedQuestionsData (line 71) | type SuggestedQuestionsData = string[];
type AnnotationData (line 73) | type AnnotationData =
type MessageAnnotation (line 81) | type MessageAnnotation = {
function getAnnotationData (line 86) | function getAnnotationData<T extends AnnotationData>(
FILE: frontend/components/ui/chat/widgets/PdfDialog.tsx
type PdfDialogProps (line 13) | interface PdfDialogProps {
function PdfDialog (line 31) | function PdfDialog(props: PdfDialogProps) {
FILE: frontend/components/ui/chat/widgets/WeatherCard.tsx
type WeatherData (line 1) | interface WeatherData {
function WeatherCard (line 169) | function WeatherCard({ data }: { data: WeatherData }) {
FILE: frontend/components/ui/command.tsx
type CommandDialogProps (line 26) | interface CommandDialogProps extends DialogProps {}
FILE: frontend/components/ui/document-preview.tsx
type DocumentPreviewProps (line 20) | interface DocumentPreviewProps {
function DocumentPreview (line 25) | function DocumentPreview(props: DocumentPreviewProps) {
function PreviewCard (line 74) | function PreviewCard(props: DocumentPreviewProps) {
function inKB (line 117) | function inKB(size: number) {
FILE: frontend/components/ui/file-upload.tsx
function GridPattern (line 192) | function GridPattern() {
FILE: frontend/components/ui/file-uploader.tsx
type FileUploaderProps (line 8) | interface FileUploaderProps {
constant DEFAULT_INPUT_ID (line 20) | const DEFAULT_INPUT_ID = "fileInput";
constant DEFAULT_FILE_SIZE_LIMIT (line 21) | const DEFAULT_FILE_SIZE_LIMIT = 1024 * 1024 * 50;
function FileUploader (line 23) | function FileUploader({
FILE: frontend/components/ui/form.tsx
type FormFieldContextValue (line 20) | type FormFieldContextValue<
type FormItemContextValue (line 67) | type FormItemContextValue = {
FILE: frontend/components/ui/input.tsx
type InputProps (line 5) | interface InputProps
FILE: frontend/components/ui/pagination.tsx
type PaginationLinkProps (line 37) | type PaginationLinkProps = {
FILE: frontend/components/ui/placeholders-and-vanish-input.tsx
function PlaceholdersAndVanishInput (line 7) | function PlaceholdersAndVanishInput({
FILE: frontend/components/ui/sheet.tsx
type SheetContentProps (line 52) | interface SheetContentProps
FILE: frontend/components/ui/skeleton.tsx
function Skeleton (line 3) | function Skeleton({
FILE: frontend/components/ui/sonner.tsx
type ToasterProps (line 6) | type ToasterProps = React.ComponentProps<typeof Sonner>
FILE: frontend/components/ui/textarea.tsx
type TextareaProps (line 5) | interface TextareaProps
FILE: frontend/components/ui/toast.tsx
type ToastProps (line 115) | type ToastProps = React.ComponentPropsWithoutRef<typeof Toast>
type ToastActionElement (line 117) | type ToastActionElement = React.ReactElement<typeof ToastAction>
FILE: frontend/components/ui/toaster.tsx
function Toaster (line 13) | function Toaster() {
FILE: frontend/components/ui/upload-image-preview.tsx
function UploadImagePreview (line 5) | function UploadImagePreview({
FILE: frontend/components/ui/use-toast.ts
constant TOAST_LIMIT (line 11) | const TOAST_LIMIT = 1
constant TOAST_REMOVE_DELAY (line 12) | const TOAST_REMOVE_DELAY = 1000000
type ToasterToast (line 14) | type ToasterToast = ToastProps & {
function genId (line 30) | function genId() {
type ActionType (line 35) | type ActionType = typeof actionTypes
type Action (line 37) | type Action =
type State (line 55) | interface State {
function dispatch (line 136) | function dispatch(action: Action) {
type Toast (line 143) | type Toast = Omit<ToasterToast, "id">
function toast (line 145) | function toast({ ...props }: Toast) {
function useToast (line 174) | function useToast() {
FILE: frontend/lib/utils.ts
function cn (line 4) | function cn(...inputs: ClassValue[]) {
FILE: frontend/types/index.d.ts
type SiteConfig (line 1) | type SiteConfig = {
Condensed preview — 191 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (552K chars).
[
{
"path": ".github/ISSUE_TEMPLATE/bug_report.md",
"chars": 834,
"preview": "---\nname: Bug report\nabout: Create a report to help us improve\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Describe the b"
},
{
"path": ".github/ISSUE_TEMPLATE/feature_request.md",
"chars": 595,
"preview": "---\nname: Feature request\nabout: Suggest an idea for this project\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Is your fea"
},
{
"path": ".github/PULL_REQUEST_TEMPLATE/pull_request_template.md",
"chars": 1421,
"preview": "# Pull Request Template\n\n## Description\n\nPlease include a summary of the change and which issue is fixed. Please also in"
},
{
"path": ".gitignore",
"chars": 85,
"preview": "/rag-venv\n/ragsaas-venvs\nbackend/poetry.lock\nbackend/rag_config.json\nbackend/data/**\n"
},
{
"path": "CODE_OF_CONDUCT.md",
"chars": 5202,
"preview": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nWe as members, contributors, and leaders pledge to make participa"
},
{
"path": "CONTRIBUTING.md",
"chars": 4209,
"preview": "# Contributing to RAG-SaaS\n\nFirst off, thank you for considering contributing to RAG-SaaS! It's people like you that mak"
},
{
"path": "LICENSE",
"chars": 46180,
"preview": "RAG-SaaS Dual License\n\nThis project is licensed under two licenses:\n\n1. Apache License, Version 2.0 (for students, devel"
},
{
"path": "LICENSING.md",
"chars": 2492,
"preview": "# Licensing for RAG-SaaS\n\nRAG-SaaS is released under a dual licensing model designed to accommodate both open-source and"
},
{
"path": "README.md",
"chars": 10858,
"preview": "<h2 align=\"center\">RAG SaaS</h2>\n<p align=\"center\">\n Ship RAG solutions quickly⚡\n</p>\n\n<p align=\"center\">\n <img alt=\"R"
},
{
"path": "backend/.gitignore",
"chars": 32,
"preview": "__pycache__\nstorage\n.env\noutput\n"
},
{
"path": "backend/Dockerfile",
"chars": 659,
"preview": "FROM python:3.11 as build\n\nWORKDIR /app\n\nENV PYTHONPATH=/app\n\n# Install Poetry\nRUN curl -sSL https://install.python-poet"
},
{
"path": "backend/README.md",
"chars": 3197,
"preview": "This is a [LlamaIndex](https://www.llamaindex.ai/) project using [FastAPI](https://fastapi.tiangolo.com/) bootstrapped w"
},
{
"path": "backend/app/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "backend/app/api/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "backend/app/api/admin/__init__.py",
"chars": 59,
"preview": "from .route import admin_router\n\n__all__ = [\"admin_router\"]"
},
{
"path": "backend/app/api/admin/route.py",
"chars": 9999,
"preview": "import os\n\nfrom grpc import Status\nimport boto3\nimport tempfile\nimport shutil\nfrom dotenv import load_dotenv\nfrom fastap"
},
{
"path": "backend/app/api/auth/__init__.py",
"chars": 57,
"preview": "from .route import auth_router\n\n__all__ = [\"auth_router\"]"
},
{
"path": "backend/app/api/auth/route.py",
"chars": 6459,
"preview": "from fastapi import APIRouter, Depends, HTTPException, Response, status, Body\nfrom fastapi.security import OAuth2Passwor"
},
{
"path": "backend/app/api/chat/__init__.py",
"chars": 174,
"preview": "from .route import chat_router\nfrom .chat_config import config_router\nfrom .upload import file_upload_router\n\n__all__ = "
},
{
"path": "backend/app/api/chat/chat_config.py",
"chars": 329,
"preview": "import logging\nimport os\n\nfrom fastapi import APIRouter\n\nfrom app.api.chat.models import ChatConfig\nfrom app.services.co"
},
{
"path": "backend/app/api/chat/engine/__init__.py",
"chars": 55,
"preview": "from .engine import get_chat_engine as get_chat_engine\n"
},
{
"path": "backend/app/api/chat/engine/engine.py",
"chars": 1597,
"preview": "import os\n\nfrom app.api.chat.engine.index import get_index\nfrom app.api.chat.engine.node_postprocessors import NodeCitat"
},
{
"path": "backend/app/api/chat/engine/generate.py",
"chars": 2459,
"preview": "# flake8: noqa: E402\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nimport logging\nimport os\n\nfrom app.api.chat.engine.l"
},
{
"path": "backend/app/api/chat/engine/index.py",
"chars": 589,
"preview": "import logging\nfrom llama_index.core.indices import VectorStoreIndex\nfrom app.api.chat.engine.vectordb import get_vector"
},
{
"path": "backend/app/api/chat/engine/loaders/__init__.py",
"chars": 1211,
"preview": "import logging\n\nimport yaml\nfrom app.api.chat.engine.loaders.db import DBLoaderConfig, get_db_documents\nfrom app.api.cha"
},
{
"path": "backend/app/api/chat/engine/loaders/db.py",
"chars": 604,
"preview": "import logging\nfrom typing import List\nfrom pydantic import BaseModel\n\nlogger = logging.getLogger(__name__)\n\n\nclass DBLo"
},
{
"path": "backend/app/api/chat/engine/loaders/file.py",
"chars": 2149,
"preview": "import os\nimport logging\nfrom typing import Dict\nfrom llama_parse import LlamaParse\nfrom pydantic import BaseModel\n\nfrom"
},
{
"path": "backend/app/api/chat/engine/loaders/web.py",
"chars": 884,
"preview": "from pydantic import BaseModel, Field\n\n\nclass CrawlUrl(BaseModel):\n base_url: str\n prefix: str\n max_depth: int "
},
{
"path": "backend/app/api/chat/engine/node_postprocessors.py",
"chars": 706,
"preview": "from typing import List, Optional\n\nfrom llama_index.core import QueryBundle\nfrom llama_index.core.postprocessor.types im"
},
{
"path": "backend/app/api/chat/engine/query_filter.py",
"chars": 934,
"preview": "from llama_index.core.vector_stores.types import MetadataFilter, MetadataFilters\n\n\ndef generate_filters(doc_ids):\n \"\""
},
{
"path": "backend/app/api/chat/engine/vectordb.py",
"chars": 1244,
"preview": "import os\nimport qdrant_client\nfrom llama_index.vector_stores.qdrant import QdrantVectorStore\n\n\ndef get_vector_store():\n"
},
{
"path": "backend/app/api/chat/events.py",
"chars": 5028,
"preview": "import json\nimport asyncio\nimport logging\nfrom typing import AsyncGenerator, Dict, Any, List, Optional\nfrom llama_index."
},
{
"path": "backend/app/api/chat/models.py",
"chars": 7398,
"preview": "import logging\nimport os\nfrom typing import Any, Dict, List, Literal, Optional\n\nfrom llama_index.core.llms import ChatMe"
},
{
"path": "backend/app/api/chat/route.py",
"chars": 7545,
"preview": "import json\nimport logging\nfrom typing import List, Dict, Any, Optional\nfrom fastapi import (\n APIRouter,\n Depends"
},
{
"path": "backend/app/api/chat/services/file.py",
"chars": 4445,
"preview": "import base64\nimport mimetypes\nimport os\nfrom io import BytesIO\nfrom pathlib import Path\nfrom typing import Any, List, T"
},
{
"path": "backend/app/api/chat/services/suggestion.py",
"chars": 2180,
"preview": "import logging\nfrom typing import List\n\nfrom app.api.chat.models import Message\nfrom llama_index.core.prompts import Pro"
},
{
"path": "backend/app/api/chat/summary.py",
"chars": 1425,
"preview": "from typing import List\nfrom app.api.chat.models import Message\nfrom llama_index.core.settings import Settings\n\n\nasync d"
},
{
"path": "backend/app/api/chat/upload.py",
"chars": 782,
"preview": "import logging\nfrom typing import List, Any\n\nfrom fastapi import APIRouter, HTTPException\nfrom pydantic import BaseModel"
},
{
"path": "backend/app/api/chat/vercel_response.py",
"chars": 3834,
"preview": "import json\n\nfrom aiostream import stream\nfrom fastapi import Request\nfrom fastapi.responses import StreamingResponse\nfr"
},
{
"path": "backend/app/api/conversation/__init__.py",
"chars": 73,
"preview": "from .route import conversation_router\n\n__all__ = [\"conversation_router\"]"
},
{
"path": "backend/app/api/conversation/route.py",
"chars": 5877,
"preview": "import logging\nfrom pydantic import BaseModel\nfrom datetime import datetime, timedelta\nfrom typing import List, Dict, An"
},
{
"path": "backend/app/config.py",
"chars": 18,
"preview": "DATA_DIR = \"data\"\n"
},
{
"path": "backend/app/core/config.py",
"chars": 778,
"preview": "from typing import List, ClassVar\n\nfrom decouple import config\nfrom pydantic import AnyHttpUrl\nfrom pydantic_settings im"
},
{
"path": "backend/app/core/security.py",
"chars": 1381,
"preview": "from datetime import datetime, timedelta\nfrom passlib.context import CryptContext\nfrom typing import Union, Any\nfrom app"
},
{
"path": "backend/app/core/user.py",
"chars": 1461,
"preview": "from datetime import datetime\nfrom fastapi import Depends, HTTPException, status\nfrom fastapi.security import OAuth2Pass"
},
{
"path": "backend/app/db.py",
"chars": 6543,
"preview": "import asyncio\nimport re\nimport os\nimport json\nimport time\nfrom motor.motor_asyncio import AsyncIOMotorClient\nfrom pymon"
},
{
"path": "backend/app/llmhub.py",
"chars": 1938,
"preview": "from llama_index.embeddings.openai import OpenAIEmbedding\nfrom llama_index.core.settings import Settings\nfrom typing imp"
},
{
"path": "backend/app/models/user_model.py",
"chars": 1472,
"preview": "from typing import Optional\nfrom datetime import datetime\nfrom uuid import UUID, uuid4\nfrom pydantic import BaseModel, F"
},
{
"path": "backend/app/observability.py",
"chars": 1521,
"preview": "import os\nfrom openinference.instrumentation.llama_index import LlamaIndexInstrumentor\nfrom opentelemetry import trace a"
},
{
"path": "backend/app/schemas/admin_schema.py",
"chars": 627,
"preview": "from pydantic import BaseModel, EmailStr\nfrom typing import List, Optional, Any\nfrom datetime import datetime\nfrom uuid "
},
{
"path": "backend/app/schemas/auth_schema.py",
"chars": 485,
"preview": "from uuid import UUID\nfrom pydantic import BaseModel\nfrom typing_extensions import Optional\n\n\nclass TokenSchema(BaseMode"
},
{
"path": "backend/app/schemas/user_schema.py",
"chars": 624,
"preview": "from typing import Optional\nfrom pydantic import BaseModel, EmailStr, Field\n\n\nclass UserAuth(BaseModel):\n email: Emai"
},
{
"path": "backend/app/services/__init__.py",
"chars": 265,
"preview": "from .user_service import user_service\nfrom .conversation_service import conversation_service\nfrom .admin_service import"
},
{
"path": "backend/app/services/admin_service.py",
"chars": 1719,
"preview": "from typing import List, Dict, Any, Optional\nfrom bson import ObjectId, Binary\nfrom datetime import datetime\nfrom app.db"
},
{
"path": "backend/app/services/config_service.py",
"chars": 1460,
"preview": "from typing import List, Dict, Any, Optional\nfrom app.db import async_mongodb\nfrom app.api.chat.models import ChatConfig"
},
{
"path": "backend/app/services/conversation_service.py",
"chars": 6171,
"preview": "import logging\nfrom pydantic import BaseModel\nfrom datetime import datetime, timedelta\nfrom typing import List, Dict, An"
},
{
"path": "backend/app/services/user_service.py",
"chars": 2350,
"preview": "from typing import Optional\nfrom uuid import UUID\nfrom app.schemas.user_schema import UserAuth, UserUpdate\nfrom app.mode"
},
{
"path": "backend/app/settings.py",
"chars": 5654,
"preview": "import os\nfrom typing import Dict\n\nfrom llama_index.core.settings import Settings\n\n\ndef init_settings():\n model_provi"
},
{
"path": "backend/config/loaders.yaml",
"chars": 169,
"preview": "file:\n # use_llama_parse: Use LlamaParse if `true`. Needs a `LLAMA_CLOUD_API_KEY` from https://cloud.llamaindex.ai set "
},
{
"path": "backend/config/tools.yaml",
"chars": 23,
"preview": "local: {}\nllamahub: {}\n"
},
{
"path": "backend/main.py",
"chars": 2896,
"preview": "# flake8: noqa: E402\nfrom dotenv import load_dotenv\n\nfrom app.config import DATA_DIR\n\nload_dotenv()\n\nimport logging\nimpo"
},
{
"path": "backend/pyproject.toml",
"chars": 1448,
"preview": "[tool]\n[tool.poetry]\nname = \"app\"\nversion = \"0.1.0\"\ndescription = \"⚡ Ship RAG Solutions Quickly\"\nauthors = [ \"Adithya S "
},
{
"path": "backend/tests/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "docker-compose.yaml",
"chars": 2193,
"preview": "version: '3.8'\n\nservices:\n qdrant:\n image: qdrant/qdrant:latest\n container_name: qdrant\n ports:\n - 6333:6"
},
{
"path": "frontend/.eslintrc.json",
"chars": 40,
"preview": "{\n \"extends\": \"next/core-web-vitals\"\n}\n"
},
{
"path": "frontend/.gitignore",
"chars": 391,
"preview": "# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.\n\n# dependencies\n/node_modules\n/.pn"
},
{
"path": "frontend/Dockerfile",
"chars": 800,
"preview": "FROM node:18-alpine AS base\n\nFROM base AS deps\n\nRUN apk add --no-cache libc6-compat\nWORKDIR /app\n\nCOPY package.json ./\n\n"
},
{
"path": "frontend/README.md",
"chars": 1383,
"preview": "This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js"
},
{
"path": "frontend/app/(auth)/signin/page.tsx",
"chars": 5783,
"preview": "'use client';\n\nimport Image from 'next/image';\nimport Link from 'next/link';\nimport { Button } from '@/components/ui/but"
},
{
"path": "frontend/app/(auth)/signup/page.tsx",
"chars": 7466,
"preview": "'use client';\n\nimport React, { Suspense, lazy, useState } from 'react';\nimport Image from 'next/image';\nimport Link from"
},
{
"path": "frontend/app/ConversationContext.tsx",
"chars": 1621,
"preview": "'use client';\n\nimport React, { createContext, useContext, useState, useEffect } from 'react';\n\ninterface Conversation {\n"
},
{
"path": "frontend/app/admin/AdminAuthProvider.tsx",
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"path": "frontend/components/ui/textarea.tsx",
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{
"path": "frontend/components/ui/toast.tsx",
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},
{
"path": "frontend/components/ui/toaster.tsx",
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"preview": "\"use client\"\n\nimport {\n Toast,\n ToastClose,\n ToastDescription,\n ToastProvider,\n ToastTitle,\n ToastViewport,\n} from"
},
{
"path": "frontend/components/ui/toggle-group.tsx",
"chars": 1753,
"preview": "\"use client\"\n\nimport * as React from \"react\"\nimport * as ToggleGroupPrimitive from \"@radix-ui/react-toggle-group\"\nimport"
},
{
"path": "frontend/components/ui/toggle.tsx",
"chars": 1449,
"preview": "\"use client\"\n\nimport * as React from \"react\"\nimport * as TogglePrimitive from \"@radix-ui/react-toggle\"\nimport { cva, typ"
},
{
"path": "frontend/components/ui/tooltip.tsx",
"chars": 1159,
"preview": "\"use client\"\n\nimport * as React from \"react\"\nimport * as TooltipPrimitive from \"@radix-ui/react-tooltip\"\n\nimport { cn } "
},
{
"path": "frontend/components/ui/upload-image-preview.tsx",
"chars": 768,
"preview": "import { XCircleIcon } from \"lucide-react\";\nimport Image from \"next/image\";\nimport { cn } from \"../../lib/utils\";\n\nexpor"
},
{
"path": "frontend/components/ui/use-toast.ts",
"chars": 3948,
"preview": "\"use client\"\n\n// Inspired by react-hot-toast library\nimport * as React from \"react\"\n\nimport type {\n ToastActionElement,"
},
{
"path": "frontend/components.json",
"chars": 344,
"preview": "{\n \"$schema\": \"https://ui.shadcn.com/schema.json\",\n \"style\": \"default\",\n \"rsc\": true,\n \"tsx\": true,\n \"tailwind\": {\n"
},
{
"path": "frontend/config/features.ts",
"chars": 2352,
"preview": "export const Features = [\n {\n title: 'Basic Authentication',\n description:\n 'Secure your RAG-based applicati"
},
{
"path": "frontend/config/site.ts",
"chars": 358,
"preview": "import { SiteConfig } from '@/types';\n\nexport const siteConfig: SiteConfig = {\n name: 'RAG SAAS',\n description: 'RAG-S"
},
{
"path": "frontend/config/tools.json",
"chars": 59,
"preview": "{\n \"local\": {\n \"duckduckgo\": {}\n },\n \"llamahub\": {}\n}"
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{
"path": "frontend/hooks/useGitHubStars.tsx",
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"preview": "'use client';\n\nimport { useState, useEffect } from 'react';\n\nconst useGitHubStars = (repoName: string) => {\n const [sta"
},
{
"path": "frontend/lib/utils.ts",
"chars": 166,
"preview": "import { type ClassValue, clsx } from \"clsx\"\nimport { twMerge } from \"tailwind-merge\"\n\nexport function cn(...inputs: Cla"
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{
"path": "frontend/next.config.mjs",
"chars": 116,
"preview": "/** @type {import('next').NextConfig} */\nconst nextConfig = {\n output: \"standalone\",\n};\n\nexport default nextConfig;"
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{
"path": "frontend/package.json",
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"preview": "{\n \"name\": \"frontend\",\n \"version\": \"0.1.0\",\n \"private\": true,\n \"scripts\": {\n \"dev\": \"next dev\",\n \"build\": \"nex"
},
{
"path": "frontend/postcss.config.mjs",
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"preview": "/** @type {import('postcss-load-config').Config} */\nconst config = {\n plugins: {\n tailwindcss: {},\n },\n};\n\nexport d"
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{
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"preview": "{\n \"name\": \"\",\n \"short_name\": \"\",\n \"icons\": [\n {\n \"src\": \"/android-chrome-192x192.png\",\n \"sizes\": \"192x1"
},
{
"path": "frontend/tailwind.config.ts",
"chars": 2640,
"preview": "import type { Config } from 'tailwindcss';\n\nconst config = {\n darkMode: ['class'],\n content: [\n './pages/**/*.{ts,t"
},
{
"path": "frontend/tsconfig.json",
"chars": 574,
"preview": "{\n \"compilerOptions\": {\n \"lib\": [\"dom\", \"dom.iterable\", \"esnext\"],\n \"allowJs\": true,\n \"skipLibCheck\": true,\n "
},
{
"path": "frontend/types/index.d.ts",
"chars": 160,
"preview": "export type SiteConfig = {\n name: string;\n description: string;\n url: string;\n ogImage: string;\n links: {\n twitt"
}
]
About this extraction
This page contains the full source code of the adithya-s-k/RAG-SaaS GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 191 files (505.7 KB), approximately 126.2k tokens, and a symbol index with 338 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.