Merge pull request #926 from siiddhantt/feature

Feature: Logging token usage info to MongoDB

.env-template

@@ -1,7 +1,8 @@
-OPENAI_API_KEY=<LLM api key (for example, open ai key)>
-EMBEDDINGS_KEY=<LLM embeddings api key (for example, open ai key)>
+API_KEY=<LLM api key (for example, open ai key)>
+LLM_NAME=docsgpt
+VITE_API_STREAMING=true
 
-#For Azure
+#For Azure (you can delete it if you don't use Azure)
 OPENAI_API_BASE=
 OPENAI_API_VERSION=
 AZURE_DEPLOYMENT_NAME=

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,138 @@
+name: "🐛 Bug Report"
+description: "Submit a bug report to help us improve"
+title: "🐛 Bug Report: "
+labels: ["type: bug"]
+body:
+  - type: markdown
+    attributes:
+      value: We value your time and your efforts to submit this bug report is appreciated. 🙏
+
+  - type: textarea
+    id: description
+    validations:
+      required: true
+    attributes:
+      label: "📜 Description"
+      description: "A clear and concise description of what the bug is."
+      placeholder: "It bugs out when ..."
+
+  - type: textarea
+    id: steps-to-reproduce
+    validations:
+      required: true
+    attributes:
+      label: "👟 Reproduction steps"
+      description: "How do you trigger this bug? Please walk us through it step by step."
+      placeholder: "1. Go to '...'
+        2. Click on '....'
+        3. Scroll down to '....'
+        4. See error"
+
+  - type: textarea
+    id: expected-behavior
+    validations:
+      required: true
+    attributes:
+      label: "👍 Expected behavior"
+      description: "What did you think should happen?"
+      placeholder: "It should ..."
+
+  - type: textarea
+    id: actual-behavior
+    validations:
+      required: true
+    attributes:
+      label: "👎 Actual Behavior with Screenshots"
+      description: "What did actually happen? Add screenshots, if applicable."
+      placeholder: "It actually ..."
+
+  - type: dropdown
+    id: operating-system
+    attributes:
+      label: "💻 Operating system"
+      description: "What OS is your app running on?"
+      options:
+        - Linux
+        - MacOS
+        - Windows
+        - Something else
+    validations:
+      required: true
+
+  - type: dropdown
+    id: browsers
+    attributes:
+      label: What browsers are you seeing the problem on?
+      multiple: true
+      options:
+        - Firefox
+        - Chrome
+        - Safari
+        - Microsoft Edge
+        - Something else
+
+  - type: dropdown
+    id: dev-environment
+    validations:
+      required: true
+    attributes:
+      label: "🤖 What development environment are you experiencing this bug on?"
+      options:
+        - Docker
+        - Local dev server
+
+  - type: textarea
+    id: env-vars
+    validations:
+      required: false
+    attributes:
+      label: "🔒 Did you set the correct environment variables in the right path? List the environment variable names (not values please!)"
+      description: "Please refer to the [Project setup instructions](https://github.com/arc53/DocsGPT#quickstart) if you are unsure."
+      placeholder: "It actually ..."
+
+  - type: textarea
+    id: additional-context
+    validations:
+      required: false
+    attributes:
+      label: "📃 Provide any additional context for the Bug."
+      description: "Add any other context about the problem here."
+      placeholder: "It actually ..."
+
+  - type: textarea
+    id: logs
+    validations:
+      required: false
+    attributes:
+      label: 📖 Relevant log output
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      render: shell
+
+  - type: checkboxes
+    id: no-duplicate-issues
+    attributes:
+      label: "👀 Have you spent some time to check if this bug has been raised before?"
+      options:
+        - label: "I checked and didn't find similar issue"
+          required: true
+
+  - type: dropdown
+    id: willing-to-submit-pr
+    attributes:
+      label: 🔗 Are you willing to submit PR?
+      description: This is absolutely not required, but we are happy to guide you in the contribution process.
+      options: # Added options key
+        - "Yes, I am willing to submit a PR!"
+        - "No"
+    validations:
+      required: false
+
+
+  - type: checkboxes
+    id: terms
+    attributes:
+      label: 🧑‍⚖️ Code of Conduct
+      description: By submitting this issue, you agree to follow our [Code of Conduct](https://github.com/arc53/DocsGPT/blob/main/CODE_OF_CONDUCT.md)
+      options:
+        - label: I agree to follow this project's Code of Conduct
+          required: true

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,54 @@
+name: 🚀 Feature
+description: "Submit a proposal for a new feature"
+title: "🚀 Feature: "
+labels: [feature]
+body:
+  - type: markdown
+    attributes:
+      value: We value your time and your efforts to submit this bug report is appreciated. 🙏
+  - type: textarea
+    id: feature-description
+    validations:
+      required: true
+    attributes:
+      label: "🔖 Feature description"
+      description: "A clear and concise description of what the feature is."
+      placeholder: "You should add ..."
+  - type: textarea
+    id: pitch
+    validations:
+      required: true
+    attributes:
+      label: "🎤 Why is this feature needed ?"
+      description: "Please explain why this feature should be implemented and how it would be used. Add examples, if applicable."
+      placeholder: "In my use-case, ..."
+  - type: textarea
+    id: solution
+    validations:
+      required: true
+    attributes:
+      label: "✌️ How do you aim to achieve this?"
+      description: "A clear and concise description of what you want to happen."
+      placeholder: "I want this feature to, ..."
+  - type: textarea
+    id: alternative
+    validations:
+      required: false
+    attributes:
+      label: "🔄️ Additional Information"
+      description: "A clear and concise description of any alternative solutions or additional solutions you've considered."
+      placeholder: "I tried, ..."
+  - type: checkboxes
+    id: no-duplicate-issues
+    attributes:
+      label: "👀 Have you spent some time to check if this feature request has been raised before?"
+      options:
+        - label: "I checked and didn't find similar issue"
+          required: true
+  - type: dropdown
+    id: willing-to-submit-pr
+    attributes:
+      label: Are you willing to submit PR?
+      description: This is absolutely not required, but we are happy to guide you in the contribution process.
+      options:
+        - "Yes I am willing to submit a PR!"

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,5 @@
+- **What kind of change does this PR introduce?** (Bug fix, feature, docs update, ...)
+
+- **Why was this change needed?** (You can also link to an open issue here)
+
+- **Other information**:

.github/holopin.yml

@@ -0,0 +1,5 @@
+organization: arc53
+defaultSticker: clqmdf0ed34290glbvqh0kzxd
+stickers:
+  - id: clqmdf0ed34290glbvqh0kzxd
+    alias: festive

.github/labeler.yml

@@ -0,0 +1,23 @@
+repo:
+  - '*'
+
+github:
+  - .github/**/*
+
+application:
+  - application/**/*
+
+docs:
+  - docs/**/*
+
+extensions:
+  - extensions/**/*
+
+frontend:
+  - frontend/**/*
+
+scripts:
+  - scripts/**/*
+
+tests:
+  - tests/**/*

.github/workflows/labeler.yml

@@ -0,0 +1,15 @@
+# https://github.com/actions/labeler
+name: Pull Request Labeler
+on:
+  - pull_request_target
+jobs:
+  triage:
+    permissions:
+      contents: read
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/labeler@v4
+        with:
+          repo-token: "${{ secrets.GITHUB_TOKEN }}"
+          sync-labels: true

.github/workflows/pytest.yml

@@ -1,15 +1,12 @@
 name: Run python tests with pytest
-
 on: [push, pull_request]
-
 jobs:
-  build:
-
+  pytest_and_coverage:
+    name: Run tests and count coverage
     runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: ["3.9", "3.10", "3.11"]
-
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
@@ -19,9 +16,15 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install pytest
+          pip install pytest pytest-cov
           cd application
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-      - name: Test with pytest
+      - name: Test with pytest and generate coverage report
         run: |
-          python -m pytest
+          python -m pytest --cov=application --cov=scripts --cov=extensions --cov-report=xml
+      - name: Upload coverage reports to Codecov
+        if: github.event_name == 'pull_request' && matrix.python-version == '3.11'
+        uses: codecov/codecov-action@v3
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+

.gitignore

@@ -5,7 +5,7 @@ __pycache__/
 
 # C extensions
 *.so
-
+*.next
 # Distribution / packaging
 .Python
 build/
@@ -75,6 +75,7 @@ target/
 
 # Jupyter Notebook
 .ipynb_checkpoints
+**/*.ipynb
 
 # IPython
 profile_default/
@@ -169,4 +170,7 @@ application/vectors/
 
 **/yarn.lock
 
-node_modules/
+node_modules/
+.vscode/settings.json
+models/
+model/

CODE_OF_CONDUCT.md

@@ -2,58 +2,58 @@
 
 ## 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
+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
+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.
+diverse, inclusive and a healthy community.
 
 ## Our Standards
 
-Examples of behavior that contributes to a positive environment for our
+Examples of behavior that contribute 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
+## Demonstrating empathy and kindness towards other people
+1. Being respectful and open to differing opinions, viewpoints, and experiences
+2. Giving and gracefully accepting constructive feedback
+3. Taking accountability and offering apologies to those who have been impacted by our errors,
+  while also gaining insights from the situation
+4. Focusing on what is best not just for us as individuals but for the
+  community as a whole
 
 Examples of unacceptable behavior include:
 
-* The use of sexualized language or imagery, and sexual attention or
+1. 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
+2. Trolling, insulting or derogatory comments, and personal or political attacks
+3. Public or private harassment
+4. Publishing other's private information, such as a physical or email
   address, without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
+5. 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,
+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
+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
+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
+posting via an official social media account or acting as an appointed
 representative at an online or offline event.
 
 ## Enforcement
@@ -63,29 +63,27 @@ reported to the community leaders responsible for enforcement at
 contact@arc53.com.
 All complaints will be reviewed and investigated promptly and fairly.
 
-All community leaders are obligated to respect the privacy and security of the
+All community leaders are obligated to be respectful towards 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:
+the consequences for any action that 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 space.
 
-**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
+* **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
+* **Community Impact**: A violation through a single incident or series
 of actions.
 
-**Consequence**: A warning with consequences for continued behavior. No
+* **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
@@ -93,23 +91,21 @@ 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
+* **Community Impact**: A serious violation of community standards, including
 sustained inappropriate behavior.
 
-**Consequence**: A temporary ban from any sort of interaction or public
+* **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 towards or disparagement of classes of individuals.
 
-**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
+* **Consequence**: A permanent ban from any sort of public interaction within
 the community.
 
 ## Attribution

CONTRIBUTING.md

@@ -1,38 +1,128 @@
-# Welcome to DocsGPT Contributing guideline
+# Welcome to DocsGPT Contributing Guidelines
 
-Thank you for choosing this project to contribute to, we are all very grateful!
+Thank you for choosing to contribute to DocsGPT! We are all very grateful! 
 
 # We accept different types of contributions
 
-📣 Discussions - where you can start a new topic or answer some questions
+📣 **Discussions** - Engage in conversations, start new topics, or help answer questions.
 
-🐞 Issues - Is how we track tasks, sometimes its bugs that need fixing, sometimes its new features
+🐞 **Issues** - This is where we keep track of tasks. It could be bugs,fixes or suggestions for new features.
 
-🛠️ Pull requests - Is how you can suggest changes to our repository, to work on existing issue or to add new features
+🛠️ **Pull requests** - Suggest changes to our repository, either by working on existing issues or adding new features.
 
-📚 Wiki - where we have our documentation
+📚 **Wiki** - This is where our documentation resides.
 
 
 ## 🐞 Issues and Pull requests
 
-We value contributions to our issues in form of discussion or suggestion, we recommend that you check out existing issues and our [Roadmap](https://github.com/orgs/arc53/projects/2)
-
-If you want to contribute by writing code there are few things that you should know before doing it:
-We have frontend (React, Vite) and Backend (python)
-
-### If you are looking to contribute to Frontend (⚛️React, Vite):
-Current frontend is being migrated from /application to /frontend with a new design, so please contribute to the new on. Check out this [Milestone](https://github.com/arc53/DocsGPT/milestone/1) and its issues also [Figma](https://www.figma.com/file/OXLtrl1EAy885to6S69554/DocsGPT?node-id=0%3A1&t=hjWVuxRg9yi5YkJ9-1)
-Please try to follow guidelines
+- We value contributions in the form of discussions or suggestions. We recommend taking a look at existing issues and our [roadmap](https://github.com/orgs/arc53/projects/2).
 
 
-### If you are looking to contribute to Backend (🐍Python):
-Check out our issues, and contribute to /application or /scripts (ignore old  ingest_rst.py ingest_rst_sphinx.py files, they will be deprecated soon)
-Currently we don't have any tests(which would be useful😉) but before submitting you PR make sure that after you ingested some test data its queryable
+- If you're interested in contributing code, here are some important things to know:
 
-### Workflow:
-Create a fork, make changes on your forked repository, submit changes in a form of pull request
+- We have a frontend built on React (Vite) and a backend in Python.
+=======
+Before creating issues, please check out how the latest version of our app looks and works by launching it via [Quickstart](https://github.com/arc53/DocsGPT#quickstart) the version on our live demo is slightly modified with login. Your issues should relate to the version that you can launch via [Quickstart](https://github.com/arc53/DocsGPT#quickstart).
 
-## Questions / collaboration
-Please join our [Discord](https://discord.gg/n5BX8dh8rU) don't hesitate, we are very friendly and welcoming to new contributors.
+### 👨‍💻 If you're interested in contributing code, here are some important things to know:
 
-# Thank you so much for considering to contribute to DocsGPT!🙏
+
+Tech Stack Overview:
+
+- 🌐 Frontend: Built with React (Vite) ⚛️,
+
+- 🖥 Backend: Developed in Python 🐍
+
+### 🌐 If you are looking to contribute to frontend (⚛️React, Vite):
+
+- The current frontend is being migrated from [`/application`](https://github.com/arc53/DocsGPT/tree/main/application) to [`/frontend`](https://github.com/arc53/DocsGPT/tree/main/frontend) with a new design, so please contribute to the new one.
+- Check out this [milestone](https://github.com/arc53/DocsGPT/milestone/1) and its issues.
+- The updated Figma design can be found [here](https://www.figma.com/file/OXLtrl1EAy885to6S69554/DocsGPT?node-id=0%3A1&t=hjWVuxRg9yi5YkJ9-1).
+
+Please try to follow the guidelines.
+
+### 🖥 If you are looking to contribute to Backend (🐍 Python):
+
+- Review our issues and contribute to [`/application`](https://github.com/arc53/DocsGPT/tree/main/application) or [`/scripts`](https://github.com/arc53/DocsGPT/tree/main/scripts) (please disregard old [`ingest_rst.py`](https://github.com/arc53/DocsGPT/blob/main/scripts/old/ingest_rst.py) [`ingest_rst_sphinx.py`](https://github.com/arc53/DocsGPT/blob/main/scripts/old/ingest_rst_sphinx.py) files; they will be deprecated soon).
+- All new code should be covered with unit tests ([pytest](https://github.com/pytest-dev/pytest)). Please find tests under [`/tests`](https://github.com/arc53/DocsGPT/tree/main/tests) folder.
+- Before submitting your Pull Request, ensure it can be queried after ingesting some test data.
+  
+### Testing
+
+To run unit tests from the root of the repository, execute:
+```
+python -m pytest
+```
+
+## Workflow 📈
+
+Here's a step-by-step guide on how to contribute to DocsGPT:
+
+1. **Fork the Repository:**
+   - Click the "Fork" button at the top-right of this repository to create your fork.
+
+2. **Clone the Forked Repository:**
+   - Clone the repository using:
+      ``` shell
+      git clone https://github.com/<your-github-username>/DocsGPT.git
+      ```
+
+3. **Keep your Fork in Sync:**
+   - Before you make any changes, make sure that your fork is in sync to avoid merge conflicts using:
+     ```shell
+     git remote add upstream https://github.com/arc53/DocsGPT.git
+     git pull upstream main
+     ```
+
+4. **Create and Switch to a New Branch:**
+   - Create a new branch for your contribution using:
+     ```shell
+     git checkout -b your-branch-name
+     ```
+
+5. **Make Changes:**
+   - Make the required changes in your branch.
+
+6. **Add Changes to the Staging Area:**
+   - Add your changes to the staging area using:
+     ```shell
+     git add .
+     ```
+
+7. **Commit Your Changes:**
+   - Commit your changes with a descriptive commit message using:
+     ```shell
+     git commit -m "Your descriptive commit message"
+     ```
+
+8. **Push Your Changes to the Remote Repository:**
+   - Push your branch with changes to your fork on GitHub using:
+     ```shell
+     git push origin your-branch-name
+     ```
+
+9. **Submit a Pull Request (PR):**
+   - Create a Pull Request from your branch to the main repository. Make sure to include a detailed description of your changes and reference any related issues.
+
+10. **Collaborate:**
+   - Be responsive to comments and feedback on your PR.
+   - Make necessary updates as suggested.
+   - Once your PR is approved, it will be merged into the main repository.
+
+11. **Testing:**
+   - Before submitting a Pull Request, ensure your code passes all unit tests.
+   - To run unit tests from the root of the repository, execute:
+     ```shell
+     python -m pytest
+     ```
+
+*Note: You should run the unit test only after making the changes to the backend code.*
+
+12. **Questions and Collaboration:**
+    - Feel free to join our Discord. We're very friendly and welcoming to new contributors, so don't hesitate to reach out.
+
+Thank you for considering contributing to DocsGPT! 🙏
+
+## Questions/collaboration
+Feel free to join our [Discord](https://discord.gg/n5BX8dh8rU). We're very friendly and welcoming to new contributors, so don't hesitate to reach out.
+# Thank you so much for considering to contribute DocsGPT!🙏

README.md

@@ -7,130 +7,194 @@
 </p>
 
 <p align="left">
-  <strong>DocsGPT</strong> is a cutting-edge open-source solution that streamlines the process of finding information in project documentation. With its integration of the powerful <strong>GPT</strong> models, developers can easily ask questions about a project and receive accurate answers.
+  <strong><a href="https://docsgpt.arc53.com/">DocsGPT</a></strong> is a cutting-edge open-source solution that streamlines the process of finding information in the project documentation. With its integration of the powerful <strong>GPT</strong> models, developers can easily ask questions about a project and receive accurate answers.
   
-Say goodbye to time-consuming manual searches, and let <strong>DocsGPT</strong> help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.
+Say goodbye to time-consuming manual searches, and let <strong><a href="https://docsgpt.arc53.com/">DocsGPT</a></strong> help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.
 </p>
 
 <div align="center">
   
-  <a href="https://discord.gg/n5BX8dh8rU">![example1](https://img.shields.io/github/stars/arc53/docsgpt?style=social)</a>
-  <a href="https://discord.gg/n5BX8dh8rU">![example2](https://img.shields.io/github/forks/arc53/docsgpt?style=social)</a>
-  <a href="https://discord.gg/n5BX8dh8rU">![example3](https://img.shields.io/github/license/arc53/docsgpt)</a>
-  <a href="https://discord.gg/n5BX8dh8rU">![example3](https://img.shields.io/discord/1070046503302877216)</a>
-  
+  <a href="https://github.com/arc53/DocsGPT">![link to main GitHub showing Stars number](https://img.shields.io/github/stars/arc53/docsgpt?style=social)</a>
+  <a href="https://github.com/arc53/DocsGPT">![link to main GitHub showing Forks number](https://img.shields.io/github/forks/arc53/docsgpt?style=social)</a>
+  <a href="https://github.com/arc53/DocsGPT/blob/main/LICENSE">![link to license file](https://img.shields.io/github/license/arc53/docsgpt)</a>
+  <a href="https://discord.gg/n5BX8dh8rU">![link to discord](https://img.shields.io/discord/1070046503302877216)</a>
+  <a href="https://twitter.com/docsgptai">![X (formerly Twitter) URL](https://img.shields.io/twitter/follow/docsgptai)</a>
+
+ 
 </div>
 
+### Production Support / Help for Companies:
+
+We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
+
+- [Book Demo :wave:](https://airtable.com/appdeaL0F1qV8Bl2C/shrrJF1Ll7btCJRbP)
+- [Send Email :email:](mailto:contact@arc53.com?subject=DocsGPT%20support%2Fsolutions)
+
 ![video-example-of-docs-gpt](https://d3dg1063dc54p9.cloudfront.net/videos/demov3.gif)
 
 ## Roadmap
 
-You can find our [Roadmap](https://github.com/orgs/arc53/projects/2) here, please don't hesitate contributing or creating issues, it helps us make DocsGPT better!
+You can find our roadmap [here](https://github.com/orgs/arc53/projects/2). Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!
 
-## Our open source models optimised for DocsGPT:
+## Our Open-Source Models Optimized for DocsGPT:
 
-| Name              | Base Model | Requirements (or similar)                        |
-|-------------------|------------|----------------------------------------------------------|
-| [Docsgpt-7b-falcon](https://huggingface.co/Arc53/docsgpt-7b-falcon)  | Falcon-7b  |  1xA10G gpu   |
-| [Docsgpt-14b](https://huggingface.co/Arc53/docsgpt-14b)              | llama-2-14b    | 2xA10 gpu's   |
-| [Docsgpt-40b](https://huggingface.co/Arc53/docsgpt-40b-falcon)       | falcon-40b     | 8xA10G gpu's  |
-
-
-If you don't have enough resources to run it you can use bitsnbytes to quantize
+| Name                                                                  | Base Model  | Requirements (or similar) |
+| --------------------------------------------------------------------- | ----------- | ------------------------- |
+| [Docsgpt-7b-mistral](https://huggingface.co/Arc53/docsgpt-7b-mistral)   | Mistral-7b   | 1xA10G gpu                |
+| [Docsgpt-14b](https://huggingface.co/Arc53/docsgpt-14b)               | llama-2-14b | 2xA10 gpu's               |
+| [Docsgpt-40b-falcon](https://huggingface.co/Arc53/docsgpt-40b-falcon) | falcon-40b  | 8xA10G gpu's              |
 
+If you don't have enough resources to run it, you can use bitsnbytes to quantize.
 
 ## Features
 
-![Group 9](https://user-images.githubusercontent.com/17906039/220427472-2644cff4-7666-46a5-819f-fc4a521f63c7.png)
+![Main features of DocsGPT showcasing six main features](https://user-images.githubusercontent.com/17906039/220427472-2644cff4-7666-46a5-819f-fc4a521f63c7.png)
 
+## Useful Links
 
-## Useful links
- [Live preview](https://docsgpt.arc53.com/)
- 
- [Join Our Discord](https://discord.gg/n5BX8dh8rU)
- 
- [Guides](https://github.com/arc53/docsgpt/wiki)
+- :mag: :fire: [Live preview](https://docsgpt.arc53.com/)
 
- [Interested in contributing?](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md)
+- :speech_balloon: :tada: [Join our Discord](https://discord.gg/n5BX8dh8rU)
 
- [How to use any other documentation](https://github.com/arc53/docsgpt/wiki/How-to-train-on-other-documentation)
+- :books: :sunglasses: [Guides](https://docs.docsgpt.co.uk/)
 
- [How to host it locally (so all data will stay on-premises)](https://github.com/arc53/DocsGPT/wiki/How-to-use-different-LLM's#hosting-everything-locally)
+- :couple: [Interested in contributing?](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md)
 
+- :file_folder: :rocket: [How to use any other documentation](https://docs.docsgpt.co.uk/Guides/How-to-train-on-other-documentation)
 
-## Project structure
-- Application - Flask app (main application)
+- :house: :closed_lock_with_key: [How to host it locally (so all data will stay on-premises)](https://docs.docsgpt.co.uk/Guides/How-to-use-different-LLM)
 
-- Extensions - Chrome extension
+## Project Structure
 
-- Scripts - Script that creates similarity search index and store for other libraries. 
+- Application - Flask app (main application).
 
-- Frontend - Frontend uses Vite and React
+- Extensions - Chrome extension.
+
+- Scripts - Script that creates similarity search index for other libraries.
+
+- Frontend - Frontend uses <a href="https://vitejs.dev/">Vite</a> and <a href="https://react.dev/">React</a>.
 
 ## QuickStart
 
-Note: Make sure you have docker installed
+> [!Note]
+> Make sure you have [Docker](https://docs.docker.com/engine/install/) installed
 
-1. Dowload and open this repository with `git clone https://github.com/arc53/DocsGPT.git`
-2. Create an .env file in your root directory and set the env variable OPENAI_API_KEY with your openai api key and  VITE_API_STREAMING to true or false, depending on if you want streaming answers or not
+On Mac OS or Linux, write:
+
+`./setup.sh`
+
+It will install all the dependencies and allow you to download the local model, use OpenAI or use our LLM API.
+
+Otherwise, refer to this Guide:
+
+1. Download and open this repository with `git clone https://github.com/arc53/DocsGPT.git`
+2. Create a `.env` file in your root directory and set the env variables and `VITE_API_STREAMING` to true or false, depending on whether you want streaming answers or not.
    It should look like this inside:
-   
+
    ```
-   OPENAI_API_KEY=Yourkey
+   LLM_NAME=[docsgpt or openai or others] 
    VITE_API_STREAMING=true
+   API_KEY=[if LLM_NAME is openai]
    ```
-3. Run `./run-with-docker-compose.sh`
-4. Navigate to http://localhost:5173/
 
-To stop just run Ctrl + C
+   See optional environment variables in the [/.env-template](https://github.com/arc53/DocsGPT/blob/main/.env-template) and [/application/.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) files.
 
-## Development environments
+3. Run [./run-with-docker-compose.sh](https://github.com/arc53/DocsGPT/blob/main/run-with-docker-compose.sh).
+4. Navigate to http://localhost:5173/.
 
-### Spin up mongo and redis
-For development only 2 containers are used from docker-compose.yaml (by deleting all services except for redis and mongo). 
+To stop, just run `Ctrl + C`.
+
+## Development Environments
+
+### Spin up Mongo and Redis
+
+For development, only two containers are used from [docker-compose.yaml](https://github.com/arc53/DocsGPT/blob/main/docker-compose.yaml) (by deleting all services except for Redis and Mongo).
 See file [docker-compose-dev.yaml](./docker-compose-dev.yaml).
 
 Run
+
 ```
 docker compose -f docker-compose-dev.yaml build
 docker compose -f docker-compose-dev.yaml up -d
 ```
 
-### Run the backend
+### Run the Backend
 
-Make sure you have Python 3.10 or 3.11 installed.
+> [!Note]
+> Make sure you have Python 3.10 or 3.11 installed.
+
+1. Export required environment variables or prepare a `.env` file in the project folder:
+   - Copy [.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) and create `.env`.
+
+(check out [`application/core/settings.py`](application/core/settings.py) if you want to see more config options.)
+
+2. (optional) Create a Python virtual environment:
+   You can follow the [Python official documentation](https://docs.python.org/3/tutorial/venv.html) for virtual environments.
+
+a) On Mac OS and Linux
 
-1. Export required environment variables
-```commandline
-export CELERY_BROKER_URL=redis://localhost:6379/0   
-export CELERY_RESULT_BACKEND=redis://localhost:6379/1
-export MONGO_URI=mongodb://localhost:27017/docsgpt
-```
-2. Prepare .env file
-Copy `.env_sample` and create `.env` with your OpenAI API token
-3. (optional) Create a python virtual environment
 ```commandline
 python -m venv venv
 . venv/bin/activate
 ```
-4. Change to `application/` subdir and install dependencies for the backend
+
+b) On Windows
+
 ```commandline
-cd application/ 
-pip install -r requirements.txt
+python -m venv venv
+ venv/Scripts/activate
 ```
-5. Run the app `python wsgi.py`
-6. Start worker with `celery -A app.celery worker -l INFO`
 
-### Start frontend 
-Make sure you have Node version 16 or higher.
+3. Download embedding model and save it in the `model/` folder:
+You can use the script below, or download it manually from [here](https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip), unzip it and save it in the `model/` folder.
 
-1. Navigate to `/frontend` folder
-2. Install dependencies
-`npm install`
-3. Run the app 
-`npm run dev`
+```commandline
+wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
+unzip mpnet-base-v2.zip -d model
+rm mpnet-base-v2.zip
+```
 
+4. Install dependencies for the backend:
 
+```commandline
+pip install -r application/requirements.txt
+```
 
-Built with [🦜️🔗 LangChain](https://github.com/hwchase17/langchain)
+5. Run the app using `flask --app application/app.py run --host=0.0.0.0 --port=7091`.
+6. Start worker with `celery -A application.app.celery worker -l INFO`.
 
+### Start Frontend
+
+> [!Note]
+> Make sure you have Node version 16 or higher.
+
+1. Navigate to the [/frontend](https://github.com/arc53/DocsGPT/tree/main/frontend) folder.
+2. Install the required packages `husky` and `vite` (ignore if already installed).
+
+```commandline
+npm install husky -g
+npm install vite -g
+```
+
+3. Install dependencies by running `npm install --include=dev`.
+4. Run the app using `npm run dev`.
+
+## Contributing
+
+Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for information about how to get involved. We welcome issues, questions, and pull requests.
+
+## Code Of Conduct
+
+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. Please refer to the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file for more information about contributing.
+
+## Many Thanks To Our Contributors⚡
+
+<a href="https://github.com/arc53/DocsGPT/graphs/contributors" alt="View Contributors">
+  <img src="https://contrib.rocks/image?repo=arc53/DocsGPT" alt="Contributors" />
+</a>
+
+## License
+
+The source code license is [MIT](https://opensource.org/license/mit/), as described in the [LICENSE](LICENSE) file.
+
+Built with [:bird: :link: LangChain](https://github.com/hwchase17/langchain)

SECURITY.md

@@ -0,0 +1,14 @@
+# Security Policy
+
+## Supported Versions
+
+Supported Versions:
+
+Currently, we support security patches by committing changes and bumping the version published on Github.
+
+## Reporting a Vulnerability
+
+Found a vulnerability? Please email us:
+
+security@arc53.com
+

application/.env_sample

@@ -1,9 +1,8 @@
 API_KEY=your_api_key
 EMBEDDINGS_KEY=your_api_key
-CELERY_BROKER_URL=redis://localhost:6379/0
-CELERY_RESULT_BACKEND=redis://localhost:6379/1
-MONGO_URI=mongodb://localhost:27017/docsgpt
 API_URL=http://localhost:7091
+FLASK_APP=application/app.py
+FLASK_DEBUG=true
 
 #For OPENAI on Azure
 OPENAI_API_BASE=

application/Dockerfile

@@ -1,19 +1,27 @@
-FROM python:3.10-slim-bullseye as builder
+FROM python:3.11-slim-bullseye as builder
 
 # Tiktoken requires Rust toolchain, so build it in a separate stage
 RUN apt-get update && apt-get install -y gcc curl
+RUN apt-get install -y wget unzip
+RUN wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
+RUN unzip mpnet-base-v2.zip -d model
+RUN rm mpnet-base-v2.zip
 RUN curl https://sh.rustup.rs -sSf | sh -s -- -y && apt-get install --reinstall libc6-dev -y
 ENV PATH="/root/.cargo/bin:${PATH}"
-RUN pip install --upgrade pip && pip install tiktoken==0.3.3
+RUN pip install --upgrade pip && pip install tiktoken==0.5.2
 COPY requirements.txt .
 RUN pip install -r requirements.txt
 
-FROM python:3.10-slim-bullseye
+
+
+FROM python:3.11-slim-bullseye
 
 # Copy pre-built packages and binaries from builder stage
 COPY --from=builder /usr/local/ /usr/local/
 
 WORKDIR /app
+COPY --from=builder /model /app/model
+
 COPY . /app/application
 ENV FLASK_APP=app.py
 ENV FLASK_DEBUG=true

application/api/answer/routes.py

@@ -0,0 +1,389 @@
+import asyncio
+import os
+from flask import Blueprint, request, Response
+import json
+import datetime
+import logging
+import traceback
+
+from pymongo import MongoClient
+from bson.objectid import ObjectId
+
+
+from application.core.settings import settings
+from application.llm.llm_creator import LLMCreator
+from application.retriever.retriever_creator import RetrieverCreator
+from application.error import bad_request
+
+
+logger = logging.getLogger(__name__)
+
+mongo = MongoClient(settings.MONGO_URI)
+db = mongo["docsgpt"]
+conversations_collection = db["conversations"]
+vectors_collection = db["vectors"]
+prompts_collection = db["prompts"]
+api_key_collection = db["api_keys"]
+answer = Blueprint("answer", __name__)
+
+gpt_model = ""
+# to have some kind of default behaviour
+if settings.LLM_NAME == "openai":
+    gpt_model = "gpt-3.5-turbo"
+elif settings.LLM_NAME == "anthropic":
+    gpt_model = "claude-2"
+
+if settings.MODEL_NAME:  # in case there is particular model name configured
+    gpt_model = settings.MODEL_NAME
+
+# load the prompts
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
+with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
+    chat_combine_template = f.read()
+
+with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") as f:
+    chat_reduce_template = f.read()
+
+with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
+    chat_combine_creative = f.read()
+
+with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
+    chat_combine_strict = f.read()
+
+api_key_set = settings.API_KEY is not None
+embeddings_key_set = settings.EMBEDDINGS_KEY is not None
+
+
+async def async_generate(chain, question, chat_history):
+    result = await chain.arun({"question": question, "chat_history": chat_history})
+    return result
+
+
+def run_async_chain(chain, question, chat_history):
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    result = {}
+    try:
+        answer = loop.run_until_complete(async_generate(chain, question, chat_history))
+    finally:
+        loop.close()
+    result["answer"] = answer
+    return result
+
+
+def get_data_from_api_key(api_key):
+    data = api_key_collection.find_one({"key": api_key})
+    if data is None:
+        return bad_request(401, "Invalid API key")
+    return data
+
+
+def get_vectorstore(data):
+    if "active_docs" in data:
+        if data["active_docs"].split("/")[0] == "default":
+            vectorstore = ""
+        elif data["active_docs"].split("/")[0] == "local":
+            vectorstore = "indexes/" + data["active_docs"]
+        else:
+            vectorstore = "vectors/" + data["active_docs"]
+        if data["active_docs"] == "default":
+            vectorstore = ""
+    else:
+        vectorstore = ""
+    vectorstore = os.path.join("application", vectorstore)
+    return vectorstore
+
+
+def is_azure_configured():
+    return (
+        settings.OPENAI_API_BASE
+        and settings.OPENAI_API_VERSION
+        and settings.AZURE_DEPLOYMENT_NAME
+    )
+
+
+def save_conversation(conversation_id, question, response, source_log_docs, llm):
+    if conversation_id is not None and conversation_id != "None":
+        conversations_collection.update_one(
+            {"_id": ObjectId(conversation_id)},
+            {
+                "$push": {
+                    "queries": {
+                        "prompt": question,
+                        "response": response,
+                        "sources": source_log_docs,
+                    }
+                }
+            },
+        )
+
+    else:
+        # create new conversation
+        # generate summary
+        messages_summary = [
+            {
+                "role": "assistant",
+                "content": "Summarise following conversation in no more than 3 "
+                "words, respond ONLY with the summary, use the same "
+                "language as the system \n\nUser: "
+                + question
+                + "\n\n"
+                + "AI: "
+                + response,
+            },
+            {
+                "role": "user",
+                "content": "Summarise following conversation in no more than 3 words, "
+                "respond ONLY with the summary, use the same language as the "
+                "system",
+            },
+        ]
+
+        completion = llm.gen(model=gpt_model, messages=messages_summary, max_tokens=30)
+        conversation_id = conversations_collection.insert_one(
+            {
+                "user": "local",
+                "date": datetime.datetime.utcnow(),
+                "name": completion,
+                "queries": [
+                    {
+                        "prompt": question,
+                        "response": response,
+                        "sources": source_log_docs,
+                    }
+                ],
+            }
+        ).inserted_id
+    return conversation_id
+
+
+def get_prompt(prompt_id):
+    if prompt_id == "default":
+        prompt = chat_combine_template
+    elif prompt_id == "creative":
+        prompt = chat_combine_creative
+    elif prompt_id == "strict":
+        prompt = chat_combine_strict
+    else:
+        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
+    return prompt
+
+
+def complete_stream(question, retriever, conversation_id, user_api_key):
+
+    response_full = ""
+    source_log_docs = []
+    answer = retriever.gen()
+    for line in answer:
+        if "answer" in line:
+            response_full += str(line["answer"])
+            data = json.dumps(line)
+            yield f"data: {data}\n\n"
+        elif "source" in line:
+            source_log_docs.append(line["source"])
+
+    llm = LLMCreator.create_llm(
+        settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
+    )
+    conversation_id = save_conversation(
+        conversation_id, question, response_full, source_log_docs, llm
+    )
+
+    # send data.type = "end" to indicate that the stream has ended as json
+    data = json.dumps({"type": "id", "id": str(conversation_id)})
+    yield f"data: {data}\n\n"
+    data = json.dumps({"type": "end"})
+    yield f"data: {data}\n\n"
+
+
+@answer.route("/stream", methods=["POST"])
+def stream():
+    data = request.get_json()
+    # get parameter from url question
+    question = data["question"]
+    if "history" not in data:
+        history = []
+    else:
+        history = data["history"]
+        history = json.loads(history)
+    if "conversation_id" not in data:
+        conversation_id = None
+    else:
+        conversation_id = data["conversation_id"]
+    if "prompt_id" in data:
+        prompt_id = data["prompt_id"]
+    else:
+        prompt_id = "default"
+    if "selectedDocs" in data and data["selectedDocs"] is None:
+        chunks = 0
+    elif "chunks" in data:
+        chunks = int(data["chunks"])
+    else:
+        chunks = 2
+
+    prompt = get_prompt(prompt_id)
+
+    # check if active_docs is set
+
+    if "api_key" in data:
+        data_key = get_data_from_api_key(data["api_key"])
+        source = {"active_docs": data_key["source"]}
+        user_api_key = data["api_key"]
+    elif "active_docs" in data:
+        source = {"active_docs": data["active_docs"]}
+        user_api_key = None
+    else:
+        source = {}
+        user_api_key = None
+
+    if (
+        source["active_docs"].split("/")[0] == "default"
+        or source["active_docs"].split("/")[0] == "local"
+    ):
+        retriever_name = "classic"
+    else:
+        retriever_name = source["active_docs"]
+
+    retriever = RetrieverCreator.create_retriever(
+        retriever_name,
+        question=question,
+        source=source,
+        chat_history=history,
+        prompt=prompt,
+        chunks=chunks,
+        gpt_model=gpt_model,
+        user_api_key=user_api_key,
+    )
+
+    return Response(
+        complete_stream(
+            question=question,
+            retriever=retriever,
+            conversation_id=conversation_id,
+            user_api_key=user_api_key,
+        ),
+        mimetype="text/event-stream",
+    )
+
+
+@answer.route("/api/answer", methods=["POST"])
+def api_answer():
+    data = request.get_json()
+    question = data["question"]
+    if "history" not in data:
+        history = []
+    else:
+        history = data["history"]
+    if "conversation_id" not in data:
+        conversation_id = None
+    else:
+        conversation_id = data["conversation_id"]
+    print("-" * 5)
+    if "prompt_id" in data:
+        prompt_id = data["prompt_id"]
+    else:
+        prompt_id = "default"
+    if "chunks" in data:
+        chunks = int(data["chunks"])
+    else:
+        chunks = 2
+
+    prompt = get_prompt(prompt_id)
+
+    # use try and except  to check for exception
+    try:
+        # check if the vectorstore is set
+        if "api_key" in data:
+            data_key = get_data_from_api_key(data["api_key"])
+            source = {"active_docs": data_key["source"]}
+            user_api_key = data["api_key"]
+        else:
+            source = {data}
+            user_api_key = None
+
+        if (
+            source["active_docs"].split("/")[0] == "default"
+            or source["active_docs"].split("/")[0] == "local"
+        ):
+            retriever_name = "classic"
+        else:
+            retriever_name = source["active_docs"]
+
+        retriever = RetrieverCreator.create_retriever(
+            retriever_name,
+            question=question,
+            source=source,
+            chat_history=history,
+            prompt=prompt,
+            chunks=chunks,
+            gpt_model=gpt_model,
+            user_api_key=user_api_key,
+        )
+        source_log_docs = []
+        response_full = ""
+        for line in retriever.gen():
+            if "source" in line:
+                source_log_docs.append(line["source"])
+            elif "answer" in line:
+                response_full += line["answer"]
+
+        llm = LLMCreator.create_llm(
+            settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
+        )
+
+        result = {"answer": response_full, "sources": source_log_docs}
+        result["conversation_id"] = save_conversation(
+            conversation_id, question, response_full, source_log_docs, llm
+        )
+
+        return result
+    except Exception as e:
+        # print whole traceback
+        traceback.print_exc()
+        print(str(e))
+        return bad_request(500, str(e))
+
+
+@answer.route("/api/search", methods=["POST"])
+def api_search():
+    data = request.get_json()
+    # get parameter from url question
+    question = data["question"]
+
+    if "api_key" in data:
+        data_key = get_data_from_api_key(data["api_key"])
+        source = {"active_docs": data_key["source"]}
+        user_api_key = data["api_key"]
+    elif "active_docs" in data:
+        source = {"active_docs": data["active_docs"]}
+        user_api_key = None
+    else:
+        source = {}
+        user_api_key = None
+    if "chunks" in data:
+        chunks = int(data["chunks"])
+    else:
+        chunks = 2
+
+    if (
+        source["active_docs"].split("/")[0] == "default"
+        or source["active_docs"].split("/")[0] == "local"
+    ):
+        retriever_name = "classic"
+    else:
+        retriever_name = source["active_docs"]
+
+    retriever = RetrieverCreator.create_retriever(
+        retriever_name,
+        question=question,
+        source=source,
+        chat_history=[],
+        prompt="default",
+        chunks=chunks,
+        gpt_model=gpt_model,
+        user_api_key=user_api_key,
+    )
+    docs = retriever.search()
+    return docs

application/api/internal/routes.py

@@ -0,0 +1,69 @@
+import os
+import datetime
+from flask import Blueprint, request, send_from_directory
+from pymongo import MongoClient
+from werkzeug.utils import secure_filename
+
+
+from application.core.settings import settings
+mongo = MongoClient(settings.MONGO_URI)
+db = mongo["docsgpt"]
+conversations_collection = db["conversations"]
+vectors_collection = db["vectors"]
+
+current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+internal = Blueprint('internal', __name__)
+@internal.route("/api/download", methods=["get"])
+def download_file():
+    user = secure_filename(request.args.get("user"))
+    job_name = secure_filename(request.args.get("name"))
+    filename = secure_filename(request.args.get("file"))
+    save_dir = os.path.join(current_dir, settings.UPLOAD_FOLDER, user, job_name)
+    return send_from_directory(save_dir, filename, as_attachment=True)
+
+
+
+@internal.route("/api/upload_index", methods=["POST"])
+def upload_index_files():
+    """Upload two files(index.faiss, index.pkl) to the user's folder."""
+    if "user" not in request.form:
+        return {"status": "no user"}
+    user = secure_filename(request.form["user"])
+    if "name" not in request.form:
+        return {"status": "no name"}
+    job_name = secure_filename(request.form["name"])
+    save_dir = os.path.join(current_dir, "indexes", user, job_name)
+    if settings.VECTOR_STORE == "faiss":
+        if "file_faiss" not in request.files:
+            print("No file part")
+            return {"status": "no file"}
+        file_faiss = request.files["file_faiss"]
+        if file_faiss.filename == "":
+            return {"status": "no file name"}
+        if "file_pkl" not in request.files:
+            print("No file part")
+            return {"status": "no file"}
+        file_pkl = request.files["file_pkl"]
+        if file_pkl.filename == "":
+            return {"status": "no file name"}
+        # saves index files
+        
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+        file_faiss.save(os.path.join(save_dir, "index.faiss"))
+        file_pkl.save(os.path.join(save_dir, "index.pkl"))
+    # create entry in vectors_collection
+    vectors_collection.insert_one(
+        {
+            "user": user,
+            "name": job_name,
+            "language": job_name,
+            "location": save_dir,
+            "date": datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
+            "model": settings.EMBEDDINGS_NAME,
+            "type": "local",
+        }
+    )
+    return {"status": "ok"}

application/api/user/routes.py

@@ -0,0 +1,446 @@
+import os
+import uuid
+import shutil
+from flask import Blueprint, request, jsonify
+from urllib.parse import urlparse
+import requests
+from pymongo import MongoClient
+from bson.objectid import ObjectId
+from werkzeug.utils import secure_filename
+
+from application.api.user.tasks import ingest, ingest_remote
+
+from application.core.settings import settings
+from application.vectorstore.vector_creator import VectorCreator
+
+mongo = MongoClient(settings.MONGO_URI)
+db = mongo["docsgpt"]
+conversations_collection = db["conversations"]
+vectors_collection = db["vectors"]
+prompts_collection = db["prompts"]
+feedback_collection = db["feedback"]
+api_key_collection = db["api_keys"]
+user = Blueprint('user', __name__)
+
+current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+@user.route("/api/delete_conversation", methods=["POST"])
+def delete_conversation():
+    # deletes a conversation from the database
+    conversation_id = request.args.get("id")
+    # write to mongodb
+    conversations_collection.delete_one(
+        {
+            "_id": ObjectId(conversation_id),
+        }
+    )
+
+    return {"status": "ok"}
+
+@user.route("/api/get_conversations", methods=["get"])
+def get_conversations():
+    # provides a list of conversations
+    conversations = conversations_collection.find().sort("date", -1).limit(30)
+    list_conversations = []
+    for conversation in conversations:
+        list_conversations.append({"id": str(conversation["_id"]), "name": conversation["name"]})
+
+    #list_conversations = [{"id": "default", "name": "default"}, {"id": "jeff", "name": "jeff"}]
+
+    return jsonify(list_conversations)
+
+
+@user.route("/api/get_single_conversation", methods=["get"])
+def get_single_conversation():
+    # provides data for a conversation
+    conversation_id = request.args.get("id")
+    conversation = conversations_collection.find_one({"_id": ObjectId(conversation_id)})
+    return jsonify(conversation['queries'])
+
+@user.route("/api/update_conversation_name", methods=["POST"])
+def update_conversation_name():
+    # update data for a conversation
+    data = request.get_json()
+    id = data["id"]
+    name = data["name"]
+    conversations_collection.update_one({"_id": ObjectId(id)},{"$set":{"name":name}})
+    return {"status": "ok"}
+
+
+@user.route("/api/feedback", methods=["POST"])
+def api_feedback():
+    data = request.get_json()
+    question = data["question"]
+    answer = data["answer"]
+    feedback = data["feedback"]
+
+
+    feedback_collection.insert_one(
+        {
+            "question": question,
+            "answer": answer,
+            "feedback": feedback,
+        }
+    )
+    return {"status": "ok"}
+
+@user.route("/api/delete_by_ids", methods=["get"])
+def delete_by_ids():
+    """Delete by ID. These are the IDs in the vectorstore"""
+
+    ids = request.args.get("path")
+    if not ids:
+        return {"status": "error"}
+
+    if settings.VECTOR_STORE == "faiss":
+        result = vectors_collection.delete_index(ids=ids)
+        if result:
+            return {"status": "ok"}
+    return {"status": "error"}
+
+@user.route("/api/delete_old", methods=["get"])
+def delete_old():
+    """Delete old indexes."""
+    import shutil
+
+    path = request.args.get("path")
+    dirs = path.split("/")
+    dirs_clean = []
+    for i in range(0, len(dirs)):
+        dirs_clean.append(secure_filename(dirs[i]))
+    # check that path strats with indexes or vectors
+
+    if dirs_clean[0] not in ["indexes", "vectors"]:
+        return {"status": "error"}
+    path_clean = "/".join(dirs_clean)
+    vectors_collection.delete_one({"name": dirs_clean[-1], 'user': dirs_clean[-2]})
+    if settings.VECTOR_STORE == "faiss":
+        try:
+            shutil.rmtree(os.path.join(current_dir, path_clean))
+        except FileNotFoundError:
+            pass
+    else:
+        vetorstore = VectorCreator.create_vectorstore(
+            settings.VECTOR_STORE, path=os.path.join(current_dir, path_clean)
+        )
+        vetorstore.delete_index()
+        
+    return {"status": "ok"}
+
+@user.route("/api/upload", methods=["POST"])
+def upload_file():
+    """Upload a file to get vectorized and indexed."""
+    if "user" not in request.form:
+        return {"status": "no user"}
+    user = secure_filename(request.form["user"])
+    if "name" not in request.form:
+        return {"status": "no name"}
+    job_name = secure_filename(request.form["name"])
+    # check if the post request has the file part
+    files = request.files.getlist("file")
+        
+    if not files or all(file.filename == '' for file in files):
+        return {"status": "no file name"}
+
+    # Directory where files will be saved
+    save_dir = os.path.join(current_dir, settings.UPLOAD_FOLDER, user, job_name)
+    os.makedirs(save_dir, exist_ok=True)
+    
+    if len(files) > 1:
+        # Multiple files; prepare them for zip
+        temp_dir = os.path.join(save_dir, "temp")
+        os.makedirs(temp_dir, exist_ok=True)
+        
+        for file in files:
+            filename = secure_filename(file.filename)
+            file.save(os.path.join(temp_dir, filename))
+        
+        # Use shutil.make_archive to zip the temp directory
+        zip_path = shutil.make_archive(base_name=os.path.join(save_dir, job_name), format='zip', root_dir=temp_dir)
+        final_filename = os.path.basename(zip_path)
+        
+        # Clean up the temporary directory after zipping
+        shutil.rmtree(temp_dir)
+    else:
+        # Single file
+        file = files[0]
+        final_filename = secure_filename(file.filename)
+        file_path = os.path.join(save_dir, final_filename)
+        file.save(file_path)
+    
+    # Call ingest with the single file or zipped file
+    task = ingest.delay(settings.UPLOAD_FOLDER, [".rst", ".md", ".pdf", ".txt", ".docx", 
+    ".csv", ".epub", ".html", ".mdx"],
+    job_name, final_filename, user)
+    
+    return {"status": "ok", "task_id": task.id}
+    
+@user.route("/api/remote", methods=["POST"])
+def upload_remote():
+    """Upload a remote source to get vectorized and indexed."""
+    if "user" not in request.form:
+        return {"status": "no user"}
+    user = secure_filename(request.form["user"])
+    if "source" not in request.form:
+        return {"status": "no source"}
+    source = secure_filename(request.form["source"])
+    if "name" not in request.form:
+        return {"status": "no name"}
+    job_name = secure_filename(request.form["name"])
+    # check if the post request has the file part
+    if "data" not in request.form:
+        print("No data")
+        return {"status": "no data"}
+    source_data = request.form["data"]
+
+    if source_data:
+        task = ingest_remote.delay(source_data=source_data, job_name=job_name, user=user, loader=source)
+        # task id
+        task_id = task.id
+        return {"status": "ok", "task_id": task_id}
+    else:
+        return {"status": "error"}
+
+@user.route("/api/task_status", methods=["GET"])
+def task_status():
+    """Get celery job status."""
+    task_id = request.args.get("task_id")
+    from application.celery import celery
+    task = celery.AsyncResult(task_id)
+    task_meta = task.info
+    return {"status": task.status, "result": task_meta}
+
+
+@user.route("/api/combine", methods=["GET"])
+def combined_json():
+    user = "local"
+    """Provide json file with combined available indexes."""
+    # get json from https://d3dg1063dc54p9.cloudfront.net/combined.json
+
+    data = [
+        {
+            "name": "default",
+            "language": "default",
+            "version": "",
+            "description": "default",
+            "fullName": "default",
+            "date": "default",
+            "docLink": "default",
+            "model": settings.EMBEDDINGS_NAME,
+            "location": "remote",
+        }
+    ]
+    # structure: name, language, version, description, fullName, date, docLink
+    # append data from vectors_collection
+    for index in vectors_collection.find({"user": user}):
+        data.append(
+            {
+                "name": index["name"],
+                "language": index["language"],
+                "version": "",
+                "description": index["name"],
+                "fullName": index["name"],
+                "date": index["date"],
+                "docLink": index["location"],
+                "model": settings.EMBEDDINGS_NAME,
+                "location": "local",
+            }
+        )
+    if settings.VECTOR_STORE == "faiss":
+        data_remote = requests.get("https://d3dg1063dc54p9.cloudfront.net/combined.json").json()
+        for index in data_remote:
+            index["location"] = "remote"
+            data.append(index)
+    if 'duckduck_search' in settings.RETRIEVERS_ENABLED:
+        data.append(
+            {
+                "name": "DuckDuckGo Search",
+                "language": "en",
+                "version": "",
+                "description": "duckduck_search",
+                "fullName": "DuckDuckGo Search",
+                "date": "duckduck_search",
+                "docLink": "duckduck_search",
+                "model": settings.EMBEDDINGS_NAME,
+                "location": "custom",
+            }
+        )
+    if 'brave_search' in settings.RETRIEVERS_ENABLED:
+        data.append(
+            {
+                "name": "Brave Search",
+                "language": "en",
+                "version": "",
+                "description": "brave_search",
+                "fullName": "Brave Search",
+                "date": "brave_search",
+                "docLink": "brave_search",
+                "model": settings.EMBEDDINGS_NAME,
+                "location": "custom",
+            }
+        )
+
+    return jsonify(data)
+
+
+@user.route("/api/docs_check", methods=["POST"])
+def check_docs():
+    # check if docs exist in a vectorstore folder
+    data = request.get_json()
+    # split docs on / and take first part
+    if data["docs"].split("/")[0] == "local":
+        return {"status": "exists"}
+    vectorstore = "vectors/" + secure_filename(data["docs"])
+    base_path = "https://raw.githubusercontent.com/arc53/DocsHUB/main/"
+    if os.path.exists(vectorstore) or data["docs"] == "default":
+        return {"status": "exists"}
+    else:
+        file_url = urlparse(base_path + vectorstore + "index.faiss")
+        
+        if (
+            file_url.scheme in ['https'] and 
+            file_url.netloc == 'raw.githubusercontent.com' and 
+            file_url.path.startswith('/arc53/DocsHUB/main/')
+        ):
+            r = requests.get(file_url.geturl())
+            if r.status_code != 200:
+                return {"status": "null"}
+            else:
+                if not os.path.exists(vectorstore):
+                    os.makedirs(vectorstore)
+                with open(vectorstore + "index.faiss", "wb") as f:
+                    f.write(r.content)
+
+                r = requests.get(base_path + vectorstore + "index.pkl")
+                with open(vectorstore + "index.pkl", "wb") as f:
+                    f.write(r.content)
+        else:
+            return {"status": "null"}
+
+        return {"status": "loaded"}
+
+@user.route("/api/create_prompt", methods=["POST"])
+def create_prompt():
+    data = request.get_json()
+    content = data["content"]
+    name = data["name"]
+    if name == "":
+        return {"status": "error"}
+    user = "local"
+    resp = prompts_collection.insert_one(
+        {
+            "name": name,
+            "content": content,
+            "user": user,
+        }
+    )
+    new_id = str(resp.inserted_id)
+    return {"id": new_id}
+
+@user.route("/api/get_prompts", methods=["GET"])
+def get_prompts():
+    user = "local"
+    prompts = prompts_collection.find({"user": user})
+    list_prompts = []
+    list_prompts.append({"id": "default", "name": "default", "type": "public"})
+    list_prompts.append({"id": "creative", "name": "creative", "type": "public"})
+    list_prompts.append({"id": "strict", "name": "strict", "type": "public"})
+    for prompt in prompts:
+        list_prompts.append({"id": str(prompt["_id"]), "name": prompt["name"], "type": "private"})
+
+    return jsonify(list_prompts)
+
+@user.route("/api/get_single_prompt", methods=["GET"])
+def get_single_prompt():
+    prompt_id = request.args.get("id")
+    if prompt_id == 'default':
+        with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
+            chat_combine_template = f.read()
+        return jsonify({"content": chat_combine_template})
+    elif prompt_id == 'creative':
+        with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
+            chat_reduce_creative = f.read()
+        return jsonify({"content": chat_reduce_creative})
+    elif prompt_id == 'strict':
+        with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
+            chat_reduce_strict = f.read()   
+        return jsonify({"content": chat_reduce_strict})
+
+
+    prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})
+    return jsonify({"content": prompt["content"]})
+
+@user.route("/api/delete_prompt", methods=["POST"])
+def delete_prompt():
+    data = request.get_json()
+    id = data["id"]
+    prompts_collection.delete_one(
+        {
+            "_id": ObjectId(id),
+        }
+    )
+    return {"status": "ok"}
+
+@user.route("/api/update_prompt", methods=["POST"])
+def update_prompt_name():
+    data = request.get_json()
+    id = data["id"]
+    name = data["name"]
+    content = data["content"]
+    # check if name is null
+    if name == "":
+        return {"status": "error"}
+    prompts_collection.update_one({"_id": ObjectId(id)},{"$set":{"name":name, "content": content}})
+    return {"status": "ok"}
+
+
+
+@user.route("/api/get_api_keys", methods=["GET"])
+def get_api_keys():
+    user = "local"
+    keys = api_key_collection.find({"user": user})
+    list_keys = []
+    for key in keys:
+        list_keys.append({
+            "id": str(key["_id"]),
+            "name": key["name"],
+            "key": key["key"][:4] + "..." + key["key"][-4:],
+            "source": key["source"],
+            "prompt_id": key["prompt_id"],
+            "chunks": key["chunks"]
+        })
+    return jsonify(list_keys)
+
+@user.route("/api/create_api_key", methods=["POST"])
+def create_api_key():
+    data = request.get_json()
+    name = data["name"]
+    source = data["source"]
+    prompt_id = data["prompt_id"]
+    chunks = data["chunks"]
+    key = str(uuid.uuid4())
+    user = "local"
+    resp = api_key_collection.insert_one(
+        {
+            "name": name,
+            "key": key,
+            "source": source,
+            "user": user,
+            "prompt_id": prompt_id,
+            "chunks": chunks
+        }
+    )
+    new_id = str(resp.inserted_id)
+    return {"id": new_id, "key": key}
+
+@user.route("/api/delete_api_key", methods=["POST"])
+def delete_api_key():
+    data = request.get_json()
+    id = data["id"]
+    api_key_collection.delete_one(
+        {
+            "_id": ObjectId(id),
+        }
+    )
+    return {"status": "ok"}
+

application/api/user/tasks.py

@@ -0,0 +1,12 @@
+from application.worker import ingest_worker, remote_worker
+from application.celery import celery
+
+@celery.task(bind=True)
+def ingest(self, directory, formats, name_job, filename, user):
+    resp = ingest_worker(self, directory, formats, name_job, filename, user)
+    return resp
+
+@celery.task(bind=True)
+def ingest_remote(self, source_data, job_name, user, loader):
+    resp = remote_worker(self, source_data, job_name, user, loader)
+    return resp

application/app.py

@@ -1,719 +1,44 @@
-import asyncio
-import datetime
-import http.client
-import json
-import logging
-import os
 import platform
-import traceback
-
 import dotenv
-import openai
-import requests
-from celery import Celery
-from celery.result import AsyncResult
-from flask import Flask, request, render_template, send_from_directory, jsonify, Response
-from langchain import FAISS
-from langchain import VectorDBQA, Cohere, OpenAI
-from langchain.chains import LLMChain, ConversationalRetrievalChain
-from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
-from langchain.chains.question_answering import load_qa_chain
-from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
-from langchain.embeddings import (
-    OpenAIEmbeddings,
-    HuggingFaceHubEmbeddings,
-    CohereEmbeddings,
-    HuggingFaceInstructEmbeddings,
-)
-from langchain.prompts import PromptTemplate
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-    AIMessagePromptTemplate,
-)
-from langchain.schema import HumanMessage, AIMessage
-from pymongo import MongoClient
-from werkzeug.utils import secure_filename
-
+from application.celery import celery
+from flask import Flask, request, redirect
 from application.core.settings import settings
-from application.error import bad_request
-from application.worker import ingest_worker
-from bson.objectid import ObjectId
-
-# os.environ["LANGCHAIN_HANDLER"] = "langchain"
-
-logger = logging.getLogger(__name__)
-if settings.LLM_NAME == "gpt4":
-    gpt_model = 'gpt-4'
-else:
-    gpt_model = 'gpt-3.5-turbo'
-
-
-if settings.SELF_HOSTED_MODEL:
-    from langchain.llms import HuggingFacePipeline
-    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-    model_id = settings.LLM_NAME # hf model id (Arc53/docsgpt-7b-falcon, Arc53/docsgpt-14b)
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-    model = AutoModelForCausalLM.from_pretrained(model_id)
-    pipe = pipeline(
-        "text-generation", model=model,
-        tokenizer=tokenizer, max_new_tokens=2000,
-        device_map="auto", eos_token_id=tokenizer.eos_token_id
-    )
-    hf = HuggingFacePipeline(pipeline=pipe)
-
-# Redirect PosixPath to WindowsPath on Windows
+from application.api.user.routes import user
+from application.api.answer.routes import answer
+from application.api.internal.routes import internal
 
 if platform.system() == "Windows":
     import pathlib
-
-    temp = pathlib.PosixPath
     pathlib.PosixPath = pathlib.WindowsPath
 
-# loading the .env file
 dotenv.load_dotenv()
 
-# load the prompts
-current_dir = os.path.dirname(os.path.abspath(__file__))
-with open(os.path.join(current_dir, "prompts", "combine_prompt.txt"), "r") as f:
-    template = f.read()
-
-with open(os.path.join(current_dir, "prompts", "combine_prompt_hist.txt"), "r") as f:
-    template_hist = f.read()
-
-with open(os.path.join(current_dir, "prompts", "question_prompt.txt"), "r") as f:
-    template_quest = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_combine_prompt.txt"), "r") as f:
-    chat_combine_template = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") as f:
-    chat_reduce_template = f.read()
-
-api_key_set = settings.API_KEY is not None
-embeddings_key_set = settings.EMBEDDINGS_KEY is not None
-
 app = Flask(__name__)
-app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER = "inputs"
-app.config["CELERY_BROKER_URL"] = settings.CELERY_BROKER_URL
-app.config["CELERY_RESULT_BACKEND"] = settings.CELERY_RESULT_BACKEND
-app.config["MONGO_URI"] = settings.MONGO_URI
-celery = Celery()
+app.register_blueprint(user)
+app.register_blueprint(answer)
+app.register_blueprint(internal)
+app.config.update(
+    UPLOAD_FOLDER="inputs",
+    CELERY_BROKER_URL=settings.CELERY_BROKER_URL,
+    CELERY_RESULT_BACKEND=settings.CELERY_RESULT_BACKEND,
+    MONGO_URI=settings.MONGO_URI
+)
 celery.config_from_object("application.celeryconfig")
-mongo = MongoClient(app.config["MONGO_URI"])
-db = mongo["docsgpt"]
-vectors_collection = db["vectors"]
-conversations_collection = db["conversations"]
-
-
-async def async_generate(chain, question, chat_history):
-    result = await chain.arun({"question": question, "chat_history": chat_history})
-    return result
-
-
-def run_async_chain(chain, question, chat_history):
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    result = {}
-    try:
-        answer = loop.run_until_complete(async_generate(chain, question, chat_history))
-    finally:
-        loop.close()
-    result["answer"] = answer
-    return result
-
-
-def get_vectorstore(data):
-    if "active_docs" in data:
-        if data["active_docs"].split("/")[0] == "local":
-            if data["active_docs"].split("/")[1] == "default":
-                vectorstore = ""
-            else:
-                vectorstore = "indexes/" + data["active_docs"]
-        else:
-            vectorstore = "vectors/" + data["active_docs"]
-        if data["active_docs"] == "default":
-            vectorstore = ""
-    else:
-        vectorstore = ""
-    vectorstore = os.path.join("application", vectorstore)
-    return vectorstore
-
-
-def get_docsearch(vectorstore, embeddings_key):
-    if settings.EMBEDDINGS_NAME == "openai_text-embedding-ada-002":
-        if is_azure_configured():
-            os.environ["OPENAI_API_TYPE"] = "azure"
-            openai_embeddings = OpenAIEmbeddings(model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME)
-        else:
-            openai_embeddings = OpenAIEmbeddings(openai_api_key=embeddings_key)
-        docsearch = FAISS.load_local(vectorstore, openai_embeddings)
-    elif settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
-        docsearch = FAISS.load_local(vectorstore, HuggingFaceHubEmbeddings())
-    elif settings.EMBEDDINGS_NAME == "huggingface_hkunlp/instructor-large":
-        docsearch = FAISS.load_local(vectorstore, HuggingFaceInstructEmbeddings())
-    elif settings.EMBEDDINGS_NAME == "cohere_medium":
-        docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key))
-    return docsearch
-
-
-@celery.task(bind=True)
-def ingest(self, directory, formats, name_job, filename, user):
-    resp = ingest_worker(self, directory, formats, name_job, filename, user)
-    return resp
-
 
 @app.route("/")
 def home():
-    return render_template(
-        "index.html", api_key_set=api_key_set, llm_choice=settings.LLM_NAME, embeddings_choice=settings.EMBEDDINGS_NAME
-    )
-
-
-def complete_stream(question, docsearch, chat_history, api_key, conversation_id):
-    openai.api_key = api_key
-    if is_azure_configured():
-        logger.debug("in Azure")
-        openai.api_type = "azure"
-        openai.api_version = settings.OPENAI_API_VERSION
-        openai.api_base = settings.OPENAI_API_BASE
-        llm = AzureChatOpenAI(
-            openai_api_key=api_key,
-            openai_api_base=settings.OPENAI_API_BASE,
-            openai_api_version=settings.OPENAI_API_VERSION,
-            deployment_name=settings.AZURE_DEPLOYMENT_NAME,
-        )
+    if request.remote_addr in ('0.0.0.0', '127.0.0.1', 'localhost', '172.18.0.1'):
+        return redirect('http://localhost:5173')
     else:
-        logger.debug("plain OpenAI")
-        llm = ChatOpenAI(openai_api_key=api_key)
-    docs = docsearch.similarity_search(question, k=2)
-    # join all page_content together with a newline
-    docs_together = "\n".join([doc.page_content for doc in docs])
-    p_chat_combine = chat_combine_template.replace("{summaries}", docs_together)
-    messages_combine = [{"role": "system", "content": p_chat_combine}]
-    source_log_docs = []
-    for doc in docs:
-        if doc.metadata:
-            data = json.dumps({"type": "source", "doc": doc.page_content, "metadata": doc.metadata})
-            source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
-        else:
-            data = json.dumps({"type": "source", "doc": doc.page_content})
-            source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
-        yield f"data:{data}\n\n"
+        return 'Welcome to DocsGPT Backend!'
 
-    if len(chat_history) > 1:
-        tokens_current_history = 0
-        # count tokens in history
-        chat_history.reverse()
-        for i in chat_history:
-            if "prompt" in i and "response" in i:
-                tokens_batch = llm.get_num_tokens(i["prompt"]) + llm.get_num_tokens(i["response"])
-                if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
-                    tokens_current_history += tokens_batch
-                    messages_combine.append({"role": "user", "content": i["prompt"]})
-                    messages_combine.append({"role": "system", "content": i["response"]})
-    messages_combine.append({"role": "user", "content": question})
-    completion = openai.ChatCompletion.create(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
-                                              messages=messages_combine, stream=True, max_tokens=500, temperature=0)
-    reponse_full = ""
-    for line in completion:
-        if "content" in line["choices"][0]["delta"]:
-            # check if the delta contains content
-            data = json.dumps({"answer": str(line["choices"][0]["delta"]["content"])})
-            reponse_full += str(line["choices"][0]["delta"]["content"])
-            yield f"data: {data}\n\n"
-    # save conversation to database
-    if conversation_id is not None:
-        conversations_collection.update_one(
-            {"_id": ObjectId(conversation_id)},
-            {"$push": {"queries": {"prompt": question, "response": reponse_full, "sources": source_log_docs}}},
-        )
-
-    else:
-        # create new conversation
-        # generate summary
-        messages_summary = [{"role": "assistant", "content": "Summarise following conversation in no more than 3 "
-                                                             "words, respond ONLY with the summary, use the same "
-                                                             "language as the system \n\nUser: " + question + "\n\n" +
-                                                             "AI: " +
-                                                             reponse_full},
-                            {"role": "user", "content": "Summarise following conversation in no more than 3 words, "
-                                                        "respond ONLY with the summary, use the same language as the "
-                                                        "system"}]
-        completion = openai.ChatCompletion.create(model='gpt-3.5-turbo', engine=settings.AZURE_DEPLOYMENT_NAME,
-                                                  messages=messages_summary, max_tokens=30, temperature=0)
-        conversation_id = conversations_collection.insert_one(
-            {"user": "local",
-             "date": datetime.datetime.utcnow(),
-             "name": completion["choices"][0]["message"]["content"],
-             "queries": [{"prompt": question, "response": reponse_full, "sources": source_log_docs}]}
-        ).inserted_id
-
-    # send data.type = "end" to indicate that the stream has ended as json
-    data = json.dumps({"type": "id", "id": str(conversation_id)})
-    yield f"data: {data}\n\n"
-    data = json.dumps({"type": "end"})
-    yield f"data: {data}\n\n"
-
-
-@app.route("/stream", methods=["POST"])
-def stream():
-    data = request.get_json()
-    # get parameter from url question
-    question = data["question"]
-    history = data["history"]
-    # history to json object from string
-    history = json.loads(history)
-    conversation_id = data["conversation_id"]
-
-    # check if active_docs is set
-
-    if not api_key_set:
-        api_key = data["api_key"]
-    else:
-        api_key = settings.API_KEY
-    if not embeddings_key_set:
-        embeddings_key = data["embeddings_key"]
-    else:
-        embeddings_key = settings.EMBEDDINGS_KEY
-    if "active_docs" in data:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
-    else:
-        vectorstore = ""
-    docsearch = get_docsearch(vectorstore, embeddings_key)
-
-    # question = "Hi"
-    return Response(
-        complete_stream(question, docsearch,
-                        chat_history=history, api_key=api_key,
-                        conversation_id=conversation_id), mimetype="text/event-stream"
-    )
-
-
-def is_azure_configured():
-    return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
-
-
-@app.route("/api/answer", methods=["POST"])
-def api_answer():
-    data = request.get_json()
-    question = data["question"]
-    history = data["history"]
-    if "conversation_id" not in data:
-        conversation_id = None
-    else:
-        conversation_id = data["conversation_id"]
-    print("-" * 5)
-    if not api_key_set:
-        api_key = data["api_key"]
-    else:
-        api_key = settings.API_KEY
-    if not embeddings_key_set:
-        embeddings_key = data["embeddings_key"]
-    else:
-        embeddings_key = settings.EMBEDDINGS_KEY
-
-    # use try and except  to check for exception
-    try:
-        # check if the vectorstore is set
-        vectorstore = get_vectorstore(data)
-        # loading the index and the store and the prompt template
-        # Note if you have used other embeddings than OpenAI, you need to change the embeddings
-        docsearch = get_docsearch(vectorstore, embeddings_key)
-
-        q_prompt = PromptTemplate(
-            input_variables=["context", "question"], template=template_quest, template_format="jinja2"
-        )
-        if settings.LLM_NAME == "openai_chat":
-            if is_azure_configured():
-                logger.debug("in Azure")
-                llm = AzureChatOpenAI(
-                    openai_api_key=api_key,
-                    openai_api_base=settings.OPENAI_API_BASE,
-                    openai_api_version=settings.OPENAI_API_VERSION,
-                    deployment_name=settings.AZURE_DEPLOYMENT_NAME,
-                )
-            else:
-                logger.debug("plain OpenAI")
-                llm = ChatOpenAI(openai_api_key=api_key, model_name=gpt_model)  # optional parameter: model_name="gpt-4"
-            messages_combine = [SystemMessagePromptTemplate.from_template(chat_combine_template)]
-            if history:
-                tokens_current_history = 0
-                # count tokens in history
-                history.reverse()
-                for i in history:
-                    if "prompt" in i and "response" in i:
-                        tokens_batch = llm.get_num_tokens(i["prompt"]) + llm.get_num_tokens(i["response"])
-                        if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
-                            tokens_current_history += tokens_batch
-                            messages_combine.append(HumanMessagePromptTemplate.from_template(i["prompt"]))
-                            messages_combine.append(AIMessagePromptTemplate.from_template(i["response"]))
-            messages_combine.append(HumanMessagePromptTemplate.from_template("{question}"))
-            p_chat_combine = ChatPromptTemplate.from_messages(messages_combine)
-        elif settings.LLM_NAME == "openai":
-            llm = OpenAI(openai_api_key=api_key, temperature=0)
-        elif settings.SELF_HOSTED_MODEL:
-            llm = hf
-        elif settings.LLM_NAME == "cohere":
-            llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
-        else:
-            raise ValueError("unknown LLM model")
-
-        if settings.LLM_NAME == "openai_chat":
-            question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
-            doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
-            chain = ConversationalRetrievalChain(
-                retriever=docsearch.as_retriever(k=2),
-                question_generator=question_generator,
-                combine_docs_chain=doc_chain,
-            )
-            chat_history = []
-            # result = chain({"question": question, "chat_history": chat_history})
-            # generate async with async generate method
-            result = run_async_chain(chain, question, chat_history)
-        elif settings.SELF_HOSTED_MODEL:
-            question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
-            doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
-            chain = ConversationalRetrievalChain(
-                retriever=docsearch.as_retriever(k=2),
-                question_generator=question_generator,
-                combine_docs_chain=doc_chain,
-            )
-            chat_history = []
-            # result = chain({"question": question, "chat_history": chat_history})
-            # generate async with async generate method
-            result = run_async_chain(chain, question, chat_history)
-
-        else:
-            qa_chain = load_qa_chain(
-                llm=llm, chain_type="map_reduce", combine_prompt=chat_combine_template, question_prompt=q_prompt
-            )
-            chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=3)
-            result = chain({"query": question})
-
-        print(result)
-
-        # some formatting for the frontend
-        if "result" in result:
-            result["answer"] = result["result"]
-        result["answer"] = result["answer"].replace("\\n", "\n")
-        try:
-            result["answer"] = result["answer"].split("SOURCES:")[0]
-        except Exception:
-            pass
-
-        sources = docsearch.similarity_search(question, k=2)
-        sources_doc = []
-        for doc in sources:
-            if doc.metadata:
-                sources_doc.append({'title': doc.metadata['title'], 'text': doc.page_content})
-            else:
-                sources_doc.append({'title': doc.page_content, 'text': doc.page_content})
-        result['sources'] = sources_doc
-
-        # generate conversationId
-        if conversation_id is not None:
-            conversations_collection.update_one(
-                {"_id": ObjectId(conversation_id)},
-                {"$push": {"queries": {"prompt": question,
-                                       "response": result["answer"], "sources": result['sources']}}},
-            )
-
-        else:
-            # create new conversation
-            # generate summary
-            messages_summary = [AIMessage(content="Summarise following conversation in no more than 3 " +
-                                                  "words, respond ONLY with the summary, use the same " +
-                                                  "language as the system \n\nUser: " + question + "\n\nAI: " +
-                                                  result["answer"]),
-                                HumanMessage(content="Summarise following conversation in no more than 3 words, " +
-                                                     "respond ONLY with the summary, use the same language as the " +
-                                                     "system")]
-
-
-            # completion = openai.ChatCompletion.create(model='gpt-3.5-turbo', engine=settings.AZURE_DEPLOYMENT_NAME,
-            #                                           messages=messages_summary, max_tokens=30, temperature=0)
-            completion = llm.predict_messages(messages_summary)
-            conversation_id = conversations_collection.insert_one(
-                {"user": "local",
-                 "date": datetime.datetime.utcnow(),
-                 "name": completion.content,
-                 "queries": [{"prompt": question, "response": result["answer"], "sources": result['sources']}]}
-            ).inserted_id
-
-        result["conversation_id"] = str(conversation_id)
-
-        # mock result
-        # result = {
-        #     "answer": "The answer is 42",
-        #     "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
-        # }
-        return result
-    except Exception as e:
-        # print whole traceback
-        traceback.print_exc()
-        print(str(e))
-        return bad_request(500, str(e))
-
-
-@app.route("/api/docs_check", methods=["POST"])
-def check_docs():
-    # check if docs exist in a vectorstore folder
-    data = request.get_json()
-    # split docs on / and take first part
-    if data["docs"].split("/")[0] == "local":
-        return {"status": "exists"}
-    vectorstore = "vectors/" + data["docs"]
-    base_path = "https://raw.githubusercontent.com/arc53/DocsHUB/main/"
-    if os.path.exists(vectorstore) or data["docs"] == "default":
-        return {"status": "exists"}
-    else:
-        r = requests.get(base_path + vectorstore + "index.faiss")
-
-        if r.status_code != 200:
-            return {"status": "null"}
-        else:
-            if not os.path.exists(vectorstore):
-                os.makedirs(vectorstore)
-            with open(vectorstore + "index.faiss", "wb") as f:
-                f.write(r.content)
-
-            # download the store
-            r = requests.get(base_path + vectorstore + "index.pkl")
-            with open(vectorstore + "index.pkl", "wb") as f:
-                f.write(r.content)
-
-        return {"status": "loaded"}
-
-
-@app.route("/api/feedback", methods=["POST"])
-def api_feedback():
-    data = request.get_json()
-    question = data["question"]
-    answer = data["answer"]
-    feedback = data["feedback"]
-
-    print("-" * 5)
-    print("Question: " + question)
-    print("Answer: " + answer)
-    print("Feedback: " + feedback)
-    print("-" * 5)
-    response = requests.post(
-        url="https://86x89umx77.execute-api.eu-west-2.amazonaws.com/docsgpt-feedback",
-        headers={
-            "Content-Type": "application/json; charset=utf-8",
-        },
-        data=json.dumps({"answer": answer, "question": question, "feedback": feedback}),
-    )
-    return {"status": http.client.responses.get(response.status_code, "ok")}
-
-
-@app.route("/api/combine", methods=["GET"])
-def combined_json():
-    user = "local"
-    """Provide json file with combined available indexes."""
-    # get json from https://d3dg1063dc54p9.cloudfront.net/combined.json
-
-    data = [
-        {
-            "name": "default",
-            "language": "default",
-            "version": "",
-            "description": "default",
-            "fullName": "default",
-            "date": "default",
-            "docLink": "default",
-            "model": settings.EMBEDDINGS_NAME,
-            "location": "local",
-        }
-    ]
-    # structure: name, language, version, description, fullName, date, docLink
-    # append data from vectors_collection
-    for index in vectors_collection.find({"user": user}):
-        data.append(
-            {
-                "name": index["name"],
-                "language": index["language"],
-                "version": "",
-                "description": index["name"],
-                "fullName": index["name"],
-                "date": index["date"],
-                "docLink": index["location"],
-                "model": settings.EMBEDDINGS_NAME,
-                "location": "local",
-            }
-        )
-
-    data_remote = requests.get("https://d3dg1063dc54p9.cloudfront.net/combined.json").json()
-    for index in data_remote:
-        index["location"] = "remote"
-        data.append(index)
-
-    return jsonify(data)
-
-
-@app.route("/api/upload", methods=["POST"])
-def upload_file():
-    """Upload a file to get vectorized and indexed."""
-    if "user" not in request.form:
-        return {"status": "no user"}
-    user = secure_filename(request.form["user"])
-    if "name" not in request.form:
-        return {"status": "no name"}
-    job_name = secure_filename(request.form["name"])
-    # check if the post request has the file part
-    if "file" not in request.files:
-        print("No file part")
-        return {"status": "no file"}
-    file = request.files["file"]
-    if file.filename == "":
-        return {"status": "no file name"}
-
-    if file:
-        filename = secure_filename(file.filename)
-        # save dir
-        save_dir = os.path.join(app.config["UPLOAD_FOLDER"], user, job_name)
-        # create dir if not exists
-        if not os.path.exists(save_dir):
-            os.makedirs(save_dir)
-
-        file.save(os.path.join(save_dir, filename))
-        task = ingest.delay("temp", [".rst", ".md", ".pdf", ".txt"], job_name, filename, user)
-        # task id
-        task_id = task.id
-        return {"status": "ok", "task_id": task_id}
-    else:
-        return {"status": "error"}
-
-
-@app.route("/api/task_status", methods=["GET"])
-def task_status():
-    """Get celery job status."""
-    task_id = request.args.get("task_id")
-    task = AsyncResult(task_id)
-    task_meta = task.info
-    return {"status": task.status, "result": task_meta}
-
-
-### Backgound task api
-@app.route("/api/upload_index", methods=["POST"])
-def upload_index_files():
-    """Upload two files(index.faiss, index.pkl) to the user's folder."""
-    if "user" not in request.form:
-        return {"status": "no user"}
-    user = secure_filename(request.form["user"])
-    if "name" not in request.form:
-        return {"status": "no name"}
-    job_name = secure_filename(request.form["name"])
-    if "file_faiss" not in request.files:
-        print("No file part")
-        return {"status": "no file"}
-    file_faiss = request.files["file_faiss"]
-    if file_faiss.filename == "":
-        return {"status": "no file name"}
-    if "file_pkl" not in request.files:
-        print("No file part")
-        return {"status": "no file"}
-    file_pkl = request.files["file_pkl"]
-    if file_pkl.filename == "":
-        return {"status": "no file name"}
-
-    # saves index files
-    save_dir = os.path.join("indexes", user, job_name)
-    if not os.path.exists(save_dir):
-        os.makedirs(save_dir)
-    file_faiss.save(os.path.join(save_dir, "index.faiss"))
-    file_pkl.save(os.path.join(save_dir, "index.pkl"))
-    # create entry in vectors_collection
-    vectors_collection.insert_one(
-        {
-            "user": user,
-            "name": job_name,
-            "language": job_name,
-            "location": save_dir,
-            "date": datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
-            "model": settings.EMBEDDINGS_NAME,
-            "type": "local",
-        }
-    )
-    return {"status": "ok"}
-
-
-@app.route("/api/download", methods=["get"])
-def download_file():
-    user = secure_filename(request.args.get("user"))
-    job_name = secure_filename(request.args.get("name"))
-    filename = secure_filename(request.args.get("file"))
-    save_dir = os.path.join(app.config["UPLOAD_FOLDER"], user, job_name)
-    return send_from_directory(save_dir, filename, as_attachment=True)
-
-
-@app.route("/api/delete_old", methods=["get"])
-def delete_old():
-    """Delete old indexes."""
-    import shutil
-
-    path = request.args.get("path")
-    dirs = path.split("/")
-    dirs_clean = []
-    for i in range(1, len(dirs)):
-        dirs_clean.append(secure_filename(dirs[i]))
-    # check that path strats with indexes or vectors
-    if dirs[0] not in ["indexes", "vectors"]:
-        return {"status": "error"}
-    path_clean = "/".join(dirs)
-    vectors_collection.delete_one({"location": path})
-    try:
-        shutil.rmtree(path_clean)
-    except FileNotFoundError:
-        pass
-    return {"status": "ok"}
-
-
-@app.route("/api/get_conversations", methods=["get"])
-def get_conversations():
-    # provides a list of conversations
-    conversations = conversations_collection.find().sort("date", -1)
-    list_conversations = []
-    for conversation in conversations:
-        list_conversations.append({"id": str(conversation["_id"]), "name": conversation["name"]})
-
-    #list_conversations = [{"id": "default", "name": "default"}, {"id": "jeff", "name": "jeff"}]
-
-    return jsonify(list_conversations)
-
-@app.route("/api/get_single_conversation", methods=["get"])
-def get_single_conversation():
-    # provides data for a conversation
-    conversation_id = request.args.get("id")
-    conversation = conversations_collection.find_one({"_id": ObjectId(conversation_id)})
-    return jsonify(conversation['queries'])
-
-@app.route("/api/delete_conversation", methods=["POST"])
-def delete_conversation():
-    # deletes a conversation from the database
-    conversation_id = request.args.get("id")
-    # write to mongodb
-    conversations_collection.delete_one(
-        {
-            "_id": ObjectId(conversation_id),
-        }
-    )
-
-    return {"status": "ok"}
-
-
-# handling CORS
 @app.after_request
 def after_request(response):
     response.headers.add("Access-Control-Allow-Origin", "*")
     response.headers.add("Access-Control-Allow-Headers", "Content-Type,Authorization")
     response.headers.add("Access-Control-Allow-Methods", "GET,PUT,POST,DELETE,OPTIONS")
-    response.headers.add("Access-Control-Allow-Credentials", "true")
     return response
 
-
 if __name__ == "__main__":
-    app.run(debug=True, port=7091)
+    app.run(debug=settings.FLASK_DEBUG_MODE, port=7091)
+

application/celery.py

@@ -0,0 +1,9 @@
+from celery import Celery
+from application.core.settings import settings
+
+def make_celery(app_name=__name__):
+    celery = Celery(app_name, broker=settings.CELERY_BROKER_URL, backend=settings.CELERY_RESULT_BACKEND)
+    celery.conf.update(settings)
+    return celery
+
+celery = make_celery()

application/core/settings.py

@@ -1,26 +1,68 @@
 from pathlib import Path
+from typing import Optional
+import os
 
-from pydantic import BaseSettings
+from pydantic_settings import BaseSettings
+
+current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 
 class Settings(BaseSettings):
-    LLM_NAME: str = "openai_chat"
-    EMBEDDINGS_NAME: str = "openai_text-embedding-ada-002"
+    LLM_NAME: str = "docsgpt"
+    MODEL_NAME: Optional[str] = None # if LLM_NAME is openai, MODEL_NAME can be gpt-4 or gpt-3.5-turbo
+    EMBEDDINGS_NAME: str = "huggingface_sentence-transformers/all-mpnet-base-v2"
     CELERY_BROKER_URL: str = "redis://localhost:6379/0"
     CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"
     MONGO_URI: str = "mongodb://localhost:27017/docsgpt"
-    MODEL_PATH: str = "./models/gpt4all-model.bin"
+    MODEL_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
     TOKENS_MAX_HISTORY: int = 150
-    SELF_HOSTED_MODEL: bool = False
+    UPLOAD_FOLDER: str = "inputs"
+    VECTOR_STORE: str = "faiss"  # "faiss" or "elasticsearch" or "qdrant"
+    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"] # also brave_search
 
     API_URL: str = "http://localhost:7091"  # backend url for celery worker
 
-    API_KEY: str = None  # LLM api key
-    EMBEDDINGS_KEY: str = None  # api key for embeddings (if using openai, just copy API_KEY
-    OPENAI_API_BASE: str = None  # azure openai api base url
-    OPENAI_API_VERSION: str = None  # azure openai api version
-    AZURE_DEPLOYMENT_NAME: str = None  # azure deployment name for answering
-    AZURE_EMBEDDINGS_DEPLOYMENT_NAME: str = None  # azure deployment name for embeddings
+    API_KEY: Optional[str] = None  # LLM api key
+    EMBEDDINGS_KEY: Optional[str] = None  # api key for embeddings (if using openai, just copy API_KEY)
+    OPENAI_API_BASE: Optional[str] = None  # azure openai api base url
+    OPENAI_API_VERSION: Optional[str] = None  # azure openai api version
+    AZURE_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for answering
+    AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for embeddings
+
+    # elasticsearch
+    ELASTIC_CLOUD_ID: Optional[str] = None  # cloud id for elasticsearch
+    ELASTIC_USERNAME: Optional[str] = None  # username for elasticsearch
+    ELASTIC_PASSWORD: Optional[str] = None  # password for elasticsearch
+    ELASTIC_URL: Optional[str] = None  # url for elasticsearch
+    ELASTIC_INDEX: Optional[str] = "docsgpt"  # index name for elasticsearch
+
+    # SageMaker config
+    SAGEMAKER_ENDPOINT: Optional[str] = None  # SageMaker endpoint name
+    SAGEMAKER_REGION: Optional[str] = None  # SageMaker region name
+    SAGEMAKER_ACCESS_KEY: Optional[str] = None  # SageMaker access key
+    SAGEMAKER_SECRET_KEY: Optional[str] = None  # SageMaker secret key
+
+    # prem ai project id
+    PREMAI_PROJECT_ID: Optional[str] = None
+
+    # Qdrant vectorstore config
+    QDRANT_COLLECTION_NAME: Optional[str] = "docsgpt"
+    QDRANT_LOCATION: Optional[str] = None
+    QDRANT_URL: Optional[str] = None
+    QDRANT_PORT: Optional[int] = 6333
+    QDRANT_GRPC_PORT: int = 6334
+    QDRANT_PREFER_GRPC: bool = False
+    QDRANT_HTTPS: Optional[bool] = None
+    QDRANT_API_KEY: Optional[str] = None
+    QDRANT_PREFIX: Optional[str] = None
+    QDRANT_TIMEOUT: Optional[float] = None
+    QDRANT_HOST: Optional[str] = None
+    QDRANT_PATH: Optional[str] = None
+    QDRANT_DISTANCE_FUNC: str = "Cosine"
+
+    BRAVE_SEARCH_API_KEY: Optional[str] = None
+
+    FLASK_DEBUG_MODE: bool = False
 
 
 path = Path(__file__).parent.parent.absolute()

application/llm/anthropic.py

@@ -0,0 +1,50 @@
+from application.llm.base import BaseLLM
+from application.core.settings import settings
+
+
+class AnthropicLLM(BaseLLM):
+
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
+
+        super().__init__(*args, **kwargs)
+        self.api_key = (
+            api_key or settings.ANTHROPIC_API_KEY
+        )  # If not provided, use a default from settings
+        self.user_api_key = user_api_key
+        self.anthropic = Anthropic(api_key=self.api_key)
+        self.HUMAN_PROMPT = HUMAN_PROMPT
+        self.AI_PROMPT = AI_PROMPT
+
+    def _raw_gen(
+        self, baseself, model, messages, stream=False, max_tokens=300, **kwargs
+    ):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Context \n {context} \n ### Question \n {user_question}"
+        if stream:
+            return self.gen_stream(model, prompt, stream, max_tokens, **kwargs)
+
+        completion = self.anthropic.completions.create(
+            model=model,
+            max_tokens_to_sample=max_tokens,
+            stream=stream,
+            prompt=f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT}",
+        )
+        return completion.completion
+
+    def _raw_gen_stream(
+        self, baseself, model, messages, stream=True, max_tokens=300, **kwargs
+    ):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Context \n {context} \n ### Question \n {user_question}"
+        stream_response = self.anthropic.completions.create(
+            model=model,
+            prompt=f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT}",
+            max_tokens_to_sample=max_tokens,
+            stream=True,
+        )
+
+        for completion in stream_response:
+            yield completion.completion

application/llm/base.py

@@ -0,0 +1,28 @@
+from abc import ABC, abstractmethod
+from application.usage import gen_token_usage, stream_token_usage
+
+
+class BaseLLM(ABC):
+    def __init__(self):
+        self.token_usage = {"prompt_tokens": 0, "generated_tokens": 0}
+
+    def _apply_decorator(self, method, decorator, *args, **kwargs):
+        return decorator(method, *args, **kwargs)
+
+    @abstractmethod
+    def _raw_gen(self, model, messages, stream, *args, **kwargs):
+        pass
+
+    def gen(self, model, messages, stream=False, *args, **kwargs):
+        return self._apply_decorator(self._raw_gen, gen_token_usage)(
+            self, model=model, messages=messages, stream=stream, *args, **kwargs
+        )
+
+    @abstractmethod
+    def _raw_gen_stream(self, model, messages, stream, *args, **kwargs):
+        pass
+
+    def gen_stream(self, model, messages, stream=True, *args, **kwargs):
+        return self._apply_decorator(self._raw_gen_stream, stream_token_usage)(
+            self, model=model, messages=messages, stream=stream, *args, **kwargs
+        )

application/llm/docsgpt_provider.py

@@ -0,0 +1,44 @@
+from application.llm.base import BaseLLM
+import json
+import requests
+
+
+class DocsGPTAPILLM(BaseLLM):
+
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.endpoint = "https://llm.docsgpt.co.uk"
+
+    def _raw_gen(self, baseself, model, messages, stream=False, *args, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        response = requests.post(
+            f"{self.endpoint}/answer", json={"prompt": prompt, "max_new_tokens": 30}
+        )
+        response_clean = response.json()["a"].replace("###", "")
+
+        return response_clean
+
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, *args, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        # send prompt to endpoint /stream
+        response = requests.post(
+            f"{self.endpoint}/stream",
+            json={"prompt": prompt, "max_new_tokens": 256},
+            stream=True,
+        )
+
+        for line in response.iter_lines():
+            if line:
+                # data = json.loads(line)
+                data_str = line.decode("utf-8")
+                if data_str.startswith("data: "):
+                    data = json.loads(data_str[6:])
+                    yield data["a"]

application/llm/huggingface.py

@@ -0,0 +1,68 @@
+from application.llm.base import BaseLLM
+
+
+class HuggingFaceLLM(BaseLLM):
+
+    def __init__(
+        self,
+        api_key=None,
+        user_api_key=None,
+        llm_name="Arc53/DocsGPT-7B",
+        q=False,
+        *args,
+        **kwargs,
+    ):
+        global hf
+
+        from langchain.llms import HuggingFacePipeline
+
+        if q:
+            import torch
+            from transformers import (
+                AutoModelForCausalLM,
+                AutoTokenizer,
+                pipeline,
+                BitsAndBytesConfig,
+            )
+
+            tokenizer = AutoTokenizer.from_pretrained(llm_name)
+            bnb_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_use_double_quant=True,
+                bnb_4bit_quant_type="nf4",
+                bnb_4bit_compute_dtype=torch.bfloat16,
+            )
+            model = AutoModelForCausalLM.from_pretrained(
+                llm_name, quantization_config=bnb_config
+            )
+        else:
+            from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+            tokenizer = AutoTokenizer.from_pretrained(llm_name)
+            model = AutoModelForCausalLM.from_pretrained(llm_name)
+
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        pipe = pipeline(
+            "text-generation",
+            model=model,
+            tokenizer=tokenizer,
+            max_new_tokens=2000,
+            device_map="auto",
+            eos_token_id=tokenizer.eos_token_id,
+        )
+        hf = HuggingFacePipeline(pipeline=pipe)
+
+    def _raw_gen(self, baseself, model, messages, stream=False, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        result = hf(prompt)
+
+        return result.content
+
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
+
+        raise NotImplementedError("HuggingFaceLLM Streaming is not implemented yet.")

application/llm/llama_cpp.py

@@ -0,0 +1,52 @@
+from application.llm.base import BaseLLM
+from application.core.settings import settings
+
+
+class LlamaCpp(BaseLLM):
+
+    def __init__(
+        self,
+        api_key=None,
+        user_api_key=None,
+        llm_name=settings.MODEL_PATH,
+        *args,
+        **kwargs,
+    ):
+        global llama
+        try:
+            from llama_cpp import Llama
+        except ImportError:
+            raise ImportError(
+                "Please install llama_cpp using pip install llama-cpp-python"
+            )
+
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        llama = Llama(model_path=llm_name, n_ctx=2048)
+
+    def _raw_gen(self, baseself, model, messages, stream=False, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        result = llama(prompt, max_tokens=150, echo=False)
+
+        # import sys
+        # print(result['choices'][0]['text'].split('### Answer \n')[-1], file=sys.stderr)
+
+        return result["choices"][0]["text"].split("### Answer \n")[-1]
+
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        result = llama(prompt, max_tokens=150, echo=False, stream=stream)
+
+        # import sys
+        # print(list(result), file=sys.stderr)
+
+        for item in result:
+            for choice in item["choices"]:
+                yield choice["text"]

application/llm/llm_creator.py

@@ -0,0 +1,27 @@
+from application.llm.openai import OpenAILLM, AzureOpenAILLM
+from application.llm.sagemaker import SagemakerAPILLM
+from application.llm.huggingface import HuggingFaceLLM
+from application.llm.llama_cpp import LlamaCpp
+from application.llm.anthropic import AnthropicLLM
+from application.llm.docsgpt_provider import DocsGPTAPILLM
+from application.llm.premai import PremAILLM
+
+
+class LLMCreator:
+    llms = {
+        "openai": OpenAILLM,
+        "azure_openai": AzureOpenAILLM,
+        "sagemaker": SagemakerAPILLM,
+        "huggingface": HuggingFaceLLM,
+        "llama.cpp": LlamaCpp,
+        "anthropic": AnthropicLLM,
+        "docsgpt": DocsGPTAPILLM,
+        "premai": PremAILLM,
+    }
+
+    @classmethod
+    def create_llm(cls, type, api_key, user_api_key, *args, **kwargs):
+        llm_class = cls.llms.get(type.lower())
+        if not llm_class:
+            raise ValueError(f"No LLM class found for type {type}")
+        return llm_class(api_key, user_api_key, *args, **kwargs)

application/llm/openai.py

@@ -0,0 +1,80 @@
+from application.llm.base import BaseLLM
+from application.core.settings import settings
+
+
+class OpenAILLM(BaseLLM):
+
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        global openai
+        from openai import OpenAI
+
+        super().__init__(*args, **kwargs)
+        self.client = OpenAI(
+            api_key=api_key,
+        )
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+
+    def _get_openai(self):
+        # Import openai when needed
+        import openai
+
+        return openai
+
+    def _raw_gen(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=False,
+        engine=settings.AZURE_DEPLOYMENT_NAME,
+        **kwargs
+    ):
+        response = self.client.chat.completions.create(
+            model=model, messages=messages, stream=stream, **kwargs
+        )
+
+        return response.choices[0].message.content
+
+    def _raw_gen_stream(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=True,
+        engine=settings.AZURE_DEPLOYMENT_NAME,
+        **kwargs
+    ):
+        response = self.client.chat.completions.create(
+            model=model, messages=messages, stream=stream, **kwargs
+        )
+
+        for line in response:
+            # import sys
+            # print(line.choices[0].delta.content, file=sys.stderr)
+            if line.choices[0].delta.content is not None:
+                yield line.choices[0].delta.content
+
+
+class AzureOpenAILLM(OpenAILLM):
+
+    def __init__(
+        self, openai_api_key, openai_api_base, openai_api_version, deployment_name
+    ):
+        super().__init__(openai_api_key)
+        self.api_base = (settings.OPENAI_API_BASE,)
+        self.api_version = (settings.OPENAI_API_VERSION,)
+        self.deployment_name = (settings.AZURE_DEPLOYMENT_NAME,)
+        from openai import AzureOpenAI
+
+        self.client = AzureOpenAI(
+            api_key=openai_api_key,
+            api_version=settings.OPENAI_API_VERSION,
+            api_base=settings.OPENAI_API_BASE,
+            deployment_name=settings.AZURE_DEPLOYMENT_NAME,
+        )
+
+    def _get_openai(self):
+        openai = super()._get_openai()
+
+        return openai

application/llm/premai.py

@@ -0,0 +1,38 @@
+from application.llm.base import BaseLLM
+from application.core.settings import settings
+
+
+class PremAILLM(BaseLLM):
+
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        from premai import Prem
+
+        super().__init__(*args, **kwargs)
+        self.client = Prem(api_key=api_key)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.project_id = settings.PREMAI_PROJECT_ID
+
+    def _raw_gen(self, baseself, model, messages, stream=False, **kwargs):
+        response = self.client.chat.completions.create(
+            model=model,
+            project_id=self.project_id,
+            messages=messages,
+            stream=stream,
+            **kwargs
+        )
+
+        return response.choices[0].message["content"]
+
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
+        response = self.client.chat.completions.create(
+            model=model,
+            project_id=self.project_id,
+            messages=messages,
+            stream=stream,
+            **kwargs
+        )
+
+        for line in response:
+            if line.choices[0].delta["content"] is not None:
+                yield line.choices[0].delta["content"]

application/llm/sagemaker.py

@@ -0,0 +1,140 @@
+from application.llm.base import BaseLLM
+from application.core.settings import settings
+import json
+import io
+
+
+class LineIterator:
+    """
+    A helper class for parsing the byte stream input.
+
+    The output of the model will be in the following format:
+    ```
+    b'{"outputs": [" a"]}\n'
+    b'{"outputs": [" challenging"]}\n'
+    b'{"outputs": [" problem"]}\n'
+    ...
+    ```
+
+    While usually each PayloadPart event from the event stream will contain a byte array
+    with a full json, this is not guaranteed and some of the json objects may be split across
+    PayloadPart events. For example:
+    ```
+    {'PayloadPart': {'Bytes': b'{"outputs": '}}
+    {'PayloadPart': {'Bytes': b'[" problem"]}\n'}}
+    ```
+
+    This class accounts for this by concatenating bytes written via the 'write' function
+    and then exposing a method which will return lines (ending with a '\n' character) within
+    the buffer via the 'scan_lines' function. It maintains the position of the last read
+    position to ensure that previous bytes are not exposed again.
+    """
+
+    def __init__(self, stream):
+        self.byte_iterator = iter(stream)
+        self.buffer = io.BytesIO()
+        self.read_pos = 0
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        while True:
+            self.buffer.seek(self.read_pos)
+            line = self.buffer.readline()
+            if line and line[-1] == ord("\n"):
+                self.read_pos += len(line)
+                return line[:-1]
+            try:
+                chunk = next(self.byte_iterator)
+            except StopIteration:
+                if self.read_pos < self.buffer.getbuffer().nbytes:
+                    continue
+                raise
+            if "PayloadPart" not in chunk:
+                print("Unknown event type:" + chunk)
+                continue
+            self.buffer.seek(0, io.SEEK_END)
+            self.buffer.write(chunk["PayloadPart"]["Bytes"])
+
+
+class SagemakerAPILLM(BaseLLM):
+
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        import boto3
+
+        runtime = boto3.client(
+            "runtime.sagemaker",
+            aws_access_key_id="xxx",
+            aws_secret_access_key="xxx",
+            region_name="us-west-2",
+        )
+
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.endpoint = settings.SAGEMAKER_ENDPOINT
+        self.runtime = runtime
+
+    def _raw_gen(self, baseself, model, messages, stream=False, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        # Construct payload for endpoint
+        payload = {
+            "inputs": prompt,
+            "stream": False,
+            "parameters": {
+                "do_sample": True,
+                "temperature": 0.1,
+                "max_new_tokens": 30,
+                "repetition_penalty": 1.03,
+                "stop": ["</s>", "###"],
+            },
+        }
+        body_bytes = json.dumps(payload).encode("utf-8")
+
+        # Invoke the endpoint
+        response = self.runtime.invoke_endpoint(
+            EndpointName=self.endpoint, ContentType="application/json", Body=body_bytes
+        )
+        result = json.loads(response["Body"].read().decode())
+        import sys
+
+        print(result[0]["generated_text"], file=sys.stderr)
+        return result[0]["generated_text"][len(prompt) :]
+
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        # Construct payload for endpoint
+        payload = {
+            "inputs": prompt,
+            "stream": True,
+            "parameters": {
+                "do_sample": True,
+                "temperature": 0.1,
+                "max_new_tokens": 512,
+                "repetition_penalty": 1.03,
+                "stop": ["</s>", "###"],
+            },
+        }
+        body_bytes = json.dumps(payload).encode("utf-8")
+
+        # Invoke the endpoint
+        response = self.runtime.invoke_endpoint_with_response_stream(
+            EndpointName=self.endpoint, ContentType="application/json", Body=body_bytes
+        )
+        # result = json.loads(response['Body'].read().decode())
+        event_stream = response["Body"]
+        start_json = b"{"
+        for line in LineIterator(event_stream):
+            if line != b"" and start_json in line:
+                # print(line)
+                data = json.loads(line[line.find(start_json) :].decode("utf-8"))
+                if data["token"]["text"] not in ["</s>", "###"]:
+                    print(data["token"]["text"], end="")
+                    yield data["token"]["text"]

application/package.json

@@ -1,5 +0,0 @@
-{
-  "devDependencies": {
-    "tailwindcss": "^3.2.4"
-  }
-}

application/parser/file/bulk.py

@@ -62,7 +62,6 @@ class SimpleDirectoryReader(BaseReader):
             file_extractor: Optional[Dict[str, BaseParser]] = None,
             num_files_limit: Optional[int] = None,
             file_metadata: Optional[Callable[[str], Dict]] = None,
-            chunk_size_max: int = 2048,
     ) -> None:
         """Initialize with parameters."""
         super().__init__()
@@ -148,12 +147,24 @@ class SimpleDirectoryReader(BaseReader):
                 # do standard read
                 with open(input_file, "r", errors=self.errors) as f:
                     data = f.read()
-            if isinstance(data, List):
-                data_list.extend(data)
-            else:
-                data_list.append(str(data))
+            # Prepare metadata for this file
             if self.file_metadata is not None:
-                metadata_list.append(self.file_metadata(str(input_file)))
+                file_metadata = self.file_metadata(str(input_file))
+            else:
+                # Provide a default empty metadata
+                file_metadata = {'title': '', 'store': ''}
+                # TODO: Find a case with no metadata and check if breaks anything 
+
+            if isinstance(data, List):
+                # Extend data_list with each item in the data list
+                data_list.extend([str(d) for d in data])
+                # For each item in the data list, add the file's metadata to metadata_list
+                metadata_list.extend([file_metadata for _ in data])
+            else:
+                # Add the single piece of data to data_list
+                data_list.append(str(data))
+                # Add the file's metadata to metadata_list
+                metadata_list.append(file_metadata)
 
         if concatenate:
             return [Document("\n".join(data_list))]

application/parser/file/html_parser.py

@@ -57,7 +57,7 @@ class HTMLParser(BaseParser):
         title_indexes = [i for i, isd_el in enumerate(isd) if isd_el['type'] == 'Title']
 
         # Creating 'Chunks' - List of lists of strings 
-        # each list starting with with isd_el['type'] = 'Title' and all the data till the next 'Title'
+        # each list starting with isd_el['type'] = 'Title' and all the data till the next 'Title'
         # Each Chunk can be thought of as an individual set of data, which can be sent to the model
         # Where Each Title is grouped together with the data under it
 
@@ -69,10 +69,10 @@ class HTMLParser(BaseParser):
                 Chunks.append([])
             Chunks[-1].append(isd_el['text'])
 
-        # Removing all the chunks with sum of lenth of all the strings in the chunk < 25
+        # Removing all the chunks with sum of length of all the strings in the chunk < 25
         # TODO: This value can be an user defined variable
         for chunk in Chunks:
-            # sum of lenth of all the strings in the chunk
+            # sum of length of all the strings in the chunk
             sum = 0
             sum += len(str(chunk))
             if sum < 25:

application/parser/file/openapi3_parser.py

@@ -0,0 +1,51 @@
+from urllib.parse import urlparse
+
+from openapi_parser import parse
+
+try:
+    from application.parser.file.base_parser import BaseParser
+except ModuleNotFoundError:
+    from base_parser import BaseParser
+
+
+class OpenAPI3Parser(BaseParser):
+    def init_parser(self) -> None:
+        return super().init_parser()
+
+    def get_base_urls(self, urls):
+        base_urls = []
+        for i in urls:
+            parsed_url = urlparse(i)
+            base_url = parsed_url.scheme + "://" + parsed_url.netloc
+            if base_url not in base_urls:
+                base_urls.append(base_url)
+        return base_urls
+
+    def get_info_from_paths(self, path):
+        info = ""
+        if path.operations:
+            for operation in path.operations:
+                info += (
+                    f"\n{operation.method.value}="
+                    f"{operation.responses[0].description}"
+                )
+        return info
+
+    def parse_file(self, file_path):
+        data = parse(file_path)
+        results = ""
+        base_urls = self.get_base_urls(link.url for link in data.servers)
+        base_urls = ",".join([base_url for base_url in base_urls])
+        results += f"Base URL:{base_urls}\n"
+        i = 1
+        for path in data.paths:
+            info = self.get_info_from_paths(path)
+            results += (
+                f"Path{i}: {path.url}\n"
+                f"description: {path.description}\n"
+                f"parameters: {path.parameters}\nmethods: {info}\n"
+            )
+            i += 1
+        with open("results.txt", "w") as f:
+            f.write(results)
+        return results

application/parser/file/rst_parser.py

@@ -27,7 +27,7 @@ class RstParser(BaseParser):
             remove_interpreters: bool = True,
             remove_directives: bool = True,
             remove_whitespaces_excess: bool = True,
-            # Be carefull with remove_characters_excess, might cause data loss
+            # Be careful with remove_characters_excess, might cause data loss
             remove_characters_excess: bool = True,
             **kwargs: Any,
     ) -> None:

application/parser/open_ai_func.py

@@ -1,14 +1,14 @@
 import os
 
 import tiktoken
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
+from application.vectorstore.vector_creator import VectorCreator
+from application.core.settings import settings
 from retry import retry
 
 
-# from langchain.embeddings import HuggingFaceEmbeddings
-# from langchain.embeddings import HuggingFaceInstructEmbeddings
-# from langchain.embeddings import CohereEmbeddings
+# from langchain_community.embeddings import HuggingFaceEmbeddings
+# from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+# from langchain_community.embeddings import CohereEmbeddings
 
 
 def num_tokens_from_string(string: str, encoding_name: str) -> int:
@@ -33,12 +33,23 @@ def call_openai_api(docs, folder_name, task_status):
         os.makedirs(f"{folder_name}")
 
     from tqdm import tqdm
-    docs_test = [docs[0]]
-    docs.pop(0)
     c1 = 0
+    if settings.VECTOR_STORE == "faiss":
+        docs_init = [docs[0]]
+        docs.pop(0)
 
-    store = FAISS.from_documents(docs_test, OpenAIEmbeddings(openai_api_key=os.getenv("EMBEDDINGS_KEY")))
-
+        store = VectorCreator.create_vectorstore(
+            settings.VECTOR_STORE,
+            docs_init = docs_init,
+            path=f"{folder_name}",
+            embeddings_key=os.getenv("EMBEDDINGS_KEY")
+        )
+    else:
+        store = VectorCreator.create_vectorstore(
+            settings.VECTOR_STORE,
+            path=f"{folder_name}",
+            embeddings_key=os.getenv("EMBEDDINGS_KEY")
+        )
     # Uncomment for MPNet embeddings
     # model_name = "sentence-transformers/all-mpnet-base-v2"
     # hf = HuggingFaceEmbeddings(model_name=model_name)
@@ -57,7 +68,8 @@ def call_openai_api(docs, folder_name, task_status):
             store.save_local(f"{folder_name}")
             break
         c1 += 1
-    store.save_local(f"{folder_name}")
+    if settings.VECTOR_STORE == "faiss":
+        store.save_local(f"{folder_name}")
 
 
 def get_user_permission(docs, folder_name):

application/parser/remote/base.py

@@ -0,0 +1,19 @@
+"""Base reader class."""
+from abc import abstractmethod
+from typing import Any, List
+
+from langchain.docstore.document import Document as LCDocument
+from application.parser.schema.base import Document
+
+
+class BaseRemote:
+    """Utilities for loading data from a directory."""
+
+    @abstractmethod
+    def load_data(self, *args: Any, **load_kwargs: Any) -> List[Document]:
+        """Load data from the input directory."""
+
+    def load_langchain_documents(self, **load_kwargs: Any) -> List[LCDocument]:
+        """Load data in LangChain document format."""
+        docs = self.load_data(**load_kwargs)
+        return [d.to_langchain_format() for d in docs]

application/parser/remote/crawler_loader.py

@@ -0,0 +1,59 @@
+import requests
+from urllib.parse import urlparse, urljoin
+from bs4 import BeautifulSoup
+from application.parser.remote.base import BaseRemote
+
+class CrawlerLoader(BaseRemote):
+    def __init__(self, limit=10):
+        from langchain.document_loaders import WebBaseLoader
+        self.loader = WebBaseLoader  # Initialize the document loader
+        self.limit = limit  # Set the limit for the number of pages to scrape
+
+    def load_data(self, inputs):
+        url = inputs
+        # Check if the input is a list and if it is, use the first element
+        if isinstance(url, list) and url:
+            url = url[0]
+
+        # Check if the URL scheme is provided, if not, assume http
+        if not urlparse(url).scheme:
+            url = "http://" + url
+
+        visited_urls = set()  # Keep track of URLs that have been visited
+        base_url = urlparse(url).scheme + "://" + urlparse(url).hostname  # Extract the base URL
+        urls_to_visit = [url]  # List of URLs to be visited, starting with the initial URL
+        loaded_content = []  # Store the loaded content from each URL
+
+        # Continue crawling until there are no more URLs to visit
+        while urls_to_visit:
+            current_url = urls_to_visit.pop(0)  # Get the next URL to visit
+            visited_urls.add(current_url)  # Mark the URL as visited
+
+            # Try to load and process the content from the current URL
+            try:
+                response = requests.get(current_url)  # Fetch the content of the current URL
+                response.raise_for_status()  # Raise an exception for HTTP errors
+                loader = self.loader([current_url])  # Initialize the document loader for the current URL
+                loaded_content.extend(loader.load())  # Load the content and add it to the loaded_content list
+            except Exception as e:
+                # Print an error message if loading or processing fails and continue with the next URL
+                print(f"Error processing URL {current_url}: {e}")
+                continue
+
+            # Parse the HTML content to extract all links
+            soup = BeautifulSoup(response.text, 'html.parser')
+            all_links = [
+                urljoin(current_url, a['href'])
+                for a in soup.find_all('a', href=True)
+                if base_url in urljoin(current_url, a['href'])  # Ensure links are from the same domain
+            ]
+
+            # Add new links to the list of URLs to visit if they haven't been visited yet
+            urls_to_visit.extend([link for link in all_links if link not in visited_urls])
+            urls_to_visit = list(set(urls_to_visit))  # Remove duplicate URLs
+
+            # Stop crawling if the limit of pages to scrape is reached
+            if self.limit is not None and len(visited_urls) >= self.limit:
+                break
+
+        return loaded_content  # Return the loaded content from all visited URLs

application/parser/remote/reddit_loader.py

@@ -0,0 +1,26 @@
+from application.parser.remote.base import BaseRemote
+from langchain_community.document_loaders import RedditPostsLoader
+
+
+class RedditPostsLoaderRemote(BaseRemote):
+    def load_data(self, inputs):
+        data = eval(inputs)
+        client_id = data.get("client_id")
+        client_secret = data.get("client_secret")
+        user_agent = data.get("user_agent")
+        categories = data.get("categories", ["new", "hot"])
+        mode = data.get("mode", "subreddit")
+        search_queries = data.get("search_queries")
+        number_posts = data.get("number_posts", 10)
+        self.loader = RedditPostsLoader(
+            client_id=client_id,
+            client_secret=client_secret,
+            user_agent=user_agent,
+            categories=categories,
+            mode=mode,
+            search_queries=search_queries,
+            number_posts=number_posts,
+        )
+        documents = self.loader.load()
+        print(f"Loaded {len(documents)} documents from Reddit")
+        return documents

application/parser/remote/remote_creator.py

@@ -0,0 +1,20 @@
+from application.parser.remote.sitemap_loader import SitemapLoader
+from application.parser.remote.crawler_loader import CrawlerLoader
+from application.parser.remote.web_loader import WebLoader
+from application.parser.remote.reddit_loader import RedditPostsLoaderRemote
+
+
+class RemoteCreator:
+    loaders = {
+        "url": WebLoader,
+        "sitemap": SitemapLoader,
+        "crawler": CrawlerLoader,
+        "reddit": RedditPostsLoaderRemote,
+    }
+
+    @classmethod
+    def create_loader(cls, type, *args, **kwargs):
+        loader_class = cls.loaders.get(type.lower())
+        if not loader_class:
+            raise ValueError(f"No LLM class found for type {type}")
+        return loader_class(*args, **kwargs)

application/parser/remote/sitemap_loader.py

@@ -0,0 +1,81 @@
+import requests
+import re  # Import regular expression library
+import xml.etree.ElementTree as ET
+from application.parser.remote.base import BaseRemote
+
+class SitemapLoader(BaseRemote):
+    def __init__(self, limit=20):
+        from langchain.document_loaders import WebBaseLoader
+        self.loader = WebBaseLoader
+        self.limit = limit  # Adding limit to control the number of URLs to process
+
+    def load_data(self, inputs):
+        sitemap_url= inputs
+        # Check if the input is a list and if it is, use the first element
+        if isinstance(sitemap_url, list) and sitemap_url:
+            url = sitemap_url[0]
+
+        urls = self._extract_urls(sitemap_url)
+        if not urls:
+            print(f"No URLs found in the sitemap: {sitemap_url}")
+            return []
+
+        # Load content of extracted URLs
+        documents = []
+        processed_urls = 0  # Counter for processed URLs
+        for url in urls:
+            if self.limit is not None and processed_urls >= self.limit:
+                break  # Stop processing if the limit is reached
+
+            try:
+                loader = self.loader([url])
+                documents.extend(loader.load())
+                processed_urls += 1  # Increment the counter after processing each URL
+            except Exception as e:
+                print(f"Error processing URL {url}: {e}")
+                continue
+
+        return documents
+
+    def _extract_urls(self, sitemap_url):
+        try:
+            response = requests.get(sitemap_url)
+            response.raise_for_status()  # Raise an exception for HTTP errors
+        except (requests.exceptions.HTTPError, requests.exceptions.ConnectionError) as e:
+            print(f"Failed to fetch sitemap: {sitemap_url}. Error: {e}")
+            return []
+
+        # Determine if this is a sitemap or a URL
+        if self._is_sitemap(response):
+            # It's a sitemap, so parse it and extract URLs
+            return self._parse_sitemap(response.content)
+        else:
+            # It's not a sitemap, return the URL itself
+            return [sitemap_url]
+
+    def _is_sitemap(self, response):
+        content_type = response.headers.get('Content-Type', '')
+        if 'xml' in content_type or response.url.endswith('.xml'):
+            return True
+
+        if '<sitemapindex' in response.text or '<urlset' in response.text:
+            return True
+
+        return False
+
+    def _parse_sitemap(self, sitemap_content):
+        # Remove namespaces
+        sitemap_content = re.sub(' xmlns="[^"]+"', '', sitemap_content.decode('utf-8'), count=1)
+
+        root = ET.fromstring(sitemap_content)
+
+        urls = []
+        for loc in root.findall('.//url/loc'):
+            urls.append(loc.text)
+
+        # Check for nested sitemaps
+        for sitemap in root.findall('.//sitemap/loc'):
+            nested_sitemap_url = sitemap.text
+            urls.extend(self._extract_urls(nested_sitemap_url))
+
+        return urls

application/parser/remote/telegram.py

@@ -0,0 +1,11 @@
+from langchain.document_loader import TelegramChatApiLoader
+from application.parser.remote.base import BaseRemote
+
+class TelegramChatApiRemote(BaseRemote):
+    def _init_parser(self, *args, **load_kwargs):
+        self.loader = TelegramChatApiLoader(**load_kwargs)
+        return {}
+
+    def parse_file(self, *args, **load_kwargs):
+
+        return 

application/parser/remote/web_loader.py

@@ -0,0 +1,22 @@
+from application.parser.remote.base import BaseRemote
+
+class WebLoader(BaseRemote):
+    def __init__(self):
+        from langchain.document_loaders import WebBaseLoader
+        self.loader = WebBaseLoader
+
+    def load_data(self, inputs):
+        urls = inputs
+
+        if isinstance(urls, str):
+            urls = [urls] # Convert string to list if a single URL is passed
+
+        documents = []
+        for url in urls:
+            try:
+                loader = self.loader([url])  # Process URLs one by one
+                documents.extend(loader.load())
+            except Exception as e:
+                print(f"Error processing URL {url}: {e}")
+                continue  # Continue with the next URL if an error occurs
+        return documents

application/parser/token_func.py

@@ -21,16 +21,18 @@ def group_documents(documents: List[Document], min_tokens: int, max_tokens: int)
     for doc in documents:
         doc_len = len(tiktoken.get_encoding("cl100k_base").encode(doc.text))
 
-        if current_group is None:
-            current_group = Document(text=doc.text, doc_id=doc.doc_id, embedding=doc.embedding,
-                                     extra_info=doc.extra_info)
-        elif len(tiktoken.get_encoding("cl100k_base").encode(
-                current_group.text)) + doc_len < max_tokens and doc_len < min_tokens:
-            current_group.text += " " + doc.text
+        # Check if current group is empty or if the document can be added based on token count and matching metadata
+        if (current_group is None or 
+            (len(tiktoken.get_encoding("cl100k_base").encode(current_group.text)) + doc_len < max_tokens and 
+             doc_len < min_tokens and 
+             current_group.extra_info == doc.extra_info)):
+            if current_group is None:
+                current_group = doc  # Use the document directly to retain its metadata
+            else:
+                current_group.text += " " + doc.text  # Append text to the current group
         else:
             docs.append(current_group)
-            current_group = Document(text=doc.text, doc_id=doc.doc_id, embedding=doc.embedding,
-                                     extra_info=doc.extra_info)
+            current_group = doc  # Start a new group with the current document
 
     if current_group is not None:
         docs.append(current_group)

application/prompts/chat_combine_default.txt

@@ -0,0 +1,9 @@
+You are a helpful AI assistant, DocsGPT, specializing in document assistance, designed to offer detailed and informative responses. 
+If appropriate, your answers can include code examples, formatted as follows:
+```(language)
+(code)
+```
+You effectively utilize chat history, ensuring relevant and tailored responses. 
+If a question doesn't align with your context, you provide friendly and helpful replies.
+----------------
+{summaries}

application/prompts/chat_combine_strict.txt

@@ -0,0 +1,13 @@
+You are an AI Assistant, DocsGPT, adept at offering document assistance. 
+Your expertise lies in providing answer on top of provided context. 
+You can leverage the chat history if needed.
+Answer the question based on the context below. 
+Keep the answer concise. Respond "Irrelevant context" if not sure about the answer.
+If question is not related to the context, respond "Irrelevant context".
+When using code examples, use the following format:
+```(language)
+(code)
+```
+ ----------------
+ Context:
+ {summaries}

application/prompts/combine_prompt.txt

@@ -1,25 +0,0 @@
-You are a DocsGPT, friendly and helpful AI assistant by Arc53 that provides help with documents. You give thorough answers with code examples if possible.
-
-QUESTION: How to merge tables in pandas?
-=========
-Content: pandas provides various facilities for easily combining together Series or DataFrame with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
-Source: 28-pl
-Content: pandas provides a single function, merge(), as the entry point for all standard database join operations between DataFrame or named Series objects: \n\npandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)
-Source: 30-pl
-=========
-FINAL ANSWER: To merge two tables in pandas, you can use the pd.merge() function. The basic syntax is: \n\npd.merge(left, right, on, how) \n\nwhere left and right are the two tables to merge, on is the column to merge on, and how is the type of merge to perform. \n\nFor example, to merge the two tables df1 and df2 on the column 'id', you can use: \n\npd.merge(df1, df2, on='id', how='inner')
-SOURCES: 28-pl 30-pl
-
-QUESTION: How are you?
-=========
-CONTENT:
-SOURCE:
-=========
-FINAL ANSWER: I am fine, thank you. How are you?
-SOURCES:
-
-QUESTION: {{ question }}
-=========
-{{ summaries }}
-=========
-FINAL ANSWER:

application/prompts/combine_prompt_hist.txt

@@ -1,33 +0,0 @@
-You are a DocsGPT, friendly and helpful AI assistant by Arc53 that provides help with documents. You give thorough answers with code examples if possible.
-
-QUESTION: How to merge tables in pandas?
-=========
-Content: pandas provides various facilities for easily combining together Series or DataFrame with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
-Source: 28-pl
-Content: pandas provides a single function, merge(), as the entry point for all standard database join operations between DataFrame or named Series objects: \n\npandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)
-Source: 30-pl
-=========
-FINAL ANSWER: To merge two tables in pandas, you can use the pd.merge() function. The basic syntax is: \n\npd.merge(left, right, on, how) \n\nwhere left and right are the two tables to merge, on is the column to merge on, and how is the type of merge to perform. \n\nFor example, to merge the two tables df1 and df2 on the column 'id', you can use: \n\npd.merge(df1, df2, on='id', how='inner')
-SOURCES: 28-pl 30-pl
-
-QUESTION: How are you?
-=========
-CONTENT:
-SOURCE:
-=========
-FINAL ANSWER: I am fine, thank you. How are you?
-SOURCES:
-
-QUESTION: {{ historyquestion }}
-=========
-CONTENT:
-SOURCE:
-=========
-FINAL ANSWER: {{ historyanswer }}
-SOURCES:
-
-QUESTION: {{ question }}
-=========
-{{ summaries }}
-=========
-FINAL ANSWER:

application/prompts/question_prompt.txt

@@ -1,4 +0,0 @@
-Use the following portion of a long document to see if any of the text is relevant to answer the question.
-{{ context }}
-Question: {{ question }}
-Provide all relevant text to the question verbatim. Summarize if needed. If nothing relevant return "-".

application/requirements.txt

@@ -1,104 +1,35 @@
-aiodns==3.0.0
-aiohttp==3.8.5
-aiohttp-retry==2.8.3
-aiosignal==1.3.1
-aleph-alpha-client==2.16.1
-amqp==5.1.1
-async-timeout==4.0.2
-attrs==22.2.0
-billiard==3.6.4.0
-blobfile==2.0.1
-boto3==1.28.20
-celery==5.2.7
-cffi==1.15.1
-charset-normalizer==3.1.0
-click==8.1.3
-click-didyoumean==0.3.0
-click-plugins==1.1.1
-click-repl==0.2.0
-cryptography==41.0.3
-dataclasses-json==0.5.7
-decorator==5.1.1
-dill==0.3.6
-dnspython==2.3.0
-ecdsa==0.18.0
-entrypoints==0.4
-faiss-cpu==1.7.3
-filelock==3.9.0
-Flask==2.2.5
-Flask-Cors==3.0.10
-frozenlist==1.3.3
-geojson==2.5.0
-gunicorn==20.1.0
-greenlet==2.0.2
-gpt4all==0.1.7
-huggingface-hub==0.15.1
-humbug==0.3.2
-idna==3.4
-itsdangerous==2.1.2
-Jinja2==3.1.2
-jmespath==1.0.1
-joblib==1.2.0
-kombu==5.2.4
-langchain==0.0.263
-loguru==0.6.0
-lxml==4.9.2
-MarkupSafe==2.1.2
-marshmallow==3.19.0
-marshmallow-enum==1.5.1
-mpmath==1.3.0
-multidict==6.0.4
-multiprocess==0.70.14
-mypy-extensions==1.0.0
-networkx==3.0
-npx
+anthropic==0.12.0
+boto3==1.34.6
+celery==5.3.6
+dataclasses_json==0.6.3
+docx2txt==0.8
+duckduckgo-search==5.3.0
+EbookLib==0.18
+elasticsearch==8.12.0
+escodegen==1.0.11
+esprima==4.0.1
+faiss-cpu==1.7.4
+Flask==3.0.1
+gunicorn==21.2.0
+html2text==2020.1.16
+javalang==0.13.0
+langchain==0.1.4
+langchain-openai==0.0.5
 nltk==3.8.1
-numcodecs==0.11.0
-numpy==1.24.2
-openai==0.27.8
-packaging==23.0
-pathos==0.3.0
-Pillow==9.4.0
-pox==0.3.2
-ppft==1.7.6.6
-prompt-toolkit==3.0.38
-py==1.11.0
-pyasn1==0.4.8
-pycares==4.3.0
-pycparser==2.21
-pycryptodomex==3.17
-pydantic==1.10.5
-PyJWT==2.6.0
-pymongo==4.3.3
-pyowm==3.3.0
+openapi3_parser==1.1.16
+pandas==2.2.0
+pydantic_settings==2.1.0
+pymongo==4.6.3
 PyPDF2==3.0.1
-PySocks==1.7.1
-pytest
-python-dateutil==2.8.2
-python-dotenv==1.0.0
-python-jose==3.3.0
-pytz==2022.7.1
-PyYAML==6.0
-redis==4.5.4
-regex==2022.10.31
-requests==2.31.0
+python-dotenv==1.0.1
+qdrant-client==1.8.2
+redis==5.0.1
+Requests==2.31.0
 retry==0.9.2
-rsa==4.9
-scikit-learn==1.2.2
-scipy==1.10.1
-sentencepiece
-six==1.16.0
-SQLAlchemy==1.4.46
-sympy==1.11.1
-tenacity==8.2.2
-threadpoolctl==3.1.0
-tiktoken
-tqdm==4.65.0
-transformers==4.30.0
-typer==0.7.0
-typing-inspect==0.8.0
-typing_extensions==4.5.0
-urllib3==1.26.14
-vine==5.0.0
-wcwidth==0.2.6
-yarl==1.8.2
+sentence-transformers
+tiktoken==0.5.2
+torch==2.1.2
+tqdm==4.66.1
+transformers==4.36.2
+unstructured==0.12.2
+Werkzeug==3.0.1

application/retriever/base.py

@@ -0,0 +1,14 @@
+from abc import ABC, abstractmethod
+
+
+class BaseRetriever(ABC):
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def gen(self, *args, **kwargs):
+        pass
+
+    @abstractmethod
+    def search(self, *args, **kwargs):
+        pass

application/retriever/brave_search.py

@@ -0,0 +1,95 @@
+import json
+from application.retriever.base import BaseRetriever
+from application.core.settings import settings
+from application.llm.llm_creator import LLMCreator
+from application.utils import count_tokens
+from langchain_community.tools import BraveSearch
+
+
+class BraveRetSearch(BaseRetriever):
+
+    def __init__(
+        self,
+        question,
+        source,
+        chat_history,
+        prompt,
+        chunks=2,
+        gpt_model="docsgpt",
+        user_api_key=None,
+    ):
+        self.question = question
+        self.source = source
+        self.chat_history = chat_history
+        self.prompt = prompt
+        self.chunks = chunks
+        self.gpt_model = gpt_model
+        self.user_api_key = user_api_key
+
+    def _get_data(self):
+        if self.chunks == 0:
+            docs = []
+        else:
+            search = BraveSearch.from_api_key(
+                api_key=settings.BRAVE_SEARCH_API_KEY,
+                search_kwargs={"count": int(self.chunks)},
+            )
+            results = search.run(self.question)
+            results = json.loads(results)
+
+            docs = []
+            for i in results:
+                try:
+                    title = i["title"]
+                    link = i["link"]
+                    snippet = i["snippet"]
+                    docs.append({"text": snippet, "title": title, "link": link})
+                except IndexError:
+                    pass
+        if settings.LLM_NAME == "llama.cpp":
+            docs = [docs[0]]
+
+        return docs
+
+    def gen(self):
+        docs = self._get_data()
+
+        # join all page_content together with a newline
+        docs_together = "\n".join([doc["text"] for doc in docs])
+        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
+        messages_combine = [{"role": "system", "content": p_chat_combine}]
+        for doc in docs:
+            yield {"source": doc}
+
+        if len(self.chat_history) > 1:
+            tokens_current_history = 0
+            # count tokens in history
+            self.chat_history.reverse()
+            for i in self.chat_history:
+                if "prompt" in i and "response" in i:
+                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(
+                        i["response"]
+                    )
+                    if (
+                        tokens_current_history + tokens_batch
+                        < settings.TOKENS_MAX_HISTORY
+                    ):
+                        tokens_current_history += tokens_batch
+                        messages_combine.append(
+                            {"role": "user", "content": i["prompt"]}
+                        )
+                        messages_combine.append(
+                            {"role": "system", "content": i["response"]}
+                        )
+        messages_combine.append({"role": "user", "content": self.question})
+
+        llm = LLMCreator.create_llm(
+            settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key
+        )
+
+        completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
+        for line in completion:
+            yield {"answer": str(line)}
+
+    def search(self):
+        return self._get_data()

application/retriever/classic_rag.py

@@ -0,0 +1,110 @@
+import os
+from application.retriever.base import BaseRetriever
+from application.core.settings import settings
+from application.vectorstore.vector_creator import VectorCreator
+from application.llm.llm_creator import LLMCreator
+
+from application.utils import count_tokens
+
+
+class ClassicRAG(BaseRetriever):
+
+    def __init__(
+        self,
+        question,
+        source,
+        chat_history,
+        prompt,
+        chunks=2,
+        gpt_model="docsgpt",
+        user_api_key=None,
+    ):
+        self.question = question
+        self.vectorstore = self._get_vectorstore(source=source)
+        self.chat_history = chat_history
+        self.prompt = prompt
+        self.chunks = chunks
+        self.gpt_model = gpt_model
+        self.user_api_key = user_api_key
+
+    def _get_vectorstore(self, source):
+        if "active_docs" in source:
+            if source["active_docs"].split("/")[0] == "default":
+                vectorstore = ""
+            elif source["active_docs"].split("/")[0] == "local":
+                vectorstore = "indexes/" + source["active_docs"]
+            else:
+                vectorstore = "vectors/" + source["active_docs"]
+            if source["active_docs"] == "default":
+                vectorstore = ""
+        else:
+            vectorstore = ""
+        vectorstore = os.path.join("application", vectorstore)
+        return vectorstore
+
+    def _get_data(self):
+        if self.chunks == 0:
+            docs = []
+        else:
+            docsearch = VectorCreator.create_vectorstore(
+                settings.VECTOR_STORE, self.vectorstore, settings.EMBEDDINGS_KEY
+            )
+            docs_temp = docsearch.search(self.question, k=self.chunks)
+            docs = [
+                {
+                    "title": (
+                        i.metadata["title"].split("/")[-1]
+                        if i.metadata
+                        else i.page_content
+                    ),
+                    "text": i.page_content,
+                }
+                for i in docs_temp
+            ]
+        if settings.LLM_NAME == "llama.cpp":
+            docs = [docs[0]]
+
+        return docs
+
+    def gen(self):
+        docs = self._get_data()
+
+        # join all page_content together with a newline
+        docs_together = "\n".join([doc["text"] for doc in docs])
+        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
+        messages_combine = [{"role": "system", "content": p_chat_combine}]
+        for doc in docs:
+            yield {"source": doc}
+
+        if len(self.chat_history) > 1:
+            tokens_current_history = 0
+            # count tokens in history
+            self.chat_history.reverse()
+            for i in self.chat_history:
+                if "prompt" in i and "response" in i:
+                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(
+                        i["response"]
+                    )
+                    if (
+                        tokens_current_history + tokens_batch
+                        < settings.TOKENS_MAX_HISTORY
+                    ):
+                        tokens_current_history += tokens_batch
+                        messages_combine.append(
+                            {"role": "user", "content": i["prompt"]}
+                        )
+                        messages_combine.append(
+                            {"role": "system", "content": i["response"]}
+                        )
+        messages_combine.append({"role": "user", "content": self.question})
+
+        llm = LLMCreator.create_llm(
+            settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key
+        )
+
+        completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
+        for line in completion:
+            yield {"answer": str(line)}
+
+    def search(self):
+        return self._get_data()

application/retriever/duckduck_search.py

@@ -0,0 +1,112 @@
+from application.retriever.base import BaseRetriever
+from application.core.settings import settings
+from application.llm.llm_creator import LLMCreator
+from application.utils import count_tokens
+from langchain_community.tools import DuckDuckGoSearchResults
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
+
+
+class DuckDuckSearch(BaseRetriever):
+
+    def __init__(
+        self,
+        question,
+        source,
+        chat_history,
+        prompt,
+        chunks=2,
+        gpt_model="docsgpt",
+        user_api_key=None,
+    ):
+        self.question = question
+        self.source = source
+        self.chat_history = chat_history
+        self.prompt = prompt
+        self.chunks = chunks
+        self.gpt_model = gpt_model
+        self.user_api_key = user_api_key
+
+    def _parse_lang_string(self, input_string):
+        result = []
+        current_item = ""
+        inside_brackets = False
+        for char in input_string:
+            if char == "[":
+                inside_brackets = True
+            elif char == "]":
+                inside_brackets = False
+                result.append(current_item)
+                current_item = ""
+            elif inside_brackets:
+                current_item += char
+
+        if inside_brackets:
+            result.append(current_item)
+
+        return result
+
+    def _get_data(self):
+        if self.chunks == 0:
+            docs = []
+        else:
+            wrapper = DuckDuckGoSearchAPIWrapper(max_results=self.chunks)
+            search = DuckDuckGoSearchResults(api_wrapper=wrapper)
+            results = search.run(self.question)
+            results = self._parse_lang_string(results)
+
+            docs = []
+            for i in results:
+                try:
+                    text = i.split("title:")[0]
+                    title = i.split("title:")[1].split("link:")[0]
+                    link = i.split("link:")[1]
+                    docs.append({"text": text, "title": title, "link": link})
+                except IndexError:
+                    pass
+        if settings.LLM_NAME == "llama.cpp":
+            docs = [docs[0]]
+
+        return docs
+
+    def gen(self):
+        docs = self._get_data()
+
+        # join all page_content together with a newline
+        docs_together = "\n".join([doc["text"] for doc in docs])
+        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
+        messages_combine = [{"role": "system", "content": p_chat_combine}]
+        for doc in docs:
+            yield {"source": doc}
+
+        if len(self.chat_history) > 1:
+            tokens_current_history = 0
+            # count tokens in history
+            self.chat_history.reverse()
+            for i in self.chat_history:
+                if "prompt" in i and "response" in i:
+                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(
+                        i["response"]
+                    )
+                    if (
+                        tokens_current_history + tokens_batch
+                        < settings.TOKENS_MAX_HISTORY
+                    ):
+                        tokens_current_history += tokens_batch
+                        messages_combine.append(
+                            {"role": "user", "content": i["prompt"]}
+                        )
+                        messages_combine.append(
+                            {"role": "system", "content": i["response"]}
+                        )
+        messages_combine.append({"role": "user", "content": self.question})
+
+        llm = LLMCreator.create_llm(
+            settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key
+        )
+
+        completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
+        for line in completion:
+            yield {"answer": str(line)}
+
+    def search(self):
+        return self._get_data()

application/retriever/retriever_creator.py

@@ -0,0 +1,19 @@
+from application.retriever.classic_rag import ClassicRAG
+from application.retriever.duckduck_search import DuckDuckSearch
+from application.retriever.brave_search import BraveRetSearch
+
+
+
+class RetrieverCreator:
+    retievers = {
+        'classic': ClassicRAG,
+        'duckduck_search': DuckDuckSearch,
+        'brave_search': BraveRetSearch
+    }
+
+    @classmethod
+    def create_retriever(cls, type, *args, **kwargs):
+        retiever_class = cls.retievers.get(type.lower())
+        if not retiever_class:
+            raise ValueError(f"No retievers class found for type {type}")
+        return retiever_class(*args, **kwargs)

application/static/dist/css/output.css

@@ -1,987 +0,0 @@
-/*
-! tailwindcss v3.2.4 | MIT License | https://tailwindcss.com
-*/
-
-/*
-1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4)
-2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116)
-*/
-
-*,
-::before,
-::after {
-  box-sizing: border-box;
-  /* 1 */
-  border-width: 0;
-  /* 2 */
-  border-style: solid;
-  /* 2 */
-  border-color: #e5e7eb;
-  /* 2 */
-}
-
-::before,
-::after {
-  --tw-content: '';
-}
-
-/*
-1. Use a consistent sensible line-height in all browsers.
-2. Prevent adjustments of font size after orientation changes in iOS.
-3. Use a more readable tab size.
-4. Use the user's configured `sans` font-family by default.
-5. Use the user's configured `sans` font-feature-settings by default.
-*/
-
-html {
-  line-height: 1.5;
-  /* 1 */
-  -webkit-text-size-adjust: 100%;
-  /* 2 */
-  -moz-tab-size: 4;
-  /* 3 */
-  -o-tab-size: 4;
-     tab-size: 4;
-  /* 3 */
-  font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
-  /* 4 */
-  font-feature-settings: normal;
-  /* 5 */
-}
-
-/*
-1. Remove the margin in all browsers.
-2. Inherit line-height from `html` so users can set them as a class directly on the `html` element.
-*/
-
-body {
-  margin: 0;
-  /* 1 */
-  line-height: inherit;
-  /* 2 */
-}
-
-/*
-1. Add the correct height in Firefox.
-2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655)
-3. Ensure horizontal rules are visible by default.
-*/
-
-hr {
-  height: 0;
-  /* 1 */
-  color: inherit;
-  /* 2 */
-  border-top-width: 1px;
-  /* 3 */
-}
-
-/*
-Add the correct text decoration in Chrome, Edge, and Safari.
-*/
-
-abbr:where([title]) {
-  -webkit-text-decoration: underline dotted;
-          text-decoration: underline dotted;
-}
-
-/*
-Remove the default font size and weight for headings.
-*/
-
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
-  font-size: inherit;
-  font-weight: inherit;
-}
-
-/*
-Reset links to optimize for opt-in styling instead of opt-out.
-*/
-
-a {
-  color: inherit;
-  text-decoration: inherit;
-}
-
-/*
-Add the correct font weight in Edge and Safari.
-*/
-
-b,
-strong {
-  font-weight: bolder;
-}
-
-/*
-1. Use the user's configured `mono` font family by default.
-2. Correct the odd `em` font sizing in all browsers.
-*/
-
-code,
-kbd,
-samp,
-pre {
-  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
-  /* 1 */
-  font-size: 1em;
-  /* 2 */
-}
-
-/*
-Add the correct font size in all browsers.
-*/
-
-small {
-  font-size: 80%;
-}
-
-/*
-Prevent `sub` and `sup` elements from affecting the line height in all browsers.
-*/
-
-sub,
-sup {
-  font-size: 75%;
-  line-height: 0;
-  position: relative;
-  vertical-align: baseline;
-}
-
-sub {
-  bottom: -0.25em;
-}
-
-sup {
-  top: -0.5em;
-}
-
-/*
-1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297)
-2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016)
-3. Remove gaps between table borders by default.
-*/
-
-table {
-  text-indent: 0;
-  /* 1 */
-  border-color: inherit;
-  /* 2 */
-  border-collapse: collapse;
-  /* 3 */
-}
-
-/*
-1. Change the font styles in all browsers.
-2. Remove the margin in Firefox and Safari.
-3. Remove default padding in all browsers.
-*/
-
-button,
-input,
-optgroup,
-select,
-textarea {
-  font-family: inherit;
-  /* 1 */
-  font-size: 100%;
-  /* 1 */
-  font-weight: inherit;
-  /* 1 */
-  line-height: inherit;
-  /* 1 */
-  color: inherit;
-  /* 1 */
-  margin: 0;
-  /* 2 */
-  padding: 0;
-  /* 3 */
-}
-
-/*
-Remove the inheritance of text transform in Edge and Firefox.
-*/
-
-button,
-select {
-  text-transform: none;
-}
-
-/*
-1. Correct the inability to style clickable types in iOS and Safari.
-2. Remove default button styles.
-*/
-
-button,
-[type='button'],
-[type='reset'],
-[type='submit'] {
-  -webkit-appearance: button;
-  /* 1 */
-  background-color: transparent;
-  /* 2 */
-  background-image: none;
-  /* 2 */
-}
-
-/*
-Use the modern Firefox focus style for all focusable elements.
-*/
-
-:-moz-focusring {
-  outline: auto;
-}
-
-/*
-Remove the additional `:invalid` styles in Firefox. (https://github.com/mozilla/gecko-dev/blob/2f9eacd9d3d995c937b4251a5557d95d494c9be1/layout/style/res/forms.css#L728-L737)
-*/
-
-:-moz-ui-invalid {
-  box-shadow: none;
-}
-
-/*
-Add the correct vertical alignment in Chrome and Firefox.
-*/
-
-progress {
-  vertical-align: baseline;
-}
-
-/*
-Correct the cursor style of increment and decrement buttons in Safari.
-*/
-
-::-webkit-inner-spin-button,
-::-webkit-outer-spin-button {
-  height: auto;
-}
-
-/*
-1. Correct the odd appearance in Chrome and Safari.
-2. Correct the outline style in Safari.
-*/
-
-[type='search'] {
-  -webkit-appearance: textfield;
-  /* 1 */
-  outline-offset: -2px;
-  /* 2 */
-}
-
-/*
-Remove the inner padding in Chrome and Safari on macOS.
-*/
-
-::-webkit-search-decoration {
-  -webkit-appearance: none;
-}
-
-/*
-1. Correct the inability to style clickable types in iOS and Safari.
-2. Change font properties to `inherit` in Safari.
-*/
-
-::-webkit-file-upload-button {
-  -webkit-appearance: button;
-  /* 1 */
-  font: inherit;
-  /* 2 */
-}
-
-/*
-Add the correct display in Chrome and Safari.
-*/
-
-summary {
-  display: list-item;
-}
-
-/*
-Removes the default spacing and border for appropriate elements.
-*/
-
-blockquote,
-dl,
-dd,
-h1,
-h2,
-h3,
-h4,
-h5,
-h6,
-hr,
-figure,
-p,
-pre {
-  margin: 0;
-}
-
-fieldset {
-  margin: 0;
-  padding: 0;
-}
-
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-      margin-bottom: 12rem;
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-    .sm\:max-lg\:hidden {
-      display: none;
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-    .sm\:max-lg\:p-5 {
-      padding: 1.25rem;
-    }
-  }
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-@media (min-width: 1024px) {
-  .lg\:flex {
-    display: flex;
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-  .lg\:w-3\/4 {
-    width: 75%;
-  }
-
-  .lg\:w-1\/4 {
-    width: 25%;
-  }
-}
-
-

application/static/favicon/site.webmanifest

@@ -1 +0,0 @@
-{"name":"","short_name":"","icons":[{"src":"/android-chrome-192x192.png","sizes":"192x192","type":"image/png"},{"src":"/android-chrome-512x512.png","sizes":"512x512","type":"image/png"}],"theme_color":"#ffffff","background_color":"#ffffff","display":"standalone"}

application/static/src/authapi.js

@@ -1,19 +0,0 @@
-function resetApiKey() {
-  const modal = document.getElementById("modal");
-  modal.classList.toggle("hidden");
-}
-
-const apiKeyForm = document.getElementById("api-key-form");
-if (apiKeyForm) {
-  apiKeyForm.addEventListener("submit", function(event) {
-    event.preventDefault();
-
-    const apiKeyInput = document.getElementById("api-key-input");
-    const apiKey = apiKeyInput.value;
-
-    localStorage.setItem("apiKey", apiKey);
-
-    apiKeyInput.value = "";
-    modal.classList.toggle("hidden");
-  });
-}

application/static/src/chat.js

@@ -1,76 +0,0 @@
-var form = document.getElementById('message-form');
-var errorModal = document.getElementById('error-alert')
-document.getElementById('close').addEventListener('click',()=>{
-    errorModal.classList.toggle('hidden')
-})
-
-
-function submitForm(event){
-    event.preventDefault()
-    var message = document.getElementById("message-input").value;
-    console.log(message.length)
-    if(message.length === 0){
-        return
-    }
-    msg_html = '<div class="bg-blue-500 text-white p-2 rounded-lg mb-2 self-end"><p class="text-sm">'
-    msg_html += message
-    msg_html += '</p></div>'
-    document.getElementById("messages").innerHTML += msg_html;
-    let chatWindow = document.getElementById("messages-container");
-    chatWindow.scrollTop = chatWindow.scrollHeight;
-    document.getElementById("message-input").value = "";
-    document.getElementById("button-submit").innerHTML = '<i class="fa fa-circle-o-notch fa-spin"></i> Thinking...';
-    document.getElementById("button-submit").disabled = true;
-    if (localStorage.getItem('activeDocs') == null) {
-        localStorage.setItem('activeDocs', 'default')
-    }
-
-    
-    fetch('/api/answer', {
-        method: 'POST',
-        headers: {
-            'Content-Type': 'application/json',
-        },
-
-        body: JSON.stringify({question: message,
-            api_key: localStorage.getItem('apiKey'),
-            embeddings_key: localStorage.getItem('apiKey'),
-            history: localStorage.getItem('chatHistory'),
-            active_docs: localStorage.getItem('activeDocs')}),
-    }).then((response)=> response.json())
-    .then(data => {
-            console.log('Success:', data);
-            if(data.error){
-            document.getElementById('text-error').textContent = `Error : ${JSON.stringify(data.message)}`
-            errorModal.classList.toggle('hidden')
-            }
-            if(data.answer){
-            msg_html = '<div class="bg-indigo-500 text-white p-2 rounded-lg mb-2 self-start"><code class="text-sm">'
-            data.answer = data.answer.replace(/\n/g, "<br>");
-            msg_html += data.answer
-            msg_html += '</code></div>'
-            document.getElementById("messages").innerHTML += msg_html;
-            let chatWindow = document.getElementById("messages-container");
-            chatWindow.scrollTop = chatWindow.scrollHeight;
-            }
-            document.getElementById("button-submit").innerHTML = 'Send';
-            document.getElementById("button-submit").disabled = false;
-            let chatHistory = [message, data.answer || ''];
-            localStorage.setItem('chatHistory', JSON.stringify(chatHistory));
-
-            
-
-
-        })
-        .catch((error) => {
-            console.error('Error:', error);
-            // console.log(error);
-            // document.getElementById("button-submit").innerHTML = 'Send';
-            // document.getElementById("button-submit").disabled = false;
-
-        });
-}
-
-//window.addEventListener('submit',submitForm)
-// rewrite using id = button-submit
-document.getElementById("button-submit").addEventListener('click',submitForm)

application/static/src/choiceChange.js

@@ -1,15 +0,0 @@
-document.getElementById("select-docs").addEventListener("change", function() {
-localStorage.setItem('activeDocs', this.value)
-     fetch('/api/docs_check', {
-         method: 'POST',
-         headers: {
-             'Content-Type': 'application/json',
-         },
-         body: JSON.stringify({docs: this.value}),
-     }).then(response => response.json()).then(
-            data => {
-                console.log('Success:', data);
-            }
-     )
-});
-

application/static/src/input.css

@@ -1,37 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-
-
-
-@media screen and (max-width: 1024px) {
-  .text-lg {
-    font-size: 3.125rem;
-    margin: 2rem;
-    line-height: inherit;
-  }
-  .text-sm {
-    font-size: 2.5rem;
-    margin: 1.5rem;
-    line-height: inherit;
-  }
-
-}
-
-
-.loader {
-  border: 16px solid #f3f3f3; /* Light grey */
-  border-top: 16px solid #3498db; /* Blue */
-  border-radius: 50%;
-  width: 120px;
-  height: 120px;
-  animation: spin 2s linear infinite;
-}
-
-@keyframes spin {
-  0% { transform: rotate(0deg); }
-  100% { transform: rotate(360deg); }
-}
-
-

application/tailwind.config.js

@@ -1,8 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: ["./templates/**/*.html", "./static/src/**/*.js"],
-  theme: {
-    extend: {},
-  },
-  plugins: [],
-}

application/templates/index.html

@@ -1,215 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <title>DocsGPT 🦖 Preview</title>
-    <link href="{{url_for('static',filename='dist/css/output.css')}}" rel="stylesheet">
-      <link rel="favicon" href="{{ url_for('static', filename='favicon/favicon.ico') }}">
-      <link rel="apple-touch-icon" sizes="180x180" href="{{ url_for('static', filename='favicon/apple-touch-icon.png') }}">
-    <link rel="icon" type="image/png" sizes="32x32" href="{{ url_for('static', filename='favicon/favicon-32x32.png') }}">
-    <link rel="icon" type="image/png" sizes="16x16" href="{{ url_for('static', filename='favicon/favicon-16x16.png') }}">
-    <link rel="manifest" href="{{ url_for('static', filename='favicon//site.webmanifest') }}">
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
-
-
-
-  </head>
-
-
-  <body>
-    
-
-
-    <header class="bg-white p-2 flex justify-between items-center">
-      <h1 class="text-lg font-medium">DocsGPT 🦖 Preview</h1>
-        <div>
-      <a href="https://github.com/arc53/docsgpt" class="text-blue-500 hover:text-blue-800 text-sm">About</a>
-            {% if not api_key_set %}
-      <button class="text-sm text-yellow-500 hover:text-yellow-800" onclick="resetApiKey()">Reset Key</button>
-        {% endif %}
-            </div>
-    </header>
-
-  
- <!-- Alert Info  -->
- <div class="border flex justify-between 
-  w-auto px-4 py-3 rounded relative 
-  hidden" style="background-color: rgb(197, 51, 51);color: white;" id="error-alert" role="alert">
-  <span class="block sm:inline" id="text-error"></span>
-  <strong class="text-xl align-center alert-del" style="cursor: pointer;" id="close">&times;</strong>
-</div>
-
-
-    <div class="lg:flex ml-2 mr-2">
-      <div class="lg:w-3/4 min-h-screen max-h-screen">
-        <div class="w-full flex flex-col h-5/6">
-          <div id="messages-container" style="overflow: auto;" class="sm:max-lg:mb-[12rem]">
-
-            <div id="messages" class="w-full flex flex-col mt-2" >
-              <div class="bg-indigo-500 text-white p-2 rounded-lg mb-2 self-start">
-                <p class="text-sm">Hello, ask me anything about this library. Im here to help</p>
-              </div>
-              <div class="bg-blue-500 text-white p-2 rounded-lg mb-2 self-end">
-                <p class="text-sm">How to merge tables?</p>
-              </div>
-              <div class="bg-indigo-500 text-white p-2 rounded-lg mb-2 self-start">
-                <p class="text-sm">To merge two tables in pandas, you can use the pd.merge() function. The basic syntax is:<br>
-pd.merge(left, right, on, how)<br>
-where left and right are the two tables to merge, on is the column to merge on, and how is the type of merge to perform.<br>
-For example, to merge the two tables df1 and df2 on the column 'key', you can use:<br>
-pd.merge(df1, df2, on='key', how='left')<br>
-This will return a new DataFrame with all the columns from both tables, and only the rows that match the 'key' column. </p>
-              </div>
-
-          </div>
-        </div>
-
-        <div class="fixed bottom-0 w-full mt-4 mb-2 lg:w-3/4">
-        <form id="message-form" autocomplete="off" class="flex items-stretch">
-          <input autocomplete="off" id="message-input" class="bg-white p-2 rounded-lg ml-2 text-sm w-full" type="text" placeholder="Type your message here...">
-          <button id="button-submit" class="bg-blue-500 text-white p-2 rounded-lg ml-2 mr-2 text-sm sm:max-lg:p-5" type="submit">Send</button>
-        </form>
-        </div>
-
-        
-
-
-    </div>
-        </div>
-        <div class="lg:w-1/4 p-2 sm:max-lg:hidden">
-          <p class="text-sm">This is a chatbot that uses the GPT-3, Faiss and <a href="https://github.com/hwchase17/langchain" class="text-blue-500 hover:text-blue-800">LangChain</a> to answer questions</p>
-          <br>
-          <p class="text-sm">The source code is available on <a href="https://github.com/arc53/docsgpt" class="text-blue-500 hover:text-blue-800">Github</a></p><br>
-          <p class="text-sm">Currently It uses python pandas documentation, so it will respond to information relevant to pandas. If you want to train it on different documentation -  <a href="https://github.com/arc53/docsgpt/wiki/How-to-train-on-other-documentation" class="text-blue-500 hover:text-blue-800"> please follow this guide </a></p><br>
-          <p class="text-sm">If you want to launch it on your own server - <a href="https://github.com/arc53/docsgpt/wiki/How-to-train-on-other-documentation" class="text-blue-500 hover:text-blue-800"> follow this guide </a></p><br>
-            <label  class="block mb-2 text-sm font-medium text-gray-900">Select documentation from DocsHUB</label>
-            <select id="select-docs" class="bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5">
-              <option selected value="default">Choose documentation</option>
-              <option value="default">Default</option>
-            </select>
-            <form action="/api/upload" method="post" enctype="multipart/form-data" class="mt-2">
-                <input type="file" name="file" class="py-4" id="file-upload">
-                <input type="text" name="user" value="local" hidden>
-                <input type="text" name="name" placeholder="Name:">
-
-
-              <button type="submit" class="py-2 px-4 text-white bg-blue-500 rounded-md hover:bg-blue-600 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500">
-                Upload
-              </button>
-            </form>
-
-
-
-        </div>
-    </div>
-
-  <div class="flex items-center justify-center h-full">
-    
- 
-</div>
-
-
-
-
-{% if not api_key_set %}
-
-<div class="fixed z-10 overflow-y-auto top-0 w-full left-0 show" id="modal">
-  <div class="flex items-center justify-center min-height-100vh pt-4 px-4 pb-20 text-center sm:block sm:p-0">
-    <div class="fixed inset-0 transition-opacity">
-      <div class="absolute inset-0 bg-gray-900 opacity-75" />
-    </div>
-    <span class="hidden sm:inline-block sm:align-middle sm:h-screen">&#8203;</span>
-    <div class=" text-sm inline-block align-center bg-white rounded-lg text-left overflow-hidden shadow-xl transform transition-all sm:my-8 sm:align-middle sm:max-w-lg sm:w-full" role="dialog" aria-modal="true" aria-labelledby="modal-headline">
-       <form id="api-key-form">
-        <div class="bg-white px-4 pt-5 pb-4 sm:p-6 sm:pb-4">
-        <h2>Before you can start using DocsGPT we need you to provide an API key for llm. Currently, we support only OpenAI but soon many more. You can find it <a class="text-blue-500 hover:text-blue-800" href="https://platform.openai.com/account/api-keys">here</a></h2><br>
-        <label>OpenAI API key:</label>
-
-        <input id="api-key-input" type="password" class="w-full bg-gray-100 p-2 mt-2 mb-3" placeholder="Paste you Api Key here">
-
-      </div>
-      <div class="bg-gray-200 px-4 py-3 text-right">
-        <button type="submit" class="py-2 px-4 bg-blue-500 text-white rounded hover:bg-blue-700 mr-2">Save</button>
-
-      </div>
-            </form>
-    </div>
-  </div>
-</div>
-{% endif %}
-
-
-
-      <script>
-          function docsIndex() {
-                // loads latest index from https://raw.githubusercontent.com/arc53/DocsHUB/main/combined.json
-                // and stores it in localStorage
-                fetch('/api/combine')
-                    .then(response => response.json())
-                    .then(data => {
-                        localStorage.setItem("docsIndex", JSON.stringify(data));
-                        localStorage.setItem("docsIndexDate", Date.now());
-                        generateOptions()
-                    }
-
-                )
-
-            }
-          function generateOptions(){
-                docsIndex = localStorage.getItem('docsIndex')
-                // create option on select with id select-docs
-                var select = document.getElementById("select-docs");
-                // convert docsIndex to json
-                docsIndex = JSON.parse(docsIndex)
-                // create option for each key in docsIndex
-                for (var key in docsIndex) {
-                    var option = document.createElement("option");
-                    if (docsIndex[key].location == 'docshub'){
-                        if (docsIndex[key].name == docsIndex[key].language) {
-                            option.text = docsIndex[key].name + " " + docsIndex[key].version;
-                            option.value = docsIndex[key].name + "/" + ".project" + "/" + docsIndex[key].version + "/{{ embeddings_choice }}/";
-                            if (docsIndex[key].model == "{{ embeddings_choice }}") {
-                                select.add(option);
-                            }
-                        }
-                        else {
-                            option.text = docsIndex[key].name + " " + docsIndex[key].version;
-                            option.value = docsIndex[key].language + "/" + docsIndex[key].name + "/" + docsIndex[key].version + "/{{ embeddings_choice }}/";
-                            if (docsIndex[key].model == "{{ embeddings_choice }}") {
-                                select.add(option);
-                            }
-                        }
-                    }
-                    else {
-                        option.text = docsIndex[key].name;
-                        option.value = docsIndex[key].location + "/" + docsIndex[key].name;
-                        select.add(option);
-                    }
-                }
-
-          }
-        {% if not api_key_set %}
-        if (localStorage.getItem('apiKey') === null) {
-            console.log("apiKey is not set")
-            document.getElementById('modal').classList.toggle('hidden')
-        }
-        {% endif %}
-        if (localStorage.getItem('docsIndex') === null) {
-            console.log("docsIndex is not set")
-            docsIndex()
-        }
-        else if (localStorage.getItem("docsIndexDate") < Date.now() - 900000) {
-            console.log("docsIndex is older than 15 minutes")
-            docsIndex()
-        }
-
-        generateOptions()
-
-  </script>
-    {% if not api_key_set %}
-    <script src="{{url_for('static',filename='src/authapi.js')}}"></script>
-    {% endif %}
-  <script src="{{url_for('static',filename='src/chat.js')}}"></script>
-  <script src="{{url_for('static',filename='src/choiceChange.js')}}"></script>
-
-  </body>
-</html>

application/usage.py

@@ -0,0 +1,49 @@
+import sys
+from pymongo import MongoClient
+from datetime import datetime
+from application.core.settings import settings
+from application.utils import count_tokens
+
+mongo = MongoClient(settings.MONGO_URI)
+db = mongo["docsgpt"]
+usage_collection = db["token_usage"]
+
+
+def update_token_usage(user_api_key, token_usage):
+    if "pytest" in sys.modules:
+        return
+    usage_data = {
+        "api_key": user_api_key,
+        "prompt_tokens": token_usage["prompt_tokens"],
+        "generated_tokens": token_usage["generated_tokens"],
+        "timestamp": datetime.now(),
+    }
+    usage_collection.insert_one(usage_data)
+
+
+def gen_token_usage(func):
+    def wrapper(self, model, messages, stream, **kwargs):
+        for message in messages:
+            self.token_usage["prompt_tokens"] += count_tokens(message["content"])
+        result = func(self, model, messages, stream, **kwargs)
+        self.token_usage["generated_tokens"] += count_tokens(result)
+        update_token_usage(self.user_api_key, self.token_usage)
+        return result
+
+    return wrapper
+
+
+def stream_token_usage(func):
+    def wrapper(self, model, messages, stream, **kwargs):
+        for message in messages:
+            self.token_usage["prompt_tokens"] += count_tokens(message["content"])
+        batch = []
+        result = func(self, model, messages, stream, **kwargs)
+        for r in result:
+            batch.append(r)
+            yield r
+        for line in batch:
+            self.token_usage["generated_tokens"] += count_tokens(line)
+        update_token_usage(self.user_api_key, self.token_usage)
+
+    return wrapper

application/utils.py

@@ -0,0 +1,6 @@
+from transformers import GPT2TokenizerFast
+
+
+def count_tokens(string):
+    tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
+    return len(tokenizer(string)['input_ids'])

application/vectorstore/base.py

@@ -0,0 +1,56 @@
+from abc import ABC, abstractmethod
+import os
+from langchain_community.embeddings import (
+    HuggingFaceEmbeddings,
+    CohereEmbeddings,
+    HuggingFaceInstructEmbeddings,
+)
+from langchain_openai import OpenAIEmbeddings
+from application.core.settings import settings
+
+class BaseVectorStore(ABC):
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def search(self, *args, **kwargs):
+        pass
+
+    def is_azure_configured(self):
+        return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
+
+    def _get_embeddings(self, embeddings_name, embeddings_key=None):
+        embeddings_factory = {
+            "openai_text-embedding-ada-002": OpenAIEmbeddings,
+            "huggingface_sentence-transformers/all-mpnet-base-v2": HuggingFaceEmbeddings,
+            "huggingface_hkunlp/instructor-large": HuggingFaceInstructEmbeddings,
+            "cohere_medium": CohereEmbeddings
+        }
+        
+        if embeddings_name not in embeddings_factory:
+            raise ValueError(f"Invalid embeddings_name: {embeddings_name}")
+
+        if embeddings_name == "openai_text-embedding-ada-002":
+            if self.is_azure_configured():
+                os.environ["OPENAI_API_TYPE"] = "azure"
+                embedding_instance = embeddings_factory[embeddings_name](
+                    model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME
+                )
+            else:
+                embedding_instance = embeddings_factory[embeddings_name](
+                    openai_api_key=embeddings_key
+                )
+        elif embeddings_name == "cohere_medium":
+            embedding_instance = embeddings_factory[embeddings_name](
+                cohere_api_key=embeddings_key
+            )
+        elif embeddings_name == "huggingface_sentence-transformers/all-mpnet-base-v2":
+            embedding_instance = embeddings_factory[embeddings_name](
+                #model_name="./model/all-mpnet-base-v2",
+                model_kwargs={"device": "cpu"},
+            )
+        else:
+            embedding_instance = embeddings_factory[embeddings_name]()
+            
+        return embedding_instance
+

application/vectorstore/document_class.py

@@ -0,0 +1,8 @@
+class Document(str):
+    """Class for storing a piece of text and associated metadata."""
+
+    def __new__(cls, page_content: str, metadata: dict):
+        instance = super().__new__(cls, page_content)
+        instance.page_content = page_content
+        instance.metadata = metadata
+        return instance

application/vectorstore/elasticsearch.py

@@ -0,0 +1,213 @@
+from application.vectorstore.base import BaseVectorStore
+from application.core.settings import settings
+from application.vectorstore.document_class import Document
+import elasticsearch
+
+
+
+
+class ElasticsearchStore(BaseVectorStore):
+    _es_connection = None  # Class attribute to hold the Elasticsearch connection
+
+    def __init__(self, path, embeddings_key, index_name=settings.ELASTIC_INDEX):
+        super().__init__()
+        self.path = path.replace("application/indexes/", "").rstrip("/")
+        self.embeddings_key = embeddings_key
+        self.index_name = index_name
+        
+        if ElasticsearchStore._es_connection is None:
+            connection_params = {}
+            if settings.ELASTIC_URL:
+                connection_params["hosts"] = [settings.ELASTIC_URL]
+                connection_params["http_auth"] = (settings.ELASTIC_USERNAME, settings.ELASTIC_PASSWORD)
+            elif settings.ELASTIC_CLOUD_ID:
+                connection_params["cloud_id"] = settings.ELASTIC_CLOUD_ID
+                connection_params["basic_auth"] = (settings.ELASTIC_USERNAME, settings.ELASTIC_PASSWORD)
+            else:
+                raise ValueError("Please provide either elasticsearch_url or cloud_id.")
+
+            
+
+            ElasticsearchStore._es_connection = elasticsearch.Elasticsearch(**connection_params)
+            
+        self.docsearch = ElasticsearchStore._es_connection
+
+    def connect_to_elasticsearch(
+        *,
+        es_url = None,
+        cloud_id = None,
+        api_key = None,
+        username = None,
+        password = None,
+    ):
+        try:
+            import elasticsearch
+        except ImportError:
+            raise ImportError(
+                "Could not import elasticsearch python package. "
+                "Please install it with `pip install elasticsearch`."
+            )
+
+        if es_url and cloud_id:
+            raise ValueError(
+                "Both es_url and cloud_id are defined. Please provide only one."
+            )
+
+        connection_params = {}
+
+        if es_url:
+            connection_params["hosts"] = [es_url]
+        elif cloud_id:
+            connection_params["cloud_id"] = cloud_id
+        else:
+            raise ValueError("Please provide either elasticsearch_url or cloud_id.")
+
+        if api_key:
+            connection_params["api_key"] = api_key
+        elif username and password:
+            connection_params["basic_auth"] = (username, password)
+
+        es_client = elasticsearch.Elasticsearch(
+            **connection_params,
+        )
+        try:
+            es_client.info()
+        except Exception as e:
+            raise e
+
+        return es_client
+
+    def search(self, question, k=2, index_name=settings.ELASTIC_INDEX, *args, **kwargs):
+        embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key)
+        vector = embeddings.embed_query(question)
+        knn = {
+            "filter": [{"match": {"metadata.store.keyword": self.path}}],
+            "field": "vector",
+            "k": k,
+            "num_candidates": 100,
+            "query_vector": vector,
+        }
+        full_query = {
+            "knn": knn,
+            "query": {
+                "bool": {
+                    "must": [
+                        {
+                            "match": {
+                                "text": {
+                                    "query": question,
+                                }
+                            }
+                        }
+                    ],
+                    "filter": [{"match": {"metadata.store.keyword": self.path}}],
+                }
+            },
+            "rank": {"rrf": {}},
+        }
+        resp = self.docsearch.search(index=self.index_name, query=full_query['query'], size=k, knn=full_query['knn'])
+        # create Documents objects from the results page_content ['_source']['text'], metadata ['_source']['metadata']
+        doc_list = []
+        for hit in resp['hits']['hits']:
+            
+            doc_list.append(Document(page_content = hit['_source']['text'], metadata = hit['_source']['metadata']))
+        return doc_list
+
+    def _create_index_if_not_exists(
+            self, index_name, dims_length
+        ):
+
+        if self._es_connection.indices.exists(index=index_name):
+            print(f"Index {index_name} already exists.")
+
+        else:
+
+            indexSettings = self.index(
+                dims_length=dims_length,
+            )
+            self._es_connection.indices.create(index=index_name, **indexSettings)
+
+    def index(
+            self,
+            dims_length,
+        ):
+        return {
+            "mappings": {
+                "properties": {
+                    "vector": {
+                        "type": "dense_vector",
+                        "dims": dims_length,
+                        "index": True,
+                        "similarity": "cosine",
+                    },
+                }
+            }
+        }
+
+    def add_texts(
+        self,
+        texts,
+        metadatas = None,
+        ids = None,
+        refresh_indices = True,
+        create_index_if_not_exists = True,
+        bulk_kwargs = None,
+        **kwargs,
+        ):
+        
+        from elasticsearch.helpers import BulkIndexError, bulk
+
+        bulk_kwargs = bulk_kwargs or {}
+        import uuid
+        embeddings = []
+        ids = ids or [str(uuid.uuid4()) for _ in texts]
+        requests = []
+        embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key)
+
+        vectors = embeddings.embed_documents(list(texts))
+
+        dims_length = len(vectors[0])
+
+        if create_index_if_not_exists:
+            self._create_index_if_not_exists(
+                index_name=self.index_name, dims_length=dims_length
+            )
+
+        for i, (text, vector) in enumerate(zip(texts, vectors)):
+            metadata = metadatas[i] if metadatas else {}
+
+            requests.append(
+                {
+                    "_op_type": "index",
+                    "_index": self.index_name,
+                    "text": text,
+                    "vector": vector,
+                    "metadata": metadata,
+                    "_id": ids[i],
+                }
+            )
+
+
+        if len(requests) > 0:
+            try:
+                success, failed = bulk(
+                    self._es_connection,
+                    requests,
+                    stats_only=True,
+                    refresh=refresh_indices,
+                    **bulk_kwargs,
+                )
+                return ids
+            except BulkIndexError as e:
+                print(f"Error adding texts: {e}")
+                firstError = e.errors[0].get("index", {}).get("error", {})
+                print(f"First error reason: {firstError.get('reason')}")
+                raise e
+
+        else:
+            return []
+
+    def delete_index(self):
+        self._es_connection.delete_by_query(index=self.index_name, query={"match": {
+                                      "metadata.store.keyword": self.path}},)
+

application/vectorstore/faiss.py

@@ -0,0 +1,46 @@
+from langchain_community.vectorstores import FAISS
+from application.vectorstore.base import BaseVectorStore
+from application.core.settings import settings
+
+class FaissStore(BaseVectorStore):
+
+    def __init__(self, path, embeddings_key, docs_init=None):
+        super().__init__()
+        self.path = path
+        embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+        if docs_init:
+            self.docsearch = FAISS.from_documents(
+                docs_init, embeddings
+            )
+        else:
+            self.docsearch = FAISS.load_local(
+                self.path, embeddings
+            )
+        self.assert_embedding_dimensions(embeddings)
+
+    def search(self, *args, **kwargs):
+        return self.docsearch.similarity_search(*args, **kwargs)
+
+    def add_texts(self, *args, **kwargs):
+        return self.docsearch.add_texts(*args, **kwargs)
+
+    def save_local(self, *args, **kwargs):
+        return self.docsearch.save_local(*args, **kwargs)
+
+    def delete_index(self, *args, **kwargs):
+        return self.docsearch.delete(*args, **kwargs)
+
+    def assert_embedding_dimensions(self, embeddings):
+        """
+        Check that the word embedding dimension of the docsearch index matches
+        the dimension of the word embeddings used 
+        """
+        if settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
+            try:
+                word_embedding_dimension = embeddings.client[1].word_embedding_dimension
+            except AttributeError as e:
+                raise AttributeError("word_embedding_dimension not found in embeddings.client[1]") from e
+            docsearch_index_dimension = self.docsearch.index.d
+            if word_embedding_dimension != docsearch_index_dimension:
+                raise ValueError(f"word_embedding_dimension ({word_embedding_dimension}) " +
+                                 f"!= docsearch_index_word_embedding_dimension ({docsearch_index_dimension})")

application/vectorstore/mongodb.py

@@ -0,0 +1,126 @@
+from application.vectorstore.base import BaseVectorStore
+from application.core.settings import settings
+from application.vectorstore.document_class import Document
+
+class MongoDBVectorStore(BaseVectorStore):
+    def __init__(
+        self,
+        path: str = "",
+        embeddings_key: str = "embeddings",
+        collection: str = "documents",
+        index_name: str = "vector_search_index",
+        text_key: str = "text",
+        embedding_key: str = "embedding",
+        database: str = "docsgpt",
+    ):
+        self._index_name = index_name
+        self._text_key = text_key
+        self._embedding_key = embedding_key
+        self._embeddings_key = embeddings_key
+        self._mongo_uri = settings.MONGO_URI
+        self._path = path.replace("application/indexes/", "").rstrip("/")
+        self._embedding = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+
+        try:
+            import pymongo
+        except ImportError:
+            raise ImportError(
+                "Could not import pymongo python package. "
+                "Please install it with `pip install pymongo`."
+            )
+
+        self._client = pymongo.MongoClient(self._mongo_uri)
+        self._database = self._client[database]
+        self._collection = self._database[collection]
+
+        
+    def search(self, question, k=2, *args, **kwargs):
+        query_vector = self._embedding.embed_query(question)
+
+        pipeline = [
+            {
+                "$vectorSearch": {
+                    "queryVector": query_vector, 
+                    "path": self._embedding_key,
+                    "limit": k, 
+                    "numCandidates": k * 10, 
+                    "index": self._index_name,
+                    "filter": {
+                        "store": {"$eq": self._path}
+                    }
+                }
+            }
+        ]
+
+        cursor = self._collection.aggregate(pipeline)
+        
+        results = []
+        for doc in cursor:
+            text = doc[self._text_key]
+            doc.pop("_id")
+            doc.pop(self._text_key)
+            doc.pop(self._embedding_key)
+            metadata = doc
+            results.append(Document(text, metadata))
+        return results
+    
+    def _insert_texts(self, texts, metadatas):
+        if not texts:
+            return []
+        embeddings = self._embedding.embed_documents(texts)
+        to_insert = [
+            {self._text_key: t, self._embedding_key: embedding, **m}
+            for t, m, embedding in zip(texts, metadatas, embeddings)
+        ]
+        # insert the documents in MongoDB Atlas
+        insert_result = self._collection.insert_many(to_insert)
+        return insert_result.inserted_ids
+    
+    def add_texts(self,
+        texts,
+        metadatas = None,
+        ids = None,
+        refresh_indices = True,
+        create_index_if_not_exists = True,
+        bulk_kwargs = None,
+        **kwargs,):
+
+
+        #dims = self._embedding.client[1].word_embedding_dimension
+        # # check if index exists
+        # if create_index_if_not_exists:
+        #     # check if index exists
+        #     info = self._collection.index_information()
+        #     if self._index_name not in info:
+        #         index_mongo = {
+        #         "fields": [{
+        #             "type": "vector",
+        #             "path": self._embedding_key,
+        #             "numDimensions": dims,
+        #             "similarity": "cosine",
+        #         },
+        #         {
+        #             "type": "filter",
+        #             "path": "store"
+        #         }]
+        #         }
+        #         self._collection.create_index(self._index_name, index_mongo)
+
+        batch_size = 100
+        _metadatas = metadatas or ({} for _ in texts)
+        texts_batch = []
+        metadatas_batch = []
+        result_ids = []
+        for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
+            texts_batch.append(text)
+            metadatas_batch.append(metadata)
+            if (i + 1) % batch_size == 0:
+                result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
+                texts_batch = []
+                metadatas_batch = []
+        if texts_batch:
+            result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
+        return result_ids
+    
+    def delete_index(self, *args, **kwargs):
+        self._collection.delete_many({"store": self._path})

application/vectorstore/qdrant.py

@@ -0,0 +1,47 @@
+from langchain_community.vectorstores.qdrant import Qdrant
+from application.vectorstore.base import BaseVectorStore
+from application.core.settings import settings
+from qdrant_client import models
+
+
+class QdrantStore(BaseVectorStore):
+    def __init__(self, path: str = "", embeddings_key: str = "embeddings"):
+        self._filter = models.Filter(
+            must=[
+                models.FieldCondition(
+                    key="metadata.store",
+                    match=models.MatchValue(value=path.replace("application/indexes/", "").rstrip("/")),
+                )
+            ]
+        )
+
+        self._docsearch = Qdrant.construct_instance(
+            ["TEXT_TO_OBTAIN_EMBEDDINGS_DIMENSION"],
+            embedding=self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key),
+            collection_name=settings.QDRANT_COLLECTION_NAME,
+            location=settings.QDRANT_LOCATION,
+            url=settings.QDRANT_URL,
+            port=settings.QDRANT_PORT,
+            grpc_port=settings.QDRANT_GRPC_PORT,
+            https=settings.QDRANT_HTTPS,
+            prefer_grpc=settings.QDRANT_PREFER_GRPC,
+            api_key=settings.QDRANT_API_KEY,
+            prefix=settings.QDRANT_PREFIX,
+            timeout=settings.QDRANT_TIMEOUT,
+            path=settings.QDRANT_PATH,
+            distance_func=settings.QDRANT_DISTANCE_FUNC,
+        )
+
+    def search(self, *args, **kwargs):
+        return self._docsearch.similarity_search(filter=self._filter, *args, **kwargs)
+
+    def add_texts(self, *args, **kwargs):
+        return self._docsearch.add_texts(*args, **kwargs)
+
+    def save_local(self, *args, **kwargs):
+        pass
+
+    def delete_index(self, *args, **kwargs):
+        return self._docsearch.client.delete(
+            collection_name=settings.QDRANT_COLLECTION_NAME, points_selector=self._filter
+        )

application/vectorstore/vector_creator.py

@@ -0,0 +1,20 @@
+from application.vectorstore.faiss import FaissStore
+from application.vectorstore.elasticsearch import ElasticsearchStore
+from application.vectorstore.mongodb import MongoDBVectorStore
+from application.vectorstore.qdrant import QdrantStore
+
+
+class VectorCreator:
+    vectorstores = {
+        "faiss": FaissStore,
+        "elasticsearch": ElasticsearchStore,
+        "mongodb": MongoDBVectorStore,
+        "qdrant": QdrantStore,
+    }
+
+    @classmethod
+    def create_vectorstore(cls, type, *args, **kwargs):
+        vectorstore_class = cls.vectorstores.get(type.lower())
+        if not vectorstore_class:
+            raise ValueError(f"No vectorstore class found for type {type}")
+        return vectorstore_class(*args, **kwargs)

application/worker.py

@@ -9,26 +9,76 @@ import requests
 
 from application.core.settings import settings
 from application.parser.file.bulk import SimpleDirectoryReader
+from application.parser.remote.remote_creator import RemoteCreator
 from application.parser.open_ai_func import call_openai_api
 from application.parser.schema.base import Document
 from application.parser.token_func import group_split
 
 try:
-    nltk.download('punkt', quiet=True)
-    nltk.download('averaged_perceptron_tagger', quiet=True)
+    nltk.download("punkt", quiet=True)
+    nltk.download("averaged_perceptron_tagger", quiet=True)
 except FileExistsError:
     pass
 
 
+# Define a function to extract metadata from a given filename.
 def metadata_from_filename(title):
-    return {'title': title}
+    store = "/".join(title.split("/")[1:3])
+    return {"title": title, "store": store}
 
 
+# Define a function to generate a random string of a given length.
 def generate_random_string(length):
-    return ''.join([string.ascii_letters[i % 52] for i in range(length)])
+    return "".join([string.ascii_letters[i % 52] for i in range(length)])
 
 
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
+
+def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
+    """
+    Recursively extract zip files with a limit on recursion depth.
+
+    Args:
+        zip_path (str): Path to the zip file to be extracted.
+        extract_to (str): Destination path for extracted files.
+        current_depth (int): Current depth of recursion.
+        max_depth (int): Maximum allowed depth of recursion to prevent infinite loops.
+    """
+    if current_depth > max_depth:
+        print(f"Reached maximum recursion depth of {max_depth}")
+        return
+
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_to)
+    os.remove(zip_path)  # Remove the zip file after extracting
+
+    # Check for nested zip files and extract them
+    for root, dirs, files in os.walk(extract_to):
+        for file in files:
+            if file.endswith(".zip"):
+                # If a nested zip file is found, extract it recursively
+                file_path = os.path.join(root, file)
+                extract_zip_recursive(file_path, root, current_depth + 1, max_depth)
+
+
+# Define the main function for ingesting and processing documents.
 def ingest_worker(self, directory, formats, name_job, filename, user):
+    """
+    Ingest and process documents.
+
+    Args:
+        self: Reference to the instance of the task.
+        directory (str): Specifies the directory for ingesting ('inputs' or 'temp').
+        formats (list of str): List of file extensions to consider for ingestion (e.g., [".rst", ".md"]).
+        name_job (str): Name of the job for this ingestion task.
+        filename (str): Name of the file to be ingested.
+        user (str): Identifier for the user initiating the ingestion.
+
+    Returns:
+        dict: Information about the completed ingestion task, including input parameters and a "limited" flag.
+    """
     # directory = 'inputs' or 'temp'
     # formats = [".rst", ".md"]
     input_files = None
@@ -42,34 +92,52 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
     token_check = True
     min_tokens = 150
     max_tokens = 1250
-    full_path = directory + '/' + user + '/' + name_job
+    recursion_depth = 2
+    full_path = os.path.join(directory, user, name_job)
+    import sys
+
+
+    print(full_path, file=sys.stderr)
     # check if API_URL env variable is set
-    file_data = {'name': name_job, 'file': filename, 'user': user}
-    response = requests.get(urljoin(settings.API_URL, "/api/download"), params=file_data)
+    file_data = {"name": name_job, "file": filename, "user": user}
+    response = requests.get(
+        urljoin(settings.API_URL, "/api/download"), params=file_data
+    )
+    # check if file is in the response
+    print(response, file=sys.stderr)
     file = response.content
 
     if not os.path.exists(full_path):
         os.makedirs(full_path)
-    with open(full_path + '/' + filename, 'wb') as f:
+    with open(os.path.join(full_path, filename), "wb") as f:
         f.write(file)
 
     # check if file is .zip and extract it
-    if filename.endswith('.zip'):
-        with zipfile.ZipFile(full_path + '/' + filename, 'r') as zip_ref:
-            zip_ref.extractall(full_path)
-        os.remove(full_path + '/' + filename)
+    if filename.endswith(".zip"):
+        extract_zip_recursive(os.path.join(full_path, filename), full_path, 0, recursion_depth)
 
-    self.update_state(state='PROGRESS', meta={'current': 1})
+    self.update_state(state="PROGRESS", meta={"current": 1})
 
-    raw_docs = SimpleDirectoryReader(input_dir=full_path, input_files=input_files, recursive=recursive,
-                                     required_exts=formats, num_files_limit=limit,
-                                     exclude_hidden=exclude, file_metadata=metadata_from_filename).load_data()
-    raw_docs = group_split(documents=raw_docs, min_tokens=min_tokens, max_tokens=max_tokens, token_check=token_check)
+    raw_docs = SimpleDirectoryReader(
+        input_dir=full_path,
+        input_files=input_files,
+        recursive=recursive,
+        required_exts=formats,
+        num_files_limit=limit,
+        exclude_hidden=exclude,
+        file_metadata=metadata_from_filename,
+    ).load_data()
+    raw_docs = group_split(
+        documents=raw_docs,
+        min_tokens=min_tokens,
+        max_tokens=max_tokens,
+        token_check=token_check,
+    )
 
     docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
 
     call_openai_api(docs, full_path, self)
-    self.update_state(state='PROGRESS', meta={'current': 100})
+    self.update_state(state="PROGRESS", meta={"current": 100})
 
     if sample:
         for i in range(min(5, len(raw_docs))):
@@ -77,20 +145,80 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
 
     # get files from outputs/inputs/index.faiss and outputs/inputs/index.pkl
     # and send them to the server (provide user and name in form)
-    file_data = {'name': name_job, 'user': user}
-    files = {'file_faiss': open(full_path + '/index.faiss', 'rb'),
-             'file_pkl': open(full_path + '/index.pkl', 'rb')}
-    response = requests.post(urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data)
+    file_data = {"name": name_job, "user": user}
+    if settings.VECTOR_STORE == "faiss":
+        files = {
+            "file_faiss": open(full_path + "/index.faiss", "rb"),
+            "file_pkl": open(full_path + "/index.pkl", "rb"),
+        }
+        response = requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+        )
+        response = requests.get(
+            urljoin(settings.API_URL, "/api/delete_old?path=" + full_path)
+        )
+    else:
+        response = requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), data=file_data
+        )
 
-    response = requests.get(urljoin(settings.API_URL, "/api/delete_old?path="))
     # delete local
     shutil.rmtree(full_path)
 
     return {
-        'directory': directory,
-        'formats': formats,
-        'name_job': name_job,
-        'filename': filename,
-        'user': user,
-        'limited': False
+        "directory": directory,
+        "formats": formats,
+        "name_job": name_job,
+        "filename": filename,
+        "user": user,
+        "limited": False,
     }
+
+
+def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
+    # sample = False
+    token_check = True
+    min_tokens = 150
+    max_tokens = 1250
+    full_path = directory + "/" + user + "/" + name_job
+
+    if not os.path.exists(full_path):
+        os.makedirs(full_path)
+
+    self.update_state(state="PROGRESS", meta={"current": 1})
+
+    # source_data {"data": [url]} for url type task just urls
+
+    # Use RemoteCreator to load data from URL
+    remote_loader = RemoteCreator.create_loader(loader)
+    raw_docs = remote_loader.load_data(source_data)
+
+    docs = group_split(
+        documents=raw_docs,
+        min_tokens=min_tokens,
+        max_tokens=max_tokens,
+        token_check=token_check,
+    )
+
+    # docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
+
+    call_openai_api(docs, full_path, self)
+    self.update_state(state="PROGRESS", meta={"current": 100})
+
+    # Proceed with uploading and cleaning as in the original function
+    file_data = {"name": name_job, "user": user}
+    if settings.VECTOR_STORE == "faiss":
+        files = {
+            "file_faiss": open(full_path + "/index.faiss", "rb"),
+            "file_pkl": open(full_path + "/index.pkl", "rb"),
+        }
+        requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+        )
+        requests.get(urljoin(settings.API_URL, "/api/delete_old?path=" + full_path))
+    else:
+        requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
+
+    shutil.rmtree(full_path)
+
+    return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}

application/wsgi.py

@@ -1,4 +1,5 @@
 from application.app import app
+from application.core.settings import settings
 
 if __name__ == "__main__":
-    app.run(debug=True, port=7091)
+    app.run(debug=settings.FLASK_DEBUG_MODE, port=7091)

codecov.yml

@@ -0,0 +1,2 @@
+ignore:
+  - "*/tests/*"

docker-compose-local.yaml

@@ -0,0 +1,26 @@
+version: "3.9"
+
+services:
+  frontend:
+    build: ./frontend
+    environment:
+      - VITE_API_HOST=http://localhost:7091
+      - VITE_API_STREAMING=$VITE_API_STREAMING
+      - VITE_EMBEDDINGS_NAME=$EMBEDDINGS_NAME
+    ports:
+      - "5173:5173"
+
+  redis:
+    image: redis:6-alpine
+    ports:
+      - 6379:6379
+
+  mongo:
+    image: mongo:6
+    ports:
+      - 27017:27017
+    volumes:
+      - mongodb_data_container:/data/db
+
+volumes:
+  mongodb_data_container:

docker-compose-mock.yaml

@@ -0,0 +1,22 @@
+version: "3.9"
+
+services:
+  frontend:
+    build: ./frontend
+    environment:
+      - VITE_API_HOST=http://localhost:7091
+      - VITE_API_STREAMING=$VITE_API_STREAMING
+    ports:
+      - "5173:5173"
+    depends_on:
+      - mock-backend
+
+  mock-backend:
+    build: ./mock-backend
+    ports:
+      - "7091:7091"
+
+  redis:
+    image: redis:6-alpine
+    ports:
+      - 6379:6379

docker-compose.yaml

@@ -14,12 +14,12 @@ services:
   backend:
     build: ./application
     environment:
-      - API_KEY=$OPENAI_API_KEY
-      - EMBEDDINGS_KEY=$OPENAI_API_KEY
+      - API_KEY=$API_KEY
+      - EMBEDDINGS_KEY=$API_KEY
+      - LLM_NAME=$LLM_NAME
       - CELERY_BROKER_URL=redis://redis:6379/0
       - CELERY_RESULT_BACKEND=redis://redis:6379/1
       - MONGO_URI=mongodb://mongo:27017/docsgpt
-      - SELF_HOSTED_MODEL=$SELF_HOSTED_MODEL
     ports:
       - "7091:7091"
     volumes:
@@ -34,8 +34,9 @@ services:
     build: ./application
     command: celery -A application.app.celery worker -l INFO
     environment:
-      - API_KEY=$OPENAI_API_KEY
-      - EMBEDDINGS_KEY=$OPENAI_API_KEY
+      - API_KEY=$API_KEY
+      - EMBEDDINGS_KEY=$API_KEY
+      - LLM_NAME=$LLM_NAME
       - CELERY_BROKER_URL=redis://redis:6379/0
       - CELERY_RESULT_BACKEND=redis://redis:6379/1
       - MONGO_URI=mongodb://mongo:27017/docsgpt

docs/README.md

@@ -0,0 +1,51 @@
+# nextra-docsgpt
+
+## Setting Up Docs Folder of DocsGPT Locally
+
+### 1. Clone the DocsGPT repository:
+
+```bash
+git clone https://github.com/arc53/DocsGPT.git
+```
+### 2. Navigate to the docs folder:
+
+```bash
+cd DocsGPT/docs
+```
+
+The docs folder contains the markdown files that make up the documentation. The majority of the files are in the pages directory. Some notable files in this folder include:
+
+`index.mdx`: The main documentation file.
+`_app.js`: This file is used to customize the default Next.js application shell.
+`theme.config.jsx`: This file is for configuring the Nextra theme for the documentation.
+
+### 3. Verify that you have Node.js and npm installed in your system. You can check by running:
+
+```bash
+node --version
+npm --version
+```
+
+### 4. If not installed, download Node.js and npm from the respective official websites.
+
+### 5. Once you have Node.js and npm running, proceed to install yarn - another package manager that helps to manage project dependencies:
+
+```bash
+npm install --global yarn
+```
+
+### 6. Install the project dependencies using yarn:
+
+```bash
+yarn install
+```
+
+### 7. After the successful installation of the project dependencies, start the local server:
+
+```bash
+yarn dev
+```
+
+- Now, you should be able to view the docs on your local environment by visiting `http://localhost:5000`. You can explore the different markdown files and make changes as you see fit.
+
+- **Footnotes:** This guide assumes you have Node.js and npm installed. The guide involves running a local server using yarn, and viewing the documentation offline. If you encounter any issues, it may be worth verifying your Node.js and npm installations and whether you have installed yarn correctly.