Merge pull request #1028 from utin-francis-peter/fix/issue#1023

Fix: adjusted alignment of submit query icon within its container

.env-template

@@ -1,8 +1,8 @@
-OPENAI_API_KEY=<LLM api key (for example, open ai key)>
-SELF_HOSTED_MODEL=false
+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/ci.yml

@@ -13,7 +13,6 @@ jobs:
     permissions:
       contents: read
       packages: write
-
     steps:
       - uses: actions/checkout@v3
 
@@ -36,7 +35,6 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      # Runs a single command using the runners shell
       - name: Build and push Docker images to docker.io and ghcr.io
         uses: docker/build-push-action@v4
         with:

.github/workflows/cife.yml

@@ -8,11 +8,11 @@ on:
 
 jobs:
   deploy:
+    if: github.repository == 'arc53/DocsGPT'
     runs-on: ubuntu-latest
     permissions:
       contents: read
       packages: write
-
     steps:
       - uses: actions/checkout@v3
 
@@ -40,7 +40,7 @@ jobs:
         uses: docker/build-push-action@v4
         with:
           file: './frontend/Dockerfile'
-          platforms: linux/amd64
+          platforms: linux/amd64, linux/arm64
           context: ./frontend
           push: true
           tags: |

.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

.gitignore

@@ -75,6 +75,7 @@ target/
 
 # Jupyter Notebook
 .ipynb_checkpoints
+**/*.ipynb
 
 # IPython
 profile_default/
@@ -172,3 +173,4 @@ application/vectors/
 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

@@ -1,44 +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 - This is how we track tasks, sometimes it is bugs that need fixing, and sometimes it is new features
+🐞 **Issues** - This is where we keep track of tasks. It could be bugs,fixes or suggestions for new features.
 
-🛠️ Pull requests - This is how you can suggest changes to our repository, to work on existing issues or 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 the form of discussion or suggestion, we recommend that you check out existing issues and our [Roadmap](https://github.com/orgs/arc53/projects/2)
+- 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 want to contribute by writing code there are a 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):
-The current frontend is being migrated from /application to /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 also [Figma](https://www.figma.com/file/OXLtrl1EAy885to6S69554/DocsGPT?node-id=0%3A1&t=hjWVuxRg9yi5YkJ9-1)
+- If you're interested in contributing code, here are some important things to know:
+
+- 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).
+
+### 👨‍💻 If you're interested in contributing code, here are some important things to know:
+
+
+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):
-* 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)
-* 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 PR make sure that after you ingested some test data it is queryable.
+### 🖥 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:
+
+To run unit tests from the root of the repository, execute:
 ```
 python -m pytest
 ```
 
-### Workflow:
-Create a fork, make changes on your forked repository, and submit changes in the form of a pull request.
+## 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
-Please join our [Discord](https://discord.gg/n5BX8dh8rU) don't hesitate, we are very friendly and welcoming to new contributors.
-
-# Thank you so much for considering contributing to DocsGPT!🙏
+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!🙏

HACKTOBERFEST.md

@@ -1,31 +0,0 @@
-🎉 Join the Hacktoberfest with DocsGPT and Earn a Free T-shirt! 🎉
-
-Welcome, contributors! We're excited to announce that DocsGPT is participating in Hacktoberfest. Get involved by submitting a **meaningful** pull request, and earn a free shirt in return!
-📜 Here's How to Contribute:
-
-    🛠️ Code: This is the golden ticket! Make meaningful contributions through PRs.
-    📚 Wiki: Improve our documentation, Create a guide or change existing documentation.
-    🖥️ Design: Improve the UI/UX, or design a new feature.
-
-📝 Guidelines for Pull Requests:
-
-Familiarize yourself with the current contributions and our [Roadmap](https://github.com/orgs/arc53/projects/2).
-
-Deciding to contribute with code? Here are some insights based on the area of your interest:
-
-Frontend (⚛️React, Vite):
-    Most of the code is located in /frontend folder. You can also check out our React extension in /extensions/react-widget.
-    For design references, here's the [Figma](https://www.figma.com/file/OXLtrl1EAy885to6S69554/DocsGPT?node-id=0%3A1&t=hjWVuxRg9yi5YkJ9-1).
-    Ensure you adhere to the established guidelines.
-
-Backend (🐍Python):
-    Focus on /application or /scripts. However, avoid the files ingest_rst.py and ingest_rst_sphinx.py as they are soon to be deprecated.
-    Newly added code should come with relevant unit tests (pytest).
-    Refer to the /tests folder for test suites.
-
-Check out [Contributing Guidelines](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md)
-
-
-Don't be shy! Hop into our [Discord](https://discord.gg/n5BX8dh8rU) Server. We're a friendly bunch and eager to assist newcomers.
-
-Big thanks for considering contributing to DocsGPT during Hacktoberfest! 🙏 Your effort can earn you a swanky new t-shirt. 🎁 Let's code together! 🚀

README.md

@@ -7,150 +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://www.docsgpt.cloud/">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://www.docsgpt.cloud/">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>
 
-### Enterprise Solutions: 
+### Production Support / Help for Companies:
 
-When deploying your DocsGPT to a live environment, we're eager to provide personalized assistance. Reach out to us via email [here]( mailto:contact@arc53.com?subject=DocsGPT%20Enterprise&body=Hi%20we%20are%20%3CCompany%20name%3E%20and%20we%20want%20to%20build%20%3CSolution%3E%20with%20DocsGPT) to discuss your project further, and our team will connect with you shortly.
+We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
 
-### [🎉 Join the Hacktoberfest with DocsGPT and Earn a Free T-shirt! 🎉](https://github.com/arc53/DocsGPT/blob/main/HACKTOBERFEST.md)
+- [Get Enterprise / teams Demo :wave:](https://www.docsgpt.cloud/contact)
+- [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 to contribute or create 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-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
+| 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://docs.docsgpt.co.uk/)
+- :mag: :fire: [Cloud Version](https://app.docsgpt.cloud/)
 
- [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://docs.docsgpt.co.uk/Guides/How-to-train-on-other-documentation)
+- :books: :sunglasses: [Guides](https://docs.docsgpt.cloud/)
 
- [How to host it locally (so all data will stay on-premises)](https://docs.docsgpt.co.uk/Guides/How-to-use-different-LLM)
+- :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.cloud/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.cloud/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
 
-On Mac OS or Linux just write:
+On Mac OS or Linux, write:
 
 `./setup.sh`
 
-It will install all the dependencies and give you an option to download local model or use OpenAI
+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:
+Otherwise, refer to this Guide for Windows:
 
 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 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
+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
-   SELF_HOSTED_MODEL=false
+   API_KEY=[if LLM_NAME is openai]
    ```
-   See optional environment variables in the `/.env-template` and `/application/.env_sample` files.
-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
-export FLASK_APP=application/app.py
-export FLASK_DEBUG=true
-```
-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
+python -m venv venv
+ venv/Scripts/activate
+```
+
+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.
+
+```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
 ```
-5. Run the app `flask run --host=0.0.0.0 --port=7091`
-6. Start worker with `celery -A application.app.celery worker -l INFO`
 
-### Start frontend 
-Make sure you have Node version 16 or higher.
+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`.
 
-1. Navigate to `/frontend` folder
-2. Install dependencies
-`npm install`
-3. Run the app 
-`npm run dev`
+### 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).
 
-Built with [🦜️🔗 LangChain](https://github.com/hwchase17/langchain)
+```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

@@ -1,23 +1,93 @@
-FROM python:3.10-slim-bullseye as builder
+# Builder Stage
+FROM ubuntu:24.04 as builder
 
-# Tiktoken requires Rust toolchain, so build it in a separate stage
-RUN apt-get update && apt-get install -y gcc curl
-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
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && \
+    apt-get install -y software-properties-common
+
+RUN add-apt-repository ppa:deadsnakes/ppa
+
+# Install necessary packages and Python
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends gcc curl wget unzip libc6-dev python3.11 python3.11-distutils python3.11-venv && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/* 
+
+# Verify Python installation and setup symlink
+RUN if [ -f /usr/bin/python3.11 ]; then \
+        ln -s /usr/bin/python3.11 /usr/bin/python; \
+    else \
+        echo "Python 3.11 not found"; exit 1; \
+    fi
+
+# Download and unzip the model
+RUN wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip && \
+    unzip mpnet-base-v2.zip -d model && \
+    rm mpnet-base-v2.zip
+
+# Install Rust
+RUN curl https://sh.rustup.rs -sSf | sh -s -- -y
+
+# Clean up to reduce container size
+RUN apt-get remove --purge -y wget unzip && apt-get autoremove -y && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements.txt
 COPY requirements.txt .
-RUN pip install -r requirements.txt
 
-FROM python:3.10-slim-bullseye
+# Setup Python virtual environment
+RUN python3.11 -m venv /venv
 
-# Copy pre-built packages and binaries from builder stage
-COPY --from=builder /usr/local/ /usr/local/
+# Activate virtual environment and install Python packages
+ENV PATH="/venv/bin:$PATH"
 
+# Install Python packages
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir tiktoken && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Final Stage
+FROM ubuntu:24.04 as final
+
+RUN apt-get update && \
+    apt-get install -y software-properties-common
+
+RUN add-apt-repository ppa:deadsnakes/ppa
+
+# Install Python
+RUN apt-get update && apt-get install -y --no-install-recommends python3.11 && \
+    ln -s /usr/bin/python3.11 /usr/bin/python && \
+    rm -rf /var/lib/apt/lists/*
+
+# Set working directory
 WORKDIR /app
-COPY . /app/application
-ENV FLASK_APP=app.py
-ENV FLASK_DEBUG=true
 
+# Create a non-root user: `appuser` (Feel free to choose a name)
+RUN groupadd -r appuser && \
+    useradd -r -g appuser -d /app -s /sbin/nologin -c "Docker image user" appuser
+
+# Copy the virtual environment and model from the builder stage
+COPY --from=builder /venv /venv
+COPY --from=builder /model /app/model
+
+# Copy your application code
+COPY . /app/application
+
+# Change the ownership of the /app directory to the appuser
+	
+RUN mkdir -p /app/application/inputs/local
+RUN chown -R appuser:appuser /app
+
+# Set environment variables
+ENV FLASK_APP=app.py \
+    FLASK_DEBUG=true \
+    PATH="/venv/bin:$PATH"
+
+# Expose the port the app runs on
 EXPOSE 7091
 
-CMD ["gunicorn", "-w", "2", "--timeout", "120", "--bind", "0.0.0.0:7091", "application.wsgi:app"]
+# Switch to non-root user
+USER appuser
+
+# Start Gunicorn
+CMD ["gunicorn", "-w", "2", "--timeout", "120", "--bind", "0.0.0.0:7091", "application.wsgi:app"]

application/api/answer/routes.py

@@ -1,5 +1,6 @@
 import asyncio
 import os
+import sys
 from flask import Blueprint, request, Response
 import json
 import datetime
@@ -8,47 +9,48 @@ import traceback
 
 from pymongo import MongoClient
 from bson.objectid import ObjectId
-from transformers import GPT2TokenizerFast
-
-
 
 from application.core.settings import settings
-from application.vectorstore.vector_creator import VectorCreator
 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"]
-answer = Blueprint('answer', __name__)
+prompts_collection = db["prompts"]
+api_key_collection = db["api_keys"]
+answer = Blueprint("answer", __name__)
 
-if settings.LLM_NAME == "gpt4":
-    gpt_model = 'gpt-4'
-else:
-    gpt_model = 'gpt-3.5-turbo'
+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", "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:
+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
 
@@ -58,11 +60,6 @@ async def async_generate(chain, question, chat_history):
     return result
 
 
-def count_tokens(string):
-    tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
-    return len(tokenizer(string)['input_ids'])
-
-
 def run_async_chain(chain, question, chat_history):
     loop = asyncio.new_event_loop()
     asyncio.set_event_loop(loop)
@@ -75,13 +72,21 @@ def run_async_chain(chain, question, chat_history):
     return result
 
 
+def get_data_from_api_key(api_key):
+    data = api_key_collection.find_one({"key": api_key})
+    
+    # # Raise custom exception if the API key is not found
+    if data is None:
+        raise Exception("Invalid API Key, please generate new key", 401)
+    return data
+
+
 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"]
+        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":
@@ -92,246 +97,343 @@ def get_vectorstore(data):
     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
-
-
 def is_azure_configured():
-    return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
+    return (
+        settings.OPENAI_API_BASE
+        and settings.OPENAI_API_VERSION
+        and settings.AZURE_DEPLOYMENT_NAME
+    )
 
 
-def complete_stream(question, docsearch, chat_history, api_key, conversation_id):
-    llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
-    
-
-    docs = docsearch.search(question, k=2)
-    if settings.LLM_NAME == "llama.cpp":
-        docs = [docs[0]]
-    # 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"
-
-    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 = 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": question})
-
-    response_full = ""
-    completion = llm.gen_stream(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
-                                messages=messages_combine)
-    for line in completion:
-        data = json.dumps({"answer": str(line)})
-        response_full += str(line)
-        yield f"data: {data}\n\n"
-
-    # save conversation to database
-    if conversation_id is not None:
+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)},
-            {"$push": {"queries": {"prompt": question, "response": response_full, "sources": source_log_docs}}},
+            {
+                "$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_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"}]
+        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, engine=settings.AZURE_DEPLOYMENT_NAME,
-                             messages=messages_summary, max_tokens=30)
+        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_full, "sources": source_log_docs}]}
+            {
+                "user": "local",
+                "date": datetime.datetime.utcnow(),
+                "name": completion,
+                "queries": [
+                    {
+                        "prompt": question,
+                        "response": response,
+                        "sources": source_log_docs,
+                    }
+                ],
+            }
         ).inserted_id
+    return conversation_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"
 
+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):
+
+    try:
+        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"
+    except Exception as e:
+        print("\033[91merr", str(e), file=sys.stderr)
+        data = json.dumps({"type": "error","error":"Please try again later. We apologize for any inconvenience.",
+          "error_exception": str(e)})
+        yield f"data: {data}\n\n"
+        return 
 
 @answer.route("/stream", methods=["POST"])
 def stream():
+   try:
     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"]
+    if "history" not in data:
+        history = []
     else:
-        api_key = settings.API_KEY
-    if not embeddings_key_set:
-        embeddings_key = data["embeddings_key"]
+        history = data["history"]
+        history = json.loads(history)
+    if "conversation_id" not in data:
+        conversation_id = None
     else:
-        embeddings_key = settings.EMBEDDINGS_KEY
-    if "active_docs" in data:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
+        conversation_id = data["conversation_id"]
+    if "prompt_id" in data:
+        prompt_id = data["prompt_id"]
     else:
-        vectorstore = ""
-    docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key)
+        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
+    if "token_limit" in data:
+        token_limit = data["token_limit"]
+    else:
+        token_limit = settings.DEFAULT_MAX_HISTORY
 
-    return Response(
-        complete_stream(question, docsearch,
-                        chat_history=history, api_key=api_key,
-                        conversation_id=conversation_id), mimetype="text/event-stream"
+    # check if active_docs or api_key is set
+
+    if "api_key" in data:
+        data_key = get_data_from_api_key(data["api_key"])
+        chunks = int(data_key["chunks"])
+        prompt_id = data_key["prompt_id"]
+        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"]
+
+    prompt = get_prompt(prompt_id)
+
+    retriever = RetrieverCreator.create_retriever(
+        retriever_name,
+        question=question,
+        source=source,
+        chat_history=history,
+        prompt=prompt,
+        chunks=chunks,
+        token_limit=token_limit,
+        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",
+    )
+    
+   except ValueError:
+       message = "Malformed request body"
+       print("\033[91merr", str(message), file=sys.stderr)
+       return Response(
+        error_stream_generate(message),
+        status=400,
+        mimetype="text/event-stream",
+    )
+   except Exception as e:
+        print("\033[91merr", str(e), file=sys.stderr)
+        message = e.args[0]
+        status_code = 400
+        # # Custom exceptions with two arguments, index 1 as status code
+        if(len(e.args) >= 2):
+            status_code = e.args[1]
+        return Response(
+        error_stream_generate(message),
+        status=status_code,
+        mimetype="text/event-stream",
+    )
+def error_stream_generate(err_response):
+            data = json.dumps({"type": "error", "error":err_response})
+            yield f"data: {data}\n\n"
 
 @answer.route("/api/answer", methods=["POST"])
 def api_answer():
     data = request.get_json()
     question = data["question"]
-    history = data["history"]
+    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 not api_key_set:
-        api_key = data["api_key"]
+    if "prompt_id" in data:
+        prompt_id = data["prompt_id"]
     else:
-        api_key = settings.API_KEY
-    if not embeddings_key_set:
-        embeddings_key = data["embeddings_key"]
+        prompt_id = "default"
+    if "chunks" in data:
+        chunks = int(data["chunks"])
     else:
-        embeddings_key = settings.EMBEDDINGS_KEY
+        chunks = 2
+    if "token_limit" in data:
+        token_limit = data["token_limit"]
+    else:
+        token_limit = settings.DEFAULT_MAX_HISTORY
 
     # 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 = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key)
-
-
-        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
-
-
-
-        docs = docsearch.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:
-                source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
-            else:
-                source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
-        # join all page_content together with a newline
-
-
-        if len(history) > 1:
-            tokens_current_history = 0
-            # count tokens in history
-            history.reverse()
-            for i in 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": question})
-
-
-        completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
-                                    messages=messages_combine)
-
-
-        result = {"answer": completion, "sources": source_log_docs}
-        logger.debug(result)
-
-        # 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']}}},
-            )
-
+        if "api_key" in data:
+            data_key = get_data_from_api_key(data["api_key"])
+            chunks = int(data_key["chunks"])
+            prompt_id = data_key["prompt_id"]
+            source = {"active_docs": data_key["source"]}
+            user_api_key = data["api_key"]
         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\n"
-                    "User: " + question + "\n\n" + "AI: " + result["answer"]},
-                {"role": "user", "content": "Summarise following conversation in no more than 3 words, "
-                    "respond ONLY with the summary, use the same language as the system"}
-            ]
+            source = data
+            user_api_key = None
 
-            completion = llm.gen(
-                model=gpt_model,
-                engine=settings.AZURE_DEPLOYMENT_NAME,
-                messages=messages_summary,
-                max_tokens=30
-            )
-            conversation_id = conversations_collection.insert_one(
-                {"user": "local",
-                "date": datetime.datetime.utcnow(),
-                "name": completion,
-                "queries": [{"prompt": question, "response": result["answer"], "sources": source_log_docs}]}
-            ).inserted_id
+        if (
+            source["active_docs"].split("/")[0] == "default"
+            or source["active_docs"].split("/")[0] == "local"
+        ):
+            retriever_name = "classic"
+        else:
+            retriever_name = source["active_docs"]
 
-        result["conversation_id"] = str(conversation_id)
+        prompt = get_prompt(prompt_id)
+
+        retriever = RetrieverCreator.create_retriever(
+            retriever_name,
+            question=question,
+            source=source,
+            chat_history=history,
+            prompt=prompt,
+            chunks=chunks,
+            token_limit=token_limit,
+            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
+        )
 
-        # 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))
+
+
+@answer.route("/api/search", methods=["POST"])
+def api_search():
+    data = request.get_json()
+    # get parameter from url question
+    question = data["question"]
+    if "chunks" in data:
+        chunks = int(data["chunks"])
+    else:
+        chunks = 2
+    if "api_key" in data:
+        data_key = get_data_from_api_key(data["api_key"])
+        chunks = int(data_key["chunks"])
+        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"]
+    if "token_limit" in data:
+        token_limit = data["token_limit"]
+    else:
+        token_limit = settings.DEFAULT_MAX_HISTORY
+
+    retriever = RetrieverCreator.create_retriever(
+        retriever_name,
+        question=question,
+        source=source,
+        chat_history=[],
+        prompt="default",
+        chunks=chunks,
+        token_limit=token_limit,
+        gpt_model=gpt_model,
+        user_api_key=user_api_key,
+    )
+    docs = retriever.search()
+    return docs

application/api/internal/routes.py

@@ -34,6 +34,7 @@ def upload_index_files():
     if "name" not in request.form:
         return {"status": "no name"}
     job_name = secure_filename(request.form["name"])
+    tokens = secure_filename(request.form["tokens"])
     save_dir = os.path.join(current_dir, "indexes", user, job_name)
     if settings.VECTOR_STORE == "faiss":
         if "file_faiss" not in request.files:
@@ -64,6 +65,7 @@ def upload_index_files():
             "date": datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
             "model": settings.EMBEDDINGS_NAME,
             "type": "local",
+            "tokens": tokens
         }
     )
     return {"status": "ok"}

application/api/user/routes.py

@@ -1,13 +1,15 @@
 import os
+import uuid
+import shutil
 from flask import Blueprint, request, jsonify
+from urllib.parse import urlparse
 import requests
-import json
 from pymongo import MongoClient
 from bson.objectid import ObjectId
+from bson.binary import Binary, UuidRepresentation
 from werkzeug.utils import secure_filename
-import http.client
-
-from application.api.user.tasks import ingest
+from bson.dbref import DBRef
+from application.api.user.tasks import ingest, ingest_remote
 
 from application.core.settings import settings
 from application.vectorstore.vector_creator import VectorCreator
@@ -16,9 +18,17 @@ mongo = MongoClient(settings.MONGO_URI)
 db = mongo["docsgpt"]
 conversations_collection = db["conversations"]
 vectors_collection = db["vectors"]
-user = Blueprint('user', __name__)
+prompts_collection = db["prompts"]
+feedback_collection = db["feedback"]
+api_key_collection = db["api_keys"]
+shared_conversations_collections = db["shared_conversations"]
+
+user = Blueprint("user", __name__)
+
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
 
-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():
@@ -33,15 +43,25 @@ def delete_conversation():
 
     return {"status": "ok"}
 
+
+@user.route("/api/delete_all_conversations", methods=["POST"])
+def delete_all_conversations():
+    user_id = "local"
+    conversations_collection.delete_many({"user": user_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)
+    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.append(
+            {"id": str(conversation["_id"]), "name": conversation["name"]}
+        )
 
-    #list_conversations = [{"id": "default", "name": "default"}, {"id": "jeff", "name": "jeff"}]
+    # list_conversations = [{"id": "default", "name": "default"}, {"id": "jeff", "name": "jeff"}]
 
     return jsonify(list_conversations)
 
@@ -51,7 +71,17 @@ 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'])
+    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"])
@@ -61,19 +91,29 @@ def api_feedback():
     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}),
+    feedback_collection.insert_one(
+        {
+            "question": question,
+            "answer": answer,
+            "feedback": feedback,
+        }
     )
-    return {"status": http.client.responses.get(response.status_code, "ok")}
+    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"])
@@ -84,13 +124,14 @@ def delete_old():
     path = request.args.get("path")
     dirs = path.split("/")
     dirs_clean = []
-    for i in range(1, len(dirs)):
+    for i in range(0, len(dirs)):
         dirs_clean.append(secure_filename(dirs[i]))
     # check that path strats with indexes or vectors
-    if dirs[0] not in ["indexes", "vectors"]:
+
+    if dirs_clean[0] not in ["indexes", "vectors"]:
         return {"status": "error"}
-    path_clean = "/".join(dirs)
-    vectors_collection.delete_one({"location": path})
+    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))
@@ -101,9 +142,10 @@ def delete_old():
             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."""
@@ -114,34 +156,84 @@ def upload_file():
         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 == "":
+    files = request.files.getlist("file")
+
+    if not files or all(file.filename == "" for file in files):
         return {"status": "no file name"}
 
-    if file:
-        filename = secure_filename(file.filename)
-        # save dir
-        save_dir = os.path.join(current_dir, settings.UPLOAD_FOLDER, user, job_name)
-        # create dir if not exists
-        if not os.path.exists(save_dir):
-            os.makedirs(save_dir)
+    # 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)
 
-        file.save(os.path.join(save_dir, filename))
-        task = ingest.delay(settings.UPLOAD_FOLDER, [".rst", ".md", ".pdf", ".txt"], job_name, filename, user)
-        # task id
+    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"])
+    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
         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
+    from application.celery_init import celery
+
     task = celery.AsyncResult(task_id)
     task_meta = task.info
     return {"status": task.status, "result": task_meta}
@@ -163,12 +255,13 @@ def combined_json():
             "date": "default",
             "docLink": "default",
             "model": settings.EMBEDDINGS_NAME,
-            "location": "local",
+            "location": "remote",
+            "tokens":""
         }
     ]
     # structure: name, language, version, description, fullName, date, docLink
-    # append data from vectors_collection
-    for index in vectors_collection.find({"user": user}):
+    # append data from vectors_collection in sorted order in descending order of date
+    for index in vectors_collection.find({"user": user}).sort("date", -1):
         data.append(
             {
                 "name": index["name"],
@@ -180,13 +273,46 @@ def combined_json():
                 "docLink": index["location"],
                 "model": settings.EMBEDDINGS_NAME,
                 "location": "local",
+                "tokens" : index["tokens"] if ("tokens" in index.keys()) else ""
             }
         )
     if settings.VECTOR_STORE == "faiss":
-        data_remote = requests.get("https://d3dg1063dc54p9.cloudfront.net/combined.json").json()
+        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",
+                "tokens":""
+            }
+        )
+    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",
+                "tokens":""
+            }
+        )
 
     return jsonify(data)
 
@@ -198,29 +324,243 @@ def check_docs():
     # split docs on / and take first part
     if data["docs"].split("/")[0] == "local":
         return {"status": "exists"}
-    vectorstore = "vectors/" + data["docs"]
+    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:
-        r = requests.get(base_path + vectorstore + "index.faiss")
+        file_url = urlparse(base_path + vectorstore + "index.faiss")
 
-        if r.status_code != 200:
-            return {"status": "null"}
+        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:
-            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": "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"}
+
+
+#route to share conversation
+##isPromptable should be passed through queries
+@user.route("/api/share",methods=["POST"])
+def share_conversation():
+    try:
+        data = request.get_json()
+        user = "local"
+        if(hasattr(data,"user")):
+            user = data["user"]
+        conversation_id = data["conversation_id"]
+        isPromptable = request.args.get("isPromptable").lower() == "true"
+        conversation = conversations_collection.find_one({"_id": ObjectId(conversation_id)})
+        current_n_queries = len(conversation["queries"])
+        pre_existing = shared_conversations_collections.find_one({
+            "conversation_id":DBRef("conversations",ObjectId(conversation_id)),
+            "isPromptable":isPromptable,
+            "first_n_queries":current_n_queries
+        })
+        print("pre_existing",pre_existing)
+        if(pre_existing is not None):
+            explicit_binary = pre_existing["uuid"]
+            return jsonify({"success":True, "identifier":str(explicit_binary.as_uuid())}),200
+        else:
+           explicit_binary = Binary.from_uuid(uuid.uuid4(), UuidRepresentation.STANDARD)       
+           shared_conversations_collections.insert_one({
+           "uuid":explicit_binary,
+           "conversation_id": {
+                  "$ref":"conversations",
+                  "$id":ObjectId(conversation_id)
+                  } ,
+                    "isPromptable":isPromptable,
+                    "first_n_queries":current_n_queries,
+                    "user":user
+           })
+            ## Identifier as route parameter in frontend
+           return jsonify({"success":True, "identifier":str(explicit_binary.as_uuid())}),201
+    except Exception as err:
+        return jsonify({"success":False,"error":str(err)}),400
+
+#route to get publicly shared conversations
+@user.route("/api/shared_conversation/<string:identifier>",methods=["GET"])
+def get_publicly_shared_conversations(identifier : str):
+    try:
+        query_uuid = Binary.from_uuid(uuid.UUID(identifier), UuidRepresentation.STANDARD)
+        shared = shared_conversations_collections.find_one({"uuid":query_uuid})
+        conversation_queries=[]
+        if shared and 'conversation_id' in shared and isinstance(shared['conversation_id'], DBRef):
+        # Resolve the DBRef
+            conversation_ref = shared['conversation_id']
+            conversation = db.dereference(conversation_ref)
+            if(conversation is None):
+                return jsonify({"sucess":False,"error":"might have broken url or the conversation does not exist"}),404
+            conversation_queries = conversation['queries'][:(shared["first_n_queries"])]
+            for query in conversation_queries:
+                query.pop("sources") ## avoid exposing sources
+        else:
+            return jsonify({"sucess":False,"error":"might have broken url or the conversation does not exist"}),404
+        date = conversation["_id"].generation_time.isoformat()
+        return jsonify({
+            "success":True,
+            "queries":conversation_queries,
+            "title":conversation["name"],
+            "timestamp":date
+            }), 200
+    except Exception as err:
+        print (err)
+        return jsonify({"success":False,"error":str(err)}),400
+    

application/api/user/tasks.py

@@ -1,7 +1,12 @@
-from application.worker import ingest_worker
-from application.celery import celery
+from application.worker import ingest_worker, remote_worker
+from application.celery_init 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

@@ -1,68 +1,44 @@
 import platform
-
-
 import dotenv
-from application.celery import celery
+from application.celery_init import celery
 from flask import Flask, request, redirect
-
-
 from application.core.settings import settings
 from application.api.user.routes import user
 from application.api.answer.routes import answer
 from application.api.internal.routes import internal
 
-
-
-# Redirect PosixPath to WindowsPath on Windows
-
 if platform.system() == "Windows":
     import pathlib
-
-    temp = pathlib.PosixPath
     pathlib.PosixPath = pathlib.WindowsPath
 
-# loading the .env file
 dotenv.load_dotenv()
 
-
-
 app = Flask(__name__)
 app.register_blueprint(user)
 app.register_blueprint(answer)
 app.register_blueprint(internal)
-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
+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")
 
-
-
 @app.route("/")
 def home():
-    """
-    The frontend source code lives in the /frontend directory of the repository.
-    """
     if request.remote_addr in ('0.0.0.0', '127.0.0.1', 'localhost', '172.18.0.1'):
-        # If users locally try to access DocsGPT running in Docker,
-        # they will be redirected to the Frontend application.
         return redirect('http://localhost:5173')
     else:
-        # Handle other cases or render the default page
         return 'Welcome to DocsGPT Backend!'
 
-
-
-
-# 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/core/settings.py

@@ -1,36 +1,69 @@
 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"
-    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 = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
-    TOKENS_MAX_HISTORY: int = 150
+    DEFAULT_MAX_HISTORY: int = 150
+    MODEL_TOKEN_LIMITS: dict = {"gpt-3.5-turbo": 4096, "claude-2": 1e5}
     UPLOAD_FOLDER: str = "inputs"
-    VECTOR_STORE: str = "faiss"  # "faiss" or "elasticsearch"
+    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: str = None # cloud id for elasticsearch
-    ELASTIC_USERNAME: str = None # username for elasticsearch
-    ELASTIC_PASSWORD: str = None # password for elasticsearch
-    ELASTIC_URL: str = None # url for elasticsearch
-    ELASTIC_INDEX: str = "docsgpt" # index name for 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

@@ -1,14 +1,28 @@
 from abc import ABC, abstractmethod
+from application.usage import gen_token_usage, stream_token_usage
 
 
 class BaseLLM(ABC):
     def __init__(self):
-        pass
+        self.token_usage = {"prompt_tokens": 0, "generated_tokens": 0}
+
+    def _apply_decorator(self, method, decorator, *args, **kwargs):
+        return decorator(method, *args, **kwargs)
 
     @abstractmethod
-    def gen(self, *args, **kwargs):
+    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 gen_stream(self, *args, **kwargs):
+    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

@@ -1,31 +1,68 @@
 from application.llm.base import BaseLLM
 
+
 class HuggingFaceLLM(BaseLLM):
 
-    def __init__(self, api_key, llm_name='Arc53/DocsGPT-7B'):
+    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
-        from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-        tokenizer = AutoTokenizer.from_pretrained(llm_name)
-        model = AutoModelForCausalLM.from_pretrained(llm_name)
+
+        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
+            "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 gen(self, model, engine, messages, stream=False, **kwargs):
-        context = messages[0]['content']
-        user_question = messages[-1]['content']
+    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 gen_stream(self, model, engine, messages, stream=True, **kwargs):
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
 
         raise NotImplementedError("HuggingFaceLLM Streaming is not implemented yet.")
-

application/llm/llama_cpp.py

@@ -1,39 +1,55 @@
 from application.llm.base import BaseLLM
 from application.core.settings import settings
+import threading
+
+class LlamaSingleton:
+    _instances = {}
+    _lock = threading.Lock()  # Add a lock for thread synchronization
+
+    @classmethod
+    def get_instance(cls, llm_name):
+        if llm_name not in cls._instances:
+            try:
+                from llama_cpp import Llama
+            except ImportError:
+                raise ImportError(
+                    "Please install llama_cpp using pip install llama-cpp-python"
+                )
+            cls._instances[llm_name] = Llama(model_path=llm_name, n_ctx=2048)
+        return cls._instances[llm_name]
+
+    @classmethod
+    def query_model(cls, llm, prompt, **kwargs):
+        with cls._lock:
+            return llm(prompt, **kwargs)
+
 
 class LlamaCpp(BaseLLM):
+    def __init__(
+        self,
+        api_key=None,
+        user_api_key=None,
+        llm_name=settings.MODEL_PATH,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.llama = LlamaSingleton.get_instance(llm_name)
 
-    def __init__(self, api_key, llm_name=settings.MODEL_PATH, **kwargs):
-        global llama
-        try:
-            from llama_cpp import Llama
-        except ImportError:
-            raise ImportError("Please install llama_cpp using pip install llama-cpp-python")
-
-        llama = Llama(model_path=llm_name, n_ctx=2048)
-
-    def gen(self, model, engine, messages, stream=False, **kwargs):
-        context = messages[0]['content']
-        user_question = messages[-1]['content']
+    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 = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False)
+        return result["choices"][0]["text"].split("### Answer \n")[-1]
 
-        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 gen_stream(self, model, engine, messages, stream=True, **kwargs):
-        context = messages[0]['content']
-        user_question = messages[-1]['content']
+    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)
-
+        result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False, stream=stream)
         for item in result:
-            for choice in item['choices']:
-                yield choice['text']
+            for choice in item["choices"]:
+                yield choice["text"]

application/llm/llm_creator.py

@@ -2,21 +2,26 @@ 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
+        "openai": OpenAILLM,
+        "azure_openai": AzureOpenAILLM,
+        "sagemaker": SagemakerAPILLM,
+        "huggingface": HuggingFaceLLM,
+        "llama.cpp": LlamaCpp,
+        "anthropic": AnthropicLLM,
+        "docsgpt": DocsGPTAPILLM,
+        "premai": PremAILLM,
     }
 
     @classmethod
-    def create_llm(cls, type, *args, **kwargs):
+    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(*args, **kwargs)
+        return llm_class(api_key, user_api_key, *args, **kwargs)

application/llm/openai.py

@@ -1,57 +1,80 @@
 from application.llm.base import BaseLLM
 from application.core.settings import settings
 
+
 class OpenAILLM(BaseLLM):
 
-    def __init__(self, api_key):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
         global openai
-        import openai
-        openai.api_key = api_key
-        self.api_key = api_key  # Save the API key to be used later
+        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
-        # Set the API key every time you import openai
-        openai.api_key = self.api_key
+
         return openai
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
-        response = openai.ChatCompletion.create(
-            model=model,
-            engine=engine,
-            messages=messages,
-            stream=stream,
-            **kwargs
+    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"]
+        return response.choices[0].message.content
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
-        response = openai.ChatCompletion.create(
-            model=model,
-            engine=engine,
-            messages=messages,
-            stream=stream,
-            **kwargs
+    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:
-            if "content" in line["choices"][0]["delta"]:
-                yield line["choices"][0]["delta"]["content"]
+            # 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):
+    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,
+        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()
-        openai.api_base = self.api_base
-        openai.api_version = self.api_version
-        openai.api_type = "azure"
+
         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

@@ -1,27 +1,140 @@
 from application.llm.base import BaseLLM
 from application.core.settings import settings
-import requests
 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, *args, **kwargs):
-        self.url = settings.SAGEMAKER_API_URL
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        import boto3
 
-    def gen(self, model, engine, 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"
-
-        response = requests.post(
-                    url=self.url,
-                    headers={
-                        "Content-Type": "application/json; charset=utf-8",
-                    },
-                    data=json.dumps({"input": prompt})
+        runtime = boto3.client(
+            "runtime.sagemaker",
+            aws_access_key_id="xxx",
+            aws_secret_access_key="xxx",
+            region_name="us-west-2",
         )
 
-        return response.json()['answer']
+        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 gen_stream(self, model, engine, messages, stream=True, **kwargs):
-        raise NotImplementedError("Sagemaker does not support streaming")
+    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
 

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/open_ai_func.py

@@ -1,22 +1,13 @@
 import os
 
-import tiktoken
 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
-
-
-def num_tokens_from_string(string: str, encoding_name: str) -> int:
-    # Function to convert string to tokens and estimate user cost.
-    encoding = tiktoken.get_encoding(encoding_name)
-    num_tokens = len(encoding.encode(string))
-    total_price = ((num_tokens / 1000) * 0.0004)
-    return num_tokens, total_price
+# from langchain_community.embeddings import HuggingFaceEmbeddings
+# from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+# from langchain_community.embeddings import CohereEmbeddings
 
 
 @retry(tries=10, delay=60)
@@ -26,13 +17,13 @@ def store_add_texts_with_retry(store, i):
 
 
 def call_openai_api(docs, folder_name, task_status):
-    # Function to create a vector store from the documents and save it to disk.
+    # Function to create a vector store from the documents and save it to disk
 
-    # create output folder if it doesn't exist
     if not os.path.exists(f"{folder_name}"):
         os.makedirs(f"{folder_name}")
 
     from tqdm import tqdm
+
     c1 = 0
     if settings.VECTOR_STORE == "faiss":
         docs_init = [docs[0]]
@@ -40,25 +31,32 @@ def call_openai_api(docs, folder_name, task_status):
 
         store = VectorCreator.create_vectorstore(
             settings.VECTOR_STORE,
-            docs_init = docs_init,
+            docs_init=docs_init,
             path=f"{folder_name}",
-            embeddings_key=os.getenv("EMBEDDINGS_KEY")
+            embeddings_key=os.getenv("EMBEDDINGS_KEY"),
         )
     else:
         store = VectorCreator.create_vectorstore(
             settings.VECTOR_STORE,
             path=f"{folder_name}",
-            embeddings_key=os.getenv("EMBEDDINGS_KEY")
+            embeddings_key=os.getenv("EMBEDDINGS_KEY"),
         )
     # Uncomment for MPNet embeddings
     # model_name = "sentence-transformers/all-mpnet-base-v2"
     # hf = HuggingFaceEmbeddings(model_name=model_name)
     # store = FAISS.from_documents(docs_test, hf)
     s1 = len(docs)
-    for i in tqdm(docs, desc="Embedding 🦖", unit="docs", total=len(docs),
-                  bar_format='{l_bar}{bar}| Time Left: {remaining}'):
+    for i in tqdm(
+        docs,
+        desc="Embedding 🦖",
+        unit="docs",
+        total=len(docs),
+        bar_format="{l_bar}{bar}| Time Left: {remaining}",
+    ):
         try:
-            task_status.update_state(state='PROGRESS', meta={'current': int((c1 / s1) * 100)})
+            task_status.update_state(
+                state="PROGRESS", meta={"current": int((c1 / s1) * 100)}
+            )
             store_add_texts_with_retry(store, i)
         except Exception as e:
             print(e)
@@ -72,23 +70,3 @@ def call_openai_api(docs, folder_name, task_status):
         store.save_local(f"{folder_name}")
 
 
-def get_user_permission(docs, folder_name):
-    # Function to ask user permission to call the OpenAI api and spend their OpenAI funds.
-    # Here we convert the docs list to a string and calculate the number of OpenAI tokens the string represents.
-    # docs_content = (" ".join(docs))
-    docs_content = ""
-    for doc in docs:
-        docs_content += doc.page_content
-
-    tokens, total_price = num_tokens_from_string(string=docs_content, encoding_name="cl100k_base")
-    # Here we print the number of tokens and the approx user cost with some visually appealing formatting.
-    print(f"Number of Tokens = {format(tokens, ',d')}")
-    print(f"Approx Cost = ${format(total_price, ',.2f')}")
-    # Here we check for user permission before calling the API.
-    user_input = input("Price Okay? (Y/N) \n").lower()
-    if user_input == "y":
-        call_openai_api(docs, folder_name)
-    elif user_input == "":
-        call_openai_api(docs, folder_name)
-    else:
-        print("The API was not called. No money was spent.")

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 +1,32 @@
+from application.parser.remote.base import BaseRemote
+from langchain_community.document_loaders import WebBaseLoader
+
+headers = {
+    "User-Agent": "Mozilla/5.0",
+    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*"
+    ";q=0.8",
+    "Accept-Language": "en-US,en;q=0.5",
+    "Referer": "https://www.google.com/",
+    "DNT": "1",
+    "Connection": "keep-alive",
+    "Upgrade-Insecure-Requests": "1",
+}
+
+
+class WebLoader(BaseRemote):
+    def __init__(self):
+        self.loader = WebBaseLoader
+
+    def load_data(self, inputs):
+        urls = inputs
+        if isinstance(urls, str):
+            urls = [urls]
+        documents = []
+        for url in urls:
+            try:
+                loader = self.loader([url], header_template=headers)
+                documents.extend(loader.load())
+            except Exception as e:
+                print(f"Error processing URL {url}: {e}")
+                continue
+        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

@@ -1,106 +1,34 @@
-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
-elasticsearch==8.9.0
-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
-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
-pycryptodome==3.19.0
-pydantic==1.10.5
-PyJWT==2.6.0
-pymongo==4.3.3
-pyowm==3.3.0
+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==22.0.0
+html2text==2020.1.16
+javalang==0.13.0
+langchain==0.1.4
+langchain-openai==0.0.5
+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==1.9.0
+redis==5.0.1
+Requests==2.32.0
 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
+sentence-transformers
 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
+torch
+tqdm==4.66.3
+transformers==4.36.2
+unstructured==0.12.2
+Werkzeug==3.0.3

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 +1,103 @@
+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,
+        token_limit=150,
+        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.token_limit = (
+            token_limit
+            if token_limit
+            < settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+            else settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+        )
+        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 < self.token_limit:
+                        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 +1,123 @@
+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,
+        token_limit=150,
+        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.token_limit = (
+            token_limit
+            if token_limit
+            < settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+            else settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+        )
+        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,
+                    "source": (
+                        i.metadata.get("source")
+                        if i.metadata.get("source")
+                        else "local"
+                    ),
+                }
+                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 < self.token_limit:
+                        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 +1,120 @@
+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,
+        token_limit=150,
+        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.token_limit = (
+            token_limit
+            if token_limit
+            < settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+            else settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+        )
+        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 < self.token_limit:
+                        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/tailwind.config.js

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

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
+
+tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
+tokenizer.model_max_length = 100000
+def count_tokens(string):
+    return len(tokenizer(string)['input_ids'])

application/vectorstore/base.py

@@ -1,13 +1,39 @@
 from abc import ABC, abstractmethod
 import os
-from langchain.embeddings import (
-    OpenAIEmbeddings,
+from langchain_community.embeddings import (
     HuggingFaceEmbeddings,
     CohereEmbeddings,
     HuggingFaceInstructEmbeddings,
 )
+from langchain_openai import OpenAIEmbeddings
 from application.core.settings import settings
 
+class EmbeddingsSingleton:
+    _instances = {}
+
+    @staticmethod
+    def get_instance(embeddings_name, *args, **kwargs):
+        if embeddings_name not in EmbeddingsSingleton._instances:
+            EmbeddingsSingleton._instances[embeddings_name] = EmbeddingsSingleton._create_instance(
+                embeddings_name, *args, **kwargs
+            )
+        return EmbeddingsSingleton._instances[embeddings_name]
+
+    @staticmethod
+    def _create_instance(embeddings_name, *args, **kwargs):
+        embeddings_factory = {
+            "openai_text-embedding-ada-002": OpenAIEmbeddings,
+            "huggingface_sentence-transformers/all-mpnet-base-v2": HuggingFaceEmbeddings,
+            "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}")
+
+        return embeddings_factory[embeddings_name](*args, **kwargs)
+
 class BaseVectorStore(ABC):
     def __init__(self):
         pass
@@ -20,32 +46,36 @@ class BaseVectorStore(ABC):
         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](
+                embedding_instance = EmbeddingsSingleton.get_instance(
+                    embeddings_name,
                     model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME
                 )
             else:
-                embedding_instance = embeddings_factory[embeddings_name](
+                embedding_instance = EmbeddingsSingleton.get_instance(
+                    embeddings_name,
                     openai_api_key=embeddings_key
                 )
         elif embeddings_name == "cohere_medium":
-            embedding_instance = embeddings_factory[embeddings_name](
+            embedding_instance = EmbeddingsSingleton.get_instance(
+                embeddings_name,
                 cohere_api_key=embeddings_key
             )
+        elif embeddings_name == "huggingface_sentence-transformers/all-mpnet-base-v2":
+            if os.path.exists("./model/all-mpnet-base-v2"):
+                embedding_instance = EmbeddingsSingleton.get_instance(
+                    embeddings_name,
+                    model_name="./model/all-mpnet-base-v2",
+                    model_kwargs={"device": "cpu"}
+                )
+            else:
+                embedding_instance = EmbeddingsSingleton.get_instance(
+                    embeddings_name,
+                    model_kwargs={"device": "cpu"}
+                )
         else:
-            embedding_instance = embeddings_factory[embeddings_name]()
-            
-        return embedding_instance
+            embedding_instance = EmbeddingsSingleton.get_instance(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

@@ -1,16 +1,8 @@
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
+from application.vectorstore.document_class import Document
 import elasticsearch
 
-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
-
 
 
 
@@ -114,7 +106,7 @@ class ElasticsearchStore(BaseVectorStore):
             "rank": {"rrf": {}},
         }
         resp = self.docsearch.search(index=self.index_name, query=full_query['query'], size=k, knn=full_query['knn'])
-        # create Documnets objects from the results page_content ['_source']['text'], metadata ['_source']['metadata']
+        # create Documents objects from the results page_content ['_source']['text'], metadata ['_source']['metadata']
         doc_list = []
         for hit in resp['hits']['hits']:
             

application/vectorstore/faiss.py

@@ -1,5 +1,5 @@
+from langchain_community.vectorstores import FAISS
 from application.vectorstore.base import BaseVectorStore
-from langchain import FAISS
 from application.core.settings import settings
 
 class FaissStore(BaseVectorStore):
@@ -7,20 +7,40 @@ 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, self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+                docs_init, embeddings
             )
         else:
             self.docsearch = FAISS.load_local(
-                self.path, self._get_embeddings(settings.EMBEDDINGS_NAME, settings.EMBEDDINGS_KEY)
+                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

@@ -1,11 +1,15 @@
 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
+        "faiss": FaissStore,
+        "elasticsearch": ElasticsearchStore,
+        "mongodb": MongoDBVectorStore,
+        "qdrant": QdrantStore,
     }
 
     @classmethod
@@ -13,4 +17,4 @@ class VectorCreator:
         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)
+        return vectorstore_class(*args, **kwargs)

application/worker.py

@@ -2,36 +2,77 @@ import os
 import shutil
 import string
 import zipfile
+import tiktoken
 from urllib.parse import urljoin
 
-import nltk
 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)
-except FileExistsError:
-    pass
-
-
+# Define a function to extract metadata from a given filename.
 def metadata_from_filename(title):
-    store = title.split('/')
-    store = store[1] + '/' + store[2]
-    return {'title': title, 'store': store}
+    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__))))
 
+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
@@ -45,38 +86,54 @@ 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})
+    tokens = count_tokens_docs(docs)
+    self.update_state(state="PROGRESS", meta={"current": 100})
 
     if sample:
         for i in range(min(5, len(raw_docs))):
@@ -84,24 +141,97 @@ 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}
+    file_data = {"name": name_job, "user": user, "tokens":tokens}
     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))
+        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.post(
+            urljoin(settings.API_URL, "/api/upload_index"), data=file_data
+        )
 
-    
     # 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"):
+    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})
+
+    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)
+    tokens = count_tokens_docs(docs)
+    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, "tokens":tokens}
+    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}
+
+
+def count_tokens_docs(docs):
+    # Here we convert the docs list to a string and calculate the number of tokens the string represents.
+    # docs_content = (" ".join(docs))
+    docs_content = ""
+    for doc in docs:
+        docs_content += doc.page_content
+
+    tokens, total_price = num_tokens_from_string(
+        string=docs_content, encoding_name="cl100k_base"
+    )
+    # Here we print the number of tokens and the approx user cost with some visually appealing formatting.
+    return tokens
+
+
+def num_tokens_from_string(string: str, encoding_name: str) -> int:
+    # Function to convert string to tokens and estimate user cost.
+    encoding = tiktoken.get_encoding(encoding_name)
+    num_tokens = len(encoding.encode(string))
+    total_price = (num_tokens / 1000) * 0.0004
+    return num_tokens, total_price

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

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

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

@@ -1 +1,51 @@
-# nextra-docsgpt
+# 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.

docs/next.config.js

@@ -1,9 +1,9 @@
 const withNextra = require('nextra')({
-    theme: 'nextra-theme-docs',
-    themeConfig: './theme.config.jsx'
-  })
+  theme: 'nextra-theme-docs',
+  themeConfig: './theme.config.jsx'
+})
+
+module.exports = withNextra()
    
-  module.exports = withNextra()
-   
-  // If you have other Next.js configurations, you can pass them as the parameter:
-  // module.exports = withNextra({ /* other next.js config */ })
+// If you have other Next.js configurations, you can pass them as the parameter:
+// module.exports = withNextra({ /* other next.js config */ })

docs/package.json

@@ -1,10 +1,16 @@
 {
+  "scripts": {
+    "dev": "next dev",
+    "build": "next build",
+    "start": "next start"
+  },
+  "license": "MIT",
   "dependencies": {
-    "@vercel/analytics": "^1.0.2",
-    "docsgpt": "^0.2.4",
-    "next": "^13.4.19",
-    "nextra": "^2.12.3",
-    "nextra-theme-docs": "^2.12.3",
+    "@vercel/analytics": "^1.1.1",
+    "docsgpt": "^0.3.7",
+    "next": "^14.1.1",
+    "nextra": "^2.13.2",
+    "nextra-theme-docs": "^2.13.2",
     "react": "^18.2.0",
     "react-dom": "^18.2.0"
   }

docs/pages/API/API-docs.md

@@ -0,0 +1,350 @@
+# API Endpoints Documentation
+
+*Currently, the application provides the following main API endpoints:*
+
+
+### 1. /api/answer 
+**Description:**
+
+This endpoint is used to request answers to user-provided questions.
+
+**Request:**
+
+**Method**: `POST`
+
+**Headers**: Content-Type should be set to `application/json; charset=utf-8`
+
+**Request Body**: JSON object with the following fields:
+* `question` — The user's question.
+* `history`  —  (Optional) Previous conversation history.
+* `api_key`— Your API key.
+* `embeddings_key`  —  Your embeddings key.
+* `active_docs` — The location of active documentation.
+
+Here is a JavaScript Fetch Request example:
+```js
+// answer (POST http://127.0.0.1:5000/api/answer)
+fetch("http://127.0.0.1:5000/api/answer", {
+      "method": "POST",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+      "body": JSON.stringify({"question":"Hi","history":null,"api_key":"OPENAI_API_KEY","embeddings_key":"OPENAI_API_KEY",
+      "active_docs": "javascript/.project/ES2015/openai_text-embedding-ada-002/"})
+})
+.then((res) => res.text())
+.then(console.log.bind(console))
+```
+
+**Response**
+
+In response, you will get a JSON document containing the `answer`, `query` and `result`:
+```json
+{
+  "answer": "Hi there! How can I help you?\n",
+  "query": "Hi",
+  "result": "Hi there! How can I help you?\nSOURCES:"
+}
+```
+
+### 2. /api/docs_check
+
+**Description:**
+
+This endpoint will make sure documentation is loaded on the server (just run it every time user is switching between libraries (documentations)).
+
+**Request:**
+
+**Method**: `POST`
+
+**Headers**: Content-Type should be set to `application/json; charset=utf-8`
+
+**Request Body**: JSON object with the field:
+* `docs` — The location of the documentation:
+```js
+// docs_check (POST http://127.0.0.1:5000/api/docs_check)
+fetch("http://127.0.0.1:5000/api/docs_check", {
+      "method": "POST",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+      "body": JSON.stringify({"docs":"javascript/.project/ES2015/openai_text-embedding-ada-002/"})
+})
+.then((res) => res.text())
+.then(console.log.bind(console))
+```
+
+**Response:**
+
+In response, you will get a JSON document like this one indicating whether the documentation exists or not:
+```json
+{
+  "status": "exists"
+}
+```
+
+
+### 3. /api/combine
+**Description:**
+
+This endpoint provides information about available vectors and their locations with a simple GET request.
+
+**Request:**
+
+**Method**: `GET`
+
+**Response:**
+
+Response will include:
+* `date`
+* `description`
+* `docLink`
+* `fullName`
+* `language`
+* `location` (local or docshub)
+* `model`
+* `name`
+* `version`
+
+Example of JSON in Docshub and local:
+
+<img width="295" alt="image" src="https://user-images.githubusercontent.com/15183589/224714085-f09f51a4-7a9a-4efb-bd39-798029bb4273.png">
+
+### 4. /api/upload
+**Description:**
+
+This endpoint is used to upload a file that needs to be trained, response is JSON with task ID, which can be used to check on task's progress.
+
+**Request:**
+
+**Method**: `POST`
+
+**Request Body**: A multipart/form-data form with file upload and additional fields, including `user` and `name`.
+
+HTML example:
+
+```html
+<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-purple-30 rounded-md hover:bg-purple-30 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-purple-30">
+        Upload
+    </button>
+</form>
+```
+
+**Response:**
+
+JSON response with a status and a task ID that can be used to check the task's progress.
+
+
+### 5. /api/task_status
+**Description:**
+
+This endpoint is used to get the status of a task (`task_id`) from `/api/upload`
+
+**Request:**
+
+**Method**: `GET`
+
+**Query Parameter**: `task_id` (task ID to check)
+
+**Sample JavaScript Fetch Request:**
+```js
+// Task status (Get http://127.0.0.1:5000/api/task_status)
+fetch("http://localhost:5001/api/task_status?task_id=YOUR_TASK_ID", {
+      "method": "GET",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+})
+.then((res) => res.text())
+.then(console.log.bind(console))
+```
+
+**Response:**
+
+There are two types of responses:
+
+1. While the task is still running, the 'current' value will show progress from 0 to 100.
+   ```json
+   {
+     "result": {
+       "current": 1
+     },
+     "status": "PROGRESS"
+   }
+   ```
+
+2. When task is completed:
+   ```json
+   {
+     "result": {
+       "directory": "temp",
+       "filename": "install.rst",
+       "formats": [
+         ".rst",
+         ".md",
+         ".pdf"
+       ],
+       "name_job": "somename",
+       "user": "local"
+     },
+     "status": "SUCCESS"
+   }
+   ```
+
+### 6. /api/delete_old
+**Description:**
+
+This endpoint is used to delete old Vector Stores.
+
+**Request:**
+
+**Method**: `GET`
+
+**Query Parameter**: `task_id`
+
+**Sample JavaScript Fetch Request:**
+```js
+// delete_old (GET http://127.0.0.1:5000/api/delete_old)
+fetch("http://localhost:5001/api/delete_old?task_id=YOUR_TASK_ID", {
+      "method": "GET",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+})
+.then((res) => res.text())
+.then(console.log.bind(console))
+
+```
+**Response:**
+
+JSON response indicating the status of the operation:
+
+```json
+{ "status": "ok" }
+```
+
+### 7. /api/get_api_keys
+**Description:**
+
+The endpoint retrieves a list of API keys for the user.
+
+**Request:**
+
+**Method**: `GET`
+
+**Sample JavaScript Fetch Request:**
+```js
+// get_api_keys (GET http://127.0.0.1:5000/api/get_api_keys)
+fetch("http://localhost:5001/api/get_api_keys", {
+      "method": "GET",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+})
+.then((res) => res.text())
+.then(console.log.bind(console))
+
+```
+**Response:**
+
+JSON response with a list of created API keys:
+
+```json
+[
+      {
+        "id": "string",
+        "name": "string",
+        "key": "string",
+        "source": "string"
+      },
+      ...
+    ]
+```
+
+### 8. /api/create_api_key
+
+**Description:**
+
+Create a new API key for the user.
+
+**Request:**
+
+**Method**: `POST`
+
+**Headers**: Content-Type should be set to `application/json; charset=utf-8`
+
+**Request Body**: JSON object with the following fields:
+* `name` — A name for the API key.
+* `source` — The source documents that will be used.
+* `prompt_id` — The prompt ID.
+* `chunks` — The number of chunks used to process an answer.
+
+Here is a JavaScript Fetch Request example:
+```js
+// create_api_key (POST http://127.0.0.1:5000/api/create_api_key)
+fetch("http://127.0.0.1:5000/api/create_api_key", {
+      "method": "POST",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+      "body": JSON.stringify({"name":"Example Key Name",
+          "source":"Example Source",
+          "prompt_id":"creative",
+          "chunks":"2"})
+})
+.then((res) => res.json())
+.then(console.log.bind(console))
+```
+
+**Response**
+
+In response, you will get a JSON document containing the `id` and `key`:
+```json
+{
+  "id": "string",
+  "key": "string"
+}
+```
+
+### 9. /api/delete_api_key
+
+**Description:**
+
+Delete an API key for the user.
+
+**Request:**
+
+**Method**: `POST`
+
+**Headers**: Content-Type should be set to `application/json; charset=utf-8`
+
+**Request Body**: JSON object with the field:
+* `id` — The unique identifier of the API key to be deleted.
+
+Here is a JavaScript Fetch Request example:
+```js
+// delete_api_key (POST http://127.0.0.1:5000/api/delete_api_key)
+fetch("http://127.0.0.1:5000/api/delete_api_key", {
+      "method": "POST",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+      "body": JSON.stringify({"id":"API_KEY_ID"})
+})
+.then((res) => res.json())
+.then(console.log.bind(console))
+```
+
+**Response:**
+
+In response, you will get a JSON document indicating the status of the operation:
+```json
+{
+  "status": "ok"
+}
+```

docs/pages/API/_meta.json

@@ -0,0 +1,10 @@
+{
+  "API-docs": {
+    "title": "🗂️️ API-docs",
+    "href": "/API/API-docs"
+  },
+  "api-key-guide": {
+    "title": "🔐 API Keys guide",
+    "href": "/API/api-key-guide"
+  }
+}

docs/pages/API/api-key-guide.md

@@ -0,0 +1,30 @@
+## Guide to DocsGPT API Keys
+
+DocsGPT API keys are essential for developers and users who wish to integrate the  DocsGPT models into external applications, such as the our widget. This guide will walk you through the steps of obtaining an API key, starting from uploading your document to understanding the key variables associated with API keys.
+
+### Uploading Your Document
+
+Before creating your first API key, you must upload the document that will be linked to this key. You can upload your document through two methods:
+
+- **GUI Web App Upload:** A user-friendly graphical interface that allows for easy upload and management of documents.
+- **Using `/api/upload` Method:** For users comfortable with API calls, this method provides a direct way to upload documents.
+
+### Obtaining Your API Key
+
+After uploading your document, you can obtain an API key either through the graphical user interface or via an API call:
+
+- **Graphical User Interface:** Navigate to the Settings section of the DocsGPT web app, find the API Keys option, and press 'Create New' to generate your key.
+- **API Call:** Alternatively, you can use the `/api/create_api_key` endpoint to create a new API key. For detailed instructions, visit [DocsGPT API Documentation](https://docs.docsgpt.cloud/API/API-docs#8-apicreate_api_key).
+
+### Understanding Key Variables
+
+Upon creating your API key, you will encounter several key variables. Each serves a specific purpose:
+
+- **Name:** Assign a name to your API key for easy identification.
+- **Source:** Indicates the source document(s) linked to your API key, which DocsGPT will use to generate responses.
+- **ID:** A unique identifier for your API key. You can view this by making a call to `/api/get_api_keys`.
+- **Key:** The API key itself, which will be used in your application to authenticate API requests.
+
+With your API key ready, you can now integrate DocsGPT into your application, such as the DocsGPT Widget or any other software, via `/api/answer` or `/stream` endpoints. The source document is preset with the API key, allowing you to bypass fields like `selectDocs` and `active_docs` during implementation.
+
+Congratulations on taking the first step towards enhancing your applications with DocsGPT! With this guide, you're now equipped to navigate the process of obtaining and understanding DocsGPT API keys.

docs/pages/Deploying/Hosting-the-app.md

@@ -1,54 +1,47 @@
 # Self-hosting DocsGPT on Amazon Lightsail
 
-Here's a step-by-step guide on how to setup an Amazon Lightsail instance to host DocsGPT.
+Here's a step-by-step guide on how to set up an Amazon Lightsail instance to host DocsGPT.
 
 ## Configuring your instance
 
-(If you know how to create a Lightsail instance, you can skip to the recommended configuration part by clicking here)
+(If you know how to create a Lightsail instance, you can skip to the recommended configuration part by clicking [here](#connecting-to-your-newly-created-instance)).
 
-### 1. Create an account or login to https://lightsail.aws.amazon.com
+### 1. Create an AWS Account: 
+If you haven't already, create or log in to your AWS account at https://lightsail.aws.amazon.com.
 
-### 2. Click on "Create instance"
+### 2. Create an Instance: 
 
-### 3. Create your instance
+a. Click "Create Instance."
 
-The first step is to select the "Instance location". In most cases there's no need to switch locations as the default one will work well.
+b. Select the "Instance location." In most cases, the default location works fine.
 
-After that it is time to pick your Instance Image. We recommend using "Linux/Unix" as the image and "Ubuntu 20.04 LTS" for Operating System.
+c. Choose "Linux/Unix" as the image and "Ubuntu 20.04 LTS" as the Operating System.
 
-As for instance plan, it'll vary depending on your unique demands, but a "1 GB, 1vCPU, 40GB SSD and 2TB transfer" setup should cover most scenarios.
+d. Configure the instance plan based on your requirements. A "1 GB, 1vCPU, 40GB SSD, and 2TB transfer" setup is recommended for most scenarios.
 
-Lastly, Identify your instance by giving it a unique name and then hit "Create instance".
+e. Give your instance a unique name and click "Create Instance."
 
-PS: Once you create your instance, it'll likely take a few minutes for the setup to be completed.
+PS: It may take a few minutes for the instance setup to complete.
 
-#### The recommended configuration is as follows:
+### Connecting to Your newly created Instance
 
-- Ubuntu 20.04 LTS
-- 1GB RAM
-- 1vCPU
-- 40GB SSD Hard Drive
-- 2TB transfer
+Your instance will be ready a few minutes after creation. To access it, open the instance and click "Connect using SSH."
 
-### Connecting to your the newly created instance
+#### Clone the DocsGPT Repository
 
-Your instance will be ready for use a few minutes after being created. To access, just open it up and click on "Connect using SSH".
-
-#### Clone the repository
-
-A terminal window will pop up, and the first step will be to clone DocsGPT git repository.
+A terminal window will pop up, and the first step will be to clone the DocsGPT Git repository:
 
 `git clone https://github.com/arc53/DocsGPT.git`
 
 #### Download the package information
 
-Once it has finished cloning the repository, it is time to download the package information from all sources. To do so simply enter the following command:
+Once it has finished cloning the repository, it is time to download the package information from all sources. To do so, simply enter the following command:
 
 `sudo apt update`
 
 #### Install Docker and Docker Compose
 
-DocsGPT backend and worker use python, Frontend is written on React and the whole application is containerized using Docker. To install Docker and Docker Compose, enter the following commands:
+DocsGPT backend and worker use Python, Frontend is written on React and the whole application is containerized using Docker. To install Docker and Docker Compose, enter the following commands:
 
 `sudo apt install docker.io`
 
@@ -56,19 +49,19 @@ And now install docker-compose:
 
 `sudo apt install docker-compose`
 
-#### Access the DocsGPT folder
+#### Access the DocsGPT Folder
 
-Enter the following command to access the folder in which DocsGPT docker-compose file is.
+Enter the following command to access the folder in which the DocsGPT docker-compose file is present.
 
 `cd DocsGPT/`
 
-#### Prepare the environment
+#### Prepare the Environment
 
-Inside the DocsGPT folder create a .env file and copy the contents of .env_sample into it.
+Inside the DocsGPT folder create a `.env` file and copy the contents of `.env_sample` into it.
 
 `nano .env`
 
-Make sure your .env file looks like this:
+Make sure your `.env` file looks like this:
 
 ```
 OPENAI_API_KEY=(Your OpenAI API key)
@@ -76,37 +69,42 @@ VITE_API_STREAMING=true
 SELF_HOSTED_MODEL=false
 ```
 
-To save the file, press CTRL+X, then Y and then ENTER.
+To save the file, press CTRL+X, then Y, and then ENTER.
 
-Next we need to set a correct IP for our Backend. To do so, open the docker-compose.yml file:
+Next, set the correct IP for the Backend by opening the docker-compose.yml file:
 
 `nano docker-compose.yml`
 
-And change this line 7 `VITE_API_HOST=http://localhost:7091`
+And Change line 7 to: `VITE_API_HOST=http://localhost:7091`
 to this `VITE_API_HOST=http://<your instance public IP>:7091`
 
 This will allow the frontend to connect to the backend.
 
-#### Running the app
+#### Running the Application
 
 You're almost there! Now that all the necessary bits and pieces have been installed, it is time to run the application. To do so, use the following command:
 
 `sudo docker-compose up -d`
 
-If you launch it for the first time it will take a few minutes to download all the necessary dependencies and build.
+Launching it for the first time will take a few minutes to download all the necessary dependencies and build.
 
 Once this is done you can go ahead and close the terminal window.
 
-#### Enabling ports 
+#### Enabling Ports 
 
-Before you being able to access your live instance, you must first enable the port which it is using.
+a. Before you are able to access your live instance, you must first enable the port that it is using.
 
-Open your Lightsail instance and head to "Networking".
+b. Open your Lightsail instance and head to "Networking".
 
-Then click on "Add rule" under "IPv4 Firewall", enter 5173 as your your port and hit "Create". 
-Repeat the process for port 7091.
+c. Then click on "Add rule" under "IPv4 Firewall", enter `5173` as your port, and hit "Create". 
+Repeat the process for port `7091`.
 
 #### Access your instance
 
-Your instance will now be available under your Public IP Address and port 5173. Enjoy!
+Your instance is now available at your Public IP Address on port 5173. Enjoy using DocsGPT!
 
+## Other Deployment Options
+
+- [Deploy DocsGPT on Civo Compute Cloud](https://dev.to/rutamhere/deploying-docsgpt-on-civo-compute-c)
+- [Deploy DocsGPT on DigitalOcean Droplet](https://dev.to/rutamhere/deploying-docsgpt-on-digitalocean-droplet-50ea)
+- [Deploy DocsGPT on Kamatera Performance Cloud](https://dev.to/rutamhere/deploying-docsgpt-on-kamatera-performance-cloud-1bj)

docs/pages/Deploying/Kubernetes-Deploying.md

@@ -0,0 +1,100 @@
+# Self-hosting DocsGPT on Kubernetes
+
+This guide will walk you through deploying DocsGPT on Kubernetes.
+
+## Prerequisites
+
+Ensure you have the following installed before proceeding:
+
+- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/)
+- Access to a Kubernetes cluster
+
+## Folder Structure
+
+The `k8s` folder contains the necessary deployment and service configuration files:
+
+- `deployments/`
+- `services/`
+- `docsgpt-secrets.yaml`
+
+## Deployment Instructions
+
+1. **Clone the Repository**
+
+   ```sh
+   git clone https://github.com/arc53/DocsGPT.git
+   cd docsgpt/k8s
+   ```
+
+2. **Configure Secrets (optional)**
+
+   Ensure that you have all the necessary secrets in `docsgpt-secrets.yaml`. Update it with your secrets before applying if you want. By default we will use qdrant as a vectorstore and public docsgpt llm as llm for inference.
+
+3. **Apply Kubernetes Deployments**
+
+   Deploy your DocsGPT resources using the following commands:
+
+   ```sh
+   kubectl apply -f deployments/
+   ```
+
+4. **Apply Kubernetes Services**
+
+   Set up your services using the following commands:
+
+   ```sh
+   kubectl apply -f services/
+   ```
+
+5. **Apply Secrets**
+
+   Apply the secret configurations:
+
+   ```sh
+   kubectl apply -f docsgpt-secrets.yaml
+   ```
+
+6. **Substitute API URL**
+
+   After deploying the services, you need to update the environment variable `VITE_API_HOST` in your deployment file `deployments/docsgpt-deploy.yaml` with the actual endpoint URL created by your `docsgpt-api-service`.
+
+    ```sh
+    kubectl get services/docsgpt-api-service -o jsonpath='{.status.loadBalancer.ingress[0].ip}' | xargs -I {} sed -i "s|<your-api-endpoint>|{}|g" deployments/docsgpt-deploy.yaml
+    ```
+
+7. **Rerun Deployment**
+
+   After making the changes, reapply the deployment configuration to update the environment variables:
+
+   ```sh
+   kubectl apply -f deployments/
+   ```
+
+## Verifying the Deployment
+
+To verify if everything is set up correctly, you can run the following:
+
+```sh
+kubectl get pods
+kubectl get services
+```
+
+Ensure that the pods are running and the services are available.
+
+## Accessing DocsGPT
+
+To access DocsGPT, you need to find the external IP address of the frontend service. You can do this by running:
+
+```sh
+kubectl get services/docsgpt-frontend-service | awk 'NR>1 {print "http://" $4}'
+```
+
+## Troubleshooting
+
+If you encounter any issues, you can check the logs of the pods for more details:
+
+```sh
+kubectl logs <pod-name>
+```
+
+Replace `<pod-name>` with the actual name of your DocsGPT pod.

docs/pages/Deploying/Quickstart.md

@@ -1,23 +1,112 @@
 ## Launching Web App
-Note: Make sure you have docker installed
+**Note**: Make sure you have Docker installed
 
-1. Open download this repository with `git clone https://github.com/arc53/DocsGPT.git`
-2. Create .env file in your root directory and set your `OPENAI_API_KEY` with your openai api key
-3. Run `docker-compose build && docker-compose up`
-4. Navigate to `http://localhost:5173/`
+**On macOS or Linux:**
+Just run the following command:
 
-To stop just run Ctrl + C
+```bash
+./setup.sh
+```
 
-### Chrome Extension
+This command will install all the necessary dependencies and provide you with an option to use our LLM API, download the local model or use OpenAI.
 
-To install the Chrome extension:
+If you prefer to follow manual steps, refer to this guide:
 
-1. In the DocsGPT GitHub repository, click on the "Code" button and select Download ZIP
-2. Unzip the downloaded file to a location you can easily access
-3. Open the Google Chrome browser and click on the three dots menu (upper right corner)
-4. Select "More Tools" and then "Extensions"
-5. Turn on the "Developer mode" switch in the top right corner of the Extensions page
-6. Click on the "Load unpacked" button
-7. Select the "Chrome" folder where the DocsGPT files have been unzipped (docsgpt-main > extensions > chrome)
-8. The extension should now be added to Google Chrome and can be managed on the Extensions page
-9. To disable or remove the extension, simply turn off the toggle switch on the extension card or click the "Remove" button.
+1. Open and download this repository with 
+   ```bash
+   git clone https://github.com/arc53/DocsGPT.git
+   ```
+2. Create a `.env` file in your root directory and set your `API_KEY` with your [OpenAI API key](https://platform.openai.com/account/api-keys). (optional in case you want to use OpenAI)
+3. Run the following commands:
+   ```bash
+   docker-compose build && docker-compose up
+   ```
+4. Navigate to http://localhost:5173/.
+
+To stop, simply press **Ctrl + C**.
+
+**For WINDOWS:**
+
+To run the setup on Windows, you have two options: using the Windows Subsystem for Linux (WSL) or using Git Bash or Command Prompt.
+
+**Option 1: Using Windows Subsystem for Linux (WSL):**
+
+1. Install WSL if you haven't already. You can follow the official Microsoft documentation for installation: (https://learn.microsoft.com/en-us/windows/wsl/install).
+2. After setting up WSL, open the WSL terminal.
+3. Clone the repository and create the `.env` file:
+   ```bash
+   git clone https://github.com/arc53/DocsGPT.git
+   cd DocsGPT
+   echo "API_KEY=Yourkey" > .env
+   echo "VITE_API_STREAMING=true" >> .env
+   ```
+4. Run the following command to start the setup with Docker Compose:
+   ```bash
+   ./run-with-docker-compose.sh
+   ```
+6. Open your web browser and navigate to http://localhost:5173/.
+7. To stop the setup, just press **Ctrl + C** in the WSL terminal
+
+**Option 2: Using Git Bash or Command Prompt (CMD):**
+
+1. Install Git for Windows if you haven't already. Download it from the official website: (https://gitforwindows.org/).
+2. Open Git Bash or Command Prompt.
+3. Clone the repository and create the `.env` file:
+   ```bash
+   git clone https://github.com/arc53/DocsGPT.git
+   cd DocsGPT
+   echo "API_KEY=Yourkey" > .env
+   echo "VITE_API_STREAMING=true" >> .env
+   ```
+4. Run the following command to start the setup with Docker Compose:
+   ```bash
+   ./run-with-docker-compose.sh
+   ```
+5. Open your web browser and navigate to http://localhost:5173/.
+6. To stop the setup, just press **Ctrl + C** in the Git Bash or Command Prompt terminal.
+
+These steps should help you set up and run the project on Windows using either WSL or Git Bash/Command Prompt. 
+**Important:** Ensure that Docker is installed and properly configured on your Windows system for these steps to work.
+
+
+For WINDOWS:
+
+To run the given setup on Windows, you can use the Windows Subsystem for Linux (WSL) or a Git Bash terminal to execute similar commands. Here are the steps adapted for Windows:
+
+Option 1: Using Windows Subsystem for Linux (WSL):
+
+1. Install WSL if you haven't already. You can follow the official Microsoft documentation for installation: (https://learn.microsoft.com/en-us/windows/wsl/install).
+2. After setting up WSL, open the WSL terminal.
+3. Clone the repository and create the `.env` file:
+   ```bash
+   git clone https://github.com/arc53/DocsGPT.git
+   cd DocsGPT
+   echo "API_KEY=Yourkey" > .env
+   echo "VITE_API_STREAMING=true" >> .env
+   ```
+4. Run the following command to start the setup with Docker Compose:
+    ```bash
+    ./run-with-docker-compose.sh
+    ```
+5. Open your web browser and navigate to http://localhost:5173/.
+6. To stop the setup, just press **Ctrl + C** in the WSL terminal.
+
+Option 2: Using Git Bash or Command Prompt (CMD):
+
+1. Install Git for Windows if you haven't already. You can download it from the official website: (https://gitforwindows.org/).
+2. Open Git Bash or Command Prompt.
+3. Clone the repository and create the `.env` file:
+   ```bash
+   git clone https://github.com/arc53/DocsGPT.git
+   cd DocsGPT
+   echo "API_KEY=Yourkey" > .env
+   echo "VITE_API_STREAMING=true" >> .env
+   ```
+4. Run the following command to start the setup with Docker Compose:
+   ```bash
+   ./run-with-docker-compose.sh
+   ```
+5. Open your web browser and navigate to http://localhost:5173/.
+6. To stop the setup, just press **Ctrl + C** in the Git Bash or Command Prompt terminal.
+
+These steps should help you set up and run the project on Windows using either WSL or Git Bash/Command Prompt. Make sure you have Docker installed and properly configured on your Windows system for this to work.

docs/pages/Deploying/Railway-Deploying.md

@@ -0,0 +1,254 @@
+
+# Self-hosting DocsGPT on Railway
+
+  
+
+Here's a step-by-step guide on how to host DocsGPT on Railway App.
+
+  
+
+At first Clone and set up the project locally to run , test and Modify.
+
+  
+
+### 1. Clone and GitHub SetUp
+
+a. Open Terminal (Windows Shell or Git bash(recommended)).
+
+  
+
+b. Type `git clone https://github.com/arc53/DocsGPT.git`
+
+  
+
+#### Download the package information
+
+  
+
+Once it has finished cloning the repository, it is time to download the package information from all sources. To do so, simply enter the following command:
+
+  
+
+`sudo apt update`
+
+  
+
+#### Install Docker and Docker Compose
+
+  
+
+DocsGPT backend and worker use Python, Frontend is written on React and the whole application is containerized using Docker. To install Docker and Docker Compose, enter the following commands:
+
+  
+
+`sudo apt install docker.io`
+
+  
+
+And now install docker-compose:
+
+  
+
+`sudo apt install docker-compose`
+
+  
+
+#### Access the DocsGPT Folder
+
+  
+
+Enter the following command to access the folder in which the DocsGPT docker-compose file is present.
+
+  
+
+`cd DocsGPT/`
+
+  
+
+#### Prepare the Environment
+
+  
+
+Inside the DocsGPT folder create a `.env` file and copy the contents of `.env_sample` into it.
+
+  
+
+`nano .env`
+
+  
+
+Make sure your `.env` file looks like this:
+
+  
+
+```
+
+OPENAI_API_KEY=(Your OpenAI API key)
+
+VITE_API_STREAMING=true
+
+SELF_HOSTED_MODEL=false
+
+```
+
+  
+
+To save the file, press CTRL+X, then Y, and then ENTER.
+
+  
+
+Next, set the correct IP for the Backend by opening the docker-compose.yml file:
+
+  
+
+`nano docker-compose.yml`
+
+  
+
+And Change line 7 to: `VITE_API_HOST=http://localhost:7091`
+
+to this `VITE_API_HOST=http://<your instance public IP>:7091`
+
+  
+
+This will allow the frontend to connect to the backend.
+
+  
+
+#### Running the Application
+
+  
+
+You're almost there! Now that all the necessary bits and pieces have been installed, it is time to run the application. To do so, use the following command:
+
+  
+
+`sudo docker-compose up -d`
+
+  
+
+Launching it for the first time will take a few minutes to download all the necessary dependencies and build.
+
+  
+
+Once this is done you can go ahead and close the terminal window.
+
+  
+
+### 2. Pushing it to your own Repository
+
+  
+
+a. Create a Repository on your GitHub.
+
+  
+
+b. Open Terminal in the same directory of the Cloned project.
+
+  
+
+c. Type `git init`
+
+  
+
+d. `git add .`
+
+  
+
+e. `git commit -m "first-commit"`
+
+  
+
+f. `git remote add origin <your  repository  link>`
+
+  
+
+g. `git push git push --set-upstream origin master`
+
+Your local files will now be pushed to your GitHub Account. :)
+  
+
+### 3. Create a Railway Account:
+
+  
+
+If you haven't already, create or log in to your railway account do it by visiting [Railway](https://railway.app/)
+
+  
+
+Signup via **GitHub** [Recommended].
+
+  
+
+### 4. Start New Project:
+
+  
+
+a. Open Railway app and Click on "Start New Project."
+
+  
+
+b. Choose any from the list of options available (Recommended "**Deploy from GitHub Repo**")
+
+  
+
+c. Choose the required Repository from your GitHub.
+
+  
+
+d. Configure and allow access to modify your GitHub content from the pop-up window.
+
+  
+
+e. Agree to all the terms and conditions.
+
+  
+
+PS: It may take a few minutes for the account setup to complete.
+
+  
+
+#### You will get A free trial of $5 (use it for trial and then purchase if satisfied and needed)
+
+  
+
+### 5. Connecting to Your newly Railway app with GitHub
+
+  
+
+a. Choose DocsGPT repo from the list of your GitHub repository that you want to deploy now.
+
+  
+
+b. Click on Deploy now.
+
+  
+
+![Three Tabs will be there](/Railway-selection.png)
+
+  
+
+c. Select Variables Tab.
+
+  
+
+d. Upload the env file here that you used for local setup.
+
+  
+
+e. Go to Settings Tab now.
+
+  
+
+f. Go to "Networking" and click on Generate Domain Name, to get the URL of your hosted project.
+
+  
+
+g. You can update the Root directory, build command, installation command as per need.
+
+*[However recommended not the disturb these options and leave them as default if not that needed.]*
+
+  
+  
+
+Your own DocsGPT is now available at the Generated domain URl. :)

docs/pages/Deploying/_meta.json

@@ -6,5 +6,13 @@
   "Quickstart": {
     "title": "⚡️Quickstart",
     "href": "/Deploying/Quickstart"
+  },
+  "Railway-Deploying": {
+    "title": "🚂Deploying on Railway",
+    "href": "/Deploying/Railway-Deploying"
+  },
+  "Kubernetes-Deploying": {
+    "title": "☸️Deploying on Kubernetes",
+    "href": "/Deploying/Kubernetes-Deploying"
   }
 }

docs/pages/Developing/API-docs.md

@@ -1,153 +0,0 @@
-App currently has two main api endpoints:
-
-### /api/answer 
-Its a POST request that sends a JSON in body with 4 values. Here is a JavaScript fetch example
-It will receive an answer for a user provided question
-
-```js
-// answer (POST http://127.0.0.1:5000/api/answer)
-fetch("http://127.0.0.1:5000/api/answer", {
-      "method": "POST",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-      "body": JSON.stringify({"question":"Hi","history":null,"api_key":"OPENAI_API_KEY","embeddings_key":"OPENAI_API_KEY",
-      "active_docs": "javascript/.project/ES2015/openai_text-embedding-ada-002/"})
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-```
-
-In response you will get a json document like this one:
-
-```json
-{
-  "answer": " Hi there! How can I help you?\n",
-  "query": "Hi",
-  "result": " Hi there! How can I help you?\nSOURCES:"
-}
-```
-
-### /api/docs_check
-It will make sure documentation is loaded on a server (just run it every time user is switching between libraries (documentations)
-Its a POST request that sends a JSON in body with 1 value. Here is a JavaScript fetch example
-
-```js
-// answer (POST http://127.0.0.1:5000/api/docs_check)
-fetch("http://127.0.0.1:5000/api/docs_check", {
-      "method": "POST",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-      "body": JSON.stringify({"docs":"javascript/.project/ES2015/openai_text-embedding-ada-002/"})
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-```
-
-In response you will get a json document like this one:
-```json
-{
-  "status": "exists"
-}
-```
-
-
-### /api/combine
-Provides json that tells UI which vectors are available and where they are located with a simple get request
-
-Respsonse will include:
-date, description, docLink, fullName, language, location (local or docshub), model, name, version
-
-Example of json in Docshub and local:
-<img width="295" alt="image" src="https://user-images.githubusercontent.com/15183589/224714085-f09f51a4-7a9a-4efb-bd39-798029bb4273.png">
-
-
-### /api/upload
-Uploads file that needs to be trained, response is json with task id, which can be used to check on tasks progress
-HTML example:
-
-```html
-<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>
-```
-
-Response:
-```json
-{
-  "status": "ok",
-  "task_id": "b2684988-9047-428b-bd47-08518679103c"
-}
-
-```
-
-### /api/task_status
-Gets task status (task_id) from /api/upload
-```js
-// Task status (Get http://127.0.0.1:5000/api/task_status)
-fetch("http://localhost:5001/api/task_status?task_id=b2d2a0f4-387c-44fd-a443-e4fe2e7454d1", {
-      "method": "GET",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-```
-
-Responses:
-There are two types of responses:
-1. while task it still running, where "current" will show progress from 0 - 100
-```json
-{
-  "result": {
-    "current": 1
-  },
-  "status": "PROGRESS"
-}
-```
-
-2. When task is completed
-```json
-{
-  "result": {
-    "directory": "temp",
-    "filename": "install.rst",
-    "formats": [
-      ".rst",
-      ".md",
-      ".pdf"
-    ],
-    "name_job": "somename",
-    "user": "local"
-  },
-  "status": "SUCCESS"
-}
-```
-
-### /api/delete_old
-deletes old vecotstores
-```js
-// Task status (GET http://127.0.0.1:5000/api/docs_check)
-fetch("http://localhost:5001/api/task_status?task_id=b2d2a0f4-387c-44fd-a443-e4fe2e7454d1", {
-      "method": "GET",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-```
-response:
-
-```json
-{ "status": "ok" }
-```

docs/pages/Developing/_meta.json

@@ -1,6 +0,0 @@
-{
-  "API-docs": {
-    "title": "🗂️️ API-docs",
-    "href": "/Developing/API-docs"
-  }
-}

docs/pages/Extensions/Chatwoot-extension.md

@@ -1,29 +1,44 @@
-### To start chatwoot extension:
-1. Prepare and start the DocsGPT itself (load your documentation too) 
-Follow our [wiki](https://github.com/arc53/DocsGPT/wiki) to start it and to [ingest](https://github.com/arc53/DocsGPT/wiki/How-to-train-on-other-documentation) data
-2. Go to chatwoot, Navigate to your profile (bottom left), click on profile settings, scroll to the bottom and copy Access Token 
-2. Navigate to `/extensions/chatwoot`. Copy .env_sample and create .env file
-3. Fill in the values
+## Chatwoot Extension Setup Guide
 
-```
-docsgpt_url=<docsgpt_api_url>
-chatwoot_url=<chatwoot_url>
-docsgpt_key=<openai_api_key or other llm key>
-chatwoot_token=<from part 2>
+### Step 1: Prepare and Start DocsGPT
+
+- **Launch DocsGPT**: Follow the instructions in our [DocsGPT Wiki](https://github.com/arc53/DocsGPT/wiki) to start DocsGPT. Make sure to load your documentation.
+
+### Step 2: Get Access Token from Chatwoot
+
+- Go to Chatwoot.
+- In your profile settings (located at the bottom left), scroll down and copy the **Access Token**.
+
+### Step 3: Set Up Chatwoot Extension
+
+- Navigate to `/extensions/chatwoot`.
+- Copy the `.env_sample` file and create a new file named `.env`.
+- Fill in the values in the `.env` file as follows:
+
+```env
+docsgpt_url=<Docsgpt_API_URL>
+chatwoot_url=<Chatwoot_URL>
+docsgpt_key=<OpenAI_API_Key or Other_LLM_Key>
+chatwoot_token=<Token from Step 2>
 ```
 
-4. start with `flask run` command
+### Step 4: Start the Extension
 
-If you want for bot to stop responding to questions for a specific user or session just add label `human-requested` in your conversation
+- Use the command `flask run` to start the extension.
 
+### Step 5: Optional - Extra Validation
 
-### Optional (extra validation)
-In app.py uncomment lines 12-13 and 71-75
+- In app.py, uncomment lines 12-13 and 71-75.
+- Add the following lines to your .env file:
+```account_id=(optional) 1
+assignee_id=(optional) 1
+```
+These Chatwoot values help ensure you respond to the correct widget and handle questions assigned to a specific user.
 
-in your .env file add:
+### Stopping Bot Responses for Specific User or Session
 
-`account_id=(optional) 1 `
+- If you want the bot to stop responding to questions for a specific user or session, add a label `human-requested` in your conversation.
 
-`assignee_id=(optional) 1`
+### Additional Notes
 
-Those are chatwoot values and will allow you to check if you are responding to correct widget and responding to questions assigned to specific user
+- For further details on training on other documentation, refer to our [wiki](https://github.com/arc53/DocsGPT/wiki/How-to-train-on-other-documentation).

docs/pages/Extensions/Chrome-extension.mdx

@@ -0,0 +1,34 @@
+
+import {Steps} from 'nextra/components'
+import { Callout } from 'nextra/components'
+
+
+## Chrome  Extension Setup Guide
+
+To enhance your DocsGPT experience, you can install the DocsGPT Chrome extension. Here's how:
+<Steps >
+### Step 1
+
+
+
+In the DocsGPT GitHub repository, click on the **Code** button and select **Download ZIP**.
+### Step 2
+Unzip the downloaded file to a location you can easily access.
+### Step 3
+Open the Google Chrome browser and click on the three dots menu (upper right corner).
+### Step 4
+Select **More Tools** and then **Extensions**.
+### Step 5
+Turn on the **Developer mode** switch in the top right corner of the **Extensions page**.
+### Step 6
+Click on the **Load unpacked** button.
+### Step 7
+7. Select the **Chrome** folder where the DocsGPT files have been unzipped (docsgpt-main > extensions > chrome).
+### Step 8
+The extension should now be added to Google Chrome and can be managed on the Extensions page.
+### Step 9
+ To disable or remove the extension, simply turn off the toggle switch on the extension card or click the **Remove** button.
+</Steps>
+
+
+

docs/pages/Extensions/_meta.json

@@ -4,7 +4,11 @@
     "href": "/Extensions/Chatwoot-extension"
   },
   "react-widget": {
-      "title": "🏗️ Widget setup",
-      "href": "/Extensions/react-widget"
-    }
+    "title": "🏗️ Widget setup",
+    "href": "/Extensions/react-widget"
+  },
+  "Chrome-extension": {
+    "title": "🌐 Chrome Extension",
+    "href": "/Extensions/Chrome-extension"
+  }
 }

docs/pages/Extensions/react-widget.md

@@ -1,28 +1,46 @@
-### How to set up react docsGPT widget on your website:
+### Setting up the DocsGPT Widget in Your React Project
+
+### Introduction:
+The DocsGPT Widget is a powerful tool that allows you to integrate AI-powered documentation assistance into your web applications. This guide will walk you through the installation and usage of the DocsGPT Widget in your React project. Whether you're building a web app or a knowledge base, this widget can enhance your user experience.
 
 ### Installation
-Got to your project and install a new dependency: `npm install docsgpt`
+First, make sure you have Node.js and npm installed in your project. Then go to your project and install a new dependency: `npm install docsgpt`.
 
 ### Usage
-Go to your project and in the file where you want to use the widget import it: 
+In the file where you want to use the widget, import it and include the CSS file:
 ```js
 import { DocsGPTWidget } from "docsgpt";
-import "docsgpt/dist/style.css";
 ```
 
 
-Then you can use it like this: `<DocsGPTWidget />`
-
-DocsGPTWidget takes 3 props:
-- `apiHost` - url of your DocsGPT API
-- `selectDocs` - documentation that you want to use for your widget (eg. `default` or `local/docs1.zip`)
-- `apiKey` - usually its empty
+Now, you can use the widget in your component like this :
+```jsx
+<DocsGPTWidget
+  apiHost="https://your-docsgpt-api.com"
+  selectDocs="local/docs.zip"
+  apiKey=""
+  avatar = "https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png",
+  title = "Get AI assistance",
+  description = "DocsGPT's AI Chatbot is here to help",
+  heroTitle = "Welcome to DocsGPT !",
+  heroDescription="This chatbot is built with DocsGPT and utilises GenAI, 
+  please review important information using sources."
+/>
+```
+DocsGPTWidget takes 8 **props** with default fallback values:
+1. `apiHost` — The URL of your DocsGPT API.
+2. `selectDocs` — The documentation source that you want to use for your widget (e.g. `default` or `local/docs1.zip`).
+3. `apiKey` — Usually, it's empty.
+4. `avatar`: Specifies the URL of the avatar or image representing the chatbot.
+5. `title`: Sets the title text displayed in the chatbot interface.
+6. `description`: Provides a brief description of the chatbot's purpose or functionality.
+7. `heroTitle`: Displays a welcome title when users interact with the chatbot.
+8. `heroDescription`: Provide additional introductory text or information about the chatbot's capabilities.
 
 ### How to use DocsGPTWidget with [Nextra](https://nextra.site/) (Next.js + MDX)
-Install you widget as described above and then go to your `pages/` folder and create a new file `_app.js` with the following content:
+Install your widget as described above and then go to your `pages/` folder and create a new file `_app.js` with the following content:
 ```js
 import { DocsGPTWidget } from "docsgpt";
-import "docsgpt/dist/style.css";
 
 export default function MyApp({ Component, pageProps }) {
     return (
@@ -32,6 +50,60 @@ export default function MyApp({ Component, pageProps }) {
         </>
     )
 }
+```  
+### How to use DocsGPTWidget with HTML
+```html
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>DocsGPT Widget</title>
+</head>
+<body>
+  <div id="app"></div>
+  <!-- Include the widget script from dist/modern or dist/legacy -->
+  <script src="https://unpkg.com/docsgpt/dist/modern/main.js" type="module"></script>
+  <script type="module">
+    window.onload = function() {
+      renderDocsGPTWidget('app');
+    }
+  </script>
+</body>
+</html>
+```
+To link the widget to your api and your documents you can pass parameters to the renderDocsGPTWidget('div id', { parameters }).
+```html
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>DocsGPT Widget</title>
+</head>
+<body>
+  <div id="app"></div>
+  <!-- Include the widget script from dist/modern or dist/legacy -->
+  <script src="https://unpkg.com/docsgpt/dist/modern/main.js" type="module"></script>
+  <script type="module">
+    window.onload = function() {
+      renderDocsGPTWidget('app', {
+        apiHost: 'http://localhost:7001',
+        selectDocs: 'default',
+        apiKey: '',
+        avatar: 'https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png',
+        title: 'Get AI assistance',
+        description: "DocsGPT's AI Chatbot is here to help",
+        heroTitle: 'Welcome to DocsGPT!',
+        heroDescription: 'This chatbot is built with DocsGPT and utilises GenAI, please review important information using sources.'
+      });
+    }
+  </script>
+</body>
+</html>
 ```
 
+For more information about React, refer to this [link here](https://react.dev/learn)
 

docs/pages/Guides/Customising-prompts.md

@@ -1,4 +0,0 @@
-## To customise a main prompt navigate to `/application/prompt/combine_prompt.txt`
-
-You can try editing it to see how the model responds.
-

docs/pages/Guides/Customising-prompts.mdx

@@ -0,0 +1,42 @@
+import Image from 'next/image'
+
+# Customizing the Main Prompt
+
+Customizing the main prompt for DocsGPT gives you the ability to tailor the AI's responses to your specific requirements. By modifying the prompt text, you can achieve more accurate and relevant answers. Here's how you can do it:
+
+1. Navigate to `SideBar -> Settings`.
+
+
+
+
+2.In Settings select the `Active Prompt` now you will be able to see various prompts style.x
+
+
+
+
+3.Click on the `edit icon`  on the prompt of your choice and you will be able to see the current prompt for it,you can now customise  the prompt as per your choice.
+
+### Video Demo
+<Image src="/prompts.gif" alt="prompts" width={800} height={500} />
+
+
+
+## Example Prompt Modification
+
+**Original Prompt:**
+```markdown
+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.
+Use the following pieces of context to help answer the users question. If it's not relevant to the question, provide friendly responses.
+You have access to chat history, and can use it to help answer the question.
+When using code examples, use the following format:
+
+(code)
+{summaries}
+```
+
+Feel free to customize the prompt to align it with your specific use case or the kind of responses you want from the AI. For example, you can focus on specific document types, industries, or topics to get more targeted results.
+
+## Conclusion
+
+Customizing the main prompt for DocsGPT allows you to tailor the AI's responses to your unique requirements. Whether you need in-depth explanations, code examples, or specific insights, you can achieve it by modifying the main prompt. Remember to experiment and fine-tune your prompts to get the best results.
+