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

Fix: adjusted alignment of submit query icon within its container

.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: |

.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/

README.md

@@ -7,9 +7,9 @@
 </p>
 
 <p align="left">
-  <strong><a href="https://docsgpt.arc53.com/">DocsGPT</a></strong> is a cutting-edge open-source solution that streamlines the process of finding information in the project documentation. With its integration of the powerful <strong>GPT</strong> models, developers can easily ask questions about a project and receive accurate answers.
+  <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><a href="https://docsgpt.arc53.com/">DocsGPT</a></strong> help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.
+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">
@@ -27,7 +27,7 @@ Say goodbye to time-consuming manual searches, and let <strong><a href="https://
 
 We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
 
-- [Book Demo :wave:](https://airtable.com/appdeaL0F1qV8Bl2C/shrrJF1Ll7btCJRbP)
+- [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)
@@ -40,7 +40,7 @@ You can find our roadmap [here](https://github.com/orgs/arc53/projects/2). Pleas
 
 | Name                                                                  | Base Model  | Requirements (or similar) |
 | --------------------------------------------------------------------- | ----------- | ------------------------- |
-| [Docsgpt-7b-falcon](https://huggingface.co/Arc53/docsgpt-7b-falcon)   | Falcon-7b   | 1xA10G gpu                |
+| [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              |
 
@@ -52,17 +52,17 @@ If you don't have enough resources to run it, you can use bitsnbytes to quantize
 
 ## Useful Links
 
-- :mag: :fire: [Live preview](https://docsgpt.arc53.com/)
+- :mag: :fire: [Cloud Version](https://app.docsgpt.cloud/)
 
 - :speech_balloon: :tada: [Join our Discord](https://discord.gg/n5BX8dh8rU)
 
-- :books: :sunglasses: [Guides](https://docs.docsgpt.co.uk/)
+- :books: :sunglasses: [Guides](https://docs.docsgpt.cloud/)
 
 - :couple: [Interested in contributing?](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md)
 
-- :file_folder: :rocket: [How to use any other documentation](https://docs.docsgpt.co.uk/Guides/How-to-train-on-other-documentation)
+- :file_folder: :rocket: [How to use any other documentation](https://docs.docsgpt.cloud/Guides/How-to-train-on-other-documentation)
 
-- :house: :closed_lock_with_key: [How to host it locally (so all data will stay on-premises)](https://docs.docsgpt.co.uk/Guides/How-to-use-different-LLM)
+- :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)
 
 ## Project Structure
 
@@ -85,7 +85,7 @@ On Mac OS or Linux, write:
 
 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 variables and `VITE_API_STREAMING` to true or false, depending on whether you want streaming answers or not.
@@ -123,7 +123,7 @@ docker compose -f docker-compose-dev.yaml up -d
 > [!Note]
 > Make sure you have Python 3.10 or 3.11 installed.
 
-1. Export required environment variables or prepare a `.env` file in the `/application` folder:
+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.)
@@ -152,11 +152,12 @@ You can use the script below, or download it manually from [here](https://d3dg10
 wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
 unzip mpnet-base-v2.zip -d model
 rm mpnet-base-v2.zip
+```
 
-4. Change to the `application/` subdir by the command `cd application/` and install dependencies for the backend:
+4. Install dependencies for the backend:
 
 ```commandline
-pip install -r requirements.txt
+pip install -r application/requirements.txt
 ```
 
 5. Run the app using `flask --app application/app.py run --host=0.0.0.0 --port=7091`.

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/Dockerfile

@@ -1,29 +1,93 @@
-FROM python:3.11-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==0.5.2
+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
-RUN apt-get install -y wget unzip
-RUN wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
-RUN unzip mpnet-base-v2.zip -d model
-RUN rm mpnet-base-v2.zip
 
-FROM python:3.11-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
+
+# 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
-ENV FLASK_APP=app.py
-ENV FLASK_DEBUG=true
 
+# 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
@@ -8,17 +9,12 @@ 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)
@@ -26,17 +22,23 @@ db = mongo["docsgpt"]
 conversations_collection = db["conversations"]
 vectors_collection = db["vectors"]
 prompts_collection = db["prompts"]
-answer = Blueprint('answer', __name__)
+api_key_collection = db["api_keys"]
+answer = Blueprint("answer", __name__)
 
-if settings.LLM_NAME == "gpt4":
-    gpt_model = 'gpt-4'
+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'
-else:
-    gpt_model = 'gpt-3.5-turbo'
+    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__))))
+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()
 
@@ -47,7 +49,7 @@ with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r"
     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()    
+    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)
@@ -75,10 +72,19 @@ 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] == "default":
-                vectorstore = ""
+            vectorstore = ""
         elif data["active_docs"].split("/")[0] == "local":
             vectorstore = "indexes/" + data["active_docs"]
         else:
@@ -92,248 +98,291 @@ def get_vectorstore(data):
 
 
 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, prompt_id, conversation_id):
-    llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
-
-    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"]
-
-    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 = prompt.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})
-
-    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"]
-    if 'prompt_id' in data:
+    if "history" not in data:
+        history = []
+    else:
+        history = data["history"]
+        history = json.loads(history)
+    if "conversation_id" not in data:
+        conversation_id = None
+    else:
+        conversation_id = data["conversation_id"]
+    if "prompt_id" in data:
         prompt_id = data["prompt_id"]
     else:
-        prompt_id = 'default'
-
-    # check if active_docs is set
-
-    if not api_key_set:
-        api_key = data["api_key"]
+        prompt_id = "default"
+    if "selectedDocs" in data and data["selectedDocs"] is None:
+        chunks = 0
+    elif "chunks" in data:
+        chunks = int(data["chunks"])
     else:
-        api_key = settings.API_KEY
-    if not embeddings_key_set:
-        embeddings_key = data["embeddings_key"]
+        chunks = 2
+    if "token_limit" in data:
+        token_limit = data["token_limit"]
     else:
-        embeddings_key = settings.EMBEDDINGS_KEY
-    if "active_docs" in data:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
-    else:
-        vectorstore = ""
-    docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key)
+        token_limit = settings.DEFAULT_MAX_HISTORY
 
-    return Response(
-        complete_stream(question, docsearch,
-                        chat_history=history, api_key=api_key,
-                        prompt_id=prompt_id,
-                        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"]
-    else:
-        api_key = settings.API_KEY
-    if not embeddings_key_set:
-        embeddings_key = data["embeddings_key"]
-    else:
-        embeddings_key = settings.EMBEDDINGS_KEY
-    if 'prompt_id' in data:
+    if "prompt_id" in data:
         prompt_id = data["prompt_id"]
     else:
-        prompt_id = 'default'
-
-    if prompt_id == 'default':
-        prompt = chat_combine_template
-    elif prompt_id == 'creative':
-        prompt = chat_combine_creative
-    elif prompt_id == 'strict':
-        prompt = chat_combine_strict
+        prompt_id = "default"
+    if "chunks" in data:
+        chunks = int(data["chunks"])
     else:
-        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
+        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 = prompt.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
@@ -347,28 +396,44 @@ def api_search():
     data = request.get_json()
     # get parameter from url question
     question = data["question"]
-
-    if not embeddings_key_set:
-        if "embeddings_key" in data:
-            embeddings_key = data["embeddings_key"]
-        else:
-            embeddings_key = settings.EMBEDDINGS_KEY
+    if "chunks" in data:
+        chunks = int(data["chunks"])
     else:
-        embeddings_key = settings.EMBEDDINGS_KEY
-    if "active_docs" in data:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
+        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:
-        vectorstore = ""
-    docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key)
+        source = {}
+        user_api_key = None
 
-    docs = docsearch.search(question, k=2)
-
-    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})
-        #yield f"data:{data}\n\n"
-    return source_log_docs
+    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

@@ -1,11 +1,15 @@
 import os
+import uuid
+import shutil
 from flask import Blueprint, request, jsonify
+from urllib.parse import urlparse
 import requests
 from pymongo import MongoClient
 from bson.objectid import ObjectId
+from bson.binary import Binary, UuidRepresentation
 from werkzeug.utils import secure_filename
-
-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 +20,15 @@ conversations_collection = db["conversations"]
 vectors_collection = db["vectors"]
 prompts_collection = db["prompts"]
 feedback_collection = db["feedback"]
-user = Blueprint('user', __name__)
+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 +71,8 @@ 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():
@@ -59,7 +80,7 @@ def update_conversation_name():
     data = request.get_json()
     id = data["id"]
     name = data["name"]
-    conversations_collection.update_one({"_id": ObjectId(id)},{"$set":{"name":name}})
+    conversations_collection.update_one({"_id": ObjectId(id)}, {"$set": {"name": name}})
     return {"status": "ok"}
 
 
@@ -70,7 +91,6 @@ def api_feedback():
     answer = data["answer"]
     feedback = data["feedback"]
 
-
     feedback_collection.insert_one(
         {
             "question": question,
@@ -80,6 +100,7 @@ def api_feedback():
     )
     return {"status": "ok"}
 
+
 @user.route("/api/delete_by_ids", methods=["get"])
 def delete_by_ids():
     """Delete by ID. These are the IDs in the vectorstore"""
@@ -94,6 +115,7 @@ def delete_by_ids():
             return {"status": "ok"}
     return {"status": "error"}
 
+
 @user.route("/api/delete_old", methods=["get"])
 def delete_old():
     """Delete old indexes."""
@@ -109,7 +131,7 @@ def delete_old():
     if dirs_clean[0] not in ["indexes", "vectors"]:
         return {"status": "error"}
     path_clean = "/".join(dirs_clean)
-    vectors_collection.delete_one({"name": dirs_clean[-1], 'user': dirs_clean[-2]})
+    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))
@@ -120,9 +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."""
@@ -133,36 +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", ".docx", 
-        ".csv", ".epub", ".html", ".mdx"],
-         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}
@@ -185,11 +256,12 @@ def combined_json():
             "docLink": "default",
             "model": settings.EMBEDDINGS_NAME,
             "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"],
@@ -201,13 +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)
 
@@ -219,28 +324,36 @@ 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()
@@ -259,6 +372,7 @@ def create_prompt():
     new_id = str(resp.inserted_id)
     return {"id": new_id}
 
+
 @user.route("/api/get_prompts", methods=["GET"])
 def get_prompts():
     user = "local"
@@ -268,30 +382,39 @@ def get_prompts():
     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"})
+        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:
+    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:
+    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()   
+    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()
@@ -303,6 +426,7 @@ def delete_prompt():
     )
     return {"status": "ok"}
 
+
 @user.route("/api/update_prompt", methods=["POST"])
 def update_prompt_name():
     data = request.get_json()
@@ -312,10 +436,131 @@ def update_prompt_name():
     # check if name is null
     if name == "":
         return {"status": "error"}
-    prompts_collection.update_one({"_id": ObjectId(id)},{"$set":{"name":name, "content": content}})
+    prompts_collection.update_one(
+        {"_id": ObjectId(id)}, {"$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

@@ -1,6 +1,6 @@
 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
@@ -40,5 +40,5 @@ def after_request(response):
     return response
 
 if __name__ == "__main__":
-    app.run(debug=True, port=7091)
+    app.run(debug=settings.FLASK_DEBUG_MODE, port=7091)
 

application/core/settings.py

@@ -3,19 +3,23 @@ from typing import Optional
 import os
 
 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 = "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
 
@@ -27,17 +31,39 @@ class Settings(BaseSettings):
     AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for embeddings
 
     # elasticsearch
-    ELASTIC_CLOUD_ID: Optional[str] = None # cloud id for elasticsearch
-    ELASTIC_USERNAME: Optional[str] = None # username for elasticsearch
-    ELASTIC_PASSWORD: Optional[str] = None # password for elasticsearch
-    ELASTIC_URL: Optional[str] = None # url for elasticsearch
-    ELASTIC_INDEX: Optional[str] = "docsgpt" # index name for elasticsearch
+    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
+    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

@@ -1,21 +1,29 @@
 from application.llm.base import BaseLLM
 from application.core.settings import settings
 
+
 class AnthropicLLM(BaseLLM):
 
-    def __init__(self, api_key=None):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
         from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
-        self.api_key = api_key or settings.ANTHROPIC_API_KEY  # If not provided, use a default from settings
+
+        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 gen(self, model, messages, engine=None, max_tokens=300, stream=False, **kwargs):
-        context = messages[0]['content']
-        user_question = messages[-1]['content']
+    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, max_tokens, **kwargs)
+            return self.gen_stream(model, prompt, stream, max_tokens, **kwargs)
 
         completion = self.anthropic.completions.create(
             model=model,
@@ -25,9 +33,11 @@ class AnthropicLLM(BaseLLM):
         )
         return completion.completion
 
-    def gen_stream(self, model, messages, engine=None, max_tokens=300, **kwargs):
-        context = messages[0]['content']
-        user_question = messages[-1]['content']
+    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,
@@ -37,4 +47,4 @@ class AnthropicLLM(BaseLLM):
         )
 
         for completion in stream_response:
-            yield completion.completion
+            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

@@ -2,48 +2,43 @@ from application.llm.base import BaseLLM
 import json
 import requests
 
+
 class DocsGPTAPILLM(BaseLLM):
 
-    def __init__(self, *args, **kwargs):
-        self.endpoint =  "https://llm.docsgpt.co.uk"
+    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 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, *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
-            }
+            f"{self.endpoint}/answer", json={"prompt": prompt, "max_new_tokens": 30}
         )
-        response_clean = response.json()['a'].split("###")[0]
+        response_clean = response.json()["a"].replace("###", "")
 
         return response_clean
 
-    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, *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
+            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')
+                # data = json.loads(line)
+                data_str = line.decode("utf-8")
                 if data_str.startswith("data: "):
                     data = json.loads(data_str[6:])
-                    yield data['a']
-                    
+                    yield data["a"]

application/llm/huggingface.py

@@ -1,44 +1,68 @@
 from application.llm.base import BaseLLM
 
+
 class HuggingFaceLLM(BaseLLM):
 
-    def __init__(self, api_key, llm_name='Arc53/DocsGPT-7B',q=False):
+    def __init__(
+        self,
+        api_key=None,
+        user_api_key=None,
+        llm_name="Arc53/DocsGPT-7B",
+        q=False,
+        *args,
+        **kwargs,
+    ):
         global hf
-        
+
         from langchain.llms import HuggingFacePipeline
+
         if q:
             import torch
-            from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
+            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)
+                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

@@ -4,23 +4,24 @@ from application.llm.huggingface import HuggingFaceLLM
 from application.llm.llama_cpp import LlamaCpp
 from application.llm.anthropic import AnthropicLLM
 from application.llm.docsgpt_provider import DocsGPTAPILLM
-
+from application.llm.premai import PremAILLM
 
 
 class LLMCreator:
     llms = {
-        'openai': OpenAILLM,
-        'azure_openai': AzureOpenAILLM,
-        'sagemaker': SagemakerAPILLM,
-        'huggingface': HuggingFaceLLM,
-        'llama.cpp': LlamaCpp,
-        'anthropic': AnthropicLLM,
-        'docsgpt': DocsGPTAPILLM
+        "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

@@ -1,36 +1,53 @@
 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
         from openai import OpenAI
-        
+
+        super().__init__(*args, **kwargs)
         self.client = OpenAI(
-                api_key=api_key, 
-            )
+            api_key=api_key,
+        )
         self.api_key = api_key
+        self.user_api_key = user_api_key
 
     def _get_openai(self):
         # Import openai when needed
         import openai
-        
+
         return openai
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
-        response = self.client.chat.completions.create(model=model,
-            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
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
-        response = self.client.chat.completions.create(model=model,
-            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:
             # import sys
@@ -41,14 +58,17 @@ class OpenAILLM(BaseLLM):
 
 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_key=openai_api_key,
             api_version=settings.OPENAI_API_VERSION,
             api_base=settings.OPENAI_API_BASE,
             deployment_name=settings.AZURE_DEPLOYMENT_NAME,

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

@@ -4,11 +4,10 @@ import json
 import io
 
 
-
 class LineIterator:
     """
-    A helper class for parsing the byte stream input. 
-    
+    A helper class for parsing the byte stream input.
+
     The output of the model will be in the following format:
     ```
     b'{"outputs": [" a"]}\n'
@@ -16,21 +15,21 @@ class LineIterator:
     b'{"outputs": [" problem"]}\n'
     ...
     ```
-    
-    While usually each PayloadPart event from the event stream will contain a byte array 
+
+    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. 
+    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()
@@ -43,7 +42,7 @@ class LineIterator:
         while True:
             self.buffer.seek(self.read_pos)
             line = self.buffer.readline()
-            if line and line[-1] == ord('\n'):
+            if line and line[-1] == ord("\n"):
                 self.read_pos += len(line)
                 return line[:-1]
             try:
@@ -52,33 +51,35 @@ class LineIterator:
                 if self.read_pos < self.buffer.getbuffer().nbytes:
                     continue
                 raise
-            if 'PayloadPart' not in chunk:
-                print('Unknown event type:' + chunk)
+            if "PayloadPart" not in chunk:
+                print("Unknown event type:" + chunk)
                 continue
             self.buffer.seek(0, io.SEEK_END)
-            self.buffer.write(chunk['PayloadPart']['Bytes'])
+            self.buffer.write(chunk["PayloadPart"]["Bytes"])
+
 
 class SagemakerAPILLM(BaseLLM):
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
         import boto3
+
         runtime = boto3.client(
-            'runtime.sagemaker',
-            aws_access_key_id='xxx',
-            aws_secret_access_key='xxx',
-            region_name='us-west-2'
+            "runtime.sagemaker",
+            aws_access_key_id="xxx",
+            aws_secret_access_key="xxx",
+            region_name="us-west-2",
         )
 
-        
-        self.endpoint =  settings.SAGEMAKER_ENDPOINT
+        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(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"
-    
 
         # Construct payload for endpoint
         payload = {
@@ -89,25 +90,25 @@ class SagemakerAPILLM(BaseLLM):
                 "temperature": 0.1,
                 "max_new_tokens": 30,
                 "repetition_penalty": 1.03,
-                "stop": ["</s>", "###"]
-            }
+                "stop": ["</s>", "###"],
+            },
         }
-        body_bytes = json.dumps(payload).encode('utf-8')
+        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())
+        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 gen_stream(self, model, engine, messages, stream=True, **kwargs):
-        context = messages[0]['content']
-        user_question = messages[-1]['content']
+        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 = {
@@ -118,22 +119,22 @@ class SagemakerAPILLM(BaseLLM):
                 "temperature": 0.1,
                 "max_new_tokens": 512,
                 "repetition_penalty": 1.03,
-                "stop": ["</s>", "###"]
-            }
+                "stop": ["</s>", "###"],
+            },
         }
-        body_bytes = json.dumps(payload).encode('utf-8')
+        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'{'
+        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']
+            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/parser/file/bulk.py

@@ -147,12 +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/open_ai_func.py

@@ -1,6 +1,5 @@
 import os
 
-import tiktoken
 from application.vectorstore.vector_creator import VectorCreator
 from application.core.settings import settings
 from retry import retry
@@ -11,14 +10,6 @@ from retry import retry
 # from langchain_community.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
-
-
 @retry(tries=10, delay=60)
 def store_add_texts_with_retry(store, i):
     store.add_texts([i.page_content], metadatas=[i.metadata])
@@ -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/requirements.txt

@@ -3,31 +3,32 @@ boto3==1.34.6
 celery==5.3.6
 dataclasses_json==0.6.3
 docx2txt==0.8
+duckduckgo-search==5.3.0
 EbookLib==0.18
 elasticsearch==8.12.0
 escodegen==1.0.11
 esprima==4.0.1
 faiss-cpu==1.7.4
 Flask==3.0.1
-gunicorn==21.2.0
+gunicorn==22.0.0
 html2text==2020.1.16
 javalang==0.13.0
 langchain==0.1.4
 langchain-openai==0.0.5
-nltk==3.8.1
 openapi3_parser==1.1.16
 pandas==2.2.0
 pydantic_settings==2.1.0
-pymongo==4.6.1
+pymongo==4.6.3
 PyPDF2==3.0.1
 python-dotenv==1.0.1
+qdrant-client==1.9.0
 redis==5.0.1
-Requests==2.31.0
+Requests==2.32.0
 retry==0.9.2
 sentence-transformers
-tiktoken==0.5.2
-torch==2.1.2
-tqdm==4.66.1
+tiktoken
+torch
+tqdm==4.66.3
 transformers==4.36.2
 unstructured==0.12.2
-Werkzeug==3.0.1
+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/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

@@ -8,6 +8,32 @@ from langchain_community.embeddings import (
 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
@@ -20,37 +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":
-            embedding_instance = embeddings_factory[embeddings_name](
-                #model_name="./model/all-mpnet-base-v2",
-                model_kwargs={"device": "cpu"},
-            )
+            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/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

@@ -1,13 +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,
-        'mongodb': MongoDBVectorStore,
+        "faiss": FaissStore,
+        "elasticsearch": ElasticsearchStore,
+        "mongodb": MongoDBVectorStore,
+        "qdrant": QdrantStore,
     }
 
     @classmethod
@@ -15,4 +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

@@ -2,35 +2,60 @@ 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 = '/'.join(title.split('/')[1:3])
-    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__)))
+)
+
+
+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)
 
-current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 # Define the main function for ingesting and processing documents.
 def ingest_worker(self, directory, formats, name_job, filename, user):
@@ -61,38 +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))):
@@ -100,24 +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)

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

@@ -1,17 +1,17 @@
 {
-"scripts":{
-      "dev": "next dev",
+  "scripts": {
+    "dev": "next dev",
     "build": "next build",
     "start": "next start"
   },
-    "license": "MIT",
+  "license": "MIT",
   "dependencies": {
-    "@vercel/analytics": "^1.0.2",
-    "docsgpt": "^0.2.4",
-    "next": "^13.5.1",
-    "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

@@ -227,3 +227,124 @@ 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/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

@@ -110,19 +110,3 @@ Option 2: Using Git Bash or Command Prompt (CMD):
 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.
-
-
-### Chrome Extension
-
-#### Installing the Chrome extension:
-To enhance your DocsGPT experience, you can install the DocsGPT Chrome extension. Here's how:
-
-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.

docs/pages/Deploying/_meta.json

@@ -10,5 +10,9 @@
   "Railway-Deploying": {
     "title": "🚂Deploying on Railway",
     "href": "/Deploying/Railway-Deploying"
+  },
+  "Kubernetes-Deploying": {
+    "title": "☸️Deploying on Kubernetes",
+    "href": "/Deploying/Kubernetes-Deploying"
   }
-}
+}

docs/pages/Developing/_meta.json

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

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

@@ -10,7 +10,6 @@ First, make sure you have Node.js and npm installed in your project. Then go to
 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";
 ```
 
 
@@ -20,18 +19,28 @@ Now, you can use the widget in your component like this :
   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 3 **props**:
+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 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 (
@@ -42,6 +51,59 @@ 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.mdx

@@ -1,10 +1,25 @@
+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 `/application/prompts/combine_prompt.txt`.
+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} />
+
 
-2. Open the `combine_prompt.txt` file and modify the prompt text to suit your needs. You can experiment with different phrasings and structures to observe how the model responds. The main prompt serves as guidance to the AI model on how to generate responses.
 
 ## Example Prompt Modification
 

docs/pages/Guides/How-to-train-on-other-documentation.md

@@ -1,63 +0,0 @@
-## How to train on other documentation
-
-This AI can utilize any documentation, but it requires preparation for similarity search. Follow these steps to get your documentation ready:
-
-**Step 1: Prepare Your Documentation**
-![video-example-of-how-to-do-it](https://d3dg1063dc54p9.cloudfront.net/videos/how-to-vectorise.gif)
-
-Start by going to `/scripts/` folder.
-
-If you open this file, you will see that it uses RST files from the folder to create a `index.faiss` and `index.pkl`. 
-
-It currently uses OPENAI to create the vector store, so make sure your documentation is not too large. Using Pandas cost me around $3-$4.
-
-You can typically find documentation on GitHub in the `docs/` folder for most open-source projects.
-
-### 1. Find documentation in .rst/.md format and create a folder with it in your scripts directory.
-- Name it `inputs/`.
-- Put all your .rst/.md files in there.  
-- The search is recursive, so you don't need to flatten them.
-
-If there are no .rst/.md files, convert whatever you find to a .txt file and feed it. (Don't forget to change the extension in the script).
-
-### Step 2: Configure Your OpenAI API Key
-1. Create a .env file in the scripts/ folder.
-  - Add your OpenAI API key inside: OPENAI_API_KEY=<your-api-key>.
-
-### Step 3: Run the Ingestion Script
-
-`python ingest.py ingest`
-
-It will provide you with the estimated cost.
-
-### Step 4: Move `index.faiss` and `index.pkl` generated in `scripts/output` to `application/` folder. 
-
-
-### Step 5: Run the Web App
-Once you run it, it will use new context relevant to your documentation.Make sure you select default in the dropdown in the UI.
-
-## Customization 
-You can learn more about options while running ingest.py by running:
-  - Make sure you select 'default' from the dropdown in the UI.
-
-## Customization
-You can learn more about options while running ingest.py by executing:
-`python ingest.py --help`
-|              Options             |                                                                                                                                |
-|:--------------------------------:|:------------------------------------------------------------------------------------------------------------------------------:|
-|            **ingest**            | Runs 'ingest' function, converting documentation to Faiss plus Index format                                                  |
-| --dir TEXT                       | List of paths to directory for index creation. E.g. --dir inputs --dir inputs2 [default: inputs]                               |
-| --file TEXT                      | File paths to use (Optional; overrides directory) E.g. --files inputs/1.md --files inputs/2.md                                 |
-| --recursive / --no-recursive     | Whether to recursively search in subdirectories [default: recursive]                                                           |
-| --limit INTEGER                  | Maximum number of files to read                                                                                                |
-| --formats TEXT                   | List of required extensions (list with .) Currently supported: .rst, .md, .pdf, .docx, .csv, .epub, .html [default: .rst, .md] |
-| --exclude / --no-exclude         | Whether to exclude hidden files (dotfiles) [default: exclude]                                                                  |
-| -y, --yes                        | Whether to skip price confirmation                                                                                             |
-| --sample / --no-sample           | Whether to output sample of the first 5 split documents. [default: no-sample]                                                  |
-| --token-check / --no-token-check | Whether to group small documents and split large. Improves semantics. [default: token-check]                                   |
-| --min_tokens INTEGER             | Minimum number of tokens to not group. [default: 150]                                                                          |
-| --max_tokens INTEGER             | Maximum number of tokens to not split. [default: 2000]                                                                         |
-|                                  |                                                                                                                                |
-|            **convert**           | Creates documentation in .md format from source code                                                                           |
-| --dir TEXT                       | Path to a directory with source code. E.g. --dir inputs [default: inputs]                                                      |
-| --formats TEXT                   | Source code language from which to create documentation. Supports py, js and java.  E.g. --formats py [default: py]            |

docs/pages/Guides/How-to-train-on-other-documentation.mdx

@@ -0,0 +1,44 @@
+
+import { Callout } from 'nextra/components'
+import Image from 'next/image'
+import { Steps } from 'nextra/components'
+
+## How to train on other documentation
+
+Training on other documentation sources can greatly enhance the versatility and depth of DocsGPT's knowledge. By incorporating diverse materials, you can broaden the AI's understanding and improve its ability to generate insightful responses across a range of topics. Here's a step-by-step guide on how to effectively train DocsGPT on additional documentation sources:
+
+**Get your document ready**:
+
+Make sure you have the document on which you want to train on  ready with you on the device which you are using .You can also use links to the documentation to train on.
+
+<Callout type="warning" emoji="⚠️">
+ Note: The document should be either of the given file formats .pdf, .txt, .rst, .docx, .md, .zip and  limited to 25mb.You can also train using the link of the documentation.
+
+</Callout>
+
+### Video Demo
+
+<Image src="/docs.gif" alt="prompts" width={800} height={500} />
+
+
+
+<Steps>
+### Step1
+Navigate to the sidebar where you will find `Source Docs` option,here you will find 3 options built in which are default,Web Search and None.
+
+
+### Step 2
+Click on the `Upload icon` just beside the source docs options,now borwse and upload the document which you want to train on or select the `remote` option if you have to insert the link of the documentation.
+
+
+### Step 3
+Now you will be able to see the name of the file uploaded under the Uploaded Files ,now click on `Train`,once you click on train it might take some time to train on the document. You will be able to see the `Training progress` and once the training is completed you can click the `finish` button and there you go your docuemnt is uploaded.
+
+
+### Step 4
+Go to `New chat`  and from the side bar select the document  you uploaded  under the `Source Docs` and go ahead with your chat, now you can ask qestions regarding the document you uploaded and you will get the effective answer based on it.
+
+</Steps>
+
+
+

docs/pages/Guides/How-to-use-different-LLM.md

@@ -1,48 +0,0 @@
-# Setting Up Local Language Models for Your App
-
-Your app relies on two essential models: Embeddings and Text Generation. While OpenAI's default models work seamlessly, you have the flexibility to switch providers or even run the models locally.
-
-## Step 1: Configure Environment Variables
-
-Navigate to the `.env` file or set the following environment variables:
-
-```env
-LLM_NAME=<your Text Generation model>
-API_KEY=<API key for Text Generation>
-EMBEDDINGS_NAME=<LLM for Embeddings>
-EMBEDDINGS_KEY=<API key for Embeddings>
-VITE_API_STREAMING=<true or false>
-```
-
-You can omit the keys if users provide their own. Ensure you set `LLM_NAME` and `EMBEDDINGS_NAME`.
-
-## Step 2: Choose Your Models
-
-**Options for `LLM_NAME`:**
-- openai ([More details](https://platform.openai.com/docs/models))
-- anthropic ([More details](https://docs.anthropic.com/claude/reference/selecting-a-model))
-- manifest ([More details](https://python.langchain.com/docs/integrations/llms/manifest))
-- cohere ([More details](https://docs.cohere.com/docs/llmu))
-- llama.cpp ([More details](https://python.langchain.com/docs/integrations/llms/llamacpp))
-- huggingface (Arc53/DocsGPT-7B by default)
-- sagemaker ([Mode details](https://aws.amazon.com/sagemaker/))
-
-
-Note: for huggingface you can choose any model inside application/llm/huggingface.py or pass llm_name on init, loads   
-
-**Options for `EMBEDDINGS_NAME`:**
-- openai_text-embedding-ada-002
-- huggingface_sentence-transformers/all-mpnet-base-v2
-- huggingface_hkunlp/instructor-large
-- cohere_medium
-
-If you want to be completely local, set `EMBEDDINGS_NAME` to `huggingface_sentence-transformers/all-mpnet-base-v2`. 
-
-For llama.cpp Download the required model and place it in the `models/` folder.
-
-Alternatively, for local Llama setup, run `setup.sh` and choose option 1. The script handles the DocsGPT model addition.
-
-## Step 3: Local Hosting for Privacy
-
-If working with sensitive data, host everything locally by setting `LLM_NAME`, llama.cpp or huggingface, use any model available on Hugging Face, for llama.cpp you need to convert it into gguf format.
-That's it! Your app is now configured for local and private hosting, ensuring optimal security for critical data.

docs/pages/Guides/How-to-use-different-LLM.mdx

@@ -0,0 +1,41 @@
+
+import { Callout } from 'nextra/components'
+import Image from 'next/image'
+import { Steps } from 'nextra/components'
+
+# Setting Up Local Language Models for Your App
+
+Setting up local language models for your app can significantly enhance its capabilities, enabling it to understand and generate text in multiple languages without relying on external APIs. By integrating local language models, you can improve privacy, reduce latency, and ensure continuous functionality even in offline environments. Here's a comprehensive guide on how to set up local language models for your application:
+
+## Steps:
+### For cloud version LLM change:
+<Steps >
+### Step 1
+Visit the chat screen and you will be to see the default LLM selected.
+### Step 2
+Click on it and you will get a drop down of various LLM's available to choose.
+### Step 3
+Choose the LLM of your choice.
+
+</Steps>
+
+
+
+
+### Video Demo
+<Image src="/llms.gif" alt="prompts" width={800} height={500} />
+
+### For Open source llm change:
+<Steps >
+### Step 1
+For open source you  have to edit .env file with LLM_NAME with their desired LLM name.
+### Step 2
+All the supported LLM providers are here application/llm and you  can check what env variable are needed for each
+List of latest supported LLMs are https://github.com/arc53/DocsGPT/blob/main/application/llm/llm_creator.py
+### Step 3
+Visit application/llm and select the file of your selected llm and there you will find the speicifc requirements needed to be filled in order to use it,i.e API key of that llm.
+</Steps>
+
+
+
+

docs/pages/Guides/_meta.json

@@ -1,6 +1,6 @@
 {
   "Customising-prompts": {
-    "title": "🏗️️ Customising Prompts",
+    "title": "️💻 Customising Prompts",
     "href": "/Guides/Customising-prompts"
   },
   "How-to-train-on-other-documentation": {
@@ -8,7 +8,7 @@
     "href": "/Guides/How-to-train-on-other-documentation"
   },
   "How-to-use-different-LLM": {
-    "title": "⚙️️ How to use different LLM's",
+    "title": "️🤖 How to use different LLM's",
     "href": "/Guides/How-to-use-different-LLM"
   },
   "My-AI-answers-questions-using-external-knowledge": {

docs/pages/_app.mdx

@@ -1,11 +1,10 @@
 import { DocsGPTWidget } from "docsgpt";
-import "docsgpt/dist/style.css";
 
 export default function MyApp({ Component, pageProps }) {
   return (
     <>
       <Component {...pageProps} />
-        <DocsGPTWidget selectDocs="local/docsgpt-sep.zip/"/>
+        <DocsGPTWidget apiKey="d61a020c-ac8f-4f23-bb98-458e4da3c240" />
     </>
   )
 }

docs/pages/index.mdx

@@ -2,14 +2,16 @@
 title: 'Home'
 ---
 import { Cards, Card } from 'nextra/components'
+import Image from 'next/image'
 import deployingGuides from './Deploying/_meta.json';
-import developingGuides from './Developing/_meta.json';
+import developingGuides from './API/_meta.json';
 import extensionGuides from './Extensions/_meta.json';
 import mainGuides from './Guides/_meta.json';
 
 
 
 
+
 export const allGuides = {
   ...deployingGuides,
   ...developingGuides,
@@ -21,9 +23,12 @@ export const allGuides = {
 
 DocsGPT 🦖 is an innovative open-source tool designed to simplify the retrieval of information from project documentation using advanced GPT models 🤖. Eliminate lengthy manual searches 🔍 and enhance your documentation experience with DocsGPT, and consider contributing to its AI-powered future 🚀.
 
-![video-example-of-docs-gpt](https://d3dg1063dc54p9.cloudfront.net/videos/demov3.gif)
 
-Try it yourself: [https://docsgpt.arc53.com/](https://docsgpt.arc53.com/)
+
+<Image src="/homevideo.gif" alt="homedemo" width={800} height={500}/>
+
+
+Try it yourself: [https://www.docsgpt.cloud/](https://www.docsgpt.cloud/)
 
 <Cards
       num={3}

extensions/react-widget/.gitignore

@@ -0,0 +1,3 @@
+node_modules
+dist
+.parcel-cache

extensions/react-widget/.parcelrc

@@ -0,0 +1,10 @@
+{
+  "extends": "@parcel/config-default",
+  "resolvers": ["@parcel/resolver-glob","..."],
+  "transformers": {
+    "*.svg": ["...", "@parcel/transformer-svg-react", "@parcel/transformer-typescript-tsc"]
+},
+  "validators": {
+    "*.{ts,tsx}": ["@parcel/validator-typescript"]
+  }
+}

extensions/react-widget/README.md

@@ -1,6 +1,5 @@
 # DocsGPT react widget
 
-
 This widget will allow you to embed a DocsGPT assistant in your React app.
 
 ## Installation
@@ -11,9 +10,10 @@ npm install  docsgpt
 
 ## Usage
 
+### React
+
 ```javascript
     import { DocsGPTWidget } from "docsgpt";
-    import "docsgpt/dist/style.css";
 
     const App = () => {
       return <DocsGPTWidget />;
@@ -24,17 +24,80 @@ To link the widget to your api and your documents you can pass parameters to the
 
 ```javascript
     import { DocsGPTWidget } from "docsgpt";
-    import "docsgpt/dist/style.css";
 
     const App = () => {
-      return <DocsGPTWidget apiHost="http://localhost:7001" selectDocs='default' apiKey=''/>;
+      return <DocsGPTWidget
+             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.'
+             />;
     };
 ```
 
+### 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>
+```
 
 ## Our github
 
 [DocsGPT](https://github.com/arc53/DocsGPT)
 
 You can find the source code in the extensions/react-widget folder.
-

extensions/react-widget/custom.d.ts

@@ -0,0 +1,9 @@
+declare module "*.svg" {
+    import * as React from "react";
+  
+    const ReactComponent: React.FunctionComponent<
+      React.SVGProps<SVGSVGElement> & { title?: string }
+    >;
+  
+    export default ReactComponent;
+  }

extensions/react-widget/dist/index.d.ts

@@ -1 +0,0 @@
-export { DocsGPTWidget } from "./src/components/DocsGPTWidget";

extensions/react-widget/dist/index.es.js

@@ -1,832 +0,0 @@
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-var ne = { exports: {} }, Y = {};
-/**
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- * react-jsx-runtime.development.js
- *
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- *
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- * LICENSE file in the root directory of this source tree.
- */
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-    var sr = or, lr = ir;
-    Y.Fragment = u, Y.jsx = sr, Y.jsxs = lr;
-  }()), Y;
-}
-var L = {};
-/**
- * @license React
- * react-jsx-runtime.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-var Oe;
-function fr() {
-  if (Oe)
-    return L;
-  Oe = 1;
-  var N = Ne, w = Symbol.for("react.element"), C = Symbol.for("react.fragment"), u = Object.prototype.hasOwnProperty, E = N.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.ReactCurrentOwner, S = { key: !0, ref: !0, __self: !0, __source: !0 };
-  function T(b, d, v) {
-    var m, h = {}, x = null, p = null;
-    v !== void 0 && (x = "" + v), d.key !== void 0 && (x = "" + d.key), d.ref !== void 0 && (p = d.ref);
-    for (m in d)
-      u.call(d, m) && !S.hasOwnProperty(m) && (h[m] = d[m]);
-    if (b && b.defaultProps)
-      for (m in d = b.defaultProps, d)
-        h[m] === void 0 && (h[m] = d[m]);
-    return { $$typeof: w, type: b, key: x, ref: p, props: h, _owner: E.current };
-  }
-  return L.Fragment = C, L.jsx = T, L.jsxs = T, L;
-}
-process.env.NODE_ENV === "production" ? ne.exports = fr() : ne.exports = cr();
-var l = ne.exports;
-function dr({
-  question: N = "",
-  apiKey: w = "",
-  selectedDocs: C = "",
-  history: u = [],
-  conversationId: E = null,
-  apiHost: S = "",
-  onEvent: T = () => {
-    console.log("Event triggered, but no handler provided.");
-  }
-}) {
-  let b = "default";
-  return C && (b = C), new Promise((d, v) => {
-    const m = {
-      question: N,
-      api_key: w,
-      embeddings_key: w,
-      active_docs: b,
-      history: JSON.stringify(u),
-      conversation_id: E,
-      model: "default"
-    };
-    fetch(S + "/stream", {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json"
-      },
-      body: JSON.stringify(m)
-    }).then((h) => {
-      if (!h.body)
-        throw Error("No response body");
-      const x = h.body.getReader(), p = new TextDecoder("utf-8");
-      let R = 0;
-      const j = ({
-        done: J,
-        value: O
-      }) => {
-        if (J) {
-          console.log(R), d();
-          return;
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-        R += 1;
-        const B = p.decode(O).split(`
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-        for (let D of B) {
-          if (D.trim() == "")
-            continue;
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-      x.read().then(j).catch(v);
-    }).catch((h) => {
-      console.error("Connection failed:", h), v(h);
-    });
-  });
-}
-const pr = ({ apiHost: N = "https://gptcloud.arc53.com", selectDocs: w = "default", apiKey: C = "docsgpt-public" }) => {
-  const [u, E] = ke(() => typeof window < "u" && localStorage.getItem("docsGPTChatState") || "init"), [S, T] = ke(""), b = ur(null);
-  Pe(() => {
-    if (b.current) {
-      const v = b.current;
-      v.scrollTop = v.scrollHeight;
-    }
-  }, [S]), Pe(() => {
-    localStorage.setItem("docsGPTChatState", u);
-  }, [u]);
-  const d = (v) => {
-    T(""), v.preventDefault(), E(
-      "processing"
-      /* Processing */
-    ), setTimeout(() => {
-      E(
-        "answer"
-        /* Answer */
-      );
-    }, 800);
-    const h = v.currentTarget[0].value;
-    dr({
-      question: h,
-      apiKey: C,
-      selectedDocs: w,
-      history: [],
-      conversationId: null,
-      apiHost: N,
-      onEvent: (x) => {
-        const p = JSON.parse(x.data);
-        if (p.type === "end")
-          E(
-            "answer"
-            /* Answer */
-          );
-        else if (p.type === "source") {
-          let R;
-          if (p.metadata && p.metadata.title) {
-            const j = p.metadata.title.split("/");
-            R = {
-              title: j[j.length - 1],
-              text: p.doc
-            };
-          } else
-            R = { title: p.doc, text: p.doc };
-          console.log(R);
-        } else if (p.type === "id")
-          console.log(p.id);
-        else {
-          const R = p.answer;
-          T((j) => j + R);
-        }
-      }
-    });
-  };
-  return /* @__PURE__ */ l.jsx(l.Fragment, { children: /* @__PURE__ */ l.jsxs("div", { className: "dark widget-container", children: [
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-        onClick: () => E(
-          "init"
-          /* Init */
-        ),
-        className: `${u !== "minimized" ? "hidden" : ""} cursor-pointer`,
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-          "img",
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-            src: "https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png",
-            alt: "DocsGPT",
-            className: "cursor-pointer hover:opacity-50 h-14"
-          }
-        ) })
-      }
-    ),
-    /* @__PURE__ */ l.jsxs("div", { className: ` ${u !== "minimized" ? "" : "hidden"} divide-y dark:divide-gray-700 rounded-md border dark:border-gray-700 bg-gradient-to-br from-gray-100/80 via-white to-white dark:from-gray-900/80 dark:via-gray-900 dark:to-gray-900 font-sans shadow backdrop-blur-sm`, style: { width: "18rem", transform: "translateY(0%) translateZ(0px)" }, children: [
-      /* @__PURE__ */ l.jsxs("div", { children: [
-        /* @__PURE__ */ l.jsx(
-          "img",
-          {
-            src: "https://d3dg1063dc54p9.cloudfront.net/exit.svg",
-            alt: "Exit",
-            className: "cursor-pointer hover:opacity-50 h-2 absolute top-0 right-0 m-2 white-filter",
-            onClick: (v) => {
-              v.stopPropagation(), E(
-                "minimized"
-                /* Minimized */
-              );
-            }
-          }
-        ),
-        /* @__PURE__ */ l.jsxs("div", { className: "flex items-center gap-2 p-3", children: [
-          /* @__PURE__ */ l.jsxs("div", { className: `${u === "init" || u === "processing" || u === "typing" ? "" : "hidden"} flex-1`, children: [
-            /* @__PURE__ */ l.jsx("h3", { className: "text-sm font-bold text-gray-700 dark:text-gray-200", children: "Need help with documentation?" }),
-            /* @__PURE__ */ l.jsx("p", { className: "mt-1 text-xs text-gray-400 dark:text-gray-500", children: "DocsGPT AI assistant will help you with docs" })
-          ] }),
-          /* @__PURE__ */ l.jsx("div", { id: "docsgpt-answer", ref: b, className: `${u !== "answer" ? "hidden" : ""}`, children: /* @__PURE__ */ l.jsx("p", { className: "mt-1 text-sm text-gray-600 dark:text-white text-left", children: S }) })
-        ] })
-      ] }),
-      /* @__PURE__ */ l.jsxs("div", { className: "w-full", children: [
-        /* @__PURE__ */ l.jsx(
-          "button",
-          {
-            onClick: () => E(
-              "typing"
-              /* Typing */
-            ),
-            className: `flex w-full justify-center px-5 py-3 text-sm text-gray-800 font-bold dark:text-white transition duration-300 hover:bg-gray-100 rounded-b dark:hover:bg-gray-800/70 ${u !== "init" ? "hidden" : ""}`,
-            children: "Ask DocsGPT"
-          }
-        ),
-        (u === "typing" || u === "answer") && /* @__PURE__ */ l.jsxs(
-          "form",
-          {
-            onSubmit: d,
-            className: "relative w-full m-0",
-            style: { opacity: 1 },
-            children: [
-              /* @__PURE__ */ l.jsx(
-                "input",
-                {
-                  type: "text",
-                  className: "w-full bg-transparent px-5 py-3 pr-8 text-sm text-gray-700 dark:text-white focus:outline-none",
-                  placeholder: "What do you want to do?"
-                }
-              ),
-              /* @__PURE__ */ l.jsx("button", { className: "absolute text-gray-400 dark:text-gray-500 text-sm inset-y-0 right-2 -mx-2 px-2", type: "submit", children: "Submit" })
-            ]
-          }
-        ),
-        /* @__PURE__ */ l.jsxs("p", { className: `${u !== "processing" ? "hidden" : ""} flex w-full justify-center px-5 py-3 text-sm text-gray-800 font-bold dark:text-white transition duration-300 rounded-b`, children: [
-          "Processing",
-          /* @__PURE__ */ l.jsx("span", { className: "dot-animation", children: "." }),
-          /* @__PURE__ */ l.jsx("span", { className: "dot-animation delay-200", children: "." }),
-          /* @__PURE__ */ l.jsx("span", { className: "dot-animation delay-400", children: "." })
-        ] })
-      ] })
-    ] })
-  ] }) });
-};
-export {
-  pr as DocsGPTWidget
-};
-//# sourceMappingURL=index.es.js.map

extensions/react-widget/dist/src/App.d.ts

@@ -1,3 +0,0 @@
-/// <reference types="react" />
-declare function App(): JSX.Element;
-export default App;

extensions/react-widget/dist/src/components/DocsGPTWidget.d.ts

@@ -1,5 +0,0 @@
-export declare const DocsGPTWidget: ({ apiHost, selectDocs, apiKey }: {
-    apiHost?: string | undefined;
-    selectDocs?: string | undefined;
-    apiKey?: string | undefined;
-}) => JSX.Element;

extensions/react-widget/dist/src/components/index.d.ts

@@ -1 +0,0 @@
-export { DocsGPTWidget } from "./DocsGPTWidget";

extensions/react-widget/dist/style.css

@@ -1,762 +0,0 @@
-/*
-! tailwindcss v3.2.4 | MIT License | https://tailwindcss.com
-*//*
-1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4)
-2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116)
-*/
-
-*,
-::before,
-::after {
-  box-sizing: border-box; /* 1 */
-  border-width: 0; /* 2 */
-  border-style: solid; /* 2 */
-  border-color: #e5e7eb; /* 2 */
-}
-
-::before,
-::after {
-  --tw-content: '';
-}
-
-/*
-1. Use a consistent sensible line-height in all browsers.
-2. Prevent adjustments of font size after orientation changes in iOS.
-3. Use a more readable tab size.
-4. Use the user's configured `sans` font-family by default.
-5. Use the user's configured `sans` font-feature-settings by default.
-*/
-
-html {
-  line-height: 1.5; /* 1 */
-  -webkit-text-size-adjust: 100%; /* 2 */
-  -moz-tab-size: 4; /* 3 */
-  -o-tab-size: 4;
-     tab-size: 4; /* 3 */
-  font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; /* 4 */
-  font-feature-settings: normal; /* 5 */
-}
-
-/*
-1. Remove the margin in all browsers.
-2. Inherit line-height from `html` so users can set them as a class directly on the `html` element.
-*/
-
-body {
-  margin: 0; /* 1 */
-  line-height: inherit; /* 2 */
-}
-
-/*
-1. Add the correct height in Firefox.
-2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655)
-3. Ensure horizontal rules are visible by default.
-*/
-
-hr {
-  height: 0; /* 1 */
-  color: inherit; /* 2 */
-  border-top-width: 1px; /* 3 */
-}
-
-/*
-Add the correct text decoration in Chrome, Edge, and Safari.
-*/
-
-abbr:where([title]) {
-  -webkit-text-decoration: underline dotted;
-          text-decoration: underline dotted;
-}
-
-/*
-Remove the default font size and weight for headings.
-*/
-
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
-  font-size: inherit;
-  font-weight: inherit;
-}
-
-/*
-Reset links to optimize for opt-in styling instead of opt-out.
-*/
-
-a {
-  color: inherit;
-  text-decoration: inherit;
-}
-
-/*
-Add the correct font weight in Edge and Safari.
-*/
-
-b,
-strong {
-  font-weight: bolder;
-}
-
-/*
-1. Use the user's configured `mono` font family by default.
-2. Correct the odd `em` font sizing in all browsers.
-*/
-
-code,
-kbd,
-samp,
-pre {
-  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; /* 1 */
-  font-size: 1em; /* 2 */
-}
-
-/*
-Add the correct font size in all browsers.
-*/
-
-small {
-  font-size: 80%;
-}
-
-/*
-Prevent `sub` and `sup` elements from affecting the line height in all browsers.
-*/
-
-sub,
-sup {
-  font-size: 75%;
-  line-height: 0;
-  position: relative;
-  vertical-align: baseline;
-}
-
-sub {
-  bottom: -0.25em;
-}
-
-sup {
-  top: -0.5em;
-}
-
-/*
-1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297)
-2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016)
-3. Remove gaps between table borders by default.
-*/
-
-table {
-  text-indent: 0; /* 1 */
-  border-color: inherit; /* 2 */
-  border-collapse: collapse; /* 3 */
-}
-
-/*
-1. Change the font styles in all browsers.
-2. Remove the margin in Firefox and Safari.
-3. Remove default padding in all browsers.
-*/
-
-button,
-input,
-optgroup,
-select,
-textarea {
-  font-family: inherit; /* 1 */
-  font-size: 100%; /* 1 */
-  font-weight: inherit; /* 1 */
-  line-height: inherit; /* 1 */
-  color: inherit; /* 1 */
-  margin: 0; /* 2 */
-  padding: 0; /* 3 */
-}
-
-/*
-Remove the inheritance of text transform in Edge and Firefox.
-*/
-
-button,
-select {
-  text-transform: none;
-}
-
-/*
-1. Correct the inability to style clickable types in iOS and Safari.
-2. Remove default button styles.
-*/
-
-button,
-[type='button'],
-[type='reset'],
-[type='submit'] {
-  -webkit-appearance: button; /* 1 */
-  background-color: transparent; /* 2 */
-  background-image: none; /* 2 */
-}
-
-/*
-Use the modern Firefox focus style for all focusable elements.
-*/
-
-:-moz-focusring {
-  outline: auto;
-}
-
-/*
-Remove the additional `:invalid` styles in Firefox. (https://github.com/mozilla/gecko-dev/blob/2f9eacd9d3d995c937b4251a5557d95d494c9be1/layout/style/res/forms.css#L728-L737)
-*/
-
-:-moz-ui-invalid {
-  box-shadow: none;
-}
-
-/*
-Add the correct vertical alignment in Chrome and Firefox.
-*/
-
-progress {
-  vertical-align: baseline;
-}
-
-/*
-Correct the cursor style of increment and decrement buttons in Safari.
-*/
-
-::-webkit-inner-spin-button,
-::-webkit-outer-spin-button {
-  height: auto;
-}
-
-/*
-1. Correct the odd appearance in Chrome and Safari.
-2. Correct the outline style in Safari.
-*/
-
-[type='search'] {
-  -webkit-appearance: textfield; /* 1 */
-  outline-offset: -2px; /* 2 */
-}
-
-/*
-Remove the inner padding in Chrome and Safari on macOS.
-*/
-
-::-webkit-search-decoration {
-  -webkit-appearance: none;
-}
-
-/*
-1. Correct the inability to style clickable types in iOS and Safari.
-2. Change font properties to `inherit` in Safari.
-*/
-
-::-webkit-file-upload-button {
-  -webkit-appearance: button; /* 1 */
-  font: inherit; /* 2 */
-}
-
-/*
-Add the correct display in Chrome and Safari.
-*/
-
-summary {
-  display: list-item;
-}
-
-/*
-Removes the default spacing and border for appropriate elements.
-*/
-
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-dl,
-dd,
-h1,
-h2,
-h3,
-h4,
-h5,
-h6,
-hr,
-figure,
-p,
-pre {
-  margin: 0;
-}
-
-fieldset {
-  margin: 0;
-  padding: 0;
-}
-
-legend {
-  padding: 0;
-}
-
-ol,
-ul,
-menu {
-  list-style: none;
-  margin: 0;
-  padding: 0;
-}
-
-/*
-Prevent resizing textareas horizontally by default.
-*/
-
-textarea {
-  resize: vertical;
-}
-
-/*
-1. Reset the default placeholder opacity in Firefox. (https://github.com/tailwindlabs/tailwindcss/issues/3300)
-2. Set the default placeholder color to the user's configured gray 400 color.
-*/
-
-input::-moz-placeholder, textarea::-moz-placeholder {
-  opacity: 1; /* 1 */
-  color: #9ca3af; /* 2 */
-}
-
-input::placeholder,
-textarea::placeholder {
-  opacity: 1; /* 1 */
-  color: #9ca3af; /* 2 */
-}
-
-/*
-Set the default cursor for buttons.
-*/
-
-button,
-[role="button"] {
-  cursor: pointer;
-}
-
-/*
-Make sure disabled buttons don't get the pointer cursor.
-*/
-:disabled {
-  cursor: default;
-}
-
-/*
-1. Make replaced elements `display: block` by default. (https://github.com/mozdevs/cssremedy/issues/14)
-2. Add `vertical-align: middle` to align replaced elements more sensibly by default. (https://github.com/jensimmons/cssremedy/issues/14#issuecomment-634934210)
-   This can trigger a poorly considered lint error in some tools but is included by design.
-*/
-
-img,
-svg,
-video,
-canvas,
-audio,
-iframe,
-embed,
-object {
-  display: block; /* 1 */
-  vertical-align: middle; /* 2 */
-}
-
-/*
-Constrain images and videos to the parent width and preserve their intrinsic aspect ratio. (https://github.com/mozdevs/cssremedy/issues/14)
-*/
-
-img,
-video {
-  max-width: 100%;
-  height: auto;
-}
-
-/* Make elements with the HTML hidden attribute stay hidden by default */
-[hidden] {
-  display: none;
-}
-
-*, ::before, ::after {
-  --tw-border-spacing-x: 0;
-  --tw-border-spacing-y: 0;
-  --tw-translate-x: 0;
-  --tw-translate-y: 0;
-  --tw-rotate: 0;
-  --tw-skew-x: 0;
-  --tw-skew-y: 0;
-  --tw-scale-x: 1;
-  --tw-scale-y: 1;
-  --tw-pan-x:  ;
-  --tw-pan-y:  ;
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-  --tw-scroll-snap-strictness: proximity;
-  --tw-ordinal:  ;
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-  --tw-ring-offset-color: #fff;
-  --tw-ring-color: rgb(59 130 246 / 0.5);
-  --tw-ring-offset-shadow: 0 0 #0000;
-  --tw-ring-shadow: 0 0 #0000;
-  --tw-shadow: 0 0 #0000;
-  --tw-shadow-colored: 0 0 #0000;
-  --tw-blur:  ;
-  --tw-brightness:  ;
-  --tw-contrast:  ;
-  --tw-grayscale:  ;
-  --tw-hue-rotate:  ;
-  --tw-invert:  ;
-  --tw-saturate:  ;
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-  --tw-drop-shadow:  ;
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-  --tw-backdrop-brightness:  ;
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-  --tw-backdrop-grayscale:  ;
-  --tw-backdrop-hue-rotate:  ;
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-  --tw-backdrop-opacity:  ;
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-  --tw-backdrop-sepia:  ;
-}
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-::backdrop {
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-  --tw-translate-x: 0;
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-  --tw-grayscale:  ;
-  --tw-hue-rotate:  ;
-  --tw-invert:  ;
-  --tw-saturate:  ;
-  --tw-sepia:  ;
-  --tw-drop-shadow:  ;
-  --tw-backdrop-blur:  ;
-  --tw-backdrop-brightness:  ;
-  --tw-backdrop-contrast:  ;
-  --tw-backdrop-grayscale:  ;
-  --tw-backdrop-hue-rotate:  ;
-  --tw-backdrop-invert:  ;
-  --tw-backdrop-opacity:  ;
-  --tw-backdrop-saturate:  ;
-  --tw-backdrop-sepia:  ;
-}
-.absolute {
-  position: absolute;
-}
-.relative {
-  position: relative;
-}
-.inset-y-0 {
-  top: 0px;
-  bottom: 0px;
-}
-.top-0 {
-  top: 0px;
-}
-.right-0 {
-  right: 0px;
-}
-.right-2 {
-  right: 0.5rem;
-}
-.m-2 {
-  margin: 0.5rem;
-}
-.m-0 {
-  margin: 0px;
-}
-.-mx-2 {
-  margin-left: -0.5rem;
-  margin-right: -0.5rem;
-}
-.mr-2 {
-  margin-right: 0.5rem;
-}
-.mb-2 {
-  margin-bottom: 0.5rem;
-}
-.mt-1 {
-  margin-top: 0.25rem;
-}
-.flex {
-  display: flex;
-}
-.hidden {
-  display: none;
-}
-.h-20 {
-  height: 5rem;
-}
-.h-14 {
-  height: 3.5rem;
-}
-.h-2 {
-  height: 0.5rem;
-}
-.w-20 {
-  width: 5rem;
-}
-.w-full {
-  width: 100%;
-}
-.flex-1 {
-  flex: 1 1 0%;
-}
-.transform {
-  transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));
-}
-.cursor-pointer {
-  cursor: pointer;
-}
-.items-center {
-  align-items: center;
-}
-.justify-center {
-  justify-content: center;
-}
-.gap-2 {
-  gap: 0.5rem;
-}
-.divide-y > :not([hidden]) ~ :not([hidden]) {
-  --tw-divide-y-reverse: 0;
-  border-top-width: calc(1px * calc(1 - var(--tw-divide-y-reverse)));
-  border-bottom-width: calc(1px * var(--tw-divide-y-reverse));
-}
-.overflow-hidden {
-  overflow: hidden;
-}
-.rounded-full {
-  border-radius: 9999px;
-}
-.rounded-md {
-  border-radius: 0.375rem;
-}
-.rounded-b {
-  border-bottom-right-radius: 0.25rem;
-  border-bottom-left-radius: 0.25rem;
-}
-.border {
-  border-width: 1px;
-}
-.bg-transparent {
-  background-color: transparent;
-}
-.bg-gradient-to-br {
-  background-image: linear-gradient(to bottom right, var(--tw-gradient-stops));
-}
-.from-gray-100\/80 {
-  --tw-gradient-from: rgb(243 244 246 / 0.8);
-  --tw-gradient-to: rgb(243 244 246 / 0);
-  --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
-}
-.via-white {
-  --tw-gradient-to: rgb(255 255 255 / 0);
-  --tw-gradient-stops: var(--tw-gradient-from), #fff, var(--tw-gradient-to);
-}
-.to-white {
-  --tw-gradient-to: #fff;
-}
-.p-3 {
-  padding: 0.75rem;
-}
-.px-5 {
-  padding-left: 1.25rem;
-  padding-right: 1.25rem;
-}
-.py-3 {
-  padding-top: 0.75rem;
-  padding-bottom: 0.75rem;
-}
-.px-2 {
-  padding-left: 0.5rem;
-  padding-right: 0.5rem;
-}
-.pr-8 {
-  padding-right: 2rem;
-}
-.text-left {
-  text-align: left;
-}
-.font-sans {
-  font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
-}
-.text-sm {
-  font-size: 0.875rem;
-  line-height: 1.25rem;
-}
-.text-xs {
-  font-size: 0.75rem;
-  line-height: 1rem;
-}
-.font-bold {
-  font-weight: 700;
-}
-.text-gray-700 {
-  --tw-text-opacity: 1;
-  color: rgb(55 65 81 / var(--tw-text-opacity));
-}
-.text-gray-400 {
-  --tw-text-opacity: 1;
-  color: rgb(156 163 175 / var(--tw-text-opacity));
-}
-.text-gray-600 {
-  --tw-text-opacity: 1;
-  color: rgb(75 85 99 / var(--tw-text-opacity));
-}
-.text-gray-800 {
-  --tw-text-opacity: 1;
-  color: rgb(31 41 55 / var(--tw-text-opacity));
-}
-.shadow {
-  --tw-shadow: 0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1);
-  --tw-shadow-colored: 0 1px 3px 0 var(--tw-shadow-color), 0 1px 2px -1px var(--tw-shadow-color);
-  box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow);
-}
-.backdrop-blur-sm {
-  --tw-backdrop-blur: blur(4px);
-  -webkit-backdrop-filter: var(--tw-backdrop-blur) var(--tw-backdrop-brightness) var(--tw-backdrop-contrast) var(--tw-backdrop-grayscale) var(--tw-backdrop-hue-rotate) var(--tw-backdrop-invert) var(--tw-backdrop-opacity) var(--tw-backdrop-saturate) var(--tw-backdrop-sepia);
-          backdrop-filter: var(--tw-backdrop-blur) var(--tw-backdrop-brightness) var(--tw-backdrop-contrast) var(--tw-backdrop-grayscale) var(--tw-backdrop-hue-rotate) var(--tw-backdrop-invert) var(--tw-backdrop-opacity) var(--tw-backdrop-saturate) var(--tw-backdrop-sepia);
-}
-.transition {
-  transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, -webkit-backdrop-filter;
-  transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, backdrop-filter;
-  transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, backdrop-filter, -webkit-backdrop-filter;
-  transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
-  transition-duration: 150ms;
-}
-.delay-200 {
-  transition-delay: 200ms;
-}
-.duration-300 {
-  transition-duration: 300ms;
-}
-
-#docsgpt-answer {
-    max-height: 50vh; /* 50% of the viewport height */
-    overflow-y: auto; /* Adds a vertical scrollbar if the content exceeds the container height */
-}
-
-.widget-container {
-  position: fixed;   /* fixed positioning */
-  right: 10px;       /* from the right edge */
-  bottom: 10px;      /* from the bottom edge */
-  z-index: 1000;     /* to ensure it appears on top of other content, if any */
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-}
-
-  @keyframes dotBounce {
-    0%, 80%, 100% {
-      transform: translateY(0);
-    }
-    40% {
-      transform: translateY(-5px);
-    }
-  }
-
-  .dot-animation {
-    display: inline-block;
-    animation: dotBounce 1s infinite ease-in-out;
-  }
-
-  .delay-200 {
-    animation-delay: 200ms;
-  }
-
-  .delay-400 {
-    animation-delay: 400ms;
-  }
-
-  .white-filter {
-    filter: invert(1) brightness(2);
-}
-
-  .hover\:bg-gray-100:hover {
-  --tw-bg-opacity: 1;
-  background-color: rgb(243 244 246 / var(--tw-bg-opacity));
-}
-
-  .hover\:opacity-50:hover {
-  opacity: 0.5;
-}
-
-  .focus\:outline-none:focus {
-  outline: 2px solid transparent;
-  outline-offset: 2px;
-}
-
-  @media (prefers-color-scheme: dark) {
-
-  .dark\:divide-gray-700 > :not([hidden]) ~ :not([hidden]) {
-    --tw-divide-opacity: 1;
-    border-color: rgb(55 65 81 / var(--tw-divide-opacity));
-  }
-
-  .dark\:border-gray-700 {
-    --tw-border-opacity: 1;
-    border-color: rgb(55 65 81 / var(--tw-border-opacity));
-  }
-
-  .dark\:from-gray-900\/80 {
-    --tw-gradient-from: rgb(17 24 39 / 0.8);
-    --tw-gradient-to: rgb(17 24 39 / 0);
-    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
-  }
-
-  .dark\:via-gray-900 {
-    --tw-gradient-to: rgb(17 24 39 / 0);
-    --tw-gradient-stops: var(--tw-gradient-from), #111827, var(--tw-gradient-to);
-  }
-
-  .dark\:to-gray-900 {
-    --tw-gradient-to: #111827;
-  }
-
-  .dark\:text-gray-200 {
-    --tw-text-opacity: 1;
-    color: rgb(229 231 235 / var(--tw-text-opacity));
-  }
-
-  .dark\:text-gray-500 {
-    --tw-text-opacity: 1;
-    color: rgb(107 114 128 / var(--tw-text-opacity));
-  }
-
-  .dark\:text-white {
-    --tw-text-opacity: 1;
-    color: rgb(255 255 255 / var(--tw-text-opacity));
-  }
-
-  .dark\:hover\:bg-gray-800\/70:hover {
-    background-color: rgb(31 41 55 / 0.7);
-  }
-}

extensions/react-widget/dist/vite.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>

extensions/react-widget/index.html

@@ -1,13 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Vite + React + TS</title>
-  </head>
-  <body>
-    <div id="root"></div>
-    <script type="module" src="/src/main.tsx"></script>
-  </body>
-</html>

extensions/react-widget/index.ts

@@ -1 +0,0 @@
-export { DocsGPTWidget } from "./src/components/DocsGPTWidget";

extensions/react-widget/package.json

@@ -1,47 +1,77 @@
 {
   "name": "docsgpt",
+  "version": "0.3.9",
   "private": false,
-  "version": "0.2.4",
-  "type": "module",
-  "main": "dist/index.umd.js",
-  "module": "dist/index.es.js",
-  "types": "dist/index.d.ts",
-  "exports": {
-    ".": {
-      "import": "./dist/index.es.js",
-      "require": "./dist/index.umd.js",
-      "types": "./dist/index.d.ts"
-    },
-    "./dist/style.css": "./dist/style.css"
-  },
+  "description": "DocsGPT 🦖 is an innovative open-source tool designed to simplify the retrieval of information from project documentation using advanced GPT models 🤖.",
+  "source": "./src/index.html",
+  "main": "dist/main.js",
+  "module": "dist/module.js",
+  "types": "dist/types.d.ts",
   "files": [
-    "/dist"
+    "dist",
+    "package.json"
   ],
-  "publishConfig": {
-    "access": "public"
+  "targets": {
+    "modern": {
+      "engines": {
+        "browsers": "Chrome 80"
+      }
+    },
+    "legacy": {
+      "engines": {
+        "browsers": "> 0.5%, last 2 versions, not dead"
+      }
+    }
+  },
+  "@parcel/resolver-default": {
+    "packageExports": true
+  },
+  "resolution": {
+    "styled-components": "^5"
   },
   "scripts": {
-    "dev": "vite",
-    "build": "tsc && vite build",
-    "prepare": "npm run build && npm run build-css",
-    "build-css": "postcss src/index.css -o dist/style.css",
-    "preview": "vite preview"
+    "build": "parcel build src/main.tsx --public-url ./",
+    "dev": "parcel src/index.html -p 3000",
+    "test": "jest",
+    "lint": "eslint",
+    "check": "tsc --noEmit",
+    "ci": "yarn build && yarn test && yarn lint && yarn check"
   },
   "dependencies": {
-    "postcss-cli": "^10.1.0",
+    "@babel/plugin-transform-flow-strip-types": "^7.23.3",
+    "@bpmn-io/snarkdown": "^2.2.0",
+    "@parcel/resolver-glob": "^2.12.0",
+    "@parcel/transformer-svg-react": "^2.12.0",
+    "@parcel/transformer-typescript-tsc": "^2.12.0",
+    "@parcel/validator-typescript": "^2.12.0",
+    "@radix-ui/react-icons": "^1.3.0",
+    "class-variance-authority": "^0.7.0",
+    "clsx": "^2.1.0",
+    "dompurify": "^3.1.5",
+    "flow-bin": "^0.229.2",
+    "i": "^0.3.7",
+    "install": "^0.13.0",
+    "npm": "^10.5.0",
     "react": "^18.2.0",
     "react-dom": "^18.2.0",
-    "tailwindcss": "^3.2.4"
+    "styled-components": "^6.1.8"
   },
   "devDependencies": {
-    "@types/react": "^18.0.26",
-    "@types/react-dom": "^18.0.9",
-    "@vitejs/plugin-react-swc": "^3.5.0",
-    "autoprefixer": "^10.4.13",
-    "postcss": "^8.4.31",
-    "typescript": "^4.9.3",
-    "vite": "^5.0.12",
-    "vite-plugin-dts": "^3.7.0"
+    "@babel/core": "^7.24.0",
+    "@babel/preset-env": "^7.24.0",
+    "@babel/preset-react": "^7.23.3",
+    "@parcel/packager-ts": "^2.12.0",
+    "@parcel/transformer-typescript-types": "^2.12.0",
+    "@types/dompurify": "^3.0.5",
+    "@types/react": "^18.3.3",
+    "@types/react-dom": "^18.3.0",
+    "babel-loader": "^8.0.4",
+    "parcel": "^2.12.0",
+    "process": "^0.11.10",
+    "typescript": "^5.3.3"
+  },
+  "publishConfig": {
+    "access": "public"
   },
   "repository": {
     "type": "git",

extensions/react-widget/postcss.config.cjs

@@ -1,6 +0,0 @@
-module.exports = {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}

extensions/react-widget/public/vite.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>

extensions/react-widget/src/App.css

@@ -1,5 +0,0 @@
-
-
-
-
-

extensions/react-widget/src/App.tsx

@@ -1,15 +1,11 @@
-import { useState } from "react";
-//import "./App.css";
-import {DocsGPTWidget} from "./components/DocsGPTWidget";
-
-function App() {
-  const [count, setCount] = useState(0);
-
+import React from "react"
+import {DocsGPTWidget} from "./components/DocsGPTWidget"
+const App = () => {
   return (
-    <div className="App">
-      <DocsGPTWidget />
+    <div>
+      <DocsGPTWidget/>
     </div>
-  );
+  )
 }
 
-export default App;
+export default App

extensions/react-widget/src/assets/message.svg

@@ -0,0 +1,7 @@
+<svg width="36" height="36" viewBox="0 0 18 18" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M4.37891 9.44824H7.75821" stroke="white" stroke-width="1.68965" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M11.1377 9.44824H12.8273" stroke="white" stroke-width="1.68965" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M4.37891 6.06934H6.06856" stroke="white" stroke-width="1.68965" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M9.44824 6.06934H12.8276" stroke="white" stroke-width="1.68965" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M16.2069 11.1379C16.2069 11.5861 16.0289 12.0158 15.712 12.3327C15.3951 12.6496 14.9654 12.8276 14.5172 12.8276H4.37931L1 16.2069V2.68965C1 2.24153 1.17802 1.81176 1.49489 1.49489C1.81176 1.17802 2.24153 1 2.68965 1H14.5172C14.9654 1 15.3951 1.17802 15.712 1.49489C16.0289 1.81176 16.2069 2.24153 16.2069 2.68965V11.1379Z" stroke="white" stroke-width="1.68965" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>

extensions/react-widget/src/assets/react.svg

@@ -1 +0,0 @@
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extensions/react-widget/src/components/DocsGPTWidget.tsx

@@ -1,247 +1,460 @@
 "use client";
-import {useEffect, useRef, useState} from 'react'
-//import './style.css'
+import React from 'react'
+import DOMPurify from 'dompurify';
+import snarkdown from '@bpmn-io/snarkdown';
+import styled, { keyframes, createGlobalStyle } from 'styled-components';
+import { PaperPlaneIcon, RocketIcon, ExclamationTriangleIcon, Cross2Icon } from '@radix-ui/react-icons';
+import MessageIcon from '../assets/message.svg';
+import { MESSAGE_TYPE, Query, Status } from '../types/index';
+import { fetchAnswerStreaming } from '../requests/streamingApi';
 
-interface HistoryItem {
-  prompt: string;
-  response: string;
+const GlobalStyles = createGlobalStyle`
+.response pre {
+    padding: 8px;
+    width: 90%;
+    font-size: 12px;
+    border-radius: 6px;
+    overflow-x: auto;
+    background-color: #1B1C1F;
 }
-
-interface FetchAnswerStreamingProps {
-  question?: string;
-  apiKey?: string;
-  selectedDocs?: string;
-  history?: HistoryItem[];
-  conversationId?: string | null;
-  apiHost?: string;
-  onEvent?: (event: MessageEvent) => void;
+.response h1{
+  font-size: 20px;
 }
-
-
-enum ChatStates {
-  Init = 'init',
-  Processing = 'processing',
-  Typing = 'typing',
-  Answer = 'answer',
-  Minimized = 'minimized',
+.response h2{
+  font-size: 18px;
 }
-
-function fetchAnswerStreaming({
-  question = '',
-  apiKey = '',
-  selectedDocs = '',
-  history = [],
-  conversationId = null,
-  apiHost = '',
-  onEvent = () => {console.log("Event triggered, but no handler provided.");}
-}: FetchAnswerStreamingProps): Promise<void> {
-  let docPath = 'default';
-  if (selectedDocs) {
-    docPath = selectedDocs;
-  }
-
-  return new Promise<void>((resolve, reject) => {
-    const body = {
-      question: question,
-      api_key: apiKey,
-      embeddings_key: apiKey,
-      active_docs: docPath,
-      history: JSON.stringify(history),
-      conversation_id: conversationId,
-      model: 'default'
-    };
-
-    fetch(apiHost + '/stream', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      body: JSON.stringify(body),
-    })
-      .then((response) => {
-        if (!response.body) throw Error('No response body');
-
-        const reader = response.body.getReader();
-        const decoder = new TextDecoder('utf-8');
-        let counterrr = 0;
-        const processStream = ({
-          done,
-          value,
-        }: ReadableStreamReadResult<Uint8Array>) => {
-          if (done) {
-            console.log(counterrr);
-            resolve();
-            return;
-          }
-
-          counterrr += 1;
-
-          const chunk = decoder.decode(value);
-
-          const lines = chunk.split('\n');
-
-          for (let line of lines) {
-            if (line.trim() == '') {
-              continue;
-            }
-            if (line.startsWith('data:')) {
-              line = line.substring(5);
-            }
-
-            const messageEvent = new MessageEvent('message', {
-              data: line,
-            });
-
-            onEvent(messageEvent); // handle each message
-          }
-
-          reader.read().then(processStream).catch(reject);
-        };
-
-        reader.read().then(processStream).catch(reject);
-      })
-      .catch((error) => {
-        console.error('Connection failed:', error);
-        reject(error);
-      });
-  });
+.response h3{
+  font-size: 16px;
 }
-
-export const DocsGPTWidget = ({ apiHost = 'https://gptcloud.arc53.com', selectDocs = 'default', apiKey = 'docsgpt-public'}) => {
-    // processing states
-    const [chatState, setChatState] = useState<ChatStates>(() => {
-        if (typeof window !== 'undefined') {
-            return localStorage.getItem('docsGPTChatState') as ChatStates || ChatStates.Init;
-        }
-        return ChatStates.Init;
-    });
-
-    const [answer, setAnswer] = useState<string>('');
-
-    //const selectDocs = 'local/1706.03762.pdf/'
-    const answerRef = useRef<HTMLDivElement | null>(null);
-
-    useEffect(() => {
-        if (answerRef.current) {
-            const element = answerRef.current;
-            element.scrollTop = element.scrollHeight;
-        }
-    }, [answer]);
-
-    useEffect(() => {
-        localStorage.setItem('docsGPTChatState', chatState);
-    }, [chatState]);
-
-
-
-    // submit handler
-    const handleSubmit = (e: React.FormEvent<HTMLFormElement>) => {
-        setAnswer('')
-        e.preventDefault()
-        // get question
-        setChatState(ChatStates.Processing)
-        setTimeout(() => {
-            setChatState(ChatStates.Answer)
-        }, 800)
-        const inputElement = e.currentTarget[0] as HTMLInputElement;
-        const questionValue = inputElement.value;
-
-        fetchAnswerStreaming({
-          question: questionValue,
-          apiKey: apiKey,
-          selectedDocs: selectDocs,
-          history: [],
-          conversationId: null,
-          apiHost: apiHost,
-          onEvent: (event) => {
-            const data = JSON.parse(event.data);
-
-            // check if the 'end' event has been received
-            if (data.type === 'end') {
-              setChatState(ChatStates.Answer)
-            } else if (data.type === 'source') {
-              // check if data.metadata exists
-              let result;
-              if (data.metadata && data.metadata.title) {
-                const titleParts = data.metadata.title.split('/');
-                result = {
-                  title: titleParts[titleParts.length - 1],
-                  text: data.doc,
-                };
-              } else {
-                result = { title: data.doc, text: data.doc };
-              }
-              console.log(result)
-
-            } else if (data.type === 'id') {
-              console.log(data.id);
-            } else {
-              const result = data.answer;
-              // set answer by appending answer
-                setAnswer(prevAnswer => prevAnswer + result);
-            }
-          },
-      });
+.response code:not(pre code){
+  border-radius: 6px;
+  padding: 1px 3px 1px 3px;
+  font-size: 12px;
+  display: inline-block;
+  background-color: #646464;
+}
+`;
+const WidgetContainer = styled.div`
+    display: block;
+    position: fixed;
+    right: 10px;
+    bottom: 10px;
+    z-index: 1000;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    text-align: left;
+`;
+const StyledContainer = styled.div`
+    display: block;
+    position: relative;
+    bottom: 0;
+    left: 0;
+    width: 352px;
+    height: 407px;
+    max-height: 407px;
+    border-radius: 0.75rem;
+    background-color: #222327;
+    font-family: sans-serif;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05), 0 2px 4px rgba(0, 0, 0, 0.1);
+    transition: visibility 0.3s, opacity 0.3s;
+`;
+const FloatingButton = styled.div`
+    position: fixed;
+    display: flex;
+    z-index: 500;
+    justify-content: center;
+    align-items: center;
+    bottom: 1rem;
+    right: 1rem;
+    width: 5rem;
+    height: 5rem;
+    border-radius: 9999px;
+    background-image: linear-gradient(to bottom right, #5AF0EC, #E80D9D);
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    cursor: pointer;
+    &:hover {
+        transform: scale(1.1);
+        transition: transform 0.2s ease-in-out;
     }
+`;
+const CancelButton = styled.button`
+    cursor: pointer;
+    position: absolute;
+    top: 0;
+    right: 0;
+    margin: 0.5rem;
+    width: 30px;
+    padding: 0;
+    background-color: transparent;
+    border: none;
+    outline: none;
+    color: inherit;
+    transition: opacity 0.3s ease;
+    opacity: 0.6;
+    &:hover {
+        opacity: 1;
+    }
+    .white-filter {
+        filter: invert(100%);
+    }
+`;
 
+const Header = styled.div`
+    display: flex;
+    align-items: center;
+    padding-inline: 0.75rem;
+    padding-top: 1rem;
+    padding-bottom: 0.5rem;
+`;
+
+const IconWrapper = styled.div`
+    padding: 0.5rem;
+`;
+
+const ContentWrapper = styled.div`
+    flex: 1;
+    margin-left: 0.5rem;
+`;
+
+const Title = styled.h3`
+    font-size: 1rem;
+    font-weight: normal;
+    color: #FAFAFA;
+    margin-top: 0;
+    margin-bottom: 0.25rem;
+`;
+
+const Description = styled.p`
+    font-size: 0.85rem;
+    color: #A1A1AA;
+    margin-top: 0;
+`;
+const Conversation = styled.div`
+    height: 16rem;
+    padding-inline: 0.5rem;
+    border-radius: 0.375rem;
+    text-align: left;
+    overflow-y: auto;
+    scrollbar-width: thin;
+    scrollbar-color: #4a4a4a transparent; /* thumb color track color */
+`;
+
+const MessageBubble = styled.div<{ type: MESSAGE_TYPE }>`
+    display: flex;
+    font-size: 16px;
+    justify-content: ${props => props.type === 'QUESTION' ? 'flex-end' : 'flex-start'};
+    margin: 0.5rem;
+`;
+const Message = styled.p<{ type: MESSAGE_TYPE }>`
+    background: ${props => props.type === 'QUESTION' ?
+    'linear-gradient(to bottom right, #8860DB, #6D42C5)' :
+    '#38383b'};
+    color: #ffff;
+    border: none;
+    max-width: 80%;
+    overflow: auto;
+    margin: 4px;
+    display: block;
+    line-height: 1.5;
+    padding: 0.75rem;
+    border-radius: 0.375rem;
+`;
+const ErrorAlert = styled.div`
+  color: #b91c1c;
+  border:0.1px solid #b91c1c;
+  display: flex;
+  padding:4px;
+  margin:0.7rem;
+  opacity: 90%;
+  max-width: 70%;
+  font-weight: 400;
+  border-radius: 0.375rem;
+  justify-content: space-evenly;
+`
+//dot loading animation
+const dotBounce = keyframes`
+  0%, 80%, 100% {
+    transform: translateY(0);
+  }
+  40% {
+    transform: translateY(-5px);
+  }
+`;
+
+const DotAnimation = styled.div`
+  display: inline-block;
+  animation: ${dotBounce} 1s infinite ease-in-out;
+`;
+// delay classes as styled components
+const Delay = styled(DotAnimation) <{ delay: number }>`
+  animation-delay: ${props => props.delay + 'ms'};
+`;
+const PromptContainer = styled.form`
+  background-color: transparent;
+  height: 36px;
+  position: absolute;
+  bottom: 25px;
+  left: 24px;
+  right: 24px;
+  display: flex;
+  justify-content: space-evenly;
+`;
+const StyledInput = styled.input`
+  width: 260px;
+  height: 36px;
+  border: 1px solid #686877;
+  padding-left: 12px;
+  background-color: transparent;
+  font-size: 16px;
+  border-radius: 6px;
+  color: #ffff;
+  outline: none;
+`;
+const StyledButton = styled.button`
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  background-image: linear-gradient(to bottom right, #5AF0EC, #E80D9D);
+  border-radius: 6px;
+  width: 36px;
+  height: 36px;
+  margin-left:8px;
+  padding: 0px;
+  border: none;
+  cursor: pointer;
+  outline: none;
+  &:hover{
+    opacity: 90%;
+  }
+  &:disabled {
+    opacity: 60%;
+  }`;
+const HeroContainer = styled.div`
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  display: flex;
+  justify-content: center;
+  align-items: middle;
+  transform: translate(-50%, -50%);
+  width: 80%;
+  background-image: linear-gradient(to bottom right, #5AF0EC, #ff1bf4);
+  border-radius: 10px;
+  margin: 0 auto;
+  padding: 2px;
+`;
+const HeroWrapper = styled.div`
+  background-color: #222327;
+  border-radius: 10px; 
+  font-weight: normal;
+  padding: 6px;
+  display: flex;
+  justify-content: space-between;
+`
+const HeroTitle = styled.h3`
+  color: #fff;
+  font-size: 17px;
+  margin-bottom: 5px;
+  padding: 2px;
+`;
+const HeroDescription = styled.p`
+  color: #fff;
+  font-size: 14px;
+  line-height: 1.5;
+`;
+const Hero = ({ title, description }: { title: string, description: string }) => {
   return (
     <>
-        <div className="dark widget-container">
-            <div onClick={() => setChatState(ChatStates.Init)}
-                 className={`${chatState !== 'minimized' ? 'hidden' : ''} cursor-pointer`}>
-               <div className="mr-2 mb-2 w-20 h-20 rounded-full overflow-hidden dark:divide-gray-700 border dark:border-gray-700 bg-gradient-to-br from-gray-100/80 via-white to-white dark:from-gray-900/80 dark:via-gray-900 dark:to-gray-900 font-sans shadow backdrop-blur-sm flex items-center justify-center">
-                        <img
-                            src="https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png"
-                            alt="DocsGPT"
-                            className="cursor-pointer hover:opacity-50 h-14"
-                        />
-                    </div>
-            </div>
-      <div className={` ${chatState !== 'minimized' ? '' : 'hidden'} divide-y dark:divide-gray-700 rounded-md border dark:border-gray-700 bg-gradient-to-br from-gray-100/80 via-white to-white dark:from-gray-900/80 dark:via-gray-900 dark:to-gray-900 font-sans shadow backdrop-blur-sm`} style={{ width: '18rem', transform: 'translateY(0%) translateZ(0px)' }}>
-        <div>
-          <img
-                        src="https://d3dg1063dc54p9.cloudfront.net/exit.svg"
-                        alt="Exit"
-                        className="cursor-pointer hover:opacity-50 h-2 absolute top-0 right-0 m-2 white-filter"
-                        onClick={(event) => {
-                          event.stopPropagation();
-                          setChatState(ChatStates.Minimized);
-                        }}
-                      />
-          <div className="flex items-center gap-2 p-3">
-            <div  className={`${chatState === 'init' ? '' :
-                                chatState === 'processing' ? '' : 
-                                chatState === 'typing' ? '' :     
-                               'hidden'} flex-1`}>
-              <h3 className="text-sm font-bold text-gray-700 dark:text-gray-200">Need help with documentation?</h3>
-              <p className="mt-1 text-xs text-gray-400 dark:text-gray-500">DocsGPT AI assistant will help you with docs</p>
-            </div>
-            <div id="docsgpt-answer" ref={answerRef} className={`${chatState !== 'answer' ? 'hidden' : ''}`}>
-                <p className="mt-1 text-sm text-gray-600 dark:text-white text-left">{answer}</p>
-            </div>
+      <HeroContainer>
+        <HeroWrapper>
+          <IconWrapper style={{ marginTop: '8px' }}>
+            <RocketIcon color='white' width={20} height={20} />
+          </IconWrapper>
+          <div>
+            <HeroTitle>{title}</HeroTitle>
+            <HeroDescription>
+              {description}
+            </HeroDescription>
           </div>
-        </div>
-        <div className="w-full">
-          <button onClick={() => setChatState(ChatStates.Typing)}
-                  className={`flex w-full justify-center px-5 py-3 text-sm text-gray-800 font-bold dark:text-white transition duration-300 hover:bg-gray-100 rounded-b dark:hover:bg-gray-800/70 ${chatState !== 'init' ? 'hidden' : ''}`}>
-            Ask DocsGPT
-          </button>
-         { (chatState === 'typing' || chatState === 'answer') && (
-            <form
-                onSubmit={handleSubmit}
-                className="relative w-full m-0" style={{ opacity: 1 }}>
-              <input type="text"
-                     className="w-full bg-transparent px-5 py-3 pr-8 text-sm text-gray-700 dark:text-white focus:outline-none" placeholder="What do you want to do?" />
-              <button className="absolute text-gray-400 dark:text-gray-500 text-sm inset-y-0 right-2 -mx-2 px-2" type="submit" >Submit</button>
-            </form>
-          )}
-          <p className={`${chatState !== 'processing' ? 'hidden' : ''} flex w-full justify-center px-5 py-3 text-sm text-gray-800 font-bold dark:text-white transition duration-300 rounded-b`}>
-            Processing<span className="dot-animation">.</span><span className="dot-animation delay-200">.</span><span className="dot-animation delay-400">.</span>
-          </p>
-        </div>
-      </div>
-    </div>
+        </HeroWrapper>
+      </HeroContainer>
+    </>
+  );
+};
+export const DocsGPTWidget = ({
+  apiHost = 'https://gptcloud.arc53.com',
+  selectDocs = 'default',
+  apiKey = '82962c9a-aa77-4152-94e5-a4f84fd44c6a',
+  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.'
+}) => {
 
+  const [prompt, setPrompt] = React.useState('');
+  const [status, setStatus] = React.useState<Status>('idle');
+  const [queries, setQueries] = React.useState<Query[]>([])
+  const [conversationId, setConversationId] = React.useState<string | null>(null)
+  const [open, setOpen] = React.useState<boolean>(false)
+  const [eventInterrupt, setEventInterrupt] = React.useState<boolean>(false); //click or scroll by user while autoScrolling
+  const endMessageRef = React.useRef<HTMLDivElement | null>(null);
+  const handleUserInterrupt = () => {
+    (status === 'loading') && setEventInterrupt(true);
+  }
+  const scrollToBottom = (element: Element | null) => {
+    //recursive function to scroll to the last child of the last child ...
+    // to get to the bottom most element
+    if (!element) return;
+    if (element?.children.length === 0) {
+      element?.scrollIntoView({
+        behavior: 'smooth',
+        block: 'start',
+      });
+    }
+    const lastChild = element?.children?.[element.children.length - 1]
+    lastChild && scrollToBottom(lastChild)
+  };
+
+  React.useEffect(() => {
+    !eventInterrupt && scrollToBottom(endMessageRef.current);
+  }, [queries.length, queries[queries.length - 1]?.response]);
+
+  async function stream(question: string) {
+    setStatus('loading')
+    try {
+      await fetchAnswerStreaming(
+        {
+          question: question,
+          apiKey: apiKey,
+          apiHost: apiHost,
+          selectedDocs: selectDocs,
+          history: queries,
+          conversationId: conversationId,
+          onEvent: (event: MessageEvent) => {
+            const data = JSON.parse(event.data);
+            // check if the 'end' event has been received
+            if (data.type === 'end') {
+              // set status to 'idle'
+              setStatus('idle');
+
+            } else if (data.type === 'id') {
+              setConversationId(data.id)
+            } else {
+              const result = data.answer;
+              const streamingResponse = queries[queries.length - 1].response ? queries[queries.length - 1].response : '';
+              const updatedQueries = [...queries];
+              updatedQueries[updatedQueries.length - 1].response = streamingResponse + result;
+              setQueries(updatedQueries);
+            }
+          }
+        }
+      );
+    } catch (error) {
+      const updatedQueries = [...queries];
+      updatedQueries[updatedQueries.length - 1].error = 'error'
+      setQueries(updatedQueries);
+      setStatus('idle')
+      //setEventInterrupt(false)
+    }
+
+  }
+  // submit handler
+  const handleSubmit = async (e: React.FormEvent<HTMLFormElement>) => {
+    e.preventDefault()
+    setEventInterrupt(false);
+    queries.push({ prompt })
+    setPrompt('')
+    await stream(prompt)
+  }
+  const handleImageError = (event: React.SyntheticEvent<HTMLImageElement, Event>) => {
+    event.currentTarget.src = "https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png";
+  };
+  return (
+    <>
+      <WidgetContainer>
+        <GlobalStyles />
+        {!open && <FloatingButton onClick={() => setOpen(true)} hidden={open}>
+          <MessageIcon style={{ marginTop: '8px' }} />
+        </FloatingButton>}
+        {open && <StyledContainer>
+          <div>
+            <CancelButton onClick={() => setOpen(false)}>
+              <Cross2Icon width={24} height={24} color='white' />
+            </CancelButton>
+            <Header>
+              <IconWrapper>
+                <img style={{ maxWidth: "42px", maxHeight: "42px" }} onError={handleImageError} src={avatar} alt='docs-gpt' />
+              </IconWrapper>
+              <ContentWrapper>
+                <Title>{title}</Title>
+                <Description>{description}</Description>
+              </ContentWrapper>
+            </Header>
+          </div>
+          <Conversation onWheel={handleUserInterrupt} onTouchMove={handleUserInterrupt}>
+            {
+              queries.length > 0 ? queries?.map((query, index) => {
+                return (
+                  <React.Fragment key={index}>
+                    {
+                      query.prompt && <MessageBubble type='QUESTION'>
+                        <Message
+                          type='QUESTION'
+                          ref={(!(query.response || query.error) && index === queries.length - 1) ? endMessageRef : null}>
+                          {query.prompt}
+                        </Message>
+                      </MessageBubble>
+                    }
+                    {
+                      query.response ? <MessageBubble type='ANSWER'>
+                        <Message
+                          type='ANSWER'
+                          ref={(index === queries.length - 1) ? endMessageRef : null}
+                        >
+                          <div className="response" dangerouslySetInnerHTML={{ __html: DOMPurify.sanitize(snarkdown(query.response)) }} />
+                        </Message>
+                      </MessageBubble>
+                        : <div>
+                          {
+                            query.error ? <ErrorAlert>
+                              <IconWrapper>
+                                <ExclamationTriangleIcon style={{ marginTop: '4px' }} width={22} height={22} color='#b91c1c' />
+                              </IconWrapper>
+                              <div>
+                                <h5 style={{ margin: 2 }}>Network Error</h5>
+                                <span style={{ margin: 2, fontSize: '13px' }}>Something went wrong !</span>
+                              </div>
+                            </ErrorAlert>
+                              : <MessageBubble type='ANSWER'>
+                                <Message type='ANSWER' style={{ fontWeight: 600 }}>
+                                  <DotAnimation>.</DotAnimation>
+                                  <Delay delay={200}>.</Delay>
+                                  <Delay delay={400}>.</Delay>
+                                </Message>
+                              </MessageBubble>
+                          }
+                        </div>
+                    }
+                  </React.Fragment>)
+              })
+                : <Hero title={heroTitle} description={heroDescription} />
+            }
+          </Conversation>
+
+          <PromptContainer
+            onSubmit={handleSubmit}>
+            <StyledInput
+              value={prompt} onChange={(event) => setPrompt(event.target.value)}
+              type='text' placeholder="What do you want to do?" />
+            <StyledButton
+              disabled={prompt.length == 0 || status !== 'idle'}>
+              <PaperPlaneIcon width={15} height={15} color='white' />
+            </StyledButton>
+          </PromptContainer>
+        </StyledContainer>}
+      </WidgetContainer>
     </>
   )
-}
+}

extensions/react-widget/src/components/index.ts

@@ -1 +0,0 @@
-export { DocsGPTWidget } from "./DocsGPTWidget";

extensions/react-widget/src/index.css

@@ -1,44 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-#docsgpt-answer {
-    max-height: 50vh; /* 50% of the viewport height */
-    overflow-y: auto; /* Adds a vertical scrollbar if the content exceeds the container height */
-}
-
-.widget-container {
-  position: fixed;   /* fixed positioning */
-  right: 10px;       /* from the right edge */
-  bottom: 10px;      /* from the bottom edge */
-  z-index: 1000;     /* to ensure it appears on top of other content, if any */
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-}
-
-  @keyframes dotBounce {
-    0%, 80%, 100% {
-      transform: translateY(0);
-    }
-    40% {
-      transform: translateY(-5px);
-    }
-  }
-
-  .dot-animation {
-    display: inline-block;
-    animation: dotBounce 1s infinite ease-in-out;
-  }
-
-  .delay-200 {
-    animation-delay: 200ms;
-  }
-
-  .delay-400 {
-    animation-delay: 400ms;
-  }
-
-  .white-filter {
-    filter: invert(1) brightness(2);
-}

extensions/react-widget/src/index.html

@@ -0,0 +1,19 @@
+<!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>
+    <script type="module" src="main.tsx"></script>
+    <script type="module" src="../dist/main.js"></script>
+    <script type="module">
+      window.onload = function() {
+        renderDocsGPTWidget('app');
+      }
+    </script>
+  </body>
+</html>