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: |

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)
@@ -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.

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

@@ -1,31 +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 apt-get install -y wget unzip
-RUN wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
-RUN unzip mpnet-base-v2.zip -d model
-RUN rm mpnet-base-v2.zip
-RUN curl https://sh.rustup.rs -sSf | sh -s -- -y && apt-get install --reinstall libc6-dev -y
-ENV PATH="/root/.cargo/bin:${PATH}"
-RUN pip install --upgrade pip && pip install tiktoken==0.5.2
+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
 
+# Setup Python virtual environment
+RUN python3.11 -m venv /venv
 
+# Activate virtual environment and install Python packages
+ENV PATH="/venv/bin:$PATH"
 
-FROM python:3.11-slim-bullseye
+# 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
 
-# Copy pre-built packages and binaries from builder stage
-COPY --from=builder /usr/local/ /usr/local/
+# 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)
@@ -27,20 +23,22 @@ conversations_collection = db["conversations"]
 vectors_collection = db["vectors"]
 prompts_collection = db["prompts"]
 api_key_collection = db["api_keys"]
-answer = Blueprint('answer', __name__)
+answer = Blueprint("answer", __name__)
 
 gpt_model = ""
 # to have some kind of default behaviour
 if settings.LLM_NAME == "openai":
-    gpt_model = 'gpt-3.5-turbo'
+    gpt_model = "gpt-3.5-turbo"
 elif settings.LLM_NAME == "anthropic":
-    gpt_model = 'claude-2'
+    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()
 
@@ -51,7 +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
@@ -62,11 +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)
@@ -78,17 +71,20 @@ def run_async_chain(chain, question, chat_history):
     result["answer"] = answer
     return result
 
+
 def get_data_from_api_key(api_key):
     data = api_key_collection.find_one({"key": api_key})
-    if data is None:
-        return bad_request(401, "Invalid API key")
-    return data
     
+    # # 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:
@@ -102,95 +98,116 @@ 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, prompt_id, conversation_id, chunks=2):
-    llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.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"]
-        
-    if chunks == 0:
-        docs = []
-    else:
-        docs = docsearch.search(question, k=chunks)
-    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"]
@@ -203,36 +220,91 @@ def stream():
         conversation_id = None
     else:
         conversation_id = data["conversation_id"]
-    if 'prompt_id' in data:
+    if "prompt_id" in data:
         prompt_id = data["prompt_id"]
     else:
-        prompt_id = 'default'
-    if 'selectedDocs' in data and data['selectedDocs'] is None:
+        prompt_id = "default"
+    if "selectedDocs" in data and data["selectedDocs"] is None:
         chunks = 0
-    elif 'chunks' in data:
+    elif "chunks" in data:
         chunks = int(data["chunks"])
     else:
         chunks = 2
+    if "token_limit" in data:
+        token_limit = data["token_limit"]
+    else:
+        token_limit = settings.DEFAULT_MAX_HISTORY
 
-    # check if active_docs is set
+    # check if active_docs or api_key is set
 
     if "api_key" in data:
         data_key = get_data_from_api_key(data["api_key"])
-        vectorstore = get_vectorstore({"active_docs": data_key["source"]})
+        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:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
+        source = {"active_docs": data["active_docs"]}
+        user_api_key = None
     else:
-        vectorstore = ""
-    docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, settings.EMBEDDINGS_KEY)
+        source = {}
+        user_api_key = None
 
-    return Response(
-        complete_stream(question, docsearch,
-                        chat_history=history,
-                        prompt_id=prompt_id,
-                        conversation_id=conversation_id,
-                        chunks=chunks), mimetype="text/event-stream"
+    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():
@@ -247,118 +319,70 @@ def api_answer():
     else:
         conversation_id = data["conversation_id"]
     print("-" * 5)
-    if 'prompt_id' in data:
+    if "prompt_id" in data:
         prompt_id = data["prompt_id"]
     else:
-        prompt_id = 'default'
-    if 'chunks' in data:
+        prompt_id = "default"
+    if "chunks" in data:
         chunks = int(data["chunks"])
     else:
         chunks = 2
-
-    if prompt_id == 'default':
-        prompt = chat_combine_template
-    elif prompt_id == 'creative':
-        prompt = chat_combine_creative
-    elif prompt_id == 'strict':
-        prompt = chat_combine_strict
+    if "token_limit" in data:
+        token_limit = data["token_limit"]
     else:
-        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
+        token_limit = settings.DEFAULT_MAX_HISTORY
 
     # use try and except  to check for exception
     try:
         # check if the vectorstore is set
         if "api_key" in data:
             data_key = get_data_from_api_key(data["api_key"])
-            vectorstore = get_vectorstore({"active_docs": data_key["source"]})
+            chunks = int(data_key["chunks"])
+            prompt_id = data_key["prompt_id"]
+            source = {"active_docs": data_key["source"]}
+            user_api_key = data["api_key"]
         else:
-            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, settings.EMBEDDINGS_KEY)
+            source = data
+            user_api_key = None
 
-
-        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)
-
-
-
-        if chunks == 0:
-            docs = []
+        if (
+            source["active_docs"].split("/")[0] == "default"
+            or source["active_docs"].split("/")[0] == "local"
+        ):
+            retriever_name = "classic"
         else:
-            docs = docsearch.search(question, k=chunks)
-        # 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}]
+            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,
+        )
         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
+        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
+        )
 
-        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})
+        result = {"answer": response_full, "sources": source_log_docs}
+        result["conversation_id"] = save_conversation(
+            conversation_id, question, response_full, source_log_docs, llm
+        )
 
-
-        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']}}},
-            )
-
-        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"}
-            ]
-
-            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
-
-        result["conversation_id"] = str(conversation_id)
-
-        # mock result
-        # result = {
-        #     "answer": "The answer is 42",
-        #     "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
-        # }
         return result
     except Exception as e:
         # print whole traceback
@@ -372,30 +396,44 @@ def api_search():
     data = request.get_json()
     # get parameter from url question
     question = data["question"]
-
-    if "api_key" in data:
-        data_key = get_data_from_api_key(data["api_key"])
-        vectorstore = data_key["source"]
-    elif "active_docs" in data:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
-    else:
-        vectorstore = ""
-    if 'chunks' in data:
+    if "chunks" in data:
         chunks = int(data["chunks"])
     else:
         chunks = 2
-    docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, settings.EMBEDDINGS_KEY)
-    if chunks == 0:
-        docs = []
+    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:
-        docs = docsearch.search(question, k=chunks)
+        source = {}
+        user_api_key = None
 
-    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

@@ -1,12 +1,14 @@
 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 bson.dbref import DBRef
 from application.api.user.tasks import ingest, ingest_remote
 
 from application.core.settings import settings
@@ -19,9 +21,14 @@ vectors_collection = db["vectors"]
 prompts_collection = db["prompts"]
 feedback_collection = db["feedback"]
 api_key_collection = db["api_keys"]
-user = Blueprint('user', __name__)
+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():
@@ -36,15 +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).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)
 
@@ -54,7 +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():
@@ -62,7 +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"}
 
 
@@ -73,7 +91,6 @@ def api_feedback():
     answer = data["answer"]
     feedback = data["feedback"]
 
-
     feedback_collection.insert_one(
         {
             "question": question,
@@ -83,6 +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"""
@@ -97,6 +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."""
@@ -112,7 +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))
@@ -123,9 +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."""
@@ -136,31 +156,51 @@ 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
-        task_id = task.id
-        return {"status": "ok", "task_id": 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:
-        return {"status": "error"}
-    
+        # 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."""
@@ -173,25 +213,27 @@ def upload_remote():
     if "name" not in request.form:
         return {"status": "no name"}
     job_name = secure_filename(request.form["name"])
-    # check if the post request has the file part
     if "data" not in request.form:
         print("No data")
         return {"status": "no data"}
     source_data = request.form["data"]
 
     if source_data:
-        task = ingest_remote.delay(source_data=source_data, job_name=job_name, user=user, loader=source)
-        # task id
+        task = 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}
@@ -214,11 +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"],
@@ -230,13 +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)
 
@@ -254,11 +330,13 @@ def check_docs():
         return {"status": "exists"}
     else:
         file_url = urlparse(base_path + vectorstore + "index.faiss")
-        
-        if file_url.scheme in ['https'] and file_url.netloc == 'raw.githubusercontent.com' and file_url.path.startswith('/arc53/DocsHUB/main/'):
-            
-            r = requests.get(file_url.geturl())
 
+        if (
+            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:
@@ -267,7 +345,6 @@ def check_docs():
                 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)
@@ -276,6 +353,7 @@ def check_docs():
 
         return {"status": "loaded"}
 
+
 @user.route("/api/create_prompt", methods=["POST"])
 def create_prompt():
     data = request.get_json()
@@ -294,6 +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"
@@ -303,30 +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()
@@ -338,6 +426,7 @@ def delete_prompt():
     )
     return {"status": "ok"}
 
+
 @user.route("/api/update_prompt", methods=["POST"])
 def update_prompt_name():
     data = request.get_json()
@@ -347,27 +436,31 @@ 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"]
-        })
+        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()
@@ -384,12 +477,13 @@ def create_api_key():
             "source": source,
             "user": user,
             "prompt_id": prompt_id,
-            "chunks": chunks
+            "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()
@@ -401,3 +495,72 @@ def delete_api_key():
     )
     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

@@ -1,5 +1,5 @@
 from application.worker import ingest_worker, remote_worker
-from application.celery import celery
+from application.celery_init import celery
 
 @celery.task(bind=True)
 def ingest(self, directory, formats, name_job, filename, user):

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

application/core/settings.py

@@ -9,15 +9,17 @@ current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__
 
 class Settings(BaseSettings):
     LLM_NAME: str = "docsgpt"
-    MODEL_NAME: Optional[str] = None # when LLM_NAME is openai, MODEL_NAME can be e.g. gpt-4-turbo-preview or gpt-3.5-turbo
+    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" or "qdrant"
+    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"] # also brave_search
 
     API_URL: str = "http://localhost:7091"  # backend url for celery worker
 
@@ -59,6 +61,8 @@ class Settings(BaseSettings):
     QDRANT_PATH: Optional[str] = None
     QDRANT_DISTANCE_FUNC: str = "Cosine"
 
+    BRAVE_SEARCH_API_KEY: Optional[str] = None
+
     FLASK_DEBUG_MODE: bool = False
 
 

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'].replace("###", "")
+        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

@@ -7,22 +7,21 @@ from application.llm.docsgpt_provider import DocsGPTAPILLM
 from application.llm.premai import PremAILLM
 
 
-
 class LLMCreator:
     llms = {
-        'openai': OpenAILLM,
-        'azure_openai': AzureOpenAILLM,
-        'sagemaker': SagemakerAPILLM,
-        'huggingface': HuggingFaceLLM,
-        'llama.cpp': LlamaCpp,
-        'anthropic': AnthropicLLM,
-        'docsgpt': DocsGPTAPILLM,
-        'premai': PremAILLM,
+        "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

@@ -1,32 +1,37 @@
 from application.llm.base import BaseLLM
 from application.core.settings import settings
 
+
 class PremAILLM(BaseLLM):
 
-    def __init__(self, api_key):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
         from premai import Prem
-        
-        self.client = Prem(
-            api_key=api_key
-        )
+
+        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 gen(self, model, engine, messages, stream=False, **kwargs):
-        response = self.client.chat.completions.create(model=model,
+    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)
+            **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,
+    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)
+            **kwargs
+        )
 
         for line in response:
             if line.choices[0].delta["content"] is not None:

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/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/web_loader.py

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

application/parser/token_func.py

@@ -22,7 +22,10 @@ def group_documents(documents: List[Document], min_tokens: int, max_tokens: int)
         doc_len = len(tiktoken.get_encoding("cl100k_base").encode(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 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:

application/requirements.txt

@@ -3,32 +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==1.7.3
+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/worker.py

@@ -2,9 +2,9 @@ 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
@@ -14,13 +14,6 @@ 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])
@@ -37,6 +30,33 @@ current_dir = os.path.dirname(
 )
 
 
+def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
+    """
+    Recursively extract zip files with a limit on recursion depth.
+
+    Args:
+        zip_path (str): Path to the zip file to be extracted.
+        extract_to (str): Destination path for extracted files.
+        current_depth (int): Current depth of recursion.
+        max_depth (int): Maximum allowed depth of recursion to prevent infinite loops.
+    """
+    if current_depth > max_depth:
+        print(f"Reached maximum recursion depth of {max_depth}")
+        return
+
+    with zipfile.ZipFile(zip_path, "r") as zip_ref:
+        zip_ref.extractall(extract_to)
+    os.remove(zip_path)  # Remove the zip file after extracting
+
+    # Check for nested zip files and extract them
+    for root, dirs, files in os.walk(extract_to):
+        for file in files:
+            if file.endswith(".zip"):
+                # If a nested zip file is found, extract it recursively
+                file_path = os.path.join(root, file)
+                extract_zip_recursive(file_path, root, current_depth + 1, max_depth)
+
+
 # Define the main function for ingesting and processing documents.
 def ingest_worker(self, directory, formats, name_job, filename, user):
     """
@@ -66,7 +86,8 @@ 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)
@@ -81,14 +102,14 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
 
     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)
+        extract_zip_recursive(
+            os.path.join(full_path, filename), full_path, 0, recursion_depth
+        )
 
     self.update_state(state="PROGRESS", meta={"current": 1})
 
@@ -111,6 +132,7 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
     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})
 
     if sample:
@@ -119,7 +141,7 @@ 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"),
@@ -150,7 +172,6 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
 
 
 def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
-    # sample = False
     token_check = True
     min_tokens = 150
     max_tokens = 1250
@@ -158,12 +179,8 @@ def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
 
     if not os.path.exists(full_path):
         os.makedirs(full_path)
-
     self.update_state(state="PROGRESS", meta={"current": 1})
 
-    # source_data {"data": [url]} for url type task just urls
-
-    # Use RemoteCreator to load data from URL
     remote_loader = RemoteCreator.create_loader(loader)
     raw_docs = remote_loader.load_data(source_data)
 
@@ -173,19 +190,19 @@ def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
         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}
+    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
         )
@@ -196,3 +213,25 @@ def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
     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

docs/package.json

@@ -8,7 +8,7 @@
   "dependencies": {
     "@vercel/analytics": "^1.1.1",
     "docsgpt": "^0.3.7",
-    "next": "^14.0.4",
+    "next": "^14.1.1",
     "nextra": "^2.13.2",
     "nextra-theme-docs": "^2.13.2",
     "react": "^18.2.0",

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

@@ -14,7 +14,7 @@ Before creating your first API key, you must upload the document that will be li
 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.co.uk/Developing/API-docs#8-apicreate_api_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
 
@@ -27,4 +27,4 @@ Upon creating your API key, you will encounter several key variables. Each serve
 
 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.
+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

@@ -7,8 +7,8 @@
     "title": "🏗️ Widget setup",
     "href": "/Extensions/react-widget"
   },
-  "api-key-guide": {
-    "title": "🔐 API Keys guide",
-    "href": "/Extensions/api-key-guide"
+  "Chrome-extension": {
+    "title": "🌐 Chrome Extension",
+    "href": "/Extensions/Chrome-extension"
   }
 }

docs/pages/Extensions/react-widget.md

@@ -51,6 +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/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/README.md

@@ -1,6 +1,5 @@
 # DocsGPT react widget
 
-
 This widget will allow you to embed a DocsGPT assistant in your React app.
 
 ## Installation
@@ -11,6 +10,8 @@ npm install  docsgpt
 
 ## Usage
 
+### React
+
 ```javascript
     import { DocsGPTWidget } from "docsgpt";
 
@@ -25,9 +26,9 @@ To link the widget to your api and your documents you can pass parameters to the
     import { DocsGPTWidget } from "docsgpt";
 
     const App = () => {
-      return <DocsGPTWidget 
+      return <DocsGPTWidget
              apiHost = 'http://localhost:7001',
-             selectDocs = 'default', 
+             selectDocs = 'default',
              apiKey = '',
              avatar = 'https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png',
              title = 'Get AI assistance',
@@ -38,10 +39,65 @@ To link the widget to your api and your documents you can pass parameters to the
     };
 ```
 
+### 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/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docsgpt",
-  "version": "0.3.7",
+  "version": "0.3.9",
   "private": false,
   "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",
@@ -11,6 +11,18 @@
     "dist",
     "package.json"
   ],
+  "targets": {
+    "modern": {
+      "engines": {
+        "browsers": "Chrome 80"
+      }
+    },
+    "legacy": {
+      "engines": {
+        "browsers": "> 0.5%, last 2 versions, not dead"
+      }
+    }
+  },
   "@parcel/resolver-default": {
     "packageExports": true
   },
@@ -18,8 +30,8 @@
     "styled-components": "^5"
   },
   "scripts": {
-    "build": "parcel build src/index.ts",
-    "dev": "parcel",
+    "build": "parcel build src/main.tsx --public-url ./",
+    "dev": "parcel src/index.html -p 3000",
     "test": "jest",
     "lint": "eslint",
     "check": "tsc --noEmit",
@@ -33,16 +45,13 @@
     "@parcel/transformer-typescript-tsc": "^2.12.0",
     "@parcel/validator-typescript": "^2.12.0",
     "@radix-ui/react-icons": "^1.3.0",
-    "@types/react": "^18.2.61",
-    "@types/react-dom": "^18.2.19",
     "class-variance-authority": "^0.7.0",
     "clsx": "^2.1.0",
-    "dompurify": "^3.0.9",
+    "dompurify": "^3.1.5",
     "flow-bin": "^0.229.2",
     "i": "^0.3.7",
     "install": "^0.13.0",
     "npm": "^10.5.0",
-    "parcel": "^2.12.0",
     "react": "^18.2.0",
     "react-dom": "^18.2.0",
     "styled-components": "^6.1.8"
@@ -54,7 +63,10 @@
     "@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"
   },

extensions/react-widget/src/components/DocsGPTWidget.tsx

@@ -1,13 +1,13 @@
 "use client";
-import { Fragment, useEffect, useRef, useState } from 'react'
-import { PaperPlaneIcon, RocketIcon, ExclamationTriangleIcon, Cross2Icon } from '@radix-ui/react-icons';
-import { MESSAGE_TYPE } from '../models/types';
-import { Query, Status } from '../models/types';
-import MessageIcon from '../assets/message.svg'
-import { fetchAnswerStreaming } from '../requests/streamingApi';
-import styled, { keyframes, createGlobalStyle } from 'styled-components';
+import React from 'react'
+import DOMPurify from 'dompurify';
 import snarkdown from '@bpmn-io/snarkdown';
-import { sanitize } from 'dompurify';
+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';
+
 const GlobalStyles = createGlobalStyle`
 .response pre {
     padding: 8px;
@@ -293,13 +293,13 @@ export const DocsGPTWidget = ({
   heroDescription = 'This chatbot is built with DocsGPT and utilises GenAI, please review important information using sources.'
 }) => {
 
-  const [prompt, setPrompt] = useState('');
-  const [status, setStatus] = useState<Status>('idle');
-  const [queries, setQueries] = useState<Query[]>([])
-  const [conversationId, setConversationId] = useState<string | null>(null)
-  const [open, setOpen] = useState<boolean>(false)
-  const [eventInterrupt, setEventInterrupt] = useState<boolean>(false); //click or scroll by user while autoScrolling
-  const endMessageRef = useRef<HTMLDivElement | null>(null);
+  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);
   }
@@ -317,7 +317,7 @@ export const DocsGPTWidget = ({
     lastChild && scrollToBottom(lastChild)
   };
 
-  useEffect(() => {
+  React.useEffect(() => {
     !eventInterrupt && scrollToBottom(endMessageRef.current);
   }, [queries.length, queries[queries.length - 1]?.response]);
 
@@ -397,7 +397,7 @@ export const DocsGPTWidget = ({
             {
               queries.length > 0 ? queries?.map((query, index) => {
                 return (
-                  <Fragment key={index}>
+                  <React.Fragment key={index}>
                     {
                       query.prompt && <MessageBubble type='QUESTION'>
                         <Message
@@ -413,7 +413,7 @@ export const DocsGPTWidget = ({
                           type='ANSWER'
                           ref={(index === queries.length - 1) ? endMessageRef : null}
                         >
-                          <div className="response" dangerouslySetInnerHTML={{ __html: sanitize(snarkdown(query.response)) }} />
+                          <div className="response" dangerouslySetInnerHTML={{ __html: DOMPurify.sanitize(snarkdown(query.response)) }} />
                         </Message>
                       </MessageBubble>
                         : <div>
@@ -437,7 +437,7 @@ export const DocsGPTWidget = ({
                           }
                         </div>
                     }
-                  </Fragment>)
+                  </React.Fragment>)
               })
                 : <Hero title={heroTitle} description={heroDescription} />
             }

extensions/react-widget/src/index.html

@@ -9,5 +9,11 @@
   <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>

extensions/react-widget/src/main.tsx

@@ -1,6 +1,11 @@
-import { createRoot } from 'react-dom/client';
-import App from './App.tsx';
 import React from 'react';
-const root = createRoot(document.getElementById('app') as HTMLElement);
+import { createRoot } from 'react-dom/client';
+import { DocsGPTWidget } from './components/DocsGPTWidget';
 
-root.render(<App />);
+const renderWidget = (elementId: string, props = {}) => {
+  const root = createRoot(document.getElementById(elementId) as HTMLElement);
+  root.render(<DocsGPTWidget {...props} />);
+};
+
+(window as any).renderDocsGPTWidget = renderWidget;
+export { DocsGPTWidget };

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

@@ -0,0 +1,13 @@
+export type MESSAGE_TYPE = 'QUESTION' | 'ANSWER' | 'ERROR';
+export type Status = 'idle' | 'loading' | 'failed';
+export type FEEDBACK = 'LIKE' | 'DISLIKE';
+
+export interface Query {
+  prompt: string;
+  response?: string;
+  feedback?: FEEDBACK;
+  error?: string;
+  sources?: { title: string; text: string }[];
+  conversationId?: string | null;
+  title?: string | null;
+}

extensions/web-widget/package-lock.json

@@ -152,12 +152,12 @@
       }
     },
     "node_modules/braces": {
-      "version": "3.0.2",
-      "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.2.tgz",
-      "integrity": "sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==",
+      "version": "3.0.3",
+      "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz",
+      "integrity": "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==",
       "dev": true,
       "dependencies": {
-        "fill-range": "^7.0.1"
+        "fill-range": "^7.1.1"
       },
       "engines": {
         "node": ">=8"
@@ -294,9 +294,9 @@
       }
     },
     "node_modules/fill-range": {
-      "version": "7.0.1",
-      "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.0.1.tgz",
-      "integrity": "sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==",
+      "version": "7.1.1",
+      "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz",
+      "integrity": "sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==",
       "dev": true,
       "dependencies": {
         "to-regex-range": "^5.0.1"

frontend/index.html

@@ -1,13 +1,17 @@
 <!DOCTYPE html>
 <html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>DocsGPT 🦖</title>
-      <link rel="shortcut icon" type="image/x-icon" href="/favicon.ico" />
-  </head>
-  <body>
-    <div id="root" class="h-screen"></div>
-    <script type="module" src="/src/main.tsx"></script>
-  </body>
-</html>
+
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0,viewport-fit=cover" />
+  <meta name="apple-mobile-web-app-capable" content="yes">
+  <title>DocsGPT 🦖</title>
+  <link rel="shortcut icon" type="image/x-icon" href="/favicon.ico" />
+</head>
+
+<body>
+  <div id="root" class="h-screen"></div>
+  <script type="module" src="/src/main.tsx"></script>
+</body>
+
+</html>

frontend/package-lock.json

@@ -10,10 +10,14 @@
       "dependencies": {
         "@reduxjs/toolkit": "^1.9.2",
         "@vercel/analytics": "^0.1.10",
+        "i18next": "^23.11.5",
+        "i18next-browser-languagedetector": "^8.0.0",
+        "prop-types": "^15.8.1",
         "react": "^18.2.0",
         "react-copy-to-clipboard": "^5.1.0",
         "react-dom": "^18.2.0",
         "react-dropzone": "^14.2.3",
+        "react-i18next": "^14.1.2",
         "react-markdown": "^8.0.7",
         "react-redux": "^8.0.5",
         "react-router-dom": "^6.8.1",
@@ -354,11 +358,12 @@
       }
     },
     "node_modules/@babel/runtime": {
-      "version": "7.20.13",
-      "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.20.13.tgz",
-      "integrity": "sha512-gt3PKXs0DBoL9xCvOIIZ2NEqAGZqHjAnmVbfQtB620V0uReIQutpel14KcneZuer7UioY8ALKZ7iocavvzTNFA==",
+      "version": "7.24.6",
+      "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.24.6.tgz",
+      "integrity": "sha512-Ja18XcETdEl5mzzACGd+DKgaGJzPTCow7EglgwTmHdwokzDFYh/MHua6lU6DV/hjF2IaOJ4oX2nqnjG7RElKOw==",
+      "license": "MIT",
       "dependencies": {
-        "regenerator-runtime": "^0.13.11"
+        "regenerator-runtime": "^0.14.0"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -1485,7 +1490,7 @@
       "version": "18.0.10",
       "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-18.0.10.tgz",
       "integrity": "sha512-E42GW/JA4Qv15wQdqJq8DL4JhNpB3prJgjgapN3qJT9K2zO5IIAQh4VXvCEDupoqAwnz0cY4RlXeC/ajX5SFHg==",
-      "devOptional": true,
+      "dev": true,
       "dependencies": {
         "@types/react": "*"
       }
@@ -2151,12 +2156,12 @@
       }
     },
     "node_modules/braces": {
-      "version": "3.0.2",
-      "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.2.tgz",
-      "integrity": "sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==",
+      "version": "3.0.3",
+      "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz",
+      "integrity": "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==",
       "dev": true,
       "dependencies": {
-        "fill-range": "^7.0.1"
+        "fill-range": "^7.1.1"
       },
       "engines": {
         "node": ">=8"
@@ -3699,9 +3704,9 @@
       "integrity": "sha512-336iVw3rtn2BUK7ORdIAHTyxHGRIHVReokCR3XjbckJMK7ms8FysBfhLR8IXnAgy7T0PTPNBWKiH514FOW/WSg=="
     },
     "node_modules/fill-range": {
-      "version": "7.0.1",
-      "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.0.1.tgz",
-      "integrity": "sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==",
+      "version": "7.1.1",
+      "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz",
+      "integrity": "sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==",
       "dev": true,
       "dependencies": {
         "to-regex-range": "^5.0.1"
@@ -4134,6 +4139,15 @@
         "react-is": "^16.7.0"
       }
     },
+    "node_modules/html-parse-stringify": {
+      "version": "3.0.1",
+      "resolved": "https://registry.npmjs.org/html-parse-stringify/-/html-parse-stringify-3.0.1.tgz",
+      "integrity": "sha512-KknJ50kTInJ7qIScF3jeaFRpMpE8/lfiTdzf/twXyPBLAGrLRTmkz3AdTnKeh40X8k9L2fdYwEp/42WGXIRGcg==",
+      "license": "MIT",
+      "dependencies": {
+        "void-elements": "3.1.0"
+      }
+    },
     "node_modules/human-signals": {
       "version": "3.0.1",
       "resolved": "https://registry.npmjs.org/human-signals/-/human-signals-3.0.1.tgz",
@@ -4158,6 +4172,38 @@
         "url": "https://github.com/sponsors/typicode"
       }
     },
+    "node_modules/i18next": {
+      "version": "23.11.5",
+      "resolved": "https://registry.npmjs.org/i18next/-/i18next-23.11.5.tgz",
+      "integrity": "sha512-41pvpVbW9rhZPk5xjCX2TPJi2861LEig/YRhUkY+1FQ2IQPS0bKUDYnEqY8XPPbB48h1uIwLnP9iiEfuSl20CA==",
+      "funding": [
+        {
+          "type": "individual",
+          "url": "https://locize.com"
+        },
+        {
+          "type": "individual",
+          "url": "https://locize.com/i18next.html"
+        },
+        {
+          "type": "individual",
+          "url": "https://www.i18next.com/how-to/faq#i18next-is-awesome.-how-can-i-support-the-project"
+        }
+      ],
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.23.2"
+      }
+    },
+    "node_modules/i18next-browser-languagedetector": {
+      "version": "8.0.0",
+      "resolved": "https://registry.npmjs.org/i18next-browser-languagedetector/-/i18next-browser-languagedetector-8.0.0.tgz",
+      "integrity": "sha512-zhXdJXTTCoG39QsrOCiOabnWj2jecouOqbchu3EfhtSHxIB5Uugnm9JaizenOy39h7ne3+fLikIjeW88+rgszw==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.23.2"
+      }
+    },
     "node_modules/ignore": {
       "version": "5.2.4",
       "resolved": "https://registry.npmjs.org/ignore/-/ignore-5.2.4.tgz",
@@ -6571,6 +6617,7 @@
       "version": "15.8.1",
       "resolved": "https://registry.npmjs.org/prop-types/-/prop-types-15.8.1.tgz",
       "integrity": "sha512-oj87CgZICdulUohogVAR7AjlC0327U4el4L6eAvOqCeudMDVU0NThNaV+b9Df4dXgSP1gXMTnPdhfe/2qDH5cg==",
+      "license": "MIT",
       "dependencies": {
         "loose-envify": "^1.4.0",
         "object-assign": "^4.1.1",
@@ -6678,6 +6725,28 @@
         "react": ">= 16.8 || 18.0.0"
       }
     },
+    "node_modules/react-i18next": {
+      "version": "14.1.2",
+      "resolved": "https://registry.npmjs.org/react-i18next/-/react-i18next-14.1.2.tgz",
+      "integrity": "sha512-FSIcJy6oauJbGEXfhUgVeLzvWBhIBIS+/9c6Lj4niwKZyGaGb4V4vUbATXSlsHJDXXB+ociNxqFNiFuV1gmoqg==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.23.9",
+        "html-parse-stringify": "^3.0.1"
+      },
+      "peerDependencies": {
+        "i18next": ">= 23.2.3",
+        "react": ">= 16.8.0"
+      },
+      "peerDependenciesMeta": {
+        "react-dom": {
+          "optional": true
+        },
+        "react-native": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/react-is": {
       "version": "16.13.1",
       "resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz",
@@ -6875,9 +6944,10 @@
       }
     },
     "node_modules/regenerator-runtime": {
-      "version": "0.13.11",
-      "resolved": "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.13.11.tgz",
-      "integrity": "sha512-kY1AZVr2Ra+t+piVaJ4gxaFaReZVH40AKNo7UCX6W+dEwBo/2oZJzqfuN1qLq1oL45o56cPaTXELwrTh8Fpggg=="
+      "version": "0.14.1",
+      "resolved": "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.14.1.tgz",
+      "integrity": "sha512-dYnhHh0nJoMfnkZs6GmmhFknAGRrLznOu5nc9ML+EJxGvrx6H7teuevqVqCuPcPK//3eDrrjQhehXVx9cnkGdw==",
+      "license": "MIT"
     },
     "node_modules/regexp.prototype.flags": {
       "version": "1.4.3",
@@ -7923,6 +7993,15 @@
         "vite": "^2.6.0 || 3 || 4 || 5"
       }
     },
+    "node_modules/void-elements": {
+      "version": "3.1.0",
+      "resolved": "https://registry.npmjs.org/void-elements/-/void-elements-3.1.0.tgz",
+      "integrity": "sha512-Dhxzh5HZuiHQhbvTW9AMetFfBHDMYpo23Uo9btPXgdYP+3T5S+p+jgNy7spra+veYhBP2dCSgxR/i2Y02h5/6w==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
     "node_modules/which": {
       "version": "2.0.2",
       "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",

frontend/package.json

@@ -21,10 +21,14 @@
   "dependencies": {
     "@reduxjs/toolkit": "^1.9.2",
     "@vercel/analytics": "^0.1.10",
+    "i18next": "^23.11.5",
+    "i18next-browser-languagedetector": "^8.0.0",
+    "prop-types": "^15.8.1",
     "react": "^18.2.0",
     "react-copy-to-clipboard": "^5.1.0",
     "react-dom": "^18.2.0",
     "react-dropzone": "^14.2.3",
+    "react-i18next": "^14.1.2",
     "react-markdown": "^8.0.7",
     "react-redux": "^8.0.5",
     "react-router-dom": "^6.8.1",

frontend/src/App.tsx

@@ -1,4 +1,5 @@
 import { Routes, Route } from 'react-router-dom';
+import { useEffect } from 'react';
 import Navigation from './Navigation';
 import Conversation from './conversation/Conversation';
 import About from './About';
@@ -6,30 +7,55 @@ import PageNotFound from './PageNotFound';
 import { inject } from '@vercel/analytics';
 import { useMediaQuery } from './hooks';
 import { useState } from 'react';
-import Setting from './Setting';
-
+import Setting from './settings';
+import './locale/i18n';
+import { Outlet } from 'react-router-dom';
+import SharedConversation from './conversation/SharedConversation';
+import { useDarkTheme } from './hooks';
 inject();
 
-export default function App() {
+function MainLayout() {
   const { isMobile } = useMediaQuery();
   const [navOpen, setNavOpen] = useState(!isMobile);
   return (
-    <div className="min-h-full min-w-full dark:bg-raisin-black">
+    <div className="dark:bg-raisin-black">
       <Navigation navOpen={navOpen} setNavOpen={setNavOpen} />
       <div
-        className={`transition-all duration-200 ${
+        className={`min-h-screen ${
           !isMobile
-            ? `ml-0 ${!navOpen ? '-mt-5 md:mx-auto lg:mx-auto' : 'md:ml-72'}`
+            ? `ml-0 ${!navOpen ? 'md:mx-auto lg:mx-auto' : 'md:ml-72'}`
             : 'ml-0 md:ml-16'
         }`}
       >
-        <Routes>
-          <Route path="/" element={<Conversation />} />
-          <Route path="/about" element={<About />} />
-          <Route path="*" element={<PageNotFound />} />
-          <Route path="/settings" element={<Setting />} />
-        </Routes>
+        <Outlet />
       </div>
     </div>
   );
 }
+
+export default function App() {
+  const [isDarkTheme] = useDarkTheme();
+  useEffect(() => {
+    localStorage.setItem('selectedTheme', isDarkTheme ? 'Dark' : 'Light');
+    if (isDarkTheme) {
+      document
+        .getElementById('root')
+        ?.classList.add('dark', 'dark:bg-raisin-black');
+    } else {
+      document.getElementById('root')?.classList.remove('dark');
+    }
+  }, [isDarkTheme]);
+  return (
+    <>
+      <Routes>
+        <Route element={<MainLayout />}>
+          <Route index element={<Conversation />} />
+          <Route path="/about" element={<About />} />
+          <Route path="/settings" element={<Setting />} />
+        </Route>
+        <Route path="/share/:identifier" element={<SharedConversation />} />
+        <Route path="/*" element={<PageNotFound />} />
+      </Routes>
+    </>
+  );
+}

frontend/src/Hero.tsx

@@ -1,191 +1,52 @@
-import { useDarkTheme, useMediaQuery } from './hooks';
+import { Fragment } from 'react';
 import DocsGPT3 from './assets/cute_docsgpt3.svg';
-
-export default function Hero({ className = '' }: { className?: string }) {
-  // const isMobile = window.innerWidth <= 768;
-  const { isMobile } = useMediaQuery();
-  const [isDarkTheme] = useDarkTheme();
+import { useTranslation } from 'react-i18next';
+export default function Hero({
+  handleQuestion,
+}: {
+  handleQuestion: ({
+    question,
+    isRetry,
+  }: {
+    question: string;
+    isRetry?: boolean;
+  }) => void;
+}) {
+  const { t } = useTranslation();
+  const demos = t('demo', { returnObjects: true }) as Array<{
+    header: string;
+    query: string;
+  }>;
   return (
     <div
-      className={`mt-14 mb-32 flex flex-col text-black-1000 dark:text-bright-gray lg:mt-6`}
+      className={`mt-16 mb-4 flex w-full flex-col justify-end text-black-1000 dark:text-bright-gray sm:w-full lg:mt-6`}
     >
-      <div className=" mb-2 flex items-center justify-center sm:mb-10">
-        <p className="mr-2 text-4xl font-semibold">DocsGPT</p>
-        <img className="mb-2 h-14" src={DocsGPT3} alt="DocsGPT" />
+      <div className="flex h-full w-full flex-col items-center justify-center">
+        <div className="flex items-center">
+          <span className="p-0 text-4xl font-semibold">DocsGPT</span>
+          <img className="mb-1 inline w-14 p-0" src={DocsGPT3} alt="docsgpt" />
+        </div>
+
+        <div className="mb-4 flex flex-col items-center justify-center dark:text-white"></div>
       </div>
-      {isMobile ? (
-        <p className="mb-3 text-center leading-6">
-          Welcome to <span className="font-bold">DocsGPT</span>, your technical
-          documentation assistant! Start by entering your query in the input
-          field below, and we&apos;ll provide you with the most relevant
-          answers.
-        </p>
-      ) : (
-        <>
-          <p className="mb-3 text-center leading-6">
-            Welcome to DocsGPT, your technical documentation assistant!
-          </p>
-          <p className="mb-3 text-center leading-6">
-            Enter a query related to the information in the documentation you
-            selected to receive
-            <br /> and we will provide you with the most relevant answers.
-          </p>
-          <p className="mb-3 text-center leading-6">
-            Start by entering your query in the input field below and we will do
-            the rest!
-          </p>
-        </>
-      )}
-      <div
-        className={`mt-0 flex flex-wrap items-center justify-center gap-2 sm:mt-1 sm:gap-4 md:gap-4 lg:gap-0`}
-      >
-        {/* first */}
-        <div className="h-auto rounded-[50px] bg-gradient-to-l from-[#6EE7B7]/70  via-[#3B82F6] to-[#9333EA]/50 p-1 dark:from-[#D16FF8] dark:via-[#48E6E0] dark:to-[#C85EF6] lg:h-60  lg:rounded-tr-none lg:rounded-br-none">
-          <div
-            className={`h-full rounded-[45px] bg-white dark:bg-dark-charcoal p-${
-              isMobile ? '3.5' : '6 py-8'
-            }  lg:rounded-tr-none lg:rounded-br-none`}
-          >
-            {/* Add Mobile check here */}
-            {isMobile ? (
-              <div className="flex justify-center">
-                <img
-                  src={
-                    isDarkTheme ? '/message-text-dark.svg' : '/message-text.svg'
-                  }
-                  alt="lock"
-                  className="h-[24px] w-[24px] "
-                />
-                <h2 className="mb-0 pl-1 text-lg font-bold">
-                  Chat with Your Data
-                </h2>
-              </div>
-            ) : (
-              <>
-                <img
-                  src={
-                    isDarkTheme ? '/message-text-dark.svg' : '/message-text.svg'
-                  }
-                  alt="lock"
-                  className="h-[24px] w-[24px]"
-                />
-                <h2 className="mt-2 mb-3 text-lg font-bold">
-                  Chat with Your Data
-                </h2>
-              </>
-            )}
-            <p
-              className={
-                isMobile
-                  ? `w-[250px] text-center text-xs text-gray-500 dark:text-bright-gray`
-                  : `w-[250px] text-xs text-gray-500 dark:text-bright-gray`
-              }
-            >
-              DocsGPT will use your data to answer questions. Whether its
-              documentation, source code, or Microsoft files, DocsGPT allows you
-              to have interactive conversations and find answers based on the
-              provided data.
-            </p>
-          </div>
-        </div>
-        {/* second */}
-        <div className="h-auto rounded-[50px] bg-gradient-to-r from-[#6EE7B7]/70 via-[#3B82F6] to-[#9333EA]/50 p-1 dark:from-[#D16FF8] dark:via-[#48E6E0] dark:to-[#C85EF6] lg:h-60  lg:rounded-none  lg:py-1 lg:px-0">
-          <div
-            className={`h-full rounded-[45px] bg-white dark:bg-dark-charcoal p-${
-              isMobile ? '3.5' : '6 py-6'
-            }  lg:rounded-none`}
-          >
-            {/* Add Mobile check here */}
-            {isMobile ? (
-              <div className="flex justify-center ">
-                <img
-                  src={isDarkTheme ? '/lock-dark.svg' : '/lock.svg'}
-                  alt="lock"
-                  className="h-[24px] w-[24px]"
-                />
-                <h2 className="mb-0 pl-1 text-lg font-bold">
-                  Secure Data Storage
-                </h2>
-              </div>
-            ) : (
-              <>
-                <img
-                  src={isDarkTheme ? '/lock-dark.svg' : '/lock.svg'}
-                  alt="lock"
-                  className="h-[24px] w-[24px]"
-                />
-                <h2 className="mt-2 mb-3 text-lg font-bold">
-                  Secure Data Storage
-                </h2>
-              </>
-            )}
-            <p
-              className={
-                isMobile
-                  ? `w-[250px] text-center text-xs text-gray-500 dark:text-bright-gray`
-                  : `w-[250px] text-xs text-gray-500 dark:text-bright-gray`
-              }
-            >
-              The security of your data is our top priority. DocsGPT ensures the
-              utmost protection for your sensitive information. With secure data
-              storage and privacy measures in place, you can trust that your
-              data is kept safe and confidential.
-            </p>
-          </div>
-        </div>
-        {/* third */}
-        <div className="h-auto rounded-[50px] bg-gradient-to-l from-[#6EE7B7]/70  via-[#3B82F6] to-[#9333EA]/50 p-1 dark:from-[#D16FF8] dark:via-[#48E6E0] dark:to-[#C85EF6] lg:h-60 lg:rounded-tl-none lg:rounded-bl-none ">
-          <div
-            className={`firefox h-full rounded-[45px] bg-white dark:bg-dark-charcoal p-${
-              isMobile ? '3.5' : '6 px-6 '
-            } lg:rounded-tl-none lg:rounded-bl-none`}
-          >
-            {/* Add Mobile check here */}
-            {isMobile ? (
-              <div className="flex justify-center">
-                <img
-                  src={
-                    isDarkTheme
-                      ? 'message-programming-dark.svg'
-                      : '/message-programming.svg'
-                  }
-                  alt="lock"
-                  className="h-[24px] w-[24px]"
-                />
-                <h2 className="mb-0 pl-1 text-lg font-bold">
-                  Open Source Code
-                </h2>
-              </div>
-            ) : (
-              <>
-                <img
-                  src={
-                    isDarkTheme
-                      ? '/message-programming-dark.svg'
-                      : '/message-programming.svg'
-                  }
-                  alt="lock"
-                  className="h-[24px] w-[24px]"
-                />
-                <h2 className="mt-2 mb-3 text-lg font-bold">
-                  Open Source Code
-                </h2>
-              </>
-            )}
-            <p
-              className={
-                isMobile
-                  ? `w-[250px] text-center text-xs text-gray-500 dark:text-bright-gray`
-                  : `w-[250px] text-xs text-gray-500 dark:text-bright-gray`
-              }
-            >
-              DocsGPT is built on open source principles, promoting transparency
-              and collaboration. The source code is freely available, enabling
-              developers to contribute, enhance, and customize the app to meet
-              their specific needs.
-            </p>
-          </div>
-        </div>
+      <div className="mb-16 grid w-full grid-cols-1 items-center gap-4 self-center text-xs sm:w-auto sm:gap-6  md:mb-0 md:text-sm lg:grid-cols-2">
+        {demos?.map(
+          (demo: { header: string; query: string }, key: number) =>
+            demo.header &&
+            demo.query && (
+              <Fragment key={key}>
+                <button
+                  onClick={() => handleQuestion({ question: demo.query })}
+                  className="w-full rounded-full border-2 border-silver px-6 py-4 text-left hover:border-gray-4000 dark:hover:border-gray-3000 xl:min-w-[24vw]"
+                >
+                  <p className="mb-1 font-semibold text-black dark:text-silver">
+                    {demo.header}
+                  </p>
+                  <span className="text-gray-400">{demo.query}</span>
+                </button>
+              </Fragment>
+            ),
+        )}
       </div>
     </div>
   );

frontend/src/Navigation.tsx

@@ -1,25 +1,21 @@
 import { useEffect, useRef, useState } from 'react';
 import { useDispatch, useSelector } from 'react-redux';
 import { NavLink, useNavigate } from 'react-router-dom';
-import PropTypes from 'prop-types';
 import DocsGPT3 from './assets/cute_docsgpt3.svg';
-import Documentation from './assets/documentation.svg';
-import DocumentationDark from './assets/documentation-dark.svg';
 import Discord from './assets/discord.svg';
-import DiscordDark from './assets/discord-dark.svg';
 import Expand from './assets/expand.svg';
 import Github from './assets/github.svg';
-import GithubDark from './assets/github-dark.svg';
 import Hamburger from './assets/hamburger.svg';
 import HamburgerDark from './assets/hamburger-dark.svg';
 import Info from './assets/info.svg';
-import InfoDark from './assets/info-dark.svg';
 import SettingGear from './assets/settingGear.svg';
-import SettingGearDark from './assets/settingGear-dark.svg';
+import Twitter from './assets/TwitterX.svg';
 import Add from './assets/add.svg';
 import UploadIcon from './assets/upload.svg';
 import { ActiveState } from './models/misc';
 import APIKeyModal from './preferences/APIKeyModal';
+import DeleteConvModal from './modals/DeleteConvModal';
+
 import {
   selectApiKeyStatus,
   selectSelectedDocs,
@@ -29,6 +25,9 @@ import {
   selectConversations,
   setConversations,
   selectConversationId,
+  selectModalStateDeleteConv,
+  setModalStateDeleteConv,
+  setSourceDocs,
 } from './preferences/preferenceSlice';
 import {
   setConversation,
@@ -36,40 +35,43 @@ import {
 } from './conversation/conversationSlice';
 import { useMediaQuery, useOutsideAlerter } from './hooks';
 import Upload from './upload/Upload';
-import { Doc, getConversations } from './preferences/preferenceApi';
+import { Doc, getConversations, getDocs } from './preferences/preferenceApi';
 import SelectDocsModal from './preferences/SelectDocsModal';
 import ConversationTile from './conversation/ConversationTile';
 import { useDarkTheme } from './hooks';
 import SourceDropdown from './components/SourceDropdown';
-
+import { useTranslation } from 'react-i18next';
 interface NavigationProps {
   navOpen: boolean;
   setNavOpen: React.Dispatch<React.SetStateAction<boolean>>;
 }
-const NavImage: React.FC<{
+/* const NavImage: React.FC<{
   Light: string | undefined;
   Dark: string | undefined;
 }> = ({ Light, Dark }) => {
   return (
     <>
       <img src={Dark} alt="icon" className="ml-2 hidden w-5 dark:block " />
-      <img src={Light} alt="icon" className="ml-2 w-5 dark:hidden " />
+      <img src={Light} alt="icon" className="ml-2 w-5 dark:hidden filter dark:invert" />
     </>
   );
 };
 NavImage.propTypes = {
   Light: PropTypes.string,
   Dark: PropTypes.string,
-};
+}; */
 export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
   const dispatch = useDispatch();
   const docs = useSelector(selectSourceDocs);
   const selectedDocs = useSelector(selectSelectedDocs);
   const conversations = useSelector(selectConversations);
+  const modalStateDeleteConv = useSelector(selectModalStateDeleteConv);
   const conversationId = useSelector(selectConversationId);
+
   const { isMobile } = useMediaQuery();
   const [isDarkTheme] = useDarkTheme();
   const [isDocsListOpen, setIsDocsListOpen] = useState(false);
+  const { t } = useTranslation();
 
   const isApiKeySet = useSelector(selectApiKeyStatus);
   const [apiKeyModalState, setApiKeyModalState] =
@@ -92,6 +94,7 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
       fetchConversations();
     }
   }, [conversations, dispatch]);
+
   async function fetchConversations() {
     return await getConversations()
       .then((fetchedConversations) => {
@@ -102,6 +105,16 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
       });
   }
 
+  const handleDeleteAllConversations = () => {
+    fetch(`${apiHost}/api/delete_all_conversations`, {
+      method: 'POST',
+    })
+      .then(() => {
+        fetchConversations();
+      })
+      .catch((error) => console.error(error));
+  };
+
   const handleDeleteConversation = (id: string) => {
     fetch(`${apiHost}/api/delete_conversation?id=${id}`, {
       method: 'POST',
@@ -112,19 +125,29 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
       .catch((error) => console.error(error));
   };
 
-  const handleDeleteClick = (index: number, doc: Doc) => {
-    const docPath = 'indexes/' + 'local' + '/' + doc.name;
+  const handleDeleteClick = (doc: Doc) => {
+    const docPath = `indexes/local/${doc.name}`;
 
     fetch(`${apiHost}/api/delete_old?path=${docPath}`, {
       method: 'GET',
     })
       .then(() => {
         // remove the image element from the DOM
-        const imageElement = document.querySelector(
-          `#img-${index}`,
-        ) as HTMLElement;
-        const parentElement = imageElement.parentNode as HTMLElement;
-        parentElement.parentNode?.removeChild(parentElement);
+        // const imageElement = document.querySelector(
+        //   `#img-${index}`,
+        // ) as HTMLElement;
+        // const parentElement = imageElement.parentNode as HTMLElement;
+        // parentElement.parentNode?.removeChild(parentElement);
+
+        return getDocs();
+      })
+      .then((updatedDocs) => {
+        dispatch(setSourceDocs(updatedDocs));
+        dispatch(
+          setSelectedDocs(
+            updatedDocs?.find((doc) => doc.name.toLowerCase() === 'default'),
+          ),
+        );
       })
       .catch((error) => console.error(error));
   };
@@ -254,13 +277,15 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
             className="opacity-80 group-hover:opacity-100"
           />
           <p className=" text-sm text-dove-gray group-hover:text-neutral-600 dark:text-chinese-silver dark:group-hover:text-bright-gray">
-            New Chat
+            {t('newChat')}
           </p>
         </NavLink>
-        <div className="mb-auto h-[56vh] overflow-y-auto overflow-x-hidden dark:text-white">
-          {conversations && (
+        <div className="mb-auto h-[78vh] overflow-y-auto overflow-x-hidden dark:text-white">
+          {conversations && conversations.length > 0 ? (
             <div>
-              <p className="ml-6 mt-3 text-sm font-semibold">Chats</p>
+              <div className=" my-auto mx-4 mt-2 flex h-6 items-center justify-between gap-4 rounded-3xl">
+                <p className="mt-1 ml-4 text-sm font-semibold">{t('chats')}</p>
+              </div>
               <div className="conversations-container">
                 {conversations?.map((conversation) => (
                   <ConversationTile
@@ -275,12 +300,14 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
                 ))}
               </div>
             </div>
+          ) : (
+            <></>
           )}
         </div>
 
         <div className="flex h-auto flex-col justify-end text-eerie-black dark:text-white">
           <div className="flex flex-col-reverse border-b-[1px] dark:border-b-purple-taupe">
-            <div className="relative my-4 flex gap-2 px-2">
+            <div className="relative my-4 mx-4 flex gap-2">
               <SourceDropdown
                 options={docs}
                 selectedDocs={selectedDocs}
@@ -295,66 +322,84 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
                 onClick={() => setUploadModalState('ACTIVE')}
               ></img>
             </div>
-            <p className="ml-6 mt-3 text-sm font-semibold">Source Docs</p>
+            <p className="ml-5 mt-3 text-sm font-semibold">{t('sourceDocs')}</p>
           </div>
           <div className="flex flex-col gap-2 border-b-[1px] py-2 dark:border-b-purple-taupe">
             <NavLink
               to="/settings"
               className={({ isActive }) =>
-                `my-auto mx-4 flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-purple-taupe ${
+                `my-auto mx-4 flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-[#28292E] ${
                   isActive ? 'bg-gray-3000 dark:bg-transparent' : ''
                 }`
               }
             >
-              <NavImage Light={SettingGear} Dark={SettingGearDark} />
+              <img
+                src={SettingGear}
+                alt="icon"
+                className="ml-2 w-5 filter dark:invert"
+              />
               <p className="my-auto text-sm text-eerie-black  dark:text-white">
-                Settings
+                {t('settings.label')}
               </p>
             </NavLink>
           </div>
-
-          <div className="flex flex-col gap-2 border-b-[1.5px] py-2 dark:border-b-purple-taupe">
+          <div className="flex justify-between gap-2 border-b-[1.5px] py-2 dark:border-b-purple-taupe">
             <NavLink
               to="/about"
               className={({ isActive }) =>
-                `my-auto mx-4 flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-purple-taupe ${
-                  isActive ? 'bg-gray-3000 dark:bg-purple-taupe' : ''
+                `my-auto mx-4 flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-[#28292E] ${
+                  isActive ? 'bg-gray-3000 dark:bg-[#28292E]' : ''
                 }`
               }
             >
-              <NavImage Light={Info} Dark={InfoDark} />
-              <p className="my-auto text-sm">About</p>
+              <img
+                src={Info}
+                alt="icon"
+                className="ml-2 w-5 filter dark:invert"
+              />
+              <p className="my-auto pr-1 text-sm">{t('about')}</p>
             </NavLink>
-
-            <a
-              href="https://docs.docsgpt.co.uk/"
-              target="_blank"
-              rel="noreferrer"
-              className="my-auto mx-4 flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-purple-taupe"
-            >
-              <NavImage Light={Documentation} Dark={DocumentationDark} />
-              <p className="my-auto text-sm ">Documentation</p>
-            </a>
-            <a
-              href="https://discord.gg/WHJdfbQDR4"
-              target="_blank"
-              rel="noreferrer"
-              className="my-auto mx-4 flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-purple-taupe"
-            >
-              <NavImage Light={Discord} Dark={DiscordDark} />
-              {/*  <img src={isDarkTheme ? DiscordDark : Discord} alt="discord-link" className="ml-2 w-5" /> */}
-              <p className="my-auto text-sm">Visit our Discord</p>
-            </a>
-
-            <a
-              href="https://github.com/arc53/DocsGPT"
-              target="_blank"
-              rel="noreferrer"
-              className="mx-4 mt-auto flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-purple-taupe"
-            >
-              <NavImage Light={Github} Dark={GithubDark} />
-              <p className="my-auto text-sm">Visit our Github</p>
-            </a>
+            <div className="flex items-center justify-evenly gap-1 px-1">
+              <NavLink
+                target="_blank"
+                to={'https://discord.gg/WHJdfbQDR4'}
+                className={
+                  'rounded-full hover:bg-gray-100 dark:hover:bg-[#28292E]'
+                }
+              >
+                <img
+                  src={Discord}
+                  alt="discord"
+                  className="m-2 w-6 self-center filter dark:invert"
+                />
+              </NavLink>
+              <NavLink
+                target="_blank"
+                to={'https://twitter.com/docsgptai'}
+                className={
+                  'rounded-full hover:bg-gray-100 dark:hover:bg-[#28292E]'
+                }
+              >
+                <img
+                  src={Twitter}
+                  alt="x"
+                  className="m-2 w-5 self-center filter dark:invert"
+                />
+              </NavLink>
+              <NavLink
+                target="_blank"
+                to={'https://github.com/arc53/docsgpt'}
+                className={
+                  'rounded-full hover:bg-gray-100 dark:hover:bg-[#28292E]'
+                }
+              >
+                <img
+                  src={Github}
+                  alt="github"
+                  className="m-2 w-6 self-center filter dark:invert"
+                />
+              </NavLink>
+            </div>
           </div>
         </div>
       </div>
@@ -370,6 +415,7 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
           />
         </button>
       </div>
+
       <SelectDocsModal
         modalState={selectedDocsModalState}
         setModalState={setSelectedDocsModalState}
@@ -380,6 +426,11 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
         setModalState={setApiKeyModalState}
         isCancellable={isApiKeySet}
       />
+      <DeleteConvModal
+        modalState={modalStateDeleteConv}
+        setModalState={setModalStateDeleteConv}
+        handleDeleteAllConv={handleDeleteAllConversations}
+      />
       <Upload
         modalState={uploadModalState}
         setModalState={setUploadModalState}

frontend/src/PageNotFound.tsx

@@ -2,11 +2,11 @@ import { Link } from 'react-router-dom';
 
 export default function PageNotFound() {
   return (
-    <div className="mx-5 grid min-h-screen md:mx-36">
-      <p className="mx-auto my-auto mt-20 flex w-full max-w-6xl flex-col place-items-center gap-6 rounded-3xl bg-gray-100 p-6 text-jet lg:p-10 xl:p-16">
+    <div className="grid min-h-screen dark:bg-raisin-black">
+      <p className="mx-auto my-auto mt-20 flex w-full max-w-6xl flex-col place-items-center gap-6 rounded-3xl bg-gray-100 p-6 text-jet dark:bg-outer-space dark:text-gray-100 lg:p-10 xl:p-16">
         <h1>404</h1>
         <p>The page you are looking for does not exist.</p>
-        <button className="pointer-cursor mr-4 flex cursor-pointer items-center justify-center rounded-full bg-blue-1000 py-2 px-4 text-white hover:bg-blue-3000">
+        <button className="pointer-cursor mr-4 flex cursor-pointer items-center justify-center rounded-full bg-blue-1000 py-2 px-4 text-white transition-colors duration-100 hover:bg-blue-3000">
           <Link to="/">Go Back Home</Link>
         </button>
       </p>

frontend/src/assets/TwitterX.svg

@@ -0,0 +1,3 @@
+<svg width="18" height="18" viewBox="0 0 18 18" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M14.175 0.843262H16.9354L10.9054 7.75269L18 17.1564H12.4457L8.09229 11.4543L3.11657 17.1564H0.353571L6.80271 9.76355L0 0.844547H5.69571L9.62486 6.05555L14.175 0.843262ZM13.2043 15.5004H14.7343L4.86 2.41312H3.21943L13.2043 15.5004Z" fill="#747474"/>
+</svg>

frontend/src/assets/red-trash.svg

@@ -0,0 +1,3 @@
+<svg width="24" height="25" viewBox="0 0 24 25" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M6.66427 17.7747C6.66427 18.2167 6.84488 18.6406 7.16637 18.9532C7.48787 19.2658 7.9239 19.4413 8.37856 19.4413H15.2357C15.6904 19.4413 16.1264 19.2658 16.4479 18.9532C16.7694 18.6406 16.95 18.2167 16.95 17.7747V7.77468H6.66427V17.7747ZM8.37856 9.44135H15.2357V17.7747H8.37856V9.44135ZM14.8071 5.27468L13.95 4.44135H9.66427L8.80713 5.27468H5.80713V6.94135H17.8071V5.27468H14.8071Z" fill="#D30000"/>
+</svg>

frontend/src/assets/share.svg

@@ -0,0 +1,3 @@
+<svg width="14" height="17" viewBox="0 0 14 17" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M2.04167 7.2997C1.96431 7.2997 1.89013 7.32976 1.83543 7.38326C1.78073 7.43677 1.75 7.50934 1.75 7.585V15.0029C1.75 15.1604 1.88067 15.2882 2.04167 15.2882H11.9583C12.0357 15.2882 12.1099 15.2581 12.1646 15.2046C12.2193 15.1511 12.25 15.0785 12.25 15.0029V7.585C12.25 7.50934 12.2193 7.43677 12.1646 7.38326C12.1099 7.32976 12.0357 7.2997 11.9583 7.2997H10.7917C10.5596 7.2997 10.337 7.20952 10.1729 7.04901C10.0089 6.8885 9.91667 6.67079 9.91667 6.44379C9.91667 6.21679 10.0089 5.99909 10.1729 5.83857C10.337 5.67806 10.5596 5.58788 10.7917 5.58788H11.9583C13.0853 5.58788 14 6.48259 14 7.585V15.0029C14 15.5325 13.7849 16.0405 13.402 16.4151C13.0191 16.7896 12.4998 17 11.9583 17H2.04167C1.50018 17 0.980877 16.7896 0.59799 16.4151C0.215104 16.0405 0 15.5325 0 15.0029V7.585C0 6.48259 0.914667 5.58788 2.04167 5.58788H3.20833C3.4404 5.58788 3.66296 5.67806 3.82705 5.83857C3.99115 5.99909 4.08333 6.21679 4.08333 6.44379C4.08333 6.67079 3.99115 6.8885 3.82705 7.04901C3.66296 7.20952 3.4404 7.2997 3.20833 7.2997H2.04167ZM6.7935 0.0838185C6.82059 0.0572492 6.85278 0.0361694 6.88821 0.0217864C6.92365 0.0074035 6.96164 0 7 0C7.03836 0 7.07635 0.0074035 7.11179 0.0217864C7.14722 0.0361694 7.17941 0.0572492 7.2065 0.0838185L10.5852 3.38877C10.6261 3.42867 10.6539 3.47955 10.6652 3.53496C10.6765 3.59037 10.6707 3.64782 10.6486 3.70001C10.6265 3.75221 10.589 3.7968 10.541 3.82815C10.4929 3.85949 10.4364 3.87617 10.3787 3.87607H7.875V10.438C7.875 10.665 7.78281 10.8827 7.61872 11.0433C7.45462 11.2038 7.23206 11.2939 7 11.2939C6.76794 11.2939 6.54538 11.2038 6.38128 11.0433C6.21719 10.8827 6.125 10.665 6.125 10.438V3.87607H3.62133C3.56357 3.87617 3.50708 3.85949 3.45902 3.82815C3.41096 3.7968 3.37349 3.75221 3.35138 3.70001C3.32926 3.64782 3.32348 3.59037 3.33478 3.53496C3.34607 3.47955 3.37394 3.42867 3.41483 3.38877L6.7935 0.0838185Z" fill="#747474"/>
+</svg>

frontend/src/assets/spinner-dark.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="1em" height="1em" viewBox="0 0 24 24"><path fill="white" d="M10.72,19.9a8,8,0,0,1-6.5-9.79A7.77,7.77,0,0,1,10.4,4.16a8,8,0,0,1,9.49,6.52A1.54,1.54,0,0,0,21.38,12h.13a1.37,1.37,0,0,0,1.38-1.54,11,11,0,1,0-12.7,12.39A1.54,1.54,0,0,0,12,21.34h0A1.47,1.47,0,0,0,10.72,19.9Z"><animateTransform attributeName="transform" dur="0.75s" repeatCount="indefinite" type="rotate" values="0 12 12;360 12 12"/></path></svg>

frontend/src/assets/spinner.svg

@@ -1,9 +1 @@
-<svg width="30" height="33" viewBox="0 0 30 33" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
-<rect width="30" height="33" fill="none"/>
-<defs>
-<pattern id="pattern0" patternContentUnits="objectBoundingBox" width="1" height="1">
-<use xlink:href="#image0_1_917" transform="scale(0.0166667 0.0151515)"/>
-</pattern>
-<image id="image0_1_917" width="60" height="66" xlink:href="data:image/png;base64,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"/>
-</defs>
-</svg>
+<svg xmlns="http://www.w3.org/2000/svg" width="1em" height="1em" viewBox="0 0 24 24"><path fill="black" d="M10.72,19.9a8,8,0,0,1-6.5-9.79A7.77,7.77,0,0,1,10.4,4.16a8,8,0,0,1,9.49,6.52A1.54,1.54,0,0,0,21.38,12h.13a1.37,1.37,0,0,0,1.38-1.54,11,11,0,1,0-12.7,12.39A1.54,1.54,0,0,0,12,21.34h0A1.47,1.47,0,0,0,10.72,19.9Z"><animateTransform attributeName="transform" dur="0.75s" repeatCount="indefinite" type="rotate" values="0 12 12;360 12 12"/></path></svg>

frontend/src/assets/three-dots.svg

@@ -0,0 +1,3 @@
+<svg width="10" height="25" viewBox="0 0 10 25" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M5 3.5C3.9 3.5 3 4.4 3 5.5C3 6.6 3.9 7.5 5 7.5C6.1 7.5 7 6.6 7 5.5C7 4.4 6.1 3.5 5 3.5ZM5 17.5C3.9 17.5 3 18.4 3 19.5C3 20.6 3.9 21.5 5 21.5C6.1 21.5 7 20.6 7 19.5C7 18.4 6.1 17.5 5 17.5ZM5 10.5C3.9 10.5 3 11.4 3 12.5C3 13.6 3.9 14.5 5 14.5C6.1 14.5 7 13.6 7 12.5C7 11.4 6.1 10.5 5 10.5Z" fill="#747474"/>
+</svg>

frontend/src/components/CopyButton.tsx

@@ -0,0 +1,45 @@
+import { useState } from 'react';
+import Copy from './../assets/copy.svg?react';
+import CheckMark from './../assets/checkmark.svg?react';
+import copy from 'copy-to-clipboard';
+
+export default function CoppyButton({ text }: { text: string }) {
+  const [copied, setCopied] = useState(false);
+  const [isCopyHovered, setIsCopyHovered] = useState(false);
+
+  const handleCopyClick = (text: string) => {
+    copy(text);
+    setCopied(true);
+    // Reset copied to false after a few seconds
+    setTimeout(() => {
+      setCopied(false);
+    }, 3000);
+  };
+
+  return (
+    <div
+      className={`flex items-center justify-center rounded-full p-2 ${
+        isCopyHovered
+          ? 'bg-[#EEEEEE] dark:bg-purple-taupe'
+          : 'bg-[#ffffff] dark:bg-transparent'
+      }`}
+    >
+      {copied ? (
+        <CheckMark
+          className="cursor-pointer stroke-green-2000"
+          onMouseEnter={() => setIsCopyHovered(true)}
+          onMouseLeave={() => setIsCopyHovered(false)}
+        />
+      ) : (
+        <Copy
+          className="cursor-pointer fill-none"
+          onClick={() => {
+            handleCopyClick(text);
+          }}
+          onMouseEnter={() => setIsCopyHovered(true)}
+          onMouseLeave={() => setIsCopyHovered(false)}
+        />
+      )}
+    </div>
+  );
+}

frontend/src/components/Dropdown.tsx

@@ -1,4 +1,4 @@
-import { useState } from 'react';
+import React from 'react';
 import Arrow2 from '../assets/dropdown-arrow.svg';
 import Edit from '../assets/edit.svg';
 import Trash from '../assets/trash.svg';
@@ -9,34 +9,59 @@ function Dropdown({
   onSelect,
   size = 'w-32',
   rounded = 'xl',
+  border = 'border-2',
+  borderColor = 'silver',
   showEdit,
   onEdit,
   showDelete,
   onDelete,
   placeholder,
-  fullWidth,
-  alignMidddle,
 }: {
   options:
     | string[]
     | { name: string; id: string; type: string }[]
-    | { label: string; value: string }[];
-  selectedValue: string | { label: string; value: string } | null;
+    | { label: string; value: string }[]
+    | { value: number; description: string }[];
+  selectedValue:
+    | string
+    | { label: string; value: string }
+    | { value: number; description: string }
+    | null;
   onSelect:
     | ((value: string) => void)
     | ((value: { name: string; id: string; type: string }) => void)
-    | ((value: { label: string; value: string }) => void);
+    | ((value: { label: string; value: string }) => void)
+    | ((value: { value: number; description: string }) => void);
   size?: string;
   rounded?: 'xl' | '3xl';
+  border?: 'border' | 'border-2';
+  borderColor?: string;
   showEdit?: boolean;
   onEdit?: (value: { name: string; id: string; type: string }) => void;
   showDelete?: boolean;
   onDelete?: (value: string) => void;
   placeholder?: string;
-  fullWidth?: boolean;
-  alignMidddle?: boolean;
 }) {
-  const [isOpen, setIsOpen] = useState(false);
+  const dropdownRef = React.useRef<HTMLDivElement>(null);
+  const [isOpen, setIsOpen] = React.useState(false);
+  const borderRadius = rounded === 'xl' ? 'rounded-xl' : 'rounded-3xl';
+  const borderTopRadius = rounded === 'xl' ? 'rounded-t-xl' : 'rounded-t-3xl';
+
+  const handleClickOutside = (event: MouseEvent) => {
+    if (
+      dropdownRef.current &&
+      !dropdownRef.current.contains(event.target as Node)
+    ) {
+      setIsOpen(false);
+    }
+  };
+
+  React.useEffect(() => {
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => {
+      document.removeEventListener('mousedown', handleClickOutside);
+    };
+  }, []);
   return (
     <div
       className={[
@@ -45,11 +70,12 @@ function Dropdown({
           : 'relative align-middle',
         size,
       ].join(' ')}
+      ref={dropdownRef}
     >
       <button
         onClick={() => setIsOpen(!isOpen)}
-        className={`flex w-full cursor-pointer items-center justify-between border-2 border-silver bg-white px-5 py-3 dark:border-chinese-silver dark:bg-transparent ${
-          isOpen ? `rounded-t-${rounded}` : `rounded-${rounded}`
+        className={`flex w-full cursor-pointer items-center justify-between ${border} border-${borderColor} bg-white px-5 py-3 dark:border-${borderColor}/40 dark:bg-transparent ${
+          isOpen ? `${borderTopRadius}` : `${borderRadius}`
         }`}
       >
         {typeof selectedValue === 'string' ? (
@@ -58,14 +84,18 @@ function Dropdown({
           </span>
         ) : (
           <span
-            className={`${
-              alignMidddle && 'flex-1'
-            } overflow-hidden text-ellipsis dark:text-bright-gray ${
+            className={`overflow-hidden text-ellipsis dark:text-bright-gray ${
               !selectedValue && 'text-silver dark:text-gray-400'
             }`}
           >
-            {selectedValue
+            {selectedValue && 'label' in selectedValue
               ? selectedValue.label
+              : selectedValue && 'description' in selectedValue
+              ? `${
+                  selectedValue.value < 1e9
+                    ? selectedValue.value + ` (${selectedValue.description})`
+                    : selectedValue.description
+                }`
               : placeholder
               ? placeholder
               : 'From URL'}
@@ -80,7 +110,9 @@ function Dropdown({
         />
       </button>
       {isOpen && (
-        <div className="absolute left-0 right-0 z-20 -mt-1 max-h-40 overflow-y-auto rounded-b-xl border-2 bg-white shadow-lg dark:border-chinese-silver dark:bg-dark-charcoal">
+        <div
+          className={`absolute left-0 right-0 z-20 -mt-1 max-h-40 overflow-y-auto rounded-b-xl ${border} border-${borderColor} bg-white shadow-lg dark:border-${borderColor}/40 dark:bg-dark-charcoal`}
+        >
           {options.map((option: any, index) => (
             <div
               key={index}
@@ -91,13 +123,19 @@ function Dropdown({
                   onSelect(option);
                   setIsOpen(false);
                 }}
-                className="ml-2 flex-1 overflow-hidden overflow-ellipsis whitespace-nowrap py-3 dark:text-light-gray"
+                className="ml-5 flex-1 overflow-hidden overflow-ellipsis whitespace-nowrap py-3 dark:text-light-gray"
               >
                 {typeof option === 'string'
                   ? option
                   : option.name
                   ? option.name
-                  : option.label}
+                  : option.label
+                  ? option.label
+                  : `${
+                      option.value < 1e9
+                        ? option.value + ` (${option.description})`
+                        : option.description
+                    }`}
               </span>
               {showEdit && onEdit && (
                 <img

frontend/src/components/Input.tsx

@@ -0,0 +1,43 @@
+import { InputProps } from './types';
+
+const Input = ({
+  id,
+  name,
+  type,
+  value,
+  isAutoFocused = false,
+  placeholder,
+  maxLength,
+  className,
+  colorVariant = 'silver',
+  children,
+  onChange,
+  onPaste,
+  onKeyDown,
+}: InputProps) => {
+  const colorStyles = {
+    silver: 'border-silver dark:border-silver/40',
+    jet: 'border-jet',
+    gray: 'border-gray-5000 dark:text-silver',
+  };
+
+  return (
+    <input
+      className={`h-[42px] w-full rounded-full border-2 px-3 outline-none dark:bg-transparent dark:text-white ${className} ${colorStyles[colorVariant]}`}
+      type={type}
+      id={id}
+      name={name}
+      autoFocus={isAutoFocused}
+      placeholder={placeholder}
+      maxLength={maxLength}
+      value={value}
+      onChange={onChange}
+      onPaste={onPaste}
+      onKeyDown={onKeyDown}
+    >
+      {children}
+    </input>
+  );
+};
+
+export default Input;

frontend/src/components/RetryIcon.tsx

@@ -0,0 +1,17 @@
+import * as React from 'react';
+import { SVGProps } from 'react';
+const RetryIcon = (props: SVGProps<SVGSVGElement>) => (
+  <svg
+    xmlns="http://www.w3.org/2000/svg"
+    xmlSpace="preserve"
+    width={16}
+    height={16}
+    fill={props.fill}
+    stroke={props.stroke}
+    viewBox="0 0 383.748 383.748"
+    {...props}
+  >
+    <path d="M62.772 95.042C90.904 54.899 137.496 30 187.343 30c83.743 0 151.874 68.13 151.874 151.874h30C369.217 81.588 287.629 0 187.343 0c-35.038 0-69.061 9.989-98.391 28.888a182.423 182.423 0 0 0-47.731 44.705L2.081 34.641v113.365h113.91L62.772 95.042zM381.667 235.742h-113.91l53.219 52.965c-28.132 40.142-74.724 65.042-124.571 65.042-83.744 0-151.874-68.13-151.874-151.874h-30c0 100.286 81.588 181.874 181.874 181.874 35.038 0 69.062-9.989 98.391-28.888a182.443 182.443 0 0 0 47.731-44.706l39.139 38.952V235.742z" />
+  </svg>
+);
+export default RetryIcon;

frontend/src/components/SourceDropdown.tsx

@@ -1,8 +1,9 @@
+import React from 'react';
 import Trash from '../assets/trash.svg';
 import Arrow2 from '../assets/dropdown-arrow.svg';
 import { Doc } from '../preferences/preferenceApi';
 import { useDispatch } from 'react-redux';
-
+import { useTranslation } from 'react-i18next';
 type Props = {
   options: Doc[] | null;
   selectedDocs: Doc | null;
@@ -21,6 +22,8 @@ function SourceDropdown({
   handleDeleteClick,
 }: Props) {
   const dispatch = useDispatch();
+  const { t } = useTranslation();
+  const dropdownRef = React.useRef<HTMLDivElement>(null);
   const embeddingsName =
     import.meta.env.VITE_EMBEDDINGS_NAME ||
     'huggingface_sentence-transformers/all-mpnet-base-v2';
@@ -30,18 +33,35 @@ function SourceDropdown({
     setIsDocsListOpen(false);
   };
 
+  const handleClickOutside = (event: MouseEvent) => {
+    if (
+      dropdownRef.current &&
+      !dropdownRef.current.contains(event.target as Node)
+    ) {
+      setIsDocsListOpen(false);
+    }
+  };
+
+  React.useEffect(() => {
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => {
+      document.removeEventListener('mousedown', handleClickOutside);
+    };
+  }, []);
   return (
-    <div className="relative w-5/6 rounded-3xl">
+    <div className="relative w-5/6 rounded-3xl" ref={dropdownRef}>
       <button
         onClick={() => setIsDocsListOpen(!isDocsListOpen)}
-        className={`flex w-full cursor-pointer items-center  border-2 bg-white p-3 dark:border-chinese-silver dark:bg-transparent ${
-          isDocsListOpen ? 'rounded-t-3xl' : 'rounded-3xl'
+        className={`flex w-full cursor-pointer items-center border border-silver bg-white p-[14px] dark:bg-transparent ${
+          isDocsListOpen
+            ? 'rounded-t-3xl dark:border-silver/40'
+            : 'rounded-3xl dark:border-purple-taupe'
         }`}
       >
         <span className="ml-1 mr-2 flex-1 overflow-hidden text-ellipsis text-left dark:text-bright-gray">
           <div className="flex flex-row gap-2">
             <p className="max-w-3/4 truncate whitespace-nowrap">
-              {selectedDocs?.name || ''}
+              {selectedDocs?.name || 'None'}
             </p>
             <p className="flex flex-col items-center justify-center">
               {selectedDocs?.version}
@@ -57,7 +77,7 @@ function SourceDropdown({
         />
       </button>
       {isDocsListOpen && (
-        <div className="absolute left-0 right-0 z-50 -mt-1 max-h-40 overflow-y-auto rounded-b-xl border-2 bg-white  shadow-lg dark:border-chinese-silver dark:bg-dark-charcoal">
+        <div className="absolute left-0 right-0 z-50 -mt-1 max-h-40 overflow-y-auto rounded-b-xl border border-silver bg-white shadow-lg dark:border-silver/40 dark:bg-dark-charcoal">
           {options ? (
             options.map((option: any, index: number) => {
               if (option.model === embeddingsName) {
@@ -86,7 +106,7 @@ function SourceDropdown({
                         id={`img-${index}`}
                         onClick={(event) => {
                           event.stopPropagation();
-                          handleDeleteClick(index, option);
+                          handleDeleteClick(option);
                         }}
                       />
                     )}
@@ -95,16 +115,14 @@ function SourceDropdown({
               }
             })
           ) : (
-            <div className="h-10 w-full cursor-pointer  border-b-[1px] hover:bg-gray-100 dark:border-b-purple-taupe dark:hover:bg-purple-taupe">
-              <p className="ml-5 py-3">No default documentation.</p>
-            </div>
+            <></>
           )}
           <div
             className="flex cursor-pointer items-center justify-between hover:bg-gray-100 dark:text-bright-gray dark:hover:bg-purple-taupe"
             onClick={handleEmptyDocumentSelect}
           >
             <span className="ml-4 flex-1 overflow-hidden overflow-ellipsis whitespace-nowrap py-3">
-              Empty
+              {t('none')}
             </span>
           </div>
         </div>

frontend/src/components/types/index.ts

@@ -0,0 +1,21 @@
+export type InputProps = {
+  type: 'text' | 'number';
+  value: string | string[] | number;
+  colorVariant?: 'silver' | 'jet' | 'gray';
+  isAutoFocused?: boolean;
+  id?: string;
+  maxLength?: number;
+  name?: string;
+  placeholder?: string;
+  className?: string;
+  children?: React.ReactElement;
+  onChange: (
+    e: React.ChangeEvent<HTMLTextAreaElement | HTMLInputElement>,
+  ) => void;
+  onPaste?: (
+    e: React.ClipboardEvent<HTMLTextAreaElement | HTMLInputElement>,
+  ) => void;
+  onKeyDown?: (
+    e: React.KeyboardEvent<HTMLTextAreaElement | HTMLInputElement>,
+  ) => void;
+};

frontend/src/conversation/Conversation.tsx

@@ -11,15 +11,23 @@ import {
   selectStatus,
   updateQuery,
 } from './conversationSlice';
+import { selectConversationId } from '../preferences/preferenceSlice';
 import Send from './../assets/send.svg';
 import SendDark from './../assets/send_dark.svg';
 import Spinner from './../assets/spinner.svg';
+import SpinnerDark from './../assets/spinner-dark.svg';
 import { FEEDBACK, Query } from './conversationModels';
 import { sendFeedback } from './conversationApi';
+import { useTranslation } from 'react-i18next';
 import ArrowDown from './../assets/arrow-down.svg';
+import RetryIcon from '../components/RetryIcon';
+import ShareIcon from '../assets/share.svg';
+import { ShareConversationModal } from '../modals/ShareConversationModal';
+
 export default function Conversation() {
   const queries = useSelector(selectQueries);
   const status = useSelector(selectStatus);
+  const conversationId = useSelector(selectConversationId);
   const dispatch = useDispatch<AppDispatch>();
   const endMessageRef = useRef<HTMLDivElement>(null);
   const inputRef = useRef<HTMLDivElement>(null);
@@ -27,6 +35,9 @@ export default function Conversation() {
   const [hasScrolledToLast, setHasScrolledToLast] = useState(true);
   const fetchStream = useRef<any>(null);
   const [eventInterrupt, setEventInterrupt] = useState(false);
+  const [lastQueryReturnedErr, setLastQueryReturnedErr] = useState(false);
+  const [isShareModalOpen, setShareModalState] = useState<boolean>(false);
+  const { t } = useTranslation();
 
   const handleUserInterruption = () => {
     if (!eventInterrupt && status === 'loading') setEventInterrupt(true);
@@ -70,6 +81,13 @@ export default function Conversation() {
     };
   }, [endMessageRef.current]);
 
+  useEffect(() => {
+    if (queries.length) {
+      queries[queries.length - 1].error && setLastQueryReturnedErr(true);
+      queries[queries.length - 1].response && setLastQueryReturnedErr(false); //considering a query that initially returned error can later include a response property on retry
+    }
+  }, [queries[queries.length - 1]]);
+
   const scrollIntoView = () => {
     endMessageRef?.current?.scrollIntoView({
       behavior: 'smooth',
@@ -77,13 +95,20 @@ export default function Conversation() {
     });
   };
 
-  const handleQuestion = (question: string) => {
+  const handleQuestion = ({
+    question,
+    isRetry = false,
+  }: {
+    question: string;
+    isRetry?: boolean;
+  }) => {
     question = question.trim();
     if (question === '') return;
     setEventInterrupt(false);
-    dispatch(addQuery({ prompt: question }));
+    !isRetry && dispatch(addQuery({ prompt: question })); //dispatch only new queries
     fetchStream.current = dispatch(fetchAnswer({ question }));
   };
+
   const handleFeedback = (query: Query, feedback: FEEDBACK, index: number) => {
     const prevFeedback = query.feedback;
     dispatch(updateQuery({ index, query: { feedback } }));
@@ -92,19 +117,32 @@ export default function Conversation() {
     );
   };
 
+  const handleQuestionSubmission = () => {
+    if (inputRef.current?.textContent && status !== 'loading') {
+      if (lastQueryReturnedErr) {
+        // update last failed query with new prompt
+        dispatch(
+          updateQuery({
+            index: queries.length - 1,
+            query: {
+              prompt: inputRef.current.textContent,
+            },
+          }),
+        );
+        handleQuestion({
+          question: queries[queries.length - 1].prompt,
+          isRetry: true,
+        });
+      } else {
+        handleQuestion({ question: inputRef.current.textContent });
+      }
+      inputRef.current.textContent = '';
+    }
+  };
+
   const prepResponseView = (query: Query, index: number) => {
     let responseView;
-    if (query.error) {
-      responseView = (
-        <ConversationBubble
-          ref={endMessageRef}
-          className={`${index === queries.length - 1 ? 'mb-32' : 'mb-7'}`}
-          key={`${index}ERROR`}
-          message={query.error}
-          type="ERROR"
-        ></ConversationBubble>
-      );
-    } else if (query.response) {
+    if (query.response) {
       responseView = (
         <ConversationBubble
           ref={endMessageRef}
@@ -119,6 +157,35 @@ export default function Conversation() {
           }
         ></ConversationBubble>
       );
+    } else if (query.error) {
+      const retryBtn = (
+        <button
+          className="flex items-center justify-center gap-3 self-center rounded-full border border-silver py-3 px-5  text-lg text-gray-500 transition-colors delay-100 hover:border-gray-500 disabled:cursor-not-allowed dark:text-bright-gray"
+          disabled={status === 'loading'}
+          onClick={() => {
+            handleQuestion({
+              question: queries[queries.length - 1].prompt,
+              isRetry: true,
+            });
+          }}
+        >
+          <RetryIcon
+            fill={isDarkTheme ? 'rgb(236 236 241)' : 'rgb(107 114 120)'}
+            stroke={isDarkTheme ? 'rgb(236 236 241)' : 'rgb(107 114 120)'}
+          />
+          Retry
+        </button>
+      );
+      responseView = (
+        <ConversationBubble
+          ref={endMessageRef}
+          className={`${index === queries.length - 1 ? 'mb-32' : 'mb-7'} `}
+          key={`${index}ERROR`}
+          message={query.error}
+          type="ERROR"
+          retryBtn={retryBtn}
+        ></ConversationBubble>
+      );
     }
     return responseView;
   };
@@ -130,86 +197,109 @@ export default function Conversation() {
   };
 
   return (
-    <div
-      onWheel={handleUserInterruption}
-      onTouchMove={handleUserInterruption}
-      className="flex w-full flex-col justify-center p-4 md:flex-row"
-    >
-      {queries.length > 0 && !hasScrolledToLast && (
-        <button
-          onClick={scrollIntoView}
-          aria-label="scroll to bottom"
-          className="fixed bottom-32 right-14 z-10 flex h-7 w-7  items-center justify-center rounded-full border-[0.5px] border-gray-alpha bg-gray-100 bg-opacity-50 dark:bg-purple-taupe md:h-9 md:w-9 md:bg-opacity-100 "
-        >
-          <img
-            src={ArrowDown}
-            alt="arrow down"
-            className="h4- w-4 opacity-50 md:h-5 md:w-5"
-          />
-        </button>
+    <div className="flex h-screen flex-col gap-7 pb-2">
+      {conversationId && (
+        <>
+          <button
+            title="Share"
+            onClick={() => {
+              setShareModalState(true);
+            }}
+            className="fixed top-4 right-20 z-30 rounded-full hover:bg-bright-gray dark:hover:bg-[#28292E]"
+          >
+            <img
+              className="m-2 h-5 w-5 filter dark:invert"
+              alt="share"
+              src={ShareIcon}
+            />
+          </button>
+          {isShareModalOpen && (
+            <ShareConversationModal
+              close={() => {
+                setShareModalState(false);
+              }}
+              conversationId={conversationId}
+            />
+          )}
+        </>
       )}
+      <div
+        onWheel={handleUserInterruption}
+        onTouchMove={handleUserInterruption}
+        className="flex h-[90%] w-full flex-1 justify-center overflow-y-auto p-4 md:h-[83vh]"
+      >
+        {queries.length > 0 && !hasScrolledToLast && (
+          <button
+            onClick={scrollIntoView}
+            aria-label="scroll to bottom"
+            className="fixed bottom-40 right-14 z-10 flex h-7 w-7  items-center justify-center rounded-full border-[0.5px] border-gray-alpha bg-gray-100 bg-opacity-50 dark:bg-purple-taupe md:h-9 md:w-9 md:bg-opacity-100 "
+          >
+            <img
+              src={ArrowDown}
+              alt="arrow down"
+              className="h-4 w-4 opacity-50 md:h-5 md:w-5"
+            />
+          </button>
+        )}
 
-      {queries.length > 0 && (
-        <div className="mt-20 mb-9 flex flex-col transition-all md:w-3/4">
-          {queries.map((query, index) => {
-            return (
-              <Fragment key={index}>
-                <ConversationBubble
-                  className={'mb-7 last:mb-28'}
-                  key={`${index}QUESTION`}
-                  message={query.prompt}
-                  type="QUESTION"
-                  sources={query.sources}
-                ></ConversationBubble>
-                {prepResponseView(query, index)}
-              </Fragment>
-            );
-          })}
-        </div>
-      )}
-      {queries.length === 0 && <Hero className="mt-24 md:mt-52"></Hero>}
-      <div className="absolute bottom-0 flex w-11/12 flex-col items-end self-center bg-white pt-4 dark:bg-raisin-black md:fixed md:w-[65%]">
-        <div className="flex h-full w-full">
+        {queries.length > 0 && (
+          <div className="mt-16 w-full md:w-8/12">
+            {queries.map((query, index) => {
+              return (
+                <Fragment key={index}>
+                  <ConversationBubble
+                    className={'mb-1 last:mb-28 md:mb-7'}
+                    key={`${index}QUESTION`}
+                    message={query.prompt}
+                    type="QUESTION"
+                    sources={query.sources}
+                  ></ConversationBubble>
+
+                  {prepResponseView(query, index)}
+                </Fragment>
+              );
+            })}
+          </div>
+        )}
+
+        {queries.length === 0 && <Hero handleQuestion={handleQuestion} />}
+      </div>
+
+      <div className="flex w-11/12 flex-col items-end self-center rounded-2xl bg-opacity-0 pb-1 sm:w-6/12">
+        <div className="flex h-full w-full items-center rounded-[40px] border border-silver bg-white py-1 dark:bg-raisin-black">
           <div
             id="inputbox"
             ref={inputRef}
             tabIndex={1}
-            placeholder="Type your message here..."
+            placeholder={t('inputPlaceholder')}
             contentEditable
             onPaste={handlePaste}
-            className={`border-000000 max-h-24 min-h-[2.6rem] w-full overflow-y-auto overflow-x-hidden whitespace-pre-wrap rounded-3xl border bg-white py-2 pl-4 pr-9 text-base leading-7 opacity-100 focus:outline-none dark:bg-raisin-black dark:text-bright-gray`}
+            className={`inputbox-style max-h-24 w-full overflow-y-auto overflow-x-hidden whitespace-pre-wrap rounded-full bg-white pt-5 pb-[22px] text-base leading-tight opacity-100 focus:outline-none dark:bg-raisin-black dark:text-bright-gray`}
             onKeyDown={(e) => {
               if (e.key === 'Enter' && !e.shiftKey) {
                 e.preventDefault();
-                if (inputRef.current?.textContent && status !== 'loading') {
-                  handleQuestion(inputRef.current.textContent);
-                  inputRef.current.textContent = '';
-                }
+                handleQuestionSubmission();
               }
             }}
           ></div>
           {status === 'loading' ? (
             <img
-              src={Spinner}
-              className="relative right-[38px] bottom-[7px] -mr-[30px] animate-spin cursor-pointer self-end bg-transparent"
+              src={isDarkTheme ? SpinnerDark : Spinner}
+              className="relative right-[38px] bottom-[24px] -mr-[30px] animate-spin cursor-pointer self-end bg-transparent"
             ></img>
           ) : (
-            <div className="relative right-[43px] bottom-[7px] -mr-[35px] h-[35px] w-[35px] cursor-pointer self-end rounded-full hover:bg-gray-3000">
+            <div className="mx-1 cursor-pointer rounded-full p-3 text-center hover:bg-gray-3000">
               <img
-                className="ml-[9px] mt-[9px] text-white"
-                onClick={() => {
-                  if (inputRef.current?.textContent) {
-                    handleQuestion(inputRef.current.textContent);
-                    inputRef.current.textContent = '';
-                  }
-                }}
+                className="ml-[4px] h-6 w-6 text-white "
+                onClick={handleQuestionSubmission}
                 src={isDarkTheme ? SendDark : Send}
               ></img>
             </div>
           )}
         </div>
-        <p className="text-gray-595959 w-[100vw] self-center bg-white bg-transparent p-5 text-center text-xs dark:bg-raisin-black dark:text-bright-gray md:w-full">
-          DocsGPT uses GenAI, please review critial information using sources.
+
+        <p className="text-gray-595959 hidden w-[100vw] self-center  bg-white bg-transparent py-2 text-center text-xs dark:bg-raisin-black dark:text-bright-gray md:inline md:w-full">
+          {t('tagline')}
         </p>
       </div>
     </div>

frontend/src/conversation/ConversationBubble.tsx

@@ -1,15 +1,14 @@
 import { forwardRef, useState } from 'react';
 import Avatar from '../components/Avatar';
+import CopyButton from '../components/CopyButton';
 import remarkGfm from 'remark-gfm';
 import { FEEDBACK, MESSAGE_TYPE } from './conversationModels';
 import classes from './ConversationBubble.module.css';
 import Alert from './../assets/alert.svg';
 import Like from './../assets/like.svg?react';
 import Dislike from './../assets/dislike.svg?react';
-import Copy from './../assets/copy.svg?react';
-import CheckMark from './../assets/checkmark.svg?react';
+
 import ReactMarkdown from 'react-markdown';
-import copy from 'copy-to-clipboard';
 import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter';
 import { vscDarkPlus } from 'react-syntax-highlighter/dist/cjs/styles/prism';
 import DocsGPT3 from '../assets/cute_docsgpt3.svg';
@@ -23,24 +22,15 @@ const ConversationBubble = forwardRef<
     className?: string;
     feedback?: FEEDBACK;
     handleFeedback?: (feedback: FEEDBACK) => void;
-    sources?: { title: string; text: string }[];
+    sources?: { title: string; text: string; source: string }[];
+    retryBtn?: React.ReactElement;
   }
 >(function ConversationBubble(
-  { message, type, className, feedback, handleFeedback, sources },
+  { message, type, className, feedback, handleFeedback, sources, retryBtn },
   ref,
 ) {
   const [openSource, setOpenSource] = useState<number | null>(null);
-  const [copied, setCopied] = useState(false);
 
-  const handleCopyClick = (text: string) => {
-    copy(text);
-    setCopied(true);
-    // Reset copied to false after a few seconds
-    setTimeout(() => {
-      setCopied(false);
-    }, 3000);
-  };
-  const [isCopyHovered, setIsCopyHovered] = useState(false);
   const [isLikeHovered, setIsLikeHovered] = useState(false);
   const [isDislikeHovered, setIsDislikeHovered] = useState(false);
   const [isLikeClicked, setIsLikeClicked] = useState(false);
@@ -52,8 +42,8 @@ const ConversationBubble = forwardRef<
     bubble = (
       <div ref={ref} className={`flex flex-row-reverse self-end ${className}`}>
         <Avatar className="mt-2 text-2xl" avatar="🧑‍💻"></Avatar>
-        <div className="mr-2 ml-10 flex items-center rounded-3xl bg-purple-30 p-3.5 text-white">
-          <ReactMarkdown className="whitespace-pre-wrap break-all">
+        <div className="ml-10 mr-2 flex items-center rounded-[28px] bg-purple-30 py-[14px] px-[19px] text-white">
+          <ReactMarkdown className="whitespace-pre-wrap break-normal leading-normal">
             {message}
           </ReactMarkdown>
         </div>
@@ -63,7 +53,7 @@ const ConversationBubble = forwardRef<
     bubble = (
       <div
         ref={ref}
-        className={`flex flex-wrap self-start ${className} group flex-col pr-20  dark:text-bright-gray`}
+        className={`flex flex-wrap self-start ${className} group flex-col  dark:text-bright-gray`}
       >
         <div className="flex flex-wrap self-start lg:flex-nowrap">
           <Avatar
@@ -78,31 +68,46 @@ const ConversationBubble = forwardRef<
           />
 
           <div
-            className={`ml-2 mr-5 flex max-w-[90vw] rounded-3xl bg-gray-1000 p-3.5 dark:bg-gun-metal md:max-w-[70vw] lg:max-w-[50vw] ${
+            className={`ml-2 mr-5 flex max-w-[90vw] rounded-[28px] bg-gray-1000 py-[14px] px-7 dark:bg-gun-metal md:max-w-[70vw] lg:max-w-[50vw] ${
               type === 'ERROR'
-                ? 'flex-row items-center rounded-full border border-transparent bg-[#FFE7E7] p-2 py-5 text-sm font-normal text-red-3000  dark:border-red-2000 dark:text-white'
+                ? 'relative flex-row items-center rounded-full border border-transparent bg-[#FFE7E7] p-2 py-5 text-sm font-normal text-red-3000  dark:border-red-2000 dark:text-white'
                 : 'flex-col rounded-3xl'
             }`}
           >
             {type === 'ERROR' && (
-              <img src={Alert} alt="alert" className="mr-2 inline" />
+              <>
+                <img src={Alert} alt="alert" className="mr-2 inline" />
+                <div className="absolute -right-32 top-1/2 -translate-y-1/2">
+                  {retryBtn}
+                </div>
+              </>
             )}
             <ReactMarkdown
-              className="whitespace-pre-wrap break-words"
+              className="whitespace-pre-wrap break-normal leading-normal"
               remarkPlugins={[remarkGfm]}
               components={{
                 code({ node, inline, className, children, ...props }) {
                   const match = /language-(\w+)/.exec(className || '');
 
                   return !inline && match ? (
-                    <SyntaxHighlighter
-                      PreTag="div"
-                      language={match[1]}
-                      {...props}
-                      style={vscDarkPlus}
-                    >
-                      {String(children).replace(/\n$/, '')}
-                    </SyntaxHighlighter>
+                    <div className="group relative">
+                      <SyntaxHighlighter
+                        PreTag="div"
+                        language={match[1]}
+                        {...props}
+                        style={vscDarkPlus}
+                      >
+                        {String(children).replace(/\n$/, '')}
+                      </SyntaxHighlighter>
+                      <div
+                        className={`absolute right-3 top-3 lg:invisible 
+                        ${type !== 'ERROR' ? 'group-hover:lg:visible' : ''} `}
+                      >
+                        <CopyButton
+                          text={String(children).replace(/\n$/, '')}
+                        />
+                      </div>
+                    </div>
                   ) : (
                     <code className={className ? className : ''} {...props}>
                       {children}
@@ -172,13 +177,19 @@ const ConversationBubble = forwardRef<
                     {sources?.map((source, index) => (
                       <div
                         key={index}
-                        className={`max-w-fit cursor-pointer rounded-[28px] py-1 px-4 ${
+                        className={`max-w-xs cursor-pointer rounded-[28px] px-4 py-1 sm:max-w-sm md:max-w-md ${
                           openSource === index
                             ? 'bg-[#007DFF]'
                             : 'bg-[#D7EBFD] hover:bg-[#BFE1FF]'
                         }`}
                         onClick={() =>
-                          setOpenSource(openSource === index ? null : index)
+                          source.source !== 'local'
+                            ? window.open(
+                                source.source,
+                                '_blank',
+                                'noopener, noreferrer',
+                              )
+                            : setOpenSource(openSource === index ? null : index)
                         }
                       >
                         <p
@@ -197,109 +208,89 @@ const ConversationBubble = forwardRef<
               </>
             )}
           </div>
-          <div className="flex justify-center">
-            <div
-              className={`relative mr-5  block items-center justify-center lg:invisible 
+        </div>
+        <div className="my-2 flex justify-start lg:ml-12">
+          <div
+            className={`relative mr-5  block items-center justify-center lg:invisible 
             ${type !== 'ERROR' ? 'group-hover:lg:visible' : ''}`}
-            >
-              <div className="absolute left-2 top-4">
-                <div
-                  className={`flex items-center justify-center rounded-full p-2 
-                ${
-                  isCopyHovered
-                    ? 'bg-[#EEEEEE] dark:bg-purple-taupe'
-                    : 'bg-[#ffffff] dark:bg-transparent'
-                }`}
-                >
-                  {copied ? (
-                    <CheckMark
-                      className="cursor-pointer stroke-green-2000"
-                      onMouseEnter={() => setIsCopyHovered(true)}
-                      onMouseLeave={() => setIsCopyHovered(false)}
-                    />
-                  ) : (
-                    <Copy
-                      className={`cursor-pointer fill-none`}
-                      onClick={() => {
-                        handleCopyClick(message);
-                      }}
-                      onMouseEnter={() => setIsCopyHovered(true)}
-                      onMouseLeave={() => setIsCopyHovered(false)}
-                    ></Copy>
-                  )}
-                </div>
-              </div>
+          >
+            <div>
+              <CopyButton text={message} />
             </div>
-            <div
-              className={`relative mr-5 flex items-center justify-center ${
-                !isLikeClicked ? 'lg:invisible' : ''
-              } ${
-                feedback === 'LIKE' || type !== 'ERROR'
-                  ? 'group-hover:lg:visible'
-                  : ''
-              }`}
-            >
-              <div className="absolute left-6 top-4">
-                <div
-                  className={`flex items-center justify-center rounded-full p-2 dark:bg-transparent ${
-                    isLikeHovered
-                      ? 'bg-[#EEEEEE] dark:bg-purple-taupe'
-                      : 'bg-[#ffffff] dark:bg-transparent'
-                  }`}
-                >
-                  <Like
-                    className={`cursor-pointer 
+          </div>
+          {handleFeedback && (
+            <>
+              <div
+                className={`relative mr-5 flex items-center justify-center ${
+                  !isLikeClicked ? 'lg:invisible' : ''
+                } ${
+                  feedback === 'LIKE' || type !== 'ERROR'
+                    ? 'group-hover:lg:visible'
+                    : ''
+                }`}
+              >
+                <div>
+                  <div
+                    className={`flex items-center justify-center rounded-full p-2 dark:bg-transparent ${
+                      isLikeHovered
+                        ? 'bg-[#EEEEEE] dark:bg-purple-taupe'
+                        : 'bg-[#ffffff] dark:bg-transparent'
+                    }`}
+                  >
+                    <Like
+                      className={`cursor-pointer 
                   ${
                     isLikeClicked || feedback === 'LIKE'
                       ? 'fill-white-3000 stroke-purple-30 dark:fill-transparent'
                       : 'fill-none  stroke-gray-4000'
                   }`}
-                    onClick={() => {
-                      handleFeedback?.('LIKE');
-                      setIsLikeClicked(true);
-                      setIsDislikeClicked(false);
-                    }}
-                    onMouseEnter={() => setIsLikeHovered(true)}
-                    onMouseLeave={() => setIsLikeHovered(false)}
-                  ></Like>
+                      onClick={() => {
+                        handleFeedback?.('LIKE');
+                        setIsLikeClicked(true);
+                        setIsDislikeClicked(false);
+                      }}
+                      onMouseEnter={() => setIsLikeHovered(true)}
+                      onMouseLeave={() => setIsLikeHovered(false)}
+                    ></Like>
+                  </div>
                 </div>
               </div>
-            </div>
-            <div
-              className={`mr-13 relative flex items-center justify-center ${
-                !isDislikeClicked ? 'lg:invisible' : ''
-              } ${
-                feedback === 'DISLIKE' || type !== 'ERROR'
-                  ? 'group-hover:lg:visible'
-                  : ''
-              }`}
-            >
-              <div className="absolute left-10 top-4">
-                <div
-                  className={`flex items-center justify-center rounded-full p-2 ${
-                    isDislikeHovered
-                      ? 'bg-[#EEEEEE] dark:bg-purple-taupe'
-                      : 'bg-[#ffffff] dark:bg-transparent'
-                  }`}
-                >
-                  <Dislike
-                    className={`cursor-pointer ${
-                      isDislikeClicked || feedback === 'DISLIKE'
-                        ? 'fill-white-3000 stroke-red-2000 dark:fill-transparent'
-                        : 'fill-none  stroke-gray-4000'
+              <div
+                className={`mr-13 relative flex items-center justify-center ${
+                  !isDislikeClicked ? 'lg:invisible' : ''
+                } ${
+                  feedback === 'DISLIKE' || type !== 'ERROR'
+                    ? 'group-hover:lg:visible'
+                    : ''
+                }`}
+              >
+                <div>
+                  <div
+                    className={`flex items-center justify-center rounded-full p-2 ${
+                      isDislikeHovered
+                        ? 'bg-[#EEEEEE] dark:bg-purple-taupe'
+                        : 'bg-[#ffffff] dark:bg-transparent'
                     }`}
-                    onClick={() => {
-                      handleFeedback?.('DISLIKE');
-                      setIsDislikeClicked(true);
-                      setIsLikeClicked(false);
-                    }}
-                    onMouseEnter={() => setIsDislikeHovered(true)}
-                    onMouseLeave={() => setIsDislikeHovered(false)}
-                  ></Dislike>
+                  >
+                    <Dislike
+                      className={`cursor-pointer ${
+                        isDislikeClicked || feedback === 'DISLIKE'
+                          ? 'fill-white-3000 stroke-red-2000 dark:fill-transparent'
+                          : 'fill-none  stroke-gray-4000'
+                      }`}
+                      onClick={() => {
+                        handleFeedback?.('DISLIKE');
+                        setIsDislikeClicked(true);
+                        setIsLikeClicked(false);
+                      }}
+                      onMouseEnter={() => setIsDislikeHovered(true)}
+                      onMouseLeave={() => setIsDislikeHovered(false)}
+                    ></Dislike>
+                  </div>
                 </div>
               </div>
-            </div>
-          </div>
+            </>
+          )}
         </div>
 
         {sources && openSource !== null && sources[openSource] && (

frontend/src/conversation/ConversationTile.tsx

@@ -5,11 +5,15 @@ import Exit from '../assets/exit.svg';
 import Message from '../assets/message.svg';
 import MessageDark from '../assets/message-dark.svg';
 import { useDarkTheme } from '../hooks';
+import ConfirmationModal from '../modals/ConfirmationModal';
 import CheckMark2 from '../assets/checkMark2.svg';
-import Trash from '../assets/trash.svg';
-
+import Trash from '../assets/red-trash.svg';
+import Share from '../assets/share.svg';
+import threeDots from '../assets/three-dots.svg';
 import { selectConversationId } from '../preferences/preferenceSlice';
-
+import { ActiveState } from '../models/misc';
+import { ShareConversationModal } from '../modals/ShareConversationModal';
+import { useTranslation } from 'react-i18next';
 interface ConversationProps {
   name: string;
   id: string;
@@ -32,22 +36,19 @@ export default function ConversationTile({
   const [isDarkTheme] = useDarkTheme();
   const [isEdit, setIsEdit] = useState(false);
   const [conversationName, setConversationsName] = useState('');
-  // useOutsideAlerter(
-  //   tileRef,
-  //   () =>
-  //     handleSaveConversation({
-  //       id: conversationId || conversation.id,
-  //       name: conversationName,
-  //     }),
-  //   [conversationName],
-  // );
-
+  const [isOpen, setOpen] = useState<boolean>(false);
+  const [isShareModalOpen, setShareModalState] = useState<boolean>(false);
+  const [deleteModalState, setDeleteModalState] =
+    useState<ActiveState>('INACTIVE');
+  const menuRef = useRef<HTMLDivElement>(null);
+  const { t } = useTranslation();
   useEffect(() => {
     setConversationsName(conversation.name);
   }, [conversation.name]);
 
   function handleEditConversation() {
     setIsEdit(true);
+    setOpen(false);
   }
 
   function handleSaveConversation(changedConversation: ConversationProps) {
@@ -59,6 +60,18 @@ export default function ConversationTile({
     }
   }
 
+  const handleClickOutside = (event: MouseEvent) => {
+    if (menuRef.current && !menuRef.current.contains(event.target as Node)) {
+      setOpen(false);
+    }
+  };
+
+  useEffect(() => {
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => {
+      document.removeEventListener('mousedown', handleClickOutside);
+    };
+  }, []);
   function onClear() {
     setConversationsName(conversation.name);
     setIsEdit(false);
@@ -69,9 +82,9 @@ export default function ConversationTile({
       onClick={() => {
         selectConversation(conversation.id);
       }}
-      className={`my-auto mx-4 mt-4 flex h-9 cursor-pointer items-center justify-between gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-purple-taupe ${
+      className={`my-auto mx-4 mt-4 flex h-9 cursor-pointer items-center justify-between gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-[#28292E] ${
         conversationId === conversation.id
-          ? 'bg-gray-100 dark:bg-purple-taupe'
+          ? 'bg-gray-100 dark:bg-[#28292E]'
           : ''
       }`}
     >
@@ -88,7 +101,7 @@ export default function ConversationTile({
           <input
             autoFocus
             type="text"
-            className="h-6 w-full px-1 text-sm font-normal leading-6 outline-[#0075FF] focus:outline-1"
+            className="h-6 w-full bg-transparent px-1 text-sm font-normal leading-6 focus:outline-[#0075FF]"
             value={conversationName}
             onChange={(e) => setConversationsName(e.target.value)}
           />
@@ -99,36 +112,108 @@ export default function ConversationTile({
         )}
       </div>
       {conversationId === conversation.id && (
-        <div className="flex text-white dark:text-[#949494]">
-          <img
-            src={isEdit ? CheckMark2 : Edit}
-            alt="Edit"
-            className="mr-2 h-4 w-4 cursor-pointer text-white hover:opacity-50"
-            id={`img-${conversation.id}`}
-            onClick={(event) => {
-              event.stopPropagation();
-              isEdit
-                ? handleSaveConversation({
+        <div className="flex text-white dark:text-[#949494]" ref={menuRef}>
+          {isEdit ? (
+            <div className="flex gap-1">
+              <img
+                src={CheckMark2}
+                alt="Edit"
+                className="mr-2 h-4 w-4 cursor-pointer text-white hover:opacity-50"
+                id={`img-${conversation.id}`}
+                onClick={(event) => {
+                  event.stopPropagation();
+                  handleSaveConversation({
                     id: conversationId,
                     name: conversationName,
-                  })
-                : handleEditConversation();
-            }}
-          />
-          <img
-            src={isEdit ? Exit : Trash}
-            alt="Exit"
-            className={`mr-4 ${
-              isEdit ? 'h-3 w-3' : 'h-4 w-4'
-            }mt-px  cursor-pointer hover:opacity-50`}
-            id={`img-${conversation.id}`}
-            onClick={(event) => {
-              event.stopPropagation();
-              isEdit ? onClear() : onDeleteConversation(conversation.id);
-            }}
-          />
+                  });
+                }}
+              />
+              <img
+                src={isEdit ? Exit : Trash}
+                alt="Exit"
+                className={`mr-4 mt-px h-3 w-3 cursor-pointer hover:opacity-50`}
+                id={`img-${conversation.id}`}
+                onClick={(event) => {
+                  event.stopPropagation();
+                  onClear();
+                }}
+              />
+            </div>
+          ) : (
+            <button onClick={() => setOpen(!isOpen)}>
+              <img src={threeDots} className="mr-4 w-2" />
+            </button>
+          )}
+          {isOpen && (
+            <div className="flex-start absolute flex w-32 translate-x-1 translate-y-5 flex-col rounded-xl bg-stone-100 text-sm text-black shadow-xl dark:bg-chinese-black dark:text-chinese-silver md:w-36">
+              <button
+                onClick={() => {
+                  setShareModalState(true);
+                  setOpen(false);
+                }}
+                className="flex-start flex items-center gap-4 rounded-t-xl p-3 hover:bg-bright-gray dark:hover:bg-dark-charcoal"
+              >
+                <img
+                  src={Share}
+                  alt="Share"
+                  width={14}
+                  height={14}
+                  className="cursor-pointer hover:opacity-50"
+                  id={`img-${conversation.id}`}
+                />
+                <span>{t('convTile.share')}</span>
+              </button>
+              <button
+                onClick={(event) => {
+                  handleEditConversation();
+                }}
+                className="flex-start flex items-center gap-4 p-3 hover:bg-bright-gray dark:hover:bg-dark-charcoal"
+              >
+                <img
+                  src={Edit}
+                  alt="Edit"
+                  width={16}
+                  height={16}
+                  className="cursor-pointer hover:opacity-50"
+                  id={`img-${conversation.id}`}
+                />
+                <span>{t('convTile.rename')}</span>
+              </button>
+              <button
+                onClick={(event) => {
+                  setDeleteModalState('ACTIVE');
+                  setOpen(false);
+                }}
+                className="flex-start flex items-center gap-3 rounded-b-xl p-2 text-red-700 hover:bg-bright-gray dark:hover:bg-dark-charcoal"
+              >
+                <img
+                  src={Trash}
+                  alt="Edit"
+                  width={24}
+                  height={24}
+                  className="cursor-pointer hover:opacity-50"
+                />
+                <span>{t('convTile.delete')}</span>
+              </button>
+            </div>
+          )}
         </div>
       )}
+      <ConfirmationModal
+        message={t('convTile.deleteWarning')}
+        modalState={deleteModalState}
+        setModalState={setDeleteModalState}
+        handleSubmit={() => onDeleteConversation(conversation.id)}
+        submitLabel={t('convTile.delete')}
+      />
+      {isShareModalOpen && conversationId && (
+        <ShareConversationModal
+          close={() => {
+            setShareModalState(false);
+          }}
+          conversationId={conversationId}
+        />
+      )}
     </div>
   );
 }

frontend/src/conversation/SharedConversation.tsx

@@ -0,0 +1,144 @@
+import { useState, useEffect } from 'react';
+import { useParams } from 'react-router-dom';
+import { useNavigate } from 'react-router-dom';
+import { Query } from './conversationModels';
+import { useTranslation } from 'react-i18next';
+import ConversationBubble from './ConversationBubble';
+import { Fragment } from 'react';
+const apiHost = import.meta.env.VITE_API_HOST || 'https://docsapi.arc53.com';
+const SharedConversation = () => {
+  const params = useParams();
+  const navigate = useNavigate();
+  const { identifier } = params; //identifier is a uuid, not conversationId
+  const [queries, setQueries] = useState<Query[]>([]);
+  const [title, setTitle] = useState('');
+  const [date, setDate] = useState('');
+  const { t } = useTranslation();
+  function formatISODate(isoDateStr: string) {
+    const date = new Date(isoDateStr);
+
+    const monthNames = [
+      'Jan',
+      'Feb',
+      'Mar',
+      'Apr',
+      'May',
+      'June',
+      'July',
+      'Aug',
+      'Sept',
+      'Oct',
+      'Nov',
+      'Dec',
+    ];
+
+    const month = monthNames[date.getMonth()];
+    const day = date.getDate();
+    const year = date.getFullYear();
+
+    let hours = date.getHours();
+    const minutes = date.getMinutes();
+    const ampm = hours >= 12 ? 'PM' : 'AM';
+
+    hours = hours % 12;
+    hours = hours ? hours : 12;
+    const minutesStr = minutes < 10 ? '0' + minutes : minutes;
+    const formattedDate = `Published ${month} ${day}, ${year} at ${hours}:${minutesStr} ${ampm}`;
+    return formattedDate;
+  }
+  const fetchQueris = () => {
+    fetch(`${apiHost}/api/shared_conversation/${identifier}`)
+      .then((res) => {
+        if (res.status === 404 || res.status === 400) navigate('/pagenotfound');
+        return res.json();
+      })
+      .then((data) => {
+        if (data.success) {
+          setQueries(data.queries);
+          setTitle(data.title);
+          setDate(formatISODate(data.timestamp));
+        }
+      });
+  };
+
+  const prepResponseView = (query: Query, index: number) => {
+    let responseView;
+    if (query.response) {
+      responseView = (
+        <ConversationBubble
+          className={`${index === queries.length - 1 ? 'mb-32' : 'mb-7'}`}
+          key={`${index}ANSWER`}
+          message={query.response}
+          type={'ANSWER'}
+        ></ConversationBubble>
+      );
+    } else if (query.error) {
+      responseView = (
+        <ConversationBubble
+          className={`${index === queries.length - 1 ? 'mb-32' : 'mb-7'} `}
+          key={`${index}ERROR`}
+          message={query.error}
+          type="ERROR"
+        ></ConversationBubble>
+      );
+    }
+    return responseView;
+  };
+  useEffect(() => {
+    fetchQueris();
+  }, []);
+
+  return (
+    <div className="flex h-full flex-col items-center justify-between gap-2 overflow-y-hidden dark:bg-raisin-black">
+      <div className="flex w-full justify-center overflow-auto">
+        <div className="mt-0 w-11/12 md:w-10/12 lg:w-6/12">
+          <div className="mb-2 w-full border-b pb-2">
+            <h1 className="font-semi-bold text-4xl text-chinese-black dark:text-chinese-silver">
+              {title}
+            </h1>
+            <h2 className="font-semi-bold text-base text-chinese-black dark:text-chinese-silver">
+              {t('sharedConv.subtitle')}{' '}
+              <a href="/" className="text-[#007DFF]">
+                DocsGPT
+              </a>
+            </h2>
+            <h2 className="font-semi-bold text-base text-chinese-black dark:text-chinese-silver">
+              {date}
+            </h2>
+          </div>
+          <div className="">
+            {queries?.map((query, index) => {
+              return (
+                <Fragment key={index}>
+                  <ConversationBubble
+                    className={'mb-1 last:mb-28 md:mb-7'}
+                    key={`${index}QUESTION`}
+                    message={query.prompt}
+                    type="QUESTION"
+                    sources={query.sources}
+                  ></ConversationBubble>
+
+                  {prepResponseView(query, index)}
+                </Fragment>
+              );
+            })}
+          </div>
+        </div>
+      </div>
+
+      <div className=" flex flex-col items-center gap-4 pb-2">
+        <button
+          onClick={() => navigate('/')}
+          className="w-fit rounded-full bg-purple-30 p-4 text-white shadow-xl transition-colors duration-200 hover:bg-purple-taupe"
+        >
+          {t('sharedConv.button')}
+        </button>
+        <span className="hidden text-xs text-dark-charcoal dark:text-silver sm:inline">
+          {t('sharedConv.meta')}
+        </span>
+      </div>
+    </div>
+  );
+};
+
+export default SharedConversation;

frontend/src/conversation/conversationApi.ts

@@ -3,31 +3,7 @@ import { Doc } from '../preferences/preferenceApi';
 
 const apiHost = import.meta.env.VITE_API_HOST || 'https://docsapi.arc53.com';
 
-export function fetchAnswerApi(
-  question: string,
-  signal: AbortSignal,
-  selectedDocs: Doc | null,
-  history: Array<any> = [],
-  conversationId: string | null,
-  promptId: string | null,
-  chunks: string,
-): Promise<
-  | {
-      result: any;
-      answer: any;
-      sources: any;
-      conversationId: any;
-      query: string;
-    }
-  | {
-      result: any;
-      answer: any;
-      sources: any;
-      query: string;
-      conversationId: any;
-      title: any;
-    }
-> {
+function getDocPath(selectedDocs: Doc | null): string {
   let docPath = 'default';
 
   if (selectedDocs) {
@@ -47,8 +23,40 @@ export function fetchAnswerApi(
         '/' +
         selectedDocs.model +
         '/';
+    } else if (selectedDocs.location === 'custom') {
+      docPath = selectedDocs.docLink;
     }
   }
+
+  return docPath;
+}
+export function fetchAnswerApi(
+  question: string,
+  signal: AbortSignal,
+  selectedDocs: Doc | null,
+  history: Array<any> = [],
+  conversationId: string | null,
+  promptId: string | null,
+  chunks: string,
+  token_limit: number,
+): Promise<
+  | {
+      result: any;
+      answer: any;
+      sources: any;
+      conversationId: any;
+      query: string;
+    }
+  | {
+      result: any;
+      answer: any;
+      sources: any;
+      query: string;
+      conversationId: any;
+      title: any;
+    }
+> {
+  const docPath = getDocPath(selectedDocs);
   //in history array remove all keys except prompt and response
   history = history.map((item) => {
     return { prompt: item.prompt, response: item.response };
@@ -66,6 +74,7 @@ export function fetchAnswerApi(
       conversation_id: conversationId,
       prompt_id: promptId,
       chunks: chunks,
+      token_limit: token_limit,
     }),
     signal,
   })
@@ -96,29 +105,10 @@ export function fetchAnswerSteaming(
   conversationId: string | null,
   promptId: string | null,
   chunks: string,
+  token_limit: number,
   onEvent: (event: MessageEvent) => void,
 ): Promise<Answer> {
-  let docPath = 'default';
-
-  if (selectedDocs) {
-    let namePath = selectedDocs.name;
-    if (selectedDocs.language === namePath) {
-      namePath = '.project';
-    }
-    if (selectedDocs.location === 'local') {
-      docPath = 'local' + '/' + selectedDocs.name + '/';
-    } else if (selectedDocs.location === 'remote') {
-      docPath =
-        selectedDocs.language +
-        '/' +
-        namePath +
-        '/' +
-        selectedDocs.version +
-        '/' +
-        selectedDocs.model +
-        '/';
-    }
-  }
+  const docPath = getDocPath(selectedDocs);
 
   history = history.map((item) => {
     return { prompt: item.prompt, response: item.response };
@@ -132,6 +122,7 @@ export function fetchAnswerSteaming(
       conversation_id: conversationId,
       prompt_id: promptId,
       chunks: chunks,
+      token_limit: token_limit,
     };
     fetch(apiHost + '/stream', {
       method: 'POST',
@@ -194,32 +185,9 @@ export function searchEndpoint(
   conversation_id: string | null,
   history: Array<any> = [],
   chunks: string,
+  token_limit: number,
 ) {
-  /*
-  "active_docs": "default",
-  "question": "Summarise",
-  "conversation_id": null,
-  "history": "[]" */
-  let docPath = 'default';
-  if (selectedDocs) {
-    let namePath = selectedDocs.name;
-    if (selectedDocs.language === namePath) {
-      namePath = '.project';
-    }
-    if (selectedDocs.location === 'local') {
-      docPath = 'local' + '/' + selectedDocs.name + '/';
-    } else if (selectedDocs.location === 'remote') {
-      docPath =
-        selectedDocs.language +
-        '/' +
-        namePath +
-        '/' +
-        selectedDocs.version +
-        '/' +
-        selectedDocs.model +
-        '/';
-    }
-  }
+  const docPath = getDocPath(selectedDocs);
 
   const body = {
     question: question,
@@ -227,6 +195,7 @@ export function searchEndpoint(
     conversation_id,
     history,
     chunks: chunks,
+    token_limit: token_limit,
   };
   return fetch(`${apiHost}/api/search`, {
     method: 'POST',

frontend/src/conversation/conversationModels.ts

@@ -17,7 +17,7 @@ export interface Answer {
   answer: string;
   query: string;
   result: string;
-  sources: { title: string; text: string }[];
+  sources: { title: string; text: string; source: string }[];
   conversationId: string | null;
   title: string | null;
 }
@@ -27,7 +27,7 @@ export interface Query {
   response?: string;
   feedback?: FEEDBACK;
   error?: string;
-  sources?: { title: string; text: string }[];
+  sources?: { title: string; text: string; source: string }[];
   conversationId?: string | null;
   title?: string | null;
 }

frontend/src/conversation/conversationSlice.ts

@@ -28,6 +28,7 @@ export const fetchAnswer = createAsyncThunk<Answer, { question: string }>(
           state.conversation.conversationId,
           state.preference.prompt.id,
           state.preference.chunks,
+          state.preference.token_limit,
 
           (event) => {
             const data = JSON.parse(event.data);
@@ -51,6 +52,7 @@ export const fetchAnswer = createAsyncThunk<Answer, { question: string }>(
                 state.conversation.conversationId,
                 state.conversation.queries,
                 state.preference.chunks,
+                state.preference.token_limit,
               ).then((sources) => {
                 //dispatch streaming sources
                 dispatch(
@@ -66,6 +68,15 @@ export const fetchAnswer = createAsyncThunk<Answer, { question: string }>(
                   query: { conversationId: data.id },
                 }),
               );
+            } else if (data.type === 'error') {
+              // set status to 'failed'
+              dispatch(conversationSlice.actions.setStatus('failed'));
+              dispatch(
+                conversationSlice.actions.raiseError({
+                  index: state.conversation.queries.length - 1,
+                  message: data.error,
+                }),
+              );
             } else {
               const result = data.answer;
               dispatch(
@@ -86,6 +97,7 @@ export const fetchAnswer = createAsyncThunk<Answer, { question: string }>(
           state.conversation.conversationId,
           state.preference.prompt.id,
           state.preference.chunks,
+          state.preference.token_limit,
         );
         if (answer) {
           let sourcesPrepped = [];
@@ -148,7 +160,7 @@ export const conversationSlice = createSlice({
       action: PayloadAction<{ index: number; query: Partial<Query> }>,
     ) {
       const { index, query } = action.payload;
-      if (query.response) {
+      if (query.response != undefined) {
         state.queries[index].response =
           (state.queries[index].response || '') + query.response;
       } else {
@@ -188,6 +200,13 @@ export const conversationSlice = createSlice({
     setStatus(state, action: PayloadAction<Status>) {
       state.status = action.payload;
     },
+    raiseError(
+      state,
+      action: PayloadAction<{ index: number; message: string }>,
+    ) {
+      const { index, message } = action.payload;
+      state.queries[index].error = message;
+    },
   },
   extraReducers(builder) {
     builder
@@ -201,7 +220,7 @@ export const conversationSlice = createSlice({
         }
         state.status = 'failed';
         state.queries[state.queries.length - 1].error =
-          'Something went wrong. Please try again later.';
+          'Something went wrong. Please check your internet connection.';
       });
   },
 });

frontend/src/hooks/index.ts

@@ -77,21 +77,23 @@ export function useDarkTheme() {
     // Set dark mode based on local storage preference
     if (savedMode === 'Dark') {
       setIsDarkTheme(true);
-      document.documentElement.classList.add('dark');
-      document.documentElement.classList.add('dark:bg-raisin-black');
+      document
+        .getElementById('root')
+        ?.classList.add('dark', 'dark:bg-raisin-black');
     } else {
       // If no preference found, set to default (light mode)
       setIsDarkTheme(false);
-      document.documentElement.classList.remove('dark');
+      document.getElementById('root')?.classList.remove('dark');
     }
   }, []);
   useEffect(() => {
     localStorage.setItem('selectedTheme', isDarkTheme ? 'Dark' : 'Light');
     if (isDarkTheme) {
-      document.documentElement.classList.add('dark');
-      document.documentElement.classList.add('dark:bg-raisin-black');
+      document
+        .getElementById('root')
+        ?.classList.add('dark', 'dark:bg-raisin-black');
     } else {
-      document.documentElement.classList.remove('dark');
+      document.getElementById('root')?.classList.remove('dark');
     }
   }, [isDarkTheme]);
   //method to toggle theme

frontend/src/index.css

@@ -22,6 +22,18 @@
   background: #b1afaf;
 }
 
+@layer utilities {
+  /* Chrome, Safari and Opera */
+  .no-scrollbar::-webkit-scrollbar {
+    display: none;
+  }
+
+  .no-scrollbar {
+    -ms-overflow-style: none; /* IE and Edge */
+    scrollbar-width: none; /* Firefox */
+  }
+}
+
 /*! normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */
 
 /* Document
@@ -398,3 +410,18 @@ template {
     padding: 16px;
   }
 }
+
+@font-face {
+  font-family: 'Inter';
+  font-weight: 100 200 300 400 500 600 700 800 900;
+  src: url('/fonts/Inter-Variable.ttf');
+}
+
+::-webkit-scrollbar {
+  width: 0;
+}
+
+.inputbox-style[contenteditable] {
+  padding-left: 36px;
+  padding-right: 36px;
+}

frontend/src/locale/en.json

@@ -0,0 +1,124 @@
+{
+  "language": "English",
+  "chat": "Chat",
+  "chats": "Chats",
+  "newChat": "New Chat",
+  "myPlan": "My Plan",
+  "about": "About",
+  "inputPlaceholder": "Type your message here...",
+  "tagline": "DocsGPT uses GenAI, please review critical information using sources.",
+  "sourceDocs": "Source Docs",
+  "none": "None",
+  "cancel": "Cancel",
+  "demo": [
+    {
+      "header": "Learn about DocsGPT",
+      "query": "What is DocsGPT?"
+    },
+    {
+      "header": "Summarize documentation",
+      "query": "Summarize current context"
+    },
+    {
+      "header": "Write Code",
+      "query": "Write code for api request to /api/answer"
+    },
+    {
+      "header": "Learning Assistance",
+      "query": "Write potential questions for context"
+    }
+  ],
+  "settings": {
+    "label": "Settings",
+    "general": {
+      "label": "General",
+      "selectTheme": "Select Theme",
+      "light": "Light",
+      "dark": "Dark",
+      "selectLanguage": "Select Language",
+      "chunks": "Chunks processed per query",
+      "prompt": "Active Prompt",
+      "deleteAllLabel": "Delete all Conversation",
+      "deleteAllBtn": "Delete all",
+      "addNew": "Add New",
+      "convHistory": "Conversational history",
+      "none": "None",
+      "low": "Low",
+      "medium": "Medium",
+      "high": "High",
+      "unlimited": "Unlimited",
+      "default": "default"
+    },
+    "documents": {
+      "label": "Documents",
+      "name": "Document Name",
+      "date": "Vector Date",
+      "type": "Type",
+      "tokenUsage": "Token Usage"
+    },
+    "apiKeys": {
+      "label": "API Keys",
+      "name": "Name",
+      "key": "API Key",
+      "sourceDoc": "Source Document",
+      "createNew": "Create New"
+    }
+  },
+  "modals": {
+    "uploadDoc": {
+      "label": "Upload New Documentation",
+      "file": "From File",
+      "remote": "Remote",
+      "name": "Name",
+      "choose": "Choose Files",
+      "info": "Please upload .pdf, .txt, .rst, .docx, .md, .zip limited to 25mb",
+      "uploadedFiles": "Uploaded Files",
+      "cancel": "Cancel",
+      "train": "Train",
+      "link": "Link",
+      "urlLink": "URL Link",
+      "reddit": {
+        "id": "Client ID",
+        "secret": "Client Secret",
+        "agent": "User agent",
+        "searchQueries": "Search queries",
+        "numberOfPosts": "Number of posts"
+      }
+    },
+    "createAPIKey": {
+      "label": "Create New API Key",
+      "apiKeyName": "API Key Name",
+      "chunks": "Chunks processed per query",
+      "prompt": "Select active prompt",
+      "sourceDoc": "Source document",
+      "create": "Create"
+    },
+    "saveKey": {
+      "note": "Please save your Key",
+      "disclaimer": "This is the only time your key will be shown.",
+      "copy": "Copy",
+      "copied": "Copied",
+      "confirm": "I saved the Key"
+    },
+    "deleteConv": {
+      "confirm": "Are you sure you want to delete all the conversations?",
+      "delete": "Delete"
+    },
+    "shareConv": {
+      "label": "Create a public page to share",
+      "note": "Source document, personal information and further conversation will remain private",
+      "create": "Create"
+    }
+  },
+  "sharedConv": {
+    "subtitle": "Created with",
+    "button": "Get Started with DocsGPT",
+    "meta": "DocsGPT uses GenAI, please review critical information using sources."
+  },
+  "convTile": {
+    "share": "Share",
+    "delete": "Delete",
+    "rename": "Rename",
+    "deleteWarning": "Are you sure you want to delete this conversation?"
+  }
+}

frontend/src/locale/es.json

@@ -0,0 +1,124 @@
+{
+  "language": "Spanish",
+  "chat": "Chat",
+  "chats": "Chats",
+  "newChat": "Nuevo Chat",
+  "myPlan": "Mi Plan",
+  "about": "Acerca de",
+  "inputPlaceholder": "Escribe tu mensaje aquí...",
+  "tagline": "DocsGPT utiliza GenAI, por favor revisa información crítica utilizando fuentes.",
+  "sourceDocs": "Documentos Fuente",
+  "none": "Nada",
+  "cancel": "Cancelar",
+  "demo": [
+    {
+      "header": "Aprende sobre DocsGPT",
+      "query": "¿Qué es DocsGPT?"
+    },
+    {
+      "header": "Resumir documentación",
+      "query": "Resumir contexto actual"
+    },
+    {
+      "header": "Escribir Código",
+      "query": "Escribir código para solicitud de API a /api/answer"
+    },
+    {
+      "header": "Asistencia de Aprendizaje",
+      "query": "Escribe posibles preguntas para el contexto"
+    }
+  ],
+  "settings": {
+    "label": "Configuración",
+    "general": {
+      "label": "General",
+      "selectTheme": "Seleccionar Tema",
+      "light": "de luz",
+      "dark": "oscura",
+      "selectLanguage": "Seleccionar Idioma",
+      "chunks": "Trozos procesados por consulta",
+      "prompt": "Prompt Activo",
+      "deleteAllLabel": "Eliminar toda la Conversación",
+      "deleteAllBtn": "Eliminar todo",
+      "addNew": "Agregar Nuevo",
+      "convHistory": "Historia conversacional",
+      "none": "ninguno",
+      "low": "Bajo",
+      "medium": "Medio",
+      "high": "Alto",
+      "unlimited": "Ilimitado",
+      "default": "predeterminada"
+    },
+    "documents": {
+      "label": "Documentos",
+      "name": "Nombre del Documento",
+      "date": "Fecha Vector",
+      "type": "Tipo",
+      "tokenUsage": "Uso de Tokens"
+    },
+    "apiKeys": {
+      "label": "Claves API",
+      "name": "Nombre",
+      "key": "Clave de API",
+      "sourceDoc": "Documento Fuente",
+      "createNew": "Crear Nuevo"
+    }
+  },
+  "modals": {
+    "uploadDoc": {
+      "label": "Subir Nueva Documentación",
+      "file": "Desde Archivo",
+      "remote": "Remota",
+      "name": "Nombre",
+      "choose": "Seleccionar Archivos",
+      "info": "Por favor, suba archivos .pdf, .txt, .rst, .docx, .md, .zip limitados a 25 MB",
+      "uploadedFiles": "Archivos Subidos",
+      "cancel": "Cancelar",
+      "train": "Entrenar",
+      "link": "Enlace",
+      "urlLink": "Enlace URL",
+      "reddit": {
+        "id": "ID de Cliente",
+        "secret": "Secreto de Cliente",
+        "agent": "Agente de Usuario",
+        "searchQueries": "Consultas de Búsqueda",
+        "numberOfPosts": "Número de publicaciones"
+      }
+    },
+    "createAPIKey": {
+      "label": "Crear Nueva Clave de API",
+      "apiKeyName": "Nombre de la Clave de API",
+      "chunks": "Fragmentos procesados por consulta",
+      "prompt": "Seleccione el prompt activo",
+      "sourceDoc": "Documento Fuente",
+      "create": "Crear"
+    },
+    "saveKey": {
+      "note": "Por favor, guarde su Clave",
+      "disclaimer": "Esta es la única vez que se mostrará su clave.",
+      "copy": "Copiar",
+      "copied": "Copiado",
+      "confirm": "He guardado la Clave"
+    },
+    "deleteConv": {
+      "confirm": "¿Está seguro de que desea eliminar todas las conversaciones?",
+      "delete": "Eliminar"
+    },
+    "shareConv": {
+      "label": "Crear una página pública para compartir",
+      "note": "El documento original, la información personal y las conversaciones posteriores permanecerán privadas",
+      "create": "Crear"
+    }
+  },
+  "sharedConv": {
+    "subtitle": "Creado con",
+    "button": "Comienza con DocsGPT",
+    "meta": "DocsGPT utiliza GenAI, por favor revise la información crítica utilizando fuentes."
+  },
+  "convTile": {
+    "share": "Compartir",
+    "delete": "Eliminar",
+    "rename": "Renombrar",
+    "deleteWarning": "¿Está seguro de que desea eliminar esta conversación?"
+  }
+}

frontend/src/locale/i18n.ts

@@ -0,0 +1,38 @@
+import i18n from 'i18next';
+import { initReactI18next } from 'react-i18next';
+import LanguageDetector from 'i18next-browser-languagedetector';
+
+import en from './en.json'; //English
+import es from './es.json'; //Spanish
+import jp from './jp.json'; //Japanese
+import zh from './zh.json'; //Mandarin
+
+i18n
+  .use(LanguageDetector)
+  .use(initReactI18next)
+  .init({
+    resources: {
+      en: {
+        translation: en,
+      },
+      es: {
+        translation: es,
+      },
+      jp: {
+        translation: jp,
+      },
+      zh: {
+        translation: zh,
+      },
+    },
+    fallbackLng: 'en',
+    detection: {
+      order: ['localStorage', 'navigator'],
+      caches: ['localStorage'],
+      lookupLocalStorage: 'docsgpt-locale',
+    },
+  });
+
+i18n.changeLanguage(i18n.language);
+
+export default i18n;

frontend/src/locale/jp.json

@@ -0,0 +1,124 @@
+{
+  "language": "日本語",
+  "chat": "チャット",
+  "chats": "チャット",
+  "newChat": "新しいチャット",
+  "myPlan": "私のプラン",
+  "about": "について",
+  "inputPlaceholder": "ここにメッセージを入力してください...",
+  "tagline": "DocsGPTはGenAIを使用しています。重要な情報はソースで確認してください。",
+  "sourceDocs": "ソースドキュメント",
+  "none": "なし",
+  "cancel": "キャンセル",
+  "demo": [
+    {
+      "header": "DocsGPTについて学ぶ",
+      "query": "DocsGPTとは何ですか?"
+    },
+    {
+      "header": "ドキュメントを要約する",
+      "query": "現在のコンテキストを要約してください"
+    },
+    {
+      "header": "コードを書く",
+      "query": "APIリクエストのコードを/api/answerに書いてください。"
+    },
+    {
+      "header": "学習支援",
+      "query": "コンテキストに対する潜在的な質問を書いてください"
+    }
+  ],
+  "settings": {
+    "label": "設定",
+    "general": {
+      "label": "一般",
+      "selectTheme": "テーマを選択",
+      "light": "ライト",
+      "dark": "ダーク",
+      "selectLanguage": "言語を選択",
+      "chunks": "クエリごとに処理されるチャンク",
+      "prompt": "アクティブプロンプト",
+      "deleteAllLabel": "すべての会話を削除",
+      "deleteAllBtn": "すべて削除",
+      "addNew": "新規追加",
+      "convHistory": "会話履歴",
+      "none": "なし",
+      "low": "低",
+      "medium": "中",
+      "high": "高",
+      "unlimited": "無制限",
+      "default": "デフォルト"
+    },
+    "documents": {
+      "label": "ドキュメント",
+      "name": "ドキュメント名",
+      "date": "ベクトル日付",
+      "type": "タイプ",
+      "tokenUsage": "トークン使用量"
+    },
+    "apiKeys": {
+      "label": "APIキー",
+      "name": "名前",
+      "key": "APIキー",
+      "sourceDoc": "ソースドキュメント",
+      "createNew": "新規作成"
+    }
+  },
+  "modals": {
+    "uploadDoc": {
+      "label": "新規書類のアップロード",
+      "file": "ファイルから",
+      "remote": "リモート",
+      "name": "名前",
+      "choose": "ファイルを選択",
+      "info": ".pdf, .txt, .rst, .docx, .md, .zipファイルを25MBまでアップロードしてください",
+      "uploadedFiles": "アップロードされたファイル",
+      "cancel": "キャンセル",
+      "train": "トレーニング",
+      "link": "リンク",
+      "urlLink": "URLリンク",
+      "reddit": {
+        "id": "クライアントID",
+        "secret": "クライアントシークレット",
+        "agent": "ユーザーエージェント",
+        "searchQueries": "検索クエリ",
+        "numberOfPosts": "投稿数"
+      }
+    },
+    "createAPIKey": {
+      "label": "新しいAPIキーを作成",
+      "apiKeyName": "APIキー名",
+      "chunks": "クエリごとに処理されるチャンク",
+      "prompt": "アクティブプロンプトを選択",
+      "sourceDoc": "ソースドキュメント",
+      "create": "作成"
+    },
+    "saveKey": {
+      "note": "キーを保存してください",
+      "disclaimer": "キーが表示されるのはこのときだけです。",
+      "copy": "コピー",
+      "copied": "コピーしました",
+      "confirm": "キーを保存しました"
+    },
+    "deleteConv": {
+      "confirm": "すべての会話を削除してもよろしいですか?",
+      "delete": "削除"
+    },
+    "shareConv": {
+      "label": "共有ページを作成して共有する",
+      "note": "ソースドキュメント、個人情報、および以降の会話は非公開のままになります",
+      "create": "作成"
+    }
+  },
+  "sharedConv": {
+    "subtitle": "作成者",
+    "button": "DocsGPT を始める",
+    "meta": "DocsGPT は GenAI を使用しています、情報源を使用して重要情報を確認してください。"
+  },
+  "convTile": {
+    "share": "共有",
+    "delete": "削除",
+    "rename": "名前変更",
+    "deleteWarning": "この会話を削除してもよろしいですか？"
+  }
+}