Merge pull request #926 from siiddhantt/feature

Feature: Logging token usage info to MongoDB

.env-template

@@ -1,4 +1,5 @@
 API_KEY=<LLM api key (for example, open ai key)>
+LLM_NAME=docsgpt
 VITE_API_STREAMING=true
 
 #For Azure (you can delete it if you don't use Azure)

.github/holopin.yml

@@ -1,5 +1,5 @@
 organization: arc53
-defaultSticker: cln9dm7qz164460gk5ksrgr034
+defaultSticker: clqmdf0ed34290glbvqh0kzxd
 stickers:
-  - id: cln9dm7qz164460gk5ksrgr034
-    alias: hacktober
+  - id: clqmdf0ed34290glbvqh0kzxd
+    alias: festive

.gitignore

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

README.md

@@ -18,7 +18,7 @@ Say goodbye to time-consuming manual searches, and let <strong><a href="https://
   <a href="https://github.com/arc53/DocsGPT">![link to main GitHub showing Forks number](https://img.shields.io/github/forks/arc53/docsgpt?style=social)</a>
   <a href="https://github.com/arc53/DocsGPT/blob/main/LICENSE">![link to license file](https://img.shields.io/github/license/arc53/docsgpt)</a>
   <a href="https://discord.gg/n5BX8dh8rU">![link to discord](https://img.shields.io/discord/1070046503302877216)</a>
-  <a href="https://twitter.com/ATushynski">![X (formerly Twitter) URL](https://img.shields.io/twitter/follow/ATushynski)</a>
+  <a href="https://twitter.com/docsgptai">![X (formerly Twitter) URL](https://img.shields.io/twitter/follow/docsgptai)</a>
 
  
 </div>
@@ -40,7 +40,7 @@ You can find our roadmap [here](https://github.com/orgs/arc53/projects/2). Pleas
 
 | Name                                                                  | Base Model  | Requirements (or similar) |
 | --------------------------------------------------------------------- | ----------- | ------------------------- |
-| [Docsgpt-7b-falcon](https://huggingface.co/Arc53/docsgpt-7b-falcon)   | Falcon-7b   | 1xA10G gpu                |
+| [Docsgpt-7b-mistral](https://huggingface.co/Arc53/docsgpt-7b-mistral)   | Mistral-7b   | 1xA10G gpu                |
 | [Docsgpt-14b](https://huggingface.co/Arc53/docsgpt-14b)               | llama-2-14b | 2xA10 gpu's               |
 | [Docsgpt-40b-falcon](https://huggingface.co/Arc53/docsgpt-40b-falcon) | falcon-40b  | 8xA10G gpu's              |
 
@@ -83,17 +83,18 @@ On Mac OS or Linux, write:
 
 `./setup.sh`
 
-It will install all the dependencies and allow you to download the local model or use OpenAI.
+It will install all the dependencies and allow you to download the local model, use OpenAI or use our LLM API.
 
 Otherwise, refer to this Guide:
 
 1. Download and open this repository with `git clone https://github.com/arc53/DocsGPT.git`
-2. Create a `.env` file in your root directory and set the env variable `API_KEY` with your [OpenAI API key](https://platform.openai.com/account/api-keys) and `VITE_API_STREAMING` to true or false, depending on whether you want streaming answers or not.
+2. Create a `.env` file in your root directory and set the env variables and `VITE_API_STREAMING` to true or false, depending on whether you want streaming answers or not.
    It should look like this inside:
 
    ```
-   API_KEY=Yourkey
+   LLM_NAME=[docsgpt or openai or others] 
    VITE_API_STREAMING=true
+   API_KEY=[if LLM_NAME is openai]
    ```
 
    See optional environment variables in the [/.env-template](https://github.com/arc53/DocsGPT/blob/main/.env-template) and [/application/.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) files.
@@ -122,8 +123,8 @@ docker compose -f docker-compose-dev.yaml up -d
 > [!Note]
 > Make sure you have Python 3.10 or 3.11 installed.
 
-1. Export required environment variables or prepare a `.env` file in the `/application` folder:
-   - Copy [.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) and create `.env` with your OpenAI API token for the `API_KEY` and `EMBEDDINGS_KEY` fields.
+1. Export required environment variables or prepare a `.env` file in the project folder:
+   - Copy [.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) and create `.env`.
 
 (check out [`application/core/settings.py`](application/core/settings.py) if you want to see more config options.)
 
@@ -144,14 +145,23 @@ python -m venv venv
  venv/Scripts/activate
 ```
 
-3. Change to the `application/` subdir by the command `cd application/` and install dependencies for the backend:
+3. Download embedding model and save it in the `model/` folder:
+You can use the script below, or download it manually from [here](https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip), unzip it and save it in the `model/` folder.
+
+```commandline
+wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
+unzip mpnet-base-v2.zip -d model
+rm mpnet-base-v2.zip
+```
+
+4. Install dependencies for the backend:
 
 ```commandline
 pip install -r application/requirements.txt
 ```
 
-4. Run the app using `flask --app application/app.py run --host=0.0.0.0 --port=7091`.
-5. Start worker with `celery -A application.app.celery worker -l INFO`.
+5. Run the app using `flask --app application/app.py run --host=0.0.0.0 --port=7091`.
+6. Start worker with `celery -A application.app.celery worker -l INFO`.
 
 ### Start Frontend
 

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

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

application/api/answer/routes.py

@@ -8,17 +8,14 @@ import traceback
 
 from pymongo import MongoClient
 from bson.objectid import ObjectId
-from transformers import GPT2TokenizerFast
-
 
 
 from application.core.settings import settings
-from application.vectorstore.vector_creator import VectorCreator
 from application.llm.llm_creator import LLMCreator
+from application.retriever.retriever_creator import RetrieverCreator
 from application.error import bad_request
 
 
-
 logger = logging.getLogger(__name__)
 
 mongo = MongoClient(settings.MONGO_URI)
@@ -26,17 +23,23 @@ db = mongo["docsgpt"]
 conversations_collection = db["conversations"]
 vectors_collection = db["vectors"]
 prompts_collection = db["prompts"]
-answer = Blueprint('answer', __name__)
+api_key_collection = db["api_keys"]
+answer = Blueprint("answer", __name__)
 
-if settings.LLM_NAME == "gpt4":
-    gpt_model = 'gpt-4'
+gpt_model = ""
+# to have some kind of default behaviour
+if settings.LLM_NAME == "openai":
+    gpt_model = "gpt-3.5-turbo"
 elif settings.LLM_NAME == "anthropic":
-    gpt_model = 'claude-2'
-else:
-    gpt_model = 'gpt-3.5-turbo'
+    gpt_model = "claude-2"
+
+if settings.MODEL_NAME:  # in case there is particular model name configured
+    gpt_model = settings.MODEL_NAME
 
 # load the prompts
-current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
 with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
     chat_combine_template = f.read()
 
@@ -47,7 +50,7 @@ with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r"
     chat_combine_creative = f.read()
 
 with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
-    chat_combine_strict = f.read()    
+    chat_combine_strict = f.read()
 
 api_key_set = settings.API_KEY is not None
 embeddings_key_set = settings.EMBEDDINGS_KEY is not None
@@ -58,11 +61,6 @@ async def async_generate(chain, question, chat_history):
     return result
 
 
-def count_tokens(string):
-    tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
-    return len(tokenizer(string)['input_ids'])
-
-
 def run_async_chain(chain, question, chat_history):
     loop = asyncio.new_event_loop()
     asyncio.set_event_loop(loop)
@@ -75,10 +73,17 @@ def run_async_chain(chain, question, chat_history):
     return result
 
 
+def get_data_from_api_key(api_key):
+    data = api_key_collection.find_one({"key": api_key})
+    if data is None:
+        return bad_request(401, "Invalid API key")
+    return data
+
+
 def get_vectorstore(data):
     if "active_docs" in data:
         if data["active_docs"].split("/")[0] == "default":
-                vectorstore = ""
+            vectorstore = ""
         elif data["active_docs"].split("/")[0] == "local":
             vectorstore = "indexes/" + data["active_docs"]
         else:
@@ -92,86 +97,99 @@ def get_vectorstore(data):
 
 
 def is_azure_configured():
-    return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
+    return (
+        settings.OPENAI_API_BASE
+        and settings.OPENAI_API_VERSION
+        and settings.AZURE_DEPLOYMENT_NAME
+    )
 
 
-def complete_stream(question, docsearch, chat_history, api_key, prompt_id, conversation_id):
-    llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
-
-    if prompt_id == 'default':
-        prompt = chat_combine_template
-    elif prompt_id == 'creative':
-        prompt = chat_combine_creative
-    elif prompt_id == 'strict':
-        prompt = chat_combine_strict
-    else:
-        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
-
-    docs = docsearch.search(question, k=2)
-    if settings.LLM_NAME == "llama.cpp":
-        docs = [docs[0]]
-    # join all page_content together with a newline
-    docs_together = "\n".join([doc.page_content for doc in docs])
-    p_chat_combine = prompt.replace("{summaries}", docs_together)
-    messages_combine = [{"role": "system", "content": p_chat_combine}]
-    source_log_docs = []
-    for doc in docs:
-        if doc.metadata:
-            data = json.dumps({"type": "source", "doc": doc.page_content, "metadata": doc.metadata})
-            source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
-        else:
-            data = json.dumps({"type": "source", "doc": doc.page_content})
-            source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
-        yield f"data:{data}\n\n"
-
-    if len(chat_history) > 1:
-        tokens_current_history = 0
-        # count tokens in history
-        chat_history.reverse()
-        for i in chat_history:
-            if "prompt" in i and "response" in i:
-                tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
-                if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
-                    tokens_current_history += tokens_batch
-                    messages_combine.append({"role": "user", "content": i["prompt"]})
-                    messages_combine.append({"role": "system", "content": i["response"]})
-    messages_combine.append({"role": "user", "content": question})
-
-    response_full = ""
-    completion = llm.gen_stream(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
-                                messages=messages_combine)
-    for line in completion:
-        data = json.dumps({"answer": str(line)})
-        response_full += str(line)
-        yield f"data: {data}\n\n"
-
-    # save conversation to database
-    if conversation_id is not None:
+def save_conversation(conversation_id, question, response, source_log_docs, llm):
+    if conversation_id is not None and conversation_id != "None":
         conversations_collection.update_one(
             {"_id": ObjectId(conversation_id)},
-            {"$push": {"queries": {"prompt": question, "response": response_full, "sources": source_log_docs}}},
+            {
+                "$push": {
+                    "queries": {
+                        "prompt": question,
+                        "response": response,
+                        "sources": source_log_docs,
+                    }
+                }
+            },
         )
 
     else:
         # create new conversation
         # generate summary
-        messages_summary = [{"role": "assistant", "content": "Summarise following conversation in no more than 3 "
-                                                             "words, respond ONLY with the summary, use the same "
-                                                             "language as the system \n\nUser: " + question + "\n\n" +
-                                                             "AI: " +
-                                                             response_full},
-                            {"role": "user", "content": "Summarise following conversation in no more than 3 words, "
-                                                        "respond ONLY with the summary, use the same language as the "
-                                                        "system"}]
+        messages_summary = [
+            {
+                "role": "assistant",
+                "content": "Summarise following conversation in no more than 3 "
+                "words, respond ONLY with the summary, use the same "
+                "language as the system \n\nUser: "
+                + question
+                + "\n\n"
+                + "AI: "
+                + response,
+            },
+            {
+                "role": "user",
+                "content": "Summarise following conversation in no more than 3 words, "
+                "respond ONLY with the summary, use the same language as the "
+                "system",
+            },
+        ]
 
-        completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
-                             messages=messages_summary, max_tokens=30)
+        completion = llm.gen(model=gpt_model, messages=messages_summary, max_tokens=30)
         conversation_id = conversations_collection.insert_one(
-            {"user": "local",
-             "date": datetime.datetime.utcnow(),
-             "name": completion,
-             "queries": [{"prompt": question, "response": response_full, "sources": source_log_docs}]}
+            {
+                "user": "local",
+                "date": datetime.datetime.utcnow(),
+                "name": completion,
+                "queries": [
+                    {
+                        "prompt": question,
+                        "response": response,
+                        "sources": source_log_docs,
+                    }
+                ],
+            }
         ).inserted_id
+    return conversation_id
+
+
+def get_prompt(prompt_id):
+    if prompt_id == "default":
+        prompt = chat_combine_template
+    elif prompt_id == "creative":
+        prompt = chat_combine_creative
+    elif prompt_id == "strict":
+        prompt = chat_combine_strict
+    else:
+        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
+    return prompt
+
+
+def complete_stream(question, retriever, conversation_id, user_api_key):
+
+    response_full = ""
+    source_log_docs = []
+    answer = retriever.gen()
+    for line in answer:
+        if "answer" in line:
+            response_full += str(line["answer"])
+            data = json.dumps(line)
+            yield f"data: {data}\n\n"
+        elif "source" in line:
+            source_log_docs.append(line["source"])
+
+    llm = LLMCreator.create_llm(
+        settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
+    )
+    conversation_id = save_conversation(
+        conversation_id, question, response_full, source_log_docs, llm
+    )
 
     # send data.type = "end" to indicate that the stream has ended as json
     data = json.dumps({"type": "id", "id": str(conversation_id)})
@@ -185,36 +203,68 @@ def stream():
     data = request.get_json()
     # get parameter from url question
     question = data["question"]
-    history = data["history"]
-    # history to json object from string
-    history = json.loads(history)
-    conversation_id = data["conversation_id"]
-    if 'prompt_id' in data:
+    if "history" not in data:
+        history = []
+    else:
+        history = data["history"]
+        history = json.loads(history)
+    if "conversation_id" not in data:
+        conversation_id = None
+    else:
+        conversation_id = data["conversation_id"]
+    if "prompt_id" in data:
         prompt_id = data["prompt_id"]
     else:
-        prompt_id = 'default'
+        prompt_id = "default"
+    if "selectedDocs" in data and data["selectedDocs"] is None:
+        chunks = 0
+    elif "chunks" in data:
+        chunks = int(data["chunks"])
+    else:
+        chunks = 2
+
+    prompt = get_prompt(prompt_id)
 
     # check if active_docs is set
 
-    if not api_key_set:
-        api_key = data["api_key"]
+    if "api_key" in data:
+        data_key = get_data_from_api_key(data["api_key"])
+        source = {"active_docs": data_key["source"]}
+        user_api_key = data["api_key"]
+    elif "active_docs" in data:
+        source = {"active_docs": data["active_docs"]}
+        user_api_key = None
     else:
-        api_key = settings.API_KEY
-    if not embeddings_key_set:
-        embeddings_key = data["embeddings_key"]
+        source = {}
+        user_api_key = None
+
+    if (
+        source["active_docs"].split("/")[0] == "default"
+        or source["active_docs"].split("/")[0] == "local"
+    ):
+        retriever_name = "classic"
     else:
-        embeddings_key = settings.EMBEDDINGS_KEY
-    if "active_docs" in data:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
-    else:
-        vectorstore = ""
-    docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key)
+        retriever_name = source["active_docs"]
+
+    retriever = RetrieverCreator.create_retriever(
+        retriever_name,
+        question=question,
+        source=source,
+        chat_history=history,
+        prompt=prompt,
+        chunks=chunks,
+        gpt_model=gpt_model,
+        user_api_key=user_api_key,
+    )
 
     return Response(
-        complete_stream(question, docsearch,
-                        chat_history=history, api_key=api_key,
-                        prompt_id=prompt_id,
-                        conversation_id=conversation_id), mimetype="text/event-stream"
+        complete_stream(
+            question=question,
+            retriever=retriever,
+            conversation_id=conversation_id,
+            user_api_key=user_api_key,
+        ),
+        mimetype="text/event-stream",
     )
 
 
@@ -222,124 +272,118 @@ def stream():
 def api_answer():
     data = request.get_json()
     question = data["question"]
-    history = data["history"]
+    if "history" not in data:
+        history = []
+    else:
+        history = data["history"]
     if "conversation_id" not in data:
         conversation_id = None
     else:
         conversation_id = data["conversation_id"]
     print("-" * 5)
-    if not api_key_set:
-        api_key = data["api_key"]
-    else:
-        api_key = settings.API_KEY
-    if not embeddings_key_set:
-        embeddings_key = data["embeddings_key"]
-    else:
-        embeddings_key = settings.EMBEDDINGS_KEY
-    if 'prompt_id' in data:
+    if "prompt_id" in data:
         prompt_id = data["prompt_id"]
     else:
-        prompt_id = 'default'
-
-    if prompt_id == 'default':
-        prompt = chat_combine_template
-    elif prompt_id == 'creative':
-        prompt = chat_combine_creative
-    elif prompt_id == 'strict':
-        prompt = chat_combine_strict
+        prompt_id = "default"
+    if "chunks" in data:
+        chunks = int(data["chunks"])
     else:
-        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
+        chunks = 2
+
+    prompt = get_prompt(prompt_id)
 
     # use try and except  to check for exception
     try:
         # check if the vectorstore is set
-        vectorstore = get_vectorstore(data)
-        # loading the index and the store and the prompt template
-        # Note if you have used other embeddings than OpenAI, you need to change the embeddings
-        docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key)
-
-
-        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
-
-
-
-        docs = docsearch.search(question, k=2)
-        # join all page_content together with a newline
-        docs_together = "\n".join([doc.page_content for doc in docs])
-        p_chat_combine = prompt.replace("{summaries}", docs_together)
-        messages_combine = [{"role": "system", "content": p_chat_combine}]
-        source_log_docs = []
-        for doc in docs:
-            if doc.metadata:
-                source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
-            else:
-                source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
-        # join all page_content together with a newline
-
-
-        if len(history) > 1:
-            tokens_current_history = 0
-            # count tokens in history
-            history.reverse()
-            for i in history:
-                if "prompt" in i and "response" in i:
-                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
-                    if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
-                        tokens_current_history += tokens_batch
-                        messages_combine.append({"role": "user", "content": i["prompt"]})
-                        messages_combine.append({"role": "system", "content": i["response"]})
-        messages_combine.append({"role": "user", "content": question})
-
-
-        completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
-                                    messages=messages_combine)
-
-
-        result = {"answer": completion, "sources": source_log_docs}
-        logger.debug(result)
-
-        # generate conversationId
-        if conversation_id is not None:
-            conversations_collection.update_one(
-                {"_id": ObjectId(conversation_id)},
-                {"$push": {"queries": {"prompt": question,
-                                       "response": result["answer"], "sources": result['sources']}}},
-            )
-
+        if "api_key" in data:
+            data_key = get_data_from_api_key(data["api_key"])
+            source = {"active_docs": data_key["source"]}
+            user_api_key = data["api_key"]
         else:
-            # create new conversation
-            # generate summary
-            messages_summary = [
-                {"role": "assistant", "content": "Summarise following conversation in no more than 3 words, "
-                    "respond ONLY with the summary, use the same language as the system \n\n"
-                    "User: " + question + "\n\n" + "AI: " + result["answer"]},
-                {"role": "user", "content": "Summarise following conversation in no more than 3 words, "
-                    "respond ONLY with the summary, use the same language as the system"}
-            ]
+            source = {data}
+            user_api_key = None
 
-            completion = llm.gen(
-                model=gpt_model,
-                engine=settings.AZURE_DEPLOYMENT_NAME,
-                messages=messages_summary,
-                max_tokens=30
-            )
-            conversation_id = conversations_collection.insert_one(
-                {"user": "local",
-                "date": datetime.datetime.utcnow(),
-                "name": completion,
-                "queries": [{"prompt": question, "response": result["answer"], "sources": source_log_docs}]}
-            ).inserted_id
+        if (
+            source["active_docs"].split("/")[0] == "default"
+            or source["active_docs"].split("/")[0] == "local"
+        ):
+            retriever_name = "classic"
+        else:
+            retriever_name = source["active_docs"]
 
-        result["conversation_id"] = str(conversation_id)
+        retriever = RetrieverCreator.create_retriever(
+            retriever_name,
+            question=question,
+            source=source,
+            chat_history=history,
+            prompt=prompt,
+            chunks=chunks,
+            gpt_model=gpt_model,
+            user_api_key=user_api_key,
+        )
+        source_log_docs = []
+        response_full = ""
+        for line in retriever.gen():
+            if "source" in line:
+                source_log_docs.append(line["source"])
+            elif "answer" in line:
+                response_full += line["answer"]
+
+        llm = LLMCreator.create_llm(
+            settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
+        )
+
+        result = {"answer": response_full, "sources": source_log_docs}
+        result["conversation_id"] = save_conversation(
+            conversation_id, question, response_full, source_log_docs, llm
+        )
 
-        # mock result
-        # result = {
-        #     "answer": "The answer is 42",
-        #     "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
-        # }
         return result
     except Exception as e:
         # print whole traceback
         traceback.print_exc()
         print(str(e))
         return bad_request(500, str(e))
+
+
+@answer.route("/api/search", methods=["POST"])
+def api_search():
+    data = request.get_json()
+    # get parameter from url question
+    question = data["question"]
+
+    if "api_key" in data:
+        data_key = get_data_from_api_key(data["api_key"])
+        source = {"active_docs": data_key["source"]}
+        user_api_key = data["api_key"]
+    elif "active_docs" in data:
+        source = {"active_docs": data["active_docs"]}
+        user_api_key = None
+    else:
+        source = {}
+        user_api_key = None
+    if "chunks" in data:
+        chunks = int(data["chunks"])
+    else:
+        chunks = 2
+
+    if (
+        source["active_docs"].split("/")[0] == "default"
+        or source["active_docs"].split("/")[0] == "local"
+    ):
+        retriever_name = "classic"
+    else:
+        retriever_name = source["active_docs"]
+
+    retriever = RetrieverCreator.create_retriever(
+        retriever_name,
+        question=question,
+        source=source,
+        chat_history=[],
+        prompt="default",
+        chunks=chunks,
+        gpt_model=gpt_model,
+        user_api_key=user_api_key,
+    )
+    docs = retriever.search()
+    return docs

application/api/user/routes.py

@@ -1,11 +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 werkzeug.utils import secure_filename
 
-from application.api.user.tasks import ingest
+from application.api.user.tasks import ingest, ingest_remote
 
 from application.core.settings import settings
 from application.vectorstore.vector_creator import VectorCreator
@@ -16,6 +19,7 @@ conversations_collection = db["conversations"]
 vectors_collection = db["vectors"]
 prompts_collection = db["prompts"]
 feedback_collection = db["feedback"]
+api_key_collection = db["api_keys"]
 user = Blueprint('user', __name__)
 
 current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -36,7 +40,7 @@ def delete_conversation():
 @user.route("/api/get_conversations", methods=["get"])
 def get_conversations():
     # provides a list of conversations
-    conversations = conversations_collection.find().sort("date", -1)
+    conversations = conversations_collection.find().sort("date", -1).limit(30)
     list_conversations = []
     for conversation in conversations:
         list_conversations.append({"id": str(conversation["_id"]), "name": conversation["name"]})
@@ -133,25 +137,64 @@ 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)
+    
+    if len(files) > 1:
+        # Multiple files; prepare them for zip
+        temp_dir = os.path.join(save_dir, "temp")
+        os.makedirs(temp_dir, exist_ok=True)
+        
+        for file in files:
+            filename = secure_filename(file.filename)
+            file.save(os.path.join(temp_dir, filename))
+        
+        # Use shutil.make_archive to zip the temp directory
+        zip_path = shutil.make_archive(base_name=os.path.join(save_dir, job_name), format='zip', root_dir=temp_dir)
+        final_filename = os.path.basename(zip_path)
+        
+        # Clean up the temporary directory after zipping
+        shutil.rmtree(temp_dir)
+    else:
+        # Single file
+        file = files[0]
+        final_filename = secure_filename(file.filename)
+        file_path = os.path.join(save_dir, final_filename)
+        file.save(file_path)
+    
+    # Call ingest with the single file or zipped file
+    task = ingest.delay(settings.UPLOAD_FOLDER, [".rst", ".md", ".pdf", ".txt", ".docx", 
+    ".csv", ".epub", ".html", ".mdx"],
+    job_name, final_filename, user)
+    
+    return {"status": "ok", "task_id": task.id}
+    
+@user.route("/api/remote", methods=["POST"])
+def upload_remote():
+    """Upload a remote source to get vectorized and indexed."""
+    if "user" not in request.form:
+        return {"status": "no user"}
+    user = secure_filename(request.form["user"])
+    if "source" not in request.form:
+        return {"status": "no source"}
+    source = secure_filename(request.form["source"])
+    if "name" not in request.form:
+        return {"status": "no name"}
+    job_name = secure_filename(request.form["name"])
+    # check if the post request has the file part
+    if "data" not in request.form:
+        print("No data")
+        return {"status": "no data"}
+    source_data = request.form["data"]
 
-        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)
+    if source_data:
+        task = ingest_remote.delay(source_data=source_data, job_name=job_name, user=user, loader=source)
         # task id
         task_id = task.id
         return {"status": "ok", "task_id": task_id}
@@ -208,6 +251,34 @@ def combined_json():
         for index in data_remote:
             index["location"] = "remote"
             data.append(index)
+    if 'duckduck_search' in settings.RETRIEVERS_ENABLED:
+        data.append(
+            {
+                "name": "DuckDuckGo Search",
+                "language": "en",
+                "version": "",
+                "description": "duckduck_search",
+                "fullName": "DuckDuckGo Search",
+                "date": "duckduck_search",
+                "docLink": "duckduck_search",
+                "model": settings.EMBEDDINGS_NAME,
+                "location": "custom",
+            }
+        )
+    if 'brave_search' in settings.RETRIEVERS_ENABLED:
+        data.append(
+            {
+                "name": "Brave Search",
+                "language": "en",
+                "version": "",
+                "description": "brave_search",
+                "fullName": "Brave Search",
+                "date": "brave_search",
+                "docLink": "brave_search",
+                "model": settings.EMBEDDINGS_NAME,
+                "location": "custom",
+            }
+        )
 
     return jsonify(data)
 
@@ -219,25 +290,32 @@ def check_docs():
     # split docs on / and take first part
     if data["docs"].split("/")[0] == "local":
         return {"status": "exists"}
-    vectorstore = "vectors/" + data["docs"]
+    vectorstore = "vectors/" + secure_filename(data["docs"])
     base_path = "https://raw.githubusercontent.com/arc53/DocsHUB/main/"
     if os.path.exists(vectorstore) or data["docs"] == "default":
         return {"status": "exists"}
     else:
-        r = requests.get(base_path + vectorstore + "index.faiss")
+        file_url = urlparse(base_path + vectorstore + "index.faiss")
+        
+        if (
+            file_url.scheme in ['https'] and 
+            file_url.netloc == 'raw.githubusercontent.com' and 
+            file_url.path.startswith('/arc53/DocsHUB/main/')
+        ):
+            r = requests.get(file_url.geturl())
+            if r.status_code != 200:
+                return {"status": "null"}
+            else:
+                if not os.path.exists(vectorstore):
+                    os.makedirs(vectorstore)
+                with open(vectorstore + "index.faiss", "wb") as f:
+                    f.write(r.content)
 
-        if r.status_code != 200:
-            return {"status": "null"}
+                r = requests.get(base_path + vectorstore + "index.pkl")
+                with open(vectorstore + "index.pkl", "wb") as f:
+                    f.write(r.content)
         else:
-            if not os.path.exists(vectorstore):
-                os.makedirs(vectorstore)
-            with open(vectorstore + "index.faiss", "wb") as f:
-                f.write(r.content)
-
-            # download the store
-            r = requests.get(base_path + vectorstore + "index.pkl")
-            with open(vectorstore + "index.pkl", "wb") as f:
-                f.write(r.content)
+            return {"status": "null"}
 
         return {"status": "loaded"}
 
@@ -317,5 +395,52 @@ def update_prompt_name():
 
 
 
+@user.route("/api/get_api_keys", methods=["GET"])
+def get_api_keys():
+    user = "local"
+    keys = api_key_collection.find({"user": user})
+    list_keys = []
+    for key in keys:
+        list_keys.append({
+            "id": str(key["_id"]),
+            "name": key["name"],
+            "key": key["key"][:4] + "..." + key["key"][-4:],
+            "source": key["source"],
+            "prompt_id": key["prompt_id"],
+            "chunks": key["chunks"]
+        })
+    return jsonify(list_keys)
 
+@user.route("/api/create_api_key", methods=["POST"])
+def create_api_key():
+    data = request.get_json()
+    name = data["name"]
+    source = data["source"]
+    prompt_id = data["prompt_id"]
+    chunks = data["chunks"]
+    key = str(uuid.uuid4())
+    user = "local"
+    resp = api_key_collection.insert_one(
+        {
+            "name": name,
+            "key": key,
+            "source": source,
+            "user": user,
+            "prompt_id": prompt_id,
+            "chunks": chunks
+        }
+    )
+    new_id = str(resp.inserted_id)
+    return {"id": new_id, "key": key}
+
+@user.route("/api/delete_api_key", methods=["POST"])
+def delete_api_key():
+    data = request.get_json()
+    id = data["id"]
+    api_key_collection.delete_one(
+        {
+            "_id": ObjectId(id),
+        }
+    )
+    return {"status": "ok"}
 

application/api/user/tasks.py

@@ -1,7 +1,12 @@
-from application.worker import ingest_worker
+from application.worker import ingest_worker, remote_worker
 from application.celery import celery
 
 @celery.task(bind=True)
 def ingest(self, directory, formats, name_job, filename, user):
     resp = ingest_worker(self, directory, formats, name_job, filename, user)
     return resp
+
+@celery.task(bind=True)
+def ingest_remote(self, source_data, job_name, user, loader):
+    resp = remote_worker(self, source_data, job_name, user, loader)
+    return resp

application/app.py

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

application/core/settings.py

@@ -1,42 +1,68 @@
 from pathlib import Path
+from typing import Optional
 import os
 
-from pydantic import BaseSettings
+from pydantic_settings import BaseSettings
+
 current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 
 class Settings(BaseSettings):
-    LLM_NAME: str = "openai"
-    EMBEDDINGS_NAME: str = "openai_text-embedding-ada-002"
+    LLM_NAME: str = "docsgpt"
+    MODEL_NAME: Optional[str] = None # if LLM_NAME is openai, MODEL_NAME can be gpt-4 or gpt-3.5-turbo
+    EMBEDDINGS_NAME: str = "huggingface_sentence-transformers/all-mpnet-base-v2"
     CELERY_BROKER_URL: str = "redis://localhost:6379/0"
     CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"
     MONGO_URI: str = "mongodb://localhost:27017/docsgpt"
     MODEL_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
     TOKENS_MAX_HISTORY: int = 150
     UPLOAD_FOLDER: str = "inputs"
-    VECTOR_STORE: str = "faiss"  # "faiss" or "elasticsearch"
+    VECTOR_STORE: str = "faiss"  # "faiss" or "elasticsearch" or "qdrant"
+    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"] # also brave_search
 
     API_URL: str = "http://localhost:7091"  # backend url for celery worker
 
-    API_KEY: str = None  # LLM api key
-    EMBEDDINGS_KEY: str = None  # api key for embeddings (if using openai, just copy API_KEY)
-    OPENAI_API_BASE: str = None  # azure openai api base url
-    OPENAI_API_VERSION: str = None  # azure openai api version
-    AZURE_DEPLOYMENT_NAME: str = None  # azure deployment name for answering
-    AZURE_EMBEDDINGS_DEPLOYMENT_NAME: str = None  # azure deployment name for embeddings
+    API_KEY: Optional[str] = None  # LLM api key
+    EMBEDDINGS_KEY: Optional[str] = None  # api key for embeddings (if using openai, just copy API_KEY)
+    OPENAI_API_BASE: Optional[str] = None  # azure openai api base url
+    OPENAI_API_VERSION: Optional[str] = None  # azure openai api version
+    AZURE_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for answering
+    AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for embeddings
 
     # elasticsearch
-    ELASTIC_CLOUD_ID: str = None # cloud id for elasticsearch
-    ELASTIC_USERNAME: str = None # username for elasticsearch
-    ELASTIC_PASSWORD: str = None # password for elasticsearch
-    ELASTIC_URL: str = None # url for elasticsearch
-    ELASTIC_INDEX: str = "docsgpt" # index name for elasticsearch
+    ELASTIC_CLOUD_ID: Optional[str] = None  # cloud id for elasticsearch
+    ELASTIC_USERNAME: Optional[str] = None  # username for elasticsearch
+    ELASTIC_PASSWORD: Optional[str] = None  # password for elasticsearch
+    ELASTIC_URL: Optional[str] = None  # url for elasticsearch
+    ELASTIC_INDEX: Optional[str] = "docsgpt"  # index name for elasticsearch
 
     # SageMaker config
-    SAGEMAKER_ENDPOINT: str = None # SageMaker endpoint name
-    SAGEMAKER_REGION: str = None # SageMaker region name
-    SAGEMAKER_ACCESS_KEY: str = None # SageMaker access key
-    SAGEMAKER_SECRET_KEY: str = None # SageMaker secret key
+    SAGEMAKER_ENDPOINT: Optional[str] = None  # SageMaker endpoint name
+    SAGEMAKER_REGION: Optional[str] = None  # SageMaker region name
+    SAGEMAKER_ACCESS_KEY: Optional[str] = None  # SageMaker access key
+    SAGEMAKER_SECRET_KEY: Optional[str] = None  # SageMaker secret key
+
+    # prem ai project id
+    PREMAI_PROJECT_ID: Optional[str] = None
+
+    # Qdrant vectorstore config
+    QDRANT_COLLECTION_NAME: Optional[str] = "docsgpt"
+    QDRANT_LOCATION: Optional[str] = None
+    QDRANT_URL: Optional[str] = None
+    QDRANT_PORT: Optional[int] = 6333
+    QDRANT_GRPC_PORT: int = 6334
+    QDRANT_PREFER_GRPC: bool = False
+    QDRANT_HTTPS: Optional[bool] = None
+    QDRANT_API_KEY: Optional[str] = None
+    QDRANT_PREFIX: Optional[str] = None
+    QDRANT_TIMEOUT: Optional[float] = None
+    QDRANT_HOST: Optional[str] = None
+    QDRANT_PATH: Optional[str] = None
+    QDRANT_DISTANCE_FUNC: str = "Cosine"
+
+    BRAVE_SEARCH_API_KEY: Optional[str] = None
+
+    FLASK_DEBUG_MODE: bool = False
 
 
 path = Path(__file__).parent.parent.absolute()

application/llm/anthropic.py

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

application/llm/base.py

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

application/llm/docsgpt_provider.py

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

application/llm/huggingface.py

@@ -1,44 +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

@@ -1,32 +1,45 @@
 from application.llm.base import BaseLLM
 from application.core.settings import settings
 
+
 class LlamaCpp(BaseLLM):
 
-    def __init__(self, api_key, llm_name=settings.MODEL_PATH, **kwargs):
+    def __init__(
+        self,
+        api_key=None,
+        user_api_key=None,
+        llm_name=settings.MODEL_PATH,
+        *args,
+        **kwargs,
+    ):
         global llama
         try:
             from llama_cpp import Llama
         except ImportError:
-            raise ImportError("Please install llama_cpp using pip install llama-cpp-python")
+            raise ImportError(
+                "Please install llama_cpp using pip install llama-cpp-python"
+            )
 
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
         llama = Llama(model_path=llm_name, n_ctx=2048)
 
-    def 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 = 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']
+        return result["choices"][0]["text"].split("### Answer \n")[-1]
+
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
 
         result = llama(prompt, max_tokens=150, echo=False, stream=stream)
@@ -35,5 +48,5 @@ class LlamaCpp(BaseLLM):
         # print(list(result), file=sys.stderr)
 
         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

@@ -3,22 +3,25 @@ from application.llm.sagemaker import SagemakerAPILLM
 from application.llm.huggingface import HuggingFaceLLM
 from application.llm.llama_cpp import LlamaCpp
 from application.llm.anthropic import AnthropicLLM
-
+from application.llm.docsgpt_provider import DocsGPTAPILLM
+from application.llm.premai import PremAILLM
 
 
 class LLMCreator:
     llms = {
-        'openai': OpenAILLM,
-        'azure_openai': AzureOpenAILLM,
-        'sagemaker': SagemakerAPILLM,
-        'huggingface': HuggingFaceLLM,
-        'llama.cpp': LlamaCpp,
-        'anthropic': AnthropicLLM
+        "openai": OpenAILLM,
+        "azure_openai": AzureOpenAILLM,
+        "sagemaker": SagemakerAPILLM,
+        "huggingface": HuggingFaceLLM,
+        "llama.cpp": LlamaCpp,
+        "anthropic": AnthropicLLM,
+        "docsgpt": DocsGPTAPILLM,
+        "premai": PremAILLM,
     }
 
     @classmethod
-    def create_llm(cls, type, *args, **kwargs):
+    def create_llm(cls, type, api_key, user_api_key, *args, **kwargs):
         llm_class = cls.llms.get(type.lower())
         if not llm_class:
             raise ValueError(f"No LLM class found for type {type}")
-        return llm_class(*args, **kwargs)
+        return llm_class(api_key, user_api_key, *args, **kwargs)

application/llm/openai.py

@@ -1,57 +1,80 @@
 from application.llm.base import BaseLLM
 from application.core.settings import settings
 
+
 class OpenAILLM(BaseLLM):
 
-    def __init__(self, api_key):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
         global openai
-        import openai
-        openai.api_key = api_key
-        self.api_key = api_key  # Save the API key to be used later
+        from openai import OpenAI
+
+        super().__init__(*args, **kwargs)
+        self.client = OpenAI(
+            api_key=api_key,
+        )
+        self.api_key = api_key
+        self.user_api_key = user_api_key
 
     def _get_openai(self):
         # Import openai when needed
         import openai
-        # Set the API key every time you import openai
-        openai.api_key = self.api_key
+
         return openai
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
-        response = openai.ChatCompletion.create(
-            model=model,
-            engine=engine,
-            messages=messages,
-            stream=stream,
-            **kwargs
+    def _raw_gen(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=False,
+        engine=settings.AZURE_DEPLOYMENT_NAME,
+        **kwargs
+    ):
+        response = self.client.chat.completions.create(
+            model=model, messages=messages, stream=stream, **kwargs
         )
 
-        return response["choices"][0]["message"]["content"]
+        return response.choices[0].message.content
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
-        response = openai.ChatCompletion.create(
-            model=model,
-            engine=engine,
-            messages=messages,
-            stream=stream,
-            **kwargs
+    def _raw_gen_stream(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=True,
+        engine=settings.AZURE_DEPLOYMENT_NAME,
+        **kwargs
+    ):
+        response = self.client.chat.completions.create(
+            model=model, messages=messages, stream=stream, **kwargs
         )
 
         for line in response:
-            if "content" in line["choices"][0]["delta"]:
-                yield line["choices"][0]["delta"]["content"]
+            # import sys
+            # print(line.choices[0].delta.content, file=sys.stderr)
+            if line.choices[0].delta.content is not None:
+                yield line.choices[0].delta.content
 
 
 class AzureOpenAILLM(OpenAILLM):
 
-    def __init__(self, openai_api_key, openai_api_base, openai_api_version, deployment_name):
+    def __init__(
+        self, openai_api_key, openai_api_base, openai_api_version, deployment_name
+    ):
         super().__init__(openai_api_key)
-        self.api_base = settings.OPENAI_API_BASE,
-        self.api_version = settings.OPENAI_API_VERSION,
-        self.deployment_name = settings.AZURE_DEPLOYMENT_NAME,
+        self.api_base = (settings.OPENAI_API_BASE,)
+        self.api_version = (settings.OPENAI_API_VERSION,)
+        self.deployment_name = (settings.AZURE_DEPLOYMENT_NAME,)
+        from openai import AzureOpenAI
+
+        self.client = AzureOpenAI(
+            api_key=openai_api_key,
+            api_version=settings.OPENAI_API_VERSION,
+            api_base=settings.OPENAI_API_BASE,
+            deployment_name=settings.AZURE_DEPLOYMENT_NAME,
+        )
 
     def _get_openai(self):
         openai = super()._get_openai()
-        openai.api_base = self.api_base
-        openai.api_version = self.api_version
-        openai.api_type = "azure"
+
         return openai

application/llm/premai.py

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

application/llm/sagemaker.py

@@ -4,11 +4,10 @@ import json
 import io
 
 
-
 class LineIterator:
     """
-    A helper class for parsing the byte stream input. 
-    
+    A helper class for parsing the byte stream input.
+
     The output of the model will be in the following format:
     ```
     b'{"outputs": [" a"]}\n'
@@ -16,21 +15,21 @@ class LineIterator:
     b'{"outputs": [" problem"]}\n'
     ...
     ```
-    
-    While usually each PayloadPart event from the event stream will contain a byte array 
+
+    While usually each PayloadPart event from the event stream will contain a byte array
     with a full json, this is not guaranteed and some of the json objects may be split across
     PayloadPart events. For example:
     ```
     {'PayloadPart': {'Bytes': b'{"outputs": '}}
     {'PayloadPart': {'Bytes': b'[" problem"]}\n'}}
     ```
-    
+
     This class accounts for this by concatenating bytes written via the 'write' function
     and then exposing a method which will return lines (ending with a '\n' character) within
-    the buffer via the 'scan_lines' function. It maintains the position of the last read 
-    position to ensure that previous bytes are not exposed again. 
+    the buffer via the 'scan_lines' function. It maintains the position of the last read
+    position to ensure that previous bytes are not exposed again.
     """
-    
+
     def __init__(self, stream):
         self.byte_iterator = iter(stream)
         self.buffer = io.BytesIO()
@@ -43,7 +42,7 @@ class LineIterator:
         while True:
             self.buffer.seek(self.read_pos)
             line = self.buffer.readline()
-            if line and line[-1] == ord('\n'):
+            if line and line[-1] == ord("\n"):
                 self.read_pos += len(line)
                 return line[:-1]
             try:
@@ -52,33 +51,35 @@ class LineIterator:
                 if self.read_pos < self.buffer.getbuffer().nbytes:
                     continue
                 raise
-            if 'PayloadPart' not in chunk:
-                print('Unknown event type:' + chunk)
+            if "PayloadPart" not in chunk:
+                print("Unknown event type:" + chunk)
                 continue
             self.buffer.seek(0, io.SEEK_END)
-            self.buffer.write(chunk['PayloadPart']['Bytes'])
+            self.buffer.write(chunk["PayloadPart"]["Bytes"])
+
 
 class SagemakerAPILLM(BaseLLM):
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
         import boto3
+
         runtime = boto3.client(
-            'runtime.sagemaker',
-            aws_access_key_id='xxx',
-            aws_secret_access_key='xxx',
-            region_name='us-west-2'
+            "runtime.sagemaker",
+            aws_access_key_id="xxx",
+            aws_secret_access_key="xxx",
+            region_name="us-west-2",
         )
 
-        
-        self.endpoint =  settings.SAGEMAKER_ENDPOINT
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.endpoint = settings.SAGEMAKER_ENDPOINT
         self.runtime = runtime
 
-
-    def gen(self, model, engine, messages, stream=False, **kwargs):
-        context = messages[0]['content']
-        user_question = messages[-1]['content']
+    def _raw_gen(self, baseself, model, messages, stream=False, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
-    
 
         # Construct payload for endpoint
         payload = {
@@ -89,25 +90,25 @@ class SagemakerAPILLM(BaseLLM):
                 "temperature": 0.1,
                 "max_new_tokens": 30,
                 "repetition_penalty": 1.03,
-                "stop": ["</s>", "###"]
-            }
+                "stop": ["</s>", "###"],
+            },
         }
-        body_bytes = json.dumps(payload).encode('utf-8')
+        body_bytes = json.dumps(payload).encode("utf-8")
 
         # Invoke the endpoint
-        response = self.runtime.invoke_endpoint(EndpointName=self.endpoint,
-                                        ContentType='application/json',
-                                        Body=body_bytes)
-        result = json.loads(response['Body'].read().decode())
+        response = self.runtime.invoke_endpoint(
+            EndpointName=self.endpoint, ContentType="application/json", Body=body_bytes
+        )
+        result = json.loads(response["Body"].read().decode())
         import sys
-        print(result[0]['generated_text'], file=sys.stderr)
-        return result[0]['generated_text'][len(prompt):]
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
-        context = messages[0]['content']
-        user_question = messages[-1]['content']
+        print(result[0]["generated_text"], file=sys.stderr)
+        return result[0]["generated_text"][len(prompt) :]
+
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
+        context = messages[0]["content"]
+        user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
-    
 
         # Construct payload for endpoint
         payload = {
@@ -118,22 +119,22 @@ class SagemakerAPILLM(BaseLLM):
                 "temperature": 0.1,
                 "max_new_tokens": 512,
                 "repetition_penalty": 1.03,
-                "stop": ["</s>", "###"]
-            }
+                "stop": ["</s>", "###"],
+            },
         }
-        body_bytes = json.dumps(payload).encode('utf-8')
+        body_bytes = json.dumps(payload).encode("utf-8")
 
         # Invoke the endpoint
-        response = self.runtime.invoke_endpoint_with_response_stream(EndpointName=self.endpoint,
-                                        ContentType='application/json',
-                                        Body=body_bytes)
-        #result = json.loads(response['Body'].read().decode())
-        event_stream = response['Body']
-        start_json = b'{'
+        response = self.runtime.invoke_endpoint_with_response_stream(
+            EndpointName=self.endpoint, ContentType="application/json", Body=body_bytes
+        )
+        # result = json.loads(response['Body'].read().decode())
+        event_stream = response["Body"]
+        start_json = b"{"
         for line in LineIterator(event_stream):
-            if line != b'' and start_json in line:
-                #print(line)
-                data = json.loads(line[line.find(start_json):].decode('utf-8'))
-                if data['token']['text'] not in ["</s>", "###"]:
-                    print(data['token']['text'],end='')
-                    yield data['token']['text']
+            if line != b"" and start_json in line:
+                # print(line)
+                data = json.loads(line[line.find(start_json) :].decode("utf-8"))
+                if data["token"]["text"] not in ["</s>", "###"]:
+                    print(data["token"]["text"], end="")
+                    yield data["token"]["text"]

application/parser/file/bulk.py

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

application/parser/open_ai_func.py

@@ -6,9 +6,9 @@ from application.core.settings import settings
 from retry import retry
 
 
-# from langchain.embeddings import HuggingFaceEmbeddings
-# from langchain.embeddings import HuggingFaceInstructEmbeddings
-# from langchain.embeddings import CohereEmbeddings
+# from langchain_community.embeddings import HuggingFaceEmbeddings
+# from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+# from langchain_community.embeddings import CohereEmbeddings
 
 
 def num_tokens_from_string(string: str, encoding_name: str) -> int:

application/parser/remote/base.py

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

application/parser/remote/crawler_loader.py

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

application/parser/remote/reddit_loader.py

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

application/parser/remote/remote_creator.py

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

application/parser/remote/sitemap_loader.py

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

application/parser/remote/telegram.py

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

application/parser/remote/web_loader.py

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

application/parser/token_func.py

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

application/requirements.txt

@@ -1,110 +1,35 @@
-aiodns==3.0.0
-aiohttp==3.8.6
-aiohttp-retry==2.8.3
-aiosignal==1.3.1
-aleph-alpha-client==2.16.1
-amqp==5.1.1
-anthropic==0.5.0
-async-timeout==4.0.2
-attrs==22.2.0
-billiard==3.6.4.0
-blobfile==2.0.1
-boto3==1.28.20
-celery==5.2.7
-cffi==1.15.1
-charset-normalizer==3.1.0
-click==8.1.3
-click-didyoumean==0.3.0
-click-plugins==1.1.1
-click-repl==0.2.0
-cryptography==41.0.4
-dataclasses-json==0.5.7
-decorator==5.1.1
+anthropic==0.12.0
+boto3==1.34.6
+celery==5.3.6
+dataclasses_json==0.6.3
 docx2txt==0.8
-dill==0.3.6
-dnspython==2.3.0
-ecdsa==0.18.0
-elasticsearch==8.9.0
-entrypoints==0.4
-faiss-cpu==1.7.3
-filelock==3.9.0
-Flask==2.2.5
-Flask-Cors==3.0.10
-frozenlist==1.3.3
-geojson==2.5.0
-gunicorn==20.1.0
-greenlet==2.0.2
-gpt4all==0.1.7
-huggingface-hub==0.15.1
-humbug==0.3.2
-idna==3.4
-itsdangerous==2.1.2
-Jinja2==3.1.2
-jmespath==1.0.1
-joblib==1.2.0
-kombu==5.2.4
-langchain==0.0.312
-loguru==0.6.0
-lxml==4.9.2
-MarkupSafe==2.1.2
-marshmallow==3.19.0
-marshmallow-enum==1.5.1
-mpmath==1.3.0
-multidict==6.0.4
-multiprocess==0.70.14
-mypy-extensions==1.0.0
-networkx==3.0
-npx
+duckduckgo-search==5.3.0
+EbookLib==0.18
+elasticsearch==8.12.0
+escodegen==1.0.11
+esprima==4.0.1
+faiss-cpu==1.7.4
+Flask==3.0.1
+gunicorn==21.2.0
+html2text==2020.1.16
+javalang==0.13.0
+langchain==0.1.4
+langchain-openai==0.0.5
 nltk==3.8.1
-numcodecs==0.11.0
-numpy==1.24.2
-openai==0.27.8
-openapi3-parser==1.1.14
-packaging==23.0
-pathos==0.3.0
-Pillow==10.0.1
-pox==0.3.2
-ppft==1.7.6.6
-prompt-toolkit==3.0.38
-py==1.11.0
-pyasn1==0.4.8
-pycares==4.3.0
-pycparser==2.21
-pycryptodomex==3.17
-pycryptodome==3.19.0
-pydantic==1.10.5
-PyJWT==2.6.0
-pymongo==4.3.3
-pyowm==3.3.0
+openapi3_parser==1.1.16
+pandas==2.2.0
+pydantic_settings==2.1.0
+pymongo==4.6.3
 PyPDF2==3.0.1
-PySocks==1.7.1
-pytest
-python-dateutil==2.8.2
-python-dotenv==1.0.0
-python-jose==3.3.0
-pytz==2022.7.1
-PyYAML==6.0
-redis==4.5.4
-regex==2022.10.31
-requests==2.31.0
+python-dotenv==1.0.1
+qdrant-client==1.8.2
+redis==5.0.1
+Requests==2.31.0
 retry==0.9.2
-rsa==4.9
-scikit-learn==1.2.2
-scipy==1.10.1
-sentencepiece
-six==1.16.0
-SQLAlchemy==1.4.46
-sympy==1.11.1
-tenacity==8.2.2
-threadpoolctl==3.1.0
-tiktoken
-tqdm==4.65.0
-transformers==4.30.0
-typer==0.7.0
-typing-inspect==0.8.0
-typing_extensions==4.5.0
-urllib3==1.26.18
-vine==5.0.0
-wcwidth==0.2.6
-yarl==1.8.2
-sentence-transformers==2.2.2
+sentence-transformers
+tiktoken==0.5.2
+torch==2.1.2
+tqdm==4.66.1
+transformers==4.36.2
+unstructured==0.12.2
+Werkzeug==3.0.1

application/retriever/base.py

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

application/retriever/brave_search.py

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

application/retriever/classic_rag.py

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

application/retriever/duckduck_search.py

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

application/retriever/retriever_creator.py

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

application/usage.py

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

application/utils.py

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

application/vectorstore/base.py

@@ -1,11 +1,11 @@
 from abc import ABC, abstractmethod
 import os
-from langchain.embeddings import (
-    OpenAIEmbeddings,
+from langchain_community.embeddings import (
     HuggingFaceEmbeddings,
     CohereEmbeddings,
     HuggingFaceInstructEmbeddings,
 )
+from langchain_openai import OpenAIEmbeddings
 from application.core.settings import settings
 
 class BaseVectorStore(ABC):
@@ -44,6 +44,11 @@ class BaseVectorStore(ABC):
             embedding_instance = embeddings_factory[embeddings_name](
                 cohere_api_key=embeddings_key
             )
+        elif embeddings_name == "huggingface_sentence-transformers/all-mpnet-base-v2":
+            embedding_instance = embeddings_factory[embeddings_name](
+                #model_name="./model/all-mpnet-base-v2",
+                model_kwargs={"device": "cpu"},
+            )
         else:
             embedding_instance = embeddings_factory[embeddings_name]()
             

application/vectorstore/document_class.py

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

application/vectorstore/elasticsearch.py

@@ -1,16 +1,8 @@
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
+from application.vectorstore.document_class import Document
 import elasticsearch
 
-class Document(str):
-    """Class for storing a piece of text and associated metadata."""
-
-    def __new__(cls, page_content: str, metadata: dict):
-        instance = super().__new__(cls, page_content)
-        instance.page_content = page_content
-        instance.metadata = metadata
-        return instance
-
 
 
 

application/vectorstore/faiss.py

@@ -1,4 +1,4 @@
-from langchain.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
 

application/vectorstore/mongodb.py

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

application/vectorstore/qdrant.py

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

application/vectorstore/vector_creator.py

@@ -1,11 +1,15 @@
 from application.vectorstore.faiss import FaissStore
 from application.vectorstore.elasticsearch import ElasticsearchStore
+from application.vectorstore.mongodb import MongoDBVectorStore
+from application.vectorstore.qdrant import QdrantStore
 
 
 class VectorCreator:
     vectorstores = {
-        'faiss': FaissStore,
-        'elasticsearch':ElasticsearchStore
+        "faiss": FaissStore,
+        "elasticsearch": ElasticsearchStore,
+        "mongodb": MongoDBVectorStore,
+        "qdrant": QdrantStore,
     }
 
     @classmethod
@@ -13,4 +17,4 @@ class VectorCreator:
         vectorstore_class = cls.vectorstores.get(type.lower())
         if not vectorstore_class:
             raise ValueError(f"No vectorstore class found for type {type}")
-        return vectorstore_class(*args, **kwargs)
+        return vectorstore_class(*args, **kwargs)

application/worker.py

@@ -9,28 +9,59 @@ import requests
 
 from application.core.settings import settings
 from application.parser.file.bulk import SimpleDirectoryReader
+from application.parser.remote.remote_creator import RemoteCreator
 from application.parser.open_ai_func import call_openai_api
 from application.parser.schema.base import Document
 from application.parser.token_func import group_split
 
 try:
-    nltk.download('punkt', quiet=True)
-    nltk.download('averaged_perceptron_tagger', quiet=True)
+    nltk.download("punkt", quiet=True)
+    nltk.download("averaged_perceptron_tagger", quiet=True)
 except FileExistsError:
     pass
 
 
 # Define a function to extract metadata from a given filename.
 def metadata_from_filename(title):
-    store = '/'.join(title.split('/')[1:3])
-    return {'title': title, 'store': store}
+    store = "/".join(title.split("/")[1:3])
+    return {"title": title, "store": store}
 
 
 # Define a function to generate a random string of a given length.
 def generate_random_string(length):
-    return ''.join([string.ascii_letters[i % 52] for i in range(length)])
+    return "".join([string.ascii_letters[i % 52] for i in range(length)])
+
+
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
+
+def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
+    """
+    Recursively extract zip files with a limit on recursion depth.
+
+    Args:
+        zip_path (str): Path to the zip file to be extracted.
+        extract_to (str): Destination path for extracted files.
+        current_depth (int): Current depth of recursion.
+        max_depth (int): Maximum allowed depth of recursion to prevent infinite loops.
+    """
+    if current_depth > max_depth:
+        print(f"Reached maximum recursion depth of {max_depth}")
+        return
+
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_to)
+    os.remove(zip_path)  # Remove the zip file after extracting
+
+    # Check for nested zip files and extract them
+    for root, dirs, files in os.walk(extract_to):
+        for file in files:
+            if file.endswith(".zip"):
+                # If a nested zip file is found, extract it recursively
+                file_path = os.path.join(root, file)
+                extract_zip_recursive(file_path, root, current_depth + 1, max_depth)
 
-current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 # Define the main function for ingesting and processing documents.
 def ingest_worker(self, directory, formats, name_job, filename, user):
@@ -61,38 +92,52 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
     token_check = True
     min_tokens = 150
     max_tokens = 1250
-    full_path = directory + '/' + user + '/' + name_job
+    recursion_depth = 2
+    full_path = os.path.join(directory, user, name_job)
     import sys
+
+
     print(full_path, file=sys.stderr)
     # check if API_URL env variable is set
-    file_data = {'name': name_job, 'file': filename, 'user': user}
-    response = requests.get(urljoin(settings.API_URL, "/api/download"), params=file_data)
+    file_data = {"name": name_job, "file": filename, "user": user}
+    response = requests.get(
+        urljoin(settings.API_URL, "/api/download"), params=file_data
+    )
     # check if file is in the response
     print(response, file=sys.stderr)
     file = response.content
 
     if not os.path.exists(full_path):
         os.makedirs(full_path)
-    with open(full_path + '/' + filename, 'wb') as f:
+    with open(os.path.join(full_path, filename), "wb") as f:
         f.write(file)
 
     # check if file is .zip and extract it
-    if filename.endswith('.zip'):
-        with zipfile.ZipFile(full_path + '/' + filename, 'r') as zip_ref:
-            zip_ref.extractall(full_path)
-        os.remove(full_path + '/' + filename)
+    if filename.endswith(".zip"):
+        extract_zip_recursive(os.path.join(full_path, filename), full_path, 0, recursion_depth)
 
-    self.update_state(state='PROGRESS', meta={'current': 1})
+    self.update_state(state="PROGRESS", meta={"current": 1})
 
-    raw_docs = SimpleDirectoryReader(input_dir=full_path, input_files=input_files, recursive=recursive,
-                                     required_exts=formats, num_files_limit=limit,
-                                     exclude_hidden=exclude, file_metadata=metadata_from_filename).load_data()
-    raw_docs = group_split(documents=raw_docs, min_tokens=min_tokens, max_tokens=max_tokens, token_check=token_check)
+    raw_docs = SimpleDirectoryReader(
+        input_dir=full_path,
+        input_files=input_files,
+        recursive=recursive,
+        required_exts=formats,
+        num_files_limit=limit,
+        exclude_hidden=exclude,
+        file_metadata=metadata_from_filename,
+    ).load_data()
+    raw_docs = group_split(
+        documents=raw_docs,
+        min_tokens=min_tokens,
+        max_tokens=max_tokens,
+        token_check=token_check,
+    )
 
     docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
 
     call_openai_api(docs, full_path, self)
-    self.update_state(state='PROGRESS', meta={'current': 100})
+    self.update_state(state="PROGRESS", meta={"current": 100})
 
     if sample:
         for i in range(min(5, len(raw_docs))):
@@ -100,24 +145,80 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
 
     # get files from outputs/inputs/index.faiss and outputs/inputs/index.pkl
     # and send them to the server (provide user and name in form)
-    file_data = {'name': name_job, 'user': user}
+    file_data = {"name": name_job, "user": user}
     if settings.VECTOR_STORE == "faiss":
-        files = {'file_faiss': open(full_path + '/index.faiss', 'rb'),
-                'file_pkl': open(full_path + '/index.pkl', 'rb')}
-        response = requests.post(urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data)
-        response = requests.get(urljoin(settings.API_URL, "/api/delete_old?path=" + full_path))
+        files = {
+            "file_faiss": open(full_path + "/index.faiss", "rb"),
+            "file_pkl": open(full_path + "/index.pkl", "rb"),
+        }
+        response = requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+        )
+        response = requests.get(
+            urljoin(settings.API_URL, "/api/delete_old?path=" + full_path)
+        )
     else:
-        response = requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
+        response = requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), data=file_data
+        )
 
-    
     # delete local
     shutil.rmtree(full_path)
 
     return {
-        'directory': directory,
-        'formats': formats,
-        'name_job': name_job,
-        'filename': filename,
-        'user': user,
-        'limited': False
+        "directory": directory,
+        "formats": formats,
+        "name_job": name_job,
+        "filename": filename,
+        "user": user,
+        "limited": False,
     }
+
+
+def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
+    # sample = False
+    token_check = True
+    min_tokens = 150
+    max_tokens = 1250
+    full_path = directory + "/" + user + "/" + name_job
+
+    if not os.path.exists(full_path):
+        os.makedirs(full_path)
+
+    self.update_state(state="PROGRESS", meta={"current": 1})
+
+    # source_data {"data": [url]} for url type task just urls
+
+    # Use RemoteCreator to load data from URL
+    remote_loader = RemoteCreator.create_loader(loader)
+    raw_docs = remote_loader.load_data(source_data)
+
+    docs = group_split(
+        documents=raw_docs,
+        min_tokens=min_tokens,
+        max_tokens=max_tokens,
+        token_check=token_check,
+    )
+
+    # docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
+
+    call_openai_api(docs, full_path, self)
+    self.update_state(state="PROGRESS", meta={"current": 100})
+
+    # Proceed with uploading and cleaning as in the original function
+    file_data = {"name": name_job, "user": user}
+    if settings.VECTOR_STORE == "faiss":
+        files = {
+            "file_faiss": open(full_path + "/index.faiss", "rb"),
+            "file_pkl": open(full_path + "/index.pkl", "rb"),
+        }
+        requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+        )
+        requests.get(urljoin(settings.API_URL, "/api/delete_old?path=" + full_path))
+    else:
+        requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
+
+    shutil.rmtree(full_path)
+
+    return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}

application/wsgi.py

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

docs/next.config.js

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

docs/package.json

@@ -1,17 +1,17 @@
 {
-"scripts":{
-      "dev": "next dev",
+  "scripts": {
+    "dev": "next dev",
     "build": "next build",
     "start": "next start"
   },
-    "license": "MIT",
+  "license": "MIT",
   "dependencies": {
-    "@vercel/analytics": "^1.0.2",
-    "docsgpt": "^0.2.4",
-    "next": "^13.4.19",
-    "nextra": "^2.12.3",
-    "nextra-theme-docs": "^2.12.3",
+    "@vercel/analytics": "^1.1.1",
+    "docsgpt": "^0.3.7",
+    "next": "^14.0.4",
+    "nextra": "^2.13.2",
+    "nextra-theme-docs": "^2.13.2",
     "react": "^18.2.0",
     "react-dom": "^18.2.0"
   }
-}
+}

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

@@ -107,3 +107,4 @@ Your instance is now available at your Public IP Address on port 5173. Enjoy usi
 
 - [Deploy DocsGPT on Civo Compute Cloud](https://dev.to/rutamhere/deploying-docsgpt-on-civo-compute-c)
 - [Deploy DocsGPT on DigitalOcean Droplet](https://dev.to/rutamhere/deploying-docsgpt-on-digitalocean-droplet-50ea)
+- [Deploy DocsGPT on Kamatera Performance Cloud](https://dev.to/rutamhere/deploying-docsgpt-on-kamatera-performance-cloud-1bj)

docs/pages/Deploying/Quickstart.md

@@ -8,7 +8,7 @@ Just run the following command:
 ./setup.sh
 ```
 
-This command will install all the necessary dependencies and provide you with an option to download the local model or use OpenAI.
+This command will install all the necessary dependencies and provide you with an option to use our LLM API, download the local model or use OpenAI.
 
 If you prefer to follow manual steps, refer to this guide:
 
@@ -16,7 +16,7 @@ If you prefer to follow manual steps, refer to this guide:
    ```bash
    git clone https://github.com/arc53/DocsGPT.git
    ```
-2. Create a `.env` file in your root directory and set your `API_KEY` with your [OpenAI API key](https://platform.openai.com/account/api-keys).
+2. Create a `.env` file in your root directory and set your `API_KEY` with your [OpenAI API key](https://platform.openai.com/account/api-keys). (optional in case you want to use OpenAI)
 3. Run the following commands:
    ```bash
    docker-compose build && docker-compose up

docs/pages/Developing/API-docs.md

@@ -227,3 +227,124 @@ JSON response indicating the status of the operation:
 ```json
 { "status": "ok" }
 ```
+
+### 7. /api/get_api_keys
+**Description:**
+
+The endpoint retrieves a list of API keys for the user.
+
+**Request:**
+
+**Method**: `GET`
+
+**Sample JavaScript Fetch Request:**
+```js
+// get_api_keys (GET http://127.0.0.1:5000/api/get_api_keys)
+fetch("http://localhost:5001/api/get_api_keys", {
+      "method": "GET",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+})
+.then((res) => res.text())
+.then(console.log.bind(console))
+
+```
+**Response:**
+
+JSON response with a list of created API keys:
+
+```json
+[
+      {
+        "id": "string",
+        "name": "string",
+        "key": "string",
+        "source": "string"
+      },
+      ...
+    ]
+```
+
+### 8. /api/create_api_key
+
+**Description:**
+
+Create a new API key for the user.
+
+**Request:**
+
+**Method**: `POST`
+
+**Headers**: Content-Type should be set to `application/json; charset=utf-8`
+
+**Request Body**: JSON object with the following fields:
+* `name` — A name for the API key.
+* `source` — The source documents that will be used.
+* `prompt_id` — The prompt ID.
+* `chunks` — The number of chunks used to process an answer.
+
+Here is a JavaScript Fetch Request example:
+```js
+// create_api_key (POST http://127.0.0.1:5000/api/create_api_key)
+fetch("http://127.0.0.1:5000/api/create_api_key", {
+      "method": "POST",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+      "body": JSON.stringify({"name":"Example Key Name",
+          "source":"Example Source",
+          "prompt_id":"creative",
+          "chunks":"2"})
+})
+.then((res) => res.json())
+.then(console.log.bind(console))
+```
+
+**Response**
+
+In response, you will get a JSON document containing the `id` and `key`:
+```json
+{
+  "id": "string",
+  "key": "string"
+}
+```
+
+### 9. /api/delete_api_key
+
+**Description:**
+
+Delete an API key for the user.
+
+**Request:**
+
+**Method**: `POST`
+
+**Headers**: Content-Type should be set to `application/json; charset=utf-8`
+
+**Request Body**: JSON object with the field:
+* `id` — The unique identifier of the API key to be deleted.
+
+Here is a JavaScript Fetch Request example:
+```js
+// delete_api_key (POST http://127.0.0.1:5000/api/delete_api_key)
+fetch("http://127.0.0.1:5000/api/delete_api_key", {
+      "method": "POST",
+      "headers": {
+            "Content-Type": "application/json; charset=utf-8"
+      },
+      "body": JSON.stringify({"id":"API_KEY_ID"})
+})
+.then((res) => res.json())
+.then(console.log.bind(console))
+```
+
+**Response:**
+
+In response, you will get a JSON document indicating the status of the operation:
+```json
+{
+  "status": "ok"
+}
+```

docs/pages/Extensions/_meta.json

@@ -4,7 +4,11 @@
     "href": "/Extensions/Chatwoot-extension"
   },
   "react-widget": {
-      "title": "🏗️ Widget setup",
-      "href": "/Extensions/react-widget"
-    }
+    "title": "🏗️ Widget setup",
+    "href": "/Extensions/react-widget"
+  },
+  "api-key-guide": {
+    "title": "🔐 API Keys guide",
+    "href": "/Extensions/api-key-guide"
+  }
 }

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

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

docs/pages/Extensions/react-widget.md

@@ -10,7 +10,6 @@ First, make sure you have Node.js and npm installed in your project. Then go to
 In the file where you want to use the widget, import it and include the CSS file:
 ```js
 import { DocsGPTWidget } from "docsgpt";
-import "docsgpt/dist/style.css";
 ```
 
 
@@ -20,18 +19,28 @@ Now, you can use the widget in your component like this :
   apiHost="https://your-docsgpt-api.com"
   selectDocs="local/docs.zip"
   apiKey=""
+  avatar = "https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png",
+  title = "Get AI assistance",
+  description = "DocsGPT's AI Chatbot is here to help",
+  heroTitle = "Welcome to DocsGPT !",
+  heroDescription="This chatbot is built with DocsGPT and utilises GenAI, 
+  please review important information using sources."
 />
 ```
-DocsGPTWidget takes 3 **props**:
+DocsGPTWidget takes 8 **props** with default fallback values:
 1. `apiHost` — The URL of your DocsGPT API.
 2. `selectDocs` — The documentation source that you want to use for your widget (e.g. `default` or `local/docs1.zip`).
 3. `apiKey` — Usually, it's empty.
+4. `avatar`: Specifies the URL of the avatar or image representing the chatbot.
+5. `title`: Sets the title text displayed in the chatbot interface.
+6. `description`: Provides a brief description of the chatbot's purpose or functionality.
+7. `heroTitle`: Displays a welcome title when users interact with the chatbot.
+8. `heroDescription`: Provide additional introductory text or information about the chatbot's capabilities.
 
 ### How to use DocsGPTWidget with [Nextra](https://nextra.site/) (Next.js + MDX)
 Install your widget as described above and then go to your `pages/` folder and create a new file `_app.js` with the following content:
 ```js
 import { DocsGPTWidget } from "docsgpt";
-import "docsgpt/dist/style.css";
 
 export default function MyApp({ Component, pageProps }) {
     return (

docs/pages/_app.mdx

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

extensions/react-widget/.gitignore

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

extensions/react-widget/.parcelrc

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

extensions/react-widget/README.md

@@ -13,7 +13,6 @@ npm install  docsgpt
 
 ```javascript
     import { DocsGPTWidget } from "docsgpt";
-    import "docsgpt/dist/style.css";
 
     const App = () => {
       return <DocsGPTWidget />;
@@ -24,10 +23,18 @@ To link the widget to your api and your documents you can pass parameters to the
 
 ```javascript
     import { DocsGPTWidget } from "docsgpt";
-    import "docsgpt/dist/style.css";
 
     const App = () => {
-      return <DocsGPTWidget apiHost="http://localhost:7001" selectDocs='default' apiKey=''/>;
+      return <DocsGPTWidget 
+             apiHost = 'http://localhost:7001',
+             selectDocs = 'default', 
+             apiKey = '',
+             avatar = 'https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png',
+             title = 'Get AI assistance',
+             description = 'DocsGPT\'s AI Chatbot is here to help',
+             heroTitle = 'Welcome to DocsGPT !',
+             heroDescription='This chatbot is built with DocsGPT and utilises GenAI, please review important information using sources.'
+             />;
     };
 ```
 

extensions/react-widget/custom.d.ts

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

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

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

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

@@ -1,832 +0,0 @@
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-var ne = { exports: {} }, Y = {};
-/**
- * @license React
- * react-jsx-runtime.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-var Ce;
-function cr() {
-  if (Ce)
-    return Y;
-  Ce = 1;
-  var N = Ne, w = Symbol.for("react.element"), C = Symbol.for("react.fragment"), u = Object.prototype.hasOwnProperty, E = N.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.ReactCurrentOwner, S = { key: !0, ref: !0, __self: !0, __source: !0 };
-  function T(b, d, v) {
-    var m, h = {}, x = null, p = null;
-    v !== void 0 && (x = "" + v), d.key !== void 0 && (x = "" + d.key), d.ref !== void 0 && (p = d.ref);
-    for (m in d)
-      u.call(d, m) && !S.hasOwnProperty(m) && (h[m] = d[m]);
-    if (b && b.defaultProps)
-      for (m in d = b.defaultProps, d)
-        h[m] === void 0 && (h[m] = d[m]);
-    return { $$typeof: w, type: b, key: x, ref: p, props: h, _owner: E.current };
-  }
-  return Y.Fragment = C, Y.jsx = T, Y.jsxs = T, Y;
-}
-var L = {};
-/**
- * @license React
- * react-jsx-runtime.development.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-var Oe;
-function fr() {
-  return Oe || (Oe = 1, process.env.NODE_ENV !== "production" && function() {
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-    function J(e) {
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-    var O = N.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED;
-    function g(e) {
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-      return !!(typeof e == "string" || typeof e == "function" || e === u || e === S || Fe || e === E || e === v || e === m || Ae || e === p || D || z || De || typeof e == "object" && e !== null && (e.$$typeof === x || e.$$typeof === h || e.$$typeof === T || e.$$typeof === b || e.$$typeof === d || // This needs to include all possible module reference object
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-      // we don't know which Flight build this will end up being used
-      // with.
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-    function k(e) {
-      if (e == null)
-        return null;
-      if (typeof e.tag == "number" && g("Received an unexpected object in getComponentNameFromType(). This is likely a bug in React. Please file an issue."), typeof e == "function")
-        return e.displayName || e.name || null;
-      if (typeof e == "string")
-        return e;
-      switch (e) {
-        case u:
-          return "Fragment";
-        case C:
-          return "Portal";
-        case S:
-          return "Profiler";
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-  let b = "default";
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-      model: "default"
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-      if (!h.body)
-        throw Error("No response body");
-      const x = h.body.getReader(), p = new TextDecoder("utf-8");
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-        done: J,
-        value: O
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-          console.log(R), d();
-          return;
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-        R += 1;
-        const B = p.decode(O).split(`
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-        for (let D of B) {
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-      x.read().then(j).catch(v);
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-      console.error("Connection failed:", h), v(h);
-    });
-  });
-}
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-      const v = b.current;
-      v.scrollTop = v.scrollHeight;
-    }
-  }, [S]), Pe(() => {
-    localStorage.setItem("docsGPTChatState", u);
-  }, [u]);
-  const d = (v) => {
-    T(""), v.preventDefault(), E(
-      "processing"
-      /* Processing */
-    ), setTimeout(() => {
-      E(
-        "answer"
-        /* Answer */
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-    }, 800);
-    const h = v.currentTarget[0].value;
-    dr({
-      question: h,
-      apiKey: C,
-      selectedDocs: w,
-      history: [],
-      conversationId: null,
-      apiHost: N,
-      onEvent: (x) => {
-        const p = JSON.parse(x.data);
-        if (p.type === "end")
-          E(
-            "answer"
-            /* Answer */
-          );
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-          let R;
-          if (p.metadata && p.metadata.title) {
-            const j = p.metadata.title.split("/");
-            R = {
-              title: j[j.length - 1],
-              text: p.doc
-            };
-          } else
-            R = { title: p.doc, text: p.doc };
-          console.log(R);
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-          console.log(p.id);
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-          const R = p.answer;
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-      }
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-  };
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-            className: "cursor-pointer hover:opacity-50 h-2 absolute top-0 right-0 m-2 white-filter",
-            onClick: (v) => {
-              v.stopPropagation(), E(
-                "minimized"
-                /* Minimized */
-              );
-            }
-          }
-        ),
-        /* @__PURE__ */ l.jsxs("div", { className: "flex items-center gap-2 p-3", children: [
-          /* @__PURE__ */ l.jsxs("div", { className: `${u === "init" || u === "processing" || u === "typing" ? "" : "hidden"} flex-1`, children: [
-            /* @__PURE__ */ l.jsx("h3", { className: "text-sm font-bold text-gray-700 dark:text-gray-200", children: "Need help with documentation?" }),
-            /* @__PURE__ */ l.jsx("p", { className: "mt-1 text-xs text-gray-400 dark:text-gray-500", children: "DocsGPT AI assistant will help you with docs" })
-          ] }),
-          /* @__PURE__ */ l.jsx("div", { id: "docsgpt-answer", ref: b, className: `${u !== "answer" ? "hidden" : ""}`, children: /* @__PURE__ */ l.jsx("p", { className: "mt-1 text-sm text-gray-600 dark:text-white text-left", children: S }) })
-        ] })
-      ] }),
-      /* @__PURE__ */ l.jsxs("div", { className: "w-full", children: [
-        /* @__PURE__ */ l.jsx(
-          "button",
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-            onClick: () => E(
-              "typing"
-              /* Typing */
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-            children: "Ask DocsGPT"
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-            onSubmit: d,
-            className: "relative w-full m-0",
-            style: { opacity: 1 },
-            children: [
-              /* @__PURE__ */ l.jsx(
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-                {
-                  type: "text",
-                  className: "w-full bg-transparent px-5 py-3 pr-8 text-sm text-gray-700 dark:text-white focus:outline-none",
-                  placeholder: "What do you want to do?"
-                }
-              ),
-              /* @__PURE__ */ l.jsx("button", { className: "absolute text-gray-400 dark:text-gray-500 text-sm inset-y-0 right-2 -mx-2 px-2", type: "submit", children: "Submit" })
-            ]
-          }
-        ),
-        /* @__PURE__ */ l.jsxs("p", { className: `${u !== "processing" ? "hidden" : ""} flex w-full justify-center px-5 py-3 text-sm text-gray-800 font-bold dark:text-white transition duration-300 rounded-b`, children: [
-          "Processing",
-          /* @__PURE__ */ l.jsx("span", { className: "dot-animation", children: "." }),
-          /* @__PURE__ */ l.jsx("span", { className: "dot-animation delay-200", children: "." }),
-          /* @__PURE__ */ l.jsx("span", { className: "dot-animation delay-400", children: "." })
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-};
-export {
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-};
-//# sourceMappingURL=index.es.js.map

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

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

extensions/react-widget/dist/src/vite-env.d.ts

@@ -1 +0,0 @@
-/// <reference types="vite/client" />

extensions/react-widget/dist/style.css

@@ -1,762 +0,0 @@
-/*
-! tailwindcss v3.2.4 | MIT License | https://tailwindcss.com
-*//*
-1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4)
-2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116)
-*/
-
-*,
-::before,
-::after {
-  box-sizing: border-box; /* 1 */
-  border-width: 0; /* 2 */
-  border-style: solid; /* 2 */
-  border-color: #e5e7eb; /* 2 */
-}
-
-::before,
-::after {
-  --tw-content: '';
-}
-
-/*
-1. Use a consistent sensible line-height in all browsers.
-2. Prevent adjustments of font size after orientation changes in iOS.
-3. Use a more readable tab size.
-4. Use the user's configured `sans` font-family by default.
-5. Use the user's configured `sans` font-feature-settings by default.
-*/
-
-html {
-  line-height: 1.5; /* 1 */
-  -webkit-text-size-adjust: 100%; /* 2 */
-  -moz-tab-size: 4; /* 3 */
-  -o-tab-size: 4;
-     tab-size: 4; /* 3 */
-  font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; /* 4 */
-  font-feature-settings: normal; /* 5 */
-}
-
-/*
-1. Remove the margin in all browsers.
-2. Inherit line-height from `html` so users can set them as a class directly on the `html` element.
-*/
-
-body {
-  margin: 0; /* 1 */
-  line-height: inherit; /* 2 */
-}
-
-/*
-1. Add the correct height in Firefox.
-2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655)
-3. Ensure horizontal rules are visible by default.
-*/
-
-hr {
-  height: 0; /* 1 */
-  color: inherit; /* 2 */
-  border-top-width: 1px; /* 3 */
-}
-
-/*
-Add the correct text decoration in Chrome, Edge, and Safari.
-*/
-
-abbr:where([title]) {
-  -webkit-text-decoration: underline dotted;
-          text-decoration: underline dotted;
-}
-
-/*
-Remove the default font size and weight for headings.
-*/
-
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
-  font-size: inherit;
-  font-weight: inherit;
-}
-
-/*
-Reset links to optimize for opt-in styling instead of opt-out.
-*/
-
-a {
-  color: inherit;
-  text-decoration: inherit;
-}
-
-/*
-Add the correct font weight in Edge and Safari.
-*/
-
-b,
-strong {
-  font-weight: bolder;
-}
-
-/*
-1. Use the user's configured `mono` font family by default.
-2. Correct the odd `em` font sizing in all browsers.
-*/
-
-code,
-kbd,
-samp,
-pre {
-  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; /* 1 */
-  font-size: 1em; /* 2 */
-}
-
-/*
-Add the correct font size in all browsers.
-*/
-
-small {
-  font-size: 80%;
-}
-
-/*
-Prevent `sub` and `sup` elements from affecting the line height in all browsers.
-*/
-
-sub,
-sup {
-  font-size: 75%;
-  line-height: 0;
-  position: relative;
-  vertical-align: baseline;
-}
-
-sub {
-  bottom: -0.25em;
-}
-
-sup {
-  top: -0.5em;
-}
-
-/*
-1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297)
-2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016)
-3. Remove gaps between table borders by default.
-*/
-
-table {
-  text-indent: 0; /* 1 */
-  border-color: inherit; /* 2 */
-  border-collapse: collapse; /* 3 */
-}
-
-/*
-1. Change the font styles in all browsers.
-2. Remove the margin in Firefox and Safari.
-3. Remove default padding in all browsers.
-*/
-
-button,
-input,
-optgroup,
-select,
-textarea {
-  font-family: inherit; /* 1 */
-  font-size: 100%; /* 1 */
-  font-weight: inherit; /* 1 */
-  line-height: inherit; /* 1 */
-  color: inherit; /* 1 */
-  margin: 0; /* 2 */
-  padding: 0; /* 3 */
-}
-
-/*
-Remove the inheritance of text transform in Edge and Firefox.
-*/
-
-button,
-select {
-  text-transform: none;
-}
-
-/*
-1. Correct the inability to style clickable types in iOS and Safari.
-2. Remove default button styles.
-*/
-
-button,
-[type='button'],
-[type='reset'],
-[type='submit'] {
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extensions/react-widget/dist/vite.svg

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extensions/react-widget/index.ts

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

extensions/react-widget/package.json

@@ -1,47 +1,65 @@
 {
   "name": "docsgpt",
+  "version": "0.3.7",
   "private": false,
-  "version": "0.2.4",
-  "type": "module",
-  "main": "dist/index.umd.js",
-  "module": "dist/index.es.js",
-  "types": "dist/index.d.ts",
-  "exports": {
-    ".": {
-      "import": "./dist/index.es.js",
-      "require": "./dist/index.umd.js",
-      "types": "./dist/index.d.ts"
-    },
-    "./dist/style.css": "./dist/style.css"
-  },
+  "description": "DocsGPT 🦖 is an innovative open-source tool designed to simplify the retrieval of information from project documentation using advanced GPT models 🤖.",
+  "source": "./src/index.html",
+  "main": "dist/main.js",
+  "module": "dist/module.js",
+  "types": "dist/types.d.ts",
   "files": [
-    "/dist"
+    "dist",
+    "package.json"
   ],
-  "publishConfig": {
-    "access": "public"
+  "@parcel/resolver-default": {
+    "packageExports": true
+  },
+  "resolution": {
+    "styled-components": "^5"
   },
   "scripts": {
-    "dev": "vite",
-    "build": "tsc && vite build",
-    "prepare": "npm run build && npm run build-css",
-    "build-css": "postcss src/index.css -o dist/style.css",
-    "preview": "vite preview"
+    "build": "parcel build src/index.ts",
+    "dev": "parcel src/index.html -p 3000",
+    "test": "jest",
+    "lint": "eslint",
+    "check": "tsc --noEmit",
+    "ci": "yarn build && yarn test && yarn lint && yarn check"
   },
   "dependencies": {
-    "postcss-cli": "^10.1.0",
+    "@babel/plugin-transform-flow-strip-types": "^7.23.3",
+    "@bpmn-io/snarkdown": "^2.2.0",
+    "@parcel/resolver-glob": "^2.12.0",
+    "@parcel/transformer-svg-react": "^2.12.0",
+    "@parcel/transformer-typescript-tsc": "^2.12.0",
+    "@parcel/validator-typescript": "^2.12.0",
+    "@radix-ui/react-icons": "^1.3.0",
+    "@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",
+    "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",
-    "tailwindcss": "^3.2.4"
+    "styled-components": "^6.1.8"
   },
   "devDependencies": {
-    "@types/react": "^18.0.26",
-    "@types/react-dom": "^18.0.9",
-    "@vitejs/plugin-react-swc": "^3.0.0",
-    "autoprefixer": "^10.4.13",
-    "postcss": "^8.4.31",
-    "typescript": "^4.9.3",
-    "vite": "^4.0.0",
-    "vite-plugin-dts": "^1.7.1"
+    "@babel/core": "^7.24.0",
+    "@babel/preset-env": "^7.24.0",
+    "@babel/preset-react": "^7.23.3",
+    "@parcel/packager-ts": "^2.12.0",
+    "@parcel/transformer-typescript-types": "^2.12.0",
+    "@types/dompurify": "^3.0.5",
+    "babel-loader": "^8.0.4",
+    "process": "^0.11.10",
+    "typescript": "^5.3.3"
+  },
+  "publishConfig": {
+    "access": "public"
   },
   "repository": {
     "type": "git",

extensions/react-widget/postcss.config.cjs

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

extensions/react-widget/public/vite.svg

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

extensions/react-widget/src/App.css

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

extensions/react-widget/src/App.tsx

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

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

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

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

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="35.93" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 228"><path fill="#00D8FF" d="M210.483 73.824a171.49 171.49 0 0 0-8.24-2.597c.465-1.9.893-3.777 1.273-5.621c6.238-30.281 2.16-54.676-11.769-62.708c-13.355-7.7-35.196.329-57.254 19.526a171.23 171.23 0 0 0-6.375 5.848a155.866 155.866 0 0 0-4.241-3.917C100.759 3.829 77.587-4.822 63.673 3.233C50.33 10.957 46.379 33.89 51.995 62.588a170.974 170.974 0 0 0 1.892 8.48c-3.28.932-6.445 1.924-9.474 2.98C17.309 83.498 0 98.307 0 113.668c0 15.865 18.582 31.778 46.812 41.427a145.52 145.52 0 0 0 6.921 2.165a167.467 167.467 0 0 0-2.01 9.138c-5.354 28.2-1.173 50.591 12.134 58.266c13.744 7.926 36.812-.22 59.273-19.855a145.567 145.567 0 0 0 5.342-4.923a168.064 168.064 0 0 0 6.92 6.314c21.758 18.722 43.246 26.282 56.54 18.586c13.731-7.949 18.194-32.003 12.4-61.268a145.016 145.016 0 0 0-1.535-6.842c1.62-.48 3.21-.974 4.76-1.488c29.348-9.723 48.443-25.443 48.443-41.52c0-15.417-17.868-30.326-45.517-39.844Zm-6.365 70.984c-1.4.463-2.836.91-4.3 1.345c-3.24-10.257-7.612-21.163-12.963-32.432c5.106-11 9.31-21.767 12.459-31.957c2.619.758 5.16 1.557 7.61 2.4c23.69 8.156 38.14 20.213 38.14 29.504c0 9.896-15.606 22.743-40.946 31.14Zm-10.514 20.834c2.562 12.94 2.927 24.64 1.23 33.787c-1.524 8.219-4.59 13.698-8.382 15.893c-8.067 4.67-25.32-1.4-43.927-17.412a156.726 156.726 0 0 1-6.437-5.87c7.214-7.889 14.423-17.06 21.459-27.246c12.376-1.098 24.068-2.894 34.671-5.345a134.17 134.17 0 0 1 1.386 6.193ZM87.276 214.515c-7.882 2.783-14.16 2.863-17.955.675c-8.075-4.657-11.432-22.636-6.853-46.752a156.923 156.923 0 0 1 1.869-8.499c10.486 2.32 22.093 3.988 34.498 4.994c7.084 9.967 14.501 19.128 21.976 27.15a134.668 134.668 0 0 1-4.877 4.492c-9.933 8.682-19.886 14.842-28.658 17.94ZM50.35 144.747c-12.483-4.267-22.792-9.812-29.858-15.863c-6.35-5.437-9.555-10.836-9.555-15.216c0-9.322 13.897-21.212 37.076-29.293c2.813-.98 5.757-1.905 8.812-2.773c3.204 10.42 7.406 21.315 12.477 32.332c-5.137 11.18-9.399 22.249-12.634 32.792a134.718 134.718 0 0 1-6.318-1.979Zm12.378-84.26c-4.811-24.587-1.616-43.134 6.425-47.789c8.564-4.958 27.502 2.111 47.463 19.835a144.318 144.318 0 0 1 3.841 3.545c-7.438 7.987-14.787 17.08-21.808 26.988c-12.04 1.116-23.565 2.908-34.161 5.309a160.342 160.342 0 0 1-1.76-7.887Zm110.427 27.268a347.8 347.8 0 0 0-7.785-12.803c8.168 1.033 15.994 2.404 23.343 4.08c-2.206 7.072-4.956 14.465-8.193 22.045a381.151 381.151 0 0 0-7.365-13.322Zm-45.032-43.861c5.044 5.465 10.096 11.566 15.065 18.186a322.04 322.04 0 0 0-30.257-.006c4.974-6.559 10.069-12.652 15.192-18.18ZM82.802 87.83a323.167 323.167 0 0 0-7.227 13.238c-3.184-7.553-5.909-14.98-8.134-22.152c7.304-1.634 15.093-2.97 23.209-3.984a321.524 321.524 0 0 0-7.848 12.897Zm8.081 65.352c-8.385-.936-16.291-2.203-23.593-3.793c2.26-7.3 5.045-14.885 8.298-22.6a321.187 321.187 0 0 0 7.257 13.246c2.594 4.48 5.28 8.868 8.038 13.147Zm37.542 31.03c-5.184-5.592-10.354-11.779-15.403-18.433c4.902.192 9.899.29 14.978.29c5.218 0 10.376-.117 15.453-.343c-4.985 6.774-10.018 12.97-15.028 18.486Zm52.198-57.817c3.422 7.8 6.306 15.345 8.596 22.52c-7.422 1.694-15.436 3.058-23.88 4.071a382.417 382.417 0 0 0 7.859-13.026a347.403 347.403 0 0 0 7.425-13.565Zm-16.898 8.101a358.557 358.557 0 0 1-12.281 19.815a329.4 329.4 0 0 1-23.444.823c-7.967 0-15.716-.248-23.178-.732a310.202 310.202 0 0 1-12.513-19.846h.001a307.41 307.41 0 0 1-10.923-20.627a310.278 310.278 0 0 1 10.89-20.637l-.001.001a307.318 307.318 0 0 1 12.413-19.761c7.613-.576 15.42-.876 23.31-.876H128c7.926 0 15.743.303 23.354.883a329.357 329.357 0 0 1 12.335 19.695a358.489 358.489 0 0 1 11.036 20.54a329.472 329.472 0 0 1-11 20.722Zm22.56-122.124c8.572 4.944 11.906 24.881 6.52 51.026c-.344 1.668-.73 3.367-1.15 5.09c-10.622-2.452-22.155-4.275-34.23-5.408c-7.034-10.017-14.323-19.124-21.64-27.008a160.789 160.789 0 0 1 5.888-5.4c18.9-16.447 36.564-22.941 44.612-18.3ZM128 90.808c12.625 0 22.86 10.235 22.86 22.86s-10.235 22.86-22.86 22.86s-22.86-10.235-22.86-22.86s10.235-22.86 22.86-22.86Z"></path></svg>

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

@@ -1,247 +1,459 @@
 "use client";
-import {useEffect, useRef, useState} from 'react'
-//import './style.css'
-
-interface HistoryItem {
-  prompt: string;
-  response: string;
+import { Fragment, useEffect, useRef, useState } from 'react'
+import { PaperPlaneIcon, RocketIcon, ExclamationTriangleIcon, Cross2Icon } from '@radix-ui/react-icons';
+import { MESSAGE_TYPE, Query, Status } from '../types/index';
+import MessageIcon from '../assets/message.svg'
+import { fetchAnswerStreaming } from '../requests/streamingApi';
+import styled, { keyframes, createGlobalStyle } from 'styled-components';
+import snarkdown from '@bpmn-io/snarkdown';
+import { sanitize } from 'dompurify';
+const GlobalStyles = createGlobalStyle`
+.response pre {
+    padding: 8px;
+    width: 90%;
+    font-size: 12px;
+    border-radius: 6px;
+    overflow-x: auto;
+    background-color: #1B1C1F;
 }
-
-interface FetchAnswerStreamingProps {
-  question?: string;
-  apiKey?: string;
-  selectedDocs?: string;
-  history?: HistoryItem[];
-  conversationId?: string | null;
-  apiHost?: string;
-  onEvent?: (event: MessageEvent) => void;
+.response h1{
+  font-size: 20px;
 }
-
-
-enum ChatStates {
-  Init = 'init',
-  Processing = 'processing',
-  Typing = 'typing',
-  Answer = 'answer',
-  Minimized = 'minimized',
+.response h2{
+  font-size: 18px;
 }
-
-function fetchAnswerStreaming({
-  question = '',
-  apiKey = '',
-  selectedDocs = '',
-  history = [],
-  conversationId = null,
-  apiHost = '',
-  onEvent = () => {console.log("Event triggered, but no handler provided.");}
-}: FetchAnswerStreamingProps): Promise<void> {
-  let docPath = 'default';
-  if (selectedDocs) {
-    docPath = selectedDocs;
-  }
-
-  return new Promise<void>((resolve, reject) => {
-    const body = {
-      question: question,
-      api_key: apiKey,
-      embeddings_key: apiKey,
-      active_docs: docPath,
-      history: JSON.stringify(history),
-      conversation_id: conversationId,
-      model: 'default'
-    };
-
-    fetch(apiHost + '/stream', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      body: JSON.stringify(body),
-    })
-      .then((response) => {
-        if (!response.body) throw Error('No response body');
-
-        const reader = response.body.getReader();
-        const decoder = new TextDecoder('utf-8');
-        let counterrr = 0;
-        const processStream = ({
-          done,
-          value,
-        }: ReadableStreamReadResult<Uint8Array>) => {
-          if (done) {
-            console.log(counterrr);
-            resolve();
-            return;
-          }
-
-          counterrr += 1;
-
-          const chunk = decoder.decode(value);
-
-          const lines = chunk.split('\n');
-
-          for (let line of lines) {
-            if (line.trim() == '') {
-              continue;
-            }
-            if (line.startsWith('data:')) {
-              line = line.substring(5);
-            }
-
-            const messageEvent = new MessageEvent('message', {
-              data: line,
-            });
-
-            onEvent(messageEvent); // handle each message
-          }
-
-          reader.read().then(processStream).catch(reject);
-        };
-
-        reader.read().then(processStream).catch(reject);
-      })
-      .catch((error) => {
-        console.error('Connection failed:', error);
-        reject(error);
-      });
-  });
+.response h3{
+  font-size: 16px;
 }
-
-export const DocsGPTWidget = ({ apiHost = 'https://gptcloud.arc53.com', selectDocs = 'default', apiKey = 'docsgpt-public'}) => {
-    // processing states
-    const [chatState, setChatState] = useState<ChatStates>(() => {
-        if (typeof window !== 'undefined') {
-            return localStorage.getItem('docsGPTChatState') as ChatStates || ChatStates.Init;
-        }
-        return ChatStates.Init;
-    });
-
-    const [answer, setAnswer] = useState<string>('');
-
-    //const selectDocs = 'local/1706.03762.pdf/'
-    const answerRef = useRef<HTMLDivElement | null>(null);
-
-    useEffect(() => {
-        if (answerRef.current) {
-            const element = answerRef.current;
-            element.scrollTop = element.scrollHeight;
-        }
-    }, [answer]);
-
-    useEffect(() => {
-        localStorage.setItem('docsGPTChatState', chatState);
-    }, [chatState]);
-
-
-
-    // submit handler
-    const handleSubmit = (e: React.FormEvent<HTMLFormElement>) => {
-        setAnswer('')
-        e.preventDefault()
-        // get question
-        setChatState(ChatStates.Processing)
-        setTimeout(() => {
-            setChatState(ChatStates.Answer)
-        }, 800)
-        const inputElement = e.currentTarget[0] as HTMLInputElement;
-        const questionValue = inputElement.value;
-
-        fetchAnswerStreaming({
-          question: questionValue,
-          apiKey: apiKey,
-          selectedDocs: selectDocs,
-          history: [],
-          conversationId: null,
-          apiHost: apiHost,
-          onEvent: (event) => {
-            const data = JSON.parse(event.data);
-
-            // check if the 'end' event has been received
-            if (data.type === 'end') {
-              setChatState(ChatStates.Answer)
-            } else if (data.type === 'source') {
-              // check if data.metadata exists
-              let result;
-              if (data.metadata && data.metadata.title) {
-                const titleParts = data.metadata.title.split('/');
-                result = {
-                  title: titleParts[titleParts.length - 1],
-                  text: data.doc,
-                };
-              } else {
-                result = { title: data.doc, text: data.doc };
-              }
-              console.log(result)
-
-            } else if (data.type === 'id') {
-              console.log(data.id);
-            } else {
-              const result = data.answer;
-              // set answer by appending answer
-                setAnswer(prevAnswer => prevAnswer + result);
-            }
-          },
-      });
+.response code:not(pre code){
+  border-radius: 6px;
+  padding: 1px 3px 1px 3px;
+  font-size: 12px;
+  display: inline-block;
+  background-color: #646464;
+}
+`;
+const WidgetContainer = styled.div`
+    display: block;
+    position: fixed;
+    right: 10px;
+    bottom: 10px;
+    z-index: 1000;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    text-align: left;
+`;
+const StyledContainer = styled.div`
+    display: block;
+    position: relative;
+    bottom: 0;
+    left: 0;
+    width: 352px;
+    height: 407px;
+    max-height: 407px;
+    border-radius: 0.75rem;
+    background-color: #222327;
+    font-family: sans-serif;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05), 0 2px 4px rgba(0, 0, 0, 0.1);
+    transition: visibility 0.3s, opacity 0.3s;
+`;
+const FloatingButton = styled.div`
+    position: fixed;
+    display: flex;
+    z-index: 500;
+    justify-content: center;
+    align-items: center;
+    bottom: 1rem;
+    right: 1rem;
+    width: 5rem;
+    height: 5rem;
+    border-radius: 9999px;
+    background-image: linear-gradient(to bottom right, #5AF0EC, #E80D9D);
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    cursor: pointer;
+    &:hover {
+        transform: scale(1.1);
+        transition: transform 0.2s ease-in-out;
     }
+`;
+const CancelButton = styled.button`
+    cursor: pointer;
+    position: absolute;
+    top: 0;
+    right: 0;
+    margin: 0.5rem;
+    width: 30px;
+    padding: 0;
+    background-color: transparent;
+    border: none;
+    outline: none;
+    color: inherit;
+    transition: opacity 0.3s ease;
+    opacity: 0.6;
+    &:hover {
+        opacity: 1;
+    }
+    .white-filter {
+        filter: invert(100%);
+    }
+`;
 
+const Header = styled.div`
+    display: flex;
+    align-items: center;
+    padding-inline: 0.75rem;
+    padding-top: 1rem;
+    padding-bottom: 0.5rem;
+`;
+
+const IconWrapper = styled.div`
+    padding: 0.5rem;
+`;
+
+const ContentWrapper = styled.div`
+    flex: 1;
+    margin-left: 0.5rem;
+`;
+
+const Title = styled.h3`
+    font-size: 1rem;
+    font-weight: normal;
+    color: #FAFAFA;
+    margin-top: 0;
+    margin-bottom: 0.25rem;
+`;
+
+const Description = styled.p`
+    font-size: 0.85rem;
+    color: #A1A1AA;
+    margin-top: 0;
+`;
+const Conversation = styled.div`
+    height: 16rem;
+    padding-inline: 0.5rem;
+    border-radius: 0.375rem;
+    text-align: left;
+    overflow-y: auto;
+    scrollbar-width: thin;
+    scrollbar-color: #4a4a4a transparent; /* thumb color track color */
+`;
+
+const MessageBubble = styled.div<{ type: MESSAGE_TYPE }>`
+    display: flex;
+    font-size: 16px;
+    justify-content: ${props => props.type === 'QUESTION' ? 'flex-end' : 'flex-start'};
+    margin: 0.5rem;
+`;
+const Message = styled.p<{ type: MESSAGE_TYPE }>`
+    background: ${props => props.type === 'QUESTION' ?
+    'linear-gradient(to bottom right, #8860DB, #6D42C5)' :
+    '#38383b'};
+    color: #ffff;
+    border: none;
+    max-width: 80%;
+    overflow: auto;
+    margin: 4px;
+    display: block;
+    line-height: 1.5;
+    padding: 0.75rem;
+    border-radius: 0.375rem;
+`;
+const ErrorAlert = styled.div`
+  color: #b91c1c;
+  border:0.1px solid #b91c1c;
+  display: flex;
+  padding:4px;
+  margin:0.7rem;
+  opacity: 90%;
+  max-width: 70%;
+  font-weight: 400;
+  border-radius: 0.375rem;
+  justify-content: space-evenly;
+`
+//dot loading animation
+const dotBounce = keyframes`
+  0%, 80%, 100% {
+    transform: translateY(0);
+  }
+  40% {
+    transform: translateY(-5px);
+  }
+`;
+
+const DotAnimation = styled.div`
+  display: inline-block;
+  animation: ${dotBounce} 1s infinite ease-in-out;
+`;
+// delay classes as styled components
+const Delay = styled(DotAnimation) <{ delay: number }>`
+  animation-delay: ${props => props.delay + 'ms'};
+`;
+const PromptContainer = styled.form`
+  background-color: transparent;
+  height: 36px;
+  position: absolute;
+  bottom: 25px;
+  left: 24px;
+  right: 24px;
+  display: flex;
+  justify-content: space-evenly;
+`;
+const StyledInput = styled.input`
+  width: 260px;
+  height: 36px;
+  border: 1px solid #686877;
+  padding-left: 12px;
+  background-color: transparent;
+  font-size: 16px;
+  border-radius: 6px;
+  color: #ffff;
+  outline: none;
+`;
+const StyledButton = styled.button`
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  background-image: linear-gradient(to bottom right, #5AF0EC, #E80D9D);
+  border-radius: 6px;
+  width: 36px;
+  height: 36px;
+  margin-left:8px;
+  padding: 0px;
+  border: none;
+  cursor: pointer;
+  outline: none;
+  &:hover{
+    opacity: 90%;
+  }
+  &:disabled {
+    opacity: 60%;
+  }`;
+const HeroContainer = styled.div`
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  display: flex;
+  justify-content: center;
+  align-items: middle;
+  transform: translate(-50%, -50%);
+  width: 80%;
+  background-image: linear-gradient(to bottom right, #5AF0EC, #ff1bf4);
+  border-radius: 10px;
+  margin: 0 auto;
+  padding: 2px;
+`;
+const HeroWrapper = styled.div`
+  background-color: #222327;
+  border-radius: 10px; 
+  font-weight: normal;
+  padding: 6px;
+  display: flex;
+  justify-content: space-between;
+`
+const HeroTitle = styled.h3`
+  color: #fff;
+  font-size: 17px;
+  margin-bottom: 5px;
+  padding: 2px;
+`;
+const HeroDescription = styled.p`
+  color: #fff;
+  font-size: 14px;
+  line-height: 1.5;
+`;
+const Hero = ({ title, description }: { title: string, description: string }) => {
   return (
     <>
-        <div className="dark widget-container">
-            <div onClick={() => setChatState(ChatStates.Init)}
-                 className={`${chatState !== 'minimized' ? 'hidden' : ''} cursor-pointer`}>
-               <div className="mr-2 mb-2 w-20 h-20 rounded-full overflow-hidden dark:divide-gray-700 border dark:border-gray-700 bg-gradient-to-br from-gray-100/80 via-white to-white dark:from-gray-900/80 dark:via-gray-900 dark:to-gray-900 font-sans shadow backdrop-blur-sm flex items-center justify-center">
-                        <img
-                            src="https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png"
-                            alt="DocsGPT"
-                            className="cursor-pointer hover:opacity-50 h-14"
-                        />
-                    </div>
-            </div>
-      <div className={` ${chatState !== 'minimized' ? '' : 'hidden'} divide-y dark:divide-gray-700 rounded-md border dark:border-gray-700 bg-gradient-to-br from-gray-100/80 via-white to-white dark:from-gray-900/80 dark:via-gray-900 dark:to-gray-900 font-sans shadow backdrop-blur-sm`} style={{ width: '18rem', transform: 'translateY(0%) translateZ(0px)' }}>
-        <div>
-          <img
-                        src="https://d3dg1063dc54p9.cloudfront.net/exit.svg"
-                        alt="Exit"
-                        className="cursor-pointer hover:opacity-50 h-2 absolute top-0 right-0 m-2 white-filter"
-                        onClick={(event) => {
-                          event.stopPropagation();
-                          setChatState(ChatStates.Minimized);
-                        }}
-                      />
-          <div className="flex items-center gap-2 p-3">
-            <div  className={`${chatState === 'init' ? '' :
-                                chatState === 'processing' ? '' : 
-                                chatState === 'typing' ? '' :     
-                               'hidden'} flex-1`}>
-              <h3 className="text-sm font-bold text-gray-700 dark:text-gray-200">Need help with documentation?</h3>
-              <p className="mt-1 text-xs text-gray-400 dark:text-gray-500">DocsGPT AI assistant will help you with docs</p>
-            </div>
-            <div id="docsgpt-answer" ref={answerRef} className={`${chatState !== 'answer' ? 'hidden' : ''}`}>
-                <p className="mt-1 text-sm text-gray-600 dark:text-white text-left">{answer}</p>
-            </div>
+      <HeroContainer>
+        <HeroWrapper>
+          <IconWrapper style={{ marginTop: '8px' }}>
+            <RocketIcon color='white' width={20} height={20} />
+          </IconWrapper>
+          <div>
+            <HeroTitle>{title}</HeroTitle>
+            <HeroDescription>
+              {description}
+            </HeroDescription>
           </div>
-        </div>
-        <div className="w-full">
-          <button onClick={() => setChatState(ChatStates.Typing)}
-                  className={`flex w-full justify-center px-5 py-3 text-sm text-gray-800 font-bold dark:text-white transition duration-300 hover:bg-gray-100 rounded-b dark:hover:bg-gray-800/70 ${chatState !== 'init' ? 'hidden' : ''}`}>
-            Ask DocsGPT
-          </button>
-         { (chatState === 'typing' || chatState === 'answer') && (
-            <form
-                onSubmit={handleSubmit}
-                className="relative w-full m-0" style={{ opacity: 1 }}>
-              <input type="text"
-                     className="w-full bg-transparent px-5 py-3 pr-8 text-sm text-gray-700 dark:text-white focus:outline-none" placeholder="What do you want to do?" />
-              <button className="absolute text-gray-400 dark:text-gray-500 text-sm inset-y-0 right-2 -mx-2 px-2" type="submit" >Submit</button>
-            </form>
-          )}
-          <p className={`${chatState !== 'processing' ? 'hidden' : ''} flex w-full justify-center px-5 py-3 text-sm text-gray-800 font-bold dark:text-white transition duration-300 rounded-b`}>
-            Processing<span className="dot-animation">.</span><span className="dot-animation delay-200">.</span><span className="dot-animation delay-400">.</span>
-          </p>
-        </div>
-      </div>
-    </div>
+        </HeroWrapper>
+      </HeroContainer>
+    </>
+  );
+};
+export const DocsGPTWidget = ({
+  apiHost = 'https://gptcloud.arc53.com',
+  selectDocs = 'default',
+  apiKey = '82962c9a-aa77-4152-94e5-a4f84fd44c6a',
+  avatar = 'https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png',
+  title = 'Get AI assistance',
+  description = 'DocsGPT\'s AI Chatbot is here to help',
+  heroTitle = 'Welcome to DocsGPT !',
+  heroDescription = 'This chatbot is built with DocsGPT and utilises GenAI, please review important information using sources.'
+}) => {
 
+  const [prompt, setPrompt] = 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 handleUserInterrupt = () => {
+    (status === 'loading') && setEventInterrupt(true);
+  }
+  const scrollToBottom = (element: Element | null) => {
+    //recursive function to scroll to the last child of the last child ...
+    // to get to the bottom most element
+    if (!element) return;
+    if (element?.children.length === 0) {
+      element?.scrollIntoView({
+        behavior: 'smooth',
+        block: 'start',
+      });
+    }
+    const lastChild = element?.children?.[element.children.length - 1]
+    lastChild && scrollToBottom(lastChild)
+  };
+
+  useEffect(() => {
+    !eventInterrupt && scrollToBottom(endMessageRef.current);
+  }, [queries.length, queries[queries.length - 1]?.response]);
+
+  async function stream(question: string) {
+    setStatus('loading')
+    try {
+      await fetchAnswerStreaming(
+        {
+          question: question,
+          apiKey: apiKey,
+          apiHost: apiHost,
+          selectedDocs: selectDocs,
+          history: queries,
+          conversationId: conversationId,
+          onEvent: (event: MessageEvent) => {
+            const data = JSON.parse(event.data);
+            // check if the 'end' event has been received
+            if (data.type === 'end') {
+              // set status to 'idle'
+              setStatus('idle');
+
+            } else if (data.type === 'id') {
+              setConversationId(data.id)
+            } else {
+              const result = data.answer;
+              const streamingResponse = queries[queries.length - 1].response ? queries[queries.length - 1].response : '';
+              const updatedQueries = [...queries];
+              updatedQueries[updatedQueries.length - 1].response = streamingResponse + result;
+              setQueries(updatedQueries);
+            }
+          }
+        }
+      );
+    } catch (error) {
+      const updatedQueries = [...queries];
+      updatedQueries[updatedQueries.length - 1].error = 'error'
+      setQueries(updatedQueries);
+      setStatus('idle')
+      //setEventInterrupt(false)
+    }
+
+  }
+  // submit handler
+  const handleSubmit = async (e: React.FormEvent<HTMLFormElement>) => {
+    e.preventDefault()
+    setEventInterrupt(false);
+    queries.push({ prompt })
+    setPrompt('')
+    await stream(prompt)
+  }
+  const handleImageError = (event: React.SyntheticEvent<HTMLImageElement, Event>) => {
+    event.currentTarget.src = "https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png";
+  };
+  return (
+    <>
+      <WidgetContainer>
+        <GlobalStyles />
+        {!open && <FloatingButton onClick={() => setOpen(true)} hidden={open}>
+          <MessageIcon style={{ marginTop: '8px' }} />
+        </FloatingButton>}
+        {open && <StyledContainer>
+          <div>
+            <CancelButton onClick={() => setOpen(false)}>
+              <Cross2Icon width={24} height={24} color='white' />
+            </CancelButton>
+            <Header>
+              <IconWrapper>
+                <img style={{ maxWidth: "42px", maxHeight: "42px" }} onError={handleImageError} src={avatar} alt='docs-gpt' />
+              </IconWrapper>
+              <ContentWrapper>
+                <Title>{title}</Title>
+                <Description>{description}</Description>
+              </ContentWrapper>
+            </Header>
+          </div>
+          <Conversation onWheel={handleUserInterrupt} onTouchMove={handleUserInterrupt}>
+            {
+              queries.length > 0 ? queries?.map((query, index) => {
+                return (
+                  <Fragment key={index}>
+                    {
+                      query.prompt && <MessageBubble type='QUESTION'>
+                        <Message
+                          type='QUESTION'
+                          ref={(!(query.response || query.error) && index === queries.length - 1) ? endMessageRef : null}>
+                          {query.prompt}
+                        </Message>
+                      </MessageBubble>
+                    }
+                    {
+                      query.response ? <MessageBubble type='ANSWER'>
+                        <Message
+                          type='ANSWER'
+                          ref={(index === queries.length - 1) ? endMessageRef : null}
+                        >
+                          <div className="response" dangerouslySetInnerHTML={{ __html: sanitize(snarkdown(query.response)) }} />
+                        </Message>
+                      </MessageBubble>
+                        : <div>
+                          {
+                            query.error ? <ErrorAlert>
+                              <IconWrapper>
+                                <ExclamationTriangleIcon style={{ marginTop: '4px' }} width={22} height={22} color='#b91c1c' />
+                              </IconWrapper>
+                              <div>
+                                <h5 style={{ margin: 2 }}>Network Error</h5>
+                                <span style={{ margin: 2, fontSize: '13px' }}>Something went wrong !</span>
+                              </div>
+                            </ErrorAlert>
+                              : <MessageBubble type='ANSWER'>
+                                <Message type='ANSWER' style={{ fontWeight: 600 }}>
+                                  <DotAnimation>.</DotAnimation>
+                                  <Delay delay={200}>.</Delay>
+                                  <Delay delay={400}>.</Delay>
+                                </Message>
+                              </MessageBubble>
+                          }
+                        </div>
+                    }
+                  </Fragment>)
+              })
+                : <Hero title={heroTitle} description={heroDescription} />
+            }
+          </Conversation>
+
+          <PromptContainer
+            onSubmit={handleSubmit}>
+            <StyledInput
+              value={prompt} onChange={(event) => setPrompt(event.target.value)}
+              type='text' placeholder="What do you want to do?" />
+            <StyledButton
+              disabled={prompt.length == 0 || status !== 'idle'}>
+              <PaperPlaneIcon width={15} height={15} color='white' />
+            </StyledButton>
+          </PromptContainer>
+        </StyledContainer>}
+      </WidgetContainer>
     </>
   )
-}
+}

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

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

extensions/react-widget/src/index.css

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

extensions/react-widget/src/index.html

@@ -2,12 +2,12 @@
 <html lang="en">
   <head>
     <meta charset="UTF-8" />
-    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Vite + React + TS</title>
+    <title>DocsGPT Widget</title>
   </head>
   <body>
-    <div id="root"></div>
-    <script type="module" src="/src/main.tsx"></script>
+    <div id="app"></div>
+    <script type="module" src="main.tsx"></script>
   </body>
 </html>

extensions/react-widget/src/index.ts

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

extensions/react-widget/src/main.tsx

@@ -1,10 +1,6 @@
-import React from 'react'
-import ReactDOM from 'react-dom/client'
-import App from './App'
-import './index.css'
+import { createRoot } from 'react-dom/client';
+import App from './App.tsx';
+import React from 'react';
+const root = createRoot(document.getElementById('app') as HTMLElement);
 
-ReactDOM.createRoot(document.getElementById('root') as HTMLElement).render(
-  <React.StrictMode>
-    <App />
-  </React.StrictMode>,
-)
+root.render(<App />);

extensions/react-widget/src/requests/streamingApi.ts

@@ -0,0 +1,92 @@
+interface HistoryItem {
+    prompt: string;
+    response?: string;
+  }
+interface FetchAnswerStreamingProps {
+    question?: string;
+    apiKey?: string;
+    selectedDocs?: string;
+    history?: HistoryItem[];
+    conversationId?: string | null;
+    apiHost?: string;
+    onEvent?: (event: MessageEvent) => void;
+  }
+export function fetchAnswerStreaming({
+    question = '',
+    apiKey = '',
+    selectedDocs = '',
+    history = [],
+    conversationId = null,
+    apiHost = '',
+    onEvent = () => {console.log("Event triggered, but no handler provided.");}
+  }: FetchAnswerStreamingProps): Promise<void> {
+    let docPath = 'default';
+    if (selectedDocs) {
+      docPath = selectedDocs;
+    }
+  
+    return new Promise<void>((resolve, reject) => {
+      const body = {
+        question: question,
+        api_key: apiKey,
+        embeddings_key: apiKey,
+        active_docs: docPath,
+        history: JSON.stringify(history),
+        conversation_id: conversationId,
+        model: 'default'
+      };
+  
+      fetch(apiHost + '/stream', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify(body),
+      })
+        .then((response) => {
+          if (!response.body) throw Error('No response body');
+  
+          const reader = response.body.getReader();
+          const decoder = new TextDecoder('utf-8');
+          let counterrr = 0;
+          const processStream = ({
+            done,
+            value,
+          }: ReadableStreamReadResult<Uint8Array>) => {
+            if (done) {
+              resolve();
+              return;
+            }
+  
+            counterrr += 1;
+  
+            const chunk = decoder.decode(value);
+  
+            const lines = chunk.split('\n');
+  
+            for (let line of lines) {
+              if (line.trim() == '') {
+                continue;
+              }
+              if (line.startsWith('data:')) {
+                line = line.substring(5);
+              }
+  
+              const messageEvent = new MessageEvent('message', {
+                data: line,
+              });
+  
+              onEvent(messageEvent); // handle each message
+            }
+  
+            reader.read().then(processStream).catch(reject);
+          };
+  
+          reader.read().then(processStream).catch(reject);
+        })
+        .catch((error) => {
+          console.error('Connection failed:', error);
+          reject(error);
+        });
+    });
+  }

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/react-widget/src/vite-env.d.ts

@@ -1 +0,0 @@
-/// <reference types="vite/client" />

extensions/react-widget/tailwind.config.cjs

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

extensions/react-widget/tsconfig.json

@@ -1,23 +1,34 @@
 {
-  "compilerOptions": {
-    "target": "ESNext",
-    "useDefineForClassFields": true,
-    "lib": ["DOM", "DOM.Iterable", "ESNext"],
-    "allowJs": false,
-    "skipLibCheck": true,
-    "esModuleInterop": false,
-    "allowSyntheticDefaultImports": true,
-    "strict": true,
-    "forceConsistentCasingInFileNames": true,
-    "module": "ESNext",
-    "moduleResolution": "Node",
-    "resolveJsonModule": true,
-    "isolatedModules": true,
-    "declaration": true,
-    "typeRoots": ["./dist/index.d.ts", "node_modules/@types"],
-    "noEmit": true,
-    "jsx": "react-jsx"
-  },
-  "include": ["src", "./index.ts"],
-  "references": [{ "path": "./tsconfig.node.json" }]
-}
+    "compilerOptions": {
+      "baseUrl": ".",
+      "paths": {
+        "@/*": ["src/*", "@/*"]
+      },
+      "target": "ES2020",
+      "useDefineForClassFields": true,
+      "lib": ["ES2020", "DOM", "DOM.Iterable"],
+      "module": "ESNext",
+      "skipLibCheck": true,
+  
+      /* Bundler mode */
+      "moduleResolution": "bundler",
+      "allowImportingTsExtensions": true,
+      "resolveJsonModule": true,
+      "isolatedModules": true,
+      "noEmit": true,
+      "jsx": "react-jsx",
+  
+      /* Linting */
+      "strict": true,
+      "noUnusedLocals": false,
+      "noUnusedParameters": true,
+      "noFallthroughCasesInSwitch": true,
+      /* The "typeRoots" configuration specifies the locations where 
+         TypeScript looks for type definitions (.d.ts files) to 
+         include in the compilation process.*/
+         "typeRoots": ["./dist/index.d.ts", "node_modules/@types"]
+        },
+        /* include /index.ts*/
+        "include": ["src/index.ts","custom.d.ts"],
+        "exclude": ["node_modules"],
+  }

extensions/react-widget/tsconfig.node.json

@@ -1,9 +0,0 @@
-{
-  "compilerOptions": {
-    "composite": true,
-    "module": "ESNext",
-    "moduleResolution": "Node",
-    "allowSyntheticDefaultImports": true
-  },
-  "include": ["vite.config.ts"]
-}

extensions/react-widget/vite.config.ts

@@ -1,26 +0,0 @@
-import { defineConfig } from "vite";
-import react from "@vitejs/plugin-react-swc";
-import dts from "vite-plugin-dts";
-import path from "path";
-
-export default defineConfig({
-  build: {
-    lib: {
-      entry: path.resolve(__dirname, "index.ts"),
-      name: "ViteButton",
-      fileName: (format) => `index.${format}.js`,
-    },
-    rollupOptions: {
-      external: ["react", "react-dom"],
-      output: {
-        globals: {
-          react: "React",
-          "react-dom": "ReactDOM",
-        },
-      },
-    },
-    sourcemap: true,
-    emptyOutDir: true,
-  },
-  plugins: [react(), dts()],
-});

frontend/package.json

@@ -37,7 +37,7 @@
     "@types/react-syntax-highlighter": "^15.5.6",
     "@typescript-eslint/eslint-plugin": "^5.51.0",
     "@typescript-eslint/parser": "^5.51.0",
-    "@vitejs/plugin-react": "^3.1.0",
+    "@vitejs/plugin-react": "^4.2.1",
     "autoprefixer": "^10.4.13",
     "eslint": "^8.33.0",
     "eslint-config-prettier": "^8.6.0",
@@ -55,7 +55,7 @@
     "prettier-plugin-tailwindcss": "^0.2.2",
     "tailwindcss": "^3.2.4",
     "typescript": "^4.9.5",
-    "vite": "^4.5.0",
-    "vite-plugin-svgr": "^2.4.0"
+    "vite": "^5.0.13",
+    "vite-plugin-svgr": "^4.2.0"
   }
 }

frontend/public/lock-dark.svg

@@ -0,0 +1,7 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="33" height="33" viewBox="0 0 33 33" fill="none">
+  <path d="M8.25 13.75V11C8.25 6.44875 9.625 2.75 16.5 2.75C23.375 2.75 24.75 6.44875 24.75 11V13.75" stroke="#ECECF1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+  <path d="M23.375 30.25H9.625C4.125 30.25 2.75 28.875 2.75 23.375V20.625C2.75 15.125 4.125 13.75 9.625 13.75H23.375C28.875 13.75 30.25 15.125 30.25 20.625V23.375C30.25 28.875 28.875 30.25 23.375 30.25Z" stroke="#ECECF1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+  <path d="M21.9951 22H22.0075" stroke="#ECECF1" stroke-width="3" stroke-linecap="round" stroke-linejoin="round"/>
+  <path d="M16.4938 22H16.5061" stroke="#ECECF1" stroke-width="3" stroke-linecap="round" stroke-linejoin="round"/>
+  <path d="M10.9924 22H11.0048" stroke="#ECECF1" stroke-width="3" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>

frontend/public/message-programming-dark.svg

@@ -0,0 +1,6 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="36" height="36" viewBox="0 0 36 36" fill="none">
+  <path d="M12.75 28.4551H12C6 28.4551 3 26.9551 3 19.4551V11.9551C3 5.95508 6 2.95508 12 2.95508H24C30 2.95508 33 5.95508 33 11.9551V19.4551C33 25.4551 30 28.4551 24 28.4551H23.25C22.785 28.4551 22.335 28.6801 22.05 29.0551L19.8 32.0551C18.81 33.3751 17.19 33.3751 16.2 32.0551L13.95 29.0551C13.71 28.7251 13.17 28.4551 12.75 28.4551Z" stroke="#ECECF1" stroke-width="2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/>
+  <path d="M12 13.05L9 16.05L12 19.05" stroke="#ECECF1" stroke-width="2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/>
+  <path d="M24 13.05L27 16.05L24 19.05" stroke="#ECECF1" stroke-width="2" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/>
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