diff --git a/README.md b/README.md
index a156de1..58f5c91 100644
--- a/README.md
+++ b/README.md
@@ -1,91 +1 @@
-# LLM RAG Project
-
-## Overview
-
-This project builds on top of the main LLM Chat app. It provides RAG functionality on top of the standard chat.
-It also features a Gradio chatbot interface for the RAG chat.
-
-This implementation is a baseline vector similarity-based RAG.
-
-## Setup
-
-Follow the installation steps described in the main branch, nothing extra is required.
-
-Retrieval and reranking models are specified in `rag_service/docker-compose.yaml` - don't forget to adjust the ENV at the bottom.
-RAG-related parameters along with a prompt template are in `rag_sevice/src/config.py`
-
-## Parametrization
-
-The parameters set during the build:
-
-Retrieval/reranker models, in `rag_service/docker-compose.yaml`:
-- `command:` pass a model as `--model-id`
-- `environment:` `EMB_MODEL`
-
-If you need to switch to a gpu set TEI containers accordingly:
-- `image:` https://github.com/huggingface/text-embeddings-inference?tab=readme-ov-file#docker-images
-
-Retrieval parameters are in `rag_service/src/config.py`
-
-Rag-gradio model is in `rag_gradio_service/src/config.py`, `OPENAI_MODEL`
-
-The default parameters of a RAG request are in `rag_service/src/main.py`, `RAGRequest`
-
-## Usage
-
-RAG endpoints use the same authorization scheme as the main chat service. 
-To utilize RAG start by adding documents to your vector DB using the dedicated endpoint:
-
-```python
-import requests
-import os
-
-docs_path = "/path/to/your/docs/dir"
-
-for doc in os.listdir(docs_path):
-    doc_path = os.path.join(docs_path, doc)
-    if os.path.isfile(doc_path):
-        text = open(doc_path).read()
-        resp = requests.post(
-            "http://0.0.0.0:8001/add_to_rag_db/",
-            json={"text": text},
-            headers={"Authorization": os.getenv("YOUR_TOKEN")}
-        )
-        if resp.status_code != 200: 
-            print(f"error uploading document {doc}")
-```
-Documents are expected to be preprocessed (chunked [1](https://towardsdatascience.com/rag-101-chunking-strategies-fdc6f6c2aaec), [2](https://antematter.io/blogs/optimizing-rag-advanced-chunking-techniques-study) etc).
-
-If you have a lot of documents (>100k) you may benefit from indexing your document table (more on this [here]()).
-You can do it as follows:
-```commandline
-curl -H "Authorization: <YOUR TOKEN>" -X GET http://0.0.0.0:8001/reindex/
-```
-
-On inference, you can send a request with the following parameters:
-```commandline
-    query: str - the text query 
-    chat_id: Optional[str] - chat id to maintain conversation history
-    model: str = "gpt-4o-mini" - name of an LLM
-    use_reranker: bool = True - whether to rerank after retreival
-    top_k_retrieve: int = 20 - num of items to retrieve
-    top_k_rank: int = 4 - num of items to return after reranking
-    max_out_tokens: int = 512 - max num of tokens. Used to drop retrieved pieces 
-        of content if total prompt length goes beyond max contex length.
-```
-Note you can optionally use a reranker ([3](https://www.pinecone.io/learn/series/rag/rerankers/)).
-Both embedding and reranking models are light enough to run on a cpu.
-
-To access the Gradio interface, open `http://0.0.0.0:7860` in a browser.
-
-## Note
-
-Every query submitted via the gradio interface goes through the context retrieval stage,
-which means multi-turn dialogue may not work as expected. E.g. for a query "and you?", what may make
-sense given the conversation history, irrelevant context will likely be retrieved, which will negatively affect generation quality.
-
-Note that calls to RAG endpoints count toward the rate limit, as they use the chat endpoint under the hood.
-
-This project is licensed under the GNU GPL v3 License. See the [LICENSE](LICENSE) file for more information.
-
-© Nebius BV, 2024
+# This branch is under construction now
diff --git a/docker-compose.yaml b/docker-compose.yaml
index 2a49850..b08a431 100755
--- a/docker-compose.yaml
+++ b/docker-compose.yaml
@@ -92,6 +92,26 @@ services:
     ports:
       - "7860:7860"
 
+  jupyter:
+    image: jupyter/datascience-notebook
+    container_name: jupyter_notebook
+    ports:
+      - "8888:8888"
+    volumes:
+      - ./notebooks:/home/jovyan/work
+      - .:/home/jovyan/project
+    environment:
+      - JUPYTER_ENABLE_LAB=yes
+      - GRANT_SUDO=yes
+    user: root
+    command: start-notebook.sh --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.ip='0.0.0.0'
+    networks:
+      - default
+
 volumes:
   postgres_data:
     driver: local
+
+networks:
+  default:
+    driver: bridge