improve service names (#54)

- embedding-server => model_server
- public-types => public_types
- chatbot-ui => chatbot_ui
- function-calling => function_calling

embedding-server/Dockerfile

@@ -1,45 +0,0 @@
-# copied from https://github.com/bergos/embedding-server
-
-FROM python:3 AS base
-
-#
-# builder
-#
-FROM base AS builder
-
-WORKDIR /src
-
-COPY requirements.txt /src/
-RUN pip install --prefix=/runtime --force-reinstall -r requirements.txt
-
-COPY . /src
-
-#
-# output
-#
-
-FROM python:3-slim AS output
-
-# specify list of models that will go into the image as a comma separated list
-# following models have been tested to work with this image
-# "sentence-transformers/all-MiniLM-L6-v2,sentence-transformers/all-mpnet-base-v2,thenlper/gte-base,thenlper/gte-large,thenlper/gte-small"
-ENV MODELS="BAAI/bge-large-en-v1.5"
-ENV NER_MODELS="urchade/gliner_large-v2.1"
-
-COPY --from=builder /runtime /usr/local
-
-COPY /app /app
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-  curl \
-  && rm -rf /var/lib/apt/lists/*
-
-# comment it out for now as we don't want to download the model every time we build the image
-# we will mount host cache to docker image to avoid downloading the model every time
-# see docker-compose file for more details
-
-# RUN python install.py && \
-#   find /root/.cache/torch/sentence_transformers/ -name onnx -exec rm -rf {} +
-
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]