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Merge pull request #2 from mafzaal/dev
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‎.github/dependabot.yml‎

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# .github/dependabot.yml
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version: 2
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updates:
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# Frontend dependencies
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- package-ecosystem: "npm"
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directory: "/frontend"
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schedule:
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interval: "weekly"
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versioning-strategy: "auto"
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labels:
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- "dependencies"
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- "frontend"
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open-pull-requests-limit: 10
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groups:
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svelte-packages:
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patterns:
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- "svelte*"
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- "@sveltejs*"
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# Backend Python dependencies
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- package-ecosystem: "uv"
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directory: "/backend"
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schedule:
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interval: "weekly"
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labels:
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- "dependencies"
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- "backend"
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open-pull-requests-limit: 10
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# Docker dependencies
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- package-ecosystem: "docker"
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directory: "/"
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schedule:
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interval: "monthly"
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labels:
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- "dependencies"
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- "docker"
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# GitHub Actions
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- package-ecosystem: "github-actions"
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directory: "/"
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schedule:
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interval: "monthly"
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labels:
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- "dependencies"
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- "ci"

‎.github/workflows/docker-build.yml‎

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name: Docker Build and Push
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on:
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workflow_dispatch:
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push:
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branches:
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- main
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tags:
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- v*
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permissions:
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contents: write
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packages: write
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id-token: write
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env:
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REGISTRY: ghcr.io
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IMAGE_NAME: ${{ github.repository }}
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4.2.2
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3.10.0
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- name: Extract metadata (tags, labels) for Docker
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
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labels: |
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org.opencontainers.image.title=${{ env.IMAGE_NAME }}
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org.opencontainers.image.description="This is a Docker image for the ${{ env.IMAGE_NAME }} project"
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org.opencontainers.image.url=${{ github.repositoryUrl }}
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org.opencontainers.image.source=${{ github.repositoryUrl }}
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org.opencontainers.image.version=${{ github.sha }}
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org.opencontainers.image.created=${{ github.event.repository.updated_at }}
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- name: Login to GitHub Container Registry
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uses: docker/login-action@v3.4.0
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Build and push Docker image
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uses: docker/build-push-action@v6.18.0
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with:
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context: .
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push: true
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tags: |
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${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:latest
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${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}

‎Dockerfile‎

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FROM langchain/langgraph-api:3.13
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# Get a distribution that has uv already installed
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FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim
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# Add user - this is the user that will run the app
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# If you do not set user, the app will run as root (undesirable)
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RUN useradd -m -u 1000 user
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USER user
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# Set the home directory and path
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# -- Adding local package . --
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ADD . /deps/lets-talk
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# -- End of local package . --
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ENV UVICORN_WS_PROTOCOL=websockets
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# -- Installing all local dependencies --
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RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
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# -- End of local dependencies install --
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ENV LANGGRAPH_HTTP='{"disable_meta": true, "disable_store": true}'
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ENV LANGSERVE_GRAPHS='{"the_data_guy_chat": "/deps/lets-talk/py-src/lets_talk/agent_v2.py:agent"}'
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# Set the working directory
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WORKDIR $HOME/app
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COPY --chown=user ./pyproject.toml $HOME/app
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COPY --chown=user ./uv.lock $HOME/app
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# -- Ensure user deps didn't inadvertently overwrite langgraph-api
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RUN mkdir -p /api/langgraph_api /api/langgraph_runtime /api/langgraph_license && touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py
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RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir --no-deps -e /api
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# -- End of ensuring user deps didn't inadvertently overwrite langgraph-api --
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# -- Removing pip from the final image ~<:===~~~ --
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RUN pip uninstall -y pip setuptools wheel && rm -rf /usr/local/lib/python*/site-packages/pip* /usr/local/lib/python*/site-packages/setuptools* /usr/local/lib/python*/site-packages/wheel* && find /usr/local/bin -name "pip*" -delete
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# -- End of pip removal --
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# Install the dependencies
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# RUN uv sync --frozen
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RUN uv sync
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# Copy the app to the container
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COPY --chown=user ./py-src/ $HOME/app
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COPY --chown=user ./.chainlit/ $HOME/app
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COPY --chown=user ./chainlit.md $HOME/app
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#TODO: Fix this to download
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#copy posts to container
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COPY --chown=user ./data/ $HOME/app/data
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# Expose the port
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EXPOSE 7860
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# Run the app
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CMD ["uv", "run", "chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]
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WORKDIR /deps/lets-talk

‎Dockerfile.chainlit‎

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# Get a distribution that has uv already installed
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FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim
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# Add user - this is the user that will run the app
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# If you do not set user, the app will run as root (undesirable)
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RUN useradd -m -u 1000 user
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USER user
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# Set the home directory and path
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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ENV UVICORN_WS_PROTOCOL=websockets
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# Set the working directory
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WORKDIR $HOME/app
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COPY --chown=user ./pyproject.toml $HOME/app
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COPY --chown=user ./uv.lock $HOME/app
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# Install the dependencies
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# RUN uv sync --frozen
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RUN uv sync
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# Copy the app to the container
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COPY --chown=user ./py-src/ $HOME/app
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COPY --chown=user ./.chainlit/ $HOME/app
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COPY --chown=user ./chainlit.md $HOME/app
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#TODO: Fix this to download
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#copy posts to container
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COPY --chown=user ./data/ $HOME/app/data
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# Expose the port
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EXPOSE 7860
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# Run the app
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CMD ["uv", "run", "chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

‎frontend/src/lib/ChatWidget.svelte‎

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inputMessages.push(userMsg.toJSON());
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const streamResponse = client.runs.stream(thread_id, assistant_id, {
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input: { messages: inputMessages },
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streamMode: 'messages-tuple'
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streamMode: 'events',
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});
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userInput = '';
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let aiMsg = new Message('ai', '');
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messages.push(aiMsg);
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for await (const chunk of streamResponse) {
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if (chunk?.event === 'messages') {
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// Handle the message event
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const [msg, _] = chunk.data;
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console.log(chunk);
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//@ts-ignore
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if (msg.type === 'AIMessageChunk') {
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//@ts-ignore
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aiMsg.content += msg.content;
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}
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if(chunk?.data?.event === 'on_chat_model_stream')
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{
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aiMsg.content += chunk?.data?.data?.chunk?.content || '';
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// Handle the message event
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}
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scrollToBottom();
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// if (chunk?.event === 'messages') {
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// // Handle the message event
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// const [msg, _] = chunk.data;
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// //@ts-ignore
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// if (msg.type === 'AIMessageChunk') {
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// //@ts-ignore
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// aiMsg.content += msg.content;
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// }
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// }
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// scrollToBottom();
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}
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} catch (err) {
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console.error(err);

‎py-src/lets_talk/agent_v2.py‎

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from langchain_core.tools import tool
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from langchain.chat_models import init_chat_model
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import lets_talk.rag as rag
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from lets_talk.tools.datetime_tool import get_current_datetime
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from lets_talk.utils import format_docs
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from lets_talk.tools import RSSFeedTool
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from lets_talk.config import AGENT_PROMPT, AGENT_PROMPT_FILE, BASE_URL, BLOG_BASE_URL, LLM_MODEL,LLM_TEMPERATURE
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@tool
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def retrive_documents(query: str) -> str:
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def retrieve_documents(query: str) -> str:
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"""Retrieve relevant documents from the knowledge base to answer user questions.
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Use this tool when you need to search for specific information, facts, or content
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from lets_talk.config import (RSS_URL)
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tools =[retrive_documents, retrieve_page_by_url]
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tools =[retrieve_documents, retrieve_page_by_url, get_current_datetime]
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if RSS_URL:
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logger.info(f"RSS URL is set to: {RSS_URL}")

‎py-src/lets_talk/rag.py‎

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logger.info("No additional retrievers added. Returning base retriever.")
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return retriever
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retriever_list.append(retriever) # type: ignore
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#retriever_list.append(retriever) # type: ignore
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equal_weighting = [1/len(retriever_list)] * len(retriever_list)
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logger.info("Creating EnsembleRetriever with %d retrievers", len(retriever_list))
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ensemble_retriever = EnsembleRetriever(

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