graph TD
A[User Query] -->|Entity Description + Embedding| B[Extracted Entities]
B --> C[Entity-Text Unit Mappings]
C --> D[Candidate Text Units]
D --> E[Ranking + Filtering]
E --> F[Prioritized Text Units]
F --> G[Response]
B --> H[Entity-Report Mappings]
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requests | |
tqdm | |
bs4 |
>>> create a simple LLM chatbot interface with python package ollama and streamlit.
Here's an example of how to create a simple LLM (Large Language Model) chatbot interface using
the `ollama` library and Streamlit.
Step 1: Install Required Libraries
First, you'll need to install the ollama
library and Streamlit. You can do this by running the
following command in your terminal:
You can manage temporary SSH configurations without touching your ~/.ssh
directory at all by using a completely separate temporary directory and leveraging the -F
option with ssh
along with the GIT_SSH_COMMAND
environment variable.
Here's how:
- Create a temporary directory outside of
~/.ssh
. For example:
mkdir -p /tmp/my_temp_ssh
- Gmail: https://github.com/jasonsum/gmail-mcp-server?tab=readme-ov-file#gmail-server-for-model-context-protocol-mcp
- Video Editing: https://github.com/burningion/video-editing-mcp?tab=readme-ov-file#using-tools-in-practice
- Rijksmuseum's collection: https://github.com/r-huijts/rijksmuseum-mcp?tab=readme-ov-file#example-use-cases
https://github.com/kimtaeyoon83/mcp-server-youtube-transcript