Docker Compose will auto load .env variable

 Docker Compose retrieves environment variables from a .env file located in the same directory as your docker-compose.yml file. This is the default and most common method.

How it works:

Placement:

Create a file named .env in the root directory of your Docker Compose project, alongside your docker-compose.yml file.

Content:

Define your environment variables within this .env file using a simple key-value pair format, one variable per line:

Code

    VARIABLE_NAME=variable_value

    ANOTHER_VAR=another_value

Referencing in docker-compose.yml: In your docker-compose.yml file, you can then reference these variables using the ${VARIABLE_NAME} syntax for interpolation:

Code

    version: '3.8'

    services:

      web:

        image: "my-app:${APP_VERSION}"

        environment:

          - DB_HOST=${DATABASE_HOST}

Automatic Loading: When you run docker compose up (or docker-compose up with older versions), Docker Compose automatically detects and loads the variables from the .env file, substituting them into your docker-compose.yml configuration before creating and starting the services.

iFrame remove scroll bar

 https://stackoverflow.com/questions/10082155/remove-scrollbar-from-iframe

dd scrolling="no" attribute to the iframe.

<iframe src="http://buythecity.com"  scrolling="no" ...>

Ubuntu set folder permission for inheritance (setfacl)

 https://serverfault.com/questions/444867/linux-setfacl-set-all-current-future-files-directories-in-parent-directory-to

sudo setfacl -Rdm g:groupnamehere:rwx /base/path/members/
sudo setfacl -Rm g:groupnamehere:rwx /base/path/members/

R is recursive, which means everything under that directory will have the rule applied to it.
d is default, which means for all future items created under that directory, have these rules apply by default. m is needed to add/modify rules.

The first command, is for new items (hence the d), the second command, is for old/existing items under the folder. Hope this helps someone out as this stuff is a bit complicated and not very intuitive.

U can add others:

sudo setfacl -d -m g:your_group:rwx,o:rx /path/to/parent
sudo setfacl -d -m g:your_group:rw,o:r /path/to/parent

ES search with embedding

 

How “Search by Embedding” Works

1. Index Phase

1. Choose an embedding model, for example text-embedding-3-small.

2. For each new question, compute its embedding and store both text and vector in Elasticsearch:

   go
   emb := EmbedText("How do I apply for a mortgage in Canada?")
   // emb is []float32, e.g. [0.20, 0.80, -0.10, …]
   
   // Then index into ES:
   PUT /qa_memory/_doc/<uuid>
   {
     "text":      "How do I apply for a mortgage in Canada?",
     "embedding": [0.20, 0.80, -0.10, …]
   }
   

2. Query Phase

1. Embed the incoming question the same way:

   go
   qEmb := EmbedText("What's the process to get a home loan in Canada?")
   // qEmb might be [0.19, 0.79, -0.05, …]
   

2. Ask Elasticsearch for the most similar vector using cosine similarity:

   json
   GET /qa_memory/_search
   {
     "size": 1,
     "query": {
       "script_score": {
         "query": { "match_all": {} },
         "script": {
           "source": "cosineSimilarity(params.vec, 'embedding') + 1.0",
           "params": { "vec": qEmb }
         }
       }
     }
   }
   

   Why + 1.0?
   Cosine similarity ranges from –1 to +1; adding 1.0 shifts it to [0, 2], keeping all scores positive.

3. Example (3-Dimensional)

ID	Text	Embedding
doc1	“Apply for a mortgage in Canada?”	[0.20, 0.80, –0.10]
doc2	“Best restaurants in Vancouver”	[–0.50, 0.10, 0.70]

• New query embedding: [0.19, 0.79, –0.05]

• Cosine similarity with doc1 ≈ 0.99 → very high → reuse doc1

• Similarity with doc2 is much lower → ignore doc2

4. Decision Threshold

• If top score > 1.8 (i.e. cosine + 1.0)
  → consider it “already answered” and reuse that stored Q&A.

• Otherwise, send the question to GPT, then embed & index its answer for future reuse.

1.8 (shifted) – 1.0 = 0.8 (raw cosine)

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