Merge branch 'development_actions007' into development

* development_actions007: (49 commits)
  ...
  bump version to 1.0.22
  add baobab mcp
  feat: Improve path normalization in `namefix`
  feat: Improve Qdrant client library
  test: Skip Jina client for now
  feat: Remove redundant Jina client code
  feat: Remove optional age field from Person struct
  feat: Improve DedupeStore and update tests
  test: Improve test coverage for fenced code block and list item parsers
  test: Improve test coverage for paragraph parsing
  test: Improve test coverage for markdown block parser
  test: Improve list parsing test cases
  feat: Improve Markdown parser list and table detection
  fix: Fix CI
  feat: Improve RadixTree debugging output
  refactor: Simplify ContactsDB methods
  feat: Add calendar VFS implementation
  feat: Add Contacts VFS module
  feat: Add contacts database and VFS implementation
  ...

# Conflicts:
#	.gitignore
#	lib/clients/qdrant/qdrant_client.v
#	lib/core/texttools/namefix.v

examples/clients/groq/.env.example

@@ -0,0 +1 @@
+export GROQ_API_KEY="your-groq-api-key-here"

examples/clients/groq/README.md

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+# Groq AI Client Example
+
+This example demonstrates how to use Groq's AI API with the herolib OpenAI client. Groq provides API compatibility with OpenAI's client libraries, allowing you to leverage Groq's fast inference speeds with minimal changes to your existing code.
+
+## Prerequisites
+
+- V programming language installed
+- A Groq API key (get one from [Groq's website](https://console.groq.com/keys))
+
+## Setup
+
+1. Copy the `.env.example` file to `.env`:
+
+```bash
+cp .env.example .env
+```
+
+2. Edit the `.env` file and replace `your-groq-api-key-here` with your actual Groq API key.
+
+3. Load the environment variables:
+
+```bash
+source .env
+```
+
+## Running the Example
+
+Execute the script with:
+
+```bash
+v run groq_client.vsh
+```
+
+Or make it executable first:
+
+```bash
+chmod +x groq_client.vsh
+./groq_client.vsh
+```
+
+## How It Works
+
+The example uses the existing OpenAI client from herolib but configures it to use Groq's API endpoint:
+
+1. It retrieves the Groq API key from the environment variables
+2. Configures the OpenAI client with the Groq API key
+3. Overrides the default OpenAI URL with Groq's API URL (`https://api.groq.com/openai/v1`)
+4. Sends a chat completion request to Groq's API
+5. Displays the response
+
+## Supported Models
+
+Groq supports various models including:
+
+- llama2-70b-4096
+- mixtral-8x7b-32768
+- gemma-7b-it
+
+For a complete and up-to-date list of supported models, refer to the [Groq API documentation](https://console.groq.com/docs/models).
+
+## Notes
+
+- The example uses the `gpt_3_5_turbo` enum from the OpenAI client, but Groq will automatically map this to an appropriate model on their end.
+- For production use, you may want to explicitly specify one of Groq's supported models.

examples/clients/groq/groq_client.vsh

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+#!/usr/bin/env -S v -n -w -gc none  -cc tcc -d use_openssl -enable-globals run
+
+module main
+
+import freeflowuniverse.herolib.clients.openai
+import os
+
+fn main() {
+	// Get API key from environment variable
+	key := os.getenv('GROQ_API_KEY')
+	if key == '' {
+		println('Error: GROQ_API_KEY environment variable not set')
+		println('Please set it by running: source .env')
+		exit(1)
+	}
+
+	// Get the configured client
+	mut client := openai.OpenAI {
+		name: 'groq'
+		api_key: key
+		server_url: 'https://api.groq.com/openai/v1'
+	}
+
+	// Define the model and message for chat completion
+	// Note: Use a model that Groq supports, like llama2-70b-4096 or mixtral-8x7b-32768
+	model := 'qwen-2.5-coder-32b'
+
+	// Create a chat completion request
+	res := client.chat_completion(model, openai.Messages{
+		messages: [
+			openai.Message{
+				role: .user
+				content: 'What are the key differences between Groq and other AI inference providers?'
+			}
+		]
+	})!
+
+	// Print the response
+	println('\nGroq AI Response:')
+	println('==================')
+	println(res.choices[0].message.content)
+	println('\nUsage Statistics:')
+	println('Prompt tokens: ${res.usage.prompt_tokens}')
+	println('Completion tokens: ${res.usage.completion_tokens}')
+	println('Total tokens: ${res.usage.total_tokens}')
+}