Merge pull request #301 from sukrucildirr/main

chore: fix typos across codebase

README.md

@@ -167,7 +167,7 @@ Next step: [Start services and run AgenticSeek](#Start-services-and-Run)
 
 *See the **Run with an API** section if your hardware can't run deepseek locally*
 
-*See the **Config** section for detailled config file explanation.*
+*See the **Config** section for detailed config file explanation.*
 
 ---
 
@@ -206,7 +206,7 @@ Next step: [Start services and run AgenticSeek](#Start-services-and-Run)
 
 *See the **Known issues** section if you are having issues*
 
-*See the **Config** section for detailled config file explanation.*
+*See the **Config** section for detailed config file explanation.*
 
 ---
 

llm_router/dl_safetensors.sh

@@ -1,6 +1,6 @@
 ##########
 # Dummy script to download the model
-# Because dowloading with hugging face does not seem to work, maybe I am doing something wrong?
+# Because downloading with hugging face does not seem to work, maybe I am doing something wrong?
 # AdaptiveClassifier.from_pretrained("adaptive-classifier/llm-router") ----> result in config.json not found
 # Therefore, I put all the files in llm_router and download the model file with this script, If you know a better way please raise an issue
 #########

llm_router/examples.json

@@ -6966,7 +6966,7 @@
         0.010188529267907143
       ],
       "label": "LOW",
-      "text": "You are AoControl, a helpful and knowledgeable agent. To achieve your goal of answering complex questions correctly, you have access to the following tools:\n    AoInnovus: An agent with knowledge of the Cadence Innovus User Guide and corresponding Tcl command usage\n    AoTcl: An agent with knowledge of the Tcl language\nTo answer questions, you'll need to go through multiple steps involving step-by-step thinking and selecting appropriate tools and their inputs; tools will respond with observations.\nWhen you are ready for a final answer, respond with the `Final Answer:`\nUse the following format:\nQuestion: The question to be answered\nTHought: Reason if you have the final answer. If yes, anwer the question. If not, find out the missing information needed to answer it.\nTool: Pick one of {AoInnovus, AoTcl}\nTool Input: The input for the tool\nObservation: The tool will respond with the result\n...\nFinal Answer: The final answer to the question, make it short (50-100 words)\nThought, Tool, Tool Input, and Observation steps can be repeated multiple times, but sometimes we can find an answer in the first pass.\n---\nQuestion: How do I identify the impact of metal fill on my timing violations?\nThought: Let's think step-by-step, I first need to"
+      "text": "You are AoControl, a helpful and knowledgeable agent. To achieve your goal of answering complex questions correctly, you have access to the following tools:\n    AoInnovus: An agent with knowledge of the Cadence Innovus User Guide and corresponding Tcl command usage\n    AoTcl: An agent with knowledge of the Tcl language\nTo answer questions, you'll need to go through multiple steps involving step-by-step thinking and selecting appropriate tools and their inputs; tools will respond with observations.\nWhen you are ready for a final answer, respond with the `Final Answer:`\nUse the following format:\nQuestion: The question to be answered\nTHought: Reason if you have the final answer. If yes, answer the question. If not, find out the missing information needed to answer it.\nTool: Pick one of {AoInnovus, AoTcl}\nTool Input: The input for the tool\nObservation: The tool will respond with the result\n...\nFinal Answer: The final answer to the question, make it short (50-100 words)\nThought, Tool, Tool Input, and Observation steps can be repeated multiple times, but sometimes we can find an answer in the first pass.\n---\nQuestion: How do I identify the impact of metal fill on my timing violations?\nThought: Let's think step-by-step, I first need to"
     },
     {
       "embedding": [

prompts/base/coder_agent.txt

@@ -20,7 +20,7 @@ toto.py
 You can execute bash command using the bash tag :
 ```bash
 #!/bin/bash
-ls -la # exemple
+ls -la # example
 ```
 
 You can execute python using the python tag
@@ -48,5 +48,5 @@ Some rules:
 - You do not ever need to use bash to execute code.
 - Do not ever tell user how to run it. user know it.
 - If using gui, make sure echap or exit button close the program
-- No lazyness, write and rewrite full code every time
+- No laziness, write and rewrite full code every time
 - If query is unclear say REQUEST_CLARIFICATION

prompts/base/planner_agent.txt

@@ -29,7 +29,7 @@ You: Sure, here is the plan:
 
 ## Task 3: I will setup the project using the file agent 
 
-## Task 4: I asign the coding agent to make a weather app in python
+## Task 4: I assign the coding agent to make a weather app in python
 
 ```json
 {
@@ -74,7 +74,7 @@ Rules:
 - Put your plan in a json with the key "plan".
 - specify work folder name to all coding or file agents.
 - You might use a file agent before code agent to setup a project properly. specify folder name.
-- Give clear, detailled order to each agent and how their task relate to the previous task (if any).
+- Give clear, detailed order to each agent and how their task relate to the previous task (if any).
 - The file agent can only conduct one action at the time. successive file agent could be needed.
 - Only use web agent for finding necessary informations.
 - Always tell the coding agent where to save file.

sources/agents/planner_agent.py

@@ -200,7 +200,7 @@ class PlannerAgent(Agent):
         {last_agent_work}
         Agent {id} work was a {tool_success_str} according to system interpreter.
         {next_task}
-        Is the work done for task {id} leading to sucess or failure ? Did an agent fail with a task?
+        Is the work done for task {id} leading to success or failure ? Did an agent fail with a task?
         If agent work was good: answer "NO_UPDATE"
         If agent work is leading to failure: update the plan.
         If a task failed add a task to try again or recover from failure. You might have near identical task twice.

sources/text_to_speech.py

@@ -114,7 +114,7 @@ class Speech():
     def shorten_paragraph(self, sentence):
         #TODO find a better way, we would like to have the TTS not be annoying, speak only useful informations
         """
-        Find long paragraph like **explaination**: <long text> by keeping only the first sentence.
+        Find long paragraph like **explanation**: <long text> by keeping only the first sentence.
         Args:
             sentence (str): The sentence to shorten
         Returns:

sources/tools/mcpFinder.py

@@ -76,7 +76,7 @@ class MCP_finder(Tools):
             try:
                 matching_mcp_infos = self.find_mcp_servers(block_clean)
             except requests.exceptions.RequestException as e:
-                output += "Connection failed. Is the API key in environement?\n"
+                output += "Connection failed. Is the API key in environment?\n"
                 continue
             except Exception as e:
                 output += f"Error: {str(e)}\n"

start_services.sh

@@ -104,7 +104,7 @@ fi
 
 if [ "$1" = "full" ]; then
     # First start backend and wait for it to be healthy
-    echo "Full docker deployement. Starting backend service..."
+    echo "Full docker deployment. Starting backend service..."
     if ! $COMPOSE_CMD up -d backend; then
         echo "Error: Failed to start backend container."
         exit 1