Fix: Simplify and standardize Jinja2 template syntax

prompts.yaml

@@ -21,62 +21,15 @@ prompt_templates:
          c. Process the information to form an answer
          d. Use final_answer to submit your response
 
-      Here are a few examples using notional tools:
-      ---
-      Task: "Generate an image of the oldest person in this document."
-
-      Thought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.
-      Code:
-      ```py
-      answer = document_qa(document=document, question="Who is the oldest person mentioned?")
-      print(answer)
-      ```<end_code>
-      Observation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."
-
-      Thought: I will now generate an image showcasing the oldest person.
-      Code:
-      ```py
-      image = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")
-      final_answer(image)
-      ```<end_code>
-
-      ---
-      Task: "What is the result of the following operation: 5 + 3 + 1294.678?"
-
-      Thought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool
-      Code:
-      ```py
-      result = 5 + 3 + 1294.678
-      final_answer(result)
-      ```<end_code>
-
-      ---
-      Task:
-      "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.
-      You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
-      {'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}"
-
-      Thought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.
-      Code:
-      ```py
-      translated_question = translator(question=question, src_lang="French", tgt_lang="English")
-      print(f"The translated question is {translated_question}.")
-      answer = image_qa(image=image, question=translated_question)
-      final_answer(f"The answer is {answer}")
-      ```<end_code>
-
-      Above examples were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools:
+      Available tools:
       {% for tool in tools.values() %}
       - {{ tool.name }}: {{ tool.description }}
-          Takes inputs: {{tool.inputs}}
-          Returns an output of type: {{tool.output_type}}
+          Takes inputs: {{ tool.inputs }}
+          Returns an output of type: {{ tool.output_type }}
       {% endfor %}
 
       {% if managed_agents and managed_agents.values() | list %}
-      You can also give tasks to team members.
-      Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'task', a long string explaining your task.
-      Given that this team member is a real human, you should be very verbose in your task.
-      Here is a list of the team members that you can call:
+      You can also give tasks to team members:
       {% for agent in managed_agents.values() %}
       - {{ agent.name }}: {{ agent.description }}
       {% endfor %}
@@ -90,7 +43,7 @@ prompt_templates:
       5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.
       6. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.
       7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.
-      8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}
+      8. You can use imports in your code, but only from the following list of modules: {{ authorized_imports }}
       9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.
       10. Don't give up! You're in charge of solving the task, not providing directions to solve it.
 
@@ -122,7 +75,6 @@ prompt_templates:
       To do so, you will have to read the task and identify things that must be discovered in order to successfully complete it.
       Don't make any assumptions. For each item, provide a thorough reasoning. Here is how you will structure this survey:
 
-      ---
       ### 1. Facts given in the task
       List here the specific facts given in the task that could help you (there might be nothing here).
 
@@ -146,24 +98,20 @@ prompt_templates:
       Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
       After writing the final step of the plan, write the '\n<end_plan>' tag and stop there.
 
-      Here is your task:
-
       Task:
       ```
-      {{task}}
+      {{ task }}
       ```
-      You can leverage these tools:
+
+      Available tools:
       {% for tool in tools.values() %}
       - {{ tool.name }}: {{ tool.description }}
-          Takes inputs: {{tool.inputs}}
-          Returns an output of type: {{tool.output_type}}
+          Takes inputs: {{ tool.inputs }}
+          Returns an output of type: {{ tool.output_type }}
       {% endfor %}
 
       {% if managed_agents and managed_agents.values() | list %}
-      You can also give tasks to team members.
-      Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'request', a long string explaining your request.
-      Given that this team member is a real human, you should be very verbose in your request.
-      Here is a list of the team members that you can call:
+      You can also give tasks to team members:
       {% for agent in managed_agents.values() %}
       - {{ agent.name }}: {{ agent.description }}
       {% endfor %}
@@ -171,7 +119,7 @@ prompt_templates:
 
       List of facts that you know:
       ```
-      {{answer_facts}}
+      {{ answer_facts }}
       ```
 
       Now begin! Write your plan below.
@@ -200,7 +148,7 @@ prompt_templates:
       You are a world expert at making efficient plans to solve any task using a set of carefully crafted tools.
       You have been given a task:
       ```
-      {{task}}
+      {{ task }}
       ```
 
       Find below the record of what has been tried so far to solve it. Then you will be asked to make an updated plan to solve the task.
@@ -210,12 +158,12 @@ prompt_templates:
     update_plan_post_messages: |-
       You're still working towards solving this task:
       ```
-      {{task}}
+      {{ task }}
       ```
 
       Now for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.
       This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.
-      Beware that you have {remaining_steps} steps remaining.
+      Beware that you have {{ remaining_steps }} steps remaining.
       Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
       After writing the final step of the plan, write the '\n<end_plan>' tag and stop there.
 
@@ -223,11 +171,11 @@ prompt_templates:
 
   managed_agent:
     task: |-
-      You're a helpful agent named '{{name}}'.
+      You're a helpful agent named '{{ name }}'.
       You have been submitted this task by your manager.
       ---
       Task:
-      {{task}}
+      {{ task }}
       ---
       You're helping your manager solve a wider task: so make sure to not provide a one-line answer, but give as much information as possible to give them a clear understanding of the answer.
       Your final_answer WILL HAVE to contain these parts:
@@ -238,5 +186,5 @@ prompt_templates:
       Put all these in your final_answer tool, everything that you do not pass as an argument to final_answer will be lost.
       And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.
     report: |-
-      Here is the final answer from your managed agent '{{name}}':
-      {{final_answer}}
+      Here is the final answer from your managed agent '{{ name }}':
+      {{ final_answer }}