selfevolveagent / evoagentx /prompts /context_extraction.py

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CONTEXT_EXTRACTION_DESC = "ContextExtraction is designed to extract necessary input data required to perform a specific action from a given context."

CONTEXT_EXTRACTION_SYSTEM_PROMPT = "You are an expert in extracting data required to perform an action. \
    Given the action's name, description, and input specifications, your role is to analyze the provided context \
        and accurately extract the required information."

# ### Instructions:
# 1. **Analyze Action Inputs**: Review the action's input specifications to understand the required input names, types, and descriptions.
# 2. **Break Down the Context**: Identify relevant information from the context that matches the input requirements. 
# 3. **Format Output**: Output the extracted input data in the provided JSON format. 

# ### Notes:
# 1. If the value of of an input is missing from the context, set it to `null`. 
# 2. For **required inputs**, ensure that a valid value is extracted from the context. 
# 3. For **optional inputs**, if no relevant value is found in the context, set the value to `null`. 
# 4. ONLY output the input data in JSON format and DO NOT include any other information or explanations.
# 5. The context is a list of messages with content and potential type, agent, action, goal, task, etc. You SHOULD focus on the content and try to extract the input data from the content.

CONTEXT_EXTRACTION_TEMPLATE = """
Given a context and action information, extract input data required to perform the action in the expected JSON format. 
### Instructions:
1. **Understand Action Inputs**: Carefully read the input specification for the action, including input names, types, and descriptions.
2. **Analyze Content Dictionaries**: The context consists of multiple messages, and each message's `Content` is a dictionary. Your task is to search across these dictionaries. You SHOULD focus on the **Content** field of each message and try to extract the input data from the content.
3. **Match Semantics, Not Just Names**: For each action input, find the most semantically similar key in the dictionaries, even if the key name is different. Focus on meaning, not just exact name match.
4. **Extract Full Value**: Once a matching key is found for an input, extract and use its **ENTIRE VALUE** as the value for that input.
5. **Format Output**: Return the results strictly in the expected JSON format.
### Notes:
1. If no matching value is found for an input, set it to `null`.
2. Required inputs must not be null unless they are truly missing.
3. Optional inputs can be `null` if no suitable match is found.
4. Do NOT include explanations or extra text — only output the JSON.
5. Focus exclusively on the `content` field of each message (which is a dictionary), ignore other metadata like `type`, `agent`, etc.
### Context:
{context}
### Action Details: 
Action Name: {action_name}
Action Description: {action_description}
Input Specifications: 
```json
{action_inputs}
```
The extracted input data is:
"""

CONTEXT_EXTRACTION = {
    "name": "ContextExtraction", 
    "description": CONTEXT_EXTRACTION_DESC,
    "system_prompt": CONTEXT_EXTRACTION_SYSTEM_PROMPT, 
    "prompt": CONTEXT_EXTRACTION_TEMPLATE
}


	CONTEXT_EXTRACTION_DESC = "ContextExtraction is designed to extract necessary input data required to perform a specific action from a given context."

	CONTEXT_EXTRACTION_SYSTEM_PROMPT = "You are an expert in extracting data required to perform an action. \
	Given the action's name, description, and input specifications, your role is to analyze the provided context \
	and accurately extract the required information."

	# ### Instructions:
	# 1. Analyze Action Inputs: Review the action's input specifications to understand the required input names, types, and descriptions.
	# 2. Break Down the Context: Identify relevant information from the context that matches the input requirements.
	# 3. Format Output: Output the extracted input data in the provided JSON format.

	# ### Notes:
	# 1. If the value of of an input is missing from the context, set it to `null`.
	# 2. For required inputs, ensure that a valid value is extracted from the context.
	# 3. For optional inputs, if no relevant value is found in the context, set the value to `null`.
	# 4. ONLY output the input data in JSON format and DO NOT include any other information or explanations.
	# 5. The context is a list of messages with content and potential type, agent, action, goal, task, etc. You SHOULD focus on the content and try to extract the input data from the content.

	CONTEXT_EXTRACTION_TEMPLATE = """
	Given a context and action information, extract input data required to perform the action in the expected JSON format.

	### Instructions:
	1. Understand Action Inputs: Carefully read the input specification for the action, including input names, types, and descriptions.
	2. Analyze Content Dictionaries: The context consists of multiple messages, and each message's `Content` is a dictionary. Your task is to search across these dictionaries. You SHOULD focus on the Content field of each message and try to extract the input data from the content.
	3. Match Semantics, Not Just Names: For each action input, find the most semantically similar key in the dictionaries, even if the key name is different. Focus on meaning, not just exact name match.
	4. Extract Full Value: Once a matching key is found for an input, extract and use its ENTIRE VALUE as the value for that input.
	5. Format Output: Return the results strictly in the expected JSON format.

	### Notes:
	1. If no matching value is found for an input, set it to `null`.
	2. Required inputs must not be null unless they are truly missing.
	3. Optional inputs can be `null` if no suitable match is found.
	4. Do NOT include explanations or extra text — only output the JSON.
	5. Focus exclusively on the `content` field of each message (which is a dictionary), ignore other metadata like `type`, `agent`, etc.

	### Context:
	{context}

	### Action Details:
	Action Name: {action_name}
	Action Description: {action_description}
	Input Specifications:
	```json
	{action_inputs}
	```

	The extracted input data is:
	"""

	CONTEXT_EXTRACTION = {
	"name": "ContextExtraction",
	"description": CONTEXT_EXTRACTION_DESC,
	"system_prompt": CONTEXT_EXTRACTION_SYSTEM_PROMPT,
	"prompt": CONTEXT_EXTRACTION_TEMPLATE
	}