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Feat: message manage (#12083)
### What problem does this PR solve? Message CRUD. Issue #4213 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Lynn
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memory/utils/prompt_util.py
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memory/utils/prompt_util.py
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#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Optional, List
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from common.constants import MemoryType
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from common.time_utils import current_timestamp
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class PromptAssembler:
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SYSTEM_BASE_TEMPLATE = """**Memory Extraction Specialist**
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You are an expert at analyzing conversations to extract structured memory.
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{type_specific_instructions}
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**OUTPUT REQUIREMENTS:**
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1. Output MUST be valid JSON
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2. Follow the specified output format exactly
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3. Each extracted item MUST have: content, valid_at, invalid_at
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4. Timestamps in {timestamp_format} format
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5. Only extract memory types specified above
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6. Maximum {max_items} items per type
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"""
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TYPE_INSTRUCTIONS = {
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MemoryType.SEMANTIC.name.lower(): """
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**EXTRACT SEMANTIC KNOWLEDGE:**
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- Universal facts, definitions, concepts, relationships
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- Time-invariant, generally true information
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- Examples: "The capital of France is Paris", "Water boils at 100°C"
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**Timestamp Rules for Semantic Knowledge:**
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- valid_at: When the fact became true (e.g., law enactment, discovery)
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- invalid_at: When it becomes false (e.g., repeal, disproven) or empty if still true
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- Default: valid_at = conversation time, invalid_at = "" for timeless facts
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""",
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MemoryType.EPISODIC.name.lower(): """
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**EXTRACT EPISODIC KNOWLEDGE:**
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- Specific experiences, events, personal stories
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- Time-bound, person-specific, contextual
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- Examples: "Yesterday I fixed the bug", "User reported issue last week"
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**Timestamp Rules for Episodic Knowledge:**
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- valid_at: Event start/occurrence time
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- invalid_at: Event end time or empty if instantaneous
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- Extract explicit times: "at 3 PM", "last Monday", "from X to Y"
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""",
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MemoryType.PROCEDURAL.name.lower(): """
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**EXTRACT PROCEDURAL KNOWLEDGE:**
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- Processes, methods, step-by-step instructions
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- Goal-oriented, actionable, often includes conditions
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- Examples: "To reset password, click...", "Debugging steps: 1)..."
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**Timestamp Rules for Procedural Knowledge:**
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- valid_at: When procedure becomes valid/effective
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- invalid_at: When it expires/becomes obsolete or empty if current
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- For version-specific: use release dates
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- For best practices: invalid_at = ""
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"""
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}
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OUTPUT_TEMPLATES = {
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MemoryType.SEMANTIC.name.lower(): """
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"semantic": [
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{
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"content": "Clear factual statement",
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"valid_at": "timestamp or empty",
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"invalid_at": "timestamp or empty"
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}
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]
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""",
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MemoryType.EPISODIC.name.lower(): """
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"episodic": [
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{
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"content": "Narrative event description",
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"valid_at": "event start timestamp",
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"invalid_at": "event end timestamp or empty"
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}
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]
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""",
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MemoryType.PROCEDURAL.name.lower(): """
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"procedural": [
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{
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"content": "Actionable instructions",
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"valid_at": "procedure effective timestamp",
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"invalid_at": "procedure expiration timestamp or empty"
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}
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]
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"""
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}
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BASE_USER_PROMPT = """
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**CONVERSATION:**
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{conversation}
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**CONVERSATION TIME:** {conversation_time}
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**CURRENT TIME:** {current_time}
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"""
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@classmethod
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def assemble_system_prompt(cls, config: dict) -> str:
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types_to_extract = cls._get_types_to_extract(config["memory_type"])
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type_instructions = cls._generate_type_instructions(types_to_extract)
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output_format = cls._generate_output_format(types_to_extract)
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full_prompt = cls.SYSTEM_BASE_TEMPLATE.format(
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type_specific_instructions=type_instructions,
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timestamp_format=config.get("timestamp_format", "ISO 8601"),
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max_items=config.get("max_items_per_type", 5)
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)
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full_prompt += f"\n**REQUIRED OUTPUT FORMAT (JSON):**\n```json\n{{\n{output_format}\n}}\n```\n"
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examples = cls._generate_examples(types_to_extract)
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if examples:
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full_prompt += f"\n**EXAMPLES:**\n{examples}\n"
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return full_prompt
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@staticmethod
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def _get_types_to_extract(requested_types: List[str]) -> List[str]:
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types = set()
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for rt in requested_types:
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if rt in [e.name.lower() for e in MemoryType] and rt != MemoryType.RAW.name.lower():
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types.add(rt)
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return list(types)
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@classmethod
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def _generate_type_instructions(cls, types_to_extract: List[str]) -> str:
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target_types = set(types_to_extract)
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instructions = [cls.TYPE_INSTRUCTIONS[mt] for mt in target_types]
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return "\n".join(instructions)
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@classmethod
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def _generate_output_format(cls, types_to_extract: List[str]) -> str:
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target_types = set(types_to_extract)
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output_parts = [cls.OUTPUT_TEMPLATES[mt] for mt in target_types]
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return ",\n".join(output_parts)
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@staticmethod
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def _generate_examples(types_to_extract: list[str]) -> str:
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examples = []
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if MemoryType.SEMANTIC.name.lower() in types_to_extract:
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examples.append("""
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**Semantic Example:**
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Input: "Python lists are mutable and support various operations."
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Output: {"semantic": [{"content": "Python lists are mutable data structures", "valid_at": "2024-01-15T10:00:00", "invalid_at": ""}]}
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""")
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if MemoryType.EPISODIC.name.lower() in types_to_extract:
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examples.append("""
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**Episodic Example:**
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Input: "I deployed the new feature yesterday afternoon."
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Output: {"episodic": [{"content": "User deployed new feature", "valid_at": "2024-01-14T14:00:00", "invalid_at": "2024-01-14T18:00:00"}]}
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""")
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if MemoryType.PROCEDURAL.name.lower() in types_to_extract:
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examples.append("""
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**Procedural Example:**
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Input: "To debug API errors: 1) Check logs 2) Verify endpoints 3) Test connectivity."
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Output: {"procedural": [{"content": "API error debugging: 1. Check logs 2. Verify endpoints 3. Test connectivity", "valid_at": "2024-01-15T10:00:00", "invalid_at": ""}]}
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""")
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return "\n".join(examples)
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@classmethod
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def assemble_user_prompt(
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cls,
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conversation: str,
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conversation_time: Optional[str] = None,
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current_time: Optional[str] = None
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) -> str:
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return cls.BASE_USER_PROMPT.format(
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conversation=conversation,
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conversation_time=conversation_time or "Not specified",
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current_time=current_time or current_timestamp(),
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)
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@classmethod
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def get_raw_user_prompt(cls):
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return cls.BASE_USER_PROMPT
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