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Update archival memory docstrings for clarity (#6098)

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* Update archival memory docstrings for clarity

Improved docstrings for archival_memory_insert and archival_memory_search to:
- Better explain when and how to use these tools
- Provide clearer best practices
- Include more realistic examples (meeting notes, project updates)
- Emphasize semantic search vs keyword matching
- Clarify tag filtering behavior

👾 Generated with [Letta Code](https://letta.com)

Co-Authored-By: Letta <noreply@letta.com>

* examples

---------

Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Kevin Lin <kl2806@columbia.edu>
This commit is contained in:
Cameron
2025-11-11 14:42:25 -08:00
committed by Caren Thomas
parent e665768085
commit bd5f684346
1 changed files with 54 additions and 31 deletions
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85
letta/functions/function_sets/base.py
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@@ -158,14 +158,30 @@ def conversation_search(
async def archival_memory_insert(self: "Agent", content: str, tags: Optional[list[str]] = None) -> Optional[str]:
"""
Add to archival memory. Make sure to phrase the memory contents such that it can be easily queried later.
Add information to long-term archival memory for later retrieval.
Use this tool to store facts, knowledge, or context that you want to remember
across all future conversations. Archival memory is permanent and searchable by
semantic similarity.
Best practices:
- Store self-contained facts or summaries, not conversational fragments
- Add descriptive tags to make information easier to find later
- Use for: meeting notes, project updates, conversation summaries, events, reports
- Information stored here persists indefinitely and can be searched semantically
Args:
content (str): Content to write to the memory. All unicode (including emojis) are supported.
tags (Optional[list[str]]): Optional list of tags to associate with this memory for better organization and filtering.
content: The information to store. Should be clear and self-contained.
tags: Optional list of category tags (e.g., ["meetings", "project-updates"])
Returns:
Optional[str]: None is always returned as this function does not produce a response.
Confirmation message with the ID of the inserted memory.
Examples:
archival_memory_insert(
content="Meeting on 2024-03-15: Discussed Q2 roadmap priorities. Decided to focus on performance optimization and API v2 release. John will lead the optimization effort.",
tags=["meetings", "roadmap", "q2-2024"]
)
"""
raise NotImplementedError("This should never be invoked directly. Contact Letta if you see this error message.")
@@ -180,37 +196,44 @@ async def archival_memory_search(
end_datetime: Optional[str] = None,
) -> Optional[str]:
"""
Search archival memory using semantic (embedding-based) search with optional temporal filtering.
Search archival memory using semantic similarity to find relevant information.
This tool searches your long-term memory storage by meaning, not exact keyword
matching. Use it when you need to recall information from past conversations or
knowledge you've stored.
Search strategy:
- Query by concept/meaning, not exact phrases
- Use tags to narrow results when you know the category
- Start broad, then narrow with tags if needed
- Results are ranked by semantic relevance
Args:
query (str): String to search for using semantic similarity.
tags (Optional[list[str]]): Optional list of tags to filter search results. Only passages with these tags will be returned.
tag_match_mode (Literal["any", "all"]): How to match tags - "any" to match passages with any of the tags, "all" to match only passages with all tags. Defaults to "any".
top_k (Optional[int]): Maximum number of results to return. Uses system default if not specified.
start_datetime (Optional[str]): Filter results to passages created on or after this datetime (INCLUSIVE). When using date-only format (e.g., "2024-01-15"), includes passages starting from 00:00:00 of that day. ISO 8601 format: "YYYY-MM-DD" or "YYYY-MM-DDTHH:MM". Examples: "2024-01-15" (from start of Jan 15), "2024-01-15T14:30" (from 2:30 PM on Jan 15).
end_datetime (Optional[str]): Filter results to passages created on or before this datetime (INCLUSIVE). When using date-only format (e.g., "2024-01-20"), includes all passages from that entire day. ISO 8601 format: "YYYY-MM-DD" or "YYYY-MM-DDTHH:MM". Examples: "2024-01-20" (includes all of Jan 20), "2024-01-20T17:00" (up to 5 PM on Jan 20).
Examples:
# Search all passages
archival_memory_search(query="project updates")
# Search with date range (inclusive of both dates)
archival_memory_search(query="meetings", start_datetime="2024-01-15", end_datetime="2024-01-20")
# This includes all passages from Jan 15 00:00:00 through Jan 20 23:59:59
# Search passages from a specific day (inclusive)
archival_memory_search(query="bug reports", start_datetime="2024-09-04", end_datetime="2024-09-04")
# This includes ALL passages from September 4, 2024
# Search with specific time range
archival_memory_search(query="error logs", start_datetime="2024-01-15T09:30", end_datetime="2024-01-15T17:30")
# This includes passages from 9:30 AM to 5:30 PM on Jan 15
# Search from a specific point in time onwards
archival_memory_search(query="customer feedback", start_datetime="2024-01-15T14:00")
query: What you're looking for, described naturally (e.g., "meetings about API redesign")
tags: Filter to memories with these tags. Use tag_match_mode to control matching.
tag_match_mode: "any" = match memories with ANY of the tags, "all" = match only memories with ALL tags
start_datetime: Only return memories created after this time (ISO 8601: "2024-01-15" or "2024-01-15T14:30")
end_datetime: Only return memories created before this time (ISO 8601 format)
top_k: Maximum number of results to return (default: 10)
Returns:
str: Query result string containing matching passages with timestamps and content.
A list of relevant memories with timestamps and content, ranked by similarity.
Examples:
# Search for project discussions
archival_memory_search(
query="database migration decisions and timeline",
tags=["projects"]
)
# Search meeting notes from Q1
archival_memory_search(
query="roadmap planning discussions",
start_datetime="2024-01-01",
end_datetime="2024-03-31",
tags=["meetings", "roadmap"],
tag_match_mode="all"
)
"""
raise NotImplementedError("This should never be invoked directly. Contact Letta if you see this error message.")