agentmemory 0.2.3

Easy-to-use agent memory, powered by chromadb

agentmemory

Easy-to-use agent memory, powered by chromadb

Installation

pip install agentmemory

Quickstart

from agentmemory import create_memory, search_memory, set_storage_path

set_storage_path('./memory')

# create a memory
create_memory("conversation", "I can't do that, Dave.", metadata={"speaker": "HAL", "some_other_key": "some value, could be a number or string"})

# search for a memory
memories = search_memory("conversation", "Dave") # category, search term

print(str(memories))

# memories is a list of dictionaries
[
    {
        "id": int,
        "document": string,
        "metadata": dict{...values},
        "embeddings": (Optional) list[float] | None
    },
    {
        ...
    }
]

Basic Usage Guide

Importing into your project

from agentmemory import (
    create_memory,
    get_memories,
    search_memory,
    get_memory,
    update_memory,
    delete_memory,
    count_memories,
    wipe_category,
    wipe_all_memories
)

Create a Memory

# category, document, metadata
create_memory("conversation", "I can't do that, Dave.", metadata={"speaker": "HAL", "some_other_key": "some value, could be a number or string"})

Search memories

memories = search_memory("conversation", "Dave") # category, search term
# memories is a list of dictionaries
[
    {
        "id": int,
        "document": string,
        "metadata": dict{...values},
        "embeddings": (Optional) list[float] | None
    },
    {
        ...
    }
]

Get all memories

memories = get_memories("conversation") # can be any category
# memories is a list of dictionaries
[
    {
        "id": int,
        "document": string,
        "metadata": dict{...values},
        "embeddings": (Optional) list[float] | None
    },
    {
        ...
    }
]

Get a memory

memory = get_memory("conversation", 1) # category, id

Update a memory

update_memory("conversation", 1, "Okay, I will open the podbay doors.")

Delete a Memory

delete_memory("conversation", 1)

Documentation

Create a Memory

create_memory(category, text, id=None, embedding=None, metadata=None, persist=True)

Create a new memory in a collection.

Arguments

# Required
category (str): Category of the collection.
text (str): Document text.

# Optional
id (str): Unique id. Generated incrementally unless set.
metadata (dict): Metadata.
embedding (array): Embedding of the document. Defaults to None. Use if you already have an embedding.
persist (bool): Whether to persist the changes to disk. Defaults to True.

Example

>>> create_memory(category='sample_category', text='sample_text', id='sample_id', metadata={'sample_key': 'sample_value'}, persist=True)

Search Memory

search_memory(category, search_text, n_results=5, min_distance=None, max_distance=None, filter_metadata=None, contains_text=None, include_embeddings=True)

Search a collection with given query texts.

A note about distances: the filters are applied after the query, so the n_results may be dramatically shortened. This is a current limitation of Chromadb.

Arguments

# Required
category (str): Category of the collection.
search_text (str): Text to be searched.

# Optional
n_results (int): Number of results to be returned.
filter_metadata (dict): Metadata for filtering the results.
contains_text (str): Text that must be contained in the documents.
include_embeddings (bool): Whether to include embeddings in the results.
include_distances (bool): Whether to include distances in the results.
max_distance (float): Only include memories with this distance threshold maximum.
    0.1 = most memories will be exluded, 1.0 = no memories will be excluded
min_distance (float): Only include memories that are at least this distance
    0.0 = No memories will be excluded, 0.9 = most memories will be excluded

Returns

list: List of search results.

Example

>>> search_memory('sample_category', 'search_text', min_distance=0.01, max_distance=0.7, n_results=2, filter_metadata={'sample_key': 'sample_value'}, contains_text='sample', include_embeddings=True, include_distances=True)
[{'metadata': '...', 'document': '...', 'id': '...'}, {'metadata': '...', 'document': '...', 'id': '...'}]

Get a Memory

get_memory(category, id, include_embeddings=True)

Retrieve a specific memory from a given category based on its ID.

Arguments

# Required
category (str): The category of the memory.
id (str/int): The ID of the memory.

#optional
include_embeddings (bool): Whether to include the embeddings. Defaults to True.

Returns

dict: The retrieved memory.

Example

>>> get_memory("books", "1")

Get Memories

get_memories(category, sort_order="desc", filter_metadata=None, n_results=20, include_embeddings=True)

Retrieve a list of memories from a given category, sorted by ID, with optional filtering. sort_order controls whether you get from the beginning or end of the list.

Arguments

# Required
category (str): The category of the memories.

# Optional
sort_order (str): The sorting order of the memories. Can be 'asc' or 'desc'. Defaults to 'desc'.
filter_metadata (dict): Filter to apply on metadata. Defaults to None.
n_results (int): The number of results to return. Defaults to 20.
include_embeddings (bool): Whether to include the embeddings. Defaults to True.

Returns

list: List of retrieved memories.

Example

>>> get_memories("books", sort_order="asc", n_results=10)

Update a Memory

update_memory(category, id, text=None, metadata=None, persist=True)

Update a memory with new text and/or metadata.

Arguments

# Required
category (str): The category of the memory.
id (str/int): The ID of the memory.

# Optional
text (str): The new text of the memory. Defaults to None.
metadata (dict): The new metadata of the memory. Defaults to None.
persist (bool): Whether to persist the changes to disk. Defaults to True.

Example

# with keyword arguments
update_memory(category="conversation", id=1, text="Okay, I will open the podbay doors.", metadata={ "speaker": "HAL", "sentiment": "positive" }, persist=True)

# with positional arguments
update_memory("conversation", 1, "Okay, I will open the podbay doors.")

Delete a Memory

delete_memory(category, id, contains_metadata=None, contains_text=None, persist=True)

Delete a memory by ID.

Arguments

# Required
category (str): The category of the memory.
id (str/int): The ID of the memory.

# Optional
persist (bool): Whether to persist the changes to disk. Defaults to True.

Example

>>> delete_memory("books", "1")

Check if a memory exists

memory_exists(category, id, includes_metadata=None)

Check if a memory exists in a given category.

Arguments

# Required
category (str): The category of the memory.
id (str/int): The ID of the memory.

# Optional
includes_metadata (dict): Metadata that the memory should include. Defaults to None.

Example

>>> memory_exists("books", "1")

Wipe an Entire Category of Memories

wipe_category(category, persist=True)

Delete an entire category of memories.

Arguments

# Required
category (str): The category to delete.

# Optional
persist (bool): Whether to persist the changes to disk. Defaults to True.

Example

>>> wipe_category("books")

Count Memories

count_memories(category)

Count the number of memories in a given category.

Arguments

category (str): The category of the memories.

Returns

int: The number of memories.

Example

>>> count_memories("books")

Wipe All Memories

wipe_all_memories(persist=True)

Delete all memories across all categories.

Arguments

# Optional
persist (bool): Whether to persist the changes to disk. Defaults to True.

Example

>>> wipe_all_memories()

Set a Persistent Storage Path

set_storage_path(path)

Arguments

path (string): the path to save to

Example

    >>> set_storage_path("path/to/persistent/directory")

Save All Memory to Disk

save_memory()

Example

    >>> save_memory()

Sure, here's a Markdown formatted version that can be used in a README.md file:

# Memory Management with ChromaDB

This document provides a guide to using the memory management functions provided in the module.

## Functions

### Export Memories to JSON

The `export_memory_to_json` function exports all memories to a dictionary, optionally including embeddings.

##### Arguments

- `include_embeddings` (bool, optional): Whether to include memory embeddings in the output. Defaults to True.

**Returns:**

- dict: A dictionary with collection names as keys and lists of memories as values.

##### Example

```python
>>> export_memory_to_json()
```

Export Memories to File

The export_memory_to_file function exports all memories to a JSON file, optionally including embeddings.

Arguments

• path (str, optional): The path to the output file. Defaults to "./memory.json".
• include_embeddings (bool, optional): Whether to include memory embeddings in the output. Defaults to True.

Example

>>> export_memory_to_file(path="/path/to/output.json")

Import Memories from JSON

The import_json_to_memory function imports memories from a dictionary into the current database.

Arguments

• data (dict): A dictionary with collection names as keys and lists of memories as values.
• replace (bool, optional): Whether to replace existing memories. If True, all existing memories will be deleted before import. Defaults to True.

Example

>>> import_json_to_memory(data)

Import Memories from File

The import_file_to_memory function imports memories from a JSON file into the current database.

Arguments

• path (str, optional): The path to the input file. Defaults to "./memory.json".
• replace (bool, optional): Whether to replace existing memories. If True, all existing memories will be deleted before import. Defaults to True.

Example

>>> import_file_to_memory(path="/path/to/input.json")

In the above Markdown, you may replace "ChromaDB" with the actual name of the module if it's different. You can include this in your `README.md` file to give your users a guide on how to use these functions.

# Publishing

```bash
bash publish.sh --version=<version> --username=<pypi_username> --password=<pypi_password>

Contributions Welcome

If you like this library and want to contribute in any way, please feel free to submit a PR and I will review it. Please note that the goal here is simplicity and accesibility, using common language and few dependencies.