From 8c707aef338f4c9d3723d7088de05a86d3aedfe0 Mon Sep 17 00:00:00 2001 From: ShiHan Wan Date: Tue, 1 Oct 2024 12:56:16 -0400 Subject: [PATCH] chore: add README and polish docstrings (#10) * Update poetry.lock * update docstring * v1 of readme * formatting * explain namespaces * add publish workflow * grant write permission to format * add install apache jena * Update README.md --- .github/workflows/black.yml | 3 + .github/workflows/publish.yml | 34 +++ README.md | 238 ++++++++++++++- memonto/memonto.py | 54 +--- poetry.lock | 555 ++++++++++++++++++---------------- 5 files changed, 579 insertions(+), 305 deletions(-) create mode 100644 .github/workflows/publish.yml diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml index f9eb046..abd460a 100644 --- a/.github/workflows/black.yml +++ b/.github/workflows/black.yml @@ -5,6 +5,9 @@ on: branches: - main +permissions: + contents: write + jobs: format: runs-on: ubuntu-latest diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..5b0f5d8 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,34 @@ +name: Publish to PyPI + +on: + release: + types: [published] + +jobs: + publish: + runs-on: ubuntu-latest + + steps: + - name: Check out the code + uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.x' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install build twine + + - name: Build the package + run: python -m build + + - name: Publish to PyPI + env: + TWINE_USERNAME: __token__ + TWINE_PASSWORD: ${{ secrets.PYPI_PUBLISH_TOKEN }} + run: | + python -m twine upload --skip-existing dist/* + diff --git a/README.md b/README.md index 695ab9f..0010792 100644 --- a/README.md +++ b/README.md @@ -1 +1,237 @@ -# MemOnto \ No newline at end of file +# MemOnto ๐Ÿง  + +`memonto` (memory + ontology) adds memory to AI agents with an emphasis on user defined ontology. Define your own [RDF](https://www.w3.org/RDF/) ontology then have `memonto` automatically extract information that maps onto that ontology. + +``` +โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +โ”‚ Message โ”‚ โ”‚ LLM โ”‚ โ”‚ Memory Graph โ”‚ +โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ {Otto von Bismarck was a โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ Prussian statesman and โ”‚ โ”‚ โ”‚ โ”‚ ... โ”‚ +โ”‚ diplomat who oversaw the โ”‚ โ”‚ [Otto von Bismarck] โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ unification of Germany...} โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ–บ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ”‚ is a [Person] who โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ”‚ โ”‚ Otto von Bismarck โ”‚ โ”‚ +โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ lives in a [Place] โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +โ”‚ Ontology โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ–บ called [Prussia] โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ–บ livesAtโ”‚ โ”‚partOf โ”‚ +โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ Person โ”‚ โ”‚ โ”‚ and participated in โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”โ”Œโ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ +โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ Prussia โ”‚โ”‚ German Unification โ”‚ โ”‚ +โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ an [Event] called โ”‚ โ”‚ โ””โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”˜โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +โ”‚ livesAtโ”‚ โ”‚partOf โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ [German Unification] โ”‚ โ”‚ โ–ผ โ–ผ โ–ผ โ–ผ โ”‚ +โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ” โ”Œโ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ โ”‚ ... ... ... ... โ”‚ +โ”‚ โ”‚ Place โ”‚ โ”‚ Event โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +## ๐Ÿš€ Install +```sh +pip install memonto +``` + +## โš™๏ธ Configure +**Ephemeral Mode** + +Use `memonto` all in memory without any data stores. + +> [!IMPORTANT] +> When in ephemeral mode, there can be performance issues if the memory data grows too large. This mode is recommended for smaller use cases. + +```python +from memonto import Memonto +from rdflib import Graph, Namespace, RDF, RDFS + +g = Graph() + +HIST = Namespace("history:") + +g.bind("hist", HIST) + +g.add((HIST.Person, RDF.type, RDFS.Class)) +g.add((HIST.Event, RDF.type, RDFS.Class)) +g.add((HIST.Place, RDF.type, RDFS.Class)) + +memonto = Memonto( + ontology=g, + namespaces={"hist": HIST}, + ephemeral=True, +) +``` + +**With Triple Store** + +A triple store enables the persistent storage of memory data. Currently supports Apache Jena Fuseki as a triple store. +```python +config = { + "triple_store": { + "provider": "apache_jena", + "config": { + "connection_url": "http://localhost:8080/", + }, + }, + "model": { + "provider": "openai", + "config": { + "model": "gpt-4o", + "api_key": "api-key", + }, + } +} + +memonto = Memonto( + ontology=g, + namespaces={"hist": HIST}, +) +memonto.configure(config) +``` + +**With Triple + Vector Stores** + +A vector store enables contextual retrieval of memory data, it must be used in conjunction with a triple store. Currently supports Chroma as a vector store. +```python +config = { + "triple_store": { + "provider": "apache_jena", + "config": { + "connection_url": "http://localhost:8080/dataset_name", + }, + }, + "vector_store": { + "provider": "chroma", + "config": { + "mode": "local", + "path": ".local", + }, + }, + "model": { + "provider": "openai", + "config": { + "model": "gpt-4o", + "api_key": "api-key", + }, + } +} + +memonto = Memonto( + ontology=g, + namespaces={"hist": HIST}, +) +memonto.configure(config) +``` + +## ๐Ÿงฐ Usage +**RDF Namespaces** + +`memonto` supports RDF namespaces as well. Just pass in a dictionary with the namespace's name along with its `rdflib.Namespace` object. +```python +memonto = Memonto( + ontology=g, + namespaces={"hist": HIST}, +) +``` + +**Memory ID** + +For when you want to associate an ontology and memories to an unique `id`. +```python +memonto = Memonto( + id="some_id_123", + ontology=g, + namespaces={"hist": HIST}, +) +``` + +**Retain** + +Extract the relevant information from a message that maps onto your ontology. It will only extract data that matches onto an entity in your ontology. +```python +memonto.retain("Otto von Bismarck was a Prussian statesman who oversaw the unification of Germany.") +``` + +**Recall** + +Get a summary of the currently stored memories. You can provide a `context` for `memonto` to only summarize the memories that are relevant to that `context`. + +> [!IMPORTANT] +> When in ephemeral mode, all memories will be returned even if a `context` is provided. +```python +# retrieve summary of memory relevant to a context +memonto.recall("Germany could unify under Prussia or Austria.") + +# retrieve summary of all stored memory +memonto.recall() +``` + +**Retrieve** + +Get the raw memory data that can be programatically accessed. Instead of a summary, get the actual stored data as a `list[dict]` that can then be manipulated in code. +> [!IMPORTANT] +> When in ephemeral mode, raw queries are not supported. +```python +# retrieve raw memory data by schema +memonto.retrieve(uri=HIST.Person) + +# retrieve raw memory data by SPARQL query +memonto.retrieve(query="SELECT ?s ?p ?o WHERE {GRAPH ?g {?s ?p ?o .}}") +``` + +**Forget** + +Forget about it. +```python +memonto.forget() +``` + +**Auto Expand Ontology** + +Enable `memonto` to automatically expand your ontology to cover new information. If `memonto` sees new information that **does not** fit onto your ontology, it will automatically add onto your ontology to cover that new information. +```python +memonto = Memonto( + id="some_id_123", + ontology=g, + namespaces={"hist": HIST}, + auto_expand=True, +) +``` + +## ๐Ÿ”€ Async Usage + +All main functionalities have an async version following this function naming pattern: **a{func_name}** +```python +async def main(): + await memonto.aretain("Some user query or message") + await memonto.arecall() + await memonto.aretrieve(uri=HIST.Person) + await memonto.aforget() +``` + +## ๐Ÿ”ง Additional Setup + +**Apache Jena** +1. Download Apache Jena Fuseki [here](https://jena.apache.org/download/index.cgi#apache-jena-fuseki). +2. Unzip to desired folder. +```sh +tar -xzf apache-jena-fuseki-X.Y.Z.tar.gz +``` +3. Run a local server. +```sh +./fuseki-server --port=8080 +``` + + +## ๐Ÿ”ฎ Current and Upcoming + +| LLM | | Vector Store | |Triple Store | | +|-----------|-----|--------------|-----|-------------|-----| +|OpenAI |โœ… |Chroma |โœ… |Apache Jena |โœ… | +|Anthropic |โœ… |Pinecone |๐Ÿ”œ | | | +|Meta llama |๐Ÿ”œ |Weaviate |๐Ÿ”œ | | | + +Feedback on what to support next is always welcomed! + +## ๐Ÿ’ฏ Requirements +Python 3.7 or higher. \ No newline at end of file diff --git a/memonto/memonto.py b/memonto/memonto.py index 761b6a5..e840a14 100644 --- a/memonto/memonto.py +++ b/memonto/memonto.py @@ -26,7 +26,6 @@ class Memonto(BaseModel): triple_store: Optional[TripleStoreModel] = None vector_store: Optional[VectorStoreModel] = None auto_expand: Optional[bool] = False - auto_forget: Optional[bool] = False ephemeral: Optional[bool] = False debug: Optional[bool] = False model_config = ConfigDict(arbitrary_types_allowed=True) @@ -38,33 +37,9 @@ def init(self) -> "Memonto": def configure(self, config: dict) -> None: """ - Configure memonto with the desired llm model and datastore. - - :param config: A dictionary containing the configuration for the LLM model and the store. - configs = { - "triple_store": { - "provider": "apache_jena", - "config": { - "connection_url": "http://localhost:3030/ds/update", - "username": "", - "password": "", - }, - }, - "vector_store": { - "provider": "chroma", - "config": { - "mode": "local", - "path": ".local/", - }, - }, - "model": { - "provider": "openai", - "config": { - "model": "gpt-4o", - "api_key": "", - }, - } - } + Configure memonto with the desired llm model and data stores. + + :param config: A dictionary containing the configuration for the LLM model and the data stores. :return: None """ @@ -73,10 +48,9 @@ def configure(self, config: dict) -> None: @require_config("llm", "triple_store") def retain(self, message: str) -> None: """ - Analyze a text for relevant information that maps onto an RDF ontology then commit them to the memory store. + Analyze a text for relevant information that maps onto an RDF ontology then add them to the memory store. - :param query: The user query that is broken down into a graph then committed to memory. - :param id[Optional]: Unique identifier for a memory. Often associated with a unique transaction or user. + :param message: The user message that is broken down into a graph then committed to memory. :return: None """ @@ -110,18 +84,20 @@ async def aretain(self, message: str) -> None: ) @require_config("llm", "triple_store", "vector_store") - def recall(self, message: str = None) -> str: + def recall(self, context: str = None) -> str: """ - Return a text summary of all memories currently stored in context. + Return a text summary of either all or only relevant memories currently in the memory store. In ephemeral mode, a summary of all memories will be returned. + + :param context[Optional]: Context to query the memory store for relevant memories only. - :return: A text summary of the entire current memory. + :return: A text summary of the memory. """ return _recall( data=self.data, llm=self.llm, triple_store=self.triple_store, vector_store=self.vector_store, - message=message, + message=context, id=self.id, ephemeral=self.ephemeral, ) @@ -142,11 +118,10 @@ async def arecall(self, message: str = None) -> str: @require_config("triple_store") def retrieve(self, uri: URIRef = None, query: str = None) -> list: """ - Perform query against the memory store to retrieve raw memory data rather than a summary. + Query against the memory store to retrieve raw memory data rather than a text summary. Raw queries are not supported in ephemeral mode since there are no data stores. - :param id[Optional]: Unique identifier for a memory. Often associated with a unique transaction or user. :param uri[Optional]: URI of the entity to query for. - :param query[Optional]: Raw query that will be performed against the datastore. If you pass in a raw query then the id and uri parameters will be ignored. + :param query[Optional]: Raw query that will be performed against the memory store. If you pass in a raw query then uri will be ignored. :return: A list of triples (subject, predicate, object). """ @@ -201,8 +176,6 @@ def remember(self) -> None: """ Load existing memories from the memory store to a memonto instance. - :param id[Optional]: Unique identifier for a memory. Often associated with a unique transaction or user. - :return: None. """ self.ontology, self.data = _remember( @@ -224,6 +197,7 @@ def _render( - "json": Return the graph in JSON-LD format. - "text": Return the graph in text format. - "image": Return the graph as a png image. + :param path: The path to save the image if format is "image". :return: A text representation of the memory. - "turtle" format returns a string in Turtle format. diff --git a/poetry.lock b/poetry.lock index 9e573e5..b6c8251 100644 --- a/poetry.lock +++ b/poetry.lock @@ -38,13 +38,13 @@ vertex = ["google-auth (>=2,<3)"] [[package]] name = "anyio" -version = "4.4.0" +version = "4.6.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, - {file = "anyio-4.4.0.tar.gz", hash = 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