diff --git a/docs/llms.mdx b/docs/components/llms.mdx similarity index 99% rename from docs/llms.mdx rename to docs/components/llms.mdx index a554dbab..0cd3f772 100644 --- a/docs/llms.mdx +++ b/docs/components/llms.mdx @@ -1,5 +1,5 @@ --- -title: 🤖 Overview +title: 🤖 LLMs --- ## Overview diff --git a/docs/components/vectordb.mdx b/docs/components/vectordb.mdx new file mode 100644 index 00000000..0631e26d --- /dev/null +++ b/docs/components/vectordb.mdx @@ -0,0 +1,64 @@ +--- +title: 🗄 Vector Databases +--- + +## Overview + +Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs. + + + + + + + +## Qdrant + +[Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data. + +To use Qdrant you can do like this: + +```python +import os +from mem0 import Memory + + +config = { + "vectordb": { + "provider": "qdrant", + "config": { + "collection_name": "test", + "host": "localhost", + "port": 6333, + } + } +} + +m = Memory.from_config(config) +m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"}) +``` + +## Chroma + +[Chroma](https://www.trychroma.com/) is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed. + +To use ChromaDB you can do like this: + +```python +import os +from mem0 import Memory + + +config = { + "vectordb": { + "provider": "chromadb", + "config": { + "collection_name": "test", + "path": "db", + } + } +} + +m = Memory.from_config(config) +m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"}) +``` diff --git a/docs/mint.json b/docs/mint.json index a2e081c2..d6c1d077 100644 --- a/docs/mint.json +++ b/docs/mint.json @@ -61,9 +61,10 @@ ] }, { - "group": "LLMs", + "group": "Components", "pages": [ - "llms" + "components/llms.mdx", + "components/vectordb.mdx" ] }, { diff --git a/mem0/memory/main.py b/mem0/memory/main.py index ffbbf58e..8bedd29a 100644 --- a/mem0/memory/main.py +++ b/mem0/memory/main.py @@ -1,4 +1,3 @@ -import json import logging import os import time @@ -21,8 +20,7 @@ from mem0.memory.utils import get_update_memory_messages from mem0.vector_stores.configs import VectorStoreConfig from mem0.llms.configs import LlmConfig from mem0.embeddings.configs import EmbedderConfig -from mem0.vector_stores.qdrant import Qdrant -from mem0.utils.factory import LlmFactory, EmbedderFactory +from mem0.utils.factory import LlmFactory, EmbedderFactory, VectorStoreFactory # Setup user config setup_config() @@ -57,37 +55,17 @@ class MemoryConfig(BaseModel): description="Path to the history database", default=os.path.join(mem0_dir, "history.db"), ) - collection_name: str = Field(default="mem0", description="Name of the collection") - embedding_model_dims: int = Field( - default=1536, description="Dimensions of the embedding model" - ) class Memory(MemoryBase): def __init__(self, config: MemoryConfig = MemoryConfig()): self.config = config self.embedding_model = EmbedderFactory.create(self.config.embedder.provider) - # Initialize the appropriate vector store based on the configuration - vector_store_config = self.config.vector_store.config - if self.config.vector_store.provider == "qdrant": - self.vector_store = Qdrant( - host=vector_store_config.host, - port=vector_store_config.port, - path=vector_store_config.path, - url=vector_store_config.url, - api_key=vector_store_config.api_key, - ) - else: - raise ValueError( - f"Unsupported vector store type: {self.config.vector_store_type}" - ) - + self.vector_store = VectorStoreFactory.create(self.config.vector_store.provider, self.config.vector_store.config) self.llm = LlmFactory.create(self.config.llm.provider, self.config.llm.config) self.db = SQLiteManager(self.config.history_db_path) - self.collection_name = self.config.collection_name - self.vector_store.create_col( - name=self.collection_name, vector_size=self.embedding_model.dims - ) + self.collection_name = self.config.vector_store.config.collection_name if "collection_name" in self.config.vector_store.config else "mem0" + capture_event("mem0.init", self) @classmethod diff --git a/mem0/utils/factory.py b/mem0/utils/factory.py index 50856b04..40583424 100644 --- a/mem0/utils/factory.py +++ b/mem0/utils/factory.py @@ -44,4 +44,18 @@ class EmbedderFactory: return embedder_instance else: raise ValueError(f"Unsupported Embedder provider: {provider_name}") - \ No newline at end of file + +class VectorStoreFactory: + provider_to_class = { + "qdrant": "mem0.vector_stores.qdrant.Qdrant", + "chromadb": "mem0.vector_stores.chroma.ChromaDB", + } + + @classmethod + def create(cls, provider_name, config): + class_type = cls.provider_to_class.get(provider_name) + if class_type: + vector_store_instance = load_class(class_type) + return vector_store_instance(**config) + else: + raise ValueError(f"Unsupported VectorStore provider: {provider_name}") diff --git a/mem0/vector_stores/chroma.py b/mem0/vector_stores/chroma.py new file mode 100644 index 00000000..0b79bc2d --- /dev/null +++ b/mem0/vector_stores/chroma.py @@ -0,0 +1,224 @@ +import logging +from typing import Optional + +from pydantic import BaseModel + +try: + import chromadb + from chromadb.config import Settings +except ImportError: + raise ImportError("Chromadb requires extra dependencies. Install with `pip install chromadb`") from None + +from mem0.vector_stores.base import VectorStoreBase + + +class OutputData(BaseModel): + id: Optional[str] # memory id + score: Optional[float] # distance + payload: Optional[dict] # metadata + + +class ChromaDB(VectorStoreBase): + def __init__( + self, + collection_name="mem0", + client=None, + host=None, + port=None, + path=None + ): + """ + Initialize the Qdrant vector store. + + Args: + client (QdrantClient, optional): Existing Qdrant client instance. Defaults to None. + host (str, optional): Host address for Qdrant server. Defaults to None. + port (int, optional): Port for Qdrant server. Defaults to None. + path (str, optional): Path for local Qdrant database. Defaults to None. + """ + if client: + self.client = client + else: + self.settings = Settings(anonymized_telemetry=False) + + if host and port: + self.settings.chroma_server_host = host + self.settings.chroma_server_http_port = port + self.settings.chroma_api_impl = "chromadb.api.fastapi.FastAPI" + else: + if path is None: + path = "db" + + self.settings.persist_directory = path + self.settings.is_persistent = True + + self.client = chromadb.Client(self.settings) + + self.collection = self.create_col(collection_name) + + def _parse_output(self, data): + """ + Parse the output data. + + Args: + data (dict): Output data. + + Returns: + list: Parsed output data. + """ + keys = ['ids', 'distances', 'metadatas'] + values = [] + + for key in keys: + value = data.get(key, []) + if isinstance(value, list) and value and isinstance(value[0], list): + value = value[0] + values.append(value) + + ids, distances, metadatas = values + max_length = max(len(v) for v in values if isinstance(v, list) and v is not None) + + result = [] + for i in range(max_length): + entry = OutputData( + id=ids[i] if isinstance(ids, list) and ids and i < len(ids) else None, + score=distances[i] if isinstance(distances, list) and distances and i < len(distances) else None, + payload=metadatas[i] if isinstance(metadatas, list) and metadatas and i < len(metadatas) else None, + ) + result.append(entry) + + return result + + def create_col(self, name, embedding_fn=None): + """ + Create a new collection. + + Args: + name (str): Name of the collection. + embedding_fn (function): Embedding function to use. + """ + # Skip creating collection if already exists + collections = self.list_cols() + for collection in collections: + if collection.name == name: + logging.debug(f"Collection {name} already exists. Skipping creation.") + + collection = self.client.get_or_create_collection( + name=name, + embedding_function=embedding_fn, + ) + return collection + + def insert(self, name, vectors, payloads=None, ids=None): + """ + Insert vectors into a collection. + + Args: + name (str): Name of the collection. + vectors (list): List of vectors to insert. + payloads (list, optional): List of payloads corresponding to vectors. Defaults to None. + ids (list, optional): List of IDs corresponding to vectors. Defaults to None. + """ + + self.collection.add(ids=ids, embeddings=vectors, metadatas=payloads) + + def search(self, name, query, limit=5, filters=None): + """ + Search for similar vectors. + + Args: + name (str): Name of the collection. + query (list): Query vector. + limit (int, optional): Number of results to return. Defaults to 5. + filters (dict, optional): Filters to apply to the search. Defaults to None. + + Returns: + list: Search results. + """ + results = self.collection.query(query_embeddings=query, where=filters, n_results=limit) + final_results = self._parse_output(results) + return final_results + + def delete(self, name, vector_id): + """ + Delete a vector by ID. + + Args: + name (str): Name of the collection. + vector_id (int): ID of the vector to delete. + """ + + self.collection.delete(ids=vector_id) + + def update(self, name, vector_id, vector=None, payload=None): + """ + Update a vector and its payload. + + Args: + name (str): Name of the collection. + vector_id (int): ID of the vector to update. + vector (list, optional): Updated vector. Defaults to None. + payload (dict, optional): Updated payload. Defaults to None. + """ + + self.collection.update(ids=vector_id, embeddings=vector, metadatas=payload) + + def get(self, name, vector_id): + """ + Retrieve a vector by ID. + + Args: + name (str): Name of the collection. + vector_id (int): ID of the vector to retrieve. + + Returns: + dict: Retrieved vector. + """ + result = self.collection.get(ids=[vector_id]) + return self._parse_output(result)[0] + + def list_cols(self): + """ + List all collections. + + Returns: + list: List of collection names. + """ + return self.client.list_collections() + + def delete_col(self, name): + """ + Delete a collection. + + Args: + name (str): Name of the collection to delete. + """ + self.client.delete_collection(name=name) + + def col_info(self, name): + """ + Get information about a collection. + + Args: + name (str): Name of the collection. + + Returns: + dict: Collection information. + """ + return self.client.get_collection(name=name) + + def list(self, name, filters=None, limit=100): + """ + List all vectors in a collection. + + Args: + name (str): Name of the collection. + filters (dict, optional): Filters to apply to the list. Defaults to None. + limit (int, optional): Number of vectors to return. Defaults to 100. + + Returns: + list: List of vectors. + """ + array = [[0 for _ in range(1536)] for _ in range(1536)] + results = self.collection.query(query_embeddings=array, where=filters, n_results=limit) + return [self._parse_output(results)] \ No newline at end of file diff --git a/mem0/vector_stores/configs.py b/mem0/vector_stores/configs.py index 37c80728..52b26688 100644 --- a/mem0/vector_stores/configs.py +++ b/mem0/vector_stores/configs.py @@ -4,6 +4,10 @@ from pydantic import BaseModel, Field, field_validator, model_validator class QdrantConfig(BaseModel): + collection_name: str = Field(default="mem0", description="Name of the collection") + embedding_model_dims: Optional[int] = Field( + default=1536, description="Dimensions of the embedding model" + ) host: Optional[str] = Field(None, description="Host address for Qdrant server") port: Optional[int] = Field(None, description="Port for Qdrant server") path: Optional[str] = Field(None, description="Path for local Qdrant database") @@ -26,14 +30,36 @@ class QdrantConfig(BaseModel): return values +class ChromaDbConfig(BaseModel): + collection_name: str = Field( + default="mem0", description="Default name for the collection" + ) + path: Optional[str] = Field( + default=None, description="Path to the database directory" + ) + host: Optional[str] = Field( + default=None, description="Database connection remote host" + ) + port: Optional[str] = Field( + default=None, description="Database connection remote port" + ) + + @model_validator(mode="before") + def check_host_port_or_path(cls, values): + host, port, path = values.get("host"), values.get("port"), values.get("path") + if not path and not (host and port): + raise ValueError("Either 'host' and 'port' or 'path' must be provided.") + return values + + class VectorStoreConfig(BaseModel): provider: str = Field( description="Provider of the vector store (e.g., 'qdrant', 'chromadb', 'elasticsearch')", default="qdrant", ) - config: QdrantConfig = Field( + config: Optional[dict] = Field( description="Configuration for the specific vector store", - default=QdrantConfig(path="/tmp/qdrant"), + default={}, ) @field_validator("config") @@ -41,5 +67,7 @@ class VectorStoreConfig(BaseModel): provider = values.data.get("provider") if provider == "qdrant": return QdrantConfig(**v.model_dump()) + elif provider == "chromadb": + return ChromaDbConfig(**v.model_dump()) else: raise ValueError(f"Unsupported vector store provider: {provider}") diff --git a/mem0/vector_stores/qdrant.py b/mem0/vector_stores/qdrant.py index dc620624..156efd86 100644 --- a/mem0/vector_stores/qdrant.py +++ b/mem0/vector_stores/qdrant.py @@ -1,9 +1,7 @@ import os import shutil import logging -from typing import Optional -from pydantic import BaseModel, Field from qdrant_client import QdrantClient from qdrant_client.models import ( Distance, @@ -19,15 +17,11 @@ from qdrant_client.models import ( from mem0.vector_stores.base import VectorStoreBase -class QdrantConfig(BaseModel): 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