Add ChromaDB support (#1612)

docs/components/llms.mdx

@@ -1,5 +1,5 @@
 ---
-title: 🤖 Overview
+title: 🤖 LLMs
 ---
 
 ## Overview

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.
+
+<CardGroup>
+    <Card title="Qdrant" href="#qdrant"></Card>
+    <Card title="Chroma" href="#chroma"></Card>
+</CardGroup>
+
+
+## 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"})
+```

docs/mint.json

@@ -61,9 +61,10 @@
       ]
     },
     {
-      "group": "LLMs",
+      "group": "Components",
       "pages": [
-        "llms"
+        "components/llms.mdx",
+        "components/vectordb.mdx"
       ]
     },
     {

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, VectorStoreFactory
-from mem0.utils.factory import LlmFactory, EmbedderFactory
 
 # 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
+        self.vector_store = VectorStoreFactory.create(self.config.vector_store.provider, self.config.vector_store.config)
-        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.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.collection_name = self.config.vector_store.config.collection_name if "collection_name" in self.config.vector_store.config else "mem0"
-        self.vector_store.create_col(
+        
-            name=self.collection_name, vector_size=self.embedding_model.dims
-        )
         capture_event("mem0.init", self)
 
     @classmethod

mem0/utils/factory.py

@@ -44,4 +44,18 @@ class EmbedderFactory:
             return embedder_instance
         else:
             raise ValueError(f"Unsupported Embedder provider: {provider_name}")
-        
+        
+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}")

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)]

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}")

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):
-    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")
-
-
 class Qdrant(VectorStoreBase):
     def __init__(
         self,
+        collection_name="mem0",
+        embedding_model_dims=1536,
         client=None,
         host="localhost",
         port=6333,
@@ -62,6 +56,8 @@ class Qdrant(VectorStoreBase):
                 params["host"] = host
                 params["port"] = port
             self.client = QdrantClient(**params)
+        
+        self.create_col(collection_name, embedding_model_dims)
 
     def create_col(self, name, vector_size, distance=Distance.COSINE):
         """

poetry.lock

@@ -324,62 +324,70 @@ protobuf = ["grpcio-tools (>=1.64.1)"]
 
 [[package]]
 name = "grpcio-tools"
-version = "1.64.1"
+version = "1.62.2"
 description = "Protobuf code generator for gRPC"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "grpcio_tools-1.64.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:6c8181318e3a21827c2f834fd0505040aa8f24fb568a154ff1c95c9802c0e3f5"},
+    {file = "grpcio-tools-1.62.2.tar.gz", hash = "sha256:5fd5e1582b678e6b941ee5f5809340be5e0724691df5299aae8226640f94e18f"},
-    {file = "grpcio_tools-1.64.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:24340327f7fb85f7406910c9484f98dd9588bdf639578b9341920d67f64306a0"},
+    {file = "grpcio_tools-1.62.2-cp310-cp310-linux_armv7l.whl", hash = "sha256:1679b4903aed2dc5bd8cb22a452225b05dc8470a076f14fd703581efc0740cdb"},
-    {file = "grpcio_tools-1.64.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:646828eec31218e1314b04d7c62c78534d3478cae6348909b6a39ee880a757b2"},
+    {file = "grpcio_tools-1.62.2-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:9d41e0e47dd075c075bb8f103422968a65dd0d8dc8613288f573ae91eb1053ba"},
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+    {file = "grpcio_tools-1.62.2-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:987e774f74296842bbffd55ea8826370f70c499e5b5f71a8cf3103838b6ee9c3"},
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+    {file = "grpcio_tools-1.62.2-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:40cd4eeea4b25bcb6903b82930d579027d034ba944393c4751cdefd9c49e6989"},
-    {file = "grpcio_tools-1.64.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:759982ba0f942995bf170386559679b9d9f3b00caf103f346f3c33b8703c3057"},
+    {file = "grpcio_tools-1.62.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b6746bc823958499a3cf8963cc1de00072962fb5e629f26d658882d3f4c35095"},
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+    {file = "grpcio_tools-1.62.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:2ed775e844566ce9ce089be9a81a8b928623b8ee5820f5e4d58c1a9d33dfc5ae"},
-    {file = "grpcio_tools-1.64.1-cp310-cp310-win32.whl", hash = "sha256:a5208855046a338c5663ca39f59fb167e24514f1287c266db42fbf2057373aa0"},
+    {file = "grpcio_tools-1.62.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bdc5dd3f57b5368d5d661d5d3703bcaa38bceca59d25955dff66244dbc987271"},
-    {file = "grpcio_tools-1.64.1-cp310-cp310-win_amd64.whl", hash = "sha256:a808aaa308e26fc5026b15008aec45bea8aa2f2662989cbaffa300601ac98fae"},
+    {file = "grpcio_tools-1.62.2-cp310-cp310-win32.whl", hash = "sha256:3a8d6f07e64c0c7756f4e0c4781d9d5a2b9cc9cbd28f7032a6fb8d4f847d0445"},
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+    {file = "grpcio_tools-1.62.2-cp310-cp310-win_amd64.whl", hash = "sha256:e33b59fb3efdddeb97ded988a871710033e8638534c826567738d3edce528752"},
-    {file = "grpcio_tools-1.64.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:0a72a2d87529329117fca6493d948489f1963e3f645d27a785a27b54b05c38cb"},
+    {file = "grpcio_tools-1.62.2-cp311-cp311-linux_armv7l.whl", hash = "sha256:472505d030135d73afe4143b0873efe0dcb385bd6d847553b4f3afe07679af00"},
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+    {file = "grpcio_tools-1.62.2-cp311-cp311-macosx_10_10_universal2.whl", hash = "sha256:ec674b4440ef4311ac1245a709e87b36aca493ddc6850eebe0b278d1f2b6e7d1"},
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+    {file = "grpcio_tools-1.62.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a34d97c62e61bfe9e6cff0410fe144ac8cca2fc979ad0be46b7edf026339d161"},
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 [package.dependencies]
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-protobuf = ">=5.26.1,<6.0dev"
+protobuf = ">=4.21.6,<5.0dev"
 setuptools = "*"
 
 [[package]]
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