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Add ChromaDB support (#1612)

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This commit is contained in:
Dev Khant
2024-08-01 22:16:35 +05:30
committed by GitHub
parent e585d3c1cc
commit 45ae1f0313
9 changed files with 452 additions and 148 deletions
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2
docs/llms.mdx → docs/components/llms.mdx
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@@ -1,5 +1,5 @@
---
title: 🤖 Overview
title: 🤖 LLMs
---
## Overview

64
docs/components/vectordb.mdx Normal file
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@@ -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"})
```

5
docs/mint.json
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@@ -61,9 +61,10 @@
]
},
{
"group": "LLMs",
"group": "Components",
"pages": [
"llms"
"components/llms.mdx",
"components/vectordb.mdx"
]
},
{

30
mem0/memory/main.py
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@@ -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

16
mem0/utils/factory.py
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@@ -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}")

224
mem0/vector_stores/chroma.py Normal file
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@@ -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)]

32
mem0/vector_stores/configs.py
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@@ -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}")

12
mem0/vector_stores/qdrant.py
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@@ -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):
"""

215
poetry.lock generated
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@@ -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 = [
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protobuf = ">=4.21.6,<5.0dev"
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