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import chromadb
import logging
from chromadb import Settings
from chromadb.utils.batch_utils import create_batches
from typing import Optional
from open_webui.retrieval.vector.main import VectorItem, SearchResult, GetResult
from open_webui.config import (
CHROMA_DATA_PATH,
CHROMA_HTTP_HOST,
CHROMA_HTTP_PORT,
CHROMA_HTTP_HEADERS,
CHROMA_HTTP_SSL,
CHROMA_TENANT,
CHROMA_DATABASE,
CHROMA_CLIENT_AUTH_PROVIDER,
CHROMA_CLIENT_AUTH_CREDENTIALS,
)
from open_webui.env import SRC_LOG_LEVELS
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])
class ChromaClient:
def __init__(self):
settings_dict = {
"allow_reset": True,
"anonymized_telemetry": False,
}
if CHROMA_CLIENT_AUTH_PROVIDER is not None:
settings_dict["chroma_client_auth_provider"] = CHROMA_CLIENT_AUTH_PROVIDER
if CHROMA_CLIENT_AUTH_CREDENTIALS is not None:
settings_dict["chroma_client_auth_credentials"] = (
CHROMA_CLIENT_AUTH_CREDENTIALS
)
if CHROMA_HTTP_HOST != "":
self.client = chromadb.HttpClient(
host=CHROMA_HTTP_HOST,
port=CHROMA_HTTP_PORT,
headers=CHROMA_HTTP_HEADERS,
ssl=CHROMA_HTTP_SSL,
tenant=CHROMA_TENANT,
database=CHROMA_DATABASE,
settings=Settings(**settings_dict),
)
else:
self.client = chromadb.PersistentClient(
path=CHROMA_DATA_PATH,
settings=Settings(**settings_dict),
tenant=CHROMA_TENANT,
database=CHROMA_DATABASE,
)
def has_collection(self, collection_name: str) -> bool:
# Check if the collection exists based on the collection name.
collection_names = self.client.list_collections()
return collection_name in collection_names
def delete_collection(self, collection_name: str):
# Delete the collection based on the collection name.
return self.client.delete_collection(name=collection_name)
def search(
self, collection_name: str, vectors: list[list[float | int]], limit: int
) -> Optional[SearchResult]:
# Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
try:
collection = self.client.get_collection(name=collection_name)
if collection:
result = collection.query(
query_embeddings=vectors,
n_results=limit,
)
# chromadb has cosine distance, 2 (worst) -> 0 (best). Re-odering to 0 -> 1
# https://docs.trychroma.com/docs/collections/configure cosine equation
distances: list = result["distances"][0]
distances = [2 - dist for dist in distances]
distances = [[dist / 2 for dist in distances]]
return SearchResult(
**{
"ids": result["ids"],
"distances": distances,
"documents": result["documents"],
"metadatas": result["metadatas"],
}
)
return None
except Exception as e:
return None
def query(
self, collection_name: str, filter: dict, limit: Optional[int] = None
) -> Optional[GetResult]:
# Query the items from the collection based on the filter.
try:
collection = self.client.get_collection(name=collection_name)
if collection:
result = collection.get(
where=filter,
limit=limit,
)
return GetResult(
**{
"ids": [result["ids"]],
"documents": [result["documents"]],
"metadatas": [result["metadatas"]],
}
)
return None
except:
return None
def get(self, collection_name: str) -> Optional[GetResult]:
# Get all the items in the collection.
collection = self.client.get_collection(name=collection_name)
if collection:
result = collection.get()
return GetResult(
**{
"ids": [result["ids"]],
"documents": [result["documents"]],
"metadatas": [result["metadatas"]],
}
)
return None
def insert(self, collection_name: str, items: list[VectorItem]):
# Insert the items into the collection, if the collection does not exist, it will be created.
collection = self.client.get_or_create_collection(
name=collection_name, metadata={"hnsw:space": "cosine"}
)
ids = [item["id"] for item in items]
documents = [item["text"] for item in items]
embeddings = [item["vector"] for item in items]
metadatas = [item["metadata"] for item in items]
for batch in create_batches(
api=self.client,
documents=documents,
embeddings=embeddings,
ids=ids,
metadatas=metadatas,
):
collection.add(*batch)
def upsert(self, collection_name: str, items: list[VectorItem]):
# Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
collection = self.client.get_or_create_collection(
name=collection_name, metadata={"hnsw:space": "cosine"}
)
ids = [item["id"] for item in items]
documents = [item["text"] for item in items]
embeddings = [item["vector"] for item in items]
metadatas = [item["metadata"] for item in items]
collection.upsert(
ids=ids, documents=documents, embeddings=embeddings, metadatas=metadatas
)
def delete(
self,
collection_name: str,
ids: Optional[list[str]] = None,
filter: Optional[dict] = None,
):
# Delete the items from the collection based on the ids.
try:
collection = self.client.get_collection(name=collection_name)
if collection:
if ids:
collection.delete(ids=ids)
elif filter:
collection.delete(where=filter)
except Exception as e:
# If collection doesn't exist, that's fine - nothing to delete
log.debug(
f"Attempted to delete from non-existent collection {collection_name}. Ignoring."
)
pass
def reset(self):
# Resets the database. This will delete all collections and item entries.
return self.client.reset()
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