diff --git a/backend/open_webui/config.py b/backend/open_webui/config.py index 8075bf123..099c49f37 100644 --- a/backend/open_webui/config.py +++ b/backend/open_webui/config.py @@ -1765,6 +1765,12 @@ MILVUS_URI = os.environ.get("MILVUS_URI", f"{DATA_DIR}/vector_db/milvus.db") MILVUS_DB = os.environ.get("MILVUS_DB", "default") MILVUS_TOKEN = os.environ.get("MILVUS_TOKEN", None) +MILVUS_INDEX_TYPE = os.environ.get("MILVUS_INDEX_TYPE", "HNSW") +MILVUS_METRIC_TYPE = os.environ.get("MILVUS_METRIC_TYPE", "COSINE") +MILVUS_HNSW_M = int(os.environ.get("MILVUS_HNSW_M", "16")) +MILVUS_HNSW_EFCONSTRUCTION = int(os.environ.get("MILVUS_HNSW_EFCONSTRUCTION", "100")) +MILVUS_IVF_FLAT_NLIST = int(os.environ.get("MILVUS_IVF_FLAT_NLIST", "128")) + # Qdrant QDRANT_URI = os.environ.get("QDRANT_URI", None) QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY", None) diff --git a/backend/open_webui/retrieval/vector/dbs/milvus.py b/backend/open_webui/retrieval/vector/dbs/milvus.py index f116c57f7..336dc056a 100644 --- a/backend/open_webui/retrieval/vector/dbs/milvus.py +++ b/backend/open_webui/retrieval/vector/dbs/milvus.py @@ -3,7 +3,6 @@ from pymilvus import FieldSchema, DataType import json import logging from typing import Optional - from open_webui.retrieval.vector.main import ( VectorDBBase, VectorItem, @@ -14,13 +13,17 @@ from open_webui.config import ( MILVUS_URI, MILVUS_DB, MILVUS_TOKEN, + MILVUS_INDEX_TYPE, + MILVUS_METRIC_TYPE, + MILVUS_HNSW_M, + MILVUS_HNSW_EFCONSTRUCTION, + MILVUS_IVF_FLAT_NLIST, ) from open_webui.env import SRC_LOG_LEVELS log = logging.getLogger(__name__) log.setLevel(SRC_LOG_LEVELS["RAG"]) - class MilvusClient(VectorDBBase): def __init__(self): self.collection_prefix = "open_webui" @@ -33,7 +36,6 @@ class MilvusClient(VectorDBBase): ids = [] documents = [] metadatas = [] - for match in result: _ids = [] _documents = [] @@ -42,11 +44,9 @@ class MilvusClient(VectorDBBase): _ids.append(item.get("id")) _documents.append(item.get("data", {}).get("text")) _metadatas.append(item.get("metadata")) - ids.append(_ids) documents.append(_documents) metadatas.append(_metadatas) - return GetResult( **{ "ids": ids, @@ -60,13 +60,11 @@ class MilvusClient(VectorDBBase): distances = [] documents = [] metadatas = [] - for match in result: _ids = [] _distances = [] _documents = [] _metadatas = [] - for item in match: _ids.append(item.get("id")) # normalize milvus score from [-1, 1] to [0, 1] range @@ -75,12 +73,10 @@ class MilvusClient(VectorDBBase): _distances.append(_dist) _documents.append(item.get("entity", {}).get("data", {}).get("text")) _metadatas.append(item.get("entity", {}).get("metadata")) - ids.append(_ids) distances.append(_distances) documents.append(_documents) metadatas.append(_metadatas) - return SearchResult( **{ "ids": ids, @@ -113,11 +109,36 @@ class MilvusClient(VectorDBBase): ) index_params = self.client.prepare_index_params() + + # Use configurations from config.py + index_type = MILVUS_INDEX_TYPE.upper() + metric_type = MILVUS_METRIC_TYPE.upper() + + log.info(f"Using Milvus index type: {index_type}, metric type: {metric_type}") + + index_creation_params = {} + if index_type == "HNSW": + index_creation_params = {"M": MILVUS_HNSW_M, "efConstruction": MILVUS_HNSW_EFCONSTRUCTION} + log.info(f"HNSW params: {index_creation_params}") + elif index_type == "IVF_FLAT": + index_creation_params = {"nlist": MILVUS_IVF_FLAT_NLIST} + log.info(f"IVF_FLAT params: {index_creation_params}") + elif index_type in ["FLAT", "AUTOINDEX"]: + log.info(f"Using {index_type} index with no specific build-time params.") + else: + log.warning( + f"Unsupported MILVUS_INDEX_TYPE: '{index_type}'. " + f"Supported types: HNSW, IVF_FLAT, FLAT, AUTOINDEX. " + f"Milvus will use its default for the collection if this type is not directly supported for index creation." + ) + # For unsupported types, pass the type directly to Milvus; it might handle it or use a default. + # If Milvus errors out, the user needs to correct the MILVUS_INDEX_TYPE env var. + index_params.add_index( field_name="vector", - index_type="HNSW", - metric_type="COSINE", - params={"M": 16, "efConstruction": 100}, + index_type=index_type, + metric_type=metric_type, + params=index_creation_params, ) self.client.create_collection( @@ -125,6 +146,8 @@ class MilvusClient(VectorDBBase): schema=schema, index_params=index_params, ) + log.info(f"Successfully created collection '{self.collection_prefix}_{collection_name}' with index type '{index_type}' and metric '{metric_type}'.") + def has_collection(self, collection_name: str) -> bool: # Check if the collection exists based on the collection name. @@ -145,84 +168,95 @@ class MilvusClient(VectorDBBase): ) -> Optional[SearchResult]: # Search for the nearest neighbor items based on the vectors and return 'limit' number of results. collection_name = collection_name.replace("-", "_") + # For some index types like IVF_FLAT, search params like nprobe can be set. + # Example: search_params = {"nprobe": 10} if using IVF_FLAT + # For simplicity, not adding configurable search_params here, but could be extended. result = self.client.search( collection_name=f"{self.collection_prefix}_{collection_name}", data=vectors, limit=limit, output_fields=["data", "metadata"], + # search_params=search_params # Potentially add later if needed ) - return self._result_to_search_result(result) def query(self, collection_name: str, filter: dict, limit: Optional[int] = None): # Construct the filter string for querying collection_name = collection_name.replace("-", "_") if not self.has_collection(collection_name): + log.warning(f"Query attempted on non-existent collection: {self.collection_prefix}_{collection_name}") return None - filter_string = " && ".join( [ f'metadata["{key}"] == {json.dumps(value)}' for key, value in filter.items() ] ) - max_limit = 16383 # The maximum number of records per request all_results = [] - if limit is None: - limit = float("inf") # Use infinity as a placeholder for no limit + # Milvus default limit for query if not specified is 16384, but docs mention iteration. + # Let's set a practical high number if "all" is intended, or handle true pagination. + # For now, if limit is None, we'll fetch in batches up to a very large number. + # This part could be refined based on expected use cases for "get all". + # For this function signature, None implies "as many as possible" up to Milvus limits. + limit = 16384 * 10 # A large number to signify fetching many, will be capped by actual data or max_limit per call. + log.info(f"Limit not specified for query, fetching up to {limit} results in batches.") + # Initialize offset and remaining to handle pagination offset = 0 remaining = limit - + try: + log.info(f"Querying collection {self.collection_prefix}_{collection_name} with filter: '{filter_string}', limit: {limit}") # Loop until there are no more items to fetch or the desired limit is reached while remaining > 0: - log.info(f"remaining: {remaining}") - current_fetch = min( - max_limit, remaining - ) # Determine how many items to fetch in this iteration - + current_fetch = min(max_limit, remaining if isinstance(remaining, int) else max_limit) + log.debug(f"Querying with offset: {offset}, current_fetch: {current_fetch}") + results = self.client.query( collection_name=f"{self.collection_prefix}_{collection_name}", filter=filter_string, - output_fields=["*"], + output_fields=["id", "data", "metadata"], # Explicitly list needed fields. Vector not usually needed in query. limit=current_fetch, offset=offset, ) - + if not results: + log.debug("No more results from query.") break - + all_results.extend(results) results_count = len(results) - remaining -= ( - results_count # Decrease remaining by the number of items fetched - ) + log.debug(f"Fetched {results_count} results in this batch.") + + if isinstance(remaining, int): + remaining -= results_count + offset += results_count - - # Break the loop if the results returned are less than the requested fetch count + + # Break the loop if the results returned are less than the requested fetch count (means end of data) if results_count < current_fetch: + log.debug("Fetched less than requested, assuming end of results for this query.") break - - log.debug(all_results) + + log.info(f"Total results from query: {len(all_results)}") return self._result_to_get_result([all_results]) except Exception as e: log.exception( - f"Error querying collection {collection_name} with limit {limit}: {e}" + f"Error querying collection {self.collection_prefix}_{collection_name} with filter '{filter_string}' and limit {limit}: {e}" ) return None def get(self, collection_name: str) -> Optional[GetResult]: - # Get all the items in the collection. + # Get all the items in the collection. This can be very resource-intensive for large collections. collection_name = collection_name.replace("-", "_") - result = self.client.query( - collection_name=f"{self.collection_prefix}_{collection_name}", - filter='id != ""', - ) - return self._result_to_get_result([result]) + log.warning(f"Fetching ALL items from collection '{self.collection_prefix}_{collection_name}'. This might be slow for large collections.") + # Using query with a trivial filter to get all items. + # This will use the paginated query logic. + return self.query(collection_name=collection_name, filter={}, limit=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. @@ -230,10 +264,15 @@ class MilvusClient(VectorDBBase): if not self.client.has_collection( collection_name=f"{self.collection_prefix}_{collection_name}" ): + log.info(f"Collection {self.collection_prefix}_{collection_name} does not exist. Creating now.") + if not items: + log.error(f"Cannot create collection {self.collection_prefix}_{collection_name} without items to determine dimension.") + raise ValueError("Cannot create Milvus collection without items to determine vector dimension.") self._create_collection( collection_name=collection_name, dimension=len(items[0]["vector"]) ) - + + log.info(f"Inserting {len(items)} items into collection {self.collection_prefix}_{collection_name}.") return self.client.insert( collection_name=f"{self.collection_prefix}_{collection_name}", data=[ @@ -253,10 +292,15 @@ class MilvusClient(VectorDBBase): if not self.client.has_collection( collection_name=f"{self.collection_prefix}_{collection_name}" ): + log.info(f"Collection {self.collection_prefix}_{collection_name} does not exist for upsert. Creating now.") + if not items: + log.error(f"Cannot create collection {self.collection_prefix}_{collection_name} for upsert without items to determine dimension.") + raise ValueError("Cannot create Milvus collection for upsert without items to determine vector dimension.") self._create_collection( collection_name=collection_name, dimension=len(items[0]["vector"]) ) - + + log.info(f"Upserting {len(items)} items into collection {self.collection_prefix}_{collection_name}.") return self.client.upsert( collection_name=f"{self.collection_prefix}_{collection_name}", data=[ @@ -276,30 +320,46 @@ class MilvusClient(VectorDBBase): ids: Optional[list[str]] = None, filter: Optional[dict] = None, ): - # Delete the items from the collection based on the ids. + # Delete the items from the collection based on the ids or filter. collection_name = collection_name.replace("-", "_") + if not self.has_collection(collection_name): + log.warning(f"Delete attempted on non-existent collection: {self.collection_prefix}_{collection_name}") + return None + if ids: + log.info(f"Deleting items by IDs from {self.collection_prefix}_{collection_name}. IDs: {ids}") return self.client.delete( collection_name=f"{self.collection_prefix}_{collection_name}", ids=ids, ) elif filter: - # Convert the filter dictionary to a string using JSON_CONTAINS. filter_string = " && ".join( [ f'metadata["{key}"] == {json.dumps(value)}' for key, value in filter.items() ] ) - + log.info(f"Deleting items by filter from {self.collection_prefix}_{collection_name}. Filter: {filter_string}") return self.client.delete( collection_name=f"{self.collection_prefix}_{collection_name}", filter=filter_string, ) + else: + log.warning(f"Delete operation on {self.collection_prefix}_{collection_name} called without IDs or filter. No action taken.") + return None + def reset(self): - # Resets the database. This will delete all collections and item entries. + # Resets the database. This will delete all collections and item entries that match the prefix. + log.warning(f"Resetting Milvus: Deleting all collections with prefix '{self.collection_prefix}'.") collection_names = self.client.list_collections() - for collection_name in collection_names: - if collection_name.startswith(self.collection_prefix): - self.client.drop_collection(collection_name=collection_name) + deleted_collections = [] + for collection_name_full in collection_names: + if collection_name_full.startswith(self.collection_prefix): + try: + self.client.drop_collection(collection_name=collection_name_full) + deleted_collections.append(collection_name_full) + log.info(f"Deleted collection: {collection_name_full}") + except Exception as e: + log.error(f"Error deleting collection {collection_name_full}: {e}") + log.info(f"Milvus reset complete. Deleted collections: {deleted_collections}")