Feat: Adding support for milvus files API

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

llama_stack/providers/inline/vector_io/milvus/config.py

@@ -8,11 +8,11 @@ from typing import Any
 
 from pydantic import BaseModel
 
+from llama_stack.schema_utils import json_schema_type
 from llama_stack.providers.utils.kvstore.config import (
     KVStoreConfig,
     SqliteKVStoreConfig,
 )
-from llama_stack.schema_utils import json_schema_type
 
 
 @json_schema_type

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -196,7 +196,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
 
         index = VectorDBWithIndex(
             vector_db=vector_db,
-            index=MilvusIndex(client=self.client, collection_name=vector_db.identifier),
+            index=MilvusIndex(client=self.client, collection_name=vector_db.identifier, kvstore=self.kvstore),
             inference_api=self.inference_api,
         )
         self.cache[vector_db_id] = index
@@ -273,20 +273,179 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
     ) -> None:
         """Save vector store file metadata to Milvus database."""
-        raise NotImplementedError("Files API not yet implemented for Milvus")
+        try:
+            if not await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files"):
+                file_schema = MilvusClient.create_schema(
+                    auto_id=False,
+                    enable_dynamic_field=True,
+                    description="Metadata for OpenAI vector store files",
+                )
+                file_schema.add_field(
+                    field_name="store_file_id", datatype=DataType.VARCHAR, is_primary=True, max_length=512
+                )
+                file_schema.add_field(field_name="store_id", datatype=DataType.VARCHAR, max_length=512)
+                file_schema.add_field(field_name="file_id", datatype=DataType.VARCHAR, max_length=512)
+                file_schema.add_field(field_name="file_info", datatype=DataType.VARCHAR, max_length=65535)
+
+                await asyncio.to_thread(
+                    self.client.create_collection,
+                    collection_name="openai_vector_store_files",
+                    schema=file_schema,
+                )
+
+            if not await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files_contents"):
+                content_schema = MilvusClient.create_schema(
+                    auto_id=False,
+                    enable_dynamic_field=True,
+                    description="Contents for OpenAI vector store files",
+                )
+                content_schema.add_field(
+                    field_name="chunk_id", datatype=DataType.VARCHAR, is_primary=True, max_length=1024
+                )
+                content_schema.add_field(field_name="store_file_id", datatype=DataType.VARCHAR, max_length=1024)
+                content_schema.add_field(field_name="store_id", datatype=DataType.VARCHAR, max_length=512)
+                content_schema.add_field(field_name="file_id", datatype=DataType.VARCHAR, max_length=512)
+                content_schema.add_field(field_name="content", datatype=DataType.VARCHAR, max_length=65535)
+
+                await asyncio.to_thread(
+                    self.client.create_collection,
+                    collection_name="openai_vector_store_files_contents",
+                    schema=content_schema,
+                )
+
+            # Save file metadata
+            file_data = [
+                {
+                    "store_file_id": f"{store_id}_{file_id}",
+                    "store_id": store_id,
+                    "file_id": file_id,
+                    "file_info": json.dumps(file_info),
+                }
+            ]
+            await asyncio.to_thread(
+                self.client.upsert,
+                collection_name="openai_vector_store_files",
+                data=file_data,
+            )
+
+            # Save file contents
+            contents_data = [
+                {
+                    "chunk_id": generate_chunk_id(file_id, content.get("chunk_id", None)),
+                    "store_file_id": f"{store_id}_{file_id}",
+                    "store_id": store_id,
+                    "file_id": file_id,
+                    "content": json.dumps(content),
+                }
+                for content in file_contents
+            ]
+            await asyncio.to_thread(
+                self.client.upsert,
+                collection_name="openai_vector_store_files_contents",
+                data=contents_data,
+            )
+
+        except Exception as e:
+            logger.error(f"Error saving openai vector store file {file_id} for store {store_id}: {e}")
 
     async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]:
         """Load vector store file metadata from Milvus database."""
-        raise NotImplementedError("Files API not yet implemented for Milvus")
+        try:
+            if not await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files"):
+                return {}
+
+            query_filter = f"store_id == '{store_id}' AND file_id == '{file_id}'"
+            results = await asyncio.to_thread(
+                self.client.query,
+                collection_name="openai_vector_store_files",
+                filter=query_filter,
+                output_fields=["file_info"],
+            )
+
+            if results:
+                try:
+                    return json.loads(results[0]["file_info"])
+                except json.JSONDecodeError as e:
+                    logger.error(f"Failed to decode file_info for store {store_id}, file {file_id}: {e}")
+                    return {}
+            return {}
+        except Exception as e:
+            logger.error(f"Error loading openai vector store file {file_id} for store {store_id}: {e}")
+            return {}
 
     async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]:
         """Load vector store file contents from Milvus database."""
-        raise NotImplementedError("Files API not yet implemented for Milvus")
+        try:
+            if not await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files_contents"):
+                return []
+
+            query_filter = f"store_id == '{store_id}' AND file_id == '{file_id}'"
+            results = await asyncio.to_thread(
+                self.client.query,
+                collection_name="openai_vector_store_files_contents",
+                filter=query_filter,
+                output_fields=["chunk_id", "store_id", "file_id", "content"],
+            )
+            print(f"\nresults from milvus = {results}\n")
+
+            contents = []
+            for result in results:
+                try:
+                    content = json.loads(result["content"])
+                    contents.append(content)
+                except json.JSONDecodeError as e:
+                    logger.error(f"Failed to decode content for store {store_id}, file {file_id}: {e}")
+            return contents
+        except Exception as e:
+            print(f"failed {e}")
+
+            logger.error(f"Error loading openai vector store file contents for {file_id} in store {store_id}: {e}")
+            return []
 
     async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None:
         """Update vector store file metadata in Milvus database."""
-        raise NotImplementedError("Files API not yet implemented for Milvus")
+        try:
+            if not await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files"):
+                return
+
+            file_data = [
+                {
+                    "store_file_id": f"{store_id}_{file_id}",
+                    "store_id": store_id,
+                    "file_id": file_id,
+                    "file_info": json.dumps(file_info),
+                }
+            ]
+            await asyncio.to_thread(
+                self.client.upsert,
+                collection_name="openai_vector_store_files",
+                data=file_data,
+            )
+        except Exception as e:
+            logger.error(f"Error updating openai vector store file {file_id} for store {store_id}: {e}")
+            raise
 
     async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
         """Delete vector store file metadata from Milvus database."""
-        raise NotImplementedError("Files API not yet implemented for Milvus")
+        print("milvus is trying to delete stuff")
+        try:
+            if not await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files"):
+                return
+
+            query_filter = f"store_id == '{store_id}' AND file_id == '{file_id}'"
+            await asyncio.to_thread(
+                self.client.delete,
+                collection_name="openai_vector_store_files",
+                filter=query_filter,
+            )
+
+            if await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files_contents"):
+                await asyncio.to_thread(
+                    self.client.delete,
+                    collection_name="openai_vector_store_files_contents",
+                    filter=query_filter,
+                )
+
+        except Exception as e:
+            logger.error(f"Error deleting openai vector store file {file_id} for store {store_id}: {e}")
+            raise