feat: implement get chat completions APIs (#2200)

# What does this PR do?
* Provide sqlite implementation of the APIs introduced in
https://github.com/meta-llama/llama-stack/pull/2145.
* Introduced a SqlStore API: llama_stack/providers/utils/sqlstore/api.py
and the first Sqlite implementation
* Pagination support will be added in a future PR.

## Test Plan
Unit test on sql store:
<img width="1005" alt="image"
src="https://github.com/user-attachments/assets/9b8b7ec8-632b-4667-8127-5583426b2e29"
/>


Integration test:
```
INFERENCE_MODEL="llama3.2:3b-instruct-fp16" llama stack build --template ollama --image-type conda --run
```
```
LLAMA_STACK_CONFIG=http://localhost:5001 INFERENCE_MODEL="llama3.2:3b-instruct-fp16" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "llama3.2:3b-instruct-fp16" -k 'inference_store and openai'
```

llama_stack/providers/utils/inference/inference_store.py

@@ -0,0 +1,123 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from llama_stack.apis.inference import (
+    ListOpenAIChatCompletionResponse,
+    OpenAIChatCompletion,
+    OpenAICompletionWithInputMessages,
+    OpenAIMessageParam,
+    Order,
+)
+from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
+
+from ..sqlstore.api import ColumnDefinition, ColumnType
+from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl
+
+
+class InferenceStore:
+    def __init__(self, sql_store_config: SqlStoreConfig):
+        if not sql_store_config:
+            sql_store_config = SqliteSqlStoreConfig(
+                db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(),
+            )
+        self.sql_store_config = sql_store_config
+        self.sql_store = None
+
+    async def initialize(self):
+        """Create the necessary tables if they don't exist."""
+        self.sql_store = sqlstore_impl(self.sql_store_config)
+        await self.sql_store.create_table(
+            "chat_completions",
+            {
+                "id": ColumnDefinition(type=ColumnType.STRING, primary_key=True),
+                "created": ColumnType.INTEGER,
+                "model": ColumnType.STRING,
+                "choices": ColumnType.JSON,
+                "input_messages": ColumnType.JSON,
+            },
+        )
+
+    async def store_chat_completion(
+        self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam]
+    ) -> None:
+        if not self.sql_store:
+            raise ValueError("Inference store is not initialized")
+
+        data = chat_completion.model_dump()
+
+        await self.sql_store.insert(
+            "chat_completions",
+            {
+                "id": data["id"],
+                "created": data["created"],
+                "model": data["model"],
+                "choices": data["choices"],
+                "input_messages": [message.model_dump() for message in input_messages],
+            },
+        )
+
+    async def list_chat_completions(
+        self,
+        after: str | None = None,
+        limit: int | None = 50,
+        model: str | None = None,
+        order: Order | None = Order.desc,
+    ) -> ListOpenAIChatCompletionResponse:
+        """
+        List chat completions from the database.
+
+        :param after: The ID of the last chat completion to return.
+        :param limit: The maximum number of chat completions to return.
+        :param model: The model to filter by.
+        :param order: The order to sort the chat completions by.
+        """
+        if not self.sql_store:
+            raise ValueError("Inference store is not initialized")
+
+        # TODO: support after
+        if after:
+            raise NotImplementedError("After is not supported for SQLite")
+        if not order:
+            order = Order.desc
+
+        rows = await self.sql_store.fetch_all(
+            "chat_completions",
+            where={"model": model} if model else None,
+            order_by=[("created", order.value)],
+            limit=limit,
+        )
+
+        data = [
+            OpenAICompletionWithInputMessages(
+                id=row["id"],
+                created=row["created"],
+                model=row["model"],
+                choices=row["choices"],
+                input_messages=row["input_messages"],
+            )
+            for row in rows
+        ]
+        return ListOpenAIChatCompletionResponse(
+            data=data,
+            # TODO: implement has_more
+            has_more=False,
+            first_id=data[0].id if data else "",
+            last_id=data[-1].id if data else "",
+        )
+
+    async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages:
+        if not self.sql_store:
+            raise ValueError("Inference store is not initialized")
+
+        row = await self.sql_store.fetch_one("chat_completions", where={"id": completion_id})
+        if not row:
+            raise ValueError(f"Chat completion with id {completion_id} not found") from None
+        return OpenAICompletionWithInputMessages(
+            id=row["id"],
+            created=row["created"],
+            model=row["model"],
+            choices=row["choices"],
+            input_messages=row["input_messages"],
+        )