feat: add durable database-backed ConversationMemory backends

- [*] I have looked for existing issues (including closed) about this

## Feature Request

Add one or more durable database-backed implementations of `ConversationMemory`.

Rig already has `rig-core::memory::ConversationMemory` and an in-process
`InMemoryConversationMemory` backend, but there does not appear to be an
official durable backend for storing conversation history in a database.

This would let agents persist conversation memory across process restarts and
share memory across multiple app instances or workers.

### Motivation

For production agents, in-memory conversation history is often not enough.

A database-backed memory backend would make it possible to:

- preserve conversation history across process restarts
- share memory across multiple app instances or workers
- use existing operational databases such as SQLite, Postgres, or MongoDB
- compose durable storage with existing `rig-memory` policies such as
`PolicyMemory`, `DemotingPolicyMemory`, and `CompactingMemory`

`ConversationMemory` already provides the right lifecycle:

- `load(conversation_id)` before sending a prompt
- `append(conversation_id, messages)` after a successful turn
- `clear(conversation_id)` to delete stored history

Agents already integrate with this lifecycle through `.memory(...)`, so this
request is mainly about providing reusable durable backends.

### Proposal

Add a durable backend implementing `ConversationMemory`, starting with a small,
well-scoped backend such as SQLite or Postgres.

Possible API shape:

```rust
use rig::client::{CompletionClient, ProviderClient};
use rig::completion::Prompt;
use rig::providers::openai;
use rig_sqlite::SqliteConversationMemory;
// or: use rig_memory_sqlite::SqliteConversationMemory;

let memory = SqliteConversationMemory::new(pool_or_connection)
.with_table("rig_conversation_messages")
.migrate()
.await?;

let agent = openai::Client::from_env()?
.agent(openai::GPT_4O)
.memory(memory)
.build();

agent
.prompt("My name is Alice.")
.conversation("user-123")
.await?;
```

A minimal schema could store:

- `conversation_id`
- monotonically increasing sequence number or creation timestamp
- serialized `rig_core::completion::Message`
- optional metadata/version column for forward compatibility

Suggested acceptance criteria:

- add at least one durable `ConversationMemory` backend
- implement `load`, `append`, and `clear`
- preserve per-conversation message order
- support concurrent appends without corrupting ordering
- map backend failures into `MemoryError::Backend`
- include tests using an in-memory or temporary database
- include a small example showing `.memory(db_memory).conversation("...")`
- document how this composes with existing `rig-memory` policies

Open design questions:

1. Should database-backed conversation memory live in `rig-memory`, or in the
existing database crates such as `rig-sqlite`, `rig-postgres`, etc.?
2. Should the first implementation target SQLite for easier local/CI testing,
or Postgres for production multi-worker deployments?
3. Should Rig provide a generic SQL memory abstraction, or keep each database
backend independent like the existing vector store crates?
4. Should the backend auto-create/migrate its table, or require users to run
migrations explicitly?
5. Is JSON serialization of `Message` acceptable as the first storage format,
with a schema/version column for future compatibility?

### Alternatives

Users can implement `ConversationMemory` themselves for their database, but
that leads to repeated custom implementations for a common production need.

Another option is to keep only `InMemoryConversationMemory` in Rig and document
how to build custom backends. The drawback is that durable memory is a common
enough use case that users may expect at least one official reference
implementation.

A third option is to store conversation history in a vector store, but that is
better suited for semantic recall than exact ordered chat history. A database
backend would preserve the full ordered message stream and still compose with
existing memory policies.

I am interested in implementing this if the maintainers agree on crate
placement and the first backend target.

Look at the existing `rig-core::memory::ConversationMemory` trait and `InMemoryConversationMemory` implementation. The new backend must implement `load`, `append`, and `clear`. Start by examining the `rig-sqlite` or `rig-postgres` crates for database integration patterns. Design a table schema with `conversation_id`, a sequence number, and a serialized `rig_core::completion::Message`. Write tests using a temporary database. The example in the issue shows the intended usage pattern.

Rédigé par le modèle d'indexation à partir du texte de l'issue.