Update Gel AI API in Python

This is the current situation with `gel.ai` in Python.

```python
db = gel.create_async_client()

# WORKS: configuration (crashes if you insert it twice though)
config_query = f"""
configure current branch
insert ext::ai::OpenAIProviderConfig {{
secret := '{api_key}',
}}
"""
await db.query(config_query)

# WORKS: vector search
vector_search_query = ai.search(default.Chunk, std.str("dummy query"))
vector_search = await db.query(vector_search_query)
```

```python
# DOESN'T WORK: ORM provider config
config = ai.OpenAIProviderConfig(
name="openai",
display_name="OpenAI",
api_url="https://api.openai.com/v1",
api_style=ai.ProviderAPIStyle.OpenAI,
secret=api_key,
)
await db.save(config)
```

```python
# DOESN'T WORK: anything related to RAG
rag = await gel.ai.create_async_rag_client(db, model="gpt-4o-mini")
chunk_rag = rag.with_context(query="Chunk")
result = await chunk_rag.query_rag("dummy rag query")

# throws 400 bad request and 500 internal server error
# no error messages provided, it's plain response.raise_for_status()
```

Proposed API update to the RAG.

```python
import gel
from models import default
from models.ext import ai

# 1. make RAG a function
result = await ai.generate(
client=client,
prompt="dummy prompt",
context_query=ai.search(default.Chunk, "search string"),
model="anthropic:claude-4-haiku",
)

# 2. take any query for search
result = await ai.generate(
client=client,
prompt ="Who are our top customers?",
context_query=default.Customer.select("*").order_by(revenue="desc").limit(10),
model="openai:gpt-4o-mini"
)

result = await ai.generate(
client=client,
system_prompt="You are a customer service agent",
prompt ="Recent customer complaints?",
context_query=default.SupportTicket.filter(
lambda t: std.fts.search(t.description, "complaint issue problem")
).order_by(created_at="desc"),
model="openai:gpt-4o-mini"
)

# 3. add configation sugar
ai.configure(
client=client,
default_model="openai:gpt-4o-mini",
providers={
"openai": {"api_key": os.getenv("OPENAI_API_KEY")},
"anthropic": {"api_key": os.getenv("ANTHROPIC_API_KEY")}
}
)

# 4. implement fallbacks (or at least elaborate errors so users can catch them)
ai.configure(
client=client,
model_fallback=[
"openai:gpt-4o-mini",
"anthropic:claude-3-sonnet",
"local:llama-7b"
],
fallback_on=["rate_limit", "model_unavailable", "context_too_large"]
)

# 5. if you have to use the RAG with the same query a bunch of times for some reason
import functools

generate = functools.partial(
ai.generate,
client=client,
system_prompt=system_prompt,
model="openai:gpt-4o-mini",
context_query=context_query,
)

# extra stuff:
# 1. context postprocessing (reranking, filtering etc)
# 2. hybrid search
```