docs: OpenAI responses endpoint#9539
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…ocs-openai-api-endpoint`)
The optimization removes the default empty list `[]` from `server_config.get("args", [])` and changes it to `server_config.get("args")`. This small change eliminates unnecessary list object creation when the "args" key is missing from server configurations.
**Key optimization:** Instead of creating a new empty list every time a server config lacks an "args" key, the code now gets `None` and relies on the existing `if args and args[-1] == sse_url:` check to handle both missing keys and empty lists correctly.
**Why this is faster:** In Python, creating list objects has overhead - even empty lists require memory allocation and object initialization. The line profiler shows this optimization saves ~140 microseconds (4.7% improvement) on the `args = server_config.get("args")` line specifically. When this line executes 6,574 times in the profiled run, these small savings compound to a meaningful 20% overall speedup.
**Test case performance:** This optimization is particularly effective for configurations with many servers that lack "args" keys, as shown in test cases like `test_large_number_of_servers_some_with_empty_args()` where half the servers have missing args. The more servers without args keys, the more list creations are avoided.
The behavior remains identical since `None and args[-1] == sse_url` still evaluates to `False` just like `[] and args[-1] == sse_url` would.
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⚡️ Codeflash found optimizations for this PR📄 21% (0.21x) speedup for
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…i-endpoint`)
The optimization replaces `pkg.split(".", 1)[0]` with `pkg.partition('.')[0]`. This change delivers a **13% speedup** because:
**Key Optimization:**
- `str.partition()` is specifically designed for splitting a string at the first occurrence of a separator, returning a 3-tuple `(before, separator, after)`
- `str.split(".", 1)` creates an intermediate list object before indexing, while `partition()` directly returns a tuple
- Tuple access is faster than list access in CPython, and no intermediate list allocation occurs
**Performance Impact:**
- Line profiler shows per-hit time improved from 681.5ns to 613.3ns (10% per-call improvement)
- The optimization is most effective for high-frequency calls, as evidenced by the 1060 hits in the profiler
**Test Case Performance:**
- Works equally well across all test scenarios - basic package names, edge cases with dots/spaces, and large-scale tests with long strings
- Maintains identical behavior for all edge cases (empty strings, leading dots, unicode characters)
- The optimization benefits any workload that processes many package names, regardless of string length or complexity
This is a classic example of choosing the right string method for the task - `partition()` is purpose-built for "split at first occurrence" operations and avoids the overhead of general-purpose `split()`.
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⚡️ Codeflash found optimizations for this PR📄 13% (0.13x) speedup for
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…s-openai-api-endpoint`) The optimization applies **caching** to the `get_settings_service()` function using `@lru_cache(maxsize=1)`. This addresses the primary performance bottleneck identified in the profiling data. **Key Change:** - Added `@lru_cache(maxsize=1)` decorator to `get_settings_service()` in `deps.py` - Added `from functools import lru_cache` import **Why This Works:** The line profiler shows `get_settings_service()` consuming 99.9% of its execution time (52.4ms out of 52.5ms total) in the `get_service()` call, which involves expensive service initialization and factory registration. This function is called repeatedly throughout the encryption process - once per `encrypt_auth_settings()` call and then again for each encrypt/decrypt operation. With caching, the expensive initialization only happens on the first call. Subsequent calls return the cached `SettingsService` instance in O(1) time, eliminating the redundant factory lookups and service manager operations. **Performance Impact:** - `get_settings_service()` time drops from ~6.7ms to ~0.89ms per call (87% reduction) - Overall function runtime improves from 3.61ms to 2.04ms (77% speedup) - The optimization is most effective for workloads with multiple encryption operations, as shown in the large-scale test cases with 900+ fields **When This Optimization Helps:** This is particularly beneficial for scenarios involving multiple authentication settings encryption in the same process, such as batch operations or applications with frequent auth setting updates, where the same settings service would otherwise be repeatedly initialized.
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Much easier to follow now. Just some touchups and consistency edits.
Approving to unblock you, but please see my suggestions and comments.
Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com>
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Add documentation for the OpenAI compatible endpoint in #9069
Include headers, request and response params, example requests, and example streaming requests.
Include the features for: