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Merge pull request #37 from nomyo-ai/dev-v0.7.x-semcache

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Dev v0.7.x semcache addtl. feature
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Alpha Nerd 2026-03-12 16:08:23 +01:00 • committed by GitHub
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1
requirements.txt
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@ -32,6 +32,7 @@ PyYAML==6.0.3
sniffio==1.3.1
starlette==0.49.1
truststore==0.10.4
tiktoken==0.12.0
tqdm==4.67.1
typing-inspection==0.4.1
typing_extensions==4.14.1

390
router.py
View file

@ -78,6 +78,107 @@ def _mask_secrets(text: str) -> str:
text = re.sub(r"(?i)(api[-_ ]key\s*[:=]\s*)([^\s]+)", r"\1***redacted***", text)
return text
# ------------------------------------------------------------------
# Context-window sliding-window helpers
# ------------------------------------------------------------------
try:
import tiktoken as _tiktoken
_tiktoken_enc = _tiktoken.get_encoding("cl100k_base")
except Exception:
_tiktoken_enc = None
def _count_message_tokens(messages: list) -> int:
"""Approximate token count for a message list.
Uses tiktoken cl100k_base when available (within ~5-15% of llama tokenizers).
Falls back to char/4 heuristic if tiktoken is unavailable.
Formula follows OpenAI's per-message overhead: 4 tokens/message + content + 2 priming.
"""
if _tiktoken_enc is None:
return sum(len(str(m.get("content", ""))) for m in messages) // 4
total = 2 # priming tokens
for msg in messages:
total += 4 # per-message role/separator overhead
content = msg.get("content", "")
if isinstance(content, str):
total += len(_tiktoken_enc.encode(content))
elif isinstance(content, list):
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
total += len(_tiktoken_enc.encode(part.get("text", "")))
return total
def _trim_messages_for_context(
messages: list,
n_ctx: int,
safety_margin: int = None,
target_tokens: int = None,
) -> list:
"""Sliding-window trim — mirrors what llama.cpp context-shift used to do.
Keeps all system messages and the most recent non-system messages that fit
within (n_ctx - safety_margin) tokens. Oldest non-system messages are dropped
first (FIFO). The last message is always preserved.
safety_margin defaults to 1/4 of n_ctx to leave headroom for the generated
response, including RAG tool results and tool call JSON synthesis.
target_tokens: if provided, overrides the (n_ctx - safety_margin) target.
Pass a calibrated value when actual n_prompt_tokens is known from the error
body so that tiktoken underestimation vs the backend tokenizer is corrected.
"""
if target_tokens is not None:
target = target_tokens
else:
if safety_margin is None:
safety_margin = n_ctx // 4
target = n_ctx - safety_margin
system_msgs = [m for m in messages if m.get("role") == "system"]
non_system = [m for m in messages if m.get("role") != "system"]
while len(non_system) > 1:
if _count_message_tokens(system_msgs + non_system) <= target:
break
non_system.pop(0) # drop oldest non-system message
# Ensure the first non-system message is a user message (chat templates require it).
# Drop any leading assistant/tool messages that were left after trimming.
while non_system and non_system[0].get("role") != "user":
non_system.pop(0)
return system_msgs + non_system
def _calibrated_trim_target(msgs: list, n_ctx: int, actual_tokens: int) -> int:
"""Return a tiktoken-scale trim target based on how much backend tokens must be shed.
actual_tokens includes messages + tool schemas + overhead as counted by the backend.
_count_message_tokens only counts message text, so we cannot derive an accurate
per-token scale from the ratio. Instead we compute the *delta* we need to remove
in backend space, then convert just that delta to tiktoken scale (×1.2 buffer).
Example: actual=17993, n_ctx=16384, headroom=4096 → need to shed 5705 backend
tokens → shed 6846 tiktoken tokens from messages.
"""
cur_tiktoken = _count_message_tokens(msgs)
headroom = n_ctx // 4 # reserve for generated output
max_prompt = n_ctx - headroom # desired max backend tokens in prompt
to_shed = max(0, actual_tokens - max_prompt) # backend tokens we must drop
# Convert to tiktoken scale with 20% buffer (tiktoken underestimates llama by ~15-20%)
tiktoken_to_shed = int(to_shed * 1.2)
return max(1, cur_tiktoken - tiktoken_to_shed)
# Per-(endpoint, model) n_ctx cache.
# Populated from two sources:
# 1. 400 exceed_context_size_error body → n_ctx field
# 2. finish_reason/done_reason == "length" in streaming → prompt_tokens + completion_tokens
# Only used for proactive pre-trimming when n_ctx <= _CTX_TRIM_SMALL_LIMIT,
# so large-context models (200k+ for coding) are never touched.
_endpoint_nctx: dict[tuple[str, str], int] = {}
_CTX_TRIM_SMALL_LIMIT = 32768 # only proactively trim models with n_ctx at or below this
# ------------------------------------------------------------------
# Globals
# ------------------------------------------------------------------
@ -707,10 +808,15 @@ class fetch:
# Check error cache with lock protection
async with _available_error_cache_lock:
if endpoint in _available_error_cache:
if _is_fresh(_available_error_cache[endpoint], 300):
# Still within the short error TTL – pretend nothing is available
err_age = time.time() - _available_error_cache[endpoint]
if err_age < 30:
# Very fresh error (<30s) – endpoint likely still down, bail fast
return set()
# Error expired – remove it
elif err_age < 300:
# Stale error (30-300s) – endpoint may have recovered, probe in background
asyncio.create_task(fetch._refresh_available_models(endpoint, api_key))
return set()
# Error expired (>300s) – remove and fall through to fresh fetch
del _available_error_cache[endpoint]
# Request coalescing: check if another request is already fetching this endpoint
@ -983,7 +1089,37 @@ async def _make_chat_request(model: str, messages: list, tools=None, stream: boo
try:
if use_openai:
start_ts = time.perf_counter()
try:
response = await oclient.chat.completions.create(**params)
except Exception as e:
_e_str = str(e)
print(f"[_make_chat_request] caught {type(e).__name__}: {_e_str[:200]}")
if "exceed_context_size_error" in _e_str or "exceeds the available context size" in _e_str:
err_body = getattr(e, "body", {}) or {}
err_detail = err_body.get("error", {}) if isinstance(err_body, dict) else {}
n_ctx_limit = err_detail.get("n_ctx", 0)
actual_tokens = err_detail.get("n_prompt_tokens", 0)
if not n_ctx_limit:
raise
msgs_to_trim = params.get("messages", [])
cal_target = _calibrated_trim_target(msgs_to_trim, n_ctx_limit, actual_tokens)
trimmed = _trim_messages_for_context(msgs_to_trim, n_ctx_limit, target_tokens=cal_target)
print(f"[_make_chat_request] Context exceeded ({actual_tokens}/{n_ctx_limit} tokens, tiktoken_target={cal_target}), dropped {len(msgs_to_trim) - len(trimmed)} oldest message(s) and retrying")
try:
response = await oclient.chat.completions.create(**{**params, "messages": trimmed})
except Exception as e2:
if "exceed_context_size_error" in str(e2) or "exceeds the available context size" in str(e2):
print(f"[_make_chat_request] Context still exceeded after trimming, also stripping tools")
params_no_tools = {k: v for k, v in params.items() if k not in ("tools", "tool_choice")}
response = await oclient.chat.completions.create(**{**params_no_tools, "messages": trimmed})
else:
raise
elif "image input is not supported" in _e_str:
print(f"[_make_chat_request] Model {model} doesn't support images, retrying with text-only messages")
params = {**params, "messages": _strip_images_from_messages(params.get("messages", []))}
response = await oclient.chat.completions.create(**params)
else:
raise
if stream:
# For streaming, we need to collect all chunks
chunks = []
@ -1212,6 +1348,22 @@ def transform_images_to_data_urls(message_list):
return message_list
def _strip_images_from_messages(messages: list) -> list:
"""Remove image_url parts from message content, keeping only text."""
result = []
for msg in messages:
content = msg.get("content")
if isinstance(content, list):
text_only = [p for p in content if p.get("type") != "image_url"]
if len(text_only) == 1 and text_only[0].get("type") == "text":
content = text_only[0]["text"]
else:
content = text_only
result.append({**msg, "content": content})
else:
result.append(msg)
return result
def _accumulate_openai_tc_delta(chunk, accumulator: dict) -> None:
"""Accumulate tool_call deltas from a single OpenAI streaming chunk.
@ -1825,23 +1977,86 @@ async def chat_proxy(request: Request):
oclient = openai.AsyncOpenAI(base_url=ep2base(endpoint), default_headers=default_headers, api_key=config.api_keys.get(endpoint, "no-key"))
else:
client = ollama.AsyncClient(host=endpoint)
# For OpenAI endpoints: make the API call in handler scope
# (try/except inside async generators is unreliable with Starlette's streaming)
start_ts = None
async_gen = None
if use_openai:
start_ts = time.perf_counter()
# Proactive trim: only for small-ctx models we've already seen run out of space
_lookup_model = _normalize_llama_model_name(model) if endpoint in config.llama_server_endpoints else model
_known_nctx = _endpoint_nctx.get((endpoint, _lookup_model))
if _known_nctx and _known_nctx <= _CTX_TRIM_SMALL_LIMIT:
_pre_target = int((_known_nctx - _known_nctx // 4) / 1.2)
_pre_est = _count_message_tokens(params.get("messages", []))
if _pre_est > _pre_target:
_pre_msgs = params.get("messages", [])
_pre_trimmed = _trim_messages_for_context(_pre_msgs, _known_nctx, target_tokens=_pre_target)
_dropped = len(_pre_msgs) - len(_pre_trimmed)
print(f"[ctx-pre] n_ctx={_known_nctx} est={_pre_est} target={_pre_target} dropped={_dropped}", flush=True)
params = {**params, "messages": _pre_trimmed}
try:
async_gen = await oclient.chat.completions.create(**params)
except Exception as e:
_e_str = str(e)
print(f"[chat_proxy] caught {type(e).__name__}: {_e_str[:200]}")
if "exceed_context_size_error" in _e_str or "exceeds the available context size" in _e_str:
err_body = getattr(e, "body", {}) or {}
err_detail = err_body.get("error", {}) if isinstance(err_body, dict) else {}
n_ctx_limit = err_detail.get("n_ctx", 0)
actual_tokens = err_detail.get("n_prompt_tokens", 0)
if not n_ctx_limit:
await decrement_usage(endpoint, tracking_model)
raise
if n_ctx_limit <= _CTX_TRIM_SMALL_LIMIT:
_endpoint_nctx[(endpoint, model)] = n_ctx_limit
msgs_to_trim = params.get("messages", [])
cal_target = _calibrated_trim_target(msgs_to_trim, n_ctx_limit, actual_tokens)
trimmed = _trim_messages_for_context(msgs_to_trim, n_ctx_limit, target_tokens=cal_target)
print(f"[chat_proxy] Context exceeded ({actual_tokens}/{n_ctx_limit} tokens, tiktoken_target={cal_target}), dropped {len(msgs_to_trim) - len(trimmed)} oldest message(s) and retrying")
try:
async_gen = await oclient.chat.completions.create(**{**params, "messages": trimmed})
except Exception as e2:
_e2_str = str(e2)
if "exceed_context_size_error" in _e2_str or "exceeds the available context size" in _e2_str:
print(f"[chat_proxy] Context still exceeded after trimming messages, also stripping tools")
params_no_tools = {k: v for k, v in params.items() if k not in ("tools", "tool_choice")}
try:
async_gen = await oclient.chat.completions.create(**{**params_no_tools, "messages": trimmed})
except Exception:
await decrement_usage(endpoint, tracking_model)
raise
else:
await decrement_usage(endpoint, tracking_model)
raise
elif "image input is not supported" in _e_str:
print(f"[chat_proxy] Model {model} doesn't support images, retrying with text-only messages")
try:
params = {**params, "messages": _strip_images_from_messages(params.get("messages", []))}
async_gen = await oclient.chat.completions.create(**params)
except Exception:
await decrement_usage(endpoint, tracking_model)
raise
else:
await decrement_usage(endpoint, tracking_model)
raise
# 3. Async generator that streams chat data and decrements the counter
async def stream_chat_response():
try:
# The chat method returns a generator of dicts (or GenerateResponse)
if use_openai:
start_ts = time.perf_counter()
async_gen = await oclient.chat.completions.create(**params)
_async_gen = async_gen # established in handler scope above
else:
if opt == True:
# Use the dedicated MOE helper function
async_gen = await _make_moe_requests(model, messages, tools, think, _format, options, keep_alive)
_async_gen = await _make_moe_requests(model, messages, tools, think, _format, options, keep_alive)
else:
async_gen = await client.chat(model=model, messages=messages, tools=tools, stream=stream, think=think, format=_format, options=options, keep_alive=keep_alive, logprobs=logprobs, top_logprobs=top_logprobs)
_async_gen = await client.chat(model=model, messages=messages, tools=tools, stream=stream, think=think, format=_format, options=options, keep_alive=keep_alive, logprobs=logprobs, top_logprobs=top_logprobs)
if stream == True:
tc_acc = {} # accumulate OpenAI tool-call deltas across chunks
content_parts: list[str] = []
async for chunk in async_gen:
async for chunk in _async_gen:
if use_openai:
_accumulate_openai_tc_delta(chunk, tc_acc)
chunk = rechunk.openai_chat_completion2ollama(chunk, stream, start_ts)
@ -1860,6 +2075,20 @@ async def chat_proxy(request: Request):
# Accumulate and store cache on done chunk — before yield so it always runs
# Works for both Ollama-native and OpenAI-compatible backends; chunks are
# already converted to Ollama format by rechunk before this point.
if getattr(chunk, "done", False):
# Detect context exhaustion mid-generation for small-ctx models
_dr = getattr(chunk, "done_reason", None)
# Only cache when no max_tokens limit was set — otherwise
# finish_reason=length might just mean max_tokens was hit,
# not that the context window was exhausted.
_req_max_tok = params.get("max_tokens") or params.get("max_completion_tokens") or params.get("num_predict")
if _dr == "length" and not _req_max_tok:
_pt = getattr(chunk, "prompt_eval_count", 0) or 0
_ct = getattr(chunk, "eval_count", 0) or 0
_inferred_nctx = _pt + _ct
if 0 < _inferred_nctx <= _CTX_TRIM_SMALL_LIMIT:
_endpoint_nctx[(endpoint, model)] = _inferred_nctx
print(f"[ctx-cache] done_reason=length → cached n_ctx={_inferred_nctx} for ({endpoint},{model})", flush=True)
if _cache is not None and not _is_moe and _cache_enabled:
if chunk.message and getattr(chunk.message, "content", None):
content_parts.append(chunk.message.content)
@ -1884,18 +2113,18 @@ async def chat_proxy(request: Request):
yield json_line.encode("utf-8") + b"\n"
else:
if use_openai:
response = rechunk.openai_chat_completion2ollama(async_gen, stream, start_ts)
response = rechunk.openai_chat_completion2ollama(_async_gen, stream, start_ts)
response = response.model_dump_json()
else:
response = async_gen.model_dump_json()
prompt_tok = async_gen.prompt_eval_count or 0
comp_tok = async_gen.eval_count or 0
response = _async_gen.model_dump_json()
prompt_tok = _async_gen.prompt_eval_count or 0
comp_tok = _async_gen.eval_count or 0
if prompt_tok != 0 or comp_tok != 0:
await token_queue.put((endpoint, tracking_model, prompt_tok, comp_tok))
json_line = (
response
if hasattr(async_gen, "model_dump_json")
else orjson.dumps(async_gen)
if hasattr(_async_gen, "model_dump_json")
else orjson.dumps(_async_gen)
)
cache_bytes = json_line.encode("utf-8") + b"\n"
yield cache_bytes
@ -2604,9 +2833,13 @@ async def ps_details_proxy(request: Request):
*[_fetch_llama_props(ep, mid) for ep, mid in props_requests]
)
for model_dict, (n_ctx, is_sleeping) in zip(llama_models_pending, props_results):
for (ep, raw_id), model_dict, (n_ctx, is_sleeping) in zip(props_requests, llama_models_pending, props_results):
if n_ctx is not None:
model_dict["context_length"] = n_ctx
if 0 < n_ctx <= _CTX_TRIM_SMALL_LIMIT:
normalized = _normalize_llama_model_name(raw_id)
_endpoint_nctx[(ep, normalized)] = n_ctx
print(f"[ctx-cache/ps] cached n_ctx={n_ctx} for ({ep},{normalized})", flush=True)
if not is_sleeping:
models.append(model_dict)
@ -2686,6 +2919,21 @@ async def openai_embedding_proxy(request: Request):
model = payload.get("model")
doc = payload.get("input")
# Normalize multimodal input: extract only text parts for embedding models
if isinstance(doc, list):
normalized = []
for item in doc:
if isinstance(item, dict):
# Multimodal content part - extract text only, skip images
if item.get("type") == "text":
normalized.append(item.get("text", ""))
# Skip image_url and other non-text types
else:
normalized.append(item)
doc = normalized if len(normalized) != 1 else normalized[0]
elif isinstance(doc, dict) and doc.get("type") == "text":
doc = doc.get("text", "")
if not model:
raise HTTPException(
status_code=400, detail="Missing required field 'model'"
@ -2819,7 +3067,7 @@ async def openai_chat_completions_proxy(request: Request):
endpoint, tracking_model = await choose_endpoint(model)
base_url = ep2base(endpoint)
oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=config.api_keys.get(endpoint, "no-key"))
# 3. Async generator that streams completions data and decrements the counter
# 3. Helpers and API call — done in handler scope so try/except works reliably
async def _normalize_images_in_messages(msgs: list) -> list:
"""Fetch remote image URLs and convert them to base64 data URLs so
Ollama/llama-server can handle them without making outbound HTTP requests."""
@ -2854,25 +3102,95 @@ async def openai_chat_completions_proxy(request: Request):
resolved.append({**msg, "content": new_content})
return resolved
async def stream_ochat_response():
try:
# The chat method returns a generator of dicts (or GenerateResponse)
try:
# For non-external endpoints (Ollama, llama-server), resolve remote
# image URLs to base64 data URLs so the server can handle them locally.
# Make the API call in handler scope — try/except inside async generators is unreliable
# with Starlette's streaming machinery, so we resolve errors here before the generator starts.
send_params = params
if not is_ext_openai_endpoint(endpoint):
resolved_msgs = await _normalize_images_in_messages(params.get("messages", []))
send_params = {**params, "messages": resolved_msgs}
# Proactive trim: only for small-ctx models we've already seen run out of space
_lookup_model = _normalize_llama_model_name(model) if endpoint in config.llama_server_endpoints else model
_known_nctx = _endpoint_nctx.get((endpoint, _lookup_model))
if _known_nctx and _known_nctx <= _CTX_TRIM_SMALL_LIMIT:
_pre_target = int(((_known_nctx - _known_nctx // 4)) / 1.2)
_pre_est = _count_message_tokens(send_params.get("messages", []))
if _pre_est > _pre_target:
_pre_msgs = send_params.get("messages", [])
_pre_trimmed = _trim_messages_for_context(_pre_msgs, _known_nctx, target_tokens=_pre_target)
_dropped = len(_pre_msgs) - len(_pre_trimmed)
print(f"[ctx-pre] n_ctx={_known_nctx} est={_pre_est} target={_pre_target} dropped={_dropped}", flush=True)
send_params = {**send_params, "messages": _pre_trimmed}
try:
async_gen = await oclient.chat.completions.create(**send_params)
except openai.BadRequestError as e:
# If tools are not supported by the model, retry without tools
if "does not support tools" in str(e):
print(f"[openai_chat_completions_proxy] Model {model} doesn't support tools, retrying without tools")
except Exception as e:
_e_str = str(e)
_is_ctx_err = "exceed_context_size_error" in _e_str or "exceeds the available context size" in _e_str
print(f"[ochat] caught={type(e).__name__} ctx={_is_ctx_err} msg={_e_str[:120]}", flush=True)
if "does not support tools" in _e_str:
# Model doesn't support tools — retry without them
print(f"[ochat] retry: no tools", flush=True)
try:
params_without_tools = {k: v for k, v in send_params.items() if k != "tools"}
async_gen = await oclient.chat.completions.create(**params_without_tools)
else:
except Exception:
await decrement_usage(endpoint, tracking_model)
raise
elif _is_ctx_err:
# Backend context limit hit — apply sliding-window trim (context-shift at message level)
err_body = getattr(e, "body", {}) or {}
err_detail = err_body.get("error", {}) if isinstance(err_body, dict) else {}
n_ctx_limit = err_detail.get("n_ctx", 0)
actual_tokens = err_detail.get("n_prompt_tokens", 0)
print(f"[ctx-trim] n_ctx={n_ctx_limit} actual={actual_tokens}", flush=True)
if not n_ctx_limit:
await decrement_usage(endpoint, tracking_model)
raise
if n_ctx_limit <= _CTX_TRIM_SMALL_LIMIT:
_endpoint_nctx[(endpoint, model)] = n_ctx_limit
msgs_to_trim = send_params.get("messages", [])
try:
cal_target = _calibrated_trim_target(msgs_to_trim, n_ctx_limit, actual_tokens)
trimmed_messages = _trim_messages_for_context(msgs_to_trim, n_ctx_limit, target_tokens=cal_target)
except Exception as _helper_exc:
print(f"[ctx-trim] helper crash: {type(_helper_exc).__name__}: {str(_helper_exc)[:100]}", flush=True)
await decrement_usage(endpoint, tracking_model)
raise
dropped = len(msgs_to_trim) - len(trimmed_messages)
print(f"[ctx-trim] target={cal_target} dropped={dropped} remaining={len(trimmed_messages)} retrying-1", flush=True)
try:
async_gen = await oclient.chat.completions.create(**{**send_params, "messages": trimmed_messages})
print(f"[ctx-trim] retry-1 ok", flush=True)
except Exception as e2:
_e2_str = str(e2)
if "exceed_context_size_error" in _e2_str or "exceeds the available context size" in _e2_str:
# Still too large — tool definitions likely consuming too many tokens, strip them too
print(f"[ctx-trim] retry-1 still exceeded, stripping tools retrying-2", flush=True)
params_no_tools = {k: v for k, v in send_params.items() if k not in ("tools", "tool_choice")}
try:
async_gen = await oclient.chat.completions.create(**{**params_no_tools, "messages": trimmed_messages})
print(f"[ctx-trim] retry-2 ok", flush=True)
except Exception:
await decrement_usage(endpoint, tracking_model)
raise
else:
await decrement_usage(endpoint, tracking_model)
raise
elif "image input is not supported" in _e_str:
# Model doesn't support images — strip and retry
print(f"[openai_chat_completions_proxy] Model {model} doesn't support images, retrying with text-only messages")
try:
async_gen = await oclient.chat.completions.create(**{**send_params, "messages": _strip_images_from_messages(send_params.get("messages", []))})
except Exception:
await decrement_usage(endpoint, tracking_model)
raise
else:
await decrement_usage(endpoint, tracking_model)
raise
# 4. Async generator — only streams the already-established async_gen
async def stream_ochat_response():
try:
if stream == True:
content_parts: list[str] = []
usage_snapshot: dict = {}
@ -2909,6 +3227,15 @@ async def openai_chat_completions_proxy(request: Request):
prompt_tok, comp_tok = llama_usage
if prompt_tok != 0 or comp_tok != 0:
await token_queue.put((endpoint, tracking_model, prompt_tok, comp_tok))
# Detect context exhaustion mid-generation for small-ctx models.
# Guard: skip if max_tokens was set in the request — finish_reason=length
# could just mean the caller's token budget was exhausted, not the context window.
_req_max_tok = send_params.get("max_tokens") or send_params.get("max_completion_tokens")
if chunk.choices and chunk.choices[0].finish_reason == "length" and not _req_max_tok:
_inferred_nctx = (prompt_tok + comp_tok) or 0
if 0 < _inferred_nctx <= _CTX_TRIM_SMALL_LIMIT:
_endpoint_nctx[(endpoint, model)] = _inferred_nctx
print(f"[ctx-cache] finish_reason=length → cached n_ctx={_inferred_nctx} for ({endpoint},{model})", flush=True)
# Cache assembled streaming response — before [DONE] so it always runs
if _cache is not None and _cache_enabled and content_parts:
assembled = orjson.dumps({
@ -3044,10 +3371,15 @@ async def openai_completions_proxy(request: Request):
oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=config.api_keys.get(endpoint, "no-key"))
# 3. Async generator that streams completions data and decrements the counter
# Make the API call in handler scope (try/except inside async generators is unreliable)
try:
async_gen = await oclient.completions.create(**params)
except Exception:
await decrement_usage(endpoint, tracking_model)
raise
async def stream_ocompletions_response(model=model):
try:
# The chat method returns a generator of dicts (or GenerateResponse)
async_gen = await oclient.completions.create(**params)
if stream == True:
text_parts: list[str] = []
usage_snapshot: dict = {}