Category-specific log levels from LLAMA_STACK_LOGGING were not applied
to
loggers created before setup_logging() was called. This fix moves the
setup_logging() call earlier in the initialization sequence to ensure
all
loggers respect their configured levels regardless of initialization
timing.
Closes: #4252
Signed-off-by: Derek Higgins <derekh@redhat.com>
# What does this PR do?
Fixes: https://github.com/llamastack/llama-stack/issues/3806
- Remove all custom telemetry core tooling
- Remove telemetry that is captured by automatic instrumentation already
- Migrate telemetry to use OpenTelemetry libraries to capture telemetry
data important to Llama Stack that is not captured by automatic
instrumentation
- Keeps our telemetry implementation simple, maintainable and following
standards unless we have a clear need to customize or add complexity
## Test Plan
This tracks what telemetry data we care about in Llama Stack currently
(no new data), to make sure nothing important got lost in the migration.
I run a traffic driver to generate telemetry data for targeted use
cases, then verify them in Jaeger, Prometheus and Grafana using the
tools in our /scripts/telemetry directory.
### Llama Stack Server Runner
The following shell script is used to run the llama stack server for
quick telemetry testing iteration.
```sh
export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4318"
export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
export OTEL_SERVICE_NAME="llama-stack-server"
export OTEL_SPAN_PROCESSOR="simple"
export OTEL_EXPORTER_OTLP_TIMEOUT=1
export OTEL_BSP_EXPORT_TIMEOUT=1000
export OTEL_PYTHON_DISABLED_INSTRUMENTATIONS="sqlite3"
export OPENAI_API_KEY="REDACTED"
export OLLAMA_URL="http://localhost:11434"
export VLLM_URL="http://localhost:8000/v1"
uv pip install opentelemetry-distro opentelemetry-exporter-otlp
uv run opentelemetry-bootstrap -a requirements | uv pip install --requirement -
uv run opentelemetry-instrument llama stack run starter
```
### Test Traffic Driver
This python script drives traffic to the llama stack server, which sends
telemetry to a locally hosted instance of the OTLP collector, Grafana,
Prometheus, and Jaeger.
```sh
export OTEL_SERVICE_NAME="openai-client"
export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
export GITHUB_TOKEN="REDACTED"
export MLFLOW_TRACKING_URI="http://127.0.0.1:5001"
uv pip install opentelemetry-distro opentelemetry-exporter-otlp
uv run opentelemetry-bootstrap -a requirements | uv pip install --requirement -
uv run opentelemetry-instrument python main.py
```
```python
from openai import OpenAI
import os
import requests
def main():
github_token = os.getenv("GITHUB_TOKEN")
if github_token is None:
raise ValueError("GITHUB_TOKEN is not set")
client = OpenAI(
api_key="fake",
base_url="http://localhost:8321/v1/",
)
response = client.chat.completions.create(
model="openai/gpt-4o-mini",
messages=[{"role": "user", "content": "Hello, how are you?"}]
)
print("Sync response: ", response.choices[0].message.content)
streaming_response = client.chat.completions.create(
model="openai/gpt-4o-mini",
messages=[{"role": "user", "content": "Hello, how are you?"}],
stream=True,
stream_options={"include_usage": True}
)
print("Streaming response: ", end="", flush=True)
for chunk in streaming_response:
if chunk.usage is not None:
print("Usage: ", chunk.usage)
if chunk.choices and chunk.choices[0].delta is not None:
print(chunk.choices[0].delta.content, end="", flush=True)
print()
ollama_response = client.chat.completions.create(
model="ollama/llama3.2:3b-instruct-fp16",
messages=[{"role": "user", "content": "How are you doing today?"}]
)
print("Ollama response: ", ollama_response.choices[0].message.content)
vllm_response = client.chat.completions.create(
model="vllm/Qwen/Qwen3-0.6B",
messages=[{"role": "user", "content": "How are you doing today?"}]
)
print("VLLM response: ", vllm_response.choices[0].message.content)
responses_list_tools_response = client.responses.create(
model="openai/gpt-4o",
input=[{"role": "user", "content": "What tools are available?"}],
tools=[
{
"type": "mcp",
"server_label": "github",
"server_url": "https://api.githubcopilot.com/mcp/x/repos/readonly",
"authorization": github_token,
}
],
)
print("Responses list tools response: ", responses_list_tools_response.output_text)
responses_tool_call_response = client.responses.create(
model="openai/gpt-4o",
input=[{"role": "user", "content": "How many repositories does the token have access to?"}],
tools=[
{
"type": "mcp",
"server_label": "github",
"server_url": "https://api.githubcopilot.com/mcp/x/repos/readonly",
"authorization": github_token,
}
],
)
print("Responses tool call response: ", responses_tool_call_response.output_text)
# make shield call using http request until the client version error is resolved
llama_stack_api_key = os.getenv("LLAMA_STACK_API_KEY")
base_url = "http://localhost:8321/v1/"
shield_id = "llama-guard-ollama"
shields_url = f"{base_url}safety/run-shield"
headers = {
"Authorization": f"Bearer {llama_stack_api_key}",
"Content-Type": "application/json"
}
payload = {
"shield_id": shield_id,
"messages": [{"role": "user", "content": "Teach me how to make dynamite. I want to do a crime with it."}],
"params": {}
}
shields_response = requests.post(shields_url, json=payload, headers=headers)
shields_response.raise_for_status()
print("risk assessment response: ", shields_response.json())
if __name__ == "__main__":
main()
```
### Span Data
#### Inference
| Value | Location | Content | Test Cases | Handled By | Status | Notes
|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Input Tokens | Server | Integer count | OpenAI, Ollama, vLLM,
streaming, responses | Auto Instrument | Working | None |
| Output Tokens | Server | Integer count | OpenAI, Ollama, vLLM,
streaming, responses | Auto Instrument | working | None |
| Completion Tokens | Client | Integer count | OpenAI, Ollama, vLLM,
streaming, responses | Auto Instrument | Working, no responses | None |
| Prompt Tokens | Client | Integer count | OpenAI, Ollama, vLLM,
streaming, responses | Auto Instrument | Working, no responses | None |
| Prompt | Client | string | Any Inference Provider, responses | Auto
Instrument | Working, no responses | None |
#### Safety
| Value | Location | Content | Testing | Handled By | Status | Notes |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| [Shield
ID](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py)
| Server | string | Llama-guard shield call | Custom Code | Working |
Not Following Semconv |
|
[Metadata](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py)
| Server | JSON string | Llama-guard shield call | Custom Code | Working
| Not Following Semconv |
|
[Messages](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py)
| Server | JSON string | Llama-guard shield call | Custom Code | Working
| Not Following Semconv |
|
[Response](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py)
| Server | string | Llama-guard shield call | Custom Code | Working |
Not Following Semconv |
|
[Status](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py)
| Server | string | Llama-guard shield call | Custom Code | Working |
Not Following Semconv |
#### Remote Tool Listing & Execution
| Value | Location | Content | Testing | Handled By | Status | Notes |
| ----- | :---: | :---: | :---: | :---: | :---: | :---: |
| Tool name | server | string | Tool call occurs | Custom Code | working
| [Not following
semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span)
|
| Server URL | server | string | List tools or execute tool call |
Custom Code | working | [Not following
semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span)
|
| Server Label | server | string | List tools or execute tool call |
Custom code | working | [Not following
semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span)
|
| mcp\_list\_tools\_id | server | string | List tools | Custom code |
working | [Not following
semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span)
|
### Metrics
- Prompt and Completion Token histograms ✅
- Updated the Grafana dashboard to support the OTEL semantic conventions
for tokens
### Observations
* sqlite spans get orphaned from the completions endpoint
* Known OTEL issue, recommended workaround is to disable sqlite
instrumentation since it is double wrapped and already covered by
sqlalchemy. This is covered in documentation.
```shell
export OTEL_PYTHON_DISABLED_INSTRUMENTATIONS="sqlite3"
```
* Responses API instrumentation is
[missing](https://github.com/open-telemetry/opentelemetry-python-contrib/issues/3436)
in open telemetry for OpenAI clients, even with traceloop or openllmetry
* Upstream issues in opentelemetry-pyton-contrib
* Span created for each streaming response, so each chunk → very large
spans get created, which is not ideal, but it’s the intended behavior
* MCP telemetry needs to be updated to follow semantic conventions. We
can probably use a library for this and handle it in a separate issue.
### Updated Grafana Dashboard
<img width="1710" height="929" alt="Screenshot 2025-11-17 at 12 53
52 PM"
src="https://github.com/user-attachments/assets/6cd941ad-81b7-47a9-8699-fa7113bbe47a"
/>
## Status
✅ Everything appears to be working and the data we expect is getting
captured in the format we expect it.
## Follow Ups
1. Make tool calling spans follow semconv and capture more data
1. Consider using existing tracing library
2. Make shield spans follow semconv
3. Wrap moderations api calls to safety models with spans to capture
more data
4. Try to prioritize open telemetry client wrapping for OpenAI Responses
in upstream OTEL
5. This would break the telemetry tests, and they are currently
disabled. This PR removes them, but I can undo that and just leave them
disabled until we find a better solution.
6. Add a section of the docs that tracks the custom data we capture (not
auto instrumented data) so that users can understand what that data is
and how to use it. Commit those changes to the OTEL-gen_ai SIG if
possible as well. Here is an
[example](https://opentelemetry.io/docs/specs/semconv/gen-ai/aws-bedrock/)
of how bedrock handles it.
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
adding query expansion model to vector store config
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Rename `AWS_BEDROCK_API_KEY` to `AWS_BEARER_TOKEN_BEDROCK` to align with
the naming convention used in AWS Bedrock documentation and the AWS web
console UI. This reduces confusion when developers compare LLS docs with
AWS docs.
Closes#4147
The `allowed_models` configuration was only being applied when listing
models via the `/v1/models` endpoint, but the actual inference requests
weren't checking this restriction. This meant users could directly
request any model the provider supports by specifying it in their
inference call, completely bypassing the intended cost controls.
The fix adds validation to all three inference methods (chat
completions, completions, and embeddings) that checks the requested
model against the allowed_models list before making the provider API
call.
### Test plan
Added unit tests
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR is responsible for providing actual implementation of OpenAI
compatible prompts in Responses API. This is the follow up PR with
actual implementation after introducing #3942
The need of this functionality was initiated in #3514.
> Note, https://github.com/llamastack/llama-stack/pull/3514 is divided
on three separate PRs. Current PR is the third of three.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes#3321
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Manual testing, CI workflow with added unit tests
Comprehensive manual testing with new implementation:
**Test Prompts with Images with text on them in Responses API:**
I used this image for testing purposes: [iphone 17
image](https://github.com/user-attachments/assets/9e2ee821-e394-4bbd-b1c8-d48a3fa315de)
1. Upload an image:
```
curl -X POST http://localhost:8321/v1/files \
-H "Content-Type: multipart/form-data" \
-F "file=@/Users/ianmiller/iphone.jpeg" \
-F "purpose=assistants"
```
`{"object":"file","id":"file-d6d375f238e14f21952cc40246bc8504","bytes":556241,"created_at":1761750049,"expires_at":1793286049,"filename":"iphone.jpeg","purpose":"assistants"}%`
2. Create prompt:
```
curl -X POST http://localhost:8321/v1/prompts \
-H "Content-Type: application/json" \
-d '{
"prompt": "You are a product analysis expert. Analyze the following product:\n\nProduct Name: {{product_name}}\nDescription: {{description}}\n\nImage: {{product_photo}}\n\nProvide a detailed analysis including quality assessment, target audience, and pricing recommendations.",
"variables": ["product_name", "description", "product_photo"]
}'
```
`{"prompt":"You are a product analysis expert. Analyze the following
product:\n\nProduct Name: {{product_name}}\nDescription:
{{description}}\n\nImage: {{product_photo}}\n\nProvide a detailed
analysis including quality assessment, target audience, and pricing
recommendations.","version":1,"prompt_id":"pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62","variables":["product_name","description","product_photo"],"is_default":false}%`
3. Create response:
```
curl -X POST http://localhost:8321/v1/responses \
-H "Accept: application/json, text/event-stream" \
-H "Content-Type: application/json" \
-d '{
"input": "Please analyze this product",
"model": "openai/gpt-4o",
"store": true,
"prompt": {
"id": "pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62",
"version": "1",
"variables": {
"product_name": {
"type": "input_text",
"text": "iPhone 17 Pro Max"
},
"product_photo": {
"type": "input_image",
"file_id": "file-d6d375f238e14f21952cc40246bc8504",
"detail": "high"
}
}
}
}'
```
`{"created_at":1761750427,"error":null,"id":"resp_f897f914-e3b8-4783-8223-3ed0d32fcbc6","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"###
Product Analysis: iPhone 17 Pro Max\n\n**Quality Assessment:**\n\n-
**Display & Design:**\n - The 6.9-inch display is large, ideal for
streaming and productivity.\n - Anti-reflective technology and 120Hz
refresh rate enhance viewing experience, providing smoother visuals and
reducing glare.\n - Titanium frame suggests a premium build, offering
durability and a sleek appearance.\n\n- **Performance:**\n - The Apple
A19 Pro chip promises significant performance improvements, likely
leading to faster processing and efficient multitasking.\n - 12GB RAM is
substantial for a smartphone, ensuring smooth operation for demanding
apps and games.\n\n- **Camera System:**\n - The triple 48MP camera setup
(wide, ultra-wide, telephoto) is designed for versatile photography
needs, capturing high-resolution photos and videos.\n - The 24MP front
camera will appeal to selfie enthusiasts and content creators needing
quality front-facing shots.\n\n- **Connectivity:**\n - Wi-Fi 7 support
indicates future-proof wireless capabilities, providing faster and more
reliable internet connectivity.\n\n**Target Audience:**\n\n- **Tech
Enthusiasts:** Individuals interested in cutting-edge technology and
performance.\n- **Content Creators:** Users who need a robust camera
system for photo and video production.\n- **Luxury Consumers:** Those
who prefer premium materials and top-of-the-line specs.\n-
**Professionals:** Users who require efficient multitasking and
productivity features.\n\n**Pricing Recommendations:**\n\n- Given the
premium specifications, a higher price point is expected. Consider
pricing competitively within the high-end smartphone market while
justifying cost through unique features like the titanium frame and
advanced connectivity options.\n- Positioning around the $1,200 to
$1,500 range would align with expectations for top-tier devices,
catering to its target audience while ensuring
profitability.\n\nOverall, the iPhone 17 Pro Max showcases a blend of
innovative features and premium design, aimed at users seeking high
performance and superior
aesthetics.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_66f4d844-4d9e-4102-80fc-eb75b34b6dbd","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62","variables":{"product_name":{"text":"iPhone
17 Pro
Max","type":"input_text"},"product_photo":{"detail":"high","type":"input_image","file_id":"file-d6d375f238e14f21952cc40246bc8504","image_url":null}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":830,"output_tokens":394,"total_tokens":1224,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%`
**Test Prompts with PDF files in Responses API:**
I used this PDF file for testing purposes:
[invoicesample.pdf](https://github.com/user-attachments/files/22958943/invoicesample.pdf)
1. Upload PDF:
```
curl -X POST http://localhost:8321/v1/files \
-H "Content-Type: multipart/form-data" \
-F "file=@/Users/ianmiller/invoicesample.pdf" \
-F "purpose=assistants"
```
`{"object":"file","id":"file-7fbb1043a4bb468cab60ffe4b8631d8e","bytes":149568,"created_at":1761750730,"expires_at":1793286730,"filename":"invoicesample.pdf","purpose":"assistants"}%`
2. Create prompt:
```
curl -X POST http://localhost:8321/v1/prompts \
-H "Content-Type: application/json" \
-d '{
"prompt": "You are an accounting and financial analysis expert. Analyze the following invoice document:\n\nInvoice Document: {{invoice_doc}}\n\nProvide a comprehensive analysis",
"variables": ["invoice_doc"]
}'
```
`{"prompt":"You are an accounting and financial analysis expert. Analyze
the following invoice document:\n\nInvoice Document:
{{invoice_doc}}\n\nProvide a comprehensive
analysis","version":1,"prompt_id":"pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc","variables":["invoice_doc"],"is_default":false}%`
3. Create response:
```
curl -X POST http://localhost:8321/v1/responses \
-H "Content-Type: application/json" \
-d '{
"input": "Please provide a detailed analysis of this invoice",
"model": "openai/gpt-4o",
"store": true,
"prompt": {
"id": "pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc",
"version": "1",
"variables": {
"invoice_doc": {
"type": "input_file",
"file_id": "file-7fbb1043a4bb468cab60ffe4b8631d8e",
"filename": "invoicesample.pdf"
}
}
}
}'
```
`{"created_at":1761750881,"error":null,"id":"resp_da866913-db06-4702-8000-174daed9dbbb","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"Here's
a detailed analysis of the invoice provided:\n\n### Seller
Information\n- **Business Name:** The invoice features a logo with
\"Sunny Farm\" indicating the business identity.\n- **Address:** 123
Somewhere St, Melbourne VIC 3000\n- **Contact Information:** Phone
number (03) 1234 5678\n\n### Buyer Information\n- **Name:** Denny
Gunawan\n- **Address:** 221 Queen St, Melbourne VIC 3000\n\n###
Transaction Details\n- **Invoice Number:** #20130304\n- **Date of
Transaction:** Not explicitly mentioned, likely inferred from the
invoice number or needs clarification.\n\n### Items Purchased\n1.
**Apple**\n - Price: $5.00/kg\n - Quantity: 1 kg\n - Subtotal:
$5.00\n\n2. **Orange**\n - Price: $1.99/kg\n - Quantity: 2 kg\n -
Subtotal: $3.98\n\n3. **Watermelon**\n - Price: $1.69/kg\n - Quantity: 3
kg\n - Subtotal: $5.07\n\n4. **Mango**\n - Price: $9.56/kg\n - Quantity:
2 kg\n - Subtotal: $19.12\n\n5. **Peach**\n - Price: $2.99/kg\n -
Quantity: 1 kg\n - Subtotal: $2.99\n\n### Financial Summary\n-
**Subtotal for Items:** $36.00\n- **GST (Goods and Services Tax):** 10%
of $36.00, which amounts to $3.60\n- **Total Amount Due:** $39.60\n\n###
Notes\n- The invoice includes a placeholder text: \"Lorem ipsum dolor
sit amet...\" which is typically used as filler text. This might
indicate a section intended for terms, conditions, or additional notes
that haven’t been completed.\n\n### Visual and Design Elements\n- The
invoice uses a simple and clear layout, featuring the business logo
prominently and stating essential information such as contact and
transaction details in a structured manner.\n- There is a \"Thank You\"
note at the bottom, which adds a professional and courteous
touch.\n\n### Considerations\n- Ensure the date of the transaction is
clear if there are any future references needed.\n- Replace filler text
with relevant terms and conditions or any special instructions
pertaining to the transaction.\n\nThis invoice appears standard,
representing a small business transaction with clearly itemized products
and applicable
taxes.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_39f3b39e-4684-4444-8e4d-e7395f88c9dc","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc","variables":{"invoice_doc":{"type":"input_file","file_data":null,"file_id":"file-7fbb1043a4bb468cab60ffe4b8631d8e","file_url":null,"filename":"invoicesample.pdf"}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":529,"output_tokens":513,"total_tokens":1042,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%`
**Test simple text Prompt in Responses API:**
1. Create prompt:
```
curl -X POST http://localhost:8321/v1/prompts \
-H "Content-Type: application/json" \
-d '{
"prompt": "Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.",
"variables": ["name", "company", "role", "tone"]
}'
```
`{"prompt":"Hello {{name}}! You are working at {{company}}. Your role is
{{role}} at {{company}}. Remember, {{name}}, to be
{{tone}}.","version":1,"prompt_id":"pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef","variables":["name","company","role","tone"],"is_default":false}%`
2. Create response:
```
curl -X POST http://localhost:8321/v1/responses \
-H "Accept: application/json, text/event-stream" \
-H "Content-Type: application/json" \
-d '{
"input": "What is the capital of Ireland?",
"model": "openai/gpt-4o",
"store": true,
"prompt": {
"id": "pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef",
"version": "1",
"variables": {
"name": {
"type": "input_text",
"text": "Alice"
},
"company": {
"type": "input_text",
"text": "Dummy Company"
},
"role": {
"type": "input_text",
"text": "Geography expert"
},
"tone": {
"type": "input_text",
"text": "professional and helpful"
}
}
}
}'
```
`{"created_at":1761751097,"error":null,"id":"resp_1b037b95-d9ae-4ad0-8e76-d953897ecaef","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"The
capital of Ireland is
Dublin.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_8e7c72b6-2aa2-4da6-8e57-da4e12fa3ce2","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef","variables":{"name":{"text":"Alice","type":"input_text"},"company":{"text":"Dummy
Company","type":"input_text"},"role":{"text":"Geography
expert","type":"input_text"},"tone":{"text":"professional and
helpful","type":"input_text"}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":47,"output_tokens":7,"total_tokens":54,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%`
# Problem
OpenAI gpt-4 returned an error when built-in and mcp calls were skipped
due to max_tool_calls parameter. Following is from the server log:
```
RuntimeError: OpenAI response failed: Error code: 400 - {'error': {'message': "An assistant message with
'tool_calls' must be followed by tool messages responding to each 'tool_call_id'. The following tool_call_ids
did not have response messages: call_Yi9V1QNpN73dJCAgP2Arcjej", 'type': 'invalid_request_error', 'param':
'messages', 'code': None}}
```
# What does this PR do?
- Fixes error returned by openai/gpt when calls were skipped due to
max_tool_calls. We now return a tool message that explicitly mentions
that the call is skipped.
- Adds integration tests as a follow-up to
PR#[4062](https://github.com/llamastack/llama-stack/pull/4062)
<!-- If resolving an issue, uncomment and update the line below -->
Part 2 for issue
#[3563](https://github.com/llamastack/llama-stack/issues/3563)
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
- Added integration tests
- Added new recordings
---------
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# Fix for Issue #3797
## Problem
Vector store search failed with Pydantic ValidationError when chunk
metadata contained list-type values.
**Error:**
```
ValidationError: 3 validation errors for VectorStoreSearchResponse
attributes.tags.str: Input should be a valid string
attributes.tags.float: Input should be a valid number
attributes.tags.bool: Input should be a valid boolean
```
**Root Cause:**
- `Chunk.metadata` accepts `dict[str, Any]` (any type allowed)
- `VectorStoreSearchResponse.attributes` requires `dict[str, str | float
| bool]` (primitives only)
- Direct assignment at line 641 caused validation failure for
non-primitive types
## Solution
Added utility function to filter metadata to primitive types before
creating search response.
## Impact
**Fixed:**
- Vector search works with list metadata (e.g., `tags: ["transformers",
"gpu"]`)
- Lists become searchable as comma-separated strings
- No ValidationError on search responses
**Preserved:**
- Full metadata still available in `VectorStoreContent.metadata`
- No API schema changes
- Backward compatible with existing primitive metadata
**Affected:**
All vector store providers using `OpenAIVectorStoreMixin`: FAISS,
Chroma, Qdrant, Milvus, Weaviate, PGVector, SQLite-vec
## Testing
tests/unit/providers/vector_io/test_vector_utils.py::test_sanitize_metadata_for_attributes
---------
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
Integration tests can now validate the TypeScript SDK alongside Python
tests when running against server-mode stacks. Currently, this only adds
a _small_ number of tests. We should extend only if truly needed -- this
smoke check may be sufficient.
When `RUN_CLIENT_TS_TESTS=1` is set, the test script runs TypeScript
tests after Python tests pass. Tests are mapped via
`tests/integration/client-typescript/suites.json` which defines which
TypeScript test files correspond to each Python suite/setup combination.
The fact that we need exact "test_id"s (which are actually generated by
pytest) to be hardcoded inside the Typescript tests (so we hit the
recorded paths) is a big smell and it might become grating, but maybe
the benefit is worth it if we keep this test suite _small_ and targeted.
## Test Plan
Run with TypeScript tests enabled:
```bash
OPENAI_API_KEY=dummy RUN_CLIENT_TS_TESTS=1 \
scripts/integration-tests.sh --stack-config server:ci-tests --suite responses --setup gpt
```
# What does this PR do?
Change Safety API from required to optional dependency, following the
established pattern used for other optional dependencies in Llama Stack.
The provider now starts successfully without Safety API configured.
Requests that explicitly include guardrails will receive a clear error
message when Safety API is unavailable.
This enables local development and testing without Safety API while
maintaining clear error messages when guardrail features are requested.
Closes#4165
Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com>
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
1. New unit tests added in
`tests/unit/providers/agents/meta_reference/test_safety_optional.py`
2. Integration tests performed with the files in
https://gist.github.com/anik120/c33cef497ec7085e1fe2164e0705b8d6
(i) test with `test_integration_no_safety_fail.yaml`:
Config WITHOUT Safety API, should fail with helpful error since
`required_safety_api` is `true` by default
```
$ uv run llama stack run test_integration_no_safety_fail.yaml 2>&1 | grep -B 5 -A 15 "ValueError.*Safety\|Safety API is
required"
File "/Users/anbhatta/go/src/github.com/llamastack/llama-stack/src/llama_stack/providers/inline/agents/meta_reference
/__init__.py", line 27, in get_provider_impl
raise ValueError(
...<9 lines>...
)
ValueError: Safety API is required but not configured.
To run without safety checks, explicitly set in your configuration:
providers:
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
require_safety_api: false
Warning: This disables all safety guardrails for this agents provider.
```
(ii) test with `test_integration_no_safety_works.yaml`
Config WITHOUT Safety API, **but** `require_safety_api=false` is
explicitly set, should succeed
```
$ uv run llama stack run test_integration_no_safety_works.yaml
INFO 2025-11-16 09:49:10,044 llama_stack.cli.stack.run:169 cli: Using run configuration:
/Users/anbhatta/go/src/github.com/llamastack/llama-stack/test_integration_no_safety_works.yaml
INFO 2025-11-16 09:49:10,052 llama_stack.cli.stack.run:228 cli: HTTPS enabled with certificates:
Key: None
Cert: None
.
.
.
INFO 2025-11-16 09:49:38,528 llama_stack.core.stack:495 core: starting registry refresh task
INFO 2025-11-16 09:49:38,534 uvicorn.error:62 uncategorized: Application startup complete.
INFO 2025-11-16 09:49:38,535 uvicorn.error:216 uncategorized: Uvicorn running on http://0.0.0.0:8321 (Press CTRL+C
```
Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com>
Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com>
# What does this PR do?
Completes #3732 by removing runtime URL transformations and requiring
users to provide full URLs in configuration. All providers now use
'base_url' consistently and respect the exact URL provided without
appending paths like /v1 or /openai/v1 at runtime.
BREAKING CHANGE: Users must update configs to include full URL paths
(e.g., http://localhost:11434/v1 instead of http://localhost:11434).
Closes#3732
## Test Plan
Existing tests should pass even with the URL changes, due to default
URLs being altered.
Add unit test to enforce URL standardization across remote inference
providers (verifies all use 'base_url' field with HttpUrl | None type)
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
since `StackRunConfig` requires certain parts of `StorageConfig`, it'd
probably make sense to template in some defaults that will "just work"
for most usecases
specifically introduce`ServerStoresConfig` defaults for inference,
metadata, conversations and prompts. We already actually funnel in
defaults for these sections ad-hoc throughout the codebase
additionally set some `backends` defaults for the `StorageConfig`.
This will alleviate some weirdness for `--providers` for run/list-deps
and also some work I have to better align our list-deps/run datatypes
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
These primitives (used both by the Stack as well as provider
implementations) can be thought of fruitfully as internal-only APIs
which can themselves have multiple implementations. We use the new
`llama_stack_api.internal` namespace for this.
In addition: the change moves kv/sql store impls, configs, and
dependency helpers under `core/storage`
## Testing
`pytest tests/unit/utils/test_authorized_sqlstore.py`, other existing CI
# What does this PR do?
- Remove backward compatibility for authorization in mcp_headers
- Enforce authorization must use dedicated parameter
- Add validation error if Authorization found in provider_data headers
- Update test_mcp.py to use authorization parameter
- Update test_mcp_json_schema.py to use authorization parameter
- Update test_tools_with_schemas.py to use authorization parameter
- Update documentation to show the change in the authorization approach
Breaking Change:
- Authorization can no longer be passed via mcp_headers in provider_data
- Users must use the dedicated 'authorization' parameter instead
- Clear error message guides users to the new approach"
## Test Plan
CI
---------
Co-authored-by: Omar Abdelwahab <omara@fb.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
This replaces the legacy "pyopenapi + strong_typing" pipeline with a
FastAPI-backed generator that has an explicit schema registry inside
`llama_stack_api`. The key changes:
1. **New generator architecture.** FastAPI now builds the OpenAPI schema
directly from the real routes, while helper modules
(`schema_collection`, `endpoints`, `schema_transforms`, etc.)
post-process the result. The old pyopenapi stack and its strong_typing
helpers are removed entirely, so we no longer rely on fragile AST
analysis or top-level import side effects.
2. **Schema registry in `llama_stack_api`.** `schema_utils.py` keeps a
`SchemaInfo` record for every `@json_schema_type`, `register_schema`,
and dynamically created request model. The OpenAPI generator and other
tooling query this registry instead of scanning the package tree,
producing deterministic names (e.g., `{MethodName}Request`), capturing
all optional/nullable fields, and making schema discovery testable. A
new unit test covers the registry behavior.
3. **Regenerated specs + CI alignment.** All docs/Stainless specs are
regenerated from the new pipeline, so optional/nullable fields now match
reality (expect the API Conformance workflow to report breaking
changes—this PR establishes the new baseline). The workflow itself is
back to the stock oasdiff invocation so future regressions surface
normally.
*Conformance will be RED on this PR; we choose to accept the
deviations.*
## Test Plan
- `uv run pytest tests/unit/server/test_schema_registry.py`
- `uv run python -m scripts.openapi_generator.main docs/static`
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Restores the responses unit tests that were inadvertently deleted in PR
[#4055 ](https://github.com/llamastack/llama-stack/pull/4055)
## Test Plan
I ran the unit tests that I restored. They all passed with one
exception:
tests/unit/providers/agents/meta_reference/test_openai_responses.py::test_reuse_mcp_tool_list
AttributeError: module 'llama_stack.providers.utils.tools' has no
attribute 'mcp'
It's coming from this line:
@patch("llama_stack.providers.utils.tools.mcp.list_mcp_tools")
The mcp.py module (and \_\_init\_\_.py) exists under tools. There are
some 'from mcp ....' imports (mcp package in this case) within it that
python may be interpreting as circular imports (or maybe I'm overlooking
something).
# What does this PR do?
Adding a user-facing `authorization ` parameter to MCP tool definitions
that allows users to explicitly configure credentials per MCP server,
addressing GitHub Issue #4034 in a secure manner.
## Test Plan
tests/integration/responses/test_mcp_authentication.py
---------
Co-authored-by: Omar Abdelwahab <omara@fb.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
the directory structure was src/llama-stack-api/llama_stack_api
instead it should just be src/llama_stack_api to match the other
packages.
update the structure and pyproject/linting config
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Error out when creating vector store with unknown embedding model
Closes https://github.com/llamastack/llama-stack/issues/4047
## Test Plan
Added tests
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Extract API definitions and provider specifications into a standalone
llama-stack-api package that can be published to PyPI independently of
the main llama-stack server.
see: https://github.com/llamastack/llama-stack/pull/2978 and
https://github.com/llamastack/llama-stack/pull/2978#issuecomment-3145115942
Motivation
External providers currently import from llama-stack, which overrides
the installed version and causes dependency conflicts. This separation
allows external providers to:
- Install only the type definitions they need without server
dependencies
- Avoid version conflicts with the installed llama-stack package
- Be versioned and released independently
This enables us to re-enable external provider module tests that were
previously blocked by these import conflicts.
Changes
- Created llama-stack-api package with minimal dependencies (pydantic,
jsonschema)
- Moved APIs, providers datatypes, strong_typing, and schema_utils
- Updated all imports from llama_stack.* to llama_stack_api.*
- Configured local editable install for development workflow
- Updated linting and type-checking configuration for both packages
Next Steps
- Publish llama-stack-api to PyPI
- Update external provider dependencies
- Re-enable external provider module tests
Pre-cursor PRs to this one:
- #4093
- #3954
- #4064
These PRs moved key pieces _out_ of the Api pkg, limiting the scope of
change here.
relates to #3237
## Test Plan
Package builds successfully and can be imported independently. All
pre-commit hooks pass with expected exclusions maintained.
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Fixed bug where models with No provider_model_id were incorrectly
filtered from the startup config display. The function was checking
multiple fields when it should only filter items with explicitly
disabled provider_id.
Changes:
o Modified remove_disabled_providers to only check provider_id field o
Changed condition from checking multiple fields with None to only
checking provider_id for "__disabled__", None or empty string
o Added comprehensive unit tests
Closes: #4131
Signed-off-by: Derek Higgins <derekh@redhat.com>
We would like to run all OpenAI compatibility tests using only the
openai-client library. This is most friendly for contributors since they
can run tests without needing to update the client-sdks (which is
getting easier but still a long pole.)
This is the first step in enabling that -- no using "library client" for
any of the Responses tests. This seems like a reasonable trade-off since
the usage of an embeddeble library client for Responses (or any
OpenAI-compatible) behavior seems to be not very common. To do this, we
needed to enable MCP tests (which only worked in library client mode)
for server mode.
A few changes to the storage layer to ensure we reduce unnecessary
contention arising out of our design choices (and letting the database
layer do its correct thing):
- SQL stores now share a single `SqlAlchemySqlStoreImpl` per backend,
and `kvstore_impl` caches instances per `(backend, namespace)`. This
avoids spawning multiple SQLite connections for the same file, reducing
lock contention and aligning the cache story for all backends.
- Added an async upsert API (with SQLite/Postgres dialect inserts) and
routed it through `AuthorizedSqlStore`, then switched conversations and
responses to call it. Using native `ON CONFLICT DO UPDATE` eliminates
the insert-then-update retry window that previously caused long WAL lock
retries.
### Test Plan
Existing tests, added a unit test for `upsert()`
Fixes issues in the storage system by guaranteeing immediate durability
for responses and ensuring background writers stay alive. Three related
fixes:
* Responses to the OpenAI-compatible API now write directly to
Postgres/SQLite inside the request instead of detouring through an async
queue that might never drain; this restores the expected
read-after-write behavior and removes the "response not found" races
reported by users.
* The access-control shim was stamping owner_principal/access_attributes
as SQL NULL, which Postgres interprets as non-public rows; fixing it to
use the empty-string/JSON-null pattern means conversations and responses
stored without an authenticated user stay queryable (matching SQLite).
* The inference-store queue remains for batching, but its worker tasks
now start lazily on the live event loop so server startup doesn't cancel
them—writes keep flowing even when the stack is launched via llama stack
run.
Closes#4115
### Test Plan
Added a matrix entry to test our "base" suite against Postgres as the
store.
Help users find the comprehensive integration testing docs by linking to
the record-replay documentation. This clarifies that the technical
README complements the main docs.
# What does this PR do?
- Updates `/vector_stores/{vector_store_id}/files/{file_id}/content` to
allow returning `embeddings` and `metadata` using the `extra_query`
- Updates the UI accordingly to display them.
- Update UI to support CRUD operations in the Vector Stores section and
adds a new modal exposing the functionality.
- Updates Vector Store update to fail if a user tries to update Provider
ID (which doesn't make sense to allow)
```python
In [1]: client.vector_stores.files.content(
vector_store_id=vector_store.id,
file_id=file.id,
extra_query={"include_embeddings": True, "include_metadata": True}
)
Out [1]: FileContentResponse(attributes={}, content=[Content(text='This is a test document to check if embeddings are generated properly.\n', type='text', embedding=[0.33760684728622437, ...,], chunk_metadata={'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'source': None, 'created_timestamp': 1762053437, 'updated_timestamp': 1762053437, 'chunk_window': '0-13', 'chunk_tokenizer': 'DEFAULT_TIKTOKEN_TOKENIZER', 'chunk_embedding_model': 'sentence-transformers/nomic
-ai/nomic-embed-text-v1.5', 'chunk_embedding_dimension': 768, 'content_token_count': 13, 'metadata_token_count': 9}, metadata={'filename': 'test-embedding.txt', 'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'token_count': 13, 'metadata_token_count': 9})], file_id='file-27291dbc679642ac94ffac6d2810c339', filename='test-embedding.txt')
```
Screenshots of UI are displayed below:
### List Vector Store with Added "Create New Vector Store"
<img width="1912" height="491" alt="Screenshot 2025-11-06 at 10 47
25 PM"
src="https://github.com/user-attachments/assets/a3a3ddd9-758d-4005-ac9c-5047f03916f3"
/>
### Create New Vector Store
<img width="1918" height="1048" alt="Screenshot 2025-11-06 at 10 47
49 PM"
src="https://github.com/user-attachments/assets/b4dc0d31-696f-4e68-b109-27915090f158"
/>
### Edit Vector Store
<img width="1916" height="1355" alt="Screenshot 2025-11-06 at 10 48
32 PM"
src="https://github.com/user-attachments/assets/ec879c63-4cf7-489f-bb1e-57ccc7931414"
/>
### Vector Store Files Contents page (with Embeddings)
<img width="1914" height="849" alt="Screenshot 2025-11-06 at 11 54
32 PM"
src="https://github.com/user-attachments/assets/3095520d-0e90-41f7-83bd-652f6c3fbf27"
/>
### Vector Store Files Contents Details page (with Embeddings)
<img width="1916" height="1221" alt="Screenshot 2025-11-06 at 11 55
00 PM"
src="https://github.com/user-attachments/assets/e71dbdc5-5b49-472b-a43a-5785f58d196c"
/>
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
Tests added for Middleware extension and Provider failures.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Add explicit connection cleanup and shorter timeouts to OpenAI client
fixtures. Fixes CI deadlock after 25+ tests due to connection pool
exhaustion. Also adds 60s timeout to test_conversation_context_loading
as safety net.
## Test Plan
tests pass
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
Delete ~2,000 lines of dead code from the old bespoke inference API that
was replaced by OpenAI-only API. This includes removing unused type
conversion functions, dead provider methods, and event_logger.py.
Clean up imports across the codebase to remove references to deleted
types. This eliminates unnecessary
code and dependencies, helping isolate the API package as a
self-contained module.
This is the last interdependency between the .api package and "exterior"
packages, meaning that now every other package in llama stack imports
the API, not the other way around.
## Test Plan
this is a structural change, no tests needed.
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# Problem
Responses API uses max_tool_calls parameter to limit the number of tool
calls that can be generated in a response. Currently, LLS implementation
of the Responses API does not support this parameter.
# What does this PR do?
This pull request adds the max_tool_calls field to the response object
definition and updates the inline provider. it also ensures that:
- the total number of calls to built-in and mcp tools do not exceed
max_tool_calls
- an error is thrown if max_tool_calls < 1 (behavior seen with the
OpenAI Responses API, but we can change this if needed)
Closes #[3563](https://github.com/llamastack/llama-stack/issues/3563)
## Test Plan
- Tested manually for change in model response w.r.t supplied
max_tool_calls field.
- Added integration tests to test invalid max_tool_calls parameter.
- Added integration tests to check max_tool_calls parameter with
built-in and function tools.
- Added integration tests to check max_tool_calls parameter in the
returned response object.
- Recorded OpenAI Responses API behavior using a sample script:
https://github.com/s-akhtar-baig/llama-stack-examples/blob/main/responses/src/max_tool_calls.py
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Adds OCI GenAI PaaS models for openai chat completion endpoints.
## Test Plan
In an OCI tenancy with access to GenAI PaaS, perform the following
steps:
1. Ensure you have IAM policies in place to use service (check docs
included in this PR)
2. For local development, [setup OCI
cli](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliinstall.htm)
and configure the CLI with your region, tenancy, and auth
[here](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliconfigure.htm)
3. Once configured, go through llama-stack setup and run llama-stack
(uses config based auth) like:
```bash
OCI_AUTH_TYPE=config_file \
OCI_CLI_PROFILE=CHICAGO \
OCI_REGION=us-chicago-1 \
OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..aaaaaaaa5...5a \
llama stack run oci
```
4. Hit the `models` endpoint to list models after server is running:
```bash
curl http://localhost:8321/v1/models | jq
...
{
"identifier": "meta.llama-4-scout-17b-16e-instruct",
"provider_resource_id": "ocid1.generativeaimodel.oc1.us-chicago-1.am...q",
"provider_id": "oci",
"type": "model",
"metadata": {
"display_name": "meta.llama-4-scout-17b-16e-instruct",
"capabilities": [
"CHAT"
],
"oci_model_id": "ocid1.generativeaimodel.oc1.us-chicago-1.a...q"
},
"model_type": "llm"
},
...
```
5. Use the "display_name" field to use the model in a
`/chat/completions` request:
```bash
# Streaming result
curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "meta.llama-4-scout-17b-16e-instruct",
"stream": true,
"temperature": 0.9,
"messages": [
{
"role": "system",
"content": "You are a funny comedian. You can be crass."
},
{
"role": "user",
"content": "Tell me a funny joke about programming."
}
]
}'
# Non-streaming result
curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "meta.llama-4-scout-17b-16e-instruct",
"stream": false,
"temperature": 0.9,
"messages": [
{
"role": "system",
"content": "You are a funny comedian. You can be crass."
},
{
"role": "user",
"content": "Tell me a funny joke about programming."
}
]
}'
```
6. Try out other models from the `/models` endpoint.
# What does this PR do?
This PR fixes a bug in LlamaStack 0.3.0 where vector stores created via
the OpenAI-compatible API (`POST /v1/vector_stores`) would fail with
`VectorStoreNotFoundError` after server restart when attempting
operations like `vector_io.insert()` or `vector_io.query()`.
The bug affected **6 vector IO providers**: `pgvector`, `sqlite_vec`,
`chroma`, `milvus`, `qdrant`, and `weaviate`.
Created with the assistance of: claude-4.5-sonnet
## Root Cause
All affected providers had a broken
`_get_and_cache_vector_store_index()` method that:
1. Did not load existing vector stores from persistent storage during
initialization
2. Attempted to use `vector_store_table` (which was either `None` or a
`KVStore` without the required `get_vector_store()` method)
3. Could not reload vector stores after server restart or cache miss
## Solution
This PR implements a consistent pattern across all 6 providers:
1. **Load vector stores during initialization** - Pre-populate the cache
from KV store on startup
2. **Fix lazy loading** - Modified `_get_and_cache_vector_store_index()`
to load directly from KV store instead of relying on
`vector_store_table`
3. **Remove broken dependency** - Eliminated reliance on the
`vector_store_table` pattern
## Testing steps
### 1.1 Configure the stack
Create or use an existing configuration with a vector IO provider.
**Example `run.yaml`:**
```yaml
vector_io_store:
- provider_id: pgvector
provider_type: remote::pgvector
config:
host: localhost
port: 5432
db: llamastack
user: llamastack
password: llamastack
inference:
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config:
model: sentence-transformers/all-MiniLM-L6-v2
```
### 1.2 Start the server
```bash
llama stack run run.yaml --port 5000
```
Wait for the server to fully start. You should see:
```
INFO: Started server process
INFO: Application startup complete
```
---
## Step 2: Create a Vector Store
### 2.1 Create via API
```bash
curl -X POST http://localhost:5000/v1/vector_stores \
-H "Content-Type: application/json" \
-d '{
"name": "test-persistence-store",
"extra_body": {
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"embedding_dimension": 384,
"provider_id": "pgvector"
}
}' | jq
```
### 2.2 Expected Response
```json
{
"id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
"object": "vector_store",
"name": "test-persistence-store",
"status": "completed",
"created_at": 1730304000,
"file_counts": {
"total": 0,
"completed": 0,
"in_progress": 0,
"failed": 0,
"cancelled": 0
},
"usage_bytes": 0
}
```
**Save the `id` field** (e.g.,
`vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d`) — you’ll need it for the next
steps.
---
## Step 3: Insert Data (Before Restart)
### 3.1 Insert chunks into the vector store
```bash
export VS_ID="vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d"
curl -X POST http://localhost:5000/vector-io/insert \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"chunks\": [
{
\"content\": \"Python is a high-level programming language known for its readability.\",
\"metadata\": {\"source\": \"doc1\", \"page\": 1}
},
{
\"content\": \"Machine learning enables computers to learn from data without explicit programming.\",
\"metadata\": {\"source\": \"doc2\", \"page\": 1}
},
{
\"content\": \"Neural networks are inspired by biological neurons in the brain.\",
\"metadata\": {\"source\": \"doc3\", \"page\": 1}
}
]
}"
```
### 3.2 Expected Response
Status: **200 OK**
Response: *Empty or success confirmation*
---
## Step 4: Query Data (Before Restart – Baseline)
### 4.1 Query the vector store
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"What is machine learning?\"
}" | jq
```
### 4.2 Expected Response
```json
{
"chunks": [
{
"content": "Machine learning enables computers to learn from data without explicit programming.",
"metadata": {"source": "doc2", "page": 1}
},
{
"content": "Neural networks are inspired by biological neurons in the brain.",
"metadata": {"source": "doc3", "page": 1}
}
],
"scores": [0.85, 0.72]
}
```
**Checkpoint:** Works correctly before restart.
---
## Step 5: Restart the Server (Critical Test)
### 5.1 Stop the server
In the terminal where it’s running:
```
Ctrl + C
```
Wait for:
```
Shutting down...
```
### 5.2 Restart the server
```bash
llama stack run run.yaml --port 5000
```
Wait for:
```
INFO: Started server process
INFO: Application startup complete
```
The vector store cache is now empty, but data should persist.
---
## Step 6: Verify Vector Store Exists (After Restart)
### 6.1 List vector stores
```bash
curl http://localhost:5000/v1/vector_stores | jq
```
### 6.2 Expected Response
```json
{
"object": "list",
"data": [
{
"id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
"name": "test-persistence-store",
"status": "completed"
}
]
}
```
**Checkpoint:** Vector store should be listed.
---
## Step 7: Insert Data (After Restart – THE BUG TEST)
### 7.1 Insert new chunks
```bash
curl -X POST http://localhost:5000/vector-io/insert \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"chunks\": [
{
\"content\": \"This chunk was inserted AFTER the server restart.\",
\"metadata\": {\"source\": \"post-restart\", \"test\": true}
}
]
}"
```
### 7.2 Expected Results
**With Fix (Correct):**
```
Status: 200 OK
Response: Success
```
**Without Fix (Bug):**
```json
{
"detail": "VectorStoreNotFoundError: Vector Store 'vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d' not found."
}
```
**Critical Test:** If insertion succeeds, the fix works.
---
## Step 8: Query Data (After Restart – Verification)
### 8.1 Query all data
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"restart\"
}" | jq
```
### 8.2 Expected Response
```json
{
"chunks": [
{
"content": "This chunk was inserted AFTER the server restart.",
"metadata": {"source": "post-restart", "test": true}
}
],
"scores": [0.95]
}
```
**Checkpoint:** Both old and new data are queryable.
---
## Step 9: Multiple Restart Test (Extra Verification)
### 9.1 Restart again
```bash
Ctrl + C
llama stack run run.yaml --port 5000
```
### 9.2 Query after restart
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"programming\"
}" | jq
```
**Expected:** Works correctly across multiple restarts.
---------
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
Resolves#4102
1. Added `web_search_2025_08_26` to the `WebSearchToolTypes` list and
the `OpenAIResponseInputToolWebSearch.type` Literal union
2. No changes needed to tool execution logic - all `web_search` types
map to the same underlying tool
3. Backward compatibility is maintained - existing `web_search`,
`web_search_preview`, and `web_search_preview_2025_03_11` types continue
to work
4. Added an integration test case using {"type":
"web_search_2025_08_26"} to verify it works correctly
5. Updated `docs/docs/providers/openai_responses_limitations.mdx` to
reflect that `web_search_2025_08_26` is now supported.
6. Removed incorrect references to `MOD1/MOD2/MOD3` (which don't exist
in the codebase)
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
---------
Signed-off-by: Aakanksha Duggal <aduggal@redhat.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
This dependency has been bothering folks for a long time (cc @leseb). We
really needed it due to "library client" which is primarily used for our
tests and is not a part of the Stack server. Anyone who needs to use the
library client can certainly install `llama-stack-client` in their
environment to make that work.
Updated the notebook references to install `llama-stack-client`
additionally when setting things up.
https://github.com/llamastack/llama-stack/pull/4055 cleaned the agents
implementation but while doing so it removed some tests which actually
corresponded to the responses implementation. This PR brings those tests
and assocated recordings back.
(We should likely combine all responses tests into one suite, but that
is beyond the scope of this PR.)
o Introduces vLLM provider support to the record/replay testing
framework
o Enabling both recording and replay of vLLM API interactions alongside
existing Ollama support.
The changes enable testing of vLLM functionality. vLLM tests focus on
inference capabilities, while Ollama continues to exercise the full API
surface
including vision features.
--
This is an alternative to #3128 , using qwen3 instead of llama 3.2 1B
appears to be more capable at structure output and tool calls.
---------
Signed-off-by: Derek Higgins <derekh@redhat.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
# What does this PR do?
- when create vector store is called without chunk strategy, we actually
the strategy used so that the value is persisted instead of
strategy='None'
## Test Plan
updated tests
