mirror of
https://github.com/meta-llama/llama-stack.git
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chore: remove /v1/inference/completion and implementations (#3622)
# What does this PR do? the /inference/completion route is gone. this removes the implementations. ## Test Plan ci
This commit is contained in:
Matthew Farrellee
GitHub
parent
ea15f2a270
commit
f7c5ef4ec0
75 changed files with 16141 additions and 17056 deletions
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806
tests/integration/recordings/responses/27ef1a50dc19.json
Normal file
806
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||||
3131
tests/integration/recordings/responses/3130f21f1bb9.json
Normal file
3131
tests/integration/recordings/responses/3130f21f1bb9.json
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -1,39 +0,0 @@
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|||
{
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},
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||||
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||||
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||||
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||||
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||||
"eval_duration": 5209000000,
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||||
"response": "This is the start of a test. I'll provide some sample data and you can try to generate metrics based on it.\n\n**Data:**\n\nLet's say we have a dataset of user interactions with an e-commerce website. The data includes:\n\n| User ID | Product Name | Purchase Date | Quantity | Price |\n| --- | --- | --- | --- | --- |\n| 1 | iPhone 13 | 2022-01-01 | 2 | 999.99 |\n| 1 | MacBook Air | 2022-01-05 | 1 | 1299.99 |\n| 2 | Samsung TV | 2022-01-10 | 3 | 899.99 |\n| 3 | iPhone 13 | 2022-01-15 | 1 | 999.99 |\n| 4 | MacBook Pro | 2022-01-20 | 2 | 1799.99 |\n\n**Task:**\n\nYour task is to generate the following metrics based on this data:\n\n1. Average order value (AOV)\n2. Conversion rate\n3. Average revenue per user (ARPU)\n4. Customer lifetime value (CLV)\n\nPlease provide your answers in a format like this:\n\n| Metric | Value |\n| --- | --- |\n| AOV | 1234.56 |\n| Conversion Rate | 0.25 |\n| ARPU | 1000.00 |\n| CLV | 5000.00 |\n\nGo ahead and generate the metrics!",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
}
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
}
|
||||
806
tests/integration/recordings/responses/3b60c09d6c4f.json
Normal file
806
tests/integration/recordings/responses/3b60c09d6c4f.json
Normal file
|
|
@ -0,0 +1,806 @@
|
|||
{
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://0.0.0.0:11434/v1/v1/embeddings",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "nomic-embed-text:137m-v1.5-fp16",
|
||||
"input": [
|
||||
"The secret string is foobazbar."
|
||||
],
|
||||
"encoding_format": "float"
|
||||
},
|
||||
"endpoint": "/v1/embeddings",
|
||||
"model": "nomic-embed-text:137m-v1.5-fp16"
|
||||
},
|
||||
"response": {
|
||||
"body": {
|
||||
"__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
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||||
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806
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806
tests/integration/recordings/responses/47264d05c3ef.json
Normal file
806
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@ -0,0 +1,806 @@
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@ -1,86 +0,0 @@
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|
||||
806
tests/integration/recordings/responses/5357765a9ac9.json
Normal file
806
tests/integration/recordings/responses/5357765a9ac9.json
Normal file
|
|
@ -0,0 +1,806 @@
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"response": "Here are some common test metrics used to evaluate the performance of a system:\n\n1. **Accuracy**: The proportion of correct predictions or classifications out of total predictions made.\n2. **Precision**: The ratio of true positives (correctly predicted instances) to the sum of true positives and false positives (incorrectly predicted instances).\n3. **Recall**: The ratio of true positives to the sum of true positives and false negatives (missed instances).\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: The average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: The average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: The square root of the mean of the squared percentage differences between predicted and actual values.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well a model fits the data, with higher values indicating better fit.\n9. **Mean Absolute Percentage Error (MAPE)**: The average absolute percentage difference between predicted and actual values.\n10. **Normalized Mean Squared Error (NMSE)**: Similar to MSE, but normalized by the mean of the actual values.\n\nThese metrics can be used for various types of data, including:\n\n* Regression problems (e.g., predicting continuous values)\n* Classification problems (e.g., predicting categorical labels)\n* Time series forecasting\n* Clustering and dimensionality reduction\n\nWhen choosing a metric, consider the specific problem you're trying to solve, the type of data, and the desired level of precision.",
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"thinking": null,
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"context": null
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}
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},
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"is_streaming": false
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}
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}
|
||||
806
tests/integration/recordings/responses/87c056adc35c.json
Normal file
806
tests/integration/recordings/responses/87c056adc35c.json
Normal file
|
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@ -0,0 +1,806 @@
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{
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"input": [
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},
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806
tests/integration/recordings/responses/9d84bd0e850f.json
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806
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806
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@ -1,221 +0,0 @@
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806
tests/integration/recordings/responses/bac8c9e59dda.json
Normal file
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File diff suppressed because it is too large
Load diff
806
tests/integration/recordings/responses/bc581d1d19f9.json
Normal file
806
tests/integration/recordings/responses/bc581d1d19f9.json
Normal file
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806
tests/integration/recordings/responses/bd656a9e3f8f.json
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806
tests/integration/recordings/responses/bd656a9e3f8f.json
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@ -1,39 +0,0 @@
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"response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n1. **Accuracy**: The proportion of correct predictions made by the model.\n2. **Precision**: The ratio of true positives (correctly predicted instances) to total positive predictions.\n3. **Recall**: The ratio of true positives to the sum of true positives and false negatives (missed instances).\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: The average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: The average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: A variation of MSE that expresses the error as a percentage.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well the model explains the variance in the data.\n9. **Mean Absolute Percentage Error (MAPE)**: The average absolute percentage difference between predicted and actual values.\n10. **Mean Squared Logarithmic Error (MSLE)**: A variation of MSE that is more suitable for skewed distributions.\n\nThese metrics can be used to evaluate different aspects of a system's performance, such as:\n\n* Classification models: accuracy, precision, recall, F1-score\n* Regression models: MSE, MAE, RMSPE, R2, MSLE\n* Time series forecasting: MAPE, RMSPE\n\nNote that the choice of metric depends on the specific problem and data.",
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@ -1,347 +0,0 @@
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|
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|
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"options": {
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|
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},
|
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|
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|
||||
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|
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|
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|
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806
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806
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Normal file
806
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@ -0,0 +1,806 @@
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|
||||
|
|
@ -1,39 +0,0 @@
|
|||
{
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://localhost:11434/api/generate",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "llama3.2:3b",
|
||||
"raw": true,
|
||||
"prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 1<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
|
||||
"options": {
|
||||
"temperature": 0.0
|
||||
},
|
||||
"stream": false
|
||||
},
|
||||
"endpoint": "/api/generate",
|
||||
"model": "llama3.2:3b"
|
||||
},
|
||||
"response": {
|
||||
"body": {
|
||||
"__type__": "ollama._types.GenerateResponse",
|
||||
"__data__": {
|
||||
"model": "llama3.2:3b",
|
||||
"created_at": "2025-08-11T15:56:10.465932Z",
|
||||
"done": true,
|
||||
"done_reason": "stop",
|
||||
"total_duration": 3745686709,
|
||||
"load_duration": 9734584,
|
||||
"prompt_eval_count": 21,
|
||||
"prompt_eval_duration": 23000000,
|
||||
"eval_count": 457,
|
||||
"eval_duration": 3712000000,
|
||||
"response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n**Primary Metrics**\n\n1. **Response Time**: The time it takes for the system to respond to a request.\n2. **Throughput**: The number of requests processed by the system per unit time (e.g., requests per second).\n3. **Error Rate**: The percentage of requests that result in an error.\n\n**Secondary Metrics**\n\n1. **Average Response Time**: The average response time for all requests.\n2. **Median Response Time**: The middle value of the response times, used to detect outliers.\n3. **99th Percentile Response Time**: The response time at which 99% of requests are completed within this time.\n4. **Request Latency**: The difference between the request arrival time and the response time.\n\n**User Experience Metrics**\n\n1. **User Satisfaction (USAT)**: Measured through surveys or feedback forms to gauge user satisfaction with the system's performance.\n2. **First Response Time**: The time it takes for a user to receive their first response from the system.\n3. **Time Spent in System**: The total amount of time a user spends interacting with the system.\n\n**System Resource Metrics**\n\n1. **CPU Utilization**: The percentage of CPU resources being used by the system.\n2. **Memory Usage**: The amount of memory being used by the system.\n3. **Disk I/O Wait Time**: The average time spent waiting for disk I/O operations to complete.\n\n**Security Metrics**\n\n1. **Authentication Success Rate**: The percentage of successful authentication attempts.\n2. **Authorization Success Rate**: The percentage of successful authorization attempts.\n3. **Error Rate (Security)**: The percentage of security-related errors.\n\n**Other Metrics**\n\n1. **Page Load Time**: The time it takes for a page to load.\n2. **Click-Through Rate (CTR)**: The percentage of users who click on a link or button after seeing an ad or notification.\n3. **Conversion Rate**: The percentage of users who complete a desired action (e.g., fill out a form, make a purchase).\n\nThese metrics can be used to evaluate the performance and effectiveness of various aspects of your system, from user experience to security and resource utilization.",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
}
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue