mirror of
https://github.com/github/awesome-copilot.git
synced 2026-04-12 19:25:55 +00:00
d79183139a
* Add 9 Arize LLM observability skills Add skills for Arize AI platform covering trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations, annotations, prompt optimization, and deep linking to the Arize UI. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add 3 Phoenix AI observability skills Add skills for Phoenix (Arize open-source) covering CLI debugging, LLM evaluation workflows, and OpenInference tracing/instrumentation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Ignoring intentional bad spelling * Fix CI: remove .DS_Store from generated skills README and add codespell ignore Remove .DS_Store artifact from winmd-api-search asset listing in generated README.skills.md so it matches the CI Linux build output. Add queston to codespell ignore list (intentional misspelling example in arize-dataset skill). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add arize-ax and phoenix plugins Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix skills into a phoenix plugin for easier installation as single packages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix skill folder structures to match source repos Move arize supporting files from references/ to root level and rename phoenix references/ to rules/ to exactly match the original source repository folder structures. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fixing file locations * Fixing readme --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
111 lines
3.4 KiB
Markdown
111 lines
3.4 KiB
Markdown
# RETRIEVER Spans
|
|
|
|
## Purpose
|
|
|
|
RETRIEVER spans represent document/context retrieval operations (vector DB queries, semantic search, keyword search).
|
|
|
|
## Required Attributes
|
|
|
|
| Attribute | Type | Description | Required |
|
|
|-----------|------|-------------|----------|
|
|
| `openinference.span.kind` | String | Must be "RETRIEVER" | Yes |
|
|
|
|
## Attribute Reference
|
|
|
|
### Query
|
|
|
|
| Attribute | Type | Description |
|
|
|-----------|------|-------------|
|
|
| `input.value` | String | Search query text |
|
|
|
|
### Document Schema
|
|
|
|
| Attribute Pattern | Type | Description |
|
|
|-------------------|------|-------------|
|
|
| `retrieval.documents.{i}.document.id` | String | Unique document identifier |
|
|
| `retrieval.documents.{i}.document.content` | String | Document text content |
|
|
| `retrieval.documents.{i}.document.score` | Float | Relevance score (0-1 or distance) |
|
|
| `retrieval.documents.{i}.document.metadata` | String (JSON) | Document metadata |
|
|
|
|
### Flattening Pattern for Documents
|
|
|
|
Documents are flattened using zero-indexed notation:
|
|
|
|
```
|
|
retrieval.documents.0.document.id
|
|
retrieval.documents.0.document.content
|
|
retrieval.documents.0.document.score
|
|
retrieval.documents.1.document.id
|
|
retrieval.documents.1.document.content
|
|
retrieval.documents.1.document.score
|
|
...
|
|
```
|
|
|
|
### Document Metadata
|
|
|
|
Common metadata fields (stored as JSON string):
|
|
|
|
```json
|
|
{
|
|
"source": "knowledge_base.pdf",
|
|
"page": 42,
|
|
"section": "Introduction",
|
|
"author": "Jane Doe",
|
|
"created_at": "2024-01-15",
|
|
"url": "https://example.com/doc",
|
|
"chunk_id": "chunk_123"
|
|
}
|
|
```
|
|
|
|
**Example with metadata:**
|
|
```json
|
|
{
|
|
"retrieval.documents.0.document.id": "doc_123",
|
|
"retrieval.documents.0.document.content": "Machine learning is a method of data analysis...",
|
|
"retrieval.documents.0.document.score": 0.92,
|
|
"retrieval.documents.0.document.metadata": "{\"source\": \"ml_textbook.pdf\", \"page\": 15, \"chapter\": \"Introduction\"}"
|
|
}
|
|
```
|
|
|
|
### Ordering
|
|
|
|
Documents are ordered by index (0, 1, 2, ...). Typically:
|
|
- Index 0 = highest scoring document
|
|
- Index 1 = second highest
|
|
- etc.
|
|
|
|
Preserve retrieval order in your flattened attributes.
|
|
|
|
### Large Document Handling
|
|
|
|
For very long documents:
|
|
- Consider truncating `document.content` to first N characters
|
|
- Store full content in separate document store
|
|
- Use `document.id` to reference full content
|
|
|
|
## Examples
|
|
|
|
### Basic Vector Search
|
|
|
|
```json
|
|
{
|
|
"openinference.span.kind": "RETRIEVER",
|
|
"input.value": "What is machine learning?",
|
|
"retrieval.documents.0.document.id": "doc_123",
|
|
"retrieval.documents.0.document.content": "Machine learning is a subset of artificial intelligence...",
|
|
"retrieval.documents.0.document.score": 0.92,
|
|
"retrieval.documents.0.document.metadata": "{\"source\": \"textbook.pdf\", \"page\": 42}",
|
|
"retrieval.documents.1.document.id": "doc_456",
|
|
"retrieval.documents.1.document.content": "Machine learning algorithms learn patterns from data...",
|
|
"retrieval.documents.1.document.score": 0.87,
|
|
"retrieval.documents.1.document.metadata": "{\"source\": \"article.html\", \"author\": \"Jane Doe\"}",
|
|
"retrieval.documents.2.document.id": "doc_789",
|
|
"retrieval.documents.2.document.content": "Supervised learning is a type of machine learning...",
|
|
"retrieval.documents.2.document.score": 0.81,
|
|
"retrieval.documents.2.document.metadata": "{\"source\": \"wiki.org\"}",
|
|
"metadata.retriever_type": "vector_search",
|
|
"metadata.vector_db": "pinecone",
|
|
"metadata.top_k": 3
|
|
}
|
|
```
|