2003 lines
206 KiB
YAML
2003 lines
206 KiB
YAML
interactions:
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- request:
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headers:
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accept:
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- application/json
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accept-encoding:
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- gzip, deflate, zstd
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connection:
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- keep-alive
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content-length:
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- '108'
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content-type:
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- application/json
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host:
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- localhost:11434
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method: POST
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parsed_body:
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encoding_format: base64
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input:
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- 'Sales report Q1: Revenue was $100,000.'
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model: qwen3-embedding:4b
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uri: http://localhost:11434/v1/embeddings
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response:
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headers:
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content-type:
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- application/json
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transfer-encoding:
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- chunked
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parsed_body:
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data:
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- embedding: 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
|
||
index: 0
|
||
object: embedding
|
||
model: qwen3-embedding:4b
|
||
object: list
|
||
usage:
|
||
prompt_tokens: 17
|
||
total_tokens: 17
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '108'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
encoding_format: base64
|
||
input:
|
||
- 'Sales report Q2: Revenue was $150,000.'
|
||
model: qwen3-embedding:4b
|
||
uri: http://localhost:11434/v1/embeddings
|
||
response:
|
||
headers:
|
||
content-type:
|
||
- application/json
|
||
transfer-encoding:
|
||
- chunked
|
||
parsed_body:
|
||
data:
|
||
- embedding: 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
|
||
index: 0
|
||
object: embedding
|
||
model: qwen3-embedding:4b
|
||
object: list
|
||
usage:
|
||
prompt_tokens: 17
|
||
total_tokens: 17
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '108'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
encoding_format: base64
|
||
input:
|
||
- 'Sales report Q3: Revenue was $200,000.'
|
||
model: qwen3-embedding:4b
|
||
uri: http://localhost:11434/v1/embeddings
|
||
response:
|
||
headers:
|
||
content-type:
|
||
- application/json
|
||
transfer-encoding:
|
||
- chunked
|
||
parsed_body:
|
||
data:
|
||
- embedding: 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
|
||
index: 0
|
||
object: embedding
|
||
model: qwen3-embedding:4b
|
||
object: list
|
||
usage:
|
||
prompt_tokens: 17
|
||
total_tokens: 17
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '8631'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
messages:
|
||
- content: |-
|
||
You are a focused execution agent. Complete the following task using the skills and instructions provided.
|
||
|
||
## Task
|
||
|
||
What is the total revenue across all quarterly reports?
|
||
|
||
## Skill instructions
|
||
|
||
# Analysis
|
||
|
||
You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.
|
||
|
||
## Tools
|
||
|
||
### execute_code
|
||
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
||
|
||
Inside the code, these functions are available (use `await`):
|
||
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
||
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
||
|
||
Available modules: `json`, `re`, `math`, `pathlib`
|
||
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
||
|
||
### search
|
||
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
||
|
||
### cite
|
||
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.
|
||
|
||
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
|
||
|
||
## Document Filesystem (inside execute_code)
|
||
|
||
All documents are mounted as a virtual filesystem at `/documents/`:
|
||
|
||
```
|
||
/documents/{document_id}/
|
||
metadata.json # {"id", "title", "uri", "created_at"}
|
||
content.txt # Full document text
|
||
items.jsonl # Structured items (one JSON object per line)
|
||
toc.json # Section tree derived from heading_level
|
||
```
|
||
|
||
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.
|
||
|
||
### Reading files
|
||
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
||
|
||
```python
|
||
from pathlib import Path
|
||
import json
|
||
|
||
# Discover documents
|
||
for doc_dir in Path('/documents').iterdir():
|
||
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
||
print(meta['title'])
|
||
|
||
# Read full text
|
||
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
||
|
||
# Read and parse items
|
||
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
||
item = json.loads(line)
|
||
if item['label'] == 'table':
|
||
print(item['text'][:200])
|
||
```
|
||
|
||
### metadata.json
|
||
Document metadata: `id`, `title`, `uri`, `created_at`.
|
||
|
||
### content.txt
|
||
Full text content. Use for regex or keyword search across a whole document.
|
||
|
||
### items.jsonl
|
||
Structured document items. Each line is a JSON object with:
|
||
- `position`: sequential position in the document
|
||
- `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
|
||
- `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
|
||
- `text`: rendered content (tables are markdown with `|` columns)
|
||
- `page_numbers`: list of page numbers where the item appears
|
||
- `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
|
||
- `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.
|
||
|
||
### toc.json
|
||
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.
|
||
|
||
### Cross-referencing search results with items
|
||
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.
|
||
|
||
## Strategy
|
||
|
||
1. Search first.
|
||
2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
|
||
3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
||
4. Call `cite` with the chunk_ids that ground your answer before writing the final response.
|
||
|
||
You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.
|
||
|
||
## Important
|
||
|
||
- Variables persist between `execute_code` calls — you can search in one call and process results in the next
|
||
- Use `print()` to output results — the output is your only feedback
|
||
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
|
||
- Use `await` for all async functions inside execute_code (search, list_documents)
|
||
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
||
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.
|
||
|
||
## Guidelines
|
||
|
||
- Follow the skill instructions carefully
|
||
- Stay focused on the specific task described above
|
||
- Provide a clear, complete result
|
||
role: system
|
||
- content: What is the total revenue across all quarterly reports?
|
||
role: user
|
||
model: gpt-oss
|
||
reasoning_effort: high
|
||
stream: true
|
||
stream_options:
|
||
include_usage: true
|
||
temperature: 0.3
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
Search the knowledge base using hybrid search (vector + full-text).
|
||
|
||
Returns ranked results with content and metadata. When picture
|
||
content is in the result set and the driving skill model is
|
||
vision-capable, picture bytes are attached as ``BinaryContent``
|
||
parts so the model sees figures alongside text.
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of results.
|
||
query:
|
||
description: The search query.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Execute Python code in a sandboxed interpreter.
|
||
|
||
The code has access to search() and list_documents() functions
|
||
and a virtual filesystem at /documents/ with document content
|
||
and structure (metadata.json, content.txt, items.jsonl, toc.json
|
||
per document).
|
||
|
||
Use print() to output results. Variables persist between calls
|
||
within the same skill invocation.
|
||
name: execute_code
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
code:
|
||
description: Python code to execute.
|
||
type: string
|
||
required:
|
||
- code
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Register chunk IDs as citations for your answer.
|
||
|
||
Call this after searching, with the chunk_id values from search
|
||
results that support your answer.
|
||
name: cite
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
chunk_ids:
|
||
description: List of chunk_id values from search results.
|
||
items:
|
||
type: string
|
||
type: array
|
||
required:
|
||
- chunk_ids
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
body:
|
||
string: |+
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" user"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" asks"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" \""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"What"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" is"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" total"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" revenue"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" across"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" all"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" quarterly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" reports"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"?\""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" to"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" find"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" revenue"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" figures"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" across"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" all"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" quarterly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" reports"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176068,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Lik"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"ely"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" there"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" are"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" multiple"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" containing"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" quarterly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" reports"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" to"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" sum"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" revenue"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" numbers"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" to"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" search"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" for"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" \""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"quarter"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"ly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" report"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"\""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" or"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" \""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"Revenue"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"\""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" or"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" \""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"Q"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"1"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"\","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" \""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"Q"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"2"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"\","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" etc"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-496","object":"chat.completion.chunk","created":1779176069,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
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
|
||
index: 0
|
||
object: embedding
|
||
model: qwen3-embedding:4b
|
||
object: list
|
||
usage:
|
||
prompt_tokens: 5
|
||
total_tokens: 5
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '9980'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
messages:
|
||
- content: |-
|
||
You are a focused execution agent. Complete the following task using the skills and instructions provided.
|
||
|
||
## Task
|
||
|
||
What is the total revenue across all quarterly reports?
|
||
|
||
## Skill instructions
|
||
|
||
# Analysis
|
||
|
||
You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.
|
||
|
||
## Tools
|
||
|
||
### execute_code
|
||
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
||
|
||
Inside the code, these functions are available (use `await`):
|
||
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
||
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
||
|
||
Available modules: `json`, `re`, `math`, `pathlib`
|
||
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
||
|
||
### search
|
||
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
||
|
||
### cite
|
||
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.
|
||
|
||
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
|
||
|
||
## Document Filesystem (inside execute_code)
|
||
|
||
All documents are mounted as a virtual filesystem at `/documents/`:
|
||
|
||
```
|
||
/documents/{document_id}/
|
||
metadata.json # {"id", "title", "uri", "created_at"}
|
||
content.txt # Full document text
|
||
items.jsonl # Structured items (one JSON object per line)
|
||
toc.json # Section tree derived from heading_level
|
||
```
|
||
|
||
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.
|
||
|
||
### Reading files
|
||
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
||
|
||
```python
|
||
from pathlib import Path
|
||
import json
|
||
|
||
# Discover documents
|
||
for doc_dir in Path('/documents').iterdir():
|
||
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
||
print(meta['title'])
|
||
|
||
# Read full text
|
||
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
||
|
||
# Read and parse items
|
||
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
||
item = json.loads(line)
|
||
if item['label'] == 'table':
|
||
print(item['text'][:200])
|
||
```
|
||
|
||
### metadata.json
|
||
Document metadata: `id`, `title`, `uri`, `created_at`.
|
||
|
||
### content.txt
|
||
Full text content. Use for regex or keyword search across a whole document.
|
||
|
||
### items.jsonl
|
||
Structured document items. Each line is a JSON object with:
|
||
- `position`: sequential position in the document
|
||
- `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
|
||
- `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
|
||
- `text`: rendered content (tables are markdown with `|` columns)
|
||
- `page_numbers`: list of page numbers where the item appears
|
||
- `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
|
||
- `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.
|
||
|
||
### toc.json
|
||
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.
|
||
|
||
### Cross-referencing search results with items
|
||
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.
|
||
|
||
## Strategy
|
||
|
||
1. Search first.
|
||
2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
|
||
3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
||
4. Call `cite` with the chunk_ids that ground your answer before writing the final response.
|
||
|
||
You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.
|
||
|
||
## Important
|
||
|
||
- Variables persist between `execute_code` calls — you can search in one call and process results in the next
|
||
- Use `print()` to output results — the output is your only feedback
|
||
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
|
||
- Use `await` for all async functions inside execute_code (search, list_documents)
|
||
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
||
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.
|
||
|
||
## Guidelines
|
||
|
||
- Follow the skill instructions carefully
|
||
- Stay focused on the specific task described above
|
||
- Provide a clear, complete result
|
||
role: system
|
||
- content: What is the total revenue across all quarterly reports?
|
||
role: user
|
||
- content: null
|
||
reasoning: |+
|
||
The user asks: "What is the total revenue across all quarterly reports?" We need to find revenue figures across all quarterly reports. Likely there are multiple documents containing quarterly reports. We need to sum revenue numbers. We need to search for "quarterly report" or "Revenue" or "Q1", "Q2", etc. We need to find all quarterly reports. The question: "total revenue across all quarterly reports". So we need to sum revenue numbers across all quarterly reports. We need to find revenue numbers in each quarterly report. We need to sum them.
|
||
|
||
We need to search the knowledge base for "quarterly report revenue". Let's search.
|
||
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"quarterly report revenue","limit":10}'
|
||
name: search
|
||
id: call_h2b1nfrg
|
||
type: function
|
||
- content: |-
|
||
[fc120d47-6f41-42c6-977a-319a5eab577d] [rank 1 of 3]
|
||
Source: "Q1 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q1: Revenue was $100,000.
|
||
|
||
---
|
||
|
||
[79e14aa1-a426-4c47-9a3b-db35ddc96ba2] [rank 2 of 3]
|
||
Source: "Q3 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q3: Revenue was $200,000.
|
||
|
||
---
|
||
|
||
[6c9af4e6-3902-4359-b585-8f2d9d8f8615] [rank 3 of 3]
|
||
Source: "Q2 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q2: Revenue was $150,000.
|
||
role: tool
|
||
tool_call_id: call_h2b1nfrg
|
||
model: gpt-oss
|
||
reasoning_effort: high
|
||
stream: true
|
||
stream_options:
|
||
include_usage: true
|
||
temperature: 0.3
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
Search the knowledge base using hybrid search (vector + full-text).
|
||
|
||
Returns ranked results with content and metadata. When picture
|
||
content is in the result set and the driving skill model is
|
||
vision-capable, picture bytes are attached as ``BinaryContent``
|
||
parts so the model sees figures alongside text.
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of results.
|
||
query:
|
||
description: The search query.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Execute Python code in a sandboxed interpreter.
|
||
|
||
The code has access to search() and list_documents() functions
|
||
and a virtual filesystem at /documents/ with document content
|
||
and structure (metadata.json, content.txt, items.jsonl, toc.json
|
||
per document).
|
||
|
||
Use print() to output results. Variables persist between calls
|
||
within the same skill invocation.
|
||
name: execute_code
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
code:
|
||
description: Python code to execute.
|
||
type: string
|
||
required:
|
||
- code
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Register chunk IDs as citations for your answer.
|
||
|
||
Call this after searching, with the chunk_id values from search
|
||
results that support your answer.
|
||
name: cite
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
chunk_ids:
|
||
description: List of chunk_id values from search results.
|
||
items:
|
||
type: string
|
||
type: array
|
||
required:
|
||
- chunk_ids
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
body:
|
||
string: |+
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" have"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" three"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" quarterly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" reports"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Q"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"1"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Q"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"2"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Q"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" total"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" revenue"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" across"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" all"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" quarterly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" reports"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176144,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Sum"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"100"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"k"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" +"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"150"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"k"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" +"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"200"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"k"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" ="},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"450"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"k"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" But"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" we"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" should"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" confirm"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" if"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" there"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" is"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" a"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Q"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"4"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" report"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Search"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" again"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176145,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176146,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","tool_calls":[{"id":"call_7g40fgg1","index":0,"type":"function","function":{"name":"search","arguments":"{\"limit\":10,\"query\":\"Q4 Report revenue\"}"}}]},"finish_reason":"tool_calls"}]}
|
||
|
||
data: {"id":"chatcmpl-649","object":"chat.completion.chunk","created":1779176146,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":2202,"completion_tokens":87,"total_tokens":2289}}
|
||
|
||
data: [DONE]
|
||
|
||
headers:
|
||
content-type:
|
||
- text/event-stream
|
||
transfer-encoding:
|
||
- chunked
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '87'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
encoding_format: base64
|
||
input:
|
||
- Q4 Report revenue
|
||
model: qwen3-embedding:4b
|
||
uri: http://localhost:11434/v1/embeddings
|
||
response:
|
||
headers:
|
||
content-type:
|
||
- application/json
|
||
transfer-encoding:
|
||
- chunked
|
||
parsed_body:
|
||
data:
|
||
- embedding: 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
|
||
index: 0
|
||
object: embedding
|
||
model: qwen3-embedding:4b
|
||
object: list
|
||
usage:
|
||
prompt_tokens: 5
|
||
total_tokens: 5
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '10859'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
messages:
|
||
- content: |-
|
||
You are a focused execution agent. Complete the following task using the skills and instructions provided.
|
||
|
||
## Task
|
||
|
||
What is the total revenue across all quarterly reports?
|
||
|
||
## Skill instructions
|
||
|
||
# Analysis
|
||
|
||
You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.
|
||
|
||
## Tools
|
||
|
||
### execute_code
|
||
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
||
|
||
Inside the code, these functions are available (use `await`):
|
||
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
||
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
||
|
||
Available modules: `json`, `re`, `math`, `pathlib`
|
||
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
||
|
||
### search
|
||
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
||
|
||
### cite
|
||
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.
|
||
|
||
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
|
||
|
||
## Document Filesystem (inside execute_code)
|
||
|
||
All documents are mounted as a virtual filesystem at `/documents/`:
|
||
|
||
```
|
||
/documents/{document_id}/
|
||
metadata.json # {"id", "title", "uri", "created_at"}
|
||
content.txt # Full document text
|
||
items.jsonl # Structured items (one JSON object per line)
|
||
toc.json # Section tree derived from heading_level
|
||
```
|
||
|
||
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.
|
||
|
||
### Reading files
|
||
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
||
|
||
```python
|
||
from pathlib import Path
|
||
import json
|
||
|
||
# Discover documents
|
||
for doc_dir in Path('/documents').iterdir():
|
||
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
||
print(meta['title'])
|
||
|
||
# Read full text
|
||
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
||
|
||
# Read and parse items
|
||
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
||
item = json.loads(line)
|
||
if item['label'] == 'table':
|
||
print(item['text'][:200])
|
||
```
|
||
|
||
### metadata.json
|
||
Document metadata: `id`, `title`, `uri`, `created_at`.
|
||
|
||
### content.txt
|
||
Full text content. Use for regex or keyword search across a whole document.
|
||
|
||
### items.jsonl
|
||
Structured document items. Each line is a JSON object with:
|
||
- `position`: sequential position in the document
|
||
- `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
|
||
- `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
|
||
- `text`: rendered content (tables are markdown with `|` columns)
|
||
- `page_numbers`: list of page numbers where the item appears
|
||
- `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
|
||
- `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.
|
||
|
||
### toc.json
|
||
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.
|
||
|
||
### Cross-referencing search results with items
|
||
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.
|
||
|
||
## Strategy
|
||
|
||
1. Search first.
|
||
2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
|
||
3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
||
4. Call `cite` with the chunk_ids that ground your answer before writing the final response.
|
||
|
||
You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.
|
||
|
||
## Important
|
||
|
||
- Variables persist between `execute_code` calls — you can search in one call and process results in the next
|
||
- Use `print()` to output results — the output is your only feedback
|
||
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
|
||
- Use `await` for all async functions inside execute_code (search, list_documents)
|
||
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
||
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.
|
||
|
||
## Guidelines
|
||
|
||
- Follow the skill instructions carefully
|
||
- Stay focused on the specific task described above
|
||
- Provide a clear, complete result
|
||
role: system
|
||
- content: What is the total revenue across all quarterly reports?
|
||
role: user
|
||
- content: null
|
||
reasoning: |+
|
||
The user asks: "What is the total revenue across all quarterly reports?" We need to find revenue figures across all quarterly reports. Likely there are multiple documents containing quarterly reports. We need to sum revenue numbers. We need to search for "quarterly report" or "Revenue" or "Q1", "Q2", etc. We need to find all quarterly reports. The question: "total revenue across all quarterly reports". So we need to sum revenue numbers across all quarterly reports. We need to find revenue numbers in each quarterly report. We need to sum them.
|
||
|
||
We need to search the knowledge base for "quarterly report revenue". Let's search.
|
||
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"quarterly report revenue","limit":10}'
|
||
name: search
|
||
id: call_h2b1nfrg
|
||
type: function
|
||
- content: |-
|
||
[fc120d47-6f41-42c6-977a-319a5eab577d] [rank 1 of 3]
|
||
Source: "Q1 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q1: Revenue was $100,000.
|
||
|
||
---
|
||
|
||
[79e14aa1-a426-4c47-9a3b-db35ddc96ba2] [rank 2 of 3]
|
||
Source: "Q3 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q3: Revenue was $200,000.
|
||
|
||
---
|
||
|
||
[6c9af4e6-3902-4359-b585-8f2d9d8f8615] [rank 3 of 3]
|
||
Source: "Q2 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q2: Revenue was $150,000.
|
||
role: tool
|
||
tool_call_id: call_h2b1nfrg
|
||
- content: null
|
||
reasoning: 'We have three quarterly reports: Q1, Q2, Q3. We need total revenue across all quarterly reports. Sum:
|
||
100k + 150k + 200k = 450k. But we should confirm if there is a Q4 report. Search again.'
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"limit":10,"query":"Q4 Report revenue"}'
|
||
name: search
|
||
id: call_7g40fgg1
|
||
type: function
|
||
- content: |-
|
||
[fc120d47-6f41-42c6-977a-319a5eab577d] [rank 1 of 3]
|
||
Source: "Q1 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q1: Revenue was $100,000.
|
||
|
||
---
|
||
|
||
[6c9af4e6-3902-4359-b585-8f2d9d8f8615] [rank 2 of 3]
|
||
Source: "Q2 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q2: Revenue was $150,000.
|
||
|
||
---
|
||
|
||
[79e14aa1-a426-4c47-9a3b-db35ddc96ba2] [rank 3 of 3]
|
||
Source: "Q3 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q3: Revenue was $200,000.
|
||
role: tool
|
||
tool_call_id: call_7g40fgg1
|
||
model: gpt-oss
|
||
reasoning_effort: high
|
||
stream: true
|
||
stream_options:
|
||
include_usage: true
|
||
temperature: 0.3
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
Search the knowledge base using hybrid search (vector + full-text).
|
||
|
||
Returns ranked results with content and metadata. When picture
|
||
content is in the result set and the driving skill model is
|
||
vision-capable, picture bytes are attached as ``BinaryContent``
|
||
parts so the model sees figures alongside text.
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of results.
|
||
query:
|
||
description: The search query.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Execute Python code in a sandboxed interpreter.
|
||
|
||
The code has access to search() and list_documents() functions
|
||
and a virtual filesystem at /documents/ with document content
|
||
and structure (metadata.json, content.txt, items.jsonl, toc.json
|
||
per document).
|
||
|
||
Use print() to output results. Variables persist between calls
|
||
within the same skill invocation.
|
||
name: execute_code
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
code:
|
||
description: Python code to execute.
|
||
type: string
|
||
required:
|
||
- code
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Register chunk IDs as citations for your answer.
|
||
|
||
Call this after searching, with the chunk_id values from search
|
||
results that support your answer.
|
||
name: cite
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
chunk_ids:
|
||
description: List of chunk_id values from search results.
|
||
items:
|
||
type: string
|
||
type: array
|
||
required:
|
||
- chunk_ids
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
body:
|
||
string: |+
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"No"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Q"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"4"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" So"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" total"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" revenue"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" across"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" all"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" quarterly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" reports"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" is"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" $"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"450"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"000"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" to"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176215,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" cite"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" chunk"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" IDs"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" for"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" each"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" revenue"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" statement"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" The"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" chunk"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" IDs"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" are"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":\n\n"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" fc"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"120"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"d"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"47"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"6"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"f"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"41"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"42"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"c"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"6"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"977"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"a"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"319"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"a"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"5"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-475","object":"chat.completion.chunk","created":1779176216,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"e"},"finish_reason":null}]}
|
||
|
||
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- text/event-stream
|
||
transfer-encoding:
|
||
- chunked
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '11586'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
messages:
|
||
- content: |-
|
||
You are a focused execution agent. Complete the following task using the skills and instructions provided.
|
||
|
||
## Task
|
||
|
||
What is the total revenue across all quarterly reports?
|
||
|
||
## Skill instructions
|
||
|
||
# Analysis
|
||
|
||
You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.
|
||
|
||
## Tools
|
||
|
||
### execute_code
|
||
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
||
|
||
Inside the code, these functions are available (use `await`):
|
||
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
||
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
||
|
||
Available modules: `json`, `re`, `math`, `pathlib`
|
||
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
||
|
||
### search
|
||
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
||
|
||
### cite
|
||
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.
|
||
|
||
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
|
||
|
||
## Document Filesystem (inside execute_code)
|
||
|
||
All documents are mounted as a virtual filesystem at `/documents/`:
|
||
|
||
```
|
||
/documents/{document_id}/
|
||
metadata.json # {"id", "title", "uri", "created_at"}
|
||
content.txt # Full document text
|
||
items.jsonl # Structured items (one JSON object per line)
|
||
toc.json # Section tree derived from heading_level
|
||
```
|
||
|
||
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.
|
||
|
||
### Reading files
|
||
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
||
|
||
```python
|
||
from pathlib import Path
|
||
import json
|
||
|
||
# Discover documents
|
||
for doc_dir in Path('/documents').iterdir():
|
||
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
||
print(meta['title'])
|
||
|
||
# Read full text
|
||
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
||
|
||
# Read and parse items
|
||
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
||
item = json.loads(line)
|
||
if item['label'] == 'table':
|
||
print(item['text'][:200])
|
||
```
|
||
|
||
### metadata.json
|
||
Document metadata: `id`, `title`, `uri`, `created_at`.
|
||
|
||
### content.txt
|
||
Full text content. Use for regex or keyword search across a whole document.
|
||
|
||
### items.jsonl
|
||
Structured document items. Each line is a JSON object with:
|
||
- `position`: sequential position in the document
|
||
- `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
|
||
- `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
|
||
- `text`: rendered content (tables are markdown with `|` columns)
|
||
- `page_numbers`: list of page numbers where the item appears
|
||
- `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
|
||
- `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.
|
||
|
||
### toc.json
|
||
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.
|
||
|
||
### Cross-referencing search results with items
|
||
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.
|
||
|
||
## Strategy
|
||
|
||
1. Search first.
|
||
2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
|
||
3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
||
4. Call `cite` with the chunk_ids that ground your answer before writing the final response.
|
||
|
||
You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.
|
||
|
||
## Important
|
||
|
||
- Variables persist between `execute_code` calls — you can search in one call and process results in the next
|
||
- Use `print()` to output results — the output is your only feedback
|
||
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
|
||
- Use `await` for all async functions inside execute_code (search, list_documents)
|
||
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
||
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.
|
||
|
||
## Guidelines
|
||
|
||
- Follow the skill instructions carefully
|
||
- Stay focused on the specific task described above
|
||
- Provide a clear, complete result
|
||
role: system
|
||
- content: What is the total revenue across all quarterly reports?
|
||
role: user
|
||
- content: null
|
||
reasoning: |+
|
||
The user asks: "What is the total revenue across all quarterly reports?" We need to find revenue figures across all quarterly reports. Likely there are multiple documents containing quarterly reports. We need to sum revenue numbers. We need to search for "quarterly report" or "Revenue" or "Q1", "Q2", etc. We need to find all quarterly reports. The question: "total revenue across all quarterly reports". So we need to sum revenue numbers across all quarterly reports. We need to find revenue numbers in each quarterly report. We need to sum them.
|
||
|
||
We need to search the knowledge base for "quarterly report revenue". Let's search.
|
||
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"quarterly report revenue","limit":10}'
|
||
name: search
|
||
id: call_h2b1nfrg
|
||
type: function
|
||
- content: |-
|
||
[fc120d47-6f41-42c6-977a-319a5eab577d] [rank 1 of 3]
|
||
Source: "Q1 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q1: Revenue was $100,000.
|
||
|
||
---
|
||
|
||
[79e14aa1-a426-4c47-9a3b-db35ddc96ba2] [rank 2 of 3]
|
||
Source: "Q3 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q3: Revenue was $200,000.
|
||
|
||
---
|
||
|
||
[6c9af4e6-3902-4359-b585-8f2d9d8f8615] [rank 3 of 3]
|
||
Source: "Q2 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q2: Revenue was $150,000.
|
||
role: tool
|
||
tool_call_id: call_h2b1nfrg
|
||
- content: null
|
||
reasoning: 'We have three quarterly reports: Q1, Q2, Q3. We need total revenue across all quarterly reports. Sum:
|
||
100k + 150k + 200k = 450k. But we should confirm if there is a Q4 report. Search again.'
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"limit":10,"query":"Q4 Report revenue"}'
|
||
name: search
|
||
id: call_7g40fgg1
|
||
type: function
|
||
- content: |-
|
||
[fc120d47-6f41-42c6-977a-319a5eab577d] [rank 1 of 3]
|
||
Source: "Q1 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q1: Revenue was $100,000.
|
||
|
||
---
|
||
|
||
[6c9af4e6-3902-4359-b585-8f2d9d8f8615] [rank 2 of 3]
|
||
Source: "Q2 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q2: Revenue was $150,000.
|
||
|
||
---
|
||
|
||
[79e14aa1-a426-4c47-9a3b-db35ddc96ba2] [rank 3 of 3]
|
||
Source: "Q3 Report"
|
||
Type: text
|
||
Content:
|
||
Sales report Q3: Revenue was $200,000.
|
||
role: tool
|
||
tool_call_id: call_7g40fgg1
|
||
- content: null
|
||
reasoning: |-
|
||
No Q4. So total revenue across all quarterly reports is $450,000. We need to cite the chunk IDs for each revenue statement. The chunk IDs are:
|
||
|
||
- fc120d47-6f41-42c6-977a-319a5eab577d (Q1)
|
||
- 79e14aa1-a426-4c47-9a3b-db35ddc96ba2 (Q3)
|
||
- 6c9af4e6-3902-4359-b585-8f2d9d8f8615 (Q2)
|
||
|
||
We need to cite these chunk IDs. Then answer: $450,000. Let's do cite.
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"chunk_ids":["fc120d47-6f41-42c6-977a-319a5eab577d","79e14aa1-a426-4c47-9a3b-db35ddc96ba2","6c9af4e6-3902-4359-b585-8f2d9d8f8615"]}'
|
||
name: cite
|
||
id: call_xyujpkia
|
||
type: function
|
||
- content: Registered 3 citation(s).
|
||
role: tool
|
||
tool_call_id: call_xyujpkia
|
||
model: gpt-oss
|
||
reasoning_effort: high
|
||
stream: true
|
||
stream_options:
|
||
include_usage: true
|
||
temperature: 0.3
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
Search the knowledge base using hybrid search (vector + full-text).
|
||
|
||
Returns ranked results with content and metadata. When picture
|
||
content is in the result set and the driving skill model is
|
||
vision-capable, picture bytes are attached as ``BinaryContent``
|
||
parts so the model sees figures alongside text.
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of results.
|
||
query:
|
||
description: The search query.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Execute Python code in a sandboxed interpreter.
|
||
|
||
The code has access to search() and list_documents() functions
|
||
and a virtual filesystem at /documents/ with document content
|
||
and structure (metadata.json, content.txt, items.jsonl, toc.json
|
||
per document).
|
||
|
||
Use print() to output results. Variables persist between calls
|
||
within the same skill invocation.
|
||
name: execute_code
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
code:
|
||
description: Python code to execute.
|
||
type: string
|
||
required:
|
||
- code
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Register chunk IDs as citations for your answer.
|
||
|
||
Call this after searching, with the chunk_id values from search
|
||
results that support your answer.
|
||
name: cite
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
chunk_ids:
|
||
description: List of chunk_id values from search results.
|
||
items:
|
||
type: string
|
||
type: array
|
||
required:
|
||
- chunk_ids
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
body:
|
||
string: |+
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"The"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" combined"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" revenue"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" reported"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" in"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" all"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" quarterly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" reports"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" is"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" **"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"$"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"450"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"000"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"**"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":"stop"}]}
|
||
|
||
data: {"id":"chatcmpl-766","object":"chat.completion.chunk","created":1779176276,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":2737,"completion_tokens":20,"total_tokens":2757}}
|
||
|
||
data: [DONE]
|
||
|
||
headers:
|
||
content-type:
|
||
- text/event-stream
|
||
transfer-encoding:
|
||
- chunked
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
version: 1
|
||
...
|