601 lines
69 KiB
YAML
601 lines
69 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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- '730'
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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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- |-
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DocLayNet Dataset - Class Labels
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DocLayNet defines 11 distinct class labels for document layout analysis:
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1. Caption - Text describing figures or tables
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2. Footnote - Notes at the bottom of pages
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3. Formula - Mathematical expressions
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4. List-item - Items in bulleted or numbered lists
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5. Page-footer - Footer content on pages
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6. Page-header - Header content on pages
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7. Picture - Images and diagrams
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8. Section-header - Headings for document sections
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9. Table - Tabular data
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10. Text - Regular paragraph text (highest count: 510,377 instances)
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11. Title - Document titles
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The Text class has the highest count with 510,377 instances in the dataset.
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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: 166
|
|
total_tokens: 166
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '1090'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
You are a search query optimizer. You MUST use the run_search tool to execute searches.
|
|
|
|
For each user request:
|
|
1. Use the run_search tool with the original query
|
|
2. Use run_search again with 1-2 alternative keyword queries
|
|
3. Keep all queries SHORT (2-5 words)
|
|
4. After all tool calls complete, respond "Search complete"
|
|
|
|
You can optionally specify a limit parameter (default 5).
|
|
|
|
IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
|
|
role: system
|
|
- content: Tell me about class labels and their counts
|
|
role: user
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Run a single search query against the knowledge base.
|
|
name: run_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
description: 'Number of results to fetch (default: 5)'
|
|
query:
|
|
description: The search query
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '516'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: Need run_search original query, then alternatives.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"class labels counts","limit":5}'
|
|
name: run_search
|
|
id: call_sq8spu1c
|
|
index: 0
|
|
type: function
|
|
created: 1769793930
|
|
id: chatcmpl-745
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 39
|
|
prompt_tokens: 269
|
|
total_tokens: 308
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '89'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- class labels counts
|
|
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: 4
|
|
total_tokens: 4
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '1444'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
You are a search query optimizer. You MUST use the run_search tool to execute searches.
|
|
|
|
For each user request:
|
|
1. Use the run_search tool with the original query
|
|
2. Use run_search again with 1-2 alternative keyword queries
|
|
3. Keep all queries SHORT (2-5 words)
|
|
4. After all tool calls complete, respond "Search complete"
|
|
|
|
You can optionally specify a limit parameter (default 5).
|
|
|
|
IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
|
|
role: system
|
|
- content: Tell me about class labels and their counts
|
|
role: user
|
|
- content: |-
|
|
<think>
|
|
Need run_search original query, then alternatives.
|
|
</think>
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"class labels counts","limit":5}'
|
|
name: run_search
|
|
id: call_sq8spu1c
|
|
type: function
|
|
- content: 'Found 1 results for: class labels counts'
|
|
role: tool
|
|
tool_call_id: call_sq8spu1c
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Run a single search query against the knowledge base.
|
|
name: run_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
description: 'Number of results to fetch (default: 5)'
|
|
query:
|
|
description: The search query
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '456'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"limit":5,"query":"label class distribution"}'
|
|
name: run_search
|
|
id: call_min0izqo
|
|
index: 0
|
|
type: function
|
|
created: 1769793931
|
|
id: chatcmpl-544
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 24
|
|
prompt_tokens: 334
|
|
total_tokens: 358
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '94'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- label class distribution
|
|
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: 4
|
|
total_tokens: 4
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '1741'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
You are a search query optimizer. You MUST use the run_search tool to execute searches.
|
|
|
|
For each user request:
|
|
1. Use the run_search tool with the original query
|
|
2. Use run_search again with 1-2 alternative keyword queries
|
|
3. Keep all queries SHORT (2-5 words)
|
|
4. After all tool calls complete, respond "Search complete"
|
|
|
|
You can optionally specify a limit parameter (default 5).
|
|
|
|
IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
|
|
role: system
|
|
- content: Tell me about class labels and their counts
|
|
role: user
|
|
- content: |-
|
|
<think>
|
|
Need run_search original query, then alternatives.
|
|
</think>
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"class labels counts","limit":5}'
|
|
name: run_search
|
|
id: call_sq8spu1c
|
|
type: function
|
|
- content: 'Found 1 results for: class labels counts'
|
|
role: tool
|
|
tool_call_id: call_sq8spu1c
|
|
- content: null
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"limit":5,"query":"label class distribution"}'
|
|
name: run_search
|
|
id: call_min0izqo
|
|
type: function
|
|
- content: 'Found 1 results for: label class distribution'
|
|
role: tool
|
|
tool_call_id: call_min0izqo
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Run a single search query against the knowledge base.
|
|
name: run_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
description: 'Number of results to fetch (default: 5)'
|
|
query:
|
|
description: The search query
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '453'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"limit":5,"query":"class frequency table"}'
|
|
name: run_search
|
|
id: call_apvul2uz
|
|
index: 0
|
|
type: function
|
|
created: 1769793933
|
|
id: chatcmpl-253
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 24
|
|
prompt_tokens: 378
|
|
total_tokens: 402
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '91'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- class frequency table
|
|
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: 4
|
|
total_tokens: 4
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '2032'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
You are a search query optimizer. You MUST use the run_search tool to execute searches.
|
|
|
|
For each user request:
|
|
1. Use the run_search tool with the original query
|
|
2. Use run_search again with 1-2 alternative keyword queries
|
|
3. Keep all queries SHORT (2-5 words)
|
|
4. After all tool calls complete, respond "Search complete"
|
|
|
|
You can optionally specify a limit parameter (default 5).
|
|
|
|
IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
|
|
role: system
|
|
- content: Tell me about class labels and their counts
|
|
role: user
|
|
- content: |-
|
|
<think>
|
|
Need run_search original query, then alternatives.
|
|
</think>
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"class labels counts","limit":5}'
|
|
name: run_search
|
|
id: call_sq8spu1c
|
|
type: function
|
|
- content: 'Found 1 results for: class labels counts'
|
|
role: tool
|
|
tool_call_id: call_sq8spu1c
|
|
- content: null
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"limit":5,"query":"label class distribution"}'
|
|
name: run_search
|
|
id: call_min0izqo
|
|
type: function
|
|
- content: 'Found 1 results for: label class distribution'
|
|
role: tool
|
|
tool_call_id: call_min0izqo
|
|
- content: null
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"limit":5,"query":"class frequency table"}'
|
|
name: run_search
|
|
id: call_apvul2uz
|
|
type: function
|
|
- content: 'Found 1 results for: class frequency table'
|
|
role: tool
|
|
tool_call_id: call_apvul2uz
|
|
model: gpt-oss
|
|
reasoning_effort: low
|
|
stream: false
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Run a single search query against the knowledge base.
|
|
name: run_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
description: 'Number of results to fetch (default: 5)'
|
|
query:
|
|
description: The search query
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '298'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: stop
|
|
index: 0
|
|
message:
|
|
content: Search complete
|
|
role: assistant
|
|
created: 1769793934
|
|
id: chatcmpl-983
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 6
|
|
prompt_tokens: 422
|
|
total_tokens: 428
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
version: 1
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