440 lines
52 KiB
YAML
440 lines
52 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:
|
|
- '490'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: Need run_search queries.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"class labels counts","limit":5}'
|
|
name: run_search
|
|
id: call_1bfuc6zy
|
|
index: 0
|
|
type: function
|
|
created: 1768998278
|
|
id: chatcmpl-145
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 35
|
|
prompt_tokens: 269
|
|
total_tokens: 304
|
|
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:
|
|
- '1418'
|
|
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 queries.
|
|
</think>
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"class labels counts","limit":5}'
|
|
name: run_search
|
|
id: call_1bfuc6zy
|
|
type: function
|
|
- content: 'Found 1 results for: class labels counts'
|
|
role: tool
|
|
tool_call_id: call_1bfuc6zy
|
|
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:
|
|
- '508'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: We need 1-2 alternative keyword queries.
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"limit":5,"query":"class label frequency"}'
|
|
name: run_search
|
|
id: call_yjdscl0e
|
|
index: 0
|
|
type: function
|
|
created: 1768998279
|
|
id: chatcmpl-776
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 40
|
|
prompt_tokens: 330
|
|
total_tokens: 370
|
|
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 label frequency
|
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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: 4
|
|
total_tokens: 4
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '1766'
|
|
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 queries.
|
|
</think>
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"class labels counts","limit":5}'
|
|
name: run_search
|
|
id: call_1bfuc6zy
|
|
type: function
|
|
- content: 'Found 1 results for: class labels counts'
|
|
role: tool
|
|
tool_call_id: call_1bfuc6zy
|
|
- content: |-
|
|
<think>
|
|
We need 1-2 alternative keyword queries.
|
|
</think>
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"limit":5,"query":"class label frequency"}'
|
|
name: run_search
|
|
id: call_yjdscl0e
|
|
type: function
|
|
- content: 'Found 1 results for: class label frequency'
|
|
role: tool
|
|
tool_call_id: call_yjdscl0e
|
|
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: 1768998279
|
|
id: chatcmpl-620
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 6
|
|
prompt_tokens: 396
|
|
total_tokens: 402
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
version: 1
|