OpenRouter 教程：用 LLM 从 PDF 中抽取表格

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OpenRouter 教程：用 LLM 从 PDF 中抽取表格

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OpenRouter æç¨ï¼ç¨ LLM ä» PDF ä¸æ½åè¡¨æ ¼ Â· AIHOT OpenRouterï¼Announcements Â· 2026-10-29 08:00 38 åéå...

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主题聚焦：OpenRouter 教程：用 LLM 从 PDF 中抽取表格
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OpenRouter æç¨ï¼ç¨ LLM ä» PDF ä¸æ½åè¡¨æ ¼ Â· AIHOT

OpenRouterï¼Announcements

Â·

2026-10-29 08:00

38 åéå

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AI è¯å

76

OpenRouter æç¨ï¼ç¨ LLM ä» PDF ä¸æ½åè¡¨æ ¼

How to Extract Tables from a PDF with an LLM

AI å¯¼è¯»

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code

import
base64
import
json
import
os
import
requests
API
=
"https://openrouter.ai/api/v1/chat/completions"
HEADERS
=
{
"Authorization"
:
f
"Bearer
{
os.environ[
'OPENROUTER_API_KEY'
]
}
"
}
def
rows_schema
(fields):
"""Wrap the fields of one row in a strict schema for {"rows": [...]}."""
return
{
"type"
:
"object"
,
"properties"
: {
"rows"
: {
"type"
:
"array"
,
"items"
: {
"type"
:
"object"
,
"properties"
: fields,
"required"
:
list
(fields),
"additionalProperties"
:
False
}}},
"required"
: [
"rows"
],
"additionalProperties"
:
False
,
}
def
extract_table
(pdf_path, schema, model
=
"anthropic/claude-haiku-5.5"
,
engine
=
None
, max_tokens
=
8000
):
with
open
(pdf_path,
"rb"
)
as
f:
data_url
=
"data:application/pdf;base64,"
+
base64.b64encode(f.read()).decode()
body
=
{
"model"
: model,
"max_tokens"
: max_tokens,
"messages"
: [{
"role"
:
"user"
,
"content"
: [
{
"type"
:
"text"
,
"text"
:
"Extract every row of the table in this PDF. "
"Include rows from every page. "
"Return only rows that are in the document."
},
{
"type"
:
"file"
,
"file"
: {
"filename"
: os.path.basename(pdf_path),
"file_data"
: data_url}},
]}],
"response_format"
: {
"type"
:
"json_schema"
,
"json_schema"
: {
"name"
:
"table"
,
"strict"
:
True
,
"schema"
: schema}},
}
if
engine:
body[
"plugins"
]
=
[{
"id"
:
"file-parser"
,
"pdf"
: {
"engine"
: engine}}]
response
=
requests.post(
API
,
headers
=
HEADERS
,
json
=
body,
timeout
=
300
)
response.raise_for_status()
data
=
response.json()
choice
=
data[
"choices"
][
0
]
if
choice[
"finish_reason"
]
==
"length"
:
raise
ValueError
(
f
"Output stopped at max_tokens=
{
max_tokens
}
. "
"Raise it or split the PDF."
)
rows
=
json.loads(choice[
"message"
][
"content"
])[
"rows"
]
if
not
rows:
raise
ValueError
(
"No rows came back. If this PDF is a scan, use a model "
"that reads PDFs or engine='mistral-ocr'."
)
return
rows, data[
"usage"
][
"cost"
]
LINE_ITEMS
=
rows_schema({
"sku"
: {
"type"
:
"string"
},
"description"
: {
"type"
:
"string"
},
"qty"
: {
"type"
:
"integer"
},
"unit_price"
: {
"type"
:
"number"
,
"description"
:
"USD, no currency symbol or thousands separator"
},
"amount"
: {
"type"
:
"number"
,
"description"
:
"USD, no currency symbol or thousands separator"
},
})

å¨åç¥¨ä¸è¿è¡è¯¥å½æ°ã

code

from
extract_tables
import
extract_table,
LINE_ITEMS
rows, cost
=
extract_table(
"invoice.pdf"
,
LINE_ITEMS
)
for
row
in
rows[:
3
]:
print
(row)
print
(
f
"
{len
(rows)
}
rows, $
{
cost
:.5f
}
"
)

code

{'sku': 'NW-4100', 'description': 'Hex bolt M8x40', 'qty': 100, 'unit_price': 60.67, 'amount': 6067.0}
{'sku': 'NW-4107', 'description': 'Flange nut M8', 'qty': 2, 'unit_price': 41.72, 'amount': 83.44}
{'sku': 'NW-4114', 'description': 'Bearing 6204-2RS', 'qty': 10, 'unit_price': 52.59, 'amount': 525.9}
10 rows, $0.00067

ç¬¬ 2 æ¥ï¼å¦ä½å¤çåå¹¶åå æ ¼ãæ¬å·åèæ³¨ï¼

code

from
extract_tables
import
extract_table, rows_schema
THOUSANDS
=
{
"type"
:
"integer"
,
"description"
:
"Thousands of USD. A value in parentheses, like (1,204), is negative: -1204."
}
QUARTERLY
=
rows_schema({
"segment"
: {
"type"
:
"string"
,
"description"
:
"Segment name without footnote markers"
},
"q1"
:
THOUSANDS
,
"q2"
:
THOUSANDS
,
"q3"
:
THOUSANDS
,
"q4"
:
THOUSANDS
,
"fy_total"
:
THOUSANDS
,
})
rows, cost
=
extract_table(
"quarterly.pdf"
,
QUARTERLY
)
for
row
in
rows:
print
(row)

code

{'segment': 'Inference API', 'q1': 1813, 'q2': 7299, 'q3': 1612, 'q4': 4422, 'fy_total': 15146}
{'segment': 'Enterprise', 'q1': 1563, 'q2': 2981, 'q3': 5544, 'q4': 7667, 'fy_total': 17755}
{'segment': 'Data products', 'q1': 3163, 'q2': 9658, 'q3': 2729, 'q4': 5854, 'fy_total': 21404}
{'segment': 'Hardware resale', 'q1': 3761, 'q2': -3878, 'q3': -9774, 'q4': -1776, 'fy_total': -11667}
{'segment': 'Professional services', 'q1': 8933, 'q2': 9511, 'q3': 7805, 'q4': 5946, 'fy_total': 32195}
{'segment': 'Total operating income', 'q1': 19233, 'q2': 25571, 'q3': 7916, 'q4': 22113, 'fy_total': 74833}

å¨ Hardware resale è¿ä¸è¡ä¸ï¼(3,878) è¿åæäº -3878ï¼èä¸ä¸¤ä¸ªçæ®µåç§°éçèæ³¨æ è®°é½ä¸è§äºã

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è½è¯»å PDF çæ¨¡åã ä¸è®¾ç½® engine ãClaude Haiku 5.5ãGPT-6 Luna å Gemini 3.8 Flash é½è½è¯»åæä»¶ï¼èä¸è¿ä¸ä¸ªæ¨¡åé½å¤çäºæä»¬çä¸¤æ¬¡æ«æãPDF æè¾å ¥ token è®¡è´¹ã

ä¸è½è¯»å PDF çæ¨¡åï¼é å OCRã ä½¿ç¨ engine="mistral-ocr" ï¼æè ä¸è®¾ç½®å®ãå®ä¼å å¯¹æ¯ä¸é¡µè¿è¡å å¦åç¬¦è¯å«ï¼OCRï¼ï¼å¨æ¨¡å token è´¹ç¨ä¹å¤ï¼æ¯ 1,000 é¡µæ¶è´¹ $2ã

ä¸è½è¯»å PDF çæ¨¡åï¼é åå è´¹è§£æå¨ã åªæå½æ¯ä¸ª PDF é½æææ¬å±æ¶æä½¿ç¨ engine="cloudflare-ai" ãå¯¼åºçåç¥¨åæ¥åæææ¬å±ãæ«æä»¶æ²¡æã

ä¸é¢æ¯ææ«æçéè´è®¢ååå«ç¨æ¯ç§è§£æå¨åéç» deepseek/deepseek-v4.1-flash çç»æãè¯¥æ¨¡åæ¥åå¾åä½ä¸æ¥åæä»¶ï¼æä»¥ PDF å¿ é¡»ç»è¿è§£æå¨ã

code

from
extract_tables
import
extract_table,
LINE_ITEMS
# A model that can't read PDFs, with OCR in front of it
rows, cost
=
extract_table(
"scanned.pdf"
,
LINE_ITEMS
,
model
=
"deepseek/deepseek-v4.1-flash"
,
engine
=
"mistral-ocr"
)
print
(
f
"mistral-ocr:
{len
(rows)
}
rows, $
{
cost
:.5f
}
"
)
# The same model with the free text parser
rows, cost
=
extract_table(
"scanned.pdf"
,
LINE_ITEMS
,
model
=
"deepseek/deepseek-v4.1-flash"
,
engine
=
"cloudflare-ai"
)

code

mistral-ocr: 6 rows, $0.00211
ValueError: No rows came back. If this PDF is a scan, use a model that reads PDFs or engine='mistral-ocr'.

code

400 {"error":{"message":"plugins.0.pdf.engine: Invalid input","code":400}}

åçå¼æåªéç¨äºè½è¯»åæä»¶çæ¨¡åãå½æä»¬è¿æ¯å¨ DeepSeek V4.1 Flash ä¸è®¾ç½® engine="native" æ¶ï¼è¯·æ±æåäºä¸æ¬¡ï¼ç¶åä¸¤æ¬¡å æä¾åéè¯¯èå¤±è´¥ã

code

400 Provider returned error: "The request was rejected. Possible causes: input exceeds the model's maximum context length, or the request contains invalid parameters."

ç¬¬ 4 æ¥ï¼ä¸ºä»ä¹æçé¿è¡¨æ ¼è¿åæ¶è¢«æªæäºï¼

code

from
extract_tables
import
extract_table, rows_schema
SHIPMENTS
=
rows_schema({
"date"
: {
"type"
:
"string"
,
"description"
:
"YYYY-MM-DD"
},
"shipment_id"
: {
"type"
:
"string"
},
"origin"
: {
"type"
:
"string"
},
"destination"
: {
"type"
:
"string"
},
"weight_kg"
: {
"type"
:
"number"
,
"description"
:
"No thousands separator"
},
"status"
: {
"type"
:
"string"
},
})
rows, cost
=
extract_table(
"shipments.pdf"
,
SHIPMENTS
,
max_tokens
=
600
)

code

ValueError: Output stopped at max_tokens=600. Raise it or split the PDF.

code

JSONDecodeError: Unterminated string starting at: line 1 column 627 (char 626)

ç¬¬ 5 æ¥ï¼å¦ä½æè¡åå ¥ CSVï¼

æ¯ä¸è¡é½æ schema ä¸å®ä¹çé®ï¼å æ¤ csv.DictWriter å¯ä»¥ä» schema ä¸è·åååï¼å¹¶æåæ ·åå ¥åè¡ãExcel å¯ä»¥ç´æ¥æå¼ç»æã

code

import
csv
from
extract_tables
import
extract_table
# Define SHIPMENTS here, copied from step 4
rows, cost
=
extract_table(
"shipments.pdf"
,
SHIPMENTS
,
model
=
"openai/gpt-6-luna"
)
with
open
(
"shipments.csv"
,
"w"
,
newline
=
""
)
as
f:
fieldnames
=
list
(
SHIPMENTS
[
"properties"
][
"rows"
][
"items"
][
"properties"
])
writer
=
csv.DictWriter(f,
fieldnames
=
fieldnames)
writer.writeheader()
writer.writerows(rows)
print
(
f
"Wrote
{len
(rows)
}
rows to shipments.csv ($
{
cost
:.5f
}
)"
)

code

Wrote 58 rows to shipments.csv ($0.00130)
date,shipment_id,origin,destination,weight_kg,status
2026-09-01,SHP-88200,Savannah,Valencia,8754.6,In transit
2026-09-01,SHP-88213,Busan,Los Angeles,2074.7,Customs hold

ä½ åºè¯¥ä½¿ç¨åªä¸ªæ¨¡åï¼

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OCR è·¯çº¿çææ¬é«äº DeepSeek ä½ token ä»·æ ¼ææç¤ºçæ°´å¹³ï¼å ä¸ºæ¯ 1,000 é¡µ 2 ç¾å ç OCR è´¹ç¨ä¼å å°æ¯ä¸ªè¯·æ±ä¸ï¼ä½ å¯ä»¥å¨ usage.cost ä¸çå°è¿ä¸ç¹ãå®çä¸¤å¤éè¯¯æ¯ Enterprise å Hardware resale ä¸çèæ³¨æ è®°ï¼å°½ç®¡ schema è¦æ±ç§»é¤å®ä»¬ï¼å®å´ä¿çäºãå è´¹è§£æå¨æ£ç¡®å¤çäºå ¨é¨ä¸ä¸ªæ°å PDFï¼ä½ä¸¤ä¸ªæ«æä»¶å®å ¨éè¯¯ã

ä»ä¹æ¶å PDF åºå°±è¶³å¤äºï¼

TypeScript ä¸çç¸åè¯·æ±

è¯·æ±ä½å¨ä»»ä½è¯è¨ä¸é½æ¯ç¸åçãå°å ¶ä¿åä¸º extract.mjs ï¼å¹¶å¨ Node.js 18 ææ´é«çæ¬ä¸ä½¿ç¨ node extract.mjs invoice.pdf è¿è¡å®ã

code

import
{ readFileSync }
from
"node:fs"
;
import
{ basename }
from
"node:path"
;
const
path
=
process.argv[
2
];
const
fileData
=
`data:application/pdf;base64,${
readFileSync
(
path
).
toString
(
"base64"
)
}`
;
const
money
=
{ type:
"number"
, description:
"USD, no currency symbol or thousands separator"
};
const
schema
=
{
type:
"object"
,
properties: { rows: { type:
"array"
, items: {
type:
"object"
,
properties: { sku: { type:
"string"
}, description: { type:
"string"
},
qty: { type:
"integer"
}, unit_price: money, amount: money },
required: [
"sku"
,
"description"
,
"qty"
,
"unit_price"
,
"amount"
],
additionalProperties:
false
} } },
required: [
"rows"
],
additionalProperties:
false
,
};
const
res
=
await
fetch
(
"https://openrouter.ai/api/v1/chat/completions"
, {
method:
"POST"
,
headers: { Authorization:
`Bearer ${
process
.
env
.
OPENROUTER_API_KEY
}`
,
"Content-Type"
:
"application/json"
},
body:
JSON
.
stringify
({
model:
"openai/gpt-6-luna"
,
messages: [{ role:
"user"
, content: [
{ type:
"text"
, text:
"Extract every row of the table in this PDF. Include rows from every page. Return only rows that are in the document."
},
{ type:
"file"
, file: { filename:
basename
(path), file_data: fileData } },
] }],
response_format: { type:
"json_schema"
, json_schema: { name:
"table"
, strict:
true
, schema } },
}),
});
if
(
!
res.ok)
throw
new
Error
(
`HTTP ${
res
.
status
}: ${
await
res
.
text
()
}`
);
const
data
=
await
res.
json
();
const
choice
=
data.choices[
0
];
if
(choice.finish_reason
!==
"stop"
)
throw
new
Error
(
`Unexpected finish_reason: ${
choice
.
finish_reason
}`
);
const
{
rows
}
=
JSON
.
parse
(choice.message.content);
if
(rows.
length
===
0
)
throw
new
Error
(
"No rows came back."
);
console.
log
(rows.
length
+
" rows, finish_reason="
+
choice.finish_reason
+
", $"
+
data.usage.cost);

code

10 rows, finish_reason=stop, $0.00020275

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