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add mcp, batching

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This commit is contained in:
Paul Walko
2025-12-07 04:35:21 +01:00
parent 30f68a9d04
commit d6bc34d138
14 changed files with 1973 additions and 138 deletions
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8
docker.sh → 9030_docker.sh
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@@ -7,11 +7,11 @@ up () {
--detach \
--name cp2-pg \
--restart unless-stopped \
--env-file $HOME/scripts-private/lech/cavepedia-v2/cp2-pg.env \
--volume /mammoth/cp2/cp2-pg/data:/var/lib/postgresql/data:rw \
--publish 127.0.0.1:4010:5432 \
--env-file $HOME/scripts-private/loser/cavepedia-v2/cp2-pg.env \
--volume /texas/cp2/cp2-pg/18/data:/var/lib/postgresql/18/docker:rw \
--publish [::1]:9030:5432 \
--network pew-net \
pgvector/pgvector:pg17
pgvector/pgvector:pg18
}
down () {

1
mcp/.python-version Normal file
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@@ -0,0 +1 @@
3.11

5
mcp/README.md Normal file
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@@ -0,0 +1,5 @@
# cavepedia-v2 mcp
# todo
- signout endpoint
- auth

BIN
mcp/__pycache__/main.cpython-311.pyc Normal file
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mcp/__pycache__/server.cpython-311.pyc Normal file
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13
mcp/pyproject.toml Normal file
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@@ -0,0 +1,13 @@
[project]
name = "cavepediav2-mcp"
version = "0.1.0"
description = "MCP for cavepediav2"
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"asyncio>=4.0.0",
"cohere>=5.20.0",
"dotenv>=0.9.9",
"fastmcp>=2.13.3",
"psycopg[binary]>=3.3.2",
]

48
mcp/search.py Normal file
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@@ -0,0 +1,48 @@
from pgvector.psycopg import register_vector, Bit
from psycopg.rows import dict_row
from urllib.parse import unquote
import anthropic
import cohere
import dotenv
import datetime
import json
import minio
import numpy as np
import os
import psycopg
import time
dotenv.load_dotenv('/home/paul/scripts-private/lech/cavepedia-v2/poller.env')
COHERE_API_KEY = os.getenv('COHERE_API_KEY')
co = cohere.ClientV2(COHERE_API_KEY)
conn = psycopg.connect(
host='127.0.0.1',
port=4010,
dbname='cavepediav2_db',
user='cavepediav2_user',
password='cavepediav2_pw',
row_factory=dict_row,
)
def embed(text, input_type):
resp = co.embed(
texts=[text],
model='embed-v4.0',
input_type=input_type,
embedding_types=['float'],
)
return resp.embeddings.float[0]
def search():
query = 'links trip with not more than 2 people'
query_embedding = embed(query, 'search_query')
rows = conn.execute('SELECT * FROM embeddings ORDER BY embedding <=> %s::vector LIMIT 5', (query_embedding,)).fetchall()
for row in rows:
print(row['bucket'])
print(row['key'])
if __name__ == '__main__':
search()

79
mcp/server.py Normal file
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@@ -0,0 +1,79 @@
from fastmcp import FastMCP
from fastmcp.server.auth.providers.auth0 import Auth0Provider
from psycopg.rows import dict_row
import cohere
import dotenv
import psycopg
import os
dotenv.load_dotenv('/home/pew/scripts-private/loser/cavepedia-v2/poller.env')
COHERE_API_KEY = os.getenv('COHERE_API_KEY')
co = cohere.ClientV2(COHERE_API_KEY)
conn = psycopg.connect(
host='::1',
port=9030,
dbname='cavepediav2_db',
user='cavepediav2_user',
password='cavepediav2_pw',
row_factory=dict_row,
)
# The Auth0Provider utilizes Auth0 OIDC configuration
auth_provider = Auth0Provider(
config_url="https://dev-jao4so0av61ny4mr.us.auth0.com/.well-known/openid-configuration",
client_id="oONcxma5PNFwYLhrDC4o0PUuAmqDekzM",
client_secret="4Z7Wl12ALEtDmNAoERQe7lK2YD9x6jz7H25FiMxRp518dnag-IS2NLLScnmbe4-b",
audience="https://dev-jao4so0av61ny4mr.us.auth0.com/me/",
base_url="https://mcp.caving.dev",
# redirect_path="/auth/callback" # Default value, customize if needed
)
mcp = FastMCP("Cavepedia MCP")
def embed(text, input_type):
resp = co.embed(
texts=[text],
model='embed-v4.0',
input_type=input_type,
embedding_types=['float'],
)
return resp.embeddings.float[0]
def search(query) -> list[dict]:
query_embedding = embed(query, 'search_query')
rows = conn.execute('SELECT * FROM embeddings WHERE embedding IS NOT NULL ORDER BY embedding <=> %s::vector LIMIT 5', (query_embedding,)).fetchall()
docs = []
for row in rows:
docs.append({ 'key': row['key'], 'content': row['content']})
return docs
@mcp.tool
def get_cave_location(cave: str, state: str, county: str) -> list[dict]:
"""Lookup cave location as coordinates. Returns up to 5 matches, ordered by most to least relevant."""
return search(f'{cave} Location, latitude, Longitude. Located in {state} and {county} county.')
@mcp.tool
def general_caving_information(query: str) -> list[dict]:
"""General purpose endpoint for any topic related to caves. Returns up to 5 mates, orderd by most to least relevant."""
return search(query)
# Add a protected tool to test authentication
@mcp.tool
async def get_token_info() -> dict:
"""Returns information about the Auth0 token."""
from fastmcp.server.dependencies import get_access_token
token = get_access_token()
return {
"issuer": token.claims.get("iss"),
"audience": token.claims.get("aud"),
"scope": token.claims.get("scope")
}
if __name__ == "__main__":
mcp.run(transport='http', host='::1', port=9031)

27
mcp/test/client.py Normal file
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@@ -0,0 +1,27 @@
import asyncio
from fastmcp import Client
client = Client("http://[::1]:8031/mcp")
async def test_get_cave_location(cave: str, state: str, county: str):
async with client:
resp = await client.call_tool("get_cave_location", {"cave": cave, "state": state, "county": county})
print()
print(cave)
for item in resp.structured_content['result']:
print(item)
async def test_general_caving_information(query: str):
async with client:
resp = await client.call_tool("general_caving_information", {"query": query})
print()
print(query)
for item in resp.structured_content['result']:
print(item)
asyncio.run(test_get_cave_location("Nellies Cave", "VA", "Montgomery"))
asyncio.run(test_get_cave_location("links cave", "VA", "Giles"))
#asyncio.run(test_get_cave_location("new river", "VA", "Giles"))
#asyncio.run(test_get_cave_location("tawneys", "VA", "Giles"))
#asyncio.run(test_get_cave_location("staty fork", "WV", "Pocahontas"))
#asyncio.run(test_general_caving_information("broken sunnto"))

1483
mcp/uv.lock generated Normal file
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53
poller/getobject.py
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@@ -1,53 +0,0 @@
from pgvector.psycopg import register_vector, Bit
from psycopg.rows import dict_row
from urllib.parse import unquote
from pypdf import PdfReader, PdfWriter
import anthropic
import cohere
import dotenv
import datetime
import io
import json
import minio
import numpy as np
import os
import psycopg
import time
import logging
from pythonjsonlogger.json import JsonFormatter
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logHandler = logging.StreamHandler()
formatter = JsonFormatter("{asctime}{message}", style="{")
logHandler.setFormatter(formatter)
logger.addHandler(logHandler)
#####
dotenv.load_dotenv('/home/paul/scripts-private/lech/cavepedia-v2/poller.env')
COHERE_API_KEY = os.getenv('COHERE_API_KEY')
MINIO_ACCESS_KEY = os.getenv('MINIO_ACCESS_KEY')
MINIO_SECRET_KEY = os.getenv('MINIO_SECRET_KEY')
s3 = minio.Minio(
's3.bigcavemaps.com',
access_key=MINIO_ACCESS_KEY,
secret_key=MINIO_SECRET_KEY,
region='kansascity',
)
def getobject():
bucket = 'cavepedia-v2'
key = 'public/var/fyi/VAR-FYI 1982-01.pdf'
with s3.get_object(bucket, key) as obj:
with open('/tmp/file.pdf', 'wb') as f:
while True:
chunk = obj.read(1024)
if not chunk:
break
f.write(chunk)
if __name__ == '__main__':
getobject()

319
poller/main.py
View File

@@ -1,8 +1,10 @@
from cohere.core.api_error import ApiError
from pgvector.psycopg import register_vector, Bit
from psycopg.rows import dict_row
from urllib.parse import unquote
from pypdf import PdfReader, PdfWriter
import anthropic
import boto3
import cohere
import dotenv
import datetime
@@ -25,22 +27,23 @@ logger.addHandler(logHandler)
#####
dotenv.load_dotenv('/home/paul/scripts-private/lech/cavepedia-v2/poller.env')
dotenv.load_dotenv('/home/pew/scripts-private/loser/cavepedia-v2/poller.env')
COHERE_API_KEY = os.getenv('COHERE_API_KEY')
MINIO_ACCESS_KEY = os.getenv('MINIO_ACCESS_KEY')
MINIO_SECRET_KEY = os.getenv('MINIO_SECRET_KEY')
S3_ACCESS_KEY = os.getenv('S3_ACCESS_KEY')
S3_SECRET_KEY = os.getenv('S3_SECRET_KEY')
s3 = minio.Minio(
's3.bigcavemaps.com',
access_key=MINIO_ACCESS_KEY,
secret_key=MINIO_SECRET_KEY,
region='kansascity',
s3 = boto3.client(
's3',
aws_access_key_id=S3_ACCESS_KEY,
aws_secret_access_key=S3_SECRET_KEY,
endpoint_url='https://s3.bigcavemaps.com',
region_name='eu',
)
co = cohere.ClientV2(COHERE_API_KEY)
co = cohere.ClientV2(api_key=COHERE_API_KEY)
conn = psycopg.connect(
host='127.0.0.1',
port=4010,
host='::1',
port=9030,
dbname='cavepediav2_db',
user='cavepediav2_user',
password='cavepediav2_pw',
@@ -51,14 +54,33 @@ conn = psycopg.connect(
# events table is created by minio up creation of event destination
def create_tables():
commands = (
"""
CREATE TABLE IF NOT EXISTS metadata (
id SERIAL PRIMARY KEY,
bucket TEXT,
key TEXT,
split BOOLEAN DEFAULT FALSE,
UNIQUE(bucket, key)
)
""",
"""
CREATE TABLE IF NOT EXISTS batches (
id SERIAL PRIMARY KEY,
platform TEXT,
batch_id TEXT,
type TEXT,
done BOOLEAN DEFAULT FALSE
)
""",
"CREATE EXTENSION IF NOT EXISTS vector",
"""
CREATE TABLE IF NOT EXISTS embeddings (
id SERIAL PRIMARY KEY,
bucket TEXT,
key TEXT,
content TEXT,
embedding vector(1536),
PRIMARY KEY (bucket, key)
UNIQUE(bucket, key)
)
""")
for command in commands:
@@ -66,53 +88,118 @@ def create_tables():
conn.commit()
register_vector(conn)
## splitting
def split_pdfs():
rows = conn.execute('SELECT * FROM events')
def import_files():
"""Scan import bucket for any new files; move them to the files bucket and add to db; delete from import bucket"""
BUCKET_IMPORT = 'cavepediav2-import'
BUCKET_FILES = 'cavepediav2-files'
# get new files; add to db, sync to main bucket; delete from import bucket
response = s3.list_objects_v2(Bucket=BUCKET_IMPORT)
if 'Contents' in response:
for obj in response['Contents']:
if obj['Key'].endswith('/'):
continue
s3.copy_object(
CopySource={'Bucket': BUCKET_IMPORT, 'Key': obj['Key']},
Bucket=BUCKET_FILES,
Key=obj['Key'],
)
conn.execute('INSERT INTO metadata (bucket, key) VALUES(%s, %s);', (BUCKET_FILES, obj['Key']))
conn.commit()
s3.delete_object(
Bucket=BUCKET_IMPORT,
Key=obj['Key'],
)
def split_files():
"""Split PDFs into single pages for easier processing"""
BUCKET_PAGES = 'cavepediav2-pages'
rows = conn.execute("SELECT COUNT(*) FROM metadata WHERE split = false")
row = rows.fetchone()
logger.info(f'Found {row["count"]} files to split.')
rows = conn.execute('SELECT * FROM metadata WHERE split = false')
for row in rows:
bucket = row['bucket']
key = row['key']
with conn.cursor() as cur:
for record in row['value']['Records']:
bucket = record['s3']['bucket']['name']
key = record['s3']['object']['key']
key = unquote(key)
key = key.replace('+',' ')
logger.info(f'SPLITTING bucket: {bucket}, key: {key}')
logger.info(f'SPLITTING bucket: {bucket}, key: {key}')
##### get pdf #####
s3.download_file(bucket, key, '/tmp/file.pdf')
##### get pdf #####
with s3.get_object(bucket, key) as obj:
with open('/tmp/file.pdf', 'wb') as f:
while True:
chunk = obj.read(1024)
if not chunk:
break
f.write(chunk)
##### split #####
with open('/tmp/file.pdf', 'rb') as f:
reader = PdfReader(f)
##### split #####
with open('/tmp/file.pdf', 'rb') as f:
reader = PdfReader(f)
for i in range(len(reader.pages)):
writer = PdfWriter()
writer.add_page(reader.pages[i])
for i in range(len(reader.pages)):
writer = PdfWriter()
writer.add_page(reader.pages[i])
with io.BytesIO() as bs:
writer.write(bs)
bs.seek(0)
s3.put_object(f'{bucket}-pages', f'{key}/page-{i + 1}.pdf', bs, len(bs.getvalue()))
cur.execute('INSERT INTO embeddings (bucket, key) VALUES (%s, %s);', (f'{bucket}-pages', f'{key}/page-{i + 1}.pdf'))
cur.execute('DELETE FROM events WHERE key = %s', (row['key'],))
with io.BytesIO() as bs:
writer.write(bs)
bs.seek(0)
s3.put_object(
Bucket=BUCKET_PAGES,
Key=f'{key}/page-{i + 1}.pdf',
Body=bs.getvalue()
)
cur.execute('INSERT INTO embeddings (bucket, key) VALUES (%s, %s);', (BUCKET_PAGES, f'{key}/page-{i + 1}.pdf'))
cur.execute('UPDATE metadata SET SPLIT = true WHERE id = %s', (row['id'],));
conn.commit()
## processing
def ocr_create_message(id, bucket, key):
"""Create message to send to claude"""
url = s3.generate_presigned_url(
'get_object',
Params={
'Bucket': bucket,
'Key': unquote(key)
},
)
message = {
'custom_id': f'doc-{id}',
'params': {
'model': 'claude-haiku-4-5',
'max_tokens': 4000,
'temperature': 1,
'messages': [
{
'role': 'user',
'content': [
{
'type': 'document',
'source': {
'type': 'url',
'url': url
}
},
{
'type': 'text',
'text': 'Extract all text from this document. Do not include any summary or conclusions of your own.'
}
]
}
]
}
}
return message
def ocr(bucket, key):
url = s3.presigned_get_object(bucket, unquote(key))
"""Gets OCR content of pdfs"""
url = s3.generate_presigned_url(
'get_object',
Params={
'Bucket': bucket,
'Key': unquote(key)
},
)
client = anthropic.Anthropic()
message = client.messages.create(
model='claude-sonnet-4-20250514',
model='claude-haiku-4-5',
max_tokens=4000,
temperature=1,
messages=[
@@ -136,42 +223,115 @@ def ocr(bucket, key):
)
return message
def process_events():
rows = conn.execute("SELECT COUNT(*) FROM embeddings WHERE embedding IS NULL")
def claude_send_batch(batch):
"""Send a batch to claude"""
client = anthropic.Anthropic()
message_batch = client.messages.batches.create(
requests=batch
)
conn.execute('INSERT INTO batches (platform, batch_id, type) VALUES(%s, %s, %s);', ('claude', message_batch.id, 'ocr'))
conn.commit()
logger.info(f'Sent batch_id {message_batch.id} to claude')
def check_batches():
"""Check batch status"""
rows = conn.execute("SELECT COUNT(*) FROM batches WHERE done = false")
row = rows.fetchone()
logger.info(f'Found {row["count"]} ready to be processed')
rows = conn.execute("SELECT * FROM embeddings WHERE embedding IS NULL")
logger.info(f'Found {row["count"]} batch(es) to process.')
rows = conn.execute("SELECT * FROM batches WHERE done = false")
client = anthropic.Anthropic()
for row in rows:
message_batch = client.messages.batches.retrieve(
row['batch_id'],
)
if message_batch.processing_status == 'ended':
results = client.messages.batches.results(
row['batch_id'],
)
with conn.cursor() as cur:
for result in results:
id = int(result.custom_id.split('-')[1])
try:
content = result.result.message.content[0].text
cur.execute('UPDATE embeddings SET content = %s WHERE id = %s;', (content, id))
except:
cur.execute('UPDATE embeddings SET content = %s WHERE id = %s;', ('ERROR', id))
cur.execute('UPDATE batches SET done = true WHERE batch_id = %s;', (row['batch_id'],))
conn.commit()
def ocr_main():
"""Checks for any non-OCR'd documents and sends them to claude in batches"""
## claude 4 sonnet ##
# tier 1 limit: 8k tokens/min
# tier 2: enough
# single pdf page: up to 2k tokens
# get docs where content is null
rows = conn.execute("SELECT COUNT(*) FROM embeddings WHERE content IS NULL LIMIT 1000")
row = rows.fetchone()
logger.info(f'Batching {row["count"]} documents to generate OCR content.')
rows = conn.execute("SELECT * FROM embeddings WHERE content IS NULL LIMIT 1000")
# batch docs; set content = WIP
batch = []
for row in rows:
id = row['id']
bucket = row['bucket']
key = row['key']
logger.info(f'PROCESSING bucket: {bucket}, key: {key}')
## claude 4 sonnet ##
# tier 1 limit: 8k tokens/min
# tier 2: enough
# single pdf page: up to 2k tokens
try:
ai_ocr = ocr(bucket, key)
text = ai_ocr.content[0].text
logger.info(f'Batching for OCR: {bucket}, key: {key}')
embedding=embed(text, 'search_document')
conn.execute('UPDATE embeddings SET content = %s, embedding = %s::vector WHERE bucket = %s AND key = %s;', (text, embedding, bucket, key))
conn.commit()
except Exception as e:
logger.error(f"An unexpected error occurred: {e}")
return True
batch.append(ocr_create_message(id, bucket, key))
conn.execute('UPDATE embeddings SET content = %s WHERE id = %s;', ('WIP', id))
conn.commit()
if len(batch) > 0:
claude_send_batch(batch)
def embeddings_main():
"""Generate embeddings"""
rows = conn.execute("SELECT COUNT(*) FROM embeddings WHERE content IS NOT NULL AND content != 'ERROR' AND content != 'WIP' AND embedding IS NULL")
row = rows.fetchone()
logger.info(f'Batching {row["count"]} documents to generate embeddings.')
rows = conn.execute("SELECT id, key, bucket, content FROM embeddings WHERE content IS NOT NULL AND content != 'ERROR' AND content != 'WIP' AND embedding IS NULL")
for row in rows:
logger.info(f'Generating embeddings for id: {row["id"]}, bucket: {row["bucket"]}, key: {row["key"]}')
embedding = embed(row['content'], 'search_document')
conn.execute('UPDATE embeddings SET embedding = %s::vector WHERE id = %s;', (embedding, row['id']))
conn.commit()
# try:
# ai_ocr = ocr(bucket, key)
# text = ai_ocr.content[0].text
#
# embedding=embed(text, 'search_document')
# conn.execute('UPDATE embeddings SET content = %s, embedding = %s::vector WHERE bucket = %s AND key = %s;', (text, embedding, bucket, key))
# conn.commit()
# except Exception as e:
# logger.error(f"An unexpected error occurred: {e}")
# return True
### embeddings
def embed(text, input_type):
resp = co.embed(
texts=[text],
model='embed-v4.0',
input_type=input_type,
embedding_types=['float'],
)
return resp.embeddings.float[0]
max_retries = 3
for attempt in range(max_retries):
try:
resp = co.embed(
texts=[text],
model='embed-v4.0',
input_type=input_type,
embedding_types=['float'],
output_dimension=1536,
)
return resp.embeddings.float[0]
except ApiError as e:
if e.status_code == 502 and attempt < max_retries - 1:
time.sleep(30 ** attempt) # exponential backoff
continue
raise Exception('cohere max retries exceeded')
def fix_pages():
i = 766
@@ -183,14 +343,13 @@ def fix_pages():
if __name__ == '__main__':
create_tables()
while True:
BACKOFF = False
import_files()
split_files()
check_batches()
ocr_main()
embeddings_main()
split_pdfs()
BACKOFF = process_events()
if BACKOFF:
logger.info('backoff detected, sleeping an extra 5 minutes')
time.sleep(5 * 60)
logger.info('sleeping 5 minutes')
time.sleep(5 * 60)

1
poller/pyproject.toml
View File

@@ -6,6 +6,7 @@ readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"anthropic>=0.52.0",
"boto3>=1.42.4",
"cohere>=5.15.0",
"minio>=7.2.15",
"mypy>=1.15.0",

74
poller/uv.lock generated
View File

@@ -1,5 +1,5 @@
version = 1
revision = 2
revision = 3
requires-python = ">=3.11"
[[package]]
@@ -76,6 +76,34 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/5a/e4/bf8034d25edaa495da3c8a3405627d2e35758e44ff6eaa7948092646fdcc/argon2_cffi_bindings-21.2.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93", size = 53104, upload-time = "2021-12-01T09:09:31.335Z" },
]
[[package]]
name = "boto3"
version = "1.42.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "botocore" },
{ name = "jmespath" },
{ name = "s3transfer" },
]
sdist = { url = "https://files.pythonhosted.org/packages/f3/31/246916eec4fc5ff7bebf7e75caf47ee4d72b37d4120b6943e3460956e618/boto3-1.42.4.tar.gz", hash = "sha256:65f0d98a3786ec729ba9b5f70448895b2d1d1f27949aa7af5cb4f39da341bbc4", size = 112826, upload-time = "2025-12-05T20:27:14.931Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/00/25/9ae819385aad79f524859f7179cecf8ac019b63ac8f150c51b250967f6db/boto3-1.42.4-py3-none-any.whl", hash = "sha256:0f4089e230d55f981d67376e48cefd41c3d58c7f694480f13288e6ff7b1fefbc", size = 140621, upload-time = "2025-12-05T20:27:12.803Z" },
]
[[package]]
name = "botocore"
version = "1.42.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jmespath" },
{ name = "python-dateutil" },
{ name = "urllib3" },
]
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