Query and export data from seekdb vector database. Supports two search modes: (1) Scalar search - metadata filtering only, (2) Hybrid search - fulltext + semantic search combined. The --query-text parameter is used for BOTH fulltext ($contains) and semantic (query_texts) search simultaneously. Can export results to CSV/Excel.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Query and export data from seekdb vector database. Supports two search modes: (1) Scalar search - metadata filtering only, (2) Hybrid search - fulltext + semantic search combined. The --query-text parameter is used for BOTH fulltext ($contains) and semantic (query_texts) search simultaneously. Can export results to CSV/Excel.
license
MIT
Query and Export Data from seekdb
Query data from seekdb vector database with support for scalar search, hybrid search (fulltext + semantic), and export to CSV/Excel files.
Path Convention
Note: All paths in this document (e.g., scripts/) are relative to THIS skill directory, not the project root.
Prerequisites
Python 3.10+ installed
Data imported into seekdb collection
Required packages:
pip install pyseekdb pandas openpyxl
⚠️ CRITICAL: Execution Workflow
MUST FOLLOW this workflow when handling user search requests:
Step 1: Get Collection Information (If Not Already Known)
Before constructing any query, you MUST understand the data structure. However, you should cache this information within the conversation.
Caching Rules:
✅ First query for a collection: Execute --info to get metadata structure
✅ Subsequent queries for the SAME collection: Use cached info from earlier in conversation, skip --info
✅ : Execute for the new collection
Query for a DIFFERENT collection
--info
✅ User explicitly asks for collection info: Execute --info
# Get collection info to see metadata fields (only if not already known)
python scripts/query_from_seekdb.py <collection_name> --info
This shows:
Total record count
Available metadata field names (e.g., source, year, category)
Parse the user's natural language request to identify:
Component
Look For
Maps To
Metadata conditions
Field-value pairs like "2023年", "来自notion", "价格<100"
--where filter
Content/Semantic search
Keywords, concepts, descriptions, questions
--query-text (used for BOTH fulltext and semantic)
Important: --query-text is used for BOTH fulltext search ($contains) and semantic search (query_texts) simultaneously. The same text is used for both.
Step 3: Choose Search Method
User Request Analysis
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Does the request involve ONLY metadata field conditions? │
│ (e.g., "year=2023", "source=notion", no content search) │
└─────────────────────────────────────────────────────────────┘
│
├── YES ──► Scalar Search: --where only
│
└── NO ───► Does it involve content/semantic search?
│
├── YES (no metadata) ──► Hybrid Search: --query-text only
│
└── YES (with metadata) ──► Scalar + Hybrid: --where + --query-text
Two Search Modes
Mode 1: Scalar Search (Metadata Only)
When to use: User wants to filter by metadata fields ONLY, no content/semantic search needed.
# Filter by metadata fields only
python scripts/query_from_seekdb.py seekdb_demo --where'{"source": "notion", "year": 2023}'
Example requests:
"找出所有来自 notion 的文档"
"显示 2023 年的记录"
"source 是 google-docs 的数据"
Mode 2: Hybrid Search (Fulltext + Semantic)
When to use: User wants to search by content - the query text is used for BOTH fulltext matching AND semantic similarity.
# Hybrid search: query text used for both fulltext ($contains) and semantic (query_texts)
python scripts/query_from_seekdb.py seekdb_demo --query-text "seekdb 教程"
# List all collections
python scripts/query_from_seekdb.py --list-collections
# Show collection info (run this first to understand data structure!)
python scripts/query_from_seekdb.py <collection_name> --info
# Scalar search (metadata filter only)
python scripts/query_from_seekdb.py <collection_name> --where'<json_filter>'# Hybrid search (fulltext + semantic, using same query text for both)
python scripts/query_from_seekdb.py <collection_name> --query-text "<text>" [-n <count>]
# Scalar + Hybrid search (metadata filter + fulltext + semantic)
python scripts/query_from_seekdb.py <collection_name> --query-text "<text>" --where'<json>'# Export to CSV/Excel
python scripts/query_from_seekdb.py <collection_name> <search_options> --output results.csv
python scripts/query_from_seekdb.py <collection_name> <search_options> --output results.xlsx
Options
Option
Short
Description
--query-text
-q
Text for hybrid search (fulltext + semantic)
--where
-w
Metadata filter as JSON string
--n-results
-n
Number of results (default: 5)
--output
-o
Export to file (.csv or .xlsx)
--json
-j
Output as JSON
--info
Show collection info
--list-collections
-l
List all collections
--include
Fields to include: documents,metadatas,embeddings
--sheet-name
-s
Sheet name for Excel export
Filter Operators
How to Construct --where Parameter
Step 1: Run --info to see available metadata fields:
python scripts/query_from_seekdb.py seekdb_demo --info
# Example output:# Collection: seekdb_demo# Total records: 2# Preview (first 3 records):# ID: doc1...# Document: python tutorial...# Metadata keys: ['source', 'year'] ← These are the metadata field names!
Step 2: Use the metadata field names to construct --where:
# From the output above, we know the collection has 'source' and 'year' fields# So we can filter by these fields:
--where'{"source": "notion"}'# source equals "notion"
--where'{"year": 2023}'# year equals 2023
--where'{"source": "notion", "year": 2023}'# both conditions (implicit AND)
Step 3: Match user request to metadata fields:
User says
Metadata field
--where value
"2023 年的"
year
'{"year": 2023}'
"来自 notion 的"
source
'{"source": "notion"}'
"价格低于 100 的"
price
'{"price": {"$lt": 100}}'
"品牌是三星或苹果的"
brand
'{"brand": {"$in": ["Samsung", "Apple"]}}'
Metadata Filter Operators
Operator
Description
Example
$eq
Equal to
{"year": {"$eq": 2023}} or {"year": 2023}
$ne
Not equal to
{"status": {"$ne": "deleted"}}
$gt
Greater than
{"score": {"$gt": 90}}
$gte
Greater than or equal
{"score": {"$gte": 90}}
$lt
Less than
{"score": {"$lt": 50}}
$lte
Less than or equal
{"score": {"$lte": 50}}
$in
In list
{"tag": {"$in": ["ml", "ai"]}}
$nin
Not in list
{"tag": {"$nin": ["old"]}}
$and
Logical AND
{"$and": [{"year": 2023}, {"source": "notion"}]}
$or
Logical OR
{"$or": [{"year": 2023}, {"year": 2024}]}
Complex Filter Examples
# Multiple conditions with implicit AND (both must be true)
--where'{"source": "notion", "year": 2023}'# Explicit AND
--where'{"$and": [{"source": "notion"}, {"year": {"$gte": 2023}}]}'# OR condition
--where'{"$or": [{"source": "notion"}, {"source": "google-docs"}]}'# Range condition (year between 2022 and 2024)
--where'{"$and": [{"year": {"$gte": 2022}}, {"year": {"$lte": 2024}}]}'# Combined AND + OR
--where'{"$and": [{"year": 2023}, {"$or": [{"source": "notion"}, {"source": "obsidian"}]}]}'
