docs(website): dedicated page per bundled + optional skill (#14929)

Generates a full dedicated Docusaurus page for every one of the 132 skills (73 bundled + 59 optional) under website/docs/user-guide/skills/{bundled,optional}/<category>/. Each page carries the skill's description, metadata (version, author, license, dependencies, platform gating, tags, related skills cross-linked to their own pages), and the complete SKILL.md body that Hermes loads at runtime. Previously the two catalog pages just listed skills with a one-line blurb and no way to see what the skill actually did — users had to go read the source repo. Now every skill has a browsable, searchable, cross-linked reference in the docs. - website/scripts/generate-skill-docs.py — generator that reads skills/ and optional-skills/, writes per-skill pages, regenerates both catalog indexes, and rewrites the Skills section of sidebars.ts. Handles MDX escaping (outside fenced code blocks: curly braces, unsafe HTML-ish tags) and rewrites relative references/*.md links to point at the GitHub source. - website/docs/reference/skills-catalog.md — regenerated; each row links to the new dedicated page. - website/docs/reference/optional-skills-catalog.md — same. - website/sidebars.ts — Skills section now has Bundled / Optional subtrees with one nested category per skill folder. - .github/workflows/{docs-site-checks,deploy-site}.yml — run the generator before docusaurus build so CI stays in sync with the source SKILL.md files. Build verified locally with `npx docusaurus build`. Only remaining warnings are pre-existing broken link/anchor issues in unrelated pages.
2026-04-26 01:01:40 +00:00 · 2026-04-23 22:22:11 -07:00 · 2026-04-23 22:22:11 -07:00 · 0f6eabb890
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+---
+title: "Qdrant Vector Search — High-performance vector similarity search engine for RAG and semantic search"
+sidebar_label: "Qdrant Vector Search"
+description: "High-performance vector similarity search engine for RAG and semantic search"
+---
+
+{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}
+
+# Qdrant Vector Search
+
+High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
+
+## Skill metadata
+
+| | |
+|---|---|
+| Source | Optional — install with `hermes skills install official/mlops/qdrant` |
+| Path | `optional-skills/mlops/qdrant` |
+| Version | `1.0.0` |
+| Author | Orchestra Research |
+| License | MIT |
+| Dependencies | `qdrant-client>=1.12.0` |
+| Tags | `RAG`, `Vector Search`, `Qdrant`, `Semantic Search`, `Embeddings`, `Similarity Search`, `HNSW`, `Production`, `Distributed` |
+
+## Reference: full SKILL.md
+
+:::info
+The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
+:::
+
+# Qdrant - Vector Similarity Search Engine
+
+High-performance vector database written in Rust for production RAG and semantic search.
+
+## When to use Qdrant
+
+**Use Qdrant when:**
+- Building production RAG systems requiring low latency
+- Need hybrid search (vectors + metadata filtering)
+- Require horizontal scaling with sharding/replication
+- Want on-premise deployment with full data control
+- Need multi-vector storage per record (dense + sparse)
+- Building real-time recommendation systems
+
+**Key features:**
+- **Rust-powered**: Memory-safe, high performance
+- **Rich filtering**: Filter by any payload field during search
+- **Multiple vectors**: Dense, sparse, multi-dense per point
+- **Quantization**: Scalar, product, binary for memory efficiency
+- **Distributed**: Raft consensus, sharding, replication
+- **REST + gRPC**: Both APIs with full feature parity
+
+**Use alternatives instead:**
+- **Chroma**: Simpler setup, embedded use cases
+- **FAISS**: Maximum raw speed, research/batch processing
+- **Pinecone**: Fully managed, zero ops preferred
+- **Weaviate**: GraphQL preference, built-in vectorizers
+
+## Quick start
+
+### Installation
+
+```bash
+# Python client
+pip install qdrant-client
+
+# Docker (recommended for development)
+docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
+
+# Docker with persistent storage
+docker run -p 6333:6333 -p 6334:6334 \
+    -v $(pwd)/qdrant_storage:/qdrant/storage \
+    qdrant/qdrant
+```
+
+### Basic usage
+
+```python
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, VectorParams, PointStruct
+
+# Connect to Qdrant
+client = QdrantClient(host="localhost", port=6333)
+
+# Create collection
+client.create_collection(
+    collection_name="documents",
+    vectors_config=VectorParams(size=384, distance=Distance.COSINE)
+)
+
+# Insert vectors with payload
+client.upsert(
+    collection_name="documents",
+    points=[
+        PointStruct(
+            id=1,
+            vector=[0.1, 0.2, ...],  # 384-dim vector
+            payload={"title": "Doc 1", "category": "tech"}
+        ),
+        PointStruct(
+            id=2,
+            vector=[0.3, 0.4, ...],
+            payload={"title": "Doc 2", "category": "science"}
+        )
+    ]
+)
+
+# Search with filtering
+results = client.search(
+    collection_name="documents",
+    query_vector=[0.15, 0.25, ...],
+    query_filter={
+        "must": [{"key": "category", "match": {"value": "tech"}}]
+    },
+    limit=10
+)
+
+for point in results:
+    print(f"ID: {point.id}, Score: {point.score}, Payload: {point.payload}")
+```
+
+## Core concepts
+
+### Points - Basic data unit
+
+```python
+from qdrant_client.models import PointStruct
+
+# Point = ID + Vector(s) + Payload
+point = PointStruct(
+    id=123,                              # Integer or UUID string
+    vector=[0.1, 0.2, 0.3, ...],        # Dense vector
+    payload={                            # Arbitrary JSON metadata
+        "title": "Document title",
+        "category": "tech",
+        "timestamp": 1699900000,
+        "tags": ["python", "ml"]
+    }
+)
+
+# Batch upsert (recommended)
+client.upsert(
+    collection_name="documents",
+    points=[point1, point2, point3],
+    wait=True  # Wait for indexing
+)
+```
+
+### Collections - Vector containers
+
+```python
+from qdrant_client.models import VectorParams, Distance, HnswConfigDiff
+
+# Create with HNSW configuration
+client.create_collection(
+    collection_name="documents",
+    vectors_config=VectorParams(
+        size=384,                        # Vector dimensions
+        distance=Distance.COSINE         # COSINE, EUCLID, DOT, MANHATTAN
+    ),
+    hnsw_config=HnswConfigDiff(
+        m=16,                            # Connections per node (default 16)
+        ef_construct=100,                # Build-time accuracy (default 100)
+        full_scan_threshold=10000        # Switch to brute force below this
+    ),
+    on_disk_payload=True                 # Store payload on disk
+)
+
+# Collection info
+info = client.get_collection("documents")
+print(f"Points: {info.points_count}, Vectors: {info.vectors_count}")
+```
+
+### Distance metrics
+
+| Metric | Use Case | Range |
+|--------|----------|-------|
+| `COSINE` | Text embeddings, normalized vectors | 0 to 2 |
+| `EUCLID` | Spatial data, image features | 0 to ∞ |
+| `DOT` | Recommendations, unnormalized | -∞ to ∞ |
+| `MANHATTAN` | Sparse features, discrete data | 0 to ∞ |
+
+## Search operations
+
+### Basic search
+
+```python
+# Simple nearest neighbor search
+results = client.search(
+    collection_name="documents",
+    query_vector=[0.1, 0.2, ...],
+    limit=10,
+    with_payload=True,
+    with_vectors=False  # Don't return vectors (faster)
+)
+```
+
+### Filtered search
+
+```python
+from qdrant_client.models import Filter, FieldCondition, MatchValue, Range
+
+# Complex filtering
+results = client.search(
+    collection_name="documents",
+    query_vector=query_embedding,
+    query_filter=Filter(
+        must=[
+            FieldCondition(key="category", match=MatchValue(value="tech")),
+            FieldCondition(key="timestamp", range=Range(gte=1699000000))
+        ],
+        must_not=[
+            FieldCondition(key="status", match=MatchValue(value="archived"))
+        ]
+    ),
+    limit=10
+)
+
+# Shorthand filter syntax
+results = client.search(
+    collection_name="documents",
+    query_vector=query_embedding,
+    query_filter={
+        "must": [
+            {"key": "category", "match": {"value": "tech"}},
+            {"key": "price", "range": {"gte": 10, "lte": 100}}
+        ]
+    },
+    limit=10
+)
+```
+
+### Batch search
+
+```python
+from qdrant_client.models import SearchRequest
+
+# Multiple queries in one request
+results = client.search_batch(
+    collection_name="documents",
+    requests=[
+        SearchRequest(vector=[0.1, ...], limit=5),
+        SearchRequest(vector=[0.2, ...], limit=5, filter={"must": [...]}),
+        SearchRequest(vector=[0.3, ...], limit=10)
+    ]
+)
+```
+
+## RAG integration
+
+### With sentence-transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+from qdrant_client import QdrantClient
+from qdrant_client.models import VectorParams, Distance, PointStruct
+
+# Initialize
+encoder = SentenceTransformer("all-MiniLM-L6-v2")
+client = QdrantClient(host="localhost", port=6333)
+
+# Create collection
+client.create_collection(
+    collection_name="knowledge_base",
+    vectors_config=VectorParams(size=384, distance=Distance.COSINE)
+)
+
+# Index documents
+documents = [
+    {"id": 1, "text": "Python is a programming language", "source": "wiki"},
+    {"id": 2, "text": "Machine learning uses algorithms", "source": "textbook"},
+]
+
+points = [
+    PointStruct(
+        id=doc["id"],
+        vector=encoder.encode(doc["text"]).tolist(),
+        payload={"text": doc["text"], "source": doc["source"]}
+    )
+    for doc in documents
+]
+client.upsert(collection_name="knowledge_base", points=points)
+
+# RAG retrieval
+def retrieve(query: str, top_k: int = 5) -> list[dict]:
+    query_vector = encoder.encode(query).tolist()
+    results = client.search(
+        collection_name="knowledge_base",
+        query_vector=query_vector,
+        limit=top_k
+    )
+    return [{"text": r.payload["text"], "score": r.score} for r in results]
+
+# Use in RAG pipeline
+context = retrieve("What is Python?")
+prompt = f"Context: {context}\n\nQuestion: What is Python?"
+```
+
+### With LangChain
+
+```python
+from langchain_community.vectorstores import Qdrant
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+vectorstore = Qdrant.from_documents(documents, embeddings, url="http://localhost:6333", collection_name="docs")
+retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+```
+
+### With LlamaIndex
+
+```python
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+from llama_index.core import VectorStoreIndex, StorageContext
+
+vector_store = QdrantVectorStore(client=client, collection_name="llama_docs")
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+query_engine = index.as_query_engine()
+```
+
+## Multi-vector support
+
+### Named vectors (different embedding models)
+
+```python
+from qdrant_client.models import VectorParams, Distance
+
+# Collection with multiple vector types
+client.create_collection(
+    collection_name="hybrid_search",
+    vectors_config={
+        "dense": VectorParams(size=384, distance=Distance.COSINE),
+        "sparse": VectorParams(size=30000, distance=Distance.DOT)
+    }
+)
+
+# Insert with named vectors
+client.upsert(
+    collection_name="hybrid_search",
+    points=[
+        PointStruct(
+            id=1,
+            vector={
+                "dense": dense_embedding,
+                "sparse": sparse_embedding
+            },
+            payload={"text": "document text"}
+        )
+    ]
+)
+
+# Search specific vector
+results = client.search(
+    collection_name="hybrid_search",
+    query_vector=("dense", query_dense),  # Specify which vector
+    limit=10
+)
+```
+
+### Sparse vectors (BM25, SPLADE)
+
+```python
+from qdrant_client.models import SparseVectorParams, SparseIndexParams, SparseVector
+
+# Collection with sparse vectors
+client.create_collection(
+    collection_name="sparse_search",
+    vectors_config={},
+    sparse_vectors_config={"text": SparseVectorParams(index=SparseIndexParams(on_disk=False))}
+)
+
+# Insert sparse vector
+client.upsert(
+    collection_name="sparse_search",
+    points=[PointStruct(id=1, vector={"text": SparseVector(indices=[1, 5, 100], values=[0.5, 0.8, 0.2])}, payload={"text": "document"})]
+)
+```
+
+## Quantization (memory optimization)
+
+```python
+from qdrant_client.models import ScalarQuantization, ScalarQuantizationConfig, ScalarType
+
+# Scalar quantization (4x memory reduction)
+client.create_collection(
+    collection_name="quantized",
+    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
+    quantization_config=ScalarQuantization(
+        scalar=ScalarQuantizationConfig(
+            type=ScalarType.INT8,
+            quantile=0.99,        # Clip outliers
+            always_ram=True      # Keep quantized in RAM
+        )
+    )
+)
+
+# Search with rescoring
+results = client.search(
+    collection_name="quantized",
+    query_vector=query,
+    search_params={"quantization": {"rescore": True}},  # Rescore top results
+    limit=10
+)
+```
+
+## Payload indexing
+
+```python
+from qdrant_client.models import PayloadSchemaType
+
+# Create payload index for faster filtering
+client.create_payload_index(
+    collection_name="documents",
+    field_name="category",
+    field_schema=PayloadSchemaType.KEYWORD
+)
+
+client.create_payload_index(
+    collection_name="documents",
+    field_name="timestamp",
+    field_schema=PayloadSchemaType.INTEGER
+)
+
+# Index types: KEYWORD, INTEGER, FLOAT, GEO, TEXT (full-text), BOOL
+```
+
+## Production deployment
+
+### Qdrant Cloud
+
+```python
+from qdrant_client import QdrantClient
+
+# Connect to Qdrant Cloud
+client = QdrantClient(
+    url="https://your-cluster.cloud.qdrant.io",
+    api_key="your-api-key"
+)
+```
+
+### Performance tuning
+
+```python
+# Optimize for search speed (higher recall)
+client.update_collection(
+    collection_name="documents",
+    hnsw_config=HnswConfigDiff(ef_construct=200, m=32)
+)
+
+# Optimize for indexing speed (bulk loads)
+client.update_collection(
+    collection_name="documents",
+    optimizer_config={"indexing_threshold": 20000}
+)
+```
+
+## Best practices
+
+1. **Batch operations** - Use batch upsert/search for efficiency
+2. **Payload indexing** - Index fields used in filters
+3. **Quantization** - Enable for large collections (>1M vectors)
+4. **Sharding** - Use for collections >10M vectors
+5. **On-disk storage** - Enable `on_disk_payload` for large payloads
+6. **Connection pooling** - Reuse client instances
+
+## Common issues
+
+**Slow search with filters:**
+```python
+# Create payload index for filtered fields
+client.create_payload_index(
+    collection_name="docs",
+    field_name="category",
+    field_schema=PayloadSchemaType.KEYWORD
+)
+```
+
+**Out of memory:**
+```python
+# Enable quantization and on-disk storage
+client.create_collection(
+    collection_name="large_collection",
+    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
+    quantization_config=ScalarQuantization(...),
+    on_disk_payload=True
+)
+```
+
+**Connection issues:**
+```python
+# Use timeout and retry
+client = QdrantClient(
+    host="localhost",
+    port=6333,
+    timeout=30,
+    prefer_grpc=True  # gRPC for better performance
+)
+```
+
+## References
+
+- **[Advanced Usage](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops/qdrant/references/advanced-usage.md)** - Distributed mode, hybrid search, recommendations
+- **[Troubleshooting](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops/qdrant/references/troubleshooting.md)** - Common issues, debugging, performance tuning
+
+## Resources
+
+- **GitHub**: https://github.com/qdrant/qdrant (22k+ stars)
+- **Docs**: https://qdrant.tech/documentation/
+- **Python Client**: https://github.com/qdrant/qdrant-client
+- **Cloud**: https://cloud.qdrant.io
+- **Version**: 1.12.0+
+- **License**: Apache 2.0