mirror of
https://github.com/NousResearch/hermes-agent.git
synced 2026-04-26 01:01:40 +00:00
refactor: reorganize skills into sub-categories
The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
This commit is contained in:
teknium1
parent
d6c710706f
commit
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217 changed files with 39 additions and 4 deletions
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# Qdrant Advanced Usage Guide
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## Distributed Deployment
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### Cluster Setup
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Qdrant uses Raft consensus for distributed coordination.
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```yaml
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# docker-compose.yml for 3-node cluster
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version: '3.8'
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services:
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qdrant-node-1:
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image: qdrant/qdrant:latest
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ports:
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- "6333:6333"
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- "6334:6334"
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- "6335:6335"
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volumes:
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- ./node1_storage:/qdrant/storage
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environment:
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- QDRANT__CLUSTER__ENABLED=true
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- QDRANT__CLUSTER__P2P__PORT=6335
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- QDRANT__SERVICE__HTTP_PORT=6333
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- QDRANT__SERVICE__GRPC_PORT=6334
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qdrant-node-2:
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image: qdrant/qdrant:latest
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ports:
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- "6343:6333"
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- "6344:6334"
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- "6345:6335"
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volumes:
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- ./node2_storage:/qdrant/storage
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environment:
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- QDRANT__CLUSTER__ENABLED=true
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- QDRANT__CLUSTER__P2P__PORT=6335
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- QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335
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depends_on:
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- qdrant-node-1
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qdrant-node-3:
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image: qdrant/qdrant:latest
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ports:
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- "6353:6333"
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- "6354:6334"
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- "6355:6335"
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volumes:
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- ./node3_storage:/qdrant/storage
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environment:
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- QDRANT__CLUSTER__ENABLED=true
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- QDRANT__CLUSTER__P2P__PORT=6335
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- QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335
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depends_on:
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- qdrant-node-1
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```
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### Sharding Configuration
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```python
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from qdrant_client import QdrantClient
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from qdrant_client.models import VectorParams, Distance, ShardingMethod
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client = QdrantClient(host="localhost", port=6333)
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# Create sharded collection
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client.create_collection(
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collection_name="large_collection",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE),
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shard_number=6, # Number of shards
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replication_factor=2, # Replicas per shard
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write_consistency_factor=1 # Required acks for write
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)
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# Check cluster status
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cluster_info = client.get_cluster_info()
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print(f"Peers: {cluster_info.peers}")
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print(f"Raft state: {cluster_info.raft_info}")
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```
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### Replication and Consistency
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```python
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from qdrant_client.models import WriteOrdering
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# Strong consistency write
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client.upsert(
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collection_name="critical_data",
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points=points,
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ordering=WriteOrdering.STRONG # Wait for all replicas
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)
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# Eventual consistency (faster)
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client.upsert(
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collection_name="logs",
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points=points,
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ordering=WriteOrdering.WEAK # Return after primary ack
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)
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# Read from specific shard
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results = client.search(
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collection_name="documents",
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query_vector=query,
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consistency="majority" # Read from majority of replicas
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)
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```
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## Hybrid Search
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### Dense + Sparse Vectors
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Combine semantic (dense) and keyword (sparse) search:
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```python
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from qdrant_client.models import (
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VectorParams, SparseVectorParams, SparseIndexParams,
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Distance, PointStruct, SparseVector, Prefetch, Query
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)
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# Create hybrid collection
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client.create_collection(
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collection_name="hybrid",
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vectors_config={
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"dense": VectorParams(size=384, distance=Distance.COSINE)
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},
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sparse_vectors_config={
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"sparse": SparseVectorParams(
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index=SparseIndexParams(on_disk=False)
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)
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}
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)
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# Insert with both vector types
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def encode_sparse(text: str) -> SparseVector:
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"""Simple BM25-like sparse encoding"""
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from collections import Counter
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tokens = text.lower().split()
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counts = Counter(tokens)
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# Map tokens to indices (use vocabulary in production)
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indices = [hash(t) % 30000 for t in counts.keys()]
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values = list(counts.values())
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return SparseVector(indices=indices, values=values)
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client.upsert(
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collection_name="hybrid",
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points=[
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PointStruct(
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id=1,
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vector={
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"dense": dense_encoder.encode("Python programming").tolist(),
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"sparse": encode_sparse("Python programming language code")
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},
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payload={"text": "Python programming language code"}
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)
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]
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)
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# Hybrid search with Reciprocal Rank Fusion (RRF)
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from qdrant_client.models import FusionQuery
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results = client.query_points(
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collection_name="hybrid",
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prefetch=[
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Prefetch(query=dense_query, using="dense", limit=20),
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Prefetch(query=sparse_query, using="sparse", limit=20)
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],
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query=FusionQuery(fusion="rrf"), # Combine results
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limit=10
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)
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```
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### Multi-Stage Search
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```python
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from qdrant_client.models import Prefetch, Query
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# Two-stage retrieval: coarse then fine
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results = client.query_points(
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collection_name="documents",
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prefetch=[
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Prefetch(
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query=query_vector,
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limit=100, # Broad first stage
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params={"quantization": {"rescore": False}} # Fast, approximate
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)
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],
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query=Query(nearest=query_vector),
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limit=10,
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params={"quantization": {"rescore": True}} # Accurate reranking
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)
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```
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## Recommendations
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### Item-to-Item Recommendations
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```python
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# Find similar items
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recommendations = client.recommend(
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collection_name="products",
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positive=[1, 2, 3], # IDs user liked
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negative=[4], # IDs user disliked
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limit=10
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)
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# With filtering
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recommendations = client.recommend(
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collection_name="products",
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positive=[1, 2],
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query_filter={
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"must": [
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{"key": "category", "match": {"value": "electronics"}},
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{"key": "in_stock", "match": {"value": True}}
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]
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},
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limit=10
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)
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```
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### Lookup from Another Collection
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```python
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from qdrant_client.models import RecommendStrategy, LookupLocation
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# Recommend using vectors from another collection
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results = client.recommend(
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collection_name="products",
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positive=[
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LookupLocation(
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collection_name="user_history",
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id="user_123"
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)
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],
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strategy=RecommendStrategy.AVERAGE_VECTOR,
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limit=10
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)
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```
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## Advanced Filtering
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### Nested Payload Filtering
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```python
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from qdrant_client.models import Filter, FieldCondition, MatchValue, NestedCondition
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# Filter on nested objects
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results = client.search(
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collection_name="documents",
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query_vector=query,
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query_filter=Filter(
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must=[
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NestedCondition(
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key="metadata",
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filter=Filter(
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must=[
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FieldCondition(
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key="author.name",
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match=MatchValue(value="John")
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)
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]
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)
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)
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]
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),
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limit=10
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)
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```
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### Geo Filtering
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```python
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from qdrant_client.models import FieldCondition, GeoRadius, GeoPoint
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# Find within radius
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results = client.search(
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collection_name="locations",
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query_vector=query,
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query_filter=Filter(
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must=[
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FieldCondition(
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key="location",
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geo_radius=GeoRadius(
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center=GeoPoint(lat=40.7128, lon=-74.0060),
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radius=5000 # meters
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)
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)
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]
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),
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limit=10
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)
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# Geo bounding box
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from qdrant_client.models import GeoBoundingBox
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results = client.search(
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collection_name="locations",
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query_vector=query,
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query_filter=Filter(
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must=[
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FieldCondition(
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key="location",
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geo_bounding_box=GeoBoundingBox(
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top_left=GeoPoint(lat=40.8, lon=-74.1),
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bottom_right=GeoPoint(lat=40.6, lon=-73.9)
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)
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)
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]
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),
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limit=10
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)
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```
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### Full-Text Search
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```python
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from qdrant_client.models import TextIndexParams, TokenizerType
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# Create text index
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client.create_payload_index(
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collection_name="documents",
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field_name="content",
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field_schema=TextIndexParams(
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type="text",
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tokenizer=TokenizerType.WORD,
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min_token_len=2,
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max_token_len=15,
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lowercase=True
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)
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)
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# Full-text filter
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from qdrant_client.models import MatchText
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results = client.search(
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collection_name="documents",
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query_vector=query,
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query_filter=Filter(
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must=[
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FieldCondition(
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key="content",
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match=MatchText(text="machine learning")
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)
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]
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),
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limit=10
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)
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```
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## Quantization Strategies
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### Scalar Quantization (INT8)
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```python
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from qdrant_client.models import ScalarQuantization, ScalarQuantizationConfig, ScalarType
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# ~4x memory reduction, minimal accuracy loss
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client.create_collection(
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collection_name="scalar_quantized",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE),
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quantization_config=ScalarQuantization(
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scalar=ScalarQuantizationConfig(
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type=ScalarType.INT8,
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quantile=0.99, # Clip extreme values
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always_ram=True # Keep quantized vectors in RAM
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)
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)
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)
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```
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|
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### Product Quantization
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```python
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from qdrant_client.models import ProductQuantization, ProductQuantizationConfig, CompressionRatio
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# ~16x memory reduction, some accuracy loss
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client.create_collection(
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collection_name="product_quantized",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE),
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quantization_config=ProductQuantization(
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product=ProductQuantizationConfig(
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compression=CompressionRatio.X16,
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always_ram=True
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)
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)
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)
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```
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|
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### Binary Quantization
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```python
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from qdrant_client.models import BinaryQuantization, BinaryQuantizationConfig
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# ~32x memory reduction, requires oversampling
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client.create_collection(
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collection_name="binary_quantized",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE),
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quantization_config=BinaryQuantization(
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binary=BinaryQuantizationConfig(always_ram=True)
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)
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)
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|
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# Search with oversampling
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results = client.search(
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collection_name="binary_quantized",
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query_vector=query,
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search_params={
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"quantization": {
|
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"rescore": True,
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"oversampling": 2.0 # Retrieve 2x candidates, rescore
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}
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},
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limit=10
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)
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```
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## Snapshots and Backups
|
||||
|
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### Create Snapshot
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```python
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# Create collection snapshot
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snapshot_info = client.create_snapshot(collection_name="documents")
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print(f"Snapshot: {snapshot_info.name}")
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# List snapshots
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snapshots = client.list_snapshots(collection_name="documents")
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for s in snapshots:
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print(f"{s.name}: {s.size} bytes")
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|
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# Full storage snapshot
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full_snapshot = client.create_full_snapshot()
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```
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### Restore from Snapshot
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```python
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# Download snapshot
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client.download_snapshot(
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collection_name="documents",
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snapshot_name="documents-2024-01-01.snapshot",
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target_path="./backup/"
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)
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|
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# Restore (via REST API)
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import requests
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response = requests.put(
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"http://localhost:6333/collections/documents/snapshots/recover",
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json={"location": "file:///backup/documents-2024-01-01.snapshot"}
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||||
)
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||||
```
|
||||
|
||||
## Collection Aliases
|
||||
|
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```python
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# Create alias
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||||
client.update_collection_aliases(
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change_aliases_operations=[
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{"create_alias": {"alias_name": "production", "collection_name": "documents_v2"}}
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||||
]
|
||||
)
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||||
|
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# Blue-green deployment
|
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# 1. Create new collection with updates
|
||||
client.create_collection(collection_name="documents_v3", ...)
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|
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# 2. Populate new collection
|
||||
client.upsert(collection_name="documents_v3", points=new_points)
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||||
|
||||
# 3. Atomic switch
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client.update_collection_aliases(
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change_aliases_operations=[
|
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{"delete_alias": {"alias_name": "production"}},
|
||||
{"create_alias": {"alias_name": "production", "collection_name": "documents_v3"}}
|
||||
]
|
||||
)
|
||||
|
||||
# Search via alias
|
||||
results = client.search(collection_name="production", query_vector=query, limit=10)
|
||||
```
|
||||
|
||||
## Scroll and Iteration
|
||||
|
||||
### Scroll Through All Points
|
||||
|
||||
```python
|
||||
# Paginated iteration
|
||||
offset = None
|
||||
all_points = []
|
||||
|
||||
while True:
|
||||
results, offset = client.scroll(
|
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collection_name="documents",
|
||||
limit=100,
|
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offset=offset,
|
||||
with_payload=True,
|
||||
with_vectors=False
|
||||
)
|
||||
all_points.extend(results)
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||||
|
||||
if offset is None:
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||||
break
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||||
|
||||
print(f"Total points: {len(all_points)}")
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||||
```
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||||
|
||||
### Filtered Scroll
|
||||
|
||||
```python
|
||||
# Scroll with filter
|
||||
results, _ = client.scroll(
|
||||
collection_name="documents",
|
||||
scroll_filter=Filter(
|
||||
must=[
|
||||
FieldCondition(key="status", match=MatchValue(value="active"))
|
||||
]
|
||||
),
|
||||
limit=1000
|
||||
)
|
||||
```
|
||||
|
||||
## Async Client
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from qdrant_client import AsyncQdrantClient
|
||||
|
||||
async def main():
|
||||
client = AsyncQdrantClient(host="localhost", port=6333)
|
||||
|
||||
# Async operations
|
||||
await client.create_collection(
|
||||
collection_name="async_docs",
|
||||
vectors_config=VectorParams(size=384, distance=Distance.COSINE)
|
||||
)
|
||||
|
||||
await client.upsert(
|
||||
collection_name="async_docs",
|
||||
points=points
|
||||
)
|
||||
|
||||
results = await client.search(
|
||||
collection_name="async_docs",
|
||||
query_vector=query,
|
||||
limit=10
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
results = asyncio.run(main())
|
||||
```
|
||||
|
||||
## gRPC Client
|
||||
|
||||
```python
|
||||
from qdrant_client import QdrantClient
|
||||
|
||||
# Prefer gRPC for better performance
|
||||
client = QdrantClient(
|
||||
host="localhost",
|
||||
port=6333,
|
||||
grpc_port=6334,
|
||||
prefer_grpc=True # Use gRPC when available
|
||||
)
|
||||
|
||||
# gRPC-only client
|
||||
from qdrant_client import QdrantClient
|
||||
|
||||
client = QdrantClient(
|
||||
host="localhost",
|
||||
grpc_port=6334,
|
||||
prefer_grpc=True,
|
||||
https=False
|
||||
)
|
||||
```
|
||||
|
||||
## Multitenancy
|
||||
|
||||
### Payload-Based Isolation
|
||||
|
||||
```python
|
||||
# Single collection, filter by tenant
|
||||
client.upsert(
|
||||
collection_name="multi_tenant",
|
||||
points=[
|
||||
PointStruct(
|
||||
id=1,
|
||||
vector=embedding,
|
||||
payload={"tenant_id": "tenant_a", "text": "..."}
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
# Search within tenant
|
||||
results = client.search(
|
||||
collection_name="multi_tenant",
|
||||
query_vector=query,
|
||||
query_filter=Filter(
|
||||
must=[FieldCondition(key="tenant_id", match=MatchValue(value="tenant_a"))]
|
||||
),
|
||||
limit=10
|
||||
)
|
||||
```
|
||||
|
||||
### Collection-Per-Tenant
|
||||
|
||||
```python
|
||||
# Create tenant collection
|
||||
def create_tenant_collection(tenant_id: str):
|
||||
client.create_collection(
|
||||
collection_name=f"tenant_{tenant_id}",
|
||||
vectors_config=VectorParams(size=384, distance=Distance.COSINE)
|
||||
)
|
||||
|
||||
# Search tenant collection
|
||||
def search_tenant(tenant_id: str, query_vector: list, limit: int = 10):
|
||||
return client.search(
|
||||
collection_name=f"tenant_{tenant_id}",
|
||||
query_vector=query_vector,
|
||||
limit=limit
|
||||
)
|
||||
```
|
||||
|
||||
## Performance Monitoring
|
||||
|
||||
### Collection Statistics
|
||||
|
||||
```python
|
||||
# Collection info
|
||||
info = client.get_collection("documents")
|
||||
print(f"Points: {info.points_count}")
|
||||
print(f"Indexed vectors: {info.indexed_vectors_count}")
|
||||
print(f"Segments: {len(info.segments)}")
|
||||
print(f"Status: {info.status}")
|
||||
|
||||
# Detailed segment info
|
||||
for i, segment in enumerate(info.segments):
|
||||
print(f"Segment {i}: {segment}")
|
||||
```
|
||||
|
||||
### Telemetry
|
||||
|
||||
```python
|
||||
# Get telemetry data
|
||||
telemetry = client.get_telemetry()
|
||||
print(f"Collections: {telemetry.collections}")
|
||||
print(f"Operations: {telemetry.operations}")
|
||||
```
|
||||
|
|
@ -0,0 +1,631 @@
|
|||
# Qdrant Troubleshooting Guide
|
||||
|
||||
## Installation Issues
|
||||
|
||||
### Docker Issues
|
||||
|
||||
**Error**: `Cannot connect to Docker daemon`
|
||||
|
||||
**Fix**:
|
||||
```bash
|
||||
# Start Docker daemon
|
||||
sudo systemctl start docker
|
||||
|
||||
# Or use Docker Desktop on Mac/Windows
|
||||
open -a Docker
|
||||
```
|
||||
|
||||
**Error**: `Port 6333 already in use`
|
||||
|
||||
**Fix**:
|
||||
```bash
|
||||
# Find process using port
|
||||
lsof -i :6333
|
||||
|
||||
# Kill process or use different port
|
||||
docker run -p 6334:6333 qdrant/qdrant
|
||||
```
|
||||
|
||||
### Python Client Issues
|
||||
|
||||
**Error**: `ModuleNotFoundError: No module named 'qdrant_client'`
|
||||
|
||||
**Fix**:
|
||||
```bash
|
||||
pip install qdrant-client
|
||||
|
||||
# With specific version
|
||||
pip install qdrant-client>=1.12.0
|
||||
```
|
||||
|
||||
**Error**: `grpc._channel._InactiveRpcError`
|
||||
|
||||
**Fix**:
|
||||
```bash
|
||||
# Install with gRPC support
|
||||
pip install 'qdrant-client[grpc]'
|
||||
|
||||
# Or disable gRPC
|
||||
client = QdrantClient(host="localhost", port=6333, prefer_grpc=False)
|
||||
```
|
||||
|
||||
## Connection Issues
|
||||
|
||||
### Cannot Connect to Server
|
||||
|
||||
**Error**: `ConnectionRefusedError: [Errno 111] Connection refused`
|
||||
|
||||
**Solutions**:
|
||||
|
||||
1. **Check server is running**:
|
||||
```bash
|
||||
docker ps | grep qdrant
|
||||
curl http://localhost:6333/healthz
|
||||
```
|
||||
|
||||
2. **Verify port binding**:
|
||||
```bash
|
||||
# Check listening ports
|
||||
netstat -tlnp | grep 6333
|
||||
|
||||
# Docker port mapping
|
||||
docker port <container_id>
|
||||
```
|
||||
|
||||
3. **Use correct host**:
|
||||
```python
|
||||
# Docker on Linux
|
||||
client = QdrantClient(host="localhost", port=6333)
|
||||
|
||||
# Docker on Mac/Windows with networking issues
|
||||
client = QdrantClient(host="127.0.0.1", port=6333)
|
||||
|
||||
# Inside Docker network
|
||||
client = QdrantClient(host="qdrant", port=6333)
|
||||
```
|
||||
|
||||
### Timeout Errors
|
||||
|
||||
**Error**: `TimeoutError: Connection timed out`
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Increase timeout
|
||||
client = QdrantClient(
|
||||
host="localhost",
|
||||
port=6333,
|
||||
timeout=60 # seconds
|
||||
)
|
||||
|
||||
# For large operations
|
||||
client.upsert(
|
||||
collection_name="documents",
|
||||
points=large_batch,
|
||||
wait=False # Don't wait for indexing
|
||||
)
|
||||
```
|
||||
|
||||
### SSL/TLS Errors
|
||||
|
||||
**Error**: `ssl.SSLCertVerificationError`
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Qdrant Cloud
|
||||
client = QdrantClient(
|
||||
url="https://cluster.cloud.qdrant.io",
|
||||
api_key="your-api-key"
|
||||
)
|
||||
|
||||
# Self-signed certificate
|
||||
client = QdrantClient(
|
||||
host="localhost",
|
||||
port=6333,
|
||||
https=True,
|
||||
verify=False # Disable verification (not recommended for production)
|
||||
)
|
||||
```
|
||||
|
||||
## Collection Issues
|
||||
|
||||
### Collection Already Exists
|
||||
|
||||
**Error**: `ValueError: Collection 'documents' already exists`
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Check before creating
|
||||
collections = client.get_collections().collections
|
||||
names = [c.name for c in collections]
|
||||
|
||||
if "documents" not in names:
|
||||
client.create_collection(...)
|
||||
|
||||
# Or recreate
|
||||
client.recreate_collection(
|
||||
collection_name="documents",
|
||||
vectors_config=VectorParams(size=384, distance=Distance.COSINE)
|
||||
)
|
||||
```
|
||||
|
||||
### Collection Not Found
|
||||
|
||||
**Error**: `NotFoundException: Collection 'docs' not found`
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# List available collections
|
||||
collections = client.get_collections()
|
||||
print([c.name for c in collections.collections])
|
||||
|
||||
# Check exact name (case-sensitive)
|
||||
try:
|
||||
info = client.get_collection("documents")
|
||||
except Exception as e:
|
||||
print(f"Collection not found: {e}")
|
||||
```
|
||||
|
||||
### Vector Dimension Mismatch
|
||||
|
||||
**Error**: `ValueError: Vector dimension mismatch. Expected 384, got 768`
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Check collection config
|
||||
info = client.get_collection("documents")
|
||||
print(f"Expected dimension: {info.config.params.vectors.size}")
|
||||
|
||||
# Recreate with correct dimension
|
||||
client.recreate_collection(
|
||||
collection_name="documents",
|
||||
vectors_config=VectorParams(size=768, distance=Distance.COSINE) # Match your embeddings
|
||||
)
|
||||
```
|
||||
|
||||
## Search Issues
|
||||
|
||||
### Empty Search Results
|
||||
|
||||
**Problem**: Search returns empty results.
|
||||
|
||||
**Solutions**:
|
||||
|
||||
1. **Verify data exists**:
|
||||
```python
|
||||
info = client.get_collection("documents")
|
||||
print(f"Points: {info.points_count}")
|
||||
|
||||
# Scroll to check data
|
||||
points, _ = client.scroll(
|
||||
collection_name="documents",
|
||||
limit=10,
|
||||
with_payload=True
|
||||
)
|
||||
print(points)
|
||||
```
|
||||
|
||||
2. **Check vector format**:
|
||||
```python
|
||||
# Must be list of floats
|
||||
query_vector = embedding.tolist() # Convert numpy to list
|
||||
|
||||
# Check dimensions
|
||||
print(f"Query dimension: {len(query_vector)}")
|
||||
```
|
||||
|
||||
3. **Verify filter conditions**:
|
||||
```python
|
||||
# Test without filter first
|
||||
results = client.search(
|
||||
collection_name="documents",
|
||||
query_vector=query,
|
||||
limit=10
|
||||
# No filter
|
||||
)
|
||||
|
||||
# Then add filter incrementally
|
||||
```
|
||||
|
||||
### Slow Search Performance
|
||||
|
||||
**Problem**: Search takes too long.
|
||||
|
||||
**Solutions**:
|
||||
|
||||
1. **Create payload indexes**:
|
||||
```python
|
||||
# Index fields used in filters
|
||||
client.create_payload_index(
|
||||
collection_name="documents",
|
||||
field_name="category",
|
||||
field_schema="keyword"
|
||||
)
|
||||
```
|
||||
|
||||
2. **Enable quantization**:
|
||||
```python
|
||||
client.update_collection(
|
||||
collection_name="documents",
|
||||
quantization_config=ScalarQuantization(
|
||||
scalar=ScalarQuantizationConfig(type=ScalarType.INT8)
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
3. **Tune HNSW parameters**:
|
||||
```python
|
||||
# Faster search (less accurate)
|
||||
client.update_collection(
|
||||
collection_name="documents",
|
||||
hnsw_config=HnswConfigDiff(ef_construct=64, m=8)
|
||||
)
|
||||
|
||||
# Use ef search parameter
|
||||
results = client.search(
|
||||
collection_name="documents",
|
||||
query_vector=query,
|
||||
search_params={"hnsw_ef": 64}, # Lower = faster
|
||||
limit=10
|
||||
)
|
||||
```
|
||||
|
||||
4. **Use gRPC**:
|
||||
```python
|
||||
client = QdrantClient(
|
||||
host="localhost",
|
||||
port=6333,
|
||||
grpc_port=6334,
|
||||
prefer_grpc=True
|
||||
)
|
||||
```
|
||||
|
||||
### Inconsistent Results
|
||||
|
||||
**Problem**: Same query returns different results.
|
||||
|
||||
**Solutions**:
|
||||
|
||||
1. **Wait for indexing**:
|
||||
```python
|
||||
client.upsert(
|
||||
collection_name="documents",
|
||||
points=points,
|
||||
wait=True # Wait for index update
|
||||
)
|
||||
```
|
||||
|
||||
2. **Check replication consistency**:
|
||||
```python
|
||||
# Strong consistency read
|
||||
results = client.search(
|
||||
collection_name="documents",
|
||||
query_vector=query,
|
||||
consistency="all" # Read from all replicas
|
||||
)
|
||||
```
|
||||
|
||||
## Upsert Issues
|
||||
|
||||
### Batch Upsert Fails
|
||||
|
||||
**Error**: `PayloadError: Payload too large`
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Split into smaller batches
|
||||
def batch_upsert(client, collection, points, batch_size=100):
|
||||
for i in range(0, len(points), batch_size):
|
||||
batch = points[i:i + batch_size]
|
||||
client.upsert(
|
||||
collection_name=collection,
|
||||
points=batch,
|
||||
wait=True
|
||||
)
|
||||
|
||||
batch_upsert(client, "documents", large_points_list)
|
||||
```
|
||||
|
||||
### Invalid Point ID
|
||||
|
||||
**Error**: `ValueError: Invalid point ID`
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Valid ID types: int or UUID string
|
||||
from uuid import uuid4
|
||||
|
||||
# Integer ID
|
||||
PointStruct(id=123, vector=vec, payload={})
|
||||
|
||||
# UUID string
|
||||
PointStruct(id=str(uuid4()), vector=vec, payload={})
|
||||
|
||||
# NOT valid
|
||||
PointStruct(id="custom-string-123", ...) # Use UUID format
|
||||
```
|
||||
|
||||
### Payload Validation Errors
|
||||
|
||||
**Error**: `ValidationError: Invalid payload`
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Ensure JSON-serializable payload
|
||||
import json
|
||||
|
||||
payload = {
|
||||
"title": "Document",
|
||||
"count": 42,
|
||||
"tags": ["a", "b"],
|
||||
"nested": {"key": "value"}
|
||||
}
|
||||
|
||||
# Validate before upsert
|
||||
json.dumps(payload) # Should not raise
|
||||
|
||||
# Avoid non-serializable types
|
||||
# NOT valid: datetime, numpy arrays, custom objects
|
||||
payload = {
|
||||
"timestamp": datetime.now().isoformat(), # Convert to string
|
||||
"vector": embedding.tolist() # Convert numpy to list
|
||||
}
|
||||
```
|
||||
|
||||
## Memory Issues
|
||||
|
||||
### Out of Memory
|
||||
|
||||
**Error**: `MemoryError` or container killed
|
||||
|
||||
**Solutions**:
|
||||
|
||||
1. **Enable on-disk storage**:
|
||||
```python
|
||||
client.create_collection(
|
||||
collection_name="large_collection",
|
||||
vectors_config=VectorParams(size=384, distance=Distance.COSINE),
|
||||
on_disk_payload=True, # Store payloads on disk
|
||||
hnsw_config=HnswConfigDiff(on_disk=True) # Store HNSW on disk
|
||||
)
|
||||
```
|
||||
|
||||
2. **Use quantization**:
|
||||
```python
|
||||
# 4x memory reduction
|
||||
client.update_collection(
|
||||
collection_name="large_collection",
|
||||
quantization_config=ScalarQuantization(
|
||||
scalar=ScalarQuantizationConfig(
|
||||
type=ScalarType.INT8,
|
||||
always_ram=False # Keep on disk
|
||||
)
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
3. **Increase Docker memory**:
|
||||
```bash
|
||||
docker run -m 8g -p 6333:6333 qdrant/qdrant
|
||||
```
|
||||
|
||||
4. **Configure Qdrant storage**:
|
||||
```yaml
|
||||
# config.yaml
|
||||
storage:
|
||||
performance:
|
||||
max_search_threads: 2
|
||||
optimizers:
|
||||
memmap_threshold_kb: 20000
|
||||
```
|
||||
|
||||
### High Memory Usage During Indexing
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Increase indexing threshold for bulk loads
|
||||
client.update_collection(
|
||||
collection_name="documents",
|
||||
optimizer_config={
|
||||
"indexing_threshold": 50000 # Delay indexing
|
||||
}
|
||||
)
|
||||
|
||||
# Bulk insert
|
||||
client.upsert(collection_name="documents", points=all_points, wait=False)
|
||||
|
||||
# Then optimize
|
||||
client.update_collection(
|
||||
collection_name="documents",
|
||||
optimizer_config={
|
||||
"indexing_threshold": 10000 # Resume normal indexing
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
## Cluster Issues
|
||||
|
||||
### Node Not Joining Cluster
|
||||
|
||||
**Problem**: New node fails to join cluster.
|
||||
|
||||
**Fix**:
|
||||
```bash
|
||||
# Check network connectivity
|
||||
docker exec qdrant-node-2 ping qdrant-node-1
|
||||
|
||||
# Verify bootstrap URL
|
||||
docker logs qdrant-node-2 | grep bootstrap
|
||||
|
||||
# Check Raft state
|
||||
curl http://localhost:6333/cluster
|
||||
```
|
||||
|
||||
### Split Brain
|
||||
|
||||
**Problem**: Cluster has inconsistent state.
|
||||
|
||||
**Fix**:
|
||||
```bash
|
||||
# Force leader election
|
||||
curl -X POST http://localhost:6333/cluster/recover
|
||||
|
||||
# Or restart minority nodes
|
||||
docker restart qdrant-node-2 qdrant-node-3
|
||||
```
|
||||
|
||||
### Replication Lag
|
||||
|
||||
**Problem**: Replicas fall behind.
|
||||
|
||||
**Fix**:
|
||||
```python
|
||||
# Check collection status
|
||||
info = client.get_collection("documents")
|
||||
print(f"Status: {info.status}")
|
||||
|
||||
# Use strong consistency for critical writes
|
||||
client.upsert(
|
||||
collection_name="documents",
|
||||
points=points,
|
||||
ordering=WriteOrdering.STRONG
|
||||
)
|
||||
```
|
||||
|
||||
## Performance Tuning
|
||||
|
||||
### Benchmark Configuration
|
||||
|
||||
```python
|
||||
import time
|
||||
import numpy as np
|
||||
|
||||
def benchmark_search(client, collection, n_queries=100, dimension=384):
|
||||
# Generate random queries
|
||||
queries = [np.random.rand(dimension).tolist() for _ in range(n_queries)]
|
||||
|
||||
# Warmup
|
||||
for q in queries[:10]:
|
||||
client.search(collection_name=collection, query_vector=q, limit=10)
|
||||
|
||||
# Benchmark
|
||||
start = time.perf_counter()
|
||||
for q in queries:
|
||||
client.search(collection_name=collection, query_vector=q, limit=10)
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
print(f"QPS: {n_queries / elapsed:.2f}")
|
||||
print(f"Latency: {elapsed / n_queries * 1000:.2f}ms")
|
||||
|
||||
benchmark_search(client, "documents")
|
||||
```
|
||||
|
||||
### Optimal HNSW Parameters
|
||||
|
||||
```python
|
||||
# High recall (slower)
|
||||
client.create_collection(
|
||||
collection_name="high_recall",
|
||||
vectors_config=VectorParams(size=384, distance=Distance.COSINE),
|
||||
hnsw_config=HnswConfigDiff(
|
||||
m=32, # More connections
|
||||
ef_construct=200 # Higher build quality
|
||||
)
|
||||
)
|
||||
|
||||
# High speed (lower recall)
|
||||
client.create_collection(
|
||||
collection_name="high_speed",
|
||||
vectors_config=VectorParams(size=384, distance=Distance.COSINE),
|
||||
hnsw_config=HnswConfigDiff(
|
||||
m=8, # Fewer connections
|
||||
ef_construct=64 # Lower build quality
|
||||
)
|
||||
)
|
||||
|
||||
# Balanced
|
||||
client.create_collection(
|
||||
collection_name="balanced",
|
||||
vectors_config=VectorParams(size=384, distance=Distance.COSINE),
|
||||
hnsw_config=HnswConfigDiff(
|
||||
m=16, # Default
|
||||
ef_construct=100 # Default
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
## Debugging Tips
|
||||
|
||||
### Enable Verbose Logging
|
||||
|
||||
```python
|
||||
import logging
|
||||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
logging.getLogger("qdrant_client").setLevel(logging.DEBUG)
|
||||
```
|
||||
|
||||
### Check Server Logs
|
||||
|
||||
```bash
|
||||
# Docker logs
|
||||
docker logs -f qdrant
|
||||
|
||||
# With timestamps
|
||||
docker logs --timestamps qdrant
|
||||
|
||||
# Last 100 lines
|
||||
docker logs --tail 100 qdrant
|
||||
```
|
||||
|
||||
### Inspect Collection State
|
||||
|
||||
```python
|
||||
# Collection info
|
||||
info = client.get_collection("documents")
|
||||
print(f"Status: {info.status}")
|
||||
print(f"Points: {info.points_count}")
|
||||
print(f"Segments: {len(info.segments)}")
|
||||
print(f"Config: {info.config}")
|
||||
|
||||
# Sample points
|
||||
points, _ = client.scroll(
|
||||
collection_name="documents",
|
||||
limit=5,
|
||||
with_payload=True,
|
||||
with_vectors=True
|
||||
)
|
||||
for p in points:
|
||||
print(f"ID: {p.id}, Payload: {p.payload}")
|
||||
```
|
||||
|
||||
### Test Connection
|
||||
|
||||
```python
|
||||
def test_connection(host="localhost", port=6333):
|
||||
try:
|
||||
client = QdrantClient(host=host, port=port, timeout=5)
|
||||
collections = client.get_collections()
|
||||
print(f"Connected! Collections: {len(collections.collections)}")
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Connection failed: {e}")
|
||||
return False
|
||||
|
||||
test_connection()
|
||||
```
|
||||
|
||||
## Getting Help
|
||||
|
||||
1. **Documentation**: https://qdrant.tech/documentation/
|
||||
2. **GitHub Issues**: https://github.com/qdrant/qdrant/issues
|
||||
3. **Discord**: https://discord.gg/qdrant
|
||||
4. **Stack Overflow**: Tag `qdrant`
|
||||
|
||||
### Reporting Issues
|
||||
|
||||
Include:
|
||||
- Qdrant version: `curl http://localhost:6333/`
|
||||
- Python client version: `pip show qdrant-client`
|
||||
- Full error traceback
|
||||
- Minimal reproducible code
|
||||
- Collection configuration
|
||||
Loading…
Add table
Add a link
Reference in a new issue