diff --git a/Dockerfile b/Dockerfile index aeade05..1dd7882 100644 --- a/Dockerfile +++ b/Dockerfile @@ -38,7 +38,6 @@ COPY ./poetry.lock /app COPY --from=build-step /app/dist /app/static RUN poetry install --no-interaction --no-ansi --no-root --without dev -RUN python -c 'from fastembed.embedding import DefaultEmbedding; DefaultEmbedding("sentence-transformers/all-MiniLM-L6-v2")' # Finally copy the application source code and install root COPY qdrant_demo /app/qdrant_demo diff --git a/poetry.lock b/poetry.lock index 3b23638..f7aac5f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. 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os.environ.get("EMBEDDINGS_MODEL", "sentence-transformers/all-MiniLM-L6-v2") +COLLECTION_NAME = os.environ.get("COLLECTION_NAME", "startups_hybrid") +EMBEDDINGS_MODEL = os.environ.get("EMBEDDINGS_MODEL", "mixedbread-ai/mxbai-embed-large-v1") +# Sparse keyword model. Qdrant/bm25 handles tokenization, stemming, and stopwords; +# IDF is applied server-side via the collection's sparse modifier. +SPARSE_EMBEDDINGS_MODEL = os.environ.get("SPARSE_EMBEDDINGS_MODEL", "Qdrant/bm25") -TEXT_FIELD_NAME = "document" +TEXT_FIELD_NAME = os.environ.get("TEXT_FIELD_NAME", "document") + +# Named vectors on the hybrid collection. Leave DENSE_VECTOR_NAME empty for a +# collection with a single unnamed vector. +DENSE_VECTOR_NAME = os.environ.get("DENSE_VECTOR_NAME", "dense") +SPARSE_VECTOR_NAME = os.environ.get("SPARSE_VECTOR_NAME", "sparse") + +# Embed the query with Qdrant Cloud server-side inference. Defaults ON: the query +# model is a 1024-d mxbai, too heavy to embed per-request on a small CPU box. +# Parse leniently since some hosts keep the surrounding quotes on the value. +CLOUD_INFERENCE = os.environ.get("CLOUD_INFERENCE", "1").strip().strip('"').strip("'").lower() in ("1", "true", "yes") +RESULT_LIMIT = int(os.environ.get("RESULT_LIMIT", "20")) +HYBRID_PREFETCH = int(os.environ.get("HYBRID_PREFETCH", "40")) diff --git a/qdrant_demo/init_collection_startups.py b/qdrant_demo/init_collection_startups.py index 176a3ce..23d8280 100644 --- a/qdrant_demo/init_collection_startups.py +++ b/qdrant_demo/init_collection_startups.py @@ -1,99 +1,95 @@ +"""Build the startups collection the search path expects: a named `dense` vector +(mxbai) plus a `sparse` bm25 keyword vector with IDF, and a text index for keyword +search. Payload fields are renamed once here to the schema the frontend reads +(`document`, `logo_url`, `homepage_url`). Both vectors are embedded by Qdrant Cloud +inference, so the query and document sides use the identical models by construction. +Documents get no mxbai prefix (the query prefix is added at search time). +Run: python -m qdrant_demo.init_collection_startups +""" import json -import os.path +import os from typing import Iterable from qdrant_client import QdrantClient, models from tqdm import tqdm -from qdrant_demo.config import DATA_DIR, QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME, TEXT_FIELD_NAME, EMBEDDINGS_MODEL +from qdrant_demo.config import ( + DATA_DIR, QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME, TEXT_FIELD_NAME, + EMBEDDINGS_MODEL, SPARSE_EMBEDDINGS_MODEL, DENSE_VECTOR_NAME, SPARSE_VECTOR_NAME, +) +DENSE_DIM = 1024 # mxbai-embed-large-v1 -def read_points() -> Iterable[models.PointStruct]: - payload_path = os.path.join(DATA_DIR, 'startups_demo.json') - with open(payload_path) as fd: - for idx, line in enumerate(fd): - obj = json.loads(line) - # Rename fields to unified schema - obj["logo_url"] = obj.pop("images") - obj["homepage_url"] = obj.pop("link") - obj["document"] = obj.pop("description") - yield models.PointStruct( - id=idx, - vector=models.Document( - text=obj["document"], - model=EMBEDDINGS_MODEL, - ), - payload=obj, - ) +def _prepare(obj: dict) -> dict: + # Rename to the unified schema the frontend and search path read. + obj["logo_url"] = obj.pop("images", None) + obj["homepage_url"] = obj.pop("link", None) + obj[TEXT_FIELD_NAME] = obj.pop("description", "") + return obj -def upload_embeddings(): - client = QdrantClient( - url=QDRANT_URL, - api_key=QDRANT_API_KEY, - prefer_grpc=True, - ) +def _records() -> Iterable[dict]: + path = os.path.join(DATA_DIR, "startups_demo.json") + with open(path, encoding="utf-8") as fd: + for line in fd: + line = line.strip() + if line: + yield _prepare(json.loads(line)) - client.set_model(EMBEDDINGS_MODEL) - payload_path = os.path.join(DATA_DIR, 'startups_demo.json') - payload = [] - documents = [] +def _doc_text(obj: dict) -> str: + # Same text the searcher matches against: name + the document body. + return f"{obj.get('name', '')}. {obj.get(TEXT_FIELD_NAME, '')}".strip() - with open(payload_path) as fd: - for line in fd: - obj = json.loads(line) - # Rename fields to unified schema - documents.append(obj.pop('description')) - obj["logo_url"] = obj.pop("images") - obj["homepage_url"] = obj.pop("link") - payload.append(obj) + +def build(): + client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY, cloud_inference=True) if client.collection_exists(COLLECTION_NAME): - print(f"Collection {COLLECTION_NAME} already exists. Remove it first.") + print(f"{COLLECTION_NAME} exists, recreating.") client.delete_collection(COLLECTION_NAME) client.create_collection( - collection_name=COLLECTION_NAME, - vectors_config=models.VectorParams( - size=client.get_embedding_size(EMBEDDINGS_MODEL), - distance=models.Distance.COSINE, - on_disk=True, - ), - # Quantization is optional, but it can significantly reduce the memory usage + COLLECTION_NAME, + vectors_config={ + DENSE_VECTOR_NAME: models.VectorParams( + size=DENSE_DIM, distance=models.Distance.COSINE, on_disk=True, + ) + }, + sparse_vectors_config={ + # bm25 values carry term frequency; IDF is applied at query time. + SPARSE_VECTOR_NAME: models.SparseVectorParams(modifier=models.Modifier.IDF) + }, quantization_config=models.ScalarQuantization( scalar=models.ScalarQuantizationConfig( - type=models.ScalarType.INT8, - quantile=0.99, - always_ram=True + type=models.ScalarType.INT8, quantile=0.99, always_ram=True, ) - ) + ), ) - - # Create a payload index for text field. - # This index enables text search by the TEXT_FIELD_NAME field. client.create_payload_index( - collection_name=COLLECTION_NAME, - field_name=TEXT_FIELD_NAME, + COLLECTION_NAME, field_name=TEXT_FIELD_NAME, field_schema=models.TextIndexParams( - type=models.TextIndexType.TEXT, - tokenizer=models.TokenizerType.WORD, - min_token_len=2, - max_token_len=20, - lowercase=True, - ) + type=models.TextIndexType.TEXT, tokenizer=models.TokenizerType.WORD, + min_token_len=2, max_token_len=20, lowercase=True, + ), ) - # Upload points to the collection - # Embeddings will be automatically generated from the Document model - client.upload_points( - collection_name=COLLECTION_NAME, - points=tqdm(read_points()), - parallel=4, - batch_size=16, - ) + def points() -> Iterable[models.PointStruct]: + for idx, obj in enumerate(_records()): + text = _doc_text(obj) + yield models.PointStruct( + id=idx, + vector={ + DENSE_VECTOR_NAME: models.Document(text=text, model=EMBEDDINGS_MODEL), + SPARSE_VECTOR_NAME: models.Document(text=text, model=SPARSE_EMBEDDINGS_MODEL), + }, + payload=obj, + ) + + client.upload_points(COLLECTION_NAME, points=tqdm(points()), batch_size=64) + print(f"built {COLLECTION_NAME}: {client.count(COLLECTION_NAME).count} points") -if __name__ == '__main__': - upload_embeddings() +if __name__ == "__main__": + build() diff --git a/qdrant_demo/neural_searcher.py b/qdrant_demo/neural_searcher.py index cc8c945..1372ade 100644 --- a/qdrant_demo/neural_searcher.py +++ b/qdrant_demo/neural_searcher.py @@ -1,27 +1,76 @@ +import os import time -from typing import List from qdrant_client import QdrantClient, models -from qdrant_demo.config import QDRANT_URL, QDRANT_API_KEY, EMBEDDINGS_MODEL +from qdrant_demo.config import ( + QDRANT_URL, QDRANT_API_KEY, EMBEDDINGS_MODEL, SPARSE_EMBEDDINGS_MODEL, + DENSE_VECTOR_NAME, SPARSE_VECTOR_NAME, RESULT_LIMIT, HYBRID_PREFETCH, + CLOUD_INFERENCE, +) class NeuralSearcher: + """Dense (semantic) and hybrid (dense + bm25 keyword, fused with RRF) search + over a collection with a named dense vector and a bm25 sparse vector.""" def __init__(self, collection_name: str): self.collection_name = collection_name - self.qdrant_client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY, prefer_grpc=True) + # Fail loudly instead of hanging: measured novel-query latency is well + # under a second, so 15s is a generous ceiling that still surfaces a real + # problem quickly rather than masking it for a minute. + timeout = int(os.environ.get("QDRANT_TIMEOUT", "15")) + self.qdrant_client = QdrantClient( + url=QDRANT_URL, api_key=QDRANT_API_KEY, + cloud_inference=CLOUD_INFERENCE, timeout=timeout, + ) + + # mxbai is an asymmetric retrieval model: the query gets a prompt prefix, the + # stored documents do not, so applying it only here needs no re-indexing. + # Qdrant Cloud inference already applies this prompt server-side, so on the + # Cloud path the prefix is a verified no-op (identical scores). It matters for + # the self-hosted / local-embedding path, where nothing else adds it. + QUERY_PREFIX = "Represent this sentence for searching relevant passages: " + + def _dense(self, text: str): + return models.Document(text=self.QUERY_PREFIX + text, model=EMBEDDINGS_MODEL) + + def _sparse(self, text: str): + return models.Document(text=text, model=SPARSE_EMBEDDINGS_MODEL) - def search(self, text: str, filter_: dict = None) -> List[dict]: - start_time = time.time() - hits = self.qdrant_client.query_points( + def _dense_only(self, text: str): + return self.qdrant_client.query_points( collection_name=self.collection_name, - query=models.Document( - text=text, - model=EMBEDDINGS_MODEL, - ), - query_filter=models.Filter(**filter_) if filter_ else None, - limit=5 - ) - print(f"Search took {time.time() - start_time} seconds") - return [hit.payload for hit in hits.points] + query=self._dense(text), + using=DENSE_VECTOR_NAME or None, + limit=RESULT_LIMIT, + ).points + + def search(self, text: str, hybrid: bool = False) -> dict: + t0 = time.perf_counter() + mode = "hybrid" if hybrid else "semantic" + + if hybrid: + hits = self.qdrant_client.query_points( + collection_name=self.collection_name, + prefetch=[ + models.Prefetch(query=self._dense(text), using=DENSE_VECTOR_NAME or None, limit=HYBRID_PREFETCH), + models.Prefetch(query=self._sparse(text), using=SPARSE_VECTOR_NAME, limit=HYBRID_PREFETCH), + ], + query=models.FusionQuery(fusion=models.Fusion.RRF), + limit=RESULT_LIMIT, + ).points + else: + hits = self._dense_only(text) + + latency_ms = round((time.perf_counter() - t0) * 1000) + results = [{**hit.payload, "score": hit.score} for hit in hits] + stats = { + "mode": mode, + "embedding_model": EMBEDDINGS_MODEL, + # RRF fusion scores (~1/60) are not on the same scale as cosine (~0..1). + "score_type": "rrf" if mode == "hybrid" else "cosine", + "latency_ms": latency_ms, + "results": len(results), + } + return {"results": results, "stats": stats} diff --git a/qdrant_demo/service.py b/qdrant_demo/service.py index 3248bd0..60f0735 100644 --- a/qdrant_demo/service.py +++ b/qdrant_demo/service.py @@ -1,14 +1,20 @@ import os +import logging +from typing import Optional -from fastapi import FastAPI +from fastapi import FastAPI, HTTPException from fastapi.staticfiles import StaticFiles -from qdrant_demo.config import COLLECTION_NAME, STATIC_DIR +from qdrant_demo.config import ( + COLLECTION_NAME, STATIC_DIR, RESULT_LIMIT, CLOUD_INFERENCE, EMBEDDINGS_MODEL, +) from qdrant_demo.neural_searcher import NeuralSearcher from qdrant_demo.text_searcher import TextSearcher from fastapi.middleware.cors import CORSMiddleware +logger = logging.getLogger(__name__) + app = FastAPI() app.add_middleware( @@ -24,11 +30,39 @@ @app.get("/api/search") -async def read_item(q: str, neural: bool = True): - return { - "result": neural_searcher.search(text=q) - if neural else text_searcher.search(query=q) - } +async def read_item(q: str, mode: Optional[str] = None, neural: Optional[bool] = None): + """mode = semantic (dense) | keyword (full-text) | hybrid (dense + keyword). + + Back-compat with the older frontend, which passes `neural` (bool): + neural=true -> semantic (its original meaning), neural=false -> keyword. + When neither is given, default to hybrid. Explicit `mode` always wins.""" + if mode is None: + mode = "semantic" if neural is True else "keyword" if neural is False else "hybrid" + if not q.strip(): + return {"result": [], "stats": {"mode": mode}} + try: + if mode == "keyword": + return {"result": text_searcher.search(query=q, top=RESULT_LIMIT), + "stats": {"mode": "keyword"}} + out = neural_searcher.search(text=q, hybrid=(mode == "hybrid")) + return {"result": out["results"], "stats": out["stats"]} + except Exception as e: + logger.exception("search failed for q=%r mode=%s", q, mode) + raise HTTPException(status_code=502, detail="Search is temporarily unavailable.") + + +@app.get("/api/stats") +async def stats(): + """Live collection size, so the frontend can show off the scale.""" + try: + count = neural_searcher.qdrant_client.count(COLLECTION_NAME).count + return { + "count": count, "collection": COLLECTION_NAME, + "cloud_inference": CLOUD_INFERENCE, "model": EMBEDDINGS_MODEL, + } + except Exception: + logger.exception("stats failed") + raise HTTPException(status_code=502, detail="Stats are temporarily unavailable.") # Mount the static files directory once the search endpoint is defined