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langchain/langchain/vectorstores/qdrant.py
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Slawomir Gonet eef62bf4e9 qdrant: search by vector (#6043) 
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Added support to `search_by_vector` to Qdrant Vector store.

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VectorStores / Retrievers / Memory
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2023-06-17 09:44:28 -07:00

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"""Wrapper around Qdrant vector database."""
from __future__ import annotations
import uuid
import warnings
from itertools import islice
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
from qdrant_client.conversions import common_types
from qdrant_client.http import models as rest
DictFilter = Dict[str, Union[str, int, bool, dict, list]]
MetadataFilter = Union[DictFilter, common_types.Filter]
class Qdrant(VectorStore):
"""Wrapper around Qdrant vector database.
To use you should have the ``qdrant-client`` package installed.
Example:
.. code-block:: python
from qdrant_client import QdrantClient
from langchain import Qdrant
client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
"""
CONTENT_KEY = "page_content"
METADATA_KEY = "metadata"
def __init__(
self,
client: Any,
collection_name: str,
embeddings: Optional[Embeddings] = None,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
embedding_function: Optional[Callable] = None, # deprecated
):
"""Initialize with necessary components."""
try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
if not isinstance(client, qdrant_client.QdrantClient):
raise ValueError(
f"client should be an instance of qdrant_client.QdrantClient, "
f"got {type(client)}"
)
if embeddings is None and embedding_function is None:
raise ValueError(
"`embeddings` value can't be None. Pass `Embeddings` instance."
)
if embeddings is not None and embedding_function is not None:
raise ValueError(
"Both `embeddings` and `embedding_function` are passed. "
"Use `embeddings` only."
)
self.embeddings = embeddings
self._embeddings_function = embedding_function
self.client: qdrant_client.QdrantClient = client
self.collection_name = collection_name
self.content_payload_key = content_payload_key or self.CONTENT_KEY
self.metadata_payload_key = metadata_payload_key or self.METADATA_KEY
if embedding_function is not None:
warnings.warn(
"Using `embedding_function` is deprecated. "
"Pass `Embeddings` instance to `embeddings` instead."
)
if not isinstance(embeddings, Embeddings):
warnings.warn(
"`embeddings` should be an instance of `Embeddings`."
"Using `embeddings` as `embedding_function` which is deprecated"
)
self._embeddings_function = embeddings
self.embeddings = None
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size:
How many vectors upload per-request.
Default: 64
Returns:
List of ids from adding the texts into the vectorstore.
"""
from qdrant_client.http import models as rest
added_ids = []
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
while batch_texts := list(islice(texts_iterator, batch_size)):
# Take the corresponding metadata and id for each text in a batch
batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
batch_ids = list(islice(ids_iterator, batch_size))
self.client.upsert(
collection_name=self.collection_name,
points=rest.Batch.construct(
ids=batch_ids,
vectors=self._embed_texts(batch_texts),
payloads=self._build_payloads(
batch_texts,
batch_metadatas,
self.content_payload_key,
self.metadata_payload_key,
),
),
)
added_ids.extend(batch_ids)
return added_ids
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score(
query,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
Returns:
List of documents most similar to the query text and cosine
distance in float for each.
Lower score represents more similarity.
"""
return self.similarity_search_with_score_by_vector(
self._embed_query(query),
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
Returns:
List of documents most similar to the query text and cosine
distance in float for each.
Lower score represents more similarity.
"""
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdrant-client "
"filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
results = self.client.search(
collection_name=self.collection_name,
query_vector=embedding,
query_filter=qdrant_filter,
search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return [
(
self._document_from_scored_point(
result, self.content_payload_key, self.metadata_payload_key
),
result.score,
)
for result in results
]
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
return self.similarity_search_with_score(query, k, **kwargs)
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self._embed_query(query)
results = self.client.search(
collection_name=self.collection_name,
query_vector=embedding,
with_payload=True,
with_vectors=True,
limit=fetch_k,
)
embeddings = [result.vector for result in results]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
return [
self._document_from_scored_point(
results[i], self.content_payload_key, self.metadata_payload_key
)
for i in mmr_selected
]
@classmethod
def from_texts(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
batch_size: int = 64,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_types.InitFrom] = None,
**kwargs: Any,
) -> Qdrant:
"""Construct Qdrant wrapper from a list of texts.
Args:
texts: A list of texts to be indexed in Qdrant.
embedding: A subclass of `Embeddings`, responsible for text vectorization.
metadatas:
An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location:
If `:memory:` - use in-memory Qdrant instance.
If `str` - use it as a `url` parameter.
If `None` - fallback to relying on `host` and `port` parameters.
url: either host or str of "Optional[scheme], host, Optional[port],
Optional[prefix]". Default: `None`
port: Port of the REST API interface. Default: 6333
grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc:
If true - use gPRC interface whenever possible in custom methods.
Default: False
https: If true - use HTTPS(SSL) protocol. Default: None
api_key: API key for authentication in Qdrant Cloud. Default: None
prefix:
If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout:
Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host:
Host name of Qdrant service. If url and host are None, set to
'localhost'. Default: None
path:
Path in which the vectors will be stored while using local mode.
Default: None
collection_name:
Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func:
Distance function. One of: "Cosine" / "Euclid" / "Dot".
Default: "Cosine"
content_payload_key:
A payload key used to store the content of the document.
Default: "page_content"
metadata_payload_key:
A payload key used to store the metadata of the document.
Default: "metadata"
batch_size:
How many vectors upload per-request.
Default: 64
shard_number: Number of shards in collection. Default is 1, minimum is 1.
replication_factor:
Replication factor for collection. Default is 1, minimum is 1.
Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor:
Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will make the collection more
resilient to inconsistencies, but will also make it fail if not enough
replicas are available.
Does not have any performance impact.
Have effect only in distributed mode.
on_disk_payload:
If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are
indexed - remain in RAM.
hnsw_config: Params for HNSW index
optimizers_config: Params for optimizer
wal_config: Params for Write-Ahead-Log
quantization_config:
Params for quantization, if None - quantization will be disabled
init_from:
Use data stored in another collection to initialize this collection
**kwargs:
Additional arguments passed directly into REST client initialization
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
3. Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
"""
try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
from qdrant_client.http import models as rest
# Just do a single quick embedding to get vector size
partial_embeddings = embedding.embed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
collection_name = collection_name or uuid.uuid4().hex
distance_func = distance_func.upper()
client = qdrant_client.QdrantClient(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
client.recreate_collection(
collection_name=collection_name,
vectors_config=rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
),
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout, # type: ignore[arg-type]
)
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
while batch_texts := list(islice(texts_iterator, batch_size)):
# Take the corresponding metadata and id for each text in a batch
batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
batch_ids = list(islice(ids_iterator, batch_size))
# Generate the embeddings for all the texts in a batch
batch_embeddings = embedding.embed_documents(batch_texts)
client.upsert(
collection_name=collection_name,
points=rest.Batch.construct(
ids=batch_ids,
vectors=batch_embeddings,
payloads=cls._build_payloads(
batch_texts,
batch_metadatas,
content_payload_key,
metadata_payload_key,
),
),
)
return cls(
client=client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
)
@classmethod
def _build_payloads(
cls,
texts: Iterable[str],
metadatas: Optional[List[dict]],
content_payload_key: str,
metadata_payload_key: str,
) -> List[dict]:
payloads = []
for i, text in enumerate(texts):
if text is None:
raise ValueError(
"At least one of the texts is None. Please remove it before "
"calling .from_texts or .add_texts on Qdrant instance."
)
metadata = metadatas[i] if metadatas is not None else None
payloads.append(
{
content_payload_key: text,
metadata_payload_key: metadata,
}
)
return payloads
@classmethod
def _document_from_scored_point(
cls,
scored_point: Any,
content_payload_key: str,
metadata_payload_key: str,
) -> Document:
return Document(
page_content=scored_point.payload.get(content_payload_key),
metadata=scored_point.payload.get(metadata_payload_key) or {},
)
def _build_condition(self, key: str, value: Any) -> List[rest.FieldCondition]:
from qdrant_client.http import models as rest
out = []
if isinstance(value, dict):
for _key, value in value.items():
out.extend(self._build_condition(f"{key}.{_key}", value))
elif isinstance(value, list):
for _value in value:
if isinstance(_value, dict):
out.extend(self._build_condition(f"{key}[]", _value))
else:
out.extend(self._build_condition(f"{key}", _value))
else:
out.append(
rest.FieldCondition(
key=f"{self.metadata_payload_key}.{key}",
match=rest.MatchValue(value=value),
)
)
return out
def _qdrant_filter_from_dict(
self, filter: Optional[DictFilter]
) -> Optional[rest.Filter]:
from qdrant_client.http import models as rest
if not filter:
return None
return rest.Filter(
must=[
condition
for key, value in filter.items()
for condition in self._build_condition(key, value)
]
)
def _embed_query(self, query: str) -> List[float]:
"""Embed query text.
Used to provide backward compatibility with `embedding_function` argument.
Args:
query: Query text.
Returns:
List of floats representing the query embedding.
"""
if self.embeddings is not None:
embedding = self.embeddings.embed_query(query)
else:
if self._embeddings_function is not None:
embedding = self._embeddings_function(query)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embedding.tolist() if hasattr(embedding, "tolist") else embedding
def _embed_texts(self, texts: Iterable[str]) -> List[List[float]]:
"""Embed search texts.
Used to provide backward compatibility with `embedding_function` argument.
Args:
texts: Iterable of texts to embed.
Returns:
List of floats representing the texts embedding.
"""
if self.embeddings is not None:
embeddings = self.embeddings.embed_documents(list(texts))
if hasattr(embeddings, "tolist"):
embeddings = embeddings.tolist()
elif self._embeddings_function is not None:
embeddings = []
for text in texts:
embedding = self._embeddings_function(text)
if hasattr(embeddings, "tolist"):
embedding = embedding.tolist()
embeddings.append(embedding)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embeddings
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