id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
0275cedc3677-4 | }
if filter:
payload["filter"] = filter
r = self._client.data().vector_search(self._table_name, payload=payload)
if r.status_code != 200:
raise Exception(f"Error running similarity search: {r.status_code} {r}")
hits = r["records"]
docs_and_scores = [
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/xata.html |
0275cedc3677-5 | ]
self._client.records().transaction(payload={"operations": operations})
else:
raise ValueError("Either ids or delete_all must be set.")
def _delete_all(self) -> None:
"""Delete all records in the table."""
while True:
r = self._client.data().query(sel... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/xata.html |
59e89252a429-0 | Source code for langchain.vectorstores.chroma
from __future__ import annotations
import base64
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
from langchain.docstore.document import Docum... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-1 | .. code-block:: python
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma("langchain_store", embeddings)
"""
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-2 | major, minor, _ = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) < 4:
client_settings.chroma_db_impl = "duckdb+parquet"
_client_settings = client_settings
elif persist_directory:
# Maintain backwards compatibility... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-3 | where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Query the chroma collection."""
try:
import chromadb # noqa: F401
except ImportError:
raise ValueError(
"Could not import chromadb python package. "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-4 | # Populate IDs
if ids is None:
ids = [str(uuid.uuid1()) for _ in uris]
embeddings = None
# Set embeddings
if self._embedding_function is not None and hasattr(
self._embedding_function, "embed_image"
):
embeddings = self._embedding_function.embe... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-5 | "langchain.vectorstores.utils.filter_complex_metadata."
)
raise ValueError(e.args[0] + "\n\n" + msg)
else:
raise e
if empty_ids:
images_without_metadatas = [uris[j] for j in empty_ids]
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-6 | embeddings = None
texts = list(texts)
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(texts)
if metadatas:
# fill metadatas with empty dicts if somebody
# did not specify metadata for all texts
length_... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-7 | else:
raise e
if empty_ids:
texts_without_metadatas = [texts[j] for j in empty_ids]
embeddings_without_metadatas = (
[embeddings[j] for j in empty_ids] if embeddings else None
)
ids_without_metadatas ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-8 | where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding (List[float]): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-9 | query_embeddings=embedding,
n_results=k,
where=filter,
where_document=where_document,
)
return _results_to_docs_and_scores(results)
[docs] def similarity_search_with_score(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-10 | The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if se... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-11 | Args:
embedding: Embedding 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.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-12 | ) -> 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 ret... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-13 | include: Optional[List[str]] = None,
) -> Dict[str, Any]:
"""Gets the collection.
Args:
ids: The ids of the embeddings to get. Optional.
where: A Where type dict used to filter results by.
E.g. `{"color" : "red", "price": 4.20}`. Optional.
limit... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-14 | # Maintain backwards compatibility with chromadb < 0.4.0
major, minor, _ = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) < 4:
self._client.persist()
[docs] def update_document(self, document_id: str, document: Document) -> None:
"""Update a document in the coll... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-15 | embeddings=batch[1],
documents=batch[3],
metadatas=batch[2],
)
else:
self._collection.update(
ids=ids,
embeddings=embeddings,
documents=text,
metadatas=metadata,
)
[doc... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-16 | Defaults to None.
Returns:
Chroma: Chroma vectorstore.
"""
chroma_collection = cls(
collection_name=collection_name,
embedding_function=embedding,
persist_directory=persist_directory,
client_settings=client_settings,
client=... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
59e89252a429-17 | collection_metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
col... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
3454d191cb44-0 | Source code for langchain.vectorstores.starrocks
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.pydantic_v1 import BaseSettin... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-1 | for idx, datum in enumerate(value):
k = columns[idx][0]
r[k] = datum
result.append(r)
debug_output(result)
cursor.close()
return result
[docs]class StarRocksSettings(BaseSettings):
"""StarRocks client configuration.
Attribute:
StarRocks_host (str) : An URL to ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-2 | "metadata": "metadata",
}
database: str = "default"
table: str = "langchain"
def __getitem__(self, item: str) -> Any:
return getattr(self, item)
class Config:
env_file = ".env"
env_prefix = "starrocks_"
env_file_encoding = "utf-8"
[docs]class StarRocks(VectorStore):
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-3 | except ImportError:
# Just in case if tqdm is not installed
self.pgbar = lambda x, **kwargs: x
super().__init__()
if config is not None:
self.config = config
else:
self.config = StarRocksSettings()
assert self.config
assert self.con... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-4 | [docs] def escape_str(self, value: str) -> str:
return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
@property
def embeddings(self) -> Embeddings:
return self.embedding_function
def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-5 | """Insert more texts through the embeddings and add to the VectorStore.
Args:
texts: Iterable of strings to add to the VectorStore.
ids: Optional list of ids to associate with the texts.
batch_size: Batch size of insertion
metadata: Optional column data to be inse... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-6 | if t:
t.join()
self._insert(transac, keys)
return [i for i in ids]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] @classmethod
def from_texts(
cls,
tex... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-7 | """Text representation for StarRocks Vector Store, prints backends, username
and schemas. Easy to use with `str(StarRocks())`
Returns:
repr: string to show connection info and data schema
"""
_repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-8 | return _repr
def _build_query_sql(
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
) -> str:
q_emb_str = ",".join(map(str, q_emb))
if where_str:
where_str = f"WHERE {where_str}"
else:
where_str = ""
q_str = f"""
SEL... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-9 | """
return self.similarity_search_by_vector(
self.embedding_function.embed_query(query), k, where_str, **kwargs
)
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
)... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-10 | return []
[docs] def similarity_search_with_relevance_scores(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Perform a similarity search with StarRocks
Args:
query (str): query string
k (int, optio... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
3454d191cb44-11 | f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}",
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata"] | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
eec294367d7f-0 | Source code for langchain.vectorstores.qdrant
from __future__ import annotations
import asyncio
import functools
import uuid
import warnings
from itertools import islice
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
AsyncGenerator,
Callable,
Dict,
Generator,
Iterab... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-1 | # by removing the first letter from the method name. For example,
# if the async method is called ``aaad_texts``, the synchronous method
# will be called ``aad_texts``.
sync_method = functools.partial(
getattr(self, method.__name__[1:]), *args, **kwargs
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-2 | "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 No... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-3 | def embeddings(self) -> Optional[Embeddings]:
return self._embeddings
[docs] 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]:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-4 | 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:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-5 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-6 | filter: Optional[MetadataFilter] = 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 N... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-7 | 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:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-8 | 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.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-9 | **kwargs,
)
[docs] 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,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-10 | - 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score_by_ve... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-11 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-12 | **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.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-13 | "filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
query_vector = embedding
if self.vector_name ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-14 | from qdrant_client import grpc # noqa
from qdrant_client.conversions.conversion import RestToGrpc
from qdrant_client.http import models as rest
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdra... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-15 | 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 do... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-16 | """
response = await self._asearch_with_score_by_vector(
embedding,
k=k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-17 | filter: Filter by metadata. Defaults to None.
search_params: Additional search params
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 hig... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-18 | lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Re... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-19 | all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
QdrantClient.async_grpc_points.Search().
Returns:
List of Documents selected by maximal marginal rele... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-20 | of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-21 | self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-22 | - 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 pr... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-23 | 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 t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-24 | results = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=filter,
search_params=search_params,
limit=fetch_k,
with_payload=True,
with_vectors=True,
score_threshold=score_thre... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-25 | 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 t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-26 | )
for i in mmr_selected
]
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-27 | vector_name: Optional[str] = VECTOR_NAME,
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.HnswCon... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-28 | 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:... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-29 | Default: None
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.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-30 | 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 langchai... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-31 | 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] = N... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-32 | 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:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-33 | '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
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-34 | 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
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-35 | content_payload_key,
metadata_payload_key,
vector_name,
shard_number,
replication_factor,
write_consistency_factor,
on_disk_payload,
hnsw_config,
optimizers_config,
wal_config,
quantization_config,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-36 | 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_typ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-37 | if force_recreate:
raise ValueError
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is being thrown.
collection_info = client.get_collectio... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-38 | f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-39 | on_disk=on_disk,
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-40 | 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 = METADAT... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-41 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-42 | raise QdrantException(
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}." # noq... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-43 | f"Existing Qdrant collection is configured for "
f"{current_vector_config.distance} " # type: ignore[union-attr]
f"similarity. Please set `distance_func` parameter to "
f"`{distance_func}` if you want to reuse it. If you want to "
f"recrea... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-44 | distance_strategy=distance_func,
vector_name=vector_name,
)
return qdrant
@staticmethod
def _cosine_relevance_score_fn(distance: float) -> float:
"""Normalize the distance to a score on a scale [0, 1]."""
return (distance + 1.0) / 2.0
def _select_relevance_score_f... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-45 | **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.si... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-46 | metadata_payload_key: str,
) -> Document:
from qdrant_client.conversions.conversion import grpc_to_payload
payload = grpc_to_payload(scored_point.payload)
return Document(
page_content=payload[content_payload_key],
metadata=payload.get(metadata_payload_key) or {},
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-47 | 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 ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-48 | embeddings = await self.embeddings.aembed_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_func... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-49 | )
for point_id, vector, payload in zip(
batch_ids,
batch_embeddings,
self._build_payloads(
batch_texts,
batch_metadatas,
self.content_payload_key,
s... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
eec294367d7f-50 | batch_texts,
batch_metadatas,
self.content_payload_key,
self.metadata_payload_key,
),
)
]
yield batch_ids, points | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
4bfae7e95860-0 | Source code for langchain.vectorstores.epsilla
"""Wrapper around Epsilla vector database."""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embedd... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
4bfae7e95860-1 | """
_LANGCHAIN_DEFAULT_DB_NAME = "langchain_store"
_LANGCHAIN_DEFAULT_DB_PATH = "/tmp/langchain-epsilla"
_LANGCHAIN_DEFAULT_TABLE_NAME = "langchain_collection"
[docs] def __init__(
self,
client: Any,
embeddings: Embeddings,
db_path: Optional[str] = _LANGCHAIN_DEFAULT_DB_PA... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
4bfae7e95860-2 | """
self._collection_name = collection_name
[docs] def clear_data(self, collection_name: str = "") -> None:
"""
Clear data in a collection.
Args:
collection_name (Optional[str]): The name of the collection.
If not provided, the default collection will be us... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
4bfae7e95860-3 | dim = len(embeddings[0])
fields: List[dict] = [
{"name": "id", "dataType": "INT"},
{"name": "text", "dataType": "STRING"},
{"name": "embeddings", "dataType": "VECTOR_FLOAT", "dimensions": dim},
]
if metadatas is not None:
field_names = [field["name... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
4bfae7e95860-4 | drop_old: Optional[bool] = False,
**kwargs: Any,
) -> List[str]:
"""
Embed texts and add them to the database.
Args:
texts (Iterable[str]): The texts to embed.
metadatas (Optional[List[dict]]): Metadata dicts
attached to each of the tex... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
4bfae7e95860-5 | metadata = metadatas[index].items()
for key, value in metadata:
record[key] = value
records.append(record)
status_code, response = self._client.insert(
table_name=collection_name, records=records
)
if status_code != 200:
log... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
4bfae7e95860-6 | return list(
map(
lambda item: Document(
page_content=item["text"],
metadata={
key: item[key] for key in item if key not in exclude_keys
},
),
response["result"],
)... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
4bfae7e95860-7 | drop_old (Optional[bool]): Whether to drop the previous collection
and create a new one. Defaults to False.
Returns:
Epsilla: Epsilla vector store.
"""
instance = Epsilla(client, embedding, db_path=db_path, db_name=db_name)
instance.add_texts(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
4bfae7e95860-8 | collection_name (Optional[str]): Which collection to use.
Defaults to "langchain_collection".
If provided, default collection name will be set as well.
drop_old (Optional[bool]): Whether to drop the previous collection
and create a new one. Default... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/epsilla.html |
1437a67e6df1-0 | Source code for langchain.vectorstores.dingo
from __future__ import annotations
import logging
import uuid
from typing import Any, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore impo... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dingo.html |
1437a67e6df1-1 | dingo_client = client
else:
try:
# connect to dingo db
dingo_client = dingodb.DingoDB(user, password, host)
except ValueError as e:
raise ValueError(f"Dingo failed to connect: {e}")
self._text_key = text_key
self._client = d... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dingo.html |
1437a67e6df1-2 | """
# Embed and create the documents
ids = ids or [str(uuid.uuid1().int)[:13] for _ in texts]
metadatas_list = []
texts = list(texts)
embeds = self._embedding.embed_documents(texts)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dingo.html |
1437a67e6df1-3 | [docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
search_params: Optional[dict] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return Dingo documents most similar to query, along with scores.... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dingo.html |
1437a67e6df1-4 | k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
search_params: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dingo.html |
1437a67e6df1-5 | for metadata in selected
]
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
search_params: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dingo.html |
1437a67e6df1-6 | host: List[str] = ["172.20.31.10:13000"],
user: str = "root",
password: str = "123123",
batch_size: int = 500,
**kwargs: Any,
) -> Dingo:
"""Construct Dingo wrapper from raw documents.
This is a user friendly interface that:
1. Embeds docum... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dingo.html |
1437a67e6df1-7 | ):
dingo_client.create_index(
index_name, dimension=dimension, auto_id=False
)
else:
if (
index_name is not None
and index_name not in dingo_client.get_index()
and index_name.upper() not in dingo_clie... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dingo.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.