id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
4acc52497869-0 | Source code for langchain.vectorstores.typesense
"""Wrapper around Typesense vector search"""
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
fro... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
4acc52497869-1 | *,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ValueError(
"Could not import typesense python package. "... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
4acc52497869-2 | ]
def _create_collection(self, num_dim: int) -> None:
fields = [
{"name": "vec", "type": "float[]", "num_dim": num_dim},
{"name": f"{self._text_key}", "type": "string"},
{"name": ".*", "type": "auto"},
]
self._typesense_client.collections.create(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
4acc52497869-3 | self,
query: str,
k: int = 4,
filter: Optional[str] = "",
) -> List[Tuple[Document, float]]:
"""Return typesense documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. De... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
4acc52497869-4 | k: Number of Documents to return. Defaults to 4.
filter: typesense filter_by expression to filter documents on
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_score = self.similarity_search_with_score(query, k=k, filter=filter)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
4acc52497869-5 | }
typesense_api_key = typesense_api_key or get_from_env(
"typesense_api_key", "TYPESENSE_API_KEY"
)
client_config = {
"nodes": [node],
"api_key": typesense_api_key,
"connection_timeout_seconds": connection_timeout_seconds,
}
return ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
60ad5c3c5adc-0 | Source code for langchain.vectorstores.singlestoredb
"""Wrapper around SingleStore DB."""
from __future__ import annotations
import json
from typing import (
Any,
ClassVar,
Collection,
Iterable,
List,
Optional,
Tuple,
Type,
)
from sqlalchemy.pool import QueuePool
from langchain.docstore.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
60ad5c3c5adc-1 | timeout: float = 30,
**kwargs: Any,
):
"""Initialize with necessary components.
Args:
embedding (Embeddings): A text embedding model.
table_name (str, optional): Specifies the name of the table in use.
Defaults to "embeddings".
content_fiel... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
60ad5c3c5adc-2 | local_infile (bool, optional): Allows local file uploads.
charset (str, optional): Specifies the character set for string values.
ssl_key (str, optional): Specifies the path of the file containing the SSL
key.
ssl_cert (str, optional): Specifies the path of the file c... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
60ad5c3c5adc-3 | .. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
host="127.0.0.1",
port=3306,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
60ad5c3c5adc-4 | {} BLOB, {} JSON);""".format(
self.table_name,
self.content_field,
self.vector_field,
self.metadata_field,
),
)
finally:
cur.close()
finally:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
60ad5c3c5adc-5 | finally:
cur.close()
finally:
conn.close()
return []
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Returns the most similar indexed documents to the query text.
Uses cosine similarity.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
60ad5c3c5adc-6 | self.vector_field,
self.table_name,
),
(
"[{}]".format(",".join(map(str, embedding))),
k,
),
)
for row in cur.fetchall():
doc = Document... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
60ad5c3c5adc-7 | )
"""
instance = cls(
embedding,
table_name=table_name,
content_field=content_field,
metadata_field=metadata_field,
vector_field=vector_field,
pool_size=pool_size,
max_overflow=max_overflow,
timeout=timeout,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
bc839bff3176-0 | Source code for langchain.vectorstores.vectara
"""Wrapper around Vectara vector database."""
from __future__ import annotations
import json
import logging
import os
from hashlib import md5
from typing import Any, Iterable, List, Optional, Tuple, Type
import requests
from pydantic import Field
from langchain.embeddings.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
bc839bff3176-1 | or self._vectara_api_key is None
):
logging.warning(
"Cant find Vectara credentials, customer_id or corpus_id in "
"environment."
)
else:
logging.debug(f"Using corpus id {self._vectara_corpus_id}")
self._session = requests.Sessi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
bc839bff3176-2 | f"{response.status_code}, reason {response.reason}, text "
f"{response.text}"
)
return False
return True
def _index_doc(self, doc: dict) -> bool:
request: dict[str, Any] = {}
request["customer_id"] = self._vectara_customer_id
request["corpus_id... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
bc839bff3176-3 | metadatas = [{} for _ in texts]
doc = {
"document_id": doc_id,
"metadataJson": json.dumps({"source": "langchain"}),
"parts": [
{"text": text, "metadataJson": json.dumps(md)}
for text, md in zip(texts, metadatas)
],
}
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
bc839bff3176-4 | {
"query": [
{
"query": query,
"start": 0,
"num_results": k,
"context_config": {
"sentences_before": n_sentence_context,
"sentences_... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
bc839bff3176-5 | self,
query: str,
k: int = 5,
lambda_val: float = 0.025,
filter: Optional[str] = None,
n_sentence_context: int = 0,
**kwargs: Any,
) -> List[Document]:
"""Return Vectara documents most similar to query, along with scores.
Args:
query: Text ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
bc839bff3176-6 | Example:
.. code-block:: python
from langchain import Vectara
vectara = Vectara.from_texts(
texts,
vectara_customer_id=customer_id,
vectara_corpus_id=corpus_id,
vectara_api_key=api_key,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
bc839bff3176-7 | ) -> None:
"""Add text to the Vectara vectorstore.
Args:
texts (List[str]): The text
metadatas (List[dict]): Metadata dicts, must line up with existing store
"""
self.vectorstore.add_texts(texts, metadatas)
By Harrison Chase
© Copyright 2023, Harrison C... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
e4b7ea968c42-0 | Source code for langchain.vectorstores.analyticdb
"""VectorStore wrapper around a Postgres/PGVector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple
import sqlalchemy
from sqlalchemy import REAL, Index
from sqlalchemy.dialects.postg... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
e4b7ea968c42-1 | """
Get or create a collection.
Returns [Collection, bool] where the bool is True if the collection was created.
"""
created = False
collection = cls.get_by_name(session, name)
if collection:
return collection, created
collection = cls(name=name, cmeta... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
e4b7ea968c42-2 | """
VectorStore implementation using AnalyticDB.
AnalyticDB is a distributed full PostgresSQL syntax cloud-native database.
- `connection_string` is a postgres connection string.
- `embedding_function` any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
- `c... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
e4b7ea968c42-3 | engine = sqlalchemy.create_engine(self.connection_string)
conn = engine.connect()
return conn
[docs] def create_tables_if_not_exists(self) -> None:
Base.metadata.create_all(self._conn)
[docs] def drop_tables(self) -> None:
Base.metadata.drop_all(self._conn)
[docs] def create_col... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
e4b7ea968c42-4 | """
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
embeddings = self.embedding_function.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
with Session(self._conn) as session:
collection = self.get_collection(sessi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
e4b7ea968c42-5 | self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
) -> 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... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
e4b7ea968c42-6 | )
.filter(filter_by)
.order_by(EmbeddingStore.embedding.op("<->")(embedding))
.join(
CollectionStore,
EmbeddingStore.collection_id == CollectionStore.uuid,
)
.limit(k)
.all()
)
docs = [
(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
e4b7ea968c42-7 | pre_delete_collection: bool = False,
**kwargs: Any,
) -> AnalyticDB:
"""
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
e4b7ea968c42-8 | or set the PGVECTOR_CONNECTION_STRING environment variable.
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
connection_string = cls.get_connection_string(kwargs)
kwargs["connection_string"] = connection_string
return cls.from_te... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
40fade8f9055-0 | Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import os
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base imp... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-1 | return faiss
def _default_relevance_score_fn(score: float) -> float:
"""Return a similarity score on a scale [0, 1]."""
# 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 ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-2 | self._normalize_L2 = normalize_L2
def __add(
self,
texts: Iterable[str],
embeddings: Iterable[List[float]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
if not isinstance(self.docstore, AddableMixi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-3 | return [_id for _, _id, _ in full_info]
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-4 | ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"add... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-5 | raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append((doc, scores[0][j]))
return docs
[docs] def similarity_search_with_score(
self, query: str, k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-6 | k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k)
return [doc for doc, _ in docs_and_scores]
[docs] def max_marginal_relevance_search_by_vector(
s... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-7 | embeddings,
k=k,
lambda_mult=lambda_mult,
)
selected_indices = [indices[0][i] for i in mmr_selected]
docs = []
for i in selected_indices:
if i == -1:
# This happens when not enough docs are returned.
continue
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-8 | embedding, k, fetch_k, lambda_mult=lambda_mult
)
return docs
[docs] def merge_from(self, target: FAISS) -> None:
"""Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the curre... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-9 | **kwargs: Any,
) -> FAISS:
faiss = dependable_faiss_import()
index = faiss.IndexFlatL2(len(embeddings[0]))
vector = np.array(embeddings, dtype=np.float32)
if normalize_L2:
faiss.normalize_L2(vector)
index.add(vector)
documents = []
if ids is None:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-10 | embeddings = OpenAIEmbeddings()
faiss = FAISS.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
**k... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-11 | ids=ids,
**kwargs,
)
[docs] def save_local(self, folder_path: str, index_name: str = "index") -> None:
"""Save FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
40fade8f9055-12 | faiss = dependable_faiss_import()
index = faiss.read_index(
str(path / "{index_name}.faiss".format(index_name=index_name))
)
# load docstore and index_to_docstore_id
with open(path / "{index_name}.pkl".format(index_name=index_name), "rb") as f:
docstore, index_to_... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
a52838bcc4b3-0 | Source code for langchain.vectorstores.supabase
from __future__ import annotations
from itertools import repeat
from typing import (
TYPE_CHECKING,
Any,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.embe... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
a52838bcc4b3-1 | embedding: Embeddings,
table_name: str,
query_name: Union[str, None] = None,
) -> None:
"""Initialize with supabase client."""
try:
import supabase # noqa: F401
except ImportError:
raise ValueError(
"Could not import supabase python pa... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
a52838bcc4b3-2 | raise ValueError("Supabase document table_name is required.")
embeddings = embedding.embed_documents(texts)
docs = cls._texts_to_documents(texts, metadatas)
_ids = cls._add_vectors(client, table_name, embeddings, docs)
return cls(
client=client,
embedding=embeddin... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
a52838bcc4b3-3 | ) -> List[Tuple[Document, float]]:
match_documents_params = dict(query_embedding=query, match_count=k)
res = self._client.rpc(self.query_name, match_documents_params).execute()
match_result = [
(
Document(
metadata=search.get("metadata", {}), # ty... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
a52838bcc4b3-4 | ) -> List[Document]:
"""Return list of Documents from list of texts and metadatas."""
if metadatas is None:
metadatas = repeat({})
docs = [
Document(page_content=text, metadata=metadata)
for text, metadata in zip(texts, metadatas)
]
return docs... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
a52838bcc4b3-5 | self,
embedding: List[float],
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
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
a52838bcc4b3-6 | **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 ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
a52838bcc4b3-7 | )
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
6199e161a542-0 | Source code for langchain.vectorstores.lancedb
"""Wrapper around LanceDB vector database"""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base i... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
6199e161a542-1 | self._id_key = id_key
self._text_key = text_key
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Turn texts into embedding and add it to the database... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
6199e161a542-2 | """
embedding = self._embedding.embed_query(query)
docs = self._connection.search(embedding).limit(k).to_df()
return [
Document(
page_content=row[self._text_key],
metadata=row[docs.columns != self._text_key],
)
for _, row in doc... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
ff2ee3f76089-0 | Source code for langchain.vectorstores.qdrant
"""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,
Opti... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-1 | 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-clien... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-2 | "Using `embeddings` as `embedding_function` which is deprecated"
)
self._embeddings_function = embeddings
self.embeddings = None
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] =... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-3 | 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,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-4 | - 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... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-5 | 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... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-6 | 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,... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-7 | 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
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-8 | 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,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-9 | 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[prefi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-10 | 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"
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-11 | **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)... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-12 | )
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_facto... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-13 | 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_pay... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-14 | 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(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
ff2ee3f76089-15 | 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"):
embed... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
388733eb6075-0 | Source code for langchain.vectorstores.base
"""Interface for vector stores."""
from __future__ import annotations
import asyncio
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import (
Any,
ClassVar,
Collection,
Dict,
Iterable,
List,
Optional,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-1 | """Run more documents through the embeddings and add to the vectorstore.
Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-2 | )
[docs] async def asearch(
self, query: str, search_type: str, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_typ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-3 | query, k=k, **kwargs
)
if any(
similarity < 0.0 or similarity > 1.0
for _, similarity in docs_and_similarities
):
warnings.warn(
"Relevance scores must be between"
f" 0 and 1, got {docs_and_similarities}"
)
s... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-4 | return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
# This is a temporary workaround to make the similarity search
# asynchr... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-5 | 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
amon... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-6 | [docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
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 marg... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-7 | texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs] @classmethod
async def afrom_documents(
cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-8 | vectorstore: VectorStore
search_type: str = "similarity"
search_kwargs: dict = Field(default_factory=dict)
allowed_search_types: ClassVar[Collection[str]] = (
"similarity",
"similarity_score_threshold",
"mmr",
)
class Config:
"""Configuration for this pydantic object.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
388733eb6075-9 | docs = self.vectorstore.max_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def aget_relevant_documents(self, query: str) -> List[Document]:
if self.se... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
e8414c2133c8-0 | Source code for langchain.vectorstores.atlas
"""Wrapper around Atlas by Nomic."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e8414c2133c8-1 | is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool): Whether to reset this project if it
already exists. Default False.
Generally userful during development and testing.
"""
try:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e8414c2133c8-2 | metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]]): An optional list of ids.
refresh(bool): Whether or not to refresh indices with the updated data.
Default True.
Returns:
List[str]: List of IDs of the added texts... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e8414c2133c8-3 | else:
if metadatas is None:
data = [
{"text": text, AtlasDB._ATLAS_DEFAULT_ID_FIELD: ids[i]}
for i, text in enumerate(texts)
]
else:
for i, text in enumerate(texts):
metadatas[i]["text"] =... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e8414c2133c8-4 | """
if self._embedding_function is None:
raise NotImplementedError(
"AtlasDB requires an embedding_function for text similarity search!"
)
_embedding = self._embedding_function.embed_documents([query])[0]
embedding = np.array(_embedding).reshape(1, -1)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e8414c2133c8-5 | ids (Optional[List[str]]): Optional list of document IDs. If None,
ids will be auto created
description (str): A description for your project.
is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e8414c2133c8-6 | ids: Optional[List[str]] = None,
name: Optional[str] = None,
api_key: Optional[str] = None,
persist_directory: Optional[str] = None,
description: str = "A description for your project",
is_public: bool = True,
reset_project_if_exists: bool = False,
index_kwargs: O... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
e8414c2133c8-7 | return cls.from_texts(
name=name,
api_key=api_key,
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
description=description,
is_public=is_public,
reset_project_if_exists=reset_project_if_exists,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
cf1e7617c4b5-0 | Source code for langchain.vectorstores.myscale
"""Wrapper around MyScale vector database."""
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 pydantic import BaseSettings
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-1 | .. code-block:: python
{
'id': 'text_id',
'vector': 'text_embedding',
'text': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-2 | config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScale Wrapper to LangChain
embedding_function (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-3 | CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map['id']} String,
{self.config.column_map['text']} String,
{self.config.column_map['vector']} Array(Float32),
{self.config.column_map['metadata']} JSON,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-4 | _data.append(f"({n})")
i_str = f"""
INSERT INTO TABLE
{self.config.database}.{self.config.table}({ks})
VALUES
{','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> N... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-5 | column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) - set(column_names)) >= 0
keys, values = zip(*column_names.items())
try:
t = None
for v in self.pgbar(
zip(*values), desc="Inserting data...", total=len(metadatas)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-6 | texts (Iterable[str]): List or tuple of strings to be added
config (MyScaleSettings, Optional): Myscale configuration
text_ids (Optional[Iterable], optional): IDs for the texts.
Defaults to None.
batch_size (int, optional): Batchsi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-7 | ).named_results():
_repr += (
f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n"
)
_repr += "-" * 51 + "\n"
return _repr
def _build_qstr(
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
) -> str:
q_emb... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-8 | of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of Documents
"""
return self.similarity_search_by_v... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-9 | ]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
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]]:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
cf1e7617c4b5-10 | ]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] def drop(self) -> None:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
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