id stringlengths 14 16 | text stringlengths 31 3.14k | source stringlengths 58 124 |
|---|---|---|
9baa826ab02b-10 | else:
search_query = _default_approximate_search_query(
embedding, size, k, vector_field
)
elif search_type == SCRIPT_SCORING_SEARCH:
space_type = _get_kwargs_value(kwargs, "space_type", "l2")
pre_filter = _get_kwargs_value(kwargs, "pre... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
9baa826ab02b-11 | return documents
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
bulk_size: int = 500,
**kwargs: Any,
) -> OpenSearchVectorSearch:
"""Construct OpenSearchVectorSearch wrapper from... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
9baa826ab02b-12 | lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new eleme... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
9baa826ab02b-13 | vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
text_field = _get_kwargs_value(kwargs, "text_field", "text")
if is_appx_search:
engine = _get_kwargs_value(kwargs, "engine", "nmslib")
space_type = _get_kwargs_value(kwargs, "space_type", "l2")
e... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
132d9862be1c-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... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-1 | passive_deletes=True,
)
@classmethod
def get_by_name(cls, session: Session, name: str) -> Optional["CollectionStore"]:
return session.query(cls).filter(cls.name == name).first()
@classmethod
def get_or_create(
cls,
session: Session,
name: str,
cmetadata: Optio... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-2 | cmetadata = sqlalchemy.Column(JSON, nullable=True)
# custom_id : any user defined id
custom_id = sqlalchemy.Column(sqlalchemy.String, nullable=True)
# The following line creates an index named 'langchain_pg_embedding_vector_idx'
langchain_pg_embedding_vector_idx = Index(
"langchain_pg_embedding_... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-3 | (default: False)
- Useful for testing.
"""
def __init__(
self,
connection_string: str,
embedding_function: Embeddings,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
collection_metadata: Optional[dict] = None,
pre_delete_collection: bool = Fals... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-4 | Base.metadata.drop_all(self._conn)
[docs] def create_collection(self) -> None:
if self.pre_delete_collection:
self.delete_collection()
with Session(self._conn) as session:
CollectionStore.get_or_create(
session, self.collection_name, cmetadata=self.collection_m... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-5 | """
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... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-6 | embedding=embedding,
k=k,
filter=filter,
)
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-7 | filter_clauses.append(filter_by_metadata)
filter_by = sqlalchemy.and_(filter_by, *filter_clauses)
results: List[QueryResult] = (
session.query(
EmbeddingStore,
func.l2_distance(EmbeddingStore.embedding, embedding).label("distance"),
)
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-8 | Returns:
List of Documents most similar to the query vector.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return [doc for doc, _ in docs_and_scores]
[docs] @classmethod
def from_texts(
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-9 | data=kwargs,
key="connection_string",
env_key="PGVECTOR_CONNECTION_STRING",
)
if not connection_string:
raise ValueError(
"Postgres connection string is required"
"Either pass it as a parameter"
"or set the PGVECTOR_CONN... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
132d9862be1c-10 | cls,
driver: str,
host: str,
port: int,
database: str,
user: str,
password: str,
) -> str:
"""Return connection string from database parameters."""
return f"postgresql+{driver}://{user}:{password}@{host}:{port}/{database}"
By Harrison Chase
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
9cca7ce59b56-0 | Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import math
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 import Addabl... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-1 | # This function converts the euclidean norm of normalized embeddings
# (0 is most similar, sqrt(2) most dissimilar)
# to a similarity function (0 to 1)
return 1.0 - score / math.sqrt(2)
[docs]class FAISS(VectorStore):
"""Wrapper around FAISS vector database.
To use, you should have the ``faiss`` pyt... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-2 | "If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-3 | metadatas: Optional list of metadatas associated with the texts.
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 ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-4 | embeddings = [te[1] for te in text_embeddings]
return self.__add(texts, embeddings, metadatas, **kwargs)
[docs] def similarity_search_with_score_by_vector(
self, embedding: List[float], k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-5 | k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function(query)
docs = self.similarity_search_with_score_by_vector(embedding, k)
return docs
[docs] def similarit... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-6 | return [doc for doc, _ in docs_and_scores]
[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 maxim... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-7 | docs = []
for i in selected_indices:
if i == -1:
# This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise V... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-8 | """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 current one
Returns:
None.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError("... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-9 | **kwargs: Any,
) -> FAISS:
faiss = dependable_faiss_import()
index = faiss.IndexFlatL2(len(embeddings[0]))
index.add(np.array(embeddings, dtype=np.float32))
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
do... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-10 | faiss = FAISS.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts,
embeddings,
embedding,
metadatas,
**kwargs,
)
[docs] @classmethod
def from_embeddings(
cls,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-11 | embeddings,
embedding,
metadatas,
**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, docsto... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
9cca7ce59b56-12 | embeddings: Embeddings to use when generating queries
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
# load index separately since it is not picklable
faiss = dependable_faiss_import()
index = faiss.read_index(
str(path... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
8131cf302e6f-0 | Source code for langchain.vectorstores.milvus
"""Wrapper around the Milvus vector database."""
from __future__ import annotations
import logging
from typing import Any, Iterable, List, Optional, Tuple, Union
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from langchain.embedd... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-1 | See the following documentation for how to run a Milvus instance:
https://milvus.io/docs/install_standalone-docker.md
If looking for a hosted Milvus, take a looka this documentation:
https://zilliz.com/cloud
IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
The... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-2 | write the client.pem path.
ca_pem_path (str): If use tls two-way authentication, need to write
the ca.pem path.
server_pem_path (str): If use tls one-way authentication, need to
write the server.pem path.
server_name (str): If use tls, need to write th... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-3 | "IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}},
"HNSW": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_SQ": {"metric_type": "L2", "params... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-4 | self._primary_field = "pk"
# In order for compatiblility, the text field will need to be called "text"
self._text_field = "text"
# In order for compatbility, the vector field needs to be called "vector"
self._vector_field = "vector"
self.fields: list[str] = []
# Create th... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-5 | uri: str = connection_args.get("uri", None)
user = connection_args.get("user", None)
# Order of use is host/port, uri, address
if host is not None and port is not None:
given_address = str(host) + ":" + str(port)
elif uri is not None:
given_address = uri.split("ht... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-6 | raise e
def _init(
self, embeddings: Optional[list] = None, metadatas: Optional[list[dict]] = None
) -> None:
if embeddings is not None:
self._create_collection(embeddings, metadatas)
self._extract_fields()
self._create_index()
self._create_search_params()
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-7 | elif dtype == DataType.VARCHAR:
fields.append(FieldSchema(key, DataType.VARCHAR, max_length=65_535))
else:
fields.append(FieldSchema(key, dtype))
# Create the text field
fields.append(
FieldSchema(self._text_field, DataType.VARCHAR, max... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-8 | # Since primary field is auto-id, no need to track it
self.fields.remove(self._primary_field)
def _get_index(self) -> Optional[dict[str, Any]]:
"""Return the vector index information if it exists"""
from pymilvus import Collection
if isinstance(self.col, Collection):
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-9 | self._vector_field,
index_params=self.index_params,
using=self.alias,
)
logger.debug(
"Successfully created an index on collection: %s",
self.collection_name,
)
except ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-10 | ) -> List[str]:
"""Insert text data into Milvus.
Inserting data when the collection has not be made yet will result
in creating a new Collection. The data of the first entity decides
the schema of the new collection, the dim is extracted from the first
embedding and the columns a... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-11 | if not isinstance(self.col, Collection):
self._init(embeddings, metadatas)
# Dict to hold all insert columns
insert_dict: dict[str, list] = {
self._text_field: texts,
self._vector_field: embeddings,
}
# Collect the metadata into the insert dict.
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-12 | k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search against the query string.
Args:
query (str): The text to search.
k (int, opt... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-13 | """Perform a similarity search against the query string.
Args:
embedding (List[float]): The embedding vector to search.
k (int, optional): How many results to return. Defaults to 4.
param (dict, optional): The search params for the index type.
Defaults to None... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-14 | Args:
query (str): The text being searched.
k (int, optional): The amount of results ot return. Defaults to 4.
param (dict): The search params for the specified index.
Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-15 | **kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Perform a search on a query string and return results with score.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/se... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-16 | )
# Organize results.
ret = []
for result in res[0]:
meta = {x: result.entity.get(x) for x in output_fields}
doc = Document(page_content=meta.pop(self._text_field), metadata=meta)
pair = (doc, result.score)
ret.append(pair)
return ret
[docs... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-17 | Returns:
List[Document]: Document results for search.
"""
if self.col is None:
logger.debug("No existing collection to search.")
return []
embedding = self.embedding_func.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-18 | Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Document]: Document resul... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-19 | timeout=timeout,
)
# Reorganize the results from query to match search order.
vectors = {x[self._primary_field]: x[self._vector_field] for x in vectors}
ordered_result_embeddings = [vectors[x] for x in ids]
# Get the new order of results.
new_ordering = maximal_marginal_r... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
8131cf302e6f-20 | embedding (Embeddings): Embedding function.
metadatas (Optional[List[dict]]): Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional): Collection name to use. Defaults to
"LangChainCollection".
connection_args (dict[str, ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
7ec076f78d89-0 | Source code for langchain.vectorstores.annoy
"""Wrapper around Annoy vector database."""
from __future__ import annotations
import os
import pickle
import uuid
from configparser import ConfigParser
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from l... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-1 | def __init__(
self,
embedding_function: Callable,
index: Any,
metric: str,
docstore: Docstore,
index_to_docstore_id: Dict[int, str],
):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
self.index = index
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-2 | docs.append((doc, dist))
return docs
[docs] def similarity_search_with_score_by_vector(
self, embedding: List[float], k: int = 4, search_k: int = -1
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-3 | docstore_index, k, search_k=search_k, include_distances=True
)
return self.process_index_results(idxs, dists)
[docs] def similarity_search_with_score(
self, query: str, k: int = 4, search_k: int = -1
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-4 | Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding, k, search_k
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search_by_index(
self, docstore_index:... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-5 | Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k, search_k)
return [doc for doc, _ in docs_and_scores]
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k:... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-6 | embeddings,
k=k,
lambda_mult=lambda_mult,
)
# ignore the -1's if not enough docs are returned/indexed
selected_indices = [idxs[i] for i in mmr_selected if i != -1]
docs = []
for i in selected_indices:
_id = self.index_to_docstore_id[i]
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-7 | embedding, k, fetch_k, lambda_mult=lambda_mult
)
return docs
@classmethod
def __from(
cls,
texts: List[str],
embeddings: List[List[float]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
metric: str = DEFAULT_METRIC,
trees: ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-8 | docstore = InMemoryDocstore(
{index_to_id[i]: doc for i, doc in enumerate(documents)}
)
return cls(embedding.embed_query, index, metric, docstore, index_to_id)
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-9 | embeddings = embedding.embed_documents(texts)
return cls.__from(
texts, embeddings, embedding, metadatas, metric, trees, n_jobs, **kwargs
)
[docs] @classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-10 | text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = Annoy.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
7ec076f78d89-11 | [docs] @classmethod
def load_local(
cls,
folder_path: str,
embeddings: Embeddings,
) -> Annoy:
"""Load Annoy index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to load index, docstore,
and index_to_docstore_id ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
ec2e1d66e9df-0 | Source code for langchain.vectorstores.weaviate
"""Wrapper around weaviate vector database."""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional, Type
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
ec2e1d66e9df-1 | if weaviate_api_key is not None
else None
)
client = weaviate.Client(weaviate_url, auth_client_secret=auth)
return client
[docs]class Weaviate(VectorStore):
"""Wrapper around Weaviate vector database.
To use, you should have the ``weaviate-client`` python package installed.
Example:
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
ec2e1d66e9df-2 | if attributes is not None:
self._query_attrs.extend(attributes)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Upload texts with metadata (properties) to Weaviate."""
from weav... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
ec2e1d66e9df-3 | if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
result = query_obj.wi... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
ec2e1d66e9df-4 | result = query_obj.with_near_vector(vector).with_limit(k).do()
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
docs.append(Document(... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
ec2e1d66e9df-5 | )
return self.max_marginal_relevance_search_by_vector(
embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult:... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
ec2e1d66e9df-6 | .with_near_vector(vector)
.with_limit(fetch_k)
.do()
)
payload = results["data"]["Get"][self._index_name]
embeddings = [result["_additional"]["vector"] for result in payload]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
ec2e1d66e9df-7 | embeddings = OpenAIEmbeddings()
weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
"""
client = _create_weaviate_client(**kwargs)
from weaviate.util import get_valid... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
ec2e1d66e9df-8 | "class_name": index_name,
}
if embeddings is not None:
params["vector"] = embeddings[i]
batch.add_data_object(**params)
batch.flush()
return cls(client, index_name, text_key, embedding, attributes)
By Harrison Chase
© Cop... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
8164e1dcb879-0 | 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.... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-1 | column_map (Dict) : Column type map to project column name onto langchain
semantics. Must have keys: `text`, `id`, `vector`,
must be same size to number of columns. For example:
.. code-block:: python
{
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-2 | MyScale can not only search with simple vector indexes,
it also supports complex query with multiple conditions,
constraints and even sub-queries.
For more information, please visit
[myscale official site](https://docs.myscale.com/en/overview/)
"""
def __init__(
self,
embeddi... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-3 | for k in ["id", "vector", "text", "metadata"]:
assert k in self.config.column_map
assert self.config.metric in ["ip", "cosine", "l2"]
# initialize the schema
dim = len(embedding.embed_query("try this out"))
index_params = (
", " + ",".join([f"'{k}={v}'" for k, v i... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-4 | """
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self.embedding_function = embedding.embed_query
self.dist_order = "ASC" if self.config.metric in ["cosine", "l2"] else "DESC"
# Create a connection to myscale
self.client = get_client(
ho... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-5 | {','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None:
_i_str = self._build_istr(transac, column_names)
self.client.command(_i_str)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: O... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-6 | }
metadatas = metadatas or [{} for _ in texts]
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... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-7 | config: Optional[MyScaleSettings] = None,
text_ids: Optional[Iterable[str]] = None,
batch_size: int = 32,
**kwargs: Any,
) -> MyScale:
"""Create Myscale wrapper with existing texts
Args:
embedding_function (Embeddings): Function to extract text embedding
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-8 | Returns:
repr: string to show connection info and data schema
"""
_repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ "
_repr += f"{self.config.host}:{self.config.port}\033[0m\n\n"
_repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n"... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-9 | SELECT {self.config.column_map['text']},
{self.config.column_map['metadata']}, dist
FROM {self.config.database}.{self.config.table}
{where_str}
ORDER BY distance({self.config.column_map['vector']}, [{q_emb_str}])
AS dist {self.dist_order}
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-10 | self,
embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search with MyScale by vectors
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-11 | [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 MyScale
Args:
query (str): query string
k (int, optional): Top K ... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
8164e1dcb879-12 | return []
[docs] def drop(self) -> None:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}"
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
dd9bdee78b52-0 | Source code for langchain.vectorstores.base
"""Interface for vector stores."""
from __future__ import annotations
import asyncio
from abc import ABC, abstractmethod
from functools import partial
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TypeVar
from pydantic import BaseModel, Field, root_vali... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
dd9bdee78b52-1 | raise NotImplementedError
[docs] def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Run more documents through the embeddings and add to the vectorstore.
Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
dd9bdee78b52-2 | [docs] 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.
"""
docs_and_similaritie... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
dd9bdee78b52-3 | # asynchronous in the vector store implementations.
func = partial(self.similarity_search, query, k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Docume... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
dd9bdee78b52-4 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
dd9bdee78b52-5 | )
return await asyncio.get_event_loop().run_in_executor(None, func)
[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]:
"""Ret... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
dd9bdee78b52-6 | cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.fr... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
dd9bdee78b52-7 | texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
raise NotImplementedError
[docs] def as_retriever(self, **kwargs: Any) -> BaseRetriever:
return... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
dd9bdee78b52-8 | 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... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
559767156993-0 | Source code for langchain.vectorstores.elastic_vector_search
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from abc import ABC
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.bas... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
559767156993-1 | # VectorStore, which defines a common interface for all vector database
# implementations. By inheriting from the ABC class, ElasticVectorSearch can be
# defined as an abstract base class itself, allowing the creation of subclasses with
# their own specific implementations. If you plan to subclass ElasticVectorSearch,
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
559767156993-2 | navigating to the "Deployments" page.
To obtain your Elastic Cloud password for the default "elastic" user:
1. Log in to the Elastic Cloud console at https://cloud.elastic.co
2. Go to "Security" > "Users"
3. Locate the "elastic" user and click "Edit"
4. Click "Reset password"
5. Follow the promp... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
559767156993-3 | ValueError: If the elasticsearch python package is not installed.
"""
def __init__(self, elasticsearch_url: str, index_name: str, embedding: Embeddings):
"""Initialize with necessary components."""
try:
import elasticsearch
except ImportError:
raise ValueError(
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
559767156993-4 | "Please install it with `pip install elasticsearch`."
)
requests = []
ids = []
embeddings = self.embedding.embed_documents(list(texts))
dim = len(embeddings[0])
mapping = _default_text_mapping(dim)
# check to see if the index already exists
try:
... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
559767156993-5 | Args:
query: Text to look up documents similar to.
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, filter=filter)
documents = [d[0]... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
559767156993-6 | def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> ElasticVectorSearch:
"""Construct ElasticVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
1. E... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
559767156993-7 | )
index_name = kwargs.get("index_name", uuid.uuid4().hex)
embeddings = embedding.embed_documents(texts)
dim = len(embeddings[0])
mapping = _default_text_mapping(dim)
# check to see if the index already exists
try:
client.indices.get(index=index_name)
e... | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
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