id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
acf5728c5ee9-11 | batch_size: int = 32,
text_key: str = "text",
index_name: Optional[str] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> Pinecone:
"""Construct Pinecone wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-12 | embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
"""
try:
import pinecone
except ImportError:
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-13 | else:
raise ValueError(
f"Index '{index_name}' not found in your Pinecone project. "
f"Did you mean one of the following indexes: {', '.join(indexes)}"
)
for i in range(0, len(texts), batch_size):
# set end position of batch
i_end =... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-14 | else:
metadata = [{} for _ in range(i, i_end)]
for j, line in enumerate(lines_batch):
metadata[j][text_key] = line
to_upsert = zip(ids_batch, embeds, metadata)
# upsert to Pinecone
index.upsert(vectors=list(to_upsert), namespace=namespace)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-15 | "Please install it with `pip install pinecone-client`."
)
return cls(
pinecone.Index(index_name), embedding.embed_query, text_key, namespace
)
[docs] def delete(self, ids: List[str]) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
3366fa642411-0 | Source code for langchain.vectorstores.tigris
from __future__ import annotations
import itertools
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple
from langchain.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.vectorstores import VectorStore
if TYPE_CHECKING:... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
3366fa642411-1 | except ImportError:
raise ValueError(
"Could not import tigrisdb python package. "
"Please install it with `pip install tigrisdb`"
)
self._embed_fn = embeddings
self._vector_store = TigrisVectorStore(client.get_search(), index_name)
@property
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
3366fa642411-2 | metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids for documents.
Ids will be autogenerated if not provided.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
3366fa642411-3 | return [doc for doc, _ in docs_with_scores]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[TigrisFilter] = None,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
Args:
query ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
3366fa642411-4 | vector=vector, k=k, filter_by=filter
)
docs: List[Tuple[Document, float]] = []
for r in result:
docs.append(
(
Document(
page_content=r.doc["text"], metadata=r.doc.get("metadata")
),
r... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
3366fa642411-5 | if not index_name:
raise ValueError("`index_name` is required")
if not client:
client = TigrisClient()
store = cls(client, embedding, index_name)
store.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return store
def _prep_docs(
self,
text... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
3366fa642411-6 | doc: TigrisDocument = {
"text": t,
"embeddings": e or [],
"metadata": m or {},
}
if _id:
doc["id"] = _id
docs.append(doc)
return docs | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
e9e16c948090-0 | Source code for langchain.vectorstores.starrocks
"""Wrapper around open source StarRocks VectorSearch capability."""
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 Base... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-1 | """
for a in args:
if a not in s:
return False
return True
def debug_output(s: Any) -> None:
"""
Print a debug message if DEBUG is True.
Args:
s: The message to print
"""
if DEBUG:
print(s)
def get_named_result(connection: Any, query: str) -> List[dict[str... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-2 | r = {}
for idx, datum in enumerate(value):
k = columns[idx][0]
r[k] = datum
result.append(r)
debug_output(result)
cursor.close()
return result
class StarRocksSettings(BaseSettings):
"""StarRocks Client Configuration
Attribute:
StarRocks_host (str) : An... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-3 | Defaults to 'vector_table'.
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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-4 | }
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):
"""Wrapper around StarRoc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-5 | [StarRocks github](https://github.com/StarRocks/starrocks)
"""
def __init__(
self,
embedding: Embeddings,
config: Optional[StarRocksSettings] = None,
**kwargs: Any,
) -> None:
"""StarRocks Wrapper to LangChain
embedding_function (Embeddings):
config (S... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-6 | 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.config.host and self.config.port
assert self.config.column_map and self.config.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-7 | ) ENGINE = OLAP PRIMARY KEY(id) DISTRIBUTED BY HASH(id) \
PROPERTIES ("replication_num" = "1")\
"""
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self.embedding_function = embedding
self.dist_order = "DESC"
debug_output(self.config)
# Create a con... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-8 | return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
embed_tuple_index = tuple(column_names).index(
self.config.column_map["embedding"]
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-9 | VALUES
{','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None:
_insert_query = self._build_insert_sql(transac, column_names)
debug_output(_insert_query)
get_named_result(self.connection, _insert_que... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-10 | batch_size: Batch size of insertion
metadata: Optional column data to be inserted
Returns:
List of ids from adding the texts into the VectorStore.
"""
# Embed and create the documents
ids = ids or [sha1(t.encode("utf-8")).hexdigest() for t in texts]
colmap... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-11 | try:
t = None
for v in self.pgbar(
zip(*values), desc="Inserting data...", total=len(metadatas)
):
assert (
len(v[keys.index(self.config.column_map["embedding"])]) == self.dim
)
transac.append(v)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-12 | 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,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
config: Opt... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-13 | config (StarRocksSettings, Optional): StarRocks configuration
text_ids (Optional[Iterable], optional): IDs for the texts.
Defaults to None.
batch_size (int, optional): Batchsize when transmitting data to StarRocks.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-14 | """
_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"
width = 25
fields = 3
_repr += "-" * (width * fields + 1)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-15 | _repr += "-" * (width * fields + 1) + "\n"
q_str = f"DESC {self.config.database}.{self.config.table}"
debug_output(q_str)
rs = get_named_result(self.connection, q_str)
for r in rs:
_repr += f"|\033[94m{r['Field']:24s}\033[0m|\033[96m{r['Type']:24s}"
_repr += f"\03... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-16 | ) -> str:
q_emb_str = ",".join(map(str, q_emb))
if where_str:
where_str = f"WHERE {where_str}"
else:
where_str = ""
q_str = f"""
SELECT {self.config.column_map['document']},
{self.config.column_map['metadata']},
cosine... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-17 | ) -> List[Document]:
"""Perform a similarity search with StarRocks
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-18 | embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search with StarRocks by vectors
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults t... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-19 | """
q_str = self._build_query_sql(embedding, k, where_str)
try:
return [
Document(
page_content=r[self.config.column_map["document"]],
metadata=json.loads(r[self.config.column_map["metadata"]]),
)
for r i... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-20 | Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
e9e16c948090-21 | ),
r["dist"],
)
for r in get_named_result(self.connection, q_str)
]
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:
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/starrocks.html |
8b68bb3e4b98-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-1 | )
"""
def __init__(
self,
vectara_customer_id: Optional[str] = None,
vectara_corpus_id: Optional[str] = None,
vectara_api_key: Optional[str] = None,
):
"""Initialize with Vectara API."""
self._vectara_customer_id = vectara_customer_id or os.environ.get(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-2 | ):
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.Session() # to reuse connections
adap... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-3 | Args:
url (str): URL of the page to delete.
doc_id (str): ID of the document to delete.
Returns:
bool: True if deletion was successful, False otherwise.
"""
body = {
"customer_id": self._vectara_customer_id,
"corpus_id": self._vectara_c... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-4 | 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"] = self._vectara_corpus_id
request["document"] = doc
resp... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-5 | return False
else:
return True
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts:... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-6 | doc = {
"document_id": doc_id,
"metadataJson": json.dumps({"source": "langchain"}),
"parts": [
{"text": text, "metadataJson": json.dumps(md)}
for text, md in zip(texts, metadatas)
],
}
succeeded = self._index_doc(doc)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-7 | """Return Vectara documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 5.
lambda_val: lexical match parameter for hybrid search.
filter: Dictionary of argument(s) to fi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-8 | "start": 0,
"num_results": k,
"context_config": {
"sentences_before": n_sentence_context,
"sentences_after": n_sentence_context,
},
"corpus_key": [
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-9 | "Query failed %s",
f"(code {response.status_code}, reason {response.reason}, details "
f"{response.text})",
)
return []
result = response.json()
responses = result["responseSet"][0]["response"]
vectara_default_metadata = ["lang", "len", "of... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-10 | 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 to look up documents similar to.
k: Number of Documents to return. Defaults t... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-11 | query,
k=k,
lambda_val=lambda_val,
filter=filter,
n_sentence_context=n_sentence_context,
**kwargs,
)
return [doc for doc, _ in docs_and_scores]
[docs] @classmethod
def from_texts(
cls: Type[Vectara],
texts: List[str],
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-12 | vectara_customer_id=customer_id,
vectara_corpus_id=corpus_id,
vectara_api_key=api_key,
)
"""
# Note: Vectara generates its own embeddings, so we ignore the provided
# embeddings (required by interface)
vectara = cls(**kwargs)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-13 | "n_sentence_context": "0",
}
)
"""Search params.
k: Number of Documents to return. Defaults to 5.
lambda_val: lexical match parameter for hybrid search.
filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be "doc.rating > 3.0 and part.lan... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
8b68bb3e4b98-14 | texts (List[str]): The text
metadatas (List[dict]): Metadata dicts, must line up with existing store
"""
self.vectorstore.add_texts(texts, metadatas) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
31f9b9315f8c-0 | Source code for langchain.vectorstores.hologres
"""VectorStore wrapper around a Hologres database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.b... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-1 | self.ndims = ndims
def create_vector_extension(self) -> None:
self.cursor.execute("create extension if not exists proxima")
self.conn.commit()
def create_table(self, drop_if_exist: bool = True) -> None:
if drop_if_exist:
self.cursor.execute(f"drop table if exists {self.table_... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-2 | '{"embedding":{"algorithm":"Graph",
"distance_method":"SquaredEuclidean",
"build_params":{"min_flush_proxima_row_count" : 1,
"min_compaction_proxima_row_count" : 1,
"max_total_size_to_merge_mb" : 2000}}}');"""
)
self.conn.commit()
def get_by_id(self, id: str) -> List[Tuple]:
statement = (
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-3 | id: Optional[str] = None,
) -> None:
self.cursor.execute(
f'insert into "{self.table_name}" '
f"values (%s, array{json.dumps(embedding)}::float4[], %s, %s)",
(id if id is not None else "null", json.dumps(metadata), document),
)
self.conn.commit()
def q... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-4 | params.append(val)
filter_clause = "where " + " and ".join(conjuncts)
sql = (
f"select document, metadata::text, "
f"pm_approx_squared_euclidean_distance(array{json.dumps(embedding)}"
f"::float4[], embedding) as distance from"
f" {self.table_name} {fil... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-5 | - `table_name` is the name of the table to store embeddings and data.
(default: langchain_pg_embedding)
- NOTE: The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `pre_delete_table` if True, will dele... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-6 | self.ndims = ndims
self.table_name = table_name
self.embedding_function = embedding_function
self.pre_delete_table = pre_delete_table
self.logger = logger or logging.getLogger(__name__)
self.__post_init__()
def __post_init__(
self,
) -> None:
"""
I... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-7 | @classmethod
def __from(
cls,
texts: List[str],
embeddings: List[List[float]],
embedding_function: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
ndims: int = ADA_TOKEN_COUNT,
table_name: str = _LANGCHAIN_DEFAULT_T... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-8 | ndims=ndims,
table_name=table_name,
pre_delete_table=pre_delete_table,
)
store.add_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
return store
[docs] def add_embeddings(
self,
texts: Iterable... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-9 | """
try:
for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
self.storage.insert(embedding, metadata, text, id)
except Exception as e:
self.logger.exception(e)
self.storage.conn.commit()
[docs] def add_texts(
self,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-10 | Returns:
List of ids from adding the texts into the vectorstore.
"""
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]
se... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-11 | filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding_function.embed_query(text=query)
return self.similarity_search_by_vector(
embedding=embedding,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-12 | 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] def similarity_search_with_score(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-13 | """
embedding = self.embedding_function.embed_query(query)
docs = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return docs
[docs] def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-14 | [docs] @classmethod
def from_texts(
cls: Type[Hologres],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ndims: int = ADA_TOKEN_COUNT,
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
ids: Optional[List[str]] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-15 | metadatas=metadatas,
ids=ids,
ndims=ndims,
table_name=table_name,
pre_delete_table=pre_delete_table,
**kwargs,
)
[docs] @classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Emb... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-16 | Postgres connection string is required
"Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
Example:
.. code-block:: python
from langchain import Hologres
from langchain.embeddings import OpenAIEmbeddings
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-17 | table_name=table_name,
pre_delete_table=pre_delete_table,
**kwargs,
)
[docs] @classmethod
def from_existing_index(
cls: Type[Hologres],
embedding: Embeddings,
ndims: int = ADA_TOKEN_COUNT,
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
pre... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-18 | pre_delete_table=pre_delete_table,
)
return store
[docs] @classmethod
def get_connection_string(cls, kwargs: Dict[str, Any]) -> str:
connection_string: str = get_from_dict_or_env(
data=kwargs,
key="connection_string",
env_key="HOLOGRES_CONNECTION_STRING... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-19 | ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> Hologres:
"""
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the HOLOGRES_CONN... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
31f9b9315f8c-20 | **kwargs,
)
[docs] @classmethod
def connection_string_from_db_params(
cls,
host: str,
port: int,
database: str,
user: str,
password: str,
) -> str:
"""Return connection string from database parameters."""
return (
f"dbname={d... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hologres.html |
f218ee937c07-0 | Source code for langchain.vectorstores.redis
"""Wrapper around Redis vector database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Mapping,
Optional,
Tuple,
Type,... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-1 | # required modules
REDIS_REQUIRED_MODULES = [
{"name": "search", "ver": 20400},
{"name": "searchlight", "ver": 20400},
]
# distance mmetrics
REDIS_DISTANCE_METRICS = Literal["COSINE", "IP", "L2"]
def _check_redis_module_exist(client: RedisType, required_modules: List[dict]) -> None:
"""Check if the correct ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-2 | error_message = (
"Redis cannot be used as a vector database without RediSearch >=2.4"
"Please head to https://redis.io/docs/stack/search/quick_start/"
"to know more about installing the RediSearch module within Redis Stack."
)
logging.error(error_message)
raise ValueError(error_mess... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-3 | def _redis_prefix(index_name: str) -> str:
"""Redis key prefix for a given index."""
return f"doc:{index_name}"
def _default_relevance_score(val: float) -> float:
return 1 - val
[docs]class Redis(VectorStore):
"""Wrapper around Redis vector database.
To use, you should have the ``redis`` python pack... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-4 | redis_url: str,
index_name: str,
embedding_function: Callable,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_relevance_score,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-5 | _check_redis_module_exist(redis_client, REDIS_REQUIRED_MODULES)
except ValueError as e:
raise ValueError(f"Redis failed to connect: {e}")
self.client = redis_client
self.content_key = content_key
self.metadata_key = metadata_key
self.vector_key = vector_key
se... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-6 | )
# Check if index exists
if not _check_index_exists(self.client, self.index_name):
# Define schema
schema = (
TextField(name=self.content_key),
TextField(name=self.metadata_key),
VectorField(
self.vector_key,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-7 | embeddings: Optional[List[List[float]]] = None,
batch_size: int = 1000,
**kwargs: Any,
) -> List[str]:
"""Add more texts to the vectorstore.
Args:
texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optio... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-8 | """
ids = []
prefix = _redis_prefix(self.index_name)
# Get keys or ids from kwargs
# Other vectorstores use ids
keys_or_ids = kwargs.get("keys", kwargs.get("ids"))
# Write data to redis
pipeline = self.client.pipeline(transaction=False)
for i, text in enum... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-9 | },
)
ids.append(key)
# Write batch
if i % batch_size == 0:
pipeline.execute()
# Cleanup final batch
pipeline.execute()
return ids
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Doc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-10 | [docs] def similarity_search_limit_score(
self, query: str, k: int = 4, score_threshold: float = 0.2, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text within the
score_threshold range.
Args:
query (str): The qu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-11 | including the match score for each document.
Note:
If there are no documents that satisfy the score_threshold value,
an empty list is returned.
"""
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [doc for doc, score in docs_and_scores if sco... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-12 | )
return_fields = [self.metadata_key, self.content_key, "vector_score"]
return (
Query(base_query)
.return_fields(*return_fields)
.sort_by("vector_score")
.paging(0, k)
.dialect(2)
)
[docs] def similarity_search_with_score(
s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-13 | # Creates Redis query
redis_query = self._prepare_query(k)
params_dict: Mapping[str, str] = {
"vector": np.array(embedding) # type: ignore
.astype(dtype=np.float32)
.tobytes()
}
# Perform vector search
results = self.client.ft(self.index_name)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-14 | """Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
if self.relevance_score_fn is None:
raise ValueError(
"relevance_score_fn must be provided to"
" Redis constructor to normalize scores"
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-15 | metadata_key: str = "metadata",
vector_key: str = "content_vector",
distance_metric: REDIS_DISTANCE_METRICS = "COSINE",
**kwargs: Any,
) -> Tuple[Redis, List[str]]:
"""Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
1. Embeds... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-16 | embeddings = OpenAIEmbeddings()
redisearch, keys = RediSearch.from_texts_return_keys(
texts,
embeddings,
redis_url="redis://username:password@localhost:6379"
)
"""
redis_url = get_from_dict_or_env(kwargs, "redis_... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-17 | embeddings = embedding.embed_documents(texts)
# Create the search index
instance._create_index(dim=len(embeddings[0]), distance_metric=distance_metric)
# Add data to Redis
keys = instance.add_texts(texts, metadatas, embeddings)
return instance, keys
[docs] @classmethod
def... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-18 | 1. Embeds documents.
2. Creates a new index for the embeddings in Redis.
3. Adds the documents to the newly created Redis index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Redis... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-19 | vector_key=vector_key,
**kwargs,
)
return instance
[docs] @staticmethod
def delete(
ids: List[str],
**kwargs: Any,
) -> bool:
"""
Delete a Redis entry.
Args:
ids: List of ids (keys) to delete.
Returns:
bool: W... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-20 | )
try:
# We need to first remove redis_url from kwargs,
# otherwise passing it to Redis will result in an error.
if "redis_url" in kwargs:
kwargs.pop("redis_url")
client = redis.from_url(url=redis_url, **kwargs)
except ValueError as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-21 | Args:
index_name (str): Name of the index to drop.
delete_documents (bool): Whether to drop the associated documents.
Returns:
bool: Whether or not the drop was successful.
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-22 | except ValueError as e:
raise ValueError(f"Your redis connected error: {e}")
# Check if index exists
try:
client.ft(index_name).dropindex(delete_documents)
logger.info("Drop index")
return True
except: # noqa: E722
# Index not exist
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-23 | except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
try:
# We need to first remove redis_url from kwargs,
# otherwise passing it to Redis will result in an ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-24 | redis_url,
index_name,
embedding.embed_query,
content_key=content_key,
metadata_key=metadata_key,
vector_key=vector_key,
**kwargs,
)
[docs] def as_retriever(self, **kwargs: Any) -> RedisVectorStoreRetriever:
return RedisVectorSto... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-25 | """Validate search type."""
if "search_type" in values:
search_type = values["search_type"]
if search_type not in ("similarity", "similarity_limit"):
raise ValueError(f"search_type of {search_type} not allowed.")
return values
def get_relevant_documents(self, ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
f218ee937c07-26 | raise NotImplementedError("RedisVectorStoreRetriever does not support async")
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add documents to vectorstore."""
return self.vectorstore.add_documents(documents, **kwargs)
async def aadd_documents(
self, doc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
7403d9ea8923-0 | Source code for langchain.vectorstores.zilliz
from __future__ import annotations
import logging
from typing import Any, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.milvus import Milvus
logger = logging.getLogger(__name__)
[docs]class Zilliz(Milvus):
def _create_index(... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
7403d9ea8923-1 | }
try:
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
# If default did not work, most likely Milvus self-hosted
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
7403d9ea8923-2 | logger.error(
"Failed to create an index on collection: %s", self.collection_name
)
raise e
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collect... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
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