id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
c16601bbdd12-9 | fetch_k=fetch_k,
**kwargs,
)
return docs
[docs] async def asimilarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-10 | k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns:
List of Documents most similar to t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-11 | [docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents simi... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-12 | Returns:
List of Documents most similar to the query.
"""
docs_and_scores = await self.asimilarity_search_with_score(
query, k, filter=filter, fetch_k=fetch_k, **kwargs
)
return [doc for doc, _ in docs_and_scores]
[docs] def max_marginal_relevance_search_with_s... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-13 | for i in indices[0]:
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):
rai... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-14 | [docs] async def amax_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
*,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, Any]] = None,
) -> List[Tuple[Document, float]]:
"""Return doc... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-15 | k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversi... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-16 | among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch before filtering to
pass to MMR algorithm.
lambda_mult: Number between... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-17 | of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self._embed_query(query)
do... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-18 | embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
return docs
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete by ID. These are the IDs in the vec... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-19 | 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("Cannot merge with this type of docstore")
# Numerica... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-20 | if distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
index = faiss.IndexFlatIP(len(embeddings[0]))
else:
# Default to L2, currently other metric types not initialized.
index = faiss.IndexFlatL2(len(embeddings[0]))
vecstore = cls(
embedding,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-21 | **kwargs,
)
[docs] @classmethod
async def afrom_texts(
cls,
texts: list[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from raw document... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-22 | 3. Initializes the FAISS database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-23 | index_name: for saving with a specific index file name
"""
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
# save index separately since it is not picklable
faiss = dependable_faiss_import()
faiss.write_index(
self.index, str(path / "{index_na... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-24 | docstore, index_to_docstore_id = pickle.load(f)
return cls(embeddings, index, docstore, index_to_docstore_id, **kwargs)
[docs] def serialize_to_bytes(self) -> bytes:
"""Serialize FAISS index, docstore, and index_to_docstore_id to bytes."""
return pickle.dumps((self.index, self.docstore, self.... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-25 | return self._euclidean_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.COSINE:
return self._cosine_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be cosine, max_inner_product,"
" or euclidean"
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c16601bbdd12-26 | **kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
# Pop score threshold so that only relevancy scores, not raw scores, are
# filtered.
relevance_score_fn = self._select_relevance_score_fn()
if relevance_sco... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
c26e5ec88402-0 | Source code for langchain.vectorstores.vectara
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 langchain.pydantic_v1 import Field
from langchain.schema import Document
from langchain.schema... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-1 | or self._vectara_corpus_id is None
or self._vectara_api_key is None
):
logger.warning(
"Can't find Vectara credentials, customer_id or corpus_id in "
"environment."
)
else:
logger.debug(f"Using corpus id {self._vectara_corpu... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-2 | data=json.dumps(body),
verify=True,
headers=self._get_post_headers(),
timeout=self.vectara_api_timeout,
)
if response.status_code != 200:
logger.error(
f"Delete request failed for doc_id = {doc_id} with status code "
f"{resp... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-3 | """
Vectara provides a way to add documents directly via our API where
pre-processing and chunking occurs internally in an optimal way
This method provides a way to use that API in LangChain
Args:
files_list: Iterable of strings, each representing a local file path.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-4 | doc_id = response.json()["document"]["documentId"]
doc_ids.append(doc_id)
else:
logger.info(f"Error indexing file {file}: {response.json()}")
return doc_ids
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-5 | for text, md in zip(texts, metadatas)
],
}
success_str = self._index_doc(doc)
if success_str == "E_ALREADY_EXISTS":
self._delete_doc(doc_id)
self._index_doc(doc)
elif success_str == "E_NO_PERMISSIONS":
print(
"""No permissio... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-6 | to add, defaults to 2
Returns:
List of Documents most similar to the query and score for each.
"""
data = json.dumps(
{
"query": [
{
"query": query,
"start": 0,
"nu... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-7 | md = {m["name"]: m["value"] for m in x["metadata"]}
doc_num = x["documentIndex"]
doc_md = {m["name"]: m["value"] for m in documents[doc_num]["metadata"]}
md.update(doc_md)
metadatas.append(md)
docs_with_score = [
(
Document(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-8 | k=k,
lambda_val=lambda_val,
filter=filter,
score_threshold=None,
n_sentence_context=n_sentence_context,
**kwargs,
)
return [doc for doc, _ in docs_and_scores]
[docs] @classmethod
def from_texts(
cls: Type[Vectara],
texts:... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-9 | files: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> Vectara:
"""Construct Vectara wrapper from raw documents.
This is intended to be a quick way to get started.
Example:
.. code-block:: pyth... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
c26e5ec88402-10 | 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.lang = 'deu'"} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
n_... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
cc4a876c20c3-0 | Source code for langchain.vectorstores.vearch
from __future__ import annotations
import os
import time
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
fro... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-1 | self.using_db_name = db_name
self.url = path_or_url
self.vearch = vearch_cluster.VearchCluster(path_or_url)
else:
if path_or_url is None:
metadata_path = os.getcwd().replace("\\", "/")
else:
metadata_path = path_or_url
i... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-2 | embedding=embedding,
metadatas=metadatas,
path_or_url=path_or_url,
table_name=table_name,
db_name=db_name,
flag=flag,
**kwargs,
)
[docs] @classmethod
def from_texts(
cls: Type[Vearch],
texts: List[str],
embedd... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-3 | engine_info = {
"index_size": 10000,
"retrieval_type": "IVFPQ",
"retrieval_param": {"ncentroids": 2048, "nsubvector": 32},
}
fields = [
vearch.GammaFieldInfo(fi["field"], type_dict[fi["type"]])
for fi in field_list
]
vector_fiel... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-4 | "text": {
"type": "string",
},
"metadata": {
"type": "string",
},
"text_embedding": {
"type": "vector",
"index": True,
"dimension": dim,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-5 | for text, metadata, embed in zip(texts, metadatas, embeddings):
profiles: dict[str, Any] = {}
profiles["text"] = text
profiles["metadata"] = metadata["source"]
embed_np = np.array(embed)
profiles["text_embedding"] = {
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-6 | docid = self.vearch.add(doc_items)
t_time = 0
while len(docid) != len(embeddings):
time.sleep(0.5)
if t_time > 6:
break
t_time += 1
self.vearch.dump()
return docid
def _load(se... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-7 | k: int = DEFAULT_TOPN,
**kwargs: Any,
) -> List[Document]:
"""
Return docs most similar to query.
"""
if self.embedding_func is None:
raise ValueError("embedding_func is None!!!")
embeddings = self.embedding_func.embed_query(query)
docs = self.simi... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-8 | "feature": embed / np.linalg.norm(embed),
}
],
"fields": [],
"is_brute_search": 1,
"retrieval_param": {"metric_type": "InnerProduct", "nprobe": 20},
"topn": k,
}
query_result = self.vearch.search(... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-9 | if self.flag:
query_data = {
"query": {
"sum": [
{
"field": "text_embedding",
"feature": (embed / np.linalg.norm(embed)).tolist(),
}
],
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-10 | tmp_res = (Document(page_content=content, metadata=meta_data), score)
results.append(tmp_res)
return results
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
return self.simil... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
cc4a876c20c3-11 | Returns:
Documents which satisfy the input conditions.
"""
results: Dict[str, Document] = {}
if ids is None or ids.__len__() == 0:
return results
if self.flag:
query_data = {"query": {"ids": ids}}
docs_detail = self.vearch.mget_by_ids(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vearch.html |
682eef8aaa20-0 | Source code for langchain.vectorstores.tair
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore import Vector... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
682eef8aaa20-1 | self,
dim: int,
distance_type: str,
index_type: str,
data_type: str,
**kwargs: Any,
) -> bool:
index = self.client.tvs_get_index(self.index_name)
if index is not None:
logger.info("Index already exists")
return False
self.client... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
682eef8aaa20-2 | **{
"TEXT": text,
self.content_key: text,
self.metadata_key: json.dumps(metadata),
},
)
else:
pipeline.tvs_hset(
self.index_name,
key,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
682eef8aaa20-3 | cls: Type[Tair],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadata",
**kwargs: Any,
) -> Tair:
try:
from tair import t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
682eef8aaa20-4 | metadata_key=metadata_key,
search_params=search_params,
**kwargs,
)
except ValueError as e:
raise ValueError(f"tair failed to connect: {e}")
# Create embeddings for documents
embeddings = embedding.embed_documents(texts)
tair_vector... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
682eef8aaa20-5 | except ImportError:
raise ValueError(
"Could not import tair python package. "
"Please install it with `pip install tair`."
)
url = get_from_dict_or_env(kwargs, "tair_url", "TAIR_URL")
try:
if "tair_url" in kwargs:
kwarg... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
7106ac669f38-0 | Source code for langchain.vectorstores.atlas
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.schema.embeddings import Embeddings
from langchain.schema.vectorstore impor... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
7106ac669f38-1 | description (str): A description for your project.
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 useful d... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
7106ac669f38-2 | """Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]]): An optional list of ids.
refresh(b... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
7106ac669f38-3 | self.project.add_embeddings(embeddings=embeddings, data=data)
# Text upload case
else:
if metadatas is None:
data = [
{"text": text, AtlasDB._ATLAS_DEFAULT_ID_FIELD: ids[i]}
for i, text in enumerate(texts)
]
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
7106ac669f38-4 | Returns:
List[Document]: List of documents most similar to the query text.
"""
if self._embedding_function is None:
raise NotImplementedError(
"AtlasDB requires an embedding_function for text similarity search!"
)
_embedding = self._embedding_f... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
7106ac669f38-5 | embedding (Optional[Embeddings]): Embedding function. Defaults to None.
metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
ids (Optional[List[str]]): Optional list of document IDs. If None,
ids will be auto created
description (str): A description ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
7106ac669f38-6 | def from_documents(
cls: Type[AtlasDB],
documents: List[Document],
embedding: Optional[Embeddings] = None,
ids: Optional[List[str]] = None,
name: Optional[str] = None,
api_key: Optional[str] = None,
persist_directory: Optional[str] = None,
description: str... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
7106ac669f38-7 | texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(
name=name,
api_key=api_key,
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
descripti... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
f83b20163cfe-0 | Source code for langchain.vectorstores.tencentvectordb
"""Wrapper around the Tencent vector database."""
from __future__ import annotations
import json
import logging
import time
from typing import Any, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.document import Document
from langch... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
f83b20163cfe-1 | index_type: str = "HNSW",
metric_type: str = "L2",
params: Optional[Dict] = None,
):
self.dimension = dimension
self.shard = shard
self.replicas = replicas
self.index_type = index_type
self.metric_type = metric_type
self.params = params
[docs]class Ten... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
f83b20163cfe-2 | for db in db_list:
if database_name == db.database_name:
db_exist = True
break
if db_exist:
self.database = self.vdb_client.database(database_name)
else:
self.database = self.vdb_client.create_database(database_name)
try:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
f83b20163cfe-3 | self.field_id, enum.FieldType.String, enum.IndexType.PRIMARY_KEY
),
vdb_index.VectorIndex(
self.field_vector,
self.index_params.dimension,
index_type,
metric_type,
params,
),
vdb_index.FilterI... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
f83b20163cfe-4 | except NotImplementedError:
embeddings = [embedding.embed_query(texts[0])]
dimension = len(embeddings[0])
if index_params is None:
index_params = IndexParams(dimension=dimension)
else:
index_params.dimension = dimension
vector_db = cls(
emb... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
f83b20163cfe-5 | metadata = json.dumps(metadatas[id])
doc = self.document.Document(
id="{}-{}-{}".format(time.time_ns(), hash(texts[id]), id),
vector=embeddings[id],
text=texts[id],
metadata=metadata,
)
docs.a... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
f83b20163cfe-6 | )
return res
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similari... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
f83b20163cfe-7 | for result in res[0]:
meta = result.get(self.field_metadata)
if meta is not None:
meta = json.loads(meta)
doc = Document(page_content=result.get(self.field_text), metadata=meta)
pair = (doc, result.get("score", 0.0))
ret.append(pair)
re... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
f83b20163cfe-8 | """Perform a search and return results that are reordered by MMR."""
filter = None if expr is None else self.document.Filter(expr)
ef = 10 if param is None else param.get("ef", 10)
res: List[List[Dict]] = self.collection.search(
vectors=[embedding],
filter=filter,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tencentvectordb.html |
0aba36bff0d0-0 | Source code for langchain.vectorstores.neo4j_vector
from __future__ import annotations
import enum
import logging
import os
import uuid
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
from langchain.docstore.document import Document
from langchain.schem... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-1 | "UNWIND nodes AS n "
"RETURN n.node AS node, (n.score / max) AS score " # We use 0 as min
"} "
"WITH node, max(score) AS score ORDER BY score DESC LIMIT $k " # dedup
),
}
return type_to_query_map[search_type]
[docs]def check_if_not_null(props: List[str], values: Lis... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-2 | Example:
.. code-block:: python
from langchain.vectorstores.neo4j_vector import Neo4jVector
from langchain.embeddings.openai import OpenAIEmbeddings
url="bolt://localhost:7687"
username="neo4j"
password="pleaseletmein"
embeddings = OpenAIEm... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-3 | # Allow only cosine and euclidean distance strategies
if distance_strategy not in [
DistanceStrategy.EUCLIDEAN_DISTANCE,
DistanceStrategy.COSINE,
]:
raise ValueError(
"distance_strategy must be either 'EUCLIDEAN_DISTANCE' or 'COSINE'"
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-4 | ],
[index_name, node_label, embedding_node_property, text_node_property],
)
self.embedding = embedding
self._distance_strategy = distance_strategy
self.index_name = index_name
self.keyword_index_name = keyword_index_name
self.node_label = node_label
se... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-5 | """
from neo4j.exceptions import CypherSyntaxError
params = params or {}
with self._driver.session(database=self._database) as session:
try:
data = session.run(query, params)
return [r.data() for r in data]
except CypherSyntaxError as e:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-6 | If the index doesn't exist, `None` is returned.
Returns:
int or None: The embedding dimension of the existing index if found.
"""
index_information = self.query(
"SHOW INDEXES YIELD name, type, labelsOrTypes, properties, options "
"WHERE type = 'VECTOR' AND (n... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-7 | "SHOW INDEXES YIELD name, type, labelsOrTypes, properties, options "
"WHERE type = 'FULLTEXT' AND (name = $keyword_index_name "
"OR (labelsOrTypes = [$node_label] AND "
"properties = $text_node_property)) "
"RETURN name, labelsOrTypes, properties, options ",
p... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-8 | }
self.query(index_query, params=parameters)
[docs] def create_new_keyword_index(self, text_node_properties: List[str] = []) -> None:
"""
This method constructs a Cypher query and executes it
to create a new full text index in Neo4j.
"""
node_props = text_node_properti... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-9 | if not embedding_dimension:
store.create_new_index()
# If the index already exists, check if embedding dimensions match
elif not store.embedding_dimension == embedding_dimension:
raise ValueError(
f"Index with name {store.index_name} already exists."
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-10 | """Add embeddings to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
embeddings: List of list of embedding vectors.
metadatas: List of metadatas associated with the texts.
kwargs: vectorstore specific parameters
"""
if ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-11 | """Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
kwargs: vectorstore specific parameters
Returns:
List of ids f... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-12 | docs = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, query=query
)
return docs
[docs] def similarity_search_with_score_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""
Perform a s... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-13 | "embedding": embedding,
"keyword_index": self.keyword_index_name,
"query": kwargs["query"],
}
results = self.query(read_query, params=parameters)
docs = [
(
Document(
page_content=result["text"],
metadata... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-14 | Neo4j credentials are required in the form of `url`, `username`,
and `password` and optional `database` parameters.
"""
embeddings = embedding.embed_documents(list(texts))
return cls.__from(
texts,
embeddings,
embedding,
metadatas=metadatas... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-15 | return cls.__from(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
distance_strategy=distance_strategy,
pre_delete_collection=pre_delete_collection,
**kwargs,
)
[docs] @classmethod
def from_existin... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-16 | raise ValueError(
"The provided embedding function and vector index "
"dimensions do not match.\n"
f"Embedding function dimension: {store.embedding_dimension}\n"
f"Vector index dimension: {embedding_dimension}"
)
if search_type == Searc... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-17 | ids=ids,
**kwargs,
)
[docs] @classmethod
def from_existing_graph(
cls: Type[Neo4jVector],
embedding: Embeddings,
node_label: str,
embedding_node_property: str,
text_node_properties: List[str],
*,
keyword_index_name: Optional[str] = "keyw... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-18 | if not retrieval_query:
retrieval_query = (
f"RETURN reduce(str='', k IN {text_node_properties} |"
" str + '\\n' + k + ': ' + coalesce(node[k], '')) AS text, "
"node {.*, `"
+ embedding_node_property
+ "`: Null, id: Null, "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-19 | if not fts_node_label:
store.create_new_keyword_index(text_node_properties)
else: # Validate that FTS and Vector index use the same information
if not fts_node_label == store.node_label:
raise ValueError(
"Vector and keyword index ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
0aba36bff0d0-20 | def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/neo4j_vector.html |
41e880cdf43f-0 | Source code for langchain.vectorstores.azuresearch
from __future__ import annotations
import base64
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
from langchain.callbacks.man... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-1 | def _get_search_client(
endpoint: str,
key: str,
index_name: str,
semantic_configuration_name: Optional[str] = None,
fields: Optional[List[SearchField]] = None,
vector_search: Optional[VectorSearch] = None,
semantic_settings: Optional[SemanticSettings] = None,
scoring_profiles: Optional[... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-2 | # Fields configuration
if fields is not None:
# Check mandatory fields
fields_types = {f.name: f.type for f in fields}
mandatory_fields = {df.name: df.type for df in default_fields}
# Check for missing keys
missing_fields = {
key: manda... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-3 | "metric": "cosine",
},
)
]
)
# Create the semantic settings with the configuration
if semantic_settings is None and semantic_configuration_name is not None:
semantic_settings = SemanticSettings(
configura... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-4 | default_scoring_profile: Optional[str] = None,
**kwargs: Any,
):
from azure.search.documents.indexes.models import (
SearchableField,
SearchField,
SearchFieldDataType,
SimpleField,
)
"""Initialize with necessary components."""
#... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-5 | self.fields = fields if fields else default_fields
@property
def embeddings(self) -> Optional[Embeddings]:
# TODO: Support embedding object directly
return None
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-6 | # Check if all documents were successfully uploaded
if not all([r.succeeded for r in response]):
raise Exception(response)
# Reset data
data = []
# Considering case where data is an exact multiple of batch-size entries
if len(data) == 0... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-7 | )
[docs] def vector_search(self, query: str, k: int = 4, **kwargs: Any) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to retur... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-8 | if FIELDS_METADATA in result
else {
k: v for k, v in result.items() if k != FIELDS_CONTENT_VECTOR
},
),
float(result["@search.score"]),
)
for result in results
]
return docs
[docs] ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-9 | k=k,
fields=FIELDS_CONTENT_VECTOR,
)
],
filter=filters,
top=k,
)
# Convert results to Document objects
docs = [
(
Document(
page_content=result.pop(FIELDS_CONTENT),
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-10 | Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.semantic_hybrid_se... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-11 | semantic_answers = results.get_answers() or []
semantic_answers_dict: Dict = {}
for semantic_answer in semantic_answers:
semantic_answers_dict[semantic_answer.key] = {
"text": semantic_answer.text,
"highlights": semantic_answer.highlights,
}
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-12 | **kwargs: Any,
) -> AzureSearch:
# Creating a new Azure Search instance
azure_search = cls(
azure_search_endpoint,
azure_search_key,
index_name,
embedding.embed_query,
)
azure_search.add_texts(texts, metadatas, **kwargs)
return ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
41e880cdf43f-13 | docs = self.vectorstore.hybrid_search(query, k=self.k, **kwargs)
elif self.search_type == "semantic_hybrid":
docs = self.vectorstore.semantic_hybrid_search(query, k=self.k, **kwargs)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
efb92b8561a3-0 | Source code for langchain.vectorstores.lancedb
from __future__ import annotations
import uuid
from typing import Any, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore import VectorStore
[docs]class LanceDB(Vect... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
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