id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
7c93fefe504b-0 | Source code for langchain.retrievers.kendra
import re
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Union
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.docstore.document import Document
from langchain.pydantic_v1 im... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
7c93fefe504b-1 | """Information that highlights the key words in the excerpt."""
BeginOffset: int
"""The zero-based location in the excerpt where the highlight starts."""
EndOffset: int
"""The zero-based location in the excerpt where the highlight ends."""
TopAnswer: Optional[bool]
"""Indicates whether the resul... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
7c93fefe504b-2 | return self.Value.TextWithHighlightsValue.Text
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
[docs]class DocumentAttributeValue(BaseModel, extra=Extra.allow): # type: ignore[call-arg]
"""Value of a document attribute."""
DateValue: Optional[str]
"""The date expressed as an ISO 8... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
7c93fefe504b-3 | Id: Optional[str]
"""The ID of the relevant result item."""
DocumentId: Optional[str]
"""The document ID."""
DocumentURI: Optional[str]
"""The document URI."""
DocumentAttributes: Optional[List[DocumentAttribute]] = []
"""The document attributes."""
[docs] @abstractmethod
def get_titl... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
7c93fefe504b-4 | [docs]class QueryResultItem(ResultItem):
"""Query API result item."""
DocumentTitle: TextWithHighLights
"""The document title."""
FeedbackToken: Optional[str]
"""Identifies a particular result from a particular query."""
Format: Optional[str]
"""
If the Type is ANSWER, then format is eit... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
7c93fefe504b-5 | [docs]class RetrieveResultItem(ResultItem):
"""Retrieve API result item."""
DocumentTitle: Optional[str]
"""The document title."""
Content: Optional[str]
"""The content of the item."""
[docs] def get_title(self) -> str:
return self.DocumentTitle or ""
[docs] def get_excerpt(self) -> st... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
7c93fefe504b-6 | Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config.
credentials_profile_name: The name of the profile in the ~/.aws/credentials
or ~/.aws/config files, which has either access keys or role information
specified. If not specified, the default cre... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
7c93fefe504b-7 | return value
@root_validator(pre=True)
def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]:
if values.get("client") is not None:
return values
try:
import boto3
if values.get("credentials_profile_name"):
session = boto3.Session(pro... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
7c93fefe504b-8 | # Retrieve API returned 0 results, fall back to Query API
response = self.client.query(**kendra_kwargs)
q_result = QueryResult.parse_obj(response)
return q_result.ResultItems
def _get_top_k_docs(self, result_items: Sequence[ResultItem]) -> List[Document]:
top_docs = [
ite... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
4957a2955e7c-0 | Source code for langchain.retrievers.wikipedia
from typing import List
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, Document
from langchain.utilities.wikipedia import WikipediaAPIWrapper
[docs]class WikipediaRetriever(BaseRetriever, WikipediaAPIWrapp... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/wikipedia.html |
fff3d795c7d3-0 | Source code for langchain.retrievers.self_query.deeplake
"""Logic for converting internal query language to a valid Chroma query."""
from typing import Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
COMPAR... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/deeplake.html |
fff3d795c7d3-1 | value = COMPARATOR_TO_TQL[func.value] # type: ignore
return f"{value}"
[docs] def visit_operation(self, operation: Operation) -> str:
args = [arg.accept(self) for arg in operation.arguments]
operator = self._format_func(operation.operator)
return "(" + (" " + operator + " ").join(arg... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/deeplake.html |
a2684c0dc1c7-0 | Source code for langchain.retrievers.self_query.timescalevector
from __future__ import annotations
from typing import TYPE_CHECKING, Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
if TYPE_CHECKING:
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/timescalevector.html |
a2684c0dc1c7-1 | "Cannot import timescale-vector. Please install with `pip install "
"timescale-vector`."
) from e
args = [arg.accept(self) for arg in operation.arguments]
return client.Predicates(*args, operator=self._format_func(operation.operator))
[docs] def visit_comparison(self, comp... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/timescalevector.html |
9d5058cd9d96-0 | Source code for langchain.retrievers.self_query.dashvector
"""Logic for converting internal query language to a valid DashVector query."""
from typing import Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/dashvector.html |
9d5058cd9d96-1 | else:
value = f"'{value}'"
return (
f"{comparison.attribute}{self._format_func(comparison.comparator)}{value}"
)
[docs] def visit_structured_query(
self, structured_query: StructuredQuery
) -> Tuple[str, dict]:
if structured_query.filter is None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/dashvector.html |
6694568e605b-0 | Source code for langchain.retrievers.self_query.qdrant
from __future__ import annotations
from typing import TYPE_CHECKING, Tuple
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
if TYPE_CHECKING:
from qdrant_client.... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/qdrant.html |
6694568e605b-1 | "Cannot import qdrant_client. Please install with `pip install "
"qdrant-client`."
) from e
self._validate_func(comparison.comparator)
attribute = self.metadata_key + "." + comparison.attribute
if comparison.comparator == Comparator.EQ:
return rest.FieldCo... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/qdrant.html |
d393c03572d6-0 | Source code for langchain.retrievers.self_query.milvus
"""Logic for converting internal query language to a valid Milvus query."""
from typing import Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
COMPARAT... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/milvus.html |
d393c03572d6-1 | value = COMPARATOR_TO_BER[func]
return f"{value}"
[docs] def visit_operation(self, operation: Operation) -> str:
if operation.operator in UNARY_OPERATORS and len(operation.arguments) == 1:
operator = self._format_func(operation.operator)
return operator + "(" + operation.argum... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/milvus.html |
d2b11cb59e83-0 | Source code for langchain.retrievers.self_query.supabase
from typing import Any, Dict, Tuple
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
[docs]class SupabaseVectorTranslator(Visitor):
"""Translate Langchain filt... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/supabase.html |
d2b11cb59e83-1 | if isinstance(value, str):
return "->>"
else:
return "->"
[docs] def visit_operation(self, operation: Operation) -> str:
args = [arg.accept(self) for arg in operation.arguments]
return f"{operation.operator.value}({','.join(args)})"
[docs] def visit_comparison(self,... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/supabase.html |
5f322ddc3d2c-0 | Source code for langchain.retrievers.self_query.base
"""Retriever that generates and executes structured queries over its own data source."""
from typing import Any, Dict, List, Optional, Type, cast
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.chains import LLMChain
from langcha... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
5f322ddc3d2c-1 | Chroma,
DashVector,
DeepLake,
ElasticsearchStore,
Milvus,
MyScale,
OpenSearchVectorSearch,
Pinecone,
Qdrant,
Redis,
SupabaseVectorStore,
TimescaleVector,
Vectara,
VectorStore,
Weaviate,
)
def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
"""Get... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
5f322ddc3d2c-2 | f"Self query retriever with Vector Store type {vectorstore.__class__}"
f" not supported."
)
[docs]class SelfQueryRetriever(BaseRetriever, BaseModel):
"""Retriever that uses a vector store and an LLM to generate
the vector store queries."""
vectorstore: VectorStore
"""The underlying v... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
5f322ddc3d2c-3 | Returns:
List of relevant documents
"""
inputs = self.llm_chain.prep_inputs({"query": query})
structured_query = cast(
StructuredQuery,
self.llm_chain.predict_and_parse(
callbacks=run_manager.get_child(), **inputs
),
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
5f322ddc3d2c-4 | chain_kwargs[
"allowed_operators"
] = structured_query_translator.allowed_operators
llm_chain = load_query_constructor_chain(
llm,
document_contents,
metadata_field_info,
enable_limit=enable_limit,
**chain_kwargs,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
248efdb28577-0 | Source code for langchain.retrievers.self_query.weaviate
from typing import Dict, Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
[docs]class WeaviateTranslator(Visitor):
"""Translate `Weaviate` interna... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/weaviate.html |
390129541e5a-0 | Source code for langchain.retrievers.self_query.redis
from __future__ import annotations
from typing import Any, Tuple
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
from langchain.vectorstores.redis import Redis
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/redis.html |
390129541e5a-1 | return RedisText(attribute)
elif attribute in [tf.name for tf in self._schema.tag or []]:
return RedisTag(attribute)
elif attribute in [tf.name for tf in self._schema.numeric or []]:
return RedisNum(attribute)
else:
raise ValueError(
f"Invalid ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/redis.html |
390129541e5a-2 | def from_vectorstore(cls, vectorstore: Redis) -> RedisTranslator:
return cls(vectorstore._schema) | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/redis.html |
86930716d637-0 | Source code for langchain.retrievers.self_query.vectara
from typing import Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
[docs]def process_value(value: Union[int, float, str]) -> str:
if isinstance(va... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/vectara.html |
86930716d637-1 | [docs] def visit_comparison(self, comparison: Comparison) -> str:
comparator = self._format_func(comparison.comparator)
processed_value = process_value(comparison.value)
attribute = comparison.attribute
return (
"( " + "doc." + attribute + " " + comparator + " " + processe... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/vectara.html |
725ff0fbaf30-0 | Source code for langchain.retrievers.self_query.elasticsearch
from typing import Dict, Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
[docs]class ElasticsearchTranslator(Visitor):
"""Translate `Elastic... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/elasticsearch.html |
725ff0fbaf30-1 | # ElasticsearchStore filters require to target
# the metadata object field
field = f"metadata.{comparison.attribute}"
is_range_comparator = comparison.comparator in [
Comparator.GT,
Comparator.GTE,
Comparator.LT,
Comparator.LTE,
]
i... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/elasticsearch.html |
ec65ea32aafd-0 | Source code for langchain.retrievers.self_query.pinecone
from typing import Dict, Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
[docs]class PineconeTranslator(Visitor):
"""Translate `Pinecone` interna... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/pinecone.html |
afc5050cfcbc-0 | Source code for langchain.retrievers.self_query.myscale
import datetime
import re
from typing import Any, Callable, Dict, Tuple
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
def _DEFAULT_COMPOSER(op_name: str) -> Call... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/myscale.html |
afc5050cfcbc-1 | map_dict = {
Operator.AND: _DEFAULT_COMPOSER("AND"),
Operator.OR: _DEFAULT_COMPOSER("OR"),
Operator.NOT: _DEFAULT_COMPOSER("NOT"),
Comparator.EQ: _DEFAULT_COMPOSER("="),
Comparator.GT: _DEFAULT_COMPOSER(">"),
Comparator.GTE: _DEFAULT_COMPOSER(">="),
Comparator.LT:... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/myscale.html |
afc5050cfcbc-2 | # convert timestamp for datetime objects
if type(value) is datetime.date:
attr = f"parseDateTime32BestEffort({attr})"
value = f"parseDateTime32BestEffort('{value.strftime('%Y-%m-%d')}')"
# string pattern match
if comp is Comparator.LIKE:
value = f"'%{value[1:-... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/myscale.html |
71c031476986-0 | Source code for langchain.retrievers.self_query.opensearch
from typing import Dict, Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
[docs]class OpenSearchTranslator(Visitor):
"""Translate `OpenSearch` i... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/opensearch.html |
71c031476986-1 | field = f"metadata.{comparison.attribute}"
if comparison.comparator in [
Comparator.LT,
Comparator.LTE,
Comparator.GT,
Comparator.GTE,
]:
return {
"range": {
field: {self._format_func(comparison.comparator): ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/opensearch.html |
e74c2c75fa38-0 | Source code for langchain.retrievers.self_query.chroma
from typing import Dict, Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
[docs]class ChromaTranslator(Visitor):
"""Translate `Chroma` internal quer... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/chroma.html |
856162346c8b-0 | Source code for langchain.retrievers.document_compressors.cohere_rerank
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, Optional, Sequence
from langchain.callbacks.manager import Callbacks
from langchain.pydantic_v1 import Extra, root_validator
from langchain.retrievers.document_compressors.b... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
856162346c8b-1 | raise ImportError(
"Could not import cohere python package. "
"Please install it with `pip install cohere`."
)
return values
[docs] def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Optional[Callback... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
ed696cd01a0e-0 | Source code for langchain.retrievers.document_compressors.chain_filter
"""Filter that uses an LLM to drop documents that aren't relevant to the query."""
from typing import Any, Callable, Dict, Optional, Sequence
from langchain.callbacks.manager import Callbacks
from langchain.chains import LLMChain
from langchain.outp... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
ed696cd01a0e-1 | """Filter down documents based on their relevance to the query."""
filtered_docs = []
for doc in documents:
_input = self.get_input(query, doc)
include_doc = self.llm_chain.predict_and_parse(
**_input, callbacks=callbacks
)
if include_doc:
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
19eea034f859-0 | Source code for langchain.retrievers.document_compressors.base
from abc import ABC, abstractmethod
from inspect import signature
from typing import List, Optional, Sequence, Union
from langchain.callbacks.manager import Callbacks
from langchain.pydantic_v1 import BaseModel
from langchain.schema import BaseDocumentTrans... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
19eea034f859-1 | accepts_callbacks = (
signature(_transformer.compress_documents).parameters.get(
"callbacks"
)
is not None
)
if accepts_callbacks:
documents = _transformer.compress_documents(
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
59ae9c35ced7-0 | Source code for langchain.retrievers.document_compressors.chain_extract
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
import asyncio
from typing import Any, Callable, Dict, Optional, Sequence
from langchain.callbacks.manager import Callbacks
f... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
59ae9c35ced7-1 | """LLM wrapper to use for compressing documents."""
get_input: Callable[[str, Document], dict] = default_get_input
"""Callable for constructing the chain input from the query and a Document."""
[docs] def compress_documents(
self,
documents: Sequence[Document],
query: str,
cal... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
59ae9c35ced7-2 | prompt: Optional[PromptTemplate] = None,
get_input: Optional[Callable[[str, Document], str]] = None,
llm_chain_kwargs: Optional[dict] = None,
) -> LLMChainExtractor:
"""Initialize from LLM."""
_prompt = prompt if prompt is not None else _get_default_chain_prompt()
_get_input ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
044a3cb2e049-0 | Source code for langchain.retrievers.document_compressors.embeddings_filter
from typing import Callable, Dict, Optional, Sequence
import numpy as np
from langchain.callbacks.manager import Callbacks
from langchain.document_transformers.embeddings_redundant_filter import (
_get_embeddings_from_stateful_docs,
get... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
044a3cb2e049-1 | if values["k"] is None and values["similarity_threshold"] is None:
raise ValueError("Must specify one of `k` or `similarity_threshold`.")
return values
[docs] def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Optional[Callbacks] = ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
95a530187ada-0 | Source code for langchain.document_transformers.doctran_text_qa
from typing import Any, Optional, Sequence
from langchain.schema import BaseDocumentTransformer, Document
from langchain.utils import get_from_env
[docs]class DoctranQATransformer(BaseDocumentTransformer):
"""Extract QA from text documents using doctra... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_qa.html |
95a530187ada-1 | from doctran import Doctran
doctran = Doctran(
openai_api_key=self.openai_api_key, openai_model=self.openai_api_model
)
except ImportError:
raise ImportError(
"Install doctran to use this parser. (pip install doctran)"
)
for... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_qa.html |
a8a7e8aae5d0-0 | Source code for langchain.document_transformers.embeddings_redundant_filter
"""Transform documents"""
from typing import Any, Callable, List, Sequence
import numpy as np
from langchain.pydantic_v1 import BaseModel, Field
from langchain.schema import BaseDocumentTransformer, Document
from langchain.schema.embeddings imp... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
a8a7e8aae5d0-1 | redundant_stacked = np.column_stack(redundant)
redundant_sorted = np.argsort(similarity[redundant])[::-1]
included_idxs = set(range(len(embedded_documents)))
for first_idx, second_idx in redundant_stacked[redundant_sorted]:
if first_idx in included_idxs and second_idx in included_idxs:
#... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
a8a7e8aae5d0-2 | )
closest_indices = []
# Loop through the number of clusters you have
for i in range(num_clusters):
# Get the list of distances from that particular cluster center
distances = np.linalg.norm(
embedded_documents - kmeans.cluster_centers_[i], axis=1
)
# Find the ind... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
a8a7e8aae5d0-3 | ) -> Sequence[Document]:
"""Filter down documents."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
self.embeddings, stateful_documents
)
included_idxs = _filter_similar_embeddings(
embedded... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
a8a7e8aae5d0-4 | """ By default duplicated results are skipped and replaced by the next closest
vector in the cluster. If remove_duplicates is true no replacement will be done:
This could dramatically reduce results when there is a lot of overlap between
clusters.
"""
class Config:
"""Configuration for thi... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
69f212dd083b-0 | Source code for langchain.document_transformers.beautiful_soup_transformer
from typing import Any, List, Sequence
from langchain.schema import BaseDocumentTransformer, Document
[docs]class BeautifulSoupTransformer(BaseDocumentTransformer):
"""Transform HTML content by extracting specific tags and removing unwanted ... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/beautiful_soup_transformer.html |
69f212dd083b-1 | Returns:
A sequence of Document objects with transformed content.
"""
for doc in documents:
cleaned_content = doc.page_content
cleaned_content = self.remove_unwanted_tags(cleaned_content, unwanted_tags)
cleaned_content = self.extract_tags(cleaned_content, ... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/beautiful_soup_transformer.html |
69f212dd083b-2 | href = element.get("href")
if href:
text_parts.append(f"{element.get_text()} ({href})")
else:
text_parts.append(element.get_text())
else:
text_parts.append(element.get_text())
return " ".j... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/beautiful_soup_transformer.html |
15c48fbe9cdc-0 | Source code for langchain.document_transformers.nuclia_text_transform
import asyncio
import json
import uuid
from typing import Any, Sequence
from langchain.schema.document import BaseDocumentTransformer, Document
from langchain.tools.nuclia.tool import NucliaUnderstandingAPI
[docs]class NucliaTextTransformer(BaseDocum... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/nuclia_text_transform.html |
215362d89fdf-0 | Source code for langchain.document_transformers.openai_functions
"""Document transformers that use OpenAI Functions models"""
from typing import Any, Dict, Optional, Sequence, Type, Union
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions import create_tagging_chain
from langchain.prompts ... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/openai_functions.html |
215362d89fdf-1 | original_documents = [
Document(page_content="Review of The Bee Movie\nBy Roger Ebert\n\This is the greatest movie ever made. 4 out of 5 stars."),
Document(page_content="Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.", metadata={"reliable"... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/openai_functions.html |
215362d89fdf-2 | """Create a DocumentTransformer that uses an OpenAI function chain to automatically
tag documents with metadata based on their content and an input schema.
Args:
metadata_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary
is passed in, it's assumed to already be a v... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/openai_functions.html |
215362d89fdf-3 | original_documents = [
Document(page_content="Review of The Bee Movie\nBy Roger Ebert\n\This is the greatest movie ever made. 4 out of 5 stars."),
Document(page_content="Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.", metadata={"reliable"... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/openai_functions.html |
38d5d6db29b8-0 | Source code for langchain.document_transformers.doctran_text_extract
from typing import Any, List, Optional, Sequence
from langchain.schema import BaseDocumentTransformer, Document
from langchain.utils import get_from_env
[docs]class DoctranPropertyExtractor(BaseDocumentTransformer):
"""Extract properties from text... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_extract.html |
38d5d6db29b8-1 | transformed_document = await qa_transformer.atransform_documents(documents)
""" # noqa: E501
[docs] def __init__(
self,
properties: List[dict],
openai_api_key: Optional[str] = None,
openai_api_model: Optional[str] = None,
) -> None:
self.properties = properties
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_extract.html |
2a7c4a399224-0 | Source code for langchain.document_transformers.html2text
from typing import Any, Sequence
from langchain.schema import BaseDocumentTransformer, Document
[docs]class Html2TextTransformer(BaseDocumentTransformer):
"""Replace occurrences of a particular search pattern with a replacement string
Arguments:
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/html2text.html |
c4e80ea3a62c-0 | Source code for langchain.document_transformers.doctran_text_translate
from typing import Any, Optional, Sequence
from langchain.schema import BaseDocumentTransformer, Document
from langchain.utils import get_from_env
[docs]class DoctranTextTranslator(BaseDocumentTransformer):
"""Translate text documents using doct... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_translate.html |
c4e80ea3a62c-1 | """Translates text documents using doctran."""
try:
from doctran import Doctran
doctran = Doctran(
openai_api_key=self.openai_api_key, openai_model=self.openai_api_model
)
except ImportError:
raise ImportError(
"Install doct... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_translate.html |
8b6a5ca19c2a-0 | Source code for langchain.document_transformers.long_context_reorder
"""Reorder documents"""
from typing import Any, List, Sequence
from langchain.pydantic_v1 import BaseModel
from langchain.schema import BaseDocumentTransformer, Document
def _litm_reordering(documents: List[Document]) -> List[Document]:
"""Los in ... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/long_context_reorder.html |
bea1d7248c75-0 | Source code for langchain.storage.encoder_backed
from typing import (
Any,
Callable,
Iterator,
List,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
)
from langchain.schema import BaseStore
K = TypeVar("K")
V = TypeVar("V")
[docs]class EncoderBackedStore(BaseStore[K, V]):
"""Wraps a s... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/encoder_backed.html |
bea1d7248c75-1 | value_serializer: Callable[[V], bytes],
value_deserializer: Callable[[Any], V],
) -> None:
"""Initialize an EncodedStore."""
self.store = store
self.key_encoder = key_encoder
self.value_serializer = value_serializer
self.value_deserializer = value_deserializer
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/encoder_backed.html |
9e08d33fdadd-0 | Source code for langchain.storage.in_memory
"""In memory store that is not thread safe and has no eviction policy.
This is a simple implementation of the BaseStore using a dictionary that is useful
primarily for unit testing purposes.
"""
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/in_memory.html |
9e08d33fdadd-1 | """
return [self.store.get(key) for key in keys]
[docs] def mset(self, key_value_pairs: Sequence[Tuple[str, Any]]) -> None:
"""Set the values for the given keys.
Args:
key_value_pairs (Sequence[Tuple[str, V]]): A sequence of key-value pairs.
Returns:
None
... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/in_memory.html |
7f43ac4fd081-0 | Source code for langchain.storage.file_system
import re
from pathlib import Path
from typing import Iterator, List, Optional, Sequence, Tuple, Union
from langchain.schema import BaseStore
from langchain.storage.exceptions import InvalidKeyException
[docs]class LocalFileStore(BaseStore[str, bytes]):
"""BaseStore int... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/file_system.html |
7f43ac4fd081-1 | Returns:
Path: The full path for the given key.
"""
if not re.match(r"^[a-zA-Z0-9_.\-/]+$", key):
raise InvalidKeyException(f"Invalid characters in key: {key}")
return self.root_path / key
[docs] def mget(self, keys: Sequence[str]) -> List[Optional[bytes]]:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/file_system.html |
7f43ac4fd081-2 | for key in keys:
full_path = self._get_full_path(key)
if full_path.exists():
full_path.unlink()
[docs] def yield_keys(self, prefix: Optional[str] = None) -> Iterator[str]:
"""Get an iterator over keys that match the given prefix.
Args:
prefix (Optio... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/file_system.html |
21ac5fd37a25-0 | Source code for langchain.storage.exceptions
from langchain.schema import LangChainException
[docs]class InvalidKeyException(LangChainException):
"""Raised when a key is invalid; e.g., uses incorrect characters.""" | https://api.python.langchain.com/en/latest/_modules/langchain/storage/exceptions.html |
0d42273d56a2-0 | Source code for langchain.storage.redis
from typing import Any, Iterator, List, Optional, Sequence, Tuple, cast
from langchain.schema import BaseStore
from langchain.utilities.redis import get_client
[docs]class RedisStore(BaseStore[str, bytes]):
"""BaseStore implementation using Redis as the underlying store.
... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/redis.html |
0d42273d56a2-1 | ttl: time to expire keys in seconds if provided,
if None keys will never expire
namespace: if provided, all keys will be prefixed with this namespace
"""
try:
from redis import Redis
except ImportError as e:
raise ImportError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/redis.html |
0d42273d56a2-2 | """Get the values associated with the given keys."""
return cast(
List[Optional[bytes]],
self.client.mget([self._get_prefixed_key(key) for key in keys]),
)
[docs] def mset(self, key_value_pairs: Sequence[Tuple[str, bytes]]) -> None:
"""Set the given key-value pairs."""... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/redis.html |
44dd9fd2d675-0 | Source code for langchain.prompts.prompt
"""Prompt schema definition."""
from __future__ import annotations
from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Optional, Union
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPromptTemplate,
_get_jin... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
44dd9fd2d675-1 | def __add__(self, other: Any) -> PromptTemplate:
"""Override the + operator to allow for combining prompt templates."""
# Allow for easy combining
if isinstance(other, PromptTemplate):
if self.template_format != "f-string":
raise ValueError(
"Addin... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
44dd9fd2d675-2 | Args:
kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
return DE... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
44dd9fd2d675-3 | Returns:
The final prompt generated.
"""
template = example_separator.join([prefix, *examples, suffix])
return cls(input_variables=input_variables, template=template, **kwargs)
[docs] @classmethod
def from_file(
cls, template_file: Union[str, Path], input_variables: Li... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
44dd9fd2d675-4 | `"foo {variable2}"`.
Returns:
The prompt template loaded from the template.
"""
if template_format == "jinja2":
# Get the variables for the template
input_variables = _get_jinja2_variables_from_template(template)
elif template_format == "f-string":
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
92393eeb1755-0 | Source code for langchain.prompts.few_shot_with_templates
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, StringPromptTemplate
from langchain.prompts.example_selector.base import BaseExampleSelector
from la... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
92393eeb1755-1 | examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_selector' should be provided"
)
if examples is None and example_selecto... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
92393eeb1755-2 | Args:
kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the example... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
92393eeb1755-3 | """Return a dictionary of the prompt."""
if self.example_selector:
raise ValueError("Saving an example selector is not currently supported")
return super().dict(**kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
4e6754f2de47-0 | Source code for langchain.prompts.chat
"""Chat prompt template."""
from __future__ import annotations
from abc import ABC, abstractmethod
from pathlib import Path
from typing import (
Any,
Callable,
Dict,
List,
Sequence,
Set,
Tuple,
Type,
TypeVar,
Union,
overload,
)
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
4e6754f2de47-1 | """
def __add__(self, other: Any) -> ChatPromptTemplate:
"""Combine two prompt templates.
Args:
other: Another prompt template.
Returns:
Combined prompt template.
"""
prompt = ChatPromptTemplate(messages=[self])
return prompt + other
[docs]clas... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
4e6754f2de47-2 | """Base class for message prompt templates that use a string prompt template."""
prompt: StringPromptTemplate
"""String prompt template."""
additional_kwargs: dict = Field(default_factory=dict)
"""Additional keyword arguments to pass to the prompt template."""
[docs] @classmethod
def from_templat... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
4e6754f2de47-3 | Args:
**kwargs: Keyword arguments to use for formatting.
Returns:
Formatted message.
"""
[docs] def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
"""Format messages from kwargs.
Args:
**kwargs: Keyword arguments to use for formatting.
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
4e6754f2de47-4 | [docs]class AIMessagePromptTemplate(BaseStringMessagePromptTemplate):
"""AI message prompt template. This is a message sent from the AI."""
[docs] def format(self, **kwargs: Any) -> BaseMessage:
"""Format the prompt template.
Args:
**kwargs: Keyword arguments to use for formatting.
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
4e6754f2de47-5 | For use in external schemas."""
messages: Sequence[AnyMessage]
[docs]class BaseChatPromptTemplate(BasePromptTemplate, ABC):
"""Base class for chat prompt templates."""
@property
def lc_attributes(self) -> Dict:
"""
Return a list of attribute names that should be included in the
s... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
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