id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
69b8a42731d2-0 | Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chain... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
69b8a42731d2-1 | ) -> GraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
entity_chain = LLMChain(llm=llm, prompt=entity_prompt)
return cls(
qa_chain=qa_chain,
entity_extraction_chain=entity_chain,
**kwargs,
)
def _call(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
9c4678175048-0 | Source code for langchain.chains.graph_qa.neptune_cypher
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.bas... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
9c4678175048-1 | output_key: str = "result" #: :meta private:
top_k: int = 10
return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the graph directly."""
@property... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
9c4678175048-2 | """Generate Cypher statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
intermediate_steps: List = []
generated_cypher = self.c... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
38e028dd246e-0 | Source code for langchain.chains.graph_qa.kuzu
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chain... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
38e028dd246e-1 | cypher_prompt: BasePromptTemplate = KUZU_GENERATION_PROMPT,
**kwargs: Any,
) -> KuzuQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
return cls(
qa_chain=qa_chain,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
38e028dd246e-2 | callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
f6df3d4a090d-0 | Source code for langchain.chains.graph_qa.cypher
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from lan... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
f6df3d4a090d-1 | """Number of results to return from the query"""
return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the graph directly."""
@property
def input_ke... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
f6df3d4a090d-2 | ) -> Dict[str, Any]:
"""Generate Cypher statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
intermediate_steps: List = []
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
30ca03d7b6a5-0 | Source code for langchain.chains.graph_qa.sparql
"""
Question answering over an RDF or OWL graph using SPARQL.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base impo... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
30ca03d7b6a5-1 | cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = SPARQL_QA_PROMPT,
sparql_select_prompt: BasePromptTemplate = SPARQL_GENERATION_SELECT_PROMPT,
sparql_update_prompt: BasePromptTemplate = SPARQL_GENERATION_UPDATE_PROMPT,
sparql_intent_prompt: BasePromptTempla... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
30ca03d7b6a5-2 | callbacks = _run_manager.get_child()
prompt = inputs[self.input_key]
_intent = self.sparql_intent_chain.run({"prompt": prompt}, callbacks=callbacks)
intent = _intent.strip()
if "SELECT" not in intent and "UPDATE" not in intent:
raise ValueError(
"I am sorry, b... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
30ca03d7b6a5-3 | callbacks=callbacks,
)
res = result[self.qa_chain.output_key]
elif intent == "UPDATE":
self.graph.update(generated_sparql)
res = "Successfully inserted triples into the graph."
else:
raise ValueError("Unsupported SPARQL query type.")
re... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
ec05c5686a64-0 | Source code for langchain.chains.graph_qa.hugegraph
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/hugegraph.html |
ec05c5686a64-1 | **kwargs: Any,
) -> HugeGraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
gremlin_generation_chain = LLMChain(llm=llm, prompt=gremlin_prompt)
return cls(
qa_chain=qa_chain,
gremlin_generation_chain=gremlin_generation_chain... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/hugegraph.html |
fabf187699f7-0 | Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic imp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-1 | elif isinstance(dialogue_turn, tuple):
human = "Human: " + dialogue_turn[0]
ai = "Assistant: " + dialogue_turn[1]
buffer += "\n" + "\n".join([human, ai])
else:
raise ValueError(
f"Unsupported chat history format: {type(dialogue_turn)}."
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-2 | """An optional function to get a string of the chat history.
If None is provided, will use a default."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
allow_population_by_field_name = True
@property
def inp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-3 | )
else:
new_question = question
accepts_run_manager = (
"run_manager" in inspect.signature(self._get_docs).parameters
)
if accepts_run_manager:
docs = self._get_docs(new_question, inputs, run_manager=_run_manager)
else:
docs = self.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-4 | if chat_history_str:
callbacks = _run_manager.get_child()
new_question = await self.question_generator.arun(
question=question, chat_history=chat_history_str, callbacks=callbacks
)
else:
new_question = question
accepts_run_manager = (
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-5 | The algorithm for this chain consists of three parts:
1. Use the chat history and the new question to create a "standalone question".
This is done so that this question can be passed into the retrieval step to fetch
relevant documents. If only the new question was passed in, then relevant context
may be... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-6 | retriever=retriever,
question_generator=question_generator_chain,
)
"""
retriever: BaseRetriever
"""Retriever to use to fetch documents."""
max_tokens_limit: Optional[int] = None
"""If set, enforces that the documents returned are less than this limit.
This is only en... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-7 | question, callbacks=run_manager.get_child()
)
return self._reduce_tokens_below_limit(docs)
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
retriever: BaseRetriever,
condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
chai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-8 | callbacks: Callbacks to pass to all subchains.
**kwargs: Additional parameters to pass when initializing
ConversationalRetrievalChain
"""
combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
doc_chain = load_qa_chain(
llm,
chain_type=cha... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-9 | run_manager: CallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
vectordbkwargs = inputs.get("vectordbkwargs", {})
full_kwargs = {**self.search_kwargs, **vectordbkwargs}
return self.vectorstore.similarity_search(
question, k=self.top_k_docs_for_context, **ful... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
fabf187699f7-10 | callbacks=callbacks,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
364a91565e65-0 | Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForChainRun
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
364a91565e65-1 | return list(np.array(embeddings).mean(axis=0))
[docs] def embed_query(self, text: str) -> List[float]:
"""Generate a hypothetical document and embedded it."""
var_name = self.llm_chain.input_keys[0]
result = self.llm_chain.generate([{var_name: text}])
documents = [generation.text for ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
ab37b6a75b79-0 | Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
from __future__ import annotations
import math
import re
import warnings
from typing import Any, Dict, List, Optional
import numexpr
from pydantic import Extra, root_validator
from langchain.callback... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
ab37b6a75b79-1 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMMathChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the from_llm "
"class method."
)
if "llm_chain" not in values and values["llm"]... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
ab37b6a75b79-2 | ) -> Dict[str, str]:
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
if text_match:
expression = text_match.group(1)
output = self._evaluate_exp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
ab37b6a75b79-3 | elif llm_output.startswith("Answer:"):
answer = llm_output
elif "Answer:" in llm_output:
answer = "Answer: " + llm_output.split("Answer:")[-1]
else:
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
def _call(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
ab37b6a75b79-4 | [docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate = PROMPT,
**kwargs: Any,
) -> LLMMathChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
5180c41ddf34-0 | Source code for langchain.chains.flare.base
from __future__ import annotations
import re
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Tuple
import numpy as np
from pydantic import Field
from langchain.callbacks.manager import (
CallbackManagerForChainRun,
)
from langchain.c... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
5180c41ddf34-1 | llm: OpenAI = Field(
default_factory=lambda: OpenAI(
max_tokens=32, model_kwargs={"logprobs": 1}, temperature=0
)
)
def _extract_tokens_and_log_probs(
self, generations: List[Generation]
) -> Tuple[Sequence[str], Sequence[float]]:
tokens = []
log_probs = [... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
5180c41ddf34-2 | end = idx + num_pad_tokens + 1
if idx - low_idx[i] < min_token_gap:
spans[-1][1] = end
else:
spans.append([idx, end])
return ["".join(tokens[start:end]) for start, end in spans]
[docs]class FlareChain(Chain):
"""Chain that combines a retriever, a question generator,
a... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
5180c41ddf34-3 | self,
questions: List[str],
user_input: str,
response: str,
_run_manager: CallbackManagerForChainRun,
) -> Tuple[str, bool]:
callbacks = _run_manager.get_child()
docs = []
for question in questions:
docs.extend(self.retriever.get_relevant_documents... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
5180c41ddf34-4 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
user_input = inputs[self.input_keys[0]]
response = ""
for i in r... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
5180c41ddf34-5 | ) -> FlareChain:
"""Creates a FlareChain from a language model.
Args:
llm: Language model to use.
max_generation_len: Maximum length of the generated response.
**kwargs: Additional arguments to pass to the constructor.
Returns:
FlareChain class wit... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
ea3a9bff58cc-0 | Source code for langchain.chains.flare.prompts
from typing import Tuple
from langchain.prompts import PromptTemplate
from langchain.schema import BaseOutputParser
[docs]class FinishedOutputParser(BaseOutputParser[Tuple[str, bool]]):
"""Output parser that checks if the output is finished."""
finished_value: str ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/prompts.html |
f3a9bed8df3d-0 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
f3a9bed8df3d-1 | )
chains = [
create_draft_answer_chain,
list_assertions_chain,
check_assertions_chain,
revised_answer_chain,
]
question_to_checked_assertions_chain = SequentialChain(
chains=chains,
input_variables=["question"],
output_variables=["revised_statement"],
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
f3a9bed8df3d-2 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMCheckerChain with an llm is deprecated. "
"Please instantiate with question_to_checked_assertions_chain "
"or using the from_llm class method."
)
if (
"que... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
f3a9bed8df3d-3 | output = self.question_to_checked_assertions_chain(
{"question": question}, callbacks=_run_manager.get_child()
)
return {self.output_key: output["revised_statement"]}
@property
def _chain_type(self) -> str:
return "llm_checker_chain"
[docs] @classmethod
def from_llm(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
1dad0d01e8a9-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1dad0d01e8a9-1 | :meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1dad0d01e8a9-2 | combine_documents_chain = load_qa_chain(
llm, chain_type=chain_type, **_chain_type_kwargs
)
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
@abstractmethod
def _get_docs(
self,
question: str,
*,
run_manager: CallbackManagerForChai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1dad0d01e8a9-3 | return {self.output_key: answer, "source_documents": docs}
else:
return {self.output_key: answer}
@abstractmethod
async def _aget_docs(
self,
question: str,
*,
run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get documents to d... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1dad0d01e8a9-4 | else:
return {self.output_key: answer}
[docs]class RetrievalQA(BaseRetrievalQA):
"""Chain for question-answering against an index.
Example:
.. code-block:: python
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.fai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1dad0d01e8a9-5 | """Vector Database to connect to."""
k: int = 4
"""Number of documents to query for."""
search_type: str = "similarity"
"""Search type to use over vectorstore. `similarity` or `mmr`."""
search_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Extra search args."""
@root_validator()
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1dad0d01e8a9-6 | self,
question: str,
*,
run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
raise NotImplementedError("VectorDBQA does not support async")
@property
def _chain_type(self) -> str:
"""Return the chain type."""
return "vecto... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
38b63a9ef782-0 | Source code for langchain.chains.query_constructor.parser
import datetime
from typing import Any, Optional, Sequence, Union
from langchain.utils import check_package_version
try:
check_package_version("lark", gte_version="1.1.5")
from lark import Lark, Transformer, v_args
except ImportError:
[docs] def v_arg... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/parser.html |
38b63a9ef782-1 | """Transforms a query string into an intermediate representation."""
def __init__(
self,
*args: Any,
allowed_comparators: Optional[Sequence[Comparator]] = None,
allowed_operators: Optional[Sequence[Operator]] = None,
**kwargs: Any,
):
super().__init__(*args, **kwa... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/parser.html |
38b63a9ef782-2 | )
return Operator(func_name)
else:
raise ValueError(
f"Received unrecognized function {func_name}. Valid functions are "
f"{list(Operator) + list(Comparator)}"
)
def args(self, *items: Any) -> tuple:
return items
def false(self)... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/parser.html |
38b63a9ef782-3 | )
transformer = QueryTransformer(
allowed_comparators=allowed_comparators, allowed_operators=allowed_operators
)
return Lark(GRAMMAR, parser="lalr", transformer=transformer, start="program") | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/parser.html |
15c8dcb04295-0 | Source code for langchain.chains.query_constructor.base
"""LLM Chain for turning a user text query into a structured query."""
from __future__ import annotations
import json
from typing import Any, Callable, List, Optional, Sequence
from langchain import FewShotPromptTemplate, LLMChain
from langchain.chains.query_const... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15c8dcb04295-1 | if not parsed.get("limit"):
parsed.pop("limit", None)
return StructuredQuery(
**{k: v for k, v in parsed.items() if k in allowed_keys}
)
except Exception as e:
raise OutputParserException(
f"Parsing text\n{text}\n raised followi... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15c8dcb04295-2 | ) -> BasePromptTemplate:
attribute_str = _format_attribute_info(attribute_info)
allowed_comparators = allowed_comparators or list(Comparator)
allowed_operators = allowed_operators or list(Operator)
if enable_limit:
schema = SCHEMA_WITH_LIMIT.format(
allowed_comparators=" | ".join(all... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15c8dcb04295-3 | ) -> LLMChain:
"""Load a query constructor chain.
Args:
llm: BaseLanguageModel to use for the chain.
document_contents: The contents of the document to be queried.
attribute_info: A list of AttributeInfo objects describing
the attributes of the document.
examples: Opt... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
95cb36993fe0-0 | Source code for langchain.chains.query_constructor.ir
"""Internal representation of a structured query language."""
from __future__ import annotations
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, List, Optional, Sequence, Union
from pydantic import BaseModel
[docs]class Visitor(ABC)... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/ir.html |
95cb36993fe0-1 | snake_case = ""
for i, char in enumerate(name):
if char.isupper() and i != 0:
snake_case += "_" + char.lower()
else:
snake_case += char.lower()
return snake_case
[docs]class Expr(BaseModel):
"""Base class for all expressions."""
[docs] def accept(self, visitor: Vis... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/ir.html |
95cb36993fe0-2 | """Filtering expression."""
limit: Optional[int]
"""Limit on the number of results.""" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/ir.html |
2a684b02ef16-0 | Source code for langchain.chains.query_constructor.schema
from pydantic import BaseModel
[docs]class AttributeInfo(BaseModel):
"""Information about a data source attribute."""
name: str
description: str
type: str
class Config:
"""Configuration for this pydantic object."""
arbitrary_t... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/schema.html |
55eef8839829-0 | Source code for langchain.chains.llm_symbolic_math.base
"""Chain that interprets a prompt and executes python code to do symbolic math."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_symbolic_math/base.html |
55eef8839829-1 | try:
import sympy
except ImportError as e:
raise ImportError(
"Unable to import sympy, please install it with `pip install sympy`."
) from e
try:
output = str(sympy.sympify(expression, evaluate=True))
except Exception as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_symbolic_math/base.html |
55eef8839829-2 | async def _aprocess_llm_result(
self,
llm_output: str,
run_manager: AsyncCallbackManagerForChainRun,
) -> Dict[str, str]:
await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
text_match = re.search(r"^```text(.*?)`... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_symbolic_math/base.html |
55eef8839829-3 | self,
inputs: Dict[str, str],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
await _run_manager.on_text(inputs[self.input_key])
llm_output = await self.llm_ch... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_symbolic_math/base.html |
23eb0300de5e-0 | Source code for langchain.chains.api.base
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForCh... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
23eb0300de5e-1 | if set(input_vars) != expected_vars:
raise ValueError(
f"Input variables should be {expected_vars}, got {input_vars}"
)
return values
@root_validator(pre=True)
def validate_api_answer_prompt(cls, values: Dict) -> Dict:
"""Check that api answer prompt expec... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
23eb0300de5e-2 | return {self.output_key: answer}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
question = inputs[self.que... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
23eb0300de5e-3 | requests_wrapper = TextRequestsWrapper(headers=headers)
get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
return cls(
api_request_chain=get_request_chain,
api_answer_chain=get_answer_chain,
requests_wrapper=requests_wrapper,
api_docs=api_doc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
6ea9b3729df4-0 | Source code for langchain.chains.api.openapi.chain
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
import json
from typing import Any, Dict, List, NamedTuple, Optional, cast
from pydantic import BaseModel, Field
from requests import Response
from la... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
6ea9b3729df4-1 | """
return [self.instructions_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, "intermediate_steps"]
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
6ea9b3729df4-2 | path = self._construct_path(args)
body_params = self._extract_body_params(args)
query_params = self._extract_query_params(args)
return {
"url": path,
"data": body_params,
"params": query_params,
}
def _get_output(self, output: str, intermediate_ste... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
6ea9b3729df4-3 | method = getattr(self.requests, self.api_operation.method.value)
api_response: Response = method(**request_args)
if api_response.status_code != 200:
method_str = str(self.api_operation.method.value)
response_text = (
f"{api_response.status_code... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
6ea9b3729df4-4 | # TODO: Handle async
) -> "OpenAPIEndpointChain":
"""Create an OpenAPIEndpoint from a spec at the specified url."""
operation = APIOperation.from_openapi_url(spec_url, path, method)
return cls.from_api_operation(
operation,
requests=requests,
llm=llm,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
6ea9b3729df4-5 | requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
ef223576e235-0 | Source code for langchain.chains.api.openapi.requests_chain
"""request parser."""
import json
import re
from typing import Any
from langchain.chains.api.openapi.prompts import REQUEST_TEMPLATE
from langchain.chains.llm import LLMChain
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import Base... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/requests_chain.html |
ef223576e235-1 | ) -> LLMChain:
"""Get the request parser."""
output_parser = APIRequesterOutputParser()
prompt = PromptTemplate(
template=REQUEST_TEMPLATE,
output_parser=output_parser,
partial_variables={"schema": typescript_definition},
input_variables=["instruct... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/requests_chain.html |
b45b7b742662-0 | Source code for langchain.chains.api.openapi.response_chain
"""Response parser."""
import json
import re
from typing import Any
from langchain.chains.api.openapi.prompts import RESPONSE_TEMPLATE
from langchain.chains.llm import LLMChain
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/response_chain.html |
b45b7b742662-1 | template=RESPONSE_TEMPLATE,
output_parser=output_parser,
input_variables=["response", "instructions"],
)
return cls(prompt=prompt, llm=llm, verbose=verbose, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/response_chain.html |
c0ad0cf107ae-0 | Source code for langchain.chains.elasticsearch_database.base
"""Chain for interacting with Elasticsearch Database."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForChainR... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
c0ad0cf107ae-1 | sample_documents_in_index_info: int = 3
return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
c0ad0cf107ae-2 | for k, v in mappings.items():
hits = self.database.search(
index=k,
query={"match_all": {}},
size=self.sample_documents_in_index_info,
)["hits"]["hits"]
hits = [str(hit["_source"]) for hit in hits]
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
c0ad0cf107ae-3 | callbacks=_run_manager.get_child(),
**query_inputs,
)
_run_manager.on_text(es_cmd, color="green", verbose=self.verbose)
intermediate_steps.append(
es_cmd
) # output: elasticsearch dsl generation (no checker)
intermediate_steps.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
c0ad0cf107ae-4 | def from_llm(
cls,
llm: BaseLanguageModel,
database: Elasticsearch,
*,
query_prompt: Optional[BasePromptTemplate] = None,
answer_prompt: Optional[BasePromptTemplate] = None,
query_output_parser: Optional[BaseLLMOutputParser] = None,
**kwargs: Any,
) ->... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
597b8de8cf15-0 | Source code for langchain.chains.conversation.base
"""Chain that carries on a conversation and calls an LLM."""
from typing import Dict, List
from pydantic import Extra, Field, root_validator
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
597b8de8cf15-1 | f"({memory_keys}) - please provide keys that don't overlap."
)
prompt_variables = values["prompt"].input_variables
expected_keys = memory_keys + [input_key]
if set(expected_keys) != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input vari... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
2738c6adba9d-0 | Source code for langchain.chat_models.base
import asyncio
import inspect
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import (
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
Sequence,
cast,
)
from pydantic import Field, root_validator... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-1 | callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
"""Callback manager to add to the run trace."""
tags: Optional[List[str]] = Field(default=None, exclude=True)
"""Tags to add to the run trace."""
metadata: Optional[Dict[str, Any]] = Field(default=None, exclude=True)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-2 | return cast(
BaseMessageChunk,
cast(
ChatGeneration,
self.generate_prompt(
[self._convert_input(input)], stop=stop, **(config or {}), **kwargs
).generations[0][0],
).message,
)
[docs] async def ainvoke(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-3 | config = config or {}
messages = self._convert_input(input).to_messages()
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop, **kwargs}
callback_manager = CallbackManager.configure(
config.get("callbacks"),
se... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-4 | else:
config = config or {}
messages = self._convert_input(input).to_messages()
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop, **kwargs}
callback_manager = AsyncCallbackManager.configure(
config.get("callback... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-5 | params["stop"] = stop
return {**params, **kwargs}
def _get_llm_string(self, stop: Optional[List[str]] = None, **kwargs: Any) -> str:
if self.lc_serializable:
params = {**kwargs, **{"stop": stop}}
param_string = str(sorted([(k, v) for k, v in params.items()]))
llm_... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-6 | self._generate_with_cache(
m,
stop=stop,
run_manager=run_managers[i] if run_managers else None,
**kwargs,
)
)
except (KeyboardInterrupt, Exception) as e:
if run... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-7 | self.verbose,
tags,
self.tags,
metadata,
self.metadata,
)
run_managers = await callback_manager.on_chat_model_start(
dumpd(self), messages, invocation_params=params, options=options
)
results = await asyncio.gather(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-8 | *[
run_manager.on_llm_end(flattened_output)
for run_manager, flattened_output in zip(
run_managers, flattened_outputs
)
]
)
if run_managers:
output.run = [
RunInfo(run_id=run_manager.run_id) for r... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
2738c6adba9d-9 | if langchain.llm_cache is None or disregard_cache:
# This happens when langchain.cache is None, but self.cache is True
if self.cache is not None and self.cache:
raise ValueError(
"Asked to cache, but no cache found at `langchain.cache`."
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_models/base.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.