id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
fa7c749cec59-1 | extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Will be whatever keys the prompt expects.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Will always return tex... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
fa7c749cec59-2 | )
return {self.output_key: result}
@property
def _chain_type(self) -> str:
return "llm_requests_chain" | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
2154a11e75c0-0 | Source code for langchain.chains.moderation
"""Pass input through a moderation endpoint."""
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.pydantic_v1 import root_validator
from langchain.utils import... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
2154a11e75c0-1 | values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
values["client"] = ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
f66c413dc0d9-0 | Source code for langchain.chains.prompt_selector
from abc import ABC, abstractmethod
from typing import Callable, List, Tuple
from langchain.chat_models.base import BaseChatModel
from langchain.llms.base import BaseLLM
from langchain.pydantic_v1 import BaseModel, Field
from langchain.schema import BasePromptTemplate
fr... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/prompt_selector.html |
f66c413dc0d9-1 | True if the language model is a BaseLLM model, False otherwise.
"""
return isinstance(llm, BaseLLM)
[docs]def is_chat_model(llm: BaseLanguageModel) -> bool:
"""Check if the language model is a chat model.
Args:
llm: Language model to check.
Returns:
True if the language model is a Ba... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/prompt_selector.html |
e6e5c70a982d-0 | Source code for langchain.chains.base
"""Base interface that all chains should implement."""
import asyncio
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, Union
import yaml
from langchain.callback... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-1 | The main methods exposed by chains are:
- `__call__`: Chains are callable. The `__call__` method is the primary way to
execute a Chain. This takes inputs as a dictionary and returns a
dictionary output.
- `run`: A convenience method that takes inputs as args/kwargs and returns th... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-2 | run_name=config.get("run_name"),
**kwargs,
)
[docs] async def ainvoke(
self,
input: Dict[str, Any],
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> Dict[str, Any]:
config = config or {}
return await self.acall(
input,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-3 | will be printed to the console. Defaults to the global `verbose` value,
accessible via `langchain.globals.get_verbose()`."""
tags: Optional[List[str]] = None
"""Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed a... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-4 | return values
@validator("verbose", pre=True, always=True)
def set_verbose(cls, verbose: Optional[bool]) -> bool:
"""Set the chain verbosity.
Defaults to the global setting if not specified by the user.
"""
if verbose is None:
return _get_verbosity()
else:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-5 | specified in `Chain.input_keys`, including any inputs added by memory.
run_manager: The callbacks manager that contains the callback handlers for
this run of the chain.
Returns:
A dict of named outputs. Should contain all outputs specified in
`Chain.output... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-6 | ) -> Dict[str, Any]:
"""Execute the chain.
Args:
inputs: Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
`Chain.input_keys` except for inputs that will be set by the chain's
memory.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-7 | name=run_name,
)
try:
outputs = (
self._call(inputs, run_manager=run_manager)
if new_arg_supported
else self._call(inputs)
)
except BaseException as e:
run_manager.on_chain_error(e)
raise e
ru... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-8 | addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags: List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-9 | self,
inputs: Dict[str, str],
outputs: Dict[str, str],
return_only_outputs: bool = False,
) -> Dict[str, str]:
"""Validate and prepare chain outputs, and save info about this run to memory.
Args:
inputs: Dictionary of chain inputs, including any inputs added by ch... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-10 | _input_keys = _input_keys.difference(self.memory.memory_variables)
if len(_input_keys) != 1:
raise ValueError(
f"A single string input was passed in, but this chain expects "
f"multiple inputs ({_input_keys}). When a chain expects "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-11 | sole positional argument.
callbacks: Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags: List of string tags to... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-12 | _output_key
]
if not kwargs and not args:
raise ValueError(
"`run` supported with either positional arguments or keyword arguments,"
" but none were provided."
)
else:
raise ValueError(
f"`run` supported with... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-13 | The chain output.
Example:
.. code-block:: python
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-14 | """Dictionary representation of chain.
Expects `Chain._chain_type` property to be implemented and for memory to be
null.
Args:
**kwargs: Keyword arguments passed to default `pydantic.BaseModel.dict`
method.
Returns:
A dictionary representation ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
e6e5c70a982d-15 | with open(file_path, "w") as f:
json.dump(chain_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(file_path, "w") as f:
yaml.dump(chain_dict, f, default_flow_style=False)
else:
raise ValueError(f"{save_path} must be json or yaml")
[doc... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/base.html |
1dd1e757ea03-0 | Source code for langchain.chains.sequential
"""Chain pipeline where the outputs of one step feed directly into next."""
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
fr... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
1dd1e757ea03-1 | overlapping_keys = set(input_variables) & set(memory_keys)
raise ValueError(
f"The input key(s) {''.join(overlapping_keys)} are found "
f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
1dd1e757ea03-2 | _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
for i, chain in enumerate(self.chains):
callbacks = _run_manager.get_child()
outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
known_values.update(outputs)
return {k... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
1dd1e757ea03-3 | """Return output key.
:meta private:
"""
return [self.output_key]
@root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that chains are all single input/output."""
for chain in values["chains"]:
if len(chain.input_keys) != 1:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
1dd1e757ea03-4 | run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, cha... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
e18fde16c33a-0 | Source code for langchain.chains.loading
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains import ReduceDocumentsChain
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.c... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-1 | """Load LLM chain from config dict."""
if "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm` or `llm_path` must be present.")
if "pro... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-2 | )
def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_p... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-3 | if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "reduce_documents_chain" in config:
reduce_documents_chain = load_chain_from_config(
config.pop("reduce_documents_chain")
)
elif "reduce_documents_chain_path" in config:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-4 | collapse_documents_chain = None
else:
collapse_documents_chain = load_chain_from_config(
collapse_document_chain_config
)
elif "collapse_documents_chain_path" in config:
collapse_documents_chain = load_chain(
config.pop("collapse_documents_chain_pa... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-5 | # its to support old configs
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-6 | list_assertions_prompt_config = config.pop("list_assertions_prompt")
list_assertions_prompt = load_prompt_from_config(list_assertions_prompt_config)
elif "list_assertions_prompt_path" in config:
list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path"))
if "check_assertions_... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-7 | llm_chain = load_chain(config.pop("llm_chain_path"))
# llm attribute is deprecated in favor of llm_chain, here to support old configs
elif "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
# llm_path attribute is deprecated in favor of llm_chain_path,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-8 | return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
def _load_pal_chain(config: dict, **kwargs: Any) -> Any:
from langchain_experimental.pal_chain import PALChain
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-9 | else:
raise ValueError(
"One of `refine_llm_chain` or `refine_llm_chain_path` must be present."
)
if "document_prompt" in config:
prompt_config = config.pop("document_prompt")
document_prompt = load_prompt_from_config(prompt_config)
elif "document_prompt_path" in conf... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-10 | chain = load_chain_from_config(llm_chain_config)
return SQLDatabaseChain(llm_chain=chain, database=database, **config)
if "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
e... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-11 | vectorstore=vectorstore,
**config,
)
def _load_retrieval_qa(config: dict, **kwargs: Any) -> RetrievalQA:
if "retriever" in kwargs:
retriever = kwargs.pop("retriever")
else:
raise ValueError("`retriever` must be present.")
if "combine_documents_chain" in config:
combine_do... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-12 | "`combine_documents_chain_path` must be present."
)
return RetrievalQAWithSourcesChain(
combine_documents_chain=combine_documents_chain,
retriever=retriever,
**config,
)
def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
if "vectorstore" in kwargs:
vec... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-13 | qa_chain_config = config.pop("qa_chain")
qa_chain = load_chain_from_config(qa_chain_config)
else:
raise ValueError("`qa_chain` must be present.")
return GraphCypherQAChain(
graph=graph,
cypher_generation_chain=cypher_generation_chain,
qa_chain=qa_chain,
**config,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-14 | requests_wrapper=requests_wrapper,
**config,
)
def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-15 | "map_rerank_documents_chain": _load_map_rerank_documents_chain,
"refine_documents_chain": _load_refine_documents_chain,
"sql_database_chain": _load_sql_database_chain,
"vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain,
"vector_db_qa": _load_vector_db_qa,
"retrieval_qa": _load_... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
e18fde16c33a-16 | # Convert file to Path object.
if isinstance(file, str):
file_path = Path(file)
else:
file_path = file
# Load from either json or yaml.
if file_path.suffix == ".json":
with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
cb7c4f6763ea-0 | Source code for langchain.chains.transform
"""Chain that runs an arbitrary python function."""
import functools
import logging
from typing import Any, Awaitable, Callable, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/transform.html |
cb7c4f6763ea-1 | """Return output keys.
:meta private:
"""
return self.output_variables
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
return self.transform_cb(inputs)
async def _acall(
se... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/transform.html |
6893ed29458b-0 | Source code for langchain.chains.mapreduce
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import Callb... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
6893ed29458b-1 | **kwargs: Any,
) -> MapReduceChain:
"""Construct a map-reduce chain that uses the chain for map and reduce."""
llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks)
stuff_chain = StuffDocumentsChain(
llm_chain=llm_chain,
callbacks=callbacks,
**... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
6893ed29458b-2 | # Split the larger text into smaller chunks.
doc_text = inputs.pop(self.input_key)
texts = self.text_splitter.split_text(doc_text)
docs = [Document(page_content=text) for text in texts]
_inputs: Dict[str, Any] = {
**inputs,
self.combine_documents_chain.input_key: ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
01f7da59136f-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
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
01f7da59136f-1 | except ImportError:
raise ImportError(
"LLMMathChain requires the numexpr package. "
"Please install it with `pip install numexpr`."
)
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMMathChain with an llm is dep... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
01f7da59136f-2 | )
# Remove any leading and trailing brackets from the output
return re.sub(r"^\[|\]$", "", output)
def _process_llm_result(
self, llm_output: str, run_manager: CallbackManagerForChainRun
) -> Dict[str, str]:
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
01f7da59136f-3 | expression = text_match.group(1)
output = self._evaluate_expression(expression)
await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
await run_manager.on_text(output, color="yellow", verbose=self.verbose)
answer = "Answer: " + output
elif llm_output.start... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
01f7da59136f-4 | stop=["```output"],
callbacks=_run_manager.get_child(),
)
return await self._aprocess_llm_result(llm_output, _run_manager)
@property
def _chain_type(self) -> str:
return "llm_math_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
3c6b7392d94f-0 | Source code for langchain.chains.sql_database.query
from typing import List, Optional, TypedDict, Union
from langchain.chains.sql_database.prompt import PROMPT, SQL_PROMPTS
from langchain.schema.language_model import BaseLanguageModel
from langchain.schema.output_parser import NoOpOutputParser
from langchain.schema.pro... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/query.html |
3c6b7392d94f-1 | db: The SQLDatabase to generate the query for
prompt: The prompt to use. If none is provided, will choose one
based on dialect. Defaults to None.
k: The number of results per select statement to return. Defaults to 5.
Returns:
A chain that takes in a question and generates a SQL ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/query.html |
0233e51d43c2-0 | 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, Sequence, Tuple
from urllib.parse import urlparse
from langchain.callbacks.manager import (
AsyncCallbackMana... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
0233e51d43c2-1 | return True
return False
[docs]class APIChain(Chain):
"""Chain that makes API calls and summarizes the responses to answer a question.
*Security Note*: This API chain uses the requests toolkit
to make GET, POST, PATCH, PUT, and DELETE requests to an API.
Exercise care in who is allowed to us... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
0233e51d43c2-2 | the server.
"""
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.question_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
return [self.output_k... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
0233e51d43c2-3 | expected_vars = {"question", "api_docs", "api_url", "api_response"}
if set(input_vars) != expected_vars:
raise ValueError(
f"Input variables should be {expected_vars}, got {input_vars}"
)
return values
def _call(
self,
inputs: Dict[str, Any],
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
0233e51d43c2-4 | run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
question = inputs[self.question_key]
api_url = await self.api_request_chain.apredict(
question=question,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
0233e51d43c2-5 | **kwargs: Any,
) -> APIChain:
"""Load chain from just an LLM and the api docs."""
get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt)
requests_wrapper = TextRequestsWrapper(headers=headers)
get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
return cls(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
919930cbf19b-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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/requests_chain.html |
919930cbf19b-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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/requests_chain.html |
4865abb63265-0 | 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 requests import Response
from langchain.callbacks.manager import Callb... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
4865abb63265-1 | :meta private:
"""
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, ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
4865abb63265-2 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
4865abb63265-3 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
4865abb63265-4 | # 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,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
4865abb63265-5 | requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
) | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
c06c0d07db07-0 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/response_chain.html |
c06c0d07db07-1 | template=RESPONSE_TEMPLATE,
output_parser=output_parser,
input_variables=["response", "instructions"],
)
return cls(prompt=prompt, llm=llm, verbose=verbose, **kwargs) | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/response_chain.html |
2550425fd5d2-0 | 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 ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/flare/prompts.html |
f94ecb61892d-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 langchain.callbacks.manager import (
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
fro... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
f94ecb61892d-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 = [... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
f94ecb61892d-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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
f94ecb61892d-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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
f94ecb61892d-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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
f94ecb61892d-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... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
88dd862d0ec3-0 | Source code for langchain.chains.qa_generation.base
from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_ge... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
88dd862d0ec3-1 | Returns:
a QAGenerationChain class
"""
_prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
chain = LLMChain(llm=llm, prompt=_prompt)
return cls(llm_chain=chain, **kwargs)
@property
def _chain_type(self) -> str:
raise NotImplementedError
@property
def i... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
3ca04cccfc92-0 | 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 langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.cyph... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
3ca04cccfc92-1 | filtered_schema = {
"node_props": {
k: v
for k, v in structured_schema.get("node_props", {}).items()
if filter_func(k)
},
"rel_props": {
k: v
for k, v in structured_schema.get("rel_props", {}).items()
if filter_func(k)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
3ca04cccfc92-2 | limit the permissions granted to the credentials used with this tool.
See https://python.langchain.com/docs/security for more information.
"""
graph: GraphStore = Field(exclude=True)
cypher_generation_chain: LLMChain
qa_chain: LLMChain
graph_schema: str
input_key: str = "query" #: :meta... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
3ca04cccfc92-3 | cypher_llm: Optional[BaseLanguageModel] = None,
qa_llm: Optional[BaseLanguageModel] = None,
exclude_types: List[str] = [],
include_types: List[str] = [],
validate_cypher: bool = False,
qa_llm_kwargs: Optional[Dict[str, Any]] = None,
cypher_llm_kwargs: Optional[Dict[str, A... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
3ca04cccfc92-4 | use_cypher_llm_kwargs = (
cypher_llm_kwargs if cypher_llm_kwargs is not None else {}
)
if "prompt" not in use_qa_llm_kwargs:
use_qa_llm_kwargs["prompt"] = (
qa_prompt if qa_prompt is not None else CYPHER_QA_PROMPT
)
if "prompt" not in use_cyphe... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
3ca04cccfc92-5 | **kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Generate Cypher statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainR... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
3ca04cccfc92-6 | _run_manager.on_text(
str(context), color="green", end="\n", verbose=self.verbose
)
intermediate_steps.append({"context": context})
result = self.qa_chain(
{"question": question, "context": context},
callbacks=callbacks,
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
e18825aab6c3-0 | Source code for langchain.chains.graph_qa.arangodb
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chai... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
e18825aab6c3-1 | output_key: str = "result" #: :meta private:
# Specifies the maximum number of AQL Query Results to return
top_k: int = 10
# Specifies the set of AQL Query Examples that promote few-shot-learning
aql_examples: str = ""
# Specify whether to return the AQL Query in the output dictionary
return_aq... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
e18825aab6c3-2 | return cls(
qa_chain=qa_chain,
aql_generation_chain=aql_generation_chain,
aql_fix_chain=aql_fix_chain,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
e18825aab6c3-3 | #########################
# Generate AQL Query #
aql_generation_output = self.aql_generation_chain.run(
{
"adb_schema": self.graph.schema,
"aql_examples": self.aql_examples,
"user_input": user_input,
},
callbacks=callbac... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
e18825aab6c3-4 | aql_error = e.error_message
_run_manager.on_text(
"AQL Query Execution Error: ", end="\n", verbose=self.verbose
)
_run_manager.on_text(
aql_error, color="yellow", end="\n\n", verbose=self.verbose
)
##... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
e18825aab6c3-5 | if self.return_aql_result:
result["aql_result"] = aql_result
return result | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
49198b6adcc8-0 | Source code for langchain.chains.graph_qa.cypher_utils
import re
from collections import namedtuple
from typing import Any, Dict, List, Optional, Tuple
Schema = namedtuple("Schema", ["left_node", "relation", "right_node"])
[docs]class CypherQueryCorrector:
"""
Used to correct relationship direction in generated... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher_utils.html |
49198b6adcc8-1 | """
Args:
query: cypher query
"""
nodes = re.findall(self.node_pattern, query)
nodes = [self.clean_node(node) for node in nodes]
res: Dict[str, Any] = {}
for node in nodes:
parts = node.split(":")
if parts == "":
continu... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher_utils.html |
49198b6adcc8-2 | node_variable_dict: dictionary of node variables
"""
splitted_node = str_node.split(":")
variable = splitted_node[0]
labels = []
if variable in node_variable_dict:
labels = node_variable_dict[variable]
elif variable == "" and len(splitted_node) > 1:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher_utils.html |
49198b6adcc8-3 | """
Args:
str_relation: relation in string format
"""
relation_direction = self.judge_direction(str_relation)
relation_type = self.relation_type_pattern.search(str_relation)
if relation_type is None or relation_type.group("relation_type") is None:
return r... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher_utils.html |
49198b6adcc8-4 | start_idx += (
len(match_dict["left_node"]) + len(match_dict["relation"]) + 2
)
continue
if relation_direction == "OUTGOING":
is_legal = self.verify_schema(
left_node_labels, relation_types, right... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher_utils.html |
49198b6adcc8-5 | )
return query
def __call__(self, query: str) -> str:
"""Correct the query to make it valid. If
Args:
query: cypher query
"""
return self.correct_query(query) | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher_utils.html |
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