id stringlengths 14 16 | text stringlengths 31 2.73k | source stringlengths 56 166 |
|---|---|---|
bf0b2db716ac-2 | @property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ai21"
def _call(self, prompt: str, stop: Optio... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/llms/ai21.html |
bf0b2db716ac-3 | optional_detail = response.json().get("error")
raise ValueError(
f"AI21 /complete call failed with status code {response.status_code}."
f" Details: {optional_detail}"
)
response_json = response.json()
return response_json["completions"][0]["data"][... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/llms/ai21.html |
bd93d3bb97e4-0 | Source code for langchain.chains.llm_requests
"""Chain that hits a URL and then uses an LLM to parse results."""
from __future__ import annotations
from typing import Dict, List
from pydantic import Extra, Field, root_validator
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_requests.html |
bd93d3bb97e4-1 | """
return [self.output_key]
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from bs4 import BeautifulSoup # noqa: F401
except ImportError:
raise ValueError(... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_requests.html |
93fd0dfb5506-0 | Source code for langchain.chains.sequential
"""Chain pipeline where the outputs of one step feed directly into next."""
from typing import Dict, List
from pydantic import Extra, root_validator
from langchain.chains.base import Chain
from langchain.input import get_color_mapping
[docs]class SequentialChain(Chain):
"... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sequential.html |
93fd0dfb5506-1 | f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
)
known_variables = set(input_variables + memory_keys)
for chain in chains:
missing_vars = set(chain.input_keys).difference(known_variables)
if mis... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sequential.html |
93fd0dfb5506-2 | chains: List[Chain]
strip_outputs: bool = False
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_ke... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sequential.html |
93fd0dfb5506-3 | if self.strip_outputs:
_input = _input.strip()
self.callback_manager.on_text(
_input, color=color_mapping[str(i)], end="\n", verbose=self.verbose
)
return {self.output_key: _input}
By Harrison Chase
© Copyright 2023, Harrison Chase.
Las... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sequential.html |
1763d3a07a66-0 | Source code for langchain.chains.moderation
"""Pass input through a moderation endpoint."""
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.chains.base import Chain
from langchain.utils import get_from_dict_or_env
[docs]class OpenAIModerationChain(Chain):
"""Pass inpu... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/moderation.html |
1763d3a07a66-1 | "OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
values["client"] = openai.Moderation
except ImportError:
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/moderation.html |
dd074a604d22-0 | Source code for langchain.chains.transform
"""Chain that runs an arbitrary python function."""
from typing import Callable, Dict, List
from langchain.chains.base import Chain
[docs]class TransformChain(Chain):
"""Chain transform chain output.
Example:
.. code-block:: python
from langchain im... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/transform.html |
8624b35b2534-0 | 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.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocume... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-1 | 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 "prompt" in config:
prompt_config = con... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-2 | )
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... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-3 | if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "combine_document_chain" in config:
combine_document_chain_config = config.pop("combine_document_chain")
combine_document_chain = load_chain_from_config(combine_document_chain_config)
elif ... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-4 | if "prompt" in config:
prompt_config = config.pop("prompt")
prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
prompt = load_prompt(config.pop("prompt_path"))
return LLMBashChain(llm=llm, prompt=prompt, **config)
def _load_llm_checker_chain(config: dict, **kwar... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-5 | check_assertions_prompt_config
)
elif "check_assertions_prompt_path" in config:
check_assertions_prompt = load_prompt(
config.pop("check_assertions_prompt_path")
)
if "revised_answer_prompt" in config:
revised_answer_prompt_config = config.pop("revised_answer_prompt")... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-6 | config: dict, **kwargs: Any
) -> MapRerankDocumentsChain:
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_path"))
else:
rais... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-7 | initial_llm_chain_config = config.pop("initial_llm_chain")
initial_llm_chain = load_chain_from_config(initial_llm_chain_config)
elif "initial_llm_chain_path" in config:
initial_llm_chain = load_chain(config.pop("initial_llm_chain_path"))
else:
raise ValueError(
"One of `initi... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-8 | elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return QAWithSourcesC... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-9 | elif "combine_documents_chain_path" in config:
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return VectorDBQAWith... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-10 | else:
raise ValueError(
"One of `api_request_chain` or `api_request_chain_path` must be present."
)
if "api_answer_chain" in config:
api_answer_chain_config = config.pop("api_answer_chain")
api_answer_chain = load_chain_from_config(api_answer_chain_config)
elif "api_a... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-11 | )
else:
return LLMRequestsChain(llm_chain=llm_chain, **config)
type_to_loader_dict = {
"api_chain": _load_api_chain,
"hyde_chain": _load_hyde_chain,
"llm_chain": _load_llm_chain,
"llm_bash_chain": _load_llm_bash_chain,
"llm_checker_chain": _load_llm_checker_chain,
"llm_math_chain": _... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8624b35b2534-12 | return chain_loader(config, **kwargs)
[docs]def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain:
"""Unified method for loading a chain from LangChainHub or local fs."""
if hub_result := try_load_from_hub(
path, _load_chain_from_file, "chains", {"json", "yaml"}, **kwargs
):
return ... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/loading.html |
8daca9d1faad-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 Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
fr... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/mapreduce.html |
8daca9d1faad-1 | )
return cls(
combine_documents_chain=combine_documents_chain, text_splitter=text_splitter
)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/mapreduce.html |
cf98e3f3986b-0 | Source code for langchain.chains.llm
"""Chain that just formats a prompt and calls an LLM."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.input import get_colored_text
from langc... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
cf98e3f3986b-1 | return self.apply([inputs])[0]
[docs] def generate(self, input_list: List[Dict[str, Any]]) -> LLMResult:
"""Generate LLM result from inputs."""
prompts, stop = self.prep_prompts(input_list)
return self.llm.generate_prompt(prompts, stop)
[docs] async def agenerate(self, input_list: List[Dic... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
cf98e3f3986b-2 | self, input_list: List[Dict[str, Any]]
) -> Tuple[List[PromptValue], Optional[List[str]]]:
"""Prepare prompts from inputs."""
stop = None
if "stop" in input_list[0]:
stop = input_list[0]["stop"]
prompts = []
for inputs in input_list:
selected_inputs = ... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
cf98e3f3986b-3 | response = await self.agenerate(input_list)
return self.create_outputs(response)
[docs] def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:
"""Create outputs from response."""
return [
# Get the text of the top generated string.
{self.output_key: gen... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
cf98e3f3986b-4 | return self.prompt.output_parser.parse(result)
else:
return result
[docs] async def apredict_and_parse(
self, **kwargs: Any
) -> Union[str, List[str], Dict[str, str]]:
"""Call apredict and then parse the results."""
result = await self.apredict(**kwargs)
if sel... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
cf98e3f3986b-5 | """Create LLMChain from LLM and template."""
prompt_template = PromptTemplate.from_template(template)
return cls(llm=llm, prompt=prompt_template)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm.html |
887111f2a7f2-0 | Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.chains.base import Chain
fro... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
887111f2a7f2-1 | combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Construct the chain from an LLM."""
llm_question_chain = LLMChain(llm=llm, prompt=question_prompt)
llm_combine_chain = LLMChain(llm=llm, prompt=combine_prompt)
combine_results_... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
887111f2a7f2-2 | """
return [self.question_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
_output_keys = [self.answer_key, self.sources_answer_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_docu... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
887111f2a7f2-3 | docs = await self._aget_docs(inputs)
answer = await self.combine_documents_chain.arun(input_documents=docs, **inputs)
if re.search(r"SOURCES:\s", answer):
answer, sources = re.split(r"SOURCES:\s", answer)
else:
sources = ""
result: Dict[str, Any] = {
s... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
b2da219d9930-0 | Source code for langchain.chains.qa_with_sources.vector_db
"""Question-answering with sources over a vector database."""
import warnings
from typing import Any, Dict, List
from pydantic import Field, root_validator
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_so... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
b2da219d9930-1 | num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = self.vectorstore.similarity_search(
question, k=self.k, **self.search_kwargs
)
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
170eb71127cf-0 | Source code for langchain.chains.qa_with_sources.retrieval
"""Question-answering with sources over an index."""
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
170eb71127cf-1 | docs = self.retriever.get_relevant_documents(question)
return self._reduce_tokens_below_limit(docs)
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = await self.retriever.aget_relevant_documents(question)
return self._re... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
c1689d876698-0 | Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_bash.prompt import P... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_bash/base.html |
c1689d876698-1 | bash_executor = BashProcess()
self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
t = llm_executor.predict(question=inputs[self.input_key])
self.callback_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
if t.startswith("```bash"):
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_bash/base.html |
a344c55b94a8-0 | Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from pathlib import Path
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sequential impor... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
a344c55b94a8-1 | revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT
are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
max_checks: int = 2
"""Maximum number of times to check the assertions. Default to doubl... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
a344c55b94a8-2 | output_key="revised_summary",
verbose=self.verbose,
),
LLMChain(
llm=self.llm,
output_key="all_true",
prompt=self.are_all_true_prompt,
verbose=self.verbose,
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
c3c6b7628530-0 | Source code for langchain.chains.constitutional_ai.base
"""Chain for applying constitutional principles to the outputs of another chain."""
from typing import Any, Dict, List, Optional
from langchain.chains.base import Chain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
from langchain.ch... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
c3c6b7628530-1 | ) -> List[ConstitutionalPrinciple]:
if names is None:
return list(PRINCIPLES.values())
else:
return [PRINCIPLES[name] for name in names]
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
chain: LLMChain,
critique_prompt: BasePro... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
c3c6b7628530-2 | raw_critique = self.critique_chain.run(
input_prompt=input_prompt,
output_from_model=response,
critique_request=constitutional_principle.critique_request,
)
critique = self._parse_critique(
output_string=raw_critique,
).... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
64f35747f483-0 | Source code for langchain.chains.sql_database.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sql_... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
64f35747f483-1 | extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
64f35747f483-2 | self.callback_manager.on_text(sql_cmd, color="green", verbose=self.verbose)
result = self.database.run(sql_cmd)
intermediate_steps.append(result)
self.callback_manager.on_text("\nSQLResult: ", verbose=self.verbose)
self.callback_manager.on_text(result, color="yellow", verbose=self.verbos... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
64f35747f483-3 | database: SQLDatabase,
query_prompt: BasePromptTemplate = PROMPT,
decider_prompt: BasePromptTemplate = DECIDER_PROMPT,
**kwargs: Any,
) -> SQLDatabaseSequentialChain:
"""Load the necessary chains."""
sql_chain = SQLDatabaseChain(
llm=llm, database=database, prompt... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
64f35747f483-4 | table_names_to_use = self.decider_chain.predict_and_parse(**llm_inputs)
self.callback_manager.on_text(
"Table names to use:", end="\n", verbose=self.verbose
)
self.callback_manager.on_text(
str(table_names_to_use), color="yellow", verbose=self.verbose
)
ne... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/sql_database/base.html |
db4e955d1b7c-0 | Source code for langchain.chains.qa_generation.base
from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR
f... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_generation/base.html |
db4e955d1b7c-1 | docs = self.text_splitter.create_documents([inputs[self.input_key]])
results = self.llm_chain.generate([{"text": d.page_content} for d in docs])
qa = [json.loads(res[0].text) for res in results.generations]
return {self.output_key: qa}
async def _acall(self, inputs: Dict[str, str]) -> Dict[s... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/qa_generation/base.html |
2faddb0c3039-0 | Source code for langchain.chains.combine_documents.base
"""Base interface for chains combining documents."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Field
from langchain.chains.base import Chain
from langchain.docstore.document import Document
from la... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/combine_documents/base.html |
2faddb0c3039-1 | # Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
output, extra_return_dict = self.combine_docs(docs, **other_keys)
extra_return_dict[self.output_key] = output
return extra_return_dict
async def _acall(self,... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/combine_documents/base.html |
2faddb0c3039-2 | docs = self.text_splitter.create_documents([document])
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
other_keys[self.combine_docs_chain.input_key] = docs
return self.combine_docs_chain(other_keys, return_onl... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/combine_documents/base.html |
e3aea6ae35c7-0 | Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, PROMPT
from langchain.cha... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/graph_qa/base.html |
e3aea6ae35c7-1 | 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(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Extract entities, look up info and answer question... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/graph_qa/base.html |
ef5c1d1fd6c7-0 | Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic import Extra, Fiel... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
ef5c1d1fd6c7-1 | extra = Extra.forbid
arbitrary_types_allowed = True
allow_population_by_field_name = True
@property
def input_keys(self) -> List[str]:
"""Input keys."""
return ["question", "chat_history"]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
ef5c1d1fd6c7-2 | """Get docs."""
async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
question = inputs["question"]
get_chat_history = self.get_chat_history or _get_chat_history
chat_history_str = get_chat_history(inputs["chat_history"])
if chat_history_str:
new_question = aw... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
ef5c1d1fd6c7-3 | if self.max_tokens_limit and isinstance(
self.combine_docs_chain, StuffDocumentsChain
):
tokens = [
self.combine_docs_chain.llm_chain.llm.get_num_tokens(doc.page_content)
for doc in docs
]
token_count = sum(tokens[:num_docs])
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
ef5c1d1fd6c7-4 | combine_docs_chain=doc_chain,
question_generator=condense_question_chain,
**kwargs,
)
[docs]class ChatVectorDBChain(BaseConversationalRetrievalChain):
"""Chain for chatting with a vector database."""
vectorstore: VectorStore = Field(alias="vectorstore")
top_k_docs_for_context... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
ef5c1d1fd6c7-5 | chain_type: str = "stuff",
**kwargs: Any,
) -> BaseConversationalRetrievalChain:
"""Load chain from LLM."""
doc_chain = load_qa_chain(
llm,
chain_type=chain_type,
prompt=qa_prompt,
)
condense_question_chain = LLMChain(llm=llm, prompt=conden... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
12b5975689ee-0 | Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Dict, List
import numpy as np
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.hyde.prompts import PROMPT_MAP... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/hyde/base.html |
12b5975689ee-1 | """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 generation in result.generations[0]]
embeddings = self.embed_documents(documents)
return self.comb... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/hyde/base.html |
ea343efb7798-0 | Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_math.prompt import PROMPT
from lan... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_math/base.html |
ea343efb7798-1 | python_executor = PythonREPL()
self.callback_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
if t.startswith("```python"):
code = t[9:-4]
output = python_executor.run(code)
self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_math/base.html |
ea343efb7798-2 | answer = "Answer: " + output
elif t.startswith("Answer:"):
answer = t
elif "Answer:" in t:
answer = "Answer: " + t.split("Answer:")[-1]
else:
raise ValueError(f"unknown format from LLM: {t}")
return {self.output_key: answer}
def _call(self, inputs:... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_math/base.html |
189813f8e9fc-0 | Source code for langchain.chains.pal.base
"""Implements Program-Aided Language Models.
As in https://arxiv.org/pdf/2211.10435.pdf.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMCh... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/pal/base.html |
189813f8e9fc-1 | else:
return [self.output_key, "intermediate_steps"]
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
code = llm_chain.predict(stop=[self.stop], **inputs)
self.callback_manager.on_text(
code, color="gree... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/pal/base.html |
189813f8e9fc-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/pal/base.html |
178f9d8cbc5f-0 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_checker.prompt import (
CHECK_ASSERTIONS_P... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_checker/base.html |
178f9d8cbc5f-1 | def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
return [self.output_key]
def _ca... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_checker/base.html |
178f9d8cbc5f-2 | return "llm_checker_chain"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/llm_checker/base.html |
cee7decc2bd9-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.chains.base imp... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
cee7decc2bd9-1 | _output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
cee7decc2bd9-2 | def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Run get_relevant_text and llm on input query.
If chain has 'return_source_documents' as 'True', returns
the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = in... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
cee7decc2bd9-3 | return {self.output_key: answer, "source_documents": docs}
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... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
cee7decc2bd9-4 | "please use `from langchain.chains import RetrievalQA`"
)
return values
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
search_type = values["search_type"]
if search_type not in... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
2299404a1857-0 | 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.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PR... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/api/base.html |
2299404a1857-1 | )
return values
@root_validator(pre=True)
def validate_api_answer_prompt(cls, values: Dict) -> Dict:
"""Check that api answer prompt expects the right variables."""
input_vars = values["api_answer_chain"].prompt.input_variables
expected_vars = {"question", "api_docs", "api_url", ... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/api/base.html |
2299404a1857-2 | self.callback_manager.on_text(
api_response, color="yellow", end="\n", verbose=self.verbose
)
answer = await self.api_answer_chain.apredict(
question=question,
api_docs=self.api_docs,
api_url=api_url,
api_response=api_response,
)
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/api/base.html |
c02c28c16735-0 | 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://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/api/openapi/chain.html |
c02c28c16735-1 | @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"]
def _construct_path(self, args: Dict[str, st... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/api/openapi/chain.html |
c02c28c16735-2 | 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_steps: dict) -> dict:
"""Return the o... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/api/openapi/chain.html |
c02c28c16735-3 | response_text = (
f"{api_response.status_code}: {api_response.reason}"
+ f"\nFor {method_str.upper()} {request_args['url']}\n"
+ f"Called with args: {request_args['params']}"
)
else:
response_text = api_response.tex... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/api/openapi/chain.html |
c02c28c16735-4 | return cls.from_api_operation(
operation,
requests=requests,
llm=llm,
return_intermediate_steps=return_intermediate_steps,
**kwargs,
)
[docs] @classmethod
def from_api_operation(
cls,
operation: APIOperation,
llm: BaseLLM... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/api/openapi/chain.html |
3de2e2649a7e-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://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/conversation/base.html |
3de2e2649a7e-1 | f"The input key {input_key} was also found in the memory keys "
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):... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/chains/conversation/base.html |
817a0012aebc-0 | Source code for langchain.vectorstores.opensearch_vector_search
"""Wrapper around OpenSearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Dict, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from la... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
817a0012aebc-1 | f"Got error: {e} "
)
return client
def _validate_embeddings_and_bulk_size(embeddings_length: int, bulk_size: int) -> None:
"""Validate Embeddings Length and Bulk Size."""
if embeddings_length == 0:
raise RuntimeError("Embeddings size is zero")
if bulk_size < embeddings_length:
ra... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
817a0012aebc-2 | vector_field: str = "vector_field",
) -> Dict:
"""For Painless Scripting or Script Scoring,the default mapping to create index."""
return {
"mappings": {
"properties": {
vector_field: {"type": "knn_vector", "dimension": dim},
}
}
}
def _default_text_ma... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
817a0012aebc-3 | return {
"size": size,
"query": {"knn": {vector_field: {"vector": query_vector, "k": k}}},
}
def _default_script_query(
query_vector: List[float],
space_type: str = "l2",
pre_filter: Dict = MATCH_ALL_QUERY,
vector_field: str = "vector_field",
) -> Dict:
"""For Script Scoring Sear... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
817a0012aebc-4 | vector_field: str = "vector_field",
) -> Dict:
"""For Painless Scripting Search, this is the default query."""
source = __get_painless_scripting_source(space_type, query_vector)
return {
"query": {
"script_score": {
"query": pre_filter,
"script": {
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
817a0012aebc-5 | bulk_size: int = 500,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
bulk_size... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
817a0012aebc-6 | Returns:
List of Documents most similar to the query.
Optional Args:
vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
me... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
817a0012aebc-7 | metadata_field = _get_kwargs_value(kwargs, "metadata_field", "metadata")
vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
if search_type == "approximate_search":
size = _get_kwargs_value(kwargs, "size", 4)
search_query = _default_approximate_search_query(
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.