id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
b1d5b0dd2841-0 | 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.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.utils import get_from_dic... | https://python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
b1d5b0dd2841-1 | values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
values["client"] = ... | https://python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
15c34e806dea-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 pydantic import Extra, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
15c34e806dea-1 | overlapping_keys = set(input_variables) & set(memory_keys)
raise ValueError(
f"The 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."
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
15c34e806dea-2 | callbacks = _run_manager.get_child()
outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
known_values.update(outputs)
return {k: known_values[k] for k in self.output_variables}
async def _acall(
self,
inputs: Dict[str, Any],
run_manage... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
15c34e806dea-3 | @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:
raise ValueError(
"Chains used in SimplePipeline should all have one... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
15c34e806dea-4 | _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.chains):
_input = ... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
22580e79479e-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.base_language import BaseLanguageModel
from langchain.callbacks.manager import (... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
22580e79479e-1 | def output_keys(self) -> List[str]:
"""Will always return text key.
:meta private:
"""
return [self.output_key]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
response = self.... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
22580e79479e-2 | """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 = {k: inputs[k] for k in self.prompt.input_variables}
prompt = self.prompt.format_prompt(**se... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
22580e79479e-3 | await run_manager.on_text(_text, end="\n", verbose=self.verbose)
if "stop" in inputs and inputs["stop"] != stop:
raise ValueError(
"If `stop` is present in any inputs, should be present in all."
)
prompts.append(prompt)
return prompts, ... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
22580e79479e-4 | except (KeyboardInterrupt, Exception) as e:
await run_manager.on_chain_error(e)
raise e
outputs = self.create_outputs(response)
await run_manager.on_chain_end({"outputs": outputs})
return outputs
[docs] def create_outputs(self, response: LLMResult) -> List[Dict[str, st... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
22580e79479e-5 | Returns:
Completion from LLM.
Example:
.. code-block:: python
completion = llm.predict(adjective="funny")
"""
return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]
[docs] def predict_and_parse(
self, callbacks: Callbacks = None... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
22580e79479e-6 | return [
self.prompt.output_parser.parse(res[self.output_key]) for res in result
]
else:
return result
[docs] async def aapply_and_parse(
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
) -> Sequence[Union[str, List[str], Dict[str, str]]... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
3bf74956b8b1-0 | 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://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-1 | """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... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-2 | llm_chain=llm_chain, base_embeddings=embeddings, **config
)
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 ... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-3 | llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "combine_document_chain" in config:
combine_docu... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-4 | elif "llm_chain_path" in config:
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 d... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-5 | if "create_draft_answer_prompt" in config:
create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt")
create_draft_answer_prompt = load_prompt_from_config(
create_draft_answer_prompt_config
)
elif "create_draft_answer_prompt_path" in config:
create_draft... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-6 | revised_answer_prompt=revised_answer_prompt,
**config,
)
def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain:
llm_chain = None
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_cha... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-7 | ) -> 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:
raise ValueError("One of `llm_ch... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-8 | prompt = load_prompt_from_config(prompt_config)
elif "prompt_path" in config:
prompt = load_prompt(config.pop("prompt_path"))
else:
raise ValueError("One of `prompt` or `prompt_path` must be present.")
if llm_chain:
return PALChain(llm_chain=llm_chain, prompt=prompt, **config)
el... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-9 | elif "document_prompt_path" in config:
document_prompt = load_prompt(config.pop("document_prompt_path"))
return RefineDocumentsChain(
initial_llm_chain=initial_llm_chain,
refine_llm_chain=refine_llm_chain,
document_prompt=document_prompt,
**config,
)
def _load_qa_with_sou... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-10 | prompt = load_prompt_from_config(prompt_config)
else:
prompt = None
return SQLDatabaseChain.from_llm(llm, database, prompt=prompt, **config)
def _load_vector_db_qa_with_sources_chain(
config: dict, **kwargs: Any
) -> VectorDBQAWithSourcesChain:
if "vectorstore" in kwargs:
vectorstore = k... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-11 | else:
raise ValueError(
"One of `combine_documents_chain` or "
"`combine_documents_chain_path` must be present."
)
return VectorDBQA(
combine_documents_chain=combine_documents_chain,
vectorstore=vectorstore,
**config,
)
def _load_api_chain(config: ... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-12 | 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:
raise ValueError("One of `llm_chain` or `llm_chain_path` must be p... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-13 | "vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain,
"vector_db_qa": _load_vector_db_qa,
}
def load_chain_from_config(config: dict, **kwargs: Any) -> Chain:
"""Load chain from Config Dict."""
if "_type" not in config:
raise ValueError("Must specify a chain Type in config")
c... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
3bf74956b8b1-14 | else:
raise ValueError("File type must be json or yaml")
# Override default 'verbose' and 'memory' for the chain
if "verbose" in kwargs:
config["verbose"] = kwargs.pop("verbose")
if "memory" in kwargs:
config["memory"] = kwargs.pop("memory")
# Load the chain from the config now.
... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
ce9053f4b632-0 | 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.base_language import BaseLanguageModel
from langchain.callback... | https://python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
ce9053f4b632-1 | 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://python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
e52a6ce4a0dd-0 | Source code for langchain.chains.sql_database.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbac... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
e52a6ce4a0dd-1 | 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 SQL table directly."""
use_query_checker: bool = False
"""Whether or not the query checker too... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
e52a6ce4a0dd-2 | :meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, INTERMEDIATE_STEPS_KEY]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
e52a6ce4a0dd-3 | intermediate_steps.append(str(result)) # output: sql exec
else:
query_checker_prompt = self.query_checker_prompt or PromptTemplate(
template=QUERY_CHECKER, input_variables=["query", "dialect"]
)
query_checker_chain = LLMChain(
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
e52a6ce4a0dd-4 | intermediate_steps.append(llm_inputs) # input: final answer
final_result = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
intermediate_steps.append(final_result) # output: final answer
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
e52a6ce4a0dd-5 | This is useful in cases where the number of tables in the database is large.
"""
decider_chain: LLMChain
sql_chain: SQLDatabaseChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
return_intermediate_steps: bool = False
[docs] @classmethod
def fr... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
e52a6ce4a0dd-6 | run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_table_names = self.sql_chain.database.get_usable_table_names()
table_names = ", ".join(_table_names)
llm_inputs = {
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
4a72707320d1-0 | 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.base_language i... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
4a72707320d1-1 | def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@classmethod
def from_llm(
... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
4a72707320d1-2 | @abstractmethod
def _get_docs(self, question: str) -> List[Document]:
"""Get documents to do question answering over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run get_relevant_text an... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
4a72707320d1-3 | the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = indexqa({'query': 'This is my query'})
answer, docs = res['result'], res['source_documents']
"""
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
4a72707320d1-4 | [docs]class VectorDBQA(BaseRetrievalQA):
"""Chain for question-answering against a vector database."""
vectorstore: VectorStore = Field(exclude=True, alias="vectorstore")
"""Vector Database to connect to."""
k: int = 4
"""Number of documents to query for."""
search_type: str = "similarity"
"... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
4a72707320d1-5 | raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_docs(self, question: str) -> List[Document]:
raise NotImplementedError("VectorDBQA does not support async")
@property
def _chain_type(self) -> str:
"""Return the chain type."""
ret... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
9e314f805e07-0 | 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
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLangua... | https://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
9e314f805e07-1 | "Directly instantiating an PALChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the one of "
"the class method constructors from_math_prompt, "
"from_colored_object_prompt."
)
if "llm_chain" not in values and v... | https://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
9e314f805e07-2 | output["intermediate_steps"] = code
return output
[docs] @classmethod
def from_math_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PALChain:
"""Load PAL from math prompt."""
llm_chain = LLMChain(llm=llm, prompt=MATH_PROMPT)
return cls(
llm_chain=llm_chain,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
4f3c862cf313-0 | 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://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
4f3c862cf313-1 | 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://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
f658b87ee737-0 | 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.base_language import BaseLan... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f658b87ee737-1 | document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
question_prompt: BasePromptTemplate = QUESTION_PROMPT,
combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Construct the chain from an LLM."""
llm_question_chain = LLMChain(l... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f658b87ee737-2 | def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
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]
... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f658b87ee737-3 | if self.return_source_documents:
result["source_documents"] = docs
return result
@abstractmethod
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
"""Get docs to run questioning over."""
async def _acall(
self,
inputs: Dict[str, Any],
r... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
f658b87ee737-4 | return inputs.pop(self.input_docs_key)
@property
def _chain_type(self) -> str:
return "qa_with_sources_chain"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
1c149242df4e-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://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
1c149242df4e-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://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
95f3c182d6c0-0 | 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.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from l... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
95f3c182d6c0-1 | ) -> 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://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
2718bc6cae95-0 | Source code for langchain.chains.graph_qa.cypher
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
2718bc6cae95-1 | **kwargs: Any,
) -> GraphCypherQAChain:
"""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,
cypher_generation_chain=cypher_generation_chain,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
2718bc6cae95-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
ff33f498153f-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.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base i... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
ff33f498153f-1 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, List]:
docs = self.text_splitter.create_documents([inputs[self.input_key]])
results = self.llm_chain.generate(
[{"text": d.page_content} for d in docs... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
434bb674e57c-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.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
434bb674e57c-1 | critique_chain: LLMChain
revision_chain: LLMChain
return_intermediate_steps: bool = False
[docs] @classmethod
def get_principles(
cls, names: Optional[List[str]] = None
) -> List[ConstitutionalPrinciple]:
if names is None:
return list(PRINCIPLES.values())
else:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
434bb674e57c-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_manager.get_child(),
)
initial_response = response
input_prompt = self.chain.prompt.format(**inputs)
... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
434bb674e57c-3 | critiques_and_revisions.append((critique, revision))
_run_manager.on_text(
text=f"Applying {constitutional_principle.name}..." + "\n\n",
verbose=self.verbose,
color="green",
)
_run_manager.on_text(
text="Critique: " + cr... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
7deecdb7b76d-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.base_language import BaseLanguageModel
from langchain.ca... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
7deecdb7b76d-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://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
7deecdb7b76d-2 | 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.question_key]
api_url = await se... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
7deecdb7b76d-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://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
dd8b973504a6-0 | 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://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
dd8b973504a6-1 | :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, ... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
dd8b973504a6-2 | 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://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
dd8b973504a6-3 | 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://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
dd8b973504a6-4 | # 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://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
dd8b973504a6-5 | api_operation=operation,
requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
22b5e8885c76-0 | 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.base_language import BaseLanguageModel
from langchain.cal... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
22b5e8885c76-1 | )
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://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
22b5e8885c76-2 | 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://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
22b5e8885c76-3 | question = inputs[self.input_key]
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"
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
ccadd780c78b-0 | Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from __future__ import annotations
import logging
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_lang... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
ccadd780c78b-1 | def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMBashChain with an llm is deprecated. "
"Please instantiate with llm_chain or using the from_llm class method."
)
if "llm_chain" n... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
ccadd780c78b-2 | )
_run_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
try:
parser = self.llm_chain.prompt.output_parser
command_list = parser.parse(t) # type: ignore[union-attr]
except OutputParserException as e:
_run_manager.on_chain_error(e, ... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
1cf0c0c470e8-0 | 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.base_lan... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
1cf0c0c470e8-1 | 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://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
1cf0c0c470e8-2 | ) -> 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://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
1cf0c0c470e8-3 | 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://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
1cf0c0c470e8-4 | [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)
By Harrison Chase
© Copyright... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
bd7c3fac3320-0 | 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.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManag... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
bd7c3fac3320-1 | :meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
"""Return the prom... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
bd7c3fac3320-2 | run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in i... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
bd7c3fac3320-3 | # Other keys are assumed to be needed for LLM prediction
other_keys: Dict = {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_only_outputs=True, callbacks=_run_manager.get_c... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
74bc8a9211ef-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://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
74bc8a9211ef-1 | 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)}."
f" Full chat history: {chat_history} "
... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
74bc8a9211ef-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
74bc8a9211ef-3 | new_question = await self.question_generator.arun(
question=question, chat_history=chat_history_str, callbacks=callbacks
)
else:
new_question = question
docs = await self._aget_docs(new_question, inputs)
new_inputs = inputs.copy()
new_inputs["quest... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
74bc8a9211ef-4 | while token_count > self.max_tokens_limit:
num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]:
docs = self.retriever.get_relevant_documents(question)
return self._re... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
74bc8a9211ef-5 | )
[docs]class ChatVectorDBChain(BaseConversationalRetrievalChain):
"""Chain for chatting with a vector database."""
vectorstore: VectorStore = Field(alias="vectorstore")
top_k_docs_for_context: int = 4
search_kwargs: dict = Field(default_factory=dict)
@property
def _chain_type(self) -> str:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
74bc8a9211ef-6 | combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
doc_chain = load_qa_chain(
llm,
chain_type=chain_type,
**combine_docs_chain_kwargs,
)
condense_question_chain = LLMChain(llm=llm, prompt=condense_question_prompt)
return cls(
vecto... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
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