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f6d931da83ae-0 | langchain.chains.constitutional_ai.models.ConstitutionalPrinciple¶
class langchain.chains.constitutional_ai.models.ConstitutionalPrinciple[source]¶
Bases: BaseModel
Class for a constitutional principle.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input d... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html |
f6d931da83ae-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html |
f6d931da83ae-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html |
4f16c76424ce-0 | langchain.chains.loading.load_chain_from_config¶
langchain.chains.loading.load_chain_from_config(config: dict, **kwargs: Any) → Chain[source]¶
Load chain from Config Dict. | https://api.python.langchain.com/en/latest/chains/langchain.chains.loading.load_chain_from_config.html |
c17f4fb79a7f-0 | langchain.chains.graph_qa.arangodb.ArangoGraphQAChain¶
class langchain.chains.graph_qa.arangodb.ArangoGraphQAChain[source]¶
Bases: Chain
Chain for question-answering against a graph by generating AQL statements.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if th... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-1 | param qa_chain: LLMChain [Required]¶
param return_aql_query: bool = False¶
param return_aql_result: bool = False¶
param 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 as arguments to the handlers... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-2 | addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Sho... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-3 | these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvok... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-4 | directly as keyword arguments.
Returns
The chain output.
Example
# 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 a multi-input chain that takes a 'question' string
# and 'context' s... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-5 | Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep co... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-6 | classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['adb_schema', 'user_input', 'aql_query', 'aql_result'], output_parser=None, partial_variables={}, template="Task: Generate a natural language `Summary` from the results of an ArangoDB Query Language query.\n\... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-7 | aql_generation_prompt: BasePromptTemplate = PromptTemplate(input_variables=['adb_schema', 'aql_examples', 'user_input'], output_parser=None, partial_variables={}, template="Task: Generate an ArangoDB Query Language (AQL) query from a User Input.\n\nYou are an ArangoDB Query Language (AQL) expert responsible for transla... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-8 | should not do:\n- Do not use any properties/relationships that can't be inferred from the `ArangoDB Schema` or the `AQL Query Examples`. \n- Do not include any text except the generated AQL Query.\n- Do not provide explanations or apologies in your responses.\n- Do not generate an AQL Query that removes or deletes any ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-9 | `Collection Schema`: Lists all Collections within the ArangoDB Database Instance, along with their document/edge properties and a document/edge example.\n\nYou will output the `Corrected AQL Query` wrapped in 3 backticks (```). Do not include any text except the Corrected AQL Query.\n\nRemember to think step by step.\n... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-10 | Initialize from LLM.
classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-11 | Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(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.
Parameters
inputs – Dictionary of chain inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-12 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
c17f4fb79a7f-13 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.arangodb.ArangoGraphQAChain.html |
5de36bd09165-0 | langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain¶
class langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain[source]¶
Bases: Chain
Chain for question-answering against a Neptune graph
by generating openCypher statements.
Example
chain = NeptuneOpenCypherQAChain.from_llm(llm=llm,
graph=grap... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-1 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param qa_chain: LLMChain [Required]¶
param return_direct: bool = False¶
Whether or not to return the result of querying the graph directly.
param return_intermediate_steps: bo... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-2 | chain will be returned. Defaults to False.
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 pass to all callbacks. These will be... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-3 | 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 pass to all callbacks. These will be passed in
addition to tags passed to the c... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-4 | 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 only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-5 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-6 | classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="You are an assistant that helps to form nice and human understandable answers.\nThe information part contains the provided informati... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-7 | include any text except the generated Cypher statement.\n\nThe question is:\n{question}', template_format='f-string', validate_template=True), **kwargs: Any) → NeptuneOpenCypherQAChain[source]¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-8 | Initialize from LLM.
classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-9 | Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(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.
Parameters
inputs – Dictionary of chain inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-10 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
5de36bd09165-11 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain.html |
b2a51e789e19-0 | langchain.chains.openai_functions.openapi.SimpleRequestChain¶
class langchain.chains.openai_functions.openapi.SimpleRequestChain[source]¶
Bases: Chain
Chain for making a simple request to an API endpoint.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
b2a51e789e19-1 | param request_method: Callable [Required]¶
Method to use for making the request.
param 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 as arguments to the handlers defined in callbacks.
You can us... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
b2a51e789e19-2 | these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
b2a51e789e19-3 | metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = N... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
b2a51e789e19-4 | directly as keyword arguments.
Returns
The chain output.
Example
# 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 a multi-input chain that takes a 'question' string
# and 'context' s... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
b2a51e789e19-5 | Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep co... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
b2a51e789e19-6 | classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
b2a51e789e19-7 | method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this cha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
b2a51e789e19-8 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedN... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.SimpleRequestChain.html |
392fd0b5e6b6-0 | langchain.chains.retrieval_qa.base.BaseRetrievalQA¶
class langchain.chains.retrieval_qa.base.BaseRetrievalQA[source]¶
Bases: Chain
Base class for question-answering chains.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
392fd0b5e6b6-1 | Return the source documents or not.
param 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 as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
392fd0b5e6b6-2 | metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
392fd0b5e6b6-3 | to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
392fd0b5e6b6-4 | # -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
392fd0b5e6b6-5 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to defaul... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
392fd0b5e6b6-6 | Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
392fd0b5e6b6-7 | Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expect... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
392fd0b5e6b6-8 | Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optiona... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html |
8294b24912ce-0 | langchain.chains.openai_functions.base.convert_to_openai_function¶
langchain.chains.openai_functions.base.convert_to_openai_function(function: Union[Dict[str, Any], Type[BaseModel], Callable]) → Dict[str, Any][source]¶
Convert a raw function/class to an OpenAI function.
Parameters
function – Either a dictionary, a pyda... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.base.convert_to_openai_function.html |
796f7058a00b-0 | langchain.chains.retrieval_qa.base.RetrievalQA¶
class langchain.chains.retrieval_qa.base.RetrievalQA[source]¶
Bases: BaseRetrievalQA
Chain for question-answering against an index.
Example
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.faiss import FAISS
from langchain.vectorst... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-1 | for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a cha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-2 | chain will be returned. Defaults to False.
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 pass to all callbacks. These will be... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-3 | 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 pass to all callbacks. These will be passed in
addition to tags passed to the c... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-4 | 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 only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-5 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-6 | Initialize from LLM.
classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-7 | Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(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.
Parameters
inputs – Dictionary of chain inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-8 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
796f7058a00b-9 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html |
210fc5b347f8-0 | langchain.chains.openai_functions.qa_with_structure.AnswerWithSources¶
class langchain.chains.openai_functions.qa_with_structure.AnswerWithSources[source]¶
Bases: BaseModel
An answer to the question, with sources.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.qa_with_structure.AnswerWithSources.html |
210fc5b347f8-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.qa_with_structure.AnswerWithSources.html |
210fc5b347f8-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.qa_with_structure.AnswerWithSources.html |
a26d1a9d4e17-0 | langchain.chains.llm.LLMChain¶
class langchain.chains.llm.LLMChain[source]¶
Bases: Chain
Chain to run queries against LLMs.
Example
from langchain import LLMChain, OpenAI, PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"], template=prompt_template
)... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-1 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: Ba... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-2 | response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-3 | async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute t... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-4 | apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]][source]¶
Utilize the LLM generate method for speed gains.
apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackM... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-5 | The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-6 | bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no othe... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-7 | classmethod from_orm(obj: Any) → Model¶
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain[source]¶
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult[source]¶
Generate LLM result from inputs.
inv... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-8 | Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-9 | Prepare prompts from inputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__c... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-10 | save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definiti... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
a26d1a9d4e17-11 | Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
Examples using LLMChain¶
Zapier Natural Language Actions API
Argilla
Comet
Aim
Weights & Biases
Rebuff
MLflow
Flyte
Chat Over Documents with... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
2fbf60e8dcac-0 | langchain.chains.graph_qa.hugegraph.HugeGraphQAChain¶
class langchain.chains.graph_qa.hugegraph.HugeGraphQAChain[source]¶
Bases: Chain
Chain for question-answering against a graph by generating gremlin statements.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-1 | param 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 as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param ve... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-2 | metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-3 | to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-4 | # -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-5 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to defaul... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-6 | # -> {“_type”: “foo”, “verbose”: False, …}
classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="You are an assistant that helps to form nice and human understandable answers.\nThe infor... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-7 | json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Cal... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-8 | Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the fi... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-9 | # and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be i... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
2fbf60e8dcac-10 | constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not t... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.hugegraph.HugeGraphQAChain.html |
3760bd4cb8bd-0 | langchain.chains.openai_functions.base.convert_python_function_to_openai_function¶
langchain.chains.openai_functions.base.convert_python_function_to_openai_function(function: Callable) → Dict[str, Any][source]¶
Convert a Python function to an OpenAI function-calling API compatible dict.
Assumes the Python function has ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.base.convert_python_function_to_openai_function.html |
5df57dbf5778-0 | langchain.chains.sql_database.query.SQLInput¶
class langchain.chains.sql_database.query.SQLInput[source]¶
Input for a SQL Chain.
question: str¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.sql_database.query.SQLInput.html |
0a72bff2aab5-0 | langchain.chains.openai_functions.openapi.openapi_spec_to_openai_fn¶
langchain.chains.openai_functions.openapi.openapi_spec_to_openai_fn(spec: OpenAPISpec) → Tuple[List[Dict[str, Any]], Callable][source]¶
Convert a valid OpenAPI spec to the JSON Schema format expected for OpenAIfunctions.
Parameters
spec – OpenAPI spec... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.openapi_spec_to_openai_fn.html |
6c85496cdbef-0 | langchain.chains.qa_with_sources.base.QAWithSourcesChain¶
class langchain.chains.qa_with_sources.base.QAWithSourcesChain[source]¶
Bases: BaseQAWithSourcesChain
Question answering with sources over documents.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the in... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-1 | Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not r... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-2 | to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
async acall(inputs: Union[Dict[str, Any], Any], return_on... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-3 | Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dic... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-4 | # -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-5 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to defaul... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-6 | classmethod from_llm(llm: BaseLanguageModel, document_prompt: BasePromptTemplate = PromptTemplate(input_variables=['page_content', 'source'], output_parser=None, partial_variables={}, template='Content: {page_content}\nSource: {source}', template_format='f-string', validate_template=True), question_prompt: BasePromptTe... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-7 | or unenforceability of any term (or part of a term) of this Agreement shall not affect the continuation in force of the remainder of the term (if any) and this Agreement.\n\n11.8 No Agency. Except as expressly stated otherwise, nothing in this Agreement shall create an agency, partnership or joint venture of any kind... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-8 | \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n\nGroups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.\nSource: 0-pl\nContent: And we won’t stop. \n\nWe ha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-9 | \n\nTo all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. \n\nAnd I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American bu... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-10 | and most prosperous nation the world has ever known. \n\nNow is the hour. \n\nOur moment of responsibility. \n\nOur test of resolve and conscience, of history itself. \n\nIt is in this moment that our character is formed. Our purpose is found. Our future is forged. \n\nWell I know this nation.\nSource: 34-pl\n=========... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
6c85496cdbef-11 | Construct the chain from an LLM.
classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html |
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