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95e601e99c3e-8 | 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, 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 final outputs.
Returns
A dict of the final chain outputs.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
validator raise_callback_manager_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
95e601e99c3e-9 | 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, they can be passed in
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:
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 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")
validator set_verbose » verbose¶
Set the chain verbosity.
Defaults to the global setting if not specified by the user.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property evaluation_name: str¶
The name of the evaluation.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”] | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
95e601e99c3e-10 | 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 the class is serializable.
property requires_input: bool¶
Whether the chain requires an input string.
property requires_reference: bool¶
Whether the chain requires a reference string.
model Config¶
Bases: object
Configuration for the QAEvalChain.
extra = 'ignore'¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
c5e28a491940-0 | langchain.evaluation.criteria.eval_chain.resolve_criteria¶
langchain.evaluation.criteria.eval_chain.resolve_criteria(criteria: Optional[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple, str]]) → Dict[str, str][source]¶
Resolve the criteria to evaluate.
Parameters
criteria (CRITERIA_TYPE) –
The criteria to evaluate the runs against. It can be:
a mapping of a criterion name to its description
a single criterion name present in one of the default criteria
a single ConstitutionalPrinciple instance
Returns
A dictionary mapping criterion names to descriptions.
Return type
Dict[str, str]
Examples
>>> criterion = "relevance"
>>> CriteriaEvalChain.resolve_criteria(criteria)
{'relevance': 'Is the submission referring to a real quote from the text?'} | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.resolve_criteria.html |
cbe50f478877-0 | langchain.evaluation.schema.PairwiseStringEvaluator¶
class langchain.evaluation.schema.PairwiseStringEvaluator[source]¶
Bases: _EvalArgsMixin, ABC
Compare the output of two models (or two outputs of the same model).
Methods
__init__()
aevaluate_string_pairs(*, prediction, ...[, ...])
Asynchronously evaluate the output string pairs.
evaluate_string_pairs(*, prediction, ...[, ...])
Evaluate the output string pairs.
Attributes
requires_input
Whether this evaluator requires an input string.
requires_reference
Whether this evaluator requires a reference label.
async aevaluate_string_pairs(*, prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict[source]¶
Asynchronously evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (Optional[str], optional) – The expected output / reference string.
input (Optional[str], optional) – The input string.
**kwargs – Additional keyword arguments, such as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/or other information.
Return type
dict
evaluate_string_pairs(*, prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict[source]¶
Evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (Optional[str], optional) – The expected output / reference string.
input (Optional[str], optional) – The input string. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.PairwiseStringEvaluator.html |
cbe50f478877-1 | input (Optional[str], optional) – The input string.
**kwargs – Additional keyword arguments, such as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/or other information.
Return type
dict
property requires_input: bool¶
Whether this evaluator requires an input string.
property requires_reference: bool¶
Whether this evaluator requires a reference label.
Examples using PairwiseStringEvaluator¶
Custom Pairwise Evaluator | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.PairwiseStringEvaluator.html |
0b4b0ddfd707-0 | langchain.evaluation.string_distance.base.StringDistanceEvalChain¶
class langchain.evaluation.string_distance.base.StringDistanceEvalChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, distance: StringDistance = StringDistance.JARO_WINKLER, normalize_score: bool = True)[source]¶
Bases: StringEvaluator, _RapidFuzzChainMixin
Compute string distances between the prediction and the reference.
Examples
>>> from langchain.evaluation import StringDistanceEvalChain
>>> evaluator = StringDistanceEvalChain()
>>> evaluator.evaluate_strings(
prediction="Mindy is the CTO",
reference="Mindy is the CEO",
)
Using the load_evaluator function:
>>> from langchain.evaluation import load_evaluator
>>> evaluator = load_evaluator("string_distance")
>>> evaluator.evaluate_strings(
prediction="The answer is three",
reference="three",
)
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param distance: StringDistance = StringDistance.JARO_WINKLER¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
0b4b0ddfd707-1 | Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
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 chain with its use case.
param normalize_score: bool = True¶
Whether to normalize the score to a value between 0 and 1.
Applies only to the Levenshtein and Damerau-Levenshtein distances.
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 verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(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]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
0b4b0ddfd707-2 | Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
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
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.
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 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 the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
0b4b0ddfd707-3 | 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
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.
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 aevaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Asynchronously evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Additional keyword arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict
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[Dict[str, str]]¶
Call the chain on all inputs in the list. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
0b4b0ddfd707-4 | Call the chain on all inputs in the list.
async arun(*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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to 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, they can be passed in
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' 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 Boise is..." | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
0b4b0ddfd707-5 | # -> "The temperature in Boise is..."
compute_metric(a: str, b: str) → float¶
Compute the distance between two strings.
Parameters
a (str) – The first string.
b (str) – The second string.
Returns
The distance between the two strings.
Return type
float
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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
..code-block:: python
chain.dict(exclude_unset=True)
# -> {“_type”: “foo”, “verbose”: False, …}
evaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Additional keyword arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
0b4b0ddfd707-6 | Chain.input_keys except for inputs that will be set by the chain’s
memory.
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, 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 final outputs.
Returns
A dict of the final chain outputs.
validator raise_callback_manager_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to 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. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
0b4b0ddfd707-7 | these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
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:
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 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")
validator set_verbose » verbose¶
Set the chain verbosity.
Defaults to the global setting if not specified by the user.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_dependencies » all fields¶
Validate that the rapidfuzz library is installed.
Parameters
values (Dict[str, Any]) – The input values.
Returns
The validated values.
Return type
Dict[str, Any]
property evaluation_name: str¶
Get the evaluation name.
Returns
The evaluation name.
Return type
str
property input_keys: List[str]¶
Get the input keys.
Returns
The input keys.
Return type
List[str]
property lc_attributes: Dict¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
0b4b0ddfd707-8 | The input keys.
Return type
List[str]
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
property metric: Callable¶
Get the distance metric function.
Returns
The distance metric function.
Return type
Callable
property output_keys: List[str]¶
Get the output keys.
Returns
The output keys.
Return type
List[str]
property requires_input: bool¶
This evaluator does not require input.
property requires_reference: bool¶
This evaluator does not require a reference.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html |
2454ff95720c-0 | langchain.evaluation.qa.eval_chain.QAEvalChain¶
class langchain.evaluation.qa.eval_chain.QAEvalChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, prompt: BasePromptTemplate, llm: BaseLanguageModel, output_key: str = 'results', output_parser: BaseLLMOutputParser = None, return_final_only: bool = True, llm_kwargs: dict = None)[source]¶
Bases: LLMChain, StringEvaluator, LLMEvalChain
LLM Chain for evaluating question answering.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param llm: BaseLanguageModel [Required]¶
Language model to call.
param llm_kwargs: dict [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-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 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: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
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 verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(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]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-2 | only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
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
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.
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 aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-3 | Call apply and then parse the results.
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 the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
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
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.
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 aevaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Asynchronously evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-4 | Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Additional keyword arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
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[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async apredict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
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")
async apredict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, str]]¶
Call apredict and then parse the results. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-5 | Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to 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, they can be passed in
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' string:
question = "What's the temperature in Boise, Idaho?" | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-6 | 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 Boise is..."
create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]¶
Create outputs from response.
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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
..code-block:: python
chain.dict(exclude_unset=True)
# -> {“_type”: “foo”, “verbose”: False, …}
evaluate(examples: Sequence[dict], predictions: Sequence[dict], question_key: str = 'query', answer_key: str = 'answer', prediction_key: str = 'result', *, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[dict][source]¶
Evaluate question answering examples and predictions.
evaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Additional keyword arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-7 | Returns
The evaluation results containing the score or value.
Return type
dict
classmethod from_llm(llm: BaseLanguageModel, prompt: Optional[PromptTemplate] = None, **kwargs: Any) → QAEvalChain[source]¶
Load QA Eval Chain from LLM.
Parameters
llm (BaseLanguageModel) – the base language model to use.
prompt ('answer' and 'result' that will be used as the) – A prompt template containing the input_variables:
'input' –
prompt –
evaluation. (for) –
PROMPT. (Defaults to) –
**kwargs – additional keyword arguments.
Returns
the loaded QA eval chain.
Return type
QAEvalChain
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
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, **kwargs: Any) → Union[str, List[str], Dict[str, Any]]¶
Call predict and then parse the results. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-8 | Call predict and then parse the results.
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
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, 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 final outputs.
Returns
A dict of the final chain outputs.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
validator raise_callback_manager_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
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.__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 | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-9 | 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 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 chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
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:
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 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")
validator set_verbose » verbose¶
Set the chain verbosity.
Defaults to the global setting if not specified by the user.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
2454ff95720c-10 | to_json_not_implemented() → SerializedNotImplemented¶
property evaluation_name: str¶
The name of the evaluation.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
property requires_input: bool¶
Whether this evaluator requires an input string.
property requires_reference: bool¶
Whether this evaluator requires a reference label.
model Config[source]¶
Bases: object
Configuration for the QAEvalChain.
extra = 'ignore'¶
Examples using QAEvalChain¶
Question Answering Benchmarking: State of the Union Address
Question Answering Benchmarking: Paul Graham Essay
Data Augmented Question Answering
SQL Question Answering Benchmarking: Chinook
Question Answering
Agent VectorDB Question Answering Benchmarking | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7b73ee23cb63-0 | langchain.evaluation.string_distance.base.StringDistance¶
class langchain.evaluation.string_distance.base.StringDistance(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: str, Enum
Distance metric to use.
DAMERAU_LEVENSHTEIN¶
The Damerau-Levenshtein distance.
LEVENSHTEIN¶
The Levenshtein distance.
JARO¶
The Jaro distance.
JARO_WINKLER¶
The Jaro-Winkler distance.
Methods
__init__(*args, **kwds)
capitalize()
Return a capitalized version of the string.
casefold()
Return a version of the string suitable for caseless comparisons.
center(width[, fillchar])
Return a centered string of length width.
count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring sub in string S[start:end].
encode([encoding, errors])
Encode the string using the codec registered for encoding.
endswith(suffix[, start[, end]])
Return True if S ends with the specified suffix, False otherwise.
expandtabs([tabsize])
Return a copy where all tab characters are expanded using spaces.
find(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
format(*args, **kwargs)
Return a formatted version of S, using substitutions from args and kwargs.
format_map(mapping)
Return a formatted version of S, using substitutions from mapping.
index(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
isalnum() | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-1 | isalnum()
Return True if the string is an alpha-numeric string, False otherwise.
isalpha()
Return True if the string is an alphabetic string, False otherwise.
isascii()
Return True if all characters in the string are ASCII, False otherwise.
isdecimal()
Return True if the string is a decimal string, False otherwise.
isdigit()
Return True if the string is a digit string, False otherwise.
isidentifier()
Return True if the string is a valid Python identifier, False otherwise.
islower()
Return True if the string is a lowercase string, False otherwise.
isnumeric()
Return True if the string is a numeric string, False otherwise.
isprintable()
Return True if the string is printable, False otherwise.
isspace()
Return True if the string is a whitespace string, False otherwise.
istitle()
Return True if the string is a title-cased string, False otherwise.
isupper()
Return True if the string is an uppercase string, False otherwise.
join(iterable, /)
Concatenate any number of strings.
ljust(width[, fillchar])
Return a left-justified string of length width.
lower()
Return a copy of the string converted to lowercase.
lstrip([chars])
Return a copy of the string with leading whitespace removed.
maketrans
Return a translation table usable for str.translate().
partition(sep, /)
Partition the string into three parts using the given separator.
removeprefix(prefix, /)
Return a str with the given prefix string removed if present.
removesuffix(suffix, /)
Return a str with the given suffix string removed if present.
replace(old, new[, count])
Return a copy with all occurrences of substring old replaced by new.
rfind(sub[, start[, end]]) | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-2 | rfind(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rindex(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rjust(width[, fillchar])
Return a right-justified string of length width.
rpartition(sep, /)
Partition the string into three parts using the given separator.
rsplit([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
rstrip([chars])
Return a copy of the string with trailing whitespace removed.
split([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
splitlines([keepends])
Return a list of the lines in the string, breaking at line boundaries.
startswith(prefix[, start[, end]])
Return True if S starts with the specified prefix, False otherwise.
strip([chars])
Return a copy of the string with leading and trailing whitespace removed.
swapcase()
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()
Return a version of the string where each word is titlecased.
translate(table, /)
Replace each character in the string using the given translation table.
upper()
Return a copy of the string converted to uppercase.
zfill(width, /)
Pad a numeric string with zeros on the left, to fill a field of the given width.
Attributes
DAMERAU_LEVENSHTEIN
LEVENSHTEIN
JARO
JARO_WINKLER
capitalize()¶
Return a capitalized version of the string.
More specifically, make the first character have upper case and the rest lower | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-3 | More specifically, make the first character have upper case and the rest lower
case.
casefold()¶
Return a version of the string suitable for caseless comparisons.
center(width, fillchar=' ', /)¶
Return a centered string of length width.
Padding is done using the specified fill character (default is a space).
count(sub[, start[, end]]) → int¶
Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are
interpreted as in slice notation.
encode(encoding='utf-8', errors='strict')¶
Encode the string using the codec registered for encoding.
encodingThe encoding in which to encode the string.
errorsThe error handling scheme to use for encoding errors.
The default is ‘strict’ meaning that encoding errors raise a
UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and
‘xmlcharrefreplace’ as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
endswith(suffix[, start[, end]]) → bool¶
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
expandtabs(tabsize=8)¶
Return a copy where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
find(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
format(*args, **kwargs) → str¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-4 | Return -1 on failure.
format(*args, **kwargs) → str¶
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces (‘{’ and ‘}’).
format_map(mapping) → str¶
Return a formatted version of S, using substitutions from mapping.
The substitutions are identified by braces (‘{’ and ‘}’).
index(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
isalnum()¶
Return True if the string is an alpha-numeric string, False otherwise.
A string is alpha-numeric if all characters in the string are alpha-numeric and
there is at least one character in the string.
isalpha()¶
Return True if the string is an alphabetic string, False otherwise.
A string is alphabetic if all characters in the string are alphabetic and there
is at least one character in the string.
isascii()¶
Return True if all characters in the string are ASCII, False otherwise.
ASCII characters have code points in the range U+0000-U+007F.
Empty string is ASCII too.
isdecimal()¶
Return True if the string is a decimal string, False otherwise.
A string is a decimal string if all characters in the string are decimal and
there is at least one character in the string.
isdigit()¶
Return True if the string is a digit string, False otherwise.
A string is a digit string if all characters in the string are digits and there
is at least one character in the string.
isidentifier()¶
Return True if the string is a valid Python identifier, False otherwise. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-5 | isidentifier()¶
Return True if the string is a valid Python identifier, False otherwise.
Call keyword.iskeyword(s) to test whether string s is a reserved identifier,
such as “def” or “class”.
islower()¶
Return True if the string is a lowercase string, False otherwise.
A string is lowercase if all cased characters in the string are lowercase and
there is at least one cased character in the string.
isnumeric()¶
Return True if the string is a numeric string, False otherwise.
A string is numeric if all characters in the string are numeric and there is at
least one character in the string.
isprintable()¶
Return True if the string is printable, False otherwise.
A string is printable if all of its characters are considered printable in
repr() or if it is empty.
isspace()¶
Return True if the string is a whitespace string, False otherwise.
A string is whitespace if all characters in the string are whitespace and there
is at least one character in the string.
istitle()¶
Return True if the string is a title-cased string, False otherwise.
In a title-cased string, upper- and title-case characters may only
follow uncased characters and lowercase characters only cased ones.
isupper()¶
Return True if the string is an uppercase string, False otherwise.
A string is uppercase if all cased characters in the string are uppercase and
there is at least one cased character in the string.
join(iterable, /)¶
Concatenate any number of strings.
The string whose method is called is inserted in between each given string.
The result is returned as a new string.
Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’
ljust(width, fillchar=' ', /)¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-6 | ljust(width, fillchar=' ', /)¶
Return a left-justified string of length width.
Padding is done using the specified fill character (default is a space).
lower()¶
Return a copy of the string converted to lowercase.
lstrip(chars=None, /)¶
Return a copy of the string with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
static maketrans()¶
Return a translation table usable for str.translate().
If there is only one argument, it must be a dictionary mapping Unicode
ordinals (integers) or characters to Unicode ordinals, strings or None.
Character keys will be then converted to ordinals.
If there are two arguments, they must be strings of equal length, and
in the resulting dictionary, each character in x will be mapped to the
character at the same position in y. If there is a third argument, it
must be a string, whose characters will be mapped to None in the result.
partition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string. If the separator is found,
returns a 3-tuple containing the part before the separator, the separator
itself, and the part after it.
If the separator is not found, returns a 3-tuple containing the original string
and two empty strings.
removeprefix(prefix, /)¶
Return a str with the given prefix string removed if present.
If the string starts with the prefix string, return string[len(prefix):].
Otherwise, return a copy of the original string.
removesuffix(suffix, /)¶
Return a str with the given suffix string removed if present.
If the string ends with the suffix string and that suffix is not empty, | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-7 | If the string ends with the suffix string and that suffix is not empty,
return string[:-len(suffix)]. Otherwise, return a copy of the original
string.
replace(old, new, count=- 1, /)¶
Return a copy with all occurrences of substring old replaced by new.
countMaximum number of occurrences to replace.
-1 (the default value) means replace all occurrences.
If the optional argument count is given, only the first count occurrences are
replaced.
rfind(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
rindex(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
rjust(width, fillchar=' ', /)¶
Return a right-justified string of length width.
Padding is done using the specified fill character (default is a space).
rpartition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string, starting at the end. If
the separator is found, returns a 3-tuple containing the part before the
separator, the separator itself, and the part after it.
If the separator is not found, returns a 3-tuple containing two empty strings
and the original string.
rsplit(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-8 | sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Splitting starts at the end of the string and works to the front.
rstrip(chars=None, /)¶
Return a copy of the string with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
split(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Note, str.split() is mainly useful for data that has been intentionally
delimited. With natural text that includes punctuation, consider using
the regular expression module.
splitlines(keepends=False)¶
Return a list of the lines in the string, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends is given and
true.
startswith(prefix[, start[, end]]) → bool¶
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
strip(chars=None, /)¶
Return a copy of the string with leading and trailing whitespace removed. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7b73ee23cb63-9 | Return a copy of the string with leading and trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
swapcase()¶
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()¶
Return a version of the string where each word is titlecased.
More specifically, words start with uppercased characters and all remaining
cased characters have lower case.
translate(table, /)¶
Replace each character in the string using the given translation table.
tableTranslation table, which must be a mapping of Unicode ordinals to
Unicode ordinals, strings, or None.
The table must implement lookup/indexing via __getitem__, for instance a
dictionary or list. If this operation raises LookupError, the character is
left untouched. Characters mapped to None are deleted.
upper()¶
Return a copy of the string converted to uppercase.
zfill(width, /)¶
Pad a numeric string with zeros on the left, to fill a field of the given width.
The string is never truncated.
DAMERAU_LEVENSHTEIN = 'damerau_levenshtein'¶
JARO = 'jaro'¶
JARO_WINKLER = 'jaro_winkler'¶
LEVENSHTEIN = 'levenshtein'¶
Examples using StringDistance¶
String Distance | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
7bd058e1fe61-0 | langchain.evaluation.loading.load_evaluators¶
langchain.evaluation.loading.load_evaluators(evaluators: Sequence[EvaluatorType], *, llm: Optional[BaseLanguageModel] = None, config: Optional[dict] = None, **kwargs: Any) → List[Chain][source]¶
Load evaluators specified by a list of evaluator types.
Parameters
evaluators (Sequence[EvaluatorType]) – The list of evaluator types to load.
llm (BaseLanguageModel, optional) – The language model to use for evaluation, if none is provided, a default
ChatOpenAI gpt-4 model will be used.
config (dict, optional) – A dictionary mapping evaluator types to additional keyword arguments,
by default None
**kwargs (Any) – Additional keyword arguments to pass to all evaluators.
Returns
The loaded evaluators.
Return type
List[Chain]
Examples
>>> from langchain.evaluation import load_evaluators, EvaluatorType
>>> evaluators = [EvaluatorType.QA, EvaluatorType.CRITERIA]
>>> loaded_evaluators = load_evaluators(evaluators, criteria="helpfulness") | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.loading.load_evaluators.html |
8499738e77bf-0 | langchain.evaluation.loading.load_evaluator¶
langchain.evaluation.loading.load_evaluator(evaluator: EvaluatorType, *, llm: Optional[BaseLanguageModel] = None, **kwargs: Any) → Chain[source]¶
Load the requested evaluation chain specified by a string.
Parameters
evaluator (EvaluatorType) – The type of evaluator to load.
llm (BaseLanguageModel, optional) – The language model to use for evaluation, by default None
**kwargs (Any) – Additional keyword arguments to pass to the evaluator.
Returns
The loaded evaluation chain.
Return type
Chain
Examples
>>> from langchain.evaluation import load_evaluator, EvaluatorType
>>> evaluator = load_evaluator(EvaluatorType.QA)
Examples using load_evaluator¶
Comparing Chain Outputs
Agent Trajectory
Pairwise Embedding Distance
Pairwise String Comparison
Criteria Evaluation
QA Correctness
String Distance
Embedding Distance | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.loading.load_evaluator.html |
ec6c80952e5a-0 | langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain¶
class langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, embeddings: Embeddings = None, distance_metric: EmbeddingDistance = EmbeddingDistance.COSINE)[source]¶
Bases: _EmbeddingDistanceChainMixin, PairwiseStringEvaluator
Use embedding distances to score semantic difference between two predictions.
Examples:
>>> chain = PairwiseEmbeddingDistanceEvalChain()
>>> result = chain.evaluate_string_pairs(prediction=”Hello”, prediction_b=”Hi”)
>>> print(result)
{‘score’: 0.5}
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 valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param distance_metric: langchain.evaluation.embedding_distance.base.EmbeddingDistance = EmbeddingDistance.COSINE¶
param embeddings: langchain.embeddings.base.Embeddings [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html |
ec6c80952e5a-1 | Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
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 chain with its use case.
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 verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(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]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html |
ec6c80952e5a-2 | memory.
return_only_outputs – Whether to return only outputs in the
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
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.
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 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 the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
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 | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html |
ec6c80952e5a-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 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. 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[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html |
ec6c80952e5a-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, they can be passed in
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' 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 Boise is..."
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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
..code-block:: python
chain.dict(exclude_unset=True)
# -> {“_type”: “foo”, “verbose”: False, …}
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html |
ec6c80952e5a-5 | only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
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, 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 final outputs.
Returns
A dict of the final chain outputs.
validator raise_callback_manager_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html |
ec6c80952e5a-6 | 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, they can be passed in
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:
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 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")
validator set_verbose » verbose¶
Set the chain verbosity.
Defaults to the global setting if not specified by the user.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property evaluation_name: str¶
property input_keys: List[str]¶
Return the input keys of the chain.
Returns
The input keys.
Return type
List[str]
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html |
ec6c80952e5a-7 | 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 the class is serializable.
property output_keys: List[str]¶
Return the output keys of the chain.
Returns
The output keys.
Return type
List[str]
model Config¶
Bases: object
Permit embeddings to go unvalidated.
arbitrary_types_allowed: bool = True¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html |
55b4780758da-0 | langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain¶
class langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, embeddings: Embeddings = None, distance_metric: EmbeddingDistance = EmbeddingDistance.COSINE)[source]¶
Bases: _EmbeddingDistanceChainMixin, StringEvaluator
Use embedding distances to score semantic difference between
a prediction and reference.
Examples
>>> chain = EmbeddingDistanceEvalChain()
>>> result = chain.evaluate_strings(prediction="Hello", reference="Hi")
>>> print(result)
{'score': 0.5}
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 valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param distance_metric: langchain.evaluation.embedding_distance.base.EmbeddingDistance = EmbeddingDistance.COSINE¶
param embeddings: langchain.embeddings.base.Embeddings [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html |
55b4780758da-1 | Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
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 chain with its use case.
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 verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(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]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html |
55b4780758da-2 | memory.
return_only_outputs – Whether to return only outputs in the
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
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.
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 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 the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
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 | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html |
55b4780758da-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 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. 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[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html |
55b4780758da-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, they can be passed in
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' 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 Boise is..."
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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
..code-block:: python
chain.dict(exclude_unset=True)
# -> {“_type”: “foo”, “verbose”: False, …}
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html |
55b4780758da-5 | only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
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, 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 final outputs.
Returns
A dict of the final chain outputs.
validator raise_callback_manager_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html |
55b4780758da-6 | 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, they can be passed in
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:
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 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")
validator set_verbose » verbose¶
Set the chain verbosity.
Defaults to the global setting if not specified by the user.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property evaluation_name: str¶
The name of the evaluation.
property input_keys: List[str]¶
Return the input keys of the chain.
Returns
The input keys.
Return type
List[str]
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html |
55b4780758da-7 | 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 the class is serializable.
property output_keys: List[str]¶
Return the output keys of the chain.
Returns
The output keys.
Return type
List[str]
property requires_reference: bool¶
Return whether the chain requires a reference.
Returns
True if a reference is required, False otherwise.
Return type
bool
model Config¶
Bases: object
Permit embeddings to go unvalidated.
arbitrary_types_allowed: bool = True¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html |
3356e88464b0-0 | langchain.evaluation.schema.LLMEvalChain¶
class langchain.evaluation.schema.LLMEvalChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None)[source]¶
Bases: Chain
A base class for evaluators that use an LLM.
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 valid model.
param callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param memory: Optional[langchain.schema.memory.BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
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, | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
3356e88464b0-1 | 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 chain with its use case.
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 verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(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]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
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
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 | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
3356e88464b0-2 | 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.
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 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 the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
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
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.
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 | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
3356e88464b0-3 | 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) → List[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to 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, they can be passed in
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..." | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
3356e88464b0-4 | # -> "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 Boise is..."
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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
..code-block:: python
chain.dict(exclude_unset=True)
# -> {“_type”: “foo”, “verbose”: False, …}
abstract classmethod from_llm(llm: BaseLanguageModel, **kwargs: Any) → LLMEvalChain[source]¶
Create a new evaluator from an LLM.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
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 | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
3356e88464b0-5 | 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 final outputs.
Returns
A dict of the final chain outputs.
validator raise_callback_manager_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
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.__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 the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to 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, they can be passed in
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..." | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
3356e88464b0-6 | # -> "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..."
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 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")
validator set_verbose » verbose¶
Set the chain verbosity.
Defaults to the global setting if not specified by the user.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
abstract property input_keys: List[str]¶
Keys expected to be in the chain input.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
abstract property output_keys: List[str]¶
Keys expected to be in the chain output.
model Config¶
Bases: object
Configuration for this pydantic object. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
3356e88464b0-7 | model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
64ffe5086344-0 | langchain.evaluation.loading.load_dataset¶
langchain.evaluation.loading.load_dataset(uri: str) → List[Dict][source]¶
Load a dataset from the LangChainDatasets HuggingFace org.
Parameters
uri – The uri of the dataset to load.
Returns
A list of dictionaries, each representing a row in the dataset.
Prerequisites
pip install datasets
Examples
from langchain.evaluation import load_dataset
ds = load_dataset("llm-math")
Examples using load_dataset¶
Question Answering Benchmarking: State of the Union Address
Question Answering Benchmarking: Paul Graham Essay
Evaluating an OpenAPI Chain
Comparing Chain Outputs
SQL Question Answering Benchmarking: Chinook
Agent VectorDB Question Answering Benchmarking | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.loading.load_dataset.html |
7e09c22228d1-0 | langchain.evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser¶
class langchain.evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser[source]¶
Bases: BaseOutputParser
Trajectory output parser.
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 valid model.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
invoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.RunnableConfig | None = None) → T¶
parse(text: str) → TrajectoryEval[source]¶
Parse the output text and extract the score and reasoning.
Parameters
text (str) – The output text to parse.
Returns
A named tuple containing the normalized score and reasoning.
Return type
TrajectoryEval
Raises
OutputParserException – If the score is not found in the output text or
if the LLM’s score is not a digit in the range 1-5.
parse_result(result: List[Generation]) → T¶
Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, whichis assumed to be the highest-likelihood Generation.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser.html |
7e09c22228d1-1 | to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – String output of a language model.
prompt – Input PromptValue.
Returns
Structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser.html |
db6e6c669f3b-0 | langchain.memory.motorhead_memory.MotorheadMemory¶
class langchain.memory.motorhead_memory.MotorheadMemory(*, chat_memory: BaseChatMessageHistory = None, output_key: Optional[str] = None, input_key: Optional[str] = None, return_messages: bool = False, url: str = 'https://api.getmetal.io/v1/motorhead', session_id: str, context: Optional[str] = None, api_key: Optional[str] = None, client_id: Optional[str] = None, timeout: int = 3000, memory_key: str = 'history')[source]¶
Bases: BaseChatMemory
Chat message memory backed by Motorhead service.
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 valid model.
param api_key: Optional[str] = None¶
param chat_memory: BaseChatMessageHistory [Optional]¶
param client_id: Optional[str] = None¶
param context: Optional[str] = None¶
param input_key: Optional[str] = None¶
param output_key: Optional[str] = None¶
param return_messages: bool = False¶
param session_id: str [Required]¶
param url: str = 'https://api.getmetal.io/v1/motorhead'¶
clear() → None¶
Clear memory contents.
delete_session() → None[source]¶
Delete a session
async init() → None[source]¶
load_memory_variables(values: Dict[str, Any]) → Dict[str, Any][source]¶
Return key-value pairs given the text input to the chain.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this conversation to buffer.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.motorhead_memory.MotorheadMemory.html |
db6e6c669f3b-1 | to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
property memory_variables: List[str]¶
The string keys this memory class will add to chain inputs.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
Examples using MotorheadMemory¶
Motörhead Memory
Motörhead Memory (Managed) | https://api.python.langchain.com/en/latest/memory/langchain.memory.motorhead_memory.MotorheadMemory.html |
e454708fef34-0 | langchain.memory.chat_message_histories.sql.create_message_model¶
langchain.memory.chat_message_histories.sql.create_message_model(table_name, DynamicBase)[source]¶
Create a message model for a given table name.
Parameters
table_name – The name of the table to use.
DynamicBase – The base class to use for the model.
Returns
The model class. | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.sql.create_message_model.html |
d3e636044c13-0 | langchain.memory.vectorstore.VectorStoreRetrieverMemory¶
class langchain.memory.vectorstore.VectorStoreRetrieverMemory(*, retriever: VectorStoreRetriever, memory_key: str = 'history', input_key: Optional[str] = None, return_docs: bool = False, exclude_input_keys: Sequence[str] = None)[source]¶
Bases: BaseMemory
VectorStoreRetriever-backed memory.
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 valid model.
param exclude_input_keys: Sequence[str] [Optional]¶
Input keys to exclude in addition to memory key when constructing the document
param input_key: Optional[str] = None¶
Key name to index the inputs to load_memory_variables.
param memory_key: str = 'history'¶
Key name to locate the memories in the result of load_memory_variables.
param retriever: langchain.vectorstores.base.VectorStoreRetriever [Required]¶
VectorStoreRetriever object to connect to.
param return_docs: bool = False¶
Whether or not to return the result of querying the database directly.
clear() → None[source]¶
Nothing to clear.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Union[List[Document], str]][source]¶
Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this conversation to buffer.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.vectorstore.VectorStoreRetrieverMemory.html |
d3e636044c13-1 | 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 the class is serializable.
property memory_variables: List[str]¶
The list of keys emitted from the load_memory_variables method.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.vectorstore.VectorStoreRetrieverMemory.html |
e352468fa1e2-0 | langchain.memory.chat_message_histories.firestore.FirestoreChatMessageHistory¶
class langchain.memory.chat_message_histories.firestore.FirestoreChatMessageHistory(collection_name: str, session_id: str, user_id: str)[source]¶
Bases: BaseChatMessageHistory
Chat message history backed by Google Firestore.
Initialize a new instance of the FirestoreChatMessageHistory class.
Parameters
collection_name – The name of the collection to use.
session_id – The session ID for the chat..
user_id – The user ID for the chat.
Methods
__init__(collection_name, session_id, user_id)
Initialize a new instance of the FirestoreChatMessageHistory class.
add_ai_message(message)
Convenience method for adding an AI message string to the store.
add_message(message)
Add a Message object to the store.
add_user_message(message)
Convenience method for adding a human message string to the store.
clear()
Clear session memory from this memory and Firestore.
load_messages()
Retrieve the messages from Firestore
prepare_firestore()
Prepare the Firestore client.
upsert_messages([new_message])
Update the Firestore document.
Attributes
messages
A list of Messages stored in-memory.
add_ai_message(message: str) → None¶
Convenience method for adding an AI message string to the store.
Parameters
message – The string contents of an AI message.
add_message(message: BaseMessage) → None[source]¶
Add a Message object to the store.
Parameters
message – A BaseMessage object to store.
add_user_message(message: str) → None¶
Convenience method for adding a human message string to the store.
Parameters
message – The string contents of a human message.
clear() → None[source]¶
Clear session memory from this memory and Firestore.
load_messages() → None[source]¶
Retrieve the messages from Firestore | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.firestore.FirestoreChatMessageHistory.html |
e352468fa1e2-1 | load_messages() → None[source]¶
Retrieve the messages from Firestore
prepare_firestore() → None[source]¶
Prepare the Firestore client.
Use this function to make sure your database is ready.
upsert_messages(new_message: Optional[BaseMessage] = None) → None[source]¶
Update the Firestore document.
messages: List[BaseMessage]¶
A list of Messages stored in-memory. | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.firestore.FirestoreChatMessageHistory.html |
b86d7f95eea3-0 | langchain.memory.readonly.ReadOnlySharedMemory¶
class langchain.memory.readonly.ReadOnlySharedMemory(*, memory: BaseMemory)[source]¶
Bases: BaseMemory
A memory wrapper that is read-only and cannot be changed.
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 valid model.
param memory: langchain.schema.memory.BaseMemory [Required]¶
clear() → None[source]¶
Nothing to clear, got a memory like a vault.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]¶
Load memory variables from memory.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Nothing should be saved or changed
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
property memory_variables: List[str]¶
Return memory variables.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
Examples using ReadOnlySharedMemory¶
Shared memory across agents and tools | https://api.python.langchain.com/en/latest/memory/langchain.memory.readonly.ReadOnlySharedMemory.html |
bc454d743a24-0 | langchain.memory.entity.ConversationEntityMemory¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
bc454d743a24-1 | class langchain.memory.entity.ConversationEntityMemory(*, chat_memory: BaseChatMessageHistory = None, output_key: Optional[str] = None, input_key: Optional[str] = None, return_messages: bool = False, human_prefix: str = 'Human', ai_prefix: str = 'AI', llm: BaseLanguageModel, entity_extraction_prompt: BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
bc454d743a24-2 | going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:', template_format='f-string', validate_template=True), entity_summarization_prompt: BasePromptTemplate = PromptTemplate(input_variables=['entity', 'summary', 'history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant helping a human keep track of facts about relevant people, places, and concepts in their life. Update the summary of the provided entity in the "Entity" section based on the last line of your conversation with the human. If you are writing the summary for the first time, return a single sentence.\nThe update should only include facts that are relayed in the last line of conversation about the provided entity, and should only contain facts about the provided entity.\n\nIf there is no new information about the provided entity or the information is not worth noting (not an important or relevant fact to remember long-term), return the existing summary unchanged.\n\nFull conversation history (for context):\n{history}\n\nEntity to summarize:\n{entity}\n\nExisting summary of {entity}:\n{summary}\n\nLast line of conversation:\nHuman: {input}\nUpdated summary:', template_format='f-string', validate_template=True), entity_cache: | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
bc454d743a24-3 | {input}\nUpdated summary:', template_format='f-string', validate_template=True), entity_cache: List[str] = [], k: int = 3, chat_history_key: str = 'history', entity_store: BaseEntityStore = None)[source]¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
bc454d743a24-4 | Bases: BaseChatMemory
Entity extractor & summarizer memory.
Extracts named entities from the recent chat history and generates summaries.
With a swappable entity store, persisting entities across conversations.
Defaults to an in-memory entity store, and can be swapped out for a Redis,
SQLite, or other entity store.
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 valid model.
param ai_prefix: str = 'AI'¶
param chat_history_key: str = 'history'¶
param chat_memory: BaseChatMessageHistory [Optional]¶
param entity_cache: List[str] = []¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
bc454d743a24-5 | param entity_extraction_prompt: langchain.schema.prompt_template.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
bc454d743a24-6 | line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:', template_format='f-string', validate_template=True)¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
bc454d743a24-7 | param entity_store: langchain.memory.entity.BaseEntityStore [Optional]¶
param entity_summarization_prompt: langchain.schema.prompt_template.BasePromptTemplate = PromptTemplate(input_variables=['entity', 'summary', 'history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant helping a human keep track of facts about relevant people, places, and concepts in their life. Update the summary of the provided entity in the "Entity" section based on the last line of your conversation with the human. If you are writing the summary for the first time, return a single sentence.\nThe update should only include facts that are relayed in the last line of conversation about the provided entity, and should only contain facts about the provided entity.\n\nIf there is no new information about the provided entity or the information is not worth noting (not an important or relevant fact to remember long-term), return the existing summary unchanged.\n\nFull conversation history (for context):\n{history}\n\nEntity to summarize:\n{entity}\n\nExisting summary of {entity}:\n{summary}\n\nLast line of conversation:\nHuman: {input}\nUpdated summary:', template_format='f-string', validate_template=True)¶
param human_prefix: str = 'Human'¶
param input_key: Optional[str] = None¶
param k: int = 3¶
param llm: langchain.schema.language_model.BaseLanguageModel [Required]¶
param output_key: Optional[str] = None¶
param return_messages: bool = False¶
clear() → None[source]¶
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]¶
Returns chat history and all generated entities with summaries if available,
and updates or clears the recent entity cache.
New entity name can be found when calling this method, before the entity | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
bc454d743a24-8 | New entity name can be found when calling this method, before the entity
summaries are generated, so the entity cache values may be empty if no entity
descriptions are generated yet.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this conversation history to the entity store.
Generates a summary for each entity in the entity cache by prompting
the model, and saves these summaries to the entity store.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property buffer: List[langchain.schema.messages.BaseMessage]¶
Access chat memory messages.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
Examples using ConversationEntityMemory¶
Entity Memory with SQLite storage | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.ConversationEntityMemory.html |
fa45f41daf86-0 | langchain.memory.entity.SQLiteEntityStore¶
class langchain.memory.entity.SQLiteEntityStore(session_id: str = 'default', db_file: str = 'entities.db', table_name: str = 'memory_store', *args: Any)[source]¶
Bases: BaseEntityStore
SQLite-backed Entity store
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 valid model.
param session_id: str = 'default'¶
param table_name: str = 'memory_store'¶
clear() → None[source]¶
Delete all entities from store.
delete(key: str) → None[source]¶
Delete entity value from store.
exists(key: str) → bool[source]¶
Check if entity exists in store.
get(key: str, default: Optional[str] = None) → Optional[str][source]¶
Get entity value from store.
set(key: str, value: Optional[str]) → None[source]¶
Set entity value in store.
property full_table_name: str¶
Examples using SQLiteEntityStore¶
Entity Memory with SQLite storage | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.SQLiteEntityStore.html |
4c86ebc00ede-0 | langchain.memory.chat_message_histories.dynamodb.DynamoDBChatMessageHistory¶
class langchain.memory.chat_message_histories.dynamodb.DynamoDBChatMessageHistory(table_name: str, session_id: str, endpoint_url: Optional[str] = None)[source]¶
Bases: BaseChatMessageHistory
Chat message history that stores history in AWS DynamoDB.
This class expects that a DynamoDB table with name table_name
and a partition Key of SessionId is present.
Parameters
table_name – name of the DynamoDB table
session_id – arbitrary key that is used to store the messages
of a single chat session.
endpoint_url – URL of the AWS endpoint to connect to. This argument
is optional and useful for test purposes, like using Localstack.
If you plan to use AWS cloud service, you normally don’t have to
worry about setting the endpoint_url.
Methods
__init__(table_name, session_id[, endpoint_url])
add_ai_message(message)
Convenience method for adding an AI message string to the store.
add_message(message)
Append the message to the record in DynamoDB
add_user_message(message)
Convenience method for adding a human message string to the store.
clear()
Clear session memory from DynamoDB
Attributes
messages
Retrieve the messages from DynamoDB
add_ai_message(message: str) → None¶
Convenience method for adding an AI message string to the store.
Parameters
message – The string contents of an AI message.
add_message(message: BaseMessage) → None[source]¶
Append the message to the record in DynamoDB
add_user_message(message: str) → None¶
Convenience method for adding a human message string to the store.
Parameters
message – The string contents of a human message.
clear() → None[source]¶
Clear session memory from DynamoDB | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.dynamodb.DynamoDBChatMessageHistory.html |
4c86ebc00ede-1 | clear() → None[source]¶
Clear session memory from DynamoDB
property messages: List[langchain.schema.messages.BaseMessage]¶
Retrieve the messages from DynamoDB
Examples using DynamoDBChatMessageHistory¶
Dynamodb Chat Message History | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.dynamodb.DynamoDBChatMessageHistory.html |
2e6e3a04008e-0 | langchain.memory.combined.CombinedMemory¶
class langchain.memory.combined.CombinedMemory(*, memories: List[BaseMemory])[source]¶
Bases: BaseMemory
Combining multiple memories’ data together.
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 valid model.
param memories: List[langchain.schema.memory.BaseMemory] [Required]¶
For tracking all the memories that should be accessed.
validator check_input_key » memories[source]¶
Check that if memories are of type BaseChatMemory that input keys exist.
validator check_repeated_memory_variable » memories[source]¶
clear() → None[source]¶
Clear context from this session for every memory.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]¶
Load all vars from sub-memories.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this session for every memory.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
property memory_variables: List[str]¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.combined.CombinedMemory.html |
2e6e3a04008e-1 | Return whether or not the class is serializable.
property memory_variables: List[str]¶
All the memory variables that this instance provides.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
Examples using CombinedMemory¶
How to use multiple memory classes in the same chain | https://api.python.langchain.com/en/latest/memory/langchain.memory.combined.CombinedMemory.html |
cdea7f5202eb-0 | langchain.memory.summary.ConversationSummaryMemory¶
class langchain.memory.summary.ConversationSummaryMemory(*, human_prefix: str = 'Human', ai_prefix: str = 'AI', llm: ~langchain.schema.language_model.BaseLanguageModel, prompt: ~langchain.schema.prompt_template.BasePromptTemplate = PromptTemplate(input_variables=['summary', 'new_lines'], output_parser=None, partial_variables={}, template='Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.\n\nEXAMPLE\nCurrent summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.\n\nNew lines of conversation:\nHuman: Why do you think artificial intelligence is a force for good?\nAI: Because artificial intelligence will help humans reach their full potential.\n\nNew summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.\nEND OF EXAMPLE\n\nCurrent summary:\n{summary}\n\nNew lines of conversation:\n{new_lines}\n\nNew summary:', template_format='f-string', validate_template=True), summary_message_cls: ~typing.Type[~langchain.schema.messages.BaseMessage] = <class 'langchain.schema.messages.SystemMessage'>, chat_memory: ~langchain.schema.memory.BaseChatMessageHistory = None, output_key: ~typing.Optional[str] = None, input_key: ~typing.Optional[str] = None, return_messages: bool = False, buffer: str = '', memory_key: str = 'history')[source]¶
Bases: BaseChatMemory, SummarizerMixin
Conversation summarizer to chat memory.
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 valid model.
param ai_prefix: str = 'AI'¶
param buffer: str = ''¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.summary.ConversationSummaryMemory.html |
cdea7f5202eb-1 | param ai_prefix: str = 'AI'¶
param buffer: str = ''¶
param chat_memory: BaseChatMessageHistory [Optional]¶
param human_prefix: str = 'Human'¶
param input_key: Optional[str] = None¶
param llm: BaseLanguageModel [Required]¶
param output_key: Optional[str] = None¶
param prompt: BasePromptTemplate = PromptTemplate(input_variables=['summary', 'new_lines'], output_parser=None, partial_variables={}, template='Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.\n\nEXAMPLE\nCurrent summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.\n\nNew lines of conversation:\nHuman: Why do you think artificial intelligence is a force for good?\nAI: Because artificial intelligence will help humans reach their full potential.\n\nNew summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.\nEND OF EXAMPLE\n\nCurrent summary:\n{summary}\n\nNew lines of conversation:\n{new_lines}\n\nNew summary:', template_format='f-string', validate_template=True)¶
param return_messages: bool = False¶
param summary_message_cls: Type[BaseMessage] = <class 'langchain.schema.messages.SystemMessage'>¶
clear() → None[source]¶
Clear memory contents.
classmethod from_messages(llm: BaseLanguageModel, chat_memory: BaseChatMessageHistory, *, summarize_step: int = 2, **kwargs: Any) → ConversationSummaryMemory[source]¶
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]¶
Return history buffer.
predict_new_summary(messages: List[BaseMessage], existing_summary: str) → str¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.summary.ConversationSummaryMemory.html |
cdea7f5202eb-2 | predict_new_summary(messages: List[BaseMessage], existing_summary: str) → str¶
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this conversation to buffer.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_prompt_input_variables » all fields[source]¶
Validate that prompt input variables are consistent.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
Examples using ConversationSummaryMemory¶
How to use multiple memory classes in the same chain | https://api.python.langchain.com/en/latest/memory/langchain.memory.summary.ConversationSummaryMemory.html |
68d2b882d26f-0 | langchain.memory.buffer.ConversationStringBufferMemory¶
class langchain.memory.buffer.ConversationStringBufferMemory(*, human_prefix: str = 'Human', ai_prefix: str = 'AI', buffer: str = '', output_key: Optional[str] = None, input_key: Optional[str] = None, memory_key: str = 'history')[source]¶
Bases: BaseMemory
Buffer for storing conversation memory.
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 valid model.
param ai_prefix: str = 'AI'¶
Prefix to use for AI generated responses.
param buffer: str = ''¶
param human_prefix: str = 'Human'¶
param input_key: Optional[str] = None¶
param output_key: Optional[str] = None¶
clear() → None[source]¶
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]¶
Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this conversation to buffer.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_chains » all fields[source]¶
Validate that return messages is not True.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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. | https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationStringBufferMemory.html |
68d2b882d26f-1 | 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.
property memory_variables: List[str]¶
Will always return list of memory variables.
:meta private:
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationStringBufferMemory.html |
07f2a0a1dbc4-0 | langchain.memory.chat_message_histories.sql.SQLChatMessageHistory¶
class langchain.memory.chat_message_histories.sql.SQLChatMessageHistory(session_id: str, connection_string: str, table_name: str = 'message_store')[source]¶
Bases: BaseChatMessageHistory
Chat message history stored in an SQL database.
Methods
__init__(session_id, connection_string[, ...])
add_ai_message(message)
Convenience method for adding an AI message string to the store.
add_message(message)
Append the message to the record in db
add_user_message(message)
Convenience method for adding a human message string to the store.
clear()
Clear session memory from db
Attributes
messages
Retrieve all messages from db
add_ai_message(message: str) → None¶
Convenience method for adding an AI message string to the store.
Parameters
message – The string contents of an AI message.
add_message(message: BaseMessage) → None[source]¶
Append the message to the record in db
add_user_message(message: str) → None¶
Convenience method for adding a human message string to the store.
Parameters
message – The string contents of a human message.
clear() → None[source]¶
Clear session memory from db
property messages: List[langchain.schema.messages.BaseMessage]¶
Retrieve all messages from db | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.sql.SQLChatMessageHistory.html |
b2ed068958ab-0 | langchain.memory.summary_buffer.ConversationSummaryBufferMemory¶
class langchain.memory.summary_buffer.ConversationSummaryBufferMemory(*, human_prefix: str = 'Human', ai_prefix: str = 'AI', llm: ~langchain.schema.language_model.BaseLanguageModel, prompt: ~langchain.schema.prompt_template.BasePromptTemplate = PromptTemplate(input_variables=['summary', 'new_lines'], output_parser=None, partial_variables={}, template='Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.\n\nEXAMPLE\nCurrent summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.\n\nNew lines of conversation:\nHuman: Why do you think artificial intelligence is a force for good?\nAI: Because artificial intelligence will help humans reach their full potential.\n\nNew summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.\nEND OF EXAMPLE\n\nCurrent summary:\n{summary}\n\nNew lines of conversation:\n{new_lines}\n\nNew summary:', template_format='f-string', validate_template=True), summary_message_cls: ~typing.Type[~langchain.schema.messages.BaseMessage] = <class 'langchain.schema.messages.SystemMessage'>, chat_memory: ~langchain.schema.memory.BaseChatMessageHistory = None, output_key: ~typing.Optional[str] = None, input_key: ~typing.Optional[str] = None, return_messages: bool = False, max_token_limit: int = 2000, moving_summary_buffer: str = '', memory_key: str = 'history')[source]¶
Bases: BaseChatMemory, SummarizerMixin
Buffer with summarizer for storing conversation memory.
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 valid model. | https://api.python.langchain.com/en/latest/memory/langchain.memory.summary_buffer.ConversationSummaryBufferMemory.html |
b2ed068958ab-1 | Raises ValidationError if the input data cannot be parsed to form a valid model.
param ai_prefix: str = 'AI'¶
param chat_memory: BaseChatMessageHistory [Optional]¶
param human_prefix: str = 'Human'¶
param input_key: Optional[str] = None¶
param llm: BaseLanguageModel [Required]¶
param max_token_limit: int = 2000¶
param memory_key: str = 'history'¶
param moving_summary_buffer: str = ''¶
param output_key: Optional[str] = None¶
param prompt: BasePromptTemplate = PromptTemplate(input_variables=['summary', 'new_lines'], output_parser=None, partial_variables={}, template='Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.\n\nEXAMPLE\nCurrent summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.\n\nNew lines of conversation:\nHuman: Why do you think artificial intelligence is a force for good?\nAI: Because artificial intelligence will help humans reach their full potential.\n\nNew summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.\nEND OF EXAMPLE\n\nCurrent summary:\n{summary}\n\nNew lines of conversation:\n{new_lines}\n\nNew summary:', template_format='f-string', validate_template=True)¶
param return_messages: bool = False¶
param summary_message_cls: Type[BaseMessage] = <class 'langchain.schema.messages.SystemMessage'>¶
clear() → None[source]¶
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]¶
Return history buffer.
predict_new_summary(messages: List[BaseMessage], existing_summary: str) → str¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.summary_buffer.ConversationSummaryBufferMemory.html |
b2ed068958ab-2 | predict_new_summary(messages: List[BaseMessage], existing_summary: str) → str¶
prune() → None[source]¶
Prune buffer if it exceeds max token limit
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this conversation to buffer.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_prompt_input_variables » all fields[source]¶
Validate that prompt input variables are consistent.
property buffer: List[langchain.schema.messages.BaseMessage]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
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 the class is serializable.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
Examples using ConversationSummaryBufferMemory¶
ConversationSummaryBufferMemory | https://api.python.langchain.com/en/latest/memory/langchain.memory.summary_buffer.ConversationSummaryBufferMemory.html |
d6eb459ed174-0 | langchain.memory.entity.RedisEntityStore¶
class langchain.memory.entity.RedisEntityStore(session_id: str = 'default', url: str = 'redis://localhost:6379/0', key_prefix: str = 'memory_store', ttl: Optional[int] = 86400, recall_ttl: Optional[int] = 259200, *args: Any, redis_client: Any = None)[source]¶
Bases: BaseEntityStore
Redis-backed Entity store.
Entities get a TTL of 1 day by default, and
that TTL is extended by 3 days every time the entity is read back.
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 valid model.
param key_prefix: str = 'memory_store'¶
param recall_ttl: Optional[int] = 259200¶
param redis_client: Any = None¶
param session_id: str = 'default'¶
param ttl: Optional[int] = 86400¶
clear() → None[source]¶
Delete all entities from store.
delete(key: str) → None[source]¶
Delete entity value from store.
exists(key: str) → bool[source]¶
Check if entity exists in store.
get(key: str, default: Optional[str] = None) → Optional[str][source]¶
Get entity value from store.
set(key: str, value: Optional[str]) → None[source]¶
Set entity value in store.
property full_key_prefix: str¶ | https://api.python.langchain.com/en/latest/memory/langchain.memory.entity.RedisEntityStore.html |
e4cff99703b3-0 | langchain.memory.chat_message_histories.momento.MomentoChatMessageHistory¶
class langchain.memory.chat_message_histories.momento.MomentoChatMessageHistory(session_id: str, cache_client: momento.CacheClient, cache_name: str, *, key_prefix: str = 'message_store:', ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True)[source]¶
Bases: BaseChatMessageHistory
Chat message history cache that uses Momento as a backend.
See https://gomomento.com/
Instantiate a chat message history cache that uses Momento as a backend.
Note: to instantiate the cache client passed to MomentoChatMessageHistory,
you must have a Momento account at https://gomomento.com/.
Parameters
session_id (str) – The session ID to use for this chat session.
cache_client (CacheClient) – The Momento cache client.
cache_name (str) – The name of the cache to use to store the messages.
key_prefix (str, optional) – The prefix to apply to the cache key.
Defaults to “message_store:”.
ttl (Optional[timedelta], optional) – The TTL to use for the messages.
Defaults to None, ie the default TTL of the cache will be used.
ensure_cache_exists (bool, optional) – Create the cache if it doesn’t exist.
Defaults to True.
Raises
ImportError – Momento python package is not installed.
TypeError – cache_client is not of type momento.CacheClientObject
Methods
__init__(session_id, cache_client, cache_name, *)
Instantiate a chat message history cache that uses Momento as a backend.
add_ai_message(message)
Convenience method for adding an AI message string to the store.
add_message(message)
Store a message in the cache.
add_user_message(message) | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.momento.MomentoChatMessageHistory.html |
e4cff99703b3-1 | add_message(message)
Store a message in the cache.
add_user_message(message)
Convenience method for adding a human message string to the store.
clear()
Remove the session's messages from the cache.
from_client_params(session_id, cache_name, ...)
Construct cache from CacheClient parameters.
Attributes
messages
Retrieve the messages from Momento.
add_ai_message(message: str) → None¶
Convenience method for adding an AI message string to the store.
Parameters
message – The string contents of an AI message.
add_message(message: BaseMessage) → None[source]¶
Store a message in the cache.
Parameters
message (BaseMessage) – The message object to store.
Raises
SdkException – Momento service or network error.
Exception – Unexpected response.
add_user_message(message: str) → None¶
Convenience method for adding a human message string to the store.
Parameters
message – The string contents of a human message.
clear() → None[source]¶
Remove the session’s messages from the cache.
Raises
SdkException – Momento service or network error.
Exception – Unexpected response.
classmethod from_client_params(session_id: str, cache_name: str, ttl: timedelta, *, configuration: Optional[momento.config.Configuration] = None, auth_token: Optional[str] = None, **kwargs: Any) → MomentoChatMessageHistory[source]¶
Construct cache from CacheClient parameters.
property messages: list[langchain.schema.messages.BaseMessage]¶
Retrieve the messages from Momento.
Raises
SdkException – Momento service or network error
Exception – Unexpected response
Returns
List of cached messages
Return type
list[BaseMessage]
Examples using MomentoChatMessageHistory¶
Momento Chat Message History | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.momento.MomentoChatMessageHistory.html |
8190f5f8535f-0 | langchain.memory.chat_message_histories.mongodb.MongoDBChatMessageHistory¶
class langchain.memory.chat_message_histories.mongodb.MongoDBChatMessageHistory(connection_string: str, session_id: str, database_name: str = 'chat_history', collection_name: str = 'message_store')[source]¶
Bases: BaseChatMessageHistory
Chat message history that stores history in MongoDB.
Parameters
connection_string – connection string to connect to MongoDB
session_id – arbitrary key that is used to store the messages
of a single chat session.
database_name – name of the database to use
collection_name – name of the collection to use
Methods
__init__(connection_string, session_id[, ...])
add_ai_message(message)
Convenience method for adding an AI message string to the store.
add_message(message)
Append the message to the record in MongoDB
add_user_message(message)
Convenience method for adding a human message string to the store.
clear()
Clear session memory from MongoDB
Attributes
messages
Retrieve the messages from MongoDB
add_ai_message(message: str) → None¶
Convenience method for adding an AI message string to the store.
Parameters
message – The string contents of an AI message.
add_message(message: BaseMessage) → None[source]¶
Append the message to the record in MongoDB
add_user_message(message: str) → None¶
Convenience method for adding a human message string to the store.
Parameters
message – The string contents of a human message.
clear() → None[source]¶
Clear session memory from MongoDB
property messages: List[langchain.schema.messages.BaseMessage]¶
Retrieve the messages from MongoDB
Examples using MongoDBChatMessageHistory¶
Mongodb Chat Message History | https://api.python.langchain.com/en/latest/memory/langchain.memory.chat_message_histories.mongodb.MongoDBChatMessageHistory.html |
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