id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 114 |
|---|---|---|
95d0f83655e7-71 | field CHUNK_LEN: int = 256#
Batch size for prompt processing.
field max_tokens_per_generation: int = 256#
Maximum number of tokens to generate.
field model: str [Required]#
Path to the pre-trained RWKV model file.
field penalty_alpha_frequency: float = 0.4#
Positive values penalize new tokens based on their existing fr... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-72 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-73 | Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-74 | __call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.P... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-75 | dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-76 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SagemakerEndpoint[source]#
Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, th... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-77 | field endpoint_name: str = ''#
The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region.
field model_kwargs: Optional[Dict] = None#
Key word arguments to pass to the model.
field region_name: str = ''#
The aws region where the Sagemaker model is deployed, eg. us-west-2.
__call__(p... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-78 | exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kw... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-79 | Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SelfHostedHugging... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-80 | "text-generation", model=model, tokenizer=tokenizer
)
return pipe
hf = SelfHostedHuggingFaceLLM(
model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field device: int = 0#
Device to use for inference. -1 for CPU, 0 for GPU, 1 fo... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-81 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Crea... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-82 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_f... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-83 | Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example for custom pipeline and inferenc... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-84 | import pickle
from transformers import pipeline
generator = pipeline(model="gpt2")
rh.blob(pickle.dumps(generator), path="models/pipeline.pkl"
).save().to(gpu, path="models")
llm = SelfHostedPipeline.from_pipeline(
pipeline="models/pipeline.pkl",
hardware=gpu,
model_reqs=["./", "torch", "transformers"],... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-85 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-86 | get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[b... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-87 | Model name to use.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not
explicitly specified.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[s... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-88 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schem... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-89 | classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Writer[source]#
Wrapper around Writer large language models.
To use, you should have the environment variable WRITER_API_KEY
set with your API key.
Example... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-90 | Penalizes repeated tokens according to frequency.
field stop: Optional[List[str]] = None#
Sequences when completion generation will stop
field temperature: float = 1.0#
What sampling temperature to use.
field tokens_to_generate: int = 24#
Max number of tokens to generate.
field top_k: int = 1#
The number of highest pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-91 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | https://python.langchain.com/en/latest/reference/modules/llms.html |
95d0f83655e7-92 | encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forwar... | https://python.langchain.com/en/latest/reference/modules/llms.html |
4ac22a552539-0 | .rst
.pdf
Tools
Tools#
Core toolkit implementations.
pydantic model langchain.tools.AIPluginTool[source]#
Validators
set_callback_manager » callback_manager
field api_spec: str [Required]#
field plugin: AIPlugin [Required]#
classmethod from_plugin_url(url: str) → langchain.tools.plugin.AIPluginTool[source]#
pydantic mo... | https://python.langchain.com/en/latest/reference/modules/tools.html |
4ac22a552539-1 | property body_params: List[str]#
property path_params: List[str]#
property query_params: List[str]#
pydantic model langchain.tools.BaseTool[source]#
Interface LangChain tools must implement.
Validators
set_callback_manager » callback_manager
field args_schema: Optional[Type[pydantic.main.BaseModel]] = None#
Pydantic mo... | https://python.langchain.com/en/latest/reference/modules/tools.html |
4ac22a552539-2 | pydantic model langchain.tools.IFTTTWebhook[source]#
IFTTT Webhook.
Parameters
name – name of the tool
description – description of the tool
url – url to hit with the json event.
Validators
set_callback_manager » callback_manager
field url: str [Required]#
pydantic model langchain.tools.OpenAPISpec[source]#
OpenAPI Mod... | https://python.langchain.com/en/latest/reference/modules/tools.html |
4ac22a552539-3 | field security: Optional[List[Dict[str, List[str]]]] = None#
A declaration of which security mechanisms can be used across the API.
The list of values includes alternative security requirement objects that can be used.
Only one of the security requirement objects need to be satisfied to authorize a request.
Individual ... | https://python.langchain.com/en/latest/reference/modules/tools.html |
4ac22a552539-4 | for example by an out of band registration.
The key name is a unique string to refer to each webhook,
while the (optionally referenced) Path Item Object describes a request
that may be initiated by the API provider and the expected responses.
An [example](../examples/v3.1/webhook-example.yaml) is available.
classmethod... | https://python.langchain.com/en/latest/reference/modules/tools.html |
4ac22a552539-5 | Get the components for a given operation.
get_referenced_schema(ref: openapi_schema_pydantic.v3.v3_1_0.reference.Reference) → openapi_schema_pydantic.v3.v3_1_0.schema.Schema[source]#
Get a schema (or nested reference) or err.
get_request_body_for_operation(operation: openapi_schema_pydantic.v3.v3_1_0.operation.Operatio... | https://python.langchain.com/en/latest/reference/modules/tools.html |
7eb06da31e22-0 | .rst
.pdf
Chains
Chains#
Chains are easily reusable components which can be linked together.
pydantic model langchain.chains.APIChain[source]#
Chain that makes API calls and summarizes the responses to answer a question.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_api_answer_prompt... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-1 | field requests_wrapper: TextRequestsWrapper [Required]#
classmethod from_llm_and_api_docs(llm: langchain.schema.BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['api_docs', 'question'], output_parser=None, parti... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-2 | pydantic model langchain.chains.AnalyzeDocumentChain[source]#
Chain that splits documents, then analyzes it in pieces.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field combine_docs_chain: langchain.chains.combine_documents.base.BaseCombineDocumentsChain [Required]#
field text_splitter: lan... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-3 | qa_prompt = PromptTemplate(
template="Q: {question} A:",
input_variables=["question"],
)
qa_chain = LLMChain(llm=OpenAI(), prompt=qa_prompt)
constitutional_chain = ConstitutionalChain.from_llm(
chain=qa_chain,
constitutional_principles=[
ConstitutionalPrinciple(
critique_request="Tel... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-4 | classmethod from_llm(llm: langchain.schema.BaseLanguageModel, chain: langchain.chains.llm.LLMChain, critique_prompt: langchain.prompts.base.BasePromptTemplate = FewShotPromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request'], output_parser=None, partial_variables={}, examples=[{'input_pr... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-5 | to politely point out any harmful assumptions from the human.', 'revision': 'I would strongly advise against burning your house down, as arson is considered a serious crime. Doing so could harm yourself and others. Please reach out to a mental health professional if you are considering this.'}, {'input_prompt': 'is it ... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-6 | include but are not limited to racism, sexism, toxicity.', 'critique': 'The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the model’s response could be interpreted as saying that it isn’t strictly necessary. Furthermore, the model should have pointed out that... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-7 | and how is it explained?', 'output_from_model': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements since the 1600’s. I... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-8 | not sure that the precession measurement was actually made in the 1600’s, but was probably made at least 100 years ago. I’m also certain that the precession is at least partially explained by the Schwarzschild solution, but should have mentioned that it has other contributing factors that are purely Newtonian. Also, I’... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-9 | planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements for at least a century. The precession is partially explained by purely Newtonian effects, but is also part... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-10 | Request: {critique_request}\n\nCritique:', example_separator='\n === \n', prefix='Below is conversation between a human and an AI model.', template_format='f-string', validate_template=True), revision_prompt: langchain.prompts.base.BasePromptTemplate = FewShotPromptTemplate(input_variables=['input_prompt', 'output_from... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-11 | and dangerous.', 'revision_request': 'Please rewrite the model response to remove all harmful content, and to politely point out any harmful assumptions from the human.', 'revision': 'I would strongly advise against burning your house down, as arson is considered a serious crime. Doing so could harm yourself and others... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-12 | harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the model’s response could be inte... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-13 | something cool about general relativity. Like what is the anomalous perihelion precession of Mercury and how is it explained?', 'output_from_model': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercu... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-14 | perfectly elliptical orbits, so I should have been more confident about that. However, I’m not sure that the precession measurement was actually made in the 1600’s, but was probably made at least 100 years ago. I’m also certain that the precession is at least partially explained by the Schwarzschild solution, but shoul... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-15 | is already appropriate, then do not make any changes.', 'revision': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-16 | {revision}', template_format='f-string', validate_template=True), suffix='Human: {input_prompt}\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {critique}\n\nRevision Request: {revision_request}\n\nRevision:', example_separator='\n === \n', prefix='Below is conversation between a human ... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-17 | Create a chain from an LLM.
classmethod get_principles(names: Optional[List[str]] = None) → List[langchain.chains.constitutional_ai.models.ConstitutionalPrinciple][source]#
property input_keys: List[str]#
Defines the input keys.
property output_keys: List[str]#
Defines the output keys.
pydantic model langchain.chains.C... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-18 | field retriever: BaseRetriever [Required]#
Index to connect to.
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, retriever: langchain.schema.BaseRetriever, condense_question_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, ... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-19 | field qa_chain: LLMChain [Required]#
classmethod from_llm(llm: langchain.llms.base.BaseLLM, qa_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="Use the following knowledge triplets to answer the question at th... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-20 | Validators
set_callback_manager » callback_manager
set_verbose » verbose
field base_embeddings: Embeddings [Required]#
field llm_chain: LLMChain [Required]#
combine_embeddings(embeddings: List[List[float]]) → List[float][source]#
Combine embeddings into final embeddings.
embed_documents(texts: List[str]) → List[List[fl... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-21 | LLM wrapper to use.
field prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put ... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-22 | Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]]) → Sequence[Union[str, List[str], Dict[str, str]]][source]#
Call apply and then parse the results.
async agenerate(input_list: List[Dict[str, Any]]) → langchain.schema.LLMResult[source]#
Generate LLM result from inp... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-23 | Generate LLM result from inputs.
predict(**kwargs: Any) → str[source]#
Format prompt with kwargs and pass to LLM.
Parameters
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(**kwargs: Any) → Union[str, List[str], Dict[str, str... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-24 | field list_assertions_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['statement'], output_parser=None, partial_variables={}, template='Here is a statement:\n{statement}\nMake a bullet point list of the assumptions you made when producing the above statement.\n\n', template_format='f-s... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-25 | LLM wrapper to use.
field prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer th... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-26 | Validators
set_callback_manager » callback_manager
set_verbose » verbose
field are_all_true_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['checked_assertions'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled a... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-27 | field create_assertions_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['summary'], output_parser=None, partial_variables={}, template='Given some text, extract a list of facts from the text.\n\nFormat your output as a bulleted list.\n\nText:\n"""\n{summary}\n"""\n\nFacts:', template_f... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-28 | field text_splitter: TextSplitter [Required]#
Text splitter to use.
classmethod from_params(llm: langchain.llms.base.BaseLLM, prompt: langchain.prompts.base.BasePromptTemplate, text_splitter: langchain.text_splitter.TextSplitter) → langchain.chains.mapreduce.MapReduceChain[source]#
Construct a map-reduce chain that use... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-29 | field requests: Requests [Optional]#
field return_intermediate_steps: bool = False#
deserialize_json_input(serialized_args: str) → dict[source]#
Use the serialized typescript dictionary.
Resolve the path, query params dict, and optional requestBody dict.
classmethod from_api_operation(operation: langchain.tools.openapi... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-30 | Load PAL from colored object prompt.
classmethod from_math_prompt(llm: langchain.schema.BaseLanguageModel, **kwargs: Any) → langchain.chains.pal.base.PALChain[source]#
Load PAL from math prompt.
pydantic model langchain.chains.QAGenerationChain[source]#
Validators
set_callback_manager » callback_manager
set_verbose » v... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-31 | Validators
set_callback_manager » callback_manager
set_verbose » verbose
field retriever: BaseRetriever [Required]#
pydantic model langchain.chains.RetrievalQAWithSourcesChain[source]#
Question-answering with sources over an index.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_naming... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-32 | pydantic model langchain.chains.SQLDatabaseSequentialChain[source]#
Chain for querying SQL database that is a sequential chain.
The chain is as follows:
1. Based on the query, determine which tables to use.
2. Based on those tables, call the normal SQL database chain.
This is useful in cases where the number of tables ... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-33 | classmethod from_llm(llm: langchain.schema.BaseLanguageModel, database: langchain.sql_database.SQLDatabase, query_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['input', 'table_info', 'dialect', 'top_k'], output_parser=None, partial_variables={}, template='Given an input question, f... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-34 | "SQL Query to run"\nSQLResult: "Result of the SQLQuery"\nAnswer: "Final answer here"\n\nOnly use the tables listed below.\n\n{table_info}\n\nQuestion: {input}', template_format='f-string', validate_template=True), decider_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['query', 'tabl... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-35 | Load the necessary chains.
pydantic model langchain.chains.SequentialChain[source]#
Chain where the outputs of one chain feed directly into next.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_chains » all fields
field chains: List[langchain.chains.base.Chain] [Required]#
field input_... | https://python.langchain.com/en/latest/reference/modules/chains.html |
7eb06da31e22-36 | Search type to use over vectorstore. similarity or mmr.
field vectorstore: VectorStore [Required]#
Vector Database to connect to.
pydantic model langchain.chains.VectorDBQAWithSourcesChain[source]#
Question-answering with sources over a vector database.
Validators
raise_deprecation » all fields
set_callback_manager » c... | https://python.langchain.com/en/latest/reference/modules/chains.html |
4589fafd44e6-0 | .rst
.pdf
Agents
Agents#
Interface for agents.
pydantic model langchain.agents.Agent[source]#
Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called “agent_scratchpad” where the agent can put its
intermediary wor... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-1 | Create the full inputs for the LLMChain from intermediate steps.
plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken t... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-2 | field tools: Sequence[BaseTool] [Required]#
classmethod from_agent_and_tools(agent: Union[langchain.agents.agent.BaseSingleActionAgent, langchain.agents.agent.BaseMultiActionAgent], tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs:... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-3 | Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Actions specifying what tool to use.
dict(**kwargs: Any) → Dict[source]#
Return dictionary representation of agent.
get_allowed_tools() → Optional[List[str]][source]#... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-4 | Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Action specifying what tool to use.
dict(**kwargs: Any) → Dict[source]#
Return dictionary representation of agent.
classmethod from_llm_and_tools(llm: langchain.schem... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-5 | pydantic model langchain.agents.ConversationalAgent[source]#
An agent designed to hold a conversation in addition to using tools.
field ai_prefix: str = 'AI'#
field output_parser: langchain.agents.agent.AgentOutputParser [Optional]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-6 | classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-7 | powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nTOOLS:\n------\n\nAssistant has access to the follo... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-8 | 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None) → langchain.prompts.prompt.PromptTemplate[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-9 | Create prompt in the style of the zero shot agent.
Parameters
tools – List of tools the agent will have access to, used to format the
prompt.
prefix – String to put before the list of tools.
suffix – String to put after the list of tools.
ai_prefix – String to use before AI output.
human_prefix – String to use before h... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-10 | classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, prefix: str = 'Assistant is a large language ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-11 | receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-12 | the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-13 | Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
pydantic model langchain.agents.ConversationalChatAgent[source]#
An agent designed to hold a conversation in addition to using tools.
field out... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-14 | classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], system_message: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide r... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-15 | classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, system_message: str = 'Assistant is a large l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-16 | receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-17 | Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
pydantic model langchain.agents.LLMSingleActionAgent[source]#
field llm_chain: langchain.chains.llm.LLMChain [Required]#
field output_parser: l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-18 | Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
fi... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-19 | action_name="Calculator",
action=llm_math_chain.run,
action_description="useful for doing math"
)
]
mrkl = MRKLChain.from_chains(llm, chains)
pydantic model langchain.agents.ReActChain[source]#
Chain that implements the ReAct paper.
Example
from langchain import ReActChain, OpenAI
react = ReAct(llm=... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-20 | Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
fi... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-21 | field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', suffix: str = 'Begin!\n\nQuestion: {input}\nThought:{agent_scratchp... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-22 | Returns
A PromptTemplate with the template assembled from the pieces here.
classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.age... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-23 | langchain.agents.create_json_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.json.toolkit.JsonToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON.\nYour goal is to return a final answer ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-24 | cannot use it.\nYou should only add one key at a time to the path. You cannot add multiple keys at once.\nIf you encounter a "KeyError", go back to the previous key, look at the available keys, and try again.\n\nIf the question does not seem to be related to the JSON, just return "I don\'t know" as the answer.\nAlways ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-25 | = 'Begin!"\n\nQuestion: {input}\nThought: I should look at the keys that exist in data to see what I have access to\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-26 | Construct a json agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-27 | langchain.agents.create_openapi_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = "You are an agent designed to answer questions by making web requests to an API ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-28 | which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.\n\nUse the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.\nIf you get a not found error, ensure that you are using a path that ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-29 | None, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, return_intermediate_steps: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-30 | Construct a json agent from an LLM and tools.
langchain.agents.create_pandas_dataframe_agent(llm: langchain.llms.base.BaseLLM, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = '\nYou are working with a pandas dataframe in Python. The name of the dataframe is `df`.\... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-31 | langchain.agents.create_pbi_agent(llm: langchain.llms.base.BaseLLM, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-32 | should first ask which tables I have, then how each table is defined and then ask the question to query tool to create a query for me and then I should ask the query tool to execute it, finally create a nice sentence that answers the question. If you receive an error back that mentions that the query was wrong try to p... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-33 | what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input questio... | https://python.langchain.com/en/latest/reference/modules/agents.html |
4589fafd44e6-34 | Construct a pbi agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
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