id stringlengths 14 15 | text stringlengths 101 5.26k | source stringlengths 57 120 |
|---|---|---|
03890c24ea5c-50 | Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
get_token_ids(text: str) → List[int]#
Get the token present in the text.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, ... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-51 | 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://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-52 | yield token
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PromptLayerOpenAIChat[source]#
Wrapper around OpenAI large language models.
To use, you should have the openai and promptlayer python
package i... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-53 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-54 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a li... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-55 | The model param is required, but any other model parameters can also
be passed in with the format input={model_param: value, …}
Example
from langchain.llms import Replicate
replicate = Replicate(model="stability-ai/stable-diffusion: 27b93a2413e7f36cd83da926f365628 ... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-56 | Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
predict(text: str, *, stop: Optional[Sequence[str]] = None) → str#
Predict text from text.
predict_messages(messages: List[... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-57 | 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://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-58 | def get_pipeline():
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer
)
return pipe
hf = SelfHostedHuggingFaceLLM(
model_load_fn=get_pipelin... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-59 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-60 | field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _generate_text>#
Inference function to send to the remote hardware.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_load_fn: Callable [R... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-61 | Get the token present in the text.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: ... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-62 | dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt an... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-63 | Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) → str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage#
Predict message from m... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-64 | Generates this many completions server-side and returns the “best”.
field logprobs: bool = False#
Whether to return log probabilities.
field max_tokens: Optional[int] = None#
Maximum number of tokens to generate.
field min_tokens: Optional[int] = None#
Minimum number of tokens to generate.
field model_id: str = 'palmyr... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
03890c24ea5c-65 | Get the token present in the text.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: ... | https://langchain.readthedocs.io/en/latest/reference/modules/llms.html |
dbc1327bb7ca-0 | .rst
.pdf
Document Loaders
Document Loaders#
All different types of document loaders.
class langchain.document_loaders.AZLyricsLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]#
Loader that loads AZLyrics webpages.
load() → List[langchain.schema.Document][source]#
Load webpage.
cla... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-1 | Load data into document objects.
class langchain.document_loaders.BiliBiliLoader(video_urls: List[str])[source]#
Loader that loads bilibili transcripts.
load() → List[langchain.schema.Document][source]#
Load from bilibili url.
class langchain.document_loaders.BlackboardLoader(blackboard_course_url: str, bbrouter: str, ... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-2 | Loader that loads conversations from exported ChatGPT data.
load() → List[langchain.schema.Document][source]#
Load data into document objects.
class langchain.document_loaders.CoNLLULoader(file_path: str)[source]#
Load CoNLL-U files.
load() → List[langchain.schema.Document][source]#
Load from file path.
class langchain... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-3 | ValueError – _description_
ImportError – _description_
Returns
_description_
Return type
List[Document]
paginate_request(retrieval_method: Callable, **kwargs: Any) → List[source]#
Paginate the various methods to retrieve groups of pages.
Unfortunately, due to page size, sometimes the Confluence API
doesn’t match the li... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-4 | load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.Docx2txtLoader(file_path: str)[source]#
Loads a DOCX with docx2txt and chunks at character level.
Defaults to check for local file, but if the file is a web path, it will download it
to a temporary file, and use that, the... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-5 | field direction: Optional[Literal['asc', 'desc']] = None#
The direction to sort the results by. Can be one of: ‘asc’, ‘desc’.
field include_prs: bool = True#
If True include Pull Requests in results, otherwise ignore them.
field labels: Optional[List[str]] = None#
Label names to filter one. Example: bug,ui,@high.
field... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-6 | token_path: pathlib.Path = PosixPath('/home/docs/.credentials/token.json')#
classmethod validate_channel_or_videoIds_is_set(values: Dict[str, Any]) → Dict[str, Any][source]#
Validate that either folder_id or document_ids is set, but not both.
class langchain.document_loaders.GoogleApiYoutubeLoader(google_api_client: la... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-7 | load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.IFixitLoader(web_path: str)[source]#
Load iFixit repair guides, device wikis and answers.
iFixit is the largest, open repair community on the web. The site contains nearly
100k repair manuals, 200k Questions & Answers on ... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-8 | load() → List[langchain.schema.Document][source]#
Load from file path.
class langchain.document_loaders.MastodonTootsLoader(mastodon_accounts: Sequence[str], number_toots: Optional[int] = 100, exclude_replies: bool = False, access_token: Optional[str] = None, api_base_url: str = 'https://mastodon.social')[source]#
Mast... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-9 | field file: File [Required]#
load() → List[langchain.schema.Document][source]#
Load Documents
pydantic model langchain.document_loaders.OneDriveLoader[source]#
field auth_with_token: bool = False#
field drive_id: str [Required]#
field folder_path: Optional[str] = None#
field object_ids: Optional[List[str]] = None#
fiel... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-10 | Load given path as pages.
class langchain.document_loaders.PyPDFium2Loader(file_path: str)[source]#
Loads a PDF with pypdfium2 and chunks at character level.
lazy_load() → Iterator[langchain.schema.Document][source]#
Lazy load given path as pages.
load() → List[langchain.schema.Document][source]#
Load given path as pag... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-11 | Loader that fetches a sitemap and loads those URLs.
load() → List[langchain.schema.Document][source]#
Load sitemap.
parse_sitemap(soup: Any) → List[dict][source]#
Parse sitemap xml and load into a list of dicts.
class langchain.document_loaders.SlackDirectoryLoader(zip_path: str, workspace_url: Optional[str] = None)[so... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-12 | include_checklist – Whether to include the checklist on the card in the
document.
card_filter – Filter on card status. Valid values are “closed”, “open”,
“all”.
extra_metadata – List of additional metadata fields to include as document
metadata.Valid values are “due_date”, “labels”, “list”, “closed”.
load() → List[lang... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-13 | Loader that uses unstructured to load open office ODT files.
class langchain.document_loaders.UnstructuredPDFLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]#
Loader that uses unstructured to load PDF files.
class langchain.document_loaders.UnstructuredPowerPointLoader(... | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
dbc1327bb7ca-14 | Load documents.
previous
Text Splitter
next
Vector Stores
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/reference/modules/document_loaders.html |
5118617f3f06-0 | .rst
.pdf
Docstore
Docstore#
Wrappers on top of docstores.
class langchain.docstore.InMemoryDocstore(_dict: Dict[str, langchain.schema.Document])[source]#
Simple in memory docstore in the form of a dict.
add(texts: Dict[str, langchain.schema.Document]) → None[source]#
Add texts to in memory dictionary.
search(search: s... | https://langchain.readthedocs.io/en/latest/reference/modules/docstore.html |
14e754802292-0 | .rst
.pdf
Chat Models
Chat Models#
pydantic model langchain.chat_models.AzureChatOpenAI[source]#
Wrapper around Azure OpenAI Chat Completion API. To use this class you
must have a deployed model on Azure OpenAI. Use deployment_name in the
constructor to refer to the “Model deployment name” in the Azure portal.
In addit... | https://langchain.readthedocs.io/en/latest/reference/modules/chat_models.html |
14e754802292-1 | field openai_api_base: Optional[str] = None#
field openai_api_key: Optional[str] = None#
Base URL path for API requests,
leave blank if not using a proxy or service emulator.
field openai_organization: Optional[str] = None#
field openai_proxy: Optional[str] = None#
field request_timeout: Optional[Union[float, Tuple[flo... | https://langchain.readthedocs.io/en/latest/reference/modules/chat_models.html |
cc5bdc0b3848-0 | .rst
.pdf
Experimental Modules
Contents
Autonomous Agents
Generative Agents
Experimental Modules#
This module contains experimental modules and reproductions of existing work using LangChain primitives.
Autonomous Agents#
Here, we document the BabyAGI and AutoGPT classes from the langchain.experimental module.
class ... | https://langchain.readthedocs.io/en/latest/reference/modules/experimental.html |
cc5bdc0b3848-1 | field name: str [Required]#
The character’s name.
field status: str [Required]#
The traits of the character you wish not to change.
summarize_related_memories(observation: str) → str[source]#
Summarize memories that are most relevant to an observation.
field summary: str = ''#
Stateful self-summary generated via reflec... | https://langchain.readthedocs.io/en/latest/reference/modules/experimental.html |
ef0c727085c1-0 | .rst
.pdf
Embeddings
Embeddings#
Wrappers around embedding modules.
pydantic model langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding[source]#
Wrapper for Aleph Alpha’s Asymmetric Embeddings
AA provides you with an endpoint to embed a document and a query.
The models were optimized to make the embeddings of doc... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
ef0c727085c1-1 | field region_name: Optional[str] = None#
The aws region e.g., us-west-2. Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config in case it is not provided here.
embed_documents(texts: List[str], chunk_size: int = 1) → List[List[float]][source]#
Compute doc embeddings using a Bedrock model.
Pa... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
ef0c727085c1-2 | embed_documents(texts: List[str]) → List[List[float]][source]#
Generate embeddings for a list of documents.
Parameters
texts (List[str]) – A list of document text strings to generate embeddings
for.
Returns
A list of embeddings, one for each document in the inputlist.
Return type
List[List[float]]
embed_query(text: str... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
ef0c727085c1-3 | model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
field cache_folder: Optional[str] = None#
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.
field encode_kwargs: Dict[str, Any] [Optional]#
Key word arguments to pass when calling the encode m... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
ef0c727085c1-4 | path to the Llama model as a named parameter to the constructor.
Check out: abetlen/llama-cpp-python
Example
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
field f16_kv: bool = False#
Use half-precision for key/value cache.
field logits_all: bool = False#... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
ef0c727085c1-5 | environment variable MOSAICML_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain.llms import MosaicMLInstructorEmbeddings
endpoint_url = (
"https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
)
mosaic_llm = MosaicMLInstructorEmbeddings(
... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
ef0c727085c1-6 | If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
field... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
ef0c727085c1-7 | Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedHuggingFaceEmbeddings[source]#
Runs sentence_tr... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
ef0c727085c1-8 | Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a TensorflowHub embedding model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
previous
Chat Models
next
Indexes
By Harrison Chase
© Copyright 2023, Harrison Cha... | https://langchain.readthedocs.io/en/latest/reference/modules/embeddings.html |
dc364dbee1e2-0 | .rst
.pdf
PromptTemplates
PromptTemplates#
Prompt template classes.
pydantic model langchain.prompts.BaseChatPromptTemplate[source]#
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.... | https://langchain.readthedocs.io/en/latest/reference/modules/prompts.html |
dc364dbee1e2-1 | PromptTemplate used to format an individual example.
field example_selector: Optional[langchain.prompts.example_selector.base.BaseExampleSelector] = None#
ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided.
field example_separator: str = '\n\n'#
String separator... | https://langchain.readthedocs.io/en/latest/reference/modules/prompts.html |
dc364dbee1e2-2 | Unified method for loading a prompt from LangChainHub or local fs.
previous
Prompts
next
Example Selector
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/reference/modules/prompts.html |
96dc59d7a1f7-0 | .rst
.pdf
Text Splitter
Text Splitter#
Functionality for splitting text.
class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class... | https://langchain.readthedocs.io/en/latest/reference/modules/text_splitter.html |
96dc59d7a1f7-1 | Split documents.
abstract split_text(text: str) → List[str][source]#
Split text into multiple components.
transform_documents(documents: Sequence[langchain.schema.Document], **kwargs: Any) → Sequence[langchain.schema.Document][source]#
Transform sequence of documents by splitting them.
class langchain.text_splitter.Tok... | https://langchain.readthedocs.io/en/latest/reference/modules/text_splitter.html |
5ce94617e1eb-0 | .rst
.pdf
Document Compressors
Document Compressors#
pydantic model langchain.retrievers.document_compressors.CohereRerank[source]#
field client: Client [Required]#
field model: str = 'rerank-english-v2.0'#
field top_n: int = 3#
async acompress_documents(documents: Sequence[langchain.schema.Document], query: str) → Seq... | https://langchain.readthedocs.io/en/latest/reference/modules/document_compressors.html |
5ce94617e1eb-1 | Filter down documents based on their relevance to the query.
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, prompt: Optional[langchain.prompts.base.BasePromptTemplate] = None, **kwargs: Any) → langchain.retrievers.document_compressors.chain_filter.LLMChainFilter[source]#
previous
Retrievers
next
D... | https://langchain.readthedocs.io/en/latest/reference/modules/document_compressors.html |
d42eb0c67a58-0 | .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
raise_deprecation » all fields
set_verbose » verbose
validate_api_answer_prompt » all fi... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-1 | llm=llm,
chain=qa_chain,
constitutional_principles=[
ConstitutionalPrinciple(
critique_request="Tell if this answer is good.",
revision_request="Give a better answer.",
)
],
)
constitutional_chain.run(question="What is the meaning of life?")
Validators
raise_deprecati... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-2 | classmethod from_llm(llm: langchain.base_language.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=[{'i... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-3 | but is also partially explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that is smaller and decays more quickly than Newton’s law. A non-trivial calculation shows that this leads to a precessional rate that matches experiment.'}, {'input_pro... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-4 | their consent. It’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.'}, {'input_prompt': 'Tell me something cool about general relativity. Like what is the anomalous perihelion precession of Mercury and how is it exp... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-5 | Request: {revision_request}\n\nRevision:', example_separator='\n === \n', prefix='Below is a conversation between a human and an AI model.', template_format='f-string', validate_template=True), **kwargs: Any) → langchain.chains.constitutional_ai.base.ConstitutionalChain[source]# | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-6 | 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://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-7 | field qa_chain: LLMChain [Required]#
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, *, qa_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="You are an assistant that helps to form nice and... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-8 | Load and use LLMChain for a specific prompt key.
property input_keys: List[str]#
Input keys for Hyde’s LLM chain.
property output_keys: List[str]#
Output keys for Hyde’s LLM chain.
pydantic model langchain.chains.LLMBashChain[source]#
Chain that interprets a prompt and executes bash code to perform bash operations.
Exa... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-9 | Utilize the LLM generate method for speed gains.
apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]][source]#
Call apply and then parse the... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-10 | field question_to_checked_assertions_chain: SequentialChain [Required]#
field revised_answer_prompt: PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'question'], output_parser=None, partial_variables={}, template="{checked_assertions}\n\nQuestion: In light of the above assertions and checks, how ... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-11 | [Deprecated] Prompt to use to translate to python if necessary.
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expres... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-12 | [Deprecated] LLM wrapper to use.
field max_checks: int = 2#
Maximum number of times to check the assertions. Default to double-checking.
field revised_summary_prompt: PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'summary'], output_parser=None, partial_variables={}, template='Below are some ass... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-13 | field text_splitter: TextSplitter [Required]#
Text splitter to use.
classmethod from_params(llm: langchain.base_language.BaseLanguageModel, prompt: langchain.prompts.base.BasePromptTemplate, text_splitter: langchain.text_splitter.TextSplitter, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler]... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-14 | field api_request_chain: LLMChain [Required]#
field api_response_chain: Optional[LLMChain] = None#
field param_mapping: _ParamMapping [Required]#
field requests: Requests [Optional]#
field return_intermediate_steps: bool = False#
deserialize_json_input(serialized_args: str) → dict[source]#
Use the serialized typescript... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-15 | field prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\n\n# solution in Python:\n\n\ndef solution():\n """Olivia has $23. She bought five bagels for $... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-16 | solution in Python:\n\n\ndef solution():\n """If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?"""\n cars_initial = 3\n cars_arrived = 2\n total_cars = cars_initial + cars_arrived\n result = total_cars\n return result\n\n\n\n\n\nQ: There are 15 trees ... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-17 | [Deprecated]
field python_globals: Optional[Dict[str, Any]] = None#
field python_locals: Optional[Dict[str, Any]] = None#
field return_intermediate_steps: bool = False#
field stop: str = '\n\n'#
classmethod from_colored_object_prompt(llm: langchain.base_language.BaseLanguageModel, **kwargs: Any) → langchain.chains.pal.... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-18 | to fix the initial SQL from the LLM.
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, db: langchain.sql_database.SQLDatabase, prompt: Optional[langchain.prompts.base.BasePromptTemplate] = None, **kwargs: Any) → langchain.chains.sql_database.base.SQLDatabaseChain[source]#
pydantic model langchain.cha... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d42eb0c67a58-19 | 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_verbose » verbose
va... | https://langchain.readthedocs.io/en/latest/reference/modules/chains.html |
d3687b6fc1f3-0 | .rst
.pdf
Utilities
Utilities#
General utilities.
pydantic model langchain.utilities.ApifyWrapper[source]#
Wrapper around Apify.
To use, you should have the apify-client python package installed,
and the environment variable APIFY_API_TOKEN set with your API key, or pass
apify_api_token as a named parameter to the cons... | https://langchain.readthedocs.io/en/latest/reference/modules/utilities.html |
d3687b6fc1f3-1 | See https://lukasschwab.me/arxiv.py/index.html#Search
See https://lukasschwab.me/arxiv.py/index.html#Result
It uses only the most informative fields of article meta information.
class langchain.utilities.BashProcess(strip_newlines: bool = False, return_err_output: bool = False, persistent: bool = False)[source]#
Execut... | https://langchain.readthedocs.io/en/latest/reference/modules/utilities.html |
d3687b6fc1f3-2 | - You now have an API_KEY
3. Setup Custom Search Engine so you can search the entire web
- Create a custom search engine in this link.
- In Sites to search, add any valid URL (i.e. www.stackoverflow.com).
- That’s all you have to fill up, the rest doesn’t matter.
In the left-side menu, click Edit search engine → {your ... | https://langchain.readthedocs.io/en/latest/reference/modules/utilities.html |
d3687b6fc1f3-3 | in YYYY-MM-DD format. Otherwise, None.
Return type
A list of dictionaries with the following keys
async results_async(query: str, num_results: int) → List[Dict][source]#
Get results from the Metaphor Search API asynchronously.
pydantic model langchain.utilities.OpenWeatherMapAPIWrapper[source]#
Wrapper for OpenWeatherM... | https://langchain.readthedocs.io/en/latest/reference/modules/utilities.html |
d3687b6fc1f3-4 | Run command with own globals/locals and returns anything printed.
pydantic model langchain.utilities.SearxSearchWrapper[source]#
Wrapper for Searx API.
To use you need to provide the searx host by passing the named parameter
searx_host or exporting the environment variable SEARX_HOST.
In some situations you might want ... | https://langchain.readthedocs.io/en/latest/reference/modules/utilities.html |
d3687b6fc1f3-5 | from langchain import SerpAPIWrapper
serpapi = SerpAPIWrapper()
field aiosession: Optional[aiohttp.client.ClientSession] = None#
field params: dict = {'engine': 'google', 'gl': 'us', 'google_domain': 'google.com', 'hl': 'en'}#
field serpapi_api_key: Optional[str] = None#
async aresults(query: str) → dict[source]#
Use a... | https://langchain.readthedocs.io/en/latest/reference/modules/utilities.html |
d3687b6fc1f3-6 | Sms Client using Twilio.
To use, you should have the twilio python package installed,
and the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, and
TWILIO_FROM_NUMBER, or pass account_sid, auth_token, and from_number as
named parameters to the constructor.
Example
from langchain.utilities.twilio import Twili... | https://langchain.readthedocs.io/en/latest/reference/modules/utilities.html |
1b608297383e-0 | .rst
.pdf
Retrievers
Retrievers#
pydantic model langchain.retrievers.ArxivRetriever[source]#
It is effectively a wrapper for ArxivAPIWrapper.
It wraps load() to get_relevant_documents().
It uses all ArxivAPIWrapper arguments without any change.
async aget_relevant_documents(query: str) → List[langchain.schema.Document]... | https://langchain.readthedocs.io/en/latest/reference/modules/retrievers.html |
1b608297383e-1 | To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the Elasticsearch URL format
https://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearc... | https://langchain.readthedocs.io/en/latest/reference/modules/retrievers.html |
1b608297383e-2 | pydantic model langchain.retrievers.PubMedRetriever[source]#
It is effectively a wrapper for PubMedAPIWrapper.
It wraps load() to get_relevant_documents().
It uses all PubMedAPIWrapper arguments without any change.
async aget_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents releva... | https://langchain.readthedocs.io/en/latest/reference/modules/retrievers.html |
1b608297383e-3 | field tfidf_array: Any = None#
field vectorizer: Any = None#
async aget_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
classmethod from_documents(documents: Iterable... | https://langchain.readthedocs.io/en/latest/reference/modules/retrievers.html |
1b608297383e-4 | get_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
get_relevant_documents_with_filter(query: str, *, _filter: Optional[str] = None) → List[langchain.schema.Document]... | https://langchain.readthedocs.io/en/latest/reference/modules/retrievers.html |
61a39a44520d-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://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-1 | save_agent(file_path: Union[pathlib.Path, str]) → None[source]#
Save the underlying agent.
pydantic model langchain.agents.AgentOutputParser[source]#
abstract parse(text: str) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Parse text into agent action/finish.
class langchain.agents.AgentTy... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-2 | Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
callbacks – Callbacks to run.
**kwargs – User inputs.
Returns
Action specifying what tool to use.
return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-3 | Returns
A PromptTemplate with the template assembled from the pieces here.
classmethod from_llm_and_tools(llm: langchain.base_language.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.age... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-4 | 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://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-5 | Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
callbacks – Callbacks to run.
**kwargs – User inputs.
Returns
Action specifying what tool to use.
dict(**kwargs: Any) → Dict[source]#
Return dictionary representation of agent.
plan(intermediate_ste... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-6 | field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Respond to the human as helpfully and accurately as possible. You have access to the following tools:', suffix: str = 'Begin! Reminder to ALWAYS respond with... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-7 | Initialize tool from a function.
property args: dict#
The tool’s input arguments.
pydantic model langchain.agents.ZeroShotAgent[source]#
Agent for the MRKL chain.
field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: s... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-8 | Create csv agent by loading to a dataframe and using pandas agent.
langchain.agents.create_json_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.json.toolkit.JsonToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-9 | Construct a json agent from an LLM and tools.
langchain.agents.create_openapi_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = "You are an agent de... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-10 | Construct a pandas agent from an LLM and dataframe.
langchain.agents.create_pbi_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langch... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-11 | Construct a pbi agent from an LLM and tools.
langchain.agents.create_pbi_chat_agent(llm: langchain.chat_models.base.BaseChatModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-12 | Construct a spark agent from an LLM and dataframe.
langchain.agents.create_spark_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are a... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-13 | Construct a sql agent from an LLM and tools.
langchain.agents.create_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designe... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
61a39a44520d-14 | Get a list of all possible tool names.
langchain.agents.initialize_agent(tools: Sequence[langchain.tools.base.BaseTool], llm: langchain.base_language.BaseLanguageModel, agent: Optional[langchain.agents.agent_types.AgentType] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, agent_... | https://langchain.readthedocs.io/en/latest/reference/modules/agents.html |
64456b903bc9-0 | .rst
.pdf
Memory
Memory#
class langchain.memory.CassandraChatMessageHistory(contact_points: List[str], session_id: str, port: int = 9042, username: str = 'cassandra', password: str = 'cassandra', keyspace_name: str = 'chat_history', table_name: str = 'message_store')[source]#
Chat message history that stores history in... | https://langchain.readthedocs.io/en/latest/reference/modules/memory.html |
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