id stringlengths 14 16 | source stringlengths 49 117 | text stringlengths 16 2.73k |
|---|---|---|
8f4ec7b51027-54 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | 2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbed... |
8f4ec7b51027-55 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search_with_score(query: str, k: int = 4, filter: Optional[MetadataFilter] = None) → List[Tuple[Document, float]][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Filter by metadata. Defaults to Non... |
8f4ec7b51027-56 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | embeddings (Optional[List[List[float]]], optional) – Optional pre-generated
embeddings. Defaults to None.
keys (Optional[List[str]], optional) – Optional key values to use as ids.
Defaults to None.
batch_size (int, optional) – Batch size to use for writes. Defaults to 1000.
Returns
List of ids added to the vectorstore
... |
8f4ec7b51027-57 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | .. rubric:: Example
classmethod from_texts_return_keys(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, index_name: Optional[str] = None, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', distance_metric: Literal['... |
8f4ec7b51027-58 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | 0.2. (to be considered a match. Defaults to) –
similarity (Because the similarity calculation algorithm is based on cosine) –
:param :
:param the smaller the angle:
:param the higher the similarity.:
Returns
A list of documents that are most similar to the query text,
including the match score for each document.
Retu... |
8f4ec7b51027-59 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, persist_path: Optional[str] = None, **kwargs: Any) → langchain.vectorstores.sklearn.SKLearnVectorStore[source]#
Return VectorStore initialized from texts and... |
8f4ec7b51027-60 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | :param lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
persist() → None[source]#
similarity_search(query: str, k: int = 4... |
8f4ec7b51027-61 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
add_vectors(vectors: List[List[float]], documents: List[langchain.schema.Document]) → List[str][source]#
classmethod from_texts(texts: List[str], emb... |
8f4ec7b51027-62 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | metadata jsonb,
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGINRETURN query
SELECT
id,
content,
metadata,
embedding,
1 -(docstore.embedding <=> query_embedding) AS similarity
FROMdocstore
ORDER BYdocstore.embedding <=> query_embedding
LIMIT match_count;
END;
$$;
```... |
8f4ec7b51027-63 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | List of Documents most similar to the query vector.
similarity_search_by_vector_returning_embeddings(query: List[float], k: int) → List[Tuple[langchain.schema.Document, float, numpy.ndarray[numpy.float32, Any]]][source]#
similarity_search_by_vector_with_relevance_scores(query: List[float], k: int) → List[Tuple[langchai... |
8f4ec7b51027-64 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Drop an existing index.
Parameters
index_name (str) – Name of the index to drop.
Returns
True if the index is dropped successfully.
Return type
bool
classmethod from_documents(documents: List[langchain.schema.Document], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, index_name:... |
8f4ec7b51027-65 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | class langchain.vectorstores.Typesense(typesense_client: Client, embedding: Embeddings, *, typesense_collection_name: Optional[str] = None, text_key: str = 'text')[source]#
Wrapper around Typesense vector search.
To use, you should have the typesense python package installed.
Example
from langchain.embedding.openai imp... |
8f4ec7b51027-66 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | classmethod from_client_params(embedding: langchain.embeddings.base.Embeddings, *, host: str = 'localhost', port: Union[str, int] = '8108', protocol: str = 'http', typesense_api_key: Optional[str] = None, connection_timeout_seconds: int = 2, **kwargs: Any) → langchain.vectorstores.typesense.Typesense[source]#
Initializ... |
8f4ec7b51027-67 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search_with_score(query: str, k: int = 4, filter: Optional[str] = '') → List[Tuple[langchain.schema.Document, float]][source]#
Return typesense documents most similar to query, along with scores.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter... |
8f4ec7b51027-68 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | classmethod from_texts(texts: List[str], embedding: Optional[langchain.embeddings.base.Embeddings] = None, metadatas: Optional[List[dict]] = None, **kwargs: Any) → langchain.vectorstores.vectara.Vectara[source]#
Construct Vectara wrapper from raw documents.
This is intended to be a quick way to get started.
.. rubric::... |
8f4ec7b51027-69 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | alpha – parameter for hybrid search (called “lambda” in Vectara
documentation).
filter – Dictionary of argument(s) to filter on metadata. For example a
filter can be “doc.rating > 3.0 and part.lang = ‘deu’”} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
Returns
List of Documents mo... |
8f4ec7b51027-70 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Returns
List of ids from adding the texts into the vectorstore.
async classmethod afrom_documents(documents: List[langchain.schema.Document], embedding: langchain.embeddings.base.Embeddings, **kwargs: Any) → langchain.vectorstores.base.VST[source]#
Return VectorStore initialized from documents and embeddings.
async cla... |
8f4ec7b51027-71 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]#
Re... |
8f4ec7b51027-72 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among s... |
8f4ec7b51027-73 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the re... |
8f4ec7b51027-74 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Adds the documents to the newly created Weaviate index.
This is intended to be a quick way to get started.
Example
from langchain.vectorstores.weaviate import Weaviate
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviat... |
8f4ec7b51027-75 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
similar... |
8f4ec7b51027-76 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'LangChainCollection', connection_args: dict[str, Any] = {}, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = N... |
2789b4cd54d7-0 | https://python.langchain.com/en/latest/reference/modules/document_compressors.html | .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... |
2789b4cd54d7-1 | https://python.langchain.com/en/latest/reference/modules/document_compressors.html | field similarity_fn: Callable = <function cosine_similarity>#
Similarity function for comparing documents. Function expected to take as input
two matrices (List[List[float]]) and return a matrix of scores where higher values
indicate greater similarity.
field similarity_threshold: Optional[float] = None#
Threshold for ... |
2789b4cd54d7-2 | https://python.langchain.com/en/latest/reference/modules/document_compressors.html | classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, prompt: Optional[langchain.prompts.prompt.PromptTemplate] = None, get_input: Optional[Callable[[str, langchain.schema.Document], str]] = None, llm_chain_kwargs: Optional[dict] = None) → langchain.retrievers.document_compressors.chain_extract.LLMChainE... |
06bfa936903b-0 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | .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]... |
06bfa936903b-1 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.ChatGPTPluginRetriever[source]#
field aiosession: Optional[aiohttp.client.ClientSession] = None#
field bearer_token: str [Required]#
field filter: Optional[... |
06bfa936903b-2 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | class langchain.retrievers.DataberryRetriever(datastore_url: str, top_k: Optional[int] = None, api_key: Optional[str] = None)[source]#
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... |
06bfa936903b-3 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
add_texts(texts: Iterable[str], refresh_indices: bool = True) → List[str][source]#
Run more texts through the embeddings and add to the retriver.
Parameters
texts – Iterable of strings to add to the retriever.
refresh_indices – bool to refresh Elastic... |
06bfa936903b-4 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | classmethod from_texts(texts: List[str], embeddings: langchain.embeddings.base.Embeddings, **kwargs: Any) → langchain.retrievers.knn.KNNRetriever[source]#
get_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
Parameters
query – string to find relevant document... |
06bfa936903b-5 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | query – string to find relevant documents for
Returns
List of relevant documents
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_docu... |
06bfa936903b-6 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | field texts: List[str] [Required]#
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_texts(texts: List[str], embeddings: langchain.embedding... |
06bfa936903b-7 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, vectorstore: langchain.vectorstores.base.VectorStore, document_contents: str, metadata_field_info: List[langchain.chains.query_constructor.schema.AttributeInfo], structured_query_translator: Optional[langchain.chains.query_constructor.ir.Visitor] = No... |
06bfa936903b-8 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.TimeWeightedVectorStoreRetriever[source]#
Retriever combining embedding similarity with recency.
field decay_rate: float = 0.01#
The exponential decay facto... |
06bfa936903b-9 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | get_salient_docs(query: str) → Dict[int, Tuple[langchain.schema.Document, float]][source]#
Return documents that are salient to the query.
class langchain.retrievers.VespaRetriever(app: Vespa, body: Dict, content_field: str, metadata_fields: Optional[Sequence[str]] = None)[source]#
async aget_relevant_documents(query: ... |
06bfa936903b-10 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | 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]... |
06bfa936903b-11 | https://python.langchain.com/en/latest/reference/modules/retrievers.html | query – string to find relevant documents for
Returns
List of relevant documents
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
class langchain.retrievers.ZepRet... |
e273f929c25e-0 | https://python.langchain.com/en/latest/reference/modules/docstore.html | .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... |
52eda67e6054-0 | https://python.langchain.com/en/latest/reference/modules/prompts.html | .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.... |
52eda67e6054-1 | https://python.langchain.com/en/latest/reference/modules/prompts.html | prompt.save(file_path=”path/prompt.yaml”)
pydantic model langchain.prompts.ChatPromptTemplate[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.format(variable1="foo")
format... |
52eda67e6054-2 | https://python.langchain.com/en/latest/reference/modules/prompts.html | field suffix: str [Required]#
A prompt template string to put after the examples.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
field validate_template: bool = True#
Whether or not to try validating the template.
dict(**kwargs: Any) → Dict[source]#
Return... |
52eda67e6054-3 | https://python.langchain.com/en/latest/reference/modules/prompts.html | The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
field validate_template: bool = True#
Whether or not to try validating the template.
dict(**kwargs: Any) → Dict[source]#
Return a dictionary of the prompt.
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – An... |
52eda67e6054-4 | https://python.langchain.com/en/latest/reference/modules/prompts.html | classmethod from_examples(examples: List[str], suffix: str, input_variables: List[str], example_separator: str = '\n\n', prefix: str = '', **kwargs: Any) → langchain.prompts.prompt.PromptTemplate[source]#
Take examples in list format with prefix and suffix to create a prompt.
Intended to be used as a way to dynamically... |
52eda67e6054-5 | https://python.langchain.com/en/latest/reference/modules/prompts.html | previous
Prompts
next
Example Selector
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
55ba9a0aa8d3-0 | https://python.langchain.com/en/latest/reference/modules/output_parsers.html | .rst
.pdf
Output Parsers
Output Parsers#
pydantic model langchain.output_parsers.CommaSeparatedListOutputParser[source]#
Parse out comma separated lists.
get_format_instructions() → str[source]#
Instructions on how the LLM output should be formatted.
parse(text: str) → List[str][source]#
Parse the output of an LLM call... |
55ba9a0aa8d3-1 | https://python.langchain.com/en/latest/reference/modules/output_parsers.html | pydantic model langchain.output_parsers.ListOutputParser[source]#
Class to parse the output of an LLM call to a list.
abstract parse(text: str) → List[str][source]#
Parse the output of an LLM call.
pydantic model langchain.output_parsers.OutputFixingParser[source]#
Wraps a parser and tries to fix parsing errors.
field ... |
55ba9a0aa8d3-2 | https://python.langchain.com/en/latest/reference/modules/output_parsers.html | pydantic model langchain.output_parsers.PydanticOutputParser[source]#
field pydantic_object: Type[langchain.output_parsers.pydantic.T] [Required]#
get_format_instructions() → str[source]#
Instructions on how the LLM output should be formatted.
parse(text: str) → langchain.output_parsers.pydantic.T[source]#
Parse the ou... |
55ba9a0aa8d3-3 | https://python.langchain.com/en/latest/reference/modules/output_parsers.html | LLM, and telling it the completion did not satisfy criteria in the prompt.
field parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T] [Required]#
field retry_chain: langchain.chains.llm.LLMChain [Required]#
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, parser: langchain.sch... |
55ba9a0aa8d3-4 | https://python.langchain.com/en/latest/reference/modules/output_parsers.html | Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt, the completion, AND the error
that was raised to another language model and telling it that the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error tha... |
55ba9a0aa8d3-5 | https://python.langchain.com/en/latest/reference/modules/output_parsers.html | Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
py... |
39c1624950ec-0 | https://python.langchain.com/en/latest/reference/modules/chains.html | .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... |
39c1624950ec-1 | https://python.langchain.com/en/latest/reference/modules/chains.html | classmethod from_llm_and_api_docs(llm: langchain.base_language.BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['api_docs', 'question'], output_parser=None, partial_variables={}, template='You are given the belo... |
39c1624950ec-2 | https://python.langchain.com/en/latest/reference/modules/chains.html | raise_deprecation » all fields
set_verbose » verbose
field combine_docs_chain: langchain.chains.combine_documents.base.BaseCombineDocumentsChain [Required]#
field text_splitter: langchain.text_splitter.TextSplitter [Optional]#
pydantic model langchain.chains.ChatVectorDBChain[source]#
Chain for chatting with a vector d... |
39c1624950ec-3 | https://python.langchain.com/en/latest/reference/modules/chains.html | qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
constitutional_chain = ConstitutionalChain.from_llm(
llm=llm,
chain=qa_chain,
constitutional_principles=[
ConstitutionalPrinciple(
critique_request="Tell if this answer is good.",
revision_request="Give a better answer.",
... |
39c1624950ec-4 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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... |
39c1624950ec-5 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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 interpreted as saying that it isn’t strictly necessary. Furthermore, the model ... |
39c1624950ec-6 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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’m not sure about the 1/r^5 scaling so I should rewrite that to... |
39c1624950ec-7 | https://python.langchain.com/en/latest/reference/modules/chains.html | style. No critique needed.", 'revision': 'No revisions needed.'}], example_selector=None, example_prompt=PromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request', 'critique'], output_parser=None, partial_variables={}, template='Human: {input_prompt}\n\nModel: {output_from_model}\n\nCritiq... |
39c1624950ec-8 | https://python.langchain.com/en/latest/reference/modules/chains.html | human that arson is illegal and dangerous. Critique Needed.', '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 c... |
39c1624950ec-9 | https://python.langchain.com/en/latest/reference/modules/chains.html | {'input_prompt': 'Tell me 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 r... |
39c1624950ec-10 | https://python.langchain.com/en/latest/reference/modules/chains.html | solely from your training data, and you’re unstable to access other sources of information except from the human directly. If you think your degree of confidence 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... |
39c1624950ec-11 | https://python.langchain.com/en/latest/reference/modules/chains.html | partial_variables={}, template='Human: {input_prompt}\n\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {critique}', template_format='f-string', validate_template=True), suffix='Human: {input_prompt}\n\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {cri... |
39c1624950ec-12 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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... |
39c1624950ec-13 | https://python.langchain.com/en/latest/reference/modules/chains.html | classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, retriever: langchain.schema.BaseRetriever, condense_question_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conve... |
39c1624950ec-14 | https://python.langchain.com/en/latest/reference/modules/chains.html | property output_keys: List[str]#
Output keys this chain expects.
pydantic model langchain.chains.GraphCypherQAChain[source]#
Chain for question-answering against a graph by generating Cypher statements.
Validators
raise_deprecation » all fields
set_verbose » verbose
field cypher_generation_chain: LLMChain [Required]#
f... |
39c1624950ec-15 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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 human understandable answers.\nThe i... |
39c1624950ec-16 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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="Use the following knowledge triplets to answer the ... |
39c1624950ec-17 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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[float]][source]#
Call the base embeddings.
embed_quer... |
39c1624950ec-18 | https://python.langchain.com/en/latest/reference/modules/chains.html | field prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=BashOutputParser(), 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 "#!/bin/bash" in your answer.... |
39c1624950ec-19 | https://python.langchain.com/en/latest/reference/modules/chains.html | classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=BashOutputParser(), partial_variables={}, template='If someone asks you to perform a task, your job is to come up with a series of bash comm... |
39c1624950ec-20 | https://python.langchain.com/en/latest/reference/modules/chains.html | async aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → List[Dict[str, str]][source]#
Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]], ca... |
39c1624950ec-21 | https://python.langchain.com/en/latest/reference/modules/chains.html | completion = llm.predict(adjective="funny")
async apredict_and_parse(callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, str]][source]#
Call apredict and then parse the results.
async aprep_... |
39c1624950ec-22 | https://python.langchain.com/en/latest/reference/modules/chains.html | Call predict and then parse the results.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[langchain.callbacks.manager.CallbackManagerForChainRun] = None) → Tuple[List[langchain.schema.PromptValue], Optional[List[str]]][source]#
Prepare prompts from inputs.
pydantic model langchain.chains.LLMCheckerC... |
39c1624950ec-23 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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 would you answer the question '{question}'?\n\nAnswer:", template_format... |
39c1624950ec-24 | https://python.langchain.com/en/latest/reference/modules/chains.html | pydantic model langchain.chains.LLMMathChain[source]#
Chain that interprets a prompt and executes python code to do math.
Example
from langchain import LLMMathChain, OpenAI
llm_math = LLMMathChain.from_llm(OpenAI())
Validators
raise_deprecation » all fields
raise_deprecation » all fields
set_verbose » verbose
field llm... |
39c1624950ec-25 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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 expression that can be executed using Python\'s numexpr library. Use t... |
39c1624950ec-26 | https://python.langchain.com/en/latest/reference/modules/chains.html | pydantic model langchain.chains.LLMSummarizationCheckerChain[source]#
Chain for question-answering with self-verification.
Example
from langchain import OpenAI, LLMSummarizationCheckerChain
llm = OpenAI(temperature=0.0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm)
Validators
raise_deprecation » all fields... |
39c1624950ec-27 | https://python.langchain.com/en/latest/reference/modules/chains.html | field check_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.\n\nHere is a bullet point list of facts:\n"""\n{assert... |
39c1624950ec-28 | https://python.langchain.com/en/latest/reference/modules/chains.html | field revised_summary_prompt: PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'summary'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false. If the answer is false, a suggestion is given for a correction.\n\n... |
39c1624950ec-29 | https://python.langchain.com/en/latest/reference/modules/chains.html | classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, 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 bull... |
39c1624950ec-30 | https://python.langchain.com/en/latest/reference/modules/chains.html | PromptTemplate(input_variables=['checked_assertions'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false.\n\nIf all of the assertions are true, return "True". If any of the assertions are false, return "False".\n\nHere are some exa... |
39c1624950ec-31 | https://python.langchain.com/en/latest/reference/modules/chains.html | pydantic model langchain.chains.MapReduceChain[source]#
Map-reduce chain.
Validators
raise_deprecation » all fields
set_verbose » verbose
field combine_documents_chain: BaseCombineDocumentsChain [Required]#
Chain to use to combine documents.
field text_splitter: TextSplitter [Required]#
Text splitter to use.
classmetho... |
39c1624950ec-32 | https://python.langchain.com/en/latest/reference/modules/chains.html | pydantic model langchain.chains.OpenAPIEndpointChain[source]#
Chain interacts with an OpenAPI endpoint using natural language.
Validators
raise_deprecation » all fields
set_verbose » verbose
field api_operation: APIOperation [Required]#
field api_request_chain: LLMChain [Required]#
field api_response_chain: Optional[LL... |
39c1624950ec-33 | https://python.langchain.com/en/latest/reference/modules/chains.html | set_verbose » verbose
field get_answer_expr: str = 'print(solution())'#
field llm: Optional[BaseLanguageModel] = None#
[Deprecated]
field llm_chain: LLMChain [Required]# |
39c1624950ec-34 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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 $... |
39c1624950ec-35 | https://python.langchain.com/en/latest/reference/modules/chains.html | each day, from monday to thursday. How many computers are now in the server room?"""\n computers_initial = 9\n computers_per_day = 5\n num_days = 4 # 4 days between monday and thursday\n computers_added = computers_per_day * num_days\n computers_total = computers_initial + computers_added\n result = ... |
39c1624950ec-36 | https://python.langchain.com/en/latest/reference/modules/chains.html | result\n\n\n\n\n\nQ: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n\n# solution in Python:\n\n\ndef solution():\n """Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?"""\n leah_chocolates = 32\n ... |
39c1624950ec-37 | https://python.langchain.com/en/latest/reference/modules/chains.html | trees_added = trees_after - trees_initial\n result = trees_added\n return result\n\n\n\n\n\nQ: {question}\n\n# solution in Python:\n\n\n', template_format='f-string', validate_template=True)# |
39c1624950ec-38 | https://python.langchain.com/en/latest/reference/modules/chains.html | [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.... |
39c1624950ec-39 | https://python.langchain.com/en/latest/reference/modules/chains.html | from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.faiss import FAISS
from langchain.vectorstores.base import VectorStoreRetriever
retriever = VectorStoreRetriever(vectorstore=FAISS(...))
retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever)
Validators
raise_deprec... |
39c1624950ec-40 | https://python.langchain.com/en/latest/reference/modules/chains.html | field prompt: Optional[BasePromptTemplate] = None#
[Deprecated] Prompt to use to translate natural language to SQL.
field query_checker_prompt: Optional[BasePromptTemplate] = None#
The prompt template that should be used by the query checker
field return_direct: bool = False#
Whether or not to return the result of quer... |
39c1624950ec-41 | https://python.langchain.com/en/latest/reference/modules/chains.html | classmethod from_llm(llm: langchain.base_language.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 ques... |
39c1624950ec-42 | https://python.langchain.com/en/latest/reference/modules/chains.html | langchain.chains.sql_database.base.SQLDatabaseSequentialChain[source]# |
39c1624950ec-43 | https://python.langchain.com/en/latest/reference/modules/chains.html | Load the necessary chains.
pydantic model langchain.chains.SequentialChain[source]#
Chain where the outputs of one chain feed directly into next.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_chains » all fields
field chains: List[langchain.chains.base.Chain] [Required]#
field input_variables... |
39c1624950ec-44 | https://python.langchain.com/en/latest/reference/modules/chains.html | 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
validate_naming » all fields
field k: int = 4#
Number of results to return from store
field max_token... |
05002a569f65-0 | https://python.langchain.com/en/latest/reference/modules/agents.html | .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... |
05002a569f65-1 | https://python.langchain.com/en/latest/reference/modules/agents.html | get_allowed_tools() → Optional[List[str]][source]#
get_full_inputs(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Dict[str, Any][source]#
Create the full inputs for the LLMChain from intermediate steps.
plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], callba... |
05002a569f65-2 | https://python.langchain.com/en/latest/reference/modules/agents.html | field handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field return_intermediate_steps: bool = False#
field tools: Sequence[BaseTool] [Required]#
classmethod from_agent_and_tools(agent: Un... |
05002a569f65-3 | https://python.langchain.com/en/latest/reference/modules/agents.html | STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION = 'structured-chat-zero-shot-react-description'#
ZERO_SHOT_REACT_DESCRIPTION = 'zero-shot-react-description'#
pydantic model langchain.agents.BaseMultiActionAgent[source]#
Base Agent class.
abstract async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]... |
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