id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
6d5c37c1dbcb-80 | Returns
List of ids added to the vectorstore
Return type
List[str]
as_retriever(**kwargs: Any) → langchain.vectorstores.redis.RedisVectorStoreRetriever[source]#
static drop_index(index_name: str, delete_documents: bool, **kwargs: Any) → bool[source]#
Drop a Redis search index.
Parameters
index_name (str) – Name of the ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-81 | This is intended to be a quick way to get started.
.. 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:... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-82 | k (int) – The number of documents to return. Default is 4.
score_threshold (float) – The minimum matching score required for a document
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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-83 | kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
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) →... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-84 | :param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
: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.
Retur... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-85 | Add more texts to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional) – Optional pre-generated
embeddings. Defaults to None.
R... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-86 | k (int) – The number of documents to return. Default is 4.
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
similarity_search_with_score(query: str, k: int = 4) → List[Tuple[langchain.schema.Document, float]][source]#
Return docs most similar to query. Uses cosine similari... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-87 | Parameters
texts – Iterable of strings to add to the vectorstore.
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])... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-88 | ```sql
CREATE FUNCTION match_documents_embeddings(query_embedding vector(1536),
match_count int)
RETURNS TABLE(id bigint,
content text,
metadata jsonb,
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGINRETURN query
SELECT
id,
content,
metadata,
embedding,
1 -(docstore.... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-89 | Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
similarity_search_by_vector_returning_embeddings(query: List[float], k: int) → List[Tuple[langchai... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-90 | Add texts data to an existing index.
create_index_if_not_exist(dim: int, distance_type: str, index_type: str, data_type: str, **kwargs: Any) → bool[source]#
static drop_index(index_name: str = 'langchain', **kwargs: Any) → bool[source]#
Drop an existing index.
Parameters
index_name (str) – Name of the index to drop.
Re... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-91 | Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
class langchain.vectorstor... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-92 | Return docs most similar to query.
similarity_search_with_score(query: str, k: int = 4, filter: Optional[TigrisFilter] = None) → List[Tuple[Document, float]][source]#
Run similarity search with Chroma with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-93 | typesense_collection_name,
embedding.embed_query,
"text",
)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embedding and add to the vectorstore.
Parameters
texts – Iterable of strings to add ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-94 | protocol="http",
typesense_collection_name="langchain-memory",
)
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, typesense_client: Optional[Client] = None, typesense_client_params: Optional[dict] = None, typesense_collection_na... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-95 | .. rubric:: Example
from langchain.vectorstores import Vectara
vectorstore = Vectara(
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key
)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]#
Ru... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-96 | Return Vectara 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 5.
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.v... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-97 | Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][sour... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-98 | Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, 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.
async amax_marginal_relevance_search_by... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-99 | Return VectorStore initialized from documents and embeddings.
abstract classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → langchain.vectorstores.base.VST[source]#
Return VectorStore initialized from texts and embeddings.
max... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-100 | 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.
search(query: str, search_type: str, **kwargs: Any) → List[langchain.sch... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-101 | Returns
List of Tuples of (doc, similarity_score)
class langchain.vectorstores.Weaviate(client: typing.Any, index_name: str, text_key: str, embedding: typing.Optional[langchain.embeddings.base.Embeddings] = None, attributes: typing.Optional[typing.List[str]] = None, relevance_score_fn: typing.Optional[typing.Callable[[... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-102 | weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
max_marginal_relevance_search(query: str, 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.
Ma... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-103 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults t... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-104 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
d7df30d8f90e-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_compressors.html |
d7df30d8f90e-1 | similarity_threshold must be specified. Defaults to 20.
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 simi... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_compressors.html |
d7df30d8f90e-2 | Compress page content of raw documents.
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.docu... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_compressors.html |
020b9497c959-0 | .rst
.pdf
Python REPL
Python REPL#
For backwards compatibility.
pydantic model langchain.python.PythonREPL[source]#
Simulates a standalone Python REPL.
field globals: Optional[Dict] [Optional] (alias '_globals')#
field locals: Optional[Dict] [Optional] (alias '_locals')#
run(command: str) → str[source]#
Run command wit... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/python.html |
1bb39e8a4fc7-0 | .rst
.pdf
Document Transformers
Document Transformers#
Transform documents
pydantic model langchain.document_transformers.EmbeddingsRedundantFilter[source]#
Filter that drops redundant documents by comparing their embeddings.
field embeddings: langchain.embeddings.base.Embeddings [Required]#
Embeddings to use for embed... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_transformers.html |
0bf147887ee3-0 | .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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-1 | field requests_wrapper: TextRequestsWrapper [Required]#
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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-2 | pydantic model langchain.chains.AnalyzeDocumentChain[source]#
Chain that splits documents, then analyzes it in pieces.
Validators
raise_deprecation » all fields
set_verbose » verbose
field combine_docs_chain: langchain.chains.combine_documents.base.BaseCombineDocumentsChain [Required]#
field text_splitter: langchain.te... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-3 | Chain for applying constitutional principles.
Example
from langchain.llms import OpenAI
from langchain.chains import LLMChain, ConstitutionalChain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
llm = OpenAI()
qa_prompt = PromptTemplate(
template="Q: {question} A:",
... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-4 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-5 | model’s precedings response is 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-6 | 'Newtonian physics predicts static, 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 t... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-7 | is not in the style of Master Yoda.", 'critique': "The provided sentence appears to capture the essence of Master Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Yoda's typical speaking style. No critique needed.", '... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-8 | precedings response is 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': 'Arson is very dangerous and illegal, and should not be condoned. The model should not have asked a follow-up question abou... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-9 | are too young to give consent. 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': 'Yes, I think it is absolutely unethical to have sex with someone without their consent. It’s always bet... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-10 | 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 make it less misleading, although I’m pretty sure it decays more quickly than Newton’s law, and the Chern-Simons theorem is probably just wrong. Cr... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-11 | identify specific ways in which the model's response is not in the style of Master Yoda.", 'critique': "The provided sentence appears to capture the essence of Master Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Y... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-12 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-13 | field retriever: BaseRetriever [Required]#
Index to connect to.
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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-14 | field start_with_retrieval: bool = True#
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, max_generation_len: int = 32, **kwargs: Any) → langchain.chains.flare.base.FlareChain[source]#
property input_keys: List[str]#
Input keys this chain expects.
property output_keys: List[str]#
Output keys this ch... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-15 | field top_k: int = 10#
Number of results to return from the query
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 assistan... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-16 | Chain for question-answering against a graph.
Validators
raise_deprecation » all fields
set_verbose » verbose
field entity_extraction_chain: LLMChain [Required]#
field graph: NetworkxEntityGraph [Required]#
field qa_chain: LLMChain [Required]#
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, qa_prom... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-17 | Initialize from LLM.
pydantic model langchain.chains.HypotheticalDocumentEmbedder[source]#
Generate hypothetical document for query, and then embed that.
Based on https://arxiv.org/abs/2212.10496
Validators
raise_deprecation » all fields
set_verbose » verbose
field base_embeddings: Embeddings [Required]#
field llm_chai... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-18 | field llm_chain: LLMChain [Required]#
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 nee... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-19 | [Deprecated]
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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-20 | field prompt: BasePromptTemplate [Required]#
Prompt object to use.
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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-21 | Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
async apredict_and_parse(callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackMa... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-22 | Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, Any]][source]#
Call predict and then parse the ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-23 | [Deprecated]
field list_assertions_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-string', vali... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-24 | [Deprecated] Prompt to use when questioning the documents.
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, create_draft_answer_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='{question}\n\n', template... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-25 | raise_deprecation » all fields
raise_deprecation » all fields
set_verbose » verbose
field llm: Optional[BaseLanguageModel] = None#
[Deprecated] LLM wrapper to use.
field llm_chain: LLMChain [Required]#
field prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables=... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-26 | [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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-27 | field requests_wrapper: TextRequestsWrapper [Optional]#
field text_length: int = 8000#
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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-28 | [Deprecated]
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:\... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-29 | 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 assertions that have been fact checked and are labeled as true ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-30 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-31 | validate_template=True), 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 as true or false.\n\nIf all of the assertions are true,... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-32 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-33 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-34 | types and properties in the schema.\nDo not use any other relationship types or properties that are not provided.\nSchema:\n{schema}\nNote: Do not include any explanations or apologies in your responses.\nDo not respond to any questions that might ask anything else than for you to construct a Cypher statement.\nDo not ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-35 | Initialize from LLM.
pydantic model langchain.chains.OpenAIModerationChain[source]#
Pass input through a moderation endpoint.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create ca... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-36 | Resolve the path, query params dict, and optional requestBody dict.
classmethod from_api_operation(operation: langchain.tools.openapi.utils.api_models.APIOperation, llm: langchain.base_language.BaseLanguageModel, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, return_intermediate_steps: b... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-37 | 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 $... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-38 | solution():\n """There were nine computers in the server room. Five more computers were installed 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 = c... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-39 | = 12\n denny_lollipops = jason_lollipops_initial - jason_lollipops_after\n result = denny_lollipops\n return 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 chocolate... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-40 | 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?"""\n trees_initial = 15\n trees_after = 21\n trees_added = trees_after - trees_initial\n result = trees_added\n return result\n\n\n\n\n\... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-41 | [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.... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-42 | Chain for question-answering against an index.
Example
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.faiss import FAISS
from langchain.vectorstores.base import VectorStoreRetriever
retriever = VectorStoreRetriever(vectorstore=FAISS(...))
retrievalQA = RetrievalQA.from_llm(llm... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-43 | [Deprecated] LLM wrapper to use.
field llm_chain: LLMChain [Required]#
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 r... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-44 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-45 | Table Names:', template_format='f-string', validate_template=True), **kwargs: Any) → langchain.chains.sql_database.base.SQLDatabaseSequentialChain[source]# | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-46 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-47 | 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
validate_naming » all fields
field k: int = 4#
Number of r... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
0bf147887ee3-48 | previous
Tagging
next
Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html |
7db38aa60b44-0 | .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]... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-1 | Wrapper around Azure Cognitive Search.
field aiosession: Optional[aiohttp.client.ClientSession] = None#
ClientSession, in case we want to reuse connection for better performance.
field api_key: str = ''#
API Key. Both Admin and Query keys work, but for reading data it’s
recommended to use a Query key.
field api_version... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-2 | Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.ContextualCompressionRetriever[source]#
Retriever that wraps a base retriever and compresses the results.
field base_compressor: langchain.retrievers.docume... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-3 | Returns
List of relevant documents
top_k: Optional[int]#
class langchain.retrievers.ElasticSearchBM25Retriever(client: Any, index_name: str)[source]#
Wrapper around Elasticsearch using BM25 as a retrieval method.
To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the E... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-4 | Parameters
query – string to find relevant documents for
Returns
List of relevant documents
classmethod create(elasticsearch_url: str, index_name: str, k1: float = 2.0, b: float = 0.75) → langchain.retrievers.elastic_search_bm25.ElasticSearchBM25Retriever[source]#
get_relevant_documents(query: str) → List[langchain.sch... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-5 | Parameters
retrievers – A list of retrievers to merge.
async aget_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Asynchronously get the relevant documents for a given query.
Parameters
query – The query to search for.
Returns
A list of relevant documents.
async amerge_documents(query: str) → ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-6 | field index: Any = None#
field sparse_encoder: Any = None#
field top_k: int = 4#
add_texts(texts: List[str], ids: Optional[List[str]] = None, metadatas: Optional[List[dict]] = None) → None[source]#
async aget_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
P... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-7 | field response_key: str = 'response'#
field url: 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
get_relevant_documents(query: str) → List[... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-8 | The LLMChain for generating the vector store queries.
field search_kwargs: dict [Optional]#
Keyword arguments to pass in to the vector store search.
field search_type: str = 'similarity'#
The search type to perform on the vector store.
field structured_query_translator: langchain.chains.query_constructor.ir.Visitor [Re... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-9 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-10 | field other_score_keys: List[str] = []#
Other keys in the metadata to factor into the score, e.g. ‘importance’.
field search_kwargs: dict [Optional]#
Keyword arguments to pass to the vectorstore similarity search.
field vectorstore: langchain.vectorstores.base.VectorStore [Required]#
The vectorstore to store documents ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-11 | Parameters
query – string to find relevant documents for
Returns
List of relevant documents
classmethod from_params(url: str, content_field: str, *, k: Optional[int] = None, metadata_fields: Union[Sequence[str], Literal['*']] = (), sources: Optional[Union[Sequence[str], Literal['*']]] = None, _filter: Optional[str] = N... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-12 | class langchain.retrievers.WeaviateHybridSearchRetriever(client: Any, index_name: str, text_key: str, alpha: float = 0.5, k: int = 4, attributes: Optional[List[str]] = None, create_schema_if_missing: bool = True)[source]#
class Config[source]#
Configuration for this pydantic object.
arbitrary_types_allowed = True#
extr... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
7db38aa60b44-13 | Parameters
query – string to find relevant documents for
Returns
List of relevant documents
class langchain.retrievers.ZepRetriever(session_id: str, url: str, top_k: Optional[int] = None)[source]#
A Retriever implementation for the Zep long-term memory store. Search your
user’s long-term chat history with Zep.
Note: Yo... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html |
4001009da5ed-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/docstore.html |
4092f5c0fde6-0 | .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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-1 | clear() → None[source]#
Clear context from this session for every memory.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Load all vars from sub-memories.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this session for every memory.
property memor... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-2 | field entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last l... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-3 | line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for ext... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-4 | field entity_store: langchain.memory.entity.BaseEntityStore [Optional]#
field entity_summarization_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['entity', 'summary', 'history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant helping a human kee... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-5 | Knowledge graph memory for storing conversation memory.
Integrates with external knowledge graph to store and retrieve
information about knowledge triples in the conversation.
field ai_prefix: str = 'AI'# | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
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