id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 54 121 |
|---|---|---|
c018b0edb28a-91 | Return pinecone documents most similar to query, along with scores.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[dict]) β Dictionary of argument(s) to filter on metadata
namespace (Optional[str]) β Namespace to search in. Default... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-92 | lambda_mult (float) β 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.
filter (Optional[dict]) β
namespace (Optional[str]) β
kwargs (Any) β
Returns
List of Documents selected by maximal marginal rel... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-93 | This is intended to be a quick way to get started.
Example
from langchain import Pinecone
from langchain.embeddings import OpenAIEmbeddings
import pinecone
# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(api_key="***", environment="...")
embeddings = OpenAIEmbe... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-94 | To use you should have the qdrant-client package installed.
Example
from qdrant_client import QdrantClient
from langchain import Qdrant
client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
Parameters
client (Any) β
collection_name (str) β
embeddings (Op... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-95 | filter (Optional[MetadataFilter]) β Filter by metadata. Defaults to None.
search_params (Optional[common_types.SearchParams]) β Additional search params
offset (int) β Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold (Optional... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-96 | May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold (Optional[float]) β Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-97 | May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold (Optional[float]) β Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-98 | If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) β Read consistency of the search... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-99 | Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding, metadatas=None, ids=None, location=None, url=None, port=6333, grpc_port=6334, prefer_grpc=False, https=None, api_key=None, prefix=None, timeout=None, host=None, path=None, collection_name=None, distance_func='Cosine', content_payload_k... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-100 | Default: False
https (Optional[bool]) β If true - use HTTPS(SSL) protocol. Default: None
api_key (Optional[str]) β API key for authentication in Qdrant Cloud. Default: None
prefix (Optional[str]) β If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-101 | Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor (Optional[int]) β Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will ... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-102 | This is intended to be a quick way to get started.
Example
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
class langchain.vectorstores.Redis(redis_url, index_name, embedding_function, content_key='... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-103 | embeddings. Defaults to None.
keys (List[str]) or ids (List[str]) β Identifiers of entries.
Defaults to None.
batch_size (int, optional) β Batch size to use for writes. Defaults to 1000.
kwargs (Any) β
Returns
List of ids added to the vectorstore
Return type
List[str]
similarity_search(query, k=4, **kwargs)[source]ο
R... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-104 | k (int) β
score_threshold (float) β
kwargs (Any) β
Note
If there are no documents that satisfy the score_threshold value,
an empty list is returned.
similarity_search_with_score(query, k=4)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of ... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-105 | Return type
Tuple[langchain.vectorstores.redis.Redis, List[str]]
classmethod from_texts(texts, embedding, metadatas=None, index_name=None, content_key='content', metadata_key='metadata', vector_key='content_vector', **kwargs)[source]ο
Create a Redis vectorstore from raw documents.
This is a user-friendly interface that... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-106 | Connect to an existing Redis index.
Parameters
embedding (langchain.embeddings.base.Embeddings) β
index_name (str) β
content_key (str) β
metadata_key (str) β
vector_key (str) β
kwargs (Any) β
Return type
langchain.vectorstores.redis.Redis
as_retriever(**kwargs)[source]ο
Parameters
kwargs (Any) β
Return type
lang... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-107 | Parameters
client (Any) β
embeddings (Embeddings) β
collection_name (str) β
text_key (str) β
embedding_key (str) β
add_texts(texts, metadatas=None, ids=None, batch_size=32, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore
Args:
texts: Iterable of strings to add to the vectorstore... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-108 | Return type
langchain.vectorstores.rocksetdb.Rockset
class DistanceFunction(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]ο
Bases: enum.Enum
COSINE_SIM = 'COSINE_SIM'ο
EUCLIDEAN_DIST = 'EUCLIDEAN_DIST'ο
DOT_PRODUCT = 'DOT_PRODUCT'ο
order_by()[source]ο
Return type
str
simila... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-109 | kwargs (Any) β
Return type
List[Document]
similarity_search_by_vector(embedding, k=4, distance_func=DistanceFunction.COSINE_SIM, where_str=None, **kwargs)[source]ο
Accepts a query_embedding (vector), and returns documents with
similar embeddings.
Parameters
embedding (List[float]) β
k (int) β
distance_func (Distance... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-110 | Return type
None
persist()[source]ο
Return type
None
add_texts(texts, metadatas=None, ids=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metad... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-111 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
Parameters
embedding (List[float]) β
k (int) β
fetch_k (int) β
lambda_mult (float) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-112 | persist_path (Optional[str]) β
kwargs (Any) β
Return type
langchain.vectorstores.sklearn.SKLearnVectorStore
class langchain.vectorstores.StarRocks(embedding, config=None, **kwargs)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around StarRocks vector database
You need a pymysql python package, and a... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-113 | List of ids from adding the texts into the VectorStore.
Return type
List[str]
classmethod from_texts(texts, embedding, metadatas=None, config=None, text_ids=None, batch_size=32, **kwargs)[source]ο
Create StarRocks wrapper with existing texts
Parameters
embedding_function (Embeddings) β Function to extract text embeddin... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-114 | Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding, k=4, where_str=None, **kwargs)[source]ο
Perform a similarity search with StarRocks by vectors
Parameters
query (str) β query string
k (int, optional) β Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], option... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-115 | Bases: langchain.vectorstores.base.VectorStore
VectorStore for a Supabase postgres database. Assumes you have the pgvector
extension installed and a match_documents (or similar) function. For more details:
https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase
You can implement your own match... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-116 | Parameters
texts (List[str]) β
embedding (Embeddings) β
metadatas (Optional[List[dict]]) β
client (Optional[supabase.client.Client]) β
table_name (Optional[str]) β
query_name (Union[str, None]) β
ids (Optional[List[str]]) β
kwargs (Any) β
Return type
SupabaseVectorStore
add_vectors(vectors, documents, ids)[sour... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-117 | **kwargs β kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
kwargs (Any) β
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_searc... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-118 | Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) β T... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-119 | Parameters
ids (List[str]) β List of ids to delete.
Return type
None
class langchain.vectorstores.Tair(embedding_function, url, index_name, content_key='content', metadata_key='metadata', search_params=None, **kwargs)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Tair Vector store.
Parameters
e... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-120 | Return type
List[Document]
classmethod from_texts(texts, embedding, metadatas=None, index_name='langchain', content_key='content', metadata_key='metadata', **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
met... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-121 | index_name (str) β
content_key (str) β
metadata_key (str) β
kwargs (Any) β
Return type
langchain.vectorstores.tair.Tair
class langchain.vectorstores.Tigris(client, embeddings, index_name)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Parameters
client (TigrisClient) β
embeddings (Embeddings) β
index_nam... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-122 | filter (Optional[TigrisFilter]) β Filter by metadata. Defaults to None.
Returns
List of documents most similar to the querytext with distance in float.
Return type
List[Tuple[Document, float]]
classmethod from_texts(texts, embedding, metadatas=None, ids=None, client=None, index_name=None, **kwargs)[source]ο
Return Vect... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-123 | typesense_client=typesense_client,
embedding=embedding,
typesense_collection_name=typesense_collection_name,
text_key="text",
)
Parameters
typesense_client (Client) β
embedding (Embeddings) β
typesense_collection_name (Optional[str]) β
text_key (str) β
add_texts(texts, metadatas=None, ids=None, **kwargs... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-124 | Minimum 10 results would be returned.
filter (Optional[str]) β typesense filter_by expression to filter documents on
kwargs (Any) β
Returns
List of Documents most similar to the query and score for each
Return type
List[langchain.schema.Document]
classmethod from_client_params(embedding, *, host='localhost', port='810... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-125 | typesense_client_params (Optional[dict]) β
typesense_collection_name (Optional[str]) β
text_key (str) β
kwargs (Any) β
Return type
Typesense
class langchain.vectorstores.Vectara(vectara_customer_id=None, vectara_corpus_id=None, vectara_api_key=None)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Implementa... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-126 | lambda_val (float) β lexical match parameter for hybrid search.
filter (Optional[str]) β Dictionary of argument(s) to filter on metadata. For example a
filter can be βdoc.rating > 3.0 and part.lang = βdeuββ} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
n_sentence_context (int) β n... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-127 | .. rubric:: Example
from langchain import Vectara
vectara = Vectara.from_texts(
texts,
vectara_customer_id=customer_id,
vectara_corpus_id=corpus_id,
vectara_api_key=api_key,
)
Parameters
texts (List[str]) β
embedding (Optional[langchain.embeddings.base.Embeddings]) β
metadatas (Optional[List[dict]]) β... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-128 | texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
List[str]
add_documents(documents, **kwargs)[source]ο
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) β Documents to add to the vectorstore.
documents (List[langchain.sch... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-129 | kwargs (Any) β
Return type
List[langchain.schema.Document]
similarity_search_with_relevance_scores(query, k=4, **kwargs)[source]ο
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) β input text
k (int) β Number of Documents to return. Defaults to 4.
**kwarg... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-130 | Return type
List[langchain.schema.Document]
async asimilarity_search_by_vector(embedding, k=4, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=2... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-131 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number o... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-132 | kwargs (Any) β
Return type
langchain.vectorstores.base.VST
abstract classmethod from_texts(texts, embedding, metadatas=None, **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) ... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-133 | embedding (Optional[Embeddings]) β
attributes (Optional[List[str]]) β
relevance_score_fn (Optional[Callable[[float], float]]) β
by_text (bool) β
add_texts(texts, metadatas=None, **kwargs)[source]ο
Upload texts with metadata (properties) to Weaviate.
Parameters
texts (Iterable[str]) β
metadatas (Optional[List[dict]... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-134 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fe... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
c018b0edb28a-135 | text and cosine distance in float for each.
Lower score represents more similarity.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[Tuple[langchain.schema.Document, float]]
classmethod from_texts(texts, embedding, metadatas=None, **kwargs)[source]ο
Construct Weaviate wrapper from raw documents.
Th... | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
ff6d0b7b0742-0 | LLMsο
Wrappers on top of large language models APIs.
class langchain.llms.AI21(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, model='j2-jumbo-instruct', temperature=0.7, maxTokens=256, minTokens=0, topP=1.0, presencePenalty=AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctua... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-1 | maxTokens (int) β
minTokens (int) β
topP (float) β
presencePenalty (langchain.llms.ai21.AI21PenaltyData) β
countPenalty (langchain.llms.ai21.AI21PenaltyData) β
frequencyPenalty (langchain.llms.ai21.AI21PenaltyData) β
numResults (int) β
logitBias (Optional[Dict[str, float]]) β
ai21_api_key (Optional[str]) β
sto... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-2 | Model name to use.
attribute numResults: int = 1ο
How many completions to generate for each prompt.
attribute presencePenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)ο
Penalizes repe... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-3 | async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)ο
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.B... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-4 | exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-5 | Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) β
Return type
int
get_token_ids(text)ο
Get the token present in the text.
Parameters
text (str) β
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-6 | save(file_path)ο
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) β Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns)ο
Try to update ForwardRefs on fields based on this Model, globalns and localns... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-7 | property lc_serializable: boolο
Return whether or not the class is serializable.
class langchain.llms.AlephAlpha(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, model='luminous-base', maximum_tokens=64, temperature=0.0, top_k=0, top_p=0.0, presence_penalty=0.0, frequency_pena... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-8 | Example
from langchain.llms import AlephAlpha
aleph_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key")
Parameters
cache (Optional[bool]) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[lang... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-9 | completion_bias_exclusion_first_token_only (bool) β
contextual_control_threshold (Optional[float]) β
control_log_additive (Optional[bool]) β
repetition_penalties_include_completion (bool) β
raw_completion (bool) β
aleph_alpha_api_key (Optional[str]) β
stop_sequences (Optional[List[str]]) β
Return type
None
attri... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-10 | The maximum number of tokens to be generated.
attribute minimum_tokens: Optional[int] = 0ο
Generate at least this number of tokens.
attribute model: Optional[str] = 'luminous-base'ο
Model name to use.
attribute n: int = 1ο
How many completions to generate for each prompt.
attribute penalty_bias: Optional[str] = Noneο
P... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-11 | Total probability mass of tokens to consider at each step.
attribute use_multiplicative_presence_penalty: Optional[bool] = Falseο
Flag deciding whether presence penalty is applied
multiplicatively (True) or additively (False).
attribute verbose: bool [Optional]ο
Whether to print out response text.
__call__(prompt, stop... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-12 | Predict text from text.
Parameters
text (str) β
stop (Optional[Sequence[str]]) β
kwargs (Any) β
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)ο
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) β
stop (Optional[Sequence[str]]) β
kwargs (Any) β
Retur... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-13 | Parameters
kwargs (Any) β
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)ο
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.b... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-14 | 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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
exclude (Optional[Union[AbstractSetIntStr, Map... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-15 | Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]ο
Return the namespace of the langchain object.
eg. [βlangchainβ, βllmsβ, βopenaiβ]
property lc_secrets: Dict[str, str]ο
Return a map of constructor ar... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-16 | output transform functions to handle formats between LLM
and the endpoint.
attribute model_kwargs: Optional[Dict] = Noneο
Key word arguments to pass to the model.
attribute tags: Optional[List[str]] = Noneο
Tags to add to the run trace.
attribute verbose: bool [Optional]ο
Whether to print out response text.
__call__(pr... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-17 | Predict text from text.
Parameters
text (str) β
stop (Optional[Sequence[str]]) β
kwargs (Any) β
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)ο
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) β
stop (Optional[Sequence[str]]) β
kwargs (Any) β
Retur... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-18 | Parameters
kwargs (Any) β
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)ο
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.b... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-19 | 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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
exclude (Optional[Union[AbstractSetIntStr, Map... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-20 | Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]ο
Return the namespace of the langchain object.
eg. [βlangchainβ, βllmsβ, βopenaiβ]
property lc_secrets: Dict[str, str]ο
Return a map of constructor ar... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-21 | # Or if you want to use the chat mode, build a few-shot-prompt, or
# put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT:
raw_prompt = "What are the biggest risks facing humanity?"
prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
response = model(prompt)
Parameters
client (Any) β
model... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-22 | Whether to stream the results.
attribute tags: Optional[List[str]] = Noneο
Tags to add to the run trace.
attribute temperature: Optional[float] = Noneο
A non-negative float that tunes the degree of randomness in generation.
attribute top_k: Optional[int] = Noneο
Number of most likely tokens to consider at each step.
at... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-23 | kwargs (Any) β
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)ο
Predict text from text.
Parameters
text (str) β
stop (Optional[Sequence[str]]) β
kwargs (Any) β
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)ο
Predict message from messages.
Parameters
mes... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-24 | Returns
new model instance
Return type
Model
dict(**kwargs)ο
Return a dictionary of the LLM.
Parameters
kwargs (Any) β
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)ο
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) β
stop (Optional[List[str]]) β
callba... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-25 | 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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
exclude (Optional[Union[AbstractSetIntStr, Map... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-26 | Parameters
prompt (str) β The prompt to pass into the model.
stop (Optional[List[str]]) β Optional list of stop words to use when generating.
Returns
A generator representing the stream of tokens from Anthropic.
Return type
Generator
Example
prompt = "Write a poem about a stream."
prompt = f"\n\nHuman: {prompt}\n\nAssi... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-27 | Service, or pass it as a named parameter to the constructor.
Example
from langchain.llms import Anyscale
anyscale = Anyscale(anyscale_service_url="SERVICE_URL",
anyscale_service_route="SERVICE_ROUTE",
anyscale_service_token="SERVICE_TOKEN")
# Use Ray for distributed processing
im... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-28 | kwargs (Any) β
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)ο
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.B... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-29 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) β
values (Any) β
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)ο
Duplicate a model, optionally choo... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-30 | Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
kwargs (Any) β
Return type
langchain.schema.... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-31 | dumps_kwargs (Any) β
Return type
unicode
predict(text, *, stop=None, **kwargs)ο
Predict text from text.
Parameters
text (str) β
stop (Optional[Sequence[str]]) β
kwargs (Any) β
Return type
str
predict_messages(messages, *, stop=None, **kwargs)ο
Predict message from messages.
Parameters
messages (List[langchain.schem... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-32 | property lc_serializable: boolο
Return whether or not the class is serializable.
class langchain.llms.Aviary(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, model='amazon/LightGPT', aviary_url=None, aviary_token=None, use_prompt_format=True, version=None)[source]ο
Bases: langchain.llms.ba... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-33 | Tags to add to the run trace.
attribute verbose: bool [Optional]ο
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)ο
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callba... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-34 | Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) β
stop (Optional[Sequence[str]]) β
kwargs (Any) β
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)ο
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated d... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-35 | Parameters
prompts (List[str]) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
tags (Optional[List[str]]) β
kwargs (Any) β
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callba... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-36 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
by_alias (bool) β
skip_defaults (Optional[bool]) β
exclude_unset (bool) β
exclude_defaults (bool) β
exclude_none (bool) β
encoder (Optional[Callable[[Any], Any]]) β
models_as_dict (b... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-37 | property lc_namespace: List[str]ο
Return the namespace of the langchain object.
eg. [βlangchainβ, βllmsβ, βopenaiβ]
property lc_secrets: Dict[str, str]ο
Return a map of constructor argument names to secret ids.
eg. {βopenai_api_keyβ: βOPENAI_API_KEYβ}
property lc_serializable: boolο
Return whether or not the class is s... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-38 | attribute content_formatter: Any = Noneο
The content formatter that provides an input and output
transform function to handle formats between the LLM and
the endpoint
attribute deployment_name: str = ''ο
Deployment Name for Endpoint. Should be passed to constructor or specified as
env var AZUREML_DEPLOYMENT_NAME.
attri... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-39 | kwargs (Any) β
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)ο
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callback... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-40 | exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-41 | Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) β
Return type
int
get_token_ids(text)ο
Get the token present in the text.
Parameters
text (str) β
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-42 | save(file_path)ο
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) β Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns)ο
Try to update ForwardRefs on fields based on this Model, globalns and localns... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-43 | property lc_serializable: boolο
Return whether or not the class is serializable.
class langchain.llms.AzureOpenAI(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, model='text-davinci-003', temperature=0.7, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, ... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-44 | max_tokens (int) β
top_p (float) β
frequency_penalty (float) β
presence_penalty (float) β
n (int) β
best_of (int) β
model_kwargs (Dict[str, Any]) β
openai_api_key (Optional[str]) β
openai_api_base (Optional[str]) β
openai_organization (Optional[str]) β
openai_proxy (Optional[str]) β
batch_size (int) β
reque... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-45 | attribute logit_bias: Optional[Dict[str, float]] [Optional]ο
Adjust the probability of specific tokens being generated.
attribute max_retries: int = 6ο
Maximum number of retries to make when generating.
attribute max_tokens: int = 256ο
The maximum number of tokens to generate in the completion.
-1 returns as many token... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-46 | supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here.
attribute top_p: float = 1ο
Total prob... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-47 | kwargs (Any) β
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)ο
Predict text from text.
Parameters
text (str) β
stop (Optional[Sequence[str]]) β
kwargs (Any) β
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)ο
Predict message from messages.
Parameters
mes... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-48 | self (Model) β
Returns
new model instance
Return type
Model
create_llm_result(choices, prompts, token_usage)ο
Create the LLMResult from the choices and prompts.
Parameters
choices (Any) β
prompts (List[str]) β
token_usage (Dict[str, int]) β
Return type
langchain.schema.LLMResult
dict(**kwargs)ο
Return a dictionary ... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-49 | Parameters
messages (List[langchain.schema.BaseMessage]) β
Return type
int
get_sub_prompts(params, prompts, stop=None)ο
Get the sub prompts for llm call.
Parameters
params (Dict[str, Any]) β
prompts (List[str]) β
stop (Optional[List[str]]) β
Return type
List[List[str]]
get_token_ids(text)ο
Get the token IDs using t... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-50 | int
Example
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
static modelname_to_contextsize(modelname)ο
Calculate the maximum number of tokens possible to generate for a model.
Parameters
modelname (str) β The modelname we want to know the context size for.
Returns
The maximum context size
Return type
int
E... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-51 | stop (Optional[List[str]]) β Optional list of stop words to use when generating.
Returns
A generator representing the stream of tokens from OpenAI.
Return type
Generator
Example
generator = openai.stream("Tell me a joke.")
for token in generator:
yield token
classmethod update_forward_refs(**localns)ο
Try to update... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-52 | Example
from langchain.llms import Banana
banana = Banana(model_key="")
Parameters
cache (Optional[bool]) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallback... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-53 | kwargs (Any) β
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)ο
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) β
stop (Optional[List[str]]) β
callbacks (Optional[Union[List[langchain.callback... | https://api.python.langchain.com/en/latest/modules/llms.html |
ff6d0b7b0742-54 | exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool... | https://api.python.langchain.com/en/latest/modules/llms.html |
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