id stringlengths 14 16 | source stringlengths 49 117 | text stringlengths 16 2.73k |
|---|---|---|
63e878eb664f-8 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html | hour to reset my phone and while I was tryin to connect my phone to my computer the computer also restarted smh does anyone else knows how I can get my phone to work… my problem is I have a black dot on the bottom left of my screen an it wont allow me to touch a certain part of my screen unless I rotate my phone and I ... |
63e878eb664f-9 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html | with the phone I had troubles with. It was my dads and turns out he carried it in his pocket. The phone itself had a little bend in it as a result. A little pressure in the opposite direction helped the issue. But it also had a tiny crack in the screen which wasnt obvious, once we added a screen protector this fixed th... |
63e878eb664f-10 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html | loader = IFixitLoader("https://www.ifixit.com/Device/Standard_iPad")
data = loader.load()
data
[Document(page_content="Standard iPad\nThe standard edition of the tablet computer made by Apple.\n== Background Information ==\n\nOriginally introduced in January 2010, the iPad is Apple's standard edition of their tablet co... |
63e878eb664f-11 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html | [Document(page_content='Banana\nTasty fruit. Good source of potassium. Yellow.\n== Background Information ==\n\nCommonly misspelled, this wildly popular, phone shaped fruit serves as nutrition and an obstacle to slow down vehicles racing close behind you. Also used commonly as a synonym for “crazy” or “insane”.\n\nBota... |
63e878eb664f-12 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html | Document(page_content="# Banana Teardown\nIn this teardown, we open a banana to see what's inside. Yellow and delicious, but most importantly, yellow.\n\n\n###Tools Required:\n\n - Fingers\n\n - Teeth\n\n - Thumbs\n\n\n###Parts Required:\n\n - None\n\n\n## Step 1\nTake one banana from the bunch.\nDon't squeeze too har... |
731b7d566d24-0 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azure_blob_storage_container.html | .ipynb
.pdf
Azure Blob Storage Container
Contents
Specifying a prefix
Azure Blob Storage Container#
Azure Blob Storage is Microsoft’s object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn’t adhere to a particular data mo... |
731b7d566d24-1 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azure_blob_storage_container.html | Specifying a prefix
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
9c166dd8917f-0 | https://python.langchain.com/en/latest/reference/indexes.html | .rst
.pdf
Indexes
Indexes#
Indexes refer to ways to structure documents so that LLMs can best interact with them.
LangChain has a number of modules that help you load, structure, store, and retrieve documents.
Docstore
Text Splitter
Document Loaders
Vector Stores
Retrievers
Document Compressors
Document Transformers
pr... |
d5c0a5e3474e-0 | https://python.langchain.com/en/latest/reference/prompts.html | .rst
.pdf
Prompts
Prompts#
The reference guides here all relate to objects for working with Prompts.
PromptTemplates
Example Selector
Output Parsers
previous
How to serialize prompts
next
PromptTemplates
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
509c88a458e1-0 | https://python.langchain.com/en/latest/reference/agents.html | .rst
.pdf
Agents
Agents#
Reference guide for Agents and associated abstractions.
Agents
Tools
Agent Toolkits
previous
Memory
next
Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
373e9c5a95a3-0 | https://python.langchain.com/en/latest/reference/installation.html | .md
.pdf
Installation
Contents
Official Releases
Installing from source
Installation#
Official Releases#
LangChain is available on PyPi, so to it is easily installable with:
pip install langchain
That will install the bare minimum requirements of LangChain.
A lot of the value of LangChain comes when integrating it wi... |
26c30c8a2d88-0 | https://python.langchain.com/en/latest/reference/models.html | .rst
.pdf
Models
Models#
LangChain provides interfaces and integrations for a number of different types of models.
LLMs
Chat Models
Embeddings
previous
API References
next
Chat Models
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
9472f0bf3ff6-0 | https://python.langchain.com/en/latest/reference/modules/chat_models.html | .rst
.pdf
Chat Models
Chat Models#
pydantic model langchain.chat_models.AzureChatOpenAI[source]#
Wrapper around Azure OpenAI Chat Completion API. To use this class you
must have a deployed model on Azure OpenAI. Use deployment_name in the
constructor to refer to the “Model deployment name” in the Azure portal.
In addit... |
9472f0bf3ff6-1 | https://python.langchain.com/en/latest/reference/modules/chat_models.html | it as a named parameter to the constructor.
Example
import anthropic
from langchain.llms import Anthropic
model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
get_num_tokens(text: str) → int[source]#
Calculate number of tokens.
pydantic model langchain.chat_models.ChatGooglePalm[source]#
Wrapper ... |
9472f0bf3ff6-2 | https://python.langchain.com/en/latest/reference/modules/chat_models.html | environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
field max_retries: int = 6#
M... |
9472f0bf3ff6-3 | https://python.langchain.com/en/latest/reference/modules/chat_models.html | Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: openai/openai-cookbook
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
get_token_ids(text: str) → List[int][source]#
Get the tokens present in the text with tiktoken package.
pydantic model langchain.chat_models.C... |
9d45f2f3cb60-0 | https://python.langchain.com/en/latest/reference/modules/python.html | .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... |
a0d1b816d525-0 | https://python.langchain.com/en/latest/reference/modules/example_selector.html | .rst
.pdf
Example Selector
Example Selector#
Logic for selecting examples to include in prompts.
pydantic model langchain.prompts.example_selector.LengthBasedExampleSelector[source]#
Select examples based on length.
Validators
calculate_example_text_lengths » example_text_lengths
field example_prompt: langchain.prompts... |
a0d1b816d525-1 | https://python.langchain.com/en/latest/reference/modules/example_selector.html | Reshuffles examples dynamically based on query similarity.
Parameters
examples – List of examples to use in the prompt.
embeddings – An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g. FAISS.
k – Number of examples to select
input_keys – If provided,... |
a0d1b816d525-2 | https://python.langchain.com/en/latest/reference/modules/example_selector.html | Reshuffles examples dynamically based on query similarity.
Parameters
examples – List of examples to use in the prompt.
embeddings – An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g. FAISS.
k – Number of examples to select
input_keys – If provided... |
d8114540836d-0 | https://python.langchain.com/en/latest/reference/modules/text_splitter.html | .rst
.pdf
Text Splitter
Text Splitter#
Functionality for splitting text.
class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class... |
d8114540836d-1 | https://python.langchain.com/en/latest/reference/modules/text_splitter.html | class langchain.text_splitter.RecursiveCharacterTextSplitter(separators: Optional[List[str]] = None, keep_separator: bool = True, **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
Recursively tries to split by different characters to find one
that works.
classmethod from_language(langua... |
d8114540836d-2 | https://python.langchain.com/en/latest/reference/modules/text_splitter.html | Text splitter that uses HuggingFace tokenizer to count length.
classmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any) → langchain.te... |
ac3008b8714c-0 | https://python.langchain.com/en/latest/reference/modules/searx_search.html | .rst
.pdf
SearxNG Search
Contents
Quick Start
Searching
Engine Parameters
Search Tips
SearxNG Search#
Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
multiple search engines and databases and
supports the OpenSearch
specification.
More detai... |
ac3008b8714c-1 | https://python.langchain.com/en/latest/reference/modules/searx_search.html | s = SearxSearchWrapper(engines=['google', 'bing'],
language='es')
Search Tips#
Searx offers a special
search syntax
that can also be used instead of passing engine parameters.
For example the following query:
s = SearxSearchWrapper("langchain library", engines=['github'])
# can also be written as:
s... |
ac3008b8714c-2 | https://python.langchain.com/en/latest/reference/modules/searx_search.html | For a list of public SearxNG instances see https://searx.space/
class langchain.utilities.searx_search.SearxResults(data: str)[source]#
Dict like wrapper around search api results.
property answers: Any#
Helper accessor on the json result.
pydantic model langchain.utilities.searx_search.SearxSearchWrapper[source]#
Wrap... |
ac3008b8714c-3 | https://python.langchain.com/en/latest/reference/modules/searx_search.html | async aresults(query: str, num_results: int, engines: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → List[Dict][source]#
Asynchronously query with json results.
Uses aiohttp. See results for more info.
async arun(query: str, engines: Optional[List[str]] = None, query_suffix: Optional[str... |
ac3008b8714c-4 | https://python.langchain.com/en/latest/reference/modules/searx_search.html | engines – List of engines to use for the query.
categories – List of categories to use for the query.
**kwargs – extra parameters to pass to the searx API.
Returns
The result of the query.
Return type
str
Raises
ValueError – If an error occured with the query.
Example
This will make a query to the qwant engine:
from la... |
1ec6062faedb-0 | https://python.langchain.com/en/latest/reference/modules/utilities.html | .rst
.pdf
Utilities
Utilities#
General utilities.
pydantic model langchain.utilities.ApifyWrapper[source]#
Wrapper around Apify.
To use, you should have the apify-client python package installed,
and the environment variable APIFY_API_TOKEN set with your API key, or pass
apify_api_token as a named parameter to the cons... |
1ec6062faedb-1 | https://python.langchain.com/en/latest/reference/modules/utilities.html | call_actor(actor_id: str, run_input: Dict, dataset_mapping_function: Callable[[Dict], langchain.schema.Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None) → langchain.document_loaders.apify_dataset.ApifyDatasetLoader[source]#
Run an Actor on the Apify plat... |
1ec6062faedb-2 | https://python.langchain.com/en/latest/reference/modules/utilities.html | ARXIV_MAX_QUERY_LENGTH – the cut limit on the query used for the arxiv tool.
load_max_docs – a limit to the number of loaded documents
load_all_available_meta –
if True: the metadata of the loaded Documents gets all available meta info(see https://lukasschwab.me/arxiv.py/index.html#Result),
if False: the metadata gets... |
1ec6062faedb-3 | https://python.langchain.com/en/latest/reference/modules/utilities.html | https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e
field bing_search_url: str [Required]#
field bing_subscription_key: str [Required]#
field k: int = 10#
results(query: str, num_results: int) → List[Dict][source]#
Run query through BingSearch and return metadata.
Paramet... |
1ec6062faedb-4 | https://python.langchain.com/en/latest/reference/modules/utilities.html | run(query: str) → str[source]#
pydantic model langchain.utilities.GooglePlacesAPIWrapper[source]#
Wrapper around Google Places API.
To use, you should have the googlemaps python package installed,an API key for the google maps platform,
and the enviroment variable ‘’GPLACES_API_KEY’’
set with your API key , or pass ‘gp... |
1ec6062faedb-5 | https://python.langchain.com/en/latest/reference/modules/utilities.html | - Select Create credentials, then select API key from the drop-down menu.
- The API key created dialog box displays your newly created key.
- You now have an API_KEY
3. Setup Custom Search Engine so you can search the entire web
- Create a custom search engine in this link.
- In Sites to search, add any valid URL (i.e.... |
1ec6062faedb-6 | https://python.langchain.com/en/latest/reference/modules/utilities.html | Wrapper around the Serper.dev Google Search API.
You can create a free API key at https://serper.dev.
To use, you should have the environment variable SERPER_API_KEY
set with your API key, or pass serper_api_key as a named parameter
to the constructor.
Example
from langchain import GoogleSerperAPIWrapper
google_serper ... |
1ec6062faedb-7 | https://python.langchain.com/en/latest/reference/modules/utilities.html | Create a lambda function using the AWS Console or CLI
Run aws configure and enter your AWS credentials
field awslambda_tool_description: Optional[str] = None#
field awslambda_tool_name: Optional[str] = None#
field function_name: Optional[str] = None#
run(query: str) → str[source]#
Invoke Lambda function and parse resul... |
1ec6062faedb-8 | https://python.langchain.com/en/latest/reference/modules/utilities.html | Create PowerBI engine from dataset ID and credential or token.
Use either the credential or a supplied token to authenticate.
If both are supplied the credential is used to generate a token.
The impersonated_user_name is the UPN of a user to be impersonated.
If the model is not RLS enabled, this will be ignored.
Valida... |
1ec6062faedb-9 | https://python.langchain.com/en/latest/reference/modules/utilities.html | Information about all tables in the database.
pydantic model langchain.utilities.PubMedAPIWrapper[source]#
Wrapper around PubMed API.
This wrapper will use the PubMed API to conduct searches and fetch
document summaries. By default, it will return the document summaries
of the top-k results of an input search.
Paramete... |
1ec6062faedb-10 | https://python.langchain.com/en/latest/reference/modules/utilities.html | run(command: str) → str[source]#
Run command with own globals/locals and returns anything printed.
pydantic model langchain.utilities.SearxSearchWrapper[source]#
Wrapper for Searx API.
To use you need to provide the searx host by passing the named parameter
searx_host or exporting the environment variable SEARX_HOST.
I... |
1ec6062faedb-11 | https://python.langchain.com/en/latest/reference/modules/utilities.html | Uses aiohttp. See results for more info.
async arun(query: str, engines: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → str[source]#
Asynchronously version of run.
results(query: str, num_results: int, engines: Optional[List[str]] = None, categories: Optional[List[str]] = None, query_suf... |
1ec6062faedb-12 | https://python.langchain.com/en/latest/reference/modules/utilities.html | str
Raises
ValueError – If an error occured with the query.
Example
This will make a query to the qwant engine:
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://my.searx.host")
searx.run("what is the weather in France ?", engine="qwant")
# the same result can be achieved ... |
1ec6062faedb-13 | https://python.langchain.com/en/latest/reference/modules/utilities.html | run(query: str, **kwargs: Any) → str[source]#
Run query through SerpAPI and parse result.
class langchain.utilities.SparkSQL(spark_session: Optional[SparkSession] = None, catalog: Optional[str] = None, schema: Optional[str] = None, ignore_tables: Optional[List[str]] = None, include_tables: Optional[List[str]] = None, s... |
1ec6062faedb-14 | https://python.langchain.com/en/latest/reference/modules/utilities.html | pydantic model langchain.utilities.TextRequestsWrapper[source]#
Lightweight wrapper around requests library.
The main purpose of this wrapper is to always return a text output.
field aiosession: Optional[aiohttp.client.ClientSession] = None#
field headers: Optional[Dict[str, str]] = None#
async adelete(url: str, **kwar... |
1ec6062faedb-15 | https://python.langchain.com/en/latest/reference/modules/utilities.html | pydantic model langchain.utilities.TwilioAPIWrapper[source]#
Sms Client using Twilio.
To use, you should have the twilio python package installed,
and the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, and
TWILIO_FROM_NUMBER, or pass account_sid, auth_token, and from_number as
named parameters to the cons... |
1ec6062faedb-16 | https://python.langchain.com/en/latest/reference/modules/utilities.html | characters in length.
to – The destination phone number in
[E.164](https://www.twilio.com/docs/glossary/what-e164) format for
SMS/MMS or
[Channel user address](https://www.twilio.com/docs/sms/channels#channel-addresses)
for other 3rd-party channels.
pydantic model langchain.utilities.WikipediaAPIWrapper[source]#
Wrappe... |
1ec6062faedb-17 | https://python.langchain.com/en/latest/reference/modules/utilities.html | Last updated on Jun 04, 2023. |
8f4ec7b51027-0 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | .rst
.pdf
Vector Stores
Vector Stores#
Wrappers on top of vector stores.
class langchain.vectorstores.AnalyticDB(connection_string: str, embedding_function: langchain.embeddings.base.Embeddings, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: ... |
8f4ec7b51027-1 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | create_collection() → None[source]#
create_tables_if_not_exists() → None[source]#
delete_collection() → None[source]#
drop_tables() → None[source]#
classmethod from_documents(documents: List[langchain.schema.Document], embedding: langchain.embeddings.base.Embeddings, collection_name: str = 'langchain', ids: Optional[Li... |
8f4ec7b51027-2 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding ve... |
8f4ec7b51027-3 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vecto... |
8f4ec7b51027-4 | 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, metric: str = 'angular', trees: int = 100, n_jobs: int = - 1, **kwargs: Any) → langchain.vectorstores.annoy.Annoy[source]#
Construct Annoy wrapper from raw documents.
Parameters
texts – List... |
8f4ec7b51027-5 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR... |
8f4ec7b51027-6 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | save_local(folder_path: str, prefault: bool = False) → None[source]#
Save Annoy index, docstore, and index_to_docstore_id to disk.
Parameters
folder_path – folder path to save index, docstore,
and index_to_docstore_id to.
prefault – Whether to pre-load the index into memory.
similarity_search(query: str, k: int = 4, se... |
8f4ec7b51027-7 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Returns
List of Documents most similar to the embedding.
similarity_search_with_score(query: str, k: int = 4, search_k: int = - 1) → List[Tuple[langchain.schema.Document, float]][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defau... |
8f4ec7b51027-8 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | class langchain.vectorstores.AtlasDB(name: str, embedding_function: Optional[langchain.embeddings.base.Embeddings] = None, api_key: Optional[str] = None, description: str = 'A description for your project', is_public: bool = True, reset_project_if_exists: bool = False)[source]#
Wrapper around Atlas: Nomic’s neural data... |
8f4ec7b51027-9 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | classmethod from_documents(documents: List[langchain.schema.Document], embedding: Optional[langchain.embeddings.base.Embeddings] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = None, persist_directory: Optional[str] = None, description: str = 'A description for your project... |
8f4ec7b51027-10 | 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, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = None, description: str = 'A description for your project', is_public: bool = True, rese... |
8f4ec7b51027-11 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | k (int) – Number of results to return. Defaults to 4.
Returns
List of documents most similar to the query text.
Return type
List[Document]
class langchain.vectorstores.Chroma(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Op... |
8f4ec7b51027-12 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, client: Optional[chromadb.Client] = None, **kwargs: Any)... |
8f4ec7b51027-13 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | persist_directory (Optional[str]) – Directory to persist the collection.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.
ids (Optional[List[str]]) – List of document IDs. Defaults to None.
client_settings (Optional[chromadb... |
8f4ec7b51027-14 | 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, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for ... |
8f4ec7b51027-15 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
:param embedding: Embedding to look up documents similar to.
:type embedding: str
:param k: Number of Documents... |
8f4ec7b51027-16 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | class langchain.vectorstores.DeepLake(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding_function: Optional[langchain.embeddings.base.Embeddings] = None, read_only: Optional[bool] = False, ingestion_batch_size: int = 1024, num_workers: int = 0, verbose: bool = True, **kwargs: Any)[source]#
Wrappe... |
8f4ec7b51027-17 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | delete(ids: Any[List[str], None] = None, filter: Any[Dict[str, str], None] = None, delete_all: Any[bool, None] = None) → bool[source]#
Delete the entities in the dataset
Parameters
ids (Optional[List[str]], optional) – The document_ids to delete.
Defaults to None.
filter (Optional[Dict[str, str]], optional) – The filte... |
8f4ec7b51027-18 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Local file system path of the form ./path/to/dataset or~/path/to/dataset or path/to/dataset.
In-memory path of the form mem://path/to/dataset which doesn’tsave the dataset, but keeps it in memory instead.
Should be used only for testing as it does not persist.
documents (List[Document]) – List of documents to add.
embe... |
8f4ec7b51027-19 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 t... |
8f4ec7b51027-20 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
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 simil... |
8f4ec7b51027-21 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | classmethod from_params(embedding: langchain.embeddings.base.Embeddings, work_dir: str, n_dim: int, dist_metric: Literal['cosine', 'ip', 'l2'] = 'cosine', max_elements: int = 1024, index: bool = True, ef_construction: int = 200, ef: int = 10, M: int = 16, allow_replace_deleted: bool = True, num_threads: int = 1, **kwar... |
8f4ec7b51027-22 | 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, work_dir: Optional[str] = None, n_dim: Optional[int] = None, **kwargs: Any) → langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch[source]#
Create an DocArrayHnswSearch store and insert d... |
8f4ec7b51027-23 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Defaults to “cosine_sim”.
**kwargs – Other keyword arguments to be passed to the get_doc_cls method.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, **kwargs: Any) → langchain.vectorstores.docarray.in_memory.DocArrayInMemorySear... |
8f4ec7b51027-24 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearch constructor as the named parameter
elasticsearch_url.
You can obtain your Elastic Cloud URL and login credentials by logging in to the
Elastic Cloud console at https://cloud.elastic.co, selecting your dep... |
8f4ec7b51027-25 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, refresh_indices: bool = True, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated ... |
8f4ec7b51027-26 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **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 to 4.
Returns
List of Documents most similar to the q... |
8f4ec7b51027-27 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of unique IDs.
Returns
List of ids from adding the texts into the vectorstore.
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]#
Run more texts... |
8f4ec7b51027-28 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the FAISS database
This is intended to be a quick way to get started.
Example
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
faiss = FAISS.from_texts(texts, ... |
8f4ec7b51027-29 | 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... |
8f4ec7b51027-30 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaul... |
8f4ec7b51027-31 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | result = vectorstore.similarity_search('text1')
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]#
Turn texts into embedding and add it to the database
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – ... |
8f4ec7b51027-32 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, timeout: Optional[int] = None, batch_size: int = 1000, **kwargs: Any) → List[str][source]#
Insert text data into Milvus.
Inserting data when the collection has not be made yet will result
in creating a new Collection. The data of the first entity d... |
8f4ec7b51027-33 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Create a Milvus collection, indexes it with HNSW, and insert data.
Parameters
texts (List[str]) – Text data.
embedding (Embeddings) – Embedding function.
metadatas (Optional[List[dict]]) – Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional) – Collection name to use. Defaults to
“LangC... |
8f4ec7b51027-34 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5
param (dict, optional) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.sea... |
8f4ec7b51027-35 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search(query: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Perform a similarity search against the query string.
Parameters
query (str) – The text to search.
k (int, optional) – How many res... |
8f4ec7b51027-36 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search_with_score(query: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]#
Perform a search on a query string and return results with score.
For more information about the search pa... |
8f4ec7b51027-37 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | k (int, optional) – The amount of results ot return. Defaults to 4.
param (dict) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.search() k... |
8f4ec7b51027-38 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | classmethod from_connection_string(connection_string: str, namespace: str, embedding: langchain.embeddings.base.Embeddings, **kwargs: Any) → langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch[source]#
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, coll... |
8f4ec7b51027-39 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search_with_score(query: str, *, k: int = 4, pre_filter: Optional[dict] = None, post_filter_pipeline: Optional[List[Dict]] = None) → List[Tuple[langchain.schema.Document, float]][source]#
Return MongoDB documents most similar to query, along with scores.
Use the knnBeta Operator available in MongoDB Atlas Se... |
8f4ec7b51027-40 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
ids – Optional list o... |
8f4ec7b51027-41 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search(query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Perform a similarity search with MyScale
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) – where ... |
8f4ec7b51027-42 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) – where condition string.
Defaults to None.
NOTE – Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use {self.metadata_column}.attribute instead of at... |
8f4ec7b51027-43 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | "description": "MyScale Client Configuration\n\nAttribute:\n myscale_host (str) : An URL to connect to MyScale backend.\n Defaults to 'localhost'.\n myscale_port (int) : URL port to connect with HTTP. Defaults to 8443.\n username (str) : Username to login. Defaults to None.\n passwor... |
8f4ec7b51027-44 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | "type": "integer"
},
"username": {
"title": "Username",
"env_names": "{'myscale_username'}",
"type": "string"
},
"password": {
"title": "Password",
"env_names": "{'myscale_password'}",
"type": "string"
},
"index_type": {
... |
8f4ec7b51027-45 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | "title": "Metric",
"default": "cosine",
"env_names": "{'myscale_metric'}",
"type": "string"
}
},
"additionalProperties": false
}
Config
env_file: str = .env
env_file_encoding: str = utf-8
env_prefix: str = myscale_
Fields
column_map (Dict[str, str])
database (str)
host (str)
index... |
8f4ec7b51027-46 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, bulk_size: int = 500, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the... |
8f4ec7b51027-47 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | Optional Keyword Args for Approximate Search:engine: “nmslib”, “faiss”, “lucene”; default: “nmslib”
space_type: “l2”, “l1”, “cosinesimil”, “linf”, “innerproduct”; default: “l2”
ef_search: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 512
ef_constru... |
8f4ec7b51027-48 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | contains a k-NN query and a filter.
subquery_clause: Query clause on the knn vector field; default: “must”
lucene_filter: the Lucene algorithm decides whether to perform an exact
k-NN search with pre-filtering or an approximate search with modified
post-filtering.
Optional Args for Script Scoring Search:search_type: “s... |
8f4ec7b51027-49 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | To use, you should have the pinecone-client python package installed.
Example
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import pinecone
# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(api_key="***", env... |
8f4ec7b51027-50 | 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, batch_size: int = 32, text_key: str = 'text', index_name: Optional[str] = None, namespace: Optional[str] = None, **kwargs: Any) → langchain.vectorstores.pine... |
8f4ec7b51027-51 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None, namespace: Optional[str] = None) → List[Tuple[langchain.schema.Document, float]][source]#
Return pinecone documents most similar to query, along with scores.
Parameters
query – Text to look up documents similar to.
k – Number of Documen... |
8f4ec7b51027-52 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | ids – Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size – How many vectors upload per-request.
Default: 64
Returns
List of ids from adding the texts into the vectorstore.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Opt... |
8f4ec7b51027-53 | https://python.langchain.com/en/latest/reference/modules/vectorstores.html | url – either host or str of “Optional[scheme], host, Optional[port],
Optional[prefix]”. Default: None
port – Port of the REST API interface. Default: 6333
grpc_port – Port of the gRPC interface. Default: 6334
prefer_grpc – If true - use gPRC interface whenever possible in custom methods.
Default: False
https – If true ... |
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