id stringlengths 14 15 | text stringlengths 17 2.72k | source stringlengths 47 115 |
|---|---|---|
14b25052c0d4-0 | HuggingFace Hub Tools
Huggingface Tools that supporting text I/O can be loaded directly using the load_huggingface_tool function.
# Requires transformers>=4.29.0 and huggingface_hub>=0.14.1
pip install --upgrade transformers huggingface_hub > /dev/null
from langchain.agents import load_huggingface_tool
tool = load_hug... | https://python.langchain.com/docs/integrations/tools/huggingface_tools |
0d65d6eae836-0 | This notebook shows how to use IFTTT Webhooks.
import os
key = os.environ["IFTTTKey"]
url = f"https://maker.ifttt.com/trigger/spotify/json/with/key/{key}"
tool = IFTTTWebhook(
name="Spotify", description="Add a song to spotify playlist", url=url
) | https://python.langchain.com/docs/integrations/tools/ifttt |
304a79fd759c-0 | Human are AGI so they can certainly be used as a tool to help out AI agent when it is confused.
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
llm = ChatOpenAI(temperature=0.0)
math_llm = Op... | https://python.langchain.com/docs/integrations/tools/human_tools |
304a79fd759c-1 | Observation: oh who said it
Thought:I can use DuckDuckGo Search to find out who said the quote
Action: DuckDuckGo Search
Action Input: "Who said 'Veni, vidi, vici'?" | https://python.langchain.com/docs/integrations/tools/human_tools |
304a79fd759c-2 | Action Input: "Who said 'Veni, vidi, vici'?"
Observation: Updated on September 06, 2019. "Veni, vidi, vici" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly "I came,... | https://python.langchain.com/docs/integrations/tools/human_tools |
304a79fd759c-3 | Thought:I now know the final answer
Final Answer: Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered". | https://python.langchain.com/docs/integrations/tools/human_tools |
304a79fd759c-4 | > Finished chain.
'Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".' | https://python.langchain.com/docs/integrations/tools/human_tools |
982e9bec6863-0 | Lemon Agent
Lemon Agent helps you build powerful AI assistants in minutes and automate workflows by allowing for accurate and reliable read and write operations in tools like Airtable, Hubspot, Discord, Notion, Slack and Github.
See full docs here.
Most connectors available today are focused on read-only operations, li... | https://python.langchain.com/docs/integrations/tools/lemonai |
982e9bec6863-1 | "tools": ["hackernews-get-user", "airtable-append-data"]
}
]
Your model will have access to these functions and will prefer them over self-selecting tools to solve a given task. All you have to do is to let the agent know that it should use a given function by including the function name in the prompt.
Include Lemon AI... | https://python.langchain.com/docs/integrations/tools/lemonai |
982e9bec6863-2 | """ Define your instruction to be given to your LLM """
prompt = f"""Read information from Hackernews for user {hackernews_username} and then write the results to
Airtable (baseId: {airtable_base_id}, tableId: {airtable_table_id}). Only write the fields "username", "karma"
and "created_at_i". Please make sure that Airt... | https://python.langchain.com/docs/integrations/tools/lemonai |
ad082c112c62-0 | Metaphor is a search engine fully designed to be used by LLMs. You can search and then get the contents for any page.
This notebook goes over how to use Metaphor search.
First, you need to set up the proper API keys and environment variables. Get 1000 free searches/month here.
Then enter your API key as an environment ... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-1 | SearchResponse(results=[Result(title='A Search Engine for Machine Intelligence', url='https://bellow.ai/', id='bdYc6hvHww_JvLv9k8NhPA', score=0.19460266828536987, published_date='2023-01-01', author=None, extract=None), Result(title='Adept: Useful General Intelligence', url='https://www.adept.ai/', id='aNBppxBZvQRZMov6... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-2 | extract=None), Result(title='AI.XYZ', url='https://www.ai.xyz/', id='A5c1ePEvsaQeml2Kui_-vA', score=0.1797989457845688, published_date='2023-01-01', author=None, extract=None), Result(title='Halist AI', url='https://halist.ai/', id='-lKPLSb4N4dgMZlTgoDvJg', score=0.17975398898124695, published_date='2023-03-01', author... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-3 | Invoking: `get_contents` with `{'ids': ['bdYc6hvHww_JvLv9k8NhPA', 'aNBppxBZvQRZMov6sFVj9g', 'jieb6sB53mId3EDo0z-SDw', 'kUiCuCjJYMD4N0NXdCtqlQ', '45iSS8KnJ9tL1ilPg3dL9A', 'nCoPMUtqWQqhUvsdTjJT6A', 'Zy0YaekZdd4rurPQKkys7A', 'A5c1ePEvsaQeml2Kui_-vA', '-lKPLSb4N4dgMZlTgoDvJg', '_XIjx1YLPfI4cKePIEc_bQ']}` | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-4 | GetContentsResponse(contents=[DocumentContent(id='bdYc6hvHww_JvLv9k8NhPA', url='https://bellow.ai/', title='A Search Engine for Machine Intelligence', extract="<div><div><h2>More Opinions</h2><p>Get responses from multiple AIs</p><p>Don't rely on a single source of truth, explore the full space of machine intelligence ... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-5 | preferred CRM vendor into our custom system. You have full control over <strong>how</strong> and <strong>when</strong> we get tickets.</p></div><p></p></div><div><p></p><div><h3>Pay per resolution</h3><p>We charge for each conversation we solve. No onboarding fees. No hourly rates. Pay for what you use.</p></div></div>... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-6 | and reduces churn.</p><p>â\x80\x8d<strong>No setup, no learning curve, just plug it in and go.</strong></p></div></div><div><div><p></p><div><p>â\x80\x9cIâ\x80\x99m able to better manage the team because I can pinpoint gaps in the teamâ\x80\x99s knowledge or training, and find room for process improvements.â\x80\x9d</p... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-7 | url='https://www.sidekickai.co/', title='Sidekick AI | Customer Service Automated', extract='<div><div><div><div><div><div><div><p>Hi, I am an AI named Jenny, working at Pizza Planet. How can I help you today?</p></div><div><p>How much are large pizzas with 1 topping?</p></div><div><p>For most toppings, a large with on... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-8 | Sidekick takes an omnichannel approach to customer service, aggregating all customer interactions across all platforms in one area. Currently most social media platforms are supported, along with website embeddings and API integration.\n </p><div><div><div><p>On the web.</p><div><p>Sidekick makes adding a live chat to ... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-9 | "23874",\n "body": "How much is a large 2 topping?"\n}</pre></div></div><div><p>Sample Response</p><div><pre>{\n "response": "A large'), DocumentContent(id='Zy0YaekZdd4rurPQKkys7A', url='https://www.hebbia.ai/', title='Hebbia - Search, Reinvented', extract="<div><div><h2>Direct to the point <br />with cutting-edge AI.<... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-10 | \n \n \n <p></p>\n \n </div><div><p>\n</p><h2><strong>Tackles info<br />overload</strong></h2>\n<p></p></div><div><p>\n</p><h4>“Like ChatGPT, but way more proactive and useful because it’s designed by me, for only me”</h4>\n<p></p></div><div>\n \n \n \n <p></p>\n \n </div><div><p>\n</p><h2><strong>Never sits<br />aroun... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-11 | AI to know.</h4>\n</div><div>\n \n \n \n <p></p>\n \n </div><div>\n<p><strong>STEP THREE</strong></p><h2><strong>Get started</strong></h2><h4>Ask your AI to help you with ideas and support throughout your day. Eventually it will be able to proactively support you.</h4>\n</div><div>\n \n \n \n <p></p>\n \n </div></div>\... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-12 | Airin clones how your top expert solves problems in as little as 2 hours. Airin creates an AI companion for the rest of your team by focusing on the patterns in your expert’s questions and hypotheses, not their answers. <a href="/how-it-works">Learn how it works </a></p></div></section><section><div><p> Your customers,... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-13 | 1. [Bellow AI](https://bellow.ai/): This startup provides a search engine for machine intelligence. It allows users to get responses from multiple AIs, exploring the full space of machine intelligence and getting highly tailored results.
2. [Adept AI](https://www.adept.ai/): Adept is focused on creating useful general... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-14 | "Here are some of the hottest AI agent startups and what they do:\n\n1. [Bellow AI](https://bellow.ai/): This startup provides a search engine for machine intelligence. It allows users to get responses from multiple AIs, exploring the full space of machine intelligence and getting highly tailored results.\n\n2. [Adept ... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-15 | This is the old way of using Metaphor - through our own in-house integration.
results takes in a Metaphor-optimized search query and a number of results (up to 500). It returns a list of results with title, url, author, and creation date.
[{'title': 'Core Views on AI Safety: When, Why, What, and How',
'url': 'https://w... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-16 | 'url': 'https://forum.effectivealtruism.org/posts/uGDCaPFaPkuxAowmH/anthropic-core-views-on-ai-safety-when-why-what-and-how',
'author': 'Jonmenaster',
'published_date': '2023-03-09'},
{'title': "[Linkpost] Sam Altman's 2015 Blog Posts Machine Intelligence Parts 1 & 2 - LessWrong",
'url': 'https://www.lesswrong.com/post... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-17 | end_crawl_date: Optional[str] - "Crawl date" refers to the date that Metaphor discovered a link, which is more granular and can be more useful than published date. If endCrawlDate is specified, results will only include links that were crawled before end_crawl_date. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:M... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
ad082c112c62-18 | llm = ChatOpenAI(model_name="gpt-4", temperature=0.7)
metaphor_tool = MetaphorSearchResults(api_wrapper=search)
agent_chain = initialize_agent(
[metaphor_tool, extract_text, navigate_tool],
llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
agent_chain.run(
"find me an interesting twee... | https://python.langchain.com/docs/integrations/tools/metaphor_search |
92d83e5bf24c-0 | Alibaba Cloud OpenSearch
Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch he... | https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch |
92d83e5bf24c-1 | embeddings = OpenAIEmbeddings()
Create opensearch settings.
settings = AlibabaCloudOpenSearchSettings(
endpoint="The endpoint of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch.",
instance_id="The identify of opensearch instance, You can find it from the console of Alibaba Cloud OpenSe... | https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch |
92d83e5bf24c-2 | # for example
# settings = AlibabaCloudOpenSearchSettings(
# endpoint="ha-cn-5yd39d83c03.public.ha.aliyuncs.com",
# instance_id="ha-cn-5yd39d83c03",
# datasource_name="ha-cn-5yd39d83c03_test",
# username="this is a user name",
# password="this is a password",
# embedding_index_name="index_embedding",
# field_name_mappi... | https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch |
3617eeab04e4-0 | Activeloop Deep Lake
Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.
This notebook ... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-1 | tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding embedding (42, 1536) float32 None
id text (42, 1) str None
metadata json (42, 1) str None
text text (42, 1) str None
To disable dataset summary printings all the time, you can specify verbose=False during VectorStore initiali... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-2 | qa = RetrievalQA.from_chain_type(
llm=OpenAIChat(model="gpt-3.5-turbo"),
chain_type="stuff",
retriever=db.as_retriever(),
)
/home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/llms/openai.py:786: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instea... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-3 | [Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justic... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-4 | Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards f... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-5 | Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-6 | Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-7 | Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards f... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-8 | Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-9 | docs = text_splitter.split_documents(documents)
embedding = OpenAIEmbeddings()
db = DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True)
ids = db.add_documents(docs)
Your Deep Lake dataset has been successfully created!
Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embeddin... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-10 | docs = text_splitter.split_documents(documents)
embedding = OpenAIEmbeddings()
db = DeepLake(
dataset_path=dataset_path,
embedding=embeddings,
overwrite=True,
runtime={"tensor_db": True},
)
ids = db.add_documents(docs)
Your Deep Lake dataset has been successfully created!
|
Dataset(path='hub://adilkhan/langchain_te... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-11 | Evaluating ingest: 100%|██████████| 1/1 [00:10<00:00
\
Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (4, 1536) float32 None
ids text (4, 1) str None
metadata jso... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
3617eeab04e4-12 | This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy
/
hub://davitbun/langchain_test_copy loaded successfully.
Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage
Dataset(path='hub://davitb... | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake |
acec95af7907-0 | This notebook shows how to use functionality related to the AnalyticDB vector database. To run, you should have an AnalyticDB instance up and running:
from langchain.document_loaders import TextLoader
loader = TextLoader("../../../state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter... | https://python.langchain.com/docs/integrations/vectorstores/analyticdb |
feee4efea085-0 | This notebook shows how to use functionality related to the Annoy vector database.
# allows for custom annoy parameters, defaults are n_trees=100, n_jobs=-1, metric="angular"
vector_store_v2 = Annoy.from_texts(
texts, embeddings_func, metric="dot", n_trees=100, n_jobs=1
)
[Document(page_content='Madam Speaker, Madam Vi... | https://python.langchain.com/docs/integrations/vectorstores/annoy |
feee4efea085-1 | Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the Unit... | https://python.langchain.com/docs/integrations/vectorstores/annoy |
feee4efea085-2 | Document(page_content='We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n\nTogether with our allies –we are right now enforcing powerful economic sanctions. \n\nWe are cutting off Russia’s largest banks from the international financial system. ... | https://python.langchain.com/docs/integrations/vectorstores/annoy |
feee4efea085-3 | metadatas = [{"x": "food"}, {"x": "food"}, {"x": "stuff"}, {"x": "animal"}]
# embeddings
embeddings = embeddings_func.embed_documents(texts)
# embedding dim
f = len(embeddings[0])
# index
metric = "angular"
index = AnnoyIndex(f, metric=metric)
for i, emb in enumerate(embeddings):
index.add_item(i, emb)
index.build(1... | https://python.langchain.com/docs/integrations/vectorstores/annoy |
657518294229-0 | Atlas
Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser.
This notebook shows you how to use functionality related to the AtlasDB vectorstore.
python3 -m spacy download en_core_we... | https://python.langchain.com/docs/integrations/vectorstores/atlas |
ce611e96f5fc-0 | AwaDB
AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.
This notebook shows how to use functionality related to the AwaDB.
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import AwaDB
from langchain.document_loaders import Text... | https://python.langchain.com/docs/integrations/vectorstores/awadb |
55067484976a-0 | Azure Cognitive Search
Azure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
Vector search is currently in public previ... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-1 | vector_store.add_documents(documents=docs)
Perform a vector similarity search
Execute a pure vector similarity search using the similarity_search() method:
# Perform a similarity search
docs = vector_store.similarity_search(
query="What did the president say about Ketanji Brown Jackson",
k=3,
search_type="similarity",... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-2 | And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Perform a vector similarity search with relevance scores
Execute a pure vector similarity search using the similarity_search_wi... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-3 | 0.8441472),
(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this cou... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-4 | 0.82153815),
(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judge... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-5 | Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President ... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-6 | fields = [
SimpleField(
name="id",
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name="content",
type=SearchFieldDataType.String,
searchable=True,
),
SearchField(
name="content_vector",
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dime... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-7 | vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embedding_function,
fields=fields,
)
Perform a query with a custom filter
# Data in the metadata dictionary with a corresponding field in the index will ... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-8 | embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
embedding_function = embeddings.embed_query
fields = [
SimpleField(
name="id",
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name="content",
type=SearchFieldDataType.String,
searchable=True,
),
SearchField... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
55067484976a-9 | vector_store.add_texts(
["Test 1", "Test 1", "Test 1"],
[
{"title": "Title 1", "source": "source1", "random": "10290", "last_update": today},
{"title": "Title 1", "source": "source1", "random": "48392", "last_update": yesterday},
{"title": "Title 1", "source": "source1", "random": "32893", "last_update": one_month_ago}... | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
42178966d239-0 | BagelDB (Open Vector Database for AI), is like GitHub for AI data. It is a collaborative platform where users can create, share, and manage vector datasets. It can support private projects for independent developers, internal collaborations for enterprises, and public contributions for data DAOs.
{'ids': ['578c6d24-376... | https://python.langchain.com/docs/integrations/vectorstores/bageldb |
efe1253d7cb7-0 | Cassandra
Apache Cassandra® is a NoSQL, row-oriented, highly scalable and highly available database.
Newest Cassandra releases natively support Vector Similarity Search.
To run this notebook you need either a running Cassandra cluster equipped with Vector Search capabilities (in pre-release at the time of writing) or a... | https://python.langchain.com/docs/integrations/vectorstores/cassandra |
efe1253d7cb7-1 | if database_mode == "C":
if CASSANDRA_CONTACT_POINTS:
cluster = Cluster(
[cp.strip() for cp in CASSANDRA_CONTACT_POINTS.split(",") if cp.strip()]
)
else:
cluster = Cluster()
session = cluster.connect()
elif database_mode == "A":
ASTRA_DB_CLIENT_ID = "token"
cluster = Cluster(
cloud={
"secure_connect_bundle": ASTRA_DB_S... | https://python.langchain.com/docs/integrations/vectorstores/cassandra |
efe1253d7cb7-2 | # docsearch_preexisting.similarity_search(query, k=2)
print(docs[0].page_content)
Maximal Marginal Relevance Searches
In addition to using similarity search in the retriever object, you can also use mmr as retriever.
retriever = docsearch.as_retriever(search_type="mmr")
matched_docs = retriever.get_relevant_documents(... | https://python.langchain.com/docs/integrations/vectorstores/cassandra |
28acd22eac49-0 | ClickHouse
ClickHouse is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like L2Distance) as well as approximate ne... | https://python.langchain.com/docs/integrations/vectorstores/clickhouse |
28acd22eac49-1 | Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President ... | https://python.langchain.com/docs/integrations/vectorstores/clickhouse |
28acd22eac49-2 | embeddings = OpenAIEmbeddings()
for i, d in enumerate(docs):
d.metadata = {"doc_id": i}
docsearch = Clickhouse.from_documents(docs, embeddings)
Inserting data...: 100%|██████████| 42/42 [00:00<00:00, 6939.56it/s]
meta = docsearch.metadata_column
output = docsearch.similarity_search_with_relevance_scores(
"What did th... | https://python.langchain.com/docs/integrations/vectorstores/clickhouse |
4d3097bf0d7d-0 | Chroma
Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.
Install Chroma with:
Chroma runs in various modes. See below for examples of each integrated with LangChain.
in-memory - in a python script or jupyter notebook
in-memory with pe... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
4d3097bf0d7d-1 | Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army v... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
4d3097bf0d7d-2 | And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Passing a Chroma Client into Langchain
You can also create a Chroma Client and pass it to LangChain. This is particularly usefu... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
4d3097bf0d7d-3 | There are 3 in the collection
Basic Example (using the Docker Container)
You can also run the Chroma Server in a Docker container separately, create a Client to connect to it, and then pass that to LangChain.
Chroma has the ability to handle multiple Collections of documents, but the LangChain interface expects one, ... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
4d3097bf0d7d-4 | And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Update and Delete
While building toward a real application, you want to go beyond adding data, and also update and delete data.... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
4d3097bf0d7d-5 | # delete the last document
print("count before", example_db._collection.count())
example_db._collection.delete(ids=[ids[-1]])
print("count after", example_db._collection.count())
{'source': '../../../state_of_the_union.txt'}
{'ids': ['1'], 'embeddings': None, 'metadatas': [{'new_value': 'hello world', 'source': '../../... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
4d3097bf0d7d-6 | Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President ... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
4d3097bf0d7d-7 | And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Other Information
Similarity search with score
The returned distance score is cosine distance. Therefore, a lower score is bet... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
4d3097bf0d7d-8 | retriever.get_relevant_documents(query)[0]
Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedica... | https://python.langchain.com/docs/integrations/vectorstores/chroma |
e2e54e02dc1a-0 | Dingo
Dingo is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently co... | https://python.langchain.com/docs/integrations/vectorstores/dingo |
e2e54e02dc1a-1 | # The OpenAI embedding model `text-embedding-ada-002 uses 1536 dimensions`
docsearch = Dingo.from_documents(docs, embeddings, client=dingo_client, index_name=index_name)
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ... | https://python.langchain.com/docs/integrations/vectorstores/dingo |
35646220f27b-0 | DashVector
DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.
This notebook shows how to use functionality related to the DashVector vector ... | https://python.langchain.com/docs/integrations/vectorstores/dashvector |
718bd2edacfd-0 | DocArray InMemorySearch
DocArrayInMemorySearch is a document index provided by Docarray that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.
This notebook shows how to use functionality related to the DocArrayInMemorySearch.
Setup
Uncomme... | https://python.langchain.com/docs/integrations/vectorstores/docarray_in_memory |
718bd2edacfd-1 | And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Similarity search with score
The returned distance score is cosine distance. Therefore, a lower score is better.
docs = db.simi... | https://python.langchain.com/docs/integrations/vectorstores/docarray_in_memory |
b0536bc7338f-0 | DocArray HnswSearch
DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite.
This notebook shows how to use functionality related to th... | https://python.langchain.com/docs/integrations/vectorstores/docarray_hnsw |
b0536bc7338f-1 | And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Similarity search with score
The returned distance score is cosine distance. Therefore, a lower score is better.
docs = db.simi... | https://python.langchain.com/docs/integrations/vectorstores/docarray_hnsw |
7510d0ce2138-0 | Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library.
This notebook shows how to use functionality related to the Elasticsearch database.
pip install elasticsearch openai tiktoken langcha... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-1 | embedding = OpenAIEmbeddings()
elastic_vector_search = ElasticsearchStore(
es_url="http://localhost:9200",
index_name="test_index",
embedding=embedding,
es_user="elastic",
es_password="changeme"
)
How to obtain a password for the default "elastic" user?
To obtain your Elastic Cloud password for the default "elastic" u... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-2 | loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = ElasticsearchStore.from_documents(
docs, embeddings, es_url="http://localhost... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-3 | query = "What did the president say about Ketanji Brown Jackson"
results = db.similarity_search(query)
print(results)
[Document(page_content='One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I n... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-4 | Metadata
ElasticsearchStore supports metadata to stored along with the document. This metadata dict object is stored in a metadata object field in the Elasticsearch document. Based on the metadata value, Elasticsearch will automatically setup the mapping by infering the data type of the metadata value. For example, if ... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-5 | db = ElasticsearchStore.from_documents(
docs, embeddings, es_url="http://localhost:9200", index_name="test-metadata"
) | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-6 | query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].metadata)
{'source': '../../modules/state_of_the_union.txt', 'date': '2016-01-01', 'rating': 2, 'author': 'John Doe'}
With metadata added to the documents, you can add metadata filtering at query time.
Exa... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-7 | print(docs[0].metadata)
Filter supports many more types of queries than above.
Read more about them in the documentation.
Distance Similarity Algorithm
Elasticsearch supports the following vector distance similarity algorithms:
cosine
euclidean
dot_product
The cosine similarity algorithm is the default.
You can specif... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-8 | db = ElasticsearchStore.from_documents(
docs,
embeddings,
es_url="http://localhost:9200",
index_name="test",
distance_strategy="COSINE"
# distance_strategy="EUCLIDEAN_DISTANCE"
# distance_strategy="DOT_PRODUCT"
)
Retrieval Strategies
Elasticsearch has big advantages over other vector only databases from its ability... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-9 | db = ElasticsearchStore.from_documents(
docs,
embeddings,
es_url="http://localhost:9200",
index_name="test",
strategy=ElasticsearchStore.ApproxRetrievalStrategy(
hybrid=True,
)
)
When hybrid is enabled, the query performed will be a combination of approximate semantic search and keyword based search.
It will use rr... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-10 | # creating a new index with the pipeline,
# not relying on langchain to create the index
db.client.indices.create(
index=APPROX_SELF_DEPLOYED_INDEX_NAME,
mappings={
"properties": {
"text_field": {"type": "text"},
"vector_query_field": {
"properties": {
"predicted_value": {
"type": "dense_vector",
"dims": 384,
"index": ... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-11 | # Perform search
db.similarity_search("hello world", k=10)
SparseVectorRetrievalStrategy (ELSER)
This strategy uses Elasticsearch's sparse vector retrieval to retrieve the top-k results. We only support our own "ELSER" embedding model for now.
NOTE This requires the ELSER model to be deployed and running in Elasticsea... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-12 | db = ElasticsearchStore.from_documents(
docs,
embeddings,
es_url="http://localhost:9200",
index_name="test",
strategy=ElasticsearchStore.ExactRetrievalStrategy()
)
Customise the Query
With custom_query parameter at search, you are able to adjust the query that is used to retrieve documents from Elasticsearch. This i... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
7510d0ce2138-13 | Results:
page_content='One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue J... | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
fb1b9029f3b3-0 | Epsilla
Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0.
This notebook shows how to use the functionalities related to the Epsilla vector database.
As a prerequisite, you need to have a running Epsilla vecto... | https://python.langchain.com/docs/integrations/vectorstores/epsilla |
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