id stringlengths 14 15 | text stringlengths 17 2.72k | source stringlengths 47 115 |
|---|---|---|
117580e35849-1 | loader = AirbyteGongLoader(config=config, record_handler=handle_record, stream_name="calls")
docs = loader.load()
Incremental loads
Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are u... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_gong |
6967bd7a3f54-0 | Apify
This notebook shows how to use the Apify integration for LangChain.
Apify is a cloud platform for web scraping and data extraction, which provides an ecosystem of more than a thousand ready-made apps called Actors for various web scraping, crawling, and data extraction use cases. For example, you can use it to ex... | https://python.langchain.com/docs/integrations/tools/apify.html |
6967bd7a3f54-1 | apify = ApifyWrapper()
Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.
Note that if you already have some results in an Apify dataset, you can load them directly using ApifyDatasetLoader, as shown in this notebook. In that notebook, you'll also f... | https://python.langchain.com/docs/integrations/tools/apify.html |
a334e5ad8217-0 | Hugging Face Hub
The Hugging Face Hub is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.
This example showcases how to connect to the Hugging Face Hub and use different mo... | https://python.langchain.com/docs/integrations/llms/huggingface_hub.html |
a334e5ad8217-1 | print(llm_chain.run(question))
The FIFA World Cup was held in the year 1994. West Germany won the FIFA World Cup in 1994
Dolly, by Databricks
See Databricks organization page for a list of available models.
repo_id = "databricks/dolly-v2-3b"
llm = HuggingFaceHub(
repo_id=repo_id, model_kwargs={"temperature": 0.5, "max... | https://python.langchain.com/docs/integrations/llms/huggingface_hub.html |
a334e5ad8217-2 | Question: Who
Camel, by Writer
See Writer's organization page for a list of available models.
repo_id = "Writer/camel-5b-hf" # See https://huggingface.co/Writer for other options
llm = HuggingFaceHub(
repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 64}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
pr... | https://python.langchain.com/docs/integrations/llms/huggingface_hub.html |
a334e5ad8217-3 | llm = HuggingFaceHub(
repo_id=repo_id, model_kwargs={"max_length": 128, "temperature": 0.5}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain.run(question)) | https://python.langchain.com/docs/integrations/llms/huggingface_hub.html |
56030b2e3f9f-0 | Page Not Found
We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken. | https://python.langchain.com/docs/integrations/llms/opaqueprompts.opaque.co/api-keys |
7c8497680237-0 | Milvus
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
This notebook shows how to use functionality related to the Milvus vector database.
To run, you should have a Milvus instance up and running.
We want to use O... | https://python.langchain.com/docs/integrations/vectorstores/milvus.html |
26a4a994192d-0 | Apify Dataset
Apify Dataset is a scaleable append-only storage with sequential access built for storing structured web scraping results, such as a list of products or Google SERPs, and then export them to various formats like JSON, CSV, or Excel. Datasets are mainly used to save results of Apify Actors—serverless cloud... | https://python.langchain.com/docs/integrations/document_loaders/apify_dataset.html |
26a4a994192d-1 | https://docs.apify.com/platform/actors, https://docs.apify.com/platform/actors/running/actors-in-store, https://docs.apify.com/platform/security, https://docs.apify.com/platform/actors/examples | https://python.langchain.com/docs/integrations/document_loaders/apify_dataset.html |
57ece35ddc04-0 | ModelScope
Let's load the ModelScope Embedding class.
from langchain.embeddings import ModelScopeEmbeddings
model_id = "damo/nlp_corom_sentence-embedding_english-base"
embeddings = ModelScopeEmbeddings(model_id=model_id)
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_results = embeddi... | https://python.langchain.com/docs/integrations/text_embedding/modelscope_hub.html |
9dabe3f4026b-0 | This notebook goes over how to use Momento Cache to store chat message history using the MomentoChatMessageHistory class. See the Momento docs for more detail on how to get set up with Momento.
Note that, by default we will create a cache if one with the given name doesn't already exist.
You'll need to get a Momento au... | https://python.langchain.com/docs/integrations/memory/momento_chat_message_history.html |
23419a09dc98-0 | MyScale
MyScale is a cloud-based database optimized for AI applications and solutions, built on the open-source ClickHouse.
This notebook shows how to use functionality related to the MyScale vector database.
Setting up envrionments
pip install clickhouse-connect
We want to use OpenAIEmbeddings so we have to get the ... | https://python.langchain.com/docs/integrations/vectorstores/myscale.html |
23419a09dc98-1 | query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
Get connection info and data schema
Filtering
You can have direct access to myscale SQL where statement. You can write WHERE clause following standard SQL.
NOTE: Please be aware of SQ... | https://python.langchain.com/docs/integrations/vectorstores/myscale.html |
a0d7dbd4a403-0 | Neo4j Vector Index
Neo4j is an open-source graph database with integrated support for vector similarity search
It supports:
approximate nearest neighbor search
L2 distance and cosine distance
This notebook shows how to use the Neo4j vector index (Neo4jVector).
See the installation instruction.
# Pip install necessary p... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector.html |
a0d7dbd4a403-1 | Requirement already satisfied: certifi>=2017.4.17 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.20->openai) (2023.7.22)
Requirement already satisfied: attrs>=17.3.0 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from aiohttp->openai) (23.1.0)
Requirement already s... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector.html |
a0d7dbd4a403-2 | Requirement already satisfied: charset-normalizer<4,>=2 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken) (3.2.0)
Requirement already satisfied: idna<4,>=2.5 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken) (3.4)
Requirem... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector.html |
a0d7dbd4a403-3 | os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Neo4jVector
from langchain.document_loaders import TextLoader
from langchain.docstore.document import Do... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector.html |
a0d7dbd4a403-4 | 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.
--------------------------------------------------------------------------------
-----------------------------------------------... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector.html |
a0d7dbd4a403-5 | We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.891287088394165
A... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector.html |
a0d7dbd4a403-6 | store = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name=index_name,
)
Add documents
We can add documents to the existing vectorstore.
store.add_documents([Document(page_content="foo")])
['2f70679a-4416-11ee-b7c3-d46a6aa24f5b']
docs_with_score = store.simil... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector.html |
a0d7dbd4a403-7 | return_only_outputs=True,
)
{'answer': "The president honored Justice Stephen Breyer, who is retiring from the United States Supreme Court, and thanked him for his service. The president also mentioned that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy of excellen... | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector.html |
d03056f2f5fe-0 | Notion DB 1/2
Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.
This notebook covers how to load documents from a Notion database dump.... | https://python.langchain.com/docs/integrations/document_loaders/notion.html |
697c20c5ce90-0 | Notion DB 2/2
Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.
NotionDBLoader is a Python class for loading content from a Notion data... | https://python.langchain.com/docs/integrations/document_loaders/notiondb.html |
697c20c5ce90-1 | Click on the three-dot menu icon in the top right corner of the database view.
Select "Copy link" from the menu to copy the database URL to your clipboard.
The database ID is the long string of alphanumeric characters found in the URL. It typically looks like this: https://www.notion.so/username/8935f9d140a04f95a872520... | https://python.langchain.com/docs/integrations/document_loaders/notiondb.html |
697c20c5ce90-2 | NOTION_TOKEN = getpass()
DATABASE_ID = getpass()
from langchain.document_loaders import NotionDBLoader
loader = NotionDBLoader(
integration_token=NOTION_TOKEN,
database_id=DATABASE_ID,
request_timeout_sec=30, # optional, defaults to 10
) | https://python.langchain.com/docs/integrations/document_loaders/notiondb.html |
afad00f76da8-0 | Page Not Found
We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken. | https://python.langchain.com/docs/integrations/llms/azure_openai_example.html |
2640e09451fe-0 | OpenLLM
🦾 OpenLLM is an open platform for operating large language models (LLMs) in production. It enables developers to easily run inference with any open-source LLMs, deploy to the cloud or on-premises, and build powerful AI apps.
Installation
Install openllm through PyPI
Launch OpenLLM server locally
To start an ... | https://python.langchain.com/docs/integrations/llms/openllm.html |
25917100cb31-0 | OpenAI
Let's load the OpenAI Embedding class.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
[-0.003186025367556387,
0.011071979803637493,
-0.004020420763285827,
-0.011658221276953042,
-0.0010534035786864363... | https://python.langchain.com/docs/integrations/text_embedding/openai.html |
85716f13eedb-0 | OpenSearch
OpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on Apache Lucene.
This notebook shows how to use functionality related to the OpenSearch... | https://python.langchain.com/docs/integrations/vectorstores/opensearch.html |
85716f13eedb-1 | # If using the default Docker installation, use this instantiation instead:
# docsearch = OpenSearchVectorSearch.from_documents(
# docs,
# embeddings,
# opensearch_url="https://localhost:9200",
# http_auth=("admin", "admin"),
# use_ssl = False,
# verify_certs = False,
# ssl_assert_hostname = False,
# ssl_show_warn = Fa... | https://python.langchain.com/docs/integrations/vectorstores/opensearch.html |
85716f13eedb-2 | query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(
"What did the president say about Ketanji Brown Jackson",
k=1,
search_type="script_scoring",
)
print(docs[0].page_content)
similarity_search using Painless Scripting
similarity_search using Painless Scripting with Cust... | https://python.langchain.com/docs/integrations/vectorstores/opensearch.html |
85716f13eedb-3 | service = 'aoss' # must set the service as 'aoss'
region = 'us-east-2'
credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()
awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)
docsearch = OpenSearchVectorSearch.from_documents(
docs,
... | https://python.langchain.com/docs/integrations/vectorstores/opensearch.html |
23f580e28571-0 | This notebook goes over how to use the OpenWeatherMap component to fetch weather information.
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
import os
os.environ["OPENAI_API_KEY"] = ""
os.environ["OPENWEATHERMAP_API_KEY"] = ""
llm = OpenAI(temperature=0)
tools ... | https://python.langchain.com/docs/integrations/tools/openweathermap.html |
3b106258757b-0 | This notebook shows how to use the Postgres vector database (PGVector).
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
# PGVector needs the connection string to the database.
CONNECTION_STRING = "postgresql+psycopg2://harrisonchase@localhost:5432/test3"
# # Alternatively, you can create it from ... | https://python.langchain.com/docs/integrations/vectorstores/pgvector.html |
3b106258757b-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.
--------------------------------------------------------------------------------
-----------------------------------------------... | https://python.langchain.com/docs/integrations/vectorstores/pgvector.html |
3b106258757b-2 | And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
... | https://python.langchain.com/docs/integrations/vectorstores/pgvector.html |
b6ee642bdd58-0 | Pinecone
Pinecone is a vector database with broad functionality.
This notebook shows how to use functionality related to the Pinecone vector database.
To use Pinecone, you must have an API key. Here are the installation instructions.
pip install pinecone-client openai tiktoken langchain
import os
import getpass
os.env... | https://python.langchain.com/docs/integrations/vectorstores/pinecone.html |
b6ee642bdd58-1 | query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
Adding More Text to an Existing Index
More text can embedded and upserted to an existing Pinecone index using the add_texts function
index = pinecone.Index("langchain-demo")
vectorstor... | https://python.langchain.com/docs/integrations/vectorstores/pinecone.html |
22fd32d5aebf-0 | Page Not Found
We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken. | https://python.langchain.com/docs/integrations/tools/dataforseo.ipynb |
a3fa40b1a0f8-0 | 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.html |
a3fa40b1a0f8-1 | client = vectordb.Client()
vector_store = Epsilla.from_documents(
documents,
embeddings,
client,
db_path="/tmp/mypath",
db_name="MyDB",
collection_name="MyCollection"
)
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_store.similarity_search(query)
print(docs[0].page_content)
In state afte... | https://python.langchain.com/docs/integrations/vectorstores/epsilla.html |
4af80e533c84-0 | 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.html |
4af80e533c84-1 | # 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.html |
3c5d665cb1ad-0 | This notebook goes over how to use the google search component.
Then we will need to set some environment variables.
"STATE OF HAWAII. 1 Child's First Name. (Type or print). 2. Sex. BARACK. 3. This Birth. CERTIFICATE OF LIVE BIRTH. FILE. NUMBER 151 le. lb. Middle Name. Barack Hussein Obama II is an American former poli... | https://python.langchain.com/docs/integrations/tools/google_search.html |
b7342a218360-0 | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities.
The golden-query langchain tool i... | https://python.langchain.com/docs/integrations/tools/golden_query.html |
b7342a218360-1 | 'properties': [{'predicateId': 'name',
'instances': [{'value': 'Analog Devices', 'citations': []}]}]},
{'id': 3941943,
'latestVersionId': 60382250,
'properties': [{'predicateId': 'name',
'instances': [{'value': 'AbbVie Inc.', 'citations': []}]}]},
{'id': 4178762,
'latestVersionId': 60542667,
'properties': [{'predicateI... | https://python.langchain.com/docs/integrations/tools/golden_query.html |
1fe97ad24614-0 | Google Drive
Google Drive is a file storage and synchronization service developed by Google.
This notebook covers how to load documents from Google Drive. Currently, only Google Docs are supported.
Prerequisites
Create a Google Cloud project or use an existing project
Enable the Google Drive API
Authorize credentials ... | https://python.langchain.com/docs/integrations/document_loaders/google_drive.html |
1fe97ad24614-1 | file_types=["document", "sheet"],
recursive=False
)
Passing in Optional File Loaders
When processing files other than Google Docs and Google Sheets, it can be helpful to pass an optional file loader to GoogleDriveLoader. If you pass in a file loader, that file loader will be used on documents that do not have a Google... | https://python.langchain.com/docs/integrations/document_loaders/google_drive.html |
1fe97ad24614-2 | text/csv
text/markdown
image/png
image/jpeg
application/epub+zip
application/pdf
application/rtf
application/vnd.google-apps.document (GDoc)
application/vnd.google-apps.presentation (GSlide)
application/vnd.google-apps.spreadsheet (GSheet)
application/vnd.google.colaboratory (Notebook colab)
application/vnd.openxmlform... | https://python.langchain.com/docs/integrations/document_loaders/google_drive.html |
1fe97ad24614-3 | print(doc.page_content.strip()[:60]+"...")
You can customize your pattern.
from langchain.prompts.prompt import PromptTemplate
loader = GoogleDriveLoader(
folder_id=folder_id,
recursive=False,
template=PromptTemplate(
input_variables=["query", "query_name"],
template="fullText contains '{query}' and name contains '{que... | https://python.langchain.com/docs/integrations/document_loaders/google_drive.html |
1fe97ad24614-4 | If you use the mode="snippet", only the description will be used for the body. Else, the metadata['summary'] has the field.
Sometime, a specific filter can be used to extract some information from the filename, to select some files with specific criteria. You can use a filter.
Sometimes, many documents are returned. It... | https://python.langchain.com/docs/integrations/document_loaders/google_drive.html |
16055b24bdc6-0 | Clarifai
Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference.
This example goes over how to use LangChain to interact with Clarifai models.
To use Clarifai, you must have an account and a Personal Access Token (PAT) key.... | https://python.langchain.com/docs/integrations/llms/clarifai.html |
16055b24bdc6-1 | llm_chain.run(question)
'Justin Bieber was born on March 1, 1994. So, we need to figure out the Super Bowl winner for the 1994 season. The NFL season spans two calendar years, so the Super Bowl for the 1994 season would have taken place in early 1995. \n\nThe Super Bowl in question is Super Bowl XXIX, which was played ... | https://python.langchain.com/docs/integrations/llms/clarifai.html |
5339f238a30f-0 | Clarifai
Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference.
This example goes over how to use LangChain to interact with Clarifai models. Text embedding models in particular can be found here.
To use Clarifai, you must ... | https://python.langchain.com/docs/integrations/text_embedding/clarifai.html |
1c82aba3dd99-0 | 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.html |
1c82aba3dd99-1 | 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.html |
1c82aba3dd99-2 | 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.html |
1c82aba3dd99-3 | 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.html |
1c82aba3dd99-4 | 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.html |
1c82aba3dd99-5 | # 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.html |
1c82aba3dd99-6 | 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.html |
1c82aba3dd99-7 | 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.html |
1c82aba3dd99-8 | 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.html |
8c53260b7f7a-0 | C Transformers
The C Transformers library provides Python bindings for GGML models.
This example goes over how to use LangChain to interact with C Transformers models.
Install
%pip install ctransformers
Load Model
from langchain.llms import CTransformers
llm = CTransformers(model="marella/gpt-2-ggml")
Generate Text
pr... | https://python.langchain.com/docs/integrations/llms/ctransformers.html |
f377837e56d2-0 | Cohere
Let's load the Cohere Embedding class.
from langchain.embeddings import CohereEmbeddings
embeddings = CohereEmbeddings(cohere_api_key=cohere_api_key)
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text]) | https://python.langchain.com/docs/integrations/text_embedding/cohere.html |
09f1da0ed403-0 | 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.html |
c4bc5254a360-0 | SQL Database
This notebook showcases an agent designed to interact with a SQL databases. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors.
Note that, as this agent is in active development, all answers might not be correct. Additi... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-1 | SELECT * FROM "public"."users"
JOIN "public"."user_permissions" ON "public"."users".id = "public"."user_permissions".user_id
JOIN "public"."projects" ON "public"."users".id = "public"."projects".user_id
JOIN "public"."events" ON "public"."projects".id = "public"."events".project_id;
For a transactional SQL database, if... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-2 | > Entering new chain...
Invoking: `list_tables_sql_db` with `{}`
Album, Artist, Track, PlaylistTrack, InvoiceLine, sales_table, Playlist, Genre, Employee, Customer, Invoice, MediaType
Invoking: `schema_sql_db` with `PlaylistTrack`
CREATE TABLE "PlaylistTrack" (
"PlaylistId" INTEGER NOT NULL,
"TrackId" INTEGER NO... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-3 | > Finished chain.
'The `PlaylistTrack` table has two columns: `PlaylistId` and `TrackId`. It is a junction table that represents the relationship between playlists and tracks. \n\nHere is the schema of the `PlaylistTrack` table:\n\n```\nCREATE TABLE "PlaylistTrack" (\n\t"PlaylistId" INTEGER NOT NULL, \n\t"TrackId"... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-4 | SELECT * FROM 'PlaylistTrack' LIMIT 3;
PlaylistId TrackId
1 3402
1 3389
1 3390
Thought: I now know the final answer
Final Answer: The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.
> Finished chain.
'The PlaylistTrack table... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-5 | SELECT * FROM 'Customer' LIMIT 3;
CustomerId FirstName LastName Company Address City State Country PostalCode Phone Fax Email SupportRepId
1 Luís Gonçalves Embraer - Empresa Brasileira de Aeronáutica S.A. Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil 12227-000 +55 (12) 3923-5555 +55 (12) 3923-5566 luisg... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-6 | SELECT * FROM 'Invoice' LIMIT 3;
InvoiceId CustomerId InvoiceDate BillingAddress BillingCity BillingState BillingCountry BillingPostalCode Total
1 2 2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98
2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96
3 8 2009-01-03 00:00:00 G... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-7 | > Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: Invoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer
Thought: I should look at the schema of the Playlist and PlaylistTrack tables to see what columns I can use.
Action: sc... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-8 | SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name
Thought: The query looks correct, I can now execute it.
Action: query_sql_db
Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) ... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-9 | > Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: MediaType, Track, Invoice, Album, Playlist, Customer, Employee, InvoiceLine, PlaylistTrack, Genre, Artist
Thought: I should look at the schema of the Artist, InvoiceLine, and Track tables to see what columns I can use.
Action... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-10 | CREATE TABLE "InvoiceLine" (
"InvoiceLineId" INTEGER NOT NULL,
"InvoiceId" INTEGER NOT NULL,
"TrackId" INTEGER NOT NULL,
"UnitPrice" NUMERIC(10, 2) NOT NULL,
"Quantity" INTEGER NOT NULL,
PRIMARY KEY ("InvoiceLineId"),
FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
FOREIGN KEY("InvoiceId") REFERENCES "Inv... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
c4bc5254a360-11 | SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity
FROM Artist
INNER JOIN Track ON Artist.ArtistId = Track.ArtistId
INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId
GROUP BY Artist.Name
ORDER BY TotalQuantity DESC
LIMIT 3;
Thought: I now know the final answer.
Action: query_sql_db
Action ... | https://python.langchain.com/docs/integrations/toolkits/sql_database.html |
747b1badf38c-0 | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key.
from langchain.utilities import GoogleSerperAPIWrapper
from langchain.llms.openai import OpenAI
from langchain.agents import initialize_agent, Tool
from lang... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-1 | self_ask_with_search = initialize_agent(
tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True
)
self_ask_with_search.run(
"What is the hometown of the reigning men's U.S. Open champion?"
)
If you would also like to obtain the results in a structured way including metadata. For this we will be using the result... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-2 | 'TV, plus explore accessories, entertainment, ...',
'sitelinks': [{'title': 'Support',
'link': 'https://support.apple.com/'},
{'title': 'iPhone',
'link': 'https://www.apple.com/iphone/'},
{'title': 'Site Map',
'link': 'https://www.apple.com/sitemap/'},
{'title': 'Business',
'link': 'https://www.apple.com/business/'},
{... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-3 | '| Britannica',
'link': 'https://www.britannica.com/topic/Apple-Inc',
'snippet': 'Apple Inc., formerly Apple Computer, Inc., American '
'manufacturer of personal computers, smartphones, '
'tablet computers, computer peripherals, and computer '
'...',
'attributes': {'Related People': 'Steve Jobs Steve Wozniak Jony '
'Iv... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-4 | 'you with your stock trading and investing.',
'position': 6}],
'peopleAlsoAsk': [{'question': 'What does Apple Inc do?',
'snippet': 'Apple Inc. (Apple) designs, manufactures and '
'markets smartphones, personal\n'
'computers, tablets, wearables and accessories '
'and sells a range of related\n'
'services.',
'title': 'A... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-5 | 'link': 'https://en.wikipedia.org/wiki/Tim_Cook'}],
'relatedSearches': [{'query': 'Who invented the iPhone'},
{'query': 'Apple iPhone'},
{'query': 'History of Apple company PDF'},
{'query': 'Apple company history'},
{'query': 'Apple company introduction'},
{'query': 'Apple India'},
{'query': 'What does Apple Inc own'},... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-6 | 'thumbnailWidth': 225,
'thumbnailHeight': 224,
'source': 'Encyclopedia Britannica',
'domain': 'www.britannica.com',
'link': 'https://www.britannica.com/animal/lion',
'position': 2},
{'title': 'African lion, facts and photos',
'imageUrl': 'https://i.natgeofe.com/n/487a0d69-8202-406f-a6a0-939ed3704693/african-lion.JPG',
... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-7 | 'position': 4},
{'title': 'How to Draw a Realistic Lion like an Artist - Studio '
'Wildlife',
'imageUrl': 'https://studiowildlife.com/wp-content/uploads/2021/10/245528858_183911853822648_6669060845725210519_n.jpg',
'imageWidth': 1431,
'imageHeight': 2048,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-8 | 'other cool facts',
'imageUrl': 'https://www.gannett-cdn.com/-mm-/b2b05a4ab25f4fca0316459e1c7404c537a89702/c=0-0-1365-768/local/-/media/2022/03/16/USATODAY/usatsports/imageForEntry5-ODq.jpg?width=1365&height=768&fit=crop&format=pjpg&auto=webp',
'imageWidth': 1365,
'imageHeight': 768,
'thumbnailUrl': 'https://encrypted-... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-9 | 'position': 8},
{'title': "Lion | Smithsonian's National Zoo",
'imageUrl': 'https://nationalzoo.si.edu/sites/default/files/styles/1400_scale/public/animals/exhibit/africanlion-005.jpg?itok=6wA745g_',
'imageWidth': 1400,
'imageHeight': 845,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSgB3z_D4d... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-10 | 'of Robyn Denholm',
'link': 'https://www.reuters.com/business/autos-transportation/iss-recommends-tesla-investors-vote-against-re-election-robyn-denholm-2023-05-04/',
'snippet': 'Proxy advisory firm ISS on Wednesday recommended Tesla '
'investors vote against re-election of board chair Robyn '
'Denholm, citing "concern... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-11 | 'date': '6 hours ago',
'source': 'Bloomberg.com',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_3Eo4VI0H-nTeIbYc5DaQn5ep7YrWnmhx6pv8XddFgNF5zRC9gEpHfDq8yQ&s',
'position': 3},
{'title': 'Joby Aviation to get investment from Tesla shareholder '
'Baillie Gifford',
'link': 'https://finance.yahoo.com/... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-12 | {'title': 'The Tesla Model 3 Long Range AWD Is Now Available in the '
'U.S. With 325 Miles of Range',
'link': 'https://www.notateslaapp.com/news/1393/tesla-reopens-orders-for-model-3-long-range-after-months-of-unavailability',
'snippet': 'Tesla has reopened orders for the Model 3 Long Range '
'RWD, which has been unava... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-13 | 'footprint in the Pacific Northwest.',
'date': '22 hours ago',
'source': 'The Business Journals',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR9kIEHWz1FcHKDUtGQBS0AjmkqtyuBkQvD8kyIY3kpaPrgYaN7I_H2zoOJsA&s',
'position': 8},
{'title': 'Tesla (TSLA) Resumes Orders for Model 3 Long Range After '
'Bac... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-14 | 'a battery plant in Oklahoma, its third in...',
'date': '53 mins ago',
'source': 'Reuters',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSSTcsXeenqmEKdiekvUgAmqIPR4nlAmgjTkBqLpza-lLfjX1CwB84MoNVj0Q&s',
'position': 1},
{'title': 'Ryder lanza solución llave en mano para vehículos '
'eléctricos en EU... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-15 | 'source': 'THE BHARAT EXPRESS NEWS',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR_X9qqSwVFBBdos2CK5ky5IWIE3aJPCQeRYR9O1Jz4t-MjaEYBuwK7AU3AJQ&s',
'position': 3}]}
qdr:h (past hour) qdr:d (past day) qdr:w (past week) qdr:m (past month) qdr:y (past year)
You can specify intermediate time periods by... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-16 | 'longitude': -73.9642373,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNbNv6jZkJ9nyVi60__8c1DQbe_eEbugRAhIYye=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 2265,
'category': 'Italian'},
{'position': 3,
'title': 'Caravaggio',
'address': '23 E 74th St',
'latitude': 40.773412799999996,
'longitude': -73.9647... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-17 | {'position': 6,
'title': 'Come Prima',
'address': '903 Madison Ave',
'latitude': 40.772124999999996,
'longitude': -73.965012,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNrX19G0NVdtDyMovCQ-M-m0c_gLmIxrWDQAAbz=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 176,
'category': 'Italian'},
{'position': 7,
'tit... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
747b1badf38c-18 | 'rating': 4.5,
'ratingCount': 113,
'category': 'Italian'},
{'position': 10,
'title': 'Barbaresco',
'address': '843 Lexington Ave #1',
'latitude': 40.7654332,
'longitude': -73.9656873,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipMb9FbPuXF_r9g5QseOHmReejxSHgSahPMPJ9-8=w92-h92-n-k-no',
'rating': 4.3,
'ratin... | https://python.langchain.com/docs/integrations/tools/google_serper.html |
60b23f2519bc-0 | Hugging Face Hub
Let's load the Hugging Face Embedding class.
from langchain.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text]) | https://python.langchain.com/docs/integrations/text_embedding/huggingfacehub.html |
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