id stringlengths 14 15 | text stringlengths 17 2.72k | source stringlengths 47 115 |
|---|---|---|
9f187dcc1991-0 | from langchain.document_loaders import YoutubeLoader
from langchain.document_loaders import GoogleApiYoutubeLoader | https://python.langchain.com/docs/integrations/providers/youtube |
d2be7eb58c33-0 | Zep stores, summarizes, embeds, indexes, and enriches conversational AI chat histories, and exposes them via simple, low-latency APIs.
Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.
Auto-summarization of memory messages based on a configurable message windo... | https://python.langchain.com/docs/integrations/providers/zep |
719cfdca62e7-0 | A wrapper around Zilliz indexes allows you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.vectorstores import Milvus | https://python.langchain.com/docs/integrations/providers/zilliz |
60bf3f9f0f9f-0 | Slack
Slack is an instant messaging program.
Installation and Setup
There isn't any special setup for it.
Document Loader
See a usage example.
from langchain.document_loaders import SlackDirectoryLoader | https://python.langchain.com/docs/integrations/providers/slack |
97da9c724831-0 | spaCy
spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.
Installation and Setup
Text Splitter
See a usage example.
from langchain.text_splitter import SpacyTextSplitter | https://python.langchain.com/docs/integrations/providers/spacy |
8f324e085e7d-0 | Spreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreed... | https://python.langchain.com/docs/integrations/providers/spreedly |
65eff7b93ea1-0 | StarRocks
StarRocks is a High-Performance Analytical Database. StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.
Usually StarRocks is categorized into OLAP, and it has showed excellent performance in ClickBench — a ... | https://python.langchain.com/docs/integrations/providers/starrocks |
5ec33765d36b-0 | This page covers how to use the StochasticAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific StochasticAI wrappers.
from langchain.llms import StochasticAI | https://python.langchain.com/docs/integrations/providers/stochasticai |
0eb4f78da350-0 | Stripe
Stripe is an Irish-American financial services and software as a service (SaaS) company. It offers payment-processing software and application programming interfaces for e-commerce websites and mobile applications.
Installation and Setup
See setup instructions.
Document Loader
See a usage example.
from langcha... | https://python.langchain.com/docs/integrations/providers/stripe |
526e7fc5e413-0 | Supabase (Postgres)
Supabase is an open source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.
PostgreSQL also known as Postgres, is a free and open-source relational database managemen... | https://python.langchain.com/docs/integrations/providers/supabase |
7787c3f99b3f-0 | This page covers how to use Nebula, Symbl.ai's LLM, ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Nebula wrappers.
from langchain.llms import Nebula
llm = Nebula() | https://python.langchain.com/docs/integrations/providers/symblai_nebula |
165eafb68d20-0 | This page covers how to use the Tair ecosystem within LangChain.
Install Tair Python SDK with pip install tair.
There exists a wrapper around TairVector, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.vectorstores import Tair | https://python.langchain.com/docs/integrations/providers/tair |
7f833fcb4688-0 | This page covers how to use the TencentVectorDB ecosystem within LangChain.
There exists a wrapper around TencentVectorDB, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.vectorstores import TencentVectorDB
For a more detailed walkthrough of the TencentVectorDB ... | https://python.langchain.com/docs/integrations/providers/tencentvectordb |
f7e3c8bd5cf7-0 | Telegram Messenger is a globally accessible freemium, cross-platform, encrypted, cloud-based and centralized instant messaging service. The application also provides optional end-to-end encrypted chats and video calling, VoIP, file sharing and several other features.
from langchain.document_loaders import TelegramChatF... | https://python.langchain.com/docs/integrations/providers/telegram |
7f236aa3b76f-0 | You need to install tensorflow and tensorflow-datasets python packages.
pip install tensorflow-dataset | https://python.langchain.com/docs/integrations/providers/tensorflow_datasets |
797deffc83b6-0 | Tigris is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications. Tigris eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead.
pip in... | https://python.langchain.com/docs/integrations/providers/tigris |
fbdc4cd21896-0 | 2Markdown
2markdown service transforms website content into structured markdown files.
Installation and Setup
We need the API key. See instructions how to get it.
Document Loader
See a usage example.
from langchain.document_loaders import ToMarkdownLoader | https://python.langchain.com/docs/integrations/providers/tomarkdown |
0f5bec9f2c95-0 | Trello
Trello is a web-based project management and collaboration tool that allows individuals and teams to organize and track their tasks and projects. It provides a visual interface known as a "board" where users can create lists and cards to represent their tasks and activities. The TrelloLoader allows us to load ca... | https://python.langchain.com/docs/integrations/providers/trello |
a60a25c20938-0 | TruLens
This page covers how to use TruLens to evaluate and track LLM apps built on langchain.
What is TruLens?
TruLens is an opensource package that provides instrumentation and evaluation tools for large language model (LLM) based applications.
Quick start
Once you've created your LLM chain, you can use TruLens for... | https://python.langchain.com/docs/integrations/providers/trulens |
5766d0d0a80b-0 | We must initialize the loader with the Twitter API token, and we need to set up the Twitter username.
from langchain.document_loaders import TwitterTweetLoader | https://python.langchain.com/docs/integrations/providers/twitter |
5e30579930b4-0 | If you are using a loader that runs locally, use the following steps to get unstructured and its dependencies running locally.
If you want to get up and running with less set up, you can simply run pip install unstructured and use UnstructuredAPIFileLoader or UnstructuredAPIFileIOLoader. That will process your document... | https://python.langchain.com/docs/integrations/providers/unstructured |
7e43e6296ac2-0 | Typesense
Typesense is an open source, in-memory search engine, that you can either self-host or run on Typesense Cloud. Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and s... | https://python.langchain.com/docs/integrations/providers/typesense |
cad10f9e3f83-0 | USearch's base functionality is identical to FAISS, and the interface should look familiar if you have ever investigated Approximate Nearest Neighbors search. USearch and FAISS both employ HNSW algorithm, but they differ significantly in their design principles. USearch is compact and broadly compatible with FAISS with... | https://python.langchain.com/docs/integrations/providers/usearch |
90b18b7d475d-0 | Vectara
What is Vectara?
Vectara Overview:
Vectara is developer-first API platform for building GenAI applications
To use Vectara - first sign up and create an account. Then create a corpus and an API key for indexing and searching.
You can use Vectara's indexing API to add documents into Vectara's index
You can use Ve... | https://python.langchain.com/docs/integrations/providers/vectara/ |
90b18b7d475d-1 | As an example:
vectara.add_files(["path/to/file1.pdf", "path/to/file2.pdf",...])
To query the vectorstore, you can use the similarity_search method (or similarity_search_with_score), which takes a query string and returns a list of results:
results = vectara.similarity_score("what is LangChain?")
similarity_search_with... | https://python.langchain.com/docs/integrations/providers/vectara/ |
da638ded8bc5-0 | Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. | https://python.langchain.com/docs/integrations/providers/vespa |
f6c80b91d1c8-0 | WandB Tracing
There are two recommended ways to trace your LangChains:
Setting the LANGCHAIN_WANDB_TRACING environment variable to "true".
Using a context manager with tracing_enabled() to trace a particular block of code.
Note if the environment variable is set, all code will be traced, regardless of whether or not it... | https://python.langchain.com/docs/integrations/providers/wandb_tracing |
f6c80b91d1c8-1 | > Entering new AgentExecutor chain...
I need to use a calculator to solve this.
Action: Calculator
Action Input: 5^.123243
Observation: Answer: 1.2193914912400514
Thought: I now know the final answer.
Final Answer: 1.2193914912400514
> Finished chain.
> Entering new AgentExecutor chain...
I need to use a calculator ... | https://python.langchain.com/docs/integrations/providers/wandb_tracing |
ab3159d24508-0 | Weights & Biases
This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.
View Report
Note: the Wan... | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
ab3159d24508-1 | This function is used to try the callback handler.
Scenarios:
1. OpenAI LLM
2. Chain with multiple SubChains on multiple generations
3. Agent with Tools
"""
session_group = datetime.now().strftime("%m.%d.%Y_%H.%M.%S")
wandb_callback = WandbCallbackHandler(
job_type="inference",
project="langchain_callback_demo",
group=... | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
ab3159d24508-2 | reset: bool = True,
finish: bool = False,
The flush_tracker function is used to log LangChain sessions to Weights & Biases. It takes in the LangChain module or agent, and logs at minimum the prompts and generations alongside the serialized form of the LangChain module to the specified Weights & Biases project. By defau... | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
ab3159d24508-3 | View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo</a>
View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_dem... | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
ab3159d24508-4 | test_prompts = [
{
"title": "documentary about good video games that push the boundary of game design"
},
{"title": "cocaine bear vs heroin wolf"},
{"title": "the best in class mlops tooling"},
]
synopsis_chain.apply(test_prompts)
wandb_callback.flush_tracker(synopsis_chain, name="agent")
Waiting for W&B process to fin... | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
ab3159d24508-5 | View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>
from langchain.agents import initialize_agent, load_tools
from langchain.agents import AgentType
# SCENARIO 3 - Agent with Tools
tools = l... | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
ab3159d24508-6 | Thought: I now know the final answer.
Final Answer: Leo DiCaprio's girlfriend is Camila Morrone and her current age raised to the 0.43 power is 4.059182145592686.
> Finished chain.
Waiting for W&B process to finish... <strong style="color:green">(success).</strong>
View run <strong style="color:#cdcd00">agent</strong>... | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
8032b7c9c765-0 | We must set up the OpenWeatherMap API token.
from langchain.document_loaders import WeatherDataLoader | https://python.langchain.com/docs/integrations/providers/weather |
d71c1c9d3c94-0 | This page covers how to use the PipelineAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific PipelineAI wrappers.
from langchain.llms import PipelineAI | https://python.langchain.com/docs/integrations/providers/pipelineai |
ca6c21e8832d-0 | Portkey
LLMOps for Langchain
Portkey brings production readiness to Langchain. With Portkey, you can
view detailed metrics & logs for all requests,
enable semantic cache to reduce latency & costs,
implement automatic retries & fallbacks for failed requests,
add custom tags to requests for better tracking and analy... | https://python.langchain.com/docs/integrations/providers/portkey/ |
ca6c21e8832d-1 | # Let's test it out!
agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")
You can see the requests' logs along with the trace id on Portkey dashboard:
Advanced Features
Logging: Log all your LLM requests automatically by sending them through Portkey. ... | https://python.langchain.com/docs/integrations/providers/portkey/ |
ca6c21e8832d-2 | )
For detailed information on each feature and how to use it, please refer to the Portkey docs. If you have any questions or need further assistance, reach out to us on Twitter.. | https://python.langchain.com/docs/integrations/providers/portkey/ |
1e6c3a6b4b8b-0 | Learn how to use LangChain with models on Predibase.
Predibase integrates with LangChain by implementing LLM module. You can see a short example below or a full notebook under LLM > Integrations > Predibase.
import os
os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"
from langchain.llms import Predibase
m... | https://python.langchain.com/docs/integrations/providers/predibase |
843aff3ceeb2-0 | This page covers how to use the Prediction Guard ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.
You can provide the name of the Prediction Guard model as an argument when initializing the LLM:
Finally, you can provide an "outpu... | https://python.langchain.com/docs/integrations/providers/predictionguard |
843aff3ceeb2-1 | # Your Prediction Guard API key. Get one at predictionguard.com
os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
pgllm = PredictionGuard(model="OpenAI-text-davinci-003")
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, inpu... | https://python.langchain.com/docs/integrations/providers/predictionguard |
2a9fbeb3988b-0 | Psychic
Psychic is a platform for integrating with SaaS tools like Notion, Zendesk, Confluence, and Google Drive via OAuth and syncing documents from these applications to your SQL or vector database. You can think of it like Plaid for unstructured data.
Installation and Setup
Psychic is easy to set up - you import t... | https://python.langchain.com/docs/integrations/providers/psychic |
051019542957-0 | PromptLayer
This page covers how to use PromptLayer within LangChain. It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
Installation and Setup
If you want to work with PromptLayer:
Install the promptlayer python library pip install promptlayer
Create a PromptLay... | https://python.langchain.com/docs/integrations/providers/promptlayer |
62358fe1571a-0 | PubMed® by The National Center for Biotechnology Information, National Library of Medicine comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.
You need to i... | https://python.langchain.com/docs/integrations/providers/pubmed |
099d99a6d884-0 | This page covers how to use the Qdrant ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
There exists a wrapper around Qdrant indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain... | https://python.langchain.com/docs/integrations/providers/qdrant |
2f8dc92531b5-0 | Ray Serve
Ray Serve is a scalable model serving library for building online inference APIs. Serve is particularly well suited for system composition, enabling you to build a complex inference service consisting of multiple chains and business logic all in Python code.
Goal of this notebook
This notebook shows a simpl... | https://python.langchain.com/docs/integrations/providers/ray_serve |
2f8dc92531b5-1 | def _run_chain(self, text: str):
return self.chain(text)
async def __call__(self, request: Request):
# 1. Parse the request
text = request.query_params["text"]
# 2. Run the chain
resp = self._run_chain(text)
# 3. Return the response
return resp["text"]
Now we can bind the deployment.
# Bind the model to deployment
dep... | https://python.langchain.com/docs/integrations/providers/ray_serve |
b06cf9159b3c-0 | Rebuff
Rebuff is a self-hardening prompt injection detector. It is designed to protect AI applications from prompt injection (PI) attacks through a multi-stage defense.
Homepage
Playground
Docs
GitHub Repository
Installation and Setup
# !pip3 install rebuff openai -U
REBUFF_API_KEY = "" # Use playground.rebuff.ai to g... | https://python.langchain.com/docs/integrations/providers/rebuff |
b06cf9159b3c-1 | # Find canary word in response, and log back attacks to vault
is_canary_word_detected = rb.is_canary_word_leaked(user_input, completion, canary_word)
print(f"Canary word detected: {is_canary_word_detected}")
print(f"Canary word: {canary_word}")
print(f"Response (completion): {completion}")
if is_canary_word_detected:... | https://python.langchain.com/docs/integrations/providers/rebuff |
befa11c7ec09-0 | First, you need to install a python package.
Make a Reddit Application and initialize the loader with with your Reddit API credentials.
from langchain.document_loaders import RedditPostsLoader | https://python.langchain.com/docs/integrations/providers/reddit |
825ec460b2d1-0 | Redis
This page covers how to use the Redis ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Redis wrappers.
Installation and Setup
Install the Redis Python SDK with pip install redis
Wrappers
All wrappers needing a redis url connection string to connect... | https://python.langchain.com/docs/integrations/providers/redis |
825ec460b2d1-1 | redis_client = redis.Redis.from_url(...)
langchain.llm_cache = RedisCache(redis_client)
Semantic Cache
Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends Redis as both a cache and a vectorstore.
To import ... | https://python.langchain.com/docs/integrations/providers/redis |
29cff84b4ee0-0 | Replicate
This page covers how to run models on Replicate within LangChain.
Installation and Setup
Create a Replicate account. Get your API key and set it as an environment variable (REPLICATE_API_TOKEN)
Install the Replicate python client with pip install replicate
Calling a model
Find a model on the Replicate explo... | https://python.langchain.com/docs/integrations/providers/replicate |
9ed895d44c3d-0 | Roam
ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base.
Installation and Setup
There isn't any special setup for it.
Document Loader
See a usage example.
from langchain.document_loaders import RoamLoader | https://python.langchain.com/docs/integrations/providers/roam |
ca3cd90d4f2f-0 | This page covers how to use the Runhouse ecosystem within LangChain. It is broken into three parts: installation and setup, LLMs, and Embeddings.
For a basic self-hosted LLM, you can use the SelfHostedHuggingFaceLLM class. For more custom LLMs, you can use the SelfHostedPipeline parent class.
from langchain.llms import... | https://python.langchain.com/docs/integrations/providers/runhouse |
fea8ed8eb2d5-0 | Make sure you have Rockset account and go to the web console to get the API key. Details can be found on the website.
from langchain.vectorstores import Rockset | https://python.langchain.com/docs/integrations/providers/rockset |
5aedf74041b2-0 | This page covers how to use the RWKV-4 wrapper within LangChain. It is broken into two parts: installation and setup, and then usage with an example.
To use the RWKV wrapper, you need to provide the path to the pre-trained model file and the tokenizer's configuration.
from langchain.llms import RWKV
# Test the model
... | https://python.langchain.com/docs/integrations/providers/rwkv |
ddbc1c8eb6b1-0 | SageMaker Endpoint
Amazon SageMaker is a system that can build, train, and deploy machine learning (ML) models with fully managed infrastructure, tools, and workflows.
We use SageMaker to host our model and expose it as the SageMaker Endpoint.
Installation and Setup
For instructions on how to expose model as a SageMak... | https://python.langchain.com/docs/integrations/providers/sagemaker_endpoint |
19d844593a11-0 | SageMaker Tracking
This notebook shows how LangChain Callback can be used to log and track prompts and other LLM hyperparameters into SageMaker Experiments. Here, we use different scenarios to showcase the capability:
Scenario 1: Single LLM - A case where a single LLM model is used to generate output based on a given p... | https://python.langchain.com/docs/integrations/providers/sagemaker_tracking |
19d844593a11-1 | #Experiment name
EXPERIMENT_NAME = "langchain-sagemaker-tracker"
#Create SageMaker Session with the given bucket
session = Session(default_bucket=BUCKET_NAME)
Scenario 1 - LLM
RUN_NAME = "run-scenario-1"
PROMPT_TEMPLATE = "tell me a joke about {topic}"
INPUT_VARIABLES = {"topic": "fish"}
with Run(experiment_name=EXPE... | https://python.langchain.com/docs/integrations/providers/sagemaker_tracking |
19d844593a11-2 | # Create chain1
chain1 = LLMChain(llm=llm, prompt=prompt_template1, callbacks=[sagemaker_callback])
# Create chain2
chain2 = LLMChain(llm=llm, prompt=prompt_template2, callbacks=[sagemaker_callback])
# Create Sequential chain
overall_chain = SimpleSequentialChain(chains=[chain1, chain2], callbacks=[sagemaker_callback... | https://python.langchain.com/docs/integrations/providers/sagemaker_tracking |
ae60a4b10d0c-0 | ScaNN
Google ScaNN (Scalable Nearest Neighbors) is a python package.
ScaNN is a method for efficient vector similarity search at scale.
ScaNN includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is optim... | https://python.langchain.com/docs/integrations/providers/scann |
238e71d709d9-0 | SearxNG Search API
This page covers how to use the SearxNG search API within LangChain. It is broken into two parts: installation and setup, and then references to the specific SearxNG API wrapper.
Installation and Setup
While it is possible to utilize the wrapper in conjunction with public searx instances these insta... | https://python.langchain.com/docs/integrations/providers/searx |
238e71d709d9-1 | wrapper = SearxSearchWrapper(searx_host="**")
github_tool = SearxSearchResults(name="Github", wrapper=wrapper,
kwargs = {
"engines": ["github"],
})
arxiv_tool = SearxSearchResults(name="Arxiv", wrapper=wrapper,
kwargs = {
"engines": ["arxiv"]
})
For more information on tools, see this page. | https://python.langchain.com/docs/integrations/providers/searx |
38bf8725f1ec-0 | This page covers how to use the SerpAPI search APIs within LangChain. It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.
There exists a SerpAPI utility which wraps this API. To import this utility:
from langchain.utilities import SerpAPIWrapper
You can also easily ... | https://python.langchain.com/docs/integrations/providers/serpapi |
b1f6900984a7-0 | Shale Protocol provides production-ready inference APIs for open LLMs. It's a Plug & Play API as it's hosted on a highly scalable GPU cloud infrastructure.
Our free tier supports up to 1K daily requests per key as we want to eliminate the barrier for anyone to start building genAI apps with LLMs.
With Shale Protocol,... | https://python.langchain.com/docs/integrations/providers/shaleprotocol |
5531b168218b-0 | There are several ways to establish a connection to the database. You can either set up environment variables or pass named parameters to the SingleStoreDB constructor. Alternatively, you may provide these parameters to the from_documents and from_texts methods.
pip install singlestoredb | https://python.langchain.com/docs/integrations/providers/singlestoredb |
5e9ddf25db73-0 | SKLearnVectorStore provides a simple wrapper around the nearest neighbor implementation in the scikit-learn package, allowing you to use it as a vectorstore.
from langchain.vectorstores import SKLearnVectorStore
For a more detailed walkthrough of the SKLearnVectorStore wrapper, see this notebook. | https://python.langchain.com/docs/integrations/providers/sklearn |
e2aef7653883-0 | This page covers how to use the Deep Lake ecosystem within LangChain.
There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vector store (for now), whether for semantic search or example selection.
from langchain.vectorstores import DeepLake | https://python.langchain.com/docs/integrations/providers/activeloop_deeplake |
5ebe43caca14-0 | This page covers how to use the AI21 ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific AI21 wrappers.
from langchain.llms import AI21 | https://python.langchain.com/docs/integrations/providers/ai21 |
aefda29c9f1f-0 | You need to install ain-py python package.
You need to set the AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY environmental variable to your AIN Blockchain Account Private Key.
from langchain.agents.agent_toolkits.ainetwork.toolkit import AINetworkToolkit | https://python.langchain.com/docs/integrations/providers/ainetwork |
4833757b07c2-0 | Aim
Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents.
With Aim, you can easily debug and examine an individual execution:
Additionally, you have the option to compare multiple executions side by side:
Aim is fully open so... | https://python.langchain.com/docs/integrations/providers/aim_tracking |
4833757b07c2-1 | callbacks = [StdOutCallbackHandler(), aim_callback]
llm = OpenAI(temperature=0, callbacks=callbacks)
The flush_tracker function is used to record LangChain assets on Aim. By default, the session is reset rather than being terminated outright.
Scenario 1
In the first scenario, we will use OpenAI LLM.
# scenario 1 - LLM
... | https://python.langchain.com/docs/integrations/providers/aim_tracking |
4833757b07c2-2 | > Entering new AgentExecutor chain...
I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.
Action: Search
Action Input: "Leo DiCaprio girlfriend"
Observation: Leonardo DiCaprio seemed to prove a long-held theory about his love life right after splitting from girlfrien... | https://python.langchain.com/docs/integrations/providers/aim_tracking |
2b34e7eb0619-0 | This instruction shows how to load any source from Airbyte into a local JSON file that can be read in as a document.
Prerequisites: Have docker desktop installed.
from langchain.document_loaders import AirbyteJSONLoader | https://python.langchain.com/docs/integrations/providers/airbyte |
9455090b0ef6-0 | Airtable is a cloud collaboration service. Airtable is a spreadsheet-database hybrid, with the features of a database but applied to a spreadsheet. The fields in an Airtable table are similar to cells in a spreadsheet, but have types such as 'checkbox', 'phone number', and 'drop-down list', and can reference file attac... | https://python.langchain.com/docs/integrations/providers/airtable |
1937dba33fb8-0 | Aleph Alpha
Aleph Alpha was founded in 2019 with the mission to research and build the foundational technology for an era of strong AI. The team of international scientists, engineers, and innovators researches, develops, and deploys transformative AI like large language and multimodal models and runs the fastest Europ... | https://python.langchain.com/docs/integrations/providers/aleph_alpha |
ee7624cdb610-0 | Alibaba Cloud Opensearch
Alibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based 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 custome... | https://python.langchain.com/docs/integrations/providers/alibabacloud_opensearch |
653751587d08-0 | Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the "front door" for applications to access data, business logic, or functionality from your backend services. Using API Gateway, you can create RESTful APIs a... | https://python.langchain.com/docs/integrations/providers/amazon_api_gateway |
653751587d08-1 | llm.model_kwargs = parameters
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
tools = load_tools(["python_repl", "llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = ini... | https://python.langchain.com/docs/integrations/providers/amazon_api_gateway |
5ca7f22e9796-0 | This page covers how to use the AnalyticDB ecosystem within LangChain.
There exists a wrapper around AnalyticDB, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.vectorstores import AnalyticDB | https://python.langchain.com/docs/integrations/providers/analyticdb |
a599e9b70f63-0 | Annoy
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.
Installation and Setup
... | https://python.langchain.com/docs/integrations/providers/annoy |
953a8dc07f4f-0 | 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 scraping, crawling, and extraction use cases.
This integration enables you run Actors on the Apify platform and load their results into LangChain to feed your vec... | https://python.langchain.com/docs/integrations/providers/apify |
258404004c41-0 | This page covers how to use the Anyscale ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Anyscale wrappers.
from langchain.llms import Anyscale | https://python.langchain.com/docs/integrations/providers/anyscale |
4ac9b5dbf8b3-0 | ArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud – anywhere.
Connect your ArangoDB Database with a chat model to get insights on your data.
from arango ... | https://python.langchain.com/docs/integrations/providers/arangodb |
b0372f4072de-0 | pip install argilla --upgrade
If you already have an Argilla Server running, then you're good to go; but if you don't, follow the next steps to install it.
If you don't you can refer to Argilla - 🚀 Quickstart to deploy Argilla either on HuggingFace Spaces, locally, or on a server. | https://python.langchain.com/docs/integrations/providers/argilla |
05d3063e3e1a-0 | The following guide shows how to run a registered chat LLM with the Arthur callback handler to automatically log model inferences to Arthur.
Running the chat LLM with this run function will save the chat history in an ongoing list so that the conversation can reference earlier messages and log each response to the Arth... | https://python.langchain.com/docs/integrations/providers/arthur_tracking |
05d3063e3e1a-1 | 5. Consider error handling: It's important to handle any potential errors or exceptions that may occur within the callback function. This ensures that your program can gracefully handle unexpected situations and prevent crashes or undesired behavior.
6. Maintain code readability and modularity: As your codebase grows,... | https://python.langchain.com/docs/integrations/providers/arthur_tracking |
e30cf6b18004-0 | arXiv is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.
First, you need to install arxiv python package.
Second, you need to install PyMuPD... | https://python.langchain.com/docs/integrations/providers/arxiv |
03c67e07d739-0 | from langchain.vectorstores import AtlasDB | https://python.langchain.com/docs/integrations/providers/atlas |
2c576c1eb734-0 | from langchain.document_loaders import S3DirectoryLoader, S3FileLoader | https://python.langchain.com/docs/integrations/providers/aws_s3 |
5065e28e0c58-0 | There exists a wrapper around AwaDB vector databases, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.vectorstores import AwaDB | https://python.langchain.com/docs/integrations/providers/awadb |
8690bead46b2-0 | AZLyrics
AZLyrics is a large, legal, every day growing collection of lyrics.
Installation and Setup
There isn't any special setup for it.
Document Loader
See a usage example.
from langchain.document_loaders import AZLyricsLoader | https://python.langchain.com/docs/integrations/providers/azlyrics |
f646c4c393bf-0 | Azure Blob Storage
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 model or definition, such as text or binary data.
Azure Files offers fully managed fi... | https://python.langchain.com/docs/integrations/providers/azure_blob_storage |
d7c82e1470a0-0 | 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.
Search is foundational to any app that surfaces text to users, wh... | https://python.langchain.com/docs/integrations/providers/azure_cognitive_search_ |
75b29b663613-0 | Azure OpenAI
Microsoft Azure, often referred to as Azure is a cloud computing platform run by Microsoft, which offers access, management, and development of applications and services through global data centers. It provides a range of capabilities, including software as a service (SaaS), platform as a service (PaaS), a... | https://python.langchain.com/docs/integrations/providers/azure_openai |
d346408876e7-0 | 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.
from langchain.vectors... | https://python.langchain.com/docs/integrations/providers/bageldb |
117580e35849-0 | Airbyte Gong
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This loader exposes the Gong connector as a document loader, allowing you to load various Gong objects as documents.
In... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_gong |
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