id stringlengths 14 15 | text stringlengths 17 2.72k | source stringlengths 47 115 |
|---|---|---|
63048aa89005-0 | Vectara
Vectara is a API platform for building GenAI applications. It provides an easy-to-use API for document indexing and querying that is managed by Vectara and is optimized for performance and accuracy. See the Vectara API documentation for more information on how to use the API.
This notebook shows how to use func... | https://python.langchain.com/docs/integrations/vectorstores/vectara |
63048aa89005-1 | Include in your environment these three variables: VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY.
For example, you can set these variables using os.environ and getpass as follows:
import os
import getpass | https://python.langchain.com/docs/integrations/vectorstores/vectara |
63048aa89005-2 | os.environ["VECTARA_CUSTOMER_ID"] = getpass.getpass("Vectara Customer ID:")
os.environ["VECTARA_CORPUS_ID"] = getpass.getpass("Vectara Corpus ID:")
os.environ["VECTARA_API_KEY"] = getpass.getpass("Vectara API Key:")
Add them to the Vectara vectorstore constructor:
vectorstore = Vectara(
vectara_customer_id=vectara_cust... | https://python.langchain.com/docs/integrations/vectorstores/vectara |
63048aa89005-3 | urls = [
[
"https://www.gilderlehrman.org/sites/default/files/inline-pdfs/king.dreamspeech.excerpts.pdf",
"I-have-a-dream",
],
[
"https://www.parkwayschools.net/cms/lib/MO01931486/Centricity/Domain/1578/Churchill_Beaches_Speech.pdf",
"we shall fight on the beaches",
],
]
files_list = []
for url, _ in urls:
name = tempf... | https://python.langchain.com/docs/integrations/vectorstores/vectara |
63048aa89005-4 | Score: 0.786569
Now let's do similar search for content in the files we uploaded
query = "We must forever conduct our struggle"
min_score = 1.2
found_docs = vectara.similarity_search_with_score(
query, filter="doc.speech = 'I-have-a-dream'", score_threshold=min_score,
)
print(f"With this threshold of {min_score} we hav... | https://python.langchain.com/docs/integrations/vectorstores/vectara |
d4696cf6ef1c-0 | This notebook shows how to use functionality related to the Weaviatevector database.
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
Weaviate instances have authentication enabled by default. You can use either a username/password combination or API key.
Sometimes we might want to perform the sea... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-1 | (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/weaviate |
d4696cf6ef1c-2 | -0.0008389079, 0.0053696632, -0.0024644958, -0.016582303, 0.0066720927, -0.005036711, -0.035514854, 0.002942706, 0.02958701, 0.032825127, 0.015694432, -0.019846536, -0.024520919, -0.021974817, -0.0063293483, -0.01081114, -0.0084282495, 0.003025944, -0.010210521, 0.008780787, 0.014793505, -0.006486031, 0.011966679, 0.01... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-3 | 0.0046482678, 0.0023241339, -0.005826656, 0.0072531262, 0.015498579, -0.0077819317, -0.011953622, -0.028934162, -0.033974137, -0.01574666, 0.0086306315, -0.029299757, 0.030213742, -0.0033148287, 0.013448641, -0.013474754, 0.015851116, 0.0076578907, -0.037421167, -0.015185213, 0.010719741, -0.014636821, 0.0001918757, 0.... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-4 | -0.01340947, 0.00091643346, 0.014884903, -0.02314994, -0.024468692, 0.0004859627, 0.018828096, 0.012906778, 0.027941836, 0.027550127, -0.015028529, 0.018606128, 0.03449641, -0.017757427, -0.016020855, -0.012142947, 0.025304336, 0.00821281, -0.0025461016, -0.01902395, -0.635507, -0.030083172, 0.0177052, -0.0104912445, 0... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-5 | -0.01298512, -0.0015350056, 0.009982024, -0.024207553, -0.003332782, 0.006283649, 0.01868447, -0.010732798, -0.00876773, -0.0075273216, -0.016530076, 0.018175248, 0.016020855, -0.00067284, 0.013461698, -0.0065904865, -0.017809656, -0.014741276, 0.016582303, -0.0088526, 0.0046482678, 0.037473395, -0.02237958, 0.01011259... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-6 | 0.0011832844, 0.0065023527, -0.027053965, 0.009198609, 0.022079272, -0.027785152, 0.005846241, 0.013500868, 0.016699815, 0.010445545, -0.025265165, -0.004396922, 0.0076774764, 0.014597651, -0.009851455, -0.03637661, 0.0004745379, -0.010112594, -0.009205136, 0.01578583, 0.015211326, -0.0011653311, -0.0015847852, 0.01489... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-7 | 0.029482553, -0.0046547963, -0.015955571, -0.018397218, -0.0102431625, 0.020577725, 0.016190596, -0.02038187, 0.030030945, -0.01115062, 0.0032560725, -0.014819618, 0.005647123, -0.0032560725, 0.0038909658, 0.013311543, 0.024285894, -0.0045699263, -0.010112594, 0.009237779, 0.008728559, 0.0423828, 0.010909067, 0.0422522... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-8 | 0.02966535, 0.006495824, 0.0011008625, -0.00024318536, -0.007011573, -0.002746852, -0.004298995, 0.007710119, 0.03407859, -0.008898299, -0.008565348, 0.030527107, -0.0003027576, 0.025082368, 0.0405026, 0.03867463, 0.0014117807, -0.024076983, 0.003933401, -0.009812284, 0.00829768, -0.0074293944, 0.0061530797, -0.0166475... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-9 | -0.021504767, -0.012834964, 0.009009283, -0.0029198565, -0.014349569, -0.020434098, 0.009838398, -0.005993132, -0.013618381, -0.031597774, -0.019206747, 0.00086583785, 0.15835446, 0.033765227, 0.00893747, 0.015119928, -0.019128405, 0.0079582, -0.026270548, -0.015877228, 0.014153715, -0.011960151, 0.007853745, 0.0069724... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-10 | -0.007031158, 0.015825002, -0.013076518, 0.00736411, -0.00075689406, 0.0076578907, -0.019337315, -0.0024187965, -0.0110331075, -0.01187528, 0.0013048771, 0.0009711094, -0.027863493, -0.020616895, -0.0024481746, -0.0040802914, 0.014571536, -0.012306159, -0.037630077, 0.012652168, 0.009068039, -0.0018263385, 0.0371078, -... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-11 | 0.017561574, -0.024847344, 0.04115545, -0.00036457402, -0.0061400225, 0.013037347, -0.005480647, 0.005947433, 0.020799693, 0.014702106, 0.03272067, 0.026701428, -0.015550806, -0.036193814, -0.021126116, -0.005412098, -0.013076518, 0.027080078, 0.012900249, -0.0073379963, -0.015119928, -0.019781252, 0.0062346854, -0.032... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-12 | 0.00372449, 0.022914914, -0.0018981516, 0.031545546, -0.01051083, 0.013801178, -0.006296706, -0.00025052988, -0.01795328, -0.026296662, 0.0017659501, 0.021883417, 0.0028937424, 0.00495837, -0.011888337, -0.008950527, -0.012058077, 0.020316586, 0.00804307, -0.0068483613, -0.0038387382, 0.019715967, -0.025069311, -0.0007... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-13 | 0.02796795, -0.039118566, 0.0023975791, -0.010608757, 0.00093438674, 0.0017382042, -0.02047327, 0.026283605, -0.020799693, 0.005947433, -0.014349569, 0.009890626, -0.022719061, -0.017248206, 0.0042565595, 0.022327352, -0.015681375, -0.013840348, 6.502964e-05, 0.015485522, -0.002678303, -0.0047984226, -0.012182118, -0.0... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-14 | 0.000769951, -0.002312709, -0.025095424, -0.010621814, 0.013207087, 0.013944804, -0.0070899143, -0.022183727, -0.0028088724, -0.011424815, 0.026087752, -0.0058625625, -0.020186016, -0.010217049, 0.015315781, -0.012580355, 0.01374895, 0.004948577, -0.0021854038, 0.023215225, 0.00207442, 0.029639237, 0.01391869, -0.01581... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-15 | -0.015106871, -0.03225062, -0.010073422, 0.007285768, 0.0056079524, -0.009002754, -0.014362626, 0.010909067, 0.009779641, -0.02796795, 0.013246258, 0.025474075, -0.001247753, 0.02442952, 0.012802322, -0.032276735, 0.0029802448, 0.014179829, 0.010321504, 0.0053337566, -0.017156808, -0.010439017, 0.034444187, -0.01039331... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-16 | -0.015028529, 0.0097469995, 0.016281994, 0.0047135525, -0.011294246, 0.011477043, 0.015485522, 0.03426139, 0.014323455, 0.011052692, -0.008362965, -0.037969556, -0.00252162, -0.013709779, -0.0030292084, -0.016569246, -0.013879519, 0.0011849166, -0.0016925049, 0.009753528, 0.008349908, -0.008245452, 0.033007924, -0.0035... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-17 | 0.0058233915, -0.0056405943, -0.009381405, 0.0064044255, 0.013905633, -0.011228961, -0.0013481282, -0.014023146, 0.00016239559, -0.0051901303, 0.0025265163, 0.023619989, -0.021517823, 0.024703717, -0.025643816, 0.040189236, 0.016295051, -0.0040411204, -0.0113595305, 0.0029981981, -0.015589978, 0.026479458, 0.0067439056... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-18 | -0.004638475, -0.012495484, 0.022836573, -0.022719061, -0.031284407, -0.022405695, -0.017352663, 0.021113059, -0.03494035, 0.002772966, 0.025643816, -0.0064240107, -0.009897154, 0.0020711557, -0.16409951, 0.009688243, 0.010393318, 0.0033262535, 0.011059221, -0.012919835, 0.0014493194, -0.021857304, -0.0075730206, -0.00... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-19 | -0.011424815, 0.007181313, 0.017600743, -0.0030226798, -0.014192886, 0.0128937205, -0.009975496, 0.0051444313, -0.0044654706, -0.008826486, 0.004158633, 0.004971427, -0.017835768, 0.025017083, -0.021792019, 0.013657551, -0.01872364, 0.009100681, -0.0079582, -0.011640254, -0.01093518, -0.0147543335, -0.005000805, 0.0234... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-20 | 0.013305014, -0.007690533, 0.058808424, -0.0016859764, -0.0044622063, -0.0037734534, 0.01578583, -0.0018459238, -0.1196015, -0.0007075225, 0.0030341048, 0.012306159, -0.0068483613, 0.01851473, 0.015315781, 0.031388864, -0.015563863, 0.04776226, -0.008199753, -0.02591801, 0.00546759, -0.004915935, 0.0050824108, 0.002701... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-21 | -0.033060152, 0.011248547, -0.0019797573, -0.007181313, 0.0018867267, 0.0070899143, 0.004077027, 0.0055328747, -0.014245113, -0.021217514, -0.006750434, -0.038230695, 0.013233202, 0.014219, -0.017692143, 0.024742888, -0.008833014, -0.00753385, -0.026923396, -0.0021527617, 0.013135274, -0.018070793, -0.013500868, -0.001... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-22 | 0.0010535312, -0.017940223, 0.0012159267, -0.011065749, 0.008258509, -0.018527785, -0.022797402, 0.012377972, -0.002087477, 0.010791554, 0.022288183, 0.0048604426, -0.032590102, 0.013709779, 0.004922463, 0.020055447, -0.0150677, -0.0057222005, -0.036246043, 0.0021364405, 0.021387255, -0.013435584, 0.010732798, 0.007553... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-23 | 0.0062640635, -0.016242823, -0.0007785196, -0.0007213955, 0.018971723, 0.021687564, 0.0039464575, -0.01574666, 0.011783881, -0.0019797573, -0.013383356, -0.002706049, 0.0037734534, 0.020394927, -0.00021931567, 0.0041814824, 0.025121538, -0.036246043, -0.019428715, -0.023802789, 0.014845733, 0.015420238, 0.019650683, 0.... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-24 | 0.00025726235, 0.008016956, -0.0042565595, 0.008447835, 0.0038191527, -0.014702106, 0.02196176, 0.0052097156, -0.010869896, 0.0051640165, 0.030840475, -0.041468814, 0.009250836, -0.018997835, 0.020107675, 0.008421721, -0.016373392, 0.004602568, 0.0327729, -0.00812794, 0.001581521, 0.019350372, 0.016112253, 0.02132197, ... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
d4696cf6ef1c-25 | 0.8154189703772676)
Anything uploaded to weaviate is automatically persistent into the database. You do not need to call any specific method or pass any param for this to happen.
In addition to using similarity search in the retriever object, you can also use mmr.
This section goes over how to do question-answering wit... | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
956325db36bf-0 | Xata
Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. Xata has a native vector type, which can be added to any table, and supports similarity search. LangChain inserts vectors directly to Xata, and queries it for the n... | https://python.langchain.com/docs/integrations/vectorstores/xata |
956325db36bf-1 | os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
Similarly, we need to get the environment variables for Xata. You can create a new API key by visiting your account settings. To find the database URL, go to the Settings page of the database that you have created. The database URL should look something ... | https://python.langchain.com/docs/integrations/vectorstores/xata |
665155f52dd1-0 | Zep
Zep is an open source long-term memory store for LLM applications. Zep makes it easy to add relevant documents, chat history memory & rich user data to your LLM app's prompts.
Note: The ZepVectorStore works with Documents and is intended to be used as a Retriever. It offers separate functionality to Zep's ZepMemory... | https://python.langchain.com/docs/integrations/vectorstores/zep |
665155f52dd1-1 | # Collection config is needed if we're creating a new Zep Collection
config = CollectionConfig(
name=collection_name,
description="<optional description>",
metadata={"optional_metadata": "associated with the collection"},
is_auto_embedded=True, # we'll have Zep embed our documents using its low-latency embedder
embeddi... | https://python.langchain.com/docs/integrations/vectorstores/zep |
665155f52dd1-2 | # print results
for d, s in docs_scores:
print(d.page_content, " -> ", s, "\n====\n")
Tables necessary to determine the places of the planets are not less
necessary than those for the sun, moon, and stars. Some notion of the
number and complexity of these tables may be formed, when we state that
the positions of the tw... | https://python.langchain.com/docs/integrations/vectorstores/zep |
665155f52dd1-3 | for d in docs:
print(d.page_content, "\n====\n")
Tables necessary to determine the places of the planets are not less
necessary than those for the sun, moon, and stars. Some notion of the
number and complexity of these tables may be formed, when we state that
the positions of the two principal planets, (and these the m... | https://python.langchain.com/docs/integrations/vectorstores/zep |
665155f52dd1-4 | await vs.aadd_documents(docs)
await wait_for_ready(collection_name)
Embedding status: 402/1692 documents embedded
Embedding status: 402/1692 documents embedded
Embedding status: 552/1692 documents embedded
Embedding status: 702/1692 documents embedded
Embedding status: 1002/1692 documents embedded
Embedding status: 10... | https://python.langchain.com/docs/integrations/vectorstores/zep |
665155f52dd1-5 | in all its relations; but above all, with Astronomy and Navigation. So
important have they been considered, that in many instances large sums
have been appropriated by the most enlightened nations in the production
of them; and yet so numerous and insurmountable have been the
difficulties attending the attainment of th... | https://python.langchain.com/docs/integrations/vectorstores/zep |
665155f52dd1-6 | “I glanced at the books upon the table, and in spite of my ignorance
of German I could see that two of them were treatises on science, the
others being volumes of poetry. Then I walked across to the window,
hoping that I might catch some glimpse of the country-side, but an oak
shutter, heavily barred, was folded across... | https://python.langchain.com/docs/integrations/vectorstores/zep |
b6114be8b55f-0 | Zilliz
Zilliz Cloud is a fully managed service on cloud for LF AI Milvus®,
This notebook shows how to use functionality related to the Zilliz Cloud managed vector database.
To run, you should have a Zilliz Cloud instance up and running. Here are the installation instructions
We want to use OpenAIEmbeddings so we have t... | https://python.langchain.com/docs/integrations/vectorstores/zilliz |
b6114be8b55f-1 | embeddings = OpenAIEmbeddings()
vector_db = Milvus.from_documents(
docs,
embeddings,
connection_args={
"uri": ZILLIZ_CLOUD_URI,
"user": ZILLIZ_CLOUD_USERNAME,
"password": ZILLIZ_CLOUD_PASSWORD,
# "token": ZILLIZ_CLOUD_API_KEY, # API key, for serverless clusters which can be used as replacements for user and password
"s... | https://python.langchain.com/docs/integrations/vectorstores/zilliz |
3edf40ad03fe-0 | There isn't any special setup for it.
from langchain.document_loaders import UnstructuredPowerPointLoader | https://python.langchain.com/docs/integrations/providers/microsoft_powerpoint |
8e281a5dfbd8-0 | There isn't any special setup for it.
from langchain.document_loaders import UnstructuredWordDocumentLoader | https://python.langchain.com/docs/integrations/providers/microsoft_word |
e179c1508643-0 | This page covers how to use the Milvus ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
There exists a wrapper around Milvus 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/milvus |
077eaa5045c5-0 | Get a Minimax api key and set it as an environment variable (MINIMAX_API_KEY) Get a Minimax group id and set it as an environment variable (MINIMAX_GROUP_ID)
There exists a Minimax LLM wrapper, which you can access with See a usage example.
from langchain.llms import Minimax | https://python.langchain.com/docs/integrations/providers/minimax |
4b9dd5cf929d-0 | MLflow AI Gateway
The MLflow AI Gateway service is a powerful tool designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface that simplifies the interaction with these services by providing a un... | https://python.langchain.com/docs/integrations/providers/mlflow_ai_gateway |
4b9dd5cf929d-1 | model = mlflow.pyfunc.load_model(model_info.model_uri)
print(model.predict([{"adjective": "funny"}]))
Embeddings Example
from langchain.embeddings import MlflowAIGatewayEmbeddings
embeddings = MlflowAIGatewayEmbeddings(
gateway_uri="http://127.0.0.1:5000",
route="embeddings",
)
print(embeddings.embed_query("hello"))... | https://python.langchain.com/docs/integrations/providers/mlflow_ai_gateway |
999db166c3bd-0 | This page covers how to use the Modal ecosystem to run LangChain custom LLMs. It is broken into two parts:
You must include a prompt. There is a rigid response structure:
from pydantic import BaseModel
import modal
CACHE_PATH = "/root/model_cache"
class Item(BaseModel):
prompt: str
stub = modal.Stub(name="example-... | https://python.langchain.com/docs/integrations/providers/modal |
fd6438fc364a-0 | MLflow
MLflow is a versatile, expandable, open-source platform for managing workflows and artifacts across the machine learning lifecycle. It has built-in integrations with many popular ML libraries, but can be used with any library, algorithm, or deployment tool. It is designed to be extensible, so you can write plugi... | https://python.langchain.com/docs/integrations/providers/mlflow_tracking |
fd6438fc364a-1 | test_prompts = [
{
"title": "documentary about good video games that push the boundary of game design"
},
]
synopsis_chain.apply(test_prompts)
mlflow_callback.flush_tracker(synopsis_chain)
from langchain.agents import initialize_agent, load_tools
from langchain.agents import AgentType
# SCENARIO 3 - Agent with Tools
to... | https://python.langchain.com/docs/integrations/providers/mlflow_tracking |
8f50a3f5a5fc-0 | This page covers how to use the modelscope ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.
from langchain.embeddings import ModelScopeEmbeddings | https://python.langchain.com/docs/integrations/providers/modelscope |
ef438cb9e438-0 | Momento Cache is the world's first truly serverless caching service. It provides instant elasticity, scale-to-zero capability, and blazing-fast performance.
With Momento Cache, you grab the SDK, you get an end point, input a few lines into your code, and you're off and running.
The Cache wrapper allows for Momento to b... | https://python.langchain.com/docs/integrations/providers/momento |
adc43b0a317b-0 | Modern Treasury
Modern Treasury simplifies complex payment operations. It is a unified platform to power products and processes that move money.
Connect to banks and payment systems
Track transactions and balances in real-time
Automate payment operations for scale
Installation and Setup
There isn't any special setup f... | https://python.langchain.com/docs/integrations/providers/modern_treasury |
86f90f506f45-0 | We need to install pymongo python package.
from langchain.vectorstores import MongoDBAtlasVectorSearch | https://python.langchain.com/docs/integrations/providers/mongodb_atlas |
714111e57312-0 | MyScale
This page covers how to use MyScale vector database within LangChain. It is broken into two parts: installation and setup, and then references to specific MyScale wrappers.
With MyScale, you can manage both structured and unstructured (vectorized) data, and perform joint queries and analytics on both types of d... | https://python.langchain.com/docs/integrations/providers/myscale |
f964f9ca7e9e-0 | First, you need to install duckdb python package.
After that, you should set up a connection string - we mostly integrate with Motherduck through SQLAlchemy. The connection string is likely in the form:
You can use the SQLChain to query data in your Motherduck instance in natural language.
from langchain import OpenAI,... | https://python.langchain.com/docs/integrations/providers/motherduck |
e889718a42f3-0 | This page covers how to use the Neo4j ecosystem within LangChain.
There exists a wrapper around Neo4j vector index, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.vectorstores import Neo4jVector
For a more detailed walkthrough of the Neo4j vector index wrapper,... | https://python.langchain.com/docs/integrations/providers/neo4j |
b8f2d41fc01a-0 | 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.
All instructions are in examples below.
We have two different loaders: NotionDirector... | https://python.langchain.com/docs/integrations/providers/notion |
3c9932d4d919-0 | This page covers how to use the NLPCloud ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers.
from langchain.llms import NLPCloud | https://python.langchain.com/docs/integrations/providers/nlpcloud |
786797426074-0 | Obsidian
Obsidian is a powerful and extensible knowledge base that works on top of your local folder of plain text files.
Installation and Setup
All instructions are in examples below.
Document Loader
See a usage example.
from langchain.document_loaders import ObsidianLoader | https://python.langchain.com/docs/integrations/providers/obsidian |
32499fe9a496-0 | OpenLLM
This page demonstrates how to use OpenLLM with LangChain.
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 and Setup
Install... | https://python.langchain.com/docs/integrations/providers/openllm |
0da9c34ee3ac-0 | OpenAI
OpenAI is American artificial intelligence (AI) research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership. OpenAI conducts AI research with the declared intention of promoting and developing a friendly AI. OpenAI systems run on an Az... | https://python.langchain.com/docs/integrations/providers/openai |
a703e62a2d3f-0 | This page covers how to use the OpenSearch ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers.
There exists a wrapper around OpenSearch vector databases, allowing you to use it as a vectorstore for semantic search using approximate vector... | https://python.langchain.com/docs/integrations/providers/opensearch |
61827b4a19d4-0 | This page covers how to use the OpenWeatherMap API within LangChain.
There exists a OpenWeatherMapAPIWrapper utility which wraps this API. To import this utility:
from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
You can also easily load this wrapper as a Tool (to use with an Agent). You can do th... | https://python.langchain.com/docs/integrations/providers/openweathermap |
9da4b21e572c-0 | This page covers how to use the Petals ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Petals wrappers.
from langchain.llms import Petals | https://python.langchain.com/docs/integrations/providers/petals |
94d508c4f72a-0 | We need to install several python packages.
pip install openai
pip install psycopg2-binary
pip install tiktoken | https://python.langchain.com/docs/integrations/providers/pg_embedding |
6dd20cef94f3-0 | This page covers how to use the Postgres PGVector ecosystem within LangChain It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.
There exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore, whether for semantic search or example se... | https://python.langchain.com/docs/integrations/providers/pgvector |
b34b3c5e54d0-0 | This page covers how to use the Pinecone ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
pip install pinecone-client
There exists a wrapper around Pinecone indexes, allowing you to use it as a vectorstore, whether for semantic search or... | https://python.langchain.com/docs/integrations/providers/pinecone |
f3225ca9c75d-0 | This page covers how to use the DeepInfra ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific DeepInfra wrappers.
DeepInfra provides a range of Open Source LLMs ready for deployment. You can list supported models here. google/flan* models can be viewed here.
... | https://python.langchain.com/docs/integrations/providers/deepinfra |
67f909e02173-0 | DataForSEO
This page provides instructions on how to use the DataForSEO search APIs within LangChain.
Installation and Setup
Get a DataForSEO API Access login and password, and set them as environment variables (DATAFORSEO_LOGIN and DATAFORSEO_PASSWORD respectively). You can find it in your dashboard.
Wrappers
Utilit... | https://python.langchain.com/docs/integrations/providers/dataforseo |
7f2b5d783985-0 | Diffbot is a service to read web pages. Unlike traditional web scraping tools, Diffbot doesn't require any rules to read the content on a page. It starts with computer vision, which classifies a page into one of 20 possible types. Content is then interpreted by a machine learning model trained to identify the key attri... | https://python.langchain.com/docs/integrations/providers/diffbot |
e448af08f2c4-0 | This page covers how to use the DeepSparse inference runtime within LangChain. It is broken into two parts: installation and setup, and then examples of DeepSparse usage.
config = {'max_generated_tokens': 256}
llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thep... | https://python.langchain.com/docs/integrations/providers/deepsparse |
dff08433727b-0 | This page covers how to use the Dingo ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Dingo wrappers.
There exists a wrapper around Dingo indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.ve... | https://python.langchain.com/docs/integrations/providers/dingo |
7cffa115fdd1-0 | DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API.
We need to install docarray python package.
LangChai... | https://python.langchain.com/docs/integrations/providers/docarray |
b9efb6b6db00-0 | Discord is a VoIP and instant messaging social platform. Users have the ability to communicate with voice calls, video calls, text messaging, media and files in private chats or as part of communities called "servers". A server is a collection of persistent chat rooms and voice channels which can be accessed via invite... | https://python.langchain.com/docs/integrations/providers/discord |
2ab5c2370afc-0 | Docugami
Docugami converts business documents into a Document XML Knowledge Graph, generating forests of XML semantic trees representing entire documents. This is a rich representation that includes the semantic and structural characteristics of various chunks in the document as an XML tree.
Installation and Setup
Doc... | https://python.langchain.com/docs/integrations/providers/docugami |
abf7a4415295-0 | First, you need to install duckdb python package.
from langchain.document_loaders import DuckDBLoader | https://python.langchain.com/docs/integrations/providers/duckdb |
b737feadd478-0 | Example: Run a single-node Elasticsearch instance with security disabled. This is not recommended for production use.
The vector store is a simple wrapper around Elasticsearch. It provides a simple interface to store and retrieve vectors.
from langchain.vectorstores import ElasticsearchStore
from langchain.document_lo... | https://python.langchain.com/docs/integrations/providers/elasticsearch |
2cf79852e5ce-0 | This page covers how to use Epsilla within LangChain. It is broken into two parts: installation and setup, and then references to specific Epsilla wrappers.
There exists a wrapper around Epsilla vector databases, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.v... | https://python.langchain.com/docs/integrations/providers/epsilla |
61cb6de74be5-0 | EverNote is intended for archiving and creating notes in which photos, audio and saved web content can be embedded. Notes are stored in virtual "notebooks" and can be tagged, annotated, edited, searched, and exported.
First, you need to install lxml and html2text python packages.
pip install lxml
pip install html2text | https://python.langchain.com/docs/integrations/providers/evernote |
fd4943740878-0 | First, you need to install pandas python package.
from langchain.document_loaders import FacebookChatLoader | https://python.langchain.com/docs/integrations/providers/facebook_chat |
729b16711451-0 | Facebook Faiss
Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.
Faiss do... | https://python.langchain.com/docs/integrations/providers/facebook_faiss |
7e654e3fc00f-0 | The Figma API requires an access token, node_ids, and a file key.
Node IDs are also available in the URL. Click on anything and look for the '?node-id={node_id}' param.
from langchain.document_loaders import FigmaFileLoader | https://python.langchain.com/docs/integrations/providers/figma |
7954f0d17d3d-0 | This page covers how to use the Fireworks models within Langchain.
Fireworks integrates with Langchain through the LLM module, which allows for standardized usage of any models deployed on the Fireworks models.
In this example, we'll work the llama-v2-13b-chat model.
from langchain.llms.fireworks import Fireworks
ll... | https://python.langchain.com/docs/integrations/providers/fireworks |
04262845cc8e-0 | This page covers how to use the ForefrontAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific ForefrontAI wrappers.
from langchain.llms import ForefrontAI | https://python.langchain.com/docs/integrations/providers/forefrontai |
82290c25daa8-0 | Flyte
Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines. It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform.
The purpose of this notebook is to demonstrate the integration of a FlyteCallback into your Flyte task, enabling... | https://python.langchain.com/docs/integrations/providers/flyte |
82290c25daa8-1 | # Set Serp API key
os.environ["SERPAPI_API_KEY"] = "<your_serp_api_key>"
Replace <your_openai_api_key> and <your_serp_api_key> with your respective API keys obtained from OpenAI and Serp API.
To guarantee reproducibility of your pipelines, Flyte tasks are containerized. Each Flyte task must be associated with an image,... | https://python.langchain.com/docs/integrations/providers/flyte |
82290c25daa8-2 | llm = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0,
callbacks=[FlyteCallbackHandler()],
)
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(
llm=llm, prompt=prompt_template, callbacks=[FlyteCallbackHandler()]
)
test_prompts = [
{
"title": "documentary abou... | https://python.langchain.com/docs/integrations/providers/flyte |
1883feb21d71-0 | First, you need to install GitPython python package.
from langchain.document_loaders import GitLoader | https://python.langchain.com/docs/integrations/providers/git |
aa2ea15189c9-0 | GitBook
GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.
Installation and Setup
There isn't any special setup for it.
Document Loader
See a usage example.
from langchain.document_loaders import GitbookLoader | https://python.langchain.com/docs/integrations/providers/gitbook |
3562ceaaf0f7-0 | 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/providers/golden |
6ab5b05634ea-0 | Google BigQuery
Google BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. BigQuery is a part of the Google Cloud Platform.
Installation and Setup
First, you need to install google-cloud-bigquery python package.
pip install google-cloud-bigquery
Doc... | https://python.langchain.com/docs/integrations/providers/google_bigquery |
a5a9f29bef7f-0 | First, you need to install google-cloud-bigquery python package.
pip install google-cloud-storage
There are two loaders for the Google Cloud Storage: the Directory and the File loaders. | https://python.langchain.com/docs/integrations/providers/google_cloud_storage |
37cc33cd1e0a-0 | Currently, only Google Docs are supported.
First, you need to install several python package.
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib | https://python.langchain.com/docs/integrations/providers/google_drive |
3c31951c4d91-0 | This page covers how to use the Google Search API within LangChain. It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper.
There exists a GoogleSearchAPIWrapper utility which wraps this API. To import this utility:
from langchain.utilities import GoogleSearchAPIW... | https://python.langchain.com/docs/integrations/providers/google_search |
72f91e6cf477-0 | This page covers how to use the Banana ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
If you want to use an available language model template you can find one here. This template uses the Palmyra-Base model by Writer. You can check out a... | https://python.langchain.com/docs/integrations/providers/bananadev |
85e742fbd06f-0 | Learn how to use LangChain with models deployed on Baseten.
Baseten integrates with LangChain through the LLM module, which provides a standardized and interoperable interface for models that are deployed on your Baseten workspace.
from langchain.llms import Baseten
wizardlm = Baseten(model="MODEL_VERSION_ID", verbose... | https://python.langchain.com/docs/integrations/providers/baseten |
64bbc9552339-0 | This page covers how to use Beam within LangChain. It is broken into two parts: installation and setup, and then references to specific Beam wrappers.
This is the environment you’ll be developing against once you start the app. It's also used to define the maximum response length from the model.
llm = Beam(model_name="... | https://python.langchain.com/docs/integrations/providers/beam |
2d7ed02ceb48-0 | Bedrock
Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.
Installation and Setup
LLM
See a usage example.
from langchain.llms.bedrock import Bedrock
Text... | https://python.langchain.com/docs/integrations/providers/bedrock |
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