id stringlengths 14 15 | text stringlengths 101 5.26k | source stringlengths 57 120 |
|---|---|---|
dfe865bf62a8-0 | .ipynb
.pdf
Pinecone
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
import os
import getpass
PINECONE_A... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pinecone.html |
ada9ac68c1ef-0 | .ipynb
.pdf
Commented out until further notice
Commented out until further notice#
MongoDB Atlas Vector Search
MongoDB Atlas is a document database managed in the cloud. It also enables Lucene and its vector search feature.
This notebook shows how to use the functionality related to the MongoDB Atlas Vector Search feat... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/mongodb_atlas_vector_search.html |
7b4624a88f1a-0 | .ipynb
.pdf
Weaviate
Contents
Weaviate
Similarity search with score
Persistance
Retriever options
Retriever options
MMR
Question Answering with Sources
Weaviate#
Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly int... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-1 | docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
Tonight. I call on the ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-2 | (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://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-3 | -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, 0.011783881, 0.0036330915, -0.... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-4 | 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, 0.010112594, 0.022549322, 9.680491e-05, -0.0059082615, 0.020747464, -0.02692339... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-5 | 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, 0.04225223, -0.031806685, -0.013696723, -0.025787441, 0.00838255, -0.008715502, 0.006776548, 0.01825359, -0.014480138, ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-6 | 0.00086583785, 0.15835446, 0.033765227, 0.00893747, 0.015119928, -0.019128405, 0.0079582, -0.026270548, -0.015877228, 0.014153715, -0.011960151, 0.007853745, 0.006972402, -0.014101488, 0.02456009, 0.015119928, -0.0018850947, 0.019010892, -0.0046188897, -0.0050954674, -0.03548874, -0.01608614, -0.00324628, 0.009466276, ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-7 | -0.036193814, -0.021126116, -0.005412098, -0.013076518, 0.027080078, 0.012900249, -0.0073379963, -0.015119928, -0.019781252, 0.0062346854, -0.03266844, 0.025278222, -0.022797402, -0.0028415148, 0.021452539, -0.023162996, 0.005170545, -0.022314297, 0.011215905, -0.009838398, -0.00033233972, 0.0019650683, 0.0026326037, 0... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-8 | 0.0042565595, 0.022327352, -0.015681375, -0.013840348, 6.502964e-05, 0.015485522, -0.002678303, -0.0047984226, -0.012182118, -0.001512972, 0.013931747, -0.009642544, 0.012652168, -0.012932892, -0.027759038, -0.01085031, 0.0050236546, -0.009675186, -0.00893747, -0.0051770736, 0.036011018, 0.003528636, -0.001008648, -0.0... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-9 | 0.014179829, 0.010321504, 0.0053337566, -0.017156808, -0.010439017, 0.034444187, -0.010393318, -0.006042096, -0.018566957, 0.004517698, -0.011228961, -0.009015812, -0.02089109, 0.022484036, 0.0029867734, -0.029064732, -0.010236635, -0.0006761042, -0.029038617, 0.004367544, -0.012293102, 0.0017528932, -0.023358852, 0.02... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-10 | 0.0029981981, -0.015589978, 0.026479458, 0.0067439056, -0.035775993, -0.010550001, -0.014767391, -0.009897154, -0.013944804, -0.0147543335, 0.015798887, -0.02456009, -0.0018850947, 0.024442578, 0.0019715966, -0.02422061, -0.02945644, -0.003443766, 0.0004945313, 0.0011522742, -0.020773578, -0.011777353, 0.008173639, -0.... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-11 | -0.0147543335, -0.005000805, 0.02345025, -0.028908048, 0.0104912445, -0.00753385, 0.017561574, -0.012025435, 0.042670052, -0.0041978033, 0.0013056932, -0.009263893, -0.010941708, -0.004471999, 0.01008648, -0.002578744, -0.013931747, 0.018619185, -0.04029369, -0.00025909848, 0.0030063589, 0.003149985, 0.011091864, 0.006... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-12 | 0.011568441, -0.03230285, 0.023646105, 0.0111114485, -0.015172156, 0.0257091, 0.0045699263, -0.00919208, 0.021517823, 0.037838988, 0.00787333, -0.007755818, -0.028281316, 0.011170205, -0.005412098, -0.016321165, 0.009929797, 0.004609097, -0.03047488, 0.002688096, -0.07264877, 0.024455635, -0.020930262, -0.015381066, -0... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-13 | 0.01774437, 0.0021233836, -0.008434778, -0.0059441687, 0.038335152, 0.022653777, -0.0066002794, 0.02149171, 0.015093814, 0.025382677, -0.007579549, 0.0030357367, -0.0014117807, -0.015341896, 0.014545423, 0.007135614, -0.0113595305, -0.04387129, 0.016308108, -0.008186696, -0.013370299, -0.014297341, 0.017431004, -0.0226... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
7b4624a88f1a-14 | 0.8154189703772676)
Persistance#
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.
Retriever options#
Retriever options#
This section goes over different options for how to use Weaviate as a retriever.
MMR#
In a... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html |
ff4c412eb27d-0 | .ipynb
.pdf
AnalyticDB
AnalyticDB#
AnalyticDB for PostgreSQL is a massively parallel processing (MPP) data warehousing service that is designed to analyze large volumes of data online.
AnalyticDB for PostgreSQL is developed based on the open source Greenplum Database project and is enhanced with in-depth extensions by ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/analyticdb.html |
eb60cb15db1a-0 | .ipynb
.pdf
Tair
Tair#
Tair is a cloud native in-memory database service developed by Alibaba Cloud.
It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open source Redis. Tair also introduces persistent memory-optimized ins... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/tair.html |
2a54a6e799c5-0 | .ipynb
.pdf
Atlas
Atlas#
Atlas is a platform for interacting with both small and internet scale unstructured datasets by Nomic.
This notebook shows you how to use functionality related to the AtlasDB vectorstore.
!pip install spacy
!python3 -m spacy download en_core_web_sm
!pip install nomic
import time
from langchain.... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/atlas.html |
8b1c5da89410-0 | .ipynb
.pdf
Qdrant
Contents
Connecting to Qdrant from LangChain
Local mode
In-memory
On-disk storage
On-premise server deployment
Qdrant Cloud
Reusing the same collection
Similarity search
Similarity search with score
Metadata filtering
Maximum marginal relevance search (MMR)
Qdrant as a Retriever
Customizing Qdrant
... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
8b1c5da89410-1 | del qdrant
import qdrant_client
client = qdrant_client.QdrantClient(
path="/tmp/local_qdrant", prefer_grpc=True
)
qdrant = Qdrant(
client=client, collection_name="my_documents",
embeddings=embeddings
)
Similarity search#
The simplest scenario for using Qdrant vector store is to perform a similarity search.... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
8b1c5da89410-2 | 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.
2. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face toge... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
8b1c5da89410-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
4ddc6c7b09e5-0 | .ipynb
.pdf
MyScale
Contents
Setting up envrionments
Get connection info and data schema
Filtering
Similarity search with score
Deleting your data
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 r... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/myscale.html |
4ddc6c7b09e5-1 | k=4, where_str=f"{meta}.doc_id<10")
for d, dist in output:
print(dist, d.metadata, d.page_content[:20] + '...')
0.252379834651947 {'doc_id': 6, 'some': ''} And I’m taking robus...
0.25022566318511963 {'doc_id': 1, 'some': ''} Groups of citizens b...
0.2469480037689209 {'doc_id': 8, 'some': ''} And so many families.... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/myscale.html |
76b03b40d8e3-0 | .ipynb
.pdf
SKLearnVectorStore
Contents
Basic usage
Load a sample document corpus
Create the SKLearnVectorStore, index the document corpus and run a sample query
Saving and loading a vector store
Clean-up
SKLearnVectorStore#
scikit-learn is an open source collection of machine learning algorithms, including some impl... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/sklearn.html |
76b03b40d8e3-1 | Clean-up#
os.remove(persist_path)
previous
SingleStoreDB vector search
next
Supabase (Postgres)
Contents
Basic usage
Load a sample document corpus
Create the SKLearnVectorStore, index the document corpus and run a sample query
Saving and loading a vector store
Clean-up
By Harrison Chase
© Copyright 2023,... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/sklearn.html |
e07c96f65d0e-0 | .ipynb
.pdf
DocArrayHnswSearch
Contents
Setup
Using DocArrayHnswSearch
Similarity search
Similarity search with score
DocArrayHnswSearch#
DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vec... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/docarray_hnsw.html |
b8483673a15f-0 | .ipynb
.pdf
Tigris
Contents
Initialize Tigris vector store
Similarity Search
Similarity Search with score (vector distance)
Tigris#
Tigris is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.
Tigris eliminates the infrastructure com... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/tigris.html |
faaf549b807d-0 | .ipynb
.pdf
DocArrayInMemorySearch
Contents
Setup
Using DocArrayInMemorySearch
Similarity search
Similarity search with score
DocArrayInMemorySearch#
DocArrayInMemorySearch is a document index provided by Docarray that stores documents in memory. It is a great starting point for small datasets, where you may not want... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/docarray_in_memory.html |
e05b847b07d0-0 | .ipynb
.pdf
Annoy
Contents
Create VectorStore from texts
Create VectorStore from docs
Create VectorStore via existing embeddings
Search via embeddings
Search via docstore id
Save and load
Construct from scratch
Annoy#
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for po... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/annoy.html |
e05b847b07d0-1 | Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the Unit... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/annoy.html |
e05b847b07d0-2 | Create VectorStore via existing embeddings#
embs = embeddings_func.embed_documents(texts)
data = list(zip(texts, embs))
vector_store_from_embeddings = Annoy.from_embeddings(data, embeddings_func)
vector_store_from_embeddings.similarity_search_with_score("food", k=3)
[(Document(page_content='pizza is great', metadata={}... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/annoy.html |
e05b847b07d0-3 | )
db_manually.similarity_search_with_score("eating!", k=3)
[(Document(page_content='pizza is great', metadata={'x': 'food'}),
1.1314140558242798),
(Document(page_content='I love salad', metadata={'x': 'food'}),
1.1668788194656372),
(Document(page_content='my car', metadata={'x': 'stuff'}), 1.226445198059082)]
pre... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/annoy.html |
27e4ea9f2ce5-0 | .ipynb
.pdf
PGVector
Contents
Similarity search with score
Similarity Search with Euclidean Distance (Default)
Working with vectorstore in PG
Uploading a vectorstore in PG
Retrieving a vectorstore in PG
PGVector#
PGVector is an open-source vector similarity search for Postgres
It supports:
exact and approximate neare... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
27e4ea9f2ce5-1 | Requirement already satisfied: requests>=2.26.0 in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from tiktoken) (2.28.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken) (3.1.0)
Require... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
27e4ea9f2ce5-2 | --------------------------------------------------------------------------------
Score: 0.6076804864602984
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... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
27e4ea9f2ce5-3 | )
retriever = store.as_retriever()
print(retriever)
vectorstore=<langchain.vectorstores.pgvector.PGVector object at 0x7fe9a1b1c670> search_type='similarity' search_kwargs={}
# When we have an existing PG VEctor
DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN
db1 = PGVector.from_existing_index(
embedding=emb... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
27e4ea9f2ce5-4 | print(docs_with_score)
[(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 se... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
27e4ea9f2ce5-5 | One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
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 ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
0c8d9ded5f65-0 | .ipynb
.pdf
Deep Lake
Contents
Retrieval Question/Answering
Attribute based filtering in metadata
Choosing distance function
Maximal Marginal relevance
Delete dataset
Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory
Creating dataset on AWS S3
Deep Lake API
Transfer local dataset to cloud
Deep Lak... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html |
0c8d9ded5f65-1 | Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storage
Dataset(path='./my_deeplake/', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (42, 1536) float32 ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html |
0c8d9ded5f65-2 | Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html |
0c8d9ded5f65-3 | Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html |
0c8d9ded5f65-4 | Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html |
0c8d9ded5f65-5 | Creating dataset on AWS S3#
dataset_path = f"s3://BUCKET/langchain_test" # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.
embedding = OpenAIEmbeddings()
db = DeepLake.from_documents(docs, dataset_path=dataset_path, embedding=embeddings, overwrite=True, creds ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html |
0c8d9ded5f65-6 | ------- ------- ------- ------- -------
embedding generic (8, 1536) float32 None
ids text (8, 1) str None
metadata json (8, 1) str None
text text (8, 1) str None
['ad42f3fe-e188-11ed-b66d-41c5f7b85421',
'ad42f3ff-e188-11ed-b66d-41c... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html |
24ad99710ee1-0 | .ipynb
.pdf
ClickHouse Vector Search
Contents
Setting up envrionments
Get connection info and data schema
Clickhouse table schema
Filtering
Deleting your data
ClickHouse Vector Search#
ClickHouse is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/clickhouse.html |
24ad99710ee1-1 | If you custimized your column_map under your setting, you search with filter like this:
from langchain.vectorstores import Clickhouse, ClickhouseSettings
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitt... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/clickhouse.html |
374093f54bbc-0 | .ipynb
.pdf
Self-querying with Weaviate
Contents
Creating a Weaviate vectorstore
Creating our self-querying retriever
Testing it out
Filter k
Self-querying with Weaviate#
Creating a Weaviate vectorstore#
First we’ll want to create a Weaviate VectorStore and seed it with some data. We’ve created a small demo set of do... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/weaviate_self_query.html |
374093f54bbc-1 | Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'genre': None, 'rating': 8.6, 'year': 2006})]
# This example specifies a query and a filter
retriever.get_relevant_documents("Has Greta Gerwig directed any movies about... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/weaviate_self_query.html |
bfa2b8fa15d5-0 | .ipynb
.pdf
VectorStore
Contents
Maximum Marginal Relevance Retrieval
Similarity Score Threshold Retrieval
Specifying top k
VectorStore#
The index - and therefore the retriever - that LangChain has the most support for is the VectorStoreRetriever. As the name suggests, this retriever is backed heavily by a VectorStor... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/vectorstore.html |
7b1288250945-0 | .ipynb
.pdf
Zep
Contents
Retriever Example
Initialize the Zep Chat Message History Class and add a chat message history to the memory store
Use the Zep Retriever to vector search over the Zep memory
Zep#
Zep - A long-term memory store for LLM applications.
More on Zep:
Zep stores, summarizes, embeds, indexes, and enr... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/zep_memorystore.html |
7b1288250945-1 | " living in a dystopian future where society has collapsed due to"
" environmental disasters, poverty, and violence."
),
},
]
for msg in test_history:
zep_chat_history.append(
HumanMessage(content=msg["content"])
if msg["role"] == "human"
else AIMessage(content=msg["c... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/zep_memorystore.html |
7b1288250945-2 | Document(page_content="Write a short synopsis of Butler's book, Parable of the Sower. What is it about?", metadata={'score': 0.8857628682610436, 'uuid': 'f6706e8c-6c91-452f-8c1b-9559fd924657', 'created_at': '2023-05-25T15:03:30.265302Z', 'role': 'human', 'token_count': 23}),
Document(page_content='Who was Octavia Butl... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/zep_memorystore.html |
21ee3ea94a99-0 | .ipynb
.pdf
Contextual Compression
Contents
Contextual Compression
Using a vanilla vector store retriever
Adding contextual compression with an LLMChainExtractor
More built-in compressors: filters
LLMChainFilter
EmbeddingsFilter
Stringing compressors and document transformers together
Contextual Compression#
This not... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
21ee3ea94a99-1 | First, beat the opioid epidemic.
----------------------------------------------------------------------------------------------------
Document 4:
Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers.
And as Wall Street firms take over more nursing homes, quality in thos... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
21ee3ea94a99-2 | compressed_docs = compression_retriever.get_relevant_documents("What did the president say about Ketanji Jackson Brown")
pretty_print_docs(compressed_docs)
Document 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 ... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
21ee3ea94a99-3 | ----------------------------------------------------------------------------------------------------
Document 2:
As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.
While it often appears that w... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
969d44ef95a6-0 | .ipynb
.pdf
Wikipedia
Contents
Installation
Examples
Running retriever
Question Answering on facts
Wikipedia#
Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWik... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/wikipedia.html |
969d44ef95a6-1 | from langchain.chains import ConversationalRetrievalChain
model = ChatOpenAI(model_name='gpt-3.5-turbo') # switch to 'gpt-4'
qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
questions = [
"What is Apify?",
"When the Monument to the Martyrs of the 1830 Revolution was created?",
"What is ... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/wikipedia.html |
4cbc97f54dcb-0 | .ipynb
.pdf
ChatGPT Plugin
Contents
Using the ChatGPT Retriever Plugin
ChatGPT Plugin#
OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT’s capabilities and allowing it to perform a wide range of actions.
Plugins can ... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/chatgpt-plugin.html |
3f4073807186-0 | .ipynb
.pdf
Time Weighted VectorStore
Contents
Low Decay Rate
High Decay Rate
Virtual Time
Time Weighted VectorStore#
This retriever uses a combination of semantic similarity and a time decay.
The algorithm for scoring them is:
semantic_similarity + (1.0 - decay_rate) ** hours_passed
Notably, hours_passed refers to t... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html |
3f4073807186-1 | print(retriever.get_relevant_documents("hello world"))
[Document(page_content='hello world', metadata={'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'created_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 279596), 'buffer_idx': 0})]
previous
TF-IDF
next
VectorStore
Contents
Low Decay Rate
High Decay Rate
Vi... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html |
88ecfcddccd5-0 | .ipynb
.pdf
Weaviate Hybrid Search
Weaviate Hybrid Search#
Weaviate is an open source vector database.
Hybrid search is a technique that combines multiple search algorithms to improve the accuracy and relevance of search results. It uses the best features of both keyword-based search algorithms with vector search techn... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/weaviate-hybrid.html |
88ecfcddccd5-1 | retriever.get_relevant_documents("the ethical implications of AI")
[Document(page_content='In her second book, Dr. Simmons delves deeper into the ethical considerations surrounding AI development and deployment. It is an eye-opening examination of the dilemmas faced by developers, policymakers, and society at large.', ... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/weaviate-hybrid.html |
e1b8f6bbf8b0-0 | .ipynb
.pdf
SVM
Contents
Create New Retriever with Texts
Use Retriever
SVM#
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn package.
Lar... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/svm.html |
2f2a65158de4-0 | .ipynb
.pdf
Arxiv
Contents
Installation
Examples
Running retriever
Question Answering on facts
Arxiv#
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 sci... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/arxiv.html |
2f2a65158de4-1 | -> **Question**: What is the ImageBind model?
**Answer**: ImageBind is an approach developed by Facebook AI Research to learn a joint embedding across six different modalities, including images, text, audio, depth, thermal, and IMU data. The approach uses the binding property of images to align each modality's embeddi... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/arxiv.html |
506c6a59f04b-0 | .ipynb
.pdf
Self-querying with Qdrant
Contents
Creating a Qdrant vectorstore
Creating our self-querying retriever
Testing it out
Filter k
Self-querying with Qdrant#
Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage ... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/qdrant_self_query.html |
506c6a59f04b-1 | query='dinosaur' filter=None limit=None
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),
Do... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/qdrant_self_query.html |
506c6a59f04b-2 | previous
PubMed Retriever
next
Self-querying
Contents
Creating a Qdrant vectorstore
Creating our self-querying retriever
Testing it out
Filter k
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/qdrant_self_query.html |
0a1209deab2b-0 | .ipynb
.pdf
Pinecone Hybrid Search
Contents
Setup Pinecone
Get embeddings and sparse encoders
Load Retriever
Add texts (if necessary)
Use Retriever
Pinecone Hybrid Search#
Pinecone is a vector database with broad functionality.
This notebook goes over how to use a retriever that under the hood uses Pinecone and Hybri... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html |
0a1209deab2b-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html |
ca67590cf6d4-0 | .ipynb
.pdf
Self-querying
Contents
Creating a Pinecone index
Creating our self-querying retriever
Testing it out
Filter k
Self-querying#
In the notebook we’ll demo the SelfQueryRetriever, which, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/self_query.html |
ca67590cf6d4-1 | type="float"
),
]
document_content_description = "Brief summary of a movie"
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)
Testing it out#
And now we can try actually using our retriever!
# This example only spec... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/self_query.html |
ca67590cf6d4-2 | metadata_field_info,
enable_limit=True,
verbose=True
)
# This example only specifies a relevant query
retriever.get_relevant_documents("What are two movies about dinosaurs")
previous
Self-querying with Qdrant
next
SVM
Contents
Creating a Pinecone index
Creating our self-querying retriever
Testing it out
Fi... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/self_query.html |
4d3f43d11cdb-0 | .ipynb
.pdf
Self-querying with Chroma
Contents
Creating a Chroma vectorstore
Creating our self-querying retriever
Testing it out
Filter k
Self-querying with Chroma#
Chroma is a database for building AI applications with embeddings.
In the notebook we’ll demo the SelfQueryRetriever wrapped around a Chroma vector store... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/chroma_self_query.html |
4d3f43d11cdb-1 | Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),
Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadat... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/chroma_self_query.html |
4d3f43d11cdb-2 | previous
ChatGPT Plugin
next
Cohere Reranker
Contents
Creating a Chroma vectorstore
Creating our self-querying retriever
Testing it out
Filter k
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/chroma_self_query.html |
2fe231df2319-0 | .ipynb
.pdf
kNN
Contents
Create New Retriever with Texts
Use Retriever
kNN#
In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression.
... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/knn.html |
4622b6704dbb-0 | .ipynb
.pdf
PubMed Retriever
PubMed Retriever#
This notebook goes over how to use PubMed as a retriever
PubMed® 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... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/pubmed.html |
db164f844f70-0 | .ipynb
.pdf
ElasticSearch BM25
Contents
Create New Retriever
Add texts (if necessary)
Use Retriever
ElasticSearch BM25#
Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/elastic_search_bm25.html |
dd3414e03431-0 | .ipynb
.pdf
TF-IDF
Contents
Create New Retriever with Texts
Create a New Retriever with Documents
Use Retriever
TF-IDF#
TF-IDF means term-frequency times inverse document-frequency.
This notebook goes over how to use a retriever that under the hood uses TF-IDF using scikit-learn package.
For more information on the d... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/tf_idf.html |
0fb76a0e9b89-0 | .ipynb
.pdf
Cohere Reranker
Contents
Set up the base vector store retriever
Doing reranking with CohereRerank
Cohere Reranker#
Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.
This notebook shows how to use Cohere’s rerank endpoint i... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
0fb76a0e9b89-1 | and the middle out, not from the top down.
Because we know that when the middle class grows, the poor have a ladder up and the wealthy do very well.
America used to have the best roads, bridges, and airports on Earth.
Now our infrastructure is ranked 13th in the world.
----------------------------------------------... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
0fb76a0e9b89-2 | Our troops in Iraq and Afghanistan faced many dangers.
----------------------------------------------------------------------------------------------------
Document 16:
When we invest in our workers, when we build the economy from the bottom up and the middle out together, we can do something we haven’t done in a long ... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
0fb76a0e9b89-3 | chain({"query": query})
{'query': 'What did the president say about Ketanji Brown Jackson',
'result': " The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she is a consensus builder who has received a broad range of support from the Fraternal Order of Police to former judges ... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
0cf09c7922d0-0 | .ipynb
.pdf
Databerry
Contents
Query
Databerry#
Databerry platform brings data from anywhere (Datsources: Text, PDF, Word, PowerPpoint, Excel, Notion, Airtable, Google Sheets, etc..) into Datastores (container of multiple Datasources).
Then your Datastores can be connected to ChatGPT via Plugins or any other Large La... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/databerry.html |
3d04b97b9cd4-0 | .ipynb
.pdf
Metal
Contents
Ingest Documents
Query
Metal#
Metal is a managed service for ML Embeddings.
This notebook shows how to use Metal’s retriever.
First, you will need to sign up for Metal and get an API key. You can do so here
# !pip install metal_sdk
from metal_sdk.metal import Metal
API_KEY = ""
CLIENT_ID = ... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/metal.html |
e44eb5993657-0 | .ipynb
.pdf
Vespa
Vespa#
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.
This notebook shows how to use Vespa.ai as a LangChain retriever.
In order to create a retriever, we use pyvespa to
create a connec... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/vespa.html |
a98d244b2588-0 | .ipynb
.pdf
Azure Cognitive Search
Contents
Set up Azure Cognitive Search
Using the Azure Cognitive Search Retriever
Azure Cognitive Search#
Azure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience ove... | https://langchain.readthedocs.io/en/latest/modules/indexes/retrievers/examples/azure_cognitive_search.html |
70805e07ee7a-0 | .ipynb
.pdf
Chat Prompt Templates
Contents
Format output
Different types of MessagePromptTemplate
Chat Prompt Templates#
Chat Models take a list of chat messages as input - this list commonly referred to as a prompt.
These chat messages differ from raw string (which you would pass into a LLM model) in that every mess... | https://langchain.readthedocs.io/en/latest/modules/prompts/chat_prompt_template.html |
70805e07ee7a-1 | ChatMessage(content='May the force be with you', additional_kwargs={}, role='Jedi')
LangChain also provides MessagesPlaceholder, which gives you full control of what messages to be rendered during formatting. This can be useful when you are uncertain of what role you should be using for your message prompt templates or... | https://langchain.readthedocs.io/en/latest/modules/prompts/chat_prompt_template.html |
a6ec384bdce9-0 | .rst
.pdf
Output Parsers
Output Parsers#
Note
Conceptual Guide
Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.
Output parsers are classes that help structure language model responses. There are two main methods an out... | https://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers.html |
40fcdcb607a4-0 | .ipynb
.pdf
Getting Started
Contents
PromptTemplates
to_string
to_messages
Getting Started#
This section contains everything related to prompts. A prompt is the value passed into the Language Model. This value can either be a string (for LLMs) or a list of messages (for Chat Models).
The data types of these prompts a... | https://langchain.readthedocs.io/en/latest/modules/prompts/getting_started.html |
bc86231a1675-0 | .rst
.pdf
Prompt Templates
Prompt Templates#
Note
Conceptual Guide
Language models take text as input - that text is commonly referred to as a prompt.
Typically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.
LangChain provides several classes and functions t... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates.html |
e6fa1e7fcbd0-0 | .rst
.pdf
Example Selectors
Example Selectors#
Note
Conceptual Guide
If you have a large number of examples, you may need to select which ones to include in the prompt. The ExampleSelector is the class responsible for doing so.
The base interface is defined as below:
class BaseExampleSelector(ABC):
"""Interface for... | https://langchain.readthedocs.io/en/latest/modules/prompts/example_selectors.html |
f4129c63ca1a-0 | .md
.pdf
How to create a custom example selector
Contents
Implement custom example selector
Use custom example selector
How to create a custom example selector#
In this tutorial, we’ll create a custom example selector that selects every alternate example from a given list of examples.
An ExampleSelector must implemen... | https://langchain.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/custom_example_selector.html |
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