id stringlengths 14 15 | text stringlengths 17 2.72k | source stringlengths 47 115 |
|---|---|---|
5da393ee57c7-4 | query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search_with_score(query, filter=rest.Filter(...))
Maximum marginal relevance search (MMR)
If you'd like to look up for some similar documents, but you'd also like to receive diverse results, MMR is method you should conside... | https://python.langchain.com/docs/integrations/vectorstores/qdrant.html |
5da393ee57c7-5 | I’ve worked on these issues a long time.
I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. | https://python.langchain.com/docs/integrations/vectorstores/qdrant.html |
5da393ee57c7-6 | Qdrant as a Retriever
Qdrant, as all the other vector stores, is a LangChain Retriever, by using cosine similarity.
retriever = qdrant.as_retriever()
retriever
VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='similarity', search_kwargs={})
It might be also... | https://python.langchain.com/docs/integrations/vectorstores/qdrant.html |
5da393ee57c7-7 | Named vectors
Qdrant supports multiple vectors per point by named vectors. Langchain requires just a single embedding per document and, by default, uses a single vector. However, if you work with a collection created externally or want to have the named vector used, you can configure it by providing its name.
Qdrant.f... | https://python.langchain.com/docs/integrations/vectorstores/qdrant.html |
d9aee4ecd873-0 | Page Not Found
We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken. | https://python.langchain.com/docs/integrations/providers/www.reddit.com |
6cca510d489f-0 | Redis
Redis vector database introduction and langchain integration guide.
What is Redis?
Most developers from a web services background are probably familiar with Redis. At it's core, Redis is an open-source key-value store that can be used as a cache, message broker, and database. Developers choice Redis because it i... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-1 | redis-py Python MIT Redis
node-redis Node.js MIT Redis
nredisstack .NET MIT Redis
Deployment Options
There are many ways to deploy Redis with RediSearch. The easiest way to get started is to use Docker, but there are are many potential options for deployment such as
Redis Cloud
Docker (Redis Stack)
Cloud marketp... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-2 | os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
Sample Data
First we will describe some sample data so that the various attributes of the Redis vector store can be demonstrated.
metadata = [
{
"user": "john",
"age": 18... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-3 | documents = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadata)]
rds = Redis.from_documents(
documents,
embeddings,
redis_url="redis://localhost:6379",
index_name="users"
)
Inspecting the Created Index
Once the Redis VectorStore object has been constructed, an index will have been created in Redis i... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-4 | Index Information:
╭──────────────┬────────────────┬───────────────┬─────────────────┬────────────╮
│ Index Name │ Storage Type │ Prefixes │ Index Options │ Indexing │
├──────────────┼────────────────┼───────────────┼─────────────────┼────────────┤
│ users │ HASH │ ['doc:users'] │ [] │ 0 │
╰──────────────┴─────────────... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-5 | Statistics:
╭─────────────────────────────┬─────────────╮
│ Stat Key │ Value │
├─────────────────────────────┼─────────────┤
│ num_docs │ 5 │
│ num_terms │ 15 │
│ max_doc_id │ 5 │
│ num_records │ 33 │
│ percent_indexed │ 1 │
│ hash_indexing_failures │ 0 │
│ number_of_uses │ 4 │
│ bytes_per_record_avg │ 4.60606 │
│ doc_... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-6 | meta = results[1].metadata
print("Key of the document in Redis: ", meta.pop("id"))
print("Metadata of the document: ", meta)
Key of the document in Redis: doc:users:a70ca43b3a4e4168bae57c78753a200f
Metadata of the document: {'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}
# with scores (distance... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-7 | Content: foo --- Similarity: 1.0
Content: foo --- Similarity: 1.0
Content: foo --- Similarity: 1.0
# you can also add new documents as follows
new_document = ["baz"]
new_metadata = [{
"user": "sam",
"age": 50,
"job": "janitor",
"credit_score": "high"
}]
# both the document and metadata must be lists
rds.add_texts(new_d... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-8 | dims: 1536
distance_metric: COSINE
initial_cap: 20000
name: content_vector
Notice, this include all possible fields for the schema. You can remove any fields that you don't need.
# now we can connect to our existing index as follows | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-9 | new_rds = Redis.from_existing_index(
embeddings,
index_name="users",
redis_url="redis://localhost:6379",
schema="redis_schema.yaml"
)
results = new_rds.similarity_search("foo", k=3)
print(results[0].metadata)
{'id': 'doc:users:8484c48a032d4c4cbe3cc2ed6845fabb', 'user': 'john', 'job': 'engineer', 'credit_score': 'high',... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-10 | rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users_modified",
index_schema=index_schema, # pass in the new index schema
)
`index_schema` does not match generated metadata schema.
If you meant to manually override the schema, please igno... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-11 | # numeric filtering
age_is_18 = RedisNum("age") == 18
age_is_not_18 = RedisNum("age") != 18
age_is_greater_than_18 = RedisNum("age") > 18
age_is_less_than_18 = RedisNum("age") < 18
age_is_greater_than_or_equal_to_18 = RedisNum("age") >= 18
age_is_less_than_or_equal_to_18 = RedisNum("age") <= 18
The RedisFilter class c... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-12 | for result in results:
print("User:", result.metadata["user"], "is", result.metadata["age"])
User: derrick is 45
User: nancy is 94
User: joe is 35
# make sure to use parenthesis around FilterExpressions
# if initializing them while constructing them
age_range = (RedisNum("age") > 18) & (RedisNum("age") < 99)
results = ... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-13 | for result in results:
print("Content:", result[0].page_content, " --- Score: ", result[1])
Content: foo --- Score: 0.0
Content: foo --- Score: 0.0
Content: foo --- Score: 0.0
retriever = rds.as_retriever(search_type="similarity", search_kwargs={"k": 4})
docs = retriever.get_relevant_documents(query)
docs
[Document(pag... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-14 | Document(page_content='foo', metadata={'id': 'doc:users_modified:009b1afeb4084cc6bdef858c7a99b48e', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}),
Document(page_content='foo', metadata={'id': 'doc:users_modified:7087cee9be5b4eca93c30fbdd09a2731', 'user': 'nancy', 'job': 'doctor', 'credit_scor... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-15 | Valid Redis Url scheme are:
redis:// - Connection to Redis standalone, unencrypted
rediss:// - Connection to Redis standalone, with TLS encryption
redis+sentinel:// - Connection to Redis server via Redis Sentinel, unencrypted
rediss+sentinel:// - Connection to Redis server via Redis Sentinel, booth connections with TLS... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
6cca510d489f-16 | # connection to sentinel at localhost with default group mymaster and db 0, no password
redis_url = "redis+sentinel://localhost:26379"
# connection to sentinel at host redis with default port 26379 and user "joe" with password "secret" with default group mymaster and db 0
redis_url = "redis+sentinel://joe:secret@redis"... | https://python.langchain.com/docs/integrations/vectorstores/redis.html |
ed0773789dd4-0 | This notebook goes over how to use Redis to store chat message history.
[AIMessage(content='whats up?', additional_kwargs={}),
HumanMessage(content='hi!', additional_kwargs={})] | https://python.langchain.com/docs/integrations/memory/redis_chat_message_history.html |
bba11073a839-0 | SerpAPI
This notebook goes over how to use the SerpAPI component to search the web.
from langchain.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
search.run("Obama's first name?")
'Barack Hussein Obama II'
Custom Parameters
You can also customize the SerpAPI wrapper with arbitrary parameters. For example, i... | https://python.langchain.com/docs/integrations/tools/serpapi.html |
cc20564cd66c-0 | Self Hosted Embeddings
Let's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes.
from langchain.embeddings import (
SelfHostedEmbeddings,
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddings,
)
import runhouse as rh
# For an on-demand ... | https://python.langchain.com/docs/integrations/text_embedding/self-hosted.html |
f092c8a9178f-0 | Runhouse
The Runhouse allows remote compute and data across environments and users. See the Runhouse docs.
This example goes over how to use LangChain and Runhouse to interact with models hosted on your own GPU, or on-demand GPUs on AWS, GCP, AWS, or Lambda.
Note: Code uses SelfHosted name instead of the Runhouse.
from... | https://python.langchain.com/docs/integrations/llms/runhouse.html |
f092c8a9178f-1 | "\n\nLet's say we're talking sports teams who won the Super Bowl in the year Justin Beiber"
You can also load more custom models through the SelfHostedHuggingFaceLLM interface:
llm = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-small",
task="text2text-generation",
hardware=gpu,
)
llm("What is the capital of Germa... | https://python.langchain.com/docs/integrations/llms/runhouse.html |
f092c8a9178f-2 | llm = SelfHostedPipeline.from_pipeline(pipeline="models/pipeline.pkl", hardware=gpu) | https://python.langchain.com/docs/integrations/llms/runhouse.html |
43849f22be32-0 | scikit-learn
scikit-learn is an open source collection of machine learning algorithms, including some implementations of the k nearest neighbors. SKLearnVectorStore wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.
This notebook shows ho... | https://python.langchain.com/docs/integrations/vectorstores/sklearn.html |
43849f22be32-1 | 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://python.langchain.com/docs/integrations/vectorstores/sklearn.html |
176ecdae61ee-0 | Airtable
from langchain.document_loaders import AirtableLoader
Get your API key here.
Get ID of your base here.
Get your table ID from the table url as shown here.
api_key = "xxx"
base_id = "xxx"
table_id = "xxx"
loader = AirtableLoader(api_key, table_id, base_id)
docs = loader.load()
Returns each table row as dict.
ev... | https://python.langchain.com/docs/integrations/document_loaders/airtable.html |
7be12d94c618-0 | Page Not Found
We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken. | https://python.langchain.com/docs/integrations/modules/indexes/vectorstores/examples/alibabacloud_opensearch.ipynb |
997bc0e0c0b1-0 | Page Not Found
We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken. | https://python.langchain.com/docs/integrations/providers/xingshaomin.xsm@alibaba-inc.com |
5110e0f363f8-0 | This notebook shows how to use functionality related to the AnalyticDB vector database. To run, you should have an AnalyticDB instance up and running:
from langchain.document_loaders import TextLoader
loader = TextLoader("../../../state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter... | https://python.langchain.com/docs/integrations/vectorstores/analyticdb.html |
56b539a93455-0 | Argilla
Argilla is an open-source data curation platform for LLMs. Using Argilla, everyone can build robust language models through faster data curation using both human and machine feedback. We provide support for each step in the MLOps cycle, from data labeling to model monitoring.
In this guide we will demonstrate h... | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
56b539a93455-1 | if parse_version(rg.__version__) < parse_version("1.8.0"):
raise RuntimeError(
"`FeedbackDataset` is only available in Argilla v1.8.0 or higher, please "
"upgrade `argilla` as `pip install argilla --upgrade`."
)
dataset = rg.FeedbackDataset(
fields=[
rg.TextField(name="prompt"),
rg.TextField(name="response"),
],
questi... | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
56b539a93455-2 | llm = OpenAI(temperature=0.9, callbacks=callbacks)
llm.generate(["Tell me a joke", "Tell me a poem"] * 3) | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
56b539a93455-3 | LLMResult(generations=[[Generation(text='\n\nQ: What did the fish say when he hit the wall? \nA: Dam.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nThe Moon \n\nThe moon is high in the midnight sky,\nSparkling like a star above.\nThe night so peaceful, so serene,\nFilling up the... | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
56b539a93455-4 | 'stop', 'logprobs': None})], [Generation(text="\n\nA poem for you\n\nOn a field of green\n\nThe sky so blue\n\nA gentle breeze, the sun above\n\nA beautiful world, for us to love\n\nLife is a journey, full of surprise\n\nFull of joy and full of surprise\n\nBe brave and take small steps\n\nThe future will be revealed wi... | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
56b539a93455-5 | Scenario 2: Tracking an LLM in a chain
Then we can create a chain using a prompt template, and then track the initial prompt and the final response in Argilla.
from langchain.callbacks import ArgillaCallbackHandler, StdOutCallbackHandler
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from lang... | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
56b539a93455-6 | argilla_callback = ArgillaCallbackHandler(
dataset_name="langchain-dataset",
api_url=os.environ["ARGILLA_API_URL"],
api_key=os.environ["ARGILLA_API_KEY"],
)
callbacks = [StdOutCallbackHandler(), argilla_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)
template = """You are a playwright. Given the title of ... | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
56b539a93455-7 | > Finished chain.
[{'text': "\n\nDocumentary about Bigfoot in Paris focuses on the story of a documentary filmmaker and their search for evidence of the legendary Bigfoot creature in the city of Paris. The play follows the filmmaker as they explore the city, meeting people from all walks of life who have had encou... | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
56b539a93455-8 | > Entering new AgentExecutor chain...
I need to answer a historical question
Action: Search
Action Input: "who was the first president of the United States of America"
Observation: George Washington
Thought: George Washington was the first president
Final Answer: George Washington was the first president of the United... | https://python.langchain.com/docs/integrations/callbacks/argilla.html |
af657d88e6e9-0 | AWS S3 Directory
Amazon Simple Storage Service (Amazon S3) is an object storage service
AWS S3 Directory
This covers how to load document objects from an AWS S3 Directory object.
from langchain.document_loaders import S3DirectoryLoader
loader = S3DirectoryLoader("testing-hwc")
Specifying a prefix
You can also specify ... | https://python.langchain.com/docs/integrations/document_loaders/aws_s3_directory.html |
b160f85171f0-0 | Azure Blob Storage Container
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 Blob Storage is des... | https://python.langchain.com/docs/integrations/document_loaders/azure_blob_storage_container.html |
2bbd7a0c52ef-0 | AwaDB
AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.
This notebook shows how to use functionality related to the AwaDB.
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import AwaDB
from langchain.document_loaders import Text... | https://python.langchain.com/docs/integrations/vectorstores/awadb.html |
2877dd23c105-0 | AWS S3 File
Amazon Simple Storage Service (Amazon S3) is an object storage service.
AWS S3 Buckets
This covers how to load document objects from an AWS S3 File object.
from langchain.document_loaders import S3FileLoader
loader = S3FileLoader("testing-hwc", "fake.docx")
[Document(page_content='Lorem ipsum dolor sit amet... | https://python.langchain.com/docs/integrations/document_loaders/aws_s3_file.html |
f8a9aeb5c0ac-0 | Azure Blob Storage File
Azure Files offers fully managed file shares in the cloud that are accessible via the industry standard Server Message Block (SMB) protocol, Network File System (NFS) protocol, and Azure Files REST API.
This covers how to load document objects from a Azure Files.
#!pip install azure-storage-blob... | https://python.langchain.com/docs/integrations/document_loaders/azure_blob_storage_file.html |
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