prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scann')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import ScaNN
from langchain_text_splitters import CharacterTextSplitter
loader = ... | HuggingFaceEmbeddings() | langchain_community.embeddings.HuggingFaceEmbeddings |
from langchain_community.document_loaders import UnstructuredRSTLoader
loader = | UnstructuredRSTLoader(file_path="example_data/README.rst", mode="elements") | langchain_community.document_loaders.UnstructuredRSTLoader |
from langchain.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
]
example_prompt = ChatPromptTemplate.from_messages(
[
("human", "{input}"),
("ai", "{output}"),
]
)
few_sh... | ChatPromptTemplate.from_messages(
[
("system", "You are a wondrous wizard of math.") | langchain.prompts.ChatPromptTemplate.from_messages |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-community')
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.schema.messages import AIMessage
from langchain_community.llms.chatglm3 import ChatGLM3
template = """{question}"""
prompt = | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', "install --upgrade --quiet langchain-openai 'deeplake[enterprise]' tiktoken")
from langchain_community.vectorstores import DeepLake
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
import getpass
import os
os.environ["OP... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain_community.document_loaders import OBSDirectoryLoader
endpoint = "your-endpoint"
config = {"ak": "your-access-key", "sk": "your-secret-key"}
loader = OBSDirectoryLoader("your-bucket-name", endpoint=endpoint, config=config)
loader.load()
loader = OBSDirectoryLoader(
"your-bucket-name", endpoin... | OBSDirectoryLoader("your-bucket-name", endpoint=endpoint, config=config) | langchain_community.document_loaders.OBSDirectoryLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 nltk')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain_experimental')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pydantic')
import os
import boto3
comprehend_client = boto3.client("comp... | BaseModerationConfig(filters=[pii_config]) | langchain_experimental.comprehend_moderation.BaseModerationConfig |
from langchain.agents import AgentType, initialize_agent
from langchain.chains import LLMMathChain
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import Tool
from langchain_openai import ChatOpenAI
get_ipython().run_line_magic('pip', 'install --upgrade --quiet numexpr')
llm = Cha... | hub.pull("hwchase17/openai-functions-agent") | langchain.hub.pull |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain nuclia')
from langchain_community.vectorstores.nucliadb import NucliaDB
API_KEY = "YOUR_API_KEY"
ndb = | NucliaDB(knowledge_box="YOUR_KB_ID", local=False, api_key=API_KEY) | langchain_community.vectorstores.nucliadb.NucliaDB |
import os
os.environ["EXA_API_KEY"] = "..."
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-exa')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePa... | AgentExecutor(agent=agent, tools=tools, verbose=True) | langchain.agents.AgentExecutor |
REGION = "us-central1" # @param {type:"string"}
INSTANCE = "test-instance" # @param {type:"string"}
DB_USER = "sqlserver" # @param {type:"string"}
DB_PASS = "password" # @param {type:"string"}
DATABASE = "test" # @param {type:"string"}
TABLE_NAME = "test-default" # @param {type:"string"}
get_ipython().run_li... | MSSQLLoader(engine=engine, table_name=TABLE_NAME) | langchain_google_cloud_sql_mssql.MSSQLLoader |
get_ipython().system(' nomic login')
get_ipython().system(' nomic login token')
get_ipython().system(' pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain')
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.lang... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-firestore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from goo... | Document(page_content="Hello, World!") | langchain_core.documents.base.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pygithub')
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.github.toolkit import GitHubToolkit
from langchain_community.utilities.github import GitHubAPIWrapper
from langchain_openai import Ch... | ChatOpenAI(temperature=0.1) | langchain_openai.ChatOpenAI |
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOpenAI
api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_conten... | hub.pull("hwchase17/openai-functions-agent") | langchain.hub.pull |
from langchain_community.vectorstores import Bagel
texts = ["hello bagel", "hello langchain", "I love salad", "my car", "a dog"]
cluster = Bagel.from_texts(cluster_name="testing", texts=texts)
cluster.similarity_search("bagel", k=3)
cluster.similarity_search_with_score("bagel", k=3)
cluster.delete_cluster()
f... | Bagel.from_texts(cluster_name="testing", texts=texts, metadatas=metadatas) | langchain_community.vectorstores.Bagel.from_texts |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet amadeus > /dev/null')
import os
os.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"
os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
from langchain_community.agent_toolkits.amadeus.toolkit impo... | AmadeusToolkit(llm=llm) | langchain_community.agent_toolkits.amadeus.toolkit.AmadeusToolkit |
meals = [
"Beef Enchiladas with Feta cheese. Mexican-Greek fusion",
"Chicken Flatbreads with red sauce. Italian-Mexican fusion",
"Veggie sweet potato quesadillas with vegan cheese",
"One-Pan Tortelonni bake with peppers and onions",
]
from langchain_openai import OpenAI
llm = OpenAI(model="gpt-3.5-t... | rl_chain.ToSelectFrom(meals) | langchain_experimental.rl_chain.ToSelectFrom |
from langchain_community.utils.openai_functions import (
convert_pydantic_to_openai_function,
)
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, validator
from langchain_openai import ChatOpenAI
class Joke(BaseModel):
"""Joke to tell user."""
... | PydanticOutputFunctionsParser(pydantic_schema=Joke) | langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet slack_sdk > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messages')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet python-dotenv > ... | hub.pull("hwchase17/react") | langchain.hub.pull |
from langchain.chains import LLMSummarizationCheckerChain
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)
text = """
Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):
... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-nvidia-ai-endpoints')
import getpass
import os
if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
nvapi_key = getpass.getpass("Enter your NVIDIA API key: ")
assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is ... | ChatNVIDIA(model="mixtral_8x7b") | langchain_nvidia_ai_endpoints.ChatNVIDIA |
get_ipython().system(' pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)')
get_ipython().system(' pip install "unstructured[all-docs]" pillow pydantic lxml pillow matplotlib chromadb tiktoken')
from langchain_text_splitters import CharacterTextSplitter
fro... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet wikipedia')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOp... | WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100) | langchain_community.utilities.WikipediaAPIWrapper |
from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointApiType,
LlamaContentFormatter,
)
from langchain_core.messages import HumanMessage
llm = AzureMLOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<you... | LlamaContentFormatter() | langchain_community.llms.azureml_endpoint.LlamaContentFormatter |
from langchain_community.document_loaders import AsyncChromiumLoader
from langchain_community.document_transformers import BeautifulSoupTransformer
loader = AsyncChromiumLoader(["https://www.wsj.com"])
html = loader.load()
bs_transformer = | BeautifulSoupTransformer() | langchain_community.document_transformers.BeautifulSoupTransformer |
import re
from typing import Union
from langchain.agents import (
AgentExecutor,
AgentOutputParser,
LLMSingleActionAgent,
)
from langchain.chains import LLMChain
from langchain.prompts import StringPromptTemplate
from langchain_community.agent_toolkits import NLAToolkit
from langchain_community.tools.plugi... | NLAToolkit.from_llm_and_ai_plugin(llm, plugin) | langchain_community.agent_toolkits.NLAToolkit.from_llm_and_ai_plugin |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai duckduckgo-search')
from langchain.tools import DuckDuckGoSearchRun
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
searc... | DuckDuckGoSearchRun() | langchain.tools.DuckDuckGoSearchRun |
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
template = """Answer the users question ... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pyvespa')
from vespa.package import ApplicationPackage, Field, RankProfile
app_package = ApplicationPackage(name="testapp")
app_package.schema.add_fields(
Field(
name="text", type="string", indexing=["index", "summary"], index="enable-bm25"... | VespaStore.from_documents(docs, embedding_function, app=vespa_app, **vespa_config) | langchain_community.vectorstores.VespaStore.from_documents |
import os
from langchain.agents import AgentExecutor, AgentType, initialize_agent, load_tools
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import GradientLLM
from getpass import getpass
if not os.environ.get("GRADIENT_ACCESS_TOKEN", None)... | load_tools(["memorize"], llm=llm) | langchain.agents.load_tools |
import zipfile
import requests
def download_and_unzip(url: str, output_path: str = "file.zip") -> None:
file_id = url.split("/")[-2]
download_url = f"https://drive.google.com/uc?export=download&id={file_id}"
response = requests.get(download_url)
if response.status_code != 200:
print("Failed ... | convert_messages_for_finetuning(alternating_sessions) | langchain.adapters.openai.convert_messages_for_finetuning |
def pretty_print_docs(docs):
print(
f"\n{'-' * 100}\n".join(
[f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]
)
)
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAI... | EmbeddingsRedundantFilter(embeddings=embeddings) | langchain_community.document_transformers.EmbeddingsRedundantFilter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 nltk')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain_experimental')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pydantic')
import os
import boto3
comprehend_client = boto3.client("comp... | FakeListLLM(responses=responses) | langchain_community.llms.fake.FakeListLLM |
from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterText... | RecursiveCharacterTextSplitter(chunk_size=400) | langchain_text_splitters.RecursiveCharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet comet_ml langchain langchain-openai google-search-results spacy textstat pandas')
get_ipython().system('{sys.executable} -m spacy download en_core_web_sm')
import comet_ml
comet_ml.init(project_name="comet-example-langchain")
import os
os.envir... | PromptTemplate(input_variables=["title"], template=template) | langchain.prompts.PromptTemplate |
import os
os.environ["LANGCHAIN_PROJECT"] = "movie-qa"
import pandas as pd
df = pd.read_csv("data/imdb_top_1000.csv")
df["Released_Year"] = df["Released_Year"].astype(int, errors="ignore")
from langchain.schema import Document
from langchain_community.vectorstores import Chroma
from langchain_openai import Op... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
]
example_prompt = ChatPromptTemplate.from_messages(
[
("human", "{input}"),
("ai", "{output}"),
]
)
few_sh... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["google-serper"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What is the weathe... | load_tools(["searx-search"], searx_host="http://localhost:8888", llm=llm) | langchain.agents.load_tools |
get_ipython().system(' pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)')
get_ipython().system(' pip install "unstructured[all-docs]" pillow pydantic lxml pillow matplotlib chromadb tiktoken')
from langchain_text_splitters import CharacterTextSplitter
fro... | InMemoryStore() | langchain.storage.InMemoryStore |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scikit-learn')
from langchain_community.retrievers import TFIDFRetriever
retriever = TFIDFRetriever.from_texts(["foo", "bar", "world", "hello", "foo bar"])
from langchain_core.documents import Document
retriever = TFIDFRetriever.from_documents(
... | Document(page_content="hello") | langchain_core.documents.Document |
import sentence_transformers
from baidubce.auth.bce_credentials import BceCredentials
from baidubce.bce_client_configuration import BceClientConfiguration
from langchain.chains.retrieval_qa import RetrievalQA
from langchain_community.document_loaders.baiducloud_bos_directory import (
BaiduBOSDirectoryLoader,
)
from... | RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores.jaguar import Jaguar
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables im... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
REGION = "us-central1" # @param {type:"string"}
INSTANCE = "test-instance" # @param {type:"string"}
DATABASE = "test" # @param {type:"string"}
TABLE_NAME = "test-default" # @param {type:"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-cloud-sql-mysql')
PROJECT_ID ... | MySQLLoader(engine=engine, table_name=TABLE_NAME) | langchain_google_cloud_sql_mysql.MySQLLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet timescale-vector')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import os
from dotenv import find_dotenv, load_dotenv
_ = load_dotenv(find... | OpenAI(temperature=0) | langchain_openai.OpenAI |
meals = [
"Beef Enchiladas with Feta cheese. Mexican-Greek fusion",
"Chicken Flatbreads with red sauce. Italian-Mexican fusion",
"Veggie sweet potato quesadillas with vegan cheese",
"One-Pan Tortelonni bake with peppers and onions",
]
from langchain_openai import OpenAI
llm = OpenAI(model="gpt-3.5-t... | rl_chain.BasedOn("Tom") | langchain_experimental.rl_chain.BasedOn |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet libdeeplake')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["ACTIVELOOP_TOKEN"] = getpass.getpass("Activeloop token:")
fr... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain_community.document_loaders import NewsURLLoader
urls = [
"https://www.bbc.com/news/world-us-canada-66388172",
"https://www.bbc.com/news/entertainment-arts-66384971",
]
loader = NewsURLLoader(urls=urls)
data = loader.load()
print("First article: ", data[0])
print("\nSecond article: ", data[1]... | NewsURLLoader(urls=urls, nlp=True) | langchain_community.document_loaders.NewsURLLoader |
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_openai import ChatOpenAI
import pandas as pd
from langchain_openai import OpenAI
df = pd.read_csv("titanic.csv")
agent = create_pandas_dataframe_agent(OpenAI(tem... | ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") | langchain_openai.ChatOpenAI |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-datastore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from goo... | DatastoreLoader(doc_ref) | langchain_google_datastore.DatastoreLoader |
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
response_schemas = [
ResponseSchema(name="answer", description="answer to the user's question"),
ResponseSchema(
name="source",
descr... | StructuredOutputParser.from_response_schemas(response_schemas) | langchain.output_parsers.StructuredOutputParser.from_response_schemas |
import os
import pprint
os.environ["SERPER_API_KEY"] = ""
from langchain_community.utilities import GoogleSerperAPIWrapper
search = GoogleSerperAPIWrapper()
search.run("Obama's first name?")
os.environ["OPENAI_API_KEY"] = ""
from langchain.agents import AgentType, Tool, initialize_agent
from langchain_commu... | GoogleSerperAPIWrapper(type="news", tbs="qdr:h") | langchain_community.utilities.GoogleSerperAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet slack_sdk > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messages')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet python-dotenv > ... | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
template = """Answer the users question ... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass()
activeloop_token = getpass("Activeloop Token:")
os.environ["ACTIVELOOP_TOKEN"] = activeloop_token
get_ipython().system('ls "../../../../../../libs"')
from langchain_community.document_loaders import TextLoader
root_dir = "../../..... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('', 'pip install --upgrade --quiet flashrank')
get_ipython().run_line_magic('', 'pip install --upgrade --quiet faiss')
get_ipython().run_line_magic('', 'pip install --upgrade --quiet faiss_cpu')
def pretty_print_docs(docs):
print(
f"\n{'-' * 100}\n".join(
[f... | FlashrankRerank() | langchain.retrievers.document_compressors.FlashrankRerank |
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import Recursiv... | InMemoryByteStore() | langchain.storage.InMemoryByteStore |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai argilla')
import os
os.environ["ARGILLA_API_URL"] = "..."
os.environ["ARGILLA_API_KEY"] = "..."
os.environ["OPENAI_API_KEY"] = "..."
import argilla as rg
from packaging.version import parse as parse_version
if parse_ve... | load_tools(["serpapi"], llm=llm, callbacks=callbacks) | langchain.agents.load_tools |
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import Recursiv... | TextLoader("../../paul_graham_essay.txt") | langchain_community.document_loaders.TextLoader |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
import dspy
colbertv2 = dspy.ColBERTv2(url="http://20.102.90.50:2017/wiki17_abstracts")
from langchain.cache import SQLiteCache
from langchain.globals import set_llm_cache
from langchain_openai import OpenAI
set_llm_cache(SQLiteCache(data... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
import requests
def get_wikipedia_page(title: str):
"""
Retrieve the full text content of a Wikipedia page.
:param title: str - Title of the Wikipedia page.
:return: str - Full text conten... | ChatPromptTemplate.from_template(
"""Answer the following question based only on the provided context:
<context>
{context}
</context>
Question: {input}"""
) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0).configurable_fields(
temperature=ConfigurableF... | ChatAnthropic(temperature=0) | langchain_community.chat_models.ChatAnthropic |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_community.chat_models import ChatAnthropic
from langchain_openai import ChatOpenAI
from unittest.mock import patch
import httpx
from openai import RateLimitError
request = httpx.Request("GET", "/")
respons... | ChatOpenAI() | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 nltk')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain_experimental')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pydantic')
import os
import boto3
comprehend_client = boto3.client("comp... | FakeListLLM(responses=responses) | langchain_community.llms.fake.FakeListLLM |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet duckdb')
from langchain_community.document_loaders import DuckDBLoader
get_ipython().run_cell_magic('file', 'example.csv', 'Team,Payroll\nNationals,81.34\nReds,82.20\n')
loader = DuckDBLoader("SELECT * FROM read_csv_auto('example.csv')")
data = load... | DuckDBLoader(
"SELECT * FROM read_csv_auto('example.csv') | langchain_community.document_loaders.DuckDBLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet O365')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet beautifulsoup4 # This is optional but is useful for parsing HTML messages')
from langchain_community.agent_toolkits import O365Toolkit
toolkit = O365Toolkit()
tools = toolkit.ge... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymilvus')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Milvus
from langchain_openai import OpenAIE... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().system('pip install databricks-sql-connector')
from langchain_community.utilities import SQLDatabase
db = | SQLDatabase.from_databricks(catalog="samples", schema="nyctaxi") | langchain_community.utilities.SQLDatabase.from_databricks |
from langchain_community.utils.openai_functions import (
convert_pydantic_to_openai_function,
)
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, validator
from langchain_openai import ChatOpenAI
class Joke(BaseModel):
"""Joke to tell user."""
... | Field(description="question to set up a joke") | langchain_core.pydantic_v1.Field |
get_ipython().run_line_magic('pip', "install --upgrade --quiet faiss-gpu # For CUDA 7.5+ Supported GPU's.")
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu # For CPU Installation')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_... | HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") | langchain_community.embeddings.huggingface.HuggingFaceEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-nvidia-ai-endpoints')
import getpass
import os
if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
nvapi_key = getpass.getpass("Enter your NVIDIA API key: ")
assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is ... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet titan-iris')
from langchain_community.llms import TitanTakeoff
llm = TitanTakeoff(
base_url="http://localhost:8000", generate_max_length=128, temperature=1.0
)
prompt = "What is the largest planet in the solar system?"
llm(prompt)
from langc... | LLMChain(llm=llm, prompt=prompt) | langchain.chains.LLMChain |
from langchain.agents import Tool
from langchain_community.tools.file_management.read import ReadFileTool
from langchain_community.tools.file_management.write import WriteFileTool
from langchain_community.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
tools = [
Tool(
name="search",
func=... | ReadFileTool() | langchain_community.tools.file_management.read.ReadFileTool |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet arxiv')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent, load_tools
from langchain_openai import ChatOpenAI
llm = | ChatOpenAI(temperature=0.0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet aphrodite-engine==0.4.2')
from langchain_community.llms import Aphrodite
llm = Aphrodite(
model="PygmalionAI/pygmalion-2-7b",
trust_remote_code=True, # mandatory for hf models
max_tokens=128,
temperature=1.2,
min_p=0.05,
mirosta... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().system(' nomic login')
get_ipython().system(' nomic login token')
get_ipython().system(' pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain')
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.lang... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet dashvector dashscope')
import getpass
import os
os.environ["DASHVECTOR_API_KEY"] = getpass.getpass("DashVector API Key:")
os.environ["DASHSCOPE_API_KEY"] = getpass.getpass("DashScope API Key:")
from langchain_community.embeddings.dashscope import Da... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
import os
import pprint
os.environ["SERPER_API_KEY"] = ""
from langchain_community.utilities import GoogleSerperAPIWrapper
search = GoogleSerperAPIWrapper()
search.run("Obama's first name?")
os.environ["OPENAI_API_KEY"] = ""
from langchain.agents import AgentType, Tool, initialize_agent
from langchain_commu... | GoogleSerperAPIWrapper(type="places") | langchain_community.utilities.GoogleSerperAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet chromadb')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import Chroma
from langchain_core.doc... | Chroma.from_documents(docs, embeddings) | langchain_community.vectorstores.Chroma.from_documents |
from langchain.agents import Tool
from langchain_community.tools.file_management.read import ReadFileTool
from langchain_community.tools.file_management.write import WriteFileTool
from langchain_community.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
tools = [
Tool(
name="search",
func=... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from langchain_community.document_transformers.openai_functions import (
create_metadata_tagger,
)
from langchain_core.documents import Document
from langchain_openai import ChatOpenAI
schema = {
"properties": {
"movie_title": {"type": "string"},
"critic": {"type": "string"},
"tone": {... | create_metadata_tagger(metadata_schema=schema, llm=llm) | langchain_community.document_transformers.openai_functions.create_metadata_tagger |
from langchain_community.llms import Ollama
llm = | Ollama(model="llama2") | langchain_community.llms.Ollama |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml')
from typing import Any
from pydantic import BaseModel
from unstructured.partition.pdf import partition_pdf
path = "/Users/rlm/Desktop/Papers/LLaVA/"
raw_pdf_elements = partition_pdf(
filename=path + "LLaVA.pdf",
extract_im... | Document(page_content=s, metadata={id_key: table_ids[i]}) | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet neo4j')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Ke... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sagemaker')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
import os
os.environ["OPENAI_API_KEY"] = "<ADD-KEY-HERE>"
os.environ[... | OpenAI(callbacks=[sagemaker_callback], **HPARAMS) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet neo4j')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Ke... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "docarray"')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import DocArrayInMemorySearch
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain.prompts import ChatMessagePromptTemplate
prompt = "May the {subject} be with you"
chat_message_prompt = ChatMessagePromptTemplate.from_template(
role="Jedi", template=prompt
)
chat_message_prompt.format(subject="force")
from langchain.prompts import (
ChatPromptTemplate,
HumanMessageProm... | HumanMessage(content="What is the best way to learn programming?") | langchain_core.messages.HumanMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rellm > /dev/null')
import logging
logging.basicConfig(level=logging.ERROR)
prompt = """Human: "What's the capital of the United States?"
AI Assistant:{
"action": "Final Answer",
"action_input": "The capital of the United States is Washington D.C."... | HuggingFacePipeline(pipeline=hf_model) | langchain_community.llms.HuggingFacePipeline |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet feedparser newspaper3k listparser')
from langchain_community.document_loaders import RSSFeedLoader
urls = ["https://news.ycombinator.com/rss"]
loader = RSSFeedLoader(urls=urls)
data = loader.load()
print(len(data))
print(data[0].page_content)
l... | RSSFeedLoader(urls=urls, nlp=True) | langchain_community.document_loaders.RSSFeedLoader |
from langchain.agents import Tool
from langchain_community.tools.file_management.read import ReadFileTool
from langchain_community.tools.file_management.write import WriteFileTool
from langchain_community.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
tools = [
Tool(
name="search",
func=... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
import os
os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
os.environ["WANDB_PROJECT"] = "langchain-tracing"
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks import wandb_tracing_enabled
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain_experimental.llm_bash.base import LLMBashChain
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pyvespa')
from vespa.package import ApplicationPackage, Field, RankProfile
app_package = ApplicationPackage(name="testapp")
app_package.schema.add_fields(
Field(
name="text", type="string", indexing=["index", "summary"], index="enable-bm25"... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain.callbacks import FileCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
from loguru import logger
logfile = "output.log"
logger.add(logfile, colorize=True, enqueue=True)
handler = FileCallbackHandler(logfile)
llm = Ope... | PromptTemplate.from_template("1 + {number} = ") | langchain.prompts.PromptTemplate.from_template |
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/state_of_t... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0, model="gpt-4-turbo-preview")
from langchain import hub
from langchain_core.prompts import PromptTemplate
select_prompt = hub.pull("hwchase17/self-discovery-select")
select_prompt.pretty_print()
adapt_prompt = hub.pull("hwchase17/self-di... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain.memory import ConversationTokenBufferMemory
from langchain_openai import OpenAI
llm = OpenAI()
memory = | ConversationTokenBufferMemory(llm=llm, max_token_limit=10) | langchain.memory.ConversationTokenBufferMemory |
import os
os.environ["EXA_API_KEY"] = "..."
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-exa')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePa... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub gpt4all chromadb')
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = | WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") | langchain_community.document_loaders.WebBaseLoader |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.