prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai.chat_models import ChatOpenAI
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You're an assistant who's good at {ability}. Respond in 20 words or fewer",
... | ChatOpenAI() | langchain_openai.chat_models.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet redis redisvl langchain-openai tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_openai import OpenAIEmbeddings
embeddings = | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from operator import itemgetter
from langchain.output_parsers import JsonOutputToolsParser
from langchain_core.runnables import Runnable, Runnabl... | JsonOutputToolsParser() | langchain.output_parsers.JsonOutputToolsParser |
import os
import yaml
get_ipython().system('wget https://raw.githubusercontent.com/openai/openai-openapi/master/openapi.yaml -O openai_openapi.yaml')
get_ipython().system('wget https://www.klarna.com/us/shopping/public/openai/v0/api-docs -O klarna_openapi.yaml')
get_ipython().system('wget https://raw.githubuserconte... | JsonSpec(dict_=data, max_value_length=4000) | langchain_community.tools.json.tool.JsonSpec |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain fleet-context langchain-openai pandas faiss-cpu # faiss-gpu for CUDA supported GPU')
from operator import itemgetter
from typing import Any, Optional, Type
import pandas as pd
from langchain.retrievers import MultiVectorRetriever
from langchai... | OpenAIEmbeddings(model="text-embedding-ada-002") | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet alibabacloud_ha3engine_vector')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import (
AlibabaCloudOpenSearch,
AlibabaCloudOpenSearchSettings,
)
from langchai... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet beautifulsoup4')
from langchain_community.document_loaders import ReadTheDocsLoader
loader = | ReadTheDocsLoader("rtdocs", features="html.parser") | langchain_community.document_loaders.ReadTheDocsLoader |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml')
path = "/Users/rlm/Desktop/Papers/LLaVA/"
from typing import Any
from pydantic import BaseModel
from unstructured.partition.pdf import partition_pdf
raw_pdf_elements = partition_pdf(
filename=path + "LLaVA.pdf",
extract_i... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from typing import List
from langchain.prompts.chat import (
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class CAMELAgent:
def __init__(
se... | ChatOpenAI(temperature=0.2) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rockset')
import os
import rockset
ROCKSET_API_KEY = os.environ.get(
"ROCKSET_API_KEY"
) # Verify ROCKSET_API_KEY environment variable
ROCKSET_API_SERVER = rockset.Regions.usw2a1 # Verify Rockset region
rockset_client = rockset.RocksetClient(ROC... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
from typing import List
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
model = | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('poetry run pip install replicate')
from getpass import getpass
REPLICATE_API_TOKEN = getpass()
import os
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.chains ... | LLMChain(llm=dolly_llm, prompt=second_prompt) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet gpt4all > /dev/null')
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import GPT4All
template = """Questi... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai.chat_models import ChatOpenAI
model = | ChatOpenAI() | langchain_openai.chat_models.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandoc')
from langchain_community.document_loaders import UnstructuredEPubLoader
loader = UnstructuredEPubLoader("winter-sports.epub")
data = loader.load()
loader = | UnstructuredEPubLoader("winter-sports.epub", mode="elements") | langchain_community.document_loaders.UnstructuredEPubLoader |
from typing import Callable, List
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name =... | ChatOpenAI(temperature=0.2) | langchain_openai.ChatOpenAI |
get_ipython().system('pip install -U openai langchain langchain-experimental')
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
chat = ChatOpenAI(model="gpt-4-vision-preview", max_tokens=256)
chat.invoke(
[
HumanMessage(
content=[
... | AgentExecutor(agent=agent, tools=tools) | langchain.agents.AgentExecutor |
from langchain_community.llms import Ollama
llm = Ollama(model="llama2")
llm("The first man on the moon was ...")
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = Ollama(
model="llama2", callback_manager=CallbackManage... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
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... | FirestoreSaver() | langchain_google_firestore.FirestoreSaver |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-api-python-client > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-auth-oauthlib > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-auth-httplib2 > /dev/null')
get_ipython().run_l... | GmailToolkit(api_resource=api_resource) | langchain_community.agent_toolkits.GmailToolkit |
from langchain_community.llms import AmazonAPIGateway
api_url = "https://<api_gateway_id>.execute-api.<region>.amazonaws.com/LATEST/HF"
llm = AmazonAPIGateway(api_url=api_url)
parameters = {
"max_new_tokens": 100,
"num_return_sequences": 1,
"top_k": 50,
"top_p": 0.95,
"do_sample": False,
"re... | load_tools(["python_repl", "llm-math"], llm=llm) | langchain.agents.load_tools |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet promptlayer --upgrade')
import promptlayer # Don't forget this 🍰
from langchain.callbacks import PromptLayerCallbackHandler
from langchain.schema import (
HumanMessage,
)
from langchain_openai import ChatOpenAI
chat_llm = ChatOpenAI(
temper... | PromptLayerCallbackHandler(pl_tags=["langchain", "gpt4all"]) | langchain.callbacks.PromptLayerCallbackHandler |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("trajectory")
import subprocess
from urllib.parse import urlparse
from langchain.agents import AgentType, initialize_agent
from langchain.tools ... | load_evaluator("trajectory", agent_tools=[ping, trace_route]) | langchain.evaluation.load_evaluator |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet cos-python-sdk-v5')
from langchain_community.document_loaders import TencentCOSDirectoryLoader
from qcloud_cos import CosConfig
conf = CosConfig(
Region="your cos region",
SecretId="your cos secret_id",
SecretKey="your cos secret_key",
)
lo... | TencentCOSDirectoryLoader(conf=conf, bucket="you_cos_bucket", prefix="fake") | langchain_community.document_loaders.TencentCOSDirectoryLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet cohere')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu')
import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:")
... | CohereRerank() | langchain.retrievers.document_compressors.CohereRerank |
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../state_of_the_union.txt", encoding... | PromptTemplate.from_template(_template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install -qU chromadb langchain langchain-community langchain-openai')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharact... | Field(description="The final answer to respond to the user") | langchain_core.pydantic_v1.Field |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet marqo')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Marqo
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = Text... | Marqo.from_documents(docs, index_name=index_name) | langchain_community.vectorstores.Marqo.from_documents |
from langchain_community.document_loaders import ArcGISLoader
URL = "https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7"
loader = ArcGISLoader(URL)
docs = loader.load()
get_ipython().run_cell_magic('time', '', '\ndocs = loader.load()\n')
docs[0].metadata
loader_geom = | ArcGISLoader(URL, return_geometry=True) | langchain_community.document_loaders.ArcGISLoader |
import getpass
import os
os.environ["TAVILY_API_KEY"] = getpass.getpass()
from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
retriever = TavilySearchAPIRetriever(k=3)
retriever.invoke("what year was breath of the wild released?")
from langchain_core.output_parsers import StrOutputPa... | RunnablePassthrough.assign(context=(lambda x: x["question"]) | retriever) | langchain_core.runnables.RunnablePassthrough.assign |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet wikibase-rest-api-client mediawikiapi')
from langchain_community.tools.wikidata.tool import WikidataAPIWrapper, WikidataQueryRun
wikidata = WikidataQueryRun(api_wrapper= | WikidataAPIWrapper() | langchain_community.tools.wikidata.tool.WikidataAPIWrapper |
from langchain.callbacks import HumanApprovalCallbackHandler
from langchain.tools import ShellTool
tool = | ShellTool() | langchain.tools.ShellTool |
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 = ... | ScaNN.from_documents(docs, embeddings) | langchain_community.vectorstores.ScaNN.from_documents |
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)
tools = load_tools(
["arxiv"],
)
prompt = hub.pull("hwchase17/reac... | AgentExecutor(agent=agent, tools=tools, verbose=True) | langchain.agents.AgentExecutor |
get_ipython().system('pip install boto3')
from langchain_experimental.recommenders import AmazonPersonalize
recommender_arn = "<insert_arn>"
client = AmazonPersonalize(
credentials_profile_name="default",
region_name="us-west-2",
recommender_arn=recommender_arn,
)
client.get_recommendations(user_id="1... | Bedrock(model_id="anthropic.claude-v2", region_name="us-west-2") | langchain.llms.bedrock.Bedrock |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
runnable = RunnableParallel(
passed=RunnablePassthrough(),
extra= | RunnablePassthrough.assign(mult=lambda x: x["num"] * 3) | langchain_core.runnables.RunnablePassthrough.assign |
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_... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | LLMChain(llm=llm, prompt=prompt, callbacks=[handler], verbose=True) | langchain.chains.LLMChain |
get_ipython().system(' pip install langchain replicate')
from langchain_community.chat_models import ChatOllama
llama2_chat = ChatOllama(model="llama2:13b-chat")
llama2_code = ChatOllama(model="codellama:7b-instruct")
from langchain_community.llms import Replicate
replicate_id = "meta/llama-2-13b-chat:f4e2de70d66... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet weaviate-client')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")
os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")
WEAVIATE_API_KEY = os... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azureml-mlflow')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet textstat')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
get_ipython().run_l... | load_tools(["serpapi", "llm-math"], llm=llm, callbacks=[mlflow_callback]) | langchain.agents.load_tools |
from langchain_community.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://www.espn.com/")
data = loader.load()
data
"""
import requests
from bs4 import BeautifulSoup
html_doc = requests.get("{INSERT_NEW_URL_HERE}")
soup = BeautifulSoup(html_doc.text, 'html.parser')
"""
loader = WebB... | WebBaseLoader(
"https://www.govinfo.gov/content/pkg/CFR-2018-title10-vol3/xml/CFR-2018-title10-vol3-sec431-86.xml"
) | langchain_community.document_loaders.WebBaseLoader |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-openai langchain-anthropic langchain-community wikipedia')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()
from langchain_community.retrievers import WikipediaRetrieve... | ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | langchain_openai.ChatOpenAI |
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 |
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(["Vegetarian", "regular dairy is ok"]) | langchain_experimental.rl_chain.BasedOn |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-openai langchain-anthropic langchain-community wikipedia')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()
from langchain_community.retrievers import WikipediaRetrieve... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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 langchain label-studio label-studio-sdk langchain-openai')
import os
os.environ["LABEL_STUDIO_URL"] = "<YOUR-LABEL-STUDIO-URL>" # e.g. http://localhost:8080
os.environ["LABEL_STUDIO_API_KEY"] = "<YOUR-LABEL-STUDIO-API-KEY>"
os.environ["OPENAI_API_KEY"... | SystemMessage(content="Always use a lot of emojis") | langchain_core.messages.SystemMessage |
from langchain_community.chat_models import ChatDatabricks
from langchain_core.messages import HumanMessage
from mlflow.deployments import get_deploy_client
client = get_deploy_client("databricks")
secret = "secrets/<scope>/openai-api-key" # replace `<scope>` with your scope
name = "my-chat" # rename this if my-cha... | Databricks(endpoint_name="dolly", transform_input_fn=transform_input) | langchain_community.llms.Databricks |
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]==0.10.19" pillow pydantic lxml pillow matplotlib tiktoken open_clip_torch torch')
path = "/Users/rlm/Desktop/cpi/"
from ... | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
import nest_asyncio
nest_asyncio.apply()
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import SurrealDBStore
from langchain_text_splitters import CharacterTextSplitter
documents = | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().system('pip install -U openai langchain langchain-experimental')
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
chat = ChatOpenAI(model="gpt-4-vision-preview", max_tokens=256)
chat.invoke(
[
HumanMessage(
content=[
... | ChatOpenAI(model="gpt-3.5-turbo-1106") | 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/... | Llamafile() | langchain_community.llms.llamafile.Llamafile |
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... | ChatOllama(model="llama2:13b-chat") | langchain_community.chat_models.ChatOllama |
import os
os.environ["SERPER_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from typing import Any, List
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_community.utilities import GoogleSerperAPIWrapper
from langchain_core.doc... | GoogleSerperAPIWrapper() | langchain_community.utilities.GoogleSerperAPIWrapper |
arthur_url = "https://app.arthur.ai"
arthur_login = "your-arthur-login-username-here"
arthur_model_id = "your-arthur-model-id-here"
from langchain.callbacks import ArthurCallbackHandler
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_core.messages import HumanMessage
fr... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain fleet-context langchain-openai pandas faiss-cpu # faiss-gpu for CUDA supported GPU')
from operator import itemgetter
from typing import Any, Optional, Type
import pandas as pd
from langchain.retrievers import MultiVectorRetriever
from langchai... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().system('pip install pettingzoo pygame rlcard')
import collections
import inspect
import tenacity
from langchain.output_parsers import RegexParser
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class GymnasiumAgent:
@classmethod
... | ChatOpenAI(temperature=1) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken langchain-openai python-dotenv datasets langchain deeplake beautifulsoup4 html2text ragas')
ORG_ID = "..."
import getpass
import os
from langchain.chains import RetrievalQA
from langchain.vectorstores.deeplake import DeepLake
from langchain_... | ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | langchain_openai.ChatOpenAI |
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... | TextContentsOptions(max_length=200) | langchain_exa.TextContentsOptions |
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
def initialize_chain(instructions, memory=None):
if memory is None:
memory = | ConversationBufferWindowMemory() | langchain.memory.ConversationBufferWindowMemory |
import xorbits.pandas as pd
from langchain_experimental.agents.agent_toolkits import create_xorbits_agent
from langchain_openai import OpenAI
data = pd.read_csv("titanic.csv")
agent = create_xorbits_agent( | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | ModerationPiiConfig(labels=["SSN"], redact=True, mask_character="X") | langchain_experimental.comprehend_moderation.ModerationPiiConfig |
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 ... | hub.pull("langchain-ai/rewrite") | langchain.hub.pull |
from langchain_community.document_loaders import WebBaseLoader
loader = | WebBaseLoader("https://www.espn.com/") | langchain_community.document_loaders.WebBaseLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet infinopy')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet matplotlib')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import datetime as dt
import json
import time
import matplotlib.dates as md
import matplot... | load_summarize_chain(llm, chain_type="stuff", verbose=False) | langchain.chains.summarize.load_summarize_chain |
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... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('poetry run pip install replicate')
from getpass import getpass
REPLICATE_API_TOKEN = getpass()
import os
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.chains ... | Replicate(
model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"
) | langchain_community.llms.Replicate |
from langchain_core.pydantic_v1 import BaseModel, Field
class Joke(BaseModel):
setup: str = | Field(description="The setup of the joke") | langchain_core.pydantic_v1.Field |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet playwright beautifulsoup4')
get_ipython().system(' playwright install')
from langchain_community.document_loaders import AsyncChromiumLoader
urls = ["https://www.wsj.com"]
loader = | AsyncChromiumLoader(urls) | langchain_community.document_loaders.AsyncChromiumLoader |
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import ForefrontAI
from getpass import getpass
FOREFRONTAI_API_KEY = getpass()
os.environ["FOREFRONTAI_API_KEY"] = FOREFRONTAI_API_KEY
llm = ForefrontAI(endpoint_url="YOUR ENDPOINT URL H... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-community')
import os
os.environ["YDC_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from langchain_community.utilities.you import YouSearchAPIWrapper
utility = YouSearchAPIWrapper(num_web_results=1)
utility
import json
response... | ChatOpenAI(model="gpt-3.5-turbo-16k") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-alloydb-pg langchain-google-vertexai')
from google.colab import auth
auth.authenticate_user()
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
get_ipython... | IVFFlatIndex() | langchain_google_alloydb_pg.indexes.IVFFlatIndex |
from langchain.chains import HypotheticalDocumentEmbedder, LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI, OpenAIEmbeddings
base_embeddings = OpenAIEmbeddings()
llm = OpenAI()
embeddings = HypotheticalDocumentEmbedder.from_llm(llm, base_embeddings, "web_search")
result ... | PromptTemplate(input_variables=["question"], template=prompt_template) | langchain.prompts.PromptTemplate |
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(["google-search"], llm=llm) | langchain.agents.load_tools |
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("criteria", criteria="conciseness")
from langchain.evaluation import EvaluatorType
evaluator = | load_evaluator(EvaluatorType.CRITERIA, criteria="conciseness") | langchain.evaluation.load_evaluator |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
import os
from langchain_community.tools.google_finance import GoogleFinanceQueryRun
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
os.environ["SERPAPI_API_KEY"] = ""
tool = GoogleFinanceQueryRu... | GoogleFinanceAPIWrapper() | langchain_community.utilities.google_finance.GoogleFinanceAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet supabase')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["SUPABASE_URL"] = getpass.getpass("Supabase URL:")
os.environ["SUPABASE_SERVICE_KEY"] = getpass.getpass("Supabase Service Key:")
fro... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet nlpcloud')
from langchain_community.embeddings import NLPCloudEmbeddings
import os
os.environ["NLPCLOUD_API_KEY"] = "xxx"
nlpcloud_embd = | NLPCloudEmbeddings() | langchain_community.embeddings.NLPCloudEmbeddings |
get_ipython().system('pip install langchain lark openai elasticsearch pandas')
import pandas as pd
details = (
pd.read_csv("~/Downloads/archive/Hotel_details.csv")
.drop_duplicates(subset="hotelid")
.set_index("hotelid")
)
attributes = pd.read_csv(
"~/Downloads/archive/Hotel_Room_attributes.csv", in... | get_query_constructor_prompt(doc_contents, attribute_info) | langchain.chains.query_constructor.base.get_query_constructor_prompt |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import chain
from langchain_openai import ChatOpenAI
prompt1 = ChatPromptTemplate... | ChatPromptTemplate.from_template("What is the subject of this joke: {joke}") | langchain_core.prompts.ChatPromptTemplate.from_template |
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... | ChatPromptTemplate.from_template(prompt_text) | langchain_core.prompts.ChatPromptTemplate.from_template |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().system(' pip install lancedb')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import LanceDB
from langchain.document_loaders import TextLoader
from langchain_text_splitters imp... | OpenAIEmbeddings() | langchain.embeddings.OpenAIEmbeddings |
from langchain.chains import GraphCypherQAChain
from langchain_community.graphs import Neo4jGraph
from langchain_openai import ChatOpenAI
graph = Neo4jGraph(
url="bolt://localhost:7687", username="neo4j", password="pleaseletmein"
)
graph.query(
"""
MERGE (m:Movie {name:"Top Gun"})
WITH m
UNWIND ["Tom Cruis... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import Vectara
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitt... | FakeEmbeddings(size=768) | langchain_community.embeddings.fake.FakeEmbeddings |
get_ipython().system('pip install --upgrade langchain langchain-google-vertexai')
project: str = "PUT_YOUR_PROJECT_ID_HERE" # @param {type:"string"}
endpoint_id: str = "PUT_YOUR_ENDPOINT_ID_HERE" # @param {type:"string"}
location: str = "PUT_YOUR_ENDPOINT_LOCAtION_HERE" # @param {type:"string"}
from langchain_... | HumanMessage(content="Hi! Who are you?") | langchain_core.messages.HumanMessage |
from langchain.prompts import PromptTemplate
prompt = (
PromptTemplate.from_template("Tell me a joke about {topic}")
+ ", make it funny"
+ "\n\nand in {language}"
)
prompt
prompt.format(topic="sports", language="spanish")
from langchain.chains import LLMChain
from langchain_openai import ChatOpenAI... | AIMessage(content="what?") | langchain_core.messages.AIMessage |
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... | WebBaseLoader(url) | langchain_community.document_loaders.WebBaseLoader |
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... | Document(page_content="bar", metadata={"id": 2}) | langchain.docstore.document.Document |
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/... | hub.pull("rlm/rag-prompt-llama") | langchain.hub.pull |
from langchain.tools import ShellTool
shell_tool = ShellTool()
print(shell_tool.run({"commands": ["echo 'Hello World!'", "time"]}))
from langchain.agents import AgentType, initialize_agent
from langchain_openai import ChatOpenAI
llm = | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.chains import OpenAIModerationChain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import OpenAI
moderate = OpenAIModerationChain()
model = OpenAI()
prompt = | ChatPromptTemplate.from_messages([("system", "repeat after me: {input}")]) | langchain_core.prompts.ChatPromptTemplate.from_messages |
get_ipython().system('poetry run pip install dgml-utils==0.3.0 --upgrade --quiet')
import os
from langchain_community.document_loaders import DocugamiLoader
DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")
docset_id = "26xpy3aes7xp"
document_ids = ["d7jqdzcj50sj", "cgd1eacfkchw"]
loader = DocugamiLoader(... | DocugamiLoader(docset_id="zo954yqy53wp") | langchain_community.document_loaders.DocugamiLoader |
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... | Chroma.from_documents(docs, embedding_function, ids=ids) | langchain_community.vectorstores.Chroma.from_documents |
from langchain_community.llms import Baseten
mistral = Baseten(model="MODEL_ID", deployment="production")
mistral("What is the Mistral wind?")
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
template = """Assistant is... | PromptTemplate(input_variables=["history", "human_input"], template=template) | langchain.prompts.PromptTemplate |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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