prompt stringlengths 51 10k | completion stringlengths 8 362 | api stringlengths 18 90 |
|---|---|---|
get_ipython().run_line_magic('pip', 'install --upgrade --quiet airbyte-source-salesforce')
from langchain_community.document_loaders.airbyte import AirbyteSalesforceLoader
config = {
}
loader = AirbyteSalesforceLoader(
config=config, stream_name="asset"
) # check the documentation linked above for a list of... | Document(page_content=record.data["title"], metadata=record.data) | langchain.docstore.document.Document |
import os
from langchain.chains import ConversationalRetrievalChain
from langchain_community.vectorstores import Vectara
from langchain_openai import OpenAI
from langchain_community.document_loaders import TextLoader
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
vectara = Vectara.from_... | LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) | langchain.chains.llm.LLMChain |
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... | SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") | langchain_community.embeddings.sentence_transformer.SentenceTransformerEmbeddings |
from langchain.output_parsers.enum import EnumOutputParser
from enum import Enum
class Colors(Enum):
RED = "red"
GREEN = "green"
BLUE = "blue"
parser = EnumOutputParser(enum=Colors)
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
prompt = | PromptTemplate.from_template(
"""What color eyes does this person have?
> Person: {person}
Instructions: {instructions}"""
) | langchain_core.prompts.PromptTemplate.from_template |
from getpass import getpass
WRITER_API_KEY = getpass()
import os
os.environ["WRITER_API_KEY"] = WRITER_API_KEY
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Writer
template = """Question: {question}
Answer: Let's think step by step."""
... | Writer() | langchain_community.llms.Writer |
get_ipython().run_line_magic('pip', 'install -qU langchain-text-splitters')
from langchain_text_splitters import HTMLHeaderTextSplitter
html_string = """
<!DOCTYPE html>
<html>
<body>
<div>
<h1>Foo</h1>
<p>Some intro text about Foo.</p>
<div>
<h2>Bar main section</h2>
... | HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on) | langchain_text_splitters.HTMLHeaderTextSplitter |
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 |
from langchain_core.pydantic_v1 import BaseModel, Field
class Joke(BaseModel):
setup: str = Field(description="The setup of the joke")
punchline: str = | Field(description="The punchline to the joke") | langchain_core.pydantic_v1.Field |
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 |
from langchain.memory import ConversationKGMemory
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
memory = | ConversationKGMemory(llm=llm) | langchain.memory.ConversationKGMemory |
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_... | OpenAIChat(model="gpt-4") | langchain_openai.OpenAIChat |
from langchain.callbacks import get_openai_callback
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-4")
with | get_openai_callback() | langchain.callbacks.get_openai_callback |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet meilisearch')
import getpass
import os
os.environ["MEILI_HTTP_ADDR"] = getpass.getpass("Meilisearch HTTP address and port:")
os.environ["MEILI_MASTER_KEY"] = getpass.getpass("Meilisearch API Key:")
os.environ["OPENAI_API_KEY"] = getpass.getpass("Op... | Meilisearch.from_texts(texts=texts, embedding=embeddings) | langchain_community.vectorstores.Meilisearch.from_texts |
import os
from langchain.chains import ConversationalRetrievalChain
from langchain_community.vectorstores import Vectara
from langchain_openai import OpenAI
from langchain_community.document_loaders import TextLoader
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
vectara = Vectara.from_... | load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT) | langchain.chains.question_answering.load_qa_chain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet googlemaps')
import os
os.environ["GPLACES_API_KEY"] = ""
from langchain.tools import GooglePlacesTool
places = | GooglePlacesTool() | langchain.tools.GooglePlacesTool |
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... | RunnablePassthrough.assign(info=(lambda x: x["question"]) | retriever1) | langchain_core.runnables.RunnablePassthrough.assign |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymysql')
get_ipython().system('pip install sqlalchemy')
get_ipython().system('pip install langchain')
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
DirectoryLoader,
UnstructuredMarkdownLoader,
)
... | ApacheDoris(embeddings, settings) | langchain_community.vectorstores.apache_doris.ApacheDoris |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai context-python')
import os
from langchain.callbacks import ContextCallbackHandler
token = os.environ["CONTEXT_API_TOKEN"]
context_callback = ContextCallbackHandler(token)
import os
from langchain.callbacks import Conte... | ContextCallbackHandler(token) | langchain.callbacks.ContextCallbackHandler |
get_ipython().run_cell_magic('writefile', 'telegram_conversation.json', '{\n "name": "Jiminy",\n "type": "personal_chat",\n "id": 5965280513,\n "messages": [\n {\n "id": 1,\n "type": "message",\n "date": "2023-08-23T13:11:23",\n "date_unixtime": "1692821483",\n "from": "Jiminy Cricket",\n "from_id": "user1... | map_ai_messages(merged_messages, sender="Jiminy Cricket") | langchain_community.chat_loaders.utils.map_ai_messages |
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... | Chroma.from_documents(documents, embeddings) | langchain_community.vectorstores.Chroma.from_documents |
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/... | GPT4AllEmbeddings() | langchain_community.embeddings.GPT4AllEmbeddings |
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="images") | langchain_community.utilities.GoogleSerperAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet predictionguard langchain')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import PredictionGuard
os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>"
os.environ["PREDICTI... | LLMChain(prompt=prompt, llm=pgllm, verbose=True) | langchain.chains.LLMChain |
import getpass
import os
os.environ["POLYGON_API_KEY"] = getpass.getpass()
from langchain_community.tools.polygon.financials import PolygonFinancials
from langchain_community.tools.polygon.last_quote import PolygonLastQuote
from langchain_community.tools.polygon.ticker_news import PolygonTickerNews
from langchain_co... | PolygonTickerNews(api_wrapper=api_wrapper) | langchain_community.tools.polygon.ticker_news.PolygonTickerNews |
import os
os.environ["GOOGLE_CSE_ID"] = ""
os.environ["GOOGLE_API_KEY"] = ""
from langchain.tools import Tool
from langchain_community.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
tool = Tool(
name="google_search",
description="Search Google for recent results.",
func=searc... | GoogleSearchAPIWrapper() | langchain_community.utilities.GoogleSearchAPIWrapper |
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("criteria", criteria="conciseness")
from langchain.evaluation import EvaluatorType
evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="conciseness")
eval_result = evaluator.evaluate_strings(
prediction="What's 2+2? That's an el... | PRINCIPLES.items() | langchain.chains.constitutional_ai.principles.PRINCIPLES.items |
STAGE_BUCKET = "<bucket-name>"
get_ipython().run_cell_magic('bash', ' -s "$STAGE_BUCKET"', '\nrm -rf data\nmkdir -p data\ncd data\necho getting org ontology and sample org instances\nwget http://www.w3.org/ns/org.ttl \nwget https://raw.githubusercontent.com/aws-samples/amazon-neptune-ontology-example-blog/main/data/e... | BedrockChat(model_id="anthropic.claude-v2", client=bedrock_client) | langchain_community.chat_models.BedrockChat |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet banana-dev')
import os
os.environ["BANANA_API_KEY"] = "YOUR_API_KEY"
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Banana
template = """Question: {question}
Answer: Let's th... | Banana(model_key="YOUR_MODEL_KEY", model_url_slug="YOUR_MODEL_URL_SLUG") | langchain_community.llms.Banana |
get_ipython().system('pip install --quiet langchain_experimental langchain_openai')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_experimental.text_splitter import SemanticChunker
from langchain_openai.embeddings import OpenAIEmbeddings
text_splitter = Semantic... | OpenAIEmbeddings() | langchain_openai.embeddings.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 langchain_core.tools import tool
@tool
def complex_tool(int_arg: int, float_arg: float, dict_arg: dict) -> int:
"""Do something complex... | JsonOutputKeyToolsParser(key_name="complex_tool", return_single=True) | langchain.output_parsers.JsonOutputKeyToolsParser |
from langchain.chains import LLMMathChain
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.tools import Tool
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain_openai import ChatOpenAI, OpenAI... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from ray import serve
from starlette.requests import Request
@serve.deployment
class LLMServe:
def __init__(self) -> None:
pass
async def __call__(self, request: Request) -> str:
return "Hello World"
deployment = LLMServe.bind()
serve.api.run(deployment)
serve.api.shutdown()
from lan... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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(
[("human", "{input}") | langchain.prompts.ChatPromptTemplate.from_messages |
from langchain_community.graphs import NeptuneGraph
host = "<neptune-host>"
port = 8182
use_https = True
graph = NeptuneGraph(host=host, port=port, use_https=use_https)
from langchain.chains import NeptuneOpenCypherQAChain
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0, model="gpt-4")
chai... | NeptuneOpenCypherQAChain.from_llm(llm=llm, graph=graph) | langchain.chains.NeptuneOpenCypherQAChain.from_llm |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiledb-vector-search')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import TileDB
from langchain_text_splitters import CharacterTextSpl... | HuggingFaceEmbeddings() | langchain_community.embeddings.HuggingFaceEmbeddings |
from typing import Any, Dict, List
from langchain.chains import ConversationChain
from langchain.schema import BaseMemory
from langchain_openai import OpenAI
from pydantic import BaseModel
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
import spacy
nlp = spacy.load("en_core_web_lg")
cl... | PromptTemplate(input_variables=["entities", "input"], template=template) | langchain.prompts.prompt.PromptTemplate |
get_ipython().system('pip install --quiet langchain_experimental langchain_openai')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_experimental.text_splitter import SemanticChunker
from langchain_openai.embeddings import OpenAIEmbeddings
text_splitter = Semantic... | OpenAIEmbeddings() | langchain_openai.embeddings.OpenAIEmbeddings |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import StripeLoader
stripe_loader = StripeLoader("charges")
index = | VectorstoreIndexCreator() | langchain.indexes.VectorstoreIndexCreator |
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... | MSSQLDocumentSaver(engine=engine, table_name=TABLE_NAME) | langchain_google_cloud_sql_mssql.MSSQLDocumentSaver |
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 = ... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from langchain_community.document_loaders import IFixitLoader
loader = IFixitLoader("https://www.ifixit.com/Teardown/Banana+Teardown/811")
data = loader.load()
data
loader = | IFixitLoader(
"https://www.ifixit.com/Answers/View/318583/My+iPhone+6+is+typing+and+opening+apps+by+itself"
) | langchain_community.document_loaders.IFixitLoader |
import os
from langchain.chains import ConversationalRetrievalChain
from langchain_community.vectorstores import Vectara
from langchain_openai import OpenAI
from langchain_community.document_loaders import TextLoader
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
vectara = | Vectara.from_documents(documents, embedding=None) | langchain_community.vectorstores.Vectara.from_documents |
from langchain_community.document_loaders.blob_loaders.youtube_audio import (
YoutubeAudioLoader,
)
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import (
OpenAIWhisperParser,
OpenAIWhisperParserLocal,
)
get_ipython().run_line_mag... | FAISS.from_texts(splits, embeddings) | langchain_community.vectorstores.FAISS.from_texts |
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.tools.you import YouSearchTool
from langchain_community.utilities.you import YouSearchAPIWrapper
api_wrapper = | YouSearchAPIWrapper(num_web_results=1) | langchain_community.utilities.you.YouSearchAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai context-python')
import os
from langchain.callbacks import ContextCallbackHandler
token = os.environ["CONTEXT_API_TOKEN"]
context_callback = ContextCallbackHandler(token)
import os
from langchain.callbacks import Conte... | HumanMessage(content="I love programming.") | langchain.schema.HumanMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sqlite-vss')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import SQLiteVSS
from langchain_text_sp... | SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") | langchain_community.embeddings.sentence_transformer.SentenceTransformerEmbeddings |
get_ipython().system('pip install -U oci')
from langchain_community.llms import OCIGenAI
llm = OCIGenAI(
model_id="MY_MODEL",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="MY_OCID",
)
response = llm.invoke("Tell me one fact about earth", temperatu... | RunnablePassthrough() | langchain.schema.runnable.RunnablePassthrough |
from langchain.chains import LLMMathChain
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.tools import Tool
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain_openai import ChatOpenAI, OpenAI... | load_agent_executor(model, tools, verbose=True) | langchain_experimental.plan_and_execute.load_agent_executor |
import kuzu
db = kuzu.Database("test_db")
conn = kuzu.Connection(db)
conn.execute("CREATE NODE TABLE Movie (name STRING, PRIMARY KEY(name))")
conn.execute(
"CREATE NODE TABLE Person (name STRING, birthDate STRING, PRIMARY KEY(name))"
)
conn.execute("CREATE REL TABLE ActedIn (FROM Person TO Movie)")
conn.exec... | KuzuGraph(db) | langchain_community.graphs.KuzuGraph |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai context-python')
import os
from langchain.callbacks import ContextCallbackHandler
token = os.environ["CONTEXT_API_TOKEN"]
context_callback = ContextCallbackHandler(token)
import os
from langchain.callbacks import Conte... | LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback]) | langchain.chains.LLMChain |
from typing import Optional
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_experimental.autonomous_agents import BabyAGI
from langchain_openai import OpenAI, OpenAIEmbeddings
get_ipython().run_line_magic('pip', 'install faiss-cpu > /dev/null')
get_ipython().run_lin... | SerpAPIWrapper() | langchain_community.utilities.SerpAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-text-splitters tiktoken')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=... | TokenTextSplitter(chunk_size=10, chunk_overlap=0) | langchain_text_splitters.TokenTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet semanticscholar')
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
instructions = """You are an expert researcher."""
base_prompt = hub.pull("langchain-ai/openai... | SemanticScholarQueryRun() | langchain_community.tools.semanticscholar.tool.SemanticScholarQueryRun |
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool, StructuredTool, tool
@tool
def search(query: str) -> str:
"""Look up things online."""
return "LangChain"
print(search.name)
print(search.description)
print(search.args)
@tool
def multiply(a: int, b: int) -> int:
... | Field(description="first number") | langchain.pydantic_v1.Field |
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.prompts import PromptTemplate
from langchain_community.llms import TitanTakeoffPro
llm = TitanTakeoffPro()
output = llm("What is the weather in London in August?")
prin... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
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... | OpenAI() | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet trubrics')
import os
os.environ["TRUBRICS_EMAIL"] = "***@***"
os.environ["TRUBRICS_PASSWORD"] = "***"
os.environ["OPENAI_API_KEY"] = "sk-***"
from langchain.callbacks import TrubricsCallbackHandler
from langchain_openai import OpenAI
llm = O... | TrubricsCallbackHandler() | langchain.callbacks.TrubricsCallbackHandler |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet annoy')
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Annoy
embeddings_func = | HuggingFaceEmbeddings() | langchain_community.embeddings.HuggingFaceEmbeddings |
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().run_line_magic('pip', 'install --upgrade --quiet playwright > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lxml')
from langchain_community.agent_toolkits import PlayWrightBrowserToolkit
from langchain_community.tools.playwright.utils import (
create_async_playwrig... | create_async_playwright_browser() | langchain_community.tools.playwright.utils.create_async_playwright_browser |
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... | DuckDuckGoSearchRun() | langchain.tools.DuckDuckGoSearchRun |
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() | langchain_community.agent_toolkits.O365Toolkit |
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 rank_bm25 > /dev/null')
from langchain.retrievers import BM25Retriever, EnsembleRetriever
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
doc_list_1 = [
"I like apples",
"I like oranges",
"Ap... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain_community.tools.edenai import (
EdenAiExplicitImageTool,
EdenAiObjectDetectionTool,
EdenAiParsingIDTool,
EdenAiParsingInvoiceTool,
EdenAiSpeechToTextTool,
EdenAiTextModerationTool,
EdenAiTextToSpeechTool,
)
from langchain.agents import AgentType, initialize_agent
from langch... | EdenAiTextToSpeechTool(providers=["amazon"], language="en", voice="MALE") | langchain_community.tools.edenai.EdenAiTextToSpeechTool |
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... | WikipediaQueryRun(api_wrapper=api_wrapper) | langchain_community.tools.WikipediaQueryRun |
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.chains import HypotheticalDocumentEmbedder, LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI, OpenAIEmbeddings
base_embeddings = | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | ChatAnthropicMessages(model_name="claude-instant-1.2") | langchain_anthropic.ChatAnthropicMessages |
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... | RunnablePassthrough.assign(query=sql_response) | langchain_core.runnables.RunnablePassthrough.assign |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2')
import os
from langchain_community.llms import HuggingFaceTextGenInference
ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
HF_TOKEN = os.getenv("HUGGINGFACEHUB_A... | SystemMessage(content="You're a helpful assistant") | langchain.schema.SystemMessage |
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... | DatetimeOutputParser() | langchain.output_parsers.DatetimeOutputParser |
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool, StructuredTool, tool
@tool
def search(query: str) -> str:
"""Look up things online."""
return "LangChain"
print(search.name)
print(search.description)
print(search.args)
@tool
def multiply(a: int, b: int) -> int:
... | Field(description="second number") | langchain.pydantic_v1.Field |
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... | render_text_description_and_args(tools) | langchain.tools.render.render_text_description_and_args |
from langchain.memory import ConversationSummaryBufferMemory
from langchain_openai import OpenAI
llm = OpenAI()
memory = | ConversationSummaryBufferMemory(llm=llm, max_token_limit=10) | langchain.memory.ConversationSummaryBufferMemory |
from typing import List
from langchain.output_parsers import YamlOutputParser
from langchain.prompts import PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)
class Joke(BaseModel):
setup: str = | Field(description="question to set up a joke") | langchain_core.pydantic_v1.Field |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-text-splitters tiktoken')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=... | NLTKTextSplitter(chunk_size=1000) | langchain_text_splitters.NLTKTextSplitter |
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool, StructuredTool, tool
@tool
def search(query: str) -> str:
"""Look up things online."""
return "LangChain"
print(search.name)
print(search.description)
print(search.args)
@tool
def multiply(a: int, b: int) -> int:
... | tool("search-tool", args_schema=SearchInput, return_direct=True) | langchain.tools.tool |
get_ipython().run_line_magic('pip', 'install -U --quiet langchain langchain_community openai chromadb langchain-experimental')
get_ipython().run_line_magic('pip', 'install --quiet "unstructured[all-docs]" pypdf pillow pydantic lxml pillow matplotlib chromadb tiktoken')
import logging
import zipfile
import requests... | PyPDFLoader("./cj/cj.pdf") | langchain_community.document_loaders.PyPDFLoader |
get_ipython().system("python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken")
import getpass
import os
from langchain_community.vectorstores import DeepLake
from langchain_openai import OpenAIEmbeddings
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
activeloop_token =... | ConversationalRetrievalChain.from_llm(model, retriever=retriever) | langchain.chains.ConversationalRetrievalChain.from_llm |
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()
redis_url = "redis://localhost:637... | Redis.delete(keys, redis_url="redis://localhost:6379") | langchain_community.vectorstores.redis.Redis.delete |
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 |
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 = PromptTempl... | AIMessage(content="欢迎问我任何问题。") | langchain.schema.messages.AIMessage |
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(host="myworkspace.cloud.databricks.com", endpoint_name="dolly") | langchain_community.llms.Databricks |
import getpass
import os
os.environ["TAVILY_API_KEY"] = getpass.getpass()
from langchain_community.tools.tavily_search import TavilySearchResults
tool = TavilySearchResults()
tool.invoke({"query": "What happened in the latest burning man floods"})
import getpass
import os
os.environ["OPENAI_API_KEY"] = ge... | TavilySearchResults() | langchain_community.tools.tavily_search.TavilySearchResults |
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)
tools = load_tools([... | wandb_tracing_enabled() | langchain.callbacks.wandb_tracing_enabled |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet dingodb')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet git+https://git@github.com/dingodb/pydingo.git')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_lo... | Dingo(embeddings, "text", client=dingo_client, index_name=index_name) | langchain_community.vectorstores.Dingo |
from langchain.output_parsers import DatetimeOutputParser
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
output_parser = | DatetimeOutputParser() | langchain.output_parsers.DatetimeOutputParser |
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 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") | langchain.chains.HypotheticalDocumentEmbedder.from_llm |
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-core langchain langchain-openai')
from langchain.utils.math import cosine_similarity
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambda... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import getpass
import os
os.environ["POLYGON_API_KEY"] = getpass.getpass()
from langchain_community.tools.polygon.financials import PolygonFinancials
from langchain_community.tools.polygon.last_quote import PolygonLastQuote
from langchain_community.tools.polygon.ticker_news import PolygonTickerNews
from langchain_co... | PolygonLastQuote(api_wrapper=api_wrapper) | langchain_community.tools.polygon.last_quote.PolygonLastQuote |
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 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 OpenAI... | AgentExecutor(agent=agent, tools=tools, verbose=True) | langchain.agents.AgentExecutor |
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-memorystore-redis')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from google.colab import auth
auth.authenticate_user()
import redis
from langchain_goo... | RedisVectorStore.drop_index(client=redis_client, index_name="my_vector_index") | langchain_google_memorystore_redis.RedisVectorStore.drop_index |
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="kosmos_2") | langchain_nvidia_ai_endpoints.ChatNVIDIA |
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[... | SageMakerCallbackHandler(run) | langchain.callbacks.SageMakerCallbackHandler |
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 |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3')
from langchain_community.document_loaders import S3DirectoryLoader
loader = | S3DirectoryLoader("testing-hwc") | langchain_community.document_loaders.S3DirectoryLoader |
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