prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
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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() | langchain_community.llms.TitanTakeoffPro |
URL = "" # Your Fiddler instance URL, Make sure to include the full URL (including https://). For example: https://demo.fiddler.ai
ORG_NAME = ""
AUTH_TOKEN = "" # Your Fiddler instance auth token
PROJECT_NAME = ""
MODEL_NAME = "" # Model name in Fiddler
from langchain_community.callbacks.fiddler_callback import ... | ChatPromptTemplate.from_messages(
[
("human", "{input}") | langchain.prompts.ChatPromptTemplate.from_messages |
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 langkit langchain-openai langchain')
from langchain.callbacks import WhyLabsCallbackHandler
from langchain_openai import OpenAI
whylabs = WhyLabsCallbackHandler.from_params()
llm = | OpenAI(temperature=0, callbacks=[whylabs]) | langchain_openai.OpenAI |
from langchain.document_loaders.csv_loader import CSVLoader
loader = | CSVLoader("data/corp_sens_data.csv") | langchain.document_loaders.csv_loader.CSVLoader |
from getpass import getpass
KAY_API_KEY = getpass()
OPENAI_API_KEY = getpass()
import os
os.environ["KAY_API_KEY"] = KAY_API_KEY
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain.chains import ConversationalRetrievalChain
from langchain.retrievers import KayAiRetriever
from langchain_openai import Chat... | ConversationalRetrievalChain.from_llm(model, retriever=retriever) | langchain.chains.ConversationalRetrievalChain.from_llm |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-robocorp')
from langchain.agents import AgentExecutor, OpenAIFunctionsAgent
from langchain_core.messages import SystemMessage
from langchain_openai import ChatOpenAI
from langchain_robocorp import ActionServerToolkit
llm = | ChatOpenAI(model="gpt-4", temperature=0) | langchain_openai.ChatOpenAI |
from langchain_community.graphs import OntotextGraphDBGraph
graph = OntotextGraphDBGraph(
query_endpoint="http://localhost:7200/repositories/langchain",
query_ontology="CONSTRUCT {?s ?p ?o} FROM <https://swapi.co/ontology/> WHERE {?s ?p ?o}",
)
graph = OntotextGraphDBGraph(
query_endpoint="http://local... | ChatOpenAI(temperature=0, model_name="gpt-4-1106-preview") | langchain_openai.ChatOpenAI |
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 --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_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 |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-core langchain-experimental langchain-openai')
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import (
ChatPromptTemplate,
)
from langchain_experimental.utilities import PythonREPL
from langchain_opena... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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... | DashScopeEmbeddings() | langchain_community.embeddings.dashscope.DashScopeEmbeddings |
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().run_line_magic('pip', 'install --upgrade --quiet clearml')
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().system('pyth... | load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks) | langchain.agents.load_tools |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet airbyte-source-hubspot')
from langchain_community.document_loaders.airbyte import AirbyteHubspotLoader
config = {
}
loader = AirbyteHubspotLoader(
config=config, stream_name="products"
) # check the documentation linked above for a list of all s... | Document(page_content=record.data["title"], metadata=record.data) | langchain.docstore.document.Document |
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 ... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub langchain-openai faiss-cpu')
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
from langchain_community.vectors... | hub.pull("hwchase17/openai-tools-agent") | langchain.hub.pull |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.model_laboratory import ModelLaboratory
from langchain.prompts import PromptTemplate
from langchain_community.llms import Cohere, HuggingFaceHub
from langchain_openai import OpenAI
import getpass
import os
o... | SerpAPIWrapper() | langchain_community.utilities.SerpAPIWrapper |
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 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="foo") | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet hdbcli')
import os
from hdbcli import dbapi
connection = dbapi.connect(
address=os.environ.get("HANA_DB_ADDRESS"),
port=os.environ.get("HANA_DB_PORT"),
user=os.environ.get("HANA_DB_USER"),
password=os.environ.get("HANA_DB_PASSWORD"),... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | 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 |
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 |
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... | ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_cell_magic('writefile', 'whatsapp_chat.txt', "[8/15/23, 9:12:33 AM] Dr. Feather: \u200eMessages and calls are end-to-end encrypted. No one outside of this chat, not even WhatsApp, can read or listen to them.\n[8/15/23, 9:12:43 AM] Dr. Feather: I spotted a rare Hyacinth Macaw yesterday in the Amazon Ra... | map_ai_messages(merged_messages, sender="Dr. Feather") | langchain_community.chat_loaders.utils.map_ai_messages |
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().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... | Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False) | langchain_community.vectorstores.Weaviate.from_documents |
from langchain.chains import LLMMathChain
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | NomicEmbeddings(model="nomic-embed-text-v1") | langchain_nomic.embeddings.NomicEmbeddings |
from langchain_community.document_loaders import IFixitLoader
loader = | IFixitLoader("https://www.ifixit.com/Teardown/Banana+Teardown/811") | langchain_community.document_loaders.IFixitLoader |
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("Collection/doc/SubCollection") | langchain_google_datastore.DatastoreLoader |
import asyncio
import os
import nest_asyncio
import pandas as pd
from langchain.docstore.document import Document
from langchain_community.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain_experimental.autonomous_agents import AutoGPT
from langchain_openai import ChatOpenAI
nest_asyncio.a... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.prompts.pipeline import PipelinePromptTemplate
from langchain.prompts.prompt import PromptTemplate
full_template = """{introduction}
{example}
{start}"""
full_prompt = PromptTemplate.from_template(full_template)
introduction_template = """You are impersonating {person}."""
introduction_prompt = Pro... | PromptTemplate.from_template(example_template) | langchain.prompts.prompt.PromptTemplate.from_template |
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_community.chat_models import JinaChat
from langchain_core.messages import HumanMessage, SystemMessage
chat = JinaChat(temperature=0)
messages = [
SystemMessage(
... | SystemMessagePromptTemplate.from_template(template) | langchain.prompts.chat.SystemMessagePromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet elasticsearch == 7.11.0')
import getpass
import os
os.environ["QIANFAN_AK"] = getpass.getpass("Your Qianfan AK:")
os.environ["QIANFAN_SK"] = getpass.getpass("Your Qianfan SK:")
from langchain_community.document_loaders import TextLoader
from langcha... | QianfanEmbeddingsEndpoint() | langchain_community.embeddings.QianfanEmbeddingsEndpoint |
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... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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_... | AsyncHtmlLoader(all_links) | langchain.document_loaders.AsyncHtmlLoader |
import re
from typing import Union
from langchain.agents import (
AgentExecutor,
AgentOutputParser,
LLMSingleActionAgent,
Tool,
)
from langchain.chains import LLMChain
from langchain.prompts import StringPromptTemplate
from langchain_community.utilities import SerpAPIWrapper
from langchain_core.agents ... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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 = | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
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_... | FAISS.from_documents(list_of_documents, embeddings) | langchain_community.vectorstores.FAISS.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymysql')
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
DirectoryLoader,
UnstructuredMarkdownLoader,
)
from langchain_community.vectorstores import StarRocks
from langchain_community.vectorstores.sta... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("... | FAISS.from_texts(["foo"], embeddings) | langchain_community.vectorstores.FAISS.from_texts |
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... | DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True) | langchain_community.vectorstores.DeepLake |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.evaluation import load_evaluator
from langchain_openai import ChatOpenAI
evaluator = load_evaluator("labeled_score_string", llm=ChatOpenAI(model="gpt-4"))
eval_result = evaluator.evaluate_strings(
predic... | ChatOpenAI(model="gpt-4") | langchain_openai.ChatOpenAI |
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(... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | FAISS.from_documents(docs, embeddings) | langchain_community.vectorstores.FAISS.from_documents |
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 |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml langchainhub')
get_ipython().system(' brew install tesseract')
get_ipython().system(' brew install poppler')
path = "/Users/rlm/Desktop/Papers/LLaMA2/"
from typing import Any
from pydantic import BaseModel
from unstructured.parti... | Document(page_content=s, metadata={id_key: table_ids[i]}) | langchain_core.documents.Document |
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... | PromptTemplate(input_variables=["title"], template=template) | langchain.prompts.PromptTemplate |
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 |
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 |
from langchain.indexes import SQLRecordManager, index
from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
collection_name = "test_index"
embedding = OpenAIEmbeddings()
vectorstore = ElasticsearchStore(
es_url="http:/... | index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source") | langchain.indexes.index |
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... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pdfminer')
from langchain_community.document_loaders.image import UnstructuredImageLoader
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg")
data = loader.load()
data[0]
loader = | UnstructuredImageLoader("layout-parser-paper-fast.jpg", mode="elements") | langchain_community.document_loaders.image.UnstructuredImageLoader |
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... | PlayWrightBrowserToolkit.from_browser(async_browser=async_browser) | langchain_community.agent_toolkits.PlayWrightBrowserToolkit.from_browser |
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),
modified=lambda... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scikit-learn')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet bson')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas pyarrow')
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass("Enter... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().system('pip3 install tcvectordb')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import TencentVectorDB
from langchain_community.vectorstores.tencentvectordb import ConnectionParams
from lan... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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) | langchain.callbacks.ContextCallbackHandler |
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... | HumanMessage(content=messages) | langchain_core.messages.HumanMessage |
from langchain.indexes import SQLRecordManager, index
from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
collection_name = "test_index"
embedding = OpenAIEmbeddings()
vectorstore = ElasticsearchStore(
es_url="http:/... | index([], record_manager, vectorstore, cleanup="incremental", source_id_key="source") | langchain.indexes.index |
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 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 |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.model_laboratory import ModelLaboratory
from langchain.prompts import PromptTemplate
from langchain_community.llms import Cohere, HuggingFaceHub
from langchain_openai import OpenAI
import getpass
import os
o... | Cohere(temperature=0) | langchain_community.llms.Cohere |
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 HERE") | langchain_community.llms.ForefrontAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet youtube_search')
from langchain.tools import YouTubeSearchTool
tool = | YouTubeSearchTool() | langchain.tools.YouTubeSearchTool |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pipeline-ai')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import PipelineAI
os.environ["PIPELINE_API_KEY"] = "YOUR_API_KEY_HERE"
llm = PipelineAI(pipeline_key="YOUR_PI... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
import re
from typing import Union
from langchain.agents import (
AgentExecutor,
AgentOutputParser,
LLMSingleActionAgent,
Tool,
)
from langchain.chains import LLMChain
from langchain.prompts import StringPromptTemplate
from langchain_community.utilities import SerpAPIWrapper
from langchain_core.agents ... | SerpAPIWrapper() | langchain_community.utilities.SerpAPIWrapper |
model_url = "http://localhost:5000"
from langchain.chains import LLMChain
from langchain.globals import set_debug
from langchain.prompts import PromptTemplate
from langchain_community.llms import TextGen
| set_debug(True) | langchain.globals.set_debug |
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(["Loves meat", "especially beef"]) | langchain_experimental.rl_chain.BasedOn |
get_ipython().run_line_magic('pip', 'install xmltodict')
from langchain_community.tools.pubmed.tool import PubmedQueryRun
tool = | PubmedQueryRun() | langchain_community.tools.pubmed.tool.PubmedQueryRun |
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... | ChatOpenAI(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 ... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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) | langchain_community.vectorstores.Bagel.from_texts |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pipeline-ai')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import PipelineAI
os.environ["PIPELINE_API_KEY"] = "YOUR_API_KEY_HERE"
llm = | PipelineAI(pipeline_key="YOUR_PIPELINE_KEY", pipeline_kwargs={...}) | langchain_community.llms.PipelineAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.model_laboratory import ModelLaboratory
from langchain.prompts import PromptTemplate
from langchain_community.llms import Cohere, HuggingFaceHub
from langchain_openai import OpenAI
import getpass
import os
o... | ModelLaboratory.from_llms(llms, prompt=prompt) | langchain.model_laboratory.ModelLaboratory.from_llms |
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... | RunnableLambda(format_docs) | langchain_core.runnables.RunnableLambda |
get_ipython().system('pip install termcolor > /dev/null')
import logging
logging.basicConfig(level=logging.ERROR)
from datetime import datetime, timedelta
from typing import List
from langchain.docstore import InMemoryDocstore
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain_commun... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install "pgvecto_rs[sdk]"')
from typing import List
from langchain.docstore.document import Document
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores.pgvecto_rs import ... | FakeEmbeddings(size=3) | langchain_community.embeddings.fake.FakeEmbeddings |
from langchain.callbacks import get_openai_callback
from langchain_openai import ChatOpenAI
llm = | ChatOpenAI(model_name="gpt-4") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet unstructured')
from langchain_community.document_loaders import UnstructuredEmailLoader
loader = | UnstructuredEmailLoader("example_data/fake-email.eml") | langchain_community.document_loaders.UnstructuredEmailLoader |
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="i worked at facebook", metadata={"namespace": "ankush"}) | langchain.docstore.document.Document |
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(docset_id=docset_id, document_ids=document_ids) | langchain_community.document_loaders.DocugamiLoader |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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="nemotron_qa_8b") | langchain_nvidia_ai_endpoints.ChatNVIDIA |
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 ... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import FakeEmbeddings
from langc... | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain.evaluation import RegexMatchStringEvaluator
evaluator = RegexMatchStringEvaluator()
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("regex_match")
evaluator.evaluate_strings(
prediction="The delivery will be made on 2024-01-05",
reference=".*\\b\\d{4}-\\d{2}-\\d{... | RegexMatchStringEvaluator(flags=re.IGNORECASE) | langchain.evaluation.RegexMatchStringEvaluator |
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... | PromptTemplate.from_template(most_similar) | langchain_core.prompts.PromptTemplate.from_template |
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... | Column("len", "INTEGER") | langchain_google_alloydb_pg.Column |
import requests
def download_drive_file(url: str, output_path: str = "chat.db") -> 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 to download the ... | merge_chat_runs(raw_messages) | langchain_community.chat_loaders.utils.merge_chat_runs |
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = load... | LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark chromadb')
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
docs = [
Document(
page_content="A bunch of scientists bring back dinosaurs and m... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet psycopg2-binary')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
... | HumanMessagePromptTemplate.from_template("{question}") | langchain.prompts.chat.HumanMessagePromptTemplate.from_template |
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... | HumanMessage(content=user_ai_msg.content) | langchain.schema.HumanMessage |
import os
os.environ["GOLDEN_API_KEY"] = ""
from langchain_community.utilities.golden_query import GoldenQueryAPIWrapper
golden_query = | GoldenQueryAPIWrapper() | langchain_community.utilities.golden_query.GoldenQueryAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet atlassian-python-api')
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.jira.toolkit import JiraToolkit
from langchain_community.utilities.jira import JiraAPIWrapper
from langchain_openai import ... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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