prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
from langchain.chains import LLMSummarizationCheckerChain
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)
text = """
Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):
... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3')
from langchain_community.document_loaders import S3DirectoryLoader
loader = S3DirectoryLoader("testing-hwc")
loader.load()
loader = | S3DirectoryLoader("testing-hwc", prefix="fake") | langchain_community.document_loaders.S3DirectoryLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet singlestoredb')
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 SingleStoreDB
from langchain_openai imp... | OpenCLIPEmbeddings() | langchain_experimental.open_clip.OpenCLIPEmbeddings |
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) | langchain.model_laboratory.ModelLaboratory.from_llms |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet aim')
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 google-search-results')
i... | load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks) | langchain.agents.load_tools |
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 |
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 = ... | google_palm.ChatGooglePalm(google_api_key="YOUR_GOOGLE_PALM_API_KEY") | langchain_community.chat_models.google_palm.ChatGooglePalm |
from langchain_community.document_loaders import RoamLoader
loader = | RoamLoader("Roam_DB") | langchain_community.document_loaders.RoamLoader |
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... | HubRunnable("rlm/rag-prompt") | langchain.runnables.hub.HubRunnable |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-cloud-sql-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_ipyth... | IVFFlatIndex() | langchain_google_cloud_sql_pg.indexes.IVFFlatIndex |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet modal')
get_ipython().system('modal token new')
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Modal
template = """Question: {question}
Answer: Let's think step by step."""
... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
import os
from langchain_community.tools.google_scholar import GoogleScholarQueryRun
from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper
os.environ["SERP_API_KEY"] = ""
tool = GoogleScholarQueryRun(... | GoogleScholarAPIWrapper() | langchain_community.utilities.google_scholar.GoogleScholarAPIWrapper |
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)
template = """Question: {question}
Answer: Let's think step by step."""
prompt = | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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... | CharacterTextSplitter(chunk_size=300, chunk_overlap=0, separator=". ") | langchain_text_splitters.CharacterTextSplitter |
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... | SystemMessage(
content="You are a web researcher who answers user questions by looking up information on the internet and retrieving contents of helpful documents. Cite your sources."
) | langchain_core.messages.SystemMessage |
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=assistant_ai_msg.content) | langchain.schema.HumanMessage |
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="foo", metadata={"id": 1}) | langchain.docstore.document.Document |
from langchain.prompts import FewShotPromptTemplate, PromptTemplate
from langchain.prompts.example_selector import (
MaxMarginalRelevanceExampleSelector,
SemanticSimilarityExampleSelector,
)
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
example_prompt = Prompt... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
template = """You are a chatbot having a conversation with a human.
{chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
... | ConversationBufferMemory(memory_key="chat_history", return_messages=True) | langchain.memory.ConversationBufferMemory |
from langchain.agents import AgentType, initialize_agent
from langchain.requests import Requests
from langchain_community.agent_toolkits import NLAToolkit
from langchain_openai import OpenAI
llm = OpenAI(
temperature=0, max_tokens=700, model_name="gpt-3.5-turbo-instruct"
) # You can swap between different core L... | NLAToolkit.from_llm_and_url(llm, "https://api.speak.com/openapi.yaml") | langchain_community.agent_toolkits.NLAToolkit.from_llm_and_url |
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... | DatastoreSaver() | langchain_google_datastore.DatastoreSaver |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lxml')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet html2text')
from langchain_community.document_loaders import EverNoteLoader
loader = | EverNoteLoader("example_data/testing.enex") | langchain_community.document_loaders.EverNoteLoader |
from langchain_community.vectorstores import AnalyticDB
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_spli... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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(chains, names=names) | langchain.model_laboratory.ModelLaboratory |
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 =... | SystemMessage(content="You can make a task more specific.") | langchain.schema.SystemMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "docarray[hnswlib]"')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import DocArrayHnswSearch
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitt... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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 langchain-core databricks-vectorsearch langchain-openai tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_loaders import TextLoader
from langchain_openai import Op... | DatabricksVectorSearch("catalog_name.schema_name.delta_sync_index") | langchain_community.vectorstores.DatabricksVectorSearch |
import os
import comet_llm
os.environ["LANGCHAIN_COMET_TRACING"] = "true"
comet_llm.init()
os.environ["COMET_PROJECT_NAME"] = "comet-example-langchain-tracing"
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["... | load_tools(["llm-math"], llm=llm) | langchain.agents.load_tools |
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(["bar"], embeddings) | langchain_community.vectorstores.FAISS.from_texts |
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 |
from langchain.callbacks import get_openai_callback
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-4")
with get_openai_callback() as cb:
result = llm.invoke("Tell me a joke")
print(cb)
with get_openai_callback() as cb:
result = llm.invoke("Tell me a joke")
result2 = llm.... | load_tools(["serpapi", "llm-math"], llm=llm) | langchain.agents.load_tools |
get_ipython().system("python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken")
import getpass
import os
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import DeepLake
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters impor... | DeepLake(dataset_path=dataset_path, read_only=True, embedding=embeddings) | langchain_community.vectorstores.DeepLake |
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... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-pinecone langchain-openai langchain')
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/stat... | PineconeVectorStore.from_documents(docs, embeddings, index_name=index_name) | langchain_pinecone.PineconeVectorStore.from_documents |
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 lark clickhouse-connect')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale URL:")
os.environ["MYSCALE_PORT"] = getpass.getpass("MyScale Port:")
os.environ["... | AttributeInfo(
name="length(genre) | langchain.chains.query_constructor.base.AttributeInfo |
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... | TextLoader("../../state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet duckduckgo-search')
from langchain.tools import DuckDuckGoSearchRun
search = DuckDuckGoSearchRun()
search.run("Obama's first name?")
from langchain.tools import DuckDuckGoSearchResults
search = DuckDuckGoSearchResults()
search.run("Obama")
... | DuckDuckGoSearchResults(backend="news") | langchain.tools.DuckDuckGoSearchResults |
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 comet_ml langchain langchain-openai google-search-results spacy textstat pandas')
get_ipython().system('{sys.executable} -m spacy download en_core_web_sm')
import comet_ml
comet_ml.init(project_name="comet-example-langchain")
import os
os.envir... | LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks) | langchain.chains.LLMChain |
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=["chatopenai"]) | langchain.callbacks.PromptLayerCallbackHandler |
get_ipython().run_cell_magic('writefile', 'discord_chats.txt', "talkingtower — 08/15/2023 11:10 AM\nLove music! Do you like jazz?\nreporterbob — 08/15/2023 9:27 PM\nYes! Jazz is fantastic. Ever heard this one?\nWebsite\nListen to classic jazz track...\n\ntalkingtower — Yesterday at 5:03 AM\nIndeed! Great choice. 🎷\nre... | ChatOpenAI() | langchain_openai.ChatOpenAI |
from typing import List, Optional
from langchain.chains.openai_tools import create_extraction_chain_pydantic
from langchain_core.pydantic_v1 import BaseModel
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-3.5-turbo-1106")
class Person(BaseModel):
"""Information about people to extract.""... | create_extraction_chain_pydantic(Person, model) | langchain.chains.openai_tools.create_extraction_chain_pydantic |
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 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... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai faiss-cpu tiktoken')
from langchain.prompts import ChatPromptTemplate
from langchain.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, Runna... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet wikipedia')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOp... | ChatOpenAI(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... | OpenAIFunctionsAgent.create_prompt(system_message) | langchain.agents.OpenAIFunctionsAgent.create_prompt |
from langchain_experimental.llm_bash.base import LLMBashChain
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
text = "Please write a bash script that prints 'Hello World' to the console."
bash_chain = LLMBashChain.from_llm(llm, verbose=True)
bash_chain.run(text)
from langchain.prompts.prompt impo... | BashOutputParser() | langchain_experimental.llm_bash.prompt.BashOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rapidfuzz')
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("string_distance")
evaluator.evaluate_strings(
prediction="The job is completely done.",
reference="The job is done",
)
evaluator.evaluate_strings(
pr... | load_evaluator("string_distance", distance=StringDistance.JARO) | langchain.evaluation.load_evaluator |
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
llm = OpenAI(temperature=0)
from pathlib import Path
relevant_parts = []
for p in Path(".").absolute().parts:
... | Tool(
name="Ruff QA System",
func=ruff.run,
description="useful for when you need to answer questions about ruff (a python linter) | langchain.agents.Tool |
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... | ChatOpenAI() | langchain_openai.ChatOpenAI |
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
import requests
def get_wikipedia_page(title: str):
"""
Retrieve the full text content of a Wikipedia page.
:param title: str - Title of the Wikipedia page.
:return: str - Full text conten... | ChatOpenAI() | langchain_openai.ChatOpenAI |
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... | PromptTemplate(input_variables=["query"], template="{query}") | langchain_core.prompts.PromptTemplate |
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... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet runhouse')
import runhouse as rh
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import SelfHostedHuggingFaceLLM, SelfHostedPipeline
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1... | SelfHostedPipeline.from_pipeline(pipeline="models/pipeline.pkl", hardware=gpu) | langchain_community.llms.SelfHostedPipeline.from_pipeline |
from langchain.agents import Tool
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from pydantic im... | RetrievalQA.from_chain_type(llm=llm, retriever=retriever) | langchain.chains.RetrievalQA.from_chain_type |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
template = """You are a chatbot having a conversation with a human.
{chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
... | ChatOpenAI() | langchain_openai.ChatOpenAI |
get_ipython().system('pip/pip3 install pyepsilla')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import Epsilla
from langchain_openai import OpenAIEmbeddings
from langchain_community.document_loaders import TextLoader
from langc... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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() | langchain_openai.ChatOpenAI |
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... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet hologres-vector')
from langchain_community.vectorstores import Hologres
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = Text... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-pinecone langchain-openai langchain')
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/stat... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().system('pip install gymnasium')
import tenacity
from langchain.output_parsers import RegexParser
from langchain.schema import (
HumanMessage,
SystemMessage,
)
class GymnasiumAgent:
@classmethod
def get_docs(cls, env):
return env.unwrapped.__doc__
def __init__(self, model,... | SystemMessage(content=self.docs) | langchain.schema.SystemMessage |
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.afrom_texts(["foo"], embeddings) | langchain_community.vectorstores.FAISS.afrom_texts |
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 |
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 RunnablePassthrough
from langchain_openai import ChatOpenAI
prompt = ChatP... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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 |
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... | ChatOpenAI(model_name="gpt-4", temperature=1.0) | langchain_openai.ChatOpenAI |
from langchain.agents import AgentExecutor, BaseMultiActionAgent, Tool
from langchain_community.utilities import SerpAPIWrapper
def random_word(query: str) -> str:
print("\nNow I'm doing this!")
return "foo"
search = SerpAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
... | AgentFinish(return_values={"output": "bar"}, log="") | langchain_core.agents.AgentFinish |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "cassio>=0.1.4"')
import os
from getpass import getpass
from datasets import (
load_dataset,
)
from langchain_community.document_loaders import PyPDFLoader
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOu... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet jsonformer > /dev/null')
import logging
logging.basicConfig(level=logging.ERROR)
import json
import os
import requests
from langchain.tools import tool
HF_TOKEN = os.environ.get("HUGGINGFACE_API_KEY")
@tool
def ask_star_coder(query: str, temperat... | JsonFormer(json_schema=decoder_schema, pipeline=hf_model) | langchain_experimental.llms.JsonFormer |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet aphrodite-engine==0.4.2')
from langchain_community.llms import Aphrodite
llm = Aphrodite(
model="PygmalionAI/pygmalion-2-7b",
trust_remote_code=True, # mandatory for hf models
max_tokens=128,
temperature=1.2,
min_p=0.05,
mirosta... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import GooseAI
from getpass import getpass
GOOSEAI_API_KEY = getpass()
os.environ["GOOSEAI_API_KEY"] = G... | GooseAI() | langchain_community.llms.GooseAI |
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=0.2) | langchain_openai.ChatOpenAI |
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... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
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... | ChatOpenAI() | 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(... | OpenAI() | langchain_openai.OpenAI |
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), data, verbose=True)
agent.run("How many rows and columns are there?")
agent.run("How m... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet elasticsearch')
from langchain.retrievers import ElasticSearchBM25Retriever
elasticsearch_url = "http://localhost:9200"
retriever = | ElasticSearchBM25Retriever.create(elasticsearch_url, "langchain-index-4") | langchain.retrievers.ElasticSearchBM25Retriever.create |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet johnsnowlabs')
from langchain_community.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings
embedder = | JohnSnowLabsEmbeddings("en.embed_sentence.biobert.clinical_base_cased") | langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sentence_transformers')
from langchain_community.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings()
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loade... | SemaDB("mycollection", 768, embeddings, DistanceStrategy.COSINE) | langchain_community.vectorstores.SemaDB |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clarifai')
from getpass import getpass
CLARIFAI_PAT = getpass()
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Clarifai
from langchain_text_splitters import CharacterTextSplitter
USER_ID = ... | TextLoader("your_local_file_path.txt") | langchain_community.document_loaders.TextLoader |
import os
from langchain_community.utilities import OpenWeatherMapAPIWrapper
os.environ["OPENWEATHERMAP_API_KEY"] = ""
weather = | OpenWeatherMapAPIWrapper() | langchain_community.utilities.OpenWeatherMapAPIWrapper |
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0, model="gpt-4-turbo-preview")
from langchain import hub
from langchain_core.prompts import PromptTemplate
select_prompt = hub.pull("hwchase17/self-discovery-select")
select_prompt.pretty_print()
adapt_prompt = hub.pull("hwchase17/self-di... | hub.pull("hwchase17/self-discovery-structure") | langchain.hub.pull |
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_... | GemmaChatLocalHF(model_name=model_name, hf_access_token=hf_access_token) | langchain_google_vertexai.GemmaChatLocalHF |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet manifest-ml')
from langchain_community.llms.manifest import ManifestWrapper
from manifest import Manifest
manifest = Manifest(
client_name="huggingface", client_connection="http://127.0.0.1:5000"
)
print(manifest.client_pool.get_current_client().ge... | PromptTemplate.from_template(_prompt) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_cell_magic('writefile', 'discord_chats.txt', "talkingtower — 08/15/2023 11:10 AM\nLove music! Do you like jazz?\nreporterbob — 08/15/2023 9:27 PM\nYes! Jazz is fantastic. Ever heard this one?\nWebsite\nListen to classic jazz track...\n\ntalkingtower — Yesterday at 5:03 AM\nIndeed! Great choice. 🎷\nre... | merge_chat_runs(raw_messages) | langchain_community.chat_loaders.utils.merge_chat_runs |
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI
template = """Human: {question}
AI Assistant: """
prompt = PromptTemplate.from_template(template)
import getpass
my_account_id = getpass.getpass("Enter ... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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: doc_ids[i]}) | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import os
import uuid
uid = uuid.uuid4().hex[:6]
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "YOUR API KEY"
from langsmith.client import Client
client = Client()
import requests
url =... | LangSmithDatasetChatLoader(dataset_name=dataset_name) | langchain_community.chat_loaders.langsmith.LangSmithDatasetChatLoader |
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... | ChatAnthropic(temperature=0.0) | langchain_community.chat_models.ChatAnthropic |
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 Cha... | ConversationalRetrievalChain.from_llm(model, retriever=retriever) | langchain.chains.ConversationalRetrievalChain.from_llm |
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... | EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76) | langchain.retrievers.document_compressors.EmbeddingsFilter |
from langchain_community.document_loaders.obs_file import OBSFileLoader
endpoint = "your-endpoint"
from obs import ObsClient
obs_client = ObsClient(
access_key_id="your-access-key",
secret_access_key="your-secret-key",
server=endpoint,
)
loader = | OBSFileLoader("your-bucket-name", "your-object-key", client=obs_client) | langchain_community.document_loaders.obs_file.OBSFileLoader |
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores.jaguar import Jaguar
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables im... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langsmith langchainhub --quiet')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai tiktoken pandas duckduckgo-search --quiet')
import os
from uuid import uuid4
unique_id = uuid4().hex[0:8]
os.environ["LANGCHAIN_T... | OpenAIToolsAgentOutputParser() | langchain.agents.output_parsers.openai_tools.OpenAIToolsAgentOutputParser |
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