prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-storage')
from langchain_community.document_loaders import GCSDirectoryLoader
loader = GCSDirectoryLoader(project_name="aist", bucket="testing-hwc")
loader.load()
loader = | GCSDirectoryLoader(project_name="aist", bucket="testing-hwc", prefix="fake") | langchain_community.document_loaders.GCSDirectoryLoader |
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."""
... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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 = PromptTempla... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
import getpass
import os
os.environ["TAVILY_API_KEY"] = getpass.getpass()
from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
retriever = TavilySearchAPIRetriever(k=3)
retriever.invoke("what year was breath of the wild released?")
from langchain_core.output_parsers import StrOutputPa... | ChatPromptTemplate.from_template(
"""Answer the question based only on the context provided.
Context: {context}
Question: {question}"""
) | langchain_core.prompts.ChatPromptTemplate.from_template |
from langchain_community.tools.edenai import (
EdenAiExplicitImageTool,
EdenAiObjectDetectionTool,
EdenAiParsingIDTool,
EdenAiParsingInvoiceTool,
EdenAiSpeechToTextTool,
EdenAiTextModerationTool,
EdenAiTextToSpeechTool,
)
from langchain.agents import AgentType, initialize_agent
from langch... | EdenAiSpeechToTextTool(providers=["amazon"]) | langchain_community.tools.edenai.EdenAiSpeechToTextTool |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sodapy')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet geopandas')
import ast
import geopandas as gpd
import pandas as pd
from langchain_community.document_loader... | OpenCityDataLoader(city_id="data.sfgov.org", dataset_id=dataset, limit=5000) | langchain_community.document_loaders.OpenCityDataLoader |
get_ipython().run_cell_magic('writefile', 'wechat_chats.txt', '女朋友 2023/09/16 2:51 PM\n天气有点凉\n\n男朋友 2023/09/16 2:51 PM\n珍簟凉风著，瑶琴寄恨生。嵇君懒书札，底物慰秋情。\n\n女朋友 2023/09/16 3:06 PM\n忙什么呢\n\n男朋友 2023/09/16 3:06 PM\n今天只干成了一件像样的事\n那就是想你\n\n女朋友 2023/09/16 3:06 PM\n[动画表情]\n')
import logging
import re
from typing import Iterator, L... | map_ai_messages(merged_messages, sender="男朋友") | langchain_community.chat_loaders.utils.map_ai_messages |
get_ipython().system('poetry run pip install dgml-utils==0.3.0 --upgrade --quiet')
import os
from langchain_community.document_loaders import DocugamiLoader
DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")
docset_id = "26xpy3aes7xp"
document_ids = ["d7jqdzcj50sj", "cgd1eacfkchw"]
loader = DocugamiLoader(... | Chroma.from_documents(documents=chunks, embedding=embedding) | langchain_community.vectorstores.chroma.Chroma.from_documents |
import os
os.environ["SERPER_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from typing import Any, List
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_community.utilities import GoogleSerperAPIWrapper
from langchain_core.doc... | OpenAI() | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 nltk')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain_experimental')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pydantic')
import os
import boto3
comprehend_client = boto3.client("comp... | ModerationPromptSafetyConfig(threshold=0.8) | langchain_experimental.comprehend_moderation.ModerationPromptSafetyConfig |
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:/... | Document(page_content="woof woof", metadata={"source": "doggy.txt"}) | langchain_core.documents.Document |
get_ipython().system('pip install -U openai langchain langchain-experimental')
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
chat = ChatOpenAI(model="gpt-4-vision-preview", max_tokens=256)
chat.invoke(
[
HumanMessage(
content=[
... | PydanticToolsParser(tools=[GetCurrentWeather]) | langchain.output_parsers.openai_tools.PydanticToolsParser |
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... | OpenAI(temperature=0.9, callbacks=callbacks) | langchain_openai.OpenAI |
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... | ChatOpenAI() | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azure-search-documents')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azure-identity')
import os
from langchain_community.vectorstores.azuresearch import AzureSearch
from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbedding... | TextLoader("../../modules/state_of_the_union.txt", encoding="utf-8") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install -qU langchain-text-splitters')
import json
import requests
json_data = requests.get("https://api.smith.langchain.com/openapi.json").json()
from langchain_text_splitters import RecursiveJsonSplitter
splitter = | RecursiveJsonSplitter(max_chunk_size=300) | langchain_text_splitters.RecursiveJsonSplitter |
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 ... | PyPDFLoader(path + "cpi.pdf") | langchain_community.document_loaders.PyPDFLoader |
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() | langchain_openai.OpenAIEmbeddings |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
import dspy
colbertv2 = dspy.ColBERTv2(url="http://20.102.90.50:2017/wiki17_abstracts")
from langchain.cache import SQLiteCache
from langchain.globals import set_llm_cache
from langchain_openai import OpenAI
set_llm_cache(SQLiteCache(data... | OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai deepeval')
get_ipython().system('deepeval login')
from deepeval.metrics.answer_relevancy import AnswerRelevancy
answer_relevancy_metric = AnswerRelevancy(minimum_score=0.5)
from langchain.callbacks.confident_callback i... | Chroma.from_documents(texts, embeddings) | langchain_community.vectorstores.Chroma.from_documents |
from langchain_community.document_loaders import WebBaseLoader
loader_web = WebBaseLoader(
"https://github.com/basecamp/handbook/blob/master/37signals-is-you.md"
)
from langchain_community.document_loaders import PyPDFLoader
loader_pdf = PyPDFLoader("../MachineLearning-Lecture01.pdf")
from langchain_community... | MergedDataLoader(loaders=[loader_web, loader_pdf]) | langchain_community.document_loaders.merge.MergedDataLoader |
from getpass import getpass
KAY_API_KEY = getpass()
import os
from langchain.retrievers import KayAiRetriever
os.environ["KAY_API_KEY"] = KAY_API_KEY
retriever = KayAiRetriever.create(
dataset_id="company", data_types=["10-K", "10-Q", "PressRelease"], num_contexts=3
)
docs = retriever.get_relevant_documents(
... | ChatOpenAI(model_name="gpt-3.5-turbo") | langchain_openai.ChatOpenAI |
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain_openai import ChatOpenAI
examples = [
{
"input": "Could the members of The Police perform law... | ChatPromptTemplate.from_template(response_prompt_template) | langchain_core.prompts.ChatPromptTemplate.from_template |
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.messages import (
AIMessageChunk,
FunctionMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessageChunk,
)
AIMessageChu... | HumanMessage(content="hello!") | langchain_core.messages.HumanMessage |
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... | HumanMessage(content=messages) | langchain_core.messages.HumanMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet ctranslate2')
get_ipython().system('ct2-transformers-converter --model meta-llama/Llama-2-7b-hf --quantization bfloat16 --output_dir ./llama-2-7b-ct2 --force')
from langchain_community.llms import CTranslate2
llm = CTranslate2(
model_path="./llam... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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... | RedisText("job") | langchain_community.vectorstores.redis.RedisText |
from langchain.agents import Tool
from langchain_community.tools.file_management.read import ReadFileTool
from langchain_community.tools.file_management.write import WriteFileTool
from langchain_community.utilities import SerpAPIWrapper
search = | SerpAPIWrapper() | langchain_community.utilities.SerpAPIWrapper |
get_ipython().system('poetry run pip install dgml-utils==0.3.0 --upgrade --quiet')
import os
from langchain_community.document_loaders import DocugamiLoader
DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")
docset_id = "26xpy3aes7xp"
document_ids = ["d7jqdzcj50sj", "cgd1eacfkchw"]
loader = DocugamiLoader(... | DocugamiLoader(docset_id="zo954yqy53wp") | langchain_community.document_loaders.DocugamiLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-experimental')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pillow open_clip_torch torch matplotlib')
import open_clip
open_clip.list_pretrained()
import numpy as np
from langchain_experimental.open_clip import OpenCLI... | OpenCLIPEmbeddings() | langchain_experimental.open_clip.OpenCLIPEmbeddings |
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
def initialize_chain(instructions, memory=None):
if memory is None:
memory = ConversationBufferWindowMemory()
memory.ai... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain_openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("Input your OpenAI API key:")
tidb_connection_string_template = "mysql+pymysql://<USER>:<PASSWORD>@<HOST>:4000/<DB>?ssl_ca=/etc/ssl/cert.pem&ssl_veri... | ChatOpenAI() | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.chains import OpenAIModerationChain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import OpenAI
moderate = | OpenAIModerationChain() | langchain.chains.OpenAIModerationChain |
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... | Document(page_content="Hello World", metadata={"source": "www.example.com/hello"})]
) | langchain.docstore.document.Document |
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
examples = [
{
"question": "Who lived longer, Muhammad Ali or Alan Turing?",
"answer": """
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Int... | PromptTemplate(
input_variables=["question", "answer"], template="Question: {question}\n{answer}"
) | langchain.prompts.prompt.PromptTemplate |
from getpass import getpass
STOCHASTICAI_API_KEY = getpass()
import os
os.environ["STOCHASTICAI_API_KEY"] = STOCHASTICAI_API_KEY
YOUR_API_URL = getpass()
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import StochasticAI
template = """Question:... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
from typing import Callable, List
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name =... | ChatOpenAI(temperature=0.2) | langchain_openai.ChatOpenAI |
import getpass
import os
os.environ["TAVILY_API_KEY"] = getpass.getpass()
from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
retriever = TavilySearchAPIRetriever(k=3)
retriever.invoke("what year was breath of the wild released?")
from langchain_core.output_parsers import StrOutputPa... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet cohere')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu')
import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:")
... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=64) | langchain_text_splitters.RecursiveCharacterTextSplitter |
from langchain_openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo-instruct", temperature=0, max_tokens=512) | langchain_openai.OpenAI |
from getpass import getpass
KAY_API_KEY = getpass()
import os
from langchain.retrievers import KayAiRetriever
os.environ["KAY_API_KEY"] = KAY_API_KEY
retriever = KayAiRetriever.create(
dataset_id="company", data_types=["10-K", "10-Q", "PressRelease"], num_contexts=3
)
docs = retriever.get_relevant_documents(
... | ConversationalRetrievalChain.from_llm(model, retriever=retriever) | langchain.chains.ConversationalRetrievalChain.from_llm |
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 --upgrade --quiet playwright beautifulsoup4')
get_ipython().system(' playwright install')
from langchain_community.document_loaders import AsyncChromiumLoader
urls = ["https://www.wsj.com"]
loader = AsyncChromiumLoader(urls)
docs = loader.load()
docs[0].page_content[0:10... | Html2TextTransformer() | langchain_community.document_transformers.Html2TextTransformer |
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... | ChatOpenAI() | langchain_openai.ChatOpenAI |
from typing import Callable, List
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name =... | ChatOpenAI(temperature=1.0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-community')
import os
os.environ["YDC_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from langchain_community.utilities.you import YouSearchAPIWrapper
utility = | YouSearchAPIWrapper(num_web_results=1) | langchain_community.utilities.you.YouSearchAPIWrapper |
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... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet embedchain')
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass()
from langchain.retrievers import EmbedchainRetriever
retriever = | EmbedchainRetriever.create() | langchain.retrievers.EmbedchainRetriever.create |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-nvidia-ai-endpoints')
import getpass
import os
if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
nvapi_key = getpass.getpass("Enter your NVIDIA API key: ")
assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is ... | ChatNVIDIA(model="mixtral_8x7b", temperature=0.1, max_tokens=100, top_p=1.0) | langchain_nvidia_ai_endpoints.ChatNVIDIA |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clickhouse-connect')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["OPENAI_API_BASE"] = getpass.getpass("OpenAI Base:")
os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale Host:")
os.environ["MY... | MyScale.from_documents(docs, embeddings) | langchain_community.vectorstores.MyScale.from_documents |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import ChatOpenAI, OpenAI
llm = | ChatOpenAI(temperature=0.0) | langchain_openai.ChatOpenAI |
from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
chat_message_history = MongoDBChatMessageHistory(
session_id="test_session",
connection_string="mongodb://mongo_user:password123@mongo:27017",
database_name="my_db",
collection_name="chat_histories",
)
chat_message_history.... | ChatOpenAI() | langchain_openai.ChatOpenAI |
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... | hub.pull("langchain-ai/openai-functions-template") | langchain.hub.pull |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scikit-learn')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.retrievers import SVMRetriever
from langchain_openai imp... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet momento langchain-openai tiktoken')
import getpass
import os
os.environ["MOMENTO_API_KEY"] = getpass.getpass("Momento API Key:")
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_loaders impor... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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 -qU langchain-community langchain-openai')
from langchain_community.tools import MoveFileTool
from langchain_core.messages import HumanMessage
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain_openai import ChatOpenAI
model = Cha... | convert_to_openai_function(t) | langchain_core.utils.function_calling.convert_to_openai_function |
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) | langchain_openai.ChatOpenAI |
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:
... | TextLoader(doc_path) | langchain_community.document_loaders.TextLoader |
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",
... | HumanMessage(content="What did Simone de Beauvoir believe about free will") | langchain_core.messages.HumanMessage |
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from operator import itemgetter
from langchain.output_parsers import JsonOutputToolsParser
from langchain_core.runnables import Runnable, Runnabl... | JsonOutputToolsParser() | langchain.output_parsers.JsonOutputToolsParser |
get_ipython().system(' pip install pdf2image')
import arxiv
from langchain_community.chat_models import ChatAnthropic
from langchain_community.document_loaders import ArxivLoader, UnstructuredPDFLoader
paper = next(arxiv.Search(query="Visual Instruction Tuning").results())
paper.download_pdf(filename="downloaded-pa... | WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") | langchain_community.document_loaders.WebBaseLoader |
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... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
import os
os.environ["OPENAI_API_KEY"] = "..."
from langchain.prompts import PromptTemplate
from langchain_experimental.smart_llm import SmartLLMChain
from langchain_openai import ChatOpenAI
hard_question = "I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?"
prompt = PromptTe... | SmartLLMChain(llm=llm, prompt=prompt, n_ideas=3, verbose=True) | langchain_experimental.smart_llm.SmartLLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet supabase')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["SUPABASE_URL"] = getpass.getpass("Supabase URL:")
os.environ["SUPABASE_SERVICE_KEY"] = getpass.getpass("Supabase Service Key:")
fro... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet nlpcloud')
from getpass import getpass
NLPCLOUD_API_KEY = getpass()
import os
os.environ["NLPCLOUD_API_KEY"] = NLPCLOUD_API_KEY
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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... | Document(page_content="Bar") | langchain.docstore.document.Document |
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_with_id) | langchain_core.runnables.RunnableLambda |
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 FewShotPromptTemplate, PromptTemplate
from langchain.prompts.example_selector import SemanticSimilarityExampleSelector
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
example_prompt = PromptTemplate(
input_variables=["input", "output"]... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-text-to-speech')
from langchain.tools import GoogleCloudTextToSpeechTool
text_to_speak = "Hello world!"
tts = | GoogleCloudTextToSpeechTool() | langchain.tools.GoogleCloudTextToSpeechTool |
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts =... | ConversationBufferMemory(memory_key="chat_history", input_key="human_input") | langchain.memory.ConversationBufferMemory |
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[... | LLMChain(llm=llm, prompt=prompt_template1, callbacks=[sagemaker_callback]) | langchain.chains.LLMChain |
from langchain.memory import ConversationSummaryBufferMemory
from langchain_openai import OpenAI
llm = | OpenAI() | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet transformers')
from langchain_community.document_loaders import ImageCaptionLoader
list_image_urls = [
"https://upload.wikimedia.org/wikipedia/commons/thumb/5/5a/Hyla_japonica_sep01.jpg/260px-Hyla_japonica_sep01.jpg",
"https://upload.wikimedia... | ImageCaptionLoader(path_images=list_image_urls) | langchain_community.document_loaders.ImageCaptionLoader |
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... | load_evaluator("labeled_criteria", criteria="correctness", prompt=prompt) | langchain.evaluation.load_evaluator |
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 |
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... | PyPDFLoader("what-is-philosophy.pdf") | langchain_community.document_loaders.PyPDFLoader |
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... | AgentExecutor(agent=runnable_agent, tools=tools, handle_parsing_errors=True) | langchain.agents.AgentExecutor |
import getpass
import os
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY") or getpass.getpass(
"OpenAI API Key:"
)
from langchain.sql_database import SQLDatabase
from langchain_openai import ChatOpenAI
CONNECTION_STRING = "postgresql+psycopg2://postgres:test@localhost:5432/vectordb" # Replace wit... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azure-ai-formrecognizer > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azure-cognitiveservices-speech > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azure-ai-textanalytics > /dev/null')
get_ipy... | AzureCognitiveServicesToolkit() | langchain_community.agent_toolkits.AzureCognitiveServicesToolkit |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai duckduckgo-search')
from langchain.tools import DuckDuckGoSearchRun
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
searc... | ChatOpenAI() | langchain_openai.ChatOpenAI |
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... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | LLMChainFilter.from_llm(llm) | langchain.retrievers.document_compressors.LLMChainFilter.from_llm |
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... | HumanMessage(content="What comes after 1,2,3 ?") | langchain.schema.HumanMessage |
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) | langchain_community.vectorstores.Bagel.from_texts |
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain_openai import ChatOpenAI
examples = [
{
"input": "Could the members of The Police perform law... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import os
import uuid
uid = uuid.uuid4().hex[:6]
project_name = f"Run Fine-tuning Walkthrough {uid}"
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "YOUR API KEY"
os.environ["LANGCHAIN_PROJECT"... | ChatPromptTemplate.from_messages(
[
("system", "You are an accounting assistant.") | langchain_core.prompts.ChatPromptTemplate.from_messages |
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... | PolygonFinancials(api_wrapper=api_wrapper) | langchain_community.tools.polygon.financials.PolygonFinancials |
from langchain_community.document_loaders import ArcGISLoader
URL = "https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7"
loader = | ArcGISLoader(URL) | langchain_community.document_loaders.ArcGISLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rellm > /dev/null')
import logging
logging.basicConfig(level=logging.ERROR)
prompt = """Human: "What's the capital of the United States?"
AI Assistant:{
"action": "Final Answer",
"action_input": "The capital of the United States is Washington D.C."... | RELLM(pipeline=hf_model, regex=pattern, max_new_tokens=200) | langchain_experimental.llms.RELLM |
from langchain.agents import Tool
from langchain_community.tools.file_management.read import ReadFileTool
from langchain_community.tools.file_management.write import WriteFileTool
from langchain_community.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
tools = [
Tool(
name="search",
func=... | FileChatMessageHistory("chat_history.txt") | langchain_community.chat_message_histories.FileChatMessageHistory |
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... | GitHubToolkit.from_github_api_wrapper(github) | langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.from_github_api_wrapper |
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... | CharacterTextSplitter() | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet opensearch-py')
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 OpenSearchVectorSearch
from langchain_... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from getpass import getpass
MOSAICML_API_TOKEN = getpass()
import os
os.environ["MOSAICML_API_TOKEN"] = MOSAICML_API_TOKEN
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import MosaicML
template = """Question: {question}"""
prompt = PromptTempla... | MosaicML(inject_instruction_format=True, model_kwargs={"max_new_tokens": 128}) | langchain_community.llms.MosaicML |
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