prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
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/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain label-studio label-studio-sdk langchain-openai')
import os
os.environ["LABEL_STUDIO_URL"] = "<YOUR-LABEL-STUDIO-URL>" # e.g. http://localhost:8080
os.environ["LABEL_STUDIO_API_KEY"] = "<YOUR-LABEL-STUDIO-API-KEY>"
os.environ["OPENAI_API_KEY"... | LabelStudioCallbackHandler(
project_config="""
<View>
<Text name="prompt" value="$prompt"/>
<TextArea name="response" toName="prompt"/>
<TextArea name="user_feedback" toName="prompt"/>
<Rating name="rating" toName="prompt"/>
<Choices name="sentiment" toName="prompt">
<Choice value="Positive"/>
<Choice value... | langchain.callbacks.LabelStudioCallbackHandler |
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... | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
from langchain.chains import GraphCypherQAChain
from langchain_community.graphs import Neo4jGraph
from langchain_openai import ChatOpenAI
graph = Neo4jGraph(
url="bolt://localhost:7687", username="neo4j", password="pleaseletmein"
)
graph.query(
"""
MERGE (m:Movie {name:"Top Gun"})
WITH m
UNWIND ["Tom Cruis... | ChatOpenAI(temperature=0, model="gpt-3.5-turbo") | langchain_openai.ChatOpenAI |
from langchain_community.document_loaders import HNLoader
loader = | HNLoader("https://news.ycombinator.com/item?id=34817881") | langchain_community.document_loaders.HNLoader |
from langchain.chains import FalkorDBQAChain
from langchain_community.graphs import FalkorDBGraph
from langchain_openai import ChatOpenAI
graph = FalkorDBGraph(database="movies")
graph.query(
"""
CREATE
(al:Person {name: 'Al Pacino', birthDate: '1940-04-25'}),
(robert:Person {name: 'Robert... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain.prompts import PromptTemplate
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_openai import Ope... | ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True) | langchain.agents.ZeroShotAgent |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet amadeus > /dev/null')
import os
os.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"
os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
from langchain_community.agent_toolkits.amadeus.toolkit impo... | hub.pull("hwchase17/react-json") | langchain.hub.pull |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet gigachat')
import os
from getpass import getpass
os.environ["GIGACHAT_CREDENTIALS"] = getpass()
from langchain_community.chat_models import GigaChat
chat = | GigaChat(verify_ssl_certs=False) | langchain_community.chat_models.GigaChat |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-spanner')
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().system('gcloud services ena... | TableColumn(name="metadata", type="JSON", is_null=True) | langchain_google_spanner.TableColumn |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_community.chat_message_histories import RedisChatMessageHistory
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_opena... | ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True) | langchain.agents.ZeroShotAgent |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-firestore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from goo... | FirestoreSaver() | langchain_google_firestore.FirestoreSaver |
get_ipython().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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.output_parsers import DatetimeOutputParser
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
output_parser = DatetimeOutputParser()
template = """Answer the users question:
{question}
{format_instructions}"""
prompt = PromptTemplate.from_template(
template,
part... | OpenAI() | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rank_bm25')
from langchain.retrievers import BM25Retriever
retriever = BM25Retriever.from_texts(["foo", "bar", "world", "hello", "foo bar"])
from langchain_core.documents import Document
retriever = BM25Retriever.from_documents(
[
Docu... | Document(page_content="world") | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pyspark')
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = spark.read.csv("example_data/mlb_teams_2012.csv", header=True)
from langchain_community.document_loaders import PySparkDataFrameLoader
loader = | PySparkDataFrameLoader(spark, df, page_content_column="Team") | langchain_community.document_loaders.PySparkDataFrameLoader |
from typing import List
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, validator
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)
class Joke(BaseModel):
setup: str = Field(d... | PydanticOutputParser(pydantic_object=Actor) | langchain.output_parsers.PydanticOutputParser |
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:")
... | RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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 kensho", metadata={"namespace": "harrison"}) | langchain.docstore.document.Document |
import os
os.environ["SCENEX_API_KEY"] = "<YOUR_API_KEY>"
from langchain.agents import load_tools
tools = load_tools(["sceneXplain"])
from langchain.tools import SceneXplainTool
tool = SceneXplainTool()
from langchain.agents import initialize_agent
from langchain.memory import ConversationBufferMemory
from l... | ConversationBufferMemory(memory_key="chat_history") | langchain.memory.ConversationBufferMemory |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain.prompts import PromptTemplate
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_openai import Ope... | GoogleSearchAPIWrapper() | langchain_community.utilities.GoogleSearchAPIWrapper |
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... | XMLOutputParser() | langchain_core.output_parsers.XMLOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet praw')
client_id = ""
client_secret = ""
user_agent = ""
from langchain_community.tools.reddit_search.tool import RedditSearchRun
from langchain_community.utilities.reddit_search import RedditSearchAPIWrapper
search = RedditSearchRun(
api_wrapper... | StructuredChatAgent(llm_chain=llm_chain, verbose=True, tools=tools) | langchain.agents.StructuredChatAgent |
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.ToSelectFrom(meals) | langchain_experimental.rl_chain.ToSelectFrom |
from langchain.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
]
example_prompt = ChatPromptTemplate.from_messages(
[
("human", "{input}"),
("ai", "{output}"),
]
)
few_sh... | ChatPromptTemplate.from_messages(
[("human", "{input}") | langchain.prompts.ChatPromptTemplate.from_messages |
from langchain.output_parsers import (
OutputFixingParser,
PydanticOutputParser,
)
from langchain.prompts import (
PromptTemplate,
)
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI, OpenAI
template = """Based on the user question, provide an Action and Actio... | Field(description="action to take") | langchain_core.pydantic_v1.Field |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain tiktoken langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet hippo-api==1.1.0.rc3')
import os
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores.hippo import Hippo
... | CharacterTextSplitter(chunk_size=500, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().system(' pip install langchain docugami==0.0.8 dgml-utils==0.3.0 pydantic langchainhub chromadb hnswlib --upgrade --quiet')
from pprint import pprint
from docugami import Docugami
from docugami.lib.upload import upload_to_named_docset, wait_for_dgml
DOCSET_NAME = "NTSB Aviation Incident Reports"
FIL... | ChatOpenAI(temperature=0, model="gpt-4") | 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,
... | AgentAction(tool="Search", tool_input=kwargs["input"], log="") | langchain_core.agents.AgentAction |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet doctran')
import json
from langchain_community.document_transformers import DoctranQATransformer
from langchain_core.documents import Document
from dotenv import load_dotenv
load_dotenv()
sample_text = """[Generated with ChatGPT]
Confidential Doc... | DoctranQATransformer() | langchain_community.document_transformers.DoctranQATransformer |
from langchain_community.chat_message_histories import StreamlitChatMessageHistory
history = StreamlitChatMessageHistory(key="chat_messages")
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
from langchain_community.chat_message_histories import StreamlitChatMessageHistory
ms... | 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 |
from langchain.output_parsers.enum import EnumOutputParser
from enum import Enum
class Colors(Enum):
RED = "red"
GREEN = "green"
BLUE = "blue"
parser = | EnumOutputParser(enum=Colors) | langchain.output_parsers.enum.EnumOutputParser |
import uuid
from pathlib import Path
import langchain
import torch
from bs4 import BeautifulSoup as Soup
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore, LocalFileStore
from langchain_community.document_loaders.recursive_url_loader import (
Recursi... | HuggingFacePipeline(pipeline=pipe) | langchain.llms.huggingface_pipeline.HuggingFacePipeline |
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... | PromptTemplate.from_template(prompt_text) | langchain.prompts.PromptTemplate.from_template |
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(... | InMemoryStore() | langchain.storage.InMemoryStore |
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(model_name="gpt-4", temperature=0.0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install -qU langchain-text-splitters')
from langchain_text_splitters import HTMLHeaderTextSplitter
html_string = """
<!DOCTYPE html>
<html>
<body>
<div>
<h1>Foo</h1>
<p>Some intro text about Foo.</p>
<div>
<h2>Bar main section</h2>
... | HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on) | langchain_text_splitters.HTMLHeaderTextSplitter |
import os
os.environ["GOOGLE_CSE_ID"] = ""
os.environ["GOOGLE_API_KEY"] = ""
from langchain.tools import Tool
from langchain_community.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
tool = Tool(
name="google_search",
description="Search Google for recent results.",
func=searc... | GoogleSearchAPIWrapper() | langchain_community.utilities.GoogleSearchAPIWrapper |
from langchain.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(loader, record_manager, vectorstore, cleanup="full", source_id_key="source") | langchain.indexes.index |
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... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
api_key = ""
from langchain_community.document_loaders import ToMarkdownLoader
loader = | ToMarkdownLoader(
url="https://python.langchain.com/docs/get_started/introduction", api_key=api_key
) | langchain_community.document_loaders.ToMarkdownLoader |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["google-serper"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What is the weathe... | load_tools(["serpapi"], llm=llm) | langchain.agents.load_tools |
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import GradientLLM
import os
from getpass import getpass
if not os.environ.get("GRADIENT_ACCESS_TOKEN", None):
os.environ["GRADIENT_ACCESS_TOKEN"] = getpass("gradient.ai access token:")
if not os.env... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai wikipedia')
from operator import itemgetter
from langchain.agents import AgentExecutor, load_tools
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import O... | AgentExecutor(agent=agent, tools=tools, verbose=True) | langchain.agents.AgentExecutor |
from langchain_community.tools.edenai import (
EdenAiExplicitImageTool,
EdenAiObjectDetectionTool,
EdenAiParsingIDTool,
EdenAiParsingInvoiceTool,
EdenAiSpeechToTextTool,
EdenAiTextModerationTool,
EdenAiTextToSpeechTool,
)
from langchain.agents import AgentType, initialize_agent
from langch... | EdenAiParsingInvoiceTool(providers=["amazon", "google"], language="en") | langchain_community.tools.edenai.EdenAiParsingInvoiceTool |
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 ChatOpenAI
api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max... | WikipediaQueryRun(api_wrapper=api_wrapper) | langchain_community.tools.WikipediaQueryRun |
from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointApiType,
LlamaContentFormatter,
)
from langchain_core.messages import HumanMessage
llm = AzureMLOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<you... | DollyContentFormatter() | langchain_community.llms.azureml_endpoint.DollyContentFormatter |
from langchain_community.document_loaders.blob_loaders.youtube_audio import (
YoutubeAudioLoader,
)
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import (
OpenAIWhisperParser,
OpenAIWhisperParserLocal,
)
get_ipython().run_line_mag... | OpenAIWhisperParser() | langchain_community.document_loaders.parsers.OpenAIWhisperParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet bibtexparser pymupdf')
from langchain_community.document_loaders import BibtexLoader
import urllib.request
urllib.request.urlretrieve(
"https://www.fourmilab.ch/etexts/einstein/specrel/specrel.pdf", "einstein1905.pdf"
)
bibtex_text = """
@a... | BibtexLoader("./biblio.bib") | langchain_community.document_loaders.BibtexLoader |
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... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=300) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet timescale-vector')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import os
from dotenv import find_dotenv, load_dotenv
_ = load_dotenv(find... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_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... | ChatOpenAI() | langchain_openai.ChatOpenAI |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet typesense openapi-schema-pydantic 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_community.vectorstores... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet annoy')
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Annoy
embeddings_func = HuggingFaceEmbeddings()
texts = ["pizza is great", "I love salad", "my car", "a dog"]
vector_store = Annoy.... | Annoy.from_documents(docs, embeddings_func) | langchain_community.vectorstores.Annoy.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tigrisdb openapi-schema-pydantic langchain-openai tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["TIGRIS_PROJECT"] = getpass.getpass("Tigris Project Name:")
os.environ["TIGRIS_CLIENT_ID"... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | merge_chat_runs(raw_messages) | langchain_community.chat_loaders.utils.merge_chat_runs |
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:")
... | CohereEmbeddings() | langchain_community.embeddings.CohereEmbeddings |
from langchain_community.document_loaders import UnstructuredODTLoader
loader = | UnstructuredODTLoader("example_data/fake.odt", mode="elements") | langchain_community.document_loaders.UnstructuredODTLoader |
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... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet doctran')
import json
from langchain_community.document_transformers import DoctranQATransformer
from langchain_core.documents import Document
from dotenv import load_dotenv
load_dotenv()
sample_text = """[Generated with ChatGPT]
Confidential Doc... | Document(page_content=sample_text) | langchain_core.documents.Document |
import os
os.environ["SCENEX_API_KEY"] = "<YOUR_API_KEY>"
from langchain.agents import load_tools
tools = load_tools(["sceneXplain"])
from langchain.tools import SceneXplainTool
tool = SceneXplainTool()
from langchain.agents import initialize_agent
from langchain.memory import ConversationBufferMemory
from l... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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 ... | ChatPromptTemplate.from_messages(
[
("system", "You are speaking to hare.") | langchain_core.prompts.ChatPromptTemplate.from_messages |
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,... | HumanMessage(content=obs_message) | langchain.schema.HumanMessage |
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... | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain_community.document_loaders import AsyncHtmlLoader
urls = ["https://www.espn.com", "https://lilianweng.github.io/posts/2023-06-23-agent/"]
loader = | AsyncHtmlLoader(urls) | langchain_community.document_loaders.AsyncHtmlLoader |
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... | VertexAIEmbeddings(model_name="textembedding-gecko@latest") | langchain_community.embeddings.VertexAIEmbeddings |
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOpenAI
api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_conten... | WikipediaQueryRun(api_wrapper=api_wrapper) | langchain_community.tools.WikipediaQueryRun |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet 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="Tell me another joke?") | langchain.schema.HumanMessage |
from langchain_community.document_loaders import TomlLoader
loader = | TomlLoader("example_data/fake_rule.toml") | langchain_community.document_loaders.TomlLoader |
from langchain_community.document_loaders.blob_loaders.youtube_audio import (
YoutubeAudioLoader,
)
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import (
OpenAIWhisperParser,
OpenAIWhisperParserLocal,
)
get_ipython().run_line_mag... | OpenAIWhisperParserLocal() | langchain_community.document_loaders.parsers.OpenAIWhisperParserLocal |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
import os
from langchain_community.tools.google_trends import GoogleTrendsQueryRun
from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
os.environ["SERPAPI_API_KEY"] = ""
tool = GoogleTrendsQueryRun(api... | GoogleTrendsAPIWrapper() | langchain_community.utilities.google_trends.GoogleTrendsAPIWrapper |
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 googlemaps')
import os
os.environ["GPLACES_API_KEY"] = ""
from langchain.tools import GooglePlacesTool
places = | GooglePlacesTool() | langchain.tools.GooglePlacesTool |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet slack_sdk > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messages')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet python-dotenv > ... | AgentExecutor(agent=agent, tools=tools, verbose=True) | langchain.agents.AgentExecutor |
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... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | hub.pull("wfh/langsmith-agent-prompt:5d466cbc") | langchain.hub.pull |
get_ipython().system('pip3 install clickhouse-sqlalchemy InstructorEmbedding sentence_transformers openai langchain-experimental')
import getpass
from os import environ
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.utilities import SQLDatabase
from langch... | OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pgvector')
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')
im... | Document(page_content="foo") | langchain.docstore.document.Document |
with open("../docs/docs/modules/state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain.chains import AnalyzeDocumentChain
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
from langchain.chains.question_answering import load_qa_chain
qa_chain =... | AnalyzeDocumentChain(combine_docs_chain=qa_chain) | langchain.chains.AnalyzeDocumentChain |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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_... | HumanMessage(content="Tips: Make sure to answer in the correct format") | langchain_core.messages.HumanMessage |
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(llm=llm, tools=tools, prompt=agent_prompt) | langchain.agents.OpenAIFunctionsAgent |
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 |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark weaviate-client')
from langchain_community.vectorstores import Weaviate
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
docs = [
Document(
page_content="A bun... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub gpt4all chromadb')
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = WebBaseLoader("https://lilianweng.github.io/posts/... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
from langchain_community.chat_models import ChatDatabricks
from langchain_core.messages import HumanMessage
from mlflow.deployments import get_deploy_client
client = get_deploy_client("databricks")
secret = "secrets/<scope>/openai-api-key" # replace `<scope>` with your scope
name = "my-chat" # rename this if my-cha... | Databricks(host="myworkspace.cloud.databricks.com", endpoint_name="dolly") | langchain_community.llms.Databricks |
get_ipython().run_line_magic('pip', 'install --quiet pypdf chromadb tiktoken openai')
get_ipython().run_line_magic('pip', 'uninstall -y langchain-fireworks')
get_ipython().run_line_magic('pip', 'install --editable /mnt/disks/data/langchain/libs/partners/fireworks')
import fireworks
print(fireworks)
import fireworks.... | FireworksEmbeddings() | langchain_fireworks.embeddings.FireworksEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet promptlayer')
import os
import promptlayer
from langchain_community.llms import PromptLayerOpenAI
from getpass import getpass
PROMPTLAYER_API_KEY = getpass()
os.environ["PROMPTLAYER_API_KEY"] = PROMPTLAYER_API_KEY
from getpass import getpass
O... | PromptLayerOpenAI(return_pl_id=True) | langchain_community.llms.PromptLayerOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai argilla')
import os
os.environ["ARGILLA_API_URL"] = "..."
os.environ["ARGILLA_API_KEY"] = "..."
os.environ["OPENAI_API_KEY"] = "..."
import argilla as rg
from packaging.version import parse as parse_version
if parse_ve... | StdOutCallbackHandler() | langchain.callbacks.StdOutCallbackHandler |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml')
from typing import Any
from pydantic import BaseModel
from unstructured.partition.pdf import partition_pdf
path = "/Users/rlm/Desktop/Papers/LLaVA/"
raw_pdf_elements = partition_pdf(
filename=path + "LLaVA.pdf",
extract_im... | ChatPromptTemplate.from_template(prompt_text) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet vald-client-python')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Vald
from langchain_text_splitters import CharacterTextSplitte... | HuggingFaceEmbeddings() | langchain_community.embeddings.HuggingFaceEmbeddings |
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_documents(docs, embeddings) | langchain_community.vectorstores.FAISS.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet redis redisvl langchain-openai tiktoken lark')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import Redis
from langchain_core.documents import Document
from langchain_... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymysql')
get_ipython().system('pip install sqlalchemy')
get_ipython().system('pip install langchain')
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
DirectoryLoader,
UnstructuredMarkdownLoader,
)
... | ApacheDoris(embeddings, settings) | langchain_community.vectorstores.apache_doris.ApacheDoris |
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass()
activeloop_token = getpass("Activeloop Token:")
os.environ["ACTIVELOOP_TOKEN"] = activeloop_token
get_ipython().system('ls "../../../../../../libs"')
from langchain_community.document_loaders import TextLoader
root_dir = "../../..... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | Document(page_content=s, metadata={id_key: doc_ids[i]}) | langchain_core.documents.Document |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.