prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
get_ipython().run_line_magic('pip', 'install --upgrade --quiet docx2txt')
from langchain_community.document_loaders import Docx2txtLoader
loader = Docx2txtLoader("example_data/fake.docx")
data = loader.load()
data
from langchain_community.document_loaders import UnstructuredWordDocumentLoader
loader = Uns... | UnstructuredWordDocumentLoader("example_data/fake.docx", mode="elements") | langchain_community.document_loaders.UnstructuredWordDocumentLoader |
import nest_asyncio
from langchain.chains.graph_qa import GremlinQAChain
from langchain.schema import Document
from langchain_community.graphs import GremlinGraph
from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
from langchain_openai import AzureChatOpenAI
cosmosdb_name = "mycos... | Document(
page_content="Matrix is a movie where Keanu Reeves, Laurence Fishburne and Carrie-Anne Moss acted."
) | langchain.schema.Document |
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 |
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.prompts import PromptTemplate
from langchain_community.llms import TitanTakeoffPro
llm = TitanTakeoffPro()
output = llm("What is the weather in London in August?")
prin... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
get_ipython().system('pip install langchain lark openai elasticsearch pandas')
import pandas as pd
details = (
pd.read_csv("~/Downloads/archive/Hotel_details.csv")
.drop_duplicates(subset="hotelid")
.set_index("hotelid")
)
attributes = pd.read_csv(
"~/Downloads/archive/Hotel_Room_attributes.csv", in... | ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_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... | SecondaryIndex(index_name="row_id_and_title", columns=["row_id", "title"]) | langchain_google_spanner.SecondaryIndex |
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... | hub.pull("hwchase17/react") | langchain.hub.pull |
from typing import Any, Dict, List, Union
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks.base import BaseCallbackHandler
from langchain_core.agents import AgentAction
from langchain_openai import OpenAI
class MyCustomHandlerOne(BaseCallbackHandler):
def on_llm_start... | load_tools(["llm-math"], llm=llm) | langchain.agents.load_tools |
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... | ConfigurableField(id="llm") | langchain_core.runnables.ConfigurableField |
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... | SQLDatabase(engine, None, metadata) | langchain_community.utilities.sql_database.SQLDatabase |
get_ipython().run_cell_magic('writefile', 'telegram_conversation.json', '{\n "name": "Jiminy",\n "type": "personal_chat",\n "id": 5965280513,\n "messages": [\n {\n "id": 1,\n "type": "message",\n "date": "2023-08-23T13:11:23",\n "date_unixtime": "1692821483",\n "from": "Jiminy Cricket",\n "from_id": "user1... | merge_chat_runs(raw_messages) | langchain_community.chat_loaders.utils.merge_chat_runs |
from typing import List
from langchain.output_parsers import PydanticOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
class Actor(BaseModel):
name: str = Field(description="name of an actor")
film_names: List[str] = Field(description="list of names ... | PydanticOutputParser(pydantic_object=Actor) | langchain.output_parsers.PydanticOutputParser |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().system(' nomic login')
get_ipython().system(' nomic login token')
get_ipython().system(' pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain')
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.lang... | ChatOpenAI(temperature=0, model="gpt-4-1106-preview") | langchain_openai.ChatOpenAI |
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="puppy", metadata={"source": "doggy.txt"}) | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azureml-mlflow')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet textstat')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
get_ipython().run_l... | MlflowCallbackHandler() | langchain.callbacks.MlflowCallbackHandler |
get_ipython().run_line_magic('pip', 'install "pgvecto_rs[sdk]"')
from typing import List
from langchain.docstore.document import Document
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores.pgvecto_rs import ... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | Document(page_content=s, metadata={id_key: doc_ids[i]}) | langchain_core.documents.Document |
import logging
from langchain.retrievers import RePhraseQueryRetriever
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
loggi... | LLMChain(llm=llm, prompt=QUERY_PROMPT) | langchain.chains.LLMChain |
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import Recursiv... | TextLoader("../../state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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.... | MessagesPlaceholder(variable_name="history") | langchain_core.prompts.MessagesPlaceholder |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-community')
import os
os.environ["YDC_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from langchain_community.tools.you import YouSearchTool
from langchain_community.utilities.you import YouSearchAPIWrapper
api_wrapper = YouSearchAP... | hub.pull("langchain-ai/openai-functions-template") | langchain.hub.pull |
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('poetry run pip install replicate')
from getpass import getpass
REPLICATE_API_TOKEN = getpass()
import os
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.chains ... | Replicate(
model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"
) | langchain_community.llms.Replicate |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai context-python')
import os
from langchain.callbacks import ContextCallbackHandler
token = os.environ["CONTEXT_API_TOKEN"]
context_callback = ContextCallbackHandler(token)
import os
from langchain.callbacks import Conte... | ChatPromptTemplate.from_messages([human_message_prompt]) | langchain.prompts.chat.ChatPromptTemplate.from_messages |
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 = "../../..... | ConversationalRetrievalChain.from_llm(model, retriever=retriever) | langchain.chains.ConversationalRetrievalChain.from_llm |
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... | ChatOpenAI(temperature=0.1, model="gpt-3.5-turbo-16k") | langchain_openai.ChatOpenAI |
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... | Chroma.from_documents(docs, embedding_function) | langchain_community.vectorstores.Chroma.from_documents |
import asyncio
from langchain.callbacks import get_openai_callback
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | create_openai_functions_agent(llm, tools, prompt) | langchain.agents.create_openai_functions_agent |
import os
from langchain.retrievers import AzureCognitiveSearchRetriever
os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"] = "<YOUR_ACS_SERVICE_NAME>"
os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"] = "<YOUR_ACS_INDEX_NAME>"
os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"] = "<YOUR_API_KEY>"
retriever = | AzureCognitiveSearchRetriever(content_key="content", top_k=10) | langchain.retrievers.AzureCognitiveSearchRetriever |
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../state_of_the_union.txt", encoding... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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
... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet weaviate-client')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")
os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")
WEAVIATE_API_KEY = os... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet fastembed')
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
embeddings = | FastEmbedEmbeddings() | langchain_community.embeddings.fastembed.FastEmbedEmbeddings |
meals = [
"Beef Enchiladas with Feta cheese. Mexican-Greek fusion",
"Chicken Flatbreads with red sauce. Italian-Mexican fusion",
"Veggie sweet potato quesadillas with vegan cheese",
"One-Pan Tortelonni bake with peppers and onions",
]
from langchain_openai import OpenAI
llm = OpenAI(model="gpt-3.5-t... | rl_chain.BasedOn("Tom") | langchain_experimental.rl_chain.BasedOn |
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,
)
... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from typing import List, Tuple
from dotenv import load_dotenv
load_dotenv()
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.v... | Document(page_content="foo") | langchain_core.documents.Document |
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../state_of_the_union.txt", encoding... | ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") | langchain_openai.ChatOpenAI |
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... | load_llm("azureml.json") | langchain_community.llms.loading.load_llm |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3')
from langchain_community.document_loaders import S3DirectoryLoader
loader = | S3DirectoryLoader("testing-hwc") | langchain_community.document_loaders.S3DirectoryLoader |
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="llama2_code_70b") | langchain_nvidia_ai_endpoints.ChatNVIDIA |
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("criteria", llm=llm, criteria="conciseness") | langchain.evaluation.load_evaluator |
from langchain_community.llms import Baseten
mistral = Baseten(model="MODEL_ID", deployment="production")
mistral("What is the Mistral wind?")
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
template = """Assistant is... | ConversationBufferWindowMemory(k=2) | langchain.memory.ConversationBufferWindowMemory |
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.... | PyPDFLoader(file_name) | langchain_community.document_loaders.PyPDFLoader |
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)
utility
import json
response... | YouRetriever(num_web_results=1) | langchain_community.retrievers.you.YouRetriever |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet duckdb')
from langchain_community.document_loaders import DuckDBLoader
get_ipython().run_cell_magic('file', 'example.csv', 'Team,Payroll\nNationals,81.34\nReds,82.20\n')
loader = DuckDBLoader("SELECT * FROM read_csv_auto('example.csv')")
data = load... | DuckDBLoader(
"SELECT Team, Payroll, Team As source FROM read_csv_auto('example.csv') | langchain_community.document_loaders.DuckDBLoader |
from langchain_community.document_transformers.openai_functions import (
create_metadata_tagger,
)
from langchain_core.documents import Document
from langchain_openai import ChatOpenAI
schema = {
"properties": {
"movie_title": {"type": "string"},
"critic": {"type": "string"},
"tone": {... | ChatPromptTemplate.from_template(
"""Extract relevant information from the following text.
Anonymous critics are actually Roger Ebert.
{input}
"""
) | langchain_core.prompts.ChatPromptTemplate.from_template |
from typing import Callable, List
import tenacity
from langchain.output_parsers import RegexParser
from langchain.prompts import PromptTemplate
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
n... | ChatOpenAI(temperature=1.0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', "install --upgrade --quiet faiss-gpu # For CUDA 7.5+ Supported GPU's.")
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu # For CPU Installation')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_... | FAISS.load_local("faiss_index", embeddings, asynchronous=True) | langchain_community.vectorstores.FAISS.load_local |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import ChatOpenAI, OpenAI
llm = ChatOpenAI(temperature=0.0)
math_llm = OpenAI(temperature=0.0)
tools = load_tools(
["human", "llm-math"],
llm=math_llm,
)
agent_chain = initialize_agent(
tools,
llm,
agent=Age... | HumanInputRun(input_func=get_input) | langchain.tools.HumanInputRun |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clearml')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet textstat')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
get_ipython().system('pyth... | StdOutCallbackHandler() | langchain.callbacks.StdOutCallbackHandler |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet predibase')
import os
os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"
from langchain_community.llms import Predibase
model = Predibase(
model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN")
)
response = model("C... | LLMChain(llm=llm, prompt=prompt_template) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet python-gitlab')
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.gitlab.toolkit import GitLabToolkit
from langchain_community.utilities.gitlab import GitLabAPIWrapper
from langchain_openai impo... | GitLabToolkit.from_gitlab_api_wrapper(gitlab) | langchain_community.agent_toolkits.gitlab.toolkit.GitLabToolkit.from_gitlab_api_wrapper |
from typing import List
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)
class Joke(BaseModel):
setup: str = Field(description... | Field(description="answer to resolve the joke") | langchain_core.pydantic_v1.Field |
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... | planner.create_openapi_agent(spotify_api_spec, requests_wrapper, llm) | langchain_community.agent_toolkits.openapi.planner.create_openapi_agent |
URL = "" # Your Fiddler instance URL, Make sure to include the full URL (including https://). For example: https://demo.fiddler.ai
ORG_NAME = ""
AUTH_TOKEN = "" # Your Fiddler instance auth token
PROJECT_NAME = ""
MODEL_NAME = "" # Model name in Fiddler
from langchain_community.callbacks.fiddler_callback import ... | OpenAI(temperature=0, streaming=True, callbacks=[fiddler_handler]) | langchain_openai.OpenAI |
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import Recursiv... | Document(page_content=s, metadata={id_key: doc_ids[i]}) | langchain_core.documents.Document |
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... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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... | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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) | langchain_openai.ChatOpenAI |
meals = [
"Beef Enchiladas with Feta cheese. Mexican-Greek fusion",
"Chicken Flatbreads with red sauce. Italian-Mexican fusion",
"Veggie sweet potato quesadillas with vegan cheese",
"One-Pan Tortelonni bake with peppers and onions",
]
from langchain_openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo-instruct") | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import os
os.environ["OUTLINE_API_KEY"] = "xxx"
os.environ["OUTLINE_INSTANCE_URL"] = "https://app.getoutline.com"
from langchain.retrievers import OutlineRetriever
retriever = | OutlineRetriever() | langchain.retrievers.OutlineRetriever |
from langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChain
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azureml-mlflow')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet textstat')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
get_ipython().run_l... | LLMChain(llm=llm, prompt=prompt_template, callbacks=[mlflow_callback]) | langchain.chains.LLMChain |
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 |
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... | ChatOpenAI(temperature=0, model="gpt-4-1106-preview") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pyvespa')
from vespa.package import ApplicationPackage, Field, RankProfile
app_package = ApplicationPackage(name="testapp")
app_package.schema.add_fields(
Field(
name="text", type="string", indexing=["index", "summary"], index="enable-bm25"... | VespaStore.from_documents(docs, embedding_function, app=vespa_app, **vespa_config) | langchain_community.vectorstores.VespaStore.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-text-splitters tiktoken')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=... | SentenceTransformersTokenTextSplitter(chunk_overlap=0) | langchain_text_splitters.SentenceTransformersTokenTextSplitter |
get_ipython().system("python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken")
import getpass
import os
from langchain_community.vectorstores import DeepLake
from langchain_openai import OpenAIEmbeddings
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
activeloop_token =... | ConversationalRetrievalChain.from_llm(model, retriever=retriever) | langchain.chains.ConversationalRetrievalChain.from_llm |
from transformers import load_tool
hf_tools = [
load_tool(tool_name)
for tool_name in [
"document-question-answering",
"image-captioning",
"image-question-answering",
"image-segmentation",
"speech-to-text",
"summarization",
"text-classification",
... | HuggingGPT(llm, hf_tools) | langchain_experimental.autonomous_agents.HuggingGPT |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "docarray"')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import DocArrayInMemorySearch
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
... | DocArrayInMemorySearch.from_documents(docs, embeddings) | langchain_community.vectorstores.DocArrayInMemorySearch.from_documents |
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... | TextLoader("../../modules/state_of_the_union.txt") | 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",
... | ChatMessageHistory() | langchain_community.chat_message_histories.ChatMessageHistory |
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)
utility
import json
response... | ChatPromptTemplate.from_template(
"""Answer the question based only on the context provided.
Context: {context}
Question: {question}"""
) | langchain_core.prompts.ChatPromptTemplate.from_template |
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... | hub.pull("hwchase17/react") | langchain.hub.pull |
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... | StrOutputParser() | langchain.schema.output_parser.StrOutputParser |
from langchain_community.llms import Ollama
llm = Ollama(model="llama2")
llm("The first man on the moon was ...")
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = Ollama(
model="llama2", callback_manager=CallbackManage... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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 =... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | merge_chat_runs(raw_messages) | langchain_community.chat_loaders.utils.merge_chat_runs |
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_... | OpenAI(temperature=0, openai_api_key=openai_api_key) | 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... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
"""For basic init and call"""
from langchain_community.chat_models import ChatSparkLLM
from langchain_core.messages import HumanMessage
chat = ChatSparkLLM(
spark_app_id="<app_id>", spark_api_key="<api_key>", spark_api_secret="<api_secret>"
)
message = HumanMessage(content="Hello")
chat([message])
chat = | ChatSparkLLM(streaming=True) | langchain_community.chat_models.ChatSparkLLM |
get_ipython().run_cell_magic('capture', '', '%pip install --upgrade --quiet python-arango # The ArangoDB Python Driver\n%pip install --upgrade --quiet adb-cloud-connector # The ArangoDB Cloud Instance provisioner\n%pip install --upgrade --quiet langchain-openai\n%pip install --upgrade --quiet langchain\n')
import... | ArangoGraph(db) | langchain_community.graphs.ArangoGraph |
from typing import Callable, List
from langchain.memory import ConversationBufferMemory
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
class DialogueAgent:
def __... | ChatOpenAI(model_name="gpt-4", temperature=0.2) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sentence-transformers > /dev/null')
from langchain.chains import LLMChain, StuffDocumentsChain
from langchain.prompts import PromptTemplate
from langchain_community.document_transformers import (
LongContextReorder,
)
from langchain_community.embeddi... | OpenAI() | langchain_openai.OpenAI |
from langchain.evaluation import RegexMatchStringEvaluator
evaluator = | RegexMatchStringEvaluator() | langchain.evaluation.RegexMatchStringEvaluator |
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... | ChatPromptTemplate.from_messages(
[("system", system) | langchain_core.prompts.ChatPromptTemplate.from_messages |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rspace_client')
from langchain_community.document_loaders.rspace import RSpaceLoader
rspace_ids = ["NB1932027", "FL1921314", "SD1932029", "GL1932384"]
for rs_id in rspace_ids:
loader = | RSpaceLoader(global_id=rs_id) | langchain_community.document_loaders.rspace.RSpaceLoader |
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 = | PromptTemplate.from_template(hard_question) | langchain.prompts.PromptTemplate.from_template |
get_ipython().system(' pip install --quiet pypdf chromadb tiktoken openai langchain-together')
from langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader("~/Desktop/mixtral.pdf")
data = loader.load()
from langchain_text_splitters import RecursiveCharacterTextSplitter
text_splitter = Recursiv... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_openai import OpenAI
search = | GoogleSearchAPIWrapper() | langchain_community.utilities.GoogleSearchAPIWrapper |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.evaluation import load_evaluator
evaluator = | load_evaluator("trajectory") | langchain.evaluation.load_evaluator |
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... | ConfigurableField(id="llm") | langchain_core.runnables.ConfigurableField |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet openlm')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
import os
from getpass import getpass
if "OPENAI_API_KEY" not in os.environ:
print("Enter your OpenAI API key:")
os.environ["OPENAI_API_KEY"] = getpass()... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | RunnablePassthrough() | langchain.schema.runnable.RunnablePassthrough |
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.messages import (
AIMessageChunk,
FunctionMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessageChunk,
)
AIMessageChu... | AIMessageChunk(content=token) | langchain_core.messages.AIMessageChunk |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.