prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
from langchain_community.document_loaders import WebBaseLoader
loader = | WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") | langchain_community.document_loaders.WebBaseLoader |
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 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_... | ChatPromptTemplate(messages=prompt_msgs) | langchain_core.prompts.ChatPromptTemplate |
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... | Meilisearch.from_documents(documents=documents, embedding=embeddings) | langchain_community.vectorstores.Meilisearch.from_documents |
get_ipython().system(' pip install langchain replicate')
from langchain_community.chat_models import ChatOllama
llama2_chat = ChatOllama(model="llama2:13b-chat")
llama2_code = ChatOllama(model="codellama:7b-instruct")
from langchain_community.llms import Replicate
replicate_id = "meta/llama-2-13b-chat:f4e2de70d66... | MessagesPlaceholder(variable_name="history") | langchain_core.prompts.MessagesPlaceholder |
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import Tair
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain import hub
from langchain.agents import AgentExecutor, tool
from langchain.agents.output_parsers import XMLAgentOutputParser
from langchain_community.chat_models import ChatAnthropic
model = ChatAnthropic(model="claude-2")
@tool
def search(query: str) -> str:
"""Search things about current events... | AgentExecutor(agent=agent, tools=tool_list, verbose=True) | langchain.agents.AgentExecutor |
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 |
get_ipython().run_line_magic('pip', "install --upgrade --quiet faiss-gpu # For CUDA 7.5+ Supported GPU's.")
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu # For CPU Installation')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_... | FAISS.afrom_documents(docs, embeddings) | langchain_community.vectorstores.FAISS.afrom_documents |
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("... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("... | FAISS.load_local("faiss_index", embeddings) | langchain_community.vectorstores.FAISS.load_local |
from typing import Any, Dict, List
from langchain.chains import ConversationChain
from langchain.schema import BaseMemory
from langchain_openai import OpenAI
from pydantic import BaseModel
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
import spacy
nlp = spacy.load("en_core_web_lg")
cl... | OpenAI(temperature=0) | 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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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": {... | create_metadata_tagger(schema, llm, prompt=prompt) | langchain_community.document_transformers.openai_functions.create_metadata_tagger |
import functools
import random
from collections import OrderedDict
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 ... | HumanMessage(content=choice_prompt) | langchain.schema.HumanMessage |
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 |
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_... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import FakeEmbeddings
from langc... | FakeEmbeddings(size=768) | langchain_community.embeddings.FakeEmbeddings |
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-4") | langchain_openai.ChatOpenAI |
get_ipython().system('pip install --quiet langchain_experimental langchain_openai')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_experimental.text_splitter import SemanticChunker
from langchain_openai.embeddings import OpenAIEmbeddings
text_splitter = Semantic... | OpenAIEmbeddings() | langchain_openai.embeddings.OpenAIEmbeddings |
from langchain.prompts.pipeline import PipelinePromptTemplate
from langchain.prompts.prompt import PromptTemplate
full_template = """{introduction}
{example}
{start}"""
full_prompt = | PromptTemplate.from_template(full_template) | langchain.prompts.prompt.PromptTemplate.from_template |
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... | PromptTemplate.from_template("Write a short poem about {topic}") | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_community.chat_models import ChatAnthropic
from langchain_openai import ChatOpenAI
from unittest.mock import patch
import httpx
from openai import RateLimitError
request = httpx.Request("GET", "/")
respons... | ChatOpenAI(model="gpt-4") | langchain_openai.ChatOpenAI |
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... | RunnableLambda(call_tool) | langchain_core.runnables.RunnableLambda |
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.vectorstores import Vectara
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
vectara = Vectara.fro... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
import re
from typing import Union
from langchain.agents import (
AgentExecutor,
AgentOutputParser,
LLMSingleActionAgent,
Tool,
)
from langchain.chains import LLMChain
from langchain.prompts import StringPromptTemplate
from langchain_community.utilities import SerpAPIWrapper
from langchain_core.agents ... | LLMChain(llm=llm, prompt=prompt) | langchain.chains.LLMChain |
import re
from typing import Union
from langchain.agents import (
AgentExecutor,
AgentOutputParser,
LLMSingleActionAgent,
Tool,
)
from langchain.chains import LLMChain
from langchain.prompts import StringPromptTemplate
from langchain_community.utilities import SerpAPIWrapper
from langchain_core.agents ... | Document(page_content=t.description, metadata={"index": i}) | langchain_core.documents.Document |
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... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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=... | WriteFileTool() | langchain_community.tools.file_management.write.WriteFileTool |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet banana-dev')
import os
os.environ["BANANA_API_KEY"] = "YOUR_API_KEY"
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Banana
template = """Question: {question}
Answer: Let's th... | Banana(model_key="YOUR_MODEL_KEY", model_url_slug="YOUR_MODEL_URL_SLUG") | langchain_community.llms.Banana |
from langchain.prompts import ChatMessagePromptTemplate
prompt = "May the {subject} be with you"
chat_message_prompt = ChatMessagePromptTemplate.from_template(
role="Jedi", template=prompt
)
chat_message_prompt.format(subject="force")
from langchain.prompts import (
ChatPromptTemplate,
HumanMessageProm... | AIMessage(
content="""\
1. Choose a programming language: Decide on a programming language that you want to learn.
2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.
3. Practice, practice, practice: The best way to learn program... | langchain_core.messages.AIMessage |
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... | FirestoreLoader("Collection/doc/SubCollection") | langchain_google_firestore.FirestoreLoader |
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_... | load_qa_chain(llm, chain_type="map_reduce") | langchain.chains.question_answering.load_qa_chain |
import os
import re
OPENAI_API_KEY = "sk-xx"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from typing import Any, Callable, Dict, List, Union
from langchain.agents import AgentExecutor, LLMSingleActionAgent, Tool
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import... | LLMChain(llm=llm, prompt=prompt, verbose=verbose) | langchain.chains.LLMChain |
import os
os.environ["SEARCHAPI_API_KEY"] = ""
from langchain_community.utilities import SearchApiAPIWrapper
search = SearchApiAPIWrapper()
search.run("Obama's first name?")
os.environ["OPENAI_API_KEY"] = ""
from langchain.agents import AgentType, Tool, initialize_agent
from langchain_community.utilities im... | SearchApiAPIWrapper(engine="google_jobs") | langchain_community.utilities.SearchApiAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.model_laboratory import ModelLaboratory
from langchain.prompts import PromptTemplate
from langchain_community.llms import Cohere, HuggingFaceHub
from langchain_openai import OpenAI
import getpass
import os
o... | HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 1}) | langchain_community.llms.HuggingFaceHub |
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... | SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") | langchain_community.embeddings.sentence_transformer.SentenceTransformerEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langkit langchain-openai langchain')
from langchain.callbacks import WhyLabsCallbackHandler
from langchain_openai import OpenAI
whylabs = | WhyLabsCallbackHandler.from_params() | langchain.callbacks.WhyLabsCallbackHandler.from_params |
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... | RedisNum("age") | langchain_community.vectorstores.redis.RedisNum |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_core.tools import tool
@tool
def multiply(first_int: int, second_int: int) -> int:
"""Multiply two integers together."""
... | RunnableLambda(call_tool) | langchain_core.runnables.RunnableLambda |
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/... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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 ... | ChatPromptTemplate.from_messages(
[("system", "You are a helpful AI assistant named Fred."), ("user", "{input}") | langchain_core.prompts.ChatPromptTemplate.from_messages |
get_ipython().system(' docker run -d -p 8123:8123 -p9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 clickhouse/clickhouse-server:23.4.2.11')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clickhouse-connect')
import getpass
import os
if not os.environ["OPENAI_API_KEY"]... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
template = """Answer the users question ... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scikit-learn')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet bson')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas pyarrow')
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass("Enter... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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() | langchain.tools.SceneXplainTool |
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 langchain langchain-openai')
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
runnable = RunnableParallel(
passed= | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lm-format-enforcer > /dev/null')
import logging
from langchain_experimental.pydantic_v1 import BaseModel
logging.basicConfig(level=logging.ERROR)
class PlayerInformation(BaseModel):
first_name: str
last_name: str
num_seasons_in_nba: int
... | LMFormatEnforcer(regex=answer_regex, pipeline=hf_model) | langchain_experimental.llms.LMFormatEnforcer |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet elevenlabs')
import os
os.environ["ELEVEN_API_KEY"] = ""
from langchain.tools import ElevenLabsText2SpeechTool
text_to_speak = "Hello world! I am the real slim shady"
tts = ElevenLabsText2SpeechTool()
tts.name
speech_file = tts.run(text_to_speak... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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 ... | ChatOpenAI(temperature=0, model="gpt-4-vision-preview", max_tokens=1024) | langchain_openai.ChatOpenAI |
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... | StreamlitChatMessageHistory(key="special_app_key") | langchain_community.chat_message_histories.StreamlitChatMessageHistory |
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.nasa.toolkit import NasaToolkit
from langchain_community.utilities.nasa import NasaAPIWrapper
from langchain_openai import OpenAI
llm = OpenAI(temperature=0, openai_api_key="")
nasa = NasaAPIWrapper()
toolkit = | NasaToolkit.from_nasa_api_wrapper(nasa) | langchain_community.agent_toolkits.nasa.toolkit.NasaToolkit.from_nasa_api_wrapper |
import os
os.environ["SEARCHAPI_API_KEY"] = ""
from langchain_community.utilities import SearchApiAPIWrapper
search = SearchApiAPIWrapper()
search.run("Obama's first name?")
os.environ["OPENAI_API_KEY"] = ""
from langchain.agents import AgentType, Tool, initialize_agent
from langchain_community.utilities im... | SearchApiAPIWrapper() | langchain_community.utilities.SearchApiAPIWrapper |
"""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") | langchain_core.messages.HumanMessage |
from langchain.agents import AgentExecutor, BaseMultiActionAgent, Tool
from langchain_community.utilities import SerpAPIWrapper
def random_word(query: str) -> str:
print("\nNow I'm doing this!")
return "foo"
search = SerpAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
... | AgentFinish(return_values={"output": "bar"}, log="") | langchain_core.agents.AgentFinish |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet semanticscholar')
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
instructions = """You are an expert researcher."""
base_prompt = hub.pull("langchain-ai/openai... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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... | RedisNum("age") | langchain_community.vectorstores.redis.RedisNum |
get_ipython().run_line_magic('reload_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from datetime import datetime
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.clickup.toolkit import ClickupToolkit
from langchain_community.utilities.clickup import... | ClickupAPIWrapper(access_token=access_token) | langchain_community.utilities.clickup.ClickupAPIWrapper |
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.... | RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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 = | RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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_... | load_qa_with_sources_chain(llm, chain_type="map_reduce") | langchain.chains.qa_with_sources.load_qa_with_sources_chain |
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool, StructuredTool, tool
@tool
def search(query: str) -> str:
"""Look up things online."""
return "LangChain"
print(search.name)
print(search.description)
print(search.args)
@tool
def multiply(a: int, b: int) -> int:
... | ToolException("The search tool1 is not available.") | langchain_core.tools.ToolException |
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
response_schemas = [
ResponseSchema(name="answer", description="answer to the user's question"),
ResponseSchema(
name="source",
descr... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
import os
from langchain_community.tools.google_finance import GoogleFinanceQueryRun
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
os.environ["SERPAPI_API_KEY"] = ""
tool = GoogleFinanceQueryRu... | load_tools(["google-scholar", "google-finance"], llm=llm) | langchain.agents.load_tools |
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 |
import os
os.environ["EXA_API_KEY"] = "..."
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-exa')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePa... | SystemMessage(
content="You are a web researcher who answers user questions by looking up information on the internet and retrieving contents of helpful documents. Cite your sources."
) | langchain_core.messages.SystemMessage |
from langchain.agents import load_tools
requests_tools = load_tools(["requests_all"])
requests_tools
requests_tools[0].requests_wrapper
from langchain_community.utilities import TextRequestsWrapper
requests = TextRequestsWrapper()
requests.get("https://www.google.com")
from langchain_community.utilities.r... | JsonRequestsWrapper() | langchain_community.utilities.requests.JsonRequestsWrapper |
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
template = """Answer the users question ... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet qdrant-client')
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 Qdrant
from langchain_openai import Op... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain.retrievers import BreebsRetriever
breeb_key = "Parivoyage"
retriever = | BreebsRetriever(breeb_key) | langchain.retrievers.BreebsRetriever |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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... | BaseModerationConfig(filters=[pii_config]) | langchain_experimental.comprehend_moderation.BaseModerationConfig |
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... | ChatOpenAI() | langchain_openai.ChatOpenAI |
import os
os.environ["BING_SUBSCRIPTION_KEY"] = "<key>"
os.environ["BING_SEARCH_URL"] = "https://api.bing.microsoft.com/v7.0/search"
from langchain_community.utilities import BingSearchAPIWrapper
search = BingSearchAPIWrapper()
search.run("python")
search = | BingSearchAPIWrapper(k=1) | langchain_community.utilities.BingSearchAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_core.tools import tool
@tool
def complex_tool(int_arg: int, float_arg: float, dict_arg: dict) -> int:
"""Do something complex... | JsonOutputKeyToolsParser(key_name="complex_tool", return_single=True) | langchain.output_parsers.JsonOutputKeyToolsParser |
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool, StructuredTool, tool
@tool
def search(query: str) -> str:
"""Look up things online."""
return "LangChain"
print(search.name)
print(search.description)
print(search.args)
@tool
def multiply(a: int, b: int) -> int:
... | Field(description="should be a search query") | langchain.pydantic_v1.Field |
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="foo") | langchain.docstore.document.Document |
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... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet bilibili-api-python')
from langchain_community.document_loaders import BiliBiliLoader
loader = | BiliBiliLoader(["https://www.bilibili.com/video/BV1xt411o7Xu/"]) | langchain_community.document_loaders.BiliBiliLoader |
get_ipython().system('pip install --upgrade langchain langchain-google-vertexai')
project: str = "PUT_YOUR_PROJECT_ID_HERE" # @param {type:"string"}
endpoint_id: str = "PUT_YOUR_ENDPOINT_ID_HERE" # @param {type:"string"}
location: str = "PUT_YOUR_ENDPOINT_LOCAtION_HERE" # @param {type:"string"}
from langchain_... | HumanMessage(content="What can you help me with?") | langchain_core.messages.HumanMessage |
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... | RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet wandb')
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('python... | LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks) | langchain.chains.LLMChain |
from langchain.chains import HypotheticalDocumentEmbedder, LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI, OpenAIEmbeddings
base_embeddings = OpenAIEmbeddings()
llm = OpenAI()
embeddings = HypotheticalDocumentEmbedder.from_llm(llm, base_embeddings, "web_search")
result ... | LLMChain(llm=llm, prompt=prompt) | langchain.chains.LLMChain |
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 ... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-openai langchain-anthropic langchain-community wikipedia')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()
from langchain_community.retrievers import WikipediaRetrieve... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
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() | langchain_community.embeddings.HuggingFaceEmbeddings |
from langchain_community.tools.edenai import (
EdenAiExplicitImageTool,
EdenAiObjectDetectionTool,
EdenAiParsingIDTool,
EdenAiParsingInvoiceTool,
EdenAiSpeechToTextTool,
EdenAiTextModerationTool,
EdenAiTextToSpeechTool,
)
from langchain.agents import AgentType, initialize_agent
from langch... | EdenAiTextModerationTool(providers=["openai"], language="en") | langchain_community.tools.edenai.EdenAiTextModerationTool |
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 rank_bm25')
from langchain.retrievers import BM25Retriever
retriever = | BM25Retriever.from_texts(["foo", "bar", "world", "hello", "foo bar"]) | langchain.retrievers.BM25Retriever.from_texts |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet predictionguard langchain')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import PredictionGuard
os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>"
os.environ["PREDICTI... | PredictionGuard(model="OpenAI-text-davinci-003") | langchain_community.llms.PredictionGuard |
from typing import Callable, List
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name =... | SystemMessage(content="You can make a task more specific.") | langchain.schema.SystemMessage |
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... | JsonKeyOutputFunctionsParser(key_name="questions") | langchain.output_parsers.openai_functions.JsonKeyOutputFunctionsParser |
from langchain.chains import HypotheticalDocumentEmbedder, LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI, OpenAIEmbeddings
base_embeddings = | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().system(' pip install langchain replicate')
from langchain_community.chat_models import ChatOllama
llama2_chat = ChatOllama(model="llama2:13b-chat")
llama2_code = ChatOllama(model="codellama:7b-instruct")
from langchain_community.llms import Replicate
replicate_id = "meta/llama-2-13b-chat:f4e2de70d66... | RunnablePassthrough.assign(query=sql_response_memory) | langchain_core.runnables.RunnablePassthrough.assign |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-elasticsearch langchain-openai tiktoken langchain')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbed... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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 spacy')
get_ipython().system('python3 -m spacy download en_core_web_sm')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet nomic')
import time
from langchain_community.document_loaders import TextLoader
from langchain_community.vector... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.