prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
from langchain.callbacks.base import BaseCallbackHandler
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
class MyCustomHandler(BaseCallbackHandler):
def on_llm_new_token(self, token: str, **kwargs) -> None:
print(f"My custom handler, token: {token}")
chat = ChatO... | HumanMessage(content="Tell me a joke") | langchain_core.messages.HumanMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet psycopg2-binary')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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 |
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0, model="gpt-4-turbo-preview")
from langchain import hub
from langchain_core.prompts import PromptTemplate
select_prompt = hub.pull("hwchase17/self-discovery-select")
select_prompt.pretty_print()
adapt_prompt = hub.pull("hwchase17/self-di... | hub.pull("hwchase17/self-discovery-reasoning") | langchain.hub.pull |
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... | Field(description="answer to resolve the joke") | langchain_core.pydantic_v1.Field |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pypdf pymongo langchain-openai tiktoken')
import getpass
MONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")
from pymongo im... | RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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... | TavilySearchResults() | langchain_community.tools.tavily_search.TavilySearchResults |
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')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("Ope... | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | YouSearchAPIWrapper(num_web_results=1) | langchain_community.utilities.you.YouSearchAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sagemaker')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
import os
os.environ["OPENAI_API_KEY"] = "<ADD-KEY-HERE>"
os.environ[... | OpenAI(callbacks=[sagemaker_callback], **HPARAMS) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.evaluation import load_evaluator
from langchain_openai import ChatOpenAI
evaluator = load_evaluator("labeled_score_string", llm=ChatOpenAI(model="gpt-4"))
eval_result = evaluator.evaluate_strings(
predic... | load_evaluator("score_string", criteria=hh_criteria) | langchain.evaluation.load_evaluator |
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 |
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... | LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks) | langchain.chains.LLMChain |
from ray import serve
from starlette.requests import Request
@serve.deployment
class LLMServe:
def __init__(self) -> None:
pass
async def __call__(self, request: Request) -> str:
return "Hello World"
deployment = LLMServe.bind()
serve.api.run(deployment)
serve.api.shutdown()
from lan... | OpenAI(openai_api_key=OPENAI_API_KEY) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "cassio>=0.1.4"')
import os
from getpass import getpass
from datasets import (
load_dataset,
)
from langchain_community.document_loaders import PyPDFLoader
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOu... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().system(' pip install langchain langchain-experimental openai elasticsearch')
from elasticsearch import Elasticsearch
from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain
from langchain_openai import ChatOpenAI
ELASTIC_SEARCH_SERVER = "https://elastic:pass@localhost:9200"
db... | ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, verbose=True) | langchain.chains.elasticsearch_database.ElasticsearchDatabaseChain.from_llm |
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(persist_directory="./chroma_db", embedding_function=embedding_function) | langchain_community.vectorstores.Chroma |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet multion langchain -q')
from langchain_community.agent_toolkits import MultionToolkit
toolkit = | MultionToolkit() | langchain_community.agent_toolkits.MultionToolkit |
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_line_magic('pip', 'install --upgrade --quiet opaqueprompts langchain')
import os
os.environ["OPAQUEPROMPTS_API_KEY"] = "<OPAQUEPROMPTS_API_KEY>"
os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"
from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.chains import LLMChain... | PromptTemplate.from_template(prompt_template) | langchain.prompts.PromptTemplate.from_template |
import os
import chromadb
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain.retrievers.merger_retriever import MergerRetriever
from langchain_community.document_transformers import (
EmbeddingsClusteringFi... | DocumentCompressorPipeline(transformers=[filter_ordered_by_retriever]) | langchain.retrievers.document_compressors.DocumentCompressorPipeline |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet llama-cpp-python')
get_ipython().system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python')
get_ipython().system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cach... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet gpt4all > /dev/null')
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import GPT4All
template = """Questi... | GPT4All(model=local_path, callbacks=callbacks, verbose=True) | langchain_community.llms.GPT4All |
get_ipython().system('pip install pettingzoo pygame rlcard')
import collections
import inspect
import tenacity
from langchain.output_parsers import RegexParser
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class GymnasiumAgent:
@classmethod
... | HumanMessage(content=obs_message) | 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.PickBest.from_llm(llm=llm, prompt=PROMPT) | langchain_experimental.rl_chain.PickBest.from_llm |
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... | StdOutCallbackHandler() | langchain.callbacks.StdOutCallbackHandler |
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
... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import os
import uuid
uid = uuid.uuid4().hex[:6]
project_name = f"Run Fine-tuning Walkthrough {uid}"
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "YOUR API KEY"
os.environ["LANGCHAIN_PROJECT"... | ChatOpenAI() | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import os
import uuid
uid = uuid.uuid4().hex[:6]
project_name = f"Run Fine-tuning Walkthrough {uid}"
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "YOUR API KEY"
os.environ["LANGCHAIN_PROJECT"... | LangSmithRunChatLoader(runs=llm_runs) | langchain_community.chat_loaders.langsmith.LangSmithRunChatLoader |
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... | ChatOpenAI() | langchain_openai.ChatOpenAI |
from langchain.callbacks import HumanApprovalCallbackHandler
from langchain.tools import ShellTool
tool = ShellTool()
print(tool.run("echo Hello World!"))
tool = ShellTool(callbacks=[HumanApprovalCallbackHandler()])
print(tool.run("ls /usr"))
print(tool.run("ls /private"))
from langchain.agents import Age... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | DatabricksEmbeddings(endpoint="databricks-bge-large-en") | langchain_community.embeddings.DatabricksEmbeddings |
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(temperature=1.0) | langchain_openai.ChatOpenAI |
import getpass
import os
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY") or getpass.getpass(
"OpenAI API Key:"
)
from langchain.sql_database import SQLDatabase
from langchain_openai import ChatOpenAI
CONNECTION_STRING = "postgresql+psycopg2://postgres:test@localhost:5432/vectordb" # Replace wit... | SQLDatabase.from_uri(CONNECTION_STRING) | langchain.sql_database.SQLDatabase.from_uri |
from langchain_community.document_loaders import IMSDbLoader
loader = | IMSDbLoader("https://imsdb.com/scripts/BlacKkKlansman.html") | langchain_community.document_loaders.IMSDbLoader |
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(model="gpt-4-vision-preview", max_tokens=1024) | langchain_openai.ChatOpenAI |
from typing import Optional
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_experimental.autonomous_agents import BabyAGI
from langchain_openai import OpenAI, OpenAIEmbeddings
get_ipython().run_line_magic('pip', 'install faiss-cpu > /dev/null')
get_ipython().run_lin... | PromptTemplate.from_template(
"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}"
) | langchain.prompts.PromptTemplate.from_template |
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... | create_openai_functions_agent(llm, tools, prompt) | langchain.agents.create_openai_functions_agent |
import os
import chromadb
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain.retrievers.merger_retriever import MergerRetriever
from langchain_community.document_transformers import (
EmbeddingsClusteringFi... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from typing import List
from langchain.prompts.chat import (
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class CAMELAgent:
def __init__(
se... | ChatOpenAI(temperature=1.0) | langchain_openai.ChatOpenAI |
from langchain_openai import OpenAIEmbeddings
from langchain_pinecone import PineconeVectorStore
all_documents = {
"doc1": "Climate change and economic impact.",
"doc2": "Public health concerns due to climate change.",
"doc3": "Climate change: A social perspective.",
"doc4": "Technological solutions t... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', "install --upgrade --quiet langchain-openai 'deeplake[enterprise]' tiktoken")
from langchain_community.vectorstores import DeepLake
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
import getpass
import os
os.environ["OP... | DeepLake(dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True) | langchain_community.vectorstores.DeepLake |
import boto3
dynamodb = boto3.resource("dynamodb")
table = dynamodb.create_table(
TableName="SessionTable",
KeySchema=[{"AttributeName": "SessionId", "KeyType": "HASH"}],
AttributeDefinitions=[{"AttributeName": "SessionId", "AttributeType": "S"}],
BillingMode="PAY_PER_REQUEST",
)
table.meta.client.ge... | ChatOpenAI() | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from operator import itemgetter
from langchain.memory import ConversationBufferMemory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnableLambda, RunnablePa... | ConversationBufferMemory(return_messages=True) | 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... | ConversationBufferMemory(memory_key="chat_history") | langchain.memory.ConversationBufferMemory |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet manifest-ml')
from langchain_community.llms.manifest import ManifestWrapper
from manifest import Manifest
manifest = Manifest(
client_name="huggingface", client_connection="http://127.0.0.1:5000"
)
print(manifest.client_pool.get_current_client().ge... | ModelLaboratory(llms) | langchain.model_laboratory.ModelLaboratory |
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... | OpenAIEmbeddings() | langchain_community.embeddings.OpenAIEmbeddings |
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... | ChatPromptTemplate.from_template(prompt_text) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark qdrant-client')
from langchain_community.vectorstores import Qdrant
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 |
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... | Chroma.from_texts(to_vectorize, embeddings, metadatas=examples) | langchain_community.vectorstores.Chroma.from_texts |
from langchain.agents import AgentType, initialize_agent
from langchain.tools import BearlyInterpreterTool
from langchain_openai import ChatOpenAI
bearly_tool = BearlyInterpreterTool(api_key="...")
bearly_tool.add_file(
source_path="sample_data/Bristol.pdf", target_path="Bristol.pdf", description=""
)
bearly_... | ChatOpenAI(model="gpt-4", temperature=0) | langchain_openai.ChatOpenAI |
from langchain.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
]
example_prompt = | ChatPromptTemplate.from_messages(
[
("human", "{input}") | langchain.prompts.ChatPromptTemplate.from_messages |
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... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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 ... | ChatOpenAI() | langchain_openai.ChatOpenAI |
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... | ModerationToxicityConfig(threshold=0.5) | langchain_experimental.comprehend_moderation.ModerationToxicityConfig |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-storage')
from langchain_community.document_loaders import GCSFileLoader
loader = | GCSFileLoader(project_name="aist", bucket="testing-hwc", blob="fake.docx") | langchain_community.document_loaders.GCSFileLoader |
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... | ChatPromptTemplate.from_messages(
[
("system", "Given an input question, convert it to a SQL query. No pre-amble.") | langchain_core.prompts.ChatPromptTemplate.from_messages |
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI
template = """Human: {question}
AI Assistant: """
prompt = | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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... | DuckDuckGoSearchRun() | langchain.tools.DuckDuckGoSearchRun |
get_ipython().run_line_magic('pip', 'install -qU langchain-community langchain-openai')
from langchain_community.tools import MoveFileTool
from langchain_core.messages import HumanMessage
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain_openai import ChatOpenAI
model = Cha... | HumanMessage(content="move file foo to bar") | langchain_core.messages.HumanMessage |
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... | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | 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... | AIMessage(content="Error processing document") | langchain_core.messages.AIMessage |
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("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | SystemMessage(content=prompt) | langchain.schema.SystemMessage |
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... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clickhouse-connect')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["OPENAI_API_BASE"] = getpass.getpass("OpenAI Base:")
os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale Host:")
os.environ["MY... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
conversation = ConversationChain(
llm=llm, verbose=True, memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there!")
conversati... | ConversationBufferMemory(human_prefix="Friend") | langchain.memory.ConversationBufferMemory |
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 ... | DuckDuckGoSearchAPIWrapper() | langchain_community.utilities.DuckDuckGoSearchAPIWrapper |
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:39f3bbd0") | langchain.hub.pull |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rockset')
import os
import rockset
ROCKSET_API_KEY = os.environ.get(
"ROCKSET_API_KEY"
) # Verify ROCKSET_API_KEY environment variable
ROCKSET_API_SERVER = rockset.Regions.usw2a1 # Verify Rockset region
rockset_client = rockset.RocksetClient(ROC... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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 |
from langchain_community.document_loaders import GitbookLoader
loader = GitbookLoader("https://docs.gitbook.com")
page_data = loader.load()
page_data
loader = | GitbookLoader("https://docs.gitbook.com", load_all_paths=True) | langchain_community.document_loaders.GitbookLoader |
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... | reduce_openapi_spec(raw_openai_api_spec) | langchain_community.agent_toolkits.openapi.spec.reduce_openapi_spec |
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... | InMemoryStore() | langchain.storage.InMemoryStore |
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.... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.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... | Cohere(temperature=0) | langchain_community.llms.Cohere |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-api-python-client > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-auth-oauthlib > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-auth-httplib2 > /dev/null')
get_ipython().run_l... | build_resource_service(credentials=credentials) | langchain_community.tools.gmail.utils.build_resource_service |
import os
os.environ["OPENAI_API_KEY"] = "...input your openai api key here..."
from langchain_experimental.agents.agent_toolkits import create_spark_dataframe_agent
from langchain_openai import OpenAI
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
csv_file_path = "titanic.csv"
df ... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
import getpass
import os
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY") or getpass.getpass(
"OpenAI API Key:"
)
from langchain.sql_database import SQLDatabase
from langchain_openai import ChatOpenAI
CONNECTION_STRING = "postgresql+psycopg2://postgres:test@localhost:5432/vectordb" # Replace wit... | ChatPromptTemplate.from_messages(
[("system", template), ("human", "{question}") | langchain_core.prompts.ChatPromptTemplate.from_messages |
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="doggy", metadata={"source": "doggy.txt"}) | langchain_core.documents.Document |
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
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 aim')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
i... | LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
prompt = ChatP... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain_community.llms.fake import FakeListLLM
from langchain.agents import AgentType, initialize_agent, load_tools
tools = | load_tools(["python_repl"]) | langchain.agents.load_tools |
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... | SemanticScholarQueryRun() | langchain_community.tools.semanticscholar.tool.SemanticScholarQueryRun |
import kuzu
db = kuzu.Database("test_db")
conn = kuzu.Connection(db)
conn.execute("CREATE NODE TABLE Movie (name STRING, PRIMARY KEY(name))")
conn.execute(
"CREATE NODE TABLE Person (name STRING, birthDate STRING, PRIMARY KEY(name))"
)
conn.execute("CREATE REL TABLE ActedIn (FROM Person TO Movie)")
conn.exec... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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"... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.evaluation import RegexMatchStringEvaluator
evaluator = RegexMatchStringEvaluator()
from langchain.evaluation import load_evaluator
evaluator = | load_evaluator("regex_match") | langchain.evaluation.load_evaluator |
get_ipython().run_line_magic('pip', 'install -qU chromadb langchain langchain-community langchain-openai')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharact... | Chroma.from_documents(texts, embeddings, collection_name="state-of-union") | langchain_community.vectorstores.Chroma.from_documents |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-datastore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from goo... | Document(page_content="Hello, World!") | langchain_core.documents.Document |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clickhouse-connect')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["OPENAI_API_BASE"] = getpass.getpass("OpenAI Base:")
os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale Host:")
os.environ["MY... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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 |
import os
embaas_api_key = "YOUR_API_KEY"
os.environ["EMBAAS_API_KEY"] = "YOUR_API_KEY"
from langchain_community.embeddings import EmbaasEmbeddings
embeddings = | EmbaasEmbeddings() | langchain_community.embeddings.EmbaasEmbeddings |
import os
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import SpreedlyLoader
spreedly_loader = SpreedlyLoader(
os.environ["SPREEDLY_ACCESS_TOKEN"], "gateways_options"
)
index = | VectorstoreIndexCreator() | langchain.indexes.VectorstoreIndexCreator |
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.AutoSelectionScorer(llm=llm, prompt=REWARD_PROMPT) | langchain_experimental.rl_chain.AutoSelectionScorer |
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