prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
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()
retriever.get_releva... | ChatOpenAI(model_name="gpt-3.5-turbo") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiledb-vector-search')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import TileDB
from langchain_text_splitters import CharacterTextSpl... | HuggingFaceEmbeddings() | langchain_community.embeddings.HuggingFaceEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymysql')
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
DirectoryLoader,
UnstructuredMarkdownLoader,
)
from langchain_community.vectorstores import StarRocks
from langchain_community.vectorstores.sta... | StarRocks(embeddings, settings) | langchain_community.vectorstores.StarRocks |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "unstructured[all-docs]"')
from langchain_community.document_loaders import UnstructuredFileLoader
loader = | UnstructuredFileLoader("./example_data/state_of_the_union.txt") | langchain_community.document_loaders.UnstructuredFileLoader |
from langchain_experimental.llm_bash.base import LLMBashChain
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
text = "Please write a bash script that prints 'Hello World' to the console."
bash_chain = LLMBashChain.from_llm(llm, verbose=True)
bash_chain.run(text)
from langchain.prompts.prompt impo... | LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True) | langchain_experimental.llm_bash.base.LLMBashChain.from_llm |
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... | PromptTemplate.from_template("Tell me about {topic}") | langchain.prompts.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... | ChatAnthropic(temperature=0) | langchain_community.chat_models.ChatAnthropic |
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... | ChatPromptTemplate.from_template(prompt_text) | langchain_core.prompts.ChatPromptTemplate.from_template |
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 |
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 ... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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 ... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
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()
tools = [
Tool(
... | ConversationBufferMemory(memory_key="chat_history") | langchain.memory.ConversationBufferMemory |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-bigquery')
from langchain_community.document_loaders import BigQueryLoader
BASE_QUERY = """
SELECT
id,
dna_sequence,
organism
FROM (
SELECT
ARRAY (
SELECT
AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp.... | BigQueryLoader(ALIASED_QUERY, metadata_columns=["source"]) | langchain_community.document_loaders.BigQueryLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-spanner')
from google.colab import auth
auth.authenticate_user()
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
get_ipython().system('gcloud services ena... | TableColumn(name="title", type="STRING(MAX)", is_null=False) | langchain_google_spanner.TableColumn |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azure-ai-formrecognizer > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azure-cognitiveservices-speech > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azure-ai-textanalytics > /dev/null')
get_ipy... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import GooseAI
from getpass import getpass
GOOSEAI_API_KEY = getpass()
os.environ["GOOSEAI_API_KEY"] = G... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
from langchain.chains import GraphCypherQAChain
from langchain_community.graphs import Neo4jGraph
from langchain_openai import ChatOpenAI
graph = Neo4jGraph(
url="bolt://localhost:7687", username="neo4j", password="pleaseletmein"
)
graph.query(
"""
MERGE (m:Movie {name:"Top Gun"})
WITH m
UNWIND ["Tom Cruis... | ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k") | langchain_openai.ChatOpenAI |
from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterText... | RecursiveCharacterTextSplitter(chunk_size=400) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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
... | SystemMessage(content=self.docs) | langchain.schema.SystemMessage |
from langchain.prompts.pipeline import PipelinePromptTemplate
from langchain.prompts.prompt import PromptTemplate
full_template = """{introduction}
{example}
{start}"""
full_prompt = PromptTemplate.from_template(full_template)
introduction_template = """You are impersonating {person}."""
introduction_prompt = Pro... | PromptTemplate.from_template(start_template) | langchain.prompts.prompt.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet apify-client')
from langchain_community.document_loaders import ApifyDatasetLoader
from langchain_community.document_loaders.base import Document
loader = ApifyDatasetLoader(
dataset_id="your-dataset-id",
dataset_mapping_function=lambda datas... | VectorstoreIndexCreator() | langchain.indexes.VectorstoreIndexCreator |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2')
import os
from langchain_community.llms import HuggingFaceTextGenInference
ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
HF_TOKEN = os.getenv("HUGGINGFACEHUB_A... | ChatHuggingFace(llm=llm) | langchain_community.chat_models.huggingface.ChatHuggingFace |
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=0.2) | langchain_openai.ChatOpenAI |
import configparser
config = configparser.ConfigParser()
config.read("./secrets.ini")
openai_api_key = config["OPENAI"]["OPENAI_API_KEY"]
import os
os.environ.update({"OPENAI_API_KEY": openai_api_key})
wikidata_user_agent_header = (
None
if not config.has_section("WIKIDATA")
else config["WIKIDATA"][... | ChatOpenAI(model_name="gpt-4", temperature=0) | langchain_openai.ChatOpenAI |
REGION = "us-central1" # @param {type:"string"}
INSTANCE = "test-instance" # @param {type:"string"}
DATABASE = "test" # @param {type:"string"}
TABLE_NAME = "test-default" # @param {type:"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-cloud-sql-mysql')
PROJECT_ID ... | MySQLLoader(engine=engine, table_name=TABLE_NAME) | langchain_google_cloud_sql_mysql.MySQLLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet marqo')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Marqo
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = Text... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain.prompts import PromptTemplate
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_openai import Ope... | ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True) | langchain.agents.ZeroShotAgent |
from langchain_community.chat_models import ChatDatabricks
from langchain_core.messages import HumanMessage
from mlflow.deployments import get_deploy_client
client = get_deploy_client("databricks")
secret = "secrets/<scope>/openai-api-key" # replace `<scope>` with your scope
name = "my-chat" # rename this if my-cha... | Databricks(cluster_driver_port="7777", model_kwargs={"temperature": 0.1}) | langchain_community.llms.Databricks |
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... | RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
from typing import Callable, List
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name =... | ChatOpenAI(temperature=0.2) | langchain_openai.ChatOpenAI |
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... | ChatOpenAI() | langchain_openai.ChatOpenAI |
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... | ClickupToolkit.from_clickup_api_wrapper(clickup_api_wrapper) | langchain_community.agent_toolkits.clickup.toolkit.ClickupToolkit.from_clickup_api_wrapper |
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")
documents = loader.load()... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | 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]==0.10.19" pillow pydantic lxml pillow matplotlib tiktoken open_clip_torch torch')
path = "/Users/rlm/Desktop/photos/"
fr... | HumanMessage(content=messages) | langchain_core.messages.HumanMessage |
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-memorystore-redis')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from google.colab import auth
auth.authenticate_user()
import redis
from langchain_goo... | RedisVectorStore.drop_index(client=redis_client, index_name="my_vector_index") | langchain_google_memorystore_redis.RedisVectorStore.drop_index |
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(ai_prefix="AI Assistant") | langchain.memory.ConversationBufferMemory |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 nltk')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain_experimental')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pydantic')
import os
import boto3
comprehend_client = boto3.client("comp... | ModerationPiiConfig(labels=["SSN"], redact=True, mask_character="X") | langchain_experimental.comprehend_moderation.ModerationPiiConfig |
get_ipython().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 ... | RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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 |
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... | ChatOpenAI(max_retries=0) | langchain_openai.ChatOpenAI |
from typing import List
from langchain.output_parsers import YamlOutputParser
from langchain.prompts import PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)
class Joke(BaseModel):
setup: str = Field(description="que... | YamlOutputParser(pydantic_object=Joke) | langchain.output_parsers.YamlOutputParser |
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 |
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=400) | langchain_text_splitters.RecursiveCharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet amadeus > /dev/null')
import os
os.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"
os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
from langchain_community.agent_toolkits.amadeus.toolkit impo... | AmadeusToolkit() | langchain_community.agent_toolkits.amadeus.toolkit.AmadeusToolkit |
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-3.5-turbo") | langchain_openai.ChatOpenAI |
from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterText... | TextLoader("../../state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("embedding_distance")
evaluator.evaluate_strings(prediction="I shall go", reference="I shan't go")
evaluator.evaluate_strings(prediction="I shall go", reference="I will go")
from langchain.evaluation import EmbeddingDistance
list(Embedd... | load_evaluator("embedding_distance", embeddings=embedding_model) | langchain.evaluation.load_evaluator |
"""For basic init and call"""
import os
from langchain_community.embeddings import VolcanoEmbeddings
os.environ["VOLC_ACCESSKEY"] = ""
os.environ["VOLC_SECRETKEY"] = ""
embed = | VolcanoEmbeddings(volcano_ak="", volcano_sk="") | langchain_community.embeddings.VolcanoEmbeddings |
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="second number") | 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... | TextLoader("../../../extras/modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | ChatOpenAI(model="gpt-4", temperature=0) | langchain_openai.ChatOpenAI |
from langchain.output_parsers import DatetimeOutputParser
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
output_parser = | DatetimeOutputParser() | langchain.output_parsers.DatetimeOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet amadeus > /dev/null')
import os
os.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"
os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
from langchain_community.agent_toolkits.amadeus.toolkit impo... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub langchain-openai faiss-cpu')
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
from langchain_community.vectors... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("criteria", criteria="conciseness")
from langchain.evaluation import EvaluatorType
evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="conciseness")
eval_result = evaluator.evaluate_strings(
prediction="What's 2+2? That's an el... | load_evaluator("labeled_criteria", criteria="correctness") | langchain.evaluation.load_evaluator |
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... | RunnableLambda(img_prompt_func) | langchain_core.runnables.RunnableLambda |
from langchain_community.document_loaders.blob_loaders.youtube_audio import (
YoutubeAudioLoader,
)
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import (
OpenAIWhisperParser,
OpenAIWhisperParserLocal,
)
get_ipython().run_line_mag... | YoutubeAudioLoader(urls, save_dir) | langchain_community.document_loaders.blob_loaders.youtube_audio.YoutubeAudioLoader |
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... | HumanMessage(content=f"{assistant_sys_msg.content}") | langchain.schema.HumanMessage |
import json
from pprint import pprint
from langchain.globals import set_debug
from langchain_community.llms import NIBittensorLLM
set_debug(True)
llm_sys = NIBittensorLLM(
system_prompt="Your task is to determine response based on user prompt.Explain me like I am technical lead of a project"
)
sys_resp = llm_sys... | ConversationBufferMemory(memory_key="chat_history") | langchain.memory.ConversationBufferMemory |
get_ipython().system('poetry run pip install dgml-utils==0.3.0 --upgrade --quiet')
import os
from langchain_community.document_loaders import DocugamiLoader
DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")
docset_id = "26xpy3aes7xp"
document_ids = ["d7jqdzcj50sj", "cgd1eacfkchw"]
loader = DocugamiLoader(... | OpenAI() | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub gpt4all chromadb')
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = WebBaseLoader("https://lilianweng.github.io/posts/... | RunnablePick("context") | langchain_core.runnables.RunnablePick |
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... | RunnableLambda(img_prompt_func) | langchain_core.runnables.RunnableLambda |
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... | JsonOutputKeyToolsParser(key_name="annotated_answer", return_single=True) | langchain.output_parsers.openai_tools.JsonOutputKeyToolsParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain fleet-context langchain-openai pandas faiss-cpu # faiss-gpu for CUDA supported GPU')
from operator import itemgetter
from typing import Any, Optional, Type
import pandas as pd
from langchain.retrievers import MultiVectorRetriever
from langchai... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
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.from_files(["state_of_the_union.txt"]) | langchain_community.vectorstores.Vectara.from_files |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scikit-learn')
from langchain_community.retrievers import TFIDFRetriever
retriever = TFIDFRetriever.from_texts(["foo", "bar", "world", "hello", "foo bar"])
from langchain_core.documents import Document
retriever = TFIDFRetriever.from_documents(
... | Document(page_content="foo bar") | langchain_core.documents.Document |
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_name="gpt-fake") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2')
import os
from langchain_community.llms import HuggingFaceTextGenInference
ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
HF_TOKEN = os.getenv("HUGGINGFACEHUB_A... | render_text_description(tools) | langchain.tools.render.render_text_description |
import json
from pprint import pprint
from langchain.globals import set_debug
from langchain_community.llms import NIBittensorLLM
set_debug(True)
llm_sys = | NIBittensorLLM(
system_prompt="Your task is to determine response based on user prompt.Explain me like I am technical lead of a project"
) | langchain_community.llms.NIBittensorLLM |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark chromadb')
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
docs = [
Document(
page_content="A bunch of scientists bring back dinosaurs and m... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.chains import ConversationChain
from langchain.memory import (
CombinedMemory,
ConversationBufferMemory,
ConversationSummaryMemory,
)
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
conv_memory = ConversationBufferMemory(
memory_key="chat_history_lines", ... | OpenAI() | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub langchain-openai faiss-cpu')
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
from langchain_community.vectors... | AgentExecutor(agent=agent, tools=tools) | langchain.agents.AgentExecutor |
import getpass
import os
os.environ["ALPHAVANTAGE_API_KEY"] = getpass.getpass()
from langchain_community.utilities.alpha_vantage import AlphaVantageAPIWrapper
alpha_vantage = | AlphaVantageAPIWrapper() | langchain_community.utilities.alpha_vantage.AlphaVantageAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub langchain-openai faiss-cpu')
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
from langchain_community.vectors... | FAISS.from_documents(texts, embeddings) | langchain_community.vectorstores.FAISS.from_documents |
import asyncio
import os
import nest_asyncio
import pandas as pd
from langchain.docstore.document import Document
from langchain_community.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain_experimental.autonomous_agents import AutoGPT
from langchain_openai import ChatOpenAI
nest_asyncio.a... | Document(page_content=result, metadata={"source": url}) | langchain.docstore.document.Document |
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... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clarifai')
from getpass import getpass
CLARIFAI_PAT = getpass()
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Clarifai
from langchain_text_splitters import CharacterTextSplitter
USER_ID = ... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet neo4j')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Ke... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | MessagesPlaceholder(variable_name="history") | langchain_core.prompts.MessagesPlaceholder |
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... | OpenAI(temperature=0.9, callbacks=callbacks) | langchain_openai.OpenAI |
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/")
data = load... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-core langchain langchain-openai')
from langchain.utils.math import cosine_similarity
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambda... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
def initialize_chain(instructions, memory=None):
if memory is None:
memory = ConversationBufferWindowMemory()
memory.ai... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet 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-functions-template") | langchain.hub.pull |
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompt_values import PromptValue
from langchain_openai import ChatOpenAI
short_context_model = ChatOpenAI(model="gpt-3.5-turbo")
long_context_model = ChatOpenAI(model="gpt-3.5-turbo-16k")
def g... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-cloud-sql-pg langchain-google-vertexai')
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_ipyth... | Column("len", "INTEGER") | langchain_google_cloud_sql_pg.Column |
import os
import pprint
os.environ["SERPER_API_KEY"] = ""
from langchain_community.utilities import GoogleSerperAPIWrapper
search = GoogleSerperAPIWrapper()
search.run("Obama's first name?")
os.environ["OPENAI_API_KEY"] = ""
from langchain.agents import AgentType, Tool, initialize_agent
from langchain_commu... | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain_community.document_loaders import ObsidianLoader
loader = | ObsidianLoader("<path-to-obsidian>") | langchain_community.document_loaders.ObsidianLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3')
from langchain.retrievers import AmazonKendraRetriever
retriever = | AmazonKendraRetriever(index_id="c0806df7-e76b-4bce-9b5c-d5582f6b1a03") | langchain.retrievers.AmazonKendraRetriever |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azureml-fsspec, azure-ai-generative')
from azure.ai.resources.client import AIClient
from azure.identity import DefaultAzureCredential
from langchain_community.document_loaders import AzureAIDataLoader
client = AIClient(
credential=DefaultAzureCred... | AzureAIDataLoader(url=data_asset.path, glob="*.pdf") | langchain_community.document_loaders.AzureAIDataLoader |
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... | ChatPromptTemplate.from_template(prompt_text) | langchain.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet praw')
client_id = ""
client_secret = ""
user_agent = ""
from langchain_community.tools.reddit_search.tool import RedditSearchRun
from langchain_community.utilities.reddit_search import RedditSearchAPIWrapper
search = RedditSearchRun(
api_wrapper... | ChatOpenAI(temperature=0, openai_api_key=openai_api_key) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scikit-learn')
from langchain_community.retrievers import TFIDFRetriever
retriever = | TFIDFRetriever.from_texts(["foo", "bar", "world", "hello", "foo bar"]) | langchain_community.retrievers.TFIDFRetriever.from_texts |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet infinopy')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet matplotlib')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import datetime as dt
import json
import time
import matplotlib.dates as md
import matplot... | WebBaseLoader(url) | langchain_community.document_loaders.WebBaseLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet trubrics')
import os
os.environ["TRUBRICS_EMAIL"] = "***@***"
os.environ["TRUBRICS_PASSWORD"] = "***"
os.environ["OPENAI_API_KEY"] = "sk-***"
from langchain.callbacks import TrubricsCallbackHandler
from langchain_openai import OpenAI
llm = O... | TrubricsCallbackHandler() | langchain.callbacks.TrubricsCallbackHandler |
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... | ChatOpenAI() | langchain_openai.ChatOpenAI |
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) | langchain_openai.ChatOpenAI |
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... | ChatPromptTemplate.from_messages(
[
(
"system",
"You're a nice assistant who always includes a compliment in your response",
) | langchain_core.prompts.ChatPromptTemplate.from_messages |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.