prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
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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=... | InMemoryDocstore({}) | langchain.docstore.InMemoryDocstore |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymysql')
get_ipython().system('pip install sqlalchemy')
get_ipython().system('pip install langchain')
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
DirectoryLoader,
UnstructuredMarkdownLoader,
)
... | ApacheDorisSettings() | langchain_community.vectorstores.apache_doris.ApacheDorisSettings |
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... | OpenAI(temperature=0, openai_api_key="") | langchain_openai.OpenAI |
def pretty_print_docs(docs):
print(
f"\n{'-' * 100}\n".join(
[f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]
)
)
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAI... | EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76) | langchain.retrievers.document_compressors.EmbeddingsFilter |
from langchain.agents import AgentExecutor, BaseMultiActionAgent, Tool
from langchain_community.utilities import SerpAPIWrapper
def random_word(query: str) -> str:
print("\nNow I'm doing this!")
return "foo"
search = SerpAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
... | AgentAction(tool="RandomWord", tool_input=kwargs["input"], log="") | langchain_core.agents.AgentAction |
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... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
from langchain_community.utils.openai_functions import (
convert_pydantic_to_openai_function,
)
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, validator
from langchain_openai import ChatOpenAI
class Joke(BaseModel):
"""Joke to tell user."""
... | Field(description="answer to resolve the joke") | langchain_core.pydantic_v1.Field |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet modal')
get_ipython().system('modal token new')
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Modal
template = """Question: {question}
Answer: Let's think step by step."""
... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | format_log_to_str(x["intermediate_steps"]) | langchain.agents.format_scratchpad.format_log_to_str |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.tools import AIPluginTool
from langchain_openai import ChatOpenAI
tool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")
llm = ChatOpenAI(temperature=0)
tools = | load_tools(["requests_all"]) | langchain.agents.load_tools |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-community')
get_ipython().run_line_magic('pip', 'install --pre --upgrade bigdl-llm[all]')
from langchain.chains import LLMChain
from langchain_community.llms.bigdl import BigdlLLM
from langchain_core.prompts import PromptTemplate
template = "U... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-community')
get_ipython().run_line_magic('pip', 'install --pre --upgrade bigdl-llm[all]')
from langchain.chains import LLMChain
from langchain_community.llms.bigdl import BigdlLLM
from langchain_core.prompts import PromptTemplate
template = "U... | PromptTemplate(template=template, input_variables=["question"]) | langchain_core.prompts.PromptTemplate |
from getpass import getpass
WRITER_API_KEY = getpass()
import os
os.environ["WRITER_API_KEY"] = WRITER_API_KEY
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Writer
template = """Question: {question}
Answer: Let's think step by step."""
... | Writer() | langchain_community.llms.Writer |
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... | OpenAIFunctionsAgentOutputParser() | langchain.agents.output_parsers.OpenAIFunctionsAgentOutputParser |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import IuguLoader
iugu_loader = | IuguLoader("charges") | langchain_community.document_loaders.IuguLoader |
get_ipython().system('pip install -U openai langchain langchain-experimental')
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
chat = ChatOpenAI(model="gpt-4-vision-preview", max_tokens=256)
chat.invoke(
[
HumanMessage(
content=[
... | ChatPromptTemplate.from_messages(
[("system", "You are a helpful assistant"), ("user", "{input}") | langchain_core.prompts.ChatPromptTemplate.from_messages |
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... | PyPDFLoader("./cj/cj.pdf") | langchain_community.document_loaders.PyPDFLoader |
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... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "optimum[onnxruntime]" langchain transformers langchain-experimental langchain-openai')
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
model_path = "laiyer/deberta-v3-base-prompt-inject... | OpenAI(temperature=0) | langchain_openai.OpenAI |
from getpass import getpass
DEEPINFRA_API_TOKEN = getpass()
import os
os.environ["DEEPINFRA_API_TOKEN"] = DEEPINFRA_API_TOKEN
from langchain_community.chat_models import ChatDeepInfra
from langchain_core.messages import HumanMessage
chat = ChatDeepInfra(model="meta-llama/Llama-2-7b-chat-hf")
messages = [
... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
get_ipython().system('pip install langchain lark openai elasticsearch pandas')
import pandas as pd
details = (
pd.read_csv("~/Downloads/archive/Hotel_details.csv")
.drop_duplicates(subset="hotelid")
.set_index("hotelid")
)
attributes = pd.read_csv(
"~/Downloads/archive/Hotel_Room_attributes.csv", in... | ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet 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... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
import os
from langchain.chains import ConversationalRetrievalChain
from langchain_community.vectorstores import Vectara
from langchain_openai import OpenAI
from langchain_community.document_loaders import TextLoader
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
vectara = Vectara.from_... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai faiss-cpu tiktoken')
from langchain.prompts import ChatPromptTemplate
from langchain.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, Runna... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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_... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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, prompt=PROMPT, verbose=True) | langchain_experimental.llm_bash.base.LLMBashChain.from_llm |
get_ipython().system('pip3 install petals')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Petals
from getpass import getpass
HUGGINGFACE_API_KEY = getpass()
os.environ["HUGGINGFACE_API_KEY"] = HUGGINGFACE_API_KEY
llm = Pet... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
URL = "" # Your Fiddler instance URL, Make sure to include the full URL (including https://). For example: https://demo.fiddler.ai
ORG_NAME = ""
AUTH_TOKEN = "" # Your Fiddler instance auth token
PROJECT_NAME = ""
MODEL_NAME = "" # Model name in Fiddler
from langchain_community.callbacks.fiddler_callback import ... | OpenAI(temperature=0, streaming=True, callbacks=[fiddler_handler]) | langchain_openai.OpenAI |
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... | 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... | ChatOpenAI(model_name="gpt-4", temperature=0) | langchain_openai.ChatOpenAI |
from langchain.indexes import SQLRecordManager, index
from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
collection_name = "test_index"
embedding = OpenAIEmbeddings()
vectorstore = ElasticsearchStore(
es_url="http:/... | Document(page_content="woof woof", metadata={"source": "doggy.txt"}) | langchain_core.documents.Document |
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 scann')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import ScaNN
from langchain_text_splitters import CharacterTextSplitter
loader = | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet airbyte-source-typeform')
from langchain_community.document_loaders.airbyte import AirbyteTypeformLoader
config = {
}
loader = AirbyteTypeformLoader(
config=config, stream_name="forms"
) # check the documentation linked above for a list of all s... | Document(page_content=record.data["title"], metadata=record.data) | langchain.docstore.document.Document |
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... | PromptTemplate(
template="answer the users question as best as possible.\n{format_instructions}\n{question}",
input_variables=["question"],
partial_variables={"format_instructions": format_instructions},
) | langchain.prompts.PromptTemplate |
from getpass import getpass
STOCHASTICAI_API_KEY = getpass()
import os
os.environ["STOCHASTICAI_API_KEY"] = STOCHASTICAI_API_KEY
YOUR_API_URL = getpass()
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import StochasticAI
template = """Question:... | StochasticAI(api_url=YOUR_API_URL) | langchain_community.llms.StochasticAI |
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 |
from langchain_community.document_loaders import UnstructuredOrgModeLoader
loader = | UnstructuredOrgModeLoader(file_path="example_data/README.org", mode="elements") | langchain_community.document_loaders.UnstructuredOrgModeLoader |
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = | TextLoader("../../state_of_the_union.txt", encoding="utf-8") | langchain_community.document_loaders.TextLoader |
from langchain.agents import create_spark_sql_agent
from langchain_community.agent_toolkits import SparkSQLToolkit
from langchain_community.utilities.spark_sql import SparkSQL
from langchain_openai import ChatOpenAI
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
schema = "langchain_e... | SparkSQLToolkit(db=spark_sql, llm=llm) | langchain_community.agent_toolkits.SparkSQLToolkit |
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... | DynamoDBChatMessageHistory(table_name="SessionTable", session_id="0") | langchain_community.chat_message_histories.DynamoDBChatMessageHistory |
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 ... | ChatOpenAI(temperature=1.0) | langchain_openai.ChatOpenAI |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-firestore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from goo... | FirestoreSaver("Collection") | langchain_google_firestore.FirestoreSaver |
from getpass import getpass
from langchain_community.document_loaders.larksuite import LarkSuiteDocLoader
DOMAIN = input("larksuite domain")
ACCESS_TOKEN = getpass("larksuite tenant_access_token or user_access_token")
DOCUMENT_ID = input("larksuite document id")
from pprint import pprint
larksuite_loader = | LarkSuiteDocLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID) | langchain_community.document_loaders.larksuite.LarkSuiteDocLoader |
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
import requests
def get_wikipedia_page(title: str):
"""
Retrieve the full text content of a Wikipedia page.
:param title: str - Title of the Wikipedia page.
:return: str - Full text conten... | create_stuff_documents_chain(llm, prompt) | langchain.chains.combine_documents.create_stuff_documents_chain |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-community')
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.schema.messages import AIMessage
from langchain_community.llms.chatglm3 import ChatGLM3
template = """{question}"""
prompt = PromptTempl... | AIMessage(content="ๆฌข่ฟ้ฎๆไปปไฝ้ฎ้ขใ") | langchain.schema.messages.AIMessage |
from langchain.evaluation import ExactMatchStringEvaluator
evaluator = | ExactMatchStringEvaluator() | langchain.evaluation.ExactMatchStringEvaluator |
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... | Document(page_content=s, metadata={id_key: doc_ids[i]}) | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet dingodb')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet git+https://git@github.com/dingodb/pydingo.git')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_lo... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | OpenAI() | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet xata langchain-openai tiktoken langchain')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
api_key = getpass.getpass("Xata API key: ")
db_url = input("Xata database URL (copy it from your DB settings):")
... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
import re
from IPython.display import Image, display
from steamship import Block, Steamship
from langchain.agents import AgentType, initialize_agent
from langchain.tools import SteamshipImageGenerationTool
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
tools = [ | SteamshipImageGenerationTool(model_name="dall-e") | langchain.tools.SteamshipImageGenerationTool |
get_ipython().system('pip install databricks-sql-connector')
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_databricks(catalog="samples", schema="nyctaxi")
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0, model_name="gpt-4")
from langchain_community.utiliti... | SQLDatabaseToolkit(db=db, llm=llm) | langchain_community.agent_toolkits.SQLDatabaseToolkit |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai context-python')
import os
from langchain.callbacks import ContextCallbackHandler
token = os.environ["CONTEXT_API_TOKEN"]
context_callback = ContextCallbackHandler(token)
import os
from langchain.callbacks import Conte... | ContextCallbackHandler(token) | langchain.callbacks.ContextCallbackHandler |
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 chain
from langchain_openai import ChatOpenAI
prompt1 = ChatPromptTemplate... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai tiktoken')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet supabase')
import getpass
import os
os.environ["SUPABASE_URL"] = getpass.... | OpenAI(temperature=0) | langchain_openai.OpenAI |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
import dspy
colbertv2 = dspy.ColBERTv2(url="http://20.102.90.50:2017/wiki17_abstracts")
from langchain.cache import SQLiteCache
from langchain.globals import set_llm_cache
from langchain_openai import OpenAI
set_llm_cache( | SQLiteCache(database_path="cache.db") | langchain.cache.SQLiteCache |
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
llm = OpenAI(temperature=0)
from pathlib import Path
relevant_parts = []
for p in Path(".").absolute().parts:
... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet openllm')
from langchain_community.llms import OpenLLM
server_url = "http://localhost:3000" # Replace with remote host if you are running on a remote server
llm = OpenLLM(server_url=server_url)
from langchain_community.llms import OpenLLM
llm = Op... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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 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 |
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.instructions) | langchain.schema.SystemMessage |
get_ipython().system('pip3 install petals')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Petals
from getpass import getpass
HUGGINGFACE_API_KEY = getpass()
os.environ["HUGGINGFACE_API_KEY"] = HUGGINGFACE_API_KEY
llm = Pet... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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/... | RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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... | HumanMessage(content="Tell me a joke") | langchain_core.messages.HumanMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet hdbcli')
import os
from hdbcli import dbapi
connection = dbapi.connect(
address=os.environ.get("HANA_DB_ADDRESS"),
port=os.environ.get("HANA_DB_PORT"),
user=os.environ.get("HANA_DB_USER"),
password=os.environ.get("HANA_DB_PASSWORD"),... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | MessagesPlaceholder(variable_name="chat_history") | langchain_core.prompts.chat.MessagesPlaceholder |
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"]... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain_community.document_loaders import OBSDirectoryLoader
endpoint = "your-endpoint"
config = {"ak": "your-access-key", "sk": "your-secret-key"}
loader = | OBSDirectoryLoader("your-bucket-name", endpoint=endpoint, config=config) | langchain_community.document_loaders.OBSDirectoryLoader |
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... | ChatOpenAI(temperature=0.9) | 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) | langchain_openai.ChatOpenAI |
from getpass import getpass
MOSAICML_API_TOKEN = getpass()
import os
os.environ["MOSAICML_API_TOKEN"] = MOSAICML_API_TOKEN
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import MosaicML
template = """Question: {question}"""
prompt = | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
import os
import comet_llm
os.environ["LANGCHAIN_COMET_TRACING"] = "true"
comet_llm.init()
os.environ["COMET_PROJECT_NAME"] = "comet-example-langchain-tracing"
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["... | CometTracer() | langchain.callbacks.tracers.comet.CometTracer |
from langchain_experimental.pal_chain import PALChain
from langchain_openai import OpenAI
llm = OpenAI(temperature=0, max_tokens=512)
pal_chain = PALChain.from_math_prompt(llm, verbose=True)
question = "Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets,... | PALChain.from_colored_object_prompt(llm, verbose=True) | langchain_experimental.pal_chain.PALChain.from_colored_object_prompt |
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms.pai_eas_endpoint import PaiEasEndpoint
template = """Question: {question}
Answer: Let's think step by step."""
prompt = | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
from langchain.memory import ConversationTokenBufferMemory
from langchain_openai import OpenAI
llm = | OpenAI() | langchain_openai.OpenAI |
import os
import comet_llm
os.environ["LANGCHAIN_COMET_TRACING"] = "true"
comet_llm.init()
os.environ["COMET_PROJECT_NAME"] = "comet-example-langchain-tracing"
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenAI
llm = | OpenAI(temperature=0) | langchain.llms.OpenAI |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import pprint
from langchain_community.utilities import SearxSearchWrapper
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")
search.run("What is the capital of France")
search = SearxSearchWrapper(
searx_host="http://127.0.0.1:8888", k=5
) # k is for max number of items
search.run("large ... | SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=1) | langchain_community.utilities.SearxSearchWrapper |
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... | TextGen(model_url=model_url, streaming=True) | langchain_community.llms.TextGen |
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.... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
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... | SerpAPIWrapper() | langchain_community.utilities.SerpAPIWrapper |
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("Anna") | langchain_experimental.rl_chain.BasedOn |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet hdbcli')
import os
from hdbcli import dbapi
connection = dbapi.connect(
address=os.environ.get("HANA_DB_ADDRESS"),
port=os.environ.get("HANA_DB_PORT"),
user=os.environ.get("HANA_DB_USER"),
password=os.environ.get("HANA_DB_PASSWORD"),... | CharacterTextSplitter(chunk_size=500, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet doctran')
import json
from langchain_community.document_transformers import DoctranPropertyExtractor
from langchain_core.documents import Document
from dotenv import load_dotenv
load_dotenv()
sample_text = """[Generated with ChatGPT]
Confidential... | Document(page_content=sample_text) | langchain_core.documents.Document |
get_ipython().system('pip/pip3 install pyepsilla')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import Epsilla
from langchain_openai import OpenAIEmbeddings
from langchain_community.document_loaders import TextLoader
from langc... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-speech')
from langchain_community.document_loaders import GoogleSpeechToTextLoader
project_id = "<PROJECT_ID>"
file_path = "gs://cloud-samples-data/speech/audio.flac"
loader = | GoogleSpeechToTextLoader(project_id=project_id, file_path=file_path) | langchain_community.document_loaders.GoogleSpeechToTextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet weaviate-client')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")
os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")
WEAVIATE_API_KEY = os... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-nvidia-ai-endpoints')
import getpass
import os
if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
nvapi_key = getpass.getpass("Enter your NVIDIA API key: ")
assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is ... | ChatNVIDIA(model="nemotron_steerlm_8b") | langchain_nvidia_ai_endpoints.ChatNVIDIA |
from langchain_community.utils.openai_functions import (
convert_pydantic_to_openai_function,
)
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, validator
from langchain_openai import ChatOpenAI
class Joke(BaseModel):
"""Joke to tell user."""
... | convert_pydantic_to_openai_function(Joke) | langchain_community.utils.openai_functions.convert_pydantic_to_openai_function |
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... | load_tools(["wikipedia", "llm-math", "terminal"], llm=llm) | langchain.agents.load_tools |
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().system("python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken")
import getpass
import os
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import DeepLake
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters impor... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sqlite-vss')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import SQLiteVSS
from langchain_text_sp... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from getpass import getpass
KAY_API_KEY = getpass()
OPENAI_API_KEY = getpass()
import os
os.environ["KAY_API_KEY"] = KAY_API_KEY
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain.chains import ConversationalRetrievalChain
from langchain.retrievers import KayAiRetriever
from langchain_openai import Cha... | ChatOpenAI(model_name="gpt-3.5-turbo") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-spanner')
from google.colab import auth
auth.authenticate_user()
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
get_ipython().system('gcloud services ena... | HNLoader("https://news.ycombinator.com/item?id=34817881") | langchain_community.document_loaders.HNLoader |
get_ipython().system(' nomic login')
get_ipython().system(' nomic login token')
get_ipython().system(' pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain')
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.lang... | ChatOllama(model=ollama_llm) | langchain_community.chat_models.ChatOllama |
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