prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
import os
os.environ["BING_SUBSCRIPTION_KEY"] = "<key>"
os.environ["BING_SEARCH_URL"] = "https://api.bing.microsoft.com/v7.0/search"
from langchain_community.utilities import BingSearchAPIWrapper
search = | BingSearchAPIWrapper() | langchain_community.utilities.BingSearchAPIWrapper |
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... | SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") | langchain_community.embeddings.sentence_transformer.SentenceTransformerEmbeddings |
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... | ReActJsonSingleInputOutputParser() | langchain.agents.output_parsers.ReActJsonSingleInputOutputParser |
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"... | SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") | langchain_community.embeddings.sentence_transformer.SentenceTransformerEmbeddings |
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... | InMemoryStore() | langchain.storage.InMemoryStore |
from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader
from bs4 import BeautifulSoup as Soup
url = "https://docs.python.org/3.9/"
loader = RecursiveUrlLoader(
url=url, max_depth=2, extractor=lambda x: Soup(x, "html.parser").text
)
docs = loader.load()
docs[0].page_content[:50... | RecursiveUrlLoader(url=url) | langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_core.tools import tool
@tool
def multiply(first_int: int, second_int: int) -> int:
"""Multiply two integers together."""
... | ChatOpenAI(model="gpt-3.5-turbo-1106") | langchain_openai.ChatOpenAI |
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOpenAI
api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max... | create_react_agent(llm, tools, prompt) | langchain.agents.create_react_agent |
from langchain.chains import LLMMathChain
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.tools import Tool
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain_openai import ChatOpenAI, OpenAI... | load_agent_executor(model, tools, verbose=True) | langchain_experimental.plan_and_execute.load_agent_executor |
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... | dumps(doc) | langchain.load.dumps |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pyairtable')
from langchain_community.document_loaders import AirtableLoader
api_key = "xxx"
base_id = "xxx"
table_id = "xxx"
loader = | AirtableLoader(api_key, table_id, base_id) | langchain_community.document_loaders.AirtableLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet airbyte-source-salesforce')
from langchain_community.document_loaders.airbyte import AirbyteSalesforceLoader
config = {
}
loader = AirbyteSalesforceLoader(
config=config, stream_name="asset"
) # check the documentation linked above for a list of... | Document(page_content=record.data["title"], metadata=record.data) | langchain.docstore.document.Document |
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 ... | ChatPromptTemplate.from_template(prompt_text) | langchain_core.prompts.ChatPromptTemplate.from_template |
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... | SQLiteVSS.create_connection(db_file="/tmp/vss.db") | langchain_community.vectorstores.SQLiteVSS.create_connection |
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... | Field(description="answer to resolve the joke") | langchain_core.pydantic_v1.Field |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-pinecone langchain-openai langchain')
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/stat... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet chromadb')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import Chroma
from langchain_core.doc... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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,
query=f"select * from `{TABLE_NAME}` where JSON_EXTRACT(langchain_metadata, '$.fruit_id') | langchain_google_cloud_sql_mysql.MySQLLoader |
get_ipython().run_line_magic('', 'pip install --upgrade --quiet flashrank')
get_ipython().run_line_magic('', 'pip install --upgrade --quiet faiss')
get_ipython().run_line_magic('', 'pip install --upgrade --quiet faiss_cpu')
def pretty_print_docs(docs):
print(
f"\n{'-' * 100}\n".join(
[f... | RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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... | ChatAnthropicMessages(model_name="claude-instant-1.2") | langchain_anthropic.ChatAnthropicMessages |
from langchain_community.document_loaders import AirbyteJSONLoader
get_ipython().system('ls /tmp/airbyte_local/json_data/')
loader = | AirbyteJSONLoader("/tmp/airbyte_local/json_data/_airbyte_raw_pokemon.jsonl") | langchain_community.document_loaders.AirbyteJSONLoader |
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",
... | MessagesPlaceholder(variable_name="history") | langchain_core.prompts.MessagesPlaceholder |
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, backend="gptj", callbacks=callbacks, verbose=True) | langchain_community.llms.GPT4All |
get_ipython().run_line_magic('pip', 'install --upgrade huggingface-hub')
from langchain_community.embeddings import HuggingFaceHubEmbeddings
embeddings = | HuggingFaceHubEmbeddings(model="http://localhost:8080") | langchain_community.embeddings.HuggingFaceHubEmbeddings |
from langchain_community.utilities import SerpAPIWrapper
search = | SerpAPIWrapper() | langchain_community.utilities.SerpAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark opensearch-py')
import getpass
import os
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
os.environ["OPENAI_API_KEY"] = getpass.getpass... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.chains import create_citation_fuzzy_match_chain
from langchain_openai import ChatOpenAI
question = "What did the author do during college?"
context = """
My name is Jason Liu, and I grew up in Toronto Canada but I was born in China.
I went to an arts highschool but in university I studied Computational... | ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-core databricks-vectorsearch 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_openai import Op... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml')
path = "/Users/rlm/Desktop/Papers/LLaVA/"
from typing import Any
from pydantic import BaseModel
from unstructured.partition.pdf import partition_pdf
raw_pdf_elements = partition_pdf(
filename=path + "LLaVA.pdf",
extract_i... | Document(page_content=s, metadata={id_key: img_ids[i]}) | langchain_core.documents.Document |
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"]... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 > /dev/null')
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
from typing import List, Optional
from langchain.chains.openai_tools import create_extraction_chain_pydantic
from langchain_core.pydantic_v1 import BaseModel
from langchain_openai import ChatOpenAI
model = | ChatOpenAI(model="gpt-3.5-turbo-1106") | langchain_openai.ChatOpenAI |
from langchain_community.chat_models.human import HumanInputChatModel
get_ipython().run_line_magic('pip', 'install wikipedia')
from langchain.agents import AgentType, initialize_agent, load_tools
tools = | load_tools(["wikipedia"]) | langchain.agents.load_tools |
from langchain_community.document_loaders import UnstructuredURLLoader
urls = [
"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023",
"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023",
]
loader = Unstructur... | PlaywrightURLLoader(urls=urls, remove_selectors=["header", "footer"]) | langchain_community.document_loaders.PlaywrightURLLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet vearch')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet vearch_cluster')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
from langchain_community... | HuggingFaceEmbeddings(model_name=embedding_path) | langchain_community.embeddings.huggingface.HuggingFaceEmbeddings |
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... | LLMChain(llm=llm, prompt=prompt_template) | langchain.chains.LLMChain |
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... | RunnableLambda(memory.load_memory_variables) | langchain_core.runnables.RunnableLambda |
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... | Dingo(embeddings, "text", client=dingo_client, index_name=index_name) | langchain_community.vectorstores.Dingo |
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="name of an actor") | langchain_core.pydantic_v1.Field |
from datetime import datetime, timedelta
import faiss
from langchain.docstore import InMemoryDocstore
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
embed... | InMemoryDocstore({}) | langchain.docstore.InMemoryDocstore |
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... | HumanMessage(content="Answer question using the following context") | langchain_core.messages.HumanMessage |
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... | MessagesPlaceholder(variable_name="history") | langchain_core.prompts.MessagesPlaceholder |
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... | Document(page_content="upserted_bak", metadata={"id": 3}) | langchain.docstore.document.Document |
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=destination, embedding=embeddings) | langchain_community.vectorstores.DeepLake |
from datetime import datetime, timedelta
import faiss
from langchain.docstore import InMemoryDocstore
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
embed... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().system('pip3 install cerebrium')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import CerebriumAI
os.environ["CEREBRIUMAI_API_KEY"] = "YOUR_KEY_HERE"
llm = | CerebriumAI(endpoint_url="YOUR ENDPOINT URL HERE") | langchain_community.llms.CerebriumAI |
import random
from docarray import BaseDoc
from docarray.typing import NdArray
from langchain.retrievers import DocArrayRetriever
from langchain_community.embeddings import FakeEmbeddings
embeddings = | FakeEmbeddings(size=32) | langchain_community.embeddings.FakeEmbeddings |
from langchain_community.llms import HuggingFaceEndpoint
get_ipython().run_line_magic('pip', 'install --upgrade --quiet huggingface_hub')
from getpass import getpass
HUGGINGFACEHUB_API_TOKEN = getpass()
import os
os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
from langchain_community.ll... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_openai import ChatOpenAI
import pandas as pd
from langchain_openai import OpenAI
df = pd.read_csv("titanic.csv")
agent = create_pandas_dataframe_agent( | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain.agents import Tool
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from pydantic im... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet playwright > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lxml')
from langchain_community.agent_toolkits import PlayWrightBrowserToolkit
from langchain_community.tools.playwright.utils import (
create_async_playwrig... | create_async_playwright_browser() | langchain_community.tools.playwright.utils.create_async_playwright_browser |
from langchain.memory import ConversationSummaryBufferMemory
from langchain_openai import OpenAI
llm = OpenAI()
memory = | ConversationSummaryBufferMemory(llm=llm, max_token_limit=10) | langchain.memory.ConversationSummaryBufferMemory |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet wikipedia')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOp... | hub.pull("hwchase17/react") | langchain.hub.pull |
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:/... | index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source") | langchain.indexes.index |
get_ipython().system('pip install -qU langchain-ibm')
import os
from getpass import getpass
watsonx_api_key = getpass()
os.environ["WATSONX_APIKEY"] = watsonx_api_key
import os
os.environ["WATSONX_URL"] = "your service instance url"
os.environ["WATSONX_TOKEN"] = "your token for accessing the CPD cluster"
os.env... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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 ... | MySQLDocumentSaver(engine=engine, table_name=TABLE_NAME) | langchain_google_cloud_sql_mysql.MySQLDocumentSaver |
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="first number") | langchain.pydantic_v1.Field |
from langchain_community.document_loaders import AZLyricsLoader
loader = | AZLyricsLoader("https://www.azlyrics.com/lyrics/mileycyrus/flowers.html") | langchain_community.document_loaders.AZLyricsLoader |
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain_openai import ChatOpenAI
examples = [
{
"input": "Could the members of The Police perform law... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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="cited_answer", return_single=True) | langchain.output_parsers.openai_tools.JsonOutputKeyToolsParser |
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... | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.messages import (
AIMessageChunk,
FunctionMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessageChunk,
)
AIMessageChu... | HumanMessage(content="Meow!") | langchain_core.messages.HumanMessage |
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 |
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... | LLMChain(llm=llm, prompt=prompt) | langchain.chains.LLMChain |
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."""
... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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... | ReadFileTool(root_dir="./data") | langchain_community.tools.file_management.read.ReadFileTool |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import getpass
import os
os.environ["POLYGON_API_KEY"] = getpass.getpass()
from langchain_community.tools.polygon.financials import PolygonFinancials
from langchain_community.tools.polygon.last_quote import PolygonLastQuote
from langchain_community.tools.polygon.ticker_news import PolygonTickerNews
from langchain_co... | PolygonLastQuote(api_wrapper=api_wrapper) | langchain_community.tools.polygon.last_quote.PolygonLastQuote |
import os
os.environ["LANGCHAIN_PROJECT"] = "movie-qa"
import pandas as pd
df = pd.read_csv("data/imdb_top_1000.csv")
df["Released_Year"] = df["Released_Year"].astype(int, errors="ignore")
from langchain.schema import Document
from langchain_community.vectorstores import Chroma
from langchain_openai import Op... | Chroma.from_documents(documents, embeddings) | langchain_community.vectorstores.Chroma.from_documents |
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOpenAI
api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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... | ChatOpenAI(temperature=0.9, callbacks=[callback]) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet elevenlabs')
import os
os.environ["ELEVEN_API_KEY"] = ""
from langchain.tools import ElevenLabsText2SpeechTool
text_to_speak = "Hello world! I am the real slim shady"
tts = | ElevenLabsText2SpeechTool() | langchain.tools.ElevenLabsText2SpeechTool |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-documentai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-documentai-toolbox')
GCS_OUTPUT_PATH = "gs://BUCKET_NAME/FOLDER_PATH"
PROCESSOR_NAME = "projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSO... | Blob(
path="gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs/2022Q1_alphabet_earnings_release.pdf"
) | langchain_community.document_loaders.blob_loaders.Blob |
import getpass
import os
os.environ["POLYGON_API_KEY"] = getpass.getpass()
from langchain_community.tools.polygon.financials import PolygonFinancials
from langchain_community.tools.polygon.last_quote import PolygonLastQuote
from langchain_community.tools.polygon.ticker_news import PolygonTickerNews
from langchain_co... | PolygonTickerNews(api_wrapper=api_wrapper) | langchain_community.tools.polygon.ticker_news.PolygonTickerNews |
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... | TokenTextSplitter(chunk_size=400, chunk_overlap=50) | langchain_text_splitters.TokenTextSplitter |
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import GrobidParser
loader = GenericLoader.from_filesystem(
"../Papers/",
glob="*",
suffixes=[".pdf"],
parser= | GrobidParser(segment_sentences=False) | langchain_community.document_loaders.parsers.GrobidParser |
get_ipython().system('poetry run pip -q install psychicapi')
from langchain_community.document_loaders import PsychicLoader
from psychicapi import ConnectorId
google_drive_loader = PsychicLoader(
api_key="7ddb61c1-8b6a-4d31-a58e-30d1c9ea480e",
connector_id=ConnectorId.gdrive.value,
connection_id="google-... | Chroma.from_documents(texts, embeddings) | langchain_community.vectorstores.Chroma.from_documents |
from langchain.chains import LLMMathChain
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
llm_math = | LLMMathChain.from_llm(llm, verbose=True) | langchain.chains.LLMMathChain.from_llm |
from langchain.globals import set_llm_cache
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()
get_ipython().run_cell_magic('time', '', 'from langchain.cache import InMemoryCache\n\nset_llm_cache(InMemoryCache())\n\n# The first time, it is not yet in cache, so it should take longer\nllm.predict("Tell me a j... | SQLiteCache(database_path=".langchain.db") | langchain.cache.SQLiteCache |
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... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain_community.document_loaders import WhatsAppChatLoader
loader = | WhatsAppChatLoader("example_data/whatsapp_chat.txt") | langchain_community.document_loaders.WhatsAppChatLoader |
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=[
... | E2BDataAnalysisTool(api_key="...") | langchain.tools.E2BDataAnalysisTool |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet weaviate-client')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")
os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")
WEAVIATE_API_KEY = os... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet 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"),... | ChatOpenAI(model_name="gpt-3.5-turbo") | langchain_openai.ChatOpenAI |
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.messages import (
AIMessageChunk,
FunctionMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessageChunk,
)
AIMessageChu... | AIMessage(content=tokens) | langchain_core.messages.AIMessage |
from langchain.output_parsers import (
OutputFixingParser,
PydanticOutputParser,
)
from langchain.prompts import (
PromptTemplate,
)
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI, OpenAI
template = """Based on the user question, provide an Action and Actio... | ChatOpenAI() | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet atlassian-python-api')
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.jira.toolkit import JiraToolkit
from langchain_community.utilities.jira import JiraAPIWrapper
from langchain_openai import ... | JiraToolkit.from_jira_api_wrapper(jira) | langchain_community.agent_toolkits.jira.toolkit.JiraToolkit.from_jira_api_wrapper |
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... | RunnableLambda(format_docs_xml) | langchain_core.runnables.RunnableLambda |
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... | RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet comet_ml langchain langchain-openai google-search-results spacy textstat pandas')
get_ipython().system('{sys.executable} -m spacy download en_core_web_sm')
import comet_ml
comet_ml.init(project_name="comet-example-langchain")
import os
os.envir... | OpenAI(temperature=0.9, callbacks=callbacks, verbose=True) | langchain_openai.OpenAI |
import os
os.environ["OPENAI_API_KEY"] = "..."
from langchain.prompts import PromptTemplate
from langchain_experimental.smart_llm import SmartLLMChain
from langchain_openai import ChatOpenAI
hard_question = "I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?"
prompt = PromptTe... | ChatOpenAI(temperature=0, model_name="gpt-4") | langchain_openai.ChatOpenAI |
import os
os.environ["GOOGLE_CSE_ID"] = ""
os.environ["GOOGLE_API_KEY"] = ""
from langchain.tools import Tool
from langchain_community.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
tool = Tool(
name="google_search",
description="Search Google for recent results.",
func=searc... | GoogleSearchAPIWrapper(k=1) | langchain_community.utilities.GoogleSearchAPIWrapper |
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/... | hub.pull("rlm/rag-prompt") | langchain.hub.pull |
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... | PromptTemplate(input_variables=["title"], template=template) | langchain.prompts.PromptTemplate |
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... | hub.pull("hwchase17/react-json") | langchain.hub.pull |
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"... | convert_pydantic_to_openai_function(Calculator) | langchain.utils.openai_functions.convert_pydantic_to_openai_function |
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 wikipedia')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOp... | create_react_agent(llm, tools, prompt) | langchain.agents.create_react_agent |
get_ipython().run_line_magic('pip', 'install --editable /mnt/disks/data/langchain/libs/partners/fireworks')
get_ipython().run_line_magic('pip', 'install langchain')
from langchain_fireworks import FireworksEmbeddings
import getpass
import os
if "FIREWORKS_API_KEY" not in os.environ:
os.environ["FIREWORKS_API_... | FireworksEmbeddings() | langchain_fireworks.FireworksEmbeddings |
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