prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
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=[
... | DuckDuckGoSearchRun() | langchain.tools.DuckDuckGoSearchRun |
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 woof", metadata={"source": "doggy.txt"}) | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-storage')
from langchain_community.document_loaders import GCSDirectoryLoader
loader = | GCSDirectoryLoader(project_name="aist", bucket="testing-hwc") | langchain_community.document_loaders.GCSDirectoryLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0).configurable_fields(
temperature=ConfigurableF... | PromptTemplate.from_template("Write a short poem about {topic}") | langchain.prompts.PromptTemplate.from_template |
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import ChatOpenAI
api_wrapper = | WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100) | langchain_community.utilities.WikipediaAPIWrapper |
from langchain.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
]
example_prompt = ChatPromptTemplate.from_messages(
[
("human", "{input}"),
("ai", "{output}"),
]
)
few_sh... | ChatAnthropic(temperature=0.0) | langchain_community.chat_models.ChatAnthropic |
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... | RunnablePassthrough.assign(info=(lambda x: x["question"]) | retriever1) | langchain_core.runnables.RunnablePassthrough.assign |
get_ipython().run_line_magic('pip', 'install -qU langchain-anthropic defusedxml')
from langchain_anthropic.experimental import ChatAnthropicTools
from langchain_core.pydantic_v1 import BaseModel
class Person(BaseModel):
name: str
age: int
model = ChatAnthropicTools(model="claude-3-opus-20240229").bind_to... | ChatAnthropicTools(model="claude-3-opus-20240229") | langchain_anthropic.experimental.ChatAnthropicTools |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("... | HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") | langchain_community.embeddings.huggingface.HuggingFaceEmbeddings |
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... | TextLoader("../../state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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-... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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... | RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100) | langchain_text_splitters.RecursiveCharacterTextSplitter |
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... | FAISS.from_documents(docs, embeddings) | langchain_community.vectorstores.FAISS.from_documents |
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader("data/corp_sens_data.csv")
documents = loader.load()
print(documents)
from langchain.document_loaders.csv_loader import CSVLoader
from langchain_community.document_loaders import PebbloSafeLoader
loader = PebbloSafeLoader(
| CSVLoader("data/corp_sens_data.csv") | langchain.document_loaders.csv_loader.CSVLoader |
from langchain import hub
from langchain.agents import AgentExecutor, tool
from langchain.agents.output_parsers import XMLAgentOutputParser
from langchain_community.chat_models import ChatAnthropic
model = ChatAnthropic(model="claude-2")
@tool
def search(query: str) -> str:
"""Search things about current events... | hub.pull("hwchase17/xml-agent-convo") | langchain.hub.pull |
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 OpenAI... | WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100) | langchain_community.utilities.WikipediaAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet psycopg2-binary')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
... | ChatPromptTemplate.from_messages(messages) | langchain.prompts.chat.ChatPromptTemplate.from_messages |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet psycopg2-binary')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
... | SystemMessagePromptTemplate.from_template(system_template) | langchain.prompts.chat.SystemMessagePromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet alibabacloud_ha3engine_vector')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import (
AlibabaCloudOpenSearch,
AlibabaCloudOpenSearchSettings,
)
from langchai... | 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/photos/"
fr... | RunnableLambda(prompt_func) | langchain_core.runnables.RunnableLambda |
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... | ChatPromptValue(messages=messages) | langchain_core.prompt_values.ChatPromptValue |
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"][... | 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."""
... | convert_pydantic_to_openai_function(Jokes) | langchain_community.utils.openai_functions.convert_pydantic_to_openai_function |
from langchain.agents import AgentType, initialize_agent
from langchain.chains import LLMMathChain
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import Tool
from langchain_openai import ChatOpenAI
get_ipython().run_line_magic('pip', 'install --upgrade --quiet numexpr')
llm = Cha... | Field() | langchain_core.pydantic_v1.Field |
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.messages import (
AIMessageChunk,
FunctionMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessageChunk,
)
AIMessageChu... | AIMessage(content="Hi there human!") | langchain_core.messages.AIMessage |
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... | InMemoryStore() | langchain.storage.InMemoryStore |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet feedparser newspaper3k listparser')
from langchain_community.document_loaders import RSSFeedLoader
urls = ["https://news.ycombinator.com/rss"]
loader = | RSSFeedLoader(urls=urls) | langchain_community.document_loaders.RSSFeedLoader |
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 = | PromptTemplate.from_template(introduction_template) | langchain.prompts.prompt.PromptTemplate.from_template |
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 Chat... | ChatOpenAI(model_name="gpt-3.5-turbo") | langchain_openai.ChatOpenAI |
import os
import chromadb
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain.retrievers.merger_retriever import MergerRetriever
from langchain_community.document_transformers import (
EmbeddingsClusteringFi... | HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") | langchain_community.embeddings.HuggingFaceEmbeddings |
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 woof", metadata={"source": "doggy.txt"}) | langchain_core.documents.Document |
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") | langchain_community.utilities.SearxSearchWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet predibase')
import os
os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"
from langchain_community.llms import Predibase
model = Predibase(
model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN")
)
response = model("C... | PromptTemplate(input_variables=["synopsis"], template=template) | langchain.prompts.PromptTemplate |
get_ipython().system('pip install --upgrade volcengine')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.document_loaders import TextLoader
from langchain.vectorstores.vikingdb import VikingDB, VikingDBConfig
from langchain_openai import OpenAIEmbeddings
f... | RecursiveCharacterTextSplitter(chunk_size=10, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sentence-transformers > /dev/null')
from langchain.chains import LLMChain, StuffDocumentsChain
from langchain.prompts import PromptTemplate
from langchain_community.document_transformers import (
LongContextReorder,
)
from langchain_community.embeddi... | LongContextReorder() | langchain_community.document_transformers.LongContextReorder |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet opaqueprompts langchain')
import os
os.environ["OPAQUEPROMPTS_API_KEY"] = "<OPAQUEPROMPTS_API_KEY>"
os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"
from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.chains import LLMChain... | StdOutCallbackHandler() | langchain.callbacks.stdout.StdOutCallbackHandler |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-api-python-client > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-auth-oauthlib > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-auth-httplib2 > /dev/null')
get_ipython().run_l... | GmailToolkit() | langchain_community.agent_toolkits.GmailToolkit |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pgvector')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet psycopg2-binary')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
im... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().system(' pip install langchain replicate')
from langchain_community.chat_models import ChatOllama
llama2_chat = ChatOllama(model="llama2:13b-chat")
llama2_code = ChatOllama(model="codellama:7b-instruct")
from langchain_community.llms import Replicate
replicate_id = "meta/llama-2-13b-chat:f4e2de70d66... | RunnablePassthrough.assign(schema=get_schema) | langchain_core.runnables.RunnablePassthrough.assign |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark pgvector psycopg2-binary')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import PGVector
from langchain_core.documents import Document
from langchain_openai impor... | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain_community.document_loaders import GutenbergLoader
loader = | GutenbergLoader("https://www.gutenberg.org/cache/epub/69972/pg69972.txt") | langchain_community.document_loaders.GutenbergLoader |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet networkx')
from langchain.indexes import GraphIndexCreator
from langchain_openai import OpenAI
index_creator = GraphIndexCreator(llm=OpenAI(temperature=0))
with open("../../../modules/state_of_the_union.txt") as f:
all_text = f.read()
text = ... | NetworkxEntityGraph.from_gml("graph.gml") | langchain.indexes.graph.NetworkxEntityGraph.from_gml |
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() | langchain.memory.ConversationBufferMemory |
get_ipython().run_cell_magic('capture', '', '%pip install --upgrade --quiet python-arango # The ArangoDB Python Driver\n%pip install --upgrade --quiet adb-cloud-connector # The ArangoDB Cloud Instance provisioner\n%pip install --upgrade --quiet langchain-openai\n%pip install --upgrade --quiet langchain\n')
import... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet python-gitlab')
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.gitlab.toolkit import GitLabToolkit
from langchain_community.utilities.gitlab import GitLabAPIWrapper
from langchain_openai impo... | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import Recursiv... | Document(page_content=s, metadata={id_key: doc_ids[i]}) | langchain_core.documents.Document |
from langchain_community.chat_models.llama_edge import LlamaEdgeChatService
from langchain_core.messages import HumanMessage, SystemMessage
service_url = "https://b008-54-186-154-209.ngrok-free.app"
chat = LlamaEdgeChatService(service_url=service_url)
system_message = | SystemMessage(content="You are an AI assistant") | langchain_core.messages.SystemMessage |
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") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet metal_sdk')
from metal_sdk.metal import Metal
API_KEY = ""
CLIENT_ID = ""
INDEX_ID = ""
metal = Metal(API_KEY, CLIENT_ID, INDEX_ID)
metal.index({"text": "foo1"})
metal.index({"text": "foo"})
from langchain.retrievers import MetalRetriever
retri... | MetalRetriever(metal, params={"limit": 2}) | langchain.retrievers.MetalRetriever |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet vald-client-python')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Vald
from langchain_text_splitters import CharacterTextSplitte... | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from typing import List, Tuple
from dotenv import load_dotenv
load_dotenv()
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.v... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-experimental langchain-openai neo4j wikipedia')
from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformer
diffbot_api_key = "DIFFBOT_API_KEY"
diffbot_nlp = DiffbotGraphTransformer(diffbot_api_key=diffbot_... | WikipediaLoader(query=query) | langchain_community.document_loaders.WikipediaLoader |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-openai langchain-anthropic langchain-community wikipedia')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()
from langchain_community.retrievers import WikipediaRetrieve... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet 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... | hub.pull("kastanday/new-github-issue") | langchain.hub.pull |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet airbyte-source-gong')
from langchain_community.document_loaders.airbyte import AirbyteGongLoader
config = {
}
loader = AirbyteGongLoader(
config=config, stream_name="calls"
) # check the documentation linked above for a list of all streams
do... | Document(page_content=record.data["title"], metadata=record.data) | langchain.docstore.document.Document |
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(["... | OpenAI(temperature=0) | langchain.llms.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet llmlingua accelerate')
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 TextLo... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from langchain_community.chat_models.llama_edge import LlamaEdgeChatService
from langchain_core.messages import HumanMessage, SystemMessage
service_url = "https://b008-54-186-154-209.ngrok-free.app"
chat = LlamaEdgeChatService(service_url=service_url)
system_message = SystemMessage(content="You are an AI assistant... | HumanMessage(content="What is the capital of France?") | langchain_core.messages.HumanMessage |
get_ipython().system('pip install -U oci')
from langchain_community.llms import OCIGenAI
llm = OCIGenAI(
model_id="MY_MODEL",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="MY_OCID",
)
response = llm.invoke("Tell me one fact about earth", temperatu... | RunnablePassthrough() | langchain.schema.runnable.RunnablePassthrough |
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 |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-nvidia-ai-endpoints')
import getpass
import os
if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
nvapi_key = getpass.getpass("Enter your NVIDIA API key: ")
assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is ... | ChatPromptTemplate.from_messages(
[("system", "You are a helpful AI assistant named Fred."), ("user", "{input}") | langchain_core.prompts.ChatPromptTemplate.from_messages |
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import Vectara
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitt... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet apify-client langchain-openai langchain chromadb tiktoken')
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders.base import Document
from langchain_community.utilities import ApifyWrapper
import os
os.envi... | VectorstoreIndexCreator() | langchain.indexes.VectorstoreIndexCreator |
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('poetry run pip install replicate')
from getpass import getpass
REPLICATE_API_TOKEN = getpass()
import os
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.chains ... | Replicate(
model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf"
) | langchain_community.llms.Replicate |
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="kitty", metadata={"source": "kitty.txt"}) | langchain_core.documents.Document |
from langchain.chains import LLMCheckerChain
from langchain_openai import OpenAI
llm = OpenAI(temperature=0.7)
text = "What type of mammal lays the biggest eggs?"
checker_chain = | LLMCheckerChain.from_llm(llm, verbose=True) | langchain.chains.LLMCheckerChain.from_llm |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.evaluation import load_evaluator
eval_chain = load_evaluator("pairwise_string")
from langchain.evaluation.loading import load_dataset
dataset = load_dataset("langchain-howto-queries")
from langchain.age... | ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") | langchain_openai.ChatOpenAI |
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... | ChatOpenAI(temperature=0, model="gpt-4-vision-preview", max_tokens=1024) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from typing import Iterator, List
from langchain.prompts.chat import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
prompt = ChatPromptTemplate.from_temp... | ChatOpenAI(temperature=0.0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet opencv-python scikit-image')
import os
from langchain_openai import OpenAI
os.environ["OPENAI_API_KEY"] = "<your-key-here>"
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.utilities.dalle_i... | LLMChain(llm=llm, prompt=prompt) | langchain.chains.LLMChain |
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=[
... | ChatOpenAI(model="gpt-3.5-turbo-1106") | langchain_openai.ChatOpenAI |
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... | GoogleSerperAPIWrapper(type="news") | langchain_community.utilities.GoogleSerperAPIWrapper |
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.messages import (
AIMessageChunk,
FunctionMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessageChunk,
)
AIMessageChu... | AIMessageChunk(content=" World!") | langchain_core.messages.AIMessageChunk |
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 ... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
import os
os.environ["EXA_API_KEY"] = "..."
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-exa')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePa... | OpenAIFunctionsAgent.create_prompt(system_message) | langchain.agents.OpenAIFunctionsAgent.create_prompt |
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=2000) | langchain_text_splitters.RecursiveCharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rank_bm25')
from langchain.retrievers import BM25Retriever
retriever = BM25Retriever.from_texts(["foo", "bar", "world", "hello", "foo bar"])
from langchain_core.documents import Document
retriever = BM25Retriever.from_documents(
[
| Document(page_content="foo") | langchain_core.documents.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 ... | InMemoryStore() | langchain.storage.InMemoryStore |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clickhouse-connect')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["OPENAI_API_BASE"] = getpass.getpass("OpenAI Base:")
os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale Host:")
os.environ["MY... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain.indexes import SQLRecordManager, index
from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
collection_name = "test_index"
embedding = OpenAIEmbeddings()
vectorstore = ElasticsearchStore(
es_url="http:/... | Document(page_content="doggy doggy the doggy", metadata={"source": "doggy.txt"}) | langchain_core.documents.Document |
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 |
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_id="0000-000000-xxxxxxxx", cluster_driver_port="7777") | langchain_community.llms.Databricks |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet annoy')
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Annoy
embeddings_func = HuggingFaceEmbeddings()
texts = ["pizza is great", "I love salad", "my car", "a dog"]
vector_store = Annoy.... | TextLoader("../../modules/state_of_the_union.txtn.txtn.txt") | langchain_community.document_loaders.TextLoader |
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.nasa.toolkit import NasaToolkit
from langchain_community.utilities.nasa import NasaAPIWrapper
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0, openai_api_key="") | langchain_openai.OpenAI |
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=[
... | convert_pydantic_to_openai_tool(GetCurrentWeather) | langchain.utils.openai_functions.convert_pydantic_to_openai_tool |
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_line_magic('pip', 'install --upgrade --quiet multion langchain -q')
from langchain_community.agent_toolkits import MultionToolkit
toolkit = MultionToolkit()
toolkit
tools = toolkit.get_tools()
tools
import multion
multion.login()
from langchain import hub
from langchain.agents import Agen... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from typing import List
from langchain.prompts.chat import (
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class CAMELAgent:
def __init__(
se... | ChatOpenAI(temperature=0.2) | langchain_openai.ChatOpenAI |
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_... | ConversationBufferMemory(memory_key="chat_history", return_messages=True) | langchain.memory.ConversationBufferMemory |
from langchain_community.vectorstores import AnalyticDB
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_spli... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from langchain_community.document_loaders import NotionDirectoryLoader
loader = | NotionDirectoryLoader("Notion_DB") | langchain_community.document_loaders.NotionDirectoryLoader |
from ray import serve
from starlette.requests import Request
@serve.deployment
class LLMServe:
def __init__(self) -> None:
pass
async def __call__(self, request: Request) -> str:
return "Hello World"
deployment = LLMServe.bind()
serve.api.run(deployment)
serve.api.shutdown()
from lan... | LLMChain(llm=llm, prompt=prompt) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("trajectory")
import subprocess
from urllib.parse import urlparse
from langchain.agents import AgentType, initialize_agent
from langchain.tools ... | ChatOpenAI(model="gpt-3.5-turbo-0613", temperature=0) | langchain_openai.ChatOpenAI |
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 ... | RunnableLambda(img_prompt_func) | langchain_core.runnables.RunnableLambda |
import asyncio
from langchain.callbacks import get_openai_callback
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
with | get_openai_callback() | langchain.callbacks.get_openai_callback |
from langchain_community.llms import AmazonAPIGateway
api_url = "https://<api_gateway_id>.execute-api.<region>.amazonaws.com/LATEST/HF"
llm = | AmazonAPIGateway(api_url=api_url) | langchain_community.llms.AmazonAPIGateway |
import os
os.environ["EXA_API_KEY"] = "..."
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-exa')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePa... | PromptTemplate.from_template(
"""Answer the following query based on the following context:
query: {query}
<context>
{context}
</context"""
) | langchain_core.prompts.PromptTemplate.from_template |
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... | RunnableLambda(lambda x: x["question"]) | langchain_core.runnables.RunnableLambda |
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 |
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