prompt
stringlengths 51
10k
| completion
stringlengths 8
362
| api
stringlengths 18
90
|
|---|---|---|
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 CharacterTextSplitter
raw_documents = | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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()
search.run("python")
search = | BingSearchAPIWrapper(k=1) | langchain_community.utilities.BingSearchAPIWrapper |
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) | langchain_community.llms.OpenLLM |
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 enable spanner.googleapis.com')
INSTANCE = "my-instance" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "vectors_search_data" # @param {type: "string"}
from langchain_google_spanner import SecondaryIndex, SpannerVectorStore, TableColumn
SpannerVectorStore.init_vector_store_table(
instance_id=INSTANCE,
database_id=DATABASE,
table_name=TABLE_NAME,
id_column="row_id",
metadata_columns=[
TableColumn(name="metadata", type="JSON", is_null=True),
TableColumn(name="title", type="STRING(MAX)", is_null=False),
],
secondary_indexes=[
| SecondaryIndex(index_name="row_id_and_title", columns=["row_id", "title"]) | langchain_google_spanner.SecondaryIndex |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet transformers --quiet')
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2",
task="text-generation",
pipeline_kwargs={"max_new_tokens": 10},
)
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10)
hf = | HuggingFacePipeline(pipeline=pipe) | langchain_community.llms.huggingface_pipeline.HuggingFacePipeline |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet arxiv')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent, load_tools
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0.0)
tools = load_tools(
["arxiv"],
)
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke(
{
"input": "What's the paper 1605.08386 about?",
}
)
from langchain_community.utilities import ArxivAPIWrapper
arxiv = | ArxivAPIWrapper() | langchain_community.utilities.ArxivAPIWrapper |
from langchain_community.document_loaders import IFixitLoader
loader = IFixitLoader("https://www.ifixit.com/Teardown/Banana+Teardown/811")
data = loader.load()
data
loader = IFixitLoader(
"https://www.ifixit.com/Answers/View/318583/My+iPhone+6+is+typing+and+opening+apps+by+itself"
)
data = loader.load()
data
loader = IFixitLoader("https://www.ifixit.com/Device/Standard_iPad")
data = loader.load()
data
data = | IFixitLoader.load_suggestions("Banana") | langchain_community.document_loaders.IFixitLoader.load_suggestions |
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
def initialize_chain(instructions, memory=None):
if memory is None:
memory = ConversationBufferWindowMemory()
memory.ai_prefix = "Assistant"
template = f"""
Instructions: {instructions}
{{{memory.memory_key}}}
Human: {{human_input}}
Assistant:"""
prompt = PromptTemplate(
input_variables=["history", "human_input"], template=template
)
chain = LLMChain(
llm=OpenAI(temperature=0),
prompt=prompt,
verbose=True,
memory= | ConversationBufferWindowMemory() | langchain.memory.ConversationBufferWindowMemory |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import IuguLoader
iugu_loader = IuguLoader("charges")
index = | VectorstoreIndexCreator() | langchain.indexes.VectorstoreIndexCreator |
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("comprehend", region_name="us-east-1")
from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain
comprehend_moderation = AmazonComprehendModerationChain(
client=comprehend_client,
verbose=True, # optional
)
from langchain.prompts import PromptTemplate
from langchain_community.llms.fake import FakeListLLM
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationPiiError,
)
template = """Question: {question}
Answer:"""
prompt = PromptTemplate.from_template(template)
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.",
]
llm = FakeListLLM(responses=responses)
chain = (
prompt
| comprehend_moderation
| {"input": (lambda x: x["output"]) | llm}
| comprehend_moderation
)
try:
response = chain.invoke(
{
"question": "A sample SSN number looks like this 123-22-3345. Can you give me some more samples?"
}
)
except ModerationPiiError as e:
print(str(e))
else:
print(response["output"])
from langchain_experimental.comprehend_moderation import (
BaseModerationConfig,
ModerationPiiConfig,
ModerationPromptSafetyConfig,
ModerationToxicityConfig,
)
pii_config = ModerationPiiConfig(labels=["SSN"], redact=True, mask_character="X")
toxicity_config = ModerationToxicityConfig(threshold=0.5)
prompt_safety_config = ModerationPromptSafetyConfig(threshold=0.5)
moderation_config = BaseModerationConfig(
filters=[pii_config, toxicity_config, prompt_safety_config]
)
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, # specify the configuration
client=comprehend_client, # optionally pass the Boto3 Client
verbose=True,
)
from langchain.prompts import PromptTemplate
from langchain_community.llms.fake import FakeListLLM
template = """Question: {question}
Answer:"""
prompt = PromptTemplate.from_template(template)
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.",
]
llm = FakeListLLM(responses=responses)
chain = (
prompt
| comp_moderation_with_config
| {"input": (lambda x: x["output"]) | llm}
| comp_moderation_with_config
)
try:
response = chain.invoke(
{
"question": "A sample SSN number looks like this 123-45-7890. Can you give me some more samples?"
}
)
except Exception as e:
print(str(e))
else:
print(response["output"])
from langchain_experimental.comprehend_moderation import BaseModerationCallbackHandler
class MyModCallback(BaseModerationCallbackHandler):
async def on_after_pii(self, output_beacon, unique_id):
import json
moderation_type = output_beacon["moderation_type"]
chain_id = output_beacon["moderation_chain_id"]
with open(f"output-{moderation_type}-{chain_id}.json", "w") as file:
data = {"beacon_data": output_beacon, "unique_id": unique_id}
json.dump(data, file)
"""
async def on_after_toxicity(self, output_beacon, unique_id):
pass
async def on_after_prompt_safety(self, output_beacon, unique_id):
pass
"""
my_callback = MyModCallback()
pii_config = ModerationPiiConfig(labels=["SSN"], redact=True, mask_character="X")
toxicity_config = ModerationToxicityConfig(threshold=0.5)
moderation_config = BaseModerationConfig(filters=[pii_config, toxicity_config])
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, # specify the configuration
client=comprehend_client, # optionally pass the Boto3 Client
unique_id="john.doe@email.com", # A unique ID
moderation_callback=my_callback, # BaseModerationCallbackHandler
verbose=True,
)
from langchain.prompts import PromptTemplate
from langchain_community.llms.fake import FakeListLLM
template = """Question: {question}
Answer:"""
prompt = PromptTemplate.from_template(template)
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.",
]
llm = FakeListLLM(responses=responses)
chain = (
prompt
| comp_moderation_with_config
| {"input": (lambda x: x["output"]) | llm}
| comp_moderation_with_config
)
try:
response = chain.invoke(
{
"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"
}
)
except Exception as e:
print(str(e))
else:
print(response["output"])
get_ipython().run_line_magic('pip', 'install --upgrade --quiet huggingface_hub')
import os
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "<YOUR HF TOKEN HERE>"
repo_id = "google/flan-t5-xxl"
from langchain.prompts import PromptTemplate
from langchain_community.llms import HuggingFaceHub
template = """{question}"""
prompt = PromptTemplate.from_template(template)
llm = HuggingFaceHub(
repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 256}
)
pii_config = ModerationPiiConfig(
labels=["SSN", "CREDIT_DEBIT_NUMBER"], redact=True, mask_character="X"
)
toxicity_config = ModerationToxicityConfig(threshold=0.5)
prompt_safety_config = ModerationPromptSafetyConfig(threshold=0.8)
moderation_config_1 = BaseModerationConfig(
filters=[pii_config, toxicity_config, prompt_safety_config]
)
moderation_config_2 = BaseModerationConfig(filters=[pii_config])
amazon_comp_moderation = AmazonComprehendModerationChain(
moderation_config=moderation_config_1,
client=comprehend_client,
moderation_callback=my_callback,
verbose=True,
)
amazon_comp_moderation_out = AmazonComprehendModerationChain(
moderation_config=moderation_config_2, client=comprehend_client, verbose=True
)
chain = (
prompt
| amazon_comp_moderation
| {"input": (lambda x: x["output"]) | llm}
| amazon_comp_moderation_out
)
try:
response = chain.invoke(
{
"question": """What is John Doe's address, phone number and SSN from the following text?
John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.
"""
}
)
except Exception as e:
print(str(e))
else:
print(response["output"])
endpoint_name = "<SAGEMAKER_ENDPOINT_NAME>" # replace with your SageMaker Endpoint name
region = "<REGION>" # replace with your SageMaker Endpoint region
import json
from langchain.prompts import PromptTemplate
from langchain_community.llms import SagemakerEndpoint
from langchain_community.llms.sagemaker_endpoint import LLMContentHandler
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: dict) -> bytes:
input_str = json.dumps({"text_inputs": prompt, **model_kwargs})
return input_str.encode("utf-8")
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json["generated_texts"][0]
content_handler = ContentHandler()
template = """From the following 'Document', precisely answer the 'Question'. Do not add any spurious information in your answer.
Document: John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.
Question: {question}
Answer:
"""
llm_prompt = PromptTemplate.from_template(template)
llm = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name=region,
model_kwargs={
"temperature": 0.95,
"max_length": 200,
"num_return_sequences": 3,
"top_k": 50,
"top_p": 0.95,
"do_sample": True,
},
content_handler=content_handler,
)
pii_config = ModerationPiiConfig(labels=["SSN"], redact=True, mask_character="X")
toxicity_config = ModerationToxicityConfig(threshold=0.5)
moderation_config_1 = | BaseModerationConfig(filters=[pii_config, toxicity_config]) | langchain_experimental.comprehend_moderation.BaseModerationConfig |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-datastore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from google.colab import auth
auth.authenticate_user()
get_ipython().system('gcloud services enable datastore.googleapis.com')
from langchain_core.documents import Document
from langchain_google_datastore import DatastoreSaver
data = [Document(page_content="Hello, World!")]
saver = DatastoreSaver()
saver.upsert_documents(data)
saver = DatastoreSaver("Collection")
saver.upsert_documents(data)
doc_ids = ["AnotherCollection/doc_id", "foo/bar"]
saver = DatastoreSaver()
saver.upsert_documents(documents=data, document_ids=doc_ids)
from langchain_google_datastore import DatastoreLoader
loader_collection = DatastoreLoader("Collection")
loader_subcollection = DatastoreLoader("Collection/doc/SubCollection")
data_collection = loader_collection.load()
data_subcollection = loader_subcollection.load()
from google.cloud import datastore
client = datastore.Client()
doc_ref = client.collection("foo").document("bar")
loader_document = | DatastoreLoader(doc_ref) | langchain_google_datastore.DatastoreLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from operator import itemgetter
from langchain.output_parsers import JsonOutputToolsParser
from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
@tool
def count_emails(last_n_days: int) -> int:
"""Multiply two integers together."""
return last_n_days * 2
@tool
def send_email(message: str, recipient: str) -> str:
"Add two integers."
return f"Successfully sent email to {recipient}."
tools = [count_emails, send_email]
model = ChatOpenAI(model="gpt-3.5-turbo", temperature=0).bind_tools(tools)
def call_tool(tool_invocation: dict) -> Runnable:
"""Function for dynamically constructing the end of the chain based on the model-selected tool."""
tool_map = {tool.name: tool for tool in tools}
tool = tool_map[tool_invocation["type"]]
return RunnablePassthrough.assign(output=itemgetter("args") | tool)
call_tool_list = RunnableLambda(call_tool).map()
chain = model | | JsonOutputToolsParser() | langchain.output_parsers.JsonOutputToolsParser |
import zipfile
import requests
def download_and_unzip(url: str, output_path: str = "file.zip") -> None:
file_id = url.split("/")[-2]
download_url = f"https://drive.google.com/uc?export=download&id={file_id}"
response = requests.get(download_url)
if response.status_code != 200:
print("Failed to download the file.")
return
with open(output_path, "wb") as file:
file.write(response.content)
print(f"File {output_path} downloaded.")
with zipfile.ZipFile(output_path, "r") as zip_ref:
zip_ref.extractall()
print(f"File {output_path} has been unzipped.")
url = (
"https://drive.google.com/file/d/1rh1s1o2i7B-Sk1v9o8KNgivLVGwJ-osV/view?usp=sharing"
)
download_and_unzip(url)
directory_path = "./hogwarts"
from langchain_community.chat_loaders.facebook_messenger import (
FolderFacebookMessengerChatLoader,
SingleFileFacebookMessengerChatLoader,
)
loader = SingleFileFacebookMessengerChatLoader(
path="./hogwarts/inbox/HermioneGranger/messages_Hermione_Granger.json",
)
chat_session = loader.load()[0]
chat_session["messages"][:3]
loader = FolderFacebookMessengerChatLoader(
path="./hogwarts",
)
chat_sessions = loader.load()
len(chat_sessions)
from langchain_community.chat_loaders.utils import (
map_ai_messages,
merge_chat_runs,
)
merged_sessions = | merge_chat_runs(chat_sessions) | langchain_community.chat_loaders.utils.merge_chat_runs |
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 OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
records = df.to_dict("records")
documents = [ | Document(page_content=d["Overview"], metadata=d) | langchain.schema.Document |
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
template = """Answer the users question based only on the following context:
<context>
{context}
</context>
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI(temperature=0)
search = DuckDuckGoSearchAPIWrapper()
def retriever(query):
return search.run(query)
chain = (
{"context": retriever, "question": | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
from langchain_community.document_loaders import AsyncChromiumLoader
from langchain_community.document_transformers import BeautifulSoupTransformer
loader = | AsyncChromiumLoader(["https://www.wsj.com"]) | langchain_community.document_loaders.AsyncChromiumLoader |
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-memorystore-redis')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from google.colab import auth
auth.authenticate_user()
import redis
from langchain_google_memorystore_redis import (
DistanceStrategy,
HNSWConfig,
RedisVectorStore,
)
redis_client = redis.from_url("redis://127.0.0.1:6379")
index_config = HNSWConfig(
name="my_vector_index", distance_strategy=DistanceStrategy.COSINE, vector_size=128
)
| RedisVectorStore.init_index(client=redis_client, index_config=index_config) | langchain_google_memorystore_redis.RedisVectorStore.init_index |
from langchain_community.document_transformers.openai_functions import (
create_metadata_tagger,
)
from langchain_core.documents import Document
from langchain_openai import ChatOpenAI
schema = {
"properties": {
"movie_title": {"type": "string"},
"critic": {"type": "string"},
"tone": {"type": "string", "enum": ["positive", "negative"]},
"rating": {
"type": "integer",
"description": "The number of stars the critic rated the movie",
},
},
"required": ["movie_title", "critic", "tone"],
}
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
document_transformer = create_metadata_tagger(metadata_schema=schema, llm=llm)
original_documents = [
Document(
page_content="Review of The Bee Movie\nBy Roger Ebert\n\nThis is the greatest movie ever made. 4 out of 5 stars."
),
Document(
page_content="Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.",
metadata={"reliable": False},
),
]
enhanced_documents = document_transformer.transform_documents(original_documents)
import json
print(
*[d.page_content + "\n\n" + json.dumps(d.metadata) for d in enhanced_documents],
sep="\n\n---------------\n\n",
)
from typing import Literal
from pydantic import BaseModel, Field
class Properties(BaseModel):
movie_title: str
critic: str
tone: Literal["positive", "negative"]
rating: int = Field(description="Rating out of 5 stars")
document_transformer = | create_metadata_tagger(Properties, llm) | langchain_community.document_transformers.openai_functions.create_metadata_tagger |
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)
loader.load()
loader = OBSDirectoryLoader(
"your-bucket-name", endpoint=endpoint, config=config, prefix="test_prefix"
)
loader.load()
config = {"get_token_from_ecs": True}
loader = | OBSDirectoryLoader("your-bucket-name", endpoint=endpoint, config=config) | langchain_community.document_loaders.OBSDirectoryLoader |
from langchain_community.document_loaders.obs_file import OBSFileLoader
endpoint = "your-endpoint"
from obs import ObsClient
obs_client = ObsClient(
access_key_id="your-access-key",
secret_access_key="your-secret-key",
server=endpoint,
)
loader = OBSFileLoader("your-bucket-name", "your-object-key", client=obs_client)
loader.load()
config = {"ak": "your-access-key", "sk": "your-secret-key"}
loader = OBSFileLoader(
"your-bucket-name", "your-object-key", endpoint=endpoint, config=config
)
loader.load()
config = {"get_token_from_ecs": True}
loader = OBSFileLoader(
"your-bucket-name", "your-object-key", endpoint=endpoint, config=config
)
loader.load()
loader = | OBSFileLoader("your-bucket-name", "your-object-key", endpoint=endpoint) | langchain_community.document_loaders.obs_file.OBSFileLoader |
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 langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
from getpass import getpass
OPENAI_API_KEY = getpass()
@serve.deployment
class DeployLLM:
def __init__(self):
llm = OpenAI(openai_api_key=OPENAI_API_KEY)
template = "Question: {question}\n\nAnswer: Let's think step by step."
prompt = PromptTemplate.from_template(template)
self.chain = | 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
eval_chain = load_evaluator("pairwise_string")
from langchain.evaluation.loading import load_dataset
dataset = load_dataset("langchain-howto-queries")
from langchain.agents import AgentType, Tool, initialize_agent
from langchain_community.utilities import SerpAPIWrapper
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
search = | SerpAPIWrapper() | langchain_community.utilities.SerpAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain nuclia')
from langchain_community.vectorstores.nucliadb import NucliaDB
API_KEY = "YOUR_API_KEY"
ndb = | NucliaDB(knowledge_box="YOUR_KB_ID", local=False, api_key=API_KEY) | langchain_community.vectorstores.nucliadb.NucliaDB |
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/"
from unstructured.partition.pdf import partition_pdf
raw_pdf_elements = partition_pdf(
filename=path + "photos.pdf",
extract_images_in_pdf=True,
infer_table_structure=True,
chunking_strategy="by_title",
max_characters=4000,
new_after_n_chars=3800,
combine_text_under_n_chars=2000,
image_output_dir_path=path,
)
tables = []
texts = []
for element in raw_pdf_elements:
if "unstructured.documents.elements.Table" in str(type(element)):
tables.append(str(element))
elif "unstructured.documents.elements.CompositeElement" in str(type(element)):
texts.append(str(element))
import os
import uuid
import chromadb
import numpy as np
from langchain_community.vectorstores import Chroma
from langchain_experimental.open_clip import OpenCLIPEmbeddings
from PIL import Image as _PILImage
vectorstore = Chroma(
collection_name="mm_rag_clip_photos", embedding_function=OpenCLIPEmbeddings()
)
image_uris = sorted(
[
os.path.join(path, image_name)
for image_name in os.listdir(path)
if image_name.endswith(".jpg")
]
)
vectorstore.add_images(uris=image_uris)
vectorstore.add_texts(texts=texts)
retriever = vectorstore.as_retriever()
import base64
import io
from io import BytesIO
import numpy as np
from PIL import Image
def resize_base64_image(base64_string, size=(128, 128)):
"""
Resize an image encoded as a Base64 string.
Args:
base64_string (str): Base64 string of the original image.
size (tuple): Desired size of the image as (width, height).
Returns:
str: Base64 string of the resized image.
"""
img_data = base64.b64decode(base64_string)
img = Image.open(io.BytesIO(img_data))
resized_img = img.resize(size, Image.LANCZOS)
buffered = io.BytesIO()
resized_img.save(buffered, format=img.format)
return base64.b64encode(buffered.getvalue()).decode("utf-8")
def is_base64(s):
"""Check if a string is Base64 encoded"""
try:
return base64.b64encode(base64.b64decode(s)) == s.encode()
except Exception:
return False
def split_image_text_types(docs):
"""Split numpy array images and texts"""
images = []
text = []
for doc in docs:
doc = doc.page_content # Extract Document contents
if is_base64(doc):
images.append(
resize_base64_image(doc, size=(250, 250))
) # base64 encoded str
else:
text.append(doc)
return {"images": images, "texts": text}
from operator import itemgetter
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_openai import ChatOpenAI
def prompt_func(data_dict):
formatted_texts = "\n".join(data_dict["context"]["texts"])
messages = []
if data_dict["context"]["images"]:
image_message = {
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{data_dict['context']['images'][0]}"
},
}
messages.append(image_message)
text_message = {
"type": "text",
"text": (
"As an expert art critic and historian, your task is to analyze and interpret images, "
"considering their historical and cultural significance. Alongside the images, you will be "
"provided with related text to offer context. Both will be retrieved from a vectorstore based "
"on user-input keywords. Please use your extensive knowledge and analytical skills to provide a "
"comprehensive summary that includes:\n"
"- A detailed description of the visual elements in the image.\n"
"- The historical and cultural context of the image.\n"
"- An interpretation of the image's symbolism and meaning.\n"
"- Connections between the image and the related text.\n\n"
f"User-provided keywords: {data_dict['question']}\n\n"
"Text and / or tables:\n"
f"{formatted_texts}"
),
}
messages.append(text_message)
return [ | HumanMessage(content=messages) | langchain_core.messages.HumanMessage |
import os
from langchain.indexes import VectorstoreIndexCreator
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_community.document_loaders.figma import FigmaFileLoader
from langchain_openai import ChatOpenAI
figma_loader = FigmaFileLoader(
os.environ.get("ACCESS_TOKEN"),
os.environ.get("NODE_IDS"),
os.environ.get("FILE_KEY"),
)
index = VectorstoreIndexCreator().from_loaders([figma_loader])
figma_doc_retriever = index.vectorstore.as_retriever()
def generate_code(human_input):
system_prompt_template = """You are expert coder Jon Carmack. Use the provided design context to create idiomatic HTML/CSS code as possible based on the user request.
Everything must be inline in one file and your response must be directly renderable by the browser.
Figma file nodes and metadata: {context}"""
human_prompt_template = "Code the {text}. Ensure it's mobile responsive"
system_message_prompt = SystemMessagePromptTemplate.from_template(
system_prompt_template
)
human_message_prompt = HumanMessagePromptTemplate.from_template(
human_prompt_template
)
gpt_4 = ChatOpenAI(temperature=0.02, model_name="gpt-4")
relevant_nodes = figma_doc_retriever.get_relevant_documents(human_input)
conversation = [system_message_prompt, human_message_prompt]
chat_prompt = | ChatPromptTemplate.from_messages(conversation) | langchain.prompts.chat.ChatPromptTemplate.from_messages |
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 not a valid key"
os.environ["NVIDIA_API_KEY"] = nvapi_key
from langchain_nvidia_ai_endpoints import ChatNVIDIA
llm = ChatNVIDIA(model="mixtral_8x7b")
result = llm.invoke("Write a ballad about LangChain.")
print(result.content)
print(llm.batch(["What's 2*3?", "What's 2*6?"]))
for chunk in llm.stream("How far can a seagull fly in one day?"):
print(chunk.content, end="|")
async for chunk in llm.astream(
"How long does it take for monarch butterflies to migrate?"
):
print(chunk.content, end="|")
ChatNVIDIA.get_available_models()
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_nvidia_ai_endpoints import ChatNVIDIA
prompt = ChatPromptTemplate.from_messages(
[("system", "You are a helpful AI assistant named Fred."), ("user", "{input}")]
)
chain = prompt | ChatNVIDIA(model="llama2_13b") | StrOutputParser()
for txt in chain.stream({"input": "What's your name?"}):
print(txt, end="")
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are an expert coding AI. Respond only in valid python; no narration whatsoever.",
),
("user", "{input}"),
]
)
chain = prompt | ChatNVIDIA(model="llama2_code_70b") | StrOutputParser()
for txt in chain.stream({"input": "How do I solve this fizz buzz problem?"}):
print(txt, end="")
from langchain_nvidia_ai_endpoints import ChatNVIDIA
llm = ChatNVIDIA(model="nemotron_steerlm_8b")
complex_result = llm.invoke(
"What's a PB&J?", labels={"creativity": 0, "complexity": 3, "verbosity": 0}
)
print("Un-creative\n")
print(complex_result.content)
print("\n\nCreative\n")
creative_result = llm.invoke(
"What's a PB&J?", labels={"creativity": 9, "complexity": 3, "verbosity": 9}
)
print(creative_result.content)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_nvidia_ai_endpoints import ChatNVIDIA
prompt = ChatPromptTemplate.from_messages(
[("system", "You are a helpful AI assistant named Fred."), ("user", "{input}")]
)
chain = (
prompt
| ChatNVIDIA(model="nemotron_steerlm_8b").bind(
labels={"creativity": 9, "complexity": 0, "verbosity": 9}
)
| StrOutputParser()
)
for txt in chain.stream({"input": "Why is a PB&J?"}):
print(txt, end="")
import IPython
import requests
image_url = "https://www.nvidia.com/content/dam/en-zz/Solutions/research/ai-playground/nvidia-picasso-3c33-p@2x.jpg" ## Large Image
image_content = requests.get(image_url).content
IPython.display.Image(image_content)
from langchain_nvidia_ai_endpoints import ChatNVIDIA
llm = ChatNVIDIA(model="playground_neva_22b")
from langchain_core.messages import HumanMessage
llm.invoke(
[
HumanMessage(
content=[
{"type": "text", "text": "Describe this image:"},
{"type": "image_url", "image_url": {"url": image_url}},
]
)
]
)
from langchain_core.messages import HumanMessage
llm.invoke(
[
HumanMessage(
content=[
{"type": "text", "text": "Describe this image:"},
{"type": "image_url", "image_url": {"url": image_url}},
]
)
],
labels={"creativity": 0, "quality": 9, "complexity": 0, "verbosity": 0},
)
import IPython
import requests
image_url = "https://picsum.photos/seed/kitten/300/200"
image_content = requests.get(image_url).content
IPython.display.Image(image_content)
import base64
from langchain_core.messages import HumanMessage
b64_string = base64.b64encode(image_content).decode("utf-8")
llm.invoke(
[
HumanMessage(
content=[
{"type": "text", "text": "Describe this image:"},
{
"type": "image_url",
"image_url": {"url": f"data:image/png;base64,{b64_string}"},
},
]
)
]
)
base64_with_mime_type = f"data:image/png;base64,{b64_string}"
llm.invoke(f'What\'s in this image?\n<img src="{base64_with_mime_type}" />')
from langchain_nvidia_ai_endpoints import ChatNVIDIA
kosmos = ChatNVIDIA(model="kosmos_2")
from langchain_core.messages import HumanMessage
def drop_streaming_key(d):
"""Takes in payload dictionary, outputs new payload dictionary"""
if "stream" in d:
d.pop("stream")
return d
kosmos = ChatNVIDIA(model="kosmos_2")
kosmos.client.payload_fn = drop_streaming_key
kosmos.invoke(
[
HumanMessage(
content=[
{"type": "text", "text": "Describe this image:"},
{"type": "image_url", "image_url": {"url": image_url}},
]
)
]
)
import base64
from io import BytesIO
from PIL import Image
img_gen = ChatNVIDIA(model="sdxl_turbo")
def to_sdxl_payload(d):
if d:
d = {"prompt": d.get("messages", [{}])[0].get("content")}
d["inference_steps"] = 4 ## why not add another argument?
return d
img_gen.client.payload_fn = to_sdxl_payload
def to_pil_img(d):
return Image.open(BytesIO(base64.b64decode(d)))
(img_gen | StrOutputParser() | to_pil_img).invoke("white cat playing")
from langchain_core.messages import ChatMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_nvidia_ai_endpoints import ChatNVIDIA
prompt = ChatPromptTemplate.from_messages(
[
ChatMessage(
role="context", content="Parrots and Cats have signed the peace accord."
),
("user", "{input}"),
]
)
llm = ChatNVIDIA(model="nemotron_qa_8b")
chain = prompt | llm | | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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-chat already exists
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "my-chat",
"external_model": {
"name": "gpt-4",
"provider": "openai",
"task": "llm/v1/chat",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
chat = ChatDatabricks(
target_uri="databricks",
endpoint=name,
temperature=0.1,
)
chat([HumanMessage(content="hello")])
from langchain_community.embeddings import DatabricksEmbeddings
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
embeddings.embed_query("hello")[:3]
from langchain_community.llms import Databricks
llm = | Databricks(endpoint_name="dolly") | langchain_community.llms.Databricks |
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 = PromptTemplate.from_template(template)
llm = TextGen(model_url=model_url)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
model_url = "ws://localhost:5005"
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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 = PromptTemplate.from_template(template)
llm = TextGen(
model_url=model_url, streaming=True, callbacks=[StreamingStdOutCallbackHandler()]
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
llm = | TextGen(model_url=model_url, streaming=True) | langchain_community.llms.TextGen |
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 ContextCallbackHandler
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
token = os.environ["CONTEXT_API_TOKEN"]
chat = ChatOpenAI(
headers={"user_id": "123"}, temperature=0, callbacks=[ | ContextCallbackHandler(token) | langchain.callbacks.ContextCallbackHandler |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet docx2txt')
from langchain_community.document_loaders import Docx2txtLoader
loader = Docx2txtLoader("example_data/fake.docx")
data = loader.load()
data
from langchain_community.document_loaders import UnstructuredWordDocumentLoader
loader = UnstructuredWordDocumentLoader("example_data/fake.docx")
data = loader.load()
data
loader = | UnstructuredWordDocumentLoader("example_data/fake.docx", mode="elements") | langchain_community.document_loaders.UnstructuredWordDocumentLoader |
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 (
EmbeddingsClusteringFilter,
EmbeddingsRedundantFilter,
)
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
all_mini = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
multi_qa_mini = HuggingFaceEmbeddings(model_name="multi-qa-MiniLM-L6-dot-v1")
filter_embeddings = OpenAIEmbeddings()
ABS_PATH = os.path.dirname(os.path.abspath(__file__))
DB_DIR = os.path.join(ABS_PATH, "db")
client_settings = chromadb.config.Settings(
is_persistent=True,
persist_directory=DB_DIR,
anonymized_telemetry=False,
)
db_all = Chroma(
collection_name="project_store_all",
persist_directory=DB_DIR,
client_settings=client_settings,
embedding_function=all_mini,
)
db_multi_qa = Chroma(
collection_name="project_store_multi",
persist_directory=DB_DIR,
client_settings=client_settings,
embedding_function=multi_qa_mini,
)
retriever_all = db_all.as_retriever(
search_type="similarity", search_kwargs={"k": 5, "include_metadata": True}
)
retriever_multi_qa = db_multi_qa.as_retriever(
search_type="mmr", search_kwargs={"k": 5, "include_metadata": True}
)
lotr = MergerRetriever(retrievers=[retriever_all, retriever_multi_qa])
filter = EmbeddingsRedundantFilter(embeddings=filter_embeddings)
pipeline = DocumentCompressorPipeline(transformers=[filter])
compression_retriever = ContextualCompressionRetriever(
base_compressor=pipeline, base_retriever=lotr
)
filter_ordered_cluster = EmbeddingsClusteringFilter(
embeddings=filter_embeddings,
num_clusters=10,
num_closest=1,
)
filter_ordered_by_retriever = EmbeddingsClusteringFilter(
embeddings=filter_embeddings,
num_clusters=10,
num_closest=1,
sorted=True,
)
pipeline = DocumentCompressorPipeline(transformers=[filter_ordered_by_retriever])
compression_retriever = ContextualCompressionRetriever(
base_compressor=pipeline, base_retriever=lotr
)
from langchain_community.document_transformers import LongContextReorder
filter = EmbeddingsRedundantFilter(embeddings=filter_embeddings)
reordering = | LongContextReorder() | langchain_community.document_transformers.LongContextReorder |
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"]:
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import Clickhouse, ClickhouseSettings
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_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
for d in docs:
d.metadata = {"some": "metadata"}
settings = ClickhouseSettings(table="clickhouse_vector_search_example")
docsearch = | Clickhouse.from_documents(docs, embeddings, config=settings) | langchain_community.vectorstores.Clickhouse.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sentence_transformers')
from langchain_community.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings()
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
print(len(docs))
import getpass
import os
os.environ["SEMADB_API_KEY"] = getpass.getpass("SemaDB API Key:")
from langchain_community.vectorstores import SemaDB
from langchain_community.vectorstores.utils import DistanceStrategy
db = | SemaDB("mycollection", 768, embeddings, DistanceStrategy.COSINE) | langchain_community.vectorstores.SemaDB |
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 import PromptTemplate
from langchain_experimental.llm_bash.prompt import BashOutputParser
_PROMPT_TEMPLATE = """If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:
Question: "copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'"
I need to take the following actions:
- List all files in the directory
- Create a new directory
- Copy the files from the first directory into the second directory
```bash
ls
mkdir myNewDirectory
cp -r target/* myNewDirectory
```
Do not use 'echo' when writing the script.
That is the format. Begin!
Question: {question}"""
PROMPT = PromptTemplate(
input_variables=["question"],
template=_PROMPT_TEMPLATE,
output_parser= | BashOutputParser() | langchain_experimental.llm_bash.prompt.BashOutputParser |
from langchain.memory import ConversationKGMemory
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
memory = ConversationKGMemory(llm=llm)
memory.save_context({"input": "say hi to sam"}, {"output": "who is sam"})
memory.save_context({"input": "sam is a friend"}, {"output": "okay"})
memory.load_memory_variables({"input": "who is sam"})
memory = ConversationKGMemory(llm=llm, return_messages=True)
memory.save_context({"input": "say hi to sam"}, {"output": "who is sam"})
memory.save_context({"input": "sam is a friend"}, {"output": "okay"})
memory.load_memory_variables({"input": "who is sam"})
memory.get_current_entities("what's Sams favorite color?")
memory.get_knowledge_triplets("her favorite color is red")
llm = OpenAI(temperature=0)
from langchain.chains import ConversationChain
from langchain.prompts.prompt import PromptTemplate
template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.
If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the "Relevant Information" section and does not hallucinate.
Relevant Information:
{history}
Conversation:
Human: {input}
AI:"""
prompt = PromptTemplate(input_variables=["history", "input"], template=template)
conversation_with_kg = ConversationChain(
llm=llm, verbose=True, prompt=prompt, memory= | ConversationKGMemory(llm=llm) | langchain.memory.ConversationKGMemory |
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 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"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]
)
)
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 RecursiveCharacterTextSplitter
documents = TextLoader(
"../../modules/state_of_the_union.txt",
).load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={"k": 20})
query = "What did the president say about Ketanji Brown Jackson"
docs = retriever.get_relevant_documents(query)
pretty_print_docs(docs)
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import FlashrankRerank
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0)
compressor = | FlashrankRerank() | langchain.retrievers.document_compressors.FlashrankRerank |
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_API_TOKEN")
llm = HuggingFaceTextGenInference(
inference_server_url=ENDPOINT_URL,
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
server_kwargs={
"headers": {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": "application/json",
}
},
)
from langchain_community.llms import HuggingFaceEndpoint
ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
llm = HuggingFaceEndpoint(
endpoint_url=ENDPOINT_URL,
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 50,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
from langchain_community.llms import HuggingFaceHub
llm = HuggingFaceHub(
repo_id="HuggingFaceH4/zephyr-7b-beta",
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 30,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_community.chat_models.huggingface import ChatHuggingFace
messages = [
SystemMessage(content="You're a helpful assistant"),
HumanMessage(
content="What happens when an unstoppable force meets an immovable object?"
),
]
chat_model = ChatHuggingFace(llm=llm)
chat_model.model_id
chat_model._to_chat_prompt(messages)
res = chat_model.invoke(messages)
print(res.content)
from langchain import hub
from langchain.agents import AgentExecutor, load_tools
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import (
ReActJsonSingleInputOutputParser,
)
from langchain.tools.render import render_text_description
from langchain_community.utilities import SerpAPIWrapper
tools = load_tools(["serpapi", "llm-math"], llm=llm)
prompt = hub.pull("hwchase17/react-json")
prompt = prompt.partial(
tools= | render_text_description(tools) | langchain.tools.render.render_text_description |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet fastembed')
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
embeddings = | FastEmbedEmbeddings() | langchain_community.embeddings.fastembed.FastEmbedEmbeddings |
get_ipython().system('pip install gymnasium')
import tenacity
from langchain.output_parsers import RegexParser
from langchain.schema import (
HumanMessage,
SystemMessage,
)
class GymnasiumAgent:
@classmethod
def get_docs(cls, env):
return env.unwrapped.__doc__
def __init__(self, model, env):
self.model = model
self.env = env
self.docs = self.get_docs(env)
self.instructions = """
Your goal is to maximize your return, i.e. the sum of the rewards you receive.
I will give you an observation, reward, terminiation flag, truncation flag, and the return so far, formatted as:
Observation: <observation>
Reward: <reward>
Termination: <termination>
Truncation: <truncation>
Return: <sum_of_rewards>
You will respond with an action, formatted as:
Action: <action>
where you replace <action> with your actual action.
Do nothing else but return the action.
"""
self.action_parser = RegexParser(
regex=r"Action: (.*)", output_keys=["action"], default_output_key="action"
)
self.message_history = []
self.ret = 0
def random_action(self):
action = self.env.action_space.sample()
return action
def reset(self):
self.message_history = [
SystemMessage(content=self.docs),
SystemMessage(content=self.instructions),
]
def observe(self, obs, rew=0, term=False, trunc=False, info=None):
self.ret += rew
obs_message = f"""
Observation: {obs}
Reward: {rew}
Termination: {term}
Truncation: {trunc}
Return: {self.ret}
"""
self.message_history.append( | HumanMessage(content=obs_message) | langchain.schema.HumanMessage |
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 google.colab import auth
auth.authenticate_user()
get_ipython().system('gcloud services enable firestore.googleapis.com')
from langchain_core.documents.base import Document
from langchain_google_firestore import FirestoreSaver
saver = FirestoreSaver()
data = [Document(page_content="Hello, World!")]
saver.upsert_documents(data)
saver = FirestoreSaver("Collection")
saver.upsert_documents(data)
doc_ids = ["AnotherCollection/doc_id", "foo/bar"]
saver = FirestoreSaver()
saver.upsert_documents(documents=data, document_ids=doc_ids)
from langchain_google_firestore import FirestoreLoader
loader_collection = FirestoreLoader("Collection")
loader_subcollection = FirestoreLoader("Collection/doc/SubCollection")
data_collection = loader_collection.load()
data_subcollection = loader_subcollection.load()
from google.cloud import firestore
client = firestore.Client()
doc_ref = client.collection("foo").document("bar")
loader_document = FirestoreLoader(doc_ref)
data = loader_document.load()
from google.cloud.firestore import CollectionGroup, FieldFilter, Query
col_ref = client.collection("col_group")
collection_group = CollectionGroup(col_ref)
loader_group = FirestoreLoader(collection_group)
col_ref = client.collection("collection")
query = col_ref.where(filter=FieldFilter("region", "==", "west_coast"))
loader_query = | FirestoreLoader(query) | langchain_google_firestore.FirestoreLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet duckdb')
from langchain_community.document_loaders import DuckDBLoader
get_ipython().run_cell_magic('file', 'example.csv', 'Team,Payroll\nNationals,81.34\nReds,82.20\n')
loader = | DuckDBLoader("SELECT * FROM read_csv_auto('example.csv')") | langchain_community.document_loaders.DuckDBLoader |
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.environ["WATSONX_PASSWORD"] = "your password for accessing the CPD cluster"
os.environ["WATSONX_USERNAME"] = "your username for accessing the CPD cluster"
os.environ["WATSONX_INSTANCE_ID"] = "your instance_id for accessing the CPD cluster"
parameters = {
"decoding_method": "sample",
"max_new_tokens": 100,
"min_new_tokens": 1,
"temperature": 0.5,
"top_k": 50,
"top_p": 1,
}
from langchain_ibm import WatsonxLLM
watsonx_llm = WatsonxLLM(
model_id="ibm/granite-13b-instruct-v2",
url="https://us-south.ml.cloud.ibm.com",
project_id="PASTE YOUR PROJECT_ID HERE",
params=parameters,
)
watsonx_llm = WatsonxLLM(
model_id="ibm/granite-13b-instruct-v2",
url="PASTE YOUR URL HERE",
username="PASTE YOUR USERNAME HERE",
password="PASTE YOUR PASSWORD HERE",
instance_id="openshift",
version="4.8",
project_id="PASTE YOUR PROJECT_ID HERE",
params=parameters,
)
watsonx_llm = WatsonxLLM(
deployment_id="PASTE YOUR DEPLOYMENT_ID HERE",
url="https://us-south.ml.cloud.ibm.com",
project_id="PASTE YOUR PROJECT_ID HERE",
params=parameters,
)
from langchain.prompts import PromptTemplate
template = "Generate a random question about {topic}: Question: "
prompt = PromptTemplate.from_template(template)
from langchain.chains import LLMChain
llm_chain = | LLMChain(prompt=prompt, llm=watsonx_llm) | langchain.chains.LLMChain |
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.vectorstores import Vectara
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
vectara = Vectara.from_files(["state_of_the_union.txt"])
summary_config = {"is_enabled": True, "max_results": 5, "response_lang": "eng"}
retriever = vectara.as_retriever(
search_kwargs={"k": 3, "summary_config": summary_config}
)
def get_sources(documents):
return documents[:-1]
def get_summary(documents):
return documents[-1].page_content
query_str = "what did Biden say?"
(retriever | get_summary).invoke(query_str)
(retriever | get_sources).invoke(query_str)
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0)
mqr = | MultiQueryRetriever.from_llm(retriever=retriever, llm=llm) | langchain.retrievers.multi_query.MultiQueryRetriever.from_llm |
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://localhost:9200", index_name="test_index", embedding=embedding
)
namespace = f"elasticsearch/{collection_name}"
record_manager = SQLRecordManager(
namespace, db_url="sqlite:///record_manager_cache.sql"
)
record_manager.create_schema()
doc1 = Document(page_content="kitty", metadata={"source": "kitty.txt"})
doc2 = Document(page_content="doggy", metadata={"source": "doggy.txt"})
def _clear():
"""Hacky helper method to clear content. See the `full` mode section to to understand why it works."""
index([], record_manager, vectorstore, cleanup="full", source_id_key="source")
_clear()
index(
[doc1, doc1, doc1, doc1, doc1],
record_manager,
vectorstore,
cleanup=None,
source_id_key="source",
)
_clear()
index([doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source")
| index([doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source") | langchain.indexes.index |
get_ipython().system('poetry run pip install dgml-utils==0.3.0 --upgrade --quiet')
import os
from langchain_community.document_loaders import DocugamiLoader
DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")
docset_id = "26xpy3aes7xp"
document_ids = ["d7jqdzcj50sj", "cgd1eacfkchw"]
loader = DocugamiLoader(docset_id=docset_id, document_ids=document_ids)
chunks = loader.load()
len(chunks)
loader.min_text_length = 64
loader.include_xml_tags = True
chunks = loader.load()
for chunk in chunks[:5]:
print(chunk)
get_ipython().system('poetry run pip install --upgrade langchain-openai tiktoken chromadb hnswlib')
loader = | DocugamiLoader(docset_id="zo954yqy53wp") | langchain_community.document_loaders.DocugamiLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet cohere')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu')
import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:")
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.embeddings import CohereEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
documents = TextLoader("../../modules/state_of_the_union.txt").load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
retriever = FAISS.from_documents(texts, CohereEmbeddings()).as_retriever(
search_kwargs={"k": 20}
)
query = "What did the president say about Ketanji Brown Jackson"
docs = retriever.get_relevant_documents(query)
pretty_print_docs(docs)
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
from langchain_community.llms import Cohere
llm = Cohere(temperature=0)
compressor = | CohereRerank() | langchain.retrievers.document_compressors.CohereRerank |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-core langchain-experimental langchain-openai')
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import (
ChatPromptTemplate,
)
from langchain_experimental.utilities import PythonREPL
from langchain_openai import ChatOpenAI
template = """Write some python code to solve the user's problem.
Return only python code in Markdown format, e.g.:
```python
....
```"""
prompt = ChatPromptTemplate.from_messages([("system", template), ("human", "{input}")])
model = ChatOpenAI()
def _sanitize_output(text: str):
_, after = text.split("```python")
return after.split("```")[0]
chain = prompt | model | StrOutputParser() | _sanitize_output | | PythonREPL() | langchain_experimental.utilities.PythonREPL |
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_image_generator import DallEAPIWrapper
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0.9) | langchain_openai.OpenAI |
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')
YBUSER = "[SANDBOX USER]"
YBPASSWORD = "[SANDBOX PASSWORD]"
YBDATABASE = "[SANDBOX_DATABASE]"
YBHOST = "trialsandbox.sandbox.aws.yellowbrickcloud.com"
OPENAI_API_KEY = "[OPENAI API KEY]"
import os
import pathlib
import re
import sys
import urllib.parse as urlparse
from getpass import getpass
import psycopg2
from IPython.display import Markdown, display
from langchain.chains import LLMChain, RetrievalQAWithSourcesChain
from langchain.docstore.document import Document
from langchain_community.vectorstores import Yellowbrick
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
yellowbrick_connection_string = (
f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YBDATABASE}"
)
YB_DOC_DATABASE = "sample_data"
YB_DOC_TABLE = "yellowbrick_documentation"
embedding_table = "my_embeddings"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
system_template = """If you don't know the answer, Make up your best guess."""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}
llm = ChatOpenAI(
model_name="gpt-3.5-turbo", # Modify model_name if you have access to GPT-4
temperature=0,
max_tokens=256,
)
chain = LLMChain(
llm=llm,
prompt=prompt,
verbose=False,
)
def print_result_simple(query):
result = chain(query)
output_text = f"""### Question:
{query}
{result['text']}
"""
display(Markdown(output_text))
print_result_simple("How many databases can be in a Yellowbrick Instance?")
print_result_simple("What's an easy way to add users in bulk to Yellowbrick?")
try:
conn = psycopg2.connect(yellowbrick_connection_string)
except psycopg2.Error as e:
print(f"Error connecting to the database: {e}")
exit(1)
cursor = conn.cursor()
create_table_query = f"""
CREATE TABLE if not exists {embedding_table} (
id uuid,
embedding_id integer,
text character varying(60000),
metadata character varying(1024),
embedding double precision
)
DISTRIBUTE ON (id);
truncate table {embedding_table};
"""
try:
cursor.execute(create_table_query)
print(f"Table '{embedding_table}' created successfully!")
except psycopg2.Error as e:
print(f"Error creating table: {e}")
conn.rollback()
conn.commit()
cursor.close()
conn.close()
yellowbrick_doc_connection_string = (
f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YB_DOC_DATABASE}"
)
conn = psycopg2.connect(yellowbrick_doc_connection_string)
cursor = conn.cursor()
query = f"SELECT path, document FROM {YB_DOC_TABLE}"
cursor.execute(query)
yellowbrick_documents = cursor.fetchall()
print(f"Extracted {len(yellowbrick_documents)} documents successfully!")
cursor.close()
conn.close()
DOCUMENT_BASE_URL = "https://docs.yellowbrick.com/6.7.1/" # Actual URL
separator = "\n## " # This separator assumes Markdown docs from the repo uses ### as logical main header most of the time
chunk_size_limit = 2000
max_chunk_overlap = 200
documents = [
Document(
page_content=document[1],
metadata={"source": DOCUMENT_BASE_URL + document[0].replace(".md", ".html")},
)
for document in yellowbrick_documents
]
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size_limit,
chunk_overlap=max_chunk_overlap,
separators=[separator, "\nn", "\n", ",", " ", ""],
)
split_docs = text_splitter.split_documents(documents)
docs_text = [doc.page_content for doc in split_docs]
embeddings = OpenAIEmbeddings()
vector_store = Yellowbrick.from_documents(
documents=split_docs,
embedding=embeddings,
connection_string=yellowbrick_connection_string,
table=embedding_table,
)
print(f"Created vector store with {len(documents)} documents")
system_template = """Use the following pieces of context to answer the users question.
Take note of the sources and include them in the answer in the format: "SOURCES: source1 source2", use "SOURCES" in capital letters regardless of the number of sources.
If you don't know the answer, just say that "I don't know", don't try to make up an answer.
----------------
{summaries}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
| HumanMessagePromptTemplate.from_template("{question}") | langchain.prompts.chat.HumanMessagePromptTemplate.from_template |
from langchain_community.tools.edenai import (
EdenAiExplicitImageTool,
EdenAiObjectDetectionTool,
EdenAiParsingIDTool,
EdenAiParsingInvoiceTool,
EdenAiSpeechToTextTool,
EdenAiTextModerationTool,
EdenAiTextToSpeechTool,
)
from langchain.agents import AgentType, initialize_agent
from langchain_community.llms import EdenAI
llm = EdenAI(
feature="text", provider="openai", params={"temperature": 0.2, "max_tokens": 250}
)
tools = [
| EdenAiTextModerationTool(providers=["openai"], language="en") | langchain_community.tools.edenai.EdenAiTextModerationTool |
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"),
("human", "{input}"),
]
)
runnable = prompt | model
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
store = {}
def get_session_history(session_id: str) -> BaseChatMessageHistory:
if session_id not in store:
store[session_id] = ChatMessageHistory()
return store[session_id]
with_message_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
)
with_message_history.invoke(
{"ability": "math", "input": "What does cosine mean?"},
config={"configurable": {"session_id": "abc123"}},
)
with_message_history.invoke(
{"ability": "math", "input": "What?"},
config={"configurable": {"session_id": "abc123"}},
)
with_message_history.invoke(
{"ability": "math", "input": "What?"},
config={"configurable": {"session_id": "def234"}},
)
from langchain_core.runnables import ConfigurableFieldSpec
store = {}
def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory:
if (user_id, conversation_id) not in store:
store[(user_id, conversation_id)] = ChatMessageHistory()
return store[(user_id, conversation_id)]
with_message_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
history_factory_config=[
ConfigurableFieldSpec(
id="user_id",
annotation=str,
name="User ID",
description="Unique identifier for the user.",
default="",
is_shared=True,
),
ConfigurableFieldSpec(
id="conversation_id",
annotation=str,
name="Conversation ID",
description="Unique identifier for the conversation.",
default="",
is_shared=True,
),
],
)
with_message_history.invoke(
{"ability": "math", "input": "Hello"},
config={"configurable": {"user_id": "123", "conversation_id": "1"}},
)
from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableParallel
chain = RunnableParallel({"output_message": ChatOpenAI()})
def get_session_history(session_id: str) -> BaseChatMessageHistory:
if session_id not in store:
store[session_id] = | ChatMessageHistory() | langchain_community.chat_message_histories.ChatMessageHistory |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-datastore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from google.colab import auth
auth.authenticate_user()
get_ipython().system('gcloud services enable datastore.googleapis.com')
from langchain_core.documents import Document
from langchain_google_datastore import DatastoreSaver
data = [Document(page_content="Hello, World!")]
saver = DatastoreSaver()
saver.upsert_documents(data)
saver = DatastoreSaver("Collection")
saver.upsert_documents(data)
doc_ids = ["AnotherCollection/doc_id", "foo/bar"]
saver = DatastoreSaver()
saver.upsert_documents(documents=data, document_ids=doc_ids)
from langchain_google_datastore import DatastoreLoader
loader_collection = DatastoreLoader("Collection")
loader_subcollection = | DatastoreLoader("Collection/doc/SubCollection") | langchain_google_datastore.DatastoreLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet supabase')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["SUPABASE_URL"] = getpass.getpass("Supabase URL:")
os.environ["SUPABASE_SERVICE_KEY"] = getpass.getpass("Supabase Service Key:")
from dotenv import load_dotenv
load_dotenv()
import os
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_openai import OpenAIEmbeddings
from supabase.client import Client, create_client
supabase_url = os.environ.get("SUPABASE_URL")
supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
supabase: Client = create_client(supabase_url, supabase_key)
embeddings = OpenAIEmbeddings()
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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
search = DuckDuckGoSearchAPIWrapper()
llm = OpenAI(temperature=0)
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events",
),
Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math",
),
]
model = ChatOpenAI(temperature=0)
planner = | load_chat_planner(model) | langchain_experimental.plan_and_execute.load_chat_planner |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from operator import itemgetter
from langchain.output_parsers import JsonOutputToolsParser
from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
@tool
def count_emails(last_n_days: int) -> int:
"""Multiply two integers together."""
return last_n_days * 2
@tool
def send_email(message: str, recipient: str) -> str:
"Add two integers."
return f"Successfully sent email to {recipient}."
tools = [count_emails, send_email]
model = ChatOpenAI(model="gpt-3.5-turbo", temperature=0).bind_tools(tools)
def call_tool(tool_invocation: dict) -> Runnable:
"""Function for dynamically constructing the end of the chain based on the model-selected tool."""
tool_map = {tool.name: tool for tool in tools}
tool = tool_map[tool_invocation["type"]]
return RunnablePassthrough.assign(output=itemgetter("args") | tool)
call_tool_list = RunnableLambda(call_tool).map()
chain = model | JsonOutputToolsParser() | call_tool_list
chain.invoke("how many emails did i get in the last 5 days?")
import json
def human_approval(tool_invocations: list) -> Runnable:
tool_strs = "\n\n".join(
json.dumps(tool_call, indent=2) for tool_call in tool_invocations
)
msg = (
f"Do you approve of the following tool invocations\n\n{tool_strs}\n\n"
"Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no."
)
resp = input(msg)
if resp.lower() not in ("yes", "y"):
raise ValueError(f"Tool invocations not approved:\n\n{tool_strs}")
return tool_invocations
chain = model | | JsonOutputToolsParser() | langchain.output_parsers.JsonOutputToolsParser |
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 CharacterTextSplitter
raw_documents = TextLoader("state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
embeddings = HuggingFaceEmbeddings()
db = Vald.from_documents(documents, embeddings, host="localhost", port=8080)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
docs[0].page_content
embedding_vector = embeddings.embed_query(query)
docs = db.similarity_search_by_vector(embedding_vector)
docs[0].page_content
docs_and_scores = db.similarity_search_with_score(query)
docs_and_scores[0]
retriever = db.as_retriever(search_type="mmr")
retriever.get_relevant_documents(query)
db.max_marginal_relevance_search(query, k=2, fetch_k=10)
import grpc
with open("test_root_cacert.crt", "rb") as root:
credentials = grpc.ssl_channel_credentials(root_certificates=root.read())
with open(".ztoken", "rb") as ztoken:
token = ztoken.read().strip()
metadata = [(b"athenz-role-auth", token)]
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Vald
from langchain_text_splitters import CharacterTextSplitter
raw_documents = TextLoader("state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
embeddings = | HuggingFaceEmbeddings() | langchain_community.embeddings.HuggingFaceEmbeddings |
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_community.utilities import GoogleSerperAPIWrapper
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
search = | GoogleSerperAPIWrapper() | langchain_community.utilities.GoogleSerperAPIWrapper |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain.prompts import PromptTemplate
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_openai import OpenAI
template = """This is a conversation between a human and a bot:
{chat_history}
Write a summary of the conversation for {input}:
"""
prompt = PromptTemplate(input_variables=["input", "chat_history"], template=template)
memory = ConversationBufferMemory(memory_key="chat_history")
readonlymemory = ReadOnlySharedMemory(memory=memory)
summary_chain = LLMChain(
llm=OpenAI(),
prompt=prompt,
verbose=True,
memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory
)
search = GoogleSearchAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events",
),
Tool(
name="Summary",
func=summary_chain.run,
description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.",
),
]
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"],
)
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True, memory=memory
)
agent_chain.run(input="What is ChatGPT?")
agent_chain.run(input="Who developed it?")
agent_chain.run(
input="Thanks. Summarize the conversation, for my daughter 5 years old."
)
print(agent_chain.memory.buffer)
template = """This is a conversation between a human and a bot:
{chat_history}
Write a summary of the conversation for {input}:
"""
prompt = PromptTemplate(input_variables=["input", "chat_history"], template=template)
memory = ConversationBufferMemory(memory_key="chat_history")
summary_chain = LLMChain(
llm=OpenAI(),
prompt=prompt,
verbose=True,
memory=memory, # <--- this is the only change
)
search = | GoogleSearchAPIWrapper() | langchain_community.utilities.GoogleSearchAPIWrapper |
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 = | RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0) | langchain_text_splitters.RecursiveCharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet runhouse')
import runhouse as rh
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import SelfHostedHuggingFaceLLM, SelfHostedPipeline
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
llm = SelfHostedHuggingFaceLLM(
model_id="gpt2", hardware=gpu, model_reqs=["pip:./", "transformers", "torch"]
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
llm = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-small",
task="text2text-generation",
hardware=gpu,
)
llm("What is the capital of Germany?")
def load_pipeline():
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
pipeline,
)
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
return pipe
def inference_fn(pipeline, prompt, stop=None):
return pipeline(prompt)[0]["generated_text"][len(prompt) :]
llm = SelfHostedHuggingFaceLLM(
model_load_fn=load_pipeline, hardware=gpu, inference_fn=inference_fn
)
llm("Who is the current US president?")
pipeline = load_pipeline()
llm = SelfHostedPipeline.from_pipeline(
pipeline=pipeline, hardware=gpu, model_reqs=["pip:./", "transformers", "torch"]
)
import pickle
rh.blob(pickle.dumps(pipeline), path="models/pipeline.pkl").save().to(
gpu, path="models"
)
llm = | SelfHostedPipeline.from_pipeline(pipeline="models/pipeline.pkl", hardware=gpu) | langchain_community.llms.SelfHostedPipeline.from_pipeline |
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 = OpenLLM(
model_name="dolly-v2",
model_id="databricks/dolly-v2-3b",
temperature=0.94,
repetition_penalty=1.2,
)
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
template = "What is a good name for a company that makes {product}?"
prompt = | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-robocorp')
from langchain.agents import AgentExecutor, OpenAIFunctionsAgent
from langchain_core.messages import SystemMessage
from langchain_openai import ChatOpenAI
from langchain_robocorp import ActionServerToolkit
llm = ChatOpenAI(model="gpt-4", temperature=0)
toolkit = ActionServerToolkit(url="http://localhost:8080", report_trace=True)
tools = toolkit.get_tools()
system_message = | SystemMessage(content="You are a helpful assistant") | langchain_core.messages.SystemMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-bigquery')
from langchain_community.document_loaders import BigQueryLoader
BASE_QUERY = """
SELECT
id,
dna_sequence,
organism
FROM (
SELECT
ARRAY (
SELECT
AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp. (strain GC14_75)." AS organism
UNION ALL
SELECT
AS STRUCT 2 AS id, "AGGCGA" AS dna_sequence, "Heimdallarchaeota archaeon (strain LC_2)." AS organism
UNION ALL
SELECT
AS STRUCT 3 AS id, "TCCGGA" AS dna_sequence, "Acidianus hospitalis (strain W1)." AS organism) AS new_array),
UNNEST(new_array)
"""
loader = BigQueryLoader(BASE_QUERY)
data = loader.load()
print(data)
loader = BigQueryLoader(
BASE_QUERY,
page_content_columns=["dna_sequence", "organism"],
metadata_columns=["id"],
)
data = loader.load()
print(data)
ALIASED_QUERY = """
SELECT
id,
dna_sequence,
organism,
id as source
FROM (
SELECT
ARRAY (
SELECT
AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp. (strain GC14_75)." AS organism
UNION ALL
SELECT
AS STRUCT 2 AS id, "AGGCGA" AS dna_sequence, "Heimdallarchaeota archaeon (strain LC_2)." AS organism
UNION ALL
SELECT
AS STRUCT 3 AS id, "TCCGGA" AS dna_sequence, "Acidianus hospitalis (strain W1)." AS organism) AS new_array),
UNNEST(new_array)
"""
loader = | BigQueryLoader(ALIASED_QUERY, metadata_columns=["source"]) | langchain_community.document_loaders.BigQueryLoader |
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 with a complex tool."""
return int_arg * float_arg
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
model_with_tools = model.bind_tools(
[complex_tool],
tool_choice="complex_tool",
)
from operator import itemgetter
from langchain.output_parsers import JsonOutputKeyToolsParser
from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough
chain = (
model_with_tools
| JsonOutputKeyToolsParser(key_name="complex_tool", return_single=True)
| complex_tool
)
chain.invoke(
"use complex tool. the args are 5, 2.1, empty dictionary. don't forget dict_arg"
)
from typing import Any
from langchain_core.runnables import RunnableConfig
def try_except_tool(tool_args: dict, config: RunnableConfig) -> Runnable:
try:
complex_tool.invoke(tool_args, config=config)
except Exception as e:
return f"Calling tool with arguments:\n\n{tool_args}\n\nraised the following error:\n\n{type(e)}: {e}"
chain = (
model_with_tools
| JsonOutputKeyToolsParser(key_name="complex_tool", return_single=True)
| try_except_tool
)
print(
chain.invoke(
"use complex tool. the args are 5, 2.1, empty dictionary. don't forget dict_arg"
)
)
chain = (
model_with_tools
| | JsonOutputKeyToolsParser(key_name="complex_tool", return_single=True) | langchain.output_parsers.JsonOutputKeyToolsParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-cloud-sql-pg langchain-google-vertexai')
from google.colab import auth
auth.authenticate_user()
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
get_ipython().system('gcloud services enable sqladmin.googleapis.com')
REGION = "us-central1" # @param {type: "string"}
INSTANCE = "my-pg-instance" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "vector_store" # @param {type: "string"}
from langchain_google_cloud_sql_pg import PostgreSQLEngine
engine = await PostgreSQLEngine.afrom_instance(
project_id=PROJECT_ID, region=REGION, instance=INSTANCE, database=DATABASE
)
from langchain_google_cloud_sql_pg import PostgreSQLEngine
await engine.ainit_vectorstore_table(
table_name=TABLE_NAME,
vector_size=768, # Vector size for VertexAI model(textembedding-gecko@latest)
)
get_ipython().system('gcloud services enable aiplatform.googleapis.com')
from langchain_google_vertexai import VertexAIEmbeddings
embedding = VertexAIEmbeddings(
model_name="textembedding-gecko@latest", project=PROJECT_ID
)
from langchain_google_cloud_sql_pg import PostgresVectorStore
store = await PostgresVectorStore.create( # Use .create() to initialize an async vector store
engine=engine,
table_name=TABLE_NAME,
embedding_service=embedding,
)
import uuid
all_texts = ["Apples and oranges", "Cars and airplanes", "Pineapple", "Train", "Banana"]
metadatas = [{"len": len(t)} for t in all_texts]
ids = [str(uuid.uuid4()) for _ in all_texts]
await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)
await store.adelete([ids[1]])
query = "I'd like a fruit."
docs = await store.asimilarity_search(query)
print(docs)
query_vector = embedding.embed_query(query)
docs = await store.asimilarity_search_by_vector(query_vector, k=2)
print(docs)
from langchain_google_cloud_sql_pg.indexes import IVFFlatIndex
index = IVFFlatIndex()
await store.aapply_vector_index(index)
await store.areindex() # Re-index using default index name
await store.aadrop_vector_index() # Delete index using default name
from langchain_google_cloud_sql_pg import Column
TABLE_NAME = "vectorstore_custom"
await engine.ainit_vectorstore_table(
table_name=TABLE_NAME,
vector_size=768, # VertexAI model: textembedding-gecko@latest
metadata_columns=[ | Column("len", "INTEGER") | langchain_google_cloud_sql_pg.Column |
from langchain_community.document_loaders import HuggingFaceDatasetLoader
dataset_name = "imdb"
page_content_column = "text"
loader = HuggingFaceDatasetLoader(dataset_name, page_content_column)
data = loader.load()
data[:15]
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders.hugging_face_dataset import (
HuggingFaceDatasetLoader,
)
dataset_name = "tweet_eval"
page_content_column = "text"
name = "stance_climate"
loader = | HuggingFaceDatasetLoader(dataset_name, page_content_column, name) | langchain_community.document_loaders.hugging_face_dataset.HuggingFaceDatasetLoader |
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."""
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
openai_functions = [convert_pydantic_to_openai_function(Joke)]
model = ChatOpenAI(temperature=0)
prompt = ChatPromptTemplate.from_messages(
[("system", "You are helpful assistant"), ("user", "{input}")]
)
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
parser = | JsonOutputFunctionsParser() | langchain.output_parsers.openai_functions.JsonOutputFunctionsParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet semanticscholar')
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
instructions = """You are an expert researcher."""
base_prompt = | hub.pull("langchain-ai/openai-functions-template") | langchain.hub.pull |
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
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = | TextLoader("./test.txt") | langchain.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymilvus')
import os
OPENAI_API_KEY = "Use your OpenAI key:)"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain_community.vectorstores import Milvus
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
docs = [
Document(
page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
metadata={"year": 1993, "rating": 7.7, "genre": "action"},
),
Document(
page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
metadata={"year": 2010, "genre": "thriller", "rating": 8.2},
),
Document(
page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",
metadata={"year": 2019, "rating": 8.3, "genre": "drama"},
),
Document(
page_content="Three men walk into the Zone, three men walk out of the Zone",
metadata={"year": 1979, "rating": 9.9, "genre": "science fiction"},
),
Document(
page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",
metadata={"year": 2006, "genre": "thriller", "rating": 9.0},
),
Document(
page_content="Toys come alive and have a blast doing so",
metadata={"year": 1995, "genre": "animated", "rating": 9.3},
),
]
vector_store = | Milvus.from_documents(
docs,
embedding=embeddings,
connection_args={"uri": "Use your uri:) | langchain_community.vectorstores.Milvus.from_documents |
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-test",
)
documents = google_drive_loader.load()
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = | Chroma.from_documents(texts, embeddings) | langchain_community.vectorstores.Chroma.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "cassio>=0.1.4"')
import os
from getpass import getpass
from datasets import (
load_dataset,
)
from langchain_community.document_loaders import PyPDFLoader
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
os.environ["OPENAI_API_KEY"] = getpass("OPENAI_API_KEY = ")
embe = OpenAIEmbeddings()
from langchain_community.vectorstores import Cassandra
from cassandra.cluster import Cluster
cluster = Cluster(["127.0.0.1"])
session = cluster.connect()
import cassio
CASSANDRA_KEYSPACE = input("CASSANDRA_KEYSPACE = ")
cassio.init(session=session, keyspace=CASSANDRA_KEYSPACE)
vstore = Cassandra(
embedding=embe,
table_name="cassandra_vector_demo",
)
ASTRA_DB_ID = input("ASTRA_DB_ID = ")
ASTRA_DB_APPLICATION_TOKEN = getpass("ASTRA_DB_APPLICATION_TOKEN = ")
desired_keyspace = input("ASTRA_DB_KEYSPACE (optional, can be left empty) = ")
if desired_keyspace:
ASTRA_DB_KEYSPACE = desired_keyspace
else:
ASTRA_DB_KEYSPACE = None
import cassio
cassio.init(
database_id=ASTRA_DB_ID,
token=ASTRA_DB_APPLICATION_TOKEN,
keyspace=ASTRA_DB_KEYSPACE,
)
vstore = Cassandra(
embedding=embe,
table_name="cassandra_vector_demo",
)
philo_dataset = load_dataset("datastax/philosopher-quotes")["train"]
docs = []
for entry in philo_dataset:
metadata = {"author": entry["author"]}
doc = Document(page_content=entry["quote"], metadata=metadata)
docs.append(doc)
inserted_ids = vstore.add_documents(docs)
print(f"\nInserted {len(inserted_ids)} documents.")
texts = ["I think, therefore I am.", "To the things themselves!"]
metadatas = [{"author": "descartes"}, {"author": "husserl"}]
ids = ["desc_01", "huss_xy"]
inserted_ids_2 = vstore.add_texts(texts=texts, metadatas=metadatas, ids=ids)
print(f"\nInserted {len(inserted_ids_2)} documents.")
results = vstore.similarity_search("Our life is what we make of it", k=3)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
results_filtered = vstore.similarity_search(
"Our life is what we make of it",
k=3,
filter={"author": "plato"},
)
for res in results_filtered:
print(f"* {res.page_content} [{res.metadata}]")
results = vstore.similarity_search_with_score("Our life is what we make of it", k=3)
for res, score in results:
print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
results = vstore.max_marginal_relevance_search(
"Our life is what we make of it",
k=3,
filter={"author": "aristotle"},
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
delete_1 = vstore.delete(inserted_ids[:3])
print(f"all_succeed={delete_1}") # True, all documents deleted
delete_2 = vstore.delete(inserted_ids[2:5])
print(f"some_succeeds={delete_2}") # True, though some IDs were gone already
get_ipython().system('curl -L "https://github.com/awesome-astra/datasets/blob/main/demo-resources/what-is-philosophy/what-is-philosophy.pdf?raw=true" -o "what-is-philosophy.pdf"')
pdf_loader = PyPDFLoader("what-is-philosophy.pdf")
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=64)
docs_from_pdf = pdf_loader.load_and_split(text_splitter=splitter)
print(f"Documents from PDF: {len(docs_from_pdf)}.")
inserted_ids_from_pdf = vstore.add_documents(docs_from_pdf)
print(f"Inserted {len(inserted_ids_from_pdf)} documents.")
retriever = vstore.as_retriever(search_kwargs={"k": 3})
philo_template = """
You are a philosopher that draws inspiration from great thinkers of the past
to craft well-thought answers to user questions. Use the provided context as the basis
for your answers and do not make up new reasoning paths - just mix-and-match what you are given.
Your answers must be concise and to the point, and refrain from answering about other topics than philosophy.
CONTEXT:
{context}
QUESTION: {question}
YOUR ANSWER:"""
philo_prompt = ChatPromptTemplate.from_template(philo_template)
llm = ChatOpenAI()
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| philo_prompt
| llm
| | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet duckdb')
from langchain_community.document_loaders import DuckDBLoader
get_ipython().run_cell_magic('file', 'example.csv', 'Team,Payroll\nNationals,81.34\nReds,82.20\n')
loader = DuckDBLoader("SELECT * FROM read_csv_auto('example.csv')")
data = loader.load()
print(data)
loader = | DuckDBLoader(
"SELECT * FROM read_csv_auto('example.csv') | langchain_community.document_loaders.DuckDBLoader |
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:
"""Multiply two numbers."""
return a * b
print(multiply.name)
print(multiply.description)
print(multiply.args)
class SearchInput(BaseModel):
query: str = Field(description="should be a search query")
@tool("search-tool", args_schema=SearchInput, return_direct=True)
def search(query: str) -> str:
"""Look up things online."""
return "LangChain"
print(search.name)
print(search.description)
print(search.args)
print(search.return_direct)
from typing import Optional, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
class SearchInput(BaseModel):
query: str = | Field(description="should be a search query") | langchain.pydantic_v1.Field |
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"),
("human", "{input}"),
]
)
runnable = prompt | model
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
store = {}
def get_session_history(session_id: str) -> BaseChatMessageHistory:
if session_id not in store:
store[session_id] = ChatMessageHistory()
return store[session_id]
with_message_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
)
with_message_history.invoke(
{"ability": "math", "input": "What does cosine mean?"},
config={"configurable": {"session_id": "abc123"}},
)
with_message_history.invoke(
{"ability": "math", "input": "What?"},
config={"configurable": {"session_id": "abc123"}},
)
with_message_history.invoke(
{"ability": "math", "input": "What?"},
config={"configurable": {"session_id": "def234"}},
)
from langchain_core.runnables import ConfigurableFieldSpec
store = {}
def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory:
if (user_id, conversation_id) not in store:
store[(user_id, conversation_id)] = ChatMessageHistory()
return store[(user_id, conversation_id)]
with_message_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
history_factory_config=[
ConfigurableFieldSpec(
id="user_id",
annotation=str,
name="User ID",
description="Unique identifier for the user.",
default="",
is_shared=True,
),
ConfigurableFieldSpec(
id="conversation_id",
annotation=str,
name="Conversation ID",
description="Unique identifier for the conversation.",
default="",
is_shared=True,
),
],
)
with_message_history.invoke(
{"ability": "math", "input": "Hello"},
config={"configurable": {"user_id": "123", "conversation_id": "1"}},
)
from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableParallel
chain = RunnableParallel({"output_message": | ChatOpenAI() | langchain_openai.chat_models.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet bibtexparser pymupdf')
from langchain_community.document_loaders import BibtexLoader
import urllib.request
urllib.request.urlretrieve(
"https://www.fourmilab.ch/etexts/einstein/specrel/specrel.pdf", "einstein1905.pdf"
)
bibtex_text = """
@article{einstein1915,
title={Die Feldgleichungen der Gravitation},
abstract={Die Grundgleichungen der Gravitation, die ich hier entwickeln werde, wurden von mir in einer Abhandlung: ,,Die formale Grundlage der allgemeinen Relativit{\"a}tstheorie`` in den Sitzungsberichten der Preu{\ss}ischen Akademie der Wissenschaften 1915 ver{\"o}ffentlicht.},
author={Einstein, Albert},
journal={Sitzungsberichte der K{\"o}niglich Preu{\ss}ischen Akademie der Wissenschaften},
volume={1915},
number={1},
pages={844--847},
year={1915},
doi={10.1002/andp.19163540702},
link={https://onlinelibrary.wiley.com/doi/abs/10.1002/andp.19163540702},
file={einstein1905.pdf}
}
"""
with open("./biblio.bib", "w") as file:
file.write(bibtex_text)
docs = | BibtexLoader("./biblio.bib") | langchain_community.document_loaders.BibtexLoader |
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
os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:")
os.environ["OPENAI_API_KEY"] = getpass.getpass("Open API Key:")
os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass("Hugging Face API Key:")
llms = [
OpenAI(temperature=0),
| Cohere(temperature=0) | langchain_community.llms.Cohere |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet infinopy')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet matplotlib')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import datetime as dt
import json
import time
import matplotlib.dates as md
import matplotlib.pyplot as plt
from infinopy import InfinoClient
from langchain.callbacks import InfinoCallbackHandler
from langchain_openai import OpenAI
get_ipython().system('docker run --rm --detach --name infino-example -p 3000:3000 infinohq/infino:latest')
client = InfinoClient()
data = """In what country is Normandy located?
When were the Normans in Normandy?
From which countries did the Norse originate?
Who was the Norse leader?
What century did the Normans first gain their separate identity?
Who gave their name to Normandy in the 1000's and 1100's
What is France a region of?
Who did King Charles III swear fealty to?
When did the Frankish identity emerge?
Who was the duke in the battle of Hastings?
Who ruled the duchy of Normandy
What religion were the Normans
What type of major impact did the Norman dynasty have on modern Europe?
Who was famed for their Christian spirit?
Who assimilted the Roman language?
Who ruled the country of Normandy?
What principality did William the conquerer found?
What is the original meaning of the word Norman?
When was the Latin version of the word Norman first recorded?
What name comes from the English words Normans/Normanz?"""
questions = data.split("\n")
handler = InfinoCallbackHandler(
model_id="test_openai", model_version="0.1", verbose=False
)
llm = OpenAI(temperature=0.1)
num_questions = 10
questions = questions[0:num_questions]
for question in questions:
print(question)
llm_result = llm.generate([question], callbacks=[handler])
print(llm_result)
def plot(data, title):
data = json.loads(data)
timestamps = [item["time"] for item in data]
dates = [dt.datetime.fromtimestamp(ts) for ts in timestamps]
y = [item["value"] for item in data]
plt.rcParams["figure.figsize"] = [6, 4]
plt.subplots_adjust(bottom=0.2)
plt.xticks(rotation=25)
ax = plt.gca()
xfmt = md.DateFormatter("%Y-%m-%d %H:%M:%S")
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dates, y)
plt.xlabel("Time")
plt.ylabel("Value")
plt.title(title)
plt.show()
response = client.search_ts("__name__", "latency", 0, int(time.time()))
plot(response.text, "Latency")
response = client.search_ts("__name__", "error", 0, int(time.time()))
plot(response.text, "Errors")
response = client.search_ts("__name__", "prompt_tokens", 0, int(time.time()))
plot(response.text, "Prompt Tokens")
response = client.search_ts("__name__", "completion_tokens", 0, int(time.time()))
plot(response.text, "Completion Tokens")
response = client.search_ts("__name__", "total_tokens", 0, int(time.time()))
plot(response.text, "Total Tokens")
query = "normandy"
response = client.search_log(query, 0, int(time.time()))
print("Results for", query, ":", response.text)
print("===")
query = "king charles III"
response = client.search_log("king charles III", 0, int(time.time()))
print("Results for", query, ":", response.text)
from langchain.chains.summarize import load_summarize_chain
from langchain_community.document_loaders import WebBaseLoader
from langchain_openai import ChatOpenAI
handler = InfinoCallbackHandler(
model_id="test_chatopenai", model_version="0.1", verbose=False
)
urls = [
"https://lilianweng.github.io/posts/2023-06-23-agent/",
"https://medium.com/lyft-engineering/lyftlearn-ml-model-training-infrastructure-built-on-kubernetes-aef8218842bb",
"https://blog.langchain.dev/week-of-10-2-langchain-release-notes/",
]
for url in urls:
loader = WebBaseLoader(url)
docs = loader.load()
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k", callbacks=[handler])
chain = | load_summarize_chain(llm, chain_type="stuff", verbose=False) | langchain.chains.summarize.load_summarize_chain |
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=100)
tool = WikipediaQueryRun(api_wrapper=api_wrapper)
tools = [tool]
prompt = hub.pull("hwchase17/react")
llm = ChatOpenAI(temperature=0)
agent = | create_react_agent(llm, tools, prompt) | langchain.agents.create_react_agent |
import requests
def download_drive_file(url: str, output_path: str = "chat.db") -> None:
file_id = url.split("/")[-2]
download_url = f"https://drive.google.com/uc?export=download&id={file_id}"
response = requests.get(download_url)
if response.status_code != 200:
print("Failed to download the file.")
return
with open(output_path, "wb") as file:
file.write(response.content)
print(f"File {output_path} downloaded.")
url = (
"https://drive.google.com/file/d/1NebNKqTA2NXApCmeH6mu0unJD2tANZzo/view?usp=sharing"
)
download_drive_file(url)
from langchain_community.chat_loaders.imessage import IMessageChatLoader
loader = IMessageChatLoader(
path="./chat.db",
)
from typing import List
from langchain_community.chat_loaders.base import ChatSession
from langchain_community.chat_loaders.utils import (
map_ai_messages,
merge_chat_runs,
)
raw_messages = loader.lazy_load()
merged_messages = | merge_chat_runs(raw_messages) | langchain_community.chat_loaders.utils.merge_chat_runs |
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"
),
Field(
name="embedding",
type="tensor<float>(x[384])",
indexing=["attribute", "summary"],
attribute=["distance-metric: angular"],
),
)
app_package.schema.add_rank_profile(
RankProfile(
name="default",
first_phase="closeness(field, embedding)",
inputs=[("query(query_embedding)", "tensor<float>(x[384])")],
)
)
from vespa.deployment import VespaDocker
vespa_docker = VespaDocker()
vespa_app = vespa_docker.deploy(application_package=app_package)
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vespa_config = dict(
page_content_field="text",
embedding_field="embedding",
input_field="query_embedding",
)
from langchain_community.vectorstores import VespaStore
db = VespaStore.from_documents(docs, embedding_function, app=vespa_app, **vespa_config)
query = "What did the president say about Ketanji Brown Jackson"
results = db.similarity_search(query)
print(results[0].page_content)
query = "What did the president say about Ketanji Brown Jackson"
results = db.similarity_search(query)
result = results[0]
result.page_content = "UPDATED: " + result.page_content
db.add_texts([result.page_content], [result.metadata], result.metadata["id"])
results = db.similarity_search(query)
print(results[0].page_content)
result = db.similarity_search(query)
db.delete(["32"])
result = db.similarity_search(query)
results = db.similarity_search_with_score(query)
result = results[0]
db = VespaStore.from_documents(docs, embedding_function, app=vespa_app, **vespa_config)
retriever = db.as_retriever()
query = "What did the president say about Ketanji Brown Jackson"
results = retriever.get_relevant_documents(query)
app_package.schema.add_fields(
Field(name="date", type="string", indexing=["attribute", "summary"]),
Field(name="rating", type="int", indexing=["attribute", "summary"]),
Field(name="author", type="string", indexing=["attribute", "summary"]),
)
vespa_app = vespa_docker.deploy(application_package=app_package)
for i, doc in enumerate(docs):
doc.metadata["date"] = f"2023-{(i % 12)+1}-{(i % 28)+1}"
doc.metadata["rating"] = range(1, 6)[i % 5]
doc.metadata["author"] = ["Joe Biden", "Unknown"][min(i, 1)]
vespa_config.update(dict(metadata_fields=["date", "rating", "author"]))
db = | VespaStore.from_documents(docs, embedding_function, app=vespa_app, **vespa_config) | langchain_community.vectorstores.VespaStore.from_documents |
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() | langchain_community.utilities.SerpAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet promptlayer --upgrade')
import promptlayer # Don't forget this 🍰
from langchain.callbacks import PromptLayerCallbackHandler
from langchain.schema import (
HumanMessage,
)
from langchain_openai import ChatOpenAI
chat_llm = ChatOpenAI(
temperature=0,
callbacks=[PromptLayerCallbackHandler(pl_tags=["chatopenai"])],
)
llm_results = chat_llm(
[
| HumanMessage(content="What comes after 1,2,3 ?") | langchain.schema.HumanMessage |
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"
),
Field(
name="embedding",
type="tensor<float>(x[384])",
indexing=["attribute", "summary"],
attribute=["distance-metric: angular"],
),
)
app_package.schema.add_rank_profile(
RankProfile(
name="default",
first_phase="closeness(field, embedding)",
inputs=[("query(query_embedding)", "tensor<float>(x[384])")],
)
)
from vespa.deployment import VespaDocker
vespa_docker = VespaDocker()
vespa_app = vespa_docker.deploy(application_package=app_package)
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vespa_config = dict(
page_content_field="text",
embedding_field="embedding",
input_field="query_embedding",
)
from langchain_community.vectorstores import VespaStore
db = | VespaStore.from_documents(docs, embedding_function, app=vespa_app, **vespa_config) | langchain_community.vectorstores.VespaStore.from_documents |
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 - For Internal Use Only
Date: July 1, 2023
Subject: Updates and Discussions on Various Topics
Dear Team,
I hope this email finds you well. In this document, I would like to provide you with some important updates and discuss various topics that require our attention. Please treat the information contained herein as highly confidential.
Security and Privacy Measures
As part of our ongoing commitment to ensure the security and privacy of our customers' data, we have implemented robust measures across all our systems. We would like to commend John Doe (email: john.doe@example.com) from the IT department for his diligent work in enhancing our network security. Moving forward, we kindly remind everyone to strictly adhere to our data protection policies and guidelines. Additionally, if you come across any potential security risks or incidents, please report them immediately to our dedicated team at security@example.com.
HR Updates and Employee Benefits
Recently, we welcomed several new team members who have made significant contributions to their respective departments. I would like to recognize Jane Smith (SSN: 049-45-5928) for her outstanding performance in customer service. Jane has consistently received positive feedback from our clients. Furthermore, please remember that the open enrollment period for our employee benefits program is fast approaching. Should you have any questions or require assistance, please contact our HR representative, Michael Johnson (phone: 418-492-3850, email: michael.johnson@example.com).
Marketing Initiatives and Campaigns
Our marketing team has been actively working on developing new strategies to increase brand awareness and drive customer engagement. We would like to thank Sarah Thompson (phone: 415-555-1234) for her exceptional efforts in managing our social media platforms. Sarah has successfully increased our follower base by 20% in the past month alone. Moreover, please mark your calendars for the upcoming product launch event on July 15th. We encourage all team members to attend and support this exciting milestone for our company.
Research and Development Projects
In our pursuit of innovation, our research and development department has been working tirelessly on various projects. I would like to acknowledge the exceptional work of David Rodriguez (email: david.rodriguez@example.com) in his role as project lead. David's contributions to the development of our cutting-edge technology have been instrumental. Furthermore, we would like to remind everyone to share their ideas and suggestions for potential new projects during our monthly R&D brainstorming session, scheduled for July 10th.
Please treat the information in this document with utmost confidentiality and ensure that it is not shared with unauthorized individuals. If you have any questions or concerns regarding the topics discussed, please do not hesitate to reach out to me directly.
Thank you for your attention, and let's continue to work together to achieve our goals.
Best regards,
Jason Fan
Cofounder & CEO
Psychic
jason@psychic.dev
"""
print(sample_text)
documents = [Document(page_content=sample_text)]
properties = [
{
"name": "category",
"description": "What type of email this is.",
"type": "string",
"enum": ["update", "action_item", "customer_feedback", "announcement", "other"],
"required": True,
},
{
"name": "mentions",
"description": "A list of all people mentioned in this email.",
"type": "array",
"items": {
"name": "full_name",
"description": "The full name of the person mentioned.",
"type": "string",
},
"required": True,
},
{
"name": "eli5",
"description": "Explain this email to me like I'm 5 years old.",
"type": "string",
"required": True,
},
]
property_extractor = | DoctranPropertyExtractor(properties=properties) | langchain_community.document_transformers.DoctranPropertyExtractor |
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
conversation = ConversationChain(
llm=llm, verbose=True, memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there!")
conversation.predict(input="What's the weather?")
from langchain.prompts.prompt import PromptTemplate
template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
{history}
Human: {input}
AI Assistant:"""
PROMPT = PromptTemplate(input_variables=["history", "input"], template=template)
conversation = ConversationChain(
prompt=PROMPT,
llm=llm,
verbose=True,
memory=ConversationBufferMemory(ai_prefix="AI Assistant"),
)
conversation.predict(input="Hi there!")
conversation.predict(input="What's the weather?")
from langchain.prompts.prompt import PromptTemplate
template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
{history}
Friend: {input}
AI:"""
PROMPT = PromptTemplate(input_variables=["history", "input"], template=template)
conversation = ConversationChain(
prompt=PROMPT,
llm=llm,
verbose=True,
memory= | ConversationBufferMemory(human_prefix="Friend") | langchain.memory.ConversationBufferMemory |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken langchain-openai python-dotenv datasets langchain deeplake beautifulsoup4 html2text ragas')
ORG_ID = "..."
import getpass
import os
from langchain.chains import RetrievalQA
from langchain.vectorstores.deeplake import DeepLake
from langchain_openai import OpenAIChat, OpenAIEmbeddings
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API token: ")
os.environ["ACTIVELOOP_TOKEN"] = getpass.getpass(
"Enter your ActiveLoop API token: "
) # Get your API token from https://app.activeloop.ai, click on your profile picture in the top right corner, and select "API Tokens"
token = os.getenv("ACTIVELOOP_TOKEN")
openai_embeddings = OpenAIEmbeddings()
db = DeepLake(
dataset_path=f"hub://{ORG_ID}/deeplake-docs-deepmemory", # org_id stands for your username or organization from activeloop
embedding=openai_embeddings,
runtime={"tensor_db": True},
token=token,
read_only=False,
)
from urllib.parse import urljoin
import requests
from bs4 import BeautifulSoup
def get_all_links(url):
response = requests.get(url)
if response.status_code != 200:
print(f"Failed to retrieve the page: {url}")
return []
soup = BeautifulSoup(response.content, "html.parser")
links = [
urljoin(url, a["href"]) for a in soup.find_all("a", href=True) if a["href"]
]
return links
base_url = "https://docs.deeplake.ai/en/latest/"
all_links = get_all_links(base_url)
from langchain.document_loaders import AsyncHtmlLoader
loader = AsyncHtmlLoader(all_links)
docs = loader.load()
from langchain.document_transformers import Html2TextTransformer
html2text = Html2TextTransformer()
docs_transformed = html2text.transform_documents(docs)
from langchain_text_splitters import RecursiveCharacterTextSplitter
chunk_size = 4096
docs_new = []
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
)
for doc in docs_transformed:
if len(doc.page_content) < chunk_size:
docs_new.append(doc)
else:
docs = text_splitter.create_documents([doc.page_content])
docs_new.extend(docs)
docs = db.add_documents(docs_new)
from typing import List
from langchain.chains.openai_functions import (
create_structured_output_chain,
)
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field
docs = db.vectorstore.dataset.text.data(fetch_chunks=True, aslist=True)["value"]
ids = db.vectorstore.dataset.id.data(fetch_chunks=True, aslist=True)["value"]
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
class Questions(BaseModel):
"""Identifying information about a person."""
question: str = Field(..., description="Questions about text")
prompt_msgs = [
SystemMessage(
content="You are a world class expert for generating questions based on provided context. \
You make sure the question can be answered by the text."
),
HumanMessagePromptTemplate.from_template(
"Use the given text to generate a question from the following input: {input}"
),
HumanMessage(content="Tips: Make sure to answer in the correct format"),
]
prompt = | ChatPromptTemplate(messages=prompt_msgs) | langchain_core.prompts.ChatPromptTemplate |
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:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d"
llama2_chat_replicate = Replicate(
model=replicate_id, input={"temperature": 0.01, "max_length": 500, "top_p": 1}
)
llm = llama2_chat
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_uri("sqlite:///nba_roster.db", sample_rows_in_table_info=0)
def get_schema(_):
return db.get_table_info()
def run_query(query):
return db.run(query)
from langchain_core.prompts import ChatPromptTemplate
template = """Based on the table schema below, write a SQL query that would answer the user's question:
{schema}
Question: {question}
SQL Query:"""
prompt = ChatPromptTemplate.from_messages(
[
("system", "Given an input question, convert it to a SQL query. No pre-amble."),
("human", template),
]
)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
sql_response = (
RunnablePassthrough.assign(schema=get_schema)
| prompt
| llm.bind(stop=["\nSQLResult:"])
| StrOutputParser()
)
sql_response.invoke({"question": "What team is Klay Thompson on?"})
template = """Based on the table schema below, question, sql query, and sql response, write a natural language response:
{schema}
Question: {question}
SQL Query: {query}
SQL Response: {response}"""
prompt_response = ChatPromptTemplate.from_messages(
[
(
"system",
"Given an input question and SQL response, convert it to a natural language answer. No pre-amble.",
),
("human", template),
]
)
full_chain = (
RunnablePassthrough.assign(query=sql_response)
| RunnablePassthrough.assign(
schema=get_schema,
response=lambda x: db.run(x["query"]),
)
| prompt_response
| llm
)
full_chain.invoke({"question": "How many unique teams are there?"})
from langchain.memory import ConversationBufferMemory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
template = """Given an input question, convert it to a SQL query. No pre-amble. Based on the table schema below, write a SQL query that would answer the user's question:
{schema}
"""
prompt = ChatPromptTemplate.from_messages(
[
("system", template),
MessagesPlaceholder(variable_name="history"),
("human", "{question}"),
]
)
memory = ConversationBufferMemory(return_messages=True)
from langchain_core.runnables import RunnableLambda
sql_chain = (
RunnablePassthrough.assign(
schema=get_schema,
history=RunnableLambda(lambda x: memory.load_memory_variables(x)["history"]),
)
| prompt
| llm.bind(stop=["\nSQLResult:"])
| StrOutputParser()
)
def save(input_output):
output = {"output": input_output.pop("output")}
memory.save_context(input_output, output)
return output["output"]
sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save
sql_response_memory.invoke({"question": "What team is Klay Thompson on?"})
template = """Based on the table schema below, question, sql query, and sql response, write a natural language response:
{schema}
Question: {question}
SQL Query: {query}
SQL Response: {response}"""
prompt_response = ChatPromptTemplate.from_messages(
[
(
"system",
"Given an input question and SQL response, convert it to a natural language answer. No pre-amble.",
),
("human", template),
]
)
full_chain = (
| RunnablePassthrough.assign(query=sql_response_memory) | langchain_core.runnables.RunnablePassthrough.assign |
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 ChatOpenAI
os.environ["GITHUB_APP_ID"] = "123456"
os.environ["GITHUB_APP_PRIVATE_KEY"] = "path/to/your/private-key.pem"
os.environ["GITHUB_REPOSITORY"] = "username/repo-name"
os.environ["GITHUB_BRANCH"] = "bot-branch-name"
os.environ["GITHUB_BASE_BRANCH"] = "main"
os.environ["OPENAI_API_KEY"] = ""
llm = ChatOpenAI(temperature=0, model="gpt-4-1106-preview")
github = GitHubAPIWrapper()
toolkit = GitHubToolkit.from_github_api_wrapper(github)
tools = toolkit.get_tools()
agent = initialize_agent(
tools,
llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
print("Available tools:")
for tool in tools:
print("\t" + tool.name)
agent.run(
"You have the software engineering capabilities of a Google Principle engineer. You are tasked with completing issues on a github repository. Please look at the existing issues and complete them."
)
from langchain import hub
gh_issue_prompt_template = hub.pull("kastanday/new-github-issue")
print(gh_issue_prompt_template.template)
def format_issue(issue):
title = f"Title: {issue.get('title')}."
opened_by = f"Opened by user: {issue.get('opened_by')}"
body = f"Body: {issue.get('body')}"
comments = issue.get("comments") # often too long
return "\n".join([title, opened_by, body])
issue = github.get_issue(33) # task to implement a RNA-seq pipeline (bioinformatics)
final_gh_issue_prompt = gh_issue_prompt_template.format(
issue_description=format_issue(issue)
)
print(final_gh_issue_prompt)
from langchain.memory.summary_buffer import ConversationSummaryBufferMemory
from langchain_core.prompts.chat import MessagesPlaceholder
summarizer_llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") # type: ignore
chat_history = MessagesPlaceholder(variable_name="chat_history")
memory = ConversationSummaryBufferMemory(
memory_key="chat_history",
return_messages=True,
llm=summarizer_llm,
max_token_limit=2_000,
)
agent = initialize_agent(
tools,
llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True, # or pass a function that accepts the error and returns a string
max_iterations=30,
max_execution_time=None,
early_stopping_method="generate",
memory=memory,
agent_kwargs={
"memory_prompts": [chat_history],
"input_variables": ["input", "agent_scratchpad", "chat_history"],
"prefix": final_gh_issue_prompt,
},
)
from langchain_core.tracers.context import tracing_v2_enabled
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGCHAIN_API_KEY"] = "ls__......"
os.environ["LANGCHAIN_PROJECT"] = "Github_Demo_PR"
os.environ["LANGCHAIN_WANDB_TRACING"] = "false"
with | tracing_v2_enabled(project_name="Github_Demo_PR", tags=["PR_bot"]) | langchain_core.tracers.context.tracing_v2_enabled |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lxml')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet html2text')
from langchain_community.document_loaders import EverNoteLoader
loader = EverNoteLoader("example_data/testing.enex")
loader.load()
loader = | EverNoteLoader("example_data/testing.enex", load_single_document=False) | langchain_community.document_loaders.EverNoteLoader |
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
from langchain.globals import set_debug, set_verbose
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain_community.llms import OpaquePrompts
from langchain_openai import OpenAI
set_debug(True)
| set_verbose(True) | langchain.globals.set_verbose |
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-turbo-instruct")
from langchain.prompts import PromptTemplate
PROMPT_TEMPLATE = """Here is the description of a meal: "{meal}".
Embed the meal into the given text: "{text_to_personalize}".
Prepend a personalized message including the user's name "{user}"
and their preference "{preference}".
Make it sound good.
"""
PROMPT = PromptTemplate(
input_variables=["meal", "text_to_personalize", "user", "preference"],
template=PROMPT_TEMPLATE,
)
import langchain_experimental.rl_chain as rl_chain
chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
response = chain.run(
meal=rl_chain.ToSelectFrom(meals),
user=rl_chain.BasedOn("Tom"),
preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]),
text_to_personalize="This is the weeks specialty dish, our master chefs \
believe you will love it!",
)
print(response["response"])
for _ in range(5):
try:
response = chain.run(
meal=rl_chain.ToSelectFrom(meals),
user=rl_chain.BasedOn("Tom"),
preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]),
text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!",
)
except Exception as e:
print(e)
print(response["response"])
print()
scoring_criteria_template = (
"Given {preference} rank how good or bad this selection is {meal}"
)
chain = rl_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
selection_scorer=rl_chain.AutoSelectionScorer(
llm=llm, scoring_criteria_template_str=scoring_criteria_template
),
)
response = chain.run(
meal=rl_chain.ToSelectFrom(meals),
user=rl_chain.BasedOn("Tom"),
preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]),
text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!",
)
print(response["response"])
selection_metadata = response["selection_metadata"]
print(
f"selected index: {selection_metadata.selected.index}, score: {selection_metadata.selected.score}"
)
class CustomSelectionScorer(rl_chain.SelectionScorer):
def score_response(
self, inputs, llm_response: str, event: rl_chain.PickBestEvent
) -> float:
print(event.based_on)
print(event.to_select_from)
selected_meal = event.to_select_from["meal"][event.selected.index]
print(f"selected meal: {selected_meal}")
if "Tom" in event.based_on["user"]:
if "Vegetarian" in event.based_on["preference"]:
if "Chicken" in selected_meal or "Beef" in selected_meal:
return 0.0
else:
return 1.0
else:
if "Chicken" in selected_meal or "Beef" in selected_meal:
return 1.0
else:
return 0.0
else:
raise NotImplementedError("I don't know how to score this user")
chain = rl_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
selection_scorer=CustomSelectionScorer(),
)
response = chain.run(
meal=rl_chain.ToSelectFrom(meals),
user=rl_chain.BasedOn("Tom"),
preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]),
text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!",
)
class CustomSelectionScorer(rl_chain.SelectionScorer):
def score_preference(self, preference, selected_meal):
if "Vegetarian" in preference:
if "Chicken" in selected_meal or "Beef" in selected_meal:
return 0.0
else:
return 1.0
else:
if "Chicken" in selected_meal or "Beef" in selected_meal:
return 1.0
else:
return 0.0
def score_response(
self, inputs, llm_response: str, event: rl_chain.PickBestEvent
) -> float:
selected_meal = event.to_select_from["meal"][event.selected.index]
if "Tom" in event.based_on["user"]:
return self.score_preference(event.based_on["preference"], selected_meal)
elif "Anna" in event.based_on["user"]:
return self.score_preference(event.based_on["preference"], selected_meal)
else:
raise NotImplementedError("I don't know how to score this user")
chain = rl_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
selection_scorer=CustomSelectionScorer(),
metrics_step=5,
metrics_window_size=5, # rolling window average
)
random_chain = rl_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
selection_scorer=CustomSelectionScorer(),
metrics_step=5,
metrics_window_size=5, # rolling window average
policy=rl_chain.PickBestRandomPolicy, # set the random policy instead of default
)
for _ in range(20):
try:
chain.run(
meal=rl_chain.ToSelectFrom(meals),
user=rl_chain.BasedOn("Tom"),
preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]),
text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!",
)
random_chain.run(
meal= | rl_chain.ToSelectFrom(meals) | langchain_experimental.rl_chain.ToSelectFrom |
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 import BaseModel, Field
class DocumentInput(BaseModel):
question: str = Field()
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
tools = []
files = [
{
"name": "alphabet-earnings",
"path": "/Users/harrisonchase/Downloads/2023Q1_alphabet_earnings_release.pdf",
},
{
"name": "tesla-earnings",
"path": "/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf",
},
]
for file in files:
loader = PyPDFLoader(file["path"])
pages = loader.load_and_split()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(pages)
embeddings = OpenAIEmbeddings()
retriever = FAISS.from_documents(docs, embeddings).as_retriever()
tools.append(
Tool(
args_schema=DocumentInput,
name=file["name"],
description=f"useful when you want to answer questions about {file['name']}",
func= | RetrievalQA.from_chain_type(llm=llm, retriever=retriever) | langchain.chains.RetrievalQA.from_chain_type |
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass()
activeloop_token = getpass("Activeloop Token:")
os.environ["ACTIVELOOP_TOKEN"] = activeloop_token
get_ipython().system('ls "../../../../../../libs"')
from langchain_community.document_loaders import TextLoader
root_dir = "../../../../../../libs"
docs = []
for dirpath, dirnames, filenames in os.walk(root_dir):
for file in filenames:
if file.endswith(".py") and "*venv/" not in dirpath:
try:
loader = TextLoader(os.path.join(dirpath, file), encoding="utf-8")
docs.extend(loader.load_and_split())
except Exception:
pass
print(f"{len(docs)}")
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
print(f"{len(texts)}")
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
embeddings
from langchain_community.vectorstores import DeepLake
username = "<USERNAME_OR_ORG>"
db = DeepLake.from_documents(
texts, embeddings, dataset_path=f"hub://{username}/langchain-code", overwrite=True
)
db
db = DeepLake(
dataset_path=f"hub://{username}/langchain-code",
read_only=True,
embedding=embeddings,
)
retriever = db.as_retriever()
retriever.search_kwargs["distance_metric"] = "cos"
retriever.search_kwargs["fetch_k"] = 20
retriever.search_kwargs["maximal_marginal_relevance"] = True
retriever.search_kwargs["k"] = 20
def filter(x):
if "something" in x["text"].data()["value"]:
return False
metadata = x["metadata"].data()["value"]
return "only_this" in metadata["source"] or "also_that" in metadata["source"]
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import ChatOpenAI
model = ChatOpenAI(
model_name="gpt-3.5-turbo-0613"
) # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',
qa = | ConversationalRetrievalChain.from_llm(model, retriever=retriever) | langchain.chains.ConversationalRetrievalChain.from_llm |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain -q')
etherscanAPIKey = "..."
import os
from langchain_community.document_loaders import EtherscanLoader
os.environ["ETHERSCAN_API_KEY"] = etherscanAPIKey
account_address = "0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b"
loader = | EtherscanLoader(account_address, filter="erc20_transaction") | langchain_community.document_loaders.EtherscanLoader |
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_community.chat_models import JinaChat
from langchain_core.messages import HumanMessage, SystemMessage
chat = | JinaChat(temperature=0) | langchain_community.chat_models.JinaChat |
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 WikipediaRetriever
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
wiki = | WikipediaRetriever(top_k_results=6, doc_content_chars_max=2000) | langchain_community.retrievers.WikipediaRetriever |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain label-studio label-studio-sdk langchain-openai')
import os
os.environ["LABEL_STUDIO_URL"] = "<YOUR-LABEL-STUDIO-URL>" # e.g. http://localhost:8080
os.environ["LABEL_STUDIO_API_KEY"] = "<YOUR-LABEL-STUDIO-API-KEY>"
os.environ["OPENAI_API_KEY"] = "<YOUR-OPENAI-API-KEY>"
from langchain.callbacks import LabelStudioCallbackHandler
from langchain_openai import OpenAI
llm = OpenAI(
temperature=0, callbacks=[LabelStudioCallbackHandler(project_name="My Project")]
)
print(llm("Tell me a joke"))
from langchain.callbacks import LabelStudioCallbackHandler
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
chat_llm = ChatOpenAI(
callbacks=[
LabelStudioCallbackHandler(
mode="chat",
project_name="New Project with Chat",
)
]
)
llm_results = chat_llm(
[
| SystemMessage(content="Always use a lot of emojis") | langchain_core.messages.SystemMessage |
from langchain.prompts import ChatMessagePromptTemplate
prompt = "May the {subject} be with you"
chat_message_prompt = ChatMessagePromptTemplate.from_template(
role="Jedi", template=prompt
)
chat_message_prompt.format(subject="force")
from langchain.prompts import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
human_prompt = "Summarize our conversation so far in {word_count} words."
human_message_template = HumanMessagePromptTemplate.from_template(human_prompt)
chat_prompt = ChatPromptTemplate.from_messages(
[MessagesPlaceholder(variable_name="conversation"), human_message_template]
)
from langchain_core.messages import AIMessage, HumanMessage
human_message = HumanMessage(content="What is the best way to learn programming?")
ai_message = | AIMessage(
content="""\
1. Choose a programming language: Decide on a programming language that you want to learn.
2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.
3. Practice, practice, practice: The best way to learn programming is through hands-on experience\
"""
) | langchain_core.messages.AIMessage |