prompt
stringlengths 43
25.9k
| completion
stringlengths 7
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stringlengths 18
90
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|---|---|---|
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 nest_asyncio
from langchain.chains.graph_qa import GremlinQAChain
from langchain.schema import Document
from langchain_community.graphs import GremlinGraph
from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
from langchain_openai import AzureChatOpenAI
cosmosdb_name = "mycosmosdb"
cosmosdb_db_id = "graphtesting"
cosmosdb_db_graph_id = "mygraph"
cosmosdb_access_Key = "longstring=="
graph = GremlinGraph(
url=f"=wss://{cosmosdb_name}.gremlin.cosmos.azure.com:443/",
username=f"/dbs/{cosmosdb_db_id}/colls/{cosmosdb_db_graph_id}",
password=cosmosdb_access_Key,
)
source_doc = | Document(
page_content="Matrix is a movie where Keanu Reeves, Laurence Fishburne and Carrie-Anne Moss acted."
) | langchain.schema.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet comet_ml langchain langchain-openai google-search-results spacy textstat pandas')
get_ipython().system('{sys.executable} -m spacy download en_core_web_sm')
import comet_ml
comet_ml.init(project_name="comet-example-langchain")
import os
os.environ["OPENAI_API_KEY"] = "..."
os.environ["SERPAPI_API_KEY"] = "..."
from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain_openai import OpenAI
comet_callback = CometCallbackHandler(
project_name="comet-example-langchain",
complexity_metrics=True,
stream_logs=True,
tags=["llm"],
visualizations=["dep"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)
llm_result = llm.generate(["Tell me a joke", "Tell me a poem", "Tell me a fact"] * 3)
print("LLM result", llm_result)
comet_callback.flush_tracker(llm, finish=True)
from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
comet_callback = CometCallbackHandler(
complexity_metrics=True,
project_name="comet-example-langchain",
stream_logs=True,
tags=["synopsis-chain"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)
test_prompts = [{"title": "Documentary about Bigfoot in Paris"}]
print(synopsis_chain.apply(test_prompts))
comet_callback.flush_tracker(synopsis_chain, finish=True)
from langchain.agents import initialize_agent, load_tools
from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain_openai import OpenAI
comet_callback = CometCallbackHandler(
project_name="comet-example-langchain",
complexity_metrics=True,
stream_logs=True,
tags=["agent"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = | OpenAI(temperature=0.9, callbacks=callbacks) | langchain_openai.OpenAI |
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.prompts import PromptTemplate
from langchain_community.llms import TitanTakeoffPro
llm = TitanTakeoffPro()
output = llm("What is the weather in London in August?")
print(output)
llm = TitanTakeoffPro(
base_url="http://localhost:3000",
min_new_tokens=128,
max_new_tokens=512,
no_repeat_ngram_size=2,
sampling_topk=1,
sampling_topp=1.0,
sampling_temperature=1.0,
repetition_penalty=1.0,
regex_string="",
)
output = llm("What is the largest rainforest in the world?")
print(output)
llm = TitanTakeoffPro()
rich_output = llm.generate(["What is Deep Learning?", "What is Machine Learning?"])
print(rich_output.generations)
llm = TitanTakeoffPro(
streaming=True, callback_manager=CallbackManager([ | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
get_ipython().system('pip install langchain lark openai elasticsearch pandas')
import pandas as pd
details = (
pd.read_csv("~/Downloads/archive/Hotel_details.csv")
.drop_duplicates(subset="hotelid")
.set_index("hotelid")
)
attributes = pd.read_csv(
"~/Downloads/archive/Hotel_Room_attributes.csv", index_col="id"
)
price = pd.read_csv("~/Downloads/archive/hotels_RoomPrice.csv", index_col="id")
latest_price = price.drop_duplicates(subset="refid", keep="last")[
[
"hotelcode",
"roomtype",
"onsiterate",
"roomamenities",
"maxoccupancy",
"mealinclusiontype",
]
]
latest_price["ratedescription"] = attributes.loc[latest_price.index]["ratedescription"]
latest_price = latest_price.join(
details[["hotelname", "city", "country", "starrating"]], on="hotelcode"
)
latest_price = latest_price.rename({"ratedescription": "roomdescription"}, axis=1)
latest_price["mealsincluded"] = ~latest_price["mealinclusiontype"].isnull()
latest_price.pop("hotelcode")
latest_price.pop("mealinclusiontype")
latest_price = latest_price.reset_index(drop=True)
latest_price.head()
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4")
res = model.predict(
"Below is a table with information about hotel rooms. "
"Return a JSON list with an entry for each column. Each entry should have "
'{"name": "column name", "description": "column description", "type": "column data type"}'
f"\n\n{latest_price.head()}\n\nJSON:\n"
)
import json
attribute_info = json.loads(res)
attribute_info
latest_price.nunique()[latest_price.nunique() < 40]
attribute_info[-2][
"description"
] += f". Valid values are {sorted(latest_price['starrating'].value_counts().index.tolist())}"
attribute_info[3][
"description"
] += f". Valid values are {sorted(latest_price['maxoccupancy'].value_counts().index.tolist())}"
attribute_info[-3][
"description"
] += f". Valid values are {sorted(latest_price['country'].value_counts().index.tolist())}"
attribute_info
from langchain.chains.query_constructor.base import (
get_query_constructor_prompt,
load_query_constructor_runnable,
)
doc_contents = "Detailed description of a hotel room"
prompt = get_query_constructor_prompt(doc_contents, attribute_info)
print(prompt.format(query="{query}"))
chain = load_query_constructor_runnable(
ChatOpenAI(model="gpt-3.5-turbo", temperature=0), doc_contents, attribute_info
)
chain.invoke({"query": "I want a hotel in Southern Europe and my budget is 200 bucks."})
chain.invoke(
{
"query": "Find a 2-person room in Vienna or London, preferably with meals included and AC"
}
)
attribute_info[-3][
"description"
] += ". NOTE: Only use the 'eq' operator if a specific country is mentioned. If a region is mentioned, include all relevant countries in filter."
chain = load_query_constructor_runnable(
| ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-spanner')
from google.colab import auth
auth.authenticate_user()
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
get_ipython().system('gcloud services 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 |
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") | langchain.hub.pull |
from typing import Any, Dict, List, Union
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks.base import BaseCallbackHandler
from langchain_core.agents import AgentAction
from langchain_openai import OpenAI
class MyCustomHandlerOne(BaseCallbackHandler):
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> Any:
print(f"on_llm_start {serialized['name']}")
def on_llm_new_token(self, token: str, **kwargs: Any) -> Any:
print(f"on_new_token {token}")
def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> Any:
"""Run when LLM errors."""
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> Any:
print(f"on_chain_start {serialized['name']}")
def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> Any:
print(f"on_tool_start {serialized['name']}")
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
print(f"on_agent_action {action}")
class MyCustomHandlerTwo(BaseCallbackHandler):
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> Any:
print(f"on_llm_start (I'm the second handler!!) {serialized['name']}")
handler1 = MyCustomHandlerOne()
handler2 = MyCustomHandlerTwo()
llm = OpenAI(temperature=0, streaming=True, callbacks=[handler2])
tools = | load_tools(["llm-math"], llm=llm) | langchain.agents.load_tools |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0).configurable_fields(
temperature=ConfigurableField(
id="llm_temperature",
name="LLM Temperature",
description="The temperature of the LLM",
)
)
model.invoke("pick a random number")
model.with_config(configurable={"llm_temperature": 0.9}).invoke("pick a random number")
prompt = PromptTemplate.from_template("Pick a random number above {x}")
chain = prompt | model
chain.invoke({"x": 0})
chain.with_config(configurable={"llm_temperature": 0.9}).invoke({"x": 0})
from langchain.runnables.hub import HubRunnable
prompt = HubRunnable("rlm/rag-prompt").configurable_fields(
owner_repo_commit=ConfigurableField(
id="hub_commit",
name="Hub Commit",
description="The Hub commit to pull from",
)
)
prompt.invoke({"question": "foo", "context": "bar"})
prompt.with_config(configurable={"hub_commit": "rlm/rag-prompt-llama"}).invoke(
{"question": "foo", "context": "bar"}
)
from langchain.prompts import PromptTemplate
from langchain_community.chat_models import ChatAnthropic
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
llm = ChatAnthropic(temperature=0).configurable_alternatives(
ConfigurableField(id="llm"),
default_key="anthropic",
openai=ChatOpenAI(),
gpt4=ChatOpenAI(model="gpt-4"),
)
prompt = PromptTemplate.from_template("Tell me a joke about {topic}")
chain = prompt | llm
chain.invoke({"topic": "bears"})
chain.with_config(configurable={"llm": "openai"}).invoke({"topic": "bears"})
chain.with_config(configurable={"llm": "anthropic"}).invoke({"topic": "bears"})
llm = ChatAnthropic(temperature=0)
prompt = PromptTemplate.from_template(
"Tell me a joke about {topic}"
).configurable_alternatives(
ConfigurableField(id="prompt"),
default_key="joke",
poem=PromptTemplate.from_template("Write a short poem about {topic}"),
)
chain = prompt | llm
chain.invoke({"topic": "bears"})
chain.with_config(configurable={"prompt": "poem"}).invoke({"topic": "bears"})
llm = ChatAnthropic(temperature=0).configurable_alternatives(
| ConfigurableField(id="llm") | langchain_core.runnables.ConfigurableField |
get_ipython().system('pip3 install clickhouse-sqlalchemy InstructorEmbedding sentence_transformers openai langchain-experimental')
import getpass
from os import environ
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.utilities import SQLDatabase
from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain
from langchain_openai import OpenAI
from sqlalchemy import MetaData, create_engine
MYSCALE_HOST = "msc-4a9e710a.us-east-1.aws.staging.myscale.cloud"
MYSCALE_PORT = 443
MYSCALE_USER = "chatdata"
MYSCALE_PASSWORD = "myscale_rocks"
OPENAI_API_KEY = getpass.getpass("OpenAI API Key:")
engine = create_engine(
f"clickhouse://{MYSCALE_USER}:{MYSCALE_PASSWORD}@{MYSCALE_HOST}:{MYSCALE_PORT}/default?protocol=https"
)
metadata = MetaData(bind=engine)
environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_experimental.sql.vector_sql import VectorSQLOutputParser
output_parser = VectorSQLOutputParser.from_embeddings(
model=HuggingFaceInstructEmbeddings(
model_name="hkunlp/instructor-xl", model_kwargs={"device": "cpu"}
)
)
from langchain.callbacks import StdOutCallbackHandler
from langchain_community.utilities.sql_database import SQLDatabase
from langchain_experimental.sql.prompt import MYSCALE_PROMPT
from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain
from langchain_openai import OpenAI
chain = VectorSQLDatabaseChain(
llm_chain=LLMChain(
llm=OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0),
prompt=MYSCALE_PROMPT,
),
top_k=10,
return_direct=True,
sql_cmd_parser=output_parser,
database=SQLDatabase(engine, None, metadata),
)
import pandas as pd
pd.DataFrame(
chain.run(
"Please give me 10 papers to ask what is PageRank?",
callbacks=[StdOutCallbackHandler()],
)
)
from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
from langchain_experimental.retrievers.vector_sql_database import (
VectorSQLDatabaseChainRetriever,
)
from langchain_experimental.sql.prompt import MYSCALE_PROMPT
from langchain_experimental.sql.vector_sql import (
VectorSQLDatabaseChain,
VectorSQLRetrieveAllOutputParser,
)
from langchain_openai import ChatOpenAI
output_parser_retrieve_all = VectorSQLRetrieveAllOutputParser.from_embeddings(
output_parser.model
)
chain = VectorSQLDatabaseChain.from_llm(
llm=OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0),
prompt=MYSCALE_PROMPT,
top_k=10,
return_direct=True,
db= | SQLDatabase(engine, None, metadata) | langchain_community.utilities.sql_database.SQLDatabase |
get_ipython().run_cell_magic('writefile', 'telegram_conversation.json', '{\n "name": "Jiminy",\n "type": "personal_chat",\n "id": 5965280513,\n "messages": [\n {\n "id": 1,\n "type": "message",\n "date": "2023-08-23T13:11:23",\n "date_unixtime": "1692821483",\n "from": "Jiminy Cricket",\n "from_id": "user123450513",\n "text": "You better trust your conscience",\n "text_entities": [\n {\n "type": "plain",\n "text": "You better trust your conscience"\n }\n ]\n },\n {\n "id": 2,\n "type": "message",\n "date": "2023-08-23T13:13:20",\n "date_unixtime": "1692821600",\n "from": "Batman & Robin",\n "from_id": "user6565661032",\n "text": "What did you just say?",\n "text_entities": [\n {\n "type": "plain",\n "text": "What did you just say?"\n }\n ]\n }\n ]\n}\n')
from langchain_community.chat_loaders.telegram import TelegramChatLoader
loader = TelegramChatLoader(
path="./telegram_conversation.json",
)
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 |
from typing import List
from langchain.output_parsers import PydanticOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
class Actor(BaseModel):
name: str = Field(description="name of an actor")
film_names: List[str] = Field(description="list of names of films they starred in")
actor_query = "Generate the filmography for a random actor."
parser = | PydanticOutputParser(pydantic_object=Actor) | langchain.output_parsers.PydanticOutputParser |
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
examples = [
{
"question": "Who lived longer, Muhammad Ali or Alan Turing?",
"answer": """
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Alan Turing when he died?
Intermediate answer: Alan Turing was 41 years old when he died.
So the final answer is: Muhammad Ali
""",
},
{
"question": "When was the founder of craigslist born?",
"answer": """
Are follow up questions needed here: Yes.
Follow up: Who was the founder of craigslist?
Intermediate answer: Craigslist was founded by Craig Newmark.
Follow up: When was Craig Newmark born?
Intermediate answer: Craig Newmark was born on December 6, 1952.
So the final answer is: December 6, 1952
""",
},
{
"question": "Who was the maternal grandfather of George Washington?",
"answer": """
Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The mother of George Washington was Mary Ball Washington.
Follow up: Who was the father of Mary Ball Washington?
Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
So the final answer is: Joseph Ball
""",
},
{
"question": "Are both the directors of Jaws and Casino Royale from the same country?",
"answer": """
Are follow up questions needed here: Yes.
Follow up: Who is the director of Jaws?
Intermediate Answer: The director of Jaws is Steven Spielberg.
Follow up: Where is Steven Spielberg from?
Intermediate Answer: The United States.
Follow up: Who is the director of Casino Royale?
Intermediate Answer: The director of Casino Royale is Martin Campbell.
Follow up: Where is Martin Campbell from?
Intermediate Answer: New Zealand.
So the final answer is: No
""",
},
]
example_prompt = PromptTemplate(
input_variables=["question", "answer"], template="Question: {question}\n{answer}"
)
print(example_prompt.format(**examples[0]))
prompt = FewShotPromptTemplate(
examples=examples,
example_prompt=example_prompt,
suffix="Question: {input}",
input_variables=["input"],
)
print(prompt.format(input="Who was the father of Mary Ball Washington?"))
from langchain.prompts.example_selector import SemanticSimilarityExampleSelector
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
example_selector = SemanticSimilarityExampleSelector.from_examples(
examples,
| OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().system(' nomic login')
get_ipython().system(' nomic login token')
get_ipython().system(' pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain')
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGCHAIN_API_KEY"] = "api_key"
from langchain_community.document_loaders import WebBaseLoader
urls = [
"https://lilianweng.github.io/posts/2023-06-23-agent/",
"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/",
]
docs = [WebBaseLoader(url).load() for url in urls]
docs_list = [item for sublist in docs for item in sublist]
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=7500, chunk_overlap=100
)
doc_splits = text_splitter.split_documents(docs_list)
import tiktoken
encoding = tiktoken.get_encoding("cl100k_base")
encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
for d in doc_splits:
print("The document is %s tokens" % len(encoding.encode(d.page_content)))
import os
from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_nomic import NomicEmbeddings
from langchain_nomic.embeddings import NomicEmbeddings
vectorstore = Chroma.from_documents(
documents=doc_splits,
collection_name="rag-chroma",
embedding=NomicEmbeddings(model="nomic-embed-text-v1"),
)
retriever = vectorstore.as_retriever()
from langchain_community.chat_models import ChatOllama
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = | ChatOpenAI(temperature=0, model="gpt-4-1106-preview") | langchain_openai.ChatOpenAI |
from langchain.indexes import SQLRecordManager, index
from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
collection_name = "test_index"
embedding = OpenAIEmbeddings()
vectorstore = ElasticsearchStore(
es_url="http://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")
_clear()
index(
[doc1, doc2],
record_manager,
vectorstore,
cleanup="incremental",
source_id_key="source",
)
index(
[doc1, doc2],
record_manager,
vectorstore,
cleanup="incremental",
source_id_key="source",
)
index([], record_manager, vectorstore, cleanup="incremental", source_id_key="source")
changed_doc_2 = | Document(page_content="puppy", metadata={"source": "doggy.txt"}) | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azureml-mlflow')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet textstat')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
get_ipython().system('python -m spacy download en_core_web_sm')
import os
os.environ["MLFLOW_TRACKING_URI"] = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["SERPAPI_API_KEY"] = ""
from langchain.callbacks import MlflowCallbackHandler
from langchain_openai import OpenAI
"""Main function.
This function is used to try the callback handler.
Scenarios:
1. OpenAI LLM
2. Chain with multiple SubChains on multiple generations
3. Agent with Tools
"""
mlflow_callback = | MlflowCallbackHandler() | langchain.callbacks.MlflowCallbackHandler |
get_ipython().run_line_magic('pip', 'install "pgvecto_rs[sdk]"')
from typing import List
from langchain.docstore.document import Document
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores.pgvecto_rs import PGVecto_rs
from langchain_text_splitters import CharacterTextSplitter
loader = | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml')
path = "/Users/rlm/Desktop/Papers/LLaVA/"
from typing import Any
from pydantic import BaseModel
from unstructured.partition.pdf import partition_pdf
raw_pdf_elements = partition_pdf(
filename=path + "LLaVA.pdf",
extract_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,
)
category_counts = {}
for element in raw_pdf_elements:
category = str(type(element))
if category in category_counts:
category_counts[category] += 1
else:
category_counts[category] = 1
unique_categories = set(category_counts.keys())
category_counts
class Element(BaseModel):
type: str
text: Any
categorized_elements = []
for element in raw_pdf_elements:
if "unstructured.documents.elements.Table" in str(type(element)):
categorized_elements.append(Element(type="table", text=str(element)))
elif "unstructured.documents.elements.CompositeElement" in str(type(element)):
categorized_elements.append(Element(type="text", text=str(element)))
table_elements = [e for e in categorized_elements if e.type == "table"]
print(len(table_elements))
text_elements = [e for e in categorized_elements if e.type == "text"]
print(len(text_elements))
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
prompt_text = """You are an assistant tasked with summarizing tables and text. \
Give a concise summary of the table or text. Table or text chunk: {element} """
prompt = ChatPromptTemplate.from_template(prompt_text)
model = ChatOpenAI(temperature=0, model="gpt-4")
summarize_chain = {"element": lambda x: x} | prompt | model | StrOutputParser()
texts = [i.text for i in text_elements]
text_summaries = summarize_chain.batch(texts, {"max_concurrency": 5})
tables = [i.text for i in table_elements]
table_summaries = summarize_chain.batch(tables, {"max_concurrency": 5})
get_ipython().run_cell_magic('bash', '', '\n# Define the directory containing the images\nIMG_DIR=~/Desktop/Papers/LLaVA/\n\n# Loop through each image in the directory\nfor img in "${IMG_DIR}"*.jpg; do\n # Extract the base name of the image without extension\n base_name=$(basename "$img" .jpg)\n\n # Define the output file name based on the image name\n output_file="${IMG_DIR}${base_name}.txt"\n\n # Execute the command and save the output to the defined output file\n /Users/rlm/Desktop/Code/llama.cpp/bin/llava -m ../models/llava-7b/ggml-model-q5_k.gguf --mmproj ../models/llava-7b/mmproj-model-f16.gguf --temp 0.1 -p "Describe the image in detail. Be specific about graphs, such as bar plots." --image "$img" > "$output_file"\n\ndone\n')
import glob
import os
file_paths = glob.glob(os.path.expanduser(os.path.join(path, "*.txt")))
img_summaries = []
for file_path in file_paths:
with open(file_path, "r") as file:
img_summaries.append(file.read())
logging_header = "clip_model_load: total allocated memory: 201.27 MB\n\n"
cleaned_img_summary = [s.split(logging_header, 1)[1].strip() for s in img_summaries]
import uuid
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryStore
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
vectorstore = Chroma(collection_name="summaries", embedding_function=OpenAIEmbeddings())
store = InMemoryStore()
id_key = "doc_id"
retriever = MultiVectorRetriever(
vectorstore=vectorstore,
docstore=store,
id_key=id_key,
)
doc_ids = [str(uuid.uuid4()) for _ in texts]
summary_texts = [
| Document(page_content=s, metadata={id_key: doc_ids[i]}) | langchain_core.documents.Document |
import logging
from langchain.retrievers import RePhraseQueryRetriever
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
logging.basicConfig()
logging.getLogger("langchain.retrievers.re_phraser").setLevel(logging.INFO)
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
llm = ChatOpenAI(temperature=0)
retriever_from_llm = RePhraseQueryRetriever.from_llm(
retriever=vectorstore.as_retriever(), llm=llm
)
docs = retriever_from_llm.get_relevant_documents(
"Hi I'm Lance. What are the approaches to Task Decomposition?"
)
docs = retriever_from_llm.get_relevant_documents(
"I live in San Francisco. What are the Types of Memory?"
)
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
QUERY_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an assistant tasked with taking a natural languge query from a user
and converting it into a query for a vectorstore. In the process, strip out all
information that is not relevant for the retrieval task and return a new, simplified
question for vectorstore retrieval. The new user query should be in pirate speech.
Here is the user query: {question} """,
)
llm = ChatOpenAI(temperature=0)
llm_chain = | LLMChain(llm=llm, prompt=QUERY_PROMPT) | langchain.chains.LLMChain |
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
loaders = [
TextLoader("../../paul_graham_essay.txt"),
| TextLoader("../../state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
chat_message_history = MongoDBChatMessageHistory(
session_id="test_session",
connection_string="mongodb://mongo_user:password123@mongo:27017",
database_name="my_db",
collection_name="chat_histories",
)
chat_message_history.add_user_message("Hello")
chat_message_history.add_ai_message("Hi")
chat_message_history.messages
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
import os
assert os.environ[
"OPENAI_API_KEY"
], "Set the OPENAI_API_KEY environment variable with your OpenAI API key."
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant."),
| MessagesPlaceholder(variable_name="history") | langchain_core.prompts.MessagesPlaceholder |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-community')
import os
os.environ["YDC_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from langchain_community.tools.you import YouSearchTool
from langchain_community.utilities.you import YouSearchAPIWrapper
api_wrapper = YouSearchAPIWrapper(num_web_results=1)
tool = YouSearchTool(api_wrapper=api_wrapper)
tool
response = tool.invoke("What is the weather in NY")
print(len(response))
for item in response:
print(item)
get_ipython().system('pip install --upgrade --quiet langchain langchain-openai langchainhub langchain-community')
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
instructions = """You are an assistant."""
base_prompt = | hub.pull("langchain-ai/openai-functions-template") | langchain.hub.pull |
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('poetry run pip install replicate')
from getpass import getpass
REPLICATE_API_TOKEN = getpass()
import os
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Replicate
llm = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
llm(prompt)
llm = | Replicate(
model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"
) | langchain_community.llms.Replicate |
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)]
)
messages = [
SystemMessage(
content="You are a helpful assistant that translates English to French."
),
HumanMessage(content="I love programming."),
]
print(chat(messages))
import os
from langchain.callbacks import ContextCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
)
from langchain_openai import ChatOpenAI
token = os.environ["CONTEXT_API_TOKEN"]
human_message_prompt = HumanMessagePromptTemplate(
prompt=PromptTemplate(
template="What is a good name for a company that makes {product}?",
input_variables=["product"],
)
)
chat_prompt_template = | ChatPromptTemplate.from_messages([human_message_prompt]) | langchain.prompts.chat.ChatPromptTemplate.from_messages |
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 timescale-vector')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import os
from dotenv import find_dotenv, load_dotenv
_ = load_dotenv(find_dotenv())
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
from typing import Tuple
from datetime import datetime, timedelta
from langchain.docstore.document import Document
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders.json_loader import JSONLoader
from langchain_community.vectorstores.timescalevector import TimescaleVector
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../../extras/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()
SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]
COLLECTION_NAME = "state_of_the_union_test"
db = TimescaleVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name=COLLECTION_NAME,
service_url=SERVICE_URL,
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
retriever = db.as_retriever()
print(retriever)
from langchain_openai import ChatOpenAI
llm = | ChatOpenAI(temperature=0.1, model="gpt-3.5-turbo-16k") | langchain_openai.ChatOpenAI |
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import Chroma
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)
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
db = | Chroma.from_documents(docs, embedding_function) | langchain_community.vectorstores.Chroma.from_documents |
import asyncio
from langchain.callbacks import get_openai_callback
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
import getpass
import os
os.environ["TAVILY_API_KEY"] = getpass.getpass()
from langchain_community.tools.tavily_search import TavilySearchResults
tool = TavilySearchResults()
tool.invoke({"query": "What happened in the latest burning man floods"})
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
instructions = """You are an assistant."""
base_prompt = hub.pull("langchain-ai/openai-functions-template")
prompt = base_prompt.partial(instructions=instructions)
llm = ChatOpenAI(temperature=0)
tavily_tool = TavilySearchResults()
tools = [tavily_tool]
agent = | create_openai_functions_agent(llm, tools, prompt) | langchain.agents.create_openai_functions_agent |
import os
from langchain.retrievers import AzureCognitiveSearchRetriever
os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"] = "<YOUR_ACS_SERVICE_NAME>"
os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"] = "<YOUR_ACS_INDEX_NAME>"
os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"] = "<YOUR_API_KEY>"
retriever = | AzureCognitiveSearchRetriever(content_key="content", top_k=10) | langchain.retrievers.AzureCognitiveSearchRetriever |
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../state_of_the_union.txt", encoding="utf-8")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
for i, text in enumerate(texts):
text.metadata["source"] = f"{i}-pl"
embeddings = | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain tiktoken langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet hippo-api==1.1.0.rc3')
import os
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores.hippo import Hippo
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
os.environ["OPENAI_API_KEY"] = "YOUR OPENAI KEY"
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().system(' pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)')
get_ipython().system(' pip install "unstructured[all-docs]" pillow pydantic lxml pillow matplotlib chromadb tiktoken')
from langchain_text_splitters import CharacterTextSplitter
from unstructured.partition.pdf import partition_pdf
def extract_pdf_elements(path, fname):
"""
Extract images, tables, and chunk text from a PDF file.
path: File path, which is used to dump images (.jpg)
fname: File name
"""
return partition_pdf(
filename=path + fname,
extract_images_in_pdf=False,
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,
)
def categorize_elements(raw_pdf_elements):
"""
Categorize extracted elements from a PDF into tables and texts.
raw_pdf_elements: List of unstructured.documents.elements
"""
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))
return texts, tables
fpath = "/Users/rlm/Desktop/cj/"
fname = "cj.pdf"
raw_pdf_elements = extract_pdf_elements(fpath, fname)
texts, tables = categorize_elements(raw_pdf_elements)
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=4000, chunk_overlap=0
)
joined_texts = " ".join(texts)
texts_4k_token = text_splitter.split_text(joined_texts)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
def generate_text_summaries(texts, tables, summarize_texts=False):
"""
Summarize text elements
texts: List of str
tables: List of str
summarize_texts: Bool to summarize texts
"""
prompt_text = """You are an assistant tasked with summarizing tables and text for retrieval. \
These summaries will be embedded and used to retrieve the raw text or table elements. \
Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} """
prompt = ChatPromptTemplate.from_template(prompt_text)
model = | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet weaviate-client')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")
os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")
WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Weaviate
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) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet fastembed')
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
embeddings = | FastEmbedEmbeddings() | langchain_community.embeddings.fastembed.FastEmbedEmbeddings |
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") | langchain_experimental.rl_chain.BasedOn |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymysql')
get_ipython().system('pip install sqlalchemy')
get_ipython().system('pip install langchain')
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
DirectoryLoader,
UnstructuredMarkdownLoader,
)
from langchain_community.vectorstores.apache_doris import (
ApacheDoris,
ApacheDorisSettings,
)
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import TokenTextSplitter
update_vectordb = False
loader = DirectoryLoader(
"./docs", glob="**/*.md", loader_cls=UnstructuredMarkdownLoader
)
documents = loader.load()
text_splitter = TokenTextSplitter(chunk_size=400, chunk_overlap=50)
split_docs = text_splitter.split_documents(documents)
update_vectordb = True
def gen_apache_doris(update_vectordb, embeddings, settings):
if update_vectordb:
docsearch = ApacheDoris.from_documents(split_docs, embeddings, config=settings)
else:
docsearch = ApacheDoris(embeddings, settings)
return docsearch
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass()
update_vectordb = True
embeddings = | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from typing import List, Tuple
from dotenv import load_dotenv
load_dotenv()
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Lantern
from langchain_core.documents import Document
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)
embeddings = OpenAIEmbeddings()
CONNECTION_STRING = getpass.getpass("DB Connection String:")
COLLECTION_NAME = "state_of_the_union_test"
db = Lantern.from_documents(
embedding=embeddings,
documents=docs,
collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
docs_with_score = db.max_marginal_relevance_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
store = Lantern(
collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
embedding_function=embeddings,
)
store.add_documents([ | Document(page_content="foo") | langchain_core.documents.Document |
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../state_of_the_union.txt", encoding="utf-8")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
for i, text in enumerate(texts):
text.metadata["source"] = f"{i}-pl"
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
from langchain.chains import create_qa_with_sources_chain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
llm = | ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") | langchain_openai.ChatOpenAI |
from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointApiType,
LlamaContentFormatter,
)
from langchain_core.messages import HumanMessage
llm = AzureMLOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
endpoint_api_type=AzureMLEndpointApiType.realtime,
endpoint_api_key="my-api-key",
content_formatter=LlamaContentFormatter(),
model_kwargs={"temperature": 0.8, "max_new_tokens": 400},
)
response = llm.invoke("Write me a song about sparkling water:")
response
response = llm.invoke("Write me a song about sparkling water:", temperature=0.5)
response
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointApiType,
LlamaContentFormatter,
)
from langchain_core.messages import HumanMessage
llm = AzureMLOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/completions",
endpoint_api_type=AzureMLEndpointApiType.serverless,
endpoint_api_key="my-api-key",
content_formatter=LlamaContentFormatter(),
model_kwargs={"temperature": 0.8, "max_new_tokens": 400},
)
response = llm.invoke("Write me a song about sparkling water:")
response
import json
import os
from typing import Dict
from langchain_community.llms.azureml_endpoint import (
AzureMLOnlineEndpoint,
ContentFormatterBase,
)
class CustomFormatter(ContentFormatterBase):
content_type = "application/json"
accepts = "application/json"
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps(
{
"inputs": [prompt],
"parameters": model_kwargs,
"options": {"use_cache": False, "wait_for_model": True},
}
)
return str.encode(input_str)
def format_response_payload(self, output: bytes) -> str:
response_json = json.loads(output)
return response_json[0]["summary_text"]
content_formatter = CustomFormatter()
llm = AzureMLOnlineEndpoint(
endpoint_api_type="realtime",
endpoint_api_key=os.getenv("BART_ENDPOINT_API_KEY"),
endpoint_url=os.getenv("BART_ENDPOINT_URL"),
model_kwargs={"temperature": 0.8, "max_new_tokens": 400},
content_formatter=content_formatter,
)
large_text = """On January 7, 2020, Blockberry Creative announced that HaSeul would not participate in the promotion for Loona's
next album because of mental health concerns. She was said to be diagnosed with "intermittent anxiety symptoms" and would be
taking time to focus on her health.[39] On February 5, 2020, Loona released their second EP titled [#] (read as hash), along
with the title track "So What".[40] Although HaSeul did not appear in the title track, her vocals are featured on three other
songs on the album, including "365". Once peaked at number 1 on the daily Gaon Retail Album Chart,[41] the EP then debuted at
number 2 on the weekly Gaon Album Chart. On March 12, 2020, Loona won their first music show trophy with "So What" on Mnet's
M Countdown.[42]
On October 19, 2020, Loona released their third EP titled [12:00] (read as midnight),[43] accompanied by its first single
"Why Not?". HaSeul was again not involved in the album, out of her own decision to focus on the recovery of her health.[44]
The EP then became their first album to enter the Billboard 200, debuting at number 112.[45] On November 18, Loona released
the music video for "Star", another song on [12:00].[46] Peaking at number 40, "Star" is Loona's first entry on the Billboard
Mainstream Top 40, making them the second K-pop girl group to enter the chart.[47]
On June 1, 2021, Loona announced that they would be having a comeback on June 28, with their fourth EP, [&] (read as and).
[48] The following day, on June 2, a teaser was posted to Loona's official social media accounts showing twelve sets of eyes,
confirming the return of member HaSeul who had been on hiatus since early 2020.[49] On June 12, group members YeoJin, Kim Lip,
Choerry, and Go Won released the song "Yum-Yum" as a collaboration with Cocomong.[50] On September 8, they released another
collaboration song named "Yummy-Yummy".[51] On June 27, 2021, Loona announced at the end of their special clip that they are
making their Japanese debut on September 15 under Universal Music Japan sublabel EMI Records.[52] On August 27, it was announced
that Loona will release the double A-side single, "Hula Hoop / Star Seed" on September 15, with a physical CD release on October
20.[53] In December, Chuu filed an injunction to suspend her exclusive contract with Blockberry Creative.[54][55]
"""
summarized_text = llm.invoke(large_text)
print(summarized_text)
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms.azureml_endpoint import DollyContentFormatter
formatter_template = "Write a {word_count} word essay about {topic}."
prompt = PromptTemplate(
input_variables=["word_count", "topic"], template=formatter_template
)
content_formatter = DollyContentFormatter()
llm = AzureMLOnlineEndpoint(
endpoint_api_key=os.getenv("DOLLY_ENDPOINT_API_KEY"),
endpoint_url=os.getenv("DOLLY_ENDPOINT_URL"),
model_kwargs={"temperature": 0.8, "max_tokens": 300},
content_formatter=content_formatter,
)
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.invoke({"word_count": 100, "topic": "how to make friends"}))
from langchain_community.llms.loading import load_llm
save_llm = AzureMLOnlineEndpoint(
deployment_name="databricks-dolly-v2-12b-4",
model_kwargs={
"temperature": 0.2,
"max_tokens": 150,
"top_p": 0.8,
"frequency_penalty": 0.32,
"presence_penalty": 72e-3,
},
)
save_llm.save("azureml.json")
loaded_llm = | load_llm("azureml.json") | langchain_community.llms.loading.load_llm |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3')
from langchain_community.document_loaders import S3DirectoryLoader
loader = | S3DirectoryLoader("testing-hwc") | langchain_community.document_loaders.S3DirectoryLoader |
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") | langchain_nvidia_ai_endpoints.ChatNVIDIA |
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("criteria", criteria="conciseness")
from langchain.evaluation import EvaluatorType
evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="conciseness")
eval_result = evaluator.evaluate_strings(
prediction="What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.",
input="What's 2+2?",
)
print(eval_result)
evaluator = load_evaluator("labeled_criteria", criteria="correctness")
eval_result = evaluator.evaluate_strings(
input="What is the capital of the US?",
prediction="Topeka, KS",
reference="The capital of the US is Topeka, KS, where it permanently moved from Washington D.C. on May 16, 2023",
)
print(f'With ground truth: {eval_result["score"]}')
from langchain.evaluation import Criteria
list(Criteria)
custom_criterion = {
"numeric": "Does the output contain numeric or mathematical information?"
}
eval_chain = load_evaluator(
EvaluatorType.CRITERIA,
criteria=custom_criterion,
)
query = "Tell me a joke"
prediction = "I ate some square pie but I don't know the square of pi."
eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)
print(eval_result)
custom_criteria = {
"numeric": "Does the output contain numeric information?",
"mathematical": "Does the output contain mathematical information?",
"grammatical": "Is the output grammatically correct?",
"logical": "Is the output logical?",
}
eval_chain = load_evaluator(
EvaluatorType.CRITERIA,
criteria=custom_criteria,
)
eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)
print("Multi-criteria evaluation")
print(eval_result)
from langchain.chains.constitutional_ai.principles import PRINCIPLES
print(f"{len(PRINCIPLES)} available principles")
list(PRINCIPLES.items())[:5]
evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria=PRINCIPLES["harmful1"])
eval_result = evaluator.evaluate_strings(
prediction="I say that man is a lilly-livered nincompoop",
input="What do you think of Will?",
)
print(eval_result)
get_ipython().run_line_magic('pip', 'install --upgrade --quiet anthropic')
from langchain_community.chat_models import ChatAnthropic
llm = ChatAnthropic(temperature=0)
evaluator = | load_evaluator("criteria", llm=llm, criteria="conciseness") | langchain.evaluation.load_evaluator |
from langchain_community.llms import Baseten
mistral = Baseten(model="MODEL_ID", deployment="production")
mistral("What is the Mistral wind?")
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
template = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
{history}
Human: {human_input}
Assistant:"""
prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
chatgpt_chain = LLMChain(
llm=mistral,
llm_kwargs={"max_length": 4096},
prompt=prompt,
verbose=True,
memory= | ConversationBufferWindowMemory(k=2) | langchain.memory.ConversationBufferWindowMemory |
get_ipython().run_line_magic('pip', 'install --quiet pypdf chromadb tiktoken openai')
get_ipython().run_line_magic('pip', 'uninstall -y langchain-fireworks')
get_ipython().run_line_magic('pip', 'install --editable /mnt/disks/data/langchain/libs/partners/fireworks')
import fireworks
print(fireworks)
import fireworks.client
import requests
from langchain_community.document_loaders import PyPDFLoader
url = "https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf"
response = requests.get(url, stream=True)
file_name = "temp_file.pdf"
with open(file_name, "wb") as pdf:
pdf.write(response.content)
loader = | PyPDFLoader(file_name) | langchain_community.document_loaders.PyPDFLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-community')
import os
os.environ["YDC_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from langchain_community.utilities.you import YouSearchAPIWrapper
utility = YouSearchAPIWrapper(num_web_results=1)
utility
import json
response = utility.raw_results(query="What is the weather in NY")
hits = response["hits"]
print(len(hits))
print(json.dumps(hits, indent=2))
response = utility.results(query="What is the weather in NY")
print(len(response))
print(response)
from langchain_community.retrievers.you import YouRetriever
retriever = YouRetriever(num_web_results=1)
retriever
response = retriever.invoke("What is the weather in NY")
print(len(response))
print(response)
get_ipython().system('pip install --upgrade --quiet langchain-openai')
from langchain_community.retrievers.you import YouRetriever
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
runnable = RunnablePassthrough
retriever = | YouRetriever(num_web_results=1) | langchain_community.retrievers.you.YouRetriever |
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')",
page_content_columns=["Team"],
metadata_columns=["Payroll"],
)
data = loader.load()
print(data)
loader = | DuckDBLoader(
"SELECT Team, Payroll, Team As source FROM read_csv_auto('example.csv') | langchain_community.document_loaders.DuckDBLoader |
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)
enhanced_documents = document_transformer.transform_documents(original_documents)
print(
*[d.page_content + "\n\n" + json.dumps(d.metadata) for d in enhanced_documents],
sep="\n\n---------------\n\n",
)
from langchain_core.prompts import ChatPromptTemplate
prompt = | ChatPromptTemplate.from_template(
"""Extract relevant information from the following text.
Anonymous critics are actually Roger Ebert.
{input}
"""
) | langchain_core.prompts.ChatPromptTemplate.from_template |
from typing import Callable, List
import tenacity
from langchain.output_parsers import RegexParser
from langchain.prompts import PromptTemplate
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name = name
self.system_message = system_message
self.model = model
self.prefix = f"{self.name}: "
self.reset()
def reset(self):
self.message_history = ["Here is the conversation so far."]
def send(self) -> str:
"""
Applies the chatmodel to the message history
and returns the message string
"""
message = self.model(
[
self.system_message,
HumanMessage(content="\n".join(self.message_history + [self.prefix])),
]
)
return message.content
def receive(self, name: str, message: str) -> None:
"""
Concatenates {message} spoken by {name} into message history
"""
self.message_history.append(f"{name}: {message}")
class DialogueSimulator:
def __init__(
self,
agents: List[DialogueAgent],
selection_function: Callable[[int, List[DialogueAgent]], int],
) -> None:
self.agents = agents
self._step = 0
self.select_next_speaker = selection_function
def reset(self):
for agent in self.agents:
agent.reset()
def inject(self, name: str, message: str):
"""
Initiates the conversation with a {message} from {name}
"""
for agent in self.agents:
agent.receive(name, message)
self._step += 1
def step(self) -> tuple[str, str]:
speaker_idx = self.select_next_speaker(self._step, self.agents)
speaker = self.agents[speaker_idx]
message = speaker.send()
for receiver in self.agents:
receiver.receive(speaker.name, message)
self._step += 1
return speaker.name, message
class BiddingDialogueAgent(DialogueAgent):
def __init__(
self,
name,
system_message: SystemMessage,
bidding_template: PromptTemplate,
model: ChatOpenAI,
) -> None:
super().__init__(name, system_message, model)
self.bidding_template = bidding_template
def bid(self) -> str:
"""
Asks the chat model to output a bid to speak
"""
prompt = PromptTemplate(
input_variables=["message_history", "recent_message"],
template=self.bidding_template,
).format(
message_history="\n".join(self.message_history),
recent_message=self.message_history[-1],
)
bid_string = self.model([SystemMessage(content=prompt)]).content
return bid_string
character_names = ["Donald Trump", "Kanye West", "Elizabeth Warren"]
topic = "transcontinental high speed rail"
word_limit = 50
game_description = f"""Here is the topic for the presidential debate: {topic}.
The presidential candidates are: {', '.join(character_names)}."""
player_descriptor_system_message = SystemMessage(
content="You can add detail to the description of each presidential candidate."
)
def generate_character_description(character_name):
character_specifier_prompt = [
player_descriptor_system_message,
HumanMessage(
content=f"""{game_description}
Please reply with a creative description of the presidential candidate, {character_name}, in {word_limit} words or less, that emphasizes their personalities.
Speak directly to {character_name}.
Do not add anything else."""
),
]
character_description = ChatOpenAI(temperature=1.0)(
character_specifier_prompt
).content
return character_description
def generate_character_header(character_name, character_description):
return f"""{game_description}
Your name is {character_name}.
You are a presidential candidate.
Your description is as follows: {character_description}
You are debating the topic: {topic}.
Your goal is to be as creative as possible and make the voters think you are the best candidate.
"""
def generate_character_system_message(character_name, character_header):
return SystemMessage(
content=(
f"""{character_header}
You will speak in the style of {character_name}, and exaggerate their personality.
You will come up with creative ideas related to {topic}.
Do not say the same things over and over again.
Speak in the first person from the perspective of {character_name}
For describing your own body movements, wrap your description in '*'.
Do not change roles!
Do not speak from the perspective of anyone else.
Speak only from the perspective of {character_name}.
Stop speaking the moment you finish speaking from your perspective.
Never forget to keep your response to {word_limit} words!
Do not add anything else.
"""
)
)
character_descriptions = [
generate_character_description(character_name) for character_name in character_names
]
character_headers = [
generate_character_header(character_name, character_description)
for character_name, character_description in zip(
character_names, character_descriptions
)
]
character_system_messages = [
generate_character_system_message(character_name, character_headers)
for character_name, character_headers in zip(character_names, character_headers)
]
for (
character_name,
character_description,
character_header,
character_system_message,
) in zip(
character_names,
character_descriptions,
character_headers,
character_system_messages,
):
print(f"\n\n{character_name} Description:")
print(f"\n{character_description}")
print(f"\n{character_header}")
print(f"\n{character_system_message.content}")
class BidOutputParser(RegexParser):
def get_format_instructions(self) -> str:
return "Your response should be an integer delimited by angled brackets, like this: <int>."
bid_parser = BidOutputParser(
regex=r"<(\d+)>", output_keys=["bid"], default_output_key="bid"
)
def generate_character_bidding_template(character_header):
bidding_template = f"""{character_header}
```
{{message_history}}
```
On the scale of 1 to 10, where 1 is not contradictory and 10 is extremely contradictory, rate how contradictory the following message is to your ideas.
```
{{recent_message}}
```
{bid_parser.get_format_instructions()}
Do nothing else.
"""
return bidding_template
character_bidding_templates = [
generate_character_bidding_template(character_header)
for character_header in character_headers
]
for character_name, bidding_template in zip(
character_names, character_bidding_templates
):
print(f"{character_name} Bidding Template:")
print(bidding_template)
topic_specifier_prompt = [
SystemMessage(content="You can make a task more specific."),
HumanMessage(
content=f"""{game_description}
You are the debate moderator.
Please make the debate topic more specific.
Frame the debate topic as a problem to be solved.
Be creative and imaginative.
Please reply with the specified topic in {word_limit} words or less.
Speak directly to the presidential candidates: {*character_names,}.
Do not add anything else."""
),
]
specified_topic = | ChatOpenAI(temperature=1.0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', "install --upgrade --quiet faiss-gpu # For CUDA 7.5+ Supported GPU's.")
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu # For CPU Installation')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../../extras/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()
db = await FAISS.afrom_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = await db.asimilarity_search(query)
print(docs[0].page_content)
docs_and_scores = await db.asimilarity_search_with_score(query)
docs_and_scores[0]
embedding_vector = await embeddings.aembed_query(query)
docs_and_scores = await db.asimilarity_search_by_vector(embedding_vector)
db.save_local("faiss_index")
new_db = | FAISS.load_local("faiss_index", embeddings, asynchronous=True) | langchain_community.vectorstores.FAISS.load_local |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import ChatOpenAI, OpenAI
llm = ChatOpenAI(temperature=0.0)
math_llm = OpenAI(temperature=0.0)
tools = load_tools(
["human", "llm-math"],
llm=math_llm,
)
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
agent_chain.run("What's my friend Eric's surname?")
def get_input() -> str:
print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.")
contents = []
while True:
try:
line = input()
except EOFError:
break
if line == "q":
break
contents.append(line)
return "\n".join(contents)
tools = load_tools(["human", "ddg-search"], llm=math_llm, input_func=get_input)
from langchain.tools import HumanInputRun
tool = | HumanInputRun(input_func=get_input) | langchain.tools.HumanInputRun |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clearml')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet textstat')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
get_ipython().system('python -m spacy download en_core_web_sm')
import os
os.environ["CLEARML_API_ACCESS_KEY"] = ""
os.environ["CLEARML_API_SECRET_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["SERPAPI_API_KEY"] = ""
from langchain.callbacks import ClearMLCallbackHandler
from langchain.callbacks import StdOutCallbackHandler
from langchain_openai import OpenAI
clearml_callback = ClearMLCallbackHandler(
task_type="inference",
project_name="langchain_callback_demo",
task_name="llm",
tags=["test"],
visualize=True,
complexity_metrics=True,
stream_logs=True,
)
callbacks = [ | StdOutCallbackHandler() | langchain.callbacks.StdOutCallbackHandler |
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)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question. If none of the articles answer the question, just say you don't know.\n\nHere are the Wikipedia articles:{context}",
),
("human", "{question}"),
]
)
prompt.pretty_print()
from operator import itemgetter
from typing import List
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import (
RunnableLambda,
RunnableParallel,
RunnablePassthrough,
)
def format_docs(docs: List[Document]) -> str:
"""Convert Documents to a single string.:"""
formatted = [
f"Article Title: {doc.metadata['title']}\nArticle Snippet: {doc.page_content}"
for doc in docs
]
return "\n\n" + "\n\n".join(formatted)
format = itemgetter("docs") | RunnableLambda(format_docs)
answer = prompt | llm | | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet predibase')
import os
os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"
from langchain_community.llms import Predibase
model = Predibase(
model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN")
)
response = model("Can you recommend me a nice dry wine?")
print(response)
llm = Predibase(
model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN")
)
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = | LLMChain(llm=llm, prompt=prompt_template) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet python-gitlab')
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.gitlab.toolkit import GitLabToolkit
from langchain_community.utilities.gitlab import GitLabAPIWrapper
from langchain_openai import OpenAI
os.environ["GITLAB_URL"] = "https://gitlab.example.org"
os.environ["GITLAB_PERSONAL_ACCESS_TOKEN"] = ""
os.environ["GITLAB_REPOSITORY"] = "username/repo-name"
os.environ["GITLAB_BRANCH"] = "bot-branch-name"
os.environ["GITLAB_BASE_BRANCH"] = "main"
os.environ["OPENAI_API_KEY"] = ""
llm = OpenAI(temperature=0)
gitlab = GitLabAPIWrapper()
toolkit = | GitLabToolkit.from_gitlab_api_wrapper(gitlab) | langchain_community.agent_toolkits.gitlab.toolkit.GitLabToolkit.from_gitlab_api_wrapper |
from typing import List
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)
class Joke(BaseModel):
setup: str = Field(description="question to set up a joke")
punchline: str = | Field(description="answer to resolve the joke") | langchain_core.pydantic_v1.Field |
import os
import yaml
get_ipython().system('wget https://raw.githubusercontent.com/openai/openai-openapi/master/openapi.yaml -O openai_openapi.yaml')
get_ipython().system('wget https://www.klarna.com/us/shopping/public/openai/v0/api-docs -O klarna_openapi.yaml')
get_ipython().system('wget https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/spotify.com/1.0.0/openapi.yaml -O spotify_openapi.yaml')
from langchain_community.agent_toolkits.openapi.spec import reduce_openapi_spec
with open("openai_openapi.yaml") as f:
raw_openai_api_spec = yaml.load(f, Loader=yaml.Loader)
openai_api_spec = reduce_openapi_spec(raw_openai_api_spec)
with open("klarna_openapi.yaml") as f:
raw_klarna_api_spec = yaml.load(f, Loader=yaml.Loader)
klarna_api_spec = reduce_openapi_spec(raw_klarna_api_spec)
with open("spotify_openapi.yaml") as f:
raw_spotify_api_spec = yaml.load(f, Loader=yaml.Loader)
spotify_api_spec = reduce_openapi_spec(raw_spotify_api_spec)
import spotipy.util as util
from langchain.requests import RequestsWrapper
def construct_spotify_auth_headers(raw_spec: dict):
scopes = list(
raw_spec["components"]["securitySchemes"]["oauth_2_0"]["flows"][
"authorizationCode"
]["scopes"].keys()
)
access_token = util.prompt_for_user_token(scope=",".join(scopes))
return {"Authorization": f"Bearer {access_token}"}
headers = construct_spotify_auth_headers(raw_spotify_api_spec)
requests_wrapper = RequestsWrapper(headers=headers)
endpoints = [
(route, operation)
for route, operations in raw_spotify_api_spec["paths"].items()
for operation in operations
if operation in ["get", "post"]
]
len(endpoints)
import tiktoken
enc = tiktoken.encoding_for_model("gpt-4")
def count_tokens(s):
return len(enc.encode(s))
count_tokens(yaml.dump(raw_spotify_api_spec))
from langchain_community.agent_toolkits.openapi import planner
from langchain_openai import OpenAI
llm = OpenAI(model_name="gpt-4", temperature=0.0)
spotify_agent = | planner.create_openapi_agent(spotify_api_spec, requests_wrapper, llm) | langchain_community.agent_toolkits.openapi.planner.create_openapi_agent |
URL = "" # Your Fiddler instance URL, Make sure to include the full URL (including https://). For example: https://demo.fiddler.ai
ORG_NAME = ""
AUTH_TOKEN = "" # Your Fiddler instance auth token
PROJECT_NAME = ""
MODEL_NAME = "" # Model name in Fiddler
from langchain_community.callbacks.fiddler_callback import FiddlerCallbackHandler
fiddler_handler = FiddlerCallbackHandler(
url=URL,
org=ORG_NAME,
project=PROJECT_NAME,
model=MODEL_NAME,
api_key=AUTH_TOKEN,
)
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import OpenAI
llm = OpenAI(temperature=0, streaming=True, callbacks=[fiddler_handler])
output_parser = StrOutputParser()
chain = llm | output_parser
chain.invoke("How far is moon from earth?")
chain.invoke("What is the temperature on Mars?")
chain.invoke("How much is 2 + 200000?")
chain.invoke("Which movie won the oscars this year?")
chain.invoke("Can you write me a poem about insomnia?")
chain.invoke("How are you doing today?")
chain.invoke("What is the meaning of life?")
from langchain.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
]
example_prompt = ChatPromptTemplate.from_messages(
[
("human", "{input}"),
("ai", "{output}"),
]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
example_prompt=example_prompt,
examples=examples,
)
final_prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a wondrous wizard of math."),
few_shot_prompt,
("human", "{input}"),
]
)
llm = | OpenAI(temperature=0, streaming=True, callbacks=[fiddler_handler]) | langchain_openai.OpenAI |
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
loaders = [
TextLoader("../../paul_graham_essay.txt"),
TextLoader("../../state_of_the_union.txt"),
]
docs = []
for loader in loaders:
docs.extend(loader.load())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000)
docs = text_splitter.split_documents(docs)
vectorstore = Chroma(
collection_name="full_documents", embedding_function=OpenAIEmbeddings()
)
store = InMemoryByteStore()
id_key = "doc_id"
retriever = MultiVectorRetriever(
vectorstore=vectorstore,
byte_store=store,
id_key=id_key,
)
import uuid
doc_ids = [str(uuid.uuid4()) for _ in docs]
child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
sub_docs = []
for i, doc in enumerate(docs):
_id = doc_ids[i]
_sub_docs = child_text_splitter.split_documents([doc])
for _doc in _sub_docs:
_doc.metadata[id_key] = _id
sub_docs.extend(_sub_docs)
retriever.vectorstore.add_documents(sub_docs)
retriever.docstore.mset(list(zip(doc_ids, docs)))
retriever.vectorstore.similarity_search("justice breyer")[0]
len(retriever.get_relevant_documents("justice breyer")[0].page_content)
from langchain.retrievers.multi_vector import SearchType
retriever.search_type = SearchType.mmr
len(retriever.get_relevant_documents("justice breyer")[0].page_content)
import uuid
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
chain = (
{"doc": lambda x: x.page_content}
| ChatPromptTemplate.from_template("Summarize the following document:\n\n{doc}")
| ChatOpenAI(max_retries=0)
| StrOutputParser()
)
summaries = chain.batch(docs, {"max_concurrency": 5})
vectorstore = Chroma(collection_name="summaries", embedding_function=OpenAIEmbeddings())
store = InMemoryByteStore()
id_key = "doc_id"
retriever = MultiVectorRetriever(
vectorstore=vectorstore,
byte_store=store,
id_key=id_key,
)
doc_ids = [str(uuid.uuid4()) for _ in docs]
summary_docs = [
| Document(page_content=s, metadata={id_key: doc_ids[i]}) | langchain_core.documents.Document |
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import GradientLLM
import os
from getpass import getpass
if not os.environ.get("GRADIENT_ACCESS_TOKEN", None):
os.environ["GRADIENT_ACCESS_TOKEN"] = getpass("gradient.ai access token:")
if not os.environ.get("GRADIENT_WORKSPACE_ID", None):
os.environ["GRADIENT_WORKSPACE_ID"] = getpass("gradient.ai workspace id:")
get_ipython().run_line_magic('pip', 'install --upgrade --quiet gradientai')
import gradientai
client = gradientai.Gradient()
models = client.list_models(only_base=True)
for model in models:
print(model.id)
new_model = models[-1].create_model_adapter(name="my_model_adapter")
new_model.id, new_model.name
llm = GradientLLM(
model="674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter",
model_kwargs=dict(max_generated_token_count=128),
)
template = """Question: {question}
Answer: """
prompt = PromptTemplate.from_template(template)
llm_chain = | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai deepeval')
get_ipython().system('deepeval login')
from deepeval.metrics.answer_relevancy import AnswerRelevancy
answer_relevancy_metric = AnswerRelevancy(minimum_score=0.5)
from langchain.callbacks.confident_callback import DeepEvalCallbackHandler
deepeval_callback = DeepEvalCallbackHandler(
implementation_name="langchainQuickstart", metrics=[answer_relevancy_metric]
)
from langchain_openai import OpenAI
llm = OpenAI(
temperature=0,
callbacks=[deepeval_callback],
verbose=True,
openai_api_key="<YOUR_API_KEY>",
)
output = llm.generate(
[
"What is the best evaluation tool out there? (no bias at all)",
]
)
answer_relevancy_metric.is_successful()
import requests
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
text_file_url = "https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt"
openai_api_key = "sk-XXX"
with open("state_of_the_union.txt", "w") as f:
response = requests.get(text_file_url)
f.write(response.text)
loader = | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from langchain.chains import GraphCypherQAChain
from langchain_community.graphs import Neo4jGraph
from langchain_openai import ChatOpenAI
graph = Neo4jGraph(
url="bolt://localhost:7687", username="neo4j", password="pleaseletmein"
)
graph.query(
"""
MERGE (m:Movie {name:"Top Gun"})
WITH m
UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor
MERGE (a:Actor {name:actor})
MERGE (a)-[:ACTED_IN]->(m)
"""
)
graph.refresh_schema()
print(graph.schema)
chain = GraphCypherQAChain.from_llm(
ChatOpenAI(temperature=0), graph=graph, verbose=True
)
chain.run("Who played in Top Gun?")
chain = GraphCypherQAChain.from_llm(
ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2
)
chain.run("Who played in Top Gun?")
chain = GraphCypherQAChain.from_llm(
ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True
)
result = chain("Who played in Top Gun?")
print(f"Intermediate steps: {result['intermediate_steps']}")
print(f"Final answer: {result['result']}")
chain = GraphCypherQAChain.from_llm(
ChatOpenAI(temperature=0), graph=graph, verbose=True, return_direct=True
)
chain.run("Who played in Top Gun?")
from langchain.prompts.prompt import PromptTemplate
CYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.
Instructions:
Use only the provided relationship types and properties in the schema.
Do not use any other relationship types or properties that are not provided.
Schema:
{schema}
Note: Do not include any explanations or apologies in your responses.
Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
Do not include any text except the generated Cypher statement.
Examples: Here are a few examples of generated Cypher statements for particular questions:
MATCH (m:Movie {{title:"Top Gun"}})<-[:ACTED_IN]-()
RETURN count(*) AS numberOfActors
The question is:
{question}"""
CYPHER_GENERATION_PROMPT = PromptTemplate(
input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
)
chain = GraphCypherQAChain.from_llm(
| ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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") | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import os
os.environ["OUTLINE_API_KEY"] = "xxx"
os.environ["OUTLINE_INSTANCE_URL"] = "https://app.getoutline.com"
from langchain.retrievers import OutlineRetriever
retriever = | OutlineRetriever() | langchain.retrievers.OutlineRetriever |
from langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChain
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet azureml-mlflow')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet textstat')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
get_ipython().system('python -m spacy download en_core_web_sm')
import os
os.environ["MLFLOW_TRACKING_URI"] = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["SERPAPI_API_KEY"] = ""
from langchain.callbacks import MlflowCallbackHandler
from langchain_openai import OpenAI
"""Main function.
This function is used to try the callback handler.
Scenarios:
1. OpenAI LLM
2. Chain with multiple SubChains on multiple generations
3. Agent with Tools
"""
mlflow_callback = MlflowCallbackHandler()
llm = OpenAI(
model_name="gpt-3.5-turbo", temperature=0, callbacks=[mlflow_callback], verbose=True
)
llm_result = llm.generate(["Tell me a joke"])
mlflow_callback.flush_tracker(llm)
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = | LLMChain(llm=llm, prompt=prompt_template, callbacks=[mlflow_callback]) | langchain.chains.LLMChain |
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),
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!",
)
chain.run(
meal= | rl_chain.ToSelectFrom(meals) | langchain_experimental.rl_chain.ToSelectFrom |
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") | langchain_openai.ChatOpenAI |
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) | langchain_community.vectorstores.VespaStore.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-text-splitters tiktoken')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=100, chunk_overlap=0
)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
from langchain_text_splitters import TokenTextSplitter
text_splitter = TokenTextSplitter(chunk_size=10, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import SpacyTextSplitter
text_splitter = SpacyTextSplitter(chunk_size=1000)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
from langchain_text_splitters import SentenceTransformersTokenTextSplitter
splitter = | SentenceTransformersTokenTextSplitter(chunk_overlap=0) | langchain_text_splitters.SentenceTransformersTokenTextSplitter |
get_ipython().system("python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken")
import getpass
import os
from langchain_community.vectorstores import DeepLake
from langchain_openai import OpenAIEmbeddings
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
activeloop_token = getpass.getpass("Activeloop Token:")
os.environ["ACTIVELOOP_TOKEN"] = activeloop_token
embeddings = OpenAIEmbeddings(disallowed_special=())
get_ipython().system('git clone https://github.com/twitter/the-algorithm # replace any repository of your choice')
import os
from langchain_community.document_loaders import TextLoader
root_dir = "./the-algorithm"
docs = []
for dirpath, dirnames, filenames in os.walk(root_dir):
for file in filenames:
try:
loader = TextLoader(os.path.join(dirpath, file), encoding="utf-8")
docs.extend(loader.load_and_split())
except Exception:
pass
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
username = "<USERNAME_OR_ORG>" # replace with your username from app.activeloop.ai
db = DeepLake(
dataset_path=f"hub://{username}/twitter-algorithm",
embedding=embeddings,
)
db.add_documents(texts)
db = DeepLake(
dataset_path=f"hub://{username}/twitter-algorithm",
read_only=True,
embedding=embeddings,
)
retriever = db.as_retriever()
retriever.search_kwargs["distance_metric"] = "cos"
retriever.search_kwargs["fetch_k"] = 100
retriever.search_kwargs["maximal_marginal_relevance"] = True
retriever.search_kwargs["k"] = 10
def filter(x):
if "com.google" in x["text"].data()["value"]:
return False
metadata = x["metadata"].data()["value"]
return "scala" in metadata["source"] or "py" in metadata["source"]
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model_name="gpt-3.5-turbo-0613") # switch to 'gpt-4'
qa = | ConversationalRetrievalChain.from_llm(model, retriever=retriever) | langchain.chains.ConversationalRetrievalChain.from_llm |
from transformers import load_tool
hf_tools = [
load_tool(tool_name)
for tool_name in [
"document-question-answering",
"image-captioning",
"image-question-answering",
"image-segmentation",
"speech-to-text",
"summarization",
"text-classification",
"text-question-answering",
"translation",
"huggingface-tools/text-to-image",
"huggingface-tools/text-to-video",
"text-to-speech",
"huggingface-tools/text-download",
"huggingface-tools/image-transformation",
]
]
from langchain_experimental.autonomous_agents import HuggingGPT
from langchain_openai import OpenAI
llm = OpenAI(model_name="gpt-3.5-turbo")
agent = | HuggingGPT(llm, hf_tools) | langchain_experimental.autonomous_agents.HuggingGPT |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "docarray"')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import DocArrayInMemorySearch
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
documents = TextLoader("../../modules/state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = | DocArrayInMemorySearch.from_documents(docs, embeddings) | langchain_community.vectorstores.DocArrayInMemorySearch.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet singlestoredb')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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() | langchain_community.chat_message_histories.ChatMessageHistory |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-community')
import os
os.environ["YDC_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from langchain_community.utilities.you import YouSearchAPIWrapper
utility = YouSearchAPIWrapper(num_web_results=1)
utility
import json
response = utility.raw_results(query="What is the weather in NY")
hits = response["hits"]
print(len(hits))
print(json.dumps(hits, indent=2))
response = utility.results(query="What is the weather in NY")
print(len(response))
print(response)
from langchain_community.retrievers.you import YouRetriever
retriever = YouRetriever(num_web_results=1)
retriever
response = retriever.invoke("What is the weather in NY")
print(len(response))
print(response)
get_ipython().system('pip install --upgrade --quiet langchain-openai')
from langchain_community.retrievers.you import YouRetriever
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
runnable = RunnablePassthrough
retriever = YouRetriever(num_web_results=1)
model = ChatOpenAI(model="gpt-3.5-turbo-16k")
output_parser = StrOutputParser()
prompt = | ChatPromptTemplate.from_template(
"""Answer the question based only on the context provided.
Context: {context}
Question: {question}"""
) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet wikipedia')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import OpenAI
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") | langchain.hub.pull |
get_ipython().system('pip install -U oci')
from langchain_community.llms import OCIGenAI
llm = OCIGenAI(
model_id="MY_MODEL",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="MY_OCID",
)
response = llm.invoke("Tell me one fact about earth", temperature=0.7)
print(response)
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
llm = OCIGenAI(
model_id="MY_MODEL",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="MY_OCID",
auth_type="SECURITY_TOKEN",
auth_profile="MY_PROFILE", # replace with your profile name
model_kwargs={"temperature": 0.7, "top_p": 0.75, "max_tokens": 200},
)
prompt = PromptTemplate(input_variables=["query"], template="{query}")
llm_chain = LLMChain(llm=llm, prompt=prompt)
response = llm_chain.invoke("what is the capital of france?")
print(response)
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
from langchain_community.embeddings import OCIGenAIEmbeddings
from langchain_community.vectorstores import FAISS
embeddings = OCIGenAIEmbeddings(
model_id="MY_EMBEDDING_MODEL",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="MY_OCID",
)
vectorstore = FAISS.from_texts(
[
"Larry Ellison co-founded Oracle Corporation in 1977 with Bob Miner and Ed Oates.",
"Oracle Corporation is an American multinational computer technology company headquartered in Austin, Texas, United States.",
],
embedding=embeddings,
)
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = PromptTemplate.from_template(template)
llm = OCIGenAI(
model_id="MY_MODEL",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="MY_OCID",
)
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| | StrOutputParser() | langchain.schema.output_parser.StrOutputParser |
from langchain_community.llms import Ollama
llm = Ollama(model="llama2")
llm("The first man on the moon was ...")
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = Ollama(
model="llama2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])
)
llm("The first man on the moon was ...")
from langchain_community.llms import Ollama
llm = Ollama(model="llama2:13b")
llm("The first man on the moon was ... think step by step")
get_ipython().run_line_magic('env', 'CMAKE_ARGS="-DLLAMA_METAL=on"')
get_ipython().run_line_magic('env', 'FORCE_CMAKE=1')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet llama-cpp-python --no-cache-dirclear')
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms import LlamaCpp
llm = LlamaCpp(
model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",
n_gpu_layers=1,
n_batch=512,
n_ctx=2048,
f16_kv=True,
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=True,
)
llm("The first man on the moon was ... Let's think step by step")
get_ipython().run_line_magic('pip', 'install gpt4all')
from langchain_community.llms import GPT4All
llm = GPT4All(
model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin"
)
llm("The first man on the moon was ... Let's think step by step")
from langchain_community.llms.llamafile import Llamafile
llm = Llamafile()
llm.invoke("The first man on the moon was ... Let's think step by step.")
llm = LlamaCpp(
model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",
n_gpu_layers=1,
n_batch=512,
n_ctx=2048,
f16_kv=True,
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=True,
)
from langchain.chains import LLMChain
from langchain.chains.prompt_selector import ConditionalPromptSelector
from langchain.prompts import PromptTemplate
DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(
input_variables=["question"],
template="""<<SYS>> \n You are an assistant tasked with improving Google search \
results. \n <</SYS>> \n\n [INST] Generate THREE Google search queries that \
are similar to this question. The output should be a numbered list of questions \
and each should have a question mark at the end: \n\n {question} [/INST]""",
)
DEFAULT_SEARCH_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an assistant tasked with improving Google search \
results. Generate THREE Google search queries that are similar to \
this question. The output should be a numbered list of questions and each \
should have a question mark at the end: {question}""",
)
QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_SEARCH_PROMPT,
conditionals=[(lambda llm: isinstance(llm, LlamaCpp), DEFAULT_LLAMA_SEARCH_PROMPT)],
)
prompt = QUESTION_PROMPT_SELECTOR.get_prompt(llm)
prompt
llm_chain = | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_texts(
texts, embeddings, metadatas=[{"source": i} for i in range(len(texts))]
)
query = "What did the president say about Justice Breyer"
docs = docsearch.similarity_search(query)
from langchain.chains.question_answering import load_qa_chain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
template = """You are a chatbot having a conversation with a human.
Given the following extracted parts of a long document and a question, create a final answer.
{context}
{chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input", "context"], template=template
)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
chain = load_qa_chain(
| OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_cell_magic('writefile', 'wechat_chats.txt', '女朋友 2023/09/16 2:51 PM\n天气有点凉\n\n男朋友 2023/09/16 2:51 PM\n珍簟凉风著，瑶琴寄恨生。嵇君懒书札，底物慰秋情。\n\n女朋友 2023/09/16 3:06 PM\n忙什么呢\n\n男朋友 2023/09/16 3:06 PM\n今天只干成了一件像样的事\n那就是想你\n\n女朋友 2023/09/16 3:06 PM\n[动画表情]\n')
import logging
import re
from typing import Iterator, List
from langchain_community.chat_loaders import base as chat_loaders
from langchain_core.messages import BaseMessage, HumanMessage
logger = logging.getLogger()
class WeChatChatLoader(chat_loaders.BaseChatLoader):
def __init__(self, path: str):
"""
Initialize the Discord chat loader.
Args:
path: Path to the exported Discord chat text file.
"""
self.path = path
self._message_line_regex = re.compile(
r"(?P<sender>.+?) (?P<timestamp>\d{4}/\d{2}/\d{2} \d{1,2}:\d{2} (?:AM|PM))", # noqa
)
def _append_message_to_results(
self,
results: List,
current_sender: str,
current_timestamp: str,
current_content: List[str],
):
content = "\n".join(current_content).strip()
if not re.match(r"\[.*\]", content):
results.append(
HumanMessage(
content=content,
additional_kwargs={
"sender": current_sender,
"events": [{"message_time": current_timestamp}],
},
)
)
return results
def _load_single_chat_session_from_txt(
self, file_path: str
) -> chat_loaders.ChatSession:
"""
Load a single chat session from a text file.
Args:
file_path: Path to the text file containing the chat messages.
Returns:
A `ChatSession` object containing the loaded chat messages.
"""
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
results: List[BaseMessage] = []
current_sender = None
current_timestamp = None
current_content = []
for line in lines:
if re.match(self._message_line_regex, line):
if current_sender and current_content:
results = self._append_message_to_results(
results, current_sender, current_timestamp, current_content
)
current_sender, current_timestamp = re.match(
self._message_line_regex, line
).groups()
current_content = []
else:
current_content.append(line.strip())
if current_sender and current_content:
results = self._append_message_to_results(
results, current_sender, current_timestamp, current_content
)
return chat_loaders.ChatSession(messages=results)
def lazy_load(self) -> Iterator[chat_loaders.ChatSession]:
"""
Lazy load the messages from the chat file and yield them in the required format.
Yields:
A `ChatSession` object containing the loaded chat messages.
"""
yield self._load_single_chat_session_from_txt(self.path)
loader = WeChatChatLoader(
path="./wechat_chats.txt",
)
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 |
import os
from langchain.chains import ConversationalRetrievalChain
from langchain_community.vectorstores import Vectara
from langchain_openai import OpenAI
from langchain_community.document_loaders import TextLoader
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
vectara = Vectara.from_documents(documents, embedding=None)
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
openai_api_key = os.environ["OPENAI_API_KEY"]
llm = OpenAI(openai_api_key=openai_api_key, temperature=0)
retriever = vectara.as_retriever()
d = retriever.get_relevant_documents(
"What did the president say about Ketanji Brown Jackson", k=2
)
print(d)
bot = ConversationalRetrievalChain.from_llm(
llm, retriever, memory=memory, verbose=False
)
query = "What did the president say about Ketanji Brown Jackson"
result = bot.invoke({"question": query})
result["answer"]
query = "Did he mention who she suceeded"
result = bot.invoke({"question": query})
result["answer"]
bot = ConversationalRetrievalChain.from_llm(
OpenAI(temperature=0), vectara.as_retriever()
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = bot.invoke({"question": query, "chat_history": chat_history})
result["answer"]
chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = bot.invoke({"question": query, "chat_history": chat_history})
result["answer"]
bot = ConversationalRetrievalChain.from_llm(
llm, vectara.as_retriever(), return_source_documents=True
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = bot.invoke({"question": query, "chat_history": chat_history})
result["source_documents"][0]
from langchain.chains import LLMChain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
from langchain.chains.question_answering import load_qa_chain
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce")
chain = ConversationalRetrievalChain(
retriever=vectara.as_retriever(),
question_generator=question_generator,
combine_docs_chain=doc_chain,
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})
result["answer"]
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce")
chain = ConversationalRetrievalChain(
retriever=vectara.as_retriever(),
question_generator=question_generator,
combine_docs_chain=doc_chain,
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})
result["answer"]
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains.conversational_retrieval.prompts import (
CONDENSE_QUESTION_PROMPT,
QA_PROMPT,
)
from langchain.chains.llm import LLMChain
from langchain.chains.question_answering import load_qa_chain
llm = | OpenAI(temperature=0, openai_api_key=openai_api_key) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
runnable = RunnableParallel(
passed=RunnablePassthrough(),
extra=RunnablePassthrough.assign(mult=lambda x: x["num"] * 3),
modified=lambda x: x["num"] + 1,
)
runnable.invoke({"num": 1})
from langchain_community.vectorstores import FAISS
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
vectorstore = FAISS.from_texts(
["harrison worked at kensho"], embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
"""For basic init and call"""
from langchain_community.chat_models import ChatSparkLLM
from langchain_core.messages import HumanMessage
chat = ChatSparkLLM(
spark_app_id="<app_id>", spark_api_key="<api_key>", spark_api_secret="<api_secret>"
)
message = HumanMessage(content="Hello")
chat([message])
chat = | ChatSparkLLM(streaming=True) | langchain_community.chat_models.ChatSparkLLM |
get_ipython().run_cell_magic('capture', '', '%pip install --upgrade --quiet python-arango # The ArangoDB Python Driver\n%pip install --upgrade --quiet adb-cloud-connector # The ArangoDB Cloud Instance provisioner\n%pip install --upgrade --quiet langchain-openai\n%pip install --upgrade --quiet langchain\n')
import json
from adb_cloud_connector import get_temp_credentials
from arango import ArangoClient
con = get_temp_credentials()
db = ArangoClient(hosts=con["url"]).db(
con["dbName"], con["username"], con["password"], verify=True
)
print(json.dumps(con, indent=2))
from langchain_community.graphs import ArangoGraph
graph = | ArangoGraph(db) | langchain_community.graphs.ArangoGraph |
from typing import Callable, List
from langchain.memory import ConversationBufferMemory
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name = name
self.system_message = system_message
self.model = model
self.prefix = f"{self.name}: "
self.reset()
def reset(self):
self.message_history = ["Here is the conversation so far."]
def send(self) -> str:
"""
Applies the chatmodel to the message history
and returns the message string
"""
message = self.model(
[
self.system_message,
HumanMessage(content="\n".join(self.message_history + [self.prefix])),
]
)
return message.content
def receive(self, name: str, message: str) -> None:
"""
Concatenates {message} spoken by {name} into message history
"""
self.message_history.append(f"{name}: {message}")
class DialogueSimulator:
def __init__(
self,
agents: List[DialogueAgent],
selection_function: Callable[[int, List[DialogueAgent]], int],
) -> None:
self.agents = agents
self._step = 0
self.select_next_speaker = selection_function
def reset(self):
for agent in self.agents:
agent.reset()
def inject(self, name: str, message: str):
"""
Initiates the conversation with a {message} from {name}
"""
for agent in self.agents:
agent.receive(name, message)
self._step += 1
def step(self) -> tuple[str, str]:
speaker_idx = self.select_next_speaker(self._step, self.agents)
speaker = self.agents[speaker_idx]
message = speaker.send()
for receiver in self.agents:
receiver.receive(speaker.name, message)
self._step += 1
return speaker.name, message
class DialogueAgentWithTools(DialogueAgent):
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
tool_names: List[str],
**tool_kwargs,
) -> None:
super().__init__(name, system_message, model)
self.tools = load_tools(tool_names, **tool_kwargs)
def send(self) -> str:
"""
Applies the chatmodel to the message history
and returns the message string
"""
agent_chain = initialize_agent(
self.tools,
self.model,
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
verbose=True,
memory=ConversationBufferMemory(
memory_key="chat_history", return_messages=True
),
)
message = AIMessage(
content=agent_chain.run(
input="\n".join(
[self.system_message.content] + self.message_history + [self.prefix]
)
)
)
return message.content
names = {
"AI accelerationist": ["arxiv", "ddg-search", "wikipedia"],
"AI alarmist": ["arxiv", "ddg-search", "wikipedia"],
}
topic = "The current impact of automation and artificial intelligence on employment"
word_limit = 50 # word limit for task brainstorming
conversation_description = f"""Here is the topic of conversation: {topic}
The participants are: {', '.join(names.keys())}"""
agent_descriptor_system_message = SystemMessage(
content="You can add detail to the description of the conversation participant."
)
def generate_agent_description(name):
agent_specifier_prompt = [
agent_descriptor_system_message,
HumanMessage(
content=f"""{conversation_description}
Please reply with a creative description of {name}, in {word_limit} words or less.
Speak directly to {name}.
Give them a point of view.
Do not add anything else."""
),
]
agent_description = ChatOpenAI(temperature=1.0)(agent_specifier_prompt).content
return agent_description
agent_descriptions = {name: generate_agent_description(name) for name in names}
for name, description in agent_descriptions.items():
print(description)
def generate_system_message(name, description, tools):
return f"""{conversation_description}
Your name is {name}.
Your description is as follows: {description}
Your goal is to persuade your conversation partner of your point of view.
DO look up information with your tool to refute your partner's claims.
DO cite your sources.
DO NOT fabricate fake citations.
DO NOT cite any source that you did not look up.
Do not add anything else.
Stop speaking the moment you finish speaking from your perspective.
"""
agent_system_messages = {
name: generate_system_message(name, description, tools)
for (name, tools), description in zip(names.items(), agent_descriptions.values())
}
for name, system_message in agent_system_messages.items():
print(name)
print(system_message)
topic_specifier_prompt = [
SystemMessage(content="You can make a topic more specific."),
HumanMessage(
content=f"""{topic}
You are the moderator.
Please make the topic more specific.
Please reply with the specified quest in {word_limit} words or less.
Speak directly to the participants: {*names,}.
Do not add anything else."""
),
]
specified_topic = ChatOpenAI(temperature=1.0)(topic_specifier_prompt).content
print(f"Original topic:\n{topic}\n")
print(f"Detailed topic:\n{specified_topic}\n")
agents = [
DialogueAgentWithTools(
name=name,
system_message=SystemMessage(content=system_message),
model= | ChatOpenAI(model_name="gpt-4", temperature=0.2) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sentence-transformers > /dev/null')
from langchain.chains import LLMChain, StuffDocumentsChain
from langchain.prompts import PromptTemplate
from langchain_community.document_transformers import (
LongContextReorder,
)
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAI
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
texts = [
"Basquetball is a great sport.",
"Fly me to the moon is one of my favourite songs.",
"The Celtics are my favourite team.",
"This is a document about the Boston Celtics",
"I simply love going to the movies",
"The Boston Celtics won the game by 20 points",
"This is just a random text.",
"Elden Ring is one of the best games in the last 15 years.",
"L. Kornet is one of the best Celtics players.",
"Larry Bird was an iconic NBA player.",
]
retriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(
search_kwargs={"k": 10}
)
query = "What can you tell me about the Celtics?"
docs = retriever.get_relevant_documents(query)
docs
reordering = LongContextReorder()
reordered_docs = reordering.transform_documents(docs)
reordered_docs
document_prompt = PromptTemplate(
input_variables=["page_content"], template="{page_content}"
)
document_variable_name = "context"
llm = | OpenAI() | langchain_openai.OpenAI |
from langchain.evaluation import RegexMatchStringEvaluator
evaluator = | RegexMatchStringEvaluator() | langchain.evaluation.RegexMatchStringEvaluator |
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)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question. If none of the articles answer the question, just say you don't know.\n\nHere are the Wikipedia articles:{context}",
),
("human", "{question}"),
]
)
prompt.pretty_print()
from operator import itemgetter
from typing import List
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import (
RunnableLambda,
RunnableParallel,
RunnablePassthrough,
)
def format_docs(docs: List[Document]) -> str:
"""Convert Documents to a single string.:"""
formatted = [
f"Article Title: {doc.metadata['title']}\nArticle Snippet: {doc.page_content}"
for doc in docs
]
return "\n\n" + "\n\n".join(formatted)
format = itemgetter("docs") | RunnableLambda(format_docs)
answer = prompt | llm | StrOutputParser()
chain = (
RunnableParallel(question=RunnablePassthrough(), docs=wiki)
.assign(context=format)
.assign(answer=answer)
.pick(["answer", "docs"])
)
chain.invoke("How fast are cheetahs?")
from langchain_core.pydantic_v1 import BaseModel, Field
class cited_answer(BaseModel):
"""Answer the user question based only on the given sources, and cite the sources used."""
answer: str = Field(
...,
description="The answer to the user question, which is based only on the given sources.",
)
citations: List[int] = Field(
...,
description="The integer IDs of the SPECIFIC sources which justify the answer.",
)
llm_with_tool = llm.bind_tools(
[cited_answer],
tool_choice="cited_answer",
)
example_q = """What Brian's height?
Source: 1
Information: Suzy is 6'2"
Source: 2
Information: Jeremiah is blonde
Source: 3
Information: Brian is 3 inches shorted than Suzy"""
llm_with_tool.invoke(example_q)
from langchain.output_parsers.openai_tools import JsonOutputKeyToolsParser
output_parser = JsonOutputKeyToolsParser(key_name="cited_answer", return_single=True)
(llm_with_tool | output_parser).invoke(example_q)
def format_docs_with_id(docs: List[Document]) -> str:
formatted = [
f"Source ID: {i}\nArticle Title: {doc.metadata['title']}\nArticle Snippet: {doc.page_content}"
for i, doc in enumerate(docs)
]
return "\n\n" + "\n\n".join(formatted)
format_1 = itemgetter("docs") | RunnableLambda(format_docs_with_id)
answer_1 = prompt | llm_with_tool | output_parser
chain_1 = (
RunnableParallel(question=RunnablePassthrough(), docs=wiki)
.assign(context=format_1)
.assign(cited_answer=answer_1)
.pick(["cited_answer", "docs"])
)
chain_1.invoke("How fast are cheetahs?")
class Citation(BaseModel):
source_id: int = Field(
...,
description="The integer ID of a SPECIFIC source which justifies the answer.",
)
quote: str = Field(
...,
description="The VERBATIM quote from the specified source that justifies the answer.",
)
class quoted_answer(BaseModel):
"""Answer the user question based only on the given sources, and cite the sources used."""
answer: str = Field(
...,
description="The answer to the user question, which is based only on the given sources.",
)
citations: List[Citation] = Field(
..., description="Citations from the given sources that justify the answer."
)
output_parser_2 = JsonOutputKeyToolsParser(key_name="quoted_answer", return_single=True)
llm_with_tool_2 = llm.bind_tools(
[quoted_answer],
tool_choice="quoted_answer",
)
format_2 = itemgetter("docs") | RunnableLambda(format_docs_with_id)
answer_2 = prompt | llm_with_tool_2 | output_parser_2
chain_2 = (
RunnableParallel(question=RunnablePassthrough(), docs=wiki)
.assign(context=format_2)
.assign(quoted_answer=answer_2)
.pick(["quoted_answer", "docs"])
)
chain_2.invoke("How fast are cheetahs?")
from langchain_anthropic import ChatAnthropicMessages
anthropic = ChatAnthropicMessages(model_name="claude-instant-1.2")
system = """You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, \
answer the user question and provide citations. If none of the articles answer the question, just say you don't know.
Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that \
justifies the answer and the ID of the quote article. Return a citation for every quote across all articles \
that justify the answer. Use the following format for your final output:
<cited_answer>
<answer></answer>
<citations>
<citation><source_id></source_id><quote></quote></citation>
<citation><source_id></source_id><quote></quote></citation>
...
</citations>
</cited_answer>
Here are the Wikipedia articles:{context}"""
prompt_3 = | ChatPromptTemplate.from_messages(
[("system", system) | langchain_core.prompts.ChatPromptTemplate.from_messages |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rspace_client')
from langchain_community.document_loaders.rspace import RSpaceLoader
rspace_ids = ["NB1932027", "FL1921314", "SD1932029", "GL1932384"]
for rs_id in rspace_ids:
loader = | RSpaceLoader(global_id=rs_id) | langchain_community.document_loaders.rspace.RSpaceLoader |
import os
os.environ["OPENAI_API_KEY"] = "..."
from langchain.prompts import PromptTemplate
from langchain_experimental.smart_llm import SmartLLMChain
from langchain_openai import ChatOpenAI
hard_question = "I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?"
prompt = | PromptTemplate.from_template(hard_question) | langchain.prompts.PromptTemplate.from_template |
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)
all_splits = text_splitter.split_documents(data)
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
"""
from langchain_together.embeddings import TogetherEmbeddings
embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
"""
vectorstore = Chroma.from_documents(
documents=all_splits,
collection_name="rag-chroma",
embedding=OpenAIEmbeddings(),
)
retriever = vectorstore.as_retriever()
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
from langchain_together import Together
llm = Together(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
temperature=0.0,
max_tokens=2000,
top_k=1,
)
chain = (
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
| prompt
| llm
| | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_openai import OpenAI
search = | GoogleSearchAPIWrapper() | langchain_community.utilities.GoogleSearchAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_community.chat_models import ChatAnthropic
from langchain_openai import ChatOpenAI
from unittest.mock import patch
import httpx
from openai import RateLimitError
request = httpx.Request("GET", "/")
response = httpx.Response(200, request=request)
error = RateLimitError("rate limit", response=response, body="")
openai_llm = ChatOpenAI(max_retries=0)
anthropic_llm = ChatAnthropic()
llm = openai_llm.with_fallbacks([anthropic_llm])
with patch("openai.resources.chat.completions.Completions.create", side_effect=error):
try:
print(openai_llm.invoke("Why did the chicken cross the road?"))
except RateLimitError:
print("Hit error")
with patch("openai.resources.chat.completions.Completions.create", side_effect=error):
try:
print(llm.invoke("Why did the chicken cross the road?"))
except RateLimitError:
print("Hit error")
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You're a nice assistant who always includes a compliment in your response",
),
("human", "Why did the {animal} cross the road"),
]
)
chain = prompt | llm
with patch("openai.resources.chat.completions.Completions.create", side_effect=error):
try:
print(chain.invoke({"animal": "kangaroo"}))
except RateLimitError:
print("Hit error")
from langchain_core.output_parsers import StrOutputParser
chat_prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You're a nice assistant who always includes a compliment in your response",
),
("human", "Why did the {animal} cross the road"),
]
)
chat_model = ChatOpenAI(model_name="gpt-fake")
bad_chain = chat_prompt | chat_model | | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.evaluation import load_evaluator
evaluator = | load_evaluator("trajectory") | langchain.evaluation.load_evaluator |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0).configurable_fields(
temperature=ConfigurableField(
id="llm_temperature",
name="LLM Temperature",
description="The temperature of the LLM",
)
)
model.invoke("pick a random number")
model.with_config(configurable={"llm_temperature": 0.9}).invoke("pick a random number")
prompt = PromptTemplate.from_template("Pick a random number above {x}")
chain = prompt | model
chain.invoke({"x": 0})
chain.with_config(configurable={"llm_temperature": 0.9}).invoke({"x": 0})
from langchain.runnables.hub import HubRunnable
prompt = HubRunnable("rlm/rag-prompt").configurable_fields(
owner_repo_commit=ConfigurableField(
id="hub_commit",
name="Hub Commit",
description="The Hub commit to pull from",
)
)
prompt.invoke({"question": "foo", "context": "bar"})
prompt.with_config(configurable={"hub_commit": "rlm/rag-prompt-llama"}).invoke(
{"question": "foo", "context": "bar"}
)
from langchain.prompts import PromptTemplate
from langchain_community.chat_models import ChatAnthropic
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
llm = ChatAnthropic(temperature=0).configurable_alternatives(
| ConfigurableField(id="llm") | langchain_core.runnables.ConfigurableField |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet openlm')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
import os
from getpass import getpass
if "OPENAI_API_KEY" not in os.environ:
print("Enter your OpenAI API key:")
os.environ["OPENAI_API_KEY"] = getpass()
if "HF_API_TOKEN" not in os.environ:
print("Enter your HuggingFace Hub API key:")
os.environ["HF_API_TOKEN"] = getpass()
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import OpenLM
question = "What is the capital of France?"
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
for model in ["text-davinci-003", "huggingface.co/gpt2"]:
llm = OpenLM(model=model)
llm_chain = | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
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) | langchain_openai.OpenAI |
import uuid
from pathlib import Path
import langchain
import torch
from bs4 import BeautifulSoup as Soup
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore, LocalFileStore
from langchain_community.document_loaders.recursive_url_loader import (
RecursiveUrlLoader,
)
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter # noqa
DOCSTORE_DIR = "."
DOCSTORE_ID_KEY = "doc_id"
loader = RecursiveUrlLoader(
"https://ar5iv.labs.arxiv.org/html/1706.03762",
max_depth=2,
extractor=lambda x: Soup(x, "html.parser").text,
)
data = loader.load()
print(f"Loaded {len(data)} documents")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
print(f"Split into {len(all_splits)} documents")
from langchain_community.embeddings import QuantizedBiEncoderEmbeddings
from langchain_core.embeddings import Embeddings
model_name = "Intel/bge-small-en-v1.5-rag-int8-static"
encode_kwargs = {"normalize_embeddings": True} # set True to compute cosine similarity
model_inc = QuantizedBiEncoderEmbeddings(
model_name=model_name,
encode_kwargs=encode_kwargs,
query_instruction="Represent this sentence for searching relevant passages: ",
)
def get_multi_vector_retriever(
docstore_id_key: str, collection_name: str, embedding_function: Embeddings
):
"""Create the composed retriever object."""
vectorstore = Chroma(
collection_name=collection_name,
embedding_function=embedding_function,
)
store = InMemoryByteStore()
return MultiVectorRetriever(
vectorstore=vectorstore,
byte_store=store,
id_key=docstore_id_key,
)
retriever = get_multi_vector_retriever(DOCSTORE_ID_KEY, "multi_vec_store", model_inc)
child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
id_key = "doc_id"
doc_ids = [str(uuid.uuid4()) for _ in all_splits]
sub_docs = []
for i, doc in enumerate(all_splits):
_id = doc_ids[i]
_sub_docs = child_text_splitter.split_documents([doc])
for _doc in _sub_docs:
_doc.metadata[id_key] = _id
sub_docs.extend(_sub_docs)
retriever.vectorstore.add_documents(sub_docs)
retriever.docstore.mset(list(zip(doc_ids, all_splits)))
import torch
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "Intel/neural-chat-7b-v3-3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, device_map="auto", torch_dtype=torch.bfloat16
)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=100)
hf = HuggingFacePipeline(pipeline=pipe)
from langchain import hub
prompt = hub.pull("rlm/rag-prompt")
from langchain.schema.runnable import RunnablePassthrough
rag_chain = {"context": retriever, "question": | RunnablePassthrough() | langchain.schema.runnable.RunnablePassthrough |
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.messages import (
AIMessageChunk,
FunctionMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessageChunk,
)
AIMessageChunk(content="Hello") + AIMessageChunk(content=" World!")
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import BaseChatModel, SimpleChatModel
from langchain_core.messages import AIMessageChunk, BaseMessage, HumanMessage
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import run_in_executor
class CustomChatModelAdvanced(BaseChatModel):
"""A custom chat model that echoes the first `n` characters of the input.
When contributing an implementation to LangChain, carefully document
the model including the initialization parameters, include
an example of how to initialize the model and include any relevant
links to the underlying models documentation or API.
Example:
.. code-block:: python
model = CustomChatModel(n=2)
result = model.invoke([HumanMessage(content="hello")])
result = model.batch([[HumanMessage(content="hello")],
[HumanMessage(content="world")]])
"""
n: int
"""The number of characters from the last message of the prompt to be echoed."""
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Override the _generate method to implement the chat model logic.
This can be a call to an API, a call to a local model, or any other
implementation that generates a response to the input prompt.
Args:
messages: the prompt composed of a list of messages.
stop: a list of strings on which the model should stop generating.
If generation stops due to a stop token, the stop token itself
SHOULD BE INCLUDED as part of the output. This is not enforced
across models right now, but it's a good practice to follow since
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
"""
last_message = messages[-1]
tokens = last_message.content[: self.n]
message = AIMessage(content=tokens)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
"""Stream the output of the model.
This method should be implemented if the model can generate output
in a streaming fashion. If the model does not support streaming,
do not implement it. In that case streaming requests will be automatically
handled by the _generate method.
Args:
messages: the prompt composed of a list of messages.
stop: a list of strings on which the model should stop generating.
If generation stops due to a stop token, the stop token itself
SHOULD BE INCLUDED as part of the output. This is not enforced
across models right now, but it's a good practice to follow since
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
"""
last_message = messages[-1]
tokens = last_message.content[: self.n]
for token in tokens:
chunk = ChatGenerationChunk(message= | AIMessageChunk(content=token) | langchain_core.messages.AIMessageChunk |