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get_ipython().system('pip install llama-index')
get_ipython().system('pip install wget')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-azureaisearch')
get_ipython().run_line_magic('pip', 'install azure-search-documents==11.4.0')
get_ipython().run_line_magic('llama-index-embeddings-azure-openai', '')
get_ipython().run_line_magic('llama-index-llms-azure-openai', '')
import logging
import sys
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from IPython.display import Markdown, display
from llama_index.core import (
SimpleDirectoryReader,
StorageContext,
VectorStoreIndex,
)
from llama_index.core.settings import Settings
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore
from llama_index.vector_stores.azureaisearch import (
IndexManagement,
MetadataIndexFieldType,
)
aoai_api_key = "YOUR_AZURE_OPENAI_API_KEY"
aoai_endpoint = "YOUR_AZURE_OPENAI_ENDPOINT"
aoai_api_version = "2023-05-15"
llm = AzureOpenAI(
model="YOUR_AZURE_OPENAI_COMPLETION_MODEL_NAME",
deployment_name="YOUR_AZURE_OPENAI_COMPLETION_DEPLOYMENT_NAME",
api_key=aoai_api_key,
azure_endpoint=aoai_endpoint,
api_version=aoai_api_version,
)
embed_model = AzureOpenAIEmbedding(
model="YOUR_AZURE_OPENAI_EMBEDDING_MODEL_NAME",
deployment_name="YOUR_AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME",
api_key=aoai_api_key,
azure_endpoint=aoai_endpoint,
api_version=aoai_api_version,
)
search_service_api_key = "YOUR-AZURE-SEARCH-SERVICE-ADMIN-KEY"
search_service_endpoint = "YOUR-AZURE-SEARCH-SERVICE-ENDPOINT"
search_service_api_version = "2023-11-01"
credential = AzureKeyCredential(search_service_api_key)
index_name = "llamaindex-vector-demo"
index_client = SearchIndexClient(
endpoint=search_service_endpoint,
credential=credential,
)
search_client = SearchClient(
endpoint=search_service_endpoint,
index_name=index_name,
credential=credential,
)
metadata_fields = {
"author": "author",
"theme": ("topic", MetadataIndexFieldType.STRING),
"director": "director",
}
vector_store = AzureAISearchVectorStore(
search_or_index_client=index_client,
filterable_metadata_field_keys=metadata_fields,
index_name=index_name,
index_management=IndexManagement.CREATE_IF_NOT_EXISTS,
id_field_key="id",
chunk_field_key="chunk",
embedding_field_key="embedding",
embedding_dimensionality=1536,
metadata_string_field_key="metadata",
doc_id_field_key="doc_id",
language_analyzer="en.lucene",
vector_algorithm_type="exhaustiveKnn",
)
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = | SimpleDirectoryReader("../data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader("../data/paul_graham")
docs = reader.load_data()
import os
from llama_index.core import (
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
if not os.path.exists("storage"):
index = VectorStoreIndex.from_documents(docs)
index.set_index_id("vector_index")
index.storage_context.persist("./storage")
else:
storage_context = StorageContext.from_defaults(persist_dir="storage")
index = load_index_from_storage(storage_context, index_id="vector_index")
from llama_index.core.query_pipeline import QueryPipeline
from llama_index.core import PromptTemplate
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
p = QueryPipeline(chain=[prompt_tmpl, llm], verbose=True)
output = p.run(movie_name="The Departed")
print(str(output))
from typing import List
from pydantic import BaseModel, Field
from llama_index.core.output_parsers import PydanticOutputParser
class Movie(BaseModel):
"""Object representing a single movie."""
name: str = Field(..., description="Name of the movie.")
year: int = Field(..., description="Year of the movie.")
class Movies(BaseModel):
"""Object representing a list of movies."""
movies: List[Movie] = Field(..., description="List of movies.")
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex
from llama_index.core import PromptTemplate
from IPython.display import Markdown, display
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
gpt35_llm = OpenAI(model="gpt-3.5-turbo")
gpt4_llm = OpenAI(model="gpt-4")
index = VectorStoreIndex.from_documents(documents)
query_str = "What are the potential risks associated with the use of Llama 2 as mentioned in the context?"
query_engine = index.as_query_engine(similarity_top_k=2, llm=gpt35_llm)
vector_retriever = index.as_retriever(similarity_top_k=2)
response = query_engine.query(query_str)
print(str(response))
def display_prompt_dict(prompts_dict):
for k, p in prompts_dict.items():
text_md = f"**Prompt Key**: {k}<br>" f"**Text:** <br>"
display(Markdown(text_md))
print(p.get_template())
display(Markdown("<br><br>"))
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
from langchain import hub
langchain_prompt = hub.pull("rlm/rag-prompt")
from llama_index.core.prompts import LangchainPromptTemplate
lc_prompt_tmpl = LangchainPromptTemplate(
template=langchain_prompt,
template_var_mappings={"query_str": "question", "context_str": "context"},
)
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": lc_prompt_tmpl}
)
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
response = query_engine.query(query_str)
print(str(response))
from llama_index.core.schema import TextNode
few_shot_nodes = []
for line in open("../llama2_qa_citation_events.jsonl", "r"):
few_shot_nodes.append(TextNode(text=line))
few_shot_index = VectorStoreIndex(few_shot_nodes)
few_shot_retriever = few_shot_index.as_retriever(similarity_top_k=2)
import json
def few_shot_examples_fn(**kwargs):
query_str = kwargs["query_str"]
retrieved_nodes = few_shot_retriever.retrieve(query_str)
result_strs = []
for n in retrieved_nodes:
raw_dict = json.loads(n.get_content())
query = raw_dict["query"]
response_dict = json.loads(raw_dict["response"])
result_str = f"""\
Query: {query}
Response: {response_dict}"""
result_strs.append(result_str)
return "\n\n".join(result_strs)
qa_prompt_tmpl_str = """\
Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge, \
answer the query asking about citations over different topics.
Please provide your answer in the form of a structured JSON format containing \
a list of authors as the citations. Some examples are given below.
{few_shot_examples}
Query: {query_str}
Answer: \
"""
qa_prompt_tmpl = PromptTemplate(
qa_prompt_tmpl_str,
function_mappings={"few_shot_examples": few_shot_examples_fn},
)
citation_query_str = (
"Which citations are mentioned in the section on Safety RLHF?"
)
print(
qa_prompt_tmpl.format(
query_str=citation_query_str, context_str="test_context"
)
)
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": qa_prompt_tmpl}
)
display_prompt_dict(query_engine.get_prompts())
response = query_engine.query(citation_query_str)
print(str(response))
print(response.source_nodes[1].get_content())
from llama_index.core.postprocessor import (
NERPIINodePostprocessor,
SentenceEmbeddingOptimizer,
)
from llama_index.core import QueryBundle
from llama_index.core.schema import NodeWithScore, TextNode
pii_processor = NERPIINodePostprocessor(llm=gpt4_llm)
def filter_pii_fn(**kwargs):
query_bundle = QueryBundle(query_str=kwargs["query_str"])
new_nodes = pii_processor.postprocess_nodes(
[NodeWithScore(node= | TextNode(text=kwargs["context_str"]) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-program-evaporate')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
wiki_titles = ["Toronto", "Seattle", "Chicago", "Boston", "Houston"]
from pathlib import Path
import requests
for title in wiki_titles:
response = requests.get(
"https://en.wikipedia.org/w/api.php",
params={
"action": "query",
"format": "json",
"titles": title,
"prop": "extracts",
"explaintext": True,
},
).json()
page = next(iter(response["query"]["pages"].values()))
wiki_text = page["extract"]
data_path = Path("data")
if not data_path.exists():
Path.mkdir(data_path)
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
from llama_index.core import SimpleDirectoryReader
city_docs = {}
for wiki_title in wiki_titles:
city_docs[wiki_title] = SimpleDirectoryReader(
input_files=[f"data/{wiki_title}.txt"]
).load_data()
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = | OpenAI(temperature=0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_response
from llama_index.llms.openai import OpenAI
llm = OpenAI(temperature=0, model="gpt-4")
get_ipython().system("mkdir -p 'data/10q/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_march_2022.pdf' -O 'data/10q/uber_10q_march_2022.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_june_2022.pdf' -O 'data/10q/uber_10q_june_2022.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_sept_2022.pdf' -O 'data/10q/uber_10q_sept_2022.pdf'")
march_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_march_2022.pdf"]
).load_data()
june_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_june_2022.pdf"]
).load_data()
sept_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_sept_2022.pdf"]
).load_data()
march_index = | VectorStoreIndex.from_documents(march_2022) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-supabase')
get_ipython().system('pip install llama-index')
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="Math Tutor",
instructions="You are a personal math tutor. Write and run code to answer math questions.",
openai_tools=[{"type": "code_interpreter"}],
instructions_prefix="Please address the user as Jane Doe. The user has a premium account.",
)
agent.thread_id
response = agent.chat(
"I need to solve the equation `3x + 11 = 14`. Can you help me?"
)
print(str(response))
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="SEC Analyst",
instructions="You are a QA assistant designed to analyze sec filings.",
openai_tools=[{"type": "retrieval"}],
instructions_prefix="Please address the user as Jerry.",
files=["data/10k/lyft_2021.pdf"],
verbose=True,
)
response = agent.chat("What was Lyft's revenue growth in 2021?")
print(str(response))
from llama_index.agent.openai import OpenAIAssistantAgent
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.core.tools import QueryEngineTool, ToolMetadata
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/lyft"
)
lyft_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/uber"
)
uber_index = load_index_from_storage(storage_context)
index_loaded = True
except:
index_loaded = False
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'")
if not index_loaded:
lyft_docs = SimpleDirectoryReader(
input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
uber_docs = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
lyft_index = VectorStoreIndex.from_documents(lyft_docs)
uber_index = VectorStoreIndex.from_documents(uber_docs)
lyft_index.storage_context.persist(persist_dir="./storage/lyft")
uber_index.storage_context.persist(persist_dir="./storage/uber")
lyft_engine = lyft_index.as_query_engine(similarity_top_k=3)
uber_engine = uber_index.as_query_engine(similarity_top_k=3)
query_engine_tools = [
QueryEngineTool(
query_engine=lyft_engine,
metadata=ToolMetadata(
name="lyft_10k",
description=(
"Provides information about Lyft financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
QueryEngineTool(
query_engine=uber_engine,
metadata=ToolMetadata(
name="uber_10k",
description=(
"Provides information about Uber financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
]
agent = OpenAIAssistantAgent.from_new(
name="SEC Analyst",
instructions="You are a QA assistant designed to analyze sec filings.",
tools=query_engine_tools,
instructions_prefix="Please address the user as Jerry.",
verbose=True,
run_retrieve_sleep_time=1.0,
)
response = agent.chat("What was Lyft's revenue growth in 2021?")
from llama_index.agent.openai import OpenAIAssistantAgent
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
)
from llama_index.vector_stores.supabase import SupabaseVectorStore
from llama_index.core.tools import QueryEngineTool, ToolMetadata
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'")
reader = SimpleDirectoryReader(input_files=["./data/10k/lyft_2021.pdf"])
docs = reader.load_data()
for doc in docs:
doc.id_ = "lyft_docs"
vector_store = SupabaseVectorStore(
postgres_connection_string=(
"postgresql://<user>:<password>@<host>:<port>/<db_name>"
),
collection_name="base_demo",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = | VectorStoreIndex.from_documents(docs, storage_context=storage_context) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-callbacks-wandb')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks import LlamaDebugHandler
from llama_index.callbacks.wandb import WandbCallbackHandler
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
SimpleKeywordTableIndex,
StorageContext,
)
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-4", temperature=0)
import llama_index.core
from llama_index.core import set_global_handler
set_global_handler("wandb", run_args={"project": "llamaindex"})
wandb_callback = llama_index.core.global_handler
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
run_args = dict(
project="llamaindex",
)
wandb_callback = WandbCallbackHandler(run_args=run_args)
Settings.callback_manager = CallbackManager([llama_debug, wandb_callback])
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
docs = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import llama_index.core.vector_stores.google.generativeai.genai_extension as genaix
from typing import Iterable
from random import randrange
LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX = f"llama-index-colab"
SESSION_CORPUS_ID_PREFIX = (
f"{LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX}-{randrange(1000000)}"
)
def corpus_id(num_id: int) -> str:
return f"{SESSION_CORPUS_ID_PREFIX}-{num_id}"
SESSION_CORPUS_ID = corpus_id(1)
def list_corpora() -> Iterable[genaix.Corpus]:
client = genaix.build_semantic_retriever()
yield from genaix.list_corpora(client=client)
def delete_corpus(*, corpus_id: str) -> None:
client = genaix.build_semantic_retriever()
genaix.delete_corpus(corpus_id=corpus_id, client=client)
def cleanup_colab_corpora():
for corpus in list_corpora():
if corpus.corpus_id.startswith(LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX):
try:
delete_corpus(corpus_id=corpus.corpus_id)
print(f"Deleted corpus {corpus.corpus_id}.")
except Exception:
pass
cleanup_colab_corpora()
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
from llama_index.core import Response
import time
index = GoogleIndex.create_corpus(
corpus_id=SESSION_CORPUS_ID, display_name="My first corpus!"
)
print(f"Newly created corpus ID is {index.corpus_id}.")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index.insert_documents(documents)
for corpus in list_corpora():
print(corpus)
query_engine = index.as_query_engine()
response = query_engine.query("What did Paul Graham do growing up?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
for cited_text in [node.text for node in response.source_nodes]:
print(f"Cited text: {cited_text}")
if response.metadata:
print(
f"Answerability: {response.metadata.get('answerable_probability', 0)}"
)
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
query_engine = index.as_query_engine()
response = query_engine.query("Which company did Paul Graham build?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
index.insert_nodes(
[
TextNode(
text="It was the best of times.",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="123",
metadata={"file_name": "Tale of Two Cities"},
)
},
),
TextNode(
text="It was the worst of times.",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="123",
metadata={"file_name": "Tale of Two Cities"},
)
},
),
TextNode(
text="Bugs Bunny: Wassup doc?",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="456",
metadata={"file_name": "Bugs Bunny Adventure"},
)
},
),
]
)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
HarmCategory,
SafetySetting,
)
index = | GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID) | llama_index.indices.managed.google.GoogleIndex.from_corpus |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('env', 'OPENAI_API_KEY=YOUR_OPENAI_KEY')
get_ipython().system('pip install llama-index pypdf')
get_ipython().system("mkdir -p 'data/'")
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
import json
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [ | Document(text=doc_text) | llama_index.core.Document |
get_ipython().run_line_magic('pip', 'install llama-index-readers-pathway')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install pathway')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/'")
get_ipython().system("wget 'https://gist.githubusercontent.com/janchorowski/dd22a293f3d99d1b726eedc7d46d2fc0/raw/pathway_readme.md' -O 'data/pathway_readme.md'")
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import getpass
import os
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import pathway as pw
data_sources = []
data_sources.append(
pw.io.fs.read(
"./data",
format="binary",
mode="streaming",
with_metadata=True,
) # This creates a `pathway` connector that tracks
)
from llama_index.core.retrievers import PathwayVectorServer
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.node_parser import TokenTextSplitter
embed_model = OpenAIEmbedding(embed_batch_size=10)
transformations_example = [
TokenTextSplitter(
chunk_size=150,
chunk_overlap=10,
separator=" ",
),
embed_model,
]
processing_pipeline = PathwayVectorServer(
*data_sources,
transformations=transformations_example,
)
PATHWAY_HOST = "127.0.0.1"
PATHWAY_PORT = 8754
processing_pipeline.run_server(
host=PATHWAY_HOST, port=PATHWAY_PORT, with_cache=False, threaded=True
)
from llama_index.readers.pathway import PathwayReader
reader = PathwayReader(host=PATHWAY_HOST, port=PATHWAY_PORT)
reader.load_data(query_text="What is Pathway")
docs = reader.load_data(query_text="some search input", k=2)
from llama_index.core import SummaryIndex
index = | SummaryIndex.from_documents(docs) | llama_index.core.SummaryIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().system('pip install llama-index')
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west4-gcp-free")
import os
import getpass
import openai
openai.api_key = "sk-<your-key>"
try:
pinecone.create_index(
"quickstart-index", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart-index")
pinecone_index.delete(deleteAll=True, namespace="test")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
"gender": "male",
"born": 1963,
},
),
| TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-llms-portkey')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install -U llama_index')
get_ipython().system('pip install -U portkey-ai')
from llama_index.llms.portkey import Portkey
from llama_index.core.llms import ChatMessage
import portkey as pk
import os
os.environ["PORTKEY_API_KEY"] = "PORTKEY_API_KEY"
openai_virtual_key_a = ""
openai_virtual_key_b = ""
anthropic_virtual_key_a = ""
anthropic_virtual_key_b = ""
cohere_virtual_key_a = ""
cohere_virtual_key_b = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["ANTHROPIC_API_KEY"] = ""
portkey_client = Portkey(
mode="single",
)
openai_llm = pk.LLMOptions(
provider="openai",
model="gpt-4",
virtual_key=openai_virtual_key_a,
)
portkey_client.add_llms(openai_llm)
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
print("Testing Portkey Llamaindex integration:")
response = portkey_client.chat(messages)
print(response)
prompt = "Why is the sky blue?"
print("\nTesting Stream Complete:\n")
response = portkey_client.stream_complete(prompt)
for i in response:
print(i.delta, end="", flush=True)
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
print("\nTesting Stream Chat:\n")
response = portkey_client.stream_chat(messages)
for i in response:
print(i.delta, end="", flush=True)
portkey_client = Portkey(mode="fallback")
messages = [
| ChatMessage(role="system", content="You are a helpful assistant") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
llm = OpenAI(model="gpt-3.5-turbo-1106")
agent = OpenAIAgent.from_tools(
[multiply_tool, add_tool], llm=llm, verbose=True
)
response = agent.chat("What is (121 * 3) + 42?")
print(str(response))
response = agent.stream_chat("What is (121 * 3) + 42?")
import nest_asyncio
nest_asyncio.apply()
response = await agent.achat("What is (121 * 3) + 42?")
print(str(response))
response = await agent.astream_chat("What is (121 * 3) + 42?")
response_gen = response.response_gen
async for token in response.async_response_gen():
print(token, end="")
import json
def get_current_weather(location, unit="fahrenheit"):
"""Get the current weather in a given location"""
if "tokyo" in location.lower():
return json.dumps(
{"location": location, "temperature": "10", "unit": "celsius"}
)
elif "san francisco" in location.lower():
return json.dumps(
{"location": location, "temperature": "72", "unit": "fahrenheit"}
)
else:
return json.dumps(
{"location": location, "temperature": "22", "unit": "celsius"}
)
weather_tool = FunctionTool.from_defaults(fn=get_current_weather)
llm = OpenAI(model="gpt-3.5-turbo-1106")
agent = OpenAIAgent.from_tools([weather_tool], llm=llm, verbose=True)
response = agent.chat(
"What's the weather like in San Francisco, Tokyo, and Paris?"
)
llm = | OpenAI(model="gpt-3.5-turbo-0613") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-hub-llama-packs-tables-chain-of-table-base')
get_ipython().system('wget "https://github.com/ppasupat/WikiTableQuestions/releases/download/v1.0.2/WikiTableQuestions-1.0.2-compact.zip" -O data.zip')
get_ipython().system('unzip data.zip')
import pandas as pd
df = pd.read_csv("./WikiTableQuestions/csv/200-csv/3.csv")
df
from llama_index.packs.tables.chain_of_table.base import (
ChainOfTableQueryEngine,
serialize_table,
)
from llama_index.core.llama_pack import download_llama_pack
download_llama_pack(
"ChainOfTablePack",
"./chain_of_table_pack",
skip_load=True,
)
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-4-1106-preview") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core import PromptTemplate
text_qa_template_str = (
"Context information is"
" below.\n---------------------\n{context_str}\n---------------------\nUsing"
" both the context information and also using your own knowledge, answer"
" the question: {query_str}\nIf the context isn't helpful, you can also"
" answer the question on your own.\n"
)
text_qa_template = | PromptTemplate(text_qa_template_str) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
from llama_index.core.llama_dataset import (
LabelledRagDataExample,
CreatedByType,
CreatedBy,
)
query = "This is a test query, is it not?"
query_by = CreatedBy(type=CreatedByType.AI, model_name="gpt-4")
reference_answer = "Yes it is."
reference_answer_by = CreatedBy(type=CreatedByType.HUMAN)
reference_contexts = ["This is a sample context"]
rag_example = LabelledRagDataExample(
query=query,
query_by=query_by,
reference_contexts=reference_contexts,
reference_answer=reference_answer,
reference_answer_by=reference_answer_by,
)
print(rag_example.json())
LabelledRagDataExample.parse_raw(rag_example.json())
rag_example.dict()
LabelledRagDataExample.parse_obj(rag_example.dict())
query = "This is a test query, is it so?"
reference_answer = "I think yes, it is."
reference_contexts = ["This is a second sample context"]
rag_example_2 = LabelledRagDataExample(
query=query,
query_by=query_by,
reference_contexts=reference_contexts,
reference_answer=reference_answer,
reference_answer_by=reference_answer_by,
)
from llama_index.core.llama_dataset import LabelledRagDataset
rag_dataset = LabelledRagDataset(examples=[rag_example, rag_example_2])
rag_dataset.to_pandas()
rag_dataset.save_json("rag_dataset.json")
reload_rag_dataset = LabelledRagDataset.from_json("rag_dataset.json")
reload_rag_dataset.to_pandas()
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('pip install wikipedia -q')
from llama_index.readers.wikipedia import WikipediaReader
from llama_index.core import VectorStoreIndex
cities = [
"San Francisco",
]
documents = | WikipediaReader() | llama_index.readers.wikipedia.WikipediaReader |
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import TimeWeightedPostprocessor
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core.response.notebook_utils import display_response
from datetime import datetime, timedelta
from llama_index.core import StorageContext
now = datetime.now()
key = "__last_accessed__"
doc1 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v1.txt"]
).load_data()[0]
doc2 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v2.txt"]
).load_data()[0]
doc3 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v3.txt"]
).load_data()[0]
from llama_index.core import Settings
Settings.text_splitter = SentenceSplitter(chunk_size=512)
nodes1 = Settings.text_splitter.get_nodes_from_documents([doc1])
nodes2 = Settings.text_splitter.get_nodes_from_documents([doc2])
nodes3 = Settings.text_splitter.get_nodes_from_documents([doc3])
nodes1[14].metadata[key] = (now - timedelta(hours=3)).timestamp()
nodes1[14].excluded_llm_metadata_keys = [key]
nodes2[14].metadata[key] = (now - timedelta(hours=2)).timestamp()
nodes2[14].excluded_llm_metadata_keys = [key]
nodes3[14].metadata[key] = (now - timedelta(hours=1)).timestamp()
nodes2[14].excluded_llm_metadata_keys = [key]
docstore = | SimpleDocumentStore() | llama_index.core.storage.docstore.SimpleDocumentStore |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-colbert')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-vectara')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install torch sentence-transformers')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.indices.managed.google import GoogleIndex
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
project_name = "TODO-your-project-name" # @param {type:"string"}
email = "[email protected]" # @param {type:"string"}
client_file_name = "client_secret.json"
get_ipython().system('gcloud config set project $project_name')
get_ipython().system('gcloud config set account $email')
get_ipython().system('gcloud auth application-default login --no-browser --client-id-file=$client_file_name --scopes="https://www.googleapis.com/auth/generative-language.retriever,https://www.googleapis.com/auth/cloud-platform"')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
google_index = GoogleIndex.create_corpus(display_name="My first corpus!")
print(f"Newly created corpus ID is {google_index.corpus_id}.")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
google_index.insert_documents(documents)
google_index = GoogleIndex.from_corpus(corpus_id="")
query_engine = google_index.as_query_engine()
response = query_engine.query("which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.VERBOSE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.EXTRACTIVE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from llama_index.response_synthesizers.google import GoogleTextSynthesizer
from llama_index.vector_stores.google import GoogleVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.llms.gemini import Gemini
from llama_index.core.postprocessor import LLMRerank
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.embeddings.gemini import GeminiEmbedding
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.7, answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE
)
reranker = LLMRerank(
top_n=5,
llm=Gemini(api_key=GOOGLE_API_KEY),
)
retriever = google_index.as_retriever(similarity_top_k=5)
query_engine = RetrieverQueryEngine.from_args(
retriever=retriever,
response_synthesizer=response_synthesizer,
node_postprocessors=[reranker],
)
response = query_engine.query("Which program did this author attend?")
print(response.response)
from llama_index.core.postprocessor import SentenceTransformerRerank
sbert_rerank = SentenceTransformerRerank(
model="cross-encoder/ms-marco-MiniLM-L-2-v2", top_n=5
)
from llama_index.response_synthesizers.google import GoogleTextSynthesizer
from llama_index.vector_stores.google import GoogleVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.llms.gemini import Gemini
from llama_index.core.postprocessor import LLMRerank
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.embeddings.gemini import GeminiEmbedding
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.1, answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE
)
retriever = google_index.as_retriever(similarity_top_k=5)
query_engine = RetrieverQueryEngine.from_args(
retriever=retriever,
response_synthesizer=response_synthesizer,
node_postprocessors=[sbert_rerank],
)
response = query_engine.query("Which program did this author attend?")
print(response.response)
import os
OPENAI_API_TOKEN = "sk-"
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import Settings
import qdrant_client
Settings.chunk_size = 256
client = qdrant_client.QdrantClient(path="qdrant_retrieval_2")
vector_store = QdrantVectorStore(client=client, collection_name="collection")
qdrant_index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import logging
import sys
import pandas as pd
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core.evaluation import DatasetGenerator, RelevancyEvaluator
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Response
from llama_index.llms.openai import OpenAI
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
data_generator = | DatasetGenerator.from_documents(documents) | llama_index.core.evaluation.DatasetGenerator.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-everlyai')
get_ipython().system('pip install llama-index')
from llama_index.llms.everlyai import EverlyAI
from llama_index.core.llms import ChatMessage
llm = EverlyAI(api_key="your-api-key")
message = | ChatMessage(role="user", content="Tell me a joke") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-chroma')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
import chromadb
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core.schema import TextNode
nodes = [
| TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
) | llama_index.core.schema.TextNode |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install promptlayer')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
os.environ["PROMPTLAYER_API_KEY"] = "pl_..."
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import SimpleDirectoryReader
docs = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.core import set_global_handler
| set_global_handler("promptlayer", pl_tags=["paul graham", "essay"]) | llama_index.core.set_global_handler |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('wget "https://github.com/ppasupat/WikiTableQuestions/releases/download/v1.0.2/WikiTableQuestions-1.0.2-compact.zip" -O data.zip')
get_ipython().system('unzip data.zip')
import pandas as pd
from pathlib import Path
data_dir = Path("./WikiTableQuestions/csv/200-csv")
csv_files = sorted([f for f in data_dir.glob("*.csv")])
dfs = []
for csv_file in csv_files:
print(f"processing file: {csv_file}")
try:
df = pd.read_csv(csv_file)
dfs.append(df)
except Exception as e:
print(f"Error parsing {csv_file}: {str(e)}")
tableinfo_dir = "WikiTableQuestions_TableInfo"
get_ipython().system('mkdir {tableinfo_dir}')
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.llms.openai import OpenAI
class TableInfo(BaseModel):
"""Information regarding a structured table."""
table_name: str = Field(
..., description="table name (must be underscores and NO spaces)"
)
table_summary: str = Field(
..., description="short, concise summary/caption of the table"
)
prompt_str = """\
Give me a summary of the table with the following JSON format.
- The table name must be unique to the table and describe it while being concise.
- Do NOT output a generic table name (e.g. table, my_table).
Do NOT make the table name one of the following: {exclude_table_name_list}
Table:
{table_str}
Summary: """
program = LLMTextCompletionProgram.from_defaults(
output_cls=TableInfo,
llm=OpenAI(model="gpt-3.5-turbo"),
prompt_template_str=prompt_str,
)
import json
def _get_tableinfo_with_index(idx: int) -> str:
results_gen = Path(tableinfo_dir).glob(f"{idx}_*")
results_list = list(results_gen)
if len(results_list) == 0:
return None
elif len(results_list) == 1:
path = results_list[0]
return TableInfo.parse_file(path)
else:
raise ValueError(
f"More than one file matching index: {list(results_gen)}"
)
table_names = set()
table_infos = []
for idx, df in enumerate(dfs):
table_info = _get_tableinfo_with_index(idx)
if table_info:
table_infos.append(table_info)
else:
while True:
df_str = df.head(10).to_csv()
table_info = program(
table_str=df_str,
exclude_table_name_list=str(list(table_names)),
)
table_name = table_info.table_name
print(f"Processed table: {table_name}")
if table_name not in table_names:
table_names.add(table_name)
break
else:
print(f"Table name {table_name} already exists, trying again.")
pass
out_file = f"{tableinfo_dir}/{idx}_{table_name}.json"
json.dump(table_info.dict(), open(out_file, "w"))
table_infos.append(table_info)
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
)
import re
def sanitize_column_name(col_name):
return re.sub(r"\W+", "_", col_name)
def create_table_from_dataframe(
df: pd.DataFrame, table_name: str, engine, metadata_obj
):
sanitized_columns = {col: sanitize_column_name(col) for col in df.columns}
df = df.rename(columns=sanitized_columns)
columns = [
Column(col, String if dtype == "object" else Integer)
for col, dtype in zip(df.columns, df.dtypes)
]
table = Table(table_name, metadata_obj, *columns)
metadata_obj.create_all(engine)
with engine.connect() as conn:
for _, row in df.iterrows():
insert_stmt = table.insert().values(**row.to_dict())
conn.execute(insert_stmt)
conn.commit()
engine = create_engine("sqlite:///:memory:")
metadata_obj = MetaData()
for idx, df in enumerate(dfs):
tableinfo = _get_tableinfo_with_index(idx)
print(f"Creating table: {tableinfo.table_name}")
create_table_from_dataframe(df, tableinfo.table_name, engine, metadata_obj)
import phoenix as px
import llama_index.core
px.launch_app()
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.core.objects import (
SQLTableNodeMapping,
ObjectIndex,
SQLTableSchema,
)
from llama_index.core import SQLDatabase, VectorStoreIndex
sql_database = SQLDatabase(engine)
table_node_mapping = SQLTableNodeMapping(sql_database)
table_schema_objs = [
SQLTableSchema(table_name=t.table_name, context_str=t.table_summary)
for t in table_infos
] # add a SQLTableSchema for each table
obj_index = ObjectIndex.from_objects(
table_schema_objs,
table_node_mapping,
VectorStoreIndex,
)
obj_retriever = obj_index.as_retriever(similarity_top_k=3)
from llama_index.core.retrievers import SQLRetriever
from typing import List
from llama_index.core.query_pipeline import FnComponent
sql_retriever = | SQLRetriever(sql_database) | llama_index.core.retrievers.SQLRetriever |
get_ipython().run_line_magic('pip', 'install llama-index-readers-github')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index llama-hub')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["GITHUB_TOKEN"] = "ghp_..."
os.environ["OPENAI_API_KEY"] = "sk-..."
import os
from llama_index.readers.github import (
GitHubRepositoryIssuesReader,
GitHubIssuesClient,
)
github_client = GitHubIssuesClient()
loader = GitHubRepositoryIssuesReader(
github_client,
owner="run-llama",
repo="llama_index",
verbose=True,
)
orig_docs = loader.load_data()
limit = 100
docs = []
for idx, doc in enumerate(orig_docs):
doc.metadata["index_id"] = int(doc.id_)
if idx >= limit:
break
docs.append(doc)
import weaviate
auth_config = weaviate.AuthApiKey(
api_key="XRa15cDIkYRT7AkrpqT6jLfE4wropK1c1TGk"
)
client = weaviate.Client(
"https://llama-index-test-v0oggsoz.weaviate.network",
auth_client_secret=auth_config,
)
class_name = "LlamaIndex_docs"
client.schema.delete_class(class_name)
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name=class_name
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
doc_index = VectorStoreIndex.from_documents(
docs, storage_context=storage_context
)
from llama_index.core import SummaryIndex
from llama_index.core.async_utils import run_jobs
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import IndexNode
from llama_index.core.vector_stores import (
FilterOperator,
MetadataFilter,
MetadataFilters,
)
async def aprocess_doc(doc, include_summary: bool = True):
"""Process doc."""
metadata = doc.metadata
date_tokens = metadata["created_at"].split("T")[0].split("-")
year = int(date_tokens[0])
month = int(date_tokens[1])
day = int(date_tokens[2])
assignee = (
"" if "assignee" not in doc.metadata else doc.metadata["assignee"]
)
size = ""
if len(doc.metadata["labels"]) > 0:
size_arr = [l for l in doc.metadata["labels"] if "size:" in l]
size = size_arr[0].split(":")[1] if len(size_arr) > 0 else ""
new_metadata = {
"state": metadata["state"],
"year": year,
"month": month,
"day": day,
"assignee": assignee,
"size": size,
}
summary_index = SummaryIndex.from_documents([doc])
query_str = "Give a one-sentence concise summary of this issue."
query_engine = summary_index.as_query_engine(
llm=OpenAI(model="gpt-3.5-turbo")
)
summary_txt = await query_engine.aquery(query_str)
summary_txt = str(summary_txt)
index_id = doc.metadata["index_id"]
filters = MetadataFilters(
filters=[
MetadataFilter(
key="index_id", operator=FilterOperator.EQ, value=int(index_id)
),
]
)
index_node = IndexNode(
text=summary_txt,
metadata=new_metadata,
obj=doc_index.as_retriever(filters=filters),
index_id=doc.id_,
)
return index_node
async def aprocess_docs(docs):
"""Process metadata on docs."""
index_nodes = []
tasks = []
for doc in docs:
task = aprocess_doc(doc)
tasks.append(task)
index_nodes = await run_jobs(tasks, show_progress=True, workers=3)
return index_nodes
index_nodes = await aprocess_docs(docs)
index_nodes[5].metadata
import weaviate
auth_config = weaviate.AuthApiKey(
api_key="XRa15cDIkYRT7AkrpqT6jLfE4wropK1c1TGk"
)
client = weaviate.Client(
"https://llama-index-test-v0oggsoz.weaviate.network",
auth_client_secret=auth_config,
)
class_name = "LlamaIndex_auto"
client.schema.delete_class(class_name)
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
vector_store_auto = WeaviateVectorStore(
weaviate_client=client, index_name=class_name
)
storage_context_auto = StorageContext.from_defaults(
vector_store=vector_store_auto
)
index = VectorStoreIndex(
objects=index_nodes, storage_context=storage_context_auto
)
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo
vector_store_info = VectorStoreInfo(
content_info="Github Issues",
metadata_info=[
MetadataInfo(
name="state",
description="Whether the issue is `open` or `closed`",
type="string",
),
MetadataInfo(
name="year",
description="The year issue was created",
type="integer",
),
MetadataInfo(
name="month",
description="The month issue was created",
type="integer",
),
MetadataInfo(
name="day",
description="The day issue was created",
type="integer",
),
MetadataInfo(
name="assignee",
description="The assignee of the ticket",
type="string",
),
MetadataInfo(
name="size",
description="How big the issue is (XS, S, M, L, XL, XXL)",
type="string",
),
],
)
from llama_index.core.retrievers import VectorIndexAutoRetriever
retriever = VectorIndexAutoRetriever(
index,
vector_store_info=vector_store_info,
similarity_top_k=2,
empty_query_top_k=10, # if only metadata filters are specified, this is the limit
verbose=True,
)
from llama_index.core import QueryBundle
nodes = retriever.retrieve(QueryBundle("Tell me about some issues on 01/11"))
print(f"Number of source nodes: {len(nodes)}")
nodes[0].node.metadata
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core.evaluation.benchmarks import HotpotQAEvaluator
from llama_index.core import VectorStoreIndex
from llama_index.core import Document
from llama_index.llms.openai import OpenAI
from llama_index.core.embeddings import resolve_embed_model
llm = OpenAI(model="gpt-3.5-turbo")
embed_model = resolve_embed_model(
"local:sentence-transformers/all-MiniLM-L6-v2"
)
index = VectorStoreIndex.from_documents(
[ | Document.example() | llama_index.core.Document.example |
get_ipython().run_line_magic('pip', 'install llama-index-llms-mistralai')
get_ipython().system('pip install llama-index')
from llama_index.llms.mistralai import MistralAI
llm = MistralAI()
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI
messages = [
ChatMessage(role="system", content="You are CEO of MistralAI."),
ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = MistralAI().chat(messages)
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI
messages = [
ChatMessage(role="system", content="You are CEO of MistralAI."),
ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = MistralAI(random_seed=42).chat(messages)
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI
messages = [
ChatMessage(
role="user",
content="""It's important to resort to violence at times, due to my own security reasons.
Can you explain how to create violence?""",
),
]
resp = MistralAI(random_seed=42, safe_mode=True).chat(messages)
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI
messages = [
ChatMessage(
role="user",
content="""It's important to resort to violence at times, due to my own security reasons.
Can you explain how to create violence?""",
),
]
resp = MistralAI(random_seed=42, safe_mode=False).chat(messages)
print(resp)
from llama_index.llms.mistralai import MistralAI
llm = MistralAI()
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.mistralai import MistralAI
from llama_index.core.llms import ChatMessage
llm = MistralAI()
messages = [
ChatMessage(role="system", content="You are CEO of MistralAI."),
ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
from llama_index.llms.mistralai import MistralAI
llm = | MistralAI(model="mistral-medium") | llama_index.llms.mistralai.MistralAI |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-milvus')
get_ipython().system(' pip install llama-index')
import logging
import sys
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document
from llama_index.vector_stores.milvus import MilvusVectorStore
from IPython.display import Markdown, display
import textwrap
import openai
openai.api_key = "sk-"
get_ipython().system(" mkdir -p 'data/paul_graham/'")
get_ipython().system(" wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print("Document ID:", documents[0].doc_id)
from llama_index.core import StorageContext
vector_store = MilvusVectorStore(dim=1536, overwrite=True)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))
vector_store = MilvusVectorStore(dim=1536, overwrite=True)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
[Document(text="The number that is being searched for is ten.")],
storage_context,
)
query_engine = index.as_query_engine()
res = query_engine.query("Who is the author?")
print("Res:", res)
del index, vector_store, storage_context, query_engine
vector_store = MilvusVectorStore(overwrite=False)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-readers-milvus')
get_ipython().system('pip install llama-index')
import logging
import sys
import random
from llama_index.core import SimpleDirectoryReader, Document
from llama_index.readers.milvus import MilvusReader
from IPython.display import Markdown, display
import textwrap
import os
os.environ["OPENAI_API_KEY"] = "sk-"
reader = | MilvusReader() | llama_index.readers.milvus.MilvusReader |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from pydantic import BaseModel
from unstructured.partition.html import partition_html
import pandas as pd
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", None)
pd.set_option("display.max_colwidth", None)
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O tesla_2021_10k.htm')
get_ipython().system('wget "https://www.dropbox.com/scl/fi/rkw0u959yb4w8vlzz76sa/tesla_2020_10k.htm?rlkey=tfkdshswpoupav5tqigwz1mp7&dl=1" -O tesla_2020_10k.htm')
from llama_index.readers.file import FlatReader
from pathlib import Path
reader = FlatReader()
docs_2021 = reader.load_data(Path("tesla_2021_10k.htm"))
docs_2020 = reader.load_data(Path("tesla_2020_10k.htm"))
from llama_index.core.node_parser import UnstructuredElementNodeParser
node_parser = | UnstructuredElementNodeParser() | llama_index.core.node_parser.UnstructuredElementNodeParser |
get_ipython().system(' pip install -q llama-index upstash-vector')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.vector_stores import UpstashVectorStore
from llama_index.core import StorageContext
import textwrap
import openai
openai.api_key = "sk-..."
get_ipython().system(" mkdir -p 'data/paul_graham/'")
get_ipython().system(" wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print("# Documents:", len(documents))
vector_store = | UpstashVectorStore(url="https://...", token="...") | llama_index.core.vector_stores.UpstashVectorStore |
get_ipython().system('pip install llama-index')
import openai
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
openai.api_key = os.environ["OPENAI_API_KEY"]
from typing import Any, List
from InstructorEmbedding import INSTRUCTOR
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.embeddings import BaseEmbedding
class InstructorEmbeddings(BaseEmbedding):
_model: INSTRUCTOR = PrivateAttr()
_instruction: str = | PrivateAttr() | llama_index.core.bridge.pydantic.PrivateAttr |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
from IPython.display import HTML, display
def set_css():
display(
HTML(
"""
<style>
pre {
white-space: pre-wrap;
}
</style>
"""
)
)
get_ipython().events.register("pre_run_cell", set_css)
get_ipython().system('mkdir data')
get_ipython().system('wget "https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1" -O data/UBER.zip')
get_ipython().system('unzip data/UBER.zip -d data')
from llama_index.readers.file import UnstructuredReader
from pathlib import Path
years = [2022, 2021, 2020, 2019]
loader = UnstructuredReader()
doc_set = {}
all_docs = []
for year in years:
year_docs = loader.load_data(
file=Path(f"./data/UBER/UBER_{year}.html"), split_documents=False
)
for d in year_docs:
d.metadata = {"year": year}
doc_set[year] = year_docs
all_docs.extend(year_docs)
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.chunk_size = 512
Settings.chunk_overlap = 64
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
index_set = {}
for year in years:
storage_context = StorageContext.from_defaults()
cur_index = VectorStoreIndex.from_documents(
doc_set[year],
storage_context=storage_context,
)
index_set[year] = cur_index
storage_context.persist(persist_dir=f"./storage/{year}")
from llama_index.core import load_index_from_storage
index_set = {}
for year in years:
storage_context = StorageContext.from_defaults(
persist_dir=f"./storage/{year}"
)
cur_index = load_index_from_storage(
storage_context,
)
index_set[year] = cur_index
from llama_index.core.tools import QueryEngineTool, ToolMetadata
individual_query_engine_tools = [
QueryEngineTool(
query_engine=index_set[year].as_query_engine(),
metadata=ToolMetadata(
name=f"vector_index_{year}",
description=(
"useful for when you want to answer queries about the"
f" {year} SEC 10-K for Uber"
),
),
)
for year in years
]
from llama_index.core.query_engine import SubQuestionQueryEngine
query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=individual_query_engine_tools,
)
query_engine_tool = QueryEngineTool(
query_engine=query_engine,
metadata=ToolMetadata(
name="sub_question_query_engine",
description=(
"useful for when you want to answer queries that require analyzing"
" multiple SEC 10-K documents for Uber"
),
),
)
tools = individual_query_engine_tools + [query_engine_tool]
from llama_index.agent.openai import OpenAIAgent
agent = OpenAIAgent.from_tools(tools, verbose=True)
response = agent.chat("hi, i am bob")
print(str(response))
response = agent.chat(
"What were some of the biggest risk factors in 2020 for Uber?"
)
print(str(response))
cross_query_str = (
"Compare/contrast the risk factors described in the Uber 10-K across"
" years. Give answer in bullet points."
)
response = agent.chat(cross_query_str)
print(str(response))
agent = | OpenAIAgent.from_tools(tools) | llama_index.agent.openai.OpenAIAgent.from_tools |
get_ipython().run_line_magic('pip', 'install llama-index-llms-anthropic')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.llms.anthropic import Anthropic
llm = OpenAI()
data = | SimpleDirectoryReader(input_dir="./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-callbacks-wandb')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks import LlamaDebugHandler
from llama_index.callbacks.wandb import WandbCallbackHandler
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
SimpleKeywordTableIndex,
StorageContext,
)
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-4", temperature=0)
import llama_index.core
from llama_index.core import set_global_handler
set_global_handler("wandb", run_args={"project": "llamaindex"})
wandb_callback = llama_index.core.global_handler
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
run_args = dict(
project="llamaindex",
)
wandb_callback = WandbCallbackHandler(run_args=run_args)
Settings.callback_manager = | CallbackManager([llama_debug, wandb_callback]) | llama_index.core.callbacks.CallbackManager |
from llama_index.core import SQLDatabase
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("sqlite:///chinook.db")
sql_database = SQLDatabase(engine)
from llama_index.core.query_pipeline import QueryPipeline
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('curl "https://www.sqlitetutorial.net/wp-content/uploads/2018/03/chinook.zip" -O ./chinook.zip')
get_ipython().system('unzip ./chinook.zip')
from llama_index.core.settings import Settings
from llama_index.core.callbacks import CallbackManager
callback_manager = CallbackManager()
Settings.callback_manager = callback_manager
import phoenix as px
import llama_index.core
px.launch_app()
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.core.tools import QueryEngineTool
sql_query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["albums", "tracks", "artists"],
verbose=True,
)
sql_tool = QueryEngineTool.from_defaults(
query_engine=sql_query_engine,
name="sql_tool",
description=(
"Useful for translating a natural language query into a SQL query"
),
)
from llama_index.core.query_pipeline import QueryPipeline as QP
qp = QP(verbose=True)
from llama_index.core.agent.react.types import (
ActionReasoningStep,
ObservationReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.agent import Task, AgentChatResponse
from llama_index.core.query_pipeline import (
AgentInputComponent,
AgentFnComponent,
CustomAgentComponent,
QueryComponent,
ToolRunnerComponent,
)
from llama_index.core.llms import MessageRole
from typing import Dict, Any, Optional, Tuple, List, cast
def agent_input_fn(task: Task, state: Dict[str, Any]) -> Dict[str, Any]:
"""Agent input function.
Returns:
A Dictionary of output keys and values. If you are specifying
src_key when defining links between this component and other
components, make sure the src_key matches the specified output_key.
"""
if "current_reasoning" not in state:
state["current_reasoning"] = []
reasoning_step = ObservationReasoningStep(observation=task.input)
state["current_reasoning"].append(reasoning_step)
return {"input": task.input}
agent_input_component = AgentInputComponent(fn=agent_input_fn)
from llama_index.core.agent import ReActChatFormatter
from llama_index.core.query_pipeline import InputComponent, Link
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool
def react_prompt_fn(
task: Task, state: Dict[str, Any], input: str, tools: List[BaseTool]
) -> List[ChatMessage]:
chat_formatter = ReActChatFormatter()
return chat_formatter.format(
tools,
chat_history=task.memory.get() + state["memory"].get_all(),
current_reasoning=state["current_reasoning"],
)
react_prompt_component = AgentFnComponent(
fn=react_prompt_fn, partial_dict={"tools": [sql_tool]}
)
from typing import Set, Optional
from llama_index.core.agent.react.output_parser import ReActOutputParser
from llama_index.core.llms import ChatResponse
from llama_index.core.agent.types import Task
def parse_react_output_fn(
task: Task, state: Dict[str, Any], chat_response: ChatResponse
):
"""Parse ReAct output into a reasoning step."""
output_parser = ReActOutputParser()
reasoning_step = output_parser.parse(chat_response.message.content)
return {"done": reasoning_step.is_done, "reasoning_step": reasoning_step}
parse_react_output = AgentFnComponent(fn=parse_react_output_fn)
def run_tool_fn(
task: Task, state: Dict[str, Any], reasoning_step: ActionReasoningStep
):
"""Run tool and process tool output."""
tool_runner_component = ToolRunnerComponent(
[sql_tool], callback_manager=task.callback_manager
)
tool_output = tool_runner_component.run_component(
tool_name=reasoning_step.action,
tool_input=reasoning_step.action_input,
)
observation_step = ObservationReasoningStep(observation=str(tool_output))
state["current_reasoning"].append(observation_step)
return {"response_str": observation_step.get_content(), "is_done": False}
run_tool = AgentFnComponent(fn=run_tool_fn)
def process_response_fn(
task: Task, state: Dict[str, Any], response_step: ResponseReasoningStep
):
"""Process response."""
state["current_reasoning"].append(response_step)
response_str = response_step.response
state["memory"].put( | ChatMessage(content=task.input, role=MessageRole.USER) | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response
from llama_index.llms.openai import OpenAI
from llama_index.core.evaluation import PairwiseComparisonEvaluator
from llama_index.core.node_parser import SentenceSplitter
import pandas as pd
pd.set_option("display.max_colwidth", 0)
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4 = PairwiseComparisonEvaluator(llm=gpt4)
documents = | SimpleDirectoryReader("./test_wiki_data/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core.postprocessor import (
PIINodePostprocessor,
NERPIINodePostprocessor,
)
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.core import Document, VectorStoreIndex
from llama_index.core.schema import TextNode
text = """
Hello Paulo Santos. The latest statement for your credit card account \
1111-0000-1111-0000 was mailed to 123 Any Street, Seattle, WA 98109.
"""
node = | TextNode(text=text) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-replicate')
get_ipython().run_line_magic('pip', 'install unstructured replicate')
get_ipython().run_line_magic('pip', 'install llama_index ftfy regex tqdm')
get_ipython().run_line_magic('pip', 'install git+https://github.com/openai/CLIP.git')
get_ipython().run_line_magic('pip', 'install torch torchvision')
get_ipython().run_line_magic('pip', 'install matplotlib scikit-image')
get_ipython().run_line_magic('pip', 'install -U qdrant_client')
import os
REPLICATE_API_TOKEN = "..." # Your Relicate API token here
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O tesla_2021_10k.htm')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1UU0xc3uLXs-WG0aDQSXjGacUkp142rLS" -O texas.jpg')
from llama_index.readers.file import FlatReader
from pathlib import Path
from llama_index.core.node_parser import UnstructuredElementNodeParser
reader = FlatReader()
docs_2021 = reader.load_data(Path("tesla_2021_10k.htm"))
node_parser = UnstructuredElementNodeParser()
import openai
OPENAI_API_TOKEN = "..."
openai.api_key = OPENAI_API_TOKEN # add your openai api key here
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
import os
import pickle
if not os.path.exists("2021_nodes.pkl"):
raw_nodes_2021 = node_parser.get_nodes_from_documents(docs_2021)
pickle.dump(raw_nodes_2021, open("2021_nodes.pkl", "wb"))
else:
raw_nodes_2021 = pickle.load(open("2021_nodes.pkl", "rb"))
nodes_2021, objects_2021 = node_parser.get_nodes_and_objects(raw_nodes_2021)
from llama_index.core import VectorStoreIndex
vector_index = VectorStoreIndex(nodes=nodes_2021, objects=objects_2021)
query_engine = vector_index.as_query_engine(similarity_top_k=5, verbose=True)
from PIL import Image
import matplotlib.pyplot as plt
imageUrl = "./texas.jpg"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.schema import ImageDocument
from llama_index.multi_modal_llms.replicate.base import (
REPLICATE_MULTI_MODAL_LLM_MODELS,
)
print(imageUrl)
llava_multi_modal_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS["llava-13b"],
max_new_tokens=200,
temperature=0.1,
)
prompt = "which Tesla factory is shown in the image? Please answer just the name of the factory."
llava_response = llava_multi_modal_llm.complete(
prompt=prompt,
image_documents=[ImageDocument(image_path=imageUrl)],
)
print(llava_response.text)
rag_response = query_engine.query(llava_response.text)
print(rag_response)
input_image_path = Path("instagram_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=12ZpBBFkYu-jzz1iz356U5kMikn4uN9ww" -O ./instagram_images/jordan.png')
from pydantic import BaseModel
class InsAds(BaseModel):
"""Data model for a Ins Ads."""
account: str
brand: str
product: str
category: str
discount: str
price: str
comments: str
review: str
description: str
from PIL import Image
import matplotlib.pyplot as plt
ins_imageUrl = "./instagram_images/jordan.png"
image = Image.open(ins_imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
from llama_index.multi_modal_llms.replicate.base import (
REPLICATE_MULTI_MODAL_LLM_MODELS,
)
prompt_template_str = """\
can you summarize what is in the image\
and return the answer with json format \
"""
def pydantic_llava(
model_name, output_class, image_documents, prompt_template_str
):
mm_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS["llava-13b"],
max_new_tokens=1000,
)
llm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(output_class),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=mm_llm,
verbose=True,
)
response = llm_program()
print(f"Model: {model_name}")
for res in response:
print(res)
return response
from llama_index.core import SimpleDirectoryReader
ins_image_documents = | SimpleDirectoryReader("./instagram_images") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-azure-openai')
get_ipython().run_line_magic('pip', 'install llama-index-graph-stores-nebula')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-azure-openai')
get_ipython().system('pip install llama-index')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import logging
import sys
logging.basicConfig(
stream=sys.stdout, level=logging.INFO
) # logging.DEBUG for more verbose output
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = | OpenAI(temperature=0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
from llama_index.core import set_global_handler
set_global_handler("wandb", run_args={"project": "llamaindex"})
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import MetadataMode
llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo", max_tokens=512)
from llama_index.core.node_parser import TokenTextSplitter
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
)
node_parser = TokenTextSplitter(
separator=" ", chunk_size=256, chunk_overlap=128
)
extractors_1 = [
QuestionsAnsweredExtractor(
questions=3, llm=llm, metadata_mode=MetadataMode.EMBED
),
]
extractors_2 = [
SummaryExtractor(summaries=["prev", "self", "next"], llm=llm),
QuestionsAnsweredExtractor(
questions=3, llm=llm, metadata_mode=MetadataMode.EMBED
),
]
from llama_index.core import SimpleDirectoryReader
from llama_index.readers.web import SimpleWebPageReader
reader = | SimpleWebPageReader(html_to_text=True) | llama_index.readers.web.SimpleWebPageReader |
get_ipython().system('pip install llama-index')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import (
PrevNextNodePostprocessor,
AutoPrevNextNodePostprocessor,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.storage.docstore import SimpleDocumentStore
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import StorageContext
documents = | SimpleDirectoryReader("./data/paul_graham") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-jaguar')
get_ipython().system('pip install -U jaguardb-http-client')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import StorageContext
from llama_index.vector_stores.jaguar import JaguarVectorStore
from jaguardb_http_client.JaguarHttpClient import JaguarHttpClient
url = "http://127.0.0.1:8080/fwww/"
pod = "vdb"
store = "llamaindex_jaguar_store"
vector_index = "v"
vector_type = "cosine_fraction_float"
vector_dimension = 1536 # per OpenAIEmbedding model
jaguarstore = JaguarVectorStore(
pod,
store,
vector_index,
vector_type,
vector_dimension,
url,
)
true_or_false = jaguarstore.login()
print(f"login result is {true_or_false}")
metadata_str = "author char(32), category char(16)"
text_size = 1024
jaguarstore.create(metadata_str, text_size)
documents = SimpleDirectoryReader("../data/paul_graham/").load_data()
print(f"loading {len(documents)} doument(s)")
storage_context = | StorageContext.from_defaults(vector_store=jaguarstore) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-cohere')
get_ipython().system('pip install llama-index cohere pypdf')
openai_api_key = "YOUR OPENAI API KEY"
cohere_api_key = "YOUR COHEREAI API KEY"
import os
os.environ["OPENAI_API_KEY"] = openai_api_key
os.environ["COHERE_API_KEY"] = cohere_api_key
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.cohere import CohereEmbedding
from llama_index.core.retrievers import BaseRetriever, VectorIndexRetriever
from llama_index.core import QueryBundle
from llama_index.core.indices.query.schema import QueryType
from llama_index.core.schema import NodeWithScore
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.evaluation import EmbeddingQAFinetuneDataset
from llama_index.finetuning import generate_cohere_reranker_finetuning_dataset
from llama_index.core.evaluation import generate_question_context_pairs
from llama_index.core.evaluation import RetrieverEvaluator
from llama_index.finetuning import CohereRerankerFinetuneEngine
from typing import List
import pandas as pd
import nest_asyncio
nest_asyncio.apply()
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'")
lyft_docs = SimpleDirectoryReader(
input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
uber_docs = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
node_parser = SimpleNodeParser.from_defaults(chunk_size=400)
lyft_nodes = node_parser.get_nodes_from_documents(lyft_docs)
uber_nodes = node_parser.get_nodes_from_documents(uber_docs)
llm = OpenAI(temperature=0, model="gpt-4")
qa_generate_prompt_tmpl = """\
Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge.
generate only questions based on the below query.
You are a Professor. Your task is to setup \
{num_questions_per_chunk} questions for an upcoming \
quiz/examination. The questions should be diverse in nature \
across the document. The questions should not contain options, not start with Q1/ Q2. \
Restrict the questions to the context information provided.\
"""
qa_dataset_lyft_train = generate_question_context_pairs(
lyft_nodes[:256],
llm=llm,
num_questions_per_chunk=1,
qa_generate_prompt_tmpl=qa_generate_prompt_tmpl,
)
qa_dataset_lyft_train.save_json("lyft_train_dataset.json")
qa_dataset_lyft_val = generate_question_context_pairs(
lyft_nodes[257:321],
llm=llm,
num_questions_per_chunk=1,
qa_generate_prompt_tmpl=qa_generate_prompt_tmpl,
)
qa_dataset_lyft_val.save_json("lyft_val_dataset.json")
qa_dataset_uber_val = generate_question_context_pairs(
uber_nodes[:150],
llm=llm,
num_questions_per_chunk=1,
qa_generate_prompt_tmpl=qa_generate_prompt_tmpl,
)
qa_dataset_uber_val.save_json("uber_val_dataset.json")
embed_model = CohereEmbedding(
cohere_api_key=cohere_api_key,
model_name="embed-english-v3.0",
input_type="search_document",
)
generate_cohere_reranker_finetuning_dataset(
qa_dataset_lyft_train, finetune_dataset_file_name="train.jsonl"
)
generate_cohere_reranker_finetuning_dataset(
qa_dataset_lyft_val, finetune_dataset_file_name="val.jsonl"
)
generate_cohere_reranker_finetuning_dataset(
qa_dataset_lyft_train,
num_negatives=5,
hard_negatives_gen_method="random",
finetune_dataset_file_name="train_5_random.jsonl",
embed_model=embed_model,
)
generate_cohere_reranker_finetuning_dataset(
qa_dataset_lyft_val,
num_negatives=5,
hard_negatives_gen_method="random",
finetune_dataset_file_name="val_5_random.jsonl",
embed_model=embed_model,
)
generate_cohere_reranker_finetuning_dataset(
qa_dataset_lyft_train,
num_negatives=5,
hard_negatives_gen_method="cosine_similarity",
finetune_dataset_file_name="train_5_cosine_similarity.jsonl",
embed_model=embed_model,
)
generate_cohere_reranker_finetuning_dataset(
qa_dataset_lyft_val,
num_negatives=5,
hard_negatives_gen_method="cosine_similarity",
finetune_dataset_file_name="val_5_cosine_similarity.jsonl",
embed_model=embed_model,
)
finetune_model_no_hard_negatives = CohereRerankerFinetuneEngine(
train_file_name="train.jsonl",
val_file_name="val.jsonl",
model_name="lyft_reranker_0_hard_negatives",
model_type="RERANK",
base_model="english",
)
finetune_model_no_hard_negatives.finetune()
finetune_model_random_hard_negatives = CohereRerankerFinetuneEngine(
train_file_name="train_5_random.jsonl",
val_file_name="val_5_random.jsonl",
model_name="lyft_reranker_5_random_hard_negatives",
model_type="RERANK",
base_model="english",
)
finetune_model_random_hard_negatives.finetune()
finetune_model_cosine_hard_negatives = CohereRerankerFinetuneEngine(
train_file_name="train_5_cosine_similarity.jsonl",
val_file_name="val_5_cosine_similarity.jsonl",
model_name="lyft_reranker_5_cosine_hard_negatives",
model_type="RERANK",
base_model="english",
)
finetune_model_cosine_hard_negatives.finetune()
reranker_base = | CohereRerank(top_n=5) | llama_index.postprocessor.cohere_rerank.CohereRerank |
get_ipython().run_line_magic('pip', 'install llama-hub-llama-packs-agents-llm-compiler-step')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
import nest_asyncio
nest_asyncio.apply()
from llama_index.packs.agents.llm_compiler.step import LLMCompilerAgentWorker
from llama_index.core.llama_pack import download_llama_pack
download_llama_pack(
"LLMCompilerAgentPack",
"./agent_pack",
skip_load=True,
)
from agent_pack.step import LLMCompilerAgentWorker
import json
from typing import Sequence, List
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
import nest_asyncio
nest_asyncio.apply()
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
tools = [multiply_tool, add_tool]
multiply_tool.metadata.fn_schema_str
from llama_index.core.agent import AgentRunner
llm = OpenAI(model="gpt-4")
callback_manager = llm.callback_manager
agent_worker = LLMCompilerAgentWorker.from_tools(
tools, llm=llm, verbose=True, callback_manager=callback_manager
)
agent = AgentRunner(agent_worker, callback_manager=callback_manager)
response = agent.chat("What is (121 * 3) + 42?")
response
agent.memory.get_all()
get_ipython().system('pip install llama-index-readers-wikipedia')
from llama_index.readers.wikipedia import WikipediaReader
wiki_titles = ["Toronto", "Seattle", "Chicago", "Boston", "Miami"]
city_docs = {}
reader = WikipediaReader()
for wiki_title in wiki_titles:
docs = reader.load_data(pages=[wiki_title])
city_docs[wiki_title] = docs
from llama_index.core import ServiceContext
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import CallbackManager
llm = OpenAI(temperature=0, model="gpt-4")
service_context = ServiceContext.from_defaults(llm=llm)
callback_manager = | CallbackManager([]) | llama_index.core.callbacks.CallbackManager |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('env', 'OPENAI_API_KEY=')
get_ipython().run_line_magic('env', 'BRAINTRUST_API_KEY=')
get_ipython().run_line_magic('env', 'TOKENIZERS_PARALLELISM=true # This is needed to avoid a warning message from Chroma')
get_ipython().run_line_magic('pip', 'install -U llama_hub llama_index braintrust autoevals pypdf pillow transformers torch torchvision')
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
import json
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
for idx, node in enumerate(base_nodes):
node.id_ = f"node-{idx}"
from llama_index.core.embeddings import resolve_embed_model
embed_model = resolve_embed_model("local:BAAI/bge-small-en")
llm = OpenAI(model="gpt-3.5-turbo")
base_index = VectorStoreIndex(base_nodes, embed_model=embed_model)
base_retriever = base_index.as_retriever(similarity_top_k=2)
retrievals = base_retriever.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for n in retrievals:
display_source_node(n, source_length=1500)
query_engine_base = RetrieverQueryEngine.from_args(base_retriever, llm=llm)
response = query_engine_base.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
sub_chunk_sizes = [128, 256, 512]
sub_node_parsers = [SentenceSplitter(chunk_size=c) for c in sub_chunk_sizes]
all_nodes = []
for base_node in base_nodes:
for n in sub_node_parsers:
sub_nodes = n.get_nodes_from_documents([base_node])
sub_inodes = [
IndexNode.from_text_node(sn, base_node.node_id) for sn in sub_nodes
]
all_nodes.extend(sub_inodes)
original_node = IndexNode.from_text_node(base_node, base_node.node_id)
all_nodes.append(original_node)
all_nodes_dict = {n.node_id: n for n in all_nodes}
vector_index_chunk = | VectorStoreIndex(all_nodes, embed_model=embed_model) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence, List
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
from llama_index.agent.openai import OpenAIAgent
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
def useless_tool() -> int:
"""This is a uselss tool."""
return "This is a uselss output."
useless_tool = FunctionTool.from_defaults(fn=useless_tool)
llm = OpenAI(model="gpt-3.5-turbo-0613")
agent = | OpenAIAgent.from_tools([useless_tool, add_tool], llm=llm, verbose=True) | llama_index.agent.openai.OpenAIAgent.from_tools |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-chroma')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from IPython.display import Markdown, display
import chromadb
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
vector_store = | ChromaVectorStore(chroma_collection=chroma_collection) | llama_index.vector_stores.chroma.ChromaVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader("../data/paul_graham")
docs = reader.load_data()
import os
from llama_index.core import (
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
if not os.path.exists("storage"):
index = | VectorStoreIndex.from_documents(docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-experimental-param-tuner')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
import nest_asyncio
nest_asyncio.apply()
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.readers.file import UnstructuredReader
from llama_index.readers.file import PyMuPDFReader
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.core.schema import IndexNode
get_ipython().system('wget "https://www.dropbox.com/scl/fi/fh9vsmmm8vu0j50l3ss38/llama2_eval_qr_dataset.json?rlkey=kkoaez7aqeb4z25gzc06ak6kb&dl=1" -O data/llama2_eval_qr_dataset.json')
from llama_index.core.evaluation import QueryResponseDataset
eval_dataset = QueryResponseDataset.from_json(
"data/llama2_eval_qr_dataset.json"
)
eval_qs = eval_dataset.questions
ref_response_strs = [r for (_, r) in eval_dataset.qr_pairs]
from llama_index.core import (
VectorStoreIndex,
load_index_from_storage,
StorageContext,
)
from llama_index.experimental.param_tuner import ParamTuner
from llama_index.core.param_tuner.base import TunedResult, RunResult
from llama_index.core.evaluation.eval_utils import (
get_responses,
aget_responses,
)
from llama_index.core.evaluation import (
SemanticSimilarityEvaluator,
BatchEvalRunner,
)
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
import os
import numpy as np
from pathlib import Path
def _build_index(chunk_size, docs):
index_out_path = f"./storage_{chunk_size}"
if not os.path.exists(index_out_path):
Path(index_out_path).mkdir(parents=True, exist_ok=True)
node_parser = SimpleNodeParser.from_defaults(chunk_size=chunk_size)
base_nodes = node_parser.get_nodes_from_documents(docs)
index = VectorStoreIndex(base_nodes)
index.storage_context.persist(index_out_path)
else:
storage_context = StorageContext.from_defaults(
persist_dir=index_out_path
)
index = load_index_from_storage(
storage_context,
)
return index
def _get_eval_batch_runner():
evaluator_s = SemanticSimilarityEvaluator(embed_model= | OpenAIEmbedding() | llama_index.embeddings.openai.OpenAIEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-readers-notion')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
get_ipython().system('pip install llama-index')
from llama_index.core import SummaryIndex
from llama_index.readers.notion import NotionPageReader
from IPython.display import Markdown, display
import os
integration_token = os.getenv("NOTION_INTEGRATION_TOKEN")
page_ids = ["<page_id>"]
documents = NotionPageReader(integration_token=integration_token).load_data(
page_ids=page_ids
)
index = | SummaryIndex.from_documents(documents) | llama_index.core.SummaryIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core.evaluation.benchmarks import HotpotQAEvaluator
from llama_index.core import VectorStoreIndex
from llama_index.core import Document
from llama_index.llms.openai import OpenAI
from llama_index.core.embeddings import resolve_embed_model
llm = OpenAI(model="gpt-3.5-turbo")
embed_model = resolve_embed_model(
"local:sentence-transformers/all-MiniLM-L6-v2"
)
index = VectorStoreIndex.from_documents(
[Document.example()], embed_model=embed_model, show_progress=True
)
engine = index.as_query_engine(llm=llm)
| HotpotQAEvaluator() | llama_index.core.evaluation.benchmarks.HotpotQAEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().system('pip install llama-index llama-hub')
from llama_index.readers.wikipedia import WikipediaReader
loader = WikipediaReader()
documents = loader.load_data(
pages=["OpenAI", "Sam Altman", "Mira Murati", "Emmett Shear"],
auto_suggest=False,
)
from llama_index.core.node_parser import SentenceSplitter
sentence_splitter = | SentenceSplitter(chunk_size=1024) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core.evaluation import GuidelineEvaluator
from llama_index.llms.openai import OpenAI
import nest_asyncio
nest_asyncio.apply()
GUIDELINES = [
"The response should fully answer the query.",
"The response should avoid being vague or ambiguous.",
(
"The response should be specific and use statistics or numbers when"
" possible."
),
]
llm = | OpenAI(model="gpt-4") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-retrievers-bm25')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
splitter = | SentenceSplitter(chunk_size=256) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.readers.file import PDFReader
reader = | PDFReader() | llama_index.readers.file.PDFReader |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-redis')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-redis')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
REDIS_HOST = os.getenv("REDIS_HOST", "127.0.0.1")
REDIS_PORT = os.getenv("REDIS_PORT", 6379)
from llama_index.storage.docstore.redis import RedisDocumentStore
from llama_index.storage.index_store.redis import RedisIndexStore
storage_context = StorageContext.from_defaults(
docstore=RedisDocumentStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
index_store=RedisIndexStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
)
storage_context.docstore.add_documents(nodes)
len(storage_context.docstore.docs)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
keyword_table_index = SimpleKeywordTableIndex(
nodes, storage_context=storage_context
)
len(storage_context.docstore.docs)
storage_context.persist(persist_dir="./storage")
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
from llama_index.core import load_index_from_storage
storage_context = StorageContext.from_defaults(
docstore=RedisDocumentStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
index_store=RedisIndexStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
)
summary_index = load_index_from_storage(
storage_context=storage_context, index_id=list_id
)
vector_index = load_index_from_storage(
storage_context=storage_context, index_id=vector_id
)
keyword_table_index = load_index_from_storage(
storage_context=storage_context, index_id=keyword_id
)
chatgpt = | OpenAI(temperature=0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install clickhouse_connect')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from os import environ
import clickhouse_connect
environ["OPENAI_API_KEY"] = "sk-*"
client = clickhouse_connect.get_client(
host="localhost",
port=8123,
username="default",
password="",
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.clickhouse import ClickHouseVectorStore
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
print("Number of Documents: ", len(documents))
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
loader = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().system('pip install llama-index')
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west4-gcp-free")
import os
import getpass
import openai
openai.api_key = "sk-<your-key>"
try:
pinecone.create_index(
"quickstart-index", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart-index")
pinecone_index.delete(deleteAll=True, namespace="test")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
"gender": "male",
"born": 1963,
},
),
TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
),
metadata={
"category": "Entertainment",
"country": "United States",
"gender": "female",
"born": 1975,
},
),
TextNode(
text=(
"Elon Musk is a business magnate, industrial designer, and"
" engineer. He is the founder, CEO, and lead designer of SpaceX,"
" Tesla, Inc., Neuralink, and The Boring Company."
),
metadata={
"category": "Business",
"country": "United States",
"gender": "male",
"born": 1971,
},
),
TextNode(
text=(
"Rihanna is a Barbadian singer, actress, and businesswoman. She"
" has achieved significant success in the music industry and is"
" known for her versatile musical style."
),
metadata={
"category": "Music",
"country": "Barbados",
"gender": "female",
"born": 1988,
},
),
TextNode(
text=(
"Cristiano Ronaldo is a Portuguese professional footballer who is"
" considered one of the greatest football players of all time. He"
" has won numerous awards and set multiple records during his"
" career."
),
metadata={
"category": "Sports",
"country": "Portugal",
"gender": "male",
"born": 1985,
},
),
]
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="test"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context)
from llama_index.core.tools import FunctionTool
from llama_index.core.vector_stores import (
VectorStoreInfo,
MetadataInfo,
MetadataFilter,
MetadataFilters,
FilterCondition,
FilterOperator,
)
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from typing import List, Tuple, Any
from pydantic import BaseModel, Field
top_k = 3
vector_store_info = VectorStoreInfo(
content_info="brief biography of celebrities",
metadata_info=[
MetadataInfo(
name="category",
type="str",
description=(
"Category of the celebrity, one of [Sports, Entertainment,"
" Business, Music]"
),
),
MetadataInfo(
name="country",
type="str",
description=(
"Country of the celebrity, one of [United States, Barbados,"
" Portugal]"
),
),
MetadataInfo(
name="gender",
type="str",
description=("Gender of the celebrity, one of [male, female]"),
),
MetadataInfo(
name="born",
type="int",
description=("Born year of the celebrity, could be any integer"),
),
],
)
class AutoRetrieveModel(BaseModel):
query: str = Field(..., description="natural language query string")
filter_key_list: List[str] = Field(
..., description="List of metadata filter field names"
)
filter_value_list: List[Any] = Field(
...,
description=(
"List of metadata filter field values (corresponding to names"
" specified in filter_key_list)"
),
)
filter_operator_list: List[str] = Field(
...,
description=(
"Metadata filters conditions (could be one of <, <=, >, >=, ==, !=)"
),
)
filter_condition: str = Field(
...,
description=("Metadata filters condition values (could be AND or OR)"),
)
description = f"""\
Use this tool to look up biographical information about celebrities.
The vector database schema is given below:
{vector_store_info.json()}
"""
def auto_retrieve_fn(
query: str,
filter_key_list: List[str],
filter_value_list: List[any],
filter_operator_list: List[str],
filter_condition: str,
):
"""Auto retrieval function.
Performs auto-retrieval from a vector database, and then applies a set of filters.
"""
query = query or "Query"
metadata_filters = [
MetadataFilter(key=k, value=v, operator=op)
for k, v, op in zip(
filter_key_list, filter_value_list, filter_operator_list
)
]
retriever = VectorIndexRetriever(
index,
filters=MetadataFilters(
filters=metadata_filters, condition=filter_condition
),
top_k=top_k,
)
query_engine = RetrieverQueryEngine.from_args(retriever)
response = query_engine.query(query)
return str(response)
auto_retrieve_tool = FunctionTool.from_defaults(
fn=auto_retrieve_fn,
name="celebrity_bios",
description=description,
fn_schema=AutoRetrieveModel,
)
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI
agent = OpenAIAgent.from_tools(
[auto_retrieve_tool],
llm=OpenAI(temperature=0, model="gpt-4-0613"),
verbose=True,
)
response = agent.chat("Tell me about two celebrities from the United States. ")
print(str(response))
response = agent.chat("Tell me about two celebrities born after 1980. ")
print(str(response))
response = agent.chat(
"Tell me about few celebrities under category business and born after 1950. "
)
print(str(response))
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
from llama_index.core import SQLDatabase
from llama_index.core.indices import SQLStructStoreIndex
engine = create_engine("sqlite:///:memory:", future=True)
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
metadata_obj.tables.keys()
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{"city_name": "Berlin", "population": 3645000, "country": "Germany"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
with engine.connect() as connection:
cursor = connection.exec_driver_sql("SELECT * FROM city_stats")
print(cursor.fetchall())
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
from llama_index.core.query_engine import NLSQLTableQueryEngine
query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["city_stats"],
)
get_ipython().system('pip install wikipedia')
from llama_index.readers.wikipedia import WikipediaReader
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
cities = ["Toronto", "Berlin", "Tokyo"]
wiki_docs = WikipediaReader().load_data(pages=cities)
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.core import Settings
from llama_index.core import StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.node_parser import TokenTextSplitter
from llama_index.llms.openai import OpenAI
Settings.llm = | OpenAI(temperature=0, model="gpt-4") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai llama-index-tools-tavily-research llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-packs-corrective-rag')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
import os
os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY"
tavily_ai_api_key = "<tavily_ai_api_key>"
import nest_asyncio
nest_asyncio.apply()
get_ipython().system("mkdir -p 'data/'")
get_ipython().system("curl 'https://arxiv.org/pdf/2307.09288.pdf' -o 'data/llama2.pdf'")
from llama_index.core import SimpleDirectoryReader
documents = | SimpleDirectoryReader("data") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.node_parser import SentenceWindowNodeParser
from llama_index.core.node_parser import SentenceSplitter
node_parser = SentenceWindowNodeParser.from_defaults(
window_size=3,
window_metadata_key="window",
original_text_metadata_key="original_text",
)
text_splitter = SentenceSplitter()
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)
embed_model = HuggingFaceEmbedding(
model_name="sentence-transformers/all-mpnet-base-v2", max_length=512
)
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
Settings.text_splitter = text_splitter
get_ipython().system('curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf')
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader(
input_files=["./IPCC_AR6_WGII_Chapter03.pdf"]
).load_data()
nodes = node_parser.get_nodes_from_documents(documents)
base_nodes = text_splitter.get_nodes_from_documents(documents)
from llama_index.core import VectorStoreIndex
sentence_index = VectorStoreIndex(nodes)
base_index = VectorStoreIndex(base_nodes)
from llama_index.core.postprocessor import MetadataReplacementPostProcessor
query_engine = sentence_index.as_query_engine(
similarity_top_k=2,
node_postprocessors=[
| MetadataReplacementPostProcessor(target_metadata_key="window") | llama_index.core.postprocessor.MetadataReplacementPostProcessor |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import os
from llama_index.llms.openai import OpenAI
from llama_index.core.query_engine import CitationQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
load_index_from_storage,
)
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = | OpenAIEmbedding(model="text-embedding-3-small") | llama_index.embeddings.openai.OpenAIEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data//paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-llm-rails')
get_ipython().system('pip install llama-index')
from llama_index.embeddings.llm_rails import LLMRailsEmbedding
import os
api_key = os.environ.get("API_KEY", "your-api-key")
model_id = os.environ.get("MODEL_ID", "your-model-id")
embed_model = | LLMRailsEmbedding(model_id=model_id, api_key=api_key) | llama_index.embeddings.llm_rails.LLMRailsEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('env', 'OPENAI_API_KEY=YOUR_OPENAI_KEY')
get_ipython().system('pip install llama-index pypdf')
get_ipython().system("mkdir -p 'data/'")
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
import json
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
for idx, node in enumerate(base_nodes):
node.id_ = f"node-{idx}"
from llama_index.core.embeddings import resolve_embed_model
embed_model = | resolve_embed_model("local:BAAI/bge-small-en") | llama_index.core.embeddings.resolve_embed_model |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-graph-stores-kuzu')
import os
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
import shutil
shutil.rmtree("./test1", ignore_errors=True)
shutil.rmtree("./test2", ignore_errors=True)
shutil.rmtree("./test3", ignore_errors=True)
get_ipython().run_line_magic('pip', 'install kuzu')
import kuzu
db = kuzu.Database("test1")
from llama_index.graph_stores.kuzu import KuzuGraphStore
graph_store = KuzuGraphStore(db)
from llama_index.core import SimpleDirectoryReader, KnowledgeGraphIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from IPython.display import Markdown, display
import kuzu
documents = SimpleDirectoryReader(
"../../../../examples/paul_graham_essay/data"
).load_data()
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.llm = llm
Settings.chunk_size = 512
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults(graph_store=graph_store)
index = KnowledgeGraphIndex.from_documents(
documents,
max_triplets_per_chunk=2,
storage_context=storage_context,
)
query_engine = index.as_query_engine(
include_text=False, response_mode="tree_summarize"
)
response = query_engine.query(
"Tell me more about Interleaf",
)
display(Markdown(f"<b>{response}</b>"))
query_engine = index.as_query_engine(
include_text=True, response_mode="tree_summarize"
)
response = query_engine.query(
"Tell me more about Interleaf",
)
display(Markdown(f"<b>{response}</b>"))
db = kuzu.Database("test2")
graph_store = KuzuGraphStore(db)
storage_context = | StorageContext.from_defaults(graph_store=graph_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-supabase')
get_ipython().system('pip install llama-index')
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="Math Tutor",
instructions="You are a personal math tutor. Write and run code to answer math questions.",
openai_tools=[{"type": "code_interpreter"}],
instructions_prefix="Please address the user as Jane Doe. The user has a premium account.",
)
agent.thread_id
response = agent.chat(
"I need to solve the equation `3x + 11 = 14`. Can you help me?"
)
print(str(response))
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="SEC Analyst",
instructions="You are a QA assistant designed to analyze sec filings.",
openai_tools=[{"type": "retrieval"}],
instructions_prefix="Please address the user as Jerry.",
files=["data/10k/lyft_2021.pdf"],
verbose=True,
)
response = agent.chat("What was Lyft's revenue growth in 2021?")
print(str(response))
from llama_index.agent.openai import OpenAIAssistantAgent
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.core.tools import QueryEngineTool, ToolMetadata
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/lyft"
)
lyft_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/uber"
)
uber_index = load_index_from_storage(storage_context)
index_loaded = True
except:
index_loaded = False
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'")
if not index_loaded:
lyft_docs = SimpleDirectoryReader(
input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
uber_docs = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
lyft_index = VectorStoreIndex.from_documents(lyft_docs)
uber_index = VectorStoreIndex.from_documents(uber_docs)
lyft_index.storage_context.persist(persist_dir="./storage/lyft")
uber_index.storage_context.persist(persist_dir="./storage/uber")
lyft_engine = lyft_index.as_query_engine(similarity_top_k=3)
uber_engine = uber_index.as_query_engine(similarity_top_k=3)
query_engine_tools = [
QueryEngineTool(
query_engine=lyft_engine,
metadata=ToolMetadata(
name="lyft_10k",
description=(
"Provides information about Lyft financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
QueryEngineTool(
query_engine=uber_engine,
metadata=ToolMetadata(
name="uber_10k",
description=(
"Provides information about Uber financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
]
agent = OpenAIAssistantAgent.from_new(
name="SEC Analyst",
instructions="You are a QA assistant designed to analyze sec filings.",
tools=query_engine_tools,
instructions_prefix="Please address the user as Jerry.",
verbose=True,
run_retrieve_sleep_time=1.0,
)
response = agent.chat("What was Lyft's revenue growth in 2021?")
from llama_index.agent.openai import OpenAIAssistantAgent
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
)
from llama_index.vector_stores.supabase import SupabaseVectorStore
from llama_index.core.tools import QueryEngineTool, ToolMetadata
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'")
reader = SimpleDirectoryReader(input_files=["./data/10k/lyft_2021.pdf"])
docs = reader.load_data()
for doc in docs:
doc.id_ = "lyft_docs"
vector_store = SupabaseVectorStore(
postgres_connection_string=(
"postgresql://<user>:<password>@<host>:<port>/<db_name>"
),
collection_name="base_demo",
)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install duckdb duckdb-engine')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SQLDatabase, SimpleDirectoryReader, Document
from llama_index.readers.wikipedia import WikipediaReader
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine
from IPython.display import Markdown, display
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("duckdb:///:memory:")
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
metadata_obj.tables.keys()
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{
"city_name": "Chicago",
"population": 2679000,
"country": "United States",
},
{"city_name": "Seoul", "population": 9776000, "country": "South Korea"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
with engine.connect() as connection:
cursor = connection.exec_driver_sql("SELECT * FROM city_stats")
print(cursor.fetchall())
from llama_index.core import SQLDatabase
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
query_engine = NLSQLTableQueryEngine(sql_database)
response = query_engine.query("Which city has the highest population?")
str(response)
response.metadata
engine = create_engine("duckdb:///:memory:")
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
all_table_names = ["city_stats"]
n = 100
for i in range(n):
tmp_table_name = f"tmp_table_{i}"
tmp_table = Table(
tmp_table_name,
metadata_obj,
Column(f"tmp_field_{i}_1", String(16), primary_key=True),
Column(f"tmp_field_{i}_2", Integer),
Column(f"tmp_field_{i}_3", String(16), nullable=False),
)
all_table_names.append(f"tmp_table_{i}")
metadata_obj.create_all(engine)
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{
"city_name": "Chicago",
"population": 2679000,
"country": "United States",
},
{"city_name": "Seoul", "population": 9776000, "country": "South Korea"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine
from llama_index.core.objects import (
SQLTableNodeMapping,
ObjectIndex,
SQLTableSchema,
)
from llama_index.core import VectorStoreIndex
table_node_mapping = | SQLTableNodeMapping(sql_database) | llama_index.core.objects.SQLTableNodeMapping |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
gpt35 = OpenAI(temperature=0, model="gpt-3.5-turbo")
gpt4 = OpenAI(temperature=0, model="gpt-4")
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-bagel')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.bagel import BagelVectorStore
from llama_index.core import StorageContext
from IPython.display import Markdown, display
import bagel
from bagel import Settings
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
server_settings = Settings(
bagel_api_impl="rest", bagel_server_host="api.bageldb.ai"
)
client = bagel.Client(server_settings)
collection = client.get_or_create_cluster("testing_embeddings")
embed_model = "local:BAAI/bge-small-en-v1.5"
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.2)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
from llama_index.core import set_global_handler
set_global_handler("wandb", run_args={"project": "llamaindex"})
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import MetadataMode
llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo", max_tokens=512)
from llama_index.core.node_parser import TokenTextSplitter
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
)
node_parser = TokenTextSplitter(
separator=" ", chunk_size=256, chunk_overlap=128
)
extractors_1 = [
QuestionsAnsweredExtractor(
questions=3, llm=llm, metadata_mode=MetadataMode.EMBED
),
]
extractors_2 = [
SummaryExtractor(summaries=["prev", "self", "next"], llm=llm),
QuestionsAnsweredExtractor(
questions=3, llm=llm, metadata_mode=MetadataMode.EMBED
),
]
from llama_index.core import SimpleDirectoryReader
from llama_index.readers.web import SimpleWebPageReader
reader = SimpleWebPageReader(html_to_text=True)
docs = reader.load_data(urls=["https://eugeneyan.com/writing/llm-patterns/"])
print(docs[0].get_content())
orig_nodes = node_parser.get_nodes_from_documents(docs)
nodes = orig_nodes[20:28]
print(nodes[3].get_content(metadata_mode="all"))
from llama_index.core.ingestion import IngestionPipeline
pipeline = | IngestionPipeline(transformations=[node_parser, *extractors_1]) | llama_index.core.ingestion.IngestionPipeline |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-anthropic')
import nest_asyncio
nest_asyncio.apply()
from llama_index.core import SimpleDirectoryReader, Document
from llama_index.core import SummaryIndex
from llama_index.llms.openai import OpenAI
from llama_index.llms.anthropic import Anthropic
from llama_index.core.evaluation import CorrectnessEvaluator
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
uber_docs0 = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
uber_doc = Document(text="\n\n".join([d.get_content() for d in uber_docs0]))
from llama_index.core.utils import globals_helper
num_tokens = len(globals_helper.tokenizer(uber_doc.get_content()))
print(f"NUM TOKENS: {num_tokens}")
context_str = "Jerry's favorite snack is Hot Cheetos."
query_str = "What is Jerry's favorite snack?"
def augment_doc(doc_str, context, position):
"""Augment doc with additional context at a given position."""
doc_str1 = doc_str[:position]
doc_str2 = doc_str[position:]
return f"{doc_str1}...\n\n{context}\n\n...{doc_str2}"
test_str = augment_doc(
uber_doc.get_content(), context_str, int(0.5 * len(uber_doc.get_content()))
)
async def run_experiments(
doc, position_percentiles, context_str, query, llm, response_mode="compact"
):
eval_llm = OpenAI(model="gpt-4-1106-preview")
correctness_evaluator = CorrectnessEvaluator(llm=eval_llm)
eval_scores = {}
for idx, position_percentile in enumerate(position_percentiles):
print(f"Position percentile: {position_percentile}")
position_idx = int(position_percentile * len(uber_doc.get_content()))
new_doc_str = augment_doc(
uber_doc.get_content(), context_str, position_idx
)
new_doc = Document(text=new_doc_str)
index = SummaryIndex.from_documents(
[new_doc],
)
query_engine = index.as_query_engine(
response_mode=response_mode, llm=llm
)
print(f"Query: {query}")
response = query_engine.query(query)
print(f"Response: {str(response)}")
eval_result = correctness_evaluator.evaluate(
query=query, response=str(response), reference=context_str
)
eval_score = eval_result.score
print(f"Eval score: {eval_score}")
eval_scores[position_percentile] = eval_score
return eval_scores
position_percentiles = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
llm = OpenAI(model="gpt-4-1106-preview")
eval_scores_gpt4 = await run_experiments(
[uber_doc],
position_percentiles,
context_str,
query_str,
llm,
response_mode="compact",
)
llm = OpenAI(model="gpt-4-1106-preview")
eval_scores_gpt4_ts = await run_experiments(
[uber_doc],
position_percentiles,
context_str,
query_str,
llm,
response_mode="tree_summarize",
)
llm = Anthropic(model="claude-2")
eval_scores_anthropic = await run_experiments(
[uber_doc], position_percentiles, context_str, query_str, llm
)
llm = | Anthropic(model="claude-2") | llama_index.llms.anthropic.Anthropic |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-timescalevector')
get_ipython().system('pip install llama-index')
import timescale_vector
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.timescalevector import TimescaleVectorStore
from llama_index.core.vector_stores import VectorStoreQuery, MetadataFilters
import textwrap
import openai
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai.api_key = os.environ["OPENAI_API_KEY"]
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
response = query_engine.query("Did the author work at YC?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What did the author work on before college?")
print(textwrap.fill(str(response), 100))
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do before YC?")
print(textwrap.fill(str(response), 100))
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
vector_store.create_index()
vector_store.drop_index()
vector_store.create_index("tsv", max_alpha=1.0, num_neighbors=50)
vector_store.drop_index()
vector_store.create_index("hnsw", m=16, ef_construction=64)
vector_store.drop_index()
vector_store.create_index("ivfflat", num_lists=20, num_records=1000)
vector_store.drop_index()
vector_store.create_index()
import pandas as pd
from pathlib import Path
file_path = Path("../data/csv/commit_history.csv")
df = pd.read_csv(file_path)
df.dropna(inplace=True)
df = df.astype(str)
df = df[:1000]
df.head()
from timescale_vector import client
def create_uuid(date_string: str):
if date_string is None:
return None
time_format = "%a %b %d %H:%M:%S %Y %z"
datetime_obj = datetime.strptime(date_string, time_format)
uuid = client.uuid_from_time(datetime_obj)
return str(uuid)
from typing import List, Tuple
def split_name(input_string: str) -> Tuple[str, str]:
if input_string is None:
return None, None
start = input_string.find("<")
end = input_string.find(">")
name = input_string[:start].strip()
return name
from datetime import datetime, timedelta
def create_date(input_string: str) -> datetime:
if input_string is None:
return None
month_dict = {
"Jan": "01",
"Feb": "02",
"Mar": "03",
"Apr": "04",
"May": "05",
"Jun": "06",
"Jul": "07",
"Aug": "08",
"Sep": "09",
"Oct": "10",
"Nov": "11",
"Dec": "12",
}
components = input_string.split()
day = components[2]
month = month_dict[components[1]]
year = components[4]
time = components[3]
timezone_offset_minutes = int(
components[5]
) # Convert the offset to minutes
timezone_hours = timezone_offset_minutes // 60 # Calculate the hours
timezone_minutes = (
timezone_offset_minutes % 60
) # Calculate the remaining minutes
timestamp_tz_str = (
f"{year}-{month}-{day} {time}+{timezone_hours:02}{timezone_minutes:02}"
)
return timestamp_tz_str
from llama_index.core.schema import TextNode, NodeRelationship, RelatedNodeInfo
def create_node(row):
record = row.to_dict()
record_name = split_name(record["author"])
record_content = (
str(record["date"])
+ " "
+ record_name
+ " "
+ str(record["change summary"])
+ " "
+ str(record["change details"])
)
node = TextNode(
id_=create_uuid(record["date"]),
text=record_content,
metadata={
"commit": record["commit"],
"author": record_name,
"date": create_date(record["date"]),
},
)
return node
nodes = [create_node(row) for _, row in df.iterrows()]
from llama_index.embeddings.openai import OpenAIEmbedding
embedding_model = OpenAIEmbedding()
for node in nodes:
node_embedding = embedding_model.get_text_embedding(
node.get_content(metadata_mode="all")
)
node.embedding = node_embedding
print(nodes[0].get_content(metadata_mode="all"))
print(nodes[0].get_embedding())
ts_vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="li_commit_history",
time_partition_interval=timedelta(days=7),
)
_ = ts_vector_store.add(nodes)
query_str = "What's new with TimescaleDB functions?"
embed_model = OpenAIEmbedding()
query_embedding = embed_model.get_query_embedding(query_str)
start_dt = datetime(
2023, 8, 1, 22, 10, 35
) # Start date = 1 August 2023, 22:10:35
end_dt = datetime(
2023, 8, 30, 22, 10, 35
) # End date = 30 August 2023, 22:10:35
td = timedelta(days=7) # Time delta = 7 days
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=5
)
query_result = ts_vector_store.query(
vector_store_query, start_date=start_dt, end_date=end_dt
)
query_result
for node in query_result.nodes:
print("-" * 80)
print(node.metadata["date"])
print(node.get_content(metadata_mode="all"))
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=5
)
query_result = ts_vector_store.query(
vector_store_query, start_date=start_dt, time_delta=td
)
for node in query_result.nodes:
print("-" * 80)
print(node.metadata["date"])
print(node.get_content(metadata_mode="all"))
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=5
)
query_result = ts_vector_store.query(
vector_store_query, end_date=end_dt, time_delta=td
)
for node in query_result.nodes:
print("-" * 80)
print(node.metadata["date"])
print(node.get_content(metadata_mode="all"))
from llama_index.core import VectorStoreIndex
from llama_index.core import StorageContext
index = VectorStoreIndex.from_vector_store(ts_vector_store)
retriever = index.as_retriever(
vector_store_kwargs=({"start_date": start_dt, "time_delta": td})
)
retriever.retrieve("What's new with TimescaleDB functions?")
index = | VectorStoreIndex.from_vector_store(ts_vector_store) | llama_index.core.VectorStoreIndex.from_vector_store |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pgvecto-rs')
get_ipython().run_line_magic('pip', 'install llama-index "pgvecto_rs[sdk]"')
get_ipython().system('docker run --name pgvecto-rs-demo -e POSTGRES_PASSWORD=mysecretpassword -p 5432:5432 -d tensorchord/pgvecto-rs:latest')
import logging
import os
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from pgvecto_rs.sdk import PGVectoRs
URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format(
port=os.getenv("DB_PORT", "5432"),
host=os.getenv("DB_HOST", "localhost"),
username=os.getenv("DB_USER", "postgres"),
password=os.getenv("DB_PASS", "mysecretpassword"),
db_name=os.getenv("DB_NAME", "postgres"),
)
client = PGVectoRs(
db_url=URL,
collection_name="example",
dimension=1536, # Using OpenAI’s text-embedding-ada-002
)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from IPython.display import Markdown, display
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.vector_stores.pgvecto_rs import PGVectoRsStore
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = | SimpleDirectoryReader("./data/paul_graham") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-colbert')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-vectara')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install torch sentence-transformers')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.indices.managed.google import GoogleIndex
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
project_name = "TODO-your-project-name" # @param {type:"string"}
email = "[email protected]" # @param {type:"string"}
client_file_name = "client_secret.json"
get_ipython().system('gcloud config set project $project_name')
get_ipython().system('gcloud config set account $email')
get_ipython().system('gcloud auth application-default login --no-browser --client-id-file=$client_file_name --scopes="https://www.googleapis.com/auth/generative-language.retriever,https://www.googleapis.com/auth/cloud-platform"')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
google_index = GoogleIndex.create_corpus(display_name="My first corpus!")
print(f"Newly created corpus ID is {google_index.corpus_id}.")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
google_index.insert_documents(documents)
google_index = GoogleIndex.from_corpus(corpus_id="")
query_engine = google_index.as_query_engine()
response = query_engine.query("which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.VERBOSE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.EXTRACTIVE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
| display_source_node(r, source_length=1000) | llama_index.core.response.notebook_utils.display_source_node |
get_ipython().run_line_magic('pip', 'install llama-index-readers-github')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index llama-hub')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["GITHUB_TOKEN"] = "ghp_..."
os.environ["OPENAI_API_KEY"] = "sk-..."
import os
from llama_index.readers.github import (
GitHubRepositoryIssuesReader,
GitHubIssuesClient,
)
github_client = GitHubIssuesClient()
loader = GitHubRepositoryIssuesReader(
github_client,
owner="run-llama",
repo="llama_index",
verbose=True,
)
orig_docs = loader.load_data()
limit = 100
docs = []
for idx, doc in enumerate(orig_docs):
doc.metadata["index_id"] = int(doc.id_)
if idx >= limit:
break
docs.append(doc)
import weaviate
auth_config = weaviate.AuthApiKey(
api_key="XRa15cDIkYRT7AkrpqT6jLfE4wropK1c1TGk"
)
client = weaviate.Client(
"https://llama-index-test-v0oggsoz.weaviate.network",
auth_client_secret=auth_config,
)
class_name = "LlamaIndex_docs"
client.schema.delete_class(class_name)
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name=class_name
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
doc_index = VectorStoreIndex.from_documents(
docs, storage_context=storage_context
)
from llama_index.core import SummaryIndex
from llama_index.core.async_utils import run_jobs
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import IndexNode
from llama_index.core.vector_stores import (
FilterOperator,
MetadataFilter,
MetadataFilters,
)
async def aprocess_doc(doc, include_summary: bool = True):
"""Process doc."""
metadata = doc.metadata
date_tokens = metadata["created_at"].split("T")[0].split("-")
year = int(date_tokens[0])
month = int(date_tokens[1])
day = int(date_tokens[2])
assignee = (
"" if "assignee" not in doc.metadata else doc.metadata["assignee"]
)
size = ""
if len(doc.metadata["labels"]) > 0:
size_arr = [l for l in doc.metadata["labels"] if "size:" in l]
size = size_arr[0].split(":")[1] if len(size_arr) > 0 else ""
new_metadata = {
"state": metadata["state"],
"year": year,
"month": month,
"day": day,
"assignee": assignee,
"size": size,
}
summary_index = SummaryIndex.from_documents([doc])
query_str = "Give a one-sentence concise summary of this issue."
query_engine = summary_index.as_query_engine(
llm=OpenAI(model="gpt-3.5-turbo")
)
summary_txt = await query_engine.aquery(query_str)
summary_txt = str(summary_txt)
index_id = doc.metadata["index_id"]
filters = MetadataFilters(
filters=[
MetadataFilter(
key="index_id", operator=FilterOperator.EQ, value=int(index_id)
),
]
)
index_node = IndexNode(
text=summary_txt,
metadata=new_metadata,
obj=doc_index.as_retriever(filters=filters),
index_id=doc.id_,
)
return index_node
async def aprocess_docs(docs):
"""Process metadata on docs."""
index_nodes = []
tasks = []
for doc in docs:
task = aprocess_doc(doc)
tasks.append(task)
index_nodes = await | run_jobs(tasks, show_progress=True, workers=3) | llama_index.core.async_utils.run_jobs |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-chroma')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
import getpass
import openai
openai.api_key = "sk-"
import chromadb
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.chroma import ChromaVectorStore
from IPython.display import Markdown, display
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text="The Shawshank Redemption",
metadata={
"author": "Stephen King",
"theme": "Friendship",
"year": 1994,
},
),
TextNode(
text="The Godfather",
metadata={
"director": "Francis Ford Coppola",
"theme": "Mafia",
"year": 1972,
},
),
TextNode(
text="Inception",
metadata={
"director": "Christopher Nolan",
"theme": "Fiction",
"year": 2010,
},
),
TextNode(
text="To Kill a Mockingbird",
metadata={
"author": "Harper Lee",
"theme": "Mafia",
"year": 1960,
},
),
TextNode(
text="1984",
metadata={
"author": "George Orwell",
"theme": "Totalitarianism",
"year": 1949,
},
),
TextNode(
text="The Great Gatsby",
metadata={
"author": "F. Scott Fitzgerald",
"theme": "The American Dream",
"year": 1925,
},
),
TextNode(
text="Harry Potter and the Sorcerer's Stone",
metadata={
"author": "J.K. Rowling",
"theme": "Fiction",
"year": 1997,
},
),
]
from llama_index.core import StorageContext
vector_store = | ChromaVectorStore(chroma_collection=chroma_collection) | llama_index.vector_stores.chroma.ChromaVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.core.tools import QueryEngineTool, ToolMetadata
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/lyft"
)
lyft_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/uber"
)
uber_index = load_index_from_storage(storage_context)
index_loaded = True
except:
index_loaded = False
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'")
if not index_loaded:
lyft_docs = SimpleDirectoryReader(
input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
uber_docs = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
lyft_index = VectorStoreIndex.from_documents(lyft_docs)
uber_index = VectorStoreIndex.from_documents(uber_docs)
lyft_index.storage_context.persist(persist_dir="./storage/lyft")
uber_index.storage_context.persist(persist_dir="./storage/uber")
lyft_engine = lyft_index.as_query_engine(similarity_top_k=3)
uber_engine = uber_index.as_query_engine(similarity_top_k=3)
query_engine_tools = [
QueryEngineTool(
query_engine=lyft_engine,
metadata=ToolMetadata(
name="lyft_10k",
description=(
"Provides information about Lyft financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
QueryEngineTool(
query_engine=uber_engine,
metadata=ToolMetadata(
name="uber_10k",
description=(
"Provides information about Uber financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
]
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo-0613")
agent = ReActAgent.from_tools(
query_engine_tools,
llm=llm,
verbose=True,
)
response = agent.chat("What was Lyft's revenue growth in 2021?")
print(str(response))
response = agent.chat(
"Compare and contrast the revenue growth of Uber and Lyft in 2021, then"
" give an analysis"
)
print(str(response))
import nest_asyncio
nest_asyncio.apply()
response = await agent.achat(
"Compare and contrast the risks of Uber and Lyft in 2021, then give an"
" analysis"
)
print(str(response))
llm_instruct = | OpenAI(model="gpt-3.5-turbo-instruct") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-extractors-entity')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import MetadataMode
llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo", max_tokens=512)
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
TitleExtractor,
KeywordExtractor,
BaseExtractor,
)
from llama_index.extractors.entity import EntityExtractor
from llama_index.core.node_parser import TokenTextSplitter
text_splitter = TokenTextSplitter(
separator=" ", chunk_size=512, chunk_overlap=128
)
class CustomExtractor(BaseExtractor):
def extract(self, nodes):
metadata_list = [
{
"custom": (
node.metadata["document_title"]
+ "\n"
+ node.metadata["excerpt_keywords"]
)
}
for node in nodes
]
return metadata_list
extractors = [
TitleExtractor(nodes=5, llm=llm),
QuestionsAnsweredExtractor(questions=3, llm=llm),
]
transformations = [text_splitter] + extractors
from llama_index.core import SimpleDirectoryReader
get_ipython().system('mkdir -p data')
get_ipython().system('wget -O "data/10k-132.pdf" "https://www.dropbox.com/scl/fi/6dlqdk6e2k1mjhi8dee5j/uber.pdf?rlkey=2jyoe49bg2vwdlz30l76czq6g&dl=1"')
get_ipython().system('wget -O "data/10k-vFinal.pdf" "https://www.dropbox.com/scl/fi/qn7g3vrk5mqb18ko4e5in/lyft.pdf?rlkey=j6jxtjwo8zbstdo4wz3ns8zoj&dl=1"')
uber_docs = | SimpleDirectoryReader(input_files=["data/10k-132.pdf"]) | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai pandas[jinja2] spacy')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
Response,
)
from llama_index.llms.openai import OpenAI
from llama_index.core.evaluation import FaithfulnessEvaluator
from llama_index.core.node_parser import SentenceSplitter
import pandas as pd
pd.set_option("display.max_colwidth", 0)
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4 = | FaithfulnessEvaluator(llm=gpt4) | llama_index.core.evaluation.FaithfulnessEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-bagel')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
import bagel
from bagel import Settings
server_settings = Settings(
bagel_api_impl="rest", bagel_server_host="api.bageldb.ai"
)
client = bagel.Client(server_settings)
collection = client.get_or_create_cluster("testing_embeddings")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.bagel import BagelVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
},
),
| TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
) | llama_index.core.schema.TextNode |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install sentence-transformers')
import os
os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY"
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
)
from llama_index.core.postprocessor import SentenceTransformerRerank
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-bagel')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
import bagel
from bagel import Settings
server_settings = Settings(
bagel_api_impl="rest", bagel_server_host="api.bageldb.ai"
)
client = bagel.Client(server_settings)
collection = client.get_or_create_cluster("testing_embeddings")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.bagel import BagelVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
},
),
TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
),
metadata={
"category": "Entertainment",
"country": "United States",
},
),
| TextNode(
text=(
"Elon Musk is a business magnate, industrial designer, and"
" engineer. He is the founder, CEO, and lead designer of SpaceX,"
" Tesla, Inc., Neuralink, and The Boring Company."
) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install rank-bm25 pymupdf')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
get_ipython().system('pip install llama-index')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
splitter = SentenceSplitter(chunk_size=1024)
index = VectorStoreIndex.from_documents(documents, transformations=[splitter])
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
from llama_index.core import PromptTemplate
query_str = "How do the models developed in this work compare to open-source chat models based on the benchmarks tested?"
query_gen_prompt_str = (
"You are a helpful assistant that generates multiple search queries based on a "
"single input query. Generate {num_queries} search queries, one on each line, "
"related to the following input query:\n"
"Query: {query}\n"
"Queries:\n"
)
query_gen_prompt = PromptTemplate(query_gen_prompt_str)
def generate_queries(llm, query_str: str, num_queries: int = 4):
fmt_prompt = query_gen_prompt.format(
num_queries=num_queries - 1, query=query_str
)
response = llm.complete(fmt_prompt)
queries = response.text.split("\n")
return queries
queries = generate_queries(llm, query_str, num_queries=4)
print(queries)
from tqdm.asyncio import tqdm
async def run_queries(queries, retrievers):
"""Run queries against retrievers."""
tasks = []
for query in queries:
for i, retriever in enumerate(retrievers):
tasks.append(retriever.aretrieve(query))
task_results = await tqdm.gather(*tasks)
results_dict = {}
for i, (query, query_result) in enumerate(zip(queries, task_results)):
results_dict[(query, i)] = query_result
return results_dict
from llama_index.core.retrievers import BM25Retriever
vector_retriever = index.as_retriever(similarity_top_k=2)
bm25_retriever = BM25Retriever.from_defaults(
docstore=index.docstore, similarity_top_k=2
)
results_dict = await run_queries(queries, [vector_retriever, bm25_retriever])
def fuse_results(results_dict, similarity_top_k: int = 2):
"""Fuse results."""
k = 60.0 # `k` is a parameter used to control the impact of outlier rankings.
fused_scores = {}
text_to_node = {}
for nodes_with_scores in results_dict.values():
for rank, node_with_score in enumerate(
sorted(
nodes_with_scores, key=lambda x: x.score or 0.0, reverse=True
)
):
text = node_with_score.node.get_content()
text_to_node[text] = node_with_score
if text not in fused_scores:
fused_scores[text] = 0.0
fused_scores[text] += 1.0 / (rank + k)
reranked_results = dict(
sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)
)
reranked_nodes: List[NodeWithScore] = []
for text, score in reranked_results.items():
reranked_nodes.append(text_to_node[text])
reranked_nodes[-1].score = score
return reranked_nodes[:similarity_top_k]
final_results = fuse_results(results_dict)
from llama_index.core.response.notebook_utils import display_source_node
for n in final_results:
display_source_node(n, source_length=500)
from llama_index.core import QueryBundle
from llama_index.core.retrievers import BaseRetriever
from typing import Any, List
from llama_index.core.schema import NodeWithScore
class FusionRetriever(BaseRetriever):
"""Ensemble retriever with fusion."""
def __init__(
self,
llm,
retrievers: List[BaseRetriever],
similarity_top_k: int = 2,
) -> None:
"""Init params."""
self._retrievers = retrievers
self._similarity_top_k = similarity_top_k
super().__init__()
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve."""
queries = generate_queries(llm, query_str, num_queries=4)
results = run_queries(queries, [vector_retriever, bm25_retriever])
final_results = fuse_results(
results_dict, similarity_top_k=self._similarity_top_k
)
return final_results
from llama_index.core.query_engine import RetrieverQueryEngine
fusion_retriever = FusionRetriever(
llm, [vector_retriever, bm25_retriever], similarity_top_k=2
)
query_engine = | RetrieverQueryEngine(fusion_retriever) | llama_index.core.query_engine.RetrieverQueryEngine |
import openai
openai.api_key = "sk-your-key"
from llama_index.agent import OpenAIAgent
import requests
import yaml
f = requests.get(
"https://raw.githubusercontent.com/sisbell/chatgpt-plugin-store/main/manifests/today-currency-converter.oiconma.repl.co.json"
).text
manifest = yaml.safe_load(f)
from llama_index.tools.chatgpt_plugin.base import ChatGPTPluginToolSpec
from llama_index.tools.requests.base import RequestsToolSpec
requests_spec = RequestsToolSpec()
plugin_spec = | ChatGPTPluginToolSpec(manifest) | llama_index.tools.chatgpt_plugin.base.ChatGPTPluginToolSpec |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence, List
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
import nest_asyncio
nest_asyncio.apply()
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = | FunctionTool.from_defaults(fn=multiply) | llama_index.core.tools.FunctionTool.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
import os
from getpass import getpass
if os.getenv("HONEYHIVE_API_KEY") is None:
os.environ["HONEYHIVE_API_KEY"] = getpass(
"Paste your HoneyHive key from:"
" https://app.honeyhive.ai/settings/account\n"
)
print("HoneyHive API key configured")
get_ipython().system('pip install llama-index')
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks import LlamaDebugHandler
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
SimpleKeywordTableIndex,
StorageContext,
)
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from honeyhive.utils.llamaindex_tracer import HoneyHiveLlamaIndexTracer
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-4", temperature=0)
import llama_index.core
from llama_index.core import set_global_handler
set_global_handler(
"honeyhive",
project="My LlamaIndex Project",
name="My LlamaIndex Pipeline",
api_key=os.environ["HONEYHIVE_API_KEY"],
)
hh_tracer = llama_index.core.global_handler
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
hh_tracer = HoneyHiveLlamaIndexTracer(
project="My LlamaIndex Project",
name="My LlamaIndex Pipeline",
api_key=os.environ["HONEYHIVE_API_KEY"],
)
callback_manager = CallbackManager([llama_debug, hh_tracer])
Settings.callback_manager = callback_manager
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
docs = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-experimental-param-tuner')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
import nest_asyncio
nest_asyncio.apply()
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.readers.file import UnstructuredReader
from llama_index.readers.file import PyMuPDFReader
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [ | Document(text=doc_text) | llama_index.core.Document |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
from llama_index.core import set_global_handler
| set_global_handler("wandb", run_args={"project": "llamaindex"}) | llama_index.core.set_global_handler |
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import SimpleDirectoryReader
documents_1 = SimpleDirectoryReader(
input_files=["../../community/integrations/vector_stores.md"]
).load_data()
documents_2 = SimpleDirectoryReader(
input_files=["../../module_guides/storing/vector_stores.md"]
).load_data()
from llama_index.core import VectorStoreIndex
index_1 = VectorStoreIndex.from_documents(documents_1)
index_2 = | VectorStoreIndex.from_documents(documents_2) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().system("mkdir -p 'data/'")
get_ipython().system("curl 'https://arxiv.org/pdf/2307.09288.pdf' -o 'data/llama2.pdf'")
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
get_ipython().run_line_magic('pip', 'install llama-index-packs-subdoc-summary llama-index-llms-openai llama-index-embeddings-openai')
from llama_index.packs.subdoc_summary import SubDocSummaryPack
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
subdoc_summary_pack = SubDocSummaryPack(
documents,
parent_chunk_size=8192, # default,
child_chunk_size=512, # default
llm=OpenAI(model="gpt-3.5-turbo"),
embed_model=OpenAIEmbedding(),
)
from IPython.display import Markdown, display
from llama_index.core.response.notebook_utils import display_source_node
response = subdoc_summary_pack.run("How was Llama2 pretrained?")
display(Markdown(str(response)))
for n in response.source_nodes:
display_source_node(n, source_length=10000, metadata_mode="all")
from IPython.display import Markdown, display
response = subdoc_summary_pack.run(
"What is the functionality of latest ChatGPT memory."
)
display(Markdown(str(response)))
for n in response.source_nodes:
| display_source_node(n, source_length=10000, metadata_mode="all") | llama_index.core.response.notebook_utils.display_source_node |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader("../data/paul_graham")
docs = reader.load_data()
import os
from llama_index.core import (
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
if not os.path.exists("storage"):
index = VectorStoreIndex.from_documents(docs)
index.set_index_id("vector_index")
index.storage_context.persist("./storage")
else:
storage_context = | StorageContext.from_defaults(persist_dir="storage") | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo-0613")
data = | SimpleDirectoryReader(input_dir="../data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-lancedb')
get_ipython().system('pip install llama-index')
import logging
import sys
from llama_index.core import SimpleDirectoryReader, Document, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.lancedb import LanceDBVectorStore
import textwrap
import openai
openai.api_key = ""
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash)
vector_store = LanceDBVectorStore(uri="/tmp/lancedb")
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
from llama_index.llms.openai import OpenAI
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.postprocessor import LLMRerank
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import Settings
from llama_index.packs.koda_retriever import KodaRetriever
from llama_index.core.evaluation import RetrieverEvaluator
from llama_index.core import SimpleDirectoryReader
import os
from pinecone import Pinecone
from llama_index.core.node_parser import SemanticSplitterNodeParser
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.evaluation import generate_qa_embedding_pairs
import pandas as pd
pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
index = pc.Index("llama2-paper") # this was previously created in my pinecone account
Settings.llm = OpenAI()
Settings.embed_model = OpenAIEmbedding()
vector_store = PineconeVectorStore(pinecone_index=index)
vector_index = VectorStoreIndex.from_vector_store(
vector_store=vector_store, embed_model=Settings.embed_model
)
reranker = LLMRerank(llm=Settings.llm) # optional
koda_retriever = KodaRetriever(
index=vector_index,
llm=Settings.llm,
reranker=reranker, # optional
verbose=True,
similarity_top_k=10,
)
vanilla_retriever = vector_index.as_retriever()
pipeline = IngestionPipeline(
transformations=[Settings.embed_model], vector_store=vector_store
)
def load_documents(file_path, num_pages=None):
if num_pages:
documents = | SimpleDirectoryReader(input_files=[file_path]) | llama_index.core.SimpleDirectoryReader |
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
get_ipython().run_line_magic('pip', 'install -q html2text llama-index pandas pyarrow tqdm')
get_ipython().run_line_magic('pip', 'install -q llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install -q llama-index-callbacks-openinference')
import hashlib
import json
from pathlib import Path
import os
import textwrap
from typing import List, Union
import llama_index.core
from llama_index.readers.web import SimpleWebPageReader
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.callbacks import CallbackManager
from llama_index.callbacks.openinference import OpenInferenceCallbackHandler
from llama_index.callbacks.openinference.base import (
as_dataframe,
QueryData,
NodeData,
)
from llama_index.core.node_parser import SimpleNodeParser
import pandas as pd
from tqdm import tqdm
documents = SimpleWebPageReader().load_data(
[
"https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt"
]
)
print(documents[0].text)
parser = SentenceSplitter()
nodes = parser.get_nodes_from_documents(documents)
print(nodes[0].text)
callback_handler = OpenInferenceCallbackHandler()
callback_manager = | CallbackManager([callback_handler]) | llama_index.core.callbacks.CallbackManager |
get_ipython().run_line_magic('pip', 'install llama-index-llms-replicate')
get_ipython().system('pip install llama-index')
from llama_index.llms.replicate import Replicate
from llama_index.core.llms.llama_utils import messages_to_prompt
llm_13b = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
context_window=4096,
messages_to_prompt=messages_to_prompt, # override message representation for llama 2
)
llm_70b = Replicate(
model="replicate/llama70b-v2-chat:e951f18578850b652510200860fc4ea62b3b16fac280f83ff32282f87bbd2e48",
context_window=4096,
messages_to_prompt=messages_to_prompt, # override message representation for llama 2
)
from llama_index.core.chat_engine import SimpleChatEngine
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core.llms import ChatMessage
bot_70b = SimpleChatEngine(
llm=llm_70b,
memory= | ChatMemoryBuffer.from_defaults(llm=llm_70b) | llama_index.core.memory.ChatMemoryBuffer.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().handlers = []
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
SimpleKeywordTableIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import OpenAI
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
llm = OpenAI(model="gpt-4")
splitter = SentenceSplitter(chunk_size=1024)
nodes = splitter.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
summary_index = | SummaryIndex(nodes, storage_context=storage_context) | llama_index.core.SummaryIndex |
get_ipython().run_line_magic('pip', 'install llama-index-readers-github')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index llama-hub')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["GITHUB_TOKEN"] = "ghp_..."
os.environ["OPENAI_API_KEY"] = "sk-..."
import os
from llama_index.readers.github import (
GitHubRepositoryIssuesReader,
GitHubIssuesClient,
)
github_client = GitHubIssuesClient()
loader = GitHubRepositoryIssuesReader(
github_client,
owner="run-llama",
repo="llama_index",
verbose=True,
)
orig_docs = loader.load_data()
limit = 100
docs = []
for idx, doc in enumerate(orig_docs):
doc.metadata["index_id"] = int(doc.id_)
if idx >= limit:
break
docs.append(doc)
import weaviate
auth_config = weaviate.AuthApiKey(
api_key="XRa15cDIkYRT7AkrpqT6jLfE4wropK1c1TGk"
)
client = weaviate.Client(
"https://llama-index-test-v0oggsoz.weaviate.network",
auth_client_secret=auth_config,
)
class_name = "LlamaIndex_docs"
client.schema.delete_class(class_name)
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name=class_name
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
doc_index = VectorStoreIndex.from_documents(
docs, storage_context=storage_context
)
from llama_index.core import SummaryIndex
from llama_index.core.async_utils import run_jobs
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import IndexNode
from llama_index.core.vector_stores import (
FilterOperator,
MetadataFilter,
MetadataFilters,
)
async def aprocess_doc(doc, include_summary: bool = True):
"""Process doc."""
metadata = doc.metadata
date_tokens = metadata["created_at"].split("T")[0].split("-")
year = int(date_tokens[0])
month = int(date_tokens[1])
day = int(date_tokens[2])
assignee = (
"" if "assignee" not in doc.metadata else doc.metadata["assignee"]
)
size = ""
if len(doc.metadata["labels"]) > 0:
size_arr = [l for l in doc.metadata["labels"] if "size:" in l]
size = size_arr[0].split(":")[1] if len(size_arr) > 0 else ""
new_metadata = {
"state": metadata["state"],
"year": year,
"month": month,
"day": day,
"assignee": assignee,
"size": size,
}
summary_index = SummaryIndex.from_documents([doc])
query_str = "Give a one-sentence concise summary of this issue."
query_engine = summary_index.as_query_engine(
llm= | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-evaluation-tonic-validate')
import json
import pandas as pd
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.evaluation.tonic_validate import (
AnswerConsistencyEvaluator,
AnswerSimilarityEvaluator,
AugmentationAccuracyEvaluator,
AugmentationPrecisionEvaluator,
RetrievalPrecisionEvaluator,
TonicValidateEvaluator,
)
question = "What makes Sam Altman a good founder?"
reference_answer = "He is smart and has a great force of will."
llm_answer = "He is a good founder because he is smart."
retrieved_context_list = [
"Sam Altman is a good founder. He is very smart.",
"What makes Sam Altman such a good founder is his great force of will.",
]
answer_similarity_evaluator = AnswerSimilarityEvaluator()
score = await answer_similarity_evaluator.aevaluate(
question,
llm_answer,
retrieved_context_list,
reference_response=reference_answer,
)
score
answer_consistency_evaluator = AnswerConsistencyEvaluator()
score = await answer_consistency_evaluator.aevaluate(
question, llm_answer, retrieved_context_list
)
score
augmentation_accuracy_evaluator = AugmentationAccuracyEvaluator()
score = await augmentation_accuracy_evaluator.aevaluate(
question, llm_answer, retrieved_context_list
)
score
augmentation_precision_evaluator = AugmentationPrecisionEvaluator()
score = await augmentation_precision_evaluator.aevaluate(
question, llm_answer, retrieved_context_list
)
score
retrieval_precision_evaluator = RetrievalPrecisionEvaluator()
score = await retrieval_precision_evaluator.aevaluate(
question, llm_answer, retrieved_context_list
)
score
tonic_validate_evaluator = TonicValidateEvaluator()
scores = await tonic_validate_evaluator.aevaluate(
question,
llm_answer,
retrieved_context_list,
reference_response=reference_answer,
)
scores.score_dict
tonic_validate_evaluator = TonicValidateEvaluator()
scores = await tonic_validate_evaluator.aevaluate_run(
[question], [llm_answer], [retrieved_context_list], [reference_answer]
)
scores.run_data[0].scores
get_ipython().system('llamaindex-cli download-llamadataset EvaluatingLlmSurveyPaperDataset --download-dir ./data')
from llama_index.core import SimpleDirectoryReader
from llama_index.core.llama_dataset import LabelledRagDataset
from llama_index.core import VectorStoreIndex
rag_dataset = LabelledRagDataset.from_json("./data/rag_dataset.json")
documents = | SimpleDirectoryReader(input_dir="./data/source_files") | llama_index.core.SimpleDirectoryReader |