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get_ipython().run_line_magic('pip', 'install llama-index-program-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-llama-api')
get_ipython().system('pip install llama-index')
from llama_index.llms.llama_api import LlamaAPI
api_key = "LL-your-key"
llm = LlamaAPI(api_key=api_key)
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
| ChatMessage(role="user", content="What is your name") | llama_index.core.llms.ChatMessage |
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() | llama_index.readers.web.SimpleWebPageReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install llama-index-readers-papers')
get_ipython().system('pip install llama_index transformers wikipedia html2text pyvis')
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 KnowledgeGraphIndex
from llama_index.readers.web import SimpleWebPageReader
from llama_index.core.graph_stores import SimpleGraphStore
from llama_index.core import StorageContext
from llama_index.llms.openai import OpenAI
from transformers import pipeline
triplet_extractor = pipeline(
"text2text-generation",
model="Babelscape/rebel-large",
tokenizer="Babelscape/rebel-large",
device="cuda:0",
)
def extract_triplets(input_text):
text = triplet_extractor.tokenizer.batch_decode(
[
triplet_extractor(
input_text, return_tensors=True, return_text=False
)[0]["generated_token_ids"]
]
)[0]
triplets = []
relation, subject, relation, object_ = "", "", "", ""
text = text.strip()
current = "x"
for token in (
text.replace("<s>", "")
.replace("<pad>", "")
.replace("</s>", "")
.split()
):
if token == "<triplet>":
current = "t"
if relation != "":
triplets.append(
(subject.strip(), relation.strip(), object_.strip())
)
relation = ""
subject = ""
elif token == "<subj>":
current = "s"
if relation != "":
triplets.append(
(subject.strip(), relation.strip(), object_.strip())
)
object_ = ""
elif token == "<obj>":
current = "o"
relation = ""
else:
if current == "t":
subject += " " + token
elif current == "s":
object_ += " " + token
elif current == "o":
relation += " " + token
if subject != "" and relation != "" and object_ != "":
triplets.append((subject.strip(), relation.strip(), object_.strip()))
return triplets
import wikipedia
class WikiFilter:
def __init__(self):
self.cache = {}
def filter(self, candidate_entity):
if candidate_entity in self.cache:
return self.cache[candidate_entity]["title"]
try:
page = wikipedia.page(candidate_entity, auto_suggest=False)
entity_data = {
"title": page.title,
"url": page.url,
"summary": page.summary,
}
self.cache[candidate_entity] = entity_data
self.cache[page.title] = entity_data
return entity_data["title"]
except:
return None
wiki_filter = WikiFilter()
def extract_triplets_wiki(text):
relations = extract_triplets(text)
filtered_relations = []
for relation in relations:
(subj, rel, obj) = relation
filtered_subj = wiki_filter.filter(subj)
filtered_obj = wiki_filter.filter(obj)
if filtered_subj is None and filtered_obj is None:
continue
filtered_relations.append(
(
filtered_subj or subj,
rel,
filtered_obj or obj,
)
)
return filtered_relations
from llama_index.core import download_loader
from llama_index.readers.papers import ArxivReader
loader = ArxivReader()
documents = loader.load_data(
search_query="Retrieval Augmented Generation", max_results=1
)
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import Document
documents = [Document(text="".join([x.text for x in documents]))]
from llama_index.core import Settings
llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo")
Settings.llm = llm
Settings.chunk_size = 256
graph_store = | SimpleGraphStore() | llama_index.core.graph_stores.SimpleGraphStore |
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 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
from IPython.display import Markdown, display
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents, chunk_size=512)
from llama_index.core.output_parsers import LangchainOutputParser
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
response_schemas = [
ResponseSchema(
name="Education",
description=(
"Describes the author's educational experience/background."
),
),
ResponseSchema(
name="Work",
description="Describes the author's work experience/background.",
),
]
lc_output_parser = StructuredOutputParser.from_response_schemas(
response_schemas
)
output_parser = LangchainOutputParser(lc_output_parser)
from llama_index.core.prompts.default_prompts import (
DEFAULT_TEXT_QA_PROMPT_TMPL,
)
fmt_qa_tmpl = output_parser.format(DEFAULT_TEXT_QA_PROMPT_TMPL)
print(fmt_qa_tmpl)
from llama_index.llms.openai import OpenAI
llm = | OpenAI(output_parser=output_parser) | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index-llms-dashscope')
get_ipython().run_line_magic('env', 'DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY')
import os
os.environ["DASHSCOPE_API_KEY"] = "YOUR_DASHSCOPE_API_KEY"
from llama_index.llms.dashscope import DashScope, DashScopeGenerationModels
dashscope_llm = DashScope(model_name=DashScopeGenerationModels.QWEN_MAX)
resp = dashscope_llm.complete("How to make cake?")
print(resp)
responses = dashscope_llm.stream_complete("How to make cake?")
for response in responses:
print(response.delta, end="")
from llama_index.core.base.llms.types import MessageRole, ChatMessage
messages = [
ChatMessage(
role=MessageRole.SYSTEM, content="You are a helpful assistant."
),
ChatMessage(role=MessageRole.USER, content="How to make cake?"),
]
resp = dashscope_llm.chat(messages)
print(resp)
responses = dashscope_llm.stream_chat(messages)
for response in responses:
print(response.delta, end="")
messages = [
ChatMessage(
role=MessageRole.SYSTEM, content="You are a helpful assistant."
),
ChatMessage(role=MessageRole.USER, content="How to make cake?"),
]
resp = dashscope_llm.chat(messages)
print(resp)
messages.append(
| ChatMessage(role=MessageRole.ASSISTANT, content=resp.message.content) | llama_index.core.base.llms.types.ChatMessage |
get_ipython().system('pip install llama-index llama-hub')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('wget "https://www.dropbox.com/s/f6bmb19xdg0xedm/paul_graham_essay.txt?dl=1" -O paul_graham_essay.txt')
from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SimpleNodeParser
reader = SimpleDirectoryReader(input_files=["paul_graham_essay.txt"])
documents = reader.load_data()
node_parser = | SimpleNodeParser.from_defaults() | llama_index.core.node_parser.SimpleNodeParser.from_defaults |
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 nest_asyncio
nest_asyncio.apply()
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
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-1106", temperature=0.2)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
from llama_index.core import Settings
Settings.chunk_size = 1024
nodes = Settings.node_parser.get_nodes_from_documents(documents)
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
from llama_index.core import SummaryIndex
from llama_index.core import VectorStoreIndex
summary_index = | SummaryIndex(nodes, storage_context=storage_context) | llama_index.core.SummaryIndex |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
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-embeddings-openai')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
domain = "docs.llamaindex.ai"
docs_url = "https://docs.llamaindex.ai/en/latest/"
get_ipython().system('wget -e robots=off --recursive --no-clobber --page-requisites --html-extension --convert-links --restrict-file-names=windows --domains {domain} --no-parent {docs_url}')
from llama_index.readers.file import UnstructuredReader
reader = | UnstructuredReader() | llama_index.readers.file.UnstructuredReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-metal')
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
from llama_index.vector_stores.metal import MetalVectorStore
from IPython.display import Markdown, display
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()
from llama_index.core import StorageContext
api_key = "api key"
client_id = "client id"
index_id = "index id"
vector_store = MetalVectorStore(
api_key=api_key,
client_id=client_id,
index_id=index_id,
)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai pandas[jinja2] spacy')
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 (
TreeIndex,
VectorStoreIndex,
SimpleDirectoryReader,
Response,
)
from llama_index.llms.openai import OpenAI
from llama_index.core.evaluation import RelevancyEvaluator
from llama_index.core.node_parser import SentenceSplitter
import pandas as pd
pd.set_option("display.max_colwidth", 0)
gpt3 = | 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-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
from llama_index.core import SimpleDirectoryReader
from llama_index.core import SummaryIndex
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
wiki_titles = ["Michael Jordan", "Elon Musk", "Richard Branson", "Rihanna"]
wiki_metadatas = {
"Michael Jordan": {
"category": "Sports",
"country": "United States",
},
"Elon Musk": {
"category": "Business",
"country": "United States",
},
"Richard Branson": {
"category": "Business",
"country": "UK",
},
"Rihanna": {
"category": "Music",
"country": "Barbados",
},
}
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)
docs_dict = {}
for wiki_title in wiki_titles:
doc = SimpleDirectoryReader(
input_files=[f"data/{wiki_title}.txt"]
).load_data()[0]
doc.metadata.update(wiki_metadatas[wiki_title])
docs_dict[wiki_title] = doc
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import LlamaDebugHandler, CallbackManager
from llama_index.core.node_parser import SentenceSplitter
llm = OpenAI("gpt-4")
callback_manager = CallbackManager([LlamaDebugHandler()])
splitter = SentenceSplitter(chunk_size=256)
import weaviate
auth_config = weaviate.AuthApiKey(api_key="<api_key>")
client = weaviate.Client(
"https://llama-index-test-v0oggsoz.weaviate.network",
auth_client_secret=auth_config,
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from IPython.display import Markdown, display
client.schema.delete_class("LlamaIndex")
from llama_index.core import StorageContext
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name="LlamaIndex"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
class_schema = client.schema.get("LlamaIndex")
display(class_schema)
index = VectorStoreIndex(
[],
storage_context=storage_context,
transformations=[splitter],
callback_manager=callback_manager,
)
for wiki_title in wiki_titles:
index.insert(docs_dict[wiki_title])
from llama_index.core.retrievers import VectorIndexAutoRetriever
from llama_index.core.vector_stores.types import MetadataInfo, VectorStoreInfo
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]"
),
),
],
)
retriever = VectorIndexAutoRetriever(
index,
vector_store_info=vector_store_info,
llm=llm,
callback_manager=callback_manager,
max_top_k=10000,
)
nodes = retriever.retrieve(
"Tell me about a celebrity from the United States, set top k to 10000"
)
print(f"Number of nodes: {len(nodes)}")
for node in nodes[:10]:
print(node.node.get_content())
nodes = retriever.retrieve(
"Tell me about the childhood of a popular sports celebrity in the United"
" States"
)
for node in nodes:
print(node.node.get_content())
nodes = retriever.retrieve(
"Tell me about the college life of a billionaire who started at company at"
" the age of 16"
)
for node in nodes:
print(node.node.get_content())
nodes = retriever.retrieve("Tell me about the childhood of a UK billionaire")
for node in nodes:
print(node.node.get_content())
from llama_index.core.schema import IndexNode
nodes = []
vector_query_engines = {}
vector_retrievers = {}
for wiki_title in wiki_titles:
vector_index = VectorStoreIndex.from_documents(
[docs_dict[wiki_title]],
transformations=[splitter],
callback_manager=callback_manager,
)
vector_query_engine = vector_index.as_query_engine(llm=llm)
vector_query_engines[wiki_title] = vector_query_engine
vector_retrievers[wiki_title] = vector_index.as_retriever()
out_path = Path("summaries") / f"{wiki_title}.txt"
if not out_path.exists():
summary_index = SummaryIndex.from_documents(
[docs_dict[wiki_title]], callback_manager=callback_manager
)
summarizer = summary_index.as_query_engine(
response_mode="tree_summarize", llm=llm
)
response = await summarizer.aquery(
f"Give me a summary of {wiki_title}"
)
wiki_summary = response.response
Path("summaries").mkdir(exist_ok=True)
with open(out_path, "w") as fp:
fp.write(wiki_summary)
else:
with open(out_path, "r") as fp:
wiki_summary = fp.read()
print(f"**Summary for {wiki_title}: {wiki_summary}")
node = | IndexNode(text=wiki_summary, index_id=wiki_title) | llama_index.core.schema.IndexNode |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
get_ipython().system('pip install llama_hub')
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 SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
index = VectorStoreIndex(base_nodes)
query_engine = index.as_query_engine(similarity_top_k=2)
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.core.node_parser import SimpleNodeParser
dataset_generator = DatasetGenerator(
base_nodes[:20],
llm=OpenAI(model="gpt-4"),
show_progress=True,
num_questions_per_chunk=3,
)
eval_dataset = await dataset_generator.agenerate_dataset_from_nodes(num=60)
eval_dataset.save_json("data/llama2_eval_qr_dataset.json")
eval_dataset = QueryResponseDataset.from_json(
"data/llama2_eval_qr_dataset.json"
)
import random
full_qr_pairs = eval_dataset.qr_pairs
num_exemplars = 2
num_eval = 40
exemplar_qr_pairs = random.sample(full_qr_pairs, num_exemplars)
eval_qr_pairs = random.sample(full_qr_pairs, num_eval)
len(exemplar_qr_pairs)
from llama_index.core.evaluation.eval_utils import get_responses
from llama_index.core.evaluation import CorrectnessEvaluator, BatchEvalRunner
evaluator_c = CorrectnessEvaluator(llm=OpenAI(model="gpt-3.5-turbo"))
evaluator_dict = {
"correctness": evaluator_c,
}
batch_runner = BatchEvalRunner(evaluator_dict, workers=2, show_progress=True)
async def get_correctness(query_engine, eval_qa_pairs, batch_runner):
eval_qs = [q for q, _ in eval_qa_pairs]
eval_answers = [a for _, a in eval_qa_pairs]
pred_responses = get_responses(eval_qs, query_engine, show_progress=True)
eval_results = await batch_runner.aevaluate_responses(
eval_qs, responses=pred_responses, reference=eval_answers
)
avg_correctness = np.array(
[r.score for r in eval_results["correctness"]]
).mean()
return avg_correctness
QA_PROMPT_KEY = "response_synthesizer:text_qa_template"
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
llm = OpenAI(model="gpt-3.5-turbo")
qa_tmpl_str = (
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Query: {query_str}\n"
"Answer: "
)
qa_tmpl = PromptTemplate(qa_tmpl_str)
print(query_engine.get_prompts()[QA_PROMPT_KEY].get_template())
meta_tmpl_str = """\
Your task is to generate the instruction <INS>. Below are some previous instructions with their scores.
The score ranges from 1 to 5.
{prev_instruction_score_pairs}
Below we show the task. The <INS> tag is prepended to the below prompt template, e.g. as follows:
```
<INS>
{prompt_tmpl_str}
```
The prompt template contains template variables. Given an input set of template variables, the formatted prompt is then given to an LLM to get an output.
Some examples of template variable inputs and expected outputs are given below to illustrate the task. **NOTE**: These do NOT represent the \
entire evaluation dataset.
{qa_pairs_str}
We run every input in an evaluation dataset through an LLM. If the LLM-generated output doesn't match the expected output, we mark it as wrong (score 0).
A correct answer has a score of 1. The final "score" for an instruction is the average of scores across an evaluation dataset.
Write your new instruction (<INS>) that is different from the old ones and has a score as high as possible.
Instruction (<INS>): \
"""
meta_tmpl = PromptTemplate(meta_tmpl_str)
from copy import deepcopy
def format_meta_tmpl(
prev_instr_score_pairs,
prompt_tmpl_str,
qa_pairs,
meta_tmpl,
):
"""Call meta-prompt to generate new instruction."""
pair_str_list = [
f"Instruction (<INS>):\n{instr}\nScore:\n{score}"
for instr, score in prev_instr_score_pairs
]
full_instr_pair_str = "\n\n".join(pair_str_list)
qa_str_list = [
f"query_str:\n{query_str}\nAnswer:\n{answer}"
for query_str, answer in qa_pairs
]
full_qa_pair_str = "\n\n".join(qa_str_list)
fmt_meta_tmpl = meta_tmpl.format(
prev_instruction_score_pairs=full_instr_pair_str,
prompt_tmpl_str=prompt_tmpl_str,
qa_pairs_str=full_qa_pair_str,
)
return fmt_meta_tmpl
def get_full_prompt_template(cur_instr: str, prompt_tmpl):
tmpl_str = prompt_tmpl.get_template()
new_tmpl_str = cur_instr + "\n" + tmpl_str
new_tmpl = PromptTemplate(new_tmpl_str)
return new_tmpl
import numpy as np
def _parse_meta_response(meta_response: str):
return str(meta_response).split("\n")[0]
async def optimize_prompts(
query_engine,
initial_instr: str,
base_prompt_tmpl,
meta_tmpl,
meta_llm,
batch_eval_runner,
eval_qa_pairs,
exemplar_qa_pairs,
num_iterations: int = 5,
):
prev_instr_score_pairs = []
base_prompt_tmpl_str = base_prompt_tmpl.get_template()
cur_instr = initial_instr
for idx in range(num_iterations):
if idx > 0:
fmt_meta_tmpl = format_meta_tmpl(
prev_instr_score_pairs,
base_prompt_tmpl_str,
exemplar_qa_pairs,
meta_tmpl,
)
meta_response = meta_llm.complete(fmt_meta_tmpl)
print(fmt_meta_tmpl)
print(str(meta_response))
cur_instr = _parse_meta_response(meta_response)
new_prompt_tmpl = get_full_prompt_template(cur_instr, base_prompt_tmpl)
query_engine.update_prompts({QA_PROMPT_KEY: new_prompt_tmpl})
avg_correctness = await get_correctness(
query_engine, eval_qa_pairs, batch_runner
)
prev_instr_score_pairs.append((cur_instr, avg_correctness))
max_instr_score_pair = max(
prev_instr_score_pairs, key=lambda item: item[1]
)
return max_instr_score_pair[0], prev_instr_score_pairs
query_engine = index.as_query_engine(similarity_top_k=2)
base_qa_prompt = query_engine.get_prompts()[QA_PROMPT_KEY]
initial_instr = """\
You are a QA assistant.
Context information is below. Given the context information and not prior knowledge, \
answer the query. \
"""
old_qa_prompt = get_full_prompt_template(initial_instr, base_qa_prompt)
meta_llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
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/").load_data()
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
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))
state["memory"].put(
ChatMessage(content=response_str, role=MessageRole.ASSISTANT)
)
return {"response_str": response_str, "is_done": True}
process_response = AgentFnComponent(fn=process_response_fn)
def process_agent_response_fn(
task: Task, state: Dict[str, Any], response_dict: dict
):
"""Process agent response."""
return (
AgentChatResponse(response_dict["response_str"]),
response_dict["is_done"],
)
process_agent_response = AgentFnComponent(fn=process_agent_response_fn)
from llama_index.core.query_pipeline import QueryPipeline as QP
from llama_index.llms.openai import OpenAI
qp.add_modules(
{
"agent_input": agent_input_component,
"react_prompt": react_prompt_component,
"llm": OpenAI(model="gpt-4-1106-preview"),
"react_output_parser": parse_react_output,
"run_tool": run_tool,
"process_response": process_response,
"process_agent_response": process_agent_response,
}
)
qp.add_chain(["agent_input", "react_prompt", "llm", "react_output_parser"])
qp.add_link(
"react_output_parser",
"run_tool",
condition_fn=lambda x: not x["done"],
input_fn=lambda x: x["reasoning_step"],
)
qp.add_link(
"react_output_parser",
"process_response",
condition_fn=lambda x: x["done"],
input_fn=lambda x: x["reasoning_step"],
)
qp.add_link("process_response", "process_agent_response")
qp.add_link("run_tool", "process_agent_response")
from pyvis.network import Network
net = Network(notebook=True, cdn_resources="in_line", directed=True)
net.from_nx(qp.clean_dag)
net.show("agent_dag.html")
from llama_index.core.agent import QueryPipelineAgentWorker, AgentRunner
from llama_index.core.callbacks import CallbackManager
agent_worker = QueryPipelineAgentWorker(qp)
agent = AgentRunner(
agent_worker, callback_manager=CallbackManager([]), verbose=True
)
task = agent.create_task(
"What are some tracks from the artist AC/DC? Limit it to 3"
)
step_output = agent.run_step(task.task_id)
step_output = agent.run_step(task.task_id)
step_output.is_last
response = agent.finalize_response(task.task_id)
print(str(response))
agent.reset()
response = agent.chat(
"What are some tracks from the artist AC/DC? Limit it to 3"
)
print(str(response))
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4-1106-preview")
from llama_index.core.agent import Task, AgentChatResponse
from typing import Dict, Any
from llama_index.core.query_pipeline import (
AgentInputComponent,
AgentFnComponent,
)
def agent_input_fn(task: Task, state: Dict[str, Any]) -> Dict:
"""Agent input function."""
if "convo_history" not in state:
state["convo_history"] = []
state["count"] = 0
state["convo_history"].append(f"User: {task.input}")
convo_history_str = "\n".join(state["convo_history"]) or "None"
return {"input": task.input, "convo_history": convo_history_str}
agent_input_component = AgentInputComponent(fn=agent_input_fn)
from llama_index.core import PromptTemplate
retry_prompt_str = """\
You are trying to generate a proper natural language query given a user input.
This query will then be interpreted by a downstream text-to-SQL agent which
will convert the query to a SQL statement. If the agent triggers an error,
then that will be reflected in the current conversation history (see below).
If the conversation history is None, use the user input. If its not None,
generate a new SQL query that avoids the problems of the previous SQL query.
Input: {input}
Convo history (failed attempts):
{convo_history}
New input: """
retry_prompt = PromptTemplate(retry_prompt_str)
from llama_index.core import Response
from typing import Tuple
validate_prompt_str = """\
Given the user query, validate whether the inferred SQL query and response from executing the query is correct and answers the query.
Answer with YES or NO.
Query: {input}
Inferred SQL query: {sql_query}
SQL Response: {sql_response}
Result: """
validate_prompt = PromptTemplate(validate_prompt_str)
MAX_ITER = 3
def agent_output_fn(
task: Task, state: Dict[str, Any], output: Response
) -> Tuple[AgentChatResponse, bool]:
"""Agent output component."""
print(f"> Inferred SQL Query: {output.metadata['sql_query']}")
print(f"> SQL Response: {str(output)}")
state["convo_history"].append(
f"Assistant (inferred SQL query): {output.metadata['sql_query']}"
)
state["convo_history"].append(f"Assistant (response): {str(output)}")
validate_prompt_partial = validate_prompt.as_query_component(
partial={
"sql_query": output.metadata["sql_query"],
"sql_response": str(output),
}
)
qp = QP(chain=[validate_prompt_partial, llm])
validate_output = qp.run(input=task.input)
state["count"] += 1
is_done = False
if state["count"] >= MAX_ITER:
is_done = True
if "YES" in validate_output.message.content:
is_done = True
return AgentChatResponse(response=str(output)), is_done
agent_output_component = AgentFnComponent(fn=agent_output_fn)
from llama_index.core.query_pipeline import (
QueryPipeline as QP,
Link,
InputComponent,
)
qp = QP(
modules={
"input": agent_input_component,
"retry_prompt": retry_prompt,
"llm": llm,
"sql_query_engine": sql_query_engine,
"output_component": agent_output_component,
},
verbose=True,
)
qp.add_link("input", "retry_prompt", src_key="input", dest_key="input")
qp.add_link(
"input", "retry_prompt", src_key="convo_history", dest_key="convo_history"
)
qp.add_chain(["retry_prompt", "llm", "sql_query_engine", "output_component"])
from pyvis.network import Network
net = Network(notebook=True, cdn_resources="in_line", directed=True)
net.from_nx(qp.dag)
net.show("agent_dag.html")
from llama_index.core.agent import QueryPipelineAgentWorker, AgentRunner
from llama_index.core.callbacks import CallbackManager
agent_worker = | QueryPipelineAgentWorker(qp) | llama_index.core.agent.QueryPipelineAgentWorker |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-cross-encoders')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install datasets --quiet')
get_ipython().system('pip install sentence-transformers --quiet')
get_ipython().system('pip install openai --quiet')
from datasets import load_dataset
import random
dataset = load_dataset("allenai/qasper")
train_dataset = dataset["train"]
validation_dataset = dataset["validation"]
test_dataset = dataset["test"]
random.seed(42) # Set a random seed for reproducibility
train_sampled_indices = random.sample(range(len(train_dataset)), 800)
train_samples = [train_dataset[i] for i in train_sampled_indices]
test_sampled_indices = random.sample(range(len(test_dataset)), 80)
test_samples = [test_dataset[i] for i in test_sampled_indices]
from typing import List
def get_full_text(sample: dict) -> str:
"""
:param dict sample: the row sample from QASPER
"""
title = sample["title"]
abstract = sample["abstract"]
sections_list = sample["full_text"]["section_name"]
paragraph_list = sample["full_text"]["paragraphs"]
combined_sections_with_paras = ""
if len(sections_list) == len(paragraph_list):
combined_sections_with_paras += title + "\t"
combined_sections_with_paras += abstract + "\t"
for index in range(0, len(sections_list)):
combined_sections_with_paras += str(sections_list[index]) + "\t"
combined_sections_with_paras += "".join(paragraph_list[index])
return combined_sections_with_paras
else:
print("Not the same number of sections as paragraphs list")
def get_questions(sample: dict) -> List[str]:
"""
:param dict sample: the row sample from QASPER
"""
questions_list = sample["qas"]["question"]
return questions_list
doc_qa_dict_list = []
for train_sample in train_samples:
full_text = get_full_text(train_sample)
questions_list = get_questions(train_sample)
local_dict = {"paper": full_text, "questions": questions_list}
doc_qa_dict_list.append(local_dict)
len(doc_qa_dict_list)
import pandas as pd
df_train = pd.DataFrame(doc_qa_dict_list)
df_train.to_csv("train.csv")
"""
The Answers field in the dataset follow the below format:-
Unanswerable answers have "unanswerable" set to true.
The remaining answers have exactly one of the following fields being non-empty.
"extractive_spans" are spans in the paper which serve as the answer.
"free_form_answer" is a written out answer.
"yes_no" is true iff the answer is Yes, and false iff the answer is No.
We accept only free-form answers and for all the other kind of answers we set their value to 'Unacceptable',
to better evaluate the performance of the query engine using pairwise comparision evaluator as it uses GPT-4 which is biased towards preferring long answers more.
https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1
So in the case of 'yes_no' answers it can favour Query Engine answers more than reference answers.
Also in the case of extracted spans it can favour reference answers more than Query engine generated answers.
"""
eval_doc_qa_answer_list = []
def get_answers(sample: dict) -> List[str]:
"""
:param dict sample: the row sample from the train split of QASPER
"""
final_answers_list = []
answers = sample["qas"]["answers"]
for answer in answers:
local_answer = ""
types_of_answers = answer["answer"][0]
if types_of_answers["unanswerable"] == False:
if types_of_answers["free_form_answer"] != "":
local_answer = types_of_answers["free_form_answer"]
else:
local_answer = "Unacceptable"
else:
local_answer = "Unacceptable"
final_answers_list.append(local_answer)
return final_answers_list
for test_sample in test_samples:
full_text = get_full_text(test_sample)
questions_list = get_questions(test_sample)
answers_list = get_answers(test_sample)
local_dict = {
"paper": full_text,
"questions": questions_list,
"answers": answers_list,
}
eval_doc_qa_answer_list.append(local_dict)
len(eval_doc_qa_answer_list)
import pandas as pd
df_test = pd.DataFrame(eval_doc_qa_answer_list)
df_test.to_csv("test.csv")
get_ipython().system('pip install llama-index --quiet')
import os
from llama_index.core import SimpleDirectoryReader
import openai
from llama_index.finetuning.cross_encoders.dataset_gen import (
generate_ce_fine_tuning_dataset,
generate_synthetic_queries_over_documents,
)
from llama_index.finetuning.cross_encoders import CrossEncoderFinetuneEngine
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import Document
final_finetuning_data_list = []
for paper in doc_qa_dict_list:
questions_list = paper["questions"]
documents = [ | Document(text=paper["paper"]) | llama_index.core.Document |
get_ipython().system('pip install -U llama-index-multi-modal-llms-dashscope')
get_ipython().run_line_magic('env', 'DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY')
from llama_index.multi_modal_llms.dashscope import (
DashScopeMultiModal,
DashScopeMultiModalModels,
)
from llama_index.core.multi_modal_llms.generic_utils import load_image_urls
image_urls = [
"https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg",
]
image_documents = | load_image_urls(image_urls) | llama_index.core.multi_modal_llms.generic_utils.load_image_urls |
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 import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.huggingface import HuggingFaceLLM
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'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.core import PromptTemplate
query_wrapper_prompt = PromptTemplate(
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{query_str}\n\n### Response:"
)
import torch
llm = HuggingFaceLLM(
context_window=2048,
max_new_tokens=256,
generate_kwargs={"temperature": 0.25, "do_sample": False},
query_wrapper_prompt=query_wrapper_prompt,
tokenizer_name="Writer/camel-5b-hf",
model_name="Writer/camel-5b-hf",
device_map="auto",
tokenizer_kwargs={"max_length": 2048},
)
Settings.chunk_size = 512
Settings.llm = llm
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
import openai
openai.api_key = "sk-your-key"
from llama_index.agent import OpenAIAgent
from llama_index.tools.tool_spec.load_and_search.base import LoadAndSearchToolSpec
from llama_index.tools.google_search.base import GoogleSearchToolSpec
google_spec = | GoogleSearchToolSpec(key="your-key", engine="your-engine") | llama_index.tools.google_search.base.GoogleSearchToolSpec |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core.agent import (
CustomSimpleAgentWorker,
Task,
AgentChatResponse,
)
from typing import Dict, Any, List, Tuple, Optional
from llama_index.core.tools import BaseTool, QueryEngineTool
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core import ChatPromptTemplate, PromptTemplate
from llama_index.core.selectors import PydanticSingleSelector
from llama_index.core.bridge.pydantic import Field, BaseModel
from llama_index.core.llms import ChatMessage, MessageRole
DEFAULT_PROMPT_STR = """
Given previous question/response pairs, please determine if an error has occurred in the response, and suggest \
a modified question that will not trigger the error.
Examples of modified questions:
- The question itself is modified to elicit a non-erroneous response
- The question is augmented with context that will help the downstream system better answer the question.
- The question is augmented with examples of negative responses, or other negative questions.
An error means that either an exception has triggered, or the response is completely irrelevant to the question.
Please return the evaluation of the response in the following JSON format.
"""
def get_chat_prompt_template(
system_prompt: str, current_reasoning: Tuple[str, str]
) -> ChatPromptTemplate:
system_msg = ChatMessage(role=MessageRole.SYSTEM, content=system_prompt)
messages = [system_msg]
for raw_msg in current_reasoning:
if raw_msg[0] == "user":
messages.append(
ChatMessage(role=MessageRole.USER, content=raw_msg[1])
)
else:
messages.append(
ChatMessage(role=MessageRole.ASSISTANT, content=raw_msg[1])
)
return ChatPromptTemplate(message_templates=messages)
class ResponseEval(BaseModel):
"""Evaluation of whether the response has an error."""
has_error: bool = Field(
..., description="Whether the response has an error."
)
new_question: str = Field(..., description="The suggested new question.")
explanation: str = Field(
...,
description=(
"The explanation for the error as well as for the new question."
"Can include the direct stack trace as well."
),
)
from llama_index.core.bridge.pydantic import PrivateAttr
class RetryAgentWorker(CustomSimpleAgentWorker):
"""Agent worker that adds a retry layer on top of a router.
Continues iterating until there's no errors / task is done.
"""
prompt_str: str = Field(default=DEFAULT_PROMPT_STR)
max_iterations: int = Field(default=10)
_router_query_engine: RouterQueryEngine = PrivateAttr()
def __init__(self, tools: List[BaseTool], **kwargs: Any) -> None:
"""Init params."""
for tool in tools:
if not isinstance(tool, QueryEngineTool):
raise ValueError(
f"Tool {tool.metadata.name} is not a query engine tool."
)
self._router_query_engine = RouterQueryEngine(
selector=PydanticSingleSelector.from_defaults(),
query_engine_tools=tools,
verbose=kwargs.get("verbose", False),
)
super().__init__(
tools=tools,
**kwargs,
)
def _initialize_state(self, task: Task, **kwargs: Any) -> Dict[str, Any]:
"""Initialize state."""
return {"count": 0, "current_reasoning": []}
def _run_step(
self, state: Dict[str, Any], task: Task, input: Optional[str] = None
) -> Tuple[AgentChatResponse, bool]:
"""Run step.
Returns:
Tuple of (agent_response, is_done)
"""
if "new_input" not in state:
new_input = task.input
else:
new_input = state["new_input"]
response = self._router_query_engine.query(new_input)
state["current_reasoning"].extend(
[("user", new_input), ("assistant", str(response))]
)
chat_prompt_tmpl = get_chat_prompt_template(
self.prompt_str, state["current_reasoning"]
)
llm_program = LLMTextCompletionProgram.from_defaults(
output_parser= | PydanticOutputParser(output_cls=ResponseEval) | llama_index.core.output_parsers.PydanticOutputParser |
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)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
keyword_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)
list_retriever = summary_index.as_retriever()
vector_retriever = vector_index.as_retriever()
keyword_retriever = keyword_index.as_retriever()
from llama_index.core.tools import RetrieverTool
list_tool = RetrieverTool.from_defaults(
retriever=list_retriever,
description=(
"Will retrieve all context from Paul Graham's essay on What I Worked"
" On. Don't use if the question only requires more specific context."
),
)
vector_tool = RetrieverTool.from_defaults(
retriever=vector_retriever,
description=(
"Useful for retrieving specific context from Paul Graham essay on What"
" I Worked On."
),
)
keyword_tool = RetrieverTool.from_defaults(
retriever=keyword_retriever,
description=(
"Useful for retrieving specific context from Paul Graham essay on What"
" I Worked On (using entities mentioned in query)"
),
)
from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector
from llama_index.core.selectors import (
PydanticMultiSelector,
PydanticSingleSelector,
)
from llama_index.core.retrievers import RouterRetriever
from llama_index.core.response.notebook_utils import display_source_node
retriever = RouterRetriever(
selector= | PydanticSingleSelector.from_defaults(llm=llm) | llama_index.core.selectors.PydanticSingleSelector.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
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.llms.openai import OpenAI
from llama_index.core.tools import QueryEngineTool, ToolMetadata
llm_35 = OpenAI(model="gpt-3.5-turbo-0613", temperature=0.3)
llm_4 = OpenAI(model="gpt-4-0613", temperature=0.3)
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/march"
)
march_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/june"
)
june_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/sept"
)
sept_index = load_index_from_storage(storage_context)
index_loaded = True
except:
index_loaded = False
if not index_loaded:
march_docs = SimpleDirectoryReader(
input_files=["../../data/10q/uber_10q_march_2022.pdf"]
).load_data()
june_docs = SimpleDirectoryReader(
input_files=["../../data/10q/uber_10q_june_2022.pdf"]
).load_data()
sept_docs = SimpleDirectoryReader(
input_files=["../../data/10q/uber_10q_sept_2022.pdf"]
).load_data()
march_index = VectorStoreIndex.from_documents(
march_docs,
)
june_index = VectorStoreIndex.from_documents(
june_docs,
)
sept_index = VectorStoreIndex.from_documents(
sept_docs,
)
march_index.storage_context.persist(persist_dir="./storage/march")
june_index.storage_context.persist(persist_dir="./storage/june")
sept_index.storage_context.persist(persist_dir="./storage/sept")
march_engine = march_index.as_query_engine(similarity_top_k=3, llm=llm_35)
june_engine = june_index.as_query_engine(similarity_top_k=3, llm=llm_35)
sept_engine = sept_index.as_query_engine(similarity_top_k=3, llm=llm_35)
from llama_index.core.tools import QueryEngineTool
query_tool_sept = QueryEngineTool.from_defaults(
query_engine=sept_engine,
name="sept_2022",
description=(
f"Provides information about Uber quarterly financials ending"
f" September 2022"
),
)
query_tool_june = QueryEngineTool.from_defaults(
query_engine=june_engine,
name="june_2022",
description=(
f"Provides information about Uber quarterly financials ending June"
f" 2022"
),
)
query_tool_march = QueryEngineTool.from_defaults(
query_engine=march_engine,
name="march_2022",
description=(
f"Provides information about Uber quarterly financials ending March"
f" 2022"
),
)
query_engine_tools = [query_tool_march, query_tool_june, query_tool_sept]
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
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-index-retrievers-bm25')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
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().handlers = []
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
SimpleDirectoryReader,
StorageContext,
VectorStoreIndex,
)
from llama_index.retrievers.bm25 import BM25Retriever
from llama_index.core.retrievers import VectorIndexRetriever
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() | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-faiss')
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 faiss
d = 1536
faiss_index = faiss.IndexFlatL2(d)
from llama_index.core import (
SimpleDirectoryReader,
load_index_from_storage,
VectorStoreIndex,
StorageContext,
)
from llama_index.vector_stores.faiss import FaissVectorStore
from IPython.display import Markdown, display
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()
vector_store = FaissVectorStore(faiss_index=faiss_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
index.storage_context.persist()
vector_store = FaissVectorStore.from_persist_dir("./storage")
storage_context = StorageContext.from_defaults(
vector_store=vector_store, persist_dir="./storage"
)
index = | load_index_from_storage(storage_context=storage_context) | llama_index.core.load_index_from_storage |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
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.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.core import SummaryIndex
Settings.llm = OpenAI()
Settings.chunk_size = 1024
nodes = Settings.node_parser.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
summary_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize",
use_async=True,
)
vector_query_engine = vector_index.as_query_engine()
from llama_index.core.tools import QueryEngineTool
summary_tool = QueryEngineTool.from_defaults(
query_engine=summary_query_engine,
name="summary_tool",
description=(
"Useful for summarization questions related to the author's life"
),
)
vector_tool = QueryEngineTool.from_defaults(
query_engine=vector_query_engine,
name="vector_tool",
description=(
"Useful for retrieving specific context to answer specific questions about the author's life"
),
)
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="QA bot",
instructions="You are a bot designed to answer questions about the author",
openai_tools=[],
tools=[summary_tool, vector_tool],
verbose=True,
run_retrieve_sleep_time=1.0,
)
response = agent.chat("Can you give me a summary about the author's life?")
print(str(response))
response = agent.query("What did the author do after RICS?")
print(str(response))
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
try:
pinecone.create_index(
"quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart")
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",
},
),
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."
),
metadata={
"category": "Business",
"country": "United States",
},
),
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",
},
),
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",
},
),
]
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="test"
)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-myscale')
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 os import environ
import clickhouse_connect
environ["OPENAI_API_KEY"] = "sk-*"
client = clickhouse_connect.get_client(
host="YOUR_CLUSTER_HOST",
port=8443,
username="YOUR_USERNAME",
password="YOUR_CLUSTER_PASSWORD",
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.myscale import MyScaleVectorStore
from IPython.display import Markdown, display
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/")
documents = loader.load_data()
for file in loader.input_files:
print(file)
from llama_index.core import StorageContext
for document in documents:
document.metadata = {"user_id": "123", "favorite_color": "blue"}
vector_store = MyScaleVectorStore(myscale_client=client)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
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)
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-file')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-postgres')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-llms-llama-cpp')
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en")
get_ipython().system('pip install llama-cpp-python')
from llama_index.llms.llama_cpp import LlamaCPP
model_url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q4_0.gguf"
llm = LlamaCPP(
model_url=model_url,
model_path=None,
temperature=0.1,
max_new_tokens=256,
context_window=3900,
generate_kwargs={},
model_kwargs={"n_gpu_layers": 1},
verbose=True,
)
get_ipython().system('pip install psycopg2-binary pgvector asyncpg "sqlalchemy[asyncio]" greenlet')
import psycopg2
db_name = "vector_db"
host = "localhost"
password = "password"
port = "5432"
user = "jerry"
conn = psycopg2.connect(
dbname="postgres",
host=host,
password=password,
port=port,
user=user,
)
conn.autocommit = True
with conn.cursor() as c:
c.execute(f"DROP DATABASE IF EXISTS {db_name}")
c.execute(f"CREATE DATABASE {db_name}")
from sqlalchemy import make_url
from llama_index.vector_stores.postgres import PGVectorStore
vector_store = PGVectorStore.from_params(
database=db_name,
host=host,
password=password,
port=port,
user=user,
table_name="llama2_paper",
embed_dim=384, # openai embedding dimension
)
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.node_parser import SentenceSplitter
text_parser = SentenceSplitter(
chunk_size=1024,
)
text_chunks = []
doc_idxs = []
for doc_idx, doc in enumerate(documents):
cur_text_chunks = text_parser.split_text(doc.text)
text_chunks.extend(cur_text_chunks)
doc_idxs.extend([doc_idx] * len(cur_text_chunks))
from llama_index.core.schema import TextNode
nodes = []
for idx, text_chunk in enumerate(text_chunks):
node = TextNode(
text=text_chunk,
)
src_doc = documents[doc_idxs[idx]]
node.metadata = src_doc.metadata
nodes.append(node)
for node in nodes:
node_embedding = embed_model.get_text_embedding(
node.get_content(metadata_mode="all")
)
node.embedding = node_embedding
vector_store.add(nodes)
query_str = "Can you tell me about the key concepts for safety finetuning"
query_embedding = embed_model.get_query_embedding(query_str)
from llama_index.core.vector_stores import VectorStoreQuery
query_mode = "default"
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=2, mode=query_mode
)
query_result = vector_store.query(vector_store_query)
print(query_result.nodes[0].get_content())
from llama_index.core.schema import NodeWithScore
from typing import Optional
nodes_with_scores = []
for index, node in enumerate(query_result.nodes):
score: Optional[float] = None
if query_result.similarities is not None:
score = query_result.similarities[index]
nodes_with_scores.append(NodeWithScore(node=node, score=score))
from llama_index.core import QueryBundle
from llama_index.core.retrievers import BaseRetriever
from typing import Any, List
class VectorDBRetriever(BaseRetriever):
"""Retriever over a postgres vector store."""
def __init__(
self,
vector_store: PGVectorStore,
embed_model: Any,
query_mode: str = "default",
similarity_top_k: int = 2,
) -> None:
"""Init params."""
self._vector_store = vector_store
self._embed_model = embed_model
self._query_mode = query_mode
self._similarity_top_k = similarity_top_k
super().__init__()
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve."""
query_embedding = embed_model.get_query_embedding(
query_bundle.query_str
)
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding,
similarity_top_k=self._similarity_top_k,
mode=self._query_mode,
)
query_result = vector_store.query(vector_store_query)
nodes_with_scores = []
for index, node in enumerate(query_result.nodes):
score: Optional[float] = None
if query_result.similarities is not None:
score = query_result.similarities[index]
nodes_with_scores.append(NodeWithScore(node=node, score=score))
return nodes_with_scores
retriever = VectorDBRetriever(
vector_store, embed_model, query_mode="default", similarity_top_k=2
)
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = | RetrieverQueryEngine.from_args(retriever, llm=llm) | llama_index.core.query_engine.RetrieverQueryEngine.from_args |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
import nest_asyncio
nest_asyncio.apply()
import os
HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
import pandas as pd
def display_eval_df(question, source, answer_a, answer_b, result) -> None:
"""Pretty print question/answer + gpt-4 judgement dataset."""
eval_df = pd.DataFrame(
{
"Question": question,
"Source": source,
"Model A": answer_a["model"],
"Answer A": answer_a["text"],
"Model B": answer_b["model"],
"Answer B": answer_b["text"],
"Score": result.score,
"Judgement": result.feedback,
},
index=[0],
)
eval_df = eval_df.style.set_properties(
**{
"inline-size": "300px",
"overflow-wrap": "break-word",
},
subset=["Answer A", "Answer B"]
)
display(eval_df)
get_ipython().system('pip install wikipedia -q')
from llama_index.readers.wikipedia import WikipediaReader
train_cities = [
"San Francisco",
"Toronto",
"New York",
"Vancouver",
"Montreal",
"Boston",
]
test_cities = [
"Tokyo",
"Singapore",
"Paris",
]
train_documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in train_cities]
)
test_documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in test_cities]
)
QUESTION_GEN_PROMPT = (
"You are a Teacher/ Professor. Your task is to setup "
"a quiz/examination. Using the provided context, formulate "
"a single question that captures an important fact from the "
"context. Restrict the question to the context information provided."
)
from llama_index.core.evaluation import DatasetGenerator
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
train_dataset_generator = DatasetGenerator.from_documents(
train_documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=llm,
show_progress=True,
num_questions_per_chunk=25,
)
test_dataset_generator = DatasetGenerator.from_documents(
test_documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=llm,
show_progress=True,
num_questions_per_chunk=25,
)
train_questions = train_dataset_generator.generate_questions_from_nodes(
num=200
)
test_questions = test_dataset_generator.generate_questions_from_nodes(num=150)
len(train_questions), len(test_questions)
train_questions[:3]
test_questions[:3]
from llama_index.core import VectorStoreIndex
from llama_index.core.retrievers import VectorIndexRetriever
train_index = VectorStoreIndex.from_documents(documents=train_documents)
train_retriever = VectorIndexRetriever(
index=train_index,
similarity_top_k=2,
)
test_index = VectorStoreIndex.from_documents(documents=test_documents)
test_retriever = VectorIndexRetriever(
index=test_index,
similarity_top_k=2,
)
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
def create_query_engine(
hf_name: str, retriever: VectorIndexRetriever, hf_llm_generators: dict
) -> RetrieverQueryEngine:
"""Create a RetrieverQueryEngine using the HuggingFaceInferenceAPI LLM"""
if hf_name not in hf_llm_generators:
raise KeyError("model not listed in hf_llm_generators")
llm = HuggingFaceInferenceAPI(
model_name=hf_llm_generators[hf_name],
context_window=2048, # to use refine
token=HUGGING_FACE_TOKEN,
)
return RetrieverQueryEngine.from_args(retriever=retriever, llm=llm)
hf_llm_generators = {
"mistral-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1",
"llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf",
}
train_query_engines = {
mdl: create_query_engine(mdl, train_retriever, hf_llm_generators)
for mdl in hf_llm_generators.keys()
}
test_query_engines = {
mdl: create_query_engine(mdl, test_retriever, hf_llm_generators)
for mdl in hf_llm_generators.keys()
}
import tqdm
import random
train_dataset = []
for q in tqdm.tqdm(train_questions):
model_versus = random.sample(list(train_query_engines.items()), 2)
data_entry = {"question": q}
responses = []
source = None
for name, engine in model_versus:
response = engine.query(q)
response_struct = {}
response_struct["model"] = name
response_struct["text"] = str(response)
if source is not None:
assert source == response.source_nodes[0].node.text[:1000] + "..."
else:
source = response.source_nodes[0].node.text[:1000] + "..."
responses.append(response_struct)
data_entry["answers"] = responses
data_entry["source"] = source
train_dataset.append(data_entry)
from llama_index.llms.openai import OpenAI
from llama_index.finetuning.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core.evaluation import PairwiseComparisonEvaluator
from llama_index.core import Settings
main_finetuning_handler = OpenAIFineTuningHandler()
callback_manager = CallbackManager([main_finetuning_handler])
Settings.callback_manager = callback_manager
llm_4 = OpenAI(temperature=0, model="gpt-4", callback_manager=callback_manager)
gpt4_judge = PairwiseComparisonEvaluator(llm=llm)
for data_entry in tqdm.tqdm(train_dataset):
final_eval_result = await gpt4_judge.aevaluate(
query=data_entry["question"],
response=data_entry["answers"][0]["text"],
second_response=data_entry["answers"][1]["text"],
reference=data_entry["source"],
)
judgement = {}
judgement["llm"] = "gpt_4"
judgement["score"] = final_eval_result.score
judgement["text"] = final_eval_result.response
judgement["source"] = final_eval_result.pairwise_source
data_entry["evaluations"] = [judgement]
display_eval_df(
question=data_entry["question"],
source=data_entry["source"],
answer_a=data_entry["answers"][0],
answer_b=data_entry["answers"][1],
result=final_eval_result,
)
main_finetuning_handler.save_finetuning_events(
"pairwise_finetuning_events.jsonl"
)
import json
with open("pairwise_finetuning_events.jsonl") as f:
combined_finetuning_events = [json.loads(line) for line in f]
finetuning_events = (
[]
) # for storing events using original order of presentation
flipped_finetuning_events = (
[]
) # for storing events using flipped order of presentation
for ix, event in enumerate(combined_finetuning_events):
if ix % 2 == 0: # we always do original ordering first
finetuning_events += [event]
else: # then we flip order and have GPT-4 make another judgement
flipped_finetuning_events += [event]
assert len(finetuning_events) == len(flipped_finetuning_events)
resolved_finetuning_events = []
for ix, data_entry in enumerate(train_dataset):
if data_entry["evaluations"][0]["source"] == "original":
resolved_finetuning_events += [finetuning_events[ix]]
elif data_entry["evaluations"][0]["source"] == "flipped":
resolved_finetuning_events += [flipped_finetuning_events[ix]]
else:
continue
with open("resolved_pairwise_finetuning_events.jsonl", "w") as outfile:
for entry in resolved_finetuning_events:
print(json.dumps(entry), file=outfile)
from llama_index.finetuning import OpenAIFinetuneEngine
finetune_engine = OpenAIFinetuneEngine(
"gpt-3.5-turbo",
"resolved_pairwise_finetuning_events.jsonl",
)
finetune_engine.finetune()
finetune_engine.get_current_job()
import random
test_dataset = []
for q in tqdm.tqdm(test_questions):
model_versus = random.sample(list(test_query_engines.items()), 2)
data_entry = {"question": q}
responses = []
source = None
for name, engine in model_versus:
response = engine.query(q)
response_struct = {}
response_struct["model"] = name
response_struct["text"] = str(response)
if source is not None:
assert source == response.source_nodes[0].node.text[:1000] + "..."
else:
source = response.source_nodes[0].node.text[:1000] + "..."
responses.append(response_struct)
data_entry["answers"] = responses
data_entry["source"] = source
test_dataset.append(data_entry)
for data_entry in tqdm.tqdm(test_dataset):
final_eval_result = await gpt4_judge.aevaluate(
query=data_entry["question"],
response=data_entry["answers"][0]["text"],
second_response=data_entry["answers"][1]["text"],
reference=data_entry["source"],
)
judgement = {}
judgement["llm"] = "gpt_4"
judgement["score"] = final_eval_result.score
judgement["text"] = final_eval_result.response
judgement["source"] = final_eval_result.pairwise_source
data_entry["evaluations"] = [judgement]
from llama_index.core.evaluation import EvaluationResult
ft_llm = finetune_engine.get_finetuned_model()
ft_gpt_3p5_judge = PairwiseComparisonEvaluator(llm=ft_llm)
for data_entry in tqdm.tqdm(test_dataset):
try:
final_eval_result = await ft_gpt_3p5_judge.aevaluate(
query=data_entry["question"],
response=data_entry["answers"][0]["text"],
second_response=data_entry["answers"][1]["text"],
reference=data_entry["source"],
)
except:
final_eval_result = EvaluationResult(
query=data_entry["question"],
response="",
passing=None,
score=0.5,
feedback="",
pairwise_source="output-cannot-be-parsed",
)
judgement = {}
judgement["llm"] = "ft_gpt_3p5"
judgement["score"] = final_eval_result.score
judgement["text"] = final_eval_result.response
judgement["source"] = final_eval_result.pairwise_source
data_entry["evaluations"] += [judgement]
gpt_3p5_llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
import nest_asyncio
nest_asyncio.apply()
import os
HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
get_ipython().system('pip install wikipedia -q')
from llama_index.readers.wikipedia import WikipediaReader
cities = [
"San Francisco",
"Toronto",
"New York",
"Vancouver",
"Montreal",
"Tokyo",
"Singapore",
"Paris",
]
documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in cities]
)
QUESTION_GEN_PROMPT = (
"You are a Teacher/ Professor. Your task is to setup "
"a quiz/examination. Using the provided context, formulate "
"a single question that captures an important fact from the "
"context. Restrict the question to the context information provided."
)
from llama_index.core.evaluation import DatasetGenerator
from llama_index.llms.openai import OpenAI
gpt_35_llm = | OpenAI(model="gpt-3.5-turbo", temperature=0.3) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().system('pip install llama-index')
import time
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["OPENAI_API_KEY"] = "[YOUR_API_KEY]"
from llama_index.core import VectorStoreIndex, download_loader
from llama_index.readers.wikipedia import WikipediaReader
loader = WikipediaReader()
documents = loader.load_data(
pages=[
"Berlin",
"Santiago",
"Moscow",
"Tokyo",
"Jakarta",
"Cairo",
"Bogota",
"Shanghai",
"Damascus",
]
)
len(documents)
start_time = time.perf_counter()
index = VectorStoreIndex.from_documents(documents)
duration = time.perf_counter() - start_time
print(duration)
start_time = time.perf_counter()
index = | VectorStoreIndex(documents, use_async=True) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().system('pip install llama-index')
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) | llama_index.core.load_index_from_storage |
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
from llama_index.core.postprocessor import LLMRerank
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/10k/'")
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'")
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_overlap = 0
Settings.chunk_size = 128
documents = SimpleDirectoryReader(
input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
index = VectorStoreIndex.from_documents(
documents,
)
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core import QueryBundle
import pandas as pd
from IPython.display import display, HTML
from copy import deepcopy
pd.set_option("display.max_colwidth", -1)
def get_retrieved_nodes(
query_str, vector_top_k=10, reranker_top_n=3, with_reranker=False
):
query_bundle = | QueryBundle(query_str) | llama_index.core.QueryBundle |
import os
print(os.listdir("./discord_dumps"))
import json
with open("./discord_dumps/help_channel_dump_05_25_23.json", "r") as f:
data = json.load(f)
print("JSON keys: ", data.keys(), "\n")
print("Message Count: ", len(data["messages"]), "\n")
print("Sample Message Keys: ", data["messages"][0].keys(), "\n")
print("First Message: ", data["messages"][0]["content"], "\n")
print("Last Message: ", data["messages"][-1]["content"])
get_ipython().system('python ./group_conversations.py ./discord_dumps/help_channel_dump_05_25_23.json')
with open("conversation_docs.json", "r") as f:
threads = json.load(f)
print("Thread keys: ", threads[0].keys(), "\n")
print(threads[0]["metadata"], "\n")
print(threads[0]["thread"], "\n")
from llama_index.core import Document
documents = []
for thread in threads:
thread_text = thread["thread"]
thread_id = thread["metadata"]["id"]
timestamp = thread["metadata"]["timestamp"]
documents.append(
| Document(text=thread_text, id_=thread_id, metadata={"date": timestamp}) | llama_index.core.Document |
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 nest_asyncio
nest_asyncio.apply()
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
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-1106", temperature=0.2)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
from llama_index.core import Settings
Settings.chunk_size = 1024
nodes = Settings.node_parser.get_nodes_from_documents(documents)
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
from llama_index.core import SummaryIndex
from llama_index.core import VectorStoreIndex
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = | VectorStoreIndex(nodes, storage_context=storage_context) | llama_index.core.VectorStoreIndex |
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')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["GITHUB_TOKEN"] = ""
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"] = doc.id_
if idx >= limit:
break
docs.append(doc)
from copy import deepcopy
import asyncio
from tqdm.asyncio import tqdm_asyncio
from llama_index.core.indices import SummaryIndex
from llama_index.core import Document, ServiceContext
from llama_index.llms.openai import OpenAI
from llama_index.core.async_utils import run_jobs
async def aprocess_doc(doc, include_summary: bool = True):
"""Process doc."""
print(f"Processing {doc.id_}")
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,
"index_id": doc.id_,
}
summary_index = SummaryIndex.from_documents([doc])
query_str = "Give a one-sentence concise summary of this issue."
query_engine = summary_index.as_query_engine(
service_context=ServiceContext.from_defaults(llm= | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index llama-index-vector-stores-qdrant -q')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data')
get_ipython().system('wget "https://arxiv.org/pdf/2402.09353.pdf" -O "./data/dorav1.pdf"')
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4")
response = llm.complete("What is DoRA?")
print(response.text)
"""Load the data.
With llama-index, before any transformations are applied,
data is loaded in the `Document` abstraction, which is
a container that holds the text of the document.
"""
from llama_index.core import SimpleDirectoryReader
loader = | SimpleDirectoryReader(input_dir="./data") | llama_index.core.SimpleDirectoryReader |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install llama-index-core')
get_ipython().system('pip install --quiet transformers torch')
get_ipython().system('pip install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index-llms-openai')
get_ipython().system('pip install llama-index-postprocessor-colbert-rerank')
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'")
import os
os.environ["OPENAI_API_KEY"] = "sk-"
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().system('pip install llama-index-multi-modal-llms-ollama')
get_ipython().system('pip install llama-index-readers-file')
get_ipython().system('pip install unstructured')
get_ipython().system('pip install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index-vector-stores-qdrant')
get_ipython().system('pip install llama-index-embeddings-clip')
from llama_index.multi_modal_llms.ollama import OllamaMultiModal
mm_model = OllamaMultiModal(model="llava:13b")
from pathlib import Path
from llama_index.core import SimpleDirectoryReader
from PIL import Image
import matplotlib.pyplot as plt
input_image_path = Path("restaurant_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1GlqcNJhGGbwLKjJK1QJ_nyswCTQ2K2Fq" -O ./restaurant_images/fried_chicken.png')
image_documents = SimpleDirectoryReader("./restaurant_images").load_data()
imageUrl = "./restaurant_images/fried_chicken.png"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from pydantic import BaseModel
class Restaurant(BaseModel):
"""Data model for an restaurant."""
restaurant: str
food: str
discount: str
price: str
rating: str
review: str
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
{query_str}
Return the answer as a Pydantic object. The Pydantic schema is given below:
"""
mm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Restaurant),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=mm_model,
verbose=True,
)
response = mm_program(query_str="Can you summarize what is in the image?")
for res in response:
print(res)
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=1THe1qqM61lretr9N3BmINc_NWDvuthYf" -O shanghai.jpg')
from pathlib import Path
from llama_index.readers.file import UnstructuredReader
from llama_index.core.schema import ImageDocument
loader = UnstructuredReader()
documents = loader.load_data(file=Path("tesla_2021_10k.htm"))
image_doc = ImageDocument(image_path="./shanghai.jpg")
from llama_index.core import VectorStoreIndex
from llama_index.core.embeddings import resolve_embed_model
embed_model = resolve_embed_model("local:BAAI/bge-m3")
vector_index = VectorStoreIndex.from_documents(
documents, embed_model=embed_model
)
query_engine = vector_index.as_query_engine()
from llama_index.core.prompts import PromptTemplate
from llama_index.core.query_pipeline import QueryPipeline, FnComponent
query_prompt_str = """\
Please expand the initial statement using the provided context from the Tesla 10K report.
{initial_statement}
"""
query_prompt_tmpl = PromptTemplate(query_prompt_str)
qp = QueryPipeline(
modules={
"mm_model": mm_model.as_query_component(
partial={"image_documents": [image_doc]}
),
"query_prompt": query_prompt_tmpl,
"query_engine": query_engine,
},
verbose=True,
)
qp.add_chain(["mm_model", "query_prompt", "query_engine"])
rag_response = qp.run("Which Tesla Factory is shown in the image?")
print(f"> Retrieval Augmented Response: {rag_response}")
rag_response.source_nodes[1].get_content()
get_ipython().system('wget "https://drive.usercontent.google.com/download?id=1qQDcaKuzgRGuEC1kxgYL_4mx7vG-v4gC&export=download&authuser=1&confirm=t&uuid=f944e95f-a31f-4b55-b68f-8ea67a6e90e5&at=APZUnTVZ6n1aOg7rtkcjBjw7Pt1D:1707010667927" -O mixed_wiki.zip')
get_ipython().system('unzip mixed_wiki.zip')
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O ./mixed_wiki/tesla_2021_10k.htm')
from llama_index.core.indices.multi_modal.base import (
MultiModalVectorStoreIndex,
)
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.embeddings.clip import ClipEmbedding
import qdrant_client
from llama_index import (
SimpleDirectoryReader,
)
client = qdrant_client.QdrantClient(path="qdrant_mm_db")
text_store = QdrantVectorStore(
client=client, collection_name="text_collection"
)
image_store = QdrantVectorStore(
client=client, collection_name="image_collection"
)
storage_context = StorageContext.from_defaults(
vector_store=text_store, image_store=image_store
)
image_embed_model = ClipEmbedding()
documents = SimpleDirectoryReader("./mixed_wiki/").load_data()
index = MultiModalVectorStoreIndex.from_documents(
documents,
storage_context=storage_context,
image_embed_model=image_embed_model,
)
from llama_index.core.prompts import PromptTemplate
from llama_index.core.query_engine import SimpleMultiModalQueryEngine
qa_tmpl_str = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"answer the query.\n"
"Query: {query_str}\n"
"Answer: "
)
qa_tmpl = PromptTemplate(qa_tmpl_str)
query_engine = index.as_query_engine(
multi_modal_llm=mm_model, text_qa_template=qa_tmpl
)
query_str = "Tell me more about the Porsche"
response = query_engine.query(query_str)
print(str(response))
from PIL import Image
import matplotlib.pyplot as plt
import os
def plot_images(image_paths):
images_shown = 0
plt.figure(figsize=(16, 9))
for img_path in image_paths:
if os.path.isfile(img_path):
image = Image.open(img_path)
plt.subplot(2, 3, images_shown + 1)
plt.imshow(image)
plt.xticks([])
plt.yticks([])
images_shown += 1
if images_shown >= 9:
break
from llama_index.core.response.notebook_utils import display_source_node
for text_node in response.metadata["text_nodes"]:
| display_source_node(text_node, source_length=200) | llama_index.core.response.notebook_utils.display_source_node |
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(model="gpt-4")
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(data) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-docarray')
get_ipython().system('pip install llama-index')
import os
import sys
import logging
import textwrap
import warnings
warnings.filterwarnings("ignore")
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from llama_index.core import (
GPTVectorStoreIndex,
SimpleDirectoryReader,
Document,
)
from llama_index.vector_stores.docarray import DocArrayHnswVectorStore
from IPython.display import Markdown, display
import os
os.environ["OPENAI_API_KEY"] = "<your openai 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].doc_hash,
)
from llama_index.core import StorageContext
vector_store = DocArrayHnswVectorStore(work_dir="hnsw_index")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = GPTVectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text="The Shawshank Redemption",
metadata={
"author": "Stephen King",
"theme": "Friendship",
},
),
TextNode(
text="The Godfather",
metadata={
"director": "Francis Ford Coppola",
"theme": "Mafia",
},
),
TextNode(
text="Inception",
metadata={
"director": "Christopher Nolan",
},
),
]
from llama_index.core import StorageContext
vector_store = DocArrayHnswVectorStore(work_dir="hnsw_filters")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = GPTVectorStoreIndex(nodes, storage_context=storage_context)
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
filters = MetadataFilters(
filters=[ | ExactMatchFilter(key="theme", value="Mafia") | llama_index.core.vector_stores.ExactMatchFilter |
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 nest_asyncio
nest_asyncio.apply()
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
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-1106", temperature=0.2)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
from llama_index.core import Settings
Settings.chunk_size = 1024
nodes = Settings.node_parser.get_nodes_from_documents(documents)
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
from llama_index.core import SummaryIndex
from llama_index.core import VectorStoreIndex
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
list_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize",
use_async=True,
)
vector_query_engine = vector_index.as_query_engine()
from llama_index.core.tools import QueryEngineTool
list_tool = QueryEngineTool.from_defaults(
query_engine=list_query_engine,
description=(
"Useful for summarization questions related to Paul Graham eassy on"
" What I Worked On."
),
)
vector_tool = QueryEngineTool.from_defaults(
query_engine=vector_query_engine,
description=(
"Useful for retrieving specific context from Paul Graham essay on What"
" I Worked On."
),
)
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector
from llama_index.core.selectors import (
PydanticMultiSelector,
PydanticSingleSelector,
)
query_engine = RouterQueryEngine(
selector=PydanticSingleSelector.from_defaults(),
query_engine_tools=[
list_tool,
vector_tool,
],
)
response = query_engine.query("What is the summary of the document?")
print(str(response))
response = query_engine.query("What did Paul Graham do after RICS?")
print(str(response))
query_engine = RouterQueryEngine(
selector= | LLMSingleSelector.from_defaults() | llama_index.core.selectors.LLMSingleSelector.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai-legacy')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence
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/march"
)
march_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/june"
)
june_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/sept"
)
sept_index = load_index_from_storage(storage_context)
index_loaded = True
except:
index_loaded = False
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'")
if not index_loaded:
march_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_march_2022.pdf"]
).load_data()
june_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_june_2022.pdf"]
).load_data()
sept_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_sept_2022.pdf"]
).load_data()
march_index = | VectorStoreIndex.from_documents(march_docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
import nest_asyncio
nest_asyncio.apply()
import os
HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
get_ipython().system('pip install wikipedia -q')
from llama_index.readers.wikipedia import WikipediaReader
cities = [
"San Francisco",
"Toronto",
"New York",
"Vancouver",
"Montreal",
"Tokyo",
"Singapore",
"Paris",
]
documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in cities]
)
QUESTION_GEN_PROMPT = (
"You are a Teacher/ Professor. Your task is to setup "
"a quiz/examination. Using the provided context, formulate "
"a single question that captures an important fact from the "
"context. Restrict the question to the context information provided."
)
from llama_index.core.evaluation import DatasetGenerator
from llama_index.llms.openai import OpenAI
gpt_35_llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
dataset_generator = DatasetGenerator.from_documents(
documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=gpt_35_llm,
num_questions_per_chunk=25,
)
qrd = dataset_generator.generate_dataset_from_nodes(num=350)
from llama_index.core import VectorStoreIndex
from llama_index.core.retrievers import VectorIndexRetriever
the_index = VectorStoreIndex.from_documents(documents=documents)
the_retriever = VectorIndexRetriever(
index=the_index,
similarity_top_k=2,
)
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
llm = HuggingFaceInferenceAPI(
model_name="meta-llama/Llama-2-7b-chat-hf",
context_window=2048, # to use refine
token=HUGGING_FACE_TOKEN,
)
query_engine = RetrieverQueryEngine.from_args(retriever=the_retriever, llm=llm)
import tqdm
train_dataset = []
num_train_questions = int(0.65 * len(qrd.qr_pairs))
for q, a in tqdm.tqdm(qrd.qr_pairs[:num_train_questions]):
data_entry = {"question": q, "reference": a}
response = query_engine.query(q)
response_struct = {}
response_struct["model"] = "llama-2"
response_struct["text"] = str(response)
response_struct["context"] = (
response.source_nodes[0].node.text[:1000] + "..."
)
data_entry["response_data"] = response_struct
train_dataset.append(data_entry)
from llama_index.llms.openai import OpenAI
from llama_index.finetuning.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core.evaluation import CorrectnessEvaluator
finetuning_handler = OpenAIFineTuningHandler()
callback_manager = CallbackManager([finetuning_handler])
gpt_4_llm = OpenAI(
temperature=0, model="gpt-4", callback_manager=callback_manager
)
gpt4_judge = CorrectnessEvaluator(llm=gpt_4_llm)
import tqdm
for data_entry in tqdm.tqdm(train_dataset):
eval_result = await gpt4_judge.aevaluate(
query=data_entry["question"],
response=data_entry["response_data"]["text"],
context=data_entry["response_data"]["context"],
reference=data_entry["reference"],
)
judgement = {}
judgement["llm"] = "gpt_4"
judgement["score"] = eval_result.score
judgement["text"] = eval_result.response
data_entry["evaluations"] = [judgement]
finetuning_handler.save_finetuning_events("correction_finetuning_events.jsonl")
from llama_index.finetuning import OpenAIFinetuneEngine
finetune_engine = OpenAIFinetuneEngine(
"gpt-3.5-turbo",
"correction_finetuning_events.jsonl",
)
finetune_engine.finetune()
finetune_engine.get_current_job()
test_dataset = []
for q, a in tqdm.tqdm(qrd.qr_pairs[num_train_questions:]):
data_entry = {"question": q, "reference": a}
response = query_engine.query(q)
response_struct = {}
response_struct["model"] = "llama-2"
response_struct["text"] = str(response)
response_struct["context"] = (
response.source_nodes[0].node.text[:1000] + "..."
)
data_entry["response_data"] = response_struct
test_dataset.append(data_entry)
for data_entry in tqdm.tqdm(test_dataset):
eval_result = await gpt4_judge.aevaluate(
query=data_entry["question"],
response=data_entry["response_data"]["text"],
context=data_entry["response_data"]["context"],
reference=data_entry["reference"],
)
judgement = {}
judgement["llm"] = "gpt_4"
judgement["score"] = eval_result.score
judgement["text"] = eval_result.response
data_entry["evaluations"] = [judgement]
from llama_index.core.evaluation import EvaluationResult
ft_llm = finetune_engine.get_finetuned_model()
ft_gpt_3p5_judge = | CorrectnessEvaluator(llm=ft_llm) | llama_index.core.evaluation.CorrectnessEvaluator |
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)
def get_table_context_str(table_schema_objs: List[SQLTableSchema]):
"""Get table context string."""
context_strs = []
for table_schema_obj in table_schema_objs:
table_info = sql_database.get_single_table_info(
table_schema_obj.table_name
)
if table_schema_obj.context_str:
table_opt_context = " The table description is: "
table_opt_context += table_schema_obj.context_str
table_info += table_opt_context
context_strs.append(table_info)
return "\n\n".join(context_strs)
table_parser_component = FnComponent(fn=get_table_context_str)
from llama_index.core.prompts.default_prompts import DEFAULT_TEXT_TO_SQL_PROMPT
from llama_index.core import PromptTemplate
from llama_index.core.query_pipeline import FnComponent
from llama_index.core.llms import ChatResponse
def parse_response_to_sql(response: ChatResponse) -> str:
"""Parse response to SQL."""
response = response.message.content
sql_query_start = response.find("SQLQuery:")
if sql_query_start != -1:
response = response[sql_query_start:]
if response.startswith("SQLQuery:"):
response = response[len("SQLQuery:") :]
sql_result_start = response.find("SQLResult:")
if sql_result_start != -1:
response = response[:sql_result_start]
return response.strip().strip("```").strip()
sql_parser_component = FnComponent(fn=parse_response_to_sql)
text2sql_prompt = DEFAULT_TEXT_TO_SQL_PROMPT.partial_format(
dialect=engine.dialect.name
)
print(text2sql_prompt.template)
response_synthesis_prompt_str = (
"Given an input question, synthesize a response from the query results.\n"
"Query: {query_str}\n"
"SQL: {sql_query}\n"
"SQL Response: {context_str}\n"
"Response: "
)
response_synthesis_prompt = PromptTemplate(
response_synthesis_prompt_str,
)
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
import nest_asyncio
nest_asyncio.apply()
import os
HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
get_ipython().system('pip install wikipedia -q')
from llama_index.readers.wikipedia import WikipediaReader
cities = [
"San Francisco",
"Toronto",
"New York",
"Vancouver",
"Montreal",
"Tokyo",
"Singapore",
"Paris",
]
documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in cities]
)
QUESTION_GEN_PROMPT = (
"You are a Teacher/ Professor. Your task is to setup "
"a quiz/examination. Using the provided context, formulate "
"a single question that captures an important fact from the "
"context. Restrict the question to the context information provided."
)
from llama_index.core.evaluation import DatasetGenerator
from llama_index.llms.openai import OpenAI
gpt_35_llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
dataset_generator = DatasetGenerator.from_documents(
documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=gpt_35_llm,
num_questions_per_chunk=25,
)
qrd = dataset_generator.generate_dataset_from_nodes(num=350)
from llama_index.core import VectorStoreIndex
from llama_index.core.retrievers import VectorIndexRetriever
the_index = VectorStoreIndex.from_documents(documents=documents)
the_retriever = VectorIndexRetriever(
index=the_index,
similarity_top_k=2,
)
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
llm = HuggingFaceInferenceAPI(
model_name="meta-llama/Llama-2-7b-chat-hf",
context_window=2048, # to use refine
token=HUGGING_FACE_TOKEN,
)
query_engine = RetrieverQueryEngine.from_args(retriever=the_retriever, llm=llm)
import tqdm
train_dataset = []
num_train_questions = int(0.65 * len(qrd.qr_pairs))
for q, a in tqdm.tqdm(qrd.qr_pairs[:num_train_questions]):
data_entry = {"question": q, "reference": a}
response = query_engine.query(q)
response_struct = {}
response_struct["model"] = "llama-2"
response_struct["text"] = str(response)
response_struct["context"] = (
response.source_nodes[0].node.text[:1000] + "..."
)
data_entry["response_data"] = response_struct
train_dataset.append(data_entry)
from llama_index.llms.openai import OpenAI
from llama_index.finetuning.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core.evaluation import CorrectnessEvaluator
finetuning_handler = OpenAIFineTuningHandler()
callback_manager = | CallbackManager([finetuning_handler]) | 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-web')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-tools-metaphor')
get_ipython().system('wget "https://images.openai.com/blob/a2e49de2-ba5b-4869-9c2d-db3b4b5dcc19/new-models-and-developer-products-announced-at-devday.jpg?width=2000" -O other_images/openai/dev_day.png')
get_ipython().system('wget "https://drive.google.com/uc\\?id\\=1B4f5ZSIKN0zTTPPRlZ915Ceb3_uF9Zlq\\&export\\=download" -O other_images/adidas.png')
from llama_index.readers.web import SimpleWebPageReader
url = "https://openai.com/blog/new-models-and-developer-products-announced-at-devday"
reader = SimpleWebPageReader(html_to_text=True)
documents = reader.load_data(urls=[url])
from llama_index.llms.openai import OpenAI
from llama_index.core import VectorStoreIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
vector_index = VectorStoreIndex.from_documents(
documents,
)
query_tool = QueryEngineTool(
query_engine=vector_index.as_query_engine(),
metadata=ToolMetadata(
name=f"vector_tool",
description=(
"Useful to lookup new features announced by OpenAI"
),
),
)
from llama_index.core.agent.react_multimodal.step import (
MultimodalReActAgentWorker,
)
from llama_index.core.agent import AgentRunner
from llama_index.core.multi_modal_llms import MultiModalLLM
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.core.agent import Task
mm_llm = OpenAIMultiModal(model="gpt-4-vision-preview", max_new_tokens=1000)
react_step_engine = MultimodalReActAgentWorker.from_tools(
[query_tool],
multi_modal_llm=mm_llm,
verbose=True,
)
agent = AgentRunner(react_step_engine)
query_str = (
"The photo shows some new features released by OpenAI. "
"Can you pinpoint the features in the photo and give more details using relevant tools?"
)
from llama_index.core.schema import ImageDocument
image_document = ImageDocument(image_path="other_images/openai/dev_day.png")
task = agent.create_task(
query_str,
extra_state={"image_docs": [image_document]},
)
def execute_step(agent: AgentRunner, task: Task):
step_output = agent.run_step(task.task_id)
if step_output.is_last:
response = agent.finalize_response(task.task_id)
print(f"> Agent finished: {str(response)}")
return response
else:
return None
def execute_steps(agent: AgentRunner, task: Task):
response = execute_step(agent, task)
while response is None:
response = execute_step(agent, task)
return response
response = execute_step(agent, task)
response = execute_step(agent, task)
print(str(response))
from llama_index.tools.metaphor import MetaphorToolSpec
from llama_index.core.agent.react_multimodal.step import (
MultimodalReActAgentWorker,
)
from llama_index.core.agent import AgentRunner
from llama_index.core.multi_modal_llms import MultiModalLLM
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.core.agent import Task
metaphor_tool_spec = MetaphorToolSpec(
api_key="<api_key>",
)
metaphor_tools = metaphor_tool_spec.to_tool_list()
mm_llm = | OpenAIMultiModal(model="gpt-4-vision-preview", max_new_tokens=1000) | llama_index.multi_modal_llms.openai.OpenAIMultiModal |
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 openai
import os
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(data) | 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-llms-cohere')
get_ipython().system('pip install llama-index')
from llama_index.llms.cohere import Cohere
api_key = "Your api key"
resp = Cohere(api_key=api_key).complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.cohere import Cohere
messages = [
ChatMessage(role="user", content="hello there"),
ChatMessage(
role="assistant", content="Arrrr, matey! How can I help ye today?"
),
ChatMessage(role="user", content="What is your name"),
]
resp = Cohere(api_key=api_key).chat(
messages, preamble_override="You are a pirate with a colorful personality"
)
print(resp)
from llama_index.llms.openai import OpenAI
llm = | Cohere(api_key=api_key) | llama_index.llms.cohere.Cohere |
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().system("pip install llama-index 'google-generativeai>=0.3.0' matplotlib qdrant_client")
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from pathlib import Path
import random
from typing import Optional
def get_image_files(
dir_path, sample: Optional[int] = 10, shuffle: bool = False
):
dir_path = Path(dir_path)
image_paths = []
for image_path in dir_path.glob("*.jpg"):
image_paths.append(image_path)
random.shuffle(image_paths)
if sample:
return image_paths[:sample]
else:
return image_paths
image_files = get_image_files("SROIE2019/test/img", sample=100)
from pydantic import BaseModel, Field
class ReceiptInfo(BaseModel):
company: str = Field(..., description="Company name")
date: str = Field(..., description="Date field in DD/MM/YYYY format")
address: str = Field(..., description="Address")
total: float = Field(..., description="total amount")
currency: str = Field(
..., description="Currency of the country (in abbreviations)"
)
summary: str = Field(
...,
description="Extracted text summary of the receipt, including items purchased, the type of store, the location, and any other notable salient features (what does the purchase seem to be for?).",
)
from llama_index.multi_modal_llms.gemini import GeminiMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
Can you summarize the image and return a response \
with the following JSON format: \
"""
async def pydantic_gemini(output_class, image_documents, prompt_template_str):
gemini_llm = GeminiMultiModal(
api_key=GOOGLE_API_KEY, model_name="models/gemini-pro-vision"
)
llm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(output_class),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=gemini_llm,
verbose=True,
)
response = await llm_program.acall()
return response
from llama_index.core import SimpleDirectoryReader
from llama_index.core.async_utils import run_jobs
async def aprocess_image_file(image_file):
print(f"Image file: {image_file}")
img_docs = SimpleDirectoryReader(input_files=[image_file]).load_data()
output = await pydantic_gemini(ReceiptInfo, img_docs, prompt_template_str)
return output
async def aprocess_image_files(image_files):
"""Process metadata on image files."""
new_docs = []
tasks = []
for image_file in image_files:
task = aprocess_image_file(image_file)
tasks.append(task)
outputs = await run_jobs(tasks, show_progress=True, workers=5)
return outputs
outputs = await aprocess_image_files(image_files)
outputs[4]
from llama_index.core.schema import TextNode
from typing import List
def get_nodes_from_objs(
objs: List[ReceiptInfo], image_files: List[str]
) -> TextNode:
"""Get nodes from objects."""
nodes = []
for image_file, obj in zip(image_files, objs):
node = TextNode(
text=obj.summary,
metadata={
"company": obj.company,
"date": obj.date,
"address": obj.address,
"total": obj.total,
"currency": obj.currency,
"image_file": str(image_file),
},
excluded_embed_metadata_keys=["image_file"],
excluded_llm_metadata_keys=["image_file"],
)
nodes.append(node)
return nodes
nodes = get_nodes_from_objs(outputs, image_files)
print(nodes[0].get_content(metadata_mode="all"))
import qdrant_client
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.gemini import GeminiEmbedding
from llama_index.llms.gemini import Gemini
from llama_index.core import Settings
client = qdrant_client.QdrantClient(path="qdrant_gemini")
vector_store = | QdrantVectorStore(client=client, collection_name="collection") | llama_index.vector_stores.qdrant.QdrantVectorStore |
get_ipython().run_line_magic('', 'pip install llama-index-llms-groq')
get_ipython().system('pip install llama-index')
from llama_index.llms.groq import Groq
llm = | Groq(model="mixtral-8x7b-32768", api_key="your_api_key") | llama_index.llms.groq.Groq |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
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.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.core import SummaryIndex
Settings.llm = OpenAI()
Settings.chunk_size = 1024
nodes = Settings.node_parser.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
summary_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize",
use_async=True,
)
vector_query_engine = vector_index.as_query_engine()
from llama_index.core.tools import QueryEngineTool
summary_tool = QueryEngineTool.from_defaults(
query_engine=summary_query_engine,
name="summary_tool",
description=(
"Useful for summarization questions related to the author's life"
),
)
vector_tool = QueryEngineTool.from_defaults(
query_engine=vector_query_engine,
name="vector_tool",
description=(
"Useful for retrieving specific context to answer specific questions about the author's life"
),
)
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="QA bot",
instructions="You are a bot designed to answer questions about the author",
openai_tools=[],
tools=[summary_tool, vector_tool],
verbose=True,
run_retrieve_sleep_time=1.0,
)
response = agent.chat("Can you give me a summary about the author's life?")
print(str(response))
response = agent.query("What did the author do after RICS?")
print(str(response))
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
try:
pinecone.create_index(
"quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart")
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",
},
),
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-vector-stores-epsilla')
get_ipython().system('pip/pip3 install pyepsilla')
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.epsilla import EpsillaVectorStore
import textwrap
import openai
import getpass
OPENAI_API_KEY = getpass.getpass("OpenAI API Key:")
openai.api_key = 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(f"Total documents: {len(documents)}")
print(f"First document, id: {documents[0].doc_id}")
print(f"First document, hash: {documents[0].hash}")
from pyepsilla import vectordb
client = vectordb.Client()
vector_store = EpsillaVectorStore(client=client, db_path="/tmp/llamastore")
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("Who is the author?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("How did the author learn about AI?")
print(textwrap.fill(str(response), 100))
vector_store = EpsillaVectorStore(client=client, overwrite=True)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
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-llms-openai')
import camelot
from llama_index.core import VectorStoreIndex
from llama_index.core.query_engine import PandasQueryEngine
from llama_index.core.schema import IndexNode
from llama_index.llms.openai import OpenAI
from llama_index.readers.file import PyMuPDFReader
from typing import List
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
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")
file_path = "billionaires_page.pdf"
reader = PyMuPDFReader()
docs = reader.load(file_path)
def get_tables(path: str, pages: List[int]):
table_dfs = []
for page in pages:
table_list = camelot.read_pdf(path, pages=str(page))
table_df = table_list[0].df
table_df = (
table_df.rename(columns=table_df.iloc[0])
.drop(table_df.index[0])
.reset_index(drop=True)
)
table_dfs.append(table_df)
return table_dfs
table_dfs = get_tables(file_path, pages=[3, 25])
table_dfs[0]
table_dfs[1]
llm = OpenAI(model="gpt-4")
df_query_engines = [
PandasQueryEngine(table_df, llm=llm) for table_df in table_dfs
]
response = df_query_engines[0].query(
"What's the net worth of the second richest billionaire in 2023?"
)
print(str(response))
response = df_query_engines[1].query(
"How many billionaires were there in 2009?"
)
print(str(response))
from llama_index.core import Settings
doc_nodes = Settings.node_parser.get_nodes_from_documents(docs)
summaries = [
(
"This node provides information about the world's richest billionaires"
" in 2023"
),
(
"This node provides information on the number of billionaires and"
" their combined net worth from 2000 to 2023."
),
]
df_nodes = [
IndexNode(text=summary, index_id=f"pandas{idx}")
for idx, summary in enumerate(summaries)
]
df_id_query_engine_mapping = {
f"pandas{idx}": df_query_engine
for idx, df_query_engine in enumerate(df_query_engines)
}
vector_index = | VectorStoreIndex(doc_nodes + df_nodes) | llama_index.core.VectorStoreIndex |
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) | llama_index.core.vector_stores.google.generativeai.genai_extension.delete_corpus |
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) | llama_index.core.ServiceContext.from_defaults |
get_ipython().system('pip install llama-index-multi-modal-llms-ollama')
get_ipython().system('pip install llama-index-readers-file')
get_ipython().system('pip install unstructured')
get_ipython().system('pip install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index-vector-stores-qdrant')
get_ipython().system('pip install llama-index-embeddings-clip')
from llama_index.multi_modal_llms.ollama import OllamaMultiModal
mm_model = OllamaMultiModal(model="llava:13b")
from pathlib import Path
from llama_index.core import SimpleDirectoryReader
from PIL import Image
import matplotlib.pyplot as plt
input_image_path = Path("restaurant_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1GlqcNJhGGbwLKjJK1QJ_nyswCTQ2K2Fq" -O ./restaurant_images/fried_chicken.png')
image_documents = SimpleDirectoryReader("./restaurant_images").load_data()
imageUrl = "./restaurant_images/fried_chicken.png"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from pydantic import BaseModel
class Restaurant(BaseModel):
"""Data model for an restaurant."""
restaurant: str
food: str
discount: str
price: str
rating: str
review: str
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
{query_str}
Return the answer as a Pydantic object. The Pydantic schema is given below:
"""
mm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Restaurant),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=mm_model,
verbose=True,
)
response = mm_program(query_str="Can you summarize what is in the image?")
for res in response:
print(res)
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=1THe1qqM61lretr9N3BmINc_NWDvuthYf" -O shanghai.jpg')
from pathlib import Path
from llama_index.readers.file import UnstructuredReader
from llama_index.core.schema import ImageDocument
loader = UnstructuredReader()
documents = loader.load_data(file=Path("tesla_2021_10k.htm"))
image_doc = | ImageDocument(image_path="./shanghai.jpg") | llama_index.core.schema.ImageDocument |
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"),
ChatMessage(role="user", content="What can you do?"),
]
llm1 = pk.LLMOptions(
provider="openai",
model="gpt-4",
retry_settings={"on_status_codes": [429, 500], "attempts": 2},
virtual_key=openai_virtual_key_a,
)
llm2 = pk.LLMOptions(
provider="openai",
model="gpt-3.5-turbo",
virtual_key=openai_virtual_key_b,
)
portkey_client.add_llms(llm_params=[llm1, llm2])
print("Testing Fallback & Retry functionality:")
response = portkey_client.chat(messages)
print(response)
portkey_client = Portkey(mode="ab_test")
messages = [
| ChatMessage(role="system", content="You are a helpful assistant") | llama_index.core.llms.ChatMessage |
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)
refine_template_str = (
"The original question is as follows: {query_str}\nWe have provided an"
" existing answer: {existing_answer}\nWe have the opportunity to refine"
" the existing answer (only if needed) with some more context"
" below.\n------------\n{context_msg}\n------------\nUsing both the new"
" context and your own knowledge, update or repeat the existing answer.\n"
)
refine_template = | PromptTemplate(refine_template_str) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-dynamodb')
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.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/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
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().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,
)
from llama_index.core import SummaryIndex
from llama_index.core.response.notebook_utils import display_response
from llama_index.llms.openai import OpenAI
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.core import Document
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
docs0 = loader.load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
llm = OpenAI(model="gpt-4")
chunk_sizes = [128, 256, 512, 1024]
nodes_list = []
vector_indices = []
for chunk_size in chunk_sizes:
print(f"Chunk Size: {chunk_size}")
splitter = SentenceSplitter(chunk_size=chunk_size)
nodes = splitter.get_nodes_from_documents(docs)
for node in nodes:
node.metadata["chunk_size"] = chunk_size
node.excluded_embed_metadata_keys = ["chunk_size"]
node.excluded_llm_metadata_keys = ["chunk_size"]
nodes_list.append(nodes)
vector_index = VectorStoreIndex(nodes)
vector_indices.append(vector_index)
from llama_index.core.tools import RetrieverTool
from llama_index.core.schema import IndexNode
retriever_dict = {}
retriever_nodes = []
for chunk_size, vector_index in zip(chunk_sizes, vector_indices):
node_id = f"chunk_{chunk_size}"
node = IndexNode(
text=(
"Retrieves relevant context from the Llama 2 paper (chunk size"
f" {chunk_size})"
),
index_id=node_id,
)
retriever_nodes.append(node)
retriever_dict[node_id] = vector_index.as_retriever()
from llama_index.core.selectors import PydanticMultiSelector
from llama_index.core.retrievers import RouterRetriever
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core import SummaryIndex
summary_index = | SummaryIndex(retriever_nodes) | llama_index.core.SummaryIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-vectara')
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.schema import TextNode
from llama_index.core.indices.managed.types import ManagedIndexQueryMode
from llama_index.indices.managed.vectara import VectaraIndex
from llama_index.indices.managed.vectara import VectaraAutoRetriever
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo
from llama_index.llms.openai import OpenAI
nodes = [
TextNode(
text=(
"A pragmatic paleontologist touring an almost complete theme park on an island "
+ "in Central America is tasked with protecting a couple of kids after a power "
+ "failure causes the park's cloned dinosaurs to run loose."
),
metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
),
TextNode(
text=(
"A thief who steals corporate secrets through the use of dream-sharing technology "
+ "is given the inverse task of planting an idea into the mind of a C.E.O., "
+ "but his tragic past may doom the project and his team to disaster."
),
metadata={
"year": 2010,
"director": "Christopher Nolan",
"rating": 8.2,
},
),
TextNode(
text="Barbie suffers a crisis that leads her to question her world and her existence.",
metadata={
"year": 2023,
"director": "Greta Gerwig",
"genre": "fantasy",
"rating": 9.5,
},
),
| TextNode(
text=(
"A cowboy doll is profoundly threatened and jealous when a new spaceman action "
+ "figure supplants him as top toy in a boy's bedroom."
) | 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-llms-langchain')
get_ipython().system('pip install llama-index')
from langchain.chat_models import ChatAnyscale, ChatOpenAI
from llama_index.llms.langchain import LangChainLLM
from llama_index.core import PromptTemplate
llm = LangChainLLM(ChatOpenAI())
stream = await llm.astream(PromptTemplate("Hi, write a short story"))
async for token in stream:
print(token, end="")
llm = LangChainLLM(ChatAnyscale())
stream = llm.stream(
| PromptTemplate("Hi, Which NFL team have most Super Bowl wins") | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_response
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine
import os
os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY"
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0.2, model="gpt-3.5-turbo")
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-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") | llama_index.core.load_index_from_storage |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-txtai')
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 txtai
txtai_index = txtai.ann.ANNFactory.create({"backend": "numpy"})
from llama_index.core import (
SimpleDirectoryReader,
load_index_from_storage,
VectorStoreIndex,
StorageContext,
)
from llama_index.vector_stores.txtai import TxtaiVectorStore
from IPython.display import Markdown, display
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()
vector_store = TxtaiVectorStore(txtai_index=txtai_index)
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().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core.agent import (
CustomSimpleAgentWorker,
Task,
AgentChatResponse,
)
from typing import Dict, Any, List, Tuple, Optional
from llama_index.core.tools import BaseTool, QueryEngineTool
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core import ChatPromptTemplate, PromptTemplate
from llama_index.core.selectors import PydanticSingleSelector
from llama_index.core.bridge.pydantic import Field, BaseModel
from llama_index.core.llms import ChatMessage, MessageRole
DEFAULT_PROMPT_STR = """
Given previous question/response pairs, please determine if an error has occurred in the response, and suggest \
a modified question that will not trigger the error.
Examples of modified questions:
- The question itself is modified to elicit a non-erroneous response
- The question is augmented with context that will help the downstream system better answer the question.
- The question is augmented with examples of negative responses, or other negative questions.
An error means that either an exception has triggered, or the response is completely irrelevant to the question.
Please return the evaluation of the response in the following JSON format.
"""
def get_chat_prompt_template(
system_prompt: str, current_reasoning: Tuple[str, str]
) -> ChatPromptTemplate:
system_msg = ChatMessage(role=MessageRole.SYSTEM, content=system_prompt)
messages = [system_msg]
for raw_msg in current_reasoning:
if raw_msg[0] == "user":
messages.append(
ChatMessage(role=MessageRole.USER, content=raw_msg[1])
)
else:
messages.append(
ChatMessage(role=MessageRole.ASSISTANT, content=raw_msg[1])
)
return ChatPromptTemplate(message_templates=messages)
class ResponseEval(BaseModel):
"""Evaluation of whether the response has an error."""
has_error: bool = Field(
..., description="Whether the response has an error."
)
new_question: str = Field(..., description="The suggested new question.")
explanation: str = Field(
...,
description=(
"The explanation for the error as well as for the new question."
"Can include the direct stack trace as well."
),
)
from llama_index.core.bridge.pydantic import PrivateAttr
class RetryAgentWorker(CustomSimpleAgentWorker):
"""Agent worker that adds a retry layer on top of a router.
Continues iterating until there's no errors / task is done.
"""
prompt_str: str = Field(default=DEFAULT_PROMPT_STR)
max_iterations: int = | Field(default=10) | llama_index.core.bridge.pydantic.Field |
import openai
openai.api_key = "sk-key"
from llama_index.agent import OpenAIAgent
from llama_index.tools.code_interpreter.base import CodeInterpreterToolSpec
code_spec = CodeInterpreterToolSpec()
tools = code_spec.to_tool_list()
agent = | OpenAIAgent.from_tools(tools, verbose=True) | llama_index.agent.OpenAIAgent.from_tools |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install duckdb')
get_ipython().system('pip install llama-index-vector-stores-duckdb')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.duckdb import DuckDBVectorStore
from llama_index.core import StorageContext
from IPython.display import Markdown, display
import os
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'")
documents = SimpleDirectoryReader("data/paul_graham/").load_data()
vector_store = DuckDBVectorStore()
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 do growing up?")
display(Markdown(f"<b>{response}</b>"))
documents = SimpleDirectoryReader("data/paul_graham/").load_data()
vector_store = DuckDBVectorStore("pg.duckdb", persist_dir="./persist/")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
vector_store = | DuckDBVectorStore.from_local("./persist/pg.duckdb") | llama_index.vector_stores.duckdb.DuckDBVectorStore.from_local |
get_ipython().run_line_magic('pip', 'install llama-index-llms-sagemaker-endpoint')
get_ipython().system(' pip install llama-index')
ENDPOINT_NAME = "<-YOUR-ENDPOINT-NAME->"
from llama_index.llms.sagemaker_endpoint import SageMakerLLM
AWS_ACCESS_KEY_ID = "<-YOUR-AWS-ACCESS-KEY-ID->"
AWS_SECRET_ACCESS_KEY = "<-YOUR-AWS-SECRET-ACCESS-KEY->"
AWS_SESSION_TOKEN = "<-YOUR-AWS-SESSION-TOKEN->"
REGION_NAME = "<-YOUR-ENDPOINT-REGION-NAME->"
llm = SageMakerLLM(
endpoint_name=ENDPOINT_NAME,
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
aws_session_token=AWS_SESSION_TOKEN,
aws_region_name=REGION_NAME,
)
from llama_index.llms.sagemaker_endpoint import SageMakerLLM
ENDPOINT_NAME = "<-YOUR-ENDPOINT-NAME->"
PROFILE_NAME = "<-YOUR-PROFILE-NAME->"
llm = SageMakerLLM(
endpoint_name=ENDPOINT_NAME, profile_name=PROFILE_NAME
) # Omit the profile name to use the default profile
resp = llm.complete(
"Paul Graham is ", formatted=True
) # formatted=True to avoid adding system prompt
print(resp)
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
| ChatMessage(role="user", content="What is your name") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-readers-elasticsearch')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-opensearch')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-ollama')
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 os import getenv
from llama_index.core import SimpleDirectoryReader
from llama_index.vector_stores.opensearch import (
OpensearchVectorStore,
OpensearchVectorClient,
)
from llama_index.core import VectorStoreIndex, StorageContext
endpoint = getenv("OPENSEARCH_ENDPOINT", "http://localhost:9200")
idx = getenv("OPENSEARCH_INDEX", "gpt-index-demo")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
text_field = "content"
embedding_field = "embedding"
client = OpensearchVectorClient(
endpoint, idx, 1536, embedding_field=embedding_field, text_field=text_field
)
vector_store = | OpensearchVectorStore(client) | llama_index.vector_stores.opensearch.OpensearchVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
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.llms.openai import OpenAI
from llama_index.core.tools import QueryEngineTool, ToolMetadata
llm_35 = OpenAI(model="gpt-3.5-turbo-0613", temperature=0.3)
llm_4 = | OpenAI(model="gpt-4-0613", temperature=0.3) | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index-multi-modal-llms-anthropic')
get_ipython().system('pip install llama-index-vector-stores-qdrant')
get_ipython().system('pip install matplotlib')
import os
os.environ["ANTHROPIC_API_KEY"] = "" # Your ANTHROPIC API key here
from PIL import Image
import matplotlib.pyplot as plt
img = Image.open("../data/images/prometheus_paper_card.png")
plt.imshow(img)
from llama_index.core import SimpleDirectoryReader
from llama_index.multi_modal_llms.anthropic import AnthropicMultiModal
image_documents = SimpleDirectoryReader(
input_files=["../data/images/prometheus_paper_card.png"]
).load_data()
anthropic_mm_llm = AnthropicMultiModal(max_tokens=300)
response = anthropic_mm_llm.complete(
prompt="Describe the images as an alternative text",
image_documents=image_documents,
)
print(response)
from PIL import Image
import requests
from io import BytesIO
import matplotlib.pyplot as plt
from llama_index.core.multi_modal_llms.generic_utils import load_image_urls
image_urls = [
"https://venturebeat.com/wp-content/uploads/2024/03/Screenshot-2024-03-04-at-12.49.41%E2%80%AFAM.png",
]
img_response = requests.get(image_urls[0])
img = Image.open(BytesIO(img_response.content))
plt.imshow(img)
image_url_documents = load_image_urls(image_urls)
response = anthropic_mm_llm.complete(
prompt="Describe the images as an alternative text",
image_documents=image_url_documents,
)
print(response)
from llama_index.core import SimpleDirectoryReader
image_documents = SimpleDirectoryReader(
input_files=["../data/images/ark_email_sample.PNG"]
).load_data()
from PIL import Image
import matplotlib.pyplot as plt
img = Image.open("../data/images/ark_email_sample.PNG")
plt.imshow(img)
from pydantic import BaseModel
from typing import List
class TickerInfo(BaseModel):
"""List of ticker info."""
direction: str
ticker: str
company: str
shares_traded: int
percent_of_total_etf: float
class TickerList(BaseModel):
"""List of stock tickers."""
fund: str
tickers: List[TickerInfo]
from llama_index.multi_modal_llms.anthropic import AnthropicMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
Can you get the stock information in the image \
and return the answer? Pick just one fund.
Make sure the answer is a JSON format corresponding to a Pydantic schema. The Pydantic schema is given below.
"""
anthropic_mm_llm = | AnthropicMultiModal(max_tokens=300) | llama_index.multi_modal_llms.anthropic.AnthropicMultiModal |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-elasticsearch')
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"]
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.elasticsearch import ElasticsearchStore
from llama_index.core.schema import TextNode
nodes = [
| TextNode(
text=(
"A bunch of scientists bring back dinosaurs and mayhem breaks"
" loose"
) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-llms-ollama')
get_ipython().system('pip install llama-index')
from llama_index.llms.ollama import Ollama
llm = Ollama(model="llama2", request_timeout=30.0)
resp = llm.complete("Who is Paul Graham?")
print(resp)
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
| ChatMessage(role="user", content="What is your name") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-chroma')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install llama-index chromadb --quiet')
get_ipython().system('pip install chromadb==0.4.17')
get_ipython().system('pip install sentence-transformers')
get_ipython().system('pip install pydantic==1.10.11')
get_ipython().system('pip install open-clip-torch')
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 openai
OPENAI_API_KEY = ""
openai.api_key = OPENAI_API_KEY
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
import requests
def get_wikipedia_images(title):
response = requests.get(
"https://en.wikipedia.org/w/api.php",
params={
"action": "query",
"format": "json",
"titles": title,
"prop": "imageinfo",
"iiprop": "url|dimensions|mime",
"generator": "images",
"gimlimit": "50",
},
).json()
image_urls = []
for page in response["query"]["pages"].values():
if page["imageinfo"][0]["url"].endswith(".jpg") or page["imageinfo"][
0
]["url"].endswith(".png"):
image_urls.append(page["imageinfo"][0]["url"])
return image_urls
from pathlib import Path
import urllib.request
image_uuid = 0
MAX_IMAGES_PER_WIKI = 20
wiki_titles = {
"Tesla Model X",
"Pablo Picasso",
"Rivian",
"The Lord of the Rings",
"The Matrix",
"The Simpsons",
}
data_path = Path("mixed_wiki")
if not data_path.exists():
Path.mkdir(data_path)
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"]
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
images_per_wiki = 0
try:
list_img_urls = get_wikipedia_images(title)
for url in list_img_urls:
if url.endswith(".jpg") or url.endswith(".png"):
image_uuid += 1
urllib.request.urlretrieve(
url, data_path / f"{image_uuid}.jpg"
)
images_per_wiki += 1
if images_per_wiki > MAX_IMAGES_PER_WIKI:
break
except:
print(str(Exception("No images found for Wikipedia page: ")) + title)
continue
from chromadb.utils.embedding_functions import OpenCLIPEmbeddingFunction
embedding_function = OpenCLIPEmbeddingFunction()
from llama_index.core.indices import MultiModalVectorStoreIndex
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import SimpleDirectoryReader, StorageContext
from chromadb.utils.data_loaders import ImageLoader
image_loader = ImageLoader()
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection(
"multimodal_collection",
embedding_function=embedding_function,
data_loader=image_loader,
)
documents = SimpleDirectoryReader("./mixed_wiki/").load_data()
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
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-elasticsearch')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-opensearch')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-ollama')
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 os import getenv
from llama_index.core import SimpleDirectoryReader
from llama_index.vector_stores.opensearch import (
OpensearchVectorStore,
OpensearchVectorClient,
)
from llama_index.core import VectorStoreIndex, StorageContext
endpoint = getenv("OPENSEARCH_ENDPOINT", "http://localhost:9200")
idx = getenv("OPENSEARCH_INDEX", "gpt-index-demo")
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-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/") | llama_index.core.SimpleDirectoryReader |
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() | llama_index.readers.file.PyMuPDFReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
get_ipython().system('pip install llama_hub')
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 SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
index = VectorStoreIndex(base_nodes)
query_engine = index.as_query_engine(similarity_top_k=2)
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.core.node_parser import SimpleNodeParser
dataset_generator = DatasetGenerator(
base_nodes[:20],
llm=OpenAI(model="gpt-4"),
show_progress=True,
num_questions_per_chunk=3,
)
eval_dataset = await dataset_generator.agenerate_dataset_from_nodes(num=60)
eval_dataset.save_json("data/llama2_eval_qr_dataset.json")
eval_dataset = QueryResponseDataset.from_json(
"data/llama2_eval_qr_dataset.json"
)
import random
full_qr_pairs = eval_dataset.qr_pairs
num_exemplars = 2
num_eval = 40
exemplar_qr_pairs = random.sample(full_qr_pairs, num_exemplars)
eval_qr_pairs = random.sample(full_qr_pairs, num_eval)
len(exemplar_qr_pairs)
from llama_index.core.evaluation.eval_utils import get_responses
from llama_index.core.evaluation import CorrectnessEvaluator, BatchEvalRunner
evaluator_c = CorrectnessEvaluator(llm=OpenAI(model="gpt-3.5-turbo"))
evaluator_dict = {
"correctness": evaluator_c,
}
batch_runner = BatchEvalRunner(evaluator_dict, workers=2, show_progress=True)
async def get_correctness(query_engine, eval_qa_pairs, batch_runner):
eval_qs = [q for q, _ in eval_qa_pairs]
eval_answers = [a for _, a in eval_qa_pairs]
pred_responses = get_responses(eval_qs, query_engine, show_progress=True)
eval_results = await batch_runner.aevaluate_responses(
eval_qs, responses=pred_responses, reference=eval_answers
)
avg_correctness = np.array(
[r.score for r in eval_results["correctness"]]
).mean()
return avg_correctness
QA_PROMPT_KEY = "response_synthesizer:text_qa_template"
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
llm = OpenAI(model="gpt-3.5-turbo")
qa_tmpl_str = (
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Query: {query_str}\n"
"Answer: "
)
qa_tmpl = PromptTemplate(qa_tmpl_str)
print(query_engine.get_prompts()[QA_PROMPT_KEY].get_template())
meta_tmpl_str = """\
Your task is to generate the instruction <INS>. Below are some previous instructions with their scores.
The score ranges from 1 to 5.
{prev_instruction_score_pairs}
Below we show the task. The <INS> tag is prepended to the below prompt template, e.g. as follows:
```
<INS>
{prompt_tmpl_str}
```
The prompt template contains template variables. Given an input set of template variables, the formatted prompt is then given to an LLM to get an output.
Some examples of template variable inputs and expected outputs are given below to illustrate the task. **NOTE**: These do NOT represent the \
entire evaluation dataset.
{qa_pairs_str}
We run every input in an evaluation dataset through an LLM. If the LLM-generated output doesn't match the expected output, we mark it as wrong (score 0).
A correct answer has a score of 1. The final "score" for an instruction is the average of scores across an evaluation dataset.
Write your new instruction (<INS>) that is different from the old ones and has a score as high as possible.
Instruction (<INS>): \
"""
meta_tmpl = PromptTemplate(meta_tmpl_str)
from copy import deepcopy
def format_meta_tmpl(
prev_instr_score_pairs,
prompt_tmpl_str,
qa_pairs,
meta_tmpl,
):
"""Call meta-prompt to generate new instruction."""
pair_str_list = [
f"Instruction (<INS>):\n{instr}\nScore:\n{score}"
for instr, score in prev_instr_score_pairs
]
full_instr_pair_str = "\n\n".join(pair_str_list)
qa_str_list = [
f"query_str:\n{query_str}\nAnswer:\n{answer}"
for query_str, answer in qa_pairs
]
full_qa_pair_str = "\n\n".join(qa_str_list)
fmt_meta_tmpl = meta_tmpl.format(
prev_instruction_score_pairs=full_instr_pair_str,
prompt_tmpl_str=prompt_tmpl_str,
qa_pairs_str=full_qa_pair_str,
)
return fmt_meta_tmpl
def get_full_prompt_template(cur_instr: str, prompt_tmpl):
tmpl_str = prompt_tmpl.get_template()
new_tmpl_str = cur_instr + "\n" + tmpl_str
new_tmpl = | PromptTemplate(new_tmpl_str) | llama_index.core.PromptTemplate |
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() | llama_index.readers.wikipedia.WikipediaReader |
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() | llama_index.evaluation.tonic_validate.AugmentationAccuracyEvaluator |
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
from llama_index.core.postprocessor import LLMRerank
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512
documents = | SimpleDirectoryReader("../../../examples/gatsby/data") | llama_index.core.SimpleDirectoryReader |
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)
query_engine = index.as_query_engine()
response = query_engine.query("<query_text>")
display(Markdown(f"<b>{response}</b>"))
database_id = "<database-id>"
documents = NotionPageReader(integration_token=integration_token).load_data(
database_id=database_id
)
print(documents)
index = | SummaryIndex.from_documents(documents) | 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-readers-file')
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-embeddings-openai')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
domain = "docs.llamaindex.ai"
docs_url = "https://docs.llamaindex.ai/en/latest/"
get_ipython().system('wget -e robots=off --recursive --no-clobber --page-requisites --html-extension --convert-links --restrict-file-names=windows --domains {domain} --no-parent {docs_url}')
from llama_index.readers.file import UnstructuredReader
reader = UnstructuredReader()
from pathlib import Path
all_files_gen = Path("./docs.llamaindex.ai/").rglob("*")
all_files = [f.resolve() for f in all_files_gen]
all_html_files = [f for f in all_files if f.suffix.lower() == ".html"]
len(all_html_files)
from llama_index.core import Document
doc_limit = 100
docs = []
for idx, f in enumerate(all_html_files):
if idx > doc_limit:
break
print(f"Idx {idx}/{len(all_html_files)}")
loaded_docs = reader.load_data(file=f, split_documents=True)
start_idx = 72
loaded_doc = Document(
text="\n\n".join([d.get_content() for d in loaded_docs[72:]]),
metadata={"path": str(f)},
)
print(loaded_doc.metadata["path"])
docs.append(loaded_doc)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
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.agent.openai import OpenAIAgent
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.node_parser import SentenceSplitter
import os
from tqdm.notebook import tqdm
import pickle
async def build_agent_per_doc(nodes, file_base):
print(file_base)
vi_out_path = f"./data/llamaindex_docs/{file_base}"
summary_out_path = f"./data/llamaindex_docs/{file_base}_summary.pkl"
if not os.path.exists(vi_out_path):
Path("./data/llamaindex_docs/").mkdir(parents=True, exist_ok=True)
vector_index = VectorStoreIndex(nodes)
vector_index.storage_context.persist(persist_dir=vi_out_path)
else:
vector_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=vi_out_path),
)
summary_index = SummaryIndex(nodes)
vector_query_engine = vector_index.as_query_engine(llm=llm)
summary_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize", llm=llm
)
if not os.path.exists(summary_out_path):
Path(summary_out_path).parent.mkdir(parents=True, exist_ok=True)
summary = str(
await summary_query_engine.aquery(
"Extract a concise 1-2 line summary of this document"
)
)
pickle.dump(summary, open(summary_out_path, "wb"))
else:
summary = pickle.load(open(summary_out_path, "rb"))
query_engine_tools = [
QueryEngineTool(
query_engine=vector_query_engine,
metadata=ToolMetadata(
name=f"vector_tool_{file_base}",
description=f"Useful for questions related to specific facts",
),
),
QueryEngineTool(
query_engine=summary_query_engine,
metadata=ToolMetadata(
name=f"summary_tool_{file_base}",
description=f"Useful for summarization questions",
),
),
]
function_llm = OpenAI(model="gpt-4")
agent = OpenAIAgent.from_tools(
query_engine_tools,
llm=function_llm,
verbose=True,
system_prompt=f"""\
You are a specialized agent designed to answer queries about the `{file_base}.html` part of the LlamaIndex docs.
You must ALWAYS use at least one of the tools provided when answering a question; do NOT rely on prior knowledge.\
""",
)
return agent, summary
async def build_agents(docs):
node_parser = | SentenceSplitter() | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gradient')
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('pip', 'install llama-index-finetuning')
get_ipython().system('pip install llama-index gradientai -q')
import os
from llama_index.llms.gradient import GradientBaseModelLLM
from llama_index.finetuning import GradientFinetuneEngine
os.environ["GRADIENT_ACCESS_TOKEN"] = os.getenv("GRADIENT_API_KEY")
os.environ["GRADIENT_WORKSPACE_ID"] = "<insert_workspace_id>"
from pydantic import BaseModel
class Album(BaseModel):
"""Data model for an album."""
name: str
artist: str
from llama_index.core.callbacks import CallbackManager, LlamaDebugHandler
from llama_index.llms.openai import OpenAI
from llama_index.llms.gradient import GradientBaseModelLLM
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
openai_handler = LlamaDebugHandler()
openai_callback = CallbackManager([openai_handler])
openai_llm = OpenAI(model="gpt-4", callback_manager=openai_callback)
gradient_handler = LlamaDebugHandler()
gradient_callback = CallbackManager([gradient_handler])
base_model_slug = "llama2-7b-chat"
gradient_llm = GradientBaseModelLLM(
base_model_slug=base_model_slug,
max_tokens=300,
callback_manager=gradient_callback,
is_chat_model=True,
)
from llama_index.core.llms import LLMMetadata
prompt_template_str = """\
Generate an example album, with an artist and a list of songs. \
Using the movie {movie_name} as inspiration.\
"""
openai_program = LLMTextCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Album),
prompt_template_str=prompt_template_str,
llm=openai_llm,
verbose=True,
)
gradient_program = LLMTextCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Album),
prompt_template_str=prompt_template_str,
llm=gradient_llm,
verbose=True,
)
response = openai_program(movie_name="The Shining")
print(str(response))
tmp = openai_handler.get_llm_inputs_outputs()
print(tmp[0][0].payload["messages"][0])
response = gradient_program(movie_name="The Shining")
print(str(response))
tmp = gradient_handler.get_llm_inputs_outputs()
print(tmp[0][0].payload["messages"][0])
from llama_index.core.program import LLMTextCompletionProgram
from pydantic import BaseModel
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import GradientAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core.output_parsers import PydanticOutputParser
from typing import List
class Song(BaseModel):
"""Data model for a song."""
title: str
length_seconds: int
class Album(BaseModel):
"""Data model for an album."""
name: str
artist: str
songs: List[Song]
finetuning_handler = GradientAIFineTuningHandler()
callback_manager = | CallbackManager([finetuning_handler]) | llama_index.core.callbacks.CallbackManager |
get_ipython().run_line_magic('pip', 'install llama-index-llms-konko')
get_ipython().system('pip install llama-index')
import os
os.environ["KONKO_API_KEY"] = "<your-api-key>"
from llama_index.llms.konko import Konko
from llama_index.core.llms import ChatMessage
llm = Konko(model="meta-llama/llama-2-13b-chat")
messages = ChatMessage(role="user", content="Explain Big Bang Theory briefly")
resp = llm.chat([messages])
print(resp)
import os
os.environ["OPENAI_API_KEY"] = "<your-api-key>"
llm = Konko(model="gpt-3.5-turbo")
message = ChatMessage(role="user", content="Explain Big Bang Theory briefly")
resp = llm.chat([message])
print(resp)
message = ChatMessage(role="user", content="Tell me a story in 250 words")
resp = llm.stream_chat([message], max_tokens=1000)
for r in resp:
print(r.delta, end="")
llm = | Konko(model="numbersstation/nsql-llama-2-7b", max_tokens=100) | llama_index.llms.konko.Konko |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
get_ipython().system('pip install llama_hub')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
from llama_index.core import Document
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
docs0 = PyMuPDFReader().load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
node_parser = | SentenceSplitter(chunk_size=1024) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-llms-fireworks')
get_ipython().run_line_magic('pip', 'install llama-index')
from llama_index.llms.fireworks import Fireworks
resp = | Fireworks() | llama_index.llms.fireworks.Fireworks |
get_ipython().run_line_magic('pip', 'install llama-index-readers-pinecone')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
api_key = "<api_key>"
get_ipython().system('pip install llama-index')
from llama_index.readers.pinecone import PineconeReader
reader = | PineconeReader(api_key=api_key, environment="us-west1-gcp") | llama_index.readers.pinecone.PineconeReader |
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) | llama_index.core.callbacks.LlamaDebugHandler |
get_ipython().run_line_magic('pip', 'install llama-index-readers-make-com')
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 import VectorStoreIndex, SimpleDirectoryReader
from llama_index.readers.make_com import MakeWrapper
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-anthropic')
get_ipython().system('pip install llama-index')
from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings
tokenizer = Anthropic().tokenizer
Settings.tokenizer = tokenizer
import os
os.environ["ANTHROPIC_API_KEY"] = "YOUR ANTHROPIC API KEY"
from llama_index.llms.anthropic import Anthropic
llm = Anthropic(model="claude-3-opus-20240229")
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.anthropic import Anthropic
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
| ChatMessage(role="user", content="Tell me a story") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
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' -O pg_essay.txt")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader(input_files=["pg_essay.txt"])
documents = reader.load_data()
from llama_index.core.query_pipeline import (
QueryPipeline,
InputComponent,
ArgPackComponent,
)
from typing import Dict, Any, List, Optional
from llama_index.core.llama_pack import BaseLlamaPack
from llama_index.core.llms import LLM
from llama_index.llms.openai import OpenAI
from llama_index.core import Document, VectorStoreIndex
from llama_index.core.response_synthesizers import TreeSummarize
from llama_index.core.schema import NodeWithScore, TextNode
from llama_index.core.node_parser import SentenceSplitter
llm = OpenAI(model="gpt-3.5-turbo")
chunk_sizes = [128, 256, 512, 1024]
query_engines = {}
for chunk_size in chunk_sizes:
splitter = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=0)
nodes = splitter.get_nodes_from_documents(documents)
vector_index = VectorStoreIndex(nodes)
query_engines[str(chunk_size)] = vector_index.as_query_engine(llm=llm)
p = | QueryPipeline(verbose=True) | llama_index.core.query_pipeline.QueryPipeline |
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
from llama_index.core.postprocessor import LLMRerank
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/10k/'")
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'")
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-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
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-west1-gcp")
pinecone.create_index(
"quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.vector_stores.pinecone import PineconeVectorStore
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
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() | llama_index.readers.file.PyMuPDFReader |
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-replicate')
import os
OPENAI_API_TOKEN = "sk-<your-openai-api-token>"
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
REPLICATE_API_TOKEN = "" # Your Relicate API token here
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from pathlib import Path
input_image_path = Path("restaurant_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1GlqcNJhGGbwLKjJK1QJ_nyswCTQ2K2Fq" -O ./restaurant_images/fried_chicken.png')
from pydantic import BaseModel
class Restaurant(BaseModel):
"""Data model for an restaurant."""
restaurant: str
food: str
discount: str
price: str
rating: str
review: str
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.core import SimpleDirectoryReader
image_documents = SimpleDirectoryReader("./restaurant_images").load_data()
openai_mm_llm = OpenAIMultiModal(
model="gpt-4-vision-preview", api_key=OPENAI_API_TOKEN, max_new_tokens=1000
)
from PIL import Image
import matplotlib.pyplot as plt
imageUrl = "./restaurant_images/fried_chicken.png"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
can you summarize what is in the image\
and return the answer with json format \
"""
openai_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Restaurant),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=openai_mm_llm,
verbose=True,
)
response = openai_program()
for res in response:
print(res)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
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_replicate(
model_name, output_class, image_documents, prompt_template_str
):
mm_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS[model_name],
temperature=0.1,
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)
pydantic_replicate("fuyu-8b", Restaurant, image_documents, prompt_template_str)
pydantic_replicate(
"llava-13b", Restaurant, image_documents, prompt_template_str
)
pydantic_replicate(
"minigpt-4", Restaurant, image_documents, prompt_template_str
)
pydantic_replicate("cogvlm", Restaurant, image_documents, prompt_template_str)
input_image_path = Path("amazon_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1p1Y1qAoM68eC4sAvvHaiJyPhdUZS0Gqb" -O ./amazon_images/amazon.png')
from pydantic import BaseModel
class Product(BaseModel):
"""Data model for a Amazon Product."""
title: str
category: str
discount: str
price: str
rating: str
review: str
description: str
inventory: str
imageUrl = "./amazon_images/amazon.png"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
amazon_image_documents = SimpleDirectoryReader("./amazon_images").load_data()
prompt_template_str = """\
can you summarize what is in the image\
and return the answer with json format \
"""
openai_program_amazon = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Product),
image_documents=amazon_image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=openai_mm_llm,
verbose=True,
)
response = openai_program_amazon()
for res in response:
print(res)
pydantic_replicate(
"fuyu-8b", Product, amazon_image_documents, prompt_template_str
)
pydantic_replicate(
"minigpt-4", Product, amazon_image_documents, prompt_template_str
)
pydantic_replicate(
"cogvlm", Product, amazon_image_documents, prompt_template_str
)
pydantic_replicate(
"llava-13b", Product, amazon_image_documents, prompt_template_str
)
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
imageUrl = "./instagram_images/jordan.png"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
ins_image_documents = | SimpleDirectoryReader("./instagram_images") | llama_index.core.SimpleDirectoryReader |
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/").load_data()
index = | VectorStoreIndex.from_documents(data) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-together')
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')
domain = "docs.llamaindex.ai"
docs_url = "https://docs.llamaindex.ai/en/latest/"
get_ipython().system('wget -e robots=off --recursive --no-clobber --page-requisites --html-extension --convert-links --restrict-file-names=windows --domains {domain} --no-parent {docs_url}')
from llama_index.readers.file import UnstructuredReader
from pathlib import Path
from llama_index.llms.openai import OpenAI
from llama_index.core import Document
reader = UnstructuredReader()
all_html_files = [
"docs.llamaindex.ai/en/latest/index.html",
"docs.llamaindex.ai/en/latest/contributing/contributing.html",
"docs.llamaindex.ai/en/latest/understanding/understanding.html",
"docs.llamaindex.ai/en/latest/understanding/using_llms/using_llms.html",
"docs.llamaindex.ai/en/latest/understanding/using_llms/privacy.html",
"docs.llamaindex.ai/en/latest/understanding/loading/llamahub.html",
"docs.llamaindex.ai/en/latest/optimizing/production_rag.html",
"docs.llamaindex.ai/en/latest/module_guides/models/llms.html",
]
doc_limit = 10
docs = []
for idx, f in enumerate(all_html_files):
if idx > doc_limit:
break
print(f"Idx {idx}/{len(all_html_files)}")
loaded_docs = reader.load_data(file=f, split_documents=True)
start_idx = 64
loaded_doc = Document(
id_=str(f),
text="\n\n".join([d.get_content() for d in loaded_docs[start_idx:]]),
metadata={"path": str(f)},
)
print(str(f))
docs.append(loaded_doc)
from llama_index.embeddings.together import TogetherEmbedding
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
api_key = "<api_key>"
embed_model = TogetherEmbedding(
model_name="togethercomputer/m2-bert-80M-32k-retrieval", api_key=api_key
)
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
from llama_index.core.storage.docstore import SimpleDocumentStore
for doc in docs:
embedding = embed_model.get_text_embedding(doc.get_content())
doc.embedding = embedding
docstore = SimpleDocumentStore()
docstore.add_documents(docs)
from llama_index.core.schema import IndexNode
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import SummaryIndex
from llama_index.core.retrievers import RecursiveRetriever
import os
from tqdm.notebook import tqdm
import pickle
def build_index(docs, out_path: str = "storage/chunk_index"):
nodes = []
splitter = SentenceSplitter(chunk_size=512, chunk_overlap=70)
for idx, doc in enumerate(tqdm(docs)):
cur_nodes = splitter.get_nodes_from_documents([doc])
for cur_node in cur_nodes:
file_path = doc.metadata["path"]
new_node = IndexNode(
text=cur_node.text or "None",
index_id=str(file_path),
metadata=doc.metadata
)
nodes.append(new_node)
print("num nodes: " + str(len(nodes)))
if not os.path.exists(out_path):
index = VectorStoreIndex(nodes, embed_model=embed_model)
index.set_index_id("simple_index")
index.storage_context.persist(f"./{out_path}")
else:
storage_context = StorageContext.from_defaults(
persist_dir=f"./{out_path}"
)
index = load_index_from_storage(
storage_context, index_id="simple_index", embed_model=embed_model
)
return index
index = build_index(docs)
from llama_index.core.retrievers import BaseRetriever
from llama_index.core.indices.query.embedding_utils import get_top_k_embeddings
from llama_index.core import QueryBundle
from llama_index.core.schema import NodeWithScore
from typing import List, Any, Optional
class HybridRetriever(BaseRetriever):
"""Hybrid retriever."""
def __init__(
self,
vector_index,
docstore,
similarity_top_k: int = 2,
out_top_k: Optional[int] = None,
alpha: float = 0.5,
**kwargs: Any,
) -> None:
"""Init params."""
super().__init__(**kwargs)
self._vector_index = vector_index
self._embed_model = vector_index._embed_model
self._retriever = vector_index.as_retriever(
similarity_top_k=similarity_top_k
)
self._out_top_k = out_top_k or similarity_top_k
self._docstore = docstore
self._alpha = alpha
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve nodes given query."""
nodes = self._retriever.retrieve(query_bundle.query_str)
docs = [self._docstore.get_document(n.node.index_id) for n in nodes]
doc_embeddings = [d.embedding for d in docs]
query_embedding = self._embed_model.get_query_embedding(
query_bundle.query_str
)
doc_similarities, doc_idxs = get_top_k_embeddings(
query_embedding, doc_embeddings
)
result_tups = []
for doc_idx, doc_similarity in zip(doc_idxs, doc_similarities):
node = nodes[doc_idx]
full_similarity = (self._alpha * node.score) + (
(1 - self._alpha) * doc_similarity
)
print(
f"Doc {doc_idx} (node score, doc similarity, full similarity): {(node.score, doc_similarity, full_similarity)}"
)
result_tups.append((full_similarity, node))
result_tups = sorted(result_tups, key=lambda x: x[0], reverse=True)
for full_score, node in result_tups:
node.score = full_score
return [n for _, n in result_tups][:out_top_k]
top_k = 10
out_top_k = 3
hybrid_retriever = HybridRetriever(
index, docstore, similarity_top_k=top_k, out_top_k=3, alpha=0.5
)
base_retriever = index.as_retriever(similarity_top_k=out_top_k)
def show_nodes(nodes, out_len: int = 200):
for idx, n in enumerate(nodes):
print(f"\n\n >>>>>>>>>>>> ID {n.id_}: {n.metadata['path']}")
print(n.get_content()[:out_len])
query_str = "Tell me more about the LLM interface and where they're used"
nodes = hybrid_retriever.retrieve(query_str)
show_nodes(nodes)
base_nodes = base_retriever.retrieve(query_str)
show_nodes(base_nodes)
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = | RetrieverQueryEngine(hybrid_retriever) | llama_index.core.query_engine.RetrieverQueryEngine |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-lantern')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install psycopg2-binary llama-index asyncpg')
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.lantern import LanternVectorStore
import textwrap
import openai
import os
os.environ["OPENAI_API_KEY"] = "<your_key>"
openai.api_key = "<your_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)
import psycopg2
connection_string = "postgresql://postgres:postgres@localhost:5432"
db_name = "postgres"
conn = psycopg2.connect(connection_string)
conn.autocommit = True
with conn.cursor() as c:
c.execute(f"DROP DATABASE IF EXISTS {db_name}")
c.execute(f"CREATE DATABASE {db_name}")
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.embed_model = | OpenAIEmbedding(model="text-embedding-3-small") | llama_index.embeddings.openai.OpenAIEmbedding |
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 import (
VectorStoreIndex,
SimpleDirectoryReader,
load_index_from_storage,
StorageContext,
)
from IPython.display import Markdown, display
import dotenv
import s3fs
import os
dotenv.load_dotenv("../../../.env")
AWS_KEY = os.environ["AWS_ACCESS_KEY_ID"]
AWS_SECRET = os.environ["AWS_SECRET_ACCESS_KEY"]
R2_ACCOUNT_ID = os.environ["R2_ACCOUNT_ID"]
assert AWS_KEY is not None and AWS_KEY != ""
s3 = s3fs.S3FileSystem(
key=AWS_KEY,
secret=AWS_SECRET,
endpoint_url=f"https://{R2_ACCOUNT_ID}.r2.cloudflarestorage.com",
s3_additional_kwargs={"ACL": "public-read"},
)
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(len(documents))
index = VectorStoreIndex.from_documents(documents, fs=s3)
index.set_index_id("vector_index")
index.storage_context.persist("llama-index/storage_demo", fs=s3)
s3.listdir("llama-index/storage_demo")
sc = StorageContext.from_defaults(
persist_dir="llama-index/storage_demo", fs=s3
)
index2 = | load_index_from_storage(sc, "vector_index") | llama_index.core.load_index_from_storage |