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"""
Build txtai workflows.
Based on this example: https://github.com/neuml/txtai/blob/master/examples/workflows.py
"""
import os
import nltk
import yaml
import pandas as pd
import streamlit as st
from txtai.embeddings import Documents, Embeddings
from txtai.pipeline import Segmentation, Summary, Tabular, Textractor, Translation
from txtai.workflow import ServiceTask, Task, UrlTask, Workflow
class Process:
"""
Container for an active Workflow process instance.
"""
@staticmethod
@st.cache_data(ttl=60 * 60, max_entries=3, allow_output_mutation=True, show_spinner=False)
def get(components, data):
"""
Lookup or creates a new workflow process instance.
Args:
components: input components
data: initial data, only passed when indexing
Returns:
Process
"""
process = Process(data)
# Build workflow
with st.spinner("Building workflow...."):
process.build(components)
return process
def __init__(self, data):
"""
Creates a new Process.
Args:
data: initial data, only passed when indexing
"""
# Component options
self.components = {}
# Defined pipelines
self.pipelines = {}
# Current workflow
self.workflow = []
# Embeddings index params
self.embeddings = None
self.documents = None
self.data = data
def build(self, components):
"""
Builds a workflow using components.
Args:
components: list of components to add to workflow
"""
# pylint: disable=W0108
tasks = []
for component in components:
component = dict(component)
wtype = component.pop("type")
self.components[wtype] = component
if wtype == "embeddings":
self.embeddings = Embeddings({**component})
self.documents = Documents()
tasks.append(Task(self.documents.add, unpack=False))
elif wtype == "segmentation":
self.pipelines[wtype] = Segmentation(**self.components[wtype])
tasks.append(Task(self.pipelines[wtype]))
elif wtype == "service":
tasks.append(ServiceTask(**self.components[wtype]))
elif wtype == "summary":
self.pipelines[wtype] = Summary(component.pop("path"))
tasks.append(Task(lambda x: self.pipelines["summary"](x, **self.components["summary"])))
elif wtype == "tabular":
self.pipelines[wtype] = Tabular(**self.components[wtype])
tasks.append(Task(self.pipelines[wtype]))
elif wtype == "textractor":
self.pipelines[wtype] = Textractor(**self.components[wtype])
tasks.append(UrlTask(self.pipelines[wtype]))
elif wtype == "translation":
self.pipelines[wtype] = Translation()
tasks.append(Task(lambda x: self.pipelines["translation"](x, **self.components["translation"])))
self.workflow = Workflow(tasks)
def run(self, data):
"""
Runs a workflow using data as input.
Args:
data: input data
"""
if data and self.workflow:
# Build tuples for embedding index
if self.documents:
data = [(x, element, None) for x, element in enumerate(data)]
# Process workflow
for result in self.workflow(data):
if not self.documents:
st.write(result)
# Build embeddings index
if self.documents:
# Cache data
self.data = list(self.documents)
with st.spinner("Building embedding index...."):
self.embeddings.index(self.documents)
self.documents.close()
# Clear workflow
self.documents, self.pipelines, self.workflow = None, None, None
def search(self, query):
"""
Runs a search.
Args:
query: input query
"""
if self.embeddings and query:
st.markdown(
"""
<style>
table td:nth-child(1) {
display: none
}
table th:nth-child(1) {
display: none
}
table {text-align: left !important}
</style>
""",
unsafe_allow_html=True,
)
limit = min(5, len(self.data))
results = []
for result in self.embeddings.search(query, limit):
# Tuples are returned when an index doesn't have stored content
if isinstance(result, tuple):
uid, score = result
results.append({"text": self.find(uid), "score": f"{score:.2}"})
else:
if "id" in result and "text" in result:
result["text"] = self.content(result.pop("id"), result["text"])
if "score" in result and result["score"]:
result["score"] = f'{result["score"]:.2}'
results.append(result)
df = pd.DataFrame(results)
st.write(df.to_html(escape=False), unsafe_allow_html=True)
def find(self, key):
"""
Lookup record from cached data by uid key.
Args:
key: id to search for
Returns:
text for matching id
"""
# Lookup text by id
text = [text for uid, text, _ in self.data if uid == key][0]
return self.content(key, text)
def content(self, uid, text):
"""
Builds a content reference for uid and text.
Args:
uid: record id
text: record text
Returns:
content
"""
if uid and uid.lower().startswith("http"):
return f"<a href='{uid}' rel='noopener noreferrer' target='blank'>{text}</a>"
return text
class Application:
"""
Main application.
"""
def __init__(self, directory):
"""
Creates a new application.
"""
# Workflow configuration directory
self.directory = directory
def default(self, names):
"""
Gets default workflow index.
Args:
names: list of workflow names
Returns:
default workflow index
"""
# Get names as lowercase to match case-insensitive
lnames = [name.lower() for name in names]
# Get default workflow param
params = st.experimental_get_query_params()
index = params.get("default")
index = index[0].lower() if index else 0
# Lookup index of workflow name, add 1 to account for "--"
if index and index in lnames:
return lnames.index(index) + 1
# Workflow not found, default to index 0
return 0
def load(self, components):
"""
Load an existing workflow file.
Args:
components: list of components to load
Returns:
(names of components loaded, workflow config)
"""
with open(os.path.join(self.directory, "config.yml"), encoding="utf-8") as f:
config = yaml.safe_load(f)
names = [row["name"] for row in config]
files = [row["file"] for row in config]
selected = st.selectbox("Load workflow", ["--"] + names, self.default(names))
if selected != "--":
index = [x for x, name in enumerate(names) if name == selected][0]
with open(os.path.join(self.directory, files[index]), encoding="utf-8") as f:
workflow = yaml.safe_load(f)
st.markdown("---")
# Get tasks for first workflow
tasks = list(workflow["workflow"].values())[0]["tasks"]
selected = []
for task in tasks:
name = task.get("action", task.get("task"))
if name in components:
selected.append(name)
elif name in ["index", "upsert"]:
selected.append("embeddings")
return (selected, workflow)
return (None, None)
def state(self, key):
"""
Lookup a session state variable.
Args:
key: variable key
Returns:
variable value
"""
if key in st.session_state:
return st.session_state[key]
return None
def appsetting(self, workflow, name):
"""
Looks up an application configuration setting.
Args:
workflow: workflow configuration
name: setting name
Returns:
app setting value
"""
if workflow:
config = workflow.get("app")
if config:
return config.get(name)
return None
def setting(self, config, name, default=None):
"""
Looks up a component configuration setting.
Args:
config: component configuration
name: setting name
default: default setting value
Returns:
setting value
"""
return config.get(name, default) if config else default
def text(self, label, component, config, name, default=None):
"""
Create a new text input field.
Args:
label: field label
component: component name
config: component configuration
name: setting name
default: default setting value
Returns:
text input field value
"""
default = self.setting(config, name, default)
if not default:
default = ""
elif isinstance(default, list):
default = ",".join(default)
elif isinstance(default, dict):
default = ",".join(default.keys())
st.caption(label)
st.code(default, language="yaml")
return default
def number(self, label, component, config, name, default=None):
"""
Creates a new numeric input field.
Args:
label: field label
component: component name
config: component configuration
name: setting name
default: default setting value
Returns:
numeric value
"""
value = self.text(label, component, config, name, default)
return int(value) if value else None
def boolean(self, label, component, config, name, default=False):
"""
Creates a new checkbox field.
Args:
label: field label
component: component name
config: component configuration
name: setting name
default: default setting value
Returns:
boolean value
"""
default = self.setting(config, name, default)
st.caption(label)
st.markdown(":white_check_mark:" if default else ":white_large_square:")
return default
def select(self, label, component, config, name, options, default=0):
"""
Creates a new select box field.
Args:
label: field label
component: component name
config: component configuration
name: setting name
options: list of dropdown options
default: default setting value
Returns:
boolean value
"""
index = self.setting(config, name)
index = [x for x, option in enumerate(options) if option == default]
# Derive default index
default = index[0] if index else default
st.caption(label)
st.code(options[default], language="yaml")
return options[default]
def split(self, text):
"""
Splits text on commas and returns a list.
Args:
text: input text
Returns:
list
"""
return [x.strip() for x in text.split(",")]
def options(self, component, workflow, index):
"""
Extracts component settings into a component configuration dict.
Args:
component: component type
workflow: existing workflow, can be None
index: task index
Returns:
dict with component settings
"""
# pylint: disable=R0912, R0915
options = {"type": component}
# Lookup component configuration
# - Runtime components have config defined within tasks
# - Pipeline components have config defined at workflow root
config = None
if workflow:
if component in ["service", "translation"]:
# Service config is found in tasks section
tasks = list(workflow["workflow"].values())[0]["tasks"]
tasks = [task for task in tasks if task.get("task") == component or task.get("action") == component]
if tasks:
config = tasks[0]
else:
config = workflow.get(component)
if component == "embeddings":
st.markdown(f"** {index + 1}.) Embeddings Index** \n*Index workflow output*")
options["path"] = self.text("Embeddings model path", component, config, "path", "sentence-transformers/nli-mpnet-base-v2")
options["upsert"] = self.boolean("Upsert", component, config, "upsert")
options["content"] = self.boolean("Content", component, config, "content")
elif component in ("segmentation", "textractor"):
if component == "segmentation":
st.markdown(f"** {index + 1}.) Segment** \n*Split text into semantic units*")
else:
st.markdown(f"** {index + 1}.) Textract** \n*Extract text from documents*")
options["sentences"] = self.boolean("Split sentences", component, config, "sentences")
options["lines"] = self.boolean("Split lines", component, config, "lines")
options["paragraphs"] = self.boolean("Split paragraphs", component, config, "paragraphs")
options["join"] = self.boolean("Join tokenized", component, config, "join")
options["minlength"] = self.number("Min section length", component, config, "minlength")
elif component == "service":
st.markdown(f"** {index + 1}.) Service** \n*Extract data from an API*")
options["url"] = self.text("URL", component, config, "url")
options["method"] = self.select("Method", component, config, "method", ["get", "post"], 0)
options["params"] = self.text("URL parameters", component, config, "params")
options["batch"] = self.boolean("Run as batch", component, config, "batch", True)
options["extract"] = self.text("Subsection(s) to extract", component, config, "extract")
if options["params"]:
options["params"] = {key: None for key in self.split(options["params"])}
if options["extract"]:
options["extract"] = self.split(options["extract"])
elif component == "summary":
st.markdown(f"** {index + 1}.) Summary** \n*Abstractive text summarization*")
options["path"] = self.text("Model", component, config, "path", "sshleifer/distilbart-cnn-12-6")
options["minlength"] = self.number("Min length", component, config, "minlength")
options["maxlength"] = self.number("Max length", component, config, "maxlength")
elif component == "tabular":
st.markdown(f"** {index + 1}.) Tabular** \n*Split tabular data into rows and columns*")
options["idcolumn"] = self.text("Id columns", component, config, "idcolumn")
options["textcolumns"] = self.text("Text columns", component, config, "textcolumns")
options["content"] = self.text("Content", component, config, "content")
if options["textcolumns"]:
options["textcolumns"] = self.split(options["textcolumns"])
if options["content"]:
options["content"] = self.split(options["content"])
if len(options["content"]) == 1 and options["content"][0] == "1":
options["content"] = options["content"][0]
elif component == "translation":
st.markdown(f"** {index + 1}.) Translate** \n*Machine translation*")
options["target"] = self.text("Target language code", component, config, "args", "en")
st.markdown("---")
return options
def yaml(self, components):
"""
Builds a yaml string for components.
Args:
components: list of components to export to YAML
Returns:
(workflow name, YAML string)
"""
data = {"app": {"data": self.state("data"), "query": self.state("query")}}
tasks = []
name = None
for component in components:
component = dict(component)
name = wtype = component.pop("type")
if wtype == "embeddings":
upsert = component.pop("upsert")
data[wtype] = component
data["writable"] = True
name = "index"
tasks.append({"action": "upsert" if upsert else "index"})
elif wtype == "segmentation":
data[wtype] = component
tasks.append({"action": wtype})
elif wtype == "service":
config = dict(**component)
config["task"] = wtype
tasks.append(config)
elif wtype == "summary":
data[wtype] = {"path": component.pop("path")}
tasks.append({"action": wtype})
elif wtype == "tabular":
data[wtype] = component
tasks.append({"action": wtype})
elif wtype == "textractor":
data[wtype] = component
tasks.append({"action": wtype, "task": "url"})
elif wtype == "translation":
data[wtype] = {}
tasks.append({"action": wtype, "args": list(component.values())})
# Add in workflow
data["workflow"] = {name: {"tasks": tasks}}
return (name, yaml.dump(data))
def data(self, workflow):
"""
Gets input data.
Args:
workflow: workflow configuration
Returns:
input data
"""
# Get default data setting
data = self.appsetting(workflow, "data")
if not self.appsetting(workflow, "query"):
data = st.text_input("Input", value=data)
# Save data state
st.session_state["data"] = data
# Wrap data as list for workflow processing
return [data]
def query(self, workflow, index):
"""
Gets input query.
Args:
workflow: workflow configuration
index: True if this is an indexing workflow
Returns:
input query
"""
default = self.appsetting(workflow, "query")
default = default if default else ""
# Get query if this is an indexing workflow
query = st.text_input("Query", value=default) if index else None
# Save query state
st.session_state["query"] = query
return query
def process(self, workflow, components, index):
"""
Processes the current application action.
Args:
workflow: workflow configuration
components: workflow components
index: True if this is an indexing workflow
"""
# Get input data and initialize query
data = self.data(workflow)
query = self.query(workflow, index)
# Get workflow process
process = Process.get(components, data if index else None)
# Run workflow process
process.run(data)
# Run search
if index:
process.search(query)
def run(self):
"""
Runs Streamlit application.
"""
with st.sidebar:
st.markdown("# Workflow builder \n*Build and apply workflows to data* ")
st.markdown("Test workflows for Station. Read more about used data on [Hugging Face](https://huggingface.co/datasets/ag_news) and in the [Docs](https://neuml.github.io/txtai/workflow/).")
st.markdown("---")
# Component configuration
components = ["embeddings", "segmentation", "service", "summary", "tabular", "textractor", "translation"]
selected, workflow = self.load(components)
if selected:
# Get selected options
components = [self.options(component, workflow, x) for x, component in enumerate(selected)]
if selected:
# Process current action
self.process(workflow, components, "embeddings" in selected)
with st.sidebar:
# Generate export button after workflow is complete
_, config = self.yaml(components)
st.download_button("Export", config, file_name="workflow.yml", help="Export the API workflow as YAML")
else:
st.info("Select a workflow from the sidebar")
if __name__ == "__main__":
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# pylint: disable=W0702
try:
nltk.sent_tokenize("This is a test. Split")
except:
nltk.download("punkt")
# Create and run application
app = Application("workflows")
app.run()
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