Merge remote-tracking branch 'origin/main' into chart-display

lida/components/summarizer.py

@@ -88,7 +88,7 @@ def get_column_properties(self, df: pd.DataFrame, n_samples: int = 3) -> list[di
 
  return properties_list
 
- def encrich(self, base_summary: dict, text_gen: TextGenerator,
+ def enrich(self, base_summary: dict, text_gen: TextGenerator,
  textgen_config: TextGenerationConfig) -> dict:
  """Enrich the data summary with descriptions"""
  logger.info(f"Enriching the data summary with descriptions")
@@ -138,7 +138,7 @@ def summarize(
 
  if summary_method == "llm":
  # two stage summarization with llm enrichment
- data_summary = self.encrich(
+ data_summary = self.enrich(
  base_summary,
  text_gen=text_gen,
  textgen_config=textgen_config)

lida/datamodel.py

@@ -1,4 +1,5 @@
 # from dataclasses import dataclass
+import base64
 from dataclasses import field
 from typing import Any, Dict, List, Optional, Union
 
@@ -204,6 +205,14 @@ def _repr_mimebundle_(self, include=None, exclude=None):
 
  return bundle
 
+ def savefig(self, path):
+ """Save the raster image to a specified path if it exists"""
+ if self.raster:
+ with open(path, 'wb') as f:
+ f.write(base64.b64decode(self.raster))
+ else:
+ raise FileNotFoundError("No raster image to save")
+
 
 @dataclass
 class SummaryUrlRequest:

lida/version.py

@@ -1 +1 @@
-VERSION = "0.0.9"
+VERSION = "0.0.10"

notebooks/tutorial.ipynb

@@ -45,7 +45,7 @@
  "text_gen = llm(\"palm\") # for palm\n",
  "text_gen = llm(provider=\"hf\", model=\"uukuguy/speechless-llama2-hermes-orca-platypus-13b\", device_map=\"auto\")\n",
  "\n",
- "lida = Manager(llm=text_gen)\n",
+ "lida = Manager(text_gen=text_gen)\n",
  "```\n",
  "\n",
  "Note that you can set your llm keys as follows\n",
@@ -57,6 +57,23 @@
  "export PALM_SERVICE_ACCOUNT_KEY_FILE=<path to gcp service account key file>\n",
  "export PALM_PROJECT_ID=<your gcp project id>\n",
  "```\n",
+ "#### Azure OpenAI\n",
+ "```python\n",
+ "from llmx.generators.text.openai_textgen import OpenAITextGenerator\n",
+ "from llmx.generators.text.textgen import sanitize_provider\n",
+ "\n",
+ "provider = sanitize_provider(\"azureopenai\")\n",
+ "models = {}\n",
+ "\n",
+ "text_gen = OpenAITextGenerator(\n",
+ " api_key=os.environ[\"AZURE_OPENAI_API_KEY\"],\n",
+ " api_base=os.environ[\"AZURE_OPENAI_BASE\"],\n",
+ " provider=provider,\n",
+ " models=models\n",
+ ")\n",
+ "lida = Manager(text_gen=text_gen)\n",
+ "```\n",
+ "\n",
  "\n",
  "### Summarization Methods \n",
  "The summarizer module works takes an `summary_method` argument which determines if the base summary is enriched by an LLM. By default, the `summary_method` argument is set to `default` for a base summary (statistics etc). Set it to `llm` to enrich/annotate the base summary with an llm.\n",