Merge pull request #803 from llmware-ai/update-prompt-sources-example

updating prompt_with_sources example

Author: doberst

Diff for: examples/Prompts/prompt_with_sources.py

@@ -10,6 +10,7 @@
 from llmware.models import PromptCatalog
 from llmware.library import Library
 from llmware.retrieval import Query
+from llmware.configs import LLMWareConfig
 
 
 def prompt_with_sources(model_name, library_name):
@@ -27,6 +28,11 @@ def prompt_with_sources(model_name, library_name):
 
     prompter = Prompt().load_model(model_name)
 
+    #   Use #1 - add_source_document - parses the document in memory, filters the text chunks by query, and then
+    #   creates a 'source' context to be passed to the model
+
+    print(f"\n#1 - add a source document file directly into a prompt")
+
     sources2 = prompter.add_source_document(ingestion_folder_path, local_file, query="base salary")
 
     prompt = "What is the base salary amount?"
@@ -35,14 +41,24 @@ def prompt_with_sources(model_name, library_name):
     print (f"- Context: {local_file}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
     prompter.clear_source_materials()
 
+    #   Use #2 - add_source_wikipedia - gets a source document from Wikipedia on Barack Obama,
+    #   and creates source context
+
+    print(f"\n#2 - add a wikipedia article by api call by topic into a prompt")
+
     prompt = "Was Barack Obama the Prime Minister of Canada?"
     wiki_topic = "Barack Obama"
     prompt_instruction = "yes_no"
     sources3 = prompter.add_source_wikipedia(wiki_topic, article_count=1)
     response = prompter.prompt_with_source(prompt=prompt, prompt_name=prompt_instruction)[0]["llm_response"]
-    print (f"- Context: {local_file}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
+    print (f"- Context: {wiki_topic}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
     prompter.clear_source_materials()
 
+    #   Use #3 - add_source_query_results - produces the same results as the first case, but runs a query on the library
+    #   and then adds the query results to the prompt which are concatenated into a source context
+
+    print(f"\n#3 - run a query on a library and then pass the query results into a prompt")
+
     query_results = Query(library).text_query("base salary")
     prompt = "What is the annual rate of the base salary?"
     sources4 = prompter.add_source_query_results(query_results)
@@ -55,9 +71,7 @@ def prompt_with_sources(model_name, library_name):
 
 if __name__ == "__main__":
 
-    #   to use API-based model for this example, set API keys in os.environ variable
-    #   e.g., see example: set_model_api_keys.py
-    #   e.g., os.environ["USER_MANAGED_OPENAI_API_KEY"] = "<insert-your-api-key>"
+    LLMWareConfig().set_active_db("sqlite")
 
     #   this model is a placeholder which will run on local laptop - swap out for higher accuracy, larger models
     model_name = "llmware/bling-1b-0.1"