Introduce search api endpoint that auto infers search type intent

- Introduce prompt for GPT to automatically extract user's search intent
- Expose new search api endpoint to use that to set SearchType being
  passed to search API
- Currently meant as an experimental API to gauge usefulness,
  extendability. Evaluating for phone or voice use-case

src/main.py

@@ -15,7 +15,7 @@ from src.utils.helpers import get_absolute_path, get_from_dict
 from src.utils.cli import cli
 from src.utils.config import SearchType, SearchModels, ProcessorConfigModel, ConversationProcessorConfigModel
 from src.utils.rawconfig import FullConfig
-from src.processor.conversation.gpt import converse, message_to_log, message_to_prompt, understand, summarize
+from src.processor.conversation.gpt import converse, extract_search_type, message_to_log, message_to_prompt, understand, summarize
 
 # Application Global State
 config = FullConfig()
@@ -96,6 +96,19 @@ def regenerate(t: Optional[SearchType] = None):
     return {'status': 'ok', 'message': 'regeneration completed'}
 
 
+@app.get('/beta/search')
+def search_beta(q: str, n: Optional[int] = 1):
+    # Extract Search Type using GPT
+    metadata = extract_search_type(q, api_key=processor_config.conversation.openai_api_key, verbose=verbose)
+    search_type = get_from_dict(metadata, "search-type")
+
+    # Search
+    search_results = search(q, n=n, t=SearchType(search_type))
+
+    # Return response
+    return {'status': 'ok', 'result': search_results, 'type': search_type}
+
+
 @app.get('/chat')
 def chat(q: str):
     # Load Conversation History

src/processor/conversation/gpt.py

@@ -39,6 +39,55 @@ def summarize(text, summary_type, user_query=None, api_key=None, temperature=0.5
     return str(story).replace("\n\n", "")
 
 
+def extract_search_type(text, api_key=None, temperature=0.5, max_tokens=100, verbose=0):
+    """
+    Extract search type from user query using OpenAI's GPT
+    """
+    # Initialize Variables
+    openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
+    understand_primer = '''
+Objective: Extract search type from user query and return information as JSON
+
+Allowed search types are listed below:
+  - search-type=["notes","ledger","image","music"]
+
+Some examples are given below for reference:
+Q:What fiction book was I reading last week about AI starship?
+A:{ "search-type": "notes" }
+Q:Play some calm classical music?
+A:{ "search-type": "music" }
+Q:How much did I spend at Subway for dinner last time?
+A:{ "search-type": "ledger" }
+Q:What was that popular Sri lankan song that Alex had mentioned?
+A:{ "search-type": "music" }
+Q:Can you recommend a movie to watch from my notes?
+A:{ "search-type": "notes" }
+Q: When did I buy Groceries last?
+A:{ "search-type": "ledger" }
+Q:When did I go surfing last?
+A:{ "search-type": "notes" }'''
+
+    # Setup Prompt with Understand Primer
+    prompt = message_to_prompt(text, understand_primer, start_sequence="\nA:", restart_sequence="\nQ:")
+    if verbose > 1:
+        print(f"Message -> Prompt: {text} -> {prompt}")
+
+    # Get Response from GPT
+    response = openai.Completion.create(
+        engine="davinci",
+        prompt=prompt,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        top_p=1,
+        frequency_penalty=0.2,
+        presence_penalty=0,
+        stop=["\n"])
+
+    # Extract, Clean Message from GPT's Response
+    story = str(response['choices'][0]['text'])
+    return json.loads(story.strip(empty_escape_sequences))
+
+
 def understand(text, api_key=None, temperature=0.5, max_tokens=100, verbose=0):
     """
     Understand user input using OpenAI's GPT