Clarity tweaks.

README.md

@@ -41,7 +41,7 @@ llm_api = openai_chat_api(base_url='http://localhost:8000')  # Update for your L
 prompt = 'Write a short birthday greeting for my star employee'
 
 # You can set model params as needed
-resp = llm_api(prompt_to_chat(prompt), temperature=0.1, max_tokens=100)
+resp = llm_api(prompt_to_chat(prompt), temperature=0.1, max_tokens=256)
 # Extract just the response text, but the entire structure is available
 print(llm_api.first_choice_message(resp))
 ```

demo/chat_web_selects.py

@@ -15,14 +15,15 @@
 Needs access to an OpenAI-like service. Default assumption is self-hosted
 via e.g. llama-cpp-python or text-generation-webui
 
-Assume for the following it's at host my-llm-host, port 8000
+Assume for the following it's at host localhost, port 8000.
+MAKE SURE YOU USE A WORKING SERVER BECAUSE THIS IS A DEMO & THE ERROR HANDLING IS SIMPLISTIC
 
 pip install prerequisites, in addition to OgbujiPT cloned dir:
 
 click sentence_transformers qdrant-client httpx html2text amara3.xml
 
 ```sh
-python demo/chat_web_selects.py --apibase http://my-llm-host:8000 "www.newworldencyclopedia.org/entry/Igbo_People"
+python demo/chat_web_selects.py --apibase http://localhost:8000 "www.newworldencyclopedia.org/entry/Igbo_People"
 ```
 
 An example question might be "Who are the neighbors of the Igbo people?"

pylib/embedding/qdrant.py

@@ -136,7 +136,10 @@ def update(self, texts, metas=None):
             # The inline prints actually turn into a cool progress indicator in jupyter 😁
             embeddings = list(map(float, self._embedding_model.encode(text)))
 
-            payload = dict(_text=text, **meta)
+            try:
+                payload = dict(_text=text, **meta)
+            except TypeError:
+                raise TypeError('metas must be a list of mappings (dicts)')
 
             # Upsert the embedded chunk and its payload into the collection
             self.db.upsert(

pylib/llm_wrapper.py

@@ -113,8 +113,11 @@ def __call__(self, prompt, api_func=None, **kwargs):
         Args:
             prompt (str): Prompt to send to the LLM
 
-            kwargs (dict, optional): Extra parameters to pass to the model via API
-
+            kwargs (dict, optional): Extra parameters to pass to the model via API.
+                See Completions.create in OpenAI API, but in short, these:
+                best_of, echo, frequency_penalty, logit_bias, logprobs, max_tokens, n
+                presence_penalty, seed, stop, stream, suffix, temperature, top_p, user
+            
         Returns:
             dict: JSON response from the LLM
         '''