Update app.py

app.py

@@ -7,13 +7,21 @@ import langchain
 import chromadb
 #import faiss
 
+# Use Chroma in Colab to create vector embeddings, I then saved them to HuggingFace so now I have to set it use them here.
+from chromadb.config import Settings
+client = chromadb.Client(Settings(
+    chroma_db_impl="duckdb+parquet",
+    persist_directory="./embeddings" # Optional, defaults to .chromadb/ in the current directory
+))
 
 
-prompt_templates = {"All Needs Gurus": "I want you to act as a needs assessment expert."}
-
 def get_empty_state():
     return {"total_tokens": 0, "messages": []}
 
+    
+#Initial prompt template, others added below from TXT file
+prompt_templates = {"All Needs Gurus": "I want you to act as a needs assessment expert."}
+
 def download_prompt_templates():
     url = "https://huggingface.co/spaces/ryanrwatkins/needs/raw/main/gurus.txt"
     try:
@@ -34,14 +42,12 @@ def download_prompt_templates():
     choices = choices[:1] + sorted(choices[1:])
     return gr.update(value=choices[0], choices=choices)
 
-
-    
-
-
 def on_prompt_template_change(prompt_template):
     if not isinstance(prompt_template, str): return
     return prompt_templates[prompt_template]
 
+
+
 def submit_message(prompt, prompt_template, temperature, max_tokens, context_length, state):
 
     openai.api_key = os.environ['openai_key']
@@ -60,17 +66,14 @@ def submit_message(prompt, prompt_template, temperature, max_tokens, context_len
 
 
     try:
-        completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
+        #completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
 
 # completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
 
-# vectordb = "./embeddings"
-
-
-        
-# completion = ChatVectorDBChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectordb, return_source_documents=True)
-# query = "Have Romeo and Juliet spent the night together? Provide a verbose answer, referencing passages from the book."
-# result = completion({"question": query, "chat_history": chat_history})
+        persist_directory = "./embeddings"
+        vectordb = Chroma.from_documents(romeoandjuliet_doc, embeddings, persist_directory=persist_directory)
+        completion = ChatVectorDBChain.from_llm(OpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), vectordb, return_source_documents=True)
+        result = completion({"question": system_prompt + history[-context_length*2:] + [prompt_msg]})
 # from https://blog.devgenius.io/chat-with-document-s-using-openai-chatgpt-api-and-text-embedding-6a0ce3dc8bc8
         
         history.append(prompt_msg)