WIP: initial commit for conversational chatbot

src/v1/database.py

@@ -0,0 +1,34 @@
+import sqlite3
+
+dbase = sqlite3.connect('deep_thought.db') # Open a database File
+print('Database opened')
+
+
+dbase.execute(''' CREATE TABLE IF NOT EXISTS chat_history(
+    ID INT PRIMARY KEY NOT NULL,
+    MESSAGE TEXT NOT NULL,
+    TYPE TEXT NOT NULL,
+    TIMESTAMP TEXT NOT NULL,
+    GCHATID TEXT NOT NULL,
+    SLACKID INT NOT NULL) ''')
+
+print('Table created')
+
+def insert_record(ID,MESSAGE,TYPE,TIMESTAMP,GCHATID,SLACKID):
+    dbase.execute(''' INSERT OR IGNORE INTO chat_history(ID,MESSAGE,TYPE,TIMESTAMP,GCHATID,SLACKID)
+        VALUES(?,?,?,?,?,?)
+''',(ID,MESSAGE,TYPE,TIMESTAMP,GCHATID,SLACKID))
+    dbase.commit()
+    print('REcord inserted')
+
+
+def read_Data():
+    # from math import *
+    data = dbase.execute(''' SELECT * FROM chat_history''')
+    for record in data:
+        print('ID : '+str(record[0]))
+        print('MESSAGE : '+str(record[1]))
+        print('TYPE : '+str(record[2]))
+        print('TIMESTAMP : '+str(record[3])+'\n')
+        print('GCHATID : '+str(record[2]))
+        print('SLACKID : '+str(record[3])+'\n')

src/v1/prompt_template.py

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+"""
+This file implements prompt template for llama based models. 
+Modify the prompt template based on the model you select. 
+This seems to have significant impact on the output of the LLM.
+"""
+
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+
+# this is specific to Llama-2.
+
+system_prompt = """You are a helpful assistant, you will use the provided context to answer user questions.
+Read the given context before answering questions and think step by step. If you can not answer a user question based on 
+the provided context, inform the user. Do not use any other information for answering user. Provide a detailed answer to the question."""
+
+
+def get_prompt_template(system_prompt=system_prompt, promptTemplate_type=None, history=False):
+    if promptTemplate_type == "llama":
+        B_INST, E_INST = "[INST]", "[/INST]"
+        B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+        SYSTEM_PROMPT = B_SYS + system_prompt + E_SYS
+        if history:
+            instruction = """
+            Context: {history} \n {context}
+            User: {question}"""
+
+            prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
+            prompt = PromptTemplate(input_variables=["history", "context", "question"], template=prompt_template)
+        else:
+            instruction = """
+            Context: {context}
+            User: {question}"""
+
+            prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
+            prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)
+    elif promptTemplate_type == "mistral":
+        B_INST, E_INST = "<s>[INST] ", " [/INST]"
+        if history:
+            prompt_template = (
+                B_INST
+                + system_prompt
+                + """
+
+            Context: {history} \n {context}
+            User: {question}"""
+                + E_INST
+            )
+            prompt = PromptTemplate(input_variables=["history", "context", "question"], template=prompt_template)
+        else:
+            prompt_template = (
+                B_INST
+                + system_prompt
+                + """
+
+            Context: {context}
+            User: {question}"""
+                + E_INST
+            )
+            prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)
+    else:
+        # change this based on the model you have selected.
+        if history:
+            prompt_template = (
+                system_prompt
+                + """
+
+            Context: {history} \n {context}
+            User: {question}
+            Answer:"""
+            )
+            prompt = PromptTemplate(input_variables=["history", "context", "question"], template=prompt_template)
+        else:
+            prompt_template = (
+                system_prompt
+                + """
+
+            Context: {context}
+            User: {question}
+            Answer:"""
+            )
+            prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)
+
+    memory = ConversationBufferMemory(input_key="question", memory_key="history")
+
+    return (
+        prompt,
+        memory,
+    )