Update chatbot/bot.py

chatbot/bot.py

@@ -1,69 +1,69 @@
-from chatbot.retriever import HybridRetrieverReranker
-from litellm import completion
-import os
-import ast
-
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-DENSE_RETRIEVER_MODEL_NAME = "all-MiniLM-L6-v2"
-CROSS_ENCODER_MODEL_NAME = "cross-encoder/ms-marco-MiniLM-L-12-v2"
-LLM_CORE_MODEL_NAME = "groq/llama3-8b-8192"
-
-
-class QuestionAnsweringBot:
-
-    def __init__(self, docs, enable_bm25=True, enable_dense=True, enable_rerank=True, top_k_bm25=60, top_n_dense=30, top_n_rerank=2) -> None:
-        self.retriever = HybridRetrieverReranker(docs)
-        self.enable_bm25 = enable_bm25
-        self.enable_dense = enable_dense
-        self.enable_rerank = enable_rerank
-        self.top_k_bm25=top_k_bm25
-        self.top_n_dense=top_n_dense
-        self.top_n_rerank=top_n_rerank
-
-    def __get_answer__(self, question: str) -> str:
-        PROMPT = """\
-            You are an intelligent assistant designed to provide accurate and relevant answers based on the provided context.
-
-            Rules:
-            - Always analyze the provided context thoroughly before answering.
-            - Respond with factual and concise information.
-            - If context is ambiguous or insufficient or you can't find answer, say 'I don't know.'
-            - Do not speculate or fabricate information beyond the provided context.
-            - Follow user instructions on the response style(default style is detailed response if user didn't provide any specifications):
-              - If the user asks for a detailed response, provide comprehensive explanations.
-              - If the user requests brevity, give concise and to-the-point answers.
-            - When applicable, summarize and synthesize information from the context to answer effectively.
-            - Avoid using information outside the given context.
-          """
-        context = self.retriever.hybrid_retrieve(question,
-                                                 enable_bm25=self.enable_bm25,
-                                                 enable_dense=self.enable_dense,
-                                                 enable_rerank=self.enable_rerank,
-                                                 top_k_bm25=self.top_k_bm25,
-                                                 top_n_dense=self.top_n_dense,
-                                                 top_n_rerank=self.top_n_rerank
-                                                 )
-
-        context_text = [doc['raw_text'] for doc in context]
-
-        response = completion(
-                                model=LLM_CORE_MODEL_NAME,
-                                temperature=0.0,
-                                messages=[
-                                    {"role": "system", "content": PROMPT},
-                                    {"role": "user", "content": f"Context: {context_text}\nQuestion: {question}"}
-                            ],
-                            api_key=GROQ_API_KEY
-                            )
-        return response, context
-
-    def form_response(self, question):
-      llm_response, context = self.__get_answer__(question)
-
-      metadata_raw = [doc['chapter_name'] for doc in context]
-      metadata_cleaned = [ast.literal_eval(item) for item in metadata_raw]
-
-      print('User:', question)
-      print('System:', llm_response.choices[0].message.content)
-
-      return f"**{llm_response.choices[0].message.content}**\n\nResources: {[chapter for doc in metadata_cleaned for chapter in doc]}"
+from chatbot.retriever import HybridRetrieverReranker
+from litellm import completion
+import os
+import ast
+
+GROQ_API_KEY = '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'
+DENSE_RETRIEVER_MODEL_NAME = "all-MiniLM-L6-v2"
+CROSS_ENCODER_MODEL_NAME = "cross-encoder/ms-marco-MiniLM-L-12-v2"
+LLM_CORE_MODEL_NAME = "groq/llama3-8b-8192"
+
+
+class QuestionAnsweringBot:
+
+    def __init__(self, docs, enable_bm25=True, enable_dense=True, enable_rerank=True, top_k_bm25=60, top_n_dense=30, top_n_rerank=2) -> None:
+        self.retriever = HybridRetrieverReranker(docs)
+        self.enable_bm25 = enable_bm25
+        self.enable_dense = enable_dense
+        self.enable_rerank = enable_rerank
+        self.top_k_bm25=top_k_bm25
+        self.top_n_dense=top_n_dense
+        self.top_n_rerank=top_n_rerank
+
+    def __get_answer__(self, question: str) -> str:
+        PROMPT = """\
+            You are an intelligent assistant designed to provide accurate and relevant answers based on the provided context.
+
+            Rules:
+            - Always analyze the provided context thoroughly before answering.
+            - Respond with factual and concise information.
+            - If context is ambiguous or insufficient or you can't find answer, say 'I don't know.'
+            - Do not speculate or fabricate information beyond the provided context.
+            - Follow user instructions on the response style(default style is detailed response if user didn't provide any specifications):
+              - If the user asks for a detailed response, provide comprehensive explanations.
+              - If the user requests brevity, give concise and to-the-point answers.
+            - When applicable, summarize and synthesize information from the context to answer effectively.
+            - Avoid using information outside the given context.
+          """
+        context = self.retriever.hybrid_retrieve(question,
+                                                 enable_bm25=self.enable_bm25,
+                                                 enable_dense=self.enable_dense,
+                                                 enable_rerank=self.enable_rerank,
+                                                 top_k_bm25=self.top_k_bm25,
+                                                 top_n_dense=self.top_n_dense,
+                                                 top_n_rerank=self.top_n_rerank
+                                                 )
+
+        context_text = [doc['raw_text'] for doc in context]
+
+        response = completion(
+                                model=LLM_CORE_MODEL_NAME,
+                                temperature=0.0,
+                                messages=[
+                                    {"role": "system", "content": PROMPT},
+                                    {"role": "user", "content": f"Context: {context_text}\nQuestion: {question}"}
+                            ],
+                            api_key=GROQ_API_KEY
+                            )
+        return response, context
+
+    def form_response(self, question):
+      llm_response, context = self.__get_answer__(question)
+
+      metadata_raw = [doc['chapter_name'] for doc in context]
+      metadata_cleaned = [ast.literal_eval(item) for item in metadata_raw]
+
+      print('User:', question)
+      print('System:', llm_response.choices[0].message.content)
+
+      return f"**{llm_response.choices[0].message.content}**\n\nResources: {[chapter for doc in metadata_cleaned for chapter in doc]}"