Update application files, fix LLM models, and create new retriever class

application/api/answer/routes.py

@@ -103,7 +103,7 @@ def is_azure_configured():
     return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
 
 def save_conversation(conversation_id, question, response, source_log_docs, llm):
-    if conversation_id is not None:
+    if conversation_id is not None and conversation_id != "None":
         conversations_collection.update_one(
             {"_id": ObjectId(conversation_id)},
             {"$push": {"queries": {"prompt": question, "response": response, "sources": source_log_docs}}},
@@ -129,6 +129,7 @@ def save_conversation(conversation_id, question, response, source_log_docs, llm)
              "name": completion,
              "queries": [{"prompt": question, "response": response, "sources": source_log_docs}]}
         ).inserted_id
+    return conversation_id
 
 def get_prompt(prompt_id):
     if prompt_id == 'default':
@@ -293,12 +294,5 @@ def api_search():
             source=source, chat_history=[], prompt="default", chunks=chunks, gpt_model=gpt_model
             )
     docs = retriever.search()
-
-    source_log_docs = []
-    for doc in docs:
-        if doc.metadata:
-            source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
-        else:
-            source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
-    return source_log_docs
+    return docs
 

application/requirements.txt

@@ -3,6 +3,7 @@ boto3==1.34.6
 celery==5.3.6
 dataclasses_json==0.6.3
 docx2txt==0.8
+duckduckgo-search=5.3.0
 EbookLib==0.18
 elasticsearch==8.12.0
 escodegen==1.0.11

application/retriever/base.py

@@ -8,3 +8,7 @@ class BaseRetriever(ABC):
     @abstractmethod
     def gen(self, *args, **kwargs):
         pass
+
+    @abstractmethod
+    def search(self, *args, **kwargs):
+        pass

application/retriever/classic_rag.py

@@ -40,23 +40,22 @@ class ClassicRAG(BaseRetriever):
             docs = []
         else:
             docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, self.vectorstore, settings.EMBEDDINGS_KEY)
-            docs = docsearch.search(self.question, k=self.chunks)
+            docs_temp = docsearch.search(self.question, k=self.chunks)
+            docs = [{"title": i.metadata['title'].split('/')[-1] if i.metadata else i.page_content, "text": i.page_content} for i in docs_temp]
         if settings.LLM_NAME == "llama.cpp":
             docs = [docs[0]]
+        
         return docs
     
     def gen(self):
         docs = self._get_data()
         
         # join all page_content together with a newline
-        docs_together = "\n".join([doc.page_content for doc in docs])
+        docs_together = "\n".join([doc["text"] for doc in docs])
         p_chat_combine = self.prompt.replace("{summaries}", docs_together)
         messages_combine = [{"role": "system", "content": p_chat_combine}]
         for doc in docs:
-            if doc.metadata:
-                yield {"source": {"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content}} 
-            else:
-                yield {"source": {"title": doc.page_content, "text": doc.page_content}}
+            yield {"source": doc}
 
         if len(self.chat_history) > 1:
             tokens_current_history = 0

application/retriever/duckduck_search.py

@@ -0,0 +1,97 @@
+import json
+import ast
+from application.retriever.base import BaseRetriever
+from application.core.settings import settings
+from application.llm.llm_creator import LLMCreator
+from application.utils import count_tokens
+from langchain_community.tools import DuckDuckGoSearchResults
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
+
+
+
+class DuckDuckSearch(BaseRetriever):
+
+    def __init__(self, question, source, chat_history, prompt, chunks=2, gpt_model='docsgpt'):
+        self.question = question
+        self.source = source
+        self.chat_history = chat_history
+        self.prompt = prompt
+        self.chunks = chunks
+        self.gpt_model = gpt_model
+
+    def _parse_lang_string(self, input_string):
+        result = []
+        current_item = ""
+        inside_brackets = False
+        for char in input_string:
+            if char == "[":
+                inside_brackets = True
+            elif char == "]":
+                inside_brackets = False
+                result.append(current_item)
+                current_item = ""
+            elif inside_brackets:
+                current_item += char
+        
+        # Check if there is an unmatched opening bracket at the end
+        if inside_brackets:
+            result.append(current_item)
+        
+        return result
+    
+    def _get_data(self):
+        if self.chunks == 0:
+            docs = []
+        else:
+            wrapper = DuckDuckGoSearchAPIWrapper(max_results=self.chunks)
+            search = DuckDuckGoSearchResults(api_wrapper=wrapper)
+            results = search.run(self.question)
+            results = self._parse_lang_string(results)
+        
+            docs = []
+            for i in results:
+                try:
+                    text = i.split("title:")[0]
+                    title = i.split("title:")[1].split("link:")[0]
+                    link = i.split("link:")[1]
+                    docs.append({"text": text, "title": title, "link": link})
+                except IndexError:
+                    pass
+        if settings.LLM_NAME == "llama.cpp":
+            docs = [docs[0]]
+        
+        return docs
+    
+    def gen(self):
+        docs = self._get_data()
+        
+        # join all page_content together with a newline
+        docs_together = "\n".join([doc["text"] for doc in docs])
+        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
+        messages_combine = [{"role": "system", "content": p_chat_combine}]
+        for doc in docs:
+            yield {"source": doc}
+
+        if len(self.chat_history) > 1:
+            tokens_current_history = 0
+            # count tokens in history
+            self.chat_history.reverse()
+            for i in self.chat_history:
+                if "prompt" in i and "response" in i:
+                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
+                    if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
+                        tokens_current_history += tokens_batch
+                        messages_combine.append({"role": "user", "content": i["prompt"]})
+                        messages_combine.append({"role": "system", "content": i["response"]})
+        messages_combine.append({"role": "user", "content": self.question})
+
+        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)
+
+        completion = llm.gen_stream(model=self.gpt_model,
+                                    messages=messages_combine)
+        for line in completion:
+            yield {"answer": str(line)}
+    
+    def search(self):
+        return self._get_data()
+        

application/retriever/retriever_creator.py

@@ -1,10 +1,12 @@
 from application.retriever.classic_rag import ClassicRAG
+from application.retriever.duckduck_search import DuckDuckSearch
 
 
 
 class RetrieverCreator:
     retievers = {
         'classic': ClassicRAG,
+        'duckduck': DuckDuckSearch
     }
 
     @classmethod