Create custom_class.py

Created `custom_class.py`

custom_class.py

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+# Wrap our custom fine-tuned model into a Langchain LLM class, and make it available for chainlit factory.
+import chainlit as cl
+import cohere
+from langchain.llms.base import LLM
+from metaphor_python import Metaphor
+
+### Caution : This should not kept in open, and I'll fix this in upcoming commits.
+
+metaphor = Metaphor("56d9ca28-e84b-43ce-8f68-75e1a8bb4dd3")   ## This is metaphor API Key
+co = cohere.Client('P21OYiQsKcOVS9XeQsacEhMVodPbbbYccpX2XWsz')    # This is COHERE_API_KEY
+
+class SourcesString:
+    """[ Custom Class to beautify methaphor.search.result(currently, only top 3 results are shown)] """
+    def __init__(self, results_list) -> None:
+        self.res_list = results_list
+
+    def get_parsed_string(self):
+
+        return "### Additional Sources:\n"+"\n".join(f"{i+1}. [{self.res_list[i].title}]({self.res_list[i].url})" for i in range(len(self.res_list)))
+
+
+class CustomLLM(LLM):
+    """[Wrapper for Cohere Chat Model, which is then appended with Metaphor Search Results to mimic RAG. ] """
+  
+    @property
+    def _llm_type(self) -> str:
+        return "custom"
+
+    def  _call(self,prompt: str,stop = None,run_manager = None,) -> str:
+        """ Cohere Chat Model is not supported as of now, so we create a custom wrapper it. """
+
+        if stop is not None:
+            raise ValueError("stop kwargs are not permitted.")
+        response = co.chat(f'{prompt}', model = 'command')
+
+        results = metaphor.search(prompt, use_autoprompt=True, num_results=3)
+        # print(results)
+        
+        print(response)
+
+        sources_text = SourcesString(results.results).get_parsed_string().strip("```")    # pretty hacky, but we need to find a good fix for this.
+        print(sources_text)
+
+        return response.text + "\n\n\n" + sources_text
+
+    @property
+    def _identifying_params(self) :
+        """Get the identifying parameters."""
+
+        return {"model_type": f'COHERE_CHAT_MODEL'}