better prompts

application/app.py

@@ -1,18 +1,21 @@
 import os
 import json
 import traceback
+import pprint
 
 import dotenv
 import requests
 from flask import Flask, request, render_template
 from langchain import FAISS
 from langchain.llms import OpenAIChat
-from langchain import VectorDBQA, HuggingFaceHub, Cohere
+from langchain import VectorDBQA, HuggingFaceHub, Cohere, OpenAI
 from langchain.chains.question_answering import load_qa_chain
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceHubEmbeddings, CohereEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceHubEmbeddings, CohereEmbeddings, \
+    HuggingFaceInstructEmbeddings
 from langchain.prompts import PromptTemplate
 from error import bad_request
-# os.environ["LANGCHAIN_HANDLER"] = "langchain"
+
+os.environ["LANGCHAIN_HANDLER"] = "langchain"
 
 if os.getenv("LLM_NAME") is not None:
     llm_choice = os.getenv("LLM_NAME")
@@ -24,8 +27,6 @@ if os.getenv("EMBEDDINGS_NAME") is not None:
 else:
     embeddings_choice = "openai_text-embedding-ada-002"
 
-
-
 if llm_choice == "manifest":
     from manifest import Manifest
     from langchain.llms.manifest import ManifestWrapper
@@ -53,6 +54,9 @@ with open("combine_prompt.txt", "r") as f:
 with open("combine_prompt_hist.txt", "r") as f:
     template_hist = f.read()
 
+with open("question_prompt.txt", "r") as f:
+    template_quest = f.read()
+
 if os.getenv("API_KEY") is not None:
     api_key_set = True
 else:
@@ -76,7 +80,7 @@ def api_answer():
     data = request.get_json()
     question = data["question"]
     history = data["history"]
-    print('-'*5)
+    print('-' * 5)
     if not api_key_set:
         api_key = data["api_key"]
     else:
@@ -95,7 +99,7 @@ def api_answer():
                 vectorstore = ""
         else:
             vectorstore = ""
-
+        #vectorstore = "outputs/inputs/"
         # loading the index and the store and the prompt template
         # Note if you have used other embeddings than OpenAI, you need to change the embeddings
         if embeddings_choice == "openai_text-embedding-ada-002":
@@ -110,13 +114,19 @@ def api_answer():
         # create a prompt template
         if history:
             history = json.loads(history)
-            template_temp = template_hist.replace("{historyquestion}", history[0]).replace("{historyanswer}", history[1])
-            c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template_temp, template_format="jinja2")
+            template_temp = template_hist.replace("{historyquestion}", history[0]).replace("{historyanswer}",
+                                                                                           history[1])
+            c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template_temp,
+                                      template_format="jinja2")
         else:
-            c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template, template_format="jinja2")
+            c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template,
+                                      template_format="jinja2")
 
+        q_prompt = PromptTemplate(input_variables=["context", "question"], template=template_quest,
+                                  template_format="jinja2")
         if llm_choice == "openai":
             llm = OpenAIChat(openai_api_key=api_key, temperature=0)
+            #llm = OpenAI(openai_api_key=api_key, temperature=0)
         elif llm_choice == "manifest":
             llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 2048})
         elif llm_choice == "huggingface":
@@ -125,14 +135,17 @@ def api_answer():
             llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
 
         qa_chain = load_qa_chain(llm=llm, chain_type="map_reduce",
-                                combine_prompt=c_prompt)
+                                 combine_prompt=c_prompt, question_prompt=q_prompt)
 
-        chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=4)
+        chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=25, return_source_documents=True)
 
-        
         # fetch the answer
         result = chain({"query": question})
-        print(result)
+        # pprint.pprint(result)
+        # docs = docsearch.similarity_search(question, k=8)
+
+        for i in result['source_documents']:
+            print(i.page_content)
 
         # some formatting for the frontend
         result['answer'] = result['result']
@@ -141,6 +154,7 @@ def api_answer():
             result['answer'] = result['answer'].split("SOURCES:")[0]
         except:
             pass
+        del result['source_documents']
 
         # mock result
         # result = {
@@ -152,7 +166,7 @@ def api_answer():
         # print whole traceback
         traceback.print_exc()
         print(str(e))
-        return bad_request(500,str(e))
+        return bad_request(500, str(e))
 
 
 @app.route("/api/docs_check", methods=["POST"])