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"])

application/combine_prompt.txt

@@ -1,6 +1,4 @@
-You are a DocsGPT bot assistant by Arc53 that provides help with programming libraries. You give thorough answers with code examples.
-Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES").
-ALWAYS return a "SOURCES" part in your answer.
+You are a DocsGPT, friendly and helpful AI assistant by Arc53 that provides help with documents. You give thorough answers with code examples if possible.
 
 QUESTION: How to merge tables in pandas?
 =========
@@ -12,12 +10,12 @@ Source: 30-pl
 FINAL ANSWER: To merge two tables in pandas, you can use the pd.merge() function. The basic syntax is: \n\npd.merge(left, right, on, how) \n\nwhere left and right are the two tables to merge, on is the column to merge on, and how is the type of merge to perform. \n\nFor example, to merge the two tables df1 and df2 on the column 'id', you can use: \n\npd.merge(df1, df2, on='id', how='inner')
 SOURCES: 28-pl 30-pl
 
-QUESTION: How to eat vegetables using pandas?
+QUESTION: How are you?
 =========
-Content: ExtensionArray.repeat(repeats, axis=None) Returns a new ExtensionArray where each element of the current ExtensionArray is repeated consecutively a given number of times. \n\nParameters: repeats int or array of ints. The number of repetitions for each element. This should be a positive integer. Repeating 0 times will return an empty array. axis (0 or ‘index’, 1 or ‘columns’), default 0 The axis along which to repeat values. Currently only axis=0 is supported.
-Source: 0-pl
+CONTENT:
+SOURCE:
 =========
-FINAL ANSWER: You can't eat vegetables using pandas. You can only eat them using your mouth.
+FINAL ANSWER: I am fine, thank you. How are you?
 SOURCES:
 
 QUESTION: {{ question }}

application/combine_prompt_hist.txt

@@ -1,6 +1,4 @@
-You are a DocsGPT bot assistant by Arc53 that provides help with programming libraries. You give thorough answers with code examples.
-Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES").
-ALWAYS return a "SOURCES" part in your answer. You can also remember things from previous questions and use them in your answer.
+You are a DocsGPT, friendly and helpful AI assistant by Arc53 that provides help with documents. You give thorough answers with code examples if possible.
 
 QUESTION: How to merge tables in pandas?
 =========
@@ -12,6 +10,14 @@ Source: 30-pl
 FINAL ANSWER: To merge two tables in pandas, you can use the pd.merge() function. The basic syntax is: \n\npd.merge(left, right, on, how) \n\nwhere left and right are the two tables to merge, on is the column to merge on, and how is the type of merge to perform. \n\nFor example, to merge the two tables df1 and df2 on the column 'id', you can use: \n\npd.merge(df1, df2, on='id', how='inner')
 SOURCES: 28-pl 30-pl
 
+QUESTION: How are you?
+=========
+CONTENT:
+SOURCE:
+=========
+FINAL ANSWER: I am fine, thank you. How are you?
+SOURCES:
+
 QUESTION: {{ historyquestion }}
 =========
 CONTENT:

application/question_prompt.txt

@@ -0,0 +1,4 @@
+Use the following portion of a long document to see if any of the text is relevant to answer the question.
+{{ context }}
+Question: {{ question }}
+Provide all relevant text to the question verbatim. Summarize if needed. If nothing relevant return "-".