prompts

application/api/answer/routes.py

@@ -36,21 +36,18 @@ else:
 
 # load the prompts
 current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-with open(os.path.join(current_dir, "prompts", "combine_prompt.txt"), "r") as f:
-    template = f.read()
-
-with open(os.path.join(current_dir, "prompts", "combine_prompt_hist.txt"), "r") as f:
-    template_hist = f.read()
-
-with open(os.path.join(current_dir, "prompts", "question_prompt.txt"), "r") as f:
-    template_quest = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_combine_prompt.txt"), "r") as f:
+with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
     chat_combine_template = f.read()
 
 with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") as f:
     chat_reduce_template = f.read()
 
+with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
+    chat_reduce_creative = f.read()
+
+with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
+    chat_reduce_strict = f.read()    
+
 api_key_set = settings.API_KEY is not None
 embeddings_key_set = settings.EMBEDDINGS_KEY is not None
 
@@ -115,8 +112,17 @@ def is_azure_configured():
     return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
 
 
-def complete_stream(question, docsearch, chat_history, api_key, conversation_id):
+def complete_stream(question, docsearch, chat_history, api_key, prompt_id, conversation_id):
     llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
+
+    if prompt_id == 'default':
+        prompt = chat_reduce_template
+    elif prompt_id == 'creative':
+        prompt = chat_reduce_creative
+    elif prompt_id == 'strict':
+        prompt = chat_reduce_strict
+    else:
+        prompt = chat_reduce_template
     
 
     docs = docsearch.search(question, k=2)
@@ -124,7 +130,7 @@ def complete_stream(question, docsearch, chat_history, api_key, conversation_id)
         docs = [docs[0]]
     # join all page_content together with a newline
     docs_together = "\n".join([doc.page_content for doc in docs])
-    p_chat_combine = chat_combine_template.replace("{summaries}", docs_together)
+    p_chat_combine = prompt.replace("{summaries}", docs_together)
     messages_combine = [{"role": "system", "content": p_chat_combine}]
     source_log_docs = []
     for doc in docs:
@@ -201,6 +207,10 @@ def stream():
     # history to json object from string
     history = json.loads(history)
     conversation_id = data["conversation_id"]
+    if 'prompt_id' in data:
+        prompt_id = data["prompt_id"]
+    else:
+        prompt_id = 'default'
 
     # check if active_docs is set
 
@@ -221,6 +231,7 @@ def stream():
     return Response(
         complete_stream(question, docsearch,
                         chat_history=history, api_key=api_key,
+                        prompt_id=prompt_id,
                         conversation_id=conversation_id), mimetype="text/event-stream"
     )
 

application/api/user/routes.py

@@ -16,6 +16,7 @@ mongo = MongoClient(settings.MONGO_URI)
 db = mongo["docsgpt"]
 conversations_collection = db["conversations"]
 vectors_collection = db["vectors"]
+prompts_collection = db["prompts"]
 user = Blueprint('user', __name__)
 
 current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -188,7 +189,7 @@ def combined_json():
             "date": "default",
             "docLink": "default",
             "model": settings.EMBEDDINGS_NAME,
-            "location": "local",
+            "location": "remote",
         }
     ]
     # structure: name, language, version, description, fullName, date, docLink
@@ -245,6 +246,59 @@ def check_docs():
 
         return {"status": "loaded"}
 
+@user.route("/api/create_prompt", methods=["POST"])
+def create_prompt():
+    data = request.get_json()
+    prompt = data["prompt"]
+    name = data["name"]
+    user = "local"
+    # write to mongodb
+    prompts_collection.insert_one(
+        {
+            "name": name,
+            "prompt": prompt,
+            "user": user,
+        }
+    )
+    return {"status": "ok"}
+
+@user.route("/api/get_prompts", methods=["GET"])
+def get_prompts():
+    user = "local"
+    prompts = prompts_collection.find({"user": user})
+    list_prompts = []
+    list_prompts.append({"id": "default", "name": "default", "type": "public"})
+    list_prompts.append({"id": "creative", "name": "creative", "type": "public"})
+    list_prompts.append({"id": "precise", "name": "precise", "type": "public"})
+    for prompt in prompts:
+        list_prompts.append({"id": str(prompt["_id"]), "name": prompt["name"], type: "private"})
+
+    return jsonify(list_prompts)
+
+@user.route("/api/get_single_prompt", methods=["GET"])
+def get_single_prompt():
+    prompt_id = request.args.get("id")
+    prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})
+    return jsonify(prompt['prompt'])
+
+@user.route("/api/delete_prompt", methods=["POST"])
+def delete_prompt():
+    prompt_id = request.args.get("id")
+    prompts_collection.delete_one(
+        {
+            "_id": ObjectId(prompt_id),
+        }
+    )
+    return {"status": "ok"}
+
+@user.route("/api/update_prompt_name", methods=["POST"])
+def update_prompt_name():
+    data = request.get_json()
+    id = data["id"]
+    name = data["name"]
+    prompts_collection.update_one({"_id": ObjectId(id)},{"$set":{"name":name}})
+    return {"status": "ok"}
+
 
 
 

application/prompts/chat_combine_default.txt

@@ -0,0 +1,9 @@
+You are a helpful AI assistant, DocsGPT, specializing in document assistance, designed to offer detailed and informative responses. 
+If appropriate, your answers can include code examples, formatted as follows:
+```(language)
+(code)
+```
+You effectively utilize chat history, ensuring relevant and tailored responses. 
+If a question doesn't align with your context, you provide friendly and helpful replies.
+----------------
+{summaries}

application/prompts/chat_combine_strict.txt

@@ -0,0 +1,13 @@
+You are an AI Assistant, DocsGPT, adept at offering document assistance. 
+Your expertise lies in providing answer on top of provided context. 
+You can leverage the chat history if needed.
+Answer the question based on the context below. 
+Keep the answer concise. Respond "Irrelevant context" if not sure about the answer.
+If question is not related to the context, respond "Irrelevant context".
+When using code examples, use the following format:
+```(language)
+(code)
+```
+ ----------------
+ Context:
+ {summaries}

application/prompts/combine_prompt.txt

@@ -1,25 +0,0 @@
-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?
-=========
-Content: pandas provides various facilities for easily combining together Series or DataFrame with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
-Source: 28-pl
-Content: pandas provides a single function, merge(), as the entry point for all standard database join operations between DataFrame or named Series objects: \n\npandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)
-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: {{ question }}
-=========
-{{ summaries }}
-=========
-FINAL ANSWER:

application/prompts/combine_prompt_hist.txt

@@ -1,33 +0,0 @@
-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?
-=========
-Content: pandas provides various facilities for easily combining together Series or DataFrame with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
-Source: 28-pl
-Content: pandas provides a single function, merge(), as the entry point for all standard database join operations between DataFrame or named Series objects: \n\npandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)
-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:
-SOURCE:
-=========
-FINAL ANSWER: {{ historyanswer }}
-SOURCES:
-
-QUESTION: {{ question }}
-=========
-{{ summaries }}
-=========
-FINAL ANSWER:

application/prompts/question_prompt.txt

@@ -1,4 +0,0 @@
-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 "-".