testings

application/api/answer/routes.py

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+import os
+from flask import Blueprint, request, jsonify, Response
+import requests
+import json
+import datetime
+
+from langchain.chat_models import AzureChatOpenAI
+from pymongo import MongoClient
+from bson.objectid import ObjectId
+from werkzeug.utils import secure_filename
+import http.client
+
+from application.app import (logger, count_tokens, chat_combine_template, gpt_model,
+                             api_key_set, embeddings_key_set, get_docsearch, get_vectorstore)
+from application.core.settings import settings
+from application.llm.openai import OpenAILLM
+
+
+mongo = MongoClient(settings.MONGO_URI)
+db = mongo["docsgpt"]
+conversations_collection = db["conversations"]
+vectors_collection = db["vectors"]
+answer = Blueprint('answer', __name__)
+
+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):
+
+    # openai.api_key = api_key
+
+    if is_azure_configured():
+        # logger.debug("in Azure")
+        # openai.api_type = "azure"
+        # openai.api_version = settings.OPENAI_API_VERSION
+        # openai.api_base = settings.OPENAI_API_BASE
+        # llm = AzureChatOpenAI(
+        #     openai_api_key=api_key,
+        #     openai_api_base=settings.OPENAI_API_BASE,
+        #     openai_api_version=settings.OPENAI_API_VERSION,
+        #     deployment_name=settings.AZURE_DEPLOYMENT_NAME,
+        # )
+        llm = OpenAILLM(api_key=api_key)
+    else:
+        logger.debug("plain OpenAI")
+        llm = OpenAILLM(api_key=api_key)
+        # llm = ChatOpenAI(openai_api_key=api_key)
+    docs = docsearch.similarity_search(question, k=2)
+    # 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)
+    messages_combine = [{"role": "system", "content": p_chat_combine}]
+    source_log_docs = []
+    for doc in docs:
+        if doc.metadata:
+            data = json.dumps({"type": "source", "doc": doc.page_content, "metadata": doc.metadata})
+            source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
+        else:
+            data = json.dumps({"type": "source", "doc": doc.page_content})
+            source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
+        yield f"data:{data}\n\n"
+
+    if len(chat_history) > 1:
+        tokens_current_history = 0
+        # count tokens in history
+        chat_history.reverse()
+        for i in 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": question})
+    # completion = openai.ChatCompletion.create(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
+    #                                           messages=messages_combine, stream=True, max_tokens=500, temperature=0)
+    import sys
+    print(api_key)
+    reponse_full = ""
+    # for line in completion:
+    #     if "content" in line["choices"][0]["delta"]:
+    #         # check if the delta contains content
+    #         data = json.dumps({"answer": str(line["choices"][0]["delta"]["content"])})
+    #         reponse_full += str(line["choices"][0]["delta"]["content"])
+    #         yield f"data: {data}\n\n"
+    # reponse_full = ""
+    print(llm)
+    completion = llm.gen_stream(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
+                   messages=messages_combine)
+    for line in completion:
+
+        data = json.dumps({"answer": str(line)})
+        reponse_full += str(line)
+        yield f"data: {data}\n\n"
+
+
+    # save conversation to database
+    if conversation_id is not None:
+        conversations_collection.update_one(
+            {"_id": ObjectId(conversation_id)},
+            {"$push": {"queries": {"prompt": question, "response": reponse_full, "sources": source_log_docs}}},
+        )
+
+    else:
+        # create new conversation
+        # generate summary
+        messages_summary = [{"role": "assistant", "content": "Summarise following conversation in no more than 3 "
+                                                             "words, respond ONLY with the summary, use the same "
+                                                             "language as the system \n\nUser: " + question + "\n\n" +
+                                                             "AI: " +
+                                                             reponse_full},
+                            {"role": "user", "content": "Summarise following conversation in no more than 3 words, "
+                                                        "respond ONLY with the summary, use the same language as the "
+                                                        "system"}]
+        # completion = openai.ChatCompletion.create(model='gpt-3.5-turbo', engine=settings.AZURE_DEPLOYMENT_NAME,
+        #                                           messages=messages_summary, max_tokens=30, temperature=0)
+        completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
+                                    messages=messages_combine, max_tokens=30)
+        conversation_id = conversations_collection.insert_one(
+            {"user": "local",
+             "date": datetime.datetime.utcnow(),
+             "name": completion["choices"][0]["message"]["content"],
+             "queries": [{"prompt": question, "response": reponse_full, "sources": source_log_docs}]}
+        ).inserted_id
+
+    # send data.type = "end" to indicate that the stream has ended as json
+    data = json.dumps({"type": "id", "id": str(conversation_id)})
+    yield f"data: {data}\n\n"
+    data = json.dumps({"type": "end"})
+    yield f"data: {data}\n\n"
+
+
+@answer.route("/stream", methods=["POST"])
+def stream():
+    data = request.get_json()
+    # get parameter from url question
+    question = data["question"]
+    history = data["history"]
+    # history to json object from string
+    history = json.loads(history)
+    conversation_id = data["conversation_id"]
+
+    # check if active_docs is set
+
+    if not api_key_set:
+        api_key = data["api_key"]
+    else:
+        api_key = settings.API_KEY
+    if not embeddings_key_set:
+        embeddings_key = data["embeddings_key"]
+    else:
+        embeddings_key = settings.EMBEDDINGS_KEY
+    if "active_docs" in data:
+        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
+    else:
+        vectorstore = ""
+    docsearch = get_docsearch(vectorstore, embeddings_key)
+
+    # question = "Hi"
+    return Response(
+        complete_stream(question, docsearch,
+                        chat_history=history, api_key=api_key,
+                        conversation_id=conversation_id), mimetype="text/event-stream"
+    )