Merge pull request #348 from arc53/feature/better-structure

Feature/better structure

.gitignore

@@ -169,4 +169,5 @@ application/vectors/
 
 **/yarn.lock
 
-node_modules/
+node_modules/
+.vscode/settings.json

application/api/answer/routes.py

@@ -0,0 +1,365 @@
+import asyncio
+import os
+from flask import Blueprint, request, Response
+import json
+import datetime
+import logging
+import traceback
+
+from pymongo import MongoClient
+from bson.objectid import ObjectId
+from transformers import GPT2TokenizerFast
+
+
+
+from application.core.settings import settings
+from application.llm.openai import OpenAILLM, AzureOpenAILLM
+from application.vectorstore.faiss import FaissStore
+from application.error import bad_request
+
+
+
+logger = logging.getLogger(__name__)
+
+mongo = MongoClient(settings.MONGO_URI)
+db = mongo["docsgpt"]
+conversations_collection = db["conversations"]
+vectors_collection = db["vectors"]
+answer = Blueprint('answer', __name__)
+
+if settings.LLM_NAME == "gpt4":
+    gpt_model = 'gpt-4'
+else:
+    gpt_model = 'gpt-3.5-turbo'
+
+if settings.SELF_HOSTED_MODEL:
+    from langchain.llms import HuggingFacePipeline
+    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+    model_id = settings.LLM_NAME  # hf model id (Arc53/docsgpt-7b-falcon, Arc53/docsgpt-14b)
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(model_id)
+    pipe = pipeline(
+        "text-generation", model=model,
+        tokenizer=tokenizer, max_new_tokens=2000,
+        device_map="auto", eos_token_id=tokenizer.eos_token_id
+    )
+    hf = HuggingFacePipeline(pipeline=pipe)
+
+# 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:
+    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()
+
+api_key_set = settings.API_KEY is not None
+embeddings_key_set = settings.EMBEDDINGS_KEY is not None
+
+
+async def async_generate(chain, question, chat_history):
+    result = await chain.arun({"question": question, "chat_history": chat_history})
+    return result
+
+
+def count_tokens(string):
+    tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
+    return len(tokenizer(string)['input_ids'])
+
+
+def run_async_chain(chain, question, chat_history):
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    result = {}
+    try:
+        answer = loop.run_until_complete(async_generate(chain, question, chat_history))
+    finally:
+        loop.close()
+    result["answer"] = answer
+    return result
+
+
+def get_vectorstore(data):
+    if "active_docs" in data:
+        if data["active_docs"].split("/")[0] == "local":
+            if data["active_docs"].split("/")[1] == "default":
+                vectorstore = ""
+            else:
+                vectorstore = "indexes/" + data["active_docs"]
+        else:
+            vectorstore = "vectors/" + data["active_docs"]
+        if data["active_docs"] == "default":
+            vectorstore = ""
+    else:
+        vectorstore = ""
+    vectorstore = os.path.join("application", vectorstore)
+    return vectorstore
+
+
+# def get_docsearch(vectorstore, embeddings_key):
+#     if settings.EMBEDDINGS_NAME == "openai_text-embedding-ada-002":
+#         if is_azure_configured():
+#             os.environ["OPENAI_API_TYPE"] = "azure"
+#             openai_embeddings = OpenAIEmbeddings(model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME)
+#         else:
+#             openai_embeddings = OpenAIEmbeddings(openai_api_key=embeddings_key)
+#         docsearch = FAISS.load_local(vectorstore, openai_embeddings)
+#     elif settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
+#         docsearch = FAISS.load_local(vectorstore, HuggingFaceHubEmbeddings())
+#     elif settings.EMBEDDINGS_NAME == "huggingface_hkunlp/instructor-large":
+#         docsearch = FAISS.load_local(vectorstore, HuggingFaceInstructEmbeddings())
+#     elif settings.EMBEDDINGS_NAME == "cohere_medium":
+#         docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key))
+#     return docsearch
+
+
+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):
+    if is_azure_configured():
+        llm = AzureOpenAILLM(
+            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,
+        )
+    else:
+        logger.debug("plain OpenAI")
+        llm = OpenAILLM(api_key=api_key)
+
+    docs = docsearch.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})
+
+    response_full = ""
+    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)})
+        response_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": response_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: " +
+                                                             response_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 = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
+                             messages=messages_summary, max_tokens=30)
+        conversation_id = conversations_collection.insert_one(
+            {"user": "local",
+             "date": datetime.datetime.utcnow(),
+             "name": completion,
+             "queries": [{"prompt": question, "response": response_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 = FaissStore(vectorstore, embeddings_key)
+
+    return Response(
+        complete_stream(question, docsearch,
+                        chat_history=history, api_key=api_key,
+                        conversation_id=conversation_id), mimetype="text/event-stream"
+    )
+
+
+@answer.route("/api/answer", methods=["POST"])
+def api_answer():
+    data = request.get_json()
+    question = data["question"]
+    history = data["history"]
+    if "conversation_id" not in data:
+        conversation_id = None
+    else:
+        conversation_id = data["conversation_id"]
+    print("-" * 5)
+    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
+
+    # use try and except  to check for exception
+    try:
+        # check if the vectorstore is set
+        vectorstore = get_vectorstore(data)
+        # 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
+        docsearch = FaissStore(vectorstore, embeddings_key)
+
+        if is_azure_configured():
+            llm = AzureOpenAILLM(
+                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,
+            )
+        else:
+            logger.debug("plain OpenAI")
+            llm = OpenAILLM(api_key=api_key)
+
+
+
+        docs = docsearch.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:
+                source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
+            else:
+                source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
+        # join all page_content together with a newline
+
+
+        if len(history) > 1:
+            tokens_current_history = 0
+            # count tokens in history
+            history.reverse()
+            for i in 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 = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
+                                    messages=messages_combine)
+
+
+        result = {"answer": completion, "sources": source_log_docs}
+        logger.debug(result)
+
+        # generate conversationId
+        if conversation_id is not None:
+            conversations_collection.update_one(
+                {"_id": ObjectId(conversation_id)},
+                {"$push": {"queries": {"prompt": question,
+                                       "response": result["answer"], "sources": result['sources']}}},
+            )
+
+        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\n"
+                    "User: " + question + "\n\n" + "AI: " + result["answer"]},
+                {"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 = llm.gen(
+                model=gpt_model,
+                engine=settings.AZURE_DEPLOYMENT_NAME,
+                messages=messages_summary,
+                max_tokens=30
+            )
+            conversation_id = conversations_collection.insert_one(
+                {"user": "local",
+                "date": datetime.datetime.utcnow(),
+                "name": completion,
+                "queries": [{"prompt": question, "response": result["answer"], "sources": source_log_docs}]}
+            ).inserted_id
+
+        result["conversation_id"] = str(conversation_id)
+
+        # mock result
+        # result = {
+        #     "answer": "The answer is 42",
+        #     "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
+        # }
+        return result
+    except Exception as e:
+        # print whole traceback
+        traceback.print_exc()
+        print(str(e))
+        return bad_request(500, str(e))