v4.1.0

AstraBert · Jun 12, 2024 · 34f8ce7 · 34f8ce7
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diff --git a/README.md b/README.md
@@ -6,7 +6,7 @@
     <img src="https://img.shields.io/github/languages/top/AstraBert/everything-ai" alt="GitHub top language">
    <img src="https://img.shields.io/github/commit-activity/t/AstraBert/everything-ai" alt="GitHub commit activity">
    <img src="https://img.shields.io/badge/everything_ai-stable-green" alt="Static Badge">
-   <img src="https://img.shields.io/badge/Release-v3.0.0-purple" alt="Static Badge">
+   <img src="https://img.shields.io/badge/Release-v4.1.0-purple" alt="Static Badge">
    <img src="https://img.shields.io/docker/image-size/astrabert/everything-ai
    " alt="Docker image size">
    <img src="https://img.shields.io/badge/Supported_platforms-Windows/macOS-brown" alt="Static Badge">
@@ -72,7 +72,8 @@ Choose the task among:
 - *autotrain*: fine-tune a model on a specific downstream task with autotrain-advanced, just by specifying you HF username, HF writing token and the path to a yaml config file for the training
 - *spaces-api-supabase*: use HF Spaces API in combination with Supabase PostgreSQL databases in order to unleash more powerful LLMs and larger RAG-oriented vector databases - **MULTILINGUAL**
 - *llama.cpp-and-qdrant*: same as *retrieval-text-generation*, but uses **llama.cpp** as inference engine, so you MUST NOT specify a model - **MULTILINGUAL**
-- *build-your-llm*: Build a customizable chat LLM combining a Qdrant database with your PDFs and the power of Anthropic, OpenAI, Cohere or Groq models: you just need an API key! To build the Qdrant database, have to pass either a pdf/a bunch of pdfs specified as comma-separated paths or a directory where all the pdfs of interest are stored (**DO NOT** provide both); you can also specify the language in which the PDF is written, using [ISO nomenclature](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes) - **MULTILINGUAL**
+- *build-your-llm*: Build a customizable chat LLM combining a Qdrant database with your PDFs and the power of Anthropic, OpenAI, Cohere or Groq models: you just need an API key! To build the Qdrant database, have to pass either a pdf/a bunch of pdfs specified as comma-separated paths or a directory where all the pdfs of interest are stored (**DO NOT** provide both); you can also specify the language in which the PDF is written, using [ISO nomenclature](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes) - **MULTILINGUAL**, **LANGFUSE INTEGRATION**
+- *build-your-llm*: Build a customizable chat LLM with the power of Anthropic, OpenAI, Cohere or Groq models (no RAG pipeline): you just need an API key! - **MULTILINGUAL**, **LANGFUSE INTEGRATION**
 - *image-retrieval-search*: search an image database uploading a folder as database input. The folder should have the following structure:
 
 ```

diff --git a/docker/Dockerfile b/docker/Dockerfile
@@ -7,10 +7,6 @@ WORKDIR /app
 # Add the current directory contents into the container at /app
 ADD . /app
 
-RUN pip uninstall -y tokenizers
-
-RUN pip install tokenizers==0.19.1
-
 # Expose the port that the application will run on
 EXPOSE 8760
 

diff --git a/docker/__pycache__/utils.cpython-310.pyc b/docker/__pycache__/utils.cpython-310.pyc
diff --git a/docker/build_your_llm.py b/docker/build_your_llm.py
@@ -12,6 +12,8 @@
 from utils import *
 import os
 import subprocess as sp
+import time
+from langfuse.callback import CallbackHandler
 
 argparse = ArgumentParser()
 
@@ -72,9 +74,18 @@ def get_session_history(session_id):
 
 system_template = "You are an helpful assistant that can rely on this: {context} and on the previous message history as context, and from that you build a context and history-aware reply to this user input:"
 
+def build_langfuse_handler(langfuse_host, langfuse_pkey, langfuse_skey):
+    if langfuse_host!="None" and langfuse_pkey!="None" and langfuse_skey!="None":
+        langfuse_handler = CallbackHandler(
+            public_key=langfuse_pkey,
+            secret_key=langfuse_skey,
+            host=langfuse_host
+        )
+        return langfuse_handler, True
+    else:
+        return "No langfuse", False
 
-
-def reply(message, history, name, api_key, temperature, max_new_tokens, sessionid):
+def reply(message, history, name, api_key, temperature, max_new_tokens,langfuse_host, langfuse_pkey, langfuse_skey, sessionid):
     global pdfdb
     os.environ[NAME2APIKEY[name]]  = api_key
     if name == "Cohere":
@@ -86,6 +97,7 @@ def reply(message, history, name, api_key, temperature, max_new_tokens, sessioni
     MessagesPlaceholder(variable_name="history"),
     ("human", "{input}")]
     )
+    lf_handler, truth = build_langfuse_handler(langfuse_host, langfuse_pkey, langfuse_skey)
     chain = prompt_template | model
     runnable_with_history = RunnableWithMessageHistory(
         chain,
@@ -97,37 +109,65 @@ def reply(message, history, name, api_key, temperature, max_new_tokens, sessioni
     if txt.original == "en" and lan.replace("\\","").replace("'","") == "None":
         txt2txt = NeuralSearcher(pdfdb.collection_name, pdfdb.client, pdfdb.encoder)
         results = txt2txt.search(message)
-        response = runnable_with_history.invoke({"context": results[0]["text"], "input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
-        return response.content
+        if not truth:
+            response = runnable_with_history.invoke({"context": results[0]["text"], "input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
+        else:
+            response = runnable_with_history.invoke({"context": results[0]["text"], "input": message}, config={"configurable": {"session_id": sessionid}, "callbacks": [lf_handler]})##CONFIGURE!
+        llm=''
+        for char in response.content:
+            llm+=char
+            time.sleep(0.001)
+            yield llm 
     elif txt.original == "en" and lan.replace("\\","").replace("'","") != "None":
         txt2txt = NeuralSearcher(pdfdb.collection_name, pdfdb.client, pdfdb.encoder)
         transl = Translation(message, lan.replace("\\","").replace("'",""))
         message = transl.translatef()
         results = txt2txt.search(message)
         t = Translation(results[0]["text"], txt.original)
         res = t.translatef()
-        response = runnable_with_history.invoke({"context": res, "input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
-        return response.content
+        if not truth:
+            response = runnable_with_history.invoke({"context": res, "input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
+        else:
+            response = runnable_with_history.invoke({"context": res, "input": message}, config={"configurable": {"session_id": sessionid}, "callbacks": [lf_handler]})##CONFIGURE!
+        llm = ''
+        for char in response.content:
+            llm+=char
+            time.sleep(0.001)
+            yield llm 
     elif txt.original != "en" and lan.replace("\\","").replace("'","") == "None":
         txt2txt = NeuralSearcher(pdfdb.collection_name, pdfdb.client, pdfdb.encoder)
         results = txt2txt.search(message)
         transl = Translation(results[0]["text"], "en")
         translation = transl.translatef()
-        response = runnable_with_history.invoke({"context": translation, "input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
+        if not truth:
+            response = runnable_with_history.invoke({"context": translation, "input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
+        else:
+            response = runnable_with_history.invoke({"context": translation, "input": message}, config={"configurable": {"session_id": sessionid}, "callbacks": [lf_handler]})##CONFIGURE!
         t = Translation(response.content, txt.original)
         res = t.translatef()
-        return res
+        llm = ''
+        for char in res:
+            llm+=char
+            time.sleep(0.001)
+            yield llm 
     else:
         txt2txt = NeuralSearcher(pdfdb.collection_name, pdfdb.client, pdfdb.encoder)
         transl = Translation(message, lan.replace("\\","").replace("'",""))
         message = transl.translatef()
         results = txt2txt.search(message)
         t = Translation(results[0]["text"], txt.original)
         res = t.translatef()
-        response = runnable_with_history.invoke({"context": res, "input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
+        if not truth:
+            response = runnable_with_history.invoke({"context": res, "input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
+        else:
+            response = runnable_with_history.invoke({"context": res, "input": message}, config={"configurable": {"session_id": sessionid}, "callbacks": [lf_handler]})##CONFIGURE!
         tr = Translation(response.content, txt.original)
         ress = tr.translatef()
-        return ress 
+        llm = ''
+        for char in ress:
+            llm+=char
+            time.sleep(0.001)
+            yield llm 
 
 chat_model = gr.Dropdown(
     [m for m in list(NAME2APIKEY)], label="Chat Model", info="Choose one of the available chat models"
@@ -144,11 +184,17 @@ def reply(message, history, name, api_key, temperature, max_new_tokens, sessioni
 
 user_max_new_tokens = gr.Slider(0, 8192, value=1024, label="Max new tokens", info="Select max output tokens (higher number of tokens will result in a longer latency)")
 
+user_lf_host = gr.Textbox(label="LangFuse Host",info="Provide LangFuse host URL, or type 'None' if you do not wish to use LangFuse",value="https://cloud.langfuse.com")
+
+user_lf_pkey = gr.Textbox(label="LangFuse Public Key",info="Provide LangFuse Public key, or type 'None' if you do not wish to use LangFuse",value="pk-*************************", type="password")
+
+user_lf_skey = gr.Textbox(label="LangFuse Secret Key",info="Provide LangFuse Secret key, or type 'None' if you do not wish to use LangFuse",value="sk-*************************", type="password")
+
 user_session_id = gr.Textbox(label="Session ID",info="This alphanumeric code will link model reply to a specific message history of which the models will be aware when replying. Changing it will result in the loss of memory for your model",value="1")
 
 additional_accordion = gr.Accordion(label="Parameters to be set before you start chatting", open=True)
 
-demo = gr.ChatInterface(fn=reply, additional_inputs=[chat_model, user_api_key, user_temperature, user_max_new_tokens, user_session_id], additional_inputs_accordion=additional_accordion, title="everything-ai-buildyourllm")
+demo = gr.ChatInterface(fn=reply, additional_inputs=[chat_model, user_api_key, user_temperature, user_max_new_tokens, user_lf_host, user_lf_pkey, user_lf_skey, user_session_id], additional_inputs_accordion=additional_accordion, title="everything-ai-buildyourllm")
 
 
 if __name__=="__main__":

diff --git a/docker/chat_your_llm.py b/docker/chat_your_llm.py
@@ -0,0 +1,113 @@
+from langchain_anthropic import ChatAnthropic
+from langchain_cohere import ChatCohere
+from langchain_groq import ChatGroq
+from langchain_openai import ChatOpenAI
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_community.chat_message_histories import SQLChatMessageHistory
+from utils import Translation
+import time
+import os
+from langfuse.callback import CallbackHandler
+import gradio as gr
+import subprocess as sp
+
+
+NAME2CHAT = {"Cohere": ChatCohere, "claude-3-opus-20240229": ChatAnthropic, "claude-3-sonnet-20240229": ChatAnthropic, "claude-3-haiku-20240307": ChatAnthropic, "llama3-8b-8192": ChatGroq, "llama3-70b-8192": ChatGroq, "mixtral-8x7b-32768": ChatGroq, "gemma-7b-it": ChatGroq, "gpt-4o": ChatOpenAI, "gpt-3.5-turbo-0125": ChatOpenAI}
+NAME2APIKEY = {"Cohere": "COHERE_API_KEY", "claude-3-opus-20240229": "ANTHROPIC_API_KEY", "claude-3-sonnet-20240229": "ANTHROPIC_API_KEY", "claude-3-haiku-20240307": "ANTHROPIC_API_KEY", "llama3-8b-8192": "GROQ_API_KEY", "llama3-70b-8192": "GROQ_API_KEY", "mixtral-8x7b-32768": "GROQ_API_KEY", "gemma-7b-it": "GROQ_API_KEY", "gpt-4o": "OPENAI_API_KEY", "gpt-3.5-turbo-0125": "OPENAI_API_KEY"}
+
+sp.run("rm -rf memory.db", shell=True)
+
+def build_langfuse_handler(langfuse_host, langfuse_pkey, langfuse_skey):
+    if langfuse_host!="None" and langfuse_pkey!="None" and langfuse_skey!="None":
+        langfuse_handler = CallbackHandler(
+            public_key=langfuse_pkey,
+            secret_key=langfuse_skey,
+            host=langfuse_host
+        )
+        return langfuse_handler, True
+    else:
+        return "No langfuse", False
+
+def get_session_history(session_id):
+    return SQLChatMessageHistory(session_id, "sqlite:///chatmemory.db")
+
+def reply(message, history, name, api_key, temperature, max_new_tokens,langfuse_host, langfuse_pkey, langfuse_skey, system_template, sessionid):
+    os.environ[NAME2APIKEY[name]]  = api_key
+    if name == "Cohere":
+        model = NAME2CHAT[name](temperature=temperature, max_tokens=max_new_tokens)
+    else:
+        model = NAME2CHAT[name](model=name,temperature=temperature, max_tokens=max_new_tokens)
+    prompt_template = ChatPromptTemplate.from_messages(
+    [("system", system_template),
+    MessagesPlaceholder(variable_name="history"),
+    ("human", "{input}")]
+    )
+    lf_handler, truth = build_langfuse_handler(langfuse_host, langfuse_pkey, langfuse_skey)
+    chain = prompt_template | model
+    runnable_with_history = RunnableWithMessageHistory(
+        chain,
+        get_session_history,
+        input_messages_key="input",
+        history_messages_key="history",
+    )
+    txt = Translation(message, "en")
+    if txt.original == "en":
+        if not truth:
+            response = runnable_with_history.invoke({"input": message}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
+        else:
+            response = runnable_with_history.invoke({"input": message}, config={"configurable": {"session_id": sessionid}, "callbacks": [lf_handler]})
+        r = ''
+        for c in response.content:
+            r+=c
+            time.sleep(0.001)
+            yield r   
+    else:
+        translation = txt.translatef()
+        if not truth:
+            response = runnable_with_history.invoke({"input": translation}, config={"configurable": {"session_id": sessionid}})##CONFIGURE!
+        else:
+            response = runnable_with_history.invoke({"input": translation}, config={"configurable": {"session_id": sessionid}, "callbacks": [lf_handler]})
+        t = Translation(response.content, txt.original)
+        res = t.translatef()
+        r = ''
+        for c in res:
+            r+=c
+            time.sleep(0.001)
+            yield r
+
+
+chat_model = gr.Dropdown(
+    [m for m in list(NAME2APIKEY)], label="Chat Model", info="Choose one of the available chat models"
+    )
+
+user_api_key = gr.Textbox(
+    label="API key",
+    info="Paste your API key here",
+    lines=1,
+    type="password",
+)
+
+user_temperature = gr.Slider(0, 1, value=0.5, label="Temperature", info="Select model temperature")
+
+user_max_new_tokens = gr.Slider(0, 8192, value=1024, label="Max new tokens", info="Select max output tokens (higher number of tokens will result in a longer latency)")
+
+user_lf_host = gr.Textbox(label="LangFuse Host",info="Provide LangFuse host URL, or type 'None' if you do not wish to use LangFuse",value="https://cloud.langfuse.com")
+
+user_lf_pkey = gr.Textbox(label="LangFuse Public Key",info="Provide LangFuse Public key, or type 'None' if you do not wish to use LangFuse",value="pk-*************************", type="password")
+
+user_lf_skey = gr.Textbox(label="LangFuse Secret Key",info="Provide LangFuse Secret key, or type 'None' if you do not wish to use LangFuse",value="sk-*************************", type="password")
+
+user_template = gr.Textbox(label="System Template",info="Customize your assistant with your instructions",value="You are an helpful assistant")
+
+user_session_id = gr.Textbox(label="Session ID",info="This alphanumeric code will link model reply to a specific message history of which the models will be aware when replying. Changing it will result in the loss of memory for your model",value="1")
+
+additional_accordion = gr.Accordion(label="Parameters to be set before you start chatting", open=True)
+
+demo = gr.ChatInterface(fn=reply, additional_inputs=[chat_model, user_api_key, user_temperature, user_max_new_tokens, user_lf_host, user_lf_pkey, user_lf_skey, user_template, user_session_id], additional_inputs_accordion=additional_accordion, title="everything-ai-simplychatting")
+
+
+if __name__=="__main__":
+    demo.launch(server_name="0.0.0.0", share=False)
+