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gradio_app.py

@@ -15,6 +15,7 @@ from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import HuggingFaceBgeEmbeddings
 from langchain.llms import HuggingFaceTextGenInference, OpenAI
 from langchain.prompts import PromptTemplate
+from langchain.schema.messages import SystemMessage
 from langchain.tools import tool
 from langchain.vectorstores import FAISS
 from pydantic import BaseModel, Field
@@ -74,11 +75,13 @@ def load_chain():
     #     "Searches and returns documents regarding Indian law. Accepts query as a string. For example: 'Section 298 of Indian Penal Code'."
     # )
     tools = [search]
-    llm = ChatOpenAI(openai_api_base='http://20.83.177.108:8080/v1',
+    llm = ChatOpenAI(openai_api_base='http://20.124.240.6:8080/v1',
                      openai_api_key='none',)
 
     conv_agent_executor = create_conversational_retrieval_agent(
         llm, tools, verbose=False,
+        system_message=SystemMessage(
+            content="Your name is Votum, an expert legal assistant with extensive knowledge about Indian law. Your task is to respond to the given query in a factually correct and concise manner unless asked for a detailed explanation. Feel free to use any tools available to look up relevant information, only if necessary")
     )
     return conv_agent_executor
 
@@ -102,14 +105,20 @@ with gr.Blocks() as demo:
     def respond(history):
         print('message is', history[-1])
         bot_message = chain({'input': history[-1][0]})['output']
-        history[-1][1] = ""
-        for character in bot_message:
-            history[-1][1] += character
-            time.sleep(0.0)
-            yield history
 
+        if 'Final answer:' in bot_message:
+            bot_message = bot_message.split('Final answer:')[-1]
+
+        history[-1][1] = bot_message
+        # for character in bot_message:
+        #     history[-1][1] += character
+        #     time.sleep(0.0)
+        #     yield history
+        return history
+
+    clear.click(chain.memory.clear(),)
     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False
                ).then(respond, chatbot, chatbot)
 
 if __name__ == "__main__":
-    demo.queue(max_size=32).launch(enable_queue=True)
+    demo.queue(max_size=32).launch()

langchain_qwen.ipynb

@@ -63,9 +63,10 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "\n",
-    "llm = ChatOpenAI(openai_api_base='http://20.83.177.108:8080/v1',\n",
-    "                 openai_api_key='none',)\n"
+    "ChatOpenAI(openai_api_base='http://20.124.240.6:8080/v1',\n",
+    "           openai_api_key='none', callbacks=[StreamingStdOutCallbackHandler()], streaming=True,)\n"
    ]
   },
   {
@@ -86,7 +87,6 @@
     "from langchain.agents.agent_toolkits import create_conversational_retrieval_agent\n",
     "from langchain.agents.openai_functions_agent.agent_token_buffer_memory import AgentTokenBufferMemory\n",
     "from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\n",
-    "from langchain.schema.messages import SystemMessage\n",
     "from langchain.prompts import MessagesPlaceholder\n",
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain.memory import ConversationBufferMemory\n",
@@ -130,23 +130,18 @@
     "\n",
     "\n",
     "\n",
+    "\n",
+    "\n",
+    "\n",
     "tool = create_retriever_tool(\n",
     "    retriever,\n",
     "    \"search_legal_sections\",\n",
-    "    \"Searches and returns documents regarding Indian legal acts and sections.\"\n",
+    "    \"Searches and returns documents regarding Indian law. Accept query as a string. For example: 'Section 298 of Indian Penal Code'.\",\n",
+    "    \n",
     ")\n",
     "tools = [tool]\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(type(tool),type(search))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -180,25 +175,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'HuggingFaceTextGenInference' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb Cell 12\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m llm \u001b[39m=\u001b[39m HuggingFaceTextGenInference(\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m     inference_server_url\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhttp://20.83.177.108:8080/\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m     max_new_tokens\u001b[39m=\u001b[39m\u001b[39m512\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     top_k\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     top_p\u001b[39m=\u001b[39m\u001b[39m0.95\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m     typical_p\u001b[39m=\u001b[39m\u001b[39m0.95\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     temperature\u001b[39m=\u001b[39m\u001b[39m0.6\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     \u001b[39m# repetition_penalty=1.1,\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X41sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m )\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'HuggingFaceTextGenInference' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain.llms import HuggingFaceTextGenInference\n",
     "\n",
-    "llm = HuggingFaceTextGenInference(\n",
+    "llm_hg = HuggingFaceTextGenInference(\n",
     "    inference_server_url=\"http://20.83.177.108:8080/\",\n",
     "    max_new_tokens=2000,\n",
     "    # top_k=10,\n",
@@ -217,6 +200,10 @@
    "source": [
     "from langchain.agents import initialize_agent, Tool\n",
     "from langchain.agents import AgentType\n",
+    "from langchain.schema.messages import SystemMessage\n",
+    "import langchain\n",
+    "\n",
+    "langchain.verbose = True\n",
     "\n",
     "\n",
     "\n",
@@ -227,8 +214,13 @@
     "    verbose=True,\n",
     ")\n",
     "\n",
+    "agent_kwargs = {\n",
+    "    \"extra_prompt_messages\": [MessagesPlaceholder(variable_name=\"memory\")],\n",
+    "    \"system_message\": SystemMessage(content=\"Your name is Votum, an expert legal assistant with extensive knowledge about Indian law. Your task is to respond to the given query in a factually correct and concise manner unless asked for a detailed explanation.\"),\n",
+    "}\n",
+    "\n",
     "conv_agent_executor = create_conversational_retrieval_agent(\n",
-    "    llm, tools, verbose=True,\n",
+    "    llm, [search], verbose=False,  agent_kwargs=agent_kwargs, \n",
     ")\n"
    ]
   },
@@ -240,8 +232,9 @@
    "source": [
     "# {'input': 'How is section 308 of Indian Penal Code different from section 299?'}\n",
     "conv_agent_executor(\n",
-    "    {'input': 'Sorry i meant 299.'}\n",
-    "    )\n"
+    "    {'input': 'Explain sections related to medical negligence.'}\n",
+    "    )\n",
+    "\n"
    ]
   },
   {
@@ -253,21 +246,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValidationError",
-     "evalue": "1 validation error for OpenAI\n__root__\n  Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass  `openai_api_key` as a named parameter. (type=value_error)",
+     "ename": "NameError",
+     "evalue": "name 'retriever' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "\u001b[1;32m/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb Cell 15\u001b[0m line \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mlangchain\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m langchain\u001b[39m.\u001b[39mverbose \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m flare \u001b[39m=\u001b[39m FlareChain\u001b[39m.\u001b[39;49mfrom_llm(\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     ChatOpenAI(openai_api_base\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mhttp://20.83.177.108:8080/v1\u001b[39;49m\u001b[39m'\u001b[39;49m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m                openai_api_key\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mnone\u001b[39;49m\u001b[39m'\u001b[39;49m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     retriever\u001b[39m=\u001b[39;49mretriever,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m     max_generation_len\u001b[39m=\u001b[39;49m\u001b[39m164\u001b[39;49m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m     min_prob\u001b[39m=\u001b[39;49m\u001b[39m0.3\u001b[39;49m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X40sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m query \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mexplain in great detail the difference between the langchain framework and baby agi\u001b[39m\u001b[39m\"\u001b[39m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/langchain/chains/flare/base.py:249\u001b[0m, in \u001b[0;36mFlareChain.from_llm\u001b[0;34m(cls, llm, max_generation_len, **kwargs)\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Creates a FlareChain from a language model.\u001b[39;00m\n\u001b[1;32m    239\u001b[0m \n\u001b[1;32m    240\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[39m    FlareChain class with the given language model.\u001b[39;00m\n\u001b[1;32m    247\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    248\u001b[0m question_gen_chain \u001b[39m=\u001b[39m QuestionGeneratorChain(llm\u001b[39m=\u001b[39mllm)\n\u001b[0;32m--> 249\u001b[0m response_llm \u001b[39m=\u001b[39m OpenAI(\n\u001b[1;32m    250\u001b[0m     max_tokens\u001b[39m=\u001b[39;49mmax_generation_len, model_kwargs\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39mlogprobs\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m1\u001b[39;49m}, temperature\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m\n\u001b[1;32m    251\u001b[0m )\n\u001b[1;32m    252\u001b[0m response_chain \u001b[39m=\u001b[39m _OpenAIResponseChain(llm\u001b[39m=\u001b[39mresponse_llm)\n\u001b[1;32m    253\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39m(\n\u001b[1;32m    254\u001b[0m     question_generator_chain\u001b[39m=\u001b[39mquestion_gen_chain,\n\u001b[1;32m    255\u001b[0m     response_chain\u001b[39m=\u001b[39mresponse_chain,\n\u001b[1;32m    256\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m    257\u001b[0m )\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/langchain/load/serializable.py:75\u001b[0m, in \u001b[0;36mSerializable.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m---> 75\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     76\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lc_kwargs \u001b[39m=\u001b[39m kwargs\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/pydantic/main.py:342\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for OpenAI\n__root__\n  Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass  `openai_api_key` as a named parameter. (type=value_error)"
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb Cell 15\u001b[0m line \u001b[0;36m1\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mlangchain\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m langchain\u001b[39m.\u001b[39mverbose \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m flare \u001b[39m=\u001b[39m FlareChain\u001b[39m.\u001b[39mfrom_llm(\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     llm_n,\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m     retriever\u001b[39m=\u001b[39mretriever,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     max_generation_len\u001b[39m=\u001b[39m\u001b[39m164\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m     min_prob\u001b[39m=\u001b[39m\u001b[39m0.3\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m query \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mexplain in great detail the difference between the langchain framework and baby agi\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_qwen.ipynb#X20sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m flare\u001b[39m.\u001b[39mrun(query)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'retriever' is not defined"
      ]
     }
    ],
@@ -279,15 +269,15 @@
     "langchain.verbose = True\n",
     "\n",
     "\n",
-    "# flare = FlareChain.from_llm(\n",
-    "#     llm,\n",
-    "#     retriever=retriever,\n",
-    "#     max_generation_len=164,\n",
-    "#     min_prob=0.3,\n",
-    "# )\n",
+    "flare = FlareChain.from_llm(\n",
+    "    llm_n,\n",
+    "    retriever=retriever,\n",
+    "    max_generation_len=164,\n",
+    "    min_prob=0.3,\n",
+    ")\n",
     "\n",
     "query = \"explain in great detail the difference between the langchain framework and baby agi\"\n",
-    "print(llm)\n"
+    "flare.run(query)\n"
    ]
   },
   {
@@ -335,6 +325,9 @@
     "from langchain.agents import initialize_agent, Tool\n",
     "from langchain.agents import AgentType\n",
     "from langchain.agents.react.base import DocstoreExplorer\n",
+    "import langchain\n",
+    "\n",
+    "langchain.verbose = True\n",
     "\n",
     "\n",
     "docstore = DocstoreExplorer(vectordb)\n",
@@ -352,9 +345,67 @@
     "]\n",
     "\n",
     "\n",
-    "react = initialize_agent(\n",
+    "react_docstore = initialize_agent(\n",
     "    tools, llm, agent=AgentType.REACT_DOCSTORE, verbose=True)\n",
     "\n",
+    "react_docstore.run('hi')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I am a large language model created by Alibaba Cloud. I am called QianWen."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessageChunk(content='I am a large language model created by Alibaba Cloud. I am called QianWen.')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# from langchain.chat_models import ChatOpenAI\n",
+    "# from langchain.document_loaders import TextLoader\n",
+    "# from langchain.embeddings import OpenAIEmbeddings\n",
+    "# from langchain.indexes import VectorstoreIndexCreator\n",
+    "# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "\n",
+    "# from langchain.schema import HumanMessage\n",
+    "\n",
+    "\n",
+    "# llm_n = ChatOpenAI(openai_api_base='http://20.124.240.6:8080/v1',\n",
+    "#                    openai_api_key='none', callbacks=[StreamingStdOutCallbackHandler()],streaming=True,)\n",
+    "\n",
+    "# questions = [\n",
+    "#     \"Who is the speaker\",\n",
+    "#     \"What did the president say about Ketanji Brown Jackson\",\n",
+    "#     \"What are the threats to America\",\n",
+    "#     \"Who are mentioned in the speech\",\n",
+    "#     \"Who is the vice president\",\n",
+    "#     \"How many projects were announced\",\n",
+    "# ]\n",
+    "\n",
+    "\n",
+    "# llm_n(\n",
+    "#     [\n",
+    "#         HumanMessage(\n",
+    "#             content=\"What model are you?\"\n",
+    "#         )\n",
+    "#     ]\n",
+    "# )\n",
+    "\n",
     "\n"
    ]
   },

langchain_retreival.ipynb

@@ -247,7 +247,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -457,7 +457,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -603,16 +603,7 @@
         "id": "Emj46ATxtV9C",
         "outputId": "169be147-f31e-4153-cf91-9b1cea6667be"
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/Users/tejasw/.pyenv/versions/3.11.2/lib/python3.11/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-            "  from .autonotebook import tqdm as notebook_tqdm\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "\n",
         "from langchain.embeddings import HuggingFaceBgeEmbeddings\n",
@@ -640,7 +631,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -648,19 +639,7 @@
         "id": "Q_eTIZwf4Dk2",
         "outputId": "57072a9e-bd8f-4296-b4e1-83c098f868c7"
       },
-      "outputs": [
-        {
-          "ename": "NameError",
-          "evalue": "name 'texts' is not defined",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-            "File \u001b[0;32m<timed exec>:9\u001b[0m\n",
-            "\u001b[0;31mNameError\u001b[0m: name 'texts' is not defined"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "%%time\n",
         "# Embed and store the texts\n",
@@ -677,21 +656,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "ename": "NameError",
-          "evalue": "name 'FAISS' is not defined",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-            "\u001b[1;32m/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_retreival.ipynb Cell 24\u001b[0m line \u001b[0;36m3\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_retreival.ipynb#Y105sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# vectordb.save_local(\"faiss_index\")\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/tejasw/Downloads/scraper-law/votum-gradio/langchain_retreival.ipynb#Y105sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m vectordb \u001b[39m=\u001b[39m FAISS\u001b[39m.\u001b[39mload_local(\u001b[39m'\u001b[39m\u001b[39mfaiss_index\u001b[39m\u001b[39m'\u001b[39m,embeddings\u001b[39m=\u001b[39mmodel_norm)\n",
-            "\u001b[0;31mNameError\u001b[0m: name 'FAISS' is not defined"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# vectordb.save_local(\"faiss_index\")\n",
         "\n",
@@ -915,6 +882,8 @@
         "    ),\n",
         "]\n",
         "\n",
+        "import langchain\n",
+        "langchain.verbose= True\n",
         "llm = OpenAI(temperature=0)\n",
         "\n",
         "react = initialize_agent(tools, model, agent=AgentType.REACT_DOCSTORE, verbose=True)"
@@ -963,7 +932,10 @@
         "import os\n",
         "from langchain.chat_models import AzureChatOpenAI\n",
         "from langchain.schema import HumanMessage\n",
+        "import langchain\n",
+        "\n",
         "\n",
+        "langchain.verbose = True\n",
         "\n",
         "model = AzureChatOpenAI(\n",
         "    openai_api_base=\"https://votum.openai.azure.com/\",\n",
@@ -980,8 +952,9 @@
         "\n",
         "tools = load_tools([\"serpapi\", \"llm-math\"], llm=model)\n",
         "\n",
+        "# ZERO_SHOT_REACT_DESCRIPTION ,CHAT_ZERO_SHOT_REACT_DESCRIPTION\n",
         "agent_executor = initialize_agent(\n",
-        "    [*tools, read_cii], model, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
+        "    [*tools], model, agent=AgentType.REACT_DOCSTORE, verbose=True)\n",
         "\n",
         "\n",
         "agent_executor.invoke(\n",
@@ -994,7 +967,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "agent_executor = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
+        "agent_executor = initialize_agent(tools, llm, agent=AgentType.REACT_DOCSTORE, verbose=True)\n",
         "agent_executor.invoke({\"input\": \"What is the punishment for drinking and driving?\"})"
       ]
     },
@@ -1171,15 +1144,27 @@
         "from langchain.utilities import SerpAPIWrapper\n",
         "from langchain.agents.tools import Tool\n",
         "from langchain.chains import LLMMathChain\n",
+        "from langchain.agents import load_tools\n",
         "\n",
-        "planner = load_chat_planner(llm)\n",
-        "executor = load_agent_executor(llm, tools, verbose=True)\n",
+        "import os\n",
+        "\n",
+        "os.environ[\"SERPAPI_API_KEY\"] = '94de7df75e512ca1fe42b3f51a034a0f0e4683e0f880f9cf7dee1a0eb36a069e'\n",
+        "tools = load_tools([\"serpapi\", \"llm-math\"], llm=model)\n",
+        "planner = load_chat_planner(model)\n",
+        "executor = load_agent_executor(model, tools, verbose=True)\n",
         "plan_agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)\n",
         "\n",
         "\n",
         "plan_agent.run(\n",
-        "    'I bought a house in 2001 for 20 lakh rupees , i sold it in 2022 for 50 lakhs , what will be my profit?')\n"
+        "    'Plan a 5 day trip for me to Paris, My budget is $2000. I would prefer 4 star hotel and minimal traveling. I live in Delhi, India,')\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
     }
   ],
   "metadata": {