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langchain_qwen.ipynb

@@ -65,7 +65,7 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "\n",
-    "ChatOpenAI(openai_api_base='http://20.124.240.6:8080/v1',\n",
+    "llm = ChatOpenAI(openai_api_base='http://20.124.240.6:8080/v1',\n",
     "           openai_api_key='none', callbacks=[StreamingStdOutCallbackHandler()], streaming=True,)\n"
    ]
   },
@@ -137,7 +137,6 @@
     "    retriever,\n",
     "    \"search_legal_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"
    ]
@@ -246,31 +245,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'retriever' 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 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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We set this so we can see what exactly is going on\n",
     "from langchain.chains import FlareChain\n",
     "import langchain\n",
+    "import os\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = 'none'\n",
+    "os.environ['OPENAI_API_BASE'] = 'http://20.124.240.6:8080/v1'\n",
+    "# os.environ['OPEN']\n",
     "\n",
     "langchain.verbose = True\n",
     "\n",
     "\n",
     "flare = FlareChain.from_llm(\n",
-    "    llm_n,\n",
+    "    llm,\n",
     "    retriever=retriever,\n",
     "    max_generation_len=164,\n",
     "    min_prob=0.3,\n",
@@ -354,27 +346,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# from langchain.chat_models import ChatOpenAI\n",
     "# from langchain.document_loaders import TextLoader\n",

langchain_retreival.ipynb

@@ -11,7 +11,7 @@
       },
       "outputs": [],
       "source": [
-        "pip -q install -U langchain huggingface_hub tiktoken PyPDF2 pypdf sentence_transformers together FlagEmbedding faiss-gpu openai text-generation"
+        "pip -q install -U langchain huggingface_hub tiktoken PyPDF2 pypdf sentence_transformers together FlagEmbedding faiss-gpu openai text-generation pymupdf"
       ]
     },
     {