Update LangChain_PDF.ipynb

LangChain_PDF.ipynb

@@ -41,6 +41,7 @@
     "from langchain.indexes import VectorstoreIndexCreator\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.embeddings import HuggingFaceEmbeddings\n",
     "from langchain.vectorstores import Chroma\n",
     "import panel as pn\n",
     "import tempfile\n"
@@ -116,7 +117,8 @@
     "    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "    texts = text_splitter.split_documents(documents)\n",
     "    # select which embeddings we want to use\n",
-    "    embeddings = OpenAIEmbeddings()\n",
+    "    #embeddings = OpenAIEmbeddings()\n",
+    "    embeddings = HuggingFaceEmbeddings()\n",
     "    # create the vectorestore to use as the index\n",
     "    db = Chroma.from_documents(texts, embeddings)\n",
     "    # expose this index in a retriever interface\n",
@@ -124,6 +126,7 @@
     "    # create a chain to answer questions \n",
     "    qa = RetrievalQA.from_chain_type(\n",
     "        llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)\n",
+             llm=HuggingFaceHub(huggingfacehub_api_token = os.environ['HUGGING_FACE_HUB_API_KEY]), repo_id=repo_id, model_kwargs={'temperature':0.2, 'max_lenght':1000}, chain_type=chain_type, retriever=retriever, return_source_documents=True)\n",
     "    result = qa({\"query\": query})\n",
     "    print(result['result'])\n",
     "    return result"