Minor change to upgrade libs

examples/rag/langgraph_self_rag.ipynb

@@ -60,7 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph"
+    "! pip install -U langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph"
    ]
   },
   {
@@ -159,10 +159,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 2,
    "id": "1fafad21-60cc-483e-92a3-6a7edb1838e3",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rlm/miniforge3/envs/llama2/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 0.3.0. Use invoke instead.\n",
+      "  warn_deprecated(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -215,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "dcd77cc1-4587-40ec-b633-5364eab9e1ec",
    "metadata": {},
    "outputs": [
@@ -223,7 +231,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave conditioned on past experience. Long-term memory provides the agent with the capability to retain and recall infinite information over extended periods. Short-term memory is utilized for in-context learning.\n"
+      "The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave conditioned on past experience and interact with other agents. Long-term memory provides the agent with the capability to retain and recall infinite information over extended periods. Short-term memory is utilized for in-context learning.\n"
      ]
     }
    ],
@@ -255,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "e78931ec-940c-46ad-a0b2-f43f953f1fd7",
    "metadata": {},
    "outputs": [
@@ -265,7 +273,7 @@
        "GradeHallucinations(binary_score='yes')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -303,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "bd62276f-bf26-40d0-8cff-e07b10e00321",
    "metadata": {},
    "outputs": [
@@ -313,7 +321,7 @@
        "GradeAnswer(binary_score='yes')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -403,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "f1617e9e-66a8-4c1a-a1fe-cc936284c085",
    "metadata": {},
    "outputs": [],
@@ -429,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "add509d8-6682-4127-8d95-13dd37d79702",
    "metadata": {},
    "outputs": [],
@@ -609,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
    "metadata": {},
    "outputs": [],
@@ -645,14 +653,14 @@
     "        \"not useful\": \"transform_query\",\n",
     "    },\n",
     ")\n",
-    "ß\n",
+    "\n",
     "# Compile\n",
     "app = workflow.compile()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "id": "fb69dbb9-91ee-4868-8c3c-93af3cd885be",
    "metadata": {},
    "outputs": [
@@ -668,24 +676,22 @@
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT NOT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
-      "\"Node 'grade_documents':\"\n",
-      "'\\n---\\n'\n",
       "---ASSESS GRADED DOCUMENTS---\n",
       "---DECISION: GENERATE---\n",
-      "---GENERATE---\n",
-      "\"Node 'generate':\"\n",
+      "\"Node 'grade_documents':\"\n",
       "'\\n---\\n'\n",
+      "---GENERATE---\n",
       "---CHECK HALLUCINATIONS---\n",
       "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
       "---GRADE GENERATION vs QUESTION---\n",
       "---DECISION: GENERATION ADDRESSES QUESTION---\n",
-      "\"Node '__end__':\"\n",
+      "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
-      "('Short-term memory is used for in-context learning and lasts for a short '\n",
-      " 'period, while long-term memory allows the agent to retain and recall '\n",
-      " 'information over extended periods. Agents can also utilize external tools '\n",
-      " 'like APIs to access additional information not stored in their model '\n",
-      " 'weights.')\n"
+      "('Short-term memory is used for in-context learning in agents, allowing them '\n",
+      " 'to learn quickly. Long-term memory enables agents to retain and recall vast '\n",
+      " 'amounts of information over extended periods. Agents can also utilize '\n",
+      " 'external tools like APIs to access additional information beyond what is '\n",
+      " 'stored in their memory.')\n"
      ]
     }
    ],
@@ -708,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "4138bc51-8c84-4b8a-8d24-f7f470721f6f",
    "metadata": {},
    "outputs": [
@@ -721,28 +727,26 @@
       "'\\n---\\n'\n",
       "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "\"Node 'grade_documents':\"\n",
-      "'\\n---\\n'\n",
       "---ASSESS GRADED DOCUMENTS---\n",
       "---DECISION: GENERATE---\n",
-      "---GENERATE---\n",
-      "\"Node 'generate':\"\n",
+      "\"Node 'grade_documents':\"\n",
       "'\\n---\\n'\n",
+      "---GENERATE---\n",
       "---CHECK HALLUCINATIONS---\n",
       "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
       "---GRADE GENERATION vs QUESTION---\n",
       "---DECISION: GENERATION ADDRESSES QUESTION---\n",
-      "\"Node '__end__':\"\n",
+      "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
-      "('Chain of thought prompting works by providing a series of prompts or '\n",
-      " 'demonstrations to guide the model through a reasoning process. This method '\n",
-      " 'involves iteratively constructing thought processes by asking follow-up '\n",
-      " 'questions or exploring multiple reasoning possibilities at each step. '\n",
-      " 'External search queries and relevant content from sources like Wikipedia can '\n",
-      " 'be integrated into the context to enhance the chain of thought reasoning.')\n"
+      "('Chain of thought prompting works by repeatedly prompting the model to ask '\n",
+      " 'follow-up questions to construct the thought process iteratively. This '\n",
+      " 'method can be combined with queries to search for relevant entities and '\n",
+      " 'content to add back into the context. It extends the thought process by '\n",
+      " 'exploring multiple reasoning possibilities at each step, creating a tree '\n",
+      " 'structure of thoughts.')\n"
      ]
     }
    ],