Cleanup

examples/rag/langgraph_crag.ipynb

@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "3a566a30-cf0e-4330-ad4d-9bf994bdfa86",
    "metadata": {},
    "outputs": [],
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "94b3945f-ef0f-458d-a443-f763903550b0",
    "metadata": {},
    "outputs": [],
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "efd639c5-82e2-45e6-a94a-6a4039646ef5",
    "metadata": {},
    "outputs": [],
@@ -279,7 +279,7 @@
     "\n",
     "    # LLM with tool and enforce invocation\n",
     "    llm_with_tool = model.bind(\n",
-    "        tools=[convert_to_openai_tool(grade_tool_oai)],\n",
+    "        tools=[grade_tool_oai],\n",
     "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
     "    )\n",
     "\n",
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "dedae17a-98c6-474d-90a7-9234b7c8cea0",
    "metadata": {},
    "outputs": [],
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "f5b7c2fe-1fc7-4b76-bf93-ba701a40aa6b",
    "metadata": {},
    "outputs": [
@@ -492,13 +492,10 @@
       "'\\n---\\n'\n",
       "\"Node '__end__':\"\n",
       "'\\n---\\n'\n",
-      "('There are several types of memory in human brains, including sensory memory, '\n",
-      " 'which retains impressions of sensory information for a few seconds after the '\n",
-      " 'original stimuli have ended. Short-term memory is utilized for in-context '\n",
-      " 'learning, while long-term memory allows the agent to retain and recall '\n",
-      " 'information over extended periods by leveraging an external vector store and '\n",
-      " 'fast retrieval. Additionally, agents can use tool use to call external APIs '\n",
-      " 'for extra information that is missing from the model weights.')\n"
+      "('Short-term memory stores information needed for complex cognitive tasks and '\n",
+      " 'lasts for 20-30 seconds. Long-term memory can store information for a long '\n",
+      " 'time and has explicit and implicit subtypes. Sensory memory retains sensory '\n",
+      " 'impressions briefly after stimuli end.')\n"
      ]
     }
    ],

examples/rag/langgraph_self_rag.ipynb

@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "565a6d44-2c9f-4fff-b1ec-eea05df9350d",
    "metadata": {},
    "outputs": [],
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "add509d8-6682-4127-8d95-13dd37d79702",
    "metadata": {},
    "outputs": [],
@@ -223,7 +223,7 @@
     "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
     "\n",
     "\n",
-    "def generate(state):a\n",
+    "def generate(state):\n",
     "    \"\"\"\n",
     "    Generate answer\n",
     "\n",
@@ -288,7 +288,7 @@
     "\n",
     "    # LLM with tool and enforce invocation\n",
     "    llm_with_tool = model.bind(\n",
-    "        tools=[convert_to_openai_tool(grade_tool_oai)],\n",
+    "        tools=[grade_tool_oai],\n",
     "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
     "    )\n",
     "\n",
@@ -444,7 +444,7 @@
     "\n",
     "    # LLM with tool and enforce invocation\n",
     "    llm_with_tool = model.bind(\n",
-    "        tools=[convert_to_openai_tool(grade_tool_oai)],\n",
+    "        tools=[grade_tool_oai],\n",
     "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
     "    )\n",
     "\n",
@@ -508,7 +508,7 @@
     "\n",
     "    # LLM with tool and enforce invocation\n",
     "    llm_with_tool = model.bind(\n",
-    "        tools=[convert_to_openai_tool(grade_tool_oai)],\n",
+    "        tools=[grade_tool_oai],\n",
     "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
     "    )\n",
     "\n",
@@ -553,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
    "metadata": {},
    "outputs": [],
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "fb69dbb9-91ee-4868-8c3c-93af3cd885be",
    "metadata": {},
    "outputs": [
@@ -638,12 +638,12 @@
       "---DECISION: USEFUL---\n",
       "\"Node '__end__':\"\n",
       "'\\n---\\n'\n",
-      "('Short-term memory is the stage of memory that stores information that we are '\n",
-      " 'currently aware of and needed to carry out complex cognitive tasks. It has a '\n",
-      " 'limited capacity and lasts for a short duration. Long-term memory, on the '\n",
-      " 'other hand, can store information for a long time and has unlimited storage '\n",
-      " 'capacity. It includes explicit/declarative memory for facts and events, and '\n",
-      " 'implicit/procedural memory for unconscious skills and routines.')\n"
+      "('Short-term memory stores information needed for immediate cognitive tasks '\n",
+      " 'and lasts for about 20-30 seconds. Long-term memory can retain information '\n",
+      " 'for extended periods, with subtypes including explicit (facts and events) '\n",
+      " 'and implicit (skills and routines) memory. Sensory memory retains sensory '\n",
+      " 'impressions briefly after stimuli end, while long-term memory stores '\n",
+      " 'information for a long time.')\n"
      ]
     }
    ],
@@ -664,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "4138bc51-8c84-4b8a-8d24-f7f470721f6f",
    "metadata": {},
    "outputs": [
@@ -696,13 +696,12 @@
       "---DECISION: USEFUL---\n",
       "\"Node '__end__':\"\n",
       "'\\n---\\n'\n",
-      "('Chain of thought prompting involves guiding the behavior of autoregressive '\n",
-      " 'language models by providing prompts or demonstrations that contain '\n",
-      " 'high-quality reasoning chains. This can be done through methods such as '\n",
-      " 'self-asking, interleaving retrieval with chain-of-thought reasoning, and '\n",
-      " 'complexity-based prompting for multi-step reasoning. These techniques aim to '\n",
-      " \"improve the model's ability to generate coherent and logical responses \"\n",
-      " 'without updating its weights.')\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 model's understanding.\")\n"
      ]
     }
    ],