Merge branch 'main' into wfh/reflexion

examples/rag/langgraph_crag_mistral.ipynb

@@ -48,13 +48,12 @@
     "If you want to run this locally (e.g., on your laptop), use [Ollama](https://ollama.ai/library/mistral/tags):\n",
     "\n",
     "* Download [Ollama app](https://ollama.ai/).\n",
-    "* Download a `Mistral` model e.g., `ollama pull mistral:7b-instruct`, from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
-    "* Download LLaMA2 `ollama pull llama2:latest` to use Ollama embeddings.\n",
+    "* Download a `Mistral` model e.g., `ollama pull mistral:instruct`, from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
     "* Set flags indicating we will run locally and the Mistral model downloaded:\n",
     "  \n",
     "```\n",
     "run_local = \"Yes\"\n",
-    "local_llm = \"mistral:7b-instruct\"\n",
+    "local_llm = \"mistral:instruct\"\n",
     "```\n",
     "\n",
     "### Tracing \n",
@@ -70,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "id": "abc064ab-7de1-4d03-a987-cd3078438d61",
    "metadata": {},
    "outputs": [],
@@ -84,14 +83,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "id": "9f644869-436e-4bf6-a267-b2465c7b5aef",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Flags for running locally\n",
     "\n",
-    "run_local = \"No\"\n",
+    "run_local = \"Yes\"\n",
     "local_llm = \"mistral:instruct\""
    ]
   },
@@ -113,10 +112,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "254ae533-79e0-42f4-b200-1ec9160e1d3d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bert_load_from_file: gguf version     = 2\n",
+      "bert_load_from_file: gguf alignment   = 32\n",
+      "bert_load_from_file: gguf data offset = 695552\n",
+      "bert_load_from_file: model name           = BERT\n",
+      "bert_load_from_file: model architecture   = bert\n",
+      "bert_load_from_file: model file type      = 1\n",
+      "bert_load_from_file: bert tokenizer vocab = 30522\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
@@ -187,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e",
    "metadata": {},
    "outputs": [],
@@ -224,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d",
    "metadata": {},
    "outputs": [],
@@ -484,12 +497,12 @@
    "source": [
     "## Build Graph\n",
     "\n",
-    "The just follows the flow we outlined in the figure above."
+    "This just follows the flow we outlined in the figure above."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06",
    "metadata": {},
    "outputs": [],
@@ -540,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "3ab1d8df-a74e-4b48-a30b-e39bbfd5925a",
    "metadata": {},
    "outputs": [
@@ -552,32 +565,45 @@
       "\"Node 'retrieve':\"\n",
       "'\\n---\\n'\n",
       "---CHECK RELEVANCE---\n",
-      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "\"Node 'grade_documents':\"\n",
       "'\\n---\\n'\n",
       "---DECIDE TO GENERATE---\n",
-      "---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\n",
-      "---TRANSFORM QUERY---\n",
-      "\"Node 'transform_query':\"\n",
-      "'\\n---\\n'\n",
-      "---WEB SEARCH---\n",
-      "\"Node 'web_search':\"\n",
-      "'\\n---\\n'\n",
+      "---DECISION: GENERATE---\n",
       "---GENERATE---\n",
       "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
       "\"Node '__end__':\"\n",
       "'\\n---\\n'\n",
-      "('In agent-based systems, episodic memory can be likened to a long-term memory '\n",
-      " \"module that records agents' experiences in natural language, with retrieval \"\n",
-      " 'based on relevance, recency, and importance. Semantic memory is similar to '\n",
-      " 'an external vector store that provides agents with the ability to retain and '\n",
-      " 'recall information over extended periods. Procedural memory can be seen as '\n",
-      " 'the reflection mechanism that synthesizes memories into higher-level '\n",
-      " \"inferences, guiding the agent's future behavior.\")\n"
+      "(' In an LLM (large language model)-powered autonomous agent system, LLM '\n",
+      " 'functions as the agent’s brain, complemented by several key components: '\n",
+      " 'planning and memory.\\n'\n",
+      " '\\n'\n",
+      " 'Planning involves breaking down large tasks into smaller subgoals for '\n",
+      " 'efficient handling of complex tasks and self-criticism and refinement to '\n",
+      " 'improve results.\\n'\n",
+      " '\\n'\n",
+      " 'Memory includes short-term memory, which utilizes in-context learning, and '\n",
+      " 'long-term memory, providing the agent with the capability to retain and '\n",
+      " 'recall information over extended periods using an external vector store and '\n",
+      " 'fast retrieval. The agent also learns to call external APIs for missing '\n",
+      " 'information.\\n'\n",
+      " '\\n'\n",
+      " 'Types of Memory:\\n'\n",
+      " '1. Sensory Memory: retains impressions of sensory information for a few '\n",
+      " 'seconds.\\n'\n",
+      " '2. Short-Term Memory (STM) or Working Memory: stores information needed for '\n",
+      " 'complex cognitive tasks and lasts for 20-30 seconds.\\n'\n",
+      " '3. Long-Term Memory (LTM): stores information for a remarkably long time, '\n",
+      " 'with two subtypes: explicit/declarative memory and implicit/procedural '\n",
+      " 'memory.\\n'\n",
+      " '\\n'\n",
+      " 'The agent uses LLM as its core controller, which can be extended beyond '\n",
+      " 'generating well-written copies, stories, essays, and programs to a powerful '\n",
+      " 'general problem solver.')\n"
      ]
     }
    ],

examples/rag/langgraph_self_rag.ipynb

@@ -223,7 +223,7 @@
     "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
     "\n",
     "\n",
-    "def generate(state):\n",
+    "def generate(state):a\n",
     "    \"\"\"\n",
     "    Generate answer\n",
     "\n",