fix dependencies issue

unit2/llama-index/agents.ipynb

@@ -21,11 +21,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-index datasets llama-index-callbacks-arize-phoenix llama-index-vector-stores-chroma llama-index-llms-huggingface-api -U -q"
+    "!pip install llama-index llama-index-vector-stores-chroma llama-index-llms-huggingface-api llama-index-embeddings-huggingface -U -q"
    ]
   },
   {
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +175,7 @@
     "\n",
     "from llama_index.core import VectorStoreIndex\n",
     "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
-    "from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding\n",
+    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
     "from llama_index.core.tools import QueryEngineTool\n",
     "from llama_index.vector_stores.chroma import ChromaVectorStore\n",
     "\n",
@@ -185,7 +185,7 @@
     "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
     "\n",
     "# Create a query engine\n",
-    "embed_model = HuggingFaceInferenceAPIEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
+    "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
     "llm = HuggingFaceInferenceAPI(model_name=\"Qwen/Qwen2.5-Coder-32B-Instruct\")\n",
     "index = VectorStoreIndex.from_vector_store(\n",
     "    vector_store=vector_store, embed_model=embed_model\n",

unit2/llama-index/components.ipynb

@@ -23,7 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-index datasets llama-index-callbacks-arize-phoenix llama-index-vector-stores-chroma llama-index-llms-huggingface-api -U -q"
+    "!pip install llama-index datasets llama-index-callbacks-arize-phoenix arize-phoenix llama-index-vector-stores-chroma llama-index-llms-huggingface-api llama-index-embeddings-huggingface -U -q"
    ]
   },
   {
@@ -113,12 +113,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we have a list of `Document` objects, we can use the `IngestionPipeline` to create nodes from the documents and prepare them for the `QueryEngine`. We will use the `SentenceSplitter` to split the documents into smaller chunks and the `HuggingFaceInferenceAPIEmbedding` to embed the chunks."
+    "Now we have a list of `Document` objects, we can use the `IngestionPipeline` to create nodes from the documents and prepare them for the `QueryEngine`. We will use the `SentenceSplitter` to split the documents into smaller chunks and the `HuggingFaceEmbedding` to embed the chunks."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +142,7 @@
     }
    ],
    "source": [
-    "from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding\n",
+    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
     "from llama_index.core.node_parser import SentenceSplitter\n",
     "from llama_index.core.ingestion import IngestionPipeline\n",
     "\n",
@@ -150,7 +150,7 @@
     "pipeline = IngestionPipeline(\n",
     "    transformations=[\n",
     "        SentenceSplitter(),\n",
-    "        HuggingFaceInferenceAPIEmbedding(model_name=\"BAAI/bge-small-en-v1.5\"),\n",
+    "        HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\"),\n",
     "    ]\n",
     ")\n",
     "\n",
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -200,7 +200,7 @@
     "pipeline = IngestionPipeline(\n",
     "    transformations=[\n",
     "        SentenceSplitter(),\n",
-    "        HuggingFaceInferenceAPIEmbedding(model_name=\"BAAI/bge-small-en-v1.5\"),\n",
+    "        HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\"),\n",
     "    ],\n",
     "    vector_store=vector_store,\n",
     ")\n",
@@ -218,14 +218,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from llama_index.core import VectorStoreIndex\n",
-    "from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding\n",
+    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
     "\n",
-    "embed_model = HuggingFaceInferenceAPIEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
+    "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
     "index = VectorStoreIndex.from_vector_store(\n",
     "    vector_store=vector_store, embed_model=embed_model\n",
     ")"

unit2/llama-index/tools.ipynb

@@ -18,11 +18,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-index datasets llama-index-callbacks-arize-phoenix llama-index-vector-stores-chroma llama-index-llms-huggingface-api -U -q"
+    "!pip install llama-index llama-index-vector-stores-chroma llama-index-llms-huggingface-api llama-index-embeddings-huggingface llama-index-tools-google -U -q"
    ]
   },
   {
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -105,14 +105,14 @@
     "\n",
     "from llama_index.core import VectorStoreIndex\n",
     "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
-    "from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding\n",
+    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
     "from llama_index.core.tools import QueryEngineTool\n",
     "from llama_index.vector_stores.chroma import ChromaVectorStore\n",
     "\n",
     "db = chromadb.PersistentClient(path=\"./alfred_chroma_db\")\n",
     "chroma_collection = db.get_or_create_collection(\"alfred\")\n",
     "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
-    "embed_model = HuggingFaceInferenceAPIEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
+    "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
     "llm = HuggingFaceInferenceAPI(model_name=\"meta-llama/Llama-3.2-3B-Instruct\")\n",
     "index = VectorStoreIndex.from_vector_store(\n",
     "    vector_store=vector_store, embed_model=embed_model\n",

unit2/llama-index/workflows.ipynb

@@ -18,11 +18,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-index datasets llama-index-callbacks-arize-phoenix llama-index-vector-stores-chroma llama-index-utils-workflow llama-index-llms-huggingface-api pyvis -U -q"
+    "!pip install llama-index llama-index-vector-stores-chroma llama-index-utils-workflow llama-index-llms-huggingface-api pyvis -U -q"
    ]
   },
   {