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"
    ]
   },
   {
@@ -321,7 +321,9 @@
     {
      "data": {
       "text/plain": [
-       "AgentOutput(response=ChatMessage(role=<MessageRole.ASSISTANT: 'assistant'>, additional_kwargs={}, blocks=[TextBlock(block_type='text', text='I have handed off the request to an agent who can help you with adding 5 and 3. Please wait for their response.')]), tool_calls=[ToolCallResult(tool_name='handoff', tool_kwargs={'to_agent': 'addition_agent', 'reason': 'Add 5 and 3'}, tool_id='call_F97vcIcsvZjfAAOBzzIifW3y', tool_output=ToolOutput(content='Agent addition_agent is now handling the request due to the following reason: Add 5 and 3.\\nPlease continue with the current request.', tool_name='handoff', raw_input={'args': (), 'kwargs': {'to_agent': 'addition_agent', 'reason': 'Add 5 and 3'}}, raw_output='Agent addition_agent is now handling the request due to the following reason: Add 5 and 3.\\nPlease continue with the current request.', is_error=False), return_direct=True), ToolCallResult(tool_name='handoff', tool_kwargs={'to_agent': 'addition_agent', 'reason': 'Add 5 and 3'}, tool_id='call_jf49ktFRs09xYdOsnApAk2zz', tool_output=ToolOutput(content='Agent addition_agent is now handling the request due to the following reason: Add 5 and 3.\\nPlease continue with the current request.', tool_name='handoff', raw_input={'args': (), 'kwargs': {'to_agent': 'addition_agent', 'reason': 'Add 5 and 3'}}, raw_output='Agent addition_agent is now handling the request due to the following reason: Add 5 and 3.\\nPlease continue with the current request.', is_error=False), return_direct=True)], raw={'id': 'chatcmpl-B6Cy54VQkvlG3VOrmdzCzgwcJmVOc', 'choices': [{'delta': {'content': None, 'function_call': None, 'refusal': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'stop', 'index': 0, 'logprobs': None}], 'created': 1740819517, 'model': 'gpt-3.5-turbo-0125', 'object': 'chat.completion.chunk', 'service_tier': 'default', 'system_fingerprint': None, 'usage': None}, current_agent_name='addition_agent')"
+       "AgentOutput(response=ChatMessage(role=<MessageRole.ASSISTANT: 'assistant'>, additional_kwargs={}, blocks=[TextBlock(block_type='text', text='5 and 3 add up to 8.')]), tool_calls=[ToolCallResult(tool_name='handoff', tool_kwargs={'to_agent': 'add_agent', 'reason': 'The user wants to add two numbers, and the add_agent is better suited for this task.'}, tool_id='831895e7-3502-4642-92ea-8626e21ed83b', tool_output=ToolOutput(content='Agent add_agent is now handling the request due to the following reason: The user wants to add two numbers, and the add_agent is better suited for this task..\n",
+       "Please continue with the current request.', tool_name='handoff', raw_input={'args': (), 'kwargs': {'to_agent': 'add_agent', 'reason': 'The user wants to add two numbers, and the add_agent is better suited for this task.'}}, raw_output='Agent add_agent is now handling the request due to the following reason: The user wants to add two numbers, and the add_agent is better suited for this task..\n",
+       "Please continue with the current request.', is_error=False), return_direct=True), ToolCallResult(tool_name='add', tool_kwargs={'a': 5, 'b': 3}, tool_id='c29dc3f7-eaa7-4ba7-b49b-90908f860cc5', tool_output=ToolOutput(content='8', tool_name='add', raw_input={'args': (), 'kwargs': {'a': 5, 'b': 3}}, raw_output=8, is_error=False), return_direct=False)], raw=ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(role='assistant', content='.', tool_call_id=None, tool_calls=None), index=0, finish_reason=None, logprobs=None)], created=1744553546, id='', model='Qwen/Qwen2.5-Coder-32B-Instruct', system_fingerprint='3.2.1-sha-4d28897', usage=None, object='chat.completion.chunk'), current_agent_name='add_agent')"
       ]
      },
      "execution_count": 33,
@@ -331,6 +333,7 @@
    ],
    "source": [
     "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
+    "from llama_index.core.agent.workflow import AgentWorkflow\n",
     "\n",
     "# Define some tools\n",
     "def add(a: int, b: int) -> int:\n",
@@ -367,7 +370,8 @@
     ")\n",
     "\n",
     "# Run the system\n",
-    "response = await workflow.run(user_msg=\"Can you add 5 and 3?\")"
+    "response = await workflow.run(user_msg=\"Can you add 5 and 3?\")\n",
+    "response"
    ]
   }
  ],