![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-llms-openai\n```\n\n\n```python\n!pip install llama-index\n```\n\n\n```python\nfrom llama_index.core import (\n SimpleDirectoryReader,\n VectorStoreIndex,\n download_loader,\n RAKEKeywordTableIndex,\n)\n```\n\n\n```python\nfrom llama_index.llms.openai import OpenAI\n\nllm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n```\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/10k/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'\n```\n\nLoad document and build index\n\n\n```python\nreader = SimpleDirectoryReader(input_files=[\"./data/10k/lyft_2021.pdf\"])\ndata = reader.load_data()\n```\n\n\n```python\nindex = VectorStoreIndex.from_documents(data)\n```\n\n\n```python\nquery_engine = index.as_query_engine(streaming=True, similarity_top_k=3)\n```\n\nStream response with page citation\n\n\n```python\nresponse = query_engine.query(\n \"What was the impact of COVID? Show statements in bullet form and show\"\n \" page reference after each statement.\"\n)\nresponse.print_response_stream()\n```\n\n \n • The ongoing COVID-19 pandemic continues to impact communities in the United States, Canada and globally (page 6). \n • The pandemic and related responses caused decreased demand for our platform leading to decreased revenues as well as decreased earning opportunities for drivers on our platform (page 6).\n • Our business continues to be impacted by the COVID-19 pandemic (page 6).\n • The exact timing and pace of the recovery remain uncertain (page 6).\n • The extent to which our operations will continue to be impacted by the pandemic will depend largely on future developments, which are highly uncertain and cannot be accurately predicted (page 6).\n • An increase in cases due to variants of the virus has caused many businesses to delay employees returning to the office (page 6).\n • We anticipate that continued social distancing, altered consumer behavior, reduced travel and commuting, and expected corporate cost cutting will be significant challenges for us (page 6).\n • We have adopted multiple measures, including, but not limited, to establishing new health and safety requirements for ridesharing and updating workplace policies (page 6).\n • We have had to take certain cost-cutting measures, including lay-offs, furloughs and salary reductions, which may have adversely affect employee morale, our culture and our ability to attract and retain employees (page 18).\n • The ultimate impact of the COVID-19 pandemic on our users, customers, employees, business, operations and financial performance depends on many factors that are not within our control (page 18).\n\nInspect source nodes\n\n\n```python\nfor node in response.source_nodes:\n print(\"-----\")\n text_fmt = node.node.get_content().strip().replace(\"\\n\", \" \")[:1000]\n print(f\"Text:\\t {text_fmt} ...\")\n print(f\"Metadata:\\t {node.node.metadata}\")\n print(f\"Score:\\t {node.score:.3f}\")\n```\n\n -----\n Text:\t Impact of COVID-19 to our BusinessThe ongoing COVID-19 pandemic continues to impact communities in the United States, Canada and globally. Since the pandemic began in March 2020,governments and private businesses - at the recommendation of public health officials - have enacted precautions to mitigate the spread of the virus, including travelrestrictions and social distancing measures in many regions of the United States and Canada, and many enterprises have instituted and maintained work from homeprograms and limited the number of employees on site. Beginning in the middle of March 2020, the pandemic and these related responses caused decreased demand for ourplatform leading to decreased revenues as well as decreased earning opportunities for drivers on our platform. Our business continues to be impacted by the COVID-19pandemic. Although we have seen some signs of demand improving, particularly compared to the dema ...\n Metadata:\t {'page_label': '6', 'file_name': 'lyft_2021.pdf'}\n Score:\t 0.821\n -----\n Text:\t will continue to be impacted by the pandemic will depend largely on future developments, which are highly uncertain and cannot beaccurately predicted, including new information which may emerge concerning COVID-19 variants and the severity of the pandemic and actions by government authoritiesand private businesses to contain the pandemic or recover from its impact, among other things. For example, an increase in cases due to variants of the virus has causedmany businesses to delay employees returning to the office. Even as travel restrictions and shelter-in-place orders are modified or lifted, we anticipate that continued socialdistancing, altered consu mer behavior, reduced travel and commuting, and expected corporate cost cutting will be significant challenges for us. The strength and duration ofthese challenges cannot b e presently estimated.In response to the COVID-19 pandemic, we have adopted multiple measures, including, but not limited, to establishing ne ...\n Metadata:\t {'page_label': '56', 'file_name': 'lyft_2021.pdf'}\n Score:\t 0.808\n -----\n Text:\t storing unrented and returned vehicles. These impacts to the demand for and operations of the different rental programs have and may continue to adversely affectour business, financial condi tion and results of operation.• The COVID-19 pandemic may delay or prevent us, or our current or prospective partners and suppliers, from being able to test, develop or deploy autonomousvehicle-related technology, including through direct impacts of the COVID-19 virus on employee and contractor health; reduced consumer demand forautonomous vehicle travel resulting from an overall reduced demand for travel; shelter-in-place orders by local, state or federal governments negatively impactingoperations, including our ability to test autonomous vehicle-related technology; impacts to the supply chains of our current or prospective partners and suppliers;or economic impacts limiting our or our current or prospective partners’ or suppliers’ ability to expend resources o ...\n Metadata:\t {'page_label': '18', 'file_name': 'lyft_2021.pdf'}\n Score:\t 0.805"}
{"tokens": 1654, "doc_id": "fbb928bd-56a2-4df4-bd74-23b68502d3d0", "name": "Auto-Retrieval from a Weaviate Vector Database", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/WeaviateIndex_auto_retriever", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Auto-Retrieval from a Weaviate Vector Database\n\nThis guide shows how to perform **auto-retrieval** in LlamaIndex with [Weaviate](https://weaviate.io/). \n\nThe Weaviate vector database supports a set of [metadata filters](https://weaviate.io/developers/weaviate/search/filters) in addition to a query string for semantic search. Given a natural language query, we first use a Large Language Model (LLM) to infer a set of metadata filters as well as the right query string to pass to the vector database (either can also be blank). This overall query bundle is then executed against the vector database.\n\nThis allows for more dynamic, expressive forms of retrieval beyond top-k semantic search. The relevant context for a given query may only require filtering on a metadata tag, or require a joint combination of filtering + semantic search within the filtered set, or just raw semantic search.\n\n## Setup \n\nWe first define imports and define an empty Weaviate collection.\n\nIf you're opening this Notebook on Colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-weaviate\n```\n\n\n```python\n!pip install llama-index weaviate-client\n```\n\n\n```python\nimport logging\nimport sys\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nlogging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n```\n\nWe will be using GPT-4 for its reasoning capabilities to infer the metadata filters. Depending on your use case, `\"gpt-3.5-turbo\"` can work as well.\n\n\n```python\n# set up OpenAI\nimport os\nimport getpass\nimport openai\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\nopenai.api_key = os.environ[\"OPENAI_API_KEY\"]\n```\n\n\n```python\nfrom llama_index.embeddings.openai import OpenAIEmbedding\nfrom llama_index.llms.openai import OpenAI\nfrom llama_index.core.settings import Settings\n\nSettings.llm = OpenAI(model=\"gpt-4\")\nSettings.embed_model = OpenAIEmbedding()\n```\n\nThis Notebook uses Weaviate in [Embedded mode](https://weaviate.io/developers/weaviate/installation/embedded), which is supported on Linux and macOS.\n\nIf you prefer to try out Weaviate's fully managed service, [Weaviate Cloud Services (WCS)](https://weaviate.io/developers/weaviate/installation/weaviate-cloud-services), you can enable the code in the comments.\n\n\n```python\nimport weaviate\nfrom weaviate.embedded import EmbeddedOptions\n\n# Connect to Weaviate client in embedded mode\nclient = weaviate.connect_to_embedded()\n\n# Enable this code if you want to use Weaviate Cloud Services instead of Embedded mode.\n\"\"\"\nimport weaviate\n\n# cloud\ncluster_url = \"\"\napi_key = \"\"\n\nclient = weaviate.connect_to_wcs(cluster_url=cluster_url,\n auth_credentials=weaviate.auth.AuthApiKey(api_key), \n)\n\n# local\n# client = weaviate.connect_to_local()\n\"\"\"\n```\n\n## Defining Some Sample Data\n\nWe insert some sample nodes containing text chunks into the vector database. Note that each `TextNode` not only contains the text, but also metadata e.g. `category` and `country`. These metadata fields will get converted/stored as such in the underlying vector db.\n\n\n```python\nfrom llama_index.core.schema import TextNode\n\nnodes = [\n TextNode(\n text=(\n \"Michael Jordan is a retired professional basketball player,\"\n \" widely regarded as one of the greatest basketball players of all\"\n \" time.\"\n ),\n metadata={\n \"category\": \"Sports\",\n \"country\": \"United States\",\n },\n ),\n TextNode(\n text=(\n \"Angelina Jolie is an American actress, filmmaker, and\"\n \" humanitarian. She has received numerous awards for her acting\"\n \" and is known for her philanthropic work.\"\n ),\n metadata={\n \"category\": \"Entertainment\",\n \"country\": \"United States\",\n },\n ),\n TextNode(\n text=(\n \"Elon Musk is a business magnate, industrial designer, and\"\n \" engineer. He is the founder, CEO, and lead designer of SpaceX,\"\n \" Tesla, Inc., Neuralink, and The Boring Company.\"\n ),\n metadata={\n \"category\": \"Business\",\n \"country\": \"United States\",\n },\n ),\n TextNode(\n text=(\n \"Rihanna is a Barbadian singer, actress, and businesswoman. She\"\n \" has achieved significant success in the music industry and is\"\n \" known for her versatile musical style.\"\n ),\n metadata={\n \"category\": \"Music\",\n \"country\": \"Barbados\",\n },\n ),\n TextNode(\n text=(\n \"Cristiano Ronaldo is a Portuguese professional footballer who is\"\n \" considered one of the greatest football players of all time. He\"\n \" has won numerous awards and set multiple records during his\"\n \" career.\"\n ),\n metadata={\n \"category\": \"Sports\",\n \"country\": \"Portugal\",\n },\n ),\n]\n```\n\n## Build Vector Index with Weaviate Vector Store\n\nHere we load the data into the vector store. As mentioned above, both the text and metadata for each node will get converted into corresopnding representations in Weaviate. We can now run semantic queries and also metadata filtering on this data from Weaviate.\n\n\n```python\nfrom llama_index.core import VectorStoreIndex, StorageContext\nfrom llama_index.vector_stores.weaviate import WeaviateVectorStore\n\nvector_store = WeaviateVectorStore(\n weaviate_client=client, index_name=\"LlamaIndex_filter\"\n)\n\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n```\n\n\n```python\nindex = VectorStoreIndex(nodes, storage_context=storage_context)\n```\n\n## Define `VectorIndexAutoRetriever`\n\nWe define our core `VectorIndexAutoRetriever` module. The module takes in `VectorStoreInfo`,\nwhich contains a structured description of the vector store collection and the metadata filters it supports.\nThis information will then be used in the auto-retrieval prompt where the LLM infers metadata filters.\n\n\n```python\nfrom llama_index.core.retrievers import VectorIndexAutoRetriever\nfrom llama_index.core.vector_stores.types import MetadataInfo, VectorStoreInfo\n\n\nvector_store_info = VectorStoreInfo(\n content_info=\"brief biography of celebrities\",\n metadata_info=[\n MetadataInfo(\n name=\"category\",\n type=\"str\",\n description=(\n \"Category of the celebrity, one of [Sports, Entertainment,\"\n \" Business, Music]\"\n ),\n ),\n MetadataInfo(\n name=\"country\",\n type=\"str\",\n description=(\n \"Country of the celebrity, one of [United States, Barbados,\"\n \" Portugal]\"\n ),\n ),\n ],\n)\n\nretriever = VectorIndexAutoRetriever(\n index, vector_store_info=vector_store_info\n)\n```\n\n## Running over some sample data\n\nWe try running over some sample data. Note how metadata filters are inferred - this helps with more precise retrieval! \n\n\n```python\nresponse = retriever.retrieve(\"Tell me about celebrities from United States\")\n```\n\n\n```python\nprint(response[0])\n```\n\n\n```python\nresponse = retriever.retrieve(\n \"Tell me about Sports celebrities from United States\"\n)\n```\n\n\n```python\nprint(response[0])\n```"}
{"tokens": 1016, "doc_id": "517359e3-f4af-44bb-8076-2f6f8f27505c", "name": "Weaviate Vector Store", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/WeaviateIndexDemo", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Weaviate Vector Store\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-weaviate\n```\n\n\n```python\n!pip install llama-index\n```\n\n#### Creating a Weaviate Client\n\n\n```python\nimport os\nimport openai\n\nos.environ[\"OPENAI_API_KEY\"] = \"\"\nopenai.api_key = os.environ[\"OPENAI_API_KEY\"]\n```\n\n\n```python\nimport logging\nimport sys\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nlogging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n```\n\n\n```python\nimport weaviate\n```\n\n\n```python\n# cloud\ncluster_url = \"\"\napi_key = \"\"\n\nclient = weaviate.connect_to_wcs(\n cluster_url=cluster_url,\n auth_credentials=weaviate.auth.AuthApiKey(api_key),\n)\n\n# local\n# client = connect_to_local()\n```\n\n#### Load documents, build the VectorStoreIndex\n\n\n```python\nfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader\nfrom llama_index.vector_stores.weaviate import WeaviateVectorStore\nfrom IPython.display import Markdown, display\n```\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham\").load_data()\n```\n\n\n```python\nfrom llama_index.core import StorageContext\n\n# If you want to load the index later, be sure to give it a name!\nvector_store = WeaviateVectorStore(\n weaviate_client=client, index_name=\"LlamaIndex\"\n)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n\n# NOTE: you may also choose to define a index_name manually.\n# index_name = \"test_prefix\"\n# vector_store = WeaviateVectorStore(weaviate_client=client, index_name=index_name)\n```\n\n#### Query Index\n\n\n```python\n# set Logging to DEBUG for more detailed outputs\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What did the author do growing up?\")\n```\n\n\n```python\ndisplay(Markdown(f\"{response}\"))\n```\n\n## Loading the index\n\nHere, we use the same index name as when we created the initial index. This stops it from being auto-generated and allows us to easily connect back to it.\n\n\n```python\ncluster_url = \"\"\napi_key = \"\"\n\nclient = weaviate.connect_to_wcs(\n cluster_url=cluster_url,\n auth_credentials=weaviate.auth.AuthApiKey(api_key),\n)\n\n# local\n# client = weaviate.connect_to_local()\n```\n\n\n```python\nvector_store = WeaviateVectorStore(\n weaviate_client=client, index_name=\"LlamaIndex\"\n)\n\nloaded_index = VectorStoreIndex.from_vector_store(vector_store)\n```\n\n\n```python\n# set Logging to DEBUG for more detailed outputs\nquery_engine = loaded_index.as_query_engine()\nresponse = query_engine.query(\"What happened at interleaf?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n## Metadata Filtering\n\nLet's insert a dummy document, and try to filter so that only that document is returned.\n\n\n```python\nfrom llama_index.core import Document\n\ndoc = Document.example()\nprint(doc.metadata)\nprint(\"-----\")\nprint(doc.text[:100])\n```\n\n\n```python\nloaded_index.insert(doc)\n```\n\n\n```python\nfrom llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n\nfilters = MetadataFilters(\n filters=[ExactMatchFilter(key=\"filename\", value=\"README.md\")]\n)\nquery_engine = loaded_index.as_query_engine(filters=filters)\nresponse = query_engine.query(\"What is the name of the file?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n# Deleting the index completely\n\nYou can delete the index created by the vector store using the `delete_index` function\n\n\n```python\nvector_store.delete_index()\n```\n\n\n```python\nvector_store.delete_index() # calling the function again does nothing\n```\n\n# Connection Termination\n\nYou must ensure your client connections are closed:\n\n\n```python\nclient.close()\n```"}
{"tokens": 1471, "doc_id": "26824bda-cde2-4903-9be7-f5288b216ca2", "name": "Neo4j vector store", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/Neo4jVectorDemo", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Neo4j vector store\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-neo4jvector\n```\n\n\n```python\n!pip install llama-index\n```\n\n\n```python\nimport os\nimport openai\n\nos.environ[\"OPENAI_API_KEY\"] = \"OPENAI_API_KEY\"\nopenai.api_key = os.environ[\"OPENAI_API_KEY\"]\n```\n\n## Initiate Neo4j vector wrapper\n\n\n```python\nfrom llama_index.vector_stores.neo4jvector import Neo4jVectorStore\n\nusername = \"neo4j\"\npassword = \"pleaseletmein\"\nurl = \"bolt://localhost:7687\"\nembed_dim = 1536\n\nneo4j_vector = Neo4jVectorStore(username, password, url, embed_dim)\n```\n\n## Load documents, build the VectorStoreIndex\n\n\n```python\nfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader\nfrom IPython.display import Markdown, display\n```\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n --2023-12-14 18:44:00-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n HTTP request sent, awaiting response... 200 OK\n Length: 75042 (73K) [text/plain]\n Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n \n data/paul_graham/pa 100%[===================>] 73,28K --.-KB/s in 0,03s \n \n 2023-12-14 18:44:00 (2,16 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n \n\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham\").load_data()\n```\n\n\n```python\nfrom llama_index.core import StorageContext\n\nstorage_context = StorageContext.from_defaults(vector_store=neo4j_vector)\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n```\n\n\n```python\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What happened at interleaf?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nAt Interleaf, they added a scripting language inspired by Emacs and made it a dialect of Lisp. They were looking for a Lisp hacker to write things in this scripting language. The author of the text worked at Interleaf and mentioned that their Lisp was the thinnest icing on a giant C cake. The author also mentioned that they didn't know C and didn't want to learn it, so they never understood most of the software at Interleaf. Additionally, the author admitted to being a bad employee and spending much of their time working on a separate project called On Lisp.\n\n\n## Hybrid search\n\nHybrid search uses a combination of keyword and vector search\nIn order to use hybrid search, you need to set the `hybrid_search` to `True`\n\n\n```python\nneo4j_vector_hybrid = Neo4jVectorStore(\n username, password, url, embed_dim, hybrid_search=True\n)\n```\n\n\n```python\nstorage_context = StorageContext.from_defaults(\n vector_store=neo4j_vector_hybrid\n)\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What happened at interleaf?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nAt Interleaf, they added a scripting language inspired by Emacs and made it a dialect of Lisp. They were looking for a Lisp hacker to write things in this scripting language. The author of the essay worked at Interleaf but didn't understand most of the software because he didn't know C and didn't want to learn it. He also mentioned that their Lisp was the thinnest icing on a giant C cake. The author admits to being a bad employee and spending much of his time working on a contract to publish On Lisp.\n\n\n## Load existing vector index\n\nIn order to connect to an existing vector index, you need to define the `index_name` and `text_node_property` parameters:\n\n- index_name: name of the existing vector index (default is `vector`)\n- text_node_property: name of the property that containt the text value (default is `text`)\n\n\n```python\nindex_name = \"existing_index\"\ntext_node_property = \"text\"\nexisting_vector = Neo4jVectorStore(\n username,\n password,\n url,\n embed_dim,\n index_name=index_name,\n text_node_property=text_node_property,\n)\n\nloaded_index = VectorStoreIndex.from_vector_store(existing_vector)\n```\n\n## Customizing responses\n\nYou can customize the retrieved information from the knowledge graph using the `retrieval_query` parameter.\n\nThe retrieval query must return the following four columns:\n\n* text:str - The text of the returned document\n* score:str - similarity score\n* id:str - node id\n* metadata: Dict - dictionary with additional metadata (must contain `_node_type` and `_node_content` keys)\n\n\n```python\nretrieval_query = (\n \"RETURN 'Interleaf hired Tomaz' AS text, score, node.id AS id, \"\n \"{author: 'Tomaz', _node_type:node._node_type, _node_content:node._node_content} AS metadata\"\n)\nneo4j_vector_retrieval = Neo4jVectorStore(\n username, password, url, embed_dim, retrieval_query=retrieval_query\n)\n```\n\n\n```python\nloaded_index = VectorStoreIndex.from_vector_store(\n neo4j_vector_retrieval\n).as_query_engine()\nresponse = loaded_index.query(\"What happened at interleaf?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nInterleaf hired Tomaz."}
{"tokens": 1782, "doc_id": "dea54c67-9b5e-47f0-adcc-c00da6a46c2f", "name": "S3/R2 Storage", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/SimpleIndexOnS3", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# S3/R2 Storage\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n!pip install llama-index\n```\n\n\n```python\nimport logging\nimport sys\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nlogging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n\nfrom llama_index.core import (\n VectorStoreIndex,\n SimpleDirectoryReader,\n load_index_from_storage,\n StorageContext,\n)\nfrom IPython.display import Markdown, display\n```\n\n INFO:numexpr.utils:Note: NumExpr detected 32 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n Note: NumExpr detected 32 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n NumExpr defaulting to 8 threads.\n\n\n /home/hua/code/llama_index/.hermit/python/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n from .autonotebook import tqdm as notebook_tqdm\n\n\n\n```python\nimport dotenv\nimport s3fs\nimport os\n\ndotenv.load_dotenv(\"../../../.env\")\n\nAWS_KEY = os.environ[\"AWS_ACCESS_KEY_ID\"]\nAWS_SECRET = os.environ[\"AWS_SECRET_ACCESS_KEY\"]\nR2_ACCOUNT_ID = os.environ[\"R2_ACCOUNT_ID\"]\n\nassert AWS_KEY is not None and AWS_KEY != \"\"\n\ns3 = s3fs.S3FileSystem(\n key=AWS_KEY,\n secret=AWS_SECRET,\n endpoint_url=f\"https://{R2_ACCOUNT_ID}.r2.cloudflarestorage.com\",\n s3_additional_kwargs={\"ACL\": \"public-read\"},\n)\n```\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\nprint(len(documents))\n```\n\n 1\n\n\n\n```python\nindex = VectorStoreIndex.from_documents(documents, fs=s3)\n```\n\n INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n > [build_index_from_nodes] Total LLM token usage: 0 tokens\n INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n > [build_index_from_nodes] Total embedding token usage: 20729 tokens\n\n\n\n```python\n# save index to disk\nindex.set_index_id(\"vector_index\")\nindex.storage_context.persist(\"llama-index/storage_demo\", fs=s3)\n```\n\n\n```python\ns3.listdir(\"llama-index/storage_demo\")\n```\n\n\n\n\n [{'Key': 'llama-index/storage_demo/docstore.json',\n 'LastModified': datetime.datetime(2023, 5, 14, 20, 23, 53, 213000, tzinfo=tzutc()),\n 'ETag': '\"3993f79a6f7cf908a8e53450a2876cf0\"',\n 'Size': 107529,\n 'StorageClass': 'STANDARD',\n 'type': 'file',\n 'size': 107529,\n 'name': 'llama-index/storage_demo/docstore.json'},\n {'Key': 'llama-index/storage_demo/index_store.json',\n 'LastModified': datetime.datetime(2023, 5, 14, 20, 23, 53, 783000, tzinfo=tzutc()),\n 'ETag': '\"5b084883bf0b08e3c2b979af7c16be43\"',\n 'Size': 3105,\n 'StorageClass': 'STANDARD',\n 'type': 'file',\n 'size': 3105,\n 'name': 'llama-index/storage_demo/index_store.json'},\n {'Key': 'llama-index/storage_demo/vector_store.json',\n 'LastModified': datetime.datetime(2023, 5, 14, 20, 23, 54, 232000, tzinfo=tzutc()),\n 'ETag': '\"75535cf22c23bcd8ead21b8a52e9517a\"',\n 'Size': 829290,\n 'StorageClass': 'STANDARD',\n 'type': 'file',\n 'size': 829290,\n 'name': 'llama-index/storage_demo/vector_store.json'}]\n\n\n\n\n```python\n# load index from s3\nsc = StorageContext.from_defaults(\n persist_dir=\"llama-index/storage_demo\", fs=s3\n)\n```\n\n\n```python\nindex2 = load_index_from_storage(sc, \"vector_index\")\n```\n\n INFO:llama_index.indices.loading:Loading indices with ids: ['vector_index']\n Loading indices with ids: ['vector_index']\n\n\n\n```python\nindex2.docstore.docs.keys()\n```\n\n\n\n\n dict_keys(['f8891670-813b-4cfa-9025-fcdc8ba73449', '985a2c69-9da5-40cf-ba30-f984921187c1', 'c55f077c-0bfb-4036-910c-6fd5f26f7372', 'b47face6-f25b-4381-bb8d-164f179d6888', '16304ef7-2378-4776-b86d-e8ed64c8fb58', '62dfdc7a-6a2f-4d5f-9033-851fbc56c14a', 'a51ef189-3924-494b-84cf-e23df673e29c', 'f94aca2b-34ac-4ec4-ac41-d31cd3b7646f', 'ad89e2fb-e0fc-4615-a380-8245bd6546af', '3dbba979-ca08-4321-b4de-be5236ac2e11', '634b2d6d-0bff-4384-898f-b521470db8ac', 'ee9551ba-7a44-493d-997b-8eeab9c04e25', 'b21fe2b5-d8e3-4895-8424-fa9e3da76711', 'bd2609e8-8b52-49e8-8ee7-41b64b3ce9e1', 'a08b739e-efd9-4a61-8517-c4f9cea8cf7d', '8d4babaf-37f1-454a-8be4-b67e1b8e428f', '05389153-4567-4e53-a2ea-bc3e020ee1b2', 'd29531a5-c5d2-4e1d-ab99-56f2b4bb7f37', '2ccb3c63-3407-4acf-b5bb-045caa588bbc', 'a0b1bebb-3dcd-4bf8-9ebb-a4cd2cb82d53', '21517b34-6c1b-4607-bf89-7ab59b85fba6', 'f2487d52-1e5e-4482-a182-218680ef306e', '979998ce-39ee-41bc-a9be-b3ed68d7c304', '3e658f36-a13e-407a-8624-0adf9e842676'])"}
{"tokens": 1815, "doc_id": "865b355c-a71d-4252-8033-1aa6c567ae16", "name": "Rockset Vector Store", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/RocksetIndexDemo", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Rockset Vector Store\n\nAs a real-time search and analytics database, Rockset uses indexing to deliver scalable and performant personalization, product search, semantic search, chatbot applications, and more.\nSince Rockset is purpose-built for real-time, you can build these responsive applications on constantly updating, streaming data. \nBy integrating Rockset with LlamaIndex, you can easily use LLMs on your own real-time data for production-ready vector search applications.\n\nWe'll walk through a demonstration of how to use Rockset as a vector store in LlamaIndex. \n\n## Tutorial\nIn this example, we'll use OpenAI's `text-embedding-ada-002` model to generate embeddings and Rockset as vector store to store embeddings.\nWe'll ingest text from a file and ask questions about the content.\n\n### Setting Up Your Environment\n1. Create a [collection](https://rockset.com/docs/collections) from the Rockset console with the [Write API](https://rockset.com/docs/write-api/) as your source.\nName your collection `llamaindex_demo`. Configure the following [ingest transformation](https://rockset.com/docs/ingest-transformation) \nwith [`VECTOR_ENFORCE`](https://rockset.com/docs/vector-functions) to define your embeddings field and take advantage of performance and storage optimizations:\n```sql\nSELECT \n _input.* EXCEPT(_meta), \n VECTOR_ENFORCE(\n _input.embedding,\n 1536,\n 'float'\n ) as embedding\nFROM _input\n```\n\n2. Create an [API key](https://rockset.com/docs/iam) from the Rockset console and set the `ROCKSET_API_KEY` environment variable.\nFind your API server [here](http://rockset.com/docs/rest-api#introduction) and set the `ROCKSET_API_SERVER` environment variable. \nSet the `OPENAI_API_KEY` environment variable.\n\n3. Install the dependencies.\n```shell\npip3 install llama_index rockset \n```\n\n4. LlamaIndex allows you to ingest data from a variety of sources. \nFor this example, we'll read from a text file named `constitution.txt`, which is a transcript of the American Constitution, found [here](https://www.archives.gov/founding-docs/constitution-transcript). \n\n### Data ingestion \nUse LlamaIndex's `SimpleDirectoryReader` class to convert the text file to a list of `Document` objects.\n\n\n```python\n%pip install llama-index-llms-openai\n%pip install llama-index-vector-stores-rocksetdb\n```\n\n\n```python\nfrom llama_index.core import SimpleDirectoryReader\n\ndocs = SimpleDirectoryReader(\n input_files=[\"{path to}/consitution.txt\"]\n).load_data()\n```\n\nInstantiate the LLM and service context.\n\n\n```python\nfrom llama_index.core import Settings\nfrom llama_index.llms.openai import OpenAI\n\nSettings.llm = OpenAI(temperature=0.8, model=\"gpt-3.5-turbo\")\n```\n\nInstantiate the vector store and storage context.\n\n\n```python\nfrom llama_index.core import StorageContext\nfrom llama_index.vector_stores.rocksetdb import RocksetVectorStore\n\nvector_store = RocksetVectorStore(collection=\"llamaindex_demo\")\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n```\n\nAdd documents to the `llamaindex_demo` collection and create an index.\n\n\n```python\nfrom llama_index.core import VectorStoreIndex\n\nindex = VectorStoreIndex.from_documents(\n docs,\n storage_context=storage_context,\n)\n```\n\n### Querying\nAsk a question about your document and generate a response.\n\n\n```python\nresponse = index.as_query_engine().query(\"What is the duty of the president?\")\n\nprint(str(response))\n```\n\n\nRun the program.\n```text\n$ python3 main.py\nThe duty of the president is to faithfully execute the Office of President of the United States, preserve, protect and defend the Constitution of the United States, serve as the Commander in Chief of the Army and Navy, grant reprieves and pardons for offenses against the United States (except in cases of impeachment), make treaties and appoint ambassadors and other public ministers, take care that the laws be faithfully executed, and commission all the officers of the United States.\n```\n\n## Metadata Filtering\nMetadata filtering allows you to retrieve relevant documents that match specific filters.\n\n1. Add nodes to your vector store and create an index.\n\n\n```python\nfrom llama_index.vector_stores.rocksetdb import RocksetVectorStore\nfrom llama_index.core import VectorStoreIndex, StorageContext\nfrom llama_index.core.vector_stores.types import NodeWithEmbedding\nfrom llama_index.core.schema import TextNode\n\nnodes = [\n NodeWithEmbedding(\n node=TextNode(\n text=\"Apples are blue\",\n metadata={\"type\": \"fruit\"},\n ),\n embedding=[],\n )\n]\nindex = VectorStoreIndex(\n nodes,\n storage_context=StorageContext.from_defaults(\n vector_store=RocksetVectorStore(collection=\"llamaindex_demo\")\n ),\n)\n```\n\n2. Define metadata filters.\n\n\n```python\nfrom llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n\nfilters = MetadataFilters(\n filters=[ExactMatchFilter(key=\"type\", value=\"fruit\")]\n)\n```\n\n3. Retrieve relevant documents that satisfy the filters.\n\n\n```python\nretriever = index.as_retriever(filters=filters)\nretriever.retrieve(\"What colors are apples?\")\n```\n\n## Creating an Index from an Existing Collection\nYou can create indices with data from existing collections.\n\n\n```python\nfrom llama_index.core import VectorStoreIndex\nfrom llama_index.vector_stores.rocksetdb import RocksetVectorStore\n\nvector_store = RocksetVectorStore(collection=\"llamaindex_demo\")\n\nindex = VectorStoreIndex.from_vector_store(vector_store)\n```\n\n## Creating an Index from a New Collection\nYou can also create a new Rockset collection to use as a vector store.\n\n\n```python\nfrom llama_index.vector_stores.rocksetdb import RocksetVectorStore\n\nvector_store = RocksetVectorStore.with_new_collection(\n collection=\"llamaindex_demo\", # name of new collection\n dimensions=1536, # specifies length of vectors in ingest tranformation (optional)\n # other RocksetVectorStore args\n)\n\nindex = VectorStoreIndex(\n nodes,\n storage_context=StorageContext.from_defaults(vector_store=vector_store),\n)\n```\n\n## Configuration\n* **collection**: Name of the collection to query (required).\n\n```python\nRocksetVectorStore(collection=\"my_collection\")\n```\n\n* **workspace**: Name of the workspace containing the collection. Defaults to `\"commons\"`.\n```python\nRocksetVectorStore(worksapce=\"my_workspace\")\n```\n\n* **api_key**: The API key to use to authenticate Rockset requests. Ignored if `client` is passed in. Defaults to the `ROCKSET_API_KEY` environment variable.\n```python\nRocksetVectorStore(api_key=\"\n\n# Postgres Vector Store\nIn this notebook we are going to show how to use [Postgresql](https://www.postgresql.org) and [pgvector](https://github.com/pgvector/pgvector) to perform vector searches in LlamaIndex\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-postgres\n```\n\n\n```python\n!pip install llama-index\n```\n\nRunning the following cell will install Postgres with PGVector in Colab.\n\n\n```python\n!sudo apt update\n!echo | sudo apt install -y postgresql-common\n!echo | sudo /usr/share/postgresql-common/pgdg/apt.postgresql.org.sh\n!echo | sudo apt install postgresql-15-pgvector\n!sudo service postgresql start\n!sudo -u postgres psql -c \"ALTER USER postgres PASSWORD 'password';\"\n!sudo -u postgres psql -c \"CREATE DATABASE vector_db;\"\n```\n\n\n```python\n# import logging\n# import sys\n\n# Uncomment to see debug logs\n# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n\nfrom llama_index.core import SimpleDirectoryReader, StorageContext\nfrom llama_index.core import VectorStoreIndex\nfrom llama_index.vector_stores.postgres import PGVectorStore\nimport textwrap\nimport openai\n```\n\n### Setup OpenAI\nThe first step is to configure the openai key. It will be used to created embeddings for the documents loaded into the index\n\n\n```python\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = \"\n\n# DashVector Vector Store\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-dashvector\n```\n\n\n```python\n!pip install llama-index\n```\n\n\n```python\nimport logging\nimport sys\nimport os\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nlogging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n```\n\n#### Creating a DashVector Collection\n\n\n```python\nimport dashvector\n```\n\n\n```python\napi_key = os.environ[\"DASHVECTOR_API_KEY\"]\nclient = dashvector.Client(api_key=api_key)\n```\n\n\n```python\n# dimensions are for text-embedding-ada-002\nclient.create(\"llama-demo\", dimension=1536)\n```\n\n\n\n\n {\"code\": 0, \"message\": \"\", \"requests_id\": \"82b969d2-2568-4e18-b0dc-aa159b503c84\"}\n\n\n\n\n```python\ndashvector_collection = client.get(\"quickstart\")\n```\n\n#### Download Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n#### Load documents, build the DashVectorStore and VectorStoreIndex\n\n\n```python\nfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader\nfrom llama_index.vector_stores.dashvector import DashVectorStore\nfrom IPython.display import Markdown, display\n```\n\n INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n NumExpr defaulting to 8 threads.\n\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham\").load_data()\n```\n\n\n```python\n# initialize without metadata filter\nfrom llama_index.core import StorageContext\n\nvector_store = DashVectorStore(dashvector_collection)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n```\n\n#### Query Index\n\n\n```python\n# set Logging to DEBUG for more detailed outputs\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What did the author do growing up?\")\n```\n\n\n```python\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nThe author worked on writing and programming outside of school. They wrote short stories and tried writing programs on the IBM 1401 computer. They also built a microcomputer and started programming on it, writing simple games and a word processor."}
{"tokens": 871, "doc_id": "49aafced-ea43-4b11-a230-f031f3453b6b", "name": "MyScale Vector Store", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/MyScaleIndexDemo", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# MyScale Vector Store\nIn this notebook we are going to show a quick demo of using the MyScaleVectorStore.\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-myscale\n```\n\n\n```python\n!pip install llama-index\n```\n\n#### Creating a MyScale Client\n\n\n```python\nimport logging\nimport sys\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nlogging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n```\n\n\n```python\nfrom os import environ\nimport clickhouse_connect\n\nenviron[\"OPENAI_API_KEY\"] = \"sk-*\"\n\n# initialize client\nclient = clickhouse_connect.get_client(\n host=\"YOUR_CLUSTER_HOST\",\n port=8443,\n username=\"YOUR_USERNAME\",\n password=\"YOUR_CLUSTER_PASSWORD\",\n)\n```\n\n#### Load documents, build and store the VectorStoreIndex with MyScaleVectorStore\n\nHere we will use a set of Paul Graham essays to provide the text to turn into embeddings, store in a ``MyScaleVectorStore`` and query to find context for our LLM QnA loop.\n\n\n```python\nfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader\nfrom llama_index.vector_stores.myscale import MyScaleVectorStore\nfrom IPython.display import Markdown, display\n```\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\nprint(\"Document ID:\", documents[0].doc_id)\nprint(\"Number of Documents: \", len(documents))\n```\n\n Document ID: a5f2737c-ed18-4e5d-ab9a-75955edb816d\n Number of Documents: 1\n\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\nYou can process your files individually using [SimpleDirectoryReader](/examples/data_connectors/simple_directory_reader.ipynb):\n\n\n```python\nloader = SimpleDirectoryReader(\"./data/paul_graham/\")\ndocuments = loader.load_data()\nfor file in loader.input_files:\n print(file)\n # Here is where you would do any preprocessing\n```\n\n ../data/paul_graham/paul_graham_essay.txt\n\n\n\n```python\n# initialize with metadata filter and store indexes\nfrom llama_index.core import StorageContext\n\nfor document in documents:\n document.metadata = {\"user_id\": \"123\", \"favorite_color\": \"blue\"}\nvector_store = MyScaleVectorStore(myscale_client=client)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n```\n\n#### Query Index\n\nNow MyScale vector store supports filter search and hybrid search\n\nYou can learn more about [query_engine](/module_guides/deploying/query_engine/index.md) and [retriever](/module_guides/querying/retriever/index.md).\n\n\n```python\nimport textwrap\n\nfrom llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n\n# set Logging to DEBUG for more detailed outputs\nquery_engine = index.as_query_engine(\n filters=MetadataFilters(\n filters=[\n ExactMatchFilter(key=\"user_id\", value=\"123\"),\n ]\n ),\n similarity_top_k=2,\n vector_store_query_mode=\"hybrid\",\n)\nresponse = query_engine.query(\"What did the author learn?\")\nprint(textwrap.fill(str(response), 100))\n```\n\n#### Clear All Indexes\n\n\n```python\nfor document in documents:\n index.delete_ref_doc(document.doc_id)\n```"}
{"tokens": 5379, "doc_id": "15e961c0-5b7c-4bf7-b87e-8ba5d415e63f", "name": "Redis Vector Store", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/RedisIndexDemo", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Redis Vector Store\n\nIn this notebook we are going to show a quick demo of using the RedisVectorStore.\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install -U llama-index llama-index-vector-stores-redis llama-index-embeddings-cohere llama-index-embeddings-openai\n```\n\n\n```python\nimport os\nimport getpass\nimport sys\nimport logging\nimport textwrap\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\n# Uncomment to see debug logs\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\n\nfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader\nfrom llama_index.vector_stores.redis import RedisVectorStore\n```\n\n### Start Redis\n\nThe easiest way to start Redis is using the [Redis Stack](https://hub.docker.com/r/redis/redis-stack) docker image or\nquickly signing up for a [FREE Redis Cloud](https://redis.com/try-free) instance.\n\nTo follow every step of this tutorial, launch the image as follows:\n\n```bash\ndocker run --name redis-vecdb -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest\n```\n\nThis will also launch the RedisInsight UI on port 8001 which you can view at http://localhost:8001.\n\n\n### Setup OpenAI\nLets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt.\n\n\n```python\noai_api_key = getpass.getpass(\"OpenAI API Key:\")\nos.environ[\"OPENAI_API_KEY\"] = oai_api_key\n```\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n --2024-04-10 19:35:33-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8003::154, 2606:50c0:8000::154, 2606:50c0:8002::154, ...\n Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8003::154|:443... connected.\n HTTP request sent, awaiting response... 200 OK\n Length: 75042 (73K) [text/plain]\n Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n \n data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.03s \n \n 2024-04-10 19:35:33 (2.15 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n \n\n\n### Read in a dataset\nHere we will use a set of Paul Graham essays to provide the text to turn into embeddings, store in a ``RedisVectorStore`` and query to find context for our LLM QnA loop.\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham\").load_data()\nprint(\n \"Document ID:\",\n documents[0].id_,\n \"Document Filename:\",\n documents[0].metadata[\"file_name\"],\n)\n```\n\n Document ID: 7056f7ba-3513-4ef4-9792-2bd28040aaed Document Filename: paul_graham_essay.txt\n\n\n### Initialize the default Redis Vector Store\n\nNow we have our documents prepared, we can initialize the Redis Vector Store with **default** settings. This will allow us to store our vectors in Redis and create an index for real-time search.\n\n\n```python\nfrom llama_index.core import StorageContext\nfrom redis import Redis\n\n# create a Redis client connection\nredis_client = Redis.from_url(\"redis://localhost:6379\")\n\n# create the vector store wrapper\nvector_store = RedisVectorStore(redis_client=redis_client, overwrite=True)\n\n# load storage context\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\n# build and load index from documents and storage context\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n# index = VectorStoreIndex.from_vector_store(vector_store=vector_store)\n```\n\n 19:39:17 llama_index.vector_stores.redis.base INFO Using default RedisVectorStore schema.\n 19:39:19 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n 19:39:19 llama_index.vector_stores.redis.base INFO Added 22 documents to index llama_index\n\n\n### Query the default vector store\n\nNow that we have our data stored in the index, we can ask questions against the index.\n\nThe index will use the data as the knowledge base for an LLM. The default setting for as_query_engine() utilizes OpenAI embeddings and GPT as the language model. Therefore, an OpenAI key is required unless you opt for a customized or local language model.\n\nBelow we will test searches against out index and then full RAG with an LLM.\n\n\n```python\nquery_engine = index.as_query_engine()\nretriever = index.as_retriever()\n```\n\n\n```python\nresult_nodes = retriever.retrieve(\"What did the author learn?\")\nfor node in result_nodes:\n print(node)\n```\n\n 19:39:22 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n 19:39:22 llama_index.vector_stores.redis.base INFO Querying index llama_index with filters *\n 19:39:22 llama_index.vector_stores.redis.base INFO Found 2 results for query with id ['llama_index/vector_adb6b7ce-49bb-4961-8506-37082c02a389', 'llama_index/vector_e39be1fe-32d0-456e-b211-4efabd191108']\n Node ID: adb6b7ce-49bb-4961-8506-37082c02a389\n Text: What I Worked On February 2021 Before college the two main\n things I worked on, outside of school, were writing and programming. I\n didn't write essays. I wrote what beginning writers were supposed to\n write then, and probably still are: short stories. My stories were\n awful. They had hardly any plot, just characters with strong feelings,\n which I ...\n Score: 0.820\n \n Node ID: e39be1fe-32d0-456e-b211-4efabd191108\n Text: Except for a few officially anointed thinkers who went to the\n right parties in New York, the only people allowed to publish essays\n were specialists writing about their specialties. There were so many\n essays that had never been written, because there had been no way to\n publish them. Now they could be, and I was going to write them. [12]\n I've wor...\n Score: 0.819\n \n\n\n\n```python\nresponse = query_engine.query(\"What did the author learn?\")\nprint(textwrap.fill(str(response), 100))\n```\n\n 19:39:25 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n 19:39:25 llama_index.vector_stores.redis.base INFO Querying index llama_index with filters *\n 19:39:25 llama_index.vector_stores.redis.base INFO Found 2 results for query with id ['llama_index/vector_adb6b7ce-49bb-4961-8506-37082c02a389', 'llama_index/vector_e39be1fe-32d0-456e-b211-4efabd191108']\n 19:39:27 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n The author learned that working on things that weren't prestigious often led to valuable discoveries\n and indicated the right kind of motives. Despite the lack of initial prestige, pursuing such work\n could be a sign of genuine potential and appropriate motivations, steering clear of the common\n pitfall of being driven solely by the desire to impress others.\n\n\n\n```python\nresult_nodes = retriever.retrieve(\"What was a hard moment for the author?\")\nfor node in result_nodes:\n print(node)\n```\n\n 19:39:27 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n 19:39:27 llama_index.vector_stores.redis.base INFO Querying index llama_index with filters *\n 19:39:27 llama_index.vector_stores.redis.base INFO Found 2 results for query with id ['llama_index/vector_adb6b7ce-49bb-4961-8506-37082c02a389', 'llama_index/vector_e39be1fe-32d0-456e-b211-4efabd191108']\n Node ID: adb6b7ce-49bb-4961-8506-37082c02a389\n Text: What I Worked On February 2021 Before college the two main\n things I worked on, outside of school, were writing and programming. I\n didn't write essays. I wrote what beginning writers were supposed to\n write then, and probably still are: short stories. My stories were\n awful. They had hardly any plot, just characters with strong feelings,\n which I ...\n Score: 0.802\n \n Node ID: e39be1fe-32d0-456e-b211-4efabd191108\n Text: Except for a few officially anointed thinkers who went to the\n right parties in New York, the only people allowed to publish essays\n were specialists writing about their specialties. There were so many\n essays that had never been written, because there had been no way to\n publish them. Now they could be, and I was going to write them. [12]\n I've wor...\n Score: 0.799\n \n\n\n\n```python\nresponse = query_engine.query(\"What was a hard moment for the author?\")\nprint(textwrap.fill(str(response), 100))\n```\n\n 19:39:29 httpx INFO HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n 19:39:29 llama_index.vector_stores.redis.base INFO Querying index llama_index with filters *\n 19:39:29 llama_index.vector_stores.redis.base INFO Found 2 results for query with id ['llama_index/vector_adb6b7ce-49bb-4961-8506-37082c02a389', 'llama_index/vector_e39be1fe-32d0-456e-b211-4efabd191108']\n 19:39:31 httpx INFO HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n A hard moment for the author was when one of his programs on the IBM 1401 mainframe didn't\n terminate, leading to a technical error and an uncomfortable situation with the data center manager.\n\n\n\n```python\nindex.vector_store.delete_index()\n```\n\n 19:39:34 llama_index.vector_stores.redis.base INFO Deleting index llama_index\n\n\n### Use a custom index schema\n\nIn most use cases, you need the ability to customize the underling index configuration\nand specification. For example, this is handy in order to define specific metadata filters you wish to enable.\n\nWith Redis, this is as simple as defining an index schema object\n(from file or dict) and passing it through to the vector store client wrapper.\n\nFor this example, we will:\n1. switch the embedding model to [Cohere](cohereai.com)\n2. add an additional metadata field for the document `updated_at` timestamp\n3. index the existing `file_name` metadata field\n\n\n```python\nfrom llama_index.core.settings import Settings\nfrom llama_index.embeddings.cohere import CohereEmbedding\n\n# set up Cohere Key\nco_api_key = getpass.getpass(\"Cohere API Key:\")\nos.environ[\"CO_API_KEY\"] = co_api_key\n\n# set llamaindex to use Cohere embeddings\nSettings.embed_model = CohereEmbedding()\n```\n\n\n```python\nfrom redisvl.schema import IndexSchema\n\n\ncustom_schema = IndexSchema.from_dict(\n {\n # customize basic index specs\n \"index\": {\n \"name\": \"paul_graham\",\n \"prefix\": \"essay\",\n \"key_separator\": \":\",\n },\n # customize fields that are indexed\n \"fields\": [\n # required fields for llamaindex\n {\"type\": \"tag\", \"name\": \"id\"},\n {\"type\": \"tag\", \"name\": \"doc_id\"},\n {\"type\": \"text\", \"name\": \"text\"},\n # custom metadata fields\n {\"type\": \"numeric\", \"name\": \"updated_at\"},\n {\"type\": \"tag\", \"name\": \"file_name\"},\n # custom vector field definition for cohere embeddings\n {\n \"type\": \"vector\",\n \"name\": \"vector\",\n \"attrs\": {\n \"dims\": 1024,\n \"algorithm\": \"hnsw\",\n \"distance_metric\": \"cosine\",\n },\n },\n ],\n }\n)\n```\n\n\n```python\ncustom_schema.index\n```\n\n\n\n\n IndexInfo(name='paul_graham', prefix='essay', key_separator=':', storage_type=\n\n# Simple Vector Store - Async Index Creation\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-readers-wikipedia\n```\n\n\n```python\n!pip install llama-index\n```\n\n\n```python\nimport time\n\n# Helps asyncio run within Jupyter\nimport nest_asyncio\n\nnest_asyncio.apply()\n\n# My OpenAI Key\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = \"[YOUR_API_KEY]\"\n```\n\n\n```python\nfrom llama_index.core import VectorStoreIndex, download_loader\n\nfrom llama_index.readers.wikipedia import WikipediaReader\n\nloader = WikipediaReader()\ndocuments = loader.load_data(\n pages=[\n \"Berlin\",\n \"Santiago\",\n \"Moscow\",\n \"Tokyo\",\n \"Jakarta\",\n \"Cairo\",\n \"Bogota\",\n \"Shanghai\",\n \"Damascus\",\n ]\n)\n```\n\n\n```python\nlen(documents)\n```\n\n\n\n\n 9\n\n\n\n9 Wikipedia articles downloaded as documents\n\n\n```python\nstart_time = time.perf_counter()\nindex = VectorStoreIndex.from_documents(documents)\nduration = time.perf_counter() - start_time\nprint(duration)\n```\n\n INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n INFO:root:> [build_index_from_documents] Total embedding token usage: 142295 tokens\n\n\n 7.691995083000052\n\n\nStandard index creation took 7.69 seconds\n\n\n```python\nstart_time = time.perf_counter()\nindex = VectorStoreIndex(documents, use_async=True)\nduration = time.perf_counter() - start_time\nprint(duration)\n```\n\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=245 request_id=314b145a07f65fd34e707f633cc1a444 response_code=200\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=432 request_id=bb9e796d0b8f9c2365b68de8a56009ff response_code=200\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=433 request_id=7a94707fe2f8916e9cdd8276a5748207 response_code=200\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=499 request_id=cda679215293c3a13ed57c2eae3dc582 response_code=200\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=527 request_id=5e1c3e74aa3f9f950e4035f81a0f0a15 response_code=200\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=585 request_id=81983fe76eab95f73f82df881ff7b2d9 response_code=200\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=574 request_id=702a182b54a29a33719205f722378c8e response_code=200\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=575 request_id=d1df11775c59a3ba403dda253081f8eb response_code=200\n INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=575 request_id=47929f13469569527505b51958cd8e71 response_code=200\n INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n INFO:root:> [build_index_from_documents] Total embedding token usage: 142295 tokens\n\n\n 2.3730635830000892\n\n\nAsync index creation took 2.37 seconds\n\n\n```python\nquery_engine = index.as_query_engine()\nquery_engine.query(\"What is the etymology of Jakarta?\")\n```\n\n INFO:root:> [query] Total LLM token usage: 4075 tokens\n INFO:root:> [query] Total embedding token usage: 8 tokens\n\n\n\n\n\n Response(response=\"\\n\\nThe name 'Jakarta' is derived from the word Jayakarta (Devanagari: जयकर्त) which is ultimately derived from the Sanskrit जय jaya (victorious), and कृत krta (accomplished, acquired), thus Jayakarta translates as 'victorious deed', 'complete act' or 'complete victory'. It was named for the Muslim troops of Fatahillah which successfully defeated and drove the Portuguese away from the city in 1527. Before it was called Jayakarta, the city was known as 'Sunda Kelapa'. Tomé Pires, a Portuguese apothecary wrote the name of the city on his magnum opus as Jacatra or Jacarta during his journey to East Indies. The city is located in a low-lying area ranging from −2 to 91 m (−7 to 299 ft) with an average elevation of 8 m (26 ft) above sea level with historically extensive swampy areas. Some parts of the city have been constructed on reclaimed tidal flats that occur around the area. Thirteen rivers flow through Jakarta, including the Ciliwung River, Kalibaru, Pesanggra\", source_nodes=[SourceNode(source_text=\"Jakarta (; Indonesian pronunciation: [dʒaˈkarta] (listen)), officially the Special Capital Region of Jakarta (Indonesian: Daerah Khusus Ibukota Jakarta), is the capital and largest city of Indonesia. Lying on the northwest coast of Java, the world's most populous island, Jakarta is the largest city in Southeast Asia and serves as the diplomatic capital of ASEAN.\\nThe city is the economic, cultural, and political centre of Indonesia. It possesses a province-level status and has a population of 10,562,088 as of mid-2021. Although Jakarta extends over only 664.01 km2 (256.38 sq mi) and thus has the smallest area of any Indonesian province, its metropolitan area covers 9,957.08 km2 (3,844.45 sq mi), which includes the satellite cities Bogor, Depok, Tangerang, South Tangerang, and Bekasi, and has an estimated population of 35 million as of 2021, making it the largest urban area in Indonesia and the second-largest in the world (after Tokyo). Jakarta ranks first among the Indonesian provinces in the human development index. Jakarta's business and employment opportunities, along with its ability to offer a potentially higher standard of living compared to other parts of the country, have attracted migrants from across the Indonesian archipelago, making it a melting pot of numerous cultures.\\nJakarta is one of the oldest continuously inhabited cities in Southeast Asia. Established in the fourth century as Sunda Kelapa, the city became an important trading port for the Sunda Kingdom. At one time, it was the de facto capital of the Dutch East Indies, when it was known as Batavia. Jakarta was officially a city within West Java until 1960 when its official status was changed to a province with special capital region distinction. As a province, its government consists of five administrative cities and one administrative regency. Jakarta is an alpha world city and is the seat of the ASEAN secretariat. Financial institutions such as the Bank of Indonesia, Indonesia Stock Exchange, and corporate headquarters of numerous Indonesian companies and multinational corporations are located in the city. In 2021, the city's GRP PPP was estimated at US$602.946 billion.\\nJakarta's main challenges include rapid urban growth, ecological breakdown, gridlocked traffic, congestion, and flooding. Jakarta is sinking up to 17 cm (6.7 inches) annually, which coupled with the rising of sea levels, has made the city more prone to flooding. Hence, it is one of the fastest-sinking capitals in the world. In response to these challenges, in August 2019, President Joko Widodo announced that the capital of Indonesia would be moved from Jakarta to the planned city of Nusantara, in the province of East Kalimantan on the island of Borneo.\\n\\n\\n== Name ==\\n\\nJakarta has been home to multiple settlements. Below is the list of names used during its existence:\\n\\nSunda Kelapa (397–1527)\\nJayakarta (1527–1619)\\nBatavia (1619–1942)\\nDjakarta (1942–1972)\\nJakarta (1972–present)The name 'Jakarta' is derived from the word Jayakarta (Devanagari: जयकर्त) which is ultimately derived from the Sanskrit जय jaya (victorious), and कृत krta (accomplished, acquired), thus Jayakarta translates as 'victorious deed', 'complete act' or 'complete victory'. It was named for the Muslim troops of Fatahillah which successfully defeated and drove the Portuguese away from the city in 1527. Before it was called Jayakarta, the city was known as 'Sunda Kelapa'. Tomé Pires, a Portuguese apothecary wrote the name of the city on his magnum opus as Jacatra or Jacarta during his journey to East Indies. \\nIn the 17th century, the city was known as Koningin van het Oosten (Queen of the Orient), a name that was given for the urban beauty of downtown Batavia's canals, mansions and ordered city layout. After expanding to the south in the 19th century, this nickname came to be more associated with the suburbs (e.g. Menteng and the area around Merdeka Square), with their wide lanes, green spaces and villas. During the Japanese occupation, the city was renamed as Jakaruta Tokubetsu-shi (ジャカルタ特別市, Jakarta Special City).\\n\\n\\n== History ==\\n\\n\\n=== Precolonial era ===\\n\\nThe north coast area of western Java including Jakarta was the location of prehistoric Buni culture that flourished from 400 BC to 100 AD. The area in and around modern Jakarta was part of the 4th-century Sundanese kingdom of Tarumanagara, one of the oldest Hindu kingdoms in Indonesia. The area of North Jakarta around Tugu became a populated settlement in the early 5th century. The Tugu inscription (probably written around 417 AD) discovered in Batutumbuh hamlet, Tugu village, Koja, North Jakarta, mentions that King Purnawarman of Tarumanagara undertook hydraulic projects; the irrigation and water drainage project of the Chandrabhaga river and the Gomati river near his capital. Following the decline of Tarumanagara, its territories, including the Jakarta area, became part of the Hindu Kingdom of Sunda. From the 7th to the early 13th century, the port of Sunda was under the Srivijaya maritime empire. According to the Chinese source, Chu-fan-chi, written circa 1225, Chou Ju-kua reported in the early 13th century that Srivijaya still ruled Sumatra, the Malay peninsula and western Java (Sunda). The source says the port of Sunda is strategic and thriving, mentioning pepper from Sunda as among the best in quality. The people worked in agriculture, and their houses were built on wooden piles. The harbour area became known as Sunda Kelapa, (Sundanese: ᮞᮥᮔ᮪ᮓ ᮊᮨᮜᮕ) and by the 14th century, it was an important trading port for the Sunda Kingdom.\\nThe first European fleet, four Portuguese ships from Malacca, arrived in 1513 while looking for a route for spices. The Sunda Kingdom made an alliance treaty with the Portuguese by allowing them to build a port in 1522 to defend against the rising power of Demak Sultanate from central Java. In 1527, Fatahillah, a Javanese general from Demak attacked and conquered Sunda Kelapa, driving out the Portuguese. Sunda Kelapa was renamed Jayakarta, and became a fiefdom of the Banten Sultanate, which became a major Southeast Asian trading centre.\\nThrough the relationship with Prince Jayawikarta of the Banten Sultanate, Dutch ships arrived in 1596. In 1602, the British East India Company's first voyage, commanded by Sir James Lancaster, arrived in Aceh and sailed on to Banten where they were allowed to build a trading post. This site became the centre of British trade in the Indonesian archipelago until 1682. Jayawikarta is thought to have made trading connections with the British merchants, rivals of the Dutch, by allowing them to build houses directly across from the Dutch buildings in 1615.\\n\\n\\n=== Colonial era ===\\n\\nWhen relations between Prince Jayawikarta and the Dutch deteriorated, his soldiers attacked the Dutch fortress. His army and the British, however, were defeated by the Dutch, in part owing to the timely arrival of Jan Pieterszoon Coen. The Dutch burned the British fort and forced them to retreat on their ships. The victory consolidated Dutch power, and they renamed the city Batavia in 1619.\\n\\nCommercial opportunities in the city attracted native and especially Chinese and Arab immigrants. This sudden population increase created burdens on the city. Tensions grew as the colonial government tried to restrict Chinese migration through deportations. Following a revolt, 5,000 Chinese were massacred by the Dutch and natives on 9 October 1740, and the following year, Chinese inhabitants were moved to Glodok outside the city walls. At the beginning of the 19th century, around 400 Arabs and Moors lived in Batavia, a number that changed little during the following decades. Among the commodities traded were fabrics, mainly imported cotton, batik and clothing worn by Arab communities.The city began to expand further south as epidemics in 1835 and 1870 forced residents to move away from the port. The Koningsplein, now Merdeka Square was completed in 1818, the housing park of Menteng was started in 1913, and Kebayoran Baru was the last Dutch-built residential area. By 1930, Batavia had more than 500,000 inhabitants, including 37,067 Europeans.On 5 March 1942, the Japanese captured Batavia from Dutch control, and the city was named Jakarta (Jakarta Special City (ジャカルタ特別市, Jakaruta tokubetsu-shi), under the special status that was assigned to the city). After the war, the Dutch name Batavia was internationally recognised until full Indonesian independence on 27 December 1949. The city, now renamed Jakarta, was officially proclaimed the national capital of Indonesia.\\n\\n\\n=== Independence era ===\\n\\nAfter World War II ended, Indonesian nationalists declared independence on 17 August 1945, and the government of Jakarta City was changed into the Jakarta National Administration in the following month. During the Indonesian National Revolution, Indonesian Republicans withdrew from Allied-occupied Jakarta and established their capital in Yogyakarta.\\nAfter securing full independence, Jakarta again became the national capital in 1950. With Jakarta selected to host the 1962 Asian Games, Soekarno, envisaging Jakarta as a great international city, instigated large government-funded projects with openly nationalistic and modernist architecture. Projects included a cloverleaf interchange, a major boulevard (Jalan MH Thamrin-Sudirman), monuments such as The National Monument, Hotel Indonesia, a shopping centre, and a new building intended to be the headquarters of CONEFO. In October 1965, Jakarta was the site of an abortive coup attempt in which six top generals were killed, precipitating a violent anti-communist purge which killed at least 500,000 people, including some ethnic Chinese. The event marked the beginning of Suharto's New Order. The first government was led by a mayor until the end of 1960 when the office was changed to that of a governor. The last mayor of Jakarta was Soediro until he was replaced by Soemarno Sosroatmodjo as governor. Based on law No. 5 of 1974 relating to regional governments, Jakarta was confirmed as the capital of Indonesia and one of the country's then 26 provinces.In 1966, Jakarta was declared a 'special capital region' (Daerah Khusus Ibukota), with a status equivalent to that of a province. Lieutenant General Ali Sadikin served as governor from 1966 to 1977; he rehabilitated roads and bridges, encouraged the arts, built hospitals and a large number of schools. He cleared out slum dwellers for new development projects — some for the benefit of the Suharto family,— and attempted to eliminate rickshaws and ban street vendors. He began control of migration to the city to stem overcrowding and poverty. Foreign investment contributed to a real estate boom that transformed the face of Jakarta. The boom ended with the 1997 Asian financial crisis, putting Jakarta at the centre of violence, protest, and political manoeuvring.\\nAfter three decades in power, support for President Suharto began to wane. Tensions peaked when four students were shot dead at Trisakti University by security forces. Four days of riots and violence in 1998 ensued that killed an estimated 1,200, and destroyed or damaged 6,000 buildings, forcing Suharto to resign. Much of the rioting targeted Chinese Indonesians. In the post-Suharto era, Jakarta has remained the focal point of democratic change in Indonesia. Jemaah Islamiah-connected bombings occurred almost annually in the city between 2000 and 2005, with another in 2009. In August 2007, Jakarta held its first-ever election to choose a governor as part of a nationwide decentralisation program that allows direct local elections in several areas. Previously, governors were elected by the city's legislative body.During the Jokowi presidency, the Government adopted a plan to move Indonesia's capital to East Kalimantan.Between 2016 and 2017, a series of terrorist attacks rocked Jakarta with scenes of multiple suicide bombings and gunfire. In suspicion to its links, the Islamic State, the perpetrator led by Abu Bakr al-Baghdadi claimed responsibility for the attacks.\\n\\n\\n== Geography ==\\n\\nJakarta covers 699.5 km2 (270.1 sq mi), the smallest among any Indonesian provinces. However, its metropolitan area covers 6,392 km2 (2,468 sq mi), which extends into two of the bordering provinces of West Java and Banten. The Greater Jakarta area includes three bordering regencies (Bekasi Regency, Tangerang Regency and Bogor Regency) and five adjacent cities (Bogor, Depok, Bekasi, Tangerang and South Tangerang).\\n\\nJakarta is situated on the northwest coast of Java, at the mouth of the Ciliwung River on Jakarta Bay, an inlet of the Java Sea. It is strategically located near the Sunda Strait. The northern part of Jakarta is plain land, some areas of which are below sea level, and subject to frequent flooding. The southern parts of the city are hilly. It is one of only two Asian capital cities located in the southern hemisphere (along with East Timor's Dili). Officially, the area of the Jakarta Special District is 662 km2 (256 sq mi) of land area and 6,977 km2 (2,694 sq mi) of sea area. The Thousand Islands, which are administratively a part of Jakarta, are located in Jakarta Bay, north of the city.\\nJakarta lies in a low and flat alluvial plain, ranging from −2 to 91 m (−7 to 299 ft) with an average elevation of 8 m (26 ft) above sea level with historically extensive swampy areas. Some parts of the city have been constructed on reclaimed tidal flats that occur around the area. Thirteen rivers flow through Jakarta. They are Ciliwung River, Kalibaru, Pesanggrahan, Cipinang, Angke River, Maja, Mookervart, Krukut, Buaran, West Tarum, Cakung, Petukangan, Sunter River and Grogol River. They flow from the Puncak highlands to the south of the city, then across the city northwards towards the Java Sea. The Ciliwung River divides the city into the western and eastern districts.\\nThese rivers, combined with the wet season rains and insufficient\", doc_id='eeb6ef32-c857-44e2-b0c5-dff6e29a9cd7', extra_info=None, node_info={'start': 0, 'end': 13970}, similarity=0.8701780916463354)], extra_info=None)"}
{"tokens": 7707, "doc_id": "59cdaf61-ecef-4925-95d6-66a712b22cbc", "name": "Azure AI Search", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/AzureAISearchIndexDemo", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Azure AI Search\n\n## Basic Example\n\nIn this notebook, we take a Paul Graham essay, split it into chunks, embed it using an Azure OpenAI embedding model, load it into an Azure AI Search index, and then query it.\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n!pip install llama-index\n!pip install wget\n%pip install llama-index-vector-stores-azureaisearch\n%pip install azure-search-documents==11.4.0\n%llama-index-embeddings-azure-openai\n%llama-index-llms-azure-openai\n```\n\n\n```python\nimport logging\nimport sys\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.search.documents import SearchClient\nfrom azure.search.documents.indexes import SearchIndexClient\nfrom IPython.display import Markdown, display\nfrom llama_index.core import (\n SimpleDirectoryReader,\n StorageContext,\n VectorStoreIndex,\n)\nfrom llama_index.core.settings import Settings\n\nfrom llama_index.llms.azure_openai import AzureOpenAI\nfrom llama_index.embeddings.azure_openai import AzureOpenAIEmbedding\nfrom llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore\nfrom llama_index.vector_stores.azureaisearch import (\n IndexManagement,\n MetadataIndexFieldType,\n)\n```\n\n## Setup Azure OpenAI\n\n\n```python\naoai_api_key = \"YOUR_AZURE_OPENAI_API_KEY\"\naoai_endpoint = \"YOUR_AZURE_OPENAI_ENDPOINT\"\naoai_api_version = \"2023-05-15\"\n\nllm = AzureOpenAI(\n model=\"YOUR_AZURE_OPENAI_COMPLETION_MODEL_NAME\",\n deployment_name=\"YOUR_AZURE_OPENAI_COMPLETION_DEPLOYMENT_NAME\",\n api_key=aoai_api_key,\n azure_endpoint=aoai_endpoint,\n api_version=aoai_api_version,\n)\n\n# You need to deploy your own embedding model as well as your own chat completion model\nembed_model = AzureOpenAIEmbedding(\n model=\"YOUR_AZURE_OPENAI_EMBEDDING_MODEL_NAME\",\n deployment_name=\"YOUR_AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME\",\n api_key=aoai_api_key,\n azure_endpoint=aoai_endpoint,\n api_version=aoai_api_version,\n)\n```\n\n## Setup Azure AI Search\n\n\n```python\nsearch_service_api_key = \"YOUR-AZURE-SEARCH-SERVICE-ADMIN-KEY\"\nsearch_service_endpoint = \"YOUR-AZURE-SEARCH-SERVICE-ENDPOINT\"\nsearch_service_api_version = \"2023-11-01\"\ncredential = AzureKeyCredential(search_service_api_key)\n\n\n# Index name to use\nindex_name = \"llamaindex-vector-demo\"\n\n# Use index client to demonstrate creating an index\nindex_client = SearchIndexClient(\n endpoint=search_service_endpoint,\n credential=credential,\n)\n\n# Use search client to demonstration using existing index\nsearch_client = SearchClient(\n endpoint=search_service_endpoint,\n index_name=index_name,\n credential=credential,\n)\n```\n\n## Create Index (if it does not exist)\n\nDemonstrates creating a vector index named \"llamaindex-vector-demo\" if one doesn't exist. The index has the following fields:\n| Field Name | OData Type | \n|------------|---------------------------| \n| id | `Edm.String` | \n| chunk | `Edm.String` | \n| embedding | `Collection(Edm.Single)` | \n| metadata | `Edm.String` | \n| doc_id | `Edm.String` | \n| author | `Edm.String` | \n| theme | `Edm.String` | \n| director | `Edm.String` | \n\n\n```python\nmetadata_fields = {\n \"author\": \"author\",\n \"theme\": (\"topic\", MetadataIndexFieldType.STRING),\n \"director\": \"director\",\n}\n\nvector_store = AzureAISearchVectorStore(\n search_or_index_client=index_client,\n filterable_metadata_field_keys=metadata_fields,\n index_name=index_name,\n index_management=IndexManagement.CREATE_IF_NOT_EXISTS,\n id_field_key=\"id\",\n chunk_field_key=\"chunk\",\n embedding_field_key=\"embedding\",\n embedding_dimensionality=1536,\n metadata_string_field_key=\"metadata\",\n doc_id_field_key=\"doc_id\",\n language_analyzer=\"en.lucene\",\n vector_algorithm_type=\"exhaustiveKnn\",\n)\n```\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n### Loading documents\nLoad the documents stored in the `data/paul_graham/` using the SimpleDirectoryReader\n\n\n```python\n# Load documents\ndocuments = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\nSettings.llm = llm\nSettings.embed_model = embed_model\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n```\n\n\n```python\n# Query Data\nquery_engine = index.as_query_engine(similarity_top_k=3)\nresponse = query_engine.query(\"What did the author do growing up?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nThe author engaged in writing and programming activities during their formative years. They initially wrote short stories and later transitioned to programming on the IBM 1401 using an early version of Fortran. Subsequently, with the advent of microcomputers, the author began programming on a TRS-80, writing simple games, a rocket flight prediction program, and a word processor.\n\n\n\n```python\nresponse = query_engine.query(\n \"What did the author learn?\",\n)\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nThe author learned that the study of philosophy in college did not live up to their expectations, as they found the courses to be boring and lacking in ultimate truths. This led them to switch their focus to AI, which was influenced by a novel featuring an intelligent computer and a PBS documentary showcasing advanced technology.\n\n\n## Use Existing Index\n\n\n```python\nindex_name = \"llamaindex-vector-demo\"\n\nmetadata_fields = {\n \"author\": \"author\",\n \"theme\": (\"topic\", MetadataIndexFieldType.STRING),\n \"director\": \"director\",\n}\nvector_store = AzureAISearchVectorStore(\n search_or_index_client=search_client,\n filterable_metadata_field_keys=metadata_fields,\n index_management=IndexManagement.VALIDATE_INDEX,\n id_field_key=\"id\",\n chunk_field_key=\"chunk\",\n embedding_field_key=\"embedding\",\n embedding_dimensionality=1536,\n metadata_string_field_key=\"metadata\",\n doc_id_field_key=\"doc_id\",\n)\n```\n\n\n```python\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(\n [],\n storage_context=storage_context,\n)\n```\n\n\n```python\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What was a hard moment for the author?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nThe author faced a challenging moment when he couldn't figure out what to do with the early computer he had access to in 9th grade. This was due to the limited options for input and the lack of knowledge in math to do anything interesting with the available resources.\n\n\n\n```python\nresponse = query_engine.query(\"Who is the author?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nPaul Graham\n\n\n\n```python\nimport time\n\nquery_engine = index.as_query_engine(streaming=True)\nresponse = query_engine.query(\"What happened at interleaf?\")\n\nstart_time = time.time()\n\ntoken_count = 0\nfor token in response.response_gen:\n print(token, end=\"\")\n token_count += 1\n\ntime_elapsed = time.time() - start_time\ntokens_per_second = token_count / time_elapsed\n\nprint(f\"\\n\\nStreamed output at {tokens_per_second} tokens/s\")\n```\n\n The author worked at Interleaf, where they learned several lessons, including the importance of product-focused leadership in technology companies, the drawbacks of code being edited by too many people, the limitations of conventional office hours for optimal hacking, and the risks associated with bureaucratic customers. Additionally, the author discovered the concept that the low end tends to dominate the high end, and that being the \"entry level\" option can be advantageous.\n \n Streamed output at 99.40073103089465 tokens/s\n\n\n## Adding a document to existing index\n\n\n```python\nresponse = query_engine.query(\"What colour is the sky?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nBlue\n\n\n\n```python\nfrom llama_index.core import Document\n\nindex.insert_nodes([Document(text=\"The sky is indigo today\")])\n```\n\n\n```python\nresponse = query_engine.query(\"What colour is the sky?\")\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nThe sky is indigo today.\n\n\n## Filtering\n\n\n```python\nfrom llama_index.core.schema import TextNode\n\nnodes = [\n TextNode(\n text=\"The Shawshank Redemption\",\n metadata={\n \"author\": \"Stephen King\",\n \"theme\": \"Friendship\",\n },\n ),\n TextNode(\n text=\"The Godfather\",\n metadata={\n \"director\": \"Francis Ford Coppola\",\n \"theme\": \"Mafia\",\n },\n ),\n TextNode(\n text=\"Inception\",\n metadata={\n \"director\": \"Christopher Nolan\",\n },\n ),\n]\n```\n\n\n```python\nindex.insert_nodes(nodes)\n```\n\n\n```python\nfrom llama_index.core.vector_stores.types import (\n MetadataFilters,\n ExactMatchFilter,\n)\n\n\nfilters = MetadataFilters(\n filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")]\n)\n\nretriever = index.as_retriever(filters=filters)\nretriever.retrieve(\"What is inception about?\")\n```\n\n\n\n\n [NodeWithScore(node=TextNode(id_='049f00de-13be-4af3-ab56-8c16352fe799', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='ad2a08d4364262546db9711b915348d43e0ccc41bd8c3c41775e133624e1fa1b', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.8120511)]\n\n\n\n## Query Mode\nFour query modes are supported: DEFAULT (vector search), SPARSE, HYBRID, and SEMANTIC_HYBRID.\n\n### Perform a Vector Search\n\n\n```python\nfrom llama_index.core.vector_stores.types import VectorStoreQueryMode\n\ndefault_retriever = index.as_retriever(\n vector_store_query_mode=VectorStoreQueryMode.DEFAULT\n)\nresponse = default_retriever.retrieve(\"What is inception about?\")\n\n# Loop through each NodeWithScore in the response\nfor node_with_score in response:\n node = node_with_score.node # The TextNode object\n score = node_with_score.score # The similarity score\n chunk_id = node.id_ # The chunk ID\n\n # Extract the relevant metadata from the node\n file_name = node.metadata.get(\"file_name\", \"Unknown\")\n file_path = node.metadata.get(\"file_path\", \"Unknown\")\n\n # Extract the text content from the node\n text_content = node.text if node.text else \"No content available\"\n\n # Print the results in a user-friendly format\n print(f\"Score: {score}\")\n print(f\"File Name: {file_name}\")\n print(f\"Id: {chunk_id}\")\n print(\"\\nExtracted Content:\")\n print(text_content)\n print(\"\\n\" + \"=\" * 40 + \" End of Result \" + \"=\" * 40 + \"\\n\")\n```\n\n Score: 0.8748552\n File Name: Unknown\n Id: bae0df75-ff37-4725-b659-b9fd8bf2ef3c\n \n Extracted Content:\n Inception\n \n ======================================== End of Result ========================================\n \n Score: 0.8155207\n File Name: paul_graham_essay.txt\n Id: ae5aee85-a083-4141-bf75-bbb872f53760\n \n Extracted Content:\n It's not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it's a sign both that there's something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren't prestigious doesn't guarantee you're on the right track, it at least guarantees you're not on the most common type of wrong one.\n \n Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.\n \n One night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn't know but would probably like. One of the guests was someone I didn't know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.\n \n Jessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.\n \n When the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.\n \n One of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won't waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they'd be able to avoid the worst of the mistakes we'd made.\n \n So I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they'd be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I'd only known), I blurted out \"But not me!\" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I'd been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn't done one angel investment.\n \n Meanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.\n \n As Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We'd start our own investment firm and actually implement the ideas we'd been talking about. I'd fund it, and Jessica could quit her job and work for it, and we'd get Robert and Trevor as partners too. [13]\n \n Once again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.\n \n There are multiple components to Y Combinator, and we didn't figure them all out at once. The part we got first was to be an angel firm.\n \n ======================================== End of Result ========================================\n \n\n\n### Perform a Hybrid Search\n\n\n```python\nfrom llama_index.core.vector_stores.types import VectorStoreQueryMode\n\nhybrid_retriever = index.as_retriever(\n vector_store_query_mode=VectorStoreQueryMode.HYBRID\n)\nhybrid_retriever.retrieve(\"What is inception about?\")\n```\n\n\n\n\n [NodeWithScore(node=TextNode(id_='bae0df75-ff37-4725-b659-b9fd8bf2ef3c', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='9792a1fd7d2e1a08f1b1d70a597357bb6b68d69ed5685117eaa37ac9e9a3565e', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03181818127632141),\n NodeWithScore(node=TextNode(id_='ae5aee85-a083-4141-bf75-bbb872f53760', embedding=None, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={\n\n# Qdrant Vector Store - Default Qdrant Filters\n\nExample on how to use Filters from the qdrant_client SDK directly in your Retriever / Query Engine\n\n\n```python\n%pip install llama-index-vector-stores-qdrant\n```\n\n\n```python\n!pip3 install llama-index qdrant_client\n```\n\n\n```python\nimport openai\nimport qdrant_client\nfrom IPython.display import Markdown, display\nfrom llama_index.core import VectorStoreIndex\nfrom llama_index.core import StorageContext\nfrom llama_index.vector_stores.qdrant import QdrantVectorStore\nfrom qdrant_client.http.models import Filter, FieldCondition, MatchValue\n\nclient = qdrant_client.QdrantClient(location=\":memory:\")\nfrom llama_index.core.schema import TextNode\n\nnodes = [\n TextNode(\n text=\"りんごとは\",\n metadata={\"author\": \"Tanaka\", \"fruit\": \"apple\", \"city\": \"Tokyo\"},\n ),\n TextNode(\n text=\"Was ist Apfel?\",\n metadata={\"author\": \"David\", \"fruit\": \"apple\", \"city\": \"Berlin\"},\n ),\n TextNode(\n text=\"Orange like the sun\",\n metadata={\"author\": \"Jane\", \"fruit\": \"orange\", \"city\": \"Hong Kong\"},\n ),\n TextNode(\n text=\"Grape is...\",\n metadata={\"author\": \"Jane\", \"fruit\": \"grape\", \"city\": \"Hong Kong\"},\n ),\n TextNode(\n text=\"T-dot > G-dot\",\n metadata={\"author\": \"George\", \"fruit\": \"grape\", \"city\": \"Toronto\"},\n ),\n TextNode(\n text=\"6ix Watermelons\",\n metadata={\n \"author\": \"George\",\n \"fruit\": \"watermelon\",\n \"city\": \"Toronto\",\n },\n ),\n]\n\nopenai.api_key = \"YOUR_API_KEY\"\nvector_store = QdrantVectorStore(\n client=client, collection_name=\"fruit_collection\"\n)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex(nodes, storage_context=storage_context)\n\n\n# Use filters directly from qdrant_client python library\n# View python examples here for more info https://qdrant.tech/documentation/concepts/filtering/\n\nfilters = Filter(\n should=[\n Filter(\n must=[\n FieldCondition(\n key=\"fruit\",\n match=MatchValue(value=\"apple\"),\n ),\n FieldCondition(\n key=\"city\",\n match=MatchValue(value=\"Tokyo\"),\n ),\n ]\n ),\n Filter(\n must=[\n FieldCondition(\n key=\"fruit\",\n match=MatchValue(value=\"grape\"),\n ),\n FieldCondition(\n key=\"city\",\n match=MatchValue(value=\"Toronto\"),\n ),\n ]\n ),\n ]\n)\n\nretriever = index.as_retriever(vector_store_kwargs={\"qdrant_filters\": filters})\n\nresponse = retriever.retrieve(\"Who makes grapes?\")\nfor node in response:\n print(\"node\", node.score)\n print(\"node\", node.text)\n print(\"node\", node.metadata)\n```"}
{"tokens": 1120, "doc_id": "fc2bf4ad-a7eb-471e-8807-ecc7b2f3b871", "name": "Pinecone Vector Store - Hybrid Search", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/PineconeIndexDemo-Hybrid", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Pinecone Vector Store - Hybrid Search\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-pinecone\n```\n\n\n```python\n!pip install llama-index>=0.9.31 pinecone-client>=3.0.0 \"transformers[torch]\"\n```\n\n#### Creating a Pinecone Index\n\n\n```python\nimport logging\nimport sys\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nlogging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n```\n\n\n```python\nfrom pinecone import Pinecone, ServerlessSpec\n```\n\n\n```python\nimport os\n\nos.environ[\n \"PINECONE_API_KEY\"\n] = #\"\n\n# Elasticsearch\n\n>[Elasticsearch](http://www.github.com/elastic/elasticsearch) is a search database, that supports full text and vector searches. \n\n\n## Basic Example\n\n\nIn this basic example, we take the a Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into Elasticsearch, and then query it. For an example using different retrieval strategies see [Elasticsearch Vector Store](https://docs.llamaindex.ai/en/stable/examples/vector_stores/ElasticsearchIndexDemo/).\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install -qU llama-index-vector-stores-elasticsearch llama-index-embeddings-huggingface llama-index\n```\n\n\n```python\n# import\nfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader\nfrom llama_index.vector_stores.elasticsearch import ElasticsearchStore\nfrom llama_index.core import StorageContext\n```\n\n\n```python\n# set up OpenAI\nimport os\nimport getpass\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n```\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget -nv 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n 2024-05-13 15:10:43 URL:https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt [75042/75042] -> \"data/paul_graham/paul_graham_essay.txt\" [1]\n\n\n\n```python\nfrom llama_index.embeddings.huggingface import HuggingFaceEmbedding\nfrom llama_index.core import Settings\n\n# define embedding function\nSettings.embed_model = HuggingFaceEmbedding(\n model_name=\"BAAI/bge-small-en-v1.5\"\n)\n```\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\n\n# define index\nvector_store = ElasticsearchStore(\n es_url=\"http://localhost:9200\", # see Elasticsearch Vector Store for more authentication options\n index_name=\"paul_graham_essay\",\n)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n```\n\n\n```python\n# Query Data\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"What did the author do growing up?\")\nprint(response)\n```\n\n The author worked on writing and programming outside of school. They wrote short stories and tried writing programs on an IBM 1401 computer. They also built a microcomputer kit and started programming on it, writing simple games and a word processor."}
{"tokens": 2090, "doc_id": "41b7c5e0-53b5-40ec-bc06-3fb09db6e847", "name": "Firestore Vector Store", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/FirestoreVectorStore", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Firestore Vector Store\n\n# Google Firestore (Native Mode)\n\n> [Firestore](https://cloud.google.com/firestore) is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging Firestore's Langchain integrations.\n\nThis notebook goes over how to use [Firestore](https://cloud.google.com/firestore) to store vectors and query them using the `FirestoreVectorStore` class.\n\n## Before You Begin\n\nTo run this notebook, you will need to do the following:\n\n* [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)\n* [Enable the Firestore API](https://console.cloud.google.com/flows/enableapi?apiid=firestore.googleapis.com)\n* [Create a Firestore database](https://cloud.google.com/firestore/docs/manage-databases)\n\nAfter confirmed access to database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts.\n\n## Library Installation\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙. For this notebook, we will also install `langchain-google-genai` to use Google Generative AI embeddings.\n\n\n```python\n%pip install --quiet llama-index\n%pip install --quiet llama-index-vector-stores-firestore llama-index-embeddings-huggingface\n```\n\n### ☁ Set Your Google Cloud Project\nSet your Google Cloud project so that you can leverage Google Cloud resources within this notebook.\n\nIf you don't know your project ID, try the following:\n\n* Run `gcloud config list`.\n* Run `gcloud projects list`.\n* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113).\n\n\n```python\n# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.\n\nPROJECT_ID = \"YOUR_PROJECT_ID\" # @param {type:\"string\"}\n\n# Set the project id\n!gcloud config set project {PROJECT_ID}\n```\n\n### 🔐 Authentication\n\nAuthenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n\n- If you are using Colab to run this notebook, use the cell below and continue.\n- If you are using Vertex AI Workbench, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env).\n\n\n```python\nfrom google.colab import auth\n\nauth.authenticate_user()\n```\n\n# Basic Usage\n\n### Initialize FirestoreVectorStore\n\n`FirestoreVectroStore` allows you to load data into Firestore and query it.\n\n\n```python\n# @markdown Please specify a source for demo purpose.\nCOLLECTION_NAME = \"test_collection\"\n```\n\n\n```python\nfrom llama_index.core import SimpleDirectoryReader\n\n# Load documents and build index\ndocuments = SimpleDirectoryReader(\n \"../../examples/data/paul_graham\"\n).load_data()\n```\n\n\n```python\nfrom llama_index.embeddings.huggingface import HuggingFaceEmbedding\nfrom llama_index.core import Settings\n\n# Set the embedding model, this is a local model\nembed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n```\n\n\n```python\nfrom llama_index.core import VectorStoreIndex\nfrom llama_index.core import StorageContext, ServiceContext\n\nfrom llama_index.vector_stores.firestore import FirestoreVectorStore\n\n# Create a Firestore vector store\nstore = FirestoreVectorStore(collection_name=COLLECTION_NAME)\n\nstorage_context = StorageContext.from_defaults(vector_store=store)\nservice_context = ServiceContext.from_defaults(\n llm=None, embed_model=embed_model\n)\n\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context, service_context=service_context\n)\n```\n\n /var/folders/mh/cqn7wzgs3j79rbg243_gfcx80000gn/T/ipykernel_29666/1668628626.py:10: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n service_context = ServiceContext.from_defaults(llm=None, embed_model=embed_model)\n\n\n LLM is explicitly disabled. Using MockLLM.\n\n\n### Perform search\n\nYou can use the `FirestoreVectorStore` to perform similarity searches on the vectors you have stored. This is useful for finding similar documents or text.\n\n\n```python\nquery_engine = index.as_query_engine()\nres = query_engine.query(\"What did the author do growing up?\")\nprint(str(res.source_nodes[0].text))\n```\n\n None\n What I Worked On\n \n February 2021\n \n Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\n \n The first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\n \n The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\n \n I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.\n \n With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]\n \n The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.\n \n Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter.\n \n Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.\n \n I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.\n \n AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world.\n\n\nYou can apply pre-filtering to the search results by specifying a `filters` argument.\n\n\n```python\nfrom llama_index.core.vector_stores.types import (\n MetadataFilters,\n ExactMatchFilter,\n MetadataFilter,\n)\n\nfilters = MetadataFilters(\n filters=[MetadataFilter(key=\"author\", value=\"Paul Graham\")]\n)\nquery_engine = index.as_query_engine(filters=filters)\nres = query_engine.query(\"What did the author do growing up?\")\nprint(str(res.source_nodes[0].text))\n```"}
{"tokens": 11494, "doc_id": "c1f3da6b-ebd5-4d21-b8cb-9912b3d62b55", "name": "set up Fireworks.ai Key", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/MongoDBAtlasVectorSearchRAGFireworks", "retrieve_doc": false, "source": "llama_index", "content": "```python\n!pip install -q llama-index llama-index-vector-stores-mongodb llama-index-embeddings-fireworks==0.1.2 llama-index-llms-fireworks\n!pip install -q pymongo datasets pandas\n```\n\n\n```python\n# set up Fireworks.ai Key\nimport os\nimport getpass\n\nfw_api_key = getpass.getpass(\"Fireworks API Key:\")\nos.environ[\"FIREWORKS_API_KEY\"] = fw_api_key\n```\n\n\n```python\nfrom datasets import load_dataset\nimport pandas as pd\n\n# https://huggingface.co./datasets/AIatMongoDB/whatscooking.restaurants\ndataset = load_dataset(\"AIatMongoDB/whatscooking.restaurants\")\n\n# Convert the dataset to a pandas dataframe\ndataset_df = pd.DataFrame(dataset[\"train\"])\n\ndataset_df.head(5)\n```\n\n /mnt/disks/data/llama_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n from .autonotebook import tqdm as notebook_tqdm\n\n\n\n\n\n\n\n\n \n
\n
\n\n\n\n\n```python\nfrom llama_index.core.settings import Settings\nfrom llama_index.llms.fireworks import Fireworks\nfrom llama_index.embeddings.fireworks import FireworksEmbedding\n\nembed_model = FireworksEmbedding(\n embed_batch_size=512,\n model_name=\"nomic-ai/nomic-embed-text-v1.5\",\n api_key=fw_api_key,\n)\nllm = Fireworks(\n temperature=0,\n model=\"accounts/fireworks/models/mixtral-8x7b-instruct\",\n api_key=fw_api_key,\n)\n\nSettings.llm = llm\nSettings.embed_model = embed_model\n```\n\n\n```python\nimport json\nfrom llama_index.core import Document\nfrom llama_index.core.schema import MetadataMode\n\n# Convert the DataFrame to a JSON string representation\ndocuments_json = dataset_df.to_json(orient=\"records\")\n# Load the JSON string into a Python list of dictionaries\ndocuments_list = json.loads(documents_json)\n\nllama_documents = []\n\nfor document in documents_list:\n # Value for metadata must be one of (str, int, float, None)\n document[\"name\"] = json.dumps(document[\"name\"])\n document[\"cuisine\"] = json.dumps(document[\"cuisine\"])\n document[\"attributes\"] = json.dumps(document[\"attributes\"])\n document[\"menu\"] = json.dumps(document[\"menu\"])\n document[\"borough\"] = json.dumps(document[\"borough\"])\n document[\"address\"] = json.dumps(document[\"address\"])\n document[\"PriceRange\"] = json.dumps(document[\"PriceRange\"])\n document[\"HappyHour\"] = json.dumps(document[\"HappyHour\"])\n document[\"review_count\"] = json.dumps(document[\"review_count\"])\n document[\"TakeOut\"] = json.dumps(document[\"TakeOut\"])\n # these two fields are not relevant to the question we want to answer,\n # so I will skip it for now\n del document[\"embedding\"]\n del document[\"location\"]\n\n # Create a Document object with the text and excluded metadata for llm and embedding models\n llama_document = Document(\n text=json.dumps(document),\n metadata=document,\n metadata_template=\"{key}=>{value}\",\n text_template=\"Metadata: {metadata_str}\\n-----\\nContent: {content}\",\n )\n\n llama_documents.append(llama_document)\n\n# Observing an example of what the LLM and Embedding model receive as input\nprint(\n \"\\nThe LLM sees this: \\n\",\n llama_documents[0].get_content(metadata_mode=MetadataMode.LLM),\n)\nprint(\n \"\\nThe Embedding model sees this: \\n\",\n llama_documents[0].get_content(metadata_mode=MetadataMode.EMBED),\n)\n```\n\n \n The LLM sees this: \n Metadata: restaurant_id=>40366661\n attributes=>{\"Alcohol\": \"'none'\", \"Ambience\": \"{'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}\", \"BYOB\": null, \"BestNights\": null, \"BikeParking\": null, \"BusinessAcceptsBitcoin\": null, \"BusinessAcceptsCreditCards\": null, \"BusinessParking\": \"None\", \"Caters\": \"True\", \"DriveThru\": null, \"GoodForDancing\": null, \"GoodForKids\": \"True\", \"GoodForMeal\": null, \"HasTV\": \"True\", \"Music\": null, \"NoiseLevel\": \"'average'\", \"RestaurantsAttire\": \"'casual'\", \"RestaurantsDelivery\": \"True\", \"RestaurantsGoodForGroups\": \"True\", \"RestaurantsReservations\": \"True\", \"RestaurantsTableService\": \"False\", \"WheelchairAccessible\": \"True\", \"WiFi\": \"'free'\"}\n cuisine=>\"Tex-Mex\"\n DogsAllowed=>None\n OutdoorSeating=>True\n borough=>\"Manhattan\"\n address=>{\"building\": \"627\", \"coord\": [-73.975981, 40.745132], \"street\": \"2 Avenue\", \"zipcode\": \"10016\"}\n _id=>{'$oid': '6095a34a7c34416a90d3206b'}\n name=>\"Baby Bo'S Burritos\"\n menu=>null\n TakeOut=>true\n PriceRange=>1.0\n HappyHour=>null\n review_count=>10\n sponsored=>None\n stars=>2.5\n -----\n Content: {\"restaurant_id\": \"40366661\", \"attributes\": \"{\\\"Alcohol\\\": \\\"'none'\\\", \\\"Ambience\\\": \\\"{'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}\\\", \\\"BYOB\\\": null, \\\"BestNights\\\": null, \\\"BikeParking\\\": null, \\\"BusinessAcceptsBitcoin\\\": null, \\\"BusinessAcceptsCreditCards\\\": null, \\\"BusinessParking\\\": \\\"None\\\", \\\"Caters\\\": \\\"True\\\", \\\"DriveThru\\\": null, \\\"GoodForDancing\\\": null, \\\"GoodForKids\\\": \\\"True\\\", \\\"GoodForMeal\\\": null, \\\"HasTV\\\": \\\"True\\\", \\\"Music\\\": null, \\\"NoiseLevel\\\": \\\"'average'\\\", \\\"RestaurantsAttire\\\": \\\"'casual'\\\", \\\"RestaurantsDelivery\\\": \\\"True\\\", \\\"RestaurantsGoodForGroups\\\": \\\"True\\\", \\\"RestaurantsReservations\\\": \\\"True\\\", \\\"RestaurantsTableService\\\": \\\"False\\\", \\\"WheelchairAccessible\\\": \\\"True\\\", \\\"WiFi\\\": \\\"'free'\\\"}\", \"cuisine\": \"\\\"Tex-Mex\\\"\", \"DogsAllowed\": null, \"OutdoorSeating\": true, \"borough\": \"\\\"Manhattan\\\"\", \"address\": \"{\\\"building\\\": \\\"627\\\", \\\"coord\\\": [-73.975981, 40.745132], \\\"street\\\": \\\"2 Avenue\\\", \\\"zipcode\\\": \\\"10016\\\"}\", \"_id\": {\"$oid\": \"6095a34a7c34416a90d3206b\"}, \"name\": \"\\\"Baby Bo'S Burritos\\\"\", \"menu\": \"null\", \"TakeOut\": \"true\", \"PriceRange\": \"1.0\", \"HappyHour\": \"null\", \"review_count\": \"10\", \"sponsored\": null, \"stars\": 2.5}\n \n The Embedding model sees this: \n Metadata: restaurant_id=>40366661\n attributes=>{\"Alcohol\": \"'none'\", \"Ambience\": \"{'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}\", \"BYOB\": null, \"BestNights\": null, \"BikeParking\": null, \"BusinessAcceptsBitcoin\": null, \"BusinessAcceptsCreditCards\": null, \"BusinessParking\": \"None\", \"Caters\": \"True\", \"DriveThru\": null, \"GoodForDancing\": null, \"GoodForKids\": \"True\", \"GoodForMeal\": null, \"HasTV\": \"True\", \"Music\": null, \"NoiseLevel\": \"'average'\", \"RestaurantsAttire\": \"'casual'\", \"RestaurantsDelivery\": \"True\", \"RestaurantsGoodForGroups\": \"True\", \"RestaurantsReservations\": \"True\", \"RestaurantsTableService\": \"False\", \"WheelchairAccessible\": \"True\", \"WiFi\": \"'free'\"}\n cuisine=>\"Tex-Mex\"\n DogsAllowed=>None\n OutdoorSeating=>True\n borough=>\"Manhattan\"\n address=>{\"building\": \"627\", \"coord\": [-73.975981, 40.745132], \"street\": \"2 Avenue\", \"zipcode\": \"10016\"}\n _id=>{'$oid': '6095a34a7c34416a90d3206b'}\n name=>\"Baby Bo'S Burritos\"\n menu=>null\n TakeOut=>true\n PriceRange=>1.0\n HappyHour=>null\n review_count=>10\n sponsored=>None\n stars=>2.5\n -----\n Content: {\"restaurant_id\": \"40366661\", \"attributes\": \"{\\\"Alcohol\\\": \\\"'none'\\\", \\\"Ambience\\\": \\\"{'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}\\\", \\\"BYOB\\\": null, \\\"BestNights\\\": null, \\\"BikeParking\\\": null, \\\"BusinessAcceptsBitcoin\\\": null, \\\"BusinessAcceptsCreditCards\\\": null, \\\"BusinessParking\\\": \\\"None\\\", \\\"Caters\\\": \\\"True\\\", \\\"DriveThru\\\": null, \\\"GoodForDancing\\\": null, \\\"GoodForKids\\\": \\\"True\\\", \\\"GoodForMeal\\\": null, \\\"HasTV\\\": \\\"True\\\", \\\"Music\\\": null, \\\"NoiseLevel\\\": \\\"'average'\\\", \\\"RestaurantsAttire\\\": \\\"'casual'\\\", \\\"RestaurantsDelivery\\\": \\\"True\\\", \\\"RestaurantsGoodForGroups\\\": \\\"True\\\", \\\"RestaurantsReservations\\\": \\\"True\\\", \\\"RestaurantsTableService\\\": \\\"False\\\", \\\"WheelchairAccessible\\\": \\\"True\\\", \\\"WiFi\\\": \\\"'free'\\\"}\", \"cuisine\": \"\\\"Tex-Mex\\\"\", \"DogsAllowed\": null, \"OutdoorSeating\": true, \"borough\": \"\\\"Manhattan\\\"\", \"address\": \"{\\\"building\\\": \\\"627\\\", \\\"coord\\\": [-73.975981, 40.745132], \\\"street\\\": \\\"2 Avenue\\\", \\\"zipcode\\\": \\\"10016\\\"}\", \"_id\": {\"$oid\": \"6095a34a7c34416a90d3206b\"}, \"name\": \"\\\"Baby Bo'S Burritos\\\"\", \"menu\": \"null\", \"TakeOut\": \"true\", \"PriceRange\": \"1.0\", \"HappyHour\": \"null\", \"review_count\": \"10\", \"sponsored\": null, \"stars\": 2.5}\n\n\n\n```python\nllama_documents[0]\n```\n\n\n\n\n Document(id_='93d3f08d-85f3-494d-a057-19bc834abc29', embedding=None, metadata={'restaurant_id': '40366661', 'attributes': '{\"Alcohol\": \"\\'none\\'\", \"Ambience\": \"{\\'romantic\\': False, \\'intimate\\': False, \\'classy\\': False, \\'hipster\\': False, \\'divey\\': False, \\'touristy\\': False, \\'trendy\\': False, \\'upscale\\': False, \\'casual\\': False}\", \"BYOB\": null, \"BestNights\": null, \"BikeParking\": null, \"BusinessAcceptsBitcoin\": null, \"BusinessAcceptsCreditCards\": null, \"BusinessParking\": \"None\", \"Caters\": \"True\", \"DriveThru\": null, \"GoodForDancing\": null, \"GoodForKids\": \"True\", \"GoodForMeal\": null, \"HasTV\": \"True\", \"Music\": null, \"NoiseLevel\": \"\\'average\\'\", \"RestaurantsAttire\": \"\\'casual\\'\", \"RestaurantsDelivery\": \"True\", \"RestaurantsGoodForGroups\": \"True\", \"RestaurantsReservations\": \"True\", \"RestaurantsTableService\": \"False\", \"WheelchairAccessible\": \"True\", \"WiFi\": \"\\'free\\'\"}', 'cuisine': '\"Tex-Mex\"', 'DogsAllowed': None, 'OutdoorSeating': True, 'borough': '\"Manhattan\"', 'address': '{\"building\": \"627\", \"coord\": [-73.975981, 40.745132], \"street\": \"2 Avenue\", \"zipcode\": \"10016\"}', '_id': {'$oid': '6095a34a7c34416a90d3206b'}, 'name': '\"Baby Bo\\'S Burritos\"', 'menu': 'null', 'TakeOut': 'true', 'PriceRange': '1.0', 'HappyHour': 'null', 'review_count': '10', 'sponsored': None, 'stars': 2.5}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='{\"restaurant_id\": \"40366661\", \"attributes\": \"{\\\\\"Alcohol\\\\\": \\\\\"\\'none\\'\\\\\", \\\\\"Ambience\\\\\": \\\\\"{\\'romantic\\': False, \\'intimate\\': False, \\'classy\\': False, \\'hipster\\': False, \\'divey\\': False, \\'touristy\\': False, \\'trendy\\': False, \\'upscale\\': False, \\'casual\\': False}\\\\\", \\\\\"BYOB\\\\\": null, \\\\\"BestNights\\\\\": null, \\\\\"BikeParking\\\\\": null, \\\\\"BusinessAcceptsBitcoin\\\\\": null, \\\\\"BusinessAcceptsCreditCards\\\\\": null, \\\\\"BusinessParking\\\\\": \\\\\"None\\\\\", \\\\\"Caters\\\\\": \\\\\"True\\\\\", \\\\\"DriveThru\\\\\": null, \\\\\"GoodForDancing\\\\\": null, \\\\\"GoodForKids\\\\\": \\\\\"True\\\\\", \\\\\"GoodForMeal\\\\\": null, \\\\\"HasTV\\\\\": \\\\\"True\\\\\", \\\\\"Music\\\\\": null, \\\\\"NoiseLevel\\\\\": \\\\\"\\'average\\'\\\\\", \\\\\"RestaurantsAttire\\\\\": \\\\\"\\'casual\\'\\\\\", \\\\\"RestaurantsDelivery\\\\\": \\\\\"True\\\\\", \\\\\"RestaurantsGoodForGroups\\\\\": \\\\\"True\\\\\", \\\\\"RestaurantsReservations\\\\\": \\\\\"True\\\\\", \\\\\"RestaurantsTableService\\\\\": \\\\\"False\\\\\", \\\\\"WheelchairAccessible\\\\\": \\\\\"True\\\\\", \\\\\"WiFi\\\\\": \\\\\"\\'free\\'\\\\\"}\", \"cuisine\": \"\\\\\"Tex-Mex\\\\\"\", \"DogsAllowed\": null, \"OutdoorSeating\": true, \"borough\": \"\\\\\"Manhattan\\\\\"\", \"address\": \"{\\\\\"building\\\\\": \\\\\"627\\\\\", \\\\\"coord\\\\\": [-73.975981, 40.745132], \\\\\"street\\\\\": \\\\\"2 Avenue\\\\\", \\\\\"zipcode\\\\\": \\\\\"10016\\\\\"}\", \"_id\": {\"$oid\": \"6095a34a7c34416a90d3206b\"}, \"name\": \"\\\\\"Baby Bo\\'S Burritos\\\\\"\", \"menu\": \"null\", \"TakeOut\": \"true\", \"PriceRange\": \"1.0\", \"HappyHour\": \"null\", \"review_count\": \"10\", \"sponsored\": null, \"stars\": 2.5}', start_char_idx=None, end_char_idx=None, text_template='Metadata: {metadata_str}\\n-----\\nContent: {content}', metadata_template='{key}=>{value}', metadata_seperator='\\n')\n\n\n\n\n```python\nfrom llama_index.core.node_parser import SentenceSplitter\n\nparser = SentenceSplitter()\nnodes = parser.get_nodes_from_documents(llama_documents)\n# 25k nodes takes about 10 minutes, will trim it down to 2.5k\nnew_nodes = nodes[:2500]\n\n# There are 25k documents, so we need to do batching. Fortunately LlamaIndex provides good batching\n# for embedding models, and we are going to rely on the __call__ method for the model to handle this\nnode_embeddings = embed_model(new_nodes)\n```\n\n\n```python\nfor idx, n in enumerate(new_nodes):\n n.embedding = node_embeddings[idx].embedding\n if \"_id\" in n.metadata:\n del n.metadata[\"_id\"]\n```\n\nEnsure your databse, collection and vector store index is setup on MongoDB Atlas for the collection or the following step won't work appropriately on MongoDB.\n\n\n - For assistance with database cluster setup and obtaining the URI, refer to this [guide](https://www.mongodb.com/docs/guides/atlas/cluster/) for setting up a MongoDB cluster, and this [guide](https://www.mongodb.com/docs/guides/atlas/connection-string/) to get your connection string. \n\n - Once you have successfully created a cluster, create the database and collection within the MongoDB Atlas cluster by clicking “+ Create Database”. The database will be named movies, and the collection will be named movies_records.\n\n - Creating a vector search index within the movies_records collection is essential for efficient document retrieval from MongoDB into our development environment. To achieve this, refer to the official [guide](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/) on vector search index creation.\n\n\n\n\n```python\nimport pymongo\n\n\ndef get_mongo_client(mongo_uri):\n \"\"\"Establish connection to the MongoDB.\"\"\"\n try:\n client = pymongo.MongoClient(mongo_uri)\n print(\"Connection to MongoDB successful\")\n return client\n except pymongo.errors.ConnectionFailure as e:\n print(f\"Connection failed: {e}\")\n return None\n\n\n# set up Fireworks.ai Key\nimport os\nimport getpass\n\nmongo_uri = getpass.getpass(\"MONGO_URI:\")\nif not mongo_uri:\n print(\"MONGO_URI not set\")\n\nmongo_client = get_mongo_client(mongo_uri)\n\nDB_NAME = \"whatscooking\"\nCOLLECTION_NAME = \"restaurants\"\n\ndb = mongo_client[DB_NAME]\ncollection = db[COLLECTION_NAME]\n```\n\n Connection to MongoDB successful\n\n\n\n```python\n# To ensure we are working with a fresh collection\n# delete any existing records in the collection\ncollection.delete_many({})\n```\n\n\n\n\n DeleteResult({'n': 0, 'electionId': ObjectId('7fffffff00000000000001ce'), 'opTime': {'ts': Timestamp(1708970193, 3), 't': 462}, 'ok': 1.0, '$clusterTime': {'clusterTime': Timestamp(1708970193, 3), 'signature': {'hash': b'\\x9a3H8\\xa1\\x1b\\xb6\\xbb\\xa9\\xc3x\\x17\\x1c\\xeb\\xe9\\x03\\xaa\\xf8\\xf17', 'keyId': 7294687148333072386}}, 'operationTime': Timestamp(1708970193, 3)}, acknowledged=True)\n\n\n\n\n```python\nfrom llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch\n\nvector_store = MongoDBAtlasVectorSearch(\n mongo_client,\n db_name=DB_NAME,\n collection_name=COLLECTION_NAME,\n index_name=\"vector_index\",\n)\nvector_store.add(new_nodes)\n```\n\n# now make sure you create the search index with the right name here\n\n\n```python\nfrom llama_index.core import VectorStoreIndex, StorageContext\n\nindex = VectorStoreIndex.from_vector_store(vector_store)\n```\n\n\n```python\n%pip install -q matplotlib\n```\n\n Note: you may need to restart the kernel to use updated packages.\n\n\n\n```python\nimport pprint\nfrom llama_index.core.response.notebook_utils import display_response\n\nquery_engine = index.as_query_engine()\n\nquery = \"search query: Anything that doesn't have alcohol in it\"\n\nresponse = query_engine.query(query)\ndisplay_response(response)\npprint.pprint(response.source_nodes)\n```\n\n\n**`Final Response:`** Based on the context provided, two restaurant options that don't serve alcohol are:\n\n1. \"Academy Restauraunt\" in Brooklyn, which serves American cuisine and has a variety of dishes such as Mozzarella sticks, Cheeseburger, Baked potato, Breadsticks, Caesar salad, Chicken parmesan, Pigs in a blanket, Chicken soup, Mac & cheese, Mushroom swiss burger, Spaghetti with meatballs, and Mashed potatoes.\n\n2. \"Gabriel'S Bar & Grill\" in Manhattan, which specializes in Italian cuisine and offers dishes like Cheese Ravioli, Neapolitan Pizza, assorted gelato, Vegetarian Baked Ziti, Vegetarian Broccoli Pizza, Lasagna, Buca Trio Platter, Spinach Ravioli, Pasta with ricotta cheese, Spaghetti, Fried calamari, and Alfredo Pizza.\n\nBoth restaurants offer outdoor seating, are kid-friendly, and have a casual dress code. They also provide take-out service and have happy hour promotions.\n\n\n [NodeWithScore(node=TextNode(id_='5405e68c-19f2-4a65-95d7-f880fa6a8deb', embedding=None, metadata={'restaurant_id': '40385767', 'attributes': '{\"Alcohol\": \"u\\'beer_and_wine\\'\", \"Ambience\": \"{\\'touristy\\': False, \\'hipster\\': False, \\'romantic\\': False, \\'divey\\': False, \\'intimate\\': None, \\'trendy\\': None, \\'upscale\\': False, \\'classy\\': False, \\'casual\\': True}\", \"BYOB\": null, \"BestNights\": \"{\\'monday\\': False, \\'tuesday\\': False, \\'friday\\': True, \\'wednesday\\': False, \\'thursday\\': False, \\'sunday\\': False, \\'saturday\\': True}\", \"BikeParking\": \"True\", \"BusinessAcceptsBitcoin\": \"False\", \"BusinessAcceptsCreditCards\": \"True\", \"BusinessParking\": \"{\\'garage\\': False, \\'street\\': False, \\'validated\\': False, \\'lot\\': True, \\'valet\\': False}\", \"Caters\": \"True\", \"DriveThru\": null, \"GoodForDancing\": \"False\", \"GoodForKids\": \"True\", \"GoodForMeal\": \"{\\'dessert\\': False, \\'latenight\\': False, \\'lunch\\': True, \\'dinner\\': True, \\'brunch\\': False, \\'breakfast\\': False}\", \"HasTV\": \"True\", \"Music\": \"{\\'dj\\': False, \\'background_music\\': False, \\'no_music\\': False, \\'jukebox\\': False, \\'live\\': False, \\'video\\': False, \\'karaoke\\': False}\", \"NoiseLevel\": \"u\\'average\\'\", \"RestaurantsAttire\": \"u\\'casual\\'\", \"RestaurantsDelivery\": \"None\", \"RestaurantsGoodForGroups\": \"True\", \"RestaurantsReservations\": \"True\", \"RestaurantsTableService\": \"True\", \"WheelchairAccessible\": \"True\", \"WiFi\": \"u\\'free\\'\"}', 'cuisine': '\"American\"', 'DogsAllowed': True, 'OutdoorSeating': True, 'borough': '\"Brooklyn\"', 'address': '{\"building\": \"69\", \"coord\": [-73.9757464, 40.687295], \"street\": \"Lafayette Avenue\", \"zipcode\": \"11217\"}', 'name': '\"Academy Restauraunt\"', 'menu': '[\"Mozzarella sticks\", \"Cheeseburger\", \"Baked potato\", \"Breadsticks\", \"Caesar salad\", \"Chicken parmesan\", \"Pigs in a blanket\", \"Chicken soup\", \"Mac & cheese\", \"Mushroom swiss burger\", \"Spaghetti with meatballs\", \"Mashed potatoes\"]', 'TakeOut': 'true', 'PriceRange': '2.0', 'HappyHour': 'true', 'review_count': '173', 'sponsored': None, 'stars': 4.5}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={| \n | restaurant_id | \nattributes | \ncuisine | \nDogsAllowed | \nembedding | \nOutdoorSeating | \nborough | \naddress | \n_id | \nname | \nmenu | \nTakeOut | \nlocation | \nPriceRange | \nHappyHour | \nreview_count | \nsponsored | \nstars | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n40366661 | \n{'Alcohol': ''none'', 'Ambience': '{'romantic'... | \nTex-Mex | \nNone | \n[-0.14520384, 0.018315623, -0.018330636, -0.10... | \nTrue | \nManhattan | \n{'building': '627', 'coord': [-73.975980999999... | \n{'$oid': '6095a34a7c34416a90d3206b'} | \nBaby Bo'S Burritos | \nNone | \nTrue | \n{'coordinates': [-73.97598099999999, 40.745132... | \n1.0 | \nNone | \n10 | \nNaN | \n2.5 | \n
| 1 | \n40367442 | \n{'Alcohol': ''beer_and_wine'', 'Ambience': '{'... | \nAmerican | \nTrue | \n[-0.11977468, -0.02157107, 0.0038846824, -0.09... | \nTrue | \nStaten Island | \n{'building': '17', 'coord': [-74.1350211, 40.6... | \n{'$oid': '6095a34a7c34416a90d3209e'} | \nBuddy'S Wonder Bar | \n[Grilled cheese sandwich, Baked potato, Lasagn... | \nTrue | \n{'coordinates': [-74.1350211, 40.6369042], 'ty... | \n2.0 | \nNone | \n62 | \nNaN | \n3.5 | \n
| 2 | \n40364610 | \n{'Alcohol': ''none'', 'Ambience': '{'touristy'... | \nAmerican | \nNone | \n[-0.1004329, -0.014882699, -0.033005167, -0.09... | \nTrue | \nStaten Island | \n{'building': '37', 'coord': [-74.138263, 40.54... | \n{'$oid': '6095a34a7c34416a90d31ff6'} | \nGreat Kills Yacht Club | \n[Mozzarella sticks, Mushroom swiss burger, Spi... | \nTrue | \n{'coordinates': [-74.138263, 40.546681], 'type... | \n1.0 | \nNone | \n72 | \nNaN | \n4.0 | \n
| 3 | \n40365288 | \n{'Alcohol': None, 'Ambience': '{'touristy': Fa... | \nAmerican | \nNone | \n[-0.11735515, -0.0397448, -0.0072645755, -0.09... | \nTrue | \nManhattan | \n{'building': '842', 'coord': [-73.970637000000... | \n{'$oid': '6095a34a7c34416a90d32017'} | \nKeats Restaurant | \n[French fries, Chicken pot pie, Mac & cheese, ... | \nTrue | \n{'coordinates': [-73.97063700000001, 40.751495... | \n2.0 | \nTrue | \n149 | \nNaN | \n4.0 | \n
| 4 | \n40363151 | \n{'Alcohol': None, 'Ambience': None, 'BYOB': No... | \nBakery | \nNone | \n[-0.096541286, -0.009661355, 0.04402167, -0.12... | \nTrue | \nManhattan | \n{'building': '120', 'coord': [-73.9998042, 40.... | \n{'$oid': '6095a34a7c34416a90d31fbd'} | \nOlive'S | \n[doughnuts, chocolate chip cookies, chocolate ... | \nTrue | \n{'coordinates': [-73.9998042, 40.7251256], 'ty... | \n1.0 | \nNone | \n7 | \nNaN | \n5.0 | \n
\n\n# Simple Vector Store\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n!pip install llama-index\n```\n\n\n```python\nimport os\nimport openai\n\nos.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\nopenai.api_key = os.environ[\"OPENAI_API_KEY\"]\n```\n\n#### Load documents, build the VectorStoreIndex\n\n\n```python\nimport nltk\n\nnltk.download(\"stopwords\")\n```\n\n [nltk_data] Downloading package stopwords to\n [nltk_data] /Users/jerryliu/nltk_data...\n [nltk_data] Package stopwords is already up-to-date!\n\n\n\n\n\n True\n\n\n\n\n```python\nimport llama_index.core\n```\n\n [nltk_data] Downloading package stopwords to /Users/jerryliu/Programmi\n [nltk_data] ng/gpt_index/.venv/lib/python3.10/site-\n [nltk_data] packages/llama_index/core/_static/nltk_cache...\n [nltk_data] Unzipping corpora/stopwords.zip.\n [nltk_data] Downloading package punkt to /Users/jerryliu/Programming/g\n [nltk_data] pt_index/.venv/lib/python3.10/site-\n [nltk_data] packages/llama_index/core/_static/nltk_cache...\n [nltk_data] Unzipping tokenizers/punkt.zip.\n\n\n\n```python\nimport logging\nimport sys\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nlogging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n\nfrom llama_index.core import (\n VectorStoreIndex,\n SimpleDirectoryReader,\n load_index_from_storage,\n StorageContext,\n)\nfrom IPython.display import Markdown, display\n```\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n --2024-02-12 13:21:13-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n HTTP request sent, awaiting response... 200 OK\n Length: 75042 (73K) [text/plain]\n Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n \n data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.02s \n \n 2024-02-12 13:21:13 (4.76 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n \n\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\n```\n\n\n```python\nindex = VectorStoreIndex.from_documents(documents)\n```\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n\n\n\n```python\n# save index to disk\nindex.set_index_id(\"vector_index\")\nindex.storage_context.persist(\"./storage\")\n```\n\n\n```python\n# rebuild storage context\nstorage_context = StorageContext.from_defaults(persist_dir=\"storage\")\n# load index\nindex = load_index_from_storage(storage_context, index_id=\"vector_index\")\n```\n\n INFO:llama_index.core.indices.loading:Loading indices with ids: ['vector_index']\n Loading indices with ids: ['vector_index']\n\n\n#### Query Index\n\n\n```python\n# set Logging to DEBUG for more detailed outputs\nquery_engine = index.as_query_engine(response_mode=\"tree_summarize\")\nresponse = query_engine.query(\"What did the author do growing up?\")\n```\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n\n\n\n```python\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nThe author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later transitioned to working with microcomputers, starting with a kit-built microcomputer and eventually acquiring a TRS-80. They wrote simple games, a program to predict rocket heights, and even a word processor. Although the author initially planned to study philosophy in college, they eventually switched to studying AI.\n\n\n**Query Index with SVM/Linear Regression**\n\nUse Karpathy's [SVM-based](https://twitter.com/karpathy/status/1647025230546886658?s=20) approach. Set query as positive example, all other datapoints as negative examples, and then fit a hyperplane.\n\n\n```python\nquery_modes = [\n \"svm\",\n \"linear_regression\",\n \"logistic_regression\",\n]\nfor query_mode in query_modes:\n # set Logging to DEBUG for more detailed outputs\n query_engine = index.as_query_engine(vector_store_query_mode=query_mode)\n response = query_engine.query(\"What did the author do growing up?\")\n print(f\"Query mode: {query_mode}\")\n display(Markdown(f\"{response}\"))\n```\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n\n\n /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n warnings.warn(\n\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n Query mode: svm\n\n\n\nThe author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later got a microcomputer and started programming on it, writing simple games and a word processor. They initially planned to study philosophy in college but ended up switching to AI.\n\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n\n\n /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n warnings.warn(\n\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n Query mode: linear_regression\n\n\n\nThe author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later got a microcomputer and started programming on it, writing simple games and a word processor. They initially planned to study philosophy in college but ended up switching to AI.\n\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n\n\n /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n warnings.warn(\n\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n Query mode: logistic_regression\n\n\n\nThe author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later got a microcomputer and started programming on it, writing simple games and a word processor. They initially planned to study philosophy in college but eventually switched to AI.\n\n\n\n```python\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nThe author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later got a microcomputer and started programming on it, writing simple games and a word processor. They initially planned to study philosophy in college but eventually switched to AI.\n\n\n\n```python\nprint(response.source_nodes[0].text)\n```\n\n What I Worked On\n \n February 2021\n \n Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\n \n The first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\n \n The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\n \n I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.\n \n With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]\n \n The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.\n \n Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter.\n \n Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.\n \n I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.\n \n AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most.\n\n\n**Query Index with custom embedding string**\n\n\n```python\nfrom llama_index.core import QueryBundle\n```\n\n\n```python\nquery_bundle = QueryBundle(\n query_str=\"What did the author do growing up?\",\n custom_embedding_strs=[\"The author grew up painting.\"],\n)\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(query_bundle)\n```\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n\n\n\n```python\ndisplay(Markdown(f\"{response}\"))\n```\n\n\nThe context does not provide information about what the author did growing up.\n\n\n**Use maximum marginal relevance**\n\nInstead of ranking vectors purely by similarity, adds diversity to the documents by penalizing documents similar to ones that have already been found based on MMR . A lower mmr_treshold increases diversity.\n\n\n```python\nquery_engine = index.as_query_engine(\n vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\": 0.2}\n)\nresponse = query_engine.query(\"What did the author do growing up?\")\n```\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n\n\n#### Get Sources\n\n\n```python\nprint(response.get_formatted_sources())\n```\n\n > Source (Doc id: c4118521-8f55-4a4d-819a-2db546b6491e): What I Worked On\n \n February 2021\n \n Before college the two main things I worked on, outside of schoo...\n \n > Source (Doc id: 74f77233-e4fe-4389-9820-76dd9f765af6): Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because tha...\n\n\n#### Query Index with Filters\n\nWe can also filter our queries using metadata\n\n\n```python\nfrom llama_index.core import Document\n\ndoc = Document(text=\"target\", metadata={\"tag\": \"target\"})\n\nindex.insert(doc)\n```\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n\n\n\n```python\nfrom llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n\nfilters = MetadataFilters(\n filters=[ExactMatchFilter(key=\"tag\", value=\"target\")]\n)\n\nretriever = index.as_retriever(\n similarity_top_k=20,\n filters=filters,\n)\n\nsource_nodes = retriever.retrieve(\"What did the author do growing up?\")\n```\n\n INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n\n\n\n```python\n# retrieves only our target node, even though we set the top k to 20\nprint(len(source_nodes))\n```\n\n 1\n\n\n\n```python\nprint(source_nodes[0].text)\nprint(source_nodes[0].metadata)\n```\n\n target\n {'tag': 'target'}"}
{"tokens": 43097, "doc_id": "264a5660-6484-4a24-b74f-50ba42fa1223", "name": "Opensearch Vector Store", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/OpensearchDemo", "retrieve_doc": false, "source": "llama_index", "content": "\n\n# Opensearch Vector Store\n\nElasticsearch only supports Lucene indices, so only Opensearch is supported.\n\n**Note on setup**: We setup a local Opensearch instance through the following doc. https://opensearch.org/docs/1.0/\n\nIf you run into SSL issues, try the following `docker run` command instead: \n```\ndocker run -p 9200:9200 -p 9600:9600 -e \"discovery.type=single-node\" -e \"plugins.security.disabled=true\" opensearchproject/opensearch:1.0.1\n```\n\nReference: https://github.com/opensearch-project/OpenSearch/issues/1598\n\nDownload Data\n\n\n```python\n%pip install llama-index-readers-elasticsearch\n%pip install llama-index-vector-stores-opensearch\n%pip install llama-index-embeddings-ollama\n```\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n\n```python\nfrom os import getenv\nfrom llama_index.core import SimpleDirectoryReader\nfrom llama_index.vector_stores.opensearch import (\n OpensearchVectorStore,\n OpensearchVectorClient,\n)\nfrom llama_index.core import VectorStoreIndex, StorageContext\n\n# http endpoint for your cluster (opensearch required for vector index usage)\nendpoint = getenv(\"OPENSEARCH_ENDPOINT\", \"http://localhost:9200\")\n# index to demonstrate the VectorStore impl\nidx = getenv(\"OPENSEARCH_INDEX\", \"gpt-index-demo\")\n# load some sample data\ndocuments = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\n```\n\n /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n from .autonotebook import tqdm as notebook_tqdm\n\n\n\n```python\n# OpensearchVectorClient stores text in this field by default\ntext_field = \"content\"\n# OpensearchVectorClient stores embeddings in this field by default\nembedding_field = \"embedding\"\n# OpensearchVectorClient encapsulates logic for a\n# single opensearch index with vector search enabled\nclient = OpensearchVectorClient(\n endpoint, idx, 1536, embedding_field=embedding_field, text_field=text_field\n)\n# initialize vector store\nvector_store = OpensearchVectorStore(client)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n# initialize an index using our sample data and the client we just created\nindex = VectorStoreIndex.from_documents(\n documents=documents, storage_context=storage_context\n)\n```\n\n\n```python\n# run query\nquery_engine = index.as_query_engine()\nres = query_engine.query(\"What did the author do growing up?\")\nres.response\n```\n\n INFO:root:> [query] Total LLM token usage: 29628 tokens\n INFO:root:> [query] Total embedding token usage: 8 tokens\n\n\n\n\n\n '\\n\\nThe author grew up writing short stories, programming on an IBM 1401, and building a computer kit from Heathkit. They also wrote programs for a TRS-80, such as games, a program to predict model rocket flight, and a word processor. After years of nagging, they convinced their father to buy a TRS-80, and they wrote simple games, a program to predict how high their model rockets would fly, and a word processor that their father used to write at least one book. In college, they studied philosophy and AI, and wrote a book about Lisp hacking. They also took art classes and applied to art schools, and experimented with computer graphics and animation, exploring the use of algorithms to create art. Additionally, they experimented with machine learning algorithms, such as using neural networks to generate art, and exploring the use of numerical values to create art. They also took classes in fundamental subjects like drawing, color, and design, and applied to two art schools, RISD in the US, and the Accademia di Belli Arti in Florence. They were accepted to RISD, and while waiting to hear back from the Accademia, they learned Italian and took the entrance exam in Florence. They eventually graduated from RISD'\n\n\n\nThe OpenSearch vector store supports [filter-context queries](https://opensearch.org/docs/latest/query-dsl/query-filter-context/).\n\n\n```python\nfrom llama_index.core import Document\nfrom llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter\nimport regex as re\n```\n\n\n```python\n# Split the text into paragraphs.\ntext_chunks = documents[0].text.split(\"\\n\\n\")\n\n# Create a document for each footnote\nfootnotes = [\n Document(\n text=chunk,\n id=documents[0].doc_id,\n metadata={\"is_footnote\": bool(re.search(r\"^\\s*\\[\\d+\\]\\s*\", chunk))},\n )\n for chunk in text_chunks\n if bool(re.search(r\"^\\s*\\[\\d+\\]\\s*\", chunk))\n]\n```\n\n\n```python\n# Insert the footnotes into the index\nfor f in footnotes:\n index.insert(f)\n```\n\n\n```python\n# Create a query engine that only searches certain footnotes.\nfootnote_query_engine = index.as_query_engine(\n filters=MetadataFilters(\n filters=[\n ExactMatchFilter(\n key=\"term\", value='{\"metadata.is_footnote\": \"true\"}'\n ),\n ExactMatchFilter(\n key=\"query_string\",\n value='{\"query\": \"content: space AND content: lisp\"}',\n ),\n ]\n )\n)\n\nres = footnote_query_engine.query(\n \"What did the author about space aliens and lisp?\"\n)\nres.response\n```\n\n\n\n\n \"The author believes that any sufficiently advanced alien civilization would know about the Pythagorean theorem and possibly also about Lisp in McCarthy's 1960 paper.\"\n\n\n\n## Use reader to check out what VectorStoreIndex just created in our index.\n\nReader works with Elasticsearch too as it just uses the basic search features.\n\n\n```python\n# create a reader to check out the index used in previous section.\nfrom llama_index.readers.elasticsearch import ElasticsearchReader\n\nrdr = ElasticsearchReader(endpoint, idx)\n# set embedding_field optionally to read embedding data from the elasticsearch index\ndocs = rdr.load_data(text_field, embedding_field=embedding_field)\n# docs have embeddings in them\nprint(\"embedding dimension:\", len(docs[0].embedding))\n# full document is stored in metadata\nprint(\"all fields in index:\", docs[0].metadata.keys())\n```\n\n embedding dimension: 1536\n all fields in index: dict_keys(['content', 'embedding'])\n\n\n\n```python\n# we can check out how the text was chunked by the `GPTOpensearchIndex`\nprint(\"total number of chunks created:\", len(docs))\n```\n\n total number of chunks: 10\n\n\n\n```python\n# search index using standard elasticsearch query DSL\ndocs = rdr.load_data(text_field, {\"query\": {\"match\": {text_field: \"Lisp\"}}})\nprint(\"chunks that mention Lisp:\", len(docs))\ndocs = rdr.load_data(text_field, {\"query\": {\"match\": {text_field: \"Yahoo\"}}})\nprint(\"chunks that mention Yahoo:\", len(docs))\n```\n\n chunks that mention Lisp: 10\n chunks that mention Yahoo: 8\n\n\n## Hybrid query for opensearch vector store\nHybrid query has been supported since OpenSearch 2.10. It is a combination of vector search and text search. It is useful when you want to search for a specific text and also want to filter the results by vector similarity. You can find more details: https://opensearch.org/docs/latest/query-dsl/compound/hybrid/. \n\n### Prepare Search Pipeline\n\nCreate a new [search pipeline](https://opensearch.org/docs/latest/search-plugins/search-pipelines/creating-search-pipeline/) with [score normalization and weighted harmonic mean combination](https://opensearch.org/docs/latest/search-plugins/search-pipelines/normalization-processor/).\n\n```\nPUT /_search/pipeline/hybrid-search-pipeline\n{\n \"description\": \"Post processor for hybrid search\",\n \"phase_results_processors\": [\n {\n \"normalization-processor\": {\n \"normalization\": {\n \"technique\": \"min_max\"\n },\n \"combination\": {\n \"technique\": \"harmonic_mean\",\n \"parameters\": {\n \"weights\": [\n 0.3,\n 0.7\n ]\n }\n }\n }\n }\n ]\n}\n```\n\n### Initialize a OpenSearch client and vector store supporting hybrid query with search pipeline details\n\n\n```python\nfrom os import getenv\nfrom llama_index.vector_stores.opensearch import (\n OpensearchVectorStore,\n OpensearchVectorClient,\n)\n\n# http endpoint for your cluster (opensearch required for vector index usage)\nendpoint = getenv(\"OPENSEARCH_ENDPOINT\", \"http://localhost:9200\")\n# index to demonstrate the VectorStore impl\nidx = getenv(\"OPENSEARCH_INDEX\", \"auto_retriever_movies\")\n\n# OpensearchVectorClient stores text in this field by default\ntext_field = \"content\"\n# OpensearchVectorClient stores embeddings in this field by default\nembedding_field = \"embedding\"\n# OpensearchVectorClient encapsulates logic for a\n# single opensearch index with vector search enabled with hybrid search pipeline\nclient = OpensearchVectorClient(\n endpoint,\n idx,\n 4096,\n embedding_field=embedding_field,\n text_field=text_field,\n search_pipeline=\"hybrid-search-pipeline\",\n)\n\nfrom llama_index.embeddings.ollama import OllamaEmbedding\n\nembed_model = OllamaEmbedding(model_name=\"llama2\")\n\n# initialize vector store\nvector_store = OpensearchVectorStore(client)\n```\n\n### Prepare the index\n\n\n```python\nfrom llama_index.core.schema import TextNode\nfrom llama_index.core import VectorStoreIndex, StorageContext\n\n\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\nnodes = [\n TextNode(\n text=\"The Shawshank Redemption\",\n metadata={\n \"author\": \"Stephen King\",\n \"theme\": \"Friendship\",\n },\n ),\n TextNode(\n text=\"The Godfather\",\n metadata={\n \"director\": \"Francis Ford Coppola\",\n \"theme\": \"Mafia\",\n },\n ),\n TextNode(\n text=\"Inception\",\n metadata={\n \"director\": \"Christopher Nolan\",\n },\n ),\n]\n\nindex = VectorStoreIndex(\n nodes, storage_context=storage_context, embed_model=embed_model\n)\n```\n\n LLM is explicitly disabled. Using MockLLM.\n\n\n### Search the index with hybrid query by specifying the vector store query mode: VectorStoreQueryMode.HYBRID with filters\n\n\n```python\nfrom llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\nfrom llama_index.core.vector_stores.types import VectorStoreQueryMode\n\nfilters = MetadataFilters(\n filters=[\n ExactMatchFilter(\n key=\"term\", value='{\"metadata.theme.keyword\": \"Mafia\"}'\n )\n ]\n)\n\nretriever = index.as_retriever(\n filters=filters, vector_store_query_mode=VectorStoreQueryMode.HYBRID\n)\n\nresult = retriever.retrieve(\"What is inception about?\")\n\nprint(result)\n```\n\n query_strWhat is inception about?\n query_modehybrid\n {'size': 2, 'query': {'hybrid': {'queries': [{'bool': {'must': {'match': {'content': {'query': 'What is inception about?'}}}, 'filter': [{'term': {'metadata.theme.keyword': 'Mafia'}}]}}, {'script_score': {'query': {'bool': {'filter': [{'term': {'metadata.theme.keyword': 'Mafia'}}]}}, 'script': {'source': 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{"tokens": 717, "doc_id": "277582b6-e0c5-4a16-82ab-5bb10a14f24f", "name": "Astra DB", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/AstraDBIndexDemo", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# Astra DB\n\n>[DataStax Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Apache Cassandra and accessed through an easy-to-use JSON API.\n\nTo run this notebook you need a DataStax Astra DB instance running in the cloud (you can get one for free at [datastax.com](https://astra.datastax.com)).\n\nYou should ensure you have `llama-index` and `astrapy` installed:\n\n\n```python\n%pip install llama-index-vector-stores-astra-db\n```\n\n\n```python\n!pip install llama-index\n!pip install \"astrapy>=0.6.0\"\n```\n\n### Please provide database connection parameters and secrets:\n\n\n```python\nimport os\nimport getpass\n\napi_endpoint = input(\n \"\\nPlease enter your Database Endpoint URL (e.g. 'https://4bc...datastax.com'):\"\n)\n\ntoken = getpass.getpass(\n \"\\nPlease enter your 'Database Administrator' Token (e.g. 'AstraCS:...'):\"\n)\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\n \"\\nPlease enter your OpenAI API Key (e.g. 'sk-...'):\"\n)\n```\n\n### Import needed package dependencies:\n\n\n```python\nfrom llama_index.core import (\n VectorStoreIndex,\n SimpleDirectoryReader,\n StorageContext,\n)\nfrom llama_index.vector_stores.astra_db import AstraDBVectorStore\n```\n\n### Load some example data:\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n### Read the data:\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\nprint(f\"Total documents: {len(documents)}\")\nprint(f\"First document, id: {documents[0].doc_id}\")\nprint(f\"First document, hash: {documents[0].hash}\")\nprint(\n \"First document, text\"\n f\" ({len(documents[0].text)} characters):\\n{'='*20}\\n{documents[0].text[:360]} ...\"\n)\n```\n\n### Create the Astra DB Vector Store object:\n\n\n```python\nastra_db_store = AstraDBVectorStore(\n token=token,\n api_endpoint=api_endpoint,\n collection_name=\"astra_v_table\",\n embedding_dimension=1536,\n)\n```\n\n### Build the Index from the Documents:\n\n\n```python\nstorage_context = StorageContext.from_defaults(vector_store=astra_db_store)\n\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n```\n\n### Query using the index:\n\n\n```python\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\"Why did the author choose to work on AI?\")\n\nprint(response.response)\n```"}
{"tokens": 1273, "doc_id": "b5f2abfe-6ee9-425f-848f-35d721aac12f", "name": "DocArray InMemory Vector Store", "url": "https://docs.llamaindex.ai/en/stable/examples/vector_stores/DocArrayInMemoryIndexDemo", "retrieve_doc": true, "source": "llama_index", "content": "\n\n# DocArray InMemory Vector Store\n\n[DocArrayInMemoryVectorStore](https://docs.docarray.org/user_guide/storing/index_in_memory/) is a document index provided by [Docarray](https://github.com/docarray/docarray) that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.\n\n\n\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-docarray\n```\n\n\n```python\n!pip install llama-index\n```\n\n\n```python\nimport os\nimport sys\nimport logging\nimport textwrap\n\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\n# stop huggingface warnings\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\n# Uncomment to see debug logs\n# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n\nfrom llama_index.core import (\n GPTVectorStoreIndex,\n SimpleDirectoryReader,\n Document,\n)\nfrom llama_index.vector_stores.docarray import DocArrayInMemoryVectorStore\nfrom IPython.display import Markdown, display\n```\n\n\n```python\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = \"\n\n# Supabase Vector Store\nIn this notebook we are going to show how to use [Vecs](https://supabase.github.io/vecs/) to perform vector searches in LlamaIndex. \nSee [this guide](https://supabase.github.io/vecs/hosting/) for instructions on hosting a database on Supabase \n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-supabase\n```\n\n\n```python\n!pip install llama-index\n```\n\n\n```python\nimport logging\nimport sys\n\n# Uncomment to see debug logs\n# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n\nfrom llama_index.core import SimpleDirectoryReader, Document, StorageContext\nfrom llama_index.core import VectorStoreIndex\nfrom llama_index.vector_stores.supabase import SupabaseVectorStore\nimport textwrap\n```\n\n### Setup OpenAI\nThe first step is to configure the OpenAI key. It will be used to created embeddings for the documents loaded into the index\n\n\n```python\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = \"[your_openai_api_key]\"\n```\n\nDownload Data\n\n\n```python\n!mkdir -p 'data/paul_graham/'\n!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n### Loading documents\nLoad the documents stored in the `./data/paul_graham/` using the SimpleDirectoryReader\n\n\n```python\ndocuments = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\nprint(\n \"Document ID:\",\n documents[0].doc_id,\n \"Document Hash:\",\n documents[0].doc_hash,\n)\n```\n\n Document ID: fb056993-ee9e-4463-80b4-32cf9509d1d8 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e\n\n\n### Create an index backed by Supabase's vector store. \nThis will work with all Postgres providers that support pgvector.\nIf the collection does not exist, we will attempt to create a new collection \n\n> Note: you need to pass in the embedding dimension if not using OpenAI's text-embedding-ada-002, e.g. `vector_store = SupabaseVectorStore(..., dimension=...)`\n\n\n```python\nvector_store = SupabaseVectorStore(\n postgres_connection_string=(\n \"postgresql://\n\n# Milvus Vector Store With Hybrid Retrieval\n\nIn this notebook we are going to show a quick demo of using the MilvusVectorStore with hybrid retrieval. (Milvus version should higher than 2.4.0)\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-milvus\n```\n\nBGE-M3 from FlagEmbedding is used as the default sparse embedding method, so it needs to be installed along with llama-index.\n\n\n```python\n! pip install llama-index\n! pip install FlagEmbedding\n```\n\n\n```python\nimport logging\nimport sys\n\n# Uncomment to see debug logs\n# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n\nfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document\nfrom llama_index.vector_stores.milvus import MilvusVectorStore\nfrom IPython.display import Markdown, display\nimport textwrap\n```\n\n### Setup OpenAI\nLets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt.\n\n\n```python\nimport openai\n\nopenai.api_key = \"sk-\"\n```\n\nDownload Data\n\n\n```python\n! mkdir -p 'data/paul_graham/'\n! wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n```\n\n --2024-04-25 17:44:59-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n HTTP request sent, awaiting response... 200 OK\n Length: 75042 (73K) [text/plain]\n Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n \n data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.07s \n \n 2024-04-25 17:45:00 (994 KB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n \n\n\n### Generate our data\nWith our LLM set, lets start using the Milvus Index. As a first example, lets generate a document from the file found in the `data/paul_graham/` folder. In this folder there is a single essay from Paul Graham titled `What I Worked On`. To generate the documents we will use the SimpleDirectoryReader.\n\n\n```python\n# load documents\ndocuments = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\n\nprint(\"Document ID:\", documents[0].doc_id)\n```\n\n Document ID: ca3f5dbc-f772-41da-9a4f-bb4884691793\n\n\n### Create an index across the data\nNow that we have a document, we can can create an index and insert the document. For the index we will use a MilvusVectorStore. MilvusVectorStore takes in a few arguments:\n\n- `uri (str, optional)`: The URI to connect to, comes in the form of \"http://address:port\". Defaults to \"http://localhost:19530\".\n- `token (str, optional)`: The token for log in. Empty if not using rbac, if using rbac it will most likely be \"username:password\". Defaults to \"\".\n- `collection_name (str, optional)`: The name of the collection where data will be stored. Defaults to \"llamalection\".\n- `dim (int, optional)`: The dimension of the embeddings. If it is not provided, collection creation will be done on first insert. Defaults to None.\n- `embedding_field (str, optional)`: The name of the embedding field for the collection, defaults to DEFAULT_EMBEDDING_KEY.\n- `doc_id_field (str, optional)`: The name of the doc_id field for the collection, defaults to DEFAULT_DOC_ID_KEY.\n- `similarity_metric (str, optional)`: The similarity metric to use, currently supports IP and L2. Defaults to \"IP\".\n- `consistency_level (str, optional)`: Which consistency level to use for a newly created collection. Defaults to \"Strong\".\n- `overwrite (bool, optional)`: Whether to overwrite existing collection with same name. Defaults to False.\n- `text_key (str, optional)`: What key text is stored in in the passed collection. Used when bringing your own collection. Defaults to None.\n- `index_config (dict, optional)`: The configuration used for building the Milvus index. Defaults to None.\n- `search_config (dict, optional)`: The configuration used for searching the Milvus index. Note that this must be compatible with the index type specified by index_config. Defaults to None.\n- `batch_size (int)`: Configures the number of documents processed in one batch when inserting data into Milvus. Defaults to DEFAULT_BATCH_SIZE.\n- `enable_sparse (bool)`: A boolean flag indicating whether to enable support\n for sparse embeddings for hybrid retrieval. Defaults to False.\n- `sparse_embedding_function (BaseSparseEmbeddingFunction, optional)`: If enable_sparse\n is True, this object should be provided to convert text to a sparse embedding.\n- `hybrid_ranker (str)`: Specifies the type of ranker used in hybrid search queries.\n Currently only supports ['RRFRanker','WeightedRanker']. Defaults to \"RRFRanker\".\n- `hybrid_ranker_params (dict)`: Configuration parameters for the hybrid ranker. \n - For \"RRFRanker\", it should include:\n - 'k' (int): A parameter used in Reciprocal Rank Fusion (RRF). This value is used \n to calculate the rank scores as part of the RRF algorithm, which combines \n multiple ranking strategies into a single score to improve search relevance.\n - For \"WeightedRanker\", it should include:\n - 'weights' (list of float): A list of exactly two weights:\n - The weight for the dense embedding component.\n - The weight for the sparse embedding component. \n \n These weights are used to adjust the importance of the dense and sparse components of the embeddings in the hybrid retrieval process.\n\n Defaults to an empty dictionary, implying that the ranker will operate with its predefined default settings.\n\nNow, let's begin creating a MilvusVectorStore for hybrid retrieval. We need to set `enable_sparse` to True to enable sparse embedding generation, and we also need to configure the RRFRanker for reranking. For more details, please refer to [Milvus Reranking](https://milvus.io/docs/reranking.md).\n\n\n```python\n# Create an index over the documnts\nfrom llama_index.core import StorageContext\nimport os\n\n\nvector_store = MilvusVectorStore(\n dim=1536,\n overwrite=True,\n enable_sparse=True,\n hybrid_ranker=\"RRFRanker\",\n hybrid_ranker_params={\"k\": 60},\n)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(\n documents, storage_context=storage_context\n)\n```\n\n Sparse embedding function is not provided, using default.\n\n\n\n Fetching 30 files: 0%| | 0/30 [00:00\n\n# Qdrant Vector Store - Metadata Filter\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-qdrant\n```\n\n\n```python\n!pip install llama-index qdrant_client\n```\n\nBuild the Qdrant VectorStore Client\n\n\n```python\nimport qdrant_client\nfrom llama_index.core import VectorStoreIndex\nfrom llama_index.vector_stores.qdrant import QdrantVectorStore\n\nclient = qdrant_client.QdrantClient(\n # you can use :memory: mode for fast and light-weight experiments,\n # it does not require to have Qdrant deployed anywhere\n # but requires qdrant-client >= 1.1.1\n location=\":memory:\"\n # otherwise set Qdrant instance address with:\n # uri=\"http://\n\n# Bagel Vector Store\n\nIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n\n\n```python\n%pip install llama-index-vector-stores-bagel\n%pip install llama-index\n%pip install bagelML\n```\n\n\n```python\nimport logging\nimport sys\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nlogging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n```\n\n\n```python\n# set up OpenAI\nimport os\nimport getpass\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\nimport openai\n\nopenai.api_key = os.environ[\"OPENAI_API_KEY\"]\n```\n\n\n```python\nimport os\n\n# Set environment variable\nos.environ[\"BAGEL_API_KEY\"] = getpass.getpass(\"Bagel API Key:\")\n```\n\n\n```python\nimport bagel\nfrom bagel import Settings\n```\n\n\n```python\nserver_settings = Settings(\n bagel_api_impl=\"rest\", bagel_server_host=\"api.bageldb.ai\"\n)\n\nclient = bagel.Client(server_settings)\n\ncollection = client.get_or_create_cluster(\n \"testing_embeddings_3\", embedding_model=\"custom\", dimension=1536\n)\n```\n\n\n```python\nfrom llama_index.core import VectorStoreIndex, StorageContext\nfrom llama_index.vector_stores.bagel import BagelVectorStore\n```\n\n\n```python\nfrom llama_index.core.schema import TextNode\n\nnodes = [\n TextNode(\n text=(\n \"Michael Jordan is a retired professional basketball player,\"\n \" widely regarded as one of the greatest basketball players of all\"\n \" time.\"\n ),\n metadata={\n \"category\": \"Sports\",\n \"country\": \"United States\",\n },\n ),\n TextNode(\n text=(\n \"Angelina Jolie is an American actress, filmmaker, and\"\n \" humanitarian. She has received numerous awards for her acting\"\n \" and is known for her philanthropic work.\"\n ),\n metadata={\n \"category\": \"Entertainment\",\n \"country\": \"United States\",\n },\n ),\n TextNode(\n text=(\n \"Elon Musk is a business magnate, industrial designer, and\"\n \" engineer. He is the founder, CEO, and lead designer of SpaceX,\"\n \" Tesla, Inc., Neuralink, and The Boring Company.\"\n ),\n metadata={\n \"category\": \"Business\",\n \"country\": \"United States\",\n },\n ),\n TextNode(\n text=(\n \"Rihanna is a Barbadian singer, actress, and businesswoman. She\"\n \" has achieved significant success in the music industry and is\"\n \" known for her versatile musical style.\"\n ),\n metadata={\n \"category\": \"Music\",\n \"country\": \"Barbados\",\n },\n ),\n TextNode(\n text=(\n \"Cristiano Ronaldo is a Portuguese professional footballer who is\"\n \" considered one of the greatest football players of all time. He\"\n \" has won numerous awards and set multiple records during his\"\n \" career.\"\n ),\n metadata={\n \"category\": \"Sports\",\n \"country\": \"Portugal\",\n },\n ),\n]\n```\n\n\n```python\nvector_store = BagelVectorStore(collection=collection)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n```\n\n\n```python\nindex = VectorStoreIndex(nodes, storage_context=storage_context)\n```\n\n\n```python\nfrom llama_index.core.retrievers import VectorIndexAutoRetriever\nfrom llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo\n\n\nvector_store_info = VectorStoreInfo(\n content_info=\"brief biography of celebrities\",\n metadata_info=[\n MetadataInfo(\n name=\"category\",\n type=\"str\",\n description=(\n \"Category of the celebrity, one of [Sports, Entertainment,\"\n \" Business, Music]\"\n ),\n ),\n MetadataInfo(\n name=\"country\",\n type=\"str\",\n description=(\n \"Country of the celebrity, one of [United States, Barbados,\"\n \" Portugal]\"\n ),\n ),\n ],\n)\nretriever = VectorIndexAutoRetriever(\n index, vector_store_info=vector_store_info\n)\n```\n\n\n```python\nretriever.retrieve(\"celebrity\")\n```"}