diff --git a/Earnings_Call_Ingestion_Script.ipynb b/Earnings_Call_Ingestion_Script.ipynb index f869720..f5f7477 100644 --- a/Earnings_Call_Ingestion_Script.ipynb +++ b/Earnings_Call_Ingestion_Script.ipynb @@ -18,7 +18,14 @@ "from sycamore.data.document import Document\n", "from sycamore.functions import HuggingFaceTokenizer, OpenAITokenizer\n", "from sycamore.llms import OpenAI, OpenAIModels\n", +<<<<<<< HEAD + "from sycamore.transforms.embed import SentenceTransformerEmbedder\n", + "from aryn_sdk.client.client import Client\n", + "from sycamore.transforms.embed import OpenAIEmbedder\n", + "from sycamore import MaterializeSourceMode" +======= "from sycamore.transforms.embed import SentenceTransformerEmbedder" +>>>>>>> main ] }, { @@ -28,6 +35,12 @@ "metadata": {}, "outputs": [], "source": [ +<<<<<<< HEAD + "## Set your api-keys. You'll need an ARYN_API_KEY and an OPENAI_API_KEY\n", + "context = sycamore.init()\n", + "paths = \"\"\n", + "initial_docset = context.read.binary(paths, binary_format=\"pdf\")" +======= "context = sycamore.init()\n", "# local file path to the SortBenchmark dataset\n", "paths = \"\"\n", @@ -43,6 +56,7 @@ "outputs": [], "source": [ "## set your keys here" +>>>>>>> main ] }, { @@ -109,9 +123,14 @@ "metadata": {}, "outputs": [], "source": [ +<<<<<<< HEAD + "partitioned_docset = (initial_docset.partition(partitioner=ArynPartitioner())\n", + " .materialize(path=\"PATH_TO_STORE_MATERIALIZED\", source_mode=MaterializeSourceMode.USE_STORED)\n", +======= "from sycamore import MaterializeSourceMode\n", "partitioned_docset = (initial_docset.partition(partitioner=ArynPartitioner())\n", " .materialize(path=\"/Users/abhijitpujare/workspace/haystack-workshop-2025/materialize/partitioned_docset\", source_mode=MaterializeSourceMode.USE_STORED)\n", +>>>>>>> main " .split_elements(tokenizer=tokenizer, max_tokens=512)\n", " .extract_properties(property_extractor=OpenAIPropertyExtractor(llm=llm, schema=schema_json, schema_name=\"earnings_calls\")))" ] @@ -175,7 +194,10 @@ "\n", " return doc\n", "\n", +<<<<<<< HEAD +======= "#filtered_Docset.map_elements(markSpeakers).show()\n", +>>>>>>> main "speakersMarkedDocSet = docset_no_orig_elements.map_elements(markSpeakers)" ] }, @@ -219,8 +241,12 @@ "def filterOnlySpeakers(elem: Element):\n", " return 'speaker' in elem.properties\n", "\n", +<<<<<<< HEAD + "finalDocSet = mergedDialogeSet.filter_elements(filterOnlySpeakers)" +======= "finalDocSet = mergedDialogeSet.filter_elements(filterOnlySpeakers)\n", "#finalDocSet.show()" +>>>>>>> main ] }, { @@ -230,11 +256,18 @@ "metadata": {}, "outputs": [], "source": [ +<<<<<<< HEAD + "model_name = \"text-embedding-3-small\"\n", + "docset_name = \"\"\n", + "myClient = Client(aryn_url=\"https://test-api.aryn.ai\", aryn_api_key=\"\")\n", + "docset = myClient.create_docset(name=docset_name)" +======= "from sycamore.transforms.embed import OpenAIEmbedder\n", "model_name = \"text-embedding-3-small\"\n", "from aryn_sdk.client.client import Client \n", "myClient = Client(aryn_url=\"https://test-api.aryn.ai\", aryn_api_key=\"\")\n", "docset = myClient.create_docset(name=\"haystack_workshop_target_correct\")" +>>>>>>> main ] }, { diff --git a/QuestionAnsweringNotebook.ipynb b/QuestionAnsweringNotebook.ipynb index 96d8819..6919e07 100644 --- a/QuestionAnsweringNotebook.ipynb +++ b/QuestionAnsweringNotebook.ipynb @@ -18,7 +18,14 @@ "from sycamore.data.document import Document\n", "from sycamore.functions import HuggingFaceTokenizer, OpenAITokenizer\n", "from sycamore.llms import OpenAI, OpenAIModels\n", +<<<<<<< HEAD + "from sycamore.transforms.embed import SentenceTransformerEmbedder\n", + "from sycamore.llms.prompts.default_prompts import LlmFilterMessagesJinjaPrompt\n", + "from sycamore.llms.prompts.prompts import JinjaPrompt\n", + "from sycamore.transforms.extract_entity import OpenAIEntityExtractor" +======= "from sycamore.transforms.embed import SentenceTransformerEmbedder" +>>>>>>> main ] }, { @@ -29,6 +36,9 @@ "outputs": [], "source": [ "context = sycamore.init()\n", +<<<<<<< HEAD + "initial_docset = context.read.aryn(aryn_url=\"https://test-api.aryn.ai/v1/storage\", docset_id=\"\", aryn_api_key=\"\")" +======= "initial_docset = context.read.aryn(aryn_url=\"https://test-api.aryn.ai/v1/storage\", docset_id=\"aryn:ds-v9tfacka0xifljqaj0l1rbh\", aryn_api_key=\"\")" ] }, @@ -58,6 +68,7 @@ "outputs": [], "source": [ "print(first_doc[0].properties['earnings_calls'])" +>>>>>>> main ] }, { @@ -87,7 +98,11 @@ "outputs": [], "source": [ "from sycamore.llms import OpenAI, OpenAIModels\n", +<<<<<<< HEAD + "oai = OpenAI(OpenAIModels.GPT_4O)" +======= "oai = OpenAI(OpenAIModels.GPT_4O, api_key=\"\")" +>>>>>>> main ] }, { @@ -97,6 +112,14 @@ "metadata": {}, "outputs": [], "source": [ +<<<<<<< HEAD + "## Cell to answer question: tell me the number of customers MongoDB had at the end of Q1\n", + "entity_extractor = OpenAIEntityExtractor(entity_name=\"num_customers\", llm=oai, num_of_elements=10,\n", + " field = \"text_representation\", use_elements=True)\n", + " \n", + "\n", + "mdb_docset = removed_orig_docset.filter( lambda doc: doc.properties['earnings_calls']['company_ticker'] == 'MDB' and doc.properties['earnings_calls']['quarter']=='Q1').extract_entity(entity_extractor)" +======= "from sycamore.llms.prompts.default_prompts import LlmFilterMessagesJinjaPrompt\n", "from sycamore.llms.prompts.prompts import JinjaPrompt\n", "from sycamore.transforms.extract_entity import OpenAIEntityExtractor\n", @@ -129,6 +152,7 @@ " '''\n", "#logical_node = LlmFilter(node_id=0, question=\"Filter all the records where the Brian Chesky spoke\", field=\"Brian Chesky\")\n", "#sycamore_operator = SycamoreLlmFilter(context, logical_node, query_id=\"test\", inputs=[exploded_docset])" +>>>>>>> main ] }, { @@ -144,6 +168,127 @@ { "cell_type": "code", "execution_count": null, +<<<<<<< HEAD + "id": "fddbb1c1-4fe4-4aa1-a959-4c9e1e624e7b", + "metadata": {}, + "outputs": [], + "source": [ + "avgo_docset = removed_orig_docset.filter( lambda doc: doc.properties['earnings_calls']['company_ticker'] == 'AVGO').llm_filter(new_field=\"_autogen_LLMFilterOutput\",\n", + " prompt=LlmFilterMessagesJinjaPrompt.set(filter_question=\"Does this mention the VMWare acquisition?\", use_elements=True),\n", + " field = \"text_representation\",\n", + " llm=oai,\n", + " keep_none=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "887bdb16-1a49-4eb8-bb6d-e663aad23d43", + "metadata": {}, + "outputs": [], + "source": [ + "## Cell to answer the question tell me the first earnings call where the VMWare acquisiton was mentioned\n", + "\n", + "from sycamore.transforms import DateTimeStandardizer\n", + "\n", + "def filterVMware(elem: Element) -> bool:\n", + " return \"VMware\" in elem.text_representation\n", + "\n", + "vwmare_docset_sorted = (removed_orig_docset\n", + " .filter(lambda doc: doc.properties['earnings_calls']['company_ticker'] == 'AVGO')\n", + " .filter_elements(filterVMware)\n", + " .map(lambda doc: DateTimeStandardizer.standardize(doc, key_path = [\"properties\",\"earnings_calls\",\"date\"]))\n", + " .sort(descending=False, field=\"properties.earnings_calls.dateTime\"))\n", + "vwmare_docset_sorted.take(1)[0].properties['earnings_calls']['day']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d3de501-27cf-452a-8366-8d0ec60c307d", + "metadata": {}, + "outputs": [], + "source": [ + "## Cell to answer question summarize the impact of the VMWare acquisiton on Broadcom's earnings\n", + "from sycamore.llms.prompts.prompts import JinjaPrompt\n", + "from sycamore.llms.llms import LLMMode\n", + "myprompt = JinjaPrompt(\n", + " system=\"You are a robot\",\n", + " user=\"\"\"Here's an earnings call. Please answer the question {{ question }}\n", + " {% for elt in doc.elements %}\n", + " {{ elt.text_representation }}\n", + " {% endfor %}\"\"\",\n", + " question=\"Summarize the impact of the vmware acquisition on broadcom's earnings\"\n", + ")\n", + "\n", + "vmware_acquistion_summary = (removed_orig_docset.filter(lambda doc: doc.properties['earnings_calls']['company_ticker'] == 'AVGO')\n", + " .filter_elements(filterVMware)\n", + " .llm_map(prompt=myprompt, output_field=\"acquisition_impact_summary\", llm=oai, llm_mode=LLMMode.ASYNC))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb56b73a-60f0-4d2b-9cb8-3aed4c0c72d8", + "metadata": {}, + "outputs": [], + "source": [ + "ls = []\n", + "for doc in vmware_acquistion_summary.take_all():\n", + " ls.append((doc.properties['earnings_calls']['quarter'], doc.properties['acquisition_impact_summary']))\n", + "print(ls)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24fe8623-a63c-45ad-a973-8575fc23d57b", + "metadata": {}, + "outputs": [], + "source": [ + "## Cell to answer the question tell me how Intuit is integrating Intuit Assist (their new AI offering) into existing products\n", + "intuit_prompt = JinjaPrompt(\n", + " system=\"You are a robot\",\n", + " user=\"\"\"Here's an earnings call. Please answer the question {{ question }}\n", + " {% for elt in doc.elements %}\n", + " {{ elt.text_representation }}\n", + " {% endfor %}\"\"\",\n", + " question=\"Summarize how Intuit Assist is being integrated into Intuit's existing products\"\n", + ")\n", + "\n", + "def filterForAssist(elem: Element) -> bool:\n", + " return \"Assist\" in elem.text_representation\n", + "\n", + "intuit_assist_summary = (removed_orig_docset.filter(lambda doc: doc.properties['earnings_calls']['company_ticker'] == 'INTU')\n", + " .filter_elements(filterForAssist)\n", + " .llm_map(prompt=intuit_prompt, output_field=\"intuit_assist_summary\", llm=oai, llm_mode=LLMMode.ASYNC))\n", + "\n", + "ls = []\n", + "for doc in intuit_assist_summary.take_all():\n", + " ls.append((doc.properties['earnings_calls']['quarter'], doc.properties['intuit_assist_summary']))\n", + "print(ls)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7c7d15d-dcfa-4bd6-a210-824aca1af220", + "metadata": {}, + "outputs": [], + "source": [ + "## Cell to answer the question 'Return all the companies that mentioned inflation and return a count of the number of times inflation was mentioned'\n", + "inflation_mentioned = (exploded_docset2.filter(lambda element: 'inflation' in element.text_representation.lower())\n", + " .groupby_count(field='properties.earnings_calls.company_name'))\n", + "inflation_mentioned.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d66caeeb-cbe3-4f92-862f-33923012e1c6", + "metadata": {}, + "outputs": [], +======= "id": "192dc7b1-1598-479f-ab99-709d0c5c81d0", "metadata": {}, "outputs": [], @@ -167,6 +312,7 @@ "id": "fddbb1c1-4fe4-4aa1-a959-4c9e1e624e7b", "metadata": {}, "outputs": [], +>>>>>>> main "source": [] } ], diff --git a/sycamore b/sycamore index 335017c..ea6be3f 160000 --- a/sycamore +++ b/sycamore @@ -1 +1 @@ -Subproject commit 335017c440bcc02d64de8010d3b9891c1bcc0875 +Subproject commit ea6be3f4e86c840fa89ffbbf8263fd67bf736d47 diff --git a/uv.lock b/uv.lock index 867c51d..db14b77 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 1 requires-python = ">=3.9.2, <3.13" resolution-markers = [ "python_full_version >= '3.12' and sys_platform == 'darwin'", @@ -1275,6 +1276,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/79/9d/0fb148dc4d6fa4a7dd1d8378168d9b4cd8d4560a6fbf6f0121c5fc34eb68/importlib_metadata-8.6.1-py3-none-any.whl", hash = "sha256:02a89390c1e15fdfdc0d7c6b25cb3e62650d0494005c97d6f148bf5b9787525e", size = 26971 }, ] +[[package]] +name = "intel-openmp" +version = "2021.4.0" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/45/18/527f247d673ff84c38e0b353b6901539b99e83066cd505be42ad341ab16d/intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9", size = 1860605 }, + { url = 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