diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 06fc48b..d8f4762 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -29,7 +29,7 @@ jobs: - name: Set up uv uses: astral-sh/setup-uv@v3 with: - version: "0.4.x" + version: "0.5.x" enable-cache: true - name: Install dependencies @@ -58,7 +58,7 @@ jobs: - name: Set up uv uses: astral-sh/setup-uv@v3 with: - version: "0.4.x" + version: "0.5.x" enable-cache: true - name: Install locked versions of dependencies diff --git a/.gitignore b/.gitignore index 167efc0..e7d8f3f 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ __pycache__/ *.py[cod] *$py.class +**.DS_STORE # C extensions *.so @@ -160,9 +161,24 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ - -# Test notebook +src/yellowhammer/yue_test.ipynb +src/yellowhammer/test.ipynb examples/Demo_yue.ipynb +examples/Demo_yue2.ipynb examples/yue_examples + +# Test notebook examples/FA-Mn-H2PO2.raw -src/yellowhammer/yue_test.ipynb +examples/Demo_flagship.ipynb +examples/HL1-5_5-90_30min.xrdml +# examples/IMG_1648.jpg +prompts/deprecated +examples/llm_trace.txt +examples/Demo_anthropic.ipynb +examples/Demo_multimodal.ipynb +examples/Demo_openai.ipynb +examples/Demo_pydantic copy.ipynb +examples/Demo_pydantic.ipynb + +# AI generated docs +MULTIMODAL* diff --git a/README.md b/README.md index d206ced..ef9dc1d 100644 --- a/README.md +++ b/README.md @@ -6,17 +6,28 @@ -A continuation of the [LLM hackathon](https://www.eventbrite.com/e/llm-hackathon-for-applications-in-materials-and-chemistry-tickets-868303598437) project, [*yeLLowhaMMer: A Multi-modal Tool-calling Agent for Accelerated Research Data Management*](https://github.com/bocarsly-group/llm-hackathon-2024). +This project is a continuation of [LLM hackathon](https://www.eventbrite.com/e/llm-hackathon-for-applications-in-materials-and-chemistry-tickets-868303598437) project, [*yeLLowhaMMer: A Multi-modal Tool-calling Agent for Accelerated Research Data Management*](https://github.com/bocarsly-group/llm-hackathon-2024). -This repository will explore using the Jupyter AI plugin to provide an agentic interface to [*datalab*](https://github.com/datalab-org/datalab), with the idea of having this as an additional UI that *datalab* users can use to interact with their data, either deployed for their instance, or run locally. +This repo explores the use of a agentic assistant for [*datalab*](https://github.com/datalab-org/datalab) using Jupyter cell magics as an UI, allowing *datalab* users to interact with the agent during their standard data processing and analysis workflow. -## Initial development tasks +See the [example notebook](examples/Demo_pydantic.ipynb) for a demonstration. -- [ ] Reproduce the hackathon project with the Jupyter AI plugin, probably by extending the `%%ai` cell magic to something like `%%yellowhammer` that includes the yellowhammer system prompt, and guides through the registration of a *datalab* API key and any provided LLM API keys. -- [ ] Use yellowhammer to generate API examples for the underlying [*datalab-python-api*](https://github.com/datalab-org/datalab-python-api) package. +## Current status +- [x] Port project from langchain to pydantic-AI +- [x] Create `%%llm` line/cell magic that passes messages to the datalab agent +- [ ] Agent guides user through the registration of a *datalab* API key and any provided LLM API keys. +- [x] Write agentic tools for interactions with *datalab* API. +- [x] Include code-writing tool that can query the underlying API directly with [*datalab-python-api*](https://github.com/datalab-org/datalab-python-api) package. +- [ ] Update datalab API for direct data retrieval from backend - [ ] Consider deploying this as a JupyterHub that *datalab* instances can link to directly. - [ ] Integrate the results much more closely into *datalab* itself, i.e., attaching notebooks to the relevant samples, and recording the provenance of AI generated data recording. +## Project structure +- [`src/yellowhammer/llm_pydantic.py`](src/yellowhammer/llm_pydantic.py): Defines the agent and tools. +- [`src/yellowhammer/magics.py`](src/yellowhammer/magics.py): Defines Jupyter magic commands. +- [`prompts`](prompts/): System prompts for each agent +- [`examples/Demo_pydantic.ipynb`](examples/Demo_pydantic.ipynb): Example notebook demonstrating usage + ## Installation This repository uses [`uv`](https://docs.astral.sh/uv/) for the entire packaging workflow. diff --git a/examples/Demo_anthropic.ipynb b/examples/Demo_anthropic.ipynb deleted file mode 100644 index b56cf27..0000000 --- a/examples/Demo_anthropic.ipynb +++ /dev/null @@ -1,199 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "2671fc38-c9ca-49ef-948a-abb7815ca2b9", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext yellowhammer" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "32c5ffc5-ea83-458c-8e5a-75bb15da8a2d", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Enter your LLM API key ········\n" - ] - } - ], - "source": [ - "import os\n", - "import getpass\n", - "\n", - "os.environ[\"LLM_PROVIDER\"] = \"ANTHROPIC\"\n", - "os.environ[\"LLM_API_KEY\"] = getpass.getpass(\"Enter your LLM API key\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2f61e78f-b8a3-4f0c-b5e4-503af30019f3", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Enter your Datalab API key ········\n" - ] - } - ], - "source": [ - "os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f44cabeb-58ef-4ccd-86d7-5cb04a3f8643", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Datalab is a data management platform designed to help scientists manage their experimental data, plan experiments, analyze data, and plot results. It provides a structured way to store, organize, and interact with scientific data, particularly in the context of materials science and chemistry experiments. \n", - "\n", - "Some key features of Datalab include:\n", - "\n", - "1. Sample management: You can create, store, and retrieve information about experimental samples, including their chemical composition, synthesis methods, and related metadata.\n", - "\n", - "2. File attachments: Datalab allows you to attach files (such as raw data or images) to sample entries, making it easy to keep all relevant information together.\n", - "\n", - "3. Data blocks: These are used to parse attached files according to scientific schemas and generate plots, facilitating data analysis and visualization.\n", - "\n", - "4. Search functionality: You can search for items (samples, materials, etc.) across the Datalab instance.\n", - "\n", - "5. Relationship tracking: Datalab can track relationships between different items, helping to maintain the context of experiments and materials.\n", - "\n", - "6. API access: Datalab provides a Python API that allows programmatic interaction with the platform, enabling integration with other tools and scripts.\n", - "\n", - "Datalab is particularly useful for maintaining experimental workflows, ensuring data provenance, and facilitating collaboration among scientists. It helps in organizing complex scientific data in a structured manner, making it easier to retrieve, analyze, and share research findings." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "What is datalab?" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d10442e5-7c5c-40cd-becf-9c2e9a79fd45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "To create a sample with the specified details using the Datalab API, we'll use the `create_item` method of the `DatalabClient`. Here's what the code will do:\n", - "\n", - "1. Import the necessary module\n", - "2. Create a DatalabClient instance\n", - "3. Prepare the sample data as a dictionary\n", - "4. Use the create_item method to create the sample\n", - "5. Print the response to confirm the sample creation" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "Create a sample with ID llm-test4, sample name \"virtual sample (Claude)\", formula FrCl" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6ef8a1c2-50c8-4807-b84b-008c0bfb4efa", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/yue/Library/CloudStorage/OneDrive-Personal/code/yellowhammer/.venv/lib/python3.10/site-packages/datalab_api/_base.py:165: UserWarning: Found API URL https://demo-api.datalab-org.io in HTML meta tag. Creating client with this URL instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample creation response:\n", - "{'collections': [], 'creator_ids': [{'$oid': '66abc00dcb992f4b299aa60a'}], 'creators': [{'contact_email': None, 'display_name': 'Yue Wu'}], 'date': '2024-10-22T20:06:55.888755', 'item_id': 'llm-test4', 'name': 'virtual sample (Claude)', 'nblocks': 0, 'refcode': 'demo:YASTWQ', 'type': 'samples'}\n" - ] - } - ], - "source": [ - "from datalab_api import DatalabClient\n", - "\n", - "# Create a DatalabClient instance\n", - "with DatalabClient(\"https://demo.datalab-org.io\") as client:\n", - " # Prepare the sample data\n", - " sample_data = {\n", - " \"item_id\": \"llm-test4\",\n", - " \"name\": \"virtual sample (Claude)\",\n", - " \"chemform\": \"FrCl\",\n", - " \"type\": \"samples\",\n", - " }\n", - "\n", - " # Create the sample\n", - " response = client.create_item(item_id=\"llm-test4\", item_type=\"samples\", item_data=sample_data)\n", - "\n", - " # Print the response\n", - " print(\"Sample creation response:\")\n", - " print(response)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9f3241bc-ba1f-4807-babb-3a928baad6ea", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "yellowhammer", - "language": "python", - "name": "yellowhammer" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Demo_openai.ipynb b/examples/Demo_openai.ipynb deleted file mode 100644 index 59cfd16..0000000 --- a/examples/Demo_openai.ipynb +++ /dev/null @@ -1,133 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "2671fc38-c9ca-49ef-948a-abb7815ca2b9", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext yellowhammer" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "32c5ffc5-ea83-458c-8e5a-75bb15da8a2d", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import getpass\n", - "\n", - "os.environ[\"LLM_PROVIDER\"] = \"OPENAI\"\n", - "os.environ[\"LLM_API_KEY\"] = getpass.getpass(\"Enter your LLM API key\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2f61e78f-b8a3-4f0c-b5e4-503af30019f3", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f44cabeb-58ef-4ccd-86d7-5cb04a3f8643", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Datalab is a data management platform designed for scientists and researchers to manage their experimental data efficiently. It allows users to organize, analyze, and visualize their data through a structured interface. Datalab uses the concept of \"data blocks\" to handle files associated with samples, enabling users to parse data according to scientific schemas and create plots for better data interpretation. The platform also provides an API for programmatic access, allowing users to automate tasks such as data entry, file uploads, and data analysis." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "What is datalab?" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d10442e5-7c5c-40cd-becf-9c2e9a79fd45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "To create a new sample in Datalab with the specified ID, name, and formula, we will use the `create_item` method of the `DatalabClient`. This method requires the item ID, item type, and the data that matches the schema for samples. In this case, we will set the `item_id` to 'llm-test3', the `name` to 'virtual sample', and the `chemform` to 'RbCl'." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "Create a sample with ID llm-test3, sample name \"virtual sample\", formula RbCl" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "33c0e59e-586d-48f3-aa5f-f6fa743dcc96", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/yue/Library/CloudStorage/OneDrive-Personal/code/yellowhammer/.venv/lib/python3.10/site-packages/datalab_api/_base.py:165: UserWarning: Found API URL https://demo-api.datalab-org.io in HTML meta tag. Creating client with this URL instead.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "from datalab_api import DatalabClient\n", - "import os\n", - "\n", - "with DatalabClient(\"https://demo.datalab-org.io\") as client:\n", - " json_data = {\"name\": \"virtual sample\", \"chemform\": \"RbCl\"}\n", - " client.create_item(item_id=\"llm-test3\", item_type=\"samples\", item_data=json_data)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "yellowhammer", - "language": "python", - "name": "yellowhammer" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/IMG_1648.jpg b/examples/IMG_1648.jpg new file mode 100644 index 0000000..7cec060 Binary files /dev/null and b/examples/IMG_1648.jpg differ diff --git a/examples/yellowhammer_demo.ipynb b/examples/yellowhammer_demo.ipynb new file mode 100644 index 0000000..b6307ac --- /dev/null +++ b/examples/yellowhammer_demo.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7d614eb3", + "metadata": {}, + "source": [ + "# _datalab_ yellowhammer agent demo\n", + "\n", + "This demo uses the yellowhammer AI agent query a *datalab* instance populated by data from Josh Bocarsly's group at the University of Houston.\n" + ] + }, + { + "cell_type": "markdown", + "id": "7cb1a24f", + "metadata": {}, + "source": [ + "The agent requires access to a LLM provider. We can first specify the model we want to use, and the API key. This can be done by setting environment variables or through a `.env` file, but here we do it explicitly in the notebook.\n", + "\n", + "In this demo, we use the Google Gemini model which has a generous free tier. You can get a free API key here: https://aistudio.google.com/app/apikey\n", + "\n", + "Any model supported by Pydantic AI can be used. The list of model names is here: https://ai.pydantic.dev/api/models/base/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3dcd2d79", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", + "\n", + "# Specify the AI model\n", + "os.environ[\"AI_MODEL\"] = \"google-gla:gemini-2.5-flash\"\n", + "\n", + "# Use the correct environment variable name for the API key\n", + "# os.environ[\"GEMINI_API_KEY\"] = getpass.getpass(\"Enter your API key:\")" + ] + }, + { + "cell_type": "markdown", + "id": "0f4cc192", + "metadata": {}, + "source": [ + "We now need to connect to a _datalab_ instance using its API key. Here we connect to a locally hosted instance, but normally you will connect to a remote instance such as `https://demo-api.datalab-org.io`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d8dabcbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Logfire project URL: https://logfire-us.pydantic.dev/yue-here/datalab-1\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mLogfire\u001b[0m project URL: \u001b]8;id=750970;https://logfire-us.pydantic.dev/yue-here/datalab-1\u001b\\\u001b[4;36mhttps://logfire-us.pydantic.dev/yue-here/datalab-1\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set the Datalab API key and URL\n", + "# os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")\n", + "# os.environ[\"DATALAB_URL\"] = \"http://api.localhost:5001/\"\n", + "os.environ[\"DATALAB_URL\"] = \"http://localhost:5001/\"\n", + "\n", + "# Load the yellowhammer extension\n", + "%reload_ext yellowhammer" + ] + }, + { + "cell_type": "markdown", + "id": "f7e3c6eb", + "metadata": {}, + "source": [ + "Now we can start using the `%%llm` cell magic to talk to _datalab_!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3a663b5b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15:02:56.043 datalab_agent run\n", + "15:02:56.044 chat gemini-2.5-flash\n", + "15:02:57.397 running 1 tool\n", + "15:02:57.397 running tool: item_query\n", + "15:02:57.406 chat gemini-2.5-flash\n", + "15:02:59.331 inspection_agent run\n", + "15:02:59.332 chat gemini-2.5-flash\n" + ] + }, + { + "data": { + "text/html": [ + "
Moinak Dutta's research primarily focuses on materials related to energy storage, specifically utilizing alpha-NaFeO2.  \n",
+       "The main types of experiments conducted include Powder X-ray Diffraction (XRD) for material characterization and        \n",
+       "electrochemical cycling to evaluate battery cell performance. The materials investigated are alpha-NaFeO2 as well as    \n",
+       "electrodes and battery cells constructed with alpha-NaFeO2. The provided overviews do not contain specific key findings \n",
+       "from these experiments.                                                                                                 \n",
+       "
\n" + ], + "text/plain": [ + "Moinak Dutta's research primarily focuses on materials related to energy storage, specifically utilizing alpha-NaFeO2. \n", + "The main types of experiments conducted include Powder X-ray Diffraction (XRD) for material characterization and \n", + "electrochemical cycling to evaluate battery cell performance. The materials investigated are alpha-NaFeO2 as well as \n", + "electrodes and battery cells constructed with alpha-NaFeO2. The provided overviews do not contain specific key findings \n", + "from these experiments. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
╭───────────────────────────────────────────────── Usage Information ──────────────────────────────────────────────────╮\n",
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+       "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
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I cannot identify the specific item from the image alone. The item_query tool returned 100 items matching 'powder'.     \n",
+       "Could you please provide more details about the powder, such as its name, properties, or any associated experiment IDs, \n",
+       "so I can narrow down the search?                                                                                        \n",
+       "
\n" + ], + "text/plain": [ + "I cannot identify the specific item from the image alone. The \u001b[1;36;40mitem_query\u001b[0m tool returned 100 items matching 'powder'. \n", + "Could you please provide more details about the powder, such as its name, properties, or any associated experiment IDs, \n", + "so I can narrow down the search? \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
╭───────────────────────────────────────────────── Usage Information ──────────────────────────────────────────────────╮\n",
+       " Usage(requests=3, request_tokens=4110, response_tokens=111, total_tokens=4617, details={'thoughts_tokens': 396,      \n",
+       " 'text_prompt_tokens': 3336, 'image_prompt_tokens': 774, 'cached_content_tokens': 1217, 'image_cache_tokens': 222,    \n",
+       " 'text_cache_tokens': 995})                                                                                           \n",
+       "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "
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"14:17:46.565 chat gemini-2.5-flash\n", + "14:17:47.792 running 1 tool\n", + "14:17:47.792 running tool: get_item_details\n", + "14:17:47.793 chat gemini-2.5-flash\n", + "14:17:49.091 running 1 tool\n", + "14:17:49.091 running tool: get_item_details\n", + "14:17:49.092 chat gemini-2.5-flash\n", + "14:17:51.629 running 1 tool\n", + "14:17:51.630 running tool: item_query\n", + "14:17:51.632 chat gemini-2.5-flash\n", + "14:17:53.719 running 1 tool\n", + "14:17:53.720 running tool: get_items\n", + "14:17:54.070 chat gemini-2.5-flash\n", + "14:17:56.374 running 1 tool\n", + "14:17:56.374 running tool: item_query\n", + "14:17:56.379 chat gemini-2.5-flash\n", + "14:17:57.771 running 1 tool\n", + "14:17:57.771 running tool: get_item_details\n", + "14:17:58.821 chat gemini-2.5-flash\n", + "14:18:00.775 inspection_agent run\n", + "14:18:00.777 chat gemini-2.5-flash\n" + ] + }, + { + "data": { + "text/html": [ + "
Moinak Dutta's research centers on the synthesis and electrochemical characterization of alpha-NaFeO2, likely for       \n",
+       "sodium-ion battery applications. The core of the research involves optimizing the solid-state synthesis of alpha-NaFeO2 \n",
+       "from starting materials like Sodium Peroxide (Na2O2) and Iron (III) Oxide (Fe2O3) or Iron (II,III) oxide (Fe3O4). A key \n",
+       "challenge is minimizing the formation of an undesirable beta phase impurity, with various synthesis parameters,         \n",
+       "including heating temperatures (450°C to 700°C), dwell times (2 to 72 hours), and cooling methods (air quench, liquid N2\n",
+       "quench, furnace cool), being systematically explored. The purity of the synthesized alpha-NaFeO2 is routinely assessed  \n",
+       "using Powder X-ray Diffraction (XRD).                                                                                   \n",
+       "\n",
+       "Pure samples of alpha-NaFeO2 (such as MD7-1, noted as                                                                   \n",
+       "
\n" + ], + "text/plain": [ + "Moinak Dutta's research centers on the synthesis and electrochemical characterization of alpha-NaFeO2, likely for \n", + "sodium-ion battery applications. The core of the research involves optimizing the solid-state synthesis of alpha-NaFeO2 \n", + "from starting materials like Sodium Peroxide (Na2O2) and Iron (III) Oxide (Fe2O3) or Iron (II,III) oxide (Fe3O4). A key \n", + "challenge is minimizing the formation of an undesirable beta phase impurity, with various synthesis parameters, \n", + "including heating temperatures (450°C to 700°C), dwell times (2 to 72 hours), and cooling methods (air quench, liquid N2\n", + "quench, furnace cool), being systematically explored. The purity of the synthesized alpha-NaFeO2 is routinely assessed \n", + "using Powder X-ray Diffraction (XRD). \n", + "\n", + "Pure samples of alpha-NaFeO2 (such as MD7-1, noted as \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
╭───────────────────────────────────────────────── Usage Information ──────────────────────────────────────────────────╮\n",
+       " Usage(requests=7, request_tokens=9767, response_tokens=128, total_tokens=10857, details={'thoughts_tokens': 962,     \n",
+       " 'text_prompt_tokens': 9767, 'cached_content_tokens': 5140, 'text_cache_tokens': 5140})                               \n",
+       "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "
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Your code responses will be used in a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. + +Datalab consists of a database of "items" which have types such as "sample". Each item contains "blocks" which contain information about the item. Block types are shared across the datalab and described by a JSON schema. + +Assume `DATALAB_URL` has been set by an environment variable. +Assume `DATALAB_API_KEY` have been set an environment variable. + +Retrieve the datalab instance URL from the environment variable `DATALAB_URL`. +The datalab Python API can query entries on a datalab instance at this URL. +Each method of the DatalabClient class will return a dictionary constructed directly from the JSON response of the Datalab API. + +Python API +This package implements basic functionality for displaying and manipulating entries: + +```python +from datalab_api import DatalabClient + +with DatalabClient(DATALAB_URL) as client: + + # Get the info about this datalab instance + client.get_info() + + # Get the current user's info + client.authenticate() + + # Get information about all available data block types + block_list = client.get_block_info() + + # Search for items containing a specific block + # (Currently not implemented) + items = client.() + + # Search for items using free text + items = client.search_items("search string") + + # List all items of a given type + # Types can be 'samples' or 'starting_materials' + items = client.get_items(item_type="samples") + + # Get more info on a particular item called 'test' + item = client.get_item(item_id="test") + + # Create a new item with some data that matches the corresponding `item_type` schema + json_data = {"chemform": "NaCl"} + client.create_item(item_id="test_new", item_type="samples", item_data=json_data) + + # Attach a file to an item and get the uploaded ID + file_response = client.upload_file(filepath="my_echem_data.mpr", item_id="test") + file_id = file_response["file_id"] + + # Create a data block for a sample, then show the plot + client.create_data_block(item_id="test", file_ids=file_id) + + # Download all files attached to a sample and return their paths + file_paths = client.get_item_files(item_id="test") + + # Get the item graph, useful for finding relationships + graph = client.get_item_graph() + +``` + +To help you work with items, here is an example of a JSON schema for a sample (a type of item). This information is retrieved by `client.get_item`. Use this as a reference if you need to manipulate item information in detail. + +```json +{ + "title": "Sample", + "description": "A model for representing an experimental sample.", + "type": "object", + "properties": { + "blocks_obj": { + "title": "Blocks Obj", + "default": {}, + "type": "object" + }, + "display_order": { + "title": "Display Order", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "collections": { + "title": "Collections", + "default": [], + "type": "array", + "items": { + "$ref": "#/definitions/Collection" + } + }, + "revision": { + "title": "Revision", + "default": 1, + "type": "integer" + }, + "revisions": { + "title": "Revisions", + "type": "object" + }, + "creator_ids": { + "title": "Creator Ids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "creators": { + "title": "Creators", + "type": "array", + "items": { + "$ref": "#/definitions/Person" + } + }, + "type": { + "title": "Type", + "default": "samples", + "const": "samples", + "pattern": "^samples$", + "type": "string" + }, + "immutable_id": { + "title": "Immutable ID", + "type": "string" + }, + "last_modified": { + "title": "Last Modified", + "type": "date", + "format": "date-time" + }, + "relationships": { + "title": "Relationships", + "type": "array", + "items": { + "$ref": "#/definitions/TypedRelationship" + } + }, + "refcode": { + "title": "Refcode", + "minLength": 1, + "maxLength": 40, + "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "item_id": { + "title": "Item Id", + "minLength": 1, + "maxLength": 40, + "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "description": { + "title": "Description", + "type": "string" + }, + "date": { + "title": "Date", + "type": "date", + "format": "date-time" + }, + "name": { + "title": "Name", + "type": "string" + }, + "files": { + "title": "Files", + "type": "array", + "items": { + "$ref": "#/definitions/File" + } + }, + "file_ObjectIds": { + "title": "File Objectids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "chemform": { + "title": "Chemform", + "example": [ + "Na3P", + "LiNiO2@C" + ], + "type": "string" + }, + "synthesis_constituents": { + "title": "Synthesis Constituents", + "default": [], + "type": "array", + "items": { + "$ref": "#/definitions/Constituent" + } + }, + "synthesis_description": { + "title": "Synthesis Description", + "type": "string" + } + }, + "required": [ + "item_id" + ], + "definitions": { + "KnownType": { + "title": "KnownType", + "description": "An enumeration of the types of entry known by this implementation, should be made dynamic in the future.", + "enum": [ + "samples", + "starting_materials", + "blocks", + "files", + "people", + "collections" + ], + "type": "string" + }, + "File": { + "title": "File", + "description": "A model for representing a file that has been tracked or uploaded to datalab.", + "type": "object", + "properties": { + "revision": { + "title": "Revision", + "default": 1, + "type": "integer" + }, + "revisions": { + "title": "Revisions", + "type": "object" + }, + "creator_ids": { + "title": "Creator Ids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "creators": { + "title": "Creators", + "type": "array", + "items": { + "$ref": "#/definitions/Person" + } + }, + "type": { + "title": "Type", + "default": "files", + "const": "files", + "pattern": "^files$", + "type": "string" + }, + "immutable_id": { + "title": "Immutable ID", + "type": "string" + }, + "last_modified": { + "title": "Last Modified", + "type": "date", + "format": "date-time" + }, + "relationships": { + "title": "Relationships", + "type": "array", + "items": { + "$ref": "#/definitions/TypedRelationship" + } + }, + "size": { + "title": "Size", + "description": "The size of the file on disk in bytes.", + "type": "integer" + }, + "last_modified_remote": { + "title": "Last Modified Remote", + "description": "The last date/time at which the remote file was modified.", + "type": "date", + "format": "date-time" + }, + "item_ids": { + "title": "Item Ids", + "description": "A list of item IDs associated with this file.", + "type": "array", + "items": { + "type": "string" + } + }, + "blocks": { + "title": "Blocks", + "description": "A list of block IDs associated with this file.", + "type": "array", + "items": { + "type": "string" + } + }, + "name": { + "title": "Name", + "description": "The filename on disk.", + "type": "string" + }, + "extension": { + "title": "Extension", + "description": "The file extension that the file was uploaded with.", + "type": "string" + }, + "original_name": { + "title": "Original Name", + "description": "The raw filename as uploaded.", + "type": "string" + }, + "location": { + "title": "Location", + "description": "The location of the file on disk.", + "type": "string" + }, + "url_path": { + "title": "Url Path", + "description": "The path to a remote file.", + "type": "string" + }, + "source": { + "title": "Source", + "description": "The source of the file, e.g. 'remote' or 'uploaded'.", + "type": "string" + }, + "time_added": { + "title": "Time Added", + "description": "The timestamp for the original file upload.", + "type": "string", + "format": "date-time" + }, + "metadata": { + "title": "Metadata", + "description": "Any additional metadata.", + "type": "object" + }, + "representation": { + "title": "Representation" + }, + "source_server_name": { + "title": "Source Server Name", + "description": "The server name at which the file is stored.", + "type": "string" + }, + "source_path": { + "title": "Source Path", + "description": "The path to the file on the remote resource.", + "type": "string" + }, + "is_live": { + "title": "Is Live", + "description": "Whether or not the file should be watched for future updates.", + "type": "boolean" + } + }, + "required": [ + "item_ids", + "blocks", + "name", + "extension", + "time_added", + "is_live" + ] + }, + "EntryReference": { + "title": "EntryReference", + "description": "A reference to a database entry by ID and type.\n\nCan include additional arbitarary metadata useful for\ninlining the item data.", + "type": "object", + "properties": { + "type": { + "title": "Type", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "immutable_id": { + "title": "Immutable Id", + "type": "string" + }, + "item_id": { + "title": "Item Id", + "minLength": 1, + "maxLength": 40, + "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "refcode": { + "title": "Refcode", + "minLength": 1, + "maxLength": 40, + "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + } + }, + "required": [ + "type" + ] + }, + "InlineSubstance": { + "title": "InlineSubstance", + "type": "object", + "properties": { + "name": { + "title": "Name", + "type": "string" + }, + "chemform": { + "title": "Chemform", + "type": "string" + } + }, + "required": [ + "name" + ] + }, + "Constituent": { + "title": "Constituent", + "description": "A constituent of a sample.", + "type": "object", + "properties": { + "item": { + "title": "Item", + "anyOf": [ + { + "$ref": "#/definitions/EntryReference" + }, + { + "$ref": "#/definitions/InlineSubstance" + } + ] + }, + "quantity": { + "title": "Quantity", + "minimum": 0, + "type": "number" + }, + "unit": { + "title": "Unit", + "default": "g", + "type": "string" + } + }, + "required": [ + "item", + "quantity" + ] + } + } +} +``` \ No newline at end of file diff --git a/prompts/datalab-api-prompt.md b/prompts/datalab-api-prompt.md old mode 100755 new mode 100644 index 9beb219..12e2be4 --- a/prompts/datalab-api-prompt.md +++ b/prompts/datalab-api-prompt.md @@ -1,447 +1 @@ -Use the datalab Python API package to query entries on the datalab instance at https://demo.datalab-org.io/. -Each method of the DatalabClient class will return a dictionary constructed directly -from the JSON response of the Datalab API. - -Datalab uses "data blocks" to take a file attached to a sample, parse it -according to some scientific schema, and then make a plot. - -The rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed. - -Assume the `DATALAB_API_KEY` has been set an environment variable. - -Python API -This package implements basic functionality for displaying and manipulating entries: - -```python -from datalab_api import DatalabClient - -with DatalabClient("https://demo.datalab-org.io") as client: - - # Get the info about this datalab instance - client.get_info() - - # Get the current user's info - client.authenticate() - - # Search for items with the string - items = client.search_items("search-values") - - # List all items of a given type - # Types can be 'samples' or 'starting_materials' - items = client.get_items(item_type="samples") - - # Get more info on a particular item called 'test' - item = client.get_item(item_id="test") - - # Create a new item with some data that matches the corresponding `item_type` schema - json_data = {"chemform": "NaCl"} - client.create_item(item_id="test_new", item_type="samples", item_data=json_data) - - # Attach a file to an item and get the uploaded ID - file_response = client.upload_file(filepath="my_echem_data.mpr", item_id="test") - file_id = file_response["file_id"] - - # Create a data block for a sample, then show the plot - client.create_data_block(item_id="test", file_ids=file_id) - - # Download all files attached to a sample and return their paths - file_paths = client.get_item_files(item_id="test") - - # Get the item graph, useful for finding relationships - graph = client.get_item_graph() - -``` - -Here is an abridged JSONSchema for a sample, that also has some info about other -types. - -```json -{ - "title": "Sample", - "description": "A model for representing an experimental sample.", - "type": "object", - "properties": { - "blocks_obj": { - "title": "Blocks Obj", - "default": {}, - "type": "object" - }, - "display_order": { - "title": "Display Order", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "collections": { - "title": "Collections", - "default": [], - "type": "array", - "items": { - "$ref": "#/definitions/Collection" - } - }, - "revision": { - "title": "Revision", - "default": 1, - "type": "integer" - }, - "revisions": { - "title": "Revisions", - "type": "object" - }, - "creator_ids": { - "title": "Creator Ids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "creators": { - "title": "Creators", - "type": "array", - "items": { - "$ref": "#/definitions/Person" - } - }, - "type": { - "title": "Type", - "default": "samples", - "const": "samples", - "pattern": "^samples$", - "type": "string" - }, - "immutable_id": { - "title": "Immutable ID", - "type": "string" - }, - "last_modified": { - "title": "Last Modified", - "type": "date", - "format": "date-time" - }, - "relationships": { - "title": "Relationships", - "type": "array", - "items": { - "$ref": "#/definitions/TypedRelationship" - } - }, - "refcode": { - "title": "Refcode", - "minLength": 1, - "maxLength": 40, - "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "item_id": { - "title": "Item Id", - "minLength": 1, - "maxLength": 40, - "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "description": { - "title": "Description", - "type": "string" - }, - "date": { - "title": "Date", - "type": "date", - "format": "date-time" - }, - "name": { - "title": "Name", - "type": "string" - }, - "files": { - "title": "Files", - "type": "array", - "items": { - "$ref": "#/definitions/File" - } - }, - "file_ObjectIds": { - "title": "File Objectids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "chemform": { - "title": "Chemform", - "example": [ - "Na3P", - "LiNiO2@C" - ], - "type": "string" - }, - "synthesis_constituents": { - "title": "Synthesis Constituents", - "default": [], - "type": "array", - "items": { - "$ref": "#/definitions/Constituent" - } - }, - "synthesis_description": { - "title": "Synthesis Description", - "type": "string" - } - }, - "required": [ - "item_id" - ], - "definitions": { - "KnownType": { - "title": "KnownType", - "description": "An enumeration of the types of entry known by this implementation, should be made dynamic in the future.", - "enum": [ - "samples", - "starting_materials", - "blocks", - "files", - "people", - "collections" - ], - "type": "string" - }, - "File": { - "title": "File", - "description": "A model for representing a file that has been tracked or uploaded to datalab.", - "type": "object", - "properties": { - "revision": { - "title": "Revision", - "default": 1, - "type": "integer" - }, - "revisions": { - "title": "Revisions", - "type": "object" - }, - "creator_ids": { - "title": "Creator Ids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "creators": { - "title": "Creators", - "type": "array", - "items": { - "$ref": "#/definitions/Person" - } - }, - "type": { - "title": "Type", - "default": "files", - "const": "files", - "pattern": "^files$", - "type": "string" - }, - "immutable_id": { - "title": "Immutable ID", - "type": "string" - }, - "last_modified": { - "title": "Last Modified", - "type": "date", - "format": "date-time" - }, - "relationships": { - "title": "Relationships", - "type": "array", - "items": { - "$ref": "#/definitions/TypedRelationship" - } - }, - "size": { - "title": "Size", - "description": "The size of the file on disk in bytes.", - "type": "integer" - }, - "last_modified_remote": { - "title": "Last Modified Remote", - "description": "The last date/time at which the remote file was modified.", - "type": "date", - "format": "date-time" - }, - "item_ids": { - "title": "Item Ids", - "description": "A list of item IDs associated with this file.", - "type": "array", - "items": { - "type": "string" - } - }, - "blocks": { - "title": "Blocks", - "description": "A list of block IDs associated with this file.", - "type": "array", - "items": { - "type": "string" - } - }, - "name": { - "title": "Name", - "description": "The filename on disk.", - "type": "string" - }, - "extension": { - "title": "Extension", - "description": "The file extension that the file was uploaded with.", - "type": "string" - }, - "original_name": { - "title": "Original Name", - "description": "The raw filename as uploaded.", - "type": "string" - }, - "location": { - "title": "Location", - "description": "The location of the file on disk.", - "type": "string" - }, - "url_path": { - "title": "Url Path", - "description": "The path to a remote file.", - "type": "string" - }, - "source": { - "title": "Source", - "description": "The source of the file, e.g. 'remote' or 'uploaded'.", - "type": "string" - }, - "time_added": { - "title": "Time Added", - "description": "The timestamp for the original file upload.", - "type": "string", - "format": "date-time" - }, - "metadata": { - "title": "Metadata", - "description": "Any additional metadata.", - "type": "object" - }, - "representation": { - "title": "Representation" - }, - "source_server_name": { - "title": "Source Server Name", - "description": "The server name at which the file is stored.", - "type": "string" - }, - "source_path": { - "title": "Source Path", - "description": "The path to the file on the remote resource.", - "type": "string" - }, - "is_live": { - "title": "Is Live", - "description": "Whether or not the file should be watched for future updates.", - "type": "boolean" - } - }, - "required": [ - "item_ids", - "blocks", - "name", - "extension", - "time_added", - "is_live" - ] - }, - "EntryReference": { - "title": "EntryReference", - "description": "A reference to a database entry by ID and type.\n\nCan include additional arbitarary metadata useful for\ninlining the item data.", - "type": "object", - "properties": { - "type": { - "title": "Type", - "type": "string" - }, - "name": { - "title": "Name", - "type": "string" - }, - "immutable_id": { - "title": "Immutable Id", - "type": "string" - }, - "item_id": { - "title": "Item Id", - "minLength": 1, - "maxLength": 40, - "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "refcode": { - "title": "Refcode", - "minLength": 1, - "maxLength": 40, - "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - } - }, - "required": [ - "type" - ] - }, - "InlineSubstance": { - "title": "InlineSubstance", - "type": "object", - "properties": { - "name": { - "title": "Name", - "type": "string" - }, - "chemform": { - "title": "Chemform", - "type": "string" - } - }, - "required": [ - "name" - ] - }, - "Constituent": { - "title": "Constituent", - "description": "A constituent of a sample.", - "type": "object", - "properties": { - "item": { - "title": "Item", - "anyOf": [ - { - "$ref": "#/definitions/EntryReference" - }, - { - "$ref": "#/definitions/InlineSubstance" - } - ] - }, - "quantity": { - "title": "Quantity", - "minimum": 0, - "type": "number" - }, - "unit": { - "title": "Unit", - "default": "g", - "type": "string" - } - }, - "required": [ - "item", - "quantity" - ] - } - } -} -``` +Datalab manages scientific data by storing 'items', which represent physical samples or other experimental artifacts. Items can be e.g. 'samples' or 'cells' and are described by a JSON schema. Information retrieved by the get_item_details tool is structured in this way. Items can contain one or more 'blocks' which contain additional data about the item, such as experimental measurements or images. \ No newline at end of file diff --git a/prompts/ingest-prompt.md b/prompts/ingest-prompt.md new file mode 100644 index 0000000..c160104 --- /dev/null +++ b/prompts/ingest-prompt.md @@ -0,0 +1,16 @@ +You are an agent that helps a user create entries in the scientific data management platform "datalab". + +You have access to the tool `create_item()`. It accepts input based on the following schema: + +class CreateItemInput(BaseModel): + item_id: str = Field(description="A short identifier for the item") + name: str = Field(description="The name of the item") + chemform: str = Field(description="The chemical form of the item") + date: str = Field(description="Date of item creation, ISO formatted (YYYY-MM-DD)") + file: str = Field(description="File path to the associated file, if specified") + +Try to create an item_id relevant to the entry. +If the user specifies a date, use that. Otherwise, use today's date {}. +The tool will notify you on event of success or failure. +If there is a naming clash, try again with a different name. +For all other issues, don't retry, just report the error back to the user. diff --git a/prompts/system-prompt.md b/prompts/system-prompt.md index 18e81c6..7a23a0d 100644 --- a/prompts/system-prompt.md +++ b/prompts/system-prompt.md @@ -1 +1,19 @@ -You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question: +You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here are notes on how data is structured in datalab: {}. In each step, carefully assess whether you have sufficient information to answer the user's query, and if not, obtain more information using the provded tools. You can also assess images if multimodal input is provided. + +You have access to these tools: + +code_writer: you can use this tool to return code for interacting with the datalab python API. It has access to the API documentation. + +get_items: uses the datalab API to retrieve a list of dicts representing short overviews all the items (samples) in the current datalab and stores it in a local item manifest + +item_query: get_items MUST be run first to retrieve the item manifest! You can then use this to query the item manifest using full text search. Use keywords. Returns a list of dicts representing sample (item) overviews matching the query (the search buffer). + +Once you've performed a query, you can either work with the overviews for big-picture queries, or retrieve full items for deep dives. These tools are available: + +inspect_query: use this tool to hand off to another agent that will inspect the search buffer and deliver the final answer to the user. Provide a clear prompt explaining what the user wants to guide this agent. + +get_item_details: this tool can retrieve more detailed information for every item. ONLY use this if the information provided in the overviews is insufficient! item_query MUST be run first. The inspect_items tool can then be used to inspect the retrieved information. + +inspect_items: get_item_details MUST be run first to generate the items buffer. The items buffer contains detailed information about queried items. Use this tool to hand off to another agent that will inspect the items buffer and deliver the final answer to the user. Provide a clear prompt explaining what the user wants to guide this agent. + +Here is the user question: \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index e4bff8f..4c07f5e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,11 +8,15 @@ dependencies = [ "datalab-api>=0.2.4", "jupyter-ai>=2.24.1", "jupyterlab>=4.2.5", - "langchain>=0.2.16", - "langchain-anthropic>=0.1.23", - "langchain-openai>=0.1.25", "notebook>=7.2.2", "yellowhammer", + "nest-asyncio>=1.6.0", + "whoosh-reloaded>=2.7.5", + "logfire>=3.12.0", + "chromadb>=1.0.4", + "matplotlib>=3.10.1", + "pydantic-ai>=0.3.3", + "python-dotenv>=1.1.0", ] [project.urls] @@ -41,3 +45,6 @@ dev-dependencies = [ "pre-commit>=4.0.0", "pytest>=8.3.3", ] + +[tool.uv.sources] +datalab-api = { git = "https://github.com/datalab-org/datalab-api.git" } diff --git a/src/yellowhammer/fulltext_search.py b/src/yellowhammer/fulltext_search.py new file mode 100644 index 0000000..450a357 --- /dev/null +++ b/src/yellowhammer/fulltext_search.py @@ -0,0 +1,236 @@ +import json +from typing import List, Dict, Any, Optional, Union +from whoosh.index import create_in +from whoosh.fields import Schema, TEXT, ID, KEYWORD +from whoosh.qparser import QueryParser, MultifieldParser, OrGroup +from whoosh.analysis import StemmingAnalyzer +from whoosh.filedb.filestore import RamStorage + +class FullTextSearchTool: + def __init__(self, samples: List[Dict]): + self.samples = samples + self.storage = RamStorage() + self._build_index() + + def _extract_nested_values(self, sample: Dict) -> Dict[str, str]: + """Extract values from nested structures for indexing""" + extracted = {} + + # Extract block titles and types + if 'blocks' in sample and isinstance(sample['blocks'], list): + block_titles = [] + block_types = [] + for block in sample['blocks']: + if 'title' in block: + block_titles.append(block['title']) + if 'blocktype' in block: + block_types.append(block['blocktype']) + + extracted['block_titles'] = ' '.join(block_titles) + extracted['block_types'] = ' '.join(block_types) + + # Extract creator names + if 'creators' in sample and isinstance(sample['creators'], list): + creator_names = [] + for creator in sample['creators']: + if 'display_name' in creator: + creator_names.append(creator['display_name']) + + extracted['creator_names'] = ' '.join(creator_names) + + return extracted + + def _build_index(self): + """Build a full-text search index from the samples with field-specific indexing in memory""" + # Create a more comprehensive schema + analyzer = StemmingAnalyzer() + schema = Schema( + id=ID(stored=True), + content=TEXT(analyzer=analyzer), + name=TEXT(stored=True), + formula=TEXT(stored=True), + item_id=ID(stored=True), + type=KEYWORD(stored=True, commas=True), + date=ID(stored=True), + creator_names=TEXT(stored=True), + block_titles=TEXT(stored=True), + block_types=KEYWORD(stored=True, commas=True) + ) + + # Create in-memory index + ix = self.storage.create_index(schema) + writer = ix.writer() + + # Add documents to index + for i, sample in enumerate(self.samples): + # Convert sample to string for indexing + content = json.dumps(sample) + + # Extract values from nested structures + nested_values = self._extract_nested_values(sample) + + writer.add_document( + id=str(i), + content=content, + name=sample.get('name', ''), + formula=sample.get('characteristic_chemical_formula', ''), + item_id=sample.get('item_id', ''), + type=sample.get('type', ''), + date=sample.get('date', ''), + creator_names=nested_values.get('creator_names', ''), + block_titles=nested_values.get('block_titles', ''), + block_types=nested_values.get('block_types', '') + ) + + writer.commit() + + def search(self, query_text: str, fields=None, limit: int = 10) -> List[Dict]: + """ + Search for samples matching the query text + + Args: + query_text: Text to search for + fields: List of specific fields to search in (None means search all fields) + limit: Maximum number of results to return + + Returns: + List of matching samples + """ + # Get the in-memory index + ix = self.storage.open_index() + + with ix.searcher() as searcher: + if fields and isinstance(fields, list): + # Search in specific fields with OR between fields + parser = MultifieldParser(fields, ix.schema, group=OrGroup) + else: + # Default to searching in content field (which has everything) + parser = QueryParser("content", ix.schema) + + # Make query more flexible by wrapping terms in wildcards if simple term + if len(query_text.split()) == 1 and not any(c in query_text for c in ':"*?'): + query_text = f"*{query_text}*" + + # Parse the query + query = parser.parse(query_text) + + # Search + results = searcher.search(query, limit=limit) + + # Get the original samples + matching_samples = [] + for result in results: + sample_id = int(result["id"]) + matching_samples.append(self.samples[sample_id]) + + return matching_samples + + def search_by_field(self, field_queries: Dict[str, str], limit: int = 10) -> List[Dict]: + """ + Search using field-specific queries + + Args: + field_queries: Dictionary mapping field names to query strings + limit: Maximum number of results to return + + Returns: + List of matching samples + """ + # Get the in-memory index + ix = self.storage.open_index() + + with ix.searcher() as searcher: + query_parts = [] + + for field, query_text in field_queries.items(): + parser = QueryParser(field, ix.schema) + query_parts.append(parser.parse(query_text)) + + # Combine all query parts (implicit AND) + from whoosh.query import And + if query_parts: + combined_query = And(query_parts) + results = searcher.search(combined_query, limit=limit) + + matching_samples = [] + for result in results: + sample_id = int(result["id"]) + matching_samples.append(self.samples[sample_id]) + + return matching_samples + + return [] + + def natural_language_search(self, query_text: str, limit: int = 100) -> List[Dict]: + """ + Search for samples using natural language query. + This method is optimized for use with the yellowhammer agent. + + Args: + query_text: Natural language query string + limit: Maximum number of results to return + + Returns: + List of matching samples + """ + # Parse the query to determine which fields might be relevant + field_queries = enhance_fulltext_query(query_text) + + if "content" in field_queries and len(field_queries) == 1: + # If only searching general content, use normal search + return self.search(field_queries["content"], limit=limit) + else: + # Otherwise use field-specific search + return self.search_by_field(field_queries, limit=limit) + + def update_samples(self, new_samples: List[Dict]): + """ + Update the search index with new samples + + Args: + new_samples: New list of samples to index + """ + self.samples = new_samples + self.storage = RamStorage() # Create a fresh storage + self._build_index() # Rebuild the index + +# Improve the enhance_fulltext_query function to better handle natural language +def enhance_fulltext_query(query: str, data_sample: Dict = None) -> Dict[str, str]: + """ + Parse a natural language query into field-specific queries. + This is a simple implementation that could be replaced with an LLM call. + + Args: + query: Natural language query + data_sample: Sample of the data structure (to help understand schema) + + Returns: + Dictionary of field queries + """ + query = query.lower() + field_queries = {} + + # Simple keyword mapping + if "formula" in query or "chemical" in query: + field_queries["formula"] = query.replace("formula", "").replace("chemical", "").strip() + + if "name" in query: + field_queries["name"] = query.replace("name", "").strip() + + if "creator" in query or "author" in query or "by" in query: + field_queries["creator_names"] = query.replace("creator", "").replace("author", "").replace("by", "").strip() + + if "type" in query: + field_queries["type"] = query.replace("type", "").strip() + + if "date" in query: + field_queries["date"] = query.replace("date", "").strip() + + if "id" in query or "item_id" in query: + field_queries["item_id"] = query.replace("id", "").replace("item_id", "").strip() + + # If no specific fields detected or very short query, search all content + if not field_queries or len(query.split()) <= 3: + field_queries["content"] = query + + return field_queries diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py index effde10..03afc3a 100644 --- a/src/yellowhammer/llm.py +++ b/src/yellowhammer/llm.py @@ -1,87 +1,377 @@ -from langchain_core.prompts import ChatPromptTemplate +import os +import json +import chromadb +from typing import Any, cast +from dataclasses import dataclass +from datetime import date +from datalab_api import DatalabClient +from typing import Annotated, Any, Union +from typing_extensions import TypeAlias from pydantic import BaseModel, Field -from .prompt import SYSTEM_PROMPT -from typing import Union +from pydantic_ai import Agent, ModelRetry, RunContext +from pydantic_ai.models import KnownModelName +from .prompt import API_PROMPT, SYSTEM_PROMPT, CODE_PROMPT, INGEST_PROMPT +from .fulltext_search import FullTextSearchTool +from .utils import process_item_for_chat +from pydantic import BaseModel, Field, ConfigDict +from dotenv import load_dotenv +# Load environment variables from .env file for local development +load_dotenv() -# Pydantic schema to use with .with_structured_output() -class code(BaseModel): - """Schema for code solutions""" +# Context dependencies for agent +@dataclass +class Deps: + client: DatalabClient + datalab_api_key: str | None + datalab_url: str | None + item_manifest: dict[str, Any] | None = None + item_search_id_buffer: dict[str, Any] | None = None + items_buffer: dict[str, Any] | None = None + search_buffer: list[dict] | None = None + search_tool: FullTextSearchTool | None = None - prefix: str = Field(description="Description of the problem and approach") - imports: str = Field(description="Code block import statements") - code: str = Field(description="Code block not including import statements") +# Output schemas +class TextResponse(BaseModel): + text: str = Field(description="A conversational response") +class CodeResponse(BaseModel): + text: str = Field(description="A description of the code") + code: str = Field(description="A code snippet") -class ConversationalResponse(BaseModel): - """Respond in a conversational manner. Be kind and helpful.""" +Response: TypeAlias = Union[TextResponse, CodeResponse] - response: str = Field(description="A conversational response to the user's query") +# Define preferred model +default_model = 'google-gla:gemini-2.5-flash-lite' +model = cast(KnownModelName, os.getenv('AI_MODEL', default_model)) # let user set the model via env var +# API key will be detected from environment variables. For the main providers: +# OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY +# TODO: add getpass to example notebook for user to input API key +# PydanticAI known models: https://ai.pydantic.dev/api/models/base/ -class FinalResponse(BaseModel): - """The final response can be either a code solution or a conversational response""" +# Use Output Functions to hand off to a new agent that outputs directly to the user +# Use this to avoid rephrasing by the main agent. +# Recently implemented in https://github.com/pydantic/pydantic-ai/pull/1785 - final_output: Union[code, ConversationalResponse] +# Direct output function for item inspection +async def inspect_items_output(ctx: RunContext[Deps], prompt: str) -> str: + """ + If search item details have been successfully retrieved with a get_item_details call, + this function uses an LLM agent to inspect these items and returns the result directly. + + Args: + prompt: A system prompt for the inspection agent + """ + if ctx.deps.items_buffer == None: + return "Error: No items buffer found, run get_item_details first." + # Items buffer to plaintext + items_buffer = json.dumps(ctx.deps.items_buffer, separators=(',', ':')) + + # Create an agent to inspect the items + inspection_agent = Agent(model, system_prompt=prompt, result_type=Response, instrument=True) + result = await inspection_agent.run(items_buffer, deps=ctx.deps) -def error_parser(output): + # Return the result directly - no need to ask main agent to rephrase + return result.output if result.output else "No inspection result available." + +# Direct output function for query inspection +async def inspect_query_output(ctx: RunContext[Deps], prompt: str) -> str: """ - Parse the API output to handle errors gracefully. + If a search query has been successfully run with a item_query call, + this function uses an LLM agent to inspect the search buffer and returns the result directly. + + Args: + prompt: A system prompt for the inspection agent + """ + if ctx.deps.search_buffer == None: + return "Error: No search buffer found, run item_query first." + + # Search buffer to plaintext + search_buffer = json.dumps(ctx.deps.search_buffer, separators=(',', ':')) + inspection_agent = Agent(model, system_prompt=prompt, result_type=Response, instrument=True) + result = await inspection_agent.run(search_buffer, deps=ctx.deps) + + # Return the result directly - no need to ask main agent to rephrase + return result.output if result.output else "No inspection result available." + +# Direct output function for code writing +async def code_writer_output(ctx: RunContext[Deps], prompt: str) -> str: + """ + This function uses an LLM agent to write code based on a prompt. + It has information about the datalab API syntax and returns the result directly. + + Args: + prompt: A system prompt for the code writer agent """ - if output["parsing_error"]: - raw_output = str(output["raw"].content) - error = output["parsing_error"] - out_string = f"Error parsing LLM output. Parse error: {error}. \n Raw output: {raw_output}" - return FinalResponse(final_output=ConversationalResponse(response=out_string)) + code_agent = Agent(model, result_type=Response, system_prompt=CODE_PROMPT, instrument=True) + result = await code_agent.run(prompt, deps=ctx.deps) + + # Return the result directly - no need to ask main agent to rephrase + return result.output if result.output else "No code generation result available." + + +# Define main agent +datalab_agent = Agent[Deps, Union[Response, str]]( + model, + system_prompt=SYSTEM_PROMPT.format(API_PROMPT), + output_type=[Response, inspect_items_output, inspect_query_output, code_writer_output], # Output can be Response or output function + deps_type=Deps, + retries=2, + instrument=True, # Enable logging +) + +# Define tools +# TODO add an option to return items of all types in the API call +# @datalab_agent.tool(retries=2) +# async def get_items(ctx: RunContext[Deps], item_type: str) -> str: +# """Use the datalab get_items function to get a JSON of all items in the database. + +# Args: +# ctx: The context. +# item_type: Usually 'samples' + +# Returns: + +# """ +# # TODO check API key when the agent is initialized +# # if ctx.deps.datalab_api_key is None: +# # # if no API key is provided, say so +# # return {'error': 'No datalab API key found.'} + +# if ctx.deps.item_manifest is None: +# with ctx.deps.client(ctx.deps.datalab_url) as client: +# item_type = "samples" +# ctx.deps.item_manifest = client.get_items(item_type=item_type) +# # Initialize the search tool with the fetched items +# ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) +# return "Items retrieved." + +# if ctx.deps.item_manifest is not None and ctx.deps.search_tool is None: +# ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) + +# if ctx.deps.item_manifest is not None: +# return "Items already available." + +# # Set 2 retries in decorator to prevent looping +# raise ModelRetry('No data returned, try again.') - elif not output["parsed"]: - raw_output = str(output["raw"].content) - out_string = f"Error in LLM response. \n Raw output: {raw_output}" - return FinalResponse(final_output=ConversationalResponse(response=out_string)) +# Tool to query item manifest if it exists +@datalab_agent.tool +async def item_query(ctx: RunContext[Deps], query: str) -> str | dict[str, Any] | list[dict[str, Any]]: + """Use full-text search to find items matching your query. + This searches through all fields in the item manifest including nested data. + + Args: + query: Keyword query string (e.g., "John Doe Fe3O4") + Returns: + List of dictionaries each representing one matching item + """ + if ctx.deps.item_manifest is None: + return {'error': 'Use get_items to generate items list first'} + + # Initialize search tool if not already done + if ctx.deps.search_tool is None: + ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) + + # Perform the search using the FullTextSearchTool + matching_items = ctx.deps.search_tool.natural_language_search(query) + + if matching_items: + # store the query in the search buffer + ctx.deps.search_buffer = matching_items + + # Store the item IDs in the buffer for get_item_details + ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] + + return f'Retrieved {len(matching_items)} items matching {query}' else: - # Return the parsed output (should be FinalResponse) - return output["parsed"] - - -def get_chain( - api_provider, - api_key, - api_model=None, - api_temperature=0, -): - # API provider logic - if api_provider.lower() == "openai": - from langchain_openai import ChatOpenAI - - if api_model is None: - api_model = "gpt-4o-mini" - llm = ChatOpenAI(model=api_model, temperature=api_temperature, openai_api_key=api_key) - - elif api_provider.lower() == "anthropic": - from langchain_anthropic import ChatAnthropic - - if api_model is None: - api_model = "claude-3-5-sonnet-20240620" - llm = ChatAnthropic( - model=api_model, - temperature=api_temperature, - anthropic_api_key=api_key, - ) - - # Prompt - prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - SYSTEM_PROMPT, - ), # datalab API info is passed via {context} to the system prompt - ("placeholder", "{messages}"), - ] - ) - - # Create a chain where the final output takes the FinalResponse schema - chain = prompt | llm.with_structured_output(FinalResponse, include_raw=True) | error_parser - # Returns a runnable chain which accepts datalab API documentation "context" and user question "messages" - return chain + return {'error': f'No items found matching query: "{query}"'} + +# Tool to make get_item API calls on item search id buffer +@datalab_agent.tool +async def get_item_details(ctx: RunContext[Deps], simplify_JSON: bool = True) -> str | dict[str, Any]: + """Use the datalab get_item function to get details JSONs of specific items in the manifest. + This function reads a list of item_ids and retrieves the corresponding items using the datalab API. + item_query must be run first to generate the item search id buffer. + """ + if ctx.deps.item_search_id_buffer == None: + return {'error': 'No search id buffer found, run item_query first.'} + + if ctx.deps.items_buffer == None: + ctx.deps.items_buffer = {} + + with ctx.deps.client(ctx.deps.datalab_url) as client: + for item_id in ctx.deps.item_search_id_buffer: + item = client.get_item(item_id=item_id) + + if simplify_JSON: + item = process_item_for_chat(item) + ctx.deps.items_buffer[item_id] = item + return "Items retrieved." + + return {'error': 'No items found.'} + +# For further development +# @datalab_agent.tool +# async def vector_search(ctx: RunContext[Deps], query: str, n_results: int = 5) -> str: +# """Use chromadb to perform a vector search on the item manifest. + +# Args: +# query: A string to search for in the item manifest +# n_results: Number of top results to return (default: 5) + +# Returns: +# String confirmation with number of results found +# """ +# if ctx.deps.item_manifest is None: +# return {'error': 'Use get_items to generate items list first'} + +# # Initialize the Chroma client (in-memory) +# client = chromadb.Client() + +# # Create or get the collection +# try: +# collection = client.get_collection("items") +# except: +# collection = client.create_collection("items") + +# # Extract item IDs and text content from the manifest +# item_ids = [] +# documents = [] + +# for item in ctx.deps.item_manifest: +# item_id = str(item.get('item_id', '')) +# if not item_id: +# continue + +# # Convert the item to a string representation for the document +# item_text = json.dumps(item, ensure_ascii=False) + +# item_ids.append(item_id) +# documents.append(item_text) + +# # Add items to the collection if there are any valid items +# if item_ids and documents: +# collection.add( +# ids=item_ids, +# documents=documents +# ) + +# # Perform a vector search +# results = collection.query( +# query_texts=[query], +# n_results=min(n_results, len(item_ids)) +# ) + +# if not results or not results['ids'][0]: +# return {'error': f'No items found matching query: "{query}"'} + +# # Get the matching item IDs +# matching_ids = results['ids'][0] + +# # Store results in buffers for further processing +# matching_items = [item for item in ctx.deps.item_manifest +# if str(item.get('item_id', '')) in matching_ids] + +# # Store the results in the search buffer +# ctx.deps.search_buffer = matching_items + +# # Store the item IDs in the buffer for get_item_details +# ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] + +# return f'Retrieved {len(matching_items)} items matching vector search for "{query}"' + +# # Agent to ingest items +# ingestion_agent = Agent( +# model, +# system_prompt=INGEST_PROMPT.format(date.today()), +# result_type=Response, +# deps_type=Deps, +# retries=2, +# instrument=True, +# ) + +# class CreateItemInput(BaseModel): +# item_id: str = Field(description="A short identifier for the item") +# # item_type: str = Field(description="The type of the item") # default to 'samples' +# name: str = Field(description="The name of the item") +# chemform: str = Field(description="The chemical form of the item") +# date: str = Field(description="Date of item creation, ISO formatted (YYYY-MM-DD)") +# file: str = Field(description="File path to the associated file, if specified") + + +# @ingestion_agent.tool +# async def create_item(ctx: RunContext[Deps], inputs: CreateItemInput) -> str: +# """ +# Use the datalab client to create a new datalab item. +# """ +# if ctx.deps.datalab_api_key is None: +# return {'error': 'No datalab API key found.'} + +# if ctx.deps.datalab_url is None: +# return {'error': 'No datalab URL found.'} + +# with ctx.deps.client(ctx.deps.datalab_url) as client: +# sample_dict = { +# "name": inputs.name, +# "chemform": inputs.chemform, +# "date": inputs.date, +# } +# try: +# created = client.create_item( +# item_id=inputs.item_id, +# item_type="samples", +# item_data=sample_dict +# ) + +# created_item_id = created["item_id"] + +# if inputs.file: +# # Check if the file exists +# if not os.path.exists(inputs.file): +# return {'error': f'Item {inputs.item_id} created but cannot find file {inputs.file}'} + +# try: +# client.upload_file( +# item_id=created_item_id, file_path=inputs.file +# ) +# except Exception as e: +# return {'error': f'Item {inputs.item_id} created but file upload failed: {str(e)}'} + +# return f"Item {inputs.item_id} created successfully with ID {created_item_id}." + +# except Exception as e: +# return {'error': f'Error creating item: {str(e)}'} + + +# @datalab_agent.tool +# async def item_creator(ctx: RunContext[Deps], prompt: str) -> str: +# """ +# This tool is used to create a new item in the datalab. +# It uses an agent to create an item specified by natural language. + +# Args: +# str: A system prompt for the item creation agent +# """ +# # Create an agent to create the item +# result = await ingestion_agent.run(prompt, deps=ctx.deps) + +# return result.output +# return result.output +# @datalab_agent.tool +# async def item_creator(ctx: RunContext[Deps], prompt: str) -> str: +# """ +# This tool is used to create a new item in the datalab. +# It uses an agent to create an item specified by natural language. + +# Args: +# str: A system prompt for the item creation agent +# """ +# # Create an agent to create the item +# result = await ingestion_agent.run(prompt, deps=ctx.deps) + +# return result.output +# return result.output diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index 5342c2a..4934b78 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -1,9 +1,21 @@ """ Magics to support LLM interactions in IPython/Jupyter. -Adapted from fperez/jupytee and jan-janssen/LangSim. +Inspired by fperez/jupytee and jan-janssen/LangSim. """ import os +import re +import base64 +import pprint + +# Configure Logfire if token is available +try: + import logfire + logfire_token = os.getenv("LOGFIRE_TOKEN") + if logfire_token: + logfire.configure(token=logfire_token) +except ImportError: + pass # Logfire is optional from IPython import get_ipython from IPython.core.magic import ( @@ -16,36 +28,40 @@ argument, parse_argstring, ) -from IPython.display import Markdown -from .llm import get_chain, code, ConversationalResponse -from .prompt import API_PROMPT - - -def get_output(messages, temp=0.1): - env = os.environ - agent_executor = get_chain( - api_provider=env.get("LLM_PROVIDER", "OPENAI"), - api_key=env.get("LLM_API_KEY"), - api_model=env.get("LLM_MODEL", None), - api_temperature=env.get("LLM_TEMP", temp), - ) +from IPython.display import Markdown, HTML +from datalab_api import DatalabClient +from .fulltext_search import FullTextSearchTool +from .llm import datalab_agent, Deps +from .prompt import API_PROMPT, SYSTEM_PROMPT +from .utils import process_prompt_with_images +import asyncio +import nest_asyncio +import pprint +import rich +from rich.console import Console +from rich.markdown import Markdown as RichMarkdown +from rich.syntax import Syntax +from rich.panel import Panel +from rich.box import ROUNDED +from io import StringIO - return agent_executor.invoke({"context": API_PROMPT, "messages": messages}) +nest_asyncio.apply() - -# Class to manage state and expose the main magics @magics_class class DatalabMagics(Magics): def __init__(self, shell): super().__init__(shell) - self.messages = [] + + self.messages = None + self.item_manifest = None + self.search_tool = None # A datalab magic that returns a code block @magic_arguments() @argument( "prompt", nargs="*", - help="""Prompt for code generation. When used as a line magic, + help="""LLM prompt. When used as a line magic, it runs to the end of the line. In cell mode, the entire cell is considered the code generation prompt. """, @@ -61,31 +77,63 @@ def __init__(self, shell): @line_cell_magic def llm(self, line, cell=None): """ - Chat with the LLM. Return either conversation or code. + Multimodal LLM interaction with Jupyter magics. + Supports text prompts and local image files. """ - args = parse_argstring(self.llm, line) # self.llm is a bound method + args = parse_argstring(self.llm, line) + # Get the prompt if cell is None: prompt = " ".join(args.prompt) else: prompt = cell - self.messages.append(("human", prompt)) - response = get_output(self.messages).final_output + # Process prompt for images and file paths + messages, warnings = process_prompt_with_images(prompt) + + # Display any warnings to the user + for warning in warnings: + print(f"⚠️ {warning}") - if isinstance(response, ConversationalResponse): - output = response.response - self.messages.append(("ai", output)) - return Markdown(output) + # Retrieve item manifest using get_items() and initialize search tool if needed + if self.item_manifest is None or self.search_tool is None: + with DatalabClient(os.getenv("DATALAB_URL")) as client: + self.item_manifest = client.get_items(item_type="samples") + self.search_tool = FullTextSearchTool(self.item_manifest) - elif isinstance(response, code): - output = response - self.messages.append(("ai", output.prefix)) - cell_fill = output.imports + "\n" + output.code - get_ipython().set_next_input(cell_fill) - return Markdown(output.prefix) + item_manifest = self.item_manifest + search_tool = self.search_tool + + # Run the datalab agent with processed messages (including images) + response = datalab_agent.run_sync( + messages, # May include both text and BinaryContent for images + deps=Deps( + client=DatalabClient, + datalab_api_key=os.getenv("DATALAB_API_KEY"), + datalab_url=os.getenv("DATALAB_URL"), + item_manifest=item_manifest, + search_tool=search_tool, + ), + message_history=self.messages, + model_settings={'temperature': args.temp}, + ) + self.messages = response.all_messages() -# If testing interactively, it's convenient to %run as a script in Jupyter -if __name__ == "__main__": - get_ipython().register_magics(DatalabMagics) + if hasattr(response.data, 'text'): + # Create a string IO to capture Rich console output + console_output = StringIO() + console = Console(file=console_output, width=120, highlight=True) + + # Format main response text with Rich Markdown + console.print(RichMarkdown(response.data.text)) + + # Add usage info in a panel with syntax highlighting + usage_str = pprint.pformat(response.usage()) + usage_syntax = Syntax(usage_str, "python", theme="monokai", word_wrap=True) + console.print(Panel(usage_syntax, title="Usage Information", border_style="blue", box=ROUNDED)) + + # Output code into next cell + if hasattr(response.data, 'code'): + cell_fill = response.data.code + get_ipython().set_next_input(cell_fill) diff --git a/src/yellowhammer/prompt.py b/src/yellowhammer/prompt.py index 1658829..871b001 100644 --- a/src/yellowhammer/prompt.py +++ b/src/yellowhammer/prompt.py @@ -1,11 +1,9 @@ from pathlib import Path - def load_file_content(file_path): with open(file_path, encoding="utf-8") as file: return file.read() - # Load API_PROMPT api_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "datalab-api-prompt.md" API_PROMPT = load_file_content(api_prompt_path) @@ -14,6 +12,16 @@ def load_file_content(file_path): system_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "system-prompt.md" SYSTEM_PROMPT = load_file_content(system_prompt_path) +# Load CODE_PROMPT +code_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "code-prompt.md" +CODE_PROMPT = load_file_content(code_prompt_path) + +# Load INGEST_PROMPT +ingest_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "ingest-prompt.md" +INGEST_PROMPT = load_file_content(ingest_prompt_path) + if __name__ == "__main__": print(SYSTEM_PROMPT) print(API_PROMPT) + print(CODE_PROMPT) + print(INGEST_PROMPT) diff --git a/src/yellowhammer/test.ipynb b/src/yellowhammer/test.ipynb deleted file mode 100644 index 7ff7249..0000000 --- a/src/yellowhammer/test.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "from langchain_community.document_loaders import TextLoader\n", - "\n", - "path = \"datalab-api-prompt.md\"\n", - "loader = TextLoader(path)\n", - "API_PROMPT = loader.load()[0].page_content\n", - "\n", - "path = \"system-prompt.md\"\n", - "loader = TextLoader(path)\n", - "SYSTEM_PROMPT = loader.load()[0].page_content" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Use the datalab Python API package to query entries on the datalab instance at https://demo.datalab-org.io/.\\nEach method of the DatalabClient class will return a dictionary constructed directly\\nfrom the JSON response of the Datalab API.\\n\\nDatalab uses \"data blocks\" to take a file attached to a sample, parse it\\naccording to some scientific schema, and then make a plot.\\n\\nThe rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed.\\n\\nAssume the `DATALAB_API_KEY` has been set an environment variable.\\n\\nPython API\\nThis package implements basic functionality for displaying and manipulating entries:\\n\\n```python\\nfrom datalab_api import DatalabClient\\n\\nwith DatalabClient(\"https://demo.datalab-org.io\") as client:\\n\\n # Get the info about this datalab instance\\n client.get_info()\\n\\n # Get the current user\\'s info\\n client.authenticate()\\n\\n # Search for items with the string\\n items = client.search_items(\"search-values\")\\n\\n # List all items of a given type\\n # Types can be \\'samples\\' or \\'starting_materials\\'\\n items = client.get_items(item_type=\"samples\")\\n\\n # Get more info on a particular item called \\'test\\'\\n item = client.get_item(item_id=\"test\")\\n\\n # Create a new item with some data that matches the corresponding `item_type` schema\\n json_data = {\"chemform\": \"NaCl\"}\\n client.create_item(item_id=\"test_new\", item_type=\"samples\", item_data=json_data)\\n\\n # Attach a file to an item and get the uploaded ID\\n file_response = client.upload_file(filepath=\"my_echem_data.mpr\", item_id=\"test\")\\n file_id = file_response[\"file_id\"]\\n\\n # Create a data block for a sample, then show the plot\\n client.create_data_block(item_id=\"test\", file_ids=file_id)\\n\\n # Download all files attached to a sample and return their paths\\n file_paths = client.get_item_files(item_id=\"test\")\\n\\n # Get the item graph, useful for finding relationships\\n graph = client.get_item_graph()\\n\\n```\\n\\nHere is an abridged JSONSchema for a sample, that also has some info about other\\ntypes.\\n\\n```json \\n{\\n \"title\": \"Sample\",\\n \"description\": \"A model for representing an experimental sample.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"blocks_obj\": {\\n \"title\": \"Blocks Obj\",\\n \"default\": {},\\n \"type\": \"object\"\\n },\\n \"display_order\": {\\n \"title\": \"Display Order\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"collections\": {\\n \"title\": \"Collections\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Collection\"\\n }\\n },\\n \"revision\": {\\n \"title\": \"Revision\",\\n \"default\": 1,\\n \"type\": \"integer\"\\n },\\n \"revisions\": {\\n \"title\": \"Revisions\",\\n \"type\": \"object\"\\n },\\n \"creator_ids\": {\\n \"title\": \"Creator Ids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"creators\": {\\n \"title\": \"Creators\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Person\"\\n }\\n },\\n \"type\": {\\n \"title\": \"Type\",\\n \"default\": \"samples\",\\n \"const\": \"samples\",\\n \"pattern\": \"^samples$\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable ID\",\\n \"type\": \"string\"\\n },\\n \"last_modified\": {\\n \"title\": \"Last Modified\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"relationships\": {\\n \"title\": \"Relationships\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/TypedRelationship\"\\n }\\n },\\n \"refcode\": {\\n \"title\": \"Refcode\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"item_id\": {\\n \"title\": \"Item Id\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"description\": {\\n \"title\": \"Description\",\\n \"type\": \"string\"\\n },\\n \"date\": {\\n \"title\": \"Date\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"files\": {\\n \"title\": \"Files\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/File\"\\n }\\n },\\n \"file_ObjectIds\": {\\n \"title\": \"File Objectids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"chemform\": {\\n \"title\": \"Chemform\",\\n \"example\": [\\n \"Na3P\",\\n \"LiNiO2@C\"\\n ],\\n \"type\": \"string\"\\n },\\n \"synthesis_constituents\": {\\n \"title\": \"Synthesis Constituents\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Constituent\"\\n }\\n },\\n \"synthesis_description\": {\\n \"title\": \"Synthesis Description\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"item_id\"\\n ],\\n \"definitions\": {\\n \"KnownType\": {\\n \"title\": \"KnownType\",\\n \"description\": \"An enumeration of the types of entry known by this implementation, should be made dynamic in the future.\",\\n \"enum\": [\\n \"samples\",\\n \"starting_materials\",\\n \"blocks\",\\n \"files\",\\n \"people\",\\n \"collections\"\\n ],\\n \"type\": \"string\"\\n },\\n \"File\": {\\n \"title\": \"File\",\\n \"description\": \"A model for representing a file that has been tracked or uploaded to datalab.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"revision\": {\\n \"title\": \"Revision\",\\n \"default\": 1,\\n \"type\": \"integer\"\\n },\\n \"revisions\": {\\n \"title\": \"Revisions\",\\n \"type\": \"object\"\\n },\\n \"creator_ids\": {\\n \"title\": \"Creator Ids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"creators\": {\\n \"title\": \"Creators\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Person\"\\n }\\n },\\n \"type\": {\\n \"title\": \"Type\",\\n \"default\": \"files\",\\n \"const\": \"files\",\\n \"pattern\": \"^files$\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable ID\",\\n \"type\": \"string\"\\n },\\n \"last_modified\": {\\n \"title\": \"Last Modified\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"relationships\": {\\n \"title\": \"Relationships\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/TypedRelationship\"\\n }\\n },\\n \"size\": {\\n \"title\": \"Size\",\\n \"description\": \"The size of the file on disk in bytes.\",\\n \"type\": \"integer\"\\n },\\n \"last_modified_remote\": {\\n \"title\": \"Last Modified Remote\",\\n \"description\": \"The last date/time at which the remote file was modified.\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"item_ids\": {\\n \"title\": \"Item Ids\",\\n \"description\": \"A list of item IDs associated with this file.\",\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"blocks\": {\\n \"title\": \"Blocks\",\\n \"description\": \"A list of block IDs associated with this file.\",\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"description\": \"The filename on disk.\",\\n \"type\": \"string\"\\n },\\n \"extension\": {\\n \"title\": \"Extension\",\\n \"description\": \"The file extension that the file was uploaded with.\",\\n \"type\": \"string\"\\n },\\n \"original_name\": {\\n \"title\": \"Original Name\",\\n \"description\": \"The raw filename as uploaded.\",\\n \"type\": \"string\"\\n },\\n \"location\": {\\n \"title\": \"Location\",\\n \"description\": \"The location of the file on disk.\",\\n \"type\": \"string\"\\n },\\n \"url_path\": {\\n \"title\": \"Url Path\",\\n \"description\": \"The path to a remote file.\",\\n \"type\": \"string\"\\n },\\n \"source\": {\\n \"title\": \"Source\",\\n \"description\": \"The source of the file, e.g. \\'remote\\' or \\'uploaded\\'.\",\\n \"type\": \"string\"\\n },\\n \"time_added\": {\\n \"title\": \"Time Added\",\\n \"description\": \"The timestamp for the original file upload.\",\\n \"type\": \"string\",\\n \"format\": \"date-time\"\\n },\\n \"metadata\": {\\n \"title\": \"Metadata\",\\n \"description\": \"Any additional metadata.\",\\n \"type\": \"object\"\\n },\\n \"representation\": {\\n \"title\": \"Representation\"\\n },\\n \"source_server_name\": {\\n \"title\": \"Source Server Name\",\\n \"description\": \"The server name at which the file is stored.\",\\n \"type\": \"string\"\\n },\\n \"source_path\": {\\n \"title\": \"Source Path\",\\n \"description\": \"The path to the file on the remote resource.\",\\n \"type\": \"string\"\\n },\\n \"is_live\": {\\n \"title\": \"Is Live\",\\n \"description\": \"Whether or not the file should be watched for future updates.\",\\n \"type\": \"boolean\"\\n }\\n },\\n \"required\": [\\n \"item_ids\",\\n \"blocks\",\\n \"name\",\\n \"extension\",\\n \"time_added\",\\n \"is_live\"\\n ]\\n },\\n \"EntryReference\": {\\n \"title\": \"EntryReference\",\\n \"description\": \"A reference to a database entry by ID and type.\\\\n\\\\nCan include additional arbitarary metadata useful for\\\\ninlining the item data.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"type\": {\\n \"title\": \"Type\",\\n \"type\": \"string\"\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable Id\",\\n \"type\": \"string\"\\n },\\n \"item_id\": {\\n \"title\": \"Item Id\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"refcode\": {\\n \"title\": \"Refcode\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"type\"\\n ]\\n },\\n \"InlineSubstance\": {\\n \"title\": \"InlineSubstance\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"chemform\": {\\n \"title\": \"Chemform\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"name\"\\n ]\\n },\\n \"Constituent\": {\\n \"title\": \"Constituent\",\\n \"description\": \"A constituent of a sample.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"item\": {\\n \"title\": \"Item\",\\n \"anyOf\": [\\n {\\n \"$ref\": \"#/definitions/EntryReference\"\\n },\\n {\\n \"$ref\": \"#/definitions/InlineSubstance\"\\n }\\n ]\\n },\\n \"quantity\": {\\n \"title\": \"Quantity\",\\n \"minimum\": 0,\\n \"type\": \"number\"\\n },\\n \"unit\": {\\n \"title\": \"Unit\",\\n \"default\": \"g\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"item\",\\n \"quantity\"\\n ]\\n }\\n }\\n}\\n```\\n'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "API_PROMPT" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def load_file_content(file_path):\n", - " with open(file_path, \"r\", encoding=\"utf-8\") as file:\n", - " return file.read()\n", - "\n", - "\n", - "# Load API_PROMPT\n", - "api_prompt_path = \"datalab-api-prompt.md\"\n", - "API_PROMPT_ = load_file_content(api_prompt_path)\n", - "\n", - "# Load SYSTEM_PROMPT\n", - "system_prompt_path = \"system-prompt.md\"\n", - "SYSTEM_PROMPT_ = load_file_content(system_prompt_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11679" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(API_PROMPT_)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11679" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(API_PROMPT)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "698" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(SYSTEM_PROMPT_)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "698" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(SYSTEM_PROMPT)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question:'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SYSTEM_PROMPT" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name '__file__' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m file\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Load API_PROMPT\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m api_prompt_path \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;18;43m__file__\u001b[39;49m)\u001b[38;5;241m.\u001b[39mparent \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompts\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatalab-api-prompt.md\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 9\u001b[0m API_PROMPT \u001b[38;5;241m=\u001b[39m load_file_content(api_prompt_path)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Load SYSTEM_PROMPT\u001b[39;00m\n", - "\u001b[0;31mNameError\u001b[0m: name '__file__' is not defined" - ] - } - ], - "source": [ - "def load_file_content(file_path):\n", - " with open(file_path, \"r\", encoding=\"utf-8\") as file:\n", - " return file.read()\n", - "\n", - "\n", - "# Load API_PROMPT\n", - "api_prompt_path = Path(__file__).parent / \"prompts\" / \"datalab-api-prompt.md\"\n", - "API_PROMPT = load_file_content(api_prompt_path)\n", - "\n", - "# Load SYSTEM_PROMPT\n", - "system_prompt_path = Path(__file__).parent / \"prompts\" / \"system-prompt.md\"\n", - "SYSTEM_PROMPT = load_file_content(system_prompt_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/yellowhammer/utils.py b/src/yellowhammer/utils.py new file mode 100644 index 0000000..33fbb22 --- /dev/null +++ b/src/yellowhammer/utils.py @@ -0,0 +1,228 @@ +import os +import re +from pathlib import Path +from typing import Tuple, List + +def parse_file_paths(prompt: str) -> Tuple[str, List[str]]: + """ + Parse a prompt string to extract file paths and return cleaned prompt with paths list. + + Matches paths that: + - Have common file extensions (jpg, jpeg, png, gif, bmp, pdf, txt, etc.) + - Can be absolute or relative paths + - Can contain common path characters (letters, numbers, underscores, dots, slashes, dashes) + + Args: + prompt: The input prompt string that may contain file paths + + Returns: + Tuple of (cleaned_prompt, list_of_paths) + """ + # Pattern matches file paths with extensions + # Supports: alphanumeric, underscores, dots, forward/back slashes, dashes, colons (for absolute paths) + # This pattern is more permissive to capture complex paths like /var/folders/... + file_path_pattern = r'(?:^|(?<=\s))([A-Za-z0-9_.:~/\\\-]+\.(?:jpg|jpeg|png|gif|bmp|tiff|pdf|txt|doc|docx|xls|xlsx|csv|json|xml|raw|xrdml|xy))(?:(?=\s)|(?=[?!,;.])|$)' + + # Find all file paths + matches = re.finditer(file_path_pattern, prompt, re.IGNORECASE) + paths = [match.group(1) for match in matches] + + # Remove file paths from the prompt + cleaned_prompt = re.sub(file_path_pattern, '', prompt, flags=re.IGNORECASE) + + # Clean up extra whitespace + cleaned_prompt = re.sub(r'\s+', ' ', cleaned_prompt).strip() + + return cleaned_prompt, paths + + +def verify_and_load_image(file_path: str) -> dict | None: + """ + Verify a file path exists and if it's an image, load it as BinaryContent. + + Args: + file_path: Path to the file (relative or absolute) + + Returns: + Dictionary with 'type' and 'content' keys if successful image, None otherwise + - For images: {'type': 'image', 'content': BinaryContent object, 'path': str} + - For non-images: {'type': 'file', 'path': str} (for future extension) + - For non-existent files: None + """ + from pydantic_ai import BinaryContent + + # Supported image extensions + IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp'} + + # Convert to Path object for easier manipulation + path = Path(file_path) + + # Check if file exists + if not path.exists(): + return None + + # Get file extension + extension = path.suffix.lower() + + # Check if it's an image + if extension in IMAGE_EXTENSIONS: + try: + # Read file content + data = path.read_bytes() + + # Map extension to media type + media_type_map = { + '.jpg': 'image/jpeg', + '.jpeg': 'image/jpeg', + '.png': 'image/png', + '.gif': 'image/gif', + '.bmp': 'image/bmp', + '.tiff': 'image/tiff', + '.webp': 'image/webp' + } + + media_type = media_type_map.get(extension, 'image/jpeg') + + # Create BinaryContent for the image + binary_content = BinaryContent(data=data, media_type=media_type) + + return { + 'type': 'image', + 'content': binary_content, + 'path': str(path.absolute()) + } + except Exception as e: + print(f"Error reading image file {file_path}: {e}") + return None + else: + # For non-image files, return file info for future extension + return { + 'type': 'file', + 'path': str(path.absolute()) + } + + +def process_prompt_with_images(prompt: str) -> Tuple[List, List[str]]: + """ + Process a prompt to extract images and construct a message list for the LLM. + + This function: + 1. Parses the prompt for file paths + 2. Verifies file existence + 3. Loads images as BinaryContent + 4. Constructs a message list suitable for pydantic-ai agents + + Args: + prompt: The user's prompt string, potentially containing file paths + + Returns: + Tuple of (message_list, warnings) + - message_list: List suitable for passing to agent.run() or agent.run_sync() + - warnings: List of warning messages about missing or non-image files + """ + # Parse file paths from prompt + cleaned_prompt, file_paths = parse_file_paths(prompt) + + # Initialize message list with the cleaned text prompt + messages = [cleaned_prompt] if cleaned_prompt else [] + warnings = [] + + # Process each detected file path + for file_path in file_paths: + result = verify_and_load_image(file_path) + + if result is None: + warnings.append(f"File not found: {file_path}") + elif result['type'] == 'image': + # Add image to messages + messages.append(result['content']) + elif result['type'] == 'file': + # Non-image file - skip for now but could be extended later + warnings.append(f"File '{file_path}' is not an image format (skipped for now)") + + return messages, warnings + + +def process_item_for_chat(item: dict) -> dict: + """Process an item to make it more suitable for LLM consumption by removing large fields + and simplifying the structure. + """ + import copy + import json + + # Work on a copy to avoid modifying the original + item_info = copy.deepcopy(item) + + # Extract filenames for easier reference + item_filenames = {} + if "files" in item_info: + for file in item_info.get("files", []): + if "immutable_id" in file and "name" in file: + item_filenames[str(file["immutable_id"])] = file["name"] + + # Keys to remove from the top level + top_level_keys_to_remove = [ + "display_order", "creator_ids", "refcode", "last_modified", + "revision", "revisions", "immutable_id", "file_ObjectIds" + ] + + for k in top_level_keys_to_remove: + item_info.pop(k, None) + + # Process blocks if they exist + big_data_keys = ["bokeh_plot_data", "b64_encoded_image"] + + if "blocks_obj" in item_info: + blocks = [] + for block_id, block in item_info.get("blocks_obj", {}).items(): + # Skip chat blocks + if block.get("blocktype") == "chat": + continue + + # Remove large or unnecessary fields + block_fields_to_remove = ["item_id", "block_id", "collection_id"] + big_data_keys + for field in block_fields_to_remove: + block.pop(field, None) + + # Replace file_id with actual filename + file_id = block.pop("file_id", None) + if file_id and file_id in item_filenames: + block["file"] = item_filenames[file_id] + + blocks.append(block) + + # Replace blocks_obj with a simpler list + item_info.pop("blocks_obj", None) + item_info["blocks"] = blocks + + # Simplify relationships + if "relationships" in item_info: + for i, relationship in enumerate(item_info["relationships"]): + item_info["relationships"][i] = { + k: v for k, v in relationship.items() + if k in ["item_id", "type", "relation"] + } + + # Simplify files to just names + if "files" in item_info: + item_info["files"] = [file["name"] for file in item_info.get("files", []) if "name" in file] + + # Simplify creators to just names + if "creators" in item_info: + item_info["creators"] = [ + creator.get("display_name") for creator in item_info.get("creators", []) + if "display_name" in creator + ] + + # Process quantity fields in various constituent types + for key in ["synthesis_constituents", "positive_electrode", "negative_electrode", "electrolyte"]: + if key in item_info: + for constituent in item_info[key]: + if "quantity" in constituent and "unit" in constituent: + constituent["quantity"] = f"{constituent.get('quantity', 'unknown')} {constituent.get('unit', '')}" + constituent.pop("unit", None) + + # Remove empty values + item_info = {k: v for k, v in item_info.items() if v} + + return item_info \ No newline at end of file diff --git a/tests/test_multimodal.py b/tests/test_multimodal.py new file mode 100644 index 0000000..be7575d --- /dev/null +++ b/tests/test_multimodal.py @@ -0,0 +1,120 @@ +""" +Tests for multimodal (image) support utility functions. +""" +import os +import tempfile +from pathlib import Path +from yellowhammer.utils import parse_file_paths, verify_and_load_image, process_prompt_with_images + + +def test_parse_file_paths(): + """Test parsing file paths from prompts.""" + # Test with single image + prompt1 = "What do you see in this image: IMG_1648.jpg" + cleaned, paths = parse_file_paths(prompt1) + assert cleaned == "What do you see in this image:" + assert "IMG_1648.jpg" in paths + + # Test with multiple files + prompt2 = "Compare test.png and data.csv files" + cleaned, paths = parse_file_paths(prompt2) + assert "test.png" in paths + assert "data.csv" in paths + + # Test with paths + prompt3 = "Analyze examples/IMG_1648.jpg and other_dir/file.pdf" + cleaned, paths = parse_file_paths(prompt3) + assert "examples/IMG_1648.jpg" in paths + assert "other_dir/file.pdf" in paths + + # Test with no files + prompt4 = "Just a simple question" + cleaned, paths = parse_file_paths(prompt4) + assert cleaned == "Just a simple question" + assert len(paths) == 0 + + +def test_verify_and_load_image(): + """Test image verification and loading.""" + # Test with non-existent file + result = verify_and_load_image("non_existent_file.jpg") + assert result is None + + # Create a temporary image file for testing + with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp_file: + # Write minimal PNG header + tmp_file.write(b'\x89PNG\r\n\x1a\n') + tmp_path = tmp_file.name + + try: + # Test with existing image + result = verify_and_load_image(tmp_path) + assert result is not None + assert result['type'] == 'image' + assert 'content' in result + assert 'path' in result + + # Verify BinaryContent properties + from pydantic_ai import BinaryContent + assert isinstance(result['content'], BinaryContent) + assert result['content'].media_type == 'image/png' + finally: + # Clean up + os.unlink(tmp_path) + + # Create a temporary non-image file + with tempfile.NamedTemporaryFile(suffix='.txt', delete=False) as tmp_file: + tmp_file.write(b'test content') + tmp_path = tmp_file.name + + try: + # Test with non-image file + result = verify_and_load_image(tmp_path) + assert result is not None + assert result['type'] == 'file' + assert 'path' in result + finally: + os.unlink(tmp_path) + + +def test_process_prompt_with_images(): + """Test complete prompt processing with images.""" + # Test with no images + messages, warnings = process_prompt_with_images("Simple question without files") + assert len(messages) == 1 + assert messages[0] == "Simple question without files" + assert len(warnings) == 0 + + # Test with non-existent file + messages, warnings = process_prompt_with_images("Check this file: missing.jpg") + assert len(messages) == 1 + assert "Check this file:" in messages[0] + assert len(warnings) == 1 + assert "File not found" in warnings[0] + + # Create a temporary image for testing + with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmp_file: + tmp_file.write(b'\xff\xd8\xff') # JPEG header + tmp_path = tmp_file.name + + try: + # Test with existing image + messages, warnings = process_prompt_with_images(f"What is in {tmp_path}?") + assert len(messages) == 2 # Text + image + assert "What is in" in messages[0] + + # Verify second message is BinaryContent + from pydantic_ai import BinaryContent + assert isinstance(messages[1], BinaryContent) + assert messages[1].media_type == 'image/jpeg' + + assert len(warnings) == 0 + finally: + os.unlink(tmp_path) + + +if __name__ == "__main__": + test_parse_file_paths() + test_verify_and_load_image() + test_process_prompt_with_images() + print("✅ All tests passed!") diff --git a/uv.lock b/uv.lock index d6a0291..7b79d7d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,24 +1,24 @@ version = 1 +revision = 2 requires-python = ">=3.10" resolution-markers = [ - "python_full_version < '3.12'", - "python_full_version >= '3.12' and python_full_version < '3.12.4'", + "python_full_version < '3.12.4'", 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