From b549cb1994ab0736602301bc028e5d1af04f23ea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jacques=20Verr=C3=A9?= Date: Wed, 4 Sep 2024 09:59:23 +0100 Subject: [PATCH] Added llama-index integration (#155) * Added llama-index integration * Updated following code review --- .../docs/cookbook/llama-index.ipynb | 239 ++++++++++++++++++ .../docs/cookbook/llama-index.md | 116 +++++++++ .../docs/tracing/integrations/llama_index.md | 109 ++++++++ .../docs/tracing/integrations/overview.md | 1 + .../documentation/sidebars.ts | 4 +- .../img/cookbook/llamaIndex_cookbook.png | Bin 0 -> 419528 bytes .../img/tracing/llama_index_integration.png | Bin 0 -> 419528 bytes .../opik/integrations/llama_index/__init__.py | 3 + .../opik/integrations/llama_index/callback.py | 136 ++++++++++ .../llama_index/event_parsing_utils.py | 111 ++++++++ 10 files changed, 717 insertions(+), 2 deletions(-) create mode 100644 apps/opik-documentation/documentation/docs/cookbook/llama-index.ipynb create mode 100644 apps/opik-documentation/documentation/docs/cookbook/llama-index.md create mode 100644 apps/opik-documentation/documentation/docs/tracing/integrations/llama_index.md create mode 100644 apps/opik-documentation/documentation/static/img/cookbook/llamaIndex_cookbook.png create mode 100644 apps/opik-documentation/documentation/static/img/tracing/llama_index_integration.png create mode 100644 sdks/python/src/opik/integrations/llama_index/__init__.py create mode 100644 sdks/python/src/opik/integrations/llama_index/callback.py create mode 100644 sdks/python/src/opik/integrations/llama_index/event_parsing_utils.py diff --git a/apps/opik-documentation/documentation/docs/cookbook/llama-index.ipynb b/apps/opik-documentation/documentation/docs/cookbook/llama-index.ipynb new file mode 100644 index 000000000..5cc470f2f --- /dev/null +++ b/apps/opik-documentation/documentation/docs/cookbook/llama-index.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Opik with LlamaIndex\n", + "\n", + "This notebook showcases how to use Opik with LlamaIndex. [LlamaIndex](https://github.com/run-llama/llama_index) is a flexible data framework for building LLM applications:\n", + "> LlamaIndex is a \"data framework\" to help you build LLM apps. It provides the following tools:\n", + ">\n", + "> - Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.).\n", + "> - Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.\n", + "> - Provides an advanced retrieval/query interface over your data: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output.\n", + "> - Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, anything else).\n", + "\n", + "For this guide we will be downloading the essays from Paul Graham and use them as our data source. We will then start querying these essays with LlamaIndex." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an account on Comet.com\n", + "\n", + "[Comet](https://www.comet.com/site) provides a hosted version of the Opik platform, [simply create an account](https://www.comet.com/signup?from=llm) and grab you API Key.\n", + "\n", + "> You can also run the Opik platform locally, see the [installation guide](https://www.comet.com/docs/opik/self-host/self_hosting_opik) for more information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", + "\n", + "os.environ[\"OPIK_API_KEY\"] = getpass.getpass(\"Opik API Key: \")\n", + "os.environ[\"OPIK_WORKSPACE\"] = input(\"Comet workspace (often the same as your username): \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are running the Opik platform locally, simply set:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import os\n", + "# os.environ[\"OPIK_URL_OVERRIDE\"] = \"http://localhost:5173/api\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing our environment\n", + "\n", + "First, we will install the necessary libraries, download the Chinook database and set up our different API keys." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install opik llama-index llama-index-agent-openai llama-index-llms-openai --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And configure the required environment variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, we will download the Paul Graham essays:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "\n", + "# Create directory if it doesn't exist\n", + "os.makedirs('./data/paul_graham/', exist_ok=True)\n", + "\n", + "# Download the file using requests\n", + "url = 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt'\n", + "response = requests.get(url)\n", + "with open('./data/paul_graham/paul_graham_essay.txt', 'wb') as f:\n", + " f.write(response.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using LlamaIndex\n", + "\n", + "### Configuring the Opik integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use the Opik callback directly by calling:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import Settings\n", + "from llama_index.core.callbacks import CallbackManager\n", + "from opik.integrations.llama_index import LlamaIndexCallbackHandler\n", + "\n", + "opik_callback_handler = LlamaIndexCallbackHandler()\n", + "Settings.callback_manager = CallbackManager([opik_callback_handler])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the callback handler is configured, all traces will automatically be logged to Opik.\n", + "\n", + "### Using LLamaIndex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first step is to load the data into LlamaIndex. We will use the `SimpleDirectoryReader` to load the data from the `data/paul_graham` directory. We will also create the vector store to index all the loaded documents." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", + "\n", + "documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()\n", + "index = VectorStoreIndex.from_documents(documents)\n", + "query_engine = index.as_query_engine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now query the index using the `query_engine` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The author worked on writing short stories and programming, starting with early attempts on an IBM 1401 in 9th grade, using an early version of Fortran. Later, the author transitioned to working with microcomputers, building a TRS-80 and writing simple games and programs. Despite enjoying programming, the author initially planned to study philosophy in college but eventually switched to AI due to a lack of interest in philosophy courses.\n" + ] + } + ], + "source": [ + "response = query_engine.query(\"What did the author do growing up?\")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now go to the Opik app to see the trace:\n", + "\n", + "![LlamaIndex trace in Opik](/img/cookbook/llamaIndex_cookbook.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312_llm_eval", + "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": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/apps/opik-documentation/documentation/docs/cookbook/llama-index.md b/apps/opik-documentation/documentation/docs/cookbook/llama-index.md new file mode 100644 index 000000000..ba040cc83 --- /dev/null +++ b/apps/opik-documentation/documentation/docs/cookbook/llama-index.md @@ -0,0 +1,116 @@ +# Using Opik with LlamaIndex + +This notebook showcases how to use Opik with LlamaIndex. [LlamaIndex](https://github.com/run-llama/llama_index) is a flexible data framework for building LLM applications: +> LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools: +> +> - Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.). +> - Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs. +> - Provides an advanced retrieval/query interface over your data: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output. +> - Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, anything else). + +For this guide we will be downloading the essays from Paul Graham and use them as our data source. We will then start querying these essays with LlamaIndex. + +## Creating an account on Comet.com + +[Comet](https://www.comet.com/site) provides a hosted version of the Opik platform, [simply create an account](https://www.comet.com/signup?from=llm) and grab you API Key. + +> You can also run the Opik platform locally, see the [installation guide](https://www.comet.com/docs/opik/self-host/self_hosting_opik) for more information. + + +```python +import os +import getpass + +os.environ["OPIK_API_KEY"] = getpass.getpass("Opik API Key: ") +os.environ["OPIK_WORKSPACE"] = input("Comet workspace (often the same as your username): ") +``` + +If you are running the Opik platform locally, simply set: + + +```python +# import os +# os.environ["OPIK_URL_OVERRIDE"] = "http://localhost:5173/api" +``` + +## Preparing our environment + +First, we will install the necessary libraries, download the Chinook database and set up our different API keys. + + +```python +%pip install opik llama-index llama-index-agent-openai llama-index-llms-openai --quiet +``` + +And configure the required environment variables: + + +```python +import os +import getpass + +os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") +``` + +In addition, we will download the Paul Graham essays: + + +```python +import os +import requests + +# Create directory if it doesn't exist +os.makedirs('./data/paul_graham/', exist_ok=True) + +# Download the file using requests +url = 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' +response = requests.get(url) +with open('./data/paul_graham/paul_graham_essay.txt', 'wb') as f: + f.write(response.content) +``` + +## Using LlamaIndex + +### Configuring the Opik integration + +You can use the Opik callback directly by calling: + + +```python +from llama_index.core import Settings +from llama_index.core.callbacks import CallbackManager +from opik.integrations.llama_index import LlamaIndexCallbackHandler + +opik_callback_handler = LlamaIndexCallbackHandler() +Settings.callback_manager = CallbackManager([opik_callback_handler]) +``` + +Now that the callback handler is configured, all traces will automatically be logged to Opik. + +### Using LLamaIndex + +The first step is to load the data into LlamaIndex. We will use the `SimpleDirectoryReader` to load the data from the `data/paul_graham` directory. We will also create the vector store to index all the loaded documents. + + +```python +from llama_index.core import VectorStoreIndex, SimpleDirectoryReader + +documents = SimpleDirectoryReader("./data/paul_graham").load_data() +index = VectorStoreIndex.from_documents(documents) +query_engine = index.as_query_engine() +``` + +We can now query the index using the `query_engine` object: + + +```python +response = query_engine.query("What did the author do growing up?") +print(response) +``` + + The author worked on writing short stories and programming, starting with early attempts on an IBM 1401 in 9th grade, using an early version of Fortran. Later, the author transitioned to working with microcomputers, building a TRS-80 and writing simple games and programs. Despite enjoying programming, the author initially planned to study philosophy in college but eventually switched to AI due to a lack of interest in philosophy courses. + + +You can now go to the Opik app to see the trace: + +![LlamaIndex trace in Opik](/img/cookbook/llamaIndex_cookbook.png) diff --git a/apps/opik-documentation/documentation/docs/tracing/integrations/llama_index.md b/apps/opik-documentation/documentation/docs/tracing/integrations/llama_index.md new file mode 100644 index 000000000..2e43fe522 --- /dev/null +++ b/apps/opik-documentation/documentation/docs/tracing/integrations/llama_index.md @@ -0,0 +1,109 @@ +--- +sidebar_position: 3 +sidebar_label: LlamaIndex +--- + +# LlamaIndex + +[LlamaIndex](https://github.com/run-llama/llama_index) is a flexible data framework for building LLM applications: + + LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools: + + - Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.). + - Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs. + - Provides an advanced retrieval/query interface over your data: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output. + - Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, anything else). + +## Getting Started + +To use the Opik integration with LlamaIndex, you'll need to have both the `opik` and `llama_index` packages installed. You can install them using pip: + +```bash +pip install opik llama-index llama-index-agent-openai llama-index-llms-openai +``` + +## Using the Opik integration + +To use the Opik integration with LLamaIndex, you can set the Opik callback handler as the global callback handler: + +```python +from llama_index.core import Settings +from llama_index.core.callbacks import CallbackManager +from opik.integrations.llama_index import LlamaIndexCallbackHandler + +opik_callback_handler = LlamaIndexCallbackHandler() +Settings.callback_manager = CallbackManager([opik_callback_handler]) +``` + + +Now that the integration is set up, all the LlamaIndex runs will be traced and logged to Opik. + +## Example + +To showcase the integration, we will create a new a query engine that will use Paul Graham's essays as the data source. + +**First step:** +Configure the Opik integration: + +```python +from llama_index.core import Settings +from llama_index.core.callbacks import CallbackManager +from opik.integrations.llama_index import LlamaIndexCallbackHandler + +opik_callback_handler = LlamaIndexCallbackHandler() +Settings.callback_manager = CallbackManager([opik_callback_handler]) +``` + + + +**Second step:** +Download the example data: + +```python +import os +import requests + +# Create directory if it doesn't exist +os.makedirs('./data/paul_graham/', exist_ok=True) + +# Download the file using requests +url = 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' +response = requests.get(url) +with open('./data/paul_graham/paul_graham_essay.txt', 'wb') as f: + f.write(response.content) +``` + +**Third step:** +We can now load the data, create an index and query engine: + +```python +from llama_index.core import VectorStoreIndex, SimpleDirectoryReader + +documents = SimpleDirectoryReader("./data/paul_graham").load_data() +index = VectorStoreIndex.from_documents(documents) +query_engine = index.as_query_engine() +``` + +Now that the query engine is set up, we can use it to query the data: + +```python +response = query_engine.query("What did the author do growing up?") +print(response) +``` + +Given that the integration with Opik has been set up, all the traces are logged to the Opik platform: + +![llama_index](/img/tracing/llama_index_integration.png) diff --git a/apps/opik-documentation/documentation/docs/tracing/integrations/overview.md b/apps/opik-documentation/documentation/docs/tracing/integrations/overview.md index d3c2bc48d..2d25a145e 100644 --- a/apps/opik-documentation/documentation/docs/tracing/integrations/overview.md +++ b/apps/opik-documentation/documentation/docs/tracing/integrations/overview.md @@ -12,5 +12,6 @@ Opik aims to make it as easy as possible to log, view and evaluate your LLM trac | ----------- | ----------- | ------------- | ------------ | | OpenAI | Log traces for all OpenAI LLM calls | [Documentation](https://www.comet.com/docs/opik/integrations/openai) | [![Open Quickstart In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/openai.ipynb) | | LangChain | Log traces for all LangChain LLM calls | [Documentation](https://www.comet.com/docs/opik/integrations/langchain) | [![Open Quickstart In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/langchain.ipynb) | +| LlamaIndex | Log traces for all LlamaIndex LLM calls | [Documentation](https://www.comet.com/docs/opik/integrations/llama_index) | [![Open Quickstart In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/opik/blob/master/apps/opik-documentation/documentation/docs/cookbook/llama-index.ipynb) | If you would like to see more integrations, please open an issue on our [GitHub repository](https://github.com/comet-ml/opik). diff --git a/apps/opik-documentation/documentation/sidebars.ts b/apps/opik-documentation/documentation/sidebars.ts index f1dd5a6b4..431c5301d 100644 --- a/apps/opik-documentation/documentation/sidebars.ts +++ b/apps/opik-documentation/documentation/sidebars.ts @@ -27,7 +27,7 @@ const sidebars: SidebarsConfig = { items: ['tracing/log_traces', 'tracing/log_distributed_traces', 'tracing/annotate_traces', { type: 'category', label: 'Integrations', - items: ['tracing/integrations/overview', 'tracing/integrations/langchain', 'tracing/integrations/openai'] + items: ['tracing/integrations/overview', 'tracing/integrations/langchain', 'tracing/integrations/openai', 'tracing/integrations/llama_index'] }], }, { @@ -50,7 +50,7 @@ const sidebars: SidebarsConfig = { type: 'category', label: 'Cookbooks', collapsed: false, - items: ['cookbook/openai', 'cookbook/langchain', 'cookbook/evaluate_hallucination_metric', 'cookbook/evaluate_moderation_metric'], + items: ['cookbook/openai', 'cookbook/langchain', 'cookbook/llama-index', 'cookbook/evaluate_hallucination_metric', 'cookbook/evaluate_moderation_metric'], }, ], }; diff --git a/apps/opik-documentation/documentation/static/img/cookbook/llamaIndex_cookbook.png b/apps/opik-documentation/documentation/static/img/cookbook/llamaIndex_cookbook.png new file mode 100644 index 0000000000000000000000000000000000000000..215d701c78a81da906f693be77e5efe38df37f18 GIT binary patch literal 419528 zcmeFZX*ky5do0pEV1 z{cLP(e1*IWbQ4!WO;e}UJFxFUG%>nl;-^T)1TzkZ!dPgl?UXxXV}`(=Jkx*o?+U!l#f zT`zupUUWf%^JeJa1T$Tu)70PMuEkB|DXsa(T|MI{-+3rmlC{?5D&mO5$yQ&MEpuzr zCgB&`TH;4wGpz@8+1NS%=}VY7rwAue-&qFjwXZh`kGemz?^Gxo$3J{AujJq)z8zLv z3OcJR?r+f)`cKXHZFtrTPQZkN!A~3#+2BtMeMSHA!vLe~3t;EioYK~RDI(5JzsljG z{!bs}_ru4Z1O|ENBIDb52>2WP45jr~rr3nC!GBohRGem~_p3o7h5q^KuyaN@0)zCW zz5>T^5}V!CPaOEG=gDGYryu^V5N&)UOM(AkY1ltrU|?2q^Z-lTKIB}DjAetLUR4$R 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z7m&_(xeb7&VSki)RXLticsZbu)}kj%CHb&QKoU7j=BSAX{d+2}BEsB7(C#KBtkxBb qlT8?TM trace.Trace: + trace = self._opik_client.trace( + name=trace_name, metadata={"created_from": "llama_index"} + ) + return trace + + def start_trace(self, trace_id: Optional[str] = None) -> None: + # When a new LLama Index trace is started, create a new trace in Opik + existing_trace = opik_context.get_current_trace() + if existing_trace: + self._opik_trace = existing_trace + else: + self._opik_trace = self._create_trace(trace_name=trace_id) + + def _get_last_event(self, trace_map: Dict[str, List[str]]) -> str: + def dfs(key: str) -> str: + if key not in trace_map or not trace_map[key]: + return key + return dfs(trace_map[key][-1]) + + start_key = next(iter(trace_map)) + return dfs(start_key) + + def end_trace( + self, + trace_id: Optional[str] = None, + trace_map: Optional[Dict[str, List[str]]] = None, + ) -> None: + if not trace_map: + return + + # When a trace finishes, we first get the last event output + last_event = self._get_last_event(trace_map) + last_event_output = self._map_event_id_to_output.get(last_event, None) + + # And then end the trace with the optional output + if self._opik_trace: + self._opik_trace.end(output=last_event_output) + self._opik_trace = None + + def on_event_start( + self, + event_type: llama_index_schema.CBEventType, + payload: Optional[Dict[str, Any]] = None, + event_id: Optional[str] = None, + parent_id: Optional[str] = None, + **kwargs: Any, + ) -> str: + if not event_id: + event_id = str(uuid.uuid4()) + + # Under some scenarios, it is possible for `start_trace` to not be called (for example if + # the callback raises an exception in a previous call). + # Unclear what the best behavior is here, so for now we'll just create a new trace when + if self._opik_trace is None: + self._opik_trace = self._create_trace(trace_name=parent_id) + + # Get parent span Id if it exists + if parent_id and parent_id in self._map_event_id_to_span: + opik_parent_id = self._map_event_id_to_span[parent_id].id + else: + opik_parent_id = None + + # Compute the span input based on the event payload + span_input = event_parsing_utils.get_span_input_from_events(event_type, payload) + + # Create a new span for this event + span = self._opik_client.span( + trace_id=self._opik_trace.id, + name=event_type.value, + parent_span_id=opik_parent_id, + type="llm" + if event_type == llama_index_schema.CBEventType.LLM + else "general", + input=span_input, + ) + self._map_event_id_to_span[event_id] = span + + # If the parent_id is a BASE_TRACE_EVENT, update the trace with the span input + if parent_id == llama_index_schema.BASE_TRACE_EVENT and span_input: + self._opik_trace.update(input=span_input) + + return event_id + + def on_event_end( + self, + event_type: llama_index_schema.CBEventType, + payload: Optional[Dict[str, Any]] = None, + event_id: Optional[str] = None, + **kwargs: Any, + ) -> None: + # Get the span output from the event and store it so we can use it if needed + # when finishing the trace + span_output = event_parsing_utils.get_span_output_from_event( + event_type, payload + ) + if event_id: + self._map_event_id_to_output[event_id] = span_output + + # Log the output to the span with the matching id + if event_id in self._map_event_id_to_span: + span = self._map_event_id_to_span[event_id] + span.end(output=span_output) diff --git a/sdks/python/src/opik/integrations/llama_index/event_parsing_utils.py b/sdks/python/src/opik/integrations/llama_index/event_parsing_utils.py new file mode 100644 index 000000000..3ad94ce9f --- /dev/null +++ b/sdks/python/src/opik/integrations/llama_index/event_parsing_utils.py @@ -0,0 +1,111 @@ +from typing import Optional, Dict, Any +from llama_index.core.callbacks import schema as llama_index_schema + + +def get_span_input_from_events( + event_type: llama_index_schema.CBEventType, payload: Optional[Dict[str, Any]] +) -> Optional[Dict[str, Any]]: + if payload is None: + return None + + payload_copy = payload.copy() + + if llama_index_schema.EventPayload.SERIALIZED in payload: + # Remove + payload_copy.pop(llama_index_schema.EventPayload.SERIALIZED) + + if ( + event_type == llama_index_schema.CBEventType.EMBEDDING + and llama_index_schema.EventPayload.CHUNKS in payload_copy + ): + chunks = payload_copy.get(llama_index_schema.EventPayload.CHUNKS, []) + return {"number_of_chunks": len(chunks)} + + if ( + event_type == llama_index_schema.CBEventType.NODE_PARSING + and llama_index_schema.EventPayload.DOCUMENTS in payload_copy + ): + documents = payload_copy.pop(llama_index_schema.EventPayload.DOCUMENTS) + payload_copy["documents"] = [doc.metadata for doc in documents] + return payload_copy + + if llama_index_schema.EventPayload.QUERY_STR in payload_copy: + return { + llama_index_schema.EventPayload.QUERY_STR: payload_copy.get( + llama_index_schema.EventPayload.QUERY_STR + ) + } + + if llama_index_schema.EventPayload.MESSAGES in payload_copy: + return {"messages": payload_copy.get(llama_index_schema.EventPayload.MESSAGES)} + + if llama_index_schema.EventPayload.PROMPT in payload_copy: + return {"prompt": payload_copy.get(llama_index_schema.EventPayload.PROMPT)} + + if payload_copy: + return {"input": payload_copy} + else: + return None + + +def get_span_output_from_event( + event_type: llama_index_schema.CBEventType, payload: Optional[Dict[str, Any]] +) -> Optional[Dict[str, Any]]: + if payload is None: + return None + + payload_copy = payload.copy() + + if llama_index_schema.EventPayload.SERIALIZED in payload_copy: + # Always pop Serialized from payload as it may contain LLM api keys + payload_copy.pop(llama_index_schema.EventPayload.SERIALIZED) + + if ( + event_type == llama_index_schema.CBEventType.EMBEDDING + and llama_index_schema.EventPayload.EMBEDDINGS in payload_copy + ): + embeddings: Any = payload_copy.get(llama_index_schema.EventPayload.EMBEDDINGS) + return {"num_embeddings": len(embeddings)} + + if ( + event_type == llama_index_schema.CBEventType.NODE_PARSING + and llama_index_schema.EventPayload.NODES in payload_copy + ): + nodes = payload_copy.pop(llama_index_schema.EventPayload.NODES) + payload_copy["num_nodes"] = len(nodes) + return {"output": payload_copy} + + if ( + event_type == llama_index_schema.CBEventType.CHUNKING + and llama_index_schema.EventPayload.CHUNKS in payload + ): + chunks = payload.pop(llama_index_schema.EventPayload.CHUNKS) + payload_copy["num_chunks"] = len(chunks) + + if llama_index_schema.EventPayload.COMPLETION in payload: + return {"output": payload_copy.get(llama_index_schema.EventPayload.COMPLETION)} + + if llama_index_schema.EventPayload.RESPONSE in payload: + response: Any = payload_copy.get(llama_index_schema.EventPayload.RESPONSE) + + # Skip streaming responses as consuming them would block the user's execution path + if "Streaming" in type(response).__name__: + return None + + if hasattr(response, "response"): + return {"output": response.response} + + if hasattr(response, "message"): + output = dict(response.message) + if "additional_kwargs" in output: + if "tool_calls" in output["additional_kwargs"]: + output["tool_calls"] = output["additional_kwargs"]["tool_calls"] + + del output["additional_kwargs"] + + return {"output": output} + + if payload_copy: + return {"output": payload_copy} + else: + return None