From 77cbaf9f21d7a0c4301e2b9d68ab484bf1cb37e1 Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Tue, 19 Nov 2024 11:51:57 -0800 Subject: [PATCH 01/31] draft --- .../evaluation/evaluate_with_attachments.mdx | 103 ++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx new file mode 100644 index 00000000..1f4fbe16 --- /dev/null +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -0,0 +1,103 @@ +import { + CodeTabs, + python, + typescript, + PythonBlock, + TypeScriptBlock, +} from "@site/src/components/InstructionsWithCode"; + +# Evaluate an LLM application with attachments + +Attachments allow you to associate large files with your examples. This allows you to evaluate RAG applications +over large internal documents, benchmark image analysis tools, etc. + +## Create a dataset with attachments + +To create a dataset with attachments, you need to use the `upsert_examples_multipart` method of the LangSmith client: + +```python +from langsmith.client import Client +from langsmith.schemas import ExampleUpsertWithAttachments + +# Pass in your api key directly, or define it in the LANGCHAIN_API_KEY environment variable +langchain_client = Client(api_key="...") + +dataset = langchain_client.create_dataset( + dataset_name="attachment-test-dataset", + description="Test dataset for evals with attachments", +) + +# Define the example +example = ExampleUpsertWithAttachments( + dataset_id=dataset.id, + inputs={"question": "What were the cumulative earnings earned from online orders in the midwest during Q2?"}, + outputs={"answer": "$123456"}, + attachments={ + # Each attachments is just a name with a mime type and the bytes content of the file + "pdf": ("application/pdf", Path("./foo_earnings.pdf").read_bytes()), + # We can pass multiple attachments (of different types!), as long as they have different names + "pptx": ("application/pptx", Path("./foo_earnings.pptx").read_bytes()), + }, +) + +# Upsert the examples +langchain_client.upsert_examples_multipart(upserts=[example]) +``` + +## Define a target function with attachments + +Now that we have a dataset that includes examples with attachments, we can define a target function to run our LLM application with the attachments. +The target function must have two positional arguments, the first must be called `inputs` and the second must be called `attachments`. + +```python +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage + +model = ChatOpenAI(model="gpt-4o-mini") + +def pdf_to_image_bytes(pdf_bytes, image_format='PNG'): + pdf_document = fitz.open(stream=pdf_bytes, filetype="pdf") + images = [] + for page in pdf_document: + pix = page.get_pixmap() + img_bytes = BytesIO() + pix.pil_save(img_bytes, format=image_format) + # Encode the bytes in base64 + base64_bytes = base64.b64encode(img_bytes.getvalue()).decode('utf-8') + images.append(base64_bytes) + pdf_document.close() + return images + + +def target(inputs, attachments): + system_message = SystemMessage( + content="The images are of the pdf that the question is referencing. Use the images to generate your answer." + ) + # The attachment tuple returned contains the S3 url first and then a reader of the bytes + pdf_s3_url, pdf_reader = attachments['pdf'] + images = pdf_to_image_bytes(pdf_reader.read()) + human_message = HumanMessage( + content=[ + {"type": "text", "text": inputs["question"]}, + ] + [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image}"}} for image in images], + ) + messages = [system_message, human_message] + return {"answer": model.invoke(messages).content} +``` + +## Run an evaluation + +We can then run an evaluation as usual, by passing in the target function to the `evaluate` method: + +```python +# We can optionally define an evaluator to use +def evaluator(run, example): + score = int(str(example.outputs["answer"]) in run.outputs["answer"]) + return {"key": "correctness", "score": score} + +evaluate( + target, + data="attachment-test-dataset", + client=langchain_client, +) +``` \ No newline at end of file From 3896271ea5b92a327102fc775bdbad1ffaf68ec4 Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Tue, 19 Nov 2024 15:28:17 -0800 Subject: [PATCH 02/31] bagatur comments --- .../evaluation/evaluate_with_attachments.mdx | 38 ++++++++++--------- 1 file changed, 21 insertions(+), 17 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 1f4fbe16..cf7448fd 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -1,28 +1,27 @@ import { - CodeTabs, python, - typescript, - PythonBlock, - TypeScriptBlock, } from "@site/src/components/InstructionsWithCode"; -# Evaluate an LLM application with attachments +# Evaluate applications with large file inputs Attachments allow you to associate large files with your examples. This allows you to evaluate RAG applications over large internal documents, benchmark image analysis tools, etc. ## Create a dataset with attachments +Attachments allow you to host large files outside of your inputs/outputs, which means faster loading times as well +as nicer rendering of files in the LangSmith UI. You can think of it like attaching a file to an email instead +of copy pasting the entire contents into your email body. To create a dataset with attachments, you need to use the `upsert_examples_multipart` method of the LangSmith client: ```python -from langsmith.client import Client +from langsmith import Client from langsmith.schemas import ExampleUpsertWithAttachments -# Pass in your api key directly, or define it in the LANGCHAIN_API_KEY environment variable -langchain_client = Client(api_key="...") +# Define the LANGCHAIN_API_KEY environment variable with your api key +langsmith_client = Client() -dataset = langchain_client.create_dataset( +dataset = langsmith_client.create_dataset( dataset_name="attachment-test-dataset", description="Test dataset for evals with attachments", ) @@ -33,15 +32,17 @@ example = ExampleUpsertWithAttachments( inputs={"question": "What were the cumulative earnings earned from online orders in the midwest during Q2?"}, outputs={"answer": "$123456"}, attachments={ - # Each attachments is just a name with a mime type and the bytes content of the file + # Each attachments is just a name with a mime type and the path or bytes content of the file "pdf": ("application/pdf", Path("./foo_earnings.pdf").read_bytes()), # We can pass multiple attachments (of different types!), as long as they have different names "pptx": ("application/pptx", Path("./foo_earnings.pptx").read_bytes()), + # Can also just pass in the path + "txt": ("text/plain", Path("./foo_earnings.txt")), }, ) # Upsert the examples -langchain_client.upsert_examples_multipart(upserts=[example]) +langsmith_client.upsert_examples_multipart(upserts=[example]) ``` ## Define a target function with attachments @@ -51,7 +52,10 @@ The target function must have two positional arguments, the first must be called ```python from langchain_openai import ChatOpenAI -from langchain_core.messages import HumanMessage +from langchain_core.messages import SystemMessage, HumanMessage +import fitz +from io import BytesIO +import base64 model = ChatOpenAI(model="gpt-4o-mini") @@ -69,7 +73,7 @@ def pdf_to_image_bytes(pdf_bytes, image_format='PNG'): return images -def target(inputs, attachments): +def pdf_qa(inputs, attachments): system_message = SystemMessage( content="The images are of the pdf that the question is referencing. Use the images to generate your answer." ) @@ -91,13 +95,13 @@ We can then run an evaluation as usual, by passing in the target function to the ```python # We can optionally define an evaluator to use -def evaluator(run, example): - score = int(str(example.outputs["answer"]) in run.outputs["answer"]) +def evaluator(outputs: dict, reference_outputs: dict): + score = int(str(outputs["answer"]) in reference_outputs["answer"]) return {"key": "correctness", "score": score} evaluate( - target, + target=pdf_qa, data="attachment-test-dataset", - client=langchain_client, + client=langsmith_client, ) ``` \ No newline at end of file From fa6e7f60282b6d30b41e5818cdb66937d28179f6 Mon Sep 17 00:00:00 2001 From: Ankush Gola Date: Tue, 19 Nov 2024 23:44:15 -0500 Subject: [PATCH 03/31] update docs --- .../evaluation/evaluate_with_attachments.mdx | 56 ++++++++++++++----- docs/evaluation/how_to_guides/index.md | 1 + 2 files changed, 43 insertions(+), 14 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index cf7448fd..d8f4245c 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -4,15 +4,30 @@ import { # Evaluate applications with large file inputs -Attachments allow you to associate large files with your examples. This allows you to evaluate RAG applications -over large internal documents, benchmark image analysis tools, etc. +In addition to supporting [file attachments with traces](../../../observability/how_to_guides/tracing/upload_files_with_traces), LangSmith supports arbitrary file attachments with your examples, which you can consume when you run experiments. + +:::tip Using separate attachments outside of inputs/outputs +When dealing with large files, it's recommended to upload them as attachments rather than embedding them in your JSON inputs or outputs via base64 encoding. + +Attachments are more efficient because base64 encoding increases data size, leading to slower uploads and downloads. +This also avoid potential performance bottlenecks with parsing large JSON payloads. + +Finally, attachments are more user-friendly in the LangSmith UI, as they are rendered as files with previews, rather than as base64-encoded strings. +::: ## Create a dataset with attachments -Attachments allow you to host large files outside of your inputs/outputs, which means faster loading times as well -as nicer rendering of files in the LangSmith UI. You can think of it like attaching a file to an email instead -of copy pasting the entire contents into your email body. -To create a dataset with attachments, you need to use the `upsert_examples_multipart` method of the LangSmith client: +### SDK +To create a dataset with attachments using the SDK, you need to use the `upsert_examples_multipart` method of the LangSmith client. +This method allows you to pass in a list of examples with attachments. + +Each attachment is represented as a dictionary, mapping the attachment name to a tuple containing the MIME type and the file content. +The file content can be either a path to the file or the bytes content of the file. + +:::note Working with large files +It is typically recommended to pass in the file path directly for large files, as reading the file content into memory can be inefficient. +When a file path is passed, LangSmith will stream the file content directly from the path, rather than loading the entire file into memory. +::: ```python from langsmith import Client @@ -33,11 +48,11 @@ example = ExampleUpsertWithAttachments( outputs={"answer": "$123456"}, attachments={ # Each attachments is just a name with a mime type and the path or bytes content of the file - "pdf": ("application/pdf", Path("./foo_earnings.pdf").read_bytes()), + "my_pdf": ("application/pdf", Path("./foo_earnings.pdf").read_bytes()), # We can pass multiple attachments (of different types!), as long as they have different names - "pptx": ("application/pptx", Path("./foo_earnings.pptx").read_bytes()), - # Can also just pass in the path - "txt": ("text/plain", Path("./foo_earnings.txt")), + "my_pptx": ("application/pptx", Path("./foo_earnings.pptx").read_bytes()), + # We can also pass in the file path directly. This is recommended for large files, as it avoids loading the entire file into memory + "my_txt": ("text/plain", Path("./foo_earnings.txt")), }, ) @@ -45,10 +60,22 @@ example = ExampleUpsertWithAttachments( langsmith_client.upsert_examples_multipart(upserts=[example]) ``` +Once you upload examples with attachments, you can view them in the LangSmith UI. Each attachment will be rendered as a file with a preview, making it easy to inspect the contents. +![](./static/attachments_with_examples.png) + + ## Define a target function with attachments -Now that we have a dataset that includes examples with attachments, we can define a target function to run our LLM application with the attachments. -The target function must have two positional arguments, the first must be called `inputs` and the second must be called `attachments`. +Now that we have a dataset that includes examples with attachments, we can define a target function to run our LLM application on these examples. +The target function must have two positional arguments in order to consume the attachments associated with the example, the first must be called `inputs` and the second must be called `attachments`. + +* The `inputs` argument is a dictionary that contains the input data for the example, excluding the attachments. +* The `attachments` argument is a dictionary that maps the attachment name to a tuple containing a presigned url and a reader of the bytes content of the file. Either can be used to read the bytes of the file: + ``` + { + "attachment_name": (presigned_url, reader) + } + ``` ```python from langchain_openai import ChatOpenAI @@ -77,8 +104,9 @@ def pdf_qa(inputs, attachments): system_message = SystemMessage( content="The images are of the pdf that the question is referencing. Use the images to generate your answer." ) - # The attachment tuple returned contains the S3 url first and then a reader of the bytes - pdf_s3_url, pdf_reader = attachments['pdf'] + # The attachment tuple returned contains the presigned url first and then a reader of the bytes + # Either can be used to read the bytes of the file + pdf_signed_url, pdf_reader = attachments['pdf'] images = pdf_to_image_bytes(pdf_reader.read()) human_message = HumanMessage( content=[ diff --git a/docs/evaluation/how_to_guides/index.md b/docs/evaluation/how_to_guides/index.md index 6ca14f90..246fa559 100644 --- a/docs/evaluation/how_to_guides/index.md +++ b/docs/evaluation/how_to_guides/index.md @@ -13,6 +13,7 @@ Write evaluations to test and improve your application. - [Evaluate an existing experiment](./how_to_guides/evaluation/evaluate_existing_experiment) - [Run a pairwise evaluation](./how_to_guides/evaluation/evaluate_pairwise) - [Run evals using the API only](./how_to_guides/evaluation/run_evals_api_only) +- [Evaluate with file attachments](./how_to_guides/evaluation/evaluate_with_attachments) ## Unit testing From d4e5d8f6dadd5296d6701dce7410bc0dab4e17c9 Mon Sep 17 00:00:00 2001 From: Ankush Gola Date: Tue, 19 Nov 2024 23:44:27 -0500 Subject: [PATCH 04/31] add screenshot --- .../static/attachments_with_examples.png | Bin 0 -> 183026 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/evaluation/how_to_guides/evaluation/static/attachments_with_examples.png diff --git a/docs/evaluation/how_to_guides/evaluation/static/attachments_with_examples.png b/docs/evaluation/how_to_guides/evaluation/static/attachments_with_examples.png new file mode 100644 index 0000000000000000000000000000000000000000..806057e75ac6baaf9079c3a77b0744f673ade4c1 GIT binary patch literal 183026 zcmaHT1zeQd_BIMg3S$w{Al*nwcb9Z`NDkd0-QCg%Qo_(ZbSNnx3=Km!LpOZmIrrRq z&+&eLe(x}w{q9)1*Lv2o30G2(e2V@W9RUI1skD@sG6Dj!Hv$6EDe4n=%>4R*HUh#U 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literal 0 HcmV?d00001 From 96b121fd13d7a13c7b031c30b8ea6b4fb72a2d86 Mon Sep 17 00:00:00 2001 From: Ankush Gola Date: Tue, 19 Nov 2024 23:44:45 -0500 Subject: [PATCH 05/31] fmt --- .../evaluation/evaluate_with_attachments.mdx | 22 +++++++++---------- 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index d8f4245c..fbdb95b7 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -1,6 +1,4 @@ -import { - python, -} from "@site/src/components/InstructionsWithCode"; +import { python } from "@site/src/components/InstructionsWithCode"; # Evaluate applications with large file inputs @@ -18,6 +16,7 @@ Finally, attachments are more user-friendly in the LangSmith UI, as they are ren ## Create a dataset with attachments ### SDK + To create a dataset with attachments using the SDK, you need to use the `upsert_examples_multipart` method of the LangSmith client. This method allows you to pass in a list of examples with attachments. @@ -63,19 +62,18 @@ langsmith_client.upsert_examples_multipart(upserts=[example]) Once you upload examples with attachments, you can view them in the LangSmith UI. Each attachment will be rendered as a file with a preview, making it easy to inspect the contents. ![](./static/attachments_with_examples.png) - ## Define a target function with attachments Now that we have a dataset that includes examples with attachments, we can define a target function to run our LLM application on these examples. The target function must have two positional arguments in order to consume the attachments associated with the example, the first must be called `inputs` and the second must be called `attachments`. -* The `inputs` argument is a dictionary that contains the input data for the example, excluding the attachments. -* The `attachments` argument is a dictionary that maps the attachment name to a tuple containing a presigned url and a reader of the bytes content of the file. Either can be used to read the bytes of the file: - ``` - { - "attachment_name": (presigned_url, reader) - } - ``` +- The `inputs` argument is a dictionary that contains the input data for the example, excluding the attachments. +- The `attachments` argument is a dictionary that maps the attachment name to a tuple containing a presigned url and a reader of the bytes content of the file. Either can be used to read the bytes of the file: + ``` + { + "attachment_name": (presigned_url, reader) + } + ``` ```python from langchain_openai import ChatOpenAI @@ -132,4 +130,4 @@ evaluate( data="attachment-test-dataset", client=langsmith_client, ) -``` \ No newline at end of file +``` From b8d846283545af4d93efd7855ce88fb129c09ce1 Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Wed, 20 Nov 2024 07:35:29 -0800 Subject: [PATCH 06/31] edits --- .../evaluation/evaluate_with_attachments.mdx | 29 ++++++++++++++++--- 1 file changed, 25 insertions(+), 4 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index fbdb95b7..cbf375c0 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -30,7 +30,8 @@ When a file path is passed, LangSmith will stream the file content directly from ```python from langsmith import Client -from langsmith.schemas import ExampleUpsertWithAttachments +from langsmith.schemas import ExampleUpsertWithAttachments, Attachment +from pathlib import Path # Define the LANGCHAIN_API_KEY environment variable with your api key langsmith_client = Client() @@ -52,6 +53,8 @@ example = ExampleUpsertWithAttachments( "my_pptx": ("application/pptx", Path("./foo_earnings.pptx").read_bytes()), # We can also pass in the file path directly. This is recommended for large files, as it avoids loading the entire file into memory "my_txt": ("text/plain", Path("./foo_earnings.txt")), + # We can also explicitly define an Attachment object if we want + "my_img": Attachment(mime_type="image/png", data=Path("./foo_earnings.img").read_bytes()) }, ) @@ -104,12 +107,16 @@ def pdf_qa(inputs, attachments): ) # The attachment tuple returned contains the presigned url first and then a reader of the bytes # Either can be used to read the bytes of the file - pdf_signed_url, pdf_reader = attachments['pdf'] - images = pdf_to_image_bytes(pdf_reader.read()) + pdf_signed_url, pdf_reader = attachments['my_pdf'] + pdf_images = pdf_to_image_bytes(pdf_reader.read()) + image_signed_url, image_reader = attachments['my_image'] human_message = HumanMessage( content=[ {"type": "text", "text": inputs["question"]}, - ] + [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image}"}} for image in images], + ] + [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image}"}} for image in pdf_images], + # Certain providers also allow you to just pass in the URL for certain files + # instead of having to convert to a base64 string first + + [{"type": "image_url", "image_url": {"url": image_signed_url}}] ) messages = [system_message, human_message] return {"answer": model.invoke(messages).content} @@ -131,3 +138,17 @@ evaluate( client=langsmith_client, ) ``` + +We could also use the asynchronous `aevaluate` method if we wanted to: + +```python +async def atarget(inputs, attachments): + # Do some async thing ... + return {"answer": "my-answer"} + +await aevaluate( + target=atarget, + data="attachment-test-dataset", + client=langsmith_client +) +``` \ No newline at end of file From 5c537cfc8b502e5f4b1d877fa9fec72238549c81 Mon Sep 17 00:00:00 2001 From: Ankush Gola Date: Wed, 20 Nov 2024 11:25:00 -0500 Subject: [PATCH 07/31] light edits --- .../evaluation/evaluate_with_attachments.mdx | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index cbf375c0..0caed8f8 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -139,16 +139,16 @@ evaluate( ) ``` -We could also use the asynchronous `aevaluate` method if we wanted to: +LangSmith also supports using the asynchronous version of the `evaluate` method, `aevaluate`, to run evaluations asynchronously: ```python async def atarget(inputs, attachments): - # Do some async thing ... + # Run an asynchronous operation return {"answer": "my-answer"} - + await aevaluate( target=atarget, data="attachment-test-dataset", client=langsmith_client ) -``` \ No newline at end of file +``` From 4f44045d11cf348e3616b18779a9989148ccfc75 Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Thu, 21 Nov 2024 09:30:52 -0800 Subject: [PATCH 08/31] pass attachments to evaluate --- .../evaluation/evaluate_with_attachments.mdx | 68 +++++++++++++++++-- 1 file changed, 63 insertions(+), 5 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 0caed8f8..fad90da9 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -127,11 +127,6 @@ def pdf_qa(inputs, attachments): We can then run an evaluation as usual, by passing in the target function to the `evaluate` method: ```python -# We can optionally define an evaluator to use -def evaluator(outputs: dict, reference_outputs: dict): - score = int(str(outputs["answer"]) in reference_outputs["answer"]) - return {"key": "correctness", "score": score} - evaluate( target=pdf_qa, data="attachment-test-dataset", @@ -152,3 +147,66 @@ await aevaluate( client=langsmith_client ) ``` + +### Use attachments in your evaluator + +In addition to using attachments inside of your target function, you can also use them inside of your evaluators as follows: + +```python +from pydantic import BaseModel, Field + +class Hallucination(BaseModel): + """Hallucination detection""" + reason: str = Field(description="Why you believe there was oor wasn't hallucination") + hallucination: int = Field(ge=0, le=1, description="1 if hallucination detected, 0 if not") + + +def hallucination_evaluator(outputs: dict, attachments: dict): + image_signed_url, image_reader = attachments['my_image'] + message = HumanMessage( + content = [ + {"type": "text", "text": f"Does this description: {} of the following image contain any hallucinations?"}, + {"type": "image_url", "image_url": {"url": image_signed_url}} + ] + ) + hallucination_model = model.with_structured_output(Hallucination) + return {"key": "hallincation", "score": hallucination_model.invoke(message).hallucination } + +evaluate( + target=pdf_qa, + data="attachment-test-dataset", + evaluators=[hallucination_evaluator] + client=langsmith_client, +) +``` + +We can also do this asynchronously: + + +```python +from pydantic import BaseModel, Field + +class Hallucination(BaseModel): + """Hallucination detection""" + reason: str = Field(description="Why you believe there was oor wasn't hallucination") + hallucination: int = Field(ge=0, le=1, description="1 if hallucination detected, 0 if not") + + +async def ahallucination_evaluator(outputs: dict, attachments: dict): + image_signed_url, image_reader = attachments['my_image'] + message = HumanMessage( + content = [ + {"type": "text", "text": f"Does this description: {} of the following image contain any hallucinations?"}, + {"type": "image_url", "image_url": {"url": image_signed_url}} + ] + ) + hallucination_model = model.with_structured_output(Hallucination) + return {"key": "hallincation", "score": hallucination_model.invoke(message).hallucination } + +await aevaluate( + target=atarget, + data="attachment-test-dataset", + evaluators=[ahallucination_evaluator] + client=langsmith_client, +) +``` \ No newline at end of file From 52e57f03c37965e1f8d0c7d548b797cf1c4cfbe3 Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Thu, 21 Nov 2024 09:48:58 -0800 Subject: [PATCH 09/31] add link --- .../how_to_guides/evaluation/evaluate_with_attachments.mdx | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index fad90da9..a0b6fcb3 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -65,6 +65,11 @@ langsmith_client.upsert_examples_multipart(upserts=[example]) Once you upload examples with attachments, you can view them in the LangSmith UI. Each attachment will be rendered as a file with a preview, making it easy to inspect the contents. ![](./static/attachments_with_examples.png) +### From existing runs + +You can also add examples with attachments from existing runs that contain attachments. +To do learn how to do so you can read [this guide](./../datasets/manage_datasets_in_application#add-runs-from-the-tracing-project-ui). + ## Define a target function with attachments Now that we have a dataset that includes examples with attachments, we can define a target function to run our LLM application on these examples. From ca0fb1420b2f915ca32193dd95eb75fb31f3fb5a Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Mon, 2 Dec 2024 14:58:51 -0800 Subject: [PATCH 10/31] edits --- .../evaluation/evaluate_with_attachments.mdx | 53 +++++++----------- .../create_example_with_attachments.png | Bin 0 -> 340348 bytes 2 files changed, 21 insertions(+), 32 deletions(-) create mode 100644 docs/evaluation/how_to_guides/evaluation/static/create_example_with_attachments.png diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index a0b6fcb3..5714712b 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -30,7 +30,7 @@ When a file path is passed, LangSmith will stream the file content directly from ```python from langsmith import Client -from langsmith.schemas import ExampleUpsertWithAttachments, Attachment +from langsmith.schemas import ExampleUploadWithAttachments, Attachment from pathlib import Path # Define the LANGCHAIN_API_KEY environment variable with your api key @@ -42,7 +42,7 @@ dataset = langsmith_client.create_dataset( ) # Define the example -example = ExampleUpsertWithAttachments( +example = ExampleUploadWithAttachments( dataset_id=dataset.id, inputs={"question": "What were the cumulative earnings earned from online orders in the midwest during Q2?"}, outputs={"answer": "$123456"}, @@ -59,7 +59,7 @@ example = ExampleUpsertWithAttachments( ) # Upsert the examples -langsmith_client.upsert_examples_multipart(upserts=[example]) +langsmith_client.upload_examples_multipart(uploads=[example]) ``` Once you upload examples with attachments, you can view them in the LangSmith UI. Each attachment will be rendered as a file with a preview, making it easy to inspect the contents. @@ -70,7 +70,18 @@ Once you upload examples with attachments, you can view them in the LangSmith UI You can also add examples with attachments from existing runs that contain attachments. To do learn how to do so you can read [this guide](./../datasets/manage_datasets_in_application#add-runs-from-the-tracing-project-ui). -## Define a target function with attachments +### From the LangSmith UI + +You can also add examples with attachments from the LangSmith UI. You can do so by clicking the `+ Example` button in the `Examples` tab of the dataset UI. +You can then upload the attachments that you want by using the "Upload Files" button: + +![](./static/create_example_with_attachments.png) + +## Running evaluations with attachments + +Once you have a dataset with attachments, you can run evaluations with attachments as follows: + +### Define a target function with attachments Now that we have a dataset that includes examples with attachments, we can define a target function to run our LLM application on these examples. The target function must have two positional arguments in order to consume the attachments associated with the example, the first must be called `inputs` and the second must be called `attachments`. @@ -127,33 +138,7 @@ def pdf_qa(inputs, attachments): return {"answer": model.invoke(messages).content} ``` -## Run an evaluation - -We can then run an evaluation as usual, by passing in the target function to the `evaluate` method: - -```python -evaluate( - target=pdf_qa, - data="attachment-test-dataset", - client=langsmith_client, -) -``` - -LangSmith also supports using the asynchronous version of the `evaluate` method, `aevaluate`, to run evaluations asynchronously: - -```python -async def atarget(inputs, attachments): - # Run an asynchronous operation - return {"answer": "my-answer"} - -await aevaluate( - target=atarget, - data="attachment-test-dataset", - client=langsmith_client -) -``` - -### Use attachments in your evaluator +### Define custom evaluators with attachments In addition 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zPQ6G$F)h%m9`;)B(2S+J2NZFg6;zVoz`aKVa$(@_1H~9E5J?Q1>#VaE3y)bNeZ~7WL zTQZFN`*t8pii$r%A zVM*Q}BUs-BG>fMy0K6IH+gBA1-kH?|%Hv~6q(Vj2_vas3fjP$fMnt6RD{eQ>p(U=J zEh#ST8Q1U^IpXJMl#`#>>Wn@VwAcbHNwe=Mv=iDmY^-0;Hbir zZd0Gjg1>~h&m-Tvj^bbhE1BxM632QzAd5G0Py9xPT}=|vVttEpfSr4$ZW$6Jo%J)3 z)CfqW3iw_6=V7MpBiCynx1<=ATGry-X>*ZrFq-f4vb@GiKwvkoL=WWq`u9fm@{D)R zyx-E{-%8(8uIN0Mi_IaLKJUIXFW<-x|7apsyT8+hKtlC>r#r_j-7?)Hb-=F`pmz!z z**6G^4o?JH+z$5;^f7e>FGc@i4MKa>(a51(LQu)m!+|!(V^(4_HvRo~cO%UidYeB- zlDsK#l-1Q3M*|4ek>!6Ie2id})Lu5>uF8#!LdhtI*9WXD9*t`TioaV-wi&^97WMCz z#@v35?G4MVYFv@l9uVp$w+dI_ba*9eC;F$mkuY3> literal 0 HcmV?d00001 From 6da15bdd3fdb9a56e8a80146e878994ccf2dfc3d Mon Sep 17 00:00:00 2001 From: Ankush Gola <9536492+agola11@users.noreply.github.com> Date: Tue, 3 Dec 2024 23:31:27 -0800 Subject: [PATCH 11/31] Update docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx --- .../how_to_guides/evaluation/evaluate_with_attachments.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 5714712b..015adb84 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -17,7 +17,7 @@ Finally, attachments are more user-friendly in the LangSmith UI, as they are ren ### SDK -To create a dataset with attachments using the SDK, you need to use the `upsert_examples_multipart` method of the LangSmith client. +To create a dataset with attachments using the SDK, you need to use the `upload_examples_multipart` method of the LangSmith client. This method allows you to pass in a list of examples with attachments. Each attachment is represented as a dictionary, mapping the attachment name to a tuple containing the MIME type and the file content. From c73b6dca7b2d4a28c05d1adcfee69d658bd9a464 Mon Sep 17 00:00:00 2001 From: Ankush Gola <9536492+agola11@users.noreply.github.com> Date: Tue, 3 Dec 2024 23:35:38 -0800 Subject: [PATCH 12/31] Update docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx --- .../how_to_guides/evaluation/evaluate_with_attachments.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 015adb84..309d7a22 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -67,7 +67,7 @@ Once you upload examples with attachments, you can view them in the LangSmith UI ### From existing runs -You can also add examples with attachments from existing runs that contain attachments. +When adding runs to a LangSmith dataset, attachments can be selectively propagated from the source run to the destination example. To do learn how to do so you can read [this guide](./../datasets/manage_datasets_in_application#add-runs-from-the-tracing-project-ui). ### From the LangSmith UI From feb7722fe6c98deb9e7013e279786a9d42ad4e5b Mon Sep 17 00:00:00 2001 From: Ankush Gola <9536492+agola11@users.noreply.github.com> Date: Tue, 3 Dec 2024 23:36:10 -0800 Subject: [PATCH 13/31] Update docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx --- .../how_to_guides/evaluation/evaluate_with_attachments.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 309d7a22..4b57f6c8 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -68,7 +68,7 @@ Once you upload examples with attachments, you can view them in the LangSmith UI ### From existing runs When adding runs to a LangSmith dataset, attachments can be selectively propagated from the source run to the destination example. -To do learn how to do so you can read [this guide](./../datasets/manage_datasets_in_application#add-runs-from-the-tracing-project-ui). +To do learn more, please see [this guide](./../datasets/manage_datasets_in_application#add-runs-from-the-tracing-project-ui). ### From the LangSmith UI From 4a6c8779c9c981fa76abf6b38c90b645f27c1ff1 Mon Sep 17 00:00:00 2001 From: Ankush Gola <9536492+agola11@users.noreply.github.com> Date: Tue, 3 Dec 2024 23:36:55 -0800 Subject: [PATCH 14/31] Update docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx --- .../how_to_guides/evaluation/evaluate_with_attachments.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 4b57f6c8..cabee2c4 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -79,7 +79,7 @@ You can then upload the attachments that you want by using the "Upload Files" bu ## Running evaluations with attachments -Once you have a dataset with attachments, you can run evaluations with attachments as follows: +Once you have a dataset that contains examples with file attachments, you can run evaluations that process these attachments. ### Define a target function with attachments From ccd44e4a851f28350a890c44af40c4bb49d462d1 Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Wed, 11 Dec 2024 09:57:26 -0800 Subject: [PATCH 15/31] wip --- .../evaluation/evaluate_with_attachments.mdx | 269 +++++++++++++++--- 1 file changed, 226 insertions(+), 43 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index cabee2c4..7dabdf6b 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -1,4 +1,9 @@ -import { python } from "@site/src/components/InstructionsWithCode"; +import { + python, + CodeTabs, + PythonBlock, + TypeScriptBlock, +} from "@site/src/components/InstructionsWithCode" # Evaluate applications with large file inputs @@ -28,10 +33,12 @@ It is typically recommended to pass in the file path directly for large files, a When a file path is passed, LangSmith will stream the file content directly from the path, rather than loading the entire file into memory. ::: -```python -from langsmith import Client + Once you upload examples with attachments, you can view them in the LangSmith UI. Each attachment will be rendered as a file with a preview, making it easy to inspect the contents. ![](./static/attachments_with_examples.png) @@ -84,70 +132,99 @@ Once you have a dataset that contains examples with file attachments, you can ru ### Define a target function with attachments Now that we have a dataset that includes examples with attachments, we can define a target function to run our LLM application on these examples. + +:::tip Python Target Function with Attachments The target function must have two positional arguments in order to consume the attachments associated with the example, the first must be called `inputs` and the second must be called `attachments`. - The `inputs` argument is a dictionary that contains the input data for the example, excluding the attachments. -- The `attachments` argument is a dictionary that maps the attachment name to a tuple containing a presigned url and a reader of the bytes content of the file. Either can be used to read the bytes of the file: +- The `attachments` argument is a dictionary that maps the attachment name to a dictionary containing a presigned url and a reader of the bytes content of the file. Either can be used to read the bytes of the file: ``` { - "attachment_name": (presigned_url, reader) + "attachment_name": { + "presigned_url": presigned_url, + "reader": reader + } } ``` +::: -```python +:::tip Javascript Target Function with Attachments +::: + +, config?: Record) { + const systemMessage = new SystemMessage({ + content: 'The images are of the data that the question is referencing. Use the images to generate your answer.', + }); + + const imageData = await fetch(config?.attachments?.["attachment.my_png"]?.presigned_url) + .then((res) => res.arrayBuffer()) + .then((arrayBuffer) => { + return Buffer.from(arrayBuffer).toString('base64'); + }); + + const humanMessage = new HumanMessage({ + content: [ + { + type: 'text', + text: inputs.question + }, + { + type: 'image_url', + image_url: { url: \`data:image/png;base64,$\{imageData\}\` }, + }, + ], + }); + + const messages = [systemMessage, humanMessage]; + const response = await model.invoke(messages); + return { answer: response.content }; +}`), + ]} + groupId="client-language" +/> ### Define custom evaluators with attachments In addition to using attachments inside of your target function, you can also use them inside of your evaluators as follows: -```python -from pydantic import BaseModel, Field + { + const imageData = await fetch(attachments["attachment.my_png"].presigned_url) + .then((res) => res.arrayBuffer()) + .then((arrayBuffer) => { + return Buffer.from(arrayBuffer).toString('base64'); + }); + + const message = new HumanMessage({ + content: [ + { type: 'text', text: \`Does this description: $\{outputs.answer\} of the following image contain any hallucinations?\` }, + { + type: 'image_url', + image_url: { url: \`data:image/png;base64,$\{imageData\}\` }, + }, + ], + }); + + const response = await structuredModel.invoke([message]); + return { key: "hallcination", score: response.hallucination }; +} + +await evaluate(imageQA, { + data: "attachment-test-dataset", + evaluators: [hallcinationEvaluator], + client: langsmithClient, + // Need to pass flag to include attachments + includeAttachments: true +});`), + ]} + groupId="client-language" +/> + +We can also do this asynchronously (python only, Javascript is async by default): ```python from pydantic import BaseModel, Field @@ -203,4 +318,72 @@ await aevaluate( ## Managing datasets with attachments -TODO: FILL THIS OUT ONCE UPDATE ENDPOINTS EXIST \ No newline at end of file +### Managing programmatically + +In the code above we saw how we could upload examples with attachments using the SDK, and it +is also possible to update these same examples using the SDK. + +When updating an example with attachments, you can update attachments in a few different ways: + +- Pass in new attachments +- Rename existing attachments +- Delete existing attachments + + + +TODO: ADD NOTES ABOUT BEHAVIOR ON ATTACHMENT CONFLICT + +### From the LangSmith UI + +TODO: FILL OUT WITH SCREENSHOTS \ No newline at end of file From 667cacaa870d27c34b0583269718dc19073ab02c Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Wed, 11 Dec 2024 10:06:57 -0800 Subject: [PATCH 16/31] nits --- .../how_to_guides/evaluation/evaluate_with_attachments.mdx | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 7dabdf6b..0476f4e3 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -240,9 +240,9 @@ def hallucination_evaluator(outputs: dict, attachments: dict): return {"key": "hallincation", "score": hallucination_model.invoke(message).hallucination } evaluate( - target=pdf_qa, + target=image_qa, data="attachment-test-dataset", - evaluators=[hallucination_evaluator] + evaluators=[hallucination_evaluator], client=langsmith_client, ) `), @@ -309,9 +309,10 @@ async def ahallucination_evaluator(outputs: dict, attachments: dict): return {"key": "hallincation", "score": hallucination_model.invoke(message).hallucination } await aevaluate( + # Some async target function target=atarget, data="attachment-test-dataset", - evaluators=[ahallucination_evaluator] + evaluators=[ahallucination_evaluator], client=langsmith_client, ) ``` From 9b562172df033d1026e0aca9c1e71d76c02ba036 Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Wed, 11 Dec 2024 10:09:17 -0800 Subject: [PATCH 17/31] nits --- .../how_to_guides/evaluation/evaluate_with_attachments.mdx | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 0476f4e3..33c11411 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -165,7 +165,8 @@ def image_qa(inputs, attachments): ) # The attachment tuple returned contains the presigned url first and then a reader of the bytes # Either can be used to read the bytes of the file - image_signed_url, image_reader = attachments['my_image'] + image_signed_url = attachments['my_img']['presigned_url'] + image_reader = attachments['my_img']['reader'] human_message = HumanMessage( content=[ {"type": "text", "text": inputs["question"]}, @@ -229,7 +230,8 @@ class Hallucination(BaseModel): def hallucination_evaluator(outputs: dict, attachments: dict): - image_signed_url, image_reader = attachments['my_image'] + image_signed_url = attachments['my_img']['presigned_url'] + image_reader = attachments['my_img']['reader'] message = HumanMessage( content = [ {"type": "text", "text": f"Does this description: {outputs['answer']} of the following image contain any hallucinations?"}, From f6d44f64258f4ae13af8c6fd483085a7476f24ab Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Wed, 11 Dec 2024 19:01:47 -0800 Subject: [PATCH 18/31] wip --- .../evaluation/evaluate_with_attachments.mdx | 483 +++++++++++------- .../evaluation/static/attachment_editing.gif | Bin 0 -> 12490701 bytes 2 files changed, 301 insertions(+), 182 deletions(-) create mode 100644 docs/evaluation/how_to_guides/evaluation/static/attachment_editing.gif diff --git a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx index 33c11411..41bf8642 100644 --- a/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx +++ b/docs/evaluation/how_to_guides/evaluation/evaluate_with_attachments.mdx @@ -35,77 +35,120 @@ When a file path is passed, LangSmith will stream the file content directly from { + const response = await fetch(url); + if (!response.ok) { + throw new Error(\`Failed to fetch ${url}: $\{response.statusText\}\`); + } + return response.arrayBuffer(); +} + +// Fetch files as ArrayBuffer +const pdfArrayBuffer = await fetchArrayBuffer(pdfUrl); +const wavArrayBuffer = await fetchArrayBuffer(wavUrl); +const pngArrayBuffer = await fetchArrayBuffer(pngUrl); + +// Create the LangSmith client (Ensure LANGCHAIN_API_KEY is set in env) const langsmithClient = new Client(); -const dataset = await langsmithClient.createDataset( - "attachment-test-dataset", - { - description: "Test dataset for evals with attachments", - } -); +// Create a unique dataset name +const datasetName = "attachment-test-dataset:" + uuid4().substring(0, 8); -// Create an example id -const exampleId = uuid4(); +// Create the dataset +const dataset = await langsmithClient.createDataset(datasetName, { + description: "Test dataset for evals with publicly available attachments", +}); -const example: ExampleUploadWithAttachments = { - id: exampleId, - inputs: { question: "What were the cumulative earnings earned from online orders in the midwest during Q2?" }, - outputs: { answer: "$123456" }, - attachments: { - // Each attachment consists of a name and a tuple of type and bytes data - my_pdf: ["application/pdf", fs.readFileSync(path.join(__dirname, './foo_earnings.pdf'))], - // We can pass multiple attachments (of different types!), as long as they have different names - my_pptx: ["application/pptx", fs.readFileSync(path.join(__dirname, './foo_earnings.pptx'))], - // Files are limitied to 50MB - my_txt: ["text/plain", fs.readFileSync(path.join(__dirname, './foo_earnings.txt'))], - my_png: ["image/png", fs.readFileSync(path.join(__dirname, './foo_earnings.png'))], +// Define the example with attachments +const exampleId = uuid4(); +const example = { + id: exampleId, + inputs: { + audio_question: "What is in this audio clip?", + image_question: "What is in this image?", + }, + outputs: { + audio_answer: + "The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.", + image_answer: "A mug with a blanket over it.", + }, + attachments: { + my_pdf: { + mimeType: "application/pdf", + data: pdfArrayBuffer + }, + my_wav: { + mimeType: "audio/wav", + data: wavArrayBuffer }, + my_img: { + mimeType: "image/png", + data: pngArrayBuffer + }, + }, }; -langsmithClient.uploadExamplesMultipart(dataset.id, [example]);`), +// Upload the example with attachments to the dataset +await langsmithClient.uploadExamplesMultipart(dataset.id, [example]);`), ]} groupId="client-language" /> @@ -153,63 +196,129 @@ The target function must have two positional arguments in order to consume the a , config?: Record) { - const systemMessage = new SystemMessage({ - content: 'The images are of the data that the question is referencing. Use the images to generate your answer.', - }); +const client: any = wrapOpenAI(new OpenAI()); + +async function fileQA(inputs: Record, config?: Record) { + const presignedUrl = config?.attachments?.["my_wav"]?.presigned_url; + if (!presignedUrl) { + throw new Error("No presigned URL provided for audio."); + } - const imageData = await fetch(config?.attachments?.["attachment.my_png"]?.presigned_url) - .then((res) => res.arrayBuffer()) - .then((arrayBuffer) => { - return Buffer.from(arrayBuffer).toString('base64'); + const response = await fetch(presignedUrl); + if (!response.ok) { + throw new Error(\`Failed to fetch audio: $\{response.statusText\}\`); + } + + const arrayBuffer = await response.arrayBuffer(); + const uint8Array = new Uint8Array(arrayBuffer); + const audioB64 = Buffer.from(uint8Array).toString("base64"); + + + const audioCompletion = await client.chat.completions.create({ + model: "gpt-4o-audio-preview", + messages: [ + { + role: "user", + content: [ + { type: "text", text: inputs["audio_question"] }, + { + type: "input_audio", + input_audio: { + data: audioB64, + format: "wav", + }, + }, + ], + }, + ], }); - const humanMessage = new HumanMessage({ - content: [ - { - type: 'text', - text: inputs.question - }, + const imageUrl = config?.attachments?.["my_img"]?.presigned_url + const imageCompletion = await client.chat.completions.create({ + model: "gpt-4o-mini", + messages: [ { - type: 'image_url', - image_url: { url: \`data:image/png;base64,$\{imageData\}\` }, + role: "user", + content: [ + { type: "text", text: inputs["image_question"] }, + { + type: "image_url", + image_url: { + url: imageUrl, + }, + }, + ], }, ], }); - const messages = [systemMessage, humanMessage]; - const response = await model.invoke(messages); - return { answer: response.content }; + return { + audio_answer: audioCompletion.choices[0].message.content, + image_answer: imageCompletion.choices[0].message.content, + }; }`), ]} groupId="client-language" @@ -221,104 +330,99 @@ In addition to using attachments inside of your target function, you can also us bool: + """Use an LLM to judge if the reasoning and the answer are consistent.""" + + instructions = """ + Does the description of the following image make sense? + Please carefully review the image and the description to determine if the description is valid.""" + + class Response(BaseModel): + description_is_valid: bool + + image_url = attachments["my_img"]["presigned_url"] + response = client.beta.chat.completions.parse( + model="gpt-4o", + messages=[ + { + "role": "system", + "content": instructions + }, + { + "role": "user", + "content": [ + {"type": "image_url", "image_url": {"url": image_url}}, + {"type": "text", "text": outputs["image_answer"]} + ] + } + ], + response_format=Response + ) + return response.choices[0].message.parsed.description_is_valid -def hallucination_evaluator(outputs: dict, attachments: dict): - image_signed_url = attachments['my_img']['presigned_url'] - image_reader = attachments['my_img']['reader'] - message = HumanMessage( - content = [ - {"type": "text", "text": f"Does this description: {outputs['answer']} of the following image contain any hallucinations?"}, - {"type": "image_url", "image_url": {"url": image_signed_url}} - ] - ) - hallucination_model = model.with_structured_output(Hallucination) - return {"key": "hallincation", "score": hallucination_model.invoke(message).hallucination } - -evaluate( - target=image_qa, - data="attachment-test-dataset", - evaluators=[hallucination_evaluator], - client=langsmith_client, +langsmith_client.evaluate( + file_qa, + data=dataset_name, + evaluators=[valid_image_description], ) `), -TypeScriptBlock(`import { z } from "zod"; +TypeScriptBlock(`import { zodResponseFormat } from 'openai/helpers/zod'; +import { z } from 'zod'; +import { evaluate } from "langsmith/evaluation"; -const hallucinationSchema = z.object({ - reason: z.string().describe("Why you believe there was or wasn't hallucination"), - hallucination: z.number().optional().describe("1 if hallucination detected, 0 if not"), +const DescriptionResponse = z.object({ + description_is_valid: z.boolean(), }); -const structuredModel = model.withStructuredOutput(hallucinationSchema); -const hallcinationEvaluator = async ({ outputs, attachments }: { outputs?: any, attachments?: any }) => { - const imageData = await fetch(attachments["attachment.my_png"].presigned_url) - .then((res) => res.arrayBuffer()) - .then((arrayBuffer) => { - return Buffer.from(arrayBuffer).toString('base64'); - }); - - const message = new HumanMessage({ - content: [ - { type: 'text', text: \`Does this description: $\{outputs.answer\} of the following image contain any hallucinations?\` }, - { - type: 'image_url', - image_url: { url: \`data:image/png;base64,$\{imageData\}\` }, - }, - ], - }); - - const response = await structuredModel.invoke([message]); - return { key: "hallcination", score: response.hallucination }; +async function validImageDescription({ + outputs, + attachments, +}: { + outputs?: any; + attachments?: any; +}): Promise<{ key: string; score: boolean}> { + const instructions = \`Does the description of the following image make sense? + Please carefully review the image and the description to determine if the description is valid.\`; + + const imageUrl = attachments?.["my_img"]?.presigned_url + + const completion = await client.beta.chat.completions.parse({ + model: "gpt-4o", + messages: [ + { + role: "system", + content: instructions, + }, + { + role: "user", + content: [ + { type: "image_url", image_url: { url: imageUrl } }, + { type: "text", text: outputs?.image_answer }, + ], + }, + ], + response_format: zodResponseFormat(DescriptionResponse, 'imageResponse'), + }); + + const score: boolean = completion.choices[0]?.message?.parsed?.description_is_valid ?? false; + return { key: "valid_image_description", score }; } -await evaluate(imageQA, { - data: "attachment-test-dataset", - evaluators: [hallcinationEvaluator], - client: langsmithClient, +const resp = await evaluate(fileQA, { + data: datasetName, // Need to pass flag to include attachments - includeAttachments: true + includeAttachments: true, + evaluators: [validImageDescription], + client: langsmithClient });`), ]} groupId="client-language" /> -We can also do this asynchronously (python only, Javascript is async by default): - -```python -from pydantic import BaseModel, Field - -class Hallucination(BaseModel): - """Hallucination detection""" - reason: str = Field(description="Why you believe there was oor wasn't hallucination") - hallucination: int = Field(ge=0, le=1, description="1 if hallucination detected, 0 if not") - - -async def ahallucination_evaluator(outputs: dict, attachments: dict): - image_signed_url, image_reader = attachments['my_image'] - message = HumanMessage( - content = [ - {"type": "text", "text": f"Does this description: {} of the following image contain any hallucinations?"}, - {"type": "image_url", "image_url": {"url": image_signed_url}} - ] - ) - hallucination_model = model.with_structured_output(Hallucination) - return {"key": "hallincation", "score": hallucination_model.invoke(message).hallucination } - -await aevaluate( - # Some async target function - target=atarget, - data="attachment-test-dataset", - evaluators=[ahallucination_evaluator], - client=langsmith_client, -) -``` - ## Managing datasets with attachments ### Managing programmatically @@ -344,13 +448,13 @@ example_update = ExampleUpdateWithAttachments( }, attachments_operations=AttachmentsOperations( # Retained attachments will stay exactly the same - retain=["my_img", "my_pdf"], + retain=["my_img"], # Renaming attachments preserves the original data rename={ - "my_pptx": "my_new_pptx", + "my_wav": "my_new_wav", } # Any attachments not in rename/retain will be deleted - # In this case, that would be "my_txt" + # In this case, that would be "my_pdf" ), ) @@ -362,17 +466,20 @@ const exampleUpdate: ExampleUpdateWithAttachments = { id: exampleId, attachments: { // These are net new attachments - "my_new_file": ["text/plain", b"foo bar"], + "my_new_file": { + mimeType: "text/plain", + data: Buffer.from("foo bar") + }, }, attachments_operations: { // Retained attachments will stay exactly the same - retain: ["my_img", "my_pdf"], + retain: ["my_img"], // Renaming attachments preserves the original data rename: { - "my_pptx": "my_new_pptx", + "my_wav": "my_new_wav", }, // Any attachments not in rename/retain will be deleted - // In this case, that would be "my_txt" + // In this case, that would be "my_pdf" }, }; @@ -385,8 +492,20 @@ await langsmithClient.updateExamplesMultipart( groupId="client-language" /> -TODO: ADD NOTES ABOUT BEHAVIOR ON ATTACHMENT CONFLICT +:::warning Attachment Operations +Currently, errors are not thrown if you pass the wrong attaachment name to `rename` or `retain`. +New attachments **ALWAYS** take precedence over existing attachments. So if you upload a new attachment +name "foo" and try to retain or rename an existing attachment to "foo", the new attachment will be used instead. +::: ### From the LangSmith UI -TODO: FILL OUT WITH SCREENSHOTS \ No newline at end of file +:::note Attachment Size Limit +Attachments are limited to 20MB in size in the UI. +::: + +When editing an example in the UI, you can upload new attachments, rename and delete attachemnts, +and there is also a quick reset button to restore the attachments to what previously existed on the example. +No changes will be saved until you click submit. + +![](../evaluation/static/attachment_editing.gif) \ No newline at end of file diff --git a/docs/evaluation/how_to_guides/evaluation/static/attachment_editing.gif b/docs/evaluation/how_to_guides/evaluation/static/attachment_editing.gif new file mode 100644 index 0000000000000000000000000000000000000000..223b7953be6f94893990bfa11c2d6ffa0b3960dd GIT binary patch literal 12490701 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