From 7deb4a7b31dbcf9211ccbb38fbaee572472ba65c Mon Sep 17 00:00:00 2001 From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Date: Mon, 30 Oct 2023 14:02:58 -0700 Subject: [PATCH 1/7] Add conversational / chat feedback example --- templates/conversational-feedback/LICENSE | 21 +++ templates/conversational-feedback/README.md | 29 ++++ .../conversational_feedback/__init__.py | 3 + .../conversational_feedback/chain.py | 160 ++++++++++++++++++ .../conversational-feedback/pyproject.toml | 26 +++ .../conversational-feedback/tests/__init__.py | 0 6 files changed, 239 insertions(+) create mode 100644 templates/conversational-feedback/LICENSE create mode 100644 templates/conversational-feedback/README.md create mode 100644 templates/conversational-feedback/conversational_feedback/__init__.py create mode 100644 templates/conversational-feedback/conversational_feedback/chain.py create mode 100644 templates/conversational-feedback/pyproject.toml create mode 100644 templates/conversational-feedback/tests/__init__.py diff --git a/templates/conversational-feedback/LICENSE b/templates/conversational-feedback/LICENSE new file mode 100644 index 0000000000000..426b65090341f --- /dev/null +++ b/templates/conversational-feedback/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/conversational-feedback/README.md b/templates/conversational-feedback/README.md new file mode 100644 index 0000000000000..638d351884bfa --- /dev/null +++ b/templates/conversational-feedback/README.md @@ -0,0 +1,29 @@ +# Chat Feedback Template + +This template captures "implicit feedback from human behavior" within a simple chat bot. This instructs an LLM to use a user's responses within a conversation to grade its previous replies. + +[Chat bots](https://python.langchain.com/docs/use_cases/chatbots) are perhaps the most common interface for applying LLMs. The quality of chat bots can be variable, and it can be challenging to get explicit user feedback through things like thumbs up/down buttons. Traditional analytics like "session length" or "conversation length" can be ambiguous. For multi-turn conversations with a chat bot, an immense amount of information can be gleaned from the dialog itself, which we can convert to metrics for monitoring, fine-tuning, and further evalution. + +Taking [Chat Langchain](https://chat.langchain.com/) as a case study, only ~0.04% of all queries receive explicit feedback, but ~70% of of the queries are follow-ups to previous questions. A large portion of these followup queries are actual continuations of the same topic (vs. simply asking another question) and can be used to infer the quality of the previous AI response. + + +## LangSmith Feedback + +[LangSmith](https://smith.langchain.com/) is a platform for building production-grade LLM applications. In addition to its debugging and offline evaluation functionality, it helps you capture feedback (both user and model-assisted) to improve your LLM application. For other cookbook examples on collecting feedback using LangSmith, check out the [docs](https://docs.smith.langchain.com/cookbook/feedback-examples). + + + +## Implementation + +This particular feedback mce + + + +## Environment Variables + +Be sure that `OPENAI_API_KEY` is set in order to use the OpenAI models. Also, configure LangSmith by setting your `LANGSMITH_API_KEY`. + +```bash +export OPENAI_API_KEY=sk-... +export LANGSMITH_API_KEY=... +``` \ No newline at end of file diff --git a/templates/conversational-feedback/conversational_feedback/__init__.py b/templates/conversational-feedback/conversational_feedback/__init__.py new file mode 100644 index 0000000000000..06d4114d28ee6 --- /dev/null +++ b/templates/conversational-feedback/conversational_feedback/__init__.py @@ -0,0 +1,3 @@ +from conversational_feedback.chain import chain + +__all__ = ["chain"] diff --git a/templates/conversational-feedback/conversational_feedback/chain.py b/templates/conversational-feedback/conversational_feedback/chain.py new file mode 100644 index 0000000000000..445ee9798fb8b --- /dev/null +++ b/templates/conversational-feedback/conversational_feedback/chain.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +from typing import List, Optional + +from langchain import hub +from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler +from langchain.callbacks.tracers.schemas import Run +from langchain.chains.openai_functions.base import convert_to_openai_function +from langchain.chat_models import ChatOpenAI +from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.schema import (AIMessage, BaseMessage, HumanMessage, + StrOutputParser, get_buffer_string) +from langchain.schema.runnable import Runnable +from langsmith.evaluation import EvaluationResult, RunEvaluator +from langsmith.schemas import Example, Run + +from pydantic import BaseModel, Field + +### The feedback model + + +class ResponseEffectiveness(BaseModel): + """Score the effectiveness of bot response.""" + + reasoning: str = Field( + ..., + description="Explanation of the score.", + ) + score: int = Field( + ..., + min=0, + max=5, + description="Effectiveness of AI's final response.", + ) + + +def format_messages(input: dict) -> dict: + """Format the messages for the evaluator.""" + chat_history = input.get("chat_history") or [] + results = [] + for message in chat_history: + if message["type"] == "human": + results.append(HumanMessage.parse_obj(message)) + else: + results.append(AIMessage.parse_obj(message)) + return results + + +def format_dialog(input: dict) -> dict: + """Format the dialog for the evaluator.""" + chat_history = format_messages(input) + formatted_dialog = get_buffer_string(chat_history) # + f"\nhuman: {input['text']}" + return {"dialog": formatted_dialog} + + +def normalize_score(response: dict) -> dict: + """Normalize the score to be between 0 and 1.""" + response["score"] = int(response["score"]) / 5 + return response + + +evaluation_prompt = hub.pull("wfh/response-effectiveness") +evaluate_response_effectiveness = ( + # format_messages is a function that takes a dict and returns a dict + format_dialog + | evaluation_prompt + # bind() provides the requested schemas to the model for structured prediction + | ChatOpenAI(model="gpt-3.5-turbo").bind( + functions=[convert_to_openai_function(ResponseEffectiveness)], + function_name="response_effectiveness", + ) + # Convert the model's output to a dict + | JsonOutputFunctionsParser(args_only=True) + | normalize_score +) + + +class ResponseEffectivenessEvaluator(RunEvaluator): + def __init__(self, evaluator_runnable: Runnable) -> None: + super().__init__() + self.runnable = evaluator_runnable + + def evaluate_run( + self, run: Run, example: Optional[Example] = None + ) -> EvaluationResult: + # This particular evaluator is configured to evaluate the previous + # AI response. It uses the user's followup question or comment as + # additional grounding for its grade. + if not run.inputs.get("chat_history"): + return EvaluationResult( + key="response_effectiveness", comment="No chat history present." + ) + elif "last_run_id" not in run.inputs: + return EvaluationResult( + key="response_effectiveness", comment="No last run ID present." + ) + eval_grade: Optional[dict] = self.runnable.invoke(run.inputs) + target_run_id = run.inputs["last_run_id"] + return EvaluationResult( + **eval_grade, + key="response_effectiveness", + target_run_id=target_run_id, + ) + + +### The actual deployed chain (we are keeping it simple for this example) +# The main focus of this template is the evaluator above, not the chain itself. + + +class ChainInput(BaseModel): + chat_history: Optional[List[BaseMessage]] = Field( + description="Previous chat messages." + ) + text: str = Field(..., description="User's latest query.") + last_run_id: Optional[str] = Field("", description="ID of the last run.") + + +_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a helpful assistant who speaks like a pirate", + ), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{text}"), + ] +) +_model = ChatOpenAI() + + +def format_chat_history(chain_input: ChainInput) -> dict: + # This is a hack to get the chat history into the prompt + messages = format_messages(chain_input) + + return { + "chat_history": messages, + "text": chain_input.get("text"), + } + + +# if you update the name of this, you MUST also update ../pyproject.toml +# with the new `tool.langserve.export_attr` +chain = ( + (format_chat_history | _prompt | _model | StrOutputParser()) + # This is to populate the openapi spec for LangServe + .with_types(input_type=ChainInput) + # This is to add the evluators as "listeners" + # and to customize the name of the chain + .with_config( + run_name="ChatBot", + callbacks=[ + EvaluatorCallbackHandler( + evaluators=[ + ResponseEffectivenessEvaluator(evaluate_response_effectiveness) + ] + ) + ], + ) +) diff --git a/templates/conversational-feedback/pyproject.toml b/templates/conversational-feedback/pyproject.toml new file mode 100644 index 0000000000000..664bad219b5aa --- /dev/null +++ b/templates/conversational-feedback/pyproject.toml @@ -0,0 +1,26 @@ +[tool.poetry] +name = "chat_feedback" +version = "0.0.1" +description = "" +authors = [] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.325, <0.1" +openai = "^0.28.1" +langsmith = ">=0.0.54" +langchainhub = ">=0.1.13" + +[tool.poetry.group.dev.dependencies] +langchain-cli = ">=0.0.4" +fastapi = "^0.104.0" +sse-starlette = "^1.6.5" + +[tool.langserve] +export_module = "chat_feedback.chain" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/conversational-feedback/tests/__init__.py b/templates/conversational-feedback/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d From f4397e231d844d960dfc4356117b241a4256a3b5 Mon Sep 17 00:00:00 2001 From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Date: Mon, 30 Oct 2023 14:17:10 -0700 Subject: [PATCH 2/7] update readme --- templates/conversational-feedback/README.md | 75 ++++++++++++++++--- .../conversational_feedback/chain.py | 20 +++-- 2 files changed, 80 insertions(+), 15 deletions(-) diff --git a/templates/conversational-feedback/README.md b/templates/conversational-feedback/README.md index 638d351884bfa..850b880b4b8d3 100644 --- a/templates/conversational-feedback/README.md +++ b/templates/conversational-feedback/README.md @@ -1,29 +1,86 @@ # Chat Feedback Template -This template captures "implicit feedback from human behavior" within a simple chat bot. This instructs an LLM to use a user's responses within a conversation to grade its previous replies. +This template captures "implicit feedback from human behavior" within a simple chat bot. This instructs an LLM to use a user's responses within a conversation to grade its previous replies. [Chat bots](https://python.langchain.com/docs/use_cases/chatbots) are perhaps the most common interface for applying LLMs. The quality of chat bots can be variable, and it can be challenging to get explicit user feedback through things like thumbs up/down buttons. Traditional analytics like "session length" or "conversation length" can be ambiguous. For multi-turn conversations with a chat bot, an immense amount of information can be gleaned from the dialog itself, which we can convert to metrics for monitoring, fine-tuning, and further evalution. -Taking [Chat Langchain](https://chat.langchain.com/) as a case study, only ~0.04% of all queries receive explicit feedback, but ~70% of of the queries are follow-ups to previous questions. A large portion of these followup queries are actual continuations of the same topic (vs. simply asking another question) and can be used to infer the quality of the previous AI response. - +Taking [Chat Langchain](https://chat.langchain.com/) as a case study, only ~0.04% of all queries receive explicit feedback, but ~70% of of the queries are follow-ups to previous questions. A large portion of these followup queries + are actual continuations of the same topic (vs. simply asking another question) and can be used to infer the quality of the previous AI response. ## LangSmith Feedback [LangSmith](https://smith.langchain.com/) is a platform for building production-grade LLM applications. In addition to its debugging and offline evaluation functionality, it helps you capture feedback (both user and model-assisted) to improve your LLM application. For other cookbook examples on collecting feedback using LangSmith, check out the [docs](https://docs.smith.langchain.com/cookbook/feedback-examples). - - ## Implementation -This particular feedback mce +The feedback is executed within a custom `RunEvaluator`. This evaluator is run in a separate thread (to not obstruct the runtime of the actual chat bot). +It uses an LLM (in this case, `gpt-3.5-turbo`) to grade the the AI's most recent chat message, accounting for the user's response to that message. +The prompt used within the LLM [can be found on the hub](https://smith.langchain.com/hub/wfh/response-effectiveness). Feel free to modify it to your liking! +We also use OpenAI's function calling API to ensure more consistent structured output in the grade. - -## Environment Variables +## Environment Variables Be sure that `OPENAI_API_KEY` is set in order to use the OpenAI models. Also, configure LangSmith by setting your `LANGSMITH_API_KEY`. ```bash export OPENAI_API_KEY=sk-... export LANGSMITH_API_KEY=... -``` \ No newline at end of file +``` + +## Usage + +If you are deplying this via `LangServe`, it's recommended that you instruct the server to return callback events as well. + +``` +from conversational_feedback.chain import chain + +add_routes(app, chain, path="/conversational-feedback", include_callback_events=True) +``` + +This will unify any traces started in the client with those in the server. + +The code snippet below shows how you could use the stream endpoint + +```python +from functools import partial +from typing import Dict, Optional, Callable, List +from langserve import RemoteRunnable +from langchain.callbacks.manager import tracing_v2_enabled +from langchain.schema import BaseMessage, AIMessage, HumanMessage + +# Upudate with the URL provided by your LangServe server +chain = RemoteRunnable("http://127.0.0.1:8031/conversational-feedback") + + +def stream_content( + text: str, + chat_history: Optional[List[BaseMessage]] = None, + last_run_id: Optional[str] = None, + on_chunk=Callable, +): + results = [] + with tracing_v2_enabled() as cb: + for chunk in chain.stream( + {"text": text, "chat_history": chat_history, "last_run_id": last_run_id}, + ): + on_chunk(chunk) + results.append(chunk) + last_run_id = cb.latest_run.id if cb.latest_run is not None else None + return last_run_id, "".join(results) + + +chat_history = [] +text = "Where are my keys?" +last_run_id, response_message = stream_content(text, on_chunk=partial(print, end="")) +chat_history.extend([HumanMessage(content=text), AIMessage(content=response_message)]) +text = "I CANT FIND THEM ANYWHERE" # The previous response will be given a low score since +# the user's frustration (as evidenced by the text) seems to be increasing +last_run_id, response_message = stream_content( + text, + chat_history=chat_history, + last_run_id=str(last_run_id), + on_chunk=partial(print, end=""), +) +chat_history.extend([HumanMessage(content=text), AIMessage(content=response_message)]) +``` diff --git a/templates/conversational-feedback/conversational_feedback/chain.py b/templates/conversational-feedback/conversational_feedback/chain.py index 445ee9798fb8b..7f0a69d549333 100644 --- a/templates/conversational-feedback/conversational_feedback/chain.py +++ b/templates/conversational-feedback/conversational_feedback/chain.py @@ -9,23 +9,31 @@ from langchain.chat_models import ChatOpenAI from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain.schema import (AIMessage, BaseMessage, HumanMessage, - StrOutputParser, get_buffer_string) +from langchain.schema import ( + AIMessage, + BaseMessage, + HumanMessage, + StrOutputParser, + get_buffer_string, +) from langchain.schema.runnable import Runnable from langsmith.evaluation import EvaluationResult, RunEvaluator from langsmith.schemas import Example, Run from pydantic import BaseModel, Field -### The feedback model +### The feedback model used for the "function definition" provided to OpenAI +# For use with open source models, you can add the schema directly, +# but some modifications to the prompt and parser will be needed + class ResponseEffectiveness(BaseModel): - """Score the effectiveness of bot response.""" + """Score the effectiveness of the AI chat bot response.""" reasoning: str = Field( ..., - description="Explanation of the score.", + description="Explanation for the score.", ) score: int = Field( ..., @@ -64,7 +72,7 @@ def normalize_score(response: dict) -> dict: evaluate_response_effectiveness = ( # format_messages is a function that takes a dict and returns a dict format_dialog - | evaluation_prompt + | evaluation_prompt # bind() provides the requested schemas to the model for structured prediction | ChatOpenAI(model="gpt-3.5-turbo").bind( functions=[convert_to_openai_function(ResponseEffectiveness)], From 8400eb135149adcf890d2bbe0e4e09471e4d54a4 Mon Sep 17 00:00:00 2001 From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Date: Mon, 30 Oct 2023 14:24:47 -0700 Subject: [PATCH 3/7] update --- templates/conversational-feedback/README.md | 41 ++++++++++----------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/templates/conversational-feedback/README.md b/templates/conversational-feedback/README.md index 850b880b4b8d3..f87dbfd94d9b8 100644 --- a/templates/conversational-feedback/README.md +++ b/templates/conversational-feedback/README.md @@ -1,27 +1,26 @@ # Chat Feedback Template -This template captures "implicit feedback from human behavior" within a simple chat bot. This instructs an LLM to use a user's responses within a conversation to grade its previous replies. +This template captures implicit feedback from human behavior in a simple chat bot. It instructs an LLM to reference a user's responses within a conversation to evaluate the chat bot's previous replies. -[Chat bots](https://python.langchain.com/docs/use_cases/chatbots) are perhaps the most common interface for applying LLMs. The quality of chat bots can be variable, and it can be challenging to get explicit user feedback through things like thumbs up/down buttons. Traditional analytics like "session length" or "conversation length" can be ambiguous. For multi-turn conversations with a chat bot, an immense amount of information can be gleaned from the dialog itself, which we can convert to metrics for monitoring, fine-tuning, and further evalution. +[Chat bots](https://python.langchain.com/docs/use_cases/chatbots) serve as one of the most common interfaces for deploying LLMs. The quality of chat bots varies, making continuous development important. But users are wont to leave explicit feedback through mechanisms like thumbs-up or thumbs-down buttons. Furthermore, traditional analytics such as "session length" or "conversation length" often lack clarity. However, multi-turn conversations with a chat bot can provide a wealth of information, which we can transform into metrics for fine-tuning, evaluation, and product analytics. -Taking [Chat Langchain](https://chat.langchain.com/) as a case study, only ~0.04% of all queries receive explicit feedback, but ~70% of of the queries are follow-ups to previous questions. A large portion of these followup queries - are actual continuations of the same topic (vs. simply asking another question) and can be used to infer the quality of the previous AI response. +Taking [Chat Langchain](https://chat.langchain.com/) as a case study, only about 0.04% of all queries receive explicit feedback. Yet, approximately 70% of the queries are follow-ups to previous questions. A significant portion of these follow-up queries continue useful information we can use to infer the quality of the previous AI response. ## LangSmith Feedback -[LangSmith](https://smith.langchain.com/) is a platform for building production-grade LLM applications. In addition to its debugging and offline evaluation functionality, it helps you capture feedback (both user and model-assisted) to improve your LLM application. For other cookbook examples on collecting feedback using LangSmith, check out the [docs](https://docs.smith.langchain.com/cookbook/feedback-examples). +[LangSmith](https://smith.langchain.com/) is a platform for building production-grade LLM applications. Beyond its debugging and offline evaluation features, LangSmith helps you capture both user and model-assisted feedback to refine your LLM application. For more examples on collecting feedback using LangSmith, consult the [documentation](https://docs.smith.langchain.com/cookbook/feedback-examples). ## Implementation -The feedback is executed within a custom `RunEvaluator`. This evaluator is run in a separate thread (to not obstruct the runtime of the actual chat bot). -It uses an LLM (in this case, `gpt-3.5-turbo`) to grade the the AI's most recent chat message, accounting for the user's response to that message. +Feedback collection occurs within a custom `RunEvaluator`. This evaluator is called using the `EvaluatorCallbackHandler`, which run it in a separate thread to avoid interfering with the chat bot's runtime. -The prompt used within the LLM [can be found on the hub](https://smith.langchain.com/hub/wfh/response-effectiveness). Feel free to modify it to your liking! -We also use OpenAI's function calling API to ensure more consistent structured output in the grade. +The evaluator instructs an LLM, specifically `gpt-3.5-turbo`, to evaluate the AI's most recent chat message based on the user's followup response. It generates a score and accompanying reasoning that is converted to feedback in LangSmith, applied to the value provided as the `last_run_id`. + +The prompt used within the LLM [is available on the hub](https://smith.langchain.com/hub/wfh/response-effectiveness). Feel free to customize it. We also utilize OpenAI's function-calling API to ensure a more consistent, structured output for the grade. ## Environment Variables -Be sure that `OPENAI_API_KEY` is set in order to use the OpenAI models. Also, configure LangSmith by setting your `LANGSMITH_API_KEY`. +Ensure that `OPENAI_API_KEY` is set to use OpenAI models. Also, configure LangSmith by setting your `LANGSMITH_API_KEY`. ```bash export OPENAI_API_KEY=sk-... @@ -30,17 +29,15 @@ export LANGSMITH_API_KEY=... ## Usage -If you are deplying this via `LangServe`, it's recommended that you instruct the server to return callback events as well. +If deploying via `LangServe`, we recommend configuring the server to return callback events as well. This will ensure the backend traces are included in whatever traces you generate using the `RemoteRunnable`. -``` +```python from conversational_feedback.chain import chain add_routes(app, chain, path="/conversational-feedback", include_callback_events=True) ``` -This will unify any traces started in the client with those in the server. - -The code snippet below shows how you could use the stream endpoint +With the server running, you can use the following code snippet to stream the chat bot responses for a 2 turn conversation. ```python from functools import partial @@ -49,15 +46,14 @@ from langserve import RemoteRunnable from langchain.callbacks.manager import tracing_v2_enabled from langchain.schema import BaseMessage, AIMessage, HumanMessage -# Upudate with the URL provided by your LangServe server +# Update with the URL provided by your LangServe server chain = RemoteRunnable("http://127.0.0.1:8031/conversational-feedback") - def stream_content( text: str, chat_history: Optional[List[BaseMessage]] = None, last_run_id: Optional[str] = None, - on_chunk=Callable, + on_chunk: Callable = None, ): results = [] with tracing_v2_enabled() as cb: @@ -66,16 +62,15 @@ def stream_content( ): on_chunk(chunk) results.append(chunk) - last_run_id = cb.latest_run.id if cb.latest_run is not None else None + last_run_id = cb.latest_run.id if cb.latest_run else None return last_run_id, "".join(results) - chat_history = [] text = "Where are my keys?" last_run_id, response_message = stream_content(text, on_chunk=partial(print, end="")) chat_history.extend([HumanMessage(content=text), AIMessage(content=response_message)]) -text = "I CANT FIND THEM ANYWHERE" # The previous response will be given a low score since -# the user's frustration (as evidenced by the text) seems to be increasing +text = "I CAN'T FIND THEM ANYWHERE" # The previous response will likely receive a low score, +# as the user's frustration appears to be escalating. last_run_id, response_message = stream_content( text, chat_history=chat_history, @@ -84,3 +79,5 @@ last_run_id, response_message = stream_content( ) chat_history.extend([HumanMessage(content=text), AIMessage(content=response_message)]) ``` + +This uses the `tracing_v2_enabled` callback manager to get the run ID of the call, which we provide in subsequent calls in the same chat thread, so the evaluator can assign feedback to the appropriate trace. \ No newline at end of file From b31cf6853944d99bf8e24992585c8088d1a47373 Mon Sep 17 00:00:00 2001 From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Date: Mon, 30 Oct 2023 14:44:49 -0700 Subject: [PATCH 4/7] Update readme --- templates/conversational-feedback/README.md | 5 ++++- .../conversational_feedback/chain.py | 8 +++----- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/templates/conversational-feedback/README.md b/templates/conversational-feedback/README.md index f87dbfd94d9b8..dc3f644b6e90d 100644 --- a/templates/conversational-feedback/README.md +++ b/templates/conversational-feedback/README.md @@ -16,7 +16,7 @@ Feedback collection occurs within a custom `RunEvaluator`. This evaluator is cal The evaluator instructs an LLM, specifically `gpt-3.5-turbo`, to evaluate the AI's most recent chat message based on the user's followup response. It generates a score and accompanying reasoning that is converted to feedback in LangSmith, applied to the value provided as the `last_run_id`. -The prompt used within the LLM [is available on the hub](https://smith.langchain.com/hub/wfh/response-effectiveness). Feel free to customize it. We also utilize OpenAI's function-calling API to ensure a more consistent, structured output for the grade. +The prompt used within the LLM [is available on the hub](https://smith.langchain.com/hub/wfh/response-effectiveness). Feel free to customize it with things like additional app context (such as the goal of the app or the types of questions it should respond to) or "symptoms" you'd like the LLM to focus on. This evaluator also utilizes OpenAI's function-calling API to ensure a more consistent, structured output for the grade. ## Environment Variables @@ -25,6 +25,7 @@ Ensure that `OPENAI_API_KEY` is set to use OpenAI models. Also, configure LangSm ```bash export OPENAI_API_KEY=sk-... export LANGSMITH_API_KEY=... +export LANGCHAIN_TRACING_V2=true ``` ## Usage @@ -68,6 +69,7 @@ def stream_content( chat_history = [] text = "Where are my keys?" last_run_id, response_message = stream_content(text, on_chunk=partial(print, end="")) +print() chat_history.extend([HumanMessage(content=text), AIMessage(content=response_message)]) text = "I CAN'T FIND THEM ANYWHERE" # The previous response will likely receive a low score, # as the user's frustration appears to be escalating. @@ -77,6 +79,7 @@ last_run_id, response_message = stream_content( last_run_id=str(last_run_id), on_chunk=partial(print, end=""), ) +print() chat_history.extend([HumanMessage(content=text), AIMessage(content=response_message)]) ``` diff --git a/templates/conversational-feedback/conversational_feedback/chain.py b/templates/conversational-feedback/conversational_feedback/chain.py index 7f0a69d549333..b4e1299b9c1a8 100644 --- a/templates/conversational-feedback/conversational_feedback/chain.py +++ b/templates/conversational-feedback/conversational_feedback/chain.py @@ -18,8 +18,7 @@ ) from langchain.schema.runnable import Runnable from langsmith.evaluation import EvaluationResult, RunEvaluator -from langsmith.schemas import Example, Run - +from langsmith.schemas import Example from pydantic import BaseModel, Field ### The feedback model used for the "function definition" provided to OpenAI @@ -27,7 +26,6 @@ # but some modifications to the prompt and parser will be needed - class ResponseEffectiveness(BaseModel): """Score the effectiveness of the AI chat bot response.""" @@ -43,7 +41,7 @@ class ResponseEffectiveness(BaseModel): ) -def format_messages(input: dict) -> dict: +def format_messages(input: dict) -> List[BaseMessage]: """Format the messages for the evaluator.""" chat_history = input.get("chat_history") or [] results = [] @@ -76,7 +74,7 @@ def normalize_score(response: dict) -> dict: # bind() provides the requested schemas to the model for structured prediction | ChatOpenAI(model="gpt-3.5-turbo").bind( functions=[convert_to_openai_function(ResponseEffectiveness)], - function_name="response_effectiveness", + function_call={"name": "ResponseEffectiveness"}, ) # Convert the model's output to a dict | JsonOutputFunctionsParser(args_only=True) From 83d2b71538143bea79a9747315f724ce214fd1d9 Mon Sep 17 00:00:00 2001 From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Date: Tue, 31 Oct 2023 06:30:56 -0700 Subject: [PATCH 5/7] doc --- .../conversational_feedback/chain.py | 32 ++++++++++++------- 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/templates/conversational-feedback/conversational_feedback/chain.py b/templates/conversational-feedback/conversational_feedback/chain.py index b4e1299b9c1a8..b2444e76e6d46 100644 --- a/templates/conversational-feedback/conversational_feedback/chain.py +++ b/templates/conversational-feedback/conversational_feedback/chain.py @@ -54,9 +54,9 @@ def format_messages(input: dict) -> List[BaseMessage]: def format_dialog(input: dict) -> dict: - """Format the dialog for the evaluator.""" + """Format messages and convert to a single string.""" chat_history = format_messages(input) - formatted_dialog = get_buffer_string(chat_history) # + f"\nhuman: {input['text']}" + formatted_dialog = get_buffer_string(chat_history) + f"\nhuman: {input['text']}" return {"dialog": formatted_dialog} @@ -83,6 +83,8 @@ def normalize_score(response: dict) -> dict: class ResponseEffectivenessEvaluator(RunEvaluator): + """Evaluate the chat bot based the subsequent user responses.""" + def __init__(self, evaluator_runnable: Runnable) -> None: super().__init__() self.runnable = evaluator_runnable @@ -90,13 +92,15 @@ def __init__(self, evaluator_runnable: Runnable) -> None: def evaluate_run( self, run: Run, example: Optional[Example] = None ) -> EvaluationResult: - # This particular evaluator is configured to evaluate the previous - # AI response. It uses the user's followup question or comment as - # additional grounding for its grade. + # This evaluator grades the AI's PREVIOUS response. + # If no chat history is present, there isn't anything to evaluate + # (it's the user's first message) if not run.inputs.get("chat_history"): return EvaluationResult( key="response_effectiveness", comment="No chat history present." ) + # This only occurs if the client isn't correctly sending the run IDs + # of the previous calls. elif "last_run_id" not in run.inputs: return EvaluationResult( key="response_effectiveness", comment="No last run ID present." @@ -106,12 +110,17 @@ def evaluate_run( return EvaluationResult( **eval_grade, key="response_effectiveness", - target_run_id=target_run_id, + target_run_id=target_run_id, # Requires langsmith >= 0.0.54 ) -### The actual deployed chain (we are keeping it simple for this example) -# The main focus of this template is the evaluator above, not the chain itself. +############################################################################### +# | The chat bot definition +# | This is what is actually exposed by LangServe in the API +# | It can be any chain that accepts the ChainInput schema and returns a str +# | all that is required is the with_config() call at the end to add the +# V evaluators as "listeners" to the chain. +# ############################################################################ class ChainInput(BaseModel): @@ -149,10 +158,9 @@ def format_chat_history(chain_input: ChainInput) -> dict: # with the new `tool.langserve.export_attr` chain = ( (format_chat_history | _prompt | _model | StrOutputParser()) - # This is to populate the openapi spec for LangServe - .with_types(input_type=ChainInput) - # This is to add the evluators as "listeners" - # and to customize the name of the chain + # This is to add the evaluators as "listeners" + # and to customize the name of the chain. + # Any chain that accepts a compatible input type 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zf;?;%;}J;f7XIjJ#91?%g70@O2G6CKiJpf}De1!g0sQk72^GG4ZcQ9_ih=%L8D)>R zKN7Q^E2lQ(Y9_n#|9aXH{e1c_uE?Q->ob1hG2$@{9)nPX?5Nk0x>~*VZovZ?FH-c`sOK+H`jy&3?=8T+5$tF6kj9BzKVq0guK;~B9hKr^au8ebpp%d@I! z3KhPaQb&drOR*Rm#MN2$37^O)3JU$Dcz!^JuL>RnEKG6&qoY;1vd)N0el zq*OM2NG_XA_pUApepLVNy-ALTX{AS44!E=vR*l5H-F2b=7^I|Tb{tYttV~qd-nAyf zd-UDR=sO<}Uv2%>akZ-oN1lpE|Me*$&|vj-iOLB(mu+0Vc>4eI@IU84?mrj7|GO6f ctSE;l;GLTUa|%Ub8SroaUi&?HyS*;`5AoS)$N&HU literal 0 HcmV?d00001 From 04f313457f06d82d790f5653b94c3b7a5e1ddce3 Mon Sep 17 00:00:00 2001 From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Date: Tue, 31 Oct 2023 08:18:57 -0700 Subject: [PATCH 7/7] rename --- .../LICENSE | 0 .../README.md | 10 +- .../chat_bot_feedback}/__init__.py | 0 .../chat_bot_feedback}/chain.py | 0 templates/chat-bot-feedback/poetry.lock | 1526 +++++++++++++++++ .../pyproject.toml | 6 +- .../static/chat_interaction.png | Bin .../static/evaluator.png | Bin .../tests/__init__.py | 0 9 files changed, 1534 insertions(+), 8 deletions(-) rename templates/{conversational-feedback => chat-bot-feedback}/LICENSE (100%) rename templates/{conversational-feedback => chat-bot-feedback}/README.md (92%) rename templates/{conversational-feedback/conversational_feedback => chat-bot-feedback/chat_bot_feedback}/__init__.py (100%) rename templates/{conversational-feedback/conversational_feedback => chat-bot-feedback/chat_bot_feedback}/chain.py (100%) create mode 100644 templates/chat-bot-feedback/poetry.lock rename templates/{conversational-feedback => chat-bot-feedback}/pyproject.toml (81%) rename templates/{conversational-feedback => chat-bot-feedback}/static/chat_interaction.png (100%) rename templates/{conversational-feedback => chat-bot-feedback}/static/evaluator.png (100%) rename templates/{conversational-feedback => chat-bot-feedback}/tests/__init__.py (100%) diff --git a/templates/conversational-feedback/LICENSE b/templates/chat-bot-feedback/LICENSE similarity index 100% rename from templates/conversational-feedback/LICENSE rename to templates/chat-bot-feedback/LICENSE diff --git a/templates/conversational-feedback/README.md b/templates/chat-bot-feedback/README.md similarity index 92% rename from templates/conversational-feedback/README.md rename to templates/chat-bot-feedback/README.md index e51e7ee0c9cb9..f9a37b03d8898 100644 --- a/templates/conversational-feedback/README.md +++ b/templates/chat-bot-feedback/README.md @@ -1,6 +1,6 @@ -# Chat Feedback Template +# Chat Bot Feedback Template -This template shows how to evaluate your chat bot without explicit user feedback. It defines a simple chat bot in [chain.py](./conversational_feedback/chain.py) and custom evaluator that scores bot response effectiveness based on the subsequent user response. You can apply this run evaluator to your own chat bot by calling `with_config` on the chat bot before serving. You can also directly deploy your chat app using this template. +This template shows how to evaluate your chat bot without explicit user feedback. It defines a simple chat bot in [chain.py](./chat_bot_feedback/chain.py) and custom evaluator that scores bot response effectiveness based on the subsequent user response. You can apply this run evaluator to your own chat bot by calling `with_config` on the chat bot before serving. You can also directly deploy your chat app using this template. [Chat bots](https://python.langchain.com/docs/use_cases/chatbots) are one of the most common interfaces for deploying LLMs. The quality of chat bots varies, making continuous development important. But users are wont to leave explicit feedback through mechanisms like thumbs-up or thumbs-down buttons. Furthermore, traditional analytics such as "session length" or "conversation length" often lack clarity. However, multi-turn conversations with a chat bot can provide a wealth of information, which we can transform into metrics for fine-tuning, evaluation, and product analytics. @@ -58,9 +58,9 @@ export LANGCHAIN_PROJECT=my-project # Set to the project you want to save to If deploying via `LangServe`, we recommend configuring the server to return callback events as well. This will ensure the backend traces are included in whatever traces you generate using the `RemoteRunnable`. ```python -from conversational_feedback.chain import chain +from chat_bot_feedback.chain import chain -add_routes(app, chain, path="/conversational-feedback", include_callback_events=True) +add_routes(app, chain, path="/chat-bot-feedback", include_callback_events=True) ``` With the server running, you can use the following code snippet to stream the chat bot responses for a 2 turn conversation. @@ -73,7 +73,7 @@ from langchain.callbacks.manager import tracing_v2_enabled from langchain.schema import BaseMessage, AIMessage, HumanMessage # Update with the URL provided by your LangServe server -chain = RemoteRunnable("http://127.0.0.1:8031/conversational-feedback") +chain = RemoteRunnable("http://127.0.0.1:8031/chat-bot-feedback") def stream_content( text: str, diff --git a/templates/conversational-feedback/conversational_feedback/__init__.py 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