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Merge branch 'master' into eugene/move_memories_2

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This commit is contained in:
Eugene Yurtsev
2024-04-23 16:11:25 -04:00
parent aaf376a681 a7c347ab35
commit 8aea083bf3
9 changed files with 165 additions and 25 deletions
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3
docs/docs/use_cases/question_answering/chat_history.ipynb
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@@ -403,16 +403,13 @@
"outputs": [],
"source": [
"import bs4\n",
"from langchain import hub\n",
"from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n",
"from langchain.chains.combine_documents import create_stuff_documents_chain\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.chat_message_histories import ChatMessageHistory\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"from langchain_core.chat_history import BaseChatMessageHistory\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",

3
libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py
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@@ -8,10 +8,9 @@ from langchain_community.tools.vectorstore.tool import (
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import BaseTool
from langchain_core.vectorstores import VectorStore
from langchain.tools import BaseTool
class VectorStoreInfo(BaseModel):
"""Information about a VectorStore."""

76
libs/langchain/langchain/chains/conversational_retrieval/base.py
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@@ -7,6 +7,7 @@ from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
from langchain_core._api import deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
@@ -233,9 +234,84 @@ class BaseConversationalRetrievalChain(Chain):
super().save(file_path)
@deprecated(
since="0.1.17",
alternative=(
"create_history_aware_retriever together with create_retrieval_chain "
"(see example in docstring)"
),
removal="0.3.0",
)
class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
"""Chain for having a conversation based on retrieved documents.
This class is deprecated. See below for an example implementation using
`create_retrieval_chain`. Additional walkthroughs can be found at
https://python.langchain.com/docs/use_cases/question_answering/chat_history
.. code-block:: python
from langchain.chains import (
create_history_aware_retriever,
create_retrieval_chain,
)
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI
retriever = ... # Your retriever
llm = ChatOpenAI()
# Contextualize question
contextualize_q_system_prompt = (
"Given a chat history and the latest user question "
"which might reference context in the chat history, "
"formulate a standalone question which can be understood "
"without the chat history. Do NOT answer the question, just "
"reformulate it if needed and otherwise return it as is."
)
contextualize_q_prompt = ChatPromptTemplate.from_messages(
[
("system", contextualize_q_system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
]
)
history_aware_retriever = create_history_aware_retriever(
llm, retriever, contextualize_q_prompt
)
# Answer question
qa_system_prompt = (
"You are an assistant for question-answering tasks. Use "
"the following pieces of retrieved context to answer the "
"question. If you don't know the answer, just say that you "
"don't know. Use three sentences maximum and keep the answer "
"concise."
"\n\n"
"{context}"
)
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", qa_system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
]
)
# Below we use create_stuff_documents_chain to feed all retrieved context
# into the LLM. Note that we can also use StuffDocumentsChain and other
# instances of BaseCombineDocumentsChain.
question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
rag_chain = create_retrieval_chain(
history_aware_retriever, question_answer_chain
)
# Usage:
chat_history = [] # Collect chat history here (a sequence of messages)
rag_chain.invoke({"input": query, "chat_history": chat_history})
This chain takes in chat history (a list of messages) and new questions,
and then returns an answer to that question.
The algorithm for this chain consists of three parts:

17
libs/langchain/langchain/chains/flare/base.py
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@@ -5,7 +5,6 @@ from abc import abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Tuple
import numpy as np
from langchain_community.llms.openai import OpenAI
from langchain_core.callbacks import (
CallbackManagerForChainRun,
)
@@ -56,11 +55,7 @@ class _ResponseChain(LLMChain):
class _OpenAIResponseChain(_ResponseChain):
"""Chain that generates responses from user input and context."""
llm: OpenAI = Field(
default_factory=lambda: OpenAI(
max_tokens=32, model_kwargs={"logprobs": 1}, temperature=0
)
)
llm: BaseLanguageModel
def _extract_tokens_and_log_probs(
self, generations: List[Generation]
@@ -118,7 +113,7 @@ class FlareChain(Chain):
question_generator_chain: QuestionGeneratorChain
"""Chain that generates questions from uncertain spans."""
response_chain: _ResponseChain = Field(default_factory=_OpenAIResponseChain)
response_chain: _ResponseChain
"""Chain that generates responses from user input and context."""
output_parser: FinishedOutputParser = Field(default_factory=FinishedOutputParser)
"""Parser that determines whether the chain is finished."""
@@ -255,6 +250,14 @@ class FlareChain(Chain):
Returns:
FlareChain class with the given language model.
"""
try:
from langchain_openai import OpenAI
except ImportError:
raise ImportError(
"OpenAI is required for FlareChain. "
"Please install langchain-openai."
"pip install langchain-openai"
)
question_gen_chain = QuestionGeneratorChain(llm=llm)
response_llm = OpenAI(
max_tokens=max_generation_len, model_kwargs={"logprobs": 1}, temperature=0

23
libs/langchain/langchain/chains/llm.py
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@@ -4,6 +4,7 @@ from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
from langchain_core._api import deprecated
from langchain_core.callbacks import (
AsyncCallbackManager,
AsyncCallbackManagerForChainRun,
@@ -34,9 +35,31 @@ from langchain_core.utils.input import get_colored_text
from langchain.chains.base import Chain
@deprecated(
since="0.1.17",
alternative="RunnableSequence, e.g., `prompt | llm`",
removal="0.3.0",
)
class LLMChain(Chain):
"""Chain to run queries against LLMs.
This class is deprecated. See below for an example implementation using
LangChain runnables:
.. code-block:: python
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"], template=prompt_template
)
llm = OpenAI()
chain = prompt | llm
chain.invoke("your adjective here")
Example:
.. code-block:: python

33
libs/langchain/langchain/chains/retrieval_qa/base.py
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@@ -6,6 +6,7 @@ import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
@@ -194,9 +195,41 @@ class BaseRetrievalQA(Chain):
return {self.output_key: answer}
@deprecated(since="0.1.17", alternative="create_retrieval_chain", removal="0.3.0")
class RetrievalQA(BaseRetrievalQA):
"""Chain for question-answering against an index.
This class is deprecated. See below for an example implementation using
`create_retrieval_chain`:
.. code-block:: python
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
retriever = ... # Your retriever
llm = ChatOpenAI()
system_prompt = (
"Use the given context to answer the question. "
"If you don't know the answer, say you don't know. "
"Use three sentence maximum and keep the answer concise."
"\n\n{context}"
)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
question_answer_chain = create_stuff_documents_chain(llm, prompt)
chain = create_retrieval_chain(retriever, question_answer_chain)
chain.invoke({"input": query})
Example:
.. code-block:: python

8
libs/langchain/langchain/evaluation/comparison/eval_chain.py
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@@ -5,8 +5,6 @@ import logging
import re
from typing import Any, Dict, List, Optional, Union
from langchain_community.chat_models.azure_openai import AzureChatOpenAI
from langchain_community.chat_models.openai import ChatOpenAI
from langchain_core.callbacks.manager import Callbacks
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseOutputParser
@@ -254,10 +252,8 @@ class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):
ValueError: If the input variables are not as expected.
"""
if not (
isinstance(llm, (ChatOpenAI, AzureChatOpenAI))
and llm.model_name.startswith("gpt-4")
):
# Check if the model is GPT-4 if not raise a warning
if not hasattr(llm, "model_name") or not llm.model_name.startswith("gpt-4"):
logger.warning(
"This chain was only tested with GPT-4. \
Performance may be significantly worse with other models."

12
libs/langchain/langchain/evaluation/criteria/eval_chain.py
View File

@@ -193,7 +193,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
Examples
--------
>>> from langchain_community.chat_models import ChatAnthropic
>>> from langchain_anthropic import ChatAnthropic
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = ChatAnthropic(temperature=0)
>>> criteria = {"my-custom-criterion": "Is the submission the most amazing ever?"}
@@ -205,7 +205,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
'score': 0,
}
>>> from langchain_community.chat_models import ChatOpenAI
>>> from langchain_openai import ChatOpenAI
>>> from langchain.evaluation.criteria import LabeledCriteriaEvalChain
>>> llm = ChatOpenAI(model="gpt-4", temperature=0)
>>> criteria = "correctness"
@@ -344,7 +344,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
Examples
--------
>>> from langchain_community.llms import OpenAI
>>> from langchain_openai import OpenAI
>>> from langchain.evaluation.criteria import LabeledCriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = {
@@ -432,7 +432,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
Examples
--------
>>> from langchain_community.llms import OpenAI
>>> from langchain_openai import OpenAI
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = "conciseness"
@@ -487,7 +487,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
Examples
--------
>>> from langchain_community.llms import OpenAI
>>> from langchain_openai import OpenAI
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = "conciseness"
@@ -568,7 +568,7 @@ class LabeledCriteriaEvalChain(CriteriaEvalChain):
Examples
--------
>>> from langchain_community.llms import OpenAI
>>> from langchain_openai import OpenAI
>>> from langchain.evaluation.criteria import LabeledCriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = {

15
libs/langchain/langchain/evaluation/loading.py
View File

@@ -1,7 +1,6 @@
"""Loading datasets and evaluators."""
from typing import Any, Dict, List, Optional, Sequence, Type, Union
from langchain_community.chat_models.openai import ChatOpenAI
from langchain_core.language_models import BaseLanguageModel
from langchain.chains.base import Chain
@@ -131,6 +130,20 @@ def load_evaluator(
evaluator_cls = _EVALUATOR_MAP[evaluator]
if issubclass(evaluator_cls, LLMEvalChain):
try:
try:
from langchain_openai import ChatOpenAI
except ImportError:
try:
from langchain_community.chat_models.openai import ChatOpenAI
except ImportError:
raise ImportError(
"Could not import langchain_openai or fallback onto "
"langchain_community. Please install langchain_openai "
"or specify a language model explicitly. "
"It's recommended to install langchain_openai AND "
"specify a language model explicitly."
)
llm = llm or ChatOpenAI( # type: ignore[call-arg]
model="gpt-4", model_kwargs={"seed": 42}, temperature=0
)