mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
9ffca3b92a
Update imports to use core for the low-hanging fruit changes. Ran
following
```bash
git grep -l 'langchain.schema.runnable' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g'
git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g'
git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g'
git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g'
git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g'
git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g'
git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g'
git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g'
git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g'
git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g'
git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g'
git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g'
git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g'
git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g'
git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g'
git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g'
git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g'
git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g'
```
145 lines
4.7 KiB
Python
145 lines
4.7 KiB
Python
import os
|
|
from operator import itemgetter
|
|
from typing import List, Tuple
|
|
|
|
from langchain.chat_models import ChatOpenAI
|
|
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
|
|
from langchain.prompts.prompt import PromptTemplate
|
|
from langchain.schema import AIMessage, HumanMessage, format_document
|
|
from langchain.vectorstores.zep import CollectionConfig, ZepVectorStore
|
|
from langchain_core.documents import Document
|
|
from langchain_core.messages import BaseMessage
|
|
from langchain_core.output_parsers import StrOutputParser
|
|
from langchain_core.pydantic_v1 import BaseModel, Field
|
|
from langchain_core.runnables import (
|
|
ConfigurableField,
|
|
RunnableBranch,
|
|
RunnableLambda,
|
|
RunnableParallel,
|
|
RunnablePassthrough,
|
|
)
|
|
from langchain_core.runnables.utils import ConfigurableFieldSingleOption
|
|
|
|
ZEP_API_URL = os.environ.get("ZEP_API_URL", "http://localhost:8000")
|
|
ZEP_API_KEY = os.environ.get("ZEP_API_KEY", None)
|
|
ZEP_COLLECTION_NAME = os.environ.get("ZEP_COLLECTION", "langchaintest")
|
|
|
|
collection_config = CollectionConfig(
|
|
name=ZEP_COLLECTION_NAME,
|
|
description="Zep collection for LangChain",
|
|
metadata={},
|
|
embedding_dimensions=1536,
|
|
is_auto_embedded=True,
|
|
)
|
|
|
|
vectorstore = ZepVectorStore(
|
|
collection_name=ZEP_COLLECTION_NAME,
|
|
config=collection_config,
|
|
api_url=ZEP_API_URL,
|
|
api_key=ZEP_API_KEY,
|
|
embedding=None,
|
|
)
|
|
|
|
# Zep offers native, hardware-accelerated MMR. Enabling this will improve
|
|
# the diversity of results, but may also reduce relevance. You can tune
|
|
# the lambda parameter to control the tradeoff between relevance and diversity.
|
|
# Enabling is a good default.
|
|
retriever = vectorstore.as_retriever().configurable_fields(
|
|
search_type=ConfigurableFieldSingleOption(
|
|
id="search_type",
|
|
options={"Similarity": "similarity", "Similarity with MMR Reranking": "mmr"},
|
|
default="mmr",
|
|
name="Search Type",
|
|
description="Type of search to perform: 'similarity' or 'mmr'",
|
|
),
|
|
search_kwargs=ConfigurableField(
|
|
id="search_kwargs",
|
|
name="Search kwargs",
|
|
description=(
|
|
"Specify 'k' for number of results to return and 'lambda_mult' for tuning"
|
|
" MMR relevance vs diversity."
|
|
),
|
|
),
|
|
)
|
|
|
|
# Condense a chat history and follow-up question into a standalone question
|
|
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
|
|
Chat History:
|
|
{chat_history}
|
|
Follow Up Input: {question}
|
|
Standalone question:""" # noqa: E501
|
|
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
|
|
|
|
# RAG answer synthesis prompt
|
|
template = """Answer the question based only on the following context:
|
|
<context>
|
|
{context}
|
|
</context>"""
|
|
ANSWER_PROMPT = ChatPromptTemplate.from_messages(
|
|
[
|
|
("system", template),
|
|
MessagesPlaceholder(variable_name="chat_history"),
|
|
("user", "{question}"),
|
|
]
|
|
)
|
|
|
|
# Conversational Retrieval Chain
|
|
DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
|
|
|
|
|
|
def _combine_documents(
|
|
docs: List[Document],
|
|
document_prompt: PromptTemplate = DEFAULT_DOCUMENT_PROMPT,
|
|
document_separator: str = "\n\n",
|
|
):
|
|
doc_strings = [format_document(doc, document_prompt) for doc in docs]
|
|
return document_separator.join(doc_strings)
|
|
|
|
|
|
def _format_chat_history(chat_history: List[Tuple[str, str]]) -> List[BaseMessage]:
|
|
buffer: List[BaseMessage] = []
|
|
for human, ai in chat_history:
|
|
buffer.append(HumanMessage(content=human))
|
|
buffer.append(AIMessage(content=ai))
|
|
return buffer
|
|
|
|
|
|
_condense_chain = (
|
|
RunnablePassthrough.assign(
|
|
chat_history=lambda x: _format_chat_history(x["chat_history"])
|
|
)
|
|
| CONDENSE_QUESTION_PROMPT
|
|
| ChatOpenAI(temperature=0)
|
|
| StrOutputParser()
|
|
)
|
|
|
|
_search_query = RunnableBranch(
|
|
# If input includes chat_history, we condense it with the follow-up question
|
|
(
|
|
RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
|
|
run_name="HasChatHistoryCheck"
|
|
),
|
|
# Condense follow-up question and chat into a standalone_question
|
|
_condense_chain,
|
|
),
|
|
# Else, we have no chat history, so just pass through the question
|
|
RunnableLambda(itemgetter("question")),
|
|
)
|
|
|
|
|
|
# User input
|
|
class ChatHistory(BaseModel):
|
|
chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}})
|
|
question: str
|
|
|
|
|
|
_inputs = RunnableParallel(
|
|
{
|
|
"question": lambda x: x["question"],
|
|
"chat_history": lambda x: _format_chat_history(x["chat_history"]),
|
|
"context": _search_query | retriever | _combine_documents,
|
|
}
|
|
).with_types(input_type=ChatHistory)
|
|
|
|
chain = _inputs | ANSWER_PROMPT | ChatOpenAI() | StrOutputParser()
|