mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
609 lines
21 KiB
Python
609 lines
21 KiB
Python
from __future__ import annotations
|
|
|
|
import json
|
|
import logging
|
|
import os
|
|
from dataclasses import dataclass, field
|
|
from hashlib import md5
|
|
from typing import Any, Iterable, List, Optional, Tuple, Type
|
|
|
|
import requests
|
|
from langchain_core.documents import Document
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.pydantic_v1 import Field
|
|
from langchain_core.vectorstores import VectorStore, VectorStoreRetriever
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@dataclass
|
|
class SummaryConfig:
|
|
"""Configuration for summary generation.
|
|
|
|
is_enabled: True if summary is enabled, False otherwise
|
|
max_results: maximum number of results to summarize
|
|
response_lang: requested language for the summary
|
|
prompt_name: name of the prompt to use for summarization
|
|
(see https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer)
|
|
"""
|
|
|
|
is_enabled: bool = False
|
|
max_results: int = 7
|
|
response_lang: str = "eng"
|
|
prompt_name: str = "vectara-summary-ext-v1.2.0"
|
|
|
|
|
|
@dataclass
|
|
class MMRConfig:
|
|
"""Configuration for Maximal Marginal Relevance (MMR) search.
|
|
|
|
is_enabled: True if MMR is enabled, False otherwise
|
|
mmr_k: number of results to fetch for MMR, defaults to 50
|
|
diversity_bias: number between 0 and 1 that determines the degree
|
|
of diversity among the results with 0 corresponding
|
|
to minimum diversity and 1 to maximum diversity.
|
|
Defaults to 0.3.
|
|
Note: diversity_bias is equivalent 1-lambda_mult
|
|
where lambda_mult is the value often used in max_marginal_relevance_search()
|
|
We chose to use that since we believe it's more intuitive to the user.
|
|
"""
|
|
|
|
is_enabled: bool = False
|
|
mmr_k: int = 50
|
|
diversity_bias: float = 0.3
|
|
|
|
|
|
@dataclass
|
|
class VectaraQueryConfig:
|
|
"""Configuration for Vectara query.
|
|
|
|
k: Number of Documents to return. Defaults to 10.
|
|
lambda_val: lexical match parameter for hybrid search.
|
|
filter Dictionary of argument(s) to filter on metadata. For example a
|
|
filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see
|
|
https://docs.vectara.com/docs/search-apis/sql/filter-overview
|
|
for more details.
|
|
score_threshold: minimal score threshold for the result.
|
|
If defined, results with score less than this value will be
|
|
filtered out.
|
|
n_sentence_context: number of sentences before/after the matching segment
|
|
to add, defaults to 2
|
|
mmr_config: MMRConfig configuration dataclass
|
|
summary_config: SummaryConfig configuration dataclass
|
|
"""
|
|
|
|
k: int = 10
|
|
lambda_val: float = 0.0
|
|
filter: str = ""
|
|
score_threshold: Optional[float] = None
|
|
n_sentence_context: int = 2
|
|
mmr_config: MMRConfig = field(default_factory=MMRConfig)
|
|
summary_config: SummaryConfig = field(default_factory=SummaryConfig)
|
|
|
|
|
|
class Vectara(VectorStore):
|
|
"""`Vectara API` vector store.
|
|
|
|
See (https://vectara.com).
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.vectorstores import Vectara
|
|
|
|
vectorstore = Vectara(
|
|
vectara_customer_id=vectara_customer_id,
|
|
vectara_corpus_id=vectara_corpus_id,
|
|
vectara_api_key=vectara_api_key
|
|
)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
vectara_customer_id: Optional[str] = None,
|
|
vectara_corpus_id: Optional[str] = None,
|
|
vectara_api_key: Optional[str] = None,
|
|
vectara_api_timeout: int = 120,
|
|
source: str = "langchain",
|
|
):
|
|
"""Initialize with Vectara API."""
|
|
self._vectara_customer_id = vectara_customer_id or os.environ.get(
|
|
"VECTARA_CUSTOMER_ID"
|
|
)
|
|
self._vectara_corpus_id = vectara_corpus_id or os.environ.get(
|
|
"VECTARA_CORPUS_ID"
|
|
)
|
|
self._vectara_api_key = vectara_api_key or os.environ.get("VECTARA_API_KEY")
|
|
if (
|
|
self._vectara_customer_id is None
|
|
or self._vectara_corpus_id is None
|
|
or self._vectara_api_key is None
|
|
):
|
|
logger.warning(
|
|
"Can't find Vectara credentials, customer_id or corpus_id in "
|
|
"environment."
|
|
)
|
|
else:
|
|
logger.debug(f"Using corpus id {self._vectara_corpus_id}")
|
|
self._source = source
|
|
|
|
self._session = requests.Session() # to reuse connections
|
|
adapter = requests.adapters.HTTPAdapter(max_retries=3)
|
|
self._session.mount("http://", adapter)
|
|
self.vectara_api_timeout = vectara_api_timeout
|
|
|
|
@property
|
|
def embeddings(self) -> Optional[Embeddings]:
|
|
return None
|
|
|
|
def _get_post_headers(self) -> dict:
|
|
"""Returns headers that should be attached to each post request."""
|
|
return {
|
|
"x-api-key": self._vectara_api_key,
|
|
"customer-id": self._vectara_customer_id,
|
|
"Content-Type": "application/json",
|
|
"X-Source": self._source,
|
|
}
|
|
|
|
def _delete_doc(self, doc_id: str) -> bool:
|
|
"""
|
|
Delete a document from the Vectara corpus.
|
|
|
|
Args:
|
|
url (str): URL of the page to delete.
|
|
doc_id (str): ID of the document to delete.
|
|
|
|
Returns:
|
|
bool: True if deletion was successful, False otherwise.
|
|
"""
|
|
body = {
|
|
"customer_id": self._vectara_customer_id,
|
|
"corpus_id": self._vectara_corpus_id,
|
|
"document_id": doc_id,
|
|
}
|
|
response = self._session.post(
|
|
"https://api.vectara.io/v1/delete-doc",
|
|
data=json.dumps(body),
|
|
verify=True,
|
|
headers=self._get_post_headers(),
|
|
timeout=self.vectara_api_timeout,
|
|
)
|
|
if response.status_code != 200:
|
|
logger.error(
|
|
f"Delete request failed for doc_id = {doc_id} with status code "
|
|
f"{response.status_code}, reason {response.reason}, text "
|
|
f"{response.text}"
|
|
)
|
|
return False
|
|
return True
|
|
|
|
def _index_doc(self, doc: dict, use_core_api: bool = False) -> str:
|
|
request: dict[str, Any] = {}
|
|
request["customer_id"] = self._vectara_customer_id
|
|
request["corpus_id"] = self._vectara_corpus_id
|
|
request["document"] = doc
|
|
|
|
api_endpoint = (
|
|
"https://api.vectara.io/v1/core/index"
|
|
if use_core_api
|
|
else "https://api.vectara.io/v1/index"
|
|
)
|
|
response = self._session.post(
|
|
headers=self._get_post_headers(),
|
|
url=api_endpoint,
|
|
data=json.dumps(request),
|
|
timeout=self.vectara_api_timeout,
|
|
verify=True,
|
|
)
|
|
|
|
status_code = response.status_code
|
|
|
|
result = response.json()
|
|
status_str = result["status"]["code"] if "status" in result else None
|
|
if status_code == 409 or status_str and (status_str == "ALREADY_EXISTS"):
|
|
return "E_ALREADY_EXISTS"
|
|
elif status_str and (status_str == "FORBIDDEN"):
|
|
return "E_NO_PERMISSIONS"
|
|
else:
|
|
return "E_SUCCEEDED"
|
|
|
|
def add_files(
|
|
self,
|
|
files_list: Iterable[str],
|
|
metadatas: Optional[List[dict]] = None,
|
|
**kwargs: Any,
|
|
) -> List[str]:
|
|
"""
|
|
Vectara provides a way to add documents directly via our API where
|
|
pre-processing and chunking occurs internally in an optimal way
|
|
This method provides a way to use that API in LangChain
|
|
|
|
Args:
|
|
files_list: Iterable of strings, each representing a local file path.
|
|
Files could be text, HTML, PDF, markdown, doc/docx, ppt/pptx, etc.
|
|
see API docs for full list
|
|
metadatas: Optional list of metadatas associated with each file
|
|
|
|
Returns:
|
|
List of ids associated with each of the files indexed
|
|
"""
|
|
doc_ids = []
|
|
for inx, file in enumerate(files_list):
|
|
if not os.path.exists(file):
|
|
logger.error(f"File {file} does not exist, skipping")
|
|
continue
|
|
md = metadatas[inx] if metadatas else {}
|
|
files: dict = {
|
|
"file": (file, open(file, "rb")),
|
|
"doc_metadata": json.dumps(md),
|
|
}
|
|
headers = self._get_post_headers()
|
|
headers.pop("Content-Type")
|
|
response = self._session.post(
|
|
f"https://api.vectara.io/upload?c={self._vectara_customer_id}&o={self._vectara_corpus_id}&d=True",
|
|
files=files,
|
|
verify=True,
|
|
headers=headers,
|
|
timeout=self.vectara_api_timeout,
|
|
)
|
|
|
|
if response.status_code == 409:
|
|
doc_id = response.json()["document"]["documentId"]
|
|
logger.info(
|
|
f"File {file} already exists on Vectara (doc_id={doc_id}), skipping"
|
|
)
|
|
elif response.status_code == 200:
|
|
doc_id = response.json()["document"]["documentId"]
|
|
doc_ids.append(doc_id)
|
|
else:
|
|
logger.info(f"Error indexing file {file}: {response.json()}")
|
|
|
|
return doc_ids
|
|
|
|
def add_texts(
|
|
self,
|
|
texts: Iterable[str],
|
|
metadatas: Optional[List[dict]] = None,
|
|
doc_metadata: Optional[dict] = None,
|
|
**kwargs: Any,
|
|
) -> List[str]:
|
|
"""Run more texts through the embeddings and add to the vectorstore.
|
|
|
|
Args:
|
|
texts: Iterable of strings to add to the vectorstore.
|
|
metadatas: Optional list of metadatas associated with the texts.
|
|
doc_metadata: optional metadata for the document
|
|
|
|
This function indexes all the input text strings in the Vectara corpus as a
|
|
single Vectara document, where each input text is considered a "section" and the
|
|
metadata are associated with each section.
|
|
if 'doc_metadata' is provided, it is associated with the Vectara document.
|
|
|
|
Returns:
|
|
document ID of the document added
|
|
|
|
"""
|
|
doc_hash = md5()
|
|
for t in texts:
|
|
doc_hash.update(t.encode())
|
|
doc_id = doc_hash.hexdigest()
|
|
if metadatas is None:
|
|
metadatas = [{} for _ in texts]
|
|
if doc_metadata:
|
|
doc_metadata["source"] = "langchain"
|
|
else:
|
|
doc_metadata = {"source": "langchain"}
|
|
|
|
use_core_api = kwargs.get("use_core_api", False)
|
|
section_key = "parts" if use_core_api else "section"
|
|
doc = {
|
|
"document_id": doc_id,
|
|
"metadataJson": json.dumps(doc_metadata),
|
|
section_key: [
|
|
{"text": text, "metadataJson": json.dumps(md)}
|
|
for text, md in zip(texts, metadatas)
|
|
],
|
|
}
|
|
|
|
success_str = self._index_doc(doc, use_core_api=use_core_api)
|
|
|
|
if success_str == "E_ALREADY_EXISTS":
|
|
self._delete_doc(doc_id)
|
|
self._index_doc(doc)
|
|
elif success_str == "E_NO_PERMISSIONS":
|
|
print( # noqa: T201
|
|
"""No permissions to add document to Vectara.
|
|
Check your corpus ID, customer ID and API key"""
|
|
)
|
|
return [doc_id]
|
|
|
|
def vectara_query(
|
|
self,
|
|
query: str,
|
|
config: VectaraQueryConfig,
|
|
**kwargs: Any,
|
|
) -> List[Tuple[Document, float]]:
|
|
"""Run a Vectara query
|
|
|
|
Args:
|
|
query: Text to look up documents similar to.
|
|
config: VectaraQueryConfig object
|
|
Returns:
|
|
A list of k Documents matching the given query
|
|
If summary is enabled, last document is the summary text with 'summary'=True
|
|
"""
|
|
if isinstance(config.mmr_config, dict):
|
|
config.mmr_config = MMRConfig(**config.mmr_config)
|
|
if isinstance(config.summary_config, dict):
|
|
config.summary_config = SummaryConfig(**config.summary_config)
|
|
|
|
data = {
|
|
"query": [
|
|
{
|
|
"query": query,
|
|
"start": 0,
|
|
"numResults": (
|
|
config.mmr_config.mmr_k
|
|
if config.mmr_config.is_enabled
|
|
else config.k
|
|
),
|
|
"contextConfig": {
|
|
"sentencesBefore": config.n_sentence_context,
|
|
"sentencesAfter": config.n_sentence_context,
|
|
},
|
|
"corpusKey": [
|
|
{
|
|
"customerId": self._vectara_customer_id,
|
|
"corpusId": self._vectara_corpus_id,
|
|
"metadataFilter": config.filter,
|
|
"lexicalInterpolationConfig": {"lambda": config.lambda_val},
|
|
}
|
|
],
|
|
}
|
|
]
|
|
}
|
|
if config.mmr_config.is_enabled:
|
|
data["query"][0]["rerankingConfig"] = {
|
|
"rerankerId": 272725718,
|
|
"mmrConfig": {"diversityBias": config.mmr_config.diversity_bias},
|
|
}
|
|
if config.summary_config.is_enabled:
|
|
data["query"][0]["summary"] = [
|
|
{
|
|
"maxSummarizedResults": config.summary_config.max_results,
|
|
"responseLang": config.summary_config.response_lang,
|
|
"summarizerPromptName": config.summary_config.prompt_name,
|
|
}
|
|
]
|
|
|
|
response = self._session.post(
|
|
headers=self._get_post_headers(),
|
|
url="https://api.vectara.io/v1/query",
|
|
data=json.dumps(data),
|
|
timeout=self.vectara_api_timeout,
|
|
)
|
|
|
|
if response.status_code != 200:
|
|
logger.error(
|
|
"Query failed %s",
|
|
f"(code {response.status_code}, reason {response.reason}, details "
|
|
f"{response.text})",
|
|
)
|
|
return [], "" # type: ignore[return-value]
|
|
|
|
result = response.json()
|
|
|
|
if config.score_threshold:
|
|
responses = [
|
|
r
|
|
for r in result["responseSet"][0]["response"]
|
|
if r["score"] > config.score_threshold
|
|
]
|
|
else:
|
|
responses = result["responseSet"][0]["response"]
|
|
documents = result["responseSet"][0]["document"]
|
|
|
|
metadatas = []
|
|
for x in responses:
|
|
md = {m["name"]: m["value"] for m in x["metadata"]}
|
|
doc_num = x["documentIndex"]
|
|
doc_md = {m["name"]: m["value"] for m in documents[doc_num]["metadata"]}
|
|
if "source" not in doc_md:
|
|
doc_md["source"] = "vectara"
|
|
md.update(doc_md)
|
|
metadatas.append(md)
|
|
|
|
res = [
|
|
(
|
|
Document(
|
|
page_content=x["text"],
|
|
metadata=md,
|
|
),
|
|
x["score"],
|
|
)
|
|
for x, md in zip(responses, metadatas)
|
|
]
|
|
|
|
if config.mmr_config.is_enabled:
|
|
res = res[: config.k]
|
|
if config.summary_config.is_enabled:
|
|
summary = result["responseSet"][0]["summary"][0]["text"]
|
|
res.append(
|
|
(Document(page_content=summary, metadata={"summary": True}), 0.0)
|
|
)
|
|
|
|
return res
|
|
|
|
def similarity_search_with_score(
|
|
self,
|
|
query: str,
|
|
**kwargs: Any,
|
|
) -> List[Tuple[Document, float]]:
|
|
"""Return Vectara documents most similar to query, along with scores.
|
|
|
|
Args:
|
|
query: Text to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 10.
|
|
any other querying variable in VectaraQueryConfig like:
|
|
- lambda_val: lexical match parameter for hybrid search.
|
|
- filter: filter string
|
|
- score_threshold: minimal score threshold for the result.
|
|
- n_sentence_context: number of sentences before/after the matching segment
|
|
- mmr_config: optional configuration for MMR (see MMRConfig dataclass)
|
|
- summary_config: optional configuration for summary
|
|
(see SummaryConfig dataclass)
|
|
Returns:
|
|
List of Documents most similar to the query and score for each.
|
|
"""
|
|
config = VectaraQueryConfig(**kwargs)
|
|
docs = self.vectara_query(query, config)
|
|
return docs
|
|
|
|
def similarity_search( # type: ignore[override]
|
|
self,
|
|
query: str,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
"""Return Vectara documents most similar to query, along with scores.
|
|
|
|
Args:
|
|
query: Text to look up documents similar to.
|
|
any other querying variable in VectaraQueryConfig
|
|
|
|
Returns:
|
|
List of Documents most similar to the query
|
|
"""
|
|
docs_and_scores = self.similarity_search_with_score(
|
|
query,
|
|
**kwargs,
|
|
)
|
|
return [doc for doc, _ in docs_and_scores]
|
|
|
|
def max_marginal_relevance_search( # type: ignore[override]
|
|
self,
|
|
query: str,
|
|
fetch_k: int = 50,
|
|
lambda_mult: float = 0.5,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
"""Return docs selected using the maximal marginal relevance.
|
|
Maximal marginal relevance optimizes for similarity to query AND diversity
|
|
among selected documents.
|
|
|
|
Args:
|
|
query: Text to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 5.
|
|
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
|
Defaults to 50
|
|
lambda_mult: Number between 0 and 1 that determines the degree
|
|
of diversity among the results with 0 corresponding
|
|
to maximum diversity and 1 to minimum diversity.
|
|
Defaults to 0.5.
|
|
kwargs: any other querying variable in VectaraQueryConfig
|
|
Returns:
|
|
List of Documents selected by maximal marginal relevance.
|
|
"""
|
|
kwargs["mmr_config"] = MMRConfig(
|
|
is_enabled=True, mmr_k=fetch_k, diversity_bias=1 - lambda_mult
|
|
)
|
|
return self.similarity_search(query, **kwargs)
|
|
|
|
@classmethod
|
|
def from_texts(
|
|
cls: Type[Vectara],
|
|
texts: List[str],
|
|
embedding: Optional[Embeddings] = None,
|
|
metadatas: Optional[List[dict]] = None,
|
|
**kwargs: Any,
|
|
) -> Vectara:
|
|
"""Construct Vectara wrapper from raw documents.
|
|
This is intended to be a quick way to get started.
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.vectorstores import Vectara
|
|
vectara = Vectara.from_texts(
|
|
texts,
|
|
vectara_customer_id=customer_id,
|
|
vectara_corpus_id=corpus_id,
|
|
vectara_api_key=api_key,
|
|
)
|
|
"""
|
|
# Notes:
|
|
# * Vectara generates its own embeddings, so we ignore the provided
|
|
# embeddings (required by interface)
|
|
# * when metadatas[] are provided they are associated with each "part"
|
|
# in Vectara. doc_metadata can be used to provide additional metadata
|
|
# for the document itself (applies to all "texts" in this call)
|
|
doc_metadata = kwargs.pop("doc_metadata", {})
|
|
vectara = cls(**kwargs)
|
|
vectara.add_texts(texts, metadatas, doc_metadata=doc_metadata, **kwargs)
|
|
return vectara
|
|
|
|
@classmethod
|
|
def from_files(
|
|
cls: Type[Vectara],
|
|
files: List[str],
|
|
embedding: Optional[Embeddings] = None,
|
|
metadatas: Optional[List[dict]] = None,
|
|
**kwargs: Any,
|
|
) -> Vectara:
|
|
"""Construct Vectara wrapper from raw documents.
|
|
This is intended to be a quick way to get started.
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.vectorstores import Vectara
|
|
vectara = Vectara.from_files(
|
|
files_list,
|
|
vectara_customer_id=customer_id,
|
|
vectara_corpus_id=corpus_id,
|
|
vectara_api_key=api_key,
|
|
)
|
|
"""
|
|
# Note: Vectara generates its own embeddings, so we ignore the provided
|
|
# embeddings (required by interface)
|
|
vectara = cls(**kwargs)
|
|
vectara.add_files(files, metadatas)
|
|
return vectara
|
|
|
|
|
|
class VectaraRetriever(VectorStoreRetriever):
|
|
"""Retriever for `Vectara`."""
|
|
|
|
vectorstore: Vectara
|
|
"""Vectara vectorstore."""
|
|
search_kwargs: dict = Field(
|
|
default_factory=lambda: {
|
|
"lambda_val": 0.0,
|
|
"k": 5,
|
|
"filter": "",
|
|
"n_sentence_context": "2",
|
|
"summary_config": SummaryConfig(),
|
|
}
|
|
)
|
|
|
|
"""Search params.
|
|
k: Number of Documents to return. Defaults to 5.
|
|
lambda_val: lexical match parameter for hybrid search.
|
|
filter: Dictionary of argument(s) to filter on metadata. For example a
|
|
filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see
|
|
https://docs.vectara.com/docs/search-apis/sql/filter-overview
|
|
for more details.
|
|
n_sentence_context: number of sentences before/after the matching segment to add
|
|
"""
|
|
|
|
def add_texts(
|
|
self,
|
|
texts: List[str],
|
|
metadatas: Optional[List[dict]] = None,
|
|
doc_metadata: Optional[dict] = None,
|
|
) -> None:
|
|
"""Add text to the Vectara vectorstore.
|
|
|
|
Args:
|
|
texts (List[str]): The text
|
|
metadatas (List[dict]): Metadata dicts, must line up with existing store
|
|
"""
|
|
self.vectorstore.add_texts(texts, metadatas, doc_metadata or {})
|