Description: This pull request aims to support generating the correct
generic relevancy scores for different vector stores by refactoring the
relevance score functions and their selection in the base class and
subclasses of VectorStore. This is especially relevant with VectorStores
that require a distance metric upon initialization. Note many of the
current implenetations of `_similarity_search_with_relevance_scores` are
not technically correct, as they just return
`self.similarity_search_with_score(query, k, **kwargs)` without applying
the relevant score function
Also includes changes associated with:
https://github.com/hwchase17/langchain/pull/6564 and
https://github.com/hwchase17/langchain/pull/6494
See more indepth discussion in thread in #6494
Issue:
https://github.com/hwchase17/langchain/issues/6526https://github.com/hwchase17/langchain/issues/6481https://github.com/hwchase17/langchain/issues/6346
Dependencies: None
The changes include:
- Properly handling score thresholding in FAISS
`similarity_search_with_score_by_vector` for the corresponding distance
metric.
- Refactoring the `_similarity_search_with_relevance_scores` method in
the base class and removing it from the subclasses for incorrectly
implemented subclasses.
- Adding a `_select_relevance_score_fn` method in the base class and
implementing it in the subclasses to select the appropriate relevance
score function based on the distance strategy.
- Updating the `__init__` methods of the subclasses to set the
`relevance_score_fn` attribute.
- Removing the `_default_relevance_score_fn` function from the FAISS
class and using the base class's `_euclidean_relevance_score_fn`
instead.
- Adding the `DistanceStrategy` enum to the `utils.py` file and updating
the imports in the vector store classes.
- Updating the tests to import the `DistanceStrategy` enum from the
`utils.py` file.
---------
Co-authored-by: Hanit <37485638+hanit-com@users.noreply.github.com>
Improve documentation for a central use-case, qa / chat over documents.
This will be merged as an update to `index.mdx`
[here](https://python.langchain.com/docs/use_cases/question_answering/).
Testing w/ local Docusaurus server:
```
From `docs` directory:
mkdir _dist
cp -r {docs_skeleton,snippets} _dist
cp -r extras/* _dist/docs_skeleton/docs
cd _dist/docs_skeleton
yarn install
yarn start
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
### Description
Created a Loader to get a list of specific logs from Datadog Logs.
### Dependencies
`datadog_api_client` is required.
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [Xorbits](https://doc.xorbits.io/en/latest/) is an open-source
computing framework that makes it easy to scale data science and machine
learning workloads in parallel. Xorbits can leverage multi cores or GPUs
to accelerate computation on a single machine, or scale out up to
thousands of machines to support processing terabytes of data.
- This PR added support for the Xorbits document loader, which allows
langchain to leverage Xorbits to parallelize and distribute the loading
of data.
- Dependencies: This change requires the Xorbits library to be installed
in order to be used.
`pip install xorbits`
- Request for review: @rlancemartin, @eyurtsev
- Twitter handle: https://twitter.com/Xorbitsio
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Adding async method for CTransformers
- Issue: I've found impossible without this code to run Websockets
inside a FastAPI micro service and a CTransformers model.
- Tag maintainer: Not necessary yet, I don't like to mention directly
- Twitter handle: @_semoal
Adding a maximal_marginal_relevance method to the
MongoDBAtlasVectorSearch vectorstore enhances the user experience by
providing more diverse search results
Issue: #7304
### Summary
Adds an `UnstructuredTSVLoader` for TSV files. Also updates the doc
strings for `UnstructuredCSV` and `UnstructuredExcel` loaders.
### Testing
```python
from langchain.document_loaders.tsv import UnstructuredTSVLoader
loader = UnstructuredTSVLoader(
file_path="example_data/mlb_teams_2012.csv", mode="elements"
)
docs = loader.load()
```
### Description
argument variable client is marked as required in commit
81e5b1ad36 which breaks the default way of
initialization providing only index_id. This commit avoid KeyError
exception when it is initialized without a client variable
### Dependencies
no dependency required
`SpacyTextSplitter` currently uses spacy's statistics-based
`en_core_web_sm` model for sentence splitting. This is a good splitter,
but it's also pretty slow, and in this case it's doing a lot of work
that's not needed given that the spacy parse is then just thrown away.
However, there is also a simple rules-based spacy sentencizer. Using
this is at least an order of magnitude faster than using
`en_core_web_sm` according to my local tests.
Also, spacy sentence tokenization based on `en_core_web_sm` can be sped
up in this case by not doing the NER stage. This shaves some cycles too,
both when loading the model and when parsing the text.
Consequently, this PR adds the option to use the basic spacy
sentencizer, and it disables the NER stage for the current approach,
*which is kept as the default*.
Lastly, when extracting the tokenized sentences, the `text` attribute is
called directly instead of doing the string conversion, which is IMO a
bit more idiomatic.
Hey @hwchase17 -
This PR adds a `ZepMemory` class, improves handling of Zep's message
metadata, and makes it easier for folks building custom chains to
persist metadata alongside their chat history.
We've had plenty confused users unfamiliar with ChatMessageHistory
classes and how to wrap the `ZepChatMessageHistory` in a
`ConversationBufferMemory`. So we've created the `ZepMemory` class as a
light wrapper for `ZepChatMessageHistory`.
Details:
- add ZepMemory, modify notebook to demo use of ZepMemory
- Modify summary to be SystemMessage
- add metadata argument to add_message; add Zep metadata to
Message.additional_kwargs
- support passing in metadata
- Description: Tiny documentation fix. In Python, when defining function
parameters or providing arguments to a function or class constructor, we
do not use the `:` character.
- Issue: N/A
- Dependencies: N/A,
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @mogaal
I just added a parameter to the method get_format_instructions, to
return directly the JSON instructions without the leading instruction
sentence. I'm planning to use it to define the structure of a JSON
object passed in input, the get_format_instructions().
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Have noticed transient ref example misalignment. I believe this is
caused by the logic of assigning an example within the thread executor
rather than before.
Current problems:
1. Evaluating LLMs or Chat models isn't smooth. Even specifying
'generations' as the output inserts a redundant list into the eval
template
2. Configuring input / prediction / reference keys in the
`get_qa_evaluator` function is confusing. Unless you are using a chain
with the default keys, you have to specify all the variables and need to
reason about whether the key corresponds to the traced run's inputs,
outputs or the examples inputs or outputs.
Proposal:
- Configure the run evaluator according to a model. Use the model type
and input/output keys to assert compatibility where possible. Only need
to specify a reference_key for certain evaluators (which is less
confusing than specifying input keys)
When does this work:
- If you have your langchain model available (assumed always for
run_on_dataset flow)
- If you are evaluating an LLM, Chat model, or chain
- If the LLM or chat models are traced by langchain (wouldn't work if
you add an incompatible schema via the REST API)
When would this fail:
- Currently if you directly create an example from an LLM run, the
outputs are generations with all the extra metadata present. A simple
`example_key` and dumping all to the template could make the evaluations
unreliable
- Doesn't help if you're not using the low level API
- If you want to instantiate the evaluator without instantiating your
chain or LLM (maybe common for monitoring, for instance) -> could also
load from run or run type though
What's ugly:
- Personally think it's better to load evaluators one by one since
passing a config down is pretty confusing.
- Lots of testing needs to be added
- Inconsistent in that it makes a separate run and example input mapper
instead of the original `RunEvaluatorInputMapper`, which maps a run and
example to a single input.
Example usage running the for an LLM, Chat Model, and Agent.
```
# Test running for the string evaluators
evaluator_names = ["qa", "criteria"]
model = ChatOpenAI()
configured_evaluators = load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer")
run_on_dataset(ds_name, model, run_evaluators=configured_evaluators)
```
<details>
<summary>Full code with dataset upload</summary>
```
## Create dataset
from langchain.evaluation.run_evaluators.loading import load_run_evaluators_for_model
from langchain.evaluation import load_dataset
import pandas as pd
lcds = load_dataset("llm-math")
df = pd.DataFrame(lcds)
from uuid import uuid4
from langsmith import Client
client = Client()
ds_name = "llm-math - " + str(uuid4())[0:8]
ds = client.upload_dataframe(df, name=ds_name, input_keys=["question"], output_keys=["answer"])
## Define the models we'll test over
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import tool
llm = OpenAI(temperature=0)
chat_model = ChatOpenAI(temperature=0)
@tool
def sum(a: float, b: float) -> float:
"""Add two numbers"""
return a + b
def construct_agent():
return initialize_agent(
llm=chat_model,
tools=[sum],
agent=AgentType.OPENAI_MULTI_FUNCTIONS,
)
agent = construct_agent()
# Test running for the string evaluators
evaluator_names = ["qa", "criteria"]
models = [llm, chat_model, agent]
run_evaluators = []
for model in models:
run_evaluators.append(load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer"))
# Run on LLM, Chat Model, and Agent
from langchain.client.runner_utils import run_on_dataset
to_test = [llm, chat_model, construct_agent]
for model, configured_evaluators in zip(to_test, run_evaluators):
run_on_dataset(ds_name, model, run_evaluators=configured_evaluators, verbose=True)
```
</details>
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
fixes https://github.com/hwchase17/langchain/issues/7289
A simple fix of the buggy output of `graph_qa`. If we have several
entities with triplets then the last entry of `triplets` for a given
entity merges with the first entry of the `triplets` of the next entity.
- Description: `MlflowCallbackHandler` fails with `KeyError: "['name']
not in index"`. See https://github.com/hwchase17/langchain/issues/5770
for more details. Root cause is that LangChain does not pass "name" as a
part of `serialized` argument to `on_llm_start()` callback method. The
commit where this change was made is probably this:
18af149e91.
My bug fix derives "name" from "id" field.
- Issue: https://github.com/hwchase17/langchain/issues/5770
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
Adding a callback handler for Context. Context is a product analytics
platform for AI chat experiences to help you understand how users are
interacting with your product.
I've added the callback library + an example notebook showing its use.
### Dependencies
Requires the user to install the `context-python` library. The library
is lazily-loaded when the callback is instantiated.
### Announcing the feature
We spoke with Harrison a few weeks ago about also doing a blog post
announcing our integration, so will coordinate this with him. Our
Twitter handle for the company is @getcontextai, and the founders are
@_agamble and @HenrySG.
Thanks in advance!
**Title:** Add verbose parameter for llamacpp
**Description:**
This pull request adds a 'verbose' parameter to the llamacpp module. The
'verbose' parameter, when set to True, will enable the output of
detailed logs during the execution of the Llama model. This added
parameter can aid in debugging and understanding the internal processes
of the module.
The verbose parameter is a boolean that prints verbose output to stderr
when set to True. By default, the verbose parameter is set to True but
can be toggled off if less output is desired. This new parameter has
been added to the `validate_environment` method of the `LlamaCpp` class
which initializes the `llama_cpp.Llama` API:
```python
class LlamaCpp(LLM):
...
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
...
model_param_names = [
...
"verbose", # New verbose parameter added
]
...
values["client"] = Llama(model_path, **model_params)
...
```
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
