Improvements
* set default num_workers for ingestion to 0
* upgraded notebooks for avoiding dataset creation ambiguity
* added `force_delete_dataset_by_path`
* bumped deeplake to 3.3.0
* creds arg passing to deeplake object that would allow custom S3
Notes
* please double check if poetry is not messed up (thanks!)
Asks
* Would be great to create a shared slack channel for quick questions
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
The detailed walkthrough of the Weaviate wrapper was pointing to the
getting-started notebook. Fixed it to point to the Weaviable notebook in
the examples folder.
This pull request adds a ChatGPT document loader to the document loaders
module in `langchain/document_loaders/chatgpt.py`. Additionally, it
includes an example Jupyter notebook in
`docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
which uses fake sample data based on the original structure of the
`conversations.json` file.
The following files were added/modified:
- `langchain/document_loaders/__init__.py`
- `langchain/document_loaders/chatgpt.py`
- `docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
-
`docs/modules/indexes/document_loaders/examples/example_data/fake_conversations.json`
This pull request was made in response to the recent release of ChatGPT
data exports by email:
https://help.openai.com/en/articles/7260999-how-do-i-export-my-chatgpt-history
Hi there！
I'm excited to open this PR to add support for using a fully Postgres
syntax compatible database 'AnalyticDB' as a vector.
As AnalyticDB has been proved can be used with AutoGPT,
ChatGPT-Retrieve-Plugin, and LLama-Index, I think it is also good for
you.
AnalyticDB is a distributed Alibaba Cloud-Native vector database. It
works better when data comes to large scale. The PR includes:
- [x] A new memory: AnalyticDBVector
- [x] A suite of integration tests verifies the AnalyticDB integration
I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
First cut of a supabase vectorstore loosely patterned on the langchainjs
equivalent. Doesn't support async operations which is a limitation of
the supabase python client.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
I have noticed a typo error in the `custom_mrkl_agents.ipynb` document
while trying the example from the documentation page. As a result, I
have opened a pull request (PR) to address this minor issue, even though
it may seem insignificant 😂.
The following calls were throwing an exception:
575b717d10/docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb (L192)575b717d10/docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb (L239)
Exception:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[14], line 1
----> 1 chain_sota = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0), chain_type="stuff", retriever=vectorstore_sota, input_key="question")
File ~/github/langchain/venv/lib/python3.9/site-packages/langchain/chains/retrieval_qa/base.py:89, in BaseRetrievalQA.from_chain_type(cls, llm, chain_type, chain_type_kwargs, **kwargs)
85 _chain_type_kwargs = chain_type_kwargs or {}
86 combine_documents_chain = load_qa_chain(
87 llm, chain_type=chain_type, **_chain_type_kwargs
88 )
---> 89 return cls(combine_documents_chain=combine_documents_chain, **kwargs)
File ~/github/langchain/venv/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for RetrievalQA
retriever
instance of BaseRetriever expected (type=type_error.arbitrary_type; expected_arbitrary_type=BaseRetriever)
```
The vectorstores had to be converted to retrievers:
`vectorstore_sota.as_retriever()` and `vectorstore_pg.as_retriever()`.
The PR also:
- adds the file `paul_graham_essay.txt` referenced by this notebook
- adds to gitignore *.pkl and *.bin files that are generated by this
notebook
Interestingly enough, the performance of the prediction greatly
increased (new version of langchain or ne version of OpenAI models since
the last run of the notebook): from 19/33 correct to 28/33 correct!
- Remove dynamic model creation in the `args()` property. _Only infer
for the decorator (and add an argument to NOT infer if someone wishes to
only pass as a string)_
- Update the validation example to make it less likely to be
misinterpreted as a "safe" way to run a repl
There is one example of "Multi-argument tools" in the custom_tools.ipynb
from yesterday, but we could add more. The output parsing for the base
MRKL agent hasn't been adapted to handle structured args at this point
in time
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
## Use `index_id` over `app_id`
We made a major update to index + retrieve based on Metal Indexes
(instead of apps). With this change, we accept an index instead of an
app in each of our respective core apis. [More details
here](https://docs.getmetal.io/api-reference/core/indexing).
## What is this PR for:
* This PR adds a commented line of code in the documentation that shows
how someone can use the Pinecone client with an already existing
Pinecone index
* The documentation currently only shows how to create a pinecone index
from langchain documents but not how to load one that already exists
- Updated `langchain/docs/modules/models/llms/integrations/` notebooks:
added links to the original sites, the install information, etc.
- Added the `nlpcloud` notebook.
- Removed "Example" from Titles of some notebooks, so all notebook
titles are consistent.
### https://github.com/hwchase17/langchain/issues/2997
Replaced `conversation.memory.store` to
`conversation.memory.entity_store.store`
As conversation.memory.store doesn't exist and re-ran the whole file.
- Most important - fixes the relevance_fn name in the notebook to align
with the docs
- Updates comments for the summary:
<img width="787" alt="image"
src="https://user-images.githubusercontent.com/130414180/232520616-2a99e8c3-a821-40c2-a0d5-3f3ea196c9bb.png">
- The new conversation is a bit better, still unfortunate they try to
schedule a followup.
- Rm the max dialogue turns argument to the conversation function
Add a time-weighted memory retriever and a notebook that approximates a
Generative Agent from https://arxiv.org/pdf/2304.03442.pdf
The "daily plan" components are removed for now since they are less
useful without a virtual world, but the memory is an interesting
component to build off.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Use numexpr evaluate instead of the python REPL to avoid malicious code
injection.
Tested against the (limited) math dataset and got the same score as
before.
For more permissive tools (like the REPL tool itself), other approaches
ought to be provided (some combination of Sanitizer + Restricted python
+ unprivileged-docker + ...), but for a calculator tool, only
mathematical expressions should be permitted.
See https://github.com/hwchase17/langchain/issues/814
Last week I added the `PDFMinerPDFasHTMLLoader`. I am adding some
example code in the notebook to serve as a tutorial for how that loader
can be used to create snippets of a pdf that are structured within
sections. All the other loaders only provide the `Document` objects
segmented by pages but that's pretty loose given the amount of other
metadata that can be extracted.
With the new loader, one can leverage font-size of the text to decide
when a new sections starts and can segment the text more semantically as
shown in the tutorial notebook. The cell shows that we are able to find
the content of entire section under **Related Work** for the example pdf
which is spread across 2 pages and hence is stored as two separate
documents by other loaders
Add SVM retriever class, based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb.
Testing still WIP, but the logic is correct (I have a local
implementation outside of Langchain working).
---------
Co-authored-by: Lance Martin <122662504+PineappleExpress808@users.noreply.github.com>
Co-authored-by: rlm <31treehaus@31s-MacBook-Pro.local>
Minor cosmetic changes
- Activeloop environment cred authentication in notebooks with
`getpass.getpass` (instead of CLI which not always works)
- much faster tests with Deep Lake pytest mode on
- Deep Lake kwargs pass
Notes
- I put pytest environment creds inside `vectorstores/conftest.py`, but
feel free to suggest a better location. For context, if I put in
`test_deeplake.py`, `ruff` doesn't let me to set them before import
deeplake
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
Note to self: Always run integration tests, even on "that last minute
change you thought would be safe" :)
---------
Co-authored-by: Mike Lambert <mike.lambert@anthropic.com>
**About**
Specify encoding to avoid UnicodeDecodeError when reading .txt for users
who are following the tutorial.
**Reference**
```
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 1205: character maps to <undefined>
```
**Environment**
OS: Win 11
Python: 3.8
Allows users to specify what files should be loaded instead of
indiscriminately loading the entire repo.
extends #2851
NOTE: for reviewers, `hide whitespace` option recommended since I
changed the indentation of an if-block to use `continue` instead so it
looks less like a Christmas tree :)
Mendable Seach Integration is Finally here!
Hey yall,
After various requests for Mendable in Python docs, we decided to get
our hands dirty and try to implement it.
Here is a version where we implement our **floating button** that sits
on the bottom right of the screen that once triggered (via press or CMD
K) will work the same as the js langchain docs.
Super excited about this and hopefully the community will be too.
@hwchase17 will send you the admin details via dm etc. The anon_key is
fine to be public.
Let me know if you need any further customization. I added the langchain
logo to it.
Fixes linting issue from #2835
Adds a loader for Slack Exports which can be a very valuable source of
knowledge to use for internal QA bots and other use cases.
```py
# Export data from your Slack Workspace first.
from langchain.document_loaders import SLackDirectoryLoader
SLACK_WORKSPACE_URL = "https://awesome.slack.com"
loader = ("Slack_Exports", SLACK_WORKSPACE_URL)
docs = loader.load()
```
Have seen questions about whether or not the `SQLDatabaseChain` supports
more than just sqlite, which was unclear in the docs, so tried to
clarify that and how to connect to other dialects.
The doc loaders index was picking up a bunch of subheadings because I
mistakenly made the MD titles H1s. Fixed that.
also the easy minor warnings from docs_build
I was testing out the WhatsApp Document loader, and noticed that
sometimes the date is of the following format (notice the additional
underscore):
```
3/24/23, 1:54_PM - +91 99999 99999 joined using this group's invite link
3/24/23, 6:29_PM - +91 99999 99999: When are we starting then?
```
Wierdly, the underscore is visible in Vim, but not on editors like
VSCode. I presume it is some unusual character/line terminator.
Nevertheless, I think handling this edge case will make the document
loader more robust.
Adds a loader for Slack Exports which can be a very valuable source of
knowledge to use for internal QA bots and other use cases.
```py
# Export data from your Slack Workspace first.
from langchain.document_loaders import SLackDirectoryLoader
SLACK_WORKSPACE_URL = "https://awesome.slack.com"
loader = ("Slack_Exports", SLACK_WORKSPACE_URL)
docs = loader.load()
```
---------
Co-authored-by: Mikhail Dubov <mikhail@chattermill.io>
This PR proposes
- An NLAToolkit method to instantiate from an AI Plugin URL
- A notebook that shows how to use that alongside an example of using a
Retriever object to lookup specs and route queries to them on the fly
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
I've added a bilibili loader, bilibili is a very active video site in
China and I think we need this loader.
Example:
```python
from langchain.document_loaders.bilibili import BiliBiliLoader
loader = BiliBiliLoader(
["https://www.bilibili.com/video/BV1xt411o7Xu/",
"https://www.bilibili.com/video/av330407025/"]
)
docs = loader.load()
```
Co-authored-by: 了空 <568250549@qq.com>
This PR adds a LangChain implementation of CAMEL role-playing example:
https://github.com/lightaime/camel.
I am sorry that I am not that familiar with LangChain. So I only
implement it in a naive way. There may be a better way to implement it.
**Description**
Add custom vector field name and text field name while indexing and
querying for OpenSearch
**Issues**
https://github.com/hwchase17/langchain/issues/2500
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
Hi,
just wanted to mention that I added `langchain` to
[conda-forge](https://github.com/conda-forge/langchain-feedstock), so
that it can be installed with `conda`/`mamba` etc.
This makes it available to some corporate users with custom
conda-servers and people who like to manage their python envs with
conda.
Took me a bit to find the proper places to get the API keys. The link
earlier provided to setup search is still good, but why not provide
direct link to the Google cloud tools that give you ability to create
keys?
`combine_docs` does not go through the standard chain call path which
means that chain callbacks won't be triggered, meaning QA chains won't
be traced properly, this fixes that.
Also fix several errors in the chat_vector_db notebook
Adds a new pdf loader using the existing dependency on PDFMiner.
The new loader can be helpful for chunking texts semantically into
sections as the output html content can be parsed via `BeautifulSoup` to
get more structured and rich information about font size, page numbers,
pdf headers/footers, etc. which may not be available otherwise with
other pdf loaders
Improvements to Deep Lake Vector Store
- much faster view loading of embeddings after filters with
`fetch_chunks=True`
- 2x faster ingestion
- use np.float32 for embeddings to save 2x storage, LZ4 compression for
text and metadata storage (saves up to 4x storage for text data)
- user defined functions as filters
Docs
- Added retriever full example for analyzing twitter the-algorithm
source code with GPT4
- Added a use case for code analysis (please let us know your thoughts
how we can improve it)
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
## Why this PR?
Fixes#2624
There's a missing import statement in AzureOpenAI embeddings example.
## What's new in this PR?
- Import `OpenAIEmbeddings` before creating it's object.
## How it's tested?
- By running notebook and creating embedding object.
Signed-off-by: letmerecall <girishsharma001@gmail.com>
Right now, eval chains require an answer for every question. It's
cumbersome to collect this ground truth so getting around this issue
with 2 things:
* Adding a context param in `ContextQAEvalChain` and simply evaluating
if the question is answered accurately from context
* Adding chain of though explanation prompting to improve the accuracy
of this w/o GT.
This also gets to feature parity with openai/evals which has the same
contextual eval w/o GT.
TODO in follow-up:
* Better prompt inheritance. No need for seperate prompt for CoT
reasoning. How can we merge them together
---------
Co-authored-by: Vashisht Madhavan <vashishtmadhavan@Vashs-MacBook-Pro.local>
Evaluation so far has shown that agents do a reasonable job of emitting
`json` blocks as arguments when cued (instead of typescript), and `json`
permits the `strict=False` flag to permit control characters, which are
likely to appear in the response in particular.
This PR makes this change to the request and response synthesizer
chains, and fixes the temperature to the OpenAI agent in the eval
notebook. It also adds a `raise_error = False` flag in the notebook to
facilitate debugging
This still doesn't handle the following
- non-JSON media types
- anyOf, allOf, oneOf's
And doesn't emit the typescript definitions for referred types yet, but
that can be saved for a separate PR.
Also, we could have better support for Swagger 2.0 specs and OpenAPI
3.0.3 (can use the same lib for the latter) recommend offline conversion
for now.
### Features include
- Metadata based embedding search
- Choice of distance metric function (`L2` for Euclidean, `L1` for
Nuclear, `max` L-infinity distance, `cos` for cosine similarity, 'dot'
for dot product. Defaults to `L2`
- Returning scores
- Max Marginal Relevance Search
- Deleting samples from the dataset
### Notes
- Added numerous tests, let me know if you would like to shorten them or
make smarter
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
The specs used in chat-gpt plugins have only a few endpoints and have
unrealistically small specifications. By contrast, a spec like spotify's
has 60+ endpoints and is comprised 100k+ tokens.
Here are some impressive traces from gpt-4 that string together
non-trivial sequences of API calls. As noted in `planner.py`, gpt-3 is
not as robust but can be improved with i) better retry, self-reflect,
etc. logic and ii) better few-shots iii) etc. This PR's just a first
attempt probing a few different directions that eventually can be made
more core.
`make me a playlist with songs from kind of blue. call it machine
blues.`
```
> Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to create a playlist with songs from Kind of Blue and name it Machine Blues
Observation: 1. GET /search to find the album ID for "Kind of Blue".
2. GET /albums/{id}/tracks to get the tracks from the "Kind of Blue" album.
3. GET /me to get the current user's ID.
4. POST /users/{user_id}/playlists to create a new playlist named "Machine Blues" for the current user.
5. POST /playlists/{playlist_id}/tracks to add the tracks from "Kind of Blue" to the newly created "Machine Blues" playlist.
Thought:I have a plan to create the playlist. Now, I will execute the API calls.
Action: api_controller
Action Input: 1. GET /search to find the album ID for "Kind of Blue".
2. GET /albums/{id}/tracks to get the tracks from the "Kind of Blue" album.
3. GET /me to get the current user's ID.
4. POST /users/{user_id}/playlists to create a new playlist named "Machine Blues" for the current user.
5. POST /playlists/{playlist_id}/tracks to add the tracks from "Kind of Blue" to the newly created "Machine Blues" playlist.
> Entering new AgentExecutor chain...
Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/search?q=Kind%20of%20Blue&type=album", "output_instructions": "Extract the id of the first album in the search results"}
Observation: 1weenld61qoidwYuZ1GESA
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/albums/1weenld61qoidwYuZ1GESA/tracks", "output_instructions": "Extract the ids of all the tracks in the album"}
Observation: ["7q3kkfAVpmcZ8g6JUThi3o"]
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/me", "output_instructions": "Extract the id of the current user"}
Observation: 22rhrz4m4kvpxlsb5hezokzwi
Thought:Action: requests_post
Action Input: {"url": "https://api.spotify.com/v1/users/22rhrz4m4kvpxlsb5hezokzwi/playlists", "data": {"name": "Machine Blues"}, "output_instructions": "Extract the id of the newly created playlist"}
Observation: 48YP9TMcEtFu9aGN8n10lg
Thought:Action: requests_post
Action Input: {"url": "https://api.spotify.com/v1/playlists/48YP9TMcEtFu9aGN8n10lg/tracks", "data": {"uris": ["spotify:track:7q3kkfAVpmcZ8g6JUThi3o"]}, "output_instructions": "Confirm that the tracks were added to the playlist"}
Observation: The tracks were added to the playlist. The snapshot_id is "Miw4NTdmMWUxOGU5YWMxMzVmYmE3ZWE5MWZlYWNkMTc2NGVmNTI1ZjY5".
Thought:I am finished executing the plan.
Final Answer: The tracks from the "Kind of Blue" album have been added to the newly created "Machine Blues" playlist. The playlist ID is 48YP9TMcEtFu9aGN8n10lg.
> Finished chain.
Observation: The tracks from the "Kind of Blue" album have been added to the newly created "Machine Blues" playlist. The playlist ID is 48YP9TMcEtFu9aGN8n10lg.
Thought:I am finished executing the plan and have created the playlist with songs from Kind of Blue, named Machine Blues.
Final Answer: I have created a playlist called "Machine Blues" with songs from the "Kind of Blue" album. The playlist ID is 48YP9TMcEtFu9aGN8n10lg.
> Finished chain.
```
or
`give me a song in the style of tobe nwige`
```
> Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to get a song in the style of Tobe Nwigwe
Observation: 1. GET /search to find the artist ID for Tobe Nwigwe.
2. GET /artists/{id}/related-artists to find similar artists to Tobe Nwigwe.
3. Pick one of the related artists and use their artist ID in the next step.
4. GET /artists/{id}/top-tracks to get the top tracks of the chosen related artist.
Thought:
I'm ready to execute the API calls.
Action: api_controller
Action Input: 1. GET /search to find the artist ID for Tobe Nwigwe.
2. GET /artists/{id}/related-artists to find similar artists to Tobe Nwigwe.
3. Pick one of the related artists and use their artist ID in the next step.
4. GET /artists/{id}/top-tracks to get the top tracks of the chosen related artist.
> Entering new AgentExecutor chain...
Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/search?q=Tobe%20Nwigwe&type=artist", "output_instructions": "Extract the artist id for Tobe Nwigwe"}
Observation: 3Qh89pgJeZq6d8uM1bTot3
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/artists/3Qh89pgJeZq6d8uM1bTot3/related-artists", "output_instructions": "Extract the ids and names of the related artists"}
Observation: [
{
"id": "75WcpJKWXBV3o3cfluWapK",
"name": "Lute"
},
{
"id": "5REHfa3YDopGOzrxwTsPvH",
"name": "Deante' Hitchcock"
},
{
"id": "6NL31G53xThQXkFs7lDpL5",
"name": "Rapsody"
},
{
"id": "5MbNzCW3qokGyoo9giHA3V",
"name": "EARTHGANG"
},
{
"id": "7Hjbimq43OgxaBRpFXic4x",
"name": "Saba"
},
{
"id": "1ewyVtTZBqFYWIcepopRhp",
"name": "Mick Jenkins"
}
]
Thought:Action: requests_get
Action Input: {"url": "https://api.spotify.com/v1/artists/75WcpJKWXBV3o3cfluWapK/top-tracks?country=US", "output_instructions": "Extract the ids and names of the top tracks"}
Observation: [
{
"id": "6MF4tRr5lU8qok8IKaFOBE",
"name": "Under The Sun (with J. Cole & Lute feat. DaBaby)"
}
]
Thought:I am finished executing the plan.
Final Answer: The top track of the related artist Lute is "Under The Sun (with J. Cole & Lute feat. DaBaby)" with the track ID "6MF4tRr5lU8qok8IKaFOBE".
> Finished chain.
Observation: The top track of the related artist Lute is "Under The Sun (with J. Cole & Lute feat. DaBaby)" with the track ID "6MF4tRr5lU8qok8IKaFOBE".
Thought:I am finished executing the plan and have the information the user asked for.
Final Answer: The song "Under The Sun (with J. Cole & Lute feat. DaBaby)" by Lute is in the style of Tobe Nwigwe.
> Finished chain.
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
