Commit Graph

15 Commits (a2b4c33bd63cb2ef787daf91143455fbd26267ca)

Author SHA1 Message Date
mochi 63284ffebf
experimental[patch], docs: refine notebook for MyScale `SelfQueryRetriever` (#22016)
- **Description:** upgrade model to `gpt-4o`
4 months ago
Leonid Ganeline 95dc90609e
experimental[patch]: `prompts` import fix (#20534)
Replaced `from langchain.prompts` with `from langchain_core.prompts`
where it is appropriate.
Most of the changes go to `langchain_experimental`
Similar to #20348
5 months ago
Leonid Ganeline e512d3c6a6
langchain: `callbacks` imports fix (#20348)
Replaced all `from langchain.callbacks` into `from
langchain_core.callbacks` .
Changes in the `langchain` and `langchain_experimental`

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
5 months ago
Leonid Ganeline 3f6bf852ea
experimental: docstrings update (#18048)
Added missed docstrings. Formatted docsctrings to the consistent format.
7 months ago
Erick Friis ed789be8f4
docs, templates: update schema imports to core (#17885)
- chat models, messages
- documents
- agentaction/finish
- baseretriever,document
- stroutputparser
- more messages
- basemessage
- format_document
- baseoutputparser

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
7 months ago
Eugene Yurtsev 780e84ae79
community[minor]: SQLDatabase Add fetch mode `cursor`, query parameters, query by selectable, expose execution options, and documentation (#17191)
- **Description:** Improve `SQLDatabase` adapter component to promote
code re-use, see
[suggestion](https://github.com/langchain-ai/langchain/pull/16246#pullrequestreview-1846590962).
  - **Needed by:** GH-16246
  - **Addressed to:** @baskaryan, @cbornet 

## Details
- Add `cursor` fetch mode
- Accept SQL query parameters
- Accept both `str` and SQLAlchemy selectables as query expression
- Expose `execution_options`
- Documentation page (notebook) about `SQLDatabase` [^1]
See [About
SQLDatabase](https://github.com/langchain-ai/langchain/blob/c1c7b763/docs/docs/integrations/tools/sql_database.ipynb).

[^1]: Apparently there hasn't been any yet?

---------

Co-authored-by: Andreas Motl <andreas.motl@crate.io>
8 months ago
Bagatur baeac236b6
langchain[patch], experimental[patch]: update utilities imports (#15438) 9 months ago
Bagatur 1678d6ca17
langchain[patch], experimental[patch], docs: update tools imports (#15433) 9 months ago
Bagatur 480626dc99
docs, community[patch], experimental[patch], langchain[patch], cli[pa… (#15412)
…tch]: import models from community

ran
```bash
git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g"
git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g"
git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g"
git checkout master libs/langchain/tests/unit_tests/llms
git checkout master libs/langchain/tests/unit_tests/chat_models
git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py
make format
cd libs/langchain; make format
cd ../experimental; make format
cd ../core; make format
```
9 months ago
Bagatur 8e0d5813c2
langchain[patch], experimental[patch]: replace langchain.schema imports (#15410)
Import from core instead.

Ran:
```bash
git grep -l 'from langchain.schema\.output_parser' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.output_parser/from\ langchain_core.output_parsers/g"
git grep -l 'from langchain.schema\.messages' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.messages/from\ langchain_core.messages/g"
git grep -l 'from langchain.schema\.document' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.document/from\ langchain_core.documents/g"
git grep -l 'from langchain.schema\.runnable' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.runnable/from\ langchain_core.runnables/g"
git grep -l 'from langchain.schema\.vectorstore' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.vectorstore/from\ langchain_core.vectorstores/g"
git grep -l 'from langchain.schema\.language_model' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.language_model/from\ langchain_core.language_models/g"
git grep -l 'from langchain.schema\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.embeddings/from\ langchain_core.embeddings/g"
git grep -l 'from langchain.schema\.storage' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.storage/from\ langchain_core.stores/g"
git checkout master libs/langchain/tests/unit_tests/schema/
make format
cd libs/experimental
make format
cd ../langchain
make format
```
9 months ago
Predrag Gruevski c9986bc3a9
Tweak type hints to match dependency's behavior. (#11355)
Needs #11353 to merge first, and a new `langchain` to be published with
those changes.
12 months ago
Nuno Campos 7b13292e35
Remove python eval from vector sql db chain (#10937)
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1 year ago
Harrison Chase 12ff780089
move embeddings to schema (#10696) 1 year ago
Harrison Chase 5442d2b1fa
Harrison/stop importing from init (#10690) 1 year ago
刘 方瑞 890ed775a3
Resolve: VectorSearch enabled SQLChain? (#10177)
Squashed from #7454 with updated features

We have separated the `SQLDatabseChain` from `VectorSQLDatabseChain` and
put everything into `experimental/`.

Below is the original PR message from #7454.

-------

We have been working on features to fill up the gap among SQL, vector
search and LLM applications. Some inspiring works like self-query
retrievers for VectorStores (for example
[Weaviate](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/weaviate_self_query.html)
and
[others](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query.html))
really turn those vector search databases into a powerful knowledge
base! 🚀🚀

We are thinking if we can merge all in one, like SQL and vector search
and LLMChains, making this SQL vector database memory as the only source
of your data. Here are some benefits we can think of for now, maybe you
have more 👀:

With ALL data you have: since you store all your pasta in the database,
you don't need to worry about the foreign keys or links between names
from other data source.
Flexible data structure: Even if you have changed your schema, for
example added a table, the LLM will know how to JOIN those tables and
use those as filters.
SQL compatibility: We found that vector databases that supports SQL in
the marketplace have similar interfaces, which means you can change your
backend with no pain, just change the name of the distance function in
your DB solution and you are ready to go!

### Issue resolved:
- [Feature Proposal: VectorSearch enabled
SQLChain?](https://github.com/hwchase17/langchain/issues/5122)

### Change made in this PR:
- An improved schema handling that ignore `types.NullType` columns 
- A SQL output Parser interface in `SQLDatabaseChain` to enable Vector
SQL capability and further more
- A Retriever based on `SQLDatabaseChain` to retrieve data from the
database for RetrievalQAChains and many others
- Allow `SQLDatabaseChain` to retrieve data in python native format
- Includes PR #6737 
- Vector SQL Output Parser for `SQLDatabaseChain` and
`SQLDatabaseChainRetriever`
- Prompts that can implement text to VectorSQL
- Corresponding unit-tests and notebook

### Twitter handle: 
- @MyScaleDB

### Tag Maintainer:
Prompts / General: @hwchase17, @baskaryan
DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev

### Dependencies:
No dependency added
1 year ago