feat(Add support for ConversationalRetrievalChain): (#37)

examples/app.py

@@ -16,4 +16,5 @@
     "examples.ex10_cc:chain",
     "examples.ex12:run",
     "examples.ex13:chain",
+    "examples.ex14:chain",
 )

examples/ex14.py

@@ -0,0 +1,26 @@
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import TextLoader
+from langchain.embeddings.fake import FakeEmbeddings
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.llms import OpenAI
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+
+loader = TextLoader("Readme.md")
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+texts = text_splitter.split_documents(documents)
+
+embeddings = FakeEmbeddings(size=1504)
+docsearch = Chroma.from_documents(texts, embeddings)
+
+chain = ConversationalRetrievalChain.from_llm(
+    llm=ChatOpenAI(model="gpt-3.5-turbo"),
+    retriever=docsearch.as_retriever(search_kwargs={"k": 1}),
+)
+
+
+if __name__ == "__main__":
+    # Run the chain only specifying the input variable.
+    print(chain.run(question="colorful socks", chat_history=[]))

langcorn/server/api.py

@@ -85,13 +85,17 @@ def derive_fields(language_app) -> (list[str], list[str]):
         return language_app.input_variables, language_app.output_variables
     elif hasattr(language_app, "prompt"):
         return language_app.prompt.input_variables, [language_app.output_key]
+    elif hasattr(language_app, "input_keys"):
+        return language_app.input_keys, [language_app.output_key]
     return [language_app.input_key], ["output"]
 
 
 def derive_class(name, fields, add_memory=False):
     annotations = {f: str for f in fields}
     if add_memory:
         annotations["memory"] = list[dict]
+    if "chat_history" in annotations:
+        annotations["chat_history"] = list[list[str]]
     return type(f"Lang{name}", (BaseModel,), {"__annotations__": annotations})
 
 
@@ -133,6 +137,11 @@ def configure_llm(chain, http_headers: dict[str, str]):
     return False
 
 
+def add_chat_history(run_params):
+    if "chat_history" in run_params:
+        run_params["chat_history"] = [tuple(t) for t in run_params["chat_history"]]
+
+
 def make_handler(request_cls, chain):
     async def handler(request: request_cls, http_request: Request):
         llm_api_key = http_request.headers.get("x-llm-api-key")
@@ -144,6 +153,7 @@ async def handler(request: request_cls, http_request: Request):
             memory = run_params.pop("memory", [])
             if chain.memory and memory and memory[0]:
                 chain.memory.chat_memory.messages = messages_from_dict(memory)
+            add_chat_history(run_params)
             with get_openai_callback() as cb:
                 if not retrieval_chain:
                     output = chain.run(run_params)

pyproject.toml

@@ -1,11 +1,9 @@
 [tool.poetry]
 name = "langcorn"
-version = "0.0.16"
+version = "0.0.17"
 description = "A Python package creating rest api interface for LangChain"
 authors = ["Alexander Miasoiedov <msoedov@gmail.com>"]
-maintainers = [
-    "Alexander Miasoiedov <msoedov@gmail.com>",
-]
+maintainers = ["Alexander Miasoiedov <msoedov@gmail.com>"]
 repository = "https://github.com/msoedov/langcorn"
 license = "MIT"
 readme = "Readme.md"
@@ -24,7 +22,7 @@ langchain = "^0.0.283"
 openai = "^0.28.0"
 fire = "^0.5.0"
 loguru = "^0.7.0"
-bs4 = "0.0.1"  # required for ex4.py
+bs4 = "0.0.1"                      # required for ex4.py
 langchain-experimental = "^0.0.14"
 certifi = "^2023.7.22"
 
@@ -35,7 +33,7 @@ httpx = "^0.24.1"
 pytest = "^7.4.1"
 types-requests = "^2.31.0.1"
 pre-commit = "^3.4.0"
-chromadb = "^0.3.2.6" # required for ex8.py
+chromadb = "^0.3.2.6"        # required for ex8.py
 
 [tool.ruff]
 line-length = 120

tests.http

@@ -25,3 +25,20 @@ Content-Type: application/json
     {}
   ]
 }
+
+###
+
+POST http://0.0.0.0:8718/examples.ex14.chain/run
+X-LLM-API-KEY: sk-invalid-key
+Content-Type: application/json
+
+{
+  "chat_history": [[
+    "string",
+    "string"
+  ]],
+  "question": "string",
+  "memory": [
+    {}
+  ]
+}