Add context to api docs

docs/api.md

@@ -38,6 +38,7 @@ Advanced parameters:
 - `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
 - `system`: system prompt to (overrides what is defined in the `Modelfile`)
 - `template`: the full prompt or prompt template (overrides what is defined in the `Modelfile`)
+- `context`: the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
 
 ### Request
 
@@ -71,6 +72,7 @@ The final response in the stream also includes additional data about the generat
 - `prompt_eval_duration`: time spent in nanoseconds evaluating the prompt
 - `eval_count`: number of tokens the response
 - `eval_duration`: time in nanoseconds spent generating the response
+- `context`: an encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory
 
 To calculate how fast the response is generated in tokens per second (token/s), divide `eval_count` / `eval_duration`.
 
@@ -78,6 +80,7 @@ To calculate how fast the response is generated in tokens per second (token/s),
 {
   "model": "llama2:7b",
   "created_at": "2023-08-04T19:22:45.499127Z",
+  "context": [1, 2, 3],
   "done": true,
   "total_duration": 5589157167,
   "load_duration": 3013701500,

examples/python/client.py

@@ -0,0 +1,38 @@
+import json
+import requests
+
+# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
+model = 'llama2' # TODO: update this for whatever model you wish to use
+
+def generate(prompt, context):
+    r = requests.post('http://localhost:11434/api/generate',
+                      json={
+                          'model': model,
+                          'prompt': prompt,
+                          'context': context,
+                      },
+                      stream=True)
+    r.raise_for_status()
+
+    for line in r.iter_lines():
+        body = json.loads(line)
+        response_part = body.get('response', '')
+        # the response streams one token at a time, print that as we recieve it
+        print(response_part, end='', flush=True)
+
+        if 'error' in body:
+            raise Exception(body['error'])
+
+        if body.get('done', False):
+            return body['context']
+
+def main():
+    context = [] # the context stores a conversation history, you can use this to make the model more context aware
+    while True:
+        user_input = input("Enter a prompt: ")
+        print()
+        context = generate(user_input, context)
+        print()
+
+if __name__ == "__main__":
+    main()